From 4429b8924f89c0c8edd146eefd0d04499cf44e21 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 1 Jun 2023 14:39:00 -0700 Subject: [PATCH 001/249] allow for red & green chip only in cal search --- .../src/query_db_nearest_master_files_framework.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/database/modules/query_db_nearest_master_files/src/query_db_nearest_master_files_framework.py b/database/modules/query_db_nearest_master_files/src/query_db_nearest_master_files_framework.py index 53e6dd2da..05394ee56 100644 --- a/database/modules/query_db_nearest_master_files/src/query_db_nearest_master_files_framework.py +++ b/database/modules/query_db_nearest_master_files/src/query_db_nearest_master_files_framework.py @@ -166,7 +166,7 @@ def _perform(self): # Query database for all cal_types. - contentbitmask_list = [7, 3] # Mask values for GREEN, RED, and CA_HK together, and then for just GREEN and RED. + contentbitmask_list = [3] # Mask values for GREEN, RED, and CA_HK together, and then for just GREEN and RED. nearest_master_files_list = [] From a716e95c21599b924e46039da1e5a8842eacf8b9 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 1 Jun 2023 15:42:35 -0700 Subject: [PATCH 002/249] revert flat settings --- modules/master_flat/configs/default.cfg | 2 -- modules/master_flat/src/master_flat_framework.py | 7 +++---- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/modules/master_flat/configs/default.cfg b/modules/master_flat/configs/default.cfg index 998e7509e..6bdf7e797 100644 --- a/modules/master_flat/configs/default.cfg +++ b/modules/master_flat/configs/default.cfg @@ -13,5 +13,3 @@ low_light_limit = 50.01 green_ccd_flat_exptime_maximum = 60.0 red_ccd_flat_exptime_maximum = 60.0 ca_hk_flat_exptime_maximum = 60.0 - - diff --git a/modules/master_flat/src/master_flat_framework.py b/modules/master_flat/src/master_flat_framework.py index e8899aa98..84a49c485 100644 --- a/modules/master_flat/src/master_flat_framework.py +++ b/modules/master_flat/src/master_flat_framework.py @@ -292,11 +292,10 @@ def _perform(self): stack_avg,stack_var,cnt,stack_unc = fs.compute() # Divide by the smoothed Flatlamp pattern. - # Nominal 2-D Gaussian blurring at sigma=2.0 to smooth pixel-to-pixel variations. - # Turned off this high-pass filtering for now but still save the smoothed version + # Nominal 2-D Gaussian blurring with width sigma to remove the large scale structure in the flats. smooth_lamp_pattern = gaussian_filter(stack_avg, sigma=self.gaussian_filter_sigma) - unnormalized_flat = stack_avg# / smooth_lamp_pattern - unnormalized_flat_unc = stack_unc# / smooth_lamp_pattern + unnormalized_flat = stack_avg / smooth_lamp_pattern + unnormalized_flat_unc = stack_unc / smooth_lamp_pattern # Apply order mask, if available for the current FITS extension. Otherwise, use the low-light pixels as a mask. From de433faa1dfbaba9e611e91a7b9d0394554e512f Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Fri, 2 Jun 2023 17:59:08 -0700 Subject: [PATCH 003/249] Fixed misnamings. --- .../query_db_one_nearest_master_file/configs/default.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/database/modules/query_db_one_nearest_master_file/configs/default.cfg b/database/modules/query_db_one_nearest_master_file/configs/default.cfg index 60eeefbd2..878a37def 100644 --- a/database/modules/query_db_one_nearest_master_file/configs/default.cfg +++ b/database/modules/query_db_one_nearest_master_file/configs/default.cfg @@ -1,7 +1,7 @@ -## Default configuration for QueryDBNearestMasterFilesFramework primitive +## Default configuration for QueryDBOneNearestMasterFileFramework primitive [LOGGER] start_log = True -log_path = logs/query_db_latest_master_files_framework_debug.log +log_path = logs/query_db_one_nearest_master_file_framework_debug.log log_level = debug log_verbose = True From b1ec0ba78aac8f61dfe0f90896f5d09f6a81958f Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Fri, 2 Jun 2023 17:59:15 -0700 Subject: [PATCH 004/249] Made container names unique. Removed explicit port specification in docker run command. Made refinements for regeneration of products cleanly and securely. --- cronjobs/kpfmasters_wls_auto.pl | 37 ++++++------ cronjobs/kpfmastersruncmd_l0.pl | 41 +++++++------- cronjobs/kpfmastersruncmd_l1.pl | 40 ++++++------- .../cronjobs/kpfmasters_register_in_db.pl | 56 ++++++++++++------- 4 files changed, 96 insertions(+), 78 deletions(-) diff --git a/cronjobs/kpfmasters_wls_auto.pl b/cronjobs/kpfmasters_wls_auto.pl index f042053f3..dbefe8d00 100755 --- a/cronjobs/kpfmasters_wls_auto.pl +++ b/cronjobs/kpfmasters_wls_auto.pl @@ -37,15 +37,7 @@ # Read KPF-related environment variables. -# Legacy KPF port for Jupyter notebook. -# E.g., 6107 -my $kpfpipeport = $ENV{KPFPIPE_PORT}; - -if (! (defined $kpfpipeport)) { - die "*** Env. var. KPFPIPE_PORT not set; quitting...\n"; -} - -# Legacy KPF directory for outputs of testing (and Jupyter notebook). +# Legacy KPF directory for outputs of testing. # E.g., /KPF-Pipeline-TestData my $testdatadir = $ENV{KPFPIPE_TEST_DATA}; @@ -94,11 +86,14 @@ die "*** Env. var. KPFCRONJOB_DOCKER_NAME_WLS not set; quitting...\n"; } +my $trunctime = time() - int(53 * 365.25 * 24 * 3600); # Subtract off number of seconds in 53 years (since 00:00:00 on January 1, 1970, UTC). +$containername .= '_' . $$ . '_' . $trunctime; # Augment container name with unique numbers (process ID and truncated seconds). + # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmasters_wls_auto.pl'; -my $version = '1.1'; +my $version = '1.2'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -122,33 +117,32 @@ print "containerimage=$containerimage\n"; print "recipe=$recipe\n"; print "config=$config\n"; -print "KPFPIPE_PORT=$kpfpipeport\n"; print "KPFPIPE_TEST_DATA=$testdatadir\n"; print "KPFPIPE_MASTERS_BASE_DIR=$mastersdir\n"; print "KPFCRONJOB_SBX=$sandbox\n"; print "KPFCRONJOB_LOGS=$logdir\n"; print "KPFCRONJOB_CODE=$codedir\n"; -print "KPFCRONJOB_DOCKER_NAME_WLS=$containername\n"; +print "Docker container name = $containername\n"; # Change directory to where the Dockerfile is located. chdir "$codedir" or die "Couldn't cd to $codedir : $!\n"; -my $script = "#! /bin/bash\nmake init\nexport PYTHONUNBUFFERED=1\ngit config --global --add safe.directory /code/KPF-Pipeline\nkpf -r $recipe -c $config --date ${procdate}\nexit\n"; +my $script = "#! /bin/bash\n" . + "make init\n" . + "export PYTHONUNBUFFERED=1\n" . + "git config --global --add safe.directory /code/KPF-Pipeline\n" . + "kpf -r $recipe -c $config --date ${procdate}\n" . + "exit\n"; my $makescriptcmd = "echo \"$script\" > $dockercmdscript"; `$makescriptcmd`; `chmod +x $dockercmdscript`; -my $dockerrmcmd = "docker rm $containername"; -print "Executing $dockerrmcmd\n"; -my $opdockerrmcmd = `$dockerrmcmd`; -print "Output from dockerrmcmd: $opdockerrmcmd\n"; - `mkdir -p $sbxdir`; `cp -pr ${mastersdir}/${procdate}/kpf_${procdate}*L1.fits $sbxdir`; -my $dockerruncmd = "docker run -d --name $containername -p 6207:6207 -e KPFPIPE_PORT=$kpfpipeport " . +my $dockerruncmd = "docker run -d --name $containername " . "-v ${codedir}:/code/KPF-Pipeline -v ${testdatadir}:/testdata -v $sandbox:/data " . "$containerimage bash ./$dockercmdscript"; print "Executing $dockerruncmd\n"; @@ -173,6 +167,11 @@ sleep(300); } +my $dockerrmcmd = "docker rm $containername"; +print "Executing $dockerrmcmd\n"; +my $opdockerrmcmd = `$dockerrmcmd`; +print "Output from dockerrmcmd: $opdockerrmcmd\n"; + # Checkpoint diff --git a/cronjobs/kpfmastersruncmd_l0.pl b/cronjobs/kpfmastersruncmd_l0.pl index 148b5b8ff..de9e803f7 100755 --- a/cronjobs/kpfmastersruncmd_l0.pl +++ b/cronjobs/kpfmastersruncmd_l0.pl @@ -40,15 +40,7 @@ # Read KPF-related environment variables. -# Legacy KPF port for Jupyter notebook. -# E.g., 6107 -my $kpfpipeport = $ENV{KPFPIPE_PORT}; - -if (! (defined $kpfpipeport)) { - die "*** Env. var. KPFPIPE_PORT not set; quitting...\n"; -} - -# Legacy KPF directory for outputs of testing (and Jupyter notebook). +# Legacy KPF directory for outputs of testing. # E.g., /KPF-Pipeline-TestData my $testdatadir = $ENV{KPFPIPE_TEST_DATA}; @@ -97,11 +89,14 @@ die "*** Env. var. KPFCRONJOB_DOCKER_NAME_L0 not set; quitting...\n"; } +my $trunctime = time() - int(53 * 365.25 * 24 * 3600); # Subtract off number of seconds in 53 years (since 00:00:00 on January 1, 1970, UTC). +$containername .= '_' . $$ . '_' . $trunctime; # Augment container name with unique numbers (process ID and truncated seconds). + # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmastersruncmd_l0.pl'; -my $version = '1.2'; +my $version = '1.3'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -125,34 +120,33 @@ print "containerimage=$containerimage\n"; print "recipe=$recipe\n"; print "config=$config\n"; -print "KPFPIPE_PORT=$kpfpipeport\n"; print "KPFPIPE_TEST_DATA=$testdatadir\n"; print "KPFPIPE_MASTERS_BASE_DIR=$mastersdir\n"; print "KPFCRONJOB_SBX=$sandbox\n"; print "KPFCRONJOB_LOGS=$logdir\n"; print "KPFCRONJOB_CODE=$codedir\n"; -print "KPFCRONJOB_DOCKER_NAME_L0=$containername\n"; +print "Docker container name = $containername\n"; # Change directory to where the Dockerfile is located. chdir "$codedir" or die "Couldn't cd to $codedir : $!\n"; -my $script = "#! /bin/bash\nmake init\nexport PYTHONUNBUFFERED=1\ngit config --global --add safe.directory /code/KPF-Pipeline\nkpf -r $recipe -c $config --date ${procdate}\nexit\n"; +my $script = "#! /bin/bash\n" . + "make init\n" . + "export PYTHONUNBUFFERED=1\n" . + "git config --global --add safe.directory /code/KPF-Pipeline\n" . + "kpf -r $recipe -c $config --date ${procdate}\n" . + "exit\n"; my $makescriptcmd = "echo \"$script\" > $dockercmdscript"; `$makescriptcmd`; `chmod +x $dockercmdscript`; -my $dockerrmcmd = "docker rm $containername"; -print "Executing $dockerrmcmd\n"; -my $opdockerrmcmd = `$dockerrmcmd`; -print "Output from dockerrmcmd: $opdockerrmcmd\n"; - `mkdir -p $sandbox/L0/$procdate`; `mkdir -p $sandbox/2D/$procdate`; `cp -pr /data/kpf/L0/$procdate/*.fits $sandbox/L0/$procdate`; -my $dockerruncmd = "docker run -d --name $containername -p 6207:6207 -e KPFPIPE_PORT=$kpfpipeport " . +my $dockerruncmd = "docker run -d --name $containername " . "-v ${codedir}:/code/KPF-Pipeline -v ${testdatadir}:/testdata -v $sandbox:/data " . "$containerimage bash ./$dockercmdscript"; print "Executing $dockerruncmd\n"; @@ -177,6 +171,11 @@ sleep(300); } +my $dockerrmcmd = "docker rm $containername"; +print "Executing $dockerrmcmd\n"; +my $opdockerrmcmd = `$dockerrmcmd`; +print "Output from dockerrmcmd: $opdockerrmcmd\n"; + # Checkpoint @@ -187,8 +186,12 @@ # Make directory to store products. +# Any existing product directory may contain files owned by user or root, +# so move existing product directory away and make a new product directory. my $destdir = "${mastersdir}/$procdate"; +my $junkdir = $destdir . '_junk'; +`mv $destdir $junkdir`; if (! (-e $destdir)) { if (! make_path($destdir)) { diff --git a/cronjobs/kpfmastersruncmd_l1.pl b/cronjobs/kpfmastersruncmd_l1.pl index 1adfa037e..fa470cedc 100755 --- a/cronjobs/kpfmastersruncmd_l1.pl +++ b/cronjobs/kpfmastersruncmd_l1.pl @@ -40,15 +40,7 @@ # Read KPF-related environment variables. -# Legacy KPF port for Jupyter notebook. -# E.g., 6107 -my $kpfpipeport = $ENV{KPFPIPE_PORT}; - -if (! (defined $kpfpipeport)) { - die "*** Env. var. KPFPIPE_PORT not set; quitting...\n"; -} - -# Legacy KPF directory for outputs of testing (and Jupyter notebook). +# Legacy KPF directory for outputs of testing. # E.g., /KPF-Pipeline-TestData my $testdatadir = $ENV{KPFPIPE_TEST_DATA}; @@ -97,6 +89,10 @@ die "*** Env. var. KPFCRONJOB_DOCKER_NAME_L1 not set; quitting...\n"; } +my $trunctime = time() - int(53 * 365.25 * 24 * 3600); # Subtract off number of seconds in 53 years (since 00:00:00 on January 1, 1970, UTC). +$containername .= '_' . $$ . '_' . $trunctime; # Augment container name with unique numbers (process ID and truncated seconds). + + # Database user for connecting to the database to run this script and insert records into the CalFiles table. # E.g., kpfporuss my $dbuser = $ENV{KPFDBUSER}; @@ -117,7 +113,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmastersruncmd_l1.pl'; -my $version = '1.4'; +my $version = '1.5'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -162,38 +158,39 @@ print "containerimage=$containerimage\n"; print "recipe=$recipe\n"; print "config=$config\n"; -print "KPFPIPE_PORT=$kpfpipeport\n"; print "KPFPIPE_TEST_DATA=$testdatadir\n"; print "KPFPIPE_MASTERS_BASE_DIR=$mastersdir\n"; print "KPFCRONJOB_SBX=$sandbox\n"; print "KPFCRONJOB_LOGS=$logdir\n"; print "KPFCRONJOB_CODE=$codedir\n"; -print "KPFCRONJOB_DOCKER_NAME_L1=$containername\n"; print "dbuser=$dbuser\n"; print "dbname=$dbname\n"; print "dbport=$dbport\n"; print "dbenvfile=$dbenvfile\n"; print "dbenvfileinside=$dbenvfileinside\n"; +print "Docker container name = $containername\n"; # Change directory to where the Dockerfile is located. chdir "$codedir" or die "Couldn't cd to $codedir : $!\n"; -my $script = "#! /bin/bash\nsource $dbenvfileinside\nmake init\nexport PYTHONUNBUFFERED=1\npip install psycopg2-binary\ngit config --global --add safe.directory /code/KPF-Pipeline\nkpf -r $recipe -c $config --date ${procdate}\nexit\n"; +my $script = "#! /bin/bash\n" . + "source $dbenvfileinside\n" . + "make init\n" . + "export PYTHONUNBUFFERED=1\n" . + "pip install psycopg2-binary\n" . + "git config --global --add safe.directory /code/KPF-Pipeline\n" . + "kpf -r $recipe -c $config --date ${procdate}\n" . + "exit\n"; my $makescriptcmd = "echo \"$script\" > $dockercmdscript"; `$makescriptcmd`; `chmod +x $dockercmdscript`; -my $dockerrmcmd = "docker rm $containername"; -print "Executing $dockerrmcmd\n"; -my $opdockerrmcmd = `$dockerrmcmd`; -print "Output from dockerrmcmd: $opdockerrmcmd\n"; - `mkdir -p $sandbox/masters/$procdate`; `cp -pr ${mastersdir}/${procdate}/kpf_${procdate}*.fits ${sandbox}/masters/$procdate`; -my $dockerruncmd = "docker run -d --name $containername -p 6207:6207 -e KPFPIPE_PORT=$kpfpipeport " . +my $dockerruncmd = "docker run -d --name $containername " . "-v ${codedir}:/code/KPF-Pipeline -v ${testdatadir}:/testdata -v $sandbox:/data -v ${mastersdir}:/masters " . "--network=host -e DBPORT=$dbport -e DBNAME=$dbname -e DBUSER=$dbuser -e DBSERVER=127.0.0.1 " . "$containerimage bash ./$dockercmdscript"; @@ -219,6 +216,11 @@ sleep(300); } +my $dockerrmcmd = "docker rm $containername"; +print "Executing $dockerrmcmd\n"; +my $opdockerrmcmd = `$dockerrmcmd`; +print "Output from dockerrmcmd: $opdockerrmcmd\n"; + # Checkpoint diff --git a/database/cronjobs/kpfmasters_register_in_db.pl b/database/cronjobs/kpfmasters_register_in_db.pl index ba35d5f30..b2416c726 100755 --- a/database/cronjobs/kpfmasters_register_in_db.pl +++ b/database/cronjobs/kpfmasters_register_in_db.pl @@ -27,15 +27,7 @@ # Read KPF-related environment variables. -# Legacy KPF port for Jupyter notebook. -# E.g., 6107 -my $kpfpipeport = $ENV{KPFPIPE_PORT}; - -if (! (defined $kpfpipeport)) { - die "*** Env. var. KPFPIPE_PORT not set; quitting...\n"; -} - -# Legacy KPF directory for outputs of testing (and Jupyter notebook). +# Legacy KPF directory for outputs of testing. # E.g., /KPF-Pipeline-TestData my $testdatadir = $ENV{KPFPIPE_TEST_DATA}; @@ -84,6 +76,10 @@ die "*** Env. var. KPFCRONJOB_DOCKER_NAME_DBSCRIPT not set; quitting...\n"; } +my $trunctime = time() - int(53 * 365.25 * 24 * 3600); # Subtract off number of seconds in 53 years (since 00:00:00 on January 1, 1970, UTC). +$containername .= '_' . $$ . '_' . $trunctime; # Augment container name with unique numbers (process ID and truncated seconds). + + # Database user for connecting to the database to run this script and insert records into the CalFiles table. # E.g., kpfporuss my $dbuser = $ENV{KPFDBUSER}; @@ -104,7 +100,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmasters_register_in_db.pl'; -my $version = '1.2'; +my $version = '1.3'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -133,6 +129,16 @@ } } +my $dbenvfilename = "db.env"; +my $dbenvfile = "$codedir/" . $dbenvfilename; +my $dbenvfileinside = "/code/KPF-Pipeline/" . $dbenvfilename; + +`touch $dbenvfile`; +`chmod 600 $dbenvfile`; +open(OUT,">$dbenvfile") or die "Could not open $dbenvfile ($!); quitting...\n"; +print OUT "export DBPASS=\"$dbpass\"\n"; +close(OUT) or die "Could not close $dbenvfile ($!); quitting...\n"; + # Print environment. @@ -147,34 +153,37 @@ print "dbuser=$dbuser\n"; print "dbname=$dbname\n"; print "dbport=$dbport\n"; -print "KPFPIPE_PORT=$kpfpipeport\n"; print "KPFPIPE_TEST_DATA=$testdatadir\n"; print "KPFPIPE_MASTERS_BASE_DIR=$mastersdir\n"; print "KPFCRONJOB_SBX=$sandbox\n"; print "KPFCRONJOB_LOGS=$logdir\n"; print "KPFCRONJOB_CODE=$codedir\n"; -print "KPFCRONJOB_DOCKER_NAME_DBSCRIPT=$containername\n"; +print "dbenvfile=$dbenvfile\n"; +print "dbenvfileinside=$dbenvfileinside\n"; +print "Docker container name = $containername\n"; # Change directory to where the Dockerfile is located. chdir "$codedir" or die "Couldn't cd to $codedir : $!\n"; -my $script = "#! /bin/bash\nmake init\nexport PYTHONUNBUFFERED=1\npip install psycopg2-binary\npython $pythonscript $procdate >& ${pylogfile}\nexit\n"; +my $script = "#! /bin/bash\n" . + "source $dbenvfileinside\n" . + "make init\n" . + "export PYTHONUNBUFFERED=1\n" . + "pip install psycopg2-binary\n" . + "git config --global --add safe.directory /code/KPF-Pipeline\n" . + "python $pythonscript $procdate >& ${pylogfile}\n" . + "exit\n"; my $makescriptcmd = "echo \"$script\" > $dockercmdscript"; `$makescriptcmd`; `chmod +x $dockercmdscript`; -my $dockerrmcmd = "docker rm $containername"; -print "Executing $dockerrmcmd\n"; -my $opdockerrmcmd = `$dockerrmcmd`; -print "Output from dockerrmcmd: $opdockerrmcmd\n"; - -my $dockerruncmd = "docker run -d --name $containername -p 6207:6207 -e KPFPIPE_PORT=$kpfpipeport " . +my $dockerruncmd = "docker run -d --name $containername " . "-v ${codedir}:/code/KPF-Pipeline -v ${mastersdir}:/masters " . - "--network=host -e DBPORT=$dbport -e DBNAME=$dbname -e DBUSER=$dbuser -e DBPASS=\"$dbpass\" -e DBSERVER=127.0.0.1 " . + "--network=host -e DBPORT=$dbport -e DBNAME=$dbname -e DBUSER=$dbuser -e DBSERVER=127.0.0.1 " . "$containerimage bash ./$dockercmdscript"; -#print "Executing $dockerruncmd\n"; # COMMENT OUT THIS LINE: DO NOT PRINT DATABASE PASSWORD TO LOGFILE! +print "Executing $dockerruncmd\n"; my $opdockerruncmd = `$dockerruncmd`; print "Output from dockerruncmd: $opdockerruncmd\n"; @@ -196,6 +205,11 @@ sleep(30); } +my $dockerrmcmd = "docker rm $containername"; +print "Executing $dockerrmcmd\n"; +my $opdockerrmcmd = `$dockerrmcmd`; +print "Output from dockerrmcmd: $opdockerrmcmd\n"; + # Checkpoint From 2cdcd7be558945617b9aa0568ff93b8805f66711 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 7 Jun 2023 18:39:36 -0700 Subject: [PATCH 005/249] daily masters --- modules/quicklook/configs/default.cfg | 1 + modules/quicklook/src/alg_nightly.py | 8 ++++++++ 2 files changed, 9 insertions(+) diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index 1c8bfe3fc..c36e390c1 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -48,3 +48,4 @@ cahk_wav=/data/masters/kpfMaster_HKwave20220909_sci.csv [Nightly] exposures_dir=/data/2D/ +masters_dir=/data/masters/ diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index d8b0fdbd2..37f6b150d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -32,9 +32,17 @@ def __init__(self,config=None,logger=None): def nightly_procedures(self,night): exposures_dir = self.config['Nightly']['exposures_dir'] + masters_dir = self.config['Nightly']['masters_dir'] + + + #plot the master files for a particular night + master_list = glob.glob(masters_dir+night+'/*master*.fits') + print(master_list) #get all exposures taken on a particular night + ''' file_list = glob.glob(exposures_dir+night+'/*.fits') for i in range(len(file_list)): file_list[i] = file_list[i][18:-8] print(file_list) + ''' From e8060f956bcfb6659a3038eca6587c1792194a8c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 7 Jun 2023 21:56:57 -0700 Subject: [PATCH 006/249] nightly summary --- modules/quicklook/configs/default.cfg | 1 + modules/quicklook/src/alg_nightly.py | 196 +++++++++++++++++++++++++- 2 files changed, 194 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index c36e390c1..f0d8066da 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -49,3 +49,4 @@ cahk_wav=/data/masters/kpfMaster_HKwave20220909_sci.csv [Nightly] exposures_dir=/data/2D/ masters_dir=/data/masters/ +output_dir=/data/QLP diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 37f6b150d..9472fc82d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -33,16 +33,206 @@ def __init__(self,config=None,logger=None): def nightly_procedures(self,night): exposures_dir = self.config['Nightly']['exposures_dir'] masters_dir = self.config['Nightly']['masters_dir'] + output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary/' + if not os.path.exists(self.config['Nightly']['output_dir']+'/'+night): + os.makedirs(self.config['Nightly']['output_dir']+'/'+night) + if not os.path.exists(output_dir): + os.makedirs(output_dir) #plot the master files for a particular night master_list = glob.glob(masters_dir+night+'/*master*.fits') print(master_list) + for i in range(len(master_list)): + if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue + print(master_list[i]) + + + L0_data = masters_dir+night+master_list[i] + hdulist = fits.open(L0_data) + print(hdulist.info()) + + #get ccd names + ccd_color=[] + ccd_list = self.config.items( "CCD_LIST") + for key, path in ccd_list: + ccd_color.append(path) + + + if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: + print('skipping empty file') + return + + + #2d plots + for i_color in range(len(ccd_color)): + counts = np.array(hdulist[ccd_color[i_color]].data,'d') + flatten_counts = np.ravel(counts) + if len(flatten_counts)<1: continue + master_flatten_counts='None' + if master_file != 'None': + hdulist1 = fits.open(master_file) + master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') + master_flatten_counts = np.ravel(master_counts) + if version == 'Dark':#scale up dark exposures + master_flatten_counts*=hdr['EXPTIME'] + + #2D image + plt.figure(figsize=(5,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') + plt.xlabel('x (pixel number)') + plt.ylabel('y (pixel number)') + plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.colorbar(label = 'Counts (e-)') + + + #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') + plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + #plt.close() + + + if master_list[i].find('bias') == True or master_list[i].find('dark'): + exptime = hdr['EXPTIME'] + print('exptime',exptime) + + # Read telemetry + from astropy.table import Table + df_telemetry = Table.read(L0_data, format='fits', hdu=11).to_pandas() # need to refer to HDU by name + num_columns = ['average', 'stddev', 'min', 'max'] + for column in df_telemetry: + df_telemetry[column] = df_telemetry[column].str.decode('utf-8') + df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0 + if column in num_columns: + df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast="float") + else: + df_telemetry[column] = df_telemetry[column].astype(str) + df_telemetry.set_index("keyword", inplace=True) + + with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also + print(df_telemetry) + if ccd_color[i_color] == 'GREEN_CCD': + coll_pressure_torr = df_telemetry.at['kpfgreen.COL_PRESS', 'average'] + ech_pressure_torr = df_telemetry.at['kpfgreen.ECH_PRESS', 'average'] + coll_current_a = df_telemetry.at['kpfgreen.COL_CURR', 'average'] + ech_current_a = df_telemetry.at['kpfgreen.ECH_CURR', 'average'] + if ccd_color[i_color] == 'RED_CCD': + coll_pressure_torr = df_telemetry.at['kpfred.COL_PRESS', 'average'] + ech_pressure_torr = df_telemetry.at['kpfred.ECH_PRESS', 'average'] + coll_current_a = df_telemetry.at['kpfred.COL_CURR', 'average'] + ech_current_a = df_telemetry.at['kpfred.ECH_CURR', 'average'] + + frame = counts + if exptime > 0: + exptype = 'dark' + timelabel = ' e$^-$ hr$^{-1}$' + frame *= (3600./exptime) # convert to e- per hour + # Bias frame + else: + exptype = 'bias' + timelabel = ' e$^-$' + reg = {'ref1': {'name': 'Reference Region 1', 'x1': 1690, 'x2': 1990, 'y1': 1690, 'y2': 1990, 'short':'ref1', 'med_elec':0, 'label':''}, + 'ref2': {'name': 'Reference Region 2', 'x1': 1690, 'x2': 1990, 'y1': 2090, 'y2': 2390, 'short':'ref2', 'med_elec':0, 'label':''}, + 'ref3': {'name': 'Reference Region 3', 'x1': 2090, 'x2': 2390, 'y1': 1690, 'y2': 1990, 'short':'ref3', 'med_elec':0, 'label':''}, + 'ref4': {'name': 'Reference Region 4', 'x1': 2090, 'x2': 2390, 'y1': 2090, 'y2': 2390, 'short':'ref4', 'med_elec':0, 'label':''}, + 'ref5': {'name': 'Reference Region 5', 'x1': 80, 'x2': 380, 'y1': 700, 'y2': 1000, 'short':'ref5', 'med_elec':0, 'label':''}, + 'ref6': {'name': 'Reference Region 6', 'x1': 80, 'x2': 380, 'y1': 3080, 'y2': 3380, 'short':'ref6', 'med_elec':0, 'label':''}, + 'amp1': {'name': 'Amplifier Region 1', 'x1': 300, 'x2': 500, 'y1': 5, 'y2': 20, 'short':'amp1', 'med_elec':0, 'label':''}, + 'amp2': {'name': 'Amplifier Region 2', 'x1': 3700, 'x2': 3900, 'y1': 5, 'y2': 20, 'short':'amp2', 'med_elec':0, 'label':''}, + 'coll': {'name': 'Ion Pump (Collimator side)', 'x1': 3700, 'x2': 4000, 'y1': 700, 'y2': 1000, 'short':'coll', 'med_elec':0, 'label':''}, + 'ech': {'name': 'Ion Pump (Echelle side)', 'x1': 3700, 'x2': 4000, 'y1': 3080, 'y2': 3380, 'short':'ech', 'med_elec':0, 'label':''} + } + for r in reg.keys(): + current_region = frame[reg[r]['y1']:reg[r]['y2'],reg[r]['x1']:reg[r]['x2']] + reg[r]['med_elec'] = np.median(current_region) + + #print(reg[r]['name'] + ': ' + str(np.round(reg[r]['med_elec'],1)) + ' e- per hour') + #print('Ion Pump pressure (Torr) - Collimator side: ' + f'{coll_pressure_torr:.1e}') + #print('Ion Pump pressure (Torr) - Echelle side: ' + f'{ech_pressure_torr:.1e}') + #print('Ion Pump current (A) - Collimator side: ' + f'{coll_current_a:.1e}') + #print('Ion Pump current (A) - Echelle side: ' + f'{ech_current_a:.1e}') + + from matplotlib.patches import Rectangle + plt.figure(figsize=(5, 4)) + plt.imshow(frame, + cmap='viridis', + origin='lower', + vmin=np.percentile(frame[300:3780,0:4080],5), + vmax=np.percentile(frame[300:3780,0:4080],95) + ) + for r in reg.keys(): + plt.gca().add_patch(Rectangle((reg[r]['x1'],reg[r]['y1']),reg[r]['x2']-reg[r]['x1'],reg[r]['y2']-reg[r]['y1'],linewidth=1,edgecolor='r',facecolor='none')) + plt.text(((reg[r]['short'] == 'ref3') or + (reg[r]['short'] == 'ref4') or + (reg[r]['short'] == 'ref5') or + (reg[r]['short'] == 'ref6') or + (reg[r]['short'] == 'amp1'))*(reg[r]['x1'])+ + ((reg[r]['short'] == 'ref1') or + (reg[r]['short'] == 'ref2') or + (reg[r]['short'] == 'ech') or + (reg[r]['short'] == 'coll') or + (reg[r]['short'] == 'amp2'))*(reg[r]['x2']), + (((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*(reg[r]['y1']-30)+ + ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*(reg[r]['y2']+30)), + str(np.round(reg[r]['med_elec'],1)) + timelabel, + weight='bold', + color='r', + ha=(((reg[r]['short'] == 'ref3') or + (reg[r]['short'] == 'ref4') or + (reg[r]['short'] == 'ref5') or + (reg[r]['short'] == 'ref6') or + (reg[r]['short'] == 'amp1'))*('left')+ + ((reg[r]['short'] == 'ref1') or + (reg[r]['short'] == 'ref2') or + (reg[r]['short'] == 'ech') or + (reg[r]['short'] == 'coll') or + (reg[r]['short'] == 'amp2'))*('right')), + va=(((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*('top')+ + ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*('bottom')) + ) + now = datetime.now() + coll_text = 'Ion Pump (Coll): \n' + (f'{coll_pressure_torr:.1e}' + ' Torr, ' + f'{coll_current_a*1e6:.1f}' + ' $\mu$A')*(coll_pressure_torr > 1e-9) + ('Off')*(coll_pressure_torr < 1e-9) + ech_text = 'Ion Pump (Ech): \n' + (f'{ech_pressure_torr:.1e}' + ' Torr, ' + f'{ech_current_a*1e6:.1f}' + ' $\mu$A')*(ech_pressure_torr > 1e-9) + ('Off')*(ech_pressure_torr < 1e-9) + #plt.text(4080, -250, now.strftime("%m/%d/%Y, %H:%M:%S"), ha='right', color='gray') + plt.text(4220, 500, coll_text, rotation=90, ha='center',fontsize = 6) + plt.text(4220, 3000, ech_text, rotation=90, ha='center',fontsize = 6) + plt.text(3950, 1500, 'Bench Side\n (blue side of orders)', rotation=90, ha='center', color='white',fontsize = 6) + plt.text( 150, 1500, 'Top Side\n (red side of orders)', rotation=90, ha='center', color='white',fontsize = 6) + plt.text(2040, 70, 'Collimator Side', rotation= 0, ha='center', color='white',fontsize = 6) + plt.text(2040, 3970, 'Echelle Side', rotation= 0, ha='center', color='white',fontsize = 6) + cbar = plt.colorbar() + cbar.set_label(timelabel)#, fontsize=18 + cbar.ax.tick_params()#labelsize=18 + cbar.ax.tick_params()#size=18 + plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.xlabel('Column (pixel number)') + plt.ylabel('Row (pixel number)')#fontsize=18 + plt.xticks()#KP.20230317.07770.97 + plt.yticks() + plt.grid(False) + plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + plt.close() + + + #get all exposures taken on a particular night - ''' + file_list = glob.glob(exposures_dir+night+'/*.fits') + date_obs = [] + temp = [] for i in range(len(file_list)): file_list[i] = file_list[i][18:-8] - print(file_list) - ''' + + hdulist = fits.open(file_list[i]) + hdr = hdulist[0].header + date_obs.append(hdr['DATE-OBS']) + temp.append(hdr['TEMP']) + + date_obs = np.array(date_obs,'str') + date_obs = Time(date_obs, format='isot', scale='utc') + plt.scatter(date_obs.jd,temp, marker = '.') + plt.xlabel('Time') + plt.ylabel('Temperature') + plt.savefig(output_dir+night+'_temperature_variation.png') + plt.close() From afa88b9c4e60f363bd5e23277b83d9246650256f Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 8 Jun 2023 10:01:44 -0700 Subject: [PATCH 007/249] Added documentation for masters pipeline and database. --- docs/source/api/pipeline/masters.rst | 173 ++++++++++++++++++ docs/source/api/pipeline/pipeline.rst | 2 +- docs/source/intro/database_setup.rst | 247 ++++++++++++++++++++++++++ docs/source/intro/start.rst | 4 + 4 files changed, 425 insertions(+), 1 deletion(-) create mode 100644 docs/source/api/pipeline/masters.rst create mode 100644 docs/source/intro/database_setup.rst diff --git a/docs/source/api/pipeline/masters.rst b/docs/source/api/pipeline/masters.rst new file mode 100644 index 000000000..1b5569355 --- /dev/null +++ b/docs/source/api/pipeline/masters.rst @@ -0,0 +1,173 @@ + +Pipelines for Master Calibration Files +================================================================ + + There are several pipelines that generate master calibration files, including for bias, dark, flat, and arclamp exposures. + Generally, images contained in L0 raw FITS files with the same value of some FITS keyword, like OBJECT and/or IMTYPE, are stacked, and + then there is possibly some form of normalization, in order to create a master calibration file. + Within a FITS file, the image data within FITS extensions GREEN_CCD, RED_CCD, and CA_HK are separately stacked. + The uncertainty images associated with the stacking are given in the product with separate extensions + for the different detectors, in which the extension names have suffix '_UNC'. + Images for the number of stack inputs at each pixel location have suffix '_CNT'. + The number of inputs and the minimum and maximum modified Julian date of the inputs are tracked in the + PRIMARY FITS header via the NFRAMES, MINMJD, and MAXMJD keywords, respectively. + + +Master Bias +------------ + + For a master bias, only exposures with FITS-keyword EXPTIME = 0.0 are considered as inputs. + The images are stacked, outliers beyond sigma = +/-2.1 are rejected, and then an average is computed. + The IMTYPE keyword is set to 'Bias' in the PRIMARY FITS header of this product. + If 1% of the pixels have less than 10 samples in the stack, then a flag in the keyword INFOBITS is set in the PRIMARY FITS header; + for GREEN_CCD, RED_CCD, and CA_HK, the flags are bits 0, 1, and 2, respectively. + + +Master Dark +------------- + + For a master dark, exposures with FITS-keywords IMTYPE = 'Dark' and EXPTIME >= 300.0 are considered as inputs. + The master bias is subtracted from the input image data, and the results are normalized by the exposure time. + The images are stacked, outliers beyond sigma = +/-2.2 are rejected, and then an average is computed. + The IMTYPE keyword is set to 'Dark' in the PRIMARY FITS header of this product. + If 1% of the pixels have less than 10 samples in the stack, then a flag in the keyword INFOBITS is set in the PRIMARY FITS header; + for GREEN_CCD, RED_CCD, and CA_HK, the flags are bits 0, 1, and 2, respectively. + + +Master Flat +------------ + + For a master flat, exposures with FITS-keywords IMTYPE = 'Flatlamp' and EXPTIME <= 60.0 are considered as inputs. + The master bias is subtracted from the input image data, and the results are normalized by the exposure time. + The master dark is then subtracted. + The images are stacked, outliers beyond sigma = +/-2.3 are rejected, and then an average is computed. + The resulting stacked image is normalized by a smooth counterpart (after applying a 2-D Gaussian blur with sigma = 2.0). + The flat normalization is done separately for each orderlet, where orderlet traces are defined by the mask file:: + + /KPF-Pipeline-TestData/order_mask_3_2_20230502.fits + + The mode statistic within each orderet is the normalization factor. + Pixel values in the master flat outside of the traces are set to unity. + Also, pixel values in the master flat with values less than 50 DN in the unnormalized image before smoothing are set to unity. + The IMTYPE keyword is set to 'Flat' in the PRIMARY FITS header of this product. + If 1% of the pixels have less than 10 samples in the stack, then a flag in the keyword INFOBITS is set in the PRIMARY FITS header; + for GREEN_CCD, RED_CCD, and CA_HK, the flags are bits 0, 1, and 2, respectively. + + +Master Arclamp +-------------- + + For a master arclamp, exposures with FITS-keywords IMTYPE = 'Arclamp' and the same OBJECT setting are considered as inputs. + For a given run of this pipeline, the desired OBJECT for the image stacking, such as 'autocal-une-sky', is given. + Only GREEN_CCD and RED_CCD FITS extensions are handled at this time. + The master bias is subtracted from the input image data, and the results are normalized by the exposure time. + The master dark is then subtracted. + The master flat is applied (via image division). + The images are stacked, outliers beyond sigma = +/-2.4 are rejected, and then an average is computed. + The IMTYPE keyword is set to 'Arclamp' in the PRIMARY FITS header of this product. + If 1% of the pixels have less than 5 samples in the stack, then a flag in the keyword INFOBITS is set in the PRIMARY FITS header; + for GREEN_CCD, RED_CCD, and CA_HK, the flags are bits 0, 1, and 2, respectively. + + +Daily Operations +---------------------- + + The pipelines for master calibration files are executed daily in a cronjob at 3 p.m. PT:: + + /KPF-Pipeline/cronjobs/runDailyPipelines.sh + + This bash script executes four different Perl scripts in succession:: + + /KPF-Pipeline/cronjobs/kpfmastersruncmd_l0.pl + /KPF-Pipeline/cronjobs/kpfmastersruncmd_l1.pl + /KPF-Pipeline/cronjobs/kpfmasters_wls_auto.pl + /KPF-Pipeline/database/cronjobs/kpfmasters_register_in_db.pl + + Each of the Perl scripts runs a docker container in the detached mode, which, in turn, executes the relevant recipe. + Here are the four steps: + + 1. Create 2D-image FITS files and then execute the bias, dark, flat, and arclamp master pipelines, as described above, + for all data taken nightly. A master bias, dark, and flat are generated, as well as a number of master arclamps for different OBJECTS. + 2. Generate the 1D spectral master products. + 3. Generate the master wavelength solution (WLS). + 4. Load records into the CalFIles table of the pipeline operations database for all master files. + + Note that the database port and password are obtained from the .pgpass file in the user's home directory. + A number of environment variables must be set for these scripts to provide other required parameters: + + KPFPIPE_TEST_DATA + Legacy KPF directory for outputs of testing. E.g., /KPF-Pipeline-TestData + + KPFPIPE_MASTERS_BASE_DIR + Base directory of master files for permanent storage. E.g., /data/kpf/masters + + KPFCRONJOB_SBX + Sandbox directory for intermediate files. E.g., /data/user/rlaher/sbx + + KPFCRONJOB_CODE + Code directory of KPF-Pipeline git repo where the docker run command is executed. E.g., /data/user/rlaher/git/KPF-Pipeline + + KPFCRONJOB_LOGS + Logs directory where log file (STDOUT) from this script goes (see runDailyPipelines.sh). + Normally this is the code directory of KPF-Pipeline git repo. + E.g., /data/user/rlaher/git/KPF-Pipeline + + KPFCRONJOB_DOCKER_NAME_L0 + Prefix of docker container name for this Perl script, a unique name so it can be monitored by docker ps command. + E.g., russkpfmastersdrpl0 + + KPFCRONJOB_DOCKER_NAME_L1 + Prefix of docker container name for this Perl script, a unique name so it can be monitored by docker ps command. + E.g., russkpfmastersdrpl1 + + KPFCRONJOB_DOCKER_NAME_WLS + Prefix of docker container name for this Perl script, a unique name so it can be monitored by docker ps command. + E.g., russkpfmasterswlsauto + + KPFCRONJOB_DOCKER_NAME_DBSCRIPT + Prefix of docker container name for this Perl script, a unique name so it can be monitored by docker ps command. + E.g., russkpfmastersregisterindb + + KPFDBUSER + Name of database user with privileges for pipeline operations (i.e., with GRANT kpfporole). + E.g., apollo + + KPFDBNAME + Name of pipeline operations database. + E.g., kpfopsdb + + + Here are the master product files generated for June 6, 2023:: + + -rw-r--r--. 1 rlaher citah 5440 Jun 6 16:36 kpfmastersruncmd_l0_20230606.out + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_201091.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_201091_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_201091_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-etalon-all-morn.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-etalon-all-morn_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-etalon-all-morn_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-thar-all-morn.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-thar-all-morn_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-thar-all-morn_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-une-all-morn.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-une-all-morn_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_autocal-une-all-morn_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_cal-LFC-morn.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_cal-LFC-morn_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_cal-LFC-morn_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_arclamp_slewcal.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_arclamp_slewcal_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_arclamp_slewcal_L2.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_bias.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_bias_L1.fits + -rw-r--r--. 1 rlaher citah 532739520 Jun 6 16:56 kpf_20230606_master_dark.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_dark_L1.fits + -rw-r--r--. 1 rlaher citah 799093440 Jun 6 16:56 kpf_20230606_master_flat.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 16:56 kpf_20230606_master_flat_L1.fits + -rw-r--r--. 1 rlaher citah 1794240 Jun 6 16:56 kpf_20230606_master_flat_L2.fits + -rw-r--r--. 1 rlaher citah 5496 Jun 6 16:56 kpfmastersruncmd_l1_20230606.out + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 17:32 kpf_20230606_master_WLS_autocal-thar-all-morn_L1.fits + -rw-r--r--. 1 rlaher citah 32993280 Jun 6 17:32 kpf_20230606_master_WLS_cal-LFC-morn_L1.fits + -rw-r--r--. 1 rlaher citah 4250 Jun 6 17:32 kpfmasters_wls_auto_20230606.out + -rw-r--r--. 1 root root 82700 Jun 6 17:32 registerCalFilesForDate_20230606.out + -rw-r--r--. 1 rlaher citah 1576 Jun 6 17:32 kpfmasters_register_in_db_20230606.out diff --git a/docs/source/api/pipeline/pipeline.rst b/docs/source/api/pipeline/pipeline.rst index ded39dfd4..d25133c1e 100644 --- a/docs/source/api/pipeline/pipeline.rst +++ b/docs/source/api/pipeline/pipeline.rst @@ -8,6 +8,6 @@ dependencies :maxdepth: 2 kpfpipe.rst + masters.rst fits_primitive.rst parse_ast.rst - \ No newline at end of file diff --git a/docs/source/intro/database_setup.rst b/docs/source/intro/database_setup.rst new file mode 100644 index 000000000..21553b797 --- /dev/null +++ b/docs/source/intro/database_setup.rst @@ -0,0 +1,247 @@ + +Installing/Configuring the Pipeline Operations Database +================================================================================= + +The pipeline operations database runs under a regular unix account, +and not a root account (in order not to involve special access and a +system administrator). +A bash script has been developed that does the initial installation and +configuration of the the pipeline operations database:: + + KPF-Pipeline/database/scripts/buildDatabase.sh + +This script also starts up an instance of the database server, so that +the database is ready for immediate use. +A database superuser will be set up with the +same name as the unix account under which the pipeline +operations database is installed. Essentially, the owner of the unix +account under which this bash script is executed will be a superuser +of this database. In the example below, the unix account is "rlaher". + + +There are parameters at the beginning of the bash script that need to +be reviewed and modified according to the particular +requirements and environment of the project. +The following are the default values coded in the script:: + + KPF_PIPELINE_GIT_REPO=/data/user/rlaher/git/KPF-Pipeline + PGPORT=6125 + DB_BUILD_DATABASE_PATH=/data/user/rlaher/test/mydb + DB_BUILD_DBNAME=kpfopsdb + DB_BUILD_PGPASSWORD=testpassword + DB_USER_APOLLO=apollo + DB_USER_APOLLO_PW=Arrow + DB_TAR_GZ_FILE_URL=https://ftp.postgresql.org/pub/source/v15.2/postgresql-15.2.tar.gz + DB_BUILD_BASE=/data/user/rlaher/test/pg15.2 + DB_BUILD_LIBS=/data/user/rlaher/db/termcap/build/lib:/data/user/rlaher/db/readline/build/lib:/usr/lib64 + DB_BUILD_INCL=/data/user/rlaher/db/termcap/build/include:/data/user/rlaher/db/readline/build/include:/usr/include + +Notes on the bash-script parameters: + + KPF_PIPELINE_GIT_REPO + Pathname to KPF-Pipeline checkout of git repository. + + PGPORT + Port number of connections from database client applications. + Do not specify 5432 (which is the default port number, normally + used by a PostgresSQL server running under root). + + DB_BUILD_DATABASE_PATH + Pathname to a directory where the low-level database files + will be stored. This includes database files for logs, + configuration, data tables, indexes, and table spaces. + + + DB_BUILD_DBNAME + Name of the pipeline operations database. + + DB_BUILD_PGPASSWORD + Superuser password for the pipeline operations database. + + DB_USER_APOLLO + Database user name for non-superuser account (default is + "apollo"), for use in pipeline operations. This account has + psql-client login privileges, is allowed an unlimited number + of database connections, and inherits from ROLE kpfporole, + which generally has table grants for INSERT, UPDATE, SELECT, + REFERENCES (no DELETE or TRUNCATE). + + DB_USER_APOLLO_PW + Password for the apollo database user. + + DB_TAR_GZ_FILE_URL + URL from where the PostgreSQL-database source code is + downloaded. + + DB_BUILD_BASE + Pathname to a directory where the database software will be + built and the bin, lib, and include directories will be + installed. + + DB_BUILD_LIBS + Locations of the lib directories for the termcap and readline + libraries, as well as other libraries required for this + database-software build. + + DB_BUILD_INCL + Locations of include include directories for the termcap and + readline libraries, as well as other libraries required for this + database-software build. + +The database superuser is encouraged to review the steps in the bash script to gain +an understanding of precisely how the database is set up. + +After the database-server instance has been started by the bash +script, several SQL commands are loaded into the pipeline operations +database, in order to set up the required tablespaces, roles, and users:: + + CREATE TABLESPACE pipeline_data_01 LOCATION '/data/user/rlaher/test/mydb/tablespacedata1'; + CREATE TABLESPACE pipeline_indx_01 LOCATION '/data/user/rlaher/test/mydb/tablespaceindx1'; + CREATE ROLE kpfadminrole LOGIN SUPERUSER CREATEDB CREATEROLE; + CREATE ROLE kpfporole; + CREATE ROLE kpfreadrole; + GRANT kpfadminrole to rlaher; + GRANT kpfporole to rlaher; + GRANT kpfreadrole to rlaher; + CREATE USER apollo CONNECTION LIMIT -1 ENCRYPTED PASSWORD 'Arrow'; + GRANT kpfporole to apollo; + +Then, the following SQL files are loaded into the +pipeline operations database (in the order listed), which contain the definitions +of the database tables and stored procedures required for pipeline operations:: + + KPF-Pipeline/database/schema/kpfOpsTables.sql + KPF-Pipeline/database/schema/kpfOpsTableGrants.sql + KPF-Pipeline/database/schema/kpfOpsProcs.sql + KPF-Pipeline/database/schema/kpfOpsProcGrants.sql + +After the bash script finishes, assuming it ran normally, the user will see the following useful +output at the very end:: + + Congratulations! Database server is running and database kpfopsdb created. + + Stop the server with this command: + /data/user/rlaher/test/pg15.2/bin/pg_ctl -D /data/user/rlaher/test/mydb/dbdata -l /data/user/rlaher/test/mydb/dblogs/log stop + + Your /data/user/rlaher/.pgpass file has been augmented with database connection parameters. + + Put these three lines in your environment to run the psql client: + export PATH=/data/user/rlaher/test/pg15.2/bin:$PATH + export LD_LIBRARY_PATH=/data/user/rlaher/test/pg15.2/lib:$LD_LIBRARY_PATH + export PGPORT=6125 + + Here is how to connect to your new database: + /data/user/rlaher/test/pg15.2/bin/psql -h localhost -d kpfopsdb -p 6125 -U rlaher + +There are currently three database tables in the pipeline operations database: CalFiles, RawFiles, and +Infobits. CalFiles stores a record for each master calibration file (many records +are inserted daily). RawFiles stores a record for each exposure. +Infobits stores definitions of the bit-wise information flags +corresponding to the infobits column in the RawFiles table (the +CalFiles infobits have not been formalized into a database table, +and are described on :doc:`../api/pipeline/masters`). +These tables are queried by the pipeline for relevant nearest-in-time +master calibration files, and generally for quality-control purposes. +Here are the table-column definitions:: + + + ----------------------------- + -- TABLE: CalFiles + ----------------------------- + + CREATE TABLE calfiles ( + cid integer NOT NULL, -- Primary key + level smallint NOT NULL, -- Product level (L0, L1, or L2) + caltype character varying(32) NOT NULL, -- FITS-header keyword: IMTYPE in extension 4-6 (lowercase) + "object" character varying(32) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT (lowercase) + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + nframes smallint, -- FITS-header keyword: NFRAMES (GREEN CCD) + minmjd double precision, -- FITS-header keyword: MINMJD (GREEN CCD) + maxmjd double precision, -- FITS-header keyword: MAXMJD (GREEN CCD) + infobits integer, -- FITS-header keyword: INFOBITS + startdate date NOT NULL, -- Start date for application of master (for earliest-in-time selection) + enddate date NOT NULL, -- End date for application of master (may not be used) + filename character varying(255) NOT NULL, -- Path and filename of master calibration file. + checksum character varying(32) NOT NULL, -- MD5 checksum + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good + createdby character varying(30) NOT NULL, -- Script that inserted the record + created timestamp without time zone NOT NULL, -- FITS-header keyword: CREATED (GREEN CCD) in Zulu time + "comment" character varying(255) -- Descriptive comment + ); + + + ----------------------------- + -- TABLE: RawFiles + ----------------------------- + + CREATE TABLE rawfiles ( + rid integer NOT NULL, -- Primary key + dateobs date NOT NULL, -- FITS-header keyword: DATE-OBS + ut time without time zone NOT NULL, -- FITS-header keyword: UT + datebeg timestamp without time zone NOT NULL, -- FITS-header keyword: DATE-BEG + mjdobs double precision NOT NULL, -- FITS-header keyword: MJD-OBS + exptime real NOT NULL, -- FITS-header keyword: EXPTIME + progname character varying(64) NOT NULL, -- FITS-header keyword: PROGNAME + imtype character varying(64) NOT NULL, -- FITS-header keyword: IMTYPE + sciobj character varying(64) NOT NULL, -- FITS-header keyword: SCI-OBJ + calobj character varying(64) NOT NULL, -- FITS-header keyword: CAL-OBJ + skyobj character varying(64) NOT NULL, -- FITS-header keyword: SKY-OBJ + "object" character varying(64) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + infobits bigint DEFAULT 0 NOT NULL, -- Bit-wise information flags + filename character varying(255) NOT NULL, -- Full path and filename + checksum character varying(32) NOT NULL, -- MD5 checksum of entire file + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good (verify automatically with + -- DATASUM and CHECKSUM keywords, or set this manually later, if necessary) + created timestamp without time zone -- Timestamp of database record INSERT or last UPDATE + DEFAULT now() NOT NULL, + targname character varying(64), -- FITS-header keyword: TARGNAME + gaiaid character varying(64), -- FITS-header keyword: GAIAID + twomassid character varying(64), -- FITS-header keyword: 2MASSID + ra double precision, -- FITS-header keyword: RA converted to decimal + dec double precision, -- FITS-header keyword: DEC converted to decimal + medgreen1 real, -- Median of GREEN_AMP1 image + p16green1 real, -- 16th percentile of GREEN_AMP1 image + p84green1 real, -- 84th percentile of GREEN_AMP1 image + medgreen2 real, -- Median of GREEN_AMP2 image + p16green2 real, -- 16th percentile of GREEN_AMP2 image + p84green2 real, -- 84th percentile of GREEN_AMP2 image + medgreen3 real, -- Median of GREEN_AMP3 image + p16green3 real, -- 16th percentile of GREEN_AMP3 image + p84green3 real, -- 84th percentile of GREEN_AMP3 image + medgreen4 real, -- Median of GREEN_AMP4 image + p16green4 real, -- 16th percentile of GREEN_AMP4 image + p84green4 real, -- 84th percentile of GREEN_AMP4 image + medred1 real, -- Median of RED_AMP1 image + p16red1 real, -- 16th percentile of RED_AMP1 image + p84red1 real, -- 84th percentile of RED_AMP1 image + medred2 real, -- Median of RED_AMP2 image + p16red2 real, -- 16th percentile of RED_AMP2 image + p84red2 real, -- 84th percentile of RED_AMP2 image + medcahk real, -- Median of CA_HK image + p16cahk real, -- 16th percentile of CA_HK image + p84cahk real, -- 84th percentile of CA_HK image + comment character varying(255), -- Reason for status=0, etc. + CONSTRAINT rawfiles_ra_check CHECK (((ra >= 0.0) AND (ra < 360.0))), + CONSTRAINT rawfiles_dec_check CHECK (((dec >= -90.0) AND (dec <= 90.0))) + ); + + + ----------------------------- + -- TABLE: Infobits + -- + -- Definitions of infobits for RawFiles table only + -- (CalFiles infobits have different definitions). + ----------------------------- + + CREATE TABLE infobits ( + bid integer NOT NULL, -- Primary key + bit smallint NOT NULL, -- Bit number (allowed range is 0-63, inclusive) + param1 real, -- Parameter 1 + param2 real, -- Parameter 2 + param3 real, -- Parameter 3 + created timestamp without time zone -- Creation timestamp of database record + DEFAULT now() NOT NULL, -- Definition of bit and parameter(s) + definition character varying(256) NOT NULL, + CONSTRAINT infobits_bit_check CHECK (((bit >= 0) AND (bit <= 63))) + ); diff --git a/docs/source/intro/start.rst b/docs/source/intro/start.rst index e05274acb..09a7881e0 100644 --- a/docs/source/intro/start.rst +++ b/docs/source/intro/start.rst @@ -45,6 +45,9 @@ To start a notebook server run the following and follow the on-screen instructio make notebook +Finally, some of the recipes interact with a PostgresSQL database, +which is the so-called *pipeline operations database*. Refer to :doc:`database_setup` +for instructions on how to initially set up the pipeline operations database. .. toctree:: :maxdepth: 1 @@ -54,3 +57,4 @@ To start a notebook server run the following and follow the on-screen instructio example_trivial.rst logging.rst config.rst + database_setup.rst From ec3980a3b856ebcd49ecf3be4a232b6229de70ad Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 8 Jun 2023 14:37:25 -0700 Subject: [PATCH 008/249] allow order trace in masters config --- configs/kpf_drp.cfg | 2 +- configs/kpf_masters_l1.cfg | 6 +++--- recipes/kpf_drp.recipe | 9 +++++++-- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index 2d60e0477..6b6bd7d38 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -113,7 +113,7 @@ hk_wavelength_path = ["masters/kpfMaster_HKwave20220909_sci.csv", "masters/kpfMa # for module process: do_l0_to_2d = True -do_order_trace = True +do_order_trace = False do_spectral_extraction = True do_rv = True do_rv_reweighting = True diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index a948e5fac..894dbfb74 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -52,10 +52,10 @@ output_lev2_suffix = _L2 ccd_list = ['GREEN_CCD', 'RED_CCD'] # for order trace: -# flat_file: flat file for order trace process. updated upon new flat file. +# flat_file: path or pattern to a flat file to use to find order locations. If specifying pattern with wildcard use relative paths # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe -flat_file = /masters/20230411/kpf_20230411_master_flat.fits -order_trace_files = [ '/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv', '/masters/20230411/kpf_20230411_master_flat_RED_CCD.csv'] +flat_file = *_master_flat.fits +order_trace_files = ['', ''] fitting_poly_degree = 3 ccd_idx = [0, 1] diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 4c746a60f..0b1fa36fe 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -313,8 +313,13 @@ end_of_night_summary = False # OrderTrace: order trace # OrderRectification: order trace rectification -ll0 = lev0_flat_pattern -input_flat_file = find_files(lev0_flat_pattern)[0] +if "*" in lev0_flat_pattern: + # for masters we need to add the date_dir part in the recipe because the config + # doesn't know about date_dir + ll0 = output_dir + date_dir + lev0_flat_pattern +else: + ll0 = lev0_flat_pattern +input_flat_file = find_files(ll0)[0] _, short_flat_file = split(input_flat_file) flat_stem, flat_ext = splitext(short_flat_file) From 0a7d9f2acfb6dac254d6c9b8a051f437ad2bbcea Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 8 Jun 2023 15:38:09 -0600 Subject: [PATCH 009/249] working on masters config --- configs/kpf_drp_local.cfg | 163 +++++++++++++++++++ configs/kpf_masters_l1.cfg | 6 +- makefile | 1 - modules/quicklook/configs/default.cfg | 2 +- recipes/kpf_drp.recipe | 2 +- static/kpfMaster_HKOrderBounds20220909.csv | 15 ++ static/kpf_20230418_order_trace_GREEN.csv | 175 +++++++++++++++++++++ static/kpf_20230418_order_trace_RED.csv | 161 +++++++++++++++++++ static/static_green_ccf_ratio_2.csv | 36 +++++ static/static_red_ccf_ratio_2.csv | 33 ++++ 10 files changed, 588 insertions(+), 6 deletions(-) create mode 100644 configs/kpf_drp_local.cfg create mode 100644 static/kpfMaster_HKOrderBounds20220909.csv create mode 100644 static/kpf_20230418_order_trace_GREEN.csv create mode 100644 static/kpf_20230418_order_trace_RED.csv create mode 100755 static/static_green_ccf_ratio_2.csv create mode 100755 static/static_red_ccf_ratio_2.csv diff --git a/configs/kpf_drp_local.cfg b/configs/kpf_drp_local.cfg new file mode 100644 index 000000000..13ce78054 --- /dev/null +++ b/configs/kpf_drp_local.cfg @@ -0,0 +1,163 @@ +## Pipeline logger configurations +[LOGGER] +start_log = True +log_path = pipeline.log +log_level = info +log_verbose = True +log_directory = /data/logs/ + +[ARGUMENT] +data_type = KPF +overwrite = True + +# path to input and output data +output_dir = /data/ +output_dir_flat = /data/ +input_dir_root = /data/2D/ +masters_dir = /data/masters/ + +# the subdirectory containing order trace result, L1 data, L2 data, L2 reweighted data, qlp, bary data +# output_trace = order_trace/ +output_trace = masters/ +output_extraction = L1/ +output_rv = L2/ +output_rv_reweighting = '' +output_qlp = QLP/ +output_barycorr = bary/ + +# output_clip containing files with polygon clipping information when rectification_method is 'normal' or vertical' +# (not 'norect') +output_clip = clip_np/ + +# suffix to 2D (L0 format), L1 and L2 data files +# for lev0 set with '_2D' suffix +output_lev0_suffix = _2D +output_lev1_suffix = _L1 +output_lev2_suffix = _L2 + +# ccd list for KPF +ccd_list = ['GREEN_CCD', 'RED_CCD'] + +# for order trace: +# flat_file: flat file for order trace process. updated upon new flat file. +# ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe +#flat_file = KP.20221107.04689.77 +flat_file = /data/masters/20230527/kpf_20230527_master_flat.fits +order_trace_files = [ '/code/KPF-Pipeline/static/kpf_20230418_order_trace_GREEN.csv', '/code/KPF-Pipeline/static/kpf_20230418_order_trace_RED.csv'] +fitting_poly_degree = 3 +ccd_idx = [0, 1] + +# for spectral extraction: +# - update orders_per_ccd, start_order, orderlet_names per new order trace result +# orders_per_ccd: [ ..] +# orderlet_names: [[], ...], each extension is related to a ccd fiber. +# start_order: [, ...], assuming there are 35 * 5 (or 32 * 5) traces for green (or red) in KPF. +# if all traces for green (or red) are identified by order trace process, start_order[0] ( or start_order[1]) is 0. +# if the first n traces for green (or red) are not in the order trace result, then start_order[0] (or start_order[1]) is -n. +# ex: start_order = [-1, -1] means the traces of sky fiber of first order for both green and red are missing in the order trace result. +# orderlet_widths_ccds: trace widths per orderlet per ccd to replace the trace widths from the order trace process +# - note: order_per_ccd, start_order, orderlet_names should (the outer list), orderlet_widths_ccds (the outer list) have size as that of ccd_list. +# the inner lists of orderlet_names and orderlet_widths_ccds are with the same size. +orders_per_ccd=[35,32] +start_order = [-1, 0] +orderlet_names = [['GREEN_SKY_FLUX', 'GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX'], ['RED_SKY_FLUX', 'RED_SCI_FLUX1', 'RED_SCI_FLUX2', 'RED_SCI_FLUX3', 'RED_CAL_FLUX']] +#orderlet_widths_ccds = [[],[]] +orderlet_widths_ccds = [[-1, -1, -1, 1, -1],[-1, -1, -1, -1, -1]] + +# rectification_method: norect|vertical|normal, method to rectify the trace. +# extraction_method: summ|optimal, method to do spectral extraction. +rectification_method = norect +extraction_method = optimal + +# - fits with wavelength calibration data +# wls_fits: [ ]. +# wave_to_ext: [ ] +wls_fits = ['masters/20230527/kpf_20230527_master_WLS_cal-LFC-eve_L1.fits', 'masters/20230527/kpf_20230527_master_WLS_cal-LFC-eve_L1.fits'] +wave_to_ext = [['GREEN_SCI_WAVE1', 'GREEN_SCI_WAVE2', 'GREEN_SCI_WAVE3', 'GREEN_SKY_WAVE', 'GREEN_CAL_WAVE'], ['RED_SCI_WAVE1', 'RED_SCI_WAVE2', 'RED_SCI_WAVE3', 'RED_SKY_WAVE', 'RED_CAL_WAVE']] +do_db_query_for_one_nearest_wls_master_file = False +cal_file_level = 1 +# contentbitmask = 3 means require at least GREEN and RED CCDs in the WLS master file database-queried nearest in time. +contentbitmask = 3 +cal_type_pairs = [['WLS','cal-lfc-morn'], ['WLS', 'cal-lfc-eve'], ['WLS', 'autocal-thar-all']] +# Maximum start-date age of WLS file relative to context.date_dir at 00:00:00 UT, otherwise fall back on wls_fits. +max_cal_file_age = '2 days' + +# for rv: +# o/ou/outtrderlet_names_rv: [] +# reweighting_method: ccf_max|ccf_mean, getting the template file for reweighting by checking the maximum or mean ccf among the orders. +# ccf_ext: [, ...] +# rv_ext: extension containing rv result table +orderlet_names_rv = [['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX', 'GREEN_SKY_FLUX'],['RED_SCI_FLUX1', 'RED_SCI_FLUX2', 'RED_SCI_FLUX3', 'RED_CAL_FLUX', 'RED_SKY_FLUX']] +rv_correct_by_cal = False +reweighting_method = ccf_static +ccf_ext = ['GREEN_CCF', 'RED_CCF'] +rv_ext = RV +#static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] +static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] + +# for ca_hk: +# hk_fiber_list: [] +# hk_extract_exts: [, ...] +# hk_wave_exts: [, ...] +# hk_bias_fits: bias fits file for bias subtraction in CA-HK extraction +# hk_dark_fits: dark fits file for dark subtraction in CA-HK extraction +# hk_trace_path: CA-HK trace file for spectrum extraction +# hk_wavelength_path: [, ...] +hk_fiber_list = ['sci', 'sky'] +hk_extract_exts = ['CA_HK_SCI', 'CA_HK_SKY'] +hk_wave_exts = ['CA_HK_SCI_WAVE', 'CA_HK_SKY_WAVE'] +hk_dark_fits = None +hk_bias_fits = None +hk_trace_path = /code/KPF-Pipeline/kpfMaster_HKOrderBounds20220909.csv +hk_wavelength_path = ["masters/kpfMaster_HKwave20220909_sci.csv", "masters/kpfMaster_HKwave20220909_sky.csv"] + +# for module process: +do_l0_to_2d = True +do_order_trace = False +do_spectral_extraction = True +do_rv = True +do_rv_reweighting = True +do_hk = True +do_wavecopy_in_sp = True +do_qlp = False +do_bk_subtraction = True +do_bc = True + +# for L0->2D process +[WATCHFOR_L0] +# Define numbers of columns and rows in data section of channel images for each detector. +channel_datasec_ncols_green = 2040 +channel_datasec_nrows_green = 2040 +channel_datasec_ncols_red = 2040 +channel_datasec_nrows_red = 4080 +channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red_2amp.txt +channel_orientation_ref_path_green = /code/KPF-Pipeline/static/kpfsim_ccd_orient_green.txt +do_db_query_for_master_files = False +masterbias_path = /data/masters/20230527/kpf_20230527_master_bias.fits +masterdark_path = /data/masters/20230527/kpf_20230527_master_dark.fits +masterflat_path = /data/masters/20230527/kpf_20230527_master_flat.fits +prescan_reg = [0,4] +# overscan_method = clippedmean is n_sigma clipping and average over entire post-overscan strip. +# overscan_method = median is computed row by row. +overscan_method = clippedmean +n_sigma = 2.1 +# overscan_clip is number of columns at start and end of each row in post-overscan strip to ignore. +overscan_clip = 5 +overscan_order = 1 +lev0_ffi_ext_red = ['RED_CCD'] +lev0_ffi_ext_green = ['GREEN_CCD'] +quicklook = False +gain_keyword = 'CCDGAIN' +input_dir = /data/L0/ +output_dir = /data/2D/ + +# config file associated with the modules +[MODULE_CONFIGS] +order_trace = modules/order_trace/configs/default_recipe_kpf_20220505.cfg +spectral_extraction = modules/spectral_extraction/configs/default_recipe_kpf_20220505.cfg +radial_velocity = modules/radial_velocity/configs/default_recipe_kpf_targ.cfg +hk_spectral_extraction = modules/ca_hk/configs/default_hk.cfg +quicklook = modules/quicklook/configs/default.cfg +bias_subtraction = modules/bias_subtraction/configs/default.cfg +flat_fielding = modules/flat_fielding/configs/default.cfg + diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index a948e5fac..17a96b1c8 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -24,7 +24,7 @@ do_order_trace = True do_spectral_extraction = True do_rv = True do_rv_reweighting = False -do_hk = False +do_hk = True do_wavecopy_in_sp = True do_qlp = False do_bk_subtraction = True @@ -54,8 +54,8 @@ ccd_list = ['GREEN_CCD', 'RED_CCD'] # for order trace: # flat_file: flat file for order trace process. updated upon new flat file. # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe -flat_file = /masters/20230411/kpf_20230411_master_flat.fits -order_trace_files = [ '/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv', '/masters/20230411/kpf_20230411_master_flat_RED_CCD.csv'] +flat_file = *_master_flat.fits +order_trace_files = ['', ''] fitting_poly_degree = 3 ccd_idx = [0, 1] diff --git a/makefile b/makefile index c7ffe5a92..962f7c701 100644 --- a/makefile +++ b/makefile @@ -24,7 +24,6 @@ notebook: docker: docker build --cache-from kpf-drp:latest --tag kpf-drp:latest . - $(if $(KPFPIPE_TEST_DATA),,$(error Must set KPFPIPE_TEST_DATA)) $(if $(KPFPIPE_DATA),,$(error Must set KPFPIPE_DATA)) $(if $(KPFPIPE_PORT),,docker run -it -v ${PWD}:/code/KPF-Pipeline -v ${KPFPIPE_TEST_DATA}:/testdata -v ${KPFPIPE_DATA}:/data -v ${KPFPIPE_DATA}/masters:/masters --network=host -e DBPORT=6125 -e DBNAME=kpfopsdb -e DBUSER=${KPFPIPE_DB_USER} -e DBPASS="${KPFPIPE_DB_PASS}" -e DBSERVER=127.0.0.1 kpf-drp:latest bash) docker run -it -p ${KPFPIPE_PORT}:${KPFPIPE_PORT} --network=host \ diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index 1c8bfe3fc..43cb4004e 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -30,7 +30,7 @@ CCF_RV_2=CCD2RV [L1] wave_soln = /data/masters/MasterLFCWLS.fits -order_trace =/data/order_trace/20221107/KP.20221107.04689.77_ +order_trace = /code/KPF-Pipeline/static/kpf_20230418_order_trace_ n_per_row = 8 diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 4c746a60f..42a605a54 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -593,7 +593,7 @@ if do_rv or do_rv_reweighting: ratio_refs = [] if 'static' in reweighting_method and do_rv_reweighting: for idx in ccd_idx: - ccf_ratio_file = output_dir + static_ccf_ratio[idx] + ccf_ratio_file = static_ccf_ratio[idx] t_segment = area_def[idx][1] - area_def[idx][0] + 1 ratio_ref = RadialVelocityReweightingRef(None, reweighting_method, t_segment, cf_hdu_name=ccf_ext_names[idx], diff --git a/static/kpfMaster_HKOrderBounds20220909.csv b/static/kpfMaster_HKOrderBounds20220909.csv new file mode 100644 index 000000000..8f3f93fbe --- /dev/null +++ b/static/kpfMaster_HKOrderBounds20220909.csv @@ -0,0 +1,15 @@ +order fiber y0 x0 yf xf +-1 sci 23 0 31 1024 +-1 sky 40 0 48 1024 +0 sci 55 0 63 1024 +0 sky 72 0 80 1024 +1 sci 88 0 96 1024 +1 sky 105 0 113 1024 +2 sci 121 0 129 1024 +2 sky 138 0 146 1024 +3 sci 154 0 162 1024 +3 sky 171 0 179 1024 +4 sci 188 0 196 1024 +4 sky 205 0 213 1024 +5 sci 223 0 231 1024 +5 sky 240 0 248 1024 diff --git a/static/kpf_20230418_order_trace_GREEN.csv b/static/kpf_20230418_order_trace_GREEN.csv new file mode 100644 index 000000000..4e73d89fe --- /dev/null +++ b/static/kpf_20230418_order_trace_GREEN.csv @@ -0,0 +1,175 @@ +,Coeff0,Coeff1,Coeff2,Coeff3,BottomEdge,TopEdge,X1,X2 +0,-0.2628397801909426,0.005226565876110638,1.5661431995534793e-06,-2.8539875823082024e-10,6.638,6.1499,100,4079 +1,15.0973244234729,0.009885921358880913,-3.96197913595942e-07,-3.784944393954837e-11,8.8501,9.5355,0,4079 +2,34.04538893418942,0.010020532467232428,-5.161128813845733e-07,-2.01544730820485e-11,8.4645,8.8985,0,4079 +3,49.20286205328118,0.009947627880786757,-4.335106348455518e-07,-3.3166783372124735e-11,4.3708,4.3044,0,4079 +4,64.75407690938395,0.009427875928821687,-3.247755337056048e-07,-3.791491767067158e-11,9.6669,9.3428,0,4079 +5,85.0310246961004,0.009510373161259888,-3.8297857439051683e-07,-2.914271841466081e-11,7.3046,7.2747,0,4079 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+154,3837.0921793033285,-0.012522717778656968,1.919529204846934e-06,-5.7058353095843996e-11,3.8041,3.9727,0,4079 +155,3943.0389259562726,-0.013138006481201924,1.759232033048091e-06,-5.074840189713734e-12,9.3114,9.856,0,4079 +156,3964.129362172303,-0.013328952162136093,1.8581013535728867e-06,-2.352167891086182e-11,6.8758,7.2685,0,4079 +157,3983.0948226118708,-0.013028065007128244,1.6599340306637043e-06,1.5294381720484477e-11,9.5624,9.853,0,4079 +158,4002.6375429086816,-0.01315119188752408,1.747492539757316e-06,-3.3703578838958604e-12,8.8431,8.6913,0,4079 +159,4018.4959237881903,-0.013686074049780589,2.051714634344501e-06,-5.411898692707659e-11,3.8194,3.968,0,4079 diff --git a/static/static_green_ccf_ratio_2.csv b/static/static_green_ccf_ratio_2.csv new file mode 100755 index 000000000..4558fec56 --- /dev/null +++ b/static/static_green_ccf_ratio_2.csv @@ -0,0 +1,36 @@ +idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso lfc thar etalon +0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +6 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +10 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +13 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +14 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +15 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +16 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +18 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +19 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +21 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +22 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +23 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +24 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +26 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +31 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +32 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +34 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 diff --git a/static/static_red_ccf_ratio_2.csv b/static/static_red_ccf_ratio_2.csv new file mode 100755 index 000000000..7a966f37f --- /dev/null +++ b/static/static_red_ccf_ratio_2.csv @@ -0,0 +1,33 @@ +idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso lfc thar etalon +0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +4 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +6 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +7 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +10 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +11 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +12 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +19 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +20 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +23 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +24 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 From 27d1fb9e102752d21845f285b9d022706d62b029 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 8 Jun 2023 15:51:30 -0600 Subject: [PATCH 010/249] move stellar masks --- .../configs/default_recipe_kpf_targ.cfg | 2 +- static/stellarmasks/F9_espresso.txt | 4653 +++++ static/stellarmasks/G2.espresso.mas | 5551 ++++++ static/stellarmasks/G2.harps.mas | 3625 ++++ static/stellarmasks/G2.neid.v1.mas | 2802 +++ static/stellarmasks/G2.neid.v2.mas | 2812 +++ static/stellarmasks/G8_espresso.txt | 5827 ++++++ static/stellarmasks/G9_espresso.txt | 6366 +++++++ static/stellarmasks/K2_espresso.txt | 6593 +++++++ static/stellarmasks/K6_espresso.txt | 8468 +++++++++ static/stellarmasks/M2_espresso.txt | 9196 ++++++++++ static/stellarmasks/Thorium_mask_031921.mas | 5201 ++++++ .../kpf_etalon_masks_11may2023.csv | 10345 +++++++++++ static/stellarmasks/kpf_lfc_mask_1025.mas | 15183 ++++++++++++++++ 14 files changed, 86623 insertions(+), 1 deletion(-) create mode 100755 static/stellarmasks/F9_espresso.txt create mode 100644 static/stellarmasks/G2.espresso.mas create mode 100644 static/stellarmasks/G2.harps.mas create mode 100644 static/stellarmasks/G2.neid.v1.mas create mode 100644 static/stellarmasks/G2.neid.v2.mas create mode 100755 static/stellarmasks/G8_espresso.txt create mode 100755 static/stellarmasks/G9_espresso.txt create mode 100755 static/stellarmasks/K2_espresso.txt create mode 100755 static/stellarmasks/K6_espresso.txt create mode 100755 static/stellarmasks/M2_espresso.txt create mode 100755 static/stellarmasks/Thorium_mask_031921.mas create mode 100644 static/stellarmasks/kpf_etalon_masks_11may2023.csv create mode 100755 static/stellarmasks/kpf_lfc_mask_1025.mas diff --git a/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg b/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg index b4b6bb746..5387f786f 100644 --- a/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg +++ b/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg @@ -21,7 +21,7 @@ obslon = -155.474719 obslat = 19.8260 obsalt = 4145.0 -stellarmask_dir = stellarmasks/ +stellarmask_dir = /code/KPF-Pipeline/static/stellarmasks/ ra = TARGRA dec = TARGDEC pmra = TARGPMRA diff --git a/static/stellarmasks/F9_espresso.txt b/static/stellarmasks/F9_espresso.txt new file mode 100755 index 000000000..57d43e1c7 --- /dev/null +++ b/static/stellarmasks/F9_espresso.txt @@ -0,0 +1,4653 @@ +3782.1209154400 0.2907223 +3782.4475780500 0.3967713 +3782.6116771800 0.2792305 +3782.8742857200 0.0181508 +3783.1819074600 0.0276274 +3783.3488712300 0.3476869 +3783.5375585500 0.6944335 +3783.7541210800 0.0155939 +3784.0278747800 0.0199331 +3784.2491032500 0.0986093 +3784.5033818600 0.0784645 +3784.7073077600 0.0209086 +3784.8459444800 0.0287527 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+8685.974003946800 1.000000000000 +8686.477355612149 1.000000000000 +8686.980765619275 1.000000000000 +8687.484233978324 1.000000000000 +8687.987760699441 1.000000000000 +8688.491345792774 1.000000000000 +8688.994989268473 1.000000000000 +8689.498691136694 1.000000000000 +8690.002451407590 1.000000000000 +8690.506270091322 1.000000000000 +8691.514082737931 1.000000000000 +8692.018076721137 1.000000000000 +8692.522129157833 1.000000000000 +8693.026240058189 1.000000000000 +8693.530409432376 1.000000000000 +8694.034637290570 1.000000000000 +8694.538923642946 1.000000000000 +8695.043268499685 1.000000000000 +8696.052133766980 1.000000000000 +8696.556654197904 1.000000000000 +8697.061233173932 1.000000000000 +8697.565870705255 1.000000000000 +8698.070566802066 1.000000000000 +8698.575321474558 1.000000000000 +8699.080134732932 1.000000000000 +8699.585006587387 1.000000000000 From e936c868e2f9541b7049f7b8a08d75de94eb54c3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 8 Jun 2023 17:38:22 -0700 Subject: [PATCH 011/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 200 ++++++++++++++++++++++++++- 1 file changed, 199 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index d8b0fdbd2..9472fc82d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -32,9 +32,207 @@ def __init__(self,config=None,logger=None): def nightly_procedures(self,night): exposures_dir = self.config['Nightly']['exposures_dir'] + masters_dir = self.config['Nightly']['masters_dir'] + output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary/' + + if not os.path.exists(self.config['Nightly']['output_dir']+'/'+night): + os.makedirs(self.config['Nightly']['output_dir']+'/'+night) + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + #plot the master files for a particular night + master_list = glob.glob(masters_dir+night+'/*master*.fits') + print(master_list) + + for i in range(len(master_list)): + if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue + print(master_list[i]) + + + L0_data = masters_dir+night+master_list[i] + hdulist = fits.open(L0_data) + print(hdulist.info()) + + #get ccd names + ccd_color=[] + ccd_list = self.config.items( "CCD_LIST") + for key, path in ccd_list: + ccd_color.append(path) + + + if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: + print('skipping empty file') + return + + + #2d plots + for i_color in range(len(ccd_color)): + counts = np.array(hdulist[ccd_color[i_color]].data,'d') + flatten_counts = np.ravel(counts) + if len(flatten_counts)<1: continue + master_flatten_counts='None' + if master_file != 'None': + hdulist1 = fits.open(master_file) + master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') + master_flatten_counts = np.ravel(master_counts) + if version == 'Dark':#scale up dark exposures + master_flatten_counts*=hdr['EXPTIME'] + + #2D image + plt.figure(figsize=(5,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') + plt.xlabel('x (pixel number)') + plt.ylabel('y (pixel number)') + plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.colorbar(label = 'Counts (e-)') + + + #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') + plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + #plt.close() + + + if master_list[i].find('bias') == True or master_list[i].find('dark'): + exptime = hdr['EXPTIME'] + print('exptime',exptime) + + # Read telemetry + from astropy.table import Table + df_telemetry = Table.read(L0_data, format='fits', hdu=11).to_pandas() # need to refer to HDU by name + num_columns = ['average', 'stddev', 'min', 'max'] + for column in df_telemetry: + df_telemetry[column] = df_telemetry[column].str.decode('utf-8') + df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0 + if column in num_columns: + df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast="float") + else: + df_telemetry[column] = df_telemetry[column].astype(str) + df_telemetry.set_index("keyword", inplace=True) + + with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also + print(df_telemetry) + if ccd_color[i_color] == 'GREEN_CCD': + coll_pressure_torr = df_telemetry.at['kpfgreen.COL_PRESS', 'average'] + ech_pressure_torr = df_telemetry.at['kpfgreen.ECH_PRESS', 'average'] + coll_current_a = df_telemetry.at['kpfgreen.COL_CURR', 'average'] + ech_current_a = df_telemetry.at['kpfgreen.ECH_CURR', 'average'] + if ccd_color[i_color] == 'RED_CCD': + coll_pressure_torr = df_telemetry.at['kpfred.COL_PRESS', 'average'] + ech_pressure_torr = df_telemetry.at['kpfred.ECH_PRESS', 'average'] + coll_current_a = df_telemetry.at['kpfred.COL_CURR', 'average'] + ech_current_a = df_telemetry.at['kpfred.ECH_CURR', 'average'] + + frame = counts + if exptime > 0: + exptype = 'dark' + timelabel = ' e$^-$ hr$^{-1}$' + frame *= (3600./exptime) # convert to e- per hour + # Bias frame + else: + exptype = 'bias' + timelabel = ' e$^-$' + reg = {'ref1': {'name': 'Reference Region 1', 'x1': 1690, 'x2': 1990, 'y1': 1690, 'y2': 1990, 'short':'ref1', 'med_elec':0, 'label':''}, + 'ref2': {'name': 'Reference Region 2', 'x1': 1690, 'x2': 1990, 'y1': 2090, 'y2': 2390, 'short':'ref2', 'med_elec':0, 'label':''}, + 'ref3': {'name': 'Reference Region 3', 'x1': 2090, 'x2': 2390, 'y1': 1690, 'y2': 1990, 'short':'ref3', 'med_elec':0, 'label':''}, + 'ref4': {'name': 'Reference Region 4', 'x1': 2090, 'x2': 2390, 'y1': 2090, 'y2': 2390, 'short':'ref4', 'med_elec':0, 'label':''}, + 'ref5': {'name': 'Reference Region 5', 'x1': 80, 'x2': 380, 'y1': 700, 'y2': 1000, 'short':'ref5', 'med_elec':0, 'label':''}, + 'ref6': {'name': 'Reference Region 6', 'x1': 80, 'x2': 380, 'y1': 3080, 'y2': 3380, 'short':'ref6', 'med_elec':0, 'label':''}, + 'amp1': {'name': 'Amplifier Region 1', 'x1': 300, 'x2': 500, 'y1': 5, 'y2': 20, 'short':'amp1', 'med_elec':0, 'label':''}, + 'amp2': {'name': 'Amplifier Region 2', 'x1': 3700, 'x2': 3900, 'y1': 5, 'y2': 20, 'short':'amp2', 'med_elec':0, 'label':''}, + 'coll': {'name': 'Ion Pump (Collimator side)', 'x1': 3700, 'x2': 4000, 'y1': 700, 'y2': 1000, 'short':'coll', 'med_elec':0, 'label':''}, + 'ech': {'name': 'Ion Pump (Echelle side)', 'x1': 3700, 'x2': 4000, 'y1': 3080, 'y2': 3380, 'short':'ech', 'med_elec':0, 'label':''} + } + for r in reg.keys(): + current_region = frame[reg[r]['y1']:reg[r]['y2'],reg[r]['x1']:reg[r]['x2']] + reg[r]['med_elec'] = np.median(current_region) + + #print(reg[r]['name'] + ': ' + str(np.round(reg[r]['med_elec'],1)) + ' e- per hour') + #print('Ion Pump pressure (Torr) - Collimator side: ' + f'{coll_pressure_torr:.1e}') + #print('Ion Pump pressure (Torr) - Echelle side: ' + f'{ech_pressure_torr:.1e}') + #print('Ion Pump current (A) - Collimator side: ' + f'{coll_current_a:.1e}') + #print('Ion Pump current (A) - Echelle side: ' + f'{ech_current_a:.1e}') + + from matplotlib.patches import Rectangle + plt.figure(figsize=(5, 4)) + plt.imshow(frame, + cmap='viridis', + origin='lower', + vmin=np.percentile(frame[300:3780,0:4080],5), + vmax=np.percentile(frame[300:3780,0:4080],95) + ) + for r in reg.keys(): + plt.gca().add_patch(Rectangle((reg[r]['x1'],reg[r]['y1']),reg[r]['x2']-reg[r]['x1'],reg[r]['y2']-reg[r]['y1'],linewidth=1,edgecolor='r',facecolor='none')) + plt.text(((reg[r]['short'] == 'ref3') or + (reg[r]['short'] == 'ref4') or + (reg[r]['short'] == 'ref5') or + (reg[r]['short'] == 'ref6') or + (reg[r]['short'] == 'amp1'))*(reg[r]['x1'])+ + ((reg[r]['short'] == 'ref1') or + (reg[r]['short'] == 'ref2') or + (reg[r]['short'] == 'ech') or + (reg[r]['short'] == 'coll') or + (reg[r]['short'] == 'amp2'))*(reg[r]['x2']), + (((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*(reg[r]['y1']-30)+ + ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*(reg[r]['y2']+30)), + str(np.round(reg[r]['med_elec'],1)) + timelabel, + weight='bold', + color='r', + ha=(((reg[r]['short'] == 'ref3') or + (reg[r]['short'] == 'ref4') or + (reg[r]['short'] == 'ref5') or + (reg[r]['short'] == 'ref6') or + (reg[r]['short'] == 'amp1'))*('left')+ + ((reg[r]['short'] == 'ref1') or + (reg[r]['short'] == 'ref2') or + (reg[r]['short'] == 'ech') or + (reg[r]['short'] == 'coll') or + (reg[r]['short'] == 'amp2'))*('right')), + va=(((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*('top')+ + ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*('bottom')) + ) + now = datetime.now() + coll_text = 'Ion Pump (Coll): \n' + (f'{coll_pressure_torr:.1e}' + ' Torr, ' + f'{coll_current_a*1e6:.1f}' + ' $\mu$A')*(coll_pressure_torr > 1e-9) + ('Off')*(coll_pressure_torr < 1e-9) + ech_text = 'Ion Pump (Ech): \n' + (f'{ech_pressure_torr:.1e}' + ' Torr, ' + f'{ech_current_a*1e6:.1f}' + ' $\mu$A')*(ech_pressure_torr > 1e-9) + ('Off')*(ech_pressure_torr < 1e-9) + #plt.text(4080, -250, now.strftime("%m/%d/%Y, %H:%M:%S"), ha='right', color='gray') + plt.text(4220, 500, coll_text, rotation=90, ha='center',fontsize = 6) + plt.text(4220, 3000, ech_text, rotation=90, ha='center',fontsize = 6) + plt.text(3950, 1500, 'Bench Side\n (blue side of orders)', rotation=90, ha='center', color='white',fontsize = 6) + plt.text( 150, 1500, 'Top Side\n (red side of orders)', rotation=90, ha='center', color='white',fontsize = 6) + plt.text(2040, 70, 'Collimator Side', rotation= 0, ha='center', color='white',fontsize = 6) + plt.text(2040, 3970, 'Echelle Side', rotation= 0, ha='center', color='white',fontsize = 6) + cbar = plt.colorbar() + cbar.set_label(timelabel)#, fontsize=18 + cbar.ax.tick_params()#labelsize=18 + cbar.ax.tick_params()#size=18 + plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.xlabel('Column (pixel number)') + plt.ylabel('Row (pixel number)')#fontsize=18 + plt.xticks()#KP.20230317.07770.97 + plt.yticks() + plt.grid(False) + plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + plt.close() + + #get all exposures taken on a particular night + file_list = glob.glob(exposures_dir+night+'/*.fits') + date_obs = [] + temp = [] for i in range(len(file_list)): file_list[i] = file_list[i][18:-8] - print(file_list) + + hdulist = fits.open(file_list[i]) + hdr = hdulist[0].header + date_obs.append(hdr['DATE-OBS']) + temp.append(hdr['TEMP']) + + date_obs = np.array(date_obs,'str') + date_obs = Time(date_obs, format='isot', scale='utc') + plt.scatter(date_obs.jd,temp, marker = '.') + plt.xlabel('Time') + plt.ylabel('Temperature') + plt.savefig(output_dir+night+'_temperature_variation.png') + plt.close() From 39d4677bda540c2455a10f66c194fa79691f0fa9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:44:57 -0700 Subject: [PATCH 012/249] turn on kpf_master_l1 quicklook --- configs/kpf_masters_l1.cfg | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index a948e5fac..8003099c3 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -144,7 +144,7 @@ overscan_clip = 5 overscan_order = 1 lev0_ffi_ext_red = ['RED_CCD'] lev0_ffi_ext_green = ['GREEN_CCD'] -quicklook = False +quicklook = True gain_keyword = 'CCDGAIN' # config file associated with the modules @@ -156,4 +156,3 @@ hk_spectral_extraction = modules/ca_hk/configs/default_hk.cfg quicklook = modules/quicklook/configs/default.cfg bias_subtraction = modules/bias_subtraction/configs/default.cfg flat_fielding = modules/flat_fielding/configs/default.cfg - From 3e58c15aa608348ed2619241aa4e926fdf01adc0 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:48:45 -0700 Subject: [PATCH 013/249] nightly_summary --- configs/kpf_masters_l1.cfg | 2 +- modules/quicklook/src/alg_nightly.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index 8003099c3..da2bd2fea 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -144,7 +144,7 @@ overscan_clip = 5 overscan_order = 1 lev0_ffi_ext_red = ['RED_CCD'] lev0_ffi_ext_green = ['GREEN_CCD'] -quicklook = True +quicklook = False gain_keyword = 'CCDGAIN' # config file associated with the modules diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 9472fc82d..792b7f42a 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -44,6 +44,7 @@ def nightly_procedures(self,night): master_list = glob.glob(masters_dir+night+'/*master*.fits') print(master_list) + ''' for i in range(len(master_list)): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue print(master_list[i]) @@ -236,3 +237,4 @@ def nightly_procedures(self,night): plt.ylabel('Temperature') plt.savefig(output_dir+night+'_temperature_variation.png') plt.close() + ''' From 1c6ea0defa7e66530c212295b633a51d47a017f7 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:50:24 -0700 Subject: [PATCH 014/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 792b7f42a..daadfebd3 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -44,7 +44,7 @@ def nightly_procedures(self,night): master_list = glob.glob(masters_dir+night+'/*master*.fits') print(master_list) - ''' + for i in range(len(master_list)): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue print(master_list[i]) @@ -53,7 +53,7 @@ def nightly_procedures(self,night): L0_data = masters_dir+night+master_list[i] hdulist = fits.open(L0_data) print(hdulist.info()) - + ''' #get ccd names ccd_color=[] ccd_list = self.config.items( "CCD_LIST") From 3926720154ad6d5b51f99656e93f42102d2a22e6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:51:14 -0700 Subject: [PATCH 015/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index daadfebd3..bb29ef726 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -50,7 +50,7 @@ def nightly_procedures(self,night): print(master_list[i]) - L0_data = masters_dir+night+master_list[i] + L0_data = master_list[i] hdulist = fits.open(L0_data) print(hdulist.info()) ''' From 0567a569ae600bff825d599ae04083b8d169195c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:57:53 -0700 Subject: [PATCH 016/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index bb29ef726..1e84d830f 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -47,13 +47,15 @@ def nightly_procedures(self,night): for i in range(len(master_list)): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue - print(master_list[i]) + exposure_name = master_list[i][23:-5] + print(master_list[i],exposure_name) + ''' L0_data = master_list[i] hdulist = fits.open(L0_data) print(hdulist.info()) - ''' + #get ccd names ccd_color=[] ccd_list = self.config.items( "CCD_LIST") @@ -218,7 +220,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - + file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] From 345518a8e7b428cd2723d95d155f620be2b746b1 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 12:59:25 -0700 Subject: [PATCH 017/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 1e84d830f..fdd565ff1 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -51,7 +51,7 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] print(master_list[i],exposure_name) - ''' + L0_data = master_list[i] hdulist = fits.open(L0_data) print(hdulist.info()) @@ -87,7 +87,7 @@ def nightly_procedures(self,night): plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') - plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.title(ccd_color[i_color]+' '+exposure_name) plt.colorbar(label = 'Counts (e-)') @@ -208,7 +208,7 @@ def nightly_procedures(self,night): cbar.set_label(timelabel)#, fontsize=18 cbar.ax.tick_params()#labelsize=18 cbar.ax.tick_params()#size=18 - plt.title(ccd_color[i_color]+' '+version +' '+exposure_name) + plt.title(ccd_color[i_color]+' '+exposure_name) plt.xlabel('Column (pixel number)') plt.ylabel('Row (pixel number)')#fontsize=18 plt.xticks()#KP.20230317.07770.97 @@ -220,7 +220,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - + ''' file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] From e4507c0457b6c597d8679438361a5404f87a870c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:01:00 -0700 Subject: [PATCH 018/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index fdd565ff1..3e19e3a62 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -74,11 +74,7 @@ def nightly_procedures(self,night): flatten_counts = np.ravel(counts) if len(flatten_counts)<1: continue master_flatten_counts='None' - if master_file != 'None': - hdulist1 = fits.open(master_file) - master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') - master_flatten_counts = np.ravel(master_counts) - if version == 'Dark':#scale up dark exposures + if master_list[i].find('dark'):#scale up dark exposures master_flatten_counts*=hdr['EXPTIME'] #2D image From ad5f8a692e1a04f175a7048a43d08157a49ca4a4 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:03:15 -0700 Subject: [PATCH 019/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 3e19e3a62..c1f2b1526 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -66,8 +66,8 @@ def nightly_procedures(self,night): if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: print('skipping empty file') return - - + print(ccd_color) + ''' #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') @@ -216,7 +216,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - ''' + file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] From 727c6aa5dc494dda018d6720b7d1fd9dafff9842 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:04:41 -0700 Subject: [PATCH 020/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index c1f2b1526..298f13cc6 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -67,7 +67,7 @@ def nightly_procedures(self,night): print('skipping empty file') return print(ccd_color) - ''' + #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') @@ -88,10 +88,11 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') - plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') + plt.savefig(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() - + ''' if master_list[i].find('bias') == True or master_list[i].find('dark'): exptime = hdr['EXPTIME'] print('exptime',exptime) From 8e4c6ad83536f591eebd2c1257795c310c964ae9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:05:16 -0700 Subject: [PATCH 021/249] nightly_summary --- examples/nightly_test.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/nightly_test.cfg b/examples/nightly_test.cfg index cb97b23cb..d061e4d5b 100644 --- a/examples/nightly_test.cfg +++ b/examples/nightly_test.cfg @@ -5,7 +5,7 @@ log_level = info log_verbose = True [ARGUMENT] -night = '20230516' +night = '20230606' [MODULE_CONFIGS] quicklook = modules/quicklook/configs/default.cfg From 81ed2054c57dac48d75fed53177befd826dcbdb9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:10:05 -0700 Subject: [PATCH 022/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 298f13cc6..315fc42c3 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -73,9 +73,9 @@ def nightly_procedures(self,night): counts = np.array(hdulist[ccd_color[i_color]].data,'d') flatten_counts = np.ravel(counts) if len(flatten_counts)<1: continue - master_flatten_counts='None' - if master_list[i].find('dark'):#scale up dark exposures - master_flatten_counts*=hdr['EXPTIME'] + #master_flatten_counts='None' + #if master_list[i].find('dark'):#scale up dark exposures + #master_flatten_counts*=hdr['EXPTIME'] #2D image plt.figure(figsize=(5,4)) From 32841c786549719f703df186a9af97717e686dba Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:15:55 -0700 Subject: [PATCH 023/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 315fc42c3..c83e4fbfa 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -33,7 +33,7 @@ def __init__(self,config=None,logger=None): def nightly_procedures(self,night): exposures_dir = self.config['Nightly']['exposures_dir'] masters_dir = self.config['Nightly']['masters_dir'] - output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary/' + output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary' if not os.path.exists(self.config['Nightly']['output_dir']+'/'+night): os.makedirs(self.config['Nightly']['output_dir']+'/'+night) @@ -83,13 +83,13 @@ def nightly_procedures(self,night): plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') - plt.title(ccd_color[i_color]+' '+exposure_name) + plt.title(ccd_color[i_color]+' '+exposure_name, fontsize = 12) plt.colorbar(label = 'Counts (e-)') #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') - print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') - plt.savefig(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png', dpi=1000) + #print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') + plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() ''' From 8ffbcf684d24dc50ea3d08b2d9dcb4f9045283d5 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:20:36 -0700 Subject: [PATCH 024/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index c83e4fbfa..a0bb7d5dd 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -83,7 +83,7 @@ def nightly_procedures(self,night): plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') - plt.title(ccd_color[i_color]+' '+exposure_name, fontsize = 12) + plt.title(ccd_color[i_color]+' '+exposure_name, fontsize = 8) plt.colorbar(label = 'Counts (e-)') From 4b0eccdbc1b86ddb101b07757f2a1a2a469f02cd Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:21:36 -0700 Subject: [PATCH 025/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index a0bb7d5dd..c25aa43e2 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -92,7 +92,7 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() - ''' + if master_list[i].find('bias') == True or master_list[i].find('dark'): exptime = hdr['EXPTIME'] print('exptime',exptime) @@ -211,13 +211,13 @@ def nightly_procedures(self,night): plt.xticks()#KP.20230317.07770.97 plt.yticks() plt.grid(False) - plt.savefig(output_dir+'/'+master_list[i][:-5]+ccd_color[i_color]+'_zoomable.png', dpi=1000) + plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) plt.close() #get all exposures taken on a particular night - + ''' file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] From c487547a78d8cac0a241877ff6037483eab50545 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:23:56 -0700 Subject: [PATCH 026/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index c25aa43e2..ed6598ef3 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -41,7 +41,7 @@ def nightly_procedures(self,night): os.makedirs(output_dir) #plot the master files for a particular night - master_list = glob.glob(masters_dir+night+'/*master*.fits') + master_list = glob.glob(masters_dir+night+'/*master_dark*.fits') print(master_list) From 177c9ffafb51f517233a4e0b9e133f09acf5a1a3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:25:13 -0700 Subject: [PATCH 027/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index ed6598ef3..ab9166967 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -54,6 +54,7 @@ def nightly_procedures(self,night): L0_data = master_list[i] hdulist = fits.open(L0_data) + hdr = hdulist[0].header print(hdulist.info()) #get ccd names From 3181126c47660b314f6a694ed263cd203d3fd7c3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:37:03 -0700 Subject: [PATCH 028/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 137 ++++----------------------- 1 file changed, 17 insertions(+), 120 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index ab9166967..900590a8f 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -93,127 +93,24 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() - - if master_list[i].find('bias') == True or master_list[i].find('dark'): - exptime = hdr['EXPTIME'] - print('exptime',exptime) - - # Read telemetry - from astropy.table import Table - df_telemetry = Table.read(L0_data, format='fits', hdu=11).to_pandas() # need to refer to HDU by name - num_columns = ['average', 'stddev', 'min', 'max'] - for column in df_telemetry: - df_telemetry[column] = df_telemetry[column].str.decode('utf-8') - df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0 - if column in num_columns: - df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast="float") - else: - df_telemetry[column] = df_telemetry[column].astype(str) - df_telemetry.set_index("keyword", inplace=True) - - with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also - print(df_telemetry) - if ccd_color[i_color] == 'GREEN_CCD': - coll_pressure_torr = df_telemetry.at['kpfgreen.COL_PRESS', 'average'] - ech_pressure_torr = df_telemetry.at['kpfgreen.ECH_PRESS', 'average'] - coll_current_a = df_telemetry.at['kpfgreen.COL_CURR', 'average'] - ech_current_a = df_telemetry.at['kpfgreen.ECH_CURR', 'average'] - if ccd_color[i_color] == 'RED_CCD': - coll_pressure_torr = df_telemetry.at['kpfred.COL_PRESS', 'average'] - ech_pressure_torr = df_telemetry.at['kpfred.ECH_PRESS', 'average'] - coll_current_a = df_telemetry.at['kpfred.COL_CURR', 'average'] - ech_current_a = df_telemetry.at['kpfred.ECH_CURR', 'average'] - - frame = counts - if exptime > 0: - exptype = 'dark' - timelabel = ' e$^-$ hr$^{-1}$' - frame *= (3600./exptime) # convert to e- per hour - # Bias frame - else: - exptype = 'bias' - timelabel = ' e$^-$' - reg = {'ref1': {'name': 'Reference Region 1', 'x1': 1690, 'x2': 1990, 'y1': 1690, 'y2': 1990, 'short':'ref1', 'med_elec':0, 'label':''}, - 'ref2': {'name': 'Reference Region 2', 'x1': 1690, 'x2': 1990, 'y1': 2090, 'y2': 2390, 'short':'ref2', 'med_elec':0, 'label':''}, - 'ref3': {'name': 'Reference Region 3', 'x1': 2090, 'x2': 2390, 'y1': 1690, 'y2': 1990, 'short':'ref3', 'med_elec':0, 'label':''}, - 'ref4': {'name': 'Reference Region 4', 'x1': 2090, 'x2': 2390, 'y1': 2090, 'y2': 2390, 'short':'ref4', 'med_elec':0, 'label':''}, - 'ref5': {'name': 'Reference Region 5', 'x1': 80, 'x2': 380, 'y1': 700, 'y2': 1000, 'short':'ref5', 'med_elec':0, 'label':''}, - 'ref6': {'name': 'Reference Region 6', 'x1': 80, 'x2': 380, 'y1': 3080, 'y2': 3380, 'short':'ref6', 'med_elec':0, 'label':''}, - 'amp1': {'name': 'Amplifier Region 1', 'x1': 300, 'x2': 500, 'y1': 5, 'y2': 20, 'short':'amp1', 'med_elec':0, 'label':''}, - 'amp2': {'name': 'Amplifier Region 2', 'x1': 3700, 'x2': 3900, 'y1': 5, 'y2': 20, 'short':'amp2', 'med_elec':0, 'label':''}, - 'coll': {'name': 'Ion Pump (Collimator side)', 'x1': 3700, 'x2': 4000, 'y1': 700, 'y2': 1000, 'short':'coll', 'med_elec':0, 'label':''}, - 'ech': {'name': 'Ion Pump (Echelle side)', 'x1': 3700, 'x2': 4000, 'y1': 3080, 'y2': 3380, 'short':'ech', 'med_elec':0, 'label':''} - } - for r in reg.keys(): - current_region = frame[reg[r]['y1']:reg[r]['y2'],reg[r]['x1']:reg[r]['x2']] - reg[r]['med_elec'] = np.median(current_region) - - #print(reg[r]['name'] + ': ' + str(np.round(reg[r]['med_elec'],1)) + ' e- per hour') - #print('Ion Pump pressure (Torr) - Collimator side: ' + f'{coll_pressure_torr:.1e}') - #print('Ion Pump pressure (Torr) - Echelle side: ' + f'{ech_pressure_torr:.1e}') - #print('Ion Pump current (A) - Collimator side: ' + f'{coll_current_a:.1e}') - #print('Ion Pump current (A) - Echelle side: ' + f'{ech_current_a:.1e}') - - from matplotlib.patches import Rectangle - plt.figure(figsize=(5, 4)) - plt.imshow(frame, - cmap='viridis', - origin='lower', - vmin=np.percentile(frame[300:3780,0:4080],5), - vmax=np.percentile(frame[300:3780,0:4080],95) - ) - for r in reg.keys(): - plt.gca().add_patch(Rectangle((reg[r]['x1'],reg[r]['y1']),reg[r]['x2']-reg[r]['x1'],reg[r]['y2']-reg[r]['y1'],linewidth=1,edgecolor='r',facecolor='none')) - plt.text(((reg[r]['short'] == 'ref3') or - (reg[r]['short'] == 'ref4') or - (reg[r]['short'] == 'ref5') or - (reg[r]['short'] == 'ref6') or - (reg[r]['short'] == 'amp1'))*(reg[r]['x1'])+ - ((reg[r]['short'] == 'ref1') or - (reg[r]['short'] == 'ref2') or - (reg[r]['short'] == 'ech') or - (reg[r]['short'] == 'coll') or - (reg[r]['short'] == 'amp2'))*(reg[r]['x2']), - (((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*(reg[r]['y1']-30)+ - ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*(reg[r]['y2']+30)), - str(np.round(reg[r]['med_elec'],1)) + timelabel, - weight='bold', - color='r', - ha=(((reg[r]['short'] == 'ref3') or - (reg[r]['short'] == 'ref4') or - (reg[r]['short'] == 'ref5') or - (reg[r]['short'] == 'ref6') or - (reg[r]['short'] == 'amp1'))*('left')+ - ((reg[r]['short'] == 'ref1') or - (reg[r]['short'] == 'ref2') or - (reg[r]['short'] == 'ech') or - (reg[r]['short'] == 'coll') or - (reg[r]['short'] == 'amp2'))*('right')), - va=(((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*('top')+ - ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*('bottom')) - ) - now = datetime.now() - coll_text = 'Ion Pump (Coll): \n' + (f'{coll_pressure_torr:.1e}' + ' Torr, ' + f'{coll_current_a*1e6:.1f}' + ' $\mu$A')*(coll_pressure_torr > 1e-9) + ('Off')*(coll_pressure_torr < 1e-9) - ech_text = 'Ion Pump (Ech): \n' + (f'{ech_pressure_torr:.1e}' + ' Torr, ' + f'{ech_current_a*1e6:.1f}' + ' $\mu$A')*(ech_pressure_torr > 1e-9) + ('Off')*(ech_pressure_torr < 1e-9) - #plt.text(4080, -250, now.strftime("%m/%d/%Y, %H:%M:%S"), ha='right', color='gray') - plt.text(4220, 500, coll_text, rotation=90, ha='center',fontsize = 6) - plt.text(4220, 3000, ech_text, rotation=90, ha='center',fontsize = 6) - plt.text(3950, 1500, 'Bench Side\n (blue side of orders)', rotation=90, ha='center', color='white',fontsize = 6) - plt.text( 150, 1500, 'Top Side\n (red side of orders)', rotation=90, ha='center', color='white',fontsize = 6) - plt.text(2040, 70, 'Collimator Side', rotation= 0, ha='center', color='white',fontsize = 6) - plt.text(2040, 3970, 'Echelle Side', rotation= 0, ha='center', color='white',fontsize = 6) - cbar = plt.colorbar() - cbar.set_label(timelabel)#, fontsize=18 - cbar.ax.tick_params()#labelsize=18 - cbar.ax.tick_params()#size=18 - plt.title(ccd_color[i_color]+' '+exposure_name) - plt.xlabel('Column (pixel number)') - plt.ylabel('Row (pixel number)')#fontsize=18 - plt.xticks()#KP.20230317.07770.97 - plt.yticks() - plt.grid(False) - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) + #histogram plt.close() + plt.figure(figsize=(5,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + + #print(np.percentile(flatten_counts,99.9),saturation_limit) + plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts)+'; Saturated? '+str(np.percentile(flatten_counts,99.99)>saturation_limit),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Fri, 9 Jun 2023 13:37:41 -0700 Subject: [PATCH 029/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 900590a8f..d48632efc 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -99,7 +99,7 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.percentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts)+'; Saturated? '+str(np.percentile(flatten_counts,99.99)>saturation_limit),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Fri, 9 Jun 2023 13:42:45 -0700 Subject: [PATCH 030/249] nightly_summary --- examples/nightly_test.cfg | 2 +- examples/nightly_test.recipe | 2 +- modules/quicklook/src/alg_nightly.py | 9 +++++++-- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/examples/nightly_test.cfg b/examples/nightly_test.cfg index d061e4d5b..14ea152c6 100644 --- a/examples/nightly_test.cfg +++ b/examples/nightly_test.cfg @@ -5,7 +5,7 @@ log_level = info log_verbose = True [ARGUMENT] -night = '20230606' +#night = '20230606' [MODULE_CONFIGS] quicklook = modules/quicklook/configs/default.cfg diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index 2a8305ad5..8f9f4f82f 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,4 +1,4 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night = config.ARGUMENT.night +night = '20230606' #config.ARGUMENT.night Nightly_summary(night) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index d48632efc..29207e21c 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -55,6 +55,8 @@ def nightly_procedures(self,night): L0_data = master_list[i] hdulist = fits.open(L0_data) hdr = hdulist[0].header + + exptime = hdr['EXPTIME'] print(hdulist.info()) #get ccd names @@ -72,11 +74,14 @@ def nightly_procedures(self,night): #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') + + if master_list[i].find('dark'):#scale up dark exposures + counts*=exptime + flatten_counts = np.ravel(counts) if len(flatten_counts)<1: continue #master_flatten_counts='None' - #if master_list[i].find('dark'):#scale up dark exposures - #master_flatten_counts*=hdr['EXPTIME'] + #2D image plt.figure(figsize=(5,4)) From 0ab44f742c7642ecaa07a65500e92307b15d5c0f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:47:07 -0700 Subject: [PATCH 031/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 29207e21c..94b8d48c2 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -120,7 +120,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - ''' + file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] @@ -139,4 +139,3 @@ def nightly_procedures(self,night): plt.ylabel('Temperature') plt.savefig(output_dir+night+'_temperature_variation.png') plt.close() - ''' From 3da2b95831cefecf047c6fc44e63860f2ce6c6d0 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:48:40 -0700 Subject: [PATCH 032/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 94b8d48c2..05cd0cb29 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -125,7 +125,7 @@ def nightly_procedures(self,night): date_obs = [] temp = [] for i in range(len(file_list)): - file_list[i] = file_list[i][18:-8] + #file_list[i] = file_list[i][18:-8] hdulist = fits.open(file_list[i]) hdr = hdulist[0].header From f9fa71f05d737d64ea1e099e67aae8af0f043200 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:50:19 -0700 Subject: [PATCH 033/249] nightly_summary --- examples/nightly_test.cfg | 1 - examples/nightly_test.recipe | 2 +- modules/quicklook/src/alg_nightly.py | 1 + 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/nightly_test.cfg b/examples/nightly_test.cfg index 14ea152c6..68e82c010 100644 --- a/examples/nightly_test.cfg +++ b/examples/nightly_test.cfg @@ -5,7 +5,6 @@ log_level = info log_verbose = True [ARGUMENT] -#night = '20230606' [MODULE_CONFIGS] quicklook = modules/quicklook/configs/default.cfg diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index 8f9f4f82f..3afdd2f44 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,4 +1,4 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night = '20230606' #config.ARGUMENT.night +night = '20230606' Nightly_summary(night) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 05cd0cb29..b56fe995b 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -129,6 +129,7 @@ def nightly_procedures(self,night): hdulist = fits.open(file_list[i]) hdr = hdulist[0].header + print(hdr) date_obs.append(hdr['DATE-OBS']) temp.append(hdr['TEMP']) From 20634f15cb4507b95ca3365b4df644b4959913b8 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 13:59:33 -0700 Subject: [PATCH 034/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index b56fe995b..699d096ac 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -131,12 +131,12 @@ def nightly_procedures(self,night): hdr = hdulist[0].header print(hdr) date_obs.append(hdr['DATE-OBS']) - temp.append(hdr['TEMP']) + temp.append(hdr['RELH']) date_obs = np.array(date_obs,'str') date_obs = Time(date_obs, format='isot', scale='utc') plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') - plt.ylabel('Temperature') - plt.savefig(output_dir+night+'_temperature_variation.png') + plt.ylabel('Relative Humidity') + plt.savefig(output_dir+night+'_Relative_Humidity_variation.png') plt.close() From 4e483162c3f0673035583156df5c87eeaaa8418d Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:01:16 -0700 Subject: [PATCH 035/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 699d096ac..6013cb1dc 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -138,5 +138,5 @@ def nightly_procedures(self,night): plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') - plt.savefig(output_dir+night+'_Relative_Humidity_variation.png') + plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From d0ecf0894c4aaf4da13740b9d30062490b374ec4 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:07:13 -0700 Subject: [PATCH 036/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 6013cb1dc..bcb397ba2 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -135,8 +135,10 @@ def nightly_procedures(self,night): date_obs = np.array(date_obs,'str') date_obs = Time(date_obs, format='isot', scale='utc') + plt.close() plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') + #plt.xlim(date_obs[0].jd,date_obs[-1].jd) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From 3fd5d63b1eb6a802778ee4262cefe267177deb92 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:09:34 -0700 Subject: [PATCH 037/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index bcb397ba2..f78eb4d27 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -114,7 +114,7 @@ def nightly_procedures(self,night): plt.legend(loc='lower right') #plt.savefig(output_dir+'fig/'+exposure_name+'_Histogram_'+ccd_color[i_color]+'.png') plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_histogram.png', dpi=200) - + plt.close() @@ -135,10 +135,10 @@ def nightly_procedures(self,night): date_obs = np.array(date_obs,'str') date_obs = Time(date_obs, format='isot', scale='utc') - plt.close() + plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') - #plt.xlim(date_obs[0].jd,date_obs[-1].jd) + plt.xlim(date_obs[0].jd,date_obs[-1].jd) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From c9cdd84f203e416edd96fc62ada3010e2b172394 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:10:56 -0700 Subject: [PATCH 038/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index f78eb4d27..d50875630 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -139,6 +139,6 @@ def nightly_procedures(self,night): plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') - plt.xlim(date_obs[0].jd,date_obs[-1].jd) + #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From 3763633765791862db0842441b6c655e26393201 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:11:57 -0700 Subject: [PATCH 039/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index d50875630..361f96f30 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -139,6 +139,6 @@ def nightly_procedures(self,night): plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') - #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) + plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From f582a891d8266e827415bb6485c8a1b350ae7d72 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:13:32 -0700 Subject: [PATCH 040/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 361f96f30..bd8d11340 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -139,6 +139,7 @@ def nightly_procedures(self,night): plt.scatter(date_obs.jd,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') + print(date_obs.jd,date_obs.utc) plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From ad81b6337695b5381350ff62eca4a71cb0d92e47 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:15:41 -0700 Subject: [PATCH 041/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index bd8d11340..616209b93 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -130,7 +130,7 @@ def nightly_procedures(self,night): hdulist = fits.open(file_list[i]) hdr = hdulist[0].header print(hdr) - date_obs.append(hdr['DATE-OBS']) + date_obs.append(hdr['DATE']) temp.append(hdr['RELH']) date_obs = np.array(date_obs,'str') @@ -140,6 +140,6 @@ def nightly_procedures(self,night): plt.xlabel('Time') plt.ylabel('Relative Humidity') print(date_obs.jd,date_obs.utc) - plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) + #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From 916d6a2bd2d3d319b0721e15cf94add79c0d7324 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:17:15 -0700 Subject: [PATCH 042/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 616209b93..9fb722278 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -135,8 +135,10 @@ def nightly_procedures(self,night): date_obs = np.array(date_obs,'str') date_obs = Time(date_obs, format='isot', scale='utc') - - plt.scatter(date_obs.jd,temp, marker = '.') + plt.close() + plt.figure(figsize=(8,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + plt.scatter(date_obs.utc,temp, marker = '.') plt.xlabel('Time') plt.ylabel('Relative Humidity') print(date_obs.jd,date_obs.utc) From c7897503d3b5fdde73fa5edffe2333a3be202340 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:18:38 -0700 Subject: [PATCH 043/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 9fb722278..1111fc3ec 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -138,10 +138,10 @@ def nightly_procedures(self,night): plt.close() plt.figure(figsize=(8,4)) plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) - plt.scatter(date_obs.utc,temp, marker = '.') - plt.xlabel('Time') + plt.scatter(date_obs.jd-2460000,temp, marker = '.') + plt.xlabel('Time (BJD-2460000)') plt.ylabel('Relative Humidity') - print(date_obs.jd,date_obs.utc) + #print(date_obs.jd,date_obs.utc) #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() From 1471bd17509afc824483f68c1ba9672f0721e3b2 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:21:24 -0700 Subject: [PATCH 044/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 1111fc3ec..42f30946d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -49,6 +49,7 @@ def nightly_procedures(self,night): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue exposure_name = master_list[i][23:-5] + version = master_list[i][23:-5] print(master_list[i],exposure_name) @@ -120,7 +121,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - + ''' file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] @@ -145,3 +146,4 @@ def nightly_procedures(self,night): #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') plt.close() + ''' From 08ffba89d6e2ae8c1a91937d9e1a539f380c0053 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 14:22:31 -0700 Subject: [PATCH 045/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 42f30946d..33a55f198 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -49,8 +49,8 @@ def nightly_procedures(self,night): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue exposure_name = master_list[i][23:-5] - version = master_list[i][23:-5] - print(master_list[i],exposure_name) + version = master_list[i][35:-5] + print(master_list[i],exposure_name,version) L0_data = master_list[i] From ebd53d7bb7012b1327a18e5b99287b16d48603f2 Mon Sep 17 00:00:00 2001 From: cwang2016 Date: Mon, 5 Jun 2023 10:55:47 -0700 Subject: [PATCH 046/249] feature/CCF_summ add extension to record original CCF before reweighting, and configuation argument to set the mask allowed for reweighting. rv error is computed based on original CCF. rv is computed based on summation of CCF across the orderlet and the segments. update mask for sky to be G2_espresso in case sci is with espresso mask. add GREEN_CCD_RW & RED_CCD_RW to contain the reweighted CCF or the original CCF if no reweighting is made. Enlarge rv window dynamically for CCF computation if needed to cover the rv window for non-espresso mask. --- configs/kpf_drp.cfg | 4 +- kpfpipe/models/metadata/KPF_definitions.py | 3 + .../configs/default_recipe_kpf_targ.cfg | 2 +- modules/radial_velocity/src/alg.py | 123 ++++-- modules/radial_velocity/src/alg_rv_init.py | 31 +- .../radial_velocity/src/radial_velocity.py | 244 ++++++++---- .../src/radial_velocity_reweighting.py | 362 ++++++++++-------- recipes/kpf_drp.recipe | 15 +- 8 files changed, 498 insertions(+), 286 deletions(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index 2d60e0477..ccffc6d32 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -21,7 +21,7 @@ masters_dir = /data/masters/ output_trace = masters/ output_extraction = L1/ output_rv = L2/ -output_rv_reweighting = '' +output_rv_reweighting='' output_qlp = QLP/ output_barycorr = bary/ @@ -90,6 +90,8 @@ max_cal_file_age = '2 days' orderlet_names_rv = [['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX', 'GREEN_SKY_FLUX'],['RED_SCI_FLUX1', 'RED_SCI_FLUX2', 'RED_SCI_FLUX3', 'RED_CAL_FLUX', 'RED_SKY_FLUX']] rv_correct_by_cal = False reweighting_method = ccf_static +#reweighting_enable_masks = [['espresso', 'lfc', 'thar', 'etalon'], ['espresso', 'lfc', 'thar', 'etalon']] +reweighting_enable_masks = [['espresso'], ['espresso']] ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV #static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] diff --git a/kpfpipe/models/metadata/KPF_definitions.py b/kpfpipe/models/metadata/KPF_definitions.py index 971df954e..bbc2b86ea 100644 --- a/kpfpipe/models/metadata/KPF_definitions.py +++ b/kpfpipe/models/metadata/KPF_definitions.py @@ -108,6 +108,9 @@ 'GREEN_CCF': fits.ImageHDU, 'RED_CCF': fits.ImageHDU, + 'GREEN_CCF_RW': fits.ImageHDU, + 'RED_CCF_RW': fits.ImageHDU, + 'RV': fits.BinTableHDU, 'ACTIVITY': fits.BinTableHDU} diff --git a/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg b/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg index b4b6bb746..e86b2fd34 100644 --- a/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg +++ b/modules/radial_velocity/configs/default_recipe_kpf_targ.cfg @@ -38,4 +38,4 @@ ccf_engine = c reweighting_ccf_method = ccf_max # km/s/pixel -veloctity_span_pixel = 0.87 +vel_span_pixel = 0.87 diff --git a/modules/radial_velocity/src/alg.py b/modules/radial_velocity/src/alg.py index 35eedc024..da696432a 100644 --- a/modules/radial_velocity/src/alg.py +++ b/modules/radial_velocity/src/alg.py @@ -82,7 +82,8 @@ class RadialVelocityAlg(RadialVelocityBase): SEGMENT_W2 = 4 SEGMENT_ORD = 5 CCF_Methods = ['ccf_max', 'ccf_mean', 'ccf_static', 'ccf_steps'] - vel_range_per_mask = {'lfc': 5.0, 'thar': 5.0, 'etalon': 5.0} + s_range = abs(RadialVelocityAlgInit.non_espresso_vel_range[0])/2.0 + vel_range_per_mask = {'lfc': s_range, 'thar': s_range, 'etalon': s_range} map_fibers_mask = {'kpf': {'sci': RadialVelocityAlgInit.KEY_SCI_OBJ, @@ -114,6 +115,8 @@ def __init__(self, spectrum_data, header, init_rv, wave_cal=None, config=None, l init_data = init_rv['data'] RadialVelocityBase.__init__(self, config, logger) + + # needs update per orderlet: spectrum_data, wave_cal, header, zb, orderletname, orderlet_mask_line self.spectrum_data = spectrum_data self.wave_cal = wave_cal self.header = header @@ -160,12 +163,11 @@ def __init__(self, spectrum_data, header, init_rv, wave_cal=None, config=None, l @staticmethod def comp_velocity_span_pixel(init_data, config, instrument): - vel_span_pixel = None if init_data is not None: vel_span_pixel = init_data[RadialVelocityAlgInit.VEL_SPAN_PIXEL] else: vel_span_pixel = \ - RadialVelocityAlg.get_config_value(config, instrument, RadialVelocityAlgInit.VEL_SPAN_PIXEL, 0.87) + RadialVelocityAlg.get_config_value(config, instrument, RadialVelocityAlgInit.VEL_SPAN_PIXEL, None) return vel_span_pixel @staticmethod @@ -179,7 +181,7 @@ def get_config_value(config, instrument, c_key, default_v): val = None if config is not None: config_param = None - if instrument is not None and config.has_section(instrument): + if instrument is not None and config.has_section(instrument.upper()): config_param = config[instrument.upper()] elif config.has_section('PARAM'): config_param = config['PARAM'] @@ -201,6 +203,8 @@ def reset_spectrum(self, spec_data, header, wave_cal, orderlet=None): self.wave_cal = wave_cal self.header = header self.orderletname = orderlet + self.orderlet_mask_line = None + self.zb = None return def get_spectrum(self): @@ -402,6 +406,7 @@ def get_redshift_kpf(self, seg=0, default=None): or ('IMTYPE' in self.header and self.header['IMTYPE'].lower() != 'object'): # if self.init_data['mask_type'] in ['lfc', 'thar'] or \ bc = 0.0 + # self.d_print("bc: ", bc) elif self.bary_corr_table is not None and np.shape(self.bary_corr_table.values)[0] > ord_idx: bc = self.bary_corr_table['BARYVEL'][ord_idx + self.start_bary_index] else: @@ -692,6 +697,7 @@ def get_mask_line(self): self.orderlet_mask_line = self.mask_line else: fiber_in_maskline = self.get_fiber_key_in_maskline() + # self.d_print("get mask line from ", fiber_in_maskline) if fiber_in_maskline is not None: self.orderlet_mask_line = self.mask_line[fiber_in_maskline] @@ -1056,7 +1062,7 @@ def gaussian_rv(v_cut, rv_mean, sd): else: g_fit2 = None - if vel_span_pixel is not None and g_fit is not None: + if vel_span_pixel is not None and vel_span_pixel != 0.0 and g_fit is not None: if g_fit2 is None or math.isnan(g_fit.mean.value): g_mean = rv_guess # use the 1st guess if the 2nd fitting fails f_wid = velocity_cut # use the 1st vel range if the 2nd fitting fails @@ -1073,11 +1079,11 @@ def gaussian_rv(v_cut, rv_mean, sd): else: return None, 0.0, None, None, 0.0 - def compute_segments_ccf(self, ccf): - total_segments = np.shape(ccf)[0] - RadialVelocityAlg.ROWS_FOR_ANALYSIS + def compute_segments_ccf(self, ccf, analysis_rows=False, pixel_span=None): + total_segments = np.shape(ccf)[0] - RadialVelocityAlg.ROWS_FOR_ANALYSIS if analysis_rows else np.shape(ccf)[0] rv_segments = np.zeros(total_segments) erv_segments = np.zeros(total_segments) - v_span = self.get_vel_span_pixel() + v_span = pixel_span mask_type = self.get_orderlet_masktype(self.spectro, self.orderletname, self.init_data) for i in range(total_segments): _, rv_segments[i], _, _, erv_segments[i] = self.fit_ccf( @@ -1088,37 +1094,57 @@ def compute_segments_ccf(self, ccf): ) return rv_segments, erv_segments - def output_ccf_to_dataframe(self, ccf, ref_head=None, ervs=None, ref_ccf=None): + def output_ccf_to_dataframe(self, ccf, ref_head=None, original_ccf=None, ccf_rw_nonnorm=None, ref_ccf=None): """Convert cross correlation data to be in the form of Pandas DataFrame. Args: - ccf (numpy.ndarray): Result of cross correlation computation and analysis. + ccf (numpy.ndarray): Result of cross correlation computation and analysis, original or reweighted, + used to compute rv. ref_head (fits.header.Header, optional): Reference fits header. Defaults to None. + original_ccf (numpy.ndarray): Original cross correlation result of all segments. + original_ccf has the same ccf for each segment as that from 'ccf', + if no reweighting is involved. + ccf_rw_nonnorm (numpy.ndarray): Reweighted non-normalized ccf, used to compute rv error instead of using + 'ccf' if 'ccf' is the reweighted ccf. + ref_ccf (numpy.ndarray): reweight ratio. Returns: Pandas.DataFrame: Result of cross correlation in form of Pandas DataFrame and the resultant radial velocity is stored as the value of attribute `CCF-RVC`. - + + Notes: if ref_ccf is not None, ccf_rw_nonnorm is set with non-normalized reweighted CCF too. + """ ccf_table = {} for i in range(self.velocity_steps): ccf_table['vel-'+str(i)] = ccf[:, i] results = pd.DataFrame(ccf_table) - - # calculate rv on ccfs summation + # calculate final rv and final rv err on ccfs summation + # based on original ccf or reweighted cc, _, rv_result, _, _, rv_error = self.fit_ccf( - ccf[-1, :], self.get_rv_guess(), self.init_data[RadialVelocityAlgInit.VELOCITY_LOOP], - self.get_orderlet_masktype(self.spectro, self.orderletname, self.init_data), - rv_guess_on_ccf=(self.spectro == 'kpf'), - vel_span_pixel=self.get_vel_span_pixel()) + ccf[-1, :], self.get_rv_guess(), self.init_data[RadialVelocityAlgInit.VELOCITY_LOOP], + self.get_orderlet_masktype(self.spectro, self.orderletname, self.init_data), + rv_guess_on_ccf=(self.spectro == 'kpf'), + vel_span_pixel=self.get_vel_span_pixel() if ref_ccf is None else None) - # compute rv and erv on each segment and overwrite erv which is computed before reweighting if needed - rv_segments, erv_segments = self.compute_segments_ccf(ccf) - if ervs is not None: - erv_segments = ervs - ratio_ccf = ref_ccf if ref_ccf is not None else None + if ref_ccf is not None and ccf_rw_nonnorm is not None: # use original ccf to get rv error + _, _, _, _, rv_error = self.fit_ccf( + ccf_rw_nonnorm[-1, :], self.get_rv_guess(), self.init_data[RadialVelocityAlgInit.VELOCITY_LOOP], + self.get_orderlet_masktype(self.spectro, self.orderletname, self.init_data), + rv_guess_on_ccf=(self.spectro == 'kpf'), + vel_span_pixel=self.get_vel_span_pixel() + ) + + # compute rv and erv on each segment + rv_segments, erv_segments = self.compute_segments_ccf(ccf, analysis_rows=True, + pixel_span=self.get_vel_span_pixel() if ref_ccf is None else None) + if ref_ccf is not None and ccf_rw_nonnorm is not None: + _, erv_segments = self.compute_segments_ccf(ccf_rw_nonnorm, analysis_rows=True, + pixel_span=self.get_vel_span_pixel()) + + ratio_ccf = ref_ccf def f_decimal(num): return float("{:.10f}".format(num)) @@ -1138,20 +1164,22 @@ def f_decimal(num): else: results.attrs['CCFJDSUM'] = self.get_obs_time(seg=-1) # exposure time from the header results.attrs['CCFJDSEG'] = self.obs_jd # an array for all segments - results.attrs['CCF-RVC'] = (f_decimal(rv_result), 'BaryC RV (km/s)') + results.attrs['CCF-RVC'] = (f_decimal(rv_result), 'BaryC RV (km/s)') # rv based on CCF summation results.attrs['CCFSTART'] = self.init_data[RadialVelocityAlgInit.VELOCITY_LOOP][0] results.attrs['CCFSTEP'] = self.rv_config[RadialVelocityAlgInit.STEP] results.attrs['TOTALSTP'] = self.velocity_steps results.attrs['STARTORD'] = self.start_order results.attrs['ENDORDER'] = self.end_order results.attrs['TOTALORD'] = self.end_order - self.start_order+1 - results.attrs['BARY'] = self.zb * 1.0e-3 # from m/sec to km/sec, an array for all segments + results.attrs['BARY'] = self.zb * 1.0e-3 # from m/sec to km/sec, an array for all segments results.attrs['STARRV'] = self.init_data[RadialVelocityAlgInit.RV_CONFIG][RadialVelocityAlgInit.STAR_RV] - results.attrs['CCF-ERV'] = f_decimal(rv_error) # error on ccf summation - results.attrs['ERV_SEGMS'] = erv_segments - results.attrs['RV_SEGMS'] = rv_segments - results.attrs['RV_MEAN'] = self.weighted_rv(rv_segments, rv_segments.size, ratio_ccf) - results.attrs['ERV_MEAN'] = self.weighted_rv_error(erv_segments, erv_segments.size, ratio_ccf) # error on mean erv + results.attrs['CCF-ERV'] = f_decimal(rv_error) # rv error on ccf summation + results.attrs['ERV_SEGMS'] = erv_segments # erv for each order (segment) + results.attrs['RV_SEGMS'] = rv_segments # rv for each segment + results.attrs['RV_MEAN'] = self.weighted_rv(rv_segments, rv_segments.size, ratio_ccf) # weighted rv + results.attrs['ERV_MEAN'] = self.weighted_rv_error(erv_segments, erv_segments.size, ratio_ccf) # weighted rv error + results.attrs['ORI_CCF'] = original_ccf if ref_ccf is not None else None + results.attrs['CCF_NONNORM'] = ccf_rw_nonnorm if ccf_rw_nonnorm is not None else original_ccf return results @@ -1228,16 +1256,25 @@ def compute_rv_by_cc(self, start_seg=None, end_seg=None, ref_ccf=None, print_pro return {'ccf_df': None, 'ccf_ary': None, 'jd': self.obs_jd, 'msg': msg} total_seg_rv = np.shape(ccf)[0] - self.ROWS_FOR_ANALYSIS - erv_segments = None - if ref_ccf is not None: - rv_segs, erv_segments = self.compute_segments_ccf(ccf) - - ccf, _ = self.reweight_ccf(ccf[0:total_seg_rv, :], end_seg-start_seg+1, ref_ccf, self.reweighting_ccf_method, - s_seg=start_seg) analyzed_ccf = self.analyze_ccf(ccf) + analyzed_rw_ccf = None + analyzed_rw_ccf_nonnorm = None - df = self.output_ccf_to_dataframe(analyzed_ccf, ervs=erv_segments, ref_ccf=ref_ccf ) + if ref_ccf is not None: + analyzed_rw_ccf, _ = self.reweight_ccf(ccf[0:total_seg_rv, :], end_seg-start_seg+1, ref_ccf, + self.reweighting_ccf_method, + s_seg=start_seg, do_analysis=True) + + # note: it is made the same as analyzed_ccf, no reweighting. + analyzed_rw_ccf_nonnorm, _ = self.reweight_ccf(ccf[0:total_seg_rv, :], end_seg-start_seg+1, ref_ccf, + self.reweighting_ccf_method, + s_seg=start_seg, normalized=False, do_analysis=True) + + df = self.output_ccf_to_dataframe((analyzed_ccf if analyzed_rw_ccf is None else analyzed_rw_ccf), + original_ccf=analyzed_ccf, + ccf_rw_nonnorm=analyzed_rw_ccf_nonnorm, + ref_ccf=ref_ccf) return {'ccf_df': df, 'ccf_ary': analyzed_ccf, 'jd': self.obs_jd} @staticmethod @@ -1435,8 +1472,8 @@ def weighted_rv_error(sigma_arr, total_segment, reweighting_table_or_ccf, s_seg= return res @staticmethod - def reweight_ccf(crt_rv, total_segment, reweighting_table_or_ccf, reweighting_method, s_seg=0, - do_analysis=False, velocities=None): + def reweight_ccf(crt_rv, total_segment, rw_table_or_ccf, reweighting_method, s_seg=0, + do_analysis=False, velocities=None, normalized=True): """Reweighting ccf orders. Reweight the CCF ordres based on the given CCF ratios or CCF orders from the observation template. @@ -1444,7 +1481,7 @@ def reweight_ccf(crt_rv, total_segment, reweighting_table_or_ccf, reweighting_me Args: crt_rv (numpy.ndarray): CCF orders. total_segment (int): Total segments for reweighting. It is in default from the first row of `crt_rv`. - reweighting_table_or_ccf (numpy.ndarray): Ratios among CCF orders or CCF data from the observation template. + rw_table_or_ccf (numpy.ndarray): Ratios among CCF orders or CCF data from the observation template. reweighting_method (str): Reweighting methods, **ccf_max**, **ccf_mean**, or **ccf_steps**. s_seg (int, optional): The start order index for reweighting. This is used to select the row from `crt_rv` in case the order index column is included in `reweighting_table_or_ccf`. Defaults to 0. @@ -1452,6 +1489,7 @@ def reweight_ccf(crt_rv, total_segment, reweighting_table_or_ccf, reweighting_me :func:`~alg.RadialVelocityAlg.analysis_ccf()` dose on CCF orders. Defaults to False. velocities (np.ndarray, optional): 1D array consisting of the velocity loop for cross-correlation computation. Used when `do_analysis` is **True**. Defaults to None. + normalized (bool): Do normalization or not for 'ccf_static' method. Returns: numpy.ndarray: 2D array containing Reweighted CCF orders and the velocity loop and the CCF summation @@ -1464,18 +1502,21 @@ def reweight_ccf(crt_rv, total_segment, reweighting_table_or_ccf, reweighting_me # the order index of crt_rv and reweighting_table_or_ccf are aligned - if reweighting_table_or_ccf is None: + if rw_table_or_ccf is None: raise Exception("no valid data from observation template") if not reweighting_method or not RadialVelocityAlg.is_good_reweighting_method(reweighting_method): raise Exception("invalid reweighting method") ny, nx = np.shape(crt_rv) + reweighting_table_or_ccf = rw_table_or_ccf.copy() total_segment = min(total_segment, ny) if reweighting_method == 'ccf_max' or reweighting_method == 'ccf_mean' or reweighting_method == 'ccf_static': # if the ratio table containing a column of order index, using s_order to select the ratio with # order index from s_order to s_order+total_order-1 + if not normalized: + reweighting_table_or_ccf[:, -1] = 1.0 if np.shape(reweighting_table_or_ccf)[1] >= 2: s_seg = 0 if s_seg is None else s_seg e_seg = s_seg + total_segment - 1 @@ -1492,7 +1533,7 @@ def reweight_ccf(crt_rv, total_segment, reweighting_table_or_ccf, reweighting_me new_crt_rv = np.zeros([ny + RadialVelocityAlg.ROWS_FOR_ANALYSIS, nx]) if reweighting_method == 'ccf_static': - ccf_sums = np.nansum(crt_rv, axis=1) # summation along each order + ccf_sums = np.nansum(crt_rv, axis=1) if normalized else np.ones(total_segment) # summation along each order for idx in range(total_segment): if ccf_sums[idx] > 0. and tval[idx] != 0.0: diff --git a/modules/radial_velocity/src/alg_rv_init.py b/modules/radial_velocity/src/alg_rv_init.py index b7898531b..7538d30d1 100644 --- a/modules/radial_velocity/src/alg_rv_init.py +++ b/modules/radial_velocity/src/alg_rv_init.py @@ -30,7 +30,6 @@ 'lfc': ('kpf_lfc_mask_1025.mas', 'vac'), 'etalon': ('kpf_etalon_masks_11may2023.csv', 'vac')} - class RadialVelocityAlgInit(RadialVelocityBase): """ Radial velocity Init. @@ -117,6 +116,8 @@ class RadialVelocityAlgInit(RadialVelocityBase): KEY_SKY_OBJ = 'SKY-OBJ' KEY_CAL_OBJ = 'CAL-OBJ' + non_espresso_vel_range = [-10, 10] + def __init__(self, config=None, logger=None, l1_data=None, bc_time=None, bc_period=380, bc_corr_path=None, bc_corr_output=None, test_data=None): RadialVelocityBase.__init__(self, config, logger) @@ -251,7 +252,8 @@ def init_star_from_header(self): if stellar_dir is None: return self.ret_status(self.STELLAR_DIR + not_defined) - for fobj in [RadialVelocityAlgInit.KEY_SKY_OBJ, RadialVelocityAlgInit.KEY_SCI_OBJ, RadialVelocityAlgInit.KEY_CAL_OBJ]: + sci_mask = None + for fobj in [RadialVelocityAlgInit.KEY_SCI_OBJ, RadialVelocityAlgInit.KEY_CAL_OBJ, RadialVelocityAlgInit.KEY_SKY_OBJ]: try: fiber_obj = self.pheader[fobj] except KeyError: @@ -283,6 +285,11 @@ def init_star_from_header(self): if default_mask not in mask_file_map: return self.ret_status('default mask of ' + default_mask + ' is not defined') + if fobj == RadialVelocityAlgInit.KEY_SCI_OBJ: + sci_mask = default_mask + if fobj == RadialVelocityAlgInit.KEY_SKY_OBJ and sci_mask is not None and 'espresso' in sci_mask.lower(): + default_mask = 'G2_espresso' + self.mask_path = self.test_data_dir + stellar_dir + mask_file_map[default_mask][0] self.mask_type = default_mask self.mask_wavelengths = mask_file_map[default_mask][1] @@ -291,6 +298,7 @@ def init_star_from_header(self): "mask_type": self.mask_type, "mask_wavelengths": self.mask_wavelengths} + self.d_print("RadialVelocityAlgInit: mask orderlet: ", self.mask_orderlet) return self.ret_status('ok') @@ -514,7 +522,7 @@ def get_reweighting_ccf_method(self, default_method='ccf_max'): self.reweighting_ccf_method = self.get_value_from_config(self.REWEIGHTING_CCF, default=default_method) return self.reweighting_ccf_method - def get_velocity_width_per_pixel(self, default=0.87): + def get_velocity_width_per_pixel(self, default=None): """Get velocity span per ccd pixel Args: default (float): default velocity span per pixel. @@ -525,6 +533,8 @@ def get_velocity_width_per_pixel(self, default=0.87): """ if self.vel_span_pixel is None: self.vel_span_pixel = self.get_value_from_config(self.VEL_SPAN_PIXEL, default=default) + if self.vel_span_pixel is not None: + self.vel_span_pixel = float(self.vel_span_pixel) return self.vel_span_pixel @@ -539,7 +549,20 @@ def get_step_range(self, default='[-80, 81]'): """ if self.STEP_RANGE not in self.rv_config: - self.rv_config[self.STEP_RANGE] = self.get_rv_config_value(self.STEP_RANGE, default=default) + srange = self.get_rv_config_value(self.STEP_RANGE, default=default) + if self.rv_config[self.START_VEL] is not None: + srange = [0, (srange[1]-srange[0])] + v_loop = np.array(srange) * self.rv_config[self.STEP] + self.rv_config[self.START_VEL] + else: + v_loop = np.array(srange) * self.rv_config[self.STEP] + self.rv_config[self.STAR_RV] + vel_range = self.non_espresso_vel_range + left_dis = vel_range[0] - v_loop[0] + right_dis = v_loop[-1] - vel_range[-1] + left_inc = int((-left_dis + 0.5) / self.rv_config[self.STEP]) if left_dis < 0 else 0 + right_inc = int((-right_dis + 0.5) / self.rv_config[self.STEP]) if right_dis < 0 else 0 + inc = max(left_inc, right_inc) + self.rv_config[self.STEP_RANGE] = [srange[0]-inc, srange[1]+inc] + return self.rv_config[self.STEP_RANGE] def get_velocity_loop(self): diff --git a/modules/radial_velocity/src/radial_velocity.py b/modules/radial_velocity/src/radial_velocity.py index 9ce9a2259..ea9bc8e14 100644 --- a/modules/radial_velocity/src/radial_velocity.py +++ b/modules/radial_velocity/src/radial_velocity.py @@ -141,13 +141,13 @@ class RadialVelocity(KPF1_Primitive): rv_col_on_orderlet = [RV_COL_ORDERLET, RV_COL_SOURCE] orderlet_key_map = {'kpf':{ - 'sci_flux1': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv':'rv1', 'erv':'erv1'}}, - 'sci_flux2': {'ccf': {'mask': 'sci_mask', 'name': 'ccf2'}, 'rv': {'rv': 'rv2', 'erv': 'erv2'}}, - 'sci_flux3': {'ccf': {'mask': 'sci_mask', 'name': 'ccf3'}, 'rv': {'rv': 'rv3', 'erv': 'erv3'}}, - 'sky_flux': {'ccf': {'mask': 'sky_mask', 'name': 'ccf5'}, 'rv': {'rv': 'rvs', 'erv': 'ervs'}}, - 'cal_flux': {'ccf': {'mask': 'cal_mask', 'name': 'ccf4'}, 'rv': {'rv': 'rvc', 'erv': 'ervc'}}}, + 'sci_flux1': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv':'rv1', 'erv':'erv1', 'ccf':'ccf1'}}, + 'sci_flux2': {'ccf': {'mask': 'sci_mask', 'name': 'ccf2'}, 'rv': {'rv': 'rv2', 'erv': 'erv2', 'ccf':'ccf2'}}, + 'sci_flux3': {'ccf': {'mask': 'sci_mask', 'name': 'ccf3'}, 'rv': {'rv': 'rv3', 'erv': 'erv3', 'ccf':'ccf3'}}, + 'sky_flux': {'ccf': {'mask': 'sky_mask', 'name': 'ccf5'}, 'rv': {'rv': 'rvs', 'erv': 'ervs', 'ccf':'ccfs'}}, + 'cal_flux': {'ccf': {'mask': 'cal_mask', 'name': 'ccf4'}, 'rv': {'rv': 'rvc', 'erv': 'ervc', 'ccf':'ccfc'}}}, 'neid': { - 'sci': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv': 'rv1', 'erv': 'erv1'}} + 'sci': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv': 'rv1', 'erv': 'erv1', 'ccf':'ccf1'}} } } orderlet_rv_col_map = {'kpf':{ @@ -206,6 +206,13 @@ def __init__(self, else: self.ins = None + self.reweighting_masks = action.args['reweighting_masks'] if 'reweighting_masks' in args_keys else None + if self.reweighting_masks is None or not self.reweighting_masks: + if self.ins.lower() == 'kpf': + self.reweighting_masks = ['espresso', 'lfc', 'thar', 'etalon'] + else: + self.reweighting_masks = None + # input configuration self.config = configparser.ConfigParser() try: @@ -269,7 +276,8 @@ def __init__(self, if not self.rv_init['data'][mod]: self.input.header[sci]['MASK'] = self.rv_init['data'][mtype] elif self.ins is not None: - self.rv_init['data'][mod][RadialVelocityAlg.get_fiber_object_in_header(self.ins, sci)][mtype] + self.input.header[sci]['MASK'] = \ + self.rv_init['data'][mod][RadialVelocityAlg.get_fiber_object_in_header(self.ins, sci)][mtype] self.total_orderlet = len(self.spectrum_data_set) do_rv_corr = False @@ -297,7 +305,7 @@ def __init__(self, self.mask_collection.append(m_type) vel_span_pixel = action.args['vel_span_pixel'] if 'vel_span_pixel' in args_keys else None - self.reweighted = 'F' + self.reweighted = {} try: if self.ins is None: @@ -315,7 +323,6 @@ def __init__(self, except Exception as e: self.alg = None - def _pre_condition(self) -> bool: """ Check for some necessary pre conditions @@ -330,32 +337,51 @@ def _post_condition(self) -> bool: """ return True - @staticmethod - def is_sci(sci_name): + def is_sci(self, sci_name): return 'sci' in sci_name.lower() - @staticmethod - def is_cal(sci_name): + def is_cal(self, sci_name): return 'cal' in sci_name.lower() - @staticmethod - def is_sky(sci_name): + def is_sky(self, sci_name): return 'sky' in sci_name.lower() - def get_ratio_ccf(self, od_name): + def get_ratio_ccf(self, od_name): ratio_ccf = None + + def is_mask_enable(mask): + return mask is not None and \ + (self.reweighting_masks is None or any([m.lower() in mask.lower() for m in self.reweighting_masks])) + if self.ref_ccf is not None: if self.reweighting_method.lower() == 'ccf_static': - for idx, r_col in enumerate(self.ref_ccf.columns): - m_type = self.input.header[od_name]['MASK'] - if m_type is not None and r_col.lower() == m_type.lower(): + m_type = self.input.header[od_name]['MASK'] + if is_mask_enable(m_type): + idx = [c.lower() for c in self.ref_ccf.columns].index(m_type.lower()) + if idx >= 0: col_idx = np.array([0, idx], dtype=int) ratio_ccf = self.ref_ccf.values[:, col_idx] - break else: ratio_ccf = self.ref_ccf.values if isinstance(self.ref_ccf, pd.DataFrame) else self.ref_ccf return ratio_ccf + def get_rv_unit(self, od_name, is_final=False): + rv_unit = 'Bary-corrected RV (km/s)' + rvc_unit = 'Cal fiber RV (km/s)' + rvs_unit = 'Sky fiber RV (km/s)' + sky_note = ', including SKY RV ' if self.is_solar_data else '' + + if self.is_cal(od_name): + unit = rvc_unit + elif self.is_sky(od_name): + unit = rvs_unit + else: + if is_final: + unit = rv_unit + sky_note + else: + unit = rv_unit + return unit + def _perform(self): """ Primitive action - @@ -385,8 +411,7 @@ def _perform(self): self.logger.info('RadialVelocity: computing radial velocity on orderlet '+ self.od_names[i] + '...') ratio_ccf = self.get_ratio_ccf(self.od_names[i]) - if ratio_ccf is not None: - self.reweighted = 'T' + self.reweighted[self.od_names[i]] = 'T' if ratio_ccf is not None else 'F' rv_results = self.alg.compute_rv_by_cc(start_seg=self.start_seg, end_seg=self.end_seg, ref_ccf=ratio_ccf) one_df = rv_results['ccf_df'] if one_df is None or one_df.empty or not one_df.values.any(): @@ -401,7 +426,6 @@ def _perform(self): if self.logger: self.logger.info("RadialVelocity: no L2 produced") return Arguments(None) - self.construct_level2_data(output_df) self.output_level2.receipt_add_entry('RadialVelocity', self.__module__, f'config_path={self.config_path}', 'PASS') @@ -419,6 +443,12 @@ def get_map_key(self, od_name): return k return '' + def add_ext_key(self, ext, key, key_val): + KEY8 = 8 + key_name = key[0:KEY8] if len(key) > KEY8 else key + if key_name not in ext: + ext[key_name] = key_val + def construct_level2_data(self, output_df): if self.output_level2 is None: self.output_level2 = KPF2.from_l1(self.input) @@ -430,13 +460,24 @@ def construct_level2_data(self, output_df): # make new rv table and append the new one to the existing one if there is new_rv_table = self.make_rv_table(output_df) self.output_level2.header[self.rv_ext]['star_rv'] = new_rv_table.attrs['star_rv'] + + # ccfxrw: if rw is done on ccfx + for od_name in self.od_names: + od_key = self.get_map_key(od_name) + ccf_key = self.orderlet_key_map[self.ins][od_key]['rv']['ccf'] + if 'rw'+ccf_key not in self.output_level2.header[self.rv_ext]: + self.output_level2.header[self.rv_ext]['rw'+ccf_key] = self.reweighted[od_name] + + # velocity range for non espresso mask + for m in RadialVelocityAlg.vel_range_per_mask.keys(): + if m in self.mask_collection: + self.add_ext_key(self.output_level2.header[self.rv_ext], 'vr_' + m, + RadialVelocityAlg.vel_range_per_mask[m]) + crt_rv_ext = self.output_level2[self.rv_ext] if hasattr(self.output_level2, self.rv_ext) else None if crt_rv_ext is None or np.shape(crt_rv_ext)[0] == 0: self.output_level2[self.rv_ext] = new_rv_table self.output_level2.header[self.rv_ext]['ccd'+str(self.rv_set_idx+1)+'row'] = 0 - for m in RadialVelocityAlg.vel_range_per_mask.keys(): - if m in self.mask_collection: - self.output_level2.header[self.rv_ext]['vr_' + m] = RadialVelocityAlg.vel_range_per_mask[m] else: first_row = np.shape(crt_rv_ext)[0] new_table_list = {} @@ -460,53 +501,73 @@ def construct_level2_data(self, output_df): od_key = self.get_map_key(od_name) rv_key = self.orderlet_key_map[self.ins][od_key]['rv']['rv'] erv_key = self.orderlet_key_map[self.ins][od_key]['rv']['erv'] - self.output_level2.header[self.rv_ext][ccd_p+rv_key] = new_rv_table.attrs[ccd_+rv_key] self.output_level2.header[self.rv_ext][ccd_p+erv_key] = new_rv_table.attrs[ccd_+erv_key] + self.output_level2.header[self.rv_ext][ccd_p+'rv'] = new_rv_table.attrs[ccd_+'rv'] self.output_level2.header[self.rv_ext][ccd_p+'erv'] = new_rv_table.attrs[ccd_+'erv'] self.output_level2.header[self.rv_ext][ccd_p+'jd'] = new_rv_table.attrs[ccd_+'jd'] self.output_level2.header[self.rv_ext]['rv'+str(self.rv_set_idx+1)+'corr'] = 'T' \ if new_rv_table.attrs['do_rv_corr'] else 'F' - self.output_level2.header[self.rv_ext]['rwccfrv']= self.reweighted # raw ccf and rv, before reweighting + return True def make_ccf_table(self, output_df): total_orderlet = len(output_df.keys()) + total_segment = 0 for o_name, o_df in output_df.items(): if o_df is not None: total_segment = np.shape(o_df.values)[0] - self.alg.ROWS_FOR_ANALYSIS break - all_ccf = np.zeros((self.total_orderlet, total_segment, + if total_segment == 0: + return True + original_ccf = np.zeros((self.total_orderlet, total_segment, + self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_STEPS])) + reweight_ccf = np.zeros((self.total_orderlet, total_segment, self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_STEPS])) for i in range(total_orderlet): # make ccf in 3d format, each ccf layer is for one SCI orderlet if output_df[self.od_names[i]] is not None: - all_ccf[i, :, :] = output_df[self.od_names[i]].values[0:total_segment, :] - - self.output_level2[self.ccf_ext] = all_ccf - self.output_level2.header[self.ccf_ext]['startseg'] = self.start_seg - self.output_level2.header[self.ccf_ext]['startv'] = \ - (self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP][0], 'km/sec') - self.output_level2.header[self.ccf_ext]['stepv'] = \ - (self.rv_init['data']['rv_config'][RadialVelocityAlgInit.STEP], 'km/sec') - self.output_level2.header[self.ccf_ext]['totalv'] = self.rv_init['data']['velocity_steps'] - self.output_level2.header[self.ccf_ext]['totalsci'] = len(self.sci_names) + reweight_ccf[i, :, :] = output_df[self.od_names[i]].values[0:total_segment, :] + if self.reweighted[self.od_names[i]] == 'T': + original_ccf[i, :, :] = output_df[self.od_names[i]].attrs['ORI_CCF'][0:total_segment, :] + else: + original_ccf[i, :, :] = output_df[self.od_names[i]].values[0:total_segment, :] + + # extension xxx_RW to contain reweighted ccf for kpf case + if self.ins.lower() == 'kpf': + ccf_exts = [self.ccf_ext, self.ccf_ext+'_RW'] + # if any(x == 'T' for x in self.reweighted.values()) else [self.ccf_ext] + self.output_level2[ccf_exts[0]] = original_ccf + if len(ccf_exts) > 1: # only assign reweighted ccf if reweighting is done on some orderlet + self.output_level2[ccf_exts[1]] = reweight_ccf + else: + ccf_exts = [self.ccf_ext] + self.output_level2[ccf_exts[0]] = reweight_ccf[i, :, :] + for ext in ccf_exts: + self.output_level2.header[ext]['startseg'] = self.start_seg + self.output_level2.header[ext]['startv'] = \ + (self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP][0], 'km/sec') + self.output_level2.header[ext]['stepv'] = \ + (self.rv_init['data']['rv_config'][RadialVelocityAlgInit.STEP], 'km/sec') + self.output_level2.header[ext]['totalv'] = self.rv_init['data']['velocity_steps'] + self.output_level2.header[ext]['totalsci'] = len(self.sci_names) for i in range(total_orderlet): map_key = self.get_map_key(self.od_names[i]) - ccf_key = self.orderlet_key_map[self.ins][map_key]['ccf']['mask'] - - if ccf_key not in self.output_level2.header[self.ccf_ext]: - self.output_level2.header[self.ccf_ext][ccf_key] = \ - RadialVelocityAlg.get_orderlet_masktype(self.ins, self.od_names[i], self.rv_init['data']) + for ext in ccf_exts: + self.output_level2.header[ext][self.orderlet_key_map[self.ins][map_key]['ccf']['name']] = \ + self.od_names[i] for i in range(total_orderlet): map_key = self.get_map_key(self.od_names[i]) - self.output_level2.header[self.ccf_ext][self.orderlet_key_map[self.ins][map_key]['ccf']['name']] = \ - self.od_names[i] + ccf_key = self.orderlet_key_map[self.ins][map_key]['ccf']['mask'] + for ext in ccf_exts: + if ccf_key not in self.output_level2.header[ext]: + self.output_level2.header[ext][ccf_key] = \ + RadialVelocityAlg.get_orderlet_masktype(self.ins, self.od_names[i], self.rv_init['data']) return True def make_rv_table(self, output_df): @@ -517,6 +578,7 @@ def f_decimal(num): for o_name, o_df in output_df.items(): if o_df is not None: total_segment = np.shape(o_df.values)[0] - self.alg.ROWS_FOR_ANALYSIS + total_vel = np.shape(o_df.values)[1] break segment_table = self.alg.get_segment_info() @@ -537,9 +599,6 @@ def f_decimal(num): orderlet_rvs = {} orderlet_ervs = {} - # summation of rv and erv across science orderlet - sum_rv_segs = np.zeros(total_segment) - sum_erv_segs = list() # cal rv per segment cal_rvs = np.zeros(total_segment) @@ -565,8 +624,11 @@ def f_decimal(num): else: continue # this should not happen unless the specified orderlet name is wrong - sum_total = 0 sci_ccf_ratio = None + ccf_summ_h_rw = np.zeros((total_segment, total_vel)) + ccf_summ_h_rw_nonnorm = np.zeros((total_segment, total_vel)) + ccf_summ_v_rw = np.zeros(total_vel) # sum CCF across orderlet per segment + ccf_summ_v_rw_nonnorm = np.zeros(total_vel) # sum CCF summation across orders for idx, od_name in enumerate(self.od_names): b_sci = od_name in self.sci_names if output_df[od_name] is None : # skip no ccf and no cal correction if some sci ccf is none @@ -583,37 +645,51 @@ def f_decimal(num): if starrv is None: starrv = output_df[od_name].attrs['STARRV'] - if sci_mask is None and b_sci: sci_mask = RadialVelocityAlg.get_orderlet_masktype(ins, od_name, self.rv_init['data']) sci_ccf_ratio = self.get_ratio_ccf(od_name) - # set rv for each orderlet + # set rv column and rv error column for each orderlet if orderlet_cols[od_name]: rv_table[orderlet_cols[od_name]][self.start_seg:self.end_seg+1] = \ output_df[od_name].attrs['RV_SEGMS'][self.start_seg:self.end_seg+1] - if erv_cols[od_name]: + if erv_cols[od_name]: # no erv column for sci orderlet rv_table[erv_cols[od_name]][self.start_seg:self.end_seg + 1] = \ output_df[od_name].attrs['ERV_SEGMS'][self.start_seg:self.end_seg + 1] - orderlet_rvs[od_name] = output_df[od_name].attrs['RV_MEAN'] # rv and erv for each orderlet - orderlet_ervs[od_name] = output_df[od_name].attrs['ERV_MEAN'] + # rv and erv for each orderlet + # method 1: get rv and rv error per orderlet by CCF summation + orderlet_rvs[od_name] = output_df[od_name].attrs['CCF-RVC'][0] # method2: output_df[od_name].attrs['RV_MEAN'] + orderlet_ervs[od_name] = output_df[od_name].attrs['CCF-ERV'] # method2: output_df[od_name].attrs['ERV_MEAN'] + # ccf summation across orderlet + # for rv, using un-reweighted or reweighted ccf + # for rv error, using un-reweighted or non-normalized reweighted ccf if b_sci or (self.is_sky(od_name) and self.is_solar_data): - sum_rv_segs[self.start_seg:self.end_seg+1] += rv_table[orderlet_cols[od_name]][self.start_seg:self.end_seg+1] - sum_erv_segs.append(output_df[od_name].attrs['ERV_SEGMS']) - sum_total += 1 + ccf_summ_h_rw[self.start_seg:self.end_seg+1, :] += output_df[od_name].values[self.start_seg:self.end_seg+1, :] + ccf_summ_h_rw_nonnorm[self.start_seg:self.end_seg+1, :] += \ + output_df[od_name].attrs['CCF_NONNORM'][self.start_seg:self.end_seg+1, :] + ccf_summ_v_rw += output_df[od_name].values[-1, :] + ccf_summ_v_rw_nonnorm += output_df[od_name].attrs['CCF_NONNORM'][-1, :] elif self.is_cal(od_name): cal_rvs = rv_table[orderlet_cols[od_name]] - cal_rv = output_df[od_name].attrs['RV_MEAN'] - - # for rv and rv error column - if sum_total > 0: - col_rv = sum_rv_segs/sum_total - for r in range(self.start_seg, self.end_seg+1): - rv_err_ary = np.array([sum_erv_segs[i][r] for i in range(sum_total)]) - col_rv_err[r] = RadialVelocityAlg.weighted_rv_error(rv_err_ary, sum_total, None) - + cal_rv = output_df[od_name].attrs['CCF-RVC'][0] # record rv per segments and final rv for cal orderlet + + # method 1: for rv and rv error column, based on summation of ccf across orderlet + for r in range(self.start_seg, self.end_seg+1): + _, col_rv[r], _, _, col_rv_err[r] = RadialVelocityAlg.fit_ccf(ccf_summ_h_rw[r, :], self.alg.get_rv_guess(), + self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP], + sci_mask, + # self.input.header[od_name]['MASK'], + rv_guess_on_ccf=(self.ins.lower() == 'kpf'), + vel_span_pixel=self.alg.get_vel_span_pixel() if sci_ccf_ratio is None else None) + if sci_ccf_ratio is not None: + _, _, _, _, col_rv_err[r] = RadialVelocityAlg.fit_ccf(ccf_summ_h_rw_nonnorm[r, :], self.alg.get_rv_guess(), + self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP], + sci_mask, + # self.input.header[od_name]['MASK'], + rv_guess_on_ccf=(self.ins.lower() == 'kpf'), + vel_span_pixel=self.alg.get_vel_span_pixel()) rv_table[self.RV_COL_RV] = col_rv rv_table[self.RV_COL_RV_ERR] = col_rv_err @@ -634,33 +710,33 @@ def f_decimal(num): results = pd.DataFrame(rv_table) - # add keys to rv table ccd1rv[1-3], ccd1erv[1-3], ccd2rv[1-3], ccd2erv[1-3] (for rv ext header) - rv_unit = 'Bary-corrected RV (km/s)' - rvc_unit = 'Cal fiber RV (km/s)' - rvs_unit = 'Sky fiber RV (km/s)' - for od_name in self.od_names: map_key = self.get_map_key(od_name) if map_key: od_rv = orderlet_rvs[od_name] - cal_rv if do_corr and self.is_sci(od_name) else orderlet_rvs[od_name] - if self.is_cal(od_name): - unit = rvc_unit - elif self.is_sky(od_name): - unit = rvs_unit - else: - unit = rv_unit + unit = self.get_rv_unit(od_name) results.attrs['ccd_'+self.orderlet_key_map[self.ins][map_key]['rv']['rv']] = (f_decimal(od_rv), unit) results.attrs['ccd_'+self.orderlet_key_map[self.ins][map_key]['rv']['erv']] = f_decimal(orderlet_ervs[od_name]) final_rv = final_rv_err = 0.0 + # compute rv & rv error for the chip if sci_mask is not None: - final_rv = RadialVelocityAlg.weighted_rv(rv_table[self.RV_COL_RV], total_segment, None) - final_rv_err = RadialVelocityAlg.weighted_rv_error(rv_table[self.RV_COL_RV_ERR], total_segment, sci_ccf_ratio) - - sky_note = ', including SKY RV ' if self.is_solar_data else '' - results.attrs['ccd_rv'] = (f_decimal(final_rv - cal_rv), rv_unit) \ - if do_corr and final_rv != 0.0 else (f_decimal(final_rv), rv_unit + sky_note) + _, final_rv, _, _, final_rv_err = RadialVelocityAlg.fit_ccf(ccf_summ_v_rw, self.alg.get_rv_guess(), + self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP], + sci_mask, + rv_guess_on_ccf=(self.ins.lower() == 'kpf'), + vel_span_pixel=self.alg.get_vel_span_pixel() if sci_ccf_ratio is None else None) + + if sci_ccf_ratio is not None: + _, _, _, _, final_rv_err = RadialVelocityAlg.fit_ccf(ccf_summ_v_rw_nonnorm, self.alg.get_rv_guess(), + self.rv_init['data'][RadialVelocityAlgInit.VELOCITY_LOOP], + sci_mask, + rv_guess_on_ccf=(self.ins.lower() == 'kpf'), vel_span_pixel=self.alg.get_vel_span_pixel()) + + unit = self.get_rv_unit(self.sci_names[0], is_final=True) + results.attrs['ccd_rv'] = (f_decimal(final_rv - cal_rv), unit) \ + if do_corr and final_rv != 0.0 else (f_decimal(final_rv), unit) results.attrs['ccd_erv'] = f_decimal(final_rv_err) results.attrs['ccd_jd'] = jd[0] results.attrs['star_rv'] = starrv diff --git a/modules/radial_velocity/src/radial_velocity_reweighting.py b/modules/radial_velocity/src/radial_velocity_reweighting.py index eec541622..1285509e5 100644 --- a/modules/radial_velocity/src/radial_velocity_reweighting.py +++ b/modules/radial_velocity/src/radial_velocity_reweighting.py @@ -75,6 +75,7 @@ from modules.radial_velocity.src.alg import RadialVelocityAlg from modules.radial_velocity.src.alg_rv_init import RadialVelocityAlgInit +from modules.radial_velocity.src.radial_velocity import RadialVelocity as RV DEFAULT_CFG_PATH = 'modules/optimal_extraction/configs/default.cfg' @@ -84,49 +85,30 @@ class RadialVelocityReweighting(KPF2_Primitive): default_agrs_val = { 'order_name': 'SCI' } - RV_COL_ORDERLET = 'orderlet' - RV_COL_START_W = 's_wavelength' - RV_COL_END_W = 'e_wavelength' - RV_COL_SEG_NO = 'segment no.' - RV_COL_ORD_NO = 'order no.' - RV_COL_RV = 'RV' - RV_COL_RV_ERR = 'RV error' - RV_COL_CAL_ERR = 'CAL error' - RV_COL_SKY_ERR = 'SKY error' - RV_COL_CCFJD = 'CCFJD' - RV_COL_BARY = 'Bary_RVC' - RV_COL_SOURCE = 'source' - RV_COL_CAL = 'CAL RV' - RV_COL_CAL_SOURCE = 'source CAL' - RV_COL_SKY = 'SKY RV' - RV_COL_SKY_SOURCE ='source SKY' - RV_WEIGHTS = 'CCF Weights' - rv_col_names = [RV_COL_ORDERLET, RV_COL_START_W, RV_COL_END_W, RV_COL_SEG_NO, RV_COL_ORD_NO, - RV_COL_RV, RV_COL_RV_ERR, RV_COL_CAL, RV_COL_CAL_ERR, RV_COL_SKY, RV_COL_SKY_ERR, - RV_COL_CCFJD, RV_COL_BARY, RV_COL_SOURCE, RV_COL_CAL_SOURCE, RV_COL_SKY_SOURCE, RV_WEIGHTS] - rv_col_on_orderlet = [RV_COL_ORDERLET, RV_COL_SOURCE] - - orderlet_key_map = {'kpf':{ - 'sci_flux1': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv':'rv1', 'erv':'erv1'}}, - 'sci_flux2': {'ccf': {'mask': 'sci_mask', 'name': 'ccf2'}, 'rv': {'rv': 'rv2', 'erv': 'erv2'}}, - 'sci_flux3': {'ccf': {'mask': 'sci_mask', 'name': 'ccf3'}, 'rv': {'rv': 'rv3', 'erv': 'erv3'}}, - 'sky_flux': {'ccf': {'mask': 'sky_mask', 'name': 'ccf5'}, 'rv': {'rv': 'rvs', 'erv': 'ervs'}}, - 'cal_flux': {'ccf': {'mask': 'cal_mask', 'name': 'ccf4'}, 'rv': {'rv': 'rvc', 'erv': 'ervc'}}}, - 'neid':{ - 'sci': {'ccf': {'mask': 'sci_mask', 'name': 'ccf1'}, 'rv': {'rv':'rv1', 'erv':'erv1'}} - } - } - orderlet_rv_col_map = {'kpf':{ - 'sci_flux1': {'orderlet':'orderlet1', 'source':'source1', 'erv': ''}, - 'sci_flux2': {'orderlet':'orderlet2', 'source':'source2', 'erv': ''}, - 'sci_flux3': {'orderlet': 'orderlet3', 'source': 'source3', 'erv': ''}, - 'sky_flux': {'orderlet': RV_COL_SKY, 'source': RV_COL_SKY_SOURCE, 'erv': RV_COL_SKY_ERR}, - 'cal_flux': {'orderlet': RV_COL_CAL, 'source': RV_COL_CAL_SOURCE, 'erv': RV_COL_CAL_ERR}}, - 'neid':{ - 'sci': {'orderlet':'orderlet1', 'source':'source1', 'erv': ''} - } - } + RV_COL_ORDERLET = RV.RV_COL_ORDERLET + RV_COL_START_W = RV.RV_COL_START_W + RV_COL_END_W = RV.RV_COL_END_W + RV_COL_SEG_NO = RV.RV_COL_SEG_NO + RV_COL_ORD_NO = RV.RV_COL_ORD_NO + RV_COL_RV = RV.RV_COL_RV + RV_COL_RV_ERR = RV.RV_COL_RV_ERR + RV_COL_CAL_ERR = RV.RV_COL_CAL_ERR + RV_COL_SKY_ERR = RV.RV_COL_SKY_ERR + RV_COL_CCFJD = RV.RV_COL_CCFJD + RV_COL_BARY = RV.RV_COL_BARY + RV_COL_SOURCE = RV.RV_COL_SOURCE + RV_COL_CAL = RV.RV_COL_CAL + RV_COL_CAL_SOURCE = RV.RV_COL_CAL_SOURCE + RV_COL_SKY = RV.RV_COL_SKY + RV_COL_SKY_SOURCE = RV.RV_COL_SKY_SOURCE + RV_WEIGHTS = RV.RV_WEIGHTS + rv_col_names = RV.rv_col_names + + rv_col_on_orderlet = RV.rv_col_on_orderlet + + orderlet_key_map = RV.orderlet_key_map + orderlet_rv_col_map = RV.orderlet_rv_col_map def __init__(self, action: Action, @@ -139,6 +121,7 @@ def __init__(self, self.ccf_start_index = action.args['ccf_start_index'] if 'ccf_start_index' in args_keys else 0 self.rv_ext = action.args['rv_ext'] if 'rv_ext' in args_keys else 'RV' self.rv_ext_idx = action.args['rv_ext_idx'] if 'rv_ext_idx' in args_keys else 0 + lev2_obj = None if isinstance(action.args[0], str): @@ -156,6 +139,16 @@ def __init__(self, else: self.instrument = None + # for the use of 'ccf_static' + self.reweighting_masks = None + if 'reweighting_masks' in args_keys: + self.reweighting_masks = action.args['reweighting_masks'] + if self.reweighting_masks is None or not self.reweighting_masks: + if self.instrument.lower() == 'kpf': + self.reweighting_masks = ['espresso', 'lfc', 'thar', 'etalon'] + else: + self.reweighting_masks = None + self.is_solar_data = p_header is not None and self.instrument == 'kpf' and \ 'OBJECT' in p_header and p_header['OBJECT'].lower() == 'sun' and \ (RadialVelocityAlgInit.KEY_SCI_OBJ in p_header and @@ -163,7 +156,7 @@ def __init__(self, (RadialVelocityAlgInit.KEY_SKY_OBJ in p_header and p_header[RadialVelocityAlgInit.KEY_SKY_OBJ].lower() == 'target') - self.ccf_data = {} + self.ccf_data = {} # original ccf in terms of orderlet name self.ccf_ext_header = None self.ccf_dim = 2 @@ -175,19 +168,26 @@ def __init__(self, self.cal_orderlets = list() self.sky_orderlets = list() self.od_names = list() + self.orderlet_ccf = {} + self.rw_ccf_exts = None # 2D or 3D containing reweighted ccf + self.is_solor_data = False if self.ccf_ext and hasattr(lev2_obj, self.ccf_ext): self.ccf_ext_header = lev2_obj.header[self.ccf_ext] ccf_ext_data = lev2_obj[self.ccf_ext] + self.original_ccf_exts = np.copy(ccf_ext_data) + self.rw_ccf_exts = np.copy(ccf_ext_data) if ccf_ext_data.any(): self.ccf_dim = self.ccf_ext_header['NAXIS'] if self.ccf_dim == 2: self.totals, self.totalv = np.shape(ccf_ext_data) self.ccf_data[self.ccf_ext_header['CCF1']] = ccf_ext_data + self.orderlet_ccf[self.ccf_ext_header['CCF1']] = 0 elif self.ccf_dim == 3: self.total_orderlet, self.totals, self.totalv = np.shape(ccf_ext_data) for i in range(self.total_orderlet): od_name = self.ccf_ext_header['CCF'+str(i+1)] + self.orderlet_ccf[od_name] = i self.ccf_data[od_name] = ccf_ext_data[i, :, :] self.od_names.append(od_name) if self.is_sci(od_name): @@ -235,7 +235,6 @@ def __init__(self, self.logger = self.context.logger self.logger.info('Loading config from: {}'.format(self.config_path)) - def _pre_condition(self) -> bool: """ Check for some necessary pre conditions @@ -270,7 +269,6 @@ def _perform(self): return Arguments(self.lev2_obj) is_rv_ext = hasattr(self.lev2_obj, self.rv_ext) and not self.lev2_obj[self.rv_ext].empty - rv_ext_header = self.lev2_obj.header[self.rv_ext] if is_rv_ext else None do_corr = False if is_rv_ext: @@ -284,7 +282,9 @@ def _perform(self): velocities = np.array([start_v + i * v_intv for i in range(ccf_w)]) # do cal rv col first - ccf_ref_list = {} + ccf_ref_list = {} # ccf ref per orderlet + + rw_ccf_ext = self.ccf_ext + '_RW' for o in range(self.total_orderlet-1, -1, -1): if self.logger: @@ -294,78 +294,51 @@ def _perform(self): result_ccf_data = self.ccf_data[od_name] ccf_ref = None if self.reweighting_method.lower() == 'ccf_static': - for idx, r_col in enumerate(self.ccf_ref.columns): - if self.mask_type[od_name] is not None and r_col.lower() == self.mask_type[od_name].lower(): - col_idx = np.array([0, idx], dtype=int) - ccf_ref = self.ccf_ref.values[:, col_idx] - break + mtype = self.mask_type[od_name] + is_mask_enable = mtype is not None and \ + (self.reweighting_masks is None or + any([m.lower() in mtype.lower() for m in self.reweighting_masks])) + if is_mask_enable: + for idx, r_col in enumerate(self.ccf_ref.columns): + if mtype is not None and r_col.lower() == mtype.lower(): + col_idx = np.array([0, idx], dtype=int) + ccf_ref = self.ccf_ref.values[:, col_idx] + break else: ccf_ref = self.ccf_ref.values if isinstance(self.ccf_ref, pd.DataFrame) else self.ccf_ref + ccf_ref_list[od_name] = ccf_ref if ccf_ref is None: if self.logger: - self.logger.info("RadialVelocityReweighting: No reweighting ratio for " + self.ccf_ext + " found") - return Arguments(self.lev2_obj) + self.logger.info("RadialVelocityReweighting: No reweighting for " + od_name) + continue - ccf_ref_list[od_name] = ccf_ref rw_ccf, new_total_seg = RadialVelocityAlg.reweight_ccf(result_ccf_data, self.total_segment, ccf_ref, self.reweighting_method, s_seg=self.ccf_start_index, do_analysis=True, velocities=velocities) - (self.ccf_data[od_name])[0:new_total_seg, :] = rw_ccf[0:new_total_seg, :] # update ccf value for each orderlet - self.lev2_obj[self.ccf_ext][o, 0:new_total_seg, :] = rw_ccf[0:new_total_seg, :] + (self.ccf_data[od_name])[0:new_total_seg, :] = rw_ccf[0:new_total_seg, :] + if self.instrument.lower() == 'kpf': + self.rw_ccf_exts[self.orderlet_ccf[od_name], 0:new_total_seg, :] = rw_ccf[0:new_total_seg, :] + else: + self.lev2_obj[self.ccf_ext][o, 0:new_total_seg, :] = rw_ccf[0:new_total_seg, :] - # if existing ccf table with summary row + # if existing ccf table with summation row if np.shape(self.ccf_data[od_name])[1] >= new_total_seg + RadialVelocityAlg.ROWS_FOR_ANALYSIS and not is_rv_ext: self.ccf_data[od_name][-1, :] = rw_ccf[-1] + if self.instrument.lower() == 'kpf': + self.create_rw_ccf(rw_ccf_ext) + # update rv table if is_rv_ext: if self.logger: self.logger.info("RadialVelocityReweighting: start updating rv for each segment") - self.update_rv_table(self.total_orderlet, velocities, do_corr, ccf_ref_list) + new_rv_table = self.update_rv_table(velocities, do_corr, ccf_ref_list) + self.lev2_obj[self.rv_ext] = new_rv_table + for att in new_rv_table.attrs: + if att in self.lev2_obj.header[self.rv_ext]: + self.lev2_obj.header[self.rv_ext][att] = new_rv_table.attrs[att] - # update rv keyword, ccd[1-2]rv[1-3], ccd[1-2]rv, ccd[1-2]rvc - cal_rv = 0.0 - ods_rv = list() - for o in range(self.total_orderlet): - map_key = self.get_map_key(self.od_names[o]) - rv_colname = self.orderlet_rv_col_map[self.instrument][map_key]['orderlet'] - ccd_rv = self.reweight_rv(rv_colname, ccf_ref_list[self.od_names[o]]) - ods_rv.append(ccd_rv) - - if self.od_names[o] in self.cal_orderlets: - cal_rv = ccd_rv - - if is_rv_ext: - done_sci = False - for o in range(self.total_orderlet): - map_key = self.get_map_key(self.od_names[o]) - rv_key = 'ccd' + str(self.rv_ext_idx + 1) + self.orderlet_key_map[self.instrument][map_key]['rv']['rv'] - if self.is_sci(self.od_names[o]) and do_corr: - self.lev2_obj.header[self.rv_ext][rv_key] = ods_rv[o] - cal_rv # ccd[1-2]rv[1-3] - else: - self.lev2_obj.header[self.rv_ext][rv_key] = ods_rv[o] # ccd[1-2]rv[1-3], ccd[1-2][rvc, rvs] - - if done_sci and self.is_sci(self.od_names[o]): - continue - - if self.is_sci(self.od_names[o]): - rv_col = self.RV_COL_RV - rv_key = 'ccd'+str(self.rv_ext_idx + 1)+'rv' - ccd_rv = self.reweight_rv(rv_col, ccf_ref_list[self.od_names[o]]) - rv_ext_header[rv_key] = self.f_decimal(ccd_rv - cal_rv) if do_corr else self.f_decimal(ccd_rv) - done_sci = True - - erv_col = self.RV_COL_RV_ERR - erv_key = 'ccd' + str(self.rv_ext_idx + 1) + 'erv' - else: - erv_col = self.orderlet_rv_col_map[self.instrument][map_key]['erv'] - erv_key = 'ccd' + str(self.rv_ext_idx + 1) + self.orderlet_key_map[self.instrument][map_key]['rv']['erv'] - - ccd_erv = self.reweight_rv(erv_col, ccf_ref_list[self.od_names[o]], is_sigma=True) - rv_ext_header[erv_key] = self.f_decimal(ccd_erv) - - rv_ext_header['rwccfrv'] = 'T' self.lev2_obj.receipt_add_entry('RadialVelocityReweighting on '+ self.ccf_ext, self.__module__, f'config_path={self.config_path}', 'PASS') @@ -376,21 +349,14 @@ def _perform(self): return Arguments(self.lev2_obj) - @staticmethod - def f_decimal(num): - return float("{:.10f}".format(num)) - - @staticmethod - def is_sci(sci_name): - return 'sci' in sci_name.lower() + def is_sci(self, sci_name): + return RV.is_sci(self, sci_name) - @staticmethod - def is_cal(sci_name): - return 'cal' in sci_name.lower() + def is_cal(self, sci_name): + return RV.is_cal(self, sci_name) - @staticmethod - def is_sky(sci_name): - return 'sky' in sci_name.lower() + def is_sky(self, sci_name): + return RV.is_sky(self, sci_name) def get_map_key(self, od_name): for k in self.orderlet_key_map[self.instrument].keys(): @@ -398,7 +364,17 @@ def get_map_key(self, od_name): return k return '' - def update_rv_table(self, total_orderlet, velocities, do_corr, ccf_ref_list): + def get_rv_unit(self, od_name, is_final=False): + return RV.get_rv_unit(self, od_name, is_final=is_final) + + def create_rw_ccf(self, rw_ext): + if not self.lev2_obj[rw_ext]: + self.lev2_obj[rw_ext] = self.rw_ccf_exts + for key in self.ccf_ext_header.keys(): + if key not in ['XTENSION', 'BITPIX', 'NAXIS', 'NAXIS1', 'NAXIS2', 'NAXIS3', 'PCOUNT', 'GCOUNT', 'EXTNAME']: + self.lev2_obj.header[rw_ext][key] = self.ccf_ext_header[key] + + def update_rv_table(self, velocities, do_corr, ccf_ref_list): rv_ext_values = self.lev2_obj[self.rv_ext].values rv_ext_columns = self.lev2_obj[self.rv_ext].columns rv_ext_header = self.lev2_obj.header[self.rv_ext] @@ -408,6 +384,9 @@ def update_rv_table(self, total_orderlet, velocities, do_corr, ccf_ref_list): rv_start_idx = (ccdrow in rv_ext_header and rv_ext_header[ccdrow]) or \ (self.processed_row is not None and self.processed_row) or 0 + def f_decimal(num): + return float("{:.10f}".format(num)) + def col_idx_rv_table(colname): if colname in rv_ext_columns: return rv_ext_columns.tolist().index(colname) @@ -417,10 +396,11 @@ def col_idx_rv_table(colname): seg_size = self.totals cal_idx = col_idx_rv_table(RadialVelocityReweighting.RV_COL_CAL) rv_idx = col_idx_rv_table(self.RV_COL_RV) + rv_err_idx = col_idx_rv_table(self.RV_COL_RV_ERR) - ods_idx = {} - scis_idx = [] - sci_ccf_ref = None + ods_idx = {} # column index for each orderlet + scis_idx = [] # column index list for sci orderlet + sci_ccf_ref = None # ccf ref for sci orderlet for od_name in self.od_names: map_key = self.get_map_key(od_name) if map_key: @@ -430,48 +410,100 @@ def col_idx_rv_table(colname): if sci_ccf_ref is None: sci_ccf_ref = ccf_ref_list[od_name] - for s in range(seg_size): - sum_rv = list() + total_vel = np.shape(self.original_ccf_exts)[2] + ccf_summ_h_rw = np.zeros((seg_size, total_vel)) + ccf_summ_h_rw_nonnorm = np.zeros((seg_size, total_vel)) + ccf_summ_v_rw = np.zeros(total_vel) + ccf_summ_v_rw_nonnorm = np.zeros(total_vel) + new_rv = {} # new rv per orderlet + new_rv_err = {} # new rv error per orderlet + new_ccd_rv = None + new_ccd_rv_err = None + new_cal_rv = None + new_cal_rvs = np.zeros(seg_size) + + def get_new_rv(ccfs, orderlet_name, is_rv_err=False): + if is_rv_err: + vel_pix = RadialVelocityAlg.comp_velocity_span_pixel(None, self.config, self.instrument) + _, _, _, _, new_rv_err = RadialVelocityAlg.fit_ccf(ccfs, + RadialVelocityAlg.get_rv_estimation(rv_ext_header), + velocities, self.mask_type[orderlet_name], + rv_guess_on_ccf=(self.instrument == 'kpf'), + vel_span_pixel=vel_pix) + return new_rv_err + else: + _, new_rv, _, _, _ = RadialVelocityAlg.fit_ccf(ccfs, + RadialVelocityAlg.get_rv_estimation(rv_ext_header), + velocities, self.mask_type[orderlet_name], + rv_guess_on_ccf=(self.instrument == 'kpf')) + return new_rv - for od_name in self.od_names: - ccf_orderlet = self.ccf_data[od_name][s, :] - _, od_rv, _, _, _ = RadialVelocityAlg.fit_ccf(ccf_orderlet, - RadialVelocityAlg.get_rv_estimation(rv_ext_header), - velocities, self.mask_type[od_name], - rv_guess_on_ccf=(self.instrument == 'kpf')) + for od_name in self.od_names: + if ccf_ref_list[od_name] is None: + continue - rv_ext_values[s + rv_start_idx, ods_idx[od_name]] = od_rv + rw_ccf_nonnorm, _ = RadialVelocityAlg.reweight_ccf( + self.original_ccf_exts[self.orderlet_ccf[od_name], 0:seg_size, :], + seg_size, + ccf_ref_list[od_name], + self.reweighting_method, + do_analysis=False, + velocities=velocities, + normalized=False) + for s in range(seg_size): + rv_ext_values[s+rv_start_idx, ods_idx[od_name]] = \ + get_new_rv(self.rw_ccf_exts[self.orderlet_ccf[od_name], s, :], od_name) + + # update rv error for non sci orderlet + if not self.is_sci(od_name): + od_rv_err = get_new_rv(rw_ccf_nonnorm[s, :], od_name, is_rv_err=True) + map_key = self.get_map_key(od_name) + rv_err_nonsci_idx = col_idx_rv_table(self.orderlet_rv_col_map[self.instrument][map_key]['erv']) + rv_ext_values[s+rv_start_idx, rv_err_nonsci_idx] = od_rv_err + + # summ ccf across sci orderlet if self.is_sci(od_name) or (self.is_sky(od_name) and self.is_solar_data): - sum_rv.append(od_rv) - rv_ext_values[s + rv_start_idx, rv_idx] = sum(sum_rv)/len(sum_rv) if len(sum_rv) > 0 else 0.0 - + ccf_summ_h_rw[s, :] += self.ccf_data[od_name][s, :] + ccf_summ_h_rw_nonnorm[s, :] += rw_ccf_nonnorm[s, :] + + # summ ccf across segments => final ccf per orderlet + ccf_summ = np.sum(self.ccf_data[od_name][0:seg_size, :], axis=0) + ccf_summ_nonnorm = np.sum(rw_ccf_nonnorm[0:seg_size, :], axis=0) + + # rv and rv error for each orderlet + new_rv[od_name] = get_new_rv(ccf_summ, od_name) + new_rv_err[od_name] = get_new_rv(ccf_summ_nonnorm, od_name, is_rv_err=True) + if self.is_cal(od_name): + new_cal_rv = new_rv[od_name] + new_cal_rvs = rv_ext_values[rv_start_idx:rv_start_idx+seg_size, cal_idx] + + # summ final ccf across orderlet + if self.is_sci(od_name) or (self.is_sky(od_name) and self.is_solar_data): + ccf_summ_v_rw += ccf_summ + ccf_summ_v_rw_nonnorm += ccf_summ_nonnorm + + # rv and rv error per ccd + if np.any(ccf_summ_v_rw): + new_ccd_rv = get_new_rv(ccf_summ_v_rw, self.sci_orderlets[0]) + new_ccd_rv_err = get_new_rv(ccf_summ_v_rw_nonnorm, self.sci_orderlets[0], is_rv_err=True) + + # update rv and rv error column based on summation across sci orderlets per segment + for s in range(seg_size): + rv_ext_values[s+rv_start_idx, rv_idx] = get_new_rv(ccf_summ_h_rw[s, :], self.sci_orderlets[0]) + rv_ext_values[s+rv_start_idx, rv_err_idx] = get_new_rv(ccf_summ_h_rw_nonnorm[s, :], + self.sci_orderlets[0], + is_rv_err=True) + # weight column ccf_weight = sci_ccf_ref if len(np.shape(sci_ccf_ref)) >= 2 else sci_ccf_ref.reshape(sci_ccf_ref.size, 1) ccf_ref_idx = col_idx_rv_table(self.RV_WEIGHTS) if ccf_ref_idx >= 0: rv_ext_values[rv_start_idx:rv_start_idx+seg_size, ccf_ref_idx] = ccf_weight[0:seg_size, -1] - # error columns - sci_done = False - for od_name in self.od_names: - if self.is_sci(od_name) and not sci_done: - err_col = self.RV_COL_RV_ERR - sci_done = True - elif not self.is_sci(od_name): - err_col = self.orderlet_rv_col_map[self.instrument][self.get_map_key(od_name)]['erv'] - else: - continue - - od_ref = ccf_ref_list[od_name] - od_weight = od_ref if len(np.shape(od_ref)) >= 2 else od_ref.reshape(od_ref.size, 1) - e_idx = col_idx_rv_table(err_col) - rv_ext_values[rv_start_idx:rv_start_idx+seg_size, e_idx] *= od_weight[0:seg_size, -1] # do correction on orderletx columns if do_corr: for s_idx in scis_idx: - rv_ext_values[rv_start_idx:rv_start_idx+seg_size, s_idx] -= \ - rv_ext_values[rv_start_idx:rv_start_idx+seg_size, cal_idx] - rv_ext_values[rv_start_idx:rv_start_idx+seg_size, rv_idx] -= \ - rv_ext_values[rv_start_idx:rv_start_idx+seg_size, cal_idx] + rv_ext_values[rv_start_idx:rv_start_idx+seg_size, s_idx] -= new_cal_rvs + rv_ext_values[rv_start_idx:rv_start_idx+seg_size, rv_idx] -= new_cal_rvs # create a new rv table to take rv_ext_values with rv update new_rv_table = {} @@ -489,8 +521,38 @@ def col_idx_rv_table(colname): if idx >= 0: new_rv_table[c_name] = rv_ext_values[:, idx].tolist() - self.lev2_obj[self.rv_ext] = pd.DataFrame(new_rv_table) # update rv table - return True + results = pd.DataFrame(new_rv_table) # update rv table + cal_rv_key = 'ccd'+str(self.rv_ext_idx + 1) + 'rvc' + if cal_rv_key in rv_ext_header: + cal_rv = rv_ext_header[cal_rv_key] if new_cal_rv is None else new_cal_rv + else: + cal_rv = 0.0 + + ccd_p = 'ccd'+str(self.rv_ext_idx+1) + + if new_ccd_rv is not None: + unit = self.get_rv_unit(self.sci_orderlets[0], is_final=True) + results.attrs[ccd_p+'rv'] = (f_decimal(new_ccd_rv - cal_rv), unit) \ + if do_corr else (f_decimal(new_ccd_rv), unit) + if new_ccd_rv_err is not None: + results.attrs[ccd_p+'erv'] = f_decimal(new_ccd_rv_err) + + for od_name in new_rv.keys(): + map_key = self.get_map_key(od_name) + if map_key: + od_rv = new_rv[od_name] - cal_rv if do_corr and self.is_sci(od_name) else new_rv[od_name] + unit = self.get_rv_unit(od_name) + + results.attrs[ccd_p+self.orderlet_key_map[self.instrument][map_key]['rv']['rv']] = \ + (f_decimal(od_rv), unit) + results.attrs[ccd_p+self.orderlet_key_map[self.instrument][map_key]['rv']['erv']] = \ + f_decimal(new_rv_err[od_name]) + + ccf_key = self.orderlet_key_map[self.instrument][map_key]['rv']['ccf'] + rwkey = 'rw'+ccf_key + rwkey = rwkey[0:8] if len(rwkey) > 8 else rwkey + results.attrs[rwkey] = 'T' if ccf_ref_list[od_name] is not None else 'F' + return results def reweight_rv(self, rv_colname, weighting_ratio, is_sigma=False): rv_table = self.lev2_obj[self.rv_ext] diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 4c746a60f..2428041a4 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -257,6 +257,7 @@ bc_path = output_dir + config.ARGUMENT.output_barycorr lev2_pattern = output_rv + '*' + lev2_stem_suffix + fits_ext reweighting_method = config.ARGUMENT.reweighting_method +reweighting_masks = config.ARGUMENT.reweighting_enable_masks data_ext_rv = config.ARGUMENT.orderlet_names_rv ccf_ext_names = config.ARGUMENT.ccf_ext rv_ext = config.ARGUMENT.rv_ext @@ -572,7 +573,6 @@ if do_spectral_extraction or do_hk or do_bc: # RadialVelocityInit: do rv init on each L1 input. # RadialVelocity: do radial velocity per ccd per L1 data. # -rv_test = do_rv or do_rv_reweighting if do_rv or do_rv_reweighting: rv_star_dir = masters_data_dir @@ -616,15 +616,17 @@ if do_rv or do_rv_reweighting: if rv_init != None: for idx in ccd_idx: ratio_ref = None + rw_mask = None # rewighting if the reweighted L2 at the same directory as unweighted L2. - if ratio_refs and output_rv == output_rv_rw: + if ratio_refs and output_rv == output_rv_rw and do_rv_reweighting: ratio_ref = ratio_refs[idx] - + rw_mask = reweighting_masks[idx] rv_data = RadialVelocity(lev1_data, rv_init, rv_data, data_ext_rv[idx], ccf_ext=ccf_ext_names[idx], rv_ext=rv_ext, area_def=area_def[idx], start_seg=area_def[idx][0], end_seg=area_def[idx][1], rv_set=idx, ccf_engine='c', start_bary_index=s_bary_idx[idx], rv_correction_by_cal=is_rv_cal, - reweighting_method=reweighting_method, input_ref=ratio_ref) + reweighting_method=reweighting_method, + input_ref=ratio_ref, reweighting_masks=rw_mask ) if rv_data != None: result = to_fits(rv_data, output_lev2_file) @@ -647,14 +649,17 @@ if do_rv or do_rv_reweighting: reweighted_output = output_rv_rw + short_lev2 for idx in ccd_idx: ratio_ref = None + rw_mask = None if ratio_refs: ratio_ref = ratio_refs[idx] + rw_mask = reweighting_masks[idx] if ratio_ref is not None: t_segment = area_def[idx][1] - area_def[idx][0] + 1 lev2_rv = RadialVelocityReweighting(lev2_rv, reweighting_method, ratio_ref, t_segment, ccf_ext=ccf_ext_names[idx], - rv_ext=rv_ext, rv_ext_idx=idx, ccf_start_index=area_def[idx][0]) + rv_ext=rv_ext, rv_ext_idx=idx, ccf_start_index=area_def[idx][0], + reweighting_masks=rw_mask) if lev2_rv is not None: result = to_fits(lev2_rv, reweighted_output) From d73f514fb33923d81b0d7a1dca109d500027b15a Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:30:29 -0700 Subject: [PATCH 047/249] nightly_summary --- modules/quicklook/configs/default.cfg | 1 + modules/quicklook/src/alg_nightly.py | 22 ++++++++++++++++++++++ 2 files changed, 23 insertions(+) diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index f0d8066da..b5fd0fb6c 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -50,3 +50,4 @@ cahk_wav=/data/masters/kpfMaster_HKwave20220909_sci.csv exposures_dir=/data/2D/ masters_dir=/data/masters/ output_dir=/data/QLP +master_master_date=20230605 diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 33a55f198..941869fba 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -34,6 +34,7 @@ def nightly_procedures(self,night): exposures_dir = self.config['Nightly']['exposures_dir'] masters_dir = self.config['Nightly']['masters_dir'] output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary' + master_master_date = self.config['Nightly']['master_master_date'] if not os.path.exists(self.config['Nightly']['output_dir']+'/'+night): os.makedirs(self.config['Nightly']['output_dir']+'/'+night) @@ -118,6 +119,27 @@ def nightly_procedures(self,night): plt.close() + order_trace_file = self.config['L1']['order_trace']+ccd_color[i_color]+'.csv' + order_trace = pd.read_csv(order_trace_file) + #print(order_trace_file,order_trace) + for i in range(np.shape(order_trace)[0]):#[50]:#range(np.shape(order_trace)[0]) + #print(order_trace.iloc[i]['X1'],int(order_trace.iloc[i]['X2']-order_trace.iloc[i]['X1'])) + x_grid = np.linspace(order_trace.iloc[i]['X1'],order_trace.iloc[i]['X2'],int(order_trace.iloc[i]['X2']-order_trace.iloc[i]['X1'])) + y_grid = order_trace.iloc[i]['Coeff0']+x_grid*order_trace.iloc[i]['Coeff1']+x_grid**2*order_trace.iloc[i]['Coeff2']+x_grid**3*order_trace.iloc[i]['Coeff3'] + plt.plot(x_grid,y_grid,color ='magenta',linewidth = 0.2) + plt.plot(x_grid,y_grid-order_trace.iloc[i]['BottomEdge'],':',color ='white',linewidth = 0.2,alpha = 1) + plt.plot(x_grid,y_grid+order_trace.iloc[i]['TopEdge'],'--',color ='black',linewidth = 0.2,alpha = 1) + #plt.fill_between(x_grid,y_grid-order_trace.iloc[i]['BottomEdge'],y_grid+order_trace.iloc[i]['TopEdge'],color ='pink',alpha = 0.2) + #print(x_grid,y_grid) + plt.xlim(3200,4000) + plt.ylim(3200,4000) + plt.title(ccd_color[i_color]+' Order Trace '+exposure_name, fontsize = 8) + #plt.title(ccd_color[i_color]+' '+version+' Order Trace ' +exposure_name) + #plt.savefig(output_dir+'fig/'+exposure_name+'_order_trace_'+ccd_color[i_color]+'.png') + plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) + plt.close() + + #get all exposures taken on a particular night From 07cbdc89cb76c1c8540eb72bbaeb35de88646612 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:33:25 -0700 Subject: [PATCH 048/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 941869fba..c3e066099 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -122,13 +122,13 @@ def nightly_procedures(self,night): order_trace_file = self.config['L1']['order_trace']+ccd_color[i_color]+'.csv' order_trace = pd.read_csv(order_trace_file) #print(order_trace_file,order_trace) - for i in range(np.shape(order_trace)[0]):#[50]:#range(np.shape(order_trace)[0]) + for j in range(np.shape(order_trace)[0]):#[50]:#range(np.shape(order_trace)[0]) #print(order_trace.iloc[i]['X1'],int(order_trace.iloc[i]['X2']-order_trace.iloc[i]['X1'])) - x_grid = np.linspace(order_trace.iloc[i]['X1'],order_trace.iloc[i]['X2'],int(order_trace.iloc[i]['X2']-order_trace.iloc[i]['X1'])) - y_grid = order_trace.iloc[i]['Coeff0']+x_grid*order_trace.iloc[i]['Coeff1']+x_grid**2*order_trace.iloc[i]['Coeff2']+x_grid**3*order_trace.iloc[i]['Coeff3'] + x_grid = np.linspace(order_trace.iloc[j]['X1'],order_trace.iloc[j]['X2'],int(order_trace.iloc[j]['X2']-order_trace.iloc[j]['X1'])) + y_grid = order_trace.iloc[j]['Coeff0']+x_grid*order_trace.iloc[j]['Coeff1']+x_grid**2*order_trace.iloc[j]['Coeff2']+x_grid**3*order_trace.iloc[j]['Coeff3'] plt.plot(x_grid,y_grid,color ='magenta',linewidth = 0.2) - plt.plot(x_grid,y_grid-order_trace.iloc[i]['BottomEdge'],':',color ='white',linewidth = 0.2,alpha = 1) - plt.plot(x_grid,y_grid+order_trace.iloc[i]['TopEdge'],'--',color ='black',linewidth = 0.2,alpha = 1) + plt.plot(x_grid,y_grid-order_trace.iloc[j]['BottomEdge'],':',color ='white',linewidth = 0.2,alpha = 1) + plt.plot(x_grid,y_grid+order_trace.iloc[j]['TopEdge'],'--',color ='black',linewidth = 0.2,alpha = 1) #plt.fill_between(x_grid,y_grid-order_trace.iloc[i]['BottomEdge'],y_grid+order_trace.iloc[i]['TopEdge'],color ='pink',alpha = 0.2) #print(x_grid,y_grid) plt.xlim(3200,4000) From 5fe14d48a069511c277d4aa6c9d158eba686e0ce Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:35:13 -0700 Subject: [PATCH 049/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 40 +++++++++++++++------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index c3e066099..30e5c84be 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -100,25 +100,6 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() - #histogram - plt.close() - plt.figure(figsize=(5,4)) - plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) - - #print(np.percentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Fri, 9 Jun 2023 16:36:51 -0700 Subject: [PATCH 050/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 30e5c84be..fa1709751 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -42,7 +42,7 @@ def nightly_procedures(self,night): os.makedirs(output_dir) #plot the master files for a particular night - master_list = glob.glob(masters_dir+night+'/*master_dark*.fits') + master_list = glob.glob(masters_dir+night+'/*master_flat*.fits') print(master_list) @@ -119,7 +119,7 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_order_trace_'+ccd_color[i_color]+'.png') plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) plt.close() - + #histogram plt.close() plt.figure(figsize=(5,4)) From 62c4f4fd92e037e489754049567f3f5340097014 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:39:09 -0700 Subject: [PATCH 051/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index fa1709751..7f53bef65 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -67,6 +67,8 @@ def nightly_procedures(self,night): for key, path in ccd_list: ccd_color.append(path) + if master_list[i].find('flat'): + ccd_color=['GREEN_CCD_STACK','RED_CCD_STACK'] if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: print('skipping empty file') From 0fdad349d1664ff894fa909d834e9718952d5683 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:40:18 -0700 Subject: [PATCH 052/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 7f53bef65..4131bbf2e 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -67,8 +67,7 @@ def nightly_procedures(self,night): for key, path in ccd_list: ccd_color.append(path) - if master_list[i].find('flat'): - ccd_color=['GREEN_CCD_STACK','RED_CCD_STACK'] + if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: print('skipping empty file') @@ -78,7 +77,8 @@ def nightly_procedures(self,night): #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') - + if master_list[i].find('flat'): + counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') if master_list[i].find('dark'):#scale up dark exposures counts*=exptime From 1f647e9c00eb8d3688065882916dc6654d5a9a4a Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:47:20 -0700 Subject: [PATCH 053/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 4131bbf2e..c8ab68e8b 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -36,6 +36,7 @@ def nightly_procedures(self,night): output_dir = self.config['Nightly']['output_dir']+'/'+night+'/nightly_summary' master_master_date = self.config['Nightly']['master_master_date'] + if not os.path.exists(self.config['Nightly']['output_dir']+'/'+night): os.makedirs(self.config['Nightly']['output_dir']+'/'+night) if not os.path.exists(output_dir): @@ -43,7 +44,9 @@ def nightly_procedures(self,night): #plot the master files for a particular night master_list = glob.glob(masters_dir+night+'/*master_flat*.fits') + master_master_list = glob.glob(masters_dir+master_master_date+'/*master_flat*.fits') print(master_list) + print(master_master_list) for i in range(len(master_list)): @@ -51,7 +54,7 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] version = master_list[i][35:-5] - print(master_list[i],exposure_name,version) + print('i',master_list[i],exposure_name,version) L0_data = master_list[i] From f4b313e7211164b5b76464069039a47a2daff0a3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:52:16 -0700 Subject: [PATCH 054/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index c8ab68e8b..8f771243d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -54,8 +54,11 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] version = master_list[i][35:-5] - print('i',master_list[i],exposure_name,version) + print('i',master_list[i],exposure_name,version,master_master_list) + for j in range(len(master_master_list)): + if master_master_list[j].find(version): + hdulist1=fits.open(master_master_list[j])#identify master by the same type L0_data = master_list[i] hdulist = fits.open(L0_data) @@ -80,12 +83,17 @@ def nightly_procedures(self,night): #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') + master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') + if master_list[i].find('flat'): counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') + master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') if master_list[i].find('dark'):#scale up dark exposures - counts*=exptime + counts*=hdulist[0].header['EXPTIME'] + master_counts*=hdulist1[0].header['EXPTIME'] flatten_counts = np.ravel(counts) + master_flatten_counts = np.ravel(master_counts) if len(flatten_counts)<1: continue #master_flatten_counts='None' @@ -132,7 +140,7 @@ def nightly_procedures(self,night): #print(np.percentile(flatten_counts,99.9),saturation_limit) plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Fri, 9 Jun 2023 16:53:17 -0700 Subject: [PATCH 055/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 8f771243d..66ab0fce0 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -54,9 +54,10 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] version = master_list[i][35:-5] - print('i',master_list[i],exposure_name,version,master_master_list) + #print(i,master_list[i],exposure_name,version,master_master_list) for j in range(len(master_master_list)): + print(j,master_master_list[i]) if master_master_list[j].find(version): hdulist1=fits.open(master_master_list[j])#identify master by the same type From 77f30bfc996394fff226fc888aff383a3e3854a9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:54:04 -0700 Subject: [PATCH 056/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 66ab0fce0..e5eef47cb 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -46,7 +46,7 @@ def nightly_procedures(self,night): master_list = glob.glob(masters_dir+night+'/*master_flat*.fits') master_master_list = glob.glob(masters_dir+master_master_date+'/*master_flat*.fits') print(master_list) - print(master_master_list) + print(masters_dir+master_master_date+'/*master_flat*.fits',master_master_list) for i in range(len(master_list)): From 5f96406a14d4a585897f97081c116805b1e2fcc3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:55:31 -0700 Subject: [PATCH 057/249] nightly_summary --- modules/quicklook/configs/default.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index b5fd0fb6c..baebec98a 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -50,4 +50,4 @@ cahk_wav=/data/masters/kpfMaster_HKwave20220909_sci.csv exposures_dir=/data/2D/ masters_dir=/data/masters/ output_dir=/data/QLP -master_master_date=20230605 +master_master_date=20230604 From ebfcd4addb3ae5a83cdab18599a75e82227cb011 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:56:24 -0700 Subject: [PATCH 058/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index e5eef47cb..4a127f7cb 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -58,6 +58,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): print(j,master_master_list[i]) + if master_master_list[i][-7:] == 'L1.fits' or master_master_list[i][-7:] == 'L2.fits': continue if master_master_list[j].find(version): hdulist1=fits.open(master_master_list[j])#identify master by the same type From b9e827c0c74662c744c877b829a94d9fc4481de6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 16:56:48 -0700 Subject: [PATCH 059/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 4a127f7cb..5e289ff2d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -58,7 +58,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): print(j,master_master_list[i]) - if master_master_list[i][-7:] == 'L1.fits' or master_master_list[i][-7:] == 'L2.fits': continue + if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue if master_master_list[j].find(version): hdulist1=fits.open(master_master_list[j])#identify master by the same type From 23570bcd289e52309292cb45cc772213e175026d Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:00:15 -0700 Subject: [PATCH 060/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 5e289ff2d..9f9895217 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -135,6 +135,26 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) plt.close() + plt.figure(figsize=(5,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + #pcrint(counts,master_counts) + counts_norm = np.percentile(counts,99) + master_counts_norm = np.percentile(master_counts,99) + if np.shape(counts)!=np.shape(master_counts): continue + difference = counts/counts_norm-master_counts/master_counts_norm + + plt.imshow(difference, vmin = np.percentile(difference,1),vmax = np.percentile(difference,99), interpolation = 'None',origin = 'lower') + plt.xlabel('x (pixel number)') + plt.ylabel('y (pixel number)') + plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name) + plt.colorbar(label = 'Fractional Difference') + + + + #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') + #print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') + plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) + #histogram plt.close() plt.figure(figsize=(5,4)) From 1acaa9882f8b3fd0082fcfe4b226789a1a767f1e Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:02:00 -0700 Subject: [PATCH 061/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 9f9895217..91d0c7fb2 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -43,10 +43,10 @@ def nightly_procedures(self,night): os.makedirs(output_dir) #plot the master files for a particular night - master_list = glob.glob(masters_dir+night+'/*master_flat*.fits') - master_master_list = glob.glob(masters_dir+master_master_date+'/*master_flat*.fits') + master_list = glob.glob(masters_dir+night+'/*master*.fits') + master_master_list = glob.glob(masters_dir+master_master_date+'/*master*.fits') print(master_list) - print(masters_dir+master_master_date+'/*master_flat*.fits',master_master_list) + print(masters_dir+master_master_date+'/*master*.fits',master_master_list) for i in range(len(master_list)): @@ -146,7 +146,7 @@ def nightly_procedures(self,night): plt.imshow(difference, vmin = np.percentile(difference,1),vmax = np.percentile(difference,99), interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') - plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name) + plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) plt.colorbar(label = 'Fractional Difference') From e3305ddf6988cda63c96e0864c1a4957061834c9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:03:49 -0700 Subject: [PATCH 062/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 91d0c7fb2..92f6932b7 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -54,10 +54,10 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] version = master_list[i][35:-5] - #print(i,master_list[i],exposure_name,version,master_master_list) + print(i,master_list[i],exposure_name,version) for j in range(len(master_master_list)): - print(j,master_master_list[i]) + #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue if master_master_list[j].find(version): hdulist1=fits.open(master_master_list[j])#identify master by the same type From 418e30be2e7a54f3d64ccadbdc43d65f62d9aed6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:04:58 -0700 Subject: [PATCH 063/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 92f6932b7..8490883e3 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -88,6 +88,7 @@ def nightly_procedures(self,night): master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat'): + print('test',master_list[i].find('flat')) counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') if master_list[i].find('dark'):#scale up dark exposures From 373677460235c6a3bcdfff0d17cc2867afc74725 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:05:58 -0700 Subject: [PATCH 064/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 8490883e3..31a4ca3ad 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -59,7 +59,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue - if master_master_list[j].find(version): + if master_master_list[j].find(version)!=-1: hdulist1=fits.open(master_master_list[j])#identify master by the same type L0_data = master_list[i] @@ -87,11 +87,11 @@ def nightly_procedures(self,night): counts = np.array(hdulist[ccd_color[i_color]].data,'d') master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') - if master_list[i].find('flat'): - print('test',master_list[i].find('flat')) + if master_list[i].find('flat')!=-1: + print('test',master_list[i].find('flat')!=-1) counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') - if master_list[i].find('dark'):#scale up dark exposures + if master_list[i].find('dark')!=-1:#scale up dark exposures counts*=hdulist[0].header['EXPTIME'] master_counts*=hdulist1[0].header['EXPTIME'] From cf6e2d49edac9b9e17efb2203284bd6e3446add9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:07:14 -0700 Subject: [PATCH 065/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 31a4ca3ad..25df3388a 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -59,6 +59,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue + print('test j',master_master_list[j].find(version)) if master_master_list[j].find(version)!=-1: hdulist1=fits.open(master_master_list[j])#identify master by the same type @@ -88,7 +89,7 @@ def nightly_procedures(self,night): master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat')!=-1: - print('test',master_list[i].find('flat')!=-1) + counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') if master_list[i].find('dark')!=-1:#scale up dark exposures From 49bf8fdfcabcc3559cc2f9d498f27451874dd703 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:07:41 -0700 Subject: [PATCH 066/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 25df3388a..e16bf4920 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -59,7 +59,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue - print('test j',master_master_list[j].find(version)) + print('test j',master_master_list[j],master_master_list[j].find(version)) if master_master_list[j].find(version)!=-1: hdulist1=fits.open(master_master_list[j])#identify master by the same type From 8be5a8d19533d4c5b74bf338267fea36b36561c4 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:07:56 -0700 Subject: [PATCH 067/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index e16bf4920..1fde17890 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -59,7 +59,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue - print('test j',master_master_list[j],master_master_list[j].find(version)) + print('test j',version,master_master_list[j],master_master_list[j].find(version)) if master_master_list[j].find(version)!=-1: hdulist1=fits.open(master_master_list[j])#identify master by the same type From 632965b2e2604f649d15569ad829801846ddb877 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:11:20 -0700 Subject: [PATCH 068/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 50 ++++++++++++++-------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 1fde17890..6e26a68fc 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -56,12 +56,15 @@ def nightly_procedures(self,night): version = master_list[i][35:-5] print(i,master_list[i],exposure_name,version) + master_master_file = 'None' for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue print('test j',version,master_master_list[j],master_master_list[j].find(version)) if master_master_list[j].find(version)!=-1: - hdulist1=fits.open(master_master_list[j])#identify master by the same type + master_master_file = master_master_list[j] + + if master_master_file != 'None': hdulist1=fits.open(master_master_list[j])#identify master by the same type L0_data = master_list[i] hdulist = fits.open(L0_data) @@ -86,18 +89,18 @@ def nightly_procedures(self,night): #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') - master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') + if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat')!=-1: counts = np.array(hdulist[ccd_color[i_color]+'_STACK'].data,'d') - master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') + if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]+'_STACK'].data,'d') if master_list[i].find('dark')!=-1:#scale up dark exposures counts*=hdulist[0].header['EXPTIME'] - master_counts*=hdulist1[0].header['EXPTIME'] + if master_master_file != 'None':master_counts*=hdulist1[0].header['EXPTIME'] flatten_counts = np.ravel(counts) - master_flatten_counts = np.ravel(master_counts) + if master_master_file != 'None': master_flatten_counts = np.ravel(master_counts) if len(flatten_counts)<1: continue #master_flatten_counts='None' @@ -137,25 +140,21 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) plt.close() - plt.figure(figsize=(5,4)) - plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) - #pcrint(counts,master_counts) - counts_norm = np.percentile(counts,99) - master_counts_norm = np.percentile(master_counts,99) - if np.shape(counts)!=np.shape(master_counts): continue - difference = counts/counts_norm-master_counts/master_counts_norm - - plt.imshow(difference, vmin = np.percentile(difference,1),vmax = np.percentile(difference,99), interpolation = 'None',origin = 'lower') - plt.xlabel('x (pixel number)') - plt.ylabel('y (pixel number)') - plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) - plt.colorbar(label = 'Fractional Difference') - - - - #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') - #print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) + if master_master_file != 'None': + plt.figure(figsize=(5,4)) + plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + #pcrint(counts,master_counts) + counts_norm = np.percentile(counts,99) + master_counts_norm = np.percentile(master_counts,99) + if np.shape(counts)!=np.shape(master_counts): continue + difference = counts/counts_norm-master_counts/master_counts_norm + + plt.imshow(difference, vmin = np.percentile(difference,1),vmax = np.percentile(difference,99), interpolation = 'None',origin = 'lower') + plt.xlabel('x (pixel number)') + plt.ylabel('y (pixel number)') + plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) + plt.colorbar(label = 'Fractional Difference') + plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) #histogram plt.close() @@ -164,7 +163,8 @@ def nightly_procedures(self,night): #print(np.percentile(flatten_counts,99.9),saturation_limit) plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Fri, 9 Jun 2023 17:13:36 -0700 Subject: [PATCH 069/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 6e26a68fc..d5f0ed514 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -60,7 +60,7 @@ def nightly_procedures(self,night): for j in range(len(master_master_list)): #print(j,master_master_list[i]) if master_master_list[j][-7:] == 'L1.fits' or master_master_list[j][-7:] == 'L2.fits': continue - print('test j',version,master_master_list[j],master_master_list[j].find(version)) + #print('test j',version,master_master_list[j],master_master_list[j].find(version)) if master_master_list[j].find(version)!=-1: master_master_file = master_master_list[j] @@ -84,11 +84,12 @@ def nightly_procedures(self,night): if len(hdulist[ccd_color[0]].data)<1 and len(hdulist[ccd_color[1]].data)<1: print('skipping empty file') return - print(ccd_color) + #print(ccd_color) #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') + print(master_master_file) if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat')!=-1: From af7d3b29c057ec0257801eb4c0b3153d022e1d68 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:15:16 -0700 Subject: [PATCH 070/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index d5f0ed514..1b74090b8 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -90,6 +90,7 @@ def nightly_procedures(self,night): for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') print(master_master_file) + print(hdulist1.info()) if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat')!=-1: From fcb57931931a931162217c1511f9429e1691ce1c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:17:11 -0700 Subject: [PATCH 071/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 1b74090b8..786c52afd 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -64,7 +64,7 @@ def nightly_procedures(self,night): if master_master_list[j].find(version)!=-1: master_master_file = master_master_list[j] - if master_master_file != 'None': hdulist1=fits.open(master_master_list[j])#identify master by the same type + if master_master_file != 'None': hdulist1=fits.open(master_master_file)#identify master by the same type L0_data = master_list[i] hdulist = fits.open(L0_data) @@ -89,7 +89,7 @@ def nightly_procedures(self,night): #2d plots for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') - print(master_master_file) + print('',master_master_file) print(hdulist1.info()) if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') From 8480fd2016fddb15903f808d6b0589f6c668f499 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 9 Jun 2023 17:18:07 -0700 Subject: [PATCH 072/249] nightly_summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 786c52afd..32f3ea25e 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -90,7 +90,7 @@ def nightly_procedures(self,night): for i_color in range(len(ccd_color)): counts = np.array(hdulist[ccd_color[i_color]].data,'d') print('',master_master_file) - print(hdulist1.info()) + #print(hdulist1.info()) if master_master_file != 'None': master_counts = np.array(hdulist1[ccd_color[i_color]].data,'d') if master_list[i].find('flat')!=-1: From 84d131c07ed2724187ea49d356ce143efe84a7ce Mon Sep 17 00:00:00 2001 From: Ryan Rubenzahl Date: Mon, 12 Jun 2023 12:39:04 -1000 Subject: [PATCH 073/249] Update alg_barycentric_corr.py Return negative BC for Sun --- modules/barycentric_correction/src/alg_barycentric_corr.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/modules/barycentric_correction/src/alg_barycentric_corr.py b/modules/barycentric_correction/src/alg_barycentric_corr.py index fbff5d09e..abbe2913f 100644 --- a/modules/barycentric_correction/src/alg_barycentric_corr.py +++ b/modules/barycentric_correction/src/alg_barycentric_corr.py @@ -304,7 +304,7 @@ def get_bc_corr(obs_config, jd): rv=None, #rv=obs_config[BarycentricCorrectionAlg.RV] ) - + return -bc_obj[0][0] else: bc_obj = get_BC_vel(JDUTC=jd, ra=obs_config[BarycentricCorrectionAlg.RA], @@ -317,5 +317,4 @@ def get_bc_corr(obs_config, jd): longi=obs_config[BarycentricCorrectionAlg.LON], alt=obs_config[BarycentricCorrectionAlg.ALT], rv=obs_config[BarycentricCorrectionAlg.RV]) - - return bc_obj[0][0] + return bc_obj[0][0] From 1d05f791eec62ab12c2ae244b5b0f9887a426498 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 09:24:37 -0700 Subject: [PATCH 074/249] adjust wls search --- configs/kpf_masters_l1.cfg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index 894dbfb74..310aa559b 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -28,7 +28,7 @@ do_hk = False do_wavecopy_in_sp = True do_qlp = False do_bk_subtraction = True -do_bc = True +do_bc = False # the subdirectory containing order trace result, L1 data, L2 data, L2 reweighted data, qlp, bary data output_trace = / @@ -88,9 +88,9 @@ do_db_query_for_one_nearest_wls_master_file = True cal_file_level = 1 # contentbitmask = 3 means require at least GREEN and RED CCDs in the WLS master file database-queried nearest in time. contentbitmask = 3 -cal_type_pairs = [['WLS','cal-lfc-morn'], ['WLS', 'cal-lfc-eve'], ['WLS', 'autocal-thar-all']] +cal_type_pairs = [['WLS','cal-LFC-eve'], ['WLS', 'cal-LFC-morn'], ['WLS', 'autocal-thar-all-eve'], ['WLS', 'autocal-thar-all-morn']] # Maximum start-date age of WLS file relative to context.date_dir at 00:00:00 UT, otherwise fall back on wls_fits. -max_cal_file_age = '2 days' +max_cal_file_age = '4 days' # for rv: # o/ou/outtrderlet_names_rv: [] From dc7f9ba92289ef4d4cd61af4a167e1740ba1e64f Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 12:31:52 -0700 Subject: [PATCH 075/249] make order traces for master files --- configs/framework_multi.cfg | 2 +- configs/kpf_drp.cfg | 4 ++-- recipes/kpf_drp.recipe | 2 -- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/configs/framework_multi.cfg b/configs/framework_multi.cfg index 12a6765d6..c58960e4f 100644 --- a/configs/framework_multi.cfg +++ b/configs/framework_multi.cfg @@ -103,5 +103,5 @@ http_defaultFile = "" # want_multiprocessing = True queue_manager_hostname = 'localhost' -queue_manager_portnr = 50102 +queue_manager_portnr = 50101 queue_manager_auth_code = b"a very long authentication code" diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index 6b6bd7d38..1e1ce9977 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -78,9 +78,9 @@ do_db_query_for_one_nearest_wls_master_file = True cal_file_level = 1 # contentbitmask = 3 means require at least GREEN and RED CCDs in the WLS master file database-queried nearest in time. contentbitmask = 3 -cal_type_pairs = [['WLS','cal-lfc-morn'], ['WLS', 'cal-lfc-eve'], ['WLS', 'autocal-thar-all']] +cal_type_pairs = [['WLS','cal-LFC-eve'], ['WLS', 'cal-LFC-morn'], ['WLS', 'autocal-thar-all-eve'], ['WLS', 'autocal-thar-all-morn']] # Maximum start-date age of WLS file relative to context.date_dir at 00:00:00 UT, otherwise fall back on wls_fits. -max_cal_file_age = '2 days' +max_cal_file_age = '4 days' # for rv: # o/ou/outtrderlet_names_rv: [] diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 0b1fa36fe..16702cb7c 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -180,7 +180,6 @@ if do_db_query_for_one_nearest_wls_master_file and do_spectral_extraction: max_cal_file_age) db_wls_exit_code = query_one_nearest_master_file_list[0] - if db_wls_exit_code == 0: db_wls_rec = query_one_nearest_master_file_list[1] @@ -193,7 +192,6 @@ if do_db_query_for_one_nearest_wls_master_file and do_spectral_extraction: for wls in wls_list: wave_fits = wave_fits + [output_dir + db_wls_master_file] - #### variables related to input/output and process conditions for order_trace, spectral extraction, CA-HK, rv and qlp From 939373d3c9b447423bfb892e5f63e917ba9aee8f Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 13:54:27 -0600 Subject: [PATCH 076/249] update ratios --- static/static_green_ccf_ratio_2.csv | 72 ++++++++++++++--------------- static/static_red_ccf_ratio_2.csv | 66 +++++++++++++------------- 2 files changed, 69 insertions(+), 69 deletions(-) mode change 100755 => 100644 static/static_green_ccf_ratio_2.csv mode change 100755 => 100644 static/static_red_ccf_ratio_2.csv diff --git a/static/static_green_ccf_ratio_2.csv b/static/static_green_ccf_ratio_2.csv old mode 100755 new mode 100644 index 4558fec56..54d445e48 --- a/static/static_green_ccf_ratio_2.csv +++ b/static/static_green_ccf_ratio_2.csv @@ -1,36 +1,36 @@ -idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso lfc thar etalon -0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -6 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -10 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -13 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -14 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -15 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -16 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -18 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -19 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -21 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -22 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -23 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -24 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -26 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -31 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -32 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -34 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso sun lfc thar etalon +0 0.307648555 0.283495588 0.294824798 0.28180766 0.2395022 0.246935302 0.02007465 0.062998322 1 1 1 +1 0.304735663 0.360093113 0.355138203 0.348961398 0.302355992 0.315120994 0.033064344 0.097609921 1 1 1 +2 0.556952834 0.553405183 0.539738842 0.532052993 0.447876846 0.470420634 0.052014892 0.17313227 1 1 1 +3 0.444956353 0.472949562 0.463979229 0.493187013 0.425056702 0.456466391 0.08685582 0.172042583 1 1 1 +4 0.456316762 0.473849249 0.496723114 0.500425596 0.42995356 0.464280958 0.113100101 0.2043902 1 1 1 +5 0.451109244 0.554589831 0.518684995 0.563029903 0.489260997 0.513242242 0.12331726 0.241064495 1 1 1 +6 0.426439812 0.520056166 0.492774214 0.541431718 0.449897712 0.476434647 0.126707168 0.250018052 1 1 1 +7 0.485198045 0.560237307 0.534058218 0.566123014 0.477834618 0.509796418 0.121204569 0.292788986 1 1 1 +8 0.483243262 0.550705232 0.541698997 0.565266142 0.506167621 0.538096699 0.123076825 0.316942327 1 1 1 +9 0.451426771 0.530383344 0.551664377 0.578832591 0.491578811 0.529034603 0.154848573 0.339397315 1 1 1 +10 0.382178387 0.50913597 0.506490195 0.539793378 0.483875554 0.52223128 0.172416006 0.336942588 1 1 1 +11 0.616767985 0.662384409 0.663259991 0.69113469 0.598239053 0.642030673 0.176687468 0.47592068 1 1 1 +12 0.576403968 0.659061264 0.634857894 0.62114864 0.541062588 0.583542887 0.253380555 0.473716424 1 1 1 +13 0.594170371 0.588001989 0.633034037 0.632519717 0.567544608 0.613344118 0.270399694 0.491462458 1 1 1 +14 0.853006835 0.936201214 0.899730215 0.92759557 0.797575695 0.841380788 0.328308518 0.761728201 1 1 1 +15 0.889365725 0.903894403 0.929682049 1 0.946575381 0.983078283 0.314432353 0.808860561 1 1 1 +16 0.664101202 0.781609092 0.764993931 0.829537049 0.925190674 0.942075016 0.347974305 0.685927913 1 1 1 +17 0.750809 0.866930733 0.860857031 0.94543385 1 1 0.309212762 0.824444728 1 1 1 +18 0.914048606 0.899177188 0.882588995 0.936904846 0.960032774 0.953049191 0.255166747 0.862853469 1 1 1 +19 1 1 1 0.979301441 0.902044692 0.920071693 0.516763837 1 1 1 1 +20 0.885099998 0.867295603 0.861799575 0.90277224 0.806964777 0.882882653 0.679170571 0.886990259 1 1 1 +21 0.653477563 0.670561883 0.717905593 0.739357434 0.656841314 0.727431828 0.494034278 0.767547679 1 1 1 +22 0.732228179 0.754558587 0.758415698 0.737075206 0.621998074 0.681070706 0.304176485 0.839460607 1 1 1 +23 0.715742433 0.749689029 0.73573155 0.771651198 0.666412546 0.747022897 0.671542735 0.872200492 1 1 1 +24 0.565488395 0.58758027 0.606134616 0.622265271 0.526124724 0.595488002 0.625443537 0.686488841 1 1 1 +25 0.615936195 0.725924643 0.728389763 0.820884656 0.716883616 0.826957069 1 0.855854189 1 1 1 +26 0.437099765 0.506457806 0.538037667 0.604085763 0.519235546 0.593808707 0.623910172 0.639493118 1 1 1 +27 0.725294903 0.749712464 0.775034343 0.814475787 0.702242317 0.807333487 0.561968503 0.941422514 1 1 1 +28 0.455399013 0.587687191 0.554288516 0.624490226 0.497359374 0.590367098 0.796943331 0.685558005 1 1 1 +29 0.501928697 0.605694713 0.64567172 0.700880864 0.587441875 0.694670457 0.70370676 0.813784527 1 1 1 +30 0.217769726 0.288811187 0.280954396 0.377338337 0.336066963 0.402591861 0.757479704 0.40393392 1 1 1 +31 0.306912143 0.435554062 0.453156039 0.55568918 0.45385083 0.539835447 0.944792292 0.575346097 1 1 1 +32 0.21979644 0.29401964 0.302841433 0.355746675 0.295935167 0.355209143 0.910750796 0.389712971 1 1 1 +33 0 0 0 0 0 0 0 0 1 1 1 +34 0 0 0 0 0 0 0.066060662 0 1 1 1 diff --git a/static/static_red_ccf_ratio_2.csv b/static/static_red_ccf_ratio_2.csv old mode 100755 new mode 100644 index 7a966f37f..34c711992 --- a/static/static_red_ccf_ratio_2.csv +++ b/static/static_red_ccf_ratio_2.csv @@ -1,33 +1,33 @@ -idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso lfc thar etalon -0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -4 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -6 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -7 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -10 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -11 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -12 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -19 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -20 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -23 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -24 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 -31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 +idx F9_espresso G2_espresso G8_espresso G9_espresso K2_espresso K6_espresso M2_espresso sun lfc thar etalon +0 0.144628749 0.173485336 0.170643724 0.146884303 0.141812298 0.144335292 0.032467697 0.144775643 1 1 1 +1 0.2492304 0.297346072 0.307383477 0.293598269 0.27571083 0.268936658 0.061438438 0.265254964 1 1 1 +2 0.871770667 1 0.993135948 0.814131876 0.77409801 0.766507557 0.116477104 0.873801877 1 1 1 +3 0.633301087 0.778685112 0.704166238 0.653708776 0.672320283 0.679402906 0.10309443 0.61685671 1 1 1 +4 1 0.997145395 1 1 1 1 0.096271099 0.929048362 1 1 1 +5 0.106527959 0.181140977 0.127034675 0.109071335 0.113962694 0.113750087 0.225288207 0.115344097 1 1 1 +6 0.929824772 0.987724239 0.967095809 0.847384452 0.74565177 0.750036601 0.636619428 0.904443726 1 1 1 +7 0.198582719 0.013368205 0.175921539 0.144251757 0.124110469 0.123691403 0.557670168 0.164265796 1 1 1 +8 0 0 0 0 0 0 0 0 1 1 1 +9 0.126637459 0.190206458 0.179508737 0.207484401 0.213324723 0.218001246 1 0.189909711 1 1 1 +10 0.339058468 0.476675469 0.44734272 0.480332166 0.426966193 0.462300569 0.775235132 0.494647329 1 1 1 +11 0.472243787 0.623166986 0.646609469 0.631043055 0.558909324 0.571793732 0.777856048 0.722736947 1 1 1 +12 0.470867106 0.618960428 0.623977739 0.594987507 0.536941431 0.546189569 0.628671722 0.724391143 1 1 1 +13 0 0 0 0 0 0 0 0 1 1 1 +14 0 0 0 0 0 0 0 0 1 1 1 +15 0 0 0 0 0 0 0 0 1 1 1 +16 0 0 0 0 0 0 0 0 1 1 1 +17 0 0 0 0 0 0 0 0 1 1 1 +18 0 0 0 0 0 0 0 0 1 1 1 +19 0.186907297 0.240856858 0.20407216 0.196833851 0.168531528 0.170495955 0 0.271862843 1 1 1 +20 0.59572158 0.755476703 0.747632854 0.75358992 0.608686759 0.616767692 0 1 1 1 1 +21 0.51828807 0.584309397 0.58459379 0.538465266 0.448981344 0.458314233 0 0.795193832 1 1 1 +22 0 0 0 0 0 0 0 0 1 1 1 +23 0.436872209 0.440325071 0.435171081 0.347326774 0.293218259 0.301010285 0 0.637217803 1 1 1 +24 0.070660409 0.081678235 0.084050137 0.085215547 0.056600586 0.058342966 0 0.125429608 1 1 1 +25 0 0 0 0 0 0 0 0 1 1 1 +26 0 0 0 0 0 0 0 0 1 1 1 +27 0 0 0 0 0 0 0 0 1 1 1 +28 0 0 0 0 0 0 0 0 1 1 1 +29 0 0 0 0 0 0 0 0 1 1 1 +30 0 0 0 0 0 0 0 0 1 1 1 +31 0 0 0 0 0 0 0 0 1 1 1 From 3c0f236e7cae8464ebc28c564e71a69b4dd1c877 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 15:11:42 -0600 Subject: [PATCH 077/249] update mask paths --- configs/kpf_drp_local.cfg | 4 ++-- modules/radial_velocity/src/alg_rv_init.py | 2 +- recipes/kpf_drp.recipe | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/configs/kpf_drp_local.cfg b/configs/kpf_drp_local.cfg index 13ce78054..504771f20 100644 --- a/configs/kpf_drp_local.cfg +++ b/configs/kpf_drp_local.cfg @@ -43,7 +43,7 @@ ccd_list = ['GREEN_CCD', 'RED_CCD'] # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe #flat_file = KP.20221107.04689.77 flat_file = /data/masters/20230527/kpf_20230527_master_flat.fits -order_trace_files = [ '/code/KPF-Pipeline/static/kpf_20230418_order_trace_GREEN.csv', '/code/KPF-Pipeline/static/kpf_20230418_order_trace_RED.csv'] +order_trace_files = ['/code/KPF-Pipeline/static/kpf_20230418_order_trace_GREEN.csv', '/code/KPF-Pipeline/static/kpf_20230418_order_trace_RED.csv'] fitting_poly_degree = 3 ccd_idx = [0, 1] @@ -113,7 +113,7 @@ hk_wavelength_path = ["masters/kpfMaster_HKwave20220909_sci.csv", "masters/kpfMa # for module process: do_l0_to_2d = True -do_order_trace = False +do_order_trace = True do_spectral_extraction = True do_rv = True do_rv_reweighting = True diff --git a/modules/radial_velocity/src/alg_rv_init.py b/modules/radial_velocity/src/alg_rv_init.py index b7898531b..0faaf4630 100644 --- a/modules/radial_velocity/src/alg_rv_init.py +++ b/modules/radial_velocity/src/alg_rv_init.py @@ -283,7 +283,7 @@ def init_star_from_header(self): if default_mask not in mask_file_map: return self.ret_status('default mask of ' + default_mask + ' is not defined') - self.mask_path = self.test_data_dir + stellar_dir + mask_file_map[default_mask][0] + self.mask_path = stellar_dir + mask_file_map[default_mask][0] self.mask_type = default_mask self.mask_wavelengths = mask_file_map[default_mask][1] self.mask_orderlet[fobj] = {"obj": fiber_obj, diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index baa242fe2..141d1ddf2 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -5,7 +5,7 @@ from modules.spectral_extraction.src.spectral_extraction import SpectralExtracti from modules.radial_velocity.src.radial_velocity_init import RadialVelocityInit from modules.radial_velocity.src.radial_velocity import RadialVelocity from modules.radial_velocity.src.radial_velocity_reweighting_ref import RadialVelocityReweightingRef -from modules.radial_velocity.src.radial_velocity_reweighting import RadialVelocityReweighting +from modules.src.radial_velocity_reweighting import RadialVelocityReweighting from modules.ca_hk.src.ca_hk_extraction import CaHKExtraction from modules.quicklook.src.quick_prim import Quicklook from modules.Utils.string_proc import str_replace @@ -64,7 +64,7 @@ if context.watch: lev0_file_path = str_replace(file_path, 'L0', '2D') lev0_file_path = str_replace(lev0_file_path, '.fits', '_2D.fits') lev1_file_path = str_replace(file_path, 'L0', 'L1') - do_order_trace = False + # do_order_trace = False # do spectral extraction if 2D data is watched, do 2D->l1->l2 if '2D' in file_path: From 0e793e567b14dd864fe88462aeb7666023cf9de3 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 15:22:47 -0600 Subject: [PATCH 078/249] normal DRP running locally --- configs/kpf_drp_local.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/kpf_drp_local.cfg b/configs/kpf_drp_local.cfg index 504771f20..942d99b45 100644 --- a/configs/kpf_drp_local.cfg +++ b/configs/kpf_drp_local.cfg @@ -92,7 +92,7 @@ rv_correct_by_cal = False reweighting_method = ccf_static ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV -#static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] +reweighting_enable_masks = [['espresso'], ['espresso']] static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] # for ca_hk: From 919c84ad81ffeab7cff74933d306d09ac86af0b2 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 13 Jun 2023 15:58:31 -0600 Subject: [PATCH 079/249] update paths in primary cfg --- docs/source/index.rst | 2 +- docs/source/intro/example_trivial.rst | 31 ++++++++++++------------ docs/source/intro/process_night.rst | 35 +++++++++++++++++++++++++++ docs/source/intro/start.rst | 10 +++----- 4 files changed, 55 insertions(+), 23 deletions(-) create mode 100644 docs/source/intro/process_night.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index 1e3c41d1f..46c8054f8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,7 +11,7 @@ Welcome! This is the documentation for the KPF Data Reduction Pipeline. :maxdepth: 2 intro/start.rst + data_exploration/start.rst develop/start.rst api/api.rst primitives/primitives.rst - data_exploration/start.rst diff --git a/docs/source/intro/example_trivial.rst b/docs/source/intro/example_trivial.rst index efaa8dd14..8b54da537 100644 --- a/docs/source/intro/example_trivial.rst +++ b/docs/source/intro/example_trivial.rst @@ -37,28 +37,27 @@ For more detail regarding the structure of KPF Pipeline configuration file, plea To run this pair of files, execute the following command:: - kpf examples/simple.recipe examples/docs_simple.cfg + kpf -r examples/simple.recipe -c examples/docs_simple.cfg If everything runs smoothly, you should see the following printed to terminal:: - 2021-02-03 18:23:17:DRPF:INFO: - 2021-02-03 18:23:17:DRPF:INFO: Initialization Framework cwd= - [KPF-Pipe][INFO]:Logger started + [KPF-Pipe][INFO]:Pipeline logger started [KPF-Pipe][INFO]:Finished initializing Pipeline - 2021-02-03 18:23:17:DRPF:INFO: Framework main loop started - [KPF-Pipe][INFO]:Module: subrecipe_depth = 0 - [KPF-Pipe][INFO]:Added KPFModExample from modules.Trivial.KPFM_Trivial to event_table - [KPF-Pipe][INFO]:Queued KPFModExample with args "name": KPFModExample_args; awaiting return. - 2021-02-03 18:23:17:DRPF:INFO: Event completed: name start_recipe, action start_recipe, arg name undef, recurr False - [KPF-Pipe][INFO]:exiting pipeline... - 2021-02-03 18:23:17:DRPF:INFO: Event failed: name exit, action exit_loop, arg name undef, recurr False + [KPF-Pipe][INFO]:Pipeline logger started + [KPF-Pipe][INFO]:Pipeline logger started + [KPF-Pipe][INFO]:Finished initializing Pipeline + [KPF-Pipe][INFO]:Finished initializing Pipeline + [KPF-Pipe][INFO]:Starting new log with path: pipeline_20230613.log + [KPF-Pipe][INFO]:Starting new log with path: pipeline_20230613.log + [pipeline_20230613.log][INFO]:*************** Executing recipe examples/simple.recipe *************** + [pipeline_20230613.log][INFO]:Module: subrecipe_depth = 0 + [pipeline_20230613.log][INFO]:Added KPFModExample from modules.Trivial.KPFM_Trivial to event_table + [pipeline_20230613.log][INFO]:Queued KPFModExample with args "name": KPFModExample_args; awaiting return. [KPFModExample] missing log configuration...not starting a new logger [KPFModExample] Performed! - 2021-02-03 18:23:17:DRPF:INFO: Event failed: name KPFModExample, action KPFModExample, arg name KPFModExample_args, recurr False - [KPF-Pipe][INFO]:Module: subrecipe_depth = 0 - 2021-02-03 18:23:17:DRPF:INFO: Event completed: name resume_recipe, action resume_recipe, arg name KPFModExample_args, recurr False - 2021-02-03 18:23:18:DRPF:INFO: No new events - do nothing - 2021-02-03 18:23:18:DRPF:INFO: Exiting main loop + [pipeline_20230613.log][INFO]:Module: subrecipe_depth = 0 + [pipeline_20230613.log][INFO]:Assign: result <- KPFModExample done, type: str + [pipeline_20230613.log][INFO]:exiting pipeline... Continue to :doc:`logging` for explanations on what these messages mean. diff --git a/docs/source/intro/process_night.rst b/docs/source/intro/process_night.rst new file mode 100644 index 000000000..cd2e9f0f4 --- /dev/null +++ b/docs/source/intro/process_night.rst @@ -0,0 +1,35 @@ +Example of processing data from a single night +============================================== + +The part of the pipeline that generates stacks for master calibration files +is not yet optimized to run on personal computers due to extremely high RAM requirements. +It is easiest to download a directory of master calibration files for the night +that you would like to process. The master calibration directory should be named +by it's date (YYYYMMDD) and placed in a subdirectory called ``masters`` within +the ``KPFPIPE_DATA`` directory (e.g. ``$KPFPIPE_DATA/masters/20230527/``). + +Once you have the master calibration files in place you'll need to edit the following variables +in ``configs/kpf_drp_local.cfg``:: + + masterbias # path to a master bias L0 file (e.g. ``$KPFPIPE_DATA/masters/20230527/kpf_20230527_master_bias.fits``) + masterdark # path to a master dark L0 file (e.g. ``$KPFPIPE_DATA/masters/20230527/kpf_20230527_master_dark.fits``) + masterflat # path to a master flat L0 file used for the flat field correction (e.g. ``$KPFPIPE_DATA/masters/20230527/kpf_20230527_master_flat.fits``) + flat_file # path to a master flat L0 file used to find the order locations (e.g. ``$KPFPIPE_DATA/masters/20230527/kpf_20230527_master_flat.fits``) + wls_file # path to a wavelength solution L1 file (e.g. ``$KPFPIPE_DATA/masters/20230527/kpf_20230527_master_WLS_cal-LFC-eve_L1.fits``) + +Now download the data from a given night and place it under ``$KPFPIPE_DATA/L0/`` (e.g. ``$KPFPIPE_DATA/L0/20230527/``). + +For single-threaded processing you can launch the pipeline with:: + + kpf --date 20230527 -r recipes/kpf_drp.recipe -c configs/kpf_drp_local.cfg + +or for parallel processing use a combination of the ``--watch``, ``--ncpus``, and ``--reprocess`` flags:: + + kpf --reprocess --watch /data/L0/20230527/ --ncpus=8 -r recipes/kpf_drp.recipe -c configs/kpf_drp_local.cfg + +In this mode the pipeline will continue to monitor the input directory for new incoming +files forever so you'll need to exit with Ctrl-C once you see that there are no more messages being reported. + +In both single-threaded and parallel modes the outputs will be saved under the appropriate date directores in ``$KPFPIPE_DATA/2D/``, +``$KPFPIPE_DATA/L1/``, and ``$KPFPIPE_DATA/L2/``. + diff --git a/docs/source/intro/start.rst b/docs/source/intro/start.rst index 09a7881e0..bfa0373ca 100644 --- a/docs/source/intro/start.rst +++ b/docs/source/intro/start.rst @@ -6,17 +6,14 @@ the ``KPF-Pipeline`` module. First, download and install `Docker Desktop `_ if you don't already have it. -Obtain a copy of the test datasets stored in ownCloud. Download and install the `ownCloud desktop client `_ and direct it to ``_. - Then clone the repository and navigate into it:: - git clone https://github.com/California-Planet-Search/KPF-Pipeline.git + git clone https://github.com/Keck-DataReductionPipelines/KPF-Pipeline.git cd KPF-Pipeline .. warning:: Refer to :doc:`install_develop` for setting up other branches -Define the ``KPFPIPE_TEST_DATA`` environment variable and point it to the ``KPF-Pipeline-TestData`` directory inside your copy of the ownCloud directory. -Define the ``KPFPIPE_DATA`` environment variable and point it to the incoming data directory. +Define the ``KPFPIPE_DATA`` environment variable and point it to a location where you want to store the input and ouput files. If you would like to work in Jupyter notebooks then also define the ``KPFPIPE_PORT`` environment variable to assign a port to use for the notebook server. Build the package into a docker container and launch an interactive bash shell:: @@ -39,7 +36,7 @@ This will prompt the use case message, which should be:: The two mandatory input arguments to ``kpf`` is: a ``recipe`` file and a ``config`` configuration file. The recipe file is expected to be a ``.recipe`` script, while the ``config`` file is expected to be a ``.cfg`` file. See :doc:`example_trivial` -for a basic example. +for a basic example and :doc:`process_night` to process a night of data. To start a notebook server run the following and follow the on-screen instructions:: @@ -55,6 +52,7 @@ for instructions on how to initially set up the pipeline operations database. install_develop.rst example_trivial.rst + process_night.rst logging.rst config.rst database_setup.rst From f86d1663b58543b556c58b27918da31de0b1575f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 13 Jun 2023 15:58:22 -0700 Subject: [PATCH 080/249] allow negative values in l1 --- examples/quicklook_test.cfg | 4 ++-- examples/quicklook_test.recipe | 2 +- modules/quicklook/src/alg.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/quicklook_test.cfg b/examples/quicklook_test.cfg index 550e6d362..7f9fe9107 100644 --- a/examples/quicklook_test.cfg +++ b/examples/quicklook_test.cfg @@ -5,8 +5,8 @@ log_level = info log_verbose = True [ARGUMENT] -path_to_twod = /data/2D/20230528/ -output_dir = '/data/QLP/20230528/' +path_to_twod = /data/2D/20230525/ +output_dir = '/data/QLP/20230525/' [MODULE_CONFIGS] diff --git a/examples/quicklook_test.recipe b/examples/quicklook_test.recipe index 8953626a8..e7c9c2b9f 100644 --- a/examples/quicklook_test.recipe +++ b/examples/quicklook_test.recipe @@ -2,7 +2,7 @@ from modules.quicklook.src.quick_prim import Quicklook path_to_twod = config.ARGUMENT.path_to_twod #lev0_files= path_to_twod + '*' + '.fits' -lev0_files= path_to_twod + 'KP.20230528.52481.18_2D.fits' +lev0_files= path_to_twod + 'KP.20230525.53471.19_2D.fits' output_dir = config.ARGUMENT.output_dir end_of_night_summary = False diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index d735ab388..333a16827 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -827,7 +827,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux)[0]/n)): low, high = np.nanpercentile(flux[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(-high*0.1, high*1.2) + ax[j].set_ylim(np.min(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux,[0.1,99.9]) From b1eef906dd536609006a5015c5091add1cb5061f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 13 Jun 2023 16:06:25 -0700 Subject: [PATCH 081/249] allow negative values in l1 --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 333a16827..296bb669d 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -827,7 +827,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux)[0]/n)): low, high = np.nanpercentile(flux[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(np.min(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux,[0.1,99.9]) From 1d1c5d462093a93650a430738dc9fd96da76fd64 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 13 Jun 2023 16:09:21 -0700 Subject: [PATCH 082/249] allow negative values in l1 --- modules/quicklook/src/alg.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 296bb669d..c12120f2a 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -862,7 +862,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux2)[0]/n)): low, high = np.nanpercentile(flux2[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux2,[0.1,99.9]) @@ -897,7 +897,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux3)[0]/n)): low, high = np.nanpercentile(flux3[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux3,[0.1,99.9]) @@ -932,7 +932,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux_cal)[0]/n)): low, high = np.nanpercentile(flux_cal[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux_cal,[0.1,99.9]) @@ -967,7 +967,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux_sky)[0]/n)): low, high = np.nanpercentile(flux_sky[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux_sky,[0.1,99.9]) From 358223d9ea34a5e73d83c64317a3e7da8ac8a9ea Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 13 Jun 2023 16:11:37 -0700 Subject: [PATCH 083/249] allow negative values in l1 --- modules/quicklook/src/alg.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index c12120f2a..cb4c62b3e 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -862,7 +862,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux2)[0]/n)): low, high = np.nanpercentile(flux2[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux2[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux2,[0.1,99.9]) @@ -897,7 +897,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux3)[0]/n)): low, high = np.nanpercentile(flux3[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux3[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux3,[0.1,99.9]) @@ -932,7 +932,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux_cal)[0]/n)): low, high = np.nanpercentile(flux_cal[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux_cal[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux_cal,[0.1,99.9]) @@ -967,7 +967,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): for j in range(int(np.shape(flux_sky)[0]/n)): low, high = np.nanpercentile(flux_sky[j*n:(j+1)*n,:],[.1,99.9]) #print(j,high*1.5) - ax[j].set_ylim(np.nanmin(flux[j*n:(j+1)*n,:])-high*0.1, high*1.2) + ax[j].set_ylim(np.nanmin(flux_sky[j*n:(j+1)*n,:])-high*0.1, high*1.2) low, high = np.nanpercentile(flux_sky,[0.1,99.9]) From 9d37e40fe0463325f0e0fa0ac476f9134a57e098 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Wed, 14 Jun 2023 08:53:02 -0700 Subject: [PATCH 084/249] Added new modules/quality_control_exposure. --- modules/quality_control_exposure/__init__.py | 0 .../configs/default.cfg | 11 + .../quality_control_exposure/src/__init__.py | 0 .../src/quality_control_exposure_framework.py | 655 ++++++++++++++++++ 4 files changed, 666 insertions(+) create mode 100644 modules/quality_control_exposure/__init__.py create mode 100644 modules/quality_control_exposure/configs/default.cfg create mode 100644 modules/quality_control_exposure/src/__init__.py create mode 100644 modules/quality_control_exposure/src/quality_control_exposure_framework.py diff --git a/modules/quality_control_exposure/__init__.py b/modules/quality_control_exposure/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/modules/quality_control_exposure/configs/default.cfg b/modules/quality_control_exposure/configs/default.cfg new file mode 100644 index 000000000..caf8273a8 --- /dev/null +++ b/modules/quality_control_exposure/configs/default.cfg @@ -0,0 +1,11 @@ +## Default configuration for QualityControlExposureFramework primitive +[LOGGER] +start_log = True +log_path = logs/quality_control_exposure_framework_debug.log +log_level = debug +log_verbose = True + + +## Module related parameters +[PARAM] +product_level = 0 diff --git a/modules/quality_control_exposure/src/__init__.py b/modules/quality_control_exposure/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/modules/quality_control_exposure/src/quality_control_exposure_framework.py b/modules/quality_control_exposure/src/quality_control_exposure_framework.py new file mode 100644 index 000000000..4bdc838f2 --- /dev/null +++ b/modules/quality_control_exposure/src/quality_control_exposure_framework.py @@ -0,0 +1,655 @@ +import os +import numpy as np +import configparser as cp +from datetime import datetime, timezone +import psycopg2 +import re +import hashlib +import ast + +# Pipeline dependencies +from kpfpipe.logger import * +from kpfpipe.models.level0 import KPF0 +from kpfpipe.primitives.level0 import KPF0_Primitive +from kpfpipe.pipelines.fits_primitives import to_fits +from keckdrpframework.models.arguments import Arguments + +# Global read-only variables +DEFAULT_CFG_PATH = 'modules/quality_control_exposure/configs/default.cfg' + +def md5(fname): + hash_md5 = hashlib.md5() + + try: + with open(fname, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() + except: + print("*** Error: Cannot open file =",fname,"; quitting...") + exit(65) + +def sextodec(ra,dec): + xstr = ra.split(":") + hr = float(xstr[0]) + min = float(xstr[1]) + sec = float(xstr[2]) + rad = (sec + 60.0 * min + 3600.0 * abs(hr)) / 3600.0 * 360.0 / 24.0; + if rad < 0.0: + rad = - rad + ystr = dec.split(":") + deg = float(ystr[0]) + dmin = float(ystr[1]) + dsec = float(ystr[2]) + decd = (dsec + 60.0 * dmin + 3600.0 * abs(deg)) / 3600.0; + if "-" in dec: + decd = -decd; + return (rad, decd) + +class QualityControlExposureFramework(KPF0_Primitive): + + """ + Description: + Analyzes an L0 FITS file. Harvests info from primary FITS header and stores it in the database L0Files table. + + Arguments: + data_type (str): Type of data (e.g., KPF). + l0_filename (str): Full path and filename of L0 FITS file within container. + actual_dir (str): Prefix of actual directory outside container that maps to /data (e.g., /data/kpf) + + + """ + + def __init__(self, action, context): + + KPF0_Primitive.__init__(self, action, context) + + self.data_type = self.action.args[0] + self.l0_filename = self.action.args[1] + self.lev0_ffi_exts = self.action.args[2] + self.actual_dir = self.action.args[3] + + try: + self.module_config_path = context.config_path['quality_control_exposure'] + print("--->",self.__class__.__name__,": self.module_config_path =",self.module_config_path) + except: + self.module_config_path = DEFAULT_CFG_PATH + + print("{} class: self.module_config_path = {}".format(self.__class__.__name__,self.module_config_path)) + + print("Starting logger...") + self.logger = start_logger(self.__class__.__name__, self.module_config_path) + + if self.logger is not None: + print("--->self.logger is not None...") + else: + print("--->self.logger is None...") + + self.logger.info('Started {}'.format(self.__class__.__name__)) + self.logger.debug('module_config_path = {}'.format(self.module_config_path)) + + module_config_obj = cp.ConfigParser() + res = module_config_obj.read(self.module_config_path) + if res == []: + raise IOError('failed to read {}'.format(self.module_config_path)) + + module_param_cfg = module_config_obj['PARAM'] + + product_level_cfg_str = module_param_cfg.get('product_level') + self.product_level_cfg = ast.literal_eval(product_level_cfg_str) + + self.logger.info('self.data_type = {}'.format(self.data_type)) + self.logger.info('self.l0_filename = {}'.format(self.l0_filename)) + + self.logger.info('self.product_level_cfg = {}'.format(self.product_level_cfg)) + + self.logger.info('Type of self.product_level_cfg = {}'.format(type(self.product_level_cfg))) + + + def _perform(self): + + """ + Returns exitcode: + 0 = Normal + 2 = Exception raised closing database connection + 64 = Cannot connect to database + 65 = Input file does not exist + 66 = Could not insert database record + """ + + quality_control_exposure_exit_code = 0 + status = 1 + comment = "" + + + # See if file exists. + + isExist = os.path.exists(self.l0_filename) + #self.logger.info('File existence = {}'.format(isExist)) + + if isExist is False: + self.logger.info('Input file does not exist...') + quality_control_exposure_exit_code = 65 + return Arguments(quality_control_exposure_exit_code) + + + # Parse date from filename. Assume filename has the following form: KP.20230529.69419.77.fits + + filename_parts = self.l0_filename.split(".") + filename_date_num = int(filename_parts[1]) + #self.logger.info('filename_date_num = {}'.format(filename_date_num)) + + + # Get database connection parameters from environment. + + dbport = os.getenv('DBPORT') + dbname = os.getenv('DBNAME') + dbuser = os.getenv('DBUSER') + dbpass = os.getenv('DBPASS') + dbserver = os.getenv('DBSERVER') + + + # Connect to database + + try: + conn = psycopg2.connect(host=dbserver,database=dbname,port=dbport,user=dbuser,password=dbpass) + except: + self.logger.info('Could not connect to database...') + quality_control_exposure_exit_code = 64 + return Arguments(quality_control_exposure_exit_code) + + # Open database cursor. + + cur = conn.cursor() + + + # Select database version. + + q1 = 'SELECT version();' + #self.logger.info('q1 = {}'.format(q1)) + cur.execute(q1) + db_version = cur.fetchone() + #self.logger.info('PostgreSQL database version = {}'.format(db_version)) + + + # Check database current_user. + + q2 = 'SELECT current_user;' + #self.logger.info('q2 = {}'.format(q2)) + cur.execute(q2) + for record in cur: + #self.logger.info('record = {}'.format(record)) + pass + + + # Get parameters for infobits. + + p1_bits = [] + p2_bits = [] + + for i in range(0, 14): + bit = i + + q_bit = 'SELECT param1, param2 from l0infobits where bit = ' + str(bit) + ' order by created desc limit 1;' + #self.logger.info('q_bit = {}'.format(q_bit)) + cur.execute(q_bit) + record = cur.fetchone() + + if record is not None: + p1_bits.append(record[0]) + p2_bits.append(record[1]) + + + # Read image data object from FITS file. + + l0_file = KPF0.from_fits(self.l0_filename,self.data_type) + + infobits = 0 + + for ffi in self.lev0_ffi_exts: + + image = np.array(l0_file[ffi]) + + image_shape = np.shape(image) + + #self.logger.info('ffi,image_shape = {},{}'.format(ffi,image_shape)) + + if image_shape[0] != 0: + median = np.nanpercentile(image, 50) + p16 = np.nanpercentile(image, 16) + p84 = np.nanpercentile(image, 84) + + if ffi == "GREEN_AMP1": + bit_num_dead = 0 + bit_num_satur = 7 + elif ffi == "GREEN_AMP2": + bit_num_dead = 1 + bit_num_satur = 8 + elif ffi == "GREEN_AMP3": + bit_num_dead = 2 + bit_num_satur = 9 + elif ffi == "GREEN_AMP4": + bit_num_dead = 3 + bit_num_satur = 10 + elif ffi == "RED_AMP1": + bit_num_dead = 4 + bit_num_satur = 11 + elif ffi == "RED_AMP2": + bit_num_dead = 5 + bit_num_satur = 12 + elif ffi == "CA_HK": + bit_num_dead = 6 + bit_num_satur = 13 + + p1_bit_dead = p1_bits[bit_num_dead] + p2_bit_dead = p2_bits[bit_num_dead] + p1_bit_satur = p1_bits[bit_num_satur] + p2_bit_satur = p2_bits[bit_num_satur] + + dead_pix = np.where(image < p2_bit_dead,1,0) + dead_count = np.sum(dead_pix, dtype=int) + + satur_pix = np.where(image > p2_bit_satur,1,0) + satur_count = np.sum(satur_pix, dtype=int) + + det_count = image_shape[0] * image_shape[1] + + #self.logger.info('ffi,dead_count,satur_count,det_count = {},{},{},{}'.format(ffi,dead_count,satur_count,det_count)) + + bit_val_dead = 2**bit_num_dead + bit_val_satur = 2**bit_num_satur + + if dead_count > p1_bit_dead * det_count / 100.0: + infobits = infobits | bit_val_dead # Set bit _dead of infobits + #self.logger.info('Setting bit _dead: ffi,infobits = {},{}'.format(ffi,infobits)) + + if satur_count > p1_bit_satur * det_count / 100.0: + infobits = infobits | bit_val_satur # Set bit _satur of infobits + #self.logger.info('Setting bit _satur: ffi,infobits = {},{}'.format(ffi,infobits)) + + else: + median = 'null' + p16 = 'null' + p84 = 'null' + + #self.logger.info('ffi,median = {},{}'.format(ffi,median)) + + if ffi == "GREEN_AMP1": + medgreen1 = median + p16green1 = p16 + p84green1 = p84 + + elif ffi == "GREEN_AMP2": + medgreen2 = median + p16green2 = p16 + p84green2 = p84 + + elif ffi == "GREEN_AMP3": + medgreen3 = median + p16green3 = p16 + p84green3 = p84 + + elif ffi == "GREEN_AMP4": + medgreen4 = median + p16green4 = p16 + p84green4 = p84 + + if ffi == "RED_AMP1": + medred1 = median + p16red1 = p16 + p84red1 = p84 + + elif ffi == "RED_AMP2": + medred2 = median + p16red2 = p16 + p84red2 = p84 + + elif ffi == "CA_HK": + medcahk = median + p16cahk = p16 + p84cahk = p84 + + + date_obs = l0_file.header['PRIMARY']['DATE-OBS'] + + date_obs_parts = date_obs.split("-") + date_obs_num = int(date_obs_parts[0] + date_obs_parts[1] + date_obs_parts[2]) + #self.logger.info('date_obs_num = {}'.format(date_obs_num)) + + if date_obs_num != filename_date_num: # Ensure filename date matches DATE-OBS. + status = 0 + comment = "Filename date does not match DATE-OBS." + + ut = l0_file.header['PRIMARY']['UT'] + date_beg = l0_file.header['PRIMARY']['DATE-BEG'] + + try: + mjd_obs_str = float(l0_file.header['PRIMARY']['MJD-OBS']) + + try: + mjd_obs = float(mjd_obs_str) + + except ValueError: + mjd_obs = -99 + + except KeyError: + mjd_obs = "NotFound" + + try: + exptime_str = float(l0_file.header['PRIMARY']['EXPTIME']) + + try: + exptime = float(exptime_str) + + except ValueError: + exptime = -99 + + except KeyError: + mjd_obs = "NotFound" + + progname = l0_file.header['PRIMARY']['PROGNAME'] + imtype = l0_file.header['PRIMARY']['IMTYPE'] + sci_obj = l0_file.header['PRIMARY']['SCI-OBJ'] + cal_obj = l0_file.header['PRIMARY']['CAL-OBJ'] + sky_obj = l0_file.header['PRIMARY']['SKY-OBJ'] + object_keyval = l0_file.header['PRIMARY']['OBJECT'] + + green_found = l0_file.header['PRIMARY']['GREEN'] + red_found = l0_file.header['PRIMARY']['RED'] + ca_hk_found = l0_file.header['PRIMARY']['CA_HK'] + + if green_found == 'YES': + green_contentbit = 1 + else: + green_contentbit = 0 + + if red_found == 'YES': + red_contentbit = 2 + else: + red_contentbit = 0 + + if ca_hk_found == 'YES': + ca_hk_contentbit = 4 + else: + ca_hk_contentbit = 0 + + contentbits = green_contentbit | red_contentbit | ca_hk_contentbit + + try: + targname = l0_file.header['PRIMARY']['TARGNAME'] + + except KeyError: + targname = "NotFound" + + try: + gaiaid = l0_file.header['PRIMARY']['GAIAID'] + + except KeyError: + gaiaid = "NotFound" + + try: + twomassid = l0_file.header['PRIMARY']['2MASSID'] + + except KeyError: + twomassid = "NotFound" + + try: + ra = l0_file.header['PRIMARY']['RA'] + dec = l0_file.header['PRIMARY']['DEC'] + (rad,decd) = sextodec(ra,dec) + except KeyError: + rad = None + decd = None + + + try: + medgreen1_str = str(medgreen1) + p16green1_str = str(p16green1) + p84green1_str = str(p84green1) + except KeyError: + medgreen1_str = "null" + p16green1_str = "null" + p84green1_str = "null" + + try: + medgreen2_str = str(medgreen2) + p16green2_str = str(p16green2) + p84green2_str = str(p84green2) + except KeyError: + medgreen2_str = "null" + p16green2_str = "null" + p84green2_str = "null" + + try: + medgreen3_str = str(medgreen3) + p16green3_str = str(p16green3) + p84green3_str = str(p84green3) + except KeyError: + medgreen3_str = "null" + p16green3_str = "null" + p84green3_str = "null" + + try: + medgreen4_str = str(medgreen4) + p16green4_str = str(p16green4) + p84green4_str = str(p84green4) + except KeyError: + medgreen4_str = "null" + p16green4_str = "null" + p84green4_str = "null" + + try: + medred1_str = str(medred1) + p16red1_str = str(p16red1) + p84red1_str = str(p84red1) + except KeyError: + medred1_str = "null" + p16red1_str = "null" + p84red1_str = "null" + + try: + medred2_str = str(medred2) + p16red2_str = str(p16red2) + p84red2_str = str(p84red2) + except KeyError: + medred2_str = "null" + p16red2_str = "null" + p84red2_str = "null" + + try: + medcahk_str = str(medcahk) + p16cahk_str = str(p16cahk) + p84cahk_str = str(p84cahk) + except KeyError: + medcahk_str = "null" + p16cahk_str = "null" + p84cahk_str = "null" + + + filename = self.l0_filename.replace("/data",self.actual_dir,1) + #self.logger.info('filename = {}'.format(filename)) + + cksum = md5(self.l0_filename) + #self.logger.info('cksum = {}'.format(cksum)) + + + # Define query template for insert statement. + + columns = 'dateobs,ut,datebeg,mjdobs,exptime,progname,imtype,sciobj,calobj,' +\ + 'skyobj,"object",contentbits,infobits,filename,checksum,status,' +\ + 'targname,gaiaid,twomassid,ra,dec,medgreen1,p16green1,p84green1,' +\ + 'medgreen2,p16green2,p84green2,medgreen3,p16green3,p84green3,' +\ + 'medgreen4,p16green4,p84green4,medred1,p16red1,p84red1,' +\ + 'medred2,p16red2,p84red2,medcahk,p16cahk,p84cahk,comment' + + values = "cast('DATEOBS' as date)," +\ + "cast('UT' as time without time zone)," +\ + "cast('DATEBEG' as timestamp without time zone)," +\ + "cast(MJDOBS as double precision)," +\ + "cast(EXPTIME as real)," +\ + "cast('PROGNAME' as character varying)," +\ + "cast('IMTYPE' as character varying)," +\ + "cast('SCIOBJ' as character varying)," +\ + "cast('CALOBJ' as character varying)," +\ + "cast('SKYOBJ' as character varying)," +\ + "cast('OBJECT' as character varying)," +\ + "cast(CONTENTBITS as integer)," +\ + "cast(INFOBITS as bigint)," +\ + "cast('FILENAME' as character varying)," +\ + "cast('CHECKSUM' as character varying)," +\ + "cast(STATUS as integer)," +\ + "cast(TARGNAME as character varying)," +\ + "cast(GAIAID as character varying)," +\ + "cast(TWOMASSID as character varying)," +\ + "cast(RAD as double precision)," +\ + "cast(DECD as double precision)," +\ + "cast(MEDGREEN1 as real)," +\ + "cast(P16GREEN1 as real)," +\ + "cast(P84GREEN1 as real)," +\ + "cast(MEDGREEN2 as real)," +\ + "cast(P16GREEN2 as real)," +\ + "cast(P84GREEN2 as real)," +\ + "cast(MEDGREEN3 as real)," +\ + "cast(P16GREEN3 as real)," +\ + "cast(P84GREEN3 as real)," +\ + "cast(MEDGREEN4 as real)," +\ + "cast(P16GREEN4 as real)," +\ + "cast(P84GREEN4 as real)," +\ + "cast(MEDRED1 as real)," +\ + "cast(P16RED1 as real)," +\ + "cast(P84RED1 as real)," +\ + "cast(MEDRED2 as real)," +\ + "cast(P16RED2 as real)," +\ + "cast(P84RED2 as real)," +\ + "cast(MEDCAHK as real)," +\ + "cast(P16CAHK as real)," +\ + "cast(P84CAHK as real)," +\ + "cast(COMMENT as character varying)" + + + query_insert_template =\ + "INSERT INTO L0Files (" + columns + ") " +\ + "VALUES (" + values + ") RETURNING rid;" + + + # Substitute values into template for record insert. + + rep = {"DATEOBS": date_obs, + "UT": ut, + "DATEBEG": date_beg, + "IMTYPE": imtype, + "SCIOBJ": sci_obj, + "CALOBJ": cal_obj, + "SKYOBJ": sky_obj, + "OBJECT": object_keyval} + + if progname == '': + rep["PROGNAME"] = "blank" + else: + rep["PROGNAME"] = progname + + if mjd_obs == "NotFound": + rep["MJDOBS"] = -99 + else: + rep["MJDOBS"] = str(mjd_obs) + + if exptime == "NotFound": + rep["EXPTIME"] = -99 + else: + rep["EXPTIME"] = str(exptime) + + rep["CONTENTBITS"] = str(contentbits) + rep["INFOBITS"] = str(infobits) + rep["FILENAME"] = filename + rep["CHECKSUM"] = cksum + rep["STATUS"] = str(status) + + if targname == "NotFound": + rep["TARGNAME"] = "null" + else: + rep["TARGNAME"] = "'" + targname + "'" + + if gaiaid == "NotFound": + rep["GAIAID"] = "null" + else: + rep["GAIAID"] = "'" + gaiaid + "'" + + if twomassid == "NotFound": + rep["TWOMASSID"] = "null" + else: + rep["TWOMASSID"] = "'" + twomassid + "'" + + if rad is None: + rep["RAD"] = 'null' + else: + rep["RAD"] = str(rad) + + if decd is None: + rep["DECD"] = 'null' + else: + rep["DECD"] = str(decd) + + rep["MEDGREEN1"] = medgreen1_str + rep["P16GREEN1"] = p16green1_str + rep["P84GREEN1"] = p84green1_str + rep["MEDGREEN2"] = medgreen2_str + rep["P16GREEN2"] = p16green2_str + rep["P84GREEN2"] = p84green2_str + rep["MEDGREEN3"] = medgreen3_str + rep["P16GREEN3"] = p16green3_str + rep["P84GREEN3"] = p84green3_str + rep["MEDGREEN4"] = medgreen4_str + rep["P16GREEN4"] = p16green4_str + rep["P84GREEN4"] = p84green4_str + + rep["MEDRED1"] = medred1_str + rep["P16RED1"] = p16red1_str + rep["P84RED1"] = p84red1_str + rep["MEDRED2"] = medred2_str + rep["P16RED2"] = p16red2_str + rep["P84RED2"] = p84red2_str + + rep["MEDCAHK"] = medcahk_str + rep["P16CAHK"] = p16cahk_str + rep["P84CAHK"] = p84cahk_str + + if comment == '': + rep["COMMENT"] = 'null' + else: + rep["COMMENT"] = "'" + comment + "'" + + rep = dict((re.escape(k), v) for k, v in rep.items()) + pattern = re.compile("|".join(rep.keys())) + query = pattern.sub(lambda m: rep[re.escape(m.group(0))], query_insert_template) + + self.logger.info('query = {}'.format(query)) + + try: + cur.execute(query) + rid = cur.fetchone() + self.logger.info('PostgreSQL database L0Image ID: rid = {}'.format(rid)) + + except (Exception, psycopg2.DatabaseError) as error: + self.logger.info('*** Error inserting record ({}); skipping...'.format(error)) + quality_control_exposure_exit_code = 66 + + + # Commit transaction. + + conn.commit() + + + # Close database cursor and then connection. + + try: + cur.close() + except (Exception, psycopg2.DatabaseError) as error: + self.logger.info('*** Error closing database connection ({}); skipping...'.format(error)) + quality_control_exposure_exit_code = 2 + finally: + if conn is not None: + conn.close() + + self.logger.info('Finished {}'.format(self.__class__.__name__)) + + return Arguments(quality_control_exposure_exit_code) From aa377181fb8006fb7220ec54bdced77022438105 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Wed, 14 Jun 2023 08:58:10 -0700 Subject: [PATCH 085/249] Added new modules/quality_control_exposure. --- configs/quality_control_exposure.cfg | 26 +++ database/schema/kpfOpsL0InfobitsInserts.sql | 23 +++ database/schema/kpfOpsTableDrops.sql | 21 ++- database/schema/kpfOpsTableGrants.sql | 73 +++++++++ database/schema/kpfOpsTables.sql | 168 +++++++++++++++++--- recipes/quality_control_exposure.recipe | 18 +++ 6 files changed, 310 insertions(+), 19 deletions(-) create mode 100644 configs/quality_control_exposure.cfg create mode 100644 database/schema/kpfOpsL0InfobitsInserts.sql create mode 100644 recipes/quality_control_exposure.recipe diff --git a/configs/quality_control_exposure.cfg b/configs/quality_control_exposure.cfg new file mode 100644 index 000000000..7d6172035 --- /dev/null +++ b/configs/quality_control_exposure.cfg @@ -0,0 +1,26 @@ +# Pipeline logger configurations +[LOGGER] +start_log = True +log_path = pipeline.log +log_level = info +log_verbose = True +log_directory = /data/logs/ + +[ARGUMENT] +data_type = KPF +# With the correct form of file glob, one can ingest into the L0Files database table +# a single L0 file, or all L0 files for certain date(s), or even all L0 files in a month. +#lev0_fits_file_glob = /data/L0/20230430/KP.20230430.05981.93.fits +#lev0_fits_file_glob = /data/L0/20230525/KP.*.fits +lev0_fits_file_glob = /data/L0/202301??/KP.*.fits +# The following is input list of all readout channels in the L0 file. +lev0_ffi_exts = ['GREEN_AMP1','GREEN_AMP2','GREEN_AMP3','GREEN_AMP4','RED_AMP1','RED_AMP2','CA_HK'] +# The following maps to the /data/kpf/L0 subdirectory in the docker-run command option: -v /data/kpf:/data +# It is stored in the database as the actual file path. +actual_dir = /data/kpf + +[MODULE_CONFIGS] +quality_control_exposure = modules/quality_control_exposure/configs/default.cfg + + + diff --git a/database/schema/kpfOpsL0InfobitsInserts.sql b/database/schema/kpfOpsL0InfobitsInserts.sql new file mode 100644 index 000000000..7b46e5346 --- /dev/null +++ b/database/schema/kpfOpsL0InfobitsInserts.sql @@ -0,0 +1,23 @@ +-------------------------------------------------------------------------------------------------------------------------- +-- kpfOpsL0InfobitsInserts.sql +-- +-- Russ Laher (laher@ipac.caltech.edu) +-- +-- 09 June 2023 +-------------------------------------------------------------------------------------------------------------------------- + +INSERT INTO l0infobits (bit, param1, param2, definition) values (0, 5.0, 10000.0, 'GREEN_AMP1 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (1, 5.0, 10000.0, 'GREEN_AMP2 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (2, 5.0, 10000.0, 'GREEN_AMP3 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (3, 5.0, 10000.0, 'GREEN_AMP4 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (4, 5.0, 10000.0, 'RED_AMP1 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (5, 5.0, 10000.0, 'RED_AMP2 Dead-detector flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (6, 5.0, 10000.0, 'CA_HK flag: More than 5% of pixels in readout channel have values less than 10000 D.N.'); + +INSERT INTO l0infobits (bit, param1, param2, definition) values (7, 15.0, 5.0e8, 'GREEN_AMP1 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (8, 15.0, 5.0e8, 'GREEN_AMP2 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (9, 15.0, 5.0e8, 'GREEN_AMP3 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (10, 15.0, 5.0e8, 'GREEN_AMP4 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (11, 15.0, 5.0e8, 'RED_AMP1 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (12, 15.0, 5.0e8, 'RED_AMP2 Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); +INSERT INTO l0infobits (bit, param1, param2, definition) values (13, 15.0, 5.0e8, 'CA_HK Saturated-detector flag: More than 15% of pixels in readout channel have values more than 5.0e8 D.N.'); diff --git a/database/schema/kpfOpsTableDrops.sql b/database/schema/kpfOpsTableDrops.sql index 11f409a92..553fe3272 100644 --- a/database/schema/kpfOpsTableDrops.sql +++ b/database/schema/kpfOpsTableDrops.sql @@ -7,6 +7,25 @@ -------------------------------------------------------------------------------------------------------------------------- -DROP TABLE calfiles; +------------------- +-- Calfiles table +------------------- +DROP TABLE calfiles; DROP SEQUENCE calfiles_cid_seq; + + +------------------- +-- L0files table +------------------- + +DROP TABLE l0files; +DROP SEQUENCE l0files_rid_seq; + + +------------------- +-- L0Infobits table +------------------- + +DROP TABLE l0infobits; +DROP SEQUENCE l0infobits; diff --git a/database/schema/kpfOpsTableGrants.sql b/database/schema/kpfOpsTableGrants.sql index a0cf9f3ae..0bdf814d8 100644 --- a/database/schema/kpfOpsTableGrants.sql +++ b/database/schema/kpfOpsTableGrants.sql @@ -7,6 +7,10 @@ -------------------------------------------------------------------------------------------------------------------------- +------------------- +-- CalFiles table +------------------- + -- kpfreadrole REVOKE ALL ON TABLE calfiles FROM kpfreadrole; @@ -31,3 +35,72 @@ GRANT INSERT,UPDATE,SELECT,REFERENCES ON TABLE calfiles TO kpfporole; REVOKE ALL ON SEQUENCE calfiles_cid_seq FROM kpfporole; GRANT USAGE ON SEQUENCE calfiles_cid_seq TO kpfporole; + + +------------------- +-- L0files table +------------------- + +-- kpfreadrole + +REVOKE ALL ON TABLE l0files FROM kpfreadrole; +GRANT SELECT ON TABLE l0files TO GROUP kpfreadrole; + +REVOKE ALL ON SEQUENCE l0files_rid_seq FROM kpfreadrole; + + +-- kpfadminrole + +REVOKE ALL ON TABLE l0files FROM kpfadminrole; +GRANT ALL ON TABLE l0files TO GROUP kpfadminrole; + +REVOKE ALL ON SEQUENCE l0files_rid_seq FROM kpfadminrole; +GRANT ALL ON SEQUENCE l0files_rid_seq TO GROUP kpfadminrole; + + +-- kpfporole + +REVOKE ALL ON TABLE l0files FROM kpfporole; +GRANT INSERT,UPDATE,SELECT,REFERENCES ON TABLE l0files TO kpfporole; + +REVOKE ALL ON SEQUENCE l0files_rid_seq FROM kpfporole; +GRANT USAGE ON SEQUENCE l0files_rid_seq TO kpfporole; + + +------------------- +-- L0infobits table +------------------- + +-- kpfreadrole + +REVOKE ALL ON TABLE l0infobits FROM kpfreadrole; +GRANT SELECT ON TABLE l0infobits TO GROUP kpfreadrole; + +REVOKE ALL ON SEQUENCE l0infobits_bid_seq FROM kpfreadrole; + + +-- kpfadminrole + +REVOKE ALL ON TABLE l0infobits FROM kpfadminrole; +GRANT ALL ON TABLE l0infobits TO GROUP kpfadminrole; + +REVOKE ALL ON SEQUENCE l0infobits_bid_seq FROM kpfadminrole; +GRANT ALL ON SEQUENCE l0infobits_bid_seq TO GROUP kpfadminrole; + + +-- kpfporole + +REVOKE ALL ON TABLE l0infobits FROM kpfporole; +GRANT INSERT,UPDATE,SELECT,REFERENCES ON TABLE l0infobits TO kpfporole; + +REVOKE ALL ON SEQUENCE l0infobits_bid_seq FROM kpfporole; +GRANT USAGE ON SEQUENCE l0infobits_bid_seq TO kpfporole; + + + + + + + + + diff --git a/database/schema/kpfOpsTables.sql b/database/schema/kpfOpsTables.sql index 452689731..9e4733fe7 100644 --- a/database/schema/kpfOpsTables.sql +++ b/database/schema/kpfOpsTables.sql @@ -8,29 +8,29 @@ ----------------------------- --- TABLE: Calfiles +-- TABLE: CalFiles ----------------------------- SET default_tablespace = pipeline_data_01; CREATE TABLE calfiles ( - cid integer NOT NULL, -- Primary key - level smallint NOT NULL, -- Product level (L0, L1, or L2) - caltype character varying(32) NOT NULL, -- FITS-header keyword: IMTYPE in extension 4-6 (lowercase) - "object" character varying(32) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT (lowercase) - contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK - nframes smallint, -- FITS-header keyword: NFRAMES (GREEN CCD) - minmjd double precision, -- FITS-header keyword: MINMJD (GREEN CCD) - maxmjd double precision, -- FITS-header keyword: MAXMJD (GREEN CCD) - infobits integer, -- FITS-header keyword: INFOBITS (GREEN CCD) - startdate date NOT NULL, -- Start date for application of master (for earliest-in-time selection) - enddate date NOT NULL, -- End date for application of master (may not be used) - filename character varying(255) NOT NULL, - checksum character varying(32) NOT NULL, - status smallint DEFAULT 0 NOT NULL, - createdby character varying(30) NOT NULL, - created timestamp without time zone NOT NULL, -- FITS-header keyword: CREATED (GREEN CCD) in Zulu time - "comment" character varying(255) + cid integer NOT NULL, -- Primary key + level smallint NOT NULL, -- Product level (L0, L1, or L2) + caltype character varying(32) NOT NULL, -- FITS-header keyword: IMTYPE in extension 4-6 (lowercase) + "object" character varying(32) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT (lowercase) + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + nframes smallint, -- FITS-header keyword: NFRAMES (GREEN CCD) + minmjd double precision, -- FITS-header keyword: MINMJD (GREEN CCD) + maxmjd double precision, -- FITS-header keyword: MAXMJD (GREEN CCD) + infobits integer, -- FITS-header keyword: INFOBITS + startdate date NOT NULL, -- Start date for application of master (for earliest-in-time selection) + enddate date NOT NULL, -- End date for application of master (may not be used) + filename character varying(255) NOT NULL, -- Path and filename of master calibration file. + checksum character varying(32) NOT NULL, -- MD5 checksum + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good + createdby character varying(30) NOT NULL, -- Script that inserted the record + created timestamp without time zone NOT NULL, -- FITS-header keyword: CREATED (GREEN CCD) in Zulu time + "comment" character varying(255) -- Descriptive comment ); ALTER TABLE calfiles OWNER TO kpfadminrole; @@ -55,3 +55,135 @@ CREATE INDEX calfiles_startdate_idx ON calfiles (startdate); CREATE INDEX calfiles_enddate_idx ON calfiles (enddate); CREATE INDEX calfiles_status_idx ON calfiles (status); + +----------------------------- +-- TABLE: L0Files +----------------------------- + +SET default_tablespace = pipeline_data_01; + +CREATE TABLE l0files ( + rid integer NOT NULL, -- Primary key + dateobs date NOT NULL, -- FITS-header keyword: DATE-OBS + ut time without time zone NOT NULL, -- FITS-header keyword: UT + datebeg timestamp without time zone NOT NULL, -- FITS-header keyword: DATE-BEG + mjdobs double precision NOT NULL, -- FITS-header keyword: MJD-OBS + exptime real NOT NULL, -- FITS-header keyword: EXPTIME + progname character varying(64) NOT NULL, -- FITS-header keyword: PROGNAME + imtype character varying(64) NOT NULL, -- FITS-header keyword: IMTYPE + sciobj character varying(64) NOT NULL, -- FITS-header keyword: SCI-OBJ + calobj character varying(64) NOT NULL, -- FITS-header keyword: CAL-OBJ + skyobj character varying(64) NOT NULL, -- FITS-header keyword: SKY-OBJ + "object" character varying(64) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + infobits bigint DEFAULT 0 NOT NULL, -- Bit-wise information flags + filename character varying(255) NOT NULL, -- Full path and filename + checksum character varying(32) NOT NULL, -- MD5 checksum of entire file + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good (verify automatically with + -- DATASUM and CHECKSUM keywords, or set this manually later, if necessary) + created timestamp without time zone -- Timestamp of database record INSERT or last UPDATE + DEFAULT now() NOT NULL, + targname character varying(64), -- FITS-header keyword: TARGNAME + gaiaid character varying(64), -- FITS-header keyword: GAIAID + twomassid character varying(64), -- FITS-header keyword: 2MASSID + ra double precision, -- FITS-header keyword: RA converted to decimal + dec double precision, -- FITS-header keyword: DEC converted to decimal + medgreen1 real, -- Median of GREEN_AMP1 image + p16green1 real, -- 16th percentile of GREEN_AMP1 image + p84green1 real, -- 84th percentile of GREEN_AMP1 image + medgreen2 real, -- Median of GREEN_AMP2 image + p16green2 real, -- 16th percentile of GREEN_AMP2 image + p84green2 real, -- 84th percentile of GREEN_AMP2 image + medgreen3 real, -- Median of GREEN_AMP3 image + p16green3 real, -- 16th percentile of GREEN_AMP3 image + p84green3 real, -- 84th percentile of GREEN_AMP3 image + medgreen4 real, -- Median of GREEN_AMP4 image + p16green4 real, -- 16th percentile of GREEN_AMP4 image + p84green4 real, -- 84th percentile of GREEN_AMP4 image + medred1 real, -- Median of RED_AMP1 image + p16red1 real, -- 16th percentile of RED_AMP1 image + p84red1 real, -- 84th percentile of RED_AMP1 image + medred2 real, -- Median of RED_AMP2 image + p16red2 real, -- 16th percentile of RED_AMP2 image + p84red2 real, -- 84th percentile of RED_AMP2 image + medcahk real, -- Median of CA_HK image + p16cahk real, -- 16th percentile of CA_HK image + p84cahk real, -- 84th percentile of CA_HK image + comment character varying(255), -- Reason for status=0, etc. + CONSTRAINT l0files_ra_check CHECK (((ra >= 0.0) AND (ra < 360.0))), + CONSTRAINT l0files_dec_check CHECK (((dec >= -90.0) AND (dec <= 90.0))) +); + +ALTER TABLE l0files OWNER TO kpfadminrole; + +CREATE SEQUENCE l0files_rid_seq + START WITH 1 + INCREMENT BY 1 + NO MAXVALUE + NO MINVALUE + CACHE 1; + +ALTER SEQUENCE l0files_rid_seq OWNER TO kpfadminrole; + +ALTER TABLE l0files ALTER COLUMN rid SET DEFAULT nextval('l0files_rid_seq'::regclass); + +SET default_tablespace = pipeline_indx_01; + +ALTER TABLE ONLY l0files ADD CONSTRAINT l0files_pkey PRIMARY KEY (rid); + +ALTER TABLE ONLY l0files ADD CONSTRAINT l0filespk UNIQUE (dateobs, ut); + +CREATE INDEX l0files_dateobs_idx ON l0files (dateobs); +CREATE INDEX l0files_ut_idx ON l0files (ut); +CREATE INDEX l0files_mjdobs_idx ON l0files (mjdobs); +CREATE INDEX l0files_status_idx ON l0files (status); +CREATE INDEX l0files_infobits_idx ON l0files (infobits); + +-- Uncomment only if Q3C EXTENSION has been installed into database. +-- Q3C is not required for normal operations, but will speed up ad-hoc cone searches on (ra, dec). +-- CREATE INDEX l0files_radec_idx ON l0files (q3c_ang2ipix(ra, dec)); +-- CLUSTER l0files_radec_idx ON l0files; +-- ANALYZE l0files; + + +----------------------------- +-- TABLE: L0infobits +-- +-- Definitions of l0infobits for L0Files table only +-- (CalFiles l0infobits have different definitions). +----------------------------- + +SET default_tablespace = pipeline_data_01; + +CREATE TABLE l0infobits ( + bid integer NOT NULL, -- Primary key + bit smallint NOT NULL, -- Bit number (allowed range is 0-63, inclusive) + param1 real, -- Parameter 1 + param2 real, -- Parameter 2 + param3 real, -- Parameter 3 + created timestamp without time zone -- Creation timestamp of database record + DEFAULT now() NOT NULL, + definition character varying(256) NOT NULL, -- Definition of bit and parameter(s) + CONSTRAINT l0infobits_bit_check CHECK (((bit >= 0) AND (bit <= 63))) +); + +ALTER TABLE l0infobits OWNER TO kpfadminrole; + +CREATE SEQUENCE l0infobits_bid_seq + START WITH 1 + INCREMENT BY 1 + NO MAXVALUE + NO MINVALUE + CACHE 1; + +ALTER SEQUENCE l0infobits_bid_seq OWNER TO kpfadminrole; + +ALTER TABLE l0infobits ALTER COLUMN bid SET DEFAULT nextval('l0infobits_bid_seq'::regclass); + +SET default_tablespace = pipeline_indx_01; + +ALTER TABLE ONLY l0infobits ADD CONSTRAINT l0infobits_pkey PRIMARY KEY (bid); + +ALTER TABLE ONLY l0infobits ADD CONSTRAINT l0infobitspk UNIQUE (bit, created); + +CREATE INDEX l0infobits_bit_idx ON l0infobits (bit); diff --git a/recipes/quality_control_exposure.recipe b/recipes/quality_control_exposure.recipe new file mode 100644 index 000000000..d0f9ce0ec --- /dev/null +++ b/recipes/quality_control_exposure.recipe @@ -0,0 +1,18 @@ +from modules.quality_control_exposure.src.quality_control_exposure_framework import QualityControlExposureFramework + +data_type = config.ARGUMENT.data_type +lev0_fits_file_glob = config.ARGUMENT.lev0_fits_file_glob +lev0_ffi_exts = config.ARGUMENT.lev0_ffi_exts +actual_dir = config.ARGUMENT.actual_dir + +do_quality_control = True + +if do_quality_control: + + for l0_filename in find_files(lev0_fits_file_glob): + + quality_control_exit_code = QualityControlExposureFramework(data_type, + l0_filename, + lev0_ffi_exts, + actual_dir) + From 5fef94be893541292dd27b7c234e1bf0825ddbef Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Wed, 14 Jun 2023 08:58:35 -0700 Subject: [PATCH 086/249] Modified both versions of stored function getCalFile to require nframes >= 5 when nframes is not null. --- database/schema/kpfOpsProcs.sql | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/database/schema/kpfOpsProcs.sql b/database/schema/kpfOpsProcs.sql index 107b2c729..e528abef6 100644 --- a/database/schema/kpfOpsProcs.sql +++ b/database/schema/kpfOpsProcs.sql @@ -170,9 +170,11 @@ create function getCalFile ( infobits_ integer; startDate_ date; r_ record; + minnframes_ smallint; begin + minnframes_ := 5; caltype__ := lower(caltype_); object__ := lower(object_); @@ -183,6 +185,7 @@ create function getCalFile ( and level = level_ and caltype = caltype__ and object = object__ + and ((nframes >= minnframes_) or (nframes is null)) and cast((contentbits & contentbitmask_) as integer) = contentbitmask_ order by abs(cast(extract(days from (cast(obsDate_ as timestamp without time zone) - cast(startDate as timestamp without time zone))) as numeric)) asc, @@ -230,9 +233,11 @@ create function getCalFile ( startDate_ date; r_ record; negmaxage_ interval; + minnframes_ smallint; begin + minnframes_ := 5; caltype__ := lower(caltype_); object__ := lower(object_); negmaxage_ := cast((cast(maxage_ as text) || ' ago') as interval); @@ -244,6 +249,7 @@ create function getCalFile ( and level = level_ and caltype = caltype__ and object = object__ + and ((nframes >= minnframes_) or (nframes is null)) and cast((contentbits & contentbitmask_) as integer) = contentbitmask_ and cast(obsDate_ as timestamp without time zone) - cast(startDate as timestamp without time zone) <= maxage_ and cast(obsDate_ as timestamp without time zone) - cast(startDate as timestamp without time zone) >= negmaxage_ From 96a928e1fa294ab2d1148a91ec00de7bb6c5a90b Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 13:20:18 -0700 Subject: [PATCH 087/249] nightly summary --- examples/nightly_test.recipe | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index 3afdd2f44..10e4ab75d 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,4 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night = '20230606' -Nightly_summary(night) +night_list = ['20230605','20230604'] +for night in night_list: + Nightly_summary(night) From db7bd1dfed635e2495430ee07fa89116b13a7f1f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 13:59:54 -0700 Subject: [PATCH 088/249] nightly summary --- examples/nightly_test.recipe | 2 +- modules/quicklook/src/alg_nightly.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index 10e4ab75d..e645d603e 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,5 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night_list = ['20230605','20230604'] +night_list = ['20230604'] for night in night_list: Nightly_summary(night) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 32f3ea25e..3c3eb14ea 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -53,7 +53,7 @@ def nightly_procedures(self,night): if master_list[i][-7:] == 'L1.fits' or master_list[i][-7:] == 'L2.fits': continue exposure_name = master_list[i][23:-5] - version = master_list[i][35:-5] + version = master_list[i][36:-5] print(i,master_list[i],exposure_name,version) master_master_file = 'None' @@ -119,7 +119,7 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') #print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) #plt.close() order_trace_file = self.config['L1']['order_trace']+ccd_color[i_color]+'.csv' @@ -139,7 +139,7 @@ def nightly_procedures(self,night): plt.title(ccd_color[i_color]+' Order Trace '+exposure_name, fontsize = 8) #plt.title(ccd_color[i_color]+' '+version+' Order Trace ' +exposure_name) #plt.savefig(output_dir+'fig/'+exposure_name+'_order_trace_'+ccd_color[i_color]+'.png') - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_order_trace.png', dpi=300) plt.close() if master_master_file != 'None': @@ -156,7 +156,7 @@ def nightly_procedures(self,night): plt.ylabel('y (pixel number)') plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) plt.colorbar(label = 'Fractional Difference') - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) #histogram plt.close() @@ -174,7 +174,7 @@ def nightly_procedures(self,night): plt.title(ccd_color[i_color]+' '+exposure_name, fontsize = 8) plt.legend(loc='lower right') #plt.savefig(output_dir+'fig/'+exposure_name+'_Histogram_'+ccd_color[i_color]+'.png') - plt.savefig(output_dir+'/'+exposure_name+'_'+ccd_color[i_color]+'_histogram.png', dpi=200) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_histogram.png', dpi=200) plt.close() @@ -207,6 +207,6 @@ def nightly_procedures(self,night): plt.ylabel('Relative Humidity') #print(date_obs.jd,date_obs.utc) #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) - plt.savefig(output_dir+'/'+night+'_Relative_Humidity_variation.png') + plt.savefig(output_dir+'/'+'diagnostics/'+night+'_Relative_Humidity_variation.png') plt.close() ''' From e8f262601f8d4a4b181573d117c89ca0e7334e40 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 14:00:13 -0700 Subject: [PATCH 089/249] nightly summary --- modules/quicklook/src/alg_nightly.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 3c3eb14ea..4b91ea843 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -184,7 +184,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - ''' + file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] @@ -207,6 +207,5 @@ def nightly_procedures(self,night): plt.ylabel('Relative Humidity') #print(date_obs.jd,date_obs.utc) #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) - plt.savefig(output_dir+'/'+'diagnostics/'+night+'_Relative_Humidity_variation.png') + plt.savefig(output_dir+'/diagnostics/'+night+'_Relative_Humidity_variation.png') plt.close() - ''' From aeb9ab7a972550282f7a19d7018d0219ffdde363 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 14:01:51 -0700 Subject: [PATCH 090/249] nightly summary --- modules/quicklook/src/alg_nightly.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 4b91ea843..35fda7d81 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -55,7 +55,8 @@ def nightly_procedures(self,night): exposure_name = master_list[i][23:-5] version = master_list[i][36:-5] print(i,master_list[i],exposure_name,version) - + if not os.path.exists(output_dir+'/'+version+'/'): + os.makedirs(output_dir+'/'+version+'/') master_master_file = 'None' for j in range(len(master_master_list)): #print(j,master_master_list[i]) From e9d1c43525b68a4d50c0aa0437b99ae5aa0b1e58 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 14:06:57 -0700 Subject: [PATCH 091/249] nightly summary --- examples/nightly_test.recipe | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index e645d603e..1d9da21e2 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,5 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night_list = ['20230604'] +night_list = ['20230603'] for night in night_list: Nightly_summary(night) From 4513e85ff4546e029291293b2ca96526a94ecc21 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 14:17:51 -0700 Subject: [PATCH 092/249] nightly summary --- modules/quicklook/src/alg_nightly.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 35fda7d81..32cabf464 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -208,5 +208,7 @@ def nightly_procedures(self,night): plt.ylabel('Relative Humidity') #print(date_obs.jd,date_obs.utc) #plt.xlim(np.min(date_obs.jd),np.max(date_obs.jd)) + if not os.path.exists(output_dir+'/diagnostics/'): + os.makedirs(output_dir+'/diagnostics/') plt.savefig(output_dir+'/diagnostics/'+night+'_Relative_Humidity_variation.png') plt.close() From 364b18515789574cc9a4065dd64dedc48826e73c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 14 Jun 2023 14:30:44 -0700 Subject: [PATCH 093/249] nightly summary --- modules/quicklook/src/alg_nightly.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 32cabf464..2365c7181 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -177,7 +177,8 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_Histogram_'+ccd_color[i_color]+'.png') plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_histogram.png', dpi=200) plt.close() - + hdulist.close() + if master_master_file != 'None':hdulist1.close() @@ -212,3 +213,4 @@ def nightly_procedures(self,night): os.makedirs(output_dir+'/diagnostics/') plt.savefig(output_dir+'/diagnostics/'+night+'_Relative_Humidity_variation.png') plt.close() + plt.close('all') From f65cf06a452552e05cefd4bdf99e3b93fcf18671 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 09:55:41 -0700 Subject: [PATCH 094/249] add nightly summary to wls recipe --- configs/framework_multi.cfg | 2 +- recipes/wls_auto.recipe | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/configs/framework_multi.cfg b/configs/framework_multi.cfg index c58960e4f..2e235d8db 100644 --- a/configs/framework_multi.cfg +++ b/configs/framework_multi.cfg @@ -103,5 +103,5 @@ http_defaultFile = "" # want_multiprocessing = True queue_manager_hostname = 'localhost' -queue_manager_portnr = 50101 +queue_manager_portnr = 50109 queue_manager_auth_code = b"a very long authentication code" diff --git a/recipes/wls_auto.recipe b/recipes/wls_auto.recipe index 3acf13cba..3222da4a5 100755 --- a/recipes/wls_auto.recipe +++ b/recipes/wls_auto.recipe @@ -1,6 +1,6 @@ from modules.wavelength_cal.src.wavelength_cal import WaveCalibrate from modules.Utils.string_proc import str_replace - +from modules.quicklook.src.nightly_prim import Nightly_summary masters_dir = config.ARGUMENT.masters_dir output_dir = config.ARGUMENT.output_dir + '/' @@ -62,4 +62,6 @@ for cal_type in ['ThAr', 'LFC']: output_filename = output_dir + date_dir + '/kpf_' + date_dir + '_master_WLS_' + obj_string_short + '_L1.fits' - result = to_fits(wave_soln, output_filename) \ No newline at end of file + result = to_fits(wave_soln, output_filename) + +Nightly_summary(date_dir) \ No newline at end of file From e07b7a55ed896816aad0dd6e561deb0e8ea77c54 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 10:09:11 -0700 Subject: [PATCH 095/249] rename order trace files --- ...der_trace_GREEN.csv => kpf_20230418_order_trace_GREEN_CCD.csv} | 0 ...8_order_trace_RED.csv => kpf_20230418_order_trace_RED_CCD.csv} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename static/{kpf_20230418_order_trace_GREEN.csv => kpf_20230418_order_trace_GREEN_CCD.csv} (100%) rename static/{kpf_20230418_order_trace_RED.csv => kpf_20230418_order_trace_RED_CCD.csv} (100%) diff --git a/static/kpf_20230418_order_trace_GREEN.csv b/static/kpf_20230418_order_trace_GREEN_CCD.csv similarity index 100% rename from static/kpf_20230418_order_trace_GREEN.csv rename to static/kpf_20230418_order_trace_GREEN_CCD.csv diff --git a/static/kpf_20230418_order_trace_RED.csv b/static/kpf_20230418_order_trace_RED_CCD.csv similarity index 100% rename from static/kpf_20230418_order_trace_RED.csv rename to static/kpf_20230418_order_trace_RED_CCD.csv From e9d475077b473608e9158653b1a7d6509b650324 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 10:13:05 -0700 Subject: [PATCH 096/249] rename order trace files --- configs/kpf_drp_local.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/kpf_drp_local.cfg b/configs/kpf_drp_local.cfg index 942d99b45..93a3dbb98 100644 --- a/configs/kpf_drp_local.cfg +++ b/configs/kpf_drp_local.cfg @@ -43,7 +43,7 @@ ccd_list = ['GREEN_CCD', 'RED_CCD'] # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe #flat_file = KP.20221107.04689.77 flat_file = /data/masters/20230527/kpf_20230527_master_flat.fits -order_trace_files = ['/code/KPF-Pipeline/static/kpf_20230418_order_trace_GREEN.csv', '/code/KPF-Pipeline/static/kpf_20230418_order_trace_RED.csv'] +order_trace_files = ['/code/KPF-Pipeline/static/kpf_20230418_order_trace_GREEN_CCD.csv', '/code/KPF-Pipeline/static/kpf_20230418_order_trace_RED_CCD.csv'] fitting_poly_degree = 3 ccd_idx = [0, 1] From b2687467a4c5df1a614c9fb06e6a61bf7120ac82 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 10:21:22 -0700 Subject: [PATCH 097/249] change paths to ccf weight files --- configs/kpf_drp.cfg | 2 +- configs/kpf_masters_l1.cfg | 2 +- modules/quicklook/src/alg.py | 8 ++++---- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index f94ebb9e4..7ab20d98c 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -95,7 +95,7 @@ reweighting_enable_masks = [['espresso'], ['espresso']] ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV #static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] -static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] +static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] # for ca_hk: # hk_fiber_list: [] diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index 7cbd3b339..249002fc0 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -102,7 +102,7 @@ rv_correct_by_cal = False reweighting_method = ccf_static ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV -static_ccf_ratio = ['masters/static_green_ccf_ratio.csv', 'masters/static_red_ccf_ratio.csv'] +static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] # for ca_hk: # hk_fiber_list: [] diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index cb4c62b3e..992fc89d3 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -1093,8 +1093,8 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #print('step',step,len(vel_grid)) - if i_color == 0: ccf_weights_file='/data/masters/static_green_ccf_ratio_2.csv' - if i_color == 1: ccf_weights_file='/data/masters/static_red_ccf_ratio_2.csv' + if i_color == 0: ccf_weights_file='/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv' + if i_color == 1: ccf_weights_file='/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv' newdata = pd.read_csv(ccf_weights_file,sep = '\s+',header = 0) ccf_weights = np.array(newdata[sci_mask],'d')#np.ones(np.shape(ccf)[0]) #if i_color == 0: ccf_weights[12] = 0 @@ -1141,8 +1141,8 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): vel_grid = startv+np.array(range(np.shape(ccf)[2]),'d')*step gamma = hdulist['RV'].header[ccf_rv[i_color]] - if i_color == 0: ccf_weights_file='/data/masters/static_green_ccf_ratio_2.csv' - if i_color == 1: ccf_weights_file='/data/masters/static_red_ccf_ratio_2.csv' + if i_color == 0: ccf_weights_file='/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv' + if i_color == 1: ccf_weights_file='/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv' newdata = pd.read_csv(ccf_weights_file,sep = '\s+',header = 0) sci_mask = hdulist[ccf_color[i_color]].header['SCI_MASK'] ccf_weights = np.array(newdata[sci_mask],'d')#np.ones(np.shape(ccf)[0]) From b0ea97a3c69c7eb0373dbc11cd9e77ae64f40bf6 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 10:22:57 -0700 Subject: [PATCH 098/249] saving files --- configs/kpf_drp.cfg | 1 - 1 file changed, 1 deletion(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index 7ab20d98c..0d011d802 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -94,7 +94,6 @@ reweighting_method = ccf_static reweighting_enable_masks = [['espresso'], ['espresso']] ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV -#static_ccf_ratio = ['masters/static_green_ccf_ratio_2.csv', 'masters/static_red_ccf_ratio_2.csv'] static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] # for ca_hk: From 7c443a5948e88d885ae5642273dd07ada8222c7b Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 10:27:36 -0700 Subject: [PATCH 099/249] update rough WLS after ThAr --- configs/framework_multi.cfg | 2 +- recipes/wls_auto.recipe | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/configs/framework_multi.cfg b/configs/framework_multi.cfg index 2e235d8db..c58960e4f 100644 --- a/configs/framework_multi.cfg +++ b/configs/framework_multi.cfg @@ -103,5 +103,5 @@ http_defaultFile = "" # want_multiprocessing = True queue_manager_hostname = 'localhost' -queue_manager_portnr = 50109 +queue_manager_portnr = 50101 queue_manager_auth_code = b"a very long authentication code" diff --git a/recipes/wls_auto.recipe b/recipes/wls_auto.recipe index 3222da4a5..8ddd71ab3 100755 --- a/recipes/wls_auto.recipe +++ b/recipes/wls_auto.recipe @@ -63,5 +63,7 @@ for cal_type in ['ThAr', 'LFC']: output_filename = output_dir + date_dir + '/kpf_' + date_dir + '_master_WLS_' + obj_string_short + '_L1.fits' result = to_fits(wave_soln, output_filename) + if cal_type == 'Thar': + master_wls_file = output_filename Nightly_summary(date_dir) \ No newline at end of file From a3d27ab3a97fee4b56c93d0e97514af42028020f Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 13:50:42 -0700 Subject: [PATCH 100/249] don't background notebook --- makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/makefile b/makefile index 962f7c701..aa4a617d0 100644 --- a/makefile +++ b/makefile @@ -19,7 +19,7 @@ clean: clear notebook: pip3 install jupyter - jupyter notebook --port ${KPFPIPE_PORT} --allow-root --ip=0.0.0.0 ""& + jupyter notebook --port ${KPFPIPE_PORT} --allow-root --ip=0.0.0.0 "" docker: docker build --cache-from kpf-drp:latest --tag kpf-drp:latest . From 0ea54f9d96189a6b0f3dce12e5d7a0f86e1fa7d2 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 15 Jun 2023 16:16:20 -0700 Subject: [PATCH 101/249] solar bc fix --- configs/framework_multi.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/framework_multi.cfg b/configs/framework_multi.cfg index c58960e4f..2e235d8db 100644 --- a/configs/framework_multi.cfg +++ b/configs/framework_multi.cfg @@ -103,5 +103,5 @@ http_defaultFile = "" # want_multiprocessing = True queue_manager_hostname = 'localhost' -queue_manager_portnr = 50101 +queue_manager_portnr = 50109 queue_manager_auth_code = b"a very long authentication code" From 35ac081c07b5a3091df86beec067df441f1a738d Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Fri, 16 Jun 2023 09:31:46 -0700 Subject: [PATCH 102/249] Bug fixes. --- configs/kpf_masters_l1.cfg | 2 ++ cronjobs/runDailyPipelines.sh | 12 +++++++----- recipes/kpf_drp.recipe | 19 +++++++++---------- 3 files changed, 18 insertions(+), 15 deletions(-) diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index 249002fc0..d37977a08 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -100,6 +100,8 @@ max_cal_file_age = '4 days' orderlet_names_rv = [['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX'],['RED_SCI_FLUX1', 'RED_SCI_FLUX2', 'RED_SCI_FLUX3', 'RED_CAL_FLUX']] rv_correct_by_cal = False reweighting_method = ccf_static +#reweighting_enable_masks = [['espresso', 'lfc', 'thar', 'etalon'], ['espresso', 'lfc', 'thar', 'etalon']] +reweighting_enable_masks = [['espresso'], ['espresso']] ccf_ext = ['GREEN_CCF', 'RED_CCF'] rv_ext = RV static_ccf_ratio = ['/code/KPF-Pipeline/static/static_green_ccf_ratio_2.csv', '/code/KPF-Pipeline/static/static_red_ccf_ratio_2.csv'] diff --git a/cronjobs/runDailyPipelines.sh b/cronjobs/runDailyPipelines.sh index b52dc7b7e..9b024bc36 100755 --- a/cronjobs/runDailyPipelines.sh +++ b/cronjobs/runDailyPipelines.sh @@ -2,12 +2,14 @@ printenv >& $KPFCRONJOB_LOGS/runDailyPipelines.env -echo "Processing date: $(date +\%Y\%m\%d)" -$KPFCRONJOB_CODE/cronjobs/kpfmastersruncmd_l0.pl $(date +\%Y\%m\%d) >& $KPFCRONJOB_LOGS/kpfmastersruncmd_l0_$(date +\%Y\%m\%d).out +procdate=$(date +\%Y\%m\%d) + +echo "Processing date: $procdate" +$KPFCRONJOB_CODE/cronjobs/kpfmastersruncmd_l0.pl $procdate >& $KPFCRONJOB_LOGS/kpfmastersruncmd_l0_$procdate.out echo Return value from kpfmastersruncmd_l0.pl = $? -$KPFCRONJOB_CODE/cronjobs/kpfmastersruncmd_l1.pl $(date +\%Y\%m\%d) >& $KPFCRONJOB_LOGS/kpfmastersruncmd_l1_$(date +\%Y\%m\%d).out +$KPFCRONJOB_CODE/cronjobs/kpfmastersruncmd_l1.pl $procdate >& $KPFCRONJOB_LOGS/kpfmastersruncmd_l1_$procdate.out echo Return value from kpfmastersruncmd_l1.pl = $? -$KPFCRONJOB_CODE/cronjobs/kpfmasters_wls_auto.pl $(date +\%Y\%m\%d) >& $KPFCRONJOB_LOGS/kpfmasters_wls_auto_$(date +\%Y\%m\%d).out +$KPFCRONJOB_CODE/cronjobs/kpfmasters_wls_auto.pl $procdate >& $KPFCRONJOB_LOGS/kpfmasters_wls_auto_$procdate.out echo Return value from kpfmasters_wls_auto.pl = $? -$KPFCRONJOB_CODE/database/cronjobs/kpfmasters_register_in_db.pl $(date +\%Y\%m\%d) >& $KPFCRONJOB_LOGS/kpfmasters_register_in_db_$(date +\%Y\%m\%d).out +$KPFCRONJOB_CODE/database/cronjobs/kpfmasters_register_in_db.pl $procdate >& $KPFCRONJOB_LOGS/kpfmasters_register_in_db_$procdate.out echo Return value from kpfmasters_register_in_db.pl = $? diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 528cf09a8..0eb46741c 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -173,11 +173,11 @@ if do_db_query_for_one_nearest_wls_master_file and do_spectral_extraction: for cal_type_pair in cal_type_pairs: if db_wls_exit_code != 0: query_one_nearest_master_file_list = QueryDBOneNearestMasterFileFramework(data_type, - date_dir_db_query, - cal_file_level, - contentbitmask, - cal_type_pair, - max_cal_file_age) + date_dir_db_query, + cal_file_level, + contentbitmask, + cal_type_pair, + max_cal_file_age) db_wls_exit_code = query_one_nearest_master_file_list[0] if db_wls_exit_code == 0: @@ -186,11 +186,10 @@ if do_db_query_for_one_nearest_wls_master_file and do_spectral_extraction: db_wls_master_file = db_wls_rec[6] wave_fits = [] for wls in wls_list: - wave_fits = wave_fits + [output_dir + db_wls_master_file] - - wave_fits = [] - for wls in wls_list: - wave_fits = wave_fits + [output_dir + db_wls_master_file] + # Remove /masters from path returned from QueryDBOneNearestMasterFileFramework + # since it is already included in output_dir. + modified_file_path = str_replace(db_wls_master_file, '/masters', '') + wave_fits = wave_fits + [output_dir + modified_file_path] #### variables related to input/output and process conditions for order_trace, spectral extraction, CA-HK, rv and qlp From 7b1ceed5c34e0103523339819058ac19eaf53b10 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 16 Jun 2023 15:22:38 -0700 Subject: [PATCH 103/249] nightly summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 2365c7181..adb5ce8cc 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -120,7 +120,7 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_2D_Frame_'+ccd_color[i_color]+'.png') #print(output_dir+'/'+exposure_name+ccd_color[i_color]+'_zoomable.png') - plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=1000) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_zoomable.png', dpi=500) #plt.close() order_trace_file = self.config['L1']['order_trace']+ccd_color[i_color]+'.csv' @@ -157,7 +157,7 @@ def nightly_procedures(self,night): plt.ylabel('y (pixel number)') plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) plt.colorbar(label = 'Fractional Difference') - plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=1000) + plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_2D_Difference_zoomable.png', dpi=500) #histogram plt.close() From 720ce3ae3b8143b386e5ddf7b5ce1cdf38833afd Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Sat, 17 Jun 2023 22:18:45 -0700 Subject: [PATCH 104/249] Create analyze_l0.py Add placeholder class for analysis of L0 images. --- modules/Utils/analyze_l0.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 modules/Utils/analyze_l0.py diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py new file mode 100644 index 000000000..bf8897cd5 --- /dev/null +++ b/modules/Utils/analyze_l0.py @@ -0,0 +1,26 @@ +import numpy as np + +class AnalyzeL0: + + """ + Description: + This class [will] contain functions to analyze L0 images (storing + them as attributes) and functions to plot the results. + Currently, this class is a placeholder for future functions to be + added + + Arguments: + L0 - an L0 object + + Attributes: + TBD + """ + + def __init__(self, L0, logger=None): + self.L0 = L0 + if logger: + self.logger = logger + self.logger.debug('AnalyzeL0 class constructor') + else: + self.logger = None + print('---->AnalyzeL0 class constructor') From d66a31de90388bd1d44fa8acc92c0848b7d8875c Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Sat, 17 Jun 2023 22:20:49 -0700 Subject: [PATCH 105/249] Create analyze_l1.py Creates class for functions to analyze L1 spectra and plot the results. --- modules/Utils/analyze_l1.py | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 modules/Utils/analyze_l1.py diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py new file mode 100644 index 000000000..c8102b38d --- /dev/null +++ b/modules/Utils/analyze_l1.py @@ -0,0 +1,219 @@ +import numpy as np + +class AnalyzeL1: + + """ + Description: + This class contains functions to analyze L1 spectra (storing them + as attributes) and functions to plot the results. + + Arguments: + L0 - an L0 object + + Attributes: + GREEN_SNR (from compute_l1_snr): Two-dimensional array of + SNR values for the Green CCD. The first array index specifies + the spectral order (0-34 = green, 0-31 = red). + The second array index specifies the orderlet: + 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3 + RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, + but for the Red CCD. + GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array + of the wavelength of the middle of the spectral orders on + the green CCD. + RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, + but for the Red CCD. + """ + + def __init__(self, L1, logger=None): + self.L1 = L1 + if logger: + self.logger = logger + self.logger.debug('AnalyzeL0 class constructor') + else: + self.logger = None + print('---->AnalyzeL0 class constructor') + + def measure_L1_snr(self, percentile=95): + + """ + Compute the signal-to-noise ratio (SNR) for each spectral order and + orderlet in an L1 spectrum from KPF. + SNR is defined as signal / sqrt(abs(variance)) and can be negative. + + Args: + percentile: percentile in the SNR distribution for each combination of + order and orderlet + + Attributes: + GREEN_SNR - Two-dimensional array of SNR values for the Green CCD. + The first array index specifies the spectral order + (0-34 = green, 0-31 = red). The second array index + specifies the orderlet: + 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3. + For example, GREEN_SNR[1,2] is the SNR for order=1 and the + SCI2 orderlet. + RED_SNR - Similar to GREEN_SNR, but for the Red CCD. + GREEN_SNR_WAV - One-dimensional array of the wavelength of the + middle of the spectral orders on the green CCD. + RED_SNR_WAV - Similar to GREEN_SNR, but for the Red CCD. + + Returns: + None + """ + + # Determine the number of orders + norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] + norders_red = (L1['RED_SKY_WAVE'].data).shape[0] + orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} + norderlets = len(orderlets) + + # Define SNR arrays (needed for operations below where VAR = 0) + GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data + GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data + GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data + GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data + GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data + GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data + RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data + RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data + RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data + RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data + RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data + RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data + + # Create Arrays + GREEN_SNR = np.zeros((norders_green, norderlets+1)) + RED_SNR = np.zeros((norders_red, norderlets+1)) + GREEN_SNR_WAV = np.zeros(norders_green) + RED_SNR_WAV = np.zeros(norders_red) + + # Compute SNR arrays for each of the orders, orderlets, and CCDs. + GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), + where=(L1['GREEN_SCI_VAR1'].data!=0)) + GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), + where=(L1['GREEN_SCI_VAR2'].data!=0)) + GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), + where=(L1['GREEN_SCI_VAR3'].data!=0)) + GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), + where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) + GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, + np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), + where=(L1['GREEN_CAL_VAR'].data!=0)) + GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, + np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), + where=(L1['GREEN_SKY_VAR'].data!=0)) + RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)), + where=(L1['RED_SCI_VAR1'].data!=0)) + RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, + np.sqrt(abs(L1['RED_SCI_VAR2'].data)), + where=(L1['RED_SCI_VAR2'].data!=0)) + RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR3'].data)), + where=(L1['RED_SCI_VAR3'].data!=0)) + RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), + where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) + RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, + np.sqrt(abs(L1['RED_CAL_VAR'].data)), + where=(L1['RED_CAL_VAR'].data!=0)) + RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, + np.sqrt(abs(L1['RED_SKY_VAR'].data)), + where=(L1['RED_SKY_VAR'].data!=0)) + + # Compute SNR per order and per orderlet + for o in range(norders_green): + GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'].data[o,2040] + GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], percentile) + GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], percentile) + GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], percentile) + GREEN_SNR[o,3] = np.percentile(GREEN_SCI_SNR3[o], percentile) + GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], percentile) + GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], percentile) + for o in range(norders_red): + RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'].data[o,2040] + RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], percentile) + RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], percentile) + RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], percentile) + RED_SNR[o,3] = np.percentile(RED_SCI_SNR3[o], percentile) + RED_SNR[o,4] = np.percentile(RED_SKY_SNR[o], percentile) + RED_SNR[o,5] = np.percentile(RED_SCI_SNR[o], percentile) + + # Save SNR arrays to the object + self.GREEN_SNR = GREEN_SNR + self.RED_SNR = RED_SNR + self.GREEN_SNR_WAV = GREEN_SNR_WAV + self.RED_SNR_WAV = RED_SNR_WAV + + def plot_L1_snr(self, fig_path=None, show_plot=False): + + """ + + Generate a plot of SNR per order as compuated using the compute_l1_snr + function. + + Args: + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + if fig_path == None: + print("No output path specified in argument 'fig_path'.") + else: + + # Make 3-panel plot. First, create the figure and subplots + fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) + + # Plot the data on each subplot + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,5], marker="8", color='darkgreen', label='SCI1+SCI2+SCI3') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,1], marker=">", color='darkgreen', label='SCI1') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,2], marker="s", color='darkgreen', label='SCI2') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,3], marker="<", color='darkgreen', label='SCI3') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,5], marker="8", color='r', label='SCI1+SCI2+SCI3') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,1], marker=">", color='r', label='SCI1') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,2], marker="s", color='r', label='SCI2') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,3], marker="<", color='r', label='SCI3') + ax1.grid() + ax2.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,4], marker="D", color='darkgreen', label='SKY') + ax2.scatter(self.RED_SNR_WAV, self.RED_SNR[:,4], marker="D", color='r', label='SKY') + ax2.grid() + ax3.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,0], marker="D", color='darkgreen', label='CAL') + ax3.scatter(self.RED_SNR_WAV, self.RED_SNR[:,0], marker="D", color='r', label='CAL') + ax3.grid() + ax3.set_xlim(4450,8700) + + # Add legend + ax1.legend(["SCI1+SCI2+SCI3","SCI1","SCI2","SCI3"], ncol=4) + + # Set titles and labels for each subplot + ax1.set_title(ObsID + ' - ' + r'$\mathrm{SNR}_{'+str(percentile)+'}$ = '+str(percentile)+'th percentile (Signal / $\sqrt{\mathrm{Variance}}$)', fontsize=18) + ax3.set_xlabel('Wavelength (Ang)', fontsize=14) + ax1.set_ylabel('SNR - SCI', fontsize=14) + ax2.set_ylabel('SNR - SKY', fontsize=14) + ax3.set_ylabel('SNR - CAL', fontsize=14) + ax3.xaxis.set_tick_params(labelsize=14) + ax1.yaxis.set_tick_params(labelsize=14) + ax2.yaxis.set_tick_params(labelsize=14) + ax3.yaxis.set_tick_params(labelsize=14) + + # Adjust spacing between subplots + plt.subplots_adjust(hspace=0) + plt.tight_layout() + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=300, facecolor='w') + if show_plot == True: + plt.show() + plt.close() From cbd2b257ae9827969bb1fd5cfe6631b6a5d49679 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Sat, 17 Jun 2023 22:28:07 -0700 Subject: [PATCH 106/249] Update analyze_l1.py --- modules/Utils/analyze_l1.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index c8102b38d..a21714d71 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -167,10 +167,6 @@ def plot_L1_snr(self, fig_path=None, show_plot=False): (e.g., in a Jupyter Notebook). """ - - if fig_path == None: - print("No output path specified in argument 'fig_path'.") - else: # Make 3-panel plot. First, create the figure and subplots fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) From 1d9896b89b3659ad76caf8af7b5781057df4bb7d Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Sat, 17 Jun 2023 22:30:35 -0700 Subject: [PATCH 107/249] Update analyze_l1.py --- modules/Utils/analyze_l1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index a21714d71..c1c6e058f 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -8,7 +8,7 @@ class AnalyzeL1: as attributes) and functions to plot the results. Arguments: - L0 - an L0 object + L1 - an L1 object Attributes: GREEN_SNR (from compute_l1_snr): Two-dimensional array of From 617d2c792f97890416a0483ba3c0911caef4c620 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Sat, 17 Jun 2023 22:32:32 -0700 Subject: [PATCH 108/249] Update analyze_l0.py --- modules/Utils/analyze_l0.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index bf8897cd5..74c31febc 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -7,7 +7,7 @@ class AnalyzeL0: This class [will] contain functions to analyze L0 images (storing them as attributes) and functions to plot the results. Currently, this class is a placeholder for future functions to be - added + added. See analyze_l1.py for examples. Arguments: L0 - an L0 object From 67aa16063a339e8e54f2060648c1d7e4acde3564 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Mon, 19 Jun 2023 11:26:03 -0700 Subject: [PATCH 109/249] Small bug fixes --- modules/Utils/analyze_l1.py | 35 +++++++++++++++++++---------------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index c1c6e058f..4e46ab1f6 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -1,4 +1,5 @@ import numpy as np +import matplotlib.pyplot as plt class AnalyzeL1: @@ -8,7 +9,7 @@ class AnalyzeL1: as attributes) and functions to plot the results. Arguments: - L1 - an L1 object + L0 - an L0 object Attributes: GREEN_SNR (from compute_l1_snr): Two-dimensional array of @@ -34,7 +35,7 @@ def __init__(self, L1, logger=None): self.logger = None print('---->AnalyzeL0 class constructor') - def measure_L1_snr(self, percentile=95): + def measure_L1_snr(self, snr_percentile=95): """ Compute the signal-to-noise ratio (SNR) for each spectral order and @@ -42,7 +43,7 @@ def measure_L1_snr(self, percentile=95): SNR is defined as signal / sqrt(abs(variance)) and can be negative. Args: - percentile: percentile in the SNR distribution for each combination of + snr_percentile: snr_percentile in the SNR distribution for each combination of order and orderlet Attributes: @@ -61,6 +62,8 @@ def measure_L1_snr(self, percentile=95): Returns: None """ + + self.snr_percentile = snr_percentile # Determine the number of orders norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] @@ -129,20 +132,20 @@ def measure_L1_snr(self, percentile=95): # Compute SNR per order and per orderlet for o in range(norders_green): GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'].data[o,2040] - GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], percentile) - GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], percentile) - GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], percentile) - GREEN_SNR[o,3] = np.percentile(GREEN_SCI_SNR3[o], percentile) - GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], percentile) - GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], percentile) + GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], snr_percentile) + GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], snr_percentile) + GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], snr_percentile) + GREEN_SNR[o,3] = np.percentile(GREEN_SCI_SNR3[o], snr_percentile) + GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], snr_percentile) + GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], snr_percentile) for o in range(norders_red): RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'].data[o,2040] - RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], percentile) - RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], percentile) - RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], percentile) - RED_SNR[o,3] = np.percentile(RED_SCI_SNR3[o], percentile) - RED_SNR[o,4] = np.percentile(RED_SKY_SNR[o], percentile) - RED_SNR[o,5] = np.percentile(RED_SCI_SNR[o], percentile) + RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], snr_percentile) + RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], snr_percentile) + RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], snr_percentile) + RED_SNR[o,3] = np.percentile(RED_SCI_SNR3[o], snr_percentile) + RED_SNR[o,4] = np.percentile(RED_SKY_SNR[o], snr_percentile) + RED_SNR[o,5] = np.percentile(RED_SCI_SNR[o], snr_percentile) # Save SNR arrays to the object self.GREEN_SNR = GREEN_SNR @@ -193,7 +196,7 @@ def plot_L1_snr(self, fig_path=None, show_plot=False): ax1.legend(["SCI1+SCI2+SCI3","SCI1","SCI2","SCI3"], ncol=4) # Set titles and labels for each subplot - ax1.set_title(ObsID + ' - ' + r'$\mathrm{SNR}_{'+str(percentile)+'}$ = '+str(percentile)+'th percentile (Signal / $\sqrt{\mathrm{Variance}}$)', fontsize=18) + ax1.set_title(ObsID + ' - ' + r'$\mathrm{SNR}_{'+str(self.snr_percentile)+'}$ = '+str(self.snr_percentile)+'th percentile (Signal / $\sqrt{\mathrm{Variance}}$)', fontsize=18) ax3.set_xlabel('Wavelength (Ang)', fontsize=14) ax1.set_ylabel('SNR - SCI', fontsize=14) ax2.set_ylabel('SNR - SKY', fontsize=14) From aabab9a98eb7e3f95c6d9908f70e25e11648e4e2 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 11:57:41 -0700 Subject: [PATCH 110/249] l1 snr --- modules/Utils/analyze_l1.py | 218 +++++++++++++++++++++++++++++++++++ modules/quicklook/src/alg.py | 1 + 2 files changed, 219 insertions(+) create mode 100644 modules/Utils/analyze_l1.py diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py new file mode 100644 index 000000000..4e46ab1f6 --- /dev/null +++ b/modules/Utils/analyze_l1.py @@ -0,0 +1,218 @@ +import numpy as np +import matplotlib.pyplot as plt + +class AnalyzeL1: + + """ + Description: + This class contains functions to analyze L1 spectra (storing them + as attributes) and functions to plot the results. + + Arguments: + L0 - an L0 object + + Attributes: + GREEN_SNR (from compute_l1_snr): Two-dimensional array of + SNR values for the Green CCD. The first array index specifies + the spectral order (0-34 = green, 0-31 = red). + The second array index specifies the orderlet: + 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3 + RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, + but for the Red CCD. + GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array + of the wavelength of the middle of the spectral orders on + the green CCD. + RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, + but for the Red CCD. + """ + + def __init__(self, L1, logger=None): + self.L1 = L1 + if logger: + self.logger = logger + self.logger.debug('AnalyzeL0 class constructor') + else: + self.logger = None + print('---->AnalyzeL0 class constructor') + + def measure_L1_snr(self, snr_percentile=95): + + """ + Compute the signal-to-noise ratio (SNR) for each spectral order and + orderlet in an L1 spectrum from KPF. + SNR is defined as signal / sqrt(abs(variance)) and can be negative. + + Args: + snr_percentile: snr_percentile in the SNR distribution for each combination of + order and orderlet + + Attributes: + GREEN_SNR - Two-dimensional array of SNR values for the Green CCD. + The first array index specifies the spectral order + (0-34 = green, 0-31 = red). The second array index + specifies the orderlet: + 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3. + For example, GREEN_SNR[1,2] is the SNR for order=1 and the + SCI2 orderlet. + RED_SNR - Similar to GREEN_SNR, but for the Red CCD. + GREEN_SNR_WAV - One-dimensional array of the wavelength of the + middle of the spectral orders on the green CCD. + RED_SNR_WAV - Similar to GREEN_SNR, but for the Red CCD. + + Returns: + None + """ + + self.snr_percentile = snr_percentile + + # Determine the number of orders + norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] + norders_red = (L1['RED_SKY_WAVE'].data).shape[0] + orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} + norderlets = len(orderlets) + + # Define SNR arrays (needed for operations below where VAR = 0) + GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data + GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data + GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data + GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data + GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data + GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data + RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data + RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data + RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data + RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data + RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data + RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data + + # Create Arrays + GREEN_SNR = np.zeros((norders_green, norderlets+1)) + RED_SNR = np.zeros((norders_red, norderlets+1)) + GREEN_SNR_WAV = np.zeros(norders_green) + RED_SNR_WAV = np.zeros(norders_red) + + # Compute SNR arrays for each of the orders, orderlets, and CCDs. + GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), + where=(L1['GREEN_SCI_VAR1'].data!=0)) + GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), + where=(L1['GREEN_SCI_VAR2'].data!=0)) + GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), + where=(L1['GREEN_SCI_VAR3'].data!=0)) + GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), + where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) + GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, + np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), + where=(L1['GREEN_CAL_VAR'].data!=0)) + GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, + np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), + where=(L1['GREEN_SKY_VAR'].data!=0)) + RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)), + where=(L1['RED_SCI_VAR1'].data!=0)) + RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, + np.sqrt(abs(L1['RED_SCI_VAR2'].data)), + where=(L1['RED_SCI_VAR2'].data!=0)) + RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR3'].data)), + where=(L1['RED_SCI_VAR3'].data!=0)) + RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), + where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) + RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, + np.sqrt(abs(L1['RED_CAL_VAR'].data)), + where=(L1['RED_CAL_VAR'].data!=0)) + RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, + np.sqrt(abs(L1['RED_SKY_VAR'].data)), + where=(L1['RED_SKY_VAR'].data!=0)) + + # Compute SNR per order and per orderlet + for o in range(norders_green): + GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'].data[o,2040] + GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], snr_percentile) + GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], snr_percentile) + GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], snr_percentile) + GREEN_SNR[o,3] = np.percentile(GREEN_SCI_SNR3[o], snr_percentile) + GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], snr_percentile) + GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], snr_percentile) + for o in range(norders_red): + RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'].data[o,2040] + RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], snr_percentile) + RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], snr_percentile) + RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], snr_percentile) + RED_SNR[o,3] = np.percentile(RED_SCI_SNR3[o], snr_percentile) + RED_SNR[o,4] = np.percentile(RED_SKY_SNR[o], snr_percentile) + RED_SNR[o,5] = np.percentile(RED_SCI_SNR[o], snr_percentile) + + # Save SNR arrays to the object + self.GREEN_SNR = GREEN_SNR + self.RED_SNR = RED_SNR + self.GREEN_SNR_WAV = GREEN_SNR_WAV + self.RED_SNR_WAV = RED_SNR_WAV + + def plot_L1_snr(self, fig_path=None, show_plot=False): + + """ + + Generate a plot of SNR per order as compuated using the compute_l1_snr + function. + + Args: + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + # Make 3-panel plot. First, create the figure and subplots + fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) + + # Plot the data on each subplot + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,5], marker="8", color='darkgreen', label='SCI1+SCI2+SCI3') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,1], marker=">", color='darkgreen', label='SCI1') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,2], marker="s", color='darkgreen', label='SCI2') + ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,3], marker="<", color='darkgreen', label='SCI3') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,5], marker="8", color='r', label='SCI1+SCI2+SCI3') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,1], marker=">", color='r', label='SCI1') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,2], marker="s", color='r', label='SCI2') + ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,3], marker="<", color='r', label='SCI3') + ax1.grid() + ax2.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,4], marker="D", color='darkgreen', label='SKY') + ax2.scatter(self.RED_SNR_WAV, self.RED_SNR[:,4], marker="D", color='r', label='SKY') + ax2.grid() + ax3.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,0], marker="D", color='darkgreen', label='CAL') + ax3.scatter(self.RED_SNR_WAV, self.RED_SNR[:,0], marker="D", color='r', label='CAL') + ax3.grid() + ax3.set_xlim(4450,8700) + + # Add legend + ax1.legend(["SCI1+SCI2+SCI3","SCI1","SCI2","SCI3"], ncol=4) + + # Set titles and labels for each subplot + ax1.set_title(ObsID + ' - ' + r'$\mathrm{SNR}_{'+str(self.snr_percentile)+'}$ = '+str(self.snr_percentile)+'th percentile (Signal / $\sqrt{\mathrm{Variance}}$)', fontsize=18) + ax3.set_xlabel('Wavelength (Ang)', fontsize=14) + ax1.set_ylabel('SNR - SCI', fontsize=14) + ax2.set_ylabel('SNR - SKY', fontsize=14) + ax3.set_ylabel('SNR - CAL', fontsize=14) + ax3.xaxis.set_tick_params(labelsize=14) + ax1.yaxis.set_tick_params(labelsize=14) + ax2.yaxis.set_tick_params(labelsize=14) + ax3.yaxis.set_tick_params(labelsize=14) + + # Adjust spacing between subplots + plt.subplots_adjust(hspace=0) + plt.tight_layout() + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=300, facecolor='w') + if show_plot == True: + plt.show() + plt.close() diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index cb4c62b3e..1608d5e51 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -3,6 +3,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches from modules.Utils.config_parser import ConfigHandler +from modules.Utils.AnalyzeL1 import AnalyzeL1 from kpfpipe.models.level0 import KPF0 from keckdrpframework.models.arguments import Arguments import os From 669f00422e750ce890b30a5f3e88c2695b10c5f7 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 12:01:22 -0700 Subject: [PATCH 111/249] l1 snr --- modules/quicklook/src/alg.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 1608d5e51..0fa3ab4ba 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches from modules.Utils.config_parser import ConfigHandler -from modules.Utils.AnalyzeL1 import AnalyzeL1 +from modules.Utils.analyze_l1 import AnalyzeL1 from kpfpipe.models.level0 import KPF0 from keckdrpframework.models.arguments import Arguments import os @@ -763,6 +763,13 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): hdulist1.close() ''' + #l1 SNR + + my_L1 = AnalyzeL1(hdulist) + my_L1.measure_L1_snr(snr_percentile=95) + my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') + plt.close() + #print(hdulist1.info()) flux_green = np.array(hdulist['GREEN_SCI_FLUX1'].data,'d') From 44f1621ccb2f1ebcb2c091d5dd4ba1eec91f02b0 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 12:04:13 -0700 Subject: [PATCH 112/249] l1 snr --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 0fa3ab4ba..393680c04 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -764,8 +764,8 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): ''' #l1 SNR - - my_L1 = AnalyzeL1(hdulist) + L1 = hdulist + my_L1 = AnalyzeL1(L1) my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From 272723dbef575f00d1c15279cd6b6bfb9cd0c8d0 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:34:18 -0700 Subject: [PATCH 113/249] l1 snr --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 393680c04..1fa7231d6 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches from modules.Utils.config_parser import ConfigHandler -from modules.Utils.analyze_l1 import AnalyzeL1 +from modules.Utils.analyze_l1 import * from kpfpipe.models.level0 import KPF0 from keckdrpframework.models.arguments import Arguments import os @@ -764,7 +764,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): ''' #l1 SNR - L1 = hdulist + L1 = fits.open(L1_data) my_L1 = AnalyzeL1(L1) my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') From 717f055087917bcf7c406cced2ddd9a774d4d6a6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:40:37 -0700 Subject: [PATCH 114/249] l1 snr --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 1fa7231d6..7d469a0d7 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -764,8 +764,8 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): ''' #l1 SNR - L1 = fits.open(L1_data) - my_L1 = AnalyzeL1(L1) + + my_L1 = AnalyzeL1(hdulist) my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From 3b319a225d5f61cb94748a909c7cf3516cb0e359 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:43:54 -0700 Subject: [PATCH 115/249] l1 snr --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 7d469a0d7..08d6933b1 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -764,9 +764,9 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): ''' #l1 SNR - + my_L1 = AnalyzeL1(hdulist) - my_L1.measure_L1_snr(snr_percentile=95) + #my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From a250a7802d15cbafb320c32f161013f7c5b84bb6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:46:22 -0700 Subject: [PATCH 116/249] l1 snr --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 08d6933b1..e7d820986 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -3,7 +3,7 @@ import matplotlib.pyplot as plt import matplotlib.patches as patches from modules.Utils.config_parser import ConfigHandler -from modules.Utils.analyze_l1 import * +from modules.Utils.analyze_l1 import AnalyzeL1 from kpfpipe.models.level0 import KPF0 from keckdrpframework.models.arguments import Arguments import os From e421604cf5e739594da51b6d7b0d8ded09b0175f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:47:41 -0700 Subject: [PATCH 117/249] l1 snr --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index e7d820986..0fa3ab4ba 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -766,7 +766,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #l1 SNR my_L1 = AnalyzeL1(hdulist) - #my_L1.measure_L1_snr(snr_percentile=95) + my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From 82bd51e68cdb56cd013059e635875904e8b49822 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:49:21 -0700 Subject: [PATCH 118/249] l1 snr --- modules/quicklook/src/alg.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 0fa3ab4ba..fcb7f0892 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -766,6 +766,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #l1 SNR my_L1 = AnalyzeL1(hdulist) + print('test',my_L1) my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From 8940809a8491175df3e0f2499c7ad24ead8d6fa9 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 13:51:39 -0700 Subject: [PATCH 119/249] l1 snr --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index fcb7f0892..7952534e9 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -766,7 +766,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #l1 SNR my_L1 = AnalyzeL1(hdulist) - print('test',my_L1) + print('test',my_L1,hdulist) my_L1.measure_L1_snr(snr_percentile=95) my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() From d6ef4eb7177356ce041bc5540dd5ead382e67a61 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 14:03:48 -0700 Subject: [PATCH 120/249] l1 snr --- modules/Utils/analyze_l1.py | 118 +++++++++++++++++------------------ modules/quicklook/src/alg.py | 7 +-- 2 files changed, 62 insertions(+), 63 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index 4e46ab1f6..a04ca1581 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -12,17 +12,17 @@ class AnalyzeL1: L0 - an L0 object Attributes: - GREEN_SNR (from compute_l1_snr): Two-dimensional array of - SNR values for the Green CCD. The first array index specifies - the spectral order (0-34 = green, 0-31 = red). - The second array index specifies the orderlet: + GREEN_SNR (from compute_l1_snr): Two-dimensional array of + SNR values for the Green CCD. The first array index specifies + the spectral order (0-34 = green, 0-31 = red). + The second array index specifies the orderlet: 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3 - RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, + RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, but for the Red CCD. - GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array - of the wavelength of the middle of the spectral orders on + GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array + of the wavelength of the middle of the spectral orders on the green CCD. - RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, + RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, but for the Red CCD. """ @@ -35,53 +35,53 @@ def __init__(self, L1, logger=None): self.logger = None print('---->AnalyzeL0 class constructor') - def measure_L1_snr(self, snr_percentile=95): + def measure_L1_snr(self,L1, snr_percentile=95): """ - Compute the signal-to-noise ratio (SNR) for each spectral order and - orderlet in an L1 spectrum from KPF. + Compute the signal-to-noise ratio (SNR) for each spectral order and + orderlet in an L1 spectrum from KPF. SNR is defined as signal / sqrt(abs(variance)) and can be negative. Args: - snr_percentile: snr_percentile in the SNR distribution for each combination of - order and orderlet + snr_percentile: snr_percentile in the SNR distribution for each combination of + order and orderlet Attributes: GREEN_SNR - Two-dimensional array of SNR values for the Green CCD. - The first array index specifies the spectral order - (0-34 = green, 0-31 = red). The second array index - specifies the orderlet: + The first array index specifies the spectral order + (0-34 = green, 0-31 = red). The second array index + specifies the orderlet: 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3. - For example, GREEN_SNR[1,2] is the SNR for order=1 and the + For example, GREEN_SNR[1,2] is the SNR for order=1 and the SCI2 orderlet. RED_SNR - Similar to GREEN_SNR, but for the Red CCD. - GREEN_SNR_WAV - One-dimensional array of the wavelength of the + GREEN_SNR_WAV - One-dimensional array of the wavelength of the middle of the spectral orders on the green CCD. RED_SNR_WAV - Similar to GREEN_SNR, but for the Red CCD. Returns: None """ - + self.snr_percentile = snr_percentile # Determine the number of orders norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] norders_red = (L1['RED_SKY_WAVE'].data).shape[0] orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} - norderlets = len(orderlets) + norderlets = len(orderlets) # Define SNR arrays (needed for operations below where VAR = 0) GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data - GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data - GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data - GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data - RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data - RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data - RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data - RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data + GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data + GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data + GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data + RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data + RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data + RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data + RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data @@ -92,41 +92,41 @@ def measure_L1_snr(self, snr_percentile=95): RED_SNR_WAV = np.zeros(norders_red) # Compute SNR arrays for each of the orders, orderlets, and CCDs. - GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), + GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), where=(L1['GREEN_SCI_VAR1'].data!=0)) - GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), + GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), where=(L1['GREEN_SCI_VAR2'].data!=0)) - GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), + GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), where=(L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), + GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, - np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), + GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, + np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), where=(L1['GREEN_CAL_VAR'].data!=0)) - GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, - np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), + GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, + np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), where=(L1['GREEN_SKY_VAR'].data!=0)) - RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)), + RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)), where=(L1['RED_SCI_VAR1'].data!=0)) - RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, - np.sqrt(abs(L1['RED_SCI_VAR2'].data)), + RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, + np.sqrt(abs(L1['RED_SCI_VAR2'].data)), where=(L1['RED_SCI_VAR2'].data!=0)) - RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR3'].data)), + RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR3'].data)), where=(L1['RED_SCI_VAR3'].data!=0)) - RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), + RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) - RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, - np.sqrt(abs(L1['RED_CAL_VAR'].data)), + RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, + np.sqrt(abs(L1['RED_CAL_VAR'].data)), where=(L1['RED_CAL_VAR'].data!=0)) - RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, - np.sqrt(abs(L1['RED_SKY_VAR'].data)), + RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, + np.sqrt(abs(L1['RED_SKY_VAR'].data)), where=(L1['RED_SKY_VAR'].data!=0)) # Compute SNR per order and per orderlet @@ -151,26 +151,26 @@ def measure_L1_snr(self, snr_percentile=95): self.GREEN_SNR = GREEN_SNR self.RED_SNR = RED_SNR self.GREEN_SNR_WAV = GREEN_SNR_WAV - self.RED_SNR_WAV = RED_SNR_WAV - - def plot_L1_snr(self, fig_path=None, show_plot=False): + self.RED_SNR_WAV = RED_SNR_WAV + + def plot_L1_snr(self,L1, fig_path=None, show_plot=False): """ - - Generate a plot of SNR per order as compuated using the compute_l1_snr + + Generate a plot of SNR per order as compuated using the compute_l1_snr function. Args: - fig_path (string) - set to the path for a SNR vs. wavelength file + fig_path (string) - set to the path for a SNR vs. wavelength file to be generated. show_plot (boolean) - show the plot in the current environment. Returns: - PNG plot in fig_path or shows the plot it the current environment + PNG plot in fig_path or shows the plot it the current environment (e.g., in a Jupyter Notebook). """ - + # Make 3-panel plot. First, create the figure and subplots fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 7952534e9..596c26075 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -765,10 +765,9 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #l1 SNR - my_L1 = AnalyzeL1(hdulist) - print('test',my_L1,hdulist) - my_L1.measure_L1_snr(snr_percentile=95) - my_L1.plot_L1_snr(fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') + L1_SNR = AnalyzeL1(hdulist) + L1_SNR.measure_L1_snr(hdulist,snr_percentile=95) + L1_SNR.plot_L1_snr(hdulist,fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() #print(hdulist1.info()) From 98bdcc89e8d7057721e86e82e7d4d9503ba9e277 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 19 Jun 2023 14:06:08 -0700 Subject: [PATCH 121/249] l1 snr --- modules/Utils/analyze_l1.py | 2 +- modules/quicklook/src/alg.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index a04ca1581..ecbfdd4de 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -153,7 +153,7 @@ def measure_L1_snr(self,L1, snr_percentile=95): self.GREEN_SNR_WAV = GREEN_SNR_WAV self.RED_SNR_WAV = RED_SNR_WAV - def plot_L1_snr(self,L1, fig_path=None, show_plot=False): + def plot_L1_snr(self,L1,ObsID, fig_path=None, show_plot=False): """ diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 596c26075..2e8b65173 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -767,7 +767,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): L1_SNR = AnalyzeL1(hdulist) L1_SNR.measure_L1_snr(hdulist,snr_percentile=95) - L1_SNR.plot_L1_snr(hdulist,fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') + L1_SNR.plot_L1_snr(hdulist,exposure_name,fig_path=output_dir+'/'+exposure_name+'/1D/'+exposure_name+'_1D_spectrum_snr.png') plt.close() #print(hdulist1.info()) From 7458b8f24d186c4c17b09ad4f9d98c5d42a50f0f Mon Sep 17 00:00:00 2001 From: Aaron Householder Date: Tue, 20 Jun 2023 13:52:19 -0400 Subject: [PATCH 122/249] Update frep_key with correct value from headers --- configs/wls_auto.cfg | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/configs/wls_auto.cfg b/configs/wls_auto.cfg index 3c4d2fb87..6c81f008c 100644 --- a/configs/wls_auto.cfg +++ b/configs/wls_auto.cfg @@ -19,8 +19,9 @@ lfc_search_string = LFC*_L1 quicklook = 0 # f0_key = 5220000000. # frep_key = 28800522000. -frep_key = 20000000000.0202 +# frep_key = 20000000000.0202 f0_key = 'LFCCEOFR' +frep_key = 'LFCFRREF' red_linelist = /data/masters/kpfMaster_ThArLines20221005_red.npy # red_linelist = kpfMaster_ThArLines20221005_red.npy From 357039d29d8bee103a41d7609cfc0d157ce87f8c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 15:59:09 -0700 Subject: [PATCH 123/249] l1 snr --- modules/Utils/analyze_l1.py | 218 ------------------------------------ 1 file changed, 218 deletions(-) delete mode 100644 modules/Utils/analyze_l1.py diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py deleted file mode 100644 index ecbfdd4de..000000000 --- a/modules/Utils/analyze_l1.py +++ /dev/null @@ -1,218 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - -class AnalyzeL1: - - """ - Description: - This class contains functions to analyze L1 spectra (storing them - as attributes) and functions to plot the results. - - Arguments: - L0 - an L0 object - - Attributes: - GREEN_SNR (from compute_l1_snr): Two-dimensional array of - SNR values for the Green CCD. The first array index specifies - the spectral order (0-34 = green, 0-31 = red). - The second array index specifies the orderlet: - 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3 - RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, - but for the Red CCD. - GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array - of the wavelength of the middle of the spectral orders on - the green CCD. - RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, - but for the Red CCD. - """ - - def __init__(self, L1, logger=None): - self.L1 = L1 - if logger: - self.logger = logger - self.logger.debug('AnalyzeL0 class constructor') - else: - self.logger = None - print('---->AnalyzeL0 class constructor') - - def measure_L1_snr(self,L1, snr_percentile=95): - - """ - Compute the signal-to-noise ratio (SNR) for each spectral order and - orderlet in an L1 spectrum from KPF. - SNR is defined as signal / sqrt(abs(variance)) and can be negative. - - Args: - snr_percentile: snr_percentile in the SNR distribution for each combination of - order and orderlet - - Attributes: - GREEN_SNR - Two-dimensional array of SNR values for the Green CCD. - The first array index specifies the spectral order - (0-34 = green, 0-31 = red). The second array index - specifies the orderlet: - 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3. - For example, GREEN_SNR[1,2] is the SNR for order=1 and the - SCI2 orderlet. - RED_SNR - Similar to GREEN_SNR, but for the Red CCD. - GREEN_SNR_WAV - One-dimensional array of the wavelength of the - middle of the spectral orders on the green CCD. - RED_SNR_WAV - Similar to GREEN_SNR, but for the Red CCD. - - Returns: - None - """ - - self.snr_percentile = snr_percentile - - # Determine the number of orders - norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] - norders_red = (L1['RED_SKY_WAVE'].data).shape[0] - orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} - norderlets = len(orderlets) - - # Define SNR arrays (needed for operations below where VAR = 0) - GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data - GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data - GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data - GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data - GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data - GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data - RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data - RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data - RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data - RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data - RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data - RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data - - # Create Arrays - GREEN_SNR = np.zeros((norders_green, norderlets+1)) - RED_SNR = np.zeros((norders_red, norderlets+1)) - GREEN_SNR_WAV = np.zeros(norders_green) - RED_SNR_WAV = np.zeros(norders_red) - - # Compute SNR arrays for each of the orders, orderlets, and CCDs. - GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), - where=(L1['GREEN_SCI_VAR1'].data!=0)) - GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), - where=(L1['GREEN_SCI_VAR2'].data!=0)) - GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), - where=(L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), - where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, - np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), - where=(L1['GREEN_CAL_VAR'].data!=0)) - GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, - np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), - where=(L1['GREEN_SKY_VAR'].data!=0)) - RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)), - where=(L1['RED_SCI_VAR1'].data!=0)) - RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, - np.sqrt(abs(L1['RED_SCI_VAR2'].data)), - where=(L1['RED_SCI_VAR2'].data!=0)) - RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR3'].data)), - where=(L1['RED_SCI_VAR3'].data!=0)) - RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), - where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) - RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, - np.sqrt(abs(L1['RED_CAL_VAR'].data)), - where=(L1['RED_CAL_VAR'].data!=0)) - RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, - np.sqrt(abs(L1['RED_SKY_VAR'].data)), - where=(L1['RED_SKY_VAR'].data!=0)) - - # Compute SNR per order and per orderlet - for o in range(norders_green): - GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'].data[o,2040] - GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], snr_percentile) - GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], snr_percentile) - GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], snr_percentile) - GREEN_SNR[o,3] = np.percentile(GREEN_SCI_SNR3[o], snr_percentile) - GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], snr_percentile) - GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], snr_percentile) - for o in range(norders_red): - RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'].data[o,2040] - RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], snr_percentile) - RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], snr_percentile) - RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], snr_percentile) - RED_SNR[o,3] = np.percentile(RED_SCI_SNR3[o], snr_percentile) - RED_SNR[o,4] = np.percentile(RED_SKY_SNR[o], snr_percentile) - RED_SNR[o,5] = np.percentile(RED_SCI_SNR[o], snr_percentile) - - # Save SNR arrays to the object - self.GREEN_SNR = GREEN_SNR - self.RED_SNR = RED_SNR - self.GREEN_SNR_WAV = GREEN_SNR_WAV - self.RED_SNR_WAV = RED_SNR_WAV - - def plot_L1_snr(self,L1,ObsID, fig_path=None, show_plot=False): - - """ - - Generate a plot of SNR per order as compuated using the compute_l1_snr - function. - - Args: - fig_path (string) - set to the path for a SNR vs. wavelength file - to be generated. - show_plot (boolean) - show the plot in the current environment. - - Returns: - PNG plot in fig_path or shows the plot it the current environment - (e.g., in a Jupyter Notebook). - - """ - - # Make 3-panel plot. First, create the figure and subplots - fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) - - # Plot the data on each subplot - ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,5], marker="8", color='darkgreen', label='SCI1+SCI2+SCI3') - ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,1], marker=">", color='darkgreen', label='SCI1') - ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,2], marker="s", color='darkgreen', label='SCI2') - ax1.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,3], marker="<", color='darkgreen', label='SCI3') - ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,5], marker="8", color='r', label='SCI1+SCI2+SCI3') - ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,1], marker=">", color='r', label='SCI1') - ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,2], marker="s", color='r', label='SCI2') - ax1.scatter(self.RED_SNR_WAV, self.RED_SNR[:,3], marker="<", color='r', label='SCI3') - ax1.grid() - ax2.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,4], marker="D", color='darkgreen', label='SKY') - ax2.scatter(self.RED_SNR_WAV, self.RED_SNR[:,4], marker="D", color='r', label='SKY') - ax2.grid() - ax3.scatter(self.GREEN_SNR_WAV, self.GREEN_SNR[:,0], marker="D", color='darkgreen', label='CAL') - ax3.scatter(self.RED_SNR_WAV, self.RED_SNR[:,0], marker="D", color='r', label='CAL') - ax3.grid() - ax3.set_xlim(4450,8700) - - # Add legend - ax1.legend(["SCI1+SCI2+SCI3","SCI1","SCI2","SCI3"], ncol=4) - - # Set titles and labels for each subplot - ax1.set_title(ObsID + ' - ' + r'$\mathrm{SNR}_{'+str(self.snr_percentile)+'}$ = '+str(self.snr_percentile)+'th percentile (Signal / $\sqrt{\mathrm{Variance}}$)', fontsize=18) - ax3.set_xlabel('Wavelength (Ang)', fontsize=14) - ax1.set_ylabel('SNR - SCI', fontsize=14) - ax2.set_ylabel('SNR - SKY', fontsize=14) - ax3.set_ylabel('SNR - CAL', fontsize=14) - ax3.xaxis.set_tick_params(labelsize=14) - ax1.yaxis.set_tick_params(labelsize=14) - ax2.yaxis.set_tick_params(labelsize=14) - ax3.yaxis.set_tick_params(labelsize=14) - - # Adjust spacing between subplots - plt.subplots_adjust(hspace=0) - plt.tight_layout() - - # Display the plot - if fig_path != None: - plt.savefig(fig_path, dpi=300, facecolor='w') - if show_plot == True: - plt.show() - plt.close() From 765fbc88916a6bc156004986a659bc9f07bd4672 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 16:00:30 -0700 Subject: [PATCH 124/249] l1 snr --- modules/Utils/analyze_l1.py | 118 ++++++++++++++++++------------------ 1 file changed, 59 insertions(+), 59 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index 4e46ab1f6..ecbfdd4de 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -12,17 +12,17 @@ class AnalyzeL1: L0 - an L0 object Attributes: - GREEN_SNR (from compute_l1_snr): Two-dimensional array of - SNR values for the Green CCD. The first array index specifies - the spectral order (0-34 = green, 0-31 = red). - The second array index specifies the orderlet: + GREEN_SNR (from compute_l1_snr): Two-dimensional array of + SNR values for the Green CCD. The first array index specifies + the spectral order (0-34 = green, 0-31 = red). + The second array index specifies the orderlet: 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3 - RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, + RED_SNR (from compute_l1_snr): Similar to GREEN_SNR, but for the Red CCD. - GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array - of the wavelength of the middle of the spectral orders on + GREEN_SNR_WAV (from compute_l1_snr): One-dimensional array + of the wavelength of the middle of the spectral orders on the green CCD. - RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, + RED_SNR_WAV (from compute_l1_snr): Similar to GREEN_SNR, but for the Red CCD. """ @@ -35,53 +35,53 @@ def __init__(self, L1, logger=None): self.logger = None print('---->AnalyzeL0 class constructor') - def measure_L1_snr(self, snr_percentile=95): + def measure_L1_snr(self,L1, snr_percentile=95): """ - Compute the signal-to-noise ratio (SNR) for each spectral order and - orderlet in an L1 spectrum from KPF. + Compute the signal-to-noise ratio (SNR) for each spectral order and + orderlet in an L1 spectrum from KPF. SNR is defined as signal / sqrt(abs(variance)) and can be negative. Args: - snr_percentile: snr_percentile in the SNR distribution for each combination of - order and orderlet + snr_percentile: snr_percentile in the SNR distribution for each combination of + order and orderlet Attributes: GREEN_SNR - Two-dimensional array of SNR values for the Green CCD. - The first array index specifies the spectral order - (0-34 = green, 0-31 = red). The second array index - specifies the orderlet: + The first array index specifies the spectral order + (0-34 = green, 0-31 = red). The second array index + specifies the orderlet: 0=CAL, 1=SCI1, 2=SCI2, 3=SCI3, 4=SKY, 5=SCI1+SCI2+SCI3. - For example, GREEN_SNR[1,2] is the SNR for order=1 and the + For example, GREEN_SNR[1,2] is the SNR for order=1 and the SCI2 orderlet. RED_SNR - Similar to GREEN_SNR, but for the Red CCD. - GREEN_SNR_WAV - One-dimensional array of the wavelength of the + GREEN_SNR_WAV - One-dimensional array of the wavelength of the middle of the spectral orders on the green CCD. RED_SNR_WAV - Similar to GREEN_SNR, but for the Red CCD. Returns: None """ - + self.snr_percentile = snr_percentile # Determine the number of orders norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] norders_red = (L1['RED_SKY_WAVE'].data).shape[0] orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} - norderlets = len(orderlets) + norderlets = len(orderlets) # Define SNR arrays (needed for operations below where VAR = 0) GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data - GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data - GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data - GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data - RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data - RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data - RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data - RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data + GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data + GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data + GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data + RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data + RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data + RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data + RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data @@ -92,41 +92,41 @@ def measure_L1_snr(self, snr_percentile=95): RED_SNR_WAV = np.zeros(norders_red) # Compute SNR arrays for each of the orders, orderlets, and CCDs. - GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), + GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), where=(L1['GREEN_SCI_VAR1'].data!=0)) - GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), + GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), where=(L1['GREEN_SCI_VAR2'].data!=0)) - GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), + GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), where=(L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), + GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, + np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, - np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), + GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, + np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), where=(L1['GREEN_CAL_VAR'].data!=0)) - GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, - np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), + GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, + np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), where=(L1['GREEN_SKY_VAR'].data!=0)) - RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)), + RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)), where=(L1['RED_SCI_VAR1'].data!=0)) - RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, - np.sqrt(abs(L1['RED_SCI_VAR2'].data)), + RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, + np.sqrt(abs(L1['RED_SCI_VAR2'].data)), where=(L1['RED_SCI_VAR2'].data!=0)) - RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR3'].data)), + RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR3'].data)), where=(L1['RED_SCI_VAR3'].data!=0)) - RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), + RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, + np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) - RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, - np.sqrt(abs(L1['RED_CAL_VAR'].data)), + RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, + np.sqrt(abs(L1['RED_CAL_VAR'].data)), where=(L1['RED_CAL_VAR'].data!=0)) - RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, - np.sqrt(abs(L1['RED_SKY_VAR'].data)), + RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, + np.sqrt(abs(L1['RED_SKY_VAR'].data)), where=(L1['RED_SKY_VAR'].data!=0)) # Compute SNR per order and per orderlet @@ -151,26 +151,26 @@ def measure_L1_snr(self, snr_percentile=95): self.GREEN_SNR = GREEN_SNR self.RED_SNR = RED_SNR self.GREEN_SNR_WAV = GREEN_SNR_WAV - self.RED_SNR_WAV = RED_SNR_WAV - - def plot_L1_snr(self, fig_path=None, show_plot=False): + self.RED_SNR_WAV = RED_SNR_WAV + + def plot_L1_snr(self,L1,ObsID, fig_path=None, show_plot=False): """ - - Generate a plot of SNR per order as compuated using the compute_l1_snr + + Generate a plot of SNR per order as compuated using the compute_l1_snr function. Args: - fig_path (string) - set to the path for a SNR vs. wavelength file + fig_path (string) - set to the path for a SNR vs. wavelength file to be generated. show_plot (boolean) - show the plot in the current environment. Returns: - PNG plot in fig_path or shows the plot it the current environment + PNG plot in fig_path or shows the plot it the current environment (e.g., in a Jupyter Notebook). """ - + # Make 3-panel plot. First, create the figure and subplots fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(10,14)) From e1e01253fe05ae34dec4876ab248bc363377ac1c Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Tue, 20 Jun 2023 17:27:15 -0700 Subject: [PATCH 125/249] Changes for two-amplifier GREEN_CCD (does not break 4-amp GREEN_CCD. --- modules/Utils/overscan_subtract.py | 42 +++++++++++++++++++++++++++++- 1 file changed, 41 insertions(+), 1 deletion(-) diff --git a/modules/Utils/overscan_subtract.py b/modules/Utils/overscan_subtract.py index 4f4ec3140..c4256fc83 100644 --- a/modules/Utils/overscan_subtract.py +++ b/modules/Utils/overscan_subtract.py @@ -455,13 +455,46 @@ def _perform(self): pass else: + + # Check if GREEN_CCD will have two or four amplifiers. + # If two amplifiers, then the detector data size for a given amplifier will be approximately 2Kx4K pixels; otherwise, + # if four amplifiers, then the detector data size for a given amplifier will be approximately 2Kx2K pixels + # Either case will result in the same size of 2D image (4080x4080 pixels). + # Trim the orientation arrays accordingly. + # Also, change self.channel_datasec_nrows accordingly. + + if self.ffi_exts[0] == 'GREEN_CCD': + naxis2 = l0_obj.header['GREEN_AMP1']['NAXIS2'] + if naxis2 >= 4000: + channels.pop(3) + channels.pop(2) + channel_keys.pop(3) + channel_keys.pop(2) + channel_rows.pop(3) + channel_rows.pop(2) + channel_cols.pop(3) + channel_cols.pop(2) + channel_exts.pop(3) + channel_exts.pop(2) + self.channel_datasec_nrows = 4080 + frames_data = [] for ext in channel_exts: data = l0_obj[ext] gain = l0_obj.header[ext][self.gain_key] data = data / (2**16) #don't make hardcoded? only ok for now, output a warning here ##### - data_gain_corr = data * gain + + self.logger.debug('---->rawfile,ext,np.shape(data),np.size(data),type(data),type(gain),gain = {},{},{},{},{},{},{}'.\ + format(self.rawfile,ext,np.shape(data),np.size(data),type(data),type(gain),gain)) + self.logger.debug('---->fitsfile,ext,np.size(data) = {},{},{}'.\ + format(l0_obj.header['PRIMARY']['OFNAME'],ext,np.size(data))) + + if np.size(data) == 0: + return Arguments(l0_obj) + else: + data_gain_corr = data * gain + frames_data.append(data_gain_corr) frames_data = np.array(frames_data) @@ -491,6 +524,13 @@ def _perform(self): for ext in channel_exts: l0_obj.del_extension(ext) + if self.ffi_exts[0] == 'GREEN_CCD': + self.logger.debug('---->{}._perform(): self.ffi_exts[0],naxis2 = {},{}'.\ + format(self.__class__.__name__,self.ffi_exts[0],naxis2)) + if naxis2 >= 4000: + l0_obj.del_extension('GREEN_AMP3') + l0_obj.del_extension('GREEN_AMP4') + self.logger.debug('---->{}._perform(): Done with overscan_subtraction.py; returning...'.\ format(self.__class__.__name__)) From dc80aeb2eeb7bd523c848690008215a6a5e754fa Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Tue, 20 Jun 2023 17:27:23 -0700 Subject: [PATCH 126/249] Call new function get_good_biases, which returns list of bias files now defined by IMTYPE=Bias and OBJECT=autocal-bias, and EXPTIME is zero. --- modules/Utils/kpf_fits.py | 54 +++++++++++++++++++ .../master_bias/src/master_bias_framework.py | 12 ++--- 2 files changed, 60 insertions(+), 6 deletions(-) diff --git a/modules/Utils/kpf_fits.py b/modules/Utils/kpf_fits.py index a8c3c7fa2..9e326ba4e 100644 --- a/modules/Utils/kpf_fits.py +++ b/modules/Utils/kpf_fits.py @@ -310,3 +310,57 @@ def get_good_arclamps(self): format(matched_fits_files)) return matched_fits_files,all_arclamp_objects + + def get_good_biases(self): + + """ + Return list of bias files defined by IMTYPE=‘bias’ and OBJECT='autocal-bias', but include only those + with EXPTIME less than or equal to the specified maximum exposure time. + """ + + exptime_maximum = 0.0 + + matched_fits_files = self.match_headers_string_lower() + + filtered_matched_fits_files = [] + for fits_file in matched_fits_files: + + flag = 'remove' + + try: + + val4 = float(fits.getval(fits_file, 'EXPTIME')) + + if (val4 <= exptime_maximum): + flag = 'keep' + filtered_matched_fits_files.append(fits_file) + + if self.logger: + self.logger.debug('flag,val4 = {},{}'.\ + format(flag,val4)) + else: + print('---->flag,val4 = {},{}'.\ + format(flag,val4)) + + except KeyError as err: + + if self.logger: + self.logger.debug('KeyError: {}; removing {} from list...'.format(err,fits_file)) + else: + print('---->KeyError: {}; removing {} from list...'.format(err,fits_file)) + + except TypeError as err: + + if self.logger: + self.logger.debug('TypeError: {}; removing {} from list...'.format(err,fits_file)) + else: + print('---->TypeError: {}; removing {} from list...'.format(err,fits_file)) + + if self.logger: + self.logger.debug('FitsHeaders.get_good_biases(): filtered_matched_fits_files = {}'.\ + format(filtered_matched_fits_files)) + else: + print('---->FitsHeaders.get_good_biases(): filtered_matched_fits_files = {}'.\ + format(filtered_matched_fits_files)) + + return filtered_matched_fits_files diff --git a/modules/master_bias/src/master_bias_framework.py b/modules/master_bias/src/master_bias_framework.py index e449e9f2f..455ed6eff 100644 --- a/modules/master_bias/src/master_bias_framework.py +++ b/modules/master_bias/src/master_bias_framework.py @@ -36,8 +36,8 @@ class MasterBiasFramework(KPF0_Primitive): all_fits_files_path (str , which can include file glob): Location of inputs (e.g., /data/KP*.fits). lev0_ffi_exts (list of str): FITS extensions to stack (e.g., ['GREEN_CCD','RED_CCD']). masterbias_path (str): Pathname of output master bias (e.g., /testdata/kpf_green_red_bias.fits). - exptime_keyword (str): FITS keyword for filtering input bias files (fixed as 'EXPTIME'). - exptime__value_str (str): Maximum value of FITS keyword (fixed as <= '0.0'). + imtype_keywords (str): FITS keyword for filtering input bias files (fixed as ['IMTYPE','OBJECT']). + imtype_values_str (str): Values of FITS keyword (fixed as ['Bias','autocal-bias']). config_path (str): Location of default config file (modules/master_bias/configs/default.cfg) logger (object): Log messages written to log_path specified in default config file. """ @@ -52,8 +52,8 @@ def __init__(self, action, context): self.lev0_ffi_exts = self.action.args[3] self.masterbias_path = self.action.args[4] - self.exptime_keyword = 'EXPTIME' # Unlikely to be changed. - self.exptime_value_str = '0.0' + self.imtype_keywords = ['IMTYPE','OBJECT'] # Unlikely to be changed. + self.imtype_values_str = ['Bias','autocal-bias'] try: self.config_path = context.config_path['master_bias'] @@ -85,8 +85,8 @@ def _perform(self): master_bias_exit_code = 0 master_bias_infobits = 0 - fh = FitsHeaders(self.all_fits_files_path,self.exptime_keyword,self.exptime_value_str,self.logger) - all_bias_files = fh.match_headers_float_le() + fh = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.imtype_values_str,self.logger) + all_bias_files = fh.get_good_biases() mjd_obs_list = [] for bias_file_path in (all_bias_files): From bb6eaf59117cc1c8af8c3d9edb646c47bb412692 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:45:44 -0700 Subject: [PATCH 127/249] l1 snr --- examples/L1_SNR_test.cfg | 5 +++ examples/L1_SNR_test.recipe | 5 +++ modules/Utils/L1_SNR.cfg | 2 ++ modules/Utils/L1_SNR_prim.py | 60 ++++++++++++++++++++++++++++++++++++ 4 files changed, 72 insertions(+) create mode 100644 examples/L1_SNR_test.cfg create mode 100644 examples/L1_SNR_test.recipe create mode 100644 modules/Utils/L1_SNR.cfg create mode 100644 modules/Utils/L1_SNR_prim.py diff --git a/examples/L1_SNR_test.cfg b/examples/L1_SNR_test.cfg new file mode 100644 index 000000000..e672db6e2 --- /dev/null +++ b/examples/L1_SNR_test.cfg @@ -0,0 +1,5 @@ +[LOGGER] +start_log = True +log_path = logs/pipe_log.log +log_level = info +log_verbose = True diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe new file mode 100644 index 000000000..3ea6f980a --- /dev/null +++ b/examples/L1_SNR_test.recipe @@ -0,0 +1,5 @@ +from modules.Utils.L1_SNR_prim import L1_SNR + +lev1_file= 'data/kpf/L1/20230525/KP.20230525.53471.19_L1.fits' +open_file = kpf1_from_fits(lev1_file,data_type='KPF') +L1_SNR(open_file) diff --git a/modules/Utils/L1_SNR.cfg b/modules/Utils/L1_SNR.cfg new file mode 100644 index 000000000..14d8f2753 --- /dev/null +++ b/modules/Utils/L1_SNR.cfg @@ -0,0 +1,2 @@ +[SNR] +snr_percentile=95 diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py new file mode 100644 index 000000000..e861aef37 --- /dev/null +++ b/modules/Utils/L1_SNR_prim.py @@ -0,0 +1,60 @@ +# Standard dependencies +import configparser +import numpy as np +import pandas as pd +from astropy.io import fits + +# Pipeline dependencies +from kpfpipe.logger import start_logger +from kpfpipe.primitives.level1 import KPF1_Primitive +from kpfpipe.models.level1 import KPF1 + +# External dependencies +from keckdrpframework.models.action import Action +from keckdrpframework.models.arguments import Arguments +from keckdrpframework.models.processing_context import ProcessingContext + +# Local dependencies +#from modules.continuum_normalization.src.alg import ContNormAlgg +from modules.Utils.analyze_l1 import AnalyzeL1 + +# Global read-only variables +DEFAULT_CFG_PATH = 'modules/Utils/L1_SNR.cfg' + +class L1_SNR(KPF1_Primitive): + + def __init__(self, action:Action, context:ProcessingContext) -> None: + + #Initialize parent class + KPF1_Primitive.__init__(self,action,context) + + #input recipe arguments + self.l1_obj=self.action.args[0] + # self.data_type=self.action.args[1] + + #Input configuration + self.config=configparser.ConfigParser() + try: + self.config_path=context.config_path['cont_norm'] + except: + self.config_path = DEFAULT_CFG_PATH + self.config.read(self.config_path) + + #Start logger + self.logger=None + #self.logger=start_logger(self.__class__.__name__,config_path) + if not self.logger: + self.logger=self.context.logger + self.logger.info('Loading config from: {}'.format(self.config_path)) + + # algorithm setup + #self.alg=ContNormAlgg(self.config,self.logger) + + #Perform + def _perform(self) -> None: + AnalyzeL1 + + L1_SNR = AnalyzeL1(self.l1_obj) + L1_SNR.measure_L1_snr(self.l1_obj,snr_percentile=95) + print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) + print(L1_SNR.RED_SNR) From c83350a7c90a3b672a39bfca0159e95cb1d51f67 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:51:56 -0700 Subject: [PATCH 128/249] l1 snr --- examples/L1_SNR_test.cfg | 3 +++ examples/L1_SNR_test.recipe | 6 +++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/examples/L1_SNR_test.cfg b/examples/L1_SNR_test.cfg index e672db6e2..7e77fdb45 100644 --- a/examples/L1_SNR_test.cfg +++ b/examples/L1_SNR_test.cfg @@ -3,3 +3,6 @@ start_log = True log_path = logs/pipe_log.log log_level = info log_verbose = True + +[ARGUMENT] +path_to_oned = /data/L1/20230525/ diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index 3ea6f980a..cb3d576df 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -1,5 +1,9 @@ from modules.Utils.L1_SNR_prim import L1_SNR -lev1_file= 'data/kpf/L1/20230525/KP.20230525.53471.19_L1.fits' + +path_to_oned = config.ARGUMENT.path_to_twod + +lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' + open_file = kpf1_from_fits(lev1_file,data_type='KPF') L1_SNR(open_file) From b05e872dd44be0f9116f5dfc82c7da97f4ea6030 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:53:31 -0700 Subject: [PATCH 129/249] l1 snr --- examples/L1_SNR_test.cfg | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/L1_SNR_test.cfg b/examples/L1_SNR_test.cfg index 7e77fdb45..f1951afed 100644 --- a/examples/L1_SNR_test.cfg +++ b/examples/L1_SNR_test.cfg @@ -6,3 +6,6 @@ log_verbose = True [ARGUMENT] path_to_oned = /data/L1/20230525/ + +[MODULE_CONFIGS] +quicklook = modules/Utils/L1_SNR.cfg From 2ccd72884fa4d52c5c1973cc143ecfc719594d86 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:54:59 -0700 Subject: [PATCH 130/249] l1 snr --- examples/L1_SNR_test.recipe | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index cb3d576df..0c366bb7b 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -1,7 +1,7 @@ from modules.Utils.L1_SNR_prim import L1_SNR -path_to_oned = config.ARGUMENT.path_to_twod +path_to_oned = config.ARGUMENT.path_to_oned lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' From decc6cd3b886cd24002badb8ee65a163d20bc501 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:57:32 -0700 Subject: [PATCH 131/249] l1 snr --- examples/L1_SNR_test.recipe | 4 ++-- modules/Utils/L1_SNR_prim.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index 0c366bb7b..ccf61d340 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -5,5 +5,5 @@ path_to_oned = config.ARGUMENT.path_to_oned lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' -open_file = kpf1_from_fits(lev1_file,data_type='KPF') -L1_SNR(open_file) +#open_file = kpf1_from_fits(lev1_file,data_type='KPF') +L1_SNR(lev1_file) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index e861aef37..8214089a1 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -29,7 +29,7 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: KPF1_Primitive.__init__(self,action,context) #input recipe arguments - self.l1_obj=self.action.args[0] + self.l1_obj=fits.open(self.action.args[0]) # self.data_type=self.action.args[1] #Input configuration From 50639010467ca4fe3c41fecccfbddeed57b4d0fe Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 21:59:00 -0700 Subject: [PATCH 132/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 8214089a1..b81db4a06 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -56,5 +56,4 @@ def _perform(self) -> None: L1_SNR = AnalyzeL1(self.l1_obj) L1_SNR.measure_L1_snr(self.l1_obj,snr_percentile=95) - print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) - print(L1_SNR.RED_SNR) + return L1_SNR.GREEN_SNR,L1_SNR.RED_SNR #(orders number, orderlet number) From b71549f124dcc3865e7b5f11abade7fde2f7e459 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 20 Jun 2023 22:00:43 -0700 Subject: [PATCH 133/249] l1 snr --- examples/L1_SNR_test.recipe | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index ccf61d340..89a704958 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -6,4 +6,4 @@ path_to_oned = config.ARGUMENT.path_to_oned lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' #open_file = kpf1_from_fits(lev1_file,data_type='KPF') -L1_SNR(lev1_file) +L1_green_snr, L1_red_snr = L1_SNR(lev1_file) From 3f4a4655d0f1b003184396e77f65b87283d4ef05 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 11:59:04 -0700 Subject: [PATCH 134/249] l1 snr --- examples/L1_SNR_test.recipe | 5 ++--- modules/Utils/L1_SNR_prim.py | 11 ++++++----- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index 89a704958..839e89234 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -4,6 +4,5 @@ from modules.Utils.L1_SNR_prim import L1_SNR path_to_oned = config.ARGUMENT.path_to_oned lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' - -#open_file = kpf1_from_fits(lev1_file,data_type='KPF') -L1_green_snr, L1_red_snr = L1_SNR(lev1_file) +open_file = kpf1_from_fits(lev1_file,data_type='KPF') +L1_SNR(open_file) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index b81db4a06..de34bc9ea 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -29,7 +29,7 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: KPF1_Primitive.__init__(self,action,context) #input recipe arguments - self.l1_obj=fits.open(self.action.args[0]) + self.l1_obj=self.action.args[0] # self.data_type=self.action.args[1] #Input configuration @@ -52,8 +52,9 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: #Perform def _perform(self) -> None: - AnalyzeL1 + L1_file = fits.open(kpf0_file.filename) - L1_SNR = AnalyzeL1(self.l1_obj) - L1_SNR.measure_L1_snr(self.l1_obj,snr_percentile=95) - return L1_SNR.GREEN_SNR,L1_SNR.RED_SNR #(orders number, orderlet number) + L1_SNR = AnalyzeL1(L1_file) + L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) + print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) + print(L1_SNR.RED_SNR) From db60cfb310d1a3b8283e73c42450317f90c87ee6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 11:59:40 -0700 Subject: [PATCH 135/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index de34bc9ea..faa747959 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -52,7 +52,7 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: #Perform def _perform(self) -> None: - L1_file = fits.open(kpf0_file.filename) + L1_file = fits.open(self.l1_obj.filename) L1_SNR = AnalyzeL1(L1_file) L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) From aadbd38dbff83c10a298d25724bbf11eaffa2fcb Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 12:03:44 -0700 Subject: [PATCH 136/249] l1 snr --- examples/L1_SNR_test.recipe | 2 +- modules/Utils/L1_SNR_prim.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/L1_SNR_test.recipe b/examples/L1_SNR_test.recipe index 839e89234..a3d50afd6 100644 --- a/examples/L1_SNR_test.recipe +++ b/examples/L1_SNR_test.recipe @@ -5,4 +5,4 @@ path_to_oned = config.ARGUMENT.path_to_oned lev1_file= path_to_oned + 'KP.20230525.53471.19_L1.fits' open_file = kpf1_from_fits(lev1_file,data_type='KPF') -L1_SNR(open_file) +L1_SNR(open_file,path_to_oned) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index faa747959..5b3d8cf55 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -30,6 +30,7 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: #input recipe arguments self.l1_obj=self.action.args[0] + self.data_dir=self.action.args[1] # self.data_type=self.action.args[1] #Input configuration @@ -52,7 +53,7 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: #Perform def _perform(self) -> None: - L1_file = fits.open(self.l1_obj.filename) + L1_file = fits.open(self.data_dir+self.l1_obj.filename) L1_SNR = AnalyzeL1(L1_file) L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) From 553e740605d1fbcce76f94f4ead57f4d989a344d Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 12:15:24 -0700 Subject: [PATCH 137/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 5b3d8cf55..9987ffd44 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -57,5 +57,19 @@ def _perform(self) -> None: L1_SNR = AnalyzeL1(L1_file) L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) - print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) - print(L1_SNR.RED_SNR) + #print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) + print(L1_SNR.GREEN_SNR[1,-1]) #all science + print(L1_SNR.GREEN_SNR[1,-2]) #sky + print(L1_SNR.GREEN_SNR[1,0]) #cal + print(L1_SNR.GREEN_SNR[25,-1]) #all science + print(L1_SNR.GREEN_SNR[25,-2]) #sky + print(L1_SNR.GREEN_SNR[25,0]) #cal” + + #print(L1_SNR.G_SNR) #(orders number, orderlet number) + print(L1_SNR.GREEN_SNR[20,-1]) #all science + print(L1_SNR.GREEN_SNR[20,-2]) #sky + print(L1_SNR.GREEN_SNR[20,0]) #cal + print(L1_SNR.GREEN_SNR[-1,-1]) #all science + print(L1_SNR.GREEN_SNR[-1,-2]) #sky + print(L1_SNR.GREEN_SNR[-1,0]) #cal” + #print(L1_SNR.RED_SNR) From cc03492df68a8bc5450ae5c4ab26a7d2af2bfe70 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 12:17:25 -0700 Subject: [PATCH 138/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 9987ffd44..86b3e01c9 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -58,18 +58,18 @@ def _perform(self) -> None: L1_SNR = AnalyzeL1(L1_file) L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) #print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) - print(L1_SNR.GREEN_SNR[1,-1]) #all science - print(L1_SNR.GREEN_SNR[1,-2]) #sky - print(L1_SNR.GREEN_SNR[1,0]) #cal - print(L1_SNR.GREEN_SNR[25,-1]) #all science - print(L1_SNR.GREEN_SNR[25,-2]) #sky - print(L1_SNR.GREEN_SNR[25,0]) #cal” + print('all science',L1_SNR.GREEN_SNR[1,-1]) #all science + print('sky',L1_SNR.GREEN_SNR[1,-2]) #sky + print('cal',L1_SNR.GREEN_SNR[1,0]) #cal + print('all science',L1_SNR.GREEN_SNR[25,-1]) #all science + print('sky',L1_SNR.GREEN_SNR[25,-2]) #sky + print('cal',L1_SNR.GREEN_SNR[25,0]) #cal” #print(L1_SNR.G_SNR) #(orders number, orderlet number) - print(L1_SNR.GREEN_SNR[20,-1]) #all science - print(L1_SNR.GREEN_SNR[20,-2]) #sky - print(L1_SNR.GREEN_SNR[20,0]) #cal - print(L1_SNR.GREEN_SNR[-1,-1]) #all science - print(L1_SNR.GREEN_SNR[-1,-2]) #sky - print(L1_SNR.GREEN_SNR[-1,0]) #cal” + print('all science',L1_SNR.GREEN_SNR[20,-1]) #all science + print('sky',L1_SNR.GREEN_SNR[20,-2]) #sky + print('cal',L1_SNR.GREEN_SNR[20,0]) #cal + print('all science',L1_SNR.GREEN_SNR[-1,-1]) #all science + print('sky',L1_SNR.GREEN_SNR[-1,-2]) #sky + print('cal',L1_SNR.GREEN_SNR[-1,0]) #cal” #print(L1_SNR.RED_SNR) From df148ddc3dae47e3fd2241fc53a1fea84110be42 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 12:24:05 -0700 Subject: [PATCH 139/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 86b3e01c9..44d022b65 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -58,6 +58,25 @@ def _perform(self) -> None: L1_SNR = AnalyzeL1(L1_file) L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) #print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) + + self.l1_obj.header['PRIMARY']['SNRSC452'] = L1_SNR.GREEN_SNR[1,-1] + self.l1_obj.header['PRIMARY']['SNRSK452'] = L1_SNR.GREEN_SNR[1,-2] + self.l1_obj.header['PRIMARY']['SNRCL452'] = L1_SNR.GREEN_SNR[1,0] + + self.l1_obj.header['PRIMARY']['SNRSC548'] = L1_SNR.GREEN_SNR[25,-1] + self.l1_obj.header['PRIMARY']['SNRSK548'] = L1_SNR.GREEN_SNR[25,-2] + self.l1_obj.header['PRIMARY']['SNRCL548'] = L1_SNR.GREEN_SNR[25,0] + + + self.l1_obj.header['PRIMARY']['SNRSC747'] = L1_SNR.RED_SNR[20,-1] + self.l1_obj.header['PRIMARY']['SNRSK747'] = L1_SNR.RED_SNR[20,-2] + self.l1_obj.header['PRIMARY']['SNRCL747'] = L1_SNR.RED_SNR[20,0] + + self.l1_obj.header['PRIMARY']['SNRSC865'] = L1_SNR.RED_SNR[-1,-1] + self.l1_obj.header['PRIMARY']['SNRSK865'] = L1_SNR.RED_SNR[-1,-2] + self.l1_obj.header['PRIMARY']['SNRCL865'] = L1_SNR.RED_SNR[-1,0] + + ''' print('all science',L1_SNR.GREEN_SNR[1,-1]) #all science print('sky',L1_SNR.GREEN_SNR[1,-2]) #sky print('cal',L1_SNR.GREEN_SNR[1,0]) #cal @@ -72,4 +91,11 @@ def _perform(self) -> None: print('all science',L1_SNR.GREEN_SNR[-1,-1]) #all science print('sky',L1_SNR.GREEN_SNR[-1,-2]) #sky print('cal',L1_SNR.GREEN_SNR[-1,0]) #cal” + ''' + + self.l1_obj.to_fits(self.data_dir+self.l1_obj.filename) + + print(self.l1_obj.header) + + #print(L1_SNR.RED_SNR) From e7be1f37eacc9dad2bc53e37388bf5f99940e990 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Wed, 21 Jun 2023 12:27:30 -0700 Subject: [PATCH 140/249] l1 snr --- modules/Utils/L1_SNR_prim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 44d022b65..6d316e6d7 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -95,7 +95,7 @@ def _perform(self) -> None: self.l1_obj.to_fits(self.data_dir+self.l1_obj.filename) - print(self.l1_obj.header) + #print(self.l1_obj.header) #print(L1_SNR.RED_SNR) From 7eff7e046a605152fc4c00b7b9eebdcaa71c0a54 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Wed, 21 Jun 2023 22:02:47 -0700 Subject: [PATCH 141/249] Added guider tutorial --- .../KPF_Data_Tutorial_L0_and_2D.ipynb | 3464 +++++++++++------ 1 file changed, 2250 insertions(+), 1214 deletions(-) diff --git a/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb b/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb index 2d24a1856..963818f71 100644 --- a/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb +++ b/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb @@ -40,7 +40,9 @@ "outputs": [], "source": [ "L0_data_dir = 'data/'\n", - "ObsID = 'KP.20221112.26598.92' # Commissioning Spectrum of 51 Pegasi\n", + "#ObsID = 'KP.20230615.53679.85' # Spectrum of HD 219134\n", + "ObsID = 'KP.20230611.30570.89' # Spectrum of HD 157214\n", + "#ObsID = 'KP.20221112.26598.92' # Commissioning Spectrum of 51 Pegasi\n", "L0_file = L0_data_dir + ObsID + '.fits'\n", "D2_file = L0_data_dir + ObsID + '_2D.fits' # 2D file (which needs to be renamed with '_2D' appended)" ] @@ -69,18 +71,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Filename: data/KP.20221112.26598.92.fits\n", + "Filename: data/KP.20230611.30570.89.fits\n", "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 301 () \n", - " 1 GREEN_AMP1 1 ImageHDU 13 (2144, 2140) int32 \n", - " 2 GREEN_AMP2 1 ImageHDU 13 (2144, 2140) int32 \n", - " 3 GREEN_AMP3 1 ImageHDU 13 (2144, 2140) int32 \n", - " 4 GREEN_AMP4 1 ImageHDU 13 (2144, 2140) int32 \n", - " 5 RED_AMP1 1 ImageHDU 13 (2144, 4180) int32 \n", - " 6 RED_AMP2 1 ImageHDU 13 (2144, 4180) int32 \n", - " 7 EXPMETER_SCI 1 BinTableHDU 855 156R x 423C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 8 EXPMETER_SKY 1 BinTableHDU 855 156R x 423C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 9 1 BinTableHDU 26 116R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n" + " 0 PRIMARY 1 PrimaryHDU 407 () \n", + " 1 GREEN_AMP1 1 ImageHDU 13 (2144, 2140) float32 \n", + " 2 GREEN_AMP2 1 ImageHDU 13 (2144, 2140) float32 \n", + " 3 GREEN_AMP3 1 ImageHDU 13 (2144, 2140) float32 \n", + " 4 GREEN_AMP4 1 ImageHDU 13 (2144, 2140) float32 \n", + " 5 RED_AMP1 1 ImageHDU 13 (2094, 4110) float32 \n", + " 6 RED_AMP2 1 ImageHDU 13 (2094, 4110) float32 \n", + " 7 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 8 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 9 guider_avg 1 ImageHDU 102 (640, 512) float32 \n", + " 10 guider_cube_origins 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n", + " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n" ] } ], @@ -111,126 +115,177 @@ "BITPIX = 8 / array data type \n", "NAXIS = 0 / number of array dimensions \n", "EXTEND = T \n", - "FAVER = '0 ' / Written by FITSAssemble version 1.3 \n", + "FAVER = '0 ' / Written by FITSAssemble version 1.4 \n", "TIMESYS = 'UTC ' \n", - "CAMERAS = 3 / Detectors in assembly request \n", + "CAMERAS = 4 / Detectors in assembly request \n", "GREEN = 'YES ' / Was this camera found? \n", "RED = 'YES ' / Was this camera found? \n", "CA_HK = 'NO ' / Was this camera found? \n", "EXPMETER= 'YES ' / Was this camera found? \n", - "DATE-MID= '2022-11-12T07:25:49.061' / Halfway point of the exposure, unweighted \n", + "GUIDE = 'YES ' / Was this camera found? \n", + "DATE-MID= '2023-06-11T08:30:57.094' / Halfway point of the exposure, unweighted \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT Location: /kroot/rel/default/bin/keyheader \n", "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2022-11-12T07:30:12.312356Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT keyheader section [ExposureBegin] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:23:19.017378Z \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT \n", - "DATE-OBS= '2022-11-12' / Date dcs1.DATE \n", - "UT = '07:23:18.73' / DCS Universal Time \n", - "DATE-BEG= '2022-11-12T07:23:18.917377' / Start of exposure from kpfexpose \n", - "EXPTIME = 300.0 / Requested exposure time \n", - "PROGNAME= 'ENG ' / Program name from kpfexpose \n", - "OBSERVER= 'a.howard' / Observer names \n", + "DATE-OBS= '2023-06-11' / Date dcs1.DATE \n", + "UT = '08:29:30.54' / DCS Universal Time \n", + "DATE-BEG= '2023-06-11T08:29:30.885517' / Start of exposure from kpfexpose \n", + "EXPTIME = 172.0 / Requested exposure time \n", + "PROGNAME= 'C007 ' / Program name from kpfexpose \n", + "OBJECT = '157214 ' / Object \n", "INSTRUME= 'KPF ' / Instrument name \n", "DATALVL = '0 ' / Data Product Base Level \n", "OBSERVAT= 'KECK ' / Observatory name \n", "OBSMODE = 'SPEC ' / Basic mode of observation \n", + "HEADVER = '125547 ' / Header config version \n", "CURRINST= 'KPF ' / Selected instrument \n", "TELESCOP= 'Keck I ' / Telescope \n", - "TARGNAME= '217014 ' / DCS Target name \n", - "TARGRA = '22:57:28.30' / [h] DCS Target RA \n", - "TARGDEC = '+20:46:09.0' / [deg] DCS Target Dec \n", + "TARGNAME= '157214 ' / KPF Target name \n", + "DCSNAME = '157214 ' / DCS Target name - may not make any sense \n", + "TARGRA = '17:20:39.80' / [h] DCS Target RA \n", + "TARGDEC = '+32:27:40.0' / [deg] DCS Target Dec \n", "TARGEPOC= 2000.0 / DCS Target epoch \n", "TARGEQUI= 2000.0 / DCS Target equinox \n", - "TARGPLAX= 0.0 / [arcsec] target parallax \n", + "TARGPLAX= 68.59999999999999 / [arcsec] target parallax \n", "TARGPMDC= 0.0 / [arcsec/yr] target proper motion dec \n", "TARGPMRA= 0.0 / [s/yr] target proper motion ra \n", - "TARGRADV= 0.0 / [km/s] target radial velocity \n", + "TARGRADV= -78.43000000000001 / [km/s] target radial velocity \n", "TARGWAVE= 0.65 / target-wavelength \n", "TARGFRAM= 'FK5 ' / target-frame \n", - "RA = '22:57:28.30' / [h] Right ascension \n", - "DEC = '+20:46:19.0' / [deg] Declination \n", + "FULLTARG= '157214 ' / Full Target name from kpfconfig \n", + "GAIAID = 'DR3 1333760419707327744' / GAIA Target name from kpfconfig \n", + "2MASSID = 'J17203955+3228056' / 2MASS Target name from kpfconfig \n", + "GAIAMAG = '5.21 ' / GAIA G band magnitude \n", + "2MASSMAG= '4.16 ' / 2MASS J band magnitude \n", + "TARGTEFF= 5750.0 / Target effective temperature (K) \n", + "RA = '17:20:39.80' / [h] Right ascension \n", + "DEC = '+32:27:40.0' / [deg] Declination \n", "EQUINOX = 2000.0 / DCS Equinox \n", - "MJD-OBS = 59895.307856 / Modified Julian day \n", + "MJD-OBS = 60106.353826 / Modified Julian day \n", "PMFM = 0.0 / PMFM value \n", - "OCTAGON = 'Th_gold ' / selected octagon value \n", + "LFCMODE = 'StandbyHigh' / LFC Operation Mode \n", + "AMPON = 1768.003 / LFC: Amount of time amplifier on \n", + "LFCFO = 250000000.0 / LFC filtered Offset Freq RR Comb counted \n", + "LFCFREF = 250000000.0 / LFC filtered Offset Freq RR Comb setpoint \n", + "LFCFR = 20000000000.0197 / LFC filtered Offset Freq RR Filter counted \n", + "LFCFRREF= 20000000000.0 / LFC filtered Offset Freq RR Filter setpoint \n", + "LFCCEOFR= 5220000000.0 / CEO Filtered Setpoint Freq \n", + "LFCCWFRF= 288005220000000.0 / CW Freq Reference \n", + "LFCCWFRQ= 288005230048093.0 / CW Freq \n", + "LFCCWFER= 10048093.0 / CW Freq Error: Ref-Actual \n", + "LFCCWMDN= 1152021 / CW mode number \n", + "LFCBIACT= 0.033 / Blue cut amp diode current (A) \n", + "LFCBISET= 0.0 / Blue cut amp diode setting (A) \n", + "OCTAGON = 'EtalonFiber' / selected octagon value \n", "CALMON = 'Out ' / Calibration intensity monitor position \n", "HATCH = 'Open ' / Status of the hatch on the exterior of the FIU \n", - "FIUMODE = 'None ' / FIU operating mode \n", + "FIUMODE = 'Observing' / FIU operating mode \n", "FOLDNAM = 'Out ' / Named Position of FIU Fold mirror \n", - "FOLDVAL = 24.7 / Position of FIU Fold mirror in mm \n", - "ADCTRACK= 'Off ' / Is the ADC Tracking \n", - "HCLSN = 'HNC0486 ' / S/N of lamp in use \n", - "CAREQ = 'No ' / Ca HK fibers (6,7) requested \n", + "FOLDVAL = 24.7 / [mm] Position of FIU Fold mirror \n", + "ADCTRACK= 'On ' / Is the ADC Tracking \n", + "HCLSN = '' / S/N of lamp in use \n", + "CAREQ = 'Yes ' / Ca HK fibers (6,7) requested \n", "EXPSCREQ= 'Yes ' / ExpM Science fiber (8ab) requested \n", "FFREQ = 'No ' / Flatfield fiber (10ab) requested \n", - "FICALREQ= 'No ' / FIU Cal fibers (4,5) requested \n", - "SICALREQ= 'No ' / SimCal fiber (3ab) requested \n", + "SICALREQ= 'Yes ' / SimCal fiber (3ab) requested \n", "VACSCREQ= 'Yes ' / Vac Sci fiber (1b) requested \n", "VACSKREQ= 'Yes ' / Vac Sci fiber (2b) requested \n", + "THDAYON = '20230610T15:05:07 HST' / ThAr Daily was last turned on at this time \n", + "THDAYTON= 4306.4 / ThAr Daily has been on for this long \n", + "THAUON = '20230503T12:39:28 HST' / ThAr Gold was last turned on at this time \n", + "THAUTON = 1465.7 / ThAr Gold has been on for this long \n", + "UDAYON = '20230610T16:17:58 HST' / UNe Daily was last turned on at this time \n", + "UDAYTON = 3251.2 / UNe Daily has been on for this long \n", + "UAUON = '19691231T14:00:00 HST' / UNe Gold was last turned on at this time \n", + "UAUTON = 0.0 / UNe Gold has been on for this long \n", + "PTHDAY = 9.84700000000000E-06 / Last ThAr Daily calibration power value (Watts)\n", + "PTHDAYT = '20230610T16:11:20 HST' / Time of ThAr Daily last power value \n", + "PTHAU = 0.0 / Last ThAr Gold calibration power value (Watts) \n", + "PTHAUT = '19691231T14:00:00 HST' / Time of ThAr Gold last power value \n", + "PUDAY = 1.328E-05 / Last UNe Daily calibration power value (Watts) \n", + "PUDAYT = '20230610T17:07:06 HST' / Time of UNe Daily last power value \n", + "PUAU = 0.0 / Last UNe Gold calibration power value (Watts) \n", + "PUAUT = '19691231T14:00:00 HST' / Time of UNe Gold last power value \n", + "PLFC = 0.0 / Last LFC calibration power value (Watts) \n", + "PLFCT = '19691231T14:00:00 HST' / Time of LFC last power value \n", + "PETAL = 4.919E-07 / Last Etalon calibration power value (Watts) \n", + "PETALT = '20230610T16:40:09 HST' / Time of Etalon last power value \n", + "PBRB = 9.77900000000000E-06 / Last Broadband calibration power value (Watts) \n", + "PBRBT = '20230610T12:31:09 HST' / Time of Broadband last power value \n", + "PSOL = 0.0 / Last SoCal-CalFib cal. power value (Watts) \n", + "PSOLT = '19691231T14:00:00 HST' / Time of SoCal-CalFib last power value \n", "COMMENT \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:23:19.017378Z \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT \n", - "COMMENT Data not available for keyword 'heart_11' \n", + "COMMENT Data not available for keyword 'heart_12' \n", + "COMMENT Data not available for keyword 'heart_33' \n", + "COMMENT Data not available for keyword 'heart_34' \n", + "COMMENT Data not available for keyword 'heart_35' \n", + "COMMENT Data not available for keyword 'heart_36' \n", "COMMENT Any value above other than 'Heartbeat active' indicates \n", "COMMENT the keyword history daemon was not capturing data from \n", "COMMENT the affected service as of the snapshot timestamp. \n", "COMMENT \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2022-11-12T07:30:12.312356Z \n", - "COMMENT Header generation end time: 2022-11-12T07:30:12.487042Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:22.860985Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT Location: /kroot/rel/default/bin/keyheader \n", "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2022-11-12T07:30:13.661035Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT keyheader section [ExposureMiddle] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:25:49.060689Z \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:30:57.093759Z \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT \n", - "SRCSHTTR= 'SciSelect,SkySelect' / Source shutters commanded for exposure \n", - "TIMSHTTR= 'Scrambler' / Timed shutters commanded for exposure \n", - "OTIMSHTR= 'Scrambler' / Timed shutters open at exposure midpoint \n", - "SCISEL = 'open ' / Science Select shutter at exposure midpoint \n", - "SKYSEL = 'open ' / Sky Select Shutter at exposure midpoint \n", - "FFSHTR = 'closed ' / Flat field fiber shutter at exposure midpoint \n", - "SCRAMSHT= 'open ' / Scrambler shutter at exposure midpoint \n", - "SIMCALSH= 'closed ' / Simult Cal shutter at exposure midpoint \n", - "TRIGTARG= 'Green,Red,ExpMeter' / Cameras that were sent triggers \n", + "SRCSHTTR= 'SciSelect,SkySelect' / Source shutters commanded \n", + "TIMSHTTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters commanded \n", + "OTIMSHTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters open exp. midpoint \n", + "SCISEL = 'open ' / Science Select shutter at exp. midpoint \n", + "SKYSEL = 'open ' / Sky Select Shutter at exp. midpoint \n", + "FFSHTR = 'closed ' / Flat field fiber shutter at exp. midpoint \n", + "SCRAMSHT= 'open ' / Scrambler shutter at exp. midpoint \n", + "SIMCALSH= 'open ' / Simult Cal shutter at exp. midpoint \n", + "TRIGTARG= 'Green,Red,ExpMeter,Guide' / Cameras that were sent triggers \n", "IMTYPE = 'Object ' / Image Type \n", - "CAL-OBJ = 'None ' / Calibration fiber source \n", + "CAL-OBJ = 'EtalonFiber' / Calibration fiber source \n", "SKY-OBJ = 'Sky ' / Sky fiber source \n", "SCI-OBJ = 'Target ' / Science fiber source \n", - "CAFBS = 'No ' / Ca HK fibers (6,7) on \n", + "CAFBS = 'Yes ' / Ca HK fibers (6,7) on \n", "EXPSCIFB= 'Yes ' / ExpM Science fiber (8ab) on \n", "EXPSKYFB= 'Yes ' / ExpM Science fiber (9) on \n", "FFFB = 'No ' / Flatfield fiber (10ab) on \n", "FICALFBS= 'No ' / FIU Cal fibers (4,5) on \n", - "SICALFB = 'No ' / SimCal fiber (3ab) on \n", + "SICALFB = 'Yes ' / SimCal fiber (3ab) on \n", "VACSCFB = 'Yes ' / Vac Sci fiber (1b) on \n", "VACSKFB = 'Yes ' / Vac Sci fiber (2b) on \n", "SCIFB = 'Yes ' / Science fiber (1a) on \n", "SKYFB = 'Yes ' / Sky fiber (2a) on \n", - "GRACFMD5= 'unknown ' / Green MD5 sum for the acf file \n", - "GRACFFLN= '-default-' / Green acf file loaded \n", - "RDACFMD5= 'unknown ' / Red MD5 sum for the acf file \n", - "RDACFFLN= '-default-' / Red acf file loaded \n", - "AGITSTA = 'Stopped ' / Agitator status kpfmot.AGITATOR \n", - "THARGD = 'On ' / Gold ThAr power status \n", + "CURRBASE= '(335.5, 258.0)' / [pix] Selected pointing origin \n", + "PIXTARG = '(337.826, 262.276)' / [pix] Selected object tip/tilt target \n", + "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / Green MD5 sum for the acf file \n", + "GRACFFLN= 'kpfGreen.20220513-0519.acf' / Green acf file loaded \n", + "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / Red MD5 sum for the acf file \n", + "RDACFFLN= 'kpf_200kHz_2amp_short_oscan.acf' / Red acf file loaded \n", + "AGITSTA = 'Running ' / Agitator status kpfmot.AGITATOR \n", + "THARGD = 'Off ' / Gold ThAr power status \n", "UNEGD = 'Off ' / Gold UNe power status \n", "THARDAY = 'Off ' / Daily ThAr power status \n", "UNEDAY = 'Off ' / Daily UNe power status \n", @@ -240,58 +295,58 @@ "SKYFBILL= 'Off ' / Sky fiber LED back illuminator power \n", "HKFBILL = 'Off ' / Ca H and K fiber LED back illuminator power \n", "EXPFBILL= 'Off ' / Exposure meter fiber LED back illuminator power\n", - "SSCALFW = 'OD 0.1 ' / Sci/Cal FW Position \n", - "SIMCALFW= 'OD 0.1 ' / Simual Cal FW Position \n", + "SSCALFW = 'OD 3.0 ' / Sci/Cal FW Position \n", + "SIMCALFW= 'OD 0.5 ' / Simual Cal FW Position \n", "FFFW = 'Blank ' / Flatfield FW Position \n", - "OCTAGON = 'Th_gold ' / selected octagon value \n", - "PRES = 619.797 / [hPa] Pressure at Vaisala kpfmet.PRES \n", - "RELH = 12.472 / Relative humidity at Vaisala kpfmet.RELH \n", + "OCTAGON = 'EtalonFiber' / selected octagon value \n", + "PRES = 623.7 / [hPa] Pressure at Vaisala kpfmet.PRES \n", + "RELH = 20.291 / Relative humidity at Vaisala kpfmet.RELH \n", "PONAME = 'KPF ' / DCS Point origin name \n", "PONAME1 = 'Opt Axis' / DCS Point origin name1 \n", "DRA = 0.0 / [s/s] DCS Diff RA rate \n", "DDEC = 0.0 / [asec/s] DCS Diff Dec rate \n", - "RABASE = '22:57:28.30' / DCS RA base \n", + "RABASE = '17:20:39.80' / DCS RA base \n", "RAOFF = 0.0 / [asec] DCS RA offset \n", - "DECBASE = '+20:46:09.0' / DCS Dec base \n", - "DECOFF = 10.0 / [asec] DCS Dec offset \n", - "HA = '+01:32:05.03' / DCS Hour angle \n", - "AIRMASS = 1.07 / DCS Airmass \n", - "PARANG = 88.87 / [deg] DCS Parallactic angle astrometric \n", - "PARANTEL= 88.81 / [deg] DCS Parallactic angle telescope \n", - "EL = 68.66 / [deg] DCS Elevation \n", - "AZ = -83.18000000000001 / [deg] DCS Azimuth \n", - "LST = '00:29:33.33' / DCS Local sidereal time \n", + "DECBASE = '+32:27:40.0' / DCS Dec base \n", + "DECOFF = 0.0 / [asec] DCS Dec offset \n", + "HA = '+22:06:05.70' / DCS Hour angle \n", + "AIRMASS = 1.14 / DCS Airmass \n", + "PARANG = -108.94 / [deg] DCS Parallactic angle astrometric \n", + "PARANTEL= -108.97 / [deg] DCS Parallactic angle telescope \n", + "EL = 61.46 / [deg] DCS Elevation \n", + "AZ = 57.97 / [deg] DCS Azimuth \n", + "LST = '15:26:45.50' / DCS Local sidereal time \n", "AXESTAT = 'tracking' / DCS axes control status \n", "TRACKING= 'yes ' / DCS Servos tracking status \n", "DTRACK = 'disabled' / DCS differential tracking status \n", "GUIDING = 'true ' / DCS Guiding status \n", "AUTACTIV= 'yes ' / DCS Guider active \n", - "AUTFWHM = 1.557763 / [pix] DCS Guider fwhm \n", - "AUTXCENT= 0.6 / [asec] DCS Guider x centroid \n", - "AUTYCENT= -1.2 / [asec] DCS Guider y centroid \n", - "SECFOCUS= -1.056 / [mm] Secondary focus \n", - "TELFOCUS= -1.328 / [mm] Telescope focus \n", + "AUTFWHM = 0.6521940000000001 / [pix] DCS Guider fwhm \n", + "AUTXCENT= 0.2 / [asec] DCS Guider x centroid \n", + "AUTYCENT= 0.4 / [asec] DCS Guider y centroid \n", + "SECFOCUS= -2.244 / [mm] Secondary focus \n", + "TELFOCUS= -1.459 / [mm] Telescope focus \n", "TUBEFLEX= -13.3 / [arcsec] Telescope tube flexure \n", - "TUBETEMP= -1.37 / [degC] Telescope tube temperature \n", - "PRIMTEMP= -0.31163 / [degC] Telescope pri temperature \n", - "SECMTEMP= -2.23386 / [degC] Telescope sec temperature \n", - "DIFFPTDW= 11.58837 / [decC] Diff between pri mirro temp dewpt \n", - "DIFFSTDW= 9.566140000000001 / [decC] Diff between sec mirro temp dewpt \n", + "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", + "PRIMTEMP= 3.530288 / [degC] Telescope pri temperature \n", + "SECMTEMP= 2.699448 / [degC] Telescope sec temperature \n", + "DIFFPTDW= 10.930288 / [decC] Diff between pri mirro temp dewpt \n", + "DIFFSTDW= 10.099448 / [decC] Diff between sec mirro temp dewpt \n", "VIGNETTE= 'false ' / dome vignette (t/f) \n", "STVIGNE = 'false ' / top shutter vignette (t/f) \n", "SBVIGNE = 'false ' / bottom shutter vignette (t/f) \n", - "SBELEV = 27.63 / [deg] bottom shutter elevation \n", - "STELEV = 104.01 / [deg] top shutter elevation \n", + "SBELEV = 26.97 / [deg] bottom shutter elevation \n", + "STELEV = 97.62 / [deg] top shutter elevation \n", "SECSTST = 'INPOS ' / DCS Secondary status string \n", - "SECTHETX= -459.3 / [asec] DCS Secondary theta x \n", - "SECTHETY= 224.6 / [asec] DCS Secondary theta y \n", + "SECTHETX= -415.1 / [asec] DCS Secondary theta x \n", + "SECTHETY= 142.9 / [asec] DCS Secondary theta y \n", "TERTSTST= 'INPOS ' / DCS Tertiary status string \n", "TERTDEST= 'lnas ' / DCS Tertiary user destination \n", "TERTPOSN= 'lnas ' / DCS Tertiary user position \n", - "DOMEPOSN= 276.72 / DCS Dome user position \n", + "DOMEPOSN= 58.04 / DCS Dome user position \n", "DOMESTST= 'TRACK ' / DCS Dome status string \n", - "CALOCAL = -27.5 / collimation-azimuth-local \n", - "CELOCAL = 14.4 / collimation-elevation-local \n", + "CALOCAL = -28.8 / collimation-azimuth-local \n", + "CELOCAL = 14.6 / collimation-elevation-local \n", "FOCALSTN= 'lnas (left nasmyth)' / focal-station \n", "INSTANGL= 312.0 / porg-to-instrument angle \n", "POXPOS = 0.4 / pointing-origin-x-position \n", @@ -299,48 +354,44 @@ "ROTCALAN= 0.0 / rotator-calibration-angle \n", "ROTZERO = 0.0 / rotator-zero-angle \n", "GUIDWAVE= 0.65 / guidestar-wavelength \n", - "TIMEERR = 'ok 2 2 {NTP time correct to within 2 ms}' / resp from time serv \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:25:49.060689Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_11' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", + "TIMEERR = 'ok 2 2 {NTP time correct to within 2 ms}' / resp time serv \n", + "ETAV1C1T= 22.984734 / Etalon v1ch1temp \n", + "ETAV1C2T= 22.702303 / Etalon v1ch2temp \n", + "ETAV1C3T= 22.49995 / Etalon v1ch3temp \n", + "ETAV1C4T= 22.799961 / Etalon v1ch4temp \n", + "ETAV2C3T= 23.026154 / Etalon v2ch3temp \n", "COMMENT \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2022-11-12T07:30:13.661035Z \n", - "COMMENT Header generation end time: 2022-11-12T07:30:13.927919Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:24.214439Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT Location: /kroot/rel/default/bin/keyheader \n", "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2022-11-12T07:30:15.068563Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT keyheader section [ExposureEnd] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:28:19.104000Z \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:32:23.202000Z \n", "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", "COMMENT \n", - "DATE = '2022-11-12T07:28:18.936695' / End of exposure from kpfexpose.ENDTIME \n", - "DATE-END= '2022-11-12T07:28:18.936695' / End of exposure from kpfexpose.ENDTIME \n", - "ELAPSED = 300.0 / Actual exposure time from kpfexpose.ELAPSED \n", - "OUTDIR = '/sdata1701/kpfdev/L0' / Output directory for file in OFNAME \n", - "OFNAME = 'KP.20221112.26598.92.fits' / Filename of output file \n", - "GREENFN = '/s/sdata1701/kpfeng/2022nov11/Green/kpf_green_3521.fits' \n", - "REDFN = '/s/sdata1701/kpfeng/2022nov11/Red/kpf_red_3521.fits' \n", - "EXPMETFN= '/s/sdata1701/kpfeng/2022nov11/ExpMeter/kpf_em_3521.fits' \n", - "FRAMENO = 3521 \n", - "GRCAMD_V= 'Sep 28 2022 08:12:41' / camerad build date Kwd green CAMD_VER \n", + "DATE = '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", + "DATE-END= '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", + "ELAPSED = 172.0 / Actual exposure time from kpfexpose.ELAPSED \n", + "OUTDIR = '/s/sdata1701/kpf1/2023jun10/L0' / Output directory \n", + "OFNAME = 'KP.20230611.30570.89.fits' / Filename of output file \n", + "GREENFN = '/s/sdata1701/kpf1/2023jun10/Green/kpf_63502.fits' \n", + "REDFN = '/s/sdata1701/kpf1/2023jun10/Red/kpf_red_63502.fits' \n", + "EXPMETFN= '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' \n", + "GUIDEFN = '/s/sdata1701/kpf1/2023jun10/CRED2/kpfguide_cube_4259.fits' \n", + "FRAMENO = 63502 \n", + "GRCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd green CAMD_VER \n", "GREXPTI = 0 / exposure time in msec Kwd green EXPTIME \n", - "GRFILENA= 'kpf_green_3521.fits' / this filename Kwd green FILENAME \n", - "GRFIRMWA= '/kroot/rel/default/data/kpfgreen/ACF/kpfGreen.20220513.acf' / control\n", + "GRFILENA= 'kpf_63502.fits' / this filename Kwd green FILENAME \n", + "GRFIRMWA= '/kroot/rel/default/data/kpfgreen/ACF/kpfGreen.20220513-0519.acf' / co\n", "GRHDRSHI= 0 / number of HDR right-shift bits Kwd green HDRSHI\n", "GRSHUTT = T / shutter was enabled Kwd green SHUTTEN \n", "GRTM_ZO = 'GMT ' / time zone Kwd green TM_ZONE \n", @@ -349,55 +400,111 @@ "GRCDS2 = '#eval PixelT - 417' / Kwd green CDS2 \n", "GRCDS3 = 950 / Kwd green CDS3 \n", "GRTEST = 123 / test fitskey from modes file Kwd green TEST \n", - "GRDATE = '2022-11-12T07:29:17.059581' / FITS file write time Kwd green DATE \n", - "GROBSERV= 'a.howard' / Observer name Kwd green OBSERVER \n", - "GROBJECT= 217014 / Object name Kwd green OBJECT \n", - "GRPROGNA= 'ENG ' / Program name Kwd green PROGNAME \n", - "GRFRAME = 3521 / Kwd green FRAMENO \n", - "GRDATE-B= '2022-11-12T07:23:18.917377' / Shutter-open time Kwd green DATE-BEG \n", - "GRDATE-E= '2022-11-12T07:28:18.936695' / Shutter-close time Kwd green DATE-END \n", - "GRELAPS = 300.019 / Shutter-elapsed time Kwd green ELAPSED \n", - "RDCAMD_V= 'Sep 28 2022 08:12:41' / camerad build date Kwd red CAMD_VER \n", + "GRDATE = '2023-06-11T08:33:20.749037' / FITS file write time Kwd green DATE \n", + "GROBSERV= 'Dai,Zhang' / Observer name Kwd green OBSERVER \n", + "GRPROGNA= 'C007 ' / Program name Kwd green PROGNAME \n", + "GRFRAME = 63502 / Kwd green FRAMENO \n", + "GRACF = '20220513-0519' / Last user-chosen ACF key Kwd green ACF \n", + "GRACFFI = 'kpfGreen.20220513-0519.acf' / ACF file from ACF key Kwd green ACFFILE\n", + "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / MD5 sum for ACFFILE; 'unknown' if\n", + "GRDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd green DATE-BEG \n", + "GRDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd green DATE-END \n", + "GRELAPS = 172.013 / Shutter-elapsed time Kwd green ELAPSED \n", + "RDCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd red CAMD_VER \n", "RDEXPTI = 0 / exposure time in msec Kwd red EXPTIME \n", - "RDFILENA= 'kpf_red_3521.fits' / this filename Kwd red FILENAME \n", - "RDFIRMWA= '/kroot/rel/default/data/kpfred/ACF/kpfRed.20220513.acf' / controller \n", + "RDFILENA= 'kpf_red_63502.fits' / this filename Kwd red FILENAME \n", + "RDFIRMWA= '/kroot/rel/default/data/kpfred/ACF/kpf_200kHz_2amp_short_oscan.acf' \n", "RDHDRSHI= 0 / number of HDR right-shift bits Kwd red HDRSHIFT\n", "RDSHUTT = T / shutter was enabled Kwd red SHUTTEN \n", "RDTM_ZO = 'GMT ' / time zone Kwd red TM_ZONE \n", - "RDCDS0 = '#eval CDump - 80' / Kwd red CDS0 \n", + "RDCDS0 = '#eval RGsettleT' / Kwd red CDS0 \n", "RDCDS1 = '#eval CDump - 1' / Kwd red CDS1 \n", - "RDCDS2 = '#eval PixelT - 80' / Kwd red CDS2 \n", + "RDCDS2 = '#eval PixelT' / 2 + SWsettleT Kwd red CDS2 \n", "RDCDS3 = 950 / Kwd red CDS3 \n", "RDTEST = 123 / test fitskey from modes file Kwd red TEST \n", - "RDDATE = '2022-11-12T07:30:11.077530' / FITS file write time Kwd red DATE \n", - "RDOBSERV= 'a.howard' / Observer name Kwd red OBSERVER \n", - "RDOBJECT= 217014 / Object name Kwd red OBJECT \n", - "RDPROGNA= 'ENG ' / Program name Kwd red PROGNAME \n", - "RDFRAME = 3521 / Kwd red FRAMENO \n", - "RDDATE-B= '2022-11-12T07:23:18.917377' / Shutter-open time Kwd red DATE-BEG \n", - "RDDATE-E= '2022-11-12T07:28:18.936695' / Shutter-close time Kwd red DATE-END \n", - "RDELAPS = 300.019 / Shutter-elapsed time Kwd red ELAPSED \n", - "EMFRAME = 3521 / Frame number from kpf_expmeter Kwd expmeter FRA\n", - "EMFILEN = '/s/sdata1701/kpfeng/2022nov11/ExpMeter/kpf_em_3521.fits' / Output fil\n", + "RDDATE = '2023-06-11T08:33:09.264258' / FITS file write time Kwd red DATE \n", + "RDOBSERV= 'Dai,Zhang' / Observer name Kwd red OBSERVER \n", + "RDPROGNA= 'C007 ' / Program name Kwd red PROGNAME \n", + "RDFRAME = 63502 / Kwd red FRAMENO \n", + "RDACF = '200kHz_2amp_short_oscan' / Last user-chosen ACF key Kwd red ACF \n", + "RDACFFI = 'kpf_200kHz_2amp_short_oscan.acf' / ACF file from ACF key Kwd red ACFF\n", + "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / MD5 sum for ACFFILE; 'unknown' if\n", + "RDDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd red DATE-BEG \n", + "RDDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd red DATE-END \n", + "RDELAPS = 172.013 / Shutter-elapsed time Kwd red ELAPSED \n", + "EMFRAME = 63502 / Frame number from kpf_expmeter Kwd expmeter FRA\n", + "EMFILEN = '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' / Output file\n", "EMSEQBEG= 1 / Sequence number of first observation Kwd expmet\n", - "EMSEQEND= 156 / Sequence number of last observation Kwd expmete\n", - "EMDATE-B= '2022-11-12T07:23:18.178' / Date-Beg of first observation Kwd expmeter\n", - "EMDATE-E= '2022-11-12T07:28:19.004' / Date-End of last observation Kwd expmeter \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2022-11-12T07:28:19.104000Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_11' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", + "EMSEQEND= 89 / Sequence number of last observation Kwd expmete\n", + "EMDATE-B= '2023-06-11T08:29:30.266' / Date-Beg of first observation Kwd expmeter\n", + "EMDATE-E= '2023-06-11T08:32:23.102' / Date-End of last observation Kwd expmeter \n", + "GCNAXIS1= 640 / Kwd guide NAXIS1 \n", + "GCNAXIS2= 512 / Kwd guide NAXIS2 \n", + "GCDATE-B= '2023-06-11T08:29:30.140' / sequence begin Kwd guide DATE-BEG \n", + "GCDATE-E= '2023-06-11T08:32:22.936' / sequence end Kwd guide DATE-END \n", + "GCOUTDIR= '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory Kwd gu\n", + "GCOFNAME= 'kpfguide_cube_4259.fits' / original output filename Kwd guide OFNAME \n", + "GCEXTNA = 'guider_avg' / HDU name Kwd guide EXTNAME \n", + "GCNFRAM = 17265 / quantity of subframes Kwd guide NFRAMES \n", + "GCSUBX0 = 274 / [pix] subframe x0 coordinate Kwd guide SUBX0 \n", + "GCSUBX1 = 402 / [pix] subframe x1 coordinate Kwd guide SUBX1 \n", + "GCSUBY0 = 198 / [pix] subframe y0 coordinate Kwd guide SUBY0 \n", + "GCSUBY1 = 326 / [pix] subframe y1 coordinate Kwd guide SUBY1 \n", + "GCFPS = 100.0 / frames per second Kwd guide FPS \n", + "GCGAIN = 'Low ' / detector gain setting Kwd guide GAIN \n", + "GCTEMPD = -40.01 / [deg C] sensor temperature Kwd guide TEMPDET \n", + "GCEXPOSI= T / continuous exposures active Kwd guide EXPOSING \n", + "GCINSTRU= 'KPF ' / Selected instrument Kwd guide INSTRUME \n", + "GCTELESC= 'Keck I ' / Telescope Kwd guide TELESCOP \n", + "GCRA = '17:20:39.80' / Right ascension Kwd guide RA \n", + "GCDEC = '+32:27:40.0' / Declination Kwd guide DEC \n", + "GCEL = 61.46 / [deg] telescope elevation Kwd guide EL \n", + "GCAZ = 57.97 / [deg] telescope azimuth Kwd guide AZ \n", + "GCPARANG= -108.94 / [deg] parallactic angle Kwd guide PARANG \n", + "GCVA = 118.5 / [deg] vertical angle Kwd guide VA \n", + "GCPA = 9.6 / [deg] sky position angle Kwd guide PA \n", + "GCMJD-O = 60106.354817 / Modified Julian day Kwd guide MJD-OBS \n", + "GCPMFM = 0.0 / PMFM value Kwd guide PMFM \n", + "GCUT = '08:30:56.22' / DCS Universal Time Kwd guide UT \n", + "GCSECFOC= -2.244 / [mm] Secondary focus Kwd guide SECFOCUS \n", + "GCTELFOC= -1.459 / [mm] Telescope focus Kwd guide TELFOCUS \n", + "GCTUBETE= 3.27 / [degC] Telescope tube temperature Kwd guide TUB\n", + "GCWCSNA = 'RA/Dec ' / Descriptive name of this WCS Kwd guide WCSNAME \n", + "GCCTYPE1= 'RA---TAN' / Coordinate type of WCS axis 1 Kwd guide CTYPE1 \n", + "GCCTYPE2= 'DEC--TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2 \n", + "GCCNAME1= 'Right Ascension' / Descriptive name of WCS axis 1 Kwd guide CNAME1\n", + "GCCNAME2= 'Declination' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", + "GCCUNIT1= 'deg ' / Physical unit of WCS axis 1 Kwd guide CUNIT1 \n", + "GCCUNIT2= 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2 \n", + "GCRADECS= 'ICRS ' / Celestial coordinate system Kwd guide RADECSYS \n", + "GCCD1_1 = 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1 \n", + "GCCD1_2 = 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2 \n", + "GCCD2_1 = 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1 \n", + "GCCD2_2 = -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2 \n", + "GCCRPIX1= 337.826 / [pix] X coordinate of center pixel Kwd guide CR\n", + "GCCRPIX2= 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", + "GCCRVAL1= 260.165833 / [deg] RA of center pixel Kwd guide CRVAL1 \n", + "GCCRVAL2= 32.461111 / [deg] DEC of center pixel Kwd guide CRVAL2 \n", + "GCWCSNAM= 'Az/El ' / Descriptive name of this WCS Kwd guide WCSNAMEY\n", + "GCCTYPE = 'TLAT-TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2Y\n", + "GCCNAME = 'Elevation' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", + "GCCUNIT = 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2Y \n", + "GCRADESY= 'ICRS ' / Celestial coordinate system Kwd guide RADESYSY \n", + "GCCD1_1R= 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1R \n", + "GCCD1_2R= 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2R \n", + "GCCD2_1R= 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1R \n", + "GCCD2_2R= -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2R \n", + "GCCRPIX = 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", + "GCCRVAL = 61.45613 / [deg] El of center pixel Kwd guide CRVAL2Y \n", + "GCCD1_1Y= -7.68756E-06 / [deg] Az per pixel in x Kwd guide CD1_1Y \n", + "GCCD1_2Y= -1.41587E-05 / [deg] Az per pixel in y Kwd guide CD1_2Y \n", + "GCCD2_1Y= -1.41587E-05 / [deg] El per pixel in x Kwd guide CD2_1Y \n", + "GCCD2_2Y= 7.68756E-06 / [deg] El per pixel in y Kwd guide CD2_2Y \n", "COMMENT \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2022-11-12T07:30:15.068563Z \n", - "COMMENT Header generation end time: 2022-11-12T07:30:15.151511Z \n", + "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:25.403001Z \n", "COMMENT ------------------------------------------------------------------------\n", "COMMENT The keywords that begin with GR come from the green detector system. \n", "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", @@ -407,6 +514,9 @@ "COMMENT The original keyword name follows the Kwd in the comment field \n", "COMMENT The keywords that begin with EM come from the expmeter detector system. \n", "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", + "COMMENT The original keyword name follows the Kwd in the comment field \n", + "COMMENT The keywords that begin with GC come from the guide detector system. \n", + "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", "COMMENT The original keyword name follows the Kwd in the comment field " ] }, @@ -438,8 +548,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Star = 217014\n", - "Exposure time = 300.019 sec\n" + "Star = 157214\n", + "Exposure time = 172.013 sec\n" ] } ], @@ -456,7 +566,7 @@ "metadata": {}, "source": [ "# Green CCD\n", - "There are four quadrants in the green CCD with separate extensions. Let's plot of them." + "There are four quadrants in the green CCD with separate extensions (when it is read in 4-amplifier mode). Let's plot of them." ] }, { @@ -476,7 +586,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -491,7 +601,7 @@ "plt.figure(tight_layout=True)\n", "plt.figure(figsize=(6, 6), tight_layout=True)\n", "plt.imshow(L0['GREEN_AMP4'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['GREEN_AMP4'].data,99))\n", - "plt.colorbar()\n", + "plt.colorbar(shrink=0.7)\n", "plt.grid(False)\n", "plt.show()" ] @@ -502,7 +612,7 @@ "metadata": {}, "source": [ "# Red CCD\n", - "There are two quadrantes for the Red CCD (when read out in 2-amplifier mode). Let's plot one of them" + "There are two halves for the Red CCD (when read out in 2-amplifier mode). Let's plot one of them" ] }, { @@ -522,7 +632,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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Zil9upnGKOIcsO3AOc+eI5s1InU0NLqzTVichzTY2bDxb24qf+1I3Ow5vA6M1rO5Wnc2cExff0CSccEHPp5zozo7yNrHpdr6/t/j5sPUVTjTKJU58KcO9GulpeGGU1+i+qbGeUHkh33EdRrSfaqb48mwgLaBPZgAwnRGkNIzjJ8NphhlBubMOYHbboTauh0UPerwoX3jWeTbBqOyR4S7iDIv+FHWZzCwK4512tq0xbaWxj+HjvIcdcyNkymwSXsxkJHr2eSNLNGLTnWezfWXnlQ572NPtzTh63LLaDexDoMsG+izMbnSYvcR2prW+EUWZ2zGupGhyxHTsZKgMtNsmTOm07BxIGDJURqxCvubxOuy8ZHjPj9x+4MZG4WvjB5sVlwznuPHPL2384I10wmnm8Z0w2idhuzy4x9canfr2pY3b283pcU7bEj89v2ERXjfF3ZRmU/7TH9p6j3Q9eKV9xbDVNoFuFIkGNjIA0XFIv4UrN+mUtOFArZ1ED+rYWSh36JVOxcQhGFGReX0rNlfHs9xk9NI6IgKLGxumVxUbUQ1NRcXbwT16s+c39AkPz+DfD5zUOpl8Xmic5UQ81B9sXm5UQ1NRUVGxCXX95b7hvEtxFRUVFRUVGSJyTUT+ooj8hoh8WkS+66S4dUZTUVFRcYGh6EV9M8CfBf66qv5TIjJjsnxZohqaioqKioq7gohcBf5h4J8FUNUVsDopfjU0FRUVFRcZGlQ8Lhg+ALwO/Nci8lHgk8CfUNWDTZHrGk1FRUVFxRSPi8gnir/vn5xvgH8Q+POq+m3AAUH8ciPqjKaioqLiAmPy0omHhTdU9TtOOf8i8KKq/nw8/oucYmjqjKaioqKi4q6gqq8CXxWRb4xBv4vwIuWNqDOaioqKiop7wb8E/Hdxx9kXgP/9SRGroamoqKi40BDcBXyXgqr+EnAavZZRqbOKioqKigeKOqOpqKiouMCICg6XGnVGU1FRUVHxQHH5ZzRJYfBewh5EnndTTkVFRcU5cBHXaO4GF9/QiMC13fA9iv+MpIITptLAU42MafwSfqNs31qQbpr/bXqV+NTY3G/D9W4I26RlclHyuhfnoToYFRUn4sIbGp01XP9HnhqU86LSn9qg6JckfsUFSdvRu+dkiJ/DSxVEkkJgkNgNeYVBS5xiXAjzVhCvmD7WyQ7p0EGB0fQ6KBoynA/5jesVpHDJ4k1BsVCzgqTpQ8Ikk5xkeHPafCyDtG6U3U2KkqIa0k0NaZLd1XEeZPlfXTfKvgjfYLDFF+EnGfZNxn9jvEl9zYZ4SVq6QOkIiMjdOQbT41TX0+KfZlhOMn6TMLEGdf7Cxrlf+U4N8Zlp7qWc8zgcZzkp95LHA4ZSZzQPHOI8115YDmp4EdqEi602qvYlA1Qo743U8+JxlustDA2qo7BkHFySnhUwLnz3Tcgv6LCH/FwrUQ9e8FbQJuSR9O5DOwgKfTG/LBM8G86rGfTpc/yY3pTnXWirt6Bl/GjktAU1oT7SDXK+vo159WQN9tCeNFOUcC4ZS1Oc6yX2FYOSodUQT0P/SD+0KysYxr7PhlaIhm7o/5EM8UmI5Sb54JxXeZ7CqSgcjHCtgqMwvxUM4mrX5Gszqk/Kxw/5BuNb5KfT8nTtXNaxL/PLaQYHJKehyCcOYqO8krOxFicmLtMw5Jc/k3NRHpfn43edOAya6jVlESafo/POhU8rYNMDsD5QSsPgXJV5noKNyqRTh6LsBJHTjxMmjstpTsvouM5iz4ULb2hQxazc6OEVp3GwHnvF6UFMx8D4gZrky+QhHaWbxt0UtummLx+aTXK7U8nc0niKhBs+lWfMyLhix/F9Y8aDbZS9LfXoU75qQJvxExm024u4qeuMRAMkw0wynkwzSG+l0K6nmB0NcYK+epFHnE0O9V03SEmTfaxzP9RXzeBQqNVxHIm1lCHtyCg4WLwR+uD4ScW3Oq7PJO1gdZj0M2Pd+pO21BgdPid5iNEQHvtFTHkPgth0MSTcqj5USp0NToIr+tWkTwUT8xIdzqugTkIaL/GZCJ94iYarNMwxDjHchbDwyejT9GC6kM52ij2G7dd7ZjeO8VsNR0/MWF0xwUmzoDbeW7Z0ZhhLLJvJNY2Omdrhehkno3ug7LtRPztoDgS3AD/TEZuRry2MnRJX9EF+JsafiLL1qvDM//hCNTbnwIU3NNpYbnzzdh400k3nm3CTl9RZ6XGv5ZNu6Oi5mF7zLCIPdBpnDjIMwBsptHL2kGc3IZ8844gzEhWwq3DsZiHMdKFOebbSk2co+TjO1lJdUxtSHVN68YkyTHFCHVOd1qm0mLek9oU2ZqMtcXSKhjvlsdavpYe96UFLjoFPHnhBeWxcEjvF2K9Ra+vpzwNRhT50nrZN1qwfIoTv2bibcXjWqodB4x7igFicK/TuRzNrITsaakwxeI1n3hDutWxgN9zPZXljIy3rA/fEkVjPa8PxaGZUXJ9y1pZmivF+FA925WkO+uAcGqE59KiAm0k0MloYmsLolIYHkOxw6DAbL4yS94pajeEa+7sw6BLCcEK/b3C7Dpl7pPHYxmGtYozHWl/6QSjQ9xaNBh4BYzzzxtFYT2MdVpTGeL78wpM8+5fkHmS57x5+401weXDhDY30jsd+eX94QDMdJpESCjdwehBOerjzA1jMKEQV38ia1508L4ielBSUmWoRNklXGhkGw5EMkltIDA9UnZ+FtMlg+jbkIz2A5GPTSz4vHvCgTTQ0WpQXH9ps+NIsIs0EYlokencGxElOr42GuDLkIV6yUfWtDpSjFsadNMMoBsvkVPvURsmDRuqTNKiszxaKw/QMlyMBxeeQbBSW2x89dPHBwC/eDHVYPhIGuXLQzDOghGkZOg4b1W1S9hodqEWaDePSmPKbzG78pj8d2lm0N63lleVnes+na6IkajfTcpFCTrTcKF6k2qZrgZlBcJrX9qT3sFwhXY8xBvtWC40Nz+PUcE++6yaGYIpMWU2OT0KkDNWYMT0mYeqkMmSwiVEL9TJg7LiOAt94dDBeM6o4ERfe0ACoNdk7NiXlNaXHRnRY+DiRNitvkE0L3AX3nIzTaMH7XjB5kPQUGm0tvpFRnPzglg+qMWsPsxoZe98xL21CWDbQJqwvhbjjaoQ8BsM6SpdouThbHG20SJcpWoHgFAxeaWmYNHv6hbEqPfqS8ig8+ZHnXpw70WClOttgWP3EyKV8T6LiSjptSu+Evl23gDqlzpLnDZCon8koJ1aRGKYq4CP9BYH+ctEqqoQyffxsdESdjfJQAgUXnQdcrHg2ttHyl3RacjaSkXKDc5I24JhVOGc6sEfKlVd7Zm+tcFsNB0+3rHYFP4uzlyY5culPh+8ydnbyrKTsw9S/XsAq0g7PcWarC9pQncHcbvA7DrPVI0YxEvrHxLx8oiVV8Kmf4wUV6xEb0gR6U8MQoAJfvMo3/NnXHzh1ptTNAA8cag3779saDyRKpsvCTRnpIi0HnsmgXgxwI6orzQrS4rwl3Nx+oNFSWtMPnq/aUI8yDpB3pqV8TR+8zEQTBJpMRzvVQnvijR3pqpKbLo8TTZYG5rTTDcjUWvJ40066kO/gBWdeuqDc8u41p5gT17ViuEuL31PjDCO6bIPhT5Cps5C8ZiZlb9rBtpGqO+Nhz+t7oRC1Zt2ww7pxn4afg15bOx8dhDXaTRjCC49fJc2yU165EbFOccAbGTmN1JLJz8TaGte0uafMIMZGenw9yo0P4jXPiMUrplPam0vswQqzbNhRWNw0uJkZ1vZGn+RNL2ljz7C+N3Y8yvBAn2vY+AKDwU+z5mKzT3Mg9N7i+9Anzmqk23TcBXEAyQZdwrUyKMZGmq2If9ye2H0VE1x4QyOrnkd+5itFwOThmC6wp7DJ9Lx8cEdxzDReMSs4NUzyNke1Js8ESjqvpHo0ziLwYbAIswcGKquVwcuNhicZDihnE4ARXIyfdpJlvjvWoaTNktEbKLBo1BLtZRloJD84ysMAkNpNHmDKNarxpoCifIY6pF1uOS/IO9qGgPWBbzwTYah3bN+w4L2WdExH+WDk5zdCxOUjYUDLbdrEgOgkryJsE222thttcj6E6xA+ad9AYbHW1+WaW6bJJgN+2UfTOoiCSbRXot5y3mPKLFNjaQu+Do5DCCvW3cqt8un7qkO6HnsgmNtH0NjRM7lGka3tGivOvx0GAYY6iayXuwFTB2vNoSjjLh/8bAZAEdwlf4nLhTc0ALrqHlpZm27Fd2TSuun3G6dg47bPcGKSr9l8fi2enHy+pOWm5/ODWRyXD7kdwkf0X+m9n7TQPvH6E+U2ninE+IXxKzdDNIcOFaF7w2aqb6DMhuMxLTfkt7bIXsaFXKe1HVQ5vyKv3JaJsU7eez4Oll+y6z6gNCxqdLyoLjGtFJG12KaecisN5MiIjSm0tfWivOssGC3TQ3OsbL3eM3trid9qOHwy7jqbBWcpL/43haNik7OjozCMTvpZwYKKIk7CjrNEPSanJVGJqc29YO9Y3I6HuRtu4zR7ie2kTzdB0amigYq0Oo4fy1l8ecb7/9+3666zc+BSGJqvSdzlzXv+nS/u7CgPABfGgEfMzxPpLo39JpzoAAwRTij7BIfgtLCpc3DadxivEW7K7zyL8zCedagivQu7+0SYfbVBGwuJqpQ02y8cEEPcyk/euDN1LjY5FmHWL4UzUsQpNviYzsc1ovb0HXlSlFM6EQXtXtLyWzfqRoDzohqaioqTcB881Xvf+vrOOAT3A6MWHy/XzssJ3y8FSuP8EGcydXtzRUVFxdcKKk12T6iGpqKiouICQ7n825sv91aGioqKiooLjzqjqaioqLjQENzGV5FfHlzu2ldUVFRUXHhUQ1NRUVFR8UBRqbOKioqKC4zwm9nLPSe43LWvqKioqLjwqDOaioqKiguOur25oqKioqLiFNQZTUVFRcUFhmrd3lxRUVFRUXEqLs+MJmq/VFRUVHytwdc1moeA9MbUJDY2Dd8U9zzhNex8YdN+fxD532teFRUVFx4Xf0bTNtz5re8HBhVBIMvHQlTFiwqOKEEOeaIYCIPQVFYYjNoSxnlwmvU3korgoHKoQS46Sh1nFUvvi/qMNTlKjY6N56azs0marGZYnj/t+6b8pnET/ERHI7kbZVy7Pqiv0cT3YgxOcwROm7GeVlZ5X1iDOn9qnHtNd69xRGQUJtag5T1wUpwHVJ9pnGnf30u+91LOuY4vSh6nHVeciQtvaNQIx9fMSLgiSxyb4VhckDVWkUGaFgZBIxMU/tSAcSHcN0HpT/pgfLQJ0sWmD+c19k7KGwlxk3yy6aJSoIQ0ofyYXkL+SXkQifroDAqFSVApHft4bFwoM8skM9QpySsjY6XDJEXs26E/TB/DmnFYEnMq4yRFR9MPaoqj8h1IP/Sr9ENb2iPFN2R56ZGMcxKXypLD5cUF02vou+JOLCWKp5LH2fgqI9nhdYXIIl4qM0oS2yMX+rExo/SiOjgcMX2SMB7qMRj24R4bG3Vxiqz6LAGugHQuDE7WhKxKo+51LUxPcizKPijrc874J8WZhomNN6v3QYTN+zX+Q2wRIDI+Tpg4LDpKIqceZxROxllp3lYeUWZ9dDw9v+H4QSO8vflykE8n4cIbGln1PPEzr54R6RyKgkl2OA0ipbpfjmPGuuJ2iK9GUGvC7KaQMFYT8/CgzViC2DchnviYvpTrBXwreaakEjTsvZVB/90E6ds84Eo4H+JNBmTCQ+xmIV0ytipgooQuGgxGltNN6oEyGFnfktOpIUvuhpmi5HPih7Y0h4Kbg5uH/BAd0gpooyN13CxTLIrpBNMJ/ZZmWV5N8sOxzcS4pdTxMMqXYePwdClUQaK8rzrBvrHAzxSurRCjqDNxjBXoDTJz43unLCbWQwQ0GjBJ95Ao6gV1QvPynP6Kh90+HL/Z0hwKx89EWXKV0F4YDHNqb75BBBofyrM6brMK6mVI52RIoyDxnKY05x2nRsZdRsf44fo3B4I9FlaP+JyudO7ES5CAToY+fxfMcnDWchofnRk/OGQJo3w1xF1zXhKjAQPjkNJH52qYuQ/pyzzEaT6XnDjSNS6ewVTG9puOKz/7xXN27Nc2Lryh0VnD67/t6dHAJr64mQrvWVwyAmQPtfSg02CXbujksYsDE+mtNNAbV8bXLCFr+mHAk76oU68DdecHTz7crIEGEyPB2/XBsNijVMdoWKLRwel4HPUa6cE0aAx0gHgNI2lqZ3qYPMiUIoPgsRfxSlovl5882+lgO/Wak9F2fiTVm/LK5WTjO+RXGnRRRRtTpB3amKR1s7RvyqeQ9wUGid9S6jdLGzO6L+yqwzeCmwdp3zR4pvO+sdkpGMn3SmGAs3FhLS4K81tKt21wiyAa3R4opvMs3ijkhIt8yuNSTFGjU+CtDvGLWWOe3ce6jCCgRvMgq42iybglo2wAUxpxDZehMOwiOhhTwBpPd2dGvzK0TxyF8xLiGRPv5eL2UBVUBe8Fr7BcNsy2Oqz1+XJ6L6z60Ph21tPEc0Y05q/xODwGrR3f250zLNqe1nis8XgV5rbHiNJ5S2sc12ZHNMZhRZnFqbyN+XbectDP8Ag7dkWnhmPXMsXMOLZsx9z0/MRnPsI3/dyEMnwguPzbmy+8oUGVZqljfW8IuuPROxYfvHSJg0zy1mHwwIbBYPD2vZVAj2kwIGkGAcEYlRRTmkWUNE+aHUCMk3pzQsOVNFk2RDHvTO1I9PI0UHIwzCYytRcHDN/GdSIvgfoqqLhAnSnGSaa3fAtqg9E1nUQaMT64vQztaxTx4TjNPHwcmIwL4cmgJUMsHtqDMJNy8+L6FLO3EU1ROAyJVjRdSD+i3ViftIy8Zhh7m+X5kVfOyNkQp8zumGhohmuXKDfjCopzkg8KxmtQWS7KGmg9IDo3szsulNOG+6058kivLG6aURun1OCIsotUztDe4lx0pEY0YSp/kteJmE4Kp/FPoYbEHYb7vB0urkwf0k0FqoIuI3swHjxD+YpaC4SLUDIEQ8TCISlhB0bBCnSFM7MUeNVGJyQ5LRuqWzovOWzkVEgeT9572z0EI/PuwIU3NNL1PPp3X77LRLLxuxpZjzf12ie888hDNxvCSw7amMGbtiYMEvHhVSMjvjpQacWxFXw8n41jpMnSQBmMk8Q1mjgql7y+lbgeE86lmVOiyoAYlgyhjGZyWpQPiWKTkYEsqbNsKHtFGhmMIQyevim8cRkMmMY4pgfpBbcYvO2R156dhOhh5xlA4Zmnz2mYSR3JQFM5oX2zwW0pfrcPcXoz0DFOYO5zXiPvvqBOkoedxsp8GRR8b5i9PGP1qEO2O1DBvD7DdNA9Hb0I0UCHTfIqvXevQts6rPU5LJQluEj3iYBzaToVZw85pgzdI0rTujzjEFE0ppnmvQkiiveSH5eDWwtYWrYeP8xpxhNeCccqQe9eBfUEavHYIou4myfF8wJ9bIfVeO0mlSipvMJZQQVxMszWEowO1xcCDZkTTfL0IY+BJmR8PHRpvh93vjrjPb8kD3ytRrn8L9W88IZG25Y3fttzBBqqvEEgU1PxuFzoZ3j2gLHXayIXGyiQSJWlRfG8+J3iSJzthDxNF+k5Dd7xlC4r10YgUm3lAFVQVeI08sJhxLAwUGFFO8X5UZ65Pd5H7nvDjR7PhUoweP9TOm1KyeXF9g15nrDgXK4prRnuTWsdm8LKtFOKzUzSSbEWNjH+G2m3nCYmWR2jjcHPbDzvhvgwrLVNPN9MnxWz45FhLZrV7vf02wY3m4XjIwcKqyvN4BmXVJmQjzPVR5wxT+KJxgc3Hjfl/V7UY8gr5OGs0qf1jzSxmhjysi7ZwOZ8h2N7x2IcHMlWbn9wBLTocy33VQQ4AS+oM3HdLFF15A0RMvOYuCYlFIawMIjW6shYeh+MsjEeK4pXYWvWYY1n1Tc01rFoehrjseIxovkPglE/6gNV1ojHI/Q+XNwy3tz2zEzI5+/tvD9sDFizRhVTXHhDI87xyG/sn8jbw/BQqpWJJxy89NF0mehVRyOjkUIadnmFPNLswjcy8uzzYmUsxzfRK/TjnVNpIVwcw06xGC/NTBCyAUhcvERDOVqEj7ROoM3G4aM1KLthZ1ux4C9K3haed7y5cbzUF1PKMZWXDHnqL4B2X/EzGegvWBv4SnqpHJBNF2Y1o7RF3LX7QYfz051pozR5bBrW6dLi7mxfUQv9XAZqTWPcol1rlNwo38LxKWZ7KQ+79IEya8IIapces/LYZUOit0aL1qOdZEN9pmtZmSrbRJMVbS7zXKMgU/xN3yd9LifFc4p4H9bWTsLG6zc4VjpxOFJZa07EJpzEWpR0nJlPnJMYxwj9Bkou2WCVMBNsKe7VGH9p4DiOPx86WFbq7Jy48IYG5zFfOmPX2f3AXfwYUE7YkbTmqRtz8vnpjrhN5yf55RnDBs+/3FW3aefcKCwu0I+oxETlTanGZJA3DArlBgnfCL41g/ebPfZht11p8FNYGuTdTEYeeH7Ap15/3ghQ5DlduJ+UB4ycDDcX/Ay6HQbHJPanePCzlHayNljmDQiRpioX9EURJyxeb+h2od8JA9HsZovp4PhJPwx4zYYZQ7GDDRVo/bDjrKDH0u6yTP0wbQuFdY99HakkKcMhO2YM3bXROqVdbiLgbreYY4M+thrlMaLhfBiQ1RX17Q2yNOh8MkB7kF4QL2ir6AbqbLQLrtj0kna5ady1GNYsx8fAZsOlBNoslz/049S5SOWmw+1XFjz35dceyjZndwKleVlw8Q3Nw8Jd3Cznnyq7s6M8AGy6JS/FbXqPv/w/0fAPEU4o7wSXeZMhh3H9ztpSH39/koy3RM9XGzvEKx2MU9YC17bhT+o3GOXx9vqB+hscBgAt1hLzLr0zaMJct2jAw++fHN12O0pbOgVlmow4E/StXaP68jb9vA5ZpJdJvuXOv5R1c0ocSbN4nTgiDIYlsQuj+jNyZDTv/lP8fLIPu+JEVENTcXFwj57hvXPkD88ROG8NL62TcL/xgF83dKZzcnLC4bsq+hBmM4rUH2xWVFRU3Hc88J1cdQH/YaIamoqKiooLDn/Jf7B5uWtfUVFRUXHhUQ1NRUVFRcUDRaXOKioqKi4wlMv/9ubLXfuKioqKiguPOqN5UKjiSBUVFfcBilz6H2zWGc2DQjUyFRUVFcA5DI2ILETkF0Tkl0XkUyLy78bwf0dEXhKRX4p/v69I8wMi8oKIfEZEfm8R/u0i8qvx3H8q9/yrqYqKioqvHXjMQ/273zgPdbYEfqeq7otIC/wdEfmJeO4/VtX/qIwsIh8Bvg/4ZuBZ4G+JyIdU1QF/Hvh+4OeAvwZ8D/ATVFRUVFS8a3GmodEgXr4fD9v4dxov9L3Aj6nqEviiiLwA/CYR+RJwVVU/DiAiPwr8AaqhqaioqDgRQXT3cq9ynKv2ImJF5JeA68BPqurPx1P/ooj8ioj8VyLySAx7DvhqkfzFGPZc/D4N31Te94vIJ0TkEyt/dP7WVFRUVFRcOJzL0KiqU9WPAc8TZiffQqDBvh74GPAK8Kdj9E3rLnpK+KbyfkhVv0NVv2NmorDSppfsvVNhFRUVFRXnxl1tb1bVmyLy08D3lGszIvJfAH81Hr4IvKdI9jzwcgx/fkP46RCQ+SxVYAhPSpGlbkZ+RfoG+2UFnapG3o2xKXeRlcfp+6btzBclLLXppDbcj7zuJf8Hme7t5P1OxpmibpOvQPCX/B3eZxoaEXkC6KKR2QJ+N/AfiMgzqvpKjPZPAr8Wv/8V4C+IyJ8hbAb4IPALqupE5I6I/Bbg54E/Avy5s8rXWcv13/PeNdVF44KS4Wzfowb6hQnqkxKkmZPglnE6KFRCVJkMQl1JFTGXZQatjXAs2E6jDoUE6eWoP2WcZhlocUHrwiyV2e2OfssG5UFVNCosikuqgjFtvG+agz7UZ2EHiWivg4pieS28glfEebSNWhiqQ7iS1QvTOTTIRUvnoqqnBHVE58Z6LMkIT9NDGOimRrqMn+vmoXchvd0wWR45ChsGz1T2JO+N8H59Pi6Kqo5fAW8nD2jhcKR4Jf0tpTzzFFMH47Tz54m/KT2M1TanUsHTOEZCnELpUawZKz/epzRrdZukEWtC356U5hzlPrQ8TynjPGVO+6LidJxnRvMM8CMiYgmP9o+r6l8Vkf9GRD5GGMK/BPzzAKr6KRH5ceDXgR7443HHGcAfA34Y2CJsAjh7I4Aq89tRNCoKNKVw8WEAFwcy06Cq1zAIJVlwVkaCRhLV+NysKCKpODayJr9suiCo5FuQnqz4KD6EEY2Vb8Aew/Z1y/EjBrcIebsFWa3Pxx5MhkkNzG/MsEvl6CnJUtCmj/WyZNJRTVBuFBfSd7tBgCnJKgdjGhroZzGZT+eE2W3oroCbK3YltLfBzUP91CQjFaSN+0WQOk4yx+JCO1OfZMlnje0nxGnvCHtf8Cx3hYPnBsEqn5QOGfqiFKQaru1wTZKkdY6rYJwE5cV84YYE4qE5gr3PO44eMxw9IeuGqJwEKyMFxUFSmZEQlig0h8ruV3sOnmrYea1n/7mGflGUHfPLMsuFU5PqlsPc0K/toWfxRsfRk7ORDPhIolqGY/FKc+Dpt0PDRpLTWpanIyGv5OgkB2xIo/m7OC3KVszSESTFTVQ0lc0S0gz5lPUf+qZwVlK53iNOB0E3vyF+knXO6SfnoiGQ4yU6awenSRWNonOj6hROzKi2k+Mkzy2b2uU9OB+cKBHoHbpc8aChXP7NAOfZdfYrwLdtCP/Dp6T5QeAHN4R/AviWu6mg9J7d37i1fpNBuOiJQks3VukRjwsff9/0E57Sk59+T/lukmAuy+16aJucRkuP2pixHroIsgpWReftKO5ImbDQO0/HfmZY+7FwVEvUpjiRnLCVxy0svg2zMLuM8stRQrnURu/nMh7EUlbJ0DRM6hMGNXuszG/2iLP41oyloKdqh2b4K4/T91G5otlg5bJNCh+MmG+Ebtuw2hOWj3m01WykSznmcZ/p+nero9tIbrdAw+2vB7douPnNHtlbkeWRKS/VYGXCLVDOTsB7CbewN7gbc65+dsGtb+1odjrE+Hx7mSi5bIxiTLjHV6uG1fVt5k8d0rY9O/MVVhQjmn85bkTpvEFV8CpY43HecNw1dF3D9mKJEbBmPF3unBk56jfe2IXesLh2zJWtJY11QUIa8EVZ6dgaT2s8RjR+H0TljCheBVXBIdw82uLW/oK9K8c8unVIr4YmWsXgu0guo3M2l7FyNrYLOtdweLDg6s88wc1v8ew8dwcRZbls6V/Z5srX3WJr1gFwtGpZrhoe2T2kNZrrDaEfUt3StVIVem846ho0nvMq3H5jh+0vzDj+yBHzRUf3hV0++Kc/X2c258CFfwWNziyv/bZHBs9eJFBbAnYJzVHw1JZ7Qa/etcHzEh8GHiR5cyG/5P37cpN2HugCdWV6zV676cJsBRlmIskr9RbS86QSPOrFTcdq1+Dj7MjP4kyI9bTiYXHLg8JqN4zCyRvNbXU69rgJ7dE0U4veZfZUC29WSprDSSjfK2al2KVDesF0MvJyxSutlfx9oOSGvgqNGbz35OmazmFvHzOfNWy/3A5lTyizRD+mMtcveuFMxJnUlEacItTBI4dLHpu1gVo8x++BdVOcifMo/SFytOLJn5uFz19YRKeBkbMxklFGcvmlEc/5e5D+GHuw5Ilf3EIbg4olSwxvklhWaI47+u0FaiTf316GIl2ayIlgY9mNwG7sP293hvu9kGu2aeYowd4+cxT6v19cwc2usEr1yjPQ5ETE49JxSN8TszAx8qaHxQoOF1e4M0/pozMgwwx2NNsdDmMgtMfC9uuO7iuW48M91CimF7ZvCPvscXseGi2dIL3w6vYWzMI0NTsI0alANKpfp2m04Pr4TJroYx5ZzAr8sWVlNDMPDwOX/aWaF97QiFOufjleUSGvi6gRpFfsMtxMdhXWOAIlFeJ4KwPN48MNm+gQ15IH8/yQWB3ianHDkx6suPaTHggL6osHDKVfGrptwc1D2W4eDZSJ9Ft64JvwwGljUAPHjxZPY8rbgOmC8SsfYNMFA5ZoHrWaqRdxybjGQS4+wO1+w2ovrA01h8Lsdku/FagzPxvWjMSDn0cD4mT8wKcZRcwznye0cXbLsPf5bQ6fMBw+Uw4cxYxCQbzgG83a6+MLHv8y7SNxFIzGstU8wynrJl5o7hiufRoOnhWOnnEkrfcMnRwTZkWisR2lMVXAB2Pe3jbsvaDcea+w+xXl5ofAbWuuXknBpcBElxXNjhRVODBOmN2CnZe3uP3+tL7IOor+MD3MbrWs9ogz17JPx9/TXwozfSjbt+O8S1ovp3WDA+Ub8jOUy1EwqkPbp/2ajos10NwfCqZTbKe4VjINXVKEY1ozPpOTcPGK6ZTFq4fM7mzRb5lMpTdHju5Kk42g6T144hqlGdqbuiGzJUU7FaQv1nEUzHKJPeroPzFHjaU5PKizmXPiwhsanGP7y7cHD3eyQC19eCLmZvAgR/EKZA//tEXms7CJQmMwbngfvNC0GG4MI/otwcbwtHjeDpdCRcIi9oi/KWg4Tz6fvd8pjUccuKyEmVHncXNLmuGZlUNbg29MoLkKr1mbYQDIdJYMRjwbW9K58LU9ciyuL5nfatl60xZGXMaeLeGhD/mN1xu0NA4yDKjJSUizyzJu3lhxBLN9R3fH4ucW3+iQX7KJybjFdFJSlKnPkmE0mh0QtcG4+xbcluJ24ihqNdcVE6dqpeGJZYvRcClTZT30Wy1maTh4Xw8LF7zr5Gmb4GEb65FIwy07y/H1Bc0zh1jrWbQ9xniMgPOB+knfnTf4SAcJsFw1eGfY2V7iVWiso7WbaSPnhevXdwFY7C3Z2zmiibQYBBpLgZl1gbbzhrntaeMxQCNupAqZ6LDeG24cbXPzcMGjuwc8sjii94aZdZkuy2nicRPprd4P+XXe8sb+DvK3H2H/Nx/x9U+/iohy42ib11/b46Nf/2WuzsJv8G6utjnqW57fucnM9LQyGJC56fJ3R+gzr4alb3hzuY0RZWYcK2/59PWnWH3uKs9826s8vrXPL37ufXzk/zrZRFGxERfe0Gjb8PpvfnQY9CKC16W0h+HGXu0Y3CwOjg3ZQ1XL4C0V3porPTtTeKZp44BN3tfgBaaZiTjyFD+HKTTHyvyWo9uO1JkN9J3pw+wq02+xHbZT2gOPeFhdMdlb05S3D3GAPLiLV+wqUGcllZGosjL/3FcemkOHb01srh8MRjReEikt6RVWsb9OMsgFXZYXhlUxncfsH9PctCyutxvSFZRYgt9QzgnxRm06IY0cr7jaWGgK6uztOBYQ6L5Vx+N/t0G6nic+Pstrgueh3kYORvm998iq4+m/Ox92BJbrcUZi/tEYqmK6ffy8QY0F047W8nRiNEMYo91v2syG+0bGaco1tL3jEN/Nt/DtNirQm7IsOJYhzVFah8uz2CGvKXUmPez2cDy/wkuzNPMu6pviFvWa5gGBOn/ksx3Sb/Hi3ntDl3XwyL7ymS9/fZg1E2aPePjy1vOTtULN40Ood/EAEhmHIv78LcPuq8rL9mle3PJsv2TXfzLxAKDFmtVlxYU3NLLqeeJnXosHMnzmmU2YQdDY4eHftABfPvweaNJAQZh9CKNBIC+o++jhF1thRaNHbkI9UnmmV5qDHrOMi+GN5MV334zjh23Og9HK3rsQ48bi43ffJsMm2C5QcoHWKNoaB+1k5LLXrtDesfRbIS+7Upoj6HaEfoeBdvODYdXyIY+75dQMFBskOiMWoEK7D3tf3OboUcPh0zK0a0M6teMZTjjByCko65DqVzoOKKPr2hwI1z7nOXjacPRkGkS08CIYf4/HI/qn3AFFCE+76e6813DlRc+trzf0O3HgpsguUTsexMuYUitoodS+dh+uvOy4/XV2tAtybcNCzMd0ML+lLPdktD4y3UFX7phL7TEuUFa+KWZcqbjpTjmF9iCsa7q5jOm2gj4r45f1mFKJw1peCDN9oM7KZ6LMq8xzVLeSHtTgVM1fO+Sx4zn9lo1xlObQ0X+pKehgzeua5dgxWh8s1iJHa4/5PEjvsQdLHv/lORjBHHeVOjsnLryhQRVu3rmrJJvGr4eJMzt1w28qds5IcuKLrjd61NMpTXy4pnFFhrpsOld8aklNbvLQA2eDrLowo5i10TtP58fp1JjBSBS0YrnArkXeowV1Q2H4JRtU45T2rWN2vzyj221HtF/2ssvjaARTviPDVnjiduXZvr7C9DMWb3aozMIa3IaddKN6x/DBY07nw2dzpJheMavU7mjYyzqU1J+Ffin026BNXJcrlhzKbedTI543wcx9NqrZ2CcqMdF2KrQ3QmO6q4ouHBTxRmtZkS4U60cL6yZanLTzLu1YU4V+2aBHDebKitm8xznBWh3FS3HXd+5F+s4bloctj/zsVd76qOfKc7dRFZbHLe61La6+/yY78xVGlINVy7JreXTnkNa6vCNO4265BOcNnkDRdc6y7Bus8Xnn3vU3rjL/wqOYb73F1e1jXnvhcT78g288FOqsbgaouHvcgxekUzf0VLizozwgTGt5kpF/kMa/4cHc2MmxfzT9NPk+vZ7oys+Fz3OrZpwUb+pgnJV2mk/ZnvLHiac5GLBxfXBjmk0/fCx/71I6JSfkNxhxj+gxsv8WT/7cHJ3FnxSoQ7pb+HmLNmFL2yJNZu0eiOAKR8XJumNggHn8C+nC+Q+sPM3BHbqfWeDbbb5+f1nXZ86JamgqKu4V95k2uTtnYhPeOQfjfuA8ZnZTHLm1vxZuN8S7X5ifHeW+QmG0seIy4nLXvqKioqLiwqMamoqKioqKB4pKnVVUVFRcaIRX91xm1BlNRUVFRcUDRZ3RVFRUVFxg1M0AFRUVFRUVZ6DOaCoqKiouOOoaTUVFRUVFxSmohqaioqKi4oGiUmcVFRUVFxgapQsuMy537SsqKioqLjwux4ym0NOoqKio+FqDu+QzmkthaEQk68eMcILo0KliRF5Pf+vu1KBNjdwmo/dOhE3fhHuR8npQ5Z0n3cOOc7f98SDzPcsZu5c0FRX3ARff0FjL8sPPcfD0LCtWAqBBq8P0ZL2PJKYkTrO0cnukzG90dLsNzaFj/sodjt67x+qqzcp94oMYU1ainAWjNr+xYnW1xc8H7RPxij1Wmv0OUXBzy+yNA8z+Me6xXaRzLJ/cHuSOJQo9LR3tK7fR7TndI4ugduk166WnejSHHWoNfmaiNDQDwelKgSgNuulHHdqYQjp6GMgkyl2vIb7ZXJIq5aoPAnGlVHRxfoRSSrs8LuPFuuF9kKqGkVT1Wl7T75sGv03tSIIlG9s4eX27KeInyCRdKSKXiihVXUVOPT5vnFCfU17Rn47L1+lPzonI+tueN6Up+nJjmk11OC0PO5Yunh6fmceGYxE5Nc+Nx6p3VcapZb7d4wcMJUhhX2ZcfENDHFAdeGEko6yGkXxz1qGXoG6Y5IqbQ8vyapBXnr3Z0F0xHF8L6pdJMdNEyeR2X1hdDel2LRw+Zul3BN8E5UlxQZt+8ZZFHKx2hT2BmYeD92xjjz1vfWMbtOVbwIBZQXMITzjl+Ik5b33I0m8PCohBj16RHrZen9NvweqaZulpP4vCXn0Ut4rqidILW69BvwOrPUUbgnytAF6C2FU0yGp1JNKVPsXB1quG7gp0V/2gZhnVBiGJb43VtKZj9AgeTCdsXReufsmhBt74BwxuOyQqRcBSHfAhIKs3TgfnWB+ZhqU0ScJ7kCsZDDggfaiTb1P/hGjNkTC7CcvHhual62J6mN9U2gPl6DGD2yIrQPqGLNZp+pC/K94fP1aJnLTBkx2MUTvc0H7xOsrHdNAeKt1ONFxpzOwLcTTdUK7CVOGyvJQjVUtgkOaO95gP9+XsVsdqr83ias2RL+TJNavQmi4o3maHELLst+k889cPAVg9vp3jmF6xxw63sON6lX0zUgHVQQk11Td+L+NI7zG3jyA6YbLscHs7aDu+ucbpkyPFJM7YoVKnmDsH6OExFWfj4hsa75l95Qbtq4WebLroZ2nCJ4+969m2Nni5Xc/e7UOuphnAxCMW59EmysKuOq60TRBnSgJQyZNy0VM3BlmuoHdc/aVjUGXrK1thoEyiTqoQb8z2+oydryzCDCI9i9EgAphlj7YW3w6KGukhFtWxyJSGGZBbNLi5GeSlJ86P2mR00yA/fBeF2e2OftvQL2Q0Eyv133O5MgxS4ZzksNJzNw7mNz3zmz2+NWy/FqSkEfB2nP9IsTFd0qReKToqb6Rvn77b+NfqJFxRGwdsJ0gn+IUfGbH+OMwGj57pwWr4g1C5TuiuWGa3DYfPefzVfuj3rR4xHhHoO4vvDPOdFSapVBrNapAiikBWc3Te4FVorctx0mTEe4NTwXuDifEFOF62rG4s2HnqgK1Zl9MdrVq2Zh2tdVjRkba82eANeBWcCtttRyMeH/XovQomplcVRBQX63L7aMHxZ/fY/fANHtsJhuLL1x9lsbViZ77iztGC5/ZusTs75iu3H2FmHe/dfYst2zG3PU6FI9fy0sE1vvqTz2N6eOb3fYXnd24ytz1vLHf4pRef4zve82WeWdwCoFPL3PSjNqQ69t7QqaVXy9I1LH2DiTrPvbe5Ta8eXOXNn34GbaDbUa59Bo7/iVv8tuc/x25zjMUzNz1WPE5Dnkvf0Onw7Fl8roNXwRGu3Qt3nuDVH3sfT/2lF9b6+P5D6hrNA4exHHz4Se4834w8tyTJ69vk3ZMHGPGBQjOd0hzB9hs9x9cs89uerRcPuPXhXZZXpYgfPEYVaI6V7opgOth9sePosYbVbpgpiQt/poetGw7xSrdj2fnqEc3NQ47fs4fpPPvPzvBtmAWZPs6ClsrV34DVEzvsPzfDW7Kuu4/3telhftvRbxn6uWAiVeZt+C4+zmyS5wvMbwpuboK2ux1maGE2NHh7Wnq8aKAcCXGaI4d4sEvZ6EFmZK+48CJhpLee0opX7GGP2T8GEdrbW2M5aIp8srdYeI4lSu+eIj6T40neOSx6wKI60IMM7af3aGvXZlHpnDiHzttx2lLNUrvwYRJXGqnWrA5ZZjoufyRRPUWhBCmqSLfEz+eoWeR0uwq+2SZJUZtUXjK2RR00qmcbYGWEVek0MHYUyvrsOeWJWyuWn3yEO+0jIPDkkeLbLbyFqw7eWlzlTQt2BSsLn5k9FWfSQz52pTz1+SVq4PrRe3l19t5wvzrYu6382iMf5ldmQ51GMtYy/j6d8aw5VwJ2CY981uVncft6z+EvXONvvvBt0SnR0MdJopoiz9K5SedLSelDy/OvXW6huYeJi29oVJndXLEzCwMpkD1pFY03c6RfCGs44gfPtj302GOP7SymU8Q57FIxXTAeyXCNHrI4aKqE/PwsDuBRY910ipuFu361I2w3BrWWbsdil8Jyz+AWgWpLA3pzAFcWLctHGg6flkD5pTrPQj3MCrq3LMtrQr+tGGdQo5Gyk2BoLEgvmf7YeblltSesdhVtCdQQgBeMk9wOtaCRVkva8ADSG7ZfWbC6pvQ7xeyhiKNWRwZ+9FROH9D4Kb2w9coW1z6/Qz8X3vyo4nZ8yLecNWiRrxZh6cE2ZBosw8vooQ8XCmiKvFOclG9vkJWgCz9qmxxa5jcsx0/HGY0M6WVlmF+3zO7AwbOK23GIj3069zlvWRlwgi7ceIAq+yR99xLySG2S2L7Y9kyr6TgPsxLaOxIo0uiYSKTUfKIMy7Jyf8ZsovOBB/Eyvge8DFReilekMSvYfs1w+EwYsFGY3Qn3t5pQB7eIjtpRuNfcfGzEjAtGx7cSP0N6hFy2aHTmIDtVqZ0QnxctwjY4VLn+Goze1hsrfBPYg9mtFXtfaFhdTw6PjPop5SsT2mxMO2rMW9n50j4V58MlMDSe9qUbNK+3TGmucL58YCJ/Ghft8/nesUjUz6pj79YBezLxrhO8D95qTLf9GcuaFrvqsNhsDHQ99D1X7xyAV7Y/24bwVF8R8B45PGbv9dtc/dRslGfp9Unn0NaiNk1zGNVTjYxmKea4Q2cNfmaDR21lrIEOhWGOZclAj6HQ7i/pty1+ZrKBVhlmfGmta41SkyIu4zDxML/ds7i+xM8syJx+YQcabuR1D99zO4XobY6/j85PL10cuNRqdnszC6JgesEtTM5HjWKPhOYQ7B2LNgPVhoZZrenIs1jfF1OAZAhMSCMCzDwSvV+R9DlpV3JiVDB2oNfy+dQ40YE6E+iWDf1bM5onj2hbF8OVbtUwm/U01mONz/lZo5kKC+UGOqx3BucN83agpRJNlqi9FBegd5Y7h3Pe+vwVFh9+i0cWSxR45fo1tq8smTU9R8sZT1zdp7WO127vsj1f8fzuTRa2pzGhrivf8OKda9wwT+MW8Nw/+hXee+Ut5qbn+vIKv/Lys3z3e7/IU/PbADgM22aFLUb9RG8lqsurcOhnHLmBVvcxvFPDSwfXeOUnn6e7Eu6Dq1+Yod/7Jr/zuc/xSHNIa3oW0mPEY+OijCOWgdJK8BKNKJ1avBocwtK3fGr/GT71X38zT/3l19dvxPsMhRElehlx8Q2NMRx941Pcea4Ni72Q+fvk/aX1gTR78A2ZTmuWytYbjuWepT3wbL+4z51v2GV1xUx2nYW07aGn2zaIKtvXe44fbei2JJcLgV7betMFGn/XcuVLB9hbRxy/7xr2sOfguQVuLtkDUhHaQ8/u527RPbbNwTOzPP3XSI+kDQ/NkY9T/aJMDR5hOk4bFwDmN3vczOAWZhjoKT0+zR7aaDdUuehswoKsGi0YAmVEkU12go2oskxtpdlACDPHHWZ/CcC1wy00bVEvKLYy7zGNVpRXeJgjaq2MW+7Om6QZ2uyjkZXx4m7voLGsUW+q4HxwKqzNi8ohsw0WRDYYeSb1GlVIhs/pjjgKA24A7xB3GCk+k8+LelQMGDtyDlJeJ1JziZabOApD2cP3qx6awwO6n9pGmyuowPuXHt8GOnTPBxqts/B4p6jd4frs0ei0SLFpQnnmq3fwreHOF5/nV9vng+PklCcOlV/b/VZ+pSnKLh2Non6j+hbP2NRZsSvlqS8sg3MhwvytFTd4jL9+7fGweSiOG+S1y/LakGd9a8sjGjaRPP3Ckq9liMiXgDuAA3pV/Y6T4l58Q+M9W597ncWL8+FBnnj5owc8nUvHvccsO+bzFukcZv+QK58X/FYzePaU03OPn4eHtrm1xB7NgqdvJe+yMStPu9+hRrCrBnPYIV1Ps99hlj2z/Za+T5sNwo3aHHuk92HgdwwPUVqkN4HKM73QLwQX6Tq1gzFMnLfpBmpQbUu3LXRXQnzfsLYLabyoz9rMY+v1htVVodtheOiKTil3q40W26fXqpglmR62Xtvm6ld6XCu8+a2GfotMW6VZyggFbaGTh35KceS0mWqSgRqUot1phtFPNgPEfOyhYfaWcPyUHyjCmK9ZCfM3De0+HD6jYddcWlebD3FlKXG2NNBpQKDIpsgUloTZUMrDE+jR1MZMH8bmLoX2zrC7MOVlV5J3Gua1i0zzFPUoqSY/GTwLaijVBci77+wKtq+3HD5pwqxRod0P66BqI5W8CPdfcxQGfbdgVCfxYJbQHMwxvXL8qET6OdzzapTVFcltSw7k2o491bgGOG5PcjrSuicafu5gVh6z8vjWYA47tt+Y0RyZtbW66drkaKK51j9hjbN96+HtOHMX9yUuv0NV3zgr0sU3NAq6fwj7YbfLSRPIsyaW6TIpYA6OTr1stvh+1uUtnnmaOwcAbH9lWrhkr719/S2u/cbZ02DZ5BmPI8SCC0+4pPg2pS/Dyt1r6UespYGe0HWjsImRH+VXnusdsupAhL1fX2TabqNjMC1TQp4j77X00pPDUWBE8cU6peM0W0o7+PLieN9jjz3drh3vzCMMOs1hh116rrzchIExnnPtMIM0ce3AzUyu03RBO3vc5azBTH5jk4yUrHvbpofmUOMaxxDH9KGwbCSLP03lly5BmsiNdv4V56e3jQaDK95w8JziFmHUnd009FuKtopZCf2VHhrF7Ft05uFKjzQ+LoUo6gV/2NActaiBt76zY7azwlrPatWgry1onj1gMe8y5Tdryl15gWYsd4CpStgZVxh0jTvoFDg8WLD7d7fCemkDu19pefV39Tz93JvszlbMbc8s7mxLf+UOvFRuEy2vV8Ej9N7w0v4eN/6HJ3n8xetUnI2Lb2jeDZj+APEcv8Y+9Yd1J+Li7YLJrdg/nDITFwrzt3k+47S3TtwnnOmErCc4O850HXKKtHZ5Ul5r1NwGxwMCTanKUz/VjB2bmP/ZTs3UWSqnuZP4uo8c3xjCu55HfmkLtS3YGU6EIxhYkml7pvRj0a49p5i3Xt3cF/cZGrdrP2Q8LiKfKI5/SFV/aBJHgb8pwRv4zzecz6iGpqLifuIhvNLl3pyQs3AeJ+U+OjKr7v7ldQrWeup4uebkXDSn54LgjdPWXCK+W1VfFpEngZ8Ukd9Q1f9tU8QLS/xVVFRUVFxcqOrL8fM68JeB33RS3DqjqaioqLjg8BdsTiAiO4BR1Tvx++8B/r2T4ldDU1FRUVFxt3gK+MtxvbAB/oKq/vWTIldDU1FRUXGBEV6VeLFWklT1C8BHzxv/Ys3HKioqKiredagzmoqKiooLjsv+Cpo6o6moqKioeKCohqaioqKi4oGiUmcVFRUVFxjhzQCXe05wuWtfUVFRUXHhUWc0FRUVFRcc7pK/KOdyzGg2vajwnQp72GUa2Rx2r3ldtHSXIU5FRcXbwsWf0TSW1Yefz0JVKoL0fv018VFsi6wnsf7G5FKZMotsuSGebBLdWssnaVYkcYri7bOT8tbySa/036AKemJ5ZZ2mCqNnpT0tvKzvJnfDTNJN9TtOCBuJq8nkNfgnhL0tI1H2QXl8ksG41zgnlbMpzoY0Yg3q/INNc9487qbuRsI1K/KY5rnxWPX89bxfZb6Dxw8SyuXf3nzhDY22lte/bYG4KOplwayiXnmpxscgTpRFigpdj1IL3BdaHDZprpdjsVBokgyKnVnVsw8aJG4GfqYk2V801impfebjoNkhLmiriwt/2hIMlwM/C4WZLgmYadSEj68qz6JlmjXns7Y847rnTx2+jxQENWi753Yl7ZPyXi76y3RRJrkso3wz+/RN7Ulcy4Zw0w/GfSq4lZRNR6JdRb6m00GCeupAlOOlGx+Pdd43h49EyqKDkttTxJ3WLTkp0/ttcFTW+2l6rbLAVhqrUn4jtdH1+Pl7mc4ppnMggm83eA0bHC/pfX4l/5oDltNtcLimaqc6eZd0dN6SjpAk2fMkQ3CaE8fQfp2c33g8buLa+akTJ5M4o+NSdRXAeaTRkapqfjyifpM4jy5X622oWMOFNzSy7Hn2p6KmhDFZhldFgkddqGSGOJJvhEFcaxInzobUhMErqe1lsappvlnMqnh4CEJXSZM+KQImCeY0QPkGEDB9SNPPBRNnUa6VwfhFvXt80DdPg7SoRsMjlLK75YCQxbUKSdo0oOeBvYkPYzpvY/zYPt9qDhupWUowrH4WzxkdGaWRGqcwqFcqaBsHzT6qXxLSJ9VLBHBxdDNDo0RCPBHFHzfQeOzMD+chCGKJYowiAs6Fipio4Jk+NVZQonBWqZrc2uG19+k8JA04jSJasGj77FHapDpKeC2I82YkxpUws47WOlQFazzOhwthzdgLdt5koa2VNzhv1uIX/kEOT+U5LxwtZxy9vg2t55En72TBsLYoy6lgRbHGoyq8ebDN3tYxW21H5yyLpmOnWeHV0BhHYwqxr1i/9FmKgR32LYf9jMcXB/n48zce58ndfR6ZH7LfzXnx1h5P7e6z1XS5j1K+Lt6ghqH/Q30NnbO01mGiWfGFJ9Qax8w4jAxt9Gro1eQyWuNYeZvrnFDm18frl9J03uJV+NIrT6A35jz1ode5OlvioyZMeb2+/Pln+Mi/++WHMLO5/LvOLryh0cZw6yPXEKeolTgLUbwdKJhBspY1T1O8Zs8ty8q6KKcs4bxv06g5lJulYp1mWk6KCCEfnz3KLB3MUC4EYyZekV4HisrFkY4iXknnpdFw4rZJpCMGj3uDR7gp3EMpfQwgzuU6hIDJ1HxKI55HPOuMOCNVzZPSTcSmRPt1tU2Ihk5AzFi6uShjPT4jbfl0P6VyR9r0k/gjpUyyr0KT6loaXhG8wPFENnuUXznDJOYfuzqpgCPQlXkUbcntUNhyytUDxTeW1e5jHCVlTjMpp3BAzApuzuBGk/oizKJz302didF3zQqdaWb92UV0Hhw0+4YvbO/hFx5xgt03vLC7O3ImRtYzhY/6X8EL0vh4mYtnT8AYj7GepvExrDyvCNBYh/eGxnoa67Ci2YAmA+aRbHhEFCvxYRGgURZNz+7sOBrGaBTF0xjPlxePUXE+XHhDgxEOnzCBappQZ2m2MKJyRvRGvF/jQ+Fja02iruIA5ebFw1Q+0Eo2SCUVlwZKNws0Vorjm2BMAjUWtNy1DcdmGSR4tdHs4asBswrffRPLMqG+mXqzBJleGCizcRXjwKDjExB06eMDPKLGPDSHEsoktEkbzbMoJNZFye3zM12j305UkIrGTC2RagxSw5k6K8d/V/S9DuNQujZmFeWVbbAk2QGgGHxTujRo6STcr+eb5Y8Z4qfwnFdMP1p38uNzZZ3TuVH88r7USRjF/cXQ59M4ZR2JToxEh0p8oDbtKjhFrWGNnizrqRLS2E7xTejbVIc1p3lal031j4bGR1bAOLBLH2lugzjFdi6UJSAqa89oiRFF6GHklWgRh7Gx3ZR+eq48zrJra+UDqnzg2CHdMf3247xpn1hrN8AH97uHtk5z2XHhDY0se579G6/Fg2KEmmjcj7zXE2RZtVy8Lrx8tWbwmAsPN5wcZkQ5j3jorQwDmB3r0yeD5BuJD2PIJw3maoeHXO2Ql4/0lXiC8Yr5QpEvZKMXBuEwcyo97rIdYY2jCCMa68KbTfXZNEM3PbiZnODpDgZoRLulMokDUUqUKK1oGIOM7jDb0+IcgG8M2nqwMcwX+UjxGakzsoc71F9jGjGlVQDb+EyRifEn6lY2MV6Zr/eSvew06XPeBK/Y+kzXDOnGuZf0nMS4iRZL4db4TJ2VmvbE494bem+4czTn8PUdZLvnycdvs2h6JHrvyWtPFFFKd+Ngm0d3DtlqOjpvmdueq+0xHmFmetpISYU0iUYbPHqAuek5cjN6NTw5v4PFc7Pf5tffeppvuPomj8/2udlt86X9R/m6KzfYsiuMKEeuxYri4izBiMeK5rpCoPp6b9lplkWfhT5c+SbW0TE3PVZCO1txdGpzHttmxaGf0UqYveza41AWPvdlp+EhaKXPYcfa8pde/DZeu7nLH/zGX+D52Q2OtaWLN/RCwsLif/m538rz/8LD2RRQ0oaXERfe0GhrufXRxzGF5yuOtUFxfbE2en6F+mwebDUsMqNgnGZvLKfNCRTTa7FWEw2J0+zJ5XQu0GPJ20xGS7zmnW2DR6mM6LHszWteg5ruKMs76kqk8s3wfW3n3MYFVxDvhzrkQibHeQFY13donUey+KTdRdPyNlFuhaHf6EzAyRRcEe9EByQ5H8W1XUuTjXWzRsvleKVBK9INdB9F/cs8J/FLx92Mw1XAidBPnAwIdvaaUx4/Uvyspdt6nKPkdJgh3jTdooM7s2vczo4PvFSsVWqxYSbXT4s6TZ493w6zpeYIri+exc/Cs2qX8NXt94QZZOGIDP0Wj9NkJ52L9cJMZtMwrBemPIXgjJSwGp4Lq4hRxGq87JrX+PIlNYNT4L2wenWb5tDw4/ptLBZdXD4NkVsb1sD2v7QH3KDibFx4QyMapvnJ81czbAYYvPgUefIwR6olPRS+LdZ1ogEyTnEpXCZ5FNRJWpDPO9j64WH0cadZojLSg5oX9F1RvzhbKWk83wwPadq1Jj1xRhQ6QdxA0ZUPv8bZwGh9oaQe0ngd4yQj3BwKviX3T6bOiHX2w6f0gp/rxjLSNUpI7YDQFnRoS6pH7otUxwnVk+uggXJMmxXGdZNR2Zl2KmZbg8Mxzr+k0aaQgmbLdJcpjid5rH3343xPpJ3yNRo6b1rPtbqVVJMOx7YL95HGDS5a1Nek6zihpGyn2NUwA4dwL5X1P40yC+EDfeftcP8H6kzys2pWOszg/ZDnSTsIh7YVTogOztqZKNcxU94ysVSjdunoGMAsjzGrHrfVgl3EfMfJnzjafyizmYuoR3O3uPCGhq7nys9+MXy/yx/TyXkWsEPE8bGZusqTOGvxN5ybxklerdlwvqAA1/Ka5KNpFbooZ1NYiDvUb40W9MkTjF63HccpZwKicfPFdDde8eyOFs3juTRTzMZaioGwcBDKsFzv1BSn0RCOw7VYPB7tRMzlFPXTorzSK7ZDvXKdm+H7NF6mCdfaPokvDOtmTMrO3wvvftR2HQzb9Puo7Dg9VmBpmN2w+Bn0j3ZI67MHnyqUx2xR1Av+oMHs9Fjr8d5grKNtwy45Y8LutHKnVpoFJMovnV85i/PCta3jQIt1LS/fvMK13SP2FsccdS1v7G/z5NV9FrbPu7e8St4RWO7+A0Z038y4TC0mOG+YNz2NOIwoMxPSN8bRiqcxjpVvMASazogyNz2NcZmim5uwStNl74pM43k1/MxLH+DOzW1+64c+z+PzfSyeZbHHf246/ufPfisf/Fce3u9pLjMuvqEpcR66poBucgvPBXd2lHcIm0zn5fZ1NuAB/jr/3M7HkODsOGc5JmeFb6IU7+YYssev1pzp7OT4IkPZE6dmbYegmFEeazSmnYEIV1S54hw0C9RuseOVK17R5mqY4QA2/q05RPE47eBuCJThprXHTjiZmtzkgAh5xj7a+RfznNbh2qHn0c7zxb1v4vO2zJNcn2duuodmZOr25oqK+427dCjuBvfufJyGi+uY3CvOMq/vOufmBGy90xV4l+Bym8mKioqKiguPOqOpqKiouMDQuK51mVFnNBUVFRUVDxR1RlNRUVFxFqZvvX7IuOw/2KwzmoqKiorT8A4bmXcD6oymoqKi4gJDufx6NHVGU1FRUXEa6mzmbaMamoqKioqKB4pKnVVUVFRccFz2NwOcWXsRWYjIL4jIL4vIp0Tk343hj4rIT4rI5+LnI0WaHxCRF0TkMyLye4vwbxeRX43n/lO56/eBVFRUVFRcNpzHTC6B36mqHwU+BnyPiPwW4N8EfkpVPwj8VDxGRD4CfB/wzcD3AP+ZiKQ31/154PuBD8a/77l/TamoqKh4F0KHF5E+rL/7jTMNjQbsx8M2/inwvcCPxPAfAf5A/P69wI+p6lJVvwi8APwmEXkGuKqqH1dVBX60SHOXtT7HiwkfRtj9zut+5f8g093POGelO2+cu63PveT7MNM8iDzOwv0u417qUPGuxbnWaOKM5JPANwD/L1X9eRF5SlVfAVDVV0TkyRj9OeDniuQvxrAufp+Gbyrv+wkzHxazPW7/wx8I4YWOiIliYlkoLKHQ3JDJbpHyNfj5XNIsifoXQfq3eD36RGtlVM+sd6JRrCyeSOY7a2/ouo6GV0zvo3ZNVFZ0fqyiyVCvDDfOSKbCZqXgmT8h3kniZmXYaYJo0zzK+ng9+fx5wk7a4XNCeWee8z68XdlveMvu1M0yRV4im8VP7PgmUDN5I7QVNGmnZGmI4bSI3NXxULeTB+5z5XGXxuZu65mPo0je6Pi0OkxF8S7IsVgTruNpxw/pzc3K5f/B5rkMjao64GMicg34yyLyLadE39Qjekr4pvJ+CPghgKu7z+nBM7Z4PTeQJJDTq79lrIMyMh4MaXzWFWHQAYFCnbKokbCmkRJ0SXQQGov5iAMTpYSDFoqipa5JUuOUJE4VyrKHQdrZLTSLQg0SzqBNEHwTF/Tt1YL0MsRpo9Knk1CmBbWKeGGT4NdUsGztimhogHGhzqYHtwDfaNEfRaeKgo0aKinLWHYQPJORBkvZvzmv1IdFnUs57DUBq1j2mjhZ2bycJ1ncy883Gbn143QNxIc+9c2kH2M844Ttl8N1O35irLEz7aKRSFwh/JXujVJkb1Mdy3DpoTmEfptoGIv26yRNcS+Pspxek9PK9JPyy/orWVBtKpI2NXRJUC4rdaYwF+6zQhZmcz1yG9edylHbN6RN4n5BME+zFED+foqjsnbtc1uU2c2e2ae+emLaigF3tetMVW+KyE8T1lZeE5Fn4mzmGeB6jPYi8J4i2fPAyzH8+Q3hp0KWPc/8zdeKgDOoklNkgbNAWArbEHddh0Py5yDPmwbQTeljuGFQAR3VJSh1ApguiXqZsQGEIAaWrk4clIKQ2DA78k1SSNQs9jWojo61OHLdYh1GhrsIz+XFQdfNw4xrJFZWiouV39mQz4YyBgdB1z1vZWzIU5qUb9GdWVisiKMmGFtIBjoYbL/QkRBZ+EzeLIPRKwwljWJmbpQmiYd1nQEW+Fbp33eEtR5jPSaWoSpBHCxKBKciky49gDUe5w1NIfxlC9ExG+M6lSz8ddw13LqzzROP3GHR9EzRGL/Gs281HY3xGJTGDGJhO81yJATWmDCq2mIq7DD03tAYn8M7tRhR+igc4+LDYvH5+xW7ZNusWJgOI55OLV4Ne/Ywt8Wr8Fq3x5vdDs/Nb+LU0JrQpjbK0pZiZCaO+m2hz24ZC7TNpKcVN1KkPPRzrtlDVmrptGFhutyWhelYqWUmjm1Z5nMWxRU3WyuOWUyzwnDDXeHf/o3v5cn/YxU+Ow/ONDQi8gTQRSOzBfxu4D8A/grwzwB/Kn7+TzHJXwH+goj8GeBZwqL/L6iqE5E7cSPBzwN/BPhzZ5Wvs4br/8iTw/FEinkqvQyDN5fkiNeUEGPcnFeYm2Yp2qn3P6WzSqGkUkp4zbsr6+uHPx9nYXalRb0GWV3xIR+1Mczr4FnFcdD0ivWaDZl4zX0yojRKKV8oPNFUwWI2omQZ3FwHMxiqsQepa+JXU0lcmdB8GynE4tx0hrFGC8IatXaqtG/QwB3URyeywOOMJueSt3vSxkjtkeNDsIKfJ13uiSXErguE5fI2lz8V4JpiW5XHvKLt1bFqaoSP6fItIHCcHaQh77HiZ3RORjP4dQdpqiSaMK3zWGp8Uj87dnxMlOAulU03OT+bnKRcz/J4KmtOyL902kbtLKXFU9oyj5SPBMcmhecZrd5c75AHgMv+ZoDzzGieAX4krtMY4MdV9a+KyMeBHxeRPwp8BfiDAKr6KRH5ceDXgR7445F6A/hjwA8T9IR+Iv6dC1rccGoEg+bv5Y0xMh4FdTC6gZJX3bBmMKZT/5IGyTelYaDRigpKktYtDJVvNdMCiWLzTaDWzDJkqFaHh9IETzwYmpAekUCZaaSxDJhVSOsbzfRhihNmSToyqsOTVfRLgozDxEmm7NyWH+dVwijYdE6HtYpomdWZfC7wV8WFNLqenxJmFJvOjeob8xtxo0O/iwlyxThBliZQkAs38QDioKghvhgN0sJKSOsFsYrYwVtVFSTOfPzSsvWFGW5L6d67xFjN+aFkmeIUXwTUD/kkmlK9DGVs5N2I/RjDVwZzYPFXXOinsv1lP+qGvs5y0NGox5lctimlvLQG+Wd8uqYMa35exvnnbk2OluRZJTaW5Yq6pDorSBfubW02X9Mp9bhOtusQb9IV2eGM1LP4eA/GcWCNxiy7MYX5IjzdXwre6Ga6r2IjzjQ0qvorwLdtCH8T+F0npPlB4Ac3hH8COG19Zw2y6nnqb53BsG2isDYtPG6Stj0vFTeVuTWnnCthZfAgGcZItSZ77GHWMJalDfUI59TI2GiuLUivL/6qkWH9auL5jj3CcTuHdYXwlPpW8HGzwog+Kz3WMqykJ3WIN10bGnvUxWdsdy5vVMdx2UzPQ15DSzNasxJ8C35mx15rrouu5ZfHwkbx0ZDKStA20m8C0hnsCrQRdGXiOtbQSPUSjBfBOIkoeZM/4VhVUBWadqDA0pKBCBjj145Xq4ZlM2fnkSNspLpK6kjKOsSGzts+x51Zl+m11oTvM9PTGE8jnsYMlFWJuRnTdEs/DB1ehcY4rCidt7TGsdscMzc923YFwH4/x4hyxS6xcYR3anir3+agn/PU/Da+oAiteFysg5VA+yW04kKY+BGNBrCQjlYcrYT6HuuMpW/Zswf5+649YiHdiPJbmI6FdDH/HqcGK4FCS+EmHndque0X/Fuf+QPIXxYejGrrgEA0vPtnNO8odNZw/Xc8S6J70kBhNtBEQ6LCmznlHlgbdGC0+Jd2rp2482zqAcGIfjNOB1osU35DfmnhO6Ur808LjiEsGIqSHkv1L3fXJSMX4hVxc+blzG3YYVd6jQP9F2knK+s7+2C0o20NadfVdAdZyvc0+uuk3XFnlHni7rtE39lixD51l9rk2EzOFRtIZBUGIJ216zvYTqzodPahm52dDXFVDKIO/D5qI8Gwqdw1h6qN6clOlCHQbCtgVTg4G6m7bNCL2U3y+Jmci+eTMzR1+EabdiDPvj5bUGqjeCm/ouw1x6No95rTQpyBlLOPifM1Xa9cWx/c4NAAbL/hUXeTirNx4Q2NOM/eF5b5OHnveey0w0xgI4ecb6bBwy/z0RF3PSTT0iuP59QMN2jy5kfrPkwMh5c4K4nctGEYyOLDqnEn3LDAPz5OdVE7zDaSgUpOZTKOgTLbEBapuU0P4FBIEe4k7whyW3E3W6pzMUCUFF3oBx3HcTKmwSJ1lte8NnkBOkmTK5VOF2Vkix3//DguTjDHgTrTmV+rf/4s8ytpIatIGzLNtinRXivDlS82uBaO3tND49d2ga0hlZPujZKaKmlYP2l80S7phObA0O/6tcF6BCXv5Ms0VmzTJooo9+sJeayFFeVu2pKf7td0UtzgKI52hPWC6cG3rLUjZzm9T0/o5+wwlXVx5B2EqOQdaKM0Kevynor36XQmXrbZdIarD0lCoM5oHjR69+C3EN6HH5Od6206p8UxG1zTTVTfWroz4kzoPT3nDr3RsRnnceIuvvipG7zhTfkOVFmRH8QBeUIlll61lJ9DHtPFblFFfJ9pxEwxFs5HKm+Tk6LG4K1d86IhGPL5Wx1qheWXQ7w1Y36Cp7w2MyjXDtMgWqTJdGucwdpj6K/YtTpvHIukGPQJjkFJeZbtT1vOy7XItXVOWd+9N3VCvNG8fidNiOvjGk0y3Mkp8b2h7wxmUVBzQqQdi26UwTpZq5kilLjGZIzPa2g2UZWieG9wKrTWZSqxsT7v7Ev5G1Fa6+Juv4GSTHFM3J3XGEcfp0e//On38dT/78FTZ+8GXHxD8zBwHzySt3+zubOjXDCcZDYvt+9199hJXy7wr9/v+bWC95puk+N0N3meFee0vj4nFbkeltYii/Wn9BnjrYrYH+lfeyhbm5UH81qYh4lqaCoq7hcusG7Jw/e6L5/jdLe4uFf74uG8S5gVFRUVFRX3hDqjqaioqLjguOzvOqszmoqKioqKB4o6o6moqKi4yNDLv725zmgqKioqKh4o6oymoqKi4gIjvMCjzmgqKioqKipORDU0FRUVFRUPFJU6q6ioqLjgqNRZRUVFRUXFKfjaMjSb3o90r2EXIa/LkPem4/PEeRj5vt006fu95DHF283jPGU87DIv2/EFRXrX2cP8u9+4+NRZY1l9y3uG4/I14lEzxfQe6Rx+1uS3+IqCOB+0VJyP4Sd3YNJJWZM4Pqd2SdBxmaSZvG8vlOGHF/lt0iLJaTfoq5TxN9WrlGXelH4TTmvfmh7MXcS92/O5DB9eyOg3vKxw6haZDflOROHOPN4QNnqNvMj6sepokNoY55TjUPe7MzbnyuM+G5u7bde74rh8hibHJfK5C/x+u4uEC29otLW89h2L/GrzrNtiwyvTTQ/NETSHympP6Bfk1webPr5m3QWdlix+VEgxjx7eqLWRdSiy5g254PKV61N9kyw0FtOVss0qYJdgjwW3COWKCxK2SYMma3comBWjV7yLhrTdFZ91NURjUabQHEn1T5K1sY2mk9Hr5jW+vj1JNqf2qC0kaj1RxG29T0aQYnA2OsSZpBMF6aE5FPptRdvJK/RJ12oQOEntLMveqKOzoV5S1APRkVLwCEU9gyonuEXMow/96JtJPSTWrSfrBaV+Fx+utZ+NX/M/lQtP7TUu3pvlfVW8Tb9s60iLSYZ6pHMpTQrLfehZb7+COI0Kr0XZqY5lXFOkj+lGui+xvE0aLmsaSNN+kKFP165xagvr5yWJ2skgGzHKN5epOXzz9Z/Ua1oP1eF5K/ppdtux+MUvb8jw/kMfwCzjYeLCGxpZ9jz3t26NvdlSgjnOasQ5dNagjRluuvLmFbJkcpZI3qBpkj2UwrMbiaNNxM9G+iK5fsPXIFAW4ps+3LCuTfULg7q3US+lyMe4wijEwcX0Src9tG0wHBIUDWUocyQMN5FSDgNjFIIrHr6cVzNuw5r4E+UDN9Y3SXo3I1XCcgCQMAD7OfiZBgMZDWIeYNuysukveQ/RkCXDnOqUDJUpRogUJooYskYJUcMkaZkQLgUi0DuhP27ZvnqcpZPb1nFlluR8Fa+CjfonB8sZrXVsz7qsXdJ5w62DLa5uH2ONz3on+drKoKfivOGoa7m6OKYRnzVRSvqi1EJJGikz0zM3DhNH4d5brrWHOAwWj8NEGsTQqaH3Nsstl7LIB26WZZfnpmfbhBfhp3drGYI+y549ZNssmYljpZZbbodts6SVPocd+jldVN5LMstXzREL02HwWRr5WNsslbwtyxy+bZZYFBfLnuFZYTj0cwAetYd4FVYYOrW86a4AcM0ecs0csyM9hmGCaoBSWNMBhyosRGkBK5K1CJ0qd1SwKI9bi1fFiOBVOVbPTW9YqmXXdKzU8LSFf/Wl38v1f3IH7d/9b6p+u7jwhkYbw61v2s0GIXkeasJgHLxHjz329DsWN0sjRxiYk8e3UdhKGAwWkOWNCw9nJBFcejzxWBwIgxc/8p6SymW826UPEstNE0bhLMFc0H2pHqafuF4a4vsmevpl/aZemhTnkjeYhecJhlmL9ud2l672hKKbtu+clMGaB+l98KKb5H5Hw1QaR9mQ9rzU2xk6JmuO4ZQ68oroCt80sQ6KSgvSbsz7qirIDJWtXP5clV2vqN2NZZTlj/OwwAxQs0sP9FNDXtRxKuzG5J6eGvk1lE5RcQ+Xgm15dqNDWMpzNPMpZlCbFCtHQm5SHJftKsrLwmtl3aYOii3CtXDGLIWi53jWMRK1S85Zdop0PNvy4SZMyrUjRdT4PGsT225h61Xhef/CCZ1dUeLCGxqMcPikCfRTkoFNF9yHm605FJojy/Ka0G8NN6zpCIbGBanYNR3yTKERn5A44E/ps+KBziqDmx4GoFQlNL3grQaZWhOUEaUX3HZQBzGOOKMhKx8Sq2JWMpo1iBdMB91VPwxcnkx7ZRnaRIt5EJXC0IC2GmcEgvQSZhM2hnkJ0supTc24HEr53lxganPMw0nMr+hnz/CweoFesIcGt+ugUSSWL3Gmol4wzWBU1BNoAy9oHAiSVZr4CKHqwsalLCn6kjibyXREusaArgysDHKlDwqNK4tYRRqf8y7L8ccNWMU0PtQPUG/g2MJ8tIhXjNpF/7jY70lqOvcvw6wt9WGhKpnzjNdCvWDaPrQp9mOWnU59nzIurqG4eB+kGWW6ltPrLYRrRcy7l3wvDXy2DBLVFHUu2xXLzf1RSliXz1Ixi5VofQdJ6viMdjG80WFWnLtGUNHwDJR96ov6pjJjWaaPz3WpcgqZDk2S0LnP7lUU7h5w2d/efOENjax6nvvr14NXV17YEXXmw2djx55yGnFUwZh1iWGRQdq4lBOeXtPC6z5JWjhBC+8zU1uNCc+UU/CKn5l8g6/JDEN4qNNsJ3tVkXaby5jPLx5qFTN4flKMAdGT8w3ZW5M4eCXaDoa4OY9EicHowZt6pdlznXjHo75O0V1Yv+hum2Bco6HP6w2a6h8NYhwsRvw4QPJGU7pUL1MYoByugzOR5Ipz2iFjSYNt2TYTjIxtxvRI0wQjsjKKtZ6mcVlKWFVYti3zRZfpuZH0cNGdzhucM2zNV1ijWOMzbTazA1XmVTKtZlDmTY9BMeLxavAIV9tjIPzmwjPeQeRVaMwgUZzC9rs5V2fHbNmORjw7zZJts6JTSyuDtLFB2baB2urUcqvfYtuuMCit6WN+gdIaLruyMB1tXLxMdJrFszCBimylx0Z6bluWAFlKGaDThtt+wUwc18xhzufQz3nTXcHiedTu85g9oI0UXEIrHpfaj+AQjtWyKx2zWMZcEnUGN/yMhTj2jMMDCxFahA7lwCsdwq4Eiu0JI/xLX/19vPUXFqiv1NlZuPCGRhvLrY8+PqK+wkJkMCDGBerMLAN1ltZDwtQ6PtyOtTWQEaYeFwS9eTcOlwmNVH5P1JMaGXZ8pcXKSE2JC9SZqI8zp6J8hoE0LObqWtnSK74d8yKZHlMdOWoj+km1MFxD/8gmOsqP81nDKTvrNua3IZ04jWtKG67HpunIg1TLndJMPrRDp7vTTrp3tGdYMyxcZz0Ojkxp9crPcraIB9NsdKROovqONzhER5t2mU2dq7LqIogqb+S1yvQJJ9J1o7zHnyetVU7XMktmYfxd1p6F8RrruN6JEk/rkFOWYRNdXtZnWk6ZXwqb0rjZoWpg8YbymP/8egH3GWFzW53RPFDk3SVmuAEG71/iDSLgDW4muNkwSzEu7u7yYUG+vBkHnnbyIOQZBGMKAEY33oj7LVDexOLDsY+L63YJeHBb4xnPJhrPJFogG1ewHXRXIoc8oafyTjOGfNJuuUS7hdkV+LzjLM4UzDCzSDuUctlpR1s5s5lSG4lBcUO91EzSxE/pheZI6Hc00oXDLCMXagsDU9A/iQ6ZXpNNmxVORLFJYBQ/5eEEc2zwOy7QOisTV5WLQuKOPjxIZwbqKeXnBXNk8FuTrUrFPSUpzIU+0dmkXkreGZho0NR2URlopBhXfBEWaZ7y/Bp1FWH6ySzUFlRXOcgXFJgkmjW1OYUVM+LsRBWz3FF/p34kOUOTeKO2hTqrTU6Qhj7uh3SjWfkmRGYyt4civqZxJt3zOtCVRfvSs5KoM7O63IP/w8SFNzR0PVf/zhfvObmcxaOedt6csKp64sxoQ3jyMqcE/5r3NxjRE/OMFOBA3617wOWxpsV/GAbHWPaa91mcK5G82zVKsYin0/AJrTgtyziPb0zwRKXwZCW0cbTonMowEJ54RnUpjX2iNjctCg8GXTaGj+rXg5s3cQt6cham7Y+fsV8znZkGYwd+ZjZ71VNP2Yc1xGmbsuOR0ymlY4IZ+0KZckyZyHjdbzqQa6yHRkOajX4yIIXxlMYPO/pgfS2tNDKTtRsrirHBqljrR1RiiVnTZ/rQFGk7b8JOu0gdQqAcD7s2h2+3YbdcohqNKI0M1OPKNfFyCTPTs7CB7utjB/XecOxaZqbnSrsc7dJzKvRxJ8KW7ThwM/baI37qhW/k2f9R0LXdOPcfdXvzw8Db+FHU27sJLjf3etqteblv2/uAS/Kr8DMdpfNlcvdpTnKy7iXf88Q7lfbb9Bx6dlgW8YahTEVwJ6RSI/TA4Qn1OwaO2T2x3jfj5215lG/sbqLuQfK67x5cDkNTUXG/cUl+0f0wvOXNuNxO1sPAw7syD+a1MA8T53BbKioqKioq7h3V0FRUVFRUPFBcXuqsfF1MRUVFxbsYl30zQJ3RVFRUVFQ8UFzeGU2dzVRUVHwNQLn8P9isM5qKioqKigeKyzujqaioqPhagA6/9b6sqDOaioqKiooHimpoKioqKioeKCp1VlFRUXHBUfVoHgLEbp54qWp+F5S+EyTmBtnntbByd9xJYXcb56R054lzP8o/b5y3m+87leYh5SHWjN+V9XbLeLvHJ9XhXo+NICJDG+/h+KG1/+2WXXEqLryh0XnLa7//A8PrwuNbbaVneIttfGNuKZ+8Jik7ffOujl8RXn6O4lHkMw1PL7ed3mvFsfh1eei1NxMXx/kt+D6mdePy1lRCi7cAh9epn3w+9I/meOIVs/K4LRtesx7fOpzlofPbhCXmocUr+89uQzih6+Hx4Rykq8d5jXR/po6cTuJAoSJa5BvrajoHIvgkn11KUZ/wo99SUhtVZOnQ1g5i9JN2rdVjLb/Ng5FO89nUplPSn7lCXAr/nYZ0Pbo+vDnb2s1p7qEeCoM+U8SGl1mPj8/TrvMiiLmE79acmXZt3nCaoXEeXa7OX5d7RLid64zmwUKCqiSQBx7fDEbHJ4ln4uC8wcCMXrcujF/dfoKByVos6dMopYZNikeZtMwXorSs5FfBJ3uYZWcLQ5mlmLPRDDK7ppNBl4SkbRPTZinmdDI2PA3QRqNcchi1NEntRu0Q6Qx2v6F/osPOXYwD6kwYo0yQWjZGEZPyMHg/lGGMBtVIo/l7+gSyomTuk3E3j2yJkZhXTNs5y7xxNNbl80W3j/Irf2fgvGHZNXS95ejWAjN3XN09GuXtvcGYYTQ3Atb4fD492AfLGYdfukbz7CGPXj2gLV5hn9pXKlYCg5IloTwbjxvxGBlULr1K/gzpg1Jm700O06iWmfIq+8F5k+uQyk1lGFFmZui3VK6NCp0m5xUUOpfe8rOf/3ps4/jur/sis0I1Myl2dt7m7318dX9q96bvZTvLeudzRT025ZPDz6CNDEO/pHa24ln6htvdgl/9/PPg4Zu/4SW2m9WozCbGL+tocpgZ9VFr3KjOf/uzH+Kb/i8v1zc4nwMX3tDIqueZv/16OCi9iU201TTOWmb34BVkpcMi7ZTJ2yQxHZHUAMvjUR4TDZip4zKSMDaFjoyEtFleegO7qEmqulBHlCwcAuId4pV+YVHTrM3UkuFUiXLPG86X+i6a5LELgzuaUU5mSOvhRRgw1+AkrMz0vIytTZk+dZXCwik7x+DbFrfYwk/0WNwk39KJSHmZHh59Uzl+6Qq3FlcGJyPLT+t4lpn7YWhLlpEu2luGlW0YMmHtnCYp6ilSPoWInBjNejHZwGYngJFDIKI4Z+D6nH6m/P3F88yidHWSpk4G1auMvOvS+KXvVkpDNjaQSYo65VfGS9LUzcSYh+7WNUMy5OkLA+qzdDTA3PQ0ix5V4Zmt2zw6O8AS8m/FZUlpI4Ox2DXHufxOLQvpcBh2zJKFdFHa2vPrTzy14WI8CFz+tzdfeEOj85YXf98TAJkW8w2ZUvKxBaXC5HRWEwLHA1k5i5mmHQZ2xoPoPWBEm22YcY3yLmdZLgxyWQExnbJF/SYzr4EeG/qqVBCFWIf4Z5eKXcJqV9BmyGOg1oq6TstwBcU0bc8Jz8SYUtsQViJSh1MhOFEd5KuZ9G/xvWyjWnAlw6HDdR5dj7I+yV/plfltR3tg8e3gOIzoxEmbzoNQznhKPC17ROGFkBNoWsnxzxyPNhm0mLa93aGNod++ujltjntym2Xi3OfHbLhk61W6n+urG/rn6/bD7OxzVz6yxjrAKc4g4R5cc5AitvZ71N25XzV/V+PCGxq8Mr8RLnbW9G5YVzaEsSfO4PmOKK/Si94w2Gl0lrPss6x/rtFvI49ci+/jeq0NzGbyOTGCpgPTS5Zenso95+9JHRGCRLBjyMgySA0XxgYVzKGhORRWj/UwjwqKcbQKNNvQPml88JBFwzmNVFw6H73qQKEFFcWSSkso6SspjgevN8RzPtAzrXXYmN6a9aFKVcY0k4bu7p2ld4bDW1u0Wx17u0eZGlMVnDcjSq70wkv1x1tHC1778jV233OLp3fvMG/6TPE0MqRvjA+0ksqIwkqedlJrhIHqKZHomQSP0IijTxrFMV1JI23KpzGONipLWnymezxCK45ts4rnPZZhVrDvFvzIC7+ZR3cO+cPP/z1m0scyQhssnpVaPAanJnv1xzobxfVxyuWKtthofVbaYPFY8TmPoS3ja5vKDPVU2liGjccJLvZHCnNILttjuO0W/D8//zuwxvPH3v+/8mRzh4V0tNLHfB2teDo1OISFOBbicPGesqJ0aujUMBfHTHyeVP4rX/rf0X2fRfuq3XMWLryhkVXPE3/ntXBQSiGX38tz58pUzh//bmSb7+b8JhnmaZppG2Esz1zQbmvSzLAWb42W8wpO0dag1qSRfy2bkp5T2TC1k/BvkG62keYbyl+fXQ5Garx+Nq3kOqU2ogJTMyezqRnBZj66UnzT4GZbgeYruiZRZ/0GRyLlZXt49rZnefUar8+vjWSmB1psUv7UKSlmxtM2jY7L/pw6L8X0K9N7k40h07yyQ1L2b1zTS2uEpfOx9VLDy1vX+H988R8fHJPoPAzlDA7LKLw4Lny4wXgbXbud19by8mOgeW2vsW60fmZF19b10qeNxr6k+g67lje+9Chqlf/K/kNstysWthtoPFFmps9GvjUur0/ZwuFwKsxNn2k5I8qnXnqGD+krPAxc9jcDXHhDo/OGl7/n6TyjgTCLKXeghYiTTya0SDkoMKGBJrTLSZRbzqvAiO4ozq3twirKKemF0YwmxfcEasoF6mY8UEzWQKa02LRNMU45KEmkvuxKscfQ7YA2sl5nz7DTDEaDbGpz2lEkE+pxSvFsCsv9Nvme4qUZ7CaM2jlKq8NxSq+KeAlUXHHdRyPiNO+Yh3FKe8chDnwrYZ1kU9y1dm0+sZn6OqUdRRgU987Gek87nfH1Og0K7cERKkK/XdAESuHMTcsr05+zvcWOw3Qt1u67u0HJYmjhW8X67Ch88PAQUeh3HmUl8Lb3icUyv/6gqxsBzokLb2ikczz9c3dGN6OWv6sxE099w6L94GmnT/KMYuNAVnrgMV25GD5djF5bxynSbzJS6wZQxt4xxEEOxOnIuPhmvPstDf5rC9JTj9qyZpTUhqez35Gw/mDG9U0ZiQ7GKvSDbohX9t+kzYZhKSH3iY49+alHH42BNoXnLYSBXoo4G69fHL08NHcsfq74K32Ia4aRLe3GS177aDIVL5I7bpi91LJ6pqO9ssLE3XUmUoTOGSR63InaC940w+K4GSiqhHIhHE5+O29b0HvTXWqjvCI9lui0cgG+MZ6Z6fNuuJnp1xbTD/o5H//8+5lvrfjt73uBRlz28uemG9W1XGwPtKOn8zZ/h0DrteLwBZWVjrsJHbgwHfO4KJ/OWRSHYIsF/pLqswXVlmi83JeR3gN4rdvjv//8t6Mq/DMf+l95IlJn5c6yVtyIgkvUXsp7qKvP+QP83z/7j/HEHzUPxdjU7c0PGs5jvvTqO12L+4dNu+HuM+Qs2u7khGfHMWfsijgtj5POndYna1TaGccw2kCQZlzZOTkt/bQeiWLtHTQ2t31M77nCjR6XsXGn4oYdimtjiNmQXiANz3bDLkUfw3xZFtCLsBI4LPMrDXx0ckThA4c9SMund751vI5p1usCU4ZAxucmjkimXyf13uRsrB/LKedOTgdhBrh3KxiC/+GR3zNspjnBaTw133Q+Ojhbr3tUb1FxNi6+oXm34SH8kljvZvvTXeNyLXw+iJ44rxm/rD5oezeR008NTvvJwQXBCXvp7h0P6a0A4XfDF7NPz4tqaCoqKu4dabAtB936WpavGYiIBT4BvKSqv/+keNXQVFRUVFxwXOAfbP4J4NOcMWGsMgEVFRUVFXcNEXke+MeB//KsuNXQVFRUVFRM8biIfKL4+/4Ncf4T4F/n5Jc+ZFTqrKKiouKC4x34weYbqvodJ50Ukd8PXFfVT4rIbz8rszqjqaioqKi4W3w38E+IyJeAHwN+p4j8tydFroamoqKi4oJD46t1Htbf2fXRH1DV51X164DvA/62qv6hk+JXQ1NRUVFR8UBR12gqKioqLjCU880y3imo6k8DP31anDqjqaioqKh4oLgchsbIg/ublrOp3NPi3O90FznOefvs7eZ7L+Xca5oHUY+7yfOdPj6rDe90/SbHYs1wbCQcF3Gn5zfmdcJfTjs9PqGsi/qqnYuIC0+d6azlxu/+QPhughKe+PBCv6TyuOnNxzB+S/JU3dF2ml/0t/EFgCl9fuX+CfsLC22b8tX86Vi8YpaKnwvepozHdREP7YHDHjlWey2+lfyywaEOmuPmpJM2Z2VMp9iVx81MeEOzMH7TcX4Nv9IcOXxrct1Oe037iRIJhP7ZmHbD60g2vSrfOD9W0yz70YX3v2clxJN27U+u0d2+wv+8L0YbvY7fK/ZghRwt8Ve30dau5++CKqjaDQNT2RbDWts2KT9uVKRUXX9h6Gl7YmM/inOoDa/rFO/DC0StCXk5H14meh6MZA0mdXm7e3O9IqsObZs1vSKFcG7W5uNz13N6anJ+o6KDKpLe1qyKHhydXf/7gMv+Up8Lb2gwcPS4IalpigriB02a9Ap736y/Kn/jm2QF8GC74tX36bX7FtTo6M21YaCXQqdG13RtwnvmN5ctPbT7QndFcfOYphk0ZpJxWLw+Y/6Wsv914BYa4thoXFTATwpI4lUj5cw4EC0tzUFDv+vRrfB24ayeWT7/naG9Pg/xticvyxRFbDSgRkfKmVORKiCLTa1dvom41YYuwnnDatmw2FqNujHpzS+7hnnb5/Tem5xfeiU/DDZNVbIq56Zyy7xDXJ/rUwppQXjFf2tc1rFPr+H3BDXNg27Gi3//WR77VeX171ny3qdu5PTptSEHqxn7R3Oe2rvDJnkA5w1z27PVdPTxxkhlrbyl85adZsV2s8rnVt5mNc9SaXPLdqPyg+JmUKpcRt3zVjwe4Y3lDn//K+/hH3zvV/EqvLS/x6tffoztJw54YveAl964xne9/wvMTVTkjHXq1ODVsNMs8+v60+v9l77hzeUOj80Pcjn7/YyrzTKojBaWdNrXQFTVjH0fX9d/5Fr+0ie/ne/65hf4yO4r4bX+UaHzRr/Dj//97+Cf+86f4dFmnyQh4CJZM5M+x3Vq6NTSaeqHfnRPOMJ5n6+Bz+1qxdGpZd8t+F9e/RB3juccHM754L+yrJo058CFNzSy6nnur18/O+J0GrvplfSbvKyT1Cs3VkY2fl9ThSzz8MFT1MYwvGY+1FdFML1HrWCOe+g9T/zSLGrMrCtijspNr27fFEdBeo+f2WDQTFK/NGsiU/Z4iZ8ZfFOeIChpymC8s+5NYbTLV7+f9Kr30ayLcZ3Luux48M32unMgsO2CI8HQ9CF/Ca/Cn6I/SdWyrKM54VzZ7uJ42hYIs+pHv6zsvNxx9MktXr363Fo/mC7Ee3l7d+zw5E5hTS9oOqv2s+h4pA7wKXMdnI6Ub3JAvMBUSROyEqYuDbPrDX/vIDAGcmjZum5Y3rnKl3d2aG5bftZ/fXAyJDgqZVc3jcNGTR9VwXvBe0PfWWbzDmOibLYzzNo+q2XmekSjK4XxTca+ND69NzRvNfzGm09yc7WVZbONKMu+wdxu+MTN9/LI7IjGBIfJFk6BEU+p59N7S6eGVvwQH79mZMo6zE2Q8D5yMw5WLave0nfnnO29XdS3Nz946Kzh5d/z5OjhFxcezBF1ZovzyuZBOkI8mBXxAY3lmPHAEwKHGcdaviflP3GeTQ/NodJvC9GhHImoiQtlLG54mmPl4CmLn41nZynO2swslaWMaDvTK2YF/fYg2DZSlkyTGwfzW5ZuWwJdx5D3ZiNXNPMkGu2UOf5oYuEZ9bNxOqYWi7hGNOqIyKD4OTKYWtCBU0pvvU5r5yeU4nrFWTPQQ71h+3pHe2fFlZda+jdlzUiYPtC9vt1Af+nJ92tJ3epUEybXY7ghpsJ6G4XhdDgvTmkPerorge6znafZ73BbFt8IdtWz2p0PSSftmqq2Qux/r6hZ5HsuHE/7ZXNnK0GIwk3a8b43lyx/7hq37bU1Ndr331hx/Wc/wGuTMqbfh/yK2W66DOXzcQrEw9Ujz1VVTKd1NnNOnGloROQ9wI8CTxOGhx9S1T8rIv8O8M8Br8eof1JV/1pM8wPAHyXcL/+yqv6NGP7twA8DW8BfA/6E6tkErl1pNCbxIfXBU8pSw0bY6CnG4zU1SIkPfeFFjii0yQO1lhdDmlGZxYCe4pkO3FzoroBvg3epTRo4SmNpaI6Uw2cVN9c1Y5Qf7FLv3cv4SYmVNp1gj4Ruz6EzP55hZWMjSC/MX7esrnn8VqxI1Jmf0m1S0G6ZPsuKkmQaKhxrDjdFnCl1b4zPNuVw2bK9WK3RYQBHq5bt+WrkYSb9+E30S6KsvAquGJWM6MiGpe+lF52860Z81qBvjGe7Wa2V0XvDQTfni3/vPex9puXw99/mg4+/QWNc9JxDx9843uFgNeM9V9/K5SQPO+RjWTQdTVa9HAavlW9YOctj80Na4zAojXEhndqsb9+KC39moJWOfZvDS9XIpMT58nKPj7/8dfxj7/s0vTd8bv9JPvWVZ/i6Z17nme3bvHDzcb7vfZ9gIR0L07FtlgAc+xaPYdcc00rPLCpurtRy023zWr/Hs+1NdsySlVruuC127RHXzGFIr21WqQQweBYSKD8b1TRTGSu1HPo5//Ynv5c//tG/wbcuvprVNhfS89X+Uf7kL/8B/rNv+2GesAdsiyP5TLN4sxnAIrh45TtVOqDTYXLYCjiFrlDaBEbKmwB3fMsP3/huDvo5X9l/BPlDFu0fgkbTmaPkxcZ5ZjQ98K+p6t8XkV3gkyLyk/Hcf6yq/1EZWUQ+Qvil6DcDzwJ/S0Q+pKoO+PPA9wM/RzA03wP8xGmFy7Ln6Z985VyN0bvYBZK8rKLip0Q+4dym8E11cH5YYI3p1rzTrgfveXI+i4O9bMxfC+rsxPqoBgno1mbjvEbvEYy0dC7Em9J1kUYc0kk0pMOsQ8WO2jxKy9gor9FnKY9yltosNlJrC0+hjDjk7WVgkIY6FWVO0Ke8J55+pscmn2U9Bidkct288sxLjvmbS+AqX97ZG6tTCkgfZmyfXzw+5DmV3i6dlqLc5Ox8piniycnp8jrixLHatF5pVsLsFvzlx78LgOZIuPoWvLL3PC8ulOZQ+HOf+Z7o4ESKTjSsGUJB5RWjoBdkJejcg40elxNofVjzE0XTWmK+VtGZKeS0R3sLnLD96QV/9vB3Y7f70BxRbONZHTfMP7vF9/d/iLZ1eU3QRqdBRLFmcEj85JfvJ63fleeSgwKwcpbXX90LM8ODhg/rF9fSV6zjTEOjqq8Ar8Tvd0Tk08BzpyT5XuDHVHUJfFFEXgB+U3wnzlVV/TiAiPwo8Ac4w9DovOGV3/tM+G6KGY1IluklznbG02PyOVinPsxquqifBtYhTs5HyVTHpgF7E1J8cdAcQb9Fpn8KyfRMNcxvKu2R5/Bxi5tJHoQDxTU8DJtkiqfUnnFgl6HMvPaS4+iIOpvdUfotwU3oujOpQVkP2/DMrqc7IZ70jPqlhOnD5o/T0o/yOoPNyLvz0jWa1G8jlZaNjI5pLw/2yGEPO7bfcHRHUwsSjIw4cDPZsONON1Jn5f2XqLNpvmdhvaxx28Qr7b4L1BlgOqU5dLi5wbcGu/J0O3ZyLxT3U+mE5Loq0ivaCCqmoDXNYHzPgek1mt08pPvFBm0m+p8eZjcPWH1inp2vTbvz1mnH8bOz9v0EtApXlw7pFXFdpc7OibtaoxGRrwO+Dfh5wkvV/kUR+SMEhbV/TVXfIhihnyuSvRjDuvh9Gr6pnO8nzHxYNFd57NeOI2U03NBpq3O6gYZF72HAHXvojAeQ+NDlRe7SY514u0N+Q1is6InedDZYHpqlYpzgG0HzekM8H+tiV4HzNX2gxjSueJfrD6MF+Q3lp7qpC55nvx0MyKR3C44+pOuuCP2C3A/ZAG/ykDd4/TrOftwH0VtdW4MwxXkVTAd+rhPvO3q6naAzHUbOtAieBi4nMPdDhxadK7EMMYkCjN6ysnGBNVCCMU3M3ohirMfaYdddWvzue0P7K1fY+3zLK7/ds/XEft6Vlzzi5bLBdZarV4+CRz3pKq9Ca12mDMudfM4Him571uWdbzDsKrOTsLQAXnrj5eJ6E2kur4ZbqwVffPkxPvK+sOvstf0r3HjpGlee2ufxKwe8emuX3/6+F5iZnlZc3tEVdm5ZrtglVsKut0TNHfoZbyyv8NT8NlfsMu7UmrPXHNHGso14LDr69GryLrC0Y2zfLTj2LZ1a/rtP/GZ+57f8Bh/aeS3HN+J5dbnHX/rFf5B/9jt/lqfaWxhRZtJj8HgMLua7MB0zcTkcAgW4UouNfZN2xDlM3P0W6ueQ/HnHbfE/X/8od7o5r+/v8J5/wTwUY/M1sxlARK4A/yPwf1LV2yLy54F/n/As/vvAnwb+D2z2hfWU8PVA1R8Cfghgr31S2994cVO0i4P7+MOt7XPEkXPOqiaJNodv+v0F5B1y95Tnec6f1mdn7Rg8JWwjfTqNdzc06Un0asnxrO4gy45rv7RA2+KRSl3oV8Epajb0ac7Tjr3uta36i1H86VpgWU+VwZjlXfFTOpPgnX9ouWK5eAqAx7zyxPIAP2vR5lGe7z2fXXxkjc7M9t6WZRZVdcoXrAwMhI4dk7KeG2lAGM1+xMM3vLXi81c/zOdmHxmlF698w42On/qr/9Da79TG9OH67+VKbKJc1+jHWJ/2KFDTj3aKujc2Z1gxwrkMjYi0BCPz36nqXwJQ1deK8/8F8Ffj4YvAe4rkzwMvx/DnN4RffpygkS72wXg7+lBWBh/CAue7CApwvDyRcYST2cjznn9QmNq0B/66kPKHT+d10rzSnhT3tHMPEic89w8C74AezX3FmfeUBPf5/wN8WlX/TBH+TBHtnwR+LX7/K8D3ichcRN4PfBD4hbjWc0dEfkvM848A/9N9aseFxDk21FVUfO3B6zBIp+9n/Z0W927yuZ9/FefGeWY03w38YeBXReSXYtifBP5pEfkYwZn7EvDPA6jqp0Tkx4FfJ+xY++NxxxnAH2PY3vwTnLER4NKj3owVFRVvE8rXwBqNqv4dNs/q/9opaX4Q+MEN4Z8AvuVuKlhRUVFRcblxOd7eXFFRUVFxaXHhX0FTUVFR8TUNZcPvAy4X6oymoqKiouKB4nLMaIzUhfWKioqvWVz2DayXY0ZTjUxFRUXFpcXlmNFUVFRUfC3jkvval2NG8zBwnl8Wb4pzlv76veZ9v8q6lzj3mu/DKOdh1OMylPlOHhu5++OLUNbbyeuscipOxeWZ0RhB2jZomJfv4VINOuddeJOqWAMp3mnvtCoVNlXDn/PQ92jvhnzK1/uflMdpcJ74psTT45f04Lo2caiXKtJYsJPXHDuHep/jS+qjTRryZT28H9IZCe9QK2UIVMPbDdIvoTfFmeSf40eIHUdSw6nnAbCyVi4U73ibni/6LMXJb2WYnE9x8nu1NuSRv2+6JmX6TenOm6ZE7NeRkNim+Ke14bTyUvhJ6Tfkf1r83Md3c5yu2YayTzs+T96q43vk7eR93rxOvZ73HfLu/8HmOw4hPIhNQ/fexzl8dsFy12QRMHsM26/3bH3xLbh5G33kKocfuMbBU81Y1bLU8VDya/LdAvDQHML8lufqF49ov/AqurfLrX/gMQ6ftPTbjN64HGQJCG8ctgyvtxdGbyoWD7Ob4OfQL4qXERpQO7wR2fTC4o0gxtbtDm9QTkqg85vK4790SPvyDW59x7Pcea/FzUP50sPihvLIr93GvPw6+sSj3PjYNQ6fNLg5g4CaJ7wZ2oY8TR/yffTX7mC+eh2uXeXWtz7G0aOhb00HzbGG/vjia6BK94Gnuf2BLZZXDX4e2mmPY1tVaQ9g7/NHtJ9/Baxl9YGnuPPeOf220BwFlcnFmz3bv/EaengMe7vc+fBjHD1q6beF2W3FzcN12Xm1Z+fTr6FHx/DYNfY/eI2jRy3tobK6IixuenY/cwt5/QbSNKze/yR33rfAdrDcFWw3rsvyQ0+z/+wMBLptQRxcebko49pVjt7/CN0Vgxph94sH2K9eh60FBx9+koOnGrSBfktoDpXdF3u2P/UKdB39e59k+fiC9qBn9tlX0Cvb3PmWJzh4yiAa5AFMr1x5xbH7K9fh8Aj/1KPI0Qo5PGb/Y89x++sa+kW4LkhQZd37/IqtT72Eeo8+8SjXf+sjHD8e7mm7DPfL/CY8+Qt3aF6+we3vfJ63PhQUWgHsETz7d/bprrQsvnKTt77jCfafNzz+yytufaBleU145LOOK18+4OY3XqHfFu58HWy/LOy+2PP6RxvcQnnsV5Xjxwzb1z03PmJYXfWMhf909Gyd+BxPv2v6Sw9MekAkvD3by+gN3ydJgGwUJxRFnIxe5ysOpBf8TDnPi+WS3o44YfsVAwqHz3rUhry3Xza850dfqGvI58DFNzRKuJDO0dw8YluE+Vs268+YTmlvL5HlCvWKLDvmby6DEZlLjrfpVfWi4NpwA9uV0hw47P4yzBy6nsWN+NryueCtZH2YYCjCIK9NoZUixRtp4+A+uxMGz2YuQ5wkeRDrlHRhwiv9ZTCKSpSCBtNFbfOVYo8VvGB6ML1iV4o4F9sUtE+C3EAQBkOC0UqyAEEDh6j5MXjDuZ+MkF/DP+qwKHXQEOoa80oNCxo/w4zHt4Z+IfRb0UN0ISyIqgk0ln5h6K4EBVLx4BbBCLhbJs9ctbV0O4bVXtDyWV0VjDNoa0MVReh3GpbXDHalHD8q2GPY2Wlog8wn3U7D8WPBiHa74drNDiw7qYx5w/KaZbkX2rj1eosFtG04ftRy+Izg5tBf8TQHQnNs2W4s9D3d1RmHTzQsWmFmLcxaDp4y3PlAuIZuy2FWAliufKZFliv63TkNIKuO/Wcttz7sYKdHOwNGMbca2oOWrd+wiCr91Tk3v0nZfv8tttue2wcLdraWvPXKVfa+uKB5zXD7fRb3HXe4duUQK8r1m1c4/syCo0ctszcX3PyQwf4Dt7jz1lXe+ljPU+95i7d4knZ/wVsfEbprnn/0O3+Fn/z0h/GzBR/8XV/g+e2b/JR+G+7rjjh6YYuP/e7f4Hse+zVacSykywqbrQQpgU4tFqWVfvSK/W3T4VUworT4/Gp+p4IVxamwEI9nrGppACtkJcwkzVSqYhLjALTAQgxzaVhqz4F6ZiIY4I5XXvdzPth0GBEsgsHQisUg9DgMBoNgZTzd+zde+xg3uy3+8+c/nsP+5Ze/k8//t4+hvr6A9izIRX/x4177pH7Xo/9UeBPy7g66swiKkNlL8ZjDFXLnEF2ukMUc3d3Gb8/QxozUHDOUNSEz0zlk1WPuHKGHR0jbontX8IsGtSYOxGMNnPL7JogqsnSBNmjMZqoFwCtm2aPGQGPGDp73SOcwtw5guUL3dtGtWa4LqsiqH9o/n6FXd6JqphnoLT/UV7wP6To3pJu16M4WpNfcR0pSjpboahXqs5ij2wswZpDVLkTZ6B1yeIweL8MsdGuBzmeBfnSpzB6OjgPNOWvRrTnMWtSYcM6aEG/ZweHREG97EehQ58Nn14eyVt24LNXQBu+HugCyvYUugquvNgzecrQcl7E1z7RkSiuNRa9shzqKQGNi3ktk/zA4JVtBHkB6F+6dpgnXYN7G8gRxGmYwt/dR75HZDLwPtOcjV/E786ixlPrSY+4cwp2D3Pfu8av4eRMMdaTEzLLHvnEbjpfoY9fo9xbhfgWk97Sv3gx1OzzGP76H25nRvLGP29vCzxvaG4fh3N4Ofmbprs5o9zvM/orVkzuogfkbR7idGfZgxfLJbXzUSh7JB6zJGhSP20QOIIStxxuFj/I6e/oxfaV/qU46ZTJ8IZOe08o4blln8cr8lkcNLPeGsWdx03Hl735xY30+fuMvcqu7fl/4rvkHntdn/70/fj+yOje+9If/5CdV9TvuV34Xf0YTqTOahu7pPQ6em7O6Ipleao5g57WerS946Dr83hUOvuEqB09Z+i3JlJc2Y0orDOIEmkGhPQhU0t7n5rRfPEb3rnDjY49w9KSh247p45RZrQb6YiX4NgqZpbrGz6R0OX9T6LfBbUXDBgQRN80Pp10Ji+tCvwXdnqJpNuEFuwp5PP3xOe1rt3jjtzzBnfeF6b9ZBcGwrdeVxz8ZqDP39CNc/85djp4S3DzUExPqnVQsm6Mwa1i8rjz5yX3MV17DP77Hm992jePHJFNn7R3lkc8cMfv8q2AMx9/wJDe+cc5qL1KOotij4Vma3YLHP3XE7DMvQ9ty8OGnuPWBln4rzsqcsvOqY/eXX4WjY/xjV7nx0Uc4fErod2B+A/ptsCvY/Ypj7++/CodHuKcf4a1vvsrR40K7r6yuCluvK4/98i3klTeQxTyXZZfK8lHBHsFjn1qy+PRLMGu589GnuP2eJsxorgb6cO8LjmufCGX4x/e49eE9Vruh/Y9+6ojZC6+gV6/w1rc/zp33GNRCvx0kjq99znHtF15GesfRNzzB4dMtizcdO7/yEv6Rq1z/rY+w/x4QH66DXcHVL8DjPxsMYPeex2huHSOHx7z+XY9z85vALXymi9rbwhO/dIW9v/vlQFs+9yhf/se2WT3T8f9n78/jLUmu+z7weyIy771vq726qpfqRjfQINAASJAUwVUWTYoWJcomaY21fcaibckca6iRZMmWLHs+pm3ZHm+SNebYsjkjmaRsiaYlmSIlgxJJi5sEggbBDVsDve/dtdfb7r2ZEWf+iIzMyLx531L1qvpVd57Pp+rdjIw4EZFLnDgnIn8/yRS2M3TkGb25wqM/nTN64QpvfuMZrn3UwUpFMHZzzON/z7J7fsSJz93glW87xfZ7HA/+/ITLXykU5wvO/vIZTj435/JHx8zOKNkHb1F+4QQbL6xx45/dZTQqyX/xFFuPeTZeWGX22zd56PTNhmytilvl1tVkbFEiEdvIlniV2qMxomTiyYyrvRmg5eVEYjMIJG1Bn6f0Fo+09MW/sc4oXoW5yxjZEiPKtMzZLkecnWwzMmWtd8UWNZlaqQaDkhlHLoH8bLsc83NfehKAb37yc6xlM7waPv6Fp/jAJ+8N8dn9Lsff0MTQWVmSv7XJid0CPwoL/SHspNjNWZiBesVs7bD6omV8fYTPTB06i6GqWifV7KaiOrZzj90pya5th1nqzpSNF6dMrue4sWmtm2gMozkfQkldSug4o/Iw2na4keDyZiExDZ1BCHONNh1uLBQrUoexREOIZ7zpyG5NwTlW3yoRzfBZCJuJUyY3HGYnzNzNbsHamx47M4GeOas8MR+ulVqpQm1hTUp2i3At5iWT6w5xgbpXnJLvKHa3CF6CD9dncj3HFoIbh2mgmTcv9mhLsTtl1Ukl23WMb1rsVMimYY0m33LBK6Gace94xjcNphRGm4Fh1BSQ7fgw46/yZdOwBpTvBAOd7wRPD8LzYeeebDeEEd2OYKeKmbuq7YqZK3YWyvqZYGZgC21ChxqupZQS2DETT02q9S1PiNeLC5OIevNFNXOux7hls/tu8CB61bZas8s10ABJuG9hkmWC12MEzcGMHWIVVxhk7PB5Vj1zgh8JrDhGq+GezmcWbwWXhza5CbBW4vKccs2Tr89x4xF+ZHATKNeUR05u8sLaBsWq4czJbTbGM95YPYHfcJSrhkfPXOfDp14LIbCaMTP8jmybHiEXx9iEdkykqLs8qdJWzawOuUWxyQUytAdvm/BzT7VD5wzkFX+SrVgx07wxxHfDr7LpVnhPfoUNM6/buiqOhMONSRVqy6vw2U3v2CzHGFG+/6Gf4pwZ4fFcm69yU8YLbTlyUe77zQD3TegMI8jaKn49hM4AEEFKH0IS27tN6GhjFT8ZhcBtSnecSBgctAlXlB4zr8IxnfCHZos7uIIh0Zb+Xj74woU86ZPcsxtMZi54OtY2AWlAvA993NoJobP1VXQyrvOIahNG6oTA6jqrnWbiktAZhHK7s1Aus7AyQeNuvcq4yKxoQmd5HsJP1i7ujIOQfzpv8o9GIXwU66/W2pjNmx2Ck3FgpawG1HhtpChDOMj50LbxKISAnEetQUoXzpeu2ZE4ysM9zWwdfltoC4S6YhivU0e9o3FehOuStjHuuFNF5kXYRFBdlxiu0+kMMSZcy3He3jUXw4GqSJbVu/5kbRWdjEL4MNkRKLuzpo74PObV/XEKJhhh2dwJ/dxYQ1fHTR+8x1zfCqHG6Qw9sY5OcsytHXQ1vEeyPQ33rApN+tURslsgszn+5CqIYG7touMMmZX4EythnW3Zs5y8H0vzxAlXi7G0V82eoeleWaqnmRCktO/tsj3veHU7sq1gIMv1vC6XbxbkT7/aW9+Rhs4ef0Qf/I/+xFGoOrC8+Ef+wrssdFaJZBnlAyfYvTChWDW1F2HnMLlSMH6p2uK8scruoyfZPZ9RTqrQWXfxPY6NVehMfNjlM76lbDy3hd2doifWuPWhM+ycN00IzjQxXjXVLDdb3HUGVeisgPH1EA5ykyYWrFXorPZoirDrrFyJu860Xmg3M2F8HR741Ijs8i2uf+wCm5cMPg99NwVMripnfjOEkdwDp7nyVSfYfUDwI/B5pcuBmYeFfDsNx5OrytnfDLvO/NlT3PjyU+yebXadjbaUk8/shF1nwOx9F7jx5Jj5ibAwrhJ2P0UZ31ROfz6EnMgydr/sArceH4WdWrvBi1p7s2TtM2/UawrXv/w0u+cNxVoIXZYrYZPDxsuOE7/+Rr1D68ZTJ9g9a8i3lGJdWLnqOfXZm8gbV5HRiN0vu8DNJ0bYGcxOCXamnPnCjPHTr0GWsf2RB9m8lOGtUGyE/p14sQnP+bOn2PzASebrof+nv7ATdh+eWOfmR8+z9bDBWyjXQrj21DOOE7/6GswLZu+7wM7FEeMbjtXfehU9tcHlj51h61LwJGOYc+NF5ewvv4ls7VA+fBa7FTzxq99wkRvvF9yk8piBfEs4+1nHiV95GZzDPXSWl79tg90LHqxidg1+7Jlctjz8c7uMXrjMlW+4yNWPgF/1qCjZLctjP7XK7HTOxhdv8Po/e5bNxz0XP3GCq09ZZg84zn76JKe+NOXyR1co1mH61C75cxucfFZ565tKZOI4+ckNdh5U1l6F619TcOLMNkYUSWZWIoo1iqosEGem+dJyMVQ2zspWyMskHonvWI7ChZfN7zHDj3WoCoU32CpcV3hD4SwnJ1Ny4xgZt1BHt26vwm6Z88zzF8AJ73vyddayOR7ht56+xFP//j0KnR1vf2BfuW8Mjc4LshfeZOPlznc0EGaGZRlmj9dusnprO+wISr/5gN7ZV81r7sMMnqIMM9zrtzj5f045EWfb3fLxTRJZOJduFqB0YE17Yb5bpvJ8ms0A0vJYpHC1R3P6U5c59blxezNAXNT3ir18gwd+ucRPsnrRvs4bv4VxzSYCc3M7hBxvbHL6N5STk6yZtZc+zH4rD2j80jXO3VxDxxa1pg5d1gZz7jA3qtBjWTJ56Qajm6v43GCKsBnA7MxhXoBX5NY2pz5v2XgphG/M1AXvUZVsc1bnMze2OPlFw/pKjikcPrfYaVm1TaEomLy2Sba7Cl7xE4uUSn6lWngvS1Ze3SLfnqAiuIlFvDK6Nm3q2N5l/cUcN8nAQHa9WujfnbH+0g6jW2PUgBsbTKFM3toN91aV/MoO64XH7BTgwoaIEy/OGW1ldXhWnGflSoHMQyjSbgZPgqJk/dU54ke4UTBMKpDNPCtvzULYTBWzNeXkc2tMrgreht11PjeMNn0Iq3rP6psF5UqOG4XwZ7arZDdn4d7P5qy97kAtk6tTNl4yjG5ZVt8qsVtz1t4YhQlctsLqG8rK1ZLV53N8nrP6lkOcYfWKZ/r8iJ0rebPtWJLF9IUF9hijpvlbT8S0XSbzrTxiOiNruvU51qGdV9q0DV/IIxgb4prqBXXCzckKxijG+O6r2zKKMVxVlgZ70yJeeO3GCUaZwxiP2el8zzbIUrl/QmeDDPJOlr4vzbvfZyz7Gn3ZbsZ4Ln58ulf5Q3zpLocNZx2lvJ11dy2b6lJv5shDZ//hPQ6dfc+7NHQ2yCDvaDnIR3/75Vl2PqbvVf4QHx3q/RLH6aI77JV+EEPbNej39EPN+3szwGBoBhlkkHem7Gd4D5L3MHoHWSqDoRlkkEEGOe5yn9u2Ab15kEEGGWSQuyqDRzPIncuyWPgggwxyNHKfv16DRzPInclgZAYZZJB9ZDA0g9yZDEZmkEEG2UeG0NkggwwyyHEWZQms9f0jg0czyCCDDDLIXZV3lkez7EOsLh3ufnnupu7byXOc9C6jOD5smbuhYy+d+x0fVZ33y/Fhr8/tHB+Fri5N9l7Htyt3oucehY6POYDLvnL8DY0IsrG2/Pxh70BELr4dKIsIQXGUd/2gXy7X+f0ihlvavigJjlqvLABF3aG8nW+C9238u/Q43m9jqvRO2fQ45ll23M3fd1zje0n/+aM6jvfvbunf6zh9drr9TY/7rsVBy3aPVWs0dqD/OC3bd7zsXLde7dS7x7n7BiXhbZZjb2h0kvPydz9Ugw2qJcQsE+ZICMdq2qFME+lKIquAwuimYqcEgq+k9zWysm2Oa4Y+qPlGukx+NXe6tHXVZbWdv0ZurtsYH+DkXIsVk7pSUwY06NmpQMAFVHTMSVs6f+uqq7YExOWAFj07A+WKLgAi1tcjFoy/Y7NlMV/NyeKlfQ26+qrM3WtWX5+FNF3I020DPtSrWdXW5J41v6XpTnwuKlK45tqlhjrRn/Y7+S1ewvNXpWfbhhNfgtlZYeeiR0favp9R6jq10w8J+n13AGRBzLbFzITydJmAtXauVWkCWKUJ58Qk10cljL2mk97Td1VBRInsxqrL8y5ItAWigV7HhhdWfUNcJkYrne0HNsynwt+isOS5a4Flei/1sa+uWWDuVqz1NYKz9+FfSKMu571hem3C+rM5W++fMzk5I8tc098qjzEB3zkiRmcVGOetZ0/xgf/i+QG9+QBy7A0NzrPxsq9nJDV5WPIuajLYt2hjq5tTD2oKoy2PKRXjbMswBY6KRFeU7iBLp55oPPoGwLRcbWRo3PSuITLg7WL769MO7Eyxc2lQp2Mf07aYMCikxkYSgyzaMaJpcxP6glRnrCs930LjrfsTwx8dvdIpZ5XWQBoHo2QAls5AnA5YIk159QHcPc9cAoytLbKoht9MAoqvCpNRiamMvjUea3xA+zUea7SGro+DmRHFeVPnh2bwURWu3Vxj9+Yauxc9J99zg43JjNw6Mgm6I7vkyJRkxteQ9BGmPrJGRokDWlYxPUIDrf/8rbPcmo75igdeY2TKAIVf6cuMw6uwXY7ZyKes2xm5uPDPlFVdhkIt63baIgoDGJsCi8dhsNVMLpdQR0yPYgltsyiu7kfDjmlR1syMiRQ4pCIhc3g1df6JlEw1w2EY4TDimVSzgbWKGO2qH3PRzphUN9ip4oCInxyp1XLAirAqlrEE/qFCHVtatNIMwkxL/rftB/mPTn0H/9VX/ji/e/UKq2bEQeXPP/pRPvNfXjxw/nezHHtDI86z9vJuzXffmnlBQxQFCyRJ8VwK2y8zh3jPyrjTdSMLOqQbDupbA1gmKfsiiSHqhMWWzvp7zkdofx1lC4ZmWfmunkjYJtMSHQfKhUNvaOnGyXvq2lPnPtfu4GRXHYssXdchVdqXYHu9hT4CrPrUkr6JwknnsZtX8ZMMP5mgZgUITo9Pmra7vJVJnZ1KOod54Tmn8PLoyf780HpnWm3uWT9JPdDee7fHPenm79Wz7PdSnXHGUP3xgaRQu7e4Mx7EtL6xoO8a2annsVsFP3DqD/BXRqbXW1/m0Y9uOSbli/t35ijkPt91duwNjR9lvPY71kLozIQwWPwNyUxdQtirNYuvQiSaVS+Sg8k1kFJD6MyShE3CbL1OixLDUsriQ5iUDRVq88aqkLDPBk/DVmGUOsxEnb/2qJIwhsRQStU/KWFyxTA95/Er2vaI0rJdrg+AGKIziuxaVt4wTC94/Iprex9xlIjhllQfzey+L2SiKr1hn+iZRF2m8kZiaCSWjd6FiK/TO6jsVWikHarw3qAKmfUtL8R5E7yQzktaFOGxzzLXdFsUW4VLRBQjjdfS5SixSf2pR3P1xjrj3zjLziXHqUdvsD6e154MsEDu5RHKygKleQxa12FEa4/GJ6PsSzdPMSsynjx3BSOhjqxzXaZlzno+Y8UWdXkjHitK4S1ehRVbkBmHxbPrRxTeMjYlmXGV5yFkxuM10DMbUbwKhdo69BX/xt9dUrJ4zuIZm7KmgPYEPfG4UFt7ZRNTVPWFdl8r13hwdIM10zDtpXTPVpSiinvn4mpPLHpWU58zSvQBzNXyU1c/wid+60m+46t/g49tPMuamddtWjMzHIJF67pSSun/8Jl/Afmee0R8dp/LsTc0pvSc+43KMRYq6mWtZ/TRABw0dJZvB2rk4q2sCXlFOuba4CQNaBkXaaV3w3hxVGw8qOR8ZPmslaV1SNUWWVx7StolDsY3Pfktgx8leiQJy6XGL14iaQyymsCKObmm2JnBjc3ijFc6upI1pV4DG4spzZpF0oZuGW+0PVuUVHc3nJZcsj4DWsdOqcJxNIZxySSBsjqwS0J3RlvGMRofCAZFjF/cE6JCuZmzOgW7bbi1ucrubBSMVromV8X7Y5NMx7hBqC+3IbCUGV+daxureZlRlpbdMkcqI5N1Ni5MXUZWLVRGAxMfWqdSD7iZBhZKp1IZrSYsGI0HgMPgtDGu0YBEQwFhfX7B+zKuNiC5OCamMXwOaZWvy8SBvgrxuUw4ZXfYMLvYKlTXHfxjCDCE8tppU82ZSJH0zTDH8sBkE8aOS5NrvHf0FmtSMMcwEVeH7UzsVyIGOLeyxUwG8rODyLE3NHjP+Oq0CVkcIvQkcc0gDYfNS3DK6LrtDwfst5trP0nzJusiC9KdBJk9zsWmRSZOK3RZRiOL5rKwUysMGENweQ9b6Z1K91r1sZp2wpM1A+hdkKW6D7nrbq9wXq3fz5BpAZkN4c2+Mn3Xe7929Jx/xHvElWh+GoASKHqe/y1gcy/1S8KYBw9fHkKWqezuDOu7N+nkMi3TJ8vet266Byk9H9jZ4WfXvoGfyb+pqa53bKAVYbDbBbZ8Y3k7jlC6+zzuNzn2hkZzy5tfu1GHy+pdZ3Hm1LfrLM4MY+gshsMUxtcVU9AKnaXlFmhpe+O/7TytmXiyu6nxNrQJ68VzqTGp+lU/w0nZWqUJobOVy8LsFLhJ4mVUYa7Uy2raWYWS4m4wo9hdw+SyMD2nuDXfhPNI61REpd+LiDP01GOod1Ukejpe2cICh1a6rCKZskDFW8/wG/sl6bWuxJcGFEzu2xsFVGpK4NQjcc6gXlqeRtfpScNltacT82q7TVThIndrxInPZ+xeVNylKXm12cCYxmuJi/lxw0EaWkslejvR4+nK5c11ZtMRj5y/zsiE3VixDASvY1rmjG1Jbt3SsN3EhmhBNwSWiccjtSdSX+tk40KqM3o5qY5YT6wzlXZ9vr4uMcxZXyeCrl2Xc2a0TS4uhNg6nlQMzTlMvfEhihXP1OchpCYeV70kW27Mp69d4tnffIgnP/oyv+3MS6yaOTPNMCirdha8LjXkVcgveoGFWn746a/l8T8xhM4OIsfe0Mis5OF/8NpCuvbNxNrk353RqRLvw8Jilng0RvrL90hrptMKsS2ZYXXCaUuxGJZ9G0PbkzOzajOAlYVQXlq+G85rtdkpdlriJhmadRdApXMcy/Xobrey3RfAFH5Rh/fBI4vhShMar2KqtbR4L5bUVRvQZhZs54rPBJ9JO88SPfWcYMm96LZhWTu6YufK6psz5i9lzL4wCduno/FPJigqgpewnDXvGPGlIcVOvfkUxqVyZWO1fqZaZas1wHSC09fmdGLSDUP39zuZLIi2JnbdnZkHun6d+Qk9fQXAg+bVWmayLtnSna6R1te8mmikk6aqn+KF7Kbh1Ivw3PxRnj7xcDPpEsLEJ06iTLssHlZezlF9i7suceJ5H8uxNzSoopvbR6vyDsouMSd3rdzd1HXX8YcO87X1cQLn7Gv3QXccemUMbBzFF+uD3LbIAUN/Wk1GLxyiTKvscXpuj7Ecf0MzyP0rhxicj5Xs156DtPe49emdKnHNtvN86WHW/KryhyoTy90TkR6X8P6SwdAMcm+kD1OtL32QQQ4j8fm50+doeA7vqgyGZpC3R4YXe5BB3jUyGJpBBhlkkOMu9/m8bOCjGWSQQQbZS4wcbmPLIAsyeDSDDDLIIMdd7nOPZjA0gwwyyCB7ybCeeMcyGJpBBhlkkOMu97mtGwzNIIPcT3KndM73sq4uHcFR0k7fKU30Ucjg6RxY7g9Dc5CXpS/PQV6645Tndnns344yt6vjMDpvo06xHeypO63jbh4bQUSaL8zT49ifvuNkC89tHR/ww8Q7rSvt753oqtt8AF3x6/6lZY1BE5Tr9Hoc6NqneQ2DsTmgHH9Dk1mmH7m08AVwQvtSSy/CqVds4fHWoFl8CJfMajov4J5EUF3p4IzVbUl1RnyvqDvBYotlxKcQ+k1fl1Z7lM95pz0Q2gNU9AwJoOiSeg/Sntb1SdKOFKcnXksjvVhc4pV8s0BzQ7GaLWJsacAKyzcL3NjiJmZBz9Ln6KCS6LNzj8+lpbM1mEbK7qScWkgR9tWE43y7xOeGciUoWACXrerMdsOAG/O12rSHtAgI+/Ir5JslolBs2EBYto9eO1fyrRI3DrD7xVqbviJ9F/sQ2zsQbIv6p0q245ifzAIGXVeSd7C/z2ne8Gd007H6qRf27thRiHYbcP/JsTc0mhkuf9W4JjxroScrLWTmUIDWwy8esm1wk/AvlokkYSlN8WLlnfpo69Z4LI0+oEGY1jaAY018BgHZOD7ZVXvECzITNNdGp4EWxzwsIiDXbUvethblcjULM51OCg1KsoJGhOfCkK2WiPF4ZwOpWO7wzpAlvO0NX3t7GpuiDacEZhHBODarS5U8yhzW+IXzToVRgkBc/2Wxnkjf7BG25iMmWclKVtT5Imrw1nzMi198AF0v+eB7XmU1m9fns4qz5MZ8hc9/4REm53b5yodeYWxLDAFJ2IhyKt8lko5FsXgy41tUyEa0Rg220lA4RzThQi2/fO1xPnjiDR4eX6dQy6qZ12UBZj7nplshF8eqmTM2BSMpayTiXEpG4rjq1vmh576eC+ub/KEHf4WJFLxRnsSr4dLoKqsSiMMKLP/L5Y8xdTl/+MIn6/TI9ZJXXCwOg1dDLgG9OPK6nDJzCjWsisNKoFAO5YUbHv6jV7+Dy7vr/BdP/F0uZUXgdEleHiOCRTDVC/Erswn/yQvfwcfOvAzAv3v+E5ysGEqPQn5qZ8x/8/K38j++98d4NFs/Ep3/5bX38nPf9iRaLvLpDNIWOTS+zz2Wk+OL+nVPfW/HY0gG2r3QkAFUkdIHlOKEC6RGRO7q69XVozfVk6ZXBGa9FNOypC6h7dGkM8D4u5fXpaed0fBIN68spplOvlhWqdChaRGmpW3tlT706NiumJbayE4eTbzVRd2LKMQLdaRGV8G4wMiK6ZTVMMtfueZxY2F2sgm9pPfKlMrKFU+xZphvSOs+QaW7r099feybqCS/8y0oV2lm250+1tQRJPctaU+sVxysXFbKiTA/VaWVVXtzahI78cLoZkifn0pQkZN2LngL8Rp3r2fdb63aIKy8KYiDnQcVzaJXnOjo1JHthjLlSsg3fcBByinWubc1MrMsOd+R/IZlclXYfsTjJ77zTicTQKMNG208biFGN5O9yYsjHv/vn+kNn33i2t/mZvHWkbgh40cv6UN/7k8fhaoDywv/j3/rV1X1tx2VvvvCo7n25adCWCB90SAMiMnD2+eViA8hAjeuYOSjF0Q0No2u1t8FRfSHCnoG0Xow0uYl7Q7q9QCeDLDiFVPSoqSuDUT3Jeo6J32kZ0sG7CgLg68mg2el3zjqa5ZSaNdtWHJd+gzNwu9uKMi0y0nS1wWj2KcvFd9vaOJ9MSXku0KxKsGIdCHnAVMI+ZZQrEK5Rj0Y1/e0h1xxXzqB5Ho14clgBHwW/tb50ntFMBiR8jutq34UqobVz7VUnrIknZck74J73tSd9jW9v3UIL3126jlM9b+r/vlwnb2GDAtcZtJ4omYe+ifV82ZmJkQAupI+H7HixAjX1y2NKCjYmWAKMLOqc4mhbkUK0khAjKBE5tbOJTPzxeYN0i/H3tBghOk5afFQpC94pDBOByWgeTEcZNsWtwJulAymcfAx7Vlqn46U72JhAK0f1uTFt8kLGgcVq4sx9+qZV+urmadg5oLPwyxKTRNWq+tLB3dg4aXvhtJaA0KiRwihNFHEaAibqSCZb4XY/DyEzkzmUWcweYfkKYa/aAaNSBSW0hhLklYX7YTR8qxN0FU4U52DUVY2ZFgJGVgk2UrFoJRq2JyN2RjPQsirQ9J1fbrCW184j56d8f5H3mRiSzxCJk0brs9Wef5zDzJ+cJsPXngjhO9Q5t4yMo6NfEqecMhnxrXCXUDFe68trvmYnlcc9jtuzCeuPs5HTr3GA6NbeDV1eAyow3A33QqrZs6amdU6LJ65WkbVQsxVt85fe+YbePL0Vb77gV9jzcx4ozgJwEP59ZoeeUfH/OhbHyMTz+8/H0JsVjxrMseIZ1Lp8yo4hImEvl/2q5yQGbn4EDozJZOqv4WCQ9jxGf/xK99B6S3/waM/wQVbYEXqOV0uBq9KLoaxBMv6iZnlL7/8u/iKU6+waub8mTNfID9CmuQf317nR17/en7w8R/nnF07Ep3//Y2H+ckf+gjq70Ho7HgHnvaV4x86G13Qr3/iX12cDkVZtiCb0sM6D9Y0adIhDTsIEM8+tM/76kvq7hKytWbxPqGs7Qvt7eWMLwvr9Z03LHpAlf4u1XLwKBKq6J5iXYK0tkHuSavqWlZvb7tSkq66XllIq4ukYcius+dgdLPEjQ3lmmmFQOtlLqeMr5eUq5ZitVmcbk0SUkNO/71aGkaMxwr5rlKOpe1tp+1OJy291yG2GcY3ggc/X688iWoc9Jm09I02w0Rkvi5tL0l67lVVn7h2FKGesCWTH/EwuRaem8hku5eHqwJ2CpPrihsH/bPT/RsI4qSydR/iI7BsKNMQmhzdUnYvCC5vrm3z3DZ5F0Oh2lvJyhvCg3/nHoXO/u0/fRSqDiwv/Ml3W+gst1z/6vPhAes8WJCEnaoBsZb4XHjIporLBZ9Fj6YZnBa2YyYhuGU7XLp1LJyTtp7Qzq5BSeqP+WOowSbpstiObnu79e7bvuRcN6TUjaPXobP4gi9lpeyvN5zrGINuO5eUC4Wbc/uGpbqnqzBk32AkJazmghvB7JRZCH1CvBchvFZUg3ZrgNuHvbLWlRoLSf4m6W4L3ERC6Kx7nZPnA6k88vR0Mg4aB+IMbgTzU2Gwjvcw3W0VImeCeGV+stP3PgNXtUN8x7B0BmzRYIyKGZgirDv1Gs/4jpnmr9uBciW0uVxhMXTW9eYPIvW1E7IpuDG4cePp1+ynPWXaE8YmR+yrHx+iHe9yOfaGRjRsfWzFtltbQPufutaD7xVRadZNUq8hyRsKtB86rdJa+bqzIaEzUCXGLx6bxtikce84WIkS1hXKakCI+TtrE8sX4hs9C+1K+tBqa/077HgLazHtEJ8pYnoYZHymy3Ul16bVVuPbA27HaNRtXhYiFFq7+paGBrsKS4FMwXatvsDcMH8xo1xX5hfmSJacr9qhc8P8uRGzsx5/bobYEP5TD2LAZq4OCRoTQmPd8GA3tGeMR1UQUawJ5wpnuH51g7WTu6yNQ+A/M77eIRf/lt4wsq61i05EURVslX9rPubF5x7AbhS878G3GFnH5nyM84YT42m9o84jfOH1BxCBD158k0lWkInHiMeKtnb0FWrIxZMZx3Y5ZmxCKLJUw8iU9TkIobbNcsIvPfdevBO++ckvsZFPgSaMODEFhbfkJoQPAT63+SCffP49nDq5zSQv+cMP/xarJoTxLJ5Cs0pHSaEZDqnLWkKfXGIZYpoRpVDLz197P7/20iX+wFO/yuPjy9XuP8+amTMxcyyKQ7AoEyla+uOuu3QHnkX59577buRvCXq/x7XugRx7Q0NRcuKXnn+7W7Egh6V9vY0KjlafWeKK3I4ctm1H0ZfDfLOShk33qjt+2Gk716YbdjXSf/0O0K+DfWvjuOivVt/89LmG8W/l0kj/VNoDq8BT5VtBl5lQAnGTsJc10vXr9/ktUGVuN5r02/02KG23Kk+Wt0CV1/MHeX3ZdUrTvefLypuVpTb8fPZVQdURvQfiPU8Wt/j06Cl+9aBh7n1kPC1Qt3sk7Xuny/E3NHAsv769/2Yxw17/4yxH+TQdma5liBwHMUZHBPlyN6ZzLVOerrMeWyrnPULn94ncH4ZmkEEGufeybCA9zAB7DCeJtZgeOJ67gYk2yGBoBhlkkHep7GUEj5uBXLo4e3/IwLA5yCCDDDLIXZXBoxlkkEEGOc6SbjG/T2XwaAYZZJBBBrmrMhiaQQYZZJBB7qrsa2hE5JKI/GMR+byIfFZE/lSVfkZEflpEvlT9PZ2U+Qsi8oyIPC0ivytJ/2oR+a3q3H8jd/1jlEEGGWSQd4DoPf53xHIQj6YE/qyqfhD4OuD7ROQp4N8BflZVnwR+tjqmOvcHgQ8B3w78dyI1Ot5fBb4XeLL69+1H2JdBBnlnSner7WGP75ZuI4vHd1J2ma698sbfb8e/QQ4s+xoaVX1dVT9d/d4EPg88DHwn8MNVth8Gvqv6/Z3Aj6rqTFWfB54BPiYiDwInVPUTGjav/0hS5g570XPTD/LyHUWevofufi+zl457ofMYHEuKFnCA4159R/RP5A6Prbk7ujv9PUxZkcXjpbpgeV4WUTrS473OHSZv3d703D00NqL39t9Ry6F2nYnIe4CvBD4JXFDV1yEYIxF5oMr2MPDLSbFXqrSi+t1N76vnewmeD5PsBOWTDy1vk68QhQ960ytYkhoLzSniPGoj7a1Hc7vntvWGCKqTqUPb3BID+E6+CgOtV3zIF4Ase/I5BSv9EB0CKJjCo1lCD5zihy30KcFl63Zrnwhnq2zahiOSLoZbC0SyrBCaDa1nYBkQqZSKeMWPTOsZAFAr7WcjxY2DFq11KsZpxU/TvnZ7XTfxWmHHNWUiFXMfQrOU4TrX5HB2uf4Ucbt9IjasuWfZlsPOPPOTOQi40eL9b/rTV1dsu/TmNS65P8v0SPtcxDcc3SgpTmT4LJLwKWqlBSoaQXOL1dBu8W3Q3CZfGCfEK9lMKVZMi103fcZaIKBVHVFfyiM1ueHZ+KXn+i/WIC05sKERkXXg7wB/WlVv7bG80ndC90hfTFT9QeAHATZOPKKv/TNrCw9nRHOOIJSaLfmmqZtW1egrumQ7FewscNWoBTuDYl3RrF02AkbWKMfxgUygnlvo0kb7GSxjj4WG2llAI6+LT2ZNRWAp9BNtaJ0JdcrUomMPmW/3U6o2eUG2RuiKQ8YBfka9LMKAVfVqaUOZkWv4aATEBM4YqaY6ETRSk4utWvXfaM1Bk/LP1HTOQAowGfV0aZgjQCW05w9pPqnAKq9vrjIZF2xMZmTGL+WpiTwyN3Yn7M5GPHrmOraq59ZsQmY8q/mc1WzOyLik/kbHVjHGI5wa7dTAkx7h9Z0TGFGeWL9KpHYOgJBaUzenfDSFWq7O13lrts77199iw04x4snFseMCjtnJbIeJNOCZrxenuFmu1FTPD+U3Ku4bXwNCNvozNt2EU3aHiUlorImAmoYbbhWD52+8+vU899o5/tWv+AXGpuBrV58N3DSJTotiqwc3F1+DTwJs+hEA523A/Irn8qo5L5TrnDc7XLRgRPCqjCWr+GnMUs6Zn9s1/PvPfCf/yqOf4OtWnmdVHJuaccaUPJJQMV93O/yvW+/je0++Fu6Rn7JuJr06o/z49jrftba1Z56DyH985QN84ne9595QOd/n25sPZGhEJCcYmf9ZVf9ulfymiDxYeTMPAm9V6a8Al5LijwCvVemP9KTvKWZe8tAvbPc1KoxwCpjldMe9HoMHzSq3u5rhxmOc4ke2MRh7iNp2hjZycg8PSvqwVOcWIPQTr0i8r+tpGy2p6KlBTXhR+2gK7Nzjc4OP3pp28yVGzVezYJt4c9EoJcfLrknj5cUy0l+mZ/bahfBfBr3vk3xewr9T8wCbP81YpCNOpJ4pF7Dh4fLKet1GU4ZJx00bPMdl7Y28Li/ZdptNEWbLr47fU6e37ldPn6UMk6TPj7+sNYNuUWdLk9+UQMXEGagueq5R6rH4xZl5KrEvk2vKuW340c98C2rgr01oIaLvd0/q9trF+wlg54LPwsSt5VB33w2aesUL2baw+obyl879i7hJmGSJW5xUmhLyW8J/eVprSgSfR8+zqVDK0DhxQr4p/FunfH2+Zo6tGhipp+v2uXjTQJyEaIOB1dcMl/wzixd3kAXZ19BUO8P+GvB5Vf3LyamfAL4H+M+qv38vSf+bIvKXgYcIi/6/oqpORDZF5OsIobc/AvzAfvWrNew8NOmBwA9usLjwkLfc5a71T1/A2tUPumyhmDLw1UB4UMtx29Vv6dHFQTXqTvO1QlB9sxHp19ENCwH4qm2tF7gIbV5oZ1JnNlPcqCHTii9ISqJVF6tpAtrpvUYnucb1hD++pAv5uyPTkt9RR196py3pNVKj+IpraNnAnrbTzIJRLVek7oeWDf2xCotcMFXbTE0g1k6PNBBuRCvk1UfREBSAqSgg3KiptxWasYv9jcZDFTRv+p/2M4pxgo8UxJ1rglYDpoRr50aKr9ruxz1wsXtNFGKoL+WOSfObil8poZeoDVnPs6uVMRBnKCeCW6nI0GxI9yOFTOsIgM4MpgC35kHBO0FHHvFSUTDHEIbUhkK8wa37QB8BwZDYOGlVqOggFoipBHxp6nzFrVH3Sr1rREQmwC8AY4Id+duq+v3L8h/Eo/lG4F8GfktEfr1K+3cJBubHROSPAi8B/xKAqn5WRH4M+Bxhx9r3qWr0Lf848EME5PKPV//2FLWGm4/bZnCTZjAwjvCQjZIXs2/wSgbHmm0wB0xg9jNzcJW3bcoqdLaMFTDOFivu9tZ4HQfASMMcZ6T12kzzwDZcM8kLmoTfAKQIP/zEV4NRM9Myuwa/4qg5zusBpTr2gtmy+NUSRlUDvFBzvlRtqUNpRdWZ3DcvmVCFzGjCYMa3wm+ooCqVY9kOrwF1GK2+jNGIGt8ej0RxKq3wmnT+xjCa80JW9ePm1oS1lTkb4zmZ8XU4LBPfqldV8Ahvba7jvOHR09freq5PV7CijLOSsS1rzhYI9Mwx3635BCPKqdFuFZrzlN7y5u4GuXFcWrvB2JR1uUjXnBtX87tY8Ux9zuX5Otfnq3XoDAJfy04Vijppd2sqZ6/CK/MzbLkx5/Itpj7nQn6LXMpA51xxqwT94Tpu+pUQOqvCbzFPqH/EDbdKLiV//aVv4tUrp/ieD/8yY1PwNSvPEzlggDpEFkN0liYkaVFu+DEW5YydYlHmapiIx0pYRnyuPMlFu8UZ42o654lYDIZV0z9QO/X8g511/vNnv50/9/jP84HR65wxczY147wpeTAJnb1ebvGT2+/nj54Iy7/X/e6eVM2FOn5i+zS/b/3W0jwHlR+4/hg/9Vc/+G6lcp4B36KqW1XE65dE5OOq+st9mY8/lfP4on7dU9+7eKIOnSkYszhz3muRHSB6QE7DIms89kBWLRTvs8Fg6WKvQG8Yry6X9GHPCrRua3ehXcoQ7tuLytiUHp/2xevyPiWotX3htS7p254PfmvW2+/RLPXEOrIfvbOZh1l5GmpK291WpphqUb0cNw0wcTOAWQx3tjcDVGlpqKsKa6mpwjqd+9E7+SFMeIyrPM7k2kYGy+5mAFNQh8x6Q2dJfaLU3uvS+qu+jG8qdubZPRMmc26U6trHG42eMU1d3dCZKcK7pclSTEqWVx932pbtKJPrntkpE6IN1WYatW2vW8qQt9iQ+rpqFtqVhi/jdRUH+Y4y35DWRDCliO4+o+naqzhqptmVy8q5jz9796mcL13SR/7Uv3kUqg4sz/3bf/bAVM4isgr8EvDHVfWTfXmOP9aZNWw/vhF2jETOeqgfdFNq2JWSvphLBkFJXoz4EJpSMUU1WIlgSqWchCl8unOpFYJJdo11dzfFB7U1QHXa013zaIWf0vZGR6QTqkHCwOMzmt0+iccX89m54vMmrNjbjujpeepdTX2e3LJ1mvSapvkX+ton3UGwE87rykLoTII3qra5Rr073+own2KKymutdinFZ6j2Mru77tLfqaFJB9N5qMCNF8t2Kbxj+8MajYSwUPLstkJnVZgz5heXrNFkSR1LDMmi8aW531XIy2cwumWYng2DeTlJ+tZnq3uMQmivLjE0wchopsvX+jrPgXjItgUI7XIriW7bfh/EhTWa+anK867WcYIx6EQVhHqNZp6s0UQDVre92njT2qAjQV+6RuMzy7nFS3Tkcre2HO8j50TkU8nxD1abtGqpvo/8VeB9wH+7zMjAfWBo1AjzdcPCTLHadSYu2fKYDNx9RgKSF6NaULTzMNDEmaUpK57z1nanrqFJ0roDq1k8V/Os0z7XndV3H6YY5ktnmbE/dt6EDNPrkg6u2a5QTprF0YXNAMksVEpBVFpUzenMU0l+d+mzVZLBv31uYb2j048WHbMu5mnakoTfVOpBxOwYdKzoyNOigu57M1WQXRsonk8GTklVYGab2LzVWo+kbQO0DNNem3sk7oxTodgNo/94bd4ODUr4a6AO6QE4b5jNM4pZxur6jNxWlMRGKZ1BRBllri7jVdiajilKy8p4jqqwMZ4jUO+0i+GsSPtcOMs4K8mNC7TQaL0jrvSW7XKEQfniixexV3NOf/AKmXVc2rhRUTqH/KH+xPur0vLq77YL4a+NbIYRj1dT5wG4PF3n5GjKRjYNu/DwZMYzMUUrnJhSR2+5MZ++fomnv/QQ73niLS6tX2dsHLsu51S+y/nRZr2L7635Br9+9RG+9eLTWPFcKdZ5IN9kphljKUM94pn5nFwc18o1PnXtUb71gS8wkbI+d9LuMNWQZ83Mqj4GyuZtP2JUhUG3/YgdP2ZiCv6H53878j++Y6mcr+zn0VRLIh8VkVPA/yYiH1bVz/TlPfaGRmYFZ3/m+Xtc6V7T8EPIUdInpxLDhgcJve2Xp7Xgwp31/Sg/YDtoO7p9PGg4skM9vLRcN19fHT3p+1E4L/smp79u5bTuLjnX/4yFTdI5yIiy53xetfkpdwW8R/MwFGzJqT3bvVB/pefWMvpsVaac4M297ksa1q2ui1HlqfIyai1X5Hyd9ZoI3S9X1r3nk+ap+viLy/THQ+/5x+Yrb/95rdp6uihR5/fPfxSyZ2jg7RVVvSEiP0dAerk/DQ1w727mkctAnzxIvxyXOfCh27HfWt8RynG5RkvluJGj3UMRkfNAURmZFeB3Av/5svz3haEZZJBBjonEwfV+GGSjMex4S0vzDXIYeRD44WqdxgA/pqp/f1nmwdAMMsgg70xJDcv9RNvcJ8esiar6mwQ4sgPJwEczyCCDDDLIXZXBoxlkkEEGOebyNmxvPlIZPJpBBhlkkEHuqgwezSCDDDLIcZf73KMZDM0ggwzyzpR0p9myHWh3suPsfthEcExkMDR3InttmVyW5yjK3A0d90LnURxD/+Bxr46Pqj93sy13ovuQZcUaevESu7p6BnQR6S/bI3vl7Z5rHXfqrdubsHPGvHvp6T13r9yMtweC5kjl/jA03Yf0Dj4YO8zDfSA5SDu6efb7alza8P/d46PQcZBjoPWiAYfS0W2niDRgplEOe3w7ZQ56HBEX9jpO3/jIkNplTE2PD5I/lYMcJ2CrCeYN+OTD5r2OI2JFeuwDQvih0Cx84DtoXc14zVJxDmwPwZkEtO4FZAXVoLtTRpYhOFQ4iL3HPfm7KbLsnJEawWGhfiNI6dB5wSD7y7E3NDrKuPHNTwRILB/oXUdXp+w8tkY5qV4KbTDGxMH4Rkm5agPfR+SWUahBFwUinW0EkTSl1lw0fUCFtlDQiuq2koja2+UOsXMNnDbVeyI+MHdmU8/sRGhzilUWKQ8i+q9ow7MDTZqdBo6Z2Ump0aaznYqvo3oBfM7yN6e+Bj24Z528LcBNbdpi5rDxsmPzEYvPQz/G15XdBwQU8m2lWAvX8MSLDjcSth4yAVctQRPeC1W4vm4O8i2Yb4Q2ZLvhd+TUiTQLphCy7QA0Oj8F5Yq2eIf66rBTQUoC38mo6mqkd+iUEyesvSrsPqAB4DHyvHSvbVI+AJUKqy9bVt5Sdh4Uig2lOO2QUhhdtczPOXTsGl01LUKsWNs4cCqoQvbmiPWXhXxbuf4UlGcLJA+YZ+oFtnLMqXnDblrdbEmYT43xgd5BhaKw+MsT7Pkp40nRYkWNj0XEUvMqlM6gKrinNygfm3LyxE6Ft9a+zqUz3Li8zsWfzXjjWwLT69rGlNOru2zPc6xRTk92uTlb4eb2ChdObgLw4vPnOfG5nNG3XeHsaiA9fP7yWVYncy6dukFWgQBGFtWIzeZVWLEFM5+xYgvyikAo4rylkotr9cmI1mm7Lmfmc9aygHm263I+e+NBXr58mm98/DnOjbZYtXP+xm98LR/8c6/ex8gl906OvaHBCLvnTfXiKpBhpzk752wY0KBBs5Uw2CAZ840AtOlWpIZWX+QCl5oXxhSCKSKgZlJ/ldfOw0BaMe3WdamAT0EvtQKzXEnALJ2Q7UK+ZZmeq0Axp+DHIY9akIqSGhMMWkRnrqZ8QEC0zXaF6VlFKwrnfNNQrigYrdoSfi8M4nHcqAf6zqyzzzgZDRw2SiCGEpBdgyksm+916EgxU0PxpmHnsTIYmpuW4kQJBuwso5zA1vvKUN4qYn3Qkwyi6URWnWDygELqS2F6bYSenYc2bObYMzOM9TWklogyLzKmN0bITOCBGWvr03oQjYCVqRTOsrszws8s440ZJ1bCgDLJy3pQTUEwp2XGW/l5Vh+7xdm1Hca2xBpPShddekNmPFkygO2UIz6fP4zanJ33zVk/vcNXPfAGW8WYp1+7wIcffoPz462Ku8bXg2FKHx15YRwmDPJq+cVTT7DtT5JvCqvvv85XXXyFk/kuuTh23Ygv3TrPl59+tR48c3F4BIPW1M4RABMCMOU/Pf04/8zFZ3lkdL1uR6w7bY9XYdNPKHzGX599A9/83i/xtSefYyQlBo9PXLBNt8JPn/ogL//aE3zoyRc5M97mA2tv8tTKq7xRnCQXx3tGl3m5OMvndx/i69efYa6WvyrfzKtXH+KPPf5Jvmz8Gh7D39/4KA+Nb/BN608zqqCdcnFYlHHVv6laNkxBoYaJOMbJMx19o/g0TOLELHkucgQjwo53TBXWjOBU2VThH648xT8evZ8/9sDPcynbYVWEX3rovQvP1l2T+zx0dvz5aEYX9Ouf+FfrYylKKB06GTVufurSqgaXtqIvxpiDhbeii2yXhA66rrhIcO8lCV/UukJIIXoZEkMBXiGzjT6Rmp2xZr+MoYd4nIg4BVU0M3WdUvrg3Uhn1I7NTtuQHO8rsW7fPhanyO4cvzom0OsqUjj8JAMP4j1qggdjtmaQ2XBuSfv6JPUOpXBoXl2z0qO5WcDbEheur1Q03Jrew77qFEyZ0mQvtq1LhWB2CvxKFvLuCRCZ/PZgd+bIzKErOT43of0eTOHwY9sLqrnXPRJVzNRhdwsoPe7kBD9qP2tSKj6PN6yru0enBzOv2tMTqlygBqj4e/KtgnI1rxlgF9sKdubIL28zf2AdhIpavAlJqZXwDLnQZlHItgrsdsHsgZXqeoOd+kDlkfU/302lNCjg9Hu0SyX1HpNjcWAKxcx9iJRUSPGjGyWjz77cq+oo+WgmD1/SR//vf+YoVB1YvvT//DMH5qM5iBx7j0bHGW986wNEzpS1Nx3jq3NuvH8l0PECKQWxmSurl0OISi2UKx2umiQsloan7Bzw4FaaMFzdhsoDiXS9MexkChp+jKi/CvGUKwQCJoK3ku2CnSqz01LXFyhqaTyanEBZ60FKaXF4iBfy7RC6mp2m5jXPbwnlWhPuiaGzvvDYMij/Bc6Z5FrhU49RyXYMJ56FG+8PbbBTYXJF2L7kEQfZlqHYCF7L6c+cYH5S2H7UB74Sq5Ux1cV6qvuIF8gqXoVSgod0ugQv2G2DO1Mi1qNeaiZQnVnspsXMheKBgmylrNWZBM4/XgDvDW57BKUgayX5pEQV8rzEGK09Gq0uyLy0FF86Ae/ZZn11SmZ9y+OJYhMPx6swd5Zrnz/L5Iph55IjP7fLI2evsj0f8eYbp3j4oWucGE9bnlD9j/i7geoPHo3h0y8+SvZs8Gj062/ygfNvcjIPLJ3bbsTLm6d46vSbGPGMTWDhjB7R2JQ1XL8RpVDLldk6n37zEb7xoedrCP4Utj83Df5z4TO23JhCLX/nN7+K3/GBL/KR9VdrNtDUC9rxI/7BGx/h9b97icf/wJcYWceHN17j8fFlXitOsWrmXMqv8lpxmi/sPsjXrD/P1Of8d8/8M2z+1ln+6D//M3zZ5HUMnp+89pV8aP1VvmrlhdqjMeIZ4RlXoTSPsGEc296wZjw51KyetnrIHEqhykQMRgSvikOxCKsmJ8OypTO8KmPJsCJs+jl/f/txfubaU/y5h36KS9azanJ+35f+BfQPmnsTOjve/sC+cuwNTZh9UhsaU4b4u7jwO+aJxMDGVflcmIxHRrw6pk8yufZVuEaqmbkH72RxRlPp1aremBZ/m3TNQZM6XVOPVO2KY6x4qBkKfXXOB81S9RUvjb3r5NNKl1RrATGTaOLgdddO4/GC1wOILtifenao1QxUJFzUeB2k+hv77CTpR/M39lO8VCG71Ip3qnSVl6eCeAn6ffwt4ATVEErV2JfqnHjACb4K96kGb6W1Pq2gzoALZdQZnAuLR6VQU087Y+oyRWGREsrSMisySu/rEJuI1gYprm0YCXTThbOYIvRBCqGcWzZnY3bnOcwN27NR9fxoHYqLx1DxtFSGx6vgvKFUg5taRvMQ6t2Z5lydrjGvZjuzMmNrOubydB0jvjJinrJ6+ON6RqSXnvmMa7NVtndHvLJzim03ahmZ1NgBgcvGjSi9QXcsr2yfYsU23DJp+G/uM97aXCefK69tnQx9RLlZrvDmbIMVW3BlvMFrs5O8uHWm1n/z1hqjHeE3bj3CtXINi+eZW+fYdTlbbkIujaGxKGNT4NVQqGXdTtlyE9bttA7nhWsZKK49hrlmTGSOx+ASl2diCnIpmfqcqY7qPFOf8wvX388Xrj7A/7b2VZzLN5lIwbOXz/EEbzHI/nJfhM6+4dK/3CSULoSh8qw9YHZ3rMSQVk8YIoYrUk4Q8VVYaknobE/+kD5+kp40cR7thM5a7T4Il0pFXV3njX1N23I30GjT0OSsQMd5Vb+GXUUVnwnOh/CjCDKdh+s1yts6DiNVGDL+1u7upSrsKN5X988u7tTq1UtD4b0fn40qMi9DCK9qy0FDkGZWQFHCKK8ojUP765BgDJcukx4+HCkcMi3C9VgdBz1pPu+XPse9z4ZXpEjCkh3p9jWGYc3OHB3n+Kw/TAdgCo+5voU/tQYiTagyDjtGqnC3DyFhwMxKzM6c8tRqEx4tQji8Fdrru25pGLqn7bH9e9Kw9wyJpvAhRDzOqmcmhPjMC2/0qjnq0Nlj/8a9DZ198d9/t4XORhlvfutDTejsrZLR9Tk337dKOWk8hMiwaQpYueoDK6epQlhG6h1mQPNCJLzqdh48mnIiC6EzADsL5X01ZqoJ4S+fJYv23dCZpfbI7G7QMTtThc5m4Cah3WpCnrAZoCnT4oVXyHeq0NmpJuSXb4W6Yv1upO1wFO01jxatb1fSl0ySULdvwoB2VzjxvHLriRCmszNhfBW2Hw7XL98Ku6tU4PQXoFgXth4NYbO48aLeTdVxbmqvJ9PKSxLym4b5aYd4we4IxWnXLmMUKQzZLYOdCbPzDp1UrqSXsAmBpF/RQ9s1SCn4dYdU+U0WPJV6p1Z18ZwzjJ5ZYXppTrZSYjOXLDlVExCVmlUzzt3K0qLPnmDlzRBadCcd6+e2mc8zisvrTC5uszaZ1wyZkng0sYuRQVNV8NW/V185w8rzI/It2PzqKY9cuM76aIYRZeYy3txc54nTV0JoybraozEoY1u2PBaA6/MVPv/mRT508fU6BBfbYcRjE1fXqbBZTPAIn/yt9/H+J1/jAyffJBdXh+UgeGM7bsQvvfkE8x+/yOi73mKSlVxav87jq1e5PN/AoFyaXOP1+Ule2DrLh0++hlfhx5/9cvQ3z/NN3/EbPL5yBSueX7z6Ph5cucVHN15iIkVdh8GzZgJbqkNYMzOmfsSamdUMmaEvzeYHh2BRcimrkKKpwnCOiZTMMUw1r+vZ8WP+8dYH+aXL7+Vfv/SLXMxusmGmfN/n/zBn/7V7FDq7z+XYGxpxntNfmlYhGyXbmiO7c06KNAueQNy6bJzHbhesjLNqF5btHVRbaxhGkFIRr9XCatqAakYVF48zU5c3rtoe3TFMptCwcBnLeg3bpwtP+UY1o3XgM6nXluJ6UXd7czrzsjOPKZViremTnXrcuJlRNsapKddraKDf2PRco3oNjDCzW7lckG+HGbopPfmWZ+WKrRZ/fdhSLrD2xhw3tkyu2Wprs1R1ylLCwMB1L/XvfNdRrIZQmZ0rxWr7fob7ANlUMaVnvmbwI9OEJeMjko6tCrYI98WNMnzljemSWbBxyspVx/TlHD/KF2i+a/2SXDNg5GH1imd0q2T1LUu5klGsnWLVwWhTmW+cYJ5uR+86wWk1SfvP3VBWrpXYqSffGnPzxINcj8+RD+uJz66eDu3p9L9vcdyUsHpLefrE+xe26i9sBIhhU4WLr3ne+MKjvLLyaG/7URjdgrNPT7n6kxfYtXB19DC/Ogr3DMDl1URsBl9aeS+isHpFWX+94Jd3voJ/Wk3s8m14KYd/MvmKdj3Saa+JzxALE6f0OnTb2VwM6nXJVG+2DfmW8hfP/qGg28Daq4rqrR6Fg3Tl2BsaSkf+uVcWkrNrN/csdlzRQkdHoGNyBDruVLr9WNkj717ngL1DffeIzfEgcuIOyu57DW5X76/fJcUHlNMHzPfg5w+ve/VThy9zz2WAoTmQHH9DM8g7X/Z7WYeX+d0pyyCIjosMz+WBZTA0gwwyyPGU7kB+rwb2vQA43y4P+z63aYOhGWSQQQZJZRkFdPw9eDKHlsHQDDLIIIMcZ1EWv3G7z+S4rpkPMsgggwzyDpHB0AwyyCCDHFaO28aEYy5D6GyQQQZ550hqAI6CRfO4yH0eOhsMzSB3LseBefPtOj5KVs57yIx522Xvp0H7kKyeh2bxvN9H/3so97ehuR364YPk2e/4XpXZbwA4Ch23e9yV/VhE76PjO2EnrQeklCr4EGWBQx+3GEPvhG30sMyjTSea4/3OQf/x7eDgRVnGJtqnNzKCphh6iezVitY5EUQcWi7yHd0Vuc9t2vE3NNbgH7uATEvKsys1/EcDZ9KWCA1jZoFfw3deHjt1IIJbsXX+VEyhNeeFSgOHYgqPnTo0M2TbBfOTI2zhKScWzWSxPRrqCkCKEUywOift+gIUjSxAjtjdivRqYmqkap8J+bbDZ0K5aip20ACnku16inUb9Ak1BIf4fqgcFRqk5Qq+I9/xlBNT9T3BYKuuRRcrTVxCk1Clp/0wDibXHbMTNpDGScBrq3HPDAn0S/teiBLunzR9aDNONtA4dZnICmAaOJLRLQWB2UmpIUa0IsMzswBtM9pUdi6Yuo3iKtqGtE1C6xrWvyNkSYdYr+4EFYabEBg4k+seIV9qXdJfvk6P1z0ZV1VAc+2BZknS+kbQdCuTF8yuwa+5ZuXWdEY3L0geYcMrFcsoJ7xgMx9ukxes9Uiiz1S/iyIA+I/GRcJK7Reaa0TJbKRMCOR1mfGsjooa4br0AcXNijJ3lstfPMfqqwb5xutc2NjCVhctoG2bFrZcw9YZDEdm2qRvpTe8vn2Csys7nBrt4NXwT55+L0/9hVcGrLMDyLE3NJpZbnxgnck1x4335eHlr17KSA2c4B8CAexydCtQHvtRe0Ad3QxlZ6fofbGznQB2CeFcHETtFMY3FDcWVt8acesxQ74N85OBOjgdKCIA6OhGhs/BjbVm4ezywdiZ1HnSdPHC6HoOCvNTHjsLb7/PlfHVET6H2fkAMGl2DcbB6HrG9AGPW/WtgUUKQXNfsWRq006Ashq8nYBV8qsZxckKuHLkkV1b0w2bUfjbAgouDGbUsEqKUYzRBliyyMienTB7qGBycoYxnt2tMWKVLHcYo2SZq1/66vKF+1kxZFqjlM6QWV8DUBbOUDrLyigAH0bgyVkRbtgkL8mMZ7fIuPxmAI+5+PB1xtZReMPIOpw3XNlcY7ozwr42ZuOpqzy0sYkRZbsYcXI0xVSw+hFuP3C8uBpsMhNHqZaZC/TBEbyxSx088xlWlDUb6YEDiM+JbJfcOMZSMjFFDX3vqhtkKxriyB9j8Vjx3HCrte4zdqtmzoxlIIBO5lK20iNsvscEUEk1FFhuuDW+OL3Il6+8zJoJbYyglLEtazLHITWzZZQIUpn+3fQTzttAw7zjczbMnLy6Jk6FSfX7TRfAeR7KdskJzBqjikMmgjAXquQiTMTiULwq17xnVeDBbL19nbVgLDk3/S7/+onfy/+59gR/+yt+mK8e3zn40+fnO7wvH5NLmFH8ydE2z8rZO9a7n3SGjPtSjr2hkcJx5levQulYeXVMzUoJS/fMidOKjXERMl5mYca2AK0ez0e48i6MfxlgwrGCzEtOPj0KefOKJbEbiqmg16lg6LvMhbEPpmLIXKAfUDDzEkTwuUXirMlI6IOVABhKmO3jFTN3uJV8sS01m2fPtepG9+bTAFZa5RdfNGV7IfRdFVJJPM1Uv1Pszi3cSo5meX2NA52BrfLn/TNupTc9Ao8GjqHV1rn1Dp3DRJWz0wIE3OQUUNH6SpiwP1x4xHvMbJNyfY2ZDQOXUbjVfb5qb3qxTX0AlHudb0joenSm1naZzvS+HXDv6DJo/HgtxSk/b7+h8Upul521KpOWb3n8yX1dRr/RCi/6nrSKD6nr0Ubd4mB0q+D9u1P+7I9/31IW0K7sdY3q6EPV1tHNknH50oH0vtvl2BsarGF66SR217Hz4LgOicQwQj3o0DwktlCyqadYNbg8PuwENNltj7cEROBumIKAAuzGzcsfof6zmZLthgdtfNOx80BGNlXm61J7WenLJE7Jt0P4xech5NW8wEn35gHpuaYfSEIj+WbwPoo1wc7Dy6q28qwmMD9RGat55UFtKtMzlReXvpQlaJZct/pE88LGgWt0C+YVeqTaigk0snZmSfnogZSCT8M2nRCPKWDt1Qk7F4RyNeTLdgWfaQgbGQLzZlK2Nn7a1Fkj8gIYDSRoSs00Wl/3srp3uaJGkUIYX5mgmTI7l4SFqpHebufYXWF8Xdh6wqErVcy9FGTUhIlEFKkoDgSQihLAGMV7qUNFC6EkQlnvQ6gmy4J+5wzGKJO8RCriswjHH4nOIiFaTKuJ0Qh0ANGLWx/Nam8LwKshM50QEEpmXGABRROdwQPbLke8vnOCxzeusWILnErtgUS9kQYgF4evHoTcOJyaOixlCKydM59xOtsBYMuNOZnt1t4awKqZ4TBcKTaw4nkgv8Wo8rBaXlhFWGZFa9h+h3DDrbFmZpy3t7CJF1hoxqqZcc2t8/2f/efZfOkEf+53/n0+Mnm5JkwDKpoAX/+O12TN+NqbCv0JUih8evYQT47e4pL1bKrnz7z4Xez+/uzerNMMHs3dFTXC7KQlz4Wd86YenFqQ+EgyQwQ7FfJtYX4iDLpBUfjnbhh8Xg3SfYZmR3ArEBk54+CW7QqjTQ2GS2DngpBvwey0BEpmk4SlAFMI5WagfvYj8KNoDOsmV+s4gh/rwnqAKIyuh7qKE4rdlYoOGTQTilWYnw4hMrsb2CLdWNi9oPhxGGSjmELwI20N6PFvHJjjg+wmlvlpByroyGN3TNBnFepBPXnqCxPw8ONAH69D1RmdG+xuzvShErMWPIvpVg65RzKPWK1ZLWvdsaxKFVYD5wRrtWaxdM7gvZBVg3tccy7L8FDkucMYz3yeMWMVzZW1C9tY4/FVSK70hu2tCfOtHNQyfmCHjdVp4HUpMtbGc1LWy9yGAcVWzJWRcrlUQ+kNE9sMkKkY8cx9hhFlNQvcKdMyJzOO1SyElDLj6oGvWzbWD9QhtFtlg+F9Nt8mrwyLQRu2WbRlAHJTtkJe6cB/rVxjLZvzwbXXycVRqK3pmWO9MTyXht+6DJYAhWZMNeeUDYZm003YsNOq7SH/RAqKiuTI4rmY3ajbGtsUQ3aFZhg8EykCKybC1Ae+mDRkiBoczTWMPXUYHKY1WPsk1OerE7l4tr3BilJ0+BSmarnq1tkod7HcYlvH3JyvMKJgkP3l2BsamZec+tU3wXtWn0nirPtBy8ddJbYKg0Upw2YAsq7PXUlkiKwbUI1gqnVZKR2nfiMPeTPbDufFYh2mz71cchXpDX/UHDjGtLw2KUqwpubGiWyRlA7Ns8XdQ90wYPdcp85ab1o2Xou+8vv0T2YFOsrQapeP+OmBQqBLRQTUt4/r9rgkzYJ6pLgZTuUWJN73qi3lDHQa2BNXcjCTpF95u6uJTa6Hl2SX3+6y9iZ5Zp2twpu17oOHpaAJZwE8v2RjzPLC/fdRvPKieV/zzB+gTcs4c+K57vrpQlP2Ob9MuqHHplKt2//QboGZ3uDv/ei38ONdxtE9nrllrJy4SndV1m4XaPnm4Rr+LpVjb2jILNP3nMVOS3YvTiovQ1rhmSj1ZoCi2oG1apqQVQwNbXl8JhSrcfBs67AzxY2ac3HXmp0r+U4oO7pZsns+J5tpINqK3kjaJg/5Tgi1hdBZp19xjJ3H8Jq0z3kYbVU7c9ZCvhiTHt0KbYyhs8gOmm8p09OmvfEAGhK1dNdZ0s64C0oNjG4qxXrlUWRhV1YMxfUxjy7sOuuE50wJq28ou+eDp6gSNlYEzyx4gq2dXNIesNRWYXftXOOenV6i1DEPzbTaNSe1Zzg760NdSr37LNsV7FQYX4fNxxS/UnlnZfDo6v6auIlCQ9nKi0IU9eFii02NX/UnYeAUUWxWeQM+bHuzcYODacJufTuEoycXm1M6U+ebjAqsaVg6tboBceNEbE4MxdW7rGg8yZnLuLGzwoWNzdozi+G3GMYbGVeH29IQHLTDfV6Fmc84le/iEWYuYy2btUJXWeVZXpuvYUQ5P9qs25qLI913FkNzubjaY7terDI2Jafz7XpzwlQzvBrGpmDLTfiRz34tvHiCf+5bP82H115hYoq6DZagr/H4wiaLHFeH4uImDIdh6nN+Y/ool/JrnM9uMdWc/9czv4eT/8o9YNjURSN+v8nxNzRQL1bGf+LjSy/tgakahMRTD0SSlINQVjyI7zc04Vyjr45nuzCoGtE6jzitWTYlDcNJlddRNWL5dM2UTR+7s0NThH6auWCLas3IgynDIGpnVRurNRpbaDA6mqwXadju7a20jUDsfuxrZXCyabNmolYwc8WXUq2ldBqvoY+poekaIykh21XyLSGOM3bWGL6wkaIqG9ukHX1VXS3jqaHtvtOmyNzYbIuG0Y1qa7Sa+prErc92Fv6NbikrlwVXMbIGA2oW2obowjWMA0Efe2V6rdLFa5ssZitht1Uq0inaTazvm8K8YtdUE6zxUi8h3e6c6pdgkM0cnlk5tbgNP+pL17fSwn3VJM+F+OaZairUsKW6CPX4PGl0pwPd/oiG5yrdFVrnixMnB2uvGlbe8vwj/1V8fPUr+7eNLzS+51gBL2Q7YT1Ss1DP2quGE/pMv55BWnL8DU1RMvrMywBsPHdI//ouyvjtbsASuRMWyHe73M5G1fSLcVkyoeh+UZ6mLyuznyx8pZ7oup0v39/JcuZn7qLye0UZcJ/ftuNvaFIZeCAGOWaSwpAcBJKkm+coYEyijv103TXIlPsJluYoZRiPDiz3l6EZZJBBjp+8nQPufpuC3ilG8D63aYOhGWSQQe5f2c/I3YkR7KNufichQt9DGQzNIIMMMkif7EXdPITNDiWDoRlkkEEGidL1YvqQ0Q+CCH/EMmxvHmSQQQY5DnJU6zVpvmV0Em+DsbmfZTA0gwwyyNHKXoPwXnxJd3Pd435fU7nPbdpgaI6L3A6R2r2u834+PgrCt7vJjNl3fJflMN/VHJp9chmyeh/xW0UU16vnqNgwk2+djuycYfBqDij3j6E5KJPk/SyHZHmEO/voDzgcA6NIO1jcd7wArX+I42VMifE3LB7vJX3sjgeVw5Y9zPO3n76DtnWffAvUE52yS4fIHr3aPZdcmwWI/z10dXHENElLy4kq6j1qE6qPzseuy/D7/AKr5kKWtr6+/vZduk4/zbREXr28RPkRijJ4NHdd8ozNb3qiOY7PeRepQtLfyQPZBynRTd8jrT70CWRKmndJ2WgUTElDN5DkaWgOkjIpOGJHbws6P8Ki9DB21m21Sb5Upyxek9HNAE0yPyltuBFZLNvX13blHbgW0zkXaYgr6B2fL9HdXf0UkFIY3RDcSqBJEE+gG8iasqaoMNQiTYCr7leKW6ZUJHChHknrTPsTx1Lb4Jp1Cd6iuNLUNAGqgVESwBhfQ/VbE7DGUioBk/wWInS/Ls1TozRXZGymwuSK5SLeWMQyM6LkxtU0AZGULU9oBXJpiOvib5/c5LEpseJrBGivhmvlGtvlmMdWrtRYYxEbLG1zJGpLMcbC+fB3JK6G/8+lJBfH1Odcc+v8xJWP8q1nPs+Hxq8CMJFAUzBVy3PzB3hy9Car1YM0Ek8uAffSA7kEaP9VgVyEXExVv3DDl0xEWJWcXCwGqRGc4+9cloDuJvLn3/won/k9F+8dnfN9LMfe0KgVNi81N30BZ4r2cYtzZdmE7gADZhd3rMbVElqGrW3gaDCtoqGpwDB9GwiYGjOrZWi0NehCM5ERaEgy+gxGMohG3K2UKqDV7qSdKPjcoKaiHYiGxkKLynfJrFBFEZX6LyT1LtyjpH9FdTI1AN0yCWUAojA3iMtwq4pfdYEdNNdAL1yJmxnIFZO74BBVDKL5KAxIAfhZMJUhSCezEbgyglKqCqrUNAbGNIO5iGIjZYE3lCYwgJoKLj8zHucNWcUQGo2HqYAx45WNhqG6dXiVVppTaV2SQmxj7BLDBywYiEwao5YaIqA2Uh6pjVWqw1cPsEfIEkMU/96aT5iWOa/tnqgN6V6StjNtYzSI8XduHDOXsVWO+eKb57k6XePC6vvxamoju+tyrk9XOTvZroE/I8hnoaY2pqW3jCuA0BQAdNuNyMQzNmUN3hk5b+LvqNOpBIqHqvyKLery//SZJ/igvrpv3we5DwyNzEse+odvHbzA7YbQ9gtX9MGmHyTEkZSLJG1LIeEPApcf+W270hPS6JUeWHUpfWhTQg9wYNj6g7R5L3qCnhDYXpDyUrjAVlqV0+AGNJmqdY6F9i+EWWx/PQe8N73Xp6+biZe6FAo/aX/Lg/WKjaHLHi9dpQ3G6Tr6yzo01GlTDwq3Lslb1yftY6lAa9+0oW3LIgfdNu+Vnl5T8cr5XeXmyhrXs07bqonftYzl17alt51PKuDN7jpS656mfY6TxyotRjceuObvGXbcsL35LovmGW/99vNNQncmX6VB9GaShzKduac6ex76BdqB9Hf0aDIWHtz0uM/DMEUTxuoNYS3o6UEHjocRAj+WT0NnnZfQW9rIw/FJ7bRTPIyvVWRwpxLSqEwXrqEmOnqvU1dSaH1owevrvOKDmYShUipvpxXG6lThCoO5vIbbcMiKQ0uDnZQ1a6Wq4MoMmzlGowIjSlFajFFWRkXtAURPI3oX0B+yip5Nbl1NdpaJp1TDqGKbBCi9Ze4t6/kMI1qToHkVVmxRM1tm0oS9okQis5ge2StddfPaUPZaH5skHFXnS0i/jCijKhS1V7jKIUykaIW+4u/o1Uyq8JQRrUnJ3nDrbPsxT+ZXGElgpawAwqt+tY3gaIkBz2nCWiGMZSjUccXP+Z9ufDV/6OSv8ni+vlDu+WKrN/1eyn974xL/+89+CPVD6Gw/OfaGBmEBnr4bpqrTjbCn8Uh0tvIctClKw41i2ulxUI7RHjXUcOko4Uon54E2tUB13Hg/i3UvhM66zkB1TcSDsZ21kiUzVvGQ7YIv25w4ahfz7zepSieAQENLEDudGCpThMVoX8Hy13X0zaijukIYXxPKmeBGWYCiH2XMs+aimgLKDIosUjkbMMp23ml93xQxfa7Siuv1nKqcl+a3ChFGnqyZCYjR4MzaxnhJdwbd4yS3HpA+SYzjAm105zqna0ghP3gvLUbTyJOT6kqdY1VZ0AMwn1u8N4zHRe/5qDvtZ5z8a6d/3bq9F8rCUl6Z8NfPfj2TSdFqd1la5rOM8STU3b2O3ktdj7WLYb2yDDTasd1dpyRek/jbuebGWespC4sYxb+wxpP+2d6+H7kMHs3dFZmVXPxHrx0g4yEsxgEk3Ukj1e62/Xbx9Cs6ZCjrsLIH6+WCLGt/ZB3tMovebv1LpBUaSRhD9w2/JSOflD4wdaY7kVJGyJ5QJSSGc7/2L4P67yZ3r2USsmuFRxursL/OZfWnhKeptWoZ6G4/eurpCdH11btXGKrJFP6pHbUiCn3197ZvWRvjZEnBTj1uPMZn47qN0Y6KV7wdN3UvmVQ2G2Laz56muzhTHUnbFjbiRH3VxHJyww/bmw8ox97Q6Cjj8u94qDqgHXJKPJp0obv70C6dMS97UbsvVvVgq22YPVtx374Htqq7Dp3ZxfO9D/aytlcvdu1NSbvelmfkQuhrzx1jMc3D+IbgLRQnk7BR6o30le22G5rYd51P23WbRpdUmwF0pM2mg265VASYG/JrlnLdoxMfvIjch80AcbZcGCRTTB4Wwr0L3kc+Klsz9WZW3+w6M8b3Rlvjgn5kp3TeIKLkFUukU8F5wyQr69BZbkM4ZWQceRWmSxfdgXrROobtoAmrmc4UNqaPk5BdynSZGVeH3SAs9sewXLrrK7JVjk1RtyHsNKt2xnWYJYE6zBZ2noV8V8oTTH3OY+Mr5FIyimHBapdZrCvKSFxdFsBjcEioD4/HYPCMxDHVnKtunZ+88hV825nP8eT4DbwaJhIYMrd1xMvFWZ4cvcEo2eUWw3pRb6GGVVOSV+lWwq60mz5n1ZSsimIJu9Li0qet7nyeGPRNX9a/V43lmnOsGeHfeuV3c/WfrN6TXWfDGs1dFnGejZfnrbQ9w117zdr2rGiPzFqxVna3Ntdl+/RRh7HCgnVH5QEXk5sCHS9gr/zRIB9koR7Idn1YWJ5U6yaehCL54BdRehZG2+WlMTQu9TQOVod4xe4WaCb4XCoWRwvJVlTx7dlqfEHVjJJGHbjKpB+xAsFqHNCCWA3/nAlpojCX5u+BNprsIa0Z9SHbvZ90N/YduJyPXoW0r+WSCGUaOe3L2312xIGdef5G/t5Axw6tSZcptUmv2rMg3fsc5zPd96in/oU2xr/S1JVtFeTlsOvsIHLsDQ2lY/ybL73drRjknSoH3aU4wMMP0pUhbHZgOf6GZpBB7qYcdrAYBpfDyTIon8FgH07u88duMDSDDDLI3ZOuYd6L4+VOZTBex1YGQzPIIIO8M+R2jdcyHMXjYriqjUD3sxxwuXiQQQYZ5B0qy7yuQY5MBo9mkEEGeefIUZGfHSM5+L7M4yv7ejQi8tdF5C0R+UyS9h+IyKsi8uvVv9+TnPsLIvKMiDwtIr8rSf9qEfmt6tx/I3eEbT/IIIO8IyVlsOz+Psi/g+ge5J7LQUJnPwR8e0/6f62qH63+/e8AIvIU8AeBD1Vl/juR+iOHvwp8L/Bk9a9P5yCD3F1ZRs17kOPuYHanZQ+qa6+y3UH2dvMeZjC/m//6+n6PZK+579s+L9Z7/O+IZd/Qmar+goi854D6vhP4UVWdAc+LyDPAx0TkBeCEqn4CQER+BPgu4OO30+h7Jt1Fwtspc6fHd0PncT7uQWI+crkTY9M93uPcXuR1KbPkQfICvcetAdAu+b3f8V7wPOmHjF0yuD6oo2XwR8vkTsntum27Q4K7vUrX5+I9dh6dzZdlHySRO1mj+RMi8keATwF/VlWvAw8Dv5zkeaVKK6rf3fReEZHvJXg/TOwGnD+dnlzIH7/wbWFMtXCXwsMkpUczQwsfy2nImzzULfyt5ItjKRyaB8Y/KX343YVI7ww8+37F3weiKbKIr6ZaweCY8DV9TO5iOBlpQ/VUbUq/bO/iry2Dlmn1oTuexEHPh6/8/agDzZ/+9KFdasDOFDeSGnQUDWnilWLNLIX5gQpax1LrahoT2mMLxc6U+boJ5GdJvxbg6HvOxfaobec386oPmeDzBsUbhXwrQBMVa516AOMAD26SoDS0ULwTTqJuf2XJxLIqb4rw2+daQ9/bmVCuaQP1I7E/yXHsaHUOARJoIBQoDIw9Yn2jY2aRicNUIKE1EGbNjQPqhfLKBDk9rwEv07wCNZfPOHNYEzmBmp5uTcds35qQjRwPnN4kq4jiIk/OyLj6OP6blVkNDzSx8xbXTtYieItwP23itwBf0xDUpYjZUa4Xq1wc3WJiirrcX/viN/DoH7+Kuv35eN7tcruG5q8Cf5HwWP5F4C8B/xr9w+kywI+lroKq/iDwgwAnNh7WV7/tXDjRAhdMCsSBooJ7UUPnhQ6DVL6plGthsFABUwbkYjcKg0HaosjaKGUDnTG6qRTrghtDvgnFBvhRU2gBVLA7aPVJa5BpdEkpGBfaBqGtZi74seLGFXOltAcI8YLmWhvH+rdJdEt18VQCbkp6Xgj4YQvYY75mmESlmTgaRUsDU4OsOqRCL1YvrcHDV2i3Jve4zRxZKbC5R1XwhYFbGWZm0YtTspHDWl8NUNT4Y16F2SxjPC4DbH+CQFyUljxz3NieMLs+4dTFm2xMAmQ/UA9CUOF1JW1L2Sjn3lJ4y0pWkImrYfIv766xMxtxcmXKqfEup8c7GJSZz/js5Ytk1vORs28wNg1mGMDl6To75Yj3n3iLsWmg9iPuWBfmP7Yv5At/vZoaM6xQy4ad4hBenZ3Gq/DEymVycdwsV3l25zxfc+L5euDMpWSqI9bMrKqjrPHAIGCCeTWsVucBpprzyvwsj42u1OUcwnOzCzw2usyamdV4aF3Z8WP+ztXfxjee/BJPjt5o9S/Wu60jRjg2zLzGc4sEYwBfKs7xT7bez4X8Fr9z/XPk+JqQrFBDLhWNQdQrcMNnVZqyYVI8tfZ80wK2mgzlnSHJiGARHFrjnYW+K16V15zwSAYTaYbMT198lKuyunAd7oa8K7HOVPXN+FtE/r/A368OXwEuJVkfAV6r0h/pSd9XpPSc+0x44PfDeUpxlRZCGl6xM48fmRrJV5xiSo+3Bs2kFc5RKxXMf5Nmpw43ttXM3OPGJnhIe2FV3WZsV7xCBeQZ21rTSbcIypK+Vx5NF1+ql3IA7ZyX5Fy3zY3ns4hXpZjSBdyp+BJ2cOHFaeVVWey8DIyeYhpyr2IOTnETGzjilyBSi9MKLbg/XLNReExZUk7WUbNRn3Y0uGTFoubOdYHNTl/HpWeioLLGzcxwI7nmZ2cOFeHl0ck2sKk29/A384eWVEgLQ6vpT3+7SNoVMN3gN01z36T0PJt/cKHswqRsr8e10u074TTjkrTu7Un0ZVPHM6MPLkfLTvvbo0c8mMKjVviJ7FuXRwJoRzK6ab3S91gdMDoXgGrbdYxulozLAR7rIHJbhkZEHlTV16vD7wbijrSfAP6miPxl4CHCov+vqKoTkU0R+Trgk8AfAX7gwPVVQIk170si3UE0GplWqIhQTq3gM6l5a6R6SX0mwdBoE1KJISvprJe4iQn5reBWTPuF7GuLNKGg3jb3pVcemGgVpiEcGxeOXS69oaXoQdWenaWhdU50RxRob6UJt6Whk47uXgj2eLl8COO4MQ1vUOc+NSGvyoOcBGK2AMwI2Xa4zvNTUhO2tUJKJoyWpgiU2GqjR1fpr0BA7a6Q7cLstOJzbfej6zW20kNe8QKehvSNkN/s5hX3jaJjHxCjq1CRvTYKXu3JAjEaOGeqOtw0A2fI1+cY66uIrbZCSrEaa3zLCzQxb4UWDeE4okJvTseMM8f6eEZmPLtFzq3pmIdO3MKI1uGjUg0TG8zryLhEf0PrPLZl7V14hKuzNS5MNiuk6ECA9tZsnYuTW+TiGJuyplVuvBLP1Of8wpvv46vOvcyj42uJ51bW9e34Mbk4TtptJiYQrqVoz8/Nz/Mbty7x8MoNvnH9izXq80gcc7VVGV8RujVeUiRs2zBz8lqf1KRsABMJHo0hoDNbBFOdDekBRzrDYjvkQW+5bR6wa620P/36b+NLv/fcPUFvvt8/2NzX0IjI3wK+GTgnIq8A3w98s4h8lND9F4D/G4CqflZEfgz4HFAC36eq8S78ccIOthXCJoADbQTQzHD5yycLM6DWbDwZPOv4foTSj4ORg9GmMt+QOn5vimrgG4d/6exPbdBpInEZVejshOByGG3C/GRjCJb5tnXoLB3ou3nSA0nq9WFgFUAKMEUVOhsRwladdYpomGq4fqtBeX0tKg/DSx27F5WQL21MOiBHMcl5kjylQWYGXStDKE4lcArHuiHE/DMPVpHNDF11SKaoEyiF7GaGeCjOz5HMV2RhQb8AYjzqDfO5xY4cNnP1QAzgnCHPHLtbY2abOeMLO6xVjJphsK6anMDxt35XzSy9wXnDOCtb4bVr26s4Z5iMCk6uTJlkFXunt7wwOcvKuODS6RthcK/CcB7h2u4qpTc8snGjjv9HSP8YDooDdVqfV6nXEkK7LB7BoIxtiVPhzd0TrGZzHphsYvHcKld4deckHzzxRh0OtHhmPmPFpjD/jUEpfJgZxLAewMxnZOJ5aHKDccWs6REKNTww2sTQph3oijXhXEo9EPvkKmoA1DLVEd6bFnVAoRk3y1V2ypwbxQqXyxM1NYEVpVBb/07ZRG/5lVA3vg73GfEUmtX5HIa86k9q3KChTkh/R+oCp6HNbxaP8VB+nVzKmtLgk289xhnd6r0Og7RF7hXn9e3KyclF/Zqv/hPhoDPrbg163W70uOVmXuJHWTITbjYI1IvonfKpRyMzh44tKoIpHD63iyG67vU8TOhM2wN+urlB6tCRtAnYbndn0J1Ict2bEJ9ZGpYR5ysvM1y3NPSHV6So1nZys3Qzhai2yMVa+qvrZEoPpa/v0WGlDz4eCHpJveXknhSBNM7nHRrYqt/B+N8ZAEc3NFRfCwjXneb56NbV2iCzRPfC9UzuV5qmdv9+xPu7lCQw9Xb72uVDOFuFsNkmFksnlnvJXvd9n+YvkNalaqPnn+TNtubY59/o1fWJa3+bm8Vbh38Ie2T1wiV98g/8maNQdWD5zR/4M7+qqr/tqPQdf2QAkTAw9TDlNXmqv3XYJwmBRYOhVehsbNvrHtXgnXJbpDvJUkNTr8tYCb8nZnEH1EL4rNOWJeG1bppxWnkdUrdDfAg5hfWQxHtLmSTTXWcJG2Cr7riGkxK2dbzF7vWrryvJ4FQZGlNS7yRr7WaKRePakgE7DTvUfEZNdZ3tKuJgfrITOkvbSNgQ4TMWNnvUobMZZDswO1Vt0uj2O84luv1LFrXEU4fdYhkzjelV+Cz3dagtv2HwFspTVawzKSfzQFCjaw6MItXuLomhutRrE215kb2PuShZFjyJ2TTHWM/qZIaIUjjL7s6Ykye2MdKE4pw3jKpwW5bs8oqhLFVhbMs63anh1mzMyfE0ELlVD8u16SpnJjtkxpEZX+/mskmbC2/5jTcf4r1nLvPQ6k0yca3NDxA8JiPKup3Vno+tdHk1vDo7xdM3L3B6ssNXn3yRiZR16K1QSy6OkZQtz2bbj2svZM3Mw6YH8fUmijT8FkndgNbGCGg2YIwiYRyNd/qGW+dSdotJ7YnCn37xu9j9/dm9CZ3d53LsDY1aw833TnrXOVqL/yQho5i3EzrLd5RiVWq2S1M02239iIakLBmkW6GzLWW+FkJv2Q4U6zTbcRNpLd7WBjKe7HRwyZxHytCeGJozJZiyamve7lvcSRbXY1pka9rJG9sX8yoLIbgFI7PYuuaXF+wcykmS3PE4jWsMDUiz3be6vuIEcSEk2ORLqqp0aVxvSthK+65hXPtJltyac719qjL60B9TtHXaaSRZAz+i2rBQPVO3qq3a3i4YbDML5dxOwpe55P5r/V/V1+Rappd1Vh3ENm7lYd1AHORT4dbqpF1P3HrVs75ZR586z4aZw7VRuw12Dm+M6O+HNmVHN4QvrJ3kc2netEis07SK1ufNHLId4WULn177stb9X+hDrNcnirrP8jJJ2hyOk2e6W07D+6jJtnaA1TeUC/7ZPSoZJMqxNzQyLzj/0y8ejTLvFz8C60s7SPnDlLsd8X7/PMdF9vtQrhvKG+SOJYa86483h+v69si9AODUHuN3n8mxNzQo6HyvTamHlT439zCur1vye5BB7r3o3dqOdA+hX+5bGVCeDyzH39AMMsgg917erkF0GRzRu13uc5s2GJpBBhnk+Ehq4A5j7A5qlFL8vPuUNuB+lMHQDDLIIPe/3I7RuI+MzLBGM8gggwxyr+VeoHwPcmQyGJpBBhnk7ZGuseim3Y6eo8w7yJHJYGgGeWfKUXDipMeHyXsUZQ+it0/2K3tcPYEDtKPm8LlL544i312TIXT2DpWDEJLdqY634xgON0jereN7McAdEcHZXqRkfcfdstj2IHVHpGQ1gnLnm6R4bKX5pqn18aJCFwpnr8/ADvKN2O2QjTkHfQjdWn2Rmy1C+aT161smcAAAk09JREFULf+G+ADnVKEoIc9a9YcPSX04n2d1mVbzkmONOosS3dxe3t5Bajn+hsYInD4RHoL48PcNUsse9jTd+/BVNyy+ZC3SruUPWbesOG3I1Or81bmK8GvZLLKGp+mSp6X1ddqpIkjpwQo+wbmCAD+z8DV/H5J1MkDZuadcsTUUT9QjqpiZx63YGpW5S+LWRTvo1lMTnlVfd/dC28R+pThSCTJzkxiQEUbbnp1ztoV2YOdgCsXOld1zpkajjudNqZgCypUGFaLVlgSKp9VWOr87qNJ1PyIIlyhrr8L8hDA/2YCeRsSKjRdg6xKUKwRQVGmQGZo2NHAXATZJ+4nL4nU3zb2UXYvdNZTn5xUwafVcXBuz8YJh8yunmMy3gEqzp1cpNjz20g7FlRXsliF7Yos8L4N6afRk1rdQn0fWLXD+RFBRoAVculvmqApv/fxjTJ+c8eHHX2U1m9cQL5+/fIHyk6d58tuf5fxkq0aOzsXhMIxNWUPHRJ01fE0FkhkhZGY+Z9XMa+gbh2BRXpqd4eM/9vWc/9ZX+c6Hfh2LsukmrNoZ/7+nv5HZMyf4Y7/7Z3gwv84JO2WullNmpwVdE2FrcnH8e89/N/YPm3tCfDZsBrjLoqOMN77lgQBzEgeKeC4d6KQNSxMhIyJEvnjIdgJxWYsUjeRlbw32nTqSfHVdPuBrlasxvc3xYqeC5uCzZmAw82oQrNCXI3x+Wme7b5pAcAgYxe4GGBw3qepzYEoJOFxd0jKo+ttgmUhpAsGZVfIbOfNzDjO1+FH1wmQKTsivjyjOl2EwSwe0OLgm10pE0arRUuX1c4tY34LPb2xRkz+U8fUEOcDoJ9dCwTtDsZuTv5mTv/9WIEerLsuNrQl+O8NuWk68/xq59TV7o6qwM8/Z3ppw5tR2jcxsROs8QD1ApoOjSX9XZF8Rgr+5tOF8Wd3E//OzT7B6boevvPg6WTXw7ZQ5AL++8QRPfuhVHl2/Tm5cwgLZDNheAxlZoYbSW8a2ZMXMW4jMENCIIQx4ceB/buccr2ye4lsffDqgJ1f6f+nKe/mSXuIPfuRTrNuAbmzFs+NH/I3ya9k4scu/9MSv8bNvfhlv3DjBH3z/r3Ima2bqEYcsoh8XFTHL2DQfUo+k7EVBDujHhuvlGoVafuj0RR544Ca/6/xn2TC7tT6D8qkTp/j6M8/xyOgaE2kQp+dqGUWjUemNad06Abb9qDYO4ZqGNlwr11AJtAirZo5XITeBDC7OvQq17PgxAFMd1f0L/W8MF8CN6Qpn2WSQ/eX4ozevPKhf9U1/qkKmbXsHfYiutaFJZ+hCRXKmgfisM1vuYqb1SV8eUTCFx43i1LUdzjBFoP9N22lKbelYShC1pP6g16OmAQIVpzVo6IF0JWRuZh6AQo1r0Glje83M4SZ7hDL2aCNQ8wiFg07mQzx2olrhTXnM3OHW8jaCcqkBKbn0rXNpOwL1trktVOcDtxHItgr8yOJHiSta9TXbnOHWR7UnehB90ONRLxHjPDjFj2xzvRXM3GF3C4qTk/Z9UMg35/iRxU0sdurAK241638X9gh37oeqHJ+N0eVd3IkR5Uq7jmzqyG7NmJ1f7fVol9GEA4thyyX5pITVV7aYXlzDTaSF1jy+UWBmjt0Lk1b92hlvUt2jm47VT73QW/WRojefv6Qf+H33Fr351/6Hdxt6s1K/AOKWoDjLYpp4bUD4qoEzwqtLJ5ykaDt81f0L+JRALQmJ+EzwI2mhNIe2VEbFSuMtiYS05LgLItmHltx9YUyhqG0I0FKStP7QTjtUVIMQSuDjmW8IptDacGkWdGY7yvxExwPs6OoCdrZCVhFMM7ahM4C0dKbXIOWwj1l88BBHt2D7kTYXj90V7DSE0LYf9kmIq/JG5oKdCcWGD/TW8ZpGLy/WFeuVhg+nvmamSTMV0nIkMgMab+7pdWYXSlbP7dRemXNVYz9zEv/UFuur21gTPCpVaXhsWl6N4FQYW8fYlrWnFQnNIj/NyJaU3mBEeXXrJJu7Ez568QVy48jE41X41BuX2H7mJL/jmz5Tc9OMTcGWG/OPPv0RNi5u8i2XvsQvvvYEWzsT/oUnP83JbHchJBXJxuKMftXMw/WvaKMjmnLjATUewA0XPJr/+uPfwQe++kW+7+FfZMPs1qGoH7v2Mf7hL32Uv/R7/ycuZdeYiKtpmx1CjmLDo4glgD+Nqgsc0poHZqqeiRhyMRgMBQ6L8Jl5zvf8rT/BH/vOf8QfO/lbAGyqZ1WE/+uXfj9ffOUCP/fNf4mH7eoC8VmffP/lD/Gpb780oDcfQI69odHccOXLVzBzcCvSGrhq6Qx2EX7eZ80548DuHiJ0BguDXUQ8rtdNfBjcytWmnoiMjICdRubJajASsHPTCr9p1iEwk2SynxiJFKE22zX4keLGVSy8DPF8n+tiGE4WB24pA+w9QLZlmJ9x2F2DGzehMymE/KZhdr7sWc/Slv4FidPX0jRhN6EOv/UyMSdtFGm3V72gTpDtjHLF4B/drQd7VJhtjjDbFjMT8ke2EQGThLdm0xHznYzJqSl5XtbNjgN9NBa2FR5sBv70WKCVHtcwChc8vzdWV8k2Cs5vbNfhwVl17upknZNrU06t7NYMlb4yNDHM5glGpnCWUg1jW9bMmDF0F8ul4lVw3qAKcx/KZuIp1VCUFnHCzWLCzGdVnY6dcoSZGqa7I17bPcnWzoRiN+fFnTOsZcGIpERpIXyodZhwZEp89TslObMSSOGiwfEqbJZj5i5jdN3wzFvn+Lvjr6qNnlfDr7z+KONrhr/55tdyerRDnhi4Ui1ZQjXg1dTt8Z0H0Kkw91lNMAcNcdwr26dYfV344S9+Hb924VE8wrTMyYzj6S88zOStjH/j0u/nxGjKyJSU3jZtrOqLvwF+/ukn+aC+yj2R4x142leOvaGRwnHu13cC8VJn0X2paOrNVPkrQiWf9+g4KPpw3Sip0yJx2sKiQjwXCcFi0TScBP0DdW8bquyRAE1okV71kYJ1ibzqcEy1C0yUQBSWm3rhPlnXRgoXwjAHlEV49djoQ1zbPfKIC0RjfiVvnRNXhF1DHnQcdzQldbo54qfVfUr7c8C+HWBn1bhq4/un1wPxV9bQ/k4APJyeXsFPRmDyxkNWpRSh7OgTVfJQjOkBQ2dnnHJWlVvZg632PlY4ZHaFrf/1IlsR7bm6x+/fvQFWuDV6iMdLh7g5NycPcTMW3jNc1nU792hcdZvfc+sq/uMZb0yeaE3sLpYee+sKN/6PS1w30rsJplVPfL+XtaWvPQoX37qG+7UJb6w80Tr15O4cu73F9JceZDfZudfHNRXremKrvCcbAd4JcuwNDVSDq7aNeu8urfqhEHAatpUmg6w3Uq3R9IekAodL8yIu6I/p8VDBOMXn0tZZvQQSQ2dZLNuEztIYcIuzpieEl25ygBg6C5TSoW+hzhYhWqd8NzYfDUq+q8zXJZCKxd1lNnhQ+Y4yOxF1Sm9IrnWt6j5Vh47ae6z73G2fJml97a3yiAczh9EtZechCZspqutidwMnjp3C9sMa6kzaY8oQdis2NGzMSNvR9c6EZuNE6xkIelvhNmk2LURbNHlmzOysh3Ozpj8u/Fj5wkl2npwzWpsHD8koqgZrPca05+ZeBe8Faz25bair0w0MALnxOA3bd69vrzDdHfOei1fJJGyI8Co8+9ZZ5JmTPP4NLzGxYTdXZjxTl/OZ33gMOTPnq9/zEr/1+kNMtyZ87fufZyNrKJEjuVnctBC9qRVbYPE4DJNqY0AMt7kqdGbFU6hl000oveEnf/prOfnhq/xfHvsV1u20Lv/Tl5/ic//kCf6V3/t/8MjoKhNT1JTLrtqIYMWTV4jpBbb+basQW/SAdvyYiRShTBLq+8LsIf6rH/0X+Ybf/Zt8zwO/BMCmn7Amc/7CF7+bN587x1/55/4GF7ObbEhBgeGMKVuhujjE5GL4vpd+N9d/32QInR1Ajr2h0cxy48k1bKGUY1kMDZEMTPWsvx06gzBQZdNAfNYXalvY1tojTYitMl4+bJt1E1kYyKEhVUtDdaboUMKm223TfnXb4RtjY2darQ1VWStytrjLrk/qLdfa5IdQZn5CsLOGZE0tVBuMKNdk0VD1ta9Tl3QNSN+1jffLN3nocR7Eh/7bWfVvl/oaBkMTromdQ77ZmURoZWhmIKW0vLZ9JdHRDdUuhDIq4736ujK6ZZhfWanbF8+tv+wx5Qg3GtV6RMMGQJ/oiX0WBSfhH3QmHPFv0pZsCifm8MaJR1pNW9uE1Tcdr2w+1v7+x8MDr3rKyYTPnf0yVm4qG1P47Oc+EJ7LpI+pp9uqGxavRSdNNDxPovDgcyXTz53lb575tpp4D4V8W3n45ZL/9bVvbd7bbl9p6+w7J0qb0E2pPXlTwCNP7/K5L36Yf3PjI/U1AJhc9zx+veQvfup7GmK99L73tGHlmmejfK6n80crcT50P8uxNzQyLzj3j186GmUH/bjsbu/Ei6Rpy8jT7iXpWbwmx3z3IbBA9tVNj3KcycDOvI11n/yFt7HyRNb2OLfya3e//tNfXH7u/Gfvfv3vBBGRS8CPABcJpv0HVfX/vSz/sTc0KOhs/na34i6I6/wd5KCyH9nXXSMDG6SRiDrxbobcv5ecPcfvkS6BP6uqnxaRDeBXReSnVfVzfZmPv6EZZJBBjp/EQfZ2+WNuV95txuyYiqq+Drxe/d4Ukc8DDwODoRlkkEHuc1lmzFIP66De1X3khcm9DwOfE5FPJcc/qKo/2JdRRN4DfCXwyWXKBkMzyCD3Qm4HlHWQw0k0Ggc1HveJkXmb5MpBkAFEZB34O8CfVtVby/INhmaQQe6FDEbm4HIYr2SQt01EJCcYmf9ZVf/uXnkHQzPIIO8UOYzX1OXFuR1unrtpCAYj00i6pfyYiIStnX8N+Lyq/uX98h+AdGKQ+0LulHvlsPqPsu69jrs0C33HR6krDb8cNu9+Ze/2v8PUleQVaZ9Lj+ut5H15O9Lddn4757r5jvrckep4dxvDbwT+ZeBbROTXq3+/Z1nmwaOBvWd03eO36eHqsvr1vriHIdQSab4COyiicZyW3Ckp1l717XV9D8I5tCSPdvUufBW6x/FeZWtIl84gpRr4j3Lb377qGyoVIcXOC+gD0uQrfUAYzm2NXLGAbNHiXKqgiGpj0m56rENNAwTb8O0schelH4k2GH1N/d0PqFNw2UUg1kZn9yPlRTSLpG175U3rauWXVp5e5IklZePHmvmW4saCm7Q/ePbVB50rbykX/t6z9yQsetw+2FTVX2LPz7bbcvwNTWaZf/hSfdj7IEP7JYH2Qw0BzRnQiFAsEtKkDQMjPnwZX9eTfB28+NDL4gNc1999uKV1W2LebBqQmMuxLD7ssMCrE3Uv6EzaJa6NEpBvK9musnvWNCRmKcxX3wvdqqdpU/clT1GS+6BjWnA7XT3J29Pm5UlhXgiQMEmd9XkviBN05MGGz+9FANOQdQGIAanAL1PYGEkQmCMkTJ0ONdyLTfQFHhtXdzMzHklQlWO+126dYPPWGk8+/BYrWVHDo2TGU3rDK5unODnZ5uxkuwbUvDJd48RoyunRTg378rnrF7mytcbXPPQCG9mUsSkDCVhVpkZYrgAmn905z8OTG5zLA0+KTWIuO37Eq7NTrNsZj46vsmpmLaiXCNliqk/rYx3bfkyhGeezWzX5V0Rmzqs8oZzW7Y71GiCXgCpnO2jLEV2Z6pzBkIulUEehjlXT8MG8HfIrs4KvGMFY8t7zP3D9MX7qJz+I+uFbuP3k2BsazQzXPjCuB/yFmSksDpBVWuuwglTxeXNOXFXWtuE1ehke6Qyk9M3mktlfz4Dd1SUK2XYgcytXkrzSnJeyOwjvrRdNDU3ADfG54DOYn2r0qOlMkfa5fkshW/rSo4HuS46QLFKlJJArsc+LYI0J/kl6ygvGBYy31j2Lf5N7oT192BeK5iDTyCXls23DZFt45tojAb07hWTxgt0RruXwXN7wE9mpoJni89AwUci2BLsr/OJzH2qeg56Gx2fXTiWgeMc3O4FqERcw4dQQSPKW3fMufIyTAFia9T3cSf2pLIOI6Rbse04SGovefPGZqeuQvdvQVyamdfqanhvdCujrEeopYu7Fd2jlLeW8f3axA3dDjplHc1g59oZG5iUXf/7qnStyngqZsEnr41zft0HRqhwQzuZ22pXW0U3bQ1IA0RaKswt8Phr50PsiXwcMcfWi5B5SX+/gbtrI022D2inXvYd2MZzU1ZWGh+qiHX39IZmOXtM5l3prST5TOEypFGu2HWqKRqXwAa/ONmUCWR+BGC3mmwXU8WLNNHkTT7o7+TEFAeTV9vArKTVHks8TLqREz16ArrXOjle6mJZcd6ifjwCumhi4lDco0VFPKm1iHerz7d+SpEn9tzoHLS/WmIAZET3YeqmlAjQ1dbriveHG5XVGG3NOrle0FASw00lWYo3nhWcu8MDHZUCiOIAce0OjmeXmh8+Aas0FD3uHqcL59gARmS0D0nKVFom5+micZXkde8V/67Yt0dE6X8WBfS4tTptUTFkRmiXox906utfDlG1umtGtQAy2+4DWdSyAb7ZCc7pYT3oN0jytcx2PI+lzqCOW6ZRVkCr01TdgRDWR7jkOEt4LrrSMxkWgdk74YtLQWQx9xXST6DRJenqc0jqnaSknjBElk0haFsNN4dyXbpzn6vYKH3v4JVZsgVPBipKJo1TLl26d58LKJufGWzU986vTU5wbbXMq36lRiz99/RLXdlf57Ree40Q2ZdXOagKy0AZfkY0phVqe3rnIeyZXOZNtJbfW4zFsuhVemZ/mpN3lsfGVGuF4JA6Db9EfxzZZlFt+QoHlrNmu6tM6ZBbzhHq0Ij8LiMkhnNYsDUbSMpugIFsEU71PIZxmmWqJV2XV5GQVlcNBiMiOWn58e51vmLzJA7Yfne37H/gQn5JLvecGacuxNzSIBKh6lfZg2MoT/ux1zhTBSAUWypBoal6XqCAp06OrOzj3ohon9faiFVczRCW44fGfKavZY1JGPCHkF8MIHX19/W2Qq6U2ZtlWmBmPbjVGtl6T6o2GLOvUYh/3lDT8EWepnTKt+vuMlLb/puWth8yBZiuUy8ahbgjmMJKW007aPpPYbAfyKXzy1EdaC8kq4b5mu/DaiGatTMEWYaE5Tcu3g5fyDzce6CfngySEFFCqfb74rMbnwhRV6Ky77BCvcZ/eCklazR55OtdkGbry0lBVcq5GiO5e61ZZ7Ulboquv/mWhtKT+fMdXYedmfEijBZPrjrXyee6FHLfNAIeVY29oZF7ywM+9URNztc7FBf4qXY7JR3G960hLRJwHSXYJdUNO+4Xo+s51y6hWxHF2ua7eta/9024rlLYsJGf2zt/d/AG0Cd9ksexCuG0/KvDupMV06j5gmE1Kxc495aptNmAk+iN1dnMueNje0vLcTaHYubZCZ0t3ZJF4wNFYJf2p1/xMNEay4JX3eeMLk4Ekn5fFepZ53nuG2oQWhxBUobM0D209tPR0vOTY8D04hPYKuakK/toIXSnJ1+etaHaWeYxRrj63wZd9ygzkZweQY29oyCybX/4A0H7gujvM9gxR0XgMXY6a6NG0YtEpEVmqr4/zXrptSQaUrs3ooZDOthU/gnJFFnTHgcHnJPHs/nBcq4xvwm2ikG8FGuvp+SS2n+7wYo8BIvapp76Fl55O29L8SwaIUKgZVOo2tQbyToyeMCioFygFGTvEamugaDUhCbnFss3vdlp9DE2ITdJdZ00ZiLvQfKuMV+HyzXXmWyMeevgKI+vQ6sJY43He8NatdU6sTlkbzet6ru2ssDYqWB/Nav0vXjvNdGfEoxevsZbPycSTGdcK38UdZ16F17ZOcm5lm9VsEfF8pxxxbbrKSlZwerLDyJTYOiQYwn8x9GcSV2HbjXAqnMhmpJTN6TXYS2zDttNqt1epj2Meh6H0BocJu+uq9G7+lMraJTOUtK6+NnbLRZ1dauyZz/jlN9/DxfVN3rN2tSprmPmMFRvu2T+YfnjPfh+pHI859G3L8Tc0gN3teXiE9sVPn5OecIm4xPtJDA0kg2q1THCgHVbLQjLdEEtfWuKi25nDWwkU093qlIr6Gfb0alr1V1TPyTczduYxhadcsQf7Dmgfz3BfN77n/J6ggH3Xco/sMXwh6msm1eRs87N+bHoom/doz9L+dcssySeqrDtFih38aG3hu6wMeNR5kAnISp1+3nsgx0uzJvBo6cHvonad0rBA+dwdVze8ZyYTZmli0u41p8CYm2Z9SScXdcZ7d6Uvb991XHZtlz1Xh9FxgPP7Rjb2fBabc+d1iiPnWXmwt9z73eXBmzmgHH9DU5SsfuqFt7sV7wg5/jf7PpeuEfcaTNwRfOR75xrePXLPJv/3KlSvB5jcHXMZxp5BBjkqWTbwHJO1w0NLZx2slsPC8Q/yrpfB0AwyyCD9shepWR/x2WFlGdTTYLwW5T6dq0QZQDUHGWSQt0f2M173qyc4yIIMhmaQQQa5O7LMMzkMCvZdkL2QpAe5OzKEzgYZZJC29IW0unLQENdBjM1e+e6GHBRu6phIsqv/vpXB0Axy7+QwdAzd49uhcjgIededlN0rb9/ieV/e4ypJ+7oUFen53nN7les510eBsezcneoQEdT7O9ehuvgcDbJUBkNzt+VOBtd7dXwvZY+ZrEhC1xDDG+k3MnfKt5MGilu/Tc0Ps6ccFqGh79pWSBDYLkbMEt1dJIYDcOP0Hi8r10JRSPOzmF6jIvTkW8jTrrcXMLUPLLWL3CDJcfodmlTlOhw63Q+N6z4kVCBdnemH2Qu60vSkfuMg33as/soL3BO5jzywPjn+hsYInDnZflGWvFDLXpReWJdIOtXNvwz6pIX6u/ylqfN3dZkuxEn6O2lbCnnSedj7oE56X554buFFSXUve8H2aF9vvm7b2u2qX+YOSnCKtrA0jcUyrd8V1UGrzvi7Kp5vCppBsa5gdLF8vFwuFNKRHhw1OPLUCAuIwWIahOCILKAqzF5ax5TC5MmbgcPG+BptIAX9DI9ckxYlM64F+JkZTyau/h2/8s+rfM2X+L4GwsyMq4E4jWj9O/LapH9zceQV2GYA8ww6cgmoAhEsdCJFVc7XIJ025aqpwD+b3+2Bc1VKRuJrEM74WI8iLiEBdBPgmveMBTaMZVVGGASPkkvPh7nATIulnDK3K1t+yl+59lE+8bveg5YDH81+cuwNjY4y3vwd5xdnFXRmPX2Dds9AiVJBqdMm/6rOL4A7dtKWAXfWEDa6WCZyWLQGwjh47dX3PtKwtF+dNrT+tvqtrXPLZn2tutO29bUzKjdt3aHdnbS0HQdMk562iyzW1ZpDdOBnFJheG6OZkp+ckcLXhL+VodKAAq0estxhbXtwT/O20xaSFqBtUvHe1PdeVXAqeNc/OPY9Gdrz8LUI3no17Q1WcZA60rq6530FdtY3l+vTGX/H896HCyJdfqRUR1Leq+CLcB2DMV+sLyRUf5xBnSCZb+lq6a7e2QXwTpUFlAQ0kO2ZXWF81fCYf2ah3XdDhjWauyxSes48PV0Ab9wT0qRPkgdmAdblNtzSA8OUQP+b3nee2+nX3vkP/IAetN6jduFvZwfQAcrUhr/cCt5kGqqqvdNEjw/gLiGf9N+rBUDRJef6wk6V2J1tMIJbGR/Mq+3o7nqUwXBJK22BM6fOR8/vpK6eiVnj/UpPWtLP7iSq257Ylr3ydHX3tAPCxK0GGDXLdGgy+QuEcq28pOW1XUd3IihUEQlNCiftG2RfOfaGBgVcMsPdK4bbOo75Oy8tBCywzLSh1FM9PS/QUv31Q5kYrSrklULxpyGihQEmrW/Jy5vmbdedtKtvMNmn/ELeThv6XvpUUvThFJl3Xwrn+Dslv0r7kYS1+kJXC2GsHiReVdBrI3TsyU/M63CWqeiXU3DNorCoCuPxvKZnjiGtCJqZhrQgAGSmXDV9aUaa49Ibfu3ZRxGjfOx9L7Bii6WAlhG8Moa24u8UhNJIoFRuhbzqEFcTroplRxXNbAhrxZBZE96Koa8oXZ6aGPoK+nyLd2YirsVHE+oJkobCUkrnVMZiyLE1pXMUgyxw0Vx3O6ya/MjDYYeRm36XH7j2lXzif3zPQOV8ADn2hkZzw7WnVhYHPDqzxc65cD45Vx2LJ/C15J38ew38Pfl6zysNKGcyoNf0r+kgf4AvmPqoopt2xPWJ1B1abOOCUVnSjzr/fnmkk1E67YI6nNa/vhTTFsNgrWpiSCNW17oPnclAt/31zFPItw2+EApGvc9ILWWob56Nm3DgQqM6FS3TtSzdCZOXczDwyeKJpj2priUhoIWLfJhZdZ9OYbnuQ+iSKnSmouGedfMt3JymvjrUHNsji+Wkq0cDfXetRhIdsU1pfRre99b71snfrmexbfGdlkSfncPohvLAvaBy7mnv/SbH3tBI4Tj/a1vNoH2noSDVEEbbb6A/AChrb5jrduih+/KkbTwEQOyhQm+HDYPdSdjsTj+S64Y5e/T1ceNItZVVjVm852n+VO+ynViw9yaTvhAXtHfWzXYQVdxKnkwMpF1m391fi8dLPfie0NjScFvPuQVPd1morbPjq89r7/W2Y98WQmfSaW/zV3z123Z0y2JeqCZ6tp1nsc7KG688ad/Xvni9NBDMiU8yDLKnHHtDA+AzQ/fl6o2PG+jbEdZ9qcQr3kpnJ1mja2FDQc/LA0teTtI06gczPNTtl3TPTQbJy7b4Uu/hLSy80O0Xtt3XvgGo//eeYbXu785OshBG1Pa5veowy2PmppDAKZQpC2RX3fCaCtn1DDdWdKMMC87VjrBYZwy7+SI0zI4cxrZDa8s4a2zrXBNq69s9BmEh+9XnzwHw2HvfYmzLpGy6Q2wxhBbTU+6XlEa6j1cm5ZSJIbU+PWl4LfTL1/UDTXguCbNFnYXaitq5Ca8t46ep6aGTPrnqJZqYImlHmyY6hucivfVVt85ISlbNjIkU4dpiWjvdohSacctNOFtRW0d9Tk0dGtxLiopiIsfhK6t/y0/4/O7DfPzVp5C/Jeg9cDd6aIDuKzn2hkatYfPxwNnRNxh3Qwi9jI+d/KbUhuq4W19qp3RxYG0y9qSl6cm5OnTWV4+0y6TvaG9Eo8q/8C6n3Wm1TQ/kdu/pKfacS6JTS8um5+Lv1DNd7qV2Ol6HepbdjLRd6QWEfNejBspx/6NeR/Bc1SaTL1z3hfYtuV/hnC5krfdrKDy+6RCnzE9eYKfbmJZeXUzz/Xlb+fvOL2tv+nFimu6XpB+gjQvnWm3WTp7+9D03x3jtf94WdCTHS/h1bqdsXd57zpRu4KM5oBx7QyNFyZmff+nwBf0RPgD3+GOpZV9CD/LOkfHb3YBB7i+5z4eEY29oUNB58Xa3YpBB3n1y3GFy3m4Z4GcOLMff0AwyyCBvj9zpQNqHCzfIu1IGQzPIIIPcHdmLOA3uP6DRt1EGZIBBBhlkkFQOazAGA/OOl8HQDDLI/S7LQlSRnmA/yoNhoD/eotzzDUlHLYOhGeTuy9vJQ3M7vDMH4aTp5n27pa8dy5gquwyXh5S7wUFzN3V0fwNLzx0q38BHc2AZDM29lKPkknmb49t9L+We0uWK6X6lv+x4L46YPb+Z2mPg3S/fHl/8A6gRpLruNVhnD73EQp29FBRp3gPk24v/pcv9Il0dSbkezMBlSAPLEAEWyqS4gPV3bYm+JR8s931gvCeaQPd8Wmdvvf1lDlIPEVXAUi+URIy+lTeEh37smXtibIY1mrstRuDcqfhZNrDk5YSFly+K9g1sUHHSJPWk+aF62JKXI9W9z4sqXgPZUmsA6Ojb64XtS+++DLG9ZsmLvUzHkrx2Hj5GKyfSAsUMdfT014Apwgd05YoswIjEPPsNFmkbalSALrdMq4y2B4mIwBvzxbps+FjVTA06VjT3NcRIRA9IATmBikumOkeaFr72984gRil2c7QU1s/u1CCc1iSIAB1kgDQtE1//tksAOFOgzsy41pf+mXHk1Zf6KZ9MXvHSRJDNKOmX/SkfTPolv62+qo+Am7mULaBNG8skv6H5it/imVSgnZbIc6M10GadTj/IZso3Y0SwBHDNCMAZgTb7QDbfLvlPr3wZ/+RvPz6Aah5Ajr2h0TzjysfOtUAwe6Fb0kGxejil+oo4ogDUX6r7cM6POox8lZ6+D9C7hqHVxp72mJI2B40u5jmIvsW+sSDaBYHslu8r16Mn2wlcG4EkLDnRwobT5n9pMJ+KNaX+3L8PVDP5276+PW1f1saFPi0Jm/jG8IpKQGZwVB5hZxLSOaiNWvdk1FcG3WZXkFLYKtZ7r+WBZI+v7mPb9y2z7Fz91X2jQ/rOd37vhQQQdS0CXSbl+8rGOcCScr2Alsvyddq9kLen/EH19eUzLnkfO+VWrnpO+ecYZH859oZGSsfpp7cbCts4YO/jSgaYiP4FUqkW1xZocVvl92nYXotzfcCdtwvSedi67yC/uBgOOsTIGfu1rMyS8NZSqKC9gC8XPNW0XPdcOsBW93oBxy7OCnrKSf9xrNdU18qNTKN3waA2Bq+PbqIf863R1ecJHiq0tKTsvmGkDsVD28Nu5w+wPY1XuaCjU9deOHbB/asmEK3rmRzXHmmFWSc0eHOJJxroIhq8OtNhPE1x5SLLqTW+5YkClN4wso5xVjYYcBLYTH/ri5c4/XP3BuvsHY8MICJ/Hfi9wFuq+uEq7QzwvwDvAV4Afr+qXq/O/QXgjwIO+JOq+g+r9K8GfghYAf534E/pgbBWJIBqWlkapoLkxUrCVDXOlukMBNVMqPZ0lnCJt/TWL0SHXhlaumv8rsiqaaU/T8/v3gGg057Wucro9g4EtF/m9HiZd2On4W+52pOvwxsTddlZyFSuVa+baQ8cSweUOm2PQaXzOw1zLaNQBlAVxHisVbwXit2cbFIwGpU1KKZAHc6SetCprkM14NRdT0Jc0zIjM57r2yt4b3j0zHVy61rhr1imGwKLgJd5K08EmmyHz1JwyhgSi9LHQQMNZXL8DeCQmo45pLdDZF1OmqC/rAEsrXhyXIuWObS9CZk5DCPSOlLenCp/Twgt9Lt+vKpzMb15+LphNapj05mVxDJOtea18RXcZtBTlT1E6G3LT1mRUW+ZP7n2NTwrZw+s690sB/Fofgj4/wA/kqT9O8DPqup/JiL/TnX850XkKeAPAh8CHgJ+RkTer6oO+KvA9wK/TDA03w58fL/KNTPcfO9KQOztG+CT41YaYbAPBqUxTKIhPiIefC6LA2pXd5q+17lO/aYkLCL2kKsttHVZ+j51pICVveGbvrQldcSZaSyXGjPR4BRJEleqmx7C8phZY9mD7iqfl7BWUie1K27dL92jL8v6pcHYuUkzEEcYeSeAh/FU8KMR872e9mVTnk66KUMbs13BKLywtt66x/2Aj3unLYarpHUunTQt09UbFtOe8kt0LA03dfP31HWgvMv01unaylfn3Se01c23X9isW09f21v6YlREhBgZiGVGN0om5W3gMB5SkvndfSv7GhpV/QUReU8n+TuBb65+/zDwc8Cfr9J/VFVnwPMi8gzwMRF5ATihqp8AEJEfAb6LAxgaKRxnf/0GB+Iz2Q+VNYoPD1wrRLQktLVvOGsv3puDymF3rdytPfURiHSvnV7L2tLhcWnJQXd7ddJvlwumVdYQGFVt0r7uBo9lG0H68sbkKvzqR7YzCehMXkzqAXe84R6PdWmIreWVdrlf+vK1dS4NlfXWIR0vtFPOJPVWupeG2JI+pV7tfptCQj0HC7NJGaiamx151e+9POJqHbFF/VCF2NJ05wzWerLMLXjBrz97hg98xgwIzgeQ212juaCqrwOo6usi8kCV/jDBY4nySpVWVL+76b0iIt9L8H6YZCdwq6OFFxbo3fmVHkcj0eWeiQO7ZmZxBi09+roc7OkLXpdp2qZCvessPXe4EF3H4+kZGOJstV5rWjpwHOycnQad5WoyONEplxwjYApAwa2EgWHpANLtRxwEYPlAAsk9PmCIjdAeTIjhqxeYWhh7zMi1Qm0AYtqDRB1ak3ZaHIDK0mCMMt0ZoU7YON3sOlOVhZ1n0PC8xIGqPu5JNyyWi+d8dQOyJFyW5ksl5cDJjK+5aprzCedLku5UWsfhFrTDiMukq3OZLo/Uf/vO7SVRp+tYfl+9XL6b3tGXlvPaP6HyKngEr8JOOSITz6jiDvIqdR3X1k/s2dYjE9W7N7m8R3LUmwH6nhLdI71XVPUHgR8EOLn6kO4+OMFbemfBvWGm2JhoUJLtvyFsVu1Gs9Lfsj2kd2bbOS/abmvMIwu7IBfDBSHjYpY+aeo+mFe1p/utYOeKeMWNu+Q5y+pXTFENaHmyhgbLPcFlurqTwt7wTk9ibcGa4xjii/fCFA61goptFW1dj2UcLK3QjTYhldKHZyhfWd6+JVwsbf3tMkkV+Epn/dhUbYxY5kv13C4Xy17t369sb/m++3WAtGUfYR5U3+2kR+mpo8BSkC+Uf0pfG7yZA8rtGpo3ReTBypt5EHirSn8FuJTkewR4rUp/pCd9fylKTnzixYO16i5Z/fuSH2b4YvnOpBvu2+t6Hhd0gHe4HLsn+h6+Y+/4NZol8hPA9wD/WfX37yXpf1NE/jJhM8CTwK+oqhORTRH5OuCTwB8BfuCglWk5fBA1yD2Wwwwi7ySjfhzhdQa57+Ug25v/FmHh/5yIvAJ8P8HA/JiI/FHgJeBfAlDVz4rIjwGfA0rg+6odZwB/nGZ788c5wEaAQQYZ5B7LftD++8kA1DlIjxxk19kfWnLqW5fk/0+A/6Qn/VPAhw/VukEGGeR4yl4GZTAyRy/3udN87JEBBhnkvpJ3AqLvQQ3FYFAGOaAMhmaQ+1OOakC/XYqCZeeW0QzcCT3BIIeWvSgEluU7NO3APZxUvFs3AwxynOSwdAN3U/dRcsl0j5cNwMsWsA+6VnAYvpaDnrvNvCLCks87lvKlHIimYZkkX7wf6KPoruz3cW/UfxhKh2X3bJ+PdLvHAm08w97PI5o09R6sbervfjxc9cPH7/SKEq7d7G/rIC25Pw3NXoPOEcihX9z98i97GQ/wdXyjoyetL98eIJRAw6WiimZ2MW+LgqFHV5eqYSk/SnJ+jy/yl1IyJOX35EyJA7RTfCbtj1djfun/6Lb91Xv7q/3w0S01jFB/mb6PTdv5980rsY/JMf3n4tfyC1/px36R5tfW+TqtPtbFD2AN/aCVpvmqvv7YNH6YmoBVGuObj08roMpAn+BbQJWZ8Vjjcd4gooxtWX3s6pnYInxgWn28mlIkxLrHpqwx4WpqhEh3kGLAoeQm0B2EfL6FITfzObmUbNgpz0wv8PFXn+Jfe/yfcjG7wUhcixLBIVwuT3Apv1rTIfx7z3839g/fA2QA5b4Pxx5/Q5NZpl/52AIqQPdr95Amze/b+Jq/m57iRHnb/vBz6cCw0Ka+fNLK16ujUz7+jjhMKY6aeBAHbtyvEw0YXQB+BNlOQAGYnm30ubH2tz1eB9sdMLU1GDZtbQYwNT2DWdWeMCI158OgpnX9bdDM5vvuLipvnGjOpznZqGA0cgvAmbFcH3hmPBcHwljGqzArM1byAmt8/XV9mj/mS7/qjwMgSd31384X9l1AzfRc+OvrL+sbYM5GPwRQSwBX3YR0IE11pXnTc14Fh6mBOlvnqzrT9PTrfbNkhToCcqbHUQq1tSHY8SOcGlbNnJlmGJSJKfpUhj6qqVEFUvFIq75YD8DMZa3yEL7uL9Qy8+Hcii14fvss126u8cmbT3Aq32nXi6H0lmvzVc6Mduo6v/TaA3zggJ8Dvtvl2BsatYbtC/niCenkk73PL823TG867vloaP7/7b1prCVJdh72nYjMu7z36lV1Ve/dM+zZSGookOJwkbVQkClbCyGK2iyQXkTAhAkZEmhBsCFKgm3JhgzKhgVLkCF5bFGiBFGiLJrmGFqoBbZowdxmOBySM+Rwpnt6enp6qe6u7S13yYw4/hFLRkRG5r33Lbfuq84DVL3MWE5E5s08EefkOd9Z0df2C/8mstuecNx2A3JAoRGQo11odHqb3BwYELU50ZJQzBmyYoyOhL1Oow2kfdy5h9mPZ9IsGOuUB9faix+XqeqK1Pd19vpYjqLftytyvjvyvykiBqbaLHY1EVQUQR/0iaL/07nl+2SvITPPzjaOutAMUlrxnLl5tlIrnMWU1tOl890LPNhimKPmpAV/lIyVhY0KzzPpHjxfMu/PczONzx58TYQYEm70SAMvSfjN4vP31IAMsCbt/EJDyxq3fuqNS2K+4kXqsl+vY1PusR9vkiF0rbZOUAjyc0lfLNJWcAgCVQrEwGRSeLszRy9X+6U0EC7NuLEZiOIXPChLNbpwwUozcrpMqGuZp8I+MAuNLinWvNKMn+mYCI4TExXgFm+OE9jZddTzBwDihm8E5ujuQaDZhcehmcr99TlWzLmwGpwQoRbXNlU5KmSQU4XiLJ5E7DWzMCcLYMxZBSlvtgIMploplDdbxSkOtDdbOdOVa5umMHCmK0cl1RDEXuu4JmaY8wgTWuLQ5qoQ0C3zlaMJVZZPOy1BiUZLTDN6umyegEklMGdgQsA1UeDnlxP8rdvfgv/22X+Kp+Q0m8nztjrBk3Lfn3/fa9+EF7/91nYCyq+25Wz3FxouJe5//ZNoId+iywyW2d2k7TK7lVZbxLt6XQSmsxXjNwx62mR4pOPn2pA28+Ei2I3VplyPA/MVmr6kyYBfEqBGjNGROZ/fYpAyzPU4SN+UrpvWdAb3fSC4htRc1lxrh9mM0Lw0K9IqA4hNaJYPRXUM1gL1vICc1CgKZesaoezOfR/Am8paAJfEfoO9qErsj6pekEx3KY6XiPjkTVihCa5VlxzHJrW26cuRAXsULdPaKsBN91ck89VMqCGgVfow9FMXYGbI19FSFdAgjESNuSpRkEYh8kLbXV+Of3gd4V/uANjUTFBaoNImQ00hNN4+3sfp6wf4A28/g3FR+z4ctJ8tS0zK2gOo3vn8TXw1f2Gj+/NupZ1faADEoJgBhQtBJJw6kRs3G9e/107QgvMLkWtG+fKV1NM+5eXmxIHQJs32w3UDJNnwNgCibkPJEihmGqSAal80/BKzYKfpbE0zTPsamxvXyMvkB+kzr60wd1GtAVGAqWjVAWh9TO03x9mFQDOYxq1ywy/s38M704btf67EgWcCQEvMdnwEXvmMXRY+3worQNY8ltH4Q03d5tvD0mmBOV4ZzT6f/bRjk5mgvxOAkesjgCc0UMyWqPavN6jrdhxJQAlgwkYWufKvOKq3ZjrbWKbsGO38QkNVjcd++ssbdlpzRdnEBh2+AOmLs6HbZdabKzheaS5L2nobe5hzJW0fmMio1oBilJPg5xexaSxNqcyJh1jo8ZXz2orMZ1GeEorNTGl/ryU1daEnWdasBuPsoEskTguUaF4rzHCJWU2o4NtcmDYh8Oxq8RONaa0py6VP4LwHWKjROY1PxNpeX3bRxhvMaXSNZkcI0xXHGpW0ZrgicJAohM3GGTg/hA4PcVk+e6hzdCiE8s4Izlss/BhfsURJCntiabJ6ei8x9t5ijpoMo42zRMO7MbM1bSyfgIeGwJxLTKjCnljgM/Pn8KOvfwR/6r3/Ck8X9xse9trmLPGWuobn5H2U1jT4X7z8h8DfJQcsxjVo5xcaFAUefMOzAHrMTBlPtNax7Zfd8WTaAbFGowsk3lXNFGMe7V1WzrTWa2pL1qWWFmVNZ65OWNOZGiVjBX1EZcp1CYyOAKoZi8fIX2Mug6m7dq/RdV5DrOll26U8geZ+RpNFmwgtrdWzInt9i2ahafqtudsM+XtNjiAU2nl50q7B5FvapKtXlNmRpj9yUpPVijLjRppnU6/dhLrah33SsXLjrBi/dz5hm5CHNQN7k3DHHJxWvRbfTFlX+yZ7pnk/RscKP3DwRxtvzuRaSHFUt60Mm2YOV1ul2f2FRjOmb8z725zVnGP55/ug+XH7As5yvDPyLZ9LZY2yVTlBXJ90jpGZJ7kOrc2xi+/p69s1TzefVf79Z80L0pkddQW/0IHjLPyzY57RPHLJwuFKpq/Ycdp72BN4RGn3FxqlUHxu8FXfVRpE3UOgTRCSByTly6MrHkS5Tdr9hWaggQaKyQm4dQXdRQvEYeHaOg3OAAMNNNC7izZZuIZFaSAMC81AAw10FhoycW6Pcg4bV4yGhWaggbZB50lHAPQL9LPWXQS9mxeZRyH30JZoWGgGOhtt8pJdVGqBvtQBm7RdJydMShchsC8q5cAqvuvWZeis+VkAnC9lQRfc07rUl64ggPjvbZPlmyl3bZUGzxfrz/GMZMKlrvaCNiw0F01dwm8LO791XvJ1XWKJKH750hd5k6DUvvZJuYnKpu5266QuAODTEyRu2yxEU6fYoB6U0geg5tIWGPdxgAthgmJtfYTnlgtepaCd6yPyZV1Bq2HwqCopgw8HD7K6Mlg10xeUzAGIcd1sjMnhyxqnTwssHmMfqySWBD1iC88ERIjebrwUwRsASxuY4vrYv0SwwakMcmUASGiL7RanJnABoy4gVYrm3OG7uRQFPoWAPS4sDlt83iBqaxaYysqnJABcegL2AaM/8tmP4APfd3sA1lyDdn+hEQL8zK2sYFqdE8XxSAQRNe3ToM8Q3oIUQ85rqEkBXYo4AFEkY2Sw2LIBokH6gnROUWCmRitSfSWara+MT8kGp4maIeeM4riCPKkglrWJEXELIRG4lNDTEvV+ieqaRDUVUCMLVulyswh4KI4YTBI2EM6kLIjmL0wdC0AuTYComnDUl5QZQ0s3aTToASFYpUA7mh7NGNAEjHQjvIjBSgALAdqvIaygU7MC4qgAbi1QjJQVYBaU0YJTAsDsdAxVC1w7nGFUKF8e5lkJhV4YMR+dB9hlgjRqlhhZ4RVG2Lt6F1VfscDb8wM8M32Asaxb6QhmqsRY1JhKAzYZ5mYJyYFQ5uD9J6JqYam56PiX57fwY//fN+F9v+5V/P5nPoWxhfJ/cf4kHi+P8ERx5KPuJTEUE7RLPRAEgi1ZouIC+2IRjSUtAKdi0ZpzSC7KP6Vcv1yagjSVAGAQAhxvdzzXJZYsMRGVvwcVy2jOmgUO9z/cOdcLpyu+lu38QsOFxN2vvRHvViNBjWx5qy6kSHh39xcVML6vsbwmUE+6xyDuGcv14eRvxKTdRdSx8HZ8OiOo06jn4C9pA55pdqdswJuk2b0bLSgNEmX/AZI0DAQ/E1jDQsKgEe7hzhh2ftrku4l21sEc5dLugosYFp4UAniX9EZn7lvmPhCb8d2u27uFakBWDDVqHnm5ZMg5UO9NDDS8m0cAkU+acb2yGsV4DEUNTxWMG42VzKk3Qj1ti+a38+eaIWrgTpG0sf1Io3WP4zacHa9zvkAUiS8qxofuzFAdPIF/NPldvo2oubVxSvm0rytjwlqhZZ/Htbf3vYwGaTfslC32+PpxDVb3zjizdxft/EJDtcJjv3S/KeiN8s/03yDKvdVGM6iqTSZKKXzZ2v3PSpdhj2X2mTWhNaB0c+xICBARBBGKQmIshTGZSRFpi33ZM/1wMigPtT1hNEWWFJnJIq0tymiZaHwJOGLWRAV4yBy/0NkFV5dkcduMoBQ1Q1YCWnLEwyVt8xA/zGYxsgtYKx1CmpKAQpNYcm6vJ+KTZML0uGkEEBOoJpOcTgRzcxt0ttfrr4EDLT/Q/DJpCUAw+GiiSUkQmqmIGPPTEfZ+8QDHH6xw7akjFBYv7WQ2xt5kiemo8jhpoQbnTFUuNYEGQTNhr1hGpqqydWw1MpiEc04rKoUyGGhWA3J4aAC89uT6uXpTpr3W1JV+oKTaHyuYZHD7tMQkSG8wIeVvqyTgT33x92P2R4oB62wN2vmFBoJQPTYNzFYUHDdtsrhnQGsn0gX0GLZtgBoZ5ZFCdSihRqIXr6xdl+e51rndpUa7xTW1tRZ2leVFdkdfnmqUDxSK4yXEUgG1jkA5eVRATUtUhwUWhxLVHkGPTJ0OzGdwJjQv5E0ZKTOmHqNJO+xMbFYrkXMjNPUotumTgkl/IBvh2JtuIDn235M1QYxUo2UJhq4EeCEhD5YQFjiyOh2BTiTErTnKUpn8LpaHE7QAcHo6BivCwbU5CmlzoFADUGnOncmr+RumCgjNZq6PS1+cAlamcP+1lri3mOLJvSMUCWglAMxViZFQGNs0qmn2TmUfljSTZ0gp6KUjzYQvnt7Ep177EJ5+7x385ie/gEJoaCZ8afYYnhwf4UAu/MISZrEsE8j/SktULLEnl2acZC657JlpnWIBBWPKmqNs9XOpALJmsuSlaZaNpt5l36xZoiCzsLkMm+H3GwD45Bffg6/m7aCWDM4Al0wsCCfPjtqCtSV4uyRvhif1lLtu1mRACljum+8UvaabDj6OV1ieM1mkZUIB7Ewia5hnItBBHbR1pjNtTUUzBTmrIRY1qFJAsBuj2mgb3tSigHJG0EWjBWQ/JqO5/0IZs5su2hsC/yml0oanjG8U6Q6zWXKtXdSYCxksyqiPUOa31IX091RWgFxq1JNJnCYhud+Hlbl/uhy3gTPdIm3bOv3Q4zVmzEjhb+Wuu7MNAGjzG94WT8TXasfPmpZ0Yi7LmcX6yoM5kdL44IO70Ht7+KXya6Mx7nS9E5k5nUtYpg42a9ImcqGllVNHnX1WPnS6vTQBV512fqGhWuGxT7ydr+xNCZypW+NBj0AirWnpQIi28LvsHcYqd8yz8mTOm80cCQFJBCkFRkKAndks5/3V4fVFzHHKgsTc5kxnEIGWGDpsBNpp1qQGxNppotkSo0nHG24eFKzpDF6DEhWDiQOPLTsPq60xWU1PMNQImQU3Nd+1zWqtxTlY1CKHilDTRqzRkoLRLO33sbAP6UDT9GPkUhj0aIqC0ZWIjucjHH5mHyfv1cDTi0YjnBcoxgrlqI5MbY6lFBpSxJqdZsKkqGOPsMBpwpnZCqF8H6fFFUJBBh5jTkPLZfv0pjvkM36mKQhCk5tigYolrsm5z+apQP7Ymdz+uxe/Dde+W1z+YpNaKa4g7fxCAxKonjgIBJz50xI49thRW4A5fsF5ZkcemtVExShOFep9YzqLdujJbj3ndZYz36XaVFcuc9LcCDVfmLm+sDq7ezbHQpkPysWpRnlcozhegpZ1pMGwENbrrEB1ODJOEFMB5fK8SHR6m4XCG2y8zgxPJPcZkAsjNNUoFnZG4wB8Tpfwmr2wDsxtaPP2zgBluFU32ppYENS+9u3FjFCcEpY3dNOe2Jv4HIm5MN9IDrQRxpnfo/0DdLRxxOGEk3aOjdW8oAm0FOCxzo9Ri2ahyD0YTlA5fkC7nSbzLxifGSAmyBmhPGWURwKL0cimIACKuYAuGPMiVtlz5lsA1oRrFu1We8BrUdHMvJaauT9heW5s7jgP55Q7T29z2s8e772lwfwSBlpNu7/QCEJ1WG7WJ6u2d7RzppWM04CoGWKhIAWB6qa+xcu5B2+QxdHUc6vOl6lAo+HM2Bnzh++rOW7HbM0v2pjLqhpUB2azMPBOEKQQKN6SmJZFHHeC4G8XufFyAXThtYU8/bxtOwGs5diR3ofcefibaB3PnxmktNHaenh4J4rctW+iOW+a+qCvz7r1fowz7LqHNAeraSvIAHzpv8Vl0+4vNFWNvU98cbtjJg/P7t+kzWndx3bFsnLlaZ378FBf8S0G/A60IQ3wM2vT1ZChww860LuVNk0JsE0agDUHWpOuxkIz0EAD7R5FZsngOIcrN9C56DxBq7tAw0JzHkrBGQca6FGndU15wwIzUEDDQnMeGhaYgYD2Dv6i0gHsqlawa/O5IFoXudrTNtMEDM4AA72raBXk/yZtdyU9wEXQupD/m6QDOOf8ugTnuaH8V/V3HofO0y30QPTwDcjzWYUCnmuTO2c2XpVlYaCOwnY5dHCgaYcw7IC8JyVLGUD7EGhZA2/fa89voBYNC815KCfwLpDWzvPR53ZrgKvMS5++8K6dq0th9XOQ/Ol5x9hcyHZ9GOyZ4ZGF+bf1WXw1XwcbrOnaB+XRuE0QZQvKyAdiBgGY7Hg18UMuVsigJQQBlpRBThDJOAFkj6gBUox6jxIoHwIxY3yPUU8J1T4ZWB43JzRjZuOZyF0nkAZ2+mtB0AcwkD9BHx+rJswxVfaejXUT1FnbyRQMMVI+uFOQwU0jYqhaQt+egJ5YYG9/DkID21MpiXGhUEjlEbDdm7RXVi2cNEEG98xB+gMG+8wFd4bBmwDwS3efxaufeA++7rd8Dh84MAHf1+QcigUmokJp4WVcsKfHU6Ma0gZvKghMqMJn58/gFx48jz/85MdxSx77QM+/8NLvw+Q/3E7AZg+o9ZWg3V9ohAA/+3hbGK2RGiCMVAcAliKOU+uJNve8goBPUozyuAJLgepa2eB32b9yoVFPpRUMIU/EPAMhT9wILVMGTN+qwIXA7HEJJsLoRENLoNpv5s8CHlamnpBHKwaMIDx4o0ZxovDghTHqKSAqw1vZtj4odQnceKnC8lDi6HkBXQZzdvMPMdfY8FITI6DSwFd5SpAVTN6SBCjSBes5wEtzHVZwOeh/mCBBlkEEnR8jObYBhlRazDG3TlrBRUGUHTNBFsqupQ2OWbhJF8JEm7u6WgswEwQxRoWCtBHxx/MxpqMK46L2eU8o+VobCkRXF6YLuH1ygKPZGO+7dQcTC+/vsLiWusCnX3wOezdmeN+tO7gxmnkBWmuJQiiLvcU+NUCIbZZSKIA1C48NJkhjoQtMZeUFq4uSr+wDVZLCm4trAICnxkee553lPjQII1HjRjmLxnO8juoJ/t/p+/F1T72G56b3ojZH9QQjUXt8Ns2EhS5Qs8S+bCcT00wem8zxD++ZvzZ7ffO6gFDA0XKC24trqLTEWNSoWKAM4f4DB/5KNy9RzQK1FhDEeO34Om7fO8BpPcJEVn7Ml77wFD6MV1pzHahNO7/QcCFx99dfNyfBIpFLG9AVRe8AWHXRlDvQyi7wzRyRAvZuS+iSMLsljLBntwgB5YxR7RvwyT4+ZrIAabIYWs01CcUoTyVYEJYHVpBqgwtWT5sdr2tLTNBlo/YTcws7ixQ8sCbXbDDULMkFIJYackEoT6iBbgn6R/dSs1loRon2Y9sWM4NAUB41c3U3w2CZBfzd1tq1cGuLjn/LrmBbd51ZDDmOyzp5MpDD/iIGJjooC/reqCz6NEyqAJMugH0/x2ORjNOMzZgsGVMNHJX78OLbz5XxwZMaupjgePQ8joP71QrYDU1R6T3oCiBO5pJuyFJysEyvCQruT9O2hSdGTb9nFwpvjt+HNzLafusdbh6VFrJH08e1oUxZ83e60HjhwTEW/+YZvCSfjTU4r1EnG6qAd1guasbTNeOdva+wMEGm8r331IB1tibt/EJj0gTcO3ukuCtLPWVyH127yEfwm7QBEALXnGkofMldlPk6NvBwnkl7WlYAEa6V9udxEe2h6cuZ7ZibFAaOFzNovgSY8fibY9MvgJmJQGs1IE7nGJcF9r84ypuoEm2RGEbQeiyvWOsjZuix7IQJypqqbF2j6YTZJAMtM9wc2LlpGfAN2oeapA6xwFJzEuy1OHyzFNRAwI9h4GzM5iDKaJlC7Yi22SrU/sQCEDWhOtQNH8CYprTA3uslloeM+roGFxo+GyWTSQfAAEkGSYNSTaLR4sjjjsEjUkdaHBoTFjNhXNbefOUwyIBGK3uwHKMUGjcmMxQWU+xoOQERY69Y4qBceC0hNGM9qMf4xGvvwdc/8wqem96LMMiO6zEOi3mUcK3S5nm/Xpx6c5aD9nfmLIdRBqAF++/KAODH734E/9cnfgP+zG/7x/jK0RsAgGtibjHLFErLEwDKYOUtyWSCUDAiZ0KEn1/cxM+cfADf89jP4oYoIGDwAP/Yl74Vt//A/nbSBAzOAJdMglDd2msLKlsHxOaoCEss2jkbQMWQTD6UbtNWulMiDUzerqAmAosbMqonDcgFo9oXkUaTCtFoV+00LW+uMlrKwWsK9YRw+qRhNHpgAB2rA4qujRQglgw1pUgAiwo4fEVBzjXuf6A0prOlkVP1NDaFyTlw87MVTp8ocPy8/R7gABnTedvrFEuCmnLwbSF4UY8FqAYWN3Vs94fTzNz3AKONedNZSi7dbzAHp1mQxcpiRYBgiKIByaJwvPC3Rip0bRdiaE0toQwAdS19m6JQXjCdzEaYTiqMCuXrU2h9Bxjp5uGUIlf2zvEeqmWBZ28+QGlTDygtQMSY1wXeLB5HcXOO528+wLSo/JxcNknFAgLs+4bjO8pB7rucMK79UknsFUsPfW94meyfrv+sLs31wJkUJZb2IWemqG84zkk1wux4jJcf3MSDatLUMWFWl+YbSzDvhSqgmTCWdcSLmSITl4Pzd3VpmWLCm29dx97LJf6nW9+K/ckSmgEpGEpTa7/p+Cgt4jHsc3F6NAaOS/zw498IKbUfs37xAB/SL7aufaA27fxCw4Jw8vQoLgwelBaGYFRnToQyT5M3C1lzSR9oZY4c+q8eEVRJsfBTgBDtjIMt0D405y5dR/OusV08NKQQKE/sjutUQ9TU+iAoLPqwmiPCZxQVY3S/hqg0pm9LqJIgamNSUyOKduzFgjG6u7QmtqK9m0/vgQZErZNF263SQDFXIA3U0/AbWdO3MZeQLQ9/sPR+9JSHJsJkQxG1zZh6WvD8XW04NxfGzZrBMnkmQ1j+pA8xt8xVz9VGG2V5w5c7vL1rmnF9dgwUAro8jMB7ic1O27Wtkun14u3Z+afnpxlMvZD27H2aiwNfNg5w6eYdGnzJjK+qZ+BiiqXYj+vsXxVsGgtrZdAZs7hRPMk1NUp56jyC5vn6QK1RHN1F/W8m0KVBePXPdqBNmz7BWBQ+o0ZrfbxmiEqh3ptE6N2jB9UW3Zu3M8xl0c4vNCZNwFvtig3ADGndh2EdAENrk72WAjG6+tRza53x0va1AgThmrSqkTOdEUUumAA8ynPL/FbVgNIoXy8bk5sDuwy9vphBswWK2xJ745E3IUXzShwxojQAqVnNmvP0qEBqVvO8+uD+Ey01i7JNgdAgZKH7dapBktP6Yi025/SQep0xwV6vMw8CXDTf/CKPro6/KX+5MHyqg8Q5wm4YprcJ1T5QHTJ06TYwsekMAjZJHDf3KM2i6RK4hY+I08AsP1loD/0fJnwjAIVUmC1GkFLjYLLwJrZZZUTHpKyxV1ZZqP9ZXeJzrz+B9z51B0/tHUUeYif1CPvFEiNRe1NbZT/mHxYzCGKU1GhrKeQ/gBbsPwBvVvvXdz6ET37ig/j2b/k4PjAx8uOanGHJBfbFwpvhXB+nJRqvM5cqoMBELPHLs/fgUw+exx964hO4IU/8GH/hpW8H/Udb8Dp7BGjnFxqQQPXUoTkOdy2h4AGiFzUVVm6hST90O9OZOQZSYdbwsENoYHynghoLLK8XXih4D7CFRrUn8t8WImFpZYN9PpU1nTnNaP+NGmoscPqEueDRkUlBXO3H10vKfpifNN8zwObj5bUvKcilxv0XSqgJQVRGOHmvM8ummAM3Pl9hdkvi5FkB7Z6IRAiHWoPzOmsEqbM/EIpT47q7uMHB79Xcv9a3jOC388I9HR/BGEEZKbKLQLsuOncqgfAjxpQZkxg+5YFZJLi5vwsClxx5+kW8nJ0snErijMAFGRPkBGZxYABMHqbea1QaEFXz4BAD7LVB+yyl15FqdkE52XF8nTb3Rdt2bNcy92xWML83A3ggG17CfsRYEnBfBGMF90Ao4OZ9xt395/BOGdf7DMkUzstunFLnBI6vwx8n5yH/csZ431tL/OxPfSN+umjW6Lammx5zi6eoGGKp8VcmH4qe0dH9Gqy+hG3QLmbYJKIfBPB7Adxm5l/f1/YKLDRAvb/BNHOw8c6sEQiRXhiNDg1IKEZxtICcSyO40TwApBlUaYxGsi3wcqYUAFBsF6qmA2mGPJoDQmB6ewQwQyyMA4IuRcSPmEG1BheBdqXZeIadLIBaYfTW1OQdth/pW1ktFYOOZ5h+scBjny4js0Hr4U5h/lv3DSCtjdZXJFI4MkslfNP7vQr6PyhzGl1La12HR195lxa8abK9iOcGO98LFiyPBCT/GWj0mYc9g0ea/jaAvwbg76xquPsLTa0w/eQXL5bnBdhVc5vZi6b0x1nx+SRL4t5RdN6xtPq6vvpdpMwm/t1JfZumEJ9sV2FtriK9yyGomPknieiFddru/kIDvOt/0IEGWkl970gu1cBFvVOXjI4xkKXta6SPE9HHg/OPMvNHz8rsaiw0Aw000G5SqjEN9KjQ28z8jRfFbFhoBhro3U4XkcBsWGQuj5zTxhWmYaEZ6NGgs6JIXyQS9CaQ/2ndLgjqXZjDOWhjmP81+3fy3WaagCtOw0IzUEMPC/L/oqiPV+jZR2Q88RwlnnjReRhXBLQ9MooeZOyU0vJ0oUlQqXPHURxVEufUiY4dHlc1oDV4Mm5ghsI4qTCGKoyBEkEbQlwX9HNeiyGemFhqyHmN5fWRj78KXY0NyKtzu6fAbR7xGPY4Qq1O4qpCJOwonilE0qaGT9952j+Ni5rcBp750c9f+mJD4F11b/77AH47zPecVwH818z8N3Ntr85Cc0aBtHbuja52osPXqwUkmJwzA0oBRdEvRILzVt6MruMeYZNFt15TkPTC7IcvqnPJprBv0CcSEradTM6jFzmFynd/yfMhzRjf15AzhePnRhZINN/eCZ3yhFGeME6fFEaIJXhn7vpaAiaF2XfEAJeh8DHBGeUJQY3ZwvKw4eXQqP394Oi+IIzLeWARGW4s3U1CMa7hgi5dEKXwGGYmaNLhlUnRIEi7gMrC1pnHxZQv6gJfeukJTF8rMP3mt/HMtSOPMO2CF/eKysPvm34apQ2MFB5vjKPjMJhyrktoJuzJpQ+s/Mm3P4jPvvgs/vA3fBzPje96HLNXlzcxUyVemLyDa3KGPbHwwZMisBW5AMo5l9AscE3OUEJhIgzqtAugjHDQ0Fy7BEdwPNKWheSALtK33XmXSqJW3Z945dtx9GMjsN4C1tkOEjN/17ptd3+hKSQWX/teAEHAm0iEIJqX35QBrUDOJCDTpw4QoZAzDUgz5IJRT7txy7LYa8FxecrYf2OB06fGWFwjRMGL4U4t5Bect+vjulxZC04nFehp23AuIq3jzD21h8ouHALtgMug/3pzd5FxSRtfZw8XhOmbBSbvSNz9Goae6Lht0h5MKO9KjO8InHyFAhcu+i5o5yNnkSdXrm1bRUCZjKsBlgV4zODpGgInCBCEBqAJVBOIGGpuI2k1UFVBzBQlf0NeEe/kQqJgRAJqwsGXBcbvMO5/5hbuTm7CpW8Ig1Zb/DPBjW68XECoC051daN7hMffYfzjV36TDwgmhsHf0wZhwS3oadBnyF8EAbT5awz6pDyC+cXz5WxdNtA1OZ7cVTiov4Ct0A5qNJvQ7i80RFgeJtPsEgwbEMMG+dkXwy5Xhr1iiIohpYZINSluHTTTCh5aOdeQJxXG9yTkoq0VpThbWT5dQwXnKf5XqyykjIrfhSuWnYt7CdOAzSgtQcwgN7/cWK1rDubqIrZpXoOUwuOfHKFTAw3GokqBag09tshaZwlEsjA90Xn4FwDqGSATeKBstsaErztUCzBRFHjLAYpFX46kSIu0C3YWpocAYo3yQQW5UNh7Z2Tx+mL4npZJymtv8eYk7kOxtgZEG7TymFGeMspjm9LCtiWLnUmBlmlQsuM8Ryn+ntFMrbYZArd6PsHmQXAAy2PakT03KNemzEHyCFvmNEenRUpiCKHNrbftX33xFn7dzwwQNOvQ7i80dY3DT74el53DHJZihXXyc7hgEXAftfukJisAEADVGjRfYjKrwKWM24QQ/JZHrK0FgqkDK8zz6xJGQGQPN3VOeASCKCdUEgHWwu1iwztn7srat8M59wgspPzcPYCBtZm8xShnjPvvF9AjNCjTwbWEwm50j1AeAyfPW6RpyQG0fyN8OBREQCC0Yg0JDNBIR/hhYIDvj8BThXKvCoRV+K8RXM3PbM5rLXB0dw+i0Lhx4wTMBGbC/njZmKgsDlmYSC3NPulQkH1ytWAVd3WndYlPfvYFlG+N8L7f+CU8v38PhVAefVmQ9knQcuYxAC28MQAoRYO2vNAlFAvsBcnL/tkbX4OXXnkS3/X1P4tnRvdR2hXmC4snUGuB90zu4JqYe1OYmwtgkpkJ0tDWdKZYYF8sbIZMg0sWmsHSdAGpiSzkHSZ7W9U2V/9nx3+w1X6gPO3+QlMUOPq6p7MC1Zwjc049debYPT9NLpOGLylGsWBUUxHnCkkEeZepi9h8H9h7o8LxcyNU++icPxDvPKO5JmWdJrK0T1CX7mpb9V7ydV9bZOZim8BMcmveEf9gF8peFepp7/gn87MMIJYEOTP3bnnI0CPO3htP2mQu1YX9jUWS1sAuHAAC7ZLsHNIfiHziOG2hdbylSQOjI4IaCaiJeZ1aBrTE3BVeJinC9Mh8CH+wNzEwPgwcBSYmz8MubJEmmI7TY/qhmnDrdUZ5zHj16L14pXyv2XvooC3F/VpzSM5bZip7n8K5jh4wnnug8U8//lvBspmbqNibzsLfOmvqcrw7NPDGbMbdfFrHvAL7LOCVQfouH9Rg9Sq2QoPp7PKJgjeXgrfMf2ehMJPjmmQf2khYBqYhUTHKkWjSNefmlSu3D6+cKRQP5jhUGmocANakJqFW/3QMbpVH15oxW3WazjK8WvySObb4McOjVIc807nk+PbhnWX6RzyYgaoGKY0nfqrESq3WIW1rDZSp6XVF3z7KaLBk0bU9jlzGgSPVeMM6qrWpL4Rf63xiOQBOs02/S4YOGrnEcmbceD7FSQ05rzG5M4YuRQMM6n7OzDfLPkeNlkbrxgz6iBoQNUPUDG0dPyBsGmWG0U47HDVSTdrXy0YTjZ05wnlxxNPw6zGxUfCXYvRrshquuS3G/Ka/sI8P/RoNLs5r0O4vNHWNg0+9trpdn1dan2DJ2dwBmFwhol3XdZx6jCkNWlYo5xUKJ+j64Pc76xr+LVt/6mJq+ehI6AQvXg8Ef5c5rWkfjONy+XR5fbWEUjhOZoyetl7LrIHJHUZ5qvHgBQk16mlv+5QPDPL1ybMEXXDbgcHfF87wScxpdqfLZSOInPCTRxJ6wuA91QgoWx9B9AcrtjNnaSao+yNAMMrrC8CazkbjqmVyc+Y289OzdWZsTGbhcfiYOFrUBe58/ibGb00hvuE+bu6fQhD7hGEAMCnqyCyWJnUL+adlmgm1FgZu3yZb0kx48Y0ngFenePJr38S10cL3vzufolYSh5M5xrJGIeKdRpjMTDNhoQowE0qpfFoCRxxcg06uPOQRtQv4A83+K9euSbDWjHd/tAciajT2yyLGELB56VQUOPnaZwCgJWjbppukntJ6d55PHeDqhQLkUqOexF5njnIfd1Mz0OhEY/rmEqfPjLHc727vrDhpvaeeNTKkcPzQqaqLR86Mti6RJv8ReCNNsmVCymhgkebWlIkaGB8pFKcak3eC36WjvzFfapSnGsVMtk2WqXkk6Qu723bnxrzEYCFaphc51yY/jc0f1DbHxJpkaKoizRBLBWJAjcYNVD2VfoyIp+aO6w1NRvbYm3vM+Z5m3JodQSwqqH8xBsvDpm+Xps1s2AQ8deu+sTEXsnWt1s3PKJjxIX0CVPeNZkn7ntdjmgFWAI0AjFCH4yTHghlTNz+dgbRdhdbNgU0spHOgdD+j7w+OAGvS7i80SmPy2qk/zQq2dSDhc2adLhOQM7ukyc02gKInpYGqxuhV2WhGrm4Vn03KLgPOHlgf0v4ctuOzQtdPPrVZ+70zjfJok3znYc/g7LQzhqotmsx2MWBzE9r9hUZriFdvP+xZbEx9j8XVfmQGGgixqXpXIHQG2lna/YVmoIEG2j1Kd/OroIsGhOd3NQ0LzUADDbQZbZpAzbUZFpmz02A6G2igdxGtCzy6LujoRaJAb5Me8UVjQG++WBoWmoukd8ODt8k1XhZ0/0XB+rvzTYXmmijRrXZr1JGLM7HHANZDml4nNqgLILarfwi/Q9Se8xpu/71AsT4eiLLlKwFiXbHWYHdtNhZpbZBYS2KhIJY16sOx8URN8BRDl3xixujuElRryC+8gcsnHjSagSxdoHlgK4jTad8cvE54HHrnaQ3IxJtuDeFxEcjSxAxaKPBYQlton1bgIqXxR2gEjf23EoZHdEPwMOViiELeAd8A5frMsPQevoejcwCQp+a+VQcMlpzpy80YQTCijxEKjg0GmDl3MSp8bwSxJPBTC0ipLT5YCLMDCNFErkihUUjt43qkh9FRDayNhcxxEDpFUOfOpT12CNIyrEMDuXOv2sNn7z2JZw/u47ga45se+yImooqQnN2x+dugUDuYmiUX+NHXPoLPvvQM/pPf+JN4//i2QZi2aNQlKeyLhYe3uaf38Gd/7g+C35riq39gwDpbh8610BDRywCOYFA3amb+RiK6CeBHALwA4GUAf4SZ79r2fwbA99j238fMP7FyEEHAzeuNq3HfbmfN+lAI+bbhbke7F5b8jrEXV6wjCtsJDdKw0cyUj/0JBWMg40XF0CWZHRasDFCmzOffWEf4ATFKrhOsHRHYYW6PSBACkEtgeltj9rhAvR+0CeYSRWaH15UK5FY5+7YtXpbkgjB5i7B4jFFf1/BQ/OG9C8v8GNxuY/+mwZQedDHiwenPY4MxU1d18sK4eeTau9G0LN58c6uN1sIHbzo8NK0Js/sTgID9GzMIsZnAC4MS3XHs5U+YnZTQDBSFghBNpVLhZkbaMBV3Ee170hovWx9X+vYMsKZGwXLwQQygEijuS7w6fQqiAj597fkW3xiaJ+hreZECJm8J3HyH8bff+VboIgxTsO2p4SFq4LEXgXLGZ3bR34gYg0YD4N9m5reD8+8H8K+Y+QeI6Pvt+Z8mog8D+E4AXwPgWQD/koi+kpn7sdWlxPFX37SQHLYseNs5fvM7ymOWq4IVRW2fZQ/AmJ9aBCuSstSNsBO1wdvi0OzhnpuQRfBAk2ZIAtSoWdRIAwKEXLwaMXuonnRexNwsdr4uEMZrPsPEDFEB43s15FKauWXbJdeUK++qC2OREsFDDIhKozhRUBMBNRatNgAM7L0ta+IPKBI4DX8nvSienzWxrQqObHjZk1TW564nU5fyC6kzhkIzSM3M5qmQaLKnBCx7UTFyZcmzU83MUCOJFqBrwCObNiPQJk0bNJqU6+M2NrmNl//brV2SAmSloUrz+1XTIrtR6UOjAIDRfcboRENNpH/nWMDDVIUwPKRNQDcNiszadBmms+8A8Nvt8Q8B+H8A/Glb/g+YeQHgC0T0eQDfDOCnerkphYPP38/XnTWocVUwY2iXXrdfQq2gTCCPHL2CB6dJ01yQ6SoonLDcbQWd+WpdKJzEfAUApBi0qDC6W4JLGWtxl2HCoqAv7MtfC9R7AvWEwCR8/z4Ym0ZLi+cVQ8278dq8WjyFE2CBGQqZ/g6Pq9U/MGmlcDcZaHsARlMKtApmAo5KsGQUh0sIqRPkaLQgbDzUPeDhZRw6tG8TaE6vvvUYtCK89+k7GMva9wlNXSF6dCE0Cpsorcv8tU4SNQBZlGhnAnP0+vIGfuXB03h+7x6O6xF+8/UXvdnLkYAZG0ALHRoA5rrE3/3yb8KLX3oS3/dNP4EXRm/bPhoneowRKexRg0b9ljrEf/nT3wEclbjxk1uAoHkE6LwLDQP452R0/P+FmT8K4Clmfh0AmPl1InrStn0OwE8HfV+1Zf1EBLU/Mt8D0t0RmvPo416yO/LtRbtvatoBzC7J7GIMqm4nP3cTAtj/FrQMGeHMkjxStO/XYzYDA3LJUOMG5FAoMzeH8xXOJRLUwfVwcE26RCQE/dyB1jxyJjUAkHNg+hZj9iSh3kt5dCdLS+9xfqyM6Szsy4BcCEzekpg/zqgPEjhfJ7TTexkeh+MwIYtpFoItpnML2URjmB/cg00EC4IT2t4klvIJri/bALCmI/btWNubVJudvKoEVC2isTh6SFOGTV1k9grbakLxToFCA188fTK+By2zABptLrwWwJirQr6tv2Tbtet8WdjGjkUAxBIoTgmfLwGqgZ/Z/5pmfIbXbhs+AV87X9LA5B3G0w8YP/hL3xYlZ/Pgu9TwEDXw3KsKcqG3YzoD2pryFaPzLjS/hZlfs4vJvyCiX+1pm3ky81sBIvpeAN8LAJPyEPOnptBFvIh4Bjmu67RpBmsViZqt6YzagnJdCl4YB5suWvX5h9ThXcklQ1Y2oZY1fwnFzb1IhyREMtTzs9DtnDG5dc67Y15yoVEe19h7u4AaiQxCc9Ink+Athy/WZ0YLcb6EYshZDT2W0IUIBMca5qzUjJXFOUsuIGtC4/zxJujVfWUp9QgZslBB3uuqL7lb+qwn5zmEaao1wAweFVGf7HdOwG8Go41gkj8phzTd2vz5jVLQNqP1OjOWexbrsUi+VXKHOS7ezJQnjNGRwuieSc4G+/4jMJ01WqxJQSGWmwiEdzeda6Fh5tfs39tE9GMwprA3iegZq808A8Dhx7wK4D1B9+cBZGGZrWb0UQC4PnqK93/17ealWPNlzZmuOvundSEG2qY8NqV1PMxWmM54lcdYeL6OK2mfSU0DYlFBPijBpeh0qPAmtXWFTGiH99optQSMqI05sd6TqCcU92v9pYBfXJdqg40bLmJB1WOSa2mDjqcTSllU6MxcAYQI0uySrvl5MIgp9hqzHeWxgfrX12pAxtD2cI8JNfD2zvkhNa25nzs8B4Cjt/eBWuDa00cohEaIGu3MZTJwQpCBKU4Qt5K1RQnaErNb62+ya3EmuHCst+YH+NK9G7i5f4pZVeLX3XzTj9lFLpGao4WW+JlXXoB6dQ8f/sYv4Nb4xI+/0AUEaYwDNOr71QS/8LMfRPmgwPs/vh3T2bsW64yI9gEIZj6yx78TwH8D4GMAvhvAD9i/P267fAzADxPRX4ZxBvgQgJ9dOZAg6MMpIET/LspMan2Tmj1vfUOA1Whsat5YmCTCKOUZCK9AFnhtInSZdRQJKGp4kjZeZ2pE/rsCKYZQxkGg1Y/i/pFgs1qV8TqjlqBt3YueMjkHpu9Yr7Np5lqie4JuytS12tu5+1M2ppLJHYnFjcZ0550oWgw7hk61KErOu6acmnV0pjziQfl+GRNSal5KnRCijZNZL8xufm6KlEtX3TFO9zHH5YnJ6sZcQ1SMeu+wh3fAQzcKmGZApZpmxL+tsbbvFbe1TR301YxnKg2W1zFWjC8X70/aBmPkNFFt2n6gWgDVCaofuoE36LGo3lGYG+krl2+CajW4Nq9J59FongLwYzbmowDww8z8z4jo5wD8QyL6HgCvAPj3AICZP01E/xDAZwDUAP74So8zACBCfTje7EN6r/mH4xfDVajgga7tw5MGxzlax7MoLNMcmyWQ7FByEO3MJiFWOAeNKDhtIyTrPvNLlykoPdds5lXVuFFItDSmFaailajV61yPnYNhSN3tVpUDl45QvTX7/UCe+iyH6xDdX78tbzM4+4o/S2deaJj5JQBflyl/B8Dv6OjzFwH8xY0GqhVGn7mkdKlXIIo/t9RtsOReCp31ru3+3R7oQikF0hzANd+1dDWQAa7AgjDQQAMl5N5bzf1lKQ2LUEyMKy8Dr8ZCM9BAAz0a9LDBQAd6KDQsNAMNdJF0FnTnVWCg52l3FnTobSBFPwqLzLsBRPeCaFhoBtpduihE53XQni9SmJ4T3bmF4NyF2BweEzWeJCnOX3js4m2cI4QQbacOIOtG3wm8Gp47l/iLQm3uc7V35c7bNHW1j6Bvmn7ruNqba0DzQVQDXJCFpTL9R/drjH/xFVw+8bvXGWCgc9A5Bdla6M65NjlU5y6B4QTNKuGSi+cJIG8iQZEKFmbrtt4WLM7zj0vZeN6tKVSIrVBIBIp3SV8HDsddjxNChKiPE0KkAXCDSRfG1wgFqNLMpwVo6i65YqiJuyb4mB+5NC7xapS40jNQHgP1vsXPK+HjdeTCMK33jTslKUAsCdWB84e2vBgY3xUY3QeW14H5kwpcWBdMG1w8eb0AS2DxTAUogpgL6IkGVfb3eWwJEoBWBJ5J0FiDFwLjx+YQgj0Ap0N2ZiCKw5FCg4IYmgblWfsYmz64G0GMpTIxLhNZt2JsxkL58xD6xvz8HEDRMEpSwTxMuWLhoWwUC7y5PMRTowe4Jueez9/8td+M9/6nA3rzOrT7Cw0R6No+wGxhaDqErBNqPbskYgaqGpACXMpIEEb5LSoFWlbgvTG4EJFrNSltMb4yAk4b4eqEHDGDaoYuRSy0An4smv4p7AkTQVQmEn95WIIYqKciK/zkQkO5qGg0AiuNpM5GRqdlAOQC4AINcCbBQOm4+wg0AjLhZe6TEZQhAGgae1TMgHoPHrHARXKDCaP7RtjOnoARgq4/oUF9Rjg2eyaiBtSYo/lEbRyFO1YAHNQTk4HdbwqiuXuqCVQTeKq8MDfMAJpL8EQBtQBKHcHSuEnRqQTv14Z1MEeemVeTpnUwB4BrAXmngDrQQKFBpfYL0HJWGLThg8rwWErQQgDXquYmEIOVQFWVEEtCvcfgPQWSbDbNytxQE/fFnjdpgBRBVOYHUPPCzLUSEHMBXgrIucACE98nCoxKN+TudvbB06SwM2zn4v6q5tlsQdYE40QxPx4lglqQN1EckY7LRQUPsuvaTW8zYjzhS6RBo7lkKiROvvoJiKWGHos4Ah1WuGuYAMsUl8w3sn8UY3S/hh4JVAcyFu6OGChPNMr7SyxujY0wDRaGYqYN/hg1i4R7kE3UOjxEjAu61AX8rjyChuH4PHv5cxMNX+8JCMU2Gr7dTtQBllrw0jHgF4YVQ0XzkksTga8Ljl5csjAwAPJQOO5eKI5jn1oxNYBcGCy3cFzHozhVEDWj+pKMrsn1TcfzgX2MBow05BnMgRIvns4gQUrKHCUBgwYfzT147HmRWoBlBqYHzfikdF7T1BbEMROrRLWNpRJJvbJ9ZDAXDaBo86fqFKg1UAiDzJxo2WJ+YvD5xoW9Fu2vhYnAhR1fq0Z7ZbYoz3b4YFPV0iCBeIOT0fYc2GgLOcLz4OY9TDZL/bl+MhuvFAEC7TKDwN6c19P2zzZQnnZ/oVEa01ePzMvlXpg0WnedAD5mE3lc1WApMMm93E541ApQCsVbRetFJ6U7g0ezsDer4GxWkU005uYbJRvzA5MXBGFZ9Bdoa3vBcZq/xwGBOiGYhZsvYrSGdBMAChbpFNkZgFAaai7apiwY/CoDYMgNQoMVErpAVgA198wAiLaTmQVCJBJuQCTkbBu/cGcFovlLyo43SupgdsFqAovKwI3gEs06JhYENXUJzZrEZnJuNg56ops5CAYpQnlPoLrGQMHgkfaaFJ1K8/vt10ZzWUqgJohrldeWBDE0E/iNfYzvCixuaRTPnKIoTOy0UgJEQPXydeipxv5zR1gsCtTzEYpJhXpZgIhx6+YxpNCYL0uczkcoS4Wqknjqxn2U0vByJisA0CCf8CyEpkkhaETQJ0R9No9QUw4Y6BhJjKmsfL1LwVaE5rAgUZpm8uO445LcfBvBUmmJ0sLOKBZ4fX4dz0zuY08sfbu/82vfDPo/t4TePGg0WyAiu4OzGk2KtqFhdnGB/T4HTwPNkKdL8KiAGss4qZlry4Cc1xCzCvWNCVSA5wUAYmlMVAg+2JodLUAOusYmOCNlBXZBZvcf7pbseNHHx0RgMgHFnFEe1Vg8VoA0UE8pEqAuZ0YxZ1RT8tcQ7rxawtRdKyEaNxTCxczA3+jgXrtvEqTsDm8czznS1lz+m9BcFoxBDMgZmuRprtAyGt8tQRqY30KTOZIQzzuYFwIWDcJ1qAZltEmR9ufINOIxx3L7BHcdSzILyYSD+8AgTRALMuU1gZ2JqxkKxAQhBdS+arDMHPtagAsYsyE348F+EyIFMMg+O6ZSzszCr+1rLRbG1KUUeQxXTQAUYXJHYPQAAAQW2EMljHpN1gI4fZOgS4nZ4hCiIoznBDUaYbwkgIE718ZmjJog54RKMkRFeH1v3z+DwW2NtGwk5Y1GSU2bxAQWllHyN9I8dTJWehyNFx97OB1O+mqzafjFxHR2664CKwflOFAf7f5CIwVmzx5AVMZ0BsS7a9LshXn6MbdpZP/UwEgS1F6Bek8gt0snzSgFoQBQHRiEYiekiM1O0yw0sdA0H3ANsjJLmIfVWReKZkfuyT3YQKNJIamHQUuWlUYxZ6sxieRtNW3lwrzo4Xxa96BjU5SyAwA51zaTZ/NNBmgWT1BsOkvNWWHit/B6fD2bRVuP3Eel2KxVzOxuctyYYmLBw1FZiIPlTWcpllYfWnOK0twLxRPMQ+u81urMViJuH5LXxnPaotb5cgBQGkjzFBHFnmTBPDijvZPWQK3i75UBL6osiOSo8PNk0ZgBXS4ihyFmnhNAl8131HATZeaVaLXhBiWj1Tabk+C9TvoCbkMTb9ZCpwynCfvPQYlWmtuQ6QATkAmgUZBmw2qmpCUOBhfntWj3FxqlsPfS3e6XGeh/KYN2pBlQyrTtMZ1Ba0BpFK9nnA9CgeO8qxzlXGNTV9pNyWJ7jaQwxx2ms9b96fIGy9Rn3U2VNmNRoB0GCM4Q6PQYc/NhQW3tMhAkBiRUJEKnERhmwYAx4fV5iKXCh2HNa93Izu6+9Ndn+CdtXQoGNW7ahhsbNUYAqtoeQywBNbXCO8joKufm+5geBX2c6ewBoT5gU1+achAgT41Gow6UHd9A2asDFbg3G7WmfLvA6B5h+RijemoJkjbZmTIbmfJLY+gRoJ+dQy8lsBDAWANLw2h8cwYhGNWyQL2QEKWGXsoopbRZPxqTlU+wRg0CtCPXzpX5c7TJ1S2VBBGjDNCjNRvtrUjMY2F9jlxb10YF/ZQWOJ6PsTdeerMgALz+uSfwxE9swXRmN19XmXZ/oQGAQjY7s4xAi9xks7739pQBmlXgUkJPCi/4wjaAMZ3RvIY6HPuPm/67wlI1ZUE8AxM86rOWRiiRBqjWxutMBsI0RYQOd2eBEDJzYZQnNZbXzUfZeiqy5qpiwTbjpNFAtHcJzgvRTjOePS5mxvwUffC3wp+UOQ7TTEe7UIY3nYUxD6npSs4Z9bTNg7RJrUsaWN5IUlf3rddWU/JJ3jL14fh+R57h4ebTMvUkx8aUaLVJistFxZALMhp36LkYmGfEMp6rdyxZWq1RBnNgY6YrT1WQbtj9uICszD1TpbnpQln36ZFMPK8Y5WmN4lShnkjUe40YIItmXB4tAQD1Xml+89p8mxRKA5qhR5PgOjWYhPnmNppGaMz+mkINMqOFmrlxW6Ps0Tz3UjBYnbSN+CRjhe2yqM6M5sFk3OQZUrpRvT64Nq9Ju7/QCIHlE/vmQS9jc5d/YZ2jQLArznn6UM2QtYaeFNDjfBYwUtw+FwDBPMiiDp9U8i+HWWh0nHpZc/ORPhSQ6csTUmLCMa7WNYp7pUFuLp0vcGzKIaXAUm6G6Bz095cUanXuGsM+OfTkHP/AEymkyGEip4m69u4FzmmCXU4XOeoqvyTk5quG2DxaUT/eyiyuJm0PvZkBvtoL2u4vNLXC+Fe/vHbzdV70lVDi9gEq3sxXr5Oo8qKp9d06Q311u0JXSwwPdOWoz0Q94Ks9NNr9hQYY1NOBBhpoPVqlZazCodvVNAZXTFNO6UosNAMNNNAjSn3AoJeNSZebS/h3nfZX/CP9tmhYaB4VGh767dJZUJr76s7S7mGgOV8GsnMX0GgH6Gi2bktERFfuO9wu0LDQDLR7dB705QukleClktrODI6s+3cH4/6yLiDT0PlCaaAszo+SLDr6WGcO51oeeWeGHnSVAoSBsfFxMyviZQCgOK7ApUC1V8Tts1A08B52Xe7oEQoEElfyFF4m5G/LUtf0FoxNiipBwPQtxlM//uLlb/AG9+Z3CZ1DoK0UVrn6TVCWw/mldRZ2B0UTkMc5IdYlmADjObdYggsJjEexC3kP+jIAQDG4dBAz7sUOBFYolICsgAmRmxvYlxiBwbsVE7xreQpPk0LPpG7lOkBSEDWs+3bQNxRmawT8tcoQzBMwUf8O7qeGQWEmeLga0gQuuCUoAaB8QNh/jXH/QxY81NdzJCzNXLkZG0mbEPXAnbvT0wIsGbRXgwT7+0uWH2sCK4J4ew96xBA3l5BFE2NiEJxd3Iy9x5ogpYbWhOpzh1ATxv4L9yHJxPDEcTVoURiH48vCOcPJZGpgazKxOikRMTjwvXdxNGF8DmX6vvy5p/D0x7YEQXPFafcXGkHA4zcat9oijmKOovudoEvrO3ZtPk7CgQsGLtK5GByxVBZoUKIVZBjuvuy8uYNXvHNqhCyAbIxNK24miZ4O41NCVITRiYZYMOY3pYfAaXjk5xPdFwByDhy+MsfysMTJ09LC3ZvGojbxOhHcjbt8ZdAKqj3y6MytexSU5WJpUuwwRw5Z2mORAXBxKToZK0tpnQtAdXO3YMe6bI/dyZLj++bm1GrnZLEN9HRxP857lTQgBEEos2i2YnoYKI+A8QON6W0JXYY/XDx2Opcc/Iube9SGTYwTBKDKIop9ce0dFFF5YmKE6snEwCHpuF3K11wjY3KnBheE5bXr0dzbsDHcQmTujMvRSXl4TxKe0fw0R+usZGDEMVJ5DpXiq05Ot+eodMXNdVdgoRGYP3cIodjgjE0a6ItIjbfaRlYwrEANkAttYGJCkEjELyZphrQwN24O6xClLwiQPLQ62z7Hv2NDZihRrQ2WWA2qNeRi1BauCa8spIsNOC3unaK4X2J8ZxTfH5sWIebLvs7FN0UvbA41OejnjzPwLVGMTx9ig6OMDMjGGaWUA2lNm/SZzKIBKT52Jqm0f9hPCHhkBaCFriAqDVpU2Htt3ACpJlpatEnpQlBOtTQ0ZaJis5gX7dw94earsO9OCLPkzE5daAsAQS4F6onA4gb5wN6VWmFXncWka42Tbc+95ZE2KP3L0NQFx5NXpnjfX7995c1a26DdX2iUxvSld+CCAMuuF3yTFb8VsLimSSwHJ7OpaSw5byFBb2Jrt8dZrQ4w9nNmr4n5vhl7e2ra8uwdtpW0eXZCMxgjDxEDUycUQ41EoPEEwg8IXvKMiQtoMKwSYSVqgymXRVdOBGhWI0xMUSkIqVyasSPTGdpjRUIKmfKwjzu1icUMRI7ZLQllzGWOCUsT4c+Fuw8cLUSjOwJ7rwP3v4oNunPzY0WCslXmz9mjOTfliIiPC6BkyD2b28btKcjs/rUmcC0g3hpBTzXoxtyYzuyFk3DmMPj+zAQhGFoTTj+7j+qQMX7+KGrnp5PsuAjwprjMdOO5Z+pDKBx3njt2bZgNZI7/3dBWUE/vFMY5YEBvXkm7v9AIgrp5AJ+HY1Q0Gk0OfTkDKeO+BbTaAkYgVg1MTGj6ikgbzYcLghonuWy8EIt5p+jAuRw5WTOSK09MZE152g7+7QoXgfGRAtXA/DEZ4WhF46Z/bZ0zTxQzxrUvCiweH+H0CRmZq0SNxpSWCgrFFseL4g+pyVh9Y4dApOEuWi4MqrROI2dzY3BynLbjuA1pGE2Om281kQbqTHrRe98w7DOdOTMZ0OzsYcdhaY7NQkSNRpfOn4HREWNyV0HUErqQiYbcjNvSoLldljNvEbNfbLWUzX0Jr8OClxYnNVgS1Hjk71V7DG6uDaZfeX8GLgXqaZDUJeobmLc0t+5rH6hqq39wnkXl6ARUzaNwOBKLO0OM35q0+wsNEarDMYTSoKWGnkhvismaLzq2Ol2mLtJt4Q2gDQVj4WTABEFtwdpJHRuRlgmnZ8PSae6JEJPbpjMxrwANjO8EeXV6xk1fPmIYCJzjGcq7I+x9KeHjxs+ZuJgBxXF++y4E5FUQOalQCE1bm0LPbNquT2Nd5T0G5DXl5DsikGxCPABmglbunAcUgxYVpm+MPXCpb5dutoK+aT6g0LTGST+XuC76thegJEeXY9UdneD5NdqliMoBYFIQdEmY3xC9eYFamuoqRwwE42T7cLav1zpDHpn0FKGZbe/LEu/9W/e2YDrj1c/pjtPuLzRKYfLi7UbArDJVOTqLKW0V7752nTb7Ndr2nG/kvurKXHltTGdUlS1HiU7UZTQvLBOAQoCE8NkWIyw3NgtJZE5Do5WQ4iZ3T/SyBoIucWfNms5cW9tf1Eab6XRw6BBErl3I05sAZTN2l9dZl1BLx2iZzoIyB0iqi8akJSqCLpsdN0s0pjN2i0Ez3OgeYe8Nxv0PAnrE2flkjxHOq+f9YEJxahLM6XGTKtrzY4A0mbQbd0voEVAdaiOY2TgwcJS22pZbbZAUcPBKgXoKzJ9s5+nxgwVaVm6O/jJa2g7iOYfaW1jGaLJrc5DaOdQse3hMb19t4b9N2v2FhgTUzUOTP0OxAZUMvcgiDzPEuzvPAy0hGgpLUWvoQjQZPL1QangQM8RCgaWAHol2auacZxni46zpLBdqkQjjTr6uPPrI7OYLlEcKQjEWN4rV5qvcNGy65b0vE6obY8xvNny8t5RM+FryyMHj2EGjU76lZibdnqMzvYjafMMQzoMro82lfPOOGFZrVYg8pqg25SwzXkctfonZpWWGQaSxudTjYcoF0mHCtWS8ZK4mS6yGmC9x61OjeNG3/KL2Yd0mCMbOJJTThIM+lLZL23SdK5vHJ0x7kWoGZ9FW1wFLPad2EAVsDo4Aa9EVWGhgUgZrgKDjfOjJXwIATc1xSskz6Ew8VGuIMDAt91JpgKramBmkbPMJKdffz2EDM1HmvDNddK7MvsxTm1cm75m1gh+zSWv9JcI0m9u+58UONdANXu5Lj7x+RITDSnDYLdCjcSd3nBjro43vKO3+QqM0xMtv+NM1NuJnok34XtYc1qGH+2Kr1U0GGmiggRLa/YVmoIEGGujdTlfcGWAXtO+BBhroUaR1wDL7js/bf5MxznI80No0aDQDDfQwaVM050sWcA6bj5lX4/SZDvnyLkDRtM+67uB94xGt553pECXcN8ugLnUhD+u76mheg958pz2fy6ArrtEMC81Ahs4AHHomQeQcBFJBtMplfBWisSOtI96cESpRH2ZQrYBlBYxK8LhsgEJDzzClTLCwEICASfHN1gvSeftl4lnSwOEGoSFoh8ZjMvRidPEscqFQHTRIzd4lPLgnTdxK2/vReTY6MFFiRjFnFDON+WMSqqQo7gQweG9MgBqjCcil2I27DyomHDv0hBzdZ1TXqO06jvCcmz6awIJjvDwy3yrlEqCaUB+w79dyLXfHDIzumniexU1tAM2A4Fo4mkdEHXXTV0q88NE7j4xzyWXS1VhoQljyVcKtr35T4Qa0XTC7hFwu9iUpJ6VNXei1FsalnGN3FQoYYoAWFbiU0KPCCz1Xb+bCKO7PoPfHUHtGiImlhjxeoD6cmPiXHEYWEL3AXVhZzXFTLheM8rjG4kYJNUraBMJLVmZ+9dTF6ATjokPYASAFTO8qLPfNZIiBao8s3EswXziPQyNQJ/c0Dl4+xvypPZw8XaCexEChcsGY3GPMHhdGQMIgJogKWB4GoKGZa/bF1mmoF80gIMejmAOjB4zZ4+1xmsbxuI2rta22cTlNLA+hPAZGDwRmTxL0KHZRNwCy5q8a593XW27qq86D+ThAUXD78g1f92PBxh3ZhS5xW5cL4+peHzc3PIeGABhX+PLIDDh5W8T8vMs6I42XySI92PLJXTUsMmvS7i80UkC/58nmPI2bAbJwMWYnCytMTOCiHheJwEz6petQbeIEsmjNvlHcp/VSBppCcVqDBRnBHrbri8FJzltoCDkhoBjlSQE1llBjkY3VkQuGWNaoro1QXZNmIZgzqFKo9wuoSQb4cVMKXlRiNvez0pBLDYoiM+NuYslmJ79MJp6JgYnGUmYhKx9ILzT0SJjFPYxlYTTIB4pRHC8h7p9iqhjlg5GJpwp/4kpDzmvsf7lsovMrDdJsEb8z/IHETTwz5zTmxvUJedUaVGuzYched895h0esAUzVJqC3LNrvQcgn3fz0UTYmLO7ngVbDTZ/ItF8VH0fwrv4sRR4FwbZzWpxYmo2e20S1NEBq2ru+hn9G+wRQnmzLC5Ov/IK2+wsNw0CZWArRjrOPvntRQ02ichHyqiUUIkrjbJTZscgi2YZ2xa1EvNoCgKraLHRFBkNpFX8gH0OT6+diaEKzUdqOGagV5NsSkzDWRWkUb4p+G7ufUJt3DqIkjKWhWqF80wq3nGmMqAkWLGRbg3NtctqfBsSyhiyb30uPZAv9OJqTRZkmzea7RLOpba6lENClXbQtwrdcaoABNRWxVkeNUMppYjmQ0Ah6BYjq5YJRLBjz6yIyH6UIBRHenNNcbCQ+C9hoffjNV3nMKGZGS9OjoM79HEtzbkxn7bnG8+B2XTJPwMxndJewvM7Qo4QnLJ+gzGwICAhNZ0F7OTMaTL2v/SLhseHImsMca00YvSPBkrG8qdqLYhZm3fIT9iAqJ0xfGeGFT9GVXwS2QVdgodGQb93LC8x14F0AL4BJNIK3y7yV2u8BADViQZi06zRnpcdLaQSVE4SRwARaNv6gf6SFdQCEun6kGfKkgp4WUKVAhNxsSS4UyrtzVDcmqPeMRiMWGqP7SyxuTaDGTiC2d3KxEE3nlxewAFDMGaMjhfnNAqrM8wUAuTSR/9U0L7DTcbzWpIwZbHkgzKLMQLVPzY407M4WXaACpncU9r5ImD23j9Mnpf8u4QRsMWOMjhizW8Kb4YpTBimgukaxxphekx8P8CjRaLePiJvy4pRQnDDmj1OzYIT3g4Oy5NzB3XjUA9dPw5sG3b8UnkVUTd/wO0c0R39tyUWkJifXTplvNMXMmjORjJuavhjtVBSBaUsszXXpkYjr0zWD7SJ3osAE1FOZ4dc2mwGIwESbOrMhGd1bbGeRYYB5CNi8XBIC9fO3/Gmf2cqUUeuDtlgYTUKN46RpprLNwo0hKg1ohh67haHdpj1+x3UwUJxW0IWwprikPvweEl5LNK8O3sn4BjlZmrFKkZ0T515eDTRICSKu7Limlu0818ZSMVeQs9qYNotQOMRmJ2HNUspm5+zOYePK7EaiZshZhem4sKYxhnaQRTlUX3etpxXE8Sn2lzWmb5QRUCUsmCotFA4njZnJacnRdQDoBAtNTGIr2zpS2pi5nBa8LjJEXzlgoGO0Ntr1GqazPJM1NnrpZjDUtHNmuY7NWraNu39hHqmezRpVxtSlx0H6aCC/oerK4YPmr6yutvDfJu3+QsOAOF025yt+W48cHJqDavOAidQE5upTci+ztnhMXWakVfZyoDF3Obt4ajLqm8c6deF8ozJtcvd0CQoLLTN+S2Ds2tmy6RsdwsdRl1faKsHjTHNWuHWmlba/YemcJvpy3gPRZoEWNWjePNaiEE3e+9wcU4FKZL/RNDZ80gxBBDUtTU4eIghlsPfUxJnmEAukRMv0wwX2/rb5rJ05VVbGO2xxXUYpqFvgngk/twFITWfe6+wUKE+10dJG7Xk701k9acq7NNWucyDWiEgDo/tAdWBMcn0mN39ocehyjhByTiAF1PtJRpjMPEgbMFImYHmD8xiDGfJpIcIyO9jeayWe+1XajlZzxc1zV2Ch0RDvPGjO19lFAbEwdD+S26V3mbYQm9T8x0bifJ+cN1piUgsfD6pqQArvEuvamPkGfFJz1LomNcdHMeS8hh4X5mN4y8QGyIVGcX9mPMysc4JcaBRHC1Q3rNcZIerbmV8nI2hymplcaJRHCoubhUFeRl44iYohVGM6C+9nRMmpqBnjewrVgfS/XTXNO0M484isTJ/Jq8DyqX3MHi9NCufA261YMMpjjcUNYebNxgwoFBsPNyvU+8ikbbZZM5O2sTaDttmJ4b20RM4JItLymtwtzuzjv3f49gw50yjmCuVp81t4MxGbe2k0tmS+kXkp0A6B2Lkhd40M81wWwmAWrpE7pjdvjNYgxY1puA8s1GqvYPZYhZ1apMc37K+nWoGv+AKwLdr9hUYIqGdvRUWdJqSOWBAxr43QH8uVwipiV5kHU5eyt906RJohZma37DyIzkT+5bMPuysPzUuaQfPapr/uMFFVCjRbogAgZzIqG1WqHzh0A0+n6IVXGlTVGH+5yG8E/HmiSfaMTakwqJXZAHSZfjKCC1qbdBTv3MNEdmi9WuMg1GwdyKEQF44YnAMV3Vu79wpK7nV5UXw3oMwdPhdt+mqe81X2NCwx69PuLzRaQ96+lynfwC697kueExjMkDkHgRXUhUBMOAPuzxl3TStfKEGge22ka7p3cS9jShf9cl74y173uazm6gag0YG2QAMywCUTA3w6f9izeDRpUPsHGmigLdDuLzQDDTTQQO9mco5EV5iGhWaggQY6O50BI29dWgtLr2ncXXeW4OPUXE6UddXmxRIDraZhoRno4dIZhRMR+e9goUCKykIXd1Nox+wQPH3CJjffHu/FLPadc91O3c79vDJlHsFAGweNHriWdXDwQmLvhdnMLYLVAcx8ZRtZIT13ThlaUhwgbL3gtKTIfdpjzmkGKbZQQfAefKJi43YdAodG/eNrTFNh+4DbYI6te0Dw6crVmKAloViwwboL2hVz4w6dpiWf3NO49m9ewlZo+EZzhekMQu5SoNO7hFTXTuosAm5VX6eel214mHWRDyLE4y4hl1xLOsesuzbagg0AqGaISpnA1JHBNyvvzgFmLG9OjXADUMxsRPhB2Xan7hCcrWh/RsvdO5pvMrcUpNJdJ2luXKdTwU9B+3B+bNASdEltUM5k3JhfvsKhImgZXwtpAxSqRuTHJeYIcSKNC3JtRG2OdRmPS9q4qusiWFwCoSlqs6jUk3BMc70hekR8YeG1xMXk3L0ZBhkh6setPqQBOdcgLcDCRvwfN+7VgKkHAXpE0dij+3VmcgPlaPcXGiLg+kF8ntaH5IIinVDv2oEm562XMrdzhH3OMi9w9qXu4JEKVS9YEpdf0h2u1dG8u+tac+iZK9UaclZB7Y1M7ERP21ZfJ4yA9d3V3Au7yVqfxmYEBWR349DaoANXGoKFBVNUYEmQ87yHWBvsMh23EVBRTEpSn5unAbBkj3CQg2bpc9+Gdjzi9Aedfbrcy8N2nVh/3Gxucu1WBBW7RTSlrnJXZ2JbxNp9es1kXW36tNMUF7BDprgUEZ5lNXgcrktXYKEBMB61irPC0r2Qmj1kh4uEzu2mWn1Dip6hFS8oJy7COXt1n4dXatrxAWMaoku49PEJqBOIMydElIaUK+zZXUgKXUJhXc+2TbTL8D65sV2ZdU8eHZ9GZicQoRBiPYy78LjHnNUJ+JnwiDQ9VxduNgKttQWxFJi0WAbzDzW9Lq0s0SJTLSvVaBy2mEM/8FpMYoZqbSrYBngC0Da3TbiJIg3oop3uAYCBS6rRaDSWn6wYakStYNsuCCqfHiMw4XXhzoX9SRssu3raoS0CKGcMLcmgGQQ0uatx+JP3OphfLPHgDHDJpBl0536ruFMsJUI782w3dEaE4pV1F2HqCnefuW8LffZ8NC8kryMEAZDWoEpBj0sTtR20i17UjEAUlTLCqJRx+0hgo9XPtMkLj9yYabmojLaiRxJ6JEC1xuidOSCA5a3GdFYe1+CCUO/JrOaVA8VsxiO/Ccn2zT0CYZn7Gd3nkCAvzKqduRfWmq3gFe3xNtEIgzl5NOeQlTYLRmiyIh18G0m1UPeqcVOmi/QGhlpn0M+WO9NZFCfL5ruJHzvsG6qLfvzG1OXNZbn7krYHAG0QK/w3ovCa7PzlwqQX4OTaiqMqM8hAOdr9hUYQ+PCgX3iHlIOYj/hl+uTarWse66Ius1nXeIjNZz6DowVtzI6dFXI9vHuIFEPMa+i9sttk0UEqxBLL3d8w4ZXgtnln3bDLRGiRMoujqDV4bjHJ5kuACOWD5veXswosCWKef9y78sSEY6Xj5+Zk+mb49pnGuvqk7ZXu15LXMXOF928TcM516s/zsTrVSh2/s2zyOur70leQ0nEm1rS/u7a0TbWtbzR8vvu7A7T7Cw0QQ4o46rQz2280Wuc3e11m1TVMPNQ17joPQU8bj6kmKAbhBNrmtE14r5pXKqgcCkKoUa1DF/ESbLKI5+5JaCYDUBydxq63gDELdjkyrPktr+Uh1ucYAXQ6R4ACT66yaeOviGIPJ7KI12vj3YVzoOQc8Lhr0bdBbZ0ESorusc91wx282W6MAGN2k00bCnjmHCdIGY1GOU8v++zJim2K6Q20SGumA2DBS9vjtYiNV5nzOstRMbcJ00YU8ZncVdj/6aMOxgOFtPsLTYfp7EJoHeHWZ17r6t/34bGrnsi8rKFA4vCDaLLjy324XPH9IQu17uqVSczGkzIWgis9zRA5M3Q6BeTMcEBjpkvnFwmWPE9Ra9BSg0cCqhQQynqdAVjenBpTh8tsKoXNu0Pdgmod6mjX5+FlUJStCc6ZzgR1fi/MpUWWSw01ClI+ZLqudDRwbbT5wfx9cGCVGgaVGo0NyeSDyY/p56+NVgDEC7Z3Nw5MaykfUrrRKMJ+tY6ejda1dGmOnRvQpG3Iz+HjRe3DsVzneINDtdoO3pn7za4w7f5CQ9Z0lpQByH9AdqazPi8S9JjB1jWtraKePrkkXGmmSJfuIPV0ORf1fE8kVtH99C+k1wAzZqCgXefVrvKC6tKeVpiznFAhZXabpftovjB288mD08al2DkIpJuGdbXTLgeIddr1lQMXCsjZha+3EQUecgP103CH1qfdX2gAA6/vaNXL5BaaFd8Z1k6LfJb6TTxENhQOFyJMgE5BcoYl9aFSZvNvaL7obDPQChoWmd2jIcPmJRMz+Pj0Yc9ioIEGGmigM9JFGWUGGmiggQYaKEu7r9EMNNBAV4cuEFjzwkA1gbZTT4q0sIkrdYAosA1QTQaufCbPYaEZaHfpHEJrbSG1qYBa1a9LKPW1WXHeigFZx/06N/4qV2zfJ8O7q316ezoccFqefl1ICL6DPc95K/bNN+SZOq3ZcqE0xKyG2m9ixrKeiM4poiNguDyuUfzalzMdB0ppWGg2pYct/FahGawZfGp4rdl2jbKVwjBXtkoorxLAPQKHO+ZAlTJBsKIjELZLcPaVOeSA3P3seF46vR7XeURCT701nqlVwbqeLa/f9iyUdb/OUcYFOe7LvR4eBHS7jlsMQVrW0fWu4xqenov5lgI2mQdngEsnAmgUZDZfNyLY+dS7AERHqxaKTV2ZN4il6Y24v8Ao6F5aZ6E8izv3WSgXfd1HHa7VnnLvYtqmViAlGgSGqP8ZPQ3P46GIFR6QFzFmlwv5Lppj1slvs0n8Wle51uBCdrfpO3fz2hoywNWn3V9oAA+QuRmRcXUm2kwLWdd9+Awv6dqzOI8L87mgQHZ017QOJh2werHOaQFnMW11lHWiC6TzT8bnXHly3gun1Gc6C/uugkWyG7MuM1d2HmnbVSaztF3G7OUDWwXaaQmStnneubbNsagZYqGg9oo2anrSx2k6uesoj2sU94/zc7hgGr7RXDYxwEFMxGXSRh8f446r21wUwgCw2U7vLEKyq926/M9hkltpytp0LgFPYvamM/ZpJDbg31WXmM5WmqA22Pj0IQ5shL3XR4SzBxu1ApmaAkpRzbsoI0RXmtnOFBxreStl02J0iL9V2HSWFy0HUM11afcXGgJo1E4T4KnvpQ01mov6OHyOfmc2nZ213SpgwnUo80F0I3JCJCfQN0lrsC7leAZ1ZHOPnFe7PJO56zx1wNlNe2dtt21a51m9iM0XACgN6jOdYfV3x61B0OwoEdHvBvBXAEgA/xsz/0BX291faFbROi+8Bi40PvyML2rna3TeF/+8Jq9dFTyO1l0s1zSxUcrzPM4SuQX9srzGVvRdyxusr0+ub991hadp5tSsxtkzp4BnlDWTutu25to3djhsrSG96azbNLeK5EkFua1g8h1zBiAiCeB/BvDvAngVwM8R0ceY+TO59ltfaDZZBQEY09nyjL7q59mJd9DG5rVV7Td1n83VdeWp6eN1kSa1rrJc/br8uuazgcBp8WIG1dqYzXJIvWfV/i5Cc1xnjPO069h0hbPuBvncoHxTTW9NVPa1E/itM5bN6hkl+TuLhqre1RrNNwP4PDO/BABE9A8AfAeAh7/QbLoKmk4AlWVStuZL7YKytuFptUn/bXl1bUo5s5O6BHPMOm3XxWLbVABrDepa3C8Ro850uWCxdFU+ECfpGs5CV+RKL4WOcPcn/iX/o8e3POyEiD4enH+UmT8anD8H4EvB+asAfmMXs21rNButgp7W2fXkhATz5malkLeUpv8uCBXYBbeuDe/0pRUEIsrXXRbtOMovWQRvrtMkRB1JiXb8ei6NLum63f33MUt1fTXu7wUsjBdJzPy7H/YcMpTbLXfesG0vNGutgkT0vQC+154u/tmrf/WXtzC3delxAG8/7EkENMynn4b59NMwn9V0ljl9xWVMZIfoVQDvCc6fB/BaV+NtLzRrrYJWRfsoABDRx5n5Gy97YuvSMJ9+GubTT8N8+mnX5gPs5px2gH4OwIeI6H0AvgzgOwH8+12Nt73QbLQKDjTQQAMNtHvEzDUR/QkAPwHj2PWDzPzprvbbXmg2WgUHGmiggQbaTWLmfwLgn6zTdqsLzaaroKWPrqjfNg3z6adhPv00zKefdm0+wG7O6UoRXYZ31EADDTTQQAM5GjJsDjTQQAMNdKk0LDQDDTTQQANdKu3sQkNEv5uIPktEnyei79/SmO8hov+biH6FiD5NRP+ZLf/zRPRlIvoF++/bgj5/xs7xs0T0uy5hTi8T0S/ZcT9uy24S0b8gos/Zv49tYz5E9FXBPfgFInpARH9y2/eHiH6QiG4T0S8HZRvfEyL6BntvP09Ef5XOCN/dMZ//gYh+lYh+kYh+jIhu2PIXiGgW3Ku/saX5bPwbXfJ8fiSYy8tE9Au2fBv3p+s9f2jP0CNPzLxz/2AcBV4E8H4AIwCfAvDhLYz7DICP2ONrAH4NwIcB/HkA/3mm/Yft3MYA3mfnLC94Ti8DeDwp++8BfL89/n4Af2lb80l+ozdgAtO2en8A/DYAHwHwy+e5JwB+FsBvgonv+qcAfs8Fzud3Aijs8V8K5vNC2C7hc5nz2fg3usz5JPX/I4D/aov3p+s9f2jP0KP+b1c1Gg9Vw8xLAA6q5lKJmV9n5p+3x0cAfgUGzaCLvgPAP2DmBTN/AcDnYeZ+2fQdAH7IHv8QgN//EObzOwC8yMxfXDHPC58PM/8kgDuZsda+J0T0DIBDZv4pNhLj7wR9zj0fZv7nzOxSMP40TMxYJ132fHroodwfR1YD+CMA/n4fjwueT9d7/tCeoUeddnWhyUHV9An8CyciegHA1wP4GVv0J6wZ5AcDlXob82QA/5yIPkEGmgcAnmLm1wHz0gB4covzcfSdiIXDw7o/jja9J8/Z423M7T+G2e06eh8RfZKI/jURfUswz8uezya/0bbuz7cAeJOZPxeUbe3+JO/5Lj9DV5p2daHZCLDtwgcnOgDwowD+JDM/APDXAXwAwG8A8DqMqg9sZ56/hZk/AuD3APjjRPTbetpu5b4R0QjA7wPwv9uih3l/VlHXHLZ1r/4cgBrA37NFrwN4LzN/PYA/BeCHiehwC/PZ9Dfa1m/3XYg3LFu7P5n3vLNpx9i78HxfCdrVheahQdUQUQnz8P09Zv4/AICZ32RmxcwawP+Kxvxz6fNk5tfs39sAfsyO/aZV251J4fa25mPp9wD4eWZ+087tod2fgDa9J68iNmdd+NyI6LsB/F4A/4E1rcCaX96xx5+Asfd/5WXP5wy/0TbuTwHgDwL4kWCeW7k/ufccO/gMPSq0qwuNh6qxu+fvBPCxyx7U2ov/JoBfYea/HJQ/EzT7AwCc98zHAHwnEY3JwOp8CObj4EXNZ5+IrrljmA/Mv2zH/W7b7LsB/Pg25hNQtAt9WPcnoY3uiTWNHBHRv2V/9z8a9Dk3kUnw96cB/D5mPg3KnyCTlwlE9H47n5e2MJ+NfqPLno+lfwfArzKzNz9t4/50vefYsWfokaKH7Y3Q9Q/At8F4g7wI4M9taczfCqP6/iKAX7D/vg3A3wXwS7b8YwCeCfr8OTvHz+KCPU5gvO4+Zf992t0HALcA/CsAn7N/b25jPpb/HoB3AFwPyrZ6f2AWudcBVDC7yu85yz0B8I0wAvdFAH8NFinjgubzeRi7vnuO/oZt+4fsb/kpAD8P4Nu3NJ+Nf6PLnI8t/9sA/ljSdhv3p+s9f2jP0KP+b4CgGWiggQYa6FJpV01nAw000EADPSI0LDQDDTTQQANdKg0LzUADDTTQQJdKw0Iz0EADDTTQpdKw0Aw00EADDXSpNCw0Aw000EADXSoNC81AAw000ECXSv8/zpdsVfXEiJsAAAAASUVORK5CYII=\n", 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" ] @@ -537,7 +647,7 @@ "plt.figure(tight_layout=True)\n", "plt.figure(figsize=(6, 12), tight_layout=True)\n", "plt.imshow(L0['RED_AMP1'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['RED_AMP1'].data,99))\n", - "plt.colorbar()\n", + "plt.colorbar(shrink=0.7)\n", "plt.grid(False)\n", "plt.show()" ] @@ -554,14 +664,49 @@ { "cell_type": "code", "execution_count": 8, + "id": "355ac676", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: data/KP.20230611.30570.89_2D.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 411 () \n", + " 1 RECEIPT 1 BinTableHDU 29 1R x 10C [26A, 6A, 7A, 40A, 3A, 9A, 1A, 21A, 40A, 4A] \n", + " 2 CONFIG 1 BinTableHDU 13 0R x 2C [D, D] \n", + " 3 GREEN_CCD 1 ImageHDU 13 (4080, 4080) float64 \n", + " 4 GREEN_VAR 1 ImageHDU 8 (0,) \n", + " 5 RED_CCD 1 ImageHDU 11 (4080, 4080) float64 \n", + " 6 RED_VAR 1 ImageHDU 7 (0,) \n", + " 7 CA_HK 1 ImageHDU 23 (0,) \n", + " 8 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 9 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 10 GUIDECAM 1 ImageHDU 7 (0,) \n", + " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n", + " 12 SOLAR_IRRADIANCE 1 BinTableHDU 9 0R x 0C [] \n", + " 13 GUIDER_AVG 1 ImageHDU 102 (640, 512) float32 \n", + " 14 GUIDER_CUBE_ORIGINS 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n" + ] + } + ], + "source": [ + "D2 = fits.open(D2_file)\n", + "D2.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "id": "965868e0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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3gHNx/xuBH1fVbxGRb4z73yAinwp8OfBpwOuAHxORt6mqA74D+Grg5widlS8E3ntqrm0Djz087Ds/EC4U491StZL2gRMVMqrQ+0z0SBEuE8WNbLO1bcpJWbZtHffb6ZeV07DK/YTeBbtRKaPWjEionLYkpmwgpaRz0PWcObsoyCkyCWWP1ri9GX5uxwN6IxtTtLQZyIF0rCSUoCCZ4gA9E0gjkoqs6AlKmTCQ686O7Y1sx5lNxgk7L8w5fJ3iF4o2OhrUJ7arvdLSPTpWyhirGOuyOsYYjQNfshLGiNJHpYw1ijUeazwz60bHZ8aN0qTwxngacVlZsnQtF2bHoa4IO7ajEY9XoTGB1vFq6NVkpUxSrYTTGkafO7bD4nGY/H95vcdHDx7i7RefwauM1CwJKayLjJrFMzc9rXF0hVImwWE2FDEr3+ZypPKm+nTesvKWVjzHrmXHdqx8w4V2qLNTwavhynoHgIXt6dXkdkiaNa/Cb156jN1ZxyO7h8xMH9Q6xfHeD6qdpI7xCFb8KNwT4j69uMiFM8ccHs/w3vDYQ/tYURQKBY7QO5OJk95ZvAqtdTkORA7CG7rG0zQ+xh9rmRJp4p3B94EgCbfkcG40xhEB3wudLZ85AxES9gVdWpzREM8HYove4Fof4q0N67YJpIkXZG3wRnE2PvojkeLGl0bOT7zgE2mUb1JG/6aXsVJmRKBMyJNCyQKBlLErsMugCsqKnn78XJBJniepckYKmvx8IkwzTYSfJfsdG9mV8Kzxs3FZRRmRsUnFc2+guDod717i/vRzKioqKioqPi5R+zkJN0XKiMhTwB8H3gX8xRj8JcDnxe3vAn4C+IYY/n2qugI+JCIfAD5bRD4MnFPVn402vxv4Um7QWdHVGv3Q02HHFD3hQhkjI/Jh8DeT40190KS02xQ12edLsT8zTNU0o2lBTWIRPH7WMFLU2MIXTfq3g9pmpAiYKFzSlKBNnzJDPLvymE5Zn7NbVQojtcOoYaFZemb7nuNHbFZ75OkCCourc/odMyhlirRTJUepLgn7w/GshmGIMxz348FPtJ2VLT2YzuNnhbKl9EdTDMKMU9qrK/qzs0A8JSTfMAS79niJ2ykufQ0NpabdUg8dOY7eINZE0GYgCK1OVDIpmirrCaEmqrzQmFzW6bktkRVUI39EcZDfShhwpvJIaAvTK/928dSGwkrL663ILile3HxyXIY0ZfpNe4zjQ1ZGZfWXBTfXTLylNHkaTTOUJR+PU3ja64YrM+WlnUei+iydpDLfSViCVaQtRuIqyOWWqwcNsg6s3gu9KdQx5Ols6qNqhsgzeME2w1keKUl6Q9+brJRJs48S0RfiKWI12zBGs/olkUkiim8lq2tKlHHXbYtIsJVIH9dbmqiU6aNSxjY+KGfWDcYoTRMIyEQMGesLJYzkfLSU7qU66lAOFPzahmmCWSkz2ACi76VIJiljlUoPfib0Cx3OmY9tUihlptOJwkki57dB9iQiJT1/nAbyKJE+rkgb49l1UN95Nw4fnl8prQ7p7zJCNW6DzK+4ZdzPfo7MZ9hPfFskBn34wNNYsu+xwheZlP7KyodP6QfMT/YnCwLoaJGCwXdZTjv1TQaTBQNK32pbFhbwijqH2Am5v6XjfaJPsVvppG/zyTbFaccqKioqKu4Laj9nwM0qZb4N+KvA2SLscVV9DkBVnxOROE+C1xO+ECU8E8O6uD0NPx2q+OXyJot5/yHT7dMcWMrm59ZyJaSNOGlKzhabO6nzY8wmSXUCQRU6bo7d2QysHQiqFLd30Fi0bcj+asrpXnbbNlGtEwilESllJBNSpT+aTECVfmYak1U1phe6XZMHad4y8iWTp4t52LnccPyIwbeTeEKeRtPuw+oiqNWx4iaN85K/mjI8+q8JKp+BVKBU4aQBZKN5vxzkG+ORwqeM9wZrB/8q1vo8cE/TUyAMfhvjcVGhYc344ZX82qTBOsDhumX/YIdHL17PcfIlVaRLCpKkpsllKwbVpcom8XEppvODiiSVFcg+YTpnsEaz2qRzlr2C0Ej98eW6DfVsgq8YExUrWpTj4PIuGGW+t8ZaX/iKGYiM0K5DmkAspJ0x2ZCvD6uYtUG9DG5U4jYwkA3xGsMJ3tox8RAT2n1L1y9y3DMHgvSWld3NhJF4wSyF9bzdbNCoLFEJxEF3il+VQCSEcvZ2IA6si9e+Qht9yvQxj6aP7R6f/FGQtenoOxZrY7oe4zjJzw1xys/4WNz3k/2SPPEwvxwaId/7Opm+tC3tpD22+axpjpXZoeLm0CwDIbvNWXlKI65QwWwp6/B8GJOZFa8ZfBv3q58TMqD8YKStHfUf8keb4sOQ2qCOVRNeMqLkKdXpw4a3JmwL+DZ+yHKKWgnTdjXeGzbYkzhlWG2c1ku4N9L0Wd/GMI3kZHyG+TbkaTrFLj3z5w9YPnmGfi8qAhOPU0z5TfFNr3kKsYrk6dCmH6bsSq/Dh4l8bGi+oLAbHl75w0B0yC+9Dm2UjynS+7G6OSqFk9N/8QpOx079SzVyIsuchj4TIOsOeofO26F8W5z/hxO0LWziyB8GIm3bKp0J09UzizA9xXYK21hQYJKfZnJv6JeKkUysnbR9Q6Q8Skf9t+u0f+pf8tW+nXCnbd+rdLdS/jsZ92aP3wg3SlPJ3oo7iBuSMiLyJ4AXVfUXROTzbsLmtu6qnhK+Lc+vJsh/WbB7E1k+wDjtZtXNz6038w560G9/mfzfm0xjbqqcPY0IG6XZPgdhgxjbFncSZ0SUlb6NJmEjwmxqezTNbKz4ygqvZGu0X6ZL2w780UCobZBzhfLrhmGc7lcpTVtL+2Zcv6kKKyvAcj6wF58Qo6lvlHnBhVUYVLt5O0x9S53vdPrTf2E7/ZfxIPhU6udNcCbtodubbV0JLQ0EShVZ8hNVxoNABPQ7TSYA957z9AuhObJkJYiCWcdpMoztJBIgTQe60TQZ05EVSBuOfjUc9+1gz/RDfCgGSpPBTd7243YIkcf/xsWybxAdWmyP/0vCZueywziTz734oPTSor1G6Zlu64Z9UcUulea4pz1sMKuotkv2/CSt1+i/SsMgNqrxklJO/DC1Ch8GnfcK1QHe3cd97+c05wIJA2Fwr5EsSNOiC5KlVM1kGBPSuKnq5YRrJw3wpwOWMk065v3mO2c6lTt9tIGs0tHDQ3au7gXFT7JnTPhoM2qdcN9pY7MdbUwgeUtlcswnq2AlTqOe2jNBvTr6GISgbUoXyKbgpw2k9/g2KFbLadhD+cjp0gel8NGniKfp+RrIpdmhx6yV5UWbfehtvKsm07s3FMjFto76C9PypHoP+/k9UrxTpr6xBhVh9N3nwvthvB8IM7NWmmPH7NlryLrDPXqe9fkZfmbwbTgn3qZ2Hd7vo/fX9N2cgkuOqXzXnPDcL+1lf3bpverIpOJUPZnaa/SeKpvVKbPrDjVCv1cUcvJekcn0VZneYum9UZRdvEaCL2zLZFyQSUCGflSOk30uxndSvHdDmSbPARju0Wxc8zMFVz4zivjRRo6X7unymVA+M4yE/ZKg9D5++BjuVynTMClvQWiOFX+6QZKyWkPThGfMSOWnw/OqnEnhx8/BkcrPufARGkZuL0Qkq/yGhJ6Nvn/fD8+6bSjrs+3YzWBK9JxEbpYLzZTb5bFEpFq73U4Zb1JXubbpxuBuofZzAm5GKfO5wH8uIl8ELIBzIvJPgBdE5Mn49ehJ4MUY/xngDUX6p4BnY/hTW8I3oKrvBt4NcE4uPugcRMWDgGkn8abSbJ+DcFsfZ249yasOcsJ2RcUrxeIu2p7fYvyTuluja16Pb68wtwhFcfUr3L3Afe3nnF88oUm9olbAWnTWxEEH44UHYExESNjXJhI3nqyCSQPC6cqJSS3iW5MHe2karvQaSA1LVsJoE4mBNMCErHpNA/g0IBcP7X7P4rkDjp88Q3euyeGlk+xyJTTTKX6W7IfjofwaprYSiGTT6zCdOH37cAyDQQYiQnwajBaNLiHcrgLplQa22iZCiRFRC4wGy+VgWtIA1/s4RTMOEEWQZQfes7szGwZo5UBrNOBKg/hIOsng1F/KQexJg7TJx5YbYfThZVsZ0sA0K4B8HpTrwSGqHtv17Lw8C9dhYyPZFkk3kUE5nZRcwkCoCeMPKkUdSl+Feb9cKCOeQzWBFEurdZbXkkRCy2dldVodlPwxYqzWjmYV3CwowVbnJausU/xEeqUp2bk9Jx+ANsi2AokES2RNLn/0HTfUZ8gvlW3jo0S6D9LzgVAubXRos7S6aC9IL0EZm/KNKmGdxXvEgHSC9CA++Hjz8+CbUZxgVhIUxK3iZz4ofleCb0McszJIL/iZovPg007WBtMHZa7OfCjDOpw3vwhxzNKEOrceszbhWRLLZLpgb/GiZX3e43Y90kWbs9AYZhkWatBGQ1hU7UovY5VubMvmUHCz4Ryp1fgMVaQX7FKGc2Nie0p8/qyFvWeE48dTmuJ505M/OubzYOL5jucgrSZbKqSzcq9U9cfp+TmNC+ekfJb5VvP1bJdhRU7fhG0kuAkwnWCX4VrpzilmLZj1cJ2JDx/tTB98+4V3QrrfYPXue+MKrfZzBtyQlFHVbwK+CSB+QfrLqvrnROTvAl8JfEv8/8GY5IeA7xWRbyU4wHsr8D5VdSKyLyLvBH4e+Arg2+9sdSoqKioqKioqbh73vZ+jhJUmiy/Mas2gfJl2WDe+6E6+BEP4chy/GKvq8CUY8pfVUVjCdNGE0CapnQa1Z6ncLPdVwTn06Jidy7vszNrwlbb4Qp6/ppdfy7OqMw3eZSAq0oqYnjC12RarXqZpWo0J06UzOVMM4iMZldQxIUJQ//hGcPOkTiAPmJLqROPgP5EAZbxM2JiBoHIzYbYfyJnji2MS4iRlJ1HBUapLsuKlGFCOkAbpyeeVjO0BecXDDcWh3xIeSbM0cEwrOJpOsWulPXDMn70OXU/32DnW52doI9GnXfBrlxSsmZiY1HmkMJmOwbaNyWQY9E6VpDmKjwNiHxIkMmbUvhoGnnRlu+qgYonH51d7tBHaQzO+nhnnN1JIldeCFnHKOp14/rQ4H8P9MY43CU+qFiWqgnRsjymBJfm4iSqTbcqcUZ0iYZkV0YnoiIqfZDe1XVJQBwJPRy4McpyshC2IUjNp4/w8oLg2FbPs8YswXM02J+qQUh22Dfl6630mDbe2s9NBoTeFKuZghT8zD3UsFD0DuSJDmzKck4xU1inZWjxXsxowxZ0qHgsVu2iskyE8iyfbiWzV1oZpmJE4znlPFJJa+AF77to9cp5XkXErqy9N8S3A94vIVwEfBf40gKr+moh8P/DrQA98bVyRAOBrGJaKfC91RYKKioqKiooTUR3g3Vfcm36OKtK72CGPPmKMCVN60uCjJCfKAUPqxBszGrj6xpCUIUBW1+RBmhn8zWic8pN8svhWslokESRpCl8aMEynnZSDoubYMXvuOutHz9DvNbE+jAag5UBbnOJmZhgsu1TmwcdNyDRlGAeY0fcNkQwIDr395kBH08Dbj6dnQZ7SUapVyvOS1EojJckUpTNmkeBTRpXzs3ZsK1XjZn2tbMEGoTWxPYIUcUt1VRl/2+C0nAIRlTLS9ejBIThHe7yi2ZmHgV9jw3XZDCoZ39o8CL+RQma0mIUJ194w9WwgvEqiJYVPF2YIChmJyq4xeZMVSJMpymlVwXDtN7i5sD4nedrvlOgK52BMKpVxpqRAVugY8FZD2Yr65GlkBWE2Oi5DOZKCIikkwjU9kC4DGTeoO7AaFDOAdG0mlYJiQwalyEzDtoJExYpoVGREVUpS3KDg5z7YWZuQx8yDE8wqKqMaRZtQGekEvIQwG5Q3spZBqeMJfv3acNwsDWqCAsceG2ZXDcvHHdJLVAJpKKOP5ZZos5cNJ/x5Zch4YppjcIvQRngZyFCraBvs5POSFCqxTQF2njvP8ZM+kLC95Knovk3tJ5EoTErF4Xz6piTUQlm3lVeT0ibl0QV76dwP/jKjumcV27IBexzVSyaoZpJ9txOVMn1x7Tjoo4cQu2RQysTrbv3cvZy+VPs5cIukjKr+BGH1AVT1ZeALToj3LsIKBtPw9wOffquFrKioqKioqKi427gf/RztOvoPfeRWi7rdVvwXGA24t4wtMTdabOAEX2qDb7RSsiDDvvf4vqd5dkZrDRs+zZLttCImDH4eShKhKL8WCw0M+RVkVbniYklU2eHLdSAHgs+a8KUbcBp82ETSwLcyGriPG4fsiHjkNyVNn3Fh1bz2wCNOWZ+3Y78nDMREieTrpJxes6EwkS12CGRRuVJcOc1lShBs+OTS8bFSNSJeMT3BEXPnaY4c7fMt0vW4R8/TnZvhWxOmrcXpbd6Sv+APbbO9zkkFseFDZRJnWm5I5fMDmcFAwNhVudpmapfBn8vIdm6HQAQ2hz0YwS3scF5GbVaoOCbnYFovYDwdLh+f1HODBCQ7mc5R9JR0U58s29pxi+on+4+ZxBvVqSQwy3ImtUV5f5aKmsl0vGxvG+mZ7G9TAybl4KpH580Qb1qfk1QvW5CmiW60z6SsOrUZ62YOl+huMSladSDLKeq/rRyyRUWz7ZylZ1raTT5yPKMFVnL6Ut1TbEufnAZq9tM1LZs2ZnwtpGelCM9f6am4t3glSpmKioqKioqKuwQFXP2CVHG7uNE8/bu82ICuVjcZ8+5iygfIlrBbM3hzqed3aDGBE0mwaVgZt7QxWThgrLSaHJsiLZGuih4vw0yUa/vMZy1iTCDTEmFmBpVXSaxtrNaZBpCJwEkqHmFQApmBzMlToraUM/mHyWocy5gEEqJvm9LeuA3zFKkmOC5enzFZjbPNQbHPypcizoQ0S3ZH52aqrjFjW6ehJNEy4ZWmuiU+M62GVhJz0e+JWqI/nDBoDyoNyYqQ4NuGwSdNdgItUUGhWSFk1hJ98ERbHYiP/mRynBjWaiTryFPNEAKRR3IuLYOPmLXkMph1KNPsqtCdIftpMXEamjaxHtF/TFIIbZwPHdrHLiX4ysnKvUjEpGmAGvJ0c81pTCe5/XdeOsfh60IZIRxLipWkLirPcZ7uVoT7NtRFfPThkxY1K8jUdD4gtJnp0/kL9Q6GBufcyReM6YZzbtdDvfsdMomZ1EN2SZ52aNehjH42XLfrF+8NRVD7OQMqKVNRUVFRUfGAosp6KyoeMNzjxQQeuCfA0dEdNScnbN8v3NDx/E2ScjeNE8i705PcQhluxv4N7MlJdTY3YftGadMKb5P8ymXcxW5hrcoyT1cdumGZTin31Ebpv6brB/9XcPPPglHeN6HsuZFvoW1ptymOTmuX6YpLk9Wmnrm23FL4u4PazwmopExFRUVFRUVFRUVFRcWNcKdXijmBvDs1yT1eQbgOmT/+oLdxXVa8MlRSpqKioqKi4gGEQl0qsqKioqKiouI1idrPGfDAkzIyn2Hf+JZi2bctzqDK/xvFg8F5VeFIKuwWzrWmNHRhX9frrVK6Umq3Nd/JMZ2WeWTMT3Z1fCxJzG5ZB1sv/IqKioqKioqKioqKioqKBwEPPCkTnFD56I1aAhmRvH4nh2Cw6UAshaX16ON2dgAWHYxRhI2cgJXL+RWOowBmVzv6HUu/Fz2022K9+IjSC30oC6PtURUL2xsev2Hk2b09VNr9nqMnWlBoloqbCz4tU5m9t0+cjiXHXwrtkadfmA3nUyE9kLz5y5AuLVGJsLGE28grfYrfF+STHZbXTEtbhhUDEikV4pve52U8s13nw/k3JnsZl+T5Pe1v8+Cfl7KMHuSny1FOPZ6XBFnadg7tHZKWEj2JRNOC5HMOXXdIuRRmEX8rcVfmn9NMSLmT5oluww2IOr2V5TgTAXivtbJ3GyUhe6fnh99snjf6fyU2bjdfuLk4CTcT537gQSnHHcD9uvNExALvBz6mqn9CRC4C/xR4M/Bh4M+o6pUY95uArwIc8HWq+iMx/B0My0O/B/h6VVURmQPfDbwDeBn4MlX98D2rXEVFRUVFRcUDgdfYCOO28eCTMiJoM1GllMuApWhex1xHufzaSQOJMu6NUA6slyvaWQvTcpVxszd52Rgk6WjJyYE0GnnGL5YlKz3Vy7qHrmd2NSwuL73Hz2xc0jESV9FrffIKn0ijRBLZlQ/e1uMyhmnJxmE7pPN2WPJx5PVbGY43ZC/y5RKIyYN58uxNXhZS43KSZKJKDRgH0kF/VlGj0V70Tt4JftfF5StjG80dYhT1wYhd9JFrU8QoIoqqYK3HOYN6Yb7oEFGshOMiihGwxuMLN/nWeKwox13D5WfP88QbLzO3Dq+CU6E1ntY6DIo1HiNK7w0r1/Di9TPIvz+PeecV3njh6ui8G9Gcj1fBRBbPq9AYP2xH5sozxAU47GbMbVii7qXDPaxR9mZrmlj+ue3xKmhRl9LGqm9GeWr8P1jOmTV9aC+TjsO6bzh4/gznntynsQ5Vwfmxc7SUX7jdBOcigeUFr4JfW8zMgQreCcZquN00xNGjBlqPtB6xOoQ7M7CcXqAXZGkRDd7zuwsFM6ggvYR4ACYSk9GzfbhRQFzYt0uhPYTHfmHJ/CMv8+Lnv46jJwQ3D9dcsDFc56UH/HTdig+rBNglzK4px49JjgeBuCyXJzXr4B1ffPByb4+VnZc953/xJaR3XP+sJzh6xODmkr3uiyevWlDWFYLHfT95ek+XDc3LgvaxDrE+5coMAPOrSr8AN99GCE8Ckk2v4RlixvHy6gXp9BV5brcTflM7ZZytZdnyyE6ErBRv97KMp9VpM0zHcSdt6v/1z20W4C5A0fu5KsHXA78BnIv73wj8uKp+i4h8Y9z/BhH5VODLgU8DXgf8mIi8TcOE9O8Avhr4OQIp84XAewkEzhVV/UQR+XLg7wBfdu+qVlFRUVFRUXG/cZ/7OQ8UHnxSBkWcH5bJO0El4yfkBTBahg8YlDKFCma67N6UxICSQAjrws+udnRnGvpdi6hmlcqo1HYgO0IZi+24n+Ma2Tw2vT5j+vm+pzn0HD3WIBqUMv1C8HG5u9EScIWdctDUHnr6HcE3SZ5TxNE44FIwTsN3zzhoSfUxacCJZoXQML2sKEdRl2w7KWooB61xAOTAt1KUR8ErpvP41sR8POIUtRJUNTFvNW1W+eQmS3ZcHOybcLn71mzEGU7GoDSYeeXhw47u7MVQb0BsaOvV5Bymuj+2UuYvXGP5K2e4ujjHoIAqSK8tXF55DZSEVV7aLy5hd60J16Hpla4RLrcMyyKW11sm2orzkOLY0n5Yym8dlzIcCLNAiO29LBweXAjL/yUbcUlFFc3lGzWmKfad4It1/qTxGOszL9qr0Cw62tbFWzeSQl5orMcaj6rQe8PyeIaI0i0bHnl0P5BnkczqnEVVaKxjbgNh41XovMGK0lqH84bjruVwOWP/6oK952Y0185w9ZNh8darPLp3xNz2iCi7zRqvhpntM0l21M84064w4vFquLza5aXDM1x66Syf8Qkf40yzojEOK8rKNfRq6L3BiPL84Tke2Tlk7S3XVgsuXTvD/kf22HnpAmbteOH3GXjLIRfPHbFoes7Nl6z6hrOzZSbsDEqvhkY817sFF2bHIzIxoSTivAoH3TzXJ5V97W0mCX/rY49z/twRj+8dbtjpJySc8wanwrpvWPWWNrW1N6G9+3AeTDpvvcGYQJCqL0jGSOT5zgSi1RYEnIZrBR//C2JE0vG0jKUSlHY6HM+kjMblNSPZJ9FuSZaRlsEkPccgK/78OM6Ql24QYq81iMhTwB8H3gX8xRj8JcDnxe3vAn4C+IYY/n2qugI+JCIfAD5bRD4MnFPVn402vxv4UgIp8yXAN0dbPwD8AxER3ZAEVlRUVFRUVFS89vHgdy0VcG6YMjPyrVIMwCf7W6eH3MYSZlv7iH1PYyzS2LHdEqaYZgXj5cgKMilMqzLjsG2KGStgTFbKLF46WSmjTSKTJK53H/OJZu3KY1cW30pezz6rahIJUJBTaWCvTfw6nxUEMnzJj/GmChhtynAdqWpGxIMPZE931of9pNDxgnQWv5OUMrEwc4dpXB7oNTMXiIQ4sE8DQWOiUkaFxbwDYNY4mjiYNBusDFhRjCjHfcMLH3uIN7zxEvOmz+qWue2ZmT6nTUqZpWt55tp53PseYu9zX+LN5y+PLwlRGvHMTZ/DVr6JSplQnt7brIZxWVUT6nzQzVk0oQ7PH57jwvyYc7Nj5sax8hYby9P5MevjVfAIy77FiM+D8V4tXoWryx322jUAZ2fLfPyon/GbTz/BW1//YlYTOTWYgjHs1WS1TCJBAHpn8QqrrmXedplYSQTDvO1x3nDdKDvzjrZx0UYY4HdqUaD3Jux3Fre24dZdWq4dFItWRoWOegmkTiQCs+JGCeoZJ9AZzLFhcd2wuNxhjlbsPiscNOc42D0T4hHjl3MSNah1MnmgICuDPTLsXhN+df9NYCNRBUGV4wZCwS6F59tAdJk1NIfCzovK7KVD8J7zv73L8bUzXFuc4YpVnm0jSWkZVDsxX6RQymxTjFDE1SGulFUqpieeeVlY7e7wkfnD0UD5jJURyZrJDxd+vS3yIpAXuemAWVTBnKjiiQRvIhTLeNM029JvtzlMZzS9TlRCupnHSba2HM8Ezr3S2upIEHov8W3AXwXOFmGPq+pzAKr6nIg8FsNfT1DCJDwTw7q4PQ1PaZ6OtnoRuQY8DFy6s9WoqKioqKioeGBx//o5DxxeBaSMh9U6khWTKUFTMiNN2Un7ENKYdMyM1DTTqUGDL5k4oshKmUJRAzT7K/y8we2E5svKmrLYNpEcqTwxfKpcmB7fwu/k4wrNkac96Fk+OgPALj1ubjK5snUgw2Sw4zVMQyIMikyeTzJWryRlEDBM+JP41djrqKzJZ0ze1iH+SPkSbY9sQhgsOQ3kUtyHkI/0PoeL1+Bs2cbzihbEWJRf5MYExAxpmlksyEBcnQQV4axTLhwf4/YuoEbSmI6jRjhoC9836V/hkbVn/tIVlr9wnhdmDw3TuaLNkrBys6EMiRQL2wVZxbAtnnyeTQcHNthwCzIptmEjli9dG6MpZvGY9HCpDSfOt+CjakY87FwyfODlp4LaIKWJU8tGSiHRzfYAcMLh3OXCmNaBgG0c1irrZYtIIKDC7RfPuyiN8YFksS4qZoLJ3igXzh5jJEwfE2BdKGXaYkpZ702ejuZVOO4aDo/nLK8uWJ+zzHfnHD2hzN5wyOPn91k0HSYrZYSZdTTiM0l1bnYMkJUyLxyc4fKlQSljRDHi6b1l7W1Wsrx4dJaLO0f03nBlucPL1/ZYf3iPh357F1S59knAmw+yUma3XdN5y9l2UMqEfMNUt+vrBedmyxxu0I3pbkkpc9jN2Wk6PJLVNknB41X47Wce58KFQx7aPd6YSueiAiadlz4rZSzrvslKGecDVdd1DapkUtT1Jk8zTNPq8nQ3L2g/VcrEC75Qw5CJEClULMV2et5EFYy4eD0q2LXgbSDD0mWa1TFF2qyEme778TGJfrGmU6JeZXhERN5f7L9bVd+ddkTkTwAvquoviMjn3YS9bQ9TPSX8tDQVFRUVFRUVFR93eOBJGe16+udfuPsZFWqXaW9RZNwDT73NdqR+mfTSjSClgqYklIp9GSljDGOVzLAviTjyHpxj9qEFeQ5IY8GYzSlekbTK5JMxgWjpHLpoItkkOd2IfJLgMyYfJw7obVTXxPC8LZMBv5KVNNmnTOG7Jk+vSUobH4iGfm/TjnHQL4KNNEDz84E4gOCvI/usmZATaXDlF1GFE+ONtktGywbFjfaG9sUF/evWSJOc+YJYj5GepMyBcBq8N7j9lgu//BBX396xuHCcp+OIKNZOnfcKPg0WhewHx0SiYTpC6XuLtYFwOD6egSjzeZ8H18PUHxMvQR3l5ZwJvnSKMFVYrxus9YhRrFGS+MH1hhU7yGOrbEuLdgrKFLKPGCD7gtGkaHIyTElxgo9zt/za0inIkWV5bFlaHQbjaUBekF3SC2YZprDNlnDpWnSkLCFu9uEiAxkVBuiSb1hRsk+Zs0ewuLTCXr7OQ79xjsNrZ3l+cTaQTzBWV8QymOSbJe2vBbuGi1eUD3zwE0ZTWqQY+APYFVyJx+0a9o5h57KnvXSAHK947N8/yfGH9jhenOHIwssN4OG5bT5lIpFW+nfaSsIWapRcn6J8qf0uXlX8bM6lRbzPZdNG2Q6JzGj6qORhEBg18XNHdrztdZx2UkbxoZ6Uj88typVcntPCU32L28w4HwmUIoFupps+AsZKHd3IuxC73VXER9edxiVV/b2nHP9c4D8XkS8CFsA5EfknwAsi8mRUyTwJvBjjPwO8oUj/FPBsDH9qS3iZ5hkRaYDzwFhaWFFRUVFRUfGaxl3q57wq8cCTMvcMp01p0ulyQzH4BlfRg/rZb1qubQIHgC2uT24ft7LCzZTgGh3aYqeMPzl+KjFWhOV4JUEG4By0zUCSxTg5fqngitCjY173g7uBLBtNQZsovSbT1DaVW+P9YKPJCiYVQe2Mm/GNNPI1E33bZD9JXgcfSNnfUog+23eszs0zGREcSA9kV54pVShzSjWYePDt8JjxTco37Ns1uJkdHEYXA/iRokcDaYcEp7mmswN5x5hk8EnVUwy20xQgcTIoH2LbZ186TXRCTdgOkqRBEeadoO1w95g1+DWYTlifU7TVwU9PoeRAAhHkZxrrITSHgnGCzlvwyuq84fgRwe1qIDJbBS+hTMXlFdROiukE397gCROnHklJJiU7xbPLzQxuR3GLRKgMeeU6DM1Lmr5k+qBCCfZCItMPJBiE9s7k6ZRISXZKZ7w6zndELiXlSumXahIvrxoXbalnUPwVpM04nW7aSaq+pKhJKsCk2rlnWlvBbRWV3D2o6jcB3wQQlTJ/WVX/nIj8XeArgW+J/z8Yk/wQ8L0i8q0ER79vBd6nqk5E9kXkncDPA18BfHuR5iuBnwX+FPCvP+79yRh7E52Kj+8mqqioqKh4reHe93MeVFRSpuLe4FY6kyeQYHDjPutG/FuLfudw5cpdM12SaPfiMTa/B3ncNk5aXe1moEoPXPz/fJSLt0Ia3kH4WPaLv/27XLwvJXgV4gEYmIoe3e8i3A98C/D9IvJVwEeBPw2gqr8mIt8P/DrQA18bV14C+BqGJbHfG38A/xj4nugU+DJh9aaPX5zZwX/WZwzO6kXCdGAT/sUNL76pQ/v8vPM+TPWN07Sl98S5hOTVKk0kFL0vwn1Q1boYP/2Seec3n7Olz75UNi3sej+Kr9Fp/+gFntKljxNew0eQAppsTnFC+IjX85PnxCmdB02kq5G8fTPpNso09VtYttu0DU86dr/TbHunbsMrsftK866oqKh4DaKSMhUVFa9ebOvA3U6n7n53BO93/hUPJJTNseU9zV/1JwirLKGqLwNfcEK8dxFWapqGvx/49C3hSyKpUwGy7pk9U8zeSmrJtJhAGZ7+jaDWDitMJpIir8ZnoDH4xmRlJUYyeaCNgI+KSlso3OK/L/yuaROOJ0WlOKK6cVBapnSulbyCoxrJDsmjL3tcS566qGbIJ/lr8imPpFCLdvLKgJpWgNSs3JxOkZSkqKMITyrSaduntpnayD7wgj27UmbXelYPNbG+uqnkm6RNZSnLkeo0LkMZR8fhhUpPVEN6p9mOTN4dWRnoy7iRJMtKw/jzw7aUpFzadg5UUeehW6PLFbQtMmuDsgs2p+rfgFg5VRDnFbxjpEwuSbHsjzASfGKG4+V2irMtP7/FXnkskYqTMotItlVul3klEnVjO9WjrNMpZd9GEm4lDG/Vdtq+lbhbyndTx0+KC5sE5nT7ThyHcdxtYTdKc6M871SaVxJ2M/nddtjJp/RO4n73cx4kVFKmouLViPpFqaLi4wJV1vvah84a1m8Iq58lJ/qJ1BDn8/S5Uc81+nVLzvCDDCaSM8lPk/fYYxcG6jAMzkXCYF0DoVKuWCZdP6hXpiqZQpUzIoIgK2pSmYBhe6K+ybamypJRo6TBbeEvbxq3JKnS/wkrXebpwGm/MZPj5LTTBRiQcB7McYdZz2NahrhCMUVYNqYIl474FRmFpSnGyf/edCVMGKYJ5xXm4rTK0SqWhmgjEkyJsFFGUzbHbTwcSws4JGfp4hXbKdKHVexml5c0L17Dn99j+egubsfiZoJvQ9l9E4kzO5QjE2mprsm3WS5rat9huz2EfhfcTMerd6b6J+frSaCV2s0O/gTztNNexk/PSM6NpuXK5HiRT75eNEw7Nh10ZweSbFSn8hqgsDPNu2j3rc4DtUinYUqwdPEcuSFP3wYbphvSaNHOaery4mVlfSH43TNr2H3Jc/1NJk9pTispjvzgxesyLz6ZOJxYfuOGOiS/cJmILNpArQzpJNVH82q6YeEKCWGe7JfP9CGuWmiPFDVDnH4uo+shXK+KidepeLBrjz32zF84QBvD+uFd3CIk8jMJ/jLz/cvIb+Zw8or69gxTmHObCNMp9KYf2iDlke8rN7Y95M3wHCLWJfnmS8+RfJ+mzItrPPrty3GJ5UjnwEggvsv2j3XxrYzLFW3n+/Xf/Az3CrWfE1BJmYqKVyMqIVNRUVHxmoCsHbNnLo/9jpX/Ccl5vzGDQgZANU5bip351mRbgXgxYZDlwwAHK1FlMvh8SisSms7j5oMTKh+VLyqStyEMuJK9NHBInX9xmgfleaAnZNVMGICFdGkAkhUyfhiAeCt5oCZ+UNuoKYgKn47pmOyYuKbb6sB8eh78OE5S/IiHZulpr7csHw6LJIz8UTHY3FgBU8pjYdBmnI7DJhg5Si/shsFdshELm3gxU6RLNp1mtY04P0x9K1UzDGF5apsP/+J82HceXa7wR0fI/gE7L++Ea9HE66wk2BIxNr12pSC8imt3ql6SzkFj4gqmW+wUX/TziqnFghTjc3CDflIk4/KqnzAmFcrBcmx7t2NHpOmIWCrIvHQvjHzlMRAA4Z4tCcSU5zg+hAF9Ov9qBhKsXFlzIAPIC2iIg/l1j+lMICs6ZXbNsXg53FfhOtZigF/UpWiS7G9wiyoNEjE1IWBIpMtgL9zfw8IMasMzQbyEhRSEvICB6eN93gp+RvAH2IOfTe5viTb7oGYzHTQroZ0ZmoMZfmZZPtKyPhPOtZtLJvvKth+tVloQid4mwqUgQjS2c1PWK5JJkczL5wGiD76izWTIc7oias4rEnDJD2OpNkykonGBlCtJr5QfxHI0ZOIw2c3X0SySeuPLLZfFzStRcq9RSZmKioqKiooHEEr9gvTxAG0t/aPngPTlU4YBZVS0hIhbVCJ9GEmJc8PUlJTe+bHqpTwWt0t/NfhJnG3qlDJ+GacMK5GnVZlNuyfZgExAbfWRkgblpbP88lixEmUeKBsiiSAbdoDiq/4wsM4kGSC9xx6tMd0iE1gjlYwZvsAP5SoGeIkEGw3IZDxwLaZjjdQJ5QAyDZqS0iIFJ4f1pdKGYsBekEDl4HL81T0QRskBe1A1KGatzF9e0rx0HX9+j+PHg/LAt+ErvG/I/1qqZkxRLjuUSQvioFw1Uzw0h0K/p8XgWycDThmpZHx00I/RsGojFGofGdW9xBZuYRQWSJchwPRBLdOdS2RYYEM2VmqkbNPEmJCd4Q/nRSJRyqh+00JJsufALk0+FupMdrZv+qh6SGWJNrs9y/KRMPhujkOey4elOCdCVnT0ceDv2SQXGc4ZTEgNKBQzRRs4MDGsJGfSv/TQLslkkgB2GQnWSDLODjSTuRBcY6X8ksonTWdM5JVdK82Rwyw7pPPML1uao5BBv0jqrvQMGZMi5fTIoa0LdU5U5oyJMClIlaEc+fqXQGBnhZEyIoHK6yK3TSJ50nRTU8RhsGFccZkVJHF+1jDc0+VUzUS0JeWMt4Jx4T/Vzay5J6j9nAGVlKmoqKioqKiouE+Q1Rr7oefDTvLRITJe2W9KOBTH1BT7IsPKf40NZENy6isyIiW0MeCDkqb0gaFNiJ+n6Sh5NT+ZbPtsj6yegVI1w3g/DgCCSiYMKHwTV4rLsv2BJIEyfHPqAgyDkjwdIJZnu78XRiRPObiaTh3J039cmBIB0O81w5SC0UBas7IGdPAFM7VHWRYdrwW7Ua5yRBwJlTQqjdPURoTdNoLsBr5kUh55mllSymT/MuEE6mqNX62Ql6+w87E5kkizRLZNyLBgVIapZ1PVy1RNk8i3Pqx4uXU1yikS0VYemvpyKcOJxJed5FuqQkZlGvbTteDmw7S3RJglFdnmFCYtwrQgJ8YETamoKQm6dCzFDdfX4EtpIOLStUG+ltM90R5qIGw82BXMr3n6nWLVyYJwSNf6ViR1WiIxEmklQzswcEah6H5sb6qagYEEykSdgnFShEWlTFSquB1GBEWapmX6ML1MnGJXwqwR2utz/DwqZfbCM6bfEXxLJgl9G8tihnIFddmg6Ekqv2FaWCD7RlPoEmEVVT/ih+eRRjVQqsMI20iZWDbTFQobs1nvROpNV+jM5yaeg9Auw3nM0+5IbRfqZ7rhHIgGVVLFvUUlZSoqXk2ovmQqKj6u4KeOCipec9De4V566b7lP73Ctl5x2wbFo+NmS5CcHKecbjK1bYaBb0k+SekzJtkq1TJixqRWmb4kDsr9NEC2W+KVCpzeIV2P3V9AnO5SEgeBGBv8xWQ/NTHOoKqRqHKI+aYv2mWcpHihIJq2qZYo8yl8uUy+/G84IYZhWhkFwRVVNMYNvmTEK6bztFeWmEvX0HN7dI/u4eY2KA6i6iCpY3wjG1M7UjtlxUOaVjGa+hM2m6PgU0bj6CSrAKZ+cYqBcik9GNW1qOMIJ3ShtvmAyeRBHCB3ZzZtZHVCMWDe8FszPQ9xsC9uOF5OaRmVRwfyIat/ChulCilNUcnT5ISsyAINPlVaMqmQfFil623qIDtPJ5xcLye16ejaK8pe5pfJjHLaTkGAJPLBOGgPfSC8Yvu4VoqpfOPypqk5ZuVpjnqaS/sggj3axe22BKWMxc9k1Oa5nIWvm3zeTjpW5JfiltNBxW2v51hNNCFhU3C61Uv/UHH63LTt8xTS6eO5ICfLaWnZfvF8Md1AHpYqHnt878YatZ8TUEmZioqKioqKBxBKlfVWPCC40ccA3fzEftOrSd9GcR5UyAnbdy/DV5DLFiLtJHj1QdTzMTC/aQIXNSXI4GSyrSTHiv0cp1wa3QwE2yhNmddp5NtQgM1yTae6MRASG76cbEEORoWRzptRvNH0uKzqGfLc5jQ6QcuwkpQzYxspblZJFW08OJwdD+DDQWiPetrDNqg8ephdXYPOizYqFB1mUlaGgfrGTVoQaWWa0v+MFmEDySUjEis7Jk6kDIxUMyomKulCon6R6jkoWaY+oOza4FuDPQxOubsLc/rd4DC825HgV2Y0bbAgJqNPmLJNkgppNCXJSHTKO9StdLSdnA8nh8V5upDTTbIwEVfT6yKpk2DDr83gZ6bw61XYy4qfRIYVCqvp+bOdxvM0+A+D4BT5XqD2cwY88KSMzGc0T7057Ew7BSNGN26ftq7WdF6239JjiMe0tFfKIVMv46T80vEiXG8iv/LY1iUDp0vrnYATl8y7Qbrt8ZWqzHjAUM9FRUVFRUVFBbyyPsEWIu1W0t2oS1l7K6cTc/dqGLqY7O/eo3zvN9Ll2cZfxa3B6uH9LsLHHR54UibPcU0M9FR6mranjt0yG1isVJD2S4duSbZXpEme3LNnd2FgvzOzOn6cajNhtxMLuW3uqBnv57IwCZt8xMjSyMkcQuOGpeTKuKO50tNjpZO4qSSx2C+XxUvH0hJs0/naJfudnG15SyZ2SvvtoUd6T79nc3uWyzeW87nLfPNKAjHPERtcxE9ztcUrzZUj/N4c35gYPu4qyIjw0i3zrwlOFJdrdGd+CiE3pJGuD19VDg6Rvd0wxz/FSWmL+dojJ4h+QvwxIQkh9Ib6HqzdLEvZU0qk4iTOBvE3JQxvdPwkO6M02/PebugWCcOcTBEjp5ORdxrqw9e60/4r7j7KNr8V3E6aKW5zLHOrUATHKyxrRUVFRUVFRcUDiPvVzxGRDwP7hB5dr6q/V0QuAv8UeDPwYeDPqOqVGP+bgK+K8b9OVX8khr8D+E5gB3gP8PWqqiIyB74beAfwMvBlqvrh08r04JMyImhbyAUnS/jBMKhO0qvMQWwbMG6zUyKpQ06av7tt9YOTVg8o451wfLQk3oa8c7ukslxSMJEZ5fKBA5lUkE622M+STYblBIUsn9zw4J/klKbYF7KDsOwwLMcNdmynuDR/Vcbx2kMLHlYXGbzwGx15Zh95a4/HMhll4xxIG9kgCXHSf5C1KuoNe7/1CIdv6WjPrzBGo286H7cV5wyzxoUwAWs8IoqRcC0ZUS7t77F84SJvetvzNMZjo87SqeFsu8SI4lVojMer8KvPPUnTeOxPvpH+D13j0x5/noXtaMXTmGFE98JxWHHjjXsvMTc9JjJXh/2cQzfjyfm1HNdhsHiMKJ1afugDn8Fnve4Z3rB7Ba+CEaUtvKp1avm1a0/yjoc+yq5Z49RgxePU0KnFisdEFssjuNi4HsGgHPkZi7heXudtzr8sz4cOH2ZmHL9nb/CHkOx/5Ogib9q9nMN7b3K5Uj29Civf4LWNl7yn9za3Z4KPd7VToY96zhTmVXL9vQoz4/J2n+oU42R7avBIDi+PaUznVTjuWhrjM7fZe8PMOrxK5gtTWo12VFO5oHMWAZw3GOM3FllYrxtEwFofbZDTq0reF9Gw74W+C/VvWjfEg4Lp3eSFks0wz72sK+DS84WwooQpyNC8ekWZnkEmrTKEZQI1xJ3KbMfz6QdHoXkVkXSM0tZYgpvtb/MhMCGJy3n3JcG8eEnpzg7PpRwvzu03a7I/g3G+Q3v23/fvqKioqKioqKioeNXi81X1UrH/jcCPq+q3iMg3xv1vEJFPBb4c+DTgdcCPicjbVNUB3wF8NfBzBFLmC4H3EgicK6r6iSLy5cDfAb7stMI8+KSMElQHpVJGxj9tCxVMUsrYqDowZkJiMJ7nmQgJW4QlEqJQymTyJM37jPESfDN2rjZV12zzsh7ilXmWaYsmmPA5YfWDYZAhjpGjr1y2xDtNnL4lGxvtrIxUPFIMakbx/CSvwss3GsZ0qNKsFLsiO74qBzXN0iMeZgdmsFusTDBS3xRl9jaVKw4GScu36UbcpK5ZvHjI+pdn9Ls7m22ghfKnPDYZiD2x9ph1x+qhJ+hTE8d5ppfsZtrXHzlUYPHsJVa/epYXdn9PiGLAt8OA1PQh0aXmzaM2TfNbf7spBtDFNYTCE1d7Pnr2bXxwMSjIRteOQrNU3rvz5u3X1rbrMm8P13vyuF+mz3NR16FOvzTbvG7NGn5lvml7dA+k6zRd00Zzvts89Q8X5ZDPyNaonUoCdVzvEG9yE0zKDyCdoM0kTzOxO82rhJPQdnGe8igtQC/BRiQZT3RKaBiYhXWQh3X9cO/k/2TDy7guZZzpA6VwApeW7RzKz6ZjwYJ8KQmQTKZsiSsx39FSrDH+9F6TafrEu6a0jtG89HE83Xx++LHNxVVHdxTmqYf8dFQO0+vE/tRmWCL2XqE6wKuoqKioqKh4reIB6ud8CfB5cfu7gJ8AviGGf5+qroAPicgHgM+OaptzqvqzACLy3cCXEkiZLwG+Odr6AeAfiIjoKVMMHnxSBmWkiumKupygYsmn9oQpF9vSvGJsXa7PbB6fqmC2HRPZXAkgpZk6GMue/82mo7GpWkYEbUrFDFlhUypihoHw4Axr6tHf2yJe+mgt4Quzi0tfagNuHr9Iw1gps28RVVYPJY/9jFQyeWm9vK9jMqvRmIcOCplCMSPxX3vD3m/vcfgJHe3ZJWI0K2SaxmUVw6zpsUa3qmSMKM9fP8PxS7t84tuexYqnMeHa8iqcaVdZ+WFQPMIvP/e6YP8nHkE/7wr/yWPPsWM7jHh2bJcVLR87voAR5Y07l2mNy8qVIz/jsJ/zxPzaSM1iUKx4OrV83wfewee8/sO8be95LIoRz0K6rGhZasv7rr6Fz7nwQeZR8ZLQqR2pXoAsH/RqMOKDUkZ6ds0qK2yCusbjY9xfO3o9c9PzlvlLIxsr3/I7x4/x1p0XMXG0u4oXgkOwsU4OYeVbWnFBQYNmFY9Tw1m7pBWXnYD5qMIx4umKOXuDkmZQ+yTFTo6TFSxS1FXoS3f6BFWQV8EjXFsvmNlwrhrxLF3DwvY5bVLbTO2nsi5dk2024rFRSZVw1M3YaTrmTT9S7JTqm15NVv44b9hfzTACe7N1TlM+xVK8EkHFM2yX/50L9RdRvBeM0XzMubjKiJKVOjl9tKEK6gW8DI9TlUD2lKqWgrDJpJESVTM6Vt3kDKQgT8sKDflsKGMY9jMp4wU1inihe6FhfZ6slElElMTleW03ONcDRo4EU97uZ+5NB0KpDvAqKioqKioqXpu4j/0cBf6ViCjwP6vqu4HHVfU5AFV9TkQei3FfT1DCJDwTw7q4PQ1PaZ6OtnoRuQY8DJTKnBEefFLGK7pcAWx6Wt/mUybtpyk6yXv6Nr8zEL9em7EfGmM2SI2TljdMONUHDYzUB9Pl1kqSZBye9oeMVMhLoKV44jT7yRmlYxgPbVPKTBUxGyjiJkVOUqRYtiSafAG3az8sxTcZVNljh3FKv2s3bGza0qGche+QoBiSYdm3UdoY5h3Nlcu4M3P8bJJXPCdBKTMf55/zCAFPdR57dEB/9hGcGVxKeGtYyTD2THjquAdpaF54jv5953hu8XvG5xjC6gFRKfPM4q15OlgodzjPv9yWCYZrCOCJqx2/febT+M320+MxRnmoDd7hf3fnk8dKmVH9h+3yP4+lzZY0hYd+s1b6HcEtxmkB7Ap+fM7YN1KKU3iAz0qQsnyiG+qa8fEiLAb5ucbpbIyxUTcdhyeFyZZ3woZSRhlIwJNQHnMFKSE6PGPK4xCUMtuQyICirNIZELjUFDfU9CvDJE15XUsZV4syJjvl1KBS2VKSJAxhpshjpJwpVDej8EiUjMJhcv+Wdm6QJoX7sd+qXPbkIwpAlZ3Lju6yGa1qAME3FySlzNjOyF+Vh48u7xCZX1FRUVFRUVFRcSfxiIi8v9h/dyRdSnyuqj4biZcfFZHfPMXetl6/nhJ+WpoT8cCTMtr3uBdevHcZThQvsm17i5NIMTINGLZfyRKBxfJ/A+k0LBWY41q7lXwCBtVNIrLMkF8mo8rtRFzZCSllBkIq+5g5SWmT/M82EpwQxzhJKdPMwlSG1TkzTL2aLEM3mroixX+yL0N8Nv41T5s685EFh68X+j0d+adJ6cUTFTeE6TMCWXUTt+VoxuKlPY7fskasR+I0FPWKaf0w7o8Kmv6lHdQqj77vCS79Xs/88SOs9dmviBHFGM/Rco4qnN1dZZUOQO8s695ydjGocACsaPYv8rsffIyzT+zz8N4RqoI1njaOLI0ozhuevnKB1z90jbntKZF8sJS2y2ONeNbe0pjgd6YvmJVGfFa/vHB0FoBHd8Ze2ns1vHh4hsf2DrLKo1SBlD5lSl83Ka9GPL64+8b+YOJ1Hes8PZ6OweDHBsZPwm1+YFK7+qQKUWHdW0w818n/0OD/RUb/YXtchj5OMVJvEFHE6CiO68ONYq0fKVDIthke+6lcNpAmMncFiSkju6PpSzr+L8ubpzqVFZAhsmYiRCJJoqM8SxLkxGlJkzIIjPzIbDgdH5E+m0qY4VhhUwlGtsQJBGdJnFq6PcHPhjIAg0+ZriC5ldHyosmuu0dLRcLg66mioqKioqKi4rWFu9LPuaSqv/e0CKr6bPx/UUT+OfDZwAsi8mRUyTwJJALiGeANRfKngGdj+FNbwss0z4hIA5wHLnMKHnhS5p7jZqYzbVlG8GYXXHm1fV/dSkrdQZy4NN9pjpNPwwmrqjzMFuLstHQnxJXgJXgaGNMU4d6H/a7j4X/WItaMCbaULjuW3kK0pThTVVi08anrj0LTxJWdFMSgxuZ4FniLO0CbGV7mG2oyJ4IzRT6FI+nkrmRdToeL+a4LldgZr6gIh/YcIxUZcL73LNuzI1XZVE2W0JXKGaBPPnJGfpomdgguWfJ+Lv/Y1nCcrcezKEKkXIQNgHlSpTEM6v1EcDXNq8xDCvJhQyklAxFw6vtoonpJig5vh8f3VOgy4umnRMXEdo4/iRvKr+N0E+JlfGyiVJmuhlamKR6h49XPCpsThdxmvhP7fpKm9FHlfLj+vWIP12hrR+pCSjVN74eLYKKySf8f3B+TnBUVFRUVFRUVFQ8+RGQPMKq6H7f/D8DfAn4I+ErgW+L/D8YkPwR8r4h8K8HR71uB96mqE5F9EXkn8PPAVwDfXqT5SuBngT8F/OvT/MlAJWUqHlTcrq+fLYRZPnQHViq+rVItl6884wcIU7pKtoTdV5y0KtqNVkm72yjzv1O+rCpuGSc9IW7ljARfb3cfYXZWVcpUVFRUVFRUvPZwn/o5jwP/PH4Eb4DvVdV/KSL/Hvh+Efkq4KPAnwZQ1V8Tke8Hfh3oga+NKy8BfA3DktjvjT+Afwx8T3QKfJmwetOpqKRMRcVrCUl9czftJ9wvYuFGxMZ4DtHJx+4H7nf+Fa86VEe/FRUVFRUVFa9V3Ot+jqp+EPjMLeEvA19wQpp3Ae/aEv5+4NO3hC+JpM7NopIyFRWvJdztQf+DQCo8CGWoqKiouEOQnQXmEz85TsmbTJeD4Mze+zBFME73S9MqxfmBjPfl9ua0u2DL5+2spB5NISwdiJd2ivAi/kiNnVa8TGVJU3onK2HqtG6jg8mRlcnb6hUxgk7j3iDdTWGbgrH8uHHS9p2Od9oHlXud/nbyqKioqKh4RaikTEXF3UbtyFRUVNwGVKuj348HqIAumjCFbupkn0DAlP6gVEDb4JxfVPFWwmp+TvFNJGuUEA5oE5xgqx0ccA+O8cMKhDmdZ7QyWSgLozjGxTiFXyzfBL9cxmn2GeXbaCfHCXmJL7Zd3FbNZfNWom0NPrQUjAs+pJqlp98xg6+vuBjAyI9XrL9dhzKPVpqckDDZIXj5it7mt8qHVSMB/NxEf1MgvQ8rHab0pf+rLbZEFXvYoU2ogz3qwuIJrdlYcTKX0elo5cXBXvzlFej8ZvopOVdu51Uqi/30cx7SIhF+QhYW+Wjy1zXKa0KKnUTCbSPPTiHethKAU/sjW9vJOVVFRLaTg9H32EYZS7LvFOIvEYjldiITb2Y755l8EKbtG5VjW5luJe5J6W4EMeDd9mnjaXtb2M0eN3a7/SlReDvHbybN7dq5Xdv3M2zj2Mmn/U6i9nMGVFKmouJuoxIyFRUVtwlfpy+95iEKso7Oo9N4bDrQBkbKleS4vY8MiAi4wWNS6UBbrSGrbLYpcVQHVUvZOS+3vQ6D9JRfaafcj+m0mXhFN2Z7/MYO+yLDSpClo+2E3qE7s2HlRyOhfjLYzCSMU7Qx+DYcnzqazw7ghUyCpdUftXBarzFNIrK6naEdRG0gpHIairzI5Smd1s+vzekXgpvBbD+s9tbtkeudyj9ydG8j8VWEJdtTJ+gprVriSpVarJCZCj7+17ipgOmhORJWD43bvlwJL23bleDmmu1ID+IF3yonOZzTwlYqpyioVcQJvtGwImbRhihIL4gLacSFfIb6aqirDnUuPesnp/tjZ/uKeKG9ZnA7OjomHuyqiF/+67A9WfhxqJct4uoJcdICrFtWICxXHxyVS8GsI+GZ4o0alsGRvg4LA6SVRtOKhHYdtv2sOA+mSF+UZ6MMhZ2NBRViGcWF+09ivPKYmkC8igZS9vwH1xw/2tLtyvg8yVCeTBabIe8UnolpCURsWKFVKFdPTPcnBII3rcqYztVwTYU0po+rLJbnWMK9Ydc+kL1G8PHaDeUJdjNRHAnv3Cap3VRHzwvxZDJbnOLmZtQGqUzi4nPASiap1QjtocO3cYXceA3YY4c2BjcXTB/zs/H+dImMTuGCXbrwnCzuN/3pn+FeofZzAiopU1FRUVFRUVFxn6Ai+EUbyAUoBoDFyneFeiaRBJAGs5IHAHnwoeQOdlClkAetmYRgGBj6Jnb0SQOmGJ7UNRJIAZO4o4Y8sPE2qV2GgQkQBgoCpleSUiapXpKKJw8YTAhXCYMHCIOkNPhIA5NmqVEpE8kOOxAqedU30sBTca2MB7ypfRM3VA6CIZNGo5XkovrFrENgi48DOM0kwfiEwnRQm1Q54pTmsMPHFeCaww61gm9jpYuV3wZ7m0TWaDA+UZeURFY6zyNyy2skpySHl0oc03voPTq3I9ujNkpEXjlNrSxvee1uQblioMa6a3ndN4lgK4iVpFwqiZGUpRkG0ImEGxIWbbFBeCl26XBzYToulJ6ByIr3XV59sSD1Rv+pPHZS/8ngflQGLcvDQCpQxIv/ifDwLaPyJnJOzRYSRIZLZ1zBeCzlURAzmUgs8k+E4WiFx7JuJpJjksoZSZYpeWNAGw33fC/Mr7ccPmnozgx1CGRdJOZ8iKdW8Q0DYevAOAEvwZ4FuwzPBrcI14rpBfGCGsW3iQSRIm18hjSK9KHg4g12CW6ezrvmZ414aA6b3P5uHuohgHSC6SS3g29AdHgeJyIxr7ppNT6/wK4FswbTgVskokjz+TRrwfThWastSBfPcwPzK5Z+Z2g38TC7bul3BDcPBJybhTzNmqw+TNeJb6E5bgLp25CfWf2/r0TJvUYlZSoqKioqKh5AKODq6kuveYj32GvHw2D3pGklU3VKqZwpw6fbsH0qR2kzDbC3HUuwdkwQlNvTuEY21DeJdBKvUd0iw+C+UOGoMXngNYV4j5+3kJQyiawyYwILAKf4mY0DvDRKlWEAWahjQr7Dfkl8pUGncYK3Qr+QEWkTvlIzUsoEe+P/hPm1oArwM5hdn+FmQr9b5s14my0qmeL05oG3FvnH9EmxMiIEisF/Wb9kzKyF5lhYn/fFKLxAVHDgwyCyJAiSgsE347xymRMREKd/+aRqIdhJagAaRUXH5FYcYEOIN1KsxEGs+DB4z/npcK60SUwjI0VAe9XSnxnIvBBHMF3RZuX/FuR2TyoYC5jyfkztI2NCoyQER/Y124IijgfTCT6SAWPb4d/0skmelNdLHMzntk7KmkiyGje+jjfKMLFX5p0UGduULviBEBieZTC77pjtCeIEkxQ2xZTHRIYEFVYMT1Mco6olTyd0oV38rLjOCqI4kU+hvortyvZLChjFdmHqozjy1EqNxJx4j28lE7Pp3IUplfE6KtQ4o3s6k2U6tIuAt4FotavivEogAQPRopHcDmGmH5697aHDzUy8BwS78phecbNgqCS5RYcpnekeFK9IH++hpDBSxa4nJ/kuofZzBrw6SBljTz52q+sc16kkFRUV9wrVn1BFRcVNQNvYHTMwTMMppvIAGDOQEGVaK4GIcJH8iIqUpIgZvnQnu3G//NpfKG3SYMc3kgdVw7YWfmaKTr4Z/MWkvIMyJviFyVMW/CDxR4IfnDL/PIhJ9gvpPoSpA2kAEurAWA1SKCnMWrPiIlSckWIkTy3o04BzILvSAFTcQJCZlQtj5ZmBNAUgqk7GhFiRTzF9IZFr5jj4lMEYZNWF89Ce0M8t7ZakXNH1lZKk24atMgk2rqMc3fswTWzWhqkWJ8RLeZeqDiCUrSDBNvIsx19xChpmPP1C0/Q82FQPTYU5yUYc9CblWLBfxItxwvZwzC573KK4rwQgTPfLxF1xn5Q+jKaEV1ag2YLYk0m8FLckVYRRHUYKnZIUUkZTa0ZTfaLKxEzUWyMFjxkIknxsqpCxRXoZ4myzR5F3mYcazWRZjuoTmTYYEIXFFcvxY8L6vIam90E5AuBmGtQbRFIhkTU2TnHTROSEn1kLviVMR7Pp/pas8AvTfwiEmZc8jSso+Ay+DeoV6QQ/D2URBCkIOtMbujM+5i2ZIBwUPmSCyXRp6qOGdk6EFUFFZLrQHmoDGdMcm6CwSfVsh2l5dmWyusasBzVYu2/wLVkJ1BybkZrKt7FNYx2SaiaV23ThpzYSo5FwdT9zCgtZcVfwwJMyYVWCt248hDcjbn8BwMkvng1MvsxszGWOtuSkr1H5JVzYK/Y3XpwbX7JOOD79ajbNr/iyxvTLGuSBofY9OIc0TYwyTXOK47RpnJNWX8jxT7BVhG9NN7E9RN4k305dieEm0t9RbHOWte3/laTZZuNWy3W36vVK87mfuNtlvpHtm2nTqb3bjVvxKkN1gPfxAF2u8L/6mzcdf/pE2XjC3OiZI5vXVHJOemK84riU9pMaZkuYRAVLtlXmITLYNxLilqqdk9Q3zkPbFIP5GDcN4gsiS5yicZrQcIw8dWc6+FebBsWTAX4cRCUfDm5RjlBBm6QOGA/6p75r0lh0tu9xC8HNhPYwfJ3vdk0cbBaEQTFw91YGPyUx36nzZyjykWGAuHWKTTmYj/vZJ0kPzRF05wrb5SWjw8DeduDa4rQmBUxT2I42SiVP9ufRDDbVhgh5ACsTu2k6TNwfkQOJCEjpYeRzJkyL0ZwokQYozPaFfpdMBKby2PWkbW8CU38xOW8ZHw87DG0JeXpSyr8kOKe+fEqH1pTbqW2mKpoyXwXbaZ66M/UNMx4HFfmmtiiuwY04SuHUekiTiTsFUZ9VIsYpux9bMrs+pztj8rQp4zSrQbb5X5lO+YJwvzdLj4rgZ4MfqKR0CT61yPe9imZiOZATqUJEUmO4r8VrvjdM5zPxl1UoGz5lJIbrUO+sqhmTdWFqk2ZfL25mIhGtYCRPJ01OxVNdtQnxzDqcMDc3oc0UzNqBFVxrcvkonmcpL7WC9DrUqThPTx/dqz5j7eckPPCkDOsO+dgLxYu9vAPHHYbcURAZdxTyi354MeeX8DTMTI6LDDLZ+KJXMZsv98J5HFn6KqOb4CTZ7JS9L9n1keO4E8KnL9z8MIhfMBJ72x4qzZGyfNgMbPH0RR3LkB8sdvP4+EuDjtKV/2XcLJMswoaEW176MbyE6aA9EFYXdPyyn77IdFLeFN3D4uXgvC7PrU9F8kNd7VIwK7KcNc9L7Qf7eV5r8aJLc2HLF2iyXZZtQwqawuNDcxp/eLEUcVP7aJg3n2S2pod+Z1K3yQszl2kSJ6+4MS1X/LdrHWSYMaw9Ch3KtCrHdG7+tMzNkac57lk91A7xy44HxXWypazpxWhWoZF8euEUeY72fXI0p0ivw1zk9EXYAPHlltui9+GFN0u94fHLMNk3y/CFM/sCSNd6+jLqlLREbfoSuBXlnP1UrrIOU9wEuWKOu2EwkvNJB09JGDsosurQeZtJ6A0y+jScRnLdjI1XQiBCkEjvH6J7O6HT0vthOsQW5OMMz8xk5yTIB/7drZfvNhAeC7WzUnGLuNF9pm4z6Ba+W9yrrvrdhEz+bwW33XG+nefaFgJtfHiLzW1pJvG2EmvhwHD9FH3pcfxJntvIu9wfN6PwkZ1b6KuX0DKPst9vB1v5Y2wxFWTkRLog6aTzYTWziU+nUikz8pGTxwKxH1+Scem/MRtTxkYfiIVhwB/fsRvKmI36pnJFIiznKUO/sxxLFPlPbZrBr/jQZy7VMaksMLrhR1MBizJRxtNiDBLbKjvpjcezCs4p7cGM40cs63OSxyZZRZLGO8lptcay63iMAmGc0RyZSMrEKX8j0qToU6dpQbmvCM1yGEtJH327JOVLTCc+EJZpDJCmFQbSJJRtpJgp+/NuyG+qSDJdSGt6DVMZfWHLDnGCn5qQl29DmF0Fo/2uZFKmPQzH+0UglNKKeaUD5DRmCTYY+ZRBwf3S7Twdbx21nzPgwSdlZi36hsc3H8xpv+jA++lDeBr3RmHboMMgCcYDJVGgn/iM3vZFekN1wricUyXNtnRTxcTN2i7aSToHXY8u5uM4Kd6WPHTqwG1qu3xRZWl08QIpbOZ2nJBWIQ3caFUE8YrplO6MHX+FmpBBiZja5rBsvu+ZXZfs/C97m/fFw9WFh5R4yek3vkgZ8HF1ALXxXZTKFJ2ClfthO5ICaZ60AFYRoznM2LBtJP6b4YcoEo8Z42O/RTlazVivGoz1eGd44uJ1rPHMjMMaT2N8bHalMY7GeLwKjXjmtqcRjxFPG/+NKJbwD9CKw4jywcNHeGR+wMPtIVY8BuXnrryFJ3eu8Sl7z9GKw8YLMNlwGLwKRpSFrPnJq5/Mf3jhKf7yJ/8QrfTMxOHU4BD2zAqAhXTY+HY3Mh4pdGpZSMcvLt9Ep5Y/sPs7+ZiPdhbS5/QAV/0O1/2Cj6wf5aw9Zq0NL3bn+MT5C5y1x+y7HT5h9iILcViUp/sL/M7qCf6zvd/KNv7D8o188uw5dk2HV8EhfO/ld2JE+YJzv47B85g9YKkN1/0CK57fWT3Bpf4su2bNJ86f5zG7jxHPUlssPpfRITgMnVqe7h7mze2lIbxgONNLa13ogVNYasPQRg3/9PnfxzsufJQ3zS+x1oZOLQduwdK3PNLu5/ZK+Xg1LH1Lp5br/YJ/87G38vue+BDnmiUr32BE8RridWrwalhFFq/3Fo/gVei9Ye2baD+Ux3mTj3sVNMbTGEdVQhyF3lm6vsFaj6pEwZ/gfYinPthABfXhGArqBeJP1obzv3GRgzcq/RmHPbRoG6XMCuJihzGSfe2BCU4BY6dNowPT5Fxw40ujwuofnzKltqKiomIbbketuIVAGx2+TRHwa4FYu1NIbbFtVCAnbFfcWcxuFOHVpsC+19imrIdNkrU8dgNYPbyDBay4GTz4pMy6Qz763Abbvk0xI6Vaxtoh3jb2HQZHcyOFzRAnS2BFBnLCEOZ1ZxJhKJcKeR6txvmxG2RDJgqkUKEMg/80z3qqiBk5oMtkBQVxUZASSXmjYYCR4rWHSnOsHEelTKmCGc0/tQNDu81ZW5KGjuailgx9wc7nU5SUKDndsPxgltVNFTaT54ZZS1DKXPSDw7Y8BzztxiUT7ZB4mBsO80uG1cM+OL8zQ1rTDc7J7FKwK+jOarZrOhlYbi2UMkRHetFOKR/FScgifzqJTTJRj2zsE+eAEkQUqtEP20a6IGk0K5gnpYyDl3f2RudjI6/Yvr4Zh0096k9VPXbJ8BUjHmsPlKcb4efmnzWqS5m+rFd7oOweev7uk1+W89+Q6BbYptoJaqZw4LvmX1Qc13G9U3hyXteRVWDGhVU50vzrNBcX4teKDr5r8UXZjF2Nv0xBWMoUgZ9cvHNDzZTspKUP3Wy4vqdKrrKOJn792NpjnpybKVlYxpvve96z85b8VaVsv6kSaVSO+AXlzIHnl/c+c7i3J0gS9W3lOmm61NRJoACpKG1ha6wW0lGaMv/NvCPp4j3zlw64+BsL3MxguvWwOs2oDGHHrn1QXPlBzjv2A7FZ/48d3LshjbsV7XxFRUVFRcVrDXX69Y2xrY1O+4j/AKH2cwIefFKmaeDxR8dhJ00DEBn6z9skj0XYjZyXpThpNCwnadlvdBNMcZJfmBulu5U8SpR17l1w3jZvx0TUNK6RwXndlngjWWgirLaobkYKmrzaAvmYNqYgciJhVU4DY0w8Ba/znn4vTMtIHsS3zZeeKmVSPrMDz+olibJGk0mnUimTBuXN0WB/ItgIg+wmLv3XSyC/4vJ1gwLHD6RZmr8sFMwNkJb8Ew1VNkENIwaats8qmayQiWoZG8MAjlYzVmsbxsFeeOyhfYworXU04rFRGWNEaYynEUevlpnpOd8umdseg25VyRjRoIARz+8cPDZSygD83OW38Prdq3zS7gu0MnzRayVoY5MCpBVHK46fuPxJ/NpLT/B1n/Rv2DMrFqbDxHtrz6xwapiJywoZy1Qp07CQjl9YvhmAd+787nBOEDq17GWPbAFTpczSt1zuz/Cm+SUetgdcdbsjpcyH+4f48PpR/tOowvEq/OLqDRtKme+5/AeYm57PP/sbN1TKfOriY1y0B1iUpTa5rWb4qJQRltrybPfQWClT3FhJCeTU4DEYPJ5BIZPC12r53ufeyedc/CBvml1irZalzjjyM47cnMfba7EtbT5HACvfsvQt19wOP/bM2/icJz7CXrPi2LW5vJ1aerU4layIcSpZLdN7w9K1hbJmUMek4xrDXHlMBeclKmUsTePw3mxVyqimbbJSBhU0Lo0pa8P5Xz/P/psVt+ewh7NBKePHPgnwQnsouEV0JJrm2CvRcaCMScZIRnW/XjsQFRUVFRUVFRUVdwYPPCmj6zX+w0/n/VPnwRZhGw7mIMy9Lea5prm42cHcdG6rGdvMviFO8EGzMVf1FOdygxpGJj5m0hKPZZyxUuaG6po0+I9fxRPh0R4rzbFn+ZCNcwsn8SO8DfMS0/JopWoHJRMw5XSh0D7D/3Qu68jxWHkcNpUyxbEyXiBKYH1+HCdEHNvb6tfGQ39ZWF0o1AKQ52fmto1L5E19rGRFR+SrKKY2pIym6oGpc7VtipUTj023IftvTvOAUZh3yk6ce2t6ON7ZHeKnuk9VMMmx3NR/y7QMpVJmrfyukUEpA7RHysv2TfzHmWxPz1jt0CyVi/uOf/TIl4ZopT+lbcqPLUoIJDjgE698z3xLvjJOmxy8mUIFbjodVi/Q4jonqGhMp3zv/I+P4gcCLxVWaY88KPy7nd8/3KPFqh2mH5YZ/F8bGV+TBFvTa8z0OvjnmdR/G7YrZZT20PPD8zcPSploK/s92oZCTXPhes+v7HzmeE70KWU6SdFyUnyrg0omlTnXJxHCJUq/OyfeHxocCPqe5uVjHv2Pc/zMBqVM8h8Ew3KoiZyJ/oKSyicratL1BhvX1Meu32XH4UON6lKRFRUVFRUVFa9J1H7OgAeelEEVXa2G3ftYlDsBOWF7HOkWvsKe4ARuq/O3iJ3pVDBukewq026bVjZdUSGFn0R6FSTXVtKrLEfv0EV7MhGWyC9T7BdLiNplT7/bDqqcqMyRRGClZTkd9LuDJ/ipTxnfBo/oY2Js/L+NUBv5mklhMQ2M02XyykQVziRtOm76MLXLN4rphe6cj87BNKcNbZh+OjAgMU5W6kTfNpL/4zGgP24wM0czc0HNI7C8skDmjt2zK4wZ/CtZMx60JnXPc1fOwItzHv3kS1jjsVFRAdBaFy+bqAqKd7sUja8qtNbxwsFZnBfeeP7qKJ9eDTPTZ6UPwNo3HPct15aLnMe15ZyH946YGcfaWx5eHNKY4Cfn6nqHy8e7fML5l7ONZw/P8/jOAQvb5/L+yotP4r3wlocv04jnTLvCq3DUz2iM48WjsxysW1rreWT3kHPtMpfRTNiU5I/lymqXhxeHOWw4Hu6d5JulTFf+J0XKB599hPPnjzi/s4z+WoS1s/TOsDsLSiKd2OidxSus+4bDZ88ye/SItnU4NzwPBtUKqDc5TCOZo0mxAgMjqAR/L6EC43AFSWoXD+IlE1kDCSqZLCr9uqTjkuIkx4E9nP/gnMMnDf1OcN6XSOgUB4b0zbHi21CGtNxvWpp37GR62Ha/de86EP5EFq2ioqKioqKi4tWN2s8JuCEpIyIL4KeAeYz/A6r6N0Tkm4H/GngpRv1rqvqemOabgK8iTP75OlX9kRj+DuA7gR3gPcDX64nrIn8c45amMW13Anerzt9eaydhKt4pcbNM5A0dj90tPIgOzcSEi+oGK0HcCA9lc7dYx6R2A3rgYQJhstwStS93Yj7WKxeLsLOFGmMOHIwsHHGBl7lSnIeFHnEoQun27PUapk45ERwwUMfHrIA9DtiLIQ64clLdjMlLxS/Y53Cbc+0boLzejfe8LT+Wbw+qOiZqtxx/RbiV5exHGd/cg029cpZwnent5nVKGayvDvBeS6j9nIqKioqKior7iZsZn66AP6yqByLSAj8tIu+Nx/5HVf17ZWQR+VTgy4FPA14H/JiIvE1VHfAdwFcDP0forHwh8F4qKioGPIj990T+3WAliJs2d29mf7wi3MQsnFPDX+14LdTr1XCdnQaFKuu9N6j9nIqKioqKinuM2s8ZcMNW0ID0IbmNv9P6618CfJ+qrlT1Q8AHgM8WkSeBc6r6s/Gr0XcDX/qKSl9RUVFRUVFR8QpQ+zkVFRUVFRUV9xM3RU2JiBWRXwReBH5UVX8+HvoLIvLLIvK/iEiamfB64Oki+TMx7PVxexpeUVFRUVFRMYESVqm6k7+K7aj9nIqKioqKinuL2s8ZcFOkjKo6VX078BTha9CnEyS6vwd4O/Ac8D/E6NtaQ08J34CIfLWIvF9E3t8VnhoqKioqKio+nuAxd/RXsR33vZ9TOrhPvyHyNPHNHdvM9ORjt4LbzeNB9JdWUVFRUXFfUfs5Abe0+pKqXhWRnwC+sJxjLSL/CPjhuPsM8IYi2VPAszH8qS3h2/J5N/BugHNy8bXg2qCioqKioqLiAcd96eece0r9O94eDngQ5zGrHnGK22nzimB5ufbovFqcH1YojGFqTQiH8TLvXgMpohp+HsR75HAJ81lYodDKpk8zD+bgCN2ZD+m9juNFR+UYA6oh/w07Hno3EE5dB9aGbVf4KkuOz1WzQ3ERGZyLewXv0OMlMms3nc+f5EjK69hBuS/iTcLVecSakRP2EK2Md0LXVH12Li7WjsqTw09zQF6WPznYn27fClQHMmy6DcM5/XjYnrbDnbb9oKa7E7YS7sfxW0lzO/ncibCKijuAm1l96VGgix2VHeCPAH9HRJ5U1editD8J/Grc/iHge0XkWwkO8N4KvE9VnYjsi8g7gZ8HvgL49jtcn4qKioqKitcEVMHVpSLvOu53P0fWPe3Hro4GjdK78H80AxuvAWMCeQJB5+wJREoMUxtJG0AFfGNyeKwoGLINUaURoT8zQ1uDm9tM4iCEn8Ls6pz1Q7O8dDyQ46iQl5nvF9GuD4SQGvBNsCEKzVLp54JaaA8VNxPcbChLOlYuXe8b8M0QRzw0R3DuQ8fsv2lBvwhp1AzlVTuUTQ2DJrwol/giTWwvCOU69+GOq584wy2Kk5TGd7oZlvJFYfdFj+2U5shz7c0t2tyEDQXxiunh7NMdy4cbul0ZpUnlFAezQ2W9J2gT218HW+KH9ja9hvYwQ/vlOIBx0O47+l1TtDHbB6ElVGM8sGsN5OF8eE6lfFJ5836sZ3PY0145ZvXEGQCaw45+t83nRbzm8gw2x21mnA/XP2CP1kjncGfmk3aeEoM6sTMlFyfVVg3XkgezXGdSUXfn43SR5JymZd1B2wxxYhlQRVbrcD83Nh8T52HdocslcvbMZruX+eV8NRCzU5KvjJPJVI8eHsHOAmm2DP8iAam9A++Qtt2ef84i5t+toWkCcTgti3pwHl2vkcX8ZGPOhXTtpFzbyMsbkZPTNpkQ1yfWx7lApKb9E4iXMUkcCegi3VCOgqyO+9r3IY/y3E7I3+2F05OvhxvFn6Y9ieia2Jeje9P3qP2cATejlHkS+C4RsYRX2/er6g+LyPeIyNsJj78PA38eQFV/TUS+H/h1wuq0XxtXJAD4GoalIt9LXZGgoqKioqLiBAh+64yYijuM+9rP0VnD+qmHgDiYc4o97sCD323zoBpC6aQfBnFqBqIkECnDYCQPSmPJJA1m0uC098jRisZ7MIamMeA0ED1OY14hjj2YxbRMSA5FRYa8ph39NHCDMPBobMh/3aFJjZLSRaXNaIAgMh5URcJK9w95+GO7wUYmqhJ5VaQt/7cMQlQEmoJQ6Bxy/ZDF82fRFH7CFDHfmKHNI+z+MrRL1zO7dgZtZCDSMgEkqEnk0ZBWvDJ/8Qi7XtAvbCbGVALxlMif5thjeoubBTvB5riICJg+Ek+RaPOJbNNw3CmIN6zPGNws5RUTx3wxwUYobzxejJ/MKpA73Zkh3uiRpUO5EyE0uzbjzHNzLn+KRQ3ML89ZPgpurpguEIv9jqKNBmItjXdFB3IqxlOBxUs7NMdw+JQfkXAqMY0ZlyHUSTnx0dp6MAq9we51qBPMs+dojgXfKv4tx4jxmRETAfWCGEXivnNCd+U884eP8+Vmbbj/nDP0H3kMd9Zhz3a5bm7ZMH96xrkPKS99bo/MHabx+dIRiWxbPNneh4qGPAsCodj23uSqu5Xl4Z+ecfntnvaxY4xRjPE0diAEnDccvrhHe8XSvHU/27V2eBMl684ZlsczZr+zw/LxnubCGmP9+DbzQv/ygkfeb3jpD4W6mll4IKkXTOMxonRX59gDy+L3XM95ei+oCt7L6Bbue5vbQNN1ZRUb8/bO4p2Mwo319L3FdwbtDdJ4TDOwpL43mBfm8LrlwGd5QdcGegNW87UkjUdEQ/kvzWivG5ZP9iGOVaTxqBPkMAyxde5D+JHl8Z8RLn2m0F/owQnMfLAf7xPpBJ17pDOoUcQJOGFxybB8xEOj0EvIy8U2aD322AQC3CpmJeAFPwvPebOK8UwkSQHfKu01g28VbcAuhX433hPxclh++4+fcIPcadR+TsINSRlV/WXgs7aE/xenpHkX8K4t4e8HPv1WCiizluaJQg18kmTMOdT7wP7eSLa2jZGcModMWNIbsbUnxVXPhux0YutESe0221vibLC5SQpcynpvQvZ6opx2HOnGcU5NX7RzlQDeHUzb99X0fydxN/K8GRu3UufTUO+Piop7gvvez1n3zKZKma4HwESlTCYIShIjEhlp8IwxWR2DkRDFxIF/7PcngkclTGlqrOD2WnxjcDODaFCmiAa1gumU9npQyoRBueRBsrg4UDfD9Ko0iA5kQIyn4K1g10q/CIPp+TVHvzBZ6SIefPownwZFiQiwMgzqVWkP4dxHllx78wI3H4iAVCe1sqmCEbJCJCtlUjkLtPvKuY+e4+ontsG2DGPcKfFRhkskHHafXwQFyqHj2lvaoU6pXnFsl/6z0kiDsuWc2ePosYZ+Z5JfIiMczA4M6zMS1CQSyJxk37ih/eyagVCRIt8Y13aKXSlNEwd/kFUt4TrRIe98LsNGKrNdK8Ypblb2N3Von4KUSfvtfs/syhLxe4iH9qBn/bEGNZLVPb6VTdIklcErph/6ou3BCuk869+abZybrWVKQbIlbrx+QBDn8W0bVF4Hx5i1QxtDd7bdeD/nc6mDHbtc4xaxTPHeSHGb/SN0VtyvgDhPs38Ns7/k/IfO4a0B2aLAmLTHaSjVQtJ72udf4OJvnKXfbYcyJaVdLP+Thx2mW7K+sJPbbkQMF20mvWd29Tpur8XNm633iD1a0b64z9mPXgjm4jNMFNSGYahddUi3Yn1+98TzPrqO8rHhvOa847kZ7s9CmaJB2QUyPEsBvNIcH4dzW9gOdn2cOiqFvRDFLjvM2tHvxbEngppw3HRB/aONCdf2umfx9DX2nj2HW1jEe7yVMI1UQxmMS9NNXSa7xSvmuMfvNKNnrfhAiCMEoj6qIMXFd0NUSEqvG/1OtQa77PP1J86Hd0wR56Urr3C8V3HLuCWfMvcV+ctH8eVCZJBlekW8R+ezEDcdT52VE/eJD6b4ApDxQwoopLDpn9HDLL3wR3EMm2nKB40Gu5rTDce1tMUkfLpf2EwP1N2XHKZTDh9v8LPwEjc9uJbhiwODrfIharrwsnXz2MnoY0epiFN2bEK+xct8iqJTVu6nTpFdk7/QDC/c8f7UhnjYfalnfcYOcumiczM79HQ7od0XLzvcjgn7FHYU2qPwoO3TMR0fH8qsW8KKzkkxHb509WjWHt8axIUHrbjwsveFxLx8cYyktWUHqNz2DFL0ss09k47QJF2CL+KkTk3yI+C3xJ+e0/LrZ4ky7TbSIX+x9aN7ayPOSXJRf4LdbWVM8aZxV2uYnSLHPY2UvBE5Mjmufb8pD76RPPV28r3LUOdB/Xap843S9j1Yi9wC8aWqOb72fai/scG/A6CrNRg5UVatfR8k4/N5SJNsdR1JWi1mMgorCfwU5D24KN/eclxeOl3WfaegVFnvxwO0sfSPnAXCM1ScIsddIEZ22tzPGPmVUUV6n9OoF/Ae03vEx+ds+s/vqomqxXvkaIm52oQBijWD9D89q51DVh3t88U1LzL4jUn3hXPjQc5U8QKDTxkYfMo0sVMynWoQ88n/5bOwd+jhEY/+7m4osynUMkkBs81O2k77MW8t723nkP0jFs+czQqajX6jnahfpkoZQFY97bUzeUAGsc9UDM5DmBT9F2X+4jGmW+AWIV0gVcYkU3PsEW/xthgsJ5vFADYMzlKHZOiz+WjLt4LpDes9wS2S6qYoa+zLJpVOqaJJMOvQ7+p3U9+YEbb1fedXLHsvtFz+1BB5frnh+HHFt7E/JeAWGtQsABa0GImLF6QzuUw7z7eYDg7fEBUuRV9Zk7pmdA0UZFP5nwrcxM5eZ2jOdHgvmGd3aA4Ft6PwpqOsioHh8jSFUqbrLOtrc3YfPoq3kaFpQmfROcP6I2dw5xztmXXuwParhtlHz3Hmo+d4+XPX2LkLCpWkxpl2u6KCJB0ftbuO4wB0q4aLP/U4L/++nsXFJSKa1TICWONx3nD9hTM01xbsvu0q1nh6b5g3DhHFecF7g9Pwv1412N86x/LJjsXF5ShvVclKmYf/ww6XP3+JqtC0LpbNYBsX1C8v72CODLO37NM0jjDMCob6QhHkvaHvDSKEeKKoSlAfdRbXG8Qqs1mPiNJ1FtdbULCNp2kd3gf1TVbUeEFXhvblGd2T68ithOtG4n9Q4cR0XVTPGEUOWuxyhntihfYGfBiImbnDdxbWFkSRuUeXlsf/7SNc+ixwD/XQmaDK6kzMT4L6pfXQ2XAtqyBrYX55h9VFh84U6QVtFFnHfxVkJeg83DNmZRAHfhbr0EtQlsWxlZ+Fdp2/tEN3LkybFAf9zlBvFFYfvkfTl6j9nIRXASkjw3zqEvGFnwgCnIe+D52N8gV7q9mdNggqX/qnYdsgZNsTtexwwAYTvdVe2k5tsuWYHC1BlZ2ndwLzmQbcjRmYUEpyaXippy8uvrXh5eoUP7P55VcSAnlOef4vtiW91CVvl0RVuv9Mr0XHIxz3DQMRZoYXvRadCtNbVucN/R742KdTSyBbDizduWBn55yhOwfrCxo7F5rttdcatFG6C34sZU3b8V+sD/+xg5BevMQXGkDTpBenYuP/9f1dzp+7Tucs63WDd4b5ouOh3WOMKK112HgBz6zDqzAzPY3xNBIknUY8bdwGMOKZmx4jiiXFGa6rNjJEXiXEifbP22OMeCzBplfDR1cXOWNXPDm7io2sSivh66zN9gciodOGs+aYThuW2vKwPWCtlj2zwqK42ID7focL5ijmNyYiXnLnuGgPmEU9fSpTwqHOmOFYxHIkOISlNuxJl/dDXTbvxU4NrXhaPJf9gkftMUu1/P3n/wj/1WM/xQWz4mW/wzlZxTp7TFGGbTLKbvLCcEguQyq3V8OuWXHV7fIj1z6DL77wi7k9QxpDF1nRbS8gj2FdsKYew9K3OMzGufA6jttpg8Pk8+5VOPJz9t2C880RnVq8GlpxOIRV/Hzr1LDyTS5XsC0c+xlOhQ/vP8zz+2f53z3xDAbN7eA15HXQz5mZHq8SwhDWLtj6rY88xd75JU+ev86qbzCiHHVtLp8RxXkTZyQI677BOYO1nr43rF/cxR4Y+od62vMrvLPMfmMHt1C6N65yZwwldIYUZs+1XPgduPLJ0F10pC/TO8+FV12/o/R7JREKzbHEDszAqbbXhPYAjh9T7Crm0QZbpoPV/zJ8ka2oeKWQdUf7oefDuzn6F9BI4tpZ8aEJgjIm7ovzefpOJiHT+z36wsCC2vA+VwtEQkFt+KJqRPA7LdraOB1H8nterWB6jz3s6M/Owrs6fViIHxF8G+KaLqgl0n76oJDUHCrQHGv2sdIcebq9+GFF4lSbOIBP73pvxwRAeky3x8qZjx5x8MZd3Iys0An5Fv2G3MDj9GryODgPQNKHteZIOfOxs1x/8zx8MDrpnE0+oKTt3UszTK/YY8/+G2dhSkERL/nbyY9zHdrKOGiWM9bnm6Ag2tIlDO0kpI9EgXgZyiTxA032KcOEdBopJ4JKpT2w4489hfImt9+knokYNJ1mHzAjtUhRr1HeHuyyx1w7YvfZQETaww631+Zpc6KgzZbKp7r5ss6KOe4GnzI34z+k7GufFM+E+8M3BlGPOboSSFAR3Nl5bNNxu2qhThdVpD/Gzwr1flK9qGIPrwXCrk3vXkXcGnO4jxwe88gvng/3uQn56LZxxZDb6G8EJROB4h3t88/x8C+fxc+bYprXOOETyxWyOqI/v5PP/aZSJhJSvdJcv4LbneHn2z9W2OUx9tI+F37nAqDheZTttjHOEuk9/dl5HptkTAjbQC7HepnN6zaMOdpQv3SPAEgzVsYV17E4T3N0RH8m1aEY6wioGYbL4T7ToIbqVkivQWET7UB45ob70+Wxk+kd86df5uxHz+F2bDhmJcdJtlNYWS97fIxbNGOn70lVE+8JH8eF4vv8ETiri5J6JhHMHuxxh5/ZXBdt7RBHlUtXq1LmXuNVQMpoIFzKTknZ8UidE+ODxHceneKVHZNSJQM3r5RJD6zi+A1VMsTtKPMdKVGK7STnHRMYMb/Szpa0WYnDkKbE7otRKfNEg5uRWVA/Y8N52lRpY9eB6EoSY9Np7jTlJH6cZuMLzQRqiw5MDgx2zBrcPHXiGDpHRZxEtkgfm1oJktujUNbyhWKc0h4o7VEgenZf8nTXhe5KklWbbHt2EIiafseMOjUnqXumSpmyflNljyg8ulTcbEHjYM9plHrvsGrOA3DM2NaG0ih3IAe5+LiDU+ano3RTKe02ma7pwsPXl+qpaedL4jUT80j+C4YvtrrxQg+rfGy+6IEozdx+LLXB6Ngk2ujFdUIfpTxmnIb7DJgdeP6vZz9j7ORxYvckTPObSqCnsvj2SPmpvd9/utFtmFwC26TW246dlDadp3x8cq9OO83ZbryG7Fq50Cm/tfdpYxt5gKHDc3kiD3/LdYeb7XE8P4NocGux2HBGWOTrhvMvXmmOO8za4xYWN58jHuYv7+NbM5YYM3S62oND7JUjzv/umeC0NKI5OA7PbWvw7bTjHDoreZqHKnblQid/bzZ0sgpV5seu37vOinsVL+9YcXPQrqd//oV7ny/Z3QywIXIYxds6iWLyHN86JJuujjSJL9OPUWX8csA1yUud59wvTlQyhVInx5+qrEdhMoQbQUSCOnC14tHf3AnOS1O8G/RB80ex1TquEOWZP7eXCbGNdFv6o6hirh1hD3bCoDkpftI7Mz57zcrRHDVxis+4H5rbR2R4F+ePWjJ8BBMQC6Yz9DsmOFwu+pnTD2ybvlpi8TuKr+ybg/dSCZ76JLPryu6LC668rQWB+RXl+FHJShnRoNZO/R41OurPiA9EerI7vxz6hEevkxB30j8e+bkpybjc507x46A+TmszPbhdjzhh8cIZ7DJ87Dt6Yz98vCvfqaWd3tBct/QXuxDuJfgBAfCweGaP7qzH7Q3vElkZdp87y9mPeF78bNCZoilN+eEwvTejKiORD1uR4kTFxeM/t8PL/4nQXYx1MEO9xSjqDPbyLrOrhuNPWOfyYjUrSIIqL/abO8OZ391h+agGPyllg8Z826tzHvnFXV54Z7yeZj6fR7WhD9JemdEcCcdv6IZ2je04qrOGdGG2QXEObKyLF3CCuGi78WG/N4MzZmXwKxTVYbIW5i/PWT7R57LhCCoTP1z/2gQ1isT6zy5b2kM4fMqBJShSeobr3QdbvlFMLzz+vke4/EmW9UM+hLfBB4y2GvzJpKmhRRfDrqA9WLA+p/hm6L8aF2YymC6UMylgwoyH4V7IY6hiXGCXgj3ewbfBll0Hv1BhGlX4X3/w3vU9aj8n4MEnZZQgwfVslf/nZ1SSmy9Xo0GCTBUq2xQrp2VfyOlvCdM0U8l8dKw3eslvvMxkczuTTWYjLOQjyPEKnGf3QzuRrY2DjtaOlDLbFDNpAOJn4ckQvoRFz/yl7LboCJRKmbRfvoR9M0hvR9O7NHzJ6TtGL/tMaBVfu6YO5vqFod8Tur0iXwt4oV9IVsqoMazPSaGU8fmFO7sWOgH9uUIpI8VDPilljOZwEQ19uKRQiWqZtk3KD8Wa0IZXDnY4f/aIdd/Q90FWOVXKJIVGm5Uyjsa4rIAxKI1x7NgOK0ojbqSSaUdzp6A1UR6rJqtkDMpZu9xQynx4+TDnmuUNlTIL6bDiWfoZF+whTg1X/S6P2f2slCmx73c4a45pxWUFTVKUPO/O8ajdZxbzC3UJx4IaxrIQRztR2Ph4bDeWzyMYNJZzyMMhOBWsKE4lK2WcCt/+0h/mqx/5SS7ajsuu5azpYp3Hg5Ftw203eWR4GNVrPypPdk3PVT/jvdc/ky859x9pizerQ+jUYFHWW15AXg1r7Gh/qe2gCipKNlXVBKVMUKuk83vd73DkZ1ywRzgNKp2glDFZKdOppVPL0rdY8biodln6lt4bfmv/cV48PMNnP/bRbDekM3g17GelTFDOJKWMV8Mvf+T1XLiwz+uiWgzgoJthS6WMCl6DM7/jdYvzhtY61r3l6MU9mn1L/3DH4twS1xuaXz9Lv6f4N0ZKUwX1oM6gXpg9u8f5D+xx5VMIHcT4AFk8exZMkMT3ez68VzR0tuyxDP4sJJzc2TWhPYTjxxW7DB1A38bHQAfreybrDe1zLyEiC+CngDmhj/ADqvo3ROSbgf8aeClG/Wuq+p6Y5puAryKM8b9OVX8khr+Dwente4CvV1UVkTnw3cA7gJeBL1PVD9+TClbcOdxMX0rd6Ydvkt/clpN2N5f2tnD9+iu38bFbT5JaSyb/U9zJzvsd0f3dRj/5icIF9kNwKoG3Ncs0LaxYanwS4eTEJynTmRCAse+upXJhunrOJG6G96PVfMp81fmQT0kaaliOna7joX+5My7nSe17Aom5rT6qih4d89CPLpA0bbAsc8qj70P55rON8cUGvIaVlZomTAfcVs443fChnz0zIiZHeToXVmlazMbHbmb2wvR/Ouabhk2PxZWaZN2N80/T5rfFT+j64Hg8uc4op9onlFPvr1zn/H/YC6tulXUUGaabTuF9yCO1b2kvKiuDL60wM0ImH782VE6RNB4JHpwP56+o73Mv380HbFG++9DPeVDxKiBlPLqOF0b6UjkiMqJcKxEcbTue2pO+NJQPgvKrx4kKmmKkdpOqmrHCRTIpkR/CKS4EksQW0rtEVJTp4/aQlhEpksMZ0qGwuNJjV46jx8ISluIJMsw2fSGJ7ZhUCeUztw+KDjcPdbOd4qYO1wrGtSzj6LSVz9xtX/Q1sL3Gge2KcCkjDX8lRJWdS475vtAvhheKCtnBXrcbrof51R43M3R7ZqMO7VF4yfY7idUJBRiVt3we66SME0XJtI7nlh4/u8AZF9o/tNkMbc6gwLqIu4z1Oiy+ho3MTdU40zYpO7bKhupmQ82SFAIimQTbWEUjpROGpVDTZekH1cy07iN1xkY5Tz6Wyj5Vb4XyjvdD4MlmhvzC9DbRsPTn155/O65lrJS53XfBtmtBgu3mWHnv7h/ael2cWl7drPO2Nt5mq2yjrHZJTgtPiBsS6NZ7VBTMWtnrPL+0+/YtZdXt92d0APkJBz1+dpb92fl8TbXxC4yN++WiGrtec/3Fg12uMJ3HzyxuPkNUaa9cRa3Qnx2W1gyS+DjV4+gYc+2Ii7+6F740R9jDg1CtxoSpmQVM5+LzeHiWmLVD1j1+dzYwcoXvpWevnT7QfJVjBfxhVT0QkRb4aRFJw6f/UVX/XhlZRD4V+HLg0wjLQ/+YiLwtrkb0HcBXAz9HIGW+kLAa0VcBV1T1E0Xky4G/A3zZPahbRUXF3cCd8H92AwJvI7o/ff+2i/EKj98RLJd3z/b+/t2zfRquXLk/+T5oeOmlG8d5AKC6vnGkijuKB56U0d7h7tcFfNIUi+n2KWz8DZn7KYO5haFnS9iJ0lwj0PWoKudnLZhiAFI6uTRj4iqTVOnFau3g3TuxsxP5brniQx702xsTWFrMExevuNaM5rBPFTXb/NSYzofBlaY4IZ5vQFyQ4/oGTGfpd4TVOdlQ9DRxilN3pnCil/IsSTAz/tdCtjpajjLbDsftURP8V3gwfRgY+wbcXux4GIIah+Lf+uBALql0JNgLTRdGyuGUaeQk/eTyiFMtohIh+VyZt8HpmYm2AK4v57TWcW6xoo3psromKXWiWqdXQ+8Nu014SB/1M87NlvTeMCsUP0aUpWtY2J7WDOEJ+/2cs82KJip6bLSf0KkZKYIALB6HYe0bdsy68K8zKIYSy25E6dRm5c/VfpcLzREOw7/66CfzOa/7MBeaI672u+zZ5FNmyCshKTnKfSD7dyn9twCsfINXYW56jt2M//jy6/nsR55mHv2tDPWz0d72Z0bpu8aroVdD521WQCW46MMloU9KlcKnzNo3HPcte806H2+Mx6vQOYuI4uJ5dd6MbLsYdu1gQXd9zkNPXAXCdZXaQ4GuazAmOMALv+BYEMB/bBe357Hn1kHuDPj1ZC6jT6wz0NssORYnzC7NaY6hO6N054KE/OyHFqiFw9f7TFKKlyy3XVza4fyHznDtLZbVheSgW1hc2s2S+H6H3KsWH6TB4gqljEJ7oMz2leNHTJQCaya4Ta/0T5+yIsYdxr2W9WpYzu8g7rbxd9o45EuA71PVFfAhEfkA8Nki8mHgnKr+LICIfDfwpQRS5kuAb47pfwD4ByIiurGUYEVFRUVFRcVrGXX6UsADT8rcV9xs//AUdv9WmfvXWo/0ZoQNt3srNsDiNtPejtT25m3feo02yLtbtXmzUtwCZ2FTbpsUWAQZdb6yjUEY/OAISw5i3M3vOWuOTGSxNsruOaAFc4JgelrWk+p9A5luQph+eB6AJ/uX+Ehzno/IhU15KWy2xc1gKo2N+xf6Q363eezW7d0qTlqRKmLmO7pIzIr3uCh1bYjT1HBBun5C+kfdlfAQm0qw4WQHiemh59LqXubkZ+mGj5nhganO52meoxWZRJC2CbLpqV3nUOc4M5WXJ+n5tnO8ZVqsqoJXLpxwnf3u6mh7fV4jEBEL/ALwicA/VNWfF5E/BvwFEfkK4P3AX1LVK8DrCUqYhGdiWBe3p+HE/6cBVLUXkWvAw8Clu1erioqKioqKiooHE5WUqfj4xN38IHuLEly4c7LbrbbvnumKigcSd/2av0eCDuVkVdUrwCMi8v5i/92q+u5RvmHq0dtF5ALwz0Xk0wlTkf52LNbfBv4H4L/i5Ml1p026u4kJeRUVFRUVFRWvZdylfs6rEpWUqaioqKioeCAhean5O4hLqvp7byaiql4VkZ8AvrD0JSMi/wj44bj7DPCGItlTwLMx/Kkt4WWaZ0SkAc4Dl2+9KhUVFRUVFRWvXtyVfs6rEpWaqqioqKioqABARB6NChlEZAf4I8BvisiTRbQ/Cfxq3P4h4MtFZC4ibwHeCrxPVZ8D9kXknRLmn30F8INFmq+M238K+NfVn0xFRUVFRUXFxyuqUqaioqKiouIBxH2S9T4JfFf0K2OA71fVHxaR7xGRt8difRj48wCq+msi8v3ArwM98LVx+hPA1zAsif3e+AP4x8D3RKfAlwmrN1VUVFRUVFR8HKFOXxpQSZmKioqKiooKAFT1l4HP2hL+X5yS5l3Au7aEvx/49C3hS+BPv7KSVlRUVFRUVFS8NlBJmYqKioqKigcUda51RUVFRUVFxWsVtZ8TUEmZioqKioqKBxCqUmW9HweQtsG+7W0ggoogyb2O6sZKX3K8gt6BKnp4iDx0AW0sqCLO52OIoOs1spgPy8N3PbpcQtOg+wdgLSKC7O3m5eK179H9A2RngbTteNl558JS902zuWRgjKfrLixhXy4n77e7CwpLz0c7xgzbU/dCW5atH9Ir6hxi7ZYIHsScuryhxrKJkbx9QsRgK22HRNvbQW4wwCjjpLqK3Lnte5HHnd5OuFd53k8bd7I97oWtG6W/neN3Ks1pdu6G7Yo7jtrPGfDAkzLSNjSPPnFyhJu4UVQV1l3oSJQdBQidhRQ2fSGf0OlIcbXvkWZLE5bpkk0jI/u5ozPtbKSOjXOxYzNcqOpc6BTFjtSGrWm91KNeNzsrsXzqFZm2R45yEw+gu7qOs742HoS3Uofpi+RB+K+497iZc7EtTonTjlVUVDxYEMHvzvCtBSOokO95tZIXEFcRmoM1ZtWDCMYY1m+8iJtb1AjSe0yvOV17fc3ykQXaCGqhOfLMXj6mPztn/qGX0PkM3Z1z8OYz+EZQIzTHnr3fvMT6qQscPT4LZQF8KzRLxa48ywsW8aBl10JBPOxc7lmfsfQ7gm9j9aKHIbWghli/kGa2r6gV1mdCRomQUpEcZ/RfNptT9l707H3kgOtvPcvyQkijqV+jIIl0UXJdcvr0aNS4nepU5KcCxoU6+DbGdbC46lmdN6iEehsH8+sOu1SOH7a5HD62UbNSXCsbdUgLwRunoU1lKJv48EM1lE+hPfT0O4Y0hjFO8VZymlRn8YN98RquD9WcX66zKuKU5rBHG4PbsZi1z+fBWwMCZuXwcxuuR68Yp7SXjzH7x6zf8BBqBLP24BW/sJQL3GeScVQXjW3p877ZXyKHx6CK7i7wF/ZG5yycIz/YYpJH75HnX4JHL6KzZnhvej+KX5KecniMtg20sS/vlUyGnvDulBhHrcnXF6rowRGyuwi71w8C2WmLwWbZr+66MIaYz8O/MWjXhTFFfG/76/vI2TOIOWXAesr7XVXzWCFtj8YLcZyQy3+8ROZzRmSm16FtynSTMc0IzuXjul6HMYu1W8dfqgpdB9aG8U65DZtl3oZJmhPhfSZ/N8Zvqb+UyOE41gLGhPHWusZ8SxI05ZXyTShtpe0y3ra8rB2d5w0SehuxfaO2KNNuKwcgyzoGuNd44EkZRGDWDtvpR3zxlmHphi/jxH9ZrUMHpLXBdWGMo0LsBI1fltm2YXQsbZu1xx53rC4u0NSJgi02Ykck3awy7pCIEl7a8Xhz5NEGZtd61uca1meGG2t26Nl59piDN+2Gl/Ikz9Q5UAumg3MfXiLOc/3NO3Q7Rf0kHN99oef4kQbxSr8jsZxFHRTsOnRA3CLVKZrwML+mmE45ejS8tJvj0Glxs6EzZXoNnbDyYZyIa1Wao2A7k6Q6dEZSZ8b05M4dhHx2X+g5eKoBD+2R0p0Z2xcXOkmuSIdh3MEjlRGapYJAP5dxZzN1XCblHraHti/bLR2za8X0oTOW2mCwFzonzaHHLUIna2te5X4+vxrOy1xyeIrXHvb41uBbGdfT6dDZTx2j2HkzztM+v0/3xFl8YzbzJ3ZEPDT7K9xOmwcP2phRm8ropQ922ecXim9Dp65sg7J+9nCFOMXtzTCrHp01uROYyzF9PxcdrG2Q3iPrHr+YbZavtHESgTGpD4B0oU46azfjbyuDav4CPvoSXqJ8KZZhxfMsh2XDMbyPo55my9fiVIfehePpP7WpneR5kq2yjZKNsgyq0PWhA3a0RHcXIcyaIY7zYxI82SzLoBqOWZNtyuFxeA9YGzrQqsi6Q3fm4zJMy1tAluusKMCYcZrUwfT+5DZM5bUG+eBPb49zF+DqF6TXPhRk2WPX/TB4tP9/9v49dpckve/DPk9Vd7/v+7ud65y57swsd3aXFMkVxYtEmhKtiy0plGIZQnwB7CQwlBhwHNhAYMQ2EMAxYgFKjDhGEiGwgsShHMSyzFiWQouWLJKyKN6Xl+Vyl8sluTu7czlz5lx/1/d9u7vqyR9V1V3d7/s7l5kzZ87O9Bc459dvd927uuqp5/nWU7Ix9ogqrGtk3YTxZ7WivH5EmS8+s+9UTpfs3NsJv71HnEfPlszKEn/3HlJVSFGwf3QWFpjOg/f423eZna2YfaMaKoWdA6/szauNMTMtVKkbdgsbvtU050WlAPlCIj1b1ag1cVGVr8CTnGf6c0K3KKflbA3Hp1w8q7sxHpvJiFmaAzkvN2zl4kOcz1QkzKHZfOmLXtlTnrQUy6KT88QrxanDNB5fBnnKFxKUXQK20S6vTh6S9Bt8V9+hYgZloJhzldDsSVDwxLb1mbJnIIvEPFDwRbwWNpVTHua3K5pdodkHu475GvBV+GeX0OwrWkRZ08HB12bs3tjn+g9bXKUUp4Jxwvqy6/OyulEW8QIuK6cPMsHuGwfsvxEUi8cvWQ6/q0EqjxS+n2Y8qI9tbnS4Dj4pefknDnjrTxjk+RW2cBgTlBHOGaz13asP62aDfvkq9VXH/NlTrPWohrCqUJ9ViPUUVUpH0dh49apgZ2/d3WsaS/krL3LyB9aIVa7+9Evc/GMN+1dOsRLiNs5iogBz9M4+1U2L+9QSfXeOXFtRfnWH1Str5ns1q5OKT/xty5v/tLD34hECFNZ1TVkVDivKWV1ijVIVLV6F1tkuXN1aFmVLYTwiivMGP3r5TgXnDUencw7+7h53/vQSt+6/U3NYcuXTt7GmlztMTCvd8ypYUZwKqsLd4x1ms4a2tcx+5oD1ZWi/84TdRU1hPdZ4nDesmoLTwwUHvzrj5FWPu9Sy+9WK1VWPvLBCjN8gu6R3rd4gongVyq/uUB94Zi+fYK3HOYMxijHhfXovtK3FtaFevhUOPj/n6NOO6rkzmrrAn5TMLi9xraVdW+avz1i92FDu19gitF/bWNrGIqLYwuNag3lrjpaK2/VQeuzcwdthweQutkG29UJxZGkPHFQec1ighcJ+i64slB68gIvfdCOIE9SEdYnuOmgFqcPYtPNNy/J5j98J73l2vUSNUj/jkFYwS4Pbc2AUnCCNQRxoFdKTuh8vzDpc+yI1MtiVUB0Kq//sH/KkMMk5AU+/UkYJgvwWDIaWc4Tybp28rjFVubnA2RJne2ajON6jdcPixuz8tHIl0f3yy8vUurAYWdfMqjIsQJJiqWnR0yUXb+2GxUWKkyunCMKEOIccnYL3XLp3EJRRIr2SynvkdMXi+iJUZ170Aov0SqYgaIFWBhXpBBAAe9YiqpSnM9QERZWvTCcgqEgQxqx0iqckMKS/dq20bRRcTP9MFIynV6QQBBk14EvFz4RmRzBxYl9fkKBAM71yShTaOVGxRhjgoLOEpTKIh+LUoAU0e8FU5Wc+Ku/oBkZGf8VouPahwKb0YcIVja9DqVclurKU+2uqWUtpHRIn6LQOvHNrn8tXD9mtGoxoN9HNbIsRpRCPkZC2V8GIclgvOKkrvv3Su6ELoBQmDNC/+ParvHjhkM8cvIvFU8T0bq33uFydsmNrnBqseJwalq7k+uoCv/rT386P/Onf5Dt2r2PEY2PDz0xDJS132j1Kcfynv/+D/MiLX+Vaecyz5SFXihNKabFoty+0pBce/s7d7+Ves2BhG37g4Ot8++zt7plXg5H++/4/v/VP8dbJBf43n/7b/Fe3vp+/ePXzPGePurJAv/d0Lm5wb6WWuTi8CjNxnZDwjfYSf+vO9/JvXPtpTEzHI5SxjrUaPLKRXnedCTFnWnDPL/ipo+/kqF3wL1/5eXZM09U9b4P8eqWWCr+xb9YRaJsO4Y3mCs8URzg1zE2DV8PrzVV2zJqL5iyW2/Buu88dt4dF+UR1G4BfOHmN1hu+d+8bWDxHfsFcagAaLbjRXOAX736S77v4TX713st838Vv8vb6AjPTcqU87dqk8ZbfO32Go3rOt1+4Ed+R0Kpl7QqMKGtv+erda7y0f7frW16Fw/WCr9+8zAuXj3jrNz7Ble+6yel6xpXdM6zxIcxyTlWEdj6rS9brkqJwzMo2UliF1hmauqAoHW1raG8tuPBly+oqNAced7mFtWH+TsHq1XX8/uiEn+5vqFRcAAiLdwztrmJXgpuHxUVnwTdBUW3W4VlYDElnPRcFaaOCuID6//lkpk6N72XCRxwiuP1ZXHynuSzMt51CIy6Sy5MGaRziFHNYsHz1UphzCf1ZWo9awbRKebhm+dwOvgyKYFMr8xtnNBfnzL8q6P4Ofqfi7KUd2lmw+ttG2fsty9mnrgSjjYK3Ye4tl4FVUe+bbqGfhm/jwpy6uO2oDwxNxpQxLVG5EP9lipL5XY8rhWZ31M9zPe1YiRCVFeKV3Rue3W+ecviZPVYXTWdQSWEQ4jgQFzipTUU2DCzJGJTCJDnGOEXaoJwIBg6YHVrWF6LcEg1B83uGYuVZXg6FUAOaCBhrwVWj954ZWxLTqLtW7a5DoHBdnXjEG9SEcUpckLGGzBR6w0tMdzCMaJ+eaDDwVEcNbmZpFwa78l06vgjjq1l73NyiNpVTmb+7wpyuecFdCH2uCYo5txgtsHRYL7q6ZcYhhfLeKrBlWsfe13a4/JUd1JigmEttkLVbl3ayL6xbZl+7yScPr9LuFCCZ0WSLbC9Oqe4e4hYlbmc+YKihweioxqA2k7fTs1bx5W73WxRm79yh+fwOANWbNzh4/SJuttvL8F67vnzttMauWpqDGXa5xM8txb1D2gsz3GyGrT2zr13nU3ev0uzs9QbirvDhz0Hsy+l3pf2zWRw7vOnvmVEzGKDysNsoi6/fZPf6xWBMTXJ+s2Z96dIwb8BGA1NKA4l6ZAPPNoq3C4xTdr9+F7c3Y/lbC3yx6NYBVkP5Lq+UxTtHNF+e0e5YZrdOaXcLmv0Z46lvgzUFoDC/taTdsdQHu73GLeaTfwtprBKn7Lx1xKXfXdDs7SAObO1pdnaCkbJVZveW1AclbjbP1kP9t6QmfPPlcY1aQQsJxlBrqQ7DPV/134FdN7iZwReWYulQA25WhLa2phtDQvkCeyyNLb4yIW/nwxhwtKbZLzuDf3myQlRp9orYN102Jyji204ZPDYKp29Pbd+4pvGYteONw3PWw48Zk5zT4+lXygi9FTVjxeiYFZOYMmOWTIq6rPHvkymTBiA1YNceu2qpL82GTJlciRAFmVyRkSslcitIul+eBitLda+lvlDQ7PaKlOrEs3N9yfErO7TzXomRp4uGMtg17H9zH2k9R9+2oF1k4QgLkL13Wk6vFYiHZk96tko28dl1mDzbhfSsHsLANL9bYlo4uxYEIbsMFpuc1WLaKJhsUYKKh+IssHR6xUkvHKU2krYXbFL7D6C90NczdMJ1t87WULFEYc7ZFqYJ5VALbmai8GszoWWYV16/TpDSrFyZkLe3VkwDrip6xpRq0DXGuM+dKM3OFU6zeon2LJhBmlFJZuIC8Qvzaxvl2z1Sbs0ucmP2ykY9fVRIDanNwYr3yutnfPEr381vVJ+LmQWhu6NJxzbdveX5+YMf6ATOrRNlVp7qpKdlf2H+uWCx2xZeAyW8apR//8q/QnXi+ff3vnvAWkpxzhPSB99DvG9apTxT/pWD72EMnw79zeIOii+bZZTIzkLhF/e+d2u8zYTov88tkPhO0/fVCVBt/10kGEdHmU6W22IVfv/s7I907zdvI+OUYqn85PxVilX4a5uQrrcMBBZbK+KVX559YlC+3DI+WynvzK4M6+DgxaXHza/xyZtLVj93mXmruGo/GYDYb7R7n7OWbsGZb4NIAn9aGBVnDeWdI9xOhZ9Z3NwiraM4W1NfrLa26YCJpEHAKY4btDJIq/gyY3fFsV28hgVtma8Gs/TiIgIrvHXyAW7dnPDxg/cUt443Fn7pb/c9OB/8wjRNeLRcsWhdbwBykSkT2XTaNOy9E2mukS2jZ0tmVYk/PoF7hxhr2H9r0bP0nMMfHbM4PGFnVvVxAdrIeky0/23bJNs2PM8NR7mpO5ffILAOjelZh2Nj1oj1HML0l4kpc2nZ4BdlL9flaaUmHRue7HbZLy1QBgsZjeNGRHnaYtdFYMXEMbc6DkyZ6iSML95KN9/ZJiTYGZ+SexqhU1aF/InlSW3HUNazhnpfurFffJBJkpw12FKW2igybAfz5khJs7hpafYiU2bZt3E7D3HtCuoLGiz8AtIKF35vj8XtHa7/kMHPPMWJRRTqy67PIxmzYj7dXyddIUQFPOx9/QJ7b+9TrDyHrxYcfVeNVA5jgyBmIitFoTOAAagP5hZ3VPHyTzzPG39aKK4tKQqPtZElEpkyiekionhvaH/rgPWzLfvPnlAkZkX8t1xWFIWjqtrwDqVnX5wuZ1zYWwZmjRdab/C/eJnV585AhYs//Sx3/8SKZy7f64wSicWiKty4fpHi1gL7yRPqGzsUV1fY37nA+ttWzHdWLI/mvPy3nuebPwr7zx8zK1zHgMlZKqu6pCwcpQ0GKecN86LFqbCsSxZVE1g2sQwA1ngaFzpKzm658Hef4e6fWdIuC6SIQsRpxUuvvUsV805pOG+w0VDoVAbd6dbJDvOqYd0UHP/UJZbPKfIdJ+wt1lRF232+67bgzt1d9n71gJNXPVxdM//yLsvnHLPnzzoDpo+sGGM07ioL7Jc07Ojv7NFc8lz4xCFl4WgiI8YYH96xNx0DyjmhXRdc+JUDDr8jMKTqdYk7Ldi5ckJTF7S1pXp9wfrFmvmFNUXhcM5Q1wXqhWrWBibVssTcrNACfOUx+w1iFd5a4GcenfugfTJKcbOivdJgZg69PUNnHlk0aGMwc4dvDDRh3JDaQCvoIjBkKBUKD2uL1MLi+g5nrzYds6Y4mmEcNBc9OLBnBrfjo3JfwQjSCjhBK484QRrpvtnEktFKwUnHlKm/MSlKnjSefqWMV3S1HvpmeUh0ez0B2haxBpEt2oHz9kbeL6/o8G4+2r6Q73+UnAFz3l4/74eU3rg/UduWeVEM93K2Dl0uuXR9d0ANHm/rwgg4jxyfgipXbu4Hxk2mjMJ7zMmKxRtx/+usDNtWoBNaQvkUUY173aPAEp/ZZYs4z+xucCQo7SMyZQgLQDfrKb6dUKG9cCG+3xKVtNMJQanWU3OT8OJdSKPdIQgRZEJQYsoQ4oqD4lSGTJlKe2FC6NkyEv8Wilg/WNiZomfKmGiOqJclWlvKvZpq1mJNCJMmG1XhnVv7XLp6PJg8AUrrmNmWygTGTM6Uubve4awp+c6LN8MrHzFlPnHxHq/t39xgylwsz1jYpkvHq3DmK948u8hv/cyn+eE//UU+s3uDUlzHlpmZBovnjtulFMeP/d4P8qdf/gpXy2M+Ud5hx6ypxGHw+Djd5syZv3Xn+zl1FZVp+aGD3+fbq+vnelr/P775Z7i13OM/+NRP8F/c+iP8C1d/ieeK49BXYnpJKMjZMD5jyjiEXWm7PL7WXOYn7n4P//ozP4ONQpxToRJPKUE5Fsoc+yRgswVArYolsK3PVLjnK/7+8Xdz5ir+pUu/xI44bEwnGRvSdUp7pYZKeqZMzr7xCI0avtle4rniEK+GeSz/681Vds2afbPswr/TXuS226OSlheKuwD83OlnAPi+na8DcOwXzCUs3Bq1vNNe4OfvfoofuPg6v3T3k/zAxW/wxuoyC1tzrTqi8QWNWjzC75w8y0kz4zsO3gkCF4HyvI6ri9oXfPXeM3xi/x6FBEHPI9xbL/j6zSu8cPmQr//6C1z73A3uLudc2z/p+tq9jClzuq6o64Ki8MzKJgq3BucNdW0pikBDbt5dcOErc1ZXoLngcZcbWFnmN2asX12jTvrvcCtTxoATdq5XNHuRKTNTtIxjSVz0mEYwNR1TRmJanYXX9UyZ5itPimorE6334wABXVTBIp/YMqoD5YXGbaV22SB1UI7IcUnz0pWOYSM+OPtNRiV7smZ9ZSf4lDGCXTnKW2e0F+dUv/8OurcDVcnZJw66Rb5plZ3ffof61ausL5bB0h4ZMbYOTJlm12TbdoJi17RBOTO719Ls2jivs8FISYzX9M3Ojj3eQrO72c83tuLAQAlrHCxutizemnHyqQusL5hB3KRQzxknkln4N5gyjkHZOgOaD3klBUuwWlvczHTWbOgZTclIYNCgcFCNbdePVZ0hgb5cXf6EsiQjTB8GitMWt7D4uA04MaPyMEBnAU+MorFiJFc0i4I9rdHC4GdFYGLFcL4wYARTO9ysV7SJV4p7S+TkjMX1y2Hh17iwnWq2zdeiDvMfbWUVBXOyQo7PQJW9L8157ud3A4tgmFD2DtN7jMye5hT71i0++8bloKDDRANbpjxMZYn3isM7+HmBn2+y3sV5VAyY/lmauq86Re3u8N3cehf30wswQvHOW1z+7Uv42cGgv6XrTy8bzGqJ26uwqzPczFIc3cHtz9GiRJqG8hs3+cyNK7jdxabyMF7v+NhnsrZUK1hgllhUEMaDGE8lLP7UCjb28xcaZfG1d9m7fgmJ33LYdlezvvQM9Xg9pJ1OILBvUjYK12qPL3bY97DzjTu43RnrX17gy52B/D9X+MTKM79+SPuFGe1OwezmMe1+RbO32Pj2c8Vs3pbzWyvcwlIfXECNUMX13GAbIP04YFpl8fYhF39vh2YvvEPTKu18PzJLYHZ3TX2hoJ3vduOcuGQMm4X+2ijFWdiqp1ZoFzNQpTpuw5qmkMC0kqDEbXYtaizlWWCy+KKM46eNY0wYQ4J7Ao+L7MWgyA6uJkzjKQ/XrH+76uQSu6rBCO0ibFMyTYMvzYDVFvp8WpclylAIG8YtRYugHDWNx64cbx6dY0V87JjknAR5oAOlDxkXymv6Q1f/ue53p+gYO1bawpTp9yQLsm7QWTnct9yxZWRjz/LGPuaR9UUah1m1tBfmQ+VIbmTNGDg9IydLM7Z9vl/YLj1qwsTb7ha0u7Z7Vpx5ZjdOWb60RzvvHb31liQ6FoFtlJ03zxDnOX15j3be56ESFheLmw2rK0XHhEn1GLAMGsU4Qnx6gSEJU6ZWlldtGOTXweHcwzJl0GDhdzMZPu8NICRfKElpAyHOzrsNJy9WQTBYKc2OGQhwpg0Da+dYLxO8xmUwLqShJip/cstUCj8SaHKMWRp5ONsECqyrhtbBPp5Snvn+fY6EhoFQOhAeo6+amemFndinyhOPFjJk2hAXoMJAWAXiQK8s3jpl+eIurjKbgnAMB8HfUbMXLIS5P6TzUJz5zrdQoG+O2iBDeeywa8/6cklxFh0aji1/43cS7w0YNFmbGacUZ556/xxfIePkTeY7YIT0rouVRxw0e5lvp/tgG0NrDNP2Dhs7xlf0A5T3neQQEnpmTWC3BHZVWkTk7RMm7uC7qDhzgQ7rsvGnq3z6djT0Axj6NYrlsms/7CdR8C9WQXCvbi+pL80xLqYT+6Y47cbfIPxo3BY5bKt8y4ZdtRS3T9FFFYTnmQWn2NOG9sJQkN7w1ZMtOsxZg5bBb4ZaE3xopDE4Lhyl9UEBvTWtnrHwS7/1n3B08tZDvPn3hxe+85L+pb/xxx9rmv/B5/7rX1XV73+siU54XziQy/pH5E893kTHC9Ex8mdjY1V09HneQQCIGRitkjEq/7vpt8kMwm8csJBks3GZjCAjmW5wAELdoKsVZm83+J3axqhOccfGqyzdzueXDWXv5MKsDlqY3ih1VuP2ZtFvDZ3/NGkczcV5p3wKccJCx1dm6E8msWbyPDJ5Kc0bnSILKM88za7JmDK9A+HB/DeaH5OT5e7WaIE7v6s0i+CvplileQd8FZRrdgXNwZAtvf9Nz+JWy7vfV+FmUJyF+/XFzBAmmdiUL5L9UM4TD7tvwt7bDtMqJy8U3PsM+LkGvzQjGTHUV7s5SVSwp4ZP/P2a6390xvoZF+JlYTDD+Cjs/W7J+rLSPNMgRSZ8qgR/H1aRym0qCJYWsxMYNOoFdYaLv1Zx+BkHBp79OeHGP6HIpTpsdQfUSWBSAP7mnPlNw/KVhurdgvqyY+9rBacveXS3xRwXvPzftrz1T5Y0z0YGxkBwi+WoDRKNhKrS5SFG8Y3BlH5TPh21AYA/LXnhp4TrPxK28ULoM+Wx0HzbqjM0dtFUAhNFBfXS19EL/qQMDBHg6s8XrC8Jx59pkXnwc9KFbQ0cF1z+guHk5cDE2v+aYX0Z1s81w/eVl3f0Pne+VtLuKfULoa3VBcq9mPABafLXkny2rA1XfkM4+hTU11qkMdgTQ3vBBRZJLexcN5w95/H7LvTV1oAHLTXUoTHgoLpnkFZwO0q775FGqO6Fb95XipsHxer8lmH1jEdLpbpjcFXo22FbpAZfMI2gRbguzoR2J8jNSZ7zhWJaYe8bwtnzipsBJqRn11BfCu1q1oKfadduWsbmimx3b6FYCa5S7Fqi31CN36FgGqgO4av/5X/E6u03JjnnCeKpZ8po2+JuvPvkMnzAAjNBiX7KxJy7e2Eg0Gxj6MBWwSZhJsIsVz5pOPpx8XsFGNMLKt0k3wsxnddzYP/1GRg7FG4AbRrmZdSgJI/vI0GlUxwlKnIm1CRnp7s78077qsZAkQk0nkARjg73xvRhExdBWvQMnLESK9/XnLTIxWnD7ChSFNdJAJANnzJqtN9eJuCTwJMJROKloxm3izgZFZlwNBCetvxN7dmxdbR7bmqDaQiDa6IYC5mgIJRHJc2BH7BzVOI1ZIydbEJuTKA4Xqy7NksTHe/O8LuO8mBNztpJfjqKItBnJabXtpa2thz84kWO/4klB/tn2OjUDgLV1YqyagtElDe/fpnZs2csZjU7VcOibIJFMCtfkTmF+/2bV2nqAls4rl444dmd49hNe+ZPwm+88RLuqOK7v+Mb/M471/j0sze5PDvtGC7Qs0xmpqdMeQ1sk1I8HmFhm+7Zu6s9vnzjOf7Yy7/fMWsAyqjZ8AT67cy0WXr99+qRzhdNo4bTdsbv3bvKWV3yA8+9EXz/nLcvid5XS2ncgCGT5+NVuLne46BcBZ84NtCP76x3KcSzU9RdWifNjJM6KCOuzAKD5o2jCzTO8tKFQwDWrqCQSNtWw0ld8fbtA65cOuHtO3tcuXzI6apCBHZmdaRsB0Hr5HROuy7YvXAC0FGFfWSfeC80hzPsXhvYXgAquNpgb89wF1v2vnKBk9daZG3QHdetLqS2JN9O0gimjg7Gk5M5BXHxfhEsy+XhjIu/t2B1STp6vWlhflM5fRGMy/aO+9HfJJN7mN8KWyXtOipJoxUpCXfiFFP3fiMG/hiU7gQWteB+78lYdZSeLj5hwiPhAQ7Qh2HclmfuoQ9Y1HP+Pkn44+Mnkk/6GpPee6T7QAlsyw8SiwcFeEg59pGxTY5Vz0v/nRkF25L/eTLwKK108ucVI1wZnTQKsPVUmcyYpXXNqz9fMTDedsE2y6WRnS7WDGVooDtlaBwvnUw0rtN6zfNVCWLQszMu/YMdZOw4PtsmODh9qSjQ9Ropy+60HX/vkE9/YQ/KcrMcY9bPqIwDf1Tb4o3bz3u4c48Lv36hM8CFE91ccNqfrx22pZEbVtrTfp1w7xjKAr+307MA6Q0htB5zeMK1nTlaFpjTJVqV6KIahtuWb3pdJ2sobLd9casyOvoyAoIC9e4xV39lB12U4D3SuHBiV2SQmbM1fmcGaQfBeKdDOqFo7YIytzDBPYYqso7jqZXOH5JZN/hZGWSNteueJUWwOO3LHQ1Ewel4vw7rnp2u0PmsUxpL3YLzaFVGBpzvyjlkBWbtksKkgx3SwQoQdlo0Ld+428vEHyQmOafHU6+UeeJ4VObQNmEmPXpEtwPjnM8ria7Xj5bw6emjhf8AIVuu3+vyJhdM7uNu+b3jgxJsHpjv41nwnWvlPAfqlWf/Ezk3/7349zLfCBf3Sd9lbfeK3unLJMJyW4Q4gXzafwVVpbGWb/P3cMZwc1v4jXeT1KTpOheElryiX+Ob9kHD3cMOh45L/jqXgLeNAcqtwt4mzhfVgzL2lNvDu8AJDhh+8Q0LgsIk3b/mowIltmPuT7IALnrPRb0OIlyJE/OVkPFGWz77MGPgOcdDdtbyjJo+UAQ/7Pg62k6qznEwVj6PheIHDLjqH3Fsvw+se3JjqnvPI+SECRM+lvigGPDnybuj+48q+25N0gNt+54UfNo+mcXkA/EYZG//pOX323ceHOYxIb3bvLts6zoP6gPnr8LOx7iHjPN42DS3lW3bPXfO8w9Tkf0gqD7iWvN9YJJzAialzIQJ5+HD2tp3H0XfIyXzHgSjB8Z5BCbZo9x/r+EenNAjpvQw9XvKt3x+2HhY5fIjpzv51p0wYcKECRMmTJjwEcSklJkwYcIHj7EiI6dkPk14Gss04WMLzRxbT5gwYcKECRMmfJQwyTk9JqXMhAkTHh6PS2kxKT8mTJgwYcKECRMmTJgwYVLKTJgwYcKECU8r/LTXesKECRMmTJjwEcUk5wRMSpkJEyZMGONp3V414WMFVTZO7JowYcKECRMmTPgoYJJzekxKmQkTJjw+jJUZT/oEq2l71YQJEyZMmDBhwoQJE76FMCllJkyY0ONxKFE+rKPEP+y8H4Sk6EllnBQ/Ex4CkwO8CRMmTJgwYcJHFZOcE/DUK2WkqiheegVaB9aAMdC0UNjeKq+KFjZG2Hyx0roQpny46krrQAS12/e4SRNOuNfCIqs6pL2YhXjGoCaUQXxcdG1bjJ3HJtBRnBzrGl2toKzACCICRaiTNg14RapyGMfHc2SN6a9H6Wu6btu+PM5DrEfXzuNyOgc2trvPyjs+u9ZvX3zqtjp6vz2v/Pm4Lg+b9n3KMoz88Gfv6sOk93AJgZjp3N+PMsZj09OsQPq4YDzWPHS8x1+UCR9zjOfZXG4YywvnPZswYcKECRMmfEviqVfK4B16fBKUBNb0C1drg1IiQqzthRmRoULBhYWuGB8UJiLDsBDCx/saFT5aRCUQoLYPL01Q2vjKYk9rANoLc6T1aGnRQlAREFARxCtqJfwTEAVx4Z5P9y2QZLEsjrd9U1RHjtmtJc3FOb4Q3NzQ7Bh8KcwOHeLg7Bnbp2NBhVgOuvKwcS+Enx0G4a7ZFexKaXcENeBLEA9qQAu6OtglNHuA6dMKbZR+h/TECeLBzbR/ZkDamGZWd3FgV0K7o6gBjGJX0qUvHtxcyZWqGtvINOBnUUDN9GkqIV8UzNrg51HxkWTZFDbJuU6QWjC10F50WUKAVTBJcSYUdwrUKm7PQ+G7fACoDZQKSc+SymwUnCBrg8499thil0J9rcUeFoiH9lIbwnuQ1qCFD2lJnzeapekBL2AVc1gwv2U4+2QTwvsQtrhX4PY8ZiW4fQdt7AtG+06gYE9seB7TFZXQhqka8beoMLthcQulPfDdvVQuFWWr767UHqN2H9RHQz5boX1YaQS1imkEX/ZltGcGLRRfpjJn+cgwHXGCcaEJiqXQ7Hvw4d7l31JOXjKsLynlkdDu6SAfAPHSvZJBXum3j8/y+gGmBuOEdq5IfAWmCel1/Ti21+yuUF/U0Ne3pG9Xgquy9vYhHfEhzf1venbfbrj36QoVWF6TmBe4efju1DDsv3kd83ekw3Dj+ubxB/FGv7t2GYcljA2DvjMuk47eaV6OjfJp+PbjeAOhvhe/tsaeNtz9jr2un6B0fcHWyt5bNacvVPiiL6hpFP+Tv7DRRh8EwlGRkwO8jzz2d/B/6HtQK0jrwUpn3CHKC2PFjAwMIRpkBpHQ3zNoJieZ1kcjlglGHOd7Gcj144e0HjUG0Zimz8Kla6fdc0jf2aicCaOyhjzcIMjAmJXSGStMvd+a1vY8/dZn6nLj1MgI8sCyx/BiglGqi3aeEegh0h+FTWmJyDDdVA4jm8alvFznGHjUKxLb+H7Xj4SUV349Lst7uX4vGCs0tyk5H/Z6rPR8GsM+iecPKt/D3nvUfB42naft3nn1eBzp3i98wmNPiyeCSc7p8fQrZaxFDvbDBGgMag3SOrSwqDFBGHG+U6RsRZo8c6bMeRYm7wMrJwk3EgQQaeJz1cDUAWxdIGcrUKVQ7dk8Y4VPEmJSnqrDCXZbudOHkpVTlmtYrrC3qq4tKItQvsjY2V3Murz1vPY4B7Ku+3ZqHZRFUEblbWXSBKxI6/FV1qbpm8rzFenq78tMwyR0QmRSoGm8J17xRWxHA9JGJUxM15eGXrkjA2HOW6FXOkmXV78Y8/gyhol5hsVoVgalEwzbRWQ+aZ9OroAqzxRvoZ1b1Np+YQuII+SloW26uAbSItHNLHYZFoCr45LiLMStj0rSwlI8oe26vDOllOnLDKAWihOhPIbiboEmhYsoxamAGEwd2lDa0L5abvkOUoIWFKVT3sR2l6iYancMbtcjB3XXNRANryClIRq7cvjtvSAC1vruXv/JKEZ0oD+xxnfPclijrJuCqnCsW8u8bDExzOHpgqps2ZuvQ57Z6t9m6SjgvGHdFBTWc3i84IXLRzgVlnXJ8eEVzl6ree6Fu9y8c8DB/hkHMc1UnsaFfl2YUB/nDU6ly6d2lsL47p4RxatwtJrRNAXX9k9pnMWIclaXtK3lYLFGVRBRWme4+84BB9dOKKxDVXDedOl4FZZnM8qqxVofhhdvcE5wrcWvLaatsHXJyUuhzzQvrdBlEZQ3uy3amNAvk3ZIswZKv9N1JjdLVPiJk14p5fu/4tIYmMWJvyVbk4Vvrs/HtFm/1s2/6bsYKGK0zzuE1S6OxPzSc9OCNB5xnmKteJeVOYaxtWJXLeVZgbfSfRKm1uGC+AOGyzV5Ez6SkMZR3j5FCxMMNoUJ8o3N3n0mE6iVToZQG+fDKEt4kYFSR7P51bgw57pZGKyT8SfBV+l7DtfiFF8IximuzMIV0n0nagnzLv03l8qTG36CkaWfi01NN1/6koHRCgh5l8EwtCGvp3nIgxaQTxi5ISsZk7omNL1yVu0ovTgvd3HS/KvDOT+l093v0tCgULfapdmPP0ng6ONrnE83Fct924qL6Xg6uaYb67IxrzpU1Aj1hb5tUv1TuU0Nbhbi+aJPL2/z3qCSvaMy/C5PQ9v6EsqTkNZA+T02Pukw/67tZPhONgwmWRn6vpClqWDXsLjpOX3edO8iT2P4XrKypTrqsFwmyved0SOP7pM8GMMkeXO8fujFncE9W4d2U9v3mbGRI2+nNFelsqRvFsC02vVb8cN6pN+hLZSBHD5aeOeGlO473tIPtmLLuxo8luH76Nopjivpd2e4Hn3bg3eVlyXK5cmgvfEeBt9FqO/Y6NPN23HcS2mJS2NpiFCsHHjCOJnyaZXyuMaXBjcvhmNdWi9GuSM3yqcyitfwHaeypbWK0ivdreDjDg1RRdokdPTtlbdLKnconx+M5ecpWlIYcb0c062vtG9fAH7953hSmOScgKdfKeMcenQ8YMro/Zgy6d6YKROtCwOmTB42v1eEeFpGxkxUDqTnUof7fl5gYxrtxQXSuIdjyqQPyQi+iINLGpyyOOFZ3xSzQ0d1Z0VzYRYGhpnQ7Bp8EZkyHs6u2l4IsnTX92XJxGaY3QuL/XZHsLXSLgKDx5d0A6wvokDioVhCs5+Xm25U79IVwuKfnikT3guddV6tdvFNI9g1tLvaCWN22ZcbBbeIjJSkH4pCkNQGrVLb5rMC/WzYCFRuKAyNGRtOkMaAA7m0DmXzYYEsuYCnoLdnUCjs1Zgyt8AJrjbYyuPdKK4o6gS/KrA7Le6wxJ4ZeH6Fi8qY+ZVlaG8vtI2lrFryMVkYzrvOCeoNxnqWd+e4hUVePsXEsqsK68UM9hvapaU8qHFtEOYL6zulhXrB2RK734BKGNNju9jCY4u+jqrQzEt07pnN28H93GCQlAvhdyjnQG7R1GGy9xTDboPG9kUUX1vEenxjOS37Vb4/LVmVnpNq3qeVCxMpIRXUSccaMmeWN9dFd+/yHaV5t+QdvYS9W3J3v+TubGjhTUykQT9Kv5WQts2ksTQRry04ePtwFtKQ0IfxsJwt+rI6obptOfL7vdQ0ai+zNKyrKnwLSThxgjihqIXqUCnOPNVRgRpo71QU61BuV5uwUEmMtdE8vrFo8DIoR7jH4BtN8cZMmTwd0V4gUxgw0DQ2l2bhB+kkQXaUJtAxcDrWVswrZ99IC+1uUGivLkmnPO3qAti1UpxWLC/bnklDWKjkzJkJE94vtLQ0z+zircG4wOgdMGUygR8XhPe0EJBWe0Hf9P1SWs1YJ1lmAnZFJpBLYMxAMHo5v7l4TNuG0+CeGCC54H8fksPmAjYYdTrDSTL+xGdBs8z5TEuT5ZeHyQ1i24xu0DOCRhZjNaPMMtbhufls+Z0vGM9FWjDm6ZxjKFbJZMQ82ZSHV4qlQ43Q7tgubJ9XXw9fSVfGoCjIFq5Z+imNpJRDwnioFlwpFCvFzXr5NPWVXO7rZExD3x6ZbJobsFIag7jQyYDYUVgL7UJo9uiVcgLeRqWFYVCvYR6xoEa7MkgTAvkqTRZZvNGrTPJmeAe5TJvfpzNc6crCzGEr34Ub9w7XGmwRjTrOoK2hmLXBANMaxATDlltbTOVC1/VBrjGdEjCbPz2IHeYjadJN9YjyoncGMX5gGAMwRoORLNZDYrlTCoXxqApl4TDRGFVYhxUdDAWNs6gKXvtrEcWKdnGTMc6K0qoEsScasZJOBmDZFJytZuwt1lRF24Vz3uC8BGOUBnm3MH5QtnVr8d5gjMc5g7Wepimw1uO9YIyyqBqcF+6+uw/A3pUzCuNpnGW9KuGNPdoDx95zJ9R1gXOGsnSUZUvTFLSNDaKfcRSFpyiCEa1tLW1j8U7wrcVWHmNbrNVOxtfWIoXHFk0YYp2hXduuI0vhw7uO/dc7QddF907ldIbO3XDgzg1qEORQG8c/J0HedATjq1GkFezSxLWasn59Yq88aTz9ShljkMUi+DuxNihmou8WbBQSvIbrhG0MEa9gdKBZPBeR2ZErfQYCQ6TdmsLCag2qlGerISMmxzatdU4fO48pM863adGzJfNZRaQa9L516ga8Z28+2yjzAwWEFDwygLAWfMY+SsIYDNs5smkGeW0TMpImPrI9Nih+mUY7LIriNrCohZA2KNVS2lqYLn01YQsYprfqpfLkk3w3rKui1vTKLqG36KVgGqwTKDQ7syzNTNCIt8plUPS181kvaBDnfBctUqld0rOOKaO4WUmxVEwD6+sLirOgZV9f3g+CEFC4aEmMTIa8rD57HUaDsLI4FqojOC128cmKBMyPheYkMGXaM9spxdoi+yZUKJdC62b9diTCgO0FmpiRzFwU1AQtDU1dDJgs0rFb+qR7tkwQPgJThgGMGUr2At2kHdLQblI3Aus2sFDqtmBWNp2celTNmc0a5mXLGCm9pPSpW9sxVU7PZly7dEzrLOvWUl+4RHO14fKzR9wt99i/sGQ/MmUSnDd4FazxnQCQBBSgY+FoDAOBuXO6qgIrZndF64KwsFxXOGfYzZky3nDMPrvXTrv2SWXXqMBYns4oZy3GaGTKBAHFt2Fib/ZK2h0ThFir+IsNfmnDVrRF3MqWW247qXj0O26L6sL5ICB0Ft0Yp1PeJOtd3mBZmpLiR2VIsKSxmZ4fpi0+hs8Ejs5SGBebuSU5sW/y77s8bjDrlvmdWafM6tJWxTZKdXfNThUU32lhaWrFNA8xjzwGhCZ+uPF7wrcupPUUt5doaYOl1YZ5d2zpTQhGnqHMo0YGBiGgZ6MSF7hR+dHNU1Y6I0tivJhkNU7h4jcTvgFw+XbmZHX1wzj51um+jJExEAdpu9Zu8ewL6RkA6XtuFV/JYPGeL/QTg0SLfrxIbFTN5r003/ZGIjrWTq5Azy38W5kcWRkGLJv83eho0Z4WVJ1yqy/HeZ/1gGmRwscybsCHca06DLJGbiQbMDA82CayWzwd+0VNlpkwYE0gYBoTtusKFCemM9KVp4Kb0fWdwcLvnHZL7QNZ+4/rSR9n8A7GCngX/pkG1HXrVMw51vYBkwbpGEm9MTDmmRgC2fvM38eAeZXqpsP3O2ZQ2bWiRTFgcI3ZO933FO+bVvFFkOVN23/DtlG8Lbs6dduOGZVh3J7ntUlej3HY86YdzZ7H6yQStBL+Qd8GidFaeCiz8QhC2ROJzGmwmeZlGBBSNcS/1IIvdmlNz06xPthGB2wp08vh1kOVlSmNcV27Z6y+yikHZx5fCO38APGKcSAuMGXc3NAu9rs0Eju/ZyrFsTBLs2sHr/GdWaDs1h9AZK6E+4Ot5KmPuX47ac6WSvFN44JxKXt53bcJGYMnG7M75XpIR7zHtK5b89w+nOScJ42nXynTOvydu3QL+OTk1RjEmv6rTQ5nc4ZMvtcVkCJTICSrSG5ZSYwZH4SFzp/MyA+NJMtFWYSur4rfXSA+7MOmMINFOJ6eThwZO6J0FhW1ZpOepvGjNn2diuOG4t4Z7f4cLQy+srQ7Fl8K1VGLeGV1pRpYGXLlxObfflICmB37wJRZCLaGdh59ymSWiE7xoFCsgv+ZIVOGXnjJBkU8+KgvSuE6pkwW37hATW0X/TNbZ/EI6eTfbxKCOr8iucCTC4YaJl9XMZhQcoZDEjJME9Jr9n3QICdhxQzTqw6Dgqfd0W4bUEeIaCTc8ynzvh7iwawFt/AUp0JxKqyedZTHgb5eX/KdMNXVayyUZQN2EMAkLi4NxVJ6RlEUQOwKtAgsHe2YG/SFop/oaQXJZmUhSayxUdcm9IFTwdSGxlUbQlV3PSizdAJSHZVM26TTwQJEhr/zPJKAbVphmSmX7EpYGjithgv0VPwuDZWeLmygPBNuHsyDPxYHl64r+JKj21eYHwvrnRln1Zaypu8jlTWz8m74lInXpgnlvrfYDc1A3FbjhOPZbj8nK+zdFdZ3DjZZXRGzZegjLpusrULpwtbL+S1lcbNm5/oMUThrZpg6hHOLqKAT+oXKGN3EntVji+A6eFe5MLvl/YWFVNbHciE1LbJk9O7y+Nl9yRdNabsGURCxIW2fCfreCW5eYGqHq2TAhAlUecE3ymxRRL9ddBkEVtGTEiCmvdYfB6gR/E7ZMVXSlqSBQB3lBryPY5ZG5q3v6e9jLXfu0yQZVzqmbgrTC+ySG4xyponXTnYZ/IXARM7DbjNK5eho8hnrMmf45AckjNLSXL4bh8mvtxm68nD5NvNt2PKsW0wmw1K2FWCAJDfmjzJWS2dgEnr5DDr20wYzI5fTBgakfnFlz1q0MLQ7drDQAzqFsrTgZnFsjAyZofzVx+kYJF5xZbguVmGh6svAtHLzuFUsl8U26LxsVdDlTBmfbetJ8QasGaOdXaBThonQ7kCzr/gii58YMqIDw9tAfoPuuwqsGg1MGQN+5oft32kIs/IXviNZSVLA+TzSMJ4uLTLz2CoYaZLBKk1uIuCcoSgcxgSmjHOG2SwwJup1gS08xnjqdRlZ0xqMLl4G28AB2taiCkUxNOCYyIbJt5EHlkgwCOU1cNm9tCU8GbK8CkYUY0LclLY1SmE88yJnTQutN7Te0DiD94bC9t/9rGgpRtvTe6NbLGdkvngVVm3Bybpif7FiHo1ejTesmiIaqELcqnBdOY7XFa2zWOOpCofzhrN1mPCbxjKfB+OdNb7byn50d4dqp+GZCycsm4JVXdI0Fnd9B3+h5eDyEat1Gdg1sxoRpW4L2tbG7fmBYVQWDhu3tXtvggGw7jusGB2897Y1+CjEpfa26XlraBsbZB+rkU0jHSPLnxXYvX6xJJFllJhT6qKmWgjb1RvTy6lGg9asNpgzi1+Ed1J/cZJznjSeeqWMeo8/Pf2wi/HYka8PBe4vIGRIGyeEwKKwQO4gbUeGHVvMOenKfT6AkdA1YAyNKb6doGKyW3n47DrlObgnw2dZPEmKsDFrKHfqPPbJk4SlgXIuux4LZJJtT8vziPRpndlR2L78KoKpW5Dg9LlT3kUFW/AN0IdNwoZG/xRhz7zBNB7Tepq9ArNuEYVmr3fYnBhAGxZDGFKVo6LPrj3liWPn3aIXtASKpaedG0wbfOUkZUHvxDo27zoo5gZCpYCKyRa+4e/sMAhtXXlHzxMGQllSYGyjLzO8txFXsvhCt+1GHANGk11Hq2yZZ7BZto5NEbe22BVhYRSVBrb22LVQLMO2OqTff5wXsKdu6/A3cZGfW2sjTB2fRcVQstgFS5kMlB7FabDIbOyfjyhWdH0kZ4iIi5bUE49ZOWZHIaKrBNMEC1A7z5guqX2zxWCudMktL7lCZLxnvy/DplJs/GwjjfGz3AKlwzC9dVCzMmofJtta0Tk1jfu77a1jZFVzSbUfv7KtE+Ic5vCU8tZO/414EO+xy00G1oQJ7xXSOIp3DzvFScdSTcYiGM5NcbuNWOn6a1IWaL4VCNPd08QsJs4b2RaoxDgdK5jztNKCOPlY04JoXY1pJpaM6Rf9w0rGZ8nqX/f+MXzRH2qQypD82AzmiXw4jwYUV2ZlMHH+NcNwOUwTy5Gk31zJHMeusfFlrIjulQNZnLj479pJ+nFzMPaxqaQYINdvjZQV4zBpnqoOg3W92R+ym3L/XqYOW45EM787+Zi/BeL6dkpMqvA7bl9KCo9srB2Mvx40+esa9a28TmnL/rlhsjkFQt9Z3HaYxob2zhkqEht4FC9/F0MjQT+n+8JuNSqE8of5R83Qn8jAlwvDuKKR6WJMx3Dp06P7lsVpV+c0F3obmTIutVH4JtTMujzStzvoM14HjJ6x75mt9aL/LvO6jeszvh60eWhKfFLuxrCFhjMqFlsUt3lczdoMwGX5i4a1zl7r2fegxaKLP1OYZfN/LqPjlYu5Qi4y6i8kpowG5lEuN+0D12oX2E3VZWbJ94qCPTtFS4ub7WSMmMxKl8scWRt2ysrxc6BbhguZYjyWxymIHfqfSeM9QRbp5cw6UxIr5Osa6BX0qoj6bB6JcQzgXbczAeDu7fvsSZ3wgeCpV8p8bHDe/ueHiuu2X8NjOWH5fZTsI41clrmfjPWwyDaePZYPswIGCqsJ7wn7H3YB3i+yseUgHhp08CEV5WlCN1K++dbW58r5bjJU1+c8efzw73tkmfC0Q+ua9vVvfqB5yDnXCXbLvfMTe8Q+eT8jEDya8WgU9lyj0X2MSV2cbUaj/L6c/7xLY2yYyg1B43RyA895hqMUr9vanS2asrCDU7XWDWoMOi96xVxEt9h2YVs40BuLzJCdM2DjRAV22hJnmuBI1JeCqRU/Gx0mkRQp3d9+4T1WkuV+FDe2UaW6miwsUdbq0lbaudDu9CzHLp+8DGOM7ydFSGS6dCzsxNpIgp2kwEnp1tdNYOhTjWEepha0BF9qz0ZNFYqGPtNKYKSbuCBvpTsltDuQwWrwm1iex+ZJBggJrGnTL/5F4++xoiVXYEUWBUKm5N0i/Xeao5AXRgNjSLRjcKTyqJPg09AlowcxrA9+cjK/N50uLB4EQWTL5Fu1XW1gbZG5g6x+Gt+fCIj1wU6t4BsTfKeUvmeXNAZdFb3ibKdFbCxrG8Kb0xItFd1tA6U7pj97d46fKc2lNmiNDMNTV7vDCoI/v67N87ZPp56mreCxrdVo3+ZekEYw66At0oJQHgnOxIkn2pq6f6F2JcEXTPZaN953esVJ0Ze5XEiKILuKzHwP9deeHHtlknMCJqXMhAkfZbwfZV+Oj4pyZ0x3nzBhwoQJj4ZHHTtHxqKNx+/DeDSN4pmCeQu2KeM+IrM5e+c9eNrllQcoKR9fNk+oHZ5QfR4K+VbB8b3x/fPCf6vgcVjd74Mb7Udvl8rTjkkpM2HChG99jI58fGDYCQ/Gw7bnhA8MwQXIU77AmDBhwoSnCU/7vPUAJeVjy2bafTLh/eAJfUaTnNNjUspMmDDhwXjahRz41ijjtxKm9nwqMDnAmzBhwoQJEyZ8VDHJOQFTK0yYMOHDxdNONZ4wYcKECRMmPH2Y5IcJEyZ8RDAxZSZM+Lhj2zaV7sxHGfph+SDL8F4wLuPE7pjwEYISjgKdMGHChAlbMM35EyZ8S2OSc3pMSpkJEz6uGB8lft7zp9kSteVUiw8M5ymuJkyYMGHChAkTJkyYMOE94oFKGRGZA/+IcGJvAfy4qv57InIZ+C+AV4HXgX9eVe/GOP8u8JcITuH/DVX9e/H+9wH/L2AB/F3g31S9/6pGjMHs7PY3VFHnwDmkyIqfH3no/cYRiOfCP5wnrAcUc5TmA8I+ovctfa9ewT8IL195O3zcGQrb2CSP6++Epw/3U1x9WNjWX+7Xpx7ELPq4fstPMaajIj94fNhyzoQJEyZMmPBxxSTnBDwMU2YN/ElVPRGREvjHIvKTwF8EfkpV/4qI/DvAvwP82yLyB4B/EfhO4AXgH4jIZ1TVAf834F8FfpEgrPxZ4Cfvm3tZIM9f636KVzhbomdL5NKFbrGhO/N+QaGKVlnVRFCJZ8pnFvWOLWXic4iHuPedQ9Nvk35n4RTUSHe+oArd8Wpd2htp0cfPoBLSGfg6kj4/8Rqf9+mL5vmMyguDxdhWZth9vgGJ+hyf670aZf9rJ5iTNWevXWJ9YGMZQ1lSvJRXufTsvLXk5OUdfNm3t5qQd7FU1IQ6+TJvjKwc8drWyv5XD3F7M9aXZ9T725VuKbzGtky/t3kRlzyfWG7TKvPbDdWNY85evUizZ7rnxZmn3THDdtNhWnbpKZaO9aUStefXSaKiLb3f+a0aVKkvVV37mLp/56ZRFr9/m/baAfXFrLE8VEcNzX6JaTxuZiCmXx3WFDcOqV+8hNsphn0DwneS+m/Xzh610vWzFG7bMdKybZnhleK0xS4b6iuL7p1vhN36PnT4XFKbtqg1+PJ8Raucs+aRViluHtNe2UOLR3ShldfVKXbZYO4e4y/t43bK4RjyAKRvNbWDOWuwh6e01w5Q25drW3oprqkd4hU3Lwb303VSBheHS/zuLLTXA5RGpnbIssFdmPc3vWJWLX4xHEMH2KIMksYhCr6yPBRSPvNi85s6r9ij12xP12AMblFuBDWrFgqD2jQGyvZ+4vX+R2Yy7L8qwG/9/DkFfLwIr3USVp4APlw5Z8KECRMmTPgYYpJzejxQKRMtPCfxZxn/KfAXgD8e7/8Y8A+Bfzve/xuquga+LiK/B/xhEXkdOFDVXwAQkb8O/LM8SFhpHdy+l5XHo3WDNu1AwJbjKJRHgVrMaMFw3uJEsoWa2R5ma9x0LzFyHmRFT2mfs90iLI63bMEQCXHTwkGkVzJBr2jKFU+j9JOyKCl+BoqaGEezcuTKDF9Il4ZxijmrkbqhOHMhrM0USgOliyCObmEblC99+gCuArXgreBm9Glkyqd0zzTKzvU59YWSs2uW1eVU7l7J0ym9pM8P0YHiSm38G9suxAmrW7WKqQ27b825uGO58f0l62sONYqoUN4rqK84sBriGBCj2apZ4ahkdrNk9eqact4iRjFGUQUTrwGcMxjjqYpwNOL1r15CPCw+e4+dqsEaz93lnKpoWZQtt88WLP5/z3L7ez2vfPs7zGwLQOMtv/v1Z3nllZvcW1d88uIdvAqnzYyvfv05nvnHL3D0oyf8gefeorKO/WJNaRwGxSNUJqTjVSjF8fXTK1yZnfFMdczMtFjxODWU4rDiMeKxcWW88iVz02DEU4rrvKf/V29/D7/7zWf4t37o7zGXJsTDU8nwGEiHoZS2Sw/AiqdWi1cT0sbz47e+n5fm9/jh/a9S4royVeIw4mky7ZdTw4FZ4RAchtfrq/x7P/0X+V/8yE/xA4uv4xDm0gzKUOG68JZQP4ew0jCmWJSvrJ/n79z4g3ztZ17l2T/6Nv/zV36WK/akq5eJWsyxB/laLQ0Wp6Yr90pL/uaNH+ALP/dp/pUf/Wlem78zaM/UHo1aHKY7KvBnDz/LjdU+f/7ab2LxnPkZRjxeDSsteLc+wKvwX33he/nuT73Jq7u3uVSe0fiQTnrPXoUmtvGv336Rt9+4wh/7rt/BiuJUeHe5z++/8wzf/dJbeDW0ajCieBVaH9JZu4LChDq33qAq3DzepV6XXLl4gvOmG7JaZ7p8AVQF74WmLnC39jBX10EvqIIq+MYgVsGH3/j4waqAix+zB/HC4u1d1MLqqo+Kqaj0dEJ1T3BzxZe9kjl1QfES0yAoMZVeweqz3xp/Z/9UoP365CP/o4QPXc6ZMGHChAkTJnys8VA+ZUTEAr8KvAb8VVX9JRF5VlWvA6jqdRFJdJYXCRaihDfjvSZej+9vy+9fJViamBcHcOVi/8wrcrZEVyvkwkG4aQy6mKHGIN73TJmMpp+spUmBAQyVFbCpsEjhM6VFz0gJVldv5cHsmC7tzXSgV1aM76dnod4x3IjtMFCEbIm/UYbuJkM2BIFtoDJUyqQ8RYNipLo7xxjD+lLJ+sD0ipCMMdIrPA2+srRzwVXSM0eSPqnpFTppEZTVrP+jYGsw65ZiaahOLKO1b6+E2SjDiC2TlTULERZnIpgGdm42zK+fcel39qnfCswY8VCeKvWe7d5lziJKi77iDGbHLWdvVmhRDdp7kKOjUx6JwovvONTA8isXaS20Ajtr8BbWAnu1cvFLRyzu7HLyay9wIn2+n7jrWF54nkLha7tXwqK0hZfuePa+fkixOuCbe6/1yqqMsZHKltrMruENS1S2bW/PjXYdvAiYHXpePfb8pz//57u2GocZvI8tz/I8qxPlqyX8/dkPPxyDIteFNvDa6yv+vz//T/NfzLLeFesI/Te5waLIfto6lOPlb55w+uVn+T9d+ue7/vYgBX+3wI99z7QwO/J88u0z/vaX/mToR9J/41vrBJRLRZzyf9/7ZJfugH3ThotPvtty++KrvFu9irey/T3F9Mul59Vjx1f/+z/Q3TYtvHDqub7/qT7olj4zfheX1op4aOdzyoyxVmYKjxzGKcWZp9md9WOaatcOA0XJFsabqFIdrvBWaPfs4Ll4sEuHryJTRvs4HWNmnKaM08/rqz0byQh2dV6jPn5MR0U+GXy4cs4+xcuvpAdhW/V4PPK6eW+83VB1EDffNZWMS909r6CegWEKwr3zwtxvS/SIYdbnM4pzv51c52wnv+/ur4fd3n1O2TUz5L2nreIpXTHbrx83tm1H/SCuU5s/7uvHned7TeNR4j3OsE/D8w8qzntN51vh3pNII+FxhH+YZ+MwHyI+LDknzvufB95S1T//OLcsi8gM+OvA9wG3gX9BVV+/X3keSikTKbnfIyIXgb8lIt91vzpuS+I+97fl99eAvwZwwV5VvXGrf+Y92rbBp0zdW7yxFhHpJu8xu2XMnIk3h7/PYcrkSpBtcQZ55de5X5tOGSObeYl0YfU8Rk0XTsLfJGCJ9OwYYzbZMrJlm1YaN/O8kuJJyFgzhAWNZEyZkxWyqpndbTBNscGUEaVb+BdLxdSOYtUvtMLWmBjWA6LBQi50aYyZMgBaaFDwLCz1nlBfkJ4hk5RVgMYe3SkVElNG+rAdUyb97t6XYmqh2SvB73D3s4b1VQcW8FDeM9RXXWDESAiPASl838MPS2a3igFTRuLqLmfKtK3B2sCU8Sq8/TsXQWD+6UMWkSlzdDZnVrbMypbD0wXeHnDr+z0vfeYdZkVguKzbgjdev8qLr9zgdF3xysW7eITaWX7n91/g6s9f4PTPHfNdz11nbhv2ihqDdswOE8uWM2WemZ9wpTxlZlpKcTRqN5gyiaER0hgyZf7LN7+X19++zL/1R35iwJSxI+2AUxmwTCCwUhJDJMX98Zvfz8uLO/zg3u91bJucYVNH7YqPHX/HrGOdDL9fX+Mv//Rf4F/7J/87vnfxeseCydkyKR2HDFg7Ky1xhHb58upF/s6NP8jXf+ZVXviRN/lffuJnuWjPunLkdUjwanAIjRbUajumzJmf8Tfe+cN86Re/jf/Zj/4DXqluYSQwX1LZARotcCpdvX723me4vd7lR699EYAzX3Xvp1HLO+sLOAx/+ze+h+9+7XVe3b3NQbHCa2ABJbZKum7V8uu3XuStNy/zx77ry12+N84O+NqNq3zni9fxBHZMYsq4yJSpve36TmDFCDeP9mgby6ULpzgvqAoiivemY9P06zRD01j8zTnmmVW4qYJ68I1FjKIdQyY8w9MxZSSyZnbenuMLWF8J32BgvgSWXnVY4ObgS+2UpqaNA0XOkslenejovsZ7Lv72gID7tXPmignfsvgw5Zz9Cy/p2WevRUMRoZ/lc3KctxLTKzFMEyu1U5om5SFp/uvncm/j/Cupn2vH/ErzMvTfQzePrxQ3E3zRfxNahOfiQlnS99KV06c8gwJ0y/DYlzE1aPrOYpq+ZLA9HEIdut/ZtzneRp3LBDL6xjfehrJ166yKYFrFNODmbIWKYJx2Y4Mf7aIUhZ2bPhhXLo6MWHlPiXXJt6Wntg4J0Y9JErZzi4d2JhsK69wIMGAJm+HvLt+cGZjFT4rrPP30fgftGMtmHIjTobEvlXu8TvS69avIyy5eeyMmdAr1bdvH03OJZRgYQLPn4zjD+ukofLxISrv4zeQuBM5dM2RxUDCNj9uJN8u9tYzENkr9dqzwAEzrOyPBwLA02o4rToMcn+77LfVsPcTt3YNvMqtvGl/yMqRxKgSIEZ0OjR9jDL61rF7j59vWQKrgtB8jYeiWIoVJutGYVmofc7ZCywKKLVus03qyKjcNdaqb5VLt13j584S8/Om517Bu9R6dz/r1XFfuURrj/pUU8yLd+lfLjIAASBsmCU11HLfNuHw5VJHWdVvq5Rs/uxnmo4d/E/htILI8+Hd4fFuW/xJwV1VfE5F/EfjfA//C/QrzSKcvqeo9EfmHMcMbIvJ8tB49D7wbg70JfCKL9hLwdrz/0pb794e1mIsX8kKgqxW6XGEO9sM9Y2BWBd8bsdN2HbVLp99mtLH1h/7vQKmR7o8UGymcaPLJkQ3SWVpdHild2bzuqpUN8APL+0jzvM0vzWDy2fadjfzUhJsMJ65RvM7niemFOO8iA6lp8VZ6PzFJyKBPW9Kk7hRba5eHZrNzsdSgPDEyYJ0MyhGDm1YpDleYxiNuxuzIbvrPSe01HugfoAHOBQHTemZ3G8p3T3jeXaDet52QWZy1NLtDPx2hDEWXR7FSyuMV698s8Zkj6k2hJMY3Ie+rd2tM41ld3e8EnytNFC6M8Eyj7P7+bS58bZfV1Wus0mvzyqcOW+qDq1xolVvzC11e33bYMnvzJnvXL/H27mu9kLbFmtExZRrlppEg/Obt+ZAQheLU8+nDmv/8v/nR+7JI7svAyYTW4szzhhH+0fyHBkLr1ngMnxkHn3njjJ/4b/8Uf3smD4y/lWFF7H8njlffuUPzs1f5qxf/+Yxpc34dx+mECKFOr10/5L/5+T+BL6W7nwvS43h2pZhW+et7rw7Cp2vjQoTXbtUcHXyC36he3mDD5WUAmK08rx03vH712/vytvDymePO/ivh95jBE++5bBEnwLOtYpziqktZwD6vXECSKDgVy5p2UQ7DeWVD+Q2xnw4bpTgJCp12rxwINKJg1g4tMqbMaJzaKqSz5TkEIS/7/fXlB2QFH0OnoyKfND4MOcesHYuv3ckLcV7h+utknBktcuQcRs1gi/Q4fPqZFmDZ/C+tC/HHCxljNufd8dw7zu+8bdp5/eJiZzu7WTq5bWNsy35vGLdy+Wlctqj8Gm+ZxgjiQvu4ygzjSwo3XLwFmSiWNc7j1WHb+fcaGJDMMI0uXejkqk1jVfjrixDQzc+X/4IRrI+vOQM2pTWOM5j/enlUbZQBC+3SkO5ZWgwKxknH/AxtqIN5qjeQJdkxyDgDmVtBi7iwtnFyicYsrCJWw7bxVE6jvYHAGfzKIoWC0WgUC/nkw78R7Sd8UaxVvJdoOAtjezIq9F07bLld7KwpjKewHms8pfGU1nVlALrt4V6FxlnunO5wefeMnbLGSF/e7u/IsOPVsHIFTg0GZe0Kdsoar8HY4RHePQkHoOzP113+CjTOUtpgRCuM59bZLhfnS5wa9ssVrVpab7rtxwDvnOxzdeeUnaKmMJ6FbXAqnLUVl6olAK0aGm+DcQdh1QYNZGEclXHsFjUAN1d7LNuSRdF08Yxo1yYAtevHksJ4CvEDR68GZacIbTUzLR7BqVD7grurHUrrWLuC0jj2ynWXd+MttbesXEntLG3s5DPbUpmWX//qK8wO1rx27RaNs51xyYjy+o0ruMbwB165TpG2o0ej1LItWbVFTMvReEPjLM/tHeJVWLuCs6bsDFQApW0pjWdmW2pvaZxl2RTcefNZ7Klh97VDdmY1pfHd1vB1U3RbxAvjMcZjBJwPfbFuS5rGYoyyunEFWqG4tqSatThnUIX1nQUYpbqw7ozAIoqLAtt5xCYA11r0sEJLD0ZZ/e+e0AHNH5KcIyIvAX8O+MvA/yre/gs8vi3LfwH438a0fhz4v4qI3M/x/8OcvvQM0ERBZQH8UwRtz98B/qfAX4l//3aM8neA/4+I/EcEbdKngV9WVScixyLyg8AvAf8T4P/yoPy1aWjfeHPrM396mhf0ARW5PzVqK5Mmizd4amSgF9nwObONITO6P4jTCQNb/NuMWTpJaNpg3mTKgjEbJxNqBsJMup+HHwlUA8FIFXP7CK0b5m+XVPeqfnLdsg3M1A5zfMbsdoGWphd8JCkAfFDIWMFXpourMtzqpCJI2xcr+KXJBJwk2HR/tz8Lceny76xwcbZWExbfrhR26x2OXyxZXwplEQ/liWF9UQZp5NtzVOL2pbuWs+eCPwsyIWy84E7CDqLsvjlDHJy8rGihqFXsygYhqFTMyvKCucjdTxecftJ1ghAKi7fmLF9oMbXFH7RB8miF+dszrv3aZd7+YUt7rUGsxxTaCSihSwVTqapgrKc+rTCVo5q1GOO7wdtExk941eFaNfVFxRrt1sR3rl9g/tYuez9wiyIKCyleDq9CaXzHJMqhKljju8mymjW8dPEQiRNoLlDkg7lXYW6beG24t17wzX/0AvM/fJuXLhxitqzAc38wuWBUR02hEeX2coc33r7Mxc9f5t7nWl75thvMi2ajLOMydeyUKFylSf6bNy6z9/mL8MfvcmlniYjSOEuRtYfGuF4FBa7f3ac5K3n2+dsANM50hi/vDct1NNP+9j7rl2rK3TVl6SJDRTrBEkAjk6W9V7F4e4fVZ1Z9268s1c2K+tloOfLSfSP42NbJQqYE/yyAPbWYGpp9HVh5O/ZKZ/WLn00Ds3sl9YVem5a23qmBnKmS0jIuS8PD/Fao8/qSdPeSb5nqRHElaLLwp3LHMuUW4lxBM7xPXLim+3HxVTwZASJU5ckLKx83fNhyjp9Zzl673M930eCTM1Hzfpq2GfcL7owtE7GNOdL5dEvzUM7GIGzJVRO/Mw3MD1vTMW3SN5+eJfZO55y+jYwcI4GJo8Qt3llls+95bFEXH7ac+oJeWZ3Nm7mSoXt3blif85TIY4X7eC7e2OYMiEtMGTk3TGi4WJckL42UwO1cOqZMPp4NjGTp/W5RIOf1QEAjcy+4hBsplzN9Q28UHOa3wVAaT2F+s5zih0ylrk0ljL3GjdpeQ8RtW0+z3DfaISjlJeYXZYzU78+T01NqXjHt5vvfxiIaPJNz7qc4EaZVfDEfKNcaoBm1a15n8XCpUdpqn8Nty5A83yyvbh4i9MMUN9Vhvw3zki+FGgZ9y0n/e9Yop3G+WuZpZPnv1J7j6jLHwtCdgipvj5gyG+9Tsn4Q20i8ctal0Yfr2mS0Jh2zmhC4teUdBmacsozvay1wnPmpzOWFjunlYR2fv3avwc1mHC0+0Y19qeu/fOYQVY4OPsHGlKswa/tvs/RQKRyWlzpZpVAokxCc1XkV26FQZd/D5eMacUpzsIvavTBuEuIvRsafnAUZ2HNhCztGKE7XoOHgBzX9Ut6uGzDgZtXGnABsGK/G9TT1GuJ3d/c2TwQfkJxzVUQ+n/3+a5GdmuM/Bv7XwH5273FuWX4ReCOm1YrIIXAFuMU5eBg12PPAj8V9Vwb4m6r6EyLyC8DfFJG/BHwT+Odixl8Skb8JfBlogX890nsA/jX6fVc/yeN0fvcga35XhHMevw/D5wNy/kiha6abNzeebRnHcID83uazHA95VgvpDc7iv/eFBynxVLn8i+99kLjyHuM9C5yrQFTP4n7Pz4nzbf+NOV/p+LB4hDyfifk+UjnvU75P+RuDMB6o75PUWXY95x4vuzfB2vvGeRD2uctn/Zugnmcz5eiDypJgs78W+LS/Htror/Zpndun4yS6f97zMXK/Bg8KA6jX998/YjofKD4oHw2PVIZQR6NnDwg44VsMH6qcY1YNu1+83t+4H61/m6xzv/lsW1pjE2kebmwwyrdDjFmWY1ZMYqTcz/BzHlJ8r2ANakzGioljme3T6tjF49PTyBRZWZm7hWNcdOZGpPFWlF7ZENiJbmY6hVlu4BmfvpmUtUEZFf5WRw4bV4W6kUevQBgYijOjEkTlRApr6N6bm2X13JZG+pdYndm9gcEI0LgdO/xguJiOi2BfglqNW9fC4QgpjrQgXvDJodg47SwtTVvAJTaWoWfVeAmHKTgJK1XJ2C52yHyRaDhKhgzvDO26wFQu2gb7+MmQpArW9tvKAawNc0tpwyecwtqMkeMVluuK3XlNYR1lNMZY46mMQ7YYixKL4vbpDs/vH7Moms7wk4fNmSTJcNRqv+X4uJmzKJqOReFVuH22i4hyYb4KhzdEg9W6LZgVLUXcUn7jbI8rizNabzioVhhR6mgAStuS3z65wHO7R1TGURjHzIRt5bUvuFAuQz18wdKVtL5noBQSmCAL21CZltZb3lnts3JlZxxrvaUwQybRyvX7/AxKEa0tiTVTGsfCNl1beISlK1m6knvrBZV1tN5QGcdOUXOxWg5YPE0s46otWbmCKr7XL371E+xcPuOzz7yL18CASYayr1+/gjrhO159s2Pp5DhrK9auCO9jPWPdFDx/cMTctl0aa1cEBk5kAlnjmRctpQnGsdOm4hvfuII5s1z41F32ZjWzoqWNzJt1W3B8NqOqWqxodxiI8wZrPOumYN0UFNZz9M4+0gr28hpjfTck14czKJSdC8uOKZMb5RKjJrFTcv2vAO1xCZUHL6xf51sZt1T1+897KCJ/HnhXVX9VRP74Q6S3bfIaq3Lz+/eLcy4e5vSl3wT+0Jb7t4E/dU6cv0ygA43vfx643z7tCROeDB5mS86H5fjqAQrEBz7fEOb9A6M8GO8lgfed6eODe5xleYS0PmTnaQ+Lp0HfMWE7pu1LHzw+bDlHZyXrTwWDnFrpfDn0x7lviZMW+EnJkFg1MGSgRLExbUXO/dAkdP4yhM6/TGKgiI9O7WM6PpYvlSFZijt2T4wTwvYW/04hIH2em0w47Zg3iSmTh++YGpLd38KUSfHOZbZstGX+o780LvhvaXPlh/bp5uwEUe0ZdFl91EifZm7B31KOrawVDf/lPj1MG95XvvVVRnnk7IO8ftu27w79amlX3rEvHs2ZMqNpsPOBkthUg/I/DKSvo0jXHzPt2vmKymwbtrSKmu2mvvsyZdj+nXXxgEtOUTvbCNvEb3Etw/AQ+sCzractdjiy9+mEcC6LQbxyOtoqdzE69le7T92VP1y09HXaaz1LexGA2+Pmi/130Xru2kubCkrfs7/E3Yclk7k5kDZsM16OxqIc25gyY59B4/Ep/3baqEmoBY5FuDFu1q7/at8ewGePz/BVwdH8E316QOHhM6vg66XZe5Z7W5g7Kbwhtr1XXHGNU9O3u1Fl5mGWMYrUSNjq7WGhymdPzxDncHtz1CxAhEKVUmFHlcutR201VCrHb2I/G8+fX54i3qNVMWDTmPo0MClLc44PofFLzN8DiGu6PO++++QEww9Bzvlh4J8RkR8F5sCBiPy/ebxbllOcN0WkAC4A2T7lTTyhDWMTJkyY8CHhQT6Gxn4ZzrM6f4soWCZ8dKBMSpmPBY7PsP/w184fe87DfdhwD9qS3WFLuEfdkr11O/Y4r4E/GXNu3l16IpvbskcHHXQsmHE6W8Lkccd+BTsmjhnFheCUs7T9gnEL86YLWpgN1k15b40WBtNWg61jYyfO+Ta1dL/fdiR9fgI+BnJV3tZZugzTCL77Uj7028hTebR/lqfVpZPSLnrFWOck2vS/uy1OKY0UN0s3sWaGbRHaXqE/fY88/aSAyBRM3d6e7J+C1BJ80nT1zcKlKXz82aTt4MnHTVdeTc0fHM83Bpm7UN9YD2MUW7iOjZP7oQFoW0t9UlHt1VRVu7H1O3SXyAiJW5VVBe/7l9A0lrKMDlw1+BdZnYXTPctZ028vl3CIhMkOmFidVcwWa7yXQRo2Y8qcns5Z7CyDLxqj3bbzxll2yib4xvGG1tmBE39rNDCFrMMaT+sNh8s5XoWqaPHRx8qYdZIYP6kNEmsopZlvC1fCYQJ1a3He0LSWsnAogVkzKwOrJOXgvIn/gj+f5CtIRDn92gX8vuPSs0fBR1B83raG9t0dTC2Yl86wtvctZGJ58veyXpe4ZUm5W1MUHmt9F6ZtLU1ddOyUonQURWDKtI3Fv71PcWKoP7nCFh5jXSAIOhu2lfvwVyKbLPQXg7GKdwZ1EhhjtxbYWmguuL5vK5RH4biK9iCyvgZ0uOwfjDTXBFcJS9M5Ul7/Rw+7j+H94cOQc1T13wX+XYDIlPm3VPVfFpH/kMe3ZTltf/4F4H8E/PT9/MnApJSZMOGjh/erPNi2MNhGgf+g8DCOmjPrwdb4D3PvvGePuj1gwoSPEERkDvwjwo66AvhxVf33PuyjIj8WeNSx+z4UyGlL9uPFeAbYNiOcpyIToHq8xXl/+DDms0fZzrw1+nss82PaRt0ld7+2M+fk5X3/7FHa/rz07lMuVR2Ucfy7TztXPOp235YPVcZh2nscbt1S+FB4mG2Y70cWbd8O9UsHv+TjrXOh3OVoWXweO2tcx7FxLzFUxu3sXHgnxXtYfueDutet7/a8E4gfoAvI0u3zuHO2fPQyfuvjr/D4tiz/P4D/LDoFvkM4vem+mJQyEyZMGGIbk+SDFOK2uYF/oI+ohwz3uDCxZCZ8SPgQmDJr4E+q6omIlMA/FpGfBP4iH+JRkRMmTHhM+DDms/e5h/pp2WI7SQITPi7QJ/jRfZiMYFX9h4RTlh7rlmVVXRGVOg+L96e6njBhwrc+cmeM2/49ifzvV5aHLee2eOfde9QyPCj+hAkfEWjASfxZxn9KON7xx+L9HyMc+wjZUZGq+nUgHRX5PPGoyEjZ/eujOCmtHwf+lNzXBD1hwoQJEyZMmPDRxcSUmTDho4SP4rrmvdbpfsqXBylmHpTmw4Sd2DUT3icU+SAsSA88KjKeQvSrwGvAX1XVXxKRD/WoyAkTJkyYMGHCRwsfkJzzLYmnXykjgLHveT9pd0zr/Y7ofZTje58EnWvkUX7CRwj5ntjHsUd2wtOLD/qd3q//bOtj47JNY8u3BPz9jo15b7jvUZEAcevR94jIReBvicj9ThPaVkC9z/37xZkwYcKECRMmfIzwAcg535J46pUyUlUUzzwbHF6VBWpN7/zKbLFad57xo3dzQJoWWa3x+7tgR1siVJFlje7MuqPfunRyZ1lCf7yYkeERb+lSwZ7VuJ1q4Nwp96Afyp1lP1pMiVPsqsXNC7TY0kk1O2kgueEQBscc5sc1dukqFMc14pXmwmz70X9GMLVHWo/b6bvGtnQluMseHrl2P8VZ125ZWnn8bXFHxwOOy9EXMGbf+K7N7LLFzyyuNOcfgwhbjw5Nz83aYVpPu1t2Yc3ahfZZlJ2H8g08rKPYUbhUx0HbZGFN7fClHZRRnA/fxDi73BnZFph1izQOvxv6glm3oIpWxeZC/xzHudJG7/jZkXx5vJFj9/DsQQqortyxDqpI3cLNO8higb+wB4UZfjfJf17rMUdnuIt7w3cTr6WJJw+UdusxjedhEDZLS0s73AC6rS+kcoxfUTpCNo055ylJUr5O+5NBnPblGh+xmdLxfdm7R3ULzqNlEcq+pZ7m6Ay/MwcDsm7Rsujae6MdUj28D6eXxLqausVXxTDcuI3G90djsjk8A2vxe7M+SOPAmM06j9JO/UYzJ4nSuHBkbTWc8sT7rs3MnWP8pT3U2o2222irr/zcuc8+SlDVeyLyDwm+YD7UoyInTJgwYcKECRM+qnjqlTI0Lf72HbAWjBl6lN62kI+MF8mfO4dvWuTkdKTAMYH54jxSFIiRYfrdAmTLcZDds9HvpsVUJYhkypMt/ihGz1SkX/DVDbIuu8VBtxiLUCO9YkiI8WOYXOGTh8nu+UK6IxHzoxjVgLGCcYZm13ZHD6oZKoK6+0J/tKL0z/KjF4fPsrCpPJagaCqS4msUj+w6e4aAtzqom6kFtwj3qntzml3F7aRFvqClhuMYs3S7IxollAOjXRua4wq7Eprn63g8ncC9BWZt0OfCcXbEYwjTcYTG+Ox3f9yhMX6gf7LGIxKO2rPxWEKARdmgwFlTslM2lMZRGI9X4Zv3LvLShTvsletQJRWO6zmX52csbIOJmiYjihGPjy/KiMeKUoijFIfD8PmbL/PO7Qv8+W//Int2za/cfoWjesaPPPcV9u0KgFJcl44RjyWkC7D2JV85fY5CPD9w8PXuHZy4OTtmzb5dYfFdeIAzP+NOu8fV4giP4aI9w6mhUcvcNBg8HsOpn3Hs5qy05MTN+e9vfpq7f+Mz3Pus8qd+5At8bu9NrtgT5qahlJYShxXP79fP8ld++c/yH/7Qj/NMcTSof6OWXzj9NGeu4s9e+E3uuD32zZJSHBaPiy/dq2GlJTtm3cU/8nMu2jNsXPXf8zv89OEf4EcvfmEQ/8wHBUKtvcJj16w59TMq6Z0c1mo59gtO/YznintY8Tg1eAxODS520NTuXg3vtBeYS0MpjuvNRQBuNXu8PLszCB/aucLF60Yta1+wdCW/ded5bh/t8tzFY77r0nU8glOh9RaPcNpWfP5XP82VT91ht6r55vXL7F1Y8szeCTPbsnYFpXHdcZ6tGhpnubecsztbYUQ5Wc04urPP3qUzvBes9V3/9iqs1wXqTej71odjI+MRmwrhujXsfvkCroLlK00cKMAeWXwJOvOkYx3FS7j2gjjCsaxOMG04mlU03CtOBNNAs0/4Zr0EBVITxgDTwuXf3ufeawWuSopROoVup9iN6TVvPZmjItEn7wBPRJ4BmqiQWQD/FMERbzre8UM5KnLChAkTJkyY8BHDhyDnPK14+pUyZYG5chkKC9aihY3Kh5GSY8SQyZUT0jjMco0/2AmW1kx5IS4wZfzebKA86ZgxERvMmDxcyl8Vu2wCi8Kew6bZllaWjvjA8nCLAl+OFESqgU2RMU62pX8eQdyuwkLCl2akqMmS0dAmplXUhPKE59pfJwXGCJ2ihy0sifG9vIznEd0fAJ+ti0TBtIqPTJni1ONngiulY3CM65n96suUvZti6ZBWafb7z6Q484hzNHvVprJrtE7L8+iZJMMwKuFstXT/NPUDp5xm7YnCxbXnsLrAPdPfM045trK9/cbvKAtTnnpeXnt++dL3owbKM0/l4Gd3nwnvPWMmbUDAW8HWQZn1hdnnurqKD2yKvK27aD4+txIZX307DVhQGsKlBXV56rn21Ztc/tIuX/rFz/Gb1R/E22FfA7CN8m3Xa/4P/+BfGuYf+1ixCmn+g8UPZ/2ajf48rrvo6BvxUCw9P7vzR7Z+d4P3nr6hUVtKZMrk9Ri/r7wcxmmnPJXIlDEOfrEYpnleOmh4x8+1ip8t+LX5c5vl9sqn3lmzvnIRZ4RXTh2+2mddHbCK9ViN+oZ4uJi+O4EDB5eXHjffC0mPlMLie0Zax2bcOJIcFjdO8KWlvlB28e26xVvpGYTaF140U9TE/tMpklWxa484xS3soK+J08AUc0p144TyeG/IxNlGXlLl9eVHWn/wPPBj0a+MAf6mqv6EiPwCH+JRkRMmTJgwYcKECR9VPP1KmabF3bodGCzGZEyT4aovMVw6cXp0Nrx3Drl3CGI2/dM4h7ld0PmVic9lpPDp8jyP9QJQN9hZ1YUNiope2dPF28Ki6bbRNC22KsO2ATsKp7qVKdPlBZ3CKf3uFnNx+0ZSuPSNleJH9otNbBq6+30ewwVqiCMdm0XtIzJlUh5ZvI24ZNc5k8ZEloshKCgGTBlLs6e4eVw8qaBFqveQLaM2hQEy9o09sphaaJ5rIlNGkbtVyOfZNab0sfkVMYEZY4xirR+wZEQUa0IeY6aMjWyZpCWeFW1gHNQVe1WNFU9hwmo7MGUO2SlqjCitNxw3cy7NzpjbBiuKU6EUP0gzMWdK8RTG4VX45Xdf4dbdfX70s19gYRs+f/tlzpqSP/rs19ixdSiLtB2LozRtaJO4SnUIXzl5nsI4/tDeNzGiVNJy7BbMTMNFe9qFtVFbcOTmHLpdni3v0WjBRXsamTIFZcwL4NTPuOd2WfuSYzfnZ25+hnf/85e4+53K/+CP/jqf23sjMmVqShxVZPT87vo5/vKv/Cj/8Q/9GNfscccgsSg1ln988lkatfyZ/S9y2++yb1ZU9GygFP7Uz9g1666PHvsFF81ZF+7Iz/mp4+/kz134DSy+q+eRn4d3nGmE59JwqhVzCYyPxIi553YCU6a8R4nDY6jVDuLWarEoDuFGc5GZaaik5e36Eh7h3XqfT8zvdAyZJjJ0zlzVpZGYMmtf8IVbL3LvZMHzl474ros3cCp4NbRqcCqcNDO+8Kuf4spnblNZxzduXGT34Iyre6fMbEvjLaVxHQvHq7BuC+4u5+zOaqzxHK9mHN3ZZfdiYMoUhet0XhqZMt6b8F0YDTvafOR4qaAefGvY+9Ie7Q6sXsqZMiW+UrTyUfkikSUD4g3ShjSMA2lAy/QMysSU2aNTwokLDBkVsDVc+p2Ce68V+IpNlszod/vF96BFfg9QnrwFSVV/E/hDW+5/qEdFTpjwLYn7bQOeMGHChI85Pgw552nF06+UKSzm4gXEGCgKsGaoyLiPX5n0V5oWWdfo7iIwbvIwqshyje7MN1g2ui3NdN9s3gcwywY/LwZKozHrJsfYkoyCjWnkvkJylsmGj5pBAeh8WEj0JyLJmuyCtbhYtueyeEztA7OoLjdZLCn/pLTK/LGc52On+z1mw+g58R/xu9ywekclll05fGnwhXSLqTF7Y9yeY8aGqVuk9bT7JUlbZNdrpPG4RYHazc+n20Z2boF163Mbn3kRPLDwSivQirCK7/7a2rGsdjnr2h+k9dwszLmslq5Oo+d7S8fB2vGbF74HBOzas9d6fmXn+zf9fQzq1/vfMW1owK9U3x2VatKxHnRbfx8zGPJ+Mu4bPjCz0MBYevbr13nm8zt86e99ji+Wf7BjZoQyhb+mVT59Y8V/+Lf+x1uZOnatiFf+u8UfG7K+sny7NsvYSNuYMnbl+bnFdqbMAOf2ffo+uS2NUTxxdIpJ4xRpQ3//wjl+p8bfoKiyu/Lstx5XXuXLs2vD8PHvp2+d0uwfoIXw2mmLmy3w5Q4rE76j1Si7UuGZ1qN2B0S44pVra4crg4KKEatPnPZ98j5+pKrbh2CguTDv6mLqFWplg8mS+lwa7/CE78xK52fGNC4wZWZ2OPb7+D16pbh1zPzd3d7fznmLKIWvnz0Bh+8Rk7Dy0YfMZ9hv+3T4kTT3ziNN9PVVhrlGXOx3cR7ZkH9Snz7HD9jG7+4gBB3OTargfZBj0t/8mXNZMtoZsDZ2oG3zaZYOS0jbx7eVLY93v8MVRuk/cAecH6V1v/B5/c/BQ+24O8ev2/mJvvexRb0iRvoDLh4ntr23dJ0Mmun6fmHvd70NjxJ2wgeP93IwxbZDCZ7EvfuV+b2m8TjDP43PBgfNnP9KHzcmOSfgqVfKaN3g3r0ZfmQnJJ17GtP9Tliy/f6S3HeMT0LFNt8xMLgvIkN/NeM8ncOUxTC+mEH4AQNnzL4BaFtMWYY4edhcEbXl/jj8WHkkyxpUsSJhwdKFo2PemMaBC05lO4Ev34qVLfB9ZOz4Qdlg7Kdm8HukIEh5eMvg+Tj8+G/HzMma3jTgZiFeeaK0C3DzNADR+63JyjBOI2frFGdg17B8hsDIAcqTClvD6ooGRcSoPImFc979bsGcnuU+bpDg00aA1kCh0cdNXHQeVeh+ixQOEQ1+OGqDmbeYyNQJAXXUfbX3cRPvn97aobg7Y/bpI0rrOLy7i65K9p87prRB4LYmsHhCN9HYxQIDqPWGeycLAJ67eIyqYEVZO4sVZVEGHzddmYC1K1i1BQfVmlYNi6LBq+C86fIEaJxl2Za4yMS4fWuf537yOY5fNrR/+JhnDk7YLWsK4yki+wfg7nqHr//Ki7z2g9/goAp+cbwKhXhaNXzz6BLLuuQ7nrnBWVtRmZYqxk2e370KtSuYx7IBrFwZ2EkonsAo+cbdS3z26ruRhaRdvJSWV8FEPz61LygiCyg9W7uCdVuwX607n0GpLRJyb/QndRX8AhnP0WqGiLJcV+wt1p2PlySHt64f57wK3gvOGdZ359iTErfnmF1ahf6jdEwV3xp2vrzP2YsOrTzVuwvaPcUvPBQenHSstPCxAF4wyxKtQr+VWqgOK5q9uGfL6GDrnmll6OxXGWy7ClvchP3XK1Tg9AVJXZriDNTS+3zpwsdrFz8zF7cy2qAkDNvNwDRKsyPddyg+MGWCTxllX+D0pQWujOmmPBTS1tH023/5PprLCRMeEb60LF+52CswTejHxTIwFJu9IijEOyUK+Cr05eQjbrzlMgnXOUM1ubvqjEGZ0hYlsmTB1uG7Sdt/XdnPn6JhrlUT0jMN+DJsaQ1KzpCOpu876UqTizwX4vqCMGZ4OkNSuteNC/miIM3ZSabIfNvl40GxDGOEm/XhxPd135p2lv627dSJpZv/zv1M2RrcPLwztaEe+Vbt1EZIqKtdZ7/H0DAu+aKv42Drcy4PpecefJHGKOnHRD/MQ2Lbq8nqo32aWmgXrqtn8tel/T21DG70xh/t2yvlGeOrzd5THHe16GWrMcTLIK3EWAQGc4e4vm06xjV9nMG7kr4NNt531sZ5XbceFEEWNpsrTBP7cJZGCpOzvgfx3Za0Y7ixfJobc8buAcZbsLu0NUsjyzufO7v8thiJ8nZP40TaWh76qGzEGZer26Ksw/bKZeI0XhSr8O2m8UK2vJdt72rb95Guu+8he2dj49iA4Z/lu7Ws0rdfijuQSbKDQ9JYWyw9Lo7Zdh2Mk51PTXN+OxqnLG61rC4VG31rqx4jG9cHt0dtNliLaRyXpY8vCv7v/8KWDCZ8JplLgAABAABJREFUkHjqlTJAJ1ygmXXmvSjL27aPP87iPST3UUP+Leff64eCR9XEP7Z8R4utRzkufZDM+yj/2Pr0MHgv+UWLrIjwbGIbPEK7X+ry7su5uE/4efwHUAHpazbZdfq9G6/3gaveo23LgRgkO33NieCAtNGo0jO+zd1AreVwSz0u6dtcgvhsTQOcnlPWdf5Dl8P0dMVLHJ4bN8EBTXadowCKeGpRk903mVU2f/sXs+vUxrmFOv2+L3Lr6Wh7Z5/IFqvneXgMFssHWXQvP1pi76ssAHsPGc74B739xwONSrwJH22YxjF/+3hwT5zCOmwlLWdxS6L3w9MOR6xgIGMzai87wfBEv/FYkNg1IoGN3MYRyxpwvmcoQwiXGDsiPaMklec8S/oWS60as+FTKvc1NTAs5enA4HS1FB6A1gdDUmGG5YgGqG1MvfN8BvbPs3sma+MEp2hp4gIrKKS0yIx5fug/zGR+uDbyj3VJz8dlyw1U+ULOdT4IdZDOgI2dLUA1GsJyxqa30rOtR0qfLv9uASsDRUO+QB8Yv9ICPoZLi/KoPxooAoYLRR2mRRavyOoYnbZjQK1GxV5afWZpp63pnn6belIInTfNZZoPsb2RKYloqhIOkUjBaoPM/Gb81PEETNwu38XROM2mtAGJB0ZY67HWd8YfFxu8jNvfVWVg+BqLga0LTvUL67HGd0Y1p0LrLK0LFU9b7GdF2xnINBqKvIYDAZw38V6sqrPsVA2LsukMSyYrS/677eIKtbMcL2es3tjn8mt3ACisY160OG945+4+Vy6cslPGLd/e0EZjlWZpp/KE33RlTvWwoti49b9xltabGE/wPmzZ9t6gCoX1FNZhTXApkNq2cQYf80/3ShuMonVb0LaWonCU1oVyOoNzBh8Viukdiihnt3ewew1GlOa4AquYWXQpYRRbuAFRxfvQ+V1rmH11wfLlBjMPa1j14TvL332qf9dQ8VrTdRv6KtHwNTAKezB1bGMTBkhx0DyhQyYnOafHt4ZSZsLHDx/W/msdL5/PufegZJ40w/Z9KLH0nOunDw5tHxzq6a5DhjFd9L0k8RiLM2HChA8HvrIsX9rPFsmCOKU8C1uNm/0iMA0yVoCvotCdFspWNqyfiSnRyeqDBXDcOpy2w0bFQWDKhDCuChZUX/SLblHtfDEhiaFKdzBBx7LIGRmpLEpkR9CzNor4PDIGBmyZzLqbW9RF2XCsH+oQmTKzwERJzIkBUyZZ6vM5OlMgdO2UHmn2O2vLbj3kYxtkTJmO/ZGUMLUMFAl2JfiqTyvPKzFJfBVuiE/vNS7mM4VHVxaftWNiPyiIN5kWKpYv+tbLlTIJaZGWmCypDuKy9jKb5R4oP7J654yMnKlkmqCk8OWwrbs4XULZdewjQlyUZmVLSiM1Ekjg+eKUsAhN/gellV5Bsw3apxd+JyXgcLYNbBHJ2jqUxZdZ3+jyHyQ1aPcNxkuetQmk6UF7KjQ5y2mEnMGV8l3LZtht7KvTMZssCzc+SEA8nBZwvO07HAmVgzJp0InteVi/cbXrzyexjLuncDrf46QYxklso/uyZQBnYJ33qXwsyepm478UxksgA+eHmaR8i6zvJzJz0cYFdLxnPMycjtpUuvFv90TxVYFKYAN5C1qUQx+ZmRyflL6mhZ13W5ZvFviqGKS/FVlbpHFu/A5zpkyueBUfGY8x/LvLc/KY8IFhUspMmPBRwIMW9h8W82jCEGNFzOT8ccIDoJMF6SMPs27Z+eotgMw3kg+MFVVmaUt0zn4Zj+nbfBo8DHsuZ8+MmTSJRZOzclLaeRoZg3HwPNtyLRkbR60Zhhn7MhA5n/05Yr/AaDHjHGpt2KIt2dZrk8XdxoQZM2fy6ua+e0aLp5Cn4qvElKFjyaSFz9iHWae8yfIZ+y3LD1voGSMyWsSlZ9Jtpx4u8jUqArQrh49nqnmb1SumlcrYbbcZKfkGW2pG5RhsKxtvLfNRGRR/h/qHlW9iv5DlpxrLFLdJdQtHFxLotikZBS99+1pFjSJJkZI3RmKjeAlbw612Bzgk1oFqUrKkdgmFE6OBKZNehygKHdMCDYoibQ127gYvQYhjeGSpiPFxS3lIx2WsirTNHAJ7wohSWEdpe2aMT2yN7C2nwyByeBWWTdkdKgF0W601Mk3S/TIyVUrTs2RaNR17oXHhOIP88IhVU7BbNezEreT54RLjv423uMhUydOwJhxkYVBK62ic5Y27F7l2cMJOWXdlbdXQODtg37gsrZwxVBrfsX2MaHdYResNhfG03rBqC+rW0sZ6FbGNSusHdVGgjmEhHNAxi+4DVnVJG7fezwpH4wx1W+Ai+8iYUD9rPM4bbt7axe60HVNGKk8xC1ZGExlR+bDifah701hOf2eH1Ss1xbzt+yndJxm6bFQQ5vKCuqhp8ulf7JNjpowoOEFcUB6LF3DQ/tRGt/rAMMk5AZNSZsKEjwMeZfH/NClwxuV+n8ySrTgvzfEi5HHlPSliJjwC/LkmsQkfFWhpaV68GBbJUdIWr5joU6bdqzrGR0JipnRbRpKPgry7jKyquW+4fCtPxyaJjBvTBGHdVdIt0NXSbcFJTt4hskQqGbAecp8NwSLchw2+S4YKhJSuOO3rlJg2uaU9Vcf323LGflfsSvFVaJ/c38OgbL6ve94+A+TD/yjc2O+EraGd9/5yUrnzNup82hF9yqRD8nIrPqFsiZ003l6Uwg6YT/TvbsP/xnj6zBgbHYtI+3tp61Lni0b7d9ltc8is7Bt9LSluxn5QPJnCL/ryou/Dg36av2+JN5PuxCkqZpBvUPalH5IpALP8c2TKpS7P0fNtfkxy5//dfR3GEQe+KId1J1NypeqktsvYCxv1pw/XyvDZelSWMVLZjOtfC8Q1edK3Znk2BupRn07lDveyjDRsx6481AbWRobf4KCNdNi/s/fsiHlmY5OocnmprKs9VuM+FOO67Pe2V9gS2msbCy0dVGAczDM/cQOlZ4yfUHlllpU/HTSwG/tY2spnVVn4zf6SxrlLZx5fVmH8rBU1Fl8WwzElaVjSX8I7mN9aUl8ocbMZYwwYQMmXDX3c9KwPn7SIdErZVKdwWEqIIB5uHz05WXWScwImpcyECR8X3M/7eno+xnvxtP84cF6+H4RC47w0J1bLhAkTngROlpj//tfPfTwQ1N7LePwQ/snu6wdtW/xR+K3+yM47PGH0bCNux0wxW/MapDcOk/lKO/cwhfz3Nt88+aEK6ff4UIU8vCpqbdgOZrekF/3ndGwdp5Cfmjiun9eBT5xOSTVQqslAuTA4lW7k72WoYJLutMRcQTDwUTNWXJClxTDsePGZtkbpKN/Op44G56Uqsc+NF6X5lGsYLPjTVq6kfAx59pn04YcFH/u4SWyeUI8k/+RpZvGytNmmDJH+njjwlY7au1dydWU3itrI6MmcMecsn1BO7Rk+RVxwq4T9P6lRyLp2poFSL0Frkvmr6WkVsZISymIK3/u10XQAgPT+ckaNIkbRxkDhMWVg/gyko8yfSWJw5JC0TU7o2UGiwdfLvQqzX3e+dyD5V8mSj/5WtoqJol36Ka9UhuQLyLfx5TvpfQxB11YS2wUFdWaopUnvIWl+Ch8dI2bpZWkh4Vl5t8DNg9KjOAvbRP3cD75Plez9R2WoaYS9byw4e15xCx30yXxbkmR558rAlJbE95hvJUv5+ri10jQZU8ZD8yuTouRJY1LKTJjwUcb9KO7nULnvG/9J4WHKBpvKm22/P+gy5Pl8EEyeCR9bqDI5wJswxHsZXx7CL9r79YM2jXqPF3LO9ccOTxNz98PCezhsYsJHBB/iEfDXn9SBBpOc02FSykyY8K2ISVAJeJBfhXTvg2b8bFN2nYdJaTPhETDttZ4wYcLHFtN8+VBK1QkTvpUxyTkBT79SRgQpqweHS4haRfX6SMcSp6NZxcjGMa0pnQcd3/q+8H60oR/UgjPfvvFxZwCM2+F+fyc8PXgUtsuTwtNUFtjef8/75qfxYMKECRMmTJgwYcKEx4qnXikjVYl96YXh3t5tf2Gwp1jWNVpYMAatyn4v6DaLtir23gm6t4OfF5jWI3WLzsqwFzgeY5byyPf1jtMRr6g13R5gfDhZQQuDlj0FUbelkznH8la6+vR7dWVjj69xyvwb92iv7lEflFmZQt7FytHu2L79uvy3tHX0+C9euyMMB44HnVIsPd4Kbm76Iw5T/HikZSrj7F5LeW/Fyat7W/PLTxJ4EO6nRE3H1u1/7YT64ozV1XIjTuedf0vcgQM2E5zQpRMFbAOu7MOaRvGlxGMYdXAMaWi/vNB0e3gHx2rm+Xuwa8XNpNt7bBvtHSyOjp808ajKdBxoqnvKa3Gzod2xtAsTjjZdK+1cBvvNcyeF3X7nWP8N6Oi+ZmEVZseOdmawjVLvmWGctE037h9XI8yOHOKg2TWogerEs74QT6p4BCWqaRXTKO2iz9PW8YjN+F3Njh27Xz/m5NsOmN+qWT1TYdee5eVi88jT1A91eD85DFzcajm7Vg6cKSbni+WZ0ixk0Pc3ypscNyoUa8UXgrdZX8uPohz3FR3uEfZ21G8JaYb96kKxUsoTx/LKQwzv8bu2deiD+fte3GzRQrArz+pygS8l9KdFP3al43HTSR6i2jk1NC72023957zi5M919HvLPfFKcerZ/cq7uCv7nL68230z4vu2TGPTIK/oLFR8diywandU7zBsyjvE0X/0C/evyGODTLTejyMmpeeECRMmTPhYYJJzEp56pQxNi795u/u51ZlcjniEo3cOjEFEEGuHz3NE4cev1sjRCSYd1egcUoTmyePLWBmUO5E770jHNpQFm0n6ZrQQHVuqjdnqgK479z7BgxydUKhiT2f985ieNA57WvbKJCFThozSysuTlEDFUDFklw6T7kXHbd0xivGYx0Ebi3QnOuQO5dJpESntscM4NsKSlZ+NtMTD/PaM5bWS0+fNFmdw/QIxP2Yy1JnsaLjg7Mrb4JDLroV2EZ2tafjt5kSlTDhxwhf9cz8bOu5KzsJQgTIeXZBeZ1pZHxVwocaUHt8YdGWx+w3qBWOUonQY48PxeHWBsZ7ZrOmyaFqLqlAUjlu/fYHmmZady8fsL9a8e7jLhf0li7LpjiA0ohhSORSnpjuSsIgaGpMdCZCOCASoncWIUsWjE3/trZe4enDK0WrG9z/3BkaUVg2VafFqKI3jTr2DQblYLflHb3yKel3wmeffZa9c84W3X+RPvPq7AOzadcxbsfT5O0z3O5XjxvqAt84u8L2X3qAUx9oXfPXkGi/t3GPPrmnU8jNvf5qjv/cM8z/7Lte/9AzPffcNrh/u8c9+5lfYtyucmu64xFJc/JwEFztIo5Y7zS4AP/nF7+J/+LnPs2fXOAxLV7IXy/tLt1/lBy+9ycI2g28pTTIOw71mwcVySesNv330HJdnZ1yuTrm13mNhG3aLkNbaFyxd2aXVekujhjpq+06aGTtFTetDGduozbl+fMCsaNkta968d4HlO3u8/Onrg4kuHSGZaKLpetUUnB7NWeytaVuLtR7nDHe/uosvoTwWlp9okZ0GDkvk0pru6EUv6MqGvh0VwbRBcyW1Qa1mikkJz/3Q2Vw4nWPzXjolBcK31jm1iyd3pDCzu4b5O3usnllw9AnbKbhMS69QbulPkEjpu/5ZUrYFh3n9O+xOj/D9887J5YQJHxQmhcyECRMmTJjwscLTr5QpC+TZq+F6zHIZe8LPYFY1ag1Yi86KYXzJWCqAeI85PMXv7aDzAmkc0jj8rAyslcpmrBXpF9y5cgOCEkRBi0wBomBXLT5jyqj0R8gN2DBOu3udp3rolQdjZg0hzo5z1M/u0+wX/dGJhMWFXXvaHTNg2fRKiVG7qXbHCQamjPRHO8byFZVBC2jnZpPF4dKCJSlwDLa0uNmoPim7c9Y1OXNjeLzc+YKqeLDLlvLUMru7hYGjdAqo3KN/f8pAH9g22tXNNOF4zWRRtzW4ql/w5acYBKZM0b33VAci40JNsdEGeCiWSrsz79rYNOBmZWfJ14wVM2+1P/o0Yh6PJ1UjXLzuWO8XtIsDljNht4F6tssqZzVtMAbyNh4hMSdSnfJ6AXt3lNPFPkWj/PLBpWHdY7juGFQL8zvKjoM3Dz6JGtg7VH72yveGrLYwQM6DacKxov/13qtd29sV/E4V0/Ewu6c885Uld1bP8MINx+nvPcuFFfzdX/0nNk+M2JKnZAqA5972/MxXfqA/CcL1rKjqGP7e3kv3P2IzMs9QKFbwRknPlLHD/ngeU0ZjW270awl1rws4tFCdwf6R587XXtj01J+VJ/Vf4+HiWnHzOYUHDJQKu9c9voRi6VnesriqoFgqzd5ikP/4uNd00oS09Mfajuqyga2MGO37X1yk5sqU9B6qY485W1OeVMzuFf33EpUrasLYNEb6vozTQfuncWxwqkbe92HzHX+AmPZaT5gwYcKECRM+qpjknICnXynTtOjbN/rf5zFlzHBFqc6RjkMcsGsSTb1LL5yR5psWuXcYWDE+MGVMWYBXrDUMvJ+nNM7zV2HM8J5zYO1Wpkx+5GFOo+/SGB/JSKZAMGERL3ePqJyn3MmYMhBYNM5RzcqwyMjYMpqzcLJibTBlTIoX8rJrjxaCWdshUwY6pkxKo1g5TOMoVooveoVNegGdoqhTOqUyMGCbdPGS8kTSdf9XPLh5Qb1nWF/MypWe50wZoV8sSmDJaDq6TsDUgi96poxbaGDS+P53sLyHhbkvezaAznyfFn0dgHDs3uD4RQUvmOMCf6HGFB7fWFgbzF5gyohVbOE6IpVrLGKVqmrDMYJA04RVqLXK2Vf2qK+1VBfX7C7WHB7tcLC/ZH9WUxiPiAamTGTLJKYMwMy2AxaNEY9XQ2Fcx1BpvemYMh7h1954iWsXTyJT5k0MSqOGUnpmy916QWE8+8Wan3vjkzR1wWdfuEFlWr78znP8sVe+BsBusR4wYoxox/RI+Vs8DsO7632unx3wBy+91TFlfvc4MGV2izWtN/zMW5/m5uIK1Z+5yfUvXeXqd7/LnaNd/plPf5EdW28wZax4mriid2oCU6bexavw01/6dv7c577IwtZ4FU7cjINiBcCv3HqF77nyJjPTsg2tN9xrdrhYntGo5atH1yJT5ox313vsFjW7tsYjrF3B2hddWo0aWm85aWd4FU6bikXR4DqmTPj77vEei6phXrS8c+eAw5tzrr327pAp4wMzpm4LSutwkSmzrgvq04pqt8Y7gxiPekP9Ozu4uVIeGc5ebmHmkeMCLgZWTzqWUpYWzZkyLnx/Zj1kyiTGWL8ViE2GDCGNLoyPY2367YYKFmlBxaDzkna3oD6QTvllHN1xo6btNSpJCSmuH3tzpoxqf+zkQJEVlasbiscPEMp0KsGECRMmTJgw4aOJSc7p8fQrZQqLuXI5XOeKBGC8tWeAuglKEGPQsmArqyZjuJijU3R3gVYl4hw0LVS9T5k8/EDxkZdBFZxCYTolCF4xtQusk7KnKySlyFZLNmTbhvIAbNwTr1TrGn9hh2a/Gigygr8SR7tICpRhO42/gc7HiNKxZtK2nKRECVblXjESypDia1iQof0iqnHYtWJaHdZlXOGsPB07ZhuL4ZzvVjyUd1fseaVYlVHhMmynbT55tqVrWt+FNa3HF9ItxGzjcWVoh+TfY8iUybVJwzYaM4tSXYuV0s6iv5LOp8yM5Lck3yph2rAo9JmPGOMSUyb4lHELQ7PYwZe7PLcKadWFUI/aOS9Dd3/cPjq6N2LKXDvyuGqH/RZ+a+9i33+yfNIi2lu4fOgRB7d2X8Fb4Zljz29c/FxX/o33MV78pvZsgw+Zn1q83L+bNbxV9unMT5SD3z3k6MYVXr5Zc/brz3BtpfzMlR/cZAVlyru8romV8fI7jl/8he/tFvyJzaIC5Sn87M7zW5lGKtL5KfFFuLYruFUEhV5KP73PxA7J33ny0wIh/JHN3kss76IO5TmzcGkNs0PH2W8+M2wzQrnnkTVmY9y5T76Hyq6fisLidlAMmVpZv25xM0uxUpqd2eBbNQ34wgz7ukTGyZZvbtzGY+SsFBk7QE8snyyv8tQjpytmtyx7e6Zj5Un3XUj/jWRjSsfoyXzIJMXLYIxNjJ1UNwPT9pIJEyZMmDBhwoQJjwtPvVJG64b2zbf6G7Jl5XMfbJzAdF589eGZkSB/e91kxEBg3nRpnbPCNwaTPVPV8LtjvcQyjJ0Xj8t5HiNnzAo6PsEcnzKvqk3FVesoo1JqwI7J/25TVMXrzkFs8lHTehDBVwXYoWIpLXY0MoLs6RpzsmJ2Z9YpePItWB1rJymwSIqB/JpOIQQZi4ZR/NQW0eqdFCW9A9dgQd+Ik4VJf00bnLBign8ZN0sLsehvpiL4uPDgSul816AER7P568m2RYT8GeQvCsUSmh3QuEg3DbgFwcpvos+a9Oobg5rg0BYJa0XjwoUWsPeNkvqC0OwrbqYUZ8EHTmLzaFqZZ/XvFsbJt066n2ND0xJ+VzcL2l3FrIT2maZPT7J4TWgEqTzFOxXSQHPZo6WjullQvxDURaZImgdyfWm4NSqPW1tkaTEX1yCKesGflMiixRQaDjO7OUPcHu/+AOy/PuPoU57y2FC/uu7zinVKrCPN7qmCr4Midfm7FWefCfHUC+oMUgTmEbdn6MUGsX5IwUzt4AVtDFL64DbquEArDX5YahPaMmlIvIR/eXvnjJFG0EI7XyuoxK2Cod/7UilPhPmtgtMXdVgWciVKr0w1rVCcCe2iVxKF8BaVoDRcXTG4ORSnQrOXdQkPtu77dueTRcLWv06Bm5gwqkOmjGbXRKVH6j65csb3fyWdlBcVNKb2SBu2nBZL3zknT06aQzztlUEpfqq/873jYqdd+qHdtCs3WRqmfnJUmUn/8zGA0Pmwe694pNMhk7yTroHEGn7suN+pbtsG+vd6/X7jv5f8zsMHmd+TrOPY1+G29/dBp/Eo8R4U9kmmte3540jzceXzXtN5v/cetiyPcu+9hH+Usj3JtM773h/12XsJ82FBP9zsnyY89UoZYPi2dItzgPtFfQwyxrdEXzk6+rBLMCZRkPx0ytfYWON/UPkXwN6jRpQnUbpvETyi0rMT7nMh/0Hh83weNt798r7fb2Dvx8PPa3ncvMwfxELkI4YLjyuhD2jmTRvHZl/4QJLv0JVelx9sRhM+VvAXdjn9k98XTveKSsXk+yw5me79nzFggib2V1JCjsP2f+/DFI3I0+lvkhkxgvAuvmdohjJIX7ZkyNGe4TdgA3oN7EkNPu/UCu1cen9RKc/EXhudfDYwtoyhWRzonXvnz4UN1m7uBDzFF69Iq9ja085tn994CBO6djeNx561tPvlpjPw+y2INuqxRYm8LfqjDKfSh7drh1m3tPvVIK2uHTql//kZbC3PRvnOCaRb3g0EdwGndWCWz8qHyOAR0XrEuZ79rhoU8esGnZW9cn5b2VqP1A26qDaf5djyPhNj9lzoOavSTDTp+2i+HtJhfvn1OJzXzfA5HsZ5/aMofsdl3Fb+Ubh0eu15kCbM9FpYZLlGF7Pz85V8HBrmOf42u+eqYZdEvrsiln/AXs7DZ+2Z3FBsVZ48CNva5bz3dc6Ycd+0xljXYTfI2Miftb988wF9fcJjx7eGUmbCe8OHrf38VsFHrY3ej5LpvSgoUpxHiZuHfT9KkY3tLQ+Z1nsp85PER61PTnjP8E/Sq/CEDwV22XDwheA7L7BKRz7l0iIg9znHpnJhgKi40SKmlStl8oMLYKjgyK0rKY5Iz3SNSo2OYSb02xSN9L7o8rQie7Xb/tltx1QQwsls0DnpTml4mymZkMwPnIx8zY3qkNZTMnquQWnki1S/LNwI4qND+ZXS7GfO9bXPNymPOgJpA/M7FWfP9gza5MPO22xMN/39vh46VIClcnT1H9UlKVm6PLRnu6bTJLO/kn4DHM0o7y1oX11hTF8ujY3RG/JT/wg3TEdpjIoiARHN/o7vK5oxUSWm0YdTrNHuXt1a7r5+Ca08z7x0rzstsjT9PG2Np8wcixlRCuOpTPCJV4inMK47/dFkmqs3Ty9ye7nD566+TmEcrbccNnN+880X+czz7/L84ohWDY3PT0YIeOv0At+8cZk/+PKb7JVr7MD/XvrrsaJUpu38wu2YGpudZpmXx4ofnHQJYLPnLnZQrwanhjNfdb43GrV4FYwoO7Zmx9TMTH8CZKOWxhd4hGM373zbLWzDwtb9uyT46rtUnrJjanL40Z7sRi2luM7f4KB8yNb4KUyjljM36064tKN636z3OWkrPrVzK8TNPujUZr9w+5OcNRXfc+VNfuJL382f+vbfiT4ELUb8oE5Xy5PON+A3l5dY2AavhoWt2bNrjGg87bJi6UrWvqBVw6998xN8zyfeZL9Ys3QlHqEQz0G5ohBHq5bGW07bqvPpVxnHbrHGq+G4nbFqy678BqWyoS8UWZ1rb6l9gVeh9aHPdSd2Rp+BTfThKND5hAS6uvrsRM3WW5wKrbPUbehFqv1pm71OTHDO4L+xi7tWM9utKYrwPTWNpapanDM4Z1j/25vfwQeFSc75/7P3p8GWJNl9H/g77hFxl7fknllZlbX0Vr0D3dhFEBQHomaokUakVpIaiZwRh1qMGkpmYyaJ/DCimQamxSTSZKKMNpRgQ4EUTaRAckBxAEGkxEWkoRtoNNALurq7qrq2rNzz7e8uEeF+5oPH4hH3vlyqsrKyquOYZb4bHr4cXyLC/e9/PyfIAMp8mOV+lLVBBvl+kXV000EGecKlnlgN8uEWN005+MKlZsHfGqaXLnOlx1rpskl0DUtmvTH9k6RxOx/vLEd5NKwcp8R2zdujuV2wRGqj3UTgA20eiRp8KpUtNiKmjyJeWh8EKzqxtl73tVfldYUx1Brzbtuv/mtKxeSecnoPpkwkplDSo5LJ3aTdWY907NgNXAMi9fNeYaPETdDZGe/mozFjdE3fJ3OPXZYsz6zZCdc15a4p/x1JH/yL2DsAqSpn9gp8IpSbZ5o2LKP+LSws+oyQe7HAora1hTJxykvjs024OOXpI8/exnPsJNJr4/a3zT3Pzzy3tj/KzRqojCU+kkIPELyXxG1wUtwYC1vTPx0nGnTjtr+1saPWAK3RfGidzUNogdJueVLlW6/0769/bDS/L/X7AODlVNqxQTdNOg/nm399dImP3C341tnPnTifq3G1wGDTTr073i7jo80KVw4d1zc+zjXT6tx/d3UYiVW943dx9wg0DduvX6dO+0V9VTd54rX7vq9+u+pf3H4p4fkJx8r1vu+O5OgIN07waWuLNOiYNb9v3H48c49hntPKAMp82GUAZL7/5Eno83sBH48SIHmYut6PPtuX+1FE36nuT0L/DDLIIE+M2FnB1tdvhYv6vdKjlYcwaeOIdJ0O1NK/F7NkTmLInLSolWBXrmOjjWgRVV17K40zgdoDY330ql4MaNIyXJo8msVDu1BSBdEK3Im8OcYK1ke7+oyVJprQsnhsV++YoXISYBICK6bMUsm3pUlTG3nvlhX+mgJGu5bZUxLsuEGHFdNk3eisHTt5XdBNu4rVi/eoz+JFnoq2ZRjfsGTEKmJ9ONlb5eV3RySHCTw/Q0xgqRijnYWRRA0S7hHdo2HB1HFjNgy0jBhrFGs81qzuhddeFmuvjk6F7716ATZKnn/mJpOkIDEeg1bsFyGzrsM4KNWQmbLx+FjnW0vNqnEqvH54jqMi4/Nnr5NWjJrjcsSXrj3Pj1x+i4ujwyadxTfMlFItrx+d5dVb5/nx515nO1k2+rSeKcNSOjGOVBwjUzI2RcNeiRkwNTvGog3rpA63dBkkdf5ehUITFpo0jBOAVByn7JyRKZq0C00r3UO+C59SqGWvmLKRLJmanLQ6K1ioxSOcT44YmYJMyqq9Wu+UrhpYhVrGUpBKicc092Y+LOTHUnQ8ZQLM/AiH4CvWSl3Pmv1TM2cOyjGFWl4Y3+20UV0Hh/C/3f0EubP84Jm3+fnf+iL/5Ke+ysiUDcMkMb7pk/Np6MulT3ljfo6RKUmMY9MuG0bPkRtxUE7wCJkpOS5H/G9vfJQfeuYqW+mC3IclciKe7WTesGuOyxGFGrwavErFPgr9fFwxb0pvGr3GlWfTmrlVe9rMK3ZM7hMKZ5s+rZ+HZZkwSkL9EuMrZpV2xp3X0H6lNxTOVn9NhyVTj39omTL5a1voUwvG0wWJDW1dlBZrPd7XcYa56uOWAZQZZJBBHq2sM2TW//1ugIn7pX0v2GEnnfd9EInPJg8yyEOJDK4ivw/EjxNmL56PvHvRHBda2Y2GDujQSMwmqRfv6+JF8eP8OvmodnZv47xqb3ImWjw2wE0d3Yc4MfOFUgJwUAMkHpJ5ic4Fu7BdtktkU2XFlsMaGysnPSJ95kxjl2Yl3mobiwabMlI4/ChpAY/KAH/ztw4DxHnsrKDczIIjhHcj8dp8Dev5fjZdYqcJLSBUgSj5ArMocdvZvY/A0bbNike6tWW2DIp6rKw9IteTZiGi8OLRMZoYyul5lgKLdelimyCRU477ick9U6e8PDrTSXP5uOSNyYu8ntTHAiuV67FLYFs8vyi5uvniqr0goI9adY6Z9aPeq83vV5X+OG6Ax1X2SoNT1VMwXzFGYv3r6M1xuwdoyxogrMvrMWU6upwwTnunl5p312+mvfKj9Mncg1e+NH6Gj+0s+frpL5zYXi2Y2wWQY+cDwYB/qzcKzx4WXN/4GNeMdN8xMbumYda0c9s+2Aw076qOw5QqXc1oqdPE9Qz4qTImtKVVxYuEV0IfnK10sqrYmH0D9PC9Rl9xij3eRUcpPkk67aFiG8cGN26ubdr3QIZ5Ti0DKDPIIN/P8l4d5TnJ+NyjKO+BJg2PuF4PaqjtfuyaQQYZZJCemOMl0197PQoQVrwuNvdMGxbbnaklvlcbqKx+d+73vT+exLyp7xnTuVd7YlQRSEwAZiIbM2vzSNo4KEgZ2B2ShoW1VkwRoT0G1diiEcJ/6xaAddx44VVHj1gozdGujn0c1scFxEGyVPKNWIeIKVMv8AhpTaGM9pX5eVMxg3og2wkMmxVbMWtEja7WhW6aTn5UC+2IjRPuK+mBIT0SZs+6AJRB27cx4yZm6VRpAzYkYdg01KPqntVouDZIQNjV79ieqW5FDBtVCTv0r23hxsrkyiHWBhstddz4r5H4d8sa6LN94nt7B1PKZcKZc4eNLRvnDXeun+LsU/ucniwaFk7MFHJquHW4ycHtTZ5//jbTNO+U56tFZX0tooyMY5SUZPE5v0pMpFdsYybW1SMYtIobmCW1DZNFmTa/x7ZgbEtS44JNEW/JvaX0tolTL3hnRcbIloySVqea0XF6NGdiCxLxHSbGSVLruHQJC5dQqmWa5Bi0KTeuV8wcCdddOzC7yykAz23sRbq1Z6c8wnfuXGSRp3zk/F1eeukKH33xBpOkoPSBdRPLqSwY41+4lDuzDSZpwciWjCv7Lrm3zIrAhEor9tW8THnljfOcvbzPRlZQVLZdUuOZJEWjx7JMyJ3t1CezDiNK7g2LIsFXaUWUxPrGPpJXwXlD6QK7xXtT2Xlux633YRypF4ytx2M03k1rrwkINmK84Ku/6qP3JBDbiQoJhPHVDZbnHTpxSOqDl1AngWHnBZyw/H8NQMnjlgGUGWSQJ0niYz2DHZQPlpzURw/ad/3d0MEe1CAMQ+D7QbR0uNu33281HkrWDcsepvFAIqzHgR6bvAff1u215byzWsoDskDeUTlr8pb7tce6Y3Vt4odO0y9PXThWI9auZ8Csq9+DtJEI5/VuYCf0PPxccLdDmJi1eRngGb/gGX8TkqTR+UQzqNV9B8xNtvbevdI9jMyxzO9T//ruhi/BCE6yzj2ryqFsctgv/37H0HvXh3IPb0j3kXGV311z9sTynvbH4DykKZ9ZvAmjDJUssER60eey0aQ7p8uqLpZCwrJXVNmovfkGK9hsacln8rcgScI46LCw2rbIfAG0RpX7esJyhTnVvV+JP8HRxEN+9LWTZ79fTiijLNvxvm6u6ZW9/eVD6fFuZJjnBHniQRmxFrt9qg3oeyEAtCjBGOQertTuK30KZPzidw7NcyTL2g9GmqxP97jknbimO0GaB7qqJ2KQNa7SVnV4FP7G9eQX1juRh9RJVcG5zsf4fpOSJo19jy2TP+jYquM9wjGxIvdzXa0e9RomUo9T6nKNvONJ70NL3yNKpMtJk7p3IiLSPJsPMibvGceY9tm433NNb2K87vl8WLCwLrPWQTX8TntuT71HyxLJsgeu/wPFcQ7NC2Q8auPG/WgEdR6KAtK0vX+Cy3XZf3zjfDCAN8gg76E8rtWAuvvHWZfsMTsIHNZGgwwyCIC+w3fWOytrmOfABwCUwRhkczP8jtE821J4ZbEMk/s0tngf/V1H163CmrOPzqGp7aSrqbxSeszeEf70JrIoAtK8NQ4U46XDj5OGets5w+t0lbJbidp2Ea2J6dFYax2quCYKrymo0P6Oz6/Su9/QZdu0NeO0MTBXnT2d7Dg2XrqNbozZ//Qp8i1p6bfQnHdvzkdWZ6prt5W1QT1x1e/K0F9N462lySeuX88IXqOjBaRH243rKd10cRxxtb7aSdPYz/NgF8Izf79gdjHh8FmDXcLRCw5NtFNWq7wiS8P5rxp2PgvuVAlOIPVg68Uhq7Reo528mqOlHoxtKbyNIT5vcLOEbCvv0H0BjPG9E0EtrXed+8mO+pXhvRrHscZTOMN8mTHOCrLEsX88oSgsaeoQUc5szrj29lk2zsw5PZ23FEwVpmmB84a3vvkUZ74lyO+9y9nJjLSictYGy6h0eW3nLMcHYz73wjXe2jvNpa1DNtNlQ+eNz5UaaV1O1lTe2o1gZkruLja4+fef4fR3PTd/Aj7+A1dJrWM7XTTpayN/cT6168G15Ymn9JaFS9hMu7sEO8sNrh1s85nz4aDtd3YukljHucmM33r1GU79Zkb2u2/z1OYh44oGnFTl18bqSjUYlIVLSIxn4VKmSY5XYX854a3d05zdnLE9WjC2BdePt9k/nvDxC3c6utRuFFWFeZny1vcu8NFP3CARz9IlFN4wToJxuXmRcrgYsTVecvdwg3Nbx03dlYiCrYLzwiJPkV87RbGpJJ8+aIzAAZTOMD8eMZ7mFHmCTVxwx1hYxCi+NNjEN2NRIVBpARR8YTEHCem+IduHw08V1LR4vJDsJJz/unL7h8BtepJ9Cxp+N5R9T/NsiYNkJphCKKeBpm8XVXYpSAnihfFduPjrM27+2BQ14Ebt41nZ5yPbV06/nLP/kQy1kJ9q9RYNedXvhPwv/hKDDDLIIIMMMsgggwzyKOTJB2XUo/NwNnCF2l9HWeZIknQpiffbNa9ZEfW1cyu7/KYGbpzHLxbIbBEoX0ZIdtPmno0Aos4utPfd898nycqZ8RPin0Qt7NM/TzjPfc8wVWSR4/f2kSzlzOEcTZOW2lbF6YBc0Hhc6NyLd9BVV86id8pvwLEoPKINdsCsftwYAKMLZjVG5iJvD2tpqYVn9NodRtembL82Rpxn+dJobZ4Q2tYUnsnbB2zc2MSNBXHgE9vxotCpTw80i++L1q5Pu+WJDy5H3Shdn3YdMNWrXj9dHeaqsFIkuJpUmJSKT4SlgWmuDbCGwPFom4t7SjFNORydaoE8hVkFcl2+5pheX3B7dJ4baReEiyU7ViZLeP3cR7FzuD46iz/pLbTuMagAxHB+Hy6+XDC+OUN0ixtvPFeBcG36PvgeY2UrxdX4lbbAYiymALtQvrZ5DoD0SCmN8FYKl+54tq7OuVuc543xhQgwbfMEGuN24sK92LCnKZXNGSyyLWZJGAs2V7ZyuLqx1dnC7Lhl9HBlx7P/lWcaMFIU8sgw3UghVzizVPLR5kp9AYxXDDAqYfPqDDeyzL+x1QUTPZwtFZ9kHeN5bX20azMiMvRXu4o0y4Jk7jC5Y/HSuOksUUjmC9Ibh2xc38anBrsMiIlLzVqdRYPLWryiWRWnrMBuqdtJsbMCc3OHy4vQdxp9K2qjfLIoMXuHjG5soMag42pgRsbx6ufzjaPHs4PUP2s+yCCDDDLIIIMM8mGRYZ7TypMPyohBJpPqdwTKxECItUiWrTJl7mXYrs+UKUp0lHTS+JopUzjM/nHLlLGGcjssJu7LlLGCt2Z1UR0DD6Z1B9lhxKwJa9I3iz7pXlMxVBo2TAxU0O42S8tWqRd4kx3H5m+l+K0pe5/ZotismTItUFAb3auZJg0jJgIXmvBqgejTth5Bwaj82KWltHoHt5PaurOMgIdmbdpPV7FR6vjiKpZOot3ya/Fgj1Ou/J2LHD2dcvyMYAo4/khJZca8+leXV+WzsJz7yml2ftDDqQItDGbkgpG7OmrMUKncTTaX0t73XkgqZkHMcCmdYTHP2N6ad/ILhuvqv3FYjxFD15hczQyp49YslElSUKrhYDlmM1syTXKuH28zz1MmWYEV5enNfb761hWePb/H5elBwyYp1ZCIJ/eWr379Y5z61pRL/9wbXJwcMjKuidcyZTzf3LnMzuEG/7sXXualvUt8dOsuW+mi4waydr9Yu2yMGS2FtkyZa/NTfO3vvMjpl7e59dscv/MLv0VmSk6noc0svsOUseIp1JKK6xjXC0MhuBVc+oRCLXOXsZ3MO3FuLrd5/egsP3LuTQC+uvMsI1tyaXzI333lExz/5oRn/o9v8PzmDiNTNm4x43otfUIqjmM3IjMlc5cysQVehZ18g+/sXOTK1j5nRjMmtuD1o7PcmW3wxXPXO30c3GMaSm+ZlRlfe/VZfvTFV0jEMyszcm8bpk/ddguXcv1wi2e2D5rw2DhhqQbnDYfLjL0vnac4pZz+zF2yyChg4SwHx2O2pkvmeYo1od+WeYIxSlka0rQFLGrjjfVY987i9lOynYz0UJh9etk+VyqYOyPOfW3M7R9RdKPE7o9QAT+NOPwKUrNvHCQzg8mh3KiZMuEloQkVUwbGt4WLXx1x48cnIODGTZFUHksZ7SqnX9ng4CMZPqmYMjXwpWDK9h1bvP34ji8NXgkGGWSQQQYZZJAPqwzznCBPPijjHXpw2Anq20/QskStvb9NmTXsmSaXNUwZiWwO+OUS2T9ojhzZyKaMidkkcVn1YeDYzkRsEyeyGbLWHsKDGE3rnmNZE/ckWsCa8GWOPzjE3Ek4d2drFeSqAbG6/HVgVz//B2bKRGljd5P3YtP000WgWKN3BCqtc/VnCk/25h0mb004uz1GnJKfGXUBrzrvarSI84yvH7D9xgZunASwyqYNs2KlnD5rIqpLAJNWmTQonCkVl41W01UgXUwa8D1Qq9W5z9oJf3wCPqnqo4GVc5QEXWyuWAfLChD79ugyZ/aU3Y0N7mZXOkBefUTt6WuO6Y0ZN/V5rmdtnftuS9OjwPz4B2d/iGSufGX8TIcp1FSIXlh0r66jzeHyKwXjW3OSxSZf/fbnQegyb3ptF63/T3QpavKwUG+YMvWwLxW7gF/evBJYHccBBLiWwuU7nun1GXfvPset0XMrQGrHfSQ0bB+J3DFKCelcuZad46oFDNgl2EL52vR8R8eYKSMenttxvH7uxSZvUeVu5BIyhCvjXLmVne3k0c9v4pTzbx/hR5b5l8902nDk4VKh+HTKtNAm/5o10zB/ev0Vfgc3kHZZYOZzTO7If20SvSvALuYktw859b3AlDF5AMY0rW3StHnVekvhEK/41AYdSl8dp5SglypmXsCdXa7sVkYEkx6jEZBliewfMn5zMzz7o2gg+bbvVIS39lc9agwyyCCDDDLIIIMMMsg7kScflBEDo8qidwVkCAQApgYKiiJYRE+6TJcmTXzd+9uwQEqHJrYLONRpS4c5PEa3N5G8ABH8xqRh0Whq0RrQadgtEpgypqLKx4aJI3p/YIxUwIPpLuS6x3GIgAhWAQPp5V2X1bNH0+oQ5UtY3GR7JZOXb6EbE44+dpp805zIlAnXEdtFaV1GejpxfBKBJITFTW2vJ7Yt0zmGUy34V46inAQ0aJd1A7S2beyaNFWd7QKeshdYnk05fMZil8rRcy3TZ7V9Axhw7uun2f2kodj2SCloqi0YULNrYnDB0LqejOoqXlDrgxs6CH8FKAVZWHSzpLG5USet4nRsyKzBw5CWuaN1NWr7NpUYozgnlHlCkjps4lnOUjS3SOZAYLq55OD6JrKdM91cIpUbSe8FYxRVuPXdbc5+c8zhb58xnS5JTGD/lM7ivMEaT2ode3sb6F7GuY/ssru/wcbGgs1RcC8Zs3rq6z4u41QonSWxjqPFiDvT05z9tnDnB4XRZ3dJrWOSlk2dM9uybWobN4lpWRd9mzUiGhgzZcIkKTqMp8N8xO29TT5yYQeAt3ZPI6JsTxe8/fo5zn11wuHvOubM1ozEeBLjGyZJXS9X2dgpvAndHOkzy1Nu7m4w2VySWM8kKziYjVkcjTh3/pDYBSKA8yE/5w13v7fF5GP7oY7O4JxpGFgQGCvOGRaHI8ZbyyoshNdYq1Y2alxu2P76Ji6D408tKzenVdle0JmFSQmFCYwyJZyVMoRnIYnGeQz8eIOUQnKYkh4J4x1l70Xase+F7GDE+W+MuPP5hGJDSY8E8YEFE+cXQCSp7EKFZ7KcVM9nQXgH1UwZhWxPOf+1lLs/sIVacFn7PjQVuJQeKdvf2+D42QlqYLktneNSMVOmvPb4Pp2DV4JBBhlkkEEGGeTDKsM8J8gTD8poWb5/riL7Xkdu3uIduyeOWDr91CvXD+q95QHt5qwkO0F/VaUsSsQI0+8lTE+yh9Pz3rI2v37ZfV3NOhRhTdwYzDnJpk5fr3vYwFnRyyu6f0CWJmyPx2HXfDxaAe1W8j085syvToLdHdVQ9kl1OokNBF1GUL8Mp8H4dKeOJ9R9TR+sZQP2wcGG/VME8FAMUhaIz1uGUZpiFkcV+JitZZnYw13M7hHb3zuH2lGnHuK0sT10vnDI8gi3ucGZ0oe4dswDS3SKZcsryZ1byMERmy+fojw96R4hhJZ1VUn/hdfcUo2jMdbAPqpFFDZU2codfnQeVHmu8AEMTDY4PTvA7B5x/qunUDtZ/+xF3prGEXuklk2nXCpmULWVyogzrkTKfHUc1Om8Iloi8xvopAKv1XXfU1Fc3FHwGhZ/ARtUpmbxKHo8C8/bpNc3qsElZVw/r+u/qOtcP6oGL0fqoSy5OIpcaHoNzMjFks3/bdSC7uu8kMWer9R340T3Gs9y3qN5wflvVSOg/26v45QlW1+1VZST38N2MTvx3iCDDDLIIIMMMsgggzyMPPGgzPsq/YVGb/HycHk9uGHI99oF4v20Vx/AsEGeAOkDTI8y63tcay88Jv2s08JX/+Tq22vv9/M2a8LfiTRP1Y2bJ+r2TmVdXhqFx21U15+rbz9Q3ivPYH005n7xvl/k+Pg9yVaL/P6RypZpdXJGj69nBgN43yfyiN/vHVkH0vYdJ6z7/TDxYnknaU6SB8nr3ZbxqMp7L34/bh3fTTp4sDzer7jvx/33Ks2Dhj1I3u9F2MPo/KjyeFT5v9N76+Rh87xXugfJ+wMqwzwnyADKDDLIkyzxi/YD/tJ9IHmYhcn3Q3sM8n0tigyTle8D0e0py9/+IzR2mYTgFUykPZYbv+6iIdF4GzNCa/w+RBCvxHad1g2lmPVoCo94pRwH+0xqpPGglh4Uweba6aw5al3fE0+zOGhsSmko3y49agWXRQ4PqvqY3GMLH8rrKCVVndbUuV7XnLR5FX0XRGmZmnFWdZhf/w05yd7Y+vJo7E0BK0fFQz0d2Rt3cE+dwU2jabermKRJj0lcL7R6TE9UA5Oylpppu06tegyohniq2OMCP06CHbmjHB1ZxClukrJ+N6Vd7DWe9ao819noW00f6m9nOZoYfGSnyx4t8eO0ZYHGi+f6Wrv5NNnG334fbJVResyywE8zpPSYgxnuzEaTp5kXUDr85qitW/w3XjCvk/58Q0Pfmb0jNE3QjXHzTJjbe/hz26Fu/fwbvXv1KX3X1lnTBoq5ewBpgt+egghSerh5By6d77Joq/gA5u4BOh2jkwyZLdHpqMtyr+LKbAm37sLli2iaIN6HazFw8Wzn2ZHSh2fcClI4cB4dp502k9Ijx3NwHn/7Dubpp9BxBqWD2zvBScu5M61nV5HOWArj1Qc7oXV9VAND9+4uejzDXL6EJhbJC9yNW9izZ9DtTUjWtIUx0buqygeQ2QJ3/Qbm1DayvRWS3N1F8xxz6QI6yoKuxrR5xX1ZutAXyxx34xYA9sJ5SCy6f4jbPyC5eD7oVds7LUqI7JE29uyKEn/rDprnIY9Rhh4d43b2sGdPI9tbwbxG36Ovq3QoSvztu/j5AnvuLDIeofN5SL+9iZw+Fdo5icajD+96nMPv7OGPZ9gL55DpBBZLypu3ER6PQ4NhntPKAMoMMsgg7508CIJ/0q7Gg+T9oNKfcJ20K/Gg+cdx77dDNcgggwxyDzG5Y/rmQWNrLX6PdAzl9xeSECbqzeKG6hgqa99ptf26ZqEVr4FFMIUPi0ArYARvK2DDa+M6vo7bgD9agSdSe2aMQAkFTQSfCuXIRDbuwu1kaTCFUkxbwEYrwKe2V9e3FbfWkL30ypUW0BFXGX9v4tHxCtkYx4/KaOz9rSuX3r3od9BXW/t6VV7JXHj6H1zk9hfGzC9EoJEP3uLKTe14lRQFn2inrWqgyI+0zbsCZWrPk41NOqLf2uqSXZ+Qn3NIKUze3mR5VrFLyC8XSFp5gazWoDZxGONRFdQHu3Mm9Rjj8c6SpGXjVTJ4jwx/obUPV3tOPLi+BWPHpUv71B4gr712nu3Lh1ze3unEj70Mlt7gVUiMb/IqvVlbxo3DLXZvnOKFj9xifz5m/tWnee6n3mSSFHiEb129jN/N+Mzn3iQRT2IcmXFMbNHx8LiRBJtrqbiO/bnaU2Fd7rEbcVCM+dI//DRuy/FTP/htNpOcuUv5tb/+eZ773a/zY2ffYCSBeVl7f3TVwKw9Pqbi2C2nvHJ4gU9u3WzaIBXH0ifsFVP+4d/4QfKznn/0J7/JVrrg9aNzXP/ZF7nwr77OT517BacGj3DoxngVjt2Iv/M//hDlp2b83k99jb/+7R/k93zy63x0EkxBLH3KoRsz8xm/8PLnefq/Ocfs39njhy68zp3lBi//pU9iSuXT/5eXeGF6t2mPl46eIjMlH5ne5dd2nufObIN/8srXOZXMKNTi1fDK7CJ/++ufQRaGT/43W3zn3x3zYy+8zhuHZ/A/90mKqXDmD1xlI8mZJjnnR0ds2iWFWkamZKfY4MZ8i49s3KVQy2E55rAYceN4m8XPf5ILv7bHt/6dKdtnZhzcPMen//OM137fJZ75nW9xZWOPzJR4NRyWI46KEWdHx1waHXJQjtkvJtycbaEqXP3GFT75n5Xc/cde4ObvLJHM89xfvsjkjUNe+iOnufiJOzy1ccB2Nif3CXvLCdvZgoVLWZYJNw63yBLH3Tee5lP/1QSc5+U/dIHitOPSP3yas/+/l7j6Bz6G+6l9zm8G5u/VO6d5+tx+Y1fw7sEGIrC8tsGLf2Eb88ZN3vyXP8rsGc/Zrwvn//LX2f3ff4IbP10y2l6yPBwx2lqSJJUdwoMxiGJujfjYXz2N+a3XuP1PfZzDF4Tt7ynnfv7rLH7sE7z1u1J8qvitEkkr1P8oqWz8Cc/98jmSr36XnX/8o+x9wrB5Vbn4P8zRImOQxyui91k0iMizwM8BTxE+AX9OVf8LEfmTwB8BaoMvf0JVf7FK88eBP0w4XfDHVPWXq/AfBv48MAF+Efi39T4KbMtZ/XH5x95R5QYZZJAnXO4FkpwkDwLc9EGTdeH30ufdllnnNYAyH0r5sv4vHOjOe761M/74M/r8f/qvP9I8v/vP/Qe/rqo/8kgz/YDL+z3P2Tp9Rb/42/9YYKb4ajFdeWZrGDCR1F7vgIYp0wAEtS0wE8CHms3SxIHVnelKTKHB2+Qo7C5rQmCCOMj2wtG//EzWACXiIgZFn8lS79gvPT4z+LQbRzzYpUdKxU1Mq1f8ul7HlInKWCd1u9RpTamNp8EmTuUEoBsY6d54eeuxMvrlr3kDrGPK2NwzeeUOy+fPUmyEvdCawWKchraJ81ZWGTA128nKStiKLjWoVI+TKl56VFJOLeIhPchxkwRxSrGZdIEkaLxC1p75TKkN6FY7fVhhcK3TA8gOSlxmcBPTgEyj3ZJi2+JSued3t+NRErospui3zZVk5shPJZhcGd9ZcPzMpNEx2y8xpbI4V6NxMcDXFhB7gzxp875pEwcbV+e4ccLifNq099bLhxx9dJNyvPrsrtPduKB/OYqe3aiMrZcPcRsps6fHqAlxt755h8PPnaeYtP0QPC9WOrx6yPL8hOWZhPFOyeJsgksjdlsVb7xTMvnWdY6++AzlxCBO2f7WDohw8Jkz+EQiEDV4nHSpkM49UkK+ZVp9q34Y3ykwhSf59pssvvgR8lMJdqlsfusWOko5evFMGF8mjOfaWQgSxpkpNTDrqABhF56h6cs7cHeX5Q9+BDcypLOS9Ddexb/4HLNnpkFX0x0jtZ3BACy3hv1Hd5bY3/gO8uzT5FdOoyKMv3Mdnc0pP/M8yzMZPpWmT+v3SN1+Jg/v6Wy/IPnG98AI7sXn8Jkle3sXd/U68umPsri8ia+8R9qFw0WsQJuHF01yVJB85y30eAaf/AhuIyO5fYh/7U3MC89SPHMal5qgQ2aad7xdOEQVOysx33sbv3+A+chz+M0Jdv8Y9+ZV7KWLlM+cQ61BM9O8+03uqnqUmDdv4Xd3sc9dwZ3ZwBwu8K+9xZf1b7Gf3xrmOY9RHoQpUwL/D1X9qohsAb8uIn+ruvenVfU/iyOLyGeA3w98Fnga+Nsi8qKqOuDPAv8a8CXCZOV3A7/0aKoyyCCDPJHSZ5KcFOdh8nuncfoAzYOCMO+0zAGYGWSQD4K8r/Mck3sm144bmn38tzb8XUvsiREIx1+qsIYJUy1EQuZ1unaB2P0d6VEfhbLavLrqRXhncSzdRbtWrJquBF1sKrhUcKP2mEINdCQLQRwUG9Jhx7QeJ2MmTP03WujW7JSY5RLXqwKOfBrlAR2mTJNPxHxpG5uWadIHxqL4DTsGGq+RMaCRHCc8XZ7j5o+OWJ7VZuEoHsxSKKf1KrnK3Ede7CrdGrAp0+DJsQZEavDGaOvJTqXLmqkkvTGiuFBCKUze3mBx0WHnBr2ywCYOawNbpvbeZ4zHiuJUWMwz0qwksZ6itKSJI0scpasWz9VK2BptvA4Gj4PC1aunkY2SSxcCKyY1ntdevsSpK/tc3DwitS4wRFAS4xrmileh7KFnMVsFWg+K1462uX1rm0+/8Ba3jze5/vVzfO63v0JmHV6Fr77xHO4o4cc/9zIGZWTLzt9aEuMYmcBuGZuCVFbtQRZqmbmMucv4n/7eF3FnS37XZ7/BRrJkv5jwa3/983zqn/ouP3r6DUamwPaQKyMeHyGON4ttXjm+wOe2rmFQxqbAIczciN1yyt/6qz/G/LLjd//Eb3I6mfHdo4u8+d98ghf+yHf5qTOvMDJF0MlnzNyI2/kW/+sv/DDu80f83he/wl//zg/wL37qy3x8fBOnhoWmzHzGbrHBz3/nCzzz559G/u+3+NFzV9nJp3zz5z6LivCFP/gNXpjcxVaD75uHT3MqnfPRyR2+vPsCN2db/N5nvsGZ5JilT3EILx1f5n/++meReconf/Yprv7fCn7yhe/y6v55dv78UyxPCRf/xTfZzhZsJDkXR4ek4ijUMjYFN5bb3Jxv88ntmwDsFRP2iwnXjk5x+y8/xbmvjXn9/+o5deqI3RvbfPraeV7/p7f46E+9wbMbu0xsQektptI5lTCejt2IuUu5u9xgWSa8+htX+ORrp7j9k5e489NLbOJ45i88w+TaEa/8SyNe/PRVLk8PyEzJfjHh6uFpnt7cr9hbhpuzTawob756gU/9mUsAvPbPblKccjz19y9z5pf2eOv/cBbzk7tc3DoC4M07Z/jYxethDHnLtb1tAOZvb/Lif3sZc/U2b/4TZ5hd9pz7zSnnr91k58cvcfN3OrLtJfksZbo9Z5SWiCj7Bxthqnpjg4/9lWew3y659TsucfgCnHplm3M/f5vjH3yGt/5xix95zHbRsN/cwQgphPTA8PwvJtivzbjz2y+z9wnYenObi7fuoOXAlHnccl+mzEoCkV8A/gzwk8DRmsnKHwdQ1f+ouv5l4E8CrwN/R1U/VYX/AeB3quo94bGBKTPIIPeRhwE0TpKHOTY0yKOVgWHzgZPHxpT52DP63H/6bzzSPF/+5/+fH8gdpMcpj32es31Ff+RH/2hr+wAitoPckxliKhsJNeARewzssGROqmtsg6X2KFfvqNYghlfsQY6oUp4aNffEB7ZHzd5o8ortZZTBpkxtNyW2cWJKj5QeN05WQKR7St+ejDkhnJrJsybsPl4uT7Qrs65N46zqfpA2Dykcydt38edP4abtQkdUkdLje9711tlsqdt2rS2XB3wT2VmBm6TB7sksR0cpeB9syqyxVdNhVJUebADPxOnK/RV9IsaUPS4qmzK2qUNyGGzK+CxiSa2rzwN+Ck3hsYuScjNFSiXdmbG8tNnkZWclJi/Jz1QeBU3bT13do/B7tWuFo41uziAxLM+OGkbG+I09lldO4UamBRLvIeIVkwdGWb/u4mHy5j6aJSwuTVEj2NwzfuUWi49fpJzYlikT2XiavLlPeWpCcSoj3c/JT2dozRjT9jnM9nOSV6+Tf/oKbmyD/t+7izjP/BMXOiwzkwfPiz4V7MJjSk+xkbS2pwApldHOMtj4efUt3GdeoNhMsYUn+95tSCzLF841oK4mpvuseEUqRkhz7cK7InvjLrqzh/vU8/jMYhcl8tJryPPPkF9qGSlxv3aeY68YF9oo3Zmh33kNe/EC5eUzYAT72g10Nkc//lx4z1lpAFHjfMtCrI96AvYoR155M9hoeeEKPkuwt/dwN25iXniW8uI2Wullli6866ie/dyDhOdSXn8bfzzHvHAFnY4we0eUb10juXwJ99QZNLXhXZHZBng3eQAMzaJA3rqJPzzEPnMZ3Zggx3Pc29cx586iT51DjQl6mPD8ShG8dEpewo07wQbO00+hW1NkvsRdvcaX/d9mv7w9zHMeozyUTRkReQH4IvBlwmTl3xKRPwh8hbDLtAs8Q9ghquVqFVZUv/vh68r51wg7TYyZgjnB2FDtpkgM77nLoqbM93nxej/GQRynjvdhWuC9l/VZ16b36+/3ezw8Kvkw1OGDKicxbAZ5ZyIPsqp7l/LgzvTevXyIXt8fBHlf5jl2i9Frd9rnPgZYpMuUCQZg4wW0rhrN7NugifPs349BBOcDyKK2Ol5Qgy0EA6AiYcGeRPmYljXTWVFWaU0RjPz61HQW6+Gex5RKsWmb4wwta6U9NtFlyrRhTX164XFcE9mU6bBnkiiPaqHSHKVgTV61jZqaPRPrYKLyTfcIDArJAi592XD381MW56RJIw5MCeWkXTw27CNblx0oSjVTxqfara9piqFr46bHtFEY3dlgec4jpTC9ucninGJyYXG5BFsxa0TBCSSBedNMuZYZpB5JPFpaJPFIxcaRKl3TBVXZ3hu8E+z1LdxYkXPLargp9vVt8osl6dYSE5VjjGIqto0CqoL31TiMh5fpzvmPj8Zwa4PRc0cs5ymTb56DH98nS0pUhb23t0kOpmx9ZgcIjJ7avk2ts62ujWhg79Cyfvr2bnJnKbzltS89g5soT332FonxzMuE3V+8SPHT+3z03F3Glc2aWhLxHbaPV+GoHHH9cJtnt3cxosGGDsrCJewup1z9pWfITytP/cR1RrbkxuEWm3/pGQ7/pX0+d+EGI1viVJiVGV6F/XzC1V+8wtFHHc9/8gYvv3yJj3/yOs9t7IYyEeYuZVZmfOPlK3zsL17hzX+j5PkLtznKMw7/h6cwBcgfuM1TG4dBb+N48+AM1nie3drj1d1zHBxO+fyV15kmBbm3wbbP8TZv/dZFzFL4+F98ipf/pQlnPrLLweGUS3/1GcqxcPDPHDHOCrLEsTVaMrIlS5dgxTMrMvbnY549vRfaJh9xnKccHo85+zef5uxXx7z8L0/hdIG5vcmLf/YiV/+J87jfts/2dEFqPNZ4MhM+0vMybfoP4M5sQp4nyDfP8pH/9w57/8gVrv+OMI4/8teeZXTtgFd/3zbZiwdsjoN9oXmeslimTMc5xniMwN7BNIzz17f5+P/nIlI6Xv+nz5GfVi792ibb//MhN3/6KXZ+omC8tUREmd+dsHnxGBGlLC3z/QAQZjemfPR/APPWDa7/408xf0o589IpTv/CXQ5/5ArXf5ulPOUwC4PfKjFZqJs/nCClML5tee4XLfLt19n9R57h4AXD1pue07+wQ/6ZK7z10yN8CuW2C8+5CvbIYEoh2xOe+bsTzG9+l/0fv8Lexy0b15Vz/9+9x8uUGeY5AA/OlBGRTeDvAT+jqn9NRC4BdwhN+R8Cl1X1XxWR/wr4FVX9i1W6nyVQeN8E/iNV/V1V+E8B/66q/p/uVe6p0SX9bU//n9dOLlgsIUkgTdrJQ3PWNZrI9Bc4tQXr2iJ2hxp8wm6NB1nmsHcI50+jWdKdAEW2McRpx6J8syMTffhDnpHV/RidX6NzQIur84c39ymeOo0ft7sO8Y5Wdu0AEkt+YaNBuZO5w2emOae5Uh5EeYRdEfHgRqbdBfPVWc9Ri2DXO3vpfkH22i3yj18i306xS085bc8vqglnp2t9xzs56Y1D5h85g104lmfTztnV/vn0ZqJUodatccFWfVOCS9swU8L0Ro7PDLOLSbedKl1Ew/nYbmNX6QtlvONC2rrZfKvL9hs52a1jdn/wNOVEGipx35BgrGOte90vo31PORbcWFbi1vU3pXL65Zz5hZT5BdNMAGv6c22MUQ3YpZIdhA/M/JzpjGNxICX4UTshhDD5m9xSfArLM9LQxhupzin7GsKtJtY2B5fRTHLRts6mpBo/tPTveue3X0fp5tspYwnnv55z+GzK/GKX3g5gcvBZGz/cqz46C5jcVo6vSDNJNbngM23ySI6FZAaLC/XY5EQRBZOHCG7S7mbXY2KtZ5Oq/VbqSlvX+liAKYTJbZg9pZ2FwcqOrYI4wZSh3y786gF3v7jN0ZVAeY93iMQJ6SGcebnkzucSkhnkZ8Dbbqbi40EaFDIlnPm24/gpy/JspHtvQVKHmQI2ryrHT4cFxJnvlux9NMGN1zRmr06TW4ot4PiydMaBKCSz0D5utKZNtPs7vha3qmPtEabdWaTpw079ovJXygBe+h//NMd33rrHaHk0Mv7YM/rcf/KId5D+hQ/mDtLjkPdrnjMwgh+BPC4g+52Cvuo7aaXedffa/H5k5Z+UXw3g9X7LurYz9ynnXu3dT1t5iemEOwfWri8bTq5DR4fYm5BHnUeSJPwuSiTL2nycC/eztJfHCeWsa+e+Tl6hCLaWSKuyvKKzGTIZB11W8l1Tniqqiqxrc1V0Ng/rh/G4ClL08AjZ2lxfhvfBPkmaIFmGFgWSpmv7VPMCf3CAOX2qKd8fHYP3mK1NsLaTb2gHE9pTdbV879E8D7apDg+Dd6M0bXQGgt4nrc8A9b7bFlX76NExulxiTm2HzXrv8PsHyGSCTCfdsXTCBq6qNv3mDo4wWYpMJoEBdHQMzmE2N9r+hBC/9/w2RICyxB8do14xG1PEGnSxxC+XmMkEGY26+dTrzloPCB6QZjPUOcxkEtq8KPDLJZJl4V+zNo3qWKVX59DlEnUuxLU2hOU5Ym3Qoe63fl+qonmOliWSZkH/KuzL/m8/PkbwMM8BHpApIyIp8FeB/05V/xqAqt6M7v/XwN+sLq8Cz0bJrwDXqvAra8LvLaXD3wnI9kkveoleGs0jWA/c/ou1emk2vzsPsWnn8Os+CGWJLpbIHTCJbfXpv1RUu67ZaoAoZrlU18FTQu/lFD98EdhTu3JjviDZOUZr94LRDpmoIrMFJJZ0zzbgkCwLTGqxqW3BiXVAkCEswn14YYi3qxS+aNEXDHQFIAofjPa1/wiobIjdMa4XgzX1LlS9u6RCtEMXgJ9214hmkdTErw36FWHhVhugM4WSHiW4kbA4U3l+qAENL81gqRf1db3q/Gwe8jl+WhojeKZo9ciOU+xyzPFlQ7lRpbVa7c4Jbtxb6AstwJGFvszvGMpNpdzwK7tbIXFYrKfHKUdXDLNnHWqjVWK9owVhV2thGd2xqMDymRwq2wAAWgoUBpmWiFW0DAfbTaIsXx8HgOXpedg9spWrPlG8M3hvSLNwjlVE8d4wXyRk4xJrPXlhUS+MxgUiynKZ4pxhY7rsYDK2Sq8qzW5aXU4Y5u2z5LxhNhtxuDdh71PK+CMHWOOr8+fhMZ4tRkzHS4yA89LsfqkKewdT8rcmbH5qp9F7thgxHeUk1jdxZgcZZ5/Za8qNXxGqrau++ly9V2nqpSqUpW3O4rdp6Eicj8Z5+3BuXoEyt2iSUTy7DDuQCupDnPBQVI3oBZwgucHkBj9OyLeF/Ixv7RAQ4plCEW8ox8FugahQTrQDMoa/2gFKxGsot1MJWgAjCosNEpoyAF/igiE8u6jyq/P2PaCjAklGh4opAuAb208QD8mi2lHsU691TV6hUxrAstGbkKYGD+vrDlBT61m7WKUN7xj8rAyUPi4ZXEU+Hnlf5zlPgnzQWbWPS3d9FzS5KG1M7n5cRO+T5APc6/eW4+MPWdn73cvZ7JHl7G7eWgnzj6AO7u7OauC71Lufpx4ewuHhO8rLLxwsFt389/ZPiH2fvHo6+Nnsoevqe/F1uUSXywdOr8tl53nWskTL8sHSFjlaPHBRj1SGeU6Q+4IyEuC5nwVeUtU/FYVfVtXr1eU/A3yz+v03gL8kIn+KYADvE8CvqqoTkUMR+QkCLfgPAv/l/TVMMBfO1YV27+VF8AmfRueR6+MkfcpvA9JI8C9f+5+nBQjuRflFFZnnmIMj9Nzp9gx0D9gIk3rfMmXq/Ov7MZOnBmWishtdJPpdpZPSh51jVYqLW7hJzZRp9RSvjPMSzRKW5yfNucz0OMGNbLAmHukTM1CashzhzKEnGOeLQZlCAyukOjtbMzZMqaRZSrmRUGwGdk2xYboMFycN+JEmAonFZ4IvDS6TlilT61aDJ5GenbOn1ZqqXnwZB1oS3EMSABRTeNQa7FJXmDXBMwW4om2Phu0hgXVil0oya/vHFDUQBemxwyxLskPFuLBorgEh8eBnPeZPvWj0NN4WxjtKuRDKI+noFospYLxbUI4FNbZjRLCmYdcAmclhtKuVDlkHbDIlSAlunKCm0pnAmti4Bm4sLBbjDtBV97v1gYUUkyTSpeBGSiltWy5SKtYHWAfH40nUWbXSbd4d6V9rcBc6uevJr1lmuhXqXrNhALsw7I6nLepT97GCPTZMbgn701PUtG5ZCsvRNABbgD20jI+FHU5HevQRFWn0l2VomINJtNvmpWtUsZ9egOIeu471eC6E5EgoDlO86esQqydQBiaLKSOAwlfrkkiPjoeDJIwVn2kF7EVlRwBM80znUI4NxSYU277z7NXx4upKKaQHwvKcIqWQb1kW5yNW0bo6V6CI2uAa9zg+6KFSMWVCHXwaP/8tSBUfJ+j8XsOUEU/LionS94GmNn4F1sQ2MhTKbz2GI1KDPDZ53+c5T4J8kAGZQQYZZJBBBvmAy4MwZX4S+FeAb4jIb1ZhfwL4AyLyBcIU9nXgXwdQ1d8Skb8CfIvg0eCPVh4JAP5NWleRv8SDeF4qS/yt6Kx1LM5Vx5BWbc6spXoBjf0ZMS11s03UjbdOl+USKUtsEhumixgz9cQmZvX0GS+9o1W6wthZAyh5bfKWw2NSY0jSpD06FYNHB8dImjDS6hiVCDIvsCMbQCDTBWaas9txHtXOeZIZvDXURv2kVOyibbtghDC4ZCMvSI5LvJVmJ7n1SCAdl4q28IEeudRgoC2vEZaWiRIbPfRIu7COFl5xB9ZHW5oytVqMWmlc23WOhVS79i7rhrdHdwSXCeWULlOmAmXKicFVLIVyWpUZM2VG2qwdG1Cu0t2n2hRWbmrlfaGue3fla3Jhcc2yOC/Mn3btgpoAdGlSo1IgcxOMBhpYPFNU58Sr/AoDpcDEYRKPL8K5J5N4ZjoODIqnFoihYZZAYIioF5LUNedyVYV8kZCOS6xRXGlwKoxGBSKwXKQ4L0w3lg0rpv5b5xthAgg0Hhdq8SrMZyPmr46ZPe3Jnj8iSVyHbTNbZGxW53xLZ0msa/Q7OJwyZ8zmswfYqsz5MmUyKkisxyvsH2ywOEw58/R+Oy46jBdpdPIVU0ahYcp4byhLUzFlKr29UBa2g/zrhA4KpTUYoRJAHUBNMISoIxeBBi34UKdXX7OfItBWAS8tEFEDEIVg8gCa2qXB5gHoaryg0MbtH9sxuZDOHNmhbY9ZngBg1ADRZCcYK5QSRnsl0xspbiRrjhhpB1CZ3HWBZVPYtpwqTrII47327nIvdkyfgdO0T83+i/Wv00TMmLjcUJY2RknbOEoyf5xMmcdW1PezvL/znEEGGWSQQQb5PpVhnhPkvqCMqv4DVvewIZyfPinNzwA/syb8K8DnHkZBrMWcOb0KyoiE83LGQP+MaHV/nU0ZNYKUAczRNTZlOAnM8YrkBRweweltNEvp2JSJi3aRrZha13oRVFvVr44Jxe4uO8yByMo3gCl9cwbQLnP81rixwh2nFQU5zsBa3EbWWP22gM9sY2xvxRq89PJw4UiST0xzbEBUMPhogbbmKfIB0KktpneM17nq+IAEcIeixC49Jvckc48p23aqV4u13RqNmQjVwitmc0Bt0K811GdKJT0sSeYBXNF+PzX2XeiM8LbNlcndEiTB2zasjju+W5DePWb7jTHluF5YV0wRp8G+Dd2860WhT8NCNTv0AfiZxEwF6RJLSti8lpPOU0a7JujSLLwVrUFJCUym7CAwqua30w47SKrjJW6UrNqUuRMsyy/enhAzZbo6d9tvnFdhBhIfxREYl7UBw0k4riVNVlEHdPPrM2dEYVLA1tsF4hNmd7YpDZSR/ZakgGXSAmV5pPdoCdObytHxGYq6X3KYZzRsqtFcSI/g+M7ZE/WK2yEpgr2U+XTSaZuibud4jFZ5Ke0rooXi6IAgENprfAd8kq6O1bjhNIBx4iCZKbJ0ZAdKdiANm6ROI6UE1tIiHCUyywqUsS3LDWgZaT32i1oJhj/jukXAaf0sOhv6vhxJY0PGjQ3lVCgn6xqzC9SINyRzZXG2ejdG95J5BYpldHQ+0fZL9c84bcZFJ07MlCECpOjG66bpvuvcS4+HKRPwppMG5HsjIvIs8HPAU4Sv1J9T1f9CRP4k8EeA21XUP6Gqv1il+ePAHyaYQP5jqvrLVfgP0wIUvwj826qqIjKqyvhh4C7w+1T19cdSwTXyvs9zBhlkkEEGGeT7UN6Pec6TKg/lfen9EC2KtWcdga6BtJOMn93DWNiKgbF1RrZiQKc2FDabN+yck4yUdQy3ielcS51vw5qpUYQozhqDVbVRZp0vsMs8sHViHSMjY4iQVrZlACgd1hq0vl5nx6Zj1Kr6lyZo1S5h19g37JvY85DMc3SxJLl7hFmMgwvIyM1ldAIEjGAPl8h8Sbq7QApHVh/lMnRZPEoFEkiTtjFuVQFaLSijFShT6eQVsyxRZ0iPfcPSiY+SAXgnHUAgBnWk1GCFvj7GUDERwrEebfJp2QMRYFLXt+n06HeVh1YAiPbqHa87DeEYik8C4yf27BCYMrHeYWGO1vZ1orgetJAQbrugjMuC3Rw37i7A6+NL6wz9IpVh5Rrg0cjQb8VY8rHxdm3r3vy+h2gF9NTsp+boUu8xberYBy7ihXrVb+ID+FGPkfpIl12uUUa7f+u04kO7dxb1/eQnLPLX6taACDC+G3yRNuBDE0/btBXrw5Qw3nOYwxkbN6aIT1aZYM6TLJTJzTmnx1PsUttjhXX5VGM5do2roU83rs4Y38kotm2n/E67RMenRncXbF4LnT66ccRoZxqOUMZxmzZoWSj2uEAKx/Zro959DW56oXXZWqdrytZOeGv7JWKz+ChOHH9N3bv5dePWv5PDnA+xlAQvQ18VkS3g10Xkb1X3/vQa99CfAX4/8FnCUZ6/LSIvVsyRP0vwMPQlAsDxuwnMkT8M7Krqx0Xk9wP/CfD7HkPdBhlkkEEGGWSQQZ44eeJBGeCeRorejYE0vX+U9emKJ2BCfnDwfmuwXnZ3gfVtG4c15u7evtZZq78XIsA6BzAPKltNRkKfHeSAjZdghcn1Hshm9e9B5fR7pMf7ISPg3DtMe/b+UZ4o2X6YyKo4IHvlNe7lvFCBjV8Lv9c+Cyd5KiB8JB70Q1HHh/BsrMHQTkx3Ek53r/f0O32Hv1vRx2UNT2GFQvZeFxlsqFyvfh+KyEuc4Na5kt8D/PequgReE5FXgB8TkdeBbVX9FQAR+Tng9xJAmd8D/Mkq/c8Df0ZERB/UHeQggwwyyCCDDPLBl/dhnvOkygcClBlkkCdC7rVe+CCuJd6Nt42+jaRBuvJBGw8fNH2/j+Q96JrzIvKV6PrPqeqfWxdRRF4AvkgwWvuTwL8lIn8Q+AqBTbNLAGy+FCW7WoUV1e9+ONXftwBUtRSRfQLueufdVW2QQQYZZJBBBvkgyTAFDTKAMoMM8v0q7+YtuO64B3SBnhi4ia+/H6Rf1+GLM8iTI3dU9UfuF0lENgkuov8dVT0QkT8L/IeEfa3/EPjPgX+VkwlO9yI+PSwp6sMvIoi1XccFzgWmsAiSpGCkOdq8llTkFdR300D3GLfX9ce6/X2av3aQEF8TjnUDQff6lnOd49mypjytdF05xl3nHX831h1P966938l4zfemdvDQ/1b107yb8JO+c3H4g8ZZ96182O/ou9kwqdur//v9ivuk3L+f/g+a5t3m86SEvZs83usyH+TeIPeW7+8v8vsiAygzyCCDPDo5CaxZd92fpK6bRJ40+X0UAE+dz7pJc1zeO817kEEehbwPQ0lEUgIg89+p6l8DUNWb0f3/Gvib1eVV4Nko+RXgWhV+ZU14nOaqiCTAKWDn0dfkgyNmNIKPv0BxYYpPDabwZDcO8a+8gdmY4F58juX5MT6T1sYale0oVVwqJHNlfGuO+earyHTC8gdeYHk2CbbWIltmwUZYa++ttsk1OvQU0zb/2F7XeM/jRtIYtRcH413HxpdehYvnOPjMGXwi2KWycXUGv/kd7KULHH3hGfJtg6uM25syDGi7VKbXF5hf/zbm9CmWn3uWcmIxpTK+eoh++xXMdErxgx9jcT7Dp4IpgvF/u/BMv3mN8vpN7MeeZ/7Rs4hTbOHJvnON8uYtkuefZf7xC5SblmQWDNlPv3ub8rU3SJ6+zOKTlxFVfGqYvHyb8vU3sefPU3zmCt4a3Miw8dJNyjffxn7seZbPncGNDXjY+NYNyjevkjx9meKFi6hA+s3X0LzAfeETLC6OcJlhtFMw/sor6GIJn/wIMs/xr70FP/AJjj6y2dhs2/7WDv7l17AXzuN2duEHXmTvxQ1qO2jTGwXpP/wmeKX4HZ9n72NZY7Nu662Syd/7LfKf+DR7n8jwVti6VrL5rbvI8Zyd3/Ecm9eWHD47Ij1Wtr69w+LZU9z9TMaZ7xTMLibkp4TTrxaoCLsvJpQbbd+fdKqgNpa/4oChHpe1Oa4YG6uPKXQ8QGhjCy327GcKwafatcsX2UGrvWxmu0Iyg9nT935RmlwY78Dx0215se7GEWyV1c+J7b16e/baOobmK9t3jZF6bdNoXc66tqwarLbv19xWSA+CvcBiQ1ubfqIdndHIYL7v3sLQOshQkDLYLLSL4NxgeVZbj6Q9j59dzwJteGPn7l5jQqVpn7qP+vk3/Vy3Se2Rs5SO/cJO3aqyxYNWzh2678AQb7QT7CPmZ7Q79mIbhULjbKWxmVjZamzi1X0W2d+rbSeeVH9xtf4a1S2ksXlwWOLXrbrjsa3Rc6CVk5NK51o38dLooUYRL6FMIl1r7xp+pfmb8Sgu/Gv6KR7TCsXP/sr6ir4XMkyZgQGUGWSQQd5LuR+wsc7z2f3SPyrGzRrvbI8871oGkGaQD4hIoGL8LPCSqv6pKPxyZW8G4J8Bvln9/hvAXxKRP0Uw9PsJ4FdV1YnIoYj8BOH40x8E/ssozR8CfgX454H/9fvdnozPC+zbNxndCd4TcQ6dzdGywB+D/d41Nq6NwZqT34ulQ+dz3GKJ8Z7xd28wGmfB8nrfs2TkoTJ4/FPIC0iDgf7GS2QFVss8D4b/kyovp8hiiR4dI2LY/largxwcUTqHHh2x8eouk3HWNdQNSOEw+8eURYnO5oze2mOUJuA9sntA6RXNc9LreySHE9QK4jToUjj08CjsgB8cMbmahnzL0GYAenTM+PoEP06QpQsLm8PjcG+xILtzHBbhiUGPZkG3Iie5OwcraGrR4zmoR2YL0p0FycgG75qzRchnucTuV7/zArwn2V8wsgafGdKDHC1KVBUzWyKLHNRjZjmj3bLybqfIfIl6RUtXXRdkR2FlJh6SWdGwoOzCkR0pVMBasnDgPSZ3wcOeVcxSg6Fz77FFMJZuSrC5D/mXwVC8ccFJgZSExRzaXfw2XgW1XSBCu1ilvl91LREIES9eO8NVO7/DAlk7HiGDJ0lwma4CGhUwobbKx1t8KuTnXJt9v1xRJDeItxQXSkh8GPrRarUsTXATWeUvVqGqr4gGslYVX1Uq4pW0961iRJswBYwoIoox7b86fv3Xe9NMD7w3Vf4w25lA6hlvL3FOmIxKEuObtLUeAF4FrxL0qvaarPEk1jX55qXFGGV2PCLfy5hcPsJajxXFGN/40FCVoLN0yXPeG1xd3yrMiOKrMFPlU8dzzpAlrlPfWmdX6SqiSJUWIC8tzhnGWVnVRZr2KEsbbO97Q5I6rPVV+4V4zhnUC7NJ8CAqFxed/mj61yjGapu2DO1v0pr5F8LqcS82PGvqpXL9SW8ItyiklAG01USb9PWzUC4rz7dpjJD0wBPXB1K0BZCoQZRexxiCg5fah0sDUHWf5RihqsE6U/Vkk7Z+/pQTwdhB3lsZQJlBBvkwynt5VGiwI/Pw8m7s9wzyfSztxPQxyk8C/wrwDRH5zSrsTwB/QES+QJiSvg786wCq+lsi8leAbxE8N/3RyvMSwL9J6xL7l6p/EECfv1AZBd4heG/6vhYzHqHPX2Z5doJPBZt7smsHyGyG2d7EffwZlucCA6OzKK52Rn0iJDMfmDJHx8ipbeafeorlmQSfVpNv0wIwDXsGmh3i0YEn3zQdr4D1vcmOI98wuFG1CCthcrdkencPffo8+586hRpIlsrGm2Pkzg5y+hSHnzzDcsvik3a3WVRJFsr07RHmxi3M5gbzF87gRgYplakxyJ27yGRC/uwZFudSfFIzZSCZe6a7B3B0DGdPMXt+G1MqdulI9w/h8BDZ3mL27Dbl1GDnYcG1cTiHOyDTKYuLG027TfcmIXw8Jr8wRY3gMsPG7TGIQbc3yC9MKCcGUyobN4KXOJlMKM5MAEiTBFWlODNhcSHDpaF9x9WxLr89wVRHsdypCfPzabVbrYxHGWIEyVLEGtz2iMVp2+6yz1MyI4Al305ZnA56IOAyQ2oMbppQTgmeGjMJ4Jq1FBODpoZyLNilQdMEnxncCLwVXBa8Jvo0MCncqPWiiGjkjTECZCIgBmmZGSeKtn/jrGpnldSL0XitWYQI9Rht2DQqDZADkMyFZAH22LRgEAQlTcuWsEshmYM5sqixXe+YgKnYHX0WRx1Pa+ZJlbfUzIZKb5VQjZrRgFYOLerwPrOobrMYRKoX0U4Y7Rs0UfKDBPFwnFaNZ0B7TBbRqhPixbfVpkxRCcqohPY6EhblVgC2GtCriuulq6PG/1rQqtNfEXAAtR4wt6u6dvSt617jGoWAhzzTTpkxywVgmbR64wHfsnPGtw0+hZwxZYuXBCaUBgDCRyCHqZgnPgtjRRwY1ybUJKSrx0eod8T2idpJajCzZlk1rBzFLCWMoaTXHjUDqWY69QDRmG0jrh2fNTNL63Su+uvb8bfS9HGAgLeKIYCtGoE0dX0e39TjfZnnPJEygDKDDNKX+CjNSX9Pku8HwOLDXr/3SoZ2+2DIOvsVfXH3j/LI5DFjear6D+hOy2v5xXuk+RngZ9aEfwX43JrwBfAvvAs1P3TiF0vs628zejtDrEGdRxcL1Dn8wRH25atsvDWmYbjEjJf6r/fo8QyXF5j9AyYvXWcyylBr2jgxYyZOGzNlTLVjX7NloGXKpNW2qioyW+JnM+zNHU45BQNSVkwX59C9fTa/O2Y6SiGpMqpc1cdMGX90zPj13VB2xZRRF1g/2dU9kr1JSF96pGbKHM9APbJ3yORqYLB0mDLHM8Y3jlumDDSMGJ3PGd2Zh/ayFSMG0GVOencGxqCJCYwY9cjRnGxnRDJKEOfDcSRA85zkYAGqjZfQ5HDJKDH4RMj282BfxznMLEeWEVNmf1Qt4DWEA1pWrJp5SXYcjlyhil24ZnfcLj3pTMOutoTrwJTx2EVgkNhlaAtUA1OmVEyhFajlQ5kRU8aUNPnVC+CwEKyAjXqQ1ovEqiubhWfFdOm8NXpvkOZUTLSQ77NfOmBNIehIW0ZMfRSn97soAmDnzpbNolOqhbAYbYZ4sbCITzGXFogJ51MkYq74irGg3jRYgYkWscZ6kiSwM2pGSsvkqFgfos2j5L3phNW/bcSSsVH5NetEVSidYe/uJiZzbGwuKEvLKC1JrMca36avmTsEtozzhtKFclPrSUyLHCzLsNzbPxoz3xtz5ul9EutJrWvq6isWS11vH9XPVUhGzXrps2RsVJbzBucNo2TVc26tq1bsHqBJu8hTSm8Yp2XD2nHeBCaQF6z1qApZUjavP+fbOGVpmadTNPNsXTpqdFcVytKgKiSJwxht2DWuDGmz1GGMp6zYOlR9mlRsH+9My0zyQZ8+Y0adQHVMrAu+KS6vQMMkQnPqPFQqMEVaYE20YirWccMRvLrM5rgc4dkNgJJUYz80tHaAQ+0+ezWGWZs2WmEArXTdeyvDniXwAQBlJE1ILjx14n0tHXp4CNYimxtIkqxOPGqpDNzpYgnLJbJVOTv2HrJqayBaOGnfON264w6AzBZoUcD2ZpjMPGA6ra6lKJHFEr+1Ea7zAjk4Qs+eaidAfd3ivEx0XxWzPwNr8VthArcOgBTnA80uDfp28ls3HV+zoOwfDWadTp4KLT5hQSpr6lOHxzsIVRlqT2rL9flCNYnZWVCcGeMm0WSzn8SHl5Ym0u5C1veqCUtN86t3GxvaN2AXAW0e3V1QbmXkp5KubhUKv/HmEfOnN5qz+W0hkZ2AGNB2YVeznjCZQtl87ZDDj23hMonasE0bl9nv22ShJHPP4oxtN1XyetdNOrtD/fPiNldOvXTI4vKU46eSMGdzNNTj7TeWFJsJx5dsm1ZYac92d7fuo/C73tGtZfOaI98y5NurenXOmldiF4pawdf+oTUqvypTTbvbMNpRbK7kW0KxJZ2JZZ2/Kaux13tbStnGHe0p3oIbCT5sopLthTbNtwW7hAu/MafYTlARNl7e4fCz5zh62uJGbfs0ukX1s8sQ7tOgf3qgFNvSxI/PO492lXPfOGLnM5sszwo+YXWyrN38s8MwxvJt6fR3egTFZjxBr9qjiHZxovKbvKs+kbLbZs0OT9WPdqFsv1HiU+Hg+WQ1r0pMXk08k/hl0P41TrHLsMPb6Fj26h3F708+mrPi/bgQnm1t4zXj9n/6hwwyyKMUk6Xw9CXKM1M0EaRUkpv7yHyO2Zjgn7tMcXaMT83aZ1qtYOeO0Z2KKbO5Qf7RS+Sn0+b7Eb/TffMtrf44JTv0FBt1/tKwZcTBaK+knNrmmyNOGe+UjO7soGe2mX1kGzXBVsw4McjtO8jmJrNntyk2LT4RxIdjM6LhiM3YWuTaTWQ8pri4hRsHmzKZ84Epk2W4c5ssz4yaNhGvJAtHemccmDKTMeWZCTgNR3juZnAEkmUUp0aUkySAGgKjnXGoa5JQbmQV7V+wo+qDYQQ/DgaV/chi63lhYvGjBDeyiDckSZgIiAi+Os4l9ZEzEwAZnxl8ZrHGgHNhzmLaD6HG8zZjguHjOsh7TFnt3Guoc32exC4dybwKE8EU4cVqZwXZcYa3YHKPFCValqRHHrMsyY48ydwhi5zkqGC8m2LnjvGepSiE7LBAjTC+bSkn8diQtfMxiL/B0ffohKleGHSr7/c4r+ZbQxhzPm3nXOsTQXYQ5jPpUbby7Y51MQWMd5TZ7nT1e0g3vqwD3SWqX9Ukpr+QrfIzCiepXdbzGANFrG9Pp9OHgIFyPCF14ExI2znOFaWXiCEBkBs68676GzddQHKsLM+cozAwi22zKB0GSH9evW5eWLdNHL/OZ3aPvluxc0IFDnpYWLrtWg9/E9It1+mskChMDxQ1hvzUmWbOLArNqaGoXRIPadXX9Vw08702sN12kJ5enflCB9Ck00/1fHHFXk48J+mBJrGtpY6tGxP1Qz1/6evXf161e7/WI8xvw7ve1OBOPY96nJtPgwAfAFCm3vlZ6y0Awm6S82HszRdoWo/4+olds+tZhHO+LJYhX+cQ79sPZg2WdECGHhgQ3yvLkEfpAqrvpXu/3tVqvAz06lLvWNXnt0XA2rBTk0Q61Yv2SpdmsS1dfcw8RVOLm6QBxGhemO3EzJRaGQc0VZzWEGBnIR9/iKI8oF2QhThdQ4Id6qehAbjW3uu/ZOp4VOmiD6z24vXL63+wVCCZwdbbCUdPW/Kt1ThNmxShPm6srT5VGeKil6S0i2e19SpfSQ/DZHbzzYTFBWF2ufoaNAtuRUrh/K9vs/M5cFsufL211aOm5DaiQGmQcRl2rEoDC8PFXznFrZ902K282YGh2pHxzmCsD+dkq3PM7RlmmB2MkZ2UyQsHmGqHYnY8xhjP5saiKbo+V1zvDC1Ly2w2wqXb7H0atj57F2uUZZGQJY7jZcr8y6dYnFNOf/Zus/tjTXVuOdpZKbyhdJZTo7DjePtwk83xkq3qGsJuyhtfexq9uOCFy3cxopTekBiPQfEIJvoieoQbB1tMsoIz47DzuXRJ2O0wntIbCmcZJWWza/T2m+cwxxYuLLh8fn/tme3DZYY1yrja9anrUO8oeRVuXDuDpJ7xNGdzskRVuHNjG4Dt88cczUaM9iYszoTxnO1tsvdxy9Enc+ykbHfkRPGunvBXuzlHSdiBHjnUCfZuijtdNGMTBZwgpaG4adm6OmJ2WZhf9OH8cv/DXO0I1hMBPzJIKeSntAkPY1soNqOFQhXfLiOg14TJxjq6rKGaUNeTjeqZb6i61T9Thl1bgWoBQmcCYXIqQ3za5F9PmsSHHWObK24pzQRMnLZtE+kuvp+/Vrr06um1m64CZ+r49ULoPRdloPV+n4hfLpGXX8em7bTMVwtrd3CEeelVsjRt5yUrmz+By655EfIqS9KvvUqa1htVppNO1sxltHRME9udC1WbPVoUYdOr3niqbL744zly9QabhzMats5sjncOv7PL9JsaNr1iQ+1QxVvgXNhYy167BYkFF+Z86hx+viB54xbJrXFg+9QAhXP4yqaM7u6TVewbSoefzYO+h4dkb1rSLA0AhRF0/yAUfTwjvb4X4lmDHh2F34slye2DMB9KE/S4skFzNCO5nZCkSRMPQBcL7O5xyLsoUFXs/jEjVTS1mONlCHceczyHihFjjhZku6P2HVWFkxeVTZmcbL9sNn3scd54qzKzguwwbd6pZhHKNbkLBo0TsIsSnA8MncIjpcfkiil904amVMRVrBkn1fEJrd7/4f2pNfoA7Zxpze/6ujlSIb04ddT+9Ca+X7VFc2TOgc/q657BX4i+N4JPK8O1thsnzt/kIWB5ftV4cKNrPf/yrQHdjh0dS2s8N9Jj5QjSSuWiykvIPz5e1OQX7XImexa14DZcMNIbH5+q7dvE6bw03/aW6lMhRCrNETEzN6QHhuXF6qNrex/u+gxWrFt/od+va9OebfvVdexIH9Do3JNQTycBXekBHvWxLdHIthG07JKqz8odAwaW5xz1UbB4blKDLPU8xJThvq9QG3HB8HJTRdPtyhhAEej0v5RVftG4qqrWbGapjfKgmsv4bljdps1GVT1f6m0ON+mr8kTb12t/46n5W8+dXDtXEsLazjjtzpke0zRnmOe08uSDMsYiW5vhdwxm1COvLKEoIE2R6QTSdBVA6cvCgOTItKICO4+OekyZdeBLPAmKJhhSOkQErQzqrbAwTC9N/34FCGl19lhqQ36xIb667D4g028PrX67avIS2bGIQSYpq92VPpMlbuL7sGc6z1DkDWItq8auKaP+GPZBn1j61ex/mDthshoGJEvP+HaOKTOKiVlJX8c3FfvVpZVecXvVk5QIXAq7Bq1C6dyjBia3CiY7CZOboWIVZtOwSk59b4H4MW5kV+vRbzqtJiepbSYqpoBT35vjsglunKxMQOrzqB3QK6rzmbmSHivzN0818baWhF2Z0cZ6faoPw+YSTr06wxZjju6eb3RaGEhKOP1yyfK04fju+ZNBOqFZVB/XTJk5HGawn3Tb4OwbyvL0hGunr0RAnvb6PerrmXCYwt5IO23R+WhZGrrn9k3BFJDfmnDr1HjtZLJlyvRnEW3Z0zvhHHM5GXF3FN5X09thrM3vpKRzYXqrwC5DPyYHCyY3J7hRhhunLVOmngtJ+00fLwRvFZ8miEK2L+RF2sSvJwZSQnYAdu5IjgkgYaKd+ncmoXX9lqFdTMT8IQJZmjYhujb1JDwYQJR4Mli1i7egtW2CKs+YxeUzoZwaXCbkp+vw1bPaNqeaNEXNHk18TRlYN27cVqrus/7kMQZkQj5hElLvEHXzbtugvq4nTu6bD3DEaZBBHkZU0SJHi3z1nnf4hYPFYvXeSdmVJe7g4BEqeI+yihx/eLgS7mcz/Gx23/R+4fBvX1sJ1yKnvH7jnmnd7i7s7q6G7+3D3v768g4P1+t7fIx/7Xg1r9u34fbtByqj/N7r7f04/I23OnHke9G9uC6A++6r2O9GesUF/MZvMfqN9rJ5Pf3mtxj/5mqe2S/fxQOj34zCX4PtL4XrSfWvljN/j3vLE3oM98L7rcAgg7yX8j7YJEx09V04yHsrTz4o4ysr+7CWLaPOo2UZ1hqzOaT1DnI9w74HU2a2aJky1XngjpeCOJ+4/D7bpSiCDosctSaAH+uYMh29ovsunAmWmuLqwm5G848aDZWwW9IBh9oyGvRXJHgPMHIyU8bKwzFlWBMeLYobsKIPAtTAgIn1aPOM6aArjBciMCqK+zBMGYBkFujEJzJlqnykqECZsa6UJWVws9dh9PgW9VapFsEWtqYZ84vC/Cnf3X0xIIUgfszdz4PbjOB4aeN0zoQpgbM69ohRtBRkbrHFmLs/6jAbReMVoFl/qrTMGOMrpkw9TJSD/TF2LyF7/qDxBnB4NMIknul0GTdJc9ZXVSic5XiWMdofs/M5YePTOxjjycuExHgWecqu2WJ+Udn81E5j0T+cb3adDZfCWZwXNrLwvN453GBjnLNZMWWMKM4brm5eRp+a8+yl3YY5UjNwaonPP9882GIzKzg9qZgyZRK8EBhP4SylN6TWkVdslGuvn8csDJxfrjBlauR+HVNGq78NU+btM2AgnRRsTHJUhaPJJgiMz81ZHmcsXk5ZnA1GNDc3R2GMXCmQiYvOtoOrdlrq+pbHSRhDqYYxZ1LKM2VoTENzBlkKwS4tbmQoN6Dc8AHI6PGEO6CHEo7rRUb0aj3q+6ayllcDEqZoB4hKy06pgbs2HR1Xi/XOTH2dzJX0yGFTYXy3fX91gBMNoIyo4m3rkrNhrvgAqNjcNy536yNNobrSiVuzZRqdKhBbXARe98oIYVq1c9hJtsvHtYVEB0QaZJBBBnlfZTBYP0if9TbIIO9WhqEEfBBAGTEwGZ98uyzRPA8uJLO0tSkDnfO7sahWlM3YjkySrGfY1Fa7TmK5nBTWf1m5Oo8ovC7P++BGsqhBGRdAnmXZLBYa+zNxlU7YsZD5EnJDIj1bMfGiq6Kx2tSugExrWWTrALE+k6Zm9axL3/TJCXnImrCVPNboZ07QOz7OlXvS3QXjOyPKSe37rZsn0Bxj8Okqm0Z8sB/TocdqN05jU2Z3iXstYXmmZTPUacQrG28eMd6ZhnPbzfgy3bh1kmoR6ZOkYZiYUtn83iHJfBM3Gq0ybCK9YiCullO5kh155me3mnibRRhnbtTumfV1SRQ2Cjj9nSPGd8ccvRyAiHF1tGvTwfabS9zYcPTKGVRaNm1p2gVyWMiHxe1hZetgulSchd3apkz159J1RzkZs3f66VVmzJrxkC6hMFA5x4goyLTMj/rYHXBuV0lnyvLUmP2tSReMq6RmkBz3qNEN1Ro4vUfVfhmuYhudPgh9tzy9xeYSNq4vyA6D7ZRkd8bpV8aYPMWPukyZGJODAEqotEyRbB+K3d7YIoAT2Z6SHhVsXMsweWDvrNjz6UkyC8d/GrYLYZylR2BcBNbWuGERtVHdlg14EekesWIEmqNMdZjzgk/DO8qN5ESbMj4NutV2MVbYK54KjGrD6/Pb62jHfZqw1MeXon6N46+mVfxjZcqc8D4cZJBBBhlkkMctAxgzyCOXYZ4DHwRQxrnmHPA6UdXAeqks3GvMdDkBlME51Llgv0AEVa2ODEXxTzLWC6ywb4ocdR7sfJUl07NTs5Z54zy6zJHKeBxlDcrkUBjUyOr57/658jhfX9nYKRzEhnFrtk0NBFRnGbE0wFNrK6IGWCRaBPdAlwhoaRf/7SosPq7SN9DbHkWhTVcvsk84ztRl7PT0iRfovd92aRA/YnE+pZia9YwcCYta8eCy1fv1MRiN6towFSoQJJlpdcxFWG4b5hfMit7iwM4nHDwf3Ff2gYUVA8MaFpg+owFlbA7p0YT9j1jcZLVdYmN5tau77hEfQ3ZgmD2lrcGzeWgXN4lXq+3aVQA82IUwuTPm6ErCwceqJnBhwS0FJMuUfEs4fKELRoVytBoP2rAmfBJWuslMKrecvi1QQZyl2ILFOW3aI9RnzaRAhWQmqNVg9BVaA67RYr5pIwXxgiaQnxbybW3jQheEgMala3NP23zdKIwJl9EY+vVplb8FTQjMtYYZFuI39lXqxX81RqrqBFZGdWLBV7Zb0plWNqqielW2VUb7ipnlZIeTYHDSrtanscdS1SGdB/sC2aE08UQhnXnKUTsga0DCxA4VhMZAdmtXRpu27wAt2tqEwSu28KS3j0GE9Kgych4xUuoyg1FyjUDsKJ/6qKZz7bu2CotFesxDGrZMLxwiJk0vTpTOHq85YjLIIIMMMsgggwwyyCDvQJ54UEZV8Q9wjlo9jUvCB857ubx/pIeRNWeUH0r6R5bv7ry7/O4hD4pzr8Mun2g88ySWjSobj/Es9Bg4dY/7Fx9BGU//z48gk3coI+DcPe6ff1yKfMCkfuE6YPoSTN+DMrwq06/38n5C6ca1zYV7OdhYJ/1aPO5aqRb3j/TICnt8RQ0yyBMt9YbSIIMMMsggHx4ZXuvABwCUGWSQh5J7TdiGydzjlb5h7ifUQOB7Kk/KmHtS9BhkkEEGeacyvMcGGWSQQQb5kMoAygwyyCDvjQwT6CeWoTLIB0iGoTPIIIMMMsggg3xYZZjnAAMoM8ggg5wksZHrRwEqnJRHnP+HhU0TM4MGQGaQdypK11rxIIN80KX/vn+nv0/K872WR6X//X7H34/HkUc/7r3S3StuP917eT+Wh7k/yCCPW9aN6UcR572M+7hkmOc0MoAygwzy/Sz9I0ZxWPxSfq8nMx+2yVLfg1ssA0gzyCCDxLI5Qb/wha4R/fh9XNvbrj2xm/A7OVzixwk+tU14xzg/rN+BFFa8HAaPeCHyujxENRgrrz1BRi7j7azAjxJ8apo84nwaL2za5pce5pQbafCqFstaT4899ZXWWDet8fK6bitp1uS5Lm9x2joliPWFjoHxOs6K04NOxpF+dTvNHcnunOLCFJe1Tik6ngX7fRfr0NPV5p5ybJvwph2qvlpnuL7v6XKlv6ATtk46Dh1MW278u76u9YodOMRG5xvPg6Y7rtS0utm5Jz0oWJ4f4RNpxmLfA+ZJ6zq7DG1VbPSsl1WG8jt6VXVqPCFq238qdJwxxPdXvEOuk6is2BFG7GSj9rjZSWZkff513J7Ti9qZQePYQAlG/yO9xVWOF07Ku6dj7UAibpfYyUTfA2O/vitNEenalxXPh2vSNb/Xxe/1Y3bkMaWSb9mmzeJnuO2H3vug1/8dHVgfv98+8Thu00fvV23HfCf9mvo28X1wsBE7cojLhRAndk4hPvT55HbB8mxKOZJqrNHxSNrGV/zf+hIfVhGRMfD3CSYyE+DnVfU/EJGzwF8GXgBeB/5FVd2t0vxx4A8TzCH+MVX95Sr8h4E/D0yAXwT+bVVVERkBPwf8MHAX+H2q+vq99BpAmUEerQzsgPdH1tlveVhQpR/nwwaUPCnysO36fuxcDPLEyNDt3wdSgQxSez2MV+I10AKtRWypwq2g1qCJtACIlWaBu7aoevFh2oXBymLa0s2jUscnbbo4fnpoKTdMM9Gvy4k9zvUXDeM9y3LbNt4O63qtLI5qACleOEYLr0bitGZNXrI+/84i29d1r677z14NPiR1Odots9avbrPeYio7yNh6Y8TeJwzlVFEL1HmYkF8njY9X6nEdFJNbkiMhP+ta0K5W2BvIPBhF6jwrfYytgDcFYyLgSMKqMN4sN0ZDOKDR6jNJHEYUI0piPa7S0xrFeUFEMQLWeBLjKb0hMZ5RUuJVUA1xnDcYCWWMbNnEdWoY2dZxx6u3z1N+b5MrP3SNc6MZpVqmSY5XIRGPRzAoiXFYUUz0/BRqeOvoDDf2t/jcU9dJooFrqroZ8VhRCm8ZmZKNZMnEFpTesPQpRdW5qXg2kiVp5SKxUMtxOcIjJOLwakiMIxXX5O1VSMXhMCx9Qirt/UJtcz21S8ZSsu8mzFyGqzrVq3AqmTM1OUZ8U7ZXw0ITjtwYg2LFMzIFU5Nj8Rz6MYVPSE1J4RPOp4eNzkufcrPYxquwaUN9UlNiadsDaK4BZj5jZApScRRqmbkRp5IZtnooCk1wCDMX2gPA9d2JAlZ80z/jKj+HYFEc0ujn1OARFj6tdPGkxjVp43xmPmPpE7zW6RMmtqjaTPk7Nz7B7nzMb3vmNUpvubvc4ML4qNHJqzRjpxanQulDXrm3+Oq6bh8jSmaqvqjq66tnpPSG3CfN2Cx9aIeiSm+r9lUVjouMg8WIaVbgqvSqgtfwPNXPh6rgvKF0hsJZ8tySZQ6R7jPqnEFVKArLaFSEvLzgnaEsLJOXxsyeL7HbS9QLWhrEetSHl6K6Cux0QvHlle57z+R9mOcsgZ9W1SMRSYF/ICK/BPyzwP+iqv+xiPz7wL8P/Hsi8hng9wOfBZ4G/raIvKiqDvizwL8GfIkAyvxu4JcIAM6uqn5cRH4/8J8Av+9eSg2gzCCPVp70FcSDUvk+yNJ3mz7IB1ek2hIXc+8duPdcjXsXrvGu9b3iyuokLcoEdQ6xNsSrrhETwtbJffTCa5tnHNdVfp+qfEUEVUXqHceyDDoYacOcb65l8Rg74wl/pQ7yCKQCU2h2b9vd1f6uav8+zS5ru/iWBhlZX1bIoxccASZqCM9LzQ6pd56ttOBK9MwnsxK7TEjTLigTMwDaMkKE9KAgOU7QpPtOOJFxEO3G99kLnThN/foVjMowdFgaTZQKlOkqFN2v3cWtAWFi9kiTLtYZSI8d2c6C7HCKz2SlD5pymrrV29b1dVsZ4xS7cBQbNYLU3ZhR207v+2yADrvjhPHQqUtdn154PTbjZjRR3b1AXgF4uYFlFDFmBKjAYVxer+3OHCmj/ZKjbz/Noa3CTZt2ncSsLFPA6aXyva0X10eO6hjYDat1r9srBjMbpkqte12m9J7ROrgac2uf7xq49HTaumGorQH9mrgxwFjlU+EFK0yZGpMyZVV+DM6uefYaNkrMdooZFo2ebTutk7XskrgufbZLnNbcu5/7bVaPj1q/0Z6y4ZVfPfVFULCF8kbay7Bmi8RgatwPEVOu856M+r3PElr3fllhi2nAT8s17eOk8lCpbb9ZDdj81NUgebctmnwd+HTSCTclTG8WLN6ylKNJV6eGIdPmdafFrd57eczzHFVVoK5hWv1T4PcAv7MK/2+Bvwv8e1X4f6+qS+A1EXkF+DEReR3YVtVfARCRnwN+LwGU+T3An6zy+nngz4iIVGWvlScelJE0ITl/CXUesS3dE0BLh2RpFHn9U6tFgR4eIaMRMp2ACZNtRLpp4rzrCXwUJkUZPnajrHuvjluUkCYr+caU0ObDbdbo209TrceAZrLWTxN2WNowsygxBzNwDnfxND5d/WD3KbfiNSxeEtNSdzsv5ShMwRQeN7GdvBrabz0JO2Fi1ae9NlV3hN2nOs4DfCTaxIIpFW/pUI9rPWyuTK8tOL4ywaVEeXfrqlJ9qAR8skrpXKERSqW3Xb3fTIJMt//tEs58Z87+xybkm1JNdLv1CjRDqkkXLM5JkxdavWyzVodYT3GQHIMbVx+imyVHTyckc+XoisHbkP/WW57lacPyVCh/tAuTu569T5hOnqIgJbiRIl4wObipdmmjHrR6DE0e0vp69zP6QKgFtYpdCmrBZdFkptPGayY5Jo6jVdkSdhaj/mg7HybXEhZPOTT14Z4nfOXiflTAaDd9p4+1M6OVhUFHHhKF3CBOUFGSo9CJ5Zbj3Fct3sLepxXdLKvyqjK8IAuLThx42PxuRnIMez9QhK9tU1eFwoAXkn1LueXaSUO1A9o0WPQhbuJ4ad8dTZtEndaZpcPoZsKpV5TD5wU3UYrTntFNGxYhAouLrvs+qttQQZxw6tuCKaDYFo6e9U0bmkKaCYWPvjamrD7+1SLIFML295Rzv7HLjZ86S7EJo13l4q/uM7+8we4nU8pJeAZMCXYZ0hcb7XukmbQs22cqPVLOf23GwQsTFmcNJldMCVtXC/DK3ieySjchO1TyzfAuOfutOcvzGfmmwVULza03c+YXUoqpUP7C32WQQR6liILWE2Kj4VnrLGIFoX1XKdX31poGLMHI+gUc7XeoYcH0vrEdhkv9HVXtHLFo6PLaeX2QHVqKDUO1qY1KxJDpLajqBcJkYlictvi0t2CLj2DV3yLp5gHtYqJhmdRzinuli8Kbdom+LfU8pPkO9RSXahdZbbjuM3M6+UeLsrptsz3L1psJu58K79kuo6f3LWvy0/ab0ISH74M9SnDniqBb9C1UL9hRy9YIXVkxWGy7S2+tr3baq2KjXflGlWgXvr5Xs2SM8c1uvq/CITAGrPGkJjAKFmXCNC2YJEWTFmiYDjXzoA4vvSGzrvn90q1LLN7Y4tM/9Drb6QKPsGHzKk3F6qjSFhWrAQKDwalwc7bNjcMtfuzymw0rxqANw6GWpQsMi+1kzsiUeITCWwq1Tf027ZKxKTDi8Wo4dGO8CiNTsvQJRgJrZWpynJqQtmLyLH3CyJQNUwJgZArGUpJKSSqOO+UWRfUBS8Wx8ClbdsHUVIwWKfEYFj5l5kcdts3IFGyYZWDw+BFeDSNTUKjltJ3hVCg0YaEpd4otPMIpOw9tglZ/fdMPFk8qJVaUQzdumC112SNTYPE4qnpW9a2lZsDUfVTrmUnZsGgCgyil0NDONUOmZv8ADUsojC3Tyb9m7Sx90mEXTW3e9MEvXvssyzLhpy+/wtKn3FpucnHUIg71+KvrnBjf5FOzb0q1LF1CoYbSW0o1jG1RjdHQxz56bnKXkNmyGxZNguZl2uRfP2+lN6S2+9w6byi8YVEkaBXXq1A6wzgrmuWf1xC39KtMmZpBky8Tjl6aMH+hIN3MQ15O6j2wENdVLyQnlH/nMSMlj1lExAK/Dnwc+K9U9csicklVrwOo6nURuVhFf4bAhKnlahVWVL/74XWat6q8ShHZB84Bd07S6YkHZXAefzwD71Fjml1LAFWFIm/jnrQL61zYcc2ruA0gE8Wvz7PWC+hwUd0L8bQMdEpx9cxJ2rRewfsAzMSgjEj4PhsJSGutew9g6uclRkK5NZpuTERRjT+Y7RlvDFD6oIfzUPo2anwWvJpgNRTpztnnCDhiFSBZPevY7pzVE70+LXplYtibGNW7A83Ecs39euchKLk60TIleCth4RdP8ARsLowmCcstwWfdOvVBDVME/WvQY50ecboGlFmj00r9vWCXUGwmLM4I+akqTqLduZ+XsGh1QjKHxflosV6BJD6ifLf6K1IK6ShM+BAhnVkWZ4X0SFicUzQNk/3s0LA8A8uzHrWKqMUUwvJcRIWudz5KwY89UgpmaXAbrp3EegEHmikomGorzI99N4/CBHDEUC0iNKSh1X2t9FcM8a2Tvhca/fO0FPDeLof4aoJNNzwUV3/p4nRCMhdcIWDA5NKAFslR9WyVlvQo0NJHdw3lvAWNpcrPLgQ/MoiD8R0lmSvjt9MVUEiK8Fykx+DGSbO7G58jrhdHHZsK/es6zEV5+zYtHsZ7no1rOckyw2VQTA3jPd/sAi7fDmDdunyNUzau5ZjCU04t0xvtZ8U4DQu7aFdNKl3Ea7MzLSWMb80xt/c4//UJbmRIjkvMzR2m85z0aAufmWoHUJHCh92/zHRAzzBePTUTzuSO5NoOp4+28NMs3HOK3T8GVbL97fC+NAZZFugoBe+xN/dIb40hrXbyVTH7x4yuj9HUkswcj01O2iIc5MMjXjGLojNvCAwY7c4negZXzfESKRxa25Tp2yipZIVVErPGvK7GiWb6TbiVTvxGjGBmOZraMK+JwkN+XWVEwyaQPc6ZTjN8csK8rZOod610mQbr4jRpV2/0MOk2amWf457siwdg2K6AUNC8j8z+jFPf3Qjt1bzz2zncCsMHom99NPfzJSZf4iZJE6mZoymoeTB24Ulj5UHlnq+nSt9JxfaY9b5x3XyiukXzUFHlqbknPTzm6G9d4dBGNmVOVKqnhlPO557vbH62G95ne1TfSLV0vytxvvVctPmWtuNwnX2euE7rwvvzXfGreTZ2X4ROH7d9DfF8MGZMrAUI4zlCbw7cl/5eTtx2/Y2wtbIyT+2+yzq6rovfhMvaSV/cFh1dIzA4PfakTvny5o829nSu224B6+bvdXifIROzB+PwJq12x3Cra9Sv8Xu0GlNGlbIeW9qmsQqbkd0m4L6sPlMqPh1357UORnePKbdSXDaq5nTae69oU/7OHo9PHv0857yIfCW6/nOq+uc6RYajR18QkdPAXxeRz90jv3UK6j3C75XmRHnyQRlrMJsbqPeIMQ1AAkBZImm16IkHf3/CURSIc8hkjEwmUAMi9QQmYsU0rdU/AmIEWQZUVMc9pkwtRYlm6Sp4UpcTv7BrkCV+0dQ6aTsxaDbY64nLmhdVPJatMWieIsbgJwk+s0262DBaPPESp8GoU2qatlh5Sdd/q49YMFJX1wUoaQz2raVDRnnUYE73vnY+OmuBkDir/neyPuHhu2lF2gWgcUFPrXec6jiRbgGUqRpp5WOy2i5dhk+vbO29NBWSBZhCsbmSLELfadEtqzbiJSWYZVjEB+UIJ1lcXU/tto8IUgo2D31g8rqs8NcUoD7s+psiADgml2ZRbFxgLHQolwqmFLQUxEk1YWj17QMdMUDUfFhUArPEhn+aSACHspYnGYDC+jlg5Ry7sdrcD8OnuzMX56NVmcWRxZxbkqZuZbcv3g201lMUtrmu48ZzhzrffJaRTgqSxLFcps0uw/wggCp2q2B+fYLPYPFCznhriTEtkuG9YTlPGU9zvBeOyi2SheA+fYxU5/frc/xFHs5Iz/YyzFaBWEW9kGZlYwsg7IKA99LYAFAF70yn/ULZ0rZzFK4ejq6PKaYjDj9CsHVwJie5niE+jNHiYhEavga4GqYO4ITtb42wubI8Lcyeb20BSM2UKas+rycshSA+jClNFCmEUy9vcr7w3PqRSWDK7Iy4lJ9j/vQGO59OAlPGKiYPY1xcxZTp0bLtkoZVlh7Bhd+wHHx0wuKcYJZhsrL95hQV2PtEGphnCWQHSrElmEI5982M+cXAlPEZ4GH7jQ3mFwNTpnj7hAXPIIO8AxEFKVy7AdSZz0Tzg0rCdyt84NSY5giQVhtO8Te2zacLYKw7FhUWpFH6eM5iu4uFtjxIDxPKiQ3vdtPNv2Gw9Oqb7WXkpxJ8Kid+Y/uGguP0TTv0FpfxsZzAnqVdZPfmE52Frg/vBpdVZZpuvkBrPLV3DKeOt1KHns7pobJ5bcrexxLcmPaoTGVPpnN05gQ968xMKcGmzBm/YrcnsGnb72k7v9DuWaOYfROLEsZdr7/Xxo2YNkRzK6i+pRLmHTbxSPUtDN+4+FurK/ZrYtbN0e0NJm9toT9wyGSU47whSxxeaWzX1IwDr3S+5dYoR/MR88MRz1y+07AggMbWRx02K1JS60iND4wFqm+yBHszdfyRLcmq+/MyxSNY8e2cwrTMn5oN4VVwakiN6zAxEvGMkwJDiH9UjFi4MKms00+TnMw6Egl2VZwKuU9YlGH9M04KMlM2bKG5Sxs7Pol4SjVs2Lxh/uTeclSMmrxrfUzFIIrnVTVDZeFSMuMw4im9ZVZmJKbdnCi9bdhD9d/Qtq7Ro86rrHTwKpQVC6m29WMkMFVi1lStW/07tF34nXuL84Y8smSsKp3+e/XGBVxheO7yHQpvOFyM2Bjlnfh1P0N3iNePgNdg08V5oXShzHge2cHuVChLU83H2vu+0j9+rOr5LKKoN+EZqcaL+sBcUS9h7iUg1RyP0kASGwKTlg0OYSM08+0cXEFyw/arG8yeUsoK5ZHStGsLJbABCe+64jfWvMg+OHJHVX/kQSKq6p6I/F2CLZibInK5YslcBm5V0a4Cz0bJrgDXqvAra8LjNFdFJAFOATv30uXJB2Wcxx8dN0wZoDn/j/eNTYAOENNnzDiH5kWTX8OUieP2UHfpASoAWoYXkCzzKH2Urizb41Q9tkzIQJvfzY5Sny1Ts25iHSEAUtAFfIKltpBfFdfkJZIXUDrsrMAUfmV3rJ2cVMCP12rhpO3ErfmIx79rwMBHx5yk2b2mphPHk5kejVolLAxrYESifJs3VTxRIn55VYiu0tGp/rsy+avzqeqkpm+tPJqU1hOealeiz7hpyupNGsWcDMp08iXU0WWgieBTaY5S9Zk24oLOxnQnVzVTQpyEMInmS013KD4RfFqBHxV7yCcS2ClJWNjXkz9NIgq1qSevURMqrfHBkyZvGsaiaMsc0YidIiphcY5BvTYLdcU0fdmHjrUpPOTj1m3fqYTw/gy42sWYHAiLuyMWafWhLQUqYKnVrWr/tQrQ3RHxQroAn2UsTQDwahsD4+PwHLiJZeOmxydQjjPcNA0npur8HIyXgh+NECdsvaEkS8VlG52xo0BaVBP5IygnSaUDzREBAOMjHasyTBEtHjpla7ub16vfeE+ZXl8y2clwmVBMMsb7vomXb4VC+14h6h23zWtzpFTKiWX5an2GQRs9JH5nKNjcNZMANUG30a059vodLn0pwY8syXGBffsOGwdzRne30DSwYqT0lecIDWC1rSYj1azIlL7zPrQ3djm3s4mfpIgL3w1zMANVJte3WqZMXgbGgSrm9h7Z22M0TQKID8jBMeM3RpAmvHpU8rjkYXeuB/ngic4X+K9/++HSUNkbiKT3yXqs8k5gyuZVdh/myX3lJJa0+vXhjyLvdyrqmazJ8352ux5aHoXej1qnSuQh+1srdpXYh6xTNT8+533n+iSZnFQ+YbEUL5iKugiiaXkU39F9PqVKr0ARAZ5OhGWntKJlVFTp5iLMV7RyoAsAlivtueyiBMC+VLNprbWah7zX1nidlL3fy57e62VtHJ+vBL0b7qkBRvewZ/kx7jTrqjGw9TCZ9+c870QexXtoJctwokIfVj/1D/xuuOWO34Fm70we9zxHRC4ARQXITIDfRTDE+zeAPwT8x9XfX6iS/A3gL4nInyIY+v0E8Kuq6kTkUER+Avgy8AeB/zJK84eAXwH+eeB/vZc9GfgggDLGIOMxeAemNdYoAGUJSbIWQGlEFS1LpCyRNEFGWQBy4jQdQOeE34CULrzoKuClY3dGwwdDa5sy/fQ12BJf95gxUIEWpgIfYhsO62zKiLTxGrBH2g/DCTRcUQ3n0dHq6IMiTsMmuJfO09G0bf0Me5DSY3uUYfGKqY9IxcDFSVTZnm7iV104dgChPtgS1REC80OTcIQpjqMSjHolRznj3STsgjX3u8eGkHAMSkU7Z+P7+cXxG1sp6wAc6O5KKdgcksOCye2EZF4xZSKqLIT+MWWIn8wV8fUuKI2NF5/JalkSFsjJseLGwmhPSQ8d4ztCslDcxODTAEqMqgW3cQY1kO3DeFcZ3zKru5qVDRvxYcHvZhXtWoKdmQAU1P0QmDhu3GV6SRkYEWrBLIMtmPV2caLdvajdGsZRDF7VgFEsFUOiPq4jpVRAo6ImYtLU8xNAbZTHSXPFCuHQxISjWUbxpQnW2SSwzMRDueVZnjLgIT/jcZs1KlHl4wWzMPiJAxWSWUK2D4vLrtWjjl8GNklyaIL9gSQAn5pp2071DkgFgqnRznVnlqi960YnGN2xeDvi8HmDy4Lu49u2AZ0W5zXsnPa9gVQ7K8XmGJNDOYWj59vGDEewpLLb0qYxpW0YXz4Jz93WmwlnlwU7n5lSbAnZ/ojzeUlxdsLuiyPKqVQADthFAHzKqawwZcyS5rlLZso5Yzi+MmV+1mCLsBu+cX2KlMr+x8YtU+bIU2yEY2WnX84otjPyLdswADfenpKfzig2DO7aY/p0RmDbIIN8aOXdOgjQNUu6E447PJK834M834N12xMr77RXtLh/nEEG+X6Rd/zOeC/eae9G3p95zmXgv63syhjgr6jq3xSRXwH+ioj8YeBN4F8AUNXfEpG/AnyLgEr+0er4E8C/SesS+5eqfwA/C/yFyijwDsF70z3liQdl1Dn8wUHLHomlQs7X9mWMiHuPliVaVCySDljSXYGuIPhRmVrZkpEkOeG+6wE+vfPVNSjTs19TXVRJDNRHtWKGTcOU6ekXM3IASoculuAddteiiWUt0BQdy6rt4Zg6bgQQtfpXfz2I92FHOQaSVLsAUf94UtxOMUOl1iU6srX2fHUM1DTpop++Zrj0QSswhccsCtJjh13WoNFqPsGIaOijQKmuV5112b34Eo78NKzJ6MiZRnk2xWh1jGhRks4qK45Sx4nOUFe2NsRDsvBtneoFrVNc2m2Xth0CkOMWkB160qOS0YEhmXvKsTSskNG+Q5zFFEGN7FDJ9ksmt9MO6AEVKJNGoMwoKlBbuyAAUgagz2URtqcEhkJ1xtcUoU19beu8Uwdp0sTt1gFiarBHqcZOtw1qBsx4z7G8FhmvXpNvfda6f665o3vj5QRsAd6aBpAzLtyzSwcKbiRsvH0MXtm4NcGNKpSxbksfxqMmIWx8Z4ZZFGy9PWVFvCLeY5dFGANGWoPaTRy/Rnft6g0NkyS2bxDXzyxKzN4RZ74+CccXsgQzLwKLBIKtlThd/NuDOZoFFqI16HQc3VMa2xiRiPMreel8gR4ecWlnH5KkMdCeJQlPvTRpWZHeo5Ve/fd1vaPa1tuhxzO2vpOxlSaVPr5hTp7/Rks7aozJe0XznJExjGKvT0VBUr2X7ezB9xcHGWSQ90GedE+QgwwyyCCDvC+iql8Hvrgm/C7wj52Q5meAn1kT/hVgxR6Nqi6oQJ0HlScelEEVXZ5MkntY5FyLVdrch1bu3vPo2iOVh5n+nERGuN+9dyMeSL/+HmV+P+mfzQcmv1FRZR+AxnsSpfZBZaP6O+qF968hmAX/MMkaqOOxyfgB4ynr+yKWBzoa8C4WIZ5VZ00Ps5fyKDd5/Wy2Gnhw8O4yXSzWh68p6kFEH9u2dkwjG2SQQQYZZJBBBvkwyTDPqeXJB2UGGeSDLvdaLH8Qd/Pe7fn/90pig9nxdS19o9rx/ZOo7o+jrg/gzaMTd5BBBhlkkEEGeXTH1AYZZJBB3mcZQJlBBhnk4eS9mAA9CvCjb1B7XZ59YOZR6/Awsu4o0CCD9GUYGoMMMsgg62X4dg4yyAdfhscYGECZQQYZ5EmQk1gtfdbLO2GUxGkeZgL3MAyWh5FhEjnIw8gwXAYZZJBBBhlkkA+rDPMcYABlBhlkkHcj65gnJwEi7yTvd5LPveI+qnzejZyU7wDWDDLIIE+a3OvY53uR7wftd5+h+ajze9C8HybdveL2090vbi33u/+4RQwr7mnWhT2qfD4oYU9K/Hd7717yIOnead7fTzJMSR+7DKDMIIN80OWkydHjmhi938eAPizyKNrtXgumeEzcy97OIE+WDN3y4ZfpBH7wBwDWexiMcW7nkcLjJwmNx0LoPMPBI9u9bWR17Cr2AfWOhzcaT4hSeZtrPCM2ngil6+1tjehJ7zfp1q/xhlfraKSjq9yjmL6tyOD1r+fVEZr34L1sS4rSeHWMvf+ZwpMe5CzPjlpPeFUdsr0cN0lwYxPaxLfe8mJPjJ26d/SXqhzFLBU3MdQeChsdfJu224eQHHuMU/JtS+2FsPbeaEolPXLk2xY1q+NgrddIaeOIwnjH4UZCsWFWvFGGiCEOAuXENPHi/uz0ba+9axnvOlwm5Jumq0etVx03boNeW8Q62bzyCOnrMRVumQLSmbI4U4+RKrkLv8WHMmtPm43XS8LYiHX3tn0e0LaMWI/Gg2apTG97ZhdCJJ9It497z4OakE58XVabl10qG7c8R09ZNInqV8VPDxW1UGx03yvpkYJCsSWIC/napVJOpPGm2Rl70l6Li/Sqrut8mzaO0tXt2NyDtV4114koSFl566yvXeg7n4W26I+pOk5dB5/SeP+MPXiaPOjnbehfPPhR2+dxe3U8hWpbp6a/quetDrNLKMe9+5UHUzTo3m9PU4b75Ualv4ImUXnS5lW3b13X2pupqfMqIi+o2m2T9FBDv5ch//rZ6Hi0dVD8d7+yvlPeCxnmOcAHBZQxD+R75L4ifXfSj0DUd0dSXcZJ4Q+b5wOnk3Vf/DV5O9eg2XHeWrmKbcL7+cUI+AOWFZe5Ns8qX/WKWNt19913ge5ciFe7te3dw95jjFT3RaR1a27vXQdVXXWPDisu1N+V3GsR3gdUVBv3xE1dT9LFV+6GozZZVxd1Doxp760Za5oXoe2sCS7l0+qVsW4sPMjYcK5bB+cgjV5DJ07c75Fvf9x4B5Hb+qZ+1aJFlzkyjvwdxe34ICybsgx/s3R9nMo19InAR17AKFstr3YdXbldBkJc1dBGxqBGkPkS0gRNkxOBNykduncAWYpsTMMkvBk3a+q4juVUubOW0kFeoFtTTrTVkxdBb2u690sHd3eR0QgtS2RjCsscPbWJRu0uqsh8id8MsxiZ5237qkLp0CxtJrq1a/DYRbi41q18s6iMFpGhTqxci/dhgWYFKRxqbbi/ZhNN8gKsQV79B6s3BxnknYqAT2313q7mEDXoES1AAcQbJPG4cRIm5nYN6BDJyqK1WeBKtbDqLSaFNly1WmiEuDUoU6dpFmb3kLoesR793/WiSnx3IREv/GLAop93J98+WGGiRVb8ujVRW5z02fHgEzoLbLuA6Y2Mo2clLKwANWGlt3EtIT8l5NvaLLZ82i07lFtX+IT+8UJyLBSnfLg2oKKISnCLJ6BWuwt/UdLdBDsXFs8U4CW8w0YBJCE3jG6lLJ8uwFbgnUS6AGIU9YJJPQKI8YiAMYr3gr62QbnhyS4dY4wi0uYjoqgKd763BYA7W3L2wi5G2tdw0yUC1nicN00evmoIr/DW62fQDcelp+4iolhRjCipDfMH50PFU+swBD1MVI/MhHgeIXeW28cbXN46ZOkSUuPIjMMj7Myn3Lh7ih96/k0S8RhRjHhmZYYRZeFSpkmOQTkoxpwdHTMyDq9CoYbSWzxC6Q1jWzIyJYlxzF3KZpXOIxTeVvX2eDXcXmzytZee54ufeYVSLedGxyTimdgcr4a5SxtdJrbA4ln6lEINXg0TmzMyJUuf8PrxOb7xW8/xw5//Lk+NDwEYmYJCLXOX8es3rzDNCn747PWmfVLx/PqdK5TO8mMX3uawHOFVePvoFC9u7XI6m+NUKL0lMY5UPIlx5D4hEcdhOWZiCxJxGFGOq/R13a0oiXhGpsSI57gcMbIlc5eSiMer4KOH0VV9b0WbtqrjlN6wu5xyZjTDiJK7hHmZsjOfcnYyY5SE+VjpTTMGli7hKM9QFZw3bI6WzZgI4zWUszcfYwRGScm8SMjLhCwpsSaMp3pc1WOz/u28YVkkWONRFbwKpTOhDRKHqnA8GzHdCF4fvTd4LzhnKOYp6gU7Lht9fGkwVnELC0uDPVXgC1M9iw71gpYGjIKT8GybCIkqBTIfwsvwTpSlQTOPVO8BcQGEQYXRrpCf0gDSmArY6rnbNK4ChAZ5rPLEN7mkKcmlS+HC+7AwKUskrWHTaqFTL7LjBViTSTXJMSffY93CNFqANJN9096T0iMHx2EnZjoG5/EbYzBgF2VYSKQJooqfZu3H10ibXzWZiicF6U7w0yqLnPL8Fm6atpOSapImGnZwxCvehgVFnE+zUyZd1Hu0s8S+8jb61AUWz2yiVjC5Z3Rnjly9iT59gcXlTdzEhHxN0C+ZeXwmmEIppqaaaLQTonrSYXPFpdLsdtmlcurbh/hpyuHzY8pxQOVtrmG34FaO5J67n50GPRXcWMj2lWJD8CPAw+nvlYxvL7j5I5vYpVJMw4QomcOZby/ZfXFEfiq8SHyFLKuFbB/Of3PBnc+PWZ6F6XUlO1R2P2lQq52JGdCg3+mBkJ/WFt1PtDuRrCdWGuqjmYZJElF+Uk3M5hYduzARqnYwyA0yCdsTYqp41QRJneAPUxh5TObwSwteGF9LkRLKzx0hAkniMNWLuSwt1nqM8Rze2sQeWLZf3MV5gwIbo7waehrmZ85y++YpknHJlQu7Tf3ryYpXYVkm3PrqJYpLBZ/8yHW++9IVTj+7x9PbwT3xvAwTh1PZnMN8zNIlPLVxgCF8yHJvSaqP+a35FqdHc77yxkdRJ3zxhbeYlRkvfecKP/kD/3/2/jPWtiTL88N+K2KbY65972W+dJWVpqqrprqb09M9MxxDI4kASUCQKECi+SJKwAAEBAKiAH0gKeiDAIEC9YWADCRgAAIiBQkSQRGiINrpIWfEnvacnu6u7uryWWlePnvfu+aYbSKWPkTENuee+0zmy1dpzgIu7j57h9+xI1b81z9W/IDaWya24SCrMOJp1ZKJw6vBI8xtNVK6krTexIlfseL5cHXMb334Fv+dd7/Lo2aKFWVqag6yMDm+v7rGf/l7v8h//6//Lrk4lr7gOFuSS6i3iSsgj9CopZSWSjMab5nZmkYtv33/bU6rCf/dN/64K0ejlnv1Pi8V57y3us61fDEqb6MWi+dRM+M3P3ibf+TNn5Ab1yltK5dz3pQ8WM95Z/9BjGP43Q++TnVW8vobJ7wyD+3+9//wXeavn/OtG3c75RHolIVFU3Dn5Jjj//w1qiNh9VcumEwa9ifVaO3VOIvzMlKQnTe0UeFdr3Nca9GHBfOfWaq/eNH1N1UJGFJUNuROiZt6yDT8uaAI2AvLG//FMYuXMyannpNvWeYfKye/CJoHSx0KphHmHwrnb3tMK8w+FtbXwndhK8GuoDnov0dTCz4PC6T0DduKbnxMY4mtx79NE5QPjdY1cYqt+sVfvlDaiXRjwcjK5JV8Ecrgbj0fQ8ETRbl6xbiTL5WI8+F9+7TQlYFlNM0tEpgyre9+d4DMJvC4KZt2liuYLiOgI+n9BjCCtGMwQe24b8qg/Bi5bEBhozsP65jyifmK10u60SXZBJq2AVPCCDjqLNxPiJPK0IFesX6mVopHNeV5EdgRg/CTBw3t3NJOe4bIkEFxCRjaVjcJZbSVD2ORDHRXYpk2ga4oxUVgyqzfyzpGjc8iINBCee5Y/zgfAF192Yd5bzJlQmFh+iCUqd7fu5x/TOfwQQC5m1lOM78x7o4xvAfaQbodOyr+vfLA005yqv2XLwNtw75pttxnAPQN2AnvT250jIQU1tRwfKH82bVvdXqyxjYIbd23kTh4L9/oSzH9NMeMmCGbbTuIY2q4edfzwx9/ExR+nNKNxoDEWhiyeoYNqbbPx67h5see733wC/xJshOlbumCDnyWw381f6WrHwJ5wG/4jf0bYbFOmEP/YPZK1xZbGSyiiEuArnblEC+oic+Sbk3fdoFtFMJ05SC9p+GPwUuMYlr4sNAubGKUvF/0c/koTQ/S9n3gIu/bJHxPoWKmCe1+bkMe0sLKDtqePs42pkwzbJdYhia23aSGppyO4lgPWSyXz4uuzEknmdShXs08C4waAqibWC5XMmV8v+5JfScwZUwfPrWLh/KRYmrpWDVJPxq9ZscloOYzk52e08nnHpTBOfzDR+N73o+srABbrURXMAm2MiBguwUZQMwmy7RTOHxVBUUpMjhMsu46D+qRaOE3A8v9MB/ZAhDpugr/ncMulmRZtr1+w+0Ij2PUDCnLqzV6sUA+VmbnC5JlX1ercP8WzE4XYCPFNQJT0rSoNYjzwUK/CYKl6wicddI6ODnFFjnHJ3PU2qD8tQ68D1Z/53hpcdTXPzOYdYvmFs0C48A+XMBqzc3KIc6hRRZAqcZjT84oHhzgJ1koX1c2MKsWe/+UV84OcdOc7LxC6pa99+cjivYm/Vpqj5/Y0fPhwLxJpx4qbpvtbpoGn5uR8mecx2dmQ5GIi0FVTOXQzKA2izRGJT9fgEL1h4FNMFQuhsrGjaXH1DXV8VGvaCcGVwxfenhr6VCb08xe6SeU4aSj8NbtJe1ezsXBG7zzoKE+OORRftSFAVhGxoJ4eL94pWv74SRma+XCwJuPwux/+/BdxMO7Dxve+/Vv9e26rR9HAHJTttGgbaPcXDh+4/CvBKtuWjzE+LZRvnGn4jf/f3859OsNevpm+sNFkRpBFLKlZ+KV/+/+f3MU3rgAkNpGI704aeI6UvJePW/5s4NfGvchH5Rt0yo/KF/p2vf1sxbTOpr5de4WNwD4xr2KZn/O3ck7I2U6XVsHX1s7Jj+7g04L1t/bQ22J2v2RQpkNlbyNRQuAtEEJsuuG7PyC9Z/OuDwQhnzzRYXLTQcOp3rbuqb86X2mP5sh64rpxwfY84r9D+ajBZ04JT+rOf5BiSjkZzVtBKPFKabxuInt2iy1tWm1s9qb1nffUHrfJjLj0m/xCr5fTIrTMGbEsc5ULb4YMMyGiooqpmrBGEz94vaiP267xk6+JCKgefgINRptEhACjIAOaeP3MLX92Ja2E12Rdkh3I7/BuDdcFHcsmYFsblNQG8MNAZwNeRwzJtQljc3jMgWmSPxWh3OCDNLdvJb+enORvsnmSeP6yCAj43STpC0SOrBKZyuY3rGBKZNrlz8Kex+UrK8Jzb529zTr42/Pc/CBx4WutEJ2YWiOErNU+3hOgpU8GYYGZc7u55gG6teasIj3gpmGOdevMvL7Ge3rFWIUsZENYLTD/IzRzsADveFHVfBeOP3xPu2hY+/mRWRy9GyZzAbWwMc/OQajmGtrXrt+2ldNFGtCul6lYx2oyuh+4ywf/fQG9rDm6y+fkInHGk9hHCZOTpnxXRpZvJeYJRCYIIlVUnvLvdUeb+2fsIhGjMCK8Xy8PORn9475y2++zzyrOkbIeTPBiFJ5Sx7TPG9LXi4vmNoaI0rrDQ5D7TMqlzHPKvai8ejClVgCUySVLbBrAnvlbrXH733/bf7R7/wAr4ZXJ6fs2aozFF24kpkJbJhcHI1aKt+vIfZshRXP2ud8/+Imv/Wn3+C//St/wCvlKS529kYtZ+2E377zFvtlxa8cfxjYRpHd8jsP3sKr8A9ff4+zdopH+Mn5db59eIfDbEXjLY1a8vjRO0xnBLtwZVe23Dgu2pLK5x1TZmobStNiI5J01k5HjJlNSUyZ1NZAx6RxKjysZ9woF0BgwZy3JXeX+7w8O+8YMK2azqjWquGsntB4i/OGg3LdGcAMSmFbvAqn9RQrnr284ryesGiKwL4aGLq0Y3D1H5pToW6zjh0G4HwIW+YtjTOcX0zZm6+7MKqB9bNeBQZPUQbURYC2sRjrWS4LdG3JDyuqOoKpue+ZMnHwUh8U08RsozFI4cN33IR1hVQmGINVgm7oBYnGsnZqqY/9GKBOw1AyQjnBv0CEYKfnBPn8gzLWYo6PRrcuMWUgKC+PYckAneJ96dkmqDG4P1J0zEbaTjGnF2BNz5TZK8Nib90i3gdQwYOf5QHUUB0pMiP2TkTJ85NlAAnWFe3LBz1NWejAoLRX2LQaFvxDy8YAGQ00135BUj6osD9y6Osvs3wtMGVs7SnurTAf3kVff4nlq/PIlOnBhnzpcUVYcDYzg89kvO8yls/Wka4bGSHZWjn60xJ3UHD25oR2GhQd2yimgdmdGtN47v/yNJRTwU2gfBj2PLoyKIJHP54zuV9z5y/NsGulnQuugGwJ17835eTbBfUBHV1YouWieAQ3/njCvV8pqY6V+UdT8gU8+la/L3e0L9THeGdCdey7d6LZQPkRelaMEiwEmXaU4E5RiooGKwsTj2S+H6Ari521SFRqxCTraKAJN2clZtpiM09bW9QLxYczxAvZL58ikcprY7y6tWTx973bh8gi59VfuIfzgVK5XwZlIU1alct4/84xk1nNuzceDBScNMFZamf58e++iX5tza9+/af83nff5fW37vPG/iMA1m2OEc9xseJhPWXtct7aO+korbXPKOLE/MHqmMN8zd/9yTdAlL/+1g84rSf8wQ+/zj/5y39E7TOmtmE/W2PxnTLgCOWf2UAFtjJeDK99zszU3f2fra/zGx+9zT/3zh9wv9nD4tnLKg7tCoCfrF7iP/r9P8/f+Ot/F0NQno7zBRNpadRixGNjGy19wczUrDWjivk0avmvHnyDi7rkn3v9H3TlqHzO/WaPG/kFP1nd4Hq+6BSyRoNyA/ComfIbH7zDP/7mj7t28iqsXMF5G5gy39q/H96Bt/y999+mWhR8/fV7vBaZMr/7B9/k8GunfPP6vUtMmdYblk3BBw8Oufaf3mR1Q6j/yjll0bI/6dlGLrJqEg28Z8pIz5RZFThn0Acl8w8nVL+6wETFW6Nlw6ugTpDbE/zMo5kfMGUM9iLjjb99k8WrGZOHnpNvW+a3Zjz8RcKCJlp2pBX2Piw5f8sjrTC7XVBdC2U1tZAtoTmICxYvmCosljaZMp11OiMyauILiuNRYMr0W55Mq5i6TyNfhD31w/37iZkoHvJFAHD0+89xG+NOvvIiTskerXsjC4x1jeF9H5gyWR7GlM5ANeySj8MMR+DMRh7p3sZ2xgRId1tojRmBylfWa5ulfeTPZONh+qkE5pCR7WDT5q2O0XxVQcb1Gd2Dy/4/kjgF25dBVIMh6KLi6IeT0VYzUcWe1fhJhi9tF94PjERDubKsUcezVQDeNsMGHZJOPx0aJ7JVhWk8zX7QjwOTOuuus8UqPOsALNlItzdUJDZ2px95KM7W+MzQ7O11dUp1T2m98ygsNttJTjt7uUvfE3ZWjCzy8ffmMv2dRy2uyKnmr7Lu6roRaADMXepng3zEB33z+8WrYyMG4bt7beH58eG3x8DcIG4X1sP72QYQGeeGAGZKl4ZxYz1/JBrmnbdOWn5w4zsA/CDv65d22Qz9xsBGHaWvt62Vt+47fvc3fjXMe92cFd/Z0rM2wn81fX1Ux2wV5rS/NX+9M8TYWvl75ZsjnXgEVkbj2YgJlNpJe3D3EhC64aNn8x1eMnZKfy+0F/w4G+dnGvhJ/urYz9DwO3F9O1wMia060Pljue5GfUEcrFPZ9TFAQWKnDOqScNuEnRzX4IpZaJNYtAKYNFFHyaNxVei2EZkGTKO009kIAO/81Qij771n9NFtNepYSS34fDywpfdUnjuamenaontvMg57e31F/XfymcnnHpTRtsXdf3Dp/tVsl8coy88YR0S2M2SSeMXXdfDLkWWoas+Iib4zJE2wV/nN2OYro6oQI2jTkj06I882XlMHRJmemfI0vjCiPw1/foF53zN/EH1EeI+u1/iLBcY75venkNmxchb9j+AjU2bTt8RAYRy1p3Pw6AybZVy/NQ/peg2KpfOwWqPe88rDo16xygyybkI+kSljThfoas1r59eQ1gcWjTGIc5iTc169vY8W0ceGMZ0SK1WDeXDG6/cP8dMce15B03L0g/nl7WwDhc3ULoBh297ZwCopOrC8d0rC+F0Y1wTFbKBEiXfh3gaDJaQZ8ldrArMoUsbz8wtQpfqtWYgzaOb5YEJ6d+UwdUt9dJ08TRQbNHM0MmWyKY+mbw4y75+Lh3fuXODmObcP3+UbDxqa/evcioyNxM65ZwI4iMJ3szcwTQ9YDZk8dw18/bwFVX66/23EK79wWvP9a7/UT8qmb9dhGz9OwRlPxMrNRcvfOvjHBkyZfgufaZRv3Vvxt/6jf6yb0NRuSZexAjF0VGcrj22V/+DgnxyVI7E2EsNke5rK6xcNf7b3S5feOV4xTvlBcbO7/+ZFizQ1bn6Nu3lo9184WeCmU+5P3768VQDIFd6qHMVHt9A8o/m9AzTLUTPr8kz9YpMG228biP+dYtol9nRF87t76KZ/r5iOXS+iAi9hodKxbTz5hw+Y/ahAmpa9n+4hy4qjH+6NKfkK9nTNjaOgqGQXNW6ad+WQxuHLnjGYGHHifNc3pPX995T6Udzmkdhz4nzHbIPQ5uJ8N55J49B8UMfhIk4VqVxQnl4gU2bnAO/LLyqEfpfmr3hv+F2nvh1ARY9P81P67kxKa7xI7mQ4P8kgfTaU/cFC/NK4nMZUI90WiqvzGS/q+gWXjO8NFp4dI8f3C79LW43SPfo0uzDDNIftu0XNGvrSuVTGKGk79IhhuFKm9ycsXrWd89FU19ntkvpAaPakmy98xqjMm2yhYT2QALiIF/IF1Pt6KW7a6jCctzoG7MPAlFm9rP22hjjYSyNMTgpWN30sk47BKI19yAnY8FuHDB0Psw+mtHOlvhE9wwrB+KQS9mZ4mHwwAYH6yKPX6o5J09XdS7g3XJDHZ+l39v6Edq7IS1U0WnmsHfqw0c6PzVC654OkmyajWubM9iucCz5skj+c9arA3S+Zf+2sY/Jk1tO60DBu6KekyZhPaoqs7bpst+XXGcq8xRqPFWXV5OHa9PNE8p/jvOFsXXD/RwdMv/UIK8rhdN0ZRpZNjqqwFw1pia2RjCXJt44RpXGW+xdzHvzggONfvs9hWXWGFq/Bn86tu4eY3HPj+LwrnzWej+4f4VV448Yj1m2GV+HkYsq1gwWTrO0MekNGlPMGa3xXv9QOdZt1bQCBydSVHVjVOdZoZzzcFNXQJ5KRcXjfKyyqgmncgu+9oXGW9apgMq27NtaNeHWdoV7w3mAz1xmSVAWJfbZtLGKULHc4Z/C1RawPLhMV1PUfq/o0WMS/AXMFQFoJ+mQ0NpmVwc829AQFUwVDvZbaFVqaMF7YtWArodlTTBPTy4Ihapuj35SmtKB5f42EtFwZv4dYd9MSmDKPLM1+r9emsWrYkJJcQbwo2ek5wBcAlBFrMUeH45vOIZtARXJieUlBGC6m5eowm4yaYbhtlisj4DzmfBGsRtMS4zw6mwSlqG5GAIVOix50GKZjL6dtTpcBiKgb/PE+7TRZNsbgQfIpExbvKR3o0HvSgjQOGl7JH64xbQsvX6d+Zb/bApQ/XGFuabj/8h7txHZAgxohWzpcYTBOcaXB5zJWjNJWnVYDiyYWJ6s88x+V+GnO4o0Z7STsY7S1R1qY3F8jjeP0W/t4G5TJthTKc6WZRqaMh4P35xT3Vjz4lQNsHazZyafE0Y+mnL4zoT6QMVPGBC/j175XcvLnZlTHwuzunOLc8+gbtt8DPGq3GO8C6oN+sEoKaKrvJlPG572Coym9OJ7bdfTobnu/NLYW3CT+ThNRUiY9ZBc5vlB8EfZ+4oXp3RJTw9mfSxuAdQDPS+ezxj4oKc6E9btV6CpeMGXb+ayBONncK9FSya6vuoYwkbbs42RWfnef6ljJ37rA/WiP5pWa6X7YXte2BhEoipamsbStZX++JotpOC/YqPws1gWTouHkgyNMbdh765SmtTQ/2mf/Oyc4b8isY5IHhcepYEU7xacYUEqH0nrTOf8zojxaTTj56IivvX2PZZN3jKJJdAb3aDXh9t+/wfV/+DZWlNpZpnnT0aFTOolGnVuH84bGG3Ljab3h1skB7TLjra/f6airrTecVwWzsuZ0OaXI2q4dUlsYgaq1XHxwwOz1i5Ey0baWtg1O4GYHq/gODNXHM/IzQ/1KQzaLCs/3D6iuO8yNCt+kLXBhgk9UVnuW8+pvvkIzFe7/BfCFosXA9KMStwgkECPsBQe6/6YOjuHyC2Hvgzknv0jHOEH7bMVB+XCCz+MCxsYJ3kO2hpu/a6gPc/JFy9mbJdMHjtN3svCd+vQNwfzjkovXLOJhen/SLWxMExh3zbz3P2HqkJcdsFxslcAkuhMZTAtDloG4AHwFJ6ZhTDS1dt+eXXl8kT5k7cCypAjZdRvba2MO2clOPo0s1+jvf/epgm6uYTevv1DyOGPS51z2HvfwGQ9D+KxFjHQHSHyaAy+eRxpPlM2jip9HWz7uEIJPcHjFk2T2hOfXrsg3BybPkI8F3thoq+HxAzP1HF0R9x0+6q5TnteeMt/yyUFGsv+M4QE2D0v53MhX6BjtD/zi512Er5x87kEZbVvc3XtPDvgJB9Vnnlw2Tpu5NEnF5xqfd+Efk88m68dHPwioh9t3sSKPZwCFRJ6u/M7h6gZZLsk+7F+/b9twus5ySfZ+RjY8lQe604hUtT+56IrTlC610bpCjGH/h3kAz9JJT4A2gTlx/N6EzkeOseEEHZE+rapC25aXPpj2eUcroq7X3Phe0bGVRuV2Hl0uefmHMySzaNOA8xz8drm9TROQlk50GvrteZJcFWZb/Mf5AopMog4sTABb04BX3phtmbaHDhVbF7b4pROGtjhbxGs4RciYwF7alh6Ed2cN5Dla3wlgqOkXrV29hycHDesY5SUN/otuNo/CIjf6XtLqY6R8zBTvnzABbpxwdFPX3GzvQ5YFpXmj7jf9CqrbkPIc9teNyba84ojXA/cgpLtx4tdBjH+4LdJAwVD3/tV9b1PSqVuD8Opc6OPp3pZ4qtqx9Y7+/ad0SnuVEhTHueNnHCs7pUp9p/QdR4V7csXYsXfFu3hh8rTHguvqsy3HTnbyVZCn/d6+aKIvykPm08lwOH0eQ+sLH563bT/byU52spMvmXzuQRng6QbiTzgJPq/J5dOk89jate1nwurSyqFV9dT3n4ssH/fscQ+fItw5IwDjkix2iG8nX2Dr5HOT8/Ofdwl6YO4TKJrPFKO97Fjvk8hzGSvTOH3VeP05W8x8HmTnAG8nO9nJV1p2YMxOdvKllp2eE+SLAcrsZCdPI7uJ++cnz7vtv6zA0WY77frsTnayk53sZCdfLfmEBpmd7GQnX1554p4fEfmaiPyXIvI9EfkTEflX4v1rIvK3ROSH8f/xIM6/LiI/EpHvi8g/Nbj/ayLyx/HZ/16u9Na7k53s5DOTtNXoef5dJWkL1hUnoj0x7JdNvuz128nzF5Xn+7eTS7LTc3ayk528UNkBMjvZSS87PQd4ClCGcFrd/1xV/xzwV4B/WUS+A/xrwN9W1W8Cfzv+Jj77F4BfBP5p4P8kIsmpwf8Z+JeAb8a/f/o51mUnO9nJ51mGgMQQgPkqrlm+qvXeybOJfgZ/O9kmOz1nJzvZyU52spMXLTs9p5MngjKq+rGq/v14fQ58D3gd+GeAfycG+3eA/168/meA/4eqVqr6U+BHwF8WkVeBA1X9LVVV4N8dxNnJTnbyeZBN4OSq50/79yz5fhXlSSDV497FTnayk+ciOz1nJzv5FPIkJuyT5rSnff684mzTcz6P955kyHqW8J9Ed3uWZ09Tnuf57HmF2Xz2vMN8krDDOE8bNsknibOTz408k08ZEXkL+AvA7wA3VfVjCAqNiLwcg70O/PYg2ofxXhOvN+/vZCdfTPkyD2q7gfvZ5WlPgLt0FOa2IzkH99IreMyr2DxFbnic5Cc6vnR4CtJVp6xdkV/3LMbbPJkOuPI0uifu9HjSKXRPEhFGJ1p9kv6tCssX+F18ga0+X0T5ueg5QjjZ7gly1TGxwyOPr7q/7VjkzWdPur+tLJ/p8cg/Txkezbx5TPMnSeNJaW8bc7eNv5/2COdt8eNJlqPrbfc2n8P20x2TXBVnQ9LJnldJd/LnINzwHnD52trLJxMa89h7w7QvPd8296T5pKvvRrj0XCSchjjM39o+7nA+etrTPpNshh+WN+W1eW94cuO2Z8N4l6p8xbvyHrG2f+Z9KJu13UmQWHvpHY6rMn63NA1SFP2pr5+031/1zQz7dTpxNYXd1meHv1N4r2hdI3l29XeVwg7TGvWN2P/qGikKsFvKOjwYYrPfbfaXVP6nEefQqkYmg1Nd2xasQR6+QLezOz0HeAZQRkT2gP8X8D9T1bPHKM/bHuhj7m/L618i0H+ZyBx7cLDRmcMkpk0bBl4j448hPSuKyx/BVaKKrsOxy2Y6gSzrOvalum5L56qBeJA+xkDToOs1sr8fy/2E8rVxMMvDq9KND028wmqNPzvHHB2GtPMcphP0YglH+33ZusWRQNMidYPuzVARxHtkVaHTEs0M0nrUmBBmkoMIGtMRH55hgDS/ZYNBfrCgVxsnSRFM61EroQ4pjGroBUZGdZMYNqVhaocawReRIZ6ipyiDuNmixRemy9eVBvHK9MNz6pfmtHOLdmUMbWgqz+TWOdWr+zT7/RHC4kANmFqZfnTO8usHuFJiO4DPBDUgTkPdUrnSelrDtYqEvJyCgLeC+JC/z/q65EtFLbSTvnx4QlsrHP60pjrOWB8bVEIZ1MZtlLENJE1sHvY+cszeO0Nzy4NfOcCV4b6bSBcPGbRjytaHctsKjn7YUJw13PvzM8Qr1TUZh5fQTjf+qKV8WHPrH53hSlCrXfoprA7nmnjfroRXf6vmwS8WrG6GOGq1K5c4wVbQzhRMbKcLwdvQLq7U4Lk9jiYpbuiPwvGfCstXheolF9tZyc4tbuop71tMA8s3W8za4KcejIIX7LlFreJnnr0fZ1y83ULhye7nzD8STn+xRWrhlb8n3Pmr4Pfbvl5Owl/uw+9WwIYCHv6Dgos3FbfvKO9aXvmdljt/Kac+8PipR1rh+LsGFE5+xUGmoUypk4iG/21MX6UfYTXkffQnGQfvtdz9ixnrV9rYl4XyrqV6yaFGERV04PJeVDBr6fuug4MfCedvhXYWH+6ZRpAGbA2TB4p4mDxymEbJz1ryjx8hXqlfO6Ldy2n2DPXc0E7BlYJmsc9L6rchP+PG92WgO+fn4KbpW6J736bt33vH+4zPhmFspfg8fqstlGeedir4LDxX08cR138D4mMaLoxT4sG0Cv/pr7OTL5/8vPSccnrE8p/61VHqptEwv8R5pRvjhiqDV7wNc0u28t0c4DMJ/X04Tsf+7W2fRvrG0ncVvj0Jc0jMS43043isiWgonytkNKYbB67o00/5Z2twZcg7zXvDuogjzot9GejKczlv3Xy2qQ/A1jex9Xksh12Dm/Tz8WYa4iFbQLNHN+el+VvtOLy4kK7P+7imHsxhg/bu4vkwpqoJbdVXFDTTrg3sSvB5uDea62JdxEkfflhHJxSnwuS+sHhDafddNyeN6jo8BsVJmH9SvYyiTsALZtKGdpAQRwCR0MmMUZo6o/jhNMw30zCo2onDZuG6PplgD2sEyHKHtR5rPF6FtrWoQvVowt5LC4woXgUjihFlUjSYWM66zbDGUzUZxni8NyyWJa9cO+viADTeoCqIKKpCYUM5LqqCvbJmUeec3D1AsjBnv/v6PT4+PeDN44c03lLaFiNKYVoK47oytWpoffgI3nt0jSJzvHP4ACNKbhxOhYum5MPzI17ZO+e7f/R1rr/9kH/oxi1A+Mn5dd6/fY1fe/t9apexl1fU3lK7jJcmF92rKE3LyuXMswofP7oPl0fcW83Zy2u+eXCPeRZOTr1f7XGU9yeOGlFycTRqabTXbwHOmwn7eYVX4bydcC1f8Cenr5IZzzf277FyObl45llFLo7KZ9yp9tnLajJx3Fod8vXZCf/g4Rv84tHH5OI4a6f8/u2vcXo64y+/+x7fvfMmq5/t80/9I/+AymW8d3GN1+ePOMgqPMJ+tmblct5bXMer8Ob8IT85v87P/s7X+eY/8RMerGb84zd/xMzW5HGCdmqotP9YG9/Xq1E7KOtBfFfKm9MTPlwfYUV5uTyn9YZGLYu25Hc/fpPXD08xorw6PaO0LY/qKVNbM88qjCj3qz2uFRe0avnDB6/xC0f3eFDN+bPf/DbX/vw9MuNj2YTr0yWZeGpv+d4PXucb37jNzenlUz9bNXzv3isUmWP5925Q/tUHvHP8AACvQu0z2th3a2/JjMeK57SaULeWus04nK6Z5zUewavgvAnfYnz3XoXaWRpnURW8Qm49jTM8Op2z/ztTln9tQV60qArNT/ZpX6qp/te7s4BetDxVi4tITlBU/m+q+h/E23dE5NVoPXoVuBvvfwh8bRD9DeBWvP/GlvuXRFX/JvA3AQ6zl3Q7wigBXTQSEEgROmDGu/AsywK6Oq7M9kqqQtMGRDPLkLKko48NQYQtIIpeBa4M73kPmUWqLABAe1MwJigf5up4UocFlS9zRBXNNuqjirEGWVfo3iwgzJMC3ZtiVGmv76E2ARQxbQNSe+yior4x78CC7DzH7RW40mLqAIrYVUu7lwfFzBDCthGAsBJABsAXERBoewVFzWDhD9hGcXkCZfqFV1IUQ9gQOKs0KDsmKJjZWvE2Luo2FKvNhX55mgVQQ+kADtMqtp5z/rWS6rDPH4IiaVeKreacvVlQH/V5pAVbtgrxT9/OcBFQljYpT3TgTVeW9N+PFTZpQ94+D0qaWvB5rwTlFwY10O5pAGMiKJGUr3yZs3zJsLoZlDtxDMAV7fJM9TKNpTwp8aVl8ZrgZopphHYaFLptIElS7DCKXQmzuxZRqK6H++uX/CWFV1po5ga7ttSHiptEhbHrp3FVkZTrIbigAaRyRWiLYZ8J2n+YnDrFcLjoTgvn0SI+aeihvNKGtjbrCF4gZCtB1JAtwzvOTi22EnwlXb/Mz0NfdUuhfKS0dzN8rhQPhcmJp/o4w9RQPqqZ3Spop0W/cHB0wFZ6F6lOe7cc4ixumlGeKOW9NfvvZTQzgy8MpoX9DxtQcGXefXcJFEhtIE5Rk8X72t9vYf/DmsmdJdcn+1Tv2y5ueeao98zGIke79jSD07PFKdM7a+Z3ShJ4KS7cF6eYRsku6hBvWUPrkKpGzy9QrxTOkRc50yLHFxmaGzQz4/HSDBaBTsFKNwaIat+XqxbN7ejbD+9fOxByJIP2wPsOZCYCqFI1aG57q+DQ8uTp00z/ne9/e49UzeU8PyPZHRX5YuTnqudMXtG9P70/LMzVbK5Ny23ScVrXh7WmN5wMafCqnXFlnCZX60YS58OhVRkQ54NuYQbfrPPhu0p17OZRj88ewwRI39nAiNMDLVeVa5DHoE0u+XjckuflMGBqjy9MbxzaDKNg1452buNcTgRlZFwWGIFdaaw1bQCxgoFrEH4w75o2lNPlg0yhM26hQY8K+pKM2qmrih8AaRv386WnOPdMHljaib3kwGBbnNF8TJxjPLg8IU6M9Kl0b9rC/I6j3je4aCTzeRH0IYWDhdJOZ50Ol/6MQhmB8b2l0uwfoQImzvNO4GIALCY9ybTgovFgXsHD+fxSXRIAJx4WMQ1Tw8NYlesPe8PP7aOvkS/h/fnh5XYYvLPUJhBAu5WFP5wGQp2avr2yJfx08hI3P/SsfniD3967ARoAy+Mz5U+/+61OF04GgR9uAoQ6ztvWofwXFj6cvtnlZ9oILuqW+IP3CL0OSTRIaBbqgcB707cDwDjQbcVHPd/07ff9IsT5L2dvdO+iOFVerpQ/u/5t5o+U6w8dv/XjX43fkXJSvtH1P43vzVZBF/moeBtbKa//ZM2tW29ja/hPDl65PP8P66fDebsPY5pe3fzTIgKfEuo51Nf2L5SH5SEAd/O+Tw0BVNPG96PBgPrd6U1MC29+WLH8wxudjVoU7md9m7/1wHFx/Dpn2eWxTFQ5WAZd/Nr75yx+cMRH5XHX1qYZ6DIabHEAuVMKF/Qyn++xNDKqfxqDXGzjzEE+bB+BicJR5SlvP2D9/X3UFKBKfrbETywn9z4lM/kZZKfnBHkiKBNPDvi3ge+p6r81ePT/Af5HwL8Z//+Hg/v/dxH5t4DXCI7ufldVnYici8hfIdCC/0Xg//CJSq06Ug76+7EXpmfqUTd4bgaKd1+/QJPzimr4CwuEZC6VnmlyxZYOMdvvXwJlWheAn7ZF1jVqzZiFs+VamrBKMnFRIJvglAsMF9+2mHUVaGhJCatq7HkVgJyklHTpOqhq8tMIaDnFLKtgrckM0rjAmFm3mMaPFIDAYjEjJS2BReJ6ax2JFZOK2yqayUhhSAONGjpmDEQFKY8KmhVs5aP1z4yUkDQxDBW3/LzFTSziA3jkSkEcZGcV0weWbG1HSpFxYGtPdl4xPcmxdXrfdNYwWyn5Wc30fh4UK8KzZBXswJHBJJPq55NCJUHpSkBTmuz8YKDOFwGMylaBSdOxYSKQVZ62AdiK1lHxdIyRDmAZABbFucOsQh8qzsDVIV9TRwts1370BQbEh2d2HdpeWo+tQpq2koRtdO8iMAh8d92XOVnZorKTGB/0931j0ExwJbiZD9Y7o72yB/jaQOEREyrX2BzNw7WZBYudq8NiQGwPGnknNPsl1XWPvlx172F9liMzh1KEsr65olpm2FmLMR71huVZAZnHzloWzYz63RV52bK4O0Otpf3Wknadsfwo5+JbNeV+FT8xpW0N3huyzGGMUjcWY0P7nLb7XLzbYvcblh+XTE5nnPyy4g4b7NThaoO3AQg5/9U1NvOI8ZcYq6kDq8qIKOMbQ/1HEw72LHf+ssG9EuqtTshvFzQ3m2DxTApJSlcFqv77klbY/+GM83fiO3EBMOqYMmthej+0X/nIYxulOG0pPgzTSvX6Ec1eRr1vaOZCO5XOEq2dRbwHLU0bQbn43oMSHfpQcSq00z5s+v7E9ZP5NoUt9EswVbRaJ6bMqdLMQDPBNPpUTJl0bRtFP855YbJTVj5z+XnrOX6ScfGdG7EwYT6zcQ7s5o9Rgen6d1osZivtFho+F1wulxZyCSgezr8dy1L6xUvHkpEBsECv6EMPMgyZLOLBF+H5cAGbraCdhHxEtQNi07xtnI7KMWTBbAIXQ6bMCAwZ6gUbv7s22AwzaAO7Dmy8IZNo1CYe8kVGfRDum4jL+ox+cSkhQXFh4vJFmk/BVEI7137ONXTzY5rnbWVQG9ifw0W0RvYGHuzSoHk0qtgEImhXXmkHTJlBBcUJ2alh8sCyeN0HZqcZh0usl66NWoPkvrO6iwHfCuqFrHSIKGICM0Yk/YU5cL0qWP1oSnWzRaYhbF62ZFmcB+/PKI/WqApZ5hCB3IZwrQ/z5+nphGs3zrFGaZ0hsz6wVWxgqiTWizWeRV10TIWHiylfO35EFpk3zhsqlwXjDoE1M8kC8+VsPeFougLgxx+9RJaHCeDbr93hvYfHvHP8kKrN2C/WFMaRGUdpHF6FRk3HWvEq/OjRDWZ5w7sH9zGilKbFI1y0Be+dXee1vVN+54++wdffvc13jm/TeMtPzq/zwb1j/vF3fkSrhrmtWbiCymXcKC86pkNiukxtbxD4YHnMx8sD9ouKb+7d7Z7dq/d4qbgIdcewcjnH+RKnZsQeAnjUzDjKl3gVHrUzruULvnv6GpOs4Vt7d7hoS0rTdiyVczfhXr3HUb5iZmo+XB/z9vQ+v/vwLf780YcYlIftjL/38Ts8OJvx3/rG9/ntW29x76MD/tm/9psdI+advfvs2aCb7Ns1S1/ww4uXadXw9vwB3z+7yZ/93Xf4lX/i+9xeHPDPvPI9rHhycTiExmcs42BjhwMTgTWTG8fa53y8PsDHen9t9pCPVkeUpuVGeYGNMMrSF/zm7bd55+gBBuX16SMA7td7ZOKZ2hqAk3rOtWJBq5Y/evAaf+n6Le5Xc/7gN3+BN3/1I7JYDo/w0uSCzDhqn/E733uHP/fu+1wrlwCYGM6K0njLn9x7hTJv+fA3Xmb21+7ztYOHsU8ZWjWBIYbi48eaieNkPWfdZqzqnOPZiknWBBYMQtVmcQNC+Fa8Co2z1N7gIusms466tZyezdn7ness/9oFZdnivVD95AD/ckX7s3G7fqay03OAp2PK/HXgfwj8sYj8g3jvf0FQUv49EfkbwPvAPwugqn8iIv8e8KeEEw3+ZdUOGvmfAP8XYAr8J/Hv2WRgYb38LJmltf+dtnRc5SPDmLAdB9+DGSID9o1czZQZlEk3wwxBkOTDwNrwSTUZWuSQDbbRXMWWiVawjikzAC4AUMU4hywydFIGUGRSoLMSaVrcfrmVKWMqh8kMzWGgfRjnwUC7X+ILg60cPjPYwgamjNBZa7qtOluYMmGBE64D9Zoubxvp2IluHR4QkN5MRsqfrUxHv+6YMhnR0jSo/iaoAJRFTjuVDrBwk7AwKx+WrK5n1AcyUvhMG0CQ8n7B6pqlPuoTE9crvOXDgtUNE5gycfHmix7YGSpzQ4BkyJQxjfQWoSYqyDm98mUCNbmd0TNABmyYes9SHwj1kcZ8BZ8Us7Sw7BaSQjs1aG7whaWdh7aQdrwVaJtFULx2imJYRIetHuH30NwSy+7H76UrR2ehJVq0ZEzD1wDymEaxFdiFQbPLC2xTSdiSFu9n54L4oC27IgxjNrV3+oTi+5s8UHxhqOpJ186TC8FNLJP7AaRa+inFSnCTPLwnD+UivCtfFOx9oCzcFJ/D/kOY3fH4fMakhvmtir3vlbhJ0ZU532DKFAPK/tGPHfmFxU0yyofK/IMFN/5gTjvJ8Xketg39LCgB2aocs5kGFqDR9YhBA/sfVhT3l4g/oN4vu3danjnq/WyU1jA90/Y3jVMmdxcc/aQMYZyPVjIfLTgeu6zBEwFdH8DmiwWop1xVlEWOlnlguURmyiULb+ojm/u3BwiUqdoeCI5hEwumM08N2GmhTjG+92HMSixBp1A3YUtoYiSMmDLpYxz8V+3YSKGeL44p86JFRL5GcFD7CqE1/6aq/u9E5Brw/wTeAt4D/jlVfRjj/OvA3wAc8D9V1f8s3v81+jn/Pwb+FVVVESljHr8GPAD+eVV97wVVcZv8XPUcs3bsfS9Q1kdbe0V6Jsw23WPAfJGm7cKqMf2WYujZaPF6nDm9rpLyjfltslBGTBmv+MSKkcG9IZs3GYFaHww3l5gZvU6RWHMjhkya2zdYOqHcEsON8xrfG0fZ/D0Eh0wbtzjajXIMGAJ27Wlnppvz03PdYOpIHDO6rWfRaOHKAUtmwDDq5sIIELtokOrIrwMjjK3DlrUu38Ec3htxNu7F+ThfKMW5i0yZrI+/0T7dHBqZE6O6RbDaRzbBVvAMmDUw/9hTfZjhi7xjJ6RdJtfOlXa2H+sX7rVmkIfC0RKq/bJrn3pD1xuKcb0OlK3hg/nhCHhLYHuq13liyjSQMPbjh6FtRZUfH71DvoAfz4/7begDfbObgwfdMl/C2sDd6WvxHdOxV20FD8pXufmB5+SHr/F3Zq8FBs0KDi6U3/zjP08yUHZtnPXl78jFg2/I1gFMPLHw48k7PTM3MdafYqGbdNe0PVmzwOpRge9Ov90ZJhOYGxjY/VZjU8NvR6bMj2fvdvnnZ8r1tfLbN/4C5alycOr5T3/016JhT/lZ+Q4aWSfexnSr8O18vxDypfL6T2p++OG3sDX8ewev9YXWjfaXdE+756ndEngqPjB6OqbM0MDjobhQfloGYPyHed+O6T0m/UitgEK+8vz9ycuIg7c+XLP8vVdH7frTrP9m3z5pOT38Go+2AewKB0uPGvjaR2es/viIu5Ep8zimk2mV0sHEKz7f793cJT0uhm1iPKMhbGoXFZg65Vrjye/eo/6jfdSEdWZ2fo4vM07uvjimzE6CPBGUUdXf4PLUluSfuCLOvwH8G1vu/z7wS89SQFTRtr3s1wWCgyKCInBJcXAOmpoE1CiMw2yKV9Q51PngR6UDVdIodznusExbyzekCUdHT9q2sFoHp37G9A27mX5Kr3Wgis2z8f2hrNb4qsKcXaB1DU2DqRt0scTejxT9IX1XBJxDqoaybukYN1WNuaggsyFfa6BuyE6LoDykASVuMxguZHqmTG/9wvRtoBIWdWlRFu5FRdHreMEFHVMnAV7SBkvRpk+ZlM7wt121aG66Z640iFOKewsOnNLsZYMFIXGS8OQPFhxmhma//ywCOCGYxlPcXXD0Y4MrTQ8m5TJShIIlsJ/AhpNomtDShJCYMEOmTLYOlqtmKv1E3KUNkwcNtsnIViZO3jqg/iZFTruyTx46zLLB1I7Z7TL4lHHBp0wHIg0VnU65JvrSgXzhyc8aypM8KqJma3hbKflFQ/GoDMrngDkUCzYCq5KCbtfh/eYLaPZlDMqkrtSGH2kblyjRUhitkXHSTHGGNPJ2FrZGtfO0FwYwJviiaYVsDc2RD20St3XhwZWBpu7mnvzCsn7Jo6XHl2Gr0/pVh9QSfPy8rLQHA9qGl8Hefg2c61jf/CJj+ZrSHDrqe5bZnQnnbxrqI8VNfASbcmwFD7+jaO77xcxQAWkksIWUzj+MeAEnNHslhz81nHzbsnqlZzBN7maBNWQHacV0RcHUA6aMg4Mfz7h4MwB/4iPLqonskxqmDwLgM3k0Q1olP2/Ib2fgHO2rx7SznGbf0k4N7SSwoRJY2ynzps+ve/ex36d3Wpwr7aQHeTtWS5sWQOOFkfjez5C4qIjF/igOiouwQPJWOiv9kF0jSQFLCxoXt25p+K93vtQO8NLx0H9fRPaB/1pE/hbwPyYcD/1visi/Rjge+l/dOB76NeDXReQXIkiRjof+bQIo808TQIq/ATxU1W+IyL8A/G+Bf/6F1nIgP289x5eW9VtBEU/GE9P4aKyQSwp5km5+kQAYpLk1+ZTZrFG3VWFYXhkYRLRX2CGlPZxse6U+MEVlPIYM5sShZJXSljJiyoSEUrk0jgVJN6B7rsMxYrgIHukAw0qO4166P6p734a2IvpCG7B/htE9ZAtDsx/n2bRg25yvok4hGoCLngUD7ZTRlo1NFpCtoyGqGCz8GSzOI1M1sQ2HjMOOEZMMJ0PWrsaF8rmhPBGWrwjtngbm6iD/7aCMju6Li3mUkdVoBnlJH9asDYhhfS1sZ0YUX2g3Z+WnlnbPxfceK5p85PiQSHZmaY/bvlPG8oqNrFlAvSBW8bUNLFlAFxn2qA6MkBjXe9OT6RuLyR1iwNUmsH6MZ3V7hka2jb1RsTwtyI/XeG/Ic0eet91r9pGlCnRMorNHU8Qq+wcrjCiZ9XgNfm8uzibM9ise7h3QvLHm6HiB84bFYoK/X3L8zoPArrWeurU4b9gr647VYo2ncZZJFtnzojxcTrlYlhjjuXaw7JhC5+uSWVl3PnSCfxvfsWSs8V26F3XBJGtx3rBuM+ZFzd2zPUTg5sE5VZuRW9f51Gm85dFqyryoKW3LyWrG9dmCDx4e8drRGVY8y6bgo7tH6FnBy2/f5e7dQ/IPCt74qx/SOMvJYsa1+ZJp1uAR5lnNqs25t5jTesNLewvuX8y5+/eOyf6RE86XJW+//KCrS2p7j2DFd/VOkurZesNpFQzPrbPcmC65v5yhKhxMqlG4W7eP2T9aYo3n+nyJFc9FXZIZTx7ZJo9WEyZ5aKuPT/a5drRg3WSc/d4R9S8tO39JqsJ8WpFZj/PCnR9fY/b1M6ZFYLM4L12f8d7w8ckcscrB7x9w+hcq9o+XXV0kvnsA53uFel3ltLXFtwZbOvLc9XYlZ7rBw9qUj+BdYMmoEtjSXvDnOdf/6xs8+Isu7Bv0hsn7B1TXHdUHvDh58XrO51K+GF58kmPZjXvJwqnGjGi1mpgpwMjz/aBDXwJB1DO0zI5kqOxfwZR5oqNfG8ARSawaY7ZbwDbTtwn0sP3zDSVJEqsn/pcsC9fWBmtwB4z0YIk0AYjRMg/giHOhncsMzW1Y8FkbfPlOQhrJYhX8M0jYfpW8hkeLmTjfgyRdXWPRB9uXUnmGC6qRw8DcdMqRWgnKipFub/KIli2DuhEUwz5c2L5k2uCPx5UGNxmkEcODQa3FF6Zz5Bvqk5ROA1Y6WniyPPmsB2U6awIDJW3DsqGD8gq9AtPlFwHutDBMi1PR0EVN8ueRfMoO6ejdPg668tnKBwuqN2QrjQqVdntjty5DtK+PrZX8vMUuayYPp8HhqTeXohgHxcOwXW5+a3bJAeRwi8nmXmBbe8r7Kw5+ZinO7BjIieXpLBSxwLZKppDI5Bn0pWGdxEcmy0dhG02SbO3xGeSLBlt51j/NYh4pLcjiti9XGCb3lhz/oAisrWVDeVKx/8EUcZ7Ze48oHx7gS9OVV7wGBl4CytJ45JXy3jluXgRwZ7Ek+/ghk3tH+DJaLlXJHyygdRz9YJ9ksQ7tNnzH2qXZsZGjhdacLuF8wdd/fIBOi+6+rJvA0mMQfxCv850S78lyzctlERZTPobxPgDf3qN1NEO1LWG7qMfVNXjF3D+hMIbSmst+v0adR0J6myckDMfu5D8syWCL6iUZWNRHJ0EAnQ+ZS3u3hvF9F3d4msco/WZ9Od8vicTThtKJQ+ciMjwe+r8Rg/07wN8B/lUGx0MDPxWRdDz0e8TjoQFE5N8lHA/9n8Q4/6uY1r8P/B9FRPRxR7B8icWsaqZ//OH4ZtIhHncqy5CR27bhOwNGDJv0exh+KCIjQHPz2ZW+ZiL795Jsy7d1Y2Zwkg1221WM4Sv9ygzjDONtXrMB0MBoPAmGFxeYP5dYL4MotQtgPUlv4JKPrGF90jZjNDALfWE22ECDcniNALEEPWmQd/d+NPjyGm0D36xXUGd6plHHvFDs2pMtGvY/LEJZtry+TV1qBApKKIOojrZl9/2nj2tapTxZ085ttxU9gIyhkbKlw03G77djfcV6ZytHM7OXwKLLjCdGfttsrbSTySVdoLt2oCYY54L/lcDkKS76QM1sQrYOfm8S63oE/Cd9YyCH68h0KouuXlaVwsNhpbiiYHa3pj7MaaaBgbNfK9nCUR1f69o432DKQLDrWA/1oL/vNXBY+6gbz3Cxz+y58N6G5wPUw4IO9FPjlTqOL4WH2grXq9AO62IP8YFtUafXrTBtFWeEhYTri+yYl1aedbkXdFanfH3hsFVNfXiNt1eO/OyC1e++gii81Hg0m7E2QmJAoXDY+GDDyva43irlrROqP5hzvVWq6ZiJkvpIC1RdR9BLYWatdu+qtftcawO7VrN59/5yVX5hWeOLwArWbEYrwtT16RnguutZtUd1g8+DDmgf3sb/Z9PBOOvBFN03fHNxgp/mYCa9rpXEw6v1RRiDTu/y2q/PRn65+twH9fMgfk3nymPz8JQrTum7lLdqWLOdX3Dz1+e9Ab9uwBge3m63p7OTz0w+96CMeo9fLJ4ccJusP5ni7Jr6yYE+rTw6fe5Jbm2nO3cv39sim59w+u02Aw6eDefFp7ne9vtZ5Wk77BZdAw/k8W+beCD/7uOfT//wKQtwlWxbBD4p3AYINxVh+rT5qXbvcP9PN9J6BvHA/h+F6/ljwjng8E8/WfqTP4TJE0N+MrE8Pu29p0hjGF8Hvx2Q/9nTlyXtZArbrZQW4IMPhyz07p3JD+P/J6S52aO67/Ypv//PQvRFjKOfsVz5pb4g6CAZep+z3BCR3x/8/pvR6ezl/D+746FfBz6IabUicgpcBwbebr86ok1L+/Htn3cxvrIyxBw27w8l4h2PDfM42aaX/DykeHKQ5yafts6fWifYwvTaen9DN5teBYQ+B9mmx5bPPZcXLFcYOtL7G+pMTytJJ4dP0I+eVteOMvz+n+aND8NvWycNwz3u+TD+C/TgclnuP7h0S/XF6HCfkZ7zhZTPPSizk518qeRpJ4lNFsMnSePT5P9p5DNQYj6VbKvzs0zYn6Y+X03D/06ep1zlROGTy31V/YtPCvQZHw/9uGc72clOdvJ85Ko5+El61dCn2E6eXj4v7fV5KcdOnk6ev57zhZSdF5+d7GQnn1y2UOF/7nLVZLzNMvakcM+a34Zz2J3s5IsojzseOj7/tMdDd3FEJAMOgZPnX5Od7GQnO9nJTnayk8+/7ECZnexkJ59Mhvv+09/nEYi4yp/CJpV56D9h8//TpL95b5s/hs8DaLWTL5boc/57gjzF8dBw+Xjof0FEShF5m/546I+BcxH5KzHNf3EjTkrrfwD8F19VfzI72clOdrKTnXyl5QXrOZ9X2W1f2slOvuryNHunt/m3uSqtn7dcBZI87vnTpvNp8978vVuH7uQJ8nPYa/0ijof+t4H/a3QKfEI4vWknO9nJTnayk518xWTnUybIDpTZyU4+DzIERh63/eYzcjz3hQBbvozyZW3XdIrS5ulI205f+kTJP3u7qdfHx7vq5KdtZW6+pO8NXsjx0Kq6JoI6O9nJTnbypZNtutyz3ts88GF470nPYPu9JM/6bFven7Q+T1uvp2nTT5PHs5b1Wcqzk518AvlCgDKSD3zFD44PVOeCkr3lWMbHOCW8LMPjrH041jUdzyr5libyvj+m8klpprBDcdGIuO04yc18No90hXA8a6w/EI7ENhLqnMqVjhYU6dtsuLjo7m1a8c34+Wa5re3b9mnq/ySGQnfs52D7Szxul3h07+jYz83oT7E4E6+wXEGeh+M6XThOXdsW2hatG7SuMdMJZBmS55Bl4QhPa5B1ja7XcO0IMnuJ6aCZ6co3PHZ8VAanSNXA3QdIUaDXDkEVP4t9Ox69J94j6wZZrNCzc1QVc3iAzibotECLDJ+FxPPbj2hvHuJLG4/HFIzzZA9XtMfTwTGTAgKm9rQzG8uZCka3/OqP3Rw8i+03/fCC+vqM6jh8D6KD48CV/nhQYXSEpnjtjjYfvTcL2UqxlbI+jkeMqsYjM1NcwnGeOjgaMroPVQOmCdc+C+mlo8Dntx3Lly0uF9RC+Uip9wUdpGHaUN69W47TtzLUEo8QV7IlNHPBTfr8QztA8SgcP12ce07ftvHoUbpjvq/9WcvipqU6CvlJPFHQ5319+n5Jl284+h0mD5TykdJOhOUrodGKM6gP+jqmuHsfKvWB4ErQDFwJeMjWIZzPIb+A6hjExXbM6Y9wJ7Shz/r+mi3hxh/XrK5n2FpZ3LTUR7G9Wzj4qef0HRN+u3BMaHfUaHrngzLaFRz/sCE/b2jnGQ++U+AzuiPX01GjSUwLxZkyeeRpJwIKqxumew/iw7fks9A24mLZU1+TcHS3rQftJWDXcPijJRdvTKj3+pcgGo5P9Zl0YcUr+SIco5mfOdY38lBP7fuO//W/ywuTnc63k8+TbFvsfNGvty08P20aT5ve4+JtpvGksF9UEbMdwE/3nvT8WdN5Ufe44t7QaDG8N0yDLeE378mWNB4XfiRP8WzYreQJ/4f12dYdu3vb0xYjneFErzjSWYyEdc8WQ8lm/GE6Vz0b/g/FvzpMSqev6vYwm+GeRR6Xz0ZlQX0f3tqr+/22Prrt+aaIebHGp52eA3wRQBkRzN48LJIBseFjVOfDQhkw89kY4NgGBJgt97Zdq6IXS2jqkOfRAZuAgFQ1WuRhgT5MIy2CBwtzcWFRigdsXEgs1mAMOiu78LLpLFQ1jF1VHQAO55AmrvBaFwCF1Rqta2R/HylyyDM0s1DkaGbQ3OKtQa0BI2HhbsOCWSUufgft4q3ERab0i8VYRTVCtvS4UvB5XGSnxX1cjIVwYbHmItbQ5ZP0hwEIEBZYIY4r+3u2Dovj9bV+4eUz6RaOaugXsgn7UfrFvPZ5pTwO3m+p9w3tRLC1kq2VfOHJzxuyh0vk3kP0tRu0RxOavYx6z9LMwoJ3eqLMPq6485emYaE+7GotuAn4XPsFd6yjDuppGpg8EF75rSn1Ucn9X8oxLSxfDYt8U4WVvV0L5UPl4Gctsx+dYFrH4lsvsXg1Y31DqPcVF88VvPl7U+79itAcObAa+luVcfD9KWff8LFRBC3CgGsvCtz1BsQjRruyiiiqgrHhnm+lrwegleX67xxx9i7o11chjBeKskVEcc7gnMEYjwhkmUME2tbgWkte9OFS3CzzrE6m2DNL8dY5qoJrDXnRYq2nbS1tY7GZp20s5aTBe+nSsNazXoZOlhUt1irOCd4bVn86Y/VuRT5pMdZz/tEcvV6R5Q5VwXvBr8J40sxzFr+8RqyirUGXluzC0h42mHkbhgQPqoK2hvxOjrQwObFcfLvuFQsv4ITykWXxulDdcGimSC2IFzRXpBE0U3AhkmkiKBPbW61iWoNpwE2EZj/2jVZo59r1+/AnuAJ8/FMTwJX0PSUQRrwiTjAtPXgSvykUbBUAnQQsZQvFrhz50mArpTwVRAPgZxulOHNM70kHiqVxogc0+nzEh++sfLDGLGvMOmP/wwAgJhAvfcfpGxan5BeObNHgM4MolKcJnO3rlOKYNo2v2uerijSe4aLFtJ7szin76z38JOvC4RVxETgfOGk26zjWVjXF/XKUljjFrp90yOVOdvL0IpMS++4vQN3A6QXcOOoMGwFo9MiqQosczbMwFya9JOkLxgRjhgfNxwYfcQ4d6Eii8buJ8cVpH6f1mLrFT3Ok9UGXMBKmk6jD4DXoFCKI85h1g58VqBFM7fC5jSD7FuPVEETYQkJL+pOohnwbT3a2prk2w9QOaT1+miG1x5eW4u4F7mCCm2bROMFlo1IsL4BdOVxpArhamKD3RLGVx9SOZj/v6zpadIKtPSrgiwAWJyNEGpOytcdUnvowi7rPYIE10H9Mq+y9t6A+KqmPMrwV8oWnPojpiozyHo2TXtn7sMauWy6+NqWZhUWurbUzRmwaXtIYb+qQdzMfl7vLK4Lfs3ue5UsGn4/b0jTK9e8uOX13hs+h2ROkBV+CqUI6PoPjHzQsXsmorgm2CmN7s9fXaai/pfyH7TO6N/iN9PMcQLaC/ExZ3xB8HuY0V9Ipgkn/7PTQZNiJQbILwc3CnDK9KzRzaPcVtNchfU6YuxUm9wzVDT/SN9VGPbUc6vFBrxMvQT+0GvK1iqhgF6ECbu5R0e5byB9aUGiuuS6d7ix2o0HXGCq5xXBiD89lbYPelwWdTBsTdMSUXm2QqUOdQG1g4vpP8n6Oz4HDJnY2+gWzgDohu1vQvlxjMh90o8Zw4zdyHn4H/MsVqDD9fklzqDTXW+y8ARXsTyZkK2H5zSroipMmFMkbNHZQE89HDrqX0Cxzrv1OzslfbDGzloPfnoLC2V9dkeUOY3QwdQvqBf1ghr6+xq2C4SybtLRnBflhhc1Ce0lsw/Wy6O7ZH02pbjjya+ugO0e9dPq9CT6H+psr1BmysqWtMjCKiGJuTZjeEfIL5eTXWmTqEFHEhvp4DR3ZmJCPc6Yful3QLcUq+QcF07vC4g2lvdFAYzj4fsbqJaV9oyK7VWIqoXqzgsZw7fcz2plw9st10GHTe2pM6Ce5j2UEXVtmPwu6z/LrbXjmw4dgT0Nd3Nxjzy2i0O47qv/N32UnL1Y+/6CMKrpa9QrK8FEEKbxzlyd+uMxQGcjjmDRa16gqIoK07SBSQBS98wEc2sY8YTyndHkNFZG6CcyWBDRdRfVX7Vk1XvFD1oxzPaPn/DwoXMZ05RJrQrqJQTNgz2yCSJ3Yq9ktQTlqwQb2yNb46Z73VwJWMFa6UruMFMTWg/PoNCE7Mcw2Re5pLESq2IcLdFKgmUGcQtOG+tQNul6jqzVyC4qTkrzMmRU5mls0M5hljVyseKW9HhTUgRInTvG56RTIvo7jIhin2EVD9sE9srsTXj09RFRpDgrwinHaMV3sssU8vICHp6hXZj8yTO5OcbMCVxp8ERpg+v45xdk+7TQozYnNML2zYP/DSSpJx1SxtaOZZb2Ckt5FUpJMFyVWLtURDn+yYvKwZP2TaaeouEJIzAKUfrEd4xVRufNZHy4BkD4T5ovIlHn/oMtXs1COTMOcoSak43OwRL0iln+vDtc+j2BgLPf++x5bl7i8RC0cnEB1fxKYMgJEkAxgftvT7k3wVhEv2HVglzSPctwk6xQvCEBIeRJAx/xcKW7nl8C34iywcrw1aA4S89FcgtJmpdNxRkwTCQpFAkvCwiJO2qZvF40KmnjF50H5dCX4LIB1Ia0w0fo8tHd9pJjE2Ml6UDMsMCKoE+uQXwj1rZzVdUu2VhavGapjBaNBma8zzt8KSmhi2YijU5aJgBGiiDdkS7DVlPw8p9nLePhNGxhErld4RyCng/KhZfIoD0wZYPWSjMps2p45ZZJyrn07igsLBzVJ8w6srKPMcP71SbcYSQq1qcdlQKG48IhCfu5YXw8LpjBegW3AffSCps4ByLyTL6/43FK9so9dt+TOU72yj8+jzmMEaT3FaUk7z3ET2y9eBwvpNP6KB1eOF/SmUnwp3fgrLo7fxG+gUdppyM80Sr5w1AcW0yg+l45Jlr4TcXQLf+OgOHdUhxZvhWztu7R9LuOFdyzr1sV2epzUizg2mBrmd3MuXsuwa7CNUu8J2UppZ8JBYVhdz6gPZGBYGqc5PKA9vwiARLZW2qmEMSyOs9lCyVawvt4zKzfZjdky3Gvm8bmln1MVspUlW8D6pbAA3wYqAJjaIG7O4hXD6qWwcC/OMtbXNaap43YZgjKt4PKS8jzn5DuW5iCABNlScKXG+SJOJFZH/cSsDLYS6mtu+1EfCuKE+meWxZsuLO4HC3NZW6YnEx59Oxjf3LW6X+SvbAibebJ1ztnb4F6pYJEhtaDHdVevzjCUmsbEBXmqZKxwCtcTppW2tYgJiP76vCA/yXCvr7C5o17nZJNmoHqGRam1vjNApfvOGVaPJuR7NcYoi2KOO3BMjtd4LxhRXGuZTBqs8ThvWOZ7TF5ddGCA94Ysc7StZVI0YQEeZbUq8N6Q5y1ZFvLPjMepsDifoCrM9iqM8VhRnArndg88zF9advVWla783pvuvveGaVnj4j0T01guJhRlQ565gMHUGdaG9YX3hnqdUU4bnDO0taWYtF2a9TRDc08xbTrgwvvUZgFQ8IViC4+xDvWGtgkGlMAmDcBIMojiBN8a1Al5EwxEVBYlOCCDAL4kY5Ub9gsvyNKSrcGeW3xjyJZR33xUUGcbk6MG/WNyKqzLkmwVxgNXZhTnQlNNaa2OwtulCYAbUD4QxGU0q1mXVuZgel/xGbjpJACQRUFR9WPD5J4wfeDJVsrsZzlq8zgmh+dZ1NvS73y4rHS97jK9q5SnjmxlaG4XmAb2PnKUJ4b1gwmT+8HAtHpYBubyz2rcxCCuGH/jbWwjm/XjTaPM7oX15OSBRa3tdKp8GXRFV1qyVWgLV1ruL55iffU8ZKfndPL5B2VEkPkMTNziMGDKsF4H4GQ27Zkyl7bjpB55GWjYGh7gYok8hiljNpkyKZ1Npgz0FqkBU8acr9DMjpgyKWy4EcGKAVNGmhZJAE1iyixX0DRjpkyeRcZMYMrop2XKdFYGIVs43MQ8limTrOUdU8bISEEaMWVcn8eIKVMFxWt9LF24xzFlOqPBBlMm3Iygws9mVIeWtnw8U6YZMmXmgY0wu++Z3qm5+xentIkpE/MwLjJlMr3MCOqbBFPD5P6Em0B9bcKD7+SIg9WrQUE2dXgvpoLJiXLwswnTHwcr5OIb11m8EpgyzR64SRhwX/6vD7n/K4bm0IdJRRRZWw5/MOfsm76rvy/DdXZhaa814R1FVH2oGEmcrLQd0o8ErQ2umHL2TdA3V8Fw44SscGhkr6gTxAblx9jQT72z+Faw+YYy4cFmnuZhSXZukTcXgcES0xRRfMe+UbwT8rINVovIlDHG06wCg8KWDmM83pswucuU5bs1dtpijOfs1gy9UWMz35U9MWVsnbN+ow59qxXM0tLuCc2RR6eRbuLiCsIJ4rPYZ4X6ejuaCMUJzczQzqHdUzTXwIAigAimjX0nYa+NBOtZYsqYfuUgsd/iQzxTR7DD98+yVQBmAqgguHVcNKzSgiZYbkQlgFA6BjNIgMRgW1e2VMqHNVBgaw+aUZzGvtnA/HaD2jwsxtr+mxXttw71LBglqzzTWwvMsiZ/lHM92xszZQYWTwjKRLaITJncgML847xrk44R0wFhg3xdP3521yndusXeP+P4bI4Wg2kvgsBqzGj8lboNedUN09mkZzOqQgROX5jslJUvvZjWU9xfIOsGVmvye0vIAgqrxiDOIcuKYlngU/8dsjDSijX1+8yM56DGo3n/oYUtoSZeh+/FF7b7HkzVkl0UmMbjoyFCIvCDIeSTjBNOsauG/KxArWAqh0bDgbfbtzJsGi1CfS7fDMwbT35WYZoJtnLgYTK1mNrjSkN5+wJbTWkfZj1zpato/Od7kNauHW5iQ90K07OBRbBrj6095Vk2uD9ITsGuA6jQToLOpxsgULZWTK2Up6YvS9KR4lQSGJCe2ccrbF1SnAcGYb50TB6Yju2zyWLptgs72P+owS5bDmZTmlkYo+06AHLdexqWP/63lSItNPcztoI+ke04u+MxtcXndgzw1TC9t2Lv/Qk+E5oHBaYOC19b0xkU9m61qLFUZ2Wcv6A9KfvymHHbqtEOtBoCUOMwYfoUT1e/6ULIz2DdTvAZ5E1YNOsQ+BOltuN2hNCOxUJwswwnMLtrqNdCvZyHRW2mSCssyiLk52Fy37I28wh6gaiwzjw4YZ1HY1jClqpQyCrPqWxUTq0GsGEROtiiNUEPi5W1Z/F+kQxrsbmyAI6plz4PFdpmwICLrAy3zKg8tHGN4lqDS/qdCr62tFnQmXxrcK3pG9sBWcxnCEhGQMwYT2uVLHNY61ENQJfPctzMU8xqvDP4oqCdKmbeMJ3XeC+00wKMYPcD4DOZ1n2ZYr2yzHXdzXnDalLQzCf46w3ZpKHe3wOB4uUlRdFiY53TNOm9YdXsU762oFrmiFXKsmX9aML0eEWeuR78AxbLkqII+uWq3qd5qWF+bdWBYU2dsVzM8IXC2wucs5STmmpddMyXxXSKLwzZApbfDCxtiECjUXxMK8uCbtw0Fht18KTrilGq96ZM7luWr3v8jQqtLO0kZ31Dca+uWX1cBub011f4xmKanHpfOrZ3agTf2KAr5pEVL+BXGeuf5qiF1ddrJPfhHatgHmUBQJ45zEVgavl5S/vrL1D52Ok5wBcBlFHFn11snbBxAQWmqrYzXx7DlBnJMK5GXy1p0bBaX0pLEzNncG+Y/2ZJut+xDj4yfJK/miuxyOQ3xhh0w7+MRhaNekXOztj0JzNkynR13MLskW0A1Wa7pfttYMp0z7e9kyEr6ApGTlJUAsMihO3YN3kWWCx1g5bFON2Nsm9Le6uoIhcr5plFMxu2KzjXg1tVhV+tMW1LfjunyHPmCeCyBqkadLHktYfXorK7AbpFejcjpsxYGRMXFF25/YDZRxmTjw/BKe6g7J6nNpF1izlfoKdneOeZNy2z96b4WVDItfMpc8r81kGwnEY2jGkbigcrrn1v2nWs5H/DNA43NZfZMTIo8wbAlso0++CMw/cmrK9PIsNh4NcjWgGSMhUsqGGhnNgggf7ej7pqBFsp+dKxuj6LN2PcAfMhgYM+T2XpwbgR+8NKVJCU+W3H9F6OKwt89CnT7AWFLdUv+aPZ/6jFNL2fE1spdg3tzNJOA/W1aw+F8mH4MX3ocWU2aj/xMHnkUGOwaxOAmAh+pPSHfk6CtVkGPmWEbAnFQmldoFVDULRtMWCLDEQckD6TgGEEoKYNwKjJ4zYnmxZiIW8X35mNz1K5fCG084xmbnClUB9I2EYYy1tcBHDQ2x5kCmVKvoCkL6NCtjLY9Yz8NMNNM87fyIJ/nbTNKgG/g/oU5xnTBxn1nsE2yvIlMwBFenZMstIPFxMdU6YdL0ZspRxYw+q1KfV8vDg1jXb9J93LF0F5yk8bqhvFaMySFvytJ/gD28lOnkF8bqhenpMtWzLvqW/OIygZ+mYAJnLcPA/jklyeYwJ7LXRiX8honLeVxxWmn480bHMhjdOt0k7CAGJrJVt5qkMbtsMUvZHGxa0sxmm37cc4pTgrqI7CVqB8pbRleOYKegUn/e+x58tbZwb3RInMG2X6wLJ4xZKtcsQHpksYpwH2WN4Mi5Nu/thUC9JcopBf2Bg/MG06lpwEw4WtlOp6YNAk67YM5oBsCRhoZiFO2v6pRuMWUEO2gtVLOmbtpHqnd7IyZKspF69ZVjcDOyY/s1TXtWfYDEGKwZaVsB01J19kPPoFaA48GLAXBjeLRhozzFP7uW9pMZV0BpreCVisoxPwQjvLWLzVQhl9F6Z3trbMb5ecfgM09+hxjVYWM2vxi7i1zirFo5yzdxW9uQ5GEC/khxVpa4oxOnpNWWSCdP1A5ZJLHhPZNG002KgKy/OS+n6BeW1FUbTUVc5kWo/SAshMSN9Ip95TtxmL0ynT/TUisDT76HHN/uEKr0JuHY2zTIvAlGmd5b454vi1U6xRnA9bXCZFw7rOmRRNV3avwsWqRFUo8pYiCwan3DrqNuPRWdB7jg8XWOMxojTOck8Dc/jajfO++3rTgQmNMxgB5wXnDXuTKoASKoFF5CxndsJsUpNnDueFus26+rfesDYF81lF4yy1yUJ7AV6FpiyQ0lNOhkyZ3hjWtpbW0oEy3hva1L82vjtxYftPXdvAKGpAGnBrC0ZZUwRsyUsHNg0NheoEXWRkK+Aso1lbDhYhr7OTKVU+YHFFkAonTB4a1sUUuwoAZ1UW5GeGdTVntcGuMUtDmwVAau+eIG3OcpH1PuQaYXovMAYvJnOMhyovo8uBkMbsnjC9H8bN9XslvijSp4qLOok4CSwdBdtGnUohc5BHtvH0jjI5deQLQ317gmlg/wPHZMSUgdXJNLhl+FlDOzWYZjIaX01LNyeERgVTK/O7DjWwupt37N/E/kMCsJqYMm2Zce9FMWV20snnH5QRCQ5YkyOjxIjxilZVCDKddEwaYLyV6CrmDGwHH1TR5Qpt2wBs7O3123qS1E1wQjuMP9jmM7wnzgfAIe35BsxyDdYE0GEbqDD0bVA3IUzruq1M2jrEO3RdQdtiZrPgoDazvYPazEZHtXFhaUy/RSiV08gIYOiemw2FL4a3qxZf2MhaGbNlkuU7KXlpH3Ji3YzSSswgrx0Nutu3HJVCU8c92TJQAIdAgg6uYWRRGYIEaXE/v1XRzjPcRDC1BovYusUuaszZEsMjuHGM35viZzntPHciDlkAAQAASURBVKOdBlZQ8ailvD/j4S8dBGVz+NrbwPJR+wTfOS2Up55rrafdL3n0zTmmVVYvmeBzpg5MG1tBee6ZfTwh94p4T/PGdaobJesjSzuL1HTg+p8YHv5CQbPfW+tMAwc/y7h4w3ZWtQRo2BU0++Oyjenv8ZYbPIuLYHFzLl61nfNZ8QTLASBeesaDgI8WGeNMZDnRhUtx1UK2EPJzYXVTunyTUp388ASfKwNQZlA2G4wsEfSJ7axCeSphC1HclpOtoZ0G5Tn1h9RfAnusV7zDQkb7PuX78OL6PE2t3USWFILgINaFvix92TtwaQBEQAQOTOjf6bsozx3FozZYXVZZBxAEnwFhfEoOb8uTJvhEyMN37PO4wKq1+2aylaOd2Wj9HGyxi+mYxo/AOFM7iluPmExL8J69WYGbRqaKU7KTBXvvz0i+JDAyZqUMWX6qSOOQ0wu0qsmt4dXbh2Mte+hLK4K00rSwrsJY5j2HZTEO632fht/IO/1PjtE1WQfDuL7343w8V2gEu4dlgjDWRlA833T27hVzseaFyc6C9KUXU7VM3jtBmhY9u2DidbxN2HtkXZPlGcXQ2fwmUyZ9f5vbh1sX9JjxCrcHO73vfM6Ic9C0TCZl74smzXmb+cVrWTfMpkGfkcZ1hoP0/7HGk8dtJReCQeN8zezWDGlcKGuZI43DFxZ7csHk9gQ/zfuxbDPNQXlNFfQYaX1gE6cx0YDUwZ+OmxXowPl3SCMGa5JPmQSOMdI5Te0xlaPdzzsdaVifLlzjmdw6Z3p7GsIaIVu1NHvZ9m3FXdygN01vr5C6pTjbi8aWsEXZ53EbV8fcHsYNwJ2tPc28HweH4Fja0jB5ULH+aR6YUsP6tcrBD86x9R5qoZmWGKe4PMMmUNAKB++tmd0vqA4mmDaUuZnNRrrHNhb1JaPXyHAY2qMYsCWvVUpxrqyPZmgmlK3ii9mlbWOJ8Dq8Zxwcr5R2UoKB6ydKOytp5hNEg2sO8bBM234Vrp0o1U+v9+UHKhPSqjawetOGOE0e0krvUzxM4hRyPp9FlmzQkfbPYp5H5UinXRmNunbQUdL1qhhMEHEet2thmWnHgpU2li09r4XFZBbnfGFVzrokZqdh6956r7i89TD6tJmeCPXiAJ9FfaQRylNl+qGlWs1Bhcl9xVZC1Ra4SdAhilMhWyrNftj6PfTBk7YQdowpACfkq2jAOzO4qWKaqB8uDVrIqG8Hq1TQHaUxiAvbxV0WGbaxb4++60HdurWK1fC5B1UGnxu8BVfGNUssd9J53URopwQWy0y7eqnRjmEFREY7wY9gatsI2IR1jUHUsLoh1Mce0wr5wrC+JqxeCazFbA3L1z3ihMlJMHIt3og+i7p+JwGIHjD4TS2RDRnDRz86+KCHI9DOlGwZ1iTNXLsdDC9EdnoO8EUAZVRxZ2ePD7Ncfnb5Pzr95HGHrJGhDL3vP0n0yT3VRXBqlO9TilxxfZXY+PeJ5XGevmH0rLjkof4ZZEsexZZgaZxGFU5DPxMgj3/DcId/+Jj8rirfRjkSpnD0O+H/wWOSTBsk5NbHTIDJljAv/Rfb4+49Jt1PKtvy/7zK49p1U2ZPDrJVrmqPnE/f/pZx/7s0N8ZxxT7F+PCs3+vmxpxhzx7idV1Rrkhn6/3bd56xNJ9P0W1j2E528glFM0v78gFm1WK90tw87H2XWUFaxZ6vA1uytONFezSEqAk+yQBcbi4BBb4wHVCPD77Qeuagx02Cc17TBGffzUE28ikjrjeemHbAlPRKfu46x7bZOmwrCn6tLm/BCWV+TGMMDQU2AOGTBxOWL+dkVdge2cyCI/J2Iuy/n7F+qaDeS453L+czPMkvXyrNTLr4OmDE5Muw/Wh9FOus/bYnCCCBXYewbip9fnGBLhq2gNo1VNcGW5s260bYBnTdCMuXM1bXDZpBfh4c4/YAxUa8+L7EwdF0Tr5wnHwrD8YWE1iVvgjbqRNTpjMWpQX5ymBrqA90lG63To2+QPY+LLj4Wu+zLYmthPJsxsNvWbyF5kAxTTgUwcbtupqBaUvO3zRU1xVTC6YV6gPflSUBEZ1ho2vHZBTZ3knUBOf1HeNoYSgeGtYvezT3mEpwU9en2zX45bTECdm5oZ0F/zrN3NLsK+1B25VJXNxmbMMKvZ1lVC+140nQaNiGPPJXIkgdXqIWHob19YK9CAVy+65PywuuCI5W6+TolwC+JLBC3CBjT0hbBy/QRxCh0G7yl7RVmtAmuja4qQ/lMGNwxC/DgQS+GPSPEe4TMurGkhhMfARonHRGLGkDGJC+veCXLvQT0d5lZqgjI1BOIsCSLYV85ShOM9xaKC5cMEo9sL2fQO3LKS6cYomazojmCkt+Afm5HRl1IRjt0gmQ8489xanQPArgRfDfFE7pDAY0G8GOYJhL7TN56CnOHPmipS0nwUDWGfl6oLM7JdT15R4ejjB51FI+apie5DSz4P9yfruimWdU71vKswbTKPNbGeI9ex+sUCOUZ5P+fUA47XXDz6U4T3nSgCqHPy1GhzTYtQvvozDY2iNecYXh7vkOKXnR8vkHZb7Iss0avO35Z5Xv51H0ilNLtt0f3rsq3vOUT9NuL6J8L0qeBOp9knbaZCI8a54vUrYBqS86/+cZbidfaNk5wPsKyGKF/OYfds4v5YMPt+3AAbauLS/JtjBPAmfzjd/PaiTN4GpD1KcVVQ4GaU43Hs/45OD6NvksjBpXyWH8+6Ty6n/8vEpyWW485tnrT5HvsxhHdvLFl/lThrv+CdK+9M3/vz9BIp9SHlu/qI9d+7uDe0O9d8gy3Hy2IVPG9S0Zj8fDsVqeVK4tcpVBcQgIBF/jnyHhYUN2ek6QHSizk53sZCzPwuTalM2J5klOtT+P8iLLuW3rzE52spOdfBHlSYao55H2Tnayk518EWQ4Zm2OX7vxbCdbZAfK7GQnO9kuTztpDK2jnwbQ+SrKrp12spOd7GQnO9nJTnayk6+07ECZnezkqy7PA0y54qStnTylPCf/Ujv5Esrute9kJzvZyU52spMvq+z0HGAHyuxkJ58v2WSdbO7R/6z8nezAlM+/fJp3tM3B9uOcZz/JufYzOt8W80nBvmd08L0pm/mm05o+aXkAEUHWu+9lJzvZyU52spOd7GQnz0c+/6CMgGRXF1Ojki1Gxgr80ywqQsT+3jYFPt0bXqeiPc0iaXhstvdo24IYxBo0HSlr7XjBpeEoVsmyED8d77pxhPfj8lcNrs21aUMeIshkw3Xf4+oO3bHjUpbhiHCRcBT4Ve0a8xkUcOO56dph9DuFcw5dLGE6QfK8P4p8o20QuczM8PFoW6/Qtuh8ejnc8ChdMz6dQqoGzSxS1V2YlEY6NrT3Cq/98eExLblYhWLsz8bHh6YjwxuPWVb4g+noCE0VwdQt9sE5/mCG2++9qGvWH1kubWiz/M4Z7Uv74Wi7WBafjh+V4HW/q9Pg5IVs6bCrlupaGY7vjkc0i+/rlS/CcZw+E7ae/ADhRI0seJb3gyMWh3JV/ORhPsUbnuhgmnBqRL5QbK0sb8RjOONJDeL6cmoWT7qI6WUr7Y7dVhOOwh4esyk+Hsstsc4afydv+LEO6ajFm7+74O6vzWn2wjPTwOyOsnhNes/9wujUDTM8ttvD9I6y97HDZ8KDX7Kd1/1wHGIsmwW12rVTcSbMP/acvmvC6Rndi4ynWnSe9cFNNbZ17yDNrgRbQXWsnVd9Nk4jEReOS/RWMa30p5J4cEU6blvY/6myuim0k3A6QflAaefh2MfykXLwXsODXypoZ8RTWWL6LeQX0M7C8e4H7zvOX7c0+33/Se9tmHc6jSAdHw6QLcNx86OTH4Z9LaWVTklIR4sPj+mWcdrz2456z9BO5XK6MT3jlHQUvM/AVuGkGdNq7DuC+8//Di9EdOcAbyc72clOnqts8+P2uHubbOLHGcyedG8nn749nib+49r+KsPn49J9ljhPCvs0feVZ/j+NfJI4L0p2ek4nn3tQRqzFHB+PF9BpceY8VBVYC1mGZFGbNwatm/DbDs4cGHbKpglAw3Tg4zrbAEfqBvIMtQap4nXKw/uwsN8GHEBY1Fs7WqBL65H7D5E8R2cTzKpCJwV+bwppkQ/IqsGsK9y1vZB3BBs0MxFcGIACSQzdohXA1I7sdI05OYWyQKcl1asHaNaH0Q4YGMRPjzUcezm5dc7qtX3y0xq1wvqlcrSgHh456XMZHTenlhGgkRaxpk0LyhA5HUeXrZWj71+wfGPG6poNRzFKX05RxbTgylhu05c9W4aFqLRQnnnO37D4YrxwtnW/gHYl/UIZKM6hncL0XlgkGwfnX5dYh34BCSG+WrqFrS+UvZ8dIh7OvjHoAqmdjGKXwuxj4eJN7RadsVJkC8ONP9rj7C3L4mu+X+RPFBVFVJAqLKBf/v0J9/8hoZ1rd1yfm/t+gVn67v3hJBzRaCC7WzJ5MOHi3RZyj1gN47KPiIRCfq+keblBco8MR0gBdaER1Qu2dHgnGBtGUnXhPiqogi3dGKuJ97U1qBfEesRoR95QFXSZIbMWc68gWxiat9cj7M/VJny+XjB5OEIyfc56WqBZPBbSKFK6rqyIQmsgi+3SmFBnq6F94jGTOMGsDeKg/kHB8nWlOQj52KXBtIbVTR9AhM1zoRVME46RBJBWyM8kHLdqhWau3fGDoiBNAD4Q7fug9mmJAxPPKE0Ahq0JR5XGd26cdKBSKk+2CgCCeImA2wCQScCF7wEu47SL24EhCuKV+Z2WrLK4IhwdW5463ERwuZAvPJM7Sw5nFlcGAEStRCBDyVbhSFxbecp7a7LFBDcx3Tfc56ndcb4JRAnHSMY6V75Lt/9cdFAfDUfSqEZQRXvgyw8i+T5tc7ZiVmZonsbxjXS9j+2s/VzRujDOuwj8WoNdbR4c/hnKTlnZyZdBti1sn/b6ReXzuMX3J4kHj1/UD59fdf00Yb9IchVr83H30vWnvQeD62e8J1fcA8RIZyAeGbGEy/c+gYzS/wTPhmE27yUW67Z7n6Cgl9/jszx73P9QyMv3Pq0Y6Y3um/8/hYgIqvpU/1+oJEP/0Ch+BVgjq+fUxk8jOz0H+AKAMuo8en4+vgcBeHGu+3hEBN1kpRhzeVJL4sLKQFfr/t4GEyV1XBHBO9dd9+EH11sGCNkM4xVdrVBrkYsM37QBdHq4wT5pW3AOe77o4m3mt/VDHtxT76Fu8BG0kixjcrYYt8O2ugwnfK/ocsns4QVa14gxFB+Vo3AdYOIVtWb8YW+255B1BJcBrdbBySn7J3P2pmUPPA3L6X0AuyAAXgnIauJKzylSN+z9bBpALHqwKS3UxCk+D0yMtAi0lcPnBruou6z235+S0AVx2gNsTsH2DBpvDcXJChXh4L3BYXMmsGQQwTSe7Lzm6Mdl12ahMGCrhvLjc6Z359Q/yDv2gM+kC2tcAF7m752TX+zji/SewBWmSyuxZtIzNQEMK8892cIxu52j1nZAWGIImBaKC0+9n+OzfmGcmCrDAdNneWTKyKVnEFkTG6Ad9ACDRvaGWjp03NTgJhnFqWJqWFbTLm8VKCLbAeiYMiaui7MluMJ2zCBf5IPyB5DCZwF8ERcK5fMADmjW18/UAcywVUN2IaixsW+E8tlqkymjI6WrA0CyOKGL4C1oFsAXTYCBjUCl1Q70C+0jNHtCfew75k+qg12bDrwRD24S8tZMA8gkSrY0mEqoj10AfRTUav+OVDpWilpF2lBfoo7pI1MGL/g8Y/mK0E6VbGWY3Dc0e+CmUDy0qNnjwXcs7Z4iTrryGifk55ZmrmRLy8HPLOdfMzQHCfSIgBJ9O+Ij0JnAz8hiylbginHfkg2dLjGfUh9O/asDzgZxxCvz27PAlJltpJXIey6lGfuHDYBYYswg4HKhvf25nzp38gUSyXPs9ZeR+SwyBDfmdxGkCR1c877vSd2E32muTQuKpPOktOomzH91g06KoD8V+ejsbKkCq7YzJg3L4LQHMonfR9WgRRbmw8wgqxqdFqiNBqi6xU8KyEzH/gTCvA7d/AmA125+1sx0ep0aGc2XEue0fn4JaZjKIU5p9/KQjg/5dGzLgYyZqjFdNzZ0BcA45GUrj6kdzX7ezUniwK4dpnZU18vO0OJtSpBu7tfhHCqM5sS+ToPxz/TzdgrfgdWD+GncQ+jnkTS0jq7H+gYKttE4N43bAejy3dZuHfs2GgRGjNQ4R4/nro106cvcsStj3UwNN/6k5eE3MuojHbXZsAzEqXB4P1uFuarLw+i4nc2gjibpgYI0gp/6WL4BqDKMmwrYKSsxDS/9/2gUEhOsHxLT0o2XLdLrDOqDYUtEEeO7AqoKItr97z8cwavgFjnFQYUx2hnPRBRjFO8FEVi9v48e1+wfrrq8vQpGFCMxT9E4PIwVuKa1NM4yK2us8V0dNsPZjd/pVRlRrPFdXdJ1ko8fHbC+NefNb9+JZdCufEmcN6yanGne4FQorMOIdnVIYZ03lFnbxfcqNM4yz4Me72Njt96gKqzbDBFlkrU4b8itY1MWdcE0byiMwxrPWTXBiDLNGmpvcd7gVHh4MePlg4tL7TKsQ6p77UI8EWVd5xjjsbGPpPp075stfQBwfrC+i+G7vFSCKjVIR7v+BN4bvBe8MxgbjK7ehY/FWB+uRVFvgqoY9cf1//Jvb63bTj47+dxrlmINcngwvjdkyjR1ADSyDCnyDkjRugm/N0GIIVPG+fGWnk2mTNMGdowRzCZTZrgFZlseEEChq5gy8ylmub6SKSNVjTue90wZ6JW1IVNmuAbfxpR5dB7KPZuwfmX/EzFllq/vUz6s8Lll/VJYJXk7UBTgElMmbRHpmDKmV05MG5kznSIT/ucr5fD7JYs391gfm25BNlTKbKMdU6bbPiM9U8Y0AVw4f91221i6fOugGNga3KTPF6A4S0yZGWmrw/mbfRmHrIO0WFOTWDfK/k8LMHD2dnrfw3aFbClM75Qs3hwoDrHt83PhxnczTt+2LN7wpC0uftIrJqYKDI+XJ4fc//OCmymJVeFmHlFBRQNTJi7ASYtfoxR3M8qHlou3XVjIW+0W+akgxT1L/ZKD3A/ABg1dOjIzUEEKhzqDJPaJl24QBzDlxkSncRJpgzYlVgOjxWgXT1cZMm1Z3yuwK6H5+rrLTxWoByhP5nvFScGcZWgeFHEsaDFgC8XyYeOPVhAXWC3iJIAWEICBtcG0gisM7Vxxs9QOBp+Dz3XETBkqx9KCjXUJCrtimtDGdmU64AMCqyb0p8H3q6EPFmdKed/0fS2+I1vTb9VxgS2WFOSOTbMCWyvNIxvYNo2i0jNU+m0+dAuL4XXaliYe5rdqJicZPhds7SnOHK4wuFLILxyT2wtekn3aiYRtPrZPOzBlBFMrk3srZnfKjikzZP+kcTK0n3blTOOYqR2amcHCrY/Tt02qV8+2CYubgaLk+rTlYhXGw6uYMqqBEZOuRQKAP9xGKkK22DFldvL8RIsMfeU6q1fm3bgw2jYrYQuqCrRz240FxXkTtpzmg62uXjGtxvk4zLv5wiOtJ1u01EcFmgnNbDzO5AuPWmgnpmeh0n/XrpTunnFKce6p9wy2VlwhTB451kc2jBmVki899b7BFdKvaZUOVO8MHglUbULaPg+/MVGH6AxGATzxAx0mpVGcBxB1fU0wbWgDn0kHHIxAoeECPSXtxiCDtP0cny/ArpT1Deni2xryi4xsBRevB72n2xabwBMzmOuFMNZszP3hffVAyFC3SID6CJSB0ZwhLobN+sX+KAwEvWAAcIgXzEoCEzlPtMW+zbuXPpRUiDSXpv8pnABtLECuW9PogIrWBPZwEaif6kL/9mvL8m7O4msec6OKi0SJDNm4UEwgQmq7+Gy9yDHzZnR/BIeIjn+r4J3ga4OduhhHNzGgrSIRdPHOIMbjvcEkUCXqS2lR3E1Xg0V0WmR7MR04IhLKqDoO61W6F69Jz2qFtrFde6bmhx4EMmuhXWUs8jLEiyzlTp/T8bIlxI0XjQEnLPK4i2AABnS6ZWzTVLfu2bAw6d7G7+zUMj8R3jc3NwrQpyE+AGYnVkdbmtnsWgkYS7qpEgHZYZhwbRqQ2EcTc/lSWWO4s8HKOG03H45X4iGv4P5kvDbt4gx1a+iNRzE9J/3n0pVTN+Jv3OvYwfF68Pqxvg8Txr/LccTHsSaOT90Wd2GrTobCvUdP+hqeo+z0HOALAMqo8/iTR+ObQ8bFkKK4wUq55ONkmG5kgogdzF5XMGVg0F8209uMsyHD/FU1ME5E4KENTB9rMflwBDDBt0rbYu9v+IDZpNM9jvamCk2Lq6rgs8ZaynsPt1LyrvQDo4ouV8zuTKBtsUAxmVydd7LMXVW+IQtn+Dv99x49PefgzoyDIg/Mmy1lGlkFhyybuOVAqoaD/dkYFINu24YkFtUQMKvbwGBaV+G38xwfzkfxu7KkGU2k6yNmUYHzXP/jWSjmUCuI6dnzivZwQmc9S89aT3bvjL2fTGmOJt3gr3asBAMUt86Y39rHlXGx7cEXAQlTIbKV6AZjjeXMljXZoqH6szIycC7323zR0OzZjiHSsaEGCi2Az2woX7KobQymPss7ZTIUkq6s0CvFI6ZME9gu+TJsf1neKvs2lP7dQa+0pryzlca2ispdYUd5b/qUSQuDNHmn8plIkirOGuYf5dT7lgAiwuSB4uJiZdOnDEQlPvm6caG8pgkKha2kY3907TDUZ5KSlsfy53RboZKolW5CNS76WpGwha5je02EbCXRp4wgXqLVVQeTfM+OERfaoGujgoFiVLC6EXzI2Aqm9w3NXHAl5BcG0844fSuj2evbt1NWzi3tLLTnYSlcvGpp9nvFbagwpLzF0VuKbf9efSG9Yrep6GjvO6cDm2K/NG6cfspvdm+Om4QF6SitAZsmxTVtWNjZ2nc+ZfCgmeDvDPcgfnYy6B47+RKLNC3m/inTddPPa5urp7oJc0eR98p5VZOXRRj70pDuB2N/ZG7IuglzY9OSPSpQa9FybISSdRsYoPmGcQrC3JzbHvhxHrNumE5ypPFoZJmW8wLNDKYJTJnpNB+zN6Er05gpE9JEJBiOIoDh03yWJM07MNI17DKsnGZ3M8T1vp+2MWXC3CTdNYTvvmOnwAAcEbKVxzSe6UnWzWum1e5+vuyZmSm/EatF+roODUzDte2QKZPmqs2yD9MK8ZSODbvBWunqNijD8JmtwnzVvZvBc328StsZArqFcowjbbzuQDe9FC+0dcxnwFJFIK9h8tAzuWtp1kEPEuL8lYq4UT6N8fO14Namy3ezDpusVgBxgmkE1xmv4vNR2AHwkeokBIOSk7jdRyAChSIKJkyqAXxJ/wOLJaXTMVwi28VaP2K+DAGcnvEgOGeoFGbzisz028wT68L5kN/D/YL8sOL60UUfNzI3EntjyHQxg+vKWerWMi+axzJltjFc0n0jikHxCIa+XkaU9x8ec3Fnzre+9RGZ8V1YGDNblk3BJGtovKW0YyNIyjM965gzami9YZo1l8KGumVkxjPNGlpvQv6xnIlpc1GXzPKaLLbTo/WUzHhmeU3lssBW8oaTxYybB+ddOwzrqSqjuq/bnNYbjCirJscaT2FdVzbd0o4pnc0+keo0YsrEvuW86drZqwSVKYZ1zuCckGWh3zhnuvRT30xMmWQMbX7/xSgfOz2nl889KCPGYPbm/Y3hNhvnA8gRQQeGDoHbNrBnrAkADIwBkrYF55FywI8fxo9sGrIsXMf0ui03zofrje01aqSn4abnMT1xDnl4Gso1ncC6Cv5eZpMe/BFBqhqzrvHHe338CEZ0k/umT5lU5nTZOORihTm9CIyhsqC9sd9t6RmG7xbgiS1DXPC0Snb3jPblA+xFhRpDc20S67aptIUtQcNFug63t0g/iXeOTgfOWSH4kJj9dEJ1c4/qKI/KyQDAgM4CGOIlJULI1r5z0pmfOZav5Lh8rCCl7Q2mDZa9IdunWChtKUxPHGoDy+D89WxkSdy0bKmRrjzz2zOMU86+NuxDdGwZu1Zm96ZcvGrHCoMEls/xDy2LVwsWr4Q2lJbeYW1C+T1cs8KjbxS4qXRt6SZ9mydK8LCcAMVpxuRhzsUbprfmbSxKy0eG6qhnByVJymJCz302Tnv4rLMWjhKI7Z5AgO699XFNRecDSEW6RXwChJKFQ3TAcIp18E1ckKTFeyzTZr5p4Z4U8KGTY1HFVrGtlw3lQx+c6xLafnrSoiYbMVO6tlMCaNRtNVPKR47JvTUqcDSdjyzFxunIYpHaKFt5yvtrivPpOJ8IEHTb72KfS30yWIeCddzUDjfJOoW9L6P2/ld8XOh5DQshGKelijldomWB5jaMW8sK0jaJpoXTc177YD/cG+5HjmBwYpbIxZL96WTEMOxYLEN2ymAbQydtux0oH8TVzXjDPevD/dFpTE4Oyze3UnZRhnE2GDODZ2a47XUnO/mUonmGe/mY6uas375i+zFADeQXwXG4m0ZA3kF+3tLs987ZgeijiZ4pAuQXHvEamDKHOa40tJPB/AgUC4+3Ehgxw+1BPoxXI6ZM2u66Z8gqxRVQPpqyPg6+4LJ1YMqsD6NvtzS20DNcO1Amzhu2CvX0eT+/BGCFwUJ6zEZJUpwGYKQ+EqTt56EOIIgyBED6m3SMk03mTGh3i62U9fVe77A15GdhS+Pi9X7+6Ry0pzwS9it0IPmmdGUy2rE40xbf0UplA2wJQJag1l9mygwTH7ZVHOhlbQK7NLFd03Ojl+1tA+VHnSCZD0xZ2zNqxSi+DgqVTUzVYTylY3b41iBGsVl0IRC3dVTrjNWHBas3WvLjdTfkJsAhgRmb4ICqsL4oKWZ1F8YYjdOK4L1gzOU4rjW41jCdNjH9cT7D7R/pfgonorSt7Ra3NlIVErAymNY78d5ccpE0BGhSudJ/jR0nLapVwTuDNoa6zmjNuC1SWQDMSmiWOY+yWdcO6mWwrYrYRmPwx7vQJtoaHhWJEjwABFJ2w7ZRoTOYRDZT/7EP1gDxVnaaMT8Rvi+vx4LrKM0QD6QxY99/Kf/EiNGBHqoDHdRHBrT2aXYslcF4mgxSKV0hfPemSiBn6L/JWNcdjhHzydZwe3LINkkGqk5/TUYiwthZC6yGeQ/aZ1jeTaZMeKaD6/H/fKD+2MGzofGqA2aj0XCUttdRWe492lq9nXyG8rkHZdQ53MOHzzfRDQV7dH+UuV6+92llc4R/nHx4RRJPk80wv5TXT6+Yt4GtTrPU4wD5UcfyJ3tezrWuEKce+z2YfdJ84qLs4Fnjb3FAVl7RJo+T6WOfwvwxzw7i35PkpU+xzfNp0v88yNHPMW8FDv/B5fvl5VtPTAdg//eeLd7kyUGuyDDk+EkG9U0dwG15dknu3P0EOX05RJ8wDjzfzF5cVjv5+YhUDeaD28zuT8eGp6G0yaP3ADVvWso8S6u7cC+BnPGemuiPxis0DXkZfcpsbNeWmP7m/S7NYb7Jp0yZh3JlFlnXzKO/GlqHNC17k2JsCEr12mS/QrfNECtd+TfZrmGL6uX2kboFVfwk74DlAGyNmSCjMqTsZbxYgcFvK0jtMY3DzfMYPrBbTe2QxnP0k5JL27TNwJgksjWPrlzKeLuaGQNRQ9ncYp5Wk/328L7tUt22sVZNG/zfpPYZMoe2MWtG7RIXweMDC+iUxCFrZ1T2TaZMMtrFxbFplaMf1di6oD7Y68uxUf7h/dQOB2twZdmHN30Qw7hOqbhGoXTgyqC1+WEbbau/6DjvaGASB60dl0th61a1rhGJ4MEQLNuo22Z/DIWEYiW0F1nP/unCh0MhACaPDE2bs677QnRgxTDR4Xa11EmdgJdgrxiWIQJd3e9B2cXEbpdtpN+hEnTzmGsM/twi0zb64KFnIdEH9Y3BZBp8+g7LPWjQ7sAJ+mHEO8Hk4/k5VcU5CZsqbKDmDkEqIlOmaQ02852fn7oJHd1mvgPH1IfDKexB74Nys8sPWS4+bj0T0QBKCgHUHITtCrpZz03Qastg1W/z2xy3CR07AS0qveuCzW1psU9LAtmA5nee8/r3cbLTc4AvACjzmcg2QOaq+1eF/azK8PPKSy87vPpU4T6tfNp8nkc5X1Rdv8jyvEHLr6JsM5+9yPFhJzvZyc9X8gxu3qB6aU5ikSQ/JUn/zlYONYKb2sByU8gWDe08j0yZaBX22vmdS1tg8kULTrHrlna/wOcGV5rR4j9bBqaML3v/NBAss5d9ygTmTjO32NrjCkNx1lIfZt2Wv2zpqQ9sx1jtGbTx/6C8ooGdisiIidkxZVJZ3CD+gPFSngcgqjo0nTXb2+0AQbfwjQuRdBKdH7B/u98mMFltpVTHpotvayVbQlYpi1f6ba09+zel3ec/Og1vUJahT5nhtuBRvf3l9LqtoJLYQ6mBTXe9CWB0VvJ1cHaveZdUyH4j7KUtBclfh2djgQ/ShPe8ufW2W2yldmhjOOvjdt+QkamF8lHOxdeE+siP/fEMyj7yYwPghWwpuLK/P2iC2CTDH73vEdMIbjqgNKj0PkpGdRgsiFN92/iNJmf5w6Db9mJsIFzJB9rlcOOsRiwQJ5haMJFFfql4kRGRrdJzG5pJN/rfBuAyytODcdKzvjbVk7S4py9Xl/dGeqN043W+gPKhYurJFiSjDy8tozHwUl46zrNnygy2CXbpRcAnkX8SEzl+c13aGsa24RgxOkAgbg1Fib60Jn3e2+qb0k8MlEEZRt/xwFfMNn8wXdqbfQNAB9vTB/n0YX2fVvJrFVnTfVl1I06YQ04evEDj006Aryoos5Od7OTTyw6Q2clOPltRtur3O/lyia7WuD/5PvYJY2qy/PcRlewZxmFVxYpggfyJoZ8sm2lsKpTPI49nkb0XnN/PK88vs+z/vAuwkxciRz/vAvy85YtgeNPVk8M8l3x2ek6SHSizk53s5MkyZG/swJjnK1ceg/Ac0tomXwRlYCe97F7XV0c+ybf5rHF23/9OdrKTnezk8yS7aQnYgTI72clXW54FYNmBMS9Gdu28k53sZCc72clOdrKTnXxlZAfK7GQnz1uGzva2ekaL8lk4kv6iy6ZzZfXje5u/P3E2g/3Y6VSjLcfFXxH5yWGGp/skp7DWgnPh/zC5x/WBLUeXb0R+fJx0glC899i8LqXzmLDb2mAz/NDx6PD346RzUJqOf0kneRhoatR5ZDJ5fNkAXa7CqXNZ1tVZVaFtkTwPp+8N6iD22fqU3H+BU+fOgrSTnexkJzt5UbLza7eTFy277gZ8EUAZESSPx1ZfpYinhU9c7Fy18FDVy8+etOjx/tIi6klxtuZvJJSzO1khHa3tUOeQPBvfU+0XCtZC04bjv4sCijyETQvUYbs4B2K6uLpehzBFjmRbXvfgyO5ROdN1era5wEpHeBsZL7Y2T1ZI6Q0XPZvHe8fjuIdlSc/kYgV5hp8VG8+vaN94LS4e0Q2YxuPKcKKEaRw+t73nf8CuWnxu8YXBVA5f2sHpAP3kNIyjEvIghusd+6Uy9o4Ip/cafCZUx+GUDG8hX3lmHyw4+8Z+CGfpHA3aRpmcOJYvZcFR4cApWzpOdPrAYyvPxashzPSBJ79wXLyW006FbKXYWtm7VfPoGyXNXCgfKrN7Le3M0MwMq5cGR5+mIzhT2YfOEIfPNpyizT9WLt4UvA1t0R/hHdrKFYpp4fp3lUfvGqpr/ebR4XHjoXKCL318Jwa1yvSOYfm6R5rgzG96D05/wYXjPAXsucUdN125Mdp51O+ObazjMeO5D68yeaOPDs/KWznT28KjX6u7I0FN7lEPfpFD6eJvYfLDCet3K8QGT/xZ0XZe6NoqQ2tLvld3ZWjuT5m/Z8kW8OgvNBz9Yc7pX1pjcx888XsTTgKA7mhQ15rwWRvft5UPjdWuMuy0RV1o33RqwcBnXAwf6ujPc+zC4q414QSDLr0Yo43HTrbSHTspqzDeaeHDSQzQObIwq+jUMtPuSFZTh+9Rcx07EIx9xFaCrYRmHo5gTI7sJDpaTI4RxQl2HY91j9+PrXrHeLPbyvSk5eRbOb6I6fRN1P02DVz/3prlzYL18WAsceE7WR8ZJqc+OCCN4sreMaCJDhjHDveGvxX3t571LK6d7GQnO9nJTj5ncpWxZFPnf9L9nexkJ59aPvegjFiDOYiu1LoFb1ohxN91E8CI6aQHRLYMNOp9ACb8wKN0Alw2gQQA58PAk2cb+cfF3lVHVyZgYhtLwppx+LrBrGv0YN6fRlA3mNahkwKcR6cFsqwwZxfo3gx/OOvTNyaABSmbxgXQIgugR/bgAjKL2yvxZazHyFP8gDEQT2rY9JavQnespXhFnOKKQSJmkFZcTG16dtcBcCRe42kRfV5qx8dGJk/n03tTXGmojm1XlkvgR7qMXs69FWyttJPwIFsr7TQAEPlSaabSn8qgUJ4VtBPBlZAvQtgEfqiRUGcPPh+/Z9Po6GjHdAJDV+0BlucKYXXDdPeLc6E4LVm+bDpQJoW3VVhQr16ScJrD0AF6BHpMY8gqoT4KdbGVQTw0e0I7I/QBC660tFPBTcCVoQ4+k+5kiq7MG++rW+gq4Bh5v0+nW4iHbKVkF4JmMopvIk5ic8E0kF+0lA8lHrcnY6devk/PZxYUsnV4xwF8Mkgb8po88oAN74f4PudFl2/qE+MTC+jeTzpdoPPG72Fy4ikfttim6KsQT1WwNXhru/44v9uwfL8IgIgHb8uuLqYNAJQrZl06xYVn8mCF1J7ZvQnzDxdM7037vhHbPp0mEMo0Vna6emjocz4rx175hzIEKBRs7TFNSzvZOOZ2kFfq40lM0/ZtkPpeAttcE58JnUf/dIpEB+zGOAl8cT5+8+aSp/8OlCVcS+P6Y2xNOHo2hTfna6RumNza74/FHbZDB/Z4zP1TilszdJL3Y6UqsqrZnxbIqh4d8asD4P1S2275bVctL0p2DvB2spOdvFDZxpTY9Cu3yQbeOI77sc+fJZ0kV6W9Yxt/tvIk0GYnn3/Z/F6e+v+LK+JOzwnyuQdlcB5dLMP1VQwV7wOzxLnLfWiDyq/GjEGZx9D6NS3cNqntm9T9bQyeKweyDWZL26LOQV1jEtjTtiHvRQZeMZlFmwZf1UjbYtf1OJ8RU8aHe4kpc7EAa8nOi7AI2SjXECy5BCYNWS9m8MzreEGTmC4bcYaAD5uvzpgRCIOREdCS8spO1/giI78oLoM/3f8YbUCQME4DOwYwtcdNAlsiWzpcaUfMlnzR4kqLzwVb+XBUqGV0LCmE4zlT+hDySKBMApL6tuzDF+ceNxFcEeNnkF8opnJkSwWTju8M9TG1YhsNgMBgsR5YOeF/AAAUU4f0TKthUTsAVFLbBBZO/J+FI0ddCe2EDuRRo2NAbTged/fDy+qYDhrAodUrHl8OmDKA1KHttPDYpWF217J4HeobbQ8ADV8YsZ5lPL5vbVGr1B9lrL5eQ23ILiyTu5aL79SY3IU2PsvJr627rmeMBoYJoCqoCq41GOvJ80C5cM7EZ+Bay9n7U6Z3S9Z/+SJ2XyXPHc6Z/z97fxZry5Lm92G/LyIz17SHM9yp7q2qrqGrqpvd7G6yuikKImQCFGw/GKANW5b8YNmwYBmGDduAHyTrxTQEATJgyTAsQAANCbQ8SBYkG5ZkCrIpmaZaZDfZ3eypups13rp1p3PuOWfPa8jMiM8PEZEZmWvtffY5995zh1ofsLHXyox55RDxj//3/1gvK8pJ230//+4B8o1LjFFUhWnV4FUwoqw2JW1TcHiwQmLfHj48ZP6DGcUaLr+94uC3F2z+4QtmkwBuOBUK43He4FUQUeq6wBilKFyHW3pvUIX1qmIybXDOYBKzRbSrT+NYeh/6Xp9NkFWF3NsgJqRV6Jgy6gSxirZZGNVlfC1UPjBohMCm8YKsCrCEcKg5U8bQh0hNv6+G39auAjDn5rGMyHwRH8JN9uE8A1NGi/46NJs+ROXBO4dMTj1Pfq7Al/SsmxyAS0yZ78y4+kLF5k7PlDFtAODWdwzTU9/dj6LQTvrPKVTpOCSpxDCSouDe27sv7e0zbrsWwrf5/GlejCUGcf45d4Xc5R6b0j5XdTJwfx1/vrVL7HbB1/fjNjZmPOcsbcJ8N2eO72SRb5WZzZ1v41Z7k+v2uMxbsM9v18Zdc/Ebxi9nm980Z79tXeOs4/l23ofB/DiOQ36vjcdkV/t2sdKvcyO+7th4k3l3R4b3/tPKTOb99jriaZaY9S5ea9laA0brirFF9rM0LRS237BOm9cpJPSgvvjfjsodu12PQEppfdis9qO8u56T14GMTqEY3VeqW5vY0np8FcfByNBDwSvsGpJuM4ouH4CpXTcuXR8I42paHzbGChPq+MP/fEfBH5Pt5znAZwGUsQZZxJ1nkeEDMmPKSGTKhGRy/QPP2lszZaSNq4ey2L6hRLYBDehvvl0PMR2CGYiEtq9r9HCOTwBR3YQ2ViU4j5+WyHKDnF/C4QJ33O/EJ3AjtVs2Dqx0N5p9XEBZ0B5Mwk2d72b7AAR0u9URpEg7xekY9ACDuMAacRPTAyrZYnwnW0bDA7/bgVY6lx/x9IBGHKOcsTOvLO3Msrlru7Q5EJOzZsSHBZOasDDbYsoIlCtLO+2ZMACTc0MzE3wF5ZUZMGk6toD2Y9ANfav92KS0+cZ9/KmrC0M7FZrDuGAuYnkTSzsPIEfHlFHAh2trF+MoZ7Go7DjvCUwWp0gbgCPTBMaHqcHWilqhWEOxjNeBhrHfKi+VSToekBpbpzpg/tChxoY+paTaM2V8YTAtzB/WtJOS5knRs266gYr5fFiQB5eXcE3OnngmJxWmUewapmctxarqALBipTSLg65e8XFRnS/UdzJl+jqnJ47qvOX88aK77tLvflwHZkr6Pv+gZfVHWTor3RhNI1Dmqt615cvnnsnjK0ztWH1/zuy9c5Y/muPtvHcDk/S7xgl9fOzkIF+6Bk2rqC2uf4GlfsX/duMwdYOvzICR0per0RXPd5MVaTaxfhlcawGw8P1zIbZZIjh53YRLWj94+XeTGz9sCwCt710ZRaD13fPJLNewqVn86LCfbPlhdkmMmCdnTH48DWzDZF6RTc1RVYZJW3yGi1c0fy4/jZatil02N6fZ296exRYz+MU/3d1DA9deGNxbA9fdHRsfuxYtoopPm0vdu5qsvlCWaXx8n+XH8zzZ+z0v3/fv/MHmSnp+dOfo3zXRio1HWmgWZkfaUZ/ydU5+3gzbKo6egbqrnDgm1XnPovWFdAzG8BwP3wP7ke5dOWjfqE3dedOfT2X2Y5TmV/GcicM0ev8mt85UVzffIis/ueoO+phtkKTPxLQxj8TPImFzQUTD1Dq6wnov+MZQTtvOFTeA/3Sf06ZAf06x6b9R6tZy/mTB/HjF4WyDNR4BztcTFpOa0niMKNb4bmMDoDQOI4oRZeMKJralEI8Rj1eDibtCRdx8ab2hMJ5CPK0aDosNHqHxlpkdPqdTXq+GlSt5b3nMVw6eUBjH/fKK0rg4vEqjlvN2ymvVOTbmc2qYmu1nv0NofMHUNKx9yeNmwWuTM6bSDup+2BwxN3X823RlWTy1Wv5k9TqXbsK35u9j8RzaNZbQdxtvptQWg6dKk4X4HcDHB4HBc+Fn3LFXXd68/w7BqenKa7SgzNrr1NBgmUpDowUOYSF1lyYdm0rqg3ZjEf4b/mD9Je7YJfftJWstmctm0AcXHyRP3AFrLTF4nrgDfm7yHn+0foMzN+MfWXwXK76rL1mjBS72w8dyHIa1L/kPHv4yf+bOT/iZySNqLaikpczGyuJxGJwKZ25Bo5Z7xSUAtYbJ7MaXrLVgbmq8Gi7clKWvaNTiVfjR1X2+OD/lqp3wcnVBo5ZSHA5D6w0T03ZterQ5YFFsurH3CKf1jA/WB7w8veSDdZjDHlcrLpsJdycr2uiyfrKZ897pEV976VF3P4RyBK/Cui27e0jjsXRegbot8AqlDWP+6PSAoghjsbmaMFnUqArGeDbLEs7m6MIhhaf+K+ztBdunHpRR5/Hn4WZ5qqbMZhMQ9WepIEejdyGZu46PEOxbiWcOAJwsv3N455DLq7CrkvriI4Ck0eWqbdG6QVZrzNnloNy8fnV+cEw3NRiheFLuBqC2+ibbaPsY5IpuU8BuTZnryh187zVlgIELVl6fXK6orGU6jwtdMzy/pS2TxiRDlk0TPqsRzKYNx23PzLHrFl8YtDSY2ocFrJGOBRMW8r1GTccGiQvCxKoZTKhM36fp4xpvDcW6ILlEFStPebLm4L0SNdGdKLEDWqjOWlxVdDozg3KjG1ax8jQLg7cBeCovHdWlpXVg11BsFNP4wMZBsE1gDUkl2E3Q8+gm8nEi2X1O/RxNjtHAsEmL9fbC0ByAr7JJJCBtGDdfBq2Q+siyuSts7veTRM3KJGb1ZZw81GEW6ieG5ReCHkhxKbip5eIripYBCCgvheZO1GQR3d4FiW1Rq4HtEUEv8eGYOGHyoGDxjuXxt12fv/CRGWLR0kGp4GHxo5KrL7exLAlskgSs1AbZGPSgZwPZJwUHby0oL5THf8Zz7/cPefJnfNBrEQ2aLamsVE5ip6RJdAJvVDBri596xEnob/Y7dWOpRDcxsJcF5ZWwueu7iXwCpgDESZiku36hZDfh2nZRt0W0BwztOrTNFyGPuHC9QgAb84VDuj9sHRgvbkYPksXzQ6ZMBONseq4E1lhKM3vimT6qOfv6NOjO+L59XX0RuDr6wYT67oT6uAcBxSuTJy31cUF50Q40o3zVu2eOwc/EjiGCvij491/QqzP99nv7/FsGyOT/x0D5YHGez1HSPZ0umNyNUHs2HVmxHRlDAALAapQBcDBsW7bRlLfJawa4DC/YtFGS152DI3btIust3nvZM/zGXfG8vBFLVVr650iWbmAK5UWLm1oSszUB98nFWoXoViuD8kPbsu/ZO6zTaRuBNh0okzfByFa6XR3MQZnhM/aa8ZGs/uvanZ9StjYBxIFPeo63mOLmZTsDpYNXl0o7PaIpoI7nqgbqAjY7xq9ve5z/eR1+j0Dl2O011B2PG8E0OuzT6Bma3oG2ht+bvEECGvOxkfiu8uNH/bi9qbxsg8W0QRstZ3FrnNsN6srHVeO7V+E/Tx7Zu0g6ef27fkvXv3t3XXN5feM+bG2YZX0at2dMdN5ltu7nrN3vseN9Zlx/XLwG1nodngd/ffqPDuob9lO3z2lwG/9/z77abYyGRFmS7LPJNsEG/R+N8cDFPM4z3ivCps530jz9mt9GXM9+kayfplXeKqTb4H0g4fNV0V/jplVerT3nsy+Bwjrf4I7j0Oz4jdOheVqnxANf2ihqyvicVLSYxjEJddlNg6sMaiyPH9/yxv+wtp/ndPapB2XECCYyYK4DNrRtwfsggruLvdIlVDC2n4nk5aQbZgxwADKiz2EMNwIbqbyBsK8MwIzuWNNCXSPzGWpNeMg4B60LTBnv0apE1jUsl8hshi5mg/I1LzPtABc23GRnl2ANOp+i5Q73pfFO25iunNIZIQno4j2+KgYMm/FYjF9u3eQoLnC06N0KuvLzdkXNigrQ0tIcVcNyze568IEJYpp+odUDLWBLg5uYuEDtH6SuMvjSUBgX3JfiojBnyuj4Mmj7tnesltHLIWluGHyWHkyj0HrsWlGrGNOPo2mUYumoLs1QuyMbp+qsxa5bppOw2z85bSguG2aT0D+79tjaUz5ZsZha3NRQXrRUJ2vKyxIthHJZXv+Czxe6DMdBVLsFwORkw+yDyRAMUToXkMREmL5zyeyDGc286H8r6Be8xBeXDdd1GqvisqY9qMJioXGYy5p7fzTrAC+zcWiub5QzJ7LF9OAF6bW7d8QpsmowVyte+r3DLl+6D6TNVi2qyNUanU46pln3HxDvA+XWDu9HWa7RtuXl/2yBXC555W/O+3trxFrpjl2na5LcE1W38of++8FxdR68C8+9seXPwYE2i9LRuvM06dygWdrXO3YN3VX2uJ27ytpKm/rqUa/c+42n9AVQr1TAZEeUrWd66Y37H0388llK2dvebmU7gZiOxTh6V9+QZ/Bejs/sDuA3fXlbwEF6BsuwnKEI/HZ9iXE3FovvFm5myMQcMGXKMA9o5iGzH9Q/bN847xajJr6HO1DGjto7esdNzoRmHt0vo9YahIWaq+K7Ovucs4Nz1kpeZtJqUzMEBsQJWmRgl/TtTe7Dgz7m7U3ldAJmWb7R+JA2JwYsGg1C70rPkjE6zGZ8577rWot3QjVtMdnx3Ez23WQMGYDCOurW8vDxIYd3lhxONx2z5Xw94Wi6obQ9K8UjGEIZlXUYFCOetSuZ2qbLm+ryKlRxNV17S2UcRjyttxyWa7waWjXMbNOVlVg2RpTWW65cxbtXx3z96BGFRKZM3DGw4ln7kks34QvV2aDfOeMiMU8gMCvmZsPST3jQHPHlyeOORZLKfLe+y6FdMzcbFmbD3GzwGAyBCfLH69dZuoqfm70LwJFddwyYSlz3OZU3Zn8kcxhsYsqYZddOm01QE8MkZ8+kcUrnGy2YSkONpVEbmTIOi7KOjBIjnmpEWU2Mlj/YfJGXi3PumCWNFqG/iaWD6ep66A5ptMDi+aA94luTd/mjzRss3YS/cPAPsHF88rbWavH0jB8ILKErP+Hfe/Bn+YfuvsnPVI+o1WJFKaXFojhkwOp50h7gMNyx4Z3uVPCRcZPa7DCcuRkbX9KopfGWt1b3eGN2ykU7DUwZbymNCwwjtYFhE8fh/c0RR8U6tl1o1HLazHi0PuCl6SUPV2HueW+y5LKdcH9yxcqVGJQnmznvnR/x9XuPWLuSeVF3bJhWLau2xEoIPNGq6c4l1kztg7t+YTwKvPvkiKoKO2nrywmTRWDwiCjrZQVnJXrQItbT/MkeKXnR9qkHZdR53Pn57RJfXX28jfkM2u4lxfPbeP1+HY46Pv6079dZav9HeaHuKss+5fxHYbPRdw9M//D26Z+WRoHOcSYxAIDJ7/cAgBKAR7ll+bex28ShSfjFs8asUYa/zS6X4A+D5SeA3gO8ufv8R2YPPwj/H1zXmB4M2tv1pt49PVGX9mNsyAuyvQDeT4ftdIdmBELkFh+GA7fgnD0wAmUM8bwPbigq2aK8c9nWweMnZ8b04Mf2BRlci3Ub+OjK6YGR8c59sUpAcvyXBxzYWdbwntgF9pjkvrQ18ZDuOSteKS895SyyfjNmjbg+GEFgyvT1XsdASX3oNnSy9IFJoVuMnh7gSb/bsB/bNgLERkDOwKV8NGbjMR0IrPthnT1TZlTPjmdR/lu42IZNHLdXrpR2dodl0Zdhaji/qdxROzRuoI1dXbuoelnbRQOoJ0lL47px1HCNmBp+c/Kl/rrMqkhjoEWf56a+4+mul45hI8N8ps2AuB0goa0DwPk3YlCJa1kuT7NsU23L1XBwTZJpBOp2/tEm466Nu3HZ+XnbaAQohSRNkG/IpTzjQAc+skfEwV+f/sVr+7erTlGluHT89elX+98OOobuFkNoB0OvS5P6rP09nOo0jefd0iBO+eNCtphGAzZOq0NvgMi6Fae8Y6XbxHxgw3PpaqQp82rtOZt8GVFlEzdq+3YpTXyeGadb8+PJaJP9641DJbADTVPjC9M/VtomuK8XQRbjyXVz1Y/B9vOcYJ96UGZve9vbc9jgBXsN62KX7WJ9fdQgwW3c/V60fVwCls86dntAZm9j218Sn3sTr9jLTfyyDc702jLjjLuBnJAnO6VEQXrJFvTbwE+KxhaERkcsk9SOXY/JuEjY0qlJdWcASVdt+t8tULLCElAx2gXS0f90rjse6/aEfiR265Bx038xreAmgcWT3JfCOARB/E73LomPZ8wXRnV2rR/r2HRlbi/Iu7SpvHwc07ER8AL0mjKS9Ly282hM17dLR+3XYV05s8aDtIKf+Wy4dAjsxPoHls5bhdZgTwvcoUOmLrjJiuLXBWbWhusgasckXRsAY313jTgnFIXHxLwSgcTEzAnsAOmOtSpMyzayBGBatoPLNbF7nAp1W3B6OeXO4YrKOhZljY2MnMCmMazbkjuT1RabJGne5GXWzlJZR+0sZ/WM+9Orjs2T8j/ZLJjahnlRMzGOma0HDJW3lndZtSVfXJxiUBbFptfakaC1k/RQgtZMP/5WRmwVNZy3U+6WQ1anQQdpTbzJPDJg3iTGx9Q0ODVsor5K6ksT/bpK03aMlzwvwI9WL7EoNrxUXtKo5cCueyZO1FZp1HLSzGnVYlBOmxlfnj3hzeV9LtsJ377zVtSUsV3bU968nRD0hTa+5G+/9xW+cucJr80ucCpYUSrTdiySNG5ehYt2ilPhKGm+IDgVWm/Z+IKZDcEcrlxF7WzHwnp4dcBL8yuWTcXRZN1pGyWtl8q4ro2n6xmTou1YYV6FZVNyvpxyONtwsQrblfNJw7opWExqXNSUWdUlV6cz7ty/pHGW0rqBbkzb2u7e8SmAg4ZAFiSNmXh/KeDOpuH+FJClRWfJnxxkbSjPDe08gGmbf/kFztX38xxgD8rsbW97y+1ZQYFs5/EzbR9X+68rd1d0k73tbW8/vTZ+Vlz37EjrKcNu8H3Euuh3dzOtuXFVHVMmE+RmG5S5FrTvFupDoGdr13hHftO44LJaF6EsOwKirrGBEPK4P507VY5MjdsNxbLBxyiN3mY7xr539wqgjFzPlNlqmAwBqTHgNUi7o13X2TVskqfq7uR5bvmqSS7KuStbj/SMC93BsogaR8V6E/QpMpfe4CI3Wnrs6pvebrwlc6ntXOWTm/zIbT5Z4ZWphzuNR4tDVITGQC7hKxrSXeS/Wccg2R7ITjRfA2PhxN7vT/o+TQ2cje+HWJ5pPcYr7xT3bu70GAsbux8zZHYM846O7RLL35X2hnzX5mkdiPDdXM7B70ibuWyLV56YuyH4gCq/WXyrO/7UdsXPX6jP2NiKH8tLu9s+zus9j83Btlu1KieR3927Vyv4lpf0CYhhrhtaMYCjzai5WYxcDrT/lpjfE6/cjW7i966j9MY+p6hd+f/d6a8pZ3B9+F5G4ylu5Ceb1e7y9vax2R6U2dve9vZ0u0ZraOf3vT3dbtKk2oM0e8tsT+v9/Juu1vjf++NPuhlba+4P82SXaz5fZ8/rqfFhzYz+J9uhXLW357DnHsfrNixyICN/d36GNoeeiut9BHU872vjc/G62c+hPhK7Fvz5GGw/zwm2B2X2trefBrvtZOWmic1nZMLzmbePepw/DxOUsTDyhxmjm8q47cT+RQ7p5+Dn29ve9ra3Z7KnsRt2pfk8vOv2trefRtvfusAelNlbso/SDeXDliMfzZ6ZjKnCt6k3RZ25ydT34cV3FXNN3xPq3NEPnQMxiDVd5Jou4ldZpMLCf2NCehjW7QI9VKztffldT0mUYnSL7xKTzARmtW5CHmvAebRtkek05GtbcC58H7dtXOYgUsjoWKpvnFY10JyzvOI8LFcwn8UIYGb3teVcGJcYdayve9T9ixV+ESSOxXu0CpR5WW4Q53H3Dgb0867tXThkxawa3KLqqLhmE5Ts3cFkpKMw7IdaE/6XhuLxivbuDF8aRJXq4RXtnfBdrWCXLW5W4MveFUGc9toJMVT75MEVzb0Z3kofsh0GFPBi7Zi8+Rh374DNS7OuXZOTDW5W0BzEiFitIl5xU4OpgzDd5PGG+m7VtSP0JZRfndSIUzYvTVATIoJV75yy/MZ9ECgvW+rDElt7xCnNYbhu7TrcB3bjQKF694zlz97r64g0e9Mqdu1pDmxXr619CE9fmT7C14BiT0+rzuj6SZjPF8Lk8RpzvmLzpTuD38u0nvKdU/zhPAioNo727qwbm8H4/r3/nL3tbW9729tnyPbuwnvb294+xfZUUEZE/g3gvwI8VNVfjMf+CvDfB2I4Ef55Vf3r8dz/AvinAQf8T1T1P47Hvw38NULQl78O/E/1FtwoMQYzm6POhZDXXLPovW4BPgp5nXUs/B8DCPmCPF9U7aoz+Wc3cSEro0XjeGGafCrH50U63z4t7PDFMV6Eeg/FdmjrZGpMH9o6lj0WCZQ2hNXVMix2thagXVmjdnYhuCWIBpqoft+EhWZaAE3efIS7e0j98mwQ3nIsjNeFkQbMRpl//zGbL9+lPiqicn0fRrO6CIDE5thSXnlMrTQHpqO8NTMzFNjTrHyhExrs+xSPj/6bJvz3JSE8ZpYfglr/4dues6+aTl3ftHF44nef7qokwhfzlxdCeQmrV5WDt+DeH61YvTbh9OuWdg6TJ3D3ew1uanj0SxZXKe2i9w+evW+wK7j4pkOtIo1w8COLr2D5hkMnHowyfbtCGlALm6+vERME8bQ2SOlDaEwIoTFVcMuC8qCmrS2Hvzvl4isePWooHlYcfx9O/gtritLhfjJn8tig3z7H2nC9FsZ3gnvhUunDYjovqAqTsu1F/ERZ1SWtNxxNN0ETUJTGGy5WU47nQXQvhb08Xc7Y/NbrmD9zRlW0HM/Wnche6w2rpsSr8OT0DrP5hlcOL7u8XqUTdGucZd0WnP7+6xTfuMA5Q31ZcfflC1abCveDOxRXwsGf/4AqhupMMgkQhAHXdcmmKdi8dZf5V0NEuLa1bN4/Qpxw/LWTcMwbmqbAWt/dxqvLCcWkpbmYYBcN09+7x9U3a6o47ge/c5+Lrzp07pBCKd6b07zUYmYt6gT1AnWveRCEFIW7v3uHi6+Cmyha+eCH77PnVitUJyWvm5c4/3LF+dcJEQk8HL5ZUN8R1vcC4GTXgqmhOVTKS8GXcPQjy+Ubgp9AitQg8XpfvGcp1srFF0O7Zh9Y7i4XnH0lhFqfPrFs7gh2bbGNsjkOz4hyGSJmFKtw/5anE1b3ixDpYwCiKOXS0CziteWgWIcoA74M0RnyaCcpEkFqH9D7occIC52fvQ9AkaR8LoQ/l9Zh6jaAXpsWswqFSeMxmdbGODT4x2bKfgfpBdknPdfZ29729jHb/jbcbU9zoc43bfdjuLeP2vbznM5uw5T5a8C/Cvybo+P/W1X93+QHRORPAf8k8AvA68DfEJFvqqoD/jXgnwF+gzBR+S8D/9GtW+q1Eygaix5152EbfEnMh/EkOs2vvY520zPhI0cH0uiuKybV5TwqPgIvfhvAGX+PrIiOYSDSsSCkHYEyIoPd+iCEt4MtkFgYeZmEBUuXMgkARvGtASvhJv/cbox9z3DIwaXWhTYlUarLK6wq07rdLULXqf/3/ZTWw8kZExGqaTlgX6gIZh2EsqbTClO3gVUSWQ5AAJiM2f0y4SlicQNGQ7/7Pmh7t3jz2LMVB2/N+/CZqd9jEKtrf6ymdkjjcYuS4nSNeXxK9e6UgzcP0MJgljXm7AqMYf72EWpNL7qoir1qEOdofzcGs3ZKcb5GrcUtyi5tcXkOLlzXzfGUtJIX7yMAla6R0CfjWlxZIB6qh09wRxO0NNirC8z5ijvfO0ZNSXlxhdQt9d+dXwvk5f3tRB5H0TiqNF72oMszAaZO0WKOFyHhCsetMn3/jPo3ZwFUK45os/GdxrIONx5fLmgmfZkQQnQmmzm482hJfXcW+t142sUxR61Sna4Q51l+594gUki6BowqBw4OvVKeN9R3Djvhx/LSoQL18d1R+NO+8nuN4osK0yi+nDJ/b8XqBxWumiMOFu8sOXpzgi8LvIXJeUt9aMPvEoU0jevBQx9DJx6+tWb+QYUvBF+YTmgwD3daLh2T9y44dgeUqwpvI8j3sKE5KqgPwvVgWo9poZkLxdrjSmHxXo1pS1zGlDEulD971GKc4osCFWF64jDLhtkTjwpMzhxIAG6SCCJAsfYRBNLQt9Zja2UUPALbBLZMApkkgi7pWkqRYBKAG06AJCKZ9Bed+ADyaAHFZYGZVLQLG0UhQ7mmUYonFe5wEp45paW5E0T+TONxVfZMsoOXxt4+H/bX+DTMdfa2t729GNsFNjzt2E+D3bAJ/VMzBh+35dfY8/wfl/Nhy0w2OP5ih+RFmoh8ifCuf42w8P+rqvq/E5F7wP8N+ArwJvDfVNWTmOeZNmJEZBLr+DbwGPgnVPXNm9r1VFBGVf+WiHzllv38y8C/raob4Eci8n3gz4nIm8CRqv6d2IF/E/ivctuJSgQBJLltpMUwwk5Xk+tYMTvT7kgf2jjMa3bkTQt+r0hVDgGLPO+gDf2xfBEvzg/dM9JNkcCPdEw1AFN21Gaf+hIWZh1bJgcl0rg1DowZ0vFjOwYRE9Ji2oaFHk4hMmS87ftp6uAWACHvtG5wLx2xfnkaGTV5HaNFeuxGsfLMW8fqa/eoD00oT/o0kzMfmB9HkSnTKvWBCQs5gWbWh78c1BXzB5AlOwYdOJTap5IxZarIlGGYxjZw8JMF5181+CpWk7FrEM2YMrG+WFd5IRRXsHpNOXhzzkvfmXH12oSzr4cQdNNHB9z5/hHtTHj0y4GJ4w7jSlVh9s4cW8PFN9oQvrIVDn8wp53C6o0WIlNm8pNDpBW0UNqvr0mh8HxtMaXHxDCNYhT1QrMsmRxuaJuC2d+/x9VXHBw12HcPOXzzkIt/dEVROpo3D6hOBfPtsy50ZGEdRgIrBsAa7c45b7qwlGk4jChXdYn3huPZumOztN5wvp5wZ7bGGj9gyjz8rXsDpkwqx3nDui1w3vDkdMF8seKVw0sghArMw2w6b1i1Be//7ksUP3dO01iaZcXdl85Yriv89w+wa+HwH+qZMo2zfQhNb1g3BZtNgX97TvWVUE/bGtz7cwAWXzlDgNYZ2tZGpkwYl/VlRTFxtBclZtEy/70Zl9+qKRcN7abg8O/PufiaD+EJC6V6r6S+H0KJqhPwAo109wtGwQnNYsrF18CXip9oBG4D2pbCzFZPLNXpIRdfrjj/WmBQ4eFwXrG5I2zuBUDEbgymhnahFFfh+hNfcvlF013T4sG4BGRYbA2rl8PNIWqYzUo2x+FeM85SHwq+CKyW5oB4v5oAZCZWj5XITJMIaBGBpXTz0blviaOLYmKaAOoE0CaCOkpXdmgToIo4xbQhn102yGpDed6GZ4CLY9U4ZLnGJsClaanic1daT5ExZUx7TZSDj9iyR+TePmb7VMx19vbpsF0L8vHn26a9zXl4OhDwLOev22z7abbknj4+1oVGkqcfy8vI3d3H/2H3sWS3cZN/DhMj6GgDOh0b/7/u3IdswO3G4qPo/zii067/edPSWmhHJKNdnhDXHd9uR7bJnm+2P81uk2drg948W/5x2mc0uXwxm0+f0DynBf7nqvo7InII/LaI/H+A/y7wn6jqvyQi/xzwzwH/7HNuxPzTwImq/qyI/JPA/xr4J25q1IfRlPkfi8g/BfxW7NgJ8EZsVLK347Emfh4fv52l3VHnorZGfrO4YVojW4f6m98P08H2zZtu3K2840Iza+q48O7L2cmWGb0gZXSDCWy7K+1ixFizfQGnB3FaUNyEakfGixnv9uYv8l1l5EyZHHxq3YCloucX2NYxv5pHoEcGIfPG7lR4kKaFJ6fMgGlVdmEx07jJugFVFrMqfPYeqrKvs9zt0rVV19jGeVwaRxmc69gfzmPOVxx/f9EBYwNti7zOmD6BXNI4pG7x8wpzsYbTc+6+OeH4jxeotZhNg5xdgjEcfe8oAEnRxQyvmGVgyrjjWRhT57EXazAGt6g6poy9Wnbj0h5N+7b4NgJioW8SmVemXuNLi7gWe/I+upiipUU2S8z5kpd/5xi1Brs8RdYN7f93MQT7njamZjgmE9UQbrOYd+NVqDJrFS1mHfPIAC85pXzyiPY/maGmRItFrCM8vA5j2ruNR02Fr14G6X/u1L4COFS49+Sc9jixH1ra2QF3PZTnF0jjWP/mcQcaVn44uT3U8DuUl0uao2kHYNpV0KOp7xzQ6Zgog2tOWkWLMjJlCiYfXFD/wRQ3mSAOZg8uuP+dCj8x+MJQXtS4mcGn3z8CLJ0rnglaNLN3Lzl6a4ovpQMy09Orb1/D5CcnFJcLZo9nHYA6fVLjJpb6qNdsMY3SzoRiHVyEpg83VFdVx5RJoAnA5LTFrh2mDVo61YXDnC05eHeGWijPHba2mCawUGw91JQpVi4AvquayanHV6NJlFPsxlN0gG8oBw3ua5LAlPEbPfseboMUGjeE+/WVRazBVxHcjnpBphB0UqJVgRYGWRvcogxFJhfNeL1ugeIfp32Od6s+I/axz3XEGMzB4fbx2ywa8jCzg7nMjoWgtSGtNbsXRdcsysb1523qPu/SWds1D0r15HOv69KNv6f3/S6mWr55VcR+OhdA3EnPvs1ZhIMNrGROEe/xk3LI6s3+55tcnVt3PGcaF1y8C9O9S+y6xU8KtMjmBq2ihQxCceflhk2kvL6+iWqkA5+TFpdpFDcZlSXDckLe9IHOFRQIob9N+hvOfUyr2AbqhTDY2Eppkqt3TG9rKNYaQPmRC3u+GYeCFtkGmLC7/Yw+5+9WF9mSieGahXEZtDO9m+M7NN80M03I74vw2Rcwf09Yvq4US8HNFDfRrm3SCL7qw2ybTSjIzT1aatg0UQlLjvQd+v+j+Opifd/v1mCq4XrDxHyamp31UYx254nncndykf58ypcNf/e9aS2TuIHmVNhsSqaThlnVDOozWRlhSiQ8eHCHV1456za0TJYe4IOLBYtpzbwMQce9Ck6FMrq/F6Zfm1kJm1nn9YT337rH6z/zmGnRduUalNwdvi/PcFlXtM4yKxus8VRpA1L6zcLk2n5RTzisNqzbkmnRdG1I539ycoe7ixVnqymvH51jje82+zzCmw/uU00aXju+GLjMF8ZjUDYuLK9L67pzzpuujov1hE1dsHk8Y/7KFXcXIfx06w3nyymqwuE8uOtvmoJ1XXIw2yCirJuCy7MZd+5esdpUGOOZVn1Ad43u9tZ66rrAtZbJtMZ7E/f142ajM3hnEFG8N7jaUEwcqoLbWNb/q3798LHbC57nqOp7wHvx84WI/DHhXf2Xgb8Yk/0fgb8J/LM830bMXwb+Sizr3wX+VRGRm9yZnxeU+deAf4EwjP8C8C8D/z12g116w/GdJiL/DAF1YiqLsADvtAGi9soO1E8km5wkMzJEx9NE5DpNgKftLOyatIgJTJ7r2DU3TUyS+UTFGIIy48mH+G0x1AGams51L3PZmlhI68PCIi34xhOPrNwBw6b1UU/GxElIzN56fNHru5SrDf7uAc29ec9QSZbyyfC43XimTUv96hHNYdEzbCT0obwID+X6uKBYemztg3BorLOdZpoyxPddAgPSS5jhhKD7nL3sTUt0k2Ewmeg0ZRrl8J0F51+OGhiESYFoYMqo0DN2sgmIChRLKC+V1SvCwTuH3PmTGZuXp5x9taSdQXWm3Pn+IW5qePLzJa4CN+uv0+kHQnmlQRfEgLRw8JMDfCGsXlNcFX7L2fuLrh9XX/ah7QqmCeyZbiwMnZZIO/dIK9z9owOu3hCaA2VyIhz/8A4P/hxoqUzfXzB9rJz+aY/ummSkgU+/a/JBMtkxUWgN4gSduH5y4sMiWKc+6KWkN/7GcOcP55x9y0OhIU9evgsTn+Lc4ieKP4iTmQQepHJUkFY4/N4xV18Mk6niSqjvekwtzN+ZUF4pT/60hvpTGfF3xIfxk0aYP5iwfC1OTDxMTipMA1ev98cSgJLKKVaCLxW7EnwFxz+wXH7R0M7CJPDO9xZcvWZw0/C7TJ9Y6iPBVbE8jRPH7LoNzJAZV6/aCMqQMU3StamUV4bZe1Oao4rVPYtaIjOmoJ0ZNoem03CxNbSzcGF4C76yuAj4SMBDME778Y0sFJHg4hM0WQJ4azcOXQqm8Zja9yDRxoEPCxUVkNWGyUndLwQ0Tna9YlcNxawMoK5XTO0IrmEmuONF5mDKRwT8OhA4/x/dNmW1QZdLppkgdtKZ0fNL5OwSYw3atlSncVKSNgTSJbHpJ0CfN/u4tVWeh9L7CdrHNtfJ5zmT2R2Wf/Hn+wVz946Ki28JC+UBu/TKB+DWhveWrT3NvJ9zdM/9yDAzjdIsDLZWmkXmnpu1OHfdHBwfHevekyawRwHa2fD9Kj6x34JoOMqAzZpAgEH5qQ7ZfVxc+HNTuuecSv+MVBOek+0stLFYhr5v7spQc07C+1NNeNendyGA2YDdwOauZgt9ugX8uI3i6MpO7xVfKH4SGIziYfLYUB8rvurfhXYtuErRKi6403ty1xXU0ZcTiqDgw/tIZz488zYGXWTvR4maWdmCHS8BAADUC1pbzCzMrXxjwnO89INFOEC7schVgb23RoyHuDjV2CZb9CCCKjSrEnNWIi+vMdYN9iRT2apBK20MKkAGHqQ+RDMRcMjbV9cFZekw2cI+ByXSQlQEfJyTjEGK9SrQnm3haDYFtvCs/Bz90or1eYXMW6pp0/WjiXVq1M1r10V4p81bijgW3hmcM5RVu0WaSu3oQJkEuig4Y8N4ZmM8HnPVbNKqis+mPSFdAHfUC4x+yz4f/RxNgVZYFdodNxvDeeU5z5cgOvwvEXiqnlgenr20PXeKc6/iSnhSKY/j/Z9Yrfnce9DMOF89eig8+uDVYf1pTkeWTyW6KIeyl+m+NbvaHMtv4bSIz7J87RCfJdUVnE+OKTbw7uxOV2/Kf3yuqJ3xaHbclZ/hbJj0mwjd/CrPP2lg5pTySmkWh2yqo67+oybMgXxxgAITp8wceBs2JRcejtdKO70bNjPN0CNBNJSdNl/Ds7jaDjutdG7jSY9PC0sKtPDkhM+yvSQiv5V9/6uq+ld3JYwM2T8D/CbwagRsUNX3ROSVmOx5NmLeAH4Sy2pF5Ay4Dzy6rtHPBcqo6oOsM/8H4D/MGvOlLOkXgXfj8S/uOH5d+X8V+KsAR+a+qvOgMRJMekI7t50PbqZo7QJbdoEwT6F5be0atW2ITLNVzg3gTldYRu/Lv+8qw+sWI2dH44Zfd5XpHSKC3IY+OKo/P9ZFEfKKzXaN/NUVcnFB9WByO/ofoM7jr64oz86pynKrT9o6UM+sKEJ0Ie+YpMhCXkMEo7yu56HtqpJHSNqdRGGzYTGZ9P2PbXjq7+0cOA9FEdhV6w0za5n/7aqPdrTZgAhf/M1J1/fO2hZ1npcnVd/HJqqaZtGXaNq+H3n0pTF1N1lG+dS25eW04+nDPXf8/6pCX50D53h1/Pt05TzFpeN6cPjGvOo8rz5tbG8rvHrNGCQ670s31DOmBT+XZfUfj05t75Pfzma3SOOBErg7Oj4BFjdlVOXgpvOj+lug+uGb3ff8SqnGRWd55Cdv71yLRO/Ewfdx/ue2x0+eO6tq+/REH5FtTaQ+fvtrfLzaKs9M6f2k7OOc6+TznOPqVT34+++kE9e/u0bvA4oisoMjQFnYYdr8/RRZtniFcnvqp7mr9E31pjam4+nZvSsAQWxDt4mWTznym3k8FbmuDRF41R2snMR8EdXAGlUNrtoedFZ2G0wDpkwMXJCvmqX1IRLfvByAYyFT+p7aLSEqXrb5ZDcejdHw0rFyuaGdFYONJtP4wJTpXLX7OsYg2JY2n8T+KbjSdu6driq6svq6xgPVLxTFg0/XQnzYDsCrWLdtFNNAczDfLg86pkw6t6jBrpTmaNYHTIBBm9LiNLFz8vZ1jNBUpuggMMQA/HNQF+M2p7LyVbJ2L40BU0bA1AJGqYvw2ZXK/ImwnE+proR2adhMi+46lUaoy/7BbNbhhHOCS+OpgBNqJx3o0rU5A6fC4cT+6EEq1XAcwNohUDYAqkxg2dsdoFRIG4IvSAyQkI5BcMtOxzZNwXxSd4yO5aZiPqmZFu2A+TJmzXgVfvLwHj/zyhMmtme0JPMqvHd+xN35inlZd8dUBWs8hfEU4vDZReAJTJY333qZn/3Kg0G5hWyv/bwaWjVc1hMab5iXDaVxHVPGZIJ1Xg0e4Xwz5c5kxVVbsShqCuMojKf1Bq/Cj07u84WDSx4t53zj+CRjw4S6vvfgZaaThjeOz7o+AR3jZuMKDEOGjtOeKXO6mnJVlyw/WHD42jn3F0sguM2frQJT5nge2DOrumRdlyym9U6mjLWeWcaUcV7YxGATTVPgWsN0NmTKqArOGZwTjFFca3GNoZi0qDe42lJ/58VNPj6Gec4jVf3Vp9YrcgD8e8D/TFXPb1iz7jqhNxy/Kc+19lygjIh8ISFJwH8N+MP4+d8H/q8i8q8QJmjfAP6uqjoRuRCRP09Aov4p4H9/q8pU0SbcyLTtnsn9WbHl8+Z73oyfYbu6ut2xZBcfX1N2mW42wwPr9e6EzwqCfVosd63z9P3YpR3wUdQ13jbb294+RfYCtFX+Ms9I6f2k7EXNddyi5OJX39ix+GZrIdkt9K887dzgbVo0K83cdOvQxIgTH3Y9xSnt1AR9p0Vy5RlWN1gg71jMdx/bvj1Jh62dJmAnpvGRIeMJEd+UwcK6Y8qMgYMdoET6LG3It4spk1xGTRPaYloorkI7NndluBCP6dQmofD+0rNrwTQZU4Y+XzdmiRGqBP22stfASpHr3ESDK4sXpo9KNnc9WmnHMrVXJX4a3V1E+zI7mmP6UUafVbroe3hBZg3qDGwMsshcTRK+lrvzeOldYVTwcRGGKK6xgfRt3RZzpdkU+GXB7P5qwF7xkSlSlm0ABuLC9XI5oT2vWLxyNdCIM9K7nqgKrTfY6FqSu90AEUQYggDJUnkA66ZgUrgBKAH9IllVun2nXPuur0O5WE0wRqmKltWmoigcy/aY+c+cc3U2Y7KomU83ndfdui4pEuMIWC4noMLBfEMZXXhaH7TlJmWLMR4fF/v5ak1j+xKIofTMnzS2QAfSpP4EbDKcc9H9pKEHKlUl5ifLL9n5cB35yNBXDZqD52VwXVEnsLFclbMuWmeebxAtxwnl44I3L14dAF85I6e4sFxMjqFIIDHD67objJBPNIBjBx8Ibz7+ImqzcrNnYkpLes41gjgCIycBeZrdAll608DDIj7LRgw+FMoreHdyD7uB785e2jp/eBb0I9+Z3u36mVuHHWXt7bAhBVsrcwd3Lj31wR3Oqztdmlkd/tflUWiLg0mrgb0PLJxytFHc5C7TxMixfT2lRqYMgbEjXvF2ugV8DPT3Or2+In5XHj/5jM7pb2kiUhIAmf+Lqv7f4+EH6b0vIl8AHsbjz7MRk/K8LSIFYS/2xt3A24TE/rcI/lUvicjbwP8S+Isi8iuEn/NN4H8AoKrfEZF/B/gjwgbo/yjumAH8D+mpzP8Re+G7ve3t82fPylDatQ7LQYsXYdftAn/cdX361qB7+zTap+cy+ai0VZ6Z0vsi7JOc69irmsPf+PGuRvXPiS2GrkMSMyayPAc6dUkEcnxeNTBsdg/C7uNjBmHOmnVRay53tR4/w294puu47GvYNjlT5kY2czqvGiJNqqJVySBiZGL4RGbQQAfO+8BanZaDtgwAszHjJ9OYSu7hFH1ABrNqgqZMph8jrQ8RFnP9vKzsnZErU1uMIOoD0GXDqk9ci69GYFuuSQOdi1ffV/DFJJ7TqJe1zRAyrSKt0s4X2yAaPRiXjh+0SrEObhkDpoz0oB0E9mRyg+sblYCvbP0/AufaDCQrPbRWaK9xUe/IMmlxLPRpI1BXRGCxLqBsg8bN/YfK8v07HK/BTSasq8OuTcZBk24hhVkT/rvprDueFv+rogcHchAi73PePvGwHhHBNAEjuwDLbrwSqDfMG85fcy5P5gTf9gMutaAYVP2gru5/VqcW4YcQm4GLmbUKTDxmkrFclEDij8CYji4q1xg2boJ7bYMtIlNo5M42GCMNgBZeMJUL+jPWj5QetAO11puCctJ2QRms7RktqrA8m1IsGtpVwfRogzE+5g3nLx/PQlCGo82gDcaENjrXA2rhnPSgFtBuCnRjKE4K2vst5UHPInKr0A87j66FraAbi0T3fW0N5rxA7zRobcAoUuXu2KCNCc+SRpDWoFPXu6sldMZLf8yDqS1+6rvP9R+8QFDmBc9zJFBi/nXgj1X1X8lO/fvAfwf4l+L//2d2/Fk3YlJZfwf4bwD/6dM2n24Tfem/tePwv35D+n8R+Bd3HP8t4BefVt/e9ra3z6g9D8iQs1LGOkmflH2cde+jYeztWe2jn6zc2tc6s49SW+Wmc5+YfZJzHT+rWP7Kl3e6mnQjk2mkBZ0qRzsLGlGmCdHF2lmISKiWTgw26LAEpoybmagtk+2qSz9H7zRlxgvvUbvGmjIq4CYy0KRJAEDQM8jKSWX51Kdh+fmicyzKa1zQR3CT9N7oxyhFZbN1ECuXFoqVYhwhItzYZSXukHvLQHvCbsJ4bu4MgYbxorbrZ9SUSWBKsQxl+gn4ImrKnAj1cdCYSfmKVdSUKenZOF2/0wAOB777nawiUU/NT0IdZiO4ecZqkFEmCQvvboHvAyPITyNrpI3uVoX2i3jCTrrZGOxKaO667bvXA0moNo3J2lBcWJp7rteUS7/VeEFo9GZtutiMwVjkW/6tCcyhEbtnwNhQOh2eNBaDsur042nQ5imCIP/qiw57YYKA78T311ptAsNJQbxgVoJ4CekiG0RcQI+0DItl8aOBS2OSN1uj9p/tzw2AtCxtxw7rutM/G1K/O2aGsFVPKqPTUmokaEP62Kc6sMgSiyS5m421WVCYnEF9UvXjmrdT4z1RRPAmnhe/4yfV/r9pYfrEs357uqWX0tWh4dmQyjPtjudN3t+sDtMoaqfB/TAP0hKF/4u14soptlFclTl5x/zllUNFcJPZcDyycvrPed7YXqeYxmM3K9zE4qMsQGhbAlxN92yTtumCeYhzmKaOv1cAblIfQjs0BPaQEBAkPRuHuj1K0uAL+ZQuSpMqOOXJoxsC3HzU9uJnAP8I8N8G/kBEfjce++cJYMy/IyL/NPAW8I8Dz7sR868D/6fIIH5CcPW+0T5M9KW97W1vn2e7DjBJx/I0H1Vdn3fLd2qfxfasmr19dHYrX+vcPmJtlWem9H7ezVysmP7NP9h5Lt9Yy/3dVZUyHktpxrpNA8t0z15gTI2P1p5H1+t5wu9+mJC9u/QBx8eu03d7wZaHQf5INNM+CfuUjOXePh47+qQb8HFZ9lx/3oX4WIppbGOM7KY7fNc51WukCj4Hpqq/zvXcsb90TZ5n2ojRMID/+LO0aw/K7G1vn5Tt0hb5NIITtxF+HJz7EJPZUV55msAv2cTyWYWrb7BcxDo5kos1O2nz6lyXvhNWTrsO1g4BFe+RqopJtBMs7/JdE91N27ZPNw4nGxoc/tdNOF5W4B0Yuzs92wu8lGbgAtFFdUvbZTuAul3XQtOE8ZhOts/lbVYNba7KYdltjHZk467NpobZdHf+Joqsp9/He1hvdqcf96tp0U2NLGbD87nwdC5+msRA375x+fvRWdo5/ITtI9ZWeWZK7+fdVBV/nVZXnu6Wx/a2t9vaHtP4KbCPao553Xz1p/vxvbcPa5+Sec6nwT4boIzZVtrvbNeOxuiYGOl2AW5c5O1a1O3a9bhFJBh1LtRl7VYkH3V+9wIvLeJg8PBT5+JiLOvTVpkhMo5U1aB9frVGRJBZHyNF2xapYlyUpo0LotGujpgQ3aEqYbVG2xZzdNi3L7f0kHYOynI4PsnfXDVEfFANx8qipwzuWtwl//SxTzr04XAfPkYOFvjjRZcupzvL5QrOLtFX7yPeIxdL3CvHYIIftzQOPylimdsLL9M4fBY23JWm61sn6nfRgAjNYRmjFgQKqXhl9uML2vszNnfDWKcQ23YdqObNQaQLizB93OCmhs2R7WnWCkc/WlEfV6xeLjCN4iYShQljUw1UF8FnuT6Q4F8e6c+dv/QyUIFNDe1CaKc9bbXz986otIH63h8bfBcQBy///ZYnf6qgOVCqU6G+o0gb6JHNoXbii2roxd8kUFiT+GQKD1o9MRz8GBYPHO//eUt9L1Ck7ZXBzxQtMp5r+hyf4MWjkuljYf2y4l7dDLcGtA//Obi1U+SBjcVGP1xV0LMq1GXAzFr8ecndP7CcfVM5eMtw9asrjA2hKtvaUlQOr8Lsd+es7ylHP4Qn/3CNKTx6VnH4fcvlt1foSYVdGoyDyWNh+avL0DZRqj+cUyzh6tdW+FYoKte/naIQnypM/mSGFlB/Pajx+9oipQ90bFHEKOoM0+9PQkjIb2wwpyX+sEUKhYsCrTxUMU8cH1zMXxuKS4MvwGVhxe3SYFqhPfA9dVkF09JRz7ufI4UbdTB5JJSXylXOoVA6anSK9CEOpo+U5jCIYwYBPKhOFTcV3DS4E8w+8Fx9wQyu0xQ9ZHISfkM3CWVICwfvtSxfKbZWrOG67+nOkzNHdVKzem3a07Q9mFojnZfunko0YgD/+LPx6nweewHaKs9M6d3b3va2t709p90EmlzHhL6JKb3L9oEL9ra3D22f+pmlWIs9PuqBAiNh8W8tOIc2LTKbDmihuqkDiJHvPnsNoaDLuCO7SyhuBzigqxWIQebTUMYYLBh/DgpS+MsrZDpFFrMQ9z2BBc6jV0s4XGyHpWxamFTBD7Bp0bIIvoPLdQAeTBJxk+Faw4BZ1sjlCvfSMRSma4t95xEUBe0X7ob6vWKWNe2dANIUpyvaO7Pgu2gIKuatByPYq5r2eEL58BJzuWLz9VdC+MY4prmYnGk8du2oj3qROABT+y5sZDuziCpm43FTm4VRlOFiOgIKQ5E2wbRxQVQIplEOW0fz8iGXX5z2IIX0i7XZB1Mqa7n6+hHiYPZeyfnPHnagRrFW6oXpwQliXhfqL5eedmrC4gxop9svptmTkHF9x/ShJAm+rdXplPW9iuXLJgrqhXN2A8VG2Rz1iz3TFLRTYXNnGLLTTQvauaFZgN0IbkIvDJgBHQGE6ccx9SO1BcA2im7CIIkLfrMpHGe4NrX3842/S/LlH1xurVKd1sweWKozqC481YUgbSizndEtekPZvW9z0hhIi93gm6vMHzSUp2vu/dEhzdx24+QLiSBcvBaMHfjnVpee6sJRH1rqgyRYSK8qnzc9be5oPy75716sfBe61RcVxUqZvX/B8Y9mVKdLlj+e9deXU9SWAXx7/xI3KyifrDh85wAVi123VCeXrL6/oFi1mNaDU+yqYf0n81gpTB9eQOtZ/4N59Pu1PfCQ2qrK5PE5aoTmzqT7rTSFtyU+z7xSnZwFkPA3J5jNGl/ZqPtQ9+Ffo04AaBgnAWnb7r73Zf9sDL7Nipaj56W7YddNQz+lcbjDHY4S+cRNwS7rXgQTMK1HNg1aWtSa8My62nD4o2n/LKcfH1k1QZvC2vDs8x5zvmL6YNbpVgwsATOArGpkU2Mv5oPnprT99rGK9GXEw6Z+gdvLL3ie+3FrqzwPpXdve9vb3vb2guy69c3TwJk9c2Zvz2v7Swb4DIAy6j3+ckd44DQ5V99R+ztzDo0ATmKUJHa0XAfI5JaDMm0bXBNSqOan5YVA3W/b8H+9HroJAFrXyGq9zTqJYJOEjiMRePLOI5dXPbCzi9HTtvi2xaw3Xb9VNYydMRRt2zNN2pbyLCzudLOhPJ9GoCqBV1HsqWmoTir0aonWDZM3A7tni+FCAJtwjmJSDc851yHokwRCtS4yZUy/2Bk9+AcLoeQ+EF0K1JogXnV+SQEcxIVlHmlANABOcrlk/s40LIjPrjh4u0SLUL5pPJOpjWX2IFNY/AvFqqWdFd1OgZskFkpKCNVpG0XICrztgRFxilk3FKuS8iqAEr4M+YqVj4BAz2qxmxCJwW4ENYH5IgrG+YGAmEomphj/XBnYLG5GDwxJ/9/bMJZmIzRH4CYgTjqBwsSCSUJ0akBtDN2p4XMXGtQEsGf+wYSLrwrNQpmcGDb3PKYJ+dujhMAQysgjD1iN4RAlfFYoTgoO36xYPCj44Nvg7gUVelkW6MxhYphGFEzpUS/hpxZFH06ZPqxYve6oXrnsLqGkdG9tCMG5K6zmZl1RVm2nqr86mSGlQ6ynmrasT6cc/8ER599yzN86wP/aOdaGtPWmoJq0eC/Ibx+xeclz+IMpF39hiTGe5nTKwQ+OqX/1kvrxFLsqMY1Qncxp/uxl37bfO8RuoP7VS7wzlFVLHjLUOYN6wf7xEX6i+K8GpoxrTBiXfCy8MPkHx6jA5mfXcDJFj5oQEeGiQiuPVIHpo4ktE0NiSm2wF0G80C16wMEsDaYW3GH/m6IgjfRMqHSbRhaMeJh+MKdYKle5qsiYKUMCT+fUR8/AlEnihBF4nJ4EAcY2MmVMA4v35yxfKbYpsRGws3W4p8sLR3Vas3510oWbFA+2zphBpgcoE9Dpf/ycLnrPYXta7972tre9vSDL3cpzGx8bAw/X5buNfZIBAJ6n3nGep31/kbbrd7rp+668z/Mfnj/v89T1ObP9PCfYpx6UEWMwB1H5OoEBibHStttMGQhASGLKJIDC+cCUqarrgZWcKZPcVK6WAShJ2gg3MWwg3DCrNf5qicxnyGy6xZThaglHB1BkOg1JG2HMlGkC0DJmyozNrGtMYsrYHsSw75qdTJnmziwsiM9WNIkpE9tvWh+iGFzVtEdTyocXyHJN/TP38TGko0r/e6gEJXO7ccGNJwEGGhY3yZXFzcLK3m48bhrAneQyMO5T5y4Ty4fgFoEGZkixVhbnK9qXD7l6fbIFyIhXZkYoW8fqC3OkVaYGrl6fdOwQu1GaeWDC+CykpYlq7OWVoZmb8F12M2XQAjWwOTId8wcPthF8VYSF5UQGTBnUYGvFTbI+WsHb3jVJYzndJRYXtMYpnrgQjw8xW4fx6tyacnzLKeUlXX+LVXTxSB4qWbhKieObR6gYhJOkTzd7sOboB3PcNDBl6ofRpUUJEUAyICm5qaSIHV0/4piXS8/sg4byZMWr9oh2VsV+Kb4oUBPCU4hqF1EkWXXpKS8a6qOCZhGfE9ov2vO+mcTu6JgyitqqO9YxZcTgixK79kwfnHL/OzPKsytW35mjNgILTlEzQVSZPjjHzUqKkxV3fnAQAL11S3lyyfr35xRXdWB/OEVWDfXfW3TjUj0+RRpH/VuLCGINNViSq015egpAexzPOx/u83SNmPC5OD8BVdzhFLO5DO53AqZpAvCYnqHj+aTzSBNct7TswWKJDB+dZAByjE4A/b0Z2gpJ0d+sW2ha/C6mDAyubbOuIysm1CHeI3UbAODSBh2e5YbjxXTnpFjWDck9UuOYmMsl87cyl80cQM4nNZsaWW0onsx7FmRysewyyPDdA5j6BUYl2Nve9vbpsV0Lo10Ls/zzx3F+7F5ym/O3OZbbJwkWfJK2q8+3AR6uGysxXCveI5GiflO+Xfmf5Vz+H24+9wlYLjExPjY+t+t7Mk3M39GxgZj1U85vlQeIHf6uAx1D9Vv5YsP63yWNba7vN/7/MVune5j9D20y/Ub86L8sfwrv/U/YPvWgjHqHv7jYPpHfDHUzzJP0XMY3Qvp8g+3SfwGCBgzciimT9F10vUHKYvCgVgjsm8hgGZgP7BiBwc2jziEnZ13bJfU/N+cCU+byaqgpc7UEY7CrVWxAAH+KD+LirqkpPqgG7JruIdG2VGWJrtf4uqFqmsDu2eHmFcQ5W8pJNTzufD/+CYSK2jmdjsuul12u8TKiRHaaMidnlOuao4uD7XJUkas1nF2y+GFc7J1fcQhhsScgjcdPexe3AVNGwsLLV/1i1MeFabcQNUJxEa696qLsmC8QFu72YsPUCOKqsOAuiAydEArPtLZLX1w5TKOI2l7nRUFqT3npKOchfKU4wRc6AKxsE0AjXzEMaRrdVXypXZt8FcClzp2oYACi9eFU6dg6eXjBBKrUdyrW94XmENqZUB8F4EMUmgPtykuuaIlVoSbVI114xPLC0s4Ms7nl5Ocsm7uB+WDXgq8UXwafKlGivkzfnupxweRJweYebF5pe/Ao1ZHCf+YwfPwBpZEQ1jKGyrQXRdBIseCnHntZcO8Pjzn9Jhz+uOLJr7iO3SONCaEuVTj+zhH1MRz+eMqjbytaKMVlwdH3Jzz5FU9xXlJeCijMPlCe/Erog1rl6Ht3qM6Ux3/WI21oT2IoATHsqXD45hQULn8mjI2pw3WQwKc0xgdvzZAWLr+ilOdCOw/9KZbhOvAFXaTTLqSmgmmE8ioAe27Wj6HdJC2iPm1yjcsjtgZNmXhbuMB0qS49V6/17lgpjah21594mJx62pnBlcRwkAFs80XQiTENTB83rF4OrpGp3Ql8LS8couAqgxYgLUwfVWzubosMd65tEViyy5bidElzfxFALgAXgOkw/tq7C8bvQOfm9kJsv4P002FRO+824uYDex6B8xsWAjm7eDwfehaW8a58W/OrweIgAz53Cbc/bVy6+cKONuYhb9M8B6BtO+F1APU+PBu6Tbg4d9G4Ebhr7pPGa5fw+o45jKw26Hw66K9sanQx68HjXQxisudOdr4LhdsEoL4H4j3mahNc1SXMb+xVmK/4WYFdNrTHk4H7+PBzP3bJhTw/Ll4pzxsQqO9WXdtn71yxfm2On/RtG8xJ0nwjO97NFbLuHv54TX23YnNsuvfV0Q+uOP/ZBa6SgW7euLxBGPP00cHxmw31oeXqteDWaxrl/h+uePynZz0T2vRl9S72bFl1Afe/s+b9Pzft2nH3u47NkeHqDennO6LDja1sjoLC/H3h+IcND36txE0DG/PV33ac/GzB+iWlCxtvdNiOOG62Fl7/z1oe/FoZ5k5GmT6wHLytPPrVPkR3yDMCPmrDa78uPPw1cEdBy684KTj6Pjz5tRZs0Ksbz5+K9yrm7wrn3950un3d3Lf7r91/1wbGb1E5mlVJOWtAlGZZMfthRfOnlhjrqZcld357wvmfXzGZNn1+Z6h+85DLrzqK+2vEKMXvHuBmin7rCms93gv8yUGYG/1SWDPW7y24/3vC6T+2wliP/9GC2QfC5s9dYq1n9XjGK79e8Oi/uKb6wQxx4H7xEmOU9eMZL/+GpTkQzr7hkfsbitKhP1zw+q+3/Pi/rkwONrg3D3j5t5UH/zAc/Nhg4nL07Fc3lG9Xkf0O5pfOhmOfbn8vgQmehjdteKV5asacTufVG8SE+Wd3POV3gl8XmGmLb2z4ffLyow5h0gQUo5E5nW6i9Ces/8p/yguz/TwH+AyAMmIMZj4fvLxVQyQUdR5xLojW5kyZzSYwZZJ7UObqRFFcP4lJL7ccRFmvQQyS3HLGLkc7JiG6XqPLFTKZIPNZmJCkF7Dz6GqFzOfbTJnWoZMgCmtccGcxzgeh3cPFbgAjtlvWNSxXcOeonzx4j3wgoc0v3Y2NC5MCfxhWXuZiFT4bg48UfdqwYy6rGr+YYk4vYbXGfeGlTnchZ8oASBt2t91iMngRmiYxZTSI6qoGsGNSDF70Y82GfCHUPeyjpowWgjSe6mKJHi1o7s+3mDbilVIEs65pjqfRZaGhPZx0rkp242nntnNX6sbHpUmh4Kv0hqbT2sh0Zocv/fQwS+PhHOJ8AFu8oq2gotjaB00K6YGgYtng62xsY3n2YoM4z0zCBMKX29dAsWrBg5vZnZOHYh0j/DQeX1l8IZ3gaZokde4gyRUsmbL1XZxSvnfK5MkhvrTYVRN+Wx/EUX28Rgbv/wSKpgV6vL5FFdk4zMUSWW34mXeO0aqI7XVRZDXeH+nd37+1kLoN+iOFRadRvLoTcx3tIOa7m8lNL58stxn7Id4/ennFy9Mpuql57T+e9eVkuxy6WoV7bLPhpb/Rp9H1mlf/w2lYALQxQlDd8Mp/kLFH6uDq+PJ/MGVrtyu11+sw+lKebrS71LlyWttHEEo7H7vK7srZvVN3Y1Ccm0KoRsD88JbRs26RiHKXoHtm5SjLDXGfBuZUMbt2iNmeJ3TfdXXL0ve2t6ebHs5xv/rLuEm/8M9dVMneLendaGuPmyQts+H7cteiNH/fJtH43P0waWylBaZpevZl0pIbLwpzNmWxUdqJdMAoJgP0odc8694BGuqMbotaCN4GRqcaCe64436MF8hCpwHX9WV8K5vtvKIRvH2scQGtiBfKSyiu4OpL/UJaPBRXQnMQAPfcNXhQvtFhO/Pv6bATDn5gufhm2y2MpBHm71quvt4E0F80/EwZHVFdWECZIoDyQ0xIUcCdzaDyVAc1IsrmfMLsR4e4X7pExIfXz9uHYbxe3WDfPqb4xgVF0b/3bKzTq2BNeL5ao92jXqNAvapQtwXrHxwhCkc//5jSeurW8vhv36f+lUtevnPZlRemizpoc27OG0x0M1YVWm9459dfY/Xlhte//JC6Ldi0ltV/chf/l064Nw/P34ltt8ozojhv8HHwvQpehcvNhPf/zkts7nve+IX3sMZzupzxRO/i/kunvHRw1eW/zlJZqsLbj+5QLGdU/8hjCuuxxvNEXmXzkucLP/8AI4o1vivPiNL6/oZrnKX1hoc/vE+xKtBfvOBwtmHTlFy+e8Tl11vuvnGGNRp/B9+NVfo9vDdcLSes/3DG+os1B/eXqApLPaC8tExfu6IoHBLzpfEN0yOh3pQ08ynuqGVyJ0R/27Qz2llBeVBj7PAdq9H12RdVD16NFvMKQxBAgcaAF5rWICtL4+Jaa2kpr2B9MqE1ir0ylJcKjyasYjASUQEHhydKs7DUdZhjHTwOz7DLxZw23qeHD4L25PmPDgBYPBJmT1rOfxT0AA/eFWaPPM0fH+AFDi5g8d6Gyz+asXhXMU459wcgcHwGB29vcDOLaSz14Qy1sHhPmT5Ycfy7R7hJxfED5eAnK5r5nMXDpmOhoxNmj33Q5RO4OD/ux6kbHCiyza7B+TRl7uav4RndbaYxWnN05Wh8dluMG61XINNkHAF0WVnp+5PzPVLyou1TD8qo87jz85sTLV9MW6616/z8lks4Odmd5vGTZ6vj/Vume/DwmY6Pl2K7bsEuzXVl088Zx0uw/Pkio89PX67dbElJyH5n9/moJ4u8/Q4ADrDfG6Z5WmDb2+6HjxeEqb6b6hgfNzuO+WvS7rLbtNWKYHddq+Pr8ymUZQfwg6FczLO0A4bXQzctfPe97vx1r4NP7DXx6BZpzs5v/g6QM//SmO9iA34O7CMNt6ofo8vQp9hPe+9r/fk3s2mYfP9Bv1GTbIe7XnesdSH9ro2h68DQzI1ac+YH9CB1v4V7Ow29PL8xw3fJmAWTjqXzzg+DJ0QwPDBAzPbLZNd7KYHvqdxx3dfl9R5ZN/jFlC7IQeMQ5/CLySC9NJE1a2QYmGDEHhnU0TEusnJUKU7XNL8zi4CPBr27q4b2zg7WSpYvMDeyjbzU/fjRbhy+FHwZQH9be4qLK9bfmXblVWeB2dLOSsrLDZu/t+jZtbvKHQFZeX1TD688rtHCsPq9eyBQKdz/8ZKrN+c0kwXNCCAcgGzSlzeIBAmg8NoPa1Y/LFjeeQ3xSuXgzvfWPGnucFrdDcls1qYxYEdWHmFB+/L3WtZ3LSfvfyEI4G/g3vc2PDy8y/vV3bjZuN3O/nv/MJ6dCwfv1Lz/2/c7Hb57P/SsTw0PN692GnwJnMsXx91nLxy+K8wet5z94IDTyQLTCnc+8KzfKTit7wawj1B+B9DGsvFgNobqwlM8KrlsAxgxfWwpr5Tzx7MA9EXGsJjRJs7aUmwU2RjqVWCimnWIFNk2FuMDXToHqrRjLYOpHMb4ePvFOuLnHChrmiB2WFYt66piOmswRllOJtQnU8qXVhSFY3U1YXM8xb52xcG06YCoxlnWP7nD+o2G+UtLRGD1+Jj2UJl97RwjildhuT4OmnbfPENVuFocUJ1ZzDcvMcZzWRzRHBjsL51hjOfi8YLZo4rml644n80BofjFcO7swQHFsqJdCOffchT31hSl4+T7h0zO5pz/6prpvObJDw+x9YzHf8azebPE1uFavvy1FVdvTbG1QY0y+aWTdGkPLk03umh91HbctaxMAJhPWoLQAWzps/cGty4opm3QHiwU73owNUXsVBd+244pky7MzOrf/LCrtNvbfp4T7FMPynwm7DYT+k/xpH9vP0V23XW4/fR/tnJvAnGuK+sZgaDPrP2UCLXt7WOw0e7V3j6f5iclm2+8ipvYLVeHLhpfZDEmwMVugtvf0G1mtEAd6Yak3W1bK+1UBnNw08ZIfrG8oLkV6u/00HYtrOP1aTfgpqEOacOCecDiyVxrUwS+oK0mYYc9prdrRQuhnY7HIftO3w6TMWXGabo+jwEGH9wyJ6fK1RvxvIfqQiiWcPXFbaZMfaT9wrhrT/+9BxnyY9r1PewSCQc/mHH5zSYstp0gjWH63pzVz26CKDtxUZvJf6gziPWI1VBszjqJi6rmbILMWqaLNSLK8nTG9K0F5pfOuoXy8kdHaKHIvRrzzpTpN88obcaUMYkpA6X1A8aMxIWvFcWpsG4KHn73Lr5SvvDNh1hRGm9492+9ivzaGa8eXVBIj8rnbhhbTBk1GHqmTO0t7//NN1h/Y8PPvPGI2llWdckH/+l9qn/sES/Pwk7srGjwCAbFyO4dAK+BNXO6nvHB4gus3mj5+Z97m8J4Hl4d8GTzCkf/2Pu8PEtMmevLAfAItbP88MFLnJ/PufPnH2BFKa3jwfoNVq+3fPNb72KNpzAekz3A60gd8yo4NTTO8uPFK9iV5eAXnnA8W7NqSq7eeoX1z4a+J8ChMg5PYBGlMWyc5Ww1ZX3nGP/FNa/eu8B5wyN/h2JVcO/1M0rrEVFaZ7vx9XEhf7Wa0MwWcFSzOApMmcu1xU0M03mN3cGU8V7YFMGt11qPyYCehIkGNo7pjrnGos7gvcGvCtYJRLgqsCtYn0+ojSJXEUw6mdKUVbjJFHDC/ROlfliwqgPwdOex0q6Ey9lRABe8cPggBLC4WByBwvwDw/yR4/wHgf2yeEeYnCon3z1Cgfm5MH/YcPEPFix+Elyaz+UIgMNT4eDdBjc1oJbmYIFaOH5Xmb234uD3DnHVlDvvK4u319z7gxnzhy2m1vjcmDF75IMrtAjn53eHbJT4OXkXpXNFfj77nzNmclZNV2b8LD6y6osy6mH2Oozp/KB8Uhm6xbx5YUyZ/Tynsz0os7e97e3ZbDeEfz2ochPY8nkFYnK7Tl9gl+0Bm73t7afOzKZl8sMPdjNTcgZI/uwYM2WeRZTV+xCAIK/GJYZKDwLtirS4VUcS3G5dX2bOXrnOVHvGTpZeWhfqHbuK77Lr9AJvMybOI3UT9F1S2qYNbZpNejAsuZaXxbVaL7uODVzKMi1Xc7nCH8y6soIAfB1E0cfMm1S0MgqukCeIIFp9hVrTBZaQZoO9PKf9G/OeTXMVGJl+VmCvTmmPpqTocqPiyKNMDvsV/s8VXjkPQEZ9915wYVO48/4T6t9c4CZz3KiZW0EqdgFo0b78zjnN35nQHL2KVTj0MH/rhOUP7nAVmTKXNhuv8Wb/CMAzLbzxzhI3L3jy8pdRE1xdXv3uGWcnr/BuJVvg3xbIl32+f6Ucf/eCx+0rNAbWAq/9uKZZWD74gy9FTT8YgIE6/G1F4dXHnsMfr3hk7/OkAjy89McbiquKR/ff6EHAsfdu+k1rOPrxmuY3ZlwcBgbW3RPl4N2WR+VLLHMGkgzrtw0cvNew+c6U5iA4/B5cCoc/8Ty6c0ib8sqQ4TF/JMwfKOsfLfAdGKrD8rOxkxaMD7p25UZwk6B7ZNfBZdBc2AjICnYDdmnwUeMH6KjUphHEJT2hAMAQI3yqUXwVwGE/Ca11s8BacbPAFmoWlmIJ7aEPDCYsbmqojz3FpQmRMO+3pIAHzYGhPjBBs/CeR0uPuILjH5Usv+Bxc4+agvkHFVdfEFAbXIxc0P9r56FMgMtvDLVPB1TxXT/sTabX5FcQL5iN4KceqQUdacqIowfTc2A9adhkgE/zt38K5uefMtuDMnvb295uZnDcYgK6t4/I9oDN3sa2/9k/96aVpXn9XhCTzxfF+eMgbZQmtkvjA7MmjxayxdoYAQVJZq/RqF/Tn0uaZYlxYhvtxOvT/y03Eel3Xm0dNWWMdNH1AlMmqz8BERoWB7ZRXCU9Uya6lASRb7bHYrTQ3tKUGbcPBu1O3/FBM2dyqiy/EHXVPEFTZqVhgRXHQTwUS2gO6YTj8wXyQMtmtJgfuL1oWEQevHXMxVcD60ZciFo4eyBcfiUtFAmATb7o8rG8jKkz/GGhOJ/jporOgwq7uZiyePuQi5+vuzzVO0GPo77vmL27YPW1OgiBxoVZcm9RlSAkSvZKyvRhVAXfCpMfHaIC7TeWQcvFGQ7/7j3O/nRDdbQZMnpuwMmSy4YxGlwwnGH2m0dcfclj3ljincU3hnu/focnf6EOQrEw0MO57tWZ66csfmvO+p7CN68QUepViS+P+OAvbZjMm0F7x4yeoYuI0D6eIf6Ax/9o3QmmNn9vxuYeuG8skcytp8sbRV0VQAXvhNVbE8RPOf3lBpk6tDVMTitOvwXu9XWXP3c/6Ui3TmBjOP5RyeWXlfZOcOp30wJTW1ZfajvXpXQN9QwMwawNzcKwuau0hzEAgTE0M6E91KEuUmJQeAnPCQta6BZY1A2ZD3Uk/SZxIE6wK+l0V+xKKJZKdWbCvb+G6soxeWyD1lSsVxxMTxy+NNhNqHD+KAQD8FUfTGDxnsduFFcFrcjqVJk/rDn+XtCUmT3yTE8cbhqixJZXyuy9NXf/aM7skcPWHrsJ0RDKS+Xgx0vcvGRyUdLMDd5aZk9aqnfPefm3X8KXlumTlvlb57wiR0ye1EFb0XnK1ZzqvA6aMgqL9/tInwOiWMZQGTJVemZMGPdsPFQHTJoeSNEYLMHjrfRMmSy9dFFIs3szy5/X+ejsBUaZ3M9zgD0os7e9fbZs7AbzNHeYZwFP9kDLbvsQoSKvC734oeobh7a0dnu3uAvlOCxLnd8dSS7Xj8i1IcY22snX9SYIjRdFf65poCwZRFoZtWu4q52vcLK+OddHYbnNuOURZZzrd9q99roVT7O2HUaYcz6UVWWqUXWDXLyY6EtpA3Jvn2+TdU3x3Z90EZh2Wh5eVT1oELMfPAvS+V35RnlJedP58Wr5WcMhp0iLuzRldr2jrhP4Ts+A8TPjw9gucSsfIlu+VPbTYHUevOfl7Bhe+0ATOTPneZqhCs7x2pgFlD+vttp5S2GuHWOsziF5ud4zCIs7Ovcs1kXqhOHz2Tleu80z+2njOL6eo3j8S3/t+ecpXYjjPEQycOf//NwFcvjvjN5foYJnKgPg6N8eHr57Xfprwmt/7f+xnfT+v3G7Jhz8u9vH7v6bT893bRuf0e6Nvs+vSbfYcezoFule+Vuj7//x8Pv93+g/jwME2B3lOeDouz/ovntVpn8wnIsv/u4wz+FHNb++5ebcLXiGt6hr/VGU8lTbz3N624My19mz3EDPOnnp6vhoFns3lp0vcnLLXx5PCY25lXX8Mk+2azHnHJRlV96uMJvh5R7CgQ9CYapHm7YPTxkKGfZvV6jLtg005HkIBTloUxfRJmtrLjSY19P54cu152RTo5NqGC7Te7QcCfJ1tGSJu2AyvG5EtunJ6ZTXQEtOfc0jVcUypPUU52t4/wPkYBGifB3OqV89jBE64k7n6LcOO4w7dkFj+8pLj68EV8XdkS5NH7kj/Z+dONZ3bBdNY3KubI6lo227afBtHQolMtQCSDs5sR2zDxQ3hc1d2d59zPJ3n+POZh7K0q5BCzJdhKFewNEPoT4SVi8rphHcXCku+rDh9Z1Iy027mNEBOIXc7tqT32ajF4zdCIc/gvOvEcJept3PqJScQlqPBQHD+IYd1bTT9NLvKY9+WUDh8E04/zoc/EQorpTTnw87srMPBFPD1RtZ+GkX0l98LTRPS+1E36oTQ3uoeAvTR4KvoDnMG9H3SeJ308Drv97gKsPjXyhoF4qpheMfeq5eMzT5bEn7PMUa2tnwXNohMm0cDwuTk9D2+ngYISyFR40/ZRdtq1iGxvkSZo899YEJu2FLTzM33e+d70Tl302rVOcONzPd9V1eOsqLhvXLkxCxzCvVSQ2/cxvp7b3t7XamrcM9q/j/3vZ2C9u11tEbzn3a7aMQj//YBeg/LlF6kd2NfxYW7adp8+3zxP59Wl8+T33d28dmn35QRgQzn4edWAghXyMgoHWNVFXY+c2BB+fwmw1mNu13H/IdJdgNRGyC859Mp9sPrhyE6ITrJPgaL1cBOJhUYbdIFcqiX3j7sJu95Z+9w09bmjb4BGdlDFT4mzb4NnehqWV3hALJRPx2+CGbdYu5WtO+dBB8uqH3LU74ReZT3InqtYpdNmzuTvo6BEztsY1HjdAsYkhjr0werdm8PA3AQFxYjRfXxVoplo7mIIRrdtG3t1gr08c1l69PwqJVMxr1WCE/LmyNg9ljR3XacPa1KWrBZesn44iLuJDfl6BFT9lOi718UT8eg7zO2QfK+qWY34ZjdjNadNK3TROIkG+uJDAiKzenjJo6hg0dLfolhscTBbuCg3enHNcNzStHmNqxenXGybcK2lkAJdToNvgBW/TTrotOmD6yuCn4KoAJqe6ttnpo3ytYvwS+CiBC+1jY3AvtRKNfr/YFSFJ8T9Tu0YIbwF2Am0igsg+onf13yeY/Jn5P1wuA2aT+x/Tph4xpJ2cu+Cp704WKLa40XCsK1Vm8JzSngUr8PaVvD+xso2ig9s8fNFQXZQxJHgCBHIAZ0lPT5zRWGkEBz/ytK6qLBShMH605fHvK5KTG1I6D96egUF4EbYT6uOpF3JxSnaw5/mGMxmGlq8cuW3xlUQPFVYtaCa4UGYCR//aiCk6pfvIYrGX68DAAkV4pnlxxfDgNtOJkKZ/zvY5EZj3lOaEtglkFirqvioEmw7A9/URHGtc9L2W96dktTdt/vknQOmlMpF1yEaRuoGkpPuh1J2S1CWW+KNvP5fa2t73tbW/jwAjPs9B/3k3kD2NP0xzcAxZ7218CwGcBlEHRukZdXHl5DRNiI2jT7qSmqyratHjWPSvjFqEdUx2ya8JtzO4Qkd6jbYvUNWLtkPWRtUdyICen2Y/MO9+njWUMUqpHMnZLd24XZRi4lo3TtuAcxcn5Vlt2MmQS08MHOv+8LLfcH9SHtk+KjA68XrN4dzpkksDwId06cI5pUYS2JAaLc+h6w72f9GTGjrWyS3gwjq2sN1A3vPTwIFwn2W8xXsx1YTdTmbFdA7BrVF/OaDGXNf6gGrJpvKKFGQrodcBRxhLJwI1UZg8C9Z+NU3whw7wxf+qPaZTqyRrOLylEkKZl5j1qF7iJwUd//cQWGIBNZsRCydgak3NHO5EAjFTpXA+MhTEM/6enHi0MrgqL/fIy6AUkcMM2slV+Emy7Ti9ATQCkXKVdmrGQ3Bg4C0wZ7U7YOkYVKXQrLx7sxrC5K6xf9pEp4yguTCeI1tx1277VMW+4djIKyfjFkn7+leXwhxMuvuHQiev8rTGRrZL+p8gdPhuMeE5aAWd4+beP+ODbAaQ5/OEB599wHPx4jl3B2Z8KZc/enyEOll90nTCeqYXDNydcfM0HELHQrr2TJxXNYfDRnj2Y4KZKc9SfH4BlmZ/4G/+/V2nnlic/b2kOFLsRjn8w4+p1Q53lD8CQYJrAXHKzvlygY6wkpgwGJk/CyfpIurEdRxro2qVQLlPY2MCU2RwFdkx55WkWZlBPaNOQKWMbpbzomTLBr9xRXDZs7k9iXZEp8/gz8Orc2972tre9fbbsOve/z7K2302AzE3nc7vOJfImF8m97e0zZp+BmaUENsyIKSMiYU00mfQslgRUNG04N59nrim73V1y0/UaxATWyxh8yb/njJfWoasVMptBWcQdZB9YLqmeFOlgDCakMkZtx/ZMmTETRhqHlrYHGsyonPi5X+Tnx7MurFrMchOYMkZ2s2S6z337TO2x65b6zmTA8LiJKbN+ZYavZLggTuUSGDF25WgPLN5mTJmVMn1Sc/nGpFvEe7utaJ+XJx7mHziqs54p48seBJAIEIgnLkzBlxFwSi4kMAgB2rV1BBqIwvRRxpRJAowjpkwCH0xLH7ozY3KMxRnzsQmiXRFUyD3G4gI2LSqLFRy8Yzk6XdC8cohdt6y+MOfsawXtlE5EMQcy0qI2iReOTbzQPA5MGTcBN8npIHnC0A5bC5u74MvAlLHrwHDROOZu2mcVje/Q8bs4/jbdT9sGppBd96yOXU1IZtowXmr69D1TRoYsFEJdh++0TM4Nk9MAGrhJCMuYxjYt6PM8g/8uK1PHn8MXW8Ps4ZqD9yp8YSPrJfsdIIYtlC5vGieIzBqniPfMfnLB/OEhqDJ5vObud6dUJ0uk8dz9frjwios14jzNUZW1VylPVtz/TkgTxii2b7XClxa1QnHV9EwZp9eKwInzFO8+AREOfnQUmTIec7bk7nyCVtnrJTF+nEIb3PtkF2vF9cekbsKxXM9lVN7gc5tCNJjgVhifwV1UmF07cjnTxivUTYhoA/3zva4p380itGzqoJvzgmxrnPa2t73tbW/btktz77No160TfprtaWOyH6Nt26U/+Uz/X1xT9/OcYJ9+UEZHTBnnQEzYLHUOn7FOEsiizodzy+VIbHLEqBlXlZgy0Y3pqTe5MYHN4RyyWkfBTd+fSzZi2Fyn1dK1IaYVkbBEywTvEutmS1MmF/+6jh0zEmPTpqV4tEOvZWxjIU7nmSVWS1ZeWuBUGVOGzYbFjyehz+Py05g4F9hGRXT5SoCTCwuiez+YDR8UY42XcfvrBt1sePkniw4Iy8N85gi7JgbPVttGx0ZuZF04y1WNziowpndlyTVl8rZ1LByGgJlkaUggTQYeeo0uVul8rCdXVE+aMqfnVJFBtrjaUF4exggdYRHeu2UNwbqhO1gC0JTqwgWmTSmdG1jHlJE+Lxo0ZYqlxVcEwOA8MCcSUNFOs/5KyJODWbs0ZWwT2mHrYZ3Jxq5wbhJBONuft5MQFlHLcb6AlpWXlmYurF8BaQiaMpfSuYZtkqZMwim1BwA7oGsMMMnwg9nA4Y8nXHwltFEyYLBn3vRAaK7f0gGKEYR7aXbM418IEQcO3yw5/6owf7+kOldOvxUKmDyuKNbK1Rt0bmLSwuFbEy6+LB1TRnz4rauzACRqCZMnAcysDzOAybMFipkWXv2tEhXh5FsVzSJEEjl+c8HyJUt9JAOACohMGcXNshd+ZKB0fYzXRXWhmFapD80AROz/6+B7sQ4F+gImJy1uFtwhyytHO4uaMi4bW9VBv6T1FFdtAKMI94tdtdiLDc3dAGSJV+zlBh5/JDJ6T7fEDNrb3vb2ydqYwTBeRFx3frzYGR+DZzt/m2PXnf882lj4ViTOWf3udPl/2H3sQzVHOlH/XFg4P3Yrwf/dhd++vU9h5T+1Kuk1IHdpQQ7rusaTYHfBfZ7naMtT7bb9ftoYjuUmniXv87Ypr+J571mR3ttg/DxI558WHCRtoj3YsSH2cdh+ntPZpx+UkRjZI9eGSZ9rkKocis4aCUyV9WbIoknnsnK3qqrj7udkcrNLT/5QcS4wbCYTpCzpNGQSaOHT9yyix7istFg3gmna7mGl4zyqmLTrm+/mQh/2LH4euOSkY5mZTYNZbXD3Dvr6ZDhGA9AgAQGNw2xa2sNJvPljksZjGo8KuEUU9vVKcbKiuTfHF7LlntO1pfbYVYubFWiRBGUFu3ZUpxvWr866/vkiLc6HoEIuljo5aSjONiy/vAg7/kUGcKSd/7gQ9oXgM/2YgaZMZuPxS3VOTxybO6Z3ASLoh7hJJk4ax3agKTMqTkffu2ri4rdzP6JfhBpHF96u2CjTwjB5PEEP5tA63PGM1ctVDFPa9zFf+KcFcN+QrG4Ptg6AjC+ing99nzqR1SiAamrF1kGLJYQR9agx3fliLYM6OncY6dsSjveMjMlp0DdpTuwwbwZIdWWlhbof9tE0fnDthXZrl696skILQ3tQIW1wwTIbF/RPNDBn8n53efMXW3eM/oWX2gKIc5iLFS8vZp0+ypYuSidCnX3PziVAUS8uOf6tEA9AV2tenk5gU6Nty/2/FSlaTRMiLU2q/pkE6HrDS5OhSG2KCNJNMHwEwPNrfkdfUY+uN6gqr/69qgfF65qFtTtdPbuydk0MRtE/uuge101kdkXvyCesme2S5dVd+dmOWuDVY7IJvAfU17vbtLe9PY8JHRu4P3aLCf9TJvk3bUqNEl5Tvtm50bS1SNq1SbSrnbm+03VpdrV93MZxX8Zzq11zvXyDZtd8TjWUe432XzcHGLOTd53PzuXafimNaYP2oJuVIya070P8Aijd/MVuwu/gZnbgchwyQmKZprDmXZnJ8z+yldVk7UpdT/8t25su43T5cRille58Sp8zdPMytsraVaaBbWZ0cFOtLoR2Cn7SP8e97fP35eo19WnfLgHNv9PXNTgW3ZjFKBilC5mdLinRbI9N8d7gVpbJ0QaThRu31nd5JcsDYEQH58LlqpjsWJcu/jeiXNUVk6JlVjaUZig07FWorMOgmEy0z6uhiGmNDN+H6fvJes7ZZsrXjh93aW1qA5ql3b1RcdVWnNYzvrQ4AaCMF2RXVgaeOQytt2x8wcqV3KuuuvS5ueym8SqDtnsVNr7Aq2Fm666fAB7BqeDVUBrHxhUUpu764+JF5tXg4+dU9iZeXBPjBnW2sWynghXFqbBsKwrjMSiFcV15HunKzdvrVWjVdue8hhDo6VwKqe4RWm/67+k8sKpLVIWycAxCqJOWoWZQfttaRJS2NRijgymZ94J6w/qf//RDBJ83+/SPuCp+sxm9wCN4or5n0ETr0Gn1uEv3TIh0h2ovl8/WRK+IuXp6wusmWU+Tgt8VAm9HWbqrnHG4zGhhYaHITzLAYpz1muZ4r9gd45rGr8jOea+D77usy9c1ud9hULZD1N3GVD2z33vOXY9nlOafPl8tH4u1qvBO//3gN29//X8UNv6tPqqx+Tjx+vRr75pSKGzpaN9k14H9mtWzffIakOIme/T4GVr18ZsmduHn1T6uaBq3sH2oyM+/6eGc+h/65RAlD7KFPqNFYsrQL8AhANkdE1IZ6I91C+6c8JpH3MvK3mYiBtfJcqXUCzNYIJsoqN5pi+UAQfofNxB8GdJPn3jW95LGmQzSXufe/LQ0efAAxn1Nmw5jUGHcXw3gha/oFv070+Vjw/BYt9BnmG5QbzxuV4JdCZv7GVVPheJKaBfa64g58NPw5ijPKlBoj31wOR4/GFSQjUFnbnid1CaMwyT6Be8CHwBqA2WWJnV/1xRi4AOc0g2PqZMAXsj26y19Vy/9SzNdL3l9Nzz72icVfuaRqQtlqyD2OcMqaWSlx4tJnQx13VI7XLwvYyO7Pl2zqyatYFeGTZtuEq7to+iO+sb9VxAvIXhC1nZIbtsMJyxZlMJuUyqvY1T+mNmKBrd008DvzF/eqnOQJyvTNNmt4ML3n1Rf2erL+HNiEKfNvsTsHrdpV1sHbuNp/ZCzytO5rN+dBMCOdgy/6+7rdJw+JfHbl8TW2A6O6fY47shrGh1sWnY/t0KVju9i5o3ri8zkbiN61+Wr8PDRi1s/7Oc5wT79oAzEp/c1k+LR8cF6Wt1zhb57UXk+Dfa87b4p3/jcs9bxkY3lJ7iQ+tTYx+GneROtchel+6Y0L4pW/UlRuHeN/5hOel26ve1tbz8VZtYNs3/w4PassF2i/nB9/ptYJ6NzOmaseA2M4BRRMm/DrmfZTrp/WCDIuuZoNumPXcdezj7vchselztmFI/TbLFcUtpx360M9PT6/KN2dUBUv9IduyOPmbJ9FMywuDLO08yLQR5Te/zERP0zDezgjjncgoCbRmZuznCOiyvxiivtoP0dU6a017JgEsjmC3vN+R0AmowArxycSwvWpKE3GM8bwLHu883AmALVudDOpYvyp0IQrx+DZIOyR4CU6KAOTAaqFb5jv6RjIj3IlI4LIMZnl6DGS0txztCsCw6O1gM2R2FdRsjSwbkxEyZ9Tgwak52HwFYRUS7rCbOyYWLbkB7FI93/qW0GeSAwLwrxHXvGZu1IaT7YHHC6nvGtOw87Bo7JUAMjN0/Yr9oJJ/WML0emjMUP+jtmuTRqqSNT5qXqsjtvr9nWcju2zVauHDBlUtmOyFiJDKHaFxTiujp6JonBZzeRU6GOQpOVaTumTc6sSWPh1XDVVh0TqBQ/YOhsMWUie6Z2xeBY3u6c4aIZ46b7rsK6LVAVCutw3mAi0wZAVXC+Z9y4yJQBaFuDtT07S1UiU0ZofrSfl75o+2yAMnvb294+PXZTZIDbAB+fBDjySfnU31TvDTsae9tbZ/tL43Nvflqy+vnXMt2weCJfQDLa+fXBJQUBaQli5tHNN3enTeUMypabF8r5IlycUqyV+sAMFuEDt9pxmel/3Jn2RQAHZk88q/uhnI4VxLjuLP/o2Fb5O9o/SKPXtK8bk/hRQ/vcRIdjodfUOzgWFzMjbbRBXXn0PgF7aTAN1PccooKKIirYywJ34FGjIdqeN4HhglKcFiDQHjtITJm8Xa0gjUFno+ihm9iwqeuZKxF8yF9PvjE900S4+ZWZgwj54cy32ceF4S7WzU17JJ3rDmGBmFgwA1Nh9WSCLlrsJPiFB++z53tYPjVXVr8qeJf9wCoknu1WWxtB1pbLAVNGRmhd1oDu/zWDs4M5k8xsTB+0QaVzLwe22RCxHd3xjD0igzSRKVPD31q8Nsrff5VU3lY/wn1lWviTKqvD5fn6/4kBKDlTZlzeiFGys86MCZLXM/gs9K7uW33pP4uL5e1yRBiXCwFMva5cxsfyjTmyCJfjenT7eFavBQ52uGObxndph+72ZIwbpfeoGLbn4YOtIj8+289zgD0os7e97e22dv0s6sW24/NktwGz9oDNT7Xtab2ffzOrhtkfvXe7Z2nSqlPto0tep710jW7SrQUnE2DcukFESaBfWV/HjElp8vSbmuOkaXVTvpGN2Tu3yp+D3nn+NCbjMn0czzF75jqWzejzjelydzQIemVO8dPhmErr8YnpEhdSaoPQuanDStZXFmzG/pEExnnEt/jCDOoyrkUlaPUhZtvVKwJwwQUuiZxvn+/KHHzeoU0jPe4QWDKyBWxtfZbh511udGOwq7xS3KQK0TpTOvuUMsd1Mmr7gO2jO8YqS8OOcxmrJrmG2I3QUPQsHOjBkzHYRywjZ+wM2D7aX3odayf897VFCo8p/FaRCiMdm3g8A7IkK1syXZvNpmSzLjg8XmFNX8aWjFSmYZIzeurWstpU3DlYDnRwcksaLUkfxXlD4wyLqrmWRXSTbdpwX5V25EmhPcOkMJ7WGwrjB2Xn6fL2tT788IXxYTwzds24NaumwMZxtcYPNGBURwPHtuZLwoyUBEzG40r2WTLJQcG1tvs9u/Z0N6Kgvs+LF7SN91QrAeTN82hIs/kXX9zcfj/PCbYHZfa2t8+Ljance3t2+4iiL+T21GgLzyPouWNhca1a//MKfO5MfkM/rnWdGOlaXdNfbVt0vUGmE8SasIDYbEAEqSqujboGoNrl7USLx8KpXVoPbdxNLoq+PW0b6jCWgQ+l82Gx1rZdenmyf3Xu7aMznRTUX39ly3Vm+ztgJAi8a6Ypo3TsmJ0aKyJD8XrJQITu++hzYuU4pVh76gNLvgBNmjK5uOouAf4k0C8uCOOv79kw78/acxM7ZleancL7g7anTAx0NnayaVI7o6bMmGlz4yJ+V93QrTCuYzzZlWA3sLmbLb4ViivBzTUELYiC+SnqYXUeCmiONGr5DFcx4kL0OzcZHje1RMF63QIsBvkbQctRu0eWsw8GB3el9Qmw8Vt1PZURslX8iCmgUJ0Z3FTxlScxP9TqkJEgWZ7smhjXPWBPJMbGrvZCxz4xOjoXURrJyhcHdgOmkT5Nqid+z9kWW2yOXWOhbLMuNOi2kLmKjdkn4/7nTJrwvR/jvPxJG4JXtNPJjv4O+2AGfQnPqImHWav4ctFNW3yWLuTV7ljUUKaKOlkutS2l97HZXX7daks51miJFRtNJLPwvcjLIBuPPH+KRJQD3NnncTpUORxLgObBHEaMlf689vOOcWCFXa7u2TG5Ls34u9/Oe1PAhdMP9gENXrTtZ5Yv0sa7Rx/V4vlpizrvQl3XpNu5YHxaqL2bmpN2hdLDTOT6aAmDcrejtaTIWl1ZcWdQne8Wblvt2pqwZREV2jYsuoyQh+nW9QaqMkTQytqoqxW0LXJwsL2LlufPFoyyXMOkCj743iPOo9NqmFc1LPZien96hjk+Qo8WaBVvy9ajEzvIJ+sWef8DuH8XnVX4aYG0Hmkc7Z0p9qrBzUp8aTo6tXGKvWqo74aIWQPBR5vazyDyVB5C3BfSTdLzOZTaMHGszh3r+5bJmaedCs2i30ryBYOJb4juEMIdqw2Li3YugYpfwuxxEIJM4bOnT8LvXi49V69ZFu87rl61tIvQ5uo8UEU3d0YRpqCPDNaGiVEKs7x8Tbp0ndiZgrc62OUc7LYR/ouCXQrtPE5wDZRXwuGbyunPMfCfFw2T79d+o+HRL5es7yv5blknJCh9WcR6kplN+OLjZBkDZi3MHgq2hssvKloq4gQfZxjXTej6TmUTstHCQnxcGEwVn0sTDCZvqazwZ9KOSzrv6NwpUh3iwa7B1lmo7ThZFBeOzx557vz+E85//i7rOwZROPpxjWk9J9+Y9tcSff2pnPJKOXxrw+WXJhSrEEp7fdcO00ZKs2lDVC/TeJqDIuy0CkyetPjK4KaCaZQULru8aGgOCsrzhvYwPB/8394Vz+ljsut+y719bkxWNeXvv3lzomsiiz2zJVbNdSyacbUxets8i2Sp44n8jfX5PoqTc4NybtmAZ0uf1/tM1WyH3H2mfu5sww35d4XUHYdq/ohsHKL5E7HPqvDi3vZ2g93mjnruu+4TZkmrtk9P9JFV9uKq+jTbpx6UEWMw88XwYL54jy96ynJ7F3cgIndz+MWet7cjXd2gbYvMZ4gI2jRBvXo6CQjv1RKZTpGq7BfpTQPOIbPZsCwRaB26qZHFrK9bpKfoiiBNG0Jfx3jzKVy3ZiJ5OwXwRnRZ2TTo2+9j7t1BD+b4WWyjKvbJJTqb4BYVWpis3Jg/C3eNCL4y21TUaGmBDXRllec1ag3NYdFHhgC6HbXRjli/qxQiPhRLT3Ng8RaqC4/4sNAyDspLT31kQpjmcTn5wtgTfe3D5+mJp1nE6A9F9HX3cPRmw/pewfqeCYtIp6gIh++0TD/Y8PhPz0OYa5OVa+ijXdj++NGPHav7hs0dwa7DgnF9P1u4aliE2lXYCTj6Scvij0uWP/sSZ18paA5CWruG5iACJnHxOX2ivPIbwukvHLM5FurjWMeFcvWGMH08oz4GN6FbPduVMH08Yfl6+m3j4t1JR6NVQ7fIzUEb02RpujGVmE4xtTD7QFi+Jkw/MLRzaA7pdvk69XwNxxJw4mahLaYVmkUUGbRgN4b6SHBVaEu7iov2RvBlBJGyiB+5z/KwjVkfWrCbCO5cKtWpdGl6cECHeg35blGsJ+UpVh436W+AYuWZP2gQXw3GEYVi45m9c8H94oj6wPRjEeu87qXb7Ty1vY9//5so5XmLOM/xDyvUhPtlsMs33r1JZQ5Cd++qVzG1R4vh82XXTlJfzrAg8bpz4SeNR7zvQcc83HfrkeUGHp1wVDccRRcHObsEVV5+fMQg1Oy4b5sWuVxy99ECmhbxymI2igWW0juPbGpQpSoLiItEWW/A2vDcjQs68QpNS1kWUDeUMZyvqV/gZGVvn3tT53AnJ590M37q7fO8LthjIrew22yU5jpw4/fRnqX80dou/cJd3z8K+zjK3NventE+9aBMYBSMouiMvqvzCE2nRv9Uu5GCn3y0ff/duS70tqb6vUcjBV7Xm5B+k+0kORfaXjf94iQJ7bkIJG02O8Ah0+c3MvwOA7/urV7s6lfboqsV/pFHzi8xiV2iHr+pkaLAFsVO9kvHeHF+i1kyGKtk+ZhBcD2wlqoYRW24ETzLHozOQxGZIk0Y60VcFNG0UBaoNcOH6FijIzhZ9qc3dWCwmMieMSG/nF8xm03QSRUWf57ASjhfwqbmlcs7qLXdOdG4iM/BsPiiNmdLDhfTAYvFLUZMGa+Y1qMC9nQJj0+ZGUN1usBNg6CftIqbhAq6Rf6ywTw84bgwuFmJmxeYxmNqz+JBRXnV0iyKELEhAiCmUcoLx/S0DLTxbji0A5O8zRgU5OCGdgyFnsIdQRobwKvpqUeNYXqiNGvB1olaEgGxPDxpFIY0mzi+daS2eyCGTSVvg0TwzAagyZXQzqFdBH9vcRKZMjqI9CBZfvGB3QJQLoTlF0aIixB8bK3fwZTpGSphoKBYGtqFjwCgUpxbDt+sOP0FH6I/xL4G4UZDsTriwa8W1C+5AeVcUjjQWM72DQ0SRRq1TPcWmJVl+rCkWMPlz8Q6vaBFEIsciPCN5xfXMWVSfQ6Kq0AL10JJtPAxO6bLpgzDcwLGyZApE+srloFi3Rz07cqZMvOHnjt/UHL+83dY3zGgcPyjA8Qrp9+Y4KOQaShP+zI0gG2Hb2+4+OKEchnCRq7v2L6vz8KUmQim1U4Mr7xsaRcF5XlNcxTAIv/wBb06x+Dg3va2t73t7eOx2y7Id7iQ3JhuD9Y8n22x3p/y/TrbBaTt+u32gRc+GdvPczr79IMyIpC7lHSHI2CgitD0mgODRDtcW/Jyb5FORAKrxTlkMQ91Nk1YyE+n4H1Y28xnoQ0Q3F1UA1g0nw4fBokF0zTIPDBldIdbjNRNAA+KtItbg0jHlNkCBOL/sa+1WdfI2+9j7t/FH83RSdm5a9jHF/j5FH9QhTbAgCWTyjJNAA98lfmUJ7eXEbsFwBdhYVid1WhpaA4iU6ZzL7mZJRNYLUq5DH7swe3EIxoWWuLD9/rIdK41uR/9TUyZ2RNPfTBiyjg4fvOI5csFmzumA0QQOPzJEZMnkSlTDZkyCAOmTPKzP/rxguXLgfFhN1BcKZv7veuPaAAlEnvj8O0ZB61j+bV7nH+5oF2EtHYD7SIAEkkFfvKk4uXlHc6+dUh9KNRHQrGCYqlcvS5MH1s2dwRf0Y2xXcP0seXqdenGWhSkicBKYsfsZMpEFyT68vJ0pgY1hvVL4TdvF0JzEMdnxJQxbfY7JwAlgjT5ot84QjRzjX7SGnyaixUUa6W4Irj+CEzO4uJZh65V+QPebEI+CKwluw4XomivqC8+uy4zpssAtdCQrlj3YBlAsVLmD2uqiyqAIllaW3sWPzzjtfaI5iDVm11DXTtHz504KTCNC9dX9lwyraM8W2FaT/P7VcdSUxvA32FEgNGbTrNj453T6EctjQtst/jceqovtffDiUwOWub9aSNTpszdihL44ZHVBj055fhyyVEEX+VyiXrPK+8esiXYmbfJOeRyyf2fzCJ70nOQuwzmrBrnA1iuyiRjI1I34X9hGfh+J4bMpqaIDB6zabbb8XHZfrKyt73t7afNPk7mwi7GZf4O+8ikBfZgzKfCxpu14883pfuwtgd4bmf7YQI+C6CMKjTbE+D891Pnoa5vFrTM7RluODUGmiaAPx39PTJlkkvRehOORWHJ5IMdog1sdnQpsn9W6/7gaLHhnY9aLAJlhV+HtElH5boebB13LjBlHjrktOw0WgD8eoNYiy2Lm8ck9dvc4AueuyqkvkSWUFkUQ9eyW+rJqPcs0phGVtKiLEEVbVsOynK7rDHrZhwhom569o1IB0bJ5ZLZpEKTC1pcaMpyja7XvPrwuF+85cynESAmqsjlksPpBJ2W4DSAcLNq5yIVA+ZihT4+Yd60TN8+CDoyxnSaMt2CXMGuGuTBE+62Hj+v8JMCvGJXDUdvziiWDW5WBE2ZCLBJ6ymuWg7ergZgWxJghGzRL0NQRnwUcoxaNANNmahDMzlzlEtLdelxpVAfmG7B7i09eBiBHDVQLnugTFwAR3wJxdpTXJlObyW4JoXziZER3OQCgOKLAPb4khELiB6AmoKfSADiLGzup3TSMVWSq1XOZNkp0qiBSdIutBur4srgy4rTb8pQ90ShWBmK5SEnP1eyennIhulAmdTu/mfukm1pykiI5jB9WGIb5eoLgi81iCp24TBzkGsE9mQhF3PxvwTU4aFYRcHLCNQN8ifWSQb8mLEnj48AnB2mtWvF1j1TJg99aZogArr4E8PqK/eo74T7fv7uGrNpufzqQX+tyrgdIWTv7N0r1q/OsRuPaT31UZmNR2L9BI2l8rxGWo+bFgFsBsqLGrWCn9gAysY89mqDn1fYi01gvAH6opgye9vb3vb2cdkuVsAYnBizB8bHcvDkumPP3b5EW/4Y7Wlz073t7cPYZ/ma2gMlL9w+/TNLkZtFYtOOprU7QZkBGHATk+baNHHh7RwyqUKetgX1MJmkWGYhYkhk9AgRREhsmlF54hxa18h83tc5Zso0bQAPjEGnFeZiGbqb7/7u6tv43KZG3nOYu3fQ+TQABZ2bzRU6qQJgkGnKdLo4qagoSOuTFoSwlSYxZVToyiouNmAM7aKMQrj0ecx2/jHToVi1NPMCDBRLh3ilPiwRVYorFxg40aWh25mPC/FcuDRFhxCF6rSlXVjUSowcEdq9eGdNfVxRH9s+soTA/EFN+WTJ+beOg0tQLDswKwKYIaoDYdzFuwds7hbUBwZbK+XKsz62g76ZRik2YbxmD6ZUdUP7hbss35jTRr2VoHhvApslLuAnF46DyxXr1w9pDguauWAbKC8rlq8UTM4KmoXgyn4wba1MTw3Ll0fhN12/cB4o8wvZgjsyMLLj3QI3Lo6TC4ppQqZio91ie6D94vsJWrHy3TE3MTEcpzA5aShWBb4Kv0t57jDOYzaO8mpC9WRNdVbhI1PFrjym8bSLUd9yRocLOikYsMuW5rDKdGMS0Dp8+wy0UnLWiVdM7fr7BZDGYc6X3P3DTPsqPpek9fD4lDd+sIBJlZWp26r66Xj2X5zfnhSrwqYOQHFZbp+D3pUwK38gWtmp8I8mvKldY8vTjc5viWHmApuDOjP3xpEIp8Z6nXNUb/6EKhO7VK8c/P41gHDWLvXKNBOznKVnzDXilqoeI6bHDGNZ47dCqmHgNOtXu9vzEZuQAWx729vetm0XYJB/3gUapPMvvK1RqPkZovzlAr03RvG7bT3XlHFzZL2ntHdrc+yWc/HrxuG2zPZxWq9dMIitfGY0x035NnV4N1/Hksi+6655dn6N3TQnzz53G3RWdssu2O28iUGuRrL5c0o3am9sk46PZ6YDPcZYXhbUIdXZ5x/+34q0lj7G4BDjHeJxW7eil23VF/qR9Ci3o7ENKyjWPuo+9m1P2n3JPf/G+sYW57vFWnGV9IzxURlAN0ff2hjM0poaDt5rufxCgR85f4xlA0zL1gZfsrQ2URM3xKRn02/ZaD4/Xh/l6fKgF8naf+/v7Cj0o7f9PKe3Tz0oo+rRekdYrtGkvQuDOs7/kTQi1JHEdruFwFWYmKtzSNRPAYYLnsur7fK8Xn8uTzN62QDXvlBvKkfbJoaWNX1+r7g8RO0uTZnRYub204hosfzithOQHUp0dnBaBwut4hZjsWtBtu0MF+qeiGGy47gHDv7QbB2/yQogl6eeXZcwWqsK777H/O89JSHgVCl/8OZWP7ba/oznP4zlsWie1ten2XV8rFTHrt/vtrFwlA//0Nvy+tlxbMsefnD9ub0f88B23Vrq3fbBp+S9lbCl3q7cT9T218Xn3qSw2Lv3RwfjOycB/jdR6xOLs8t7u8XhYAE3Zpkac7uFpRmVNz43qq93YZXhQk2yBWGa3m0t5PrvaXOn8xjNNn5yN+mx23TuMj10q95OJx7KVXB57piiWf60sREWjTI4N/ifMyGT227WVlPD/KFy8aUYEKGrS2O/+3rH37uFYB7FT7L8edqkXSYaf7c0rwQx2qWXlE9BrGIknJN4Lv0PP6lmP69SGB+PadxvDPmt8Thv8CrMywZr0vzQd58BGm9ZFDUm0jpNLNMQyjHSYGPdToVSfGgf8NblXb58cMLM1qFO+jLS/1IcpTiseBq1/Lvf/xX+wpd+yNdmj7o6LYpD2PgSg2LFd/nKTEQtlWvx2Cy6QK3FzmMLs+nSA5QS3j+NWox4Tt2CRi1fKh+HdFn+Rgsetke8VpwOjidzEQ2wsZyF2WDweAxODVY8Tg1XvmLpJ7xcXGyVsVVmvOC8Gkpx3fdkPkMgHIZGLe/Wd/liFdqf6l5r2fV1mF+6vq21wKvp+uER1n47n0f6NCo4DK03/P7pG3zt8BEz2+BV8Go4bWZ4FWa2wYjSqsGpYEVxMY1XwSMxj3R5PULrDW+d3OVwtmZRRA3RmKarX4XWWTatRaC7lsfpVssJqz+csf5TK8pJXxaAc0kMMSAjbm0xlQv3bNIeVAlTgSa9FxTqILeACfnESZiMxmAcg1DhHqTdsc4jPMfGYcVR2QKPPlbbz3OAzwAog9K5rtyY7AUoy9+0MFBPYNCM7aa236JfH5XdZgw/C/bMi65nKvyGBdqLWLw9y0Pp0/oAGy8YPup23sbN7rbl7Ep/m93TXZTs6yjbzyocuLe97e2nzvxswvpXvjLYHMlZpFvHgMTYBPrIhl26DLzYtSeSl6OjY9oDHE/bSd7Vxp1t3VXnM+4vda6eozHo6hoDN7vqvKY917Y1AhNbUe3i+sk40OSqmQTw8+q1/40GXRnVV2yUyYlDTXHzmO8yGX7odtvdro7doiwdfb5tEaPrSBwDTTMX01gP6wyk2mqCh4voZj3WJtu1w5+brZXfr77UpTPt09+rrzxx/OHRL/P7xfZw5bv3A9DuOsvHLzPjdDCW1/0s6VpJrrpdmUAXJdLKANAbWwITdzI24vVoXMYeGU9lrrkXdta5oz/i6HQcu2O6nX88jkmIPy8v14rcyjO+h5bK3519KQN0s/ZYnjr2u0wUpitlXR2zukG9IV3XCPicqBXrM8BRrRy823J5Og3BBbJE3XM84S1jpkysI/UnpTUtnSRAam9+/Y3ZJymq7Hjs0gb8IJACGjQd9/ZC7dMPyuxtb3v7bNt1QM2Y+js+/2Hr+ajT35Tvul3qsavls4I1e/uptz2t9/NvZt0w++P3wpcb3CeeqpmWmCrXuTPvKvO6siVjsgy0ulIdpj+3iwkTy9xysdgFMo3ZMbL9GRgGGBAZMVBG33e4M+euDd2iNUujqUyyYza6LpgsX0wb3Hvp3DXGDJZQ5+jYjv92LbhKuPiyRBF83S4rfc7GUg0wTtuxYbLvKU8eTXDAolHEaGTLxJ8osUuMj8Qp7RgwPWNGBz+tEaWwgfkiGTtFgMJ4msiUmRUtlQmrS2t8ZMCE9Ju24LBaD9gthXhK4zK2THSpV9Md9whvXd3lawePmJi2Y8qY7AFqxXfMl5Tn3/ret/lLP/NdvjJ9vJMpk5gapTgmpqGKTBkT2S6ewCKx5KwY27F50nGHYSpNrNt37YHAFDF4Tt2cRgveKE8GdQRWT8HD9pDXirOO9ZIzZm5kyiCUOBosV37C2pe8VpyxyzymyxfaLQOmTarTqdlKu/Yl7zYZU0YNDhMZL7s3hj2GWouYXsJYiMerYa1FHFsdsHYcQuOLjjXTqOV3Tr7Ezx5+wMw2WVmWs2YWmDIoTYZU5SyZ0J+eOdPGdLUr+NHJPe4tlhxWmwGbBghsmMioWbdFuOZEQxneRGZYSHe5nLD+wwOaP7VkMo1tjNqJbRu1LQXUC826wFaeHKVSH8rRxoSHgCEyZRRsRK86ADn7DCEARceUGU4oEoMmVKUDAPpFMmX285xge1Bmb3vb27PZdWDKdWlvOva0858n24M1e9vb3naYn5Usf/H1awCLLOH40dgxZXbvIG+BFLmlrxkDpVsDdO4zNzyXbtKduOb4tbvUu46Pd3l1e/d/K5LjTXXJ9W0djMW4GfnQar9TDYq0oI4oPn+NjdqxawyKFUxPHb60N7I1+kLyBDs6uiuL3FDWdbvrY6bMrstgR/qcHJSGq4ZOG+/KwFVcHybdj648B2f5zv9NNmq3aZTfKn9m6ze7Kf+rJy2/c/hn+e0ddY5ZTrf6bUbtGma4Ob1ovKZ2sDKCTp924OMuBtagvKfUNajjpilXZNfsZNztMNPu0JQZtWcn2+iaNlx7z47GuFh7/n71pa12dm1PeMTTnkVdxjDPPa6VpjjisZUb85iomdhm3kV5U+atMnu4Yv1HE1w51BodM4JMG5lMu4YpY9WIUxBzvabMdXnzZ6XSMdrGz4j3Xox03t4y24Mye9vbZ8U+LFjxYSMhfJi2JOHB/Pt1dp1AYTyehA+vFT18moZRpqvUfR9pOO0UPYw7w+pcH8Usd4PqdpVj/d73n53rNaeyczeKK0bLo7lp0/bjKAYpC6Qoolj4SBvKObRpkdl0OCZGhvoU6boYCw5mEeW2duujwDneh361bS88nEJKOw/ljvw7hDZzPQtpWjRFhMtDWUdhZC12zVh3LB7zvK3rfz9rCCLMYbmgZTHII86HNDvq7wS937qtitGHtNGCaW+fTzPLDbPf+fHo4PDZsOtZkUS2JWfLdPmvETzNv4+ffWNmzTXPJ90RnGCr3B3Hb9Ka2Xl+1IbuOZELnUbwaKcejWRlJlDG9Gml9WhhtnVnskXc/5+9P4m5JcnSw8DvmPlw7/2n9168iMiMyKzMqqyJVcVBXWyBQPdCgjbqhgBqIZHURloUQEAQQC5ZXEkbNqiNFi0CAqohokQCksgW0BDRTTYBks2WSBVZLLLJKtaUlVUZGXO8+R/u4O5mdnpxzMzN7Pq9///mF5H3AD9+v+Y2uflgZ/jOOdXaQfc6CTY6CmdRSE4CkU7Frkn/00SZq0SxZpsiqGkqQFJevqv/bWSOR9IUCKAUdRNi0my1BaJ0ySmyBsk5P0YknSJucuQNOwI7gq4clPbxXhIEDiDxNeraShkEKaOUy+LThP/OxwcJx09Wc9w5WqHWFhXlaJ3yv/If1n/1vW/iW9/8Au8sLjNUDQD0VkcUT0UOrTYRrSP9jDxNnRwH9IVKPt4OhLnqs3kEGlijJosLM4NxGu+2F1v1HBMeDkd4u7mKqJVdtLZ1RAsFxEhNFgNrdK6CYY1b1SqeD2SLyJFpTJRKuaxuiOeSIoGM07jXHeOd9iq77s5WW9ec9ZWgVYzPzGFYYsWUayD/1VYcmI8e38Jbxys02sa6zITVUKPWNupk0nFD3Bf5L79jGQDnFJZP5qhmBnVj/HmPcPF1GQA7ghuUIMi0k1gxHrHCjgSlstE4/e4cFz9uwW3wQfLnQtwYBcleuVHgisd3LUGvBFQLE6AG/63RiJpQYjkOcWGCS1NIyFEG/03Ph7FCmX1FbM6BzxnpoJR5XnphQu7ThdG9Ngr/TQXTstkuxi5kS9H7HCshQlPfi8DY1BljyJtOhMimRhnEcGTOivJ+ABsjWawUSYrxtgUplUfTT/8HRnMYxrKZhLllrSSjzaYDHy9yYa7SwGBA3QA+WYDWnQiATT36N1cqj5YfpnvVwZ3OBTWoVc4ohrn5uvpK0reb4xqqs9CrAVwp9Lfa7cCFCgg+/plfbGQsk2OE3/7QR4NXhnH62+cAgPOfu5VlZrINRiYsWEch/ujtE4fNbQXXyIYxf+CwekdJ8MMlY3NHPvh6zbCtHNuZzJcsYFtg/oCxeYsi7Pv4E8b6LsE1knYZPhV12GWqFfD1/22N8+/MsL5LGE4ZZAnHHzFu/84Kn/0fjjCcMBafAZs7hOGMow/14nPC+h2GbWUzU73fwNvRCiAXx6CBUF/Jxtrftph/pmHnjOGE/SbnN0SXNpTrOvsu4eI7gJtJBqn2kYbTjOGU43UQA/W5gp0zWDEWnyps3maAZSOtLwUaOpxKpqqwiZIj6A3ixqkGQHdAc85olg5HP7iCfngJMMOdHWH1I6dYvaNh5hKULULvK6C+YJx9YPDgD9ewjfSvN4BZ5Fav4DecQlXVADTnQHd7LButKuLLXC2Bas0wC0J9KWmp+1NCf0ogAzSXjP6MRuuL/wvjhYwFWUBxBtoLxjCX553smG1AD5JS28wo1h2zaCH/7zim6iZmVCsHVxPIQv4bhu4cyDHMQsc2xIDuLFx4h9MxEms4f3bNd/AF0l6L6IG+EsSzBsPPfGPb1QeJUiFQ8lMyHlJuHS729mwPmigv4zhkY+xhM/bG+gCmLeuJMLDXOr/V2XSbrTk8jSUcQKb9magvVvZxfeX7wuPyWCnMQ87x5OEURW+intGcDwBPS0HE1/d5LXIi7Ye3z03NawohFc8VSRTiN56AjCEJ4zLFDIbyjOocUhMUjI4B0tvjJ4fxOlKEDQPHxgE6TbEgfTgAcDIcMY+Z9Jjxh1aPgKbFpfbohSjc8uT15xkFp40AW9kQw3XtqxuOHeNzdXs7C6Kfz8d0p5jDRAbF7OfEfNkBdDIe72h7bSbGiXrMPa6o3c7KGN4al0j/Y6tk7MLNqby+cjq+3TfxYPL8bEf2xd0dPv2GuyvD41Tf795E1nsVQVJvQB+6PcloXjAd+ByhN14pQ0RQR8VHNhWKnQN3HaiqQE2yoV1nRS/9p8vzwVpfZj4AJGiuT41NbZMrKlKL1d60frRVtpX9oOwvkPMW6NS6NGVZ2nU+6bdkXKIw4usEgUp1BlwpuFZPQw8hQoteD9CfPADaBsPXb0vqYAXQ4FD/4D7c7VN07x7B1TusU0jKAMwe9aiebLD6xjFYEWb3N+jutDALlSsvClKW0T42cBWBLGNzVyROVwG6B+b3eizfayIMVBkR9qoNozk3WH6txuKBgW0V+iPlg2pJarww7/ARIQcs7hss362yoFupEButZA6YPWrgKkJ3i1AvGe2TFq4mrN5RmVIk9EU2Z3AzGHfChJYB+kaGj8CtMEBm5v3jvWacq7wtBeWA8UJwJ/eVHFCvHOorgjJyzEp8W6sNw9XCdNiaskBysycW1dojFIiwuDegXmq4irI0goHZrTaM+t4lzhQwf1DDzKW/+YMe1b0LvPWbDexMoX00wBxpmLmK824fD+hPqwjlV4MoTcprBABlHFTnvCJJob7o4RoF2wTuO1lDHgPQkWU0D9e487szSafOgF5vwIrgmjTtOUNtbHz+9UUPt/BaD8dQGwNo3yYI/v4cDePuRM4BlkFdLwiSqyXceiPnrpY4Pl/iaDEDa53FdmCloLoBdLnC+1+cxRSbZBy41v5+hDF4/BaE751lUG/g5tNOxWQZNFjA96d6AxgLnjdwjZZnqDdwbbVt8ffKDdY0yfSpzoCbKnsuQAQ4J/NvdmxbBXMbg9oBUL18v8AM1hrknMwfQNXWGQNOxvl0oZQz1Ul/e90VDnSgp6WrNdT/+usvpq83hKF/KnrD3TZflcF48YrGeS2UGsCmjp+TbvoEvdlP2oHeaLrueb1JJs0vQ8bHA702euOVMgzszxzk2KfEFuUMkCtQbkQlbB/IXQ/8POLLaD3snVlSYe+DDT8j7bsGZgbpcQwCsA8ZE3uacNPYgv0TTfflLECEqioeGf8BilpxY+CWK0BrVMt1dJVg6+CWS9Bqjdnj+ahsug423fXgfsDR5VrGWa1x9PlsdDkolVaJawp1gkgBM9rPfaJmLZBlWm1QPzkahSwnwh4NFrTq0D6cQy07cK3BbR0FRFerXJnlr7+67FAt56NVLYFIA6NSBgDqiwHQhOaqgl47VKsBrlIg10SFFLEoNVgTyPnjVGmlMFpICYmCCqMCzvPmyvpjldQBwBoSBT60T5EyBtCDQnebIqJFWY31u4KCqK8UNndFYVKtCbaVixyRMtJuuF9hcxceQcQw8xrrtwl2znBVomDySpBqqdCcn+HJjzdYvy1IGLLAyQcz3MEt3Pvf1YKU+XSGzduM4SykKyAsPp1h/TUH1zqAANXJhbpZIaQQg4xCdVkBDAy3LeafzDEcMcyp3zB1Yj5OkTKGcPa7t3Dx4y4iZZoHDbgGhlObDEGoH7cwCwYUsPhkjs1djmtbX9AkUgZ+PQPnqIygW9pzRr10OP7AI2UAuLMjLH/kBMt3NewsQcqwoF6ac8bpD87w4I/UgoJhoNqHlEmi/aseaC48UiazUI5tqqUo5Myc0HikTHdG6E+84u5KkDKyjohKLjWkSplgkRz7by6kz6CQDMpPPTDUwDAzr4ibQMlIzAKOisEwdrW0cI3yilX5rzcWxAwzr6LijSxD9Q5cSYBSQc+whxhz/A5kMTxeNh2kiB8OumHa9wMd6EtJ+9AhX0VKXYMP9PqodJfe9bxdp0wp+3vW8weapsOyAfgSKGWIyMdM2KF0cA7okcdWiI0n2pRKgLJeGm+ijL3giYcB0BpkjKBzSqTM5BjTqJkplMckSiZVNjgHNGLBznywp+aaomTSTaKwjAeLNU8gZQBArwe4pgI3ahJiHeD9ej1Af3wfmM8wfO3WNlLmrVN0b08jZWJXyXK2D3pUl50gZYgwu7dGd3cGswjoi/J6EYWr9rEBV6KIikgZLekn5/d7XL3XRvSKMjwiZS4WWL1TYfZwDjtTGBYEVpJpwbaj21B0D7KC6ghImaDcSH07U6j4vBbf8e5MobliNBdKkDJv63EdfHo9ceHIs0ogRcqEW+WQw/8yWLAIsOzEfUMNiSCbIhW8YgTw7iuXziuGpO/ZYwtAQw2Meu1QLzWIGbqTwG7E8EgZhrIi5M8eW8we6tj//N6A+cNqCykT1qbaWLSfnOPOcILhowqulTm3DzvUnz3B1371bdhGoXnSwxzXsDOK19E86TCcVjGdpPJIiCxeSUSkOFSbQRQYFaG+2sBVakS7JGudQr6JGdXDNe7+yxm4UoKIWYvy0dXJg8ssbjCV3KjqYjOiTiwLckOToD680B/nZ0qkjAP1gyBRrpZwfS9KhCfnOH5whKN5K9/H8tszGNDlEosfnEWXO3ISQ2Eyxkv6jWVBynC7I/y+R63AWEHt9YPEYmnreE00WDmeQvql4xVME3WDoHlKJsoBZO30ua35JfcNAPpB5mndGPNmEGV/7b+lsS9jgYmYMmkMIjKFou9ABzrQgQ705aDy257+fpHKlINC5vXTDtnrRnWfhQ5KmQM9B73xShlmvh4pE5ArNrFUEwnTf40/4hYVMVR4QhnE1gr6gxmkN9sf9JuOkYwzGRx0F03U2YWsyUr3uXQlwUMnyVooRdsxZUrhyFrY9Qb0hFA9uQBCQEBmQdBcXKL9bLZT4VVeF/cDMPQ4ujcHlAJvNjj6ZCaCVTmHcg36IY49m7ejkss50LrD7c/m+fwrLQJw16P9dAG12oC1ArdNDMCFShWZLrwC4HKD9sFcEDJJEMStmDIO0CtB8MxOGuiNiTFlquUYU0YQJD6OjHdRSn3bZYwRgZOOs6WwsQw4B2Ucqo2L6JgQqyZvL/9chag4CcG+bEsYjuSiWAuKBkyilPFxZwQpI0gJ24qSprvjY8pogFWNzR0FO0N0o8qDKirM7h/j6pst1m8pDCdSfvSpxm3j8OinG/QnwOKeFlTGmXc5YWD+uaBy7IwRYsoQQ2LMZAsCkAGqZQ1lge4WY36vhZ1BYsogUQwychcrB5x9b4bLb1GMVdM8kTXsTxJXF/ZomEb6Wny+wOaO3BvdA9VSAkQOpyhiygCq88oy8jFleqC5cqiWFvOPL6GeCFKGj+bo3jvD5m4tbmkeTUUsa1svGScfbvDop+cxXky1AcwsV1Rpj1xxyW6gfEyY7jSkShzXA/BImQ2j2jiYuUJzaUGGYecK/bECWZlzf6Jiu6AIVJbjfR/RLeMQ9aWBaxVYiYJvRHwJisW2ASkDUVIzkjE4tgmBgQFArSugUt7dSgkCplcgy6IsC2gYZvm2J+/rqNx5PYqYQ6rIAx3oQF862oc8mDr3qpEl+4T1gzLlQM9KX5Vn5xUrlw58jtAbr5QhYMx0kp1IhGl24s6jdSbU73QB2oWmScuDm88EooaMEaWCtUDdZK5EOxUM+z74U8ia9PeEpZkrPSoZyvq7XIISmkTQFHNO66iNxGRIrdRpPBoRkBzUxkB98QDUtnB3z6JVnowDffYAODmCvXviFQw0KivCeECmSaqedFDLDfp3TgBNqB+sMNyaw86S1JETyiYyjPqyF/cfy+jPmqgwUb1D/XiD/u35iKwxLO4NvUN92WNzd4bmSQuuFMxCS7vBwTUJksijWcgBTaXQ3W6RZ0yg0TUjRV74jA12rgBUPoaFkr4x9s2EXCngOCY6YOLtj1ga/yI5pwYHdbUBrMXi3hGcVlF4dZUakTTJfMky9FWPxaKW++RdtBaftTEmiluIMKsGF+ODcOjbMlgT1FUPno2fGXWxBs8bUR75ezOO6+OU3H+MO5/MwU0t6AZm0GoDPr/Ee49uCUqt6+UdqCv4sPlSlsRaIutyJq8MjhfOVxroeqmn9dgmQdWE9swMdB3u1s3oDhjcDiYUlOGbwcaM3wnn+3EOpHURiM9tzxPw9RmOnbhr+phX1R8QjsusS7Gt1Lv7q0/hTnnDeBT7AtvNk+PZVIXnjHlx4xC7yTpuvSoTZVP1dnfd3XQWz0eMg+XtQAd6XXSdS8MuhMVNym7SBsjPf9kEvqdBJVDQghdGQnb5/+edUpK9cW8Wx3ReN51DGiKgDBVQdhsMu1NrVBpmn9bYe6MxJuQC67xB2/NCU+32eSBMjD9ROFl37zUTCXr1pn3tKN/yTNj3Xt2k74CoRSK/3JT8466Wa7ij+Xbcz+vmkdIUKMBfGysVYwpu1S8phDkIxqjf/cfXj/0i6MDnRHrjlTLMDLe5hgEOggqG7AP7sm5xKpBc+0F/SWPvGvfGUcCfkq7zdmcZXOqFj9ynnxXnGbhHwO9nuotr+7UA1O+N8yDc7MGNBnRm1MWHLSsrPgYMoPXHhJsH+WuvrxKJivrl7xdNAWumfvBR9u3fx+IEXVKhg8rOheNAZVkpfpfPERf/I93fManHj3dPOKVdTPCLppfZ93XE9ksZ0/NABzrQBF3HfN9AIH0qfuQ64bKsvgfRmlTa0d81yN4gxGo9LeCVCopCEb0XdZue0xNlSIS1UqhJv++JAIaApgNy4xawJQSRcXCzKjc60XRmxn1pvOV80pYgLrC6bIvEMFTUT/+H6y7L0z5iPf+nt/va1W4bxTt9fjzmJKPkWB6zM060IQtwzUkZZ/OW+8OAI5z9VoXV1xjDuwOgWG5XTNnN8jr4NNykHLQeU3VTqJOk6Q6/y1TdcdikLgA4JknVrZykyvZtwp88SgxFLqZ1BoDK+6UrcjBOx7TcadrtSllokjaWCTW5LF13SH1tWGOu+iw1uE64tP/3R38IWjH+9+9+iHebC9Q0zkMn/VlWGFhjYI2zagU9IW0pup45cXvSeqdkQXGM0G/aduUatD44nmOVpQt32cOAeC4tz+r7VN9p6uyBtU+9rTCwgk7us2XC2tYxJboDwfq6oY9NkhI89AMAS9PgD/7V+/jmz32OWSXzZ6aY7jvUD2m6Y6puf846BWMVNr1AoavKIqT1FjunAvu68ltSdIdU3vD1EM4NSt4XS4BR6P4vz68EPdDT0RuvlAFwswB4PqL1qxZSXpdQ9MYLY7sE1dcmwE6Me9DMvhmUMsIv6p5MQaNf1Bj7YM8vaowDHcjTAdb7Q0BHc/Af+yP7hfVQzJynbHfsXU0psdpiFEwLIR9IhV6KfQKA05S0GeexLeiXwnbR/0T9qXmFMmKgWjHMguBCWC/G6K7rFQ66ZzgtrrQAxuD0Zf/p/LauZ0LpMDEvMMCaoQYvALU8XZ+Tdjz2H+K8VStxs2Uv9G+tUzpmuk1l8+eiYLuvreOxp61ynmpb/nYAFWMKQpey/zel0FdoU/YtFjia/t5NQB0lFiBlhh9CeJ4BOIprRJZQXzGaCwJXY+KGtK9yHMfbZVNzCrdm7G/iGlJoJuVtqYjOkLvjFnNIrGE7dR7J2Nk80viGxRxC8P3ZY1GM/erRW1lsx3INYvpzRm7Zu2attuY1MVdlePc17OifUovh1NhT53b1tXXvJEZiFl+w6IccsrABW/dmxxzIMb7zaI3uH7yDtVdGTt5X/wqntySNdXjLsEeqJ89/SnuM9Wldcs5/yxnkDB492tnshdOBzxG6VilDRH8VwL8H4B4z/5wvuwPgbwD4NoAPAPwpZn7sz/1FAL8AMYr/OWb+u7785wH8MgTd/rcB/HneTmR/oAMd6EDbtAvm/ax9TdGLhIdf18/h03egA71R9Dp5HRos6s+e7P82pf8z922eRq7cFMIPxIDvMY7TNd/YvQkKbjCH+B1PXW6NHd2yp9orEpfTgHiZchsoUCtbwVyT//uSJGSu0WFtKrV9vkRKpn0GDxzrYGfVqAyaysxYKJ+AbcVZKrXkyJQCvYL03Ngnq7JdIkhO1B+vKW+7hYIp+03mXqJeUjRMieoBeTmZ8vrb44xKJqsBrt1431VAygQJ3x9bQn/aYPMWw73bgRSLnKwEzSJ9poiY8F+OlXJ7ETI6OR8QEeFchpRRHilDHFExGVImlo9tKiUGZ02MwWm0yqBSFsbpeK5VJrYBgJps1kdAyjimWDcgX1SinfhbH/4cZrXB//HtD/FOfenHHZE6gZxHylhWONGbeC5FzNSltmmCLFKEyvY3rETF2B2al5VtMQtpJJN6JWompSmkTCiLvwMixSNnHBMG1luol7Wt471wrGBYRbQMIEiZSrmkjfR72c/wvV//Br79hz/FvBrieevy+RinIlom1vGIGOsU1n0NpVwcI9RlAM6pESXjkTHOyUuVloEBN2h5NzxSpv/+5NId6CXSTZAyvwzgrwD4a0nZLwL4+8z8l4noF/3vv0BEPwPgzwD4WQDvAfh7RPSTzGwB/DcA/iyAfwJhVP5dAH/nRV3IgQ50oFdIL8vH/SY+6C/Lr/5V+us/y1hfxpgCr5N2xQB4EbEJXmX24oP+7lXRL+M18Tpcawzv3d5CyWQKh8S6nQabJ8eSzbAU9DMBfRTMs7KtfpG03+7raVAyW+1CnakyBuoVS6bDNIldqiAgQPWCCnJNPsYuQX9LSYGiXjqngHJRifJDScB4KMA2nK8pEprog4wgJvSaMJy6PIPi1vz2oF5iXZ4+X0I2pmgfMia14u8yV+/adjIUw45KT4NUuMm3zoUFCboUHxuGCTDJQEGHQAAZQr0UpMymaWRKnIFppueR6tus9B3rF4gGKtplaBDevj20Y+3KdgDG7JoZqmGqn6JOMnY8R0ndYrzZIwejCP/L0buSubToOwTpD0kP0msq0S/70DBjHZ5uU7SbOkeOxzUofOu3+p1CrsR+eee5nfMviByP62B5XO/Qf1j3clwH/OT5E5jTO+hK3i6ZVymo52vBuB1+3+Q9DVWt2+7L9SNv74DH916hS8aBzwFwA6UMM/8vRPTtovhPAvi3/PF/B+AfAvgLvvx/ZImC+H0i+h6Af5OIPgBwysy/AgBE9NcA/Ps4KGUO9KbSyxR+bygQ7o0PMNVHKWyG376fEPQtHG9ZV0NA2yJgdhyytFpOzScd01qgqvI5pVm3ALB1UlaOF1I8T43Z9cCsDZMahQdv5WUdAnJbCYo9a4vg3kmbwrJJmw68aKGuNnCLmQS2LiypGWPlRFESUl+ncw2Q5mhVTuGtRjZxrscU86lBR1mWYNRvSTBqZRnNx08wfO0MtlXRVWEcI3Q8IWyRMApgwLXj5Jtzg+Gogqu3n7PAqGxZTZPrJgvozqE/0XFj38l0ApGRSq3samBwsguRGefNFSRmgJL/1crCLHTMTBXmRo6hNw62UWP/CYMEosg0kWU0Dzcwt1q4Wkn2Jg89jgxsiNtlGWQl9bdrfcDp8Hz92v+6vSgvgQi7GcGXNuZLRowQUQtRfPw8gIcA/jQzf/CKLm8nvU5eJyJl0vlcg2iJv3n85gWGmp4lttwepW85l8DQ35husp9aC2i9jdaZmmMZC2bXOPtQN7vWqNwriiQIW7Fk9o0D+IxvehvFM4XKQfH9Lt3Z/LgZoqb4Rqf98VYf2GqX7x0KW8ouYDJL4xYSZ/LcOOa0so62kTa76k/Mixji7haRRsjuCyuOe6TugGol7mTjGoU+eezXx8+JiBz/58hl65edDwgdXx4fAZXHmSGFiLThHcic9FYDgtIJt44BVMpJe6YMsaNVHkMmjXuSImVqbbfi2IRxv/vRuyDFeP+dJzhuuoi8CPWkLxfRII4Ji6qPY6QUUD9T53ZRGselnHuKTgFypEtvNRpts1g7Kapksj2reC3ZHCbah//MBMMqzimUd1bH9S7jvjATBqviZyV8dpgJvakw/OZtqJ++Ql0LsiggWwIxE5yPAxPLkrgw7BRcrwFiqMpJPQbAEj9GJhNeOMRzyQWLQpMBMhRdL9VA6P6vN06r8Fz0OvicN5WeNabMu8z8GQAw82dE9I4vfx9iHQr0sS8b/HFZPklE9GchlibMsJiqsH92z2pRvi41dBrIZVfMipChaRLe6zd1n7qbdgS125k1CpjO6BQD0enkVMGABPIpxMPYbCVzFQ8GNAtpo9UYx6eqxr4iU+Dn4Nx0hPaQVSmUDwao9DQ0OWplHdDUnoGgCUE4vZ7i2slnclIEtR7g2sqnBVYCOWbRXKvNADertzI3MY0+tbZVGZSYNaKW3VWUWbuqlYNZKCkPzELCMIWPW+hDGREA+2PpgCxi+uKjzy2WX9NgJWmL+1PaZjYg6ZFdLW1YJ+UdcPqhxeOf0DKeBU4/cFi9O6aVZgU0lwAZYP3uyISwD08w/4KgemD5TYarGOQIrDljtkpLohoIrkk0+ksFpwHXOrQPNDbvmZF5cQToYGaRi1JXGlwxeG4luFgYrEoYIIacqx0wKCy+X2P1LSOWqsbFvqAY6JWM4Qj1E41qSVj/WDfy1mFzSh/ZUG4Iiw8rrL5pcPL7d7B+h2HO/HvggwVmpiEHqE4Wz/l5kCGQlXUTZUihxLHyLOgNgRxg5uPzBRqP9YZw8kGLix8lsGJUa8I7//wOHv1Mg2GByEwGn/SoVAjXE+5PmH4nfdsWMQ317IGW56zCqJAI9e1ohQrjRN97ln6VYVQVYVj4FNNblqzQjuOYAKLCR9aCQQMis6DsqDwJ2bnC2NVqkMxeJr1AAJahOwtdKREWlXwvQhYwuVf+/liGulhJ5jwl3wZYzt0NQgYsJ98lsj7bXeiHSBR+X136ZbxcxMgvAHjMzD9ORH8GwH8J4E+/kit7enppvE7G59RnsLePsn1TBMSEEffPNACwVsl76ZUyKbKGgBJlE/rL5J5EsSv9jmNFwTkR2tNz0yiUZO7puWTMLaHcU712GOZq3GO3lM1+/6tkD5wU8AlbcyvH3KVASH/H9VCAGqQsjJldH8Z25TH5OBTVBuhPsLWXb7kj7eiHJ8puTOm6P0c3wdC/1dG+BuVUeOJ82Aum+mTa3tM46YsxZn4I+xMwxmhhxFkrC1Rri+ZCxWDFed+UzTnuXRNjZ+OjqBfLGCVaZvr6d/THRVkwFARESHk+Wc9w7JJxLcucFMuS2dBXOl8A37kSvsrO38LaZ90ckUHJveLxGnsq+onxeYp7u4WK4bzPrM54EVSWheOUn2DGJlnesX3RtuwDyBXYU+f9sfb9VXBb9RbwrlPBsFn0teW9GuflgP4jkX2m5pDWza7PXVtn0mPWue2ynZmYHB5sVtPnDvTS6EUH+p361vOe8kli5l8C8EsAcKbvsmpno+Uc2Gnhj+llvcUlKBNCSjg2BlRVk4qQzNI/9TtJYwtrxcKfvhhag6oKVFejomIHQoBXa/k9n2eKlDBvTKXYTq01KdJAEWiQXYjTF3vK1cN/vFQ/SOpgpaC6HtzUUFcr8OmxKE8o6bOtCuYuOXYOUGpUooR0aqGer0vrAVxrcO0VVp6RjAyhk1TIbl5vMZKTlK4JF3WJPEqDx4++txzSYEHaa6xTCxjghT+GGlLsNMd6GeLBn9OdhWv0tH996DeBNSrjAMeZ1T28HdWTNdqHkkxYDRaurbYYP2KIgOiVXul5ZRz04xUWn3rG3jGqR0ucnczgGh3XSW0MiBl2UTwrzNDLHrAMe9rGsmiR3fW2pmkfeUyPzYqg1gb2qM7GCLD8OHTfCz8UnvmoYAzMPcWNnjWB2KA6X8KctVJWFUKLM/JOMEN1Hcg4DKfN1ju/ZX2ECOP1xRX6swbto0sMJw3szCtSi+wcISAbWQsQRrQJh1TjXkBgzoWA5H4BgG2Kd5m98DE4zO73qJcNWBF07zD77Aq3mlMYL7ik1kLeYvhDp3KsjBzbJswLmD2xUFaUiuMkkDB9kq48U/j4OvHZTeYOXz+uJ+cvp0CwRdkY0fQBCRN/j+8ca0oCMDLqVmE4VnItNF6zKLkcXEOioKrG5yb0r4w8y2QZs1qju9NGeDZZjmuPRJGjrCiAyDq4Vo91KwJ/8mosSGXcjVcz5EtHjPxJAP+F7+t/AvBXiIi+ZDHmnpvXyfic5h3Wnz6cHqlYljTF7Y2WLE3Ru4tukjlg31jXIXP29R/QP8l1hd/bdX0/KQ84JWhsjX+TddrdzzOt+dPQc2RueCkZN9/4TBLPRsevewJfEnpO596npn1P8JdpU3gq+hJsd69sS34NfM6bSs+qlPmCiL7uLUdfB3DPl38M4JtJvW8A+NSXf2Oi/FpidnC9aCGvS/cYla2OQYpKd0hf3uOZ4wn4jYodT2sqrQX3Q+YukjUP8Fev8KB+2FaaFG4lJSMQ+0wtaGFTDgqeyfSOIwqIrYuKHzYGqCqZt3MgpUQmcw5EJEqscsxUKeKVRDtTSgKgwYileQIpMwrdDiqsUUDCFBbDaAnz7gtjP6EvgCsFVykJVlXLcdTqO4Y9qmHmOraPLhBG+rStGvuEF/yCUltTJgwHy56rS6QMxl3NI2UAQbOQA/oTrzTwFh1XAceftbh8T4IBVmtGd8v71sd5+j56wDbSZ0hTKW2Akw8XePITCq4SIfz0D+ZYvqcwHMPDfBnNuQi663fGNWQtmoDF50cgAyy/6aLriKuAqCkICJlwbQyQoRElwgR9peBqBjeM5qFG97UBabA9VMm9c4C6agukjL/YOiBlGHBKsjI0DtxrLL4/z5EywVJDADotY1hC/biF3hA23+qzMbPnBl654Ag0aMw/Psb6GxYn3zvB6msMc2qTeVDshywVSBmO5UExIL/zZ4jsGGeAGDAzzqU4b6HTa4WTH8xw+W155vQGqC+P8OTHKpgFoqIn+JqXSJlU6RGePTjAtf68A5RV6E9IlDL+HYloFisKlPDsRaRLcvtkbM79zDEel77VghQbFTfBJ3zsn+N7GNyQRgshQ68M6qtq23WCRdmISkUkS1QC2tDelzsHdbHCfDkDKxWtbLE+swQTBeR76NEyXOnRpSJRWr8KekNgvS8SMfI+gI98X4aIzgG8BeDBy5v+M9Mr4XV4MDCfff6Cpnygl0Fvxmt4oJdOz4KyP9CXk1JFQGkAD2VTyoLUyJh6ZpReGgdFw43pDeFzXjs9q1LmbwH4TwD8Zf//f07K/3si+q8gUOafAPCrzGyJ6JKI/gSAfwrgPwbwX99kICIF1dQjQmQqy4BHbISYGfS0SJkyDkdaViJlANAwvDykTDm/IMAUfs3Zi+/dobiu8nPlfy900KYHt+IqRJsePGtAHinDWomw0g9iJW+r7fHiWrFXyAQETGIpD4oVZqiN8UiZCRQKvAIoxG0IFr0Ctj3+yP+P7gpedh4sVIgtEoQ851EwG1HuqT74ZYxfAfLIhck5hnoJOoUYUJ3ZRsrQKEjGGBTBam8E9ihIGZk7sSh79Pka809n0sXg4Hy2hi1Ej5m2YJF1UOcrnP7+cUQfqfMVbi9acFONwmQ3iHCeImX8/NVVD3IObtFs3aOdVNQh41PqaQJtzDjOxH0ly6BhJfW9m1m6jgByNIiWPvTVOdyRxJQRKHKohHy9Owsa7Ij8KadOQLmBVlc9zHGD6rKDm9eSNQOI6JuoGIxKDI8mSSHR7BUxiqbH8etEDNhGJQrA0T2BLNA+3GD+YAb4mCqzTy5w153CzlRUEAKILnIy1rgWUoCIFCGXI3Oac4N6pXOkDMZ2QReXpXoMr4JXyOiNS1BCybjZGsuxq8jPddQ0UqZgpTymTOI6CAZqRTALnQVwDHX1RmLjBLen+N00PmZRcItyQKMUzEkL14jCVgX0Wbhu/46Jkkjcl1xT+fvi5Bv52StCyrwcuktEv5b8/iWP2ngWmvpATDwJsXxfmzeRXhmvc6ADfeVol1D7KsbcJSgf6ECBpp6LsmzXszPljXB45g70nHSTlNj/AwS2fJeIPgbwn0MYlL9JRL8A4EMA/yEAMPNvEtHfBPBbAAyA/8z7lgPAf4ox6N/fwQ2D/LIbkTJbcwtCT7Cm7vlNikakzDNSBhMtkTLWSlyWrlDGBEoFd69EwXozHT9mSvEE5C/5BHyXdCEoZMqCESnjfBwZkILziirX996VhEBJTBlKY8qUcwjzSFAtcdy0bDCgSoLHlgqM6B5kHaivs3bRLSqtW5Z7lylK+woWbceAR5swvEJPkQT4pFyADS4Lti3nOB5GQdiX6XUFOx/dP0SAJaR+6gHFAAh6hRyjP1IRSRD6XdxrsXq7AmtByvSnNCJh0uUefDyZKkHtEKB64PSjIzz5TjXGlPnwCKu7CsOJCMNOA80lQw3A+p1RwRRcceYP5Nzy6yRIjyDsJ8JvFlOGSebTcNSeVGs5dA3QPgbWb/MY/8QiHgMi4OuV3Esz5xhjB0zgiuOaipuQlJEFjj86xvLr/ppq6YicxF5RPcXrqZdAtWQs36eoYUiRG+ONDWMAi8/nWL9DOP5khu6WoIzSeWT+3E7uB4AMGZPe8yzqfxjHsSBXWNYpVYAENIoeADIN1ndFOacGRvtFg+FUw8zUqOxg8ZfPfeRHhAol10aO/TPox+jE1UxpSuoGpdBEsFCviIJHwJB1UBuD+qL2bQJiytcP7gDBLzwgV1RR5ucMYESpFOXkGBgM2kpH5XIkx4AxouDe8scWRWhUzDMDXYc6DUBdxgpL/b29Mr4qlWqbZ99HnppevEzzgJn/+FO2eZGIkdDmYyKqAJwBePSU83nh9Lp5nQMd6IeSXobwuo9nPdCBXjZNKWzeVCoRP6Ux+pXN49UO96bSTbIv/Uc7Tv07O+r/JQB/aaL81wD83FPNDiJsU+0VAzuUFeSRMtE6GoI4ht+hXojZskvpsY+cG4PsWStKmFT5QyqiZGSuhXImRX5sOmk3nyHNjlPGwrnOtSmeNx5KXxWolhIRFISNvgfVXgESjhWBjo/G/kMgy3r7EcmyI6SuS+W8EpQPV8m6ly976Tbg2+76nMXyVDgLY/VDdDWQ4L8q1qNugKo0dLimLKaMF96qCQt4QESkKA/vrpbF+Jn6mKXHfgwEwTJdw02HResRKt6lbGuNABlzCvXhHNB1OPrVNhM+T7QGVHJNzo6IrLL/wYCZcbc8Nw6S/y6E9hhzKe1zF8Iq9pHECNjh0z8VJO3u9Awn5/r2tVWfYzd4yb73t9KhAMz/5XN0NrHJNs/RHSA60fIOT63mV22/5a9ozIU99CIRI6GvXwHwHwD4B29CPJnXzesc6CtEU0JNgbqdQuJOIjyep+xNoNc5j2CMLJN4pGXpfyA/fuZhqTAGP8Ua3GTsCeTxzu4CP+iPA4UkH9LfBAJ/a8wd8lfwUNjbds+5Uk7KFGrFmOm92RfLceqay3ckjqnKxuP/69a37MtYCQ0x9R6W8lwI/zDV59Q7DYwyjXMSFsInR7mRAiWVdyaDByeGMC+f0Gf1dr0DvVR60YF+XzgxM7jr3mimPnzreXhK6+ly+eIn8zz08LUbKw/0VaSbbBq7FFv7NvqSCZ2qP6Uo27U5P41c+CyM5sHf+EDPQK8hJfbLRoz8twD+ug8K/AiSvelAL5JetCD8lELqtUJoKQgDOwWgG6GJoyFqOx7fZN/7xqVtg1pE4qZj7EJjpIJl8Z/Lc9GAJ39b6bavQQ6nWSRjHcJ4zrdxMWU6fD1Et++xbnpe+gnI4CzjVvxP2W9B9vp56KIuxv556z9Nn0ujCvj+Zw8Zm7sEVyObM9kiI1dCXHF+fTz2L//HZAzNuUJ/ywlSlyCo4BATL9yXLFW2T38dygDEFNgEkGJ/O1iOk3TZRAxjFOragpkw9BXmC0lDrRXDMfDk/Ah3bi3RVCYuYZpe2jGhUg61HrMBpimuQ93f+/RdEDF+9v3PAAC90/jeP/sWjn/qMe4eL3FU9ZhVAxplYmrnzlaY6yEZz/k+R4NElnYbYzrqzlZotYECY2kbzHWPisY5Olb4YHkHx3WHr80usvul4bL01eF4YI3zYY479Sg3WSgYp+FAWf8A0LkKS9PitN7E1Nydq6DIxXnbmNZ6fNim0nI7ECwTHCs86eeoyKHRJmtjnMb3Ht7Fou3xteNLOCbcWx7j9myNtakxrwY4SHruZd/gatPiztEqjhfmYp2CsTIf4xRC6vOQEpuZsNnU4C9mqN5b+TKAnb9KJrCTenAkbKYj/wcfP1H6Il8WkjCQBcgAyggKfvi/Pa/Z7uZ0iCkj9MYrZQ50oAN9iam0UuyjXVaF6/reV39Xf1P1X7ZFb9ccDsqZA+0ixtNncnneIV8yYoSZN/BKnQMJ0ayF/vGfmhbKk/9bgrn/nwrnaTrrQFEZoJC57ZYxqJ5KWE/apeMwSeYyF919x/mp4LKrCPWVRXdLg2ySFQ65UJ0K99IuzDuZf6o8SAX5tKxQDkymxCbEmFZ5ym2eWIfxN/t+QtBycfnl7esp5pfWQTafifKp+lNSjFc65O2S9mFCxInywMV6tKvPcBiuKXqZJicdSTIAR6DKbbeZsJtcyxJYAlsVr6n/pEb/tgHaRABnAgYC6nB94c8LoVWKXEYWsF8m5M8PCm5N4IZFGcMAMUn+6HCjKekn/R/HpXH85OdWPT+s3hAGnwig6gmbZj5OywHzK8LF0Wxr3ODmXdzSneOcPASggO/e+g7AkjDi3e9bLD+4g3vNnfE9S5YjPM9p2VT/U+czV2+f3TCNxaeMuOnf04TvVXn76fTo8k9Z3kbm+z5jAoHo8ox4zybXx9cJiQq2rqW8Xt93TN5QsosMvLN2cHqBJ81tkAMWhrGpbkM5xiZ58GeWMXcMrhYxf0YQxskhxhyMcwjvNcI6OKj1csxuOjHP+CBmLvTBtb14J0IbN4Z/IMv49OIVJTR4DXzOm0oHpcyBDnSgl0M3UYQ8zfmvKh3QMwc60A83EY0BsFPiwFR7QWNX813lweU6Veak5MY6U8RP8U3OBKOQ7QzIv2mJizINBou29rHf1Pb3P7j2brkbJGPase+d9XYo76fqS0BwytdtYtwphVgMjK7TCU7ModBOcFknKM5KRZhXmMU2W+1D/+m404q0ncgXlfeXKb/SMmzXiQHugRiLjtNr8XWjQFuOH689LwvrCnikzKqCq6vsmsj62Ha0vR4xg2SyPuP8EtQLQ961sH4EMFgUTUEJ5pEyRAA88kWUWaNCa1R2MZTieC6gZChRiA1DhaY2YAB9X+F41kOr8fyjJ8e4e/sSlRL0iPJtU7SMIkatbDwO/1Uiqf/rT76OqrL4I+9JWK/eVvjXv/pjuPMz93F3scRps8lQMgqMzgWkjNtCxKRomTCm9tK/hULvKjQ+vejStJjrHq3/HZAhv3/1Nk6bNb7ukTKOKZ4rrw8Q1MiTYYE7zTKWhzYWKo4f5mCcxtrWOKq6WH9ta9Tk4vwzhIx/QAIiJp1riZRplMHMI2UC+sU4he8+eAfHsw7vHZ/DsMa95THuzFdYDQ3mlQQgZCacdzOs+hp3FutxDCavmyAYKyEHjFUZWsgygZmwXjfge0eo31uCCHCOwE7OMQB2og1kXw7/G44kk6kJLwfFDJ5k/XFAyvRA/8lBRfCq6bDiBzrQDxPtc935KilFntEn/Ma+37t8ndnlGdUSv+/Mnzv4YE/4FLO1o592yOSWUtFmL0w/zJUdeNOBmsYHxC3mOYkcmoD4lxm0iMDDACgd/dPZOgnsPTXnEP8rv4DtsQOFuE2h3r66u2JoTRBvOpljU4/1gp9+iNHFvB2nK0z581foa33Q0331KSpfgJi5EP5/EGxvgpABENxX0r5ZK5+VzBftErKnUDKx3m7hPiID/GtPVo6dTuaEXIlSrxy6My1Z4WqaRN+4JNB8FNZTtE8pxCdzjgqGCUE/Q94k66CMlIUA8jdByMS23oLOmvNxsraJEiCWc7bOkRI3GLnW4nci/IcBSCVl8TEZ27GjsSwZK7rgpMOnx+m5HX4G7BRIOTinoNRupMxOyqLVj+ScivPpP5rDfn2DqjGZu4btFVTthL0JrhteKFUJaoeZwJbG1yMZj62C7Qi8sCDFMt/05bAUUTMBiBDGHzvx3bod+1SKfmBADYReQ5IG9ITzdpwPWXGnelggZShpH3UjlJTFMShe4skDeZd+4+ynYt9v/4HD8rO38WHzdqaMozg35OMmSJMMdZKNiZgEIcxJ0C35GhADugM+UcBvVhN9AmNSgtgmQalgHLNMpjD2xTHpwiTSB8Xjlj3iOZJElBbsjxlLYOSlQz+O8fXOgXWLJ9WZrLtx6PQZKscwySNx4hinhuHq+bZSvEC2TM7XMfT6AtZnNyXHMdFC1i7ZVygty+I6TpzzaJlPz6eT7LwUOvA5AA5KmR8+ehGC9w0F3r0C7lQfQbAtgpjtFGaLAM+R0nOBfIBcKgMXp4GWM4taioOWeUWhkxnk+2bnZD66CGRsDDBroyAZgiNzahEM/uTWyXy1Bl1cAfMZeN76FNB+DpUaGVGf8neEmlNkxFVvAeNgjxufrlnqj5axhDHXIwNeXQ0wRxVYEZqLAf2tOkt1TA6oVhb9WQUmoNo4DEc6s5LpgVF5RlsPjGEh44qlqmCQE0pTMgdoa4CzxwxUKVM+wfACwnQQJ8x4IiyEevUlMJwC9QVQbRjDEWE4Ru537i1lzTnBNpIhydXjjkEO4MqPwUC1Jpz9vsOTnyTMPycsv8mSAcsAxx8Cl9/GCGn3kOP6nDCccMwyFdewI5z9HrB6lzC/x7j6JsE1HNNDB5hxZDSmNrLA9w/CHLpa1vSdf25x/mMVutsMvSGYOWdrJBmVRDjSnVgYs0xSLMxcGCPQ0WcM2wLdbfFJri8Zw/HIEIY1AwOqR7yvcf6xDucpty1Qr1ieHw7tKJkvR4h1zDblcoZsLGekLuftww6u1ZJi258HhPGqVt5/3zi4eSUZo4Jw6aRvfnTYOg/0AolofMZSCkw/4AVCIH35CKOCYpQzE6Y7MOVTfYdx07F28QclIw/EzIcp0x/QPmQcWKlRkZQKBqGffsCiqWX/01P8wMR8JuY3heahMmj801wnIHNPKbmOSRQNkCGFsjZTc9iB2Nnqo+yncGeT9mndQjmX1JXvZKowoti/tEl4hKR95tLm25V7cEAXhe9zRKKkiBxgPC76KBVrabuIQFLA7NyhO2nhmnacGyHGlNm6diDyH1l/hcIu7Ef1JdA/bGTPC+xcxn/kirrYR7be6brxVvtUKecqRPcqpwA3c2MdAD0Ae2YB7b8DpdKNpYySuDeyvKOmhAg4/3wBEKP9+gpEDOcI53SC9Y/2qI96NI2NaJyggLNOodLWhzySMhX7LH4jJ8sE7RE9vdVbcW+YCQ+XC9Ta4vZiPd6rAikTYuYAgpRZDzWOmj5D0oS4KzpRBDomWKcwOIVZZWL9zlRQxFnd4IKXIlamwvi7gFLpayhiVDqvZR3hyaNjqNrh5FhixSxXLWazDYZBo66tHw/ouxqm02iP+iRWDOKL4pyST65VCYPj5+sI6DSa+wv0XwsGJAgKxh9TRMYg8pwxdowVPg008llgiplDyYpCTA3A8NcPgX5fNb35nCUBVGaD2aUUeJpAcbHNDay2wVrrLctEBLYW3A+gWYuYbamc2z5hv7A2AxijhSuVW4eZ41h5muvdx1lAuam6YazBgOdtsnHTaKkrywITlpo+iMCVGushMGcqfiNAXiFRjf2xT8ObbtjVysDOK7haCVNXKQ8HpvjV152D0wTXElTPcA2JNS4stRMhn6xsWk7LB6l9YkCWsXqnjpukMj69dAIJJi+gOU1RA19tJHVwfyTj6A6wM6nfXDJcJcI7e+GRLDB/7LB8V2XQXTIcU1mH8QBA95Ii2cwJt3/3DOu3a6zeUXGOYJ86GXIc4b9+/VKqrxh6A6zfpmj1y+p7UonFAQzMHlXoT2RuR19orN5RcDUinJEc0J4rbO7IPalXKgregNSp1oymJnS3SHylj2hk1AvliGwQiRCe+AGTSeCrgfFIFRDF7yB8h1TYKWVR5lmEfDMn1EsHvXFwjYKZeaVZ7F/mVS3lvnN4RtjPs1h31TFm99eYPZyjuRhw9oOQIhpoHm1w9sGssEIxqpWFbVX+PjuGMoz6wQq3jxuoVY9bvzeLSrXAsKf/J7mHuJZ+rl7JWX/2GIsPT+BmFZRxkho+a5fcB5sw7xj7CAxyamFSVxtwrcFtLWvXG3BTZd+ZeD+My63+e5IYkXOgwY4CUvrtSa91yjWg6DsTBolAVytwU6Np6rwNS5a2cMx1hSxLQlgf8+qyL+0wTB/oq0TOgZYbOS4F7yg8U1YW99sUQRP27DTgrWLZo2+ArEndYva60YT5ANl3IsSOICvf/fA77DXKjAqNoOQnx3A15WOHZYnCffhPxe/kvyrnkrfZp9iPwr2RfjJDAuX9lqic+G336IA0PkcU4ifGyvrDRB3ffmvctG5JW3X2fDxox/8J4h39UIkkCXziRBJL6SiMw9l+PlkxfK7tOMb8c43uLc6MJKLsJ3DNsV0UShm5UovH+7R1LV44NUejwmMrxokXcDPkRaInydpkmpqkDsY6IYYRWOK8uFoh5WuqJcPO68zwMLbnLZRIOa/Qpr2Qiv3xcez7+JMO3e9UcNUi4wXDPPO+Oee5smPO5gwGqoTvmk8gPxQD7/ZygvVxvB6d9un8rfNtagZqxwAdZWM3CR8Zx/DthVeaRb6mSZXVwDSKJDtfljuAN0iJkjpfNz4IcVAy28vRyLw1v8RwXGYtjYcpP5P24fIsrfvqpeTPTSY7nJjDh6vVdr2XRAc+R+iNV8oQSCD3aercyYp7dpSyzY6+dilv2DP+mfLFTlYtxil2HUVASEyRPv9FGy6tRsySgvu6VH1pyr+bEDuwdcBVNaJSJqple3yBWinLQvnkWgYhrUS6hHb9gDqkzk7PpX0FBVlaZ1dK7vR40wHMmP3BbCz37blUnhWCHhkr96TxWmPrJK01AHS9zKHSWXva9Dg+mY/Ms/KMasochzW38oF2TYX6i3O092Y4+YFXlDn2jN5odWRFOXOaQM51Z0GDw+JBLfyJJrFa+U02DVDGifWrvrJoLjVAQPPEAFwLhDvZ2JsrJ20UoDvOGRLnkTKdw9ALUsb6Rz3zL0/GDwfCAFNkPNSAmF2hRMkkzfIyFjQLOYp+4mL9okwp1Zwr9KeM9kkFvQGGI2A45SSgJIeGaB7XsDOWudT+eokkon01QnKrFeHs907w5CeBxec1rn7EgbUwiscfnuDyR92IiIEwjM2TBv2ZA+t0AxVl1tl3W6zfIcweMpbfEIUcK/j7ydn1bMFvCeIn7NcRLOurDPDuP2vx+Ccq9B4pY2ecK1hYfImdZuhufG6iZS9YVShh2Bg4+vQYZg70twhkRDE4nFD27ERIdIqUKaDJZX0yI1JmtJZSwkCNfeTj8MhUBubVeObJPy/t/QZuXmFYVLly0DK0R8oo42BnFcg6ef94rMef3PAb+yLoEGfoq08BbTl1r0OZzc/t4XgyIYOmeIq03119lO5/u+qngkzYA62bdidM6rIxmNX11n67c5wS/XKDuU0LH7uuw419pbzATdqXws+ecXZmf991ffvu05412Ztl/mmCak5d297qL/B79ZRjH2g/HRW/33gB8Ia064nja86/NvqS7Ol7vyEvfrBXN9YbTG/8O8kAeEgiQE9t9p52ImJ23Wzv0hKr7VLSOAfUdT6uVwaMrisTigAAJTKGphQGob+wwaZxJIJ7S+oOk7adgOJGlExiOStdguS6vLJn1ub+6okVrvRjjwqMRPsrLjn5nDKkjgJgGRysADQiWALcmQmorwzMUQWnKWZvEIXEiC6p1g6uIriGoAaGbdQo7MMrHrRH5ngFAhiYPRTr9/qdOgrzyogve2btYkSkTMgUUW3kd38sdauNoC0AoLlg2EZSNAbhkQwwe+KRMlHhA6hBUDUlrFn1DN0DZg7c/t0K63dqrN6WNSX/6Lt6nB8nVqjSglJfVag2jPVdJUigxCc/25k4ubcMzB4p9KeiGFlUhNXbKrrMqCG4gqi4BkyAnSWKKys31lWCLHEmscoGgdklj6/liAJK5yHjeUWNX7fYRyji4newiCgCWbeFoknb1UsDM1eolg66d7Ctgp2pLSQLANTLXq5HiYJCGc6VW35eundoHqxw+oMF6ose/b9u5NtgGPX5Bm/9VomUAfTGwDU6Ua6wV3o4VA+XcMct1KqHO555N7MRIZPSZJDO4IoThCCvDNVfPMHx7x3BtTXI7XYvYKIxUOd1AhMzaNWJcnNWyzUMRhAmiWvDeK9c/L7sCzAqc3KAsdvfsb1tUiXXjmMAtO4ArVA3BTyXGRT2G8fQASlTrvtwE638gQ50M+JNB/u733vd0zjQgX64aZ9x90Bffkr4oUn+5qZ9HOhAL4neeKUMARIH5DUiZaLCJEXKODcKANYmUr3XAoyDyT8fWPPa1zlkIkjJI2VgDG6ElJmiEIR0ol+qqvw8qb1WuK3xpuaUBgadoinhyhhUSm8pkDJ0jlfKxUCoqcAWrqlUdgHgfgDYYT6b5WOm9zRQGDvMPdz7EI/Gx38BMCr2lBqtkd4F4mQxy13Ygu986haGIJA7cK2hHl+h/ajB2dEsE1wDwgZAFNKlMZL+xLpPncXxWRsF7JiO0NeJsPMUKbMyMPMKrIDmfEBzOR1TRvdVRMoMKxUVNBEpsxI0jShyZNygYBFXNOQKIvKKI4woCKaADqGnQMoEZAdt+YmnCOvmQqE/BZpLjWqVxJTRSV+e6gvt48kkSiIExR8E5cIEvQFu/X6NJ99RmN9rsHpP4uioATj6pMHVN2k7psxFC3NU9OuRJGe/P8f6bYX5fYerbygfMBOT8XImkTIJ0iTEvyEL3P2NOS7fr9HdJokZ02BLWRQC6el+jA+QIWWCwicxQS0eODgNdKcK5IBmKfGE0vmFdip4BqlkzATdkrmkOUa1dpl7YbpWab/BtSqPN8MxBkwIxBeoOq/AlYKb1aMiLkCmu8F/Gx24GRVYGcT5FTLvB1jvgQ50oAM9Bz2vIH6grwbtcg19lj6elQ5KnUk68DlCb7xShsHgvt8+cZ2Lzg2yqGy53UwpfEjix5DWYGvFbUmp+GKxd6eJFnlVmoZ9n7synaRIGuelX63HuflNhIlAbVu0TZUPxfWmyooS5RKqBKRM2+SW6LLdDtRMjCmTWtyBMbBgoiyQmDIqKjyCO0Dq567XA9ysjjFlQiyNqFRgSCBbTbC1gjKCPOFkrBgPJshPPrJ9czEAjtG91UbhO/q9p/7PAQmh4X2MBQnBimAWgqjQHcO2MqdqKY6vtlHR3UNZRnNusHmrztZ89LNHprdThqE7Qd+cfFCjP2uweUss+MpHfHcVReGxvI8p1SsH3Tl0tyoZr4wBhFEYTgV4NSjx61dBGTKiGSQAGEMZ6ZuJoDfBN2l8lnTnoNcGZCrowaFe6vH+Fx/cGMk+QX8EFxY1uBh/KCItsrlzJnxnsU5KpEQRV4R6A64UqDOC6Kj06HqlTqZLAADmr0lEQVRW+BNTN2zHZgrCfkrOga+WuPUrc3Dfi7ulr8ubDd4J7230U3ZjgOjY7ahcQNfhltZgY3Ar+AyXfsnAtpI1Oc/JWEGpzMbg7j43iQQKfy1sdcJv+SR8y66BnD8VvP0lwddtuD6SFJLpetwEA8PcvYxpHehABzrQV59K5cirUJQ8jyB+oAO9KHpTn78pFNGBXjm98UoZgHKFxnW1UwTFHtoZ6KhM3WqtxHmBKGYAiCLDWhEYrBXh62aT23++FBTScufA681Uq+k+bkAMRIXTjWiH//K1n5gg5F7XxjEUADUlgBakAWAqxs4OIS4IgvMdCo2bCIrNVOEeobH0370JMYDa/z0P7W1fPiP+A5y2KfBEkdp9Y4R+/H/1HJvPzpbJO5IVF/8nm071tQ9lVZbfhHHc1ecbstEdttmC3nTGI0H/HOhAB/oS0dT3Pi2b2heetexVKjdeFJUGxoDWTv8/7xCKwI6fPhNooBS1Xbr/x6nvkTlSQ+9155P+UhozjhZzKOedhVfQ22U7+t+a177kJ1t1c+NxGoaB62r7+Sx5o/A8lyEg0rrlemTG6HzerPfMdaK8RN+mSPKtsWLFG/SZoNmj4fEGr6VkTM3rlnPQnYXaDDCnbTRi2nZbhlN2RBnXj9Ywt2ag3sEcSQxRNbiIlq/WFtWTNfp3jkYjsAPoH/+j6yf9IujA50R685UyzODu5lbJmwhnZd2n7TsrM2ai9MtFP0zvwiFu3AS9KMG0QJq8VIF3ivl8lvEmYqQA2Gaon+aaSiZkyu3lTVcGHOiNIAKuj71zoB9eKgX15+7v6QXhvQJv2m+y+UYjSLkhk5L+UrTwPsHWn9sSNBVhKxOm0vnvtN2Ey3OGNg4Us6o4oK58PL2ij2DQ0yGoP23F7AOwlelS3HvH+HxTmbCiS3KIxZcIodEd2f/FZAAaWYbKEDC9dAtun1iAgfVdcVFWhhP07FgvdaFlCtkJkfWXzSPUx/h7MoMVFemjVYiJx1v1wb4fHyg/BPePwmz4jbE9hbqOoGoLUgARg5SDUpzd6s1Fi2puMF+I7KFDSuWhwrztoX1qah3SSMuUQABqbaF8OmgFSTFdKYeKHCqV4zAr5aDA6J2kjW6UycazTPjg4i7eWVzirN7gfneMW80Kx1WPimQcx4SlaXGrXsWsla0y0CR9A4ADQYGhPaRfgWNZSpoclK/TuRor2+BYd1k5ADhW2LgaC90hBGX4pLsFRYzfPP86Bqfxb7/9W1joDp2rocihJgvHCoocBtb4aHMHl2aGpWnwJ259X64XBC0m8Fg3HW9gjZpkDQfW8Xosq2Ru4420UFmq67VroMColIVjgklg8hfDDK2S9Nkm6c/G9sqnyyYYp2CcjseKOKbmDqOHcxtTofXpwB0TLFM+R6dgfarabvAIeeXgnEJIPc5McE5Scm+ezNDcm8P+6BpsCXzRQt0SbxJnSRJROAI2taSsYuDkd27j6tsW9bnCcMeBiaHXVXTDbx+0OPlgjsc/A5AF7IyhBoL5/70aFcGBzxnpzVfKHOhAB/pq0T4rXzj/rP0+K00peW6KjnmWeZWWzcOGdKAD/dASzVro7/yk/5EI7cAouMfK04J+maoaANR6ACsFN6/yOoWwPwrsQcEQfieTnHLFTc/v+k4mMZ3IiQtyddlBPbkCbzofK88rYhZz2FvHMLdamLn2SgFxMQ4xxkK8MGVFaJdg7OOcyWGMS5YK8RVtKQ12xiwLccOSBALKANZDRetLwM7ld5TffHu9kVhhtuWxnJApJsq4asAo1HNaJyxhuF3GX3/F2Xk4ipn8SlfhqVhcW2m5GVh8WkNZ4OqbDiBAdwTbcB6I3+btJAbZM+5d2cMj9zOME/5LMoC8njxLoRHlVsUSmOTSZxY+Vln+XMQ+k/ZnS8A2DWy9ACAurcSANkBX5XXHsfycbX4N2byTeUaX6xQlQEUbx9Ab4Pv11yTZwAB8osf5KyNroWzyHKaUXN+UG/k+CnHdSnf5NOFB+iyEdPd6I///n0fvTz7naf2wZv/32U/kY09awBGTb6TXtvsCpvuYeq/CmDzVZqpr/xzdhGrHsOk3GTkoKBXCj0KCEpqeDzGgNw7VqkN/qwEY0J2DmTd+XjzOz473bvbgCt1vt6g2A8xCg4mgjI3fv2o1oH68wdEXRxICoibAAQ/Wh4QGr5oOSpkDHehAz0bPo6yYslC+Ttj1DaCuL3XcFzXOC4B8SzdeMLwO+h3G3Bf0O5APdh5dE8tMcKnQtwvCPUWpy2mo610y2bufUl1sdWmw96dwKw153ql7hSmxD+i+Hw7y8bEC7bMcbj2xO9wuqesBIqir52D1inmQddvK6qm5pmURGcPi/t33cJtOjoN7MxH4agl1cYn2sxpt5d0fyjh3hUI7BthPy4MrRTZxyt0t/P/JmHnh+vx5ck5SklcemTNYcKXGAPyJwousA2uVB+enZMygCMvmlivEghtFaBeE8BhkXuWKKMCjYbL+8rZhflERpfJz80cGrAj1pWS0VAPgmqAISxR3iSIrZNDMAtBTiaYJ15z8J55Ayoxtp9Azci55ppRY+b2ZfVRkqaQsLDsxyJezI6hKEC4gQcgIUkb+mAnnFzPUix5H8x5EHJExndE4bntokpAFtXIZmoGI0SgLCkiZBDGjyMXfgSpfZlihIheRGilC5HsXd/H1xQVuNWt8vj7BW+0KR1WH2iNljFO4si1u1WtoSH8pSgYQ1EZNNkPLAMjQLwCgwRlSZuNqnOjNVr0SKWNB+KS7DUWM33jyHqxT+D+/87tYqB4brqDBqMlmSJgPNndxPswxOI0/fvZBLE/HCOMOrNG5GpYVFrqDY4WBdYYEAgQxkx5LPxQRM2vbRNTO4FEyzte7MC2Oq963pVge0DElYiagaUakjI7oo4CSsU5hbWrMKiPzcEmfLGEq0vJ1X8vzRgzrUTfsny0AcI6wejxHfa8Ff3sNZoI9r1HdEkQXAwATnCXwRgO1JKE5/q1jXP2YQf2kxXBHeBi10WDNgpS5P8PJBy0e/yxDGYKdMWggmH9y89Ahz00HPgfAQSlzoK8KvQzo9g6/5p1C6nW+yWV174sMx6DAWKqCwYztc8EzxjvSOrZl64PHaj0KySFQbDq3cF0hi5V1QNeBjha5kJoysb1Pm6M1eLMBHS1Gn+HgO9v1PhVyCG6dMqy0ZfGNgbV9uVoP4FpLqmi/ZikzqHqJ32TnNUAkgaEbHVNLZ3WNQ3Xeobs7hzIMNTj0p3VMtc46yWzlr7FaWgwnOmaBUgNj8cEFrn78DLZJGO/AnCbH5IDm3KK7pVGvHYa5wNAjo+yZTD1ICvUS0g0AZL0l1GcmMmlgn1CXx/O6A2aPLVZ3dUyZPhkEOi1KeNqTTw1Wb2nYmaSQjxbEpE19JVbfkAI+pqoHYmDmkMZcDz7rEsm1yHyCdUfmunxXQ1mgXjqYmYKrJdNTSDsf1iasabhHzZWDaVVMO19mniInFiMQoNcOdibr35wb1PdXWP/ISYT6i/XRgSzDLELqr9CvWPUpscip3it7KkJ10YkV/btpdKWXSwdY7w8J+YD5GQoGyBURSIT3wvVFyhIlAwC96mOWsQwlo4q+CqWAnBv7HMf2VTxSJvZXns9QASOCgwwLUuZiA3W+BG86UFB0VhWwmMPePsJwNoOd+eDzQehX5N1Z/HfFioIiIGUimiQgaJpkT2CMyJcJ5UKpHCCHOJZUln5tK8f1JWAWMsbYn0cKrEky9nmkTNZ/WJPkOzvelwTGEpQMxbqqgbwghew8HAQ9g6IcnF1DjlrIUQdHH1UAActvWP8dVbAzlz+LZf8Wcn4CGZLRBARhL3qjRJXYMDQJ+iWgNAhbwlw2UnkuoGzSa2JhidhnRoQDTjxSJkPF+GfgIkEObfWdIjFKFI6fj+xryO5HQAll68HwGQiB79bvwWlA98CHCYILkP62rim0t8lYKPqObtUT8/T7YcYTIK+XoWiY4T2vUK0dyDH+X4v3ttYp1e2QkaQJZBn/j9lP+r4LJAxh+9kq1rckCpkYA6WK7gLdkyOZGJMxZHZQqF/uz2XiitYyoDxCZldnfl3OpmJGFaSGHmq9hD2WjxENa88D+zb+wSJnY/vqySXM6QyqN3AzYRbJuvh9VN0S6nyFO795DGLAVZJt8uFyuPF6PC8d+Byhr4ZS5nndDK6zLhfC+d7gYfvSQ6fVbmD1ZWOEUUkD8e7zoQ7jl8qE0jpUzjWdX9cBVQWq6zFri3M+bXbCHKZ9Ju4XXOl8rHA+HaPwqxYrlPMC/Xg+ZlUKTGeSyjljREOfqQUqFdL9x91VY/vZ5yvYoxr97SaHfZKcJ8eoLy3MXKO+Muju1MIIOsC0o+BY+maTQ8yuFATlDC4d+C0jDF1zyVjcM3jyY3WEaQN52+g/DaBah2sBjj916E4J67flumYPRbBev01onwjT3J0hsU7J/OoLwM6kTzsDqhVw+7sWD39Ww85GJi6F+M7vE1QPmCPg1vcsHv2UxnDGI9SagaNPCf0ZYI4YNGDL13zMkDQep+s+u0dwDdCfssBxCdkuVp8TlAW62/Ksze/NYWYyXrpRkwOqJeH4kxYX31ao1oBeA5u7wtSwkvUViCdipq32sUZ3y2+oThQJ1fII59+uYL2CJGWmUuUAeVj55i0Fe0UYFt7/X1PccIkZvKEIqy+ZJWJRerBnpnTc3NNrGwUc3ckzOgsZyBLhJ2O0eBw/MreO0d7foFq1cDX5TGOcj+cY1dqKMFSr2C7cb8l8Je3ISnauUC+cjxm8Bgu16jH/Yib1NwaoJDAfGSeWZ6XGrG1FDB/aDEClt1NTB2IGjBfujI3fIdr04PUaR8t1tKiHrHNwjLautvsp+7bhBlLMBEjdlz+W2IHeMIrvH++1GlJSdy85j5RRCqrdE/rdFoPt6jspDwLZVia6NHhn+J+iZAJSpuvh+n4LKYPLK6jzC8yaZttAAEyj68o6YVzPK03OM0x3V3DRqd8p+sZn3dzibQBZT60ypM4kGifhgbJzmKrji/y+mcaeya4lVbalPFDSxxYCBwAIqC86EAPd7UZcZYwZ3ccS/mXLOJGhYnJ+aBItQ9jJP23VC+P44y1eTY9zGOtw1iZrH/gpPYHASfgdu1CwM4abeWYlnDMENE7QOEBE31DUVEiZLJNs6uTPCyLHxVsLyG8CYDzKpvLIG+XbMBMeXywwX3RYNAOuNi3mzYBZZWJdZkJnNRb1EFE4MXaM/+2YJH6N/x0QJiUCJkX39FajdxUWVR/RPiltbIWZHvfBR90CzIRPL05hrcLPvPs5ZtpERElFDo4pxsH5Yn2CjanQmwo/cft+LA+UxrxJ47g0fsyAUBljxqisbbju9H9nq3EuyGO7rE0VkU/WjbFoRnbI/w4oFyY4BozVcR7O30f255mBYdDQIZ4RI5bD1wEEAQMAbhDfNFKce7YzRb5TXbaY3VNY/YgBWUJ1qTCced4n1HOA3ii4ShRUp9+bY/kNoLoi4dtJjHohXlT7hHD6wQke/5QCGcDOJaaM/e3nTTlyoKelN14pQ0pBzRf+R775sTEjMsCjBjLIamqBASaVI2ydIA2mlBihzqYT+Ltjgca3jWy8od8Aywe2lSXp731RzcP8Q5kxcBeXUKcnQFOPjE0J1U37KuJzsFajNnsKapyW+fZ0pYC2ATd1rEvGjr93rBGcZ7iCUuaGSrKgkIG1ouRKXBhpKkU5EiZmV59TwVsDQ+XnRedX0Oc16sdtpu2OWm7nRKCrK1DXo/nCpzW2brzGycmNY0+mWA/zC8EA+wG02mD28bE/mTBqoXliBSHjYt/qagVuanDQfG8Gue/zRoRGReCmyvqMaaFreda4UiIwny9x9OFJwUiOc1abQayjtY51XVtl1tfqsoOd1+BGeZ/8wEkm9yx9DAkZg1hfDnCNgplpYT4TmDXgkRCOYY60ZyIt7EzBtmrCr5/RPOlRbVrojkGGMX+kYxp1VyHutgEZ01w5NJdqHK9nVJcd5g8bmHZkbreYXPJKlJ6hehZkjvcrF+ULj2nGqyR4YtGfMoAdEJV6dp4w3JEpHxnpas1wVY3VOypByoR62/cQwGgpYuBkobB6R8PMvaUuOReRMpcMV49zITMywqE/iRMhljIzJ78eHNcWAKoVY/7Y4uprHilzxTAz8kiZcC2Aq8UaHhVenmdsL53UT1A6mdXNiZIKBFQrBzOXhWjODZoHS6y+eRKVsuQAvbZQBVJmtA5y/N4STyBlAPDzuIM8DSVKtQN9hYlZ9psJ95qch9g+Pxl7Jm1TabhZtXUuM3SU3+dCGZDPAeLKo4u2Oyjuxz5Gheot1NUG6nIlqMugFKoq0GIGd3YEczKDnWmPgvFomYiSoajcD0FsU6E8xNewTeJSxAnyJRHWS2VCNCCUMWV8PBLbSl/1JcPMSZAzxXdcr8XgYtN0jRN7xlT5zpgv4fwg6+/C58draWS+nPWV9hdpEsok5YvPKhADq6/LuukN4No9/fnvc9x7eXe9fKzr60wZFgJyI90Xsn7if9pql8+ZwaRGpCcD8DCm0Eb2I4KrdD6+BdgHjM7nkO9HU3MvjSZTxpasX19+2jm46gggwsI4QM2z20iOsfB7t2OZi4njczSchLg40jePYxTK06yOZUEGJYaleI4Za4znlBO3xm+aDcCMVX0bq9DvhOFjZh1mbAE34FyfFfParh/mN6CerhdQ5BEtIheYZtpt3Yj80EWQ8XbK3TLOYUJLXtTZmdG3pF1K7x3ZbSf7Tt29b5A59iYZTtgxThLAATvGp3Z5fd8vgl4Tn0NEfxXAvwfgHjP/nC+7A+BvAPg2gA8A/ClmfuzP/UUAvwCRVP8cM/9dX/7zAH4ZwBzA3wbw55mZiagF8NcA/DyAhwD+NDN/sG9Ob7xShp2D23STqBS2Vj6uyvgHyRRt/UdGiXVzEsXCDrwvtRzgfaCHsX6wlk4wI7sE8Z3uKFvVgjaWwf0Ad7UcXVvSa7gO/eLrpM/5JDpnVNnLuH3vLeoOMQWfc/IxLmHUZTo8ALChvz1MY1FGgwVZDddUWT2uRktXZtUJ6eYSZioymB66GIMQEvlNeFQSsCLMP69hThp0t+roYhEg0sKUMZpLBzsj1JcWmzuVWPUtw3jXCOkrZ/ACk0IsaIzSH1saeTeWltCcMxb3DZ58p86yHLgKYxtGVMponxXdVcDJx7ewuUXY3JU5z+/LPdi8RWjOAa6A/nTsMygP6gsJUsgkGvFqSbj9e6eClGk5ZxS9kD6/R1CDR8r83gke/XSOlCEGjj6eo7sFmIUI6BLZHRmaJtRNFU3h/PyLGnYGDCcMMogWs8AkNOcVyALdLSmY369gFgH1M3KzgpQBjj9VgpRZicC+eYugO1k7rkZGW+4bYfaI0J2RzMsB1Zpw9EmDzW0VAzxOMVRg739f0Rh00tdV1jN+XkmhO8i7QmPb0Kcy8lyE58guUyZoZApD37pzaJ8MqDa1R8qM6xwYTmBkZOWYY1n7YI3ZwxauUVCD22onz5vEGeBaRaEq4+t5hCDDOKmXjCNzF6QMrXscfeCRMr0Baw14pAwGH+wzVXgmVnfqBkG/TKBo4hhBuDN2dJ3rB/BqjaPHV/l33VjAOYlbkdIkUiYsOoGHIc7nQAd60UQlamUXpaiVPefgY8ro8jmfqrurLBUWgvFiUgBJ30eX1R/RMqJYZ2PgBrMtLDxRoPsVtNaodqGBp8a7hibnm/W1Z91vKmzddKzYx4sRpvLqyXPhBaunaPxUYx3oq0n7npjXIDd/9egNdtOJn+0fnk/BLwP4KxDFSaBfBPD3mfkvE9Ev+t9/gYh+BsCfAfCzAN4D8PeI6CeZ2QL4bwD8WQD/BKKU+XcB/B2IAucxM/84Ef0ZAP8lgD+9b0JvvFKGlIKaBYmo2JSNkYCOROKXnCBliEjSVRPFoI9TSBk4LtyDJhQtmw7UNBFqS20jL1ZIsbgLKXOdIqZEryRKDzIGzjmo46NtpMwuy1TRH2fKnERBM4WUCdWsBWZtjpQZzPh7F6MUtOGquCbmkWlMUTCZIseBjIUyRaRvPa24upZS3F9At4T+gpB3sYR+WKOZNdN9WAfqRRCkbkA7b8e+p5AyYbyUqd51nxKkDA0GWG8w/4McKbO1roECM0gEWnfi0tH6yOu9R8q0jdwzRUBdWEiZRwGYSNbEOtDFFU5++0QE5UAJ1Jo2g4xdadDlCiffPRYlWmKdVcsO3FSCwkme50mLLIAAvw7KM70exFVmXo1Im6ggIuhOXE7sQhA61cqAawXb6i04tu4d6icbtI8XUIOD7hz6WzXIiEuRq4M1bfQjrpcWw5EWiywDyjCqx2ucfNzAtjnUW+Y0/iYLNBcWrIFqI8HSMkWgV3Apw6Nl17cN/Th4ZZ4WxZKdFevlz8dr7DTIAus7ehIpM+HGnyFljusF1nc07AyjX3qiPJM1ESi7mULKBEWRk+vSPTBEpEy+VrpjzB4OWL1biwvdysE2ch/UIDFlWAPWw+VHhY/0UV85uQde+ZTGswlz0R7RotcWtlWAIlRXA6oHV+jeO4tKZGKG6sVlyi7q3F3EK5lS7lMNNn5P9bIHjAUvX9XWWbz/B/pKEnc9zB988Lqn8UYQD/3rnsJXgn6IBKunpxuguCPdEPV9oK8opftv+RxkPkZJnak9e1f5ded+KOj18DnM/L8Q0beL4j8J4N/yx/8dgH8I4C/48v+RmTsA3yei7wH4N4noAwCnzPwrAEBEfw3Avw9RyvxJAP+F7+t/AvBXiIh4j+b+jVfKBKTMjpMFysVbMT1CZETK7P6g7s0u4uOzsGNQ1431Nx0m47YUGUSyrvZ91H3sllJpw30P99hOxpSZ7HPLP3oiZkwmFG+7bHEnKSpjDBkAbKwoD0po9BRSJvEDz9ymUpRNGY8GXgbyyo7ouqPUeJzBtYEsoGEh3McPpUepBLSBi65fQFNruEWD/qwZ50AY2zhGvTQwM4kp09+qo296dJdhLwTHsTEiLxiwNVC67oRzahA3lnrpML/X4+LbMwRfaxHmMZpAeWyr/avAGjj6wqA/1tjckbWYPXZQFljfITSXgsboTyj6bcsiAM0VwzYylm0J1YZx9gdnePyTjQS0TckL6bNHDD2Iy8nJhz0uvt2gP6GszuKew3BMMDMfoyQgfwqKyI3i+trHLVgThiNspd4EJPaNHjiO2z6p4GoZL5IX1KtOhOvNHY2qU6jWDt2pgvbuQU7Du95QvG+psiQoG8g5L+x7Ze+EMiDEaqkvepCrUW0sbK3GGDoeCQMAqrcRARb7C3FZnAQkDkitWC+FFQelnBMFqrraYLGYRXRP1m+J+nB5Ga02OK4rUTyGjCppfSAiSuI3aAdcmJlHF8w4Vm5h537ALCiYrc2+S2yDr31SVqJVSiV2SonlmpkRv5iO4dih+r7ahvuyQ1UGwN5BYZ+woa3d7Kz7omkyEOaBDnSgA/2wUcprhv3gZStNDgqZH27ad/+n3EX3tblJXymVBuap/18ReoP4nHeZ+TMAYObPiOgdX/4+BAkT6GNfNvjjsjy0+cj3ZYjoHMBbAB7sGvyNV8oQEdRWPBNRYrB3sSGtwdaOiJiANgmw84iU2ZYQKSAWpl4Ir2jhvgfqOrrxUBAsdIGUSZUye1++aTeqkBEHgA9E6SQjToAep1l6rtso2M9vQtmzt5kx0i7Gy2FAuWmBKMlQEtEfiQLpRluZb8fOxRgysV1QVpXH1/W3VcTbCrP1BlprzHfCuh1gDGpSgLWo6vEebAUbLNvtoqRuFDh9QOc7/2LPqzglCPs+ZgDOEpc3eL/QyblOzTf0ZS3e+f9SVERunS/a3dk92+vpFZrwzpLjm+TLKetYAO1v33y8gLva+8bt2VC5+D91Lv19MIa+OjpYng90oAN9pWlqX5oSAqcExFc5x32/D3Sgm1KaGGXf/6etf13ymBvP74b/XxZ9uXmeu0T0a8nvX2LmX3qO/qZWm/eU72uzk954pQwzw232WyX3CTIvjNI5rF7mQHvGPdBXlrjbgQa7SdsXNYcv9wf4y0VfIQvHgV4yHZ6VAx3o1dI+5UR6vKvspc3rZsLetRlCrxMcb5IxdJ87fnE+a5shtyeQisFQNoH0jv2kiMo0O2h6LjWQJpm4sjrpvAp3//T/FGI7lvuMgdQbqIcX4JMF7Nk8G2srYHaIp1cgvqcyZIEZHAJeFwkN0vTLrAjVysFVFN2N65XzaGI1Iq6TJAPjHMNv5KngPQo6TSUv44b5joUh0P7siYNpCZs7KsYPLDNx5tfqx5yieJ3bZVvzSc/7eWfjyELl9dWOvgOy2QC3f5sxHBMuf0SuI5uez95Zit5kt9cso0JkD5I92XEtqhXBNZIAgdI2nqorwvEnjPMf3553Oc5UwO8smHYSdzBt0//Vf7zjAl4CvXg+5wEz//FnaPcFEX3do2S+DuCeL/8YwDeTet8A8Kkv/8ZEedrmYyKqIHbiR/sGf+OVMgc60IEO9MbRVxRCeqADHeg1kZqWTPa5X08K1kX9NHlA+jtzm86E5glEMdHo4hxcDasqyXpJuTtJuBZFgDESl+/4aBw7yTTJ6zWgNGgxQ+bmTDTGgXI8BvoOYw1GYqppPQrMzKPbMxAFZgC52zNhdK8OAjKNrr5lZkAgFSpDe8R+Ujfd9Dc5ycI0lSo6S8nsf1cbyfBkG4qClhoYzQVj85aSjE6Z63QiZCdJB6Q8JDgI9zVcZ2jLmTCuOoKdM6AZsD6jU8OS1SlJC62uJPsh3+4BAogYbBVU7Ubpjwm6sgipoOV/cQxAxXLGo49vQV9p3P5DD6F9auLap3EO6aLlMuS3ZQUFxu9/cRffeucRTuoOihwcKyhyMU1xoy2qrdTPDjpJ8zywQqtsTBGtiTE4jVpZXJkGrbJofSKRUKcc558/+CaWf/tbOP8Zg//Tz/865qpPxuOYDjr87lyFmmyWtjpNGR3IQkHDbZUPrOFYwYFwMcxwq17jn37xLRy3Hf6NW5+jcxX+xb33cTrr8HO3vsDFMAMA1MrCMmUppA2rbH5hLo4JG1tjpoc41nYqaoJlhc/OT6GUw6MfnIEXFu9/8x6MU1h2DZrKQPtkDDENNMsxJ/2wPx9SRIdzIY10/J2cj9wXE9gldQaviErSSY/aKPmj7JycIjeW8UAxWyf7xB0yWf/q0PgbwJgIwkpqbDI0ZgxLx5g4JocYs49YMlZuZaNMFCfVWpKEWJ8FNWYnw1hnO8uXxP8DOM9mJguf12XgozV+GOlvAfhPAPxl//9/Tsr/eyL6ryCBfn8CwK8ysyWiSyL6EwD+KYD/GMB/XfT1KwD+AwD/YF88GeCglDnQgQ70sul1QJxfBe3yJ/5hpRcF2U0pWE9J7RdOd1GY05RluCwrYcr7aHhF97xktA701aSjOfBzP5MHRE+s4aWlXZQUpZU9bePrq7FtfTGArEN3ZxbrVGsLO5Pg5mVabFZeUHBihQ8p5WePRNjc3GlQLy1so2Db8X2oNg5mpmIWwubCov30ClffOZ2My3b0yQZcKSzfa3LlRoICUCZJce2t4M1SLPKpAiNkP9xKPe2FjF2ppqNQVlrOQzXfNhWmpvoIMeWC4kQZxCDsW+QSoSjokDoGWQl+HsbRPaO5chKgPnjoJ/MJ81V2Ym5+/SJNge3DOANLHDzQKLwR+bh843i6kw+SbcckCeEZkf4S5FBJzDvneevcQNkB3T++Fe8905gfovzkBgHym0uLfvF1PGKe7DdHVNBWeVzHqagCNH5/U7TBFLqiXjm894OHuPvrR/hXf/+PZrH8yucuHZOTd3p7bun7nfwvr41FmG+uGKuG8Cvz9+QduWJctYR/MntvfDb9+wNgvGehL8rnGuepx9/SDlvPUb2W8nceOph5hSff+5p8PyzQV8V7mV5Teh00cT/inESRx5RUL9unlDzrk2uG7bKtV9uFDJqUn1Tjz3BdlD5+jr0StOhxlzgedJ5mTHe/hZQpqLoiABUuv4XsOcjmX6BfxlSo4VtGmVKorGtfFVDmNfE5RPQ/QIL63iWijwH85xBlzN8kol8A8CGA/xAAmPk3iehvAvgtAAbAf+YzLwHAf4oxJfbf8X8A8N8C+Os+KPAjSPamvXRQyhzoQM9CL0IQL2HEeyDF1wqk+9K6+9gyOYR4rM+DAYWYOQnUV34W4yrKx9qCAxdCro+TlMGOU0tssNQqH0/JujG7VdeLRbWpC+ZhZP7IMbiuRutpCTeO4wCoisDRwXJsGdQNsCezHCptvaWxUsgCSSeMVDovZR2qx2uY23MJ9BuyGYXl8Axe+9kF7Nkc5qgGE+AalQdjBnLm2R+zFgZHDQwzH4Ue1TPsjArGg+I1HH20QvfWDMOJBllGfWXRn1ViHTWM+ecdhrMatlGolxab21Vk3NonBt0t+d1cWOiNQ3XV4+pbi5gJSfnYUq6izDLDJIw+GJJlyTCac4PNHXnW9CCZqOJYjw3MQqyw5BjV0qL94AG4qdG/fwYz05KW3kO1JT239Ks3VgRHn83JzlS2yVdXg2TqagKkmlGtZT+1M1mX6qqXZ8IBbl7l2etCVqZwP/7lP8QrowMS6ytP1A+oPrxXFO7+5rNz03Hw9rQL6dwXn4yRs9gYieV2Xdy59Nu66UBaoWkboB/E3STJbAjr8iyRXQ/edDi5Gv2+I+oFAF0ugapC89l8/I5PKPFj4HM/F+oNuNaS7S91Z9HbSQDiOTXuCxliBhiz5VGi/KHkWx8QJ5S0pSCgpmMgU54EKTITqv33PBVymeT7aVuMSi4CVC9Z5zZvkSBlCkGTdSIkxzlxPn/K64NY2iToGd0R7MKBlSiG4AjcOiD05ZEO6qIS9MxZD/Ln2BJ0LegWAGAmVLWFUi6xXXCGFAkImbCFfvLBbdBAeOsnBSmjPTpGe7SM9h905fsJSI7f//xt/Ni7n+Ok2WSIlIocDCs0BQLGMqEmh0qN2T6N05jrwfc/ImEqZXE5zHBUdREpE+YQECNSl/C/3ftRPPnb7+L8j/X4k3/sn8f6jgm1T0cY6mtyWNkGrTLxumLfYDhQ/G9ZxTrWayocEwb/QA6scTnMcKte4R9/8WN4a77CHzr9HANr/JMvvo23F0v85Ok9LI2893Pdw7HCwAo1OT8GwTidIWAGVnCssLEVGr9WASkTyHnETe80Pjk/Q6Ut7n1wGzgZ8J1v3kNnKmxMhUU9QCuXtWOWvjj5Hf5bj4Sxfiz2z1SKmBlRNIjlCHUAsKORzfN1ojLKyfONBBUTXxAHQdA4gAzB1sJXuNpliSBk4pT/Dq+dxWhPSs8V9UPWy5hYIkHK6LV8j1zlz6X9sCBpFvctzGwU4wOPMonKSXizLXQMY0xikczt42ePqvD09HqyL/1HO079Ozvq/yUAf2mi/NcA/NxE+QZeqXNTOihlnpWeRyi/gUX5WshyYkEGcKOsT8w8BkPOK43HJXOWBkKe8gVOgsqyMZIuPMzPM1bkfYRT1BYplUGYtzIyOSfMXZ0IRwW0OYNMh1TUlRYm8GjmyxnqySWgFdztk8h0BcaLHEOtephb8yxbVJmOOfOtVQQyfjNulFgoHgmUt7/VwDXkodC+vWfa6isLs1DR4sgENFcOw0KBHGN+f8DlN1uQ45gaWbT0wHAsmwdroF4ymkuH5bsauhfNum0pfpBVD9gZwTWA6oCTTwwuvlXJxz1cg2f+dA/c+a0BV+9XGI4Idu419YGJ83WDRY81wJozC0fm58yImYCqFcG2LJZCf44wjk8s2n5JuSwnqzXBzqT/o48JtgU274zjpZuN3hCqNdDd5nHsMC+vEAnz1gGaPTaXqn6es4eM5fu+0GdlUr2M55qi73BcbHC6A04+bHH1vsDMAw9H3iLKJOt9W51ic7fG5pZnPNrxWQt1RzgsZ9mq1CDWVJOkntYdRyY+zwolc+BKRSurQiJ0xM2Xo7JH9Q6Vt4SSBaqNBV9J3/XSQHUWajWgPbcjZNaE3T+MOyov1CDfgaBE0Vcd9FqYQzW4mDIc/lzTVGKZdQ60GcBXS0BrNERoKlHKpGsjbd2Y5l0pcamoqyzbFPWDuDUkwiMNBnCMuq6kj87DzZmhg6AZqMxc5QXcAx3oRRA3Ncy335XjBPGSud4k8SjIMFytkn0p3a+QZzbzh815DyZCf6uJyIdqY6OyM0XIpN86ZRiuGl2JZg96cEXobteyp80VXE0JkkKy9IX5to8GtPdXuPyxUwSFq5mpWP/4ow3sTGP5tXoSXRDQOrambF7NUsYJWQ5DeUC7bCFjwt6HRDjx4wQF+jaigaKwUsbfyL514Xfop0DKpKiftO9QFjI+KgvwkH9DdQfUSwdXqWhFjwr/dF8qKRXEuGiHUUANayEZIZWvxx51lLiC+fJ6LQ2MR8oEgTLfoznnK1Per5S9fPsfezKADKO7czvO1Sm/lZdW/GQtv7Wy6OZfx0Ztr3GekXB7Lum5EiEV0UGWM8RZWUfGYRxvLO58+im+9o+O8Bt3/mhev+DPS5RTptRLeKvymnfda3LCmx2tHTbqDL82kxAXJ2uLdXWGfzH7RlzDSVTc1nzSyWJb8ThR77QXPu30yoK1wtWt9wEGKgY2evxGTbUty7QvU2k9mq47VTaFiJpEzWD/OXLA4sGA5koB0NvxY/y6l30FxF46v9RYtet5Izs+spSyHCEk0PhZgG29sqhNxpq8uIl3Lk4MyTeCtuq6g4bgldObv+REoLYtiqYfPGb2yoPEVzrpJ1KheNibWjooH4LvdFCG6Nw6sxepAIhFSas8IJlKlA2T86TtsvB7MOBhAC3mUWmRQUhL1wpmqH4Az9rtuU2NH74Gpd92+GIUH2kyDupqBXfrGEE9Tb0Baw3X6lgnZGziSkUEQkmyETpQZ8Hzevo7U/rNDzaiIRQA18qjTQF5QSRjKsoUSeFaybhcEZbKXCmsNGz8yXhMgD5fA4ok5fEOBZnaDOCmEgE1METrAdxqWb/LNarLI685H+cGZnBTRUaHNgbU9Vh8OJcMY/7aKGTDMi6iQshaqMs1jj5YbK01a5JxH11i8YMFWClpFwTfxH8WGJnH8t5v9es3HRrsuObZQozrT4MDNMHV8owo4yJjqC86cK1hjybw30TyjBgnqIYSxu+PR2aU4RoV55ciZdTA0GuD0w+abMMlK+sdlGipAipl0MPzqXtG+7BDtW7haoppvVM4LlmgebgRhdRGgxVFWH5AvsgAyJkyGtvrgWPcAoGZb0PkwyYOJrAW9wLbEtgIqsbMpb0aCHZeoT/xykKqsLmlo6DCCtjclnvTNgp641BXCst3a7HoeJi7WIfz4HEgWRNBysg82ycN1m9Jf3rILcrt4xbmSMZWllFfWsz6ATxvsfnGKWwrc0yRMvH+bSTAIWtBzthWZXDw+srA1gqu8QgaZlQrj5SZa5Bh1JdeKeMc3KyOz2F8FqJVicGPd/kkvATaxVAd6CtD1BtUHxVZMpOU8yVFXifrhMpKsS4A4UEAVGk2S2vRaL2Xp9rK4tcPgCK0dR2zNVKKpEkyKQIADwO463H6xWLsIzEO8WaDigiz784L3ouy73RmlPFzh9bC/5T70RRyEpAYM0m9LUSlUlGpte3O5ftLkJIBaZMqJFhTLCM3Ks+mYtJw1rcotFztETF+3sowdC8uXKVrCfx3N0fJIPudGViQjJueD3vBLFyEfCddzRKkNawDEaqlXE9/6uI4cADXnO1ZXPvnN6J2eKxPEl8mjWtTfdJCbwj9j6+FnSaOSJup2DThsVjdO8L87RXa2kSkTkDgOAa04ojMAQBmkpg1GJErliX2THwMPGKkUg7roUatbUSLpP2kx9+/fwenf//rePJTjB/9Nz5Brbb7S5FCvdWoPApoitKYLWUdxxRRJIZVRKN8/4u30DQG798+x2A1fnD/Npp2wLtnl9gY4YlbbSPKyN+q2Fc6riBXgMHPE77eqNsa61mrsLloAcVoPmlgW4DeX8M5ghsk3hCpUSsR0CuxH07LPf+ZIFsyJEtUJFA8DudHVAgniry03D9u4TiLqeJ5e8aIXjGAmQn/ZBYJj8ajEqU0GAaXrdBHphBlbCNSinaBx6w2wlcFRHLZruoYs3sdXDULCzkdqyZx60sD+sbycC2BX3JjP9XmFTIfBz4HwJdBKQOWzTcvwWR8gPRciSTZP0JOU0J1YEzCg3uNO0nJ5MT03VsKoD3z24XGCdZg58CrMRJTlIWmFDuO4ZgjamW7zx1jecZHmK7cbSV1SWHnwP0AZfy98mmulVJ5eu1gQdEeMk20rUjySjUaDHhockRMykilTJpL7r9z25rhUFfRyFxpz9wOFexitNKNTBmSMcOx/JVImVmto/VQLHo5UiZFxbh63GyapcOwICgDzO8vcPEjTYQqOu39xAEMR3JxXBHqS0Z76XD1Ne2ZONGaK79BBCSFqyVw4PGnFuff9lp+RkS/2EaQG2/95jEuv1nBLAgmRcp4xgg8BitjDXDF05ZI8v17Jq1aCeol9BcfUD8+GKivCFwBZia7ZbUSRAsDOP7oCGYObO5y1iZQtSHoDdDd4rz/dEref131MpfskeBxnu3jBqv34Blp71MfZPQ6udZxSXIIKnukzIxw9b6OKKSg3AiWRDUAzfkM67s1+lN5rtJ4DLLRC3IlnWNQ6iivLIxrml5susED0fKqBotqI9BbZYHqyqLxwokyjGo5oCWAK4VqaZINnFFfDFC9CHHNxQDVW6hlh+NGj0qhwb973rUIXukh/TtB0VUKZBz0skfzpJWN37rx/WWGuuq8K5qsAboe/OQCuKow6wcR5ALShWhEx/lvBZQoAMk6cY9IXY4GI9+R8L1lBoyV/5WWOfbhhjN0padRg74tdQekzIFeIFUa7u1buZKgMKykCBiyDFeNSJkxFk3SLnXTAVCdd4ACzGkby/XGwM6qiACdstyTcV65LmO1jzuwVujPatRLAzPTmQJTDc67ZMr3obkYUN9fYvmtMwQrsrhbyvdi8ckattVYv9vmcT0SNKsoCHIepV46UTYnCJo0pswUUmbyOPzGWBYRMthRP20TmvL4nQ/3RdnE2sx53a1+vGAkCEvyQhZDDaJAbiqKe/iW8j7rm5P+kr6DEBbqp8Ib/H2rxucrtfZnCNVOvpuu0eN43oCRKq4zzUkqCAKRj451maHWV2JsPBqFzHGNQrukn6hsvD8Gm04MbVP/qTwfjjN0TV6n4SID6oSylJnx4/YT8HqDd7zLddbE13exC442TjmXuzClhkBmRi4ByRzCq9IAaPxcfsLdl0KlUAH4Cfd5bDJLmu+NNer2nJua69Zpv9Y+A9gLpdeYIvT0tY28h5hx/FL7f5Wphg8EfCmUMrRbkQBkjDNbu42U2YGQIaI8G8EWemRC6ZOWZenzcmVFOgbgBX/ve02pVaec301RMoAIGcYAizH1XkTK7EC+ZEiZXRao8mPtkTIoYKxAENr92IMFLdcjUsY50GDhtPf5BsQlwXgBLiBlijmG32QcMFi4eT2pnErRM8QAjGeiUhQHc7RwS6wIB+JCMebdJKpllWu840DTm0p6HSCCOl8BRNBXrdzXkikAoNYeKZOMr7pB1scy1HKN5uFRrryyDHJO6gSkTG9Amx7HRzlSJiJsnIvIJjIOtFzj+LtHEekUGBpWCmQt6Mkljn5nJoJspQUxU15vsLqWlsW4jhNrZK13GdnxjPt1ZKIM2cPeiqqWG4AIbtHmY4Y+LEv9ts4Eli2kDPlx6sQ6mwgfZB30aoA5a3OodoiVEgQOgkcR8SjAFMJL83iD2aOZR2qMY0WkjAOaB0voTYvhRJ7toJSJ73HCPKfCQXBnUwND90lMmYFBTuWCSJhTtH7IXFmx91P2CkgFgcU3SuKwGIXhaBT2yDKGEx0FDb3RqJnR3a68BYej4jCLo+CvWw+iYLKNzL15UqO7Iwyr6nlMMclAc95K0FGvlKmWBs0glvj+a2dyrkqUMlHxk8aUUVC9jei8IAxUqwGsFVytouJIdUbcImaVuFYtk5gyrc5iyohyLCiBADx5dVsn7WOgD/TVoH4A/eCzUe6fEj4KQa1K+Iud5JJ+vHGrLmJ66V3Kx2Ssch4EYOaF4F3ZbMcOHKxjzL67vY/LaRFQj1OXq2cV5l6H0PaK3s83jVm/9r6XlPJ5/vvNoTxVPOwyRh7oS0W8pUn6EtJh731ldOBzhN607/wESaySnZSiYoAbI2WmDBVPTU+TDcRahKCnKe0M1rd3XFE6sXWgi8tYnF3HlCLDWqDeht3vnIMiGcMrrWhfgFdmuH4ArRKrgrMgpaGCIoo5MngqxJRJ+0j79JZsvaoxiaZJkC8yligaqNKgTe+DF3rB2oq1noxY5mNcmfQ69YigYWxbHEv3GOWt9LYWpUPjT/e32zErRlga78NbL624YNRjIMFq6WDn4moxuz/D1TdmMaZMcNEgB/RHIkw6TahXDs2Fxeqd2gdLDTFl/HQHhm0EKaN74OizAZffqMHVaEkUlxKBRt/+3ROsvtZgWBDMTJAr0doXhHOP9nAakz608GsWfNbJAaoTlEnqWpMhTpwEK2PlYdMA9MbHcCFg8cUJnCZs7o7xClKrou4Z1RoRcZIq06KF0Cuh1DDOI40TEGLBzJ5YrN7WSRtBpQj8nKatnKn1jwHdK+ihQX9Wwfq1DfVjADaTPLvBsmgT66RXCoRnIJRH5ZJlqMGh8pB4cgzq3ajgZE7mJ0iR6t4F1NUMs5m4wKlVj/ZBE/tW50vU92ug0qB1h/bzWVTc0brDfObrdoNYMvsep0+Okvm7kdH240ZmJj3HDO46tMEdtURADkPeh7Ww6w2ICPWDR6jLeFjhG+/G7woRyXeuFDT9WDpVRnvhswrf09SKq2jafd/fRx5eYQS8A7PylSe2Fvbx49c9jZdK1+lLXqMR/ECvgqaQL+Xx1O/npfSbf129Ax0opRfxTJSosQNN02F9AHwplDI0qczISXtmfHQR4oCIuC6+THhhrokzs+XDXWahCWV5J+MY3uIrWQnUZP2s/6mPQYoKMgZkDGg2yyH5W+5RidLCGKBtpq+/bBeEHGOjq1EaxwEYLfsAJL7HegM+XozCWnAp8Nl0yLoRdhqCbk6NG5A2QJaloZwbAmw2EQzZB/2kTRfHZB/AU11Wcd4jgsABXY9q0+8epzwGRmHTW9N50wGOsfh0Yn0DGbP1rLExI2qr63D792ejH3+s5OTZiXO2wGAwb2qZR5lpqcjoxP0gdcP57BLFbeMsxEgqMyPtuv6pOkW/0/VLBmw3J57CYMuyp6Ibcvvzp+95khbXnHcQRU4z9Y7fcGPaoRebpFKlnapCGJMgp+vpi2dp9GzEALDZXFft1dKBgTjQgQ50oOenILC+TKXIQeFyoNdFhRvqG0ep0mhLBno9U/phpjdfKcMsls9roHAMROGLPVImomb2NnQ3q/Mm0r6N7FmFhq+yRvf+/dc9gxvQ+cvp9hrX0Df5jr+pr99z01f1PTvQi6Nn1pod6EAHeuNpit/aV1a6nV9XNuEy9EYKhy9iTgEhn/4Hpssmm0sMlL2ZT6fG2kU7jK5b8SWfJq7klPH4utALU9ezFRZhTwiG0ri9K6TC1Fj7DM1GsiRmiSDCWFP9BANksQZlWIUt9/odc8jaacLOhBRld8bFxBT5ieRwKpREHHeyWwlCbtmHRNhRhxHR1FzRFuMe4ztpyRDL1URHIebfde8cM/DP/9H+Oi+KDnxOpDdfKQMIMuBpKHVmvIlj45fZ+fFFC3YHQfFABzrQgd4IIvDB1/pAbz49r1B9g4QMW8LyrjYTwlSOcvbtvMtijFk4IfAGV0gK8aUCajtav6eF2ThmKtAyS9KDIEgX2TdTF20yklY4CsFJNqkyGHTMYklJoOYke5TqDdTFGubuyejm6l2yy+QGTtO2u7aS//Au1RJbzKFaWWzu1jHrHllxmw7u2pI5BlngY4nLBh+MWfrKMlGF35SPHX47Heae1FU82TZmdbok6B44+cjh8psKm7cZrmLpR/n/VfRFHgXimDVKyonG8xT+K/ZJqZL2TCDFUIpRVdY/agStRersfuMW7IJx+hOPoRVLJihirLoGx7MuDh8yHsH3XykHlUjhWrmYEUrRmMWp/C+3m/PfNPati3oAsLY1Wm2gwKiUhSbGk34OB8KdZjVZbxe5QsNgmeBY4V/cex8/dec+TuoNXBLhO8zNsYqZoADgi80JFDHeapexn1AvjsUEB8qyS4Xy2CcIxinJR8HBq0Luoy3GDOdDf52pcLGc4faJn4NTsE4yYekkQ1XXV2iaAWmGKnES8P05Ajs5jt7mFnCrGtQ6efYY2MpIZeUBp57ATUDOI2aiooGgOoJrGXpDcLVHmzuvwGFE1/z0vSY7hj+I/w1gf+upI0c9Ex34nJG+HEqZAx3oQAe6KZXWwXBc1rluE3gTLYpvCk0JRDdBHV7brc/Y4Pu6kdXyRc0nZO8ox0zLwnF3eDYO9OKIqgr67jtyPAV3Ty3UaQy20gqc/nZOspmV7RK34SB8EzPo/Ap8svCB08MYyNqzLlJFx37SeRVCPY2Cf4jnlgvRVAjg4+80+L7TlAn5Wdw2lZanfZf/xzbNJWNxz+DJd2qJN1bWBzB7yGANdLe9kiEqQ6bHibfCSdYl9mmAlZVMgcMJbykNsl3IC0n1JcE1gG1FWAzC1WQ2qdDPjk9Sc044/f1TPP5ZwM4kPZSk7uZRWVLOoSQvuIXrqc8rzB7WuPxRESDJEvRGwbbs12jstMxSyDqMwbHvqfFCW7K0XSec39EuHU8ZQPUSa09vJNtimm55VyasnecwcS6MaeGzZ+XlOkEBvPOxhasJq++/BQPvXsxAZRirKn8oygxgyiAK1lPzmprTFO2rp2ySfj2pT04UBR+liRtShNYumuCziIGTlcPHza3d7cp5GQYTTcbY30LLbI3H47fOjdktb0pE4jJ+5BjHFnD1LK8Qnutkrm4KpQL4+IwTN4ZDuzGxxPRz75N/pPcBQfkifbtKYho6TVk/UXfoePxmhziExZzIAZ+sD/CVV00HpcyBDvS89KKgt9dW2TPODWG0U0Gd2TqxBKaBVKcycii1DZd1PKajHAfJ58UOVFV5tjNArJRKbQscoa+ulxhIRep0chzPZXGJmCVTWOiHRgGCuh48b8YsYomlkYwbGQf2AaGtg7lzJMKKHdN/hmtmTVC9HedjWbI3qZHpBwC9NnCtBhlG/WiFzdePY+amamPBRDALjZBGXISRUSghm2z2YXOlnAkhPx8Zz8HOBP6qBtmcWcOn+GbYWS5E6c5hdq/D5l0JviuCD3kG1mE4ztc3pH21dRDmENPWzh8MMAsNVhIwWubm16FjNJcDhkUFs1AgBqqljZmtFh9eoHvnCK5WqC8H1PevYO4ew8x0zHilO59qlwCuFFTvRmGvSp5Xfy+rldwfO5d4TDH1ay9Z4LhRUJ0FnL+/HvKbETPU2oB9NicarGTx+vX/D14ZHSxIX3nipoL7xts3qzwV+8xB4O+BLMuz2ur8+zjVHgBbhtYKrMP75k9Ivt5QC2SmZfZsemnMt2vG3XlNcWI8Xt/UFnfTvddOCIb9AFp3mH18nCuf0jrrHtAK3FTTiv3k/6QbRfrtNHkWxbxtaBP2KwuuxwyVWaIDn0UxjLFLARb6051F9XiN48+OM0GR1bg3bCnEMmXTGEg/CHnVWpAy8wdVzNaojEfKBAVZ8B4qFEeSVCEZs0DDZL/jWhflUQnH2/34dXFKzqtexrMNYTgG+lMnyJjYHyf8QIKOiUgZBqkEGRPKAI+YYc9ueMUZE5RiKOWgVFrm4JzC/d84w3DqcOvHH6DWgpJRxFj3NU7aHoo4lqXoFp2gYYARSSMomBwpE4+RlrlJVEw4L/+lz7Wt0Sobf2tiXAwzGFaTSJnqBloOxwqKXESr/LMvfgQ/d/czHOl+rFN8XQY38l1fbE5QkcXtZr1V1yYPS0DYBMSMlKXnKc4hn58gaAJxgbphJmxMhfPlHLeOV1DEsE5hsArOKVQ6IGUI667BrBmkXdJfiZgZjwF2CmZVQc3M+NlwADt56JkBGCXIGKPBtQPcqMQjR4Ah6I5gW0a1FqRMTMDBlCkqM6SMLxcFqvSpDGD+1fMZ2Z6KDnwOgINS5uXSlMX+Ru2KF2GHxbcU0rPAqKXPa2rp9b9LH1dYK1DeKR/XCf/XVMCPgZDD2CGzSrDUhRdO6XE+joGhB+omCdDMgLXg9QZsDNTxEahpxiDJYS5psFwlaY3hfHalpo7H6fgB5kuDBayTVMs6v0dq2cEtGrhGXg01WNDGAD4Vr2sruLaKGZZYjWtLliPToQbJ2pNmYgpWtlToTi2MgSkhK/6oemCYNjAwFNsHbbikGQZsg5humVyS7ch/bF0FPw9kzJGyQLVknH3Q4er9Bqt3VZIqOu+zWsn9626PEOlqI5mLhiOfMSm1iPl5sgbqK6C7M55jJfDJagUMx5C0yFHZAGFyDOHoE8b6HYJrw7rJ+uqOcPQpY/W1ESIdLFSuLvuSMedfsGRxCpZWjHPU3dieDFBtGNWacfFtFc+H7FBkxwxR9SXH7E3KMNZ3VexfLA9Ae97AtmKpO/1Q4fw7Tbw39bICMdCfUNwo0+cg3TypXNske5QyFDNqqZ62smORk2dKMjelZ0TxojYG1aoeU30rguod9MZADZUfL4wv/QR/5iiAWUb1ZINqJu9bULaQY1GIDA5q1aFqqyjgqI2JyiT18AKzQdK706YHL1eo+wFV8v5GSD/8O5MGmU6/r0H5N/gFDcrGMFdjpW2l5RhAFiw9WyLJ/heVhz4FPfUDDnSgF0U0GOhPH+Z771QshKkshJ7KwPlkLLhP3GVC/VSJEIZzAHwgfk4VIFl9jG4xW/sWbSkI4hhA3CdTIT8qoIGIntilFADzKMzTjrq7XGComI//tjaXjPlDQcqkGQfT7+/8AcO2QH9G4jqT7J9T7jLpt5+rUWGgBoLqgOGUR8VBstfGe+jb1xcEO2PZVzEiZTJlRepqs4u9ZKB+UuP0D2Z49IcZ3HLcpyNShooGU50wMqRM81ijeayx/FELJgdyBLVWcHM3ugKlXTJAJjzDyZi75LBJhED6e3S3yFAAXqikIGA6EqTMIHu07oBqPRE/xBM5+OyHNPaZXEM2Tjm2/52iUDiUebuPcsDdH1iYGWH9g7sRJUMsvMeFLsaM5/NOp5AyZPO5xT17a648Oe+4BmG+VJZJJtCPaWyXIT5i2VSf2/XO1hYftD8deZcpilkoHUN5xep5uHdFmy33l+eQ8UPcFgBQzPFz2DBwZh1YnyTjhAtPFswxoAo0zS5K4j2R6UZ3xHBuYh3JcoGUGccl68BaiUyk1PhNyuq57NueXUfy+7OrPZmPD/RS6M1XyhCB6ma7fA9qIAr3QUkRfHOB7UxMfgwAogwwRpQAiRJiK+vS1oBFf1Pw9zLrUki7uguqHNs68HIFtC0oyZzEfe+VFUndTSfKkVkrL+NqDVrMM0GGjAWMBc/bcbjVBqg0+PIKuH22jXwwVgSY8BInSqaIRBiM9OE/FLQZhJFatKMA5SDKjUDMoOVG6oS+LIsF6/wStN6ATo7BR3NhyIJwVeqMvKKFjBNFSavH9NchIFdx3wkyl/jBShVHDlAePUGDlWtzCrAOyvpMT1MftOTjTIMVC5Km/GO3y+KXypfhg2lcvl6xrhstoz6oV7bGqaIpjDf18XVy3+jiCs3HC9yeF5mj0iUerNzPNslvbRzIeuSAyu/r2FDWws2LVOxTltyEyScG1NUG7qgFVyoTBMg6qKse9qTNhAMkiJFSSKiuepjjZsuHHgDU4BefATIOyjiQZbRPZpEZcXUq7DBcrVCtrSi3jCgqmstakCnhdWNBg9iZghoY9XmHxf0qMvHVSpAeZD0Kw8l7lcLkgxIrhfGHNQrMPStRvAGAawimpSjw2Box25juGWY2QmOZgHqtUG0abO5IB5LynCTd+EajO9GRgQ7wZVH8BcliPLdYVBiONVhB5gBR/AGSvrx5MsNwomHmSoSiK0kRDwaOlcL660dgTaivDJp7Dfq3j2DmVXwedGdhG4+a0oIEikJeqohyYX0tQIBtxTqdImVUb+FaLUgZhjxjmuJ3I3zjyDHUZsiUtK7W4PPieX6ZdLAgfeWJ6wr2/bvbJ6ZQLcW3IO49alSqk5Pvq2snWDzPf2SMuldScq3jnrM1DiDCUbrXxUnd8BkN79ZTouJT9wPA75HPQ5ZBXQ9ad1h8cLR731t3wj955XA2h6l1CFQa4pglUGntM0CW/ZQ0mJj1EkC+r8WGCQ+WKtdK9nawUJdrvPXrxyMvCHgl3thX6YKWzbNQaqmuh94Y9L8+i8+JMka+z6kRKWkX7uGIkkmVc3uUbUmdsjwiUyfqh+NqxdA9UC8d2icKXI0Ktgx1k6BvAgKH0zpI+g0ImBSlA9+WSVA2aqwHTu4LVTAzYP2jvefNBGnDgwK1FjFeTZhLGNYjY0bRhCM6JyByyKNsrB2RGwF5E86P+lmO59PfRIzBamgl6JvwiHSmgnWE43ZEtlgm6ASZQ8QxRktJoTzEaPn+/Vt4+85jzOshni8pRcCcr2dQxDjy4+efpbFe6D/053i77/Ecxb44aTOC/Cj+Zwas0TDLFtXRACKWcZwCWwJp/0w4Anca1Nj4EY1xYVKFXkC5JGgXvVJwrdz/WNe3DYo7cgTyBkhK6hDkvO4ItvFImSA6e74t1I1IGYx9ZH0xQBYwf/AKVQQHPgfAl0EpA8YkUmRPalyOvqryUWIL2RAD2R0bOjtBmwy5dnCXVv1paQpZMnkVpVLGOqDrQIl7CVsrv9NN2qNT+ErH37xaZ2M7ZsA5aeuvy4XU1X0PNZgR7QLENdxC3ARlTCh33oIcAuEZWUN1WecKj3ANgaHoeqg0HTUzYExEymC9kVTalR4ZitTdJfjVe6SM7GMJUoYZrJQwBHDjuEpF//jR8kSg3sDNqggbplpDkw+m55EyduaRMsHyF24XI0KC9caBKxImBRgZAAj017YKtiXPWPi93CsRlBVfUN1zVicV1JUFbCsfYdtK/+GjGxUIXqGQMiEZUsYIUubWH5zi4kdarN9Nns9Ql4NyQcq722GtAL32SJnjfINIoZCsPFLm9jh31oIcmULKhLHJAkefHOdIGc+k6A1h8ZkgZUDIkDK2SfoKl+OAxb0W67sB2TTeL3KCfCEra6oGRrWW/5ffUrLJ9R4p4zcqvQFcC9SXGmCg6sQHe31XIaBtw6bWnosrj+6Bat1gfWd8txuPXhqOZLNWlsbrTPoY78MozERLIHulkPOKl42LD4ruHXQla0RGlE/VOgmoxj5g45MN5tq/I/6Z1oOD6iya83GLCG5a4i4l70PkvRyjOl+jbSphqoMy0QtPNFjQukfb1BHCT718d8AMenSOxXIjSvCuB18t0SzXaLQeU9j7oJuACBQUUC7ANNIlfMcrPZY7+f7BWgm66b9TEX1XCnvMYOugEqShUgTqXhFShnHISvBDQNQNUN//NCkIH4GEFwiIzFKBkBiQst/WQlXVuEdm/YZ90PfpGLzeQCklcWgmEDml2+eWUiCrO845VahvoWpKIXwCTRP3BTUqXyNKNT23QzifEvKJgebKYfZwwPmPtkWsmrHf2WOGrQn9CcUAtSk6ZaqNrGeIKeOXwe8bwzG2ryu9nf5bX18JGtM2MtfJmDLpHHYQscSnOf3A4fFPK1gfHDTszRlKp5hGRn4vCtfTnBPaJ4yrb/rKDtAbQfdE5QZyQS/b3zLrQlI3nQhPlKXlBfO8JVwmdQNaVA1yHzJva193RHjQiAAp5jD2TTvOBQSPf6880iEYJ8DA4osNWBM2381RqMoyWNXjXh/6DcJ00l+Ohsn34WBICoqwrD9X1E/HSfbI7fOME8vym+bI3ArjOvJWmyinTZz76eGBlwdoZ50wHwA4cZ0fKJEn0v062fu5aDuemK6/VbdM+ekm+s7Ob/MN2Tyvq7+v711znOzg6ZgFvqa/H9jlU/X3zHTgcyJ9CZQyiYJgF+Rwh5WDrbRJlRlJo+0yK24t1NSRQY9tnzbtXGklUTn6hAJSpkTtTDBWvFwB8xmorsV64liQMrM2Y46w3mRIGaw3wHyWwZqVh+5zO6KPIlLmailImapYL5+6bos8o0bMEU2TxvAAABfQF1aEwRj7wX/Q1WoDtxhhfsQM9IPcBwA0n4EXM2kXBLLy3imAOhutZ66pBNmhx8wEbNM1lUBZNFh5dlLkyGYYlTDw/faDoK2sg9pU0Cu9DR10yaYECMpF0/ZzFjazFHUUysMaWL+hWjuig9Jl932wUhLwSwVXETf2HdeYc6a8nMtggMsl3v5gPqKnMmSPP7ZWNvq2HvsKbiCpa1lonyKqBgM09fa5wfiUiKnggXieVh141iaWW2HmyThBWB3N5N76GAjZ8xWY+oCGuerhjpstAUEUHy6uu5hDGKo3OPpMYsrAsvj3+2c9IGVUbyO0lqzD/H4jPvvJWldrA1uLG1R9f4WTVkfGWncOYMAcBUnD/0v4lKDokwmOdVLhghzn+5k/J0KMCC/yuVI+pswo+LiaoNcNBo+IYSUKPD0w9FpjONGJ9X0cz9a0BaGeN0qQLQqCgCG/pgBUz6gvB9iZhjnS3iVujCkzJ8JwdwFbK1Qrg+p+BXP7CG7uU2YyoDrrvwOAq3w8GD0Kl2kgPxCgNhbEDNfqjBkl6yRmQ6vl/WYG13KdWwH42LtZJa6PXCng/EuwdR7oy0NNDX7/nelz6bcUhSyaClupYsTyiGJM94MpchBep+vBdSVKmXT4AJlnBqJw6bbOj/1tv0OTdMPy6aCYPH1c/t41Fxa3RHQd3vmd+c72PHjla+Dfdglu+8YMdcuA4bFpuX5u7O8pBbtpgZEBdjjdF3fuBsLclAA3ge16pr5fFU1g7l8bHb/uCeyhqSfuGnXADw8dEB0Heon0JeAsZUMBqZ1aQt71uWAH2Ou1gWl9dgx0IzKHkVhE92Xl2Ec3rYdtBVOIsYL1ZkzLCIzBWcu6jqO7FlsLusiFegZGQTr2JRZoHgxovdl275qYV7TaAYgxZLSW3z4+DRyLgivrJ1ewcT+Alqs4xlZMmcsrgRmHmA5Jasg4/+A/6dExajDiV6kIlMa08UFeyYoLF3mGdSvTBNGIyqlF2cGVArQGt1piymiVWfviXAK81zi4SkWhM0WexHSUaQBX35ZVsJiQBGZtg0VwIqZM7WPKJC4q0bXE3+wQBDa4twRBnkmsN9WacfzRGut3Z1i9XTxParQy6Y301d0a6+hOfLTNfHRnIeYspozTQL0Un3wAMV6OMoxqBZgF4CqKlp2wHmQZR/cc1rdVjJETlBO6ZyzuW6zfEkE6IGX0IGtRGuGIgfmjGTZneoQZY7RQ6V7mrIz4LauOUW0srt5rInomrClZhvYBdKuVKFvUIC5P4rozWsjIAq5XXoE3CgMB5aJ6Yb51Jcw+JYq9VAEUAzsGC1dh3SLjn3UGqBtEkacgisnwrjgf3LZSCdpGlKl0tUL1IPF/DvUHEwU0cV1K/kpBzzF406EOLpnFebYOMAa1IkGoOBZ0XXjW1hvUn9So/beE+x7qUw3l3UfZI/xKFOFe8shBVSBgYsDp8DsoyP11lOQStCAH9KXZ7B/7BdIhVeRXn3i9gfuN3z0w/K+Dnpy/7hkc6E2hHQberfeyUJQe6A2np/2u7ruv+xSlB3pmOvA5Ql8CpYyn69KbTik9LKRNGrtlsq0XnK0FoQh2W0KDEYp39Dc1j6kMNSmCYQ/qhhyDNxtRblRVHJeHQWLKpAFvu04EN1/Omw1oNhvrKBJ0g7WS1Sa023QgpcDrNejkJEuZCWCMKRPWI7XiBAquBaFsMGKNmjXxOsCc+TSTY6Dqs7mQczKekfaoKkFZJH1zsT75miIqaMglwAIfB4bCXI2NCAnp1NfcdKBNLYJcuPZhGBE1WkMH5VBKQWjdtR5TcMty7PhzRKdMBn0urXKp9S28J0GQLBWCpYXOObAxOCbCcYnKycYJUc9yt5SdcMsXALNkx1jsaT+fWps9NN97dpvOnrL+3v5Z0CzNv94+teerdiPi5D9PlH9paPW6J/AU9KVb3AO98XRgSg90oKenEpX+MhQlU30eFDJfHnrR9+p13/sSjV7+v0md61ysDvRa6M1XynCKdNmTdm3qFDs5QepmPHSwgt4UWXNTmlIoPQ2k82k2mqmX8ro6Kb1pVqPVl0lS+/LSl/KzzNenYTzQgb70dGCaDnSgA92EpoSsKQHsaQW5qXNvIr2p85qiMkPqM3VBYMfZ/+ce6ylQ/WydoNF3VxqPy2dJ69FVLzEI7jR4l0bD1J0wCxK0w3D8lEb9OI+p8Bdl38D2e+HR+RkNgzfQJ8b09VqM5z5EgsTcSgytPn5fZuQtyTlpW+1wV933Xkyhnz3amT5+hQ5/Bz4HwJdBKQNkMPdnoqcV3l6GsPe8fT7NA7vP3/pZ+jvQgQ50oAO9BuLDt/pAL4++CpbSF6UouKHgOin43lDgi4LehIt4Nv80U2RWHnxy1bbgls5jasx0bKI8DpzxyN5EqBtjmSXuqEFgDG7eRDF23lQg6F0BnwHvXqvEVdzVPvGCzhMvZEGRHTC7v8bm7TlcQ9H/NGZ6AjB7MGA4rjAcS9D9LNvixP+dgaEh7hRpNsUQ6031gJ1jDOI8SLKFfWnSQZyVVUuCmTO4RkwOAMUTbu7jLQoZnQCAQzr09HxaRgxYwvyjGutv96CKwYag2gk5xI9HKuhHfPA4AM5qOEPgjUZ1MsBciS95c9bBGo3Zr8/R/dEV2plkI4oZnAD0Q4W6NtC+7MnDYxzdWmOzqWGXNW6/e4HBaqx//xTz71ygqfIEK2UIJ+0zTF0sZzg9EvfhzVBhvWoxm/doKhPbOKfQGw2tHaxVmDVjGApmwmA1KuXgmLBeN5jNJQamtQrGaLTtgJB5KWSMihmbfLakNJsUAHSrGqpi6MrCGg27qqCPhtFNmgn0yQx27kC3fMzNXuPkNxtcfceCFwa0rCSlfOUAo0CdAs8t1JWGmzlAc/5QeHh0faFRnxO6OwzXSop6vfYhEjh5pnwbAnwcRkANJK72lfzWG0ncwQR0v/Sqskwe+JxAz6WUIaIPAFxCcCqGmf84Ed0B8DcAfBvABwD+FDM/9vX/IoBf8PX/HDP/3ecZ/0AHOtCBnpveVOvac1jQxi72X9ukdW2XO2XpKle0m8ouN/m7EEjYx37Zyia3j6aEGhvyb+udZVMxu8p5TtXbmuv6+e/Nm0wvam8nop8H8MsQ776/DeDP87UpJt48eum8DhGobqaF6ZTK535CkJ8U4oFJgX0UtvNzWVyodNxyTsHVObGuSvyzQqAPwrxXILBSeQy3INAnwn8I1E6WoVY9zNlM+tWjgH+TzE0xfls4Voht9MCoriw2b9VZeUy1DBTpmhGF5TL701b6ZozHpZCepXdGci5tlwrzURjP2xED9QVhOGXJ0FQoBMY2qdU8Pw8AzUMF2wDm1E4L+yHN866+fD0q24U6jLxPAHjSylpqBh0ZYMsBNyfuNE5/8xQXP21AcxHg2REozMsRFr8zQ3eHYd7uC41G2tGestDGAbD+mfTTIibJMrVWsAsX6+mNgp2lrvCUXT6K/yL4js+Xsn48JO0cRmUJA+Qou+exv/RW+HbK+nYWOPrcoVo1EqvQIMYxHDNNjWOE3yGWYOhD+axVtq0kM6VhmPYIygLHH3dYfjaHq+Zbc5mFbF3+Ot5bOZjZKU57hjKMYXEHjWO89cii+xen2e0KmScpZIJK+n63Z7haHNtnlnHHMFzV5M+Xbx/G5oQliNms/Lk7hsHah1kwLMkJVIEQSbRDE9m+AUgcSVEQ1jJvY+DqHGVTXV2Baw3ns7KSs6gfPIS5NffJI3ysOv9NDXEA1SAxKstkD+FYDRbUG3Bbw1UqizV4HcV4gUEZaZzEQSTCZ+cHNPqrpheBlPm3mflB8vsXAfx9Zv7LRPSL/vdfIKKfAfBnAPwsgPcA/D0i+knmgw/CgV4zPY9QfgPBeS+kNKV9EMpYZwdTPhHzKARwpmjd8rGTQuau2KdGdKfTOh8zWM2MBa9WoKYBHR95k4oaGe6kPu9KwapIIJbnl8CdsxiQNk7jcg2eNXCzJre4pXNJYKlpdiUXsuP4DaY638AeNTDHslmTZajegmsVsyG1n19iePsI///2/jXGliVLD8O+FZGZ+1FV55x77u3bt/t2z/RoZkw9aEmmCZoGDYGwZXlEG6b8wwBhGOIPAQMYEmDDMGwKAgzZgADZgAU/IXgsCyBt0wQhi9CAEC1RtgQbBs2ZoTVDzavJnu7p7tv3fc859dp7Z2ZELP9YEZER+dh713nUqarOBRRq78x4Z+6I9fjWWmYp2bRC0F4myAFpGcuPLrD5ucdwZZLdx49BNQyzUpERDsxDDMwcrkH6Xzw32Hy99OnNJZBvCOjLfuyRuQ/z9wyT8sGFUyafnAgVMn9CdeXQnChUVw5mSSh2jN07Kh7ckdmC1C92jJNPWmzfL+EKYP1pi/ppCbOQftL03oAEhQ4p2pfnFmapYqp2cmFcBN2yZ9xkvZQBFi8MmseFZIrS0n5gDBfPWhTXBttvLGXe3K1lx5hyTMkaAl3H9fF9l5cS4Ls9K2NWrOq8AZxD+3iZPbsuSDJ3jAszlJEsKSH2FYXg6b4/tW0BY8E/uiVYL+NtWpBex9n+rwP4ZQD/X4hS5pcA/I3bnMRrpDfG69ByAfwjv5grJdC3lHdfsgyEmSAchEp5Z2KWwFEFT/I5TW3rOAaoBxD3p2y8xomsXvg9sXUx+P1gfMk4wx43Of44v+6zbh1gHNxCTwoZxJCMU1K711jyu0+uAT5I+6aFbt34mEP7ITNf0t++VNSj4/PPZGw95caBNhzn7hphT2osXKW7eH39dtK+xlzzCVBbAxRK2qGkXvKOZEkNOK+fBunvFGTUndNhXElZvTNR+HNlkSu5EmVbmuBg9dkGZx8tYKuqS1qgujZPPmvQPNJoTqrsrBygYkKdvsItVTI5gIue4gwBKdOtkzKAXehO0aaAPjJm0HYYd+i/6CnUIMqqtF8ogIMCCkiYi/DHeR+WYJc5UoYqF9EsfWUZeSSK1i4iQJxVsEaBa43yrEZ7uQCIsXhUwxiNzW+tsfvHNlgsWs8KdmiZtoeU+eKrU5w9vcBms4DdFnj6/nO0VuPLP3iM9c+fY1Hk22Noh5MfmmPgapMjZXYe6VJqG9E0xiq0VkMrB+sUVh4pI+whwTiFQjkwE663FVZLuW+dgrUKVWk6XVrSPzPtR8qULqJz7KZAsTYd0gYAfXQKu/ZIGQZcq3H22+/i8hcNaGWB6yW4dPI+GBKkzNLlSBnX+4EzUJxrVBeE+p0RpEyfnH/knsdSLUGZHCljK3kvmo8mXLdeN71dPudO0ZtwX/qzAP60//wXAfxHAP5H/vpfYeYawA+I6HsA/gSAv/UGxnB79LIC/asI81Nt9AO+egE7WoPTIK19/8SxQMZTFnRrJShyVXX9Eg3Hw07SWy8WkhEqCPjOSTYlrQFjwJst6OxUxmCMpKE0Rvwsy1IgtcGn0md5AgAutNwzNqYKR6HjQR8tcEBMnas2DdyqhKv8q0/oBGifCplLDbfQknLXMVwl34MgzAo+1TDFQ1DVwqSatRzOxdbBrFVnmYNYHGxJMVMOuMvqA+TMRRhbekgDCZNCwgy4smNamARKS07gtKtnDKclM1J7ImNWLePxD1q8+PlS5kJyX9dikWjXlB3WQZBffsV453cusPvaGs/+wTJZh45pCRYaVyRj7u2zxRZ493cf48s/uoRddMI3MXD2Y4vmVKF+ko8hwCyD9aOfIlVZwC7IHzAMZYCTzyo0Zxq7d+S6bkQJ4QqCXcg6qfYE1x8sYFaSBloZoKg5zkEZoHpWoX6kJUMUs1cCyPPTxLCVF0q8laqz7HRZlqR/B11blNvkkPMKhWhBG6Rv7dpTrYupoSMT5RjUeuFIEfS2xWpRQO1acCnpnpdflIOMTXHdWgv14hrVs5W8G+cbLD5fdGnYw885JB/yaeZBBNrUgPclTxVLMq7Ev5lZ0tvXDVaLKrPIhDFR3QBNi+LLJKzzyAGdrU/fRZNI9hrnUIZ+APCuBgAsv1yMCyQ+Y1vWVj/LUygHgI0FWDJT/RTSjc52jy55xMx/CwCI6C8B+Gdwf5UyfXp9vE7TQn3/4+77MXEdRgX7/Yr+MYW7lOvXU+Nj6P12Ix/hnJzHKTIm7T9cY96LkmHV8R1BMCfjQK2Fahy42I+SiUobldyPqJSkvP+vW0axKbB7WiRC8rDcmGDtEvTNAD0TlunAtYHgniFiOpTMGB8AKSJImccMV3DWV6dM6AnrcRydgL/4Yg2zAuypHRf6gQQp092j+FnuBQGUKNXyJR+T14efV+KqoRn6tBnokrLdmgl2p3H2O2tc/kMt9Fr2X2cJSnNs8+p3Vth+4KDeG2bHYz/MVMiOms9Ey8YMEXwNAWXv3LQE2inwysXvaqdEYE7LMQRZk/WT3LMKbsFREO5H6Fdt+L10vEXKU8U64RHFtql7bAZYf8IgJ0gZsoiZKsnlbcSMmdwtR0TKmICUKVFsvdJjWUEZxtmPW1w9GyJllAWWLTKF2jevHczyCU5bhmoZ7fpdVFaQMrvfeJwZjbJ59eZ40jjYUtIqLC2gawdW1SA7aVizwKvmbXaDfdoyuPBIGSs8HnukDPWei1zr3olUWUs2/HZkn1OmFmOhL0zMUJvngBLlp4y1hTp/Bne6AgoFmF23N7KgdrhQoNZ2+2afHKAaI8lVykLkH+f5sP4ZMVBOc5ekJCr0O/7n0+c/lXzOW6VXVcowgH+fRKX5v2fmXwHwdWb+BACY+RMiet+X/RBiLQv0kb82ICL6ZYh1DUusBda7j1JlRPiefJ704w0ZZbQID9yaLoVyWt5KalkaC/iU+viOXZcJ9SY48stKy1ufASowMcxdSujwg7FWMhOlfYQ26kayM61WXfCoNABU08r3oNwIkGVvxSLrrV4TDBw1LahuwCcrqTMWXIpZshttd+CTlUdFyHOhppU6SoGaFmhb8GohSqSm7VB5Zek3GRXHRM7F9M+yVq57jj5oGLnEygd0rhaOQXUDZSxUsCr1BD1qDVgraD8WAN4C2DGf2XpEmKGVZxVSCCfphCOlwl4IrjWGfBnTGI8F7Uoh42k53y7VTcf4lkVEqtDlNZY/PO3qhHcE6MacIlWYQdsGeH6O9ecrLD85i31kltiEUQ/X+xZBZRz0F+f4+vZJcmAJFS924FUJc1ImjCNlTN3QQicfIxzUihKjerZDsalQbAvfL8fU1rZSUIahrxpUFwV0K/7n5CQ9NitBnigrEFBlOB+D75c1InoljC0oSPpWVN3KQbl9qjvmO331PNOUMvepwkq1nbIrQ8o0fv4FUF0u0J4Qqqsl2hWh3HJUSsU+wmcCih1w+vESm/cLuIJw8skKu3eLiJSJVhZfJ0XKrJ47tGuKSkdlOwSMahm6BcxS+laGsXxuUT/RMU26oJek3eVzg+Kqxeaby07YcCMMouOuXsogeearvDQgx2hPi/guTCFlwv6RImXIyZ5FluO7OYmUub7FcGw3iAl/JL1HRL+RfP8Vf3an9DrO9tZ/7l+/j/TaeZ2Mzykfw/38h+GGdJjuIamiAujtR92X+Pux7PcKfz707SX9dnq/p4CAGZQPw7EOcIIoJBblMAd4/Uj76e+aFTpXI/QEn/68IQptYsCcFEOkTA+9Aq/EZgfQgfD1xICuLfTGgPp7fGx/qm6+j2S07/faO9sF2Tne9iSl/AwDVIsSfsCDHaLkmaptK8+sdyYPED3p7R4aNkWG5m2k9RM+sr1CUMRx2UPojPVpDcrnl2h/bQUuVRxbqsirnl/CLgs5A4ICKcwjfO4p6eIYAz+RrGNEp8axIbryRGObYdhKj7ieIVPypfeKHcMuVIzdkY4rHWe2doShMi/rj8FpXBoNuAoSu6aQc9OVSb3+8/Eok2hoIwZZAhmCagFz4lBspLA5syBDYFXg/I+wKKXieAJvThEBBAD6RQH7yIBqBb1VMO+1gCOsflhi+7MtULihnjljAmSwvNWgdSvzbhSo1uCFBRUsc2CAHQFGCbLEEiiNpcMAWwXSYpXknQbCfUvyVw3d0eLv1Kl8XMFdbacE3eT7jG5uyR67+vgUZIHth1Z4GkN4/PtnuPoOYNYOxUaJcrUAyACqIbgFQ28IroK0nyjewpjKK0J5ATRPAFfJGmRIGerKZso4FuPjVEyZ5i/daz7nXtKrrvifYuaPPTPyN4no9/eUPfrI8wzPrwDAI/WUD2YqSn9vjkFMMYMS8aGjGZJ+GRB3j7SvoDwJWZluGuPhyCjmADILVj/OAeAXKhXgx9AuXQOCZNnVGPMzZ2tFwZS4tHR9+LXrB45Lm3dWtLLGgtmJAkk6kf9BecQMrr0FJI0XYYzU0UrasVYszxKhq1MsBKt1HEtgMJVYebxWmIwSbbJlOdx1Mo6wXN7PUl0VcOtSNNVxvHJwkmWo1sJ5GK/eeWtMpWEXstYRfq0Cw+ubaGW85kSYi2IjSJn0oJfDO3FrYX9IAh2iJnmcfetYdo26Q7bPMIDlMF59xbAlwHqIlHn+CxWU5QFSxqyGcGMwsPrS4cnvrLD7YI3nf6SMVpfMV9cLy5zuKqkuiQG9ZTz9/RJf/SOClAnX4YBHP16gfqRQP6Z4PSgfAqJEWY5WnU4AEaQMObHoKAucKEL9REtbDOjaI2VKwJbiYrN4VqI9VTAr5X2uWd6dwIy1iIioXDkQFCWMQslzT+/BWw0j0sWJ8F+9aBIllr8flAqQuaXPWfyq5YBVrevqxvUWpZFUJqhNC7csoDeNIGUag9N1NekPTa2FOr/G4uMVoAnqYoPVaiHCFYCoaItIGSPKXAC0rRMFHuXKTedEierLwjnQrsFqWXUuWkgEmroBNy0ef7zKBYsRVI2015fi/HdjwMwoE2U1Ny3gLJbLJSbT0Sd9sFfCKK/Mj+FPQp/Oyn7V3J4F6aCQdnP6kpn/+IEyr+Nsv4GYe+fptfM6KZ/zuPgaq+/9OD9vj8nQOMWThLqHXGQn+AeokUwn6PiIGHspNTKkaNw+WqaHyjkmiCzgz2W/fywaM0DZSBmgH0g2CtcZ8oWiQijUc4UClQp2qTLX0fGYMpSdyQFZI5+7e5MuM1ldGgrYY2X719D1Dch+XlwD5qQTuDOUTb9uj6cIL+XiuSBIzUknRGdIm9Bn0k7/fh+JEykIhLFd4dGLcxXRtvZk6NlHTLEsAFBNePLdFc5/AZ0SwFKH4HHAo++doX4K1E9ToYCSNpNJJwvQR0SQI5BNAqWGcg4othKkN3zXOwW74K6dMN60L86FYS4ItsqfY1qGnMxHJZ8zwdrl9WLdpC+ywPoLC73TwhsaMYgQJ/Ud5+05JLyO56ksQzUMVxF0bT1/Kfzy6tMtHv1o2Z3pHk0MQIyywajHgN7u4CoFZQyodbDLAuQcivMN7Kl3WY+xbtL1TNpkBrU7pKh/iZ2SGGID72AZUMIn9V3PI0LWI3nj5wm+o3OX5pwXSfmD/vXUCNtH2noehZmBuhYDewg0nlKf/+jzWoDUcTwZH29AfT6oXy4Z90fb7Xgbb4DeAJ9zL+mVlDLM/LH//zkR/TUIRPczIvqGtxx9A8DnvvhHAL6dVP8WgI9xkChnLA4oOkgn/x2L2wwwRCQEci4iZRCQMn1EjHeZGXXpAcYZo5TpmCobfmAJw8PMoB5SJraTziFFyvShwgEps15HdESKlKGbImV6c6CmlT5SpEygsKF59AgRgU9W3ZoygxolAh1Rp7DROiJhJilsdEHo82tG3n2JrAMa1W1MycaqwpjqBlSX0CkzGcg5aYsIXGgRQgMipNDD8kn7Y0iZwXvkN7uI9Olv/jexdIV3o/9+BaWWUoKICsxreP7M4KsNvvm9k1g/PHMA+bMM70sr0Ei+3uDkkwrr75/J/R4DHWg0jkECjVRfXeCD54+9ZbUroy53OC1FaZYx2Vnjob28/RADgfxhXFzWqF6UWJ5KW2RYFBuFIGXIMooXW6wLglkVwny5TjHiCh9jZmdQXVnYavgbV0Yg44FBJXSMa2fRlTVXluC2Gu2p6uDroUSifMqseGE+DGiPlOkYdM8wGfnd2oqwuKjQnCpUl+KSVW4cdu/ouGYhjk0g3QDrjyvs3l/AFYTV5ys0jwvYRTJG6hg2XbOPrQMszi3skqJwEeK9CPMnTJxdyiRVy6guWjRnEgAvvB+B0SkvDIqLHbYfnGSufRJwL7w3nDCiOZMGK++x3rag1sKcVPFd0Zc1qDUwj1cJ09oxUClDR8yAcdEVi2NfSfm6FUXy5pZ8rd8Svaaz/SP/uX/93tEb53XKAvwz3xg9cyNaYQylMCjrL4WYMn13oJTSr+mxZl23N6f8RyzLMb5JUOAOAlGmbad1Pb/TR8PsI2UclFKwp4tJdE0YVxwf+0mZER1BgOwDoNZB7Rqoxubz7bfpevXHBLOkHCVjGba1596YEDhRJgqCPnbcYPwpL+WvZ4Ja7MNFpOwAdR7r9/iy3n2e6rc/h16ZNLD6qBA5Mo6vAeMCLNAZY8M7P4ZkOpaOUYreA1q9wbYZwOJgqY7SLSwIn9y7fkyfN/k+3kiyr83KgEniB/IbuE/00koZIjoBoJj50n/+pwD8TwH8KoA/D+Bf9f//HV/lVwH8ZSL61yDB734RwK8d7onzzXFPWODRDfiYl2oKKZO0S2oCcfMaMqSMkhoyMoOux6xcQLT20vUmd3kK5dNDPFH0cPJ/X0wZDjFldrXU0Wq8XIgpY2weU8ZYkHdfYmPATQva7kQRZQzYCHomPBeSxjpXAptySMEP3cNZtepiYoTYMix+7BSUEKtK4L5hvoSoIJNgsBqu0lC1KGVcVYArFdE08Ja01E1FtwLnNmtRfuidhVlpdJYqQcq4kmKwUkHKJAz1KFImZ4ajtQ6IAm4q4Csj5VxJWLywYEVwJQkCRsn90x9tcfmzKxkDifWkqMWyJu4piaLEMk5/tBO3o+crtE9PcPHzqyiEp3ByFfxxtVcaRCbSj5fFPej0DzSufv4Utkz8nx2w+ryCXWo0ZyqWD/VTFxZK9oAMbs+iECDH0DsjiCevrNGWs7bE+uRArYNWVp6Ple/CJZD/DW1RPVvK9x7DTLUFL3TH4DuvIEjH5v/ISltlEjelL/APmPVQNyDI+kq7YOUJSri2FeWmMSClwMbipKdMzBhfa8F1g7NK4FrctKi0Gt/TvDUmfg17SKAxRjxheNharKf2K+vA7LD4/UTRsY+ZntjT2fFAHnShPKnR/TtlOm7CmrGrb1D6FemWmcbXdbYzsyWiSyL6kwD+NoB/FsD/5lYn8xroNngdrhvwd38wvHFIqOz9FjI9S6/uQT1I0taxOpNQLvvdHfG+3kAnA6ADCty03iFi7GUpJ+vM9Hrpda/prcuSryrcT5yNkXrn6VFjmDLcvQyN9Zu2f0w/t3WO3eRZzAqZu0PzswDwakiZrwP4a14xUAD4y8z8fyeiXwfwV4nonwPwIwD/TQBg5t8hor8K4HcBGAD//EtlXtqjBKG+8dIHu433+xb9REsfyxRFLmwQSQCnESXGVLsAjkPU9JAykfrxYsKG14c2T8yNW2/NXS079E2qfPHCGxUTj3+faxQgypK2lUC8Lh9HtklbK30nqBwRICfcCJwD+yChbJ0ImSyp5QI0j41HoAQUFJEEDg7zDOibEWJmoGlBRZEHUe5ZjEiRIGu88kel8OtgZevDCL3QWmodP1e9PlJ44cAqNIUQOrLcwAI2BnMMZa3F4187fFBzui4Q5pUAPP7bByDzh9oFcPKb4/c0gFfKaxOUABDry5gVJ7ytYT77MA8GAP3hj8a7uuH3n3Y6uB7mDboE3dckf4zDgvnrp9d5tv930KXE/hu4n0F+3zyvwwyub1HRN9NMP810E8H9WKXG61B+HGr7UB8DNNxrGtNUO8eO63WO5Ri6zb5uSn1F1qH/x9a5z/R2+Jw7SS+tlGHm7wP4x0aufwXgvzRR518B8K/crCMvHEYf6d6DOxhvpuOFph55vM4Si+Wt0+v6gV1cvJ52boHc5eXbHsJPFb2SJem+CrgzzTTTQXqdZzsz/waAP/q6x3ibdGu8zkwzvU3aJ+TtEwQPISZugqa4TXqbYwnuV+n/V26SOlS//7+3/5TSMRyKhZmGheiTUoKiJxq670+FkMA06n/veBxLHz2jKTctaFHF7wODNiCx4ZSva63cT1330gQxMsBh/2F+SUbYybLBSOpcjMsX+zIG8Gjll1ZujV2rG2l3qk3nwFfXoJO1PJv+79M50MevZCKd6SXoFkMrvwK5NGjXGxYI77vGcaaZZppppgdCPJ9JM830qjQVH+fG7RwWnieF4UNZN4EM8Sv3c9R2ds0jmjPhMcv6mQiaRCL8kQKqMkfsGgOqqnyf0XooqJVJ9qsUIRHHRdn3NNV5vJbEkcsCNAMYC9Ic26HkfxJ8P8Ywii7liGWLjYVdKbjg4p24fscAzRoxa2AaDHk0k1LoD+iCQifXTj6x2LyvYdYj9ZDUDXV69cN3Vhy/kyVU58D2fUZMjQ4gpjl3JAkfKrkPBqpzBVUDu687qIZQXhB2H9ikD479gxhqq3H6hwoX/6ARP8GQeShdX++fqC4KnH1f4fyPSvYjqlw3Fki51e8vYU4Z5md3IAJso6BKB6WG7sV9Yibvjd0hnsfKm10Buiyg3qtB1JW1VyXOvlvi6h/dQZUO9rwCSge96uRGZwn0VQX3pJVX80UJt7agpQUbBewUsHAgnbhrh4/9TEyGUJwXMI96cmmSlSleagjFRsGc+fV1gKoJ1XOF+msuz14W1hz5NXIU75FvIwsOHTIzOeDkY8b2a5Rl0UwDROsaeOe7Lc6/U8KsJUlIKCsBrRntv12OrP6boJnPCXQ/lDIzzTTT/aTbtka9CtM80g5bK+VTBrdfdGqOYwz12D3rg0ymmdLG+gpuaWEsAUEYMptZJ21VyUGq9aD+wI2zlrzatFwM4sRw04BWI6ECg7th24IWC2F427Zz/zRGXDAH8aZGLKzG5tamNA7OhAUHSgE7n7Wg0HItxNepynEBzAfjjkGvQ5ye1IprLPjyElgsZD361i1jxRL34raYlZl+aqiX8WjU4j1l4Z7YL0iroYV6ZC9KhXTqx5UaS1oQLPzxd67FRbrfV/jeDwpP1KXrDn+hH9PtYTE2GfvMikn2pRAUnAk+MQGi4B2ChcfUyb7p4qqFOS0lcDvlQnwqjJOTYO9phqS+IJ4GXZdKiBmZ+oK3MhKoPaRYBiBZYbQITWk/qpVMSpIpyi9fDRH2Idd1C6gGMKsu61KxBexC+tE7wC0ApzuBef2JCHSbDzlm4VEtsP4EuPpZQO+kIBnJwuQqhqopCn3mlIcBmlOBfKB8mBayyJJXMrBvhjplxBhFAbUnFEOEXS4490NmYPFlhfbMwZVBYeEruU5w9QEL8z7i9byfmFXJ+XmH8tYLsRuF5jFg1r32OKmbCt3hPyeCNUTwDsKzaoHFC84zL3K3DmTlL8TUAwPVpWShLK8UdMMorx0WL3SWyCDN1qRrYP15i+q8kGQDMdRAOn75XG4cVl/ssPpKEBRO+8QWHJIOAKvPJctS/VjKKJv8HtLnlq0tYjsZ9QFbTn43qnHQbQ2zTEI4MKBai/LZFZrfXINJQ7etxFVM+BBJ+7yDXWg//x1cqSSTq0/2wUWnLKQY1w8DxQE5hmq2ks01u0GDsvDjS8uSY6idgV2Vg/XpE/nYhNJW7z8wCEZOmxq8qsZ5cGbAMtSLS6x/eCrKUssShzO0YRy+fzmj4m+bZqXMTHeLjvbfPV74zhjbsZSjI0ztQNDeF2NnylrlGVduWtBqGRoeMrzWSqyb9SoTzietUBGS6T/vaslwlVqvnOugi17w5WWZB3buWbKYIFkomGHXVcbkhnZVK0Fxi4sdmvdPwEQoz3cAgObdVZc6NHTDYtkKDKuuGeV5g+03lrHdYifPoV0rEAOnP9xg8+EKJsneYyuKDO0gVThL20Ut5XTDMIuu7NlPDMg4bL5eoj1J0lyiG1tqDYpWoZbx9Ls7XH24wO6Jisxv2m9g4MgIExyu65olaLLqmI+UoY0Zlxg4+cSBFbB7oqBrRvuox3j6fsorn9J76TNDNYxiJ4y4LQnlNWP1zOLyQw1lhWlvTygGJtYNOgHAM+SsgNNPDYqNxfl3FhljoFvG+tMW19+sYnaokJJcGYauGdWLFvW7JVxBWDwzYpksCXrr4EoFu5C5pOm+hYnlGPxa76wEZl5owDLsUsd04DFjlH8m5BjUSna44nwrQbmXpaT3bizgHNy6EuYlDY4MgBp5D7j0ShnjurQPYSuwFtjVoKoCLxcAM6g1XVY1aiVD3G0qG2cL0oMnWi6gv/Nz8p4l76RySSYkSlAFSn4fdLUFn67yNPYAqG6hLrewT09jUHtqJTYea+qUHSFLU6FkTzAOtlT5vh8HmQsa5BjF+Q72RALnc+HPWyKQcV27QL5vIlFkZO37qdVW9gIAMbi7cZ2CJa0SYoilaAuEOgynVSxDjgHjoBq3V/gB0AX0Jx/c/8BvMN3jc2GTfXB67hIKJH30r2XXQ7vs558+Y+NA4d1Qki2PWgcU+edUeFNXNaAI7mTRPUtmqE0Dd7rs4tU5yFprBdXa2AaXerDOff6gj26RL+nkOkVbRLsEPi1Fv9BIO76xHMUigjRrgtPJeAgorw3atUr4BkraTdvI/8tYkCvsVO9+b5wAUF06mBVBtb1058jbjUq+IJuTIEjGEDSqBcgR6ne5N87uhSPnkTL+XvVCQbXA7n0LvVMorhTqr9scIROeCwFqq3D6w7JDypReYzNW9rzA2Q+WCVLGdr85YrAjrH5/ifYRw35rBxDgGg1V2o7tTX4g/V2A/YIp5QQ1A0AFJEyyIZldAbqqoN5tpF+/FvaqwNnfewdX/+naI2VKoBpDyiwjUgbnS/DaghYeKVMHpIzrPexkG2CSNWkV9KWGfWTGGbyEBkgZTpAy7zlRSjKNKu5yBE2nVKRw36X/5Z04+ZixfY/ARaiHqGAjFv7w6e+f4fznSpiV5weL7r7eMtrPbzHL5MznAPhpVsq8DFP9OqzwUxDWkYC/fSirWLF0PvaeZSqWtXKYiuXaX2taUFXGQLnRit33JQ0Wdd82GyvfQ2Dg1HqdWsKNFYtyOqbWZ7ZaVl0d1YPVAqDzK2EuHp14QTlfi9RCFhiKNPPPIIUye1gqgPL5FvakglsVXRkGissaatui/uCsO+gjYxAYTBEoxWqWMFQ+RS8rn0EpZTaoy7SkWsbqo2tcfee0O4xV1z6YUW4cFl/scP0z6zjmbt7J/h6WLEs/DKy+aLB5v/JCq5TRtUN74pUaBig2TrIa7Xs9GSi3DqplNKc9q62TNKPF1sU13L1beiZ0AXKM5lGXflnqeEY4WRsAUeNPvoxqRTgoSBQIVLcoLyqo2mdSakTA5yQDkrQDsQb7S6qRlNfUCrMa+iu/3ADOobxYipXCvz/puRmY37AOsm4O+vMXePLVCXhVDs5cABkjG60Mfn6ZdasXwye1CNHVVt79qhQGsxw5CJlBtQEK7QUoYdRhnWQcUwrUGlDdYP2DtRe48raE2e8Y5/BdXW0A6/DeF6ddf0Hou95i8ek6ExQpoFtYUsyXXy5FMbmtRajUSn73ZdEJEq5rN7QRGGq0xqfh1fJ7CEiXFCkDdIKEnzNdb8FVCbX1Gd7CmFrbpYtNLfCt8YJZDymTknPiLusEdRQzX4Xn/DYYh5lZefjUGuCzL0AjRoP02kCIaRpQWeT1AMBaOGOgXlwMzvXsnU9jLrAgzVSItYCOn5iytnLboijLzt0FyPmZxMCQub6k7fZRNMaCF0XepxWkTN+IEPoLqJv07JYzizP3F10o2IX2Z3YiPCeuLzI3xMyG0dAwIrT3Bf1MGE+E6EmkTOGVCYnQrxrAVYliAIJ8yZAyjbRpllIWBBQb+c4FQ28JdsU+E6Kcs+uPZe+//qZcJy8Urj4Hrr7N0LUXiA3QeqSMrikK5GadCOt+jZDNMz38Mf45kAMm8yD3rRipBSMQd4OglsCafXvdGV59qdE+doKiScccuyEwcfYfQJ6Oi4VZHHMriUP1grLeEbbvM+yydz9xMUmnlwvKImj30SOqBfSOUVx1fCsAkKWYRZIMcqTMhUfKXGuoGiivGdWFHoy74xUZ688NdK0lsyWl40In7AMoN4z15zssnwkKhlWRrSs5xsmnNexSoX4sViplUkRNwhem68GIxpfAVwdFg5Tp82YM1bSwyzwhimodqq+eC1JGa+hNA3h3t7Qttb2OiBVdN3CFl0nYigJU93jlkSM4KLVVI+iXvjK2Q2aFtXGdUSiUsQxqWjGY7qORGJBDVBFn/2lbg6uRdsN968CX1zj53RW40NF41/Xp8IMX7f5xvU6a+RwA90EpQwQqu2BDbK0oJva4EmQ+uYH2BJkK/UQKwX7L4Qs97aowcr3PYI1BggOFH4RWMesPtAI1LRCstNYCZS/w0tQ6GCOMXlV2/VoHlBVIq3yNUvQFAIbL58kOcKpbF+e68iYt5mSMCXErP2pq267PRCERy222ovhp2716g2MpU2btdtBlhaLnSsFtC1iL5fOL6YbGUoqnY08VU/1NzTOnvNng0fPTrjx1TCQAscRvd3j07AQDl5fwfFVnJSTmznrqHNTVDuWzVV7PuKhIEHSBxaKPfvFKgywFdm1BzNDbMlNUSTlR9oAAVRtR0CgSyyPkoAztdZam8NkrzdjBLgu0Jyq2X2yF4WhXokipXiyw/VoJs5KDVDeCOnEFcoY3LLdnhvVOGFRdC3MaXqSzH5cgy7j+oIA5ESY4IFz6jHjarmqAd3+vxOW3F9i9Q4KUSUjQHFKHrCBWIsqk7hjrdJyREobk5ONHcAWheSQon+aM4r1QlhxQXrKgYhZiCdENQ3ukjCsJ5ZVHynxLQxkPb193DJVqAC46/jYgZU4+OYOuHS5/psp4X9UyTj49xdU3q/jeBah2dSW/8+pFi917FVgTFs9bmJUGa4LeWriFgl0oBGs3MGTIlGHorYWqTWRs7EJHyDArypkF6yG8ilAQwVUF3LKAaq0oYyyDV6VXpIT31691rSUjnNZR6RTe18jgtKYTEJUSZj0ob2amYaY3RWUBfOP9+G4CEGHOdciuDCWSIGXc2VpQEQlFpMy7Z+BCzg7VGLBWncARBCFArgVERqk75UbfdTA9ngxDn+9gTyu4BCkT7oU4H0CnvMiMxxMuV7p2sm+EMo7FyFFQruhG9xseQ5uQY7hSDcoSewHcOox6zPj9iqd4qyA8TrgO5G0FAYijwScaEwKf1x+7cYO+qRWkEICIHAp8CWuSd6W1HinTZQzNXDA2kt2LV10QVLIM2tZw66Xsc/46aw3oDvEEIIsHE4XOKX44uZ6iu+K9sbpZHWRrkxncEoMZvCGHFUVkk5QB1K6GW2iPDENncPNtdMiRoLhL+FEd2uKOT0jGlbmyyQJj9XmL1VclzIKGyjkkn5Prg7gzKr+mLFBeAXaJTkGIrp3As7jEjmMXMpd2DahKCjeP+n2LoouVKN5Uq3H9TUBZgq24K5t0xyQuUWa5wIs/ImN1XuGV/q53P1jALoHNh16p3BK45CxWTkaUfAiKoCQOSmg/KM8AQNUaxRWhfcfFdokV1Ebh8fcqvPiHWJSTVyW4YLhVotRxQPViifZMxl5cEuwScAsHsgRdE+zCI7J6iJe+0owMobwktGfcKbEIYMUgmyClPa9YXBPMWTcW1RAWL4DdU69UTBV2aX99BE1QmqUxZXrXTz512D31CO8RJZtqGU++1+DqWxXatRiSo/smCz/d/vXZTfu26e4rZTjJiOQtR2yRa7PT4r3/x1KKcAmpgGkkRStL4f2NHUDL7I80rjqrrBYhAraDkMLm6Z8Hgd0CWSsKLKsROQ9vBWYAYAfWWtpID0jlEQZEMVZF/B5QN6GOV1QEwUngtDmShwJSxlu/WakcXeOZTfVCYkO4xyfJoZvMLUHPBMbG9aHRA3SNfC+fbWAeLQUSTZ0QVlw2UNsW22+cZoqHjDlQiKiXdDwRKaOR3cuQMoVHyvzkGtffOY3tce/1Ka8dFl/ucP3t9YDpHBPqg1tHcLFZfrXG9mul798vV81oTqUj3bJHyujuIJ+g8tqBLKM9VZkgqsJP0K+T3mrxyw/XAvKB2VsQ/DvhlRasZL301qK4brE47+DpxVYaLxeClCjOd1gvtDDUniEX33uKii8Kgj4na95YURIFS4en4tk1YB2WX6w6f95kDYLFozv0wu/fQX15jurTNdyywsDVJbWGBH/cpC7vyzaQImU24v7FVSm/9XJkW3bejSaNERH9n8VKTa0B6gYn3115htsJciW1oKRKUa9soG0NtC0WH53mvyHnQJsdFj9OXOPCnH37aFqUn1RgrUC7BgsfDJKsfwZa530n6xs+k7Edyo4ZRZh/n3EPCCffNm1r6EJDlYWMxceV4Z3/LfQEK7JOEDVhj/LtZ78458DWx8pp/Nhb05WxDhyU0rdBXoCb6YFTa4BPPt+LlKHedTgGGwOVoGFjEevgrIV6/qK7GN6jKQNS6s7bpymepjXQWkOHODQpKsfXywwxU6654RogxiP/Gw2/34ik68eo8fXGgsoiNV74a2rToADkbEmRNQnqJSoACJ1AnyJlCIOgsjKORHBOygsP4ZEywV0Wsj+FILTROEAQV9gy8Ar+vK0ZdkURjaMb9kgZim61euuFdx9TJhXkiYH1p+ICs/mgc+HVLbD8gnH9TYJufP/Wx6qpOMaZASTODNDJqBmCNyx5+m5Q7396y+UKi2gQmaL+/YTRVwYREZR2vXhGaE85GnNGkSq9PoSvSOMSJXVTwTYpHz4zldh+TXVImb4g3K+XtEWuu58K28owdMMoNhNtOuHN0lhI5caJm/iVgm6B8tpi+Vx3bcc25PeuG8byywbLF5WgsHXXduwzuEDvHKpnW5z+xBsBk997aLt8vgM0wZx6Y45JEKnc47M44YViGwm/AkzyW5lbcbhnHehqg6//h55nCbJjQNwG/qw13V7iPQEyJG5fRuvxLJGcAxsbs1Fxdq/3gnGHbEYy50H2plA2pV5b3Oc/XK88IO2O8aBJWbYOT/0ePWgTQFFvBtfeCM18TqS7r5QhEmQMAEDvRcpkbj6ZdWePEmVEQRKsDIMfStLHgN4QUgYJUgYJUibWnmKWGl+mSB6xUoDSHinTC/gZBCDnZP1CfwDYR+2Pm2dPkRPPVeeiy1Ns1liAHagoxhFMvm+32YK0Am138XIsOeLaBceT6NcB1bUIer1AimgbsHVYfr4c1kn7G+s/uT6YU7o+zODdDo8+XY0zpICs+W6Hx5+sBwqrqIhKmdBUuGaxdFWfrPIxWod1VXbuIq3BalXlVqu0fYJHKgl8szqp8r59nyGmjL7YYbESAVxvW8AyynWRMKTJBqt8BwRvtUXnDpYwZWYtc1+sK7QnhXxnYT5dKUiZzFKUnpOKfEwZJIESpfDJSQHVOmzfK9GeqBjkEJwww34Mabu6BR5/r8T2gyV2j9UkUibC0xMQm2rQxaDpKdUiM+bvrb+0IAfUj1WmTAt9ANJPuRHfebMQhZcyEo8nxNspdozlswbXHyy82xfDrFQSNJC7+XqGDwQsvmqhrxpsv3XSwYYBkGEsv9hi97VVrBPiI6gQbO+qhj1dwJUKxXkNtxQBUTUWrlDRfSmiXQIj5ve4gOKibdO5b1VekeTjOvQD2LGx3d4YEC3B+h3KuaA4Sl4SYyUwKSC/FdtjZJg9k+WVMIFRsUm9gEAcY4JmmullqdDAe0+7FK9A54KXGDpyw4CD2uzgTted62Q4k+sWtNnBPTmNrr/UdiixeEYFNxI1gpSZcKuO/VuGutrBLSvwQg/ce4FuD87OmayR/KwEEN0BYhH2Yw/jSqtP8fEeicIJgihF3MlnznmMtM0x18YxITG93v+cluu3F4S/PgWlc38tUwEr1O0rrcOeFITRNEsTc0QtR54wBIo3Bl8r/J6bCkZpMHkAmeIu1I/DPiAkHrg/JhD2+xhQX3Ad3H4JIe9Am4fo8SvVnqazl6iTcrWLyVIdjYTzHyXHjHKPYTk8S/0S6NJ9pWeR/c3R5NrO6OBbp7uvlGEGmzb/3ldwhFvxgwMfEf9lus/x9gEcl7klr7C/zb6/d1qVCGwdqOm0sNS0aYFeW0n7PiYCUkVIK0IMa42AlBm0E4LTeqvXqMIhdelRKdPjMgYSAEh7lM+iEoFKq15/5JeBAK3Bp4liog+BVRjU7UO6M8WFH5s634BXFdyqzBAt6qoB7WqY9x8NmcZEeUHGw6DTro1nmLWP7N4T7ENMEbKM6pML7D70xzWhF2tEAp2WzzbYfeOsu5daNXvvBgWYoSKo1qF8vkPzdBXnJbBIC7sKbm8MvTOwPqbOvgj3ETUaLJTBShLQEa2VqbYGeiuCqto0gHUoX6ieFYSHsVRaC3W9xUltYrkQA4TLQvxun1/g5MXaZ9XhLjtRn6ll7rm2+HLWZb8r3m4BZixC7IO49i5vL37kyIzydoez3y1wphN0RfLsUqt11qf/3eWWk3FGNSAB1ykCLiuWWFX6lO4r7MCO8Whij9rHpDI7LH+rtz+xA2M/Q+e9wBBiJ/f1W6MjSdca95PZYr6trATJOzbTw6XWgD/6ZHiGTxCzD245ZmV1PnCotaCPdVLn8HvkbiDIMrsMsDz2Wx/oYKYby76OcV+T+8ke6pc/5ld77Arcx31rppleCx0jrN9EoH8JBc5MD4lmPifQ3VfKEBKkjGc0igk/tz0KjqzJPVpeoBOCqM/sHKgnY5hQBgUlxsQYYp+JIEfe4kFai7DmHKhMrBx75sjS6AQyxXkBb4JFSRQCwtwlFhh/fUxAHBUog+vVCOpI+pK2nbdE04vzfqPT9ZTq/KBHxhKLWguQAinKLGPMDs4x1A/HAqvm7fafairgHlL/WXYo/t6BMgCK3zvQ0AQ55uEPmQhFb+2OjaPOmJ5TaNECwN9PPvfu72t73nrHiXv/X7qdl13gW1M0zHQjmpnVB0/sHNxud7jgGI24WXftzr/pmWa6VZpSMIygwgbXbvL/Nuk2+n7Tc3qd6/yybR373H8a6ad13j26+0oZhrjPpJfa5lWbfK3l7hW9LON3S8R1fft9PkTGdd7gZppppplmmmmmV6WbCpbA3VAmHEvBNSv9f7DKkXMZC2OwJ1HJXuobfaOhdDjeg6EW+nPsj4moK9M28tmj2KkohmUPjTGQtXl8yvR9CfV770rfDXKy7cFY8j64X74/1JDhciQD7MF+/ZipbsGLMk9hHwztjRH37H1ts09TfzKCUR7j60PbtQ83sfDxgyxDvbiCe3wiaHQAbl0N64+0KwHAG+BH/5/pcc70RujuK2VmmmmmmWbaT2+T2e0zdylU51g30oQRvrGLaH8sY0z1viCmNykTqL2l9WbMAfBmmqlPryLkv6zAfUiIxYggnMaRS79Plqe8nyygcU+Yb1qgKiWOX78fIhGgKRdmB2nJg5txQGD769HNuzXgZRnjEo3FoYNj6MsdzJMkUYFPQ5ylDNdJPaJhkGDyAYgJwwyOWTn/Pw20TIhlsv++je4a9frM60j6ZoCL3v0wlvjgkI1/kBUp1uX4WRnC6jPG9bek/eDiy5Bgva5I+ojB5PI5jwVPjv2kuoiG4EIWpdCR9QUKzlKB99uL35MBnn6/AFmgfodBjlB/PU27OlLf+yWTJUlJ7q8RE8oXCu0jh5htKLySjWSVdAsGGckMBSafUCO0QdnyhHTv5AA46tbB5eUGWY0wvE4OqM4lE5OtuKvf67OLFZXPVftslssvGfU7lGUyCmWqC4Y56bKAjWVUIgusvnLYvqvivS7jG7L/YdzEPkkHA+1a+bh/wOmPK2y+sUCxdSDDqJ8UWYiBQap23xcZoDpvwZ+8QhiQm9DM50SalTIzzXSXqB835WWF7QOM5yHBlx0PAmqnDOSoi59n8kKMFKq8Vt65oQtiNZHaPfTRtOCmkXKLRYxuH/vwcV+oKgVdpbVYb1TCuDqWuDRpu5stsKgk01HfopMywK0ByqJjRsMUNzV4uZA0tYCPk0RdwMyECabWAsbBhSwEtZVAuNSVy7J9KUhwW6AL3Am5Vlw1UF+8gPn2e3JpKVu3aizKT16g/tl34ZKUtGOunLq2sKWsT2AKdO1AxsEudfxuTvYcC8wodhLs2VUKemNgToouhhIDemNQXOzAWsOelF3brZPUrlrBlV3gcDKM6qNnsE9OYR9170Ua/wmWk2wf+W8kpIjtBxWO2cAsgxpJdZ2ldfX1w/8QG4nXC8Sgp/0UvD6TBH1vwuI000wvQ4TO+nxk9qO4H6cBXZUanhkhBpx3I6YsmPxI8oG0fqIIIOrdn8qklH4nn52xllh4vFpMlk2t02Qc6Nk5+MmZWHeDAsGngE6FfTJOAgPrJMZcX1hP1i9mQApFrc+gGKbjQkZFdPWjUiMRxrlTDKjWC9W6Xy8X3oGhgB0FNCcB8EU5INkbYwY5304aqP3Rjy027ym0p+mcc4G1e45dfymRlaD0Zu2/cyfoqhaozkXAdCWS/ddX5q6NRz86weW3FVyRCJpIyk0J7mEc6Wc3vN8vG7M3Ib8eynaZGfM2CTwpqAOdYB3aVhadUJzOeaRuP11yXo6hW8bieYvTT8tBOUlH3JtQbx2zWIBBGE8F2V5myvgb4bz+AC3S64NM2o98Ll5cyFePwrCrPamSU2QI58+JLEM1Bq4a8hgh+D9rFTNqhjEN1qY/h3RtiG4u4Pt1Uo2RgOIpLxi6oJF3uzd+JoLeNLDravT3pmo/94ESLLngGHrT4DRJttENojeA5KvaGRCz8JhO+CL97AqnuxNQbUHMWDw7DikDy1CbGtTOwQZum2alzEOlPfC6w3UPC/TsWAT7qVSa7GIcmVEYZSoQp1avkMJO646J7NdPFQEpI8ksGZacFaG6KrOsTzGLVFVmTN6AkTQ+Dk5ZdCmIA8MbGFo/fknnfSmZDR6fgZci7NO2Aa+qTgg0Dup6B15V3YZNXepgLii2J+ktDfSza9TfftIJ2wljKP+FaQMhlll81cAuNdqzImMcg3CpGwdVM5pHWqxSkMOmunZQLcMuFFTLOPn+Oa5+4THqxxpOe0uOt2IBwOorC1cQdu/kAZBVA6w/Nyg3Bs9/YQlioNgy6scUGaP1lw6br6mY7jtlRgFhKE4/sTj5/gV2H5zi4mdK2IqiZUXvGMWOoVtg857C6acylvoxiZVL++xEjc+I5BlCssDZRwb1Y436iTDwlDAyYS3IAdUVwyw6ZhTegrH+wqJ+oiQDEotlLdQJTLGywlCUG4di57B7osWSsXEwi3y9upSYfhzO1w+CkGOoRhhJ9aKQ4M2MqOgAhIlh7RUOnDCdIcWkkTTl1DrooDwKGZlqA2olDToxy+FtfGakJMBwTFXpywAACgWqWxSXRff7ZQbtWkmzrQjqqkQZ0nJbn8JbUQdRhmdmLi6hmxb6vOoYhBQqn8aOyq5zHNcABh3Ip62EIgkontYPa8gssTkcd8IxO1A/40joZ08cj9dOsyvigydaLKC+/W35Es63VBCZgO1n2ZhCtsRyJGukcRJMOM3Ad0OFfyZzeoXnPgEPQFRoq424nEf4/B5eP7QbdRdWUjnL/Cz6WSPJOKiROadl0kxI2ZoxD+L9DeiYTGsh1W1f4btPOBzLwBn4C2NzviiUT8d+vcXZetll1IrtpH2EwpS/Myn7ZTm+M5ngyQzaGbh16RM0SDv9NOLEjOL5FqvP15kSOyjDOmRLpwyLCrPksXFSNlWGZdcShUlUVqWKMiSfafyzi2NP6nhllzEAFzmvE8ebjgvT7cuYggIkrrJHymhByqi8ffIp08NzSdEvB1EyQK4tYBIlYUDKhDo9pExGYz9jzz+CgbM/WIMJaB9JvfprNlP4jNZn5EgZyPfiXME8dhlah5hADQm/WrGMlTjWiUiZ3sRT9ErKgw0Vb73y4V5YIv958YJgln7tkvqUtDOaFt3zhwCwfLbG7ill6xPKVOcsSlRK6o60s/pqFZEy5MJ9zsrFdiNSRhQx7Ylk3CTLOPtRietvLlBuhMern+hBf6GdVJFJlrF4UcF9ektIGWDmczzdD6XMa1Ak5EUn2ptq40D6bVgbGXlmHqIHxmhC4RCD5SYKD25a0GKRW7+16sabBN6lfuYi3w+shbu8Ap2egMoyP+hD37ta5lGV0+P2qRb5agM6XUsa26YVwSdAYLUG7WrweillU413igoIzEPymepGhLTVQhQrhe6YTMseFuut7Myd36YTpo2rAtQYSdW5XsItC7F4A2KtI/JKEfIpQhMGNYyzbsX6tvAppZXqyvb9XZmBQkvfZdEhKAJ0Nwiy1oKMBVoLtesEOmotUORrFMdwvUX1ZWIlDP1lSizO1lBdb8GFxrIqu/Lp+9AakLFYL6t8Dk0bFURkHfjFBc42O5ym7aQM4a4GlMKjZaJ5d8Lg0mYHWIv3Pzn1TLMFL6vuHd3VOFsv8/77jPZmB764wurZJZY/XufvkbXCBDuHRydLqMutKCYWFdL0rmDuMoz59dHn11itl+BV2TE0SYar0IfaGXCpcmQFM/RFjcXZQq4HZItHynCwwHolkPIKDr2t4vdy0aWbHWT6CnPnjkElhliNvU9wZrWD9AMlyBNRbFHsP5QPn3WpvAWYumu1BhmGXSmABSnTnibpaPvyCAN65/2TK4Via2HWukMKMVBsLIrzGigUjEfKMEnbqhX0iatUfB+UZSx+CNinp2gfLbp9IjIuPsuW72PwWzEi/ITfckyDHepbB9UIugeF8sgmXzcom+AFR2MFKQMAxuWIJcizYCLgao+l8HXTzKw8fGpa8Cefy+cbxIcgIOcLgM5YkpIP7p+hH6OgOe2Wsxcdk12n4TWPkgEAan2mvjRBwT5FQXJuhXOX/d6eIWUYUMbBLnT8rXYImfA/n5OLZz68QMxwlTrgDhPaTq4BA6RMZ0AZlh0I70l70ThRdnsYF9Slt/P1MqTMjyyuv65g1pT3k/QVaJiK3P8zMna7yssSi4GluvBIGY+AGbQDMdic/WiBy59Rosw4QGNImrG2R8sFPtH1hG9/P/vfb6MvyKIn6ELWIlOY8Ejd3nhy4ZYHgnj4rIzD4rnB6lmKlAlnYDIf7rWTIIf6iJ3RuTIL8safc92a5Qua8RHpOZ9cD23q8wtBYXiEi1sWXqE5LJu1kfKroVwt6e0zGSCMh1n4+35mzBFDSkavAOjI1qE1IlvtU9JOUWjHuxWOylBeptkrFwKgbS0y0DH9JW2TY/DCv1+OQVcblJ8uBYXkHNaLAwjf5LnRrokZVm+FZj4HwH1QyhCNZ1vy6V9pzKr00l29ehsAcsvL2IvmnChfkO2p0SLDQAZJZv+DGsCUR4iBXNgN35nBTQNY2wmqfbJW3DWm7qdjNAa02YC0luxGyZiJCM5ayRRFCn1eYTRLVtjcrZU5lEVM90kqaHclVkSqVFPpOjBLADFrwW0LVTeZRR7GAFpDb4oM/RKhih41Q3XrDzbbbdDOgTxDGObK6TqH72E8SgGFgivEwqSUArcWbl0J9DNYmYwDl6pjFP3cimuDggi7b5zmzCLQIVxUZ60M6JnVlyuYVYHmTJQJ5BhOU2yj2AnyonkkQnxghBaXokm3FUE3jJM/WOHyP/UYu8ddmutoPWJg/ZWFLQm7p/l7qBrg9BMDXVu8+IUF4IAyRcqwIGWu31dx/BEpE96RBCmz/cYpLr/tkTK+SLEVpIxqGdv3FE4/OYOtFOozEl9thQi/tpVn4JSgWs4+WqN+olE/olgOGDJb5bWshSuRWA+A9RcV6idaxsMCOwZ1fQBSDiTj1LVD80geWHmtYZaUMRgDeLXlQVsKSiyV8AxEgC07gRqTsdC1BRkaZRAD06FqG9sJ762qjbyDntmixqC8KLIyfUszNca/fwrUtCirUpioMK/GI2WIoKsS7F3IyFj5zRGJ8i9BufD5JfRmC71cjO+ZdoLrCpbuPUiZqDgHEN0x+hZsdtJH2LeAbn9K/wf0TNOOj2emmV6GqhLq61+Ts2gKKZPwJxlCJewVraArBi56QIaUGcLyJ/ieMRbDjdwbq58aDphzpMwhPosZaIzESgllnfN9O7GqJ2WptdDxfm8Y/b1kzADQer5nD422k14LyJZjeMi+wBn6cDlCcuCygZ5gvdni0XLR8TBZweN5WbJO9ui+oc6xxJRZJAIm0fDd88agx7+7yo0gPSVdqhxLDQN99E16f0yxliFlfF9Z7JoJ5VqoM3Ar83WYCMp6fkmPK8/y+DR5P9ln5PUDUtdWhOsPVIydE8lBeJek/HScmuQ60nLdu6Fagis5a0P4L7keqddWn9jfOvnRGsow6qcEssDu3d7voV8/4WvSWDwEoDwntI88+i2pohqAHMEuGGQ9H+f5roFbXF85xlI3W490ScaUa/1rDqguAbsQvrFfrh//Jb0vxjMZ8/IZo/Go7X4MmOqSYVbJmnDebpjv8rkTFDrQU8qF/9z7DhRbB2VYYsoEfvUnK+zeXwlCvnFonhRdWy6dB2froSyjvGjgXtx9FcFDo7u/4tzFqMive+VAuJUUOSpQ5AgqhqOD58hhuO+Qo06zSs713GtGDnutJb11L2ZHmoo6Ex6aBrRMfLGt66KX+7HFGB+pUiAc/v6zu7iEOjsFynJoLQPA252gaDw6YoxBoNZ0SJmzE3BZQKVIGSKg0FBB09tHyqSW52BdD58B0K6W/pZVF9cjRcqkSAgALqw7M2AcuNSg1kJtBCnDCy2Wc+9WAa3gvD+soF8SJsKvh/LrxYtSxq6ps5DrTqCO4w4IEWuBtoMfU2OgjIp90a4RXsW7h2RxK/qM4k6QMsuPVLZGEdqdWiISRpOut6i0xirRlqdErclRTaFe02ZMNJ9f4NHlNc78u0DWZe8D6kbKVmXnRgXIO7erwcbg6394GtcCwYUFAJoWj1JLAnOuLADEUnB9jdVnC6z//trDp2VsQesP6/B4tYiuMjFOjEp/J0lMGWbQ9RarsgAvF+OCRViP1oIL1SGxPKmrGqtV1b2Tydr3mU7VWlBrUb0QQUQ1Fq5MlIBANob4XHuom+CHjdZAhQj7OnkWHklCCcMJiNJEnn/3brlCZePUpQK1TtyiAOidHo8pkygmOqSMht4a2HXR7REsrncSU4ZgTxZw3g9dtc7/5pJ19WMvAbjTNeyjRccJRqQZor+5NNQJDWEN0pgy8K5Ycd9xbhhTJryLibIJdSOormChMjZX6gLdnnp9bIL5V6We8DfTw6SmhfvsC/mcnt1TNMJXsLXJvpLf5zSmTF9hE9tMpZlp9Izcp7zcgbgzIYOmvqxGkTIpqgbwPEddgzYllCnFOJIZP7o+JJ4VumtByFck8lMUmv3erChH3FmGK1UnCKtOSTCIB6MoEzKDC41qOcal6dcdFe7jvJM1Ze/CAsTAr6lQK2NDRFSc/sRg+16Bdp22nz5DjH7OY3wAumGYVa8ey5yqS0bziCS+DecCdhToLHD2scHlN4tO2ZDK/UGIpbxeXib5MiYE974PkDIjwvbgXs9VORVMycoYlPGuPclYRw0dfYE29JMJ2d7tN0GEFhc1Tj7pEBABASoxYNI5dG0O3Ih926NnGOB50TwmS2dc6c7x2M7If+rf39Vg5wRJ77z8kd5PP48oHTktZwyQGlrTsv3x9cv4dUiJ++fj2N45YRwfFjtw1h6KVdMf6+A2R/mUD7S1fsl++viaZfJ5hRsQM4g3N6nxCjTzOYHuvlKGKHcH8hSUMXcGKRMQJCyuKpEpGlMoeaTMWOyDSH2kTNpmDymTtjL5WjsHNkZQLVMoG+v9tVOIMnPefkCXtAZ0fS3MR7BWJ/XYo1zGxrlvnZlZNn+toytXfMYj6QQpZQ6ddyOzVpA8FwVUkrqPvWCn+zFlgA5+TSTCmHMx9kx06Ulhjalg7Zysi/Fz7ituFMTtqtBgrSUooXeVUcYJmkbl1iK1LaGdg3l3BRf8ucM8Akxbdf0E5rB6vgJXCu1pgRAzhVWX1UDVgt5oH+kMxl1ci2uJqxSUYSx/uMD2249Rv1OAVWdBCrR8bsCKsHtHZ0yYbhnLL1sU1y0uf+4EAFBsHJpHKjIyy+cGu6dFFlMm9S0nB6w+b7H46AXs0xNcf7iKgReJhYnUO9H8109LLL9swQpoz4po4SLuGOXAwCnLWH62gl0XaB51MXcypoqEQSs2NroEpYxYVWqYdSnuN5GJQ/RnlvEHbg1QzOBSiaJljOmyHNdOMUcFYnw/rXd92zbg3Q76+Ub6rUqviLTgyyuUn5ZdPILA3ITPgdFqTfeOR/SYvOtleK+NQdlnuMLv239m6xBjRhmDQunut+kRJ9yIZbzQulNSs4u/wX7QaNs0ICLoMZTemFIku82DfS9leGS5A+eaSlQuL5O4fxximNg2e++/NmIcFtBnuv9UFlDvPfWKw15w8v7n9HvK9wS0Rj+2ib8X3UiPRcpM0GgsmbD1jFVwEHdXIrjgYrrnlSZmiQFFHiGYKuydk73DodvnnINyrifIHfEZQSh2+ZpMle+3n95LFWK9ejzWRl8YHbR/hCDatlgd2i9jfdcr0hvTGHpwbBwjFPbKowS+I9p7rXTHBD0GMKXOP+ZXuG82d2umM03Rbf8E7gXNfE6ku6+UYQabaag4m+RBvmJcmcGmdkyMGQ9p575D7JSLkVd6RLh/P+AtIPeJcqRMVfngt575KIu8Xjh8iIbjDu1cX4NWy15MmURpstsBRZHfBzohEYguQLzdglYrQSIYK0xJEOYKDexqYLkYICCyeaZj9p9VLagbLCoRIgvduT8EtEZqvU7aCjBcMlaQHFUpiBD/Y6e67RAVRF3Q0bQdJEJ6Os7Qvrfsp8R1DRgD2hTRdx51AwpoEkCE57qBKjTUZdkxQM5BjzDF3LbAdofqeps/h/hxgqlqG4AUylRAT8t5wXjRey6Zi5xzcE2LxY8LLLQebpaJkL5MFYVpW+xw9nd17HeVWWccqrHfViokO4b1gv7JsGSkMqlXhjYPnHoFXmHjY75RXUYPkHNE+b3Xvno2XvHLr24wqrtJd4WpnJmmmW6buGlhP/5s5MbxQvGBQi8xqn4bb+EX+sUXt9/nTDPNNNNMM70FuvtKGUIvcK5YY1OrZnZvjFIr7r5yx1LPosGOQdQJnBzan4qDEMbUv9+z3HaXGfSK2T7YC+TY1fvHBOxfx4yev9KYunbvijj2ivTi/PW3eXHx+ts8krh9RTTA68hQwyNIs9dRdqaZ7gs9lP1xpmlifvX9dqaZZvrppl4spzv1/6eFgqyZ/r+vdJss9cznALgPShlG9EcevX1HrJrs3uzb+9rmeYzgOgu3M80000wzzTTTTA+TXrfAvEf4vFGcxxRV26s36vo+hjbv3RvUO2TMTbOb1rUg04sibyf93M9KFtvoIc7T4NW1JN7AeiVx+YL7YdNKiID1Mou/FLKO9mMqjboj9uPlpXHvgOiSH2PgaTWaDZKcZAHrx0CKbvEhXlNYhsZJNsZef6EuOUhm0yp3yQ9JE/rZIbN5KeRxp1iSPax+eAn7eInde1WXdj1bj1A++ezj+EjGymFf0t/QHTFtQ7fSv1kpjAYXHm2067+rkN+T+xxjNaqG4Soa1gttpe73yf/qooVZ6S7+YLgfYhPxsO8s4C8A/Pr/e89kZnoTdPeVMjPNNNP9p9dpKTlgeegzgGlwtb1tjMUqmqIxJmwkjhKAzJWxS1nfQ+71xxdc0lJ3xDQT0NgYJxhWrmsJ4r1c+NgHCnTiw8h5ppyvrkAnJ11g5KaVuDCtAT1+FMfCW0l3TkUhAcOLCQ95IgnUDQhDWzede2TI8OaDRMcqjiVDzE4CN8cxAsB2B77eyPWzs859M7jSaS2xpDY70MlKXCqNAVZLqWsM6LTnCJfGhLq6FhfRwgekTgJLD2ItOAf6bE6JPdNMGb3sHp+6nSb78qggf2Df7oYydM9Obg6F9f6ZYV23V+teCtswhrYB72rQ6Ynsh75dNkbaLUv57VoLLKpcOE5j0oUxBZf1RNhOBXAyEqzZLcosqDGAYWYjoEuqoAlwgN404FLDroqYhSikajZLjS5AMrrMRTEBAjLBPAQs1jsGF4BZqOx6liWI4dOYd23pRgRiVyRlk/5UC5QbydrYT2Ee5xuaT8aYpvrWDaM9yQMzT2UbCgGalQWKDWDWiDHvHn/fYvuuQv2OF+B5WD+MKQLmaagEUC1idkgwsP6MUV0xzn9O0rFX5wBrYPWVAyvg8tvdwJUFnEZMnZ1mZ+pn78lj1/UHCZ+pCFA15D1iH2B6IKBLXL7w7NK4eaphWTNNCHH5yALVlax52l/6X1lp0wYlA3djKnYMs8iVLtmYQxxRBiRBgPQdgl/3lTyhPoXvaaQLK/H7VMj62VtDSsJidBkyUw8Jn7jAOpSL4KYf7nFWL49RhSyV+CBwswuKEY7XYxKDdPwTAZplHL7tukERkq/04mTlc+FhO6GNZk6Jfds0K2Vmuns0FnPmYJ0bWmn2CfaJ8JsJvlMWmSAYlmUsz8y5AD6WvSJYqfz9WNc6sc4sFhK8eLUCmMG7XSdYWp9NI81i5LMqkXVwX3wF9fgR+GydMW9xyCHjE5Fkhnp2Dj5dA1UJVxUSnDaxWHGhYhpmV2kJuHu+gz3z2XUi88FwWqG8bKCudsCXL0AnKzTfegpXabhKrArLzzcwZwu0p0XGjJEDVh9fY/fBGmYlF5XhGKBYtQ7br1ddhouQjcIBZz/c4PrDlaSdBqBbGc/iucHu3RK2Ak4+aaGMw+W3FjEN9+qZhVkpOA3YBaEfHipYeIqdMCKqZbQnKo43MlmfGzSPtTA4jGjpWJw7sQBpoDntImLqhqGspF90WgISh8O2vGa4AjHAcVxfn247TRk5FoxFhQDCBCy/bFH95AXMe2fCmCuF7bdOJGuRFUZw/YNz7D44g/VrXl5IBiV1ucXuO++C/VpVz3ZQmwZ2XcEtCljPkPTTb8MBeme8EFFAX+6gLgpwSJFNBHtS5UyJzzIVfq/uzIeOtCyGPx88mJdVl80rBOpUSnhyrSSuFDNgKX/3R5gT6UjaYec6N9EUep2m3GYeD9L+xmhEKTTTgyNSCmrdUxqOKBS6M8Knbu9lP/KFus8jmRoH5UYUzNy2omjoBx32v6csFlvfYt+P09a0UicE+fXXqbWgi2u4p2dgraEuN3L++LTZY4qGLItSuk/7a3or8Qfb0yoR4OWDakWqCWdQpF6qZWUYxWUDtyoAh8zyLwVFoKte1NCfPkf7M+/BnJaiPCCgvDZgIpi1lnNja9CeFENFAHVB5IPSI8uumAq0BDn/jJO2sobyr7KvizCdWsdVq+EKJZmmAlkvfFk5SFRyngzTgGNAeicB/0Mw+4Gwl4w/bUO11icgUF1f0YIvgii1Fm5ZTPN//fE5SKxGPy9yrsuSmC3QHn6SGdSYLisnAHV5jcfLBXjRG0v6e+m1mQnW4RyxLAqy8P5vapCxOPv7J4ACqLaS6XNTA0rhyXdXnaKn5wo0+tuIAx7jYYZzJtsFuB5Dw6SJIjJlmCKQSZQycc6Ari2Mz+TYR8nIReFNIiIm3PNKjqJSuZItpb4yjOW3SnYE+UHduoyNgxz5rGtT78JYzMO8DZUgZY6K0py0ERVcI7+NoOBiBa/MS8aZvPJTCBm5B5QXDcy6OBopM2gPAD+7LRXBzOcEmpUyx9DLWICO8SPsxXDpx8cJqcAHqbPjsPqbcRJ0NYVJpve8NT9THhD10nj3mKp0PuwFlBDU11oJDBwoZlphcNMCzgJKgwqxjMdxpIeWUl3GidAvM3BxJT/Udx4htSJxyqwqYSi4UHLoFSpPux3aI5LDtiq6Ay2ByEYm01/S21aUCKUePbCAjonSWwN11aD92jqmBVatg12oznIRmGIvLLuSYuaeftrl4qpF+dU1zJM19KbB9hunUK1D9eUG2w9OwIpQ7CyodTAnXXakcCDq2mKx2cJ+7Qnq91cDhlKZxPrhD9JqW8OdruCWBdxCxxSYknJZDl+yXmtfqggmADqBHE4OemWFsUFr5Pm3Bvq6ha6tZJoCoC53KC1D75ItyIkGXz+7wtIBXMr6wTNo1BrAWhSXI3kemKGeXeLR5Ulcz2BZpG2NxScLsFJQl9eAYzz9Yt0xR9s6KgowFpjatw/jf482SSmeMEu02WGdZjcJ171QAqIubTwgvx3HXd8Jk0mt6bJ99RWAqUIvpX4Acn+NNjvwxRXKrQSkBhFONnXe3osLLHwKegCgXSMIk7bF4idF/O3R9RYwFnpTQFUlimIosETySBlVFpK2fCdMKPy89IDpZ6nj4dvRSuNY9q2gKLUOrLpsUINMKEFQ1Fpg2h6Zw4XO1y08H+W6AOehjNZdqvfQfnhfnBvdj2ea6aVJa6h3nuwvk/72mzZmB9xbpW3l/Z/IuDhoHxD+wRjh2fvZI8NvYKru4DLH2GJU9NhNa+F8QHwCxBChNVR1JAqt7+LifF+KUPm+sixvIfPbvuQDfi5sDArfRtVHyoQydQ233aG0FmV4FkSyhxFhERTHxqJclPm+E/iZQCHVNzB0PQn/jRN+41rWR5IeJII5JXWYO57GI2WoleyaQaAOwjsrAhdqmP47RcukQm78DhTbAq4kQT5QIgCHzySCZVBYRYSNR8rYsiufCv66AYoto34SDC/p/HJBO4wnJWWC8WQ4rvR/H2FBboiUefK9x9i8r1A/GfKAsY0xCkK3J90IIoW19LP+hFFuGRffkbWvzgFXAuvPxYhz9WE352D8CZkkR5UA3JUDEBExY4gaMKBrRMOOK5GnGPd9qgZdRstEkaDaLqtnRLs4Rnldou1nZojKhq5NVyFJ5S3Xda3iezSa4jwR2mNbLYNLMfZJ3Y6vFUTISDte6UG2u3/IFSi2x906qdaBWgddd7ILuQSFsgc5008WIuWTlOchEQwzqLVRSTiJkInz7K7TZtchZUbqDMYwgrq5VaTMTADug1KGRg7z7P6BoLRjKQND1T0MDVsnlqiUAky+TwcYnql+3HYnQkZVSbmUWSAF3m5FOFksDgsB6Q8vdX1I2gO7mD0pIDTImNx/dopSJYZLtJrWgSlh3LzgGvpi60BkwEYDbYuBcieMvQ9hZieuEsygXkAd6mfZCYolz7QO7gdKIcmp+0j4ntaxDloRoHKF2JjLCBsLtA0WXy7z56BHrIxhzmEcLtVu+HKtAe92KC83gDFYbWqxamx3WO0kgxRaI4fgwiu6PPNXVCXIWkmffH6NJXOHigl9hLTmQYPeWqBpJfNVmJZVSFOXppbRoHhircGVitBnAB4pI8KstgxVluDVAu07S7hKRcTIyjq0TxZoT3VnGWE5uE6IsPlwBbMUZkUZRrHrkDLXH1QZgxcsOo9+UOHq2wlSppFDd/ncYPteAVsSTj85BRnG5bermIZ79cyiXSmwhsBnqRuPTAodUoaE4QsQ3cD0EAPrzwzqd3ScP/v/ixfCZLkCEWEDALrej5RhDdgqt3oF+LKyHdM45sOsjFemKWD1eYPykwLmvTOoTQPWGtsPA1JGxrn+AaH54EyeJYDyskXxYge12aH+5qP4/MtnJdSugVtWcKtC0rsjYQaC5ZUBtWsBB7hlgYJILJdBMauVWMSBjkkwTpbe7/dcahEmVK6AisKI65gLWQtBsZCxghCw1n+WP2rNuPBonbgiACDbKW7IZyKL7RKJkJnuf2+aGOC7EjhtpjdH1sKdX8Svk7xJEML9O5uhMAdnk5IzuCgAr8Qk6pUdQ39qLUrKPlKGemdAWie9fwgp4+9RY6AuN3DvPgIrBX1+DV4t4FZlFK6OQsok89abBlAK5qRMBG/5oBpBItiAFEnO9WyPNYziuhUEKACzKgYxI8g4Qcp89gLNzwpSJpxj5bWB00qQMpZR7GyGlEndacghO2f2ka4dyLIgd1Lh2e9pEWEDyLntzwcQosEkRasS+y6tKJ0J5AV2v68mnRDDG016Y0qRMkA2rji2MBbbCZ2qsWIY8rxEvw4ZB2oM3LKMe30UInuC6KA/QFxMg7Eu8F1hLGm5lMI1n/UzCsWXGzxaVmJUBKYTeOwzmPgxpb8T2tVA0+LJ3xEDEXllHhrhw95dL/N2eu7Lo32MGB2630tePmaABTrDYL/NwCtm40DkGwYGjtbmhs8wrKS+Mi5Ha4W6RpBNY7/rtO9sDoZBV1uUzoHMyo+p6y/G4AmKnoS3izFlpmjMpRyIxnMmQbcHPlrGvf9HnKJVBv2kSqdMAZav1z4EW4ZUXpVwS+8iPqIYOtgGAP78llQEM58T6e4rZRjjKR+j//FEUNrwgPcwz3vZanYRqZISqZtrDnlC4cHWyvCDi0h/Ewj9B0jmlH90oCBE+M80sqEzc7dtkOr6SHVNI5BoQdMQmL0yxzNrrKhDyqTWH8fgppGDNJQpiw6umligudBDpI5j0AvPpL77JLafBTGLiAMGCpk7ay2WoZE1otZGIXIs0Jl8EGE2IGXYI2WAHmOIjgnS1y3UpkXz3jr62KrWBzPzxJoiU0PMcKUCGeGK+gdDeWVQfHkF884aatui/voJVOtQfrVB/cGpWJK8gsKcyAFIVpQWdqWha4vy2Tns4xM07y6zNQUEPhp8ygFA7xR0WchhutBgD3GmRKNPHrJN1kEZRMubqi0Kh2hFIOvARNCXNdT1Flw3ICJUX23EGufXWT+/htq1qBaJVdQ5OUyeXeCkbjuh3AlSBq0BGYvyqyFShiyDXlziyVfrDu3g03zTrsFytQC0Al1tAeew+KhDENGuju9fRGyNoDhiIL5UOZsyYrsaJ2PW67rpyqeK3jDGfkBAQJSbIf17nCTl4zpgJY9jqxvw5RWKq42Pd6Bwen7SrblS4ItLLDY7VAEp42O7cF1jwdz9bncNuG6gFxV0oTvE0AiRsQCzIGWaFny9ATVlVAqrtreX+jVm76ZEmyRTnEdJwcrYsn3dOZBBp5AesfiMukKmn7UWhW14TlFpmsDrieSnnzCzM830WqjQUE8e7y+T7jVtK2fqofewVaLknBLgpvqxVvaeCXZnslc3ZKzZG2NUiob1aJSIlFEEDkiZfkBVoFOGHiKPyglImczwEhTefWPVYMBiUNL+XjllFGwbuF2NcrPJy/i4XUu/h7B1WFQJj5T+D7THHSzwheRdlrka4beSehniJlUCbBug0LnSy7erqiIqvIJrMIAOdZwIuqDuOpcKTitYH1MmlksUUOTg0RZBYPZImZIi79OPRaNaFqTMY5UoALv7sQ4SJVf6WK0EYm3XXZ9pmQHyORHWiy3DrLo5PPpDg+27Gs3jnlvQvp9e/x571EnRjXf9uUO5cbj4diExZS7EELN8LjFlrr6ZuIUn7iyj8WR8H8fElJF7HJEyYBnXsAxABhnyJpBq4QPvdv2QA8qNQ7tOzsx+e6k7TnadoWvOkDJ5rBzuyrvuGhlG8Ux411E3w7AYLpF7fH8U3KLTsbq0bvc5G0dozwpfqrdFXm4EtdK/P7gXPkfDd4KUMTaXm8bqZe17ebJpoMPen5YPe3Rv/+esjOf7E0PtTLdDd18pQ+jcetIDlEeuZeQZgGPcflLyig2JCXKEP+pLomTYC3FQBLVYIItBAuC1I2VCsR5SBsA0UiZhglj57z6Vd7xjzLTSLCJlSFyZkn44IGXCWJUeMFC83cmYE+XYYEa9jWRvlH9rI6M1oL622lpByozFgwHytbUObAyqTysMED89xVbY+LL3oo+oMYKUKc6X4LbF6sWJKLm2W6wuNp1iwDqUSbDU6FbjBFWjP3+O5e4kMmaZ9SCJKQPjxLVEK+imBC1K6KKzpgGAK1RUzLh07I69kozgiIS51gTVFuC2FIGgKmFPqsxKt2gd7EmJ9qyMzFyA1a6YUb+/jr66qmWo2kG3DtQ47N5fZMxfGOfJj0psP1jDeqSMan3wufMW9dMSriSsvmigaovrD1eRsVy8MLAL5ZEpasDwCHRX0DqsAdUwzFoNECurLxu0pwLnDrFawEB1buAKWRdz0lk5deNARhR0AdIc7hUbC1boFHuBUQkHvuPu+Yz8/tKYMsWLGhoAPz6NVrj2/UfCKBoHVyiUzsE9OY1KS7VpobQWr7RH6+gyqLxiIsR1iT77KSMSlK3MXvGZ7tvdfbJueD0gW5g7hoDFVYD9dWx2ohQOFuKgXLUWXDdxj4L1wX6bVjL4TQl1VhRBPBakGRgKmkFovS2afa0fPrUG7osvu+9TfEU0RNjB+zqWYSYGrE3PvX3uyeGeR8CS5q5cGntmDBUzpUxovRIgVVh7tKe62oAfn4nC/HorMadWVSI0EwZuPdm9fE5q0wBEsOsupkxqlIEiuLBnZexkck4bB33dwi0LELMExk3ve2NF8WIH9eU5zIfvwq7LeJ7ojbhQmZXElNE7A7MuxvkIYNwNK6EgaOuduLvaVRH39lTITpEycj4kMWosowAk0O+yEyKj4JnyKYy455HJBdF4P5St2wx1OwhGGv7iXP291uQ8al8gdA7UGpyUxeB+JpiOCadBMR8C72f3hkq6gTDqbPbb4LrBSVDYZzFzeudCyi/3FZ5pXLLQt3Ng66TttI5v5+zQ+zJGR6ANRvn1V2jvtdCUgvgICidx9XpGcjSN6bz69+87Md+i+9LM5wC4D0oZTjeRhBGO8VgmHmQvXkuvySP6dZMIl5vSlKIgIHGc/89dhex+PMDG2p5SMO0LSkumux+CZPYUQl07yWHb17iG+2kQwXjoKOEbyHVImZihgLqDk1nGMhZTJvx/8mjI9KXMYEAbJKiDQVpAoItiHtpKrOcDpssjNVhrQeGE5RtZbrVrQZsa7p3TDhFjfZC5FG4KRBguF8oH14OP79IxVGrbQr24ksC7dQPz9BRkHfTzAvbdM1EGNBYwDhwC4Xk0CVeF+I5rDSyqzkUk9o+BpZ+ckyCn1oFbI6jn1gdO9cyECigs66C0knlsdlCbIkOXSLwPEkTFbgfebIG6RtlDhPD1FrrQnUXT1wczeLPF6tOqY6isFcHbicB++v3FcAP37m4nf1DlzC3LnErP3LFHXDz63W5d2MdZmaQeQxfjMGHI0I06SyaMzWK6lzfGLDEAwwz8pLtGf9CNnzA0iDsk/PVnn8frt6iKAL56Nn59s9lf73Li+qF6N6Bbhdq+AsM60z2hYiSmTHpGp9eck1gx5RGxV4wdj6EyQgNBkh04+8EfQCXnjXUfAyKub+SyFs4Y0GYryhLv5tQ3htHY2AA58/p9+rXSI2sX2igS40jeXi5wh1WbYpKdYzh2wCefje778emwQ9l3l34ZCuN/yeqh97G34YDJLx/GxOd95WaaJp7BCDPNNPM5nu6+UgZTSo0xFMuRSJq+b+YYOR4wM6kwdjBwXk/oHxCzZBMh1cWu6bcdYPxVJ0AeTNWLjvkYhwC7zLUp/gwygdCOfoz1mSNMOLVGZD8pJ5bqOJZ+sLyEgZqyTnMIEHg9LkwNrRGcuekMyztkSJYJcjfU2LqRto5hVtIyJAOU9jB0l2Z4wfn7iGUOtvv8+d7+b5UuLg6XeR10fX1cud3ulbq598fHfADONNOdIW4NzCefvu1h/FTQa9/5+AhV9TFlZppppplm+qmme6GUGYvtMk6ZBkH+9xUBN+l3RIP9JkQZbpv99+t67/37QK+ybkE5M9NMM830U0Wp8nqmmWaaaabbo9uKHXYDVP5eF/2Xaf8G7Y0ahY/J7Ja5+4c4loeTphw0Qh9jYB+MpTfel1nPYIy+6fuRAQfc/ueSenvcdhDcYEx/MZ0o57X3N/M5AO6JUuaVrLqzRXimmWYK9CaYrPTQPJK52stYHZMJbarqQSZGga0djZfFPu00lSOBQYM7YS+GRcxKFAKD9pB42Xj7sRNC08FtIQkaHGJRARDmZyxTGkRhy00DFeJuxXha/pk4HjJ/fQanh54bpM513K1ZcNU8fz2urTPNNNM9oZc9O/bs5wcF7Cmkd1rkGEF5AuE9Foco6ztFHvdjwKhhcgbA75upC3oaiyj936sX20+DFof91seZ4bIQF+nE3Z2MlVh6/fhGvv+pmERkXB5bD8jLOkjSiD7ivL/caTyeVoKyslbQ17Vkvyn1ZF3JHDjsYyreUVdxeKlPo/Wm2ngDYtJ4hiugfLGT+HpnnTM3WR73qwvkEOPohRTv0t6wj2MyDI2OcQQhnwdS7mIZqY1koEwzlI62GT735+dG1mesXjquqfu9drJU13vKZTE9p9Jjz3zOrdP9UMrMNNNtUj92zk2YsQkGbJT5CoJjv04q4Csad5nbw4j1BWJuWlBVysabZsuJTFIvyGNqeTAGVFXg7Ra0WnkBWQKv0mrlA6y6LoWkb5f7DFLaJzOoaaWd5QIodMYM0baRcbYGvF6AlepSG/aZJR8fR724hn16KrF6jMQxYq2gaiNZtPrBLdnH9WGGiynJh0uqdxZmpSVjFRG4oGEgYEVYfLmFq7QPXBwYSiDNIhDqqVbG5zRl2RKy/knqpgEdy42R9Kz9V4zhxydjkWwInK27ah1CiuzyqkW7logHymfLAgHV59egbY32g8cxHWw44PXGSEDikJqxl6VA7STgIzUGXBVwIU5SCMLrYynFuE6BLEO/uAKI4B6tOwvNppb3WRG41BKkM00fH+Mv1eAX56Anj4Vh9xmyYnY1b9FiRR2zksboSpmSGIBYYhiRz3LCxkiAdJ8Vhntr+8ZpNizMFOhVLLN7i02cT306pBCYOqP2KQPSzJAhxpjqnRn98aTnYlpmrP9+DLqx8THLvhB+8xeXoNMTcFXGfYSXi1wxMSbI55OcLjMxjn7g4u66VyR4hXEq6Gf1+v2n8wOyzJTUunw/V8n/tBnjY98VXey7EKA+lmGWlNeWwT44fax3QPEkCpcJtEMUhAHVGLiq6HgByHqopsuqKWPxH4Ll/aByouMJoqBsWdYiBemH5sJ8++MM75ASpRTtatCmGCrIjhGo9wnsx1w/VjA/hkaCI78sMTPQtCBFeTzBY9AuE0adt0LGQE1lZRujsfkdi/C5YTiF0dIvi7SZY+fdOs1KmbtML8uAjcHeSI0yXhwyFo0oBkLgUSrzFJXsI9tTUSBmqRqxokdK02RbKzFyfLYUt92JlTst579Tj/kJGV1gDLhpoR6dRcENgGSM2G7B2x2oKECPzoAkOxGIxKoSFAjOAZfXwNkJUBagy40oCE5WcnBcbSRY7uMTOaS1KBQk60U3LtoZqOcXcO88AgqficABbllEQZvDPDi3jOidgV37n6ETJqP44hJkHezjE5jHCx8IOHk2ioYWF5tkVOoeFABg+ckVmnfXIMdofV/kBfQx0rXzGYeA4qpB83SJ5UeXqN8/gatE6VF9tUP9/iqWl5SYFIX0kKY7jDXNEkQOKC8bUGthHi86pQiE2SkuC7hSQ+0M7EnZWUaCwJ4oMsh2WYE4jQHlGAQHMk7uhTGEdQnP0zK0TpRwvXSF1BjoUndBmXsBkuPnyw10oVF8NRJ8MzBsYX0CY50qCsaoz0y1JqasHpRLGZbRbGjdGLhtUQSGwnXZkXi3k6DIm22HLgkps42R4Jl9BjPcb1qQVuAQCHpi72LnJGh0Mi7e7QBSUJttd9mYuCcprRBT4nphLMSTYr8X4OoaINXFjVIk+5ezEmAcCbMS4NN9pWjYK62T/S/97lFEcS+5RQaCZ1jvgyfSGvrxO92FMQYemM6UeGyWyf75sEdxMiiffg/B4qOy4QheZR8/Y6xkWawOpPk+9nfX3wPj9wmh11qwsXFP56aVLG/Gyl5krRgSRogOjCsEU58cZx8tMjb+2NjNEJmjNGZ8GaF+MPs8kQPnWSPTYPhpxqNQZ1TJNvIu9hAyaftkHdQYitNa6P71I5AyGXqBKGYYjKSR8XlpXTehPAtIGSiCuirAqzJTXo0hUAcGo5QmHvMABTPxGNOMmwfbeAUaRcWMDsgjZQoFc5rEygz86562yPC4gm8fWmaqzEgXg36T32AffaOua0HKpLxjrDfSZi+5hvBsGCBtJlNl98ZzEAET2ptCwByoF/t6cXtImZnPEbofSplR5uLmL8s+qOikcqIr0DHxWo8yPDEQ8BR0NI1zsy+VZaD+eMeyIvX7YQb7wLhUVXKgtq2gGoIgxt3ByVfXoiSpyvwQblqwDzSszk6zMXNdi+Lj9ATkkRSj/qH9sTknyo5FJd/bVjJLeetUmF8a2Dgj58SKPbjuBU7r0+k6J+4UbRuzLfuJSRth7a3t0m4rBd7VkpqzbsDGwu12oF0NVTfx2TEzVB/Oagzcdgeqa1F++es6URKkQYkzBZcx0EFo9IIn73YivD4/R5U8lyHE9cisGpstFl+IoqsqpmG+3Xq67rqx0M8WwOU1lpudKLRYMi+trndSzlpBtqTzStEtejh+2tSAcyg8YiVlyNS2BRkHagxQKLiArAjVGUmbiO8mFwpcSFpuaEHrsCK4RYeUicoZFssffCas9F6q8Cq2Fmats0xZIR03OY6fl58v4FYF2pNEEZdCr5PvqvWoFp23kaY4jSmw/RqSYxQbGUtsN32njGdAR9A5xJD19HMrro2kVUWOlCm/2EBtdjlSxgjjOoqU4S6lqtq0QCFIGVf6MaaQdvblWyvPM2GM9yJlALiyEIHNBSUfiaIEANWNCExnpx06Bug+G9uhw8J6tUm2rfTd94qrKIQtFrKXkFfiZL8dzDTT6yMnBoUpmgreP1ZmjAZGlQNlJmmKtzlQbq+bDNAJATudn0+xgVTh3ilaB2fjFFIm7XPsPC00SHlehBnUNKD1UpAyxvMUASkz0f5AyNV77qfL0xP6R1Evft9TRgwmGdqw18ZY/RTVGa8F1KJCZ6S4AVImfE/bG0XKFP1n3Rscc+fGM0HkeICICdeptmIAy+brUTuE4TkU+iQ3jnJKDDPs5CDO3VySrF99gbo1McskbWvJZtlDd0o9jvzoKKKpN8cB9YXrMYF6qvyheyGZx6DYnjYOJNmQBvxiNcKXjxqY9tE+JeJtpFNOUCPR+DRG+2LAHJFo5HXSjTJ6jpGbA5TfNt19pQwBpHVUmkTlyQ3p6Jdz4sci/Qp6hDCiGAjlbjowUq/PnTPAgEMq7WDx9SmaUytzVMAYI/D8WuepDoOCgxTc5ZVvP1FmtEas0xGyGRiQCcVREPJbk2R88n1Eq7h3dTAdBDTWDf/9H2klLjNpOatECGtFUUQrYaoyS43xsS+0knIA+NGpuD5cXANlAXe6BO1aqEsNrJawj08kbbRXNGRCJQCqW6ivLuDee+yZHAUwwy47VAqQCP2KIhOgNwbmpPTlhNkpvrgEGQv7zgnMo4UoF9JXfgwpE9pNyQvNi4+v0HxtDTIMc5LE7QgaK0am8NC1k9+dZRTXLZp3Flj+pMLu66dwJUG1jOrZFvX7a5lDbWEXAVmRIGV68w7tk2MUmkCWYU6rTtD3SgjxHddQLBDp6JLTmxcgjKA8ewtVG6Cl7hqRPJtad+8fEJmuwGCm6cgzBscB1LTCPIxBoW2ClLneAlqhrHpIGc9wZpS2tc9a4dczXjcGizHIbGDugO432rc6JO2yMSi8YjljtOoarjUoLi5H09eqEGNgjEJacet8TIFp5Tb17oVA5nSZZM5qJTMdFIGU7piDEYUo72rgxUWHjgGAgK6xnQDXzSWsFeWMXlxrJ3FlQjmfVp3Ss+EWeMDY0S2icmZ6S6SUGE7i9/w9J38WIxUEer+jMb7ooKJl1Do/IvCnystgYLkJUubQOIyVPo4R1PpGhKkyab99IXZkT2ZjZL0CT1M3XrFrxJ3R7BdQOkF9XKjtup+4N7Bq70k1fkCQP4qmeOIphVnquhXcUsfGQZQLpnvOjdQQmCFxxgxO1kYDVt+IpFP36XC/h74BekqpNJ7LhOEr/qYm7qdKHQaBWAviRkFQ12UBJAq0vuKOuEOIjCFyunr9xcCki9sk3URsehkUTf93OfFeFuc7MBHsI4+QT4xP+ygoJLN5H4oD0x/fvmv7YsOk5ZlB2wa8qAbKvNH6gaccIGX28H4Tn18a+QKMI23CuMYUcwCwuS2kzMznBLr7ShlGVDKwhSgxDihYwkaaKnAO+UyTmoCFhvrEXqkR/qdWDzUUiqY2mPAjGIv/kfaZBqwcuz9GfaTMciHMRNuCTtYIgdkoQcrg6hq0XIDKMrbBzoGaVoQlraFO1tmYua4Bj5TBDZEyrHKkDGvdCU1hrvt8NYM1OzBJKtlcrBVhzlpB+Wx3OSMVhNdUubTZyllFJMKdIqjtDty2cHUD2tXQm20HQR5TCgakzG4X2wJz53OabYY9dwnrUKWMNjN4Vwsi51kpyJZjUWFT78V2h+qzBcDOt9d7d8OYA4WgrQC4abH6dAHebLA8v4pCAW92WL1YdQy61kPIcfif9SfvNjUt4BzKeikoF+oUVbRrReHWGmjm7j6AqZgy5JVBjrzikcSaqJwGlzq6NnVIGZZrzLnVsbeEamfhljoiRgL6Rhrr6pTPFkCh0J51KK/gHkauKwegQ90oypmR/uNLmBViht4a2GUR62XljKwDa3+vzyBYF5lQfd3ArbwiMNQjQL/YQF1vRblYJe5NRFDb1iOREqQMPOPBLM/MKzqjshQYMg3G5gw3c1TG8MnKrxcDTdW9N4XuhLagYPWWRm5bERCqUtBuznXMfVi7FInWpxSerjAuqESr5h4G6E0RT4xppodFLHG6pjJQHIOUScvdJh0TXHykUv49jDvhIw4GoR29PlIm8nAj5VIldvidp2W0AlCAKEGDBkXCAYE97lUj/e5FzWSol7xc3K+zM2uPAN9XOI8gZVLjxBgpb+RwSZ9knZyf6dSMnDHOx7shK+jMUG9UyPXnNjhHygwCrHqUJZe5gYWsR19WRVI3GFUmjB/hJ2btOJ8e66d7Pg/Pst6cyAkPGs+fXS18zphi8JjYMfsMK/uQMZnxYI9g3i87cn90P5lyMzmyrPPGDtVXPO8b51iZI9EmL4sW4Rv08cp0RxUSzLeU+XbmcyLdfaUM4aWRMgfLpXC00G7/R2iT+0zd/5vMYd8QXrbiVGq1EAuGHciYLi6MuRgV7tlacbvpK1VC/BdFcG3iS+2ztwRXIYkps5u0cPQZKw6KE1Lej1ssUmnZNC5Efp1jbAc4N36okkeqKCVuSr0AtDEGhlaisLEOWC0FKePL83IhblXXG3EBe3SSaZS5j5RpJKYMv/MoutHAMnih8wM51E+UMGrXwi1LUfRZmZf+6lLKnaxgTxcD5ifMZRAnZYLRLb64hHssriFuUXRMygRDolr/G3AOatPAPF6h+LyAfedE1s0x1HkJ+1iUddSYPPBggjSRSSKDBAsD7EAG/llQLC9oIf9eGQvUgsLKLAQJ5Dco3fh6Iy7gijpFHFEGJ86YrSlLQS+oHRsjvw3/mxrAT3099vEGFkWxl9HhsX5665/XTfYo5ukN+1AMAk8EwKXjSe7ZUPaTzwbN2CkE4dR4puhNMR+Xl2+m3Ql6G8LvTA+YCF2QW2CIlIkIMD16XwrR0Bi+z6gz4g4wUIT0z69APePSoPxNyRjZ88uRmFx92nPWTdLYfhv4CUCQcc6CG3/LWqBpRBkcEb2RGRxvK+tvv/BLvTJTwu9gNYMyoFeu3/Z4e8NrFNrikb5Cl26kz5HvMry8HZ4oh16ZYTvDq6Pljrw200wzzXQf6O4rZfpImeR/pBvGl7mR+1OIc6JEIAv/b5yW9iZomhHL0l6rUQ+C7+paLBBVKRYQayWYbgzA5qKgydutIF3KBBYasg34mDIUkDJhLN5lgFYrgVVHC9LIc0ghrM7KAd1DwgzcKEK98LyBzlLtXQlgrQQIDQKzr8NNG++7K4D6fvoJY+KYwU0D2mxEORCUT2UJDi5aikDPng+F92yoDs4Y4PmLw/NyedA/Tt05/FqZNMCYyhntVOu/9y1OmMD053LMmx/XO6lrevdtv/yx7c5092mwwc70Vuk2MyDM9FaIw7md0hTCclj56D5uMKDjy+5t5x7v+tfXh8vMNNNMM8306jTzOQDug1IGwGTq4JekUeaEXXSPyskmL4vt/k+lMcZbED4nGB9um/jZ9hm+lHa7/e1P3T9U70jaO7Z9dKB/PrLdQbleu8e2A5ND/V7HezDvUzPNNNNMD5yC+9JMM800023RyyLbXrnfNxer5GVijt6gcfn/GuXRt0L9LG5TgsYteS/N1NH9UMq4KYjMG6Bj+5ityTPNNBPw5hibVzz0p9w9uYfECkrvCFM/FB+ql2WBrZU6U+i9PoWgpcdkshu5Dscx/XYcj7VJGm/O4355lCKHIMS99mPWM2tBZZFfH2mPrQPa22FmGTdEOMw000w/vfSqZ9GRZ85BwXdfOy8TkHofuvxQpi9AgjiH7HkTcSMHFDKD9mITZRTSpIcsmQNE+x73vrFxNo0g1/elyT7QRgw0PRbnL5xp/ThIfRT4ke8R7WpJpuFjAIJIPgOCuNe95zbm5ndTJUprJKZRmlThYOanMUP8NPo93JdkKdzFWAyu9GPunPv6PejeOIxXFGksTEOvzN4YQ0CudAlum2N8Xq8unR/gBV8TzXxOR/dDKTPTTPeR9sVumazTy1owWmQoZHdZAiYCIGLMBW4iAGMIiOz9+w+5znHTDDOH0Mh4WDJZoChARSGpxC+voFZLqR+EZ2NBhZYUsaSE4UljDYQAjN5NDVoL89QaGfui6rIqhPJAd7j2n0k/5kw/0GKfr9rUwJfPgG+8LwFxx+IuOJ8e1MfaGQR2TOPtJH2rrQ9cywz3aDV6+KdBfdW2BVcFmAj6xRXc4xNJJd4auEcr6G0Lt/DP0VoJxni5Aftr7kwyaJFz0J8/B4oC7p3T4ZxscmAqvwZlAapb8MUl8M7jzo2RGbRrYh9hPnH8VxugKMBtC1qvJM166AsQRigEDO4zKMZGBpubVlwiyxLuegN1su7iTpECVWV0GWXnJHZWWYlCJ6DaSIHaJrp9qvVa3gfmGLhR4mYlGaXqGmRuycLIPMPlZro/9LJKgSOUAS+lCDiQ9acrd2RsHC/sjrfRj8ezZzxjAYlH2hkNTpyVHVFWHBzXnu9jioKJ+EVHXQvnx0Gly7RihBx3Z8nLkHVQ11sJJD+VxnhESKZtDV5Ww3cjDe67rQXltqymswdNCOxjmXTI+NAIgU+Zam9fzCDj+aCi6AXGvWHcpanAwkl/3DYSjsCHJwAgCUXYjfO+U8qJQ0qVWF8CcrtwJqdtHElHx4NzrnPlT3nC4zs6flBTMQZvSMfPbUQZNrKO/JrGdZBmPifSrJR5yPQyDFI8REfi2iQpZzMG6ZBVJNkABlmrxmgkAwMbI7FxVsu8X04s4GnK0HQePSaHrQNfXkpMnOUij/cTrCmO5XALmaLCOIKm3DkRJoNAnWqdkznT5QZ8spLUiF4YV1ebaF0I2QSotaCrDdyTU9BGovbbp49iykT97MqnV9QihJdaAv1aB325k/YU4JZll4paJwdJCIxr3TBzQ1grxwABqjYS4PcdiSUUU3mnjzzoL1qL4osLtB88iYF8B0yc70O1DupiC15VMKsSettC/aEBPX0H5r0zWS/joLYtbFVAP7uQ/tdL2FUpgZD9WFkT1M4HiS4L8KIE1a2kXD9Z+nGomB0IgCgWFERZQhTvxXKBkoOIAPFaTIIDU93CtQZqW4vyIJTtByNOkBndg5g4NNNsQr4dfb0dKee6DEPwFkCtoYgkk9hmK++tY6iLK8AYKJXEkmKG29URDaNeXMRxua0E7KbNZr8FCZB5+/2AmwYU09r75lJLTO+wdU0r96wFtrsBc8CO9yNlrJWsbdbKemgNblq48NzC+1zr2A4zAz5TE/t1lAVQch2QdkPQ8zStrXNg2wX3Zq+omWmm10aEPNBvvD7iJr1H8T6ofiwCYVjxRuX39rNvjMcgM25qSX9TyMUQ125KsA9lpmgqexvbScFycPUYweWmgm4c0u3uaWnMvan3h5klWDDR4fdgz7vkmkbOxqlMXf02WFKkY1eN9hvPX392yPl3BG97SAHmnOcj3GSZUSUXERBY0DDePlIGyNOA7xvbMdcB0GYnCjOtQa2RpBRpRjPVi7/ZzwJmfdKGqWcbZIcU3dsa4YurA4q6KWXY1P1U+ZIaiBKkzL6ye/u/JaRMLHUILRMSWPRpgJS5xy5a95Tuv1LmdcI1JxAKR/soHmmpmWRgfGaj6D6wz5rjN7zJlNpjYwsKjD7zlwYJ3mxARSEQyrFyjkUjXhTDvnvIEG7biIiIbgpE0wdWArscQAN3O9mMT07y/oyRtNxhrEXRIT3StsY2vThvKSsZf0yO2ECTjVeEdDt0hehZNsImyXUN1A1UUjYKfkp375a1koJ7swPaBs46qM0O8IGA3W4XFU+kCJQK2k0b3xk9xTCO+cKOHZZAzDZRPFv4ZR6x7oV5Ogfe7lDukngIKslOFf4rEoG4bqCqEsqnOXa7Gri4RBEyKjFLKuxCS2p3UqJ42hVhYb1SRkmGLOOzYiRpttEIg0TkMusVMYNdGA/kMxFQqCy48SGkjCICXVVwp+vxDFsAYBnkXJepK2uzU2oxIf6uiAHatiD/PpqzZVSGZSlCjYsIG7Vt4ZaCUNHPrmDfORFllbFwj1ZQmyT9dStIGXVxLRZAQNA4gFz//DlQlXBPTsd/Mw5xLVKkDC6vgXcedcHCDyBl1NVGgmk3Dehk3WUjSxghClmzekTHIGWMkd9WoRMmxQFbAhaLDinDLFa94AplHWixkH6Da1RAyhDJb9Yrtg4Ku6+R3gasl4h+CcD/CsLq/xvM/K/e+iB+qojGmeSUptK17yE+ZNe1Rwj5WYOvKQtboFdx0Tw2C+cNFUyTfFcvUUNuYNoj4B/qg1R+xuxDuO7jCfchYA6hXaiXuavX1kuhZdLzdOIZxKtTiKZg9EgTUWQDG0Fj9O87OXP4dJ0b7faRY1E4rJejiILAL9BW+B5eVofHceA6OeEZsoyhx7QTDE/hXsgSliKokxTcBEwbXRJDxDF9c90AOzFkcnBLDnJCX4F8TNaylCYUCyGlNml1lNLxKGXjBCLkpRWVBxAmN2q3l8jllekGiBR2txemY+ZzhG5dKfNSi5AKr2+Q2PnD9qZKmJgeN1HqhA1pDBI2xcJY5xUnI4GEp8Z83EiTCg48pTzycRUk81I7yhyklo3Y9x4lCwcUilKdgJwqOaaUSr0xctNE5Uwn8KuoQGB/OBPyDY+Ctr4/j6A4SJQ2BGQHInmfYg7uLVLZu6Z4pIwX4Lh/kIbxXFyDz9ZwZXdfnYtQzGWClGkM1MUG9t0zqMsdVGtgn57GPlOkjFsW0S1GhOwteFkCRCKA+3eOdYcAyZAyZeIWMoaU2RqouoV5sjrIaKjWQX9xDvPBE7nmOEfixLKAai3UxRZuvYBbFlDbFnR9DXp0BvvuGVgLYii45Ch/GPF6KQJ+YDz881N1Kz7dWoNXomSAtV167lRZkp5DzCCLbg3ahKnpWTMHs/eMmGsaqMtrYJMIUqliiTn3qY5lOuZobD25aeM+Urwos3vxYxpLJUHCcF1DXVzG7Gnqq1Luh3fc7y8RqQJAfdkdAW67FUXv+UW3h00QW0HrOJ/Wnna77LfsEuVynwHhtu0Un1fXUbkcFZnAuHUoUIKUEdciDW4NnA3MqHdZSoVc9jFjjJHfQlDchnH4++zXIGsn/D6CX3lrHjRShog0gP8dgP8ygI8A/DoR/Soz/+7bHdn9oZvzORzTM3eNHOABXhUlA0yf3dLAjepMKz/2jOMV59gbwPFlx8ZwKHhnEFBetp8pBUJw9Yj99G5nYzhi37kJmmaqiRsJjW/Q7WDEUHIU9QyYbC1wcTkotu834qyVmBp7UOHOu8HGeDV548NG9xhuGRB+pmmHCNtRPnnC/TwYJIkEHTymrFMEjOXivAmCBn79qjJHm8fYbkPee0ATv4mASA/KwBSFTNYKL3MIKRPKp/30DU1jSsSUgkFpjI87RlGyDwEDTLqMDWLETBlQY71p17OxeoKw3rfXeTnv8uEiZe4qn3OrSpmXXoSQGemVB3BogwjxB9R+xUy/nVGf4NzqdbS1JmxkR0Ah9/o09xVG/jsbI7EWYn/+B+83LHd1LUiZ5UKsyKHd4I4zhZQZQQu8VqSMIvEvXqbuSyKUspKDk4hkXClSJqxlH8US7od0635zJGtzpIy1goIJ4/PjmQoa2rXvFQrbHbCrs/eJtztBGRAhpsN2Fq5poa6ugaaFY4a6vIrr6eoGAcKrUpcsx/JMff+6LMbhy+kBcojR9W0Wn5VDi16f2MFtd7mrTRpktfccuW1B5wWKogCshd3uoJ49h27a+Jy4baGKAuzhxlQ3oEWVz0UpsQi1rTzzVuKoZIdc398+fU6pW106Vp1b9TpFInKkyFUBXi7Ai8SClzKQKVImXEsVYGPMJjNo18Z3052uhuWZxY0LECXWrhX3LU1Qz6/Aj07kd9K0cKdrUN2IYpFI3m1jBani9wA+8bGArAO9uBDlyKORmDLpuhKJ61ahBblyeQl6/ChxV/KoJd8HOZczBJuN7C3GyD7j0W1xJayV317v9xXjxfi4RDBGxl2VgNsIysXXh6IceWAt0EDKksoQMGyt7BnB5SowmFqUl5TuyXzE7+d10+37Wv8JAN9j5u8DABH9FQB/FsCslDmCXo7PocNIGQBwIyjHCToY/BG4OVJmX3/xw1uMDXAT5M1LKLXiWX9MDJoRPmcvumZfANpQL7gkHxuHpj+2Q+MOxpvJMU6s2Vg7Y8jTgy5I+f2AlOHy5UUVsg7YbIH1qkNljlFPkFXbuotPl7aXKgjqRs6K5WIo9O9pe/SaR+BQ2eO7jqqbZH/15y31UudMxYXJCx2BZukL90HJM8ZrDpqaHvegbNJ3rph0A557bx9j9LqUm7HoKxhp3vR++QoGJL7NvXzmcwDcPlLmpRbhYDaQ10R7kTKpkmNfOrGAjulZXfaiY/JB5G2PD1T+HcOA9NE7ADh1NendZ2vBrZGgmK9qxeopALJsKH3qH+L9MtZKTJl+30n8CU4FsdB3L9ZFzNQS2kzic0RmyyNoiLmLMZMK7uyjsnt0DLk9SBmlRPBNmAq6qkSQLrT8JwK1RiC2T85AW4kp456cicsNkaAytO7qlJ01Ql1ufUwZ6hAlwCBWCoexpmsblA1ALEuNxEtxj086hA0ncViSOZJxUM/O4d590pXTCYoq/WlYK5DgqhQmq2lBl1eg5VLWyLs4Ua3ifSgSV5eykDWPjfXW2jn5s7aDPGdImQ5lkyo4UrREvBab7SFlwsHbNnDXW6jPvsj3pgxlExijnCYZhoDKSOKV5G13c3fpGBNGyFoLfJpbfdNDlft10O11HPYsAPj8y/ExhnYH1xg4v5iuM1b+TVAvlf0obfbfjgGAD9EtIWUu8fzf+w/433rvNTe7JKLfSL7/CjP/SvL9QwA/Tr5/BOA/95rH8JDp5nwOM7g98t0DQLzn7D1WMXFsEFxg+syebPuGaJo948kbuKHl9nUqUK31rh9qr+A5SYeEjnA/tDlhiDwkdE4G5zy0Z+0TkPfV3YsUeMOC7bHtT6GU0vsz3Suan9jDoZnP6ei2lTIvtQijjPLLQkjHqKdcCcqZnOxQWbPvQDgiGN+4JYaGlpgjUTajyJme9ppbAxqzOniljNvuQFqLRXkqEJRPZXvwGYRgUmnQz4FFpe8C1js8g7KoboQh6kMWrfPoFhoybNHHlXPGwceEQWvE3cnPi4Fc2ZPG90mIU2EeGAr2YR7wENjnL7Jn5XyQ1MzdI1jqv/yqW+MvvoyMiu2/a8m6Ge87nGZhCvX6B9foQRbe+7F7vXqDMoGZ+eTTPbX3k7u+Bj797KXrvy2yL5o32v5tMB2jW9jLwBJnpvaNEDP/0lvodmxjnx/w8XRzPofoOOPTPqNN3436EE0J/aPdvqbH/6asofP+M9Mhmt+RmWa6kzTzOR3dtlLmqEUgol8G8Mv+a/0f8L/124dr3QK9ugvVewD2mKHvEF0fVer253PcuI6joUz99p9PO/H5ENnef6G3P5/XRw9pLsA8n7tOh+bzs7c1kLdAHwH4dvL9WwA+fktjuY/0UnzO32z+8pDPub/0kPaDhzQXYJ7PXaeHNJ+HNBfgp28+M59zy3TbSpmjFsFDjH4FAIjoN5j5j9/O8N4sPaS5APN87jo9pPk8pLkA83zuOj20+dyQfh3ALxLRzwH4CYA/B+C/9XaHdK/op5rPAR7WfB7SXIB5PnedHtJ8HtJcgHk+D4zuJJ9z26GV4yIQUQVZhF+95THMNNNMM80000wjxMwGwL8A4N8D8HsA/ioz/87bHdW9opnPmWmmmWaaaaY7SneVz7lVpAwzGyIKi6AB/Jt3YRFmmmmmmWaaaSYhZv53Afy7b3sc95FmPmemmWaaaaaZ7jbdRT7ntt2XXmYRfuVwkXtDD2kuwDyfu04PaT4PaS7APJ+7Tg9tPjPdIv2U8znAw5rPQ5oLMM/nrtNDms9Dmgswz2emN0x0VE73mWaaaaaZZppppplmmmmmmWaaaaaZXivddkyZmWaaaaaZZppppplmmmmmmWaaaaaZcIeVMkT0S0T0XSL6HhH9hbc9nmOJiP6QiP4TIvpNIvoNf+0pEf1NIvr7/v87Sfl/0c/xu0T0X3l7I4/j+TeJ6HMi+u3k2o3HT0T/Wb8O3yOi/zURjaUJfRtz+ZeJ6Cf++fwmEf2Z+zAXP45vE9F/SES/R0S/Q0T/XX/93j2fPXO5l8+HiJZE9GtE9Ft+Pv8Tf/3ePZsD87mXz8ePQxPRf0xEf91/v5fPZqaHQzTzOW+F6AHxOX4cD4bXoQfE5xyYz717PjTzOXd6PslYZl7nvhIz37k/SHC8PwDwDwCoAPwWgH/4bY/ryLH/IYD3etf+5wD+gv/8FwD8z/znf9jPbQHg5/yc9Vse/z8B4I8B+O1XGT+AXwPwnwdAAP4GgH/6jszlXwbwPxgpe6fn4sfxDQB/zH8+A/D3/Ljv3fPZM5d7+Xx836f+cwngbwP4k/fx2RyYz718Pn4c/30AfxnAX/ff7+Wzmf8exh9mPudtjv/B8Dl75nMv92o8ID7nwHzu3fPBzOfc6fkk45x5nXv6d1eRMn8CwPeY+fvM3AD4KwD+7Fse06vQnwXwF/3nvwjgn0mu/xVmrpn5BwC+B5n7WyNm/n8BeNa7fKPxE9E3ADxi5r/F8uv+S0mdW6OJuUzRnZ4LADDzJ8z8//OfLyFp3D7EPXw+e+YyRXd2LgDAQlf+a+n/GPfw2QB75zNFd3o+RPQtAP9VAP9Gb8z37tnM9GBo5nPeEj0kPgd4WLzOQ+JzgIfF68x8zt2eDzDzOved7qpS5kMAP06+f4T9m9hdIgbw7xPR3yGiX/bXvs7MnwCyQQN431+/L/O86fg/9J/71+8K/QtE9HdJIL8Bxnev5kJE3wHwn4Fo9u/18+nNBbinz8dDRn8TwOcA/iYz3+tnMzEf4H4+n/8lgP8hAJdcu7fPZqYHQffl/B+jmc+5H/vBfdyrIz0kPgd4GLzOzOfc7flg5nXuNd1VpcyY79o+7eVdoj/FzH8MwD8N4J8non9iT9n7PE9gevx3eV7/OoCfB/CPA/gEwP/CX783cyGiUwD/NwD/PWa+2Fd05NqdmtPIXO7t82Fmy8z/OIBvQawNf3RP8fs6n3v3fIjovwbgc2b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156 rows × 423 columns

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89 rows × 418 columns

\n", "" ], "text/plain": [ - " Date-Beg Date-End 872.5 \\\n", - "0 b'2022-11-12T07:23:18.178' b'2022-11-12T07:23:19.316' 7507.967104 \n", - "1 b'2022-11-12T07:23:20.112' b'2022-11-12T07:23:21.250' 18201.904825 \n", - "2 b'2022-11-12T07:23:22.032' b'2022-11-12T07:23:23.169' 27987.923441 \n", - "3 b'2022-11-12T07:23:23.951' b'2022-11-12T07:23:25.094' 27526.988735 \n", - "4 b'2022-11-12T07:23:25.892' b'2022-11-12T07:23:27.030' 35475.219898 \n", - ".. ... ... ... \n", - "151 b'2022-11-12T07:28:10.118' b'2022-11-12T07:28:11.247' 37936.870914 \n", - "152 b'2022-11-12T07:28:12.044' b'2022-11-12T07:28:13.183' 39086.931970 \n", - "153 b'2022-11-12T07:28:13.975' b'2022-11-12T07:28:15.118' 27035.628301 \n", - "154 b'2022-11-12T07:28:15.925' b'2022-11-12T07:28:17.062' 24092.111511 \n", - "155 b'2022-11-12T07:28:17.869' b'2022-11-12T07:28:19.004' 28287.243498 \n", + " Date-Beg Date-End 872.5 \\\n", + "0 b'2023-06-11T08:29:30.266' b'2023-06-11T08:29:31.403' 65288.482868 \n", + "1 b'2023-06-11T08:29:32.211' b'2023-06-11T08:29:33.348' 145037.293213 \n", + "2 b'2023-06-11T08:29:34.161' b'2023-06-11T08:29:35.291' 102230.586317 \n", + "3 b'2023-06-11T08:29:36.109' b'2023-06-11T08:29:37.246' 124723.655282 \n", + "4 b'2023-06-11T08:29:38.074' b'2023-06-11T08:29:39.204' 158318.758731 \n", + ".. ... ... ... \n", + "84 b'2023-06-11T08:32:14.167' b'2023-06-11T08:32:15.297' 124354.741489 \n", + "85 b'2023-06-11T08:32:16.113' b'2023-06-11T08:32:17.252' 123058.241489 \n", + "86 b'2023-06-11T08:32:18.060' b'2023-06-11T08:32:19.201' 143975.551834 \n", + "87 b'2023-06-11T08:32:20.007' b'2023-06-11T08:32:21.149' 148698.069075 \n", + "88 b'2023-06-11T08:32:21.962' b'2023-06-11T08:32:23.102' 127710.327696 \n", "\n", - 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"execution_count": 10, + "execution_count": 11, "id": "41c889a3", "metadata": {}, "outputs": [ @@ -1005,7 +1156,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_92988/1314969845.py:26: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/1493378176.py:27: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", " int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample)\n" ] } @@ -1017,10 +1168,11 @@ "# Define wavelength arrays and disperion at each wavelength (nm per pixel)\n", "wav_SCI_str = df_SCI_EM.columns[2:] # string (center) wavelengths of each pixel\n", "wav_SCI = df_SCI_EM.columns[2:].astype(float) # float (center) wavelengths of each pixel\n", - "disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1\n", + "disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1\n", + "disp_SCI_smooth = np.polyval(np.polyfit(wav_SCI,disp_SCI, deg=6),wav_SCI)\n", "\n", "# define normalized flux array (e- / nm / time)\n", - "df_SCI_EM_norm = df_SCI_EM[wav_SCI_str] * EM_gain /disp_SCI\n", + "df_SCI_EM_norm = df_SCI_EM[wav_SCI_str] * EM_gain / disp_SCI_smooth\n", "\n", "# define time arrays\n", "date_beg = np.array(df_SCI_EM[\"Date-Beg\"], dtype=np.datetime64)\n", @@ -1053,13 +1205,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "e6d96249", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1096,13 +1248,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "244dfde0", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1138,13 +1290,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "7ed04a87", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1181,15 +1333,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "489f8080", "metadata": {}, "outputs": [], "source": [ - "# this HDU should be named 'TELEMETRY' instead of index=9 (or whatever is the last value)\n", - "# the bug was fixed in the L0 Assembly code on 11/22/22. \n", - "# A future version up this notebook should update this.\n", - "df_telemetry = Table.read(L0_file, format='fits', hdu=9).to_pandas() \n", + "df_telemetry = Table.read(L0_file, format='fits', hdu='TELEMETRY').to_pandas() \n", "num_columns = ['average', 'stddev', 'min', 'max']\n", "for column in df_telemetry:\n", " df_telemetry[column] = df_telemetry[column].str.decode('utf-8')\n", @@ -1203,7 +1352,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "89f8ef3c", "metadata": { "scrolled": true @@ -1214,18 +1363,18 @@ "output_type": "stream", "text": [ "\n", - "Index: 116 entries, kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS to kpfpower.OUTLET_A1_Amps\n", + "Index: 123 entries, kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS to kpfpower.OUTLET_A1_Amps\n", "Data columns (total 6 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 average 116 non-null float32\n", - " 1 stddev 116 non-null float32\n", - " 2 min 116 non-null float32\n", - " 3 max 116 non-null float32\n", - " 4 units 116 non-null object \n", - " 5 description 116 non-null object \n", + " 0 average 123 non-null float32\n", + " 1 stddev 123 non-null float32\n", + " 2 min 123 non-null float32\n", + " 3 max 123 non-null float32\n", + " 4 units 123 non-null object \n", + " 5 description 123 non-null object \n", "dtypes: float32(4), object(2)\n", - "memory usage: 4.5+ KB\n" + "memory usage: 4.8+ KB\n" ] } ], @@ -1243,7 +1392,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "c0c3069a", "metadata": {}, "outputs": [ @@ -1288,584 +1437,584 @@ " \n", " \n", " kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS\n", - " 1.743070e+01\n", - " 4.817150e-04\n", - " 1.743000e+01\n", - " 1.743200e+01\n", + " 1.597510e+01\n", + " 1.963010e+02\n", + " 1.597500e+01\n", + " 1.597600e+01\n", " degC\n", " Bench Bottom Between Cameras C2 c- double degC...\n", " \n", " \n", " kpfmet.BENCH_BOTTOM_COLLIMATOR\n", - " 1.803690e+01\n", - " 5.135500e-04\n", - " 1.803600e+01\n", - " 1.803800e+01\n", + " 1.647870e+01\n", + " 2.024890e+02\n", + " 1.647700e+01\n", + " 1.648000e+01\n", " degC\n", " Bench Bottom Coll C3 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.BENCH_BOTTOM_DCUT\n", - " 1.781270e+01\n", - " 6.067910e-04\n", - " 1.781200e+01\n", - " 1.781400e+01\n", + " 1.624350e+01\n", + " 1.995990e+02\n", + " 1.624300e+01\n", + " 1.624400e+01\n", " degC\n", " Bench Bottom D-cut C4 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.BENCH_BOTTOM_ECHELLE\n", - " 1.795330e+01\n", - " 4.740290e-04\n", - " 1.795200e+01\n", - " 1.795400e+01\n", + " 1.638640e+01\n", + " 2.013550e+02\n", + " 1.638600e+01\n", + " 1.638700e+01\n", " degC\n", " Bench Bottom Echelle Cam B c- double degC {%.3f}\n", " \n", " \n", " kpfmet.BENCH_TOP_BETWEEN_CAMERAS\n", - " 1.733170e+01\n", - " 4.920710e-04\n", - " 1.733000e+01\n", - " 1.733200e+01\n", + " 1.588580e+01\n", + " 1.952040e+02\n", + " 1.588500e+01\n", + " 1.588700e+01\n", " degC\n", " Bench Top Between Cameras D4 c- double degC {%...\n", " \n", " \n", " kpfmet.BENCH_TOP_COLL\n", - " 1.813210e+01\n", - " 5.333490e-04\n", - " 1.813100e+01\n", - " 1.813300e+01\n", + " 1.657790e+01\n", + " 2.037080e+02\n", + " 1.657700e+01\n", + " 1.657900e+01\n", " degC\n", " Bench Top Coll D5 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.BENCH_TOP_DCUT\n", - " 1.784160e+01\n", - " 5.604510e-04\n", - " 1.784100e+01\n", - " 1.784300e+01\n", + " 1.628050e+01\n", + " 2.000540e+02\n", + " 1.627900e+01\n", + " 1.628100e+01\n", " degC\n", " Bench Top D-cut D3 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.BENCH_TOP_ECHELLE_CAM\n", - " 1.789740e+01\n", - " 4.959300e-04\n", - " 1.789700e+01\n", - " 1.789800e+01\n", + " 1.629190e+01\n", + " 2.001940e+02\n", + " 1.629100e+01\n", + " 1.629300e+01\n", " degC\n", " Bench Top Echelle Cam D1 c- double degC {%.3f}\n", " \n", " \n", + " kpfmet.CALEM_SCMBLR_CHMBR_END\n", + " 1.736520e+01\n", + " 2.133830e+02\n", + " 1.736400e+01\n", + " 1.736600e+01\n", + " degC\n", + " Cal EM Scrammbler Chamber End C1 c- double deg...\n", + " \n", + " \n", + " kpfmet.CALEM_SCMBLR_FIBER_END\n", + " 1.721330e+01\n", + " 2.115160e+02\n", + " 1.721200e+01\n", + " 1.721500e+01\n", + " degC\n", + " Cal EM Scrambler Fiber End D1 c- double degC {...\n", + " \n", + " \n", " kpfmet.CAL_BENCH\n", - " 2.130000e+01\n", - " 0.000000e+00\n", - " 2.130000e+01\n", - " 2.130000e+01\n", + " 2.020000e+01\n", + " 2.482160e+02\n", + " 2.020000e+01\n", + " 2.020000e+01\n", " degC\n", " Cal_Bench temperature c- double degC {%.1f}\n", " \n", " \n", " kpfmet.CAL_BENCH_BB_SRC\n", - " 2.160000e+01\n", - " 0.000000e+00\n", - " 2.160000e+01\n", - " 2.160000e+01\n", + " 2.050000e+01\n", + " 2.519030e+02\n", + " 2.050000e+01\n", + " 2.050000e+01\n", " degC\n", " CAL_Bench_BB_Src temperature c- double degC {%...\n", " \n", " \n", " kpfmet.CAL_BENCH_BOT\n", - " 2.190000e+01\n", - " 1.440850e-06\n", - " 2.190000e+01\n", - " 2.190000e+01\n", + " 2.360000e+01\n", + " 2.899950e+02\n", + " 2.360000e+01\n", + " 2.360000e+01\n", " degC\n", " Cal_Bench_Bot temperature c- double degC {%.1f}\n", " \n", " \n", " kpfmet.CAL_BENCH_ENCL_AIR\n", - " 2.120000e+01\n", - " 2.059970e-06\n", - " 2.120000e+01\n", - " 2.120000e+01\n", + " 2.018220e+01\n", + " 2.479980e+02\n", + " 2.010000e+01\n", + " 2.020000e+01\n", " degC\n", " Cal_Bench_Encl_Air temperature c- double degC ...\n", " \n", " \n", " kpfmet.CAL_BENCH_OCT_MOT\n", - " 2.120000e+01\n", - " 2.059970e-06\n", - " 2.120000e+01\n", - " 2.120000e+01\n", + " 1.990000e+01\n", + " 2.445300e+02\n", + " 1.990000e+01\n", + " 1.990000e+01\n", " degC\n", " Cal_Bench_Oct_Mot temperature c- double degC {...\n", " \n", " \n", " kpfmet.CAL_BENCH_TRANS_STG_MOT\n", - " 2.124490e+01\n", - " 4.983560e-02\n", - " 2.120000e+01\n", - " 2.130000e+01\n", + " 2.010000e+01\n", + " 2.469880e+02\n", + " 2.010000e+01\n", + " 2.010000e+01\n", " degC\n", " Cal_Bench_Trans_Stg_Mot temperature c- double ...\n", " \n", " \n", " kpfmet.CAL_RACK_TOP\n", - " 2.620910e+01\n", - " 2.875360e-02\n", - " 2.620000e+01\n", - " 2.630000e+01\n", + " 2.301710e+01\n", + " 2.828330e+02\n", + " 2.300000e+01\n", + " 2.310000e+01\n", " degC\n", " Cal_Rack_Top temperature c- double degC {%.1f}\n", " \n", " \n", - " kpfmet.ECHELLE_BOTTOM\n", - " 1.796950e+01\n", - " 5.514370e-04\n", - " 1.796900e+01\n", - " 1.797100e+01\n", + " kpfmet.CHAMBER_EXT_BOTTOM\n", + " 1.570260e+01\n", + " 1.929530e+02\n", + " 1.570100e+01\n", + " 1.570400e+01\n", " degC\n", - " Echelle Bottom D1 c- double degC {%.3f}\n", + " Chamber Exterior Bottom B c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.ECHELLE_TOP\n", - " 1.786290e+01\n", - " 3.750040e-04\n", - " 1.786200e+01\n", - " 1.786400e+01\n", - " degC\n", - " Echelle Top C1 c- double degC {%.3f}\n", - " \n", - " \n", - " kpfmet.FF_SRC\n", - " 2.004720e+01\n", - " 5.002260e-02\n", - " 2.000000e+01\n", - " 2.010000e+01\n", + " kpfmet.CHAMBER_EXT_TOP\n", + " -2.731500e+02\n", + " 3.356450e+03\n", + " -2.731500e+02\n", + " -2.731500e+02\n", " degC\n", - " FF_Src temperature c- double degC {%.1f}\n", + " Chamber Exterior Top C1 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_A\n", + " kpfmet.CRYOSTAT_G1\n", " -2.731500e+02\n", - " 0.000000e+00\n", + " 3.356450e+03\n", " -2.731500e+02\n", " -2.731500e+02\n", " degC\n", - " Green A c- double degC {%.3f}\n", + " Within cryostat green D2 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_B\n", - " 1.875340e+01\n", - " 6.393470e-04\n", - " 1.875200e+01\n", - " 1.875500e+01\n", + " kpfmet.CRYOSTAT_G2\n", + " -2.731500e+02\n", + " 3.356450e+03\n", + " -2.731500e+02\n", + " -2.731500e+02\n", " degC\n", - " Green B c- double degC {%.3f}\n", + " Within cryostat green D3 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_C1\n", - " 1.869870e+01\n", - " 6.944730e-04\n", - " 1.869700e+01\n", - " 1.870000e+01\n", + " kpfmet.CRYOSTAT_G3\n", + " 1.668370e+01\n", + " 2.050090e+02\n", + " 1.668300e+01\n", + " 1.668500e+01\n", " degC\n", - " Green C1 c- double degC {%.3f}\n", + " Within cryostat green D4 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_CAMERA_BOTTOM\n", - " 1.811600e+01\n", - " 6.472810e-04\n", - " 1.811500e+01\n", - " 1.811800e+01\n", + " kpfmet.CRYOSTAT_R1\n", + " 1.610210e+01\n", + " 1.978620e+02\n", + " 1.610000e+01\n", + " 1.610300e+01\n", " degC\n", - " Green Camera Bottom C3 c- double degC {%.3f}\n", + " Within Cryostat red D2 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_CAMERA_COLLIMATOR\n", - " 1.815550e+01\n", - " 6.195310e-04\n", - " 1.815400e+01\n", - " 1.815700e+01\n", + " kpfmet.CRYOSTAT_R2\n", + " -2.731500e+02\n", + " 3.356450e+03\n", + " -2.731500e+02\n", + " -2.731500e+02\n", " degC\n", - " Green Camera Collimator C4 c- double degC {%.3f}\n", + " Within Cryostat red D3 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_CAMERA_ECHELLE\n", - " 1.802870e+01\n", - " 6.073550e-04\n", - " 1.802800e+01\n", - " 1.803000e+01\n", + " kpfmet.CRYOSTAT_R3\n", + " 1.564080e+01\n", + " 1.921940e+02\n", + " 1.564000e+01\n", + " 1.564200e+01\n", " degC\n", - " Green Camera Echelle D5 c- double degC {%.3f}\n", + " Within Cryostat red D4 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_CAMERA_TOP\n", - " 1.794530e+01\n", - " 5.890810e-04\n", - " 1.794400e+01\n", - " 1.794700e+01\n", + " kpfmet.ECHELLE_BOTTOM\n", + " 1.641060e+01\n", + " 2.016530e+02\n", + " 1.641000e+01\n", + " 1.641200e+01\n", " degC\n", - " Green Camera Top C2 c- double degC {%.3f}\n", + " Echelle Bottom D1 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_D1\n", - " 1.853170e+01\n", - " 5.801500e-04\n", - " 1.853100e+01\n", - " 1.853300e+01\n", + " kpfmet.ECHELLE_TOP\n", + " 1.628290e+01\n", + " 2.000840e+02\n", + " 1.628200e+01\n", + " 1.628400e+01\n", " degC\n", - " Green D1 c- double degC {%.3f}\n", + " Echelle Top C1 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_D2\n", - " 1.650110e+01\n", - " 7.394430e-04\n", - " 1.650000e+01\n", - " 1.650200e+01\n", + " kpfmet.FF_SRC\n", + " 1.957500e+01\n", + " 2.405370e+02\n", + " 1.950000e+01\n", + " 1.960000e+01\n", " degC\n", - " Green D2 c- double degC {%.3f}\n", + " FF_Src temperature c- double degC {%.1f}\n", " \n", " \n", - " kpfmet.GREEN_D3\n", - " -2.731500e+02\n", - " 0.000000e+00\n", - " -2.731500e+02\n", - " -2.731500e+02\n", + " kpfmet.GREEN_CAMERA_BOTTOM\n", + " 1.677540e+01\n", + " 2.061350e+02\n", + " 1.677400e+01\n", + " 1.677600e+01\n", " degC\n", - " Green D3 c- double degC {%.3f}\n", + " Green Camera Bottom C3 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_D4\n", - " 1.645530e+01\n", - " 6.231880e-04\n", - " 1.645400e+01\n", - " 1.645700e+01\n", + " kpfmet.GREEN_CAMERA_COLLIMATOR\n", + " 1.682040e+01\n", + " 2.066880e+02\n", + " 1.681900e+01\n", + " 1.682200e+01\n", " degC\n", - " Green D4 c- double degC {%.3f}\n", + " Green Camera Collimator C4 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.GREEN_GRISM_TOP\n", - " 1.785480e+01\n", - " 7.172570e-04\n", - " 1.785400e+01\n", - " 1.785700e+01\n", + " kpfmet.GREEN_CAMERA_ECHELLE\n", + " 1.671060e+01\n", + " 2.053390e+02\n", + " 1.670900e+01\n", + " 1.671200e+01\n", " degC\n", - " Green Grism Top C5 c- double degC {%.3f}\n", + " Green Camera Echelle D5 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.PRIMARY_COLLIMATOR_TOP\n", - " 1.842500e+01\n", - " 4.988680e-04\n", - " 1.842400e+01\n", - " 1.842600e+01\n", + " kpfmet.GREEN_CAMERA_TOP\n", + " 1.661770e+01\n", + " 2.041970e+02\n", + " 1.661700e+01\n", + " 1.661900e+01\n", " degC\n", - " Primary Col Top D2 c- double degC {%.3f}\n", + " Green Camera Top C2 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_A\n", - " 1.446180e+01\n", - " 1.418040e-02\n", - " 1.443200e+01\n", - " 1.448900e+01\n", + " kpfmet.GREEN_GRISM_TOP\n", + " 1.639170e+01\n", + " 2.014200e+02\n", + " 1.639000e+01\n", + " 1.639300e+01\n", " degC\n", - " Red A c- double degC {%.3f}\n", + " Green Grism Top C5 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_B\n", - " 1.730100e+01\n", - " 4.271390e-03\n", - " 1.729300e+01\n", - " 1.730800e+01\n", + " kpfmet.GREEN_LN2_FLANGE\n", + " 9.791590e+00\n", + " 1.203190e+02\n", + " 9.746000e+00\n", + " 9.848000e+00\n", " degC\n", - " Red B c- double degC {%.3f}\n", + " Green LN2 Flange A c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_C1\n", - " -2.731500e+02\n", - " 0.000000e+00\n", - " -2.731500e+02\n", - " -2.731500e+02\n", + " kpfmet.PRIMARY_COLLIMATOR_TOP\n", + " 1.689680e+01\n", + " 2.076270e+02\n", + " 1.689600e+01\n", + " 1.689800e+01\n", " degC\n", - " Red C1 c- double degC {%.3f}\n", + " Primary Col Top D2 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.RED_CAMERA_BOTTOM\n", - " 1.767610e+01\n", - " 6.662820e-04\n", - " 1.767500e+01\n", - " 1.767700e+01\n", + " 1.630110e+01\n", + " 2.003070e+02\n", + " 1.630000e+01\n", + " 1.630300e+01\n", " degC\n", " Red Camera Bottom D5 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.RED_CAMERA_COLLIMATOR\n", - " 1.802560e+01\n", - " 6.901120e-04\n", - " 1.802400e+01\n", - " 1.802700e+01\n", + " 1.664290e+01\n", + " 2.045070e+02\n", + " 1.664200e+01\n", + " 1.664400e+01\n", " degC\n", " Red Camera Coll C3 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.RED_CAMERA_ECHELLE\n", - " 1.798090e+01\n", - " 6.532630e-04\n", - " 1.798000e+01\n", - " 1.798200e+01\n", + " 1.659400e+01\n", + " 2.039060e+02\n", + " 1.659300e+01\n", + " 1.659500e+01\n", " degC\n", " Red Camera Ech C4 c- double degC {%.3f}\n", " \n", " \n", " kpfmet.RED_CAMERA_TOP\n", - " 1.792610e+01\n", - " 6.733910e-04\n", - " 1.792400e+01\n", - " 1.792700e+01\n", + " 1.654670e+01\n", + " 2.033240e+02\n", + " 1.654500e+01\n", + " 1.654800e+01\n", " degC\n", " Red Camera Top C5 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_D1\n", - " 1.335860e+01\n", - " 1.665850e-02\n", - " 1.332500e+01\n", - " 1.339500e+01\n", + " kpfmet.RED_GRISM_TOP\n", + " 1.636990e+01\n", + " 2.011530e+02\n", + " 1.636900e+01\n", + " 1.637200e+01\n", " degC\n", - " Red D1 c- double degC {%.3f}\n", + " Red Grism Top C2 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_D2\n", - " 1.612960e+01\n", - " 6.835860e-04\n", - " 1.612800e+01\n", - " 1.613100e+01\n", + " kpfmet.RED_LN2_FLANGE\n", + " 6.245890e+00\n", + " 7.675000e+01\n", + " 6.199000e+00\n", + " 6.292000e+00\n", " degC\n", - " Red D2 c- double degC {%.3f}\n", + " Red LN2 Flange D1 c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_D3\n", - " -2.731500e+02\n", - " 0.000000e+00\n", - " -2.731500e+02\n", - " -2.731500e+02\n", + " kpfmet.REFORMATTER\n", + " 1.660480e+01\n", + " 2.040380e+02\n", + " 1.660400e+01\n", + " 1.660600e+01\n", " degC\n", - " Red D3 c- double degC {%.3f}\n", + " Reformatter A c- double degC {%.3f}\n", " \n", " \n", - " kpfmet.RED_D4\n", - " 1.577720e+01\n", - " 6.775850e-04\n", - " 1.577600e+01\n", - " 1.577800e+01\n", + " kpfmet.SCIENCE_CAL_FIBER_STG\n", + " 2.010000e+01\n", + " 2.469880e+02\n", + " 2.010000e+01\n", + " 2.010000e+01\n", " degC\n", - " Red D4 c- double degC {%.3f}\n", + " Science_Cal_Fiber_Stg temperature c- double de...\n", " \n", " \n", - " kpfmet.RED_GRISM_TOP\n", - " 1.785020e+01\n", - " 7.324240e-04\n", - " 1.784900e+01\n", - " 1.785200e+01\n", + " kpfmet.SCISKY_SCMBLR_CHMBR_END\n", + " -2.731500e+02\n", + " 3.356450e+03\n", + " -2.731500e+02\n", + " -2.731500e+02\n", " degC\n", - " Red Grism Top C2 c- double degC {%.3f}\n", + " SciSky Scrambler Chamber End A c- double degC ...\n", " \n", " \n", - " kpfmet.REFORMATTER\n", - " 1.816180e+01\n", - " 4.937130e-04\n", - " 1.816100e+01\n", - " 1.816300e+01\n", + " kpfmet.SCISKY_SCMBLR_FIBER_END\n", + " 1.747050e+01\n", + " 2.146770e+02\n", + " 1.746900e+01\n", + " 1.747200e+01\n", " degC\n", - " Reformatter A c- double degC {%.3f}\n", - " \n", - " \n", - " kpfmet.SCIENCE_CAL_FIBER_STG\n", - " 2.127990e+01\n", - " 4.013800e-02\n", - " 2.120000e+01\n", - " 2.130000e+01\n", - " degC\n", - " Science_Cal_Fiber_Stg temperature c- double de...\n", + " SciSky Scrammbler Fiber End B c- double degC {...\n", " \n", " \n", " kpfmet.SIMCAL_FIBER_STG\n", - " 2.120000e+01\n", - " 2.059970e-06\n", - " 2.120000e+01\n", - " 2.120000e+01\n", + " 2.010000e+01\n", + " 2.469880e+02\n", + " 2.010000e+01\n", + " 2.010000e+01\n", " degC\n", " SimCal_Fiber_Stg temperature c- double degC {%...\n", " \n", " \n", " kpfmet.SKYCAL_FIBER_STG\n", - " 2.110000e+01\n", - " 0.000000e+00\n", - " 2.110000e+01\n", - " 2.110000e+01\n", + " 1.990000e+01\n", + " 2.445300e+02\n", + " 1.990000e+01\n", + " 1.990000e+01\n", " degC\n", " SkyCal_Fiber_Stg temperature c- double degC {%...\n", " \n", " \n", " kpfmet.TEMP\n", - " 2.146190e+01\n", - " 2.623280e-02\n", - " 2.141400e+01\n", - " 2.150100e+01\n", + " 1.968480e+01\n", + " 2.418860e+02\n", + " 1.967300e+01\n", + " 1.969700e+01\n", " degC\n", " Vaisala Temperature c- double degC {%.3f}\n", " \n", " \n", " kpfmet.TH_DAILY\n", - " 2.147640e+01\n", - " 4.255980e-02\n", - " 2.140000e+01\n", - " 2.150000e+01\n", + " 2.000000e+01\n", + " 2.457590e+02\n", + " 2.000000e+01\n", + " 2.000000e+01\n", " degC\n", " Th_daily temperature c- double degC {%.1f}\n", " \n", " \n", " kpfmet.TH_GOLD\n", - " 2.550000e+01\n", - " 0.000000e+00\n", - " 2.550000e+01\n", - " 2.550000e+01\n", + " 1.990000e+01\n", + " 2.445300e+02\n", + " 1.990000e+01\n", + " 1.990000e+01\n", " degC\n", " Th_gold temperature c- double degC {%.1f}\n", " \n", " \n", " kpfmet.U_DAILY\n", - " 2.149020e+01\n", - " 2.984770e-02\n", - " 2.140000e+01\n", - " 2.150000e+01\n", + " 2.010000e+01\n", + " 2.469880e+02\n", + " 2.010000e+01\n", + " 2.010000e+01\n", " degC\n", " U_daily temperature c- double degC {%.1f}\n", " \n", " \n", " kpfmet.U_GOLD\n", - " 2.110000e+01\n", - " 0.000000e+00\n", - " 2.110000e+01\n", - " 2.110000e+01\n", + " 1.990000e+01\n", + " 2.445300e+02\n", + " 1.990000e+01\n", + " 1.990000e+01\n", " degC\n", " U_gold temperature c- double degC {%.1f}\n", " \n", " \n", " kpfgreen.BPLANE_TEMP\n", - " 3.867850e+01\n", - " 2.657240e-02\n", - " 3.862500e+01\n", - " 3.875000e+01\n", + " 3.582320e+01\n", + " 4.401930e+02\n", + " 3.581200e+01\n", + " 3.587500e+01\n", " degC\n", " Backplane temperature c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.BRD10_DRVR_T\n", - " 4.279390e+01\n", - " 5.419160e-02\n", - " 4.275000e+01\n", - " 4.293700e+01\n", + " 4.050370e+01\n", + " 4.977070e+02\n", + " 4.050000e+01\n", + " 4.056200e+01\n", " degC\n", " Board 10 (Driver) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.BRD11_DRVR_T\n", - " 4.113130e+01\n", - " 1.883080e-02\n", - " 4.112500e+01\n", - " 4.118700e+01\n", + " 3.875000e+01\n", + " 4.761580e+02\n", + " 3.875000e+01\n", + " 3.875000e+01\n", " degC\n", " Board 11 (Driver) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.BRD12_LVXBIAS_T\n", - " 3.695960e+01\n", - " 3.026760e-02\n", - " 3.693700e+01\n", - " 3.700000e+01\n", + " 3.475000e+01\n", + " 4.270060e+02\n", + " 3.475000e+01\n", + " 3.475000e+01\n", " degC\n", " Board 12 (LVxBias) temperature c- double degC ...\n", " \n", " \n", " kpfgreen.BRD1_HTRX_T\n", - " 3.539790e+01\n", - " 2.999430e-02\n", - " 3.537500e+01\n", - " 3.543700e+01\n", + " 3.184100e+01\n", + " 3.912610e+02\n", + " 3.181200e+01\n", + " 3.187500e+01\n", " degC\n", " Board 1 (HeaterX) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.BRD2_XVBIAS_T\n", - " 3.699970e+01\n", - " 1.620680e-02\n", - " 3.693700e+01\n", - " 3.706200e+01\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 0.000000e+00\n", + " 0.000000e+00\n", " degC\n", " Board 2 (XV Bias) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.BRD3_LVDS_T\n", - " 3.628370e+01\n", - " 3.094450e-02\n", - " 3.625000e+01\n", - " 3.631200e+01\n", + " 3.342310e+01\n", + " 4.107010e+02\n", + " 3.337500e+01\n", + " 3.343700e+01\n", " degC\n", " Board 3 (LVDS) temperature c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.BRD4_DRVR_T\n", - " 3.918090e+01\n", - " 1.850560e-02\n", - " 3.912500e+01\n", - " 3.918700e+01\n", + " 3.719820e+01\n", + " 4.570890e+02\n", + " 3.718700e+01\n", + " 3.725000e+01\n", " degC\n", " Board 4 (Driver) temperature c- double degC {%...\n", " \n", " \n", " kpfgreen.BRD5_AD_T\n", - " 3.594530e+01\n", - " 3.121400e-02\n", - " 3.587500e+01\n", - " 3.600000e+01\n", + " 3.381530e+01\n", + " 4.155200e+02\n", + " 3.381200e+01\n", + " 3.387500e+01\n", " degC\n", " Board 5 (AD) temperature c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.BRD7_HTRX_T\n", - " 3.996460e+01\n", - " 3.544480e-02\n", - " 3.987500e+01\n", - " 4.000000e+01\n", + " 3.784640e+01\n", + " 4.650540e+02\n", + " 3.781200e+01\n", + " 3.787500e+01\n", " degC\n", " Board 7 (HeaterX) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.BRD9_HVXBIAS_T\n", - " 3.848160e+01\n", - " 2.868240e-02\n", - " 3.843700e+01\n", - " 3.850000e+01\n", + " 3.627570e+01\n", + " 4.457540e+02\n", + " 3.625000e+01\n", + " 3.631200e+01\n", " degC\n", " Board 9 (HVxBias) temperature c- double degC {...\n", " \n", " \n", " kpfgreen.CF_BASE_2WT\n", - " -1.270670e+02\n", - " 5.641800e-04\n", - " -1.270680e+02\n", - " -1.270660e+02\n", + " -1.270330e+02\n", + " 1.560970e+03\n", + " -1.270340e+02\n", + " -1.270310e+02\n", " degC\n", " tip cold finger (2 wire) c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.CF_BASE_T\n", " -2.731500e+02\n", - " 0.000000e+00\n", + " 3.356450e+03\n", " -2.731500e+02\n", " -2.731500e+02\n", " degC\n", @@ -1873,53 +2022,53 @@ " \n", " \n", " kpfgreen.CF_BASE_TRG\n", - " -1.293000e+02\n", - " 1.152370e-05\n", - " -1.293000e+02\n", - " -1.293000e+02\n", + " -1.275000e+02\n", + " 1.566710e+03\n", + " -1.275000e+02\n", + " -1.275000e+02\n", " degC\n", " base cold finger heater 1A, target temp c2 dou...\n", " \n", " \n", " kpfgreen.CF_TIP_T\n", - " -1.278000e+02\n", - " 1.171170e-03\n", - " -1.278020e+02\n", - " -1.277980e+02\n", + " -1.280000e+02\n", + " 1.572860e+03\n", + " -1.280010e+02\n", + " -1.279990e+02\n", " degC\n", " tip cold finger c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.CF_TIP_TRG\n", - " -1.278000e+02\n", - " 1.144400e-05\n", - " -1.278000e+02\n", - " -1.278000e+02\n", + " -1.280000e+02\n", + " 1.572860e+03\n", + " -1.280000e+02\n", + " -1.280000e+02\n", " degC\n", " tip cold finger heater 1B, target temp c2 doub...\n", " \n", " \n", " kpfgreen.COL_PRESS\n", - " 8.614570e-08\n", - " 5.884600e-10\n", - " 8.600000e-08\n", - " 9.000000e-08\n", + " 4.000000e-08\n", + " 4.915170e-07\n", + " 4.000000e-08\n", + " 4.000000e-08\n", " Torr\n", " Current ion pump pressure c- double Torr {%.3e}\n", " \n", " \n", " kpfgreen.CRYOBODY_T\n", - " 1.888900e+01\n", - " 1.517390e-03\n", - " 1.888600e+01\n", - " 1.889200e+01\n", + " 1.899600e+01\n", + " 2.334220e+02\n", + " 1.899200e+01\n", + " 1.900000e+01\n", " degC\n", " Cryo Body Temperature c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.CRYOBODY_TRG\n", " 1.900000e+01\n", - " 0.000000e+00\n", + " 2.334710e+02\n", " 1.900000e+01\n", " 1.900000e+01\n", " degC\n", @@ -1928,7 +2077,7 @@ " \n", " kpfgreen.CURRTEMP\n", " -1.400000e+02\n", - " 8.475030e-04\n", + " 1.720310e+03\n", " -1.400020e+02\n", " -1.399970e+02\n", " degC\n", @@ -1937,7 +2086,7 @@ " \n", " kpfgreen.ECH_PRESS\n", " 1.000000e-11\n", - " 0.000000e+00\n", + " 1.228790e-10\n", " 1.000000e-11\n", " 1.000000e-11\n", " Torr\n", @@ -1945,18 +2094,18 @@ " \n", " \n", " kpfgreen.KPF_CCD_T\n", - " -1.010180e+02\n", - " 1.145050e-03\n", - " -1.010210e+02\n", - " -1.010160e+02\n", + " -1.010900e+02\n", + " 1.242190e+03\n", + " -1.010920e+02\n", + " -1.010890e+02\n", " degC\n", " SSL Detector temperature c- double degC {%.3f}\n", " \n", " \n", " kpfgreen.STA_CCD_T\n", - " -9.999980e+01\n", - " 1.419380e-03\n", - " -1.000030e+02\n", + " -1.000000e+02\n", + " 1.228800e+03\n", + " -1.000020e+02\n", " -9.999800e+01\n", " degC\n", " STA Detector temperature c- double degC {%.3f}\n", @@ -1964,7 +2113,7 @@ " \n", " kpfgreen.STA_CCD_TRG\n", " -1.000000e+02\n", - " 0.000000e+00\n", + " 1.228790e+03\n", " -1.000000e+02\n", " -1.000000e+02\n", " degC\n", @@ -1973,7 +2122,7 @@ " \n", " kpfgreen.TEMPSET\n", " -1.400000e+02\n", - " 0.000000e+00\n", + " 1.720310e+03\n", " -1.400000e+02\n", " -1.400000e+02\n", " degC\n", @@ -1981,116 +2130,116 @@ " \n", " \n", " kpfred.BPLANE_TEMP\n", - " 4.437860e+01\n", - " 2.720140e-02\n", - " 4.431200e+01\n", - " 4.443700e+01\n", + " 4.380440e+01\n", + " 5.382650e+02\n", + " 4.375000e+01\n", + " 4.387500e+01\n", " degC\n", " Backplane temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.BRD10_DRVR_T\n", - " 4.840920e+01\n", - " 7.985580e-02\n", - " 4.831200e+01\n", - " 4.862500e+01\n", + " 4.811190e+01\n", + " 5.911970e+02\n", + " 4.806200e+01\n", + " 4.818700e+01\n", " degC\n", " Board 10 (Driver) temperature c- double degC {...\n", " \n", " \n", " kpfred.BRD11_DRVR_T\n", - " 4.791390e+01\n", - " 3.783700e-02\n", - " 4.787500e+01\n", - " 4.800000e+01\n", + " 4.735680e+01\n", + " 5.819170e+02\n", + " 4.731200e+01\n", + " 4.737500e+01\n", " degC\n", " Board 11 (Driver) temperature c- double degC {...\n", " \n", " \n", " kpfred.BRD12_LVXBIAS_T\n", - " 4.410970e+01\n", - " 4.174030e-02\n", - " 4.406200e+01\n", - " 4.418700e+01\n", + " 4.354730e+01\n", + " 5.351070e+02\n", + " 4.343700e+01\n", + " 4.362500e+01\n", " degC\n", " Board 12 (LVxBias) temperature c- double degC ...\n", " \n", " \n", " kpfred.BRD1_HTRX_T\n", - " 4.159920e+01\n", - " 3.476950e-02\n", - " 4.156200e+01\n", - " 4.168700e+01\n", + " 4.136050e+01\n", + " 5.082350e+02\n", + " 4.131200e+01\n", + " 4.137500e+01\n", " degC\n", " Board 1 (HeaterX) temperature c- double degC {...\n", " \n", " \n", " kpfred.BRD2_XVBIAS_T\n", - " 4.316360e+01\n", - " 3.747500e-02\n", + " 4.314250e+01\n", + " 5.301330e+02\n", " 4.312500e+01\n", - " 4.325000e+01\n", + " 4.318700e+01\n", " degC\n", " Board 2 (XV Bias) temperature c- double degC {...\n", " \n", " \n", " kpfred.BRD3_LVDS_T\n", - " 4.294280e+01\n", - " 2.971860e-02\n", - " 4.287500e+01\n", + " 4.298010e+01\n", + " 5.281370e+02\n", + " 4.293700e+01\n", " 4.300000e+01\n", " degC\n", " Board 3 (LVDS) temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.BRD4_DRVR_T\n", - " 4.590750e+01\n", - " 3.102650e-02\n", - " 4.587500e+01\n", - " 4.593700e+01\n", + " 4.606210e+01\n", + " 5.660080e+02\n", + " 4.600000e+01\n", + " 4.612500e+01\n", " degC\n", " Board 4 (Driver) temperature c- double degC {%...\n", " \n", " \n", " kpfred.BRD5_AD_T\n", - " 4.114020e+01\n", - " 6.210450e-02\n", - " 4.106200e+01\n", - " 4.125000e+01\n", + " 4.077440e+01\n", + " 5.010340e+02\n", + " 4.068700e+01\n", + " 4.081200e+01\n", " degC\n", " Board 5 (AD) temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.BRD7_HTRX_T\n", - " 4.594510e+01\n", - " 4.483830e-02\n", - " 4.587500e+01\n", - " 4.600000e+01\n", + " 4.536200e+01\n", + " 5.574060e+02\n", + " 4.531200e+01\n", + " 4.543700e+01\n", " degC\n", " Board 7 (HeaterX) temperature c- double degC {...\n", " \n", " \n", " kpfred.BRD9_HVXBIAS_T\n", - " 4.515130e+01\n", - " 5.530950e-02\n", - " 4.506200e+01\n", - " 4.525000e+01\n", + " 4.526700e+01\n", + " 5.562380e+02\n", + " 4.518700e+01\n", + " 4.531200e+01\n", " degC\n", " Board 9 (HVxBias) temperature c- double degC {...\n", " \n", " \n", " kpfred.CF_BASE_2WT\n", - " -1.263260e+02\n", - " 5.548990e-04\n", - " -1.263260e+02\n", - " -1.263240e+02\n", + " -1.257290e+02\n", + " 1.544940e+03\n", + " -1.257300e+02\n", + " -1.257260e+02\n", " degC\n", " tip cold finger (2 wire) c- double degC {%.3f}\n", " \n", " \n", " kpfred.CF_BASE_T\n", " -2.731500e+02\n", - " 0.000000e+00\n", + " 3.356450e+03\n", " -2.731500e+02\n", " -2.731500e+02\n", " degC\n", @@ -2098,53 +2247,53 @@ " \n", " \n", " kpfred.CF_BASE_TRG\n", - " -1.323000e+02\n", - " 1.068640e-05\n", - " -1.323000e+02\n", - " -1.323000e+02\n", + " -1.257500e+02\n", + " 1.545210e+03\n", + " -1.257500e+02\n", + " -1.257500e+02\n", " degC\n", " base cold finger heater 1A, target temp c2 dou...\n", " \n", " \n", " kpfred.CF_TIP_T\n", - " -1.278000e+02\n", - " 9.388130e-04\n", - " -1.278010e+02\n", - " -1.277980e+02\n", + " -1.272500e+02\n", + " 1.563640e+03\n", + " -1.272510e+02\n", + " -1.272480e+02\n", " degC\n", " tip cold finger c- double degC {%.3f}\n", " \n", " \n", " kpfred.CF_TIP_TRG\n", - " -1.278000e+02\n", - " 1.144400e-05\n", - " -1.278000e+02\n", - " -1.278000e+02\n", + " -1.272500e+02\n", + " 1.563640e+03\n", + " -1.272500e+02\n", + " -1.272500e+02\n", " degC\n", " tip cold finger heater 1B, target temp c2 doub...\n", " \n", " \n", " kpfred.COL_PRESS\n", - " 6.206300e-08\n", - " 2.434280e-10\n", - " 6.200000e-08\n", - " 6.300000e-08\n", + " 3.715130e-08\n", + " 4.565340e-07\n", + " 3.700000e-08\n", + " 3.800000e-08\n", " Torr\n", " Current ion pump pressure c- double Torr {%.3e}\n", " \n", " \n", " kpfred.CRYOBODY_T\n", - " 1.894050e+01\n", - " 1.123760e-03\n", - " 1.893800e+01\n", - " 1.894200e+01\n", + " 1.899880e+01\n", + " 2.334560e+02\n", + " 1.899700e+01\n", + " 1.900100e+01\n", " degC\n", " Cryo Body Temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.CRYOBODY_TRG\n", " 1.900000e+01\n", - " 0.000000e+00\n", + " 2.334710e+02\n", " 1.900000e+01\n", " 1.900000e+01\n", " degC\n", @@ -2152,44 +2301,44 @@ " \n", " \n", " kpfred.CURRTEMP\n", - " -1.410000e+02\n", - " 8.221560e-04\n", - " -1.410020e+02\n", - " -1.409990e+02\n", + " -1.405000e+02\n", + " 1.726450e+03\n", + " -1.405010e+02\n", + " -1.404980e+02\n", " degC\n", " Current cold head temperature c- double degC {...\n", " \n", " \n", " kpfred.ECH_PRESS\n", - " 8.077950e-08\n", - " 4.153840e-10\n", - " 8.000000e-08\n", - " 8.100000e-08\n", + " 1.000000e-11\n", + " 1.228790e-10\n", + " 1.000000e-11\n", + " 1.000000e-11\n", " Torr\n", " Current ion pump pressure c- double Torr {%.3e}\n", " \n", " \n", " kpfred.KPF_CCD_T\n", - " -1.008270e+02\n", - " 1.113970e-03\n", - " -1.008290e+02\n", - " -1.008250e+02\n", + " -1.008710e+02\n", + " 1.239500e+03\n", + " -1.008720e+02\n", + " -1.008700e+02\n", " degC\n", " SSL Detector temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.STA_CCD_T\n", " -1.000000e+02\n", - " 1.344790e-03\n", + " 1.228800e+03\n", " -1.000030e+02\n", - " -9.999700e+01\n", + " -9.999900e+01\n", " degC\n", " STA Detector temperature c- double degC {%.3f}\n", " \n", " \n", " kpfred.STA_CCD_TRG\n", " -1.000000e+02\n", - " 0.000000e+00\n", + " 1.228790e+03\n", " -1.000000e+02\n", " -1.000000e+02\n", " degC\n", @@ -2197,136 +2346,199 @@ " \n", " \n", " kpfred.TEMPSET\n", - " -1.410000e+02\n", - " 0.000000e+00\n", - " -1.410000e+02\n", - " -1.410000e+02\n", + " -1.405000e+02\n", + " 1.726460e+03\n", + " -1.405000e+02\n", + " -1.405000e+02\n", " degC\n", " Set point for the cold head temperature c2 dou...\n", " \n", " \n", " kpfexpose.BENCH_C\n", - " 9.587400e+00\n", - " 6.999960e-02\n", - " 9.400000e+00\n", - " 9.800000e+00\n", + " 1.522700e+01\n", + " 1.871100e+02\n", + " 1.510000e+01\n", + " 1.540000e+01\n", " degC\n", " rtd bench c- double degC {%.1f} { -100.0 .. 10...\n", " \n", " \n", " kpfexpose.CAMBARREL_C\n", - " 9.324410e+00\n", - " 9.957740e-02\n", - " 9.100000e+00\n", - " 9.600000e+00\n", + " 1.602630e+01\n", + " 1.969330e+02\n", + " 1.580000e+01\n", + " 1.620000e+01\n", " degC\n", " rtd camera barrel c- double degC {%.1f} { -100...\n", " \n", " \n", " kpfexpose.DET_XTRN_C\n", - " 1.136500e+01\n", - " 6.707170e-02\n", - " 1.120000e+01\n", - " 1.150000e+01\n", + " 2.017700e+01\n", + " 2.479350e+02\n", + " 2.000000e+01\n", + " 2.040000e+01\n", " degC\n", " rtd detector extermal c- double degC {%.1f} { ...\n", " \n", " \n", " kpfexpose.ECHELLE_C\n", - " 8.336610e+00\n", - " 8.410160e-02\n", - " 8.100000e+00\n", - " 8.500000e+00\n", + " 1.376320e+01\n", + " 1.691240e+02\n", + " 1.350000e+01\n", + " 1.400000e+01\n", " degC\n", " rtd echelle c- double degC {%.1f} { -100.0 .. ...\n", " \n", " \n", " kpfexpose.ENCLOSURE_C\n", - " 1.044650e+01\n", - " 6.447760e-02\n", - " 1.030000e+01\n", - " 1.060000e+01\n", + " 1.863160e+01\n", + " 2.289460e+02\n", + " 1.850000e+01\n", + " 1.880000e+01\n", " degC\n", " rtd enclosure c- double degC {%.1f} { -100.0 ....\n", " \n", " \n", " kpfexpose.RACK_AIR_C\n", - " 1.471650e+01\n", - " 5.446660e-02\n", - " 1.460000e+01\n", - " 1.490000e+01\n", + " 1.857040e+01\n", + " 2.281930e+02\n", + " 1.840000e+01\n", + " 1.870000e+01\n", " degC\n", " rtd rack air c- double degC {%.1f} { -100.0 .....\n", " \n", " \n", " kpfvac.PUMP_TEMP\n", - " 4.500000e+01\n", - " 0.000000e+00\n", - " 4.500000e+01\n", - " 4.500000e+01\n", + " 3.900000e+01\n", + " 4.792300e+02\n", + " 3.900000e+01\n", + " 3.900000e+01\n", " degC\n", " Motor temperature c- double degC {%.2f}\n", " \n", " \n", " kpf_hk.COOLTARG\n", - " -2.000000e+01\n", - " 0.000000e+00\n", - " -2.000000e+01\n", - " -2.000000e+01\n", + " -6.000000e+01\n", + " 7.372760e+02\n", + " -6.000000e+01\n", + " -6.000000e+01\n", " degC\n", " temperature target c2 int degC\n", " \n", " \n", " kpf_hk.CURRTEMP\n", - " -2.019000e+01\n", - " 1.322110e-06\n", - " -2.019000e+01\n", - " -2.019000e+01\n", + " -5.978000e+01\n", + " 7.345730e+02\n", + " -5.978000e+01\n", + " -5.978000e+01\n", " degC\n", " current temperature c- double degC {%.2f}\n", " \n", " \n", + " kpfgreen.COL_CURR\n", + " 1.100000e-06\n", + " 1.351670e-05\n", + " 1.100000e-06\n", + " 1.100000e-06\n", + " A\n", + " Current ion pump current c- double A {%.3e}\n", + " \n", + " \n", + " kpfgreen.ECH_CURR\n", + " 8.910000e-11\n", + " 1.094860e-09\n", + " 8.910000e-11\n", + " 8.910000e-11\n", + " A\n", + " Current ion pump current c- double A {%.3e}\n", + " \n", + " \n", + " kpfred.COL_CURR\n", + " 9.986180e-07\n", + " 1.227110e-05\n", + " 9.800000e-07\n", + " 1.000000e-06\n", + " A\n", + " Current ion pump current c- double A {%.3e}\n", + " \n", + " \n", + " kpfred.ECH_CURR\n", + " 5.600000e-12\n", + " 6.881240e-11\n", + " 5.600000e-12\n", + " 5.600000e-12\n", + " A\n", + " Current ion pump current c- double A {%.3e}\n", + " \n", + " \n", + " kpfcal.IRFLUX\n", + " 9.999000e+03\n", + " 1.228670e+05\n", + " 9.999000e+03\n", + " 9.999000e+03\n", + " Counts\n", + " LFC Fiberlock IR Intensity c- int Counts {%d}\n", + " \n", + " \n", + " kpfcal.VISFLUX\n", + " 2.553950e+01\n", + " 3.138880e+02\n", + " 2.500000e+01\n", + " 2.600000e+01\n", + " Counts\n", + " LFC Fiberlock Vis Intensity c- int Counts {%d}\n", + " \n", + " \n", + " kpfcal.BLUECUTIACT\n", + " 3.326320e-02\n", + " 4.111620e-01\n", + " 2.300000e-02\n", + " 5.300000e-02\n", + " A\n", + " Blue cut amplifier 0 measured current c- doubl...\n", + " \n", + " \n", " kpfmot.AGITSPD\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " 0.000000e+00\n", + " -3.900470e+03\n", + " 4.822300e+04\n", + " -4.699000e+03\n", + " -1.880000e+03\n", " motor_counts/s\n", " agit raw velocity c2 int motor counts/s { -750...\n", " \n", " \n", " kpfmot.AGITTOR\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " 0.000000e+00\n", - " 0.000000e+00\n", + " -1.102970e+00\n", + " 1.590140e+01\n", + " -2.271000e+00\n", + " 1.440000e-01\n", " V\n", " agit motor torque c- double V {%.3f}\n", " \n", " \n", " kpfmot.AGITAMBI_T\n", - " 2.594540e+01\n", - " 2.104980e-02\n", - " 2.588000e+01\n", - " 2.600000e+01\n", + " 2.580130e+01\n", + " 3.170470e+02\n", + " 2.548000e+01\n", + " 2.603000e+01\n", " degC\n", " Agitator ambient temperature c- double degC {%...\n", " \n", " \n", " kpfmot.AGITMOT_T\n", - " 2.604210e+01\n", - " 2.185060e-02\n", - " 2.599000e+01\n", - " 2.610000e+01\n", + " 2.606950e+01\n", + " 3.203420e+02\n", + " 2.582000e+01\n", + " 2.622000e+01\n", " degC\n", " Agitator motor temperature c- double degC {%.2...\n", " \n", " \n", " kpfpower.OUTLET_A1_Amps\n", - " 2.697640e+01\n", - " 1.521680e-01\n", - " 2.600000e+01\n", + " 5.070390e+01\n", + " 6.235380e+02\n", " 2.700000e+01\n", + " 5.300000e+01\n", " milliamps\n", " Outlet A1 current amperage c- int milliamps\n", " \n", @@ -2337,241 +2549,255 @@ "text/plain": [ " average stddev min \\\n", "keyword \n", - "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.743070e+01 4.817150e-04 1.743000e+01 \n", - "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.803690e+01 5.135500e-04 1.803600e+01 \n", - "kpfmet.BENCH_BOTTOM_DCUT 1.781270e+01 6.067910e-04 1.781200e+01 \n", - "kpfmet.BENCH_BOTTOM_ECHELLE 1.795330e+01 4.740290e-04 1.795200e+01 \n", - "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.733170e+01 4.920710e-04 1.733000e+01 \n", - "kpfmet.BENCH_TOP_COLL 1.813210e+01 5.333490e-04 1.813100e+01 \n", - "kpfmet.BENCH_TOP_DCUT 1.784160e+01 5.604510e-04 1.784100e+01 \n", - "kpfmet.BENCH_TOP_ECHELLE_CAM 1.789740e+01 4.959300e-04 1.789700e+01 \n", - "kpfmet.CAL_BENCH 2.130000e+01 0.000000e+00 2.130000e+01 \n", - "kpfmet.CAL_BENCH_BB_SRC 2.160000e+01 0.000000e+00 2.160000e+01 \n", - "kpfmet.CAL_BENCH_BOT 2.190000e+01 1.440850e-06 2.190000e+01 \n", - "kpfmet.CAL_BENCH_ENCL_AIR 2.120000e+01 2.059970e-06 2.120000e+01 \n", - "kpfmet.CAL_BENCH_OCT_MOT 2.120000e+01 2.059970e-06 2.120000e+01 \n", - "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.124490e+01 4.983560e-02 2.120000e+01 \n", - "kpfmet.CAL_RACK_TOP 2.620910e+01 2.875360e-02 2.620000e+01 \n", - "kpfmet.ECHELLE_BOTTOM 1.796950e+01 5.514370e-04 1.796900e+01 \n", - "kpfmet.ECHELLE_TOP 1.786290e+01 3.750040e-04 1.786200e+01 \n", - "kpfmet.FF_SRC 2.004720e+01 5.002260e-02 2.000000e+01 \n", - "kpfmet.GREEN_A -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfmet.GREEN_B 1.875340e+01 6.393470e-04 1.875200e+01 \n", - "kpfmet.GREEN_C1 1.869870e+01 6.944730e-04 1.869700e+01 \n", - "kpfmet.GREEN_CAMERA_BOTTOM 1.811600e+01 6.472810e-04 1.811500e+01 \n", - "kpfmet.GREEN_CAMERA_COLLIMATOR 1.815550e+01 6.195310e-04 1.815400e+01 \n", - "kpfmet.GREEN_CAMERA_ECHELLE 1.802870e+01 6.073550e-04 1.802800e+01 \n", - "kpfmet.GREEN_CAMERA_TOP 1.794530e+01 5.890810e-04 1.794400e+01 \n", - "kpfmet.GREEN_D1 1.853170e+01 5.801500e-04 1.853100e+01 \n", - "kpfmet.GREEN_D2 1.650110e+01 7.394430e-04 1.650000e+01 \n", - "kpfmet.GREEN_D3 -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfmet.GREEN_D4 1.645530e+01 6.231880e-04 1.645400e+01 \n", - "kpfmet.GREEN_GRISM_TOP 1.785480e+01 7.172570e-04 1.785400e+01 \n", - "kpfmet.PRIMARY_COLLIMATOR_TOP 1.842500e+01 4.988680e-04 1.842400e+01 \n", - "kpfmet.RED_A 1.446180e+01 1.418040e-02 1.443200e+01 \n", - "kpfmet.RED_B 1.730100e+01 4.271390e-03 1.729300e+01 \n", - "kpfmet.RED_C1 -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfmet.RED_CAMERA_BOTTOM 1.767610e+01 6.662820e-04 1.767500e+01 \n", - "kpfmet.RED_CAMERA_COLLIMATOR 1.802560e+01 6.901120e-04 1.802400e+01 \n", - "kpfmet.RED_CAMERA_ECHELLE 1.798090e+01 6.532630e-04 1.798000e+01 \n", - "kpfmet.RED_CAMERA_TOP 1.792610e+01 6.733910e-04 1.792400e+01 \n", - "kpfmet.RED_D1 1.335860e+01 1.665850e-02 1.332500e+01 \n", - "kpfmet.RED_D2 1.612960e+01 6.835860e-04 1.612800e+01 \n", - "kpfmet.RED_D3 -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfmet.RED_D4 1.577720e+01 6.775850e-04 1.577600e+01 \n", - "kpfmet.RED_GRISM_TOP 1.785020e+01 7.324240e-04 1.784900e+01 \n", - "kpfmet.REFORMATTER 1.816180e+01 4.937130e-04 1.816100e+01 \n", - "kpfmet.SCIENCE_CAL_FIBER_STG 2.127990e+01 4.013800e-02 2.120000e+01 \n", - "kpfmet.SIMCAL_FIBER_STG 2.120000e+01 2.059970e-06 2.120000e+01 \n", - "kpfmet.SKYCAL_FIBER_STG 2.110000e+01 0.000000e+00 2.110000e+01 \n", - "kpfmet.TEMP 2.146190e+01 2.623280e-02 2.141400e+01 \n", - "kpfmet.TH_DAILY 2.147640e+01 4.255980e-02 2.140000e+01 \n", - "kpfmet.TH_GOLD 2.550000e+01 0.000000e+00 2.550000e+01 \n", - "kpfmet.U_DAILY 2.149020e+01 2.984770e-02 2.140000e+01 \n", - "kpfmet.U_GOLD 2.110000e+01 0.000000e+00 2.110000e+01 \n", - "kpfgreen.BPLANE_TEMP 3.867850e+01 2.657240e-02 3.862500e+01 \n", - "kpfgreen.BRD10_DRVR_T 4.279390e+01 5.419160e-02 4.275000e+01 \n", - "kpfgreen.BRD11_DRVR_T 4.113130e+01 1.883080e-02 4.112500e+01 \n", - "kpfgreen.BRD12_LVXBIAS_T 3.695960e+01 3.026760e-02 3.693700e+01 \n", - "kpfgreen.BRD1_HTRX_T 3.539790e+01 2.999430e-02 3.537500e+01 \n", - "kpfgreen.BRD2_XVBIAS_T 3.699970e+01 1.620680e-02 3.693700e+01 \n", - "kpfgreen.BRD3_LVDS_T 3.628370e+01 3.094450e-02 3.625000e+01 \n", - "kpfgreen.BRD4_DRVR_T 3.918090e+01 1.850560e-02 3.912500e+01 \n", - "kpfgreen.BRD5_AD_T 3.594530e+01 3.121400e-02 3.587500e+01 \n", - "kpfgreen.BRD7_HTRX_T 3.996460e+01 3.544480e-02 3.987500e+01 \n", - "kpfgreen.BRD9_HVXBIAS_T 3.848160e+01 2.868240e-02 3.843700e+01 \n", - "kpfgreen.CF_BASE_2WT -1.270670e+02 5.641800e-04 -1.270680e+02 \n", - "kpfgreen.CF_BASE_T -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfgreen.CF_BASE_TRG -1.293000e+02 1.152370e-05 -1.293000e+02 \n", - "kpfgreen.CF_TIP_T -1.278000e+02 1.171170e-03 -1.278020e+02 \n", - "kpfgreen.CF_TIP_TRG -1.278000e+02 1.144400e-05 -1.278000e+02 \n", - "kpfgreen.COL_PRESS 8.614570e-08 5.884600e-10 8.600000e-08 \n", - "kpfgreen.CRYOBODY_T 1.888900e+01 1.517390e-03 1.888600e+01 \n", - "kpfgreen.CRYOBODY_TRG 1.900000e+01 0.000000e+00 1.900000e+01 \n", - "kpfgreen.CURRTEMP -1.400000e+02 8.475030e-04 -1.400020e+02 \n", - "kpfgreen.ECH_PRESS 1.000000e-11 0.000000e+00 1.000000e-11 \n", - "kpfgreen.KPF_CCD_T -1.010180e+02 1.145050e-03 -1.010210e+02 \n", - "kpfgreen.STA_CCD_T -9.999980e+01 1.419380e-03 -1.000030e+02 \n", - "kpfgreen.STA_CCD_TRG -1.000000e+02 0.000000e+00 -1.000000e+02 \n", - "kpfgreen.TEMPSET -1.400000e+02 0.000000e+00 -1.400000e+02 \n", - "kpfred.BPLANE_TEMP 4.437860e+01 2.720140e-02 4.431200e+01 \n", - "kpfred.BRD10_DRVR_T 4.840920e+01 7.985580e-02 4.831200e+01 \n", - "kpfred.BRD11_DRVR_T 4.791390e+01 3.783700e-02 4.787500e+01 \n", - "kpfred.BRD12_LVXBIAS_T 4.410970e+01 4.174030e-02 4.406200e+01 \n", - "kpfred.BRD1_HTRX_T 4.159920e+01 3.476950e-02 4.156200e+01 \n", - "kpfred.BRD2_XVBIAS_T 4.316360e+01 3.747500e-02 4.312500e+01 \n", - "kpfred.BRD3_LVDS_T 4.294280e+01 2.971860e-02 4.287500e+01 \n", - "kpfred.BRD4_DRVR_T 4.590750e+01 3.102650e-02 4.587500e+01 \n", - "kpfred.BRD5_AD_T 4.114020e+01 6.210450e-02 4.106200e+01 \n", - "kpfred.BRD7_HTRX_T 4.594510e+01 4.483830e-02 4.587500e+01 \n", - "kpfred.BRD9_HVXBIAS_T 4.515130e+01 5.530950e-02 4.506200e+01 \n", - "kpfred.CF_BASE_2WT -1.263260e+02 5.548990e-04 -1.263260e+02 \n", - "kpfred.CF_BASE_T -2.731500e+02 0.000000e+00 -2.731500e+02 \n", - "kpfred.CF_BASE_TRG -1.323000e+02 1.068640e-05 -1.323000e+02 \n", - "kpfred.CF_TIP_T -1.278000e+02 9.388130e-04 -1.278010e+02 \n", - "kpfred.CF_TIP_TRG -1.278000e+02 1.144400e-05 -1.278000e+02 \n", - "kpfred.COL_PRESS 6.206300e-08 2.434280e-10 6.200000e-08 \n", - "kpfred.CRYOBODY_T 1.894050e+01 1.123760e-03 1.893800e+01 \n", - "kpfred.CRYOBODY_TRG 1.900000e+01 0.000000e+00 1.900000e+01 \n", - "kpfred.CURRTEMP -1.410000e+02 8.221560e-04 -1.410020e+02 \n", - "kpfred.ECH_PRESS 8.077950e-08 4.153840e-10 8.000000e-08 \n", - "kpfred.KPF_CCD_T -1.008270e+02 1.113970e-03 -1.008290e+02 \n", - "kpfred.STA_CCD_T -1.000000e+02 1.344790e-03 -1.000030e+02 \n", - "kpfred.STA_CCD_TRG -1.000000e+02 0.000000e+00 -1.000000e+02 \n", - "kpfred.TEMPSET -1.410000e+02 0.000000e+00 -1.410000e+02 \n", - "kpfexpose.BENCH_C 9.587400e+00 6.999960e-02 9.400000e+00 \n", - "kpfexpose.CAMBARREL_C 9.324410e+00 9.957740e-02 9.100000e+00 \n", - "kpfexpose.DET_XTRN_C 1.136500e+01 6.707170e-02 1.120000e+01 \n", - "kpfexpose.ECHELLE_C 8.336610e+00 8.410160e-02 8.100000e+00 \n", - "kpfexpose.ENCLOSURE_C 1.044650e+01 6.447760e-02 1.030000e+01 \n", - "kpfexpose.RACK_AIR_C 1.471650e+01 5.446660e-02 1.460000e+01 \n", - "kpfvac.PUMP_TEMP 4.500000e+01 0.000000e+00 4.500000e+01 \n", - "kpf_hk.COOLTARG -2.000000e+01 0.000000e+00 -2.000000e+01 \n", - "kpf_hk.CURRTEMP -2.019000e+01 1.322110e-06 -2.019000e+01 \n", - "kpfmot.AGITSPD 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "kpfmot.AGITTOR 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "kpfmot.AGITAMBI_T 2.594540e+01 2.104980e-02 2.588000e+01 \n", - "kpfmot.AGITMOT_T 2.604210e+01 2.185060e-02 2.599000e+01 \n", - "kpfpower.OUTLET_A1_Amps 2.697640e+01 1.521680e-01 2.600000e+01 \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597510e+01 1.963010e+02 1.597500e+01 \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.647870e+01 2.024890e+02 1.647700e+01 \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.624350e+01 1.995990e+02 1.624300e+01 \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.638640e+01 2.013550e+02 1.638600e+01 \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588580e+01 1.952040e+02 1.588500e+01 \n", + "kpfmet.BENCH_TOP_COLL 1.657790e+01 2.037080e+02 1.657700e+01 \n", + "kpfmet.BENCH_TOP_DCUT 1.628050e+01 2.000540e+02 1.627900e+01 \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629190e+01 2.001940e+02 1.629100e+01 \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736520e+01 2.133830e+02 1.736400e+01 \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.721330e+01 2.115160e+02 1.721200e+01 \n", + "kpfmet.CAL_BENCH 2.020000e+01 2.482160e+02 2.020000e+01 \n", + "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 2.519030e+02 2.050000e+01 \n", + "kpfmet.CAL_BENCH_BOT 2.360000e+01 2.899950e+02 2.360000e+01 \n", + "kpfmet.CAL_BENCH_ENCL_AIR 2.018220e+01 2.479980e+02 2.010000e+01 \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.CAL_RACK_TOP 2.301710e+01 2.828330e+02 2.300000e+01 \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.570260e+01 1.929530e+02 1.570100e+01 \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G3 1.668370e+01 2.050090e+02 1.668300e+01 \n", + "kpfmet.CRYOSTAT_R1 1.610210e+01 1.978620e+02 1.610000e+01 \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_R3 1.564080e+01 1.921940e+02 1.564000e+01 \n", + "kpfmet.ECHELLE_BOTTOM 1.641060e+01 2.016530e+02 1.641000e+01 \n", + "kpfmet.ECHELLE_TOP 1.628290e+01 2.000840e+02 1.628200e+01 \n", + "kpfmet.FF_SRC 1.957500e+01 2.405370e+02 1.950000e+01 \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.677540e+01 2.061350e+02 1.677400e+01 \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682040e+01 2.066880e+02 1.681900e+01 \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.671060e+01 2.053390e+02 1.670900e+01 \n", + "kpfmet.GREEN_CAMERA_TOP 1.661770e+01 2.041970e+02 1.661700e+01 \n", + "kpfmet.GREEN_GRISM_TOP 1.639170e+01 2.014200e+02 1.639000e+01 \n", + "kpfmet.GREEN_LN2_FLANGE 9.791590e+00 1.203190e+02 9.746000e+00 \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689680e+01 2.076270e+02 1.689600e+01 \n", + "kpfmet.RED_CAMERA_BOTTOM 1.630110e+01 2.003070e+02 1.630000e+01 \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.664290e+01 2.045070e+02 1.664200e+01 \n", + "kpfmet.RED_CAMERA_ECHELLE 1.659400e+01 2.039060e+02 1.659300e+01 \n", + "kpfmet.RED_CAMERA_TOP 1.654670e+01 2.033240e+02 1.654500e+01 \n", + "kpfmet.RED_GRISM_TOP 1.636990e+01 2.011530e+02 1.636900e+01 \n", + "kpfmet.RED_LN2_FLANGE 6.245890e+00 7.675000e+01 6.199000e+00 \n", + "kpfmet.REFORMATTER 1.660480e+01 2.040380e+02 1.660400e+01 \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747050e+01 2.146770e+02 1.746900e+01 \n", + "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.TEMP 1.968480e+01 2.418860e+02 1.967300e+01 \n", + "kpfmet.TH_DAILY 2.000000e+01 2.457590e+02 2.000000e+01 \n", + "kpfmet.TH_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.U_DAILY 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.U_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfgreen.BPLANE_TEMP 3.582320e+01 4.401930e+02 3.581200e+01 \n", + "kpfgreen.BRD10_DRVR_T 4.050370e+01 4.977070e+02 4.050000e+01 \n", + "kpfgreen.BRD11_DRVR_T 3.875000e+01 4.761580e+02 3.875000e+01 \n", + "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 4.270060e+02 3.475000e+01 \n", + "kpfgreen.BRD1_HTRX_T 3.184100e+01 3.912610e+02 3.181200e+01 \n", + "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "kpfgreen.BRD3_LVDS_T 3.342310e+01 4.107010e+02 3.337500e+01 \n", + "kpfgreen.BRD4_DRVR_T 3.719820e+01 4.570890e+02 3.718700e+01 \n", + "kpfgreen.BRD5_AD_T 3.381530e+01 4.155200e+02 3.381200e+01 \n", + "kpfgreen.BRD7_HTRX_T 3.784640e+01 4.650540e+02 3.781200e+01 \n", + "kpfgreen.BRD9_HVXBIAS_T 3.627570e+01 4.457540e+02 3.625000e+01 \n", + "kpfgreen.CF_BASE_2WT -1.270330e+02 1.560970e+03 -1.270340e+02 \n", + "kpfgreen.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 1.566710e+03 -1.275000e+02 \n", + "kpfgreen.CF_TIP_T -1.280000e+02 1.572860e+03 -1.280010e+02 \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 1.572860e+03 -1.280000e+02 \n", + "kpfgreen.COL_PRESS 4.000000e-08 4.915170e-07 4.000000e-08 \n", + "kpfgreen.CRYOBODY_T 1.899600e+01 2.334220e+02 1.899200e+01 \n", + "kpfgreen.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", + "kpfgreen.CURRTEMP -1.400000e+02 1.720310e+03 -1.400020e+02 \n", + "kpfgreen.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", + "kpfgreen.KPF_CCD_T -1.010900e+02 1.242190e+03 -1.010920e+02 \n", + "kpfgreen.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000020e+02 \n", + "kpfgreen.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", + "kpfgreen.TEMPSET -1.400000e+02 1.720310e+03 -1.400000e+02 \n", + "kpfred.BPLANE_TEMP 4.380440e+01 5.382650e+02 4.375000e+01 \n", + "kpfred.BRD10_DRVR_T 4.811190e+01 5.911970e+02 4.806200e+01 \n", + "kpfred.BRD11_DRVR_T 4.735680e+01 5.819170e+02 4.731200e+01 \n", + "kpfred.BRD12_LVXBIAS_T 4.354730e+01 5.351070e+02 4.343700e+01 \n", + "kpfred.BRD1_HTRX_T 4.136050e+01 5.082350e+02 4.131200e+01 \n", + "kpfred.BRD2_XVBIAS_T 4.314250e+01 5.301330e+02 4.312500e+01 \n", + "kpfred.BRD3_LVDS_T 4.298010e+01 5.281370e+02 4.293700e+01 \n", + "kpfred.BRD4_DRVR_T 4.606210e+01 5.660080e+02 4.600000e+01 \n", + "kpfred.BRD5_AD_T 4.077440e+01 5.010340e+02 4.068700e+01 \n", + "kpfred.BRD7_HTRX_T 4.536200e+01 5.574060e+02 4.531200e+01 \n", + "kpfred.BRD9_HVXBIAS_T 4.526700e+01 5.562380e+02 4.518700e+01 \n", + "kpfred.CF_BASE_2WT -1.257290e+02 1.544940e+03 -1.257300e+02 \n", + "kpfred.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfred.CF_BASE_TRG -1.257500e+02 1.545210e+03 -1.257500e+02 \n", + "kpfred.CF_TIP_T -1.272500e+02 1.563640e+03 -1.272510e+02 \n", + "kpfred.CF_TIP_TRG -1.272500e+02 1.563640e+03 -1.272500e+02 \n", + "kpfred.COL_PRESS 3.715130e-08 4.565340e-07 3.700000e-08 \n", + "kpfred.CRYOBODY_T 1.899880e+01 2.334560e+02 1.899700e+01 \n", + "kpfred.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", + "kpfred.CURRTEMP -1.405000e+02 1.726450e+03 -1.405010e+02 \n", + "kpfred.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", + "kpfred.KPF_CCD_T -1.008710e+02 1.239500e+03 -1.008720e+02 \n", + "kpfred.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000030e+02 \n", + "kpfred.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", + "kpfred.TEMPSET -1.405000e+02 1.726460e+03 -1.405000e+02 \n", + "kpfexpose.BENCH_C 1.522700e+01 1.871100e+02 1.510000e+01 \n", + "kpfexpose.CAMBARREL_C 1.602630e+01 1.969330e+02 1.580000e+01 \n", + "kpfexpose.DET_XTRN_C 2.017700e+01 2.479350e+02 2.000000e+01 \n", + "kpfexpose.ECHELLE_C 1.376320e+01 1.691240e+02 1.350000e+01 \n", + "kpfexpose.ENCLOSURE_C 1.863160e+01 2.289460e+02 1.850000e+01 \n", + "kpfexpose.RACK_AIR_C 1.857040e+01 2.281930e+02 1.840000e+01 \n", + "kpfvac.PUMP_TEMP 3.900000e+01 4.792300e+02 3.900000e+01 \n", + "kpf_hk.COOLTARG -6.000000e+01 7.372760e+02 -6.000000e+01 \n", + "kpf_hk.CURRTEMP -5.978000e+01 7.345730e+02 -5.978000e+01 \n", + "kpfgreen.COL_CURR 1.100000e-06 1.351670e-05 1.100000e-06 \n", + "kpfgreen.ECH_CURR 8.910000e-11 1.094860e-09 8.910000e-11 \n", + "kpfred.COL_CURR 9.986180e-07 1.227110e-05 9.800000e-07 \n", + "kpfred.ECH_CURR 5.600000e-12 6.881240e-11 5.600000e-12 \n", + "kpfcal.IRFLUX 9.999000e+03 1.228670e+05 9.999000e+03 \n", + "kpfcal.VISFLUX 2.553950e+01 3.138880e+02 2.500000e+01 \n", + "kpfcal.BLUECUTIACT 3.326320e-02 4.111620e-01 2.300000e-02 \n", + "kpfmot.AGITSPD -3.900470e+03 4.822300e+04 -4.699000e+03 \n", + "kpfmot.AGITTOR -1.102970e+00 1.590140e+01 -2.271000e+00 \n", + "kpfmot.AGITAMBI_T 2.580130e+01 3.170470e+02 2.548000e+01 \n", + "kpfmot.AGITMOT_T 2.606950e+01 3.203420e+02 2.582000e+01 \n", + "kpfpower.OUTLET_A1_Amps 5.070390e+01 6.235380e+02 2.700000e+01 \n", "\n", " max units \\\n", "keyword \n", - "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.743200e+01 degC \n", - "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.803800e+01 degC \n", - "kpfmet.BENCH_BOTTOM_DCUT 1.781400e+01 degC \n", - "kpfmet.BENCH_BOTTOM_ECHELLE 1.795400e+01 degC \n", - "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.733200e+01 degC \n", - "kpfmet.BENCH_TOP_COLL 1.813300e+01 degC \n", - "kpfmet.BENCH_TOP_DCUT 1.784300e+01 degC \n", - "kpfmet.BENCH_TOP_ECHELLE_CAM 1.789800e+01 degC \n", - "kpfmet.CAL_BENCH 2.130000e+01 degC \n", - "kpfmet.CAL_BENCH_BB_SRC 2.160000e+01 degC \n", - "kpfmet.CAL_BENCH_BOT 2.190000e+01 degC \n", - "kpfmet.CAL_BENCH_ENCL_AIR 2.120000e+01 degC \n", - "kpfmet.CAL_BENCH_OCT_MOT 2.120000e+01 degC \n", - "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.130000e+01 degC \n", - "kpfmet.CAL_RACK_TOP 2.630000e+01 degC \n", - "kpfmet.ECHELLE_BOTTOM 1.797100e+01 degC \n", - "kpfmet.ECHELLE_TOP 1.786400e+01 degC \n", - "kpfmet.FF_SRC 2.010000e+01 degC \n", - "kpfmet.GREEN_A -2.731500e+02 degC \n", - "kpfmet.GREEN_B 1.875500e+01 degC \n", - "kpfmet.GREEN_C1 1.870000e+01 degC \n", - "kpfmet.GREEN_CAMERA_BOTTOM 1.811800e+01 degC \n", - "kpfmet.GREEN_CAMERA_COLLIMATOR 1.815700e+01 degC \n", - "kpfmet.GREEN_CAMERA_ECHELLE 1.803000e+01 degC \n", - "kpfmet.GREEN_CAMERA_TOP 1.794700e+01 degC \n", - "kpfmet.GREEN_D1 1.853300e+01 degC \n", - "kpfmet.GREEN_D2 1.650200e+01 degC \n", - "kpfmet.GREEN_D3 -2.731500e+02 degC \n", - "kpfmet.GREEN_D4 1.645700e+01 degC \n", - "kpfmet.GREEN_GRISM_TOP 1.785700e+01 degC \n", - "kpfmet.PRIMARY_COLLIMATOR_TOP 1.842600e+01 degC \n", - "kpfmet.RED_A 1.448900e+01 degC \n", - "kpfmet.RED_B 1.730800e+01 degC \n", - "kpfmet.RED_C1 -2.731500e+02 degC \n", - "kpfmet.RED_CAMERA_BOTTOM 1.767700e+01 degC \n", - "kpfmet.RED_CAMERA_COLLIMATOR 1.802700e+01 degC \n", - "kpfmet.RED_CAMERA_ECHELLE 1.798200e+01 degC \n", - "kpfmet.RED_CAMERA_TOP 1.792700e+01 degC \n", - "kpfmet.RED_D1 1.339500e+01 degC \n", - "kpfmet.RED_D2 1.613100e+01 degC \n", - "kpfmet.RED_D3 -2.731500e+02 degC \n", - "kpfmet.RED_D4 1.577800e+01 degC \n", - "kpfmet.RED_GRISM_TOP 1.785200e+01 degC \n", - "kpfmet.REFORMATTER 1.816300e+01 degC \n", - "kpfmet.SCIENCE_CAL_FIBER_STG 2.130000e+01 degC \n", - "kpfmet.SIMCAL_FIBER_STG 2.120000e+01 degC \n", - "kpfmet.SKYCAL_FIBER_STG 2.110000e+01 degC \n", - "kpfmet.TEMP 2.150100e+01 degC \n", - "kpfmet.TH_DAILY 2.150000e+01 degC \n", - "kpfmet.TH_GOLD 2.550000e+01 degC \n", - "kpfmet.U_DAILY 2.150000e+01 degC \n", - "kpfmet.U_GOLD 2.110000e+01 degC \n", - "kpfgreen.BPLANE_TEMP 3.875000e+01 degC \n", - "kpfgreen.BRD10_DRVR_T 4.293700e+01 degC \n", - "kpfgreen.BRD11_DRVR_T 4.118700e+01 degC \n", - "kpfgreen.BRD12_LVXBIAS_T 3.700000e+01 degC \n", - "kpfgreen.BRD1_HTRX_T 3.543700e+01 degC \n", - "kpfgreen.BRD2_XVBIAS_T 3.706200e+01 degC \n", - "kpfgreen.BRD3_LVDS_T 3.631200e+01 degC \n", - "kpfgreen.BRD4_DRVR_T 3.918700e+01 degC \n", - "kpfgreen.BRD5_AD_T 3.600000e+01 degC \n", - "kpfgreen.BRD7_HTRX_T 4.000000e+01 degC \n", - "kpfgreen.BRD9_HVXBIAS_T 3.850000e+01 degC \n", - "kpfgreen.CF_BASE_2WT -1.270660e+02 degC \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597600e+01 degC \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.648000e+01 degC \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.624400e+01 degC \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.638700e+01 degC \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588700e+01 degC \n", + "kpfmet.BENCH_TOP_COLL 1.657900e+01 degC \n", + "kpfmet.BENCH_TOP_DCUT 1.628100e+01 degC \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629300e+01 degC \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736600e+01 degC \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.721500e+01 degC \n", + "kpfmet.CAL_BENCH 2.020000e+01 degC \n", + "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 degC \n", + "kpfmet.CAL_BENCH_BOT 2.360000e+01 degC \n", + "kpfmet.CAL_BENCH_ENCL_AIR 2.020000e+01 degC \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 degC \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 degC \n", + "kpfmet.CAL_RACK_TOP 2.310000e+01 degC \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.570400e+01 degC \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G3 1.668500e+01 degC \n", + "kpfmet.CRYOSTAT_R1 1.610300e+01 degC \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_R3 1.564200e+01 degC \n", + "kpfmet.ECHELLE_BOTTOM 1.641200e+01 degC \n", + "kpfmet.ECHELLE_TOP 1.628400e+01 degC \n", + "kpfmet.FF_SRC 1.960000e+01 degC \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.677600e+01 degC \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682200e+01 degC \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.671200e+01 degC \n", + "kpfmet.GREEN_CAMERA_TOP 1.661900e+01 degC \n", + "kpfmet.GREEN_GRISM_TOP 1.639300e+01 degC \n", + "kpfmet.GREEN_LN2_FLANGE 9.848000e+00 degC \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689800e+01 degC \n", + "kpfmet.RED_CAMERA_BOTTOM 1.630300e+01 degC \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.664400e+01 degC \n", + "kpfmet.RED_CAMERA_ECHELLE 1.659500e+01 degC \n", + "kpfmet.RED_CAMERA_TOP 1.654800e+01 degC \n", + "kpfmet.RED_GRISM_TOP 1.637200e+01 degC \n", + "kpfmet.RED_LN2_FLANGE 6.292000e+00 degC \n", + "kpfmet.REFORMATTER 1.660600e+01 degC \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 degC \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 degC \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747200e+01 degC \n", + "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 degC \n", + "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 degC \n", + "kpfmet.TEMP 1.969700e+01 degC \n", + "kpfmet.TH_DAILY 2.000000e+01 degC \n", + "kpfmet.TH_GOLD 1.990000e+01 degC \n", + "kpfmet.U_DAILY 2.010000e+01 degC \n", + "kpfmet.U_GOLD 1.990000e+01 degC \n", + "kpfgreen.BPLANE_TEMP 3.587500e+01 degC \n", + "kpfgreen.BRD10_DRVR_T 4.056200e+01 degC \n", + "kpfgreen.BRD11_DRVR_T 3.875000e+01 degC \n", + "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 degC \n", + "kpfgreen.BRD1_HTRX_T 3.187500e+01 degC \n", + "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 degC \n", + "kpfgreen.BRD3_LVDS_T 3.343700e+01 degC \n", + "kpfgreen.BRD4_DRVR_T 3.725000e+01 degC \n", + "kpfgreen.BRD5_AD_T 3.387500e+01 degC \n", + "kpfgreen.BRD7_HTRX_T 3.787500e+01 degC \n", + "kpfgreen.BRD9_HVXBIAS_T 3.631200e+01 degC \n", + "kpfgreen.CF_BASE_2WT -1.270310e+02 degC \n", "kpfgreen.CF_BASE_T -2.731500e+02 degC \n", - "kpfgreen.CF_BASE_TRG -1.293000e+02 degC \n", - "kpfgreen.CF_TIP_T -1.277980e+02 degC \n", - "kpfgreen.CF_TIP_TRG -1.278000e+02 degC \n", - "kpfgreen.COL_PRESS 9.000000e-08 Torr \n", - "kpfgreen.CRYOBODY_T 1.889200e+01 degC \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 degC \n", + "kpfgreen.CF_TIP_T -1.279990e+02 degC \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 degC \n", + "kpfgreen.COL_PRESS 4.000000e-08 Torr \n", + "kpfgreen.CRYOBODY_T 1.900000e+01 degC \n", "kpfgreen.CRYOBODY_TRG 1.900000e+01 degC \n", "kpfgreen.CURRTEMP -1.399970e+02 degC \n", "kpfgreen.ECH_PRESS 1.000000e-11 Torr \n", - "kpfgreen.KPF_CCD_T -1.010160e+02 degC \n", + "kpfgreen.KPF_CCD_T -1.010890e+02 degC \n", "kpfgreen.STA_CCD_T -9.999800e+01 degC \n", "kpfgreen.STA_CCD_TRG -1.000000e+02 degC \n", "kpfgreen.TEMPSET -1.400000e+02 degC \n", - "kpfred.BPLANE_TEMP 4.443700e+01 degC \n", - "kpfred.BRD10_DRVR_T 4.862500e+01 degC \n", - "kpfred.BRD11_DRVR_T 4.800000e+01 degC \n", - "kpfred.BRD12_LVXBIAS_T 4.418700e+01 degC \n", - "kpfred.BRD1_HTRX_T 4.168700e+01 degC \n", - "kpfred.BRD2_XVBIAS_T 4.325000e+01 degC \n", + "kpfred.BPLANE_TEMP 4.387500e+01 degC \n", + "kpfred.BRD10_DRVR_T 4.818700e+01 degC \n", + "kpfred.BRD11_DRVR_T 4.737500e+01 degC \n", + "kpfred.BRD12_LVXBIAS_T 4.362500e+01 degC \n", + "kpfred.BRD1_HTRX_T 4.137500e+01 degC \n", + "kpfred.BRD2_XVBIAS_T 4.318700e+01 degC \n", "kpfred.BRD3_LVDS_T 4.300000e+01 degC \n", - "kpfred.BRD4_DRVR_T 4.593700e+01 degC \n", - "kpfred.BRD5_AD_T 4.125000e+01 degC \n", - "kpfred.BRD7_HTRX_T 4.600000e+01 degC \n", - "kpfred.BRD9_HVXBIAS_T 4.525000e+01 degC \n", - "kpfred.CF_BASE_2WT -1.263240e+02 degC \n", + "kpfred.BRD4_DRVR_T 4.612500e+01 degC \n", + "kpfred.BRD5_AD_T 4.081200e+01 degC \n", + "kpfred.BRD7_HTRX_T 4.543700e+01 degC \n", + "kpfred.BRD9_HVXBIAS_T 4.531200e+01 degC \n", + "kpfred.CF_BASE_2WT -1.257260e+02 degC \n", "kpfred.CF_BASE_T -2.731500e+02 degC \n", - "kpfred.CF_BASE_TRG -1.323000e+02 degC \n", - "kpfred.CF_TIP_T -1.277980e+02 degC \n", - "kpfred.CF_TIP_TRG -1.278000e+02 degC \n", - "kpfred.COL_PRESS 6.300000e-08 Torr \n", - "kpfred.CRYOBODY_T 1.894200e+01 degC \n", + "kpfred.CF_BASE_TRG -1.257500e+02 degC \n", + "kpfred.CF_TIP_T -1.272480e+02 degC \n", + "kpfred.CF_TIP_TRG -1.272500e+02 degC \n", + "kpfred.COL_PRESS 3.800000e-08 Torr \n", + "kpfred.CRYOBODY_T 1.900100e+01 degC \n", "kpfred.CRYOBODY_TRG 1.900000e+01 degC \n", - "kpfred.CURRTEMP -1.409990e+02 degC \n", - "kpfred.ECH_PRESS 8.100000e-08 Torr \n", - "kpfred.KPF_CCD_T -1.008250e+02 degC \n", - "kpfred.STA_CCD_T -9.999700e+01 degC \n", + "kpfred.CURRTEMP -1.404980e+02 degC \n", + "kpfred.ECH_PRESS 1.000000e-11 Torr \n", + "kpfred.KPF_CCD_T -1.008700e+02 degC \n", + "kpfred.STA_CCD_T -9.999900e+01 degC \n", "kpfred.STA_CCD_TRG -1.000000e+02 degC \n", - "kpfred.TEMPSET -1.410000e+02 degC \n", - "kpfexpose.BENCH_C 9.800000e+00 degC \n", - "kpfexpose.CAMBARREL_C 9.600000e+00 degC \n", - "kpfexpose.DET_XTRN_C 1.150000e+01 degC \n", - "kpfexpose.ECHELLE_C 8.500000e+00 degC \n", - "kpfexpose.ENCLOSURE_C 1.060000e+01 degC \n", - "kpfexpose.RACK_AIR_C 1.490000e+01 degC \n", - "kpfvac.PUMP_TEMP 4.500000e+01 degC \n", - "kpf_hk.COOLTARG -2.000000e+01 degC \n", - "kpf_hk.CURRTEMP -2.019000e+01 degC \n", - "kpfmot.AGITSPD 0.000000e+00 motor_counts/s \n", - "kpfmot.AGITTOR 0.000000e+00 V \n", - "kpfmot.AGITAMBI_T 2.600000e+01 degC \n", - "kpfmot.AGITMOT_T 2.610000e+01 degC \n", - "kpfpower.OUTLET_A1_Amps 2.700000e+01 milliamps \n", + "kpfred.TEMPSET -1.405000e+02 degC \n", + "kpfexpose.BENCH_C 1.540000e+01 degC \n", + "kpfexpose.CAMBARREL_C 1.620000e+01 degC \n", + "kpfexpose.DET_XTRN_C 2.040000e+01 degC \n", + "kpfexpose.ECHELLE_C 1.400000e+01 degC \n", + "kpfexpose.ENCLOSURE_C 1.880000e+01 degC \n", + "kpfexpose.RACK_AIR_C 1.870000e+01 degC \n", + "kpfvac.PUMP_TEMP 3.900000e+01 degC \n", + "kpf_hk.COOLTARG -6.000000e+01 degC \n", + "kpf_hk.CURRTEMP -5.978000e+01 degC \n", + "kpfgreen.COL_CURR 1.100000e-06 A \n", + "kpfgreen.ECH_CURR 8.910000e-11 A \n", + "kpfred.COL_CURR 1.000000e-06 A \n", + "kpfred.ECH_CURR 5.600000e-12 A \n", + "kpfcal.IRFLUX 9.999000e+03 Counts \n", + "kpfcal.VISFLUX 2.600000e+01 Counts \n", + "kpfcal.BLUECUTIACT 5.300000e-02 A \n", + "kpfmot.AGITSPD -1.880000e+03 motor_counts/s \n", + "kpfmot.AGITTOR 1.440000e-01 V \n", + "kpfmot.AGITAMBI_T 2.603000e+01 degC \n", + "kpfmot.AGITMOT_T 2.622000e+01 degC \n", + "kpfpower.OUTLET_A1_Amps 5.300000e+01 milliamps \n", "\n", " description \n", "keyword \n", @@ -2583,6 +2809,8 @@ "kpfmet.BENCH_TOP_COLL Bench Top Coll D5 c- double degC {%.3f} \n", "kpfmet.BENCH_TOP_DCUT Bench Top D-cut D3 c- double degC {%.3f} \n", "kpfmet.BENCH_TOP_ECHELLE_CAM Bench Top Echelle Cam D1 c- double degC {%.3f} \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END Cal EM Scrammbler Chamber End C1 c- double deg... \n", + "kpfmet.CALEM_SCMBLR_FIBER_END Cal EM Scrambler Fiber End D1 c- double degC {... \n", "kpfmet.CAL_BENCH Cal_Bench temperature c- double degC {%.1f} \n", "kpfmet.CAL_BENCH_BB_SRC CAL_Bench_BB_Src temperature c- double degC {%... \n", "kpfmet.CAL_BENCH_BOT Cal_Bench_Bot temperature c- double degC {%.1f} \n", @@ -2590,36 +2818,34 @@ "kpfmet.CAL_BENCH_OCT_MOT Cal_Bench_Oct_Mot temperature c- double degC {... \n", "kpfmet.CAL_BENCH_TRANS_STG_MOT Cal_Bench_Trans_Stg_Mot temperature c- double ... \n", "kpfmet.CAL_RACK_TOP Cal_Rack_Top temperature c- double degC {%.1f} \n", + "kpfmet.CHAMBER_EXT_BOTTOM Chamber Exterior Bottom B c- double degC {%.3f} \n", + "kpfmet.CHAMBER_EXT_TOP Chamber Exterior Top C1 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G1 Within cryostat green D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G2 Within cryostat green D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G3 Within cryostat green D4 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R1 Within Cryostat red D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R2 Within Cryostat red D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R3 Within Cryostat red D4 c- double degC {%.3f} \n", "kpfmet.ECHELLE_BOTTOM Echelle Bottom D1 c- double degC {%.3f} \n", "kpfmet.ECHELLE_TOP Echelle Top C1 c- double degC {%.3f} \n", "kpfmet.FF_SRC FF_Src temperature c- double degC {%.1f} \n", - "kpfmet.GREEN_A Green A c- double degC {%.3f} \n", - "kpfmet.GREEN_B Green B c- double degC {%.3f} \n", - "kpfmet.GREEN_C1 Green C1 c- double degC {%.3f} \n", "kpfmet.GREEN_CAMERA_BOTTOM Green Camera Bottom C3 c- double degC {%.3f} \n", "kpfmet.GREEN_CAMERA_COLLIMATOR Green Camera Collimator C4 c- double degC {%.3f} \n", "kpfmet.GREEN_CAMERA_ECHELLE Green Camera Echelle D5 c- double degC {%.3f} \n", "kpfmet.GREEN_CAMERA_TOP Green Camera Top C2 c- double degC {%.3f} \n", - "kpfmet.GREEN_D1 Green D1 c- double degC {%.3f} \n", - "kpfmet.GREEN_D2 Green D2 c- double degC {%.3f} \n", - "kpfmet.GREEN_D3 Green D3 c- double degC {%.3f} \n", - "kpfmet.GREEN_D4 Green D4 c- double degC {%.3f} \n", "kpfmet.GREEN_GRISM_TOP Green Grism Top C5 c- double degC {%.3f} \n", + "kpfmet.GREEN_LN2_FLANGE Green LN2 Flange A c- double degC {%.3f} \n", "kpfmet.PRIMARY_COLLIMATOR_TOP Primary Col Top D2 c- double degC {%.3f} \n", - "kpfmet.RED_A Red A c- double degC {%.3f} \n", - "kpfmet.RED_B Red B c- double degC {%.3f} \n", - "kpfmet.RED_C1 Red C1 c- double degC {%.3f} \n", "kpfmet.RED_CAMERA_BOTTOM Red Camera Bottom D5 c- double degC {%.3f} \n", "kpfmet.RED_CAMERA_COLLIMATOR Red Camera Coll C3 c- double degC {%.3f} \n", "kpfmet.RED_CAMERA_ECHELLE Red Camera Ech C4 c- double degC {%.3f} \n", "kpfmet.RED_CAMERA_TOP Red Camera Top C5 c- double degC {%.3f} \n", - "kpfmet.RED_D1 Red D1 c- double degC {%.3f} \n", - "kpfmet.RED_D2 Red D2 c- double degC {%.3f} \n", - "kpfmet.RED_D3 Red D3 c- double degC {%.3f} \n", - "kpfmet.RED_D4 Red D4 c- double degC {%.3f} \n", "kpfmet.RED_GRISM_TOP Red Grism Top C2 c- double degC {%.3f} \n", + "kpfmet.RED_LN2_FLANGE Red LN2 Flange D1 c- double degC {%.3f} \n", "kpfmet.REFORMATTER Reformatter A c- double degC {%.3f} \n", "kpfmet.SCIENCE_CAL_FIBER_STG Science_Cal_Fiber_Stg temperature c- double de... \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END SciSky Scrambler Chamber End A c- double degC ... \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END SciSky Scrammbler Fiber End B c- double degC {... \n", "kpfmet.SIMCAL_FIBER_STG SimCal_Fiber_Stg temperature c- double degC {%... \n", "kpfmet.SKYCAL_FIBER_STG SkyCal_Fiber_Stg temperature c- double degC {%... \n", "kpfmet.TEMP Vaisala Temperature c- double degC {%.3f} \n", @@ -2686,6 +2912,13 @@ "kpfvac.PUMP_TEMP Motor temperature c- double degC {%.2f} \n", "kpf_hk.COOLTARG temperature target c2 int degC \n", "kpf_hk.CURRTEMP current temperature c- double degC {%.2f} \n", + "kpfgreen.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfgreen.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfcal.IRFLUX LFC Fiberlock IR Intensity c- int Counts {%d} \n", + "kpfcal.VISFLUX LFC Fiberlock Vis Intensity c- int Counts {%d} \n", + "kpfcal.BLUECUTIACT Blue cut amplifier 0 measured current c- doubl... \n", "kpfmot.AGITSPD agit raw velocity c2 int motor counts/s { -750... \n", "kpfmot.AGITTOR agit motor torque c- double V {%.3f} \n", "kpfmot.AGITAMBI_T Agitator ambient temperature c- double degC {%... \n", @@ -2707,12 +2940,12 @@ "id": "d587d5b6", "metadata": {}, "source": [ - "Print the average and standard deviation of one keyword." + "Print the average and standard deviation of one keyword. (As of June 21, 2023, there's a problem with the standard deviation. This will probably get fixed soon in the L0 Assembly code.)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "989f592d", "metadata": {}, "outputs": [ @@ -2720,13 +2953,816 @@ "name": "stdout", "output_type": "stream", "text": [ - "Red CCD Temperature = -100.0 +/- 0.00134479 degC\n" + "Red CCD Temperature = -100.0 +/- 1228.8 degC\n" ] } ], "source": [ "print('Red CCD Temperature = ', df_telemetry.at['kpfred.STA_CCD_T', 'average'], '+/-', df_telemetry.at['kpfred.STA_CCD_T', 'stddev'], df_telemetry.at['kpfred.STA_CCD_T', 'units'])" ] + }, + { + "cell_type": "markdown", + "id": "81db59c1", + "metadata": {}, + "source": [ + "# Guider Image\n", + "The guide camera images are from a CRED-2 camera with 0.056 arcsec pixels in a 950-1200nm passband (approximately z+J band). First, we will fit the image with a Moffat function (assuming a point source)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "52e2ffef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "amplitude = 3228.7076865733984\n", + "seeing = 0.4789702532980194 arcsec\n", + "beta = 2.5682683730057922\n", + "x0 = 337.54450870956305 pixels\n", + "y0 = 262.21891711604087 pixels\n" + ] + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "guider_im = D2['GUIDER_AVG'].data - np.median(D2['GUIDER_AVG'].data)\n", + "guider_header = D2['GUIDER_AVG'].header\n", + "pixel_scale = 0.056 # arcsec per pixel for the CRED-2 imager on the KPF FIU\n", + "\n", + "def moffat_2D(xy, amplitude, x0, y0, alpha, beta):\n", + " x, y = xy\n", + " return amplitude * (1 + ((x - x0) ** 2 + (y - y0) ** 2) / alpha ** 2) ** -beta\n", + "\n", + "x = np.arange(guider_im.shape[1])\n", + "y = np.arange(guider_im.shape[0])\n", + "X, Y = np.meshgrid(x, y)\n", + "x_flat = X.flatten()\n", + "y_flat = Y.flatten()\n", + "image_data_flat = guider_im.flatten()\n", + "p0 = [1, guider_header['CRPIX1'], guider_header['CRPIX2'], 5/0.056, 2.5] # Initial guess for the parameters\n", + "#p0 = [1, guider_im.shape[1] / 2, guider_im.shape[0] / 2, 2/0.056, 2] # Initial guess for the parameters\n", + "popt, pcov = curve_fit(moffat_2D, (x_flat, y_flat), image_data_flat, p0=p0)\n", + "amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit = popt\n", + "print('amplitude = ' + str(amplitude_fit))\n", + "print('seeing = ' + str(alpha_fit*0.056) + ' arcsec')\n", + "print('beta = ' + str(beta_fit))\n", + "print('x0 = ' + str(x0_fit) + ' pixels')\n", + "print('y0 = ' + str(y0_fit) + ' pixels')\n", + "\n", + "image_fit = moffat_2D((X, Y), amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit)\n", + "resid_im = guider_im - image_fit" + ] + }, + { + "cell_type": "markdown", + "id": "9ad8db27", + "metadata": {}, + "source": [ + "# Plot Guider Image and Model Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bf83beaf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:27: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:48: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:67: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.ticker as ticker\n", + "\n", + "# Plot the original image and residuals\n", + "guider_im_zoom = guider_im[255-38:255+38, 320-38:320+38]\n", + "resid_im_zoom = resid_im[255-38:255+38, 320-38:320+38]\n", + "#subx0 = D2['GUIDER_AVG'].header['SUBX0']\n", + "#subx1 = D2['GUIDER_AVG'].header['SUBX1']\n", + "#suby0 = D2['GUIDER_AVG'].header['SUBY0']\n", + "#suby1 = D2['GUIDER_AVG'].header['SUBY1']\n", + "#guider_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", + "#resid_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "# Left panel - full image\n", + "im1 = axs[0].imshow(guider_im, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "axs[1].set_aspect(640/512)\n", + "image_size_pixels = guider_im.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", + "axs[0].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", + "axs[0].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[0].get_xticks()\n", + "yticks = axs[0].get_yticks()\n", + "axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", + "axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", + "axs[0].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[0].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[0].set_title('Guider Image - '+str(ObsID)+'\\n' + \n", + " f\"{alpha_fit*pixel_scale:.2f}\" + '\" seeing (z+J-band)\\n' +\n", + " f\"{alpha_fit*pixel_scale*(1100/550)**0.2:.2f}\" + '\" seeing (V-band, scaled)', fontsize=14)\n", + "axs[0].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar1 = plt.colorbar(im1, ax=axs[0], shrink=0.6)\n", + "#cbar1.set_label('Intensity', fontsize=14)\n", + "\n", + "# Middle panel - zoomed image\n", + "im2 = axs[1].imshow(guider_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "axs[1].set_aspect('equal')\n", + "image_size_pixels = guider_im_zoom.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[1].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[1].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[1].get_xticks()\n", + "yticks = axs[1].get_yticks()\n", + "axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "axs[1].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[1].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[1].set_title('Guider Image (zoomed in)', fontsize=14)\n", + "axs[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar2 = plt.colorbar(im2, ax=axs[1], shrink=0.6)\n", + "#cbar2.set_label('Intensity', fontsize=14)\n", + "\n", + "# Right panel - zoomed image of residuals to model\n", + "im2 = axs[2].imshow(resid_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "image_size_pixels = guider_im_zoom.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[2].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[2].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[2].get_xticks()\n", + "yticks = axs[2].get_yticks()\n", + "axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "axs[2].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[2].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[2].set_title('Residuals to Moffat Function Model', fontsize=14)\n", + "axs[2].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar3 = plt.colorbar(im2, ax=axs[2], shrink=0.6)\n", + "#cbar3.set_label('Intensity', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "#plt.savefig('guider_image.png',dpi=144)\n", + "plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "1fee8abf", + "metadata": {}, + "source": [ + "# Examine and Plot Guider Performance Time Series" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "92c05f6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..................................................................
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0 4927.0 ... \n", + "... ... ... ... ... ... \n", + "17260 459291.0 1 0 8911.0 ... \n", + "17261 458436.0 1 0 8987.0 ... \n", + "17262 457964.0 1 0 9620.0 ... \n", + "17263 458240.0 1 0 10218.0 ... \n", + "17264 460913.0 1 0 8697.0 ... \n", + "\n", + " object3_tnpix object3_npix object3_peak object3_x object3_y \\\n", + "0 0 0 0.0 -999.0 -999.0 \n", + "1 0 0 0.0 -999.0 -999.0 \n", + "2 0 0 0.0 -999.0 -999.0 \n", + "3 0 0 0.0 -999.0 -999.0 \n", + "4 0 0 0.0 -999.0 -999.0 \n", + "... ... ... ... ... ... \n", + "17260 0 0 0.0 -999.0 -999.0 \n", + "17261 0 0 0.0 -999.0 -999.0 \n", + "17262 0 0 0.0 -999.0 -999.0 \n", + "17263 0 0 0.0 -999.0 -999.0 \n", + "17264 0 0 0.0 -999.0 -999.0 \n", + "\n", + " object3_a object3_b object3_theta object3_errxy object3_thresh \n", + "0 0.0 0.0 0.0 0.0 -999.0 \n", + "1 0.0 0.0 0.0 0.0 -999.0 \n", + "2 0.0 0.0 0.0 0.0 -999.0 \n", + "3 0.0 0.0 0.0 0.0 -999.0 \n", + "4 0.0 0.0 0.0 0.0 -999.0 \n", + "... ... ... ... ... ... \n", + "17260 0.0 0.0 0.0 0.0 -999.0 \n", + "17261 0.0 0.0 0.0 0.0 -999.0 \n", + "17262 0.0 0.0 0.0 0.0 -999.0 \n", + "17263 0.0 0.0 0.0 0.0 -999.0 \n", + "17264 0.0 0.0 0.0 0.0 -999.0 \n", + "\n", + "[17265 rows x 39 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_GUIDER = Table.read(L0_file, format='fits',hdu='guider_cube_origins').to_pandas()\n", + "df_GUIDER" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a0868e98", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 100)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# Create the figure and subplots\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 4), gridspec_kw={'width_ratios': [2, 1]})\n", + "plt.style.use('seaborn-whitegrid')\n", + "\n", + "# Plot the data\n", + "im1 = axes[1].hist2d(df_GUIDER.command_x*pixel_scale*1000, \n", + " df_GUIDER.command_y*pixel_scale*1000, \n", + " bins=40, cmap='viridis')\n", + "axes[1].set_title(\"Guiding Corrections - RMS-2D = \" + f'{int(np.std((df_GUIDER.command_x*pixel_scale**2+df_GUIDER.command_y*pixel_scale**2)**0.5*1000)*10)/10}'+' mas', fontsize=14)\n", + "axes[1].set_xlabel('x (mas)', fontsize=14)\n", + "axes[1].set_ylabel('y (mas)', fontsize=14)\n", + "axes[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar = plt.colorbar(im1[3])\n", + "cbar.set_label('Samples', fontsize=12)\n", + "\n", + "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_x*pixel_scale*1000, color='royalblue')\n", + "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_y*pixel_scale*1000, color='orange')\n", + "axes[0].set_title(\"Guiding Correction Time Series\", fontsize=14)\n", + "axes[0].set_xlabel(\"Time (sec)\", fontsize=14)\n", + "axes[0].set_ylabel(\"Guiding Correction (mas)\", fontsize=14)\n", + "axes[0].legend(['X - RMS = ' + f'{int(np.std(df_GUIDER.command_x*pixel_scale*1000)*10)/10}' + ' mas', \n", + " 'Y - RMS = ' + f'{int(np.std(df_GUIDER.command_y*pixel_scale*1000)*10)/10}' + ' mas'], \n", + " fontsize=12, loc='best') \n", + "\n", + "# Set the font size of tick mark labels\n", + "axes[0].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[1].tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + "# Display the plots\n", + "plt.show()\n", + "#plt.savefig('guider_corrections.png',dpi=144)" + ] + }, + { + "cell_type": "markdown", + "id": "6e449f79", + "metadata": {}, + "source": [ + "# Plot Flux Time from Guider and Exposure Meter Series" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e3f34337", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-whitegrid')\n", + "plt.figure(figsize=(12, 5), tight_layout=True)\n", + "plt.plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.object1_flux/max(df_GUIDER.object1_flux), color='royalblue')\n", + "plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec / max(int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec), marker='o', color='k')\n", + "plt.title(\"Guider and Exposure Meter Flux Time Series\", fontsize=14)\n", + "plt.xlabel(\"Seconds since \" + str(guider_header['DATE-BEG']), fontsize=14)\n", + "plt.ylabel(\"Flux (fractional)\", fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.xlim(min(df_GUIDER.timestamp-min(df_GUIDER.timestamp)), max(df_GUIDER.timestamp-min(df_GUIDER.timestamp)))\n", + "plt.legend(['Guider Flux', 'Exposure Meter Flux'], fontsize=12, loc='best') \n", + "plt.show()\n", + "#plt.savefig('guider_xy_times.png',dpi=144)\n" + ] + }, + { + "cell_type": "markdown", + "id": "41a98e41", + "metadata": {}, + "source": [ + "# Show Header for Guider Data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ce034a22", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "XTENSION= 'IMAGE ' / Image extension \n", + "BITPIX = -32 / array data type \n", + "NAXIS = 2 / number of array dimensions \n", + "NAXIS1 = 640 \n", + "NAXIS2 = 512 \n", + "PCOUNT = 0 / number of parameters \n", + "GCOUNT = 1 / number of groups \n", + "DATE-BEG= '2023-06-11T08:29:30.140' / sequence begin \n", + "DATE-END= '2023-06-11T08:32:22.936' / sequence end \n", + "OUTDIR = '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory \n", + "OFNAME = 'kpfguide_cube_4259.fits' / original output filename \n", + "EXTNAME = 'GUIDER_AVG' / HDU name \n", + "NFRAMES = 17265 / quantity of subframes \n", + "SUBX0 = 274 / [pix] subframe x0 coordinate \n", + "SUBX1 = 402 / [pix] subframe x1 coordinate \n", + "SUBY0 = 198 / [pix] subframe y0 coordinate \n", + "SUBY1 = 326 / [pix] subframe y1 coordinate \n", + "FPS = 100.0 / frames per second \n", + "GAIN = 'Low ' / detector gain setting \n", + "TEMPDET = -40.01 / [deg C] sensor temperature \n", + "EXPOSING= T / continuous exposures active \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT Location: /kroot/rel/default/sbin/kpftiptiltd \n", + "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", + "COMMENT Hostname: kpffiuserver.keck.hawaii.edu \n", + "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [kpfguide] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:30:56.538520Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "INSTRUME= 'KPF ' / Selected instrument \n", + "TELESCOP= 'Keck I ' / Telescope \n", + "RA = '17:20:39.80' / Right ascension \n", + "DEC = '+32:27:40.0' / Declination \n", + "EL = 61.46 / [deg] telescope elevation \n", + "AZ = 57.97 / [deg] telescope azimuth \n", + "PARANG = -108.94 / [deg] parallactic angle \n", + "VA = 118.5 / [deg] vertical angle \n", + "PA = 9.6 / [deg] sky position angle \n", + "MJD-OBS = 60106.354817 / Modified Julian day \n", + "PMFM = 0.0 / PMFM value \n", + "UT = '08:30:56.22' / DCS Universal Time \n", + "SECFOCUS= -2.244 / [mm] Secondary focus \n", + "TELFOCUS= -1.459 / [mm] Telescope focus \n", + "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", + "WCSNAME = 'RA/Dec ' / Descriptive name of this WCS \n", + "CTYPE1 = 'RA---TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2 = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1 = 'Right Ascension' / Descriptive name of WCS axis 1 \n", + "CNAME2 = 'Declination' / Descriptive name of WCS axis 2 \n", + "CUNIT1 = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2 = 'deg ' / Physical unit of WCS axis 2 \n", + "RADECSYS= 'ICRS ' / Celestial coordinate system \n", + "CD1_1 = 2.68683E-06 / [deg] RA per pixel in x \n", + "CD1_2 = 1.58855E-05 / [deg] RA per pixel in y \n", + "CD2_1 = 1.58855E-05 / [deg] DEC per pixel in x \n", + "CD2_2 = -2.68683E-06 / [deg] DEC per pixel in y \n", + "CRPIX1 = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2 = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1 = 260.165833 / [deg] RA of center pixel \n", + "CRVAL2 = 32.461111 / [deg] DEC of center pixel \n", + "WCSNAMER= 'RA/Dec ' / Descriptive name of this WCS \n", + "CTYPE1R = 'RA---TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2R = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1R = 'Right Ascension' / Descriptive name of WCS axis 1 \n", + "CNAME2R = 'Declination' / Descriptive name of WCS axis 2 \n", + "CUNIT1R = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2R = 'deg ' / Physical unit of WCS axis 2 \n", + "RADESYSR= 'ICRS ' / Celestial coordinate system \n", + "CD1_1R = 2.68683E-06 / [deg] RA per pixel in x \n", + "CD1_2R = 1.58855E-05 / [deg] RA per pixel in y \n", + "CD2_1R = 1.58855E-05 / [deg] DEC per pixel in x \n", + "CD2_2R = -2.68683E-06 / [deg] DEC per pixel in y \n", + "CRPIX1R = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2R = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1R = 260.165833 / [deg] RA of center pixel \n", + "CRVAL2R = 32.461111 / [deg] DEC of center pixel \n", + "WCSNAMEY= 'Az/El ' / Descriptive name of this WCS \n", + "CTYPE1Y = 'TLON-TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2Y = 'TLAT-TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1Y = 'Azimuth ' / Descriptive name of WCS axis 1 \n", + "CNAME2Y = 'Elevation' / Descriptive name of WCS axis 2 \n", + "CUNIT1Y = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2Y = 'deg ' / Physical unit of WCS axis 2 \n", + "RADESYSY= 'ICRS ' / Celestial coordinate system \n", + "CD1_1Y = -7.68756E-06 / [deg] Az per pixel in x \n", + "CD1_2Y = -1.41587E-05 / [deg] Az per pixel in y \n", + "CD2_1Y = -1.41587E-05 / [deg] El per pixel in x \n", + "CD2_2Y = 7.68756E-06 / [deg] El per pixel in y \n", + "CRPIX1Y = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2Y = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1Y = 57.971911 / [deg] Az of center pixel \n", + "CRVAL2Y = 61.45613 / [deg] El of center pixel \n", + "COMMENT \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT End of keyheader output \n", + "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", + "COMMENT Header generation end time: 2023-06-11T08:32:23.299838Z \n", + "COMMENT ------------------------------------------------------------------------" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D2['GUIDER_AVG'].header" + ] } ], "metadata": { From bc198c17f0c3ec9aaa65db094905a2753fae84d4 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Wed, 21 Jun 2023 22:04:44 -0700 Subject: [PATCH 142/249] fixed typo --- .../KPF_Data_Tutorial_L0_and_2D.ipynb | 7578 ++++++++--------- 1 file changed, 3789 insertions(+), 3789 deletions(-) diff --git a/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb b/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb index 963818f71..df52cb75f 100644 --- a/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb +++ b/docs/source/tutorials/KPF_Data_Tutorial_L0_and_2D.ipynb @@ -1,3789 +1,3789 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "874cf8c2", - "metadata": {}, - "source": [ - "# Import Packages" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "eb18fd32", - "metadata": {}, - "outputs": [], - "source": [ - "from astropy.io import fits\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from astropy.time import Time\n", - "from astropy.table import Table" - ] - }, - { - "cell_type": "markdown", - "id": "b7481710", - "metadata": {}, - "source": [ - "# Define Directories and Filenames\n", - "One needs to have the L0 file KP.20221112.26598.92.fits and the 2D file KP.20221112.26598.92_2D.fits in the data/ directory of the folder from which this notebook is running. These files can be downloaded from Jump or shrek.caltech.edu. Note that as of 11/23/2022, the 2D file will have the name KP.20221112.26598.92.fits and will need to be renamed KP.20221112.26598.92_2D.fits for the code below to work. One can also modify the code below to work with other files or paths." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7f5cf66a", - "metadata": {}, - "outputs": [], - "source": [ - "L0_data_dir = 'data/'\n", - "#ObsID = 'KP.20230615.53679.85' # Spectrum of HD 219134\n", - "ObsID = 'KP.20230611.30570.89' # Spectrum of HD 157214\n", - "#ObsID = 'KP.20221112.26598.92' # Commissioning Spectrum of 51 Pegasi\n", - "L0_file = L0_data_dir + ObsID + '.fits'\n", - "D2_file = L0_data_dir + ObsID + '_2D.fits' # 2D file (which needs to be renamed with '_2D' appended)" - ] - }, - { - "cell_type": "markdown", - "id": "544a5262", - "metadata": {}, - "source": [ - "# Open File and Show Information\n", - "Note that there are exensions for:\n", - " * Header keywords (PRIMARY)\n", - " * Four quadrants of the green CCD (GREEN_AMP1, GREEN_AMP2, GREEN_AMP3, GREEN_AMP4)\n", - " * Two halves of the red CCD (RED_AMP1, RED_AMP2)\n", - " * Spectral time series for the Exposure Meter (EXPMETER_SCI, EXPMETER_SKY)\n", - " * Telemetry (HDU needs to be named! It just has the index 9 here. This was fixed in the L0 Assembler on 11/22/22.)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a650ceba", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: data/KP.20230611.30570.89.fits\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 407 () \n", - " 1 GREEN_AMP1 1 ImageHDU 13 (2144, 2140) float32 \n", - " 2 GREEN_AMP2 1 ImageHDU 13 (2144, 2140) float32 \n", - " 3 GREEN_AMP3 1 ImageHDU 13 (2144, 2140) float32 \n", - " 4 GREEN_AMP4 1 ImageHDU 13 (2144, 2140) float32 \n", - " 5 RED_AMP1 1 ImageHDU 13 (2094, 4110) float32 \n", - " 6 RED_AMP2 1 ImageHDU 13 (2094, 4110) float32 \n", - " 7 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 8 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 9 guider_avg 1 ImageHDU 102 (640, 512) float32 \n", - " 10 guider_cube_origins 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n", - " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n" - ] - } - ], - "source": [ - "L0 = fits.open(L0_file)\n", - "L0.info()" - ] - }, - { - "cell_type": "markdown", - "id": "1fbd2b16", - "metadata": {}, - "source": [ - "# Primary Extension (header keywords)\n", - "Here is a list of keywords. Later data products (2D, L1, L2 add keywords to this list)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0ee65727", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SIMPLE = T / conforms to FITS standard \n", - "BITPIX = 8 / array data type \n", - "NAXIS = 0 / number of array dimensions \n", - "EXTEND = T \n", - "FAVER = '0 ' / Written by FITSAssemble version 1.4 \n", - "TIMESYS = 'UTC ' \n", - "CAMERAS = 4 / Detectors in assembly request \n", - "GREEN = 'YES ' / Was this camera found? \n", - "RED = 'YES ' / Was this camera found? \n", - "CA_HK = 'NO ' / Was this camera found? \n", - "EXPMETER= 'YES ' / Was this camera found? \n", - "GUIDE = 'YES ' / Was this camera found? \n", - "DATE-MID= '2023-06-11T08:30:57.094' / Halfway point of the exposure, unweighted \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureBegin] \n", - "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "DATE-OBS= '2023-06-11' / Date dcs1.DATE \n", - "UT = '08:29:30.54' / DCS Universal Time \n", - "DATE-BEG= '2023-06-11T08:29:30.885517' / Start of exposure from kpfexpose \n", - "EXPTIME = 172.0 / Requested exposure time \n", - "PROGNAME= 'C007 ' / Program name from kpfexpose \n", - "OBJECT = '157214 ' / Object \n", - "INSTRUME= 'KPF ' / Instrument name \n", - "DATALVL = '0 ' / Data Product Base Level \n", - "OBSERVAT= 'KECK ' / Observatory name \n", - "OBSMODE = 'SPEC ' / Basic mode of observation \n", - "HEADVER = '125547 ' / Header config version \n", - "CURRINST= 'KPF ' / Selected instrument \n", - "TELESCOP= 'Keck I ' / Telescope \n", - "TARGNAME= '157214 ' / KPF Target name \n", - "DCSNAME = '157214 ' / DCS Target name - may not make any sense \n", - "TARGRA = '17:20:39.80' / [h] DCS Target RA \n", - "TARGDEC = '+32:27:40.0' / [deg] DCS Target Dec \n", - "TARGEPOC= 2000.0 / DCS Target epoch \n", - "TARGEQUI= 2000.0 / DCS Target equinox \n", - "TARGPLAX= 68.59999999999999 / [arcsec] target parallax \n", - "TARGPMDC= 0.0 / [arcsec/yr] target proper motion dec \n", - "TARGPMRA= 0.0 / [s/yr] target proper motion ra \n", - "TARGRADV= -78.43000000000001 / [km/s] target radial velocity \n", - "TARGWAVE= 0.65 / target-wavelength \n", - "TARGFRAM= 'FK5 ' / target-frame \n", - "FULLTARG= '157214 ' / Full Target name from kpfconfig \n", - "GAIAID = 'DR3 1333760419707327744' / GAIA Target name from kpfconfig \n", - "2MASSID = 'J17203955+3228056' / 2MASS Target name from kpfconfig \n", - "GAIAMAG = '5.21 ' / GAIA G band magnitude \n", - "2MASSMAG= '4.16 ' / 2MASS J band magnitude \n", - "TARGTEFF= 5750.0 / Target effective temperature (K) \n", - "RA = '17:20:39.80' / [h] Right ascension \n", - "DEC = '+32:27:40.0' / [deg] Declination \n", - "EQUINOX = 2000.0 / DCS Equinox \n", - "MJD-OBS = 60106.353826 / Modified Julian day \n", - "PMFM = 0.0 / PMFM value \n", - "LFCMODE = 'StandbyHigh' / LFC Operation Mode \n", - "AMPON = 1768.003 / LFC: Amount of time amplifier on \n", - "LFCFO = 250000000.0 / LFC filtered Offset Freq RR Comb counted \n", - "LFCFREF = 250000000.0 / LFC filtered Offset Freq RR Comb setpoint \n", - "LFCFR = 20000000000.0197 / LFC filtered Offset Freq RR Filter counted \n", - "LFCFRREF= 20000000000.0 / LFC filtered Offset Freq RR Filter setpoint \n", - "LFCCEOFR= 5220000000.0 / CEO Filtered Setpoint Freq \n", - "LFCCWFRF= 288005220000000.0 / CW Freq Reference \n", - "LFCCWFRQ= 288005230048093.0 / CW Freq \n", - "LFCCWFER= 10048093.0 / CW Freq Error: Ref-Actual \n", - "LFCCWMDN= 1152021 / CW mode number \n", - "LFCBIACT= 0.033 / Blue cut amp diode current (A) \n", - "LFCBISET= 0.0 / Blue cut amp diode setting (A) \n", - "OCTAGON = 'EtalonFiber' / selected octagon value \n", - "CALMON = 'Out ' / Calibration intensity monitor position \n", - "HATCH = 'Open ' / Status of the hatch on the exterior of the FIU \n", - "FIUMODE = 'Observing' / FIU operating mode \n", - "FOLDNAM = 'Out ' / Named Position of FIU Fold mirror \n", - "FOLDVAL = 24.7 / [mm] Position of FIU Fold mirror \n", - "ADCTRACK= 'On ' / Is the ADC Tracking \n", - "HCLSN = '' / S/N of lamp in use \n", - "CAREQ = 'Yes ' / Ca HK fibers (6,7) requested \n", - "EXPSCREQ= 'Yes ' / ExpM Science fiber (8ab) requested \n", - "FFREQ = 'No ' / Flatfield fiber (10ab) requested \n", - "SICALREQ= 'Yes ' / SimCal fiber (3ab) requested \n", - "VACSCREQ= 'Yes ' / Vac Sci fiber (1b) requested \n", - "VACSKREQ= 'Yes ' / Vac Sci fiber (2b) requested \n", - "THDAYON = '20230610T15:05:07 HST' / ThAr Daily was last turned on at this time \n", - "THDAYTON= 4306.4 / ThAr Daily has been on for this long \n", - "THAUON = '20230503T12:39:28 HST' / ThAr Gold was last turned on at this time \n", - "THAUTON = 1465.7 / ThAr Gold has been on for this long \n", - "UDAYON = '20230610T16:17:58 HST' / UNe Daily was last turned on at this time \n", - "UDAYTON = 3251.2 / UNe Daily has been on for this long \n", - "UAUON = '19691231T14:00:00 HST' / UNe Gold was last turned on at this time \n", - "UAUTON = 0.0 / UNe Gold has been on for this long \n", - "PTHDAY = 9.84700000000000E-06 / Last ThAr Daily calibration power value (Watts)\n", - "PTHDAYT = '20230610T16:11:20 HST' / Time of ThAr Daily last power value \n", - "PTHAU = 0.0 / Last ThAr Gold calibration power value (Watts) \n", - "PTHAUT = '19691231T14:00:00 HST' / Time of ThAr Gold last power value \n", - "PUDAY = 1.328E-05 / Last UNe Daily calibration power value (Watts) \n", - "PUDAYT = '20230610T17:07:06 HST' / Time of UNe Daily last power value \n", - "PUAU = 0.0 / Last UNe Gold calibration power value (Watts) \n", - "PUAUT = '19691231T14:00:00 HST' / Time of UNe Gold last power value \n", - "PLFC = 0.0 / Last LFC calibration power value (Watts) \n", - "PLFCT = '19691231T14:00:00 HST' / Time of LFC last power value \n", - "PETAL = 4.919E-07 / Last Etalon calibration power value (Watts) \n", - "PETALT = '20230610T16:40:09 HST' / Time of Etalon last power value \n", - "PBRB = 9.77900000000000E-06 / Last Broadband calibration power value (Watts) \n", - "PBRBT = '20230610T12:31:09 HST' / Time of Broadband last power value \n", - "PSOL = 0.0 / Last SoCal-CalFib cal. power value (Watts) \n", - "PSOLT = '19691231T14:00:00 HST' / Time of SoCal-CalFib last power value \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_12' \n", - "COMMENT Data not available for keyword 'heart_33' \n", - "COMMENT Data not available for keyword 'heart_34' \n", - "COMMENT Data not available for keyword 'heart_35' \n", - "COMMENT Data not available for keyword 'heart_36' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", - "COMMENT Header generation end time: 2023-06-11T08:33:22.860985Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureMiddle] \n", - "COMMENT Snapshot timestamp: 2023-06-11T08:30:57.093759Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "SRCSHTTR= 'SciSelect,SkySelect' / Source shutters commanded \n", - "TIMSHTTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters commanded \n", - "OTIMSHTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters open exp. midpoint \n", - "SCISEL = 'open ' / Science Select shutter at exp. midpoint \n", - "SKYSEL = 'open ' / Sky Select Shutter at exp. midpoint \n", - "FFSHTR = 'closed ' / Flat field fiber shutter at exp. midpoint \n", - "SCRAMSHT= 'open ' / Scrambler shutter at exp. midpoint \n", - "SIMCALSH= 'open ' / Simult Cal shutter at exp. midpoint \n", - "TRIGTARG= 'Green,Red,ExpMeter,Guide' / Cameras that were sent triggers \n", - "IMTYPE = 'Object ' / Image Type \n", - "CAL-OBJ = 'EtalonFiber' / Calibration fiber source \n", - "SKY-OBJ = 'Sky ' / Sky fiber source \n", - "SCI-OBJ = 'Target ' / Science fiber source \n", - "CAFBS = 'Yes ' / Ca HK fibers (6,7) on \n", - "EXPSCIFB= 'Yes ' / ExpM Science fiber (8ab) on \n", - "EXPSKYFB= 'Yes ' / ExpM Science fiber (9) on \n", - "FFFB = 'No ' / Flatfield fiber (10ab) on \n", - "FICALFBS= 'No ' / FIU Cal fibers (4,5) on \n", - "SICALFB = 'Yes ' / SimCal fiber (3ab) on \n", - "VACSCFB = 'Yes ' / Vac Sci fiber (1b) on \n", - "VACSKFB = 'Yes ' / Vac Sci fiber (2b) on \n", - "SCIFB = 'Yes ' / Science fiber (1a) on \n", - "SKYFB = 'Yes ' / Sky fiber (2a) on \n", - "CURRBASE= '(335.5, 258.0)' / [pix] Selected pointing origin \n", - "PIXTARG = '(337.826, 262.276)' / [pix] Selected object tip/tilt target \n", - "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / Green MD5 sum for the acf file \n", - "GRACFFLN= 'kpfGreen.20220513-0519.acf' / Green acf file loaded \n", - "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / Red MD5 sum for the acf file \n", - "RDACFFLN= 'kpf_200kHz_2amp_short_oscan.acf' / Red acf file loaded \n", - "AGITSTA = 'Running ' / Agitator status kpfmot.AGITATOR \n", - "THARGD = 'Off ' / Gold ThAr power status \n", - "UNEGD = 'Off ' / Gold UNe power status \n", - "THARDAY = 'Off ' / Daily ThAr power status \n", - "UNEDAY = 'Off ' / Daily UNe power status \n", - "OCTBB = 'Off ' / Octagon broad band power status \n", - "FFSOURCE= 'Off ' / Flat field broad band power status \n", - "SCIFBILL= 'Off ' / Science fiber LED back illuminator power \n", - "SKYFBILL= 'Off ' / Sky fiber LED back illuminator power \n", - "HKFBILL = 'Off ' / Ca H and K fiber LED back illuminator power \n", - "EXPFBILL= 'Off ' / Exposure meter fiber LED back illuminator power\n", - "SSCALFW = 'OD 3.0 ' / Sci/Cal FW Position \n", - "SIMCALFW= 'OD 0.5 ' / Simual Cal FW Position \n", - "FFFW = 'Blank ' / Flatfield FW Position \n", - "OCTAGON = 'EtalonFiber' / selected octagon value \n", - "PRES = 623.7 / [hPa] Pressure at Vaisala kpfmet.PRES \n", - "RELH = 20.291 / Relative humidity at Vaisala kpfmet.RELH \n", - "PONAME = 'KPF ' / DCS Point origin name \n", - "PONAME1 = 'Opt Axis' / DCS Point origin name1 \n", - "DRA = 0.0 / [s/s] DCS Diff RA rate \n", - "DDEC = 0.0 / [asec/s] DCS Diff Dec rate \n", - "RABASE = '17:20:39.80' / DCS RA base \n", - "RAOFF = 0.0 / [asec] DCS RA offset \n", - "DECBASE = '+32:27:40.0' / DCS Dec base \n", - "DECOFF = 0.0 / [asec] DCS Dec offset \n", - "HA = '+22:06:05.70' / DCS Hour angle \n", - "AIRMASS = 1.14 / DCS Airmass \n", - "PARANG = -108.94 / [deg] DCS Parallactic angle astrometric \n", - "PARANTEL= -108.97 / [deg] DCS Parallactic angle telescope \n", - "EL = 61.46 / [deg] DCS Elevation \n", - "AZ = 57.97 / [deg] DCS Azimuth \n", - "LST = '15:26:45.50' / DCS Local sidereal time \n", - "AXESTAT = 'tracking' / DCS axes control status \n", - "TRACKING= 'yes ' / DCS Servos tracking status \n", - "DTRACK = 'disabled' / DCS differential tracking status \n", - "GUIDING = 'true ' / DCS Guiding status \n", - "AUTACTIV= 'yes ' / DCS Guider active \n", - "AUTFWHM = 0.6521940000000001 / [pix] DCS Guider fwhm \n", - "AUTXCENT= 0.2 / [asec] DCS Guider x centroid \n", - "AUTYCENT= 0.4 / [asec] DCS Guider y centroid \n", - "SECFOCUS= -2.244 / [mm] Secondary focus \n", - "TELFOCUS= -1.459 / [mm] Telescope focus \n", - "TUBEFLEX= -13.3 / [arcsec] Telescope tube flexure \n", - "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", - "PRIMTEMP= 3.530288 / [degC] Telescope pri temperature \n", - "SECMTEMP= 2.699448 / [degC] Telescope sec temperature \n", - "DIFFPTDW= 10.930288 / [decC] Diff between pri mirro temp dewpt \n", - "DIFFSTDW= 10.099448 / [decC] Diff between sec mirro temp dewpt \n", - "VIGNETTE= 'false ' / dome vignette (t/f) \n", - "STVIGNE = 'false ' / top shutter vignette (t/f) \n", - "SBVIGNE = 'false ' / bottom shutter vignette (t/f) \n", - "SBELEV = 26.97 / [deg] bottom shutter elevation \n", - "STELEV = 97.62 / [deg] top shutter elevation \n", - "SECSTST = 'INPOS ' / DCS Secondary status string \n", - "SECTHETX= -415.1 / [asec] DCS Secondary theta x \n", - "SECTHETY= 142.9 / [asec] DCS Secondary theta y \n", - "TERTSTST= 'INPOS ' / DCS Tertiary status string \n", - "TERTDEST= 'lnas ' / DCS Tertiary user destination \n", - "TERTPOSN= 'lnas ' / DCS Tertiary user position \n", - "DOMEPOSN= 58.04 / DCS Dome user position \n", - "DOMESTST= 'TRACK ' / DCS Dome status string \n", - "CALOCAL = -28.8 / collimation-azimuth-local \n", - "CELOCAL = 14.6 / collimation-elevation-local \n", - "FOCALSTN= 'lnas (left nasmyth)' / focal-station \n", - "INSTANGL= 312.0 / porg-to-instrument angle \n", - "POXPOS = 0.4 / pointing-origin-x-position \n", - "POYPOS = -0.8 / pointing-origin-y-position \n", - "ROTCALAN= 0.0 / rotator-calibration-angle \n", - "ROTZERO = 0.0 / rotator-zero-angle \n", - "GUIDWAVE= 0.65 / guidestar-wavelength \n", - "TIMEERR = 'ok 2 2 {NTP time correct to within 2 ms}' / resp time serv \n", - "ETAV1C1T= 22.984734 / Etalon v1ch1temp \n", - "ETAV1C2T= 22.702303 / Etalon v1ch2temp \n", - "ETAV1C3T= 22.49995 / Etalon v1ch3temp \n", - "ETAV1C4T= 22.799961 / Etalon v1ch4temp \n", - "ETAV2C3T= 23.026154 / Etalon v2ch3temp \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", - "COMMENT Header generation end time: 2023-06-11T08:33:24.214439Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureEnd] \n", - "COMMENT Snapshot timestamp: 2023-06-11T08:32:23.202000Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "DATE = '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", - "DATE-END= '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", - "ELAPSED = 172.0 / Actual exposure time from kpfexpose.ELAPSED \n", - "OUTDIR = '/s/sdata1701/kpf1/2023jun10/L0' / Output directory \n", - "OFNAME = 'KP.20230611.30570.89.fits' / Filename of output file \n", - "GREENFN = '/s/sdata1701/kpf1/2023jun10/Green/kpf_63502.fits' \n", - "REDFN = '/s/sdata1701/kpf1/2023jun10/Red/kpf_red_63502.fits' \n", - "EXPMETFN= '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' \n", - "GUIDEFN = '/s/sdata1701/kpf1/2023jun10/CRED2/kpfguide_cube_4259.fits' \n", - "FRAMENO = 63502 \n", - "GRCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd green CAMD_VER \n", - "GREXPTI = 0 / exposure time in msec Kwd green EXPTIME \n", - "GRFILENA= 'kpf_63502.fits' / this filename Kwd green FILENAME \n", - "GRFIRMWA= '/kroot/rel/default/data/kpfgreen/ACF/kpfGreen.20220513-0519.acf' / co\n", - "GRHDRSHI= 0 / number of HDR right-shift bits Kwd green HDRSHI\n", - "GRSHUTT = T / shutter was enabled Kwd green SHUTTEN \n", - "GRTM_ZO = 'GMT ' / time zone Kwd green TM_ZONE \n", - "GRCDS0 = '#eval CDump - 417' / Kwd green CDS0 \n", - "GRCDS1 = '#eval CDump - 1' / Kwd green CDS1 \n", - "GRCDS2 = '#eval PixelT - 417' / Kwd green CDS2 \n", - "GRCDS3 = 950 / Kwd green CDS3 \n", - "GRTEST = 123 / test fitskey from modes file Kwd green TEST \n", - "GRDATE = '2023-06-11T08:33:20.749037' / FITS file write time Kwd green DATE \n", - "GROBSERV= 'Dai,Zhang' / Observer name Kwd green OBSERVER \n", - "GRPROGNA= 'C007 ' / Program name Kwd green PROGNAME \n", - "GRFRAME = 63502 / Kwd green FRAMENO \n", - "GRACF = '20220513-0519' / Last user-chosen ACF key Kwd green ACF \n", - "GRACFFI = 'kpfGreen.20220513-0519.acf' / ACF file from ACF key Kwd green ACFFILE\n", - "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / MD5 sum for ACFFILE; 'unknown' if\n", - "GRDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd green DATE-BEG \n", - "GRDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd green DATE-END \n", - "GRELAPS = 172.013 / Shutter-elapsed time Kwd green ELAPSED \n", - "RDCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd red CAMD_VER \n", - "RDEXPTI = 0 / exposure time in msec Kwd red EXPTIME \n", - "RDFILENA= 'kpf_red_63502.fits' / this filename Kwd red FILENAME \n", - "RDFIRMWA= '/kroot/rel/default/data/kpfred/ACF/kpf_200kHz_2amp_short_oscan.acf' \n", - "RDHDRSHI= 0 / number of HDR right-shift bits Kwd red HDRSHIFT\n", - "RDSHUTT = T / shutter was enabled Kwd red SHUTTEN \n", - "RDTM_ZO = 'GMT ' / time zone Kwd red TM_ZONE \n", - "RDCDS0 = '#eval RGsettleT' / Kwd red CDS0 \n", - "RDCDS1 = '#eval CDump - 1' / Kwd red CDS1 \n", - "RDCDS2 = '#eval PixelT' / 2 + SWsettleT Kwd red CDS2 \n", - "RDCDS3 = 950 / Kwd red CDS3 \n", - "RDTEST = 123 / test fitskey from modes file Kwd red TEST \n", - "RDDATE = '2023-06-11T08:33:09.264258' / FITS file write time Kwd red DATE \n", - "RDOBSERV= 'Dai,Zhang' / Observer name Kwd red OBSERVER \n", - "RDPROGNA= 'C007 ' / Program name Kwd red PROGNAME \n", - "RDFRAME = 63502 / Kwd red FRAMENO \n", - "RDACF = '200kHz_2amp_short_oscan' / Last user-chosen ACF key Kwd red ACF \n", - "RDACFFI = 'kpf_200kHz_2amp_short_oscan.acf' / ACF file from ACF key Kwd red ACFF\n", - "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / MD5 sum for ACFFILE; 'unknown' if\n", - "RDDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd red DATE-BEG \n", - "RDDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd red DATE-END \n", - "RDELAPS = 172.013 / Shutter-elapsed time Kwd red ELAPSED \n", - "EMFRAME = 63502 / Frame number from kpf_expmeter Kwd expmeter FRA\n", - "EMFILEN = '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' / Output file\n", - "EMSEQBEG= 1 / Sequence number of first observation Kwd expmet\n", - "EMSEQEND= 89 / Sequence number of last observation Kwd expmete\n", - "EMDATE-B= '2023-06-11T08:29:30.266' / Date-Beg of first observation Kwd expmeter\n", - "EMDATE-E= '2023-06-11T08:32:23.102' / Date-End of last observation Kwd expmeter \n", - "GCNAXIS1= 640 / Kwd guide NAXIS1 \n", - "GCNAXIS2= 512 / Kwd guide NAXIS2 \n", - "GCDATE-B= '2023-06-11T08:29:30.140' / sequence begin Kwd guide DATE-BEG \n", - "GCDATE-E= '2023-06-11T08:32:22.936' / sequence end Kwd guide DATE-END \n", - "GCOUTDIR= '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory Kwd gu\n", - "GCOFNAME= 'kpfguide_cube_4259.fits' / original output filename Kwd guide OFNAME \n", - "GCEXTNA = 'guider_avg' / HDU name Kwd guide EXTNAME \n", - "GCNFRAM = 17265 / quantity of subframes Kwd guide NFRAMES \n", - "GCSUBX0 = 274 / [pix] subframe x0 coordinate Kwd guide SUBX0 \n", - "GCSUBX1 = 402 / [pix] subframe x1 coordinate Kwd guide SUBX1 \n", - "GCSUBY0 = 198 / [pix] subframe y0 coordinate Kwd guide SUBY0 \n", - "GCSUBY1 = 326 / [pix] subframe y1 coordinate Kwd guide SUBY1 \n", - "GCFPS = 100.0 / frames per second Kwd guide FPS \n", - "GCGAIN = 'Low ' / detector gain setting Kwd guide GAIN \n", - "GCTEMPD = -40.01 / [deg C] sensor temperature Kwd guide TEMPDET \n", - "GCEXPOSI= T / continuous exposures active Kwd guide EXPOSING \n", - "GCINSTRU= 'KPF ' / Selected instrument Kwd guide INSTRUME \n", - "GCTELESC= 'Keck I ' / Telescope Kwd guide TELESCOP \n", - "GCRA = '17:20:39.80' / Right ascension Kwd guide RA \n", - "GCDEC = '+32:27:40.0' / Declination Kwd guide DEC \n", - "GCEL = 61.46 / [deg] telescope elevation Kwd guide EL \n", - "GCAZ = 57.97 / [deg] telescope azimuth Kwd guide AZ \n", - "GCPARANG= -108.94 / [deg] parallactic angle Kwd guide PARANG \n", - "GCVA = 118.5 / [deg] vertical angle Kwd guide VA \n", - "GCPA = 9.6 / [deg] sky position angle Kwd guide PA \n", - "GCMJD-O = 60106.354817 / Modified Julian day Kwd guide MJD-OBS \n", - "GCPMFM = 0.0 / PMFM value Kwd guide PMFM \n", - "GCUT = '08:30:56.22' / DCS Universal Time Kwd guide UT \n", - "GCSECFOC= -2.244 / [mm] Secondary focus Kwd guide SECFOCUS \n", - "GCTELFOC= -1.459 / [mm] Telescope focus Kwd guide TELFOCUS \n", - "GCTUBETE= 3.27 / [degC] Telescope tube temperature Kwd guide TUB\n", - "GCWCSNA = 'RA/Dec ' / Descriptive name of this WCS Kwd guide WCSNAME \n", - "GCCTYPE1= 'RA---TAN' / Coordinate type of WCS axis 1 Kwd guide CTYPE1 \n", - "GCCTYPE2= 'DEC--TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2 \n", - "GCCNAME1= 'Right Ascension' / Descriptive name of WCS axis 1 Kwd guide CNAME1\n", - "GCCNAME2= 'Declination' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", - "GCCUNIT1= 'deg ' / Physical unit of WCS axis 1 Kwd guide CUNIT1 \n", - "GCCUNIT2= 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2 \n", - "GCRADECS= 'ICRS ' / Celestial coordinate system Kwd guide RADECSYS \n", - "GCCD1_1 = 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1 \n", - "GCCD1_2 = 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2 \n", - "GCCD2_1 = 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1 \n", - "GCCD2_2 = -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2 \n", - "GCCRPIX1= 337.826 / [pix] X coordinate of center pixel Kwd guide CR\n", - "GCCRPIX2= 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", - "GCCRVAL1= 260.165833 / [deg] RA of center pixel Kwd guide CRVAL1 \n", - "GCCRVAL2= 32.461111 / [deg] DEC of center pixel Kwd guide CRVAL2 \n", - "GCWCSNAM= 'Az/El ' / Descriptive name of this WCS Kwd guide WCSNAMEY\n", - "GCCTYPE = 'TLAT-TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2Y\n", - "GCCNAME = 'Elevation' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", - "GCCUNIT = 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2Y \n", - "GCRADESY= 'ICRS ' / Celestial coordinate system Kwd guide RADESYSY \n", - "GCCD1_1R= 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1R \n", - "GCCD1_2R= 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2R \n", - "GCCD2_1R= 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1R \n", - "GCCD2_2R= -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2R \n", - "GCCRPIX = 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", - "GCCRVAL = 61.45613 / [deg] El of center pixel Kwd guide CRVAL2Y \n", - "GCCD1_1Y= -7.68756E-06 / [deg] Az per pixel in x Kwd guide CD1_1Y \n", - "GCCD1_2Y= -1.41587E-05 / [deg] Az per pixel in y Kwd guide CD1_2Y \n", - "GCCD2_1Y= -1.41587E-05 / [deg] El per pixel in x Kwd guide CD2_1Y \n", - "GCCD2_2Y= 7.68756E-06 / [deg] El per pixel in y Kwd guide CD2_2Y \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", - "COMMENT Header generation end time: 2023-06-11T08:33:25.403001Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT The keywords that begin with GR come from the green detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field \n", - "COMMENT The keywords that begin with RD come from the red detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field \n", - "COMMENT The keywords that begin with EM come from the expmeter detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field \n", - "COMMENT The keywords that begin with GC come from the guide detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L0['PRIMARY'].header" - ] - }, - { - "cell_type": "markdown", - "id": "d8cb534f", - "metadata": {}, - "source": [ - "# Extract Keywords\n", - "Here we extra the name of the target star and the exposure time and assign them to variables." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "350bcd5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Star = 157214\n", - "Exposure time = 172.013 sec\n" - ] - } - ], - "source": [ - "starname = L0['PRIMARY'].header['TARGNAME']\n", - "Texp = L0['PRIMARY'].header['GRELAPS'] # actually, this is the green CCD exposure time\n", - "print('Star = ' + starname)\n", - "print('Exposure time = ' + str(Texp) + ' sec')" - ] - }, - { - "cell_type": "markdown", - "id": "a8d9e0fd", - "metadata": {}, - "source": [ - "# Green CCD\n", - "There are four quadrants in the green CCD with separate extensions (when it is read in 4-amplifier mode). Let's plot of them." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2513006d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(tight_layout=True)\n", - "plt.figure(figsize=(6, 6), tight_layout=True)\n", - "plt.imshow(L0['GREEN_AMP4'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['GREEN_AMP4'].data,99))\n", - "plt.colorbar(shrink=0.7)\n", - "plt.grid(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "1e33cb61", - "metadata": {}, - "source": [ - "# Red CCD\n", - "There are two halves for the Red CCD (when read out in 2-amplifier mode). Let's plot one of them" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bb4acd97", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(tight_layout=True)\n", - "plt.figure(figsize=(6, 12), tight_layout=True)\n", - "plt.imshow(L0['RED_AMP1'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['RED_AMP1'].data,99))\n", - "plt.colorbar(shrink=0.7)\n", - "plt.grid(False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0fef6f2b", - "metadata": {}, - "source": [ - "# 2D File\n", - "The 2D file is similar to L0 except that the image segments of the green and red CCDs have been assembled (prescan and overcal removed) and bias has been subtracted. The header information has also been removed." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "355ac676", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: data/KP.20230611.30570.89_2D.fits\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 411 () \n", - " 1 RECEIPT 1 BinTableHDU 29 1R x 10C [26A, 6A, 7A, 40A, 3A, 9A, 1A, 21A, 40A, 4A] \n", - " 2 CONFIG 1 BinTableHDU 13 0R x 2C [D, D] \n", - " 3 GREEN_CCD 1 ImageHDU 13 (4080, 4080) float64 \n", - " 4 GREEN_VAR 1 ImageHDU 8 (0,) \n", - " 5 RED_CCD 1 ImageHDU 11 (4080, 4080) float64 \n", - " 6 RED_VAR 1 ImageHDU 7 (0,) \n", - " 7 CA_HK 1 ImageHDU 23 (0,) \n", - " 8 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 9 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", - " 10 GUIDECAM 1 ImageHDU 7 (0,) \n", - " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n", - " 12 SOLAR_IRRADIANCE 1 BinTableHDU 9 0R x 0C [] \n", - " 13 GUIDER_AVG 1 ImageHDU 102 (640, 512) float32 \n", - " 14 GUIDER_CUBE_ORIGINS 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n" - ] - } - ], - "source": [ - "D2 = fits.open(D2_file)\n", - "D2.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "965868e0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "D2 = fits.open(D2_file)\n", - "fig, axs = plt.subplots(1, 2, figsize=(16,10), tight_layout=True)\n", - "im1 = axs[0].imshow(D2['GREEN_CCD'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(D2['GREEN_CCD'].data,99))\n", - "axs[0].set_title('Green CCD')\n", - "fig.colorbar(im1, ax=axs[0], shrink=0.6)\n", - "im2 = axs[1].imshow(D2['RED_CCD'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(D2['RED_CCD'].data,99))\n", - "axs[1].set_title('Red CCD')\n", - "fig.colorbar(im2, ax=axs[1], shrink=0.6)\n", - "axs[0].grid(False)\n", - "axs[1].grid(False)\n", - "plt.subplots_adjust(wspace=0.4)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "c9cec7fe", - "metadata": {}, - "source": [ - "# Exposure Meter\n", - "Back in the L0 file, there are extensions for the EM SCI and EM SKY fibers. The example below is for EM SCI, but EM SKY is similar. Here we create a Pandas data frame with the spectral time series. Each row is a spectrum for light collected between Date-Beg and Date-End." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "257eb60d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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89 rows × 418 columns

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" - ], - "text/plain": [ - " Date-Beg Date-End 872.5 \\\n", - "0 b'2023-06-11T08:29:30.266' b'2023-06-11T08:29:31.403' 65288.482868 \n", - "1 b'2023-06-11T08:29:32.211' b'2023-06-11T08:29:33.348' 145037.293213 \n", - "2 b'2023-06-11T08:29:34.161' b'2023-06-11T08:29:35.291' 102230.586317 \n", - "3 b'2023-06-11T08:29:36.109' b'2023-06-11T08:29:37.246' 124723.655282 \n", - "4 b'2023-06-11T08:29:38.074' b'2023-06-11T08:29:39.204' 158318.758731 \n", - ".. ... ... ... \n", - "84 b'2023-06-11T08:32:14.167' b'2023-06-11T08:32:15.297' 124354.741489 \n", - "85 b'2023-06-11T08:32:16.113' b'2023-06-11T08:32:17.252' 123058.241489 \n", - "86 b'2023-06-11T08:32:18.060' b'2023-06-11T08:32:19.201' 143975.551834 \n", - "87 b'2023-06-11T08:32:20.007' b'2023-06-11T08:32:21.149' 148698.069075 \n", - "88 b'2023-06-11T08:32:21.962' b'2023-06-11T08:32:23.102' 127710.327696 \n", - "\n", - " 869.1 865.8 862.4 859.2 855.9 \\\n", - "0 67002.043378 68390.296032 69528.805586 70656.291786 71491.506223 \n", - "1 149624.853723 151774.106377 153927.615931 156864.102130 159370.316568 \n", - "2 105320.146826 107596.399480 108443.909034 111245.395234 112633.609671 \n", - "3 128458.215792 130836.468446 132898.978000 135887.464199 137469.678637 \n", - "4 162879.319240 165962.571894 168799.081448 171753.567648 174870.782085 \n", - ".. ... ... ... ... ... \n", - "84 127902.301999 129678.554653 132025.064207 134457.550406 136400.764844 \n", - "85 126251.801999 128754.054653 130790.564207 133562.050406 135477.264844 \n", - "86 147620.112344 150533.364997 152976.874552 156002.360751 158174.575189 \n", - "87 153000.629585 155536.882239 158088.391793 161398.877993 164144.092430 \n", - "88 131171.888206 133226.140860 136405.650414 138327.136613 140634.351051 \n", - "\n", - " 852.7 849.6 ... 447.8 447.4 \\\n", - "0 71954.559302 72587.187752 ... 5703.675072 5743.436159 \n", - "1 160914.369646 161398.998096 ... 12800.485416 12607.246504 \n", - "2 113536.662750 114419.291200 ... 8841.778520 8620.539608 \n", - "3 137876.731715 139276.360165 ... 10785.847485 10477.608573 \n", - "4 176089.835164 177499.463614 ... 14599.950934 14220.712021 \n", - ".. ... ... ... ... ... \n", - "84 137870.817922 138140.446372 ... 10249.933692 10183.694780 \n", - "85 136684.317922 137419.946372 ... 10874.433692 10508.194780 \n", - "86 159627.628267 160418.256717 ... 12968.744037 12835.505125 \n", - "87 165108.145508 165504.773959 ... 13531.261278 13506.022366 \n", - "88 141900.404129 142219.032579 ... 11709.519899 11361.280987 \n", - "\n", - " 447.1 446.8 446.4 446.1 445.8 \\\n", - "0 5598.096457 5476.871403 5578.843803 5467.344864 5443.966945 \n", - "1 12358.906801 12037.681748 12279.654147 12212.155209 11889.777290 \n", - "2 8504.199905 8425.974852 8379.947251 8414.448312 8247.070393 \n", - "3 10258.268870 10212.043817 9951.016216 10084.517278 10006.139359 \n", - "4 13647.372319 13832.147266 13758.119665 13724.620726 13457.242807 \n", - ".. ... ... ... ... ... \n", - "84 9927.355077 9671.130024 9683.102423 9695.603485 9860.225566 \n", - "85 10214.855077 10097.630024 10194.602423 10075.103485 9831.725566 \n", - "86 12670.165422 12430.940369 12258.912768 12292.413830 12218.035910 \n", - "87 13153.682663 12955.457610 12900.430010 12634.931071 12658.553152 \n", - "88 11285.941284 10958.716231 10878.688630 10939.189692 10860.811772 \n", - "\n", - " 445.4 445.1 444.8 \n", - "0 5373.393696 5186.548686 4939.809832 \n", - "1 11869.204041 11649.359031 11570.620177 \n", - "2 8242.497145 7905.652134 7840.913281 \n", - "3 9927.566110 9701.721100 9694.982246 \n", - "4 13266.669559 13152.824548 12826.085694 \n", - ".. ... ... ... \n", - "84 9425.652317 9331.807307 9160.068453 \n", - "85 9752.152317 9731.307307 9590.568453 \n", - "86 11989.462662 11920.617651 11626.878798 \n", - "87 12569.979903 12480.134893 12043.396039 \n", - "88 10749.238524 10530.393513 10247.654660 \n", - "\n", - "[89 rows x 418 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_SCI_EM = Table.read(L0_file, format='fits',hdu='EXPMETER_SCI').to_pandas()\n", - "df_SCI_EM" - ] - }, - { - "cell_type": "markdown", - "id": "dea6829e", - "metadata": {}, - "source": [ - "# Convert Exposure Meter Data into Flux Measurements" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "41c889a3", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/1493378176.py:27: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", - " int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample)\n" - ] - } - ], - "source": [ - "# Gain of CCD in KPF Exposure Meter\n", - "EM_gain = 1.48424 # e-/ADU\n", - "\n", - "# Define wavelength arrays and disperion at each wavelength (nm per pixel)\n", - "wav_SCI_str = df_SCI_EM.columns[2:] # string (center) wavelengths of each pixel\n", - "wav_SCI = df_SCI_EM.columns[2:].astype(float) # float (center) wavelengths of each pixel\n", - "disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1\n", - "disp_SCI_smooth = np.polyval(np.polyfit(wav_SCI,disp_SCI, deg=6),wav_SCI)\n", - "\n", - "# define normalized flux array (e- / nm / time)\n", - "df_SCI_EM_norm = df_SCI_EM[wav_SCI_str] * EM_gain / disp_SCI_smooth\n", - "\n", - "# define time arrays\n", - "date_beg = np.array(df_SCI_EM[\"Date-Beg\"], dtype=np.datetime64)\n", - "date_end = np.array(df_SCI_EM[\"Date-End\"], dtype=np.datetime64)\n", - "tdur_sec = (date_end-date_beg).astype(float)/1000. # array exposure durations in sec\n", - "time = (date_beg-date_beg[0])/1000 # array of times since beginning in sec\n", - "\n", - "# define indices for wavelength\n", - "ind_550m = np.where((wav_SCI < 550))\n", - "ind_550_650 = np.where((wav_SCI >= 550) & (wav_SCI < 650))\n", - "ind_650_750 = np.where((wav_SCI >= 650) & (wav_SCI < 750))\n", - "ind_750p = np.where((wav_SCI >= 750))\n", - "\n", - "# define flux arrays\n", - "int_SCI_spec = df_SCI_EM_norm[:5].sum(axis=0) / np.sum(tdur_sec[:5]) # flux vs. wavelength per sec (use first five samples)\n", - "int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample)\n", - "int_SCI_flux_550m = df_SCI_EM[wav_SCI_str[np.where((wav_SCI < 550))]].sum(axis=1)\n", - "int_SCI_flux_550_650 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 550) & (wav_SCI < 650))]].sum(axis=1)\n", - "int_SCI_flux_650_750 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 650) & (wav_SCI < 750))]].sum(axis=1)\n", - "int_SCI_flux_750p = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 750))]].sum(axis=1)" - ] - }, - { - "cell_type": "markdown", - "id": "8c52f9fc", - "metadata": {}, - "source": [ - "# Plot EM SCI Spectrum" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e6d96249", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('seaborn-whitegrid')\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.axvspan(440, 550, alpha=0.5, color='b') \n", - "plt.axvspan(550, 650, alpha=0.5, color='g') \n", - "plt.axvspan(650, 750, alpha=0.5, color='orange') \n", - "plt.axvspan(750, 880, alpha=0.5, color='red') \n", - "plt.plot(wav_SCI, int_SCI_spec, marker='o', color='k')\n", - "plt.xlabel(\"Wavelength (nm)\", fontsize=14)\n", - "plt.ylabel(\"Average Exposure Meter Flux (e-/nm/s)\", fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.xlim(445,870)\n", - "plt.legend([\"EM SCI\"], fontsize=12, loc='best') \n", - "plt.show()\n", - "#plt.savefig('expmeter_flux_vs_wavelength.png',dpi=144)" - ] - }, - { - "cell_type": "markdown", - "id": "b813282b", - "metadata": {}, - "source": [ - "# Plot EM SCI Time Series" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "244dfde0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LTOPKfsYzKc7kb5f8jcKaQr488qVLr2MtnVmLmJYpZw1b11+1ZdFvi9rVYIlss8DVHDp3iB/Tf+Rvl/xN/zwU4RcBiABLYJoODbAyMjK45ZZbyM01XBnesWMHWVlZPP/88wwYMICkpCRGjx7N119/DcCqVasYPHgw99xzDwMGDGDZsmXk5+ezY8cOAD799FPuuOMOpk6dyvDhw1m8eDHffvstNTU11NbWsmrVKp555hmGDRvGlVdeyd13383KlSutuvb5jq5/i643iTMdc6ICotBKWptvUCsPrcRD6cHNQ282eUxCmAiwBNaRMjXFwOYfwE3hJmx3ewi21tgdLDhISV2JS+WBOq7sdyVDwobw1s63Ot31tLNrETdmbWR4+HD9g6sxRLZZ0Bm88vsr+Hn68eDFD+q36TJY52rOdda0BF2cDg2w9uzZw/jx4/nf//5nsP3gwYMMGTIEPz8//bYxY8Zw4MAB/f6LL75Yv0+lUjF06FD279+PRqPh8OHDBvtHjRqFRqPh+PHjnDhxgsbGRsaMGWMw9uHDh1Gr1RavLZCDLJWHisfGPeZUxxx7emFptBq+OPwFMwfNJFgVbPK4hNAECqoLKKsrc3iegp7NVf2uQiEp8PXwRYECf09/tJKWsZHOtecWdA621thtyNwAmM+kOAuFQsEjYx9hf8F+tuVuc/n1zNGZtYj16nq25W6z+DsX2WZBR5NVlsWXR77k3jH30kvVS789wldksATmcbd8iPO47bbbjG4vKioiPDzcYFtISAgFBQVm9587d47KykoaGhoM9ru7uxMUFERBQQEeHh4EBgbi5eWl3x8aGkpTUxOlpaUWr92W48e7Rlakvr6+w+ZSp66jurEaapz7+htLGwHYeXwnfpV+Fo6W2X5uO/nV+UwOnmx2Lv71/gCs37Oe0aGjHZ+sDXTkeyOwDWPvzRuH3kAtqflu6nf0DehLUV0R09dN5/HvH+eVca900kzPP1z1uTGX9TB2vTWH1zAwcCDlZ8opp9zp82nLxV4XE+AZwAsbX+DNy950+fVMYe735Op72o5zO6hX13OBxwVmr/Pg4Ad5bs9z1Gvq9du83bx5cPCD5+09V3zfuJYX9r2AQqFgVsgsg99zg0aumzySdYTjKuO/f/HedF064r3p0ADLFHV1dXh4GPYa8fT0pKmpSb/f09Oz3f7Gxkbq6+v1Pxvbr1arje4D2fzC0rXbkpCQYOOrcw3Hjx/vsLnklMuGIkPjhzr1mkFVQbARlIFKi+OmHk4lOS2ZnIocFCgIDAs0e45nhCdsgzrfug5/zzryvRHYRtv3pqyujC+/+5KbhtzE1ZdcDUACCTxc8jCvbn+Vl2a+xNDwoZ013fMKV31uYgNjyanIabc9OiC63fXq1fXs+2Yf9425r0M/w/cV3Mdrf7yGTx8f4oLi7B5Hd5/MrcglNjCWlKkpVikOKhsq8XTz1D80tkZC4sGdD5IyLYWzVWdJ3mT7+Jb4/OznuCvdSbw8EX8vf5PHJSQkEBkVqX+NEhK3DbuNJ6c/6fAcuivi+8Z1FNYU8s033zBv5Dwmj5ncbn/A2gAkH8nk71+8N10XZ743e/fuNbq9S9i0e3l5tQtoGhsb8fb21u9vbGw0ul+XmTK339g+kKWGlq4tgKLaIqBFc+wswn3DcVO4WXQSbF0bAM1f+OseNFsbEB8Uj5ebl6jDEpjl7V1vU9VYRfIEQxnUU+Ofws/Tj8WbF3fOxAROw1iNHYC/p387Sdzvub9Tr653Wf8rUzw49kEUKHhn9zt2j2FvDVVxbTFTP5tKo6YRTzfDxUiVu4rbh91OekU6V3x2BXO/m+uSGq2NmRu5JOoSs8GVjsThiWQ/mo3mOQ0jI0ZypOiIw9cXCIzx1o63aFA38ORlxgP4CN8ICmuFRFBgnC4RYEVERFBUVGSwrbi4mLCwMIv7g4KC8PLyori4WL9PrVZTXl5OeHg4ERERVFZWGgRZRUVFeHp6EhgYaPHaghaNcZivc38nbko3evv1ttgLy57aADelG4NDBwur9i5AZzqTmaO6sZo3d77JrEGzGNl7pMG+EJ8QHhv3GF8f+5r9+fs7aYYCZ5A4PJFw33C83b1RoCAuMI57x9zLiZITXJ16tSx/bmZj5kbcle5Mip/UoXOMDYxlTJ8xvLb9Nbs/J/bcJ/Mq85j030kcKTzC97d/z8fXfUxcYJz+97Ti2hV8MfsLNszcQC/vXkYd/J7e+LT+Z3s+62V1ZezN32tzUKtQKFgwegF7zu7h0LlDNp0rEJii9d/wi9te5OLIi7kg9AKjx4b7hosaLIFJukSANXLkSE6cOGHQu2rv3r2MGjVKv3/fvn36fXV1dRw7doxRo0ahVCoZPny4QYruwIEDuLm5kZCQQEJCAh4eHuzfv99g7KFDh+Lu7m7x2gIoqnFNBgtkJ0FLGSx7naOEk2Dn01HOZPY82H2w5wNK60rbZa90PHbpYwR5B/Hcb885da6CjqWopoizVWd5buJzaBdpyX40m/dnvU/qjalsy93GtJXTqKivAGSDi0ujL8XP07qaUGeRejiVg+cOIjX/z57Pibn7pDGHwlOlp7j8k8vJrchlfeJ6Zg2apc8O6X5POvmfl5sX5fXlRsc/XXmaSz+6lOu/vJ6/rvmrzZ/137J/Qytp7coaJg5PxNPNk0/2f2LzuQJBW9p+X0lIHCo8ZPJvONw3nHPVwkVQYJwuEWCNHTuWyMhIFi5cSHp6OsuXL+fgwYPcfLNswz179mwOHjzIe++9R0ZGBsnJyURGRnLppZcCMGfOHD7++GN++eUXDh8+zJIlS5g9eza+vr6oVCquv/56lixZwqFDh0hLS+Pjjz9m7ty5Vl1b0JLBckWAFekfadFF0F7nqITQBHLKc9qt6go6jo5wJrMniKtX1/PaH68xte9UxkWPM3pMkHcQT172JGv/XMvOMzudNl9Bx7IlZwsAk+MnG2y/bdht/O+m/7ErbxcXfnAhUa9HsTd/L4fOmX6gchXJacnt6p9s/ZyYuh9KSIz7aBw/nPyB1EMtCxGD3h5EUU0Rv877td3vxpbxA7wCAFhzco1dr2Fj5kZ8PXwZG2W7a2eITwjXXXAdKw+vpFHTaPkEgcAMxr6v6tX1Jv+GI3wjRAZLYJIuEWC5ubnx7rvvUlpayo033siaNWt4++23iY6OBiA6Opr//Oc/rFmzhtmzZ1NcXMy7776LUilPf+bMmdx///0sXryY+fPnM2zYMBYuXKgf/+mnn2b48OHMmzePRYsW8eCDD3L11VdbdW2BHGB5u3vj6+Hr9LGj/C1nsFKmpuDl5mWwzZpmxwmhCUhInCw+6fA8nUVXlcu5io7oW2NPEPfx/o8pqC4wmb3S8cgljxDqE8qzvz7rlLkKOp7fsn/Dx8OHiyIvardv9pDZPDL2ETLLM/X3oYqGig7t/wTO+ZykTE1BgWFjZB93HxaMWkBhTSHXfnktd357p34hQitp0UgaTpZYd3801XT+3Znv8sdf/2h3bWtfQ1pWGpPiJ7Wr/7KWBaMXUFxbzNo/19p1vkCgw9bPYbhvOMW1xWi0GldOS9BN6TQXwZMnDW/qcXFx+ua/xpg0aRKTJpnWxSclJZGUlGR0n0ql4uWXX+bll182ut/Stc93imqLCPcNR6Ew/gXqCJH+kZTVl1HXVIfKQ2X0mMThiXx5+EvWpq9FgcJq96ohYUMAOFZ0jNF9Otaq3Ri6TIsuGNBlWgCn9RbraphycHNm3xpbvxSbNE288vsrXBZzmcWVez9PPxaOX8gTG55gS84WJsZNdHS6gg5mc85mxseMx8PNw+j+1cdXt9umC9A76nPpjM/JtYOuRUIi0CuQyoZKg/tkk6aJPv/qQ0ldicE5utV5a16n7hhTLoX2vIbTFac5WXKSe8fca/XrbMtV/a4iyj+Kj/d/zI0JN9o9jkBg699wuG84EhLFtcVmG2QLzk+6RAZL0LUprCkkzMc1ph9R/lEAFrNY2RXZXNH3ina1AeYYGDIQN4Vbl6nD6sxGnp3F3y75W7tt1mQfbSHEJ8TodpWHyqg8NPVwKjkVOSRPSLZq0eD+i++nj18f/rnpn0ZrWQRdl+LaYg4XHmZSnOnFuY7IslrCVHbIls/JgYIDAHwx+4t290kPNw9K60qNnmfL6zRVo2XqNShQ8OxE09nftKw0AKb2s7+ps5vSjXkj57E+Y73F7xGBwBy2fg51QZWQCQqMIQIsgUV0GSxXEOkfCWDWSfB0xWmOFB7h6gFX2zS2p5sn/YP7d5kAqys8yHUkGq2G1cdXo3JT6QNpBQrevfpdp2UGTlecpraxFqXC8FbmofSgtqmW8R+PJ6ssy2BOy7YuY3Tv0cwYMMOqa/h4+PDMhGfYmruV3v/qfd7IO3sCW3O2Aph1BbS3xtOZJA5PZPk1y4kLlHtg+Xj4sPya5TZ9Tvac3QPAmD5jjO539ets/RoUKIjwjUBC0gd+xkjLSiPcN5xh4cMcuvb80fPRSlo+O/iZQ+MIzm90f8O6koS4wDizn0Pdc9G5GmF0IWiPCLAEFimsKXRZgBUVYDmDtT5jPQAzBlr3QNyaIWFDOF7UNQKsrvAg15G8/sfr/H76d5Zfu5wzj5/hh9t/QEIiOsA59Y0arYY7vr0DpVLJq1e9amAv/cn1n/DjnB/JLs/mohUXsXDjQuLfjGf418NJL01nQuwEmySv/p7+KFBQWFNo0kjDUn3d+VZ/1xXYnLMZlbuKiyMvNnmMM7JHzkCXHbph8A1E+kfavAixJ38P0QHRJqVKHfE6W2e4Cp4o4JGxj/DO7nf4Pff3dsdKksTGzI1M7Tu13QKJrQwIHsDEuIl8vP9jkWUWOETicLnZ9T0X3mNRLaN7LhIZLIExRIAlMIskSS6VCOozWGacBNdnrCcuMI6EUNu7bieEJpBemk6TpsnywS7m+SnPt9vm7e7d4Q9yHcGRwiP889d/csPgG/RfUJPjJ+Pp5slPGT855RrLti5jS84W3rn6HR6/9PF20qWrB17N7nt24+Puw8u/v2ygrf9w/4c2BTiLfluEhOGDW21TLU9teAqNVmPRybCj7OoFhvyW/RuXxlyKl7uXyWPaZl4srVq7mrFRY8kozTAp6TPF3rN7TWavoHNeZ8rUFGIDY7n7h7upV9cb7DtWdIyC6gKm9rVfHtiaBaMWkF6azvbT250ynuD8pKS2hOLaYi4IMd77qjURvkIiKDCNCLAEZqlpqqFeXe+yDFagVyA+Hj4mM1iNmkY2Zm5kxoAZdplsJIQmoNaqySjNcHSqDqPL3IT5hKFAgVKhJMIngpuHWN8SoDtkQRo1jcz9di6BXoF8MOsD/fvm5+nH5bGX89MpxwOs7ae3s2TzEuYMn8OdI+40edyA4AFG/25srX0zJeM8W3UW32W+zP9uvtH6uqQfkpj5xUwWrFlw3tXfdTZldWUcOnfIbP2VDnO1RR2Nzq58d95uq8+pbKjkZMlJo06Jreno1+nn6ccHsz7gRPEJUrYYLiTp6q/s6X9ljJuG3ISfpx8f7//YKeMJzk90rpqDQwdbPDbIOwh3pbvohSUwigiwBGbRrcyE+bomg6VQKOReWCZqsLblbqO6sZqrB9pWf6UjIUzOenWFOqy1f67Fy82LzL9lol2k5ZtbviGnMocXtrxg1fndJQvywpYX2F+wn+XXLG/3dzO9/3SOFB6x2PvMHOX15cxZPYfYwFjem/mexcD7TOUZo9ttqX0zJeMMUYXw8NiHadIaz5DWNtVSUF1gskdPT62/62xSD6dywdsXICHx7u53u9xnxBwXRV6EAgW78nZZfc7+/P2A6fqrzmTagGncOeJOXvr9JQ6dO6TfvjFzI/179ScuKM4p1/H19OXWobfyv6P/o7qx2iljCs4/dG1drAmwFAoF4b7hIoMlMIoIsARmKaopAlzTZFhHpH+kyQzW+vT1eLp5ckXfK+waW3eT7Ap1WGv/XMuUvlPw8/QD4LrB1zFv5DyWbV1m1cNUd3Ah3J23m2VblzF35FyuH3x9u/3TB0wH4OdTP9s1viRJ3Lf2Ps5UnuGL2V/om5yawxm1b6bqV96a8Rav/uVVvTlBW+IC49ibtNfk/p5af9eZ6BYiimrle9e5mnNdciHCFAFeASSEJbDrrPUBlt7gIrLrBVgAb0x7g17evbj7+7vRaDWotWp+y/7NadkrHQtGL6CmqYavjn7l1HEF5w8nik/g6eZJfFC8VcdH+EZQWCsCLEF7RIAlMItuZcaVAVaUf5TJDNa6jHVMjJuIr6d9TY79PP2IDYzlWPExR6boMCeLT5Jems41g64x2P7W9LeI9I9k7rdzqWuqMztGV3UhbC1bvOzjywjwCuCt6W8ZPXZY+DAi/SNtrsPSX+N5Jf87+j9uTLiRcdHjrDrXGcX9lupXLF2jqxgpnA90h4UIS4yNGsvOMzutNmzYk7+H2MBYl96nHSHEJ4R/z/g3u8/u5q2db7E7bzdVjVVOD7Aujb6UC0Iu4OMDQibYlu4gL+8KnCg5wcDggbgp3aw6Ptw3XEgEBUYRAZbALHqJoItMLqAlg9X2YSKnPIdjRcdstmdvS0JoQqdnsNb+uRaAmQNnGmwP9A7kk+s+4WTJSZ5Oe9rk+Z8f/Nzkvs7MgrSVLaq1amqbavkx/UejxysUCqb1n8bGzI2otWqbr6Hjx/QfrX5AcFZxv7n6FUvX0O3v49cHgFBVaKcaKfRkuupChC2MjRxLUW2R0aanxrBkcNEVuHXorcwaNIuFGxZy1edXAfDYz4859UFfoVAwqvcotuVu63GBhCMBkrPk5edDkHay+CQXhFo2uNAhJIICU4gAS2AWnczGVTVYIGew6tX1lNWXGWx3xJ69NUPChnCi+ARaSevQOI7ww58/MCJihNF6g6n9pvLQxQ/x1s63+DXrV4N9NY01LFizgLnfzWVQ8CBU7iqD/Z2dBTGWLWjQNJjNFkwfMJ2y+jKri/idkZHQBUdHbznqsuJ+SwYCicMT+fPhPwF44rInRHDlInpCOwSd0YU10uGK+grSS9MtGlx0NgqFgr/0+wtNUhM1TTWAXB/pTPlm6uFUvj/5PUCXrlO1FUcDJGfcQ7tLDbAjNGmaOFV2isEhluuvdET4RsjtO0R7AEEbRIAlMEthTSG+Hr7t5E3ORGfV3rYOa33GevoG9bXKLtUcCaEJ1KnryCm3bjXY2ZTVlbEtdxuzBs4yeczLV73MwOCB3PzVzcS+EYtyiZLIf0Uy6O1B/PfAf/nnhH9y5MEjrLh2BUHeQQDEBMR0ehbEnmzBlf2uRKlQWi0T7AkZCR1+nn4EeQeZNN4QOE7K1BTcle4G2zp7IcJWhkcMx8vNy6oAa1/+PqBrGly05V9//KvdNmfKN5PTkqlTG0qtu5s81BiOBkjOuIf2BOmtJTLLMlFr1TZnsOrUdcJYxQn0tAypCLAEZnFlk2EdumbDrZ3lGtQNpGWm2W3P3prOdhL8KeMnNJKGWYNMB1g+Hj4kDk+kpK6E05WnkZDIr87nbNVZnhr/FEuvWIq70p3E4Ym8fOXLAGz/6/ZOz4LYky0IVgVzSdQlVtu194SMRGuiA6I5XXm6s6fRY0kcnkioKhRvd+8u0dfKHjzdPBndZ7RVAVZXN7hojasXS3rSYkxrHH1dzriH9tTfbWtOFJ8ArHMQ1CGaDTuHnpghFQGWwCxFtUUuD7CMZbC25m6lpqnGbnv21ugaFHdWHdba9LWE+YTpZT+m+OTAJ0a3f3nkS4OfQ1QhADY3InUFKVNT7JItTus/jd15uympLbF4jXkj57Xb1t0yEq2JCYjp0QFWZ69Cnq44TUFNAcuuWNYl+lrZy9jIsezN32uxVnFvvuxSGeoT2kEzsx9XL5Z01cUYRz8Tjr4ue+/TzpxDd0DXA8sW1UyEn2g27Ax6YoZUBFgCsxTWFLq0/gpaAqzWToLr0tfh5ebFlL5THB4/xCeEcN/wTslgqbVq1qev5+qBV1t0JbJ2hTDERw6wrAlOXE3i8ETuveheAJuyBdMHTEdCYkPmBovX2F+wH18PX2ICYrptRqI10QHRPVYi2BVWIZ3dwLazuCT6EmqbajlWZN4Bdc/ZPV2+/kqHq900u6JbpzM+EylTU1BgqOSw5XUlDk8kaUySwbY3pr1h0z00ZWoKbgrD7zAFCpIndN8H4LacKD5Bb7/eBHoHWn2ObgH6XI1wEnSEnpghFQGWwCxFNUWE+7g2g+Xt7k2wKtggg7U+Yz2T4yc7rfYrITTB4oOKJexZhdx+ejtl9WXt7NmNYe0KYbAqGICSus4PsAD8Pf1RKpRUP1NtdbbgosiLCFYFW6zDOlZ0jB/+/IEnL3uS3Mdyu3VGQkdMQAyFNYU0qBs6eypOpyusQm7M3Ei4bzjDwod12DVdgTVGF2V1ZZwqO9VtAixnOXpaGj/KX5adh6hCOn0xxhmfidG9RyMh0cu7FyB/Z9r6utRaNb4evqTNlRcgbHUGnjNsDoHegajcVShQEOErZ26+OfENGq3GprG6KidLTtpc8y0kgs4hJjDG6PbunCEVAZbAJJIkdUgGC2QnQV2AlVWWxYniE8wY4Jh7YGsSQhM4Xnzcbqcfe1ch1/65Fg+lB1f1v8riNaxdfe1KEkGQJUpDwobYFAy7Kd34S/+/8POpn82+J69ufxWVu4oHxz7ojKl2CXRfJD0xi9XZq5CSJJGWlcbUvlMdrt3sbPr36k8v715mA6zuZHChw5LbpjPGP/GQXEvzj/H/6PTFGGd8Jr45/g0ARx44wgMXPYCbwo1bhtxi0zx+zf6Vy2Mv5/LYy/Hx8OHX7F8tn9SKU2WnKK0r5V9/+RfaRVoKnijg/Vnv81PGT/xj4z9sGqurcqL4hE31VyACLGcxJb69Wqmzs8+OIgIsgUkqGipo0jZ1SPPKSP9IvURQZ8/ujPorHUPChlBeX253Gt/eVcgf/vyBSfGTCPAKsHgNa1d39RmsLiARlCTJ7h480/pPo6C6gEPnDhndf6byDKmHUrn7wru7RX2JtUQHRAM9M8AytdrY2693h1z/WNExCqoLur08EGRb87FRY9mZt9PkMd3J4KIj8fXwxcvNi+La4s6eilNql1YfX82l0ZcS6R/J1H5TqWmqscoARUdBdQHHio5xRd8r8HTz5PLYy9mUtcnq80HODIOh9DZpTBIPXvwg//rjX3x64FObxutqFNcWU1pXanMGy9PNkyDvINFs2AHK6spY++daEkIT9J+LQK/ATs8+O4oIsAQmKaqRe2B1RIDVOoO1PmM9/Xv1Z2DIQKeNr3cStNPowp5VyIzSDE4Un7BKHqjDmtVdlYcKlbuqS2Swzlad5VzNOS7sc6HN507rPw3ApEzwjT/eQCtpefzSxx2aY1cjJkDOYPVEowtTdRqFNYW8s+sdl/eKMfYQ2J0ZGzWWI4VHqGmsMbp/b/5e+gb11S+6CGQUCgUhPiFdQkZtTJng6eZp9cp8ZlkmBwoOMDthNgCT4yejQKGvNbSG37J/A1qyBFfEX8HRoqM2BQUbMzcSGxjLgOABBtvfmPYGV/S9gqS1SWw/vd3q8boa9jgI6gj3DaewVmSw7GXplqWU1pXyf7P/j5xHcxgYPJBpA6Z16+AKRIAlMIMu5W2rVtseIv0jKaguoKaxhrTMNKdmr6DFSdDeOiydU1BbzK1C/vjnjwBm7dntpas8POzN3wvYJ1Hq49+HkREjjdq1l9WVsXzfcm4bdhvxQfGOTrNLoctgna7oeQHWnGFzCPAKwMfdR5+Ffffqd/lL/7/w0PqHuOmrm1ixd4XLXAY3Zm1kQPCAbq3bb83YqLFoJa1eCtiW7mRw0dGE+oR2iQyWTpmgc/FzV7rTN7Cv1Q+POnngjQk3ArKCYXSf0TYFWJuyNhHoFcjoPqMB9OZRusDLEhqthk1Zm4xKbz3cPFh10ypiAmKYsXIG0a9Hd8s+RroAy5YeWDoifCNEBstO/iz5k//s+g93X3g3I3uPBGBQyCD+LPmzk2fmOCLAEpikqNaJGaysVPguHr5Qyv9mGd54owKi0Epavjr2FXXqOqfWX4EcwAV4BdjlJPh77u+U15W3c3FyV7qbXYX84c8fGBI2hH69+tl8TUsEq4K7RoB1di9KhZJRvUfZdf70AdP5Pfd3qhqqDLa/u/tdqhureWr8U06YZdfC19OXXt69eqRE8GTJScrqy3hj+hv6LOx9F9/H2jlrefWqV/n2+Lfcu/Zel7gMNmma+C37N67s2zOyVwAXR14MGDe6KKktIas8q1vVX3UkIaqusQgFcpB1UeRFTIqbxNIpSzlZepL0knSrzv3m+DeM7j2avr366rdN7TuVP07/0U62bopfs39lYtxEfQPuC/tcSIBXgNUywQMFByirLzOZGQ7xCSFpTBKVjZXkVeV1yz5GJ4tP4uXmRVxgnM3nhvuGixosO/n7L39H5a5i6ZSl+m0DgwfyZ8mfLlc8uBoRYAlMos9gOWpykZUKu5KgNgeQ5H93JRkEWTqr9g/3fYi3uzeT4yc7ds02KBQKvdGFLfxy6hf+svIvxATG8Ob0N/X1Ub4evqi1apMOi5UNlWzO2cysgc7PXoH88NAVJIL7CvYxOHQwvp6+dp0/rf80mrRNBgXXdU11vLXzLaYPmM6IiBHOmmqXIiawZ/bC2nBKtt2/qp+hqYtSoeSJy54gwjcCCcMvTWe5DO4+u5vqxuoeIw8EOXMeFxjHrrPtAyxdVktksIwT6hPaJepUdZyrOUe4bzhzR85FqVDy3wP/tXhOXmUef5z5Qy8P1DG171SatE1sy91mcYzTFafJKM3gir5X6Le5K92ZGDfRaqMLnfR2at+pJo95d/e77bZ1pz5GJ0pOMDBkoMV2KsYQAZZ9/HLqF9b+uZZnJz5roBIaFDKI2qZaA2fp7ogIsAQmcZpE8GAyaNqstGlq5e3N6Gx1fz/9O1Pip6DyMGyK6Aw83Tz5Lfs3q+UL3x7/lmv+7xoGBA9g6/ytPHLJI/r6qHNPnCMhNIE7v73TqDTg54yfUWvVLpEHQrNEsAs8PNhrcKFjfOx4fD18DeqwPj34KUW1RfxjfM9wpjJGT202vCFzA/179TdYbW+NKZMZZ7gMbszciAKFU3rndSUuib7EaAZLZ3BhT/3j+UCIKqRLSAR1FNYUEuEbQaR/JDMGzOC/B/9r0d78uxPfAS3yQB2Xx16Oh9KDtEzLMkFdENXWpe2K+CtIL023KpO+MWsjw8OHm5TKQ+c7iDrKyeKTdtVfgSwRLKkroUnT5ORZ9VzUWjWP//w4/Xr145FLHjHYNyhkEEC3lwmKAEtgkqKaIgK8AvBy93JsoFoTN9hW23ec2WHw/50tK0g9nMqOMzvQSlqT8oXWfa5CXwll9qrZXNjnQn6b91u7LxZfT19W3byKioYK7vz2TrSS1mD/2vS1BKuCuTTmUqe+Dh3B3p0vEcyvyie/Ot+hAMvTzZOp/abyU8ZPSJKERqvhte2vMTZqLJPiJjlxtl2Lzmo2bE8vN2vRSfTaZq9a4wxHNVNszNzImMgxPc7wYWzkWLLLs9utkO/N3ytbuat6ddLMujYhPnKWv+29uTNoUDdQXl+ul9vPHzWfs1Vn+eXUL2bPW318NQmhCXqTJh2+nr6Mix5nVR3WpqxNhKhCGB4x3GC7biHi1yzzWax6dT3bcreZzV6Baz/brqZR00hmWSaDQ+wLsHTva1cK6Ls6K/au4GjRUV676rV2z5giwBL0eAprC51Tf+Vj4gbbvD31cCpPbWyptSmrL3O6djs5LZkmreHqUmv5Qts+VyV1JSgUCu658B6TDzDDwofx1vS32JC5gVd+f0W/XaPVsC59HTMGzNBr3p2N7uGhMzXKOoMLR1fQp/efTlZ5FhmlGaw+vppTZaf4x/h/dPs+RuaICYihuLaYuqa6Drumvb3crGVn3k6qGqvM9nwz5qjmofRwuNdJdWM1f5z5w+JDYHdE13B4d95ug+3C4MI8oT6haCQNFfUVnT0VfT2zbqHumguuIdQnlE8OfGLynOLaYjbnbG6XvdIxte9U9uXvo6LR9OuTJIlfs39lcvxklArDx70RESMIVgWzKdt8Hdb209upV9dblN4a+2yr3FXdoo/RqdJTaCSNXQYXIHph2UpZXRnP/vosk+Mnc/3g69vtjwqIQuWuOj8CrO3bt/PSSy+RmJjI9OnTmTlzJnPnzuWVV15h1y7rezEIuhdFNUXOCbCGL2q/zc0HRso3Xmd0ureEKZlCTkUOQ94Zwl/X/LXdHLSSluc3P2923HsuvIebh9zMPzf9kz9O/wHAodJDFNcW22TPbishqhDUWjVVjVWWD3YRe8/uRYFC70xlL9MGyHbt6zPW8/LvLzMoZBDXXXCdM6bYZemMZsOu/pxtOLUBpUJpUOvRlra93nw8fNBoNYyMGOnQtbfmbEWtVfeo+isdF/a5EKVCaSATLK4tJqciRxhcmEHXkL2zM/2AXkau+z71dPPkjuF38N2J70xmPdacWINW0rarv9Ixtd9UJCR2F+42uh9ki/fcilyjn0mlQsnk+MlsytpkdqFuY+ZGfc2WOdp+tgHGRY3rFlbbjli0Q0vgbG+fzfMFnYIi+BVZgXNVv6uMLqQqFUoGhgzkz9IeHGB9++23TJs2jaeeeoqSkhImTpzI/PnzSUxMZNy4cZw5c4a///3vzJgxg2+//baj5izoIAprCp1j0e4ZKP+r8JD/9YmFscuhr3zj7QjttimZgp+nHwlhCTRoGuyag0KhYMU1K4gJjOG21bdRVlfGb2d/w03hpg8cXEFXaDa8N38vF4RegJ+nn0Pj9OvVjwjfCB7/+XH25e+juLaYL49+6aRZdk06o9mwqz9nGzI3cHHkxQR5B5k9zqDX29+yCfYJZsGaBai1aruvvTFzI15uXoyPGW/3GF0VX09fhoUPM2g4vPesnD0WGSzT6JqTdwXZli6z0XrBcv7o+TRpm/ji8BdGz/nmxDfEB8WbdGgdGzUWHw8fdhTuMLofTNdf6bgi/gpyK3LJKs8yOUZaVhqXRF2Cv5e/yWN0tP5s3zvmXn4/8zt5lXkWz+tsTpacBFqkabYiMliWaa2g0JGyNcWkgqInWLWbDLDuvPNOdu7cyauvvsq2bdt49dVXuffee7n11luZM2cODzzwAP/+97/ZunUrixYtYsuWLSQmdv2VCoH1FNY4SSKY+zV4hcCoZfLP0/fqgyvoGO22MfmCj4cP7896n9W3rDZpzWrNHAK9A/nfTf/jdMVpol6PYsWJFbgr3fkx/UenzN0YIT7y6mxnOgnuzXfM4EJH6uFUSupK0EhywXdpXWm3sve1h85oNuzKz1lFfQW78naZrb8yRphvGG/PeJvdZ3fz6Z+f2n39jVkbuTz2cpeY43QFxkaOZVfeLn2mQRhcWEZ3j+wKZkC6B+8I35Za3hERIxjTZ4xRmWBFfQUbTm1gdsJsk1JpTzdPJsZNZMc50wHWpqxN9PbrbTIzo8tsmarDKqsrY8/ZPXZlhp8a/xQarYZ//fEvm8/taE4Un9C3crEHEWBZxlYFxaDgQWSWZXZr4xCTAdaiRYt46aWXGDHCsk3yuHHjeOONN1i8eLEz5yboRLSSluLaYsczWOo6yPsBom8A/2Z9c/Upg0NMBT/O1G63lS/EBcax/JrlevmCo3NIL03HXelOnVquqWnQNLg0SOhs+UtBdQFnq846JcBKTktul73oTva+9tAZzYZd+Tn7NftXNJLGbP2VKW4ZegvXD76e/xz5DyeLT9p8fmFNIYfOHeqR9Vc6xkaNpay+jFNl8r1zb/5eBgYPJNA7sJNn1nXpShksnXSs7YLlgtELOFBwgP35+w22/5j+I03aJpP1Vzqm9p1KZlWm0SyRrv5qSvwUk0Ha4NDB9PbrbbIO67fs39BKWrsCrH69+nH78Nv5YO8HXeI9MMfJkpNcEGJf/RVAoFcgnm6eotmwGWxVUAwKGYRaqya7PNuFs3ItJgOsAQMGmDypsbGRQ4cOUVVlWP8xcOBA581M0KmU1ZWhkTSOZ7AKfgF1NcTeDP795W1tAixLwY+zMJAmPZptML6jc7BkouFsdBLBzshgpR5OZcR78sLLy7+/7HAQ2d3tfe1B5aEiRBXSoRLBxOGJ/Hv6v/U/+3r4Ou1ztuHUBnw9ZGczW1EoFLx79bt4u3nz1+//arPrm65Zak+sv9JxSfQlQEvDYWFwYZnOXoRqTWFNISp3VTs59e3DbsfLzYuP939ssH318dX08etj8fOkW1Qw1s/qRPEJCqoLzNZEKhQKpsRPMVmHtTFzI36eflwSdYnZeZji6cufpraplrd2vGXX+R2BJEmcKD5hd/0VyL/HcN9wCmtFBssUtiooeoKToFUmF1lZWdx2223s27eP6upqbrzxRm655RYmT57Mnj17XD1HQSegcz1yOMDK/Ro8e0HEFPBt7o1TdardYeaCn47CkTl0dJDQWfIXnY5a9/dxruacw5m67mzv6wid0Wz44qiLAdndK9w33Gmfsw2ZG5gUPwlPN0+7zu/j34eFoxfy++nfeWfXOzaduzFzI0HeQT1aLjckbAg+Hj7syttFYU0hpytPC4MLCwR4BeCudO8S2RNdk+G2maReql7ckHADqYdTqVfXA/LC3Pr09dww+IZ2zn9tGdl7JIGegUbt2i3VX+mYEj+FguoCfR1Sa9Ky0pgYNxEPNw+zY5hiSNgQbky4kf/s+g+VDZV2jeFqCmsKKa8vdyiDBaLZsCVSpqbg7e5tsM2cguK8CbCWLl1KeHg48fHxrF69mtLSUjZv3syCBQt46aWXXD1HQSegbzLs64BEUNMAed9D9PWk/t8q4gckoLwD4v/yMqmpPau+pqODhF7esnV8R6/OusKJriMkol2Rzmg2nFUmF7PfmHAjWeVZTsmg5ZTnkF6abnP9VVuui7uO6QOmszBtoX6elpAkiY2ZG7mi7xW4Kd0cun5Xxl3pzpg+Y9iVt0sYXFiJQqEgRNU1GrIX1hSabNK7YNQCyurL+P7k9wD8lPETdeo6Zg8x7h7YGqVCySXhl5CWmdYuA7UpaxOxgbH069XP7Bi6DJcuE6zjdMVpTpac5Mq+jmWGn7n8GSoaKnh397sOjeMqdIGlIxkskOvrhETQNInDE7n7wrsBrFIJhfiEEKwK7vkB1r59+3jyyScJDg4mLS2NqVOnEhERwfXXX096erqr5yiwgCuahxpzPbKZgo3QVEnqnhCSkpLIyclBkiCnoIakpKQeFWR1dJDg4eZBgFdAh0sEXZGp6yiJaFejM5oNZ5ZlAnDXqLsA2d7cUTZkbgBwOMBSKBR8MOsDNFoNCe8kWHU/yyzLJKcip0fXX+kYGzWWffn7+OOM3A7C0fYI5wOhPqEU13WBDFb1OZPfpVf0vYLYwFi9TPCb498QogqxaIuuY1z4OE5XniajNEO/TStp+S37N7P1Vzr69epHTEBMO5mhLivmqPR2TOQYpvWfxut/vN5uca4roLNot7cHlg6RwbJMb9/eAFQ9XWWVSmhQyKBubdVuVYDl4+NDZWUlpaWl7Nu3j0mTJgGQnZ1NUFCQK+cnsICrmocW1cgSMIdMLk5/DR6BJL+6itraNlmP2lqSk3uOiUFnBAkhqpAOz2C5KlPXFSSiHU1MQAyldaUd+tCRWZZJoFcgU+Kn4O/pz5acLQ6PuSFzA5H+kQwJG+LwWFtztyIh0aBpsOp+tjFzI9Cz6690jI0aS4Omgc8OfsYFIRfY7Xh2PhHi04UyWL7GM1huSjfmjZzHL6d+4VTpKX748weuu+A6q5vUj4uQ67RaZ6AOnztMSV2J2forHQqFgiv6XsGvWb8a1D+mZaUR7hvOsPBhVs3DHM9MeIai2iI+2veRw2M5m5PFJ/F293b4O0wXYJnrKXa+k1GWQaR/JL6evlYdPyhkEOkl3TeJY1WAddVVV/HYY48xb948wsPDmThxIuvWreOpp57iuut6dkPQro6rmofqVmJ0Tkw2o2mE099B9HXknjYug8rN7VkmBrog4egtRzskSAhWBXf4w0PK1JR2X/zng5zPFXSGk2BmeSb9evXDTenG5bGXsyXXsQBLK2lJy0zjyn5XWlwpt4bktGQaNY0G28zdzzZmbSQmIIaBwT3fYOls1VlAbo5+uvJ0j25j4CxCfUI7vQZLK2kttjy5a9RdSEgMfXcolQ2V/PDnD1a/v3F+cUQHRBvUYVlbf6VjSvwUSupKOFJ4BGiR3k7tO9Upn+uJcRO5PPZyXt3+arvPd2dzouQEg0IGWax3s0SEbwQNmoYuW2vWFUgvSbfpXj0weCCnK093ycynNVj1F/Xss88yZ84cxo0bxyeffIKnpyeNjY3cf//9PPbYY66eo8AMrjJXKKotIlgVbHdxK+c2QVM5xNxEbKyJrIeJ7QLrCPEJ6XCJYOLwRKL8ovBy8zqv5HyuICZQ7oXVkTLBzLJMfU3GxLiJHCs6ps9W28OBggOU1JU4LA/UYcv9TCtp2ZS1yWnBXVcm9XAqyZtagszaptoe3yvOGXRGlr8tOkdeUxksgD/O/IFSodQ3vC+qLbL6/dVloDZlbdJnoDZlbWJA8AD9PcYSU/pO0Z8HcKzoGAXVBU7NDD9z+TOcrjzNykMrnTamMzhZfNLh+isQvbCsIaM0gwHBph3K26Izumgtf+1OmAywnn76adasWUNhYSHu7u7cddddJCcnExcnN2S9/vrrueOOO3r8F1tXx1WSrcKaQsflge7+0Ocqli5d2m63j8qLlBSR9XCEznh4OFt1lpzKHBZNWnReyflcQUc3G9ZKWrLKsvQB1oTYCQBsy91m95gbTsn1V856EDN131IqlO2K8A8UHKC0rvS8qL9ylVKhp6MzuehM2ZapHlitSU5LbteewJb3d2rfqZTUlXDo3CHUWjWbczZbnb0C+XM3IHiAPvPlCunt9AHTiQuM49619zq1XtwRGtQNZJVnOewgCCLAskRlQyXnas7ZlMHq7k6CJgOs8ePHs3PnTm677TauvvpqXnjhBTZu3Niu95Wgc0mZmoKXm5fBNmdItixJGsyibYLT30LUNeDmTXS0LIVyc5NdvuJCYfmSm0hMFA/mjtAZEsGfMn4C4OqBV3fodXsiUQFRQMdlsPKr8mnQNOgDrIsiL8Lb3duhOqwNmRsYHj6c3n69nTJHY2Yx3u7ehPmEMfWzqTzxyxM0qOVVft1D4NR+PT/AOh97xTmDUJ9QmrRNVDV23nOL7oHblIsgOP7+6hYZNmVtYn/+fiobKq2qv2rNlPgpbM7ejEarIS0rjQHBA5zqgvvFkS/Ir85HrVU7tV7cETJKM9BKWqdksHTvry6gFhhyqlRuz2NLBkt3bI8LsGbNmsWyZcvYtGkT7733Hv379+f7779n2rRp3Hzzzbzxxhv88ccfNDZ2LT3t+Ubi8EQevPhB/c/OkmwV1RbZb9FeuBkaSyH2JgBWr16NSqXSm1ocetWPxCnBDs1PIK/OlteXo9FqOuya69LXEeUfxYiIER12zZ6KLnDoqBosnYOgLsDycvdiXPQ4tuba5yRY11THttxtTpMHgnGzmA+v/ZCMRzK4/6L7+dcf/2Lsh2N5edvLLPptEQDjPhzX6SvhruZ87RXnKJ3VL7A1OutucwuWjr6/UQFRXBByAWlZafos1OT4yTbN84q+V1DRUMGuvF38lv2bw/bsbbG1vrIj0DsIigyWy0kvlc0qBoZYn8Hy8/Qjyj+q5wVYrYmLi+P222/n3//+N7///jvPPfccfn5+rFixggkTJrh6jgILXBpzKQB/Hf1Xp0m2CmsKCfexM4OV+zW4+0Kf6Wi1Wr755humT5/O8OHDAcisjoTq9s2GBbYRrApGQqK8vrxDrtekaeKXU79w9cCrhTTYSXRks+G2ARbAxNiJ7C/Yb1dh9tbcrTRoGriqv/MCLDDuKOnr6cu7M99l7e1rySrLYmHaQn1j1q6wEg5AVip8Fw9fKOV/s5w3n/O1V5yj6EyaOtPowpqWJ854f6/oewVbcrbw86mfGRI2xOassi4ge/n3l6lqrHK6M2dXzMLqemA5atEOLY7LoheWcXR1VP179bfpvEEhg3p2gNUahULB8OHDueeee/j444/ZutXxPioCx2jSNAFy1skZaLQaSmpL7JMIajVw+huInAXuKnbs2EF+fj6zZ8+mXz/5wS6zPFgEWE5AtzrbUUYXv5/+narGKiEPdCId2QsrsywTpUJpsCo+MW4iWknL9tPbbR5vw6kNeLp5Wt2vxxnMHDSTQO/Adts7eyWcrFTYlQS1OYAk/7sryWlB1vnaK85RQlTNGaxONLo4V3MOpUKpn4sxnPH+eig9qG6sZlPWJnIrcm1ecOjt15tI/0jWnFwDwKM/P+rURYuumIU9UXyCKP8o/Dz9HB7Lw82DYFWwyGCZIL003SaLdh3dOcCyqtHCvn37SElJITMzk6ampnb7jxw54vSJCaynSdscYDngBtaakroSJCT7JIJFW6ChyEAe6OHhwaxZs/SHZJZ4QXUWSFpw0Br1fKb1w8NAXG9TvS59HR5Kj/PCVKCjiAmIcUovKmvILM8kJiAGTzdP/bZx0eNwV7qzJWcL0wdMt2m8DZkbGB8zvt3Ku6vJq8wzut2lK+FZqXAwGWpzwScWRqZA/O1QeQKKfoe9j4GmjZWwplY+p69zgqDE4YkioLKRrpLBCvUJxU3pZvY4R97f1MOprNi3Qv9zdWM1ST8k6ce1dozWwcGZyjM2j2GOlKkpJP2QZGDW4unm2alZ2BPFJ5xSf6Uj3DecwloRYBnDVgdBHYNCBlFSV0JJbYl+Ubm7YFWA9fTTTxMfH8/999+Pl5eX5RMEHYpO1+ysLxFrJA0myf0a3FQQOQNJkli9ejVXXXUVgYHyqnNwcDCnzkmgbYTaPPC1zkZW0J5glVzH1lH1BevS1zExbiL+Xv4dcr3zgZiAGMrry6lurHbKKqo5Wlu06/D19GVMnzE2B3nnqs9x8NxBll2xzJlTtIrYwFhyKnKMbncJuuyULoCqzYEd82DXvaCpMX9urTCh6Ey6Qg2WuSbDziI5LZk6dZ3BNl1W19rgKDktGbVW7dAY5tCNkZyWTG5FLu5KdwK9ArllyC0Oj20PkiRxsuQkd46402ljRvhGCImgCdJL0pk1aJblA9ugcxJML03vdgGWVemDwsJC/vGPf3DllVcyYcKEdv8JOhdnSwR1mTCbbdr18sCZ4O7Lvn37yMnJYfbs2fpD+vXrR+bZ5ocSIRN0iI6UCOaU53C06KiQBzoZXbPhjpAJGguwQJYJ7j67m7qmOiNnGUfX1NTZdRrW0OH1SAeT22enJA0ogEs+glkn5KyWMUxtF3QIQd5BKBXKTs1gnas5Z78jr5U4o76pI2qkWtdXfnfbdxTVFvHx/o+dNr4tFFQXUNlQ6RSDCx3hvuFCImiEqoYqztWcszuDBd3TSdCqAGvWrFn89NNPrp6LwE50Gazy+nJ9sOUIdmewirdDfYGBPNDNzY3rrrtOf0j//v3JPNP8ZScCLIfoyPqC9RnrAWHP7mx0jUBd7SRY21RLQXWByQCrUdPIrrxdVo2VejhVLx266aubOtxcosPrkUxlodS10H8BBFwAI5eBW1uppBJGtO8BKOg4lAql3M6iE2uwCmsKzVq0OwNn1Dd1dI3UjAEzuCzmMpZuWWrT4o6z0BlcOF0iKAKsdugMLmzpgaWjb1Bf3BRu3TLAskoimJSUxOzZs/n666+Jiopq5yD22WefuWRyAuvQ1WCB/LDtaE8aXSbM6gBLX5/QLNtpqtbLAydPnkxISEtat1+/fqxevRq11g336kyH5nm+E+gdiFKh7BD5y7r0dfQN6uvU1T5BxzUbzirLAjAaYI2PGY8CBVtytjApfpLZcXTBla6OIrci16l1GtbSofVIPrEt97a223Xo6qx0dVqewdBYAhWiPrmzCfUJ7dwMVvU5+x15rcRYfZOtWV1njGELCoWClCtSmPLpFN7b8x6PX/q4S65jCr1FuxMcBHVE+EZQVl9Go6bRoNb1fEcXYNmTwfJw86Bfr37dMsCyKoP1xBNP0KtXL6ZNm8aYMWO48MILDf4TdC6ts1bOMLoorClEgUJf42MWA/esZvY+wtGfX+bPP/80kAeCHGCp1WrO1EVBlRMzWC60SO6qKBVKenn3crlEsF5dT1pWmrBndwGR/pGA6yWCxizadfRS9WJExAi25Fquw0pOSzZ4AIMu4ODnakamgMLDcJubj7y9NX0T4fpsmKOFm4phwH1w/BXIXd1hUxW0J0QV0mkZrJrGGmqaalyewXJGVrcznConx0/myn5X8uK2F6lq6Nhm0CeLT+Lj4aOXaTsD3aK0swzHegq6Hlj2BFjQfZ0ErcpgnThxgm+++Yb+/W3zrxd0DK2b9zmjDsta1yPAeH2CppbVn76EQqHghhtuMNil+xvKrAwj3lkSQWNF6LvkVXVnOXh1VTpC/rIlZwu1TbVCHugCvNy9iPCNcLlE0FyABTAhdgIfH/iYJk0THm4eRo+BrtnLxuX0TYTDS6AmGyR1i4ugpXvLmDehbD/suAsCh0BgQgdMVtCWEJ8QssuzO+XaDhlG2Ygzsrqd4VSZckUKl3x4CW/tfIt/Tvxnh133RMkJBoUMQulEJ+PWzYajAqKcNm53J6M0gz5+fWy2aNcxKGQQv2b/ilbSOvX9cjVWzXTMmDGkp6e7ei4CO2ktEXSGFKKotsh6i3YT9Qmrf69g/Pjx9O5tKFfU9cI6VeILzpIImgjyONiDV9WbCfFx/ersuvR1eLt76xtRCpxLRzQbzizLxN/T32QvnolxE6ltqmV/wX6z4/RS9TK6vTN72bicmtNQnQ4jFsvZqeuzrVu4cfOCCV+Duw9svRGaOnaFvtviZDVCqCq001wEdQGWq10EuzNjo8Zy7QXX8tr21yirK+uw654sPunU+itAn6k8VyOcBFuTXprOwBD7W8kMDB5IbVMtZ6vOOnFWrseqDNall15KcnIyP//8MzExMbi5GWY2/va3v7lkcgLrMMhgOUkiaPWKm6oP1Bn+0acXwOHT8Mbjs9sdHh0djYeHB5lFbtBYCo3l4Bnk2IRNFaGfBxbJIaoQ8qvzXXqNdenrmBI/pcP7HZ0vRAdEk17i2gWszHLZQdCUxHNCnOwGuyVnC2Ojxho9prSulAZ1A0qFEq2k1W93qYNfV+D01/K/MTfbfq5PNIz/H2y6EtKmQv05qD1tfRbsfMMFagTdIpQkSR0ucdY9aHdEBqs7s3TKUka9P4pXt7/Ksqmub/1Q11RHdnk2c0fOdeq4rTNYghYySjO4eoD9Chi9VXtJulMlna7GqgzWli1bGDJkCMXFxezfv589e/bo/9u7d6+r5yiwQJOmCZW7CnCeRNCqL4S6AmiVPdOxeo8sMbrxxhvb7XNzcyM+Pp7MggZ5gzNkguexRXKwKtilq7PpJemkl6YLeaALiQnomAyWKXkgQG+/3gwKGWS2H9Zzvz5HnbqOF6a80KF1Gp1OziroNRoCzK/ApqamEh8fj1KpJD4+ntTU5sxLxGSIvQVKdzcv+kgtgcN5UCtqEy5QI4T6hFKvrm9XO9gR6DNYLq7B6u6MiBjBrcNu5a2db7m8j1Tq4VT6/7s/EhLv7HrHqS6oIsBqT1VDFQXVBQ5lsLqrVbtVGazPP//c1fMQOECTtgmVhwpvd2/nSARriiz3wGqqhN9mgLoGhi2BzI/lhwefWFYfUXLxxaHExhoPcPr168epM80PlFWnIHiMYxMe9ozc9LM1xorQeyAhqhCXmlysS18HCHt2VxITEENlQyWVDZUEeAU4fXxJksgsy2R6/+lmj5sYO5Gvj39tVOd+sOAg7+15j/svup+nJzzN0xOedvo8uyQ1OVCyg9TTt5B8fzy5ubnExsaSkpJCYmJLUJmamkpSUhK1tfJDfE5ODklJze6KiYlQ9Hv7sXWBg8hiteACNULrdhb21oDYiy5YsLmn5HnIkslL+OroV7y47UXenP6mS67R1gW1uK7YPhdUvXNyrkE22t/TH293b9FsuBWnyuRFdHsNLgCiAqJQuat6ZoAFcOrUKY4cOYJarUaSJP12hULRzilO0LE0ahrxUHoQ6B3ocAarSdNEWX2Z+QyWpgG23ADlR2DSDxA5HUY8B8gPFnsOx/Pii0kmT+/fvz+7du2Uf3BGHVbZAflf7z5Qnw8KNxi7/Lx4cAnxCaGqscpltrDrMtYxOHSw2eyHwDFaNxseEjbE6eMXVBdQr663+B5OiJvAh/s/5EjhEUZEjNBvlySJh9c/TLAqmOenPO/0+XVpcr8m9XdI+uR7auvqgZbg6dy5c/Tu3Zvdu3fz3nvv0dDQYHBqbW0tycnJcoBVayJDeR7ImG3CGkt8Gwn1CQXk+uSOrhUsrCnE39MflYeqQ6/bHRkUMoi7Rt3F27ve5utjX3O26iyxgbGkTE1xWobcnAuq1dcwI2NV9E2Ue2HVigyWDp383Z4eWDqUCiUDQwbyZ2kPDLCWL1/O66+/TmBgIL6+hitAIsDqfJq0TXi6eRLmE+ZwDZYuA2ZyxU3Swh9z4dwmuPQzObhqxTfffANg9m+iX79+lJWVU6YOpZejEsHSvZD+Pgx6BC56C07+G/b+TZblnAforPRL60od7n/WlprGGn7L/o2HLn7IqeMKDGndbNgVAZYlB0EdE+MmArA1Z6tBgPV/R/6PrblbWT5ruXWtG3oSuatI/tpDH1zpqK2t5e9//zsAKpWqXXClPz23OYByQeDQIxmZAjvmgaQx3B42we4hQ3yaM1idYHRxruackAfawIiIEWgkDXlVeQDkVOQ4tc+eU1xQzclYdQGWkAjq0fXA6h/smAv5oJBBHDp3yBlT6jCsqsH68ssveeyxx9i5cyebNm0y+C8tLc3VcxRYQGet7IyGikZtZVu7On0VBLmrYNQr0PfOduevXr2a4cOHM3Cg6dUKnZNgZmVvx2qwJC3sfgC8w2FE88p6SHOBfslu+8ftRujkL66QCW7K2kSjpvH8kAd2Yh81XQbLVXVY1gZYcYFxxATEGPTDqmqo4skNTzKmzxgWjF7gkvl1WaqzoWQXuUXt60x1HDhwgMrKSuLi4ozu18ukR6bIsuXWnCcyZpuIuxWU3uDmCyjkALTXRZCzEv58x64hW2ewOhqbDKMEvP7H6+22ObPPnqkMpk2ZTQsy1gjfCCERbEV6aTp9/Prg5+nn0DiDggeRWZZp0Pe1q2NVgFVZWcm0adNcPReBnejkYWE+YQ5LBNsFWAaNhCVQV4HCHVSR7c7Nz89n+/btFjOa+l5Y5UGOSQRPfQQlu2D0a+AZKG/rNQqUHlCy0/5xuxG6jIIrVmfXpa/Dz9OPy2Mvd/rYXYq2f+MdbEAQ5R+FAoXLmg1nlmXKhhRBxoMAHQqFgolxE9mSs0UvA39hywucrTrL21e/bV1fvJ5Es3tgbHT7ex1AXFwcI0eOxN3dnZSUFHx8DAMohULBokWL5B/6JsqyZZ84oNnJLv6O80LGbBPFf4CmBi79pNkSPwf+8jtEXwd7HpLVCjbSugbLWaQeTiX+zXiUS5TEvxlv0ijhXM05YdFuA67us5cyNaWdlN5mF1RvE+9nczZaZLAMySjNcKj+SsegkEGotepO62lnD1YFWNdddx1ffvmlQe2VoOvQpG3CQ+lBmG8YxbXFDr1PugBN3wfLWDpcUhu4OuncsyIjI5EkCW9vb7PX6Nu3LwCnijzl2oRWNvNWU18MBxZC+ESIb/WQ4uYNQSPlwOs8QC9/cXIvLEmSWJexjiv7XYmXu5dTx+5ydHIfNQ83D3r79XZZs+Gs8iyiAqLwdjf/uQRZJlhQXUBGaQYni0/yxo43uGvUXYyLHueSuXVpclZB8EWkvPhKu3uaj48PKSktD2WJiYksX76cuLg4FAoFYWFhSJLEr7/+2nI/7pso99C6XQO9LoSCDfbd+3oyed/LC2R9Wi3ounnC+FUQOQt23w87/mpTtrmXqhcKFE7LYOmMEnIqcpCQ9DI2Y0GWyGDZhlMyTGZIHJ7I4JDBuCvd7XNBlSTwCGq/vVU2WhdgiedlmfTSdIfqr3R0RydBqwKssrIyVq5cyeWXX84tt9xCYmKiwX+CzqVR06iXCKq1aioaKuweq10Gy0I6XOeelZPTUl/w/PPPt1gUG8Hf35+wsDAyz2lkmV9Ntu0TPfgMNFXARe+AQmFokTz/BKnf/S6P3cNxlUTwWNExcityHepd0W3oAn3UogOiXSoRtNakZEKsXOuyOWczj/78KCoPFS9Nfckl8+pQbJWAVmfJtuqx8vfdlVdeCchZqbi4OJYvX97uuy8xMZHs7Gy0Wi2FhYUsWbKEzz//nHfffddwbIUCRr4ANVmQ+YnzXmNP4Mz3ED4FPNq4abp5yk2bg0Y2O9Zan212V7oT5B3ktCy/OaOE1qi1akpqS0QGywZSpqa067fozD571Y3VnCg5wcNjH0a7SEv2o9m21Xad+Q6qTkDf+eDe7EfgE2dgqhXhG0GTtony+nKnzLk7U91YTUF1gdMyWNC9AiyrTC769evHfffd5+q5COykSdNicgGyzXqQd5BdYxXVFOGmcGs530JxdnJyst6aWIeBe5YJ+vfvT+bZavmH6lMQMMj6SRbvhFMfwuDHIGhYe4vkgmqSlgMj/0Xi3U9aP243xBUSwdTDqTy0Tja2WLJ5CT6ePj27z1EXMCCICYzheNFxl4ydWZbJVf2vsurYffn7UCqU3PPDPQAkDkvs/kX69jSvzf1K/jf2ZiRJ4siRI8yYMYN169ZZfdl//vOf7N69m0cffZRRo0Yxfvz4lp19pkPopXBkKfSbJ2fez3cqT0LVnzDoYeP73bzk5vRtscLuPsQnhOI652SwrJWxFdcWIyGJDJYN6L5n5n83nyZtE3GBcU51Efw542caNY1cd8F1tp+saYT9T0LgELhkOfhGw5EX4Np0OevajOiF1YLO4MIZAVaITwjBquBuFWCZzGC9/fbb7NmzB7VazUMPPWT2P0Hn0loiCI41Gy6sKSTMN6ylD46F4my9S1YbTG3X0a9fP07lNt+AbKnD0mpkYwtVHxi+GDAR5DVC8qKXrR+3m+Ln6YeH0sNpEkGd/EW3+pZXlWdS/tJjGJkiW/sboOxQAwJds2Fny0rq1fXkVeXRL8hyBiv1cCpJa5PQtsr8fnvy2+7/3tsjAc1dJRvm+MWze/dusrOzufXWW226rFKp5PPPPyc+Pp6bbrqJ/Pz8lp0KBYx4AeryIGO5TeP2WPJ+ACD1d43xhs0AtSbqFC1km0N9Qp22CGVKrqZQKPj3zn/ToG4g9XAqF35wIQCLNy/u/p+hDiRxeCJX9ruSMX3G2J5hssCak2sIVgUzPna85YPbkv6OvBg8+l+gdAefGECCurMGh+kCrHM1wuhCF2A50mS4NYNCBnUrq3aTAZaHhwf/+c9/uPzyy7nnnnv45JNPOHHiREfOTWAlOpMLZ7glFda20YzrirN1fypt0uExMTFGxzHVZFhHv379yD2dR5PkLTcbtoRO4vOlO5Ttg+gbwMMfMBPk5Xe8LW9Ho1AoCPFxXrNha+UvPYrg0bIttLs/oADPYEALCqsU1E4hOiCa6sZqh+S9xtAVBFsjEeyx772tEtCqU3L7h9hbAFi1ahUeHh5cd53tq95BQUF8++23VFZWMmnSJOLi4loCh7R8iJgCR5fJDdvPd858T+r+GJIefoacnBwkSdL3HNMHWaayyhayzSGqEKfVYBmTsXm7eXNByAX87ae/EfV6FAvWLCC/Wg6oi2uLe/4ilZNxhWRarVXzY/qPzBw4E3el1S1gZRpK4PDzcm2grjWNT/OzT5ugX5fxFxmslh5Y/Xs5ZtGuY1DIoJ6Rwbr33nv59NNP2bx5M/Pnz6ekpITk5GTGjx/Po48+yqpVqzh92jU1AwLb0Nm0t5YI2ktRTVH7HlixNwFaGP68XKTdHFxJksTgwYPbjdG2ANwY/fv3R6vVklsXY9mq3cDlrZnMT/S6e1PBXGyY8xvvdkWCVcFOy2C52sWpS3JgIXgEwnVZsnPZjYUQfBHsexwanRvwmCImQP6ydraToLUW7dCD33tbH8r18sCb0Gq1rFq1imnTphEUFGTX5YcNG8aCBQtIT08nNzfXMHBInwD15+y2IHc6lmrVHN1vivpiKP6d5NRKk5JzwISiQmUx2xzqE+q0e2Ti8ESen9zScDsuMI4Pr/uQow8c5Zc7fqGqQW78bvAaesJCRQcSExBDYU0hDWrj/eXs4ffc3ymtK7VPHnj4eVBXytkrmo29LlmAMhHiR19vkGUVEsEWMkoz6O3XG38vf6eMNzB4IGcqz1DT2D0WpCwu0Xp5eXHZZZfxxBNPsHr1atatW8eMGTM4duwYd999N1dccUVHzFNghkZNo1Mlgu0047oUuE+0webFixfzyy+/MH36dGJjY80WgLdF1wvrVHmIZYmgBYnPM8880+4UH293Um5qAnWd+bF7ACEq52Swvjn+DRLGJWoOuTh1Yo8pixRukaVJQxaCl2wYgtINLn5PfvA9vKhDptG62bAzsSXAcrWDV6dhVAKKXANljNxVEHIJ+Maxc+dOTp8+bbM8sC0//PBDu221tbUkv/SpXI91/BVoqnToGg5jqV2Bo/vNcXYdSFpyC4z/DvQqBWN296HjLdrdOzODBZAQlgDAtvnb9DI2hULBVf2voklrvE9Pt1+o6EB090Ndw2FnsObkGjzdPJk2wMaWQ5UnIf1d6J8EQUNbjL3O5CMBOXnFBlnWUJ9QFChELyxkB0Fn1F/p0Bld6KSHXR2bNTAZGRlMmTKFxYsX8/PPP5t1ixN0DE1a2eTCx8MHHw8fh75IimqNZLB0KXCfKP2mN998k+eff5758+ezbt06cnJy0Gq1ZGdnGw+uUlMhPh6USoiPp9+BAwBklvjIAZa52hMLEp+TJ08C0KdPH0CWzS1/+SESx0tQdsDSS+72hPiEOFxf8PWxr7nlq1sY0GsAKneVwT6HXJw6uceUWSQJ9j8Fqii44G+G+0IugoH3w5//6ZC/IVc1G84sy8THw8eqQntXO3h1GrE3Ae7g7oe+eW3IOMj9UpbntaYqA8r2G8gDvby8uPbaax2agtla1RFLZQnSibccuobDmFrI2vMgwYX/hb2PGN+/71E567fvUfvbHeT9AKo+xFojOdfZ3c/RwoB7oWgz1FiuwaptqqVeXW95LlZwqlRWXRh7eOyxCxUdiP5+6KQFJ0mSWHNyDVP7TrW94e3+J+Us6YglgHljL5BdK0N8QkQGCzkQcoZFu47u5iRoc4B1zz33cO5cS2Sue6gVdB46iSDIXyT2ZrDq1fVUNlS2fxirbV5FUskB1ieffMJjjz3GjTfeyPLly1EoFOYHTk2FpCTIyZEfanNyiFy4EC8PDzKLlPKXcH2B6fPNSHwyMzP5z3/+w4IFCzh79iwffvghkiQxdspN8jHnQT+sYG/HJIKrjq7itq9v45LoS9h7715WXLuCuMA4+/qEtKWTe0yZ5fRquSH1iOehTVAJyFbaniGw636XW/5H+keiVCgtSgStbXCqQ2fRbvEziix9Wn7Ncue9912Fc7+B1ADjv2xpXnvVVrnR78FkOLykZYGnjTzwq6++YsaMGQQEBJgc3hpMyphjY+VgPvo6OPEaNDiYiXYkW2xqIaupgoi8V4w7+AE0FMO2W+R/bRlXh6YB8n+CqGtIWbYMd/f29TF33XWX8XOHPgMo2gfKbdD3C3SS0cWpslP4evgaXbjosQsVHYhOMu2sBadjRcfILMu0XR5YkCYH/8OSwVt+r60x9gr3Daew9vwOsKobq8mvzndqBks3Vo8NsETztK6HzuQCIMwnzO4aLN15bb80Ur/6nvi/gTJ4OGFhYSxYsICrrrqKL774wuiXYTuSk6HNio+yro6+ksSp/Obt5owuRqbQ7k+12clw4cKFeHh4sHTpUgAuvvhiAHYdypYljSU7Lc+vI3GBXM4Rk4svj3zJnNVzuDTmUn5K/IkArwAShyeS/Wi2fX1C2tIFekwZRdsk91ILHAp95xk/xrMXjH4NSna4vF+Ru9KdPn59zD5Q2NLgVIctPbAA5773XYW87+X7RUQrObvSHcb9F/rdBYcXw5Yb4Ls4+W9C6QmFW9m+fTt5eXnccsstDk8hJSUFHx/Dh26VStVSqzr8eVki+H1f++8N9maLJQkyPzO93yeWkyN26hfY2uHdB64+Iju7mjjfLOd+A3U1RF3LnDlz8PPzQ6VSoVAoiIqKIjw8nDfeeIM9e/a0P9c3FvrfDac+gupsk5dwhgFUazJKM+gf3N/owkWPXajoQHQZLGfVpK45uQaAay64xvLBrb+jf50BnqEGCgdrjL3CfcPPe4mgLsvrzAyWn6cfUf5R3cZJsONssgQuQ2fTDhDmG2b3l4gu86Wr5YLmRsLPfUVOsRxcFxcXo1Qque222/Dy8rJuYBMrPv3UajJPl8k/mKvD6jUK0DZ3UFfonQz/KOjHV199xVNPPUVkZCQAQ4YMwcfHh127dsk2y10pg+UiuVywKph6dX07BzhjtM6AhL0Sxu2rb2d87HjWJ653WiGqAT7Gv4yc3mPK1sA1YwVUpcOol0HpZtiourU1dN87IWyCLCWsd14NhzGiA6LNSmJsdfmTJEkOsKywaO+xSJIcYPWZ1j5LqXSDSz6SG9vmrWkJ+rWNsCuJ/614Hm9vb2bNmuXwNBITE1m+fDlxcXH6h/LLLrusRU5dfliuE2uqxO57gz3Z4vpi2HYz7JgH/oNkKVRr3Hxg5DK0bv7yZ8VYy47Rr0LQUBj1avv9CnfL7Q5aBcB79+6lvLyc9957D61Wy5kzZ9izZw/BwcH85S9/4UCztNyAoU/Lv7ujL5i8hK4hu7OMLk6VnTK7Mt8jFyo6EF9PX3p593KaRHDNyTWMjRpLpH+k+QPbfkdLTbK5Re5q/SE333xzu9PaGntF+Eac9xLB9FLZQdCZGazUw6kU1xbz2cHPrFJwdDY2B1j33XcfgYGBrpiLwE6aNC0BliMSQd0NoXUGKzk5mdp6tcFxWq2W559/HqsxIY/p7+9PZk6erM4x5yR4aoXcyO+aP5slPtlI8XN4/PHH6dOnD0888YT+UHd3d8aMGcPu3bvlQvXqU3J9Q1fARXI5/cODBflL2wxIcV0xSoWSeSPn2a5Lt5aAhPbbWvVRcwq2Bq5NVXBkCYRPgsirW4qWjVlDKxRw8bvQVAEHn3benI0QExhjdsXWVpe/otoiappqbMpg9TjK9ss1pNEmaqgUSqP3Hk1TLV//sImZM2fi7++chYfExESys7PRarU8+OCD/Pbbby2tTw4my60CDCZh473BZLY4p0Uh0HohYnUEfN9fDnBGvQwzj8LYFS0GEm1acrQzmLC0390fJDUEtnea1SNJsgSrz1/AXcWaNWtQKpUGQW1MTAybNm3Cz8+Pq666iiNHjhiO4RMt12Jl/tekEsKZGSyNVkNmWabTrKcFxokJjHGKRPBs1Vl25e2yTh5o7Dta26j/HKrVatatW0fv3r3x9pabg8eFwvL33zWoPQ/3DT/vAyxnNhmGlueXBo3sLGmNgqOzsSnA2rt3L6GhoSiVSv78808aGxstn2QDFRUVPPHEE4wdO5YJEybw2muvodHIXzp5eXksWLCAUaNGMWPGDDZv3mxw7o4dO7jmmmsYOXIkd955Jzk5OQb7P//8cyZOnMjo0aN5+umnDYoUGxsbefbZZ7n44osZP348K1ascOrrcjXOlgi2Nrmwt5GwAffd136bjw/9rruOyspKSjRRpgMsTT1kfS73vfJumddXX33Fjh07eOGFF/D19TU4ZezYsezbt4+mALnRIyW7rZ+rK3GGXM5IpkZXX2BJJmgsA6KVtDy/2YZg2Rayv4SCn6H39OaHrmaG/tOi65dN2Bq4Hn8N6gth1CugUFgsWiZoGAx+DE59CN/0dpkboqVmw7qHxLaYKp63xUGwx3Lme0ABkTNNH1Pb/iFu20koKNM4RR5ojEWLFuHj48PChQub5+CEe4O5rPAPA+CHQbBzQctCREMhqKtg2BIY8pSc0WttINGqJYcem/afBu8I2P2g6RrG8oPy7z9Klm599913TJgwgZCQEMNh+/Zl06ZNeHh4cNlllxEVFWWYbR66UF6EM5HFcmYNVl5VHo2aRhFguZiYAPMLTtay9s+1AFx7gRVGNRY+hx999BHHjx/n/fff56677iI4yJfstyDxhokGh4f7hlPRUKEPBs5H0kvSifCNcJoypjv2abQqwCopKeHmm2/mr3/9K8899xxlZWW88cYbzJgxo10g4whLlizh3LlzrFy5kldffZXvvvuOTz75BEmSeOCBBwgKCuLrr7/mhhtu4JFHHtH34crPz+f+++/n2muvZfXq1YSGhvLAAw+g1co39V9++YU333yTRYsW8dlnn3H48GFeeukl/XVfeeUV9u/fzyeffMKSJUt47733+PHHH532ulxNk7bF5CLMJ4yaphrqmmy3JzeWwTJbnG0tpaVyJkCX+YyKguXL6decas+s6m1aInj6G7m4esA9+k0NDQ0sXLiQESNGMG9e+/qZiy++mIaGBg6f9QIUXacOy6RZR7Tx7W0xkakJLt8LWJa/dGifo4pjsOtuCBsPk7+XH7puPAcoQHLuwozZlXtdA9fmwHTw/qFwZCkEj4XQsYCViwj+CYBCtm43lSVzsL4uOiCa2qZayurL2u0rrSulQdOAQmdN3Yy54nkRYCFnZ8IuI3X1L8YloGD0c/m/HeDjpWDmTDOBmQOEhYWxcOFC1qxZw9atW+1uoGvAsH+23+bmA2P+DaNeguoseTXeAAkyPrD+GrbgGdhcw7jTdA1jqwA4IyODI0eOcP311xs9dMCAATz22GNUVVVx9uxZw2zzN5tgwP2Q9RlUprc7V5fld0YGy5yDoMB5OKvZ8JqTa+jXqx9Dw4ZaPtjM57CqqornnnuOiRMncu211xITE0NpeQ21DbRvNuwrNxsuddS0phuTUZbBwBDn1V91xz6NVgVYS5cuJTIykh07duDpKWdKXn31VS644AJeeMG07tlWNm/ezLx58xg0aBDjxo1j1qxZ7Nixgx07dpCVlcXzzz/PgAEDSEpKYvTo0Xz99deAbKU7ePBg7rnnHgYMGMCyZcvIz89nx44dAHz66afccccdTJ06leHDh7N48WK+/fZbampqqK2tZdWqVTzzzDMMGzaMK6+8krvvvpuVK1c67XW5Gl0fLHBMClFYU4inmycBXi2OWSkpL+DTpl+vNY2E9Wg0sovgrFmwulnH/OmnkJhI//7yCmBmWYDpDFbGCvDta1Cg/vbbb5OVlcVrr72Gm1v7/jZjx8oPzrv2HYXAIV2nDqutFbgOCfMmHzoOPmM0UxOSLT+8WFqd7TD74KYq2Dob3H1h/Cp5ZRlkF6aw8XDmO+ddqzZPNiwwxbeRsGmaHOzV5qBAAiSoOGS5UXXr7Ueel89rjaZWbkZcl++U+jpzzYYf+/kxaptqWTplKXGBcjbQQ+nB8lmmi+d1AVZ8ULzVc+hR1JyGsv2k7o8xLQGFds1r1RpYvRtmXTm2XXbcmTz66KNERUXxxBNPII14wXh9ky1SWl0fN+8IDCR8FzwMQ/7RXoKow5WGM/GJcg3jgX8Yd0jM+x5Cx4EqgjVrZCOC664zLeV65532DZn12eYh/wClV/Nn1RAPNw8CvAKcUoOlkz71DxYZLFcSExBDcW2xXYvFOqobq0nLTOO6C66zyknVeCNr+XP4yiuvUFhYyGuvvYZCodCbXZwuoV0WXLdIXVp//gZY6SXO7YHVHdsfWBVg/fHHHzz88MN6zSmAn58ff//739m3b5/TJhMUFMT3339PXV0d586dY+vWrQwdOpSDBw8yZMgQ/Pxa6kTGjBmjL3g9ePCg3j0OZHemoUOHsn//fjQaDYcPHzbYP2rUKDQaDcePH+fEiRM0NjYyZswYg7EPHz6MWm1Ye9QVkSQJtVbdIhF0oNmwrgdW6xtR4o1XsfxucFPK26xtJKxn0yY4exbmzoWhzStIR48CsuwD4FShuyzZaqoyPLcyHQp/gwF3y7USyNnUF154gRkzZnDVVVcZvWR8fDyhoaEtdVglu8z32eoo1M1NNFVR6B+AEp4ETTX8NAby1hrPgqhrIP19kw9CIU35gGWJYIfYB0sS7Lwbqv6E8f8DnzZFxdHXy32lzDh+WU3ZAfj5EpCU8oNVa9x8YNhzsvSo4BdZatoaTb1eQvjss88aHf7hhx9u+cHUQ2hDoRzE/THXskzRQobLVLPhH//8kc8OfsbC8QtJnphM9qPZfDDrA5q0Tfq+IMbILMsk0j8SlYcRC/qeTlYqrB8JQPJrX5mXgLapHdqcFU5hBdwy/0mXTtHHx4elS5eya9cuvtrj2WoOgMLDsL7JGvJ/kuuerj9tXMLnjCyZrSgUcNHb0FjeXrJbmweleyFKlm599913jBw5Uv+9YAyz2WZVBAx6CHK+gIoT7Y5xVrPhU2Wn8FB66BdEug1dueG7EZzRbPjnjJ9p0DRYb8+uuxfolALNixRnPCbxr3/9izlz5uifJfUBVikmA6xiU60Lejg1jTXkV+c71UGwO7Y/sCrAUiqV1NW1X0UoKiqy3knOChYtWsSuXbu48MILmThxIqGhoTz88MMUFRURHm5oHR4SEkJBQYF+Hsb2nzt3jsrKShoaGgz2u7u7ExQUREFBAUVFRQQGBhq8jtDQUJqamigt7fqrD2qtHAS2lggCdtVhFdYUtu/rUXeGW8eBhIJnn33WdCNhU3z2mSwNnDULIiIgOFgfYPn4+NC7d28yzzUHsm1lgqc+lN2h+s3Xu7yFhoZSXl7OpEmTTF5SoVBw8cUXtzgJNhRDTZb1c3YFkgRZKyFiCtxwpuUBaPQrMH0v+PWHzdfAjrsMsyA77oKvw2D3/S2ZoDYE+8kPSJZWZ3X2we7NGR+X2AeffAtyV8HIFyFicvv90c1fdGfWOHadvLWw4XI58J6+S3aDa1t8P2IJXLYSMLFy2Rw0ZWdnA9C7d28UCgXR0dGoVCrWrl3bUg9l6iHUOwLGvAWYqDGpzYFtt8LWm2HnX81muIxZE1fUV3Dv2nsZFj6Mf05skYDdOvRWVO4qPjlg2j4+syyTvkGmH1Z7LLpsYqMstcwtMp65MXhYb1U7tCrvenx9fbn66qtdPtW5c+cyfPhwnn76aRoib2q+J7wmu5eFX279QJIEZ9dD7ytN3ifMrc7rMOmm6Qi9RsCgh2UpYunelu15cm0M0ddSWFjI9u3bTcoDdVjMNic0N4U1ksUK9Ql1SgbrVNkp+vbqi5uyvXqiy9KVG76bwBnNhr//83uCVcGMjx1v/Umh4wBJ/g5pXqR49tln0Wg0BsodfYBVrpKz5a2I8GuWCJ6nGSxnG1xA92x/YEUTI5g1axYvvPACS5YsQaFQUF1dze+//87zzz/v1C+h3NxchgwZwoMPPkh1dTVLly7l5Zdfpq6uDg8Pwy8NT09PmpqaAKirq9NLF1vvb2xspL6+Xv+zsf1qtdroPsCoicfx48cde5FOor6+nuPHj1Orlldmy0vKOX78OBWVFQAczDhIbJNtK5O5JbkEeAQYvEa/ih0oymXnQDc3N5tev6KmhkGrV1MxaxYFWXKAExcfD3v2kNM8TmRkJEcz5H4RZ05soSqo+b3QNjIw/SPqAibz/tsree655/TvJcDixYvbuU21pm/fvvz0008cyvdlBJB36Fsqe7n+gQla3pvWeNccpG91BmeD51Fh5HeoiP2IgRWX4aZts5AhqdHiTu7AVDwaztDn9CKUUsvvQavwpiTiIVTHFpF+Jt3i+zNUMRStpOWBIQ/w0LCHAMf/pgNK1xJ29g08mjNp9aohZDMLTIzb13sAmj+/IFf6ix3XKEDj5o+bppJ61VDO9HsXdYEHcCFcsL7lhHr01+/v0RvP5rm1ptGjN1vT0nj11VeZOXMmr776qn7fqlWrWLx4McuWLePGG28kIOxB+uQ+1+53nx/xdyq1V9Hfo4/Ra2gVXqgL/sCz0chDgqaWxj1PcqpeNmPRaDW4KdzYn7mf477y3J/d/SwF1QW8cckbZKYbLkBcGXUlKw+u5J7Ye/B29243/Mmik4wNG9tl7lmWMPa5sYf+R57Es1U2MTYUcowsJIeEhLS7XlNTE6tWrWLy5Mn6wNvVPPzwwyQlJbF48WLmzp2LZ30C/YGCvR9SFjbHqjE869LpX3ua/JC7KTf5O7yQgOjF+s9Rk0dviiIfo7L+Qjh+nLVr1xrcZ3Nycrj77rs5e/YsV155Jcebj3njjTcoKCigd+/ePPbYYwb3YFP7lV6309/9c5q2zCd70P+BQknMqS/w9IzhVB6s/mY5Wq2W4cOHm/0bePDBB9t9F3h4ePDggw/qz4vyuRj/nP+DnC9bXmPwLLy0XpwpOePw39iRs0eI8I7o8M9V63tg69dlzeem7WcCaHf/cWQOrqChSjaI2HliJ73re9t8vlqrZs3xNUzqM4n0k+3r8kzhX7aeaCCrIpj648c5fvw4n376KfPnz6eurk7/u9Y9H2aVqqgqPM6ZVu/B15ly+Ury7mTePvo2jw1/jFlxrvk9dUV+O/MbAG4Vtj0zWuJC9wtZP229wTZ7x3fW941ZJCtoaGiQXnzxRWnYsGHSBRdcIF1wwQXS0KFDpUWLFkn19fXWDGGRnJwcKSEhQcrPz9dv++OPP6SEhATpueeekx5++GGD41NTU6UZM2ZIkiRJV199tfT5558b7P/b3/4mLVq0SCopKZEGDRoknTx50mD/pZdeKv3444/SunXrpLFjxxrsy8jIkAYNGiQVFxcbbN+zZ4/Dr9NZHDt2TJIkSSqrK5NYjPT69tclSZKkktoSicVIb/zxhs1jxr8ZL93xzR2GG/98V/pjsVy4snbtWtsG/OwzSQJJ2rq1Zdu990pSr16SpNVKkiRJd955pxQTEyVJqUjSsVdbjsv5St525kcpLi6uuXDG8L+4uDiTl/7xxx8lQPpt00ZJ+lIlSXses23uDqB7bwzY9aAkfektSQ3lpk9MVcivud1/ipZjMldK0lfB8vZvouSfJUmKfj1auuu7uyzO7VDBIYnFSP93+P9sfVnGyVwpSV/6GM73S5V+XpIkSStXrpTi4uIkhUIhxcXFSSuXXSdJXyglqb7Y9LiWrvGFmyRlfOTAHH0kKXOlNGfOHMnb21vKyckxOEWj0Ujjx4+XgoODpcLCwpZxvo2T349v4wxeo7lrSJJk3Xsrye/j3G/nSpIkST+l/ySxGGnhhoVGX1ZaZprEYqQvDn3Rbl99U72kWKyQFv26yLrfURfA6OfGHtr8rlc+gKRUGN47FAqF5OHhIX377bcGp/78888S0G67q7nqqquk4OBgqaysTN7w/UBJ2jTd+gGOvSq/1o/eMvysrVxp+dxmTN1nfXx8pL/+9a/SXXfdJXl5ebXbp7vGypUrJR8fH5P7pczP5fckfYUkNVZJ0v95SdKeRyVJkqRrrrlGio2NlbTN3wvmaH0/8fLykoKCgqS6urrma6yU7z9GPod3fHOH1PfNvtb/To2g1Wol/2X+0sPrHrZ8sDMxc3+x6nNj5f3H3jm4gtrGWonFSClbUuw6/7es3yQWI3199GvbTtz3lCT9n6ckqRskrVYrTZ061fCz2Yrw8HDp7qujJWndhfptKw+tlHxSfCQWo//PJ8VHWnnINb+nrsiLW1+UWIxUWV/Z2VMxidO+byTTsYFVEkFPT08WLlzI7t27+eGHH/juu+/YuXMnixcvdppE8MiRI/j6+tK7d8tKxbBhw9BoNISFhVFUZCh5Ky4uJixMlsNFRESY3B8UFISXlxfFxS1LmGq1mvLycsLDw4mIiKCystIgW1VUVISnp2e36PfVqJHnrZMIBnkH4aZws1si2NqiHYDaM+SVy38m0dFWut3p+Owz6NsXxrdKzw8ZAmVl0Czv7NevH2fOnKVBEWRo9JCxQm5S22eaXVbxOp307r37IfhCKO1EowtNI+R+KdcaeJr5m7KmRqJvoiyHA5i0Rl9jEaIKscqC+ESxXJuQEGqkP5U9GLVIr9PXWxjtMbX0Z1K3aSHPSqdOY9eQNHDYSnv5VjU2UisJ4c7CAXzxxRc8/vjj7aRHSqWS5cuXU1VVxeOPP94yjimLaks9gqysf9FZE1c2VHLPD/eQEJrAosmLjJ46OX4y8UHxfHzg43b7dL3OOsVBsLNrPdr8TqePAK0EAT4KFAoFcXFxvP3224wePZobb7yRl19+WS8FXbVqFf7+/kyfPr1Dp/zqq69SWlpKXFycLM9LKiB19QZoqrZugLPrSd0XRdLDT5s28zBDRUWFSUfg2tpaPvvsM/773//S0NDQbt/cuXPp378/8+fPN1/rpjO82PsorIkDbQPkfEH1kY/45ZdfuP76660yImjdT2z9+vWUl5ezfPlyeefBZPn+05rmWkhn1GAV1xZT1VjV8RbtjvZQdEb9nYv6OJpC5aEiRBVit0Rwzck1eLp5Mm3ANNtOLN0DQSPAzZOffvqJtLQ0Fi1aRFBQULtDY2Ji2tVgdUc7cWeTUZrhVIv27orJAOuLL77Q96DS4e3tzcCBAxk8eHA7d6WmpiaHnPfCw8OprKwkP79FZnPqlPzA3a9fP06cOGFw8967dy+jRo0CYOTIkQZmG3V1dRw7doxRo0ahVCoZPnw4e/e2aL8PHDiAm5sbCQkJJCQk4OHhwf79+w3GHjp0KO7uVikoO5UmjSyT1JlcKBVKQnxs/yKpaayhtqm2fQ1WbR5nKuUPSVRUlPUD5uVBWhrceadc6KxDZ3Rx7BgA/fv3R5IksmuiW2qwqrOhYAP0WwBKN7us4sPCwoiPj5frsILHytp/bZP183cm+T/LzY773mn+OCtqJIBmlzCg7px+U4hPiEWTC5ADLAUK59mnWugbYrTHVF09yV+5We8maEWPIIu1I83B0YnRRw0aVUdERLT0ImrDkCFDWLhwIStXrmTDhg2W52kuALOm/uVwKgfPHWRT1iaiXo/idOVpPr7uY6PyP5A/63eNvIu0zDRyyg0fjjvNor0r1HqMWGrw49rmW3vaF0vQarVkZ2fzwAMP8Ntvv3HrrbeycOFCJk6cSGxsLB999BFarZbVOsfTDuLIkSO4ublRWVkpB0cFVSSt0JD6/nOWT26qhqKtJH9RZT7AMcH69esZNmyYyf1xcXHs37/fZPCj1Wq57LLL9JL9tugXwhQKiLwaNDVy6w2A+kJ++eQBGhoaLNZfGWPKlClMnjyZF198Ua4TN3OvCPUJpaqxSr8oaQ92Owg6uujgaJ+0kSlGjIBUtrlUOqNXm43Y02w49XAqcW/G8caON1AqlKw5aUO9rySRuuYP4uefQKlUcu211xIeHs59xnp50hxgFTVAQ5HeSKk72ok7m/RSOx0EO3txzsmYDLCKi4uZOXMm77//PhkZGSYHOHXqFP/+97+ZMWOGQZbIVkaNGkVCQgJPP/00J06c4MCBAzz77LNcd911TJs2jcjISBYuXEh6ejrLly/n4MGD3NzcR2n27NkcPHiQ9957j4yMDJKTk4mMjOTSSy8FYM6cOXz88cf88ssvHD58mCVLljB79mx8fX1RqVRcf/31LFmyhEOHDpGWlsbHH3/M3Llz7X4tHUlTc9Dg0aqwOcwnzGYXQd3x7U0u8jhT4YOXl1e75o9mSU2VC6/vuMNwexsnwX795AfAzPLgFqv2U80Zmv4LAEhJSWn35W6NVfzYsWNbjC409VB+xPr5O5Psz8ErFPpMMx8IWMqC6FA1Z3nrWwKsYFWwVQXcx4uPExcU186Nx24srIyazD4Wa2TXM3Wt0f2GY5lw62q+htEsmYWV+6+++ort27fzwgsv4O9vepXtmWeeYdCgQdx3333tHl5tQv/eNr8Wj0CD91bXpV638lndWI270p1TZebt++8adRcAnx781GB7pwVYHbzKbRTf5r9Jr1BAwZoDKqJ692LMtYZ9olQqFV988QWzZ89m27Zt+r6KNTU1Vmd+nEVycnK7Bc3aRkhettzyyec2gbaJ3IIqo7tbfwZb339iYmKYPHkyV199NQEBASxZsgQfnzYuXc33WaVSaXJBKy4ujs8//5y4uDij+w3OS3+/3f7vdjXSy0/JhAkTLL1SoyxZsoSCggLef/99s/cjXS8saxaiTKH7PNr08OiMRQdHM1B9E6G3znW3+bs0/k7bXCpNzsF1boq65uvWoruP6oKZenU9ST8kkXrYut916sdvkvRBHTkF1bJDs1pNRUUFX331lfH5xcRw+lzz565Wdjvsjnbiziaj1I4eWF1hcc7JmAywHnnkET744APOnj3LzTffzIQJE7jrrrt47LHH+Nvf/sbcuXMZN24cs2fPpqioiBUrVvDoo4/aPRF3d3c++OADAgMDmTdvHg899BBjx47l+eefx83NjXfffZfS0lJuvPFG1qxZw9tvv62XrEVHR/Of//yHNWvWMHv2bIqLi3n33XdRKuWXN3PmTO6//34WL17M/PnzGTZsmMGq9dNPP83w4cOZN28eixYt4sEHH+wQBylnoFuN02WwQLZqtznAapYUGpUIVrgTFRVlXR8JkAOrzz+HSy+FgW0+ZBER0KuXPsDS98Iq8YaaHDkQyvwY+kzXPyj169cPSZIIDg7WS3yssYofO3YsOTk5FErNq42d0Q+rsUJuphl3G6n/t8pyIGAuC6JDl8GqL9BvskUi6DR5IMgroIo2blqtMjOtJb+tiY0Ml6U8BRstX0P/YGD8GkazZGZW7uvr6/nHP/7BiBEjmD9/vtlLe3t788EHH5CZmcnzz1spSTRF30S4Phd84yBypsF7a0xWotaqLcpK4oLiuKLvFfz3wH/RSi1OhpllmXi7e9Pbz/bicIfohFXuduT9AEpPuDaTuhtq+PmwgmtvuM3o/UuhULBnz552263J/DgTkwsR52qg1ftqlLPrwd2P2BjjD7qSJDFq1Chuu+027rnnHv3958yZM2zevJlrr72WvXv38txzz7F8+XLi4uKM3mdTUlJMBmCm9isUCv75z1aBbZu/A7VGzjDOGqW1WzEyceJErrzySl566SVqBj5rMlPsSI9IHadKT6FAYZs7pzMWHSzcZ62iJkvuJ3m7Rg6W6tub8licg7FHRs/Q9i1WnER0QLTRvoCmcFSel7xoGbVtEpwNDQ0m7wUxMTFUVddTUYteJtgd7cSdSU1jDWerzjKgl40ZrK6wOOdkzNZgxcXF8fzzz7Nt2zaWLl3KRRddRFBQECEhIYwbN46XX36ZHTt2sHTpUrO9K6wlIiKCt956i507d7Jt2zb++c9/6ntvxcXFsXLlSg4fPsyPP/7I5ZcbWthOmjSJn376iYMHD/LZZ5+1W21LSkpi+/bt7NmzhxdffNGgp5dKpeLll19m//79bNu2jQULFjj8WjoKnURQV4MFsh2trV8ihTWFgJEMVm0eZ0o0ttVfHTwIR47I8sC2KBRyFqtZIhgREYFKpeLUOQkkNWQsh7qzMCBJf8qKFSvw8/PT6+6ttYrXNRzeffScvJpdstP61+AsTn8t1xrE32FzIGASdx9w9zPIYIWoZImgZKbfl1bScrLkJINDB9t2PXPE3QZKVfNDjWHWrbS0FLVa3e7BVqVSkfLiK3IWx5JMUF0L+evBb0DzCmr7zJ6tNXpvvfUW2dnZvP7660YbVbdl8uTJLFiwgFdeeYXIyEjHLaz9B8l9wlrP1QFZyYLRC8gqz2Jz9mb9Np1Fu1JhVZmt8+iMXkutkSS5BUDEFeDhT1paGrW1tWab19pT4+lsTMqgQ4BSM70mJUn+fPSeSsqyZUY/a3feeSfe3t7873//M9pu5eDBg/rvw9b1TW3vs4mJiWYDsLb7e/fujSRJBvL8tn8HW09AWQ1cf1mbhT0bWbJkCYWFhby7rrQ5U9x8nVaZ4hAfOYNlzUKUKTLKMogJjMHL3Ybac2csOsRcD7jJ932Q5X229EmryYGKo/LCjkIBMTdC/i+2BUZhlwJa8AhCfx/uOw8qDsotM2qM1/A5QkxADKV1pe2CJlM4Ks/LPWv8ucnUvUD3XCQ3G5YDQZ2deKCXXG8dGxDb5e3EnYkuy2tzBqsrLM45Gau+fX19fZk8eTIPPfQQixYt4rnnnuOBBx5g0qRJBoGKoOPRm1y0lQjaaHKhC7B0jYoBaKoEdRV5xfW2BViffQYeHnDrrcb3Dx0qZ7AkCYVCQb9+/cjMb76BHnkBvHtD1ExALr7+8ssvuf32281KuYxx4YUXolQq2bV7tywT7IwMVtZK8B8IIWOd8iCnl/jcVk38zBX6h/xgVTAaSUNlQ6XJc89UnqG2qda5AVbx73Kj5HGfGGTdNBoNt99+O+Xl5SxatEj/0AVw1113kXjnPPnLPu8H0BrvUwTAn/+RA+5xH8H1OUYze7169TJ6qpubG59//jlqtVr/exs6dChPP/00o0ePZurUqVa/zEsuuQRJksjPzzeZfbS6h5AuwGoVDDsiK7lh8A0EegUa9MTKLMvsHIMLYyvtABf8rWOuX3kSqjMgWm5eu2bNGgICApgyZYrJU+yp8XQ2xrNDKlJuQf6MmKLypPxg22c6I0aMQJIkQkJC9AHQihUr+Oyzz9ixY4dJBYIt9x9zAVjb/fn5+Tz++OO8//77/Pbbb/IBbWoRv9sL3h4wbe6LVs/BGJdddhnTp0/n5Zdfpir0Wvle4R0Bsbfo7xXOymDZbHDhjEWHM9+D1AiT1sLgx+XFyCgbbL91hkLN36vE3Cgv/J1db/qctmR/If979cGW+/Cl/4XJ66AmG34eC4cWO7WGxlTzdVM4Ks+LDTMeOJu6F+h7YZVgYHSRODyR1/7yGgCb528+b4IrgPQS2RLf5hoslYka/45anHMBHby8KXA2uhosA4mgTxildaVozD24tsFoDVZtHpIEeeeqrDe4UKvhiy/kxsLBwcaP0TkJnpMzMP379yczs9ngoqFIzlrkrAJks5W6ujqSkpKMj2UGX19fhg4d2lyHdQlUHHOZlMEoNblQ+BvE3wHNDWyNERkZadVwhrVGkHOuVv+Qr1+dNVOHdbxI7vng1ADr9Ldy8XTkDIPN//znP/nll1945513WLRoEdnZ2ajVaiIjI/UNwom5Xm4CXbzd+NiNZXD0JbkwPnyi0UNefPFFSktL22WiPD096dOnD3PnziUqKooFCxbopVGSJHH8+HGbMlDLli1rt622tpaHH36YdevW8a9//cui/FMfgF3yDvEPVJL6aUs9iiOyEpWHituH3c7Xx76mor4CSZLIKs/qnAAr9mZaVtoVoIoEN184tUKWy7qavO/lf6OuQaPR8P333zNjxox2vQ5bY0n61hG0zv6ALJlfvnwFidePb2nIa4z85gfkyBn89NNPgGziZCwA6oxAcunSpfTv35+7775bzt63dvSU4Lt9blw1cTS+Q//q8LWWLFlCSUkJb7/9trzBuzfUtcjgdDVYjjQbzijNsD3AMmZwo/S2Td6X/QX4REP4BIiZLRs2mfu7aMvZH+Vm9v6D5J9DLwPvcDj9jXXnSxJkp8oukL5t/l76/AX+skM+5sgSp9bQGGu+bo6UqSmo3FUG26yW50kSKbcq8fE2lKqauxfoA6wKH4MACyDSX/5ez6+yUYrZzbGrybCkBU8jz4u2ymC7GCLA6uaYkghKSFYX86YeTuWFLS8AMPTdoS0FoXV5lFRDQ2OT9RmsDRvkwMmYPFBHW6OLkEYyc/JbFvTVlbArCSlzJcuXL2f06NGMGTPGuuu3QWd0IQVfDEiyBWtHoVvx6ysbfVxzzTVGD6uqqjJwwTTFM888Y1JiGKySb07m3nOnW7RLEpz5FnpfCR4t2cWvvvqKl156iaSkJO655x79dqVSyY033sj69euprq6W6+yUnqZlgkdfgqYKGNl+dVuSJP7xj3/wzDPP6E1sWkuXPv74Y7Kzs/nuu+8oKytr1zS8vr7eJmmmqVX+srIyZs6cyRNPPGH0vXnyySdpbGxsHxwXQ9IDj+kDMEe71M8fPZ86dR2rjq6itK6UyobKzgmwiv+QV9ovWymvct+QB5N/gKp0+P1289lKZ5D3PfS6kNRvNxMVFUVhYSFpaWlmg2lL0reOQpf9ee2111Cr1bLpQ+QsKNunL6Bvx9n1EJAAvnH8/PPPDB061OS9ujMCSR8fHz766CNOnTrFs88+K29srjM9MGQfuUUarp/zkFOuNXbsWGbNmsWrr75KZWUlqPoY1qk2L0LZm8GqaqiiqLbI9pV5XVDppnvwV0DopdbL++qLZUOguNtBoYTQcfLCxWkrnS7VtbIRStSsFkdfpRtEXy8HXpp6s6cDUHYAKk/IVvvGCExo71IIDtfQxAQ0BzBWGl0kDk/k0UseBbD9PlqdSeK4Opa/ME+f7bV0L4iMjEShUHCm0k8vEdTRx68PAPnV51eAlV6aTrhvOAFeAdafdPI/UHFIlpzqMlbufrbJYLsgIsDq5pgyuQCsMrrQue5UNcqZndyK3BbXndo8zjQv9lmdwfr8czlzZc4kpG2A5baTmgYobK1u09Sy95snOXDgAPfcc4/1BhttGDt2LKWlpWRVNmfmOkomKEmye2DYePCTH3RPnTpFSEgIsbGx+ge5l19+maCgICZOnMj69cblGlqtljVr1piVGOpXZ83UF5woPkGwKlgvlXGY8oOyPCnmBv2mI0eOMH/+fC699FL+/e9/tztl9uzZ1NfXy6/Vw18Ozs58ZyCXA+Qvqz//LWf/eo0w2KXRaLjvvvt45ZVXuP/++/n888+ZO3duO+mSUqnkuuuuQ61WG52+LdIoU6v80dHRbNu2zeR5+fn5+Pr6Gu8RVGdYPJ04PJHsR7PRLtKS/Wi2TbKSiyMvZmjYUD4+8HHnOQgCFPwiSwQjWknyIqbARW/L2Zb9T7ru2vVFULSd1IPxJCUlca45Q15cXGzRFdCS9I3UVIiPB6VS/tfYWNYcYwUzZsjZ4PXr17fIwM4a6RmnroHCzRA5g5qaGrZu3cq0aaZ7/nRWIDlp0iTuv/9+3njjDXbs2KHfvmbNGhQKBbNm2SB1s8DixYspKyvjrbfekt1WW2WwvN298fXwtbsGS1dbYrNFO8gPif4DIeoa6DcPSndbr6Y4/bUsCYyfI/+sUDbXUK23rk/auU2gqSd1h7ehhHlXEKirrTMayk4FpUdzhtoEdSYWARyoodFlsGzphdXbXzb2Ofv3s7bdR5sXX8dNvhZJklixYoXFem8PDw/69OnD6XLPdhmsPv7NAdZ5lMFKPZzKykMrKawpJP7NeOvcG8sOwoGn5M/GuE9keW/4ZAgc2q2DK7AywCovLze579ChQ86ai8AOTNm0A1bVYZl13ak9Q16ZvM2qDFZlJXz3nVx7Za4Btc5JUNcLq5d8kcxCw8OWryvAx8eHOXPmWL62CXQNh3cdTJclEo4EWLb0aCg7IEsS4+Xs1dmzZ9mwYQP3338/OTk5+ge5p556ih07djBo0CCuueYa7r77bv2XYFxcHPfddx/Dhw/n+uuvN2nIEBsbq1+dNZvBKjnB4NDBdger7Tj9LSiUpG5t0s959OjRuLm58fXXXxttQj5hwgTCwsJa+gxFXy/3P6toY6F/eIksGxghO/e1rm8KCAhg+fLlPP3007zzzjt6t1BTOEMaZWr1/6WXXmL8+PEmLapDQ0N58sknLfcIchCFQsH8UfPZcWYHP6bLD+NGAyxX9xnJ/0Venfdos3o58F4Y9DCcfKOlDYOzOfsjIJH8znbnmMnoSE2FpCTIyZEXAnJy5J9bB1DWHGMlCQkJxMbGygFW4FDwjTcuBzv3K2gbIXIGmzdvprGx0WKDZIuBpIt46aWXiI6OZsGCBfpmxd999x3jx48nPDzcwtnWM2bMGC688EIWLVqE8rL/Ep+UR2pqS3/OEJ8Qiuvsy2Dpe2DZ22S49ows8xtwrxzY5Pyfdedlp0LgEFLXHW0JkG74itQt9S0SUXPk/UjqH54kPfUfQwnzP94mdYfKskxQq5Hn2mcGqV+vN11n6gKDGy93L8J9w21yEjxaeJRgVTARvhG2Xax0Lyg92fOnHPhaq5qJiYnhdInULsAK8wlDqVCeNxks3WJ9g0b+fOdU5Fi2yFfXysoGrxC45OOWDGuvkVB+2LKDahfHqgBr5syZ/PLLLwbb6urqeOGFF7j99ttdMjGBdZiSCIJ1Ugizrjt1eZyplB8qzWawdCu3gYFQVycHUObQOQnqMlixslb5VIspHlV18MUfCm699VYCAwMtvg5TDBs2DG9vb7kOyytcdhiz5+HS1h4NWZ83r/jdAsgBglarNdpfrU+fPmzevJmhQ4fy0Ucf6b8Ec3Nz+eCDD6isrCQ1NZVPPvmk/UO+SkVKSopeImipBmtwiBPrr858S+qhgSQ9+Hf9nNVqNQ0NDfz6669GT3Fzc+OGG27gxx9/lB3Noq4BFHD6u5aDKk7IVv0D7we/+HZ9rmpra/Hw8GDo0KFWBYvOkEZZWv03dY0333yTZcuWWdcjyEHuGHEH7kp33tjxBkB7K2lX9xlpKJEfUnr/xfj+C1+X9+1Mgm96Oz/IO/M9qKLIzTtndLfdwWxyMrTtgVZbCw89BK++Kv/30EPGj7EjqFMoFMyYMYO0tDQam5rkLFbBRlC3cQA8+5NcoxA2gZ9//hmVSmV3LylXo1sUOX78OEuXLiUrK4uDBw/a1VzYHKmpqRw7dqy51rJZitsqexnqE2p/BqvUgQyWulZuruwTI9cDBw2XHXMtUZMDRdtIPTLEsMbzzDmSPoTUD183f74kwdm1JH/lZnzRYZVS/k7UGs/yA3KWtO4sqQdizdeZGq0183S4hiY6INqmXlhHi44yNMy67wYDSvdC0Aj27j+Il5cXQ3VKGwu0NBsuMfiMuindCPEK4WzVWdvm0U2xyyJ//xNQeRwu/Qy8WylrgkbI8tIq830guzpWBVjz5s3jqaee4rHHHqOsrIzNmzdz9dVXs2XLFt577z1Xz1FgBkclgmZdd2rzOFPph1KpNNnPyGDlVscrr1heuW3lJBh/5QsoFIYZrC93elJTL9llbtEaDw8PLrzwQnZt+VGWAEga7Hq4PPiM9T0aJLW84hc5E7yCkSSJTz/9lEsvvZSBbfuCNePv728yU6xUKpkzZw533nkny5cvJ7jZPCSqFyx/8xkSExNbAiwTDw9ldWWcqzlHQpgT6q+yUuHbKCg/TPKH6e2+uM31DQFZJlhdXS0v2qh6yzUFreuwDiXLxghD5TGM2ds3NTVZnZFwljTKEQtrowGYl8Kp9S8RfhGMCB+hd5I0qKcE1/cZKUgDJFJ/1xpf5Va6Q+xNgNTcYsCJQZ6mHvJ/huhrnW/mYCowKy+Hp56S/zOl8rAzqJs+fTrV1dWy/DRyltwz7tymlgN09uwRV4CbFz///HOXd/WdPn068+bNY9myZQwfPhyA119/3akNnZOTk6mvN6wpqq2t098rQlQhdtdgnSo7RZhPmG21JfpJNAcIPjHyAmP/JPmBvnSv+fNyvgQwnpVthOT3dqLQmqmhKj8MtWfILWxvzw+QW9gc+BVuMT1Gdiq4+5H8+hrzmeFWBiagkIMrN5Xe0dNebGk2LEmSPsCyCUmS34vgi9izZw8jRowwa4xjML+YGE4XVMoq9zZ1WGGqsPMmg5VTYdym36RF/pk1kP4eJDwplwq0Jqi5LKC8eyvkrAqwkpKSWLNmDUVFRUydOpUHH3yQm2++mbVr1zJxonF3L0HHYEwiqMtgWSMRTJmagre74Zey3nWn9gx55R707t3bdBNIU6u7lh5+WzkJeg+eT1TvYDJLfdH111i+I4phw4ZxySWXWHwNlrj44ovZdzgdtbqNTMvah8vCLWZ6NORAg6Esz7dqh1xc3Vc2+ti/fz9Hjx5l3rx5Zi9z+rTxL5HW2xMTE/nvf/8LwLePQeLMCwFwV7oT6BVoUiJ4suQk4AQHQV0WpE5elcstNp7CN5ctmDJlCr169TKUCZbtg5rTULxDlqwkPAHeYWbHssde+ujRoy6TRlkTgOkywcGB3iy/W0niHOcpAFIPp3K06Kj+53YSDVf3GSn4hdQdKpL+/orpVe4jKUCbejtnBHkFm+Rxoq4lJSWlnWzUITMHU4FZTAxUV0NVlfz/bTnXAlOnTsXDw0OWCUZMBndfQ5lgVbosrY2cQXZ2NidPnjRbf9VVuOyyy5AkiZqaGkCWTluqj7MFS/eKUJ9Qu10EM0ozbDe4aCZ15X+J/xso+8+VFx12eMnBh6UsVnYqhF5K7hnjD+m5xRK+lb+bPr+5di822rgCJTYmWp6HKZmgpl6uAYu5kdzTxmV6Br/zZgMT5mjhyi2ySdGRpabnZwUxATFWSwTzq/Mpry9naLiNAVb1KWiqQOp1Ifv27bPJVCsmJoa6+kZKq2knEwz3Du/xNViSJLHktyUm9xss4reWqG+5UQ7GR7yg360vBQgbS/zfcOriS2dgtcnFkSNHOHPmDH369EGlUrF3794Wu2VBp6Hvg9VKIujp5kmAV4BVK3WJwxN54KIHACOuO3V5nCm1UH9l6iHX0sNvW6OLgcM41TQa5mjZH/ctew5nkZSU5JR6obFjx1LXKHHU2D26Nle2vIX29Skn3oJtt8LGScZ7++j4vi8cXgoZH8F38cScSgIU+iLmTz/9FC8vL2655Raz87R25V2XTSyowMAlK1gVbPLhwWkW7W2yILEm/DLMZQs8PDy49tpr+f7772V3v+jr5R3rR8IvlwJKULX8zZn6++vIPkXOIDExkZycHLy8vFhw00QSL9O0+0J2hOS0ZL3+XYeBRMOVTYAlCfJ/IXkV5le5XRXk5f0gu05FTGHixIlotVqCgoKcY+aQktK+ptTHB158EXx9wc9P/v9tMpR4ecnn2oG/vz+XX365bL3u5iVLK8+ubTGD0fUvipzBzz//DNAtAixT7Q7sro9rg6V7qKMZLHvkgampqST9/XVyimlZdHjgUVJPXCQ7zZoyuyg/LP8XN8f06wpV4l++wfTFz/4IwWNIefHldhkZlUpFyrIXZTfXM98ar3fJ+1HuhxmfaHtmOPQS6LcATrwhy77tJDogmvL6cqobLRt6HC2UnydszmA1ZxJPVYRTUVHBRRddZPWppnphQc/PYKm1apJ+SGLx5sVMiJlgvtVIW4k6Wmg4B7lfAW3b0EiyvHfJ9906yLIqwLrjjjt45plnuO2221izZg0//vgjKpWKWbNmyW49gk5DV4PVWiIIzc2GrZAIQkvvq9J/lLa47mgaob6QvJJG8wGWvSu3Q4bI/+oCrH799L2wVqxYgbe3N3fccYdV87fE2LFjAdhlVM4r/T971x0eRbl+z256D+k9oSR0iFQRC4KA9G6LiqCCiu1er169sUtULFhAUERBICgoIL0JF0V6S0JCgCSQ3uumb/t+f7w7W2d2Z0so98d5nn0gszOzszsz33zv+573HGBrHPDXDODk04b9KWdfJiGHvu8CQ7435Zc7eZKEeOhI4PzbwMmngJYCSLj9nnoOiss/Yf369Zg8ebKgIS4Hsb1C2gCrHhqqFSHQM1AwwLpYfRGuTq6I848zewwWYTQRTnkAcDGKPcVUC2bOnImGhgYcPHgQqDkFQEK+VwAANXDmBS1tbNiwYSbbX2ufIkfByckJ3bp1Q04JZfAhu+ywfZvtpwQ0PRKGHjEO8xmRXQJaioSpSFzCRSiYc/KExJi+KBaMkTx7+FjAyQ2///47AODEiROOEXNISgL69iV1QIkEiI0FVqyg5frrrFhB70kkgKsrHVf//jZ/7Lhx45CZmUkV7MiJRD+qT6c3y/YAvt0B787Yu3cvoqOj0aOHA/srOwiiqtF2qDHyjqHuLtqxIsgzCPVt9VCa6zniQbuyHUUNRTYJXCQnJ6OlzdAmoqWlBckrL2vELn7h3zB/PSX2Yh8Qrso+Pxw+sv/S89rkoGvINiFiApKSkjBw4EBIpVJt0nLGjBl0X0RPJ0YCnwBUwXoybA4dyXsMANCrVy+o1QJiBIkfUfX1zIumSrEiYY3ZMFfBt7qCZaPABaAXYNXChCIY5B6EquYq7TztfwlN8iZM+WUKVp5biTfvehN/zvnTvNUIL0W9Tcte4GsFaGlXOSz5cj0gKsCSSCT4/fff8cwzz8DZ2RkhISFYunQpFi9ejM2bRRrV3UKHgI8iCFAfltgA62LNRYR5h8Hf3V+3sK0MAENxRZN5gYvbbzdd5ulpOXMbFmaoJNi1K0pLS1FdXY3U1FTMmjXLYkAiFl27dkUnPy+cvGoUCTh5Aj1eIb5v8WbqczCGewjQ9x2g6xxDfrlnLP3d+3Xg7i1kamkMVQt2r34V1dXVFumBgPheIU51q6LR1SDACvAIEKQIXqy5iITABDhLBaieYmE0QU4aDnQNpSDLmmrB6NGj4ePjQzTB9GQI0caqq6uxZ88eJCYmXnefIkchPj4el/M1DYeNjguwzPZTAkTf6a3/sJICg791jBRuOYkgxUTxm2Zrs9x8jfASF0DVjLjLD1OgZi3qztIEMZJ6PbZs2YJevXohISHB+n3xoaoKSEsDXn4ZUKuB/HzD4IpDUhK9p1YDV66QXcWUKUCtOD9CY3By7Xv27KF+ToBogspWMjAPvx8KhQIHDhzA/fff7zh10A6ExSqInWqM3BjKTXr9PIEVyaO0Y4UYtVU+XK2/CgZmE0VQMKgsqRQWu2Bq6uMNGw24h2DEiBFQq9VawSc3NzcaA596DU6qRqDigOk+SvfQfiImQKFQ4MKFC5g9ezbUajV69+6to55HTgQkzpRM1Ie8nq632IcAqTPuvfde7TFIJBLExMRg9OjR2L17Nx599FETr0EA9Pzs9z5Qvl/Y79ACOC8sMTTBrMosBHkGaZPGolF7GvDvb7XABaBvNuxlWsFyDwYDQ0Uzv/DOtUTq+VTEfRkH6XtS8RLqAttHL45Gv+X9sCd3D76d8C0+GPkBJBKJeasRC+wFR7QC3GgQFWCtXbsWnTt3Nll+3333YedOHn+OW7hm0Ipc7LvdQJUryDNINBUiuyrb1Hy2pQSNrYCsqVW4grV1K/DrrxRkxcQIZ3f5YKwk2IUkpRctWgSZTGZgUGsvJBIJBg8djlNlkaYB0oDPgHt30zI+tOopAOnzy6fmG05M2/gH0J/+qEJISAjGjBFQVjOCGBllNzc3dOrUCeVNHkCrnpGmR6CgyMXF6ov20wMBTbVDN2zUNwM55cC/5k2yqlrg5uaGiRMnYsuWLVA28jfHoqUQH3zwAZqampCamnpd5KU7AgkJCci7UgCV1NOhFayUUSnmKRoA4KWZyPZ5C4Aa8LGtp8QEZfsAn3ikfPSJSb+mRCLBa6+9Rn8YN8J7xpL3yb174aysBvYMAk69YJ2UfPE28gaKGI/q6mr89ddfmDZtmvltrMH69YBSCcyZI36byEhg0yagqAh4+GHa3kpwpsG7d+8GPEKBwCE04a08RJnf8HE4ceIEZDLZTUEPBERU6W3t6dVDUlISCgsL4e/vj0dH+CHpXn/te1x/srVKgloFQRsqWGaDyq7zaHJfa2Q0X32MFAQ1xr4cTerUqVN49913IZfLMXLkSCBsNFRSL37T4dKdFOAEDsJff/2FhoYGTJ5MSYjp06fj8OHDqKqqAlz9gbBR1IelX2Uq2kQ2AJpj+O233wAAx44dg1qtRkFBAfbu3YuPPvoIP//8MyZMmEAGz8aIfw7w6wOc/YepEqYIaL2wRAhd2C5wcRYIGIjTp0+jf//+cHFxsbydBqGhoXB2duYPsDyoj/h692FxEuoFDQVgYOIk1M1sX9xYjKv1V/Hy0Jcxf9B8cQdhgaIuZNcQE+k4G4drDVEB1muvvSb4ev/99zv6GG/BDBRVZNzo0loMfVWuYJVMlMgFY4x/8t1aovXA4q1gZWRQEDVoEHDwIGUazWV3+dCrl1ZJMCcnBwDw2WefwdnZ2fqshQVayZAhQ5B5uQTNo7P4AyR7+1N41qttArafowe+NQO2GISFhaFc5mxIEfTgpwjKVXLk1eY5RqI94n4ATONzJMG+y0FQqYEJSf+2elczZsxATU0N/rrKP4DmNoRj2bJleOqpp9CLo5T+DyA+Ph5yuRyFrXEOrWAl9U0yT9EAyJtN6gIkvEhZ65Kt9n+wqp08mcLGICkpCUFBQXB3d4dEItFOPpYuXarr2eVLVISPwdXuv1ElOGepdVLyJduAoOGAexC2b98OlUrl2ABr1Soa5/r0sW67O+4Ali0D9u0D/vMfqz+Wk2v/448/qDoQMZFoXFd+Iqpn6D3Yu3cvnJycMGrUKKv3fz1gsUpva08vD8LDw1EmczYwG9YaslspdKH1wLKhByslJQWeboYJPHd3dwoqOz/KL3aRn6pR4JtiokI7c+ZMMMawZcsWwMkNTX4jqDqkT3tUK6lPL2I8IJFi27ZtcHd3x+jRowFQgKVWq7Ft2zZaP3o60JRr6EeYnwp4dwMCqB9pw4YN6Nu3L3r21CVjJRIJXn/9daxevRr//e9/0bdvX0RHRxsqiEqdgUFLKGC8sMjq3y/SNxISSCxSBG1WENQIXKj9b7Na4AIg2ndkZCSK6lxMKIIhHvRsu959WDZJqFvYHgA2ZfME9kLonwJIjXpZNRT1uro6KJVKkyq8pyuQ8vI48Z9xg0FUgOXk5GTw4jx69u7dKyzffQvXBPISqiC66F+XqhYEy9JQ1VIFZoH3XNFcgYb2Bp4KVjGKNSwKkwpWZSUwaRLg70/Gwh5GfR1i0bs3UFeH1OXLsWiRbuBVKpXWKUuJoJUMGTIEKpUK586d498HH3XJqD9F3+zWxGSRZ/tfTrhAoQSv95W9CA0NRYUMJhTB+rZ6qNQqg3Vza3OhYirHVLCKtwFgwKiDwCNq7KgYh4CAANzORxW1gPvvvx8eHh7YlNOP97f/z44ouLm54d1337X/uG8gcNS1y7VBDg2wAJinaABAQxbgk0CeIyH3aM6nnag+RpTO8DHIy8tDeXk5Fi1aBLVajfLycvzxxx8oLCzEiBEjUFoq7AmjdA2njLkxzKkMNheSqXfkJABED4yJicGAAQPs/14AcO4ckJ5uXfVKH089BTz7LHllrV9v9ebjxo1DY2Mjjh49qhHaYUDhBs2/m7B3714MHToU/v7+th3fdYDZKr1Q764Ngjbh4eEoq4eBEJA1HpH6yKvLg4+rD4I9g60+jqSkJKyY5w5Pd11l96677qLv7eoPxD5IwYxCI+KgVgCFG4ny6uKDs2fPaul9APU89ejRQ1tRavQfQ/1WlX/qPrT6GKCoByImgDGGrVu3YvTo0fDy8gIA9O/fH3FxcboWj8gpID9Czd8tJUDFIapeSSQoLCzE0aNH8dBDD/F+x9mzZ+Of//wnCgsLUVxcbKogGjoCiHkQyF4ENF216vdzdXJFqHeoRYpgSWMJZO0y2/qvAOQ2hEImk4kTuDBK6Ea7ufGbDbvfGBUsi/25Hbw9AEqkxXBCXzoWEYt7BE8//TQaGhrw7rvvaj0jPT09sWK+B5LuchPe5w0OUQHWRx99ZPD69NNP8csvv+Dll19GWdn/rkLKzQCFvB4A4GrEcAtiMshVcovKO4Lqci0lKKmnqotBBau9HZg+nfoStm4FIvh7LkRBw3NOfu89Mp3V/3hrlKVE0EoGDx4MAGQ4zAc+6tKQFdoql4nCjbH8tN72TLP9mrMx6NevHxITE8V9DysQFhaG8jqlweSB6y+oa6szWPdiNSk4OcQDq2gT4BULdBoAlUqF3bt3Y9y4cXByMqOyKAAvLy+MHz8em//IhHrQtwa//XGX1/DrrpP417/+hfDwcPuP2xLsaKy3FlyAlVPtCTTn8zeodxQaLgB+mmpg1BQyeZTl2LfP8v00+Q8doaWMT5gwQfv23XffjT179qCkpAQjRoxASUmJ8L6EVBWF+Psl2+nfqMlab7WpU6c6rh9p1SoSrHjYDjn9L78E7rwTmD0bCA+36hobNWoUnJ2dsfvnz4EsPaqnqg3VfzyN06dP3zT0QFFISQH4LEHuv9/qXYWHh6O8VmFYwdKMkVZTBDUKgjZdV4pGJN3eir4J4bjvvvvw+OOP4+jRo2hoaKD3u87TiF38TH+X7aeASUPNW7NmjYEKrUQiwcyZM/Hnn3+isrISTb53UoJKnyZYupMq1GGjkZGRgYKCAi09kNvH9OnT8ccffxCtzyMUCL5TF2AV/AyAaY9h48aNAIAHH3xQ8Gty6+jD4Dk+4DNArQJ29rbaZFyM2bDtCoKnAakbzlwmeqPFChZPQjc6Lw9FxQ0k1KRs1q4a6B4ICSTXvYJlsT+3g7fXwskVcAsCHlZp2QsrVqzApk2b8OGHH+Ltt99Gfn4+Zs6ciYiICCRNHnxTe2GJlmnnw+jRo/HHH3846lhuwQYonKnp1fiRFKyhQlgSuhCcfLeWoFjmAwCIPHxYN/kMDASOHAF++gmwspRuAg3tq7Cykvdt0TRBEbSSsLAwREdH49SpU8L7MdNjxatwYxwEara/eFsWLvXcixNpeR1SvQI0AVZNKw3omgm6lv5iNHngznFCoJ1N/woZTaajpgMSCU6dOoXq6mqDybS1mDFjBsrLy3G0rLP2t2dTruLVL/5AaGgo/vWvf9l3zGJgZ2O9tQgNDYW3tzcul6moCb3pSod8jgmULfRZfpoJiKbqgxI7q1hl+4CgYYCLL3bu3Inu3buja1dDKtWdd96JvXv3ory8HAMGDEBUVBR/JViIkuvqz69CVryNKnK+3bFnzx60t7dj+vTp9n0fDu3tdA1MnUqCPLaCC9BUKqC83KprzNfXF3feeSd2791rosC1P70VjLH/rQDr4YdJJMnTk/p0o6PpObFiBfD991btKiwsDGU1LWByGV370I2R1law7PHA4pIGxZXNiI6Oxosvvojm5matnyGCbqceJY4mmJ8KuAYA4WOhUCh4VWhnzZoFtVqN33//HUzqAUSMI5EKjr1QsgMIuRtw9cO2bdsgkUgwadIkg8OaNm0a5HI5du3aRQuip9NktjGXjiFgMOAbD4DogQMHDjS5r/VhUaSgQlNhU7XCWpNxMWbDdikI+vfDmXPpcHd3t0xH50noRqtUKK5uhloNA5qgi9QFQZ5B172ClTIqBR7Ohkwjk/7cDtxei7p0wL8/3dsAzp8/j5dffhljx47FK6+8ol0tMTERubm5aHTpSVYFfBYCNwFEBVhqtdrkJZPJ8NNPPzlM6e0WbIM8+G64QHu9Epw8ERz/BADLZsPZ1dnwdvVGpI9Rn1VLMUpk7gjw9obH88/rJp/NzYCLC8CnGGQtNEqCMd7evG9b9DlijGg3QllFHv+o3377jX9iZwZ1dXUoKBBwKRd4qKxZswZSqbTDxBhCQ0PR1CJHcxuAdgpQAzwCAJgqZGVXZyPaNxrervy/s2iU7CQKV8wMAMCOHTvg5ORk1wRvwoQJcHV11ZkOA9i6dSv+/vtvvPfee/AWuDYcCgc01lsDiUSChIQE5BRpqssOpgkKQnYJqUcY4sYtpXugzwiknou2L8Bqq6YJStgYNDU14dChQ4IB9x133IFXXnkFlZWVKCkp4a8E86oMOlEi4cSTJO7AQSEDKv8LROnUA4OCgnDnnXfa/n30sX07KQDaSg/UxyefmAaIIq+xcePG4XyBAsVGRZe9GUCAN6zy7LnhceIEIJNRMKVWU5Ls9Glg3DgKSJcuFb2r8PBwtLUr0dACbaXf08UT7s7uVvVgqdQqXK27apPABQCguQhKFVBWWY/o6GgMHDgQd9xxB5YsWULy5hIJ0G0+VVIq/6Z+qphZgJMrdu/ejerqapNEXd++fREfH6+lCSJ6Bn3H6qPU69SQpVWe3Lp1K26//XaEhoYa7GPYsGEIDQ3V0QSjNX2LuxOJdtuYC1xNRV5eHk6fPm22egWIUIhMTwaYkVy5SJPxaN9oiz1YmZWZCPEK0dJARcEWgQueZ340AAUDKmUwqcKH+4SjtEmYGn0tkNQ3CW/d/Zb2b6lEiu8mfmdKITez/ZO3PQmAxy9VLNQq6vHz7wcAaG5uxoMPPgg/Pz/89NNPBhYAHOsno9yfqrtW0kpvFIgKsHr16oXevXsbvIYOHYpff/0V//znPzv6GG/BDBQ+CXBx0qtfaahtQZ1nAbCcqeMELkyoDy0lKK6VIKq93XTyqVA4ZvKpURJMiYiw7P9kTOH65BNg5EgS1IiNBdzdYbQDA6n41NRUpKWlQalU8k/seMAYw/r16836y7i5uZESkx7UajXWrl2LsWPHdliPIrffChm0SoJa+kuraQXLIf1XRZtIhCCIfKl27tyJO+64AwEBATbv0tfXF2PGjMHmzZvBGINCocC///1v9OjRA08++aT9xywGDmysF4v4+HhcvqrJal6jACt1zQrMWwkUlFTr7oGl5Ujd8hcFSrag4gAABoSPwYEDByCXy81WNFetWmWyzKASzKsyuJpUD6+sAvbfrcsQl+2jfpXIyZDL5dixYwcmT57MT1e1hQK6ejWpAWqEAeyCHdeYVq5djynDGLDvPDA60dMmeu4Niy1bKIGnfw15eACbN1Ml8YUXqMol4lxy1OKyemjHSIlEYrXZcLGsGAq1wvYAq7UYpXX0XOD6mV988UXk5eWRQiRAYhcSZ+DACAo6in8HrqZizZo1CA4ONklicTTBgwcPoq6uDoicQAICRZsoEQYAkRNQXFyMM2fOGNADOTg5OWHq1KnYtWsX2traKLiDREdxU9QBJ+dhw/I3AEBLURQCn0KkVCrVPcftMBmP8o1Co7wRsnYelUIN7BK46DQAZ8+eFZes4PH+5JYU1QBoNgqwvMOvewULAPqHkSff/IHzoWZqq38rmVyGAI8AKN5S8Pf3WkJjDiXIOtFxvPzyy7h48SLWrVtnEvxzAVZagaZydZPSBEUFWD/99JPBa82aNfj555/x999/Y+LEiR19jLdgBgqVAi5Sjclw6L1aaluwFzVXWqIIZldnm06+GSMVwRolohQK/g2tnHwKzm969UJSVZV5ZSk+Cte//w2cPAksXw7k5AArV1KgxWHJEgM1w+TkZCiMvktLSwv+o6fupS9iERkZib59+yIpKQmxsbFYuHChycPDxcUFCoUCiYmJ+PNPXYPxyZMnUVRUJMr7ylbwmQ1z9Bf9ChanEmkiYmItlC2kShU9Danrf0ZUVBTS0tJw/vx5u53WZ86cicLCQpw+fRorV67E5cuXsWjRIhO57w6DAxvrxSIhIQH5BYWQSwMdKtVuDsmfrEeLUeG5pU2B5I0ASnfZttOyfYCLPxAwCDt37oSPj4/ZCpIorxMTqu6j5KVz12bqGdszEEh/Gzj2BK1/9FEcXP8mZDIZv3qgLRTQsjJg927g8ccBRwQwdlxjffr0QWRYJ+zO0B3H+SIKHMZOunntCkzAGAVY994LaPyetHBzAzZuJEuQX34RdS65AIvGSEOhC2sqWJyCoM0UweYimnhD55k0ffp0RERE4Ouvv6Y3SnYCYADTUPzaKlB78Gls375VUIV25syZUKlUZNbu4guEj6EeqpIdpP7nk4Dt26lHccqUKbyHNm3aNDQ3N2P//v2CfoQbNv2OYcOGacUHhGCsEBkQEAC1Wq0T2rJDqdeS2TBjDBeqLlgfYNWcBgDk1AWjsbFRnIIgj+VKtCbBy2c2HOETcd17sACgREa9r1wlak/uHtHbqpkau3N24/5u98NJauN4WJ+O1CNA3D1vQCqVYuXKlZg4cSLuu+8+k1UjIiIQFBSEtEtVACRELbwJISrAGjp0KIYMGaJ9DR48GImJideGvnMLZiFXyeHKXfB6zbyc2pE5imBjeyOKZcWmk+/2akAtR3FVMyKFzrEVk0+z8xuNkmDSqFHCylJ8FC6AjDyfeYYmQJzJJ9cTaFQ5MjexGz9+PGbMmIEnn3xSK2JRWlqKrKwszJ49G8eOHUNycrJJELhq1SqcPn0aXl5eGDlyJGbMmIHY2FjMnTsXEonEpGfLkeAyPhUN0AZYHEVQvwertLEUTfIm+ytYZdQDknrKH/PmzdMKFdTX11un+MiDyZMnQyKRYOTIkXjuuefg5uaGxsZG+47XGqSk8E+i/2299LxYJCQkQK1W40pT9DWrYBWW82d/C2tgG02QMTIYDrsPTCLFrl27MGbMGLi6ugpuIkQjEvJAMUD0NGDsCYBJgawPAJUm095SiC3rvoC3lzvvw9omCujatURRe+IJy8clBikpVFXXh7OzZUN2aOTaJ87AHxdcoXCNASDB3mx/AMCYpHccc3yOgj1iMVlZQG4uICSx7+IC8KlQCpxLLglFFSxDoQtrKlh5dRoPLBsk2un4ilDc5A9AF2C5uLjg2Wefxb59+5Cdna2hzxmqv2440gq5XCnYx3vbbbehc+fO2Lt3Ly1wDyd6Wtlueibkr8fWrVvRrVs3QRbGvffeCz8/P5J856kkXSwFMvIVFumBHPQVIquqqjBkyBC88sorqK+vF6b/9rd8D3Bmw0J9WIUNhWiSN1nff1V3xjqBi6YmYMcOoFs3QKPIiNhYRC9eTMfX4G1KEfQOR0VThYm677VGsawYEkiQGJaI28Juw+7c3aK3PVN6BlUtVRjfbbzNn5+6fj0xKIrLtEH3gQMHeOcOEokEiYmJSMvIBHzi//cqWPfccw9GjBgh6nUL1w8KtQIuHHdV7yHi7eoNVydXsw+SSzWXAPAoCLaWQK4EKmuaEDV2rOnEwIh+Zwlm5zdcQ6nGcJgXQtUyPjWyO+6gbOcBQ2d7oYmdl5cXCgsLsXnzZrS3t5u8f+jQIS0Fh09eODExEWfOnMHtt9+OzZs3awM5xhief/55u6s7QtBWsPQCLD93P0glUoPsbHa1gEqktSjaDLgGIHlRqmWxD2NYmHTt2rULEokETU3Uk9Te3m530GYVYmNJgMDPj2ir4eEUcO3dyy+s4ADEx1Pz+OW6gGsWYMUE8VcEY0K9gbI9hv1NYiC7SNna8DHIyMhASUmJRcETPhqRRCJBbW0tSZFbgl8vwMkwm69SA1tPKzGuvxTuxlRhwHp6HmOkHjh8OJBgpzAMh6QkEmqIjaVrzNeXzIeFTNyNMG7cOMiaWnE0eA3wiBp7SwZSZYvPo/B6wV6xmN9/p99GoNoCgIyb+cBzLrUUwQaJgSF7kGeQVSqCebV5cHVyNe1TFouWIhRpBKP0LU/mzZsHV1dXLF26lDe4WXMY6BsNQRVaiUSCWbNm4fjx46hLWwHkr9W9qWyE7NDTOHjwD0yZMkVQ/dDV1RUTJ07E1q1boXQzpb5tOEanZNasWVZ8YYJUKsW3336L6upqej4Y039d/Cio9LSsRMyZDQtJtWsFLqxWELRS4OLTT0moZu1anbddZiaCnnkG7u7uvGbD4T7hUDGV1cIqlpB6PhVxX8ZB+p4UcV/GWTQNLmksQah3KFycXDCu2zgcLTqK+rZ6UZ+1M2cnJJBgbDfb+62Tv9pvyqAwM3dITEzE+fPnofDp+78XYL300kuiX7dw/SBXySFPUyLuJUD6QAPiYmOQmpoKiUSCYM9gsxRBrYKgiQdWCUo5k+Fx44DvvtO9FxtLEwUrxBvMzm80Uu1mAyxr6DUeHhRkHTxosJhvYufp6YnvvvsOmZmZgg8gMUqGPj4+KC42Hfitkpq3EsHBwZBIJCiXuWonD1KJFJ3cOxlQBLlzbFeApZKTHHbUFBQKTHAEfycRk67k5GRq9tZDR/52BlCrgVdeIbuBkhL6u7QUWLSIbAg4pS8HgwuwcirdKDHCeeB0FJStSJmlhIebYZDl6emJlORnqe+i4r/W7bNsH/0bNlorz871CwmBz2h26dKl6NWrF5555hkDsRNBGFFwjudQJXf6AIGKsdD4wdNLAYCEFi5edIy4hT64KrtaTRTEzp3pXmizHNjed999JNe+ezeam5tx+PDhG0890F6xmC1biAJozpbBimeBn58f3N3dUdboAbQZmg1bM9nNrctFl05dbKdGtRShqM4VPj4+8NOjPoaEhODhhx/GTz/9hAZmGGhfKgWO5wKPj/Q3Kw0/c+ZMKJVKbPvxPxp1Ph32prVCoVAJ0gM5TJ8+HbW1tfirLcmgwsQY8MtxCe4e0gMRNtqx3HbbbXj++eexfPlynD592pD+O60M8OoMnH6eeinNIMInwqzZsFai3ZoKFlNTgBU4CKdPn0ZiYqJ5WnpxMQVYDz5I12k3DWU0Lw8SiQRRUVEoqnMyGZ/Cvel6Lm10nNBF6vlUzNs+DwUNBWBgKGgowLzt88wGWSWNJdpAdVz8OKiYCn9cEacCvitnF4ZGDbVOQMQIhZWt/MsF5g6JiYlob2/HpbpwTa/cNWS1OAiCAdbIkSMxbdo0Ua9buH7IOZSDmt+aUVBN7OmCwiJt9j/Yy3yAlV2VDSeJkyn1wdhkeNQo+mPZMpogWKmMZ/aZqFESxIULwjvQ65PSwlwVbdQoIC0NqNY9RPkmdvp9XhYVkCygyNrAw044OzsjKCgIFU3uBmbDgZ6BBhWsi9UX4efmhzBvO8Q2Kg4CigYgerr1v5OISZeovpyOwoYN1MuXkqKjfADAP/4BjBgBvPgicNXxCkYBAQEICgrC5VIlLWi004vKEhovIWk48OIcwwrTF198gaRnPwCcva03HS7fT/QN7zjs3LkTAwcOFCXqYlwJfu6553Do0CH07dsXDzzwAObNmyds6A2Y9GxsOQ24OgPjhwkETCkp/EqjYWFURTLGqlWUqLEhay8anp6UuLp8WRQbwNfXF8OHD8fu3btx6NAhyOVy3G+DN5TdEKpG5+RQ8oQPYu7jggLg7FkSsjAHK6iWEomEvLAa3UwqWHVtdaIpW3m1ebYLXDAyny2u1dED9fHCCy+gubkZqy7eYxDcrP0bkEpA96YZDBo0COHh4fj1sGlFbusZINCb1ALNYezYsfDw8MCW440GFabzVeG4WMrw4OwXxX1XAbz//vsIDQ3FM888A5VK7zd39gAGfkXefJe+NrsPFycXhPuEC1IEs6qyEOYdpqXJi0JjHqCQQe1/mziBizffpOTIxx/T35xkfR5RSKOjo1FUreatYAFwaB9W8oFktCiMmCSKFiQfEE5mlMhKtFXY26Nuh7+7P3bnWKYJVjRV4FTpKUyIt92OBe21iAnkf0to7qAVuijSMBbqM23//OsEwQBr2LBhqKkxvGn3799vYgh7C9cX6evTTZRPuex/kGeQ2UzdxZqL6BbQDa5ORj0TrSUoqaMJSWRkpG6CGRdn0zG+/bbpMm18JJEQTdBcBesiVWEQHk7rW6qicQHhfw0z8nwUPw5CFa4UkVRIewM0WxAWFoZymbNhgOURaEB/EVSJtAZFmwBnHyDsPqSkpJiolpn9nUTQs67HbweAKgdvvAEkJpKYgT6kUqpeSaVkEKtyPH8+Pj4elwvr6Y+OpgnW0/0VGt0dALQVJw8PD8DJDQgfS31YYv1GVO1U8Qobg5qaGhw/ftwuPzR/f398//336NWrF77//nthQ2/AoJeDMQqwRvWRwnfYR/w7j4+nFQMDdePHI49QYP3YY4ZBVmsriSjMnEk0vo7E6NH0+R9/DGRanjyMGzcOGRkZWLVqFTw8PBwnRy8WfNXoOXOALl3MUynF3Mdbt9K/lhK2xlRLb286fwLPprCwMJTVSw17sDwCoWZqUfQoxhhya3NtD7AUDYCyGUXVct4Aa+DAgRg+fDiWrD8K1UAyW1ergbVHnTDm7n4IH/q82d1LJBKMGTMG+zJBcvTcxyqBneeAiYO9LIoFeXl5YezYsdiyZQvUsQ9rK0wbquZAKpVixowZtnxzLfz8/PDFF1/gzJkz+E6fDQMAUZNITv78u0CL+QpPlG+UWYqgTfRAAJdrg9DU1GS+/+rsWfL+fOkl3bXGF2BVtdI516u2RPhQ9c+RSoKFDQJJSYHlANEruQDLWeqM0V1GY0/eHp0IiQD25lGP3/h42/uvUJ+BlAcAV1ceBoXA3KF79+5wc3NDWl6Tdh83GwQDLL4f/d///jeqqx3LI70F+9BaLVx2DfYMNitykV3FoyAIkES7Pmc8P5+Wd+5s0zFyPe+cEFJMjFF81Ls3BVh8N3peHnmfPPUUUbfUastVtEGDAB8fE5qgOViqcFmC6ADNniZwo+3DcnJQXtluoJAV4BFgQBHkVYm0BmolSQZHTgCc3LVmlT4+PuJ+JxGUHnuDW5vx1Vc0Sfz8czofxoiNBb7+Gjh8GNA0MTsSCQkJyLmq6SPsaCXBhixA4oysvCoEBwfj/vvvR0REBLZyE9uoKUBrKXnCiEH1MZKTDh+DPXv2QK1W2xVgAXTOZTJTIQ5eQ29Npj2jELhSCUx7YI6BMbgBNmygQSg3Vzd+pKaS1cMvvxgG0Fu2kA+To+mBQli8GPD3B55+2mIQz9FoN23aBMaYODqlI8FXjVYoiDr12Wd0rxhXl1xcxPXrbtlCzwENddYs9KmWpaU0ljz5JC/VMjw8HGV1KhMVQcDUzoIPlc2VaFY0220yXFTeaNB/pY8XX3wRV65cwe6LnYCp+fgz8iAKq1R4fP7roj5i7NixUCiB7Wm6ROnfl4D6FmDyrKdE7WP69OkoKSkhGh9o/rdhwwaMGjVKnACNBTz44IMYNWoU/vOf/6C8vNzwzYFfkb9i2mtm9yFkNqxmatsUBI0ELgQrWIwRjTwoyJBN4+9PQlt6AVZppQxKFQyqWBx7xFEVLMYYPF08ed+L8eN/3rYqWlHXVodIX10f4bhu41DaWIqMCvOBy66cXQjzDkNiWKLNx4y6dCQNB8aNoeS3mLmDs7Mz+vbti7SsK6SSWX/zKQmKUhHkYCnSvYVrD/dAnqZuUPbfXA+WQqVAbm0uv3x3SzFKZB7w9PQkzjhXwbIg0yqEtWtp088+o7+PHzeKjzRKgqioMN34jTfoIf3+++I/0NkZuOceE6ELSzBX4RKzrcUAzd4mcKPtQ9vaUFHRDOjx0vUpgrJ2GUobS+2TaK/6m1QloymLuW3bNqhUKuzdu1fc78QjaWtM77Q3uLUJVVXAhx8CkyaRn5oQHn+csupvvglkODaDFh8fj5KSUjRLIzu+giW7APjEIzPrAnr37g2pVIrJkydjz5495IETMR6QSIHirZb3dTUV+FPT13H6eezc8A1CQkIcYngrmmqr6eXY0v4OJBIJJs8VmMSr1STvPXYsTYj08eqrdA2sX0/jRWwsDUxOTvwCOh2BoCDgiy9oUFy+XHC11NRULFy4UPt3W1tbxwjB8CWAGINHWpowBVCppAnoCy8YVpfc3Mif0BLtr7oa+Osvy9UrPvj4kCnxpUvAe++ZvB0eHo6ymjaq8msogVq/QBFCF3YrCDYXQa4EKqobeCtYAEmlR0ZGaiXb16xZA19fX0y19Ltp0K9fP0RFReG3y3219L6tGT5wc3XBmMcWWtweACZOnAhnZ2et6fCZM2eQl5cnWj3QEiQSCZYtW4ampiZ069bNkP7r0xXo+RqQnwpU/iW4D85s2HgeWlBfgBZFi/UKgjWngU79cfpsGjw8PIT9LrdtAw4douvL2D6ga1eDAEutZmQLoNeH5e7sjk7unRxWwUo+mIxmRTNcpIZiP54unkgZxT8OljTSeMb1YAHA/d2IYmxOTVCpVmJv3l6M6zYOUolV4YIh6jMAt2CoJa7o1auX6DlWYmIi0tLSwPxuTqELO36xW7gR0GVmF0iMGABc9j/IMwiydhnkKrnJdlfqrkChVvBXN1pLUFznhKioKKKW5ecDoaHUl2AlSktJOf3RRwFu/MoxbjcRUhI8dgz49VeaCJlrfObDqFH0QUKqUx0ALkDLysriHzyE+pH0s2LmKlxvvGGwfRiA8haAoZkoWzCkCF6qFlCJtAZFmwAndyCCxAs2btyI6OhoDB061PK2Z84Aa9YAPXsaVrIWLTKpQNoT3NqEd98FmpupimEOEgn1ynTqROansbG2Vx+NkKChVeXKrkGAVZ8F5tsLFy5QgAWQN05zczMOHDgAuAUCwXdalmu/mgqcnAcoKeurbCzCnoPHMO6u7pDyVQGthBAtNDCQn8C/ZcsWDB8+3MSoUoujR6nCIjRRfOMNogMeOaKjrapUwPz5dp9f0UhKogDwX/8iVUGe6ys5Odl69U5rIUQBjIxE3COP8PexAYb3tn516eBBoLER+OYb85+7fTutLzKgMMGYMcDcuSRAcOaMwVvh4eGob2xDW7sKkNO4yPkFihG6sNsDS2MyzBgTDLBcXFzw3HPPYf/+/Th9+jR+++03zJo1i+i7IsDR+Pb8lYnGUefBHlZhW1YQ7hs9RrSVTqdOnXDvvfdqDd83bNgAZ2dnh/bYnzp1ClKpFM3Nzab0395vAF6xGsELnr5IUHDQrGhGQ3uDwXKrFQSvpgK/xwKVh4CGCzhzZLewwIVcTvOPHj3o3jBGt25UGYeux468sEz7sBxRwVpyYgk++vsjzBswD6umrNLSDzu5d8KKSSsEzX85aqW+Ema4TzgSwxLNBljHi4+jvq3ePnogQNWnTv2Rnp6O/v37i94sMTERNTU1KJF3A+oyxFPYbxAIPhElEol9fRu3cE0QODQQEcN15eLYMC9t9p8zG+Z7kGgVBIP5KlglKKnVuc7j6lWb6YHr19Oz87HHdDT9y8ZzST4lQcZowhEWRv9aC64qYWUVq0Nhrh9p2DASVZgzx3CCM3s2BShRUSbBYhiANjUgawXQVgmAKILNima0K9vtVxBkaqBoCxB+P+Dshfr6euzduxezZs2yPJmurgZmzABCQoA//6TvwgmZOGAibheysyloeuYZXdRvDsHBlCEoLqZzZUv1kQdcgHW5xp8ogh3FEFC2As1XUNQahcbGRm2Ade+998LHx0dHE4ycQlnCpnzhfaUnEzVQg+O5QF0zMKFztkMOlY8uKpVKUV1djYcffhh1dXXa5VeuXEFGRgamT58uvMMNG6iKMnmy8DqnTpkus0b9zl5IJMD99wPt7VQ547m+RAvB2ENBFqIA1tai9IMPqFJkjWXHHXfQ91q0iGiXQvj9dwrSBgwQf6zG+PxzGmvmzqVJsQYGdhaaPixrKIJ5tXmQSqSI84+z7biai1BUS/MoIYogADz99NNwcnLC7bffjqamJuzYscOq6uTMmTPR3t6OHTt2IDMzE1evXsVk42vewrUxbdo05OTkICsrCxs3bsSYMWMQEGCFaIQFJCcnQ6EwbBjXJgmcPYEBXwD154HL/AG5kNmwVQqCXIJII4uvkjfh3PlLGJTgw7/+t99Sovazz4gZY4yuXemZINf12BXVAGg29cKyV0VwY9ZGvLTnJUztMRXLJixDUr8klPyzBPEB8RgSOUQwuAJ0JsP6FEGAaIJHCo+goa2BbzPsytkFJ4kTRncZbfuBq5VAfSbqpAkoLCxEv379RG+qFboo9QaUjUCzQBX9BoXZHqz33nsPr732mvalUCiwaNEig2WvvWaeN3sLHQuFWoGAzm4AgBnDOyF//Z3a7L85s2HOH6l7YHfDN5TNgKIexVVtOo+V/HybBS7WrgWGDAG6dydVZDc3ngCLT0lw82bKPn/wATUyW4s+fWhibEUfVodDqB/J25smgYcP04RGHyoVBbijRpnQE7icvb7ZMJedrW2tRXZ1NpylzujSqYttx1tzEmgtMaAHyuVyPPDAA+a3U6mAhx8mKepNm+g8ABQodu9O/RaOhjUTy1dfJcXAd6wwaf3tN9NlxpNwKye33TQyv5crnAFFPdAu3pvHKjReApgaWWWUEecCLDc3N9x///3Yvn079fdEaSZkQlUsxoAWwwfcznOAsxMwprtjenP56KKrV6/GwoUL8dtvv6Fv375ac0quKf3zzz/nn4yqVHTexo8nKpkQrPXJ6gh8+aXpMr3rS5QQjD0U5PZ2YQqgXI6GGTOoz0mfAijGsuP994HaWup35ENzM7BvH1Wv7Eno+vvTZDgjgwI6DbReWPXQKglyFEExFay8ujxE+0abCkGJRUsRiho7AeBXEeSwbx9ZHnAqexUVFVZRQO+44w6Eh4fjt99+w7ZtdP9y/bIARF0bHCVx8ODBKCwsxIkTJxxKQbWYJIiaSmI7aa8DW6KB9VLg9zgKiiBsNpxVlYUInwj4u/tbPgijBNHlMqCpDRjoq1f51B/H//EPYtiMF6jgdO1KGeSCAl2AJfMBWo2k2u2sYP336n/x2JbHMDxmONZPX29gGTAhfgIO5R9Cs7xZcHuOImjs5Taum3m59l05u3BnzJ3wc/fjfV8UGnMAdTsyymgf1lSwuGAs7apmXnST0QQFA6xp06bB09MTTk5O2tekSZPg4+NjsMxYUewWri3kKjmkSsp8l+ll6QBdpk6oghXhE2F647SUUO9wlaYpV6WiiYYNFaz0dHrecQJtTk5UUTcJsIyVBOVy4N//psqWrc3mUilVsQ4c6LjKgLV48knTZZ6eNDH473+Fj1MuJwWjb74xyCBzgtjl9dAGWJxMbW1rLS5WX0R8QDxcjIxZRaNoMyB1ASInAiB6YExMDIYMGWJ+u7feIl7oN98Agwcbvjd1KvHZ9SoRdkPMxFLz0OzRqxewcycwbpwu8BMDocl2QQHRIJcts3py6+XlhcjISOSUagyuO4om2ECJi6wiekhxARZANMHy8nKcPHkS8OlGRr58cu3ttcBfpn46O9OAOxMAvyDb+jP5YEwXfeyxx5CcnIxjx47B29sb9913H5544gnU19cDAEpKSkwno6mp5G1WXk79PeYmitb47HUULAR5ooRgbKEgr1xJNFlz47sQBVCMZcfgwVQ9/Pxz/nt+zx4Sp3AEFW3yZOChhygpp1FlNAiwNEIXPq4+cJG6iOrByq3NtZ0eCJBEu4wShOYqWMnJyYYS5rCOAiqVStGnTx9s3rwZb775JlxdXXFQP7kowi7j4MGDkEql1JMJoKamxqF9fhaTBBIJEDoaULdpAhRNQufkPOBqqqDZsFUKgkaGzmc07eWDojTXgvGzRK0GrlwhKg4f9JQE/fz84O3tjaJ6T5MKVoR3BMqbykXrGOibCEd8HoFxqeMQHxCPbQ9tg4eLIXV0QsIEtKvaceCqMFunRFYCXzdf+LgZJpqGRQ+Dn5sfL02wRFaC9Ip0++mBdSROkVFI9D5rAiwfHx9069YNaZcqAEi0+7pZIBhgffTRR6Jft3D9oFApINHQUktrlAYBFkcR5BO6EFSXay1BpQxQKlVUwSotpaqKDRWstWupqq7f/pCQwBNgAYZKgt9+S42jn35KUZmtGDmSjv/SJdv34UgUFpJgR3Q0fwbY0kSPkyjW0DbCNOpO5Q3QTh60DdytNVqJdpvAGPVfhY4CXP1RV1eHffv24YEHHjBPHd6yBfjoI1JFe4pHwWraNGqK37XLtuPig9Dk4V//ogfljz9qH5raI9++3Tr6lNC54WTcFyywyWQ1Pj4elws0qo8dFmCRgmBmbiXCwsIM+pnGjx8PJycnPZrgZKDyT0Ber9u+6iiwOxEo2wPEPqqVSC+qAc4XAeMHuJB0egdj0KBBOHv2LLy9vaE08q8ymIxyk6RKos2iutp8sMvnrWSO+tYRsHDvixKCMRekzZ9P2finnzZMAjz9NCWz+vShfzvid3j/faChgV+Jc8sWks93lOT8118TTWLgQEAqRbiGJldeD+2zUSKRINBTnNlwXp2AB9bVVKquGFVZTNBSjKI6V/j5+cHHTBXVXi/A1NRU/PWXTiBCLpcbBkciqrQdbfguRP81SBJcXmK6oaoFSE9GuE84pBKpAUVQzdTIrsoWH2AZeeidvgp4ugE9umqW8z1L2tqEx3G9AEsikZBUe50Tbw+WXCU3UPgVgrGJcFlTGeQqOeYPnI9OHp1M1r879m54u3pj5+Wdgvssbiw2qV4BGrn2rqOxJ9dUrp0LuhzSfyVxRvrlCgQFBWmTHmKRmJiItIxMwLvr/04F6xZuDshVckgUdGOUVreAtVVpXdGFKIKMMVysviioIGhgMsxJtFsZYCmVNJ+ZMIGEsjgkJFBPqIkica9elOHk1KDuu4/4+/aA88O6EWiCDQ2UBXv8cXqo8WWAxUz0kpIocgUQuno1AH6KYEVTBXJrc20LsK6mAlsigaYrRBO8moqtW7dCoVBgFp/5qn5WfMYMyoYv4XlQApTRDg93HE1QJhOmNpWX03E9+aRNwY8BhM7NTz+RR4oQLEyQEhISkHOlCJA4d5xUewMpCGZdyDaoXgHU3D5ixAhdgCV1A5gS+K0TNYL//RDwx91UyRx9FBi+ViuRviuNNpnw+EJDiXR7rQjMwNPTE83N/FQY7WRURLbeAMbeSmKob46GiHvfohCMUIXEwwP4+WeiIfL5WIaHE03v44875nfo359Mm7/80sD8HQoFsGMHKXla8GoSjX37qOIvlwOMIbi4GFIAZXVOJlLtlnqwGtoaUN1SbaogqO3jKYBxlcUAGpPhohphgQsO9noBJicno7293WCZQXAkokorKsiz4942ThJ06tQJarXaUFyiRWC8bCmEs9QZET4RBhTBq3VX0apsFa8g2D8F+qpgZ64CibFSOA34kBZYSxcOD6f7S0/ooqhaZaAiCFAPFiBOqp3PRJiB4fNjn/Ou7+rkijFdx2BX7i7BClmJrMSk/4rDuG7jUNJYgvOV5w2W78zZiRi/GOvl741RnwH49UR6Rib69etntbZDYmIi8vLyIHPtdSvAuoVrC4VaAYmKbiq5QoW6ZhjQxSSQmGTqyprKIGuXCXpglWgCLAOTYSspggcO0Pz2sccMl8fH03PVZE7MeWP07Emc/XvvtY+TD5ABZmzsjSF0sW4dTfKeeUZ4HbETPU1vXGBTE5ycnFDe6Aq0GlIET5WeElaJNAdu8tCmeRDIa4GT8/DrT18iNjYWg40pf8aUCsboXPL1LAH0YJ4yhahB1piWGz/Yv/iCMvJmaDcIDgZ++EH4fWt6bITOzaOPArfdJmxh4OfH68/DISEhAdXV1ahlsR1awVL79DRQENTHlClTkJ2djZyDnwPZn+reaCkECjcAnQYC958FAjUy7BqJ9J1VkxAXF4eeo1/VbWOvFYEIWJyM2tJTZS31jQd2xZX61xcHHrVNs+CzG/D0JHGKmhrh8VTfl8gBvwMvONXOT/Wur0OHKPHkQKU6JCcbiFw4AQgBUFahNjEbtlTB4iTaTSiCRn08ALRVFgPIawFVK4qq2i0GWKIooGYuMIvBUUoK9fnqw8PDIIC3eF854N7WTxJUVVVh4MCBePnll7V0X+MKkxaa5VG+UQYBltUKgtFTAakr4OQBlRo4my/BoDvu0yWIrKULSyQmUu1FlS0kyCDXCUeE+1CAJUbowhYT4QnxE1AsKxb0tCppLOGtYAF6cu05Oppgu7Idf1z5A+O7jbdf7K4uHUrvvsjMzLSKHsiBE7rIqAgCGnNJJ+Amwa0A6yaHQqUAlLqyfmkdtI7oTlInBHgEmFAEzarLtZaguIE4vtoKlkRidT/C2rXUdzxxouFyXiXB1FTTJuiUFPsnZRIJVbH++1+LJp4dCsbI42bQIHqZg5gJjiaokJaWIjQ0FOWN7roKloYieKToCABY74HFM3mok7Vg3+F0fnogX7WgtdV8dWjaNJps/cHfWGsCvgf7P/9J18zkyURB4sv+f/EFqYoJBT/W9tiYOzd8FQgnJ6C+nqqzmzbxTpDiNcaqOfVhHRNgqdqApjwUNEeipaUFffr0MVmFUxvbumah6cQRoMy/q2GvZltbGw4cOIAJEyYYXhPWVo9sgMXJ6HXoqXJIXMldX5psuFV9iowBp0/TdcWXoHFxub69Zr16AY88QpVtLqDbsoXumdF2KJQZgyfQCAdQVs2srmDl1Wo8sIwpgoJVlgJdFfpqKrCzLwCguKQE0f7CSRZABAXUwgVmMThKSiI6CaALtB96yGAMs3hfOfjednJywnfffYfKykpdpa1/ipaCrFvRU0tBjvaNNujB4hQEewX3EveheatojBv5By4NyEJLO8PAe61kkRjDKMCqqGmCXAkDmqC2giXCC0vILFhoOaCj8e3MMaUJqtQqlDWWGXhg6SPCJwL9Q/sb9GH9Xfg3muRN9tMD22uA1hLkyiLQ1tZmV4CVVugEgAH1mfYd0zWEqADrt99+Q22tZe7oLVx7yFVyQKUrC1Mzr2EfllCAJWgy3OgFZ2dncnC/epWaxd3cRB9TYyM9Ox980HQzLsAy8MJKTjatZjhqUjZyJE1U0tLs35etOHKE+svMVa+sQUAA/bAlJQgNDUVFo47+4uXiBVcnV5wuPQ0A6B7U3dyeTMEzefj9NKBUgV890JZqwYgRVNn5/Xdxx8T3YAfouly3jkQ1zFX+rkWPDV+F66efgP37SbFw5kyihxpNkBI0/YE5tT6ktuRonw+ZRkGwnLLXfBWs2NhYJCYm4vdj9fz7MOonAIBDhw6hpaUFE7hJG4droMhncTK6cKFptcbO822pOuXQuWfXrhR0fP+9+MTQ33/TGPPmm9YlAa5lr9k771B16eOP6fi2biUauA3+ioLgCTTCAZQ3SqyuYHEeWCYUQaEqCwDs6A5sCgWOPwG0laFdAVQ2METJ/wKmBJktb5qlgFq4wERVwPLzgdtvp2uqZ08TQ0qL91UH3NsDBw7E888/j+XLl5PQTuckoiA7aa4Jz1j6W1NhMjYbzqrKQpRvlDiVO7UKuPQlEHg7EHwHzmg80wwM0pOSgLff1v0thibbtSsJYajViI6OBmOMWEB6NEGugiWGIpgyKgWeLkbn0oyJMACEeYdhYPhA3gCrorkCKqYSrGABGrn2oiOQtZOdwq6cXXB1csXIzjxVcWugofSlF5PQli0BVkREBIKCgpCWq6kI1t88QheiAqxFixZBZs7H4hauGxRqBaDUBVildTBREjR+kGRXZcPH1UdrUmeAlhIU17sgIiKCvI5skGjfvJnGfmN6IEBWJb6+RhWsjpyUcbSZ69mH9e239KUfesgx+5NIqIpVXIywsDCU1zNtBUsikSDAIwBylRwRPhHwdfO1bt/upsp6G48DnUOdtbLYBhCivpjLiru6UjZ12zZxE0ih66BUj25hrrqkF/ywjuyx4TuG++4Dzp2joNiogRwtLeiydCmkUikul0up2tRSYv4zxDbXc9AoCGYWEG2qVy/+TO+UKVNwNAeo5LNDMZpQpqamaoPt+fPnG6qMXaMqidnJaHw8BbGBgQ7pJRIqHixbRj7ozz8v3AZo8xA2fz753u0WNgE1wPLllLQwN8Zc716z+HhKMixdSsb1paXkkedIQ2eeIDLMyQll7a5amXZAU8FqqRHsWUk9n4qUwzSh7bOsD1LP6x1j/xRdAMDBSePjNGgpoJBRHyOg7WeODlQBI2tsL29aeEZaDI6Ki8mEecoUOvePPkpBOddjrYHZ+0roHnZyIksVG/HBBx8gPDwc8+fPJ/GazklA95eo73NyrkF/Z5RvFFqVrVqxCKsUBEu2AU15QM9XAACnT5+Gl5cXunc3SkJyYiR5eeJost26EQ28rEyrFFlUA4PElLerN3xcfURVsJL6JmHFpBWQSmh6HusXa9ZEmMOE+Ak4XnzcRB1TyANLH+Pix0GpVmrl2nfl7sKIuBHwcvWyeLxmoVH9S89rhrOzM3r2tMCo4clkSSQSErrIygOcfchw+CaBqABr2LBh2LJli4mT/C1cfyhUCjClGs5OlLE1DrCCPYNNRC4u1pC6HC+3tpV6sLSSsvn5VvdfrV1L7U933GH6nkTCoyTYkZOy8HCip1yvPqzqapqFPf44VTIchchIoKSEAqw6hTbAAnRCF1b3XzUXAYpWQKe1h5pG4I8s4IGp9/NfL3z+IGKy4lOn0m9z5Ijl44oUeDBYc31ogp+LWVmO7S0RA2dnQbqXa1ER4uLikFOiqeCaowmKba7XR0MWIHFCVl4lIiMj4e/vz7valClTwBiwI93I70ePngNQcDVv3jw0NjYCAIqKigzVylJSTJU/3dyurSLf5s30m+fmOqSXSKh4sGAB8MADwOrVpu0tHGwewiZPJn/A776zvG5lJfU8zp5teYzpqB4rsejXj5IqnNhFTY1je/S4IJL74X18ED5xIipkcqjkjdr+jUDPQKiYCg3tphkFTsWtWUHrFjQUYN72ebogq3MS0P2fmrUluipLj5eBhAWAWic2UaSZ60YHAtCJd1pf3hTxjDQbHG3fTv9O0VgtPPII/SskP86HuXNNl7m5UWB/553AK68Aq1ZZ3Yjo6+uLr7/+GmlpaVjCiSP5JJBYl5GxLGc2XCwrhkqtwsXqi+IDrIufA15x5LcF4MyZM7jttttMrYb+/JMSmGLnPZySYG6uzgurVsKrJCjWC+u+zvdBzdT4cuyXyH8532JwBZBcu5qpsSd3j8FyzgNLiCIIAMOihsHXzRe7c3bjSt0VXKy+iPHd7KQHAlRtcg9B+oVc9OjRA27mmFBmaLCJiYnIzMyEwqfPTSV0ISrAqqiowHfffYeBAwdi2LBhGDFihMHrFq4f5Co5oGTw9XKBv78/ypo8TAMsI4pgdpWARLtaCbSVo7i6nQQulErKolpRwSoupmLRY48J91SbBFgdTV0ZOZJMfPWan68ZVq+mz50/37H71VSwQkNDUVnbBnVbHaCiBzsndGFV/5WqHfh7JiBhQOIimjRAgt/PBxA9cN57pts0N9ODu3NnetBbkxW//356OIuhCfbta7rsWsto2wszE6SEhARcztdMOM0FWGKb6/XRkKVRELzISw/kkJiYiJiYGGzN66M998b0HIDUyowTbQZqZUlJ1Hzp4UHXg5MTVZIsmVM7CoxRgDVqFB2HA2CuCnX8OMXOK1fyt+AtXGjjh7q4kPrlrl2Wy2A//EDKQY6iIHckLBgqOwRJSTQ5694dGDsW4aNHQ61mqJJBZzasSUIZZ/vblG34x55/mKi4tShakHxA7xg9yCID00qAqfmGKpp6FV+tIm8AAOOWL2vKmykpFLTow5oxcOtWqiD20Dz34+KA4cNpUivWJzIvj8ZsfZuRH36gNoL580mG/8knbWpEnD59OsaPH4+33noLRUVFgK+ml8BoPNQ3G75SdwVtyjZxCoLVJ4CqI0D3l5H68wbExsbiyJEjSE9PN6zAM0biK/fcI15kS0+qXWc27M2rJCg2wMqspD6jPiGmPbNCGBQxCCFeISY0QW0FywxF0MXJBaO7jMbu3N3YlUMWKnb3XwFUbfLvj4yMDMv0QDM02MTERLS3t+NSQzQFWDeKt6kFiAqwHnroIXz00Uf48MMP8dprr+Gll14yeN3C9YNCrQBTMri6SBEeHo7SBlcTL6yalhqoNb0dje2NKGks4Z98t5WDqdUoqWqmClZxMWUbrQiwuPGajx7IISGBxl6tuJoDqCtmeyRGjaIb9cQJ0ftzCNRqykDfeSf5zDgSXAUrNBQKJaceWYnU86na/qv159cbUlvM4cyLJMk+7Ceg16s0aXhEjY05g9C1a1fcdtttunW5H9vbGygp0fUWWZMV9/Eh+tyWLeYHy9xcEsO4557rK6NtL8wkEeLj43E59yqY1MO8VLsZCWNBNFyAyrsnsrOzeQUuOEgkEkyePBn7j2SjZcwF4BG16cQRQIEAF06rVpafT1WJTz6h62HTJqKCffON8DE6EufP0zUzfbrDdikUG8fGAkOHUixkPIT5+dHQefy4HXOBp5+mjVeuFF5HpaIxZsQI6qu50XENevS0iI4Gioq0vjv6XlicJHX8knjEfhmL1/a/hid+fwKhn4Xy+kYCRipu9RmAWzDgHma6op5Qg7aC5Q1go9F61pQ3hw+n+0k/afDpp+LGQJmMsp6TJxsGDY8+Cly4AKSL6GmpqgJ++YWuSWObER8foqiGhJhe7CKDZ4lEgm+++QZqtRrTp09H3MCHIE0C4gYnGQRAXBWmqKHIOgXBi4sBFz+knvTGvHnztONVY2OjYQX+0iWqCN9zj+V9coiJoWxKXh68vb3h7++PonoP3gqWGBVBwLYASyqRYly3cdiTuwdKtc4nsFhWDBepi9YXVQid3DuhpLEEL+x+Ac5SZ5wsPSn6s3mhVgINWaiVJqC4uNhygGVmbNAKXRR7AooG88+8GwiiAqxp06Zh2rRpmDJlCgYNGoRJkyZhwoQJ2uW3cH2gUqugZmowpRpuLs6IiIhAaZ3EQOQiyDMIKqZCfVs9AAsKgi0laGgBmlvabZJoZwxYs4aogVxShw8JCbSuRniHYAd1xaKC14gRFHlda5rgwYM02euIzHJkJNDejjANJai8AUhNX4152+ehVUl0s7q2OkNqixDyfgRyVwC9/g1E6yam1dXVOHDggKF6oP6PzeHTT22j+EydSuc6w0zJ/9//pp6tn3++vtQme2FMXfL21gaJCQkJaGpqQoWys/kKlmsA/3KhpntVG9CUi6tNpOBkroIFEE2wtbUV+/fvN3mPMYaFZsoxWrWyQ4foX47ZMHkyMHYsCRxUVPBt6lhs3kyTSI4K5QCkpJiK9fAVD/SHsLo68rn+5hvg1VdtDLJiY4Fx46hKYGSsrMWePXQvPvusDR9wHXAtlQyjooCiIoSFURBEAlDlSD2fim9OUsDPwFDYUIhPj36KjZkbMaPnDIR4hfAfor6KW10G4N+Pv8rBCTV4xqKoFgjwlsBzgwug36ZkJJFuERs10dmZMyRmApi1fzDAnj1U4TS+J2bNIirtunWW97FyJTExFiwQXqeKPzAVGzzHxcVh8uTJOH36NAqKisEAFJTWGQRAYd5hcJY6o1hWLF5BsCkfKPoN6DYfyW9/YL4C/+ef9K81zCwXF7pX9aXaa03NhiO8I1DWWCbY96ePzMpMBHkGCV6LQpgQPwF1bXU4Xnxcu6yksURr0iyE1POpBvMEpVopbu5gDo2XAXU70kupp61fv37m1zfTz929e3e4ubkh7YqGfnuT9GGJCrCUSiUWLVqE/v37Y+zYsSgrK8Orr76KV1555VZf1nWEQmMorFYyuLk6ISIiAmV1ShOKIKAzG9YqCAY73mT43DlKiJmrXgECUu12wKKCl78/MGDAtQ+wli8netSMGY7ft6ZHLlTzgK9oAJKPLbFMbTFGzWng1HNA6Cign+EEesuWLVCpVIbqgY6US+MyqkI0wb/+ognz669TL93NDo66dM89RHvUBImcVPvl+hDhClbTFUDRBJMh26hHygCyywBTI7OUogNLAdY999wDPz8/nemwBowx/Pvf/8Zbb72Fu+66y7xa2aFD5CzOfZZEQnL6ra3AG2+Y/XyHYPNmqhiHhjpsl0lJ9HWkUvEFVImEinjPPw98/rmhMJlVmD+fKoA7dvC/v3w5fdepU238gGuMa6lkGB0NlJUhXON0X1YPoLUcyQeS0aYyDU6CvYLx45QfsXjsYvMqbmoV0JBJAZYQNF5xxR6TENWlL/DUKsMoffx465JEGzeSxUeXLtRTPGQI9TuJidy3bqV70rgpOjCQAviffzYvNqRU0nU2apSOYsgHBwTPx44dM1mmHwA5SZ20ZsNZVVmI8YuBj5uP+Z1e+gqAFOj+gmW/sD//pGdNt2686wmiWzfDAKtaSRRBvfMT7hOOVmWrVqnPHM5XnkffkL5We1CN6ToGzlJn7LysowmWNJaY7b8CyNyYS8xysDh3sARO4CKfri2zFSzG+K8Tzdjg7OyMvn37Iu2SZm57kygJigqwvvrqK/z999/48ccftU1qjz32GC5cuICPP/64Qw/wFoShUOkCLFcXZ6IIVreCtZbTQwDQloU5JcHs6mw4S51NvT0AErjQ9OJrK1hSqXBmQQOOMcaJzBlTxY2hmU86LMASxToZNYr4Ok1NjvlQSygtpQfb3LnCHfD2QCP8EKZ5MJbXA4VNIqgt+mirBg7PANxDgeE/A1Jng7c3btyI+Ph4w4HRkRSfkBCivmzZYvqeWk1eV1FR9O//EhITqWqnOXcJmoxDTrUX0HyVmrv1oVYARx4BnN1JqcyT8/WSAAO/NKHxadFAGd6sQj0FQTNcWhcXF0yYMAHbt2+HSnNsarUazz33HD799FMsWLAAhw4dElYrY4w850aMMMzsd+8OvPwyTQhP2kk7MYecHKIIOpAeCBALNj2dKlHWFFC52PKpp6gXa9YsG4yIx4+ne/3bb03fy8+nHq2nn6Yq782Aa6lkGB0NMIZwzSS3rIHYHULjIWdgy6m4xfrFQgKJqYpbUy5S/2pF3OQ1kEqliIuLM+zj0d9nURH15SQlAZ060fNg5EjKRoota+bmUuXqwQd1y+bMATIzaT/moFDQNTJxoqn4DEA0QU7NUQjbt1Mv9vPPm/8sIS9AK4LnoiJTSwjA0Eg52jdaG2BZpAfK64G8lUDsg4BnlHm/MFv6rzh07ar1ryOz4WZA2QypShdMab2wLPRhqZkaWVVZVtEDOfi5++HOmDsN+rCKZcVm+68A28yNLaI+HZC6ICOnEiEhIdpKMi/ef59ULadP52V5ANQnnHbuNBicgIy3xKnoXmeICrB27tyJd999F4MHD9YuGzRoED788ENeOsktXBvIVTRxogoWUQTlChXqmtRAOwVUQZ6UveN45RerL6JbQDe4OLmY7rClBMV1NAhrK1hRUVQCFwAfY+wf/zA/efD1paSrkQ2HzRCVOBs1ijJxf//tmA+1hB9+oAn0vHkds39NBStM4x9W3gDEePrzrmpAbTGQ+Y4mWfC7NhnIs6empiI6Ohp//PEHKioqsF5facrRFJ+pU2n2ytFRdQdBk4qPPjJ9aN/sSEwkgRBNxjMmJgaurq64XMYApgKajH6L8+8BNSc0SmUvUm/U/WcAMJKEFkLDBa2CYExMDHy2bbPohhsQEIDq6mq4uLggNjYWd911F7799lu88cYbWLJkCaRSqbBaWX4+Bdp89Jo33yRVvOefN5WrdxS4QN3BtHXuVn76aeu3lUopNho+nIT+rO7/d3amD963z/QeWbGCJoIdNcZ0FCzQwS15jomGJjHoXl0Nf39/lDd6Aq3looxck/omIf/lfKjfUZuouKWu/gbzVgIFpSTzXlBQYNjHowdtgNXSQgbLXboATzxBvklin0UcPVCfScCZTK5aZX7bw4fJ7FyIMjtpEvVQmaMJLl1K4/vEieY/yzh49venG0ehML+dHiwaJoP6sPLr88UpCOZ+DyibgB6UpEvRVEP0oa3A5+YCZWXW9V9x6NqVfufaWkRHR6OmvgUt7YCLQmcNoPXCsiDVXthQiCZ5k00BFkA0wfOV51HYUEieXLISiwGWLebGFlGXAfj2RHrGefPVqzVrgHffJRVUbpAcOZKqpXpjQ2KUAjV1TSip0VRbxajoXmeICrDq6uoQGBhostzDwwNtYnnAt+BwGFIEXbTNvKV10PZhGVMEs6sFFAQBjckwldsjIiJEeWDZyhgzURK0AykpInxFhw+nLO+1oAkqlfSgGT3aeqqBWISFAVIp/Gpr4erqiopGV6QkDDFPbTGW+Va3AVInMqPVgJPhLi4mBSSZTGZZhtseig9HbdKnCTY3E51s8GCdnPD/EjQNu5z5tZOTE7p27YrLxZrqqn4fVsWfQNaHQJe5QKzeBCtgABByN3Dpa2om5kNDFuDTDVkXLpLAhYWbNTU1FT/88AMAogUWFhbi6NGjmDVrFj788EPLdBXj/it9+PoSZ+7UKVLW7Ahs3kxl9NhYy+uKhEpF7SejR5vvKzUHJycqABhDNLP2ySdpgPv+e92y9naK/CZOtMgwuJlgsZ/WGnBWI5o+rDKZC9BaZpORqz6SP16DFiNBWoM+Hr1lNTU1FGBxwXGXLpSl9/YmI3Ix2LgRGDbMMInVqRONnevXQ2JOHXfrVmJQjB7N/76HBx3Ppk38PV0XLlAv8bPPUrBvCfrBc3U1BSsvvmiaHBAAr2Gyh7uBYXK0bzTy6/MhV8nNKwiqFcDlr4GQETReApgxYwbc3d3h4eFhWoG3ov/KJAlQfDe9oackWFwLuMj1AixNBcuS0IUtAhf6mBA/AQCZBcvaZWhWNJv1wAJsMze2iPp0KL37IisrS7j/6tAhKvHfe68uYQQQBTY93eCaTHQl+fk0fZ0lSyq61xmifbC+//57g+a8xsZGLF68GLfffnuHHdwtmIeuggW4VZQhQkMh0PfC4ipY1S3VUKgUyK3NFZbvbi1Bcb0bQkJC4OrqSoOiBYELWxljjgywQkPpYRygpwHwxRdGiVFPT3q4ffWVA1KjAtCMuj369SMFRgFTV4fA2RkIC4OE88JqdENSoL95agufzLdabjBAWZThnj6dZoze3o6h+HTtSv1I+gHW558TL2vxYst805sRPXvS+dMEWADRBHPyK+kPrg+rvRY49ijgEw8M/Mp0P93/QWpKxb/zf47sApRePXHxokai3cLNmpycjNbWVpO3T4ql9R06BAQHC1/3jz5KfSCvv07ZXkeiuJhUQh1MD9y9m4Ije10WBJhP4pi1UVEUSP3wg85qYvNmUju7WcQtRMKRLZ7awFOjJFhWLwHayi1TAC2gsNzUOwsgGlt7OzXhp6amansrFy9ejNQff6SVunQhr7JZsyhwstTDfukSTTT5bA7mzAFqa+HNJTaMwRgFWPfdZ94f7dFHSWmQr89v2TKqlD35pPnj5IOTky6IfPxxUabynGEy14oSGwSs+Hi+gacX54UFWFAQLPyV+qA0xsIAsGHDBjQ1NWH79u2mFfg//6TJhLHxsBF4kwDfDkQqHjaUaq8Boq48q6WyaStYFiiC5ytI4dLWAKtHUA909u+MHZd3iPLAAkTQYq1FWzXQWopL9eFob2/nr2BlZxPboFs3Gs/0ac5DhlDlU0/hsl8IiSQZBFjADa0oKGr28s477+DSpUsYNmwY2tvbMX/+fNx9990oKyszydrcwrUD14OlVAKuKhUiNMvL6gH8l+gyHi4e8HLxQlVLFfLq8qBUK81UsEpQUi8leqBcTpNcMxWstDTh+a8lxlhCAomKNfA/q6zCxx9TX2ppKbWAADzetKmpRMlSKByQGuWB3qirzfN//73jgzh96JsNy5yAtgqz1BYxMt8Wm4D37aNrY9Mmxyn6TZtGdJmqKjqJixYBM2eSWMH/ItzcKAgxCrBy865C7dyJKliMASefJgPp4esBF2/T/UROAry7ABe/MH1P1Q405iK3IQxyuZwCLAv0Tovn3hyE+q/0IZEAS5bQeY6JcWyigwvQHRxgffcdFYsnT7ZvP3Yza595hgIqToBk+XKarI8ZY9+B3WBwqIq7nx/R37gAS08Ayuw4aQExQTy9TKCqb2hoKO6++248+eSTKC2lSkVNTQ3mLVmCVIDOGUB0qMZG/v5TfWzYQPfNrFmm7913HxARAT+hfWRk0HPOkqLmvffSA9SYJiiTUYD00EOUOLEFsbFEMfz7b+Czz0RtkpSUhMceewzBwUHI/wpIGmXYu3Ol7or2/zM2zjBVuruaCvweCxxNAiTO1IcFOj9LlixBr169MHLkSMNtGKMA6+67LfZf8SYB2qRIxocUYOEsAAqwJICWyuZXugMezh4WKYKZVZmI8YuBr5uv2fWEIJFIMDFhIg5ePYicGurDsEQRBOy7J0ygMQPOKKb2Em2ApV/669uXgu5du0w9C4cMoX/1kns+gbHoFsoTYAmp6N4AEBVghYaG4rfffsMXX3yB5ORkPPjgg/jqq6+wdetWmozfwnUBRxFUKQE3CcDprJXWAdj/q3a9YC8yG9YqCPJVsBgDWotRXK0kgYvCQlomUMFav56S0T4+phoOYhhjnJKgvX1Yp04R6++f/6R565AhVBw4csRoxeRkUy64talRc80BDk29ioSe2XBFA6PJuDkIDUR6y4MFHqRaDvymTURPufdeW46YH1OnUrC2fTv16iiVFDX/LyMx0SDAio+PR3t7O4oqmoDc74BNQUDRZqD/h0DAQO16qampiIuLo+b6Ll2ReukOoPooUG1UZWq8DDAVssro5uzduzfdlB4ehuvp3axi+h8EcfUqlWks0Wuys+kGbWx0bKJj82YKWs2pnFmJoiJ69s+da7YNVRT4+v9dXKxg1o4ZQ5PV774jcYPDh6ms9j9W4RW61GxmQUZHA8XFCA8PR3ltK1hbhVYAyibIG5AySwUPN8MLwtPTE6+++iqmTp2Kv//+W1vJ4tCiUCBZIiE1PwC46y56tlqiy27cSIkmk4whqEL0+OPw/vtv6h0yxtatFCxMmmT+M5ycKIjatQuordUtX7OGRKEsiVtYwmOPUcLsrbcsi3JokJCQgKqqatSrQw0o06nnU/Hd6e+0fxfJigzlxLU0eE1EzpTAyfnA1VQcP34cZ86cwfPPP29Kd+bGLxH9V4JJAMQAubmIqvqajk3vp4SqBZKMNxHuY9lsOLMy0+bqFYcJ8RPQqmzV/i6WKIIOh0blL/1KI1xcXNCjRw/T0h/Xn2cyWQNd7xERhqJI/VOQGOeENP3f35yK7g0AUaPz448/jsbGRgwbNgxJSUmYPXs27r77btTX12NGR0hQ34IocBRBlQpwA+ABwB9AaSUA1GvXC/YMRnVLNbKrsgEA3YN4SuDyOkDVhpKqFhOJdv24IjZWpzA7cCDNl1autF4UylFS7R9/TMkPjsLj6UmK7Cb9w/amRvl4AU8/TYoer79uqPJhy/5tgX4Fq1YOtJabX1/PAFMLvQHqyJEjqKurM3n4aJuA5XJg2zZK59s749RHYiJJBs+fT03b7u6k+Pi/jMREmhRpvKESfOj6uVyqSQLIawFIDYxMuf64goICXXP9u5uQeswNuPSl4f7rOQVBmuj17NmTbkrjydIHH2hvVt7+B30JdnMw13+lj+RkU0+nlhZDsyhrVQ6qqyn77ODq1cqVdEi2iFsYw7j/39OTfgbR8aCTE/UkHjhAmV+A+tr+x5CSwh8z9uxpozaKxmw4LCwMrW1KyFoY0M6vtioKDZlIGg784+mpAGDQx/PJJ59gtZmAqZAxXXVEKiXa3IEDwvzRrCx68dEDOTzxBCQqFf89snUrcPvt4iwLHn2UJru//UZ/M0aVp6FDSR7eHkgkpPQSFESfI6JvX6us2hBhEGDxSewbyInz0eA1fTpLliyBn58fHuPzkbGi/0pIDC/GrRLIy4O7sgjBvjqTad2BFiLc23yApVApcLH6IvoE2xdg3RN3DzxdPPH7xd8BABE+EeY3cDTq0gH3MKRn5aJnz57UcsKXhG5rE05CDxliGGB1TkLindORVwHIWkBqukNWCKvo3gAQDLD+/PNPfPXVV/jqq69w6tQpfPPNN9q/uVdKSoq2Gf4Wrj0MKIKaZeEAyqoBROoy1UGeQahqrsLFmouI9InkLz23lqBVDtTUN1MFSxNgpab1MogrCgupL2H0aHo2hIXZ5hHctSuNu/YEWBcvEsPi+eepksbhzjupsmWQRBRKjUok9CCRy81P7P7zH9PBobUV+PJL6hUSkknuCANNDpGRQH09wgICUFXfBlV7PVHDhNA5Ceiv53WlN0ClpaVhwoQJiIuLw9KlS/lluA8eJE6no5Mq69fTfrmJt0zmWPrmjQhO6ELDMY9vWQ0AuGzw7FUD6W9q/+Lvj2tF8m+uun4DDrILgESKrLwqdOnSBV5cDwY3Kb9wga5zvYw11//Ae+4tgeu/6inQ38lBKOFQVob4O+4AbruNekusUTnYto0GHwcGWEolBVhjx4q2AbQI/XGyqIhYWY89RsOIRaSmAjt3Gi575ZX/uXukf3/6fTp1oqE5Jga4/35g716am5vTc+CFxmyYE4DizIZthsbbJySa+gzLysoM+3hgphJsXD1+/HG6xteu5f+sDRvoHp05U/h4undHS2IiVcL0Zd+LioCzZ8Ubbt92G0X7HE3wwAHq/7K3esUhMBD48Ucad0JCLCZPtN6A1X4G3oAW5cQFaPBlJQX49ddfMWfOHHh789Ct//yTAkALfdNKJb/riqcnkDLkd2pD8IxBdAC0nqK6lWKogmWGIphTmwO5So6+oX3NHocluDu7o0dgDy3LqcfSHvaZBluL+gzAvx/S09N19EBrk9xDhtAEsa5Ouyjx3tkAgIweh0lN9wYOrgAzAVbXrl1x5swZnD59GowxpKWl4fTp09rXmTNn0NzcjEWLFl3L470FPXA3j1JDEQSACGgogr10JWGOIphdZUFBUN9k+OpVwNkZyYuDePtwL1+2z3rFzY3GV3sCrE8+ocHuxRcNl995JyVGzp7VW8jH0XF3p4bWF16gTOfcuYYTu6eeohnQtGnCg4BEQgHBjz9eOwNNDpzZsJsb1GqG6kYAbZXmt3HTUADHpWsHqMuXL2Ps2LHw8fHBH3/8geeee45fhnvTJhK3EFKkshVCVY1r2N/pMGloseAeOhqaYLhbCbzcgBxjlqeY/riKJgBq4PI3uoUNWYB3N2RdyDY0GD57lozoevak6OGnnwyazwUl2M1BTP8VB6GEQ0AAmkaOJPqbtVTezZvppHFBqwOwcye1A9orbiGEgAAq1mZnU+7GIpKTTSOxa3yPXAv88AMVxy9fpkCroICYax9/TH64kyZZaWUYHQ1UVCBco4JcXg9tH5ZNqM8AXPyRnl2AkJAQhPJUh3grwQBSjKsjXbpQz89PP5l6YjFG9MB77hEumWjQMHUqVbpOn9Yt3L6d/hUbYEkklAE4fJh+9KVLKWHC1/tlK2pqRNODu3btColEgpxKZ6o4avqoLMqJC9DgvzvsB6VSieeee47/2ET2X33+OU2Nnn+eHoWAHmtnTDUNGj3fQXSQk2EFS8MUCfcON6siaK+CIIfU86nIrMrU/l3QUGBIpexIqBVAQxaqEI+ysjJdgGVtI+rQofSv3nWdqBnj0/To9TcyBAOsqKgorFmzBmvXrsW0adOwcuVKrF27Vvtas2YNvv32W4wQUVK9hY4BRxFUKIkiCAARXl4ok7sB3rpJSrBnMFWwqi8KKwi2GJkM5+cD0dEoLOIfcBzBfEtIsL0Hq6iIEn9PPWXaf8sZ1hvQBPkMLleupAfTzp2UJTFOj7a1UUYvPV1YhSkmhgI1vf2zjjbQ5MCZDWs4NeX1sJydrT4GOPsAfjTpLioqwujRo6FWq7F//37hfhuVioQEJk50vHGyQzvbrYdDpaHFIiCArh3Ng0LiFYuEcOMKFgwmDFwW3hgxMTFA1FTq3VJqsiENWZB79sSlS5cMA6wzZ4hDC1ClqLiYKpP24MoV2o+YZwFfosPTE/j6a5SlpAgrjQldCzIZsH8/Va+sNQc1g+++owqTJesfezBmDLBgARXBLZ6Ca3SPXPNEgx7a22lMnzpV16oE0Gn9978ph3XgANCvH8VNoo5R07wVrrGWKKuHZSq1OdRnAJ36IeP8eUHpaZNKcGQkVgBImjDBdOUnnqBo0pgSnZFBFSRz9EANZOPG0ZisT0/cupUesNb0JHKWGH360Pbt7TrKoCNgRSLNzc0NsbGxuFyiYWQ00kTBopx4/xRAapj5lTMPfHcQGDdunLYyZoCCAprvWOi/ungReOcdGmq+/lqXGMnM1DzmOR8HxWBE9xqJolrNeOQRqWWKRPhEoFHeiGZ5M+9nZFZmQiqRCifCRSL5QLJ2fsjBgErZkZBdAtRyZJQSrUgbYKWkmGblzSWhBw6km//ECe2iQ4cOQSqV4oUXXjBr8H2jQFQP1kcffQQXFxds27YNS5YsQX19PY4fP46qKju4zLdgN/Qpgm4MwD33IHzBApTWK8FaSrVZsSDPILQqW9Eob+S/ca+mAuf+pS1pR+GcVqLd0b6y+oiPp2eLWEN7fSxeTP++8orpe6GhtG+T3kk+LqNEQk1lxgM/B4mEJpDffWe5QqXZ/8WsLMeo61kCZzasOfbyBlgUukj9dQfiXlBA6uyC6OhoDBkyBPX19di7dy81ogrh8GHqdXFwnwsAx5sXW4nroU8CwFDoon8K4sOckKM/99Prj2ttbYWUpzlF2yPV/WXqo7y6VqsgmFMfCqVSqQuwqqtpQj5QI5oxeTJxsSyZlVoC138lRviEL9Ghn4gQOuf6M2597NpFiREHXpf5+cCePaRMLcb6xx588gnNg594woJy/TW4R65LokEP27ZRkUNIEXzOHOCll+jRVFws8hg1AVaYJnlmF0WQqYH681pvH3PmqQaV4I0bkQToFAT1MXMmPUeMPbE2bKC+OxF0bLWPD13/P/9MScGGBqooWyt9eewYRa1cidDRVG0rkwQJCQnIKdRkfTU0QYty4p2TgBBOIVACeMbit6o5KK9qwAsvvMD/+SL6r1QqIrh4eQHffENDF3c6uXZ1bYCVl4fo3vehoYWhsRXAHWu1VDbOC0uoDyuzMhPxAfFwd7YviWmRStmR0CgIphfQvESbiEhKoqqUVCquYd/PjxIEmj4srgdZrWnGNGfwfaNAVIBVUFCAcePG4euvv8Z3332HxsZG/PLLL5g4cSIyMzMt7+AWOgT6FSxXBqC4GBEREZArVKiVKWjCBZ3ZMAD0DDaqYHGqO4oGLUUwsvgdoNMFIC5OMOHsCOZbQgKN4ZUWWG3GqKmh+/KRR4Q9RYcPpwBLdPBmaQJjaWJ4PaCpYIVqqEMVFgKs1DU/Yt5XhSiobANjDMXFxSgvL8eLL76IAVxVQwibNlGWdNw4Rx29Dh15kYnAdSugJSZSlrqlBeichIRBE3G1CpArYdLA+/LLL6O4uBivvfYaYjUXvZOTE7799lui8QXfSWqDl76kDKKegmCfPhq6CceZ5QIsNze6ibZsTVg72AAAtuRJREFUsc+X6tAh6qsQmy0317TJdy1IpRQc/vyz6b42byYK1bBhZj/SmsqMxmsZTz0l4rvYCU9PqtqUlppSnQ1wDe6R65Zo0OCHHygeuu8+4XU2bTJdZvYYNUko/7o6uLm5oUzmajtFsDkfUDYhpz4UbW1twuapxriikRXnC7B8fCiI+uUXHQWUMQqwRo4UL4/+xBPEwti2jbIDCoV4eiCH5GRTJRFHXgBWJgkSEhJwOa8QDBIDoQuLcuJKGRB0B/CIGpiaj6UbzyE+Ph5jx47l//w//yRGQR9hWt6SJRR/fvWVjrHJCSxzp9cgwNLzwkJTvnY/Wi8sgT6s85Xn7e6/AkRQKTsKV1OBk+TNl/7HcoQF+yEkJET3fmUlMGGC+IZ9TuiCMcsenTcgRAVYH3zwAUaOHIn9+/fDRaMetnjxYowdOxYffvhhhx7gLQhDoVYATI8iWFyMcM3dT1QIuomDvXSDtEkFS091p7gW8PUAfFxbgTENQOfO2riCY984Mq6wVUlwyRIa9197TXidO++kOZnofYuZwNii5tGR8PQEOnVCmEwGgOsvEM7OJie/gRaeJvG1Qk3WHNRqmsjef7+OeO5IXOfgVUgCusMdKBIT6bfVJKniB0+HSg1cHXrRoIH3l19+wYoVK/D6669j0aJFyM/Px5YtW6BSqbSGnJBIqIoluwhcIl+srMI2SKVSXWXyzBn697bbdMcwZw5lvX/5xbbvwBgFWGL6r8RA6Fq4+27dexxaW6mCNXWqWblyayozCgVN9MeNE07eOBpDhtAcdu1amk/zBoEi7xF7KH5iEg0dRSEsLCSLvTlzqHBjzzEaQHNzSzip9kZ32ytYdRpvnyK61qwKsLhzxofZs6nqtG0b/X32LG0jgh6oxciRNGCtXk30vuBgi0kHE3R0ponvGSuRAO+/z7t6fHw8ZDIZKpXRBgGWWagVQN1ZIJD6d86cOYNjx45hwYIFvAwAABRg3XWX4BiSm0t0wAkTDG83LsC6elWzICCAJI31AqzCWgnQfFW7jbkKVouiBXm1eXYrCAIiqJQdAS5Zr6T5SMbVZvSPaKTlAAkqXbpEypZiMWQIKe0WFdnn03idICrASktLQ1JSkoF8s1QqxVNPPYXs7OwOO7hbMA+FSgFoWhbcGID2dkRoeoVK6wC00U18tkyn9jDsh2GGjY56TfQldUBUgOaPQGjls8aNo4nJ4sWOjStsCbCamijAmjIF0G8tMQbnUWsi1y6EG7FCJQaRkfCuqoKXlxfKG13NVrAKS/hLhRYHqBMnKMXekZYMdgav9kz8hBKXMTG20VdFgxNl0NAEtdLEeo2JOTk5ePrpp3HHHXfgfb2JyKRJkxAfH4/PPvsMjDvImAcAFz/gymoAQNbfa9E1JgTuXM/c2bOURe/USXcMAwaQ7LetNMG8PPH9V2JhfC08+SRJl44bR6oTn39OJzg2FmhupuDfzAm3pjKzYwep53eUuIUQunbVFeoEg0AL94itFD+ZjJwmhK51xoBHHonF3LkkWd8RFEJOBO+JJ8yvZzVT0tubJr2aAKusQWp7Bas+HYAEGVfq4ezsTNYHYnDlCrENhHpX772XAkGuh2rDBuKmWkN7dXKiyvTu3VTpbWmxPmnS0TRU42dscDCd9FJ+0QfteFgfbqAkaBb15wFVGxBIRrVLliyBl5cXnhC6sIqLaQwTGL/Uaqpku7hQl4B+DikoiCiD2gBLIqEbOS8PZzTJrHGLGOLGLNbS2LgKFp/QRXZVNhiY3QIXgAgqZUdAL1mvUAIXSoD+0WpaDugk160NsDTb2uXTeJ0gKsDy9PTk7be6fPkyfP8HvThuFshVcm2A5aqp7HNuB6V1AFrLkHo+FYuO6JQeCxsKDdVk9Jroi2uBSG7uVQNtgMVxjB2d0Y2JoZ5HMQEWN4H28aFEiH4Sng8JCTQAig6wgBuvQiUG+mbDTW5mA6yYEP4HvMUBavNmesJ0ZMe/HbCnd2TvXiqCjBplGFtPn04U0++/78ADj4sj2XSjAOuy5oZoa2vDgw8+CFdXV/zyyy9a9gBA9MB//vOfOHXqFA4fPkwLC3/ViVwAyMxvRe/gKl0GUV/ggoNEQmWDkydJQtlaiPW/shceHkRlnDUL+Ne/6Ji5Z1JlpdkTLpQ/KCig8YEx3fgyfTrNVRsaOuZrCOHtt+1nZ4kJJI09DZ96ivpVFy2ipJSxkriHB3nQNjVJsWpVxwgZqtUU348aJehrrwVfEcTJyQJTUs8Lq6xObbvIRX0G4NMN6eez0aNHD1312BKuXDH/xThPrH37gJISUg8cPZoqImKRmkqDGYfmZuuj32tB1dZ/xlZWUvX5vfcoyDGCTqrdW2OcLiLbVaOZxAcNRVVVFX755Rc8/vjj8PPz41+f678SELj49ltaZfFiU69nrg9LG2ABQNeuSD13Dv/RkwYtqGjW9goFegTCRerCSxF0lIIgB4tUSmtxNRX4PQ5YL6V/uedKaxmQ+z3QUqBd9WIpUd37xUCXxOd6/AYPFv+Z/frRJPHkSft8Gq8TRAVYDz30EN5++2388ccfAIC8vDxs3LgRb7/9Nmaa82i4hQ6FQq0ANNoMbpqxJ1xj/sRRBJMPJKNNacaYr38KIKUHRUktEBUIgLkCG6F9KOh5DjsUTk5At26WAyz9CTSHTz4x/+yQSHR9WP/T0DcbbpAK01+YGikPSuHpbsi/sThAMUaND/fdR5lgG9DRymS29o5UVNC8pndvUjXWj61//ZVUzF94wUju35GQSKiKpfHCCggIQEBAgLaC9eqrr+LcuXNYvXq1lnKij8cffxxBQUH4/PPPaUF6MsBI+KZdAeRWAL0jVLS8ro5mAlz/lT6SkihjbsYkVRCHDpGqjDVqZbbC1ZWy815eVkm5m7PAu+suUgvkrLcAamh/5plrq6DnCHaWuUBy3jy6lvUrUIWFRIf086P4+vBhSijoJxq+/55+8q1brwoyQAsKKHZXq2271//7X12h0hKMiyC+vnS+zBaT9LywymrbtcwOq1FH3j4ZGRni6YEABVh8/Vf6mD2bfsD4ePpBT5607gJMTjY18LU2+r0eLI4lSyh59+yzJgFUbGwsXFxccLlcAiibxFE7a04AbkGAVxy+//57tLe343lzXl5//kk3gN751L+Gn3+eGA5z5/Jv3rmzXg8WAHTtiuTKSsFeIYlEQl5YPBTB85Xn4ebkhm4B3Sx/z2sNjv7XUgCA0b/H5wBbuwJbIug9iW5uka4Zi/rHQJfEP36cfkx901JLcHWlbPrJk/b5NF4niAqwnnvuOSQlJWHhwoVobW3FM888g6+++gpz5szBggULOvoYb0EA+hUsN03206OqCv7+/ihtcAFayyyryXROAmIegFJFQVlkiB9QNAY47UozD3RcgAVQpclSgGXrBHr4cJKBrzAvrHdzIzISKC9HWEgIyuuZcAVLdhlJQ1uw/P3ZWqqvqAEqLY0m5jaqtF0LZTJbJqdqNdGRZDJi0hhn7qVSUugPCSGhLz2vQ8eif38KsNRqpKamoqmpCd9++y2Cg4OxdOlS/POf/8SkSZN4N/X09MSCBQuwbds2XLp0yYDue6kMUKmBPtGg5cYCF/oICaEGg7VrhdU0+eDo/isxcHIyHQw4CJzw994zXebpSS4Nq1aRvoe11luOhiPYWULrenhQUWTpUn5T4/Z2XVJZqIjPmf4KYdAgyr/Mnm39vf7DD8RanTbNwheE6TEWFhLT7B//MFPg0FSwwsPDUSdrR3trM6CwxkwLtH5THuqk8SgqKjKrIGiAtjaqSlkKsE6epEGHO0E1NdYNlI7qn7rWLI6oKODDD8lqYf16g7ecnZ3RtWtX5JRofpPGHJ4dGKHmJFLPRSE2Lg7Jyclwd3fHuXPnhNfn+q80jX/GzyvGqLhmdGhadO5Mj0fttde1K4R+cY6KH+7NH2BlVmaiV3AvOEnNNCFeL+jR/7RgCqC1GOi3EBh/Hrj9J1K+BZBRCLg6A92jPCiJr1ZTq4E19EAOQ4aQF5ZKZZtP43WEqAALAB577DEcOnQIZ8+exalTp3DkyBE8/fTTwo2Dt9DhMOjBcnahQYLjmje6Aa1l4tRkpC4obw2AmgFRoxYB5zwpe6U5t/n5lHTQb91wFBISaAATsr8BbH92cH1YN1IVy+HVnKgogDGE+vigvLYdaBUIsKqPAgD6DBkPxhh++eUXcQPUpk10sNYqUmlwLZTJBOyhzPpzfvkliW0tXizcgxUURBPToiKqcHRIP1ZiItDcjNQvvsC8efMg18hJV1dXQyqVWsyUP/fcc3B3d8fixYsN6L5ZxfRv70jQck7gQkgtcs4coLycfhSxyM2lyWNH0wONYWU0wrGDgoMNE/Nz51KQbWx/x+Fa9k47gp315pumyzw9qQpVXS0cAxcV2XeM33wDrFlDcyjjcdzSvV5XRwzkpCTb7PX8/IAPPgD++ov2w4voaKC6GuEaqX+bzIYbsgAwZJSSyI/oChaXnbQUYNmr4HedrS7swrPPknz3yy9TYKmH+Ph4XM6vpj8s9WEpZEjddQHzPs/SBjNtbW3CUt5lZZTd1Ru/+J5Xra3Cp6FzZ2JjVmsOEd26QegX56j44T7hghRBR9EDeSFE8RODFoHBUK0A+iQD/n0oWT9kBeAZi/RCoHeMK1zu+J6WX7xIvGtrhVcAujaam8mV/SaDYHR07Ngx3ld6ejoyMzMNlt3C9YE+RdDV2RWIiNBKtZfWS4G2MnFqMnVpKFFQWVprMqxXrioooD87IkmdkEAZVHMPeVufHQMGkBL1jRJgdUg1hzMbdnNDrawd8tZ68kEyRvUxwLUTjmcSzeJ2sZmkTZuIny5WLtgIHS1MVVhoyozhUFkJfPSRaVHm7Flq6p86lahg5jBsGPDZZyTMlZRkPji2FDzzvq8Rukj++GMTWolarcY777xj9vhCQkIwe/Zs/PTTT6iMfE2bQcwqBpykQAKXQTx7liKLwED+HY0fT+fYGrGLa9V/ZQwro5GffiIWY2kpf2L+Rpib6rOzAMqVWcvO4hJgoaGmDC9nZ/u/pxCD7LnngMces7qwCIDugfZ2cfRAITz5JCVJXn2V9mUCzgtL32zYWiVBjbdPRgGVOh0i0a4PewfK62x1YRe4i72+nk6iHhISEpB7pQBquFpWEqw5jeSNQEubYTlaUMqbp//K2tPAnVZ9qfYUAJ5GhrqeHm5aKj5fBauutQ4ljSUdF2DxUfxOzhMfZHkKDBLGyzsnAVPzkV4Zin53PaxVwtUaadtawQIMDIdvFggGWHPmzMHcuXO1rzlz5vC+5gqRU2+hw2FAEXRy0QoeREREoLRWCbSWWVaTUSuAhkwUt1IZICoqimreegGWUbzlUHDG6uZogikppgqqYp4dbm50b1oldNGB6JBqDmc2rPmBKoW8sKqPAkHDcPzECYSFhYlT3snOpsyTHeqBQnGZIyav5eXUGK9S0bWgP/FbvpwoR//5D1FFP/2UruHevXtg6FBq41m5UlzS4MUX6Tr6+Wfh4NhS8Cz4fkYfwNkZhdoUqCHESND+4x//gFwuxzc7KrQZxMxiID7cGW7DNRnEM2f46YEcXFyARx+lZjSBYzHBoUNUJuzeXdz6joIVvSJVVaQM+NhjwqbBN8rclGNnffstXdOWhHyMsWuXVjBPtMWYtd/THINM6J4W0hcAiB44YIBOUNMWODtTJfrqVfIpMoEmwArXVIj0LUxEoz4DcPZBenYRgoKCEC5UNjeG2ACro6LfG5xCpUW/fsArr1CCh0vcgAKstrY2FLfHigiwTqBQYOjiHUf//JOoOXoXn7WnwUSqPSICSW5uWHHffQjQiJREdgJWLHxUyxYJ9w5HbWutQW98VlUWAKBviP0eWLzgo/ipWnQKf5bQPwWQGpWYnTxpuREqKipQUVFhSKM9fpwGJ0462hp060bbciqENxEEA6xx48bBx8cH/fv3x6uvvop9+/bh4sWLJq9bMu3XD4YUQTdtgBUeHo6ymnawFpICNasm05ANqOUoaaSnYFSnTjQr0YwcjNGDtKM8YcRItXN2QX5+1j877ryTkvdC2dVriQ6p5nBmwxpuTjlfgCWvBxouUIB1/Dhuv/12A8sFQXCunmKbI4ywaxcxPow/yt3d/slrbS0wZgyxPHbvpkBKf+L3zDNE7/v5ZyArizzTKLiRQKkk2odYNpxEQp9jjJYWYrfMmUPiAXzB81NPkfii0PvJ77oCPXsiRoAfJSYQ7t69OyZPnoxvvvkGLaHTgKn5yGroht63T6HgqqGB6HyWzKTnzKFmJKGGA31cj/4rfYjsFVm/niqYs2eb39WNNDflvLx37RK/DWO0/pgxwoFkR39PIYW/+nqiLxpTbM+epRZPe6pXHEaPpvts4UKenltNEooTgKIx0soKVl064N8XGefPo1+/fuLGT4ACLA8PKiuaQ0dHvzcD3n6b5h0PP0wRjVSK+LffBgBcrguxTBGsOYmYYP6L32Ac5agE335L492GDdq3rD0NXOJZG2BJpUCXLkjSKL8CwPrnnZA0Snf+I3xI67m8SXcNnq84D8BxCoImEKL4CS03RuckIGK85g8J4BlLybzOptdYRgZVe00CrNtvN+tXKAiJRGc4fJNB8Nt+8cUXOHLkCBYsWICCggI8+uijmDRpEr7++mtcvHjxWh7jLQhArpIbUgS5ClZ4OOQKFWrrmwFFo/md1KUh9Qjw1hc0mR44bBhSAe3IUV9PQgAdVcEKDaUkkrkA68svaVKem2v9s2P4cJpg3Qj3ppBxrTXVHBOa2Z4AwN0dYRqeXAVfgFVN5fkaaW/k5ORYRw+84w6inlqJ7duJgte/Pz3HuEmdRELjrD3P/sZGmoReukTUPSFat0RCEtN8vYPt7dZVDouLhY/l4EF+8QCA6Ivl5cLvFxYCSExEiru7XRK0//rXv1BTU4OffvoJra2tyMvLQx+uuYxr8jZXwQLID2vgQHE0wZwc4txda3qglVi9mr6SUJ8dhxtpbhoTQ8e7e7f4bdLS6DobP978eh35PfkCuFWrKIBKSTEVovjhBxrXH3nEMZ//2Wd0n2nm5DpoBt4QmQxSqRRl9RLrpNoZA+ozoPLpg8zMTNsUBC0FZDdalH894OlJ5srl5dQzwBgSNNFyzvkGoCkXUAs0azMG1JxAynND4GpMz9MfR40lidvaDKgGSUmUrAPEnQZvb2JpGEu1Iy8PcZpJ09WGTkCTbgXOC0u/DyuzMhO+br6I8u0gd3uxFD9zaMoFgocDj6iBqfm8wRUApGtUcbX3iUwGZGbaRg/kMGQIcP78jZEptwJmw0kXFxfcddddeO+99/DXX39h4cKFUCqVeOWVVzBy5Eh89NFHOHXq1LU61lswgkKtgMS4gtXcjHANJ0MMFSJ1/c+YtxKQNTYDAArLyjAPQKom4ulIBUGABjFzSoJVVSRuNns2iQ5YizvuoH9vBJrg3XebLpNIqLoiBvw0MwlSfZ9BWCMF0uUNMJ08VB8DJFKcyCWKjMUAKzWVKmNpaUQTtLJJbMsWYhUmJgJ//EHHzE3qnnqKqNRGvcwWoR9YBgcDp05RhWrUKMvblpTwL7emcigUBMfG0nkQqvDGxpIAktD7MTEAEhORVF+PFZ99ZrME7fDhwzF06FAsXrwYFy5cAGMMvTknbksCF/ro04fOuyUVluvVfyUSqamUF0hLoznutZRcdwTGjyfZdJlM3Ppctev++zvumMTAOIB77DES2XjpJaLvPf00jecxMcCyZXSZ7dzpmM/u3p1ktVeuBDRJdIKnJxAYCKeSEgQHB6Os0dM6qfaWIkDRgFxZOFpbW8UrCALiJNo5iDCS7ki7ixsCRubIEQA8AVz+O4faGfS8lgzQWkItEQ8/iOHDh0MqlfKPoyJ4+pwbSX6+uCREly5GUu3dugFXriAmOhoSiQT59T5Ac7727XBvTYCl14eVWUUCF6Iro9aifwrgZCSVK0Dx44Ush2iy0ebbBVJTU/Huu+8CAAYNGkTiIqdO0YTF3gBLpdIlC28WMBvQ2trKVq1axQYMGMB69Ohhyy5uSpw+ffp6H4IWFy5cYK/te405PyplANixvn0Z27CBMYAdXr2aAWB7/w3Gyg+Z3U9siBsDYPKKjYpijDG2ZQuJlXbkV3/4YcY6d+Z/77336POzs23ff+/ejN1/v+3bW4sLFy6YLFMoGIuLY6x7d8ZiYxmTSBgLC2PMxYWxAQMYk8ks7zc2lhOONXzFupWx1uHDGQC2cBYYO7/QcMMD9zG2K5G99dZbTCqVsqamJuEPWbeOMU9Pww/w9KTlZjbhvlNQEP17++2M1debrpuRQbtctMjy9zV3SK6uZg/JAIK/W6x9x6D/s9j1/oEDtGDfPnEHwv3YsbEGP8Kvv/7KALDp06czACwrK4veeOQRxjT3s8V9e3hYPvf6XyYmRvyJsAC++8YW2HAJ33A4dIiOe/NmcevfcQdjgwZ13PHYe27UasbefJO+k5NTx52b2lrGvLwYc3c3ukUSExmbMIElJiayCUP8GDtoxQOheDtjqWAbV37AALAzZ86YvQ+1UKvpYF56ye7vZe6aFntuxBzytdiHWUgkJgN1f4CN9wJjqWCsZDf/doWb6P2q46xv377sfqEHPs/+GUDLNXjkEcYiI+n0icFDDzHWpYvegiVLaJ9lZSw0NJTNHt+NsU1h2rfLGssY3gVbemIpY4wxtVrNAhYFsPnb54v7QFuR/jb9RtwrZ4X4bTM/om2aCgRXWbduHfP09DSYR3p6erJ1M2fS71FXZ/uxl5XRPhYvtn0fRnDU84Yx4dhAdIBVVVXFNm7cyJ555hmWmJjIRo8ezT788EN24sQJhx3kjY4bLcB6effLzP0RZwaAnR00iLEjRxgDWN4PPzAAbNU8MHb1Z+GdqNVMIjENrgAwiWbA+eILuq6rqzvuu7zzDmNSKWNtbYbL29oYCw1lbNw4+/Y/fz5jvr6MKZX27Ucs+G7cX3+l33HLFsPlO3bQhGP0aMba283vV/DZABVjXbowPz8/9sL9roydekG3kUrJ2AYfxk4+y0aPHs0SExPNf4iV0Qjfg18qZez774U/4p57aHdiz4e9AZKjJtyWJhdi3o+Ops/389N7v7qaFn7yiV1fRKlUsqCgIO09HBMTw9atW0dR/ZQplr+g0A/t708ndNMmxpKTafbaATNkRz3wHBFQX2/I5TRmPfWU5XVrauiee/vtjjseR50bf/+OPTfr1lHSyuTyTPyUsX792Lhx49jABD/GdvYXv9PMFMZSwd5841Xm5OTEWn/80fKAsm4dJTUAxjp1svv+MHdNizk3jhgDr0nigueLzgJYvKszTfAvfs2/3dnXGPvZhcnqKplEImHvvvuu6P0bX4CxsYzNmiX+kN94gzFnZ73n2a5dtM/Dh9mAAQPYPYPi6NgVLYwxxpQqJZO+J2X/+eM/jDHGSmQlDO+CLTmxRPyH2oK8VXQc+b/Qv5e/Fb/t7kGM7R5sdpXo6GjeuWSshwdjPXuK+hizz9CYGIpmHYTrHmBdvHiRLVu2jM2cOZP17NmTzZgxgy1btoxdunTJYQd2M+FGC7AW7FzAvB50YQBY5vDhjBUUMAawlqVLGQD24QNgLNtMxN+Uz2KD+AOsWM2A89JLlIQTm82xBampNB4ZX++rVtHy/fvt2/+aNbSftDT79iM2e8d34w4bRlkuvqDixx/p+B55hDGVyvT9mhrGXnyR/7kAMBbrW8uYmxvr3r07mzXcm7HDek+H2nTGUsFUuT8xX19f9swzz5j/kiIyfPqwZTK7aRN/sOmgQ+KF7typOybzagViY6lqa4DoaLoALG1o5sdet24dc3FxMbiPPT082DqASsGWIPRDi3k5YIbsqAeeI66XGwEzZjAWEWF57P35Z/p+x4933LHcLOdG8BbxrmYsIIDNnTuXRQR7MrYpVPxODz/I2O+d2aRJk1ivXr2EPyQ8nLGmpg6JRMz9bmLOjSOSDtckccHz2/3H2Zk5SaVMvt6HsVPP82+3fwRjuwezAwcOMABsz549/Ot9/LHpF9A7N8XFtOiLL8Qf8ooVtE1+vmbBpUu0YPVqNmnSJBYTEUgBTb2OhhP+WTib8/scxhhje3P3MrwL9t+r/xX/obYgLZmx9U6MqeSM7ejN2J7bxW3XeJWOP+tj3rcLCwvZq6++yjuPBMAkAGNz5lj8GIu3zaxZRqVC+3AtAizBHqyRI0di5syZOHXqFKZOnYoDBw5g48aNmD9/Prp16wa1Wq19OQIKhQIfffQRhg4diqFDh+Kdd97Rmm6WlJRg7ty5SExMxLhx4/An51+gwfHjxzFp0iT0798fjz32GAoKDHm6a9euxd13343bbrsNb7zxhoHfjFwux1tvvYXBgwdj+PDh+P777x3yfa4FFCoFnLgeLHd3clyVSOBRWQl/f3/ywjLXg1WXhpQHAFdXF4PFnlKptimUk2jvSKEwPiVBxkh6t29fcX025uAIw2F7PKyOHweOHSMfRScek/Y5c8jMfv16YNIkHc8+NpaMUOPjgaVLgZEjSZBKH56eQMqUE0B7O0IDAlDeIDUUuagmn7rsmmDIZDLL/VcaSWMTCDQh2aKMOHkyfczXX5s/FA5CGhvWiINw7Q1ZWRevu4hBr17AhQtGCxMTqWHIHCz82MnJyVAojDxgWluRDFgWuACEf9DoaPqMc+eEBwK9Y7vevSI3gq+VIzB+POmIGPQT8WDXLrI3GzTo2hyXPejocyN4izQFALW1CAsMREVtK1QtFYBayb+yMerTgU79kJGRQY37Qh9SVkaqB48/btLnk9oyBXGz77H5nrD3d3OEgm1HexoC0Il9+PjQ3zExSJg7Fyq1GvktsfxKgmoVUHsaCByC4xq/pSGcd5I+FApq3PXxoX51HhULztZ1+HDxh2zihcUNfnl5iIqKQnF5HRRKGPRhRfhEaHuwMiszAdigIGitcXBjLuAVB0hdgC5PADXHAdkly59TRA7eqcdcEBcXB6lUiri4OHzwwQd4+OGH0blzZ3z++ecmIk0cYgBR/VcW2+OGDKEfWayNyA0AwQCrtLQUCoUCR48excKFCzFy5Ej07t2b9+UIfPLJJ9i/fz+WLVuG5cuX4/Dhw/jmm2/AGMNzzz0Hf39//Pbbb5g2bRpefPFFFGmcacvKyvDss89i8uTJ2LRpE4KCgvDcc89pA799+/bhyy+/xDvvvIM1a9bg/Pnz+Pjjjw0+99y5c1i1ahXee+89LF++HDsd1XXbwZCr5XBS04THzdOTvGzCwrRS7aUyd/MBVu05JN0pxdgxowGAmkKlUqy4915tU2hHemBx4PPCOniQRGNeftn+4C4ujibp9ghd2ONh9cUXJDE/Z47wOq+/TlLDu3bpgrjCQjJJDQujue2BA9QwznnFRkZqng1TSKAkzMcHFQ1qowDrKOAeguMZpPRgMcDie7KY0am15cHv7EzmpP/9L4kLmQNjuqZjkYd0w6NnT1JAVOkLYiUmkueYkNwgYFGGUsgzqxAQF2AJaRR/9BEFWYmJFk94h5hpW4lnnzVddjNeL5xghTm5drWa1Abvv58/eXOjoaM9xwQvz0AaI8Pd3KBSMVQ3AmivsrxDZSvQeBkNTgkoKCgggQuhDwkKokyZUdI5FQ9jHr5HgSrK5nvi9ddNl1nzuzkisDWXf3EokpKAJUvo/3v2IOGJJwAAl2sD+b2wZNmAsgkIGorjx4+jR48e6MQnHfvRR+QNsHo1qRTyiIkcOUJJTGt82Uy8sFxd6cfKy0NERATUajWKa2EodOETrlURzKzMRJh3GII8rVDxssE4OHXbccQ9XUIB0tgvkXpEAlz5yfJnFW1C6rlozHvxLRQUFIAxhoKCArz99tv4/fff8fLLL+PKlStYsWKFqRKuqytSAGGpXz1YDOC5oPlGkIQWC6GS14kTJ0S/7EVDQwPr3bs3+/vvv7XLNm3axJ588kl29OhR1rdvX9bY2Kh9b/bs2Wyxptntyy+/ZA/p8TJbWlrYbbfdxo4cOcIYY+yRRx7RrssYY6dOnWJ9+vRhTU1NrLm5mfXt21e7LmOMffPNNwb708eNRhFM2pTEAqe4MgCsnCMNDxnC2JgxbNSoUWxYT2/G/hglvJM/pzC2vQcbPXo0u+222xhraKC6rJ4CgZ8fYwsWdOhXYYwxFhJi2G8wYQIta211zP5nzSIKry1Qq4WZUXzUFv3S89Wr1B/x2muWPycmhv8zjI97714txZtw/DhjAHtx0iTm6+XK2EY/3cpbuzH25xT21FNPsU6dOjEVHweRQ1ER8UETE+lDRXQyr1tHghPWsmGqqqiVZ76Fvt7ly2mfc+Y4prnakbQAW7FyJX2nvDy9hRxv8uRJ4Q1HjzZLb4mNjeWlaMRKpeIPTkwjmRkehz00Ikedm+efp77GqKgObMa/RrjtNsbuukv4/RMn6PdNTe3Y43DkfdORQgmCl2dyFmMA++3ttxkAlvYhGKs5Y3mHNacZSwX769f3GQC2c+dO+hBzfYhGN0EsrtpNrXv1VR0LEaBnypo19J7YHiw3N/tYi+vW8VMVe/a0T7+AF+nptPP161lVVRUDwBa/OpaxVAljSqNJQe5KxlLB1PXZLDg4mD3xxBOm+zt7lhqlLNCwhwxh7O67rTtUuZzOx5tv6i0cNYqxoUPZqlWrGAB2INmZ+sQ0eHrb0yzk0xDGGGODVgxi9625z7oP3RJjKFjBvbbE8q6+bt065ulq+FzwdHNi6172pz5tITQXM5YKFhvux/tsiY6ONvmc2NhYJpFIWGxsLFt3332MeXuLarj287Pw7GhspB/6nXfE/EIWcV0pgkOGDBH9shdnzpyBu7s77uA0tQFMnz4dK1euRHp6Onr16gVvb2/tewMHDkSahk6Tnp6OwYMHa9/z8PBA7969ce7cOahUKpw/f97g/cTERKhUKmRnZ+PixYuQy+UYqJfdHThwIM6fPw+lUiR94DpCrpLDSaWpYHHcMc4LKyICpXVq83K0dWlg/v1x9uxZDBgwQKfJrknJ1NeTR2lHV7AAQ6n2ixdJuve558gnxRG4807KhFhLZ6isJD8nIVjKAC5ZQhW455+3/FmaoqzF5VwhQ+vNxJkNS6WQNcvR2twAqNqAtiryrgi6Q2swLDVn9PePf1BWb/NmSrOKMMtJSgKGDiVGhDX2LUFB5H+zdi1QV8e/zpUrwL/+RZW9H364cXyK7EXPnvSvAU2QS5kK0QT/+gvYvx8YO1Z30bm4GPzYKSkpphlEiQQp1vj2WDJKsuDXYy4LyVXsOpJC2NQErFlD/mcCSeqbCuPHA0ePCt8ju3fTaRg79toelz24Fl5c3C3i66u5POe4AQDCNc91sjAR4YVVR/zMjALarl+/fvQhMzRy1XyDnlGZrhDW0auNUVFBFPGkJKKMbtlCvx1fkUYISUnkwMCN0wBVeq357RMSaMobEKD72s88Q/6Ud9xh5AVlL3r0oEpQWhoCAwPRqVMn5JSpADCgMc9w3ZqTgIs/rlY7o6qqypSl0d5OPi/BwbrKGA9aW6nAZQ09EKBhODraSKq9a1cgNxeRmmdzvizARKq9qrkK7cp2ZFVmoU+wFfTAllKrjYOT33gdLXKjVdtVSF5XD1QcEP6soi0AgMJyfr+IYiODyKSkJOTn50OtViM/Px9JtbVUebJQXl+9muaaxqsZVGm9vYHevW+qCpYNtsqOR2FhISIjI7Fjxw5MmDAB9957LxYtWgS5XI6qqiqEhIQYrB8YGIjychochd6vqKiATCZDe3u7wfvOzs7w9/dHeXk5qqqq4OfnBzc3N+37QUFBUCgUqK2t7cBv7BiQDxYDALh5edFCTYAVHh6Ospo2sJZS/o3ldUBzAYraOqOmpsYwwNJEVFwr27UOsL76CnBz46f62Apu0DTXh2U88fvXv6gHbM8eCgb4KMZz5wrvTyYjT5YHHhBHoxBL4zAJsMLCAKkUYRpqCpkNV2r7r2Ru/ZCVlWWeHrhnD/Dbb8Cbb+o4DyLAGD1gZ860fsL0wgtEs/zxR9P31Gr6bZ2cKLjqyB7Aaw0uwMrO1lsYF0e9AXwBVksLubV27kzmzwUF9ERSKAC9sS0pKQkrVqzQeWlFR2MFY0iaPNmxX8DMDFnoGmaMWkRHjKCv0lEUwp9/pvvuueccs7/rjfHjKTDdv5///V27qL2Bow3fAl2OBQU05k6dqrk8NYNmmIbnXVYPcV5Y9RmAkycyLpUhICBAO2GGQkEXO9+gZ5SEiHHifwaLped9/DEglwPvvEN/T5hAt/0PP4jbHiAvwLQ04NVXyVs3KEg4oSeEdevouZyXp/vay5cD+/ZR+9nttwPvv++g5ImrK02mz52DRCJBfHw8Lhc20HvGNMHqE0DgYBw/QRNvk+fc++9Tv8H331N0KIDTpwGlUuedaQ26dOExG66pQYSXF5ycnHC1zsvEbJiB4XjxcbQqW8X1XzEGXFkN7DTTliNgHFxYzG8GWVgD8zTBok2Ab0/ECFysQssB0HMrPd0iPfDgQfLHGzmSrmnOM9LZmSdZO2QIBViMmd3njQLn630AANDc3Izi4mKsW7cO7733Hpqbm/Hee+9BqVSitbUVLi6GIgyurq7aZu7W1lYT525XV1fI5XK0tbVp/+Z7X6lU8r4HQCuwYYxsg1nR9UNbWxvqGuogUdCF1tjSgvzsbAS4uCBUJoOzUgm5Qo3a2npUZaWBSd0MtvdsPIFYAHvPUmYiICAA5cePIwzAZbkcquxsHD7sDSAaavVVZGe3dej38fcPRHl5CPbvz8Xq1V0wcaIMNTVlVhvSCsHVFfDw6I4dO+qRmFhh8v6OHb54++1wtLVRzqGgAPj8cyAsTI6NG4uRkNCOxERffPFFMMrLXRASokR7uwTffsswevRV+PvrGmra2tqQnZ2NNWs6QSYLw7Rp4n6/BQsMjwEA3N3VWLCgDNnZhhkkT88EZGQ0IDubvku3oCB4a36s8nqAZR+HT8N+BMIZmw7lgzGGiIgI3utX0taGLvPmgXXujKsTJoBZcY3n5LihrKwL+vYtRXZ2g+jtAHpYDxwYiy+/dMbYsXkG2au1azvhzz/DsHBhKZqaGuCo2447N9cbQUHxOHq0CdnZuklebHw8cPw4CoyOL+TTTxGYm4uCH39EiybtLUlMRLegILS9/z6K9HqzBgwYgN27dwMAPM6dQ1xSEopCQtB0jb7z/Pm++M9/IgDoImJ3dzVmzKhDXZ0zdu/2BWOG0XJLC/Dqq3Ls3GnfuSFhnM7o3h3w97/qsGvmesLXF/Dzi8fPPzehb1/DgKCmxgmnTsXjhReqkJ3toIFSADfKfWMNgoJicekSQ3Y23TPxgYFw19w/ZfVAZWEGauTmv1NMyTFI3bri+IkT6NatGy5evAgA6PbXX2gZMAClQr/JgAFUXgSwYIcEb7+tNhjXAYYnnihHdna92c+vqHDGsmVdMWmSDEplmfaanjAhBGvXBuDw4Rx4e1s+N8uWBUGlCsbIkbnIy1Ng7NhQ/PqrP44fz4Gfn2WhMoUCWLcuHvfc04KyshKU6V2KoaHAunWueOyxWLzzjhO4e7+gAHjqKTVKS8swcaJIx2w9hHfuDO9Dh5Bz4QJCQ0Nx+hQFUJV5f6OmqQcAQKJqQff6TNSEPo1du3bBw8MDUqlU+3u4Z2Qg7uOP0TBtGsq6dIG5QWHLlkAAIQgIuIzsbJXgenzo1Ckcf/3ljezsHACAj7s7ogA45ecjNDQUl0tVUMrykKP5fFU97f/nkz8DALxbvA3OoW/tDgSXfgEXRTkULmGoCXkCPo1/w1t2GC1eAyELHomQsiWQMt28Qi1xR1nwAsh4vmNEiB9KKupNlkcGe0JduAk5fi9B7eRj8J6TogbxlX+hJnQeFizoYiKi5O7ujgULFgheex6nTyNOpUJRZKTg8yc31xVJSXGIjVXiww/z4eurxu7dwMqVgVi8OARxcYbnwj86GuE1Ncjdvx8KO5v/rsmY5jASoh347rvvWEJCAiso0JmY7d69m/Xt25e9/fbb7IUXXjBYPzU1lY3TmCONHz+erV271uD9l156ib3zzjuspqaGJSQkmMjKDxs2jO3cuZPt2rWLDRkyxOC93NxclpCQwKp5jJ9utB6s0WtGs4hRrsxJAvKnYYyx9esZA9iGzz9nAFjGRyCZTWNkL2YsFeyt1//BpFIpa25uZuzllw002b/8kjiwlZUd/302b6bPmjGD/j1/3vGf0bs3eaTwcf+FekfM9W2dOkX9R2PHGlKML1y4oDUWNtc/wQex/Qk9etBvpcWQIez07bczAGzLP8BY0TbG9t/N2O7BbOHChQwAqxMiyr/zDn3ZP/6w7mAZY599RpsWFVm9KWOMsY0bafutW3XLLl0iv9uJEx1vD3Aj9GAxxti99zI2dKjRwuefJ766fp/ciRPEO583z3QnCxfSj5eZyf8hX39N7xcXO+y4LYGzXAgJ4b+G7ZWbNodjx2hfy5fb9x1uNDz0EP2exu2TnP3EtXgs3Sj3jTV48EHGunXTWzBgAGP336/nFygg+c1BrWbst0CmOvok8/T0ZC+++CItLyykH/6rr0Qfi36vang43dJJSZa3W7CAWoeuXDFcnp1N+/r0U8vnRqGgfsSxY3XLTp2i7b/7Ttzx79hhOk4bg7P9sqfXzADc+FVSwt5/n3rgWtaHMHb8Sd06FX9R71HRNjZ48GA2YsQI3XstLfSgjI7md703wqRJZBloCz74gA61uVmz4MMPGQOYGmAj3NzY8IQgOk45aQmcLD7J8C7YkO+HMLwL1tiu0xhgV9Yx9ounaX/VehfyAVOrdOttidW8LyWfKwGs+3Aq8zDuwfL0ZOu+fV/YdDjnO3qvNo0xxli/fv2Yk5OTrr/KUgPfokVmJ5Dl5TRHCg2lXnV9HD5Mm/7+u9FG587RGz+b8XcVievag8WHiooKHD9+HG1tbah2oFRiSEgInJ2dDcqNnTt3Rnt7O4KDg1FVZaj2U11djeDgYABAaGio4Pv+/v5wc3MzOFalUon6+nqEhIQgNDQUMpnMoFpVVVUFV1dX+Pn5Oez7dRQUagUkSgY3Z+ialTTZ7AgNp4q45jxUiLo0wCMcZ89fRs+ePal34+pVA032/HyixQVZIW5jKzh64KZN9FXS0x27/9RU+gyFQkdNevJJorbNmKGjQxrDHI1i0CCidO/dCyxcaPje77/T7/ePf1h3nGL7E6KiiPahRWQkwjS01ooGAK3FQM0pIJj6r3r27Al/Pjm+nBzioDz8sE16+Pv2key4kMCdJUydSi1kHDVepSJpend34Lvv/reogfro2ZOSqQZMh8REaiLiyPzt7cSTjIgAPvnEdCfz55Pk1Rdf8H/I2bPEJdLTue9oCfWvviK6b1kZ/zXckTLdy5cTTf9m7bcSwvjx1At69qzh8l27qHpw223X57hudGjY8rp7LDpaR59vdDOvsAvQ++01yJOFo6WlhRQEAZ2WtxVcsqQkYqfNnUt9VG+/Tffe9u3C2xQWEqtt7lxT1naPHvTxP/xgmS21axf9Ds88o1s2cCCN22vWiDv+deuIhsopW/KhhJ+FZruMO9eXeu4c4jVSw7mNUYZS7TVU1Wr17Itz584RPZAb5Dw9qaH7kUdIxtcMGKNeR2v7rzhw5yc/H/T5H3wAgGp5ce3tyM/XzEGbaaIR7hMOADhVcgqd/TvD21WnMYD0ZEBlJFkMAO7BQPcXAIlm2t45CZiaD4zcD0ANSF1Nt9Eg6W53JM/y1/4tlUqxbNkyJM17E/DtCVzloQkWbgK8uwL+/VBdXY0LFy7glVde0fVXWRpojx8HunWj3jcN9J8/MTF0zWzfbtqGMmgQsY5MlJ/Pn6dJwcMPXx8PECshKsBqbm7Giy++iHvuuQdz585FVVUV3nrrLTz00EOocQCHKzExEUqlEpcu6TT58/Ly4OXlhcTERFy8eNHAu+rMmTNI1Nx8/fuTSAOH1tZWXLhwAYmJiZBKpejbty/OnDmjfT8tLQ1OTk7o2bMnevbsCRcXF5w7d85g371794az8w3BnjQLuUoOiRJwcwLxrQBdgKUJGkvrwM81r0sD/BNx9uxZnchHfr7BSH4tPLAAukfef1/3d1ub46Wdk5MpuNJHezsFdBkZpv5SHCxN/J5+mmxP3nuPAi0OX3xBvGxHt79w4CYPWkRGIqSC6ILl9QDK9gGqVrDAYVqBCxMwRuobbm7Eh7QSra2kvTBmjE1fAQA1CD/3HPDHHxQHODvT/OWhh4T9r/4X0KsX9QqV6rdnGAtdpKQAWVkUafJNEIKCqHl73TrqhDfGmTM0k9LcwB0toX7iBNHjX3iBHqB84JPpBoAFC+z77JoaYMMGuhd9fCyvfzNh7Fg6hRrGGQDqFdm7Fxg3Tvi3/v+O6Gh6lmjzq9HRQFERwsLCUFYvBdosiFzUawQuiqlFoR8nFnP0KD0wuIBLBFpbgfJy3eP1jTeov3f+fBKT4gPX3C9kBTJ3LsUP6ekCDy8Nvv2WxtKJE3XLJBK6V44coZ4qc5DJKGH44IM06RWCw5Mn3O+bloYEjVlmTm0nwx6smhOAVyzOXSyFUqnE7e3tukGOw5IlFge5y5dpDLGl/wrQeWFdvQo6YXp2G50BlMqBdgWAZurDCvUKhQQSMDDT/ishAQuhhEDoSAqEcr4VPsDGXMR3oRPx8ccfQ61Ww8fHhy6ELk8AVUcAWY5u/fZaoOIgED0DkEiwadMmKJVKPPzww8KfoQ/G6EGuN+8wfv7I5TR26dvzcHB3pyDLoGc+NZWyBFxG4Xp4gFgJUUPzokWLUFdXhwMHDmgFIV7XGDMsNE7d24C4uDiMGjUKb7zxBjIzM3H69Gl89tlneOCBBzBs2DBERETg9ddfR05ODlasWIH09HTMmjULADBjxgykp6dj+fLlyM3NRXJyMiIiIjBM01j3yCOP4Mcff8S+fftw/vx5vPfee5gxYwa8vLzg4eGBqVOn4r333kNGRgYOHDiAH3/8EY8//rjd3+laQKFSACoGVyl0AZZmVhreTL4fpfUwvTFV7UDDBZQpu6GsrIwELgAT06uCgmsjcGGPx5RYCGXRJBIq4nz/vW3+LBIJZc779KFKWFQU0KtXD202rKO8aaKiqEqgFbuMioJLQwMCAwJQ3ugKlFG0d6UxAtXV1fwB1m+/UQlq4UJSILAShw/TBMZeBTOu71if1//TTzf0uGk3eIUuevemCyYtjUq4H30EPPYYlTCE8PLLlClYvtxweWsryRTqKaR29H321VfUMzR7tvA6xiKEkZEUEH3/PdDQYHuksGoV/QyOFMa5URASAgwebOiHdfIkKQuauzT+v4Nr0dAmoqKjgYYGhAcFoaxOZb6CdTUVOEKTyYx9n0Eqleg8P48epRNi1BtuDsaCUa6udM1WVgKvvGK6/pUrJP7z9NPCAcoDDwBeXsCmTf6Cn5ufT/pFTz1FySt9JCXRPbh2rflj37SJxvnHHjO/Hl/yRCrVFnOsh68viUWkpWkrWJcrXMjnUa7p9605CQQO1RoMD/31V5sGuaNH6V9bAywDLyyjyUYciJNXWAOgKR8A4OLkovW96hvS13BnAkIVgsslUqDbPKDqMNBg7GAPCkgac5BbTYH4M888g/DwcKxevVpzgI/SPvSrWCXbAKYEYmYCAH7++Wd0795dV8W1hMJCyijozTv4nj/t7cKnZvhwyhG2cW1m12Ki6GCIeqIdPHgQb7zxhk5BB0BsbCzeffddHDEny2YFPvnkE3Tv3h2zZ8/GggULMHr0aLzyyitwcnLCsmXLUFtbi+nTp2Pr1q1YunQpojSVmqioKCxZsgRbt27FjBkzUF1djWXLlmnlqCdMmIBnn30W7777LubMmYM+ffpog0MAeOONN9C3b1/Mnj0b77zzDhYsWIDxN8lTS6FWAEpGFSyOIujmBoSEwKOiAv7+/iirl5g+SBqyAKbEuRJSHhwwYAA9rY002a+FyTBwbRziLWXXLKhPm4WnJ00qm5s5mgRVDH77reOChKgootNpCxeaezMsMBAVja6AqhXwjMLxDPoRtQGWfo3+4Yfpi9o4K923jyYKd99t33fR8/3W4gYfN+1Gr170r0GAtWkTnZeUFJrAmaP/cejenVLTy5YZmhRnZNAFwiVP0LH3WUkJ8OuvRLu1VEHSp8EWF1NlJj8f+Oc/I2GLO4ZaTVn6O++kRMf/IsaPpwohV43ZtYti8dGjr+9x3cjgAiwtzVuzINzLC+W1bWAtZfz8Os7EVVEPAEjPa0RCGOBRvpnusXPnrJ6Jcwpz+lS/gQOB116jQGrfPsP1P/iAzu9//iO8Tx8fqirt3u2Dpib+db7/np5nTz1l+l5UFLHC16wx8UY2wLp1xPQaOlR4HcD0GRoYSPvlq1CIRmIikJYGHx8fhIWF4XKppp2jMQdorSDKXeAQHD9+HLGxsQizkad49CjJ3nfvbtthhobScH31KkwmG9wpv1oGrVR76vlU1LfVAwBWnFmB1PN6E4X+KYCTUaTq5EnL9ZCamoq4uDgyDr5/CVKPSoHcFaYH114DKBqQU84QHh4OPz8/PP7449i1axcqKioAzwggbAxwdQ11jQFED/SMAQIGoaSkBH/99RcefvhhSMTSmTQBr36AZe3zZ/hwqnKdPm3jDm4EiGngGjhwILt8+TJjjLHExERWWFjIGGMsPT2dDRo0yEFtYjc+bjSRi17f9GJRtzmxeD8wtnq17s0BAxgbP5717NmTTb/dnbFjcww3zv2BsVSwD958mQFgMpmMjPgAMjxl1BMKGHgOdxjsMScVCwv+qHbjWnwHfWzf/n/tnXl8TOf3x9+TVYLYEyKy2PdYvmqnpSiKtpQSVaWNb1td/FptSWlpo3x1+aq2Wr66MaqtFt0XpWhRJARFa8lijZBEkD1zf388mclMMpN1skyc9+uVVzLPvXPvk7lzn/uc55zzOer4e/bkNmzbpmmgDQ4O1Hq30qnk1PWe2swpQ7SaNWtq2dnZ1j+EGjVK/SF07KjqKZaVwoQP7E1VSdY3GDStbl1N+/e/cxusXRs3t+Jdm9xrr600S1R+5x3VZiYcVJ7f0RdeUNfLonhyCVi9WvXFqCNQEoyFt8u72G5lsnev+h+NX4euXUteELUsVJX7piRcuKA+s7ffzm3YsUPTQFv60EMaoF1dhaZlXi34xo3NLMQFghqhje+JEhTIPYb29dcl6su775r0GixIS1M6DP7+mpaSotr++UeJYDz1VNHH/f13ddwPPii4LSNDCQiMHm37/WvWqPfv2GF9+5kz6r5+6aWi+2KNBx9U7//pp9K936QecfWqNmDAAK1fr27qukTrlZCTHk2L36n5+/trEyZMKPUg1769po0cWco+mh3jrru0AmN5XK6oxPvT6mjajnu0tYfWap7hnhovYfrxDPfU1h4yG+v/fseyePBpy+fA2rVrNU9PT0vRCndnbe3jHpqWlWrZsYTdmqZH63dLe21A7qBx9OhRDdBee+01tU/MenWuC1vUPfGpm6btV1/AN954QwO048ePF//DePJJpVSVmWlqKumluXRJbV+8uJQHKIIqI3IxePBgXn/9dVJS8qQ2Y2JiePnll7n11lvtbfMJxSQrR3mw0rLqEPjMuLzEdSaZig1fuOpc0IOVdBBcahL5VwytW7dWsbjGJbZKqIFlLbSgOOF5JaEsHqriUNGLK1aLDfcBn5pxXEzOXZXNSWXP779yS+cAnJ2drbvY09NL7CrS69X5jxxRLvyyeunKU/igqqLT5QldANavTWZm8a7NwIFK6eDNN/OWoiMj1RKymZRteHjBXEN73Gfp6SpNbNSovFyEkjJtGkyZcoW33lK140rCu++qPGpj7dfqSPfu6n/8/nuVt3fggMq/Emzj7a2i+PJ7sBrneq0uJANf1IVNgaq+0NnN8MckSM1TNkpJhegE6OyPyo0xClwUVlPQCtHRKrikcWPL9ho1lAfrzBkwBtYsWKD2NQu0sUmfPhAUlGG1JtbmzSrCwVzcIj93363CDG2JXej1ahY7eXLRfbHG22+ryOfJk/PlmxYXo4LLoUOqFtbps4BOCV1c2Qs6Z86nNyEuLk5FaYSHF4yFLGKQS0xU0dSlDQ80EhSUO40yTjZyxR18fXxwdXYmOq0BXI8h7NcwUrMsx/rUrFTCfjUb6z1yE5CH/KGELIIsJyphYWEWugSQWzj40zSI+9yyY9dOAnAyNoGWLVsC0K5dO3r27MlHH32EpmngNwZc66j74Ny3YMg0hQeuX7+erl270qYk7r09e1QSlVkYbXh4wRy+wi5No0ZKMMkUJFcRE0U7UywDa968ebi6utKzZ0/S0tIYM2YMw4cPp27duoRV5zieKk6WIYsbVxpzLjWI2Ms18xLXD81Ef6onvr6+nE80WDGwDkDdYCIjD6jwQL0+L4ZgzBjQ6/PXHC5Xytv4MT9PcRT6SkNFGwlGA8sUEdG0KYyHxnUMxKeoh2JaJhyMNdDLN9cKs4MVaExUNZ43ObnseaYOOG7ahfbtzQysslwbnQ7+7//UwYxKK/kELkB932fOtHzrsmVlvw8+/RQSEuDJJ8t2nGeeucSwYSrxP7d2dpFCUWfOKBWq6dPz0lCrI05OSsHtxx/zcrEcJJK90nByUuOkycBq2hR0Opq4KDGtC8kAGqTGwp4HYcddcPFncMlTdDuc+95gf1TI1K5d0KqVhTJacYiOVs81a4IkvXurVMp331VrInq9mpdu2VL0cXU6uOeeZP74QwlemPPee+r+KUyEqGZNpaT7+eeWEcagniFr1qj+tWhRdF+s4empQodTU1VEeolDgM2Ef1q3bs2lS5e4SjMldHHlT6jbiT8jlBhJr1691GDm7a0s12JOJozRbPYysDQNdb6dOwFw/s9/8A8MJOaKE9yIJu6q9THdov3yLnByhfrdCuyXkpJCrA3Z47grwMn3LRuvneB6ho6L8XkGFsCDDz7IkSNHlEiccw2o9y+I0cOuEJWTdT2aU6dOsXfv3uKLW4BKrDpwoMAiREhI3nexuPO8vn3VLWf6TCtiomhHimVgXbhwgbfeeouff/6Z9957j//85z98++23rFq1yrr0s1AhZOZkcvVSczQsl6VTs90JuzFXJfMmZqClmS0daQZIiuKKrh2xsbF0AzVDTkpS28+ehdBQYjaowNeKMLCgfI2fiqCijYQGDdSE0uTB8vSEhtC4LqRmwPV0iIyG7BzoFZDrebaDFVgeeaYOOG7ahXbtVJL7lSuU/dqMH68Ebt54Qz3gjhyxELgwYhyuf/xR/S5Bnr5VNE0ZaR07wm23le1YLi7KC6VpauW9OEqHq1ap/WbMKNu5HQEvL7Xa/vDDKj/n8OHK7lHVJ1c4UOHqCo0b06SpUhW+mL8uurs33H0BerxnyoE5lPvezkE1VA7Mrl2lmolHRxeUWjenUyc19uVW2iAlpfgLV6NHX8XZWYlmGPnnH9i6Ne+7UhhTpqjzff21ZXtUlBIxLUrcoijatlXG3o4d8OKLJXyzr69SSz1wIE9J8GoTSPlblSHJzb9yc3Oja9euSinp/HklS1zMycSuXeoz6tGjdP+fkaAg9Tkap1I0b47m4gJ//01gYCDRFzMhMwl/L+s1TfzrmI31l3dDvW7K8DFjx44dhQpN+Depp96bdCiv8dpJTiYr16m5gTVhwgRq1KjBhx9+qPIOL/+BijZEzRP3zuCzFXNM+xa7xseBAyr6IldozpwbN5Rjq7jzvL591fPRJDDuYBPFYhlYU6ZM4a+//qJZs2bceuut3H777bQo7ZKGYDeycrIwZOuAgku3cfjj6+lJZpaBxMsJYMhdOroeDdnXOHDeC4BuW7danTHHbjqAh0eJF+puWiyNBK3cjQSdzopUe4orPnXUnxevwh4VGUDPTrkDenh4nhiKkRJageUVCulg46ZdsFASLKuF7uam9NG3bFEPvuxsC4ELI5GRajV66FA1Ifj007L9Dzt3qonYE0/Yp5xDeHhB3QFbBnxWljKwRoyouIWgykKvV6FkRnJyqrxCcZXAwsAC8POjiY9y1SgPlhkZCcprEBQCt6wEzwAOxUHdmk40G74KDL2Uq9bKxLEo8lVAKcCCBcX/3uenUaMc7rxTKa8aS5GsXKkWLKZNK/r9t96qPqeP85VCWrNG2aTjxxd9jKKYPFkFySxapAQhil2HT6dTYYLmSoJXvFQUTlaySUGwa9euSuH611/V+26/vdh9++MPdYqaNUv1r5kwhkcbyxji6kqmnx/8/TdBQUHEXFAWfXjvR/B0tRzrPV09CR+cO9bnZELifmiYZ8inp6fzzDPPcOutt+Ls7MyLL76oapeaH8PTk/BFi8HJ3dKLdf0kJ5PVRM74GQLUrVuXu+++m3Xr1pGxfy4Y0i2OR04qn36xib59++K/c2fxanzo9Xmu9ccft9huMKjAipIYssa6ZAXqYTkIxTKwGjdurNRGhCpFZk4mTs4pQMHiFP7E4Zu7dKVqYeVev6SDAESeVgZX10uXrB475lr9CqmBVZ0wGgl//XW8QoyEAgbWoXY0rqUuWHyugRXkrcNn4OK8DhofPKV0Fd2M+VLlhVFJ8OhR7OPGCw1VMyJjuO+TTxZ4AJpHDt53n7LH8tVpLxHLlimZ/fLOZYyNhXXrMKml6fVqcfviRSVZXt0NjXyldYDqr7RpD5o1U+HMJpW8Zs2om+WMuytcSMq3s7kMdm4R16jU3nTu3g9d88ml1vK+dk2twhe2CFDWhatp05TX9/vvVU7khx+q/Kr8OV/WcHJSXqqfflL3EygDft06NVdu0KB4fSiKPn3UuHPpUgnr8HXpAkeO0MLfH51Ox4mYeIyeluzIMPbtNavzuGWL6nAx5cSzstT4UdbwQMgn1Z5LZlAQHD9OYGAg8QlXSc2AEL/2rBy1koA6AejQEVAngJWjVhLSKXcQTY5CvyOdwDFrcHJywtfXl1atWvH6668zY8YMDh48yEsvvcTKlSsJCAgAVOHgFStWEPJAKPjfC9FrICt3sLx2gpNXVJRTfsfI1KlTSUpK4uvfC37RjpyBI3FZ3HfffbZDV/7v/9QFhbz8AaML79w5iwv8zz/Kw1cSA6tNG3U57SRWXuEUy8Bq06YNTzzxBOPGjePJJ5/k2WeftfgRKocsQxY1vWLRYRnn41nDQDhzaZKTA+SrhZV0AHTORBw7T2BgIPVzb9D8xLi1xsYmoYrQtGk+AyurBz571DLcxWTYc8qZXj175SXIapoKHRs2rNSuooULCxrdN0O+VHng768+O1MeVlndeD/8kKetBCpUxuwBl5ioDmt0bN13n5pIbdhQuv7HxKgCpA8/bL14cGmwZag7O+elV/TqpXKujJLlCQnV35vjiArFVQE/PzWJNq0PN2uG7ksXGtfRWXqwrMhgGwwGDh8+bFlg2Msrb2WkmFiTaM9PWReuRoxQxtQHH6j7OTGxcHGL/Nx/vxp21q1Tr3/9VRlbZQ0PNKfUXrouXSAzkxrR0fj7NuCf40dMmw6fuEhaeia9mmerg2/ZAoMGFbv69qFDqg/lamCdPElQ7mQq9jJwPZqQTiHEPBWD4UUDMU/F5BlXgP7D5YT+D2LPXUbTNC5cuMDZs2eZPXs2K1asoFYtlSMYEhJCTEwMX3/9NQaDAS8vFZVEyxmQfQ1i16uCwZlJnLig4ePjowTNzBg8eDB+fn589EfBYtXrd6uP8d5777U90Fy6pFySjRqpQbmQ/IF9+1RTSQwsnU55saq1gaXT6Rg9ejStWrXC09MTZ2dnix+hcsjKycLNLZkgj7wcq4AAWPluFiF8im9GBpC7UmcysA6CVzsiD0QpgYvw8ILZ4Z6exLi1rvZhN46On1++1Vk/Pxr/rlatIjyf5+yVHHoPMUtO3b9fzYonTCj1OV1d1XOsUaObK1+qPHByUvkJFrWwykJYWMEscrMH3IEDqsloYHXqpOaKpQ0TfOcd9R147LFS9tcKtiIlP/pI5XBMnaoe1LlDm4nq7s0Rz3HpsFpseFsGTZoGcfF6DUAHngEqJDCfUltMTAzXr1/PM7B271bFoEo45zEKRhVmYJU1QtjFReW2fP21MopcXCwLtxdF27Zwyy15aoJr1qh8zZEji3+Moij1IoFRSfDgQVo3vM4/F/KKdhnD4HvV2KRcJOfOlSg8sKwFhs3x8lLefFOIIJARFAQZGQTmzrGiE91NtbBsEfbal6RmFmz//PPPCzYCw4cPx9fXl5Urc2tgNeoLdTqoMEGjguC5GxbhgUacnZ2ZMmUKPx5I50JKnpGlabB+j45BvTvi4+Nje6Dx9lbqtXfdVXBQNpJ7gffvV9/ptm2t72aLvn3hxIk8R5kjUSwD69VXXy30R6h4NE0jy5CFIRsauanY3g4dche+H3SH+vVpkiurfz4ZSM8zsK66tufkyZPKwAoJyRtFc2fMKf/9gMTr7mJgVXH8/FQuqXEln6ZNaYgKF9i8eTNgVmAY4LPPlIV0112lOp+mqbG0dWu1unkz5UuVF+3a5YYI2oMiZjARKrffZGDpdErZa+fOfJ7QItDr1fP2tdfU2syOHWXocz5sRUpOngz9+yulNWu1YaF6e3NuVqXNslKg2HCu/GqThkFcyG4BkwxWZbABoqKiAJSowLVrSlWklAIXUHiIYFkjhPV6S9XB7OySe3UfeEDlU+7aBV99BffeWzBltyyUepGgdWtVX+LgQVp7p3PiYt4YsOck+NSBgJrn8j6AEuZfNWtmUc2iTDRvbsWDBQTlGh8xV+urPPhCiLuUar3dxgDn4uLC9OnT+fHHH5W6oE6nvFiJ+yHuMwBOxlkqCJrzwAMPYDBorIkZoxYb0LH/fGNOxWtMnDZL7WQrf/uNN5QE5qpV2Ax5yr3A+/apZ09+Ff2iMOZhGY1hR6JYBtayZcsK/REqnmxNrVTn5IBOUzlYxvxDAPz88Lh4kbp166pQiLQLkJ4Aaec4eFEFVXczzrTS0pQMWO6MObaX8nCIgVW1KSDV7ueHM9Cobl2OHTuGu7t7nuKQpikt3qFDVcn6UrB7txokn3yy2BEYQhG0a6cmf8bcojJRxAwmMlI9A81zKu67T/3+7LPincIYZm+csKam2j88r6hIyZvRm3OzKm2WlQIGlrEWVo0aXCjExaPX63nwwQcBGDduHPpXX1VfyFIaWDVrKjG8wihLhHBYmMq9MqekXt0JE9S4PnCgeu/XX9v3vra2SABq/CgUZ2flbj9wgFbN6nE1FRJyhXH3nIReLUFXM0AZWIGBJSrGV0pRSJuYamHlYjSwfOLjcXd3JzrRo3APVup5/G3kvPkXMsBNnz4dgA+MSjhB94POFY6/wY10OH/hEi0b3LD63tatW9O3b18++joKbUw0TDLw6YWJuLq6cs8996idipO/XcgqUFaWiqAojVJj9+5Kw8kRwwSLNU3av3+/xc+ff/7Jl19+yYcffijiF5VElkHJBeVkA5pyP1+/biYRmquA4Ovry/mrbsrAMgpcRCsrrFu3bmrivXevig/IpSKLDAulx2qxYaBxbpx19+7dcTNW9tuzR80yyhAe+N//qrCRBx4o9SGEfBjTOfLXsCkVRbg5IiMLCgu2bKlCi4obJlgeMv0l5Wb15tyMSptlpUEDtfCe38Bq4uxMYmIiGVbCmvR6PaGhoVy9qiJDzpw5Q+hrr6EHFSJYQowKguUpGGWPHL0ff1R9NEYZx8fbd/Ek/yKBn596nqxalVuqojBylQRb91MCPicuwpVr8M8F6NXaFTq9DNu2lch7deaM+rG3gRUbqxa+AXLq1YP69XE6cYLAwEBiLjspA8uWG/7ybsLHg4eHZdqGp6cn4YUMcAEBAdxxxx2sXr2a7OxsOPcdoEIpT+WG1rXM+ArGNLQq4Th16lSOHTvGvn37MBgMfPbZZ6Zat4AadA4dUsl+tgagQlaB/vpLLQCUxsCqUUM9o6qtgbVmzRqLn3Xr1rFjxw4mTpxIzbJqWwqlIjtXdt3cwII848hoYDVp0oTzV10sDay/r9C0aVMVWxsTo0Y3MwOrIosMC6WngIHl54ce+DvXpXX48GH0xkH0s89UPNeYMaU6V2wsfPmleuDKLW8/jFLtdgkTLOQBd/WqimO3UhqLiRNV+OCJE0WfoiqILYg3RyguOl0+qfYmTcDJidjcL6yHhweBgYGmcfLq1avMmjWL1HyrCKlZWYS5ukKdOiXuQ3R0+T9L7eHVDQvLMwyM2HvxxHyR4MwZpVx4/jxMmlTw3BZ06QLJybRucScA/yQ2YO8ptannnbPhShu4erXS8q+MBAWpsP3zZqVHadPGpCQYfTEdslIgM7+EZS6XdxPS352nnlBV23U6HQEBAaxcuZKQIga40NBQzp07xw8//ABRYaCpD/Rkrg+kVeMcGHTFqoTj+PHj8fDw4MMPP2Tnzp2cP3/esrjwnj1qkDeGPNjCxirQflVWlX/9q/C326JvX/WMyu+lreqUKdBn0qRJbNy40V59EUqA0cDKzga0PJl2CwPr0iV8fXy4kGTIM7A8mxEZ9VdeeODevep3PgOrRg2VvyhUXby9VfSE0cDS//ADoUB67hLktWvXCA0NRb92LXzxBdxxh8rELQVvv60mKzNn2qnzAqBqUrm42FHowsYD7uBBtdlKaSwmTFDXdv36og/fpIn19ooOzxNvjlBcLAwsFxf0deqw9pAqxKppGrGxsTz44IO0adOGBg0akGCjbkGcschUCdC0oosM2wN7eHUrY/HklluUWM7PP8O8eYXs2KULAAGXL+Pi4sI/tR5mj8d8nJyc+Neo5/PyrwYNKva5d+1Sn1ExFd2LhTE60TxMkLZt82phnc+tbm0rTPDybqjfHc9aKgolMTGRmJiYIo0rgJEjR9KkSRMldpGad9FO5krvt/ABzMMPzaxnLy8vunXrxvvvv8+tt96KTqezXGRYv15NCku5QLtvn/JW2kgDK5J+/ZThajTUHIUyGVg//vgjNeyZBSkUmyxDFmhq1UfT8q6BhYEFNKlVi/NXMtHSzkPyQW7U6Mjx48ctDawaNVQOVi4xMXmrw0LVxdlZ1QIyGlhhYWHkT49NTU0l7Jln1JJaKcMDr19XYRzjxtkvGVhQuLqqHG67CV3YIL/AhTlNmyoBiU8/tR25AkruOr/gKNwc4XmC45K/2HDYjRtkmKRXFVlZWURHR/Pcc8+pyA4r+JeiIFRiotLHKG8Dyx5e3crKbXzoIeVMefVVJa5hlc6dwckJl0OHaNGiBSdOnGDPnj107NhRSY9v2aIspUaNin3eXbuUgefqWvS+xcWaVDtt2sDFiwQ2bsyVpGtcS8O60EVOBiRGQMPe7Nq1iw4dOuSF6BUDV1dXHnzwQb7//nvOpvqa2k9chEZeUMcTyB+KmWs96/V69u/fj5b7ANA0jccff1x5dnNyVP72yJGlXqDdt095r0o7pzR6GR2t4HCxDKyBAwdy6623Wvz06NGDN954g4eMRS2FCiVbywZDbtkbPGjQQAnt5DewfN3dyco2cOXSBUg5zqGExhgMBksDq2tXi1EmJkbCAx0F82LDtlSG4uLjlRE9alSpzvHhhyr6Ytas0vZSKIx27ezowbJBZKQypGzMHZk4UfXh8GHbx3jpJTVxePJJCc8THIdmzdT6kjG3KC7TigY2kJ2dTXh4OK+//jqe+dxBnkD4nDklPndxJNrtRVm9upWZ2/jWWyq97YEHbCw2eXqqlaiDB2nVqhXHjx/nzz//VCq5qakqQaeY4YFGFdT9+9W4aE8hD39/NS4WMLCAoNw5VkwC1j1YSQfAkIGhfk/27NlD7969S3z+hx56CIPBwAd/9VK13VAhgi19gAwgv9J7rvUcFhZWIB8xNTWVsLAw2L5dJeQVFR5og/R09VwpTf6VkYYN1cfoaHlYxRJMfOqppyxe63Q6XF1d6dSpU6HKJkL5kWXIgtwHhgEPPDzU4k0BAyt3yeBCkoGGtSEyVtXw6Natm3riREaqSqFmxMSUPlZWqFj8/FTuKSiVoVjTFyAPfycntfqUW6CwJBgMsGyZKu5aivxuoRi0bw8bN6oyItY8RPbAmsCFOePGqfDPTz9Vi8X52blTrTBPm6bETv773/LppyDYm2bN1Dh24YL62792bWKvXSuwn3EuYwzHCgsLIy4uDn9PT8KBkP/7vxKfuzgS7VUFo0EWFqYcG/7+yriqiMUTd3dVILl7dxg8WK33nj2brw9dusDu3bQeO5Zvv/0WyC1D8scfKn6sGAaWUQXVGP2WkpKnYmiP/9PdXS1kmdfCMhZ+Csw17KOTPOlkzYN1eTcAfyd6k5SURJ9SJIcFBQUxdOhQ/vflXsIeew/nI/M4GR/LoPbu8FE27DJLdPPwMFnPNhdn4+JUeGCtWkrgohQcPKimmmWdU/btqwrbGwyOo2JcrG7efffdFj8jRoygWbNm1Cul3LNQdrIMWWBUqqEmHh5qNblAiGBu3Pj5ZNUc+c9VGjVqRNOmTdVSUWqqRf7V9etK88IRHghCngdL0yA8PBzPfEUmPN3dCTcYSh0e+O23cOqUeK/Kk3bt1EPjn3/K5/jXryuVwsIMrIYNYcgQ9SzNHyZ49aoqXtq8uTK2BcGRMIoBGcMEw0ePJr9aeH6VtpCQEGJiYjAYDMQ0a0bIbbeVKr7JaGBVhAfLHlRmbqOfnzJ2Ll5U16qAFkPXrhAbS9LFi6b3hIWFoX/rLWWR9e9f5DkqQgU1fy0smjcHZ2eCclUpY67Wte7Burwbagaw+6BS7yiNgQVK7OLMmTP89E8D0oYd42witBwaBgGT1A7G7/GgQaYLbMtR4t+smVK3GjPGusZ+MTDmTZXFgwXKwEpMhL//LttxKpJiGVjR0dHcd999REZGcv36de655x7Gjx/Prbfeyn5HyzqrJmQbsk0GlgFPatTIZ2DVrg1eXvjeULUPLuSK1kT+volu7Rqj0+nyBC7Mvvki0e5Y+PnBjRtqEhwSEsLKiRMJwEx9qG9fQjw984pJl5D//let+hrLYQj2x6gkWF5hglFRarJiTUHQnIkT1aTqzz8t2x97TBnxen2pnKCCUKkY80aNodQhd93FSiCgSZOiVdoSE9XqRCknuzExquxgKcQHb0rWrCnYZjKAunRBD6z74gvTtgsXLhD63XfomzcvlrxtRQh55K+FhZsbNG9Ow7g4PD09c2th2fBg5eZf1a9fn9atW5fq/KNHj8bHx4eVK1dy6pQy1lq2bKmM0EaNVE7VxInw228mffzw8PCCYbGenoTfe6+6B0oZHggq/8rHJ2+ho7QYCw47UphgsQysl19+GW9vbwIDA/nyyy9JTExk+/btTJs2jcWLF5d3HwUrmIcI5mieJg/W5ctqwg2Anx9Nah0BlAcrIwuOxGXRrd4xiNYrAyuftIsxZtxWUW6hapFfqj3krruIAQyRkcScPEnIoUMq96oUq08HD6rSIo8/XvLq60LxadNGLSqWl4EVGal+F+bBArjrLhXiYl4T69NPlWE1f76EiAqOibViwyFAzMqVykNVmErbnj3qd758GL1eLUJaKSlkQUVItFcnCjWAgoMJAzLyqTmmahphZl6twqgIIY+gIJXzZyEp3rYtun/+UUqCCcD1GMtQgRtnIPWsycDq3bu3WgQvDBtfQldXV6ZOncq3337Ljh07AGjVqpV6oHfpoh42YWFqopgb6x0SEsLKlSsJCAiwXHSIj1crBEOHlvrz2LdPreGXVTStdWsVaVHtDKzIyEhmz55N/fr1+fXXXxk8eDA+Pj7cddddnChO8RTB7mRreR6sbGqaPFhgNkj5+eHR6Qh1PeF8Ehw5C9k50C0gW9VJ2LdPhQeaffOlBpZjkVtbuECxYc6dU9bR5culDg9ctkwtCoqOTfni4aEeyuWlJBgRoST9fX0L38/LSzk6P/9cLXLGxsIjj6jF+7lzy6dvglDe1KmjPK/5iw1bSAvaYtcuJddqFuVhzOOJjbVaUsiCipBor04UagD5+GDL0RSXklKs44eHq8tpjr2FPIKC8r4XJtq0gRMnCAwIICY+HXJSIeNy3vbc/Ksklw4cO3aM3p6ehVvwRXwJH3roIXJycnjllVcAaOHvD0eOmOTu6dBBhaW89RYkJwP5wmJjYgi55x6V9DR2rPLClYJr15QD2B45/Tqd8mJVOwPL09OTlJQUEhMTiYyMZODAgQDExMSUSEZSsB/mIYLZmqWBZZGHVTsT33pwIRkic73S3YJQdRIOH7bIvwJlYLm721YbE6oW1ooNmxo++0zNLO64o0TH1OvVHOSjj9Sg9v33duuuYIPyVBKMjFThgcVZQfTzUzkQrq5qTpCeDmvXigdTcFwKFBv28VFf6OIYWLt3q0mpWfhZcfN4NE09T8XAKj5FKRn62ygL5F/M+iF9+6rFIy+v8lNBtVoLq00byMggqGFDos8lqzZzoYvLu8G5BnuOqfCjPl9/bWk8PfwwLFyo6qXMnAnTpxf6JWzZsiXt27fnwoULAHTt0gV9ZmaegQXwwgtK5ePtt63/I99/rxJ4yxAeGBmp/oWy5l8Z6dsXTpyAS5fsc7zyplgG1pAhQ5g1axYPPPAAjRo1YsCAAXz//fc8++yzjCll4TGhbJiHCGZTyxQiCHleKPz84DI0qas8WBHRqhZCUCPA2VuNNPm++bGx6jiOotJys9OkiXpQnDuX2+Djoy5eTIwqKjJmjHKRFBPjwpjRYLt+3fbqrGA/2rdXIhdGKWl7kZamPGNFhQeCusarVqm/NU2pGmqaWsQXBEfGwsDKX0DQFtnZKiExX3igrTC22Ng8RVejFHh6OnzwgYyfxcVYz8uY6+nvb2kAhQ8dWlCgxNmZ8EWLinX8VavU4/Hw4fIT8rBaC8uoJOjmxtWUGyTfwFLo4vJuqN+D3Xv34wT0yCeZTloavPiiehh/8okanK1hVtfq5MmTpubYCxcIBfTm3/muXeHOO+HNN5WrKT/r16v5xK23FuO/ts6+feq3PQ0scBwvVrGm0fPmzWPSpEn06tWLjz76CDc3NzIzM3nkkUeYJfJilYK5Bysr14Pl66sW5iw8WJ+Db31nzidBZAx0CwSdiyck5kqa5vvmSw0sx8LNTY2BpnHTxQUaN1ZP9KSkEocHPvdc+assCQVp1049My0eynbg0CG1jlIcAyssTD3HzcnMlGsvOD75iw0XbLDC4cMqTyWfwEVh+TrBwSpXZNq0vDE5KUkWqUpCSIgqCQHKgWhuAIVMnqwEShoroa4AYOWkSbZz6MzIzIT//U+FQZdndaEmTVQUkIVUu7EWVo6atEUnkCd0kZMOSZGm/KtgwKqWkE6nJH2Tk20nyZvVtcrMV+8tFQh75x3L/V94QYlYrFhh2X7tmpIQvvfegjGVJWDfPtXVEtR/LhRjRtI99xSe+1hVKJaB5eLiwtSpU3nyySe5fv06hw4d4rbbbmPy5MlFJ+IJ5YK5BytLUzLtzs7KprIwsHZBk8Y9uXAVDp2Bbq284JaVsNOgHjJNmlgcVwwsx8O82DB6vcq7OnNGDciJiRb7WsuLzchQeTd33GHmCcuHPVWWhIK0b69+2ztM0ChwUZSCIFSMwpYgVAbNmqlaqaY5Z1EGll6vCjIBzJ5tMZMLDy9Yr87TE95/H15/XS2S5K9lLItUJcOmsmrXrkqg5JVXMKxaRQwQ8vzzxTrmxo0qtOzf/7ZjR63g5KSMCovFsoYNoV49AnNzxWKSaymhC4DESDBkkVPvFv7880/61K5t/cD+/ir+0MnJeixljRpF17XK/53v2VPV53j9dcuV1a+/Vu7XMoQHgjKw7FVTVa+HRx/Ne11Y7mNVoVgGVmZmJi+//DK9evVi7NixjB8/nr59+/Lcc88VsJKFiiFLy6uDlWmohTE0OTDQSrFhz1ZkZSsVwW7jV0BQiFIQzJd/deMGJCSIgqCjYTKwjPF9xntS09SIlDsCWcuLnToVGjRQjq6jR23LCUs98fIlN4KkSKGL4qqXGYmMhPr1i3f9KkJhSxAqg2bN1JhnWkBq1iyvgGB+jANlUm5tk3PnLGZyISEq7x8s83hCQ+H//k95jK0hCxXFx6aB1by5ih88eBC2bFELxMadi2DFCjVmDhtmz55ap0AtLJ0O2rYlKFftMDq5bp4HK1fg4kh8Pa5fv07vyZMLhvXnV+IwxlIGBKhjOzmp2MRJqtaVzbpW1tpfeEFZnsb4cFDysc2aFQiPLQlXrqjPwF7hgRVRw8zeFMvAWrJkCTt27GDFihXs37+fvXv38s4773DgwAHefPPN8u6jYAXzEMHMXA8WWC827Gv2EOnevbv65p86ZTX/CsSD5Wg0bZprYBUxAlnbnJ2t5hg//aQGw3feKTzJWCgf6tRRIb6FebBKol5mJCJChQcWJ9CgqARzQXBU8hcb5uJF5bp3drZcqTh3Dp58ssiZnKapY1rL45GFirLTpIkaEwuMh05OKg4zMhJ+/VV5GYsxuB07Btu3w4wZZYp4KzYFamEBtGlD3VOn8PLyIibRPS8H6/IuqBnErkhVQbfP7Nl5brbClDjMq0K/9Zb6JzdtAmzUtXJ1tSimbWLAAPXzn/8or1ViopoQTJhQpmR8exUYNuKIERbF+vS+++47wsPD6d+/P7Vq1cLLy4uBAwfy8ssv8/XXX5d3HwUrmIcIZuTUNHmwAgJUDYbMTFSNK09PDpuVvh46dCj6JUvUCysKgiAGlqPh56fCsm/EXra+Q+4IZGsgSktTZS6cnQsujJWHypJgnfbtCzewSrqCl5GhlHmLEx4Icu2F6ouFMrter2KiwdKV36yZGkxzi68WwGwANdb2sYYsVJQdna4QZdWuXVVyVkIC3H57sY733ntKGXXaNPv20xZBQcoBmpJiNsVu0wbdxYsEBQQQfUnLrYVlUB6sRn3YvXs3Pj4+BAYGKkOnZk31uzhKHDNmKOn1Z56BjIyCda2AlXPn2s5VmzdPTRw/+kiJY2Vnq2LEZcAocFHc509ROOLCRbEMLE3TqFevXoH2unXrkpr/iS9UCOYerBwsDSxNy/Vo6HTovbx4zfhNR8Xmhv73v+ihwDdfPFiOiXF19pyvjSd+7ghU3AHKfGGsPFSWBOsYJxTWopag5Ct4f/0FWVnFE7gwItdeqI4YDSyTpz+/Clt2tspdXbq0QF6yidyBMikJTp60nVsiCxX2waaBlZ6eN0iGhRUZJ33jBnz8sQrr9Pa2fz+tYVQSPHvWNa8xV+gisF49YuLTwJABV/ZC2gWTwEWfPn2UrsFPP8FttxW//pSLi1IDPH1aFbDErK7VggUqV+3pp22/f/BgaNFCScA//LA6XhkTgvftU4IvttIOSoojLlwUy8Dq1asXr732GtfMpBxTUlJ444036NmzZ7l1TrBNliEvBwvcLUIEIc9YCktKIs1gsHhvalYWYS4uqhiEGTEx6n5u3Ljcui2UA6bSVw+EFToCOeIAdTPRrp2SxbemHp2aWjCx3ogtwzkiQv221wqiIDgqtWqpgI4zZ7C9IpGRoTwAS5cWOlAa76vCQp9koaLstGunIjmNqXCAMqbWrs17nS8/zhrr18PVq6poekVhDBoaNy4oLwI1N9E2yMOD6LNJykaM+RSAS1obTp06RZ8+fZT1fvp0yZPFhgxRsuuvvKIUXYwcPAgtW4It8QyAdevUg8eYQJidXWYFicK8vKXBERcuimVgzZ07l5iYGAYMGMCYMWMYM2YMAwcO5NKlS8yfP7+8+yhYIduQbQoRBDcLDxbkGVhxNuolxFkpuBMTIzWwHBGTgdX29kJHoJAQ+O9/897nCAPUzYQtJcGUFBg+XC3c5l/QLMxAjoxUq4fGwpeCcDNjEg4sypVfxEzOGBBiL3U0wTpWhS7CwtRAaE4RSgfvvaei5/r3t38fraHXm4/Jurxc2b2twNmZQE3jRmo6V64DcZ+Bsye7jyp1wd69eyvvFZROjeP119XnY/55HDxoWWDYGta8umVQkDh/Hi5csK+BBY63cOFSnJ18fHz49ttv2bFjB6dPn8bd3Z0WLVrkuTOFCidby7bqwTKGQhgNLH8vL2JzpUHN8bcS8mk0sATHomlT9fvsWWBuSKGjjnEOsWVLngqxUDUwTiiOHlU5caDSQYYPhwMH1EpsdrZ65sXFqSiZu++2fbkjI1W6ggzRgmBmYIWHqxmveXqDNZU2GzfW/v0qmsrKI1SwI+YGlqkUWQnjpPfvVz/Ll1fcOGitnmBqKoS96EJIUBBB168DEH0JGtaOB++B7PrlT1xdXZUI2ZIlKsawZcuSn7x1a3j8cRUu+NhjanXt9Omik8/srCBh7wLDjkqxfRWurq40bdqUpk2b0qhRI5o0aSLGVSWSP0TQ6MFyd1ch5EYDK/yeewpWPgfCrRSIlhpYjomHh5LithZalp/du5WHMp++iVAFaNRISeYbV2wvXoRbb1XFgr/6Sok6GVfwcnKU4fXNN2q//GRlQVSUhAcKghGTgVXGWCN7hz4J1gkMVPMZCw9WCZUOVqxQtvP999u9ezYp1FZp25bAS5cAiLmcO39OPMCurZvo1q0bNZycYNs25b0q7fx63jz1IHnqKfUQgKI9WHZWkNi3T4lmFXXa6k6xDKxLly4xYcIE7r77bhYsWMD8+fMZOXIk06ZNI8WKd0Qof8xVBM0NLLCUag+55x7LyudeXqx0dibkuecsjpeaqkohiIHlmFgUGy6E3buhY8fCw7GFymHdOpWDtXKlup7BwUrq97vvYNQoy311OhXumZYG1upsHjumIj5KInAhCNWZZs2UjkVaGqWONYqPV0aaGFjlj7Oz0oWwMLBKkEiclKTKOU2aZD+hheJQqK3Spg2BPkq/PfqSEurITE9hf9Q/9OlQD3btUg+BshTrqltX5WHt2JEXCjFjRuH5VHZO0N63T4Vl5j/kzUaxDKywsDDc3d355Zdf+PPPP9m3bx8//vgjmZmZLFiwoLz7KFgh25CNUw446QCcLerS5a+FFQLEvPMOBoOBmK5dCenevUAyh3HVRQwsx6Q4BpbBAH/+WabagUI5YaxxZQyDP3dOLXg884ztUM7WreHpp5VC1u7dltsiI9VvMbAEQWGhJFhKjLV9JP+qYiigJFgC7+OaNcqYrkhxCyjCVmnTBq97c6hfC2Jyq6ocjIX0LOjTYL/Kv3JxgUGDytYJT0/1+Zg/UAoTrbCjgoSmqftEFiGKaWDt37+f+fPn42fMpgcCAgJ44YUX2LZtW7l1TrBNliELJwO45ipS5PdgnTmjJtR5Cgi5CjEREVbjw6QGlmPj56fG0MI4dkypKYmBVfWwVuMKVFmSot7XtKlS183JyWuPiFDKaa1b27WbguCwWNTCKiX79qn5pyxcVAzt26u5iUVOUxHeR71ezYGefFKtI5dRbbzEGG2VBg3Ua19fM1ulbVtoCIENVQ4WwO4T6ndv/8vKwOrdu4DCc4mZN69gvY+iRCvspCARHa1qFYuBVUwDKyAggGNWvqXnzp2jqTHDXqhQsrVsnHJ0JgMrvwcrMzM3N6NhQzXKnD0Lx48r93M+A0uvz7uXJkwokzKnUEn4+SmPhw3RSCDPyyEGVtWjtDnGtWrBa68pj9X//pfXHhmp4t9FEVQQFMa1xrIYWPv3K69KrVr26ZNQOO3aKTvBKHteFMZIAOO4mZlZZrXxUhESAps3q79XrTKzVdq0gcsQ1CjPg7XrH/BvAE19/ZSaUVnCA43YWbSiJIjARR7FevzeddddLFy4kFdffZUffviBX375hbfeeos5c+bQp08fNmzYYPoRKoYsQxZO2eBsw4MFuWGCOl1e/NjevWqD2TffOCAlJqrXxSgrIVRBjJOH8+dt77Nnj1pVa9WqYvokFJ+y5BhPmAADB8LcuUp1MCdHKfPKKrsg5GEezFEaNE0ELioaq1LthWAtEqAMauNlwvicPXHCrLFRI/jWg8CGOmIS1Hdq1wno3doZUu9U+9jDwLKzaEVx0etVnWJQCrc3+zyyWDLtn3zyCbVr1+aXX37hl19+MbXXqlXLok2n0zFu3Ljy6algQbYhG10OuOjUJbRlYPXujaWB5eVlETdU2IBU1WsMCHmYS7Ubq8jnZ/du6NVLZLurIsVRjraFTqdkiLt2VZEhM2eq44iCoCDk4eGhAjpK68E6c0ZFCYiBVXG0aqW88MU1sCrRcVOARo2gZs0cTpxwzmvU6eB6MEG6aNKz4tl/Gs4mQp8hk+Cn6+oLao+VsbI8UEqJcbHeeMq4OPUabt65ZLEMrK1bt9rclpOTg7Ozs83tQvlgFLlw0anPPn+IIFgKXbB7t/rm9+hhETdUlQYkofQUtTqbnKzqK02cWGFdEkqA8QFkrHHl76+ehcV9MHXqpMqevPUWrF2r2p5/Xilx3awPN0HIj0mqvRSIwEXF4+6uao4dPVq8/f39zeY9+dorGqUVkcmJEx6WG9q0IfCbfwD4NGkW8Ca9RzwGo0fDkCH2iesu6wOlFMhifUGKrSJ4Pbc4mjlHjhxh7Nixdu+UUDSqDpaGkxUPVu3aqgiihYF19qyqiZAv/6qSPMmCnSnKwPrzT/Vb8q+qLmXNMe7YUf2+dk39vnBBwn0FwZyyGFj79imBt+Bg+/ZJKJwCSoKFEB4Orq6WbeXsuCmUgIBMTp7M19imDUG5ORnr16/Hw8ODLjqdco/aIzzQiJ1EK4qLLNYXpFgG1uHDhxk5ciQ7duwAID09nVdffZUJEybQvHnzcu2gYJ1sLRtdNiYDyyPfIklgYJ4yIH5+qvJodnaB+IbwcLVKZE5lDkhC6fDyUoa1LSVBKTBc/bF2z1ZW/oEgVEXKamB17my5mCmUP+3aqTym7Oyi9w0JUcqDLi5lVhu3CwEBWcTEKLENE23akBtkxIULF+jRoweuxigxY92qckCvV/NCJyf1214Lbzk5sGBBQdFCIzfzYn2xQgS/+uorVq5cyeOPP86gQYM4dOgQ7u7u/O9//6O3LIlXClmGLHQ54IRarsk/6AcEwD//5L44fTpvw+OPq1lX7ogTEqIKnH7/vRqQKsCTLJQThdXCkgLD1R9ZQRSEwmnWTIVLX79eMiVAY22fCRPKrWuCDdq1U+vDp04pEb7CMO738MPw7rsV07/CCAjIxGBQ0uWmvrdtyybASafDoGkcPHgQfUICIZ07Q5Mm5dKP/PlRsbH2yY+6eFG9f+tW6NtXqdeaS+rf7Iv1xfJgubi4MGXKFAYOHMgPP/xAfHw8oaGhYlxVIipEMM/Ayu+FMhYb1tbq4b338jZYkQlMSID+/SvMkyyUE7YMLCkwfHMg4b6CUDilrYV18qSqISgCFxVPSZQE9+9XxnNZ6/Tai4AA5boyVxLU79tHKGDIdfmkpKQQeuwYeuOXsxywh7pifg/YnDkqXHb3bvjgA9i5U0nS26FWcbWhWAbWl19+ybBhw4iOjkav1zNnzhxeeeUVpk6dymlz74hQYWQbstGyQYcbNdwNBZThAgLgxg1InLMU0tMtN5rdWSkpqijpwIEV1HGh3Gja1LqBJQWGbw7Cw9WKoTk3+wqiIJhTWgNLBC4qj5IYWMZIu1tvLbfulAh//4IGVtiLL5K/pnwqEGYsIFUOlDW6wegBi41V3tzYWFi8WIVi7tsHDz6ojKoKTvuq8hTLwHrppZeYOHEiX331Fd27dyckJITvvvsOT09PxowZU959FKxg9GChuVHDveB2k5LgWRsKj7l31h9/qJtBDCzHx89PCRvkj1WXAsM3ByEhasVQVhAFwTqlNbD27VNh+B062L9PQuHUrq2ebcU1sIKDldp5VaBevRzq1LE0sOJsWDVxCQnl1o+yRjdY84CBUqmVe8I2xTKwNm3axMyZM3E1k2fx8fHh3Xff5fXXXy+3zgm2yTZkoeWADnc8PApmF5oMrEY2ltxy76zt29UqhEy+HR8/P2UsX7xo2b57txQYvlmQFURBsI2vr1p8KI2B1bVrQYU6oWIojpJgerpaMK4q4YGgvmutWmGhJOhvw6qx1W4PwsMLCqGVJLrBlqertEW7bxZsGli//vorWVlZALRo0cLqPjdu3ODgwYPl0jGhcLIMWWjZoGnu1KhRsHKsycAa9nChcUPbt6u48po1y7vHQnljlGrPryQoBYYFQRDAzQ18fEo2MczJUcn7Eh5YeRgNLFtKdaCecxkZVcvAAmVgmXuwwsPD8XRzs9jH09WV8HKM5Q4JUQXozXnuueIvwEl+b+mwaWDNnDmTlJQUi7Zbb72Vc2azt7S0ND788MPy651gk2yjgYU7Hp4FtzdooOyo2Ib/shk3dOOGii2vKvHKQtmwVgsrKUk9mMRDKQiCUHKp9mPHVHiUCFxUHu3aqZzywq7b1q0qZG3AgIrrV3Fo1Up5gDIy1OuQkBBWPvssAYAOCABWLlpESAnDDUoqu+7rq36fPKkUNE+dKv65qlp9MUfBpoGlWVkquHr1KgaDoVw7JBSPbEMWhhzQtBrU8CjomjDaUrGx2Iwb2rVL5etI/lX1wJqBJQWGBUEQ8iipgWUUuBADq/IojtDF1q3Ky+jlVTF9Ki6tWqmpl7keXMjMmcQABicnYgICCHn66RId05roRFFF5Y8fh3r1oHlzmDIFPvtMKUgXh3vvVUaZu7vk95aEYuVgCVWPLEMmhmwwaB5WQwTBzMCywW+/qRWfPn3Kp49CxVK/vkrENjewpMCwIAhCHkYDq7BwM3P27VNCC61bl2+/BNsUZWBduwZ791a98ECAli3Vb/MwQbZsUZaKwQBXrqhipCWgNLLrf/8Nbduq0z72mPKorV5dvPOtW6eiYTZvlvzekiAGloOSrWUrA4saBZIXjRRlYG3fDt27S/HZ6oJOV1Cqffdu6NSpZEU1BUEQqivNmqlaSVevFm//ffvUc9JJZkuVRqNGagHRloH1++8qGqcqGlhGcSmTgWV0Pxkt/OvXi3Y/5cPWvK4w2fXjx/OKHbdvD7fdBitWqBzDwjAYYMkS6NIFhg4tdhcFxMByWLIN2RhywKB5UqOG9X0CAtTiyI0bBbelpqoVHwkPrF6YFxuWAsOCIAiWlESqPTMToqJE4KKy0emUUWDLwNq6VQmYVMVonAYNVGieSUmwDFV/MzJgxgzb222JTly9qtSF27bNa5s5Uxlk335b+Dm//loZZ88/L0JZJcWlsI3ffvstNc3k5QwGAz/88AP169cH4Pr16+XbO8EmmVmZaBrkaJ6FerBArXa0b2+5bc8eyMoSgYvqhp+fyq0D9TBKSREDSxAEwYi5gdWpU+H7Hj6sjCzJv6p82rWDjRutb9u6VRlX+QWTqwoWSoKlrPp74QKMHauiUkaPVlGG5nZaYaITf/+tfpsbWKNHq/nCO++ArXK2mgavvgotWqhzCyXDpoHl6+vLxx9/bNHWoEED1q9fb9HWpEmT8umZUCjZ2cqvm1OEBwusG1jbt6uQh379yrGTQoXj56dk2g2GvALDvXpVbp8EQRCqCiXxYInARdWhXTtYtQouX7YsJJyYCAcOwIIFlde3omjVCnbuzH3h7289xs/M/aTXK4dWXJxqfvBBeP995Yn6/HMlOmHcJzZWeZbeftt2XtTx4+q3MUQQVP3Tf/8bXnhBGWDm24xs364inVasUPsLJcPmR7Z169aK7IdQQjKzsgHI1moWy8DKz2+/qcKJVU1xRygbfn5qxfXyZSkwLAiCkJ/GjdXiYnEMrH371BgaGFju3RKKwFzoon//vPbt25WnpSrmXxlp1UoJRaSnQ43wcJVzZcP9ZEzRMm6OjYWXXlJG5e7d0Lmzag8JUT87dqhUj8JEW/7+WxlI+UvaPvSQMkzffReWLSv4vsWLVd24qVNL/a/f1EgOloOSnWk0sGyHCDZpomoX5Dew0tNVbo7kX1U/zKXapcCwIAiCJS4uqiZQcYoN79un8q9kDK18jAbW0aOW7Vu3Qs2aVdvL2KqVMoBOnUJZRTZqk4L1FC1QCsFG48qc/v2hQwflZbLF8ePKuMpfy8rHB8aPh48+Ulob5hw4AD/9BE89hc1FfKFwxMByQHIMOWi5yi+ZWi2bX35nZxUOkd/A+vNPlSwpBlb1o2lT9fvwYSkwLAiCYI3i1MJKTYW//qraE/ebiWbNlKMnv9DFr78qI8PNrXL6VRwKSLXbqE0KtlOxzp2z3q7TwaOPqnDWffus72OuIJifxx5Tudpr11q2L1miIpweecT6+4SiEQPLAckyZIFyYJGt1bTpwQLrUu3bt6ub0tzNLlQPjB6sL79Uv8XAEgRBsKQ4BtbBg0rCWhQEqwZOTkqkwdzAunBBva7K4YGQF6ZvUhIsBFtKgLbaASZPVqVY3n234LbsbHVec4ELc3r1gm7dVA6XMczw5En44gtlXNWpU3SfBeuIgeWAZOVkgal2gXuh7tuAALVAYs727RAcrKRDheqFt7cKgfnpJykwLAiCYI2iig3r9XDnnervxx4rUYkioRxp187SwNq2Tf2u6gZWvXoql8+i2LANwsMLqiEWphAIytM0eTKsX69EP8yJiVF52bY8WMbCw3/9pfK5AF57TYUTPvlk0f0VbCMGlgOSmZNZIgPrwgV1g4EKDdy9W8IDqyvOziq/IDNTCgwLgiBYIz5e5SI7OysBC3MDyigykJSkXp87V+I6sEI50a6dMoyN+UJbt0LduqoIblXHQqq9EEJCYOHCvNf5UrRs8sgj6jv90UeW7dYk2vMzcaIq5Pz222q++OGHSthCRMLLhhhYDoh5iCC4FRkiqGl54RD79kFamhhY1RW9Xk0eQCXUyqRAEAQhD71ehT+BejbGxsLDDytxgfffVxPVUtaBFcoZY7kZo+z41q2qlqezc6V1qdgU18CCvIXR48cLpGjZpHNnVXZnxQqV2mXEmkR7fjw8VKjghg15C7StWxevr4JtRNneASlpiCCoh0iLFio8ECT/qjpiXHnNyFCvr19Xr6F4A7QgVDnyF4QJD7fbl3nTpk18+OGHptfXrl0jPj6e7du307BhQ3r27Enjxo1N26dPn87o0aOJiYkhLCyMpKQkPD09WbJkCS3y6x/nsn79etasWYOTkxN+fn6Eh4dTv3597rvvPtLS0kz7RUdHM378eF544QWioqJYuHAhqampeHt7s3TpUry9ve3yPwvq62QcI42kpcGiRYW/r4g6sEIFYC7V3qABREfDrFmV26fi0qoVrFmjjPWiCiJv26YMnZIaOY8+CpMmqSLEQ4eqtuPHlcR7gwa236fX54VbGpk3T6kMytyhDGhCsdm/f39ld0HTNE07eeWkxmQ0QIM/tPXrC9n3pKaBpn3wgXo9ZIimdexYMf28WTl69GilnDcgQF3r/D8BAZXSnSpJZV0boWgKXJu1azXN09Pyy+zpqdrtTGZmpjZ+/Hjt008/1TRN006dOqUNHTrU6r5jx47Vvv76a03TNO23337TRo4cqRkMhgL7xcXFabfccouWmJioaZqmvfzyy9pLL71UYL8tW7Zow4cP11JSUrSMjAxtwIABpmeNXq/XHnroIbv8j2WhOt03Op31cVKn07TYWE3z93escbQ6XZuiyMzUNBcXTZszR9NWr1bX5ciRyu6Vbcyvzbp1qr+HDhX+HoNB07y9NS0kpOTnS09X7x0zJq+tf39N69ev8PfdjHMHe943tmwD8WA5IJYhgoV7sJo1U0mMsbGQlQW7dqmq4EL1w9YKq6y8ClWKTz6BDz4o0Oyff2l3z56CrobUVJg+HVatKvwc06bBlCnF7tKqVatMniWAAwcO4OTkxKRJk7h27RrDhg3jkUce4fLly5w+fZqRI0cCMHDgQBYsWMDRo0fp0KGDxTENBgPZ2dncuHGDOnXqkJ6eTq18SZHJycm8+OKLrFixgtq1axMREUGtWrXo3r07AOPGjWPRokUkJSVRz0yV6OzZs0ydOpWBAwcSFRVFSkoKs2fPZsiQISxfvpy4uDji4+NJSEigQ4cO9OzZk02bNnH27Flmz57NnUYFh5sQf/+CyrrGdn9/5ckqpA6sUIm4uipP0LFj6rnm7Z0XNljVMVcS7NTJ9n5Hj8KlS6UT7nB3V8PjkiUqLaRZM+XBGjOm8PfJ3KF8kBwsB8RS5MKtUAPLzU0lKsbGQkQE3Lgh+VfVldLIuwpClSW/cVVUeylJTEzkww8/ZO7cuaa2nJwc+vTpw//+9z/0ej2///47a9as4cKFC3h7e+PklPfo9PHx4eLFiwWOGxAQwPTp07njjjvo168f+/btY8aMGRb7rFq1ioEDB9Ipd8Z18eJFi7BENzc36tevT7wxsdKMM2fO0K9fPzZs2MDTTz/NIrMYt4iICN555x02btzIjh07OHXqFHq9nnnz5rF8+fLSf1jVgKJU2oqoAytUMu3aKSNk61ZlhDhKEWijgVVUHtbWrer3bbeV7jwzZij/08qVSlEwIaHw/CuQuUN5IR4sByR/DlZhIheQVwvLmH81YEB59k6oLMLDZeVVcACmTLHqXYo7dox2xiQLUPJu1lwNAQHw2292687nn3/O4MGDadasmalt/PjxFvs8+OCDrFmzhs6dO6PLN6PTNA1nK1n2v//+Oz///DPbt2+nXr16LF26lDlz5vDee+8BkJGRweeff85XX31leo/BYCj28V1dXRmYu1rWvn17kpOTTdv69OlD7dq1AfD29qZ/btKtv7+/xX43I0ZDqbDUvpAQMaiqKu3agfGWqery7ObUqQONGhVtYG3bpoa+oKDSnScgQJUYWLUKbr9dtRWmIAgydygvxIPlgJQkRBDyDKzfflODk+RLV09k5VWoVpSmIEwp+P7777nnnnss2jZt2sRxo/wWyshxcXHB19eXhIQENLMCSpcuXaJx48aEhYUxZswYxowZw6effsrWrVsZNGgQDRo0wMnJiZCQEP7880/T+3bs2EHbtm0tDLsmTZpw6dIl0+usrCySk5Px8fEp0G9XV1eTJy2/Uebm5mbx2sVF1lLNCQlR6mwGQ/FV2oSqgXmdp5deciyl3KKUBHNy1DyttN4rI48+qtSEX31VvS7KgyVzh/JBDCwHJH+IYHE8WGfOwB9/SHhgdUcmDkK1oQKe+levXiUuLo6uXbtatJ84cYK33nqLnJwc0tPT0ev1jBgxgsaNG+Pv78/3338PwM6dO3FycqJ169aEh4ezefNmNm/ezMSJE2nfvj2//fYbN27cAODnn38mODjYdI69e/fSu3dvi/MGBweTnJxMZGQkAF9++SVdunTBy8vLbv+zIDgqer2q0WTk/HnHqlFWlIEVFaXqr5XVMzd0qPKW/fSTen377UV/RjJ3sD9V0sAKCwvj/vvvN70+d+4c06ZNo0uXLgwfPpztxli3XPbs2cOoUaMIDg7m/vvvJzZfWMmaNWsYMGAAXbt2Zc6cOaSa+UEzMzOZN28ePXr0oG/fvqwqKnm6ClASmXZQ85KsLLh2TQwsQRAciHJ+6sfGxtKoUSNcXV0t2mfOnEmdOnUYNWoUo0ePpmvXrtx7770AvPHGG6xfv54777yTN998k2XLllnkZBkZO3YsAwcO5J577mHUqFHs3buXxYsXW5y7adOmFu9xdXXl7bffZtGiRYwcOZJvvvmGV43L0IJwkxMWporpmuNINcpatlRGYe6aSwGMUull9WB9+imYRwLHxTmWIVptsJtOoZ3YtWuX1rp1a23y5MmapmmawWDQRo8erc2aNUs7ceKE9v7772udO3fW4uLiNE3TtPPnz2tdunTRVq5cqZ04cUJ76qmntBEjRmg5OTmapmnaTz/9pHXr1k3bsmWLdujQIW3kyJHavHnzTOd7+eWXtZEjR2qHDx/WfvnlF61r167at99+a7VvVUWm/YcTP2jcbpRpv66dOVP4/s88kye72bRpuagcC2bcTLK5joZcm6qLXJuqi1ybqsvNdG0Kk9iviuS/NuvXq/5GRVnff8QITWvduuznvRll10tKRci0VykPVmpqKvPmzaNbt26mtj179hAdHc3ChQtp2bIloaGhdO3alQ0bNgAqQblt27Y8/PDDtGzZkkWLFnHhwgX27NkDwMcff8zkyZMZPHgwnTp14qWXXmLjxo3cuHGD1NRUPv/8c+bOnUvHjh25/fbbeeihh1i7dm2l/P/FQX9Yz5SNU4rtwdLr4e23816fOycrGYIgCIIgOBaOrnZXmJJgVhbs2FF27xWI7HpVoUoZWG+++Sa33HILt9xyi6ktKiqK9u3bW9QP6d69OwcPHjRt79Gjh2mbh4cHHTp04MCBA+Tk5HD48GGL7V26dCEnJ4djx45x/PhxMjMzTTVHjMc+fPgw2dkmFYkqg/6wntBvQklITTAzsJzZfOozm+9xdJe6IAiCIAhCBenelBuFGVgREXD9un2UER3dEK0uVBkD68CBA/z4448899xzFu0JCQl455O9a9CgganuiK3t8fHxpKSkkJGRYbHdxcWFunXrcvHiRRISEqhTpw7u7u6m7Q0bNiQrK4tEc6maKkLYr2GkZuXmj2UDTi6AjoW/z7X5HlnJEARBEATB0XF0tbvatcHHx7qBZcy/uvXWsp/H0Q3R6kKV0G7NzMwkLCyMuXPnUqdOHYttaWlpBRKQ3dzcyMrKMm3PL0nr5uZGZmYm6bmuG1vbs7OzrW4z9skax44dK+F/Zz/irppZRTmAzgWcsjhzPdpmvxo3bsGFC25W2jM5duxUOfX05iY9Pb1SvyeCbeTaVF3k2lRd5NpUXW62a9OtG/zwg2VbVf33rV2bpk0DiIqCY8csxdi++aYZrVq5cOVKNFeulO283brBSy958eabjbh40ZXGjbOYNSuBbt1SquxnVdFUxH1TJQysd955h4CAAIYPH15gm7u7O9evX7doy8zMpEZu4pG7u3sBYygzM5O6deuaPFPWtteoUQOdTmd1G6hQQ2tYFMKsYPzr+BN7NfemzEYZWM5p+Nfxt9mvpUutF5BbutStUv+X6syx/AVThSqDXJuqi1ybqotcm6qLXJuqi7VrExysDETz9owMOHgQHnrIfnPMdu1g9mzjKzegae6PAPa9byIiIqy2V4kQwW+++Ybff/+drl270rVrV1avXs3+/fvp2rUrPj4+JCQkWOx/+fJlGjVqBFDodqORdfnyZdO27OxskpOT8fb2xsfHh5SUFAsjKyEhATc3twKetKpA+OBwPF1z/b5GD5ZrOuGDbft9Hd2lLgiCIAiCUB1o2RIuXlT5Vkb+/BPS0uyTfyVUHaqEB2vNmjUWohIfffQRR44c4bXXXuP8+fO8//77pKam4pkbVBoREUGXLl0AVZhx//79pvempaVx9OhRHnnkEZycnOjUqRMRERH06dMHgIMHD+Ls7Ey7du1wcnLC1dWVAwcO0LNnT9OxO3TogItLlfhoLAjppKyisF/DiM2JRadzo76Hu6nd5vtCxKASBEEQBEGoTIxCFydPQu40lm3b1AK41CmtXlQJKyJ/sUUvLy9q1KhBQEAAfn5++Pr68vzzz/P444+zbds2oqKiCM/N1hs7diyrV69mxYoVDBkyhHfffRdfX1969+4NwKRJk3jhhRdo06YNTZo0YcGCBYwdO5aaNWsCcNddd7FgwQIWL15MQkICH3zwAS+//HLFfgAlIKRTCCGdQrhH78GvuNKgZhFVhgVBEByVaD1EhUFqHHj6Q3A4BNlvtejvv//mlVde4dq1azg5ObFw4UI6duwIQM+ePWncuLFp3+nTpzN69GhiYmIICwsjKSkJT09PlixZQosWLQoce+XKlXz33Xem14mJidy4cYPIyEiuXbtGnz59aN68uWn7nDlz6NWrF1FRUSxcuJDU1FS8vb1ZunRpASEnQRAcE3MlQaOBtXUrdO0K9epVWreE8sBulbbsyBtvvGEqNKxpmhYTE6OFhIRoHTt21EaMGKHt3LnTYv/ffvtNGzZsmNa5c2ft/vvv12JjYy22v//++1rv3r217t27a88//7yWlpZm2paamqo9++yzWpcuXbS+fftqq1evttmvqlJoWNM0bWQXN62OW5DWuU16ZXdFyMfNVPjR0ZBrU3UpcG1Or9W09Z6apifvZ72narcDqampWt++fbXffvtN0zRN++WXX7Rhw4ZpmqZpp06d0oYOHWr1fWPHjtW+/vprTdPUs2fkyJGawWAo9FxXr17Vhg4dajrXjh07tAcffLDAfhkZGdqAAQNMzxq9Xq899NBDpfsH7YjcN1UXuTZVF2vX5to1VfQ3PFy9vnFD09zcNO3ppyu4czc5FVFouEp4sPIza9Ysi9cBAQGFFv8dOHAgAwvxrYaGhhIaGmp1m4eHB0uWLGHJkiWl62wlkZltANzw8CxyV0EQhKrD6U/g9AcFmv1TU+Gc2YB2eQ8YMix3ykmFP6fDqVWFn6P5NGg+pdBd/vjjD5o1a2Z6dgwePBg/Pz9AlQ1xcnJi0qRJXLt2jWHDhvHII49w+fJlTp8+zciRIwH17FmwYAFHjx6lQ4cONs+1ZMkS+vfvbzrXgQMHSE5OZvz48WRmZjJ+/HgmTZrE4cOHqVWrlqk247hx41i0aBFJSUnUM1vePnv2LFOnTmXgwIFERUWRkpLC7NmzGTJkCMuXLycuLo74+HgSEhLo0KEDPXv2ZNOmTZw9e5bZs2dz5513Fv75CYJQLtSqBU2a5Em179oFmZmSf1UdqZIGllA0GVka4E6NGrrK7oogCIL9yW9cFdVeQqKjo2nUqBFz587l+PHjeHl5MTtXdisnJ4c+ffrw9NNPk52dTWhoKLVq1aJLly54e3vj5JSnD+Xj48PFixdtGlgnT55ky5YtbNmyxdTm7OzMoEGDCA0NJSkpiSlTpuDt7U1GRoZFWKKbmxv169cnPj7ewsACOHPmDP369WPevHn89NNPLFq0iCFDhgAql3jz5s24uroyYMAAGjZsiF6vZ8uWLSxdulQMLEGoRFq1yjOwtm0DZ2fo379y+yTYHzGwHJTMbA1Nc6eGpxhYgiA4EM2nWPUuxeWXzd0UCKmxBfbDMwBu/63M3cjOzmb79u188sknBAcHs2XLFkJDQ9m2bRvjx4+32PfBBx9kzZo1dO7cGZ3OcszVNA1nZ2eb5/n444+ZPHkytWvXNrU99thjpr99fHyYMGECv/zyC/369Sv28V1dXU0esfbt25OcnGza1qdPH9P5vL296Z87e/P397fYTxCEiqdlSzCmZ27dCj16qCLEQvWiSsi0CyVHGVg18PCUSygIQjUkOByc88VAO3uqdjvg7e1NixYtCA4OBuD2228nJyeHM2fOsGnTJo4fP27aV9M0XFxc8PX1JSEhAU3TTNsuXbpE48aNCQsLY8yYMYwZM4ZPP/0UUJ6wn3/+mbvvvtvi3GvWrOH8+fMFjt+kSRMuXbpkas/KyiI5ORkfH58C/Xd1dTV50vIbZW5ulsXlq6IqriDcrLRqBfHxcP487NsHt91W2T0SygOZnTsoGdkaGjWoIQaWIAjVkaAQuGWl8lihU79vWWk3FcEBAwZw9uxZjhw5AsC+ffvQ6XT4+flx4sQJ3nrrLXJyckhPT0ev1zNixAgaN26Mv78/33//PQA7d+7EycmJ1q1bEx4ezubNm9m8eTMTJ04E4J9//sHLy8uU22UkIiKC1atXA5CcnMyGDRsYMWIEwcHBJCcnExkZCcCXX35Jly5d8PLyssv/LAhC5WNUEvzwQ8jJkfyr6oosazkimkZGNhg0DzwkRFAQhOpKUIhdZdnNadSoEe+88w4LFiwgLS0NNzc3li9fjru7OzNnzmThwoWMGjWK7Oxs7rjjDu69914A3njjDebNm8eKFStwc3Nj2bJlFjlZ5sTExBQoQwIwf/585s+fz8iRI8nOziYkJIS+ffsC8Pbbb7Nw4ULS0tKoW7euwwkwCYJQOEYDa9UqcHWF3DKtQjVDp5nHOgiFEhERYVJ3qlQM2QR4uxKfNIFpM9bz7ruV3SHBnGP5c0mEKoNcm6qLXJuqi1ybqotcm6qLrWuTmgq5pVgZMAC2b6/gjgl2vW9s2QYSX+aIGDLIyIYczRMPj8rujCAIgiAIglAcNm5UyoEAUVGg11duf4TyQQwsR8SQSUaWMrBq1KjszgiCIAiCIAhFoddDaKjKvQK4elW9FiOr+iEGliOSk0FmNkrkQgwsQRAEQRCEKk9YmAoRNCc1VbUL1QsxsBwRQyYZ2QDuEiIoCIIgCILgAMTFlaxdcFzEwHJAcjJTyTEAuIsHSxAEQRAEwQHw9y9Zu+C4iIHlgGSkXc/9SzxYgiAIgiAIjkB4OHjmq5/u6anaheqFGFgOSGaGMYDXTTxYgiAIgiAIDkBICKxcCQEBoNOp3ytXqnaheiGFhh0Qcw+WGFiCIAilY/Hixfz444/UqVMHgKCgIP773/8C0LNnTxo3bmzad/r06YwePZqYmBjCwsJISkrC09OTJUuW0KJFC6vH37dvH0uXLiU9PZ3atWuzePFimjVrxrVr1+jTpw/Nmzc37Ttnzhx69epFVFQUCxcuJDU1FW9vb5YuXYq3t3f5fQiCIFQoISFiUN0MiIHlgGSk38j9S0IEBUGovugP6wn7NYy4q3H41/EnfHA4IZ3sNzM5cOAAb7zxBt26dbNoP336NHXr1mXz5s0F3vPMM8/wwAMPMGrUKLZv386TTz7JN998g06ns9jv4sWLzJw5kw8++IAOHTrw8ccf89JLL7F69WoOHjxIjx49+OCDDyzek5mZyRNPPMEbb7xB9+7dWbduHWFhYaxatcpu/7MgCIJQ/oiB5YBkmgwsCREUBKF6oj+sJ/SbUFKzVEh07NVYQr8JBbCLkZWZmcnRo0f53//+x5kzZwgMDGTOnDn4+vpy4MABnJycmDRpEteuXWPYsGE88sgjXL58mdOnTzNy5EgABg4cyIIFCzh69CgdOnSwOP6PP/5I//79Te333Xcf/fr1A5Rhl5yczPjx48nMzGT8+PFMmjSJw4cPU6tWLbp37w7AuHHjWLRoEUlJSdSrV8907LNnzzJ16lQGDhxIVFQUKSkpzJ49myFDhrB8+XLi4uKIj48nISGBDh060LNnTzZt2sTZs2eZPXs2d955Z5k/P0EQBME2YmA5GtF6Mvb+X+4LdzySfgKGVWaPBEEQis0nUZ/wwYEPCrSnpqbi+Wde9vees3vIyMmw3Ccrlembp7MqonCPzrSu05gSPKXQfeLj4+nVqxdPPfUUrVq1YvXq1Tz66KNs3LiRnJwc+vTpw9NPP012djahoaHUqlWLLl264O3tjZNTXvqyj48PFy9eLGBgxcTE4OnpyaxZs4iOjqZJkybMnTsXAGdnZwYNGkRoaChJSUlMmTIFb29vMjIyLMIS3dzcqF+/PvHx8RYGFsCZM2fo168f8+bN46effmLRokUMGTIEgIiICDZv3oyrqysDBgygYcOG6PV6tmzZwtKlS8XAEgRBKGfEwHIkovWwN5SMG0aRC3dqnFgA0ZchSAJ6BUGoPuQ3ropqLynNmjWzCL2bPn067777LmfPnmX8+PEW+z744IOsWbOGzp07FwgF1DQNZ2fnAsfPzs5m27Zt6PV6AgMD+eSTT5g5cyabN2/mscceM+3n4+PDhAkT+OWXX+jXr1+xj+/q6srAgQMBaN++PcnJyaZtffr0oXbt2gB4e3vTv39/APz9/S32EwRBEMoHMbAciagwyEnNLTIM4E4N5yTVLgaWIAgOwJTgKVa9S8eOHaNdu3am14H/DST2amyB/QLqBPDb1N/K3I/jx49z/Phx7rrrLlObpmm4urqyadMm2rZtS9u2bU3tLi4u+Pr6kpCQgKZpJkPo0qVLNG7cmLCwMI4cOQKocEBvb2+6detGYGAgoML9wsPDSU9P54svvmDw4MH4+vpaHL9JkyZcunTJ1J+srCySk5Px8fEp0H9XV1eTJy2/Uebm5mbx2sVFHvWCIAgVici0OxKpqtR3psnAcsPDLc3ULgiCUF0IHxyOp6tlwRhPV0/CB9unYIyTkxPh4eGcOXMGgHXr1tGmTRsaN27MiRMneOutt8jJySE9PR29Xs+IESNo3Lgx/v7+fP/99wDs3LkTJycnWrduTXh4OJs3b2bz5s1MnDiRIUOGEBkZaTr+zz//TKtWrahRowYRERGsXr0agOTkZDZs2MCIESMIDg4mOTmZyMhIAL788ku6dOmCl5eXXf5nQRAEoWKQZS1HwtMfUmPJyDI2uFPDNV21C4IgVCOMQhblpSLYunVrXnjhBR555BFycnJo3Lgxb7zxBgAzZ85k4cKFjBo1iuzsbO644w7uvfdeAN544w3mzZvHihUrcHNzY9myZRY5WUbatWvHiy++yMyZM8nOzsbLy4tly5YBMH/+fObPn8/IkSPJzs4mJCSEvn37AvD222+zcOFC0tLSqFu3LkuWLLHL/ysIgiBUHDpN07TK7oSjEBERYVJ3qhSi9ejfeJBZH2eRcA2gMaseTuWhOe9KiGAVIn+ok1B1kGtTdZFrU3WRa1N1kWtTdZFrU3Wx57WxZRtIiKADod8Foat1ucYVwEWeXJOOfldl9koQBEEQBEEQBCNiYDkQYWFhpKZlWrSlpmcSFhZWST0SBEEQBEEQBMEcMbAciLg462IWttoFQRAEQRAEQahYxMByIPz9rYtZ2GoXBEEQBEEQBKFiEQPLgQgPD8fTM59ssacn4eH2kS0WBEEQBEEQBKFsiIHlQISEhLBy5UoCmjQBwMWpGStXriQkRBQEBUEQBEEQBKEqIHWwHIyQkBBCWrbk3l5xHGt2ByEhtSu7S4IgCIIgCIIg5CIeLEckI4M0PKjhLiXMBEEQBEEQBKEqIQaWo6HXw733kk4NakQfU68FQRCqIXo9BAaCk5P6XV7D3ZYtW+jatavVbTNnzmThwoWm11u3buWWW25hzJgxpp/r169bfe/jjz/OkCFDTPstWrQIgGvXrtGpUyeLY+zZsweAqKgoxo4dy/Dhw3nggQe4dOmSnf9bQRAEobyREEFHQq+H0FBITSUNDzyzUtRrAMnDEgShGmE23AEQG1s+w11MTAxLliyxum3VqlXs37+fESNGmNoOHDjAtGnT+Pe//13ksQ8cOMCXX36Jj4+PRfvBgwfp0aMHH3zwgUV7ZmYmTzzxBG+88Qbdu3dn3bp1hIWFsWrVqlL8Z4IgCEJlIQaWIxEWZpptpFOD+iSq12FhYmAJguAQfPIJ5LMrAEhN9cdcJHXPHsjIyL8PTJ8ORdkb06bBlClF9yUtLY3Zs2fz/PPP88wzz1hs+/PPP9m5cyf33XcfKSkppvYDBw7g4uLC999/T61atZg1axY9evQocOwzZ85w48YN5s2bx4ULF+jYsSPPPfccdevW5cCBAyQnJzN+/HgyMzMZP348kyZN4vDhw9SqVYvu3bsDMG7cOBYtWkRSUhL16tUzHfvs2bNMnTqVgQMHEhUVRUpKCrNnz2bIkCEsX76cuLg44uPjSUhIoEOHDvTs2ZNNmzZx9uxZZs+ezZ133ln0hyMIgiCUGgkRdCTMCgqn4YEHaQXaBUEQqgP5jaui2kvD/PnzmTBhAm3atLFoj4+PJzw8nNdeew1nZ2eLbXXr1uW+++5j8+bN/N///R8zZ87k4sWLBY6dmJhInz59WLBgAZs2bcLT05O5c+cC4OzszKBBg1i7di3vv/8+H3/8MVu2bOHixYs0btzYdAw3Nzfq169PfHx8geOfOXOGfv36sWHDBp5++mlT+CFAREQE77zzDhs3bmTHjh2cOnUKvV7PvHnzWL58eZk+M0EQBKFoxIPlSPj7qzgZlAerBul57YIgCA7AlCnWvUvHjsXRrl070+vAQNNwZ0FAAPz2W9n7odfrcXFxYdy4cZw9e9bUnpWVxdNPP82cOXPw9vYu8L63337b9Pe//vUvunbtyh9//MHYsWMt9gsODuadd94xvZ45cyb9+vUjMzOTxx57zNTu4+PDhAkT+OWXX+jXrx86nc7iOJqmFTDyAFxdXRk4cCAA7du3Jzk52bStT58+1K6tFGa9vb3p378/oIrSm+8nCIIglA9iYDkS4eGmpASTgeXpqdoFQRCqEWbDnQl7DncbN24kPT2dMWPGkJWVZfp7/vz5nDlzhsWLFwNw+fJlcnJyyMjI4LnnnmPdunXMmDHDZAhpmoaLiwthYWEcOXIEgPvuu49WrVpx9epVBg8ebNpPp9Ph7OzMmjVrGDx4ML6+vhbHaNKkiYWoRVZWFsnJyQVyuEAZWE5OKgglv1Hm5uZm8drFRR71giAIFYmMuo6EMc8qLIy0WA88arvCipWSfyUIQrXDbLgjLk456sPD7TfcbdiwwfT32bNnGTVqFJs3bwZg+/btpm3Lly8nKSmJ+fPnk5OTg16vJygoiGHDhnH06FEOHTrEq6++ypgxYyyOv337dl555RW6d+9O3bp1Wb16NcOGDcPZ2ZmIiAhiYmKYN28eycnJbNiwgRdeeIHg4GCSk5OJjIykW7dufPnll3Tp0gUvLy/7/NOCIAhChSAGlqMREgIhIaTVMFDjkQdBbCtBEKopucNdlcHZ2Zl3332XV155heXLl+Ps7Mybb75J/fr1C+w7cOBA7r//fiZOnIjBYKBNmza8/PLLgMr9mj9/PiNHjiQ7O5uQkBD69u0LqBDEhQsXkpaWRt26dW0qHAqCIAhVF52maVKttphERESY1J0qE01TdWFefBFeeqmyeyPk59ixYxa5JELVQa5N1UWuTdVFrk3VRa5N1UWuTdXFntfGlm0gKoIOiFFFq0aNyu2HIAiCIAiCIAiWiIHlgKTnigeKgSUIgiAIgiAIVQsxsByQtNzyVx4eldsPQRAEQRAEQRAsEQPLAREPliAIgiAIgiBUTcTAckDEgyUIgiAIgiAIVRMxsBwQ8WAJgiAIgiAIQtVEDCwHRAwsQRAEQRAEQaiaiIHlYOj1cPfd6u8pU9RrQRCE6oherycwMBAnJycCAwPRl9OAt2XLFrp27Wp128yZM1m4cKHp9datW7nlllsYM2aM6ef69etW37t+/XpGjhzJqFGjeOSRR0hMTATgvvvus3h/586deeWVVwCIiopi7NixDB8+nAceeIBLly7Z+b8VBEEQyhsxsBwIvR5CQ8H4vI2PV6/FyBIEobqh1+sJDQ0lNjYWTdOIjY0lNDTU7kZWTEwMS5Yssbpt1apV7N+/36LtwIEDTJs2jc2bN5t+atWqVeC9Z86c4c0332Tt2rV88803NG3alOXLlwPK8DK+94knnsDPz48nn3ySzMxMnnjiCebOncsPP/zAsGHDCAsLs+v/KwiCIJQ/LpXdAaH4hIVBaqplW2qqag8JqZw+CYIglIRPPvmEDz74oEB7amoqnp6eptd79uwhw1hV3Wyf6dOns2rVqkLPMW3aNKZMmVJkX9LS0pg9ezbPP/88zzzzjMW2P//8k507d3LfffeRkpJiaj9w4AAuLi58//331KpVi1mzZtGjR48CxzYYDGRnZ3Pjxg3q1KlDenp6AUMsOTmZF198kRUrVlC7dm0iIiKoVasW3bt3B2DcuHEsWrSIpKQk6tWrZ3rf2bNnmTp1KgMHDiQqKoqUlBRmz57NkCFDWL58OXFxccTHx5OQkECHDh3o2bMnmzZt4uzZs8yePZs777yzyM9GEARBKD1iYDkQcXElaxcEQXBU8htXRbWXhvnz5zNhwgTatGlj0R4fH094eDj/+9//+Oyzzyy21a1blzvvvJNhw4YRERHBY489xubNm2ncuLHFfgEBAUyfPp077rgDLy8vateuzfr16y32WbVqFQMHDqRTp04AXLx40eI4bm5u1K9fn/j4eAsDC5SHrF+/fsybN4+ffvqJRYsWMWTIEAAiIiLYvHkzrq6uDBgwgIYNG6LX69myZQtLly4VA0sQBKGcEQPLgfD3h9hY6+2CIAiOwJQpU6x6l44dO0a7du1MrwMDA4m1MuAFBATw22+/lbkfer0eFxcXxo0bx9mzZ03tWVlZPP3008yZMwdvb+8C73v77bdNf//rX/+ia9eu/PHHH4wdO9Ziv99//52ff/6Z7du3U69ePZYuXcqcOXN47733AGUofv7553z11Vem9xgMBnQ6ncVxNE3D2dm5QD9cXV0ZOHAgAO3btyc5Odm0rU+fPtSuXRsAb29v+vfvD4C/v7/FfoIgCEL5IAaWAxEernKuzMMEPT1VuyAIQnUiPDyc0NBQUs0GPE9PT8LtNOBt3LiR9PR0xowZQ1ZWlunv+fPnc+bMGRYvXgzA5cuXycnJISMjg+eee45169YxY8YMkyGkaRouLi6EhYVx5MgRQIlYnDhxgkGDBtGgQQMAQkJCGDVqlOn8O3bsoG3btjRr1szU1qRJEwtRi6ysLJKTk/Hx8SnQf1dXV5ycVBp1fqPMzc3N4rWLizzqBUEQKhIZdR0IY55VWBjExWn4++sID5f8K0EQqh8huQNbWFgYcXFx+Pv7Ex4ebmovKxs2bDD9ffbsWUaNGsXmzZsB2L59u2nb8uXLSUpKYv78+eTk5KDX6wkKCmLYsGEcPXqUQ4cO8eqrrzJmzJgCx1+3bh3Tp0+nZs2a/PzzzwQHB5u27927l969e1u8Jzg4mOTkZCIjI+nWrRtffvklXbp0wcvLyy7/syAIglAxiIHlYISEqJ9jx45bhNMIgiBUN0JCQuxmUNkDZ2dn3n33XV555RWWL1+Os7Mzb775JvXr1y+w79ixYzl37hz33HMPbm5uNG3a1OQVA4iNjaVjx44W73F1deXtt99m4cKFpKWlUbduXZsKh4IgCELVRadpmlbZnXAUIiIiTOpOlU3+fAWh6iDXpuoi16bqItem6iLXpuoi16bqItem6mLPa2PLNpA6WIIgCIIgCIIgCHZCDCxBEARBEARBEAQ7IQaWIAiCIAiCIAiCnRADSxAEQRAEQRAEwU6IgSUIgiAIgiAIgmAnxMASBEEQBEEQBEGwE2JgCYIgCIIgCIIg2AkxsARBEARBEARBEOyEGFiCIAiCIAiCIAh2QgwsQRAEQRAEQRAEOyEGliAIgiAIgiAIgp3QaZqmVXYnHIWIiIjK7oIgCIIgCIIgCFWE7t27F2gTA0sQBEEQBEEQBMFOSIigIAiCIAiCIAiCnRADSxAEQRAEQRAEwU6IgSUIgiAIgiAIgmAnxMByMDIzM5k3bx49evSgb9++rFq1qrK7dNMSFxfHv//9b3r06MGAAQNYvHgxGRkZAMybN482bdpY/Hz00UeV2+GbiG+++abA5//oo48CcO7cOaZNm0aXLl0YPnw427dvr+Te3jx89dVXBa6L8ef8+fNy31QSmZmZ3HnnnezatcvUVtR9smfPHkaNGkVwcDD3338/sbGxFd3tmwJr12b37t2MHTuWrl27MmzYML744guL9wwbNqzAfXTs2LGK7nq1x9q1KWoMk/umYsh/bZ5//nmrz53Bgweb3mPv+8alzP+FUKH85z//4cCBA3z44YdcvHiRZ599Fl9fX0aOHFnZXbupyMzM5N///jctW7Zk/fr1XLlyhblz5wLqRj558iTPPvsso0ePNr2nVq1aldXdm46TJ08yZMgQXnzxRVObu7s7mqbx6KOP0qJFCzZs2MDWrVt54okn+Pbbb2nWrFkl9vjmYMSIEfTv39/02mAw8Mgjj+Dn54evr6/cN5VARkYGTz/9NCdOnDC1FXWfXLhwgUceeYRHH32U2267jXfeeYdHH32Ub775BicnWbe1F9auTUxMDDNmzODRRx9l+PDhREVFERYWRoMGDRg0aBCZmZmcOXOGTz/91GJMq1evXmX8C9UWa9cGKHQMk/umYrB2bcLCwnj66adNr69cucKkSZN48MEHAcrlvhEDy4FITU3l888/57333qNjx4507NiRhx56iLVr14qBVcEcOnSIuLg4vvjiC2rWrEmLFi148sknWbx4Mc8//zynT5+mY8eONGrUqLK7elNy6tQp2rRpU+Dz3717N9HR0ej1emrVqkXLli3ZtWsXGzZsYNasWZXU25uHGjVqUKNGDdPrtWvXcv78eT788EMAuW8qmJMnT/L000+TX0x4z549hd4nn3/+OW3btuXhhx8GYNGiRfTt25c9e/bQp0+fyvhXqh22rs33339Pu3bt+Pe//w1AQEAA+/bt45tvvmHQoEGcPn0anU5Hp06dcHV1rYyuV3tsXRsofAyT+6b8sXVtateuTe3atU2vX375ZYKDg5k8eTJAudw3YjI7EMePHyczM9NCb7979+4cPnyY7OzsSuzZzUfz5s1ZuXIlNWvWNLXpdDoyMzNJSEggOTmZoKCgSuzhzc3Jkyetfv5RUVG0b9/ewivSvXt3Dh48WIG9EwCuX7/O22+/zRNPPEGdOnXkvqkE9u/fT9++ffnss88s2ou6T6KioujRo4dpm4eHBx06dODAgQMV0u+bAVvXZvjw4cybN8+iTafTmcLTT506hZ+fnxhX5Yita1PUGCb3Tflj69qYc+DAAbZs2cKcOXNMbeVx34gHy4FISEigTp06uLu7m9oaNmxIVlYWiYmJeHt7V2Lvbi7q169vseJkMBhYu3Yt3bt35+TJk7i4uLBs2TJ27NhBvXr1mDp1Kvfcc08l9vjmwejq37ZtG2+99RYGg4E77riDJ554goSEhAL3SYMGDbh48WIl9fbm5bPPPsPNzY17770XQO6bSuC+++6z2l7UfWJre3x8fPl09CbE1rXJP3m/fPky3333HTNnzgTUfeTs7MxDDz3EsWPHCAoKYvbs2QQHB5d7n28WbF2bosYwuW/KH1vXxpz33nuPoUOH0rp1a1Nbedw3YmA5EGlpabi5uVm0GV9nZmZWRpeEXF599VWOHTvGhg0b2Lt3LwBt27bl/vvvZ+/evcyfPx8PDw+GDx9eyT2t/sTGxpKdnY2npydvvfUWcXFxhIeHc+PGDTIyMgqsULm5uZGVlVVJvb050TSNzz77jMmTJ5uux+nTpwG5b6oCaWlphd4ntp5F8hyqWFJTU5k5cybe3t6mieWpU6dISUnhmWeewcfHh88//5wHHniAb7/9Fj8/v0rucfWmqDFM7pvK5/z58+zYsYP169dbtJfHfSMGlgPh7u5e4EY0vvbw8KiMLt30aJpGeHg4n376KcuWLaNVq1a0bNmSkSNHUrduXUANtrGxsXz66acyUawAWrVqxZ49e0zJqW3btkXTNJ5++mnuvfderl+/brF/ZmamRV6QUP789ddfxMXFMWbMGFPbpEmT5L6pIri7uxd6n9h6FhmvnVD+XLt2jRkzZnD27FnWrVtnmgO8/vrrZGRkmMI7X3rpJSIjI9m0aZPJyyWUD0WNYXLfVD4//vgj/v7+BTxT5XHfSA6WA+Hj40NKSorFDZqQkICbmxt16tSpxJ7dnBgMBubOncv69et58803uf322wEVD59/wGzevLmEAVQg+ZV/WrRoQVZWFt7e3iQkJFhsu3z5sogqVDA7duwgODgYHx8fU5vcN1UHHx+fQu+TorYL5UtiYiJTpkzhzJkzfPLJJ/j7+5u2ubq6WuTO6XQ6mjdvzqVLlyqjqzcVRY1hct9UPjt27GDo0KEF2svjvhEDy4Fo164drq6uFgmRERERdOjQARcXcUZWNIsXL+abb75h+fLlFjfs4sWLmTFjhsW+x44do3nz5hXdxZuSn3/+mT59+lgsRBw9ehQvLy+6dOnC8ePHSU1NNW2LiIigS5culdDTm5f8yd4g901VIjg4uND7JDg4mMjISNO2tLQ0jh49KvdRBWAsEZKUlIRery9wf4wbN46VK1eaXhsMBv7++2+5jyqAosYwuW8qF03TOHToUIFnD5TPfSMGlgPh4eHBXXfdxYIFCzh06BC//vorH3zwAVOmTKnsrt10HDx4kI8//pgnnniCjh07kpCQYPoZNGgQO3bs4JNPPiEuLg69Xs+mTZuYPn16ZXf7pqBHjx5omsb8+fOJjo7mt99+4z//+Q/Tp0/nlltuwdfXl+eff54TJ06wcuVKoqKiTEILQsVw4sQJWrZsadEm903Voaj7ZOzYsURFRbFixQpOnjxJWFgYvr6+9O7du5J7Xv356KOP+Ouvv3j11Vfx8PAwPXeSk5MBuPXWW1m9ejXbt2/n9OnTvPTSS1y9epWxY8dWbsdvAooaw+S+qVzOnTvHjRs3aNWqVYFt5XLfaIJDkZqaqj377LNaly5dtL59+2qrV6+u7C7dlCxevFhr3bq11Z+srCztu+++00aOHKl17NhRGz58uPbTTz9VdpdvKv766y9t8uTJWpcuXbR+/fppy5cv1wwGg6ZpmhYTE6OFhIRoHTt21EaMGKHt3Lmzknt789GpUydt27ZtBdrlvqk8Wrdurf3xxx+m10XdJ7/99ps2bNgwrXPnztr999+vxcbGVnSXbxrMr83dd99t9blz3333aZqmadnZ2dqyZcu0gQMHap06ddImT56sHT9+vDK7X63Jf98UNYbJfVNx5L82Bw8e1Fq3bq3duHGjwL7lcd/oNM1KpTRBEARBEARBEAShxEiIoCAIgiAIgiAIgp0QA0sQBEEQBEEQBMFOiIElCIIgCIIgCIJgJ8TAEgRBEARBEARBsBNiYAmCIAiCIAiCINgJMbAEQRAEQRAEQRDshBhYgiAIgkPx/PPP06ZNG5s/X331FW3atGHXrl0V1qdnn32W3377rdyOn5iYyF133UVGRka5nUMQBEGwD1IHSxAEQXAorl27Rnp6OgD79+/nqaee4vfffzdtr127NteuXaNOnTq4ubmVe3/279/Pa6+9xvr168v1PMuWLcPJyYnHH3+8XM8jCIIglA2Xyu6AIAiCIJSE2rVrU7t2bQDq1KkDQKNGjSz2qVGjRoX1591332XixInlfp5JkyYxYsQIpk2bRs2aNcv9fIIgCELpkBBBQRAEodphHiI4aNAgPvvsM8aOHUvnzp2ZPn06586dY+bMmQQHB3PXXXdx6tQp03v379/PuHHj6Ny5MyNHjmTTpk02zxMbG8vevXu59dZbTW0//vgjI0aMoFOnTgwdOpQvv/zStO3atWs899xzdO/enb59+zJv3jyuX79u2n706FEmT55McHAwgwcPZsOGDaZtjRo1IjAwkG+//dYOn5AgCIJQXoiBJQiCIFR73nrrLWbNmoVer+fIkSPcfffd9O/fny+++AInJyf++9//ApCQkEBoaCijRo3im2++4bHHHuOVV15h69atVo+7c+dOOnToYPKkXblyhWeeeYapU6fy448/MmPGDF544QWTATd37lySkpLQ6/W8//77REdHM2fOHEDlWU2dOpXmzZuzceNGZs2axUsvvcT+/ftN5+vTpw87duwox09KEARBKCsSIigIgiBUe8aMGUO/fv0AuOWWW0hKSmLChAkAjB49mi+++AIAvV5Pz549eeCBBwAICAjg9OnTfPzxxwwaNKjAcY8cOULz5s1Nr+Pj48nKysLHx4emTZsyduxYfH19adiwIXFxcfzyyy/s2bOHunXrArBkyRIGDRrEhQsX2Lp1KzVr1uTFF1/E2dmZ5s2bk5ycjMFgMB2/ZcuWbNy4sVw+I0EQBME+iIElCIIgVHuaNWtm+tvd3R1fX1+L15mZmQCcPn2anTt30rVrV9P27Oxs6tevb/W4V65coVWrVqbX7dq148477yQ0NBR/f39uu+027rnnHurUqUNkZCSapnHbbbcVOE5MTAwnT56kbdu2ODs7m9onT55ssV/dunVJTEws4X8vCIIgVCRiYAmCIAjVHhcXy8edk5P1CPns7GxGjhzJo48+Wqz9dTod5mK8Op2O119/nWnTprF161a2bt3KunXreO+998jJycHT09NqTlejRo3Ytm1bkf+HwWCw2RdBEAShaiCjtCAIgiDkEhQURExMDAEBAaaf33//3UJswpyGDRuSlJRken3q1CleffVVOnTowOOPP87GjRv517/+xS+//EJQUBCpqank5OSYjg3w6quvcv36dQICAvj7778tQgLnzJnDsmXLTK+TkpJo2LBhOf33giAIgj0QA0sQBEEQcpk0aRLHjh3j9ddfJyYmhh9//JGlS5fi4+Njdf8OHTrw999/m157eXnx2WefsXz5cs6cOcOePXv4+++/6dixIy1atKB///48++yzREVFcfz4cZ577jmuXLmCt7c3o0eP5saNGyxatIjo6Gi+/fZbvv32W/r37286vvFYgiAIQtVFDCxBEARByKVp06a8//777Nq1izvvvJMlS5bw+OOPM2nSJKv7DxgwgH/++Ydr164BKtRv+fLl/Prrr4wcOZLZs2czceJExo0bB8B//vMfAgICmDZtGpMnT8bb25t3330XUPW9Vq5cyaFDhxg9ejTLly9n0aJFdOvWzXS+iIgIBg4cWM6fgiAIglAWdJp58LggCIIgCCVi6tSpjBw5knvvvbdcz3PmzBnGjh3Ltm3bpNCwIAhCFUY8WIIgCIJQBmbMmMH69evL/Tzr168nJCREjCtBEIQqjhhYgiAIglAGevfuTWBgIL/++mu5nSMxMZEdO3YwY8aMcjuHIAiCYB8kRFAQBEEQBEEQBMFOiAdLEARBEARBEATBToiBJQiCIAiCIAiCYCfEwBIEQRAEQRAEQbATYmAJgiAIgiAIgiDYCTGwBEEQBEEQBEEQ7MT/A0Vtz/IcAmdUAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('seaborn-whitegrid')\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot(time, int_SCI_flux_750p / ((847+4.8/2)-750) / tdur_sec, marker='o', color='r')\n", - "plt.plot(time, int_SCI_flux_650_750 / (750-650) / tdur_sec, marker='o', color='orange')\n", - "plt.plot(time, int_SCI_flux_550_650 / (650-550) / tdur_sec, marker='o', color='g')\n", - "plt.plot(time, int_SCI_flux_550m / (550-(450.1-0.4/2)) / tdur_sec, marker='o', color='b')\n", - "plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec, marker='o', color='k')\n", - "plt.xlabel(\"Time (sec)\", fontsize=14)\n", - "plt.ylabel(\"Exposure Meter Flux (e-/nm/s)\", fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.legend([\"750-870 nm\",\"650-750 nm\",\"550-650 nm\",\"445-550 nm\",\"445-870 nm\",], fontsize=12, loc='best') \n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "426df078", - "metadata": {}, - "source": [ - "# Plot Fraction of Flux Time Series in Each Color Bin\n", - "This is a measure of the ADC performance." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "7ed04a87", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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LWVlZcHFxwcyZMw0//tuiRQsEBwcblh0+fDieeuopXLx4EdOmTcPt27fh7e2N+Ph4PPTQQybr3rBhAw4dOmT4+9atW7hz5w6OHDmCrKwstG7dGrVr1zbMnzJlClq2bImjR49i5syZyMnJQVBQEBYsWICgoCCr7bM58fHxaNGiBdq3by87/7PPPsP69euxZcsWo+k6nQ7R0dF44oknMHz4cADAmjVrsGzZMgQGBgIAKlSogC+++EJ2vUuWLMG2bdvg6uqKBg0aYObMmbh79y6GDh1qtNyff/6JSZMm4YUXXsCePXuwcOFC6HQ61K1bF3PmzIGPj085j4CNiAfc4cOH73cSjJw8efJ+J4EsYB45B+aT42MeOQfmk+P71+TR+QQh1noLkYh7n7Xe0nQryMnJEW3atBF79uwRQgixY8cO0a1bNyGEEOfOnRNdu3aV/d7TTz8tvvnmGyGEEHv27BE9e/YUer3eZLni+fTPP/+Irl27Grb1008/iRdeeMHkO3fv3hWPP/64ob6amJgoRowYUY69LJ3evXuLvLw82XmHDx8Wbdq0ET179jSZN2PGDNGiRQvxySefGKaNHz/ecJzMOXDggOjWrZvIzc0Ver1evPzyy2LFihUmy61evVpER0cLnU4nbt68KVq2bCkuXLgghBBi/vz5YsaMGep2shhrX0tKcQZ7sIiIiIjIds6vBs6vtLzcjQOA/q7xtMIc4OBw4NwK89+t/SJQe5jZRfbv348aNWqgXbt2AIBOnTqhevXqAIDk5GS4uLhgyJAhyMrKQrdu3TBmzBjcuHED58+fR8+ePQEA7dq1Q2xsLE6ePIkGDRoobis+Ph5t27Y1bCs5ORkZGRkYMGAAdDodBgwYgCFDhuD48ePw8fFBs2bNAAD9+/fHnDlzcPv2bVSuXNmwvtTUVDz//PNo164djh49iszMTEycOBFdunTBkiVLcOnSJaSlpSE9PR0NGjRAixYtsGnTJqSmpmLixIl48sknTdJ49uxZhIWFwcPDw2TejRs3EBcXh0mTJmH58uVG8zZt2oSsrCyTXq/k5GRkZ2dj+fLlCAoKwqRJk1C3bl2Tdev1euh0OuTl5cHFxQV37941SUNKSgqWLl2K9evXw93dHfv27UOjRo1Qs2ZNAMDgwYPRu3dvzJgxAxqNxvC9gwcPYtGiRahRowbOnj2LgoICxMbGolmzZpg8eTJyc3ORnp6OmzdvomPHjqhUqRJ+/PFHpKenY9asWWjVqpVinpYGAywiIiIiuv9KBleWppfShQsXUKVKFUydOhWnT59GxYoVMXHiRABAYWEhWrdujQkTJqCgoACjRo2Cj48PmjRpgqCgILi43HttQdWqVXHt2jXFAOuvv/7Czp07sXPnTsM0V1dXdOzYEaNGjcLt27cxbNgwBAUF4e7du0bDErVaLfz9/ZGWlmYUYAHA5cuXERUVhZiYGPzwww+YM2cOunTpAgBISkrC5s2b4e7ujscffxyBgYFITEzEzp07sWDBAtkAa+fOnejYsaPJ9MLCQkyYMAETJ06Em5txqHDmzBmsXr0aCQkJmDlzpmF6Tk4OateujZEjR6J58+bYtm0bRo4cie+++w4VKlQwWkerVq3QunVrdOjQAe7u7qhVqxYGDhxotMyiRYvw7LPPIjQ0FABw7do1o+MUHByM7Oxs3Llzx2SY4LFjxzBjxgxERERg5cqVWLRoERISEgAA58+fx9dff42MjAxERUXhrbfewtq1a/H5559jxYoVDLCIiIiIyAnUHmaxdwkAsKkmkJNiOt07HOi8p9zJKCgowN69e7F69WpERkZi586dGDVqFH788UcMGDDAaNkXXngBa9asQePGjY16SABACAFXV1fF7Xz++ed49tln4evra5g2duxYw/+rVq2KgQMHYseOHYiKilK9fnd3d0OPWP369ZGRkWGY17p1a8P2goKC0LZtWwBAWFiY0XLF7d27F8uWLTOZvnDhQjRv3hxt2rTBwYMHDdOzsrLw5ptv4p133oG3t7fRd7y9vfHpp58a/u7RoweWLl2K48ePo2XLlkbLrl+/Hqmpqfj555+h1WoxZcoUxMfHIyYmBgBw9epV7Nu3D7NmzTJ8R6/XmxwnAEaBb5HQ0FBEREQYjtPGjRsN85o3bw53d3dUqVIF3t7eqo5TWTDAIiIiIqL7L3I28NsoaVhgEVdvaboVBAUF4aGHHkJkZCQAoHPnznjrrbdw+fJlHD9+HPXq1UO9evUASEGOm5sbQkNDkZ6eDiGEoYJ//fp1BAcHY9q0aThx4gQAYNCgQWjSpAkKCwuxfft2bNiwwWjba9asQadOnQw9MkXrDwkJwfXr1w3L5efnIyMjA1WrVjVJv7u7uyGgKBlsaLVao79L9jyVlJaWBk9PT1SqVMlk3jfffAN/f3/s2LEDOTk5SEtLQ+/evfHSSy8hMzMTEyZMACAFQvv370d2djb69++P3bt3G72komgfR44cadjHcePGYceOHejVq5eh52nAgAGIi4szfO+HH35Aly5djHqmQkJCcPToUaP0+/n5mQR6AODp6Wn4v0ajgRDC8Le7u3upjlNZMcAiIiIiovuv6G2BNnqL4OOPP474+HicOHECDRs2xKFDh6DRaFC9enV8/fXX2L59O5YsWYL8/HwkJiaiV69eCA4ORlhYGLZt24aePXvi559/houLC+rUqYPZs40Dv1OnTuHPP/9ExYoVDc92FUlKSsLFixcRExODjIwMrF+/Hm+99RYiIyORkZGBI0eO4JFHHsGGDRvQpEkTVKxY0Sr7rGTXrl2ywwMBYN++fYb/Hzx4EHFxcdi8eTMAqWeqyOTJk/Hwww9j+PDhuHXrFhYvXozIyEg0btwYe/fuRW5uLho3bowVK4yfnztx4gR27NiB3r17w9XVFTt27DAEvQDw22+/oVu3bkbfiYqKQnx8PC5evIiaNWti7dq16NSpU7mPg60wwCIiIiIix1Ar2qqvZS+uSpUq+PDDDxEbG4vc3FxotVosWbIEHh4eeOWVVzBz5kz06tULBQUFeOKJJ/DMM88AAN59913ExMRg6dKl0Gq1eO+992SHpgHAxYsXUa1aNZPp06dPx/Tp09GzZ08UFBQgOjoabdq0AQB88MEHmDlzJnJzc1GpUiXEx8fbZP+L27VrF2JjY622Pn9/fyxevBjTp09Hfn4+fHx88OGHH5r0rAHA6NGjMXfuXPTs2RNarRZ169bFjBkzDPNTUlJMjmFAQADmzp2LcePGIT8/H2FhYXY5TmWlEcX7zR5ASUlJhjezOIJTp04ZxoWSY2IeOQfmk+NjHjkH5pPjYx45B+aT47N2HinFGfLhNxEREREREZUaAywiIiIiIiIrYYBFRERERERkJQywiIiIiIiIrMSuAZZOp0NMTIzhx8tKvraxuA0bNqBLly5o3LgxBg0ahGPHjhnm6fV6NG7cGHXr1jX6ZGZm2mM3iIiIiIiIZNn1Ne3z589HcnIyVq1ahWvXrmHSpEkIDQ1Fz549jZbbv38/YmNjMW/ePDRs2BCJiYkYOXIkdu3aBR8fH1y+fBk6nQ67d+82ev1j8V/MJiIiIiIisje79WDl5ORg3bp1mDp1Kho2bIjOnTtjxIgRSEhIMFn2xo0bePXVV9GjRw+EhYXh1VdfRUZGBv78808AwF9//YXQ0FBUq1YNVapUMXxK/qo1ERERERGRPdmtB+v06dPQ6XRG74pv1qwZPvroIxQUFMDN7V5Sevfubfh/Xl4ePvvsMwQEBKBOnToAgHPnzqFWrVr2SjoREREREZEqdguw0tPT4efnBw8PD8O0wMBA5Ofn49atWwgKCjL5zs8//4yRI0cCAN555x34+PgAkHqw7ty5g+joaKSkpCAiIgJTpkxB7dq17bMzRERERGR1iccTMW3XNFz65xLC/MIwu9NsRDeKttr6582bh++//x5+fn4AgFq1amHx4sUAgBYtWiA4ONiw7PDhw/HUU0/h4sWLmDZtGm7fvg1vb2/Ex8fjoYcekl3/oUOHsGDBAuTl5cHX1xfz5s1DjRo1kJWVhdatWxvVVadMmYKWLVvi6NGjmDlzJnJychAUFIQFCxbI1ottIT4+Hi1atED79u1l53/22WdYv349tmzZYjRdp9MhOjoaTzzxBIYPHw4AWLNmDZYtW4bAwEAAQIUKFfDFF1/IrnfJkiXYtm0bXF1d0aBBA8ycORN3797F0KFDjZb7888/MWnSJLzwwgvYs2cPFi5cCJ1Oh7p162LOnDmG2MDR2C3Ays3NNXpeCoDhb51OJ/udevXqYePGjdi1axcmT56M6tWro0mTJjh37hxycnIwffp0VKhQAcuXL8ewYcPw3XffyT6HderUKevvUBnl5eU5VHrIFPPIOTCfHB/zyDkwnxzfvyWPtqRswfTD05FXmAcASPknBSM2j8CVv6/gyfAnrbKNX375Ba+99hrq1atnmHbq1Cn8/fff8Pb2xrx584yWP3XqFCZOnIgnn3wS7dq1Q1JSEkaPHo333nvP5NGUv//+G5MnT8bbb7+Nhx56CN9++y0mTpyIGTNmIDk5GREREYiNjTX6zrFjxzBmzBj83//9HyIiIvDdd9/htddew/Tp062yv5b8+OOPeOKJJ2TPr1OnTmHZsmXw9fU1mb9s2TJcvHgR169fN8zbu3cvhg4dinbt2hmto6Tjx49j48aNePfdd6HVajFv3jwsXLgQffv2NTr+W7ZsgaurKx599FEcOHAAkyZNwty5cxEaGorPP/8c06ZNw+jRo0u1v/a6luwWYHl4eJgEUkV/e3l5yX6n6NmqiIgIJCcnY+3atWjSpAm++OILFBYWwtvbGwCwcOFCtGvXDrt27UKfPn1M1hMREWHdnSmHU6dOOVR6yBTzyDkwnxwf88g5MJ8cn7Pn0eqjq7EyeaXF5Q6kHsDdwrtG0/IK8xBzOAZbr201+90Xm76IYZHDzC6j0+lw4cIF7NixAytXrkTNmjUxZcoUhIaG4uTJk/D09ERcXByysrLQrVs3jBkzBjdu3MCVK1cwatQouLi4ICIiAitXroQQAvXr1zda/zfffIP27dvjySelYLB27dpITU3FQw89hB07dqCgoAAzZsyATqfDgAEDMGTIECQlJaFy5cro168fAOChhx7CI488guDgYFSuXNmw7tTUVDz//PNo164djh49iszMTEycOBFdunTBkiVLcOnSJaSlpSE9PR0NGjRAixYtsGnTJqSmphoCxJLOnj2LOnXqIDIy0mTejRs3MGXKFEydOhXLly83Ov82bdoENzc3dOrUCUFBQYZ5586dg4uLC7Zu3YqgoCBMmjQJdevWNVl3RkYGAKBmzZrw9vaGVqtFjRo1jLaRkpKCjRs3Yv369QgNDcU333yDJk2aoFOnTgCAV155Bb1798bixYuNAt2DBw9i0aJFqFGjBs6ePYuCggLExsaiWbNmmDx5MnJzc5Geno6bN2+iY8eOqFSpEn788Uekp6dj1qxZaNWqldlzqKSkpCTZ6XZ7yUXVqlWRmZlpFGSlp6dDq9UaummLJCcn4/Tp00bT/vOf/+D27dsApGCtKLgq+rt69epIS0uz4R4QERERka2UDK4sTS+ttLQ0tGzZEq+//jq++eYbREZG4uWXX4YQAoWFhWjdujU++eQTJCYmYt++fVizZg2uXr2KoKAguLjcqzJXrVoV165dM1n/lStX4O3tjfHjx6NPnz54/fXXDaO1XF1d0bFjRyQkJODjjz/G559/jp07d+LatWtGwxK1Wi38/f1l67SXL19GVFQU1q9fjwkTJmDOnDmGeUlJSfjwww+xceNG/PTTTzh37hwSExMRExODJUuWyB6PnTt3omPHjibTCwsLMWHCBEycOBFVq1Y1mnfmzBmsXr0acXFxRtNzcnJQu3ZtjBw5Et9++y2efvppjBw5Enfu3DFZf6tWrdC6dWt06NABUVFRyMrKwsCBA42WWbRoEZ599lmEhoYCgMlxCg4ORnZ2tuz6jx07hhdffBGbNm1Cv379sGjRIsO88+fP4/PPP0dCQgJWrlwJb29vrF27FsOGDTP781GlZbcerIiICLi7uyM5ORktWrQAIJ0MDRo0MHrBBQAkJiYiOzsby5YtM0z7448/0KhRIxQUFKBDhw6YPHmy4fXud+7cQUpKCp/BIiIiInIwwyKHWexdAoCai2si5Z8Uk+nhfuHY8/yecqejRo0aRpXo4cOH46OPPkJqaioGDBhgtOwLL7yANWvWoHHjxiZDAYUQcHV1NVl/YWEhdu3ahcTERNSsWROrV6/GK6+8gs2bN2Ps2LGG5apWrYqBAwdix44diIqKUr1+d3d3w/C7+vXrG3qCAKB169aGx2SCgoLQtm1bAEBYWJjRcsXt3bvXqK5dZOHChYbfrD148KBhelZWFt5880288847Rh0dAODt7Y1PP/3U8HePHj2wdOlSHD9+HC1btjRadv369UhNTcXPP/8MrVaLKVOmID4+HjExMQCAq1evYt++fZg1a5bhO3q9XvZt4cUD3yKhoaGG3rD69etj48aNhnnNmzeHu7s7qlSpAm9vb1XHqSzsFmB5eXmhT58+ht+3Sk9Px8qVKw0RcHp6Onx9feHp6Yno6GhER0cjISEBUVFR2LhxI/744w+88847cHNzQ1RUFBYtWoSgoCD4+flh0aJFqFKlCjp06GCv3SEiIiIiK5rdaTZGfTsKOfk5hmne7t6Y3Wm2VdZ/+vRpnD592uhxEiEE3N3dsWnTJtSrV8/wbJYQAm5ubggNDUV6ejqEEIYK/vXr1xEcHIxp06bhxIkTAIBBgwbB398fjzzyCGrWrAkA6N+/P2bPno28vDz897//RadOnQw9MkXrDwkJwfXr1w3pyc/PR0ZGhknPESAFWEUBRclgo+R7Dkp2XpSUlpYGT09PVKpUyWTeN998A39/f+zYsQM5OTlIS0tD79698dJLLyEzMxMTJkwAIAVC+/fvR3Z2Nvr374/du3cbvaSiaB9Hjhxp2Mdx48Zhx44d6NWrl+EFFQMGDDDqEfvhhx/QpUsXoxdYhISE4OjRo0bp9/PzMwn0AMDT09Pwf41GAyGE4W93d/dSHaeystsQQUB6W0qjRo3w3HPPYcaMGRg7dix69OgBAIiKisK2bdsAAE2bNsV7772HL7/8Er169cL+/fvx6aefGroGY2Ji0K5dO4wfP97Q4vDJJ5/Y7CARERERkW1FN4rG8l7LEe4XDg00CPcLx/Jey632FkEXFxfMnj0bly9fBgB88cUXqFu3LoKDg3H27Fm8//77KCwsRF5eHhITE9GjRw8EBwcjLCzMUEf9+eef4eLigjp16mD27NnYvHkzNm/ejMGDB6NFixY4cuSIYf3bt2/Hww8/DE9PTyQlJRl6eDIyMrB+/Xr06NEDkZGRyMjIwJEjRwAAGzZsQJMmTVCxYkWr7LOSXbt2yQ4PBIB9+/bhm2++webNmzFr1iyEhYVh8+bN6NGjB3bv3m3Y544dO+L555/Ha6+9Bi8vLyxevBjHjh0DIPWO5ebmonHjxlixYoXhO506dUL9+vUNz6QJIbBjxw6j58B+++03k16vqKgoHD16FBcvXgQArF271vA8liOya0Ti5eWF+Ph4xMfHm8w7c+aM0d9dunRBly5dZNfj7e2NmJgYQ1ciERERETm/6EbRVn0te3F16tTBW2+9hTFjxqCwsBDBwcF49913AUgvTZg5cyZ69eqFgoICPPHEE3jmmWcAAO+++y5iYmKwdOlSaLVavPfee7JD02rXro0ZM2bglVdeQUFBASpWrIj33nsPADB9+nRMnz4dPXv2REFBAaKjo9GmTRsAwAcffICZM2ciNzcXlSpVkq0nW9uuXbtM3mhYHv7+/li8eDGmT5+O/Px8+Pj44MMPPzTpWQOA0aNHY+7cuejZsye0Wi3q1q2LGTNmGOanpKSgWrVqRt8JCAjA3LlzMW7cOOTn5yMsLMwux6msNKJ4v9kDKCkpyejHje83Z38T0L8B88g5MJ8cH/PIOTCfHB/zyDkwnxyftfNIKc6w6xBBIiIiIiKiBxkDLCIiIiIiIithgEVERERERGQlDLCIiIiIiIishAEWERERERGRlTDAIiIiIiIishIGWERERERERFbCAIuIiIiIHEJiIlCzJuDiIv2bmGib7ezcuRNNmzaVnVf0o8NFdu/ejcceewy9e/c2fLKzs2W/++qrr6JLly6G5ebMmQMAyMrKQqNGjYzWceDAAQDA0aNH8fTTT6N79+547rnncP36dSvvrbL4+Hjs2bNHcf5nn32GJ5980mS6TqfDM888g08//dQwbc2aNWjTpo1h/4YMGaK43iVLlqB79+548skn8eabb+Lu3bvIzMw0Oj69e/dGREQEVq1aBQDYs2cPevXqhW7dumHcuHGKeeAI3O53AoiIiIiIEhOBUaOAnBzp75QU6W8AiI623nYuXryI+Ph42XkrVqzA4cOH0aNHD8O05ORkvPjiixg9erTFdScnJ2PDhg2oWrWq0fTff/8dzZs3x8qVK42m63Q6jBs3Du+++y6aNWuGL774AtOmTcOKFSvKsGel9+uvv+L111+XnZeUlIRPPvkElSpVMpk3Z84cXL582WhacnIyJk+ejF69epnd5sGDB7F161Zs2rQJHh4eeOWVV7BmzRqMGDECmzdvNiy3Zs0a/PDDD3j22Wdx69YtTJkyBV9++SVq1qyJBQsW4J133sHbb79d2l22CwZYRERERGQzq1cDJeIKWQcOAHfvGk/LyQGGDwcsxRsvvggMG2Z5G7m5uZg4cSImT56M//u//zOad/DgQfz8888YNGgQMjMzDdOTk5Ph5uaGbdu2wcfHB+PHj0fz5s1N1p2WloY7d+4gJiYGV69eRcOGDfHmm2+iUqVKSE5ORkZGBgYMGACdTocBAwZgyJAhOH78OHx8fNCsWTMAQP/+/TFnzhzcvn0blStXNqw7NTUVzz//PNq1a4ejR48iMzMTEydORJcuXbBkyRJcunQJaWlpSE9PR4MGDdCiRQts2rQJqampmDhxomwv1NmzZxEWFgYPDw+TeTdu3EBcXBwmTZqE5cuXG83btGkTsrKy0L59e6PpycnJyM7OxvLlyxEUFIRJkyahbt26JuvW6/XQ6XTIy8uDi4sL7t69a5KGlJQULF26FOvXr4e7uzv27duHRo0aoWbNmgCAwYMHo3fv3pgxYwY0Go1RHi5atAg1atTA2bNnUVBQgNjYWDRr1gyTJ09Gbm4u0tPTcfPmTXTs2BGVKlXCjz/+iPT0dMyaNQutWrUySW9ZcIggEREREd13JYMrS9PLYvr06Rg4cKBJxT8tLQ2zZ8/GO++8A1dXV6N5lSpVwqBBg7B582a88cYbeOWVV3Dt2jWTdf/zzz9o3bo1YmNjsWnTJnh7e2Pq1KkAAFdXV3Ts2BEJCQn4+OOP8fnnn2Pnzp24du0agoODDevQarXw9/dHWlqayfovX76MqKgorF+/HhMmTDAMPwSk3qYPP/wQGzduxE8//YRz584hMTERMTExWLJkieyx2LlzJzp27GgyvbCwEBMmTMDEiRNNeuLOnDmD1atXIy4uzmh6Tk4OateujZEjR+Lbb7/F008/jZEjR+LOnTsm62/VqhVat26NDh06ICoqCllZWRg4cKDRMosWLcKzzz6L0NBQADA5TsHBwcjOzpZd/7Fjx/Diiy9i06ZN6NevHxYtWmSYd/78eXz++edISEjAypUr4e3tjbVr12LYsGFW7TVkDxYRERER2cywYep6l2rWlIYFlhQeDph5TEi1xMREuLm5oX///khNTTVMz8/Px4QJEzBlyhQEBQWZfO+DDz4w/P/RRx9F06ZNsX//fjz99NNGy9WpUwe9e/c2/P3KK68gKioKOp0OY8eONUyvWrUqBg4ciB07diAqKsqoBwYAhBAmQR4AuLu7o127dgCA+vXrIyMjwzCvdevW8PX1BQAEBQWhbdu2AICwsDCj5Yrbu3cvli1bZjJ94cKFaN68Odq0aYODBw8apmdlZeHNN9/EO++8A29vb6PveHt7Gz2P1aNHDyxduhTHjx9Hy5YtjZZdv349UlNT8fPPP0Or1WLKlCmIj49HTEwMAODq1avYt28fZs2aZfiOXq83OU4A4OJi2lcUGhqKiIgIw3HauHGjYV7z5s3h7u6OKlWqwNvbW9VxKgsGWERERER0382ebfwMFgB4e0vTrWHjxo3Iy8tD7969kZ+fb/j/9OnTcfnyZcybNw+ANDyusLAQd+/exZtvvokvvvgCL730kqGCL4SAm5sbpk2bhhMnTgAABg0aBHd3d1y5cgWdOnUyLKfRaODq6oo1a9agU6dOhh6ZonWEhIQYvdQiPz8fGRkZJj1HgBRgFQUUJYMNrVZr9Lebm/kqflpaGjw9PWWfr/rmm2/g7++PHTt2ICcnB2lpaejduzdeeuklZGZmYsKECQCkQGj//v3Izs5G//79sXv3bgwdOtSwnqJ9HDlypGEfx40bhx07dqBXr17w8fEBAAwYMMCoR+yHH35Aly5dDPMBICQkBEePHjVKv5+fn0mgBwCenp6G/2s0GgghDH+7u7uX6jiVFQMsIiIiIrrvil5kMW0acOkSEBYmBVfWesHF+vXrDf9PTU1Fr169DC9V2Lt3r2HekiVLcPv2bUyfPh2FhYVITExErVq10K1bN5w8eRLHjh3D3LlzjXqrACAhIQGzZs1Cs2bNUKlSJXz66afo1q0bXF1dkZSUhIsXLyImJgYZGRlYv3493nrrLURGRiIjIwNHjhzBI488gg0bNqBJkyaoWLGidXZawa5du2SHBwLAvn37DP8/ePAg4uLiDMep+Ms/Jk+ejIcffhjDhw/HrVu3sHjxYkRGRqJx48bYu3cvcnNz0bhxY5OhdydOnMCOHTvQu3dvuLq6YseOHYiMjDTM/+2339CtWzej70RFRSE+Ph4XL15EzZo1sXbtWkMg64gYYBERERGRQ4iOtu4bA8vL1dUVH330EWbNmoUlS5bA1dUVixYtgr+/v8myzZo1g06nw+DBg6HX61G3bl1Dz8z06dMxffp09OzZEwUFBYiOjkabNm0ASEMQZ86cidzcXFSqVEnxDYfWtGvXLsTGxlptff7+/li8eDGmT5+O/Px8+Pj44MMPPzTpWQOA0aNHY+7cuejZsye0Wi3q1q2LGTNmGOanpKSgWrVqRt8JCAjA3LlzMW7cOOTn5yMsLMwux6msNKJ4v9kDKCkpyfBmFkdw6tQpw7hQckzMI+fAfHJ8zCPnwHxyfMwj58B8cnzWziOlOINvESQiIiIiIrISBlhERERERERWwgCLiIiIiIjIShhgERERERERWQkDLPr3uJAIbKoJfOEi/Xsh8X6niIiIiIgeMAywyDFYI/gxt44LicBvo4CcFABC+ve3UQyyqOwelIDd1tceWQfz6cHAPCD6V2CARfefNYIfc+vIzwaSJwKFOcbfKcwBjk6z5p7Yj6PepMubLkfdr5IelIDd1tdeadPiiHlvKV1q0m2N68LSMVaTTnucs46aj47gQSk3HjTWuMapfB7AY8wA60HmLCfs0WkKwc/Ue3+b2xd9AfD7JPl1/DoM+K8vkHdVfts5l4D8LKNt1EtuYLuKkjXY6iZ9vyuBavdLTT6Vd18tzVc8Z50sYD86pfz7YY1jcT/P6fKck2rP+fLum9IxPvIGkP4LcGI28NsI420cHA4cHgec/Rj4Yy5waIzlfLrfZYC10mEtpU2HpeV/n2ydPHCU4+MsbH2Nk2VWvPc7y3nPHxq2M7v9CF3RCVu8MHf1Bh5bDtSKNl7u6DQp0PAOAyJnl26+2rTIrePuLSDlS+DwK8rfDXgM0GiBmwcBkX9vusYN8Gso7d+dC4A+X3kdkXOB0wuBuzfk57t6AhUbAP8cB/S6YtNLHC+1x9TWNoUBOZdNp3uHAX1SpP+XNt/U7Ju5derzgU3h8oGsZ1Wgy37g+k9SXhffhosn8NBwwCMQOLXAtPJRtEzt5wGf2kBOKvDXckCfV7Z0qtlXpfkNpkrpvPkbcH6lwoHUAEP06tJhDWq2IbdMlTbA2Y+kY66k/fdASFdAo1HeTuafwJa6iqtIrbUY1ZsNA7SVzad1U7g0vSTvcKDPReV9Ce0BXNkmv06157TJMl5A5Dyganvgx65AXppputx8gfABwMUvlc/Zqu0AjTuQtgsozDW/b0rHRp8PXNsJ7OmhcIStI8O/HypV9gcuJgCFZq4tc2kFgI3Vgdy/TTfgGQL0vgBcWl/GPCnlNW4NpU2Huz9QmFXiHuIJ1BwGiAIgfR+Q9afy9hpOBwrvAn++b3y+/G+bp/IekeoO1jo+5b3322Mb1iB3vFw8gLABgKsHcGG1cZ4V0bgClZsA/5y0fP0WY9M6XlmOlyPch5TK2UaxQHBnYE93+XLWqxrQ84RU3qastVr9y14/NMwAy17+dwKKnEvQWKoYlPhOmQqnjTWA3FTTdHgEAm03AN41pArvoZdLX9EszQmtVLhVigQyfpcKNhd3+QDJzVcKsK7vAUSh6XyNO1C9N+D7H+CvFYDupukyRYWg0r7UmwDk/wOc/VB+G24+QK3npIL4r0+AgkzlbahVngLvyvdSYaSkRn9AGwBcXC17k1bczqaa/2s9KsEzBHjiN+DqDpngSAsEdZSOye1k+ZuQWhoXQOiV52v9Ad0t5fmu3sDDY4DcK8DlDcY3TBdPIOL/gKDHAf1d4MAL8sG2tjLQMAY4PhPIz1DelkcAkH/HOMgrrnofwCsUOL/Kch6U5xpXumk9tsL8MhpX6VzXuErXolyAUJQffg2BwNb/q3iXyHvvcCD7rPJxggaAkNZVoTZwJ0WmkaQRIO5KlRglVTsCPrUAXSbw92b5ylDxdIUPBirWA07OBfJlrlcXTyCguXQu3E423zhjjleodL4p8W8uVaxvJysvEzkbKMwHTs03LSOrRAEZR/93rv7vWJbkWRVo+fn/ygS5W7kG6JMKePgD39aTv8ZdvVAALdwK/5FPo0cQ0O2gdM+Qq+QUpfVOCpD9l/K+alyl9IgC03nulYA6LwMFd4Bzn0j/mqSjCtB+m9TAUXLIty0aupTKRK9QoNdZ4PJG02OhRFtZOkbXf5YvW1zcpWtSqQx0q4gbAYMRGBQKnF4kv46iBrYyNywUW+b8GuDQSyXKr2JlizW2Ya0GS3NlpBDAxmrKI1gs3VeK6mlKnjonNfwVS4ehjmftQKYsx8sejcKydTxP4OHR0rWSdfZ/Qezdcm5IoQwsbf0LDLCsxiECLLkTsKSik75GX6AgG7iYCPw+tURLvRfQ9F2g9tD/Fe4vlag4uQOVmwJ306SbXVm5egHVegF/b5FPc2lOaKWbFFyAuq9KPRMZf5gvBL5wgWLloXhvQXla9RS3AakQ1t+Vv+kX6bJPCgZT1qloyRlZuuAHAPLSgSPjpfNC4yZfSXHzAdwrKlf6zLWYy+aRGhqgSmvA/zHg4hr5wMUjCGg6HzjwvPI6BuYoVwCL0q37B1hfGYr55OpVviDPIg3w1F9AhVrAxS/kK5pB7YF/jgG5Cjf0kr2Mpa2AaNyl3icXNyBtj/x5AA1QIUw67v+ckD8m7hWBHiekRha5NDT/SPr/6YVAxnGFw+EGRM4B3LyB5Eky61iKizddUNPjT+BkvEIrsTtQrSdwbRdQkGU639UbqNRY6qWWa+Esq6odpGDs6g/Ky0T9Fzg0Frh73XRe0TmpVL4Vv9aUltG4GwecpgtIrew1BwO6DPONYWrSYeZ8O5XbFBG/N4TitQVI57fQK6RZA4T2BNL3A/m3TWd7BAL/eQn4Y7aZ3XWVyrB8hUDPkuLXFlD2xos7l4FL64Dk/7OwQYUKn9xygwukhgZz13z1p4D/+imuU8AVGsg0AhZXsR6QfVG+8UdbWeoxgACOvw3oZPJJ4y4F4+auNa9qQN51+fPAvRLQaIZUJvw+Vb7R062idE5fWFP++oVSI1Pt4UDhHeDqdvkeVQBSvhQCm2uZv3YU6zD/U7kp4PMf4Mq3lnt/1eyP3DkpBLCpuvy93dx5r3GRbzj2rAo8dV7Kp/L2cCmNqCniEag8egga4PGNwMFR8uWsNgBo+JbUUHZ8hvI6hphpnJXBAMtKHCLAsnSBWpPGVerFuLpd/kbnFQK0/Ey6IA6OUF5PxbpA5hnl+c0/AkK6Az415S9Q/0ekoS1J45QSanxRmB0+VNNy5cHSOiwpT0XJsEseAAqMCzSNu9QC7+YtpetWMgCZwsCrGtA3VWY/akitaJf/KxUy9adILWZKla2ag4Ev3aB446/xNAAX4O9v1LUoeQRKx/G3l5R2Wn2Qa+kYqwmSza2j9wXgS1coBuOdf5J6Ivf2lm/R9KoO9DwObGusMART5fkm9ObzwK+B1Ot6bbdCUOEl9RzdPiJ/c4SL1ANz86D8+gGg5lDphqUYQKgcyiiE+WNqYR2GG5mlRhI1eW+uEaTkOgfcAbbUszzssLznZHlb8kO6AF8HK+xXKcrIcg4JP3XqFCLOdFfoxa4KNI6ThredekcmncXSWuYyoAbQO0UajqrYmx4MPLYM+KmPQhog3ZOCO0pD7f6YU/rGC5+aUos7oDyyQhsg9YgfnaKcDqN9K8V9ysz5eKrud4ioWwf4ppZ8+eTmKw3pvbxBXbqUPDQSOLdCeX7tF6Te+fLwrGo+iHsmC3D3sbwec/dk90rS8LO03fK9VOW9xiNnS+XzpfXAzQPyaShvsOiiBSo/Aty5CORdU/7uw2OBak9Kyxwaq65XVeMqjVDJu2p8n1FTbtSfJDXyXd4EXP1OaQNA/1uAtpLt7/0O2oPFl1zYg9xNXkmTecCjH1hYJl55ntADUWuBR5dIJ2hxrt5AkwVSIfzQcOnElOMdDjx5Wnm+xlWq4H9TS+p+P/C88cOJvw4FttaXgiuNq8I2woz/rhUtXSRD9NK/xS/uyNny+xJZojXU3DosUbMNpWWafyy1dru6m1aIRT5wbbtUOfEIgmxwBUitbNuaAHt6Sw+nG47nJeCvZYBbJeCJI0DjWKD2c1LB4x0OQCP9W1QQaVxMj23xtN4+KgVrssGVxnT5RxYD/xll5lwptq1a0crpAiwfY0vft7QOjUZ5373DgKAoKTBpukDh2pgn3Qwi55bvfDOXB26+UnCVdU4+uAKkHidtZYXgCgAE0O2A+eu39Wqgw/fq803purF0TNWso+SyctPV5L3SOuTW6eYl9a6V9ZpWe06qSbe5ZTyD1B3fovUoHWM16bC0DqVj0XQh8J+R0nVj6XwqcxkwVzrXzKbjHWlIuFIa3Hykns7kicCxt+RfJnFoNPDbaOnfkvNFvtT703gW8OSfQItV8ulo9h7QYLJyOkz2rRT3KUvno4urcvnUfCnQdr1yuryqA/3SgadvSP+X4x0OtFhuvmxpudL8edD/tjQs1StUeZl+18wfv81hwNEYqadM7sUGedelHjDFBk8N8HQ60Pa/QLP3bXON13sdiJgAdPsVJvfOIqWp+x2danpO6nXArd+kRlqtv/z3XL2kgHdPd+DAi+qCK48q0jPFd2+Y3mcKc6Q63e5uUs/SzwOAgy8a1/EOj5UaXbPOSPc0Od5h0v0UsP2930GxB8se1PZglaZlVe2QEFs87P/YciDgUWlscslhjEW0/sATh4D0X20/ztpa1IyhLtMww2It0Ur55l5JeqD2+h75tJUcBmBpP8o05BLS+VOeFnI1abPSS1Nk88lRXu6iqkUu3HwPizVa/Wz1HKXKdRhaCm2VjpJs8bC/rTnAy3OM8qk89ww1ypsnltKQcwXYVE15+2Z7T8rZa6hxl4bf6m6Vu3xT7A0u7/FRs4yte27NrSNikvTsYeqmew20RkOhXXCvobL4/4ux5ugWNczV8WpGS896/3PSNA1hz0gjfS59JT2nJEtF73DY09KQcXPPZ5f8jqW6gH9zaZ/yrsvP9wwB+v4tP1y+LPdcNayUjxwiaCUOEWCVtWJwvx8QVTNf7fNR97MCU0plvvjK+RyE6ufN1LDGkMvSrPM+UMwnR0mnLRs47B1AlHEdqiuFZU2HmpcFOYP7fM6WqsxzhOvLUhqs0UBpjXRYkdXzqLz3fltvI/MM8P2j0jPpJbn7AZ12S0FLyWfR78ebfZWeBavSDrix73/7UCIYLP6SIXc/KYiUe867NI3mis97ukqjm8pSF2AdzyIGWPdbyVZ3W79F0F6sOC7WUZT54rNG74k9jqcDtJhbg91+8sCWnOEaL4cHIo/+BR64fLJXA6UdPXB5pIYzVe6VRlboMqQXaci9+dG1AtDmS+mxDTU/YaAmDaVZh52ffXIU9gqw3Ky2BTKvVjRQKxqnS5Ox//tOmefbQ+Rs5QdA/22K8sJSYW8u3+xxPNWmk2zPGa5xImdjqYxjGegclN5wW/L5T0fIN6U6nraS8psxC3OA6r3ufR8o3zlZ2nWoWZ51vDJjgEXlwxuVsfIW9vY6no5yUyIisgU2Xji/B6VyryZQBKxzTpZ2HWquE4B1vDKwa4Cl0+kQFxeH77//HlqtFs8//zxGjhwpu+yGDRuwbNkypKWloX79+pg6dSoaN25smL9t2zYsWrQI169fR+vWrTFr1iwEBATYa1eoON6orIvHk4iI/u0elMq9sweKrJOUiV1f0z5//nwkJydj1apViI2NxdKlS7F161aT5fbv34/Y2FiMHz8eW7ZsQWRkJEaOHInsbOlhx2PHjmHy5MkYM2YMvvrqK2RnZ2PSpEn23BUiIiIisqVa0WX/+RVHUSta3c8o0APFbgFWTk4O1q1bh6lTp6Jhw4bo3LkzRowYgYSEBJNlb9y4gVdffRU9evRAWFgYXn31VWRkZODPP/8EACQkJKBr167o168f6tWrh/nz52Pfvn1ISbHTj/kSEREREanxIASKVCp2C7BOnz4NnU5n9KaNZs2a4fjx4ygoKDBatnfv3oahg3l5efjss88QEBCAOnXqAACOHj2K5s2bG5YPCQlBtWrVkJycbIc9ISIiIiIikme3Z7DS09Ph5+cHDw8Pw7TAwEDk5+fj1q1bCAoKMvnOzz//bAi03nnnHfj4+AAArl+/brJ8QEAA0tLkfzzw1KlT1tqNcsvLy3Oo9JAp5pFzYD45PuaRc2A+OT7mkXNgPjk+e+WR3QKs3NxcaLVao2lFf+t0Otnv1KtXDxs3bsSuXbswefJkVK9eHU2aNEFeXp7supTW40i/HfGv/C0LJ8M8cg7MJ8fHPHIOzCfHxzxyDswnx2eL38GSY7cAy8PDwyQAKvrby8tL9jtVqlRBlSpVEBERgeTkZKxduxZNmjRRXJenp6dtEk9ERERERKSC3Z7Bqlq1KjIzM40Co/T0dGi1Wvj5+Rktm5ycjNOnTxtN+89//oPbt28b1nXjxg2j+Tdu3ECVKlVslHoiIiIiIiLL7BZgRUREwN3d3ehFFElJSWjQoAHc3Iw70hITE7F48WKjaX/88Qdq164NAIiMjDTqkrt69SquXLmCJk2a2Cz9REREREREltgtwPLy8kKfPn0QGxuLY8eOYdeuXVi5ciWGDRsGQOrNysvLAwBER0fjp59+QkJCAi5evIhFixbhjz/+wHPPPQcAGDx4MLZs2YJ169bhzJkzePPNN/H444+jZs2a9todIiIiIiIiE3b9oeEpU6agUaNGeO655zBjxgyMHTsWPXr0AABERUVh27ZtAICmTZvivffew5dffolevXph//79+PTTTxEcHGyYHxcXh6VLl2LQoEHw9fVFfHy8PXeFiIiIiIjIhN1ecgFIvVjx8fGywdCZM2eM/u7SpQu6dOmiuK6+ffuib9++Vk8jERERERFRWdm1B4uIiIiIiOhBxgCLiIiIiIjIShhgERERERERWQkDLCIiIiIiIitR/ZKLnJwcbNq0CefPn0dhYSFq1aqFJ598Ev7+/rZMHxERERERkdNQ1YN1+vRpdO3aFStWrEBaWhrS0tLw6aefokePHjh37pyt00hEREREROQUVPVgzZ49G23btkVcXBzc3KSvFBQUICYmBrNmzcKqVatsmkgiIiIiIiJnoKoH69ixYxg5cqQhuAIANzc3jBw5Er///rut0kZERERERORUVAVYQUFBuHTpksn0ixcvwsfHx+qJIiIiIiIickaqhggOHDgQb731Fl599VU0btwYAHD06FF88MEHGDRokE0TSERERERE5CxUBVjDhw9Hbm4u3n33Xfzzzz8AgCpVqmD48OF4/vnnbZk+IiIiIiIip6EqwNJoNHj11Vfx6quv4ubNm/Dw8ODQQCIiIiIiohIUA6z169fjqaeeglarxfr1682upH///lZPGBERERERkbNRDLA++ugjdOrUCVqtFh999JHiCjQaDQMsIiIiIiIimAmwdu/eLfv/km7dumXdFBERERERETkpVa9pj4iIkA2kUlNT0alTJ6snioiIiIiIyBkp9mBt3LjR8OyVEAJjxowx+qFhAEhPT0dQUJBtU0hEREREROQkFAOsbt264e+//wYAJCUl4ZFHHkGFChWMlqlQoQK6du1q2xQSERERERE5CcUAy9vbG6+88goAoFq1aujZsye0Wq3dEkZERERERORsVP0OVt++fXH8+HH89ddfKCwsBCANG9TpdDh16hRmzZpl00QSERERERE5A1UB1vvvv4+PPvoIgYGBuHnzJqpWrYobN26gsLCQQwSJiIiIiIj+R9VbBNetW4fY2Fjs27cPISEhWLNmDX755Re0bt0a1apVs3UaiYiIiIiInIKqACsjIwNt27YFIL2y/ciRI6hYsSLGjx+P77//3qYJJCIiIiIichaqAqzg4GBcvnwZAPDQQw/h5MmTAKS3CN6+fdt2qSMiIiKif6XE44moubgmXGJdUHNxTSQeT7zfSSqTB2U/SD1VAdaAAQMwfvx47N27F507d8a6deuwfPlyxMXFoV69erZOI9G/CgtiIqLysVSOspx1fInHEzHq21FI+ScFAgIp/6Rg1LejjPLKGfJRzX5Yc1ulOR5qlneGY+yIVAVYo0aNwuTJk+Hl5YXGjRtj6tSp+OGHH6DRaDB79mxbp/FfizeIfx9rFcQ8d+4/a9y4rJFPzGv6t7FUjtqrnHVkzpD2abumISc/x2haTn4OJu+YjEJ9oV0Dl/KYsnOK7H68/t3rOHvzLIQQAMp/P5A7Hi9segGB8wNlv6M2gH0Qgtz7QVWABQBPPfUUHnvsMQDAM888gw0bNuDTTz+FXq+3WeIeZGW5UGxxg7DXvjhDJbFoGw3WNbBpOpWWKdQX4v+2/59sQTx111TV2ynrufPy1pdZSFqJNW5c1rixOVJF0hrlxIN6I39Q98tWLB0vpQrti5tfRKOljfDcxufkK7zfv47UzFRVFd7SVmbtuf/2KBds2YB0MeMi3vnlHaT8kyK77dSsVHjM8lDMx9LcL62lZP0h4VgCfrzwI57f9DwuZ16W/c6N3Buo80Ed+M/3R8OPGuL5Tc8b5cnIb0Zi9dHVhvWXzLOR34zE1F1T8dGhj/Dqtlcx4psRJscjX5+Pm7k3jfP5WCJu597GxO0TZY/f6C2j8fym5/HMf5+RXWdOfg7e+OENpGamIvGY49RFHY1GFJUkMg4fPoydO3fC1dUV3bp1Q+PGjQ3z7ty5gyVLliAhIQEnTpywS2LLIikpCc2aNbvfyUDi8URM2zUNl/65BH8vf2TpsqAr1Bnme7t746MeH6FJcBMcv34cY7eNRebdTJP1aF21aFW9FQ7+fRB5BXkm88P9wnHx9Ysm2wzzC8PsTrMR3ShaMV1Ky1jar1HfjjK6AL3dvbG813JEN4q2ON8a27AGtdswd7zUrENuGa2rFk2qNsFft//Crdxbimkc+chI9KrTCzdybuCV716R3U7/iP6o/V5tXMm+YvL9Sh6V8GbUm4jfH4+MvAyT+RpoIHCvOJBLe3nPp/LOd4Z15Bfmo8aiGki7k2ZyjN1d3FG/Sn24urjixPUTRmVAEVeNK4IqBCHtThr0wrQBq6JHRbzb9V1cyLiAd399F7kFuffyzM0bczvPRZsabXD9znUM3TgUN3NvmqyjNOWE2vNabh2nTp1CRESEVcoJuWXcXdxR0aMibuXeKnM+lpety1C12ynPOVs8nxz9Glc6XtMfnw43FzdsP78d289tVzzefev1xcbTG83mSYhPCEJ8QnD8+nHk6/MN07WuWgxuMBj1qtTD3H1zZe/RxVkzH8t7LS3ruQwda3VEamYqnvzySdzIuWGSXrXlQlmvV0vpdHdxR/WK1XEh44J0vF200OlNy8jKnpUxtvlYzPpZ+TdYW1RrgUdCHoGuUIfE44lG9aXS3tvKUkYW3U99tb7QCz3u5N8xSWOITwhmdpiJQ38fwqrfVxmda8V5u3vjbsFdFIpCxf311foiS5elOL+0alSsgQraCjh947TZ5UrWG4qE+YUh5fV7AbI97ttqFV1L1qIUZygGWF9++SViY2MRHh4OrVaLc+fOYfHixejatSt27NiB2NhYZGZmYvjw4XjttdesllBrc4QAS+7iK4/Hwx/HTyk/Kc4fFjkMbho3fHHiC4uFSlkrTkIIXMi4gMdWPCZbgfN298awxsPwxYkvZG9CxQtyS9upsagG/s7622Qd1ryAay6uKdtaFugViI97fQwvNy/su7QPC39diLuFdw3z3V3c0blWZ/h5+WHjqY1G84q4alzxcMDD8NH64HjaccVlhkUOwzdnvpE/nm7ecHVxNVuAumpcoRd62cKurKpWqIqv+n+Fned3YsEvC4zS7unmiemPT8dTdZ+Cq4srtp3dhrd2v2VU4fdy88L73d/HkEZD8N+T/8XLW1+2eiXbUdbh7uKOeoH1cP72edmbaZHedXujQF+ArWe3Ki4zvOlwfJr8qeJ8axjccDA00GDDqQ1G+erl5oUZ7WagebXmuJZ9Da9sewW380xfZhTqE4rzr53H+lPrZY/Xe0+8B98cX+T55GHc9+Nky4EK7hXwYtMXsfroavxz9x+T+f5e/pjbaS4K9YWYtnuabDqKK0vjTnnKDWtso/q71WXLtxCfEPw17i94u3vb/Lx/xO0RHCk4Ijt/Sfcl6FWnF3Lyc7Duj3WYvme60X3Fy80LHz/5MYZGDrVpOj/s8SF6PtwTTT5ugitZpg1IRSICI5CamSpbVhbdd5TK+2CfYExrOw0H/z6ItSfWokBfoLgdtUJ9QpE8OhmVPStj3cl1pT4+Xm5eiG0fi+r66qgUXAnDNg7DjVzT4KiiR0UMbzocnxz5pFwV7UENB0Gv12Pzmc0m5f3rLV5H05CmGLN1jGxjYAX3Cugb0Rd6ocem05tk6zxebl7o8XAPbDu7zeheUUTrqkVchzj0r98fv6b+avZ4KeWjr9YXzUKb4cjVI4pBcGXPyljVexVO3TiFmXtnGjdUqTxnez7cE79f+x39vuonWzYFegUiZXwKNp7eaPG8d4l1Ubx3T2g1AQt/XSg7TwMNLo+/jFDfUNR6r5Zir19JC7suxNyf58qeS8XrZ0rHOKhCEN5u9zZe3vay4jaaBjdFo6qNkF+Yj69PfW10Pnm7e2P5k8sR3dg69+3SuO8BVrdu3dC5c2dMnDgRAPDJJ59g8+bN6N+/P+bOnYv27dtj2rRpqFGjhtUSaQuOEGApnaByvnz6SzSu2hjdE7vj0j+XTOZbukF4uXnBR+uD9Jx02fV7uXmhc+3OAICd53fKFnCBXoH474D/4vCVw5j+43SjZdxd3NEspBkuZV4ye5MDgEDvQNlWsiLtwtvhYf+HkXU3CxvPbDRqzXfVuCLcLxw3cm+YbSWMCIxA3cC6KCgswPbz243W4eXmhaU9l2JYpBToyfUcdf9Pd+gKdfjur+/M7os5GmjwkP9D+OvWX4rLPFP/GWTrshW3o4EG+hl6s4XIM/Wfwd6Le9E1QfnHvac/Ph0fHvpQNkgLqxiGM6+eQZ0ldRSHLNwPnm6eGNhgIDae2ohMnWle+3n4IbpRNG7n3cbG0xtle25dNC4IqhCE63euy/b6uGhcEOgdCAC4kXNDdhl3F3c0r9YcSVeSZINgLzcvdH2oKwQEdpzbIXvtuLm44aVmL+GrP76y2EKsdA1busbD/MLw43M/4j/v/0fxhrxp4CYEVQhC///2l71Ovdy8EOgdWO7zQAMNXDQuZltWLankWUm2R7WsXDWuiAyOxMn0k7LnShXvKviq/1fw8/TDnot7FBsFno54Guv+WIfxP4w3mu/p5omYx2PQuXZnPPXlU7I9lSE+ITgw4gB8tD749s9vTRoW3F3cERkciWvZ15Camaq4LxpoEF4pHNeyr8nuS6B3IJY/uRwvbXlJtswP9A7Esp7LoBd6vLztZdlzsoJ7BbQOao2frv0ke96r5aP1QW5+ruy54O3mjT4RfbD59GbZxgdvN290qNUBeQV52HdpX5nTcen1S6jhV8MqgZ5ShVcDDe5MvYN6H9aTvUebo9Ta76pxRahvKP7O+lu2bFLLR+uDbF224vylPZeiesXqGPXtKFzNvmoy38vNC0EVglTXV+TUrlwbLhoXs/fDBlUa4I/0P2TnFd0Li5SngUMv9HCb6VamRkcfdx+MfnQ0PjnyCTLuZpjMd9W4Wiz3iu+LpYacst4PSvY6qmnML/qONQIbc0FuqxqtcDztuOy5VsTNxU2xIcMFLqjoWRFCCGTezZTNx5KN9WrYK8BSfAbr6tWreOaZZwx/P/fcczh37hyWLl2Kd999F8uWLXP44MpRqC2Ew/3CMajhINSvUh9zOs2Bt7u30Xxvd2/M7iS9VGR2p9my81c8tQJp/5cGDTSy28gtyEVqZipSM1NlK4iANC64w+cdMHHHRJNl8vX5OHTlENqFt8NHPT5CiE+I4r6kT0xHmF+Y7PwK7hVQoC/At39+i69OfmUyVKpQFOLvrL8xtPFQVPasLLuOih4VUTewLs7cOIMtZ7eYrCO3IBfPb34e2llaDP16qEmhoyvUYfOZzUjNTEUF9wqy2wj1CcXvL/2OA8MPKB5TADj76lmE+4XLzgv3C8e6Z9ZhW/Q2xWWKjlN0o2gs77Uc4X7hUuXKL9xQkGldtejyUBez24ntEIv3ur8ne27M6TwHnm6emNt5rsl8pX2rWqEqdg3bpThfAw3W9V+HL/p9ITu/SHzneMV5eQV52H1ht2xwBQD/3P0Ha/9Yi0NXDslWMgFAL/ToVaeXYuVEL/ToV68f+tbrq7hMvj4fXm5eipW73IJcXMi4gEv/XFK8dgr1hfigxwdY/MRis9cvoHwNW7rG53Sag9qVayteW+F+4ehdrzda1WiF+V3mK5YTl8ZfMpuvPz73I06NPYXqFavLLhPgFYDp7aabrWS83+Z9nB572mxab795W3F+Nd9q+PuNv3F1wlXFdJRUKAoR7BOseK6k56Sj4+qOaLa8GSZsn2CSl7kFuRj57Uj4z/fH6K2jTebnFeRh2u5paPFJC9ngCgCuZl9F+OJwBMwPwPObnpd9LuL3a7/j8fDHFcu3QO9AvN3+bbSs3lJxX27k3EC/df0UG9Ru5NxA///2x4D1AxQbu+7k38H5rPNmg5oPun+AlU+tNFsGjmg6QvFcyCnIwcHUg4o9uzkFObiafRW5Bblm07Gk+xIEeAXIzgv3C0cNP6lOYq4cVTMfgOI5GeYXBi93L9l7tJwArwAs6b4EM9vPVKzoF4pCdK7dWbFs0kCDj9t+jF+H/4pQn1DF/c+akmX2/jD60dF4ss6TWNB1gWK5cPH1i2bLhRNjTqCabzXFbZwbd87i/fDEyycs3guLRDeKxsXXL0I/Q4+Lr180yiNL+eiicVHMx+oVq+O3Eb8p7mt2fjY+PPShbHAFSHk2r9M8fB/9Par7ypdNxbdtbj+Ast8Pit9TSh6PAK8AaF21it9Rcx1YWkYpXUufXIofnv0BVyZcMVtuTGo9SXGeHnoMbTwUz0U+p3jtlLaRw54UAyydTgdfX1/D3+7u7vDw8EBMTAx69Ohhl8Q9KJQu8OIsXSiluUFoNBqzFZojLx3BkZeOKBZwIT4h2Dl0p2Ja9UKPL57+AmOaj1EsqIv2RSlQ/LjXx9j34j5c+79rihefrlCHD3p8gCU9lsiu46OeH2HjwI04OfakxQtY6eLUQINjY47h414fy25jftf5iAyORIvqLczecAF1BaDaQrI8BXFZzp3Rj46WXefCbgvRsVZHs/v+TINnMLjRYLM31EltJpmdf2n8JbPbuDnppsWbdtE+Kc1f+uRSLHtymdlldg7baXb+0dFHkfxScrkC5SLlrQSW5YZbch3mjnn7mu1RL7Ae5nWeJ7ud97q/h7fbv232eHWu1hl1A+tabDBSmh/fJR6hvqEI9gmWTYfSdrcO2aqYrmCfYOwethubBm4yu55F3RYpztNAg61DtiKoQpDs/EDvQHz61KdY3G2x4joK9YVI7JeoWL4tfmIxprebji+f/tJsWZ38UrJiQ1eITwiOjT6G42OOm20M+/aJb83m49jHxuKFpi+Yva8semKR2XX8Ne4vs/OTRiVh/4v7zS7zymOvKDYgFT/vAcvlqLXLWaXK7Hvd38Mrj72CmHYxZvdtZe+VZsuVtiFt0bJ6S8zvKt9oUp6KeGnKhQZBDRDfJd5mDUgl89GSsubjvM7z0Lxac7PndM60HNSoKN+REO4Xjjej3kS3/3TDvC7yZWRp9sUajQIlj8eNSTcM55W5+5C542dpmfI0VoT7hWN2p9lmr4v3u7+P97q/pzogdySq3yJYpFGjRrZIxwNN7gJ3d3FHgFeA6gultDeI8lT2F3RdgE61O6k6oW3dUljedVi6gEuzjfIGNmqXsUTtdkpz7nzU86NyV+bLe0NVqmTP6TTHatuw1zqA8t+4LM0vyw23LOWEPQK9slw7llpnldL1Ttd30KFWB/Su19vsjf31lq+bLTd6PNwD73Z7VzE4erHpi3it5Ws2LXsWdF2AJsFNFBu6FnRdgEZVG6FhUEOLjWH2uHassQ1rlKFqlLacVVOZtUUvhTUq4qUtF+zRgGQt5S2/5EZ8lDZPSpPW8jQKlGWd1mCP3jlrBeR2JRTUq1dP3Lx502ha06ZNxaVLl5S+4pAOHz58v5MghBAi4ViCCF8ULjRva0T4onCRcCzBIbZpbpmEYwnCe7a3wNswfLxne1s97dbYjqV1lGYbJ0+eNLsde+ejoyjv+WSN+c60Dmdhjf1QWoe5a8karHG+lbfcKO827LWv5pYpyidnucadWVmPj62vpdKm80FijXO2iL3zyVk4Urlg7TxSijMUX3JRr149BAUFwcXlXidXWloaAgMD4erqarTsnj17VAVzOp0OcXFx+P7776HVavH8889j5MiRsstu27YNH330EVJTUxEWFobXX38dHTt2BADo9Xo0adIEd+8aj9U+dOgQKlasaDTNEV5yUZy1H66zNXu85tha27HGa0AB58ujfyvmk+NzhjyyVrlRnm3cb86QT/92zCPnwHxyfPZ6yYWb0hfmzp1rtY0XmT9/PpKTk7Fq1Spcu3YNkyZNQmhoKHr27Gm03OHDhzFp0iRMnz4dLVq0wN69e/Hqq6/iv//9L+rXr4/Lly9Dp9Nh9+7d0GrvDREp/swYWUd0o2i7VASssR1L67DXvhCR87BHucGyh4jo30UxwOrbt69VN5STk4N169Zh2bJlaNiwIRo2bIgRI0YgISHBJMDauHEjunbtigEDBgAAhg0bhj179mDbtm2oX78+/vrrL4SGhqJaNfk32RAREREREd0PigGWtZ0+fRo6nc6oG61Zs2b46KOPUFBQADe3e0kZOnSo0d8AoNFoDEMCz507h1q1atkn4URERERERCrZLcBKT0+Hn58fPDw8DNMCAwORn5+PW7duISjo3utu69WrZ/Tds2fP4tdff8XAgQMBAH/99Rfu3LmD6OhopKSkICIiAlOmTEHt2rVlt33q1Ckb7FHZ5OXlOVR6yBTzyDkwnxwf88g5MJ8cH/PIOTCfHJ+98shuAVZubq7R81IADH/rdDq5rwAAbt68iVdeeQXNmjVD586dAUg9WDk5OZg+fToqVKiA5cuXY9iwYfjuu+9kn8NypAcO+QCk42MeOQfmk+NjHjkH5pPjYx45B+aT47PFSy7kqPodrIyMDMV5x44dU5UADw8Pk0Cq6G8vLy/Z71y7dg1Dhw6Fi4sL3n//fcMbDb/44gts2LABrVq1QuPGjbFw4UIUFBRg165dqtJCRERERERkC6oCrJ49e2L79u1G03JzczFr1iwMHjxY1YaqVq2KzMxMoyArPT0dWq0Wfn5+JstfvnwZQ4YMgUajwZo1a1C5cmXDPA8PD3h7exv9Xb16daSlpalKCxERERERkS2oCrCee+45TJo0CePHj8ft27exd+9e9OjRAz/99BOWLl2qakMRERFwd3dHcnKyYVpSUhIaNGhg8kKLjIwMvPDCC/D19cWaNWsQGBhomFdQUIC2bdti69athml37txBSkqK4jNYRERERERE9qDqGaxRo0ahW7dumDZtGjp16gSdToeXX34ZI0aMMHmuSomXlxf69OmD2NhYzJs3D+np6Vi5ciXi4uIASL1Zvr6+8PT0xKJFi3D79m0sWbIEhYWFSE9PBwB4enrC19cXUVFRWLRoEYKCguDn54dFixahSpUq6NChQxkPAxERERERUfmp6sECgBMnTiA1NRUhISHw8vJCUlISrl27VqqNTZkyBY0aNcJzzz2HGTNmYOzYsejRowcAICoqCtu2bQMAfP/998jOzkafPn0QFRVl+MTGxgIAYmJi0K5dO4wfP97wW1mffPKJSU8YERERERGRPamKSJ599lkcP34cY8aMwYgRI3Dr1i3MnDkTTz75JIYPH47XXntN1ca8vLwQHx+P+Ph4k3lnzpwx/P/gwYNm1+Pt7Y2YmBjExMSo2i4REREREZE9qOrB0mg02LRpE0aPHg03NzcEBQXhgw8+wLvvvouvv/7a1mkkIiIiInJKiYlAzZqAi4v0b2Li/U4R2ZqqHqw1a9bITu/cuTNatmxp1QQRERERET0IEhOBUaOAnBzp75QU6W8AiI6+f+ki21IVYE2aNMns/Pnz51slMUREREREgBScTJsGXLoEhIUBs2c7X1Aybdq94KpITo403dn2hdRTNUTQ1dXV6COEwKVLl/DDDz8gODjY1mkkIiIion+Rop6flBRAiHs9P842vO7SpdJNpweDqgBr7ty5Rp8FCxZg7dq1eP3113H16lVbp5HoX4VjtYmI6N/OXM+PM6lcWX66mxvw++/W3ZYt6g+sk5SN6te0y+nSpQt27txprbQQ/es9KC12RET3k6VKISuNjs/Ze36EAObOBW7dAlxdjed5eABeXkDz5sD06cDdu+Xfni3qD6yTlJ2qAEuv15t8MjMz8fnnn6OyUmhO/xq8UVmPvVrsmGdE9KCyVClkpdHxbd8u5Y0cIYC+fYHjxx33XiYE8OabwNSpwJAhwMqVQHg4oNFI/376KXDhAjB4MBAXBzRrJv1bnn0pS/3B0vGbOvXB6EW8L4QKdevWFfXq1TP5REZGim+//VbNKu6bw4cP3+8kGDl58uT9ToJVJSQI4e0thFScSB9vb2m6sypPHiUkCBEeLoRGI/1bmuNw7ZrxcSz+0WjKnCTZND4IefYgXEvlOV+cwYOQR/8GD1o+hYfLl6MVKwrx+utC+PjIzw8Pv98pV/ag5ZE527YJ4eEhRI0aQnh5GeeRl5cQTz8t5SUghKurY93LTp48KQoKhBg+XErP2LFCFBaa/86WLUJUqmR6PpZmX/R66T5SmvqDXF3Ay0uIceOEmDJFiG7d7FMnsTdrX0tKcYaqAOvAgQPi4MGDhs9vv/0mkpOTRVZWllUTaQuOEmDdq0jpVVekLFW+HKFypnQjc+QblSVlvfjKGrjo9UKsXClE5crKhVlYWJmSJOtByTNnr3CoOV8c4RovD2fPo3+LBy2flCqagBC+vs5ZaXzQ8kjJt98KodUK0bSpEDduKJeBN2/eC7Ic4V5WvI5XVK7HxEj3dzVq1Cj7vpw5I0SXLsrntdI6lOoCgBBubkJERgpRoYL8/MqVhbh7V92+ORqHCrCcmSMEWHIVKXd3IQICzAdP5ipfjtALcfu28sUJCJGRcS+tzlRJLOvFp1RYlQyOih+P0FAh6teXlouKEmL+fNN8LZqntqAuuY2iY373rhB79jhn5UKOo1c45PIgN1fKg7ffllpolSqAn34qxMyZpq23ZbnGrXH9lXUdjp5HjuJ+l5EPSj799ZcQTz1luaKpVFZ7ekrrUMue+VaaPFKTLnukvbTb2LhRqhs9+qgQt25ZXn9pe2xsRamOV5pjaq5R4PTpe9spfjxXrhTirbekgLRiRSGGDjVNh4uLaV1Trxdi3z7zdYHcXOV9c3GR/q1dW4gvvxRizZp/Rx1PSakDrMcff1y0a9dO1ceROUKAZa6VoHjBHhsrdY2vWaPcm+HhIcRjj0kX7/1quSkslC7sKlXM75OXlxQYlKxI3u8ufEvKcvFlZZk/Fq1bC/HGG1LXe8lKMyDECy/cG0ZQshDt109aZu5cdWlRKhC1WvNptHTu3O9KYEmOXClUyoOi4SzmbqaWPqW5xq3REFOedRTPI0c7fxyFIzSWWbvybmsl0/DJJ0JMniyVcT4+QgwcWPoGSnd36V7l6SnErFlSg5S5fbV3vqnNI0vp0umEWLpU2s/ylguWRtiUpoc+MFD6t0ULqfFWDaW6lbu7EPv3q9+X8rLGqBBL9cRHHjGtSxXdR6Kjhbh6VVpP8WMqV090d5cads3dh0qmWy6vv/tOiMaN5dfzINbxzCl1gLVhwwbx9ddfq/o4MkcIsMpTmZL7WBoXa2m8b3GlHYY4a5YQLVtK22rVSvpbrhCdOVOIUaPUX8COQM0wzpLH4+OPpd4If3/lPPH1lQIspR4LS8dDrxdi8GBpOTWXm1JB7eMjtRCuWCHfS9aqleNUJtRQKiQdofVWKQ98faUhMLdvm+/x/PNP8+XG2rVCZGYq78eFC1IvmFw+W+vGr2YdRXlkrfOn5P6OGXP/K/vl5QhDdq1VebcHuTQUfZ57TogrV+4tV9oh9qmpQvTvL60rNFS+cXDlSiGSkqReAbk0uLra5nxU01hRWHiv8iyXLqXhXkWfqlWNey5Kcz/w9JSC3K+/FuLzz5UbiYODhbh4UTqOco1QK1aoPyZy6dBq7z3PNGyY9GyzLcv7zEzz9bHy7Iu3txAffijE9On3eo3k8kyJ0rBDrVa6P8jdI0pzPRcWKl8HjljHK3LfA6zbapsQHJwjBFhqerCKLsZffpHG01avbv6kNbfOhg2lbtvVq0vfwuTlZb6Vr6iSuGqVco9L8e04She+JWpb25Ru7L16CTFjhvl13L1b9uORkyO17Hl7C3HkiPJyFy6oK+yL51lYmBAPPWS6vJeXFEBv22a9QtS6w9VMA+Gy5qM1n33S6dTngbl0KF3jRTdaV1fTB7xdXS33LJf2+ivPNVx0I1MTRJSlRbzkpyz5eL97ZByhjCweCMsdC71eiD/+UK40l7ZXtTx5olRpDA4uy57L27LF9Noqy8eaQ3rNNVa4uUlDzf38zKfnjTekxk9zy2i1QtSta9r74e4uRI8eQjzzjOXREOX5WOOekpUlBXvu7lLgV3JfrNEooNdLwaRSXc1a+1KkLOWEmu+Ut/wz1xB46NC9Rxuse+8vX1l93wOsevXqiRs3bhhN2759u8jJybFqwmzNEQIsNRUDucpGWZ7BGjNGiIiIexdR8fmenkK89poQs2cL8eyz5gtJPz/lG0z16ur3XaliFRhY+mN4P3ocPD2lm0rPnvJD+wAhQkLUp7M8rdVXr0qVi2rVhPj7b+N5WVlCTJ0qtbiWpdcwLKzsN8RPP1XXamyr4WpFgeD69fJvYiqqHDRuLH2UhtdWrizEpk3le/bpxAlpKIfaG25Zjtfq1UL8/LPyw/peXkK8/75UIVbKVx+fey3VSgoLhViwQHlf/P0t95afPHlSXL1q/vzZskW+V9XbW4hly4TYvl2IefPUlaFFZcsff0jHyRrBtq3cvCnE//2f8n54eQlx6ZLt0yGElE9yx8LDQ4gOHZSDmuKfomdmSntOe3lJ+ZyZKURenjREXm74XsuWQtSpo7x9awek5iqO69ZJ5b6a87G8Q3q1WmmY+JAhN8WAAcojIbRaIUaPVtcYpnQfqlJFiAkTzNcNLOVBcrL0HJtS4FGlijSk0x75eOaM6VDIsuZL8XN68WKpURWQ7ilvv237cqQsdQd79I5b6kBo2FCIIUMs31OtMeRUrfseYNWtW9ckwGratKm4ZK8S30ocIcASwrjVPSDAtACTO1HKMsRBCKnSExho/qS3dMN87TXrFIByF0VRC/yzz957EUZp12HtwsvczfTRR4Vo1sx2x6M0+3L0qDTMo1YtKQ81GumGWtRyGR0tVa5Luw1zLV2//GJ+2EnxQluu1fOJJ4QYNEi5YlCjhvHxMXfOV6tm/rw19+ndW4g+fcr+fXMvKwkLk4ZxenhI195rr9lmSFxpWzSVWruLzuuLF+W3m5IiRPv20nLNmpneHIuu4RYtpOFScum8eFGqFJobGns/Pv7+Ug98YqJyOVnanrXS9MjUqCHEgAHSNavRCNG2renxdXeXPhUqCLFokRD5+eU7Vyw5efKkYkVJo5Eq+MuXm7/+vLyEePxx0wqtl5f0fPGyZcqvR1fzcXWVrl+lXhprD0myVDlV23Cqdti+Xi/1wimtp2LFAlG3rvntKKWrtA0LlsoWtT3SZemht3Y+mtuXO3fupbU0jQJF1+iCBdKIBeN1qH9TdGmUpe5gj7qT0jaWL5eu+cceUz5nAwOlETIzZpiWGx4eQrz6qvS8YFyccoNiWc4XhwywmjRpwgCrnCwNxbAWc4VKZqa0jKUCzloFYMl9Xb1aavFxcRGiZk3pbTZKxyMlRXlYSmCg8ZuGynpM9+xR7q1T0+pn72Fyb7xhmgYXF+mYlnUbZalMFBXUR49KL+BQqkhrNEL85z/mKyG9ekm/GyL3myevvy79loilysWRI5aH1prb1+rVhTh82HywHR0txFdfKT/H9sgj0ph/a+SzJWrPR7l0bNokvXUqIECIN980DhTHjJEqsT4+0nMSer38OtasESIoSNpmUdBW9Cl6DsXNTS+GDxdi4UL582flSiF27TJ/buzaJfX2WGopLfqEhEhljJplzX0GD5beSGeuMayslVlAiCZNhDh2TDmPLlyQes+Lzqu4uPL3DsttJyNDiLi4v81eW5a2M3u2+Wdu1XwWLJDWYykd9up1VJu3RcfT3JDChx+WnqW5c8c0Dz7+WGoUK3phgNK+W3O4raVlynM/ULsNe+WjuXLDx0eINm3kn7V7/32pcVGpEaZaNfnt2fLlS2W5p9j6PqRmG9Z+D4FS+aQWAywrcdQAy9as0cJk6wLwl1+knhhAvuejaJ65j4uL1Irep0/p31aYkyPE+PHSBRoUZPn793M4UXG2aPkrbWWiNIWopVZPX1/LlecKFaTKpqVnQKzRequUlgoVlIffFH2s+XtllpT3fPzzT+WA9OGHhTh3zvI6bt9W7pXw9RVi164/jdJb1gqd0v6W/KjJx2rVhDh/XohTp5SHeXl4mC9/NBppPUrDTT08pB7CiAh1jTdK9HppOJrc7/0UtfAmJCifl6GhQmRnKx+/4s/xlQySldJZ1l7VCxeUh6yWtiHLHpXG0m5H6Xp85RUhmje/V4YoHedmzZRflhQebv0Xxljab1s+p2rNdajZhty+TJ0qxIgRZa/8K1XsHfnttveL0jUdEiLEr7+aLzeuXJGeX7dmvee+B1j16tUTN2/eNJrGAKv87HXxWaOFSc388vrnH+U3G2m10rMXSsPTqlaV3q5T9FZDuY/S8LPg4HvDMcaOlSoharr47XVjN8dWD8WXd9/K0+pp6Vfoi37Q0Fo3/rK2rBYUWP79EHsqb54pDRUuTaBoLt+s/Xa6kvtr7i2C1gi2zVW+XnjBfAXsiSeEePpp65wrap6BMvfx9VWu2Pv6CvHll+etUnG3Rs+HozRklYXS9ajXS89NWnqO19y+2/snDxzhXmctZWkUAITYulW5EUapYs8Ay1RZGzVL0yFQGvc9wKpbt64YN26cmDhxouHToEEDMXbsWKNpEydOtGpCre3fGmAJ4TwFpKWAQc2FZa6QjIwUonNn06E+Go30pqGSHL2AtEUPljWUN/gp/ZA324x1t5TO0qTV0VkjWDd3LO737yvZeqiUmvPAGueKuXz680/lZ6MCAoSYM0fq6VIqH4sHwuXNA3s0gDgztc9Nyu27o9+XnJW1hkMWYT7JK2ujptp1lMZ9D7AmT56s+uPI/s0BlrOwxpAQpXX4+UnBldKNTa6S4+h55MgtvOUpAJ3pRubIeVAa1qj8q211d0aW8tlePTLWqARaKxC25EENjqyhPNebs19LjsrawyGZT2Vjz3LjvgdYDwoGWI7PGhWQsg71kWupd4Y8elArMc50I3sQ8sBageKD3OpuKZ/t0SNjjUrggxwIO4vyXG/MI9uxZlnOfHJ8DLCshAGWc7BGAWeN4WdCMI+cBfPJOmwZKDKPrMeWZSTzyX7Kmo/MI+fAfHJ89gqw3EDkAKKjpY+t1jF7NjBqFJCTc2+at7c0nejfzBrXHtmerctIsg/mAdG/g8v9TgCRPURHA8uXA+HhgEYj/bt8OW90RERERGRdqgKs9evX49atW7ZOC5FNRUcDFy8Cer30L4MrIiIiIrI2VQFWfHw8MjMzbZ0WIiIiIiIip6YqwGrVqhU2btyInOIPsBAREREREZERVS+5SEtLw/bt27F8+XJUqlQJHh4eRvP37Nlji7QRERERERE5FVUB1qBBgzBo0CBbp4WIiIiIiMipqQqw+vbtCwDQ6/X4+++/ERISAr1eD61Wa9PEERERERERORNVz2AVFBQgPj4ekZGR6NatG65evYqJEydiwoQJfC6LiIiIiIjof1QFWO+99x727duHlStXGp6/Gjp0KE6ePIl58+bZNIFEVpOYCNSsCbi4SP8mJt7vFBERERHRA0ZVgLV161a8/fbbaN68uWHao48+ijlz5mDHjh02Sxz9i1gj+DG3jsREYNQoICUFEEL6d9QoBlmOhkEwORpL56Sac9bW5Zs900FERBapCrBu376NgIAAk+leXl7Iy8uzeqLIgdjjhmyN4EduHSNGAGPGANOnS/NKDmfNyQGmTbPuvlDZOVIQ/G+qrNqj8n+/lDcfLZ2Tas5ZW5Vv9yMdatNa3kDwQfZv339HxHOWbEGoMGbMGDF16lSh1+tFkyZNxKVLl0RmZqYYOXKkGD16tJpVCCGEuHv3rnjrrbfEo48+Klq3bi2WL1+uuOzWrVtFz549RWRkpOjVq5fYtWuXyfzOnTuLxo0bi9GjR4sbN27Irufw4cOq02cPJ0+etN/GEhKECA8XQqOR/k1IKN0yCQlCeHsLId2OpY+3t/x6yiM01HgbRZ+wMHXpLCgQIjhYfh2AEC4uyvM0GpPk2DWPykpN3t4PltJlbn54uHwehYfLrkNflm2o3QdL5729rg1bs8Z+mFlHqa4la5/TlvZNrxfi00+F8PIyXkarFWLUKCHef1+ISpXkz0kfHyFGjpT+lZvv7S1Enz5C9OghhKenuvPaHKVrw99fiA8/FKJyZfn5FSsK8cYbQrz6qnJaw8Lu5ZOtrx2155ujlm/lpbZskdl3p7gvOSNrnbP/w3xSUJ66QWmWUcHaeaQUZ6gKsK5duyaefvpp0aJFCxERESG6d+8umjRpIp588klx+fJl1YmIi4sTPXv2FMePHxc7duwQTZs2FVu2bDFZ7tChQ6JBgwbiq6++EhcvXhSff/65qF+/vvjjjz+EEEIcPXpUNGrUSGzYsEGcOnVKPPvss+LFF1+U3abDBFiWKoW22F5ZKomenkK8/roQs2aZzitLxcCcpCQh+vdXDn4AIV57TYi33jJNi5eXEC+9JMTgwUIEBJgPoPLylCsogBDPPCPEn3+qyyNHuPHbqnJf3gKwLDcqLy+p8jd+vPnz4MUXhVi6VIiZM21fgVM6V3x9hXj2WSG6dhXC3d2214a9qA1qzQkLU1yH6htZWfNtzBj5fNTrhahWTT5drq5SYOLqav6cs/Qx16gDCNG4sRCPPmq+bFIjK6t86axQQTlQ/N8nq3VrIXr2lIJLW1w7QUFCbNsm/Ss3v1o1aT/1+nIFIfedUrr0eiFOnVIOhAMChDhyRIhVq9Q1Vthj/x31GFub0jnr6SlE27alLusZYMmwRhBrxXqPQwVYRX755ReRkJAgPvvsM7F3715RWFio+rt37twRjRo1Evv37zdM+/DDD8WgQYNMlp06daoYP3680bQXXnhBLFiwQAghxMSJE8WECRMM865cuSLq1KkjLl68aLIuhwiw5E4Md3epULVV4aVUaGg0QlSpIkStWsoFh5pPbm7p9r+ooA4LkwKmbt2k9VSsKH3ktuHlJYSHh/l0BAUJMWyYEIGB5gtBpcp9v35SBUSjEcLNzT4XeHlvXNaoFMulqawF4J07Qpw7J0TVqvLpqlRJCoz8/JTz0dNTOa89Pc0H0YDUOj9hgvK5FBgoxNdfC7FlixCTJ5tuy81Nqgw/8oj57dSsKUSLFsrz1VaaraW855JGo7wvmzdLjRNK29m3T+rpMXO8/p47V6o4W0qr0jldshdbqdGneLnapIlyRb7oM3asENOmmc/H9HSzwaPZdBe/Fs017jz7rFTxljs2Fy8KMXGi+eCoWjUhrl0Tonr1sqejQgWRGxGhvI3KlYX44QchrlxRbpQbMUKI0aPNH3M1HxcX5XMyMFCI336TGlocsfdY7thotUJ06KB8Hqn9VKsmTh47prwdaweg1tqGNXotrEFuO4cOSXURc8e9fXvz8//+22RTDLBkKJ3/7u5C/Oc/yqOMNBqpLK9Rw7R+JlfGqeRQAdbQoUNFZmamyfSbN2+Kfv36qUpAUlKSqFu3rsgrumELIQ4cOCAaNGgg8vPzjZY9deqUOHv2rNG0F198UcyaNUsIIUTXrl3F2rVrjeZ36NBBbNy40WS7DhFgmbvB2uIGkZRkflujRwsxdKjyfI1GiMxM8+kOCxNi9WohCgvNF5JKlSJfXyHmzBEiI8N8YZ6ZqXzD1Wik7SttR+0N4epV80N9evQQonVr5Qtc7VBGtem0xNzxKCulvC4KQpUqtuYq6Go/Go0Qd++aPzZ6vRAXLpjfXoUK5UuHm5sQ3bsrnwtqKqtarRD2KnPKcy6dPClEr17Kx6LohufnJ7XilgxIi/LB21v53CjqIfL2lq6fkuvw8JDOrQEDzOeLv78QDz9s2rtiLh+ff176Xlnz0VzDjDVaXr28pHKlaHrJ3rSiv11dpR72GTNs2gJ88uRJddeyueHWFSsqN5IEBwvx66/KvX7+/kLEx5sPei19bNF7XJoAwFyjZt++QixbptyrGhoqxLp1ZvdP7+Ii3WuUjnG1atJweWvcY0JC5Lfh6yvE4sVCjBtnOvS1tA2SthqJUZLcdorOdRcX5eNpqREFkMqk0aOle5M9RimVJSC9nz2R2dlCLF9u/rodPNj8/JdeEuKFF5Tnl6Hec98DrD179ojFixeLxYsXi3r16om5c+ca/i76vPHGG+Kxxx5TlYDvv//eZNm//vpL1KlTR6SlpZn97p9//ikiIiLEDz/8IIQQokmTJmL37t1Gy/Tv318sW7bM5LsOEWCprYSGhkoVySKlbf2ZM0ca92/uRmiNysWUKfda+uUKfG9vaYjhypVSgSy3jRo1zO+LmtZthWdzylSQmMujRx8VonNn83k3YoT0rEPJ5zmKbhgZGVKLmaWeNku2bDGf1vbthVi7VojPPivduWNu3xo2ND9/7lxpaItSr0FYmBD5+ZZ7A9TkoaVzQWkbISFCJCcLcfCg5QC1rJVVrVaqZGo0Qrz8shAff1z+G5vS8dDplI935cpCnDhxrywpvo7q1YXo1EmquFesKMTAgfLn7OefC/Hdd0I895zy8QoIkBpAzByvC2vWSDdIc+fsQw+ZpqHo4+cn9TYNGmT+HLRmPpa2pb48LfnXryv3ulasKERKil3ScfLkSeVrq3p1IX78UYglS8wf86LGtvJWqpXSERIixKZNlvPdWkp7bqhJl6V1Ku27v79It9QwCkj3faWhr5buufHxQrz9thANGqi/1uT2NSBACqSV0uHicm/0jtx8awfKNWool1/p6WU/Z999V+rBd3eX9qXk/lo7WCxLQGrPILb4uTR/vhSEF5VtloZZlmckgAP3YGmEEELu5RepqamYOnUqhBA4dOgQmjRpAnd3d8N8jUYDb29vDBo0CO3bt7f4Mo1NmzZh4cKF+Pnnnw3TLl++jM6dO2PXrl2oXr267Pdu3ryJIUOGICgoCJ9//jlcXFwQERGBTz75BG3atDEsFx0djZYtW+LVV181+n5SUhK8vb0tps+WHurUCdqrV1UtqwsLQ1aHDtBrtQhYswYuxd7SqPf0xNWZM5H55JOouGULQqZPN5oPAHoPD9wcORL5VaogeO5cxe8DkF2H3DJVFi2C+7VryA8ORvr48dI8vR4Vt21D6JQp0BQWKu6PAKCRm67R4PQff6g6JmrSWV5KeaQLCcG5XbvMLqP39IRwc4NrdrbsuoWLCzR6vdntqzkeldesQdX4eOQHB8Pt5k243L17Lw0eHsjq2BFex49Dm5pqctz1Hh64OmMGMvv0UTx35BTtv5rjYymfrJGP1tiG2n2RPe9LpKXkMtnt2qHKkiWonJAAoEQeyOyrue3I7otWi9zGjeFx9izc/vnH7LEqCAiArnp1eJ48CZf8fMN0AeBOq1a4smABCv39Le5rvQYNoJG5TRQ/Z5XWkZeXB09PT4vrKE++lWSNfLRWuaKGmuNra3l5eQjaudMu146a+ebSoZQG4eKCvxcsQNYTTwAaubtOKQiBhx9/HG43b5rM0mu1yOzeHS7Z2fD56Seja0tO8WMDlOGa/9++X+/cGZ6enor7X+Dnh4zBgxGwbJn8PReArmZN3K1TBxACPnv2mKRdAMh59FHF8kUXEoKLGzbg4TZt5M9ZALcHD4amoACV/vtfxXTcHjIElb/4otx1A3M0OTmo/OWXCFq40OJ2ynPOul27htq9esH1zh2TbZTM+/JQc+1Z4zulpVgXdXFBVvfuuD14MNz//hshM2aU675tzXpg0X3JWnJyctCsWTPTGWqis8mTJ4usonH0ZbRt2zbFHiylNwBevXpVdO/eXTzxxBPi1q1bhumRkZGyPVhybyV0iB4sNc8OAFLLc/fu5p878vSUWqCVlqle3Xi75R0jbYm53oDTp9X1Wqg9hrbs5i5va/fdu+ZbMePjpWeAlIZe+Pnde96lpPx8IV55RVqud2+p213peBQWmn/+xMvL/Hjn0rbilbYV3Rr5aGkohj2GaVpibojNggXStqZMMR1m4+kpDQfbuVN6VlIpn6KjledXqya9HS86Wl1PtiXlaDk0tBSqWUdZ8q3kxxGexSktK7bMlpXqtwjerxZxS+Wwh8e949ixoxB//FG2skavF2L7diGiosyfZ0q9ItY4H831Mirtv5peMD8/aajif/6jnN5q1cq3jdL0OCjNr1TJeCRPaY/XqlXSMMai+6A13uJpibWG7cvl/YkTQrz3nnKemduGuTpJaqryNktD6V5XvC6qZjv2qK/+z30fIlhSXl6e2Lx5s3j//ffF7du3xa+//iquX7+uOgFJSUmiXr164u7du4Zpv/76q2jYsKHJM1hCCHHp0iXRoUMH0aNHD5Genm40r2vXrmLdunVG09q3b++4z2AJYVwpDAgw/7amrCzzF0br1mW72GyhvM8wOBI1Y6jLO5RR7ngUDS2oW1eqWBffRo0a0tvIAOklDgUFlvfD3LkzcaL5CoGtgyMrKlchaet9scazaZaucTXXljVu/OW4hlVXCkuTluL5pvQWQWfiAGXkfX2dflnIpaGgQHplfaVK6l9aVLSOsDAh/u//7r3Apnp15Tf+FZXnloYFWvnYqH6LoLXKhfJuoyxD74ruhcOHS8OgLZFbR9G+dewoxP799rm+lO79oaHq12GpAUnpGXBXVyEWLpSGaxetpyjfLN2HwsNNh++paThdulQK+po2tXyfckAOFWBdvHhRdOjQQXTq1Ek0aNBAXLp0Sbz22mviscceE8ePH1eVgJycHNGoUSNx4MABw7QPPvhADBw40GTZ27dvi06dOomnnnpK3Lx502T+xIkTxZtvvmn4u+gtghcuXDBZ1mECrP9R3VJY1tYfO7Z6CiFKP07dCSpBZb74ytPL89130rMoxW8yxT8KP0Mgy1nOnXJy6Lc1KR3jsDAh/vlH6t01V8n56SflFwKUptfHWnldxmvY7q+Wdlb3+dg49LVUWtevK7+oxt9fiK++kt4kKtezERAgPTuZl2edXhwrsmoQbI20W6PHoeT8NWuEiIm5FyAVG7kkS2k/goJKn9byUAqOKlSQynI1lF6AEhBw7yUacj23RW8ArVRJegZf6XnW4ufwvHnSR2kklI+P9Gx1QoL0pkWldTZrZrkhwgE5VIA1fPhwERcXZ/RDw4WFhSImJkYMHjxYdSJiYmJE9+7dxdGjR8XOnTvFI488IrZu3SqEEOL69esi93+v/p4+fbp45JFHxMmTJ8X169cNn6I3GR45csTwO1mnT58WQ4cOFSNGjCjVjt8vVvtdGAdo9TRK6wNUcbpvPSM5OcqvMi/tjc9Zzp1ycOhKoZpjbI/e3/uc1w6dR2TwwOVTWXuQi78RVojy9+JYkVXzyNHvAZ9/LvWs1KsnvUyiZB5cvizEokXK+Xg/ek9KjoB55x0h6tSRRiutXq38vRs3pNEpavZF6Xw8cEAa/qm0DldX+XO4PCMtQkLupcmRzyUZDhVgNWvWTJw/f14IIQwBlhBCpKSkiCZNmqhORE5Ojpg0aZJo0qSJaNOmjfj0008N8+rUqSM2bNgghBDiscceE3Xq1DH5FP/tq6+//lq0b99eNGnSRLz88suyPV1COHGAJYR1xqxSqd3XyoYtx3KXZr4TcPhKoZo8sEfv733Ma4fPIxJCPID5pNR4Ua2a9EyLvcpZK7J6Hjn6PWDvXvmf3ij+XKkD/uC7UT7dvCn9DhogPT8dFnbveH/yiRCxsdJzuRqN8s+MlGZfSntem2vky8qSfqOvvMNJHZBDBVht27YVBw8eFEIYB1g7duwQjz/+uJWSaBtOHWDRfXFf8+gBGb5nDw/EteRkN6bSeiDy6F/ggcsnBxveZw0PXB6pERoqn0+VKgnx558O2Xtikk937wrRrp1yT1CfPlLQb419Ke15bY2RFk7IXgGWi5pXEA4aNAjTp0/Hzp07AQDnzp3DunXrMH36dPTv399qrzok+tebPRso+bMC3t7SdHrwREcDFy8Cer30b3T0/U4RkfOLjgaWLwfCw6VXtoeHS38XXV8sZ52D0s8y/PMP8PDDlvPZEWi1wIUL8vOCg4GNG4EGDayzL6U9r9Vsk9dKmbmpWejll1+Gr68vZs2ahdzcXIwePRoBAQF44YUXMHz4cFunkejfo6hgmzYNuHQJCAuTCjJHumEQETm66GjlcpPlrHMICwNSUuSnFzGXz47i8mX56Wlpxn+Xd1/Kcl5b2iavlTJTFWABwNChQzF06FDk5OSgsLAQvr6+tkwX0b+XM9wwiIicGctZxzd7NjBqFJCTc2+aM/aeqAkUrcUW5zWvlTJRDLB+/fVX1Stp1aqVVRJDRERERPTA9J48KIEilYpigPXCCy9Ao9EY/hZCyC6n0Whw6tQp66eMiIiIiP69HoTekwclUKRSUQywunfvjv3796N27dro2rUrOnfujDBbdGcSERERET2oHoRAkUpFMcBatGgR8vPzceDAAezcuRPPPvss/Pz80KVLF3Tt2hX16tWzZzqJiIiIiIgcntmXXLi7u6Nt27Zo27YtYmNjcfToUezatQsTJkxAbm4uunTpgs6dO6N58+b2Si8REREREZHDUvU7WEUiIyPxxhtvYMOGDRg2bBjWr1+PYcOG2SptRERERERETkX1a9pv3LiBH3/8Ebt378aBAwdQpUoV9O/fH506dbJl+oiIiIiIiJyG2QDrzJkz2L17N3bv3o0//vgD9evXR6dOnTB+/HjUqVPHXmkkIiIiIiJyCooBVseOHZGeno7mzZujT58+eP/991G1alXDfL1eb/i/i0upRhoSERERERE9kBQDrCtXrgAAfvnlF/z666+YNWuW4kr4O1hERERERERmAqzVq1fbMx1EREREREROTzHAeuyxx+yZDiIiIiIiIqfHh6eIiIiIiIishAEWERERERGRlTDAIiIiIiIispJSBVhpaWk4cOAA8vLycOPGDVuliYiIiIiIyCmpCrDu3LmDcePGoV27dnjxxReRnp6OmJgYDBo0CDdv3rR1GomIiIiIiJyCqgArPj4et2/fxq5du+Dh4QEAmDx5MgCY/X0sIiIiIiKifxNVAdbu3bsxZcoUVKtWzTAtPDwcb7/9Nvbv32+zxBERERERETkTVQFWXl4e3N3dTabrdDoIIayeKCIiIiIiImekKsDq1KkTFi5ciMzMTMO0ixcvIi4uDu3bt7dV2oiIiIiIiJyKqgArJiYG7u7uaNGiBXJzc9G7d290794dlSpVwrRp02ydRiIiIiIiIqfgpmahq1ev4v3330dqairOnTuHgoIC1KpVCw899JCt00dEREREROQ0VAVYw4YNw4oVK9CwYUPUqFHD1mkiIiIiIiJySqqGCAYHByMtLc3WaSEiIiIiInJqqnqw6tati3HjxiEiIgLVqlUz/BZWkfnz59skcURERERERM5EVYCl0Wjw1FNP2TotRERERERETk1VgDV37lxbp4OIiIiIiMjpqQqw3nvvPbPzX3vtNaskhoiIiIiIyJmpCrAOHz5s9HdhYSFSU1ORmZmJHj162CRhREREREREzkZVgLVmzRrZ6fHx8SgoKLBqgoiIiIiIiJyVqte0KxkyZAg2btyoenmdToeYmBg0b94cbdq0wYoVKyx+5/Dhw2jfvr3RNL1ej8aNG6Nu3bpGn8zMzNLuAhERERERkdWo6sFS8v3338PT01P18vPnz0dycjJWrVqFa9euYdKkSQgNDUXPnj1llz9z5gxee+01uLq6Gk2/fPkydDoddu/eDa1Wa5ju6+tbth0hIiIiIiKyAlUBVrt27aDRaIym3blzB9nZ2XjzzTdVbSgnJwfr1q3DsmXL0LBhQzRs2BAjRoxAQkKCbIC1du1axMfHo0aNGsjIyDCa99dffyE0NBTVqlVTtW0iIiIiIiJ7UBVgvf7660Z/azQauLu7o1GjRggLC1O1odOnT0On06FZs2aGac2aNcNHH32EgoICuLkZJ+WXX37B/PnzkZWVhcWLFxvNO3fuHGrVqqVqu0RERERERPaiKsDq27ev0d86nQ6nT59G5cqVVW8oPT0dfn5+8PDwMEwLDAxEfn4+bt26haCgIKPl33//fQDA119/bbKuv/76C3fu3EF0dDRSUlIQERGBKVOmoHbt2qrTQ0REREREZG2qAqwLFy5gypQpmDRpEurUqYNBgwbhr7/+QoUKFfDxxx/j0UcftbiO3Nxco+elABj+1ul0pUr0uXPnkJOTg+nTp6NChQpYvnw5hg0bhu+++072OaxTp06Vav22lJeX51DpIVPMI+fAfHJ8zCPnwHxyfMwj58B8cnz2yiNVAVZcXByCgoJQs2ZNbNiwAbdu3cLevXuxfv16zJs3D+vXr7e4Dg8PD5NAquhvLy+vUiX6iy++QGFhIby9vQEACxcuRLt27bBr1y706dPHZPmIiIhSrd+WTp065VDpIVPMI+fAfHJ8zCPnwHxyfMwj58B8cnzWzqOkpCTZ6ape037kyBFMnDgR/v7+2LVrFzp16oSqVauiT58+OHv2rKoEVK1aFZmZmUZBVnp6OrRaLfz8/FSto4iHh4chuCr6u3r16khLSyvVeoiIiIiIiKxJVYDl7e2NzMxM3Lp1C0eOHEG7du0AABcvXkSlSpVUbSgiIgLu7u5ITk42TEtKSkKDBg1MXnBhTkFBAdq2bYutW7capt25cwcpKSl8BouIiIiIiO4rVZFNly5dMH78eHh4eKBKlSp4/PHHsW3bNsyePRtPP/20qg15eXmhT58+iI2Nxbx585Ceno6VK1ciLi4OgNSb5evra/F3tdzc3BAVFYVFixYhKCgIfn5+WLRoEapUqYIOHTqoSgsREREREZEtqOrBiomJwZAhQ9CyZUt89tln0Gq10Ol0GDNmDMaPH696Y1OmTEGjRo3w3HPPYcaMGRg7dix69OgBAIiKisK2bdtUrScmJgbt2rXD+PHjMWDAAADAJ598UqqeMCIiIiIiImvTCCGE2oWzs7ORkpKCwsJChIeHl/rZqfshKSnJ6Le37jc+AOn4mEfOgfnk+JhHzoH55PiYR86B+eT4bPGSC7k4Q1WXj06nQ3x8PL766isUFBRIX3RzQ8+ePREXF2fy+nUiIiIiIqJ/I1VDBOPj4/HTTz9h6dKlOHz4MH777Td8+OGHSE5OxqJFi2ydRiIiIiIiIqegqgdr69ateP/99/HYY48ZprVr1w6enp5444038Oabb9osgURERERERM5CVQ+WEAKVK1c2mV6pUiXk5ORYPVFERERERETOSFWA1bJlS7zzzjvIysoyTMvMzMS7776LFi1a2CxxREREREREzkTVEMGpU6di2LBhePzxxxEWFgYAuHTpEmrWrIkPP/zQpgkkIiIiIiJyFqoCrKpVq2LLli346aefcP78eXh4eOChhx5C69atodFobJ1GIiIiIiIip6D6l3nd3d1RrVo13L17FxqNBiEhIQyuiIiIiIiIilEVYF2/fh2vvvoqjh07hooVK0Kv1yM7OxutWrXC4sWLUbFiRVunk4iIiIiIyOGpesnFtGnT4OHhgR07duDgwYM4dOgQvv/+e+h0OsTGxto6jURERERERE5BVQ/W4cOH8d///hfVq1c3TAsPD8dbb72FIUOG2CxxREREREREzkRVD1Z4eDhOnTplMv3vv/9GtWrVrJ4oIiIiIiIiZ6SqB6tPnz6YOXMmTpw4gSZNmsDNzQ2nTp1CQkIC+vbti/Xr1xuW7d+/v80SS0RERERE5MhUBVirV6+Gr68vduzYgR07dhim+/j4GE3TaDQMsIiIiIiI6F9LVYC1e/duxXmFhYVwdXW1WoKIiIiIiIicleq3CGZnZ5tMP3HiBJ5++mmrJ4qIiIiIiMgZqQqwjh8/jp49e+Knn34CAOTl5WHu3LkYOHAgateubdMEEhEREREROQtVQwS//vprLF++HK+++io6duyIY8eOwcPDA5988glatWpl6zQSERERERE5BVUBlpubG4YNG4bTp0/ju+++g5ubG2bNmsXgioiIiIiIqBhVQwQ3bNiAbt264cKFC0hMTMSUKVMwa9YsPP/88zh//ryt00hEREREROQUVAVYb7/9NgYPHoyvv/4azZo1Q3R0NLZu3Qpvb2/07t3b1mkkIiIiIiJyCqqGCG7atAkPPfSQ0bSqVavio48+wvbt222SMCIiIiIiImej2IO1a9cu5OfnA4BJcFXkzp07+P33322SMCIiIiIiImejGGC98soryMzMNJrWvn17/P3334a/c3NzsWrVKtuljoiIiIiIyIkoBlhCCJNp//zzD/R6vU0TRERERERE5KxUveSCiIiIiIiILGOARUREREREZCUMsIiIiIiIiKzE7Gvat2zZggoVKhj+1uv1+O677+Dv7w8AyM7Otm3qiIiIiIiInIhigBUaGorPP//caFpAQADWrl1rNC0kJMQ2KSMiIiIiInIyigHW7t277ZkOIiIiIiIip8dnsIiIiIiIiKyEARYREREREZGVMMAiIiIiIiKyErsGWDqdDjExMWjevDnatGmDFStWWPzO4cOH0b59e5Pp27ZtQ5cuXRAZGYkxY8bg5s2bNkgxERERERGRenYNsObPn4/k5GSsWrUKsbGxWLp0KbZu3aq4/JkzZ/Daa69Br9cbTT927BgmT56MMWPG4KuvvkJ2djYmTZpk6+QTERERERGZZbcAKycnB+vWrcPUqVPRsGFDdO7cGSNGjEBCQoLs8mvXrsWgQYMQEBBgMi8hIQFdu3ZFv379UK9ePcyfPx/79u1DSkqKrXeDiIiIiIhIkd0CrNOnT0On06FZs2aGac2aNcPx48dRUFBgsvwvv/yC+fPn4/nnnzeZd/ToUTRv3tzwd0hICKpVq4bk5GSbpJ2IiIiIiEgNuwVY6enp8PPzg4eHh2FaYGAg8vPzcevWLZPl33//fXTp0kV2XdevX0dQUJDRtICAAKSlpVk30URERERERKWg+EPD1pabmwutVms0rehvnU5XqnXl5eXJrktpPadOnSrV+m0pLy/PodJDpphHzoH55PiYR86B+eT4mEfOgfnk+OyVR3YLsDw8PEwCoKK/vby8rLIuT09P2eUjIiJKtX5bOnXqlEOlh0wxj5wD88nxMY+cA/PJ8TGPnAPzyfFZO4+SkpJkp9ttiGDVqlWRmZlpFBilp6dDq9XCz8+v1Ou6ceOG0bQbN26gSpUqVkkrERERERFRWdgtwIqIiIC7u7vRiyiSkpLQoEEDuLmVriMtMjLSKGK8evUqrly5giZNmlgruURERERERKVmtwDLy8sLffr0QWxsLI4dO4Zdu3Zh5cqVGDZsGACpNysvL0/VugYPHowtW7Zg3bp1OHPmDN588008/vjjqFmzpg33gIiIiIiIyDy7/tDwlClT0KhRIzz33HOYMWMGxo4dix49egAAoqKisG3bNlXradq0KeLi4rB06VIMGjQIvr6+iI+Pt2XSiYiIiIiILLLbSy4AqRcrPj5eNhg6c+aM7Hf69euHfv36mUzv27cv+vbta/U0EhERERERlZVde7CIiIiIiIgeZAywiIiIiIiIrIQBFhERERERkZUwwCIiIiIiIrISBlhERERERERWwgCLiIiIiIjIShhgERERERERWQkDLCIiIiIiIithgEVERERERGQlDLCIiIiIiIishAEWERERERGRlTDAIiIiIiIishIGWERERERERFbCAIuIiIiIiMhKGGARERERERFZCQMsIiIiIiIiK2GARUREREREZCUMsIiIiIiIiKyEARYREREREZGVMMAiIiIiIiKyEgZYREREREREVsIAi4iIiIiIyEoYYBEREREREVkJAywiIiIiIiIrYYBFRERERERkJQywiIiIiIiIrIQBFhERERERkZUwwCIiIiIiIrISBlhERERERERWwgCLiIiIiIjIShhgERERERERWQkDLCIiIiIiIithgEVERERERGQlDLCIiIiIiIisxK4Blk6nQ0xMDJo3b442bdpgxYoVisuePn0aAwcORGRkJPr164djx44Z5un1ejRu3Bh169Y1+mRmZtpjN4iIiIiIiGS52XNj8+fPR3JyMlatWoVr165h0qRJCA0NRc+ePY2Wy8nJwYgRI9CjRw/MmTMHa9euxUsvvYQdO3bAx8cHly9fhk6nw+7du6HVag3f8/X1tefuEP1/e/ce3NOd/3H8FZdEXBptJZnFusS9SSRk6dT9ti4JGkLrzmCbFjHMKmEbZWwb7GqLbVW27faCCi12XXetS8NaqyJCSVIJwtISt0ryFUnk8/vD+I5vE1K//eZ8o3k+Zr4zzufzyTmfk/e8z8nbOed7AAAAAAeWXcGy2Wxat26d5syZo4CAAPXq1UsTJ07UqlWrio3dtm2bqlatqujoaDVp0kRz5sxRrVq1tH37dklSenq66tatq3r16snb29v+cXNzs2p3AAAAAKAYywqs1NRU5efnKyQkxN4WEhKi48ePq7Cw0GFscnKy2rZtq0qV7k7Pzc1Nbdu2VVJSkiQpIyNDjRs3tmrqAAAAAPCTWFZgZWVlycvLSx4eHva2OnXqqKCgQNeuXSs21sfHx6Ht6aef1qVLlyTdvYKVm5urkSNHqlOnTvrNb36j06dPl/1OAAAAAMBDWPYM1q1btxyel5JkX87Pz/9JY++Ny8jIkM1m09y5c1WjRg3FxcVpzJgx2r59e4nPYaWkpDhzV/4neXl55Wo+KI4YPR6IU/lHjB4PxKn8I0aPB+JU/lkVI8sKLA8Pj2KF1L1lT0/PnzS2WrVqkqQ1a9bozp07ql69uiRpyZIl6tq1q3bt2qXw8PBi227VqpWzduN/lpKSUq7mg+KI0eOBOJV/xOjxQJzKP2L0eCBO5Z+zY5SYmFhiu2UFlq+vr27evKn8/Hz71amsrCy5u7vLy8ur2NisrCyHtitXrsjb21uSHG4zvLdcv359+y2EAAAAAOAKlj2D1apVK1WtWtX+RRXS3arP399fVao41nlBQUFKSkqSMUaSZIxRUlKSgoODVVhYqM6dO2vr1q328bm5ucrMzJSfn581OwMAAAAAJbCswPL09FR4eLjmz5+vY8eOadeuXfroo480ZswYSXevZuXl5UmS+vbtK5vNpgULFig9PV2xsbHKyclRaGioqlSpok6dOuntt9/W119/rW+//VYzZsyQt7e3unfvbtXuAAAAAEAxlhVYkjR79mwFBgZq7Nixev311zV58mSFhoZKkjp16qRt27ZJkmrWrKmVK1cqKSlJgwYN0pEjRxQXF6eaNWtKkmJiYtS1a1dNnz5dL7zwgiTpgw8+KHYlDAAAAACsZGlF4unpqUWLFmnRokXF+tLS0hyWW7durY0bN5a4nurVqysmJkYxMTFlMk8AAAAA+P+w9AoWAAAAAPycUWABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNQYAEAAACAk1BgAQAAAICTUGABAAAAgJNYWmDl5+crJiZG7dq1U8eOHfXnP//5gWNTU1P14osvKigoSIMHD9axY8cc+rdt26Zf//rXCgoK0iuvvKKrV6+W9fQBAAAA4KEsLbAWL16spKQk/eUvf9H8+fO1YsUKbd26tdg4m82miRMnKigoSBs2bFBISIgiIyOVk5MjSTp27Jiio6P1yiuvKD4+Xjk5OZo5c6aVuwIAAAAAxVhWYNlsNq1bt05z5sxRQECAevXqpYkTJ2rVqlXFxm7btk1Vq1ZVdHS0mjRpojlz5qhWrVravn27JGnVqlXq3bu3Bg8erJYtW2rx4sXav3+/MjMzrdodAAAAACjGsgIrNTVV+fn5CgkJsbeFhITo+PHjKiwsdBibnJystm3bqlKlu9Nzc3NT27ZtlZSUZO9v166dffwvfvEL1atXz94PAAAAAK5QxaoNZWVlycvLSx4eHva2OnXqqKCgQNeuXZOPj4/D2MaNGzv8/NNPP63U1FRJ0uXLlx3G3+u/dOlSidtOTEx01m44RXmbD4ojRo8H4lT+EaPHA3Eq/4jR44E4lX9WxMiyAuvWrVtyd3d3aLu3nJ+f/5PG3huXl5f30P773X/FDAAAAADKkmW3CHp4eBQrgO4te3p6/qSx1apV+0n9AAAAAOAKlhVYvr6+unnzpkNhlJWVJXd3d3l5eRUbm5WV5dB25coVeXt72/uvXLnywH4AAAAAcAXLCqxWrVqpatWqDl9EkZiYKH9/f1Wp4ninYlBQkJKSkmSMkSQZY5SUlKTg4GB7//33T3733Xe6ePGivR8AAAAAXMGyAsvT01Ph4eGaP3++jh07pl27dumjjz7SmDFjJN29mpWXlydJ6tu3r2w2mxYsWKD09HTFxsYqJydHoaGhkqThw4dry5YtWrdundLS0jRr1ix16dJFjRo1smp3HtmjvGQZ1jh37pxefvlltWvXTl26dNHChQt1+/ZtSVJMTIxatGjh8Pn4449dO+EKavPmzcViMWnSJEnShQsXNH78eAUHB6tfv3766quvXDzbimfDhg3F4nPvc/HiRXKpHMjPz1f//v114MABe1tpuXPw4EENGDBAQUFBGj16NK9BKWMlxejf//63IiIi1KZNG/Xp00fr1693+Jk+ffoUy62UlBSrp16hlBSn0o5x5JK1fhyj6OjoEs9PPXv2tP9MmeSSsZDNZjMzZ840wcHBpmPHjubDDz+09zVv3tx8+eWX9uXk5GQTHh5uAgICTEREhDl+/LjDujZs2GC6detmgoODzaRJk8zVq1ct24//jwULFpiwsDBz/Phxs3PnTtOmTRuzZcsWV0+rwrp9+7bp16+fiYqKMunp6eY///mP6dmzp4mNjTXGGDNs2DDzwQcfmMuXL9s/NpvNxbOumN566y0zefJkh1j88MMPpqioyAwcONBMnz7dnDp1yqxcudK0bt3anDt3ztVTrlBu3brlEJvvv//eDBo0yERFRRljyCVXy8vLM5MnTzbNmzc3//rXv4wxptTcuXjxogkODjZxcXHm1KlTZtq0aSY0NNTcuXPHlbvys1VSjM6cOWMCAwPNihUrzNmzZ81f//pXExAQYHbt2mWMuXsOa9WqlUlMTHTIrYKCAlfuys9aSXEy5uHHOHLJWiXF6ObNmw6xSUlJMW3atDGfffaZMabscsnSAquiys3NNYGBgQ4J+e6775phw4a5cFYV29dff238/f1NTk6Ove1vf/ub6dChgzHGmPbt25uDBw+6anq4z+TJk82yZcuKtR84cMAEBgaa7Oxse9vYsWPNW2+9ZeX08COfffaZefbZZ82NGzeMMeSSK506dcoMHDjQDBgwwOEPjtJy55133nE4P9lsNtOmTRuHcxic40Exevfdd80LL7zgMPa1114z06ZNM8YYk5KSYp555hmTn59v+ZwrogfFyZiHH+PIJes8LEb3i4qKMuPGjbMvl1UuWXaLYEX2KC9ZhjX8/PwUFxenGjVq2Nvc3NyUn5+vrKws3bhxo9i72OAa6enpJcYiOTlZzzzzjGrWrGlvCwkJ0dGjRy2cHe6Xk5OjP/3pT5o6daq8vLzIJRc7fPiwOnbsqPj4eIf20nInOTlZ7dq1s/d5enrK39/f4RlqOMeDYtSvXz/FxMQ4tLm5udlvY8/IyFD9+vVVtWpVy+ZakT0oTqUd48gl6zwoRvdLSkrSP//5T82ePdveVla5ZNl7sCqyR3nJMqzx1FNPqUOHDvbloqIirVq1SiEhIUpPT1eVKlW0dOlSJSQk6Mknn9S4ceM0ePBgF864YsrPz9f58+e1Z88eLVu2TEVFRerbt6+mTp2qrKysEl84/v3337totoiPj5e7u7uGDh0qSeSSiw0bNqzE9tJy50H9ly5dKpuJVmAPitGP/2C/cuWKtm7dqilTpki6m1uVK1fWxIkTlZKSosaNG+vVV19VUFBQmc+5InpQnEo7xpFL1nlQjO73/vvvq3fv3mrevLm9raxyiQLLAo/ykmW4RmxsrFJSUvTFF1/o0KFDkqSWLVtq9OjROnTokObOnStPT0/169fPxTOtWDIzM1VYWKjq1atr2bJlOnfunN544w3l5ubq9u3bxf7Hyd3dXQUFBS6abcVmjFF8fLxGjRplj8vp06clkUvlza1btx6aOw86Z3G+cg2bzaYpU6bIx8fH/kdkRkaGbt68qRkzZsjX11fr1q3T2LFjtWXLFtWvX9/FM644SjvGkUvlx8WLF5WQkKC1a9c6tJdVLlFgWeBRXrIMaxlj9MYbb+jzzz/X0qVL1axZMzVt2lRhYWGqXbu2pLsHzszMTH3++ef8UWixZs2a6eDBg3ryyScl3Y2FMUa//e1vNXToUOXk5DiM54XjrnPixAmdO3dOzz//vL1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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('seaborn-whitegrid')\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot(time, (int_SCI_flux_550m / int_SCI_flux ) , marker='o', color='r')\n", - "plt.plot(time, (int_SCI_flux_550_650 / int_SCI_flux ) , marker='o', color='orange')\n", - "plt.plot(time, (int_SCI_flux_650_750 / int_SCI_flux ) , marker='o', color='g')\n", - "plt.plot(time, (int_SCI_flux_750p / int_SCI_flux ) , marker='o', color='b')\n", - "plt.xlabel(\"Time (sec)\", fontsize=14)\n", - "plt.ylabel(\"Exposure Meter Flux Ratio\", fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.ylim(0,0.4)\n", - "plt.legend([\"750-870 nm / 445-870 nm\",\"650-750 nm / 445-870 nm\",\"550-650 nm / 445-870 nm\",\"445-550 nm / 445-870 nm\"], fontsize=12, loc='best') \n", - "#plt.show()\n", - "plt.savefig('EM_flux_ratio_51peg.png')" - ] - }, - { - "cell_type": "markdown", - "id": "d154a953", - "metadata": {}, - "source": [ - "# Telemetry\n", - "Each KPF L0 file contains telemetry on >100 sensors or state values with average, standard deviation, minimum, and maximum values listed." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "489f8080", - "metadata": {}, - "outputs": [], - "source": [ - "df_telemetry = Table.read(L0_file, format='fits', hdu='TELEMETRY').to_pandas() \n", - "num_columns = ['average', 'stddev', 'min', 'max']\n", - "for column in df_telemetry:\n", - " df_telemetry[column] = df_telemetry[column].str.decode('utf-8')\n", - " df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0\n", - " if column in num_columns:\n", - " df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast=\"float\")\n", - " else:\n", - " df_telemetry[column] = df_telemetry[column].astype(str)\n", - "df_telemetry.set_index(\"keyword\", inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "89f8ef3c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Index: 123 entries, kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS to kpfpower.OUTLET_A1_Amps\n", - "Data columns (total 6 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 average 123 non-null float32\n", - " 1 stddev 123 non-null float32\n", - " 2 min 123 non-null float32\n", - " 3 max 123 non-null float32\n", - " 4 units 123 non-null object \n", - " 5 description 123 non-null object \n", - "dtypes: float32(4), object(2)\n", - "memory usage: 4.8+ KB\n" - ] - } - ], - "source": [ - "df_telemetry.info()" - ] - }, - { - "cell_type": "markdown", - "id": "961e0a1a", - "metadata": {}, - "source": [ - "Print the entire table of telemetry." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c0c3069a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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averagestddevminmaxunitsdescription
keyword
kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS1.597510e+011.963010e+021.597500e+011.597600e+01degCBench Bottom Between Cameras C2 c- double degC...
kpfmet.BENCH_BOTTOM_COLLIMATOR1.647870e+012.024890e+021.647700e+011.648000e+01degCBench Bottom Coll C3 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_DCUT1.624350e+011.995990e+021.624300e+011.624400e+01degCBench Bottom D-cut C4 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_ECHELLE1.638640e+012.013550e+021.638600e+011.638700e+01degCBench Bottom Echelle Cam B c- double degC {%.3f}
kpfmet.BENCH_TOP_BETWEEN_CAMERAS1.588580e+011.952040e+021.588500e+011.588700e+01degCBench Top Between Cameras D4 c- double degC {%...
kpfmet.BENCH_TOP_COLL1.657790e+012.037080e+021.657700e+011.657900e+01degCBench Top Coll D5 c- double degC {%.3f}
kpfmet.BENCH_TOP_DCUT1.628050e+012.000540e+021.627900e+011.628100e+01degCBench Top D-cut D3 c- double degC {%.3f}
kpfmet.BENCH_TOP_ECHELLE_CAM1.629190e+012.001940e+021.629100e+011.629300e+01degCBench Top Echelle Cam D1 c- double degC {%.3f}
kpfmet.CALEM_SCMBLR_CHMBR_END1.736520e+012.133830e+021.736400e+011.736600e+01degCCal EM Scrammbler Chamber End C1 c- double deg...
kpfmet.CALEM_SCMBLR_FIBER_END1.721330e+012.115160e+021.721200e+011.721500e+01degCCal EM Scrambler Fiber End D1 c- double degC {...
kpfmet.CAL_BENCH2.020000e+012.482160e+022.020000e+012.020000e+01degCCal_Bench temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_BB_SRC2.050000e+012.519030e+022.050000e+012.050000e+01degCCAL_Bench_BB_Src temperature c- double degC {%...
kpfmet.CAL_BENCH_BOT2.360000e+012.899950e+022.360000e+012.360000e+01degCCal_Bench_Bot temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_ENCL_AIR2.018220e+012.479980e+022.010000e+012.020000e+01degCCal_Bench_Encl_Air temperature c- double degC ...
kpfmet.CAL_BENCH_OCT_MOT1.990000e+012.445300e+021.990000e+011.990000e+01degCCal_Bench_Oct_Mot temperature c- double degC {...
kpfmet.CAL_BENCH_TRANS_STG_MOT2.010000e+012.469880e+022.010000e+012.010000e+01degCCal_Bench_Trans_Stg_Mot temperature c- double ...
kpfmet.CAL_RACK_TOP2.301710e+012.828330e+022.300000e+012.310000e+01degCCal_Rack_Top temperature c- double degC {%.1f}
kpfmet.CHAMBER_EXT_BOTTOM1.570260e+011.929530e+021.570100e+011.570400e+01degCChamber Exterior Bottom B c- double degC {%.3f}
kpfmet.CHAMBER_EXT_TOP-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCChamber Exterior Top C1 c- double degC {%.3f}
kpfmet.CRYOSTAT_G1-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin cryostat green D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_G2-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin cryostat green D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_G31.668370e+012.050090e+021.668300e+011.668500e+01degCWithin cryostat green D4 c- double degC {%.3f}
kpfmet.CRYOSTAT_R11.610210e+011.978620e+021.610000e+011.610300e+01degCWithin Cryostat red D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_R2-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin Cryostat red D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_R31.564080e+011.921940e+021.564000e+011.564200e+01degCWithin Cryostat red D4 c- double degC {%.3f}
kpfmet.ECHELLE_BOTTOM1.641060e+012.016530e+021.641000e+011.641200e+01degCEchelle Bottom D1 c- double degC {%.3f}
kpfmet.ECHELLE_TOP1.628290e+012.000840e+021.628200e+011.628400e+01degCEchelle Top C1 c- double degC {%.3f}
kpfmet.FF_SRC1.957500e+012.405370e+021.950000e+011.960000e+01degCFF_Src temperature c- double degC {%.1f}
kpfmet.GREEN_CAMERA_BOTTOM1.677540e+012.061350e+021.677400e+011.677600e+01degCGreen Camera Bottom C3 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_COLLIMATOR1.682040e+012.066880e+021.681900e+011.682200e+01degCGreen Camera Collimator C4 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_ECHELLE1.671060e+012.053390e+021.670900e+011.671200e+01degCGreen Camera Echelle D5 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_TOP1.661770e+012.041970e+021.661700e+011.661900e+01degCGreen Camera Top C2 c- double degC {%.3f}
kpfmet.GREEN_GRISM_TOP1.639170e+012.014200e+021.639000e+011.639300e+01degCGreen Grism Top C5 c- double degC {%.3f}
kpfmet.GREEN_LN2_FLANGE9.791590e+001.203190e+029.746000e+009.848000e+00degCGreen LN2 Flange A c- double degC {%.3f}
kpfmet.PRIMARY_COLLIMATOR_TOP1.689680e+012.076270e+021.689600e+011.689800e+01degCPrimary Col Top D2 c- double degC {%.3f}
kpfmet.RED_CAMERA_BOTTOM1.630110e+012.003070e+021.630000e+011.630300e+01degCRed Camera Bottom D5 c- double degC {%.3f}
kpfmet.RED_CAMERA_COLLIMATOR1.664290e+012.045070e+021.664200e+011.664400e+01degCRed Camera Coll C3 c- double degC {%.3f}
kpfmet.RED_CAMERA_ECHELLE1.659400e+012.039060e+021.659300e+011.659500e+01degCRed Camera Ech C4 c- double degC {%.3f}
kpfmet.RED_CAMERA_TOP1.654670e+012.033240e+021.654500e+011.654800e+01degCRed Camera Top C5 c- double degC {%.3f}
kpfmet.RED_GRISM_TOP1.636990e+012.011530e+021.636900e+011.637200e+01degCRed Grism Top C2 c- double degC {%.3f}
kpfmet.RED_LN2_FLANGE6.245890e+007.675000e+016.199000e+006.292000e+00degCRed LN2 Flange D1 c- double degC {%.3f}
kpfmet.REFORMATTER1.660480e+012.040380e+021.660400e+011.660600e+01degCReformatter A c- double degC {%.3f}
kpfmet.SCIENCE_CAL_FIBER_STG2.010000e+012.469880e+022.010000e+012.010000e+01degCScience_Cal_Fiber_Stg temperature c- double de...
kpfmet.SCISKY_SCMBLR_CHMBR_END-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCSciSky Scrambler Chamber End A c- double degC ...
kpfmet.SCISKY_SCMBLR_FIBER_END1.747050e+012.146770e+021.746900e+011.747200e+01degCSciSky Scrammbler Fiber End B c- double degC {...
kpfmet.SIMCAL_FIBER_STG2.010000e+012.469880e+022.010000e+012.010000e+01degCSimCal_Fiber_Stg temperature c- double degC {%...
kpfmet.SKYCAL_FIBER_STG1.990000e+012.445300e+021.990000e+011.990000e+01degCSkyCal_Fiber_Stg temperature c- double degC {%...
kpfmet.TEMP1.968480e+012.418860e+021.967300e+011.969700e+01degCVaisala Temperature c- double degC {%.3f}
kpfmet.TH_DAILY2.000000e+012.457590e+022.000000e+012.000000e+01degCTh_daily temperature c- double degC {%.1f}
kpfmet.TH_GOLD1.990000e+012.445300e+021.990000e+011.990000e+01degCTh_gold temperature c- double degC {%.1f}
kpfmet.U_DAILY2.010000e+012.469880e+022.010000e+012.010000e+01degCU_daily temperature c- double degC {%.1f}
kpfmet.U_GOLD1.990000e+012.445300e+021.990000e+011.990000e+01degCU_gold temperature c- double degC {%.1f}
kpfgreen.BPLANE_TEMP3.582320e+014.401930e+023.581200e+013.587500e+01degCBackplane temperature c- double degC {%.3f}
kpfgreen.BRD10_DRVR_T4.050370e+014.977070e+024.050000e+014.056200e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfgreen.BRD11_DRVR_T3.875000e+014.761580e+023.875000e+013.875000e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfgreen.BRD12_LVXBIAS_T3.475000e+014.270060e+023.475000e+013.475000e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfgreen.BRD1_HTRX_T3.184100e+013.912610e+023.181200e+013.187500e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfgreen.BRD2_XVBIAS_T0.000000e+000.000000e+000.000000e+000.000000e+00degCBoard 2 (XV Bias) temperature c- double degC {...
kpfgreen.BRD3_LVDS_T3.342310e+014.107010e+023.337500e+013.343700e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfgreen.BRD4_DRVR_T3.719820e+014.570890e+023.718700e+013.725000e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfgreen.BRD5_AD_T3.381530e+014.155200e+023.381200e+013.387500e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfgreen.BRD7_HTRX_T3.784640e+014.650540e+023.781200e+013.787500e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfgreen.BRD9_HVXBIAS_T3.627570e+014.457540e+023.625000e+013.631200e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfgreen.CF_BASE_2WT-1.270330e+021.560970e+03-1.270340e+02-1.270310e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfgreen.CF_BASE_T-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfgreen.CF_BASE_TRG-1.275000e+021.566710e+03-1.275000e+02-1.275000e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfgreen.CF_TIP_T-1.280000e+021.572860e+03-1.280010e+02-1.279990e+02degCtip cold finger c- double degC {%.3f}
kpfgreen.CF_TIP_TRG-1.280000e+021.572860e+03-1.280000e+02-1.280000e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfgreen.COL_PRESS4.000000e-084.915170e-074.000000e-084.000000e-08TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.CRYOBODY_T1.899600e+012.334220e+021.899200e+011.900000e+01degCCryo Body Temperature c- double degC {%.3f}
kpfgreen.CRYOBODY_TRG1.900000e+012.334710e+021.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfgreen.CURRTEMP-1.400000e+021.720310e+03-1.400020e+02-1.399970e+02degCCurrent cold head temperature c- double degC {...
kpfgreen.ECH_PRESS1.000000e-111.228790e-101.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.KPF_CCD_T-1.010900e+021.242190e+03-1.010920e+02-1.010890e+02degCSSL Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_T-1.000000e+021.228800e+03-1.000020e+02-9.999800e+01degCSTA Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_TRG-1.000000e+021.228790e+03-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfgreen.TEMPSET-1.400000e+021.720310e+03-1.400000e+02-1.400000e+02degCSet point for the cold head temperature c2 dou...
kpfred.BPLANE_TEMP4.380440e+015.382650e+024.375000e+014.387500e+01degCBackplane temperature c- double degC {%.3f}
kpfred.BRD10_DRVR_T4.811190e+015.911970e+024.806200e+014.818700e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfred.BRD11_DRVR_T4.735680e+015.819170e+024.731200e+014.737500e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfred.BRD12_LVXBIAS_T4.354730e+015.351070e+024.343700e+014.362500e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfred.BRD1_HTRX_T4.136050e+015.082350e+024.131200e+014.137500e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfred.BRD2_XVBIAS_T4.314250e+015.301330e+024.312500e+014.318700e+01degCBoard 2 (XV Bias) temperature c- double degC {...
kpfred.BRD3_LVDS_T4.298010e+015.281370e+024.293700e+014.300000e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfred.BRD4_DRVR_T4.606210e+015.660080e+024.600000e+014.612500e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfred.BRD5_AD_T4.077440e+015.010340e+024.068700e+014.081200e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfred.BRD7_HTRX_T4.536200e+015.574060e+024.531200e+014.543700e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfred.BRD9_HVXBIAS_T4.526700e+015.562380e+024.518700e+014.531200e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfred.CF_BASE_2WT-1.257290e+021.544940e+03-1.257300e+02-1.257260e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfred.CF_BASE_T-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfred.CF_BASE_TRG-1.257500e+021.545210e+03-1.257500e+02-1.257500e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfred.CF_TIP_T-1.272500e+021.563640e+03-1.272510e+02-1.272480e+02degCtip cold finger c- double degC {%.3f}
kpfred.CF_TIP_TRG-1.272500e+021.563640e+03-1.272500e+02-1.272500e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfred.COL_PRESS3.715130e-084.565340e-073.700000e-083.800000e-08TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.CRYOBODY_T1.899880e+012.334560e+021.899700e+011.900100e+01degCCryo Body Temperature c- double degC {%.3f}
kpfred.CRYOBODY_TRG1.900000e+012.334710e+021.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfred.CURRTEMP-1.405000e+021.726450e+03-1.405010e+02-1.404980e+02degCCurrent cold head temperature c- double degC {...
kpfred.ECH_PRESS1.000000e-111.228790e-101.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.KPF_CCD_T-1.008710e+021.239500e+03-1.008720e+02-1.008700e+02degCSSL Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_T-1.000000e+021.228800e+03-1.000030e+02-9.999900e+01degCSTA Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_TRG-1.000000e+021.228790e+03-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfred.TEMPSET-1.405000e+021.726460e+03-1.405000e+02-1.405000e+02degCSet point for the cold head temperature c2 dou...
kpfexpose.BENCH_C1.522700e+011.871100e+021.510000e+011.540000e+01degCrtd bench c- double degC {%.1f} { -100.0 .. 10...
kpfexpose.CAMBARREL_C1.602630e+011.969330e+021.580000e+011.620000e+01degCrtd camera barrel c- double degC {%.1f} { -100...
kpfexpose.DET_XTRN_C2.017700e+012.479350e+022.000000e+012.040000e+01degCrtd detector extermal c- double degC {%.1f} { ...
kpfexpose.ECHELLE_C1.376320e+011.691240e+021.350000e+011.400000e+01degCrtd echelle c- double degC {%.1f} { -100.0 .. ...
kpfexpose.ENCLOSURE_C1.863160e+012.289460e+021.850000e+011.880000e+01degCrtd enclosure c- double degC {%.1f} { -100.0 ....
kpfexpose.RACK_AIR_C1.857040e+012.281930e+021.840000e+011.870000e+01degCrtd rack air c- double degC {%.1f} { -100.0 .....
kpfvac.PUMP_TEMP3.900000e+014.792300e+023.900000e+013.900000e+01degCMotor temperature c- double degC {%.2f}
kpf_hk.COOLTARG-6.000000e+017.372760e+02-6.000000e+01-6.000000e+01degCtemperature target c2 int degC
kpf_hk.CURRTEMP-5.978000e+017.345730e+02-5.978000e+01-5.978000e+01degCcurrent temperature c- double degC {%.2f}
kpfgreen.COL_CURR1.100000e-061.351670e-051.100000e-061.100000e-06ACurrent ion pump current c- double A {%.3e}
kpfgreen.ECH_CURR8.910000e-111.094860e-098.910000e-118.910000e-11ACurrent ion pump current c- double A {%.3e}
kpfred.COL_CURR9.986180e-071.227110e-059.800000e-071.000000e-06ACurrent ion pump current c- double A {%.3e}
kpfred.ECH_CURR5.600000e-126.881240e-115.600000e-125.600000e-12ACurrent ion pump current c- double A {%.3e}
kpfcal.IRFLUX9.999000e+031.228670e+059.999000e+039.999000e+03CountsLFC Fiberlock IR Intensity c- int Counts {%d}
kpfcal.VISFLUX2.553950e+013.138880e+022.500000e+012.600000e+01CountsLFC Fiberlock Vis Intensity c- int Counts {%d}
kpfcal.BLUECUTIACT3.326320e-024.111620e-012.300000e-025.300000e-02ABlue cut amplifier 0 measured current c- doubl...
kpfmot.AGITSPD-3.900470e+034.822300e+04-4.699000e+03-1.880000e+03motor_counts/sagit raw velocity c2 int motor counts/s { -750...
kpfmot.AGITTOR-1.102970e+001.590140e+01-2.271000e+001.440000e-01Vagit motor torque c- double V {%.3f}
kpfmot.AGITAMBI_T2.580130e+013.170470e+022.548000e+012.603000e+01degCAgitator ambient temperature c- double degC {%...
kpfmot.AGITMOT_T2.606950e+013.203420e+022.582000e+012.622000e+01degCAgitator motor temperature c- double degC {%.2...
kpfpower.OUTLET_A1_Amps5.070390e+016.235380e+022.700000e+015.300000e+01milliampsOutlet A1 current amperage c- int milliamps
\n", - "
" - ], - "text/plain": [ - " average stddev min \\\n", - "keyword \n", - "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597510e+01 1.963010e+02 1.597500e+01 \n", - "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.647870e+01 2.024890e+02 1.647700e+01 \n", - "kpfmet.BENCH_BOTTOM_DCUT 1.624350e+01 1.995990e+02 1.624300e+01 \n", - "kpfmet.BENCH_BOTTOM_ECHELLE 1.638640e+01 2.013550e+02 1.638600e+01 \n", - "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588580e+01 1.952040e+02 1.588500e+01 \n", - "kpfmet.BENCH_TOP_COLL 1.657790e+01 2.037080e+02 1.657700e+01 \n", - "kpfmet.BENCH_TOP_DCUT 1.628050e+01 2.000540e+02 1.627900e+01 \n", - "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629190e+01 2.001940e+02 1.629100e+01 \n", - "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736520e+01 2.133830e+02 1.736400e+01 \n", - "kpfmet.CALEM_SCMBLR_FIBER_END 1.721330e+01 2.115160e+02 1.721200e+01 \n", - "kpfmet.CAL_BENCH 2.020000e+01 2.482160e+02 2.020000e+01 \n", - "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 2.519030e+02 2.050000e+01 \n", - "kpfmet.CAL_BENCH_BOT 2.360000e+01 2.899950e+02 2.360000e+01 \n", - "kpfmet.CAL_BENCH_ENCL_AIR 2.018220e+01 2.479980e+02 2.010000e+01 \n", - "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 2.445300e+02 1.990000e+01 \n", - "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 2.469880e+02 2.010000e+01 \n", - "kpfmet.CAL_RACK_TOP 2.301710e+01 2.828330e+02 2.300000e+01 \n", - "kpfmet.CHAMBER_EXT_BOTTOM 1.570260e+01 1.929530e+02 1.570100e+01 \n", - "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfmet.CRYOSTAT_G1 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfmet.CRYOSTAT_G2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfmet.CRYOSTAT_G3 1.668370e+01 2.050090e+02 1.668300e+01 \n", - "kpfmet.CRYOSTAT_R1 1.610210e+01 1.978620e+02 1.610000e+01 \n", - "kpfmet.CRYOSTAT_R2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfmet.CRYOSTAT_R3 1.564080e+01 1.921940e+02 1.564000e+01 \n", - "kpfmet.ECHELLE_BOTTOM 1.641060e+01 2.016530e+02 1.641000e+01 \n", - "kpfmet.ECHELLE_TOP 1.628290e+01 2.000840e+02 1.628200e+01 \n", - "kpfmet.FF_SRC 1.957500e+01 2.405370e+02 1.950000e+01 \n", - "kpfmet.GREEN_CAMERA_BOTTOM 1.677540e+01 2.061350e+02 1.677400e+01 \n", - "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682040e+01 2.066880e+02 1.681900e+01 \n", - "kpfmet.GREEN_CAMERA_ECHELLE 1.671060e+01 2.053390e+02 1.670900e+01 \n", - "kpfmet.GREEN_CAMERA_TOP 1.661770e+01 2.041970e+02 1.661700e+01 \n", - "kpfmet.GREEN_GRISM_TOP 1.639170e+01 2.014200e+02 1.639000e+01 \n", - "kpfmet.GREEN_LN2_FLANGE 9.791590e+00 1.203190e+02 9.746000e+00 \n", - "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689680e+01 2.076270e+02 1.689600e+01 \n", - "kpfmet.RED_CAMERA_BOTTOM 1.630110e+01 2.003070e+02 1.630000e+01 \n", - "kpfmet.RED_CAMERA_COLLIMATOR 1.664290e+01 2.045070e+02 1.664200e+01 \n", - "kpfmet.RED_CAMERA_ECHELLE 1.659400e+01 2.039060e+02 1.659300e+01 \n", - "kpfmet.RED_CAMERA_TOP 1.654670e+01 2.033240e+02 1.654500e+01 \n", - "kpfmet.RED_GRISM_TOP 1.636990e+01 2.011530e+02 1.636900e+01 \n", - "kpfmet.RED_LN2_FLANGE 6.245890e+00 7.675000e+01 6.199000e+00 \n", - "kpfmet.REFORMATTER 1.660480e+01 2.040380e+02 1.660400e+01 \n", - "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", - "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747050e+01 2.146770e+02 1.746900e+01 \n", - "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", - "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 2.445300e+02 1.990000e+01 \n", - "kpfmet.TEMP 1.968480e+01 2.418860e+02 1.967300e+01 \n", - "kpfmet.TH_DAILY 2.000000e+01 2.457590e+02 2.000000e+01 \n", - "kpfmet.TH_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", - "kpfmet.U_DAILY 2.010000e+01 2.469880e+02 2.010000e+01 \n", - "kpfmet.U_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", - "kpfgreen.BPLANE_TEMP 3.582320e+01 4.401930e+02 3.581200e+01 \n", - "kpfgreen.BRD10_DRVR_T 4.050370e+01 4.977070e+02 4.050000e+01 \n", - "kpfgreen.BRD11_DRVR_T 3.875000e+01 4.761580e+02 3.875000e+01 \n", - "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 4.270060e+02 3.475000e+01 \n", - "kpfgreen.BRD1_HTRX_T 3.184100e+01 3.912610e+02 3.181200e+01 \n", - "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "kpfgreen.BRD3_LVDS_T 3.342310e+01 4.107010e+02 3.337500e+01 \n", - "kpfgreen.BRD4_DRVR_T 3.719820e+01 4.570890e+02 3.718700e+01 \n", - "kpfgreen.BRD5_AD_T 3.381530e+01 4.155200e+02 3.381200e+01 \n", - "kpfgreen.BRD7_HTRX_T 3.784640e+01 4.650540e+02 3.781200e+01 \n", - "kpfgreen.BRD9_HVXBIAS_T 3.627570e+01 4.457540e+02 3.625000e+01 \n", - "kpfgreen.CF_BASE_2WT -1.270330e+02 1.560970e+03 -1.270340e+02 \n", - "kpfgreen.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfgreen.CF_BASE_TRG -1.275000e+02 1.566710e+03 -1.275000e+02 \n", - "kpfgreen.CF_TIP_T -1.280000e+02 1.572860e+03 -1.280010e+02 \n", - "kpfgreen.CF_TIP_TRG -1.280000e+02 1.572860e+03 -1.280000e+02 \n", - "kpfgreen.COL_PRESS 4.000000e-08 4.915170e-07 4.000000e-08 \n", - "kpfgreen.CRYOBODY_T 1.899600e+01 2.334220e+02 1.899200e+01 \n", - "kpfgreen.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", - "kpfgreen.CURRTEMP -1.400000e+02 1.720310e+03 -1.400020e+02 \n", - "kpfgreen.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", - "kpfgreen.KPF_CCD_T -1.010900e+02 1.242190e+03 -1.010920e+02 \n", - "kpfgreen.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000020e+02 \n", - "kpfgreen.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", - "kpfgreen.TEMPSET -1.400000e+02 1.720310e+03 -1.400000e+02 \n", - "kpfred.BPLANE_TEMP 4.380440e+01 5.382650e+02 4.375000e+01 \n", - "kpfred.BRD10_DRVR_T 4.811190e+01 5.911970e+02 4.806200e+01 \n", - "kpfred.BRD11_DRVR_T 4.735680e+01 5.819170e+02 4.731200e+01 \n", - "kpfred.BRD12_LVXBIAS_T 4.354730e+01 5.351070e+02 4.343700e+01 \n", - "kpfred.BRD1_HTRX_T 4.136050e+01 5.082350e+02 4.131200e+01 \n", - "kpfred.BRD2_XVBIAS_T 4.314250e+01 5.301330e+02 4.312500e+01 \n", - "kpfred.BRD3_LVDS_T 4.298010e+01 5.281370e+02 4.293700e+01 \n", - "kpfred.BRD4_DRVR_T 4.606210e+01 5.660080e+02 4.600000e+01 \n", - "kpfred.BRD5_AD_T 4.077440e+01 5.010340e+02 4.068700e+01 \n", - "kpfred.BRD7_HTRX_T 4.536200e+01 5.574060e+02 4.531200e+01 \n", - "kpfred.BRD9_HVXBIAS_T 4.526700e+01 5.562380e+02 4.518700e+01 \n", - "kpfred.CF_BASE_2WT -1.257290e+02 1.544940e+03 -1.257300e+02 \n", - "kpfred.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", - "kpfred.CF_BASE_TRG -1.257500e+02 1.545210e+03 -1.257500e+02 \n", - "kpfred.CF_TIP_T -1.272500e+02 1.563640e+03 -1.272510e+02 \n", - "kpfred.CF_TIP_TRG -1.272500e+02 1.563640e+03 -1.272500e+02 \n", - "kpfred.COL_PRESS 3.715130e-08 4.565340e-07 3.700000e-08 \n", - "kpfred.CRYOBODY_T 1.899880e+01 2.334560e+02 1.899700e+01 \n", - "kpfred.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", - "kpfred.CURRTEMP -1.405000e+02 1.726450e+03 -1.405010e+02 \n", - "kpfred.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", - "kpfred.KPF_CCD_T -1.008710e+02 1.239500e+03 -1.008720e+02 \n", - "kpfred.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000030e+02 \n", - "kpfred.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", - "kpfred.TEMPSET -1.405000e+02 1.726460e+03 -1.405000e+02 \n", - "kpfexpose.BENCH_C 1.522700e+01 1.871100e+02 1.510000e+01 \n", - "kpfexpose.CAMBARREL_C 1.602630e+01 1.969330e+02 1.580000e+01 \n", - "kpfexpose.DET_XTRN_C 2.017700e+01 2.479350e+02 2.000000e+01 \n", - "kpfexpose.ECHELLE_C 1.376320e+01 1.691240e+02 1.350000e+01 \n", - "kpfexpose.ENCLOSURE_C 1.863160e+01 2.289460e+02 1.850000e+01 \n", - "kpfexpose.RACK_AIR_C 1.857040e+01 2.281930e+02 1.840000e+01 \n", - "kpfvac.PUMP_TEMP 3.900000e+01 4.792300e+02 3.900000e+01 \n", - "kpf_hk.COOLTARG -6.000000e+01 7.372760e+02 -6.000000e+01 \n", - "kpf_hk.CURRTEMP -5.978000e+01 7.345730e+02 -5.978000e+01 \n", - "kpfgreen.COL_CURR 1.100000e-06 1.351670e-05 1.100000e-06 \n", - "kpfgreen.ECH_CURR 8.910000e-11 1.094860e-09 8.910000e-11 \n", - "kpfred.COL_CURR 9.986180e-07 1.227110e-05 9.800000e-07 \n", - "kpfred.ECH_CURR 5.600000e-12 6.881240e-11 5.600000e-12 \n", - "kpfcal.IRFLUX 9.999000e+03 1.228670e+05 9.999000e+03 \n", - "kpfcal.VISFLUX 2.553950e+01 3.138880e+02 2.500000e+01 \n", - "kpfcal.BLUECUTIACT 3.326320e-02 4.111620e-01 2.300000e-02 \n", - "kpfmot.AGITSPD -3.900470e+03 4.822300e+04 -4.699000e+03 \n", - "kpfmot.AGITTOR -1.102970e+00 1.590140e+01 -2.271000e+00 \n", - "kpfmot.AGITAMBI_T 2.580130e+01 3.170470e+02 2.548000e+01 \n", - "kpfmot.AGITMOT_T 2.606950e+01 3.203420e+02 2.582000e+01 \n", - "kpfpower.OUTLET_A1_Amps 5.070390e+01 6.235380e+02 2.700000e+01 \n", - "\n", - " max units \\\n", - "keyword \n", - "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597600e+01 degC \n", - "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.648000e+01 degC \n", - "kpfmet.BENCH_BOTTOM_DCUT 1.624400e+01 degC \n", - "kpfmet.BENCH_BOTTOM_ECHELLE 1.638700e+01 degC \n", - "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588700e+01 degC \n", - "kpfmet.BENCH_TOP_COLL 1.657900e+01 degC \n", - "kpfmet.BENCH_TOP_DCUT 1.628100e+01 degC \n", - "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629300e+01 degC \n", - "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736600e+01 degC \n", - "kpfmet.CALEM_SCMBLR_FIBER_END 1.721500e+01 degC \n", - "kpfmet.CAL_BENCH 2.020000e+01 degC \n", - "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 degC \n", - "kpfmet.CAL_BENCH_BOT 2.360000e+01 degC \n", - "kpfmet.CAL_BENCH_ENCL_AIR 2.020000e+01 degC \n", - "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 degC \n", - "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 degC \n", - "kpfmet.CAL_RACK_TOP 2.310000e+01 degC \n", - "kpfmet.CHAMBER_EXT_BOTTOM 1.570400e+01 degC \n", - "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 degC \n", - "kpfmet.CRYOSTAT_G1 -2.731500e+02 degC \n", - "kpfmet.CRYOSTAT_G2 -2.731500e+02 degC \n", - "kpfmet.CRYOSTAT_G3 1.668500e+01 degC \n", - "kpfmet.CRYOSTAT_R1 1.610300e+01 degC \n", - "kpfmet.CRYOSTAT_R2 -2.731500e+02 degC \n", - "kpfmet.CRYOSTAT_R3 1.564200e+01 degC \n", - "kpfmet.ECHELLE_BOTTOM 1.641200e+01 degC \n", - "kpfmet.ECHELLE_TOP 1.628400e+01 degC \n", - "kpfmet.FF_SRC 1.960000e+01 degC \n", - "kpfmet.GREEN_CAMERA_BOTTOM 1.677600e+01 degC \n", - "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682200e+01 degC \n", - "kpfmet.GREEN_CAMERA_ECHELLE 1.671200e+01 degC \n", - "kpfmet.GREEN_CAMERA_TOP 1.661900e+01 degC \n", - "kpfmet.GREEN_GRISM_TOP 1.639300e+01 degC \n", - "kpfmet.GREEN_LN2_FLANGE 9.848000e+00 degC \n", - "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689800e+01 degC \n", - "kpfmet.RED_CAMERA_BOTTOM 1.630300e+01 degC \n", - "kpfmet.RED_CAMERA_COLLIMATOR 1.664400e+01 degC \n", - "kpfmet.RED_CAMERA_ECHELLE 1.659500e+01 degC \n", - "kpfmet.RED_CAMERA_TOP 1.654800e+01 degC \n", - "kpfmet.RED_GRISM_TOP 1.637200e+01 degC \n", - "kpfmet.RED_LN2_FLANGE 6.292000e+00 degC \n", - "kpfmet.REFORMATTER 1.660600e+01 degC \n", - "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 degC \n", - "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 degC \n", - "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747200e+01 degC \n", - "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 degC \n", - "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 degC \n", - "kpfmet.TEMP 1.969700e+01 degC \n", - "kpfmet.TH_DAILY 2.000000e+01 degC \n", - "kpfmet.TH_GOLD 1.990000e+01 degC \n", - "kpfmet.U_DAILY 2.010000e+01 degC \n", - "kpfmet.U_GOLD 1.990000e+01 degC \n", - "kpfgreen.BPLANE_TEMP 3.587500e+01 degC \n", - "kpfgreen.BRD10_DRVR_T 4.056200e+01 degC \n", - "kpfgreen.BRD11_DRVR_T 3.875000e+01 degC \n", - "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 degC \n", - "kpfgreen.BRD1_HTRX_T 3.187500e+01 degC \n", - "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 degC \n", - "kpfgreen.BRD3_LVDS_T 3.343700e+01 degC \n", - "kpfgreen.BRD4_DRVR_T 3.725000e+01 degC \n", - "kpfgreen.BRD5_AD_T 3.387500e+01 degC \n", - "kpfgreen.BRD7_HTRX_T 3.787500e+01 degC \n", - "kpfgreen.BRD9_HVXBIAS_T 3.631200e+01 degC \n", - "kpfgreen.CF_BASE_2WT -1.270310e+02 degC \n", - "kpfgreen.CF_BASE_T -2.731500e+02 degC \n", - "kpfgreen.CF_BASE_TRG -1.275000e+02 degC \n", - "kpfgreen.CF_TIP_T -1.279990e+02 degC \n", - "kpfgreen.CF_TIP_TRG -1.280000e+02 degC \n", - "kpfgreen.COL_PRESS 4.000000e-08 Torr \n", - "kpfgreen.CRYOBODY_T 1.900000e+01 degC \n", - "kpfgreen.CRYOBODY_TRG 1.900000e+01 degC \n", - "kpfgreen.CURRTEMP -1.399970e+02 degC \n", - "kpfgreen.ECH_PRESS 1.000000e-11 Torr \n", - "kpfgreen.KPF_CCD_T -1.010890e+02 degC \n", - "kpfgreen.STA_CCD_T -9.999800e+01 degC \n", - "kpfgreen.STA_CCD_TRG -1.000000e+02 degC \n", - "kpfgreen.TEMPSET -1.400000e+02 degC \n", - "kpfred.BPLANE_TEMP 4.387500e+01 degC \n", - "kpfred.BRD10_DRVR_T 4.818700e+01 degC \n", - "kpfred.BRD11_DRVR_T 4.737500e+01 degC \n", - "kpfred.BRD12_LVXBIAS_T 4.362500e+01 degC \n", - "kpfred.BRD1_HTRX_T 4.137500e+01 degC \n", - "kpfred.BRD2_XVBIAS_T 4.318700e+01 degC \n", - "kpfred.BRD3_LVDS_T 4.300000e+01 degC \n", - "kpfred.BRD4_DRVR_T 4.612500e+01 degC \n", - "kpfred.BRD5_AD_T 4.081200e+01 degC \n", - "kpfred.BRD7_HTRX_T 4.543700e+01 degC \n", - "kpfred.BRD9_HVXBIAS_T 4.531200e+01 degC \n", - "kpfred.CF_BASE_2WT -1.257260e+02 degC \n", - "kpfred.CF_BASE_T -2.731500e+02 degC \n", - "kpfred.CF_BASE_TRG -1.257500e+02 degC \n", - "kpfred.CF_TIP_T -1.272480e+02 degC \n", - "kpfred.CF_TIP_TRG -1.272500e+02 degC \n", - "kpfred.COL_PRESS 3.800000e-08 Torr \n", - "kpfred.CRYOBODY_T 1.900100e+01 degC \n", - "kpfred.CRYOBODY_TRG 1.900000e+01 degC \n", - "kpfred.CURRTEMP -1.404980e+02 degC \n", - "kpfred.ECH_PRESS 1.000000e-11 Torr \n", - "kpfred.KPF_CCD_T -1.008700e+02 degC \n", - "kpfred.STA_CCD_T -9.999900e+01 degC \n", - "kpfred.STA_CCD_TRG -1.000000e+02 degC \n", - "kpfred.TEMPSET -1.405000e+02 degC \n", - "kpfexpose.BENCH_C 1.540000e+01 degC \n", - "kpfexpose.CAMBARREL_C 1.620000e+01 degC \n", - "kpfexpose.DET_XTRN_C 2.040000e+01 degC \n", - "kpfexpose.ECHELLE_C 1.400000e+01 degC \n", - "kpfexpose.ENCLOSURE_C 1.880000e+01 degC \n", - "kpfexpose.RACK_AIR_C 1.870000e+01 degC \n", - "kpfvac.PUMP_TEMP 3.900000e+01 degC \n", - "kpf_hk.COOLTARG -6.000000e+01 degC \n", - "kpf_hk.CURRTEMP -5.978000e+01 degC \n", - "kpfgreen.COL_CURR 1.100000e-06 A \n", - "kpfgreen.ECH_CURR 8.910000e-11 A \n", - "kpfred.COL_CURR 1.000000e-06 A \n", - "kpfred.ECH_CURR 5.600000e-12 A \n", - "kpfcal.IRFLUX 9.999000e+03 Counts \n", - "kpfcal.VISFLUX 2.600000e+01 Counts \n", - "kpfcal.BLUECUTIACT 5.300000e-02 A \n", - "kpfmot.AGITSPD -1.880000e+03 motor_counts/s \n", - "kpfmot.AGITTOR 1.440000e-01 V \n", - "kpfmot.AGITAMBI_T 2.603000e+01 degC \n", - "kpfmot.AGITMOT_T 2.622000e+01 degC \n", - "kpfpower.OUTLET_A1_Amps 5.300000e+01 milliamps \n", - "\n", - " description \n", - "keyword \n", - "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS Bench Bottom Between Cameras C2 c- double degC... \n", - "kpfmet.BENCH_BOTTOM_COLLIMATOR Bench Bottom Coll C3 c- double degC {%.3f} \n", - "kpfmet.BENCH_BOTTOM_DCUT Bench Bottom D-cut C4 c- double degC {%.3f} \n", - "kpfmet.BENCH_BOTTOM_ECHELLE Bench Bottom Echelle Cam B c- double degC {%.3f} \n", - "kpfmet.BENCH_TOP_BETWEEN_CAMERAS Bench Top Between Cameras D4 c- double degC {%... \n", - "kpfmet.BENCH_TOP_COLL Bench Top Coll D5 c- double degC {%.3f} \n", - "kpfmet.BENCH_TOP_DCUT Bench Top D-cut D3 c- double degC {%.3f} \n", - "kpfmet.BENCH_TOP_ECHELLE_CAM Bench Top Echelle Cam D1 c- double degC {%.3f} \n", - "kpfmet.CALEM_SCMBLR_CHMBR_END Cal EM Scrammbler Chamber End C1 c- double deg... \n", - "kpfmet.CALEM_SCMBLR_FIBER_END Cal EM Scrambler Fiber End D1 c- double degC {... \n", - "kpfmet.CAL_BENCH Cal_Bench temperature c- double degC {%.1f} \n", - "kpfmet.CAL_BENCH_BB_SRC CAL_Bench_BB_Src temperature c- double degC {%... \n", - "kpfmet.CAL_BENCH_BOT Cal_Bench_Bot temperature c- double degC {%.1f} \n", - "kpfmet.CAL_BENCH_ENCL_AIR Cal_Bench_Encl_Air temperature c- double degC ... \n", - "kpfmet.CAL_BENCH_OCT_MOT Cal_Bench_Oct_Mot temperature c- double degC {... \n", - "kpfmet.CAL_BENCH_TRANS_STG_MOT Cal_Bench_Trans_Stg_Mot temperature c- double ... \n", - "kpfmet.CAL_RACK_TOP Cal_Rack_Top temperature c- double degC {%.1f} \n", - "kpfmet.CHAMBER_EXT_BOTTOM Chamber Exterior Bottom B c- double degC {%.3f} \n", - "kpfmet.CHAMBER_EXT_TOP Chamber Exterior Top C1 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_G1 Within cryostat green D2 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_G2 Within cryostat green D3 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_G3 Within cryostat green D4 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_R1 Within Cryostat red D2 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_R2 Within Cryostat red D3 c- double degC {%.3f} \n", - "kpfmet.CRYOSTAT_R3 Within Cryostat red D4 c- double degC {%.3f} \n", - "kpfmet.ECHELLE_BOTTOM Echelle Bottom D1 c- double degC {%.3f} \n", - "kpfmet.ECHELLE_TOP Echelle Top C1 c- double degC {%.3f} \n", - "kpfmet.FF_SRC FF_Src temperature c- double degC {%.1f} \n", - "kpfmet.GREEN_CAMERA_BOTTOM Green Camera Bottom C3 c- double degC {%.3f} \n", - "kpfmet.GREEN_CAMERA_COLLIMATOR Green Camera Collimator C4 c- double degC {%.3f} \n", - "kpfmet.GREEN_CAMERA_ECHELLE Green Camera Echelle D5 c- double degC {%.3f} \n", - "kpfmet.GREEN_CAMERA_TOP Green Camera Top C2 c- double degC {%.3f} \n", - "kpfmet.GREEN_GRISM_TOP Green Grism Top C5 c- double degC {%.3f} \n", - "kpfmet.GREEN_LN2_FLANGE Green LN2 Flange A c- double degC {%.3f} \n", - "kpfmet.PRIMARY_COLLIMATOR_TOP Primary Col Top D2 c- double degC {%.3f} \n", - "kpfmet.RED_CAMERA_BOTTOM Red Camera Bottom D5 c- double degC {%.3f} \n", - "kpfmet.RED_CAMERA_COLLIMATOR Red Camera Coll C3 c- double degC {%.3f} \n", - "kpfmet.RED_CAMERA_ECHELLE Red Camera Ech C4 c- double degC {%.3f} \n", - "kpfmet.RED_CAMERA_TOP Red Camera Top C5 c- double degC {%.3f} \n", - "kpfmet.RED_GRISM_TOP Red Grism Top C2 c- double degC {%.3f} \n", - "kpfmet.RED_LN2_FLANGE Red LN2 Flange D1 c- double degC {%.3f} \n", - "kpfmet.REFORMATTER Reformatter A c- double degC {%.3f} \n", - "kpfmet.SCIENCE_CAL_FIBER_STG Science_Cal_Fiber_Stg temperature c- double de... \n", - "kpfmet.SCISKY_SCMBLR_CHMBR_END SciSky Scrambler Chamber End A c- double degC ... \n", - "kpfmet.SCISKY_SCMBLR_FIBER_END SciSky Scrammbler Fiber End B c- double degC {... \n", - "kpfmet.SIMCAL_FIBER_STG SimCal_Fiber_Stg temperature c- double degC {%... \n", - "kpfmet.SKYCAL_FIBER_STG SkyCal_Fiber_Stg temperature c- double degC {%... \n", - "kpfmet.TEMP Vaisala Temperature c- double degC {%.3f} \n", - "kpfmet.TH_DAILY Th_daily temperature c- double degC {%.1f} \n", - "kpfmet.TH_GOLD Th_gold temperature c- double degC {%.1f} \n", - "kpfmet.U_DAILY U_daily temperature c- double degC {%.1f} \n", - "kpfmet.U_GOLD U_gold temperature c- double degC {%.1f} \n", - "kpfgreen.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", - "kpfgreen.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", - "kpfgreen.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", - "kpfgreen.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", - "kpfgreen.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", - "kpfgreen.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", - "kpfgreen.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", - "kpfgreen.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", - "kpfgreen.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", - "kpfgreen.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", - "kpfgreen.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", - "kpfgreen.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", - "kpfgreen.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", - "kpfgreen.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", - "kpfgreen.CF_TIP_T tip cold finger c- double degC {%.3f} \n", - "kpfgreen.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", - "kpfgreen.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", - "kpfgreen.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", - "kpfgreen.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", - "kpfgreen.CURRTEMP Current cold head temperature c- double degC {... \n", - "kpfgreen.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", - "kpfgreen.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", - "kpfgreen.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", - "kpfgreen.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", - "kpfgreen.TEMPSET Set point for the cold head temperature c2 dou... \n", - "kpfred.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", - "kpfred.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", - "kpfred.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", - "kpfred.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", - "kpfred.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", - "kpfred.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", - "kpfred.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", - "kpfred.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", - "kpfred.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", - "kpfred.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", - "kpfred.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", - "kpfred.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", - "kpfred.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", - "kpfred.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", - "kpfred.CF_TIP_T tip cold finger c- double degC {%.3f} \n", - "kpfred.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", - "kpfred.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", - "kpfred.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", - "kpfred.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", - "kpfred.CURRTEMP Current cold head temperature c- double degC {... \n", - "kpfred.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", - "kpfred.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", - "kpfred.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", - "kpfred.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", - "kpfred.TEMPSET Set point for the cold head temperature c2 dou... \n", - "kpfexpose.BENCH_C rtd bench c- double degC {%.1f} { -100.0 .. 10... \n", - "kpfexpose.CAMBARREL_C rtd camera barrel c- double degC {%.1f} { -100... \n", - "kpfexpose.DET_XTRN_C rtd detector extermal c- double degC {%.1f} { ... \n", - "kpfexpose.ECHELLE_C rtd echelle c- double degC {%.1f} { -100.0 .. ... \n", - "kpfexpose.ENCLOSURE_C rtd enclosure c- double degC {%.1f} { -100.0 .... \n", - "kpfexpose.RACK_AIR_C rtd rack air c- double degC {%.1f} { -100.0 ..... \n", - "kpfvac.PUMP_TEMP Motor temperature c- double degC {%.2f} \n", - "kpf_hk.COOLTARG temperature target c2 int degC \n", - "kpf_hk.CURRTEMP current temperature c- double degC {%.2f} \n", - "kpfgreen.COL_CURR Current ion pump current c- double A {%.3e} \n", - "kpfgreen.ECH_CURR Current ion pump current c- double A {%.3e} \n", - "kpfred.COL_CURR Current ion pump current c- double A {%.3e} \n", - "kpfred.ECH_CURR Current ion pump current c- double A {%.3e} \n", - "kpfcal.IRFLUX LFC Fiberlock IR Intensity c- int Counts {%d} \n", - "kpfcal.VISFLUX LFC Fiberlock Vis Intensity c- int Counts {%d} \n", - "kpfcal.BLUECUTIACT Blue cut amplifier 0 measured current c- doubl... \n", - "kpfmot.AGITSPD agit raw velocity c2 int motor counts/s { -750... \n", - "kpfmot.AGITTOR agit motor torque c- double V {%.3f} \n", - "kpfmot.AGITAMBI_T Agitator ambient temperature c- double degC {%... \n", - "kpfmot.AGITMOT_T Agitator motor temperature c- double degC {%.2... \n", - "kpfpower.OUTLET_A1_Amps Outlet A1 current amperage c- int milliamps " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n", - " display(df_telemetry)" - ] - }, - { - "cell_type": "markdown", - "id": "d587d5b6", - "metadata": {}, - "source": [ - "Print the average and standard deviation of one keyword. (As of June 21, 2023, there's a problem with the standard deviation. This will probably get fixed soon in the L0 Assembly code.)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "989f592d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Red CCD Temperature = -100.0 +/- 1228.8 degC\n" - ] - } - ], - "source": [ - "print('Red CCD Temperature = ', df_telemetry.at['kpfred.STA_CCD_T', 'average'], '+/-', df_telemetry.at['kpfred.STA_CCD_T', 'stddev'], df_telemetry.at['kpfred.STA_CCD_T', 'units'])" - ] - }, - { - "cell_type": "markdown", - "id": "81db59c1", - "metadata": {}, - "source": [ - "# Guider Image\n", - "The guide camera images are from a CRED-2 camera with 0.056 arcsec pixels in a 950-1200nm passband (approximately z+J band). First, we will fit the image with a Moffat function (assuming a point source)." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "52e2ffef", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "amplitude = 3228.7076865733984\n", - "seeing = 0.4789702532980194 arcsec\n", - "beta = 2.5682683730057922\n", - "x0 = 337.54450870956305 pixels\n", - "y0 = 262.21891711604087 pixels\n" - ] - } - ], - "source": [ - "from scipy.optimize import curve_fit\n", - "\n", - "guider_im = D2['GUIDER_AVG'].data - np.median(D2['GUIDER_AVG'].data)\n", - "guider_header = D2['GUIDER_AVG'].header\n", - "pixel_scale = 0.056 # arcsec per pixel for the CRED-2 imager on the KPF FIU\n", - "\n", - "def moffat_2D(xy, amplitude, x0, y0, alpha, beta):\n", - " x, y = xy\n", - " return amplitude * (1 + ((x - x0) ** 2 + (y - y0) ** 2) / alpha ** 2) ** -beta\n", - "\n", - "x = np.arange(guider_im.shape[1])\n", - "y = np.arange(guider_im.shape[0])\n", - "X, Y = np.meshgrid(x, y)\n", - "x_flat = X.flatten()\n", - "y_flat = Y.flatten()\n", - "image_data_flat = guider_im.flatten()\n", - "p0 = [1, guider_header['CRPIX1'], guider_header['CRPIX2'], 5/0.056, 2.5] # Initial guess for the parameters\n", - "#p0 = [1, guider_im.shape[1] / 2, guider_im.shape[0] / 2, 2/0.056, 2] # Initial guess for the parameters\n", - "popt, pcov = curve_fit(moffat_2D, (x_flat, y_flat), image_data_flat, p0=p0)\n", - "amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit = popt\n", - "print('amplitude = ' + str(amplitude_fit))\n", - "print('seeing = ' + str(alpha_fit*0.056) + ' arcsec')\n", - "print('beta = ' + str(beta_fit))\n", - "print('x0 = ' + str(x0_fit) + ' pixels')\n", - "print('y0 = ' + str(y0_fit) + ' pixels')\n", - "\n", - "image_fit = moffat_2D((X, Y), amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit)\n", - "resid_im = guider_im - image_fit" - ] - }, - { - "cell_type": "markdown", - "id": "9ad8db27", - "metadata": {}, - "source": [ - "# Plot Guider Image and Model Fit" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "bf83beaf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:27: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:48: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:67: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", - "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.ticker as ticker\n", - "\n", - "# Plot the original image and residuals\n", - "guider_im_zoom = guider_im[255-38:255+38, 320-38:320+38]\n", - "resid_im_zoom = resid_im[255-38:255+38, 320-38:320+38]\n", - "#subx0 = D2['GUIDER_AVG'].header['SUBX0']\n", - "#subx1 = D2['GUIDER_AVG'].header['SUBX1']\n", - "#suby0 = D2['GUIDER_AVG'].header['SUBY0']\n", - "#suby1 = D2['GUIDER_AVG'].header['SUBY1']\n", - "#guider_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", - "#resid_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", - "\n", - "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", - "\n", - "# Left panel - full image\n", - "im1 = axs[0].imshow(guider_im, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", - "axs[1].set_aspect(640/512)\n", - "image_size_pixels = guider_im.shape\n", - "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", - "x_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", - "axs[0].xaxis.set_major_locator(x_tick_locator)\n", - "y_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", - "axs[0].yaxis.set_major_locator(y_tick_locator)\n", - "xticks = axs[0].get_xticks()\n", - "yticks = axs[0].get_yticks()\n", - "axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", - "axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", - "axs[0].set_xlabel('Arcseconds', fontsize=14)\n", - "axs[0].set_ylabel('Arcseconds', fontsize=14)\n", - "axs[0].set_title('Guider Image - '+str(ObsID)+'\\n' + \n", - " f\"{alpha_fit*pixel_scale:.2f}\" + '\" seeing (z+J-band)\\n' +\n", - " f\"{alpha_fit*pixel_scale*(1100/550)**0.2:.2f}\" + '\" seeing (V-band, scaled)', fontsize=14)\n", - "axs[0].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", - "cbar1 = plt.colorbar(im1, ax=axs[0], shrink=0.6)\n", - "#cbar1.set_label('Intensity', fontsize=14)\n", - "\n", - "# Middle panel - zoomed image\n", - "im2 = axs[1].imshow(guider_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", - "axs[1].set_aspect('equal')\n", - "image_size_pixels = guider_im_zoom.shape\n", - "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", - "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", - "axs[1].xaxis.set_major_locator(x_tick_locator)\n", - "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", - "axs[1].yaxis.set_major_locator(y_tick_locator)\n", - "xticks = axs[1].get_xticks()\n", - "yticks = axs[1].get_yticks()\n", - "axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", - "axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", - "axs[1].set_xlabel('Arcseconds', fontsize=14)\n", - "axs[1].set_ylabel('Arcseconds', fontsize=14)\n", - "axs[1].set_title('Guider Image (zoomed in)', fontsize=14)\n", - "axs[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", - "cbar2 = plt.colorbar(im2, ax=axs[1], shrink=0.6)\n", - "#cbar2.set_label('Intensity', fontsize=14)\n", - "\n", - "# Right panel - zoomed image of residuals to model\n", - "im2 = axs[2].imshow(resid_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", - "image_size_pixels = guider_im_zoom.shape\n", - "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", - "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", - "axs[2].xaxis.set_major_locator(x_tick_locator)\n", - "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", - "axs[2].yaxis.set_major_locator(y_tick_locator)\n", - "xticks = axs[2].get_xticks()\n", - "yticks = axs[2].get_yticks()\n", - "axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", - "axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", - "axs[2].set_xlabel('Arcseconds', fontsize=14)\n", - "axs[2].set_ylabel('Arcseconds', fontsize=14)\n", - "axs[2].set_title('Residuals to Moffat Function Model', fontsize=14)\n", - "axs[2].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", - "cbar3 = plt.colorbar(im2, ax=axs[2], shrink=0.6)\n", - "#cbar3.set_label('Intensity', fontsize=14)\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "#plt.savefig('guider_image.png',dpi=144)\n", - "plt.close()" - ] - }, - { - "cell_type": "markdown", - "id": "1fee8abf", - "metadata": {}, - "source": [ - "# Examine and Plot Guider Performance Time Series" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "92c05f6c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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111.686472e+09337.876262.3160.025459-0.007844457186.0104383.0...000.0-999.0-999.00.00.00.00.0-999.0
221.686472e+09337.876262.316-0.018932-0.025774459704.0104228.0...000.0-999.0-999.00.00.00.00.0-999.0
331.686472e+09337.876262.3160.015672-0.013233458274.0104375.0...000.0-999.0-999.00.00.00.00.0-999.0
441.686472e+09337.876262.3160.0605600.035182456977.0104927.0...000.0-999.0-999.00.00.00.00.0-999.0
..................................................................
17260172601.686472e+09337.778262.2380.047377-0.020259459291.0108911.0...000.0-999.0-999.00.00.00.00.0-999.0
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0 4927.0 ... \n", - "... ... ... ... ... ... \n", - "17260 459291.0 1 0 8911.0 ... \n", - "17261 458436.0 1 0 8987.0 ... \n", - "17262 457964.0 1 0 9620.0 ... \n", - "17263 458240.0 1 0 10218.0 ... \n", - "17264 460913.0 1 0 8697.0 ... \n", - "\n", - " object3_tnpix object3_npix object3_peak object3_x object3_y \\\n", - "0 0 0 0.0 -999.0 -999.0 \n", - "1 0 0 0.0 -999.0 -999.0 \n", - "2 0 0 0.0 -999.0 -999.0 \n", - "3 0 0 0.0 -999.0 -999.0 \n", - "4 0 0 0.0 -999.0 -999.0 \n", - "... ... ... ... ... ... \n", - "17260 0 0 0.0 -999.0 -999.0 \n", - "17261 0 0 0.0 -999.0 -999.0 \n", - "17262 0 0 0.0 -999.0 -999.0 \n", - "17263 0 0 0.0 -999.0 -999.0 \n", - "17264 0 0 0.0 -999.0 -999.0 \n", - "\n", - " object3_a object3_b object3_theta object3_errxy object3_thresh \n", - "0 0.0 0.0 0.0 0.0 -999.0 \n", - "1 0.0 0.0 0.0 0.0 -999.0 \n", - "2 0.0 0.0 0.0 0.0 -999.0 \n", - "3 0.0 0.0 0.0 0.0 -999.0 \n", - "4 0.0 0.0 0.0 0.0 -999.0 \n", - "... ... ... ... ... ... \n", - "17260 0.0 0.0 0.0 0.0 -999.0 \n", - "17261 0.0 0.0 0.0 0.0 -999.0 \n", - "17262 0.0 0.0 0.0 0.0 -999.0 \n", - "17263 0.0 0.0 0.0 0.0 -999.0 \n", - "17264 0.0 0.0 0.0 0.0 -999.0 \n", - "\n", - "[17265 rows x 39 columns]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_GUIDER = Table.read(L0_file, format='fits',hdu='guider_cube_origins').to_pandas()\n", - "df_GUIDER" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a0868e98", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(0, 10, 100)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)\n", - "\n", - "# Create the figure and subplots\n", - "fig, axes = plt.subplots(1, 2, figsize=(16, 4), gridspec_kw={'width_ratios': [2, 1]})\n", - "plt.style.use('seaborn-whitegrid')\n", - "\n", - "# Plot the data\n", - "im1 = axes[1].hist2d(df_GUIDER.command_x*pixel_scale*1000, \n", - " df_GUIDER.command_y*pixel_scale*1000, \n", - " bins=40, cmap='viridis')\n", - "axes[1].set_title(\"Guiding Corrections - RMS-2D = \" + f'{int(np.std((df_GUIDER.command_x*pixel_scale**2+df_GUIDER.command_y*pixel_scale**2)**0.5*1000)*10)/10}'+' mas', fontsize=14)\n", - "axes[1].set_xlabel('x (mas)', fontsize=14)\n", - "axes[1].set_ylabel('y (mas)', fontsize=14)\n", - "axes[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", - "cbar = plt.colorbar(im1[3])\n", - "cbar.set_label('Samples', fontsize=12)\n", - "\n", - "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_x*pixel_scale*1000, color='royalblue')\n", - "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_y*pixel_scale*1000, color='orange')\n", - "axes[0].set_title(\"Guiding Correction Time Series\", fontsize=14)\n", - "axes[0].set_xlabel(\"Time (sec)\", fontsize=14)\n", - "axes[0].set_ylabel(\"Guiding Correction (mas)\", fontsize=14)\n", - "axes[0].legend(['X - RMS = ' + f'{int(np.std(df_GUIDER.command_x*pixel_scale*1000)*10)/10}' + ' mas', \n", - " 'Y - RMS = ' + f'{int(np.std(df_GUIDER.command_y*pixel_scale*1000)*10)/10}' + ' mas'], \n", - " fontsize=12, loc='best') \n", - "\n", - "# Set the font size of tick mark labels\n", - "axes[0].tick_params(axis='both', which='major', labelsize=14)\n", - "axes[1].tick_params(axis='both', which='major', labelsize=14)\n", - "\n", - "# Display the plots\n", - "plt.show()\n", - "#plt.savefig('guider_corrections.png',dpi=144)" - ] - }, - { - "cell_type": "markdown", - "id": "6e449f79", - "metadata": {}, - "source": [ - "# Plot Flux Time from Guider and Exposure Meter Series" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e3f34337", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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bb76Bo6MjPv/8c5w5cwbx8fF48uQJVqxYgYULF6JFixYASoQmIyMjbN68GfHx8dizZw+ePHkit86hQ4fiyZMn2Lx5M16/fo3ly5cjOTlZcn/s2LHYt28fjh8/jri4OGzevBlHjx6Ft7e3Un1mtG5Pnz7FixcvMG/ePKSlpSkV8c3MzAwDBgxASEgIHjx4gFu3bmHr1q0Vfu79+/dIS0uT+Zefnw+RSIQFCxagf//+6NKlC2bPno0NGzbg7du3ks9v3rwZt2/fxsOHDxESEoIBAwbA3Nwc/fr1g1gsxo8//ohXr17h0qVL2LRpE4YPHw4ul4vk5GQsXboU9+7dQ3x8PE6fPg0XFxdYW1ujSZMmeP78OU6ePImYmBj8+OOP5W6aO3ToAHd3d8yePRtPnz7F/fv3sWDBAoWatNIYGhqiffv22LRpk9zw32ZmZhg6dCiWLl2Kmzdv4tWrV5g7dy6eP38OLy8vACXhy1+/fo2srCwMGDAAQqFQEhTh+vXrWLJkicbMy5o0aYKcnBzs2LED8fHxWLVqFd6/fy+5v3fvXsTHx2PWrFmYPXs24uPjZXy7NI2qY/LVV1/h1q1b+P777xEVFYX4+HhcvHgRP/74I0aOHAmBQFDpcR4zZgw2btyIixcvIjY2FsHBwbhx4wa8vb1hYWGBgwcPYtOmTYiPj8etW7fw7NkzpUxuKRQKhYGaCFIolGqHkZER9u7di9DQUOzYsQOxsbHgcrlo1KgRli1bhgEDBgAo2UAvXboU69evx+HDh9GtWzeMGjVKSnBi8PLywi+//ILly5dj+/btkjDhDH369EFGRgY2b96M1NRUeHt7Y8uWLTJ+S4qYNm0afvjhBwwfPhxmZmbo2LEjAgMDFQp8Zfnxxx+xdOlSjBs3TuLgv2rVqnI/o8gnZ+HChRAKhYiPj8fOnTsBAEOGDMGRI0ewePFibN++HQAwaNAg/PDDD3j//j369euH+fPnAygROnbu3Illy5bB398fNjY2GDVqFCZPngwA+Pbbb5GTk4OpU6ciNzcXzZo1wy+//AIej4d27dph/PjxWLZsGbhcLkaPHl1utD0ej4etW7ciJCQEw4cPh6GhIbp37y4Jm14RXbt2let/xfDDDz9g1apVmDlzJgoLC+Hn54ddu3bByMgIABAYGIiVK1ciISEBmzZtws6dO7F8+XIMGjQIFhYWGDRoEGbOnKlUX1TF09MTc+fOxc6dO/HLL79g8ODB6Nu3L4AS00AmqiWTTHnGjBlYvXo1unfvDjc3N430qSxmZmYqjUnz5s2xb98+bN26FRMnTkRubi5cXV3x+eefS/zSVK2zLOPHj0d+fj6Cg4ORnZ2NBg0aYNeuXXB0dARQEuZ97dq12LFjBywtLTFixAgMGTKElfGgUCg1Aw6pyIaCQqFQKJQy1KtXD3v27MEnn3yi665QKBQKhaJXUBNBCoVCoVAoFAqFQmEJKmBRKBQKhUKhUCgUCktQE0EKhUKhUCgUCoVCYQmqwaJQKBQKhUKhUCgUlqjyUQQjIyN13QUKhUKhUCgUCoVSA2FSv5SmygtYgPwvRtEc0dHRSoeeprADHXPtQsdb+9Ax1z50zLUPHXPtQ8dcu9S08Vak6KEmghQKhUKhUCgUCoXCElTAolAoFAqFQqFQKBSWoAIWhUKhUCgUCoVCobAEFbAoFAqFQqFQKBQKhSWogEWhUCgUCoVCoVAoLEEFLAqFQqFQKBQKhUJhCSpgUSgUCoVCoVAoFApL6ETAEgqF6NevH27cuKGwzNOnTzFs2DA0a9YMgwcPRlRUlBZ7SKFQKBQKhUKhUCiqo3UBq7CwELNmzcKLFy8UlsnLy8OECRPQrFkzHDt2DC1atMBXX32FnJwcLfZUe4SFhcHLywtcLhdeXl4ICwvTdZcoFAqFQqFQKBSKGmhVwHr58iU+//xzxMXFlVsuPDwcfD4f8+bNQ+3atTF//nyYm5vj7NmzWuqp9ggLC8OkSZMQGxsLQghiY2MxadIkKmRRKBQKhUKhUChVEK0KWHfv3kX79u1x8ODBcss9fPgQfn5+4HJLusfhcODn54f79+9ro5taJSgoCHl5eVLX8vLyEBQUBEIICCEynxEWEeTkiVnrQ+l23ueIUCAUQyQmSM8qZq0NCoVC0Wcys0W4/jAPhULl5lZhUcm8WVQsO0dTKBQKpWZjoM3Ghg8frlS5tLQ01KpVS+qara0tnj59Krd8dHR0pfumKxRp8+Li4tB1ajwAYPn4XAgMgN/OG+LRm48/2Zbpuaz0Yd1RI7x6y8O6ybmY9YspAMDKTIysHC46NytCh8ZFcLL5uIkoKChQeswJAQgALqf8ciIRcPKmAD1aCGFmrO43qb6oMuaUykPHW/toc8w/5AN5BRzYWRLwuMDpm3xE3BVI7g/7tBBNvEUwMSQw4AH5hZCal4RFwMz/z5UAMGNwPnxc2Tv0UpYCIXDxHh/n7vKxcWpehfMsABy/zsebJB5mDSmgz7kOoGOufeiYaxc63iVoVcBSlvz8fAgEAqlrAoEAQqFQbvkGDRpoo1tKkZsvxvtcMVzsDPAsthD2VgawseQpLO/h4YHY2FiZ6wJTF8n//7DLVOY+ABhZ1kYtF4Hce8ogFhPciMrHq7fpACARrgAgK6dEe/jXQz7+esjH5a0eknvR0dFKj/m4pUmISSqCu6MBfl/korDc3/fycOl+OuLSTfDrD87qfJ1qjSpjrm1EIoKIW7no1c4UPGV2eGpACMGrhCLUcVf/eS9Ldq4IeQUEZsZcXL2fhz7tzST39Hm8qyuVHfOsDyJEPi1A11by50sAOH8rB/+9FuLPfz7683473BoRd99JlTv4tyEO/i392TpufMwdZYvIpwX45ViW1L0Nx4wxrJs5rMx5+PV4FvYsdIanMx8AkPlehFPXPmBUH0twWXw/CCGSQzgAiE52xe3/CmBixEHwJHupsgVCMfrMSJC6tuB3c7zLLhmr3Qud4fX//mqTYhFBXoEYHA4H5iY1I6gxnVu0j76M+X+vCtGwlkBqHth/PhtONjzUdhOAywWcbA3w/abUkv+OtNVhb9VHX8ZbExSLCAqEJfsGhsjISLll9VLAMjQ0lBGmhEIhjIyMdNQj5RmzJAkZ70VS185ucIOhQP7iERISgjFjx6G46OP35RoYw7vN9xW2NX5ZMi5sdldrU3vtQR4WbU9XunzEzRz0amdWccEyxCQVAQDiU4rRZUocDv7kgmHz32LRBDt86mciKZf1oWTMXsQX4d/H+WjdiKqxqgrdp5ds8lIyi1EoJDh86QOOrXSFlbnigwV55OaLMemnJEz0t0J+IUHPtqbgcjnILxDj8OUP+O3P9/hqkBWGdbeosK60rGIMm/8WSyfbwZDPQcTNXPww2hY8Xsm7Iiwi8J+TKPUZQwGn3M15TYYQApEYMOBpRoBWl/iUIiSmFcPeiod1f2QiOkaIveHvMcnfCu2bmeDKvTxERufD1pKHWi4CrNibKVPHxgPv5NQsy8uEIkz8KVnh/YMXP0j+f+zSJFze6oGbj/IRtC0NALA3PBurptujZQP157ZXCUKsDs3EuhkO+LWMkLf1yMe/h81PxP5lLuByORCLCfaGZ8vU9S77o8Zt3NIknFzjJlfIuXo/D1ci83DlXokp+9xRNujZVvFaUFRM8O/jfCz8NR1b5jiiQS1DqfvxKUUwNuQgJVOE6WtSpO61b2aMyOgCtGhghOsP8zGqjwW+6GmJXt+WzDH7l7ng8MVspGSKsHiSncYOdPSNrA8imJtyq9z3jY4pRFxyEbq3NmX1cKEqIBYTBO9MR/umxvjUzwS9/3/AUceNj42zHNF3VkIFNRQi4mYupn9ujUGfmWu+wzWQ14lCEALUdlN8cFsoFCP9vQiu9iUHUD3+v98prXRQBIfIc/LRAvXq1cOePXvwySefyNxbuHAh8vPzsWbNGsm1uXPngsfj4aeffpIqGxkZiRYtWlTYXkJqERxtDMA30NxLHvm0AHN+TpV7b9YXNujXoWRRSkwtQkqmCE9jCrHz1HvcOdwPOemPAAB8YzvU+WQhnOr6K9XmtKHWGNxZ+ZfvfY4Iy3ZnIPJpgdKfKc0fS10QG/MCvk3qQcDngMP5OJ73nhVg9sZU9GxrihE9LPD3vTzs+fO9wro2zHKAlTkP8clFWPirtLDXqbkxFk+0V/BJ+YRfz0EddwHqeqin5SgUivFXZB56tjWV+l76gConQiIxgVgM8A04SM4ohqMNDxwOB49fF0o2NMsm2+HN2yJ85mcCVwflTq4JKdF4ersK4GxngIW/puH6w3y5ZYd2NUcdNwG6t5EWWHLyxbj/rABNahvidWIR/OobISWzGA7WPPx88B1OXv2oWWjXxBgNvATYfVr2GbIw5WJsP0u0aWwMJ1vpc6K45CKMWZKk8Hus/dYB322U/56eWusGM2Ou3pzAJaQW4b9XhfB05sPWggcHG/bOxIqKidLz4eFL2dh2NEvhJlweuflicDiAiZFy5c/9/Qx+TWvDxpKHfeHvMegzc5ibcFEoJDAuU4dITLBqbwYu/JunoDZgdF9L/H5G8fyjaZxseUjOEMlcXzzRDp2am8j5hGIeviiAAY8jeX+XT7XHD1vSKvzc7oXOGLdU8btQFnmbhi5TZM3YvxlmDf9Ppded7FwRjA25GLnoLVLfffzel7d64Ndj73Dw4gfsWuCE8ctKhNSJ/lbYcSJL6b6VpV8HMwzoaMaqVlsbqDSXiwjCzmXjt/+vowd/coGlKQ9iQmCk4NCW4fHrQtT1ELCy5xGLCQqLCPrOTEAzH0Osn+lY4WcWbU/DtQcl68MkfysM71HxwZi6PI0phIu9ASxM5R/q6WI+P3n1g9KHNxXWpcK8qw/oy/pZEYO/T0BWjhgXNrvjbnQBftiShuBJdujo+3F+/vHXNPwjZ59Teq5UJIfopYB15MgRbNu2DRcvXgSHwwEhBD179sSECRPw+eefS5WtSMB6m16MBdvSJJoUAJg/xhbdWrN7Ul3WXEMel7d6QFhEJKdxACAqysM/vzWHjVsnpMecR71Pl8Ol4RcAgAub3RH1ohDOdgb4YuFbAECT2oawtuDi6v18qXorIvO9CLkFYowOrnixjdjojjvR+Th9LQf/Pi5fEJsx3BrtmhrD3soAgQsTkSRnQ6Eupb9XWlYxElOL4Vv3oxZz39n3uHo/D68SijC2n6VEmFNmPMIi3qNpHUM0qfOxvs2HMnHsSg5WTrNHq4b6pUGLjo5GDvGCmTEXaVkitGtirHDhLLshauAlgIkRV6FQXcuFD2c7A4zsbYF6noZyy7zPEWHQ94ly75VH60ZGmPOlLWwteTh4IRu/Hs9SuY6K2DjLQfI7KvMelkc9TwG2zXXS2gKRVyDGi3ghmvmU9D8nTwwu96NQUva3nDLECkO6VLxRuf+sAM/ihPi8q7nck2NGszK2nyVG9rGUW8fTmEII+BxMCPmotannIcC2eU5KfbfSfR/8mRmmDLGW6cvZGzlo2cAI9tYGkvK925ni7M1cuDsaID6lJNDOmfVu4HCAQiGBoYAjY+7GBl8HWMHGgoeoF4Wo7cZHSqYI+8/Lan4Y2jY2QuTTAhSpEQvoj6UuMgcDith29B0OX/pQcUEW+O1HZ5gYcWBnZYDZG1Nw71mhwrIjelhgor8VAOCn39JxsRxhVxuUnfdz88XIeC+ChxMfP+1Jx5MYIUKDFZupawt5c0tyRjGSM4rh5mCAlEwRsj6I0Li2Yblz7t7FznBz4EsOdn8cbwtXBz7uPS2Qmme9nPnYvbByZvdl56GlX9mhfTPFhwT3nxXIPcRSZm1WFWbOr+PGx/b58r+ntjf8F//NxU+/ZbBapybGTlNUFQFL3uERAOwLdoaFKQ/PYgvx/Sb5B1lHV7rC+v9WOnovYKWlpcHc3BxGRkbIyclB9+7d0bt3b3zxxRc4dOgQ/vzzT5w/fx5mZtKmCaW/GCEEhy5+QOTTAnzmZ4I1YbLmIADANwDO/czewyoSE3yzJgXRMfJ9xMoj7fVZ/HduMpr1D0PUmbGo12YCJk1bimmfW0ttoEUigk2H3mFYdws42xlIPRjnfnaXKksIQVKGCOYmXES/KQSPx1GoWQOAOSNt4GxrAHMTroyqVNEDqC1+mecEB2seBs8tWWga1zZE0FhbnLmeg9Czijc/AGBtwcX6GY7wcJLW0ES/KcTU1R9NUwZ0NMPgLuYY83/hM2isrd6Zip2+9Azrj8oKfb51DeHlzMeD54VShwjqcvAnF9hbGYAQgpcJRfjrbi74Bhzsq2CsKyJorC1C9rC74JTlyApXrNqXUeGhQEWcWeeGmDfPNL5AnPj7A34+WHLCuXKaPURiYP7Wksl8+ufWaN3ICCMXyR6IlLfQ3nqUj/nbPi4IZsYctG1sDFMTLk7+nYO6HgIsn2qPXSezEH6jJEiOhSkX2blihG9ww+FLHzCihwUMeByF7/7cUTbo0tJUoYD/Il6IbUfe4cEL2c358qn2+OtuHtKyinH//5t3B2se1s90ROCPbxV+L/9PzXDib3bzIE4bao2/7+Xh6wArmJtw4WJvoFBzXVZw3zDLAU3/L9QnZxTj7I0cld+Rb4ZZo0V9I7g7KtYgP48TYvIKxWaJlaFDM2N0a5aBxXtlN8rzx9gqvUFs08gItyv5zrHFmm8d4Fev5Hdhnt/pn1tj06GS90wfNqllN59l1yNNsP0HJ4WavjVhGTA14mJEDwsUiQjsrUoEf5GYYOySJCSkyj9BYLT98lA0d5SnIe3f0QwzR9hU+F3KUlRM0POb8s22tLXhLz2ns40+PLvKoq8CVm6+GP2/S4CpMQe5+ZUXfZjfRO8FrHr16mH58uUYPHgwACAqKgqLFi3Cy5cvUa9ePSxevBiNGzeWqaf0F1NFGGDzYX30sgDfrvsowLRpZISmPkZKmT88ufgtMuP/xpGI15g2ujXatvbFoUOHKvzc2/RifFlqQ8J8H5GIYMXeDFy68/E0sWEtAZ68kS/8KTMOGw9m4mQlNzeXt3rg4YsCCPgcTF0lfzH5+TtHfLOW/YWmfwczzPyiZOJWVhOzb7FzuRsubTFvSypMjbl4+CwHmR+0YyIw8FMzvEksQtRLxafXZfGta4jYpCK8+6D9SGpsMLyHBQ78X1vxdYAVGrskanyBUPfwouw7m5pZjIIigv3nsnHuVsWRRW0teTJ+oqWp487Hy/iKhfWy/UjLKsbOE1nlmu3pA74+hnjwolDGFEQZ8gvE4PE4EPDlzwspmcUYsUCxoFgWLge4uEX+HJz2rhjDgiqu6891bjAx4oIQgskrkvGinN/uzDo3CAQciS9PdHQ0vGrVU8IfRLvMG22LFb+rdyAzY7g13Bz5mC1Hg/LLPCe1TcjZgtl8hl/PUXgIrAkUCVll56HBn5nBzIQr12+vNJ/5meDHCXZS1zKzRQjZky45PJFH6XlDLCboNu3jwcX6mQ6YuT4VW793xNajWfjvVSHaNTFGyNfy3QVy8sR48LwAP/7fn7yRtwA/f+cos25rY8P/5q1QYv5alo6+xhjVxxKX7uZJ1hkA6POJKcb0s4SdlQFeJwqRliVCm0bGuBKZC7/6RviQK8bIxSWHbBc2uUt8iPUdfRWw2FYYrJpuj7M3c9GnWax+CVhsERkZiTm7VPPVAdgVsMr+aKXrTkgtwqjF8s3yxCIhbu3zw4jhQ7Bn92707t0baWlpuHv3rsrt/jjeFp2am2DqqhQ8i1NOk3ZmnZuMb4MiHjwvwLG/Psi1RVW1nbiUIpy/lYs/zn2caDbNdkQjb0ONngCd3eAmcTRVhtF9LTG6r3zzKUXkF4rx88F3mDzYCpZmsvbghBBk54rl3pOHJjSI3VubsLoJLmtS+d+rQtYF5TXfOsDLiQ8DAyDtnQgcDqRM19TBwZqHUX0s4WDDQ8sGxjJj3aaREZZPdZD72ZTMYsQlF6ltSlrWVFgVDi93xdAfSg4J7Kx4SM9izyxXFUr7lQLAmtAMiVZMW+xb7IzHb4QwEnDgZGsAUyMOCCA1526Y5QBbCx7GLUtC49qGWDXdAZHRBWjV0EgjByjpWcUIPZuNU9dKDqWYwBaKfnNDPgfhG9wk5vCJacVY+Gs6YpXQSF/c7C5jdlksIsjNF0sdJK35xgF+9WWDRDEboYrmmX4dzNDR1xitGhrjelQeFv5SfoCk+l4CPFXBomPfYmf8+U8OArqYw+7/GpS4lCKMCU5C8CQ7+LgL8CpBCCtzHl7GC7FRzTVi4ThbdG6pvnUCIQQbD7yDrRUPI3tb4ujlbGw5koVOzY2xaIKd5Hl690GEN4lFSEwrQv+OH33V0rKKcSvyFXp2qqv2+18ZfvvRWWLRseNEVrkmsMpwfJWrZC0rrUmq6DNiAgTMVd7kfMZwawzoJOtrPmVVssxzJs8CRRsb/rIH7QDw4wQ7fOYne4gjLCIQFhGYKeFXxbyblX12y+Or5Ul4EV+EeaNs0KOcADbKoksBKzdfjKmrkhE0rmTeYCgvRoIyMPv614lCmb3H6vFpNVfAYgYmMbVIchqgKQFLXvQ0eb4nl7a449y5c+jduzdOnz6Nfv36YerUqdi/fz8yM5U71aqMnW/ZKH7Kkp0rwsHw13iVaqm0KZaisWZ8CxaMs0WXUhOHOkKFgw0PqZnsbzRVeU72n8/GoYvZeJ9TosWZOcJasrhm54pgyOfg8t08rA7NxI75ThJzTGERQX6hrND1NKYQUxRo+8rjwDIXmJtw8f3mVBQIS8KbA8CKqfZoXs9IYtrFhNAP6GKOo5eV8/GwMuMiK0eMPu1NMbSLBeyseDCVYyZy8d9c7DyVJfc3+XaYtdQGycyYgx/G2CEpvRjujgYSoUVYRCp05i7vWWE2n4QQTFmVgtTMYikNW1nHYXmmOt8F2qBvqfDtH/LEeJctkgTR8HbhY+cC1f0b3n0QqbTB0CW/zHOCpRkX9lY8qdNmoMQP6acpJULojHUpMlrPL3paSB2kKIv/p2aIelGI12/lCxk/fW2PxnUMFZooRdws8e2yMufpLPIhIURGgCt7Yl+arwOssO1oltx7tVz42Pq9I/IKicTuvyIu3clFyJ4M7FrgpDCdB7MRUuS7OKCjGb4dbi3zPbafyJI6iWf4dpg1rC14kiAejEkOw/Kp9nC2NYCHEx/FIoJnsUL4uAsUagQrQlXfUDsrHg795KpWW0D5JmABXcwR0NkcJ/7+gEOlIkqeWe8GY8OS57TPjHgUCHW75RrYyQwX/81FboFq/QgaawtHGwOZwzN5vuUM4/pbol0T43KjbypLt1YmaN/MBME70/HTFHuJObU8yq7bbG/4Nx/KxNv0YsncB8g+G2ztMZk1bmx/S4zsrdqBb0XEpRTh+02pUut02f2YOuhKwMrJE+PPf3Kw/f/WY6V/A2X2lREb3eU+x2UjgQ+Zl4DMUlFYa4yANbSrOQ5f+oDWjYwwbag13MpESGMGWRXtDQAcvZwNN0c+2sgJH176h5P3UpVdvJiN3aRJk7B//36kpaXByMgIa9aswZw5c5CZmQlra+sK+1QoFKukkSmvj6rAvDz7z2dLmUEyL+bNR/loUd9I7UVTJCbormATUpYTq11hasSVqM5PXv2A5PRiNPUxkoRHVsSWOY4wNeFKfK/kCRqXtrgrdcqdky/GgO9kf4stcxyR+k6E4J3pqOXCx5v/bxhbNzLCiv9rR+ZtScW/jwukfpeyGxMGGwsufpriACMBB4VFBG/eFkEsJvBw4iP8Rg6++8JGrVP587dzsetkFtLK0YZ0a2WC+WPtFN6XR1lT1tAlLnCxYzc7hDxB9Mhy13LzzylC3iTM/C6KNqG13fio5ynA27RiLJ9irzAlQ2n+vpeH4J0Vp0lg/BLK25jL4/uRNjAScLBkl+IDGL96huUGMQCA02vdpARoeeNzMMQFj18XyrTFjFtOnhhDfkiEsKjipaZsSGKRqCR62e9n3mNYNwucuZ6Dbq1N4czyM6RN9pzOUslfK6CLOaYOqXg9kEdegbjcKI6lN0KMxoihbWMjhHxtr3A+GTg7AR/yPm4yKlpX5AmcbOE/JwHZucqZJ1dm/VPXouDgTy64dCcP28sJ8rNssh0+aWqCXSez8CpRiFv/FUiC7rzPESElUyRZU8tGhvxxfEm+pOb1jLD71HtMGmSFZ3FCNKtjiLmbUyt8z8ujcW1DrJxqL9kvnbr6ARtKRce7tMVd7rwYsdFd0t/KBh9SlbLrNlsb/st3c7Fst+yc+tsiZ6l3h7HIYYOK9peVYUJIEl4nyh5isbFHNLKsjdjkYrkavMrw8HkBGtU2lHtwJu/9nDrECltKpbAASt7H57FCFIkgt39X7+dh8Y6S9bn0AYmitmqEgDXR3wojeligWETA40LuZM4Mys/fOaJxbeVegNKTQ8RGd/ANPtb95E0hpv3/1HtgJzN8O1y+k2Z+oRgC/kfbd5FIBBcXF3Tu3BkHDhwAABw7dgwBAQGIjIyEn5+fskOg9KRftu/qoo3TibfpxTh+5UO5mpVe7UzLTcQnL7kmg4UpFydWu8lcLyvcKXq5yqLOwhu+wQ1nb+RKHLB/nGAHazMu1u3PlERPK82wbub4arB6Gy1lKWtysXKaPeZuTkP7psZYOll1U1xtMnlFMp7HCdUKg80wavFbGafuX39wAt+AgxtRedh5suKw38osTqWfl0mDrCQbrw2zHJCeJUIzHyPEJhdJHPaBEmf08OsVm+CVtdVf/0cmXiQI8TRGCHMTLtZ+6wAPJz4EfA62H3+HAxc+YNEEOxmBT542PiapSKmw3+tmOEhF/GQoG4QDKNF6nLudiwauHzA90KfCuqsDD58XYOYG5cxVNOncLm8uvxFVkvfqhzG2Fa4VN6LycOTSB6z+1kGnOZpEIoITf3+Q2UgBJSZpb9OKJdppdcdTU9Ec137rgOb1VMvxyWjA54+xRePahhVGpFRlfWJSALg7GmDXAme5G9lB3ydILDXksX6mgyQyKsPqfRk4e1N2/rq81QPZuSKZvISqwqxVgKz5ckV7FkIIbjzKR7vGxgrzdSk7hsqGsVeW0msS23OBou+0ZY4j6nsJ1N4rRkdHY+qmEi3Ylu8dUdtVfS21VL0xhRIffnn+lMr8RmWfDXV5nyNCwLxEfOZngt7V2QfL3rWpTKQ4RTA/wJyRNuitZOJcxs6aoXNLEywcZ4esDyJJdDtAvj28Iq5du4ZOnTrh4MGDktDzDx48QPPmzXH48GEMGTJEqXoA+Q+Vm4MB2jc1xqRBVigqBisPN4Mu7WuZ72prycO+YOcKc4EQQrA6NBMRpSb2k2vcYGrEUfhblT7JPbzcFbYKNCH5hWKJ8KXpaIubp+WiYUPtjHlRcUmCxP4dzeRqbPUVQkoyrCsjEJfHn//kYN0f6jufV7QIvkoQSpnMKLtoludEzVCZfCn5BWKkvRchJaMY9tYG8HKWP6cq86xX9J2S0ovxIU8stUDqq2O0JikWEUniSnmUF/2NDarjmD94XoA1YZnw/9QMrvZ8tGtSMocxzy0TKVUV0rOK8fl85YOXKIO7owF+X6S9sPHlvbfnN7mrZEr7IU+MgbPlH14qSkEgT4tV2q/oyZtCHLqYjalDrTExJBnZuWL8vshZqdQyRoYchK93V6jtqeg5Z+Z8eUl9V+/LwOPXhYiTc+gpD7aFoIz3IonPrbYELABYMskOHVQMAsRQWsACSrSgP39XOaHzdaIQPx98J2WGXno8kjOKJemMykMTB1aKoghWncxl5aCscFWa1fuU30CVPRX7624eNh7IlBKuAKiUqfzYsWMwNDRE7969Jddq1aoFAHjz5o3S9QAl+bIAwN6Kh/ZNjfHnOjfsXeyCrwaX2M6zKVzpmp+/c8Rvi5xxeLlrhcIVUKKt+36kLZrW+aitNDfhlvtbeTjy0al5yaJ85rr86InnbuWg78wExCUXQZNnFKP6WGBfsDO0GcyQb8DBssn2VUq4Akp+68oKVwAqfbq15cg73H6cjy5T4tBlShwu3ZE+tS0tXP2iZE4pAKjlIoCF6cfv93WAldT9+WNsK5WM0tiICw9HPlo1NFYoXAElPn7lEbqk4k2js52BzqO56QMGPA4u/n/+LsuBZS5VLomuPuBb1wihwS4Y0sVCIlyVZpgagtLqUOn9grNtyaFb8CQ7XN7qgfZNVZsr+QYlASe0ya4F0nMNlwM42vBwYJmLyn6K5iZcDP5Mdp7cMd9JoTaNw+Fg6/clm+yx/S2xcpq9VNCGhrUMsXiiPeytDHBitRsub/UoN4UBw4VN7ghf7y75TgzvPijvk80cqG069A5pWR8FKUIIzt7MVVq46tqKXXM4AFIHvGzuNe5GfwxY5mpvgMPLpX0TmciMynLrv3zk5svXav73Sn0TVYYJIckyPr4z1qWgWFQyJqXjHFib64doox+90CJLJqnmQ6KIk1fVD1tOCMGxY8fQo0cPmJt/PC2xtLSEra0tXr9+rVJ9PC4Hl7d64OBPrlg62b5cm/uqTuPahvBQYtIty4ZZjriwyR1nN8iaBcrjsxYlE/9vf8qahInEBCv3lkzIc35OxfQ1H/1/Jgy0xNGVriptnMtjTD8ruNqr/n0plWP95PJN8ZgJvHtr2QX16OUP+GHLRzO40vm/RGLpBVJVIePwclc4WPPw6w9OGNrVAkdWuOLyVg9c3urBevJ0RTjYGEjaLMvFze6s+9hVd7hcjtTmlxlbBxs6jvrCnSfSAZ3Clpa8d0yY/6WT7dG83sdDvJIoslxMGypr0t2kVjGOr3LTegqQWi4CnN/kjpXT7LEv2Bkn17hh/zJXtZ+zaZ9Lu0Osmm4vk0ezLPW9DHF5qwdG9rZUOgIrI5TJ49IWaXPoP9d/XN93ncxSqv6r96Uj6g6b/xbxKUUQiRX7jY3tb4ltcx3x6w/S6/ycLxW7LLABE7CKDUon0N0X7AJbSx5mDJd+Xk9dVc4kNiWzGPO3puGn3zIgLCL4UE6w6YW/pGHD/o8HFrcf56NAWL7/ZE6e/PtRLwsxfU0K8gvF+Pvex99x72L5h3yrv5EfFVhT1LgZ/I2KyVgfvlAuUt72H5TfUN+7dw9xcXEIDg6Wueft7a2ygEVRDh6Po3QeiRalQhqXdc7eXyoqWlqWSCowxBc9S6L8WJvz0L6ZMa6XCWt/YbM7/vwnBxsPVBxmuOzkTdEeAn6JqYsik4PlUx0kwtGFf5U3Dy29UDBaUlXgG3BwIOTjSaONheqBPNhk1hc2ktPfU2vcVNLiUz5Sy0WA8A1uMKxG1gb6RukgRo9eFqBJHeX8nl4lSIcBP7pSfhTCtd864uCFbLg5GqB9UxN83s0CALD5sPRcP7lfoc4OQQ14HLVTS5THV4Os0LKBZiwe6nsZ4rcfnWFjyZMEkpo7ygYtGxjLCKmlrVrCb+RithICDxPMoDSKzBJ/X+SMnDyxlH/S5a0e2Hf2PXq2MdW4tdCNR/msaLUV5UEc0MlcKoDJhgPv5IbHL83hS9mS6Kc3H+VjzqZUPHope9jHHC5ejyrZEzFpLACgaR1DbJilWJAeoMAcFQCexQrRd6b0fRMjDhaMs5UKSKKLHHjVV9WhgC4tPp44xyghbM1cr5wjsrer8lqGY8eOgcfjoX///rL1UAFLLyhtarWjTGCDCAXJXMPKmEZ1+v/p5sBPzVDXQ4BjK13B43IwUM6ENW+UDeq48dG/Y4nZhf+nZlI5HCjax8nWAJe2uOPzbh9/r3oeApmJ+vgqVxxd6YqpQ6wU1sWYdpQW2OaPYUebrkv6dTCTaFyUyelCUYyRgKvzxObVma8HW0n+/9t1qUjOKJaKgiiPrA8iKZPeLi1Nyg2TP6y7Bdo3ldZqn1jtimMrXRVqfasy5352x84gJwzrbqHRdjyc+DAz5krGsGdbM4W+0aWFHCZy6X9veEjOkDXze/NW+VxtAODuyEeDWoYy7+nI3pZa0ThfuK16nkGRmODy3VyIS1lPMD5dlSWvQCyTWuLRS/nmgN2nxSuMDl3W9K80J/5WPbAMh8ORCjV/fpO7TkzSa5wGy8X+41f+5dg7rJjqIBWS8Y+lLngaUwgBn4N6ntJRBi9v9cAXCxORnCEt/a/+xkFl/6tPP/0Utraypyve3t44evQoRCIReDzdnk5TSjhwPhuT/K0kf79Nk2+PXTZ0dPc2pmhSR36Epz0LnfHgeQGa+hjC2c4ARgKuJMFfr7am1PdCT+BwOJg82BqTy4neyOQvC+hiITeKGQDcflyAto2NkV/4cZGrTr6RFIq+U3aNZg47fppij7aNpbUvz+OEmLwiWWq/AACzv5QfJbg8LEyr7zrON+DA21W/1qqIjR+DXfjPSUD4Bnds+9MI2/58KyPgVhQ0iMHGgovNc3RnUdLMxxAPXxQiUcHeQxF5BWIMmZeIAiHBzUf5CJKTZqWsO8OFze5SglBMUpFCn1x5QivbyMs7166JMW4+km+HeO7njz6tB39ygbEhV2e5EGvckWPpk4d/HxegQCiWUhEv3pGOJbsysOCXdLlS/ra5si9ZaXOyioiOjsbTp08xePBgufe9vb1RXFyMhATV81tRNEdRMcFvf2ZhvYrR5RQ5/Ho68zHwU3PUchHIBOtoUMtQkgyYUrVYME6+Scr8rWlYE/rRXGH9DO3aglMoFKCJnNQs87emyWiyJq8o2XiXPUxTJrASRX8oEBJJEASgJHeoIhys5QvCAzqa4cgKtwpD4WuSsf3VSzDcb1aCJLH1pTt5csuU1ezwuBx8Uipoy7ilSQoDhkwIqXwCaYbSwUUUwWgwQ762R+tG8vfdpfdO9lYGCpPRa4MaOVs42X58kcrmSXoeJ19lPKpPiQrc0oyHL3t/VIdHbJQfAUoRx48fBwD4+/vLve/t7Q0A1ExQDygdDW38siTsDc/G6X9kg5vYWfFweq1ywTMo1ZsuLU2ltJ092nw0Uwi/8dG8o2x+KQqFonlWTJOfy2/g7ATkF5Rsvl/Gy98DGAnooVdVpHQahN6l9ntlI9sdCHGVu5+bMUJ1rSXbqBPYa+VexUnmK6JsMLiAueqZFKoSzS+0VPL17FwRpq1OLteEM3iirDZOXUFUU9RIAavPJ6qHYR7a9aNQNa6/Fcb2s8REfyuVzXyOHTuGtm3bwtVVvqMsFbD0B16pt6Ns8lmgJBnf5a0eOPSTK0x1eEpC0S+GdS/x2RrX3xLd5EQZBEpy4FD0h7CwMHh5eYHL5cLLywthYWG67hJFA5SXxuFteskcP2m57Km8q70BwjeodphK0R3leWwwvkjfrE2RuVd6P7d3sbPe+MyV1sqIRBWHal+8Iw3n5PiKlxVYFAVakufycutRPrYdfYf1f2QiLqUIF/+Vrr9sqoJvhllXmOetnudH7dnpazlYvKMksuGF27l48kY672PpKJ0AYCjgolupsPgDO5lhZG/9ErBq5Co/tKs5dp+WDb+tCFMjjswGemQf1X/I2NhYREZGYtWqVQrLuLm5wcDAgApYeoAikwGGBrVkzU0oFA6HI1mYFS2GNNqe/hAWFoZJkyYhL6/EhCY2NhaTJk0CAAQGBuqyaxQtcuTyB4U+VvuCtZcQmFJ5zm9yRzcFARW6T4/HwvHS2o9mPh/X8gPLXGBpxoWhHpmDlhawuk+Px+WtHjh3Kwfujnw0lLMPuXpfvn/S+GXJUkLj4onyNbpASX6x7qW0f/O3fQzrXtaS58cJdvB0MpBECDQSEPh/WnLQKM9fat5oWzhY8+Bb10gq2fHV+/kKkx/Li345d7QtLv7f9HH654r9pHWF/jxBWkTVF+foSnbMvxjzwEGDBiksY2BgAA8PDypg6QEcDge92snPLaQoOSiFUhp5aQEubaHPjrZQpJn68OEDrly5glWrVmHixIkS4YohLy8PQUFBuugyRcP0aS9/Tj93K1dhlDNK1YLL5eDH8fL9YQkBluyUDs2+5tuPPrEONgZ6JVwBsgGR0rKKsXJvJqatTpGKDqgMWUomYFY2pQ0AfOZnAkEpIXDZmI/zacjX9ri0xV2irRr8mRl6tDGFb90SH6qfv1Mcnp2hcW1DDOsmG32Zx+XgyHJXbJrtqJeHlvr1FGmR/cukT6QO/qT4hIqtaF/Hjh1D06ZNUadOnXLL0VDt+sP3I2Un6dmBNnr5MlP0kzVlkhvSUNzagdFMxcbGghCC2NhYjB49Gu7u7rCyskLnzp0xd+5c5OfLP+2Ni1M+vxml6jA70BZrvnXAwRAXTBigXyZFFPbo1FzaPNu3rnyLk17tTMGrYuv5sPkf030o0tQpYtYG5VIPAeXviyV9+b/g4+rAx5yRNji+yhXGZYaaw+Fg21wnXN7qIZOg2sOpYkO6lg2MFK6bNpY8NPLWT2uiGitgOZbJWWBvZYDwDbKaKj5LRpQpKSn4559/ytVeMVABS3/xdDJAn/aq+/BRai5+9Y1weasHdsx3wh9LqamRtggKCpLRTIlEIqSnp+PHH39EeHg40tLS4OnpKffzHh764X9BYR+/ekawtzbAoM6Kk6geXu6KWV/YqBzIiqIfcLkc7F3sjO8CbbB5Wi7WzZCvKZkdqPsgFpUlr+BjdMR7Twuk7nVuIS1oKpP/lcHequINsHupABy925lJ0pYoizJpDIZrOM+apqixAhYA9O9QslE+vqok4ISRoCSR3a4FH0Oxn17LzuR68uRJEEIUhmcvjbe3N9LT0/Hhg+oJ1ijsc3yVK4LG2qKOOx87g5x13R1KFaW2m0CnoX5rGoo0UIWFhVi0aBF69+4NOzs7hISEwMREehPC5XKxePFiLfSSokuMDbkKUyvYWvLQr4MZzVdXhXFz4KNvezOUZzRQVaxRFk1QnJi+dK6okD0fzR+/GWaNhePtcH6T5g4JarupHuFQWSYMsETERvcq+w7WaAFr5hc2uLTFXUbiruUiwL7Fzjiz3o21H/b48eOoXbs2mjRpUmFZJpLgmzdvWGmbUjkszXjo2soU239wVskumUKh6A5FGqiy1wMDA7F9+3Z4enqCw+HA3t4eYrEYN27c0EY3KTqmS0tTDO0qrcnaMIvmqauOMOl2GEyNqs56/qmf/Ii0AHD+9seIfu8+fNRmMRGz5SXaHdhJOUucwZ8pLvfnOjfU89SMeV7Ptqb4opdllRWugBouYAGK/SFcHfjlhnRVlrCwMLi7uyMiIgJpaWn4448/KvwMDdVOoVAolSMkJAQCgXQSTRMTE4SEhMiUDQwMRExMDMRiMVJTUzFv3jzs2LEDe/bs0VZ3KTpk8mArqb8b0Qix1ZIBHaUF6d0/Vi2LlFlfKG/O2KS2YbnCibJ+S2V9pkpjYsSOCLF/mQt+meeEnm0/BqCZO0q+ZrkqUeMFLE3COFknJJQkt8vOzsakSZMqzLGiaQGL5nyhUCjVncDAQNStWxcGBgbgcDjw9PTE9u3blQq9vnTpUnTp0gVTpkzB/fv3tdBbii4pfdAasdGdWipUU2wseRhcyu9OGR8jfaJfB2lt0sZSmtZ94dKph8pqZctiY6G8r5SLfck47VrghBnD2Q+H7mhjgLoeAswdZYvjq1xxZIX8PLFVjar1dFUx5DlZM+F/y1vkra2tYWVlpREBi+Z8oVAoNYGMjAxER0dj9uzZWLFihUqfNTAwwP79++Hn54eAgABERkbC2lr/8qxQ2GPhOFuIxOxFDaboJ1MCrNCxmTGa/T9MeFVj4KdmOPl3DkK+tkeTOh+/w54/38PB5qPQ1MFXsUkhUBL6XFlCS+WBc3fk4+r9PAT20kwETlWDZOgzVIOlQRQ5WSsT/ldTkQTLE/ooFAqlunDixAmIRCJ8/vnnan3ewcEBR44cQUJCAr788kuIxeKKP0SpsnRuaYpureXnyKJUH7hcTpUVrgDg22E2uLzVA+2ayCbeXbk3U+Hnymq01D1IMOBxsOZbRzSvV3XHUFtQAUuDODjId5RVJvyvpgSsygh9FAqFUlU4dOgQvL290bx5c7XraNu2LTZs2IDw8HAsW7aMxd7VbKiZOoWiXb4OsMbiiXawMuNix3ynij9AqTRUwNIQ9+/fx/v372WCaChysi6Lt7c33rx5w/qpqbu7/HCdNOcLhUKpLmRkZODSpUsYOnRopRM7f/311/jyyy+xaNEiODo6UqGgkshLAK2MbzKFQpGlmY+sqd9ZOTldgZLky8dWuaG2m0DufQq7UAFLA7x48QK9evWCg4MDNm7cKAn/q4qTtbe3N4RCId6+fVthWVXo1auXzDVlhT4KhUKpCjDmgUOHDq10XRwOB126dAGHw0FqaioVCioJNVOn1GTY1t7O/lI2yp+hgG7t9QEa5IJl3r59ix49ekAsFuP8+fOoV68epk+frnI9tWrVAlASSdDNTf5phKrk5+cjPDwctWrVQlFRERISEmBlZYXNmzfTABcUCqXacPjwYXh7e8PPz4+V+oKDg0EIkbqmTMAiiizUTJ1SU9FEkDFXe80l+qVUDirmskhmZiZ69uyJ9PR0REREoF69emrXpYlQ7Rs2bEBCQgL27NmD+Ph4eHp6okePHnSDQKFQqg0ZGRm4ePEiK+aBDFQoYA9F5uiKfJYplOoC1d7WLKiAxRK5ubno168fnj9/jlOnTqFFixaVqs/DwwNcLpc1ASstLQ3Lly9H//798emnnwIAWrVqhTt37rBSP4VCoegDbJoHMigSCqjvquqEhITA0FDab4TD4eDdu3e4efOmjnpFoWgeTR3UbP3eUfL/mshTRVEPrQpYQqEQCxcuRKtWrdC+fXvs2LFDYdmbN29i0KBBaN68OUaMGIGoqCgt9lQ5StvS2tnZ4ebNmzhw4AA6d+5c6boFAgHc3d1ZE7CWLl2KvLw8rFy5UnKtVatWePPmDdLT01lpg0KhUHTN4cOHUatWLdbMA4ESocDERDqvjEAgoL6rahAYGIiuXbsCgMQ3edOmTfDw8EDv3r1x7949HfeQQtEMitw9KntQ4+bw0UxwQKfyEwxTtIdWBaxVq1bh/v372LNnD4KDg7Ft2zacOXNGptyrV68wceJEfPLJJzh69Ci6dOmCMWPGIDk5WZvdLZeykZAKCgogEAhk1L+VgYkkWFlevHiBbdu2YcKECWjQoIHkeqtWrQAAd+/erXQbFIqq0FDNFLZhM3pgaQIDA7F9+3ZJwCKBQAAzMzMEBASw1kZNgRCCFy9eoHv37hCLxYiJicHUqVNx6dIlWFlZoUePHvjvv/903U0KhXXkWTaxEWTMzISL4T0ssHKafaXqobCL1gSsvLw8HDp0CPPnz0fjxo3RrVs3TJgwAaGhoTJl//jjDzRq1Ahz5syBt7c3Jk6ciObNm+vVBkyeLa1QKGTVlpatXFg//PADDA0NsXjxYqnrLVq0AIfDoWaCFK1DQzVTNMGJEydQXFysdnLh8ggMDERMTAzEYjEiIiKQmZmJTZs2sd5OdefZs2d48eIFBg4cKHXdw8MDly5dgqGhIbp164bnz5/rqIcUCvu8ffsW586dQ9u2bSEQlIRJVyWydEVM8rdCq4ayyYcpukNrAtbTp08hFAqlJPgWLVrg0aNHKC4uliobHx8PX19fqWv169fHgwcPtNBT5dCG07O3tzeSk5MrpRW7ceMGjh49iu+//x5OTtLJ5SwsLFCvXj0qYFG0DnX2pWgCTZgHyqNz587o3bs3fvrpJ2RmZmq0rerGyZMnAQADBgyQuVe7dm1cunQJYrEYXbt2xYYNG6iWm1ItWLRoEYqLixEWFoaBAweiXr16iImJoUHGqjFaE7DS0tJgaWkp5dxqZ2eHoqIimQXK1tZWxhzw7du3ePfunVb6qgzacHpmIgmqayZICMGcOXPg5OSEWbNmyS3DBLooG4KYQtEkNCobhW0yMzM1Yh6oiJUrV+L9+/dYvny5xtuqTpw8eRJ+fn4Kk97Xr18fFy9eRGZmJmbNmkW13JQqz+PHj7F7925MnToV3t7ecHNzQ3x8PN13VXO0lgcrPz9fohZlYP4WCoVS1/v27YtJkybh3Llz6Nq1K/755x9cvnxZRgPDEB0drZlOl8PUqVMxf/58Ke2bkZERpk6dylp/mE3C33//DS5XdVn4woULuHHjBoKDgxEfHy+3jLu7O5KTk3HlyhWF41uWgoICnYx5Taa6jbmTkxOSkpLkXteH71ndxrsqUHrM//zzT6xfvx7JyclwcnLCzJkz0a9fv3I/f/ToURQXF6Nly5Za+e0MDAwwcOBA/Pzzz+jZsydcXV013ibbaPs5T09Px61btzBlypRy2+Xz+TAxMZGr5Z4zZ47GNZSahM4t2kfXYz5t2jSYmpri888/R3R0tMRf/9atW7CystJZvzSFrsdbbyBaIjw8nLRu3Vrq2suXL0ndunVJenq6TPnt27eTJk2akPr165PBgweTlStXkkGDBsmUu3v3rsb6XBH9+vUjAAiHwyGenp4kNDSU1frT0tIIALJhwwaVPysUComPjw9p2LAhKSoqUlju5s2bBAA5duyY0nU/efJE5f5QKkd1G/Pdu3cTDodDAEj+8Xg81t8hdalu410VYMY8NDSUmJiYSD0bJiYmFT4bPXv2JLVq1SJisVgb3SWEEBIXF0eMjIzIyJEjtdYmm2j7Od+5cycBQO7fv19h2bLzA/OPw+FovqMahM4t2keXY/7XX38RAGTFihWSa4cOHSIAyMOHD3XWL01S055xRXKI1kwEHR0dkZ2dLaWtSktLg0AggKWlpUz5iRMnIjIyElevXsXRo0cBKA5xqSssLS3h5eUliYTEti2tra0tzM3N1Qp08euvv+LFixdYtWoVDAwUKyp9fX1hYGBA/bAoWiUlJQWEEDg6OoLD4cDKygoikUjuXECpWajjn6dt80AGd3d3fPPNNwgNDcXDhw+11m5V5eTJk/D09ESzZs0qLEtzj1GqOmKxGHPmzJHMEwyMeawiyyJK9UBrAlaDBg3A5/Nx//59ybXIyEg0atRIRgA4c+YMlixZAj6fD3t7exBCcOXKFbRp00Zb3VWKxMREjZqFcDgc1KpVSyUBKywsDB4eHpg+fToMDQ2RlZVVbnkjIyM0adKEClgUrZGamoqffvoJAwcORHJyMsRiMVJSUtCwYUNMnToVubm5uu4iRYeo45/HRA9kM7mwssybNw9WVlaYO3eu1ttWBn1Jh5Cbm4sLFy5gwIABSgnB8nKPsRHSmkLRFocOHcLdu3exbNkyGBt/jPDHCFgJCQm66hpFC2hNwDI2Noa/vz+Cg4MRFRWFS5cuYffu3Rg1ahSAEm1WQUEBgJLgDocOHcKZM2cQFxeHBQsWID8/H4MGDdJWd5VC0wIWoFqodib0NXMqUlhYqJRTcKtWrXD37l3qcEnRCosXL0Z+fr5U0muBQIBff/0VcXFxCA4O1mHvKLpGkYbC3l5xjpfDhw/Dy8tLbp4ZTWNtbY2goCCcO3cOly5d0nr75aFKOgRlBLHKCGsXL15EQUGBTHh2RZTOPcawdOlSGnWNUiUoLCzE/Pnz0axZM5ln1snJCTwej2qwqjtaNFMkeXl55Pvvvye+vr6kffv2ZNeuXZJ7devWJUePHpX8ffz4cdKlSxfi6+tLxo8fT2JiYuTWqSsfLLFYTExMTMjMmTM12s6sWbOIkZGRUn4Fnp6ecm3WPT09y/3cjh07CADy/PlzpfpU0+xr9YHyxjw0NJR4enpqzBeQTZ48eUJ4PB6ZNm2a3Pvjx48nPB5P57bp9BnXPqV9sMr633A4HMLhcMiWLVtkPpeRkUEMDAzI999/r+0uS8jPzyceHh7Ez8+PiEQinfWjLIrWBEtLS/Lbb7+RvXv3kri4OLJv374K/d7U9Y1jGDt2LLG0tCRCoVDl75GUlESMjIzI6NGjVf6svkHnFu2jizFfv349AUDOnTsn9767u3u1eJ7lUdOecUVyiFYFLE2gKwHr3bt3BABZu3atRtvZvHkzAUDevn1bYVl1nYIfPHhAAJCwsDCl+lTTXh59QNGYV3bTo2369etHLCwsSFpamtz7GRkZxN7enrRt21anG1X6jGsfZswfP35MABAbGxvJocHOnTslQYW++eYbUlxcLPncrl27CABy584dXXWdEELI3r17CQDyxx9/sFIfGwcnitYEZf9ZWFiQpUuXknXr1hEbGxu1DvAIIaS4uJjY29uTESNGqDESJcycOZNwuVylDwL1FTq3aB9tj/m7d++IjY0N6d69u8Iy7dq1I126dNFir7RHTXvGqYDFMo8ePSIAyIEDBzTaTnh4OAFA/vnnnwrLenh4qLUAFhUVEWNjYzJjxgyl+lTTXh59QNGYq6u11AWXLl0iAMjKlSvLLcdsVLdt26alnslCn3Htw4z58uXLCQCSkJAgdb+4uJjMnDmTACB9+vQhO3fulDz/+hCBUiQSEXd3d8Lj8SqtTa7swYlQKCTBwcEKBScPDw/y/PlzsmPHDvLLL79USghTJqrftWvXKr1eJiUlEWNjYzJq1Ci169AH6NyifbQ95nPnziUcDofcu3dPYZnPP/+c+Pj4aLFX2qOmPeNUwGKZiIgIAoBcu3ZNo+08ffqUACD79u2rsOzChQtlFj9lF+VPPvmEfPLJJ0r1SVcvT1UyhWMbRWOui1DG6vwOxcXFxNfXl3h6epL8/Pxyy4rFYtKlSxdiaWlJkpKS2Oq2StS0BUIfYMa8bdu2pGXLlgrLbdu2TWIyqE+a29DQUGJoaMhKnypzcPLo0SPi5+dHAJB27doRY2NjhX1ixry89goLC0lmZiZxdXVVu09z5swhfD6fvH//XuWxKM2sWbOqvBaLzi3aRxtjXnpdBEDat29fbvnvvvuOGBsbazWthLaoac84FbBYhjFLef36tUbbyc/PJxwOhwQHB1dYdvTo0cTQ0JC4u7urLIR8++23xNjYuNycWQy6eHmqmikc2ygac2dnZ61qsNT9HX777TcCgOzfv1+pdp49e0YEAgEZPnw4G91WmZq2QOgDT548IUlJSYTD4ZAlS5aUW9bBwUHvNLdsapPLM+07ceIEKSgoIIRIb+o8PDzI0KFDiUAgIPb29hKf5vIORFTJPSavjLGxsVJzcN26dUmPHj1UHoeyVActFp1btI+mx1ydd4Px0ZKXB7aqU9OecSpgscySJUsIAMlCp0nc3NwqdIbMysoixsbGZNKkSWq1ERYWRgCQBw8eVFhWFy9PVTKF0wTyxjwzM5PY29urrbVUB3V+h9zcXOLq6kpat26t0mnd4sWLCQASERHBQs9Vo6YtEPrAkydPJIloK5qH9DEJLZt9UvSecblcAoBYW1uTzp07y2jMAJDWrVuT1NRUpdop/Zwro5kuXYbD4ZAmTZpU+E5HR0cTAGTz5s2qDYICGC3Ws2fPWKlP29C5RftoeszVWRePHDmi9J6rqlHTnnGdJxqubiQmJsLe3h6GhoYab0uZUO379+9Hfn4+Jk6cqFYbrVq1AgC9zYelTm6c6oxYLMaXX36JrKwsLFq0SBLKWCAQYPv27RoLZazO77B27VokJiZi7dq1KiWBnTdvHurWrYuRI0fCw8ND53l8KJrn1KlT8PT0RNOmTcstp49JaNnsU0hICLhc6eXZxMQEe/bsQXh4OPr06YMrV66gsLBQ5rPJycnlhrRXRGBgIGJiYiAWixETEyN3DildZuPGjXj06BFCQ0PLrffkyZMAgAEDBqjcJ3l8//33MDQ0xLJly1ipj0KpLOqsi25ubgBosuHqDBWw1CQhIUHjObAYlBGwdu7ciWbNmqmdB6ZOnTqwsrLSWwFL0SaFmaRURV+Sb6rLkiVLEB4ejo0bN2Lx4sWIiYnBsmXLIBQK0blzZ420mZ2dDYFAIPcej8fDgQMHIBKJpK4nJydj5cqVGDx4MDp06KBSe4aGhhgyZAjS0tIQHx9fbh6fqv57UoD8/HylE9HqYxJaNvvUv39/EEJgYWEBDocDT09PbN++HaNGjULv3r3LFWq0tWGbMmUK2rVrhxkzZiA1NVVhuVOnTsHPz0+SXLWyODo6YsqUKQgLC8Pz589ZqZNCqQzqHK4w7wMVsKoxWtWjaQBdmQj6+vqSvn37aqUtxhxRUXCAe/fuEQBk06ZNlWqnW7dupHnz5hWW05UPljxzmPr165N3796pXFdV8+cqPeanT58mAMjo0aOlzHOePHlCAMjNFVRZ0tPTSatWrQiHwyECgUBq7AQCAXFxcZH8HmFhYWTv3r1SZhPqpjNQZHrh5OREsrOzCSGa+T1rmomDPsCkpLhw4YJS5fUx6A3TJ+Y5/OWXX9Sq5/jx4wQA+fvvvxWWYcNsurLP+ePHj4lAICDDhg2Tez85OVlpH2JVSE5OJsbGxuTLL79ktV5tQOcW7aMLH6yK1qDi4mJiYGBA5s+fr9G+6YKa9oxTHyyWsbe3V9vfSVVCQ0MJABIdHS33/pQpU4ihoSHJzMysVDs//PADMTAwqDDKm65enk8//VRi++/p6UmmTJlC+Hw+adSoEYmLi1O6nqroz8WM+YsXL4ilpSVp3rw5ycvLkylXv3590rVrV1bbTkpKIo0bNyaGhobk1KlTcje2IpGIHDx4kDRq1Ejid8KGwFNRHh8nJye5gre831OVDXlNWyD0gYCAAGJhYUEKCwt13ZVKc/v2bQIoH9SlLF9//TUxMzMrdyzYOFhg4zlnDgBPnTolc09Znzp1+O677wiXyyVPnz5lvW5NQucW7aONMd+0aZPU2qPMe+jh4UFGjhyp8b5pm5r2jFMBi0UKCgoIANZP5RRx/fp1AoCcOXNG5l5ubi6xtLQkgYGBlW7n2LFjBAC5efNmueV08fLk5eURc3NzMmbMGKnrly5dIhYWFsTV1ZU8evSownqYBNHy/unSQb4injx5QnJyckjjxo2JjY0NefPmjdxy8+fPJzwej7XIRDExMaROnTrE1NSUXLx4scLyIpGI2NnZsSbAKhKG7ezsyE8//UTGjh1brgD21Vdfkc2bN5MFCxaUG666LDVtgdAloaGhkhx++q5JVpbi4mJia2tbYXAiRdSuXZv079+/wnKV1eKx8ZwXFhaSxo0bE1dXV5KVlSV1b8CAAcTT01MjoahTUlKqpBZLn+YWfdQCawJtjPmpU6cIoFrqnvbt25POnTtrsFe6QZ+ecW1ABSwWefPmDQFAdu3apZX2kpKSFJoAMklZr1y5Uul24uPjCQDy888/l1tOFy/P/v37CQBy6dIlmXsPHjwgzs7OxNLSUuE4pKamkvnz5xMLCwuFm3F91mA9fvyYDB8+nHC5XHL+/HmF5e7evUsAkN27d1e6zWfPnhF3d3diaWlJbty4ofTn2IympswpvSIhzNDQkFhZWZUrgCn6zWvaAqErqqK5rrKMGDGCODo6EpFIpNLnXr58qXC+Zxu2nvPbt28TLpdLJk+eLLmWm5tLjI2NyfTp01lpQx6zZ8+uclosfZlbqvO7VxZtjPnixYsJh8MhHz58UPozw4YNI3Xq1NFgr3SDvjzj2oIKWCzCZKXXVvhosVhMjI2NycyZM2XuderUidSpU4eVE0KxWEycnJwqVFnr4uXp06cPcXNzU7hZiYmJIQ0aNCACgYBMnz5dcirn6upKevbsSYyNjQmHwyFDhgwhy5YtqzILS1mfjqFDh5ZbXiwWEw8PD9KvXz+12+JwOMTZ2ZlYWFgQe3t7cv/+fZXqYdsEs6JT1vI2CmKxmMTHx6ss9NW0BUJXVEVzXWX5/fffCQCV35+tW7cSAFoJQ87mcz5r1iypw74TJ04QAEppvtUlJSWF8Pl8YmJiUmW0MJUdc7a0Tmy+e/quCdPGfN6/f3/SoEEDlT4ze/ZsYmRkVO2SDde09ZMKWCxy4MABAkApkzS2aNSoERk4cKDUtWfPnhEAZMWKFay1079/f1K/fv1yy2j75UlOTiY8Ho/Mmzev3HIZGRnEx8dH7qLRsWNHKR+20NBQYmZmppK9tLZR94RxxowZRCAQSIJAqNsWh8Mhq1at0lq/K0NFC7yqm4matkDoCn3MZ8UWjOXB8uXLVfqcv78/8fLy0sqmi83nPCcnh9SqVYv4+PiQvLw8MnbsWGJlZUWEQiFrbZQlNDSUGBgYVInDMobKjDmbcytb715V0IRpYz53cXFR2Vx1w4YNBABJS0vTUK90Q01bP6mAxSJr164lACodVEIV+vfvT5o0aSJ1bc6cOYTH45GkpCTW2lmyZAnhcDjk/fv3Csto++VhMp4/fvy4wrLu7u5Kb6SDg4MJoJ1k0eqg7gkjo2E9cOCAxttShL6daKq6CahpC4SuqM4aLEIIad68Ofn000+VLi8UComFhYXWAiix/ZxfvHiRAJCYYmt6o10Vn5/KjDmb35etuqrCb6Dp+Zw5TFm3bp1Knzt69CgBQO7du6ehnumGmrZ+0kTDLJKYmAhjY2NYWVlprU1vb2+8efMGhBAAgFAoxO+//47+/fvDycmJtXZatWoFQggiIyNZq7Oy7Nu3D35+fmjYsGGFZRMSEuRel5fwjxm38nK46BJ1kyu3a9cOjo6OOHbsmMbbUoQySUu1SWBgILZv3w4XFxcAgI2NjUYTMusr+pYvLCQkBHw+X+qarvNZsUmvXr1w/fp1ZGdnK1X+33//RXZ2Nnr06KHhnmmG5ORk8Hg8yffNy8uTm7eOLWpaAno2v29ISAh4PJ7UNXXevZr2G8jj3r17AKByHlImF5aifQtFM2hrHaQClhokJibCzc2twmSYbOLt7Y2cnBykp6cDAP7880+kpqZi4sSJrLbTsmVLANCbhMOPHz/GvXv3MGrUKKXKq5Lwz9HREQCQkpKifgc1iDrJC4GSpL/+/v44c+YMCgoKNNpWVSIwMBBxcXHg8/mYOHFijRSuJk2ahNjY2HKTNmuTwMBAeHh4QCAQSCXUrS6/Ta9evVBcXIzLly8rVf7cuXPgcrno0qWLhnumGYKCgmSSjefl5SEoKEgj7VWleYvZ1DVq1EjtTR2b33fYsGEwNDSUJMc2MTFR691T1Lazs7PKfaqqREZGgsPhoHnz5ip9zs3NDQBNNqxNtLkOUgFLDRITE+Hq6qrVNr29vQEAr1+/BgDs3LkTrq6u6NmzJ6vt2NnZoVatWnojYO3btw88Hg8jRoxQqnxISIhkwWBQdCrHaLD0VcBatmyZzDVlTxgHDx6M3NxcnD9/Xum2yh4YVCdNAgOPx0Pt2rXx/PlzXXdF6wQFBSEvL0/qmiY3v8qQlJSEV69eYeHChXj8+LFeaDvZpF27djA3N0dERIRS5c+fP482bdrA2tpawz3TDNrWZqgy3+sStjZ1ISEhEAgEUtfU/b43btxAXl4e9u7di3HjxsHAwABDhw5VuZ6QkBAYGRnJXM/JycGDBw9Urq8qEhkZibp168Lc3Fylzzk6OsLAwIAKWFpEm+sgFbDUICEhQacCVnx8PCIiIjBu3DgZFT8btGrVSi8ELLFYjLCwMPTs2RMODg5KfYYxBfP09KzwRJzRYCUnJ7Pab7aoX78+AMDW1lbl0/3PPvsMVlZWSpsJ8vl8EELUaquq4ePjgxcvXui6G1pHH015Tp06BQDw9/fXWR80CZ/PR7du3RARESEx71ZEZmYm7ty5U2XNAwHta5SY+Z4RSN3c3PRy3mJrUxcYGIiGDRtKrfshISFqfd9Tp05BIBCgR48e8Pf3R3Z2Nv766y+V6wkMDET37t0BQLJ2rFq1CpaWlujUqROuXLmicp1VjcjISPj5+an8OS6XC1dXV2oiqEW0uQ5SAUtFCCF4+/at1gUsLy8vACUC1u7duwEA48aN00hbrVq1QmxsLNLS0jRSv7JcuXIFCQkJSpsHMijr/6PvJoLh4eHgcDiIjo5W+XRfIBCgf//+OHXqFIqKisotKxQKMX/+fDRt2hQpKSl64zelKXx8fPDy5UuIxWJdd0WrKNrkMmYquuDEiROoU6cOGjVqpLM+aJpevXohNjYWz549K7fc5cuXIRaLq7SApQuNUmBgIH755RcAwNmzZ/Vy3mJrU5eamopHjx5h9uzZePnyJQCoPY+dOnUKnTt3hrm5Obp16wZTU1OcOHFC5XoIIfjvv//Qs2dPydoxZ84cXL9+He7u7ujZsyeOHj2qVh+rAqmpqUhISFDZ/4rB3d2darC0iCLTVU0cAlEBS0XS09MhFAq1LmCZmJjA2dkZL168wO7du9GtWzeJ0MU2rVq1AqB7P6y9e/fCwsICAwYM0Ej9xsbGsLCw0FsN1pkzZ9CmTRvY29ur9fnBgwfj3bt3+Pvvv8st9+uvv+L169dYsWKFRjSi+oaPjw8KCgqQmJio665oFXkBJYASYVzZIAxskp2djUuXLsHf31+r/qzahjHjrshM8Ny5c7C0tETr1q210S2NoIoFAZsw5t76Opezpdk7cOAARCIRRo4cidq1a8PX11ct4eXZs2d48eKFZG01NjZGr169cPLkSZUFtsjISLx58wbDhg2Tuu7u7o5r166hZcuWGDp0qEQIrm6oG+CCwd3dnWqwtERhYaHcNVBTh0BUwFIRZlOm7VPfsLAwZGZm4vfff0dcXJzEfEwT+Pn5gcPh6FTAys3NxdGjRzFkyBAYGxtrrB1HR0e91GClpqbizp076NOnj9p19OzZEyYmJuWaCWZnZ2PJkiXo3LkzevXqpXZbVQkfHx8AqHFmgoGBgXBzc5MKKDFjxgzExsaiZ8+eWheywsPDUVRUVG3NAxk8PT3RoEGDcgUsQgjOnz+Prl27wsDAQIu9Yx9dRBBlTqWTkpI03pY6sOU7tXfvXjRv3lyi8Q0ICMCNGzdUPixiTHP79esnuTZo0CAkJSXh33//VamuQ4cOgc/ny32PbWxscOHCBfTp0wdff/01AgIC4OnpqTdRTNmAibisaoALBjc3NyQkJFRoQkypPEFBQYiNjcXMmTMle3grKyuNHQJRAUtFmIlMmxosxkG2sLBQcm3Xrl0am5zMzc3RoEED1gQsdUJinjhxAjk5OSqbB6qKvgpY586dAyGkUgKWsbEx+vTpg+PHjys8lVyzZg3S09OxatWqaq1FKE1NFbBSUlLw5s0bLFy4ULL5Xb9+PQ4ePIi7d++iV69eWhWyTpw4AQcHB7Rt21ZrbeqKXr164e+//0Z+fr7c+8+fP0dcXFyVNg/UJfquwQoMDJQJILFy5UqVNnXR0dGIjIzEyJEjJdcCAgIAAMePH1epP6dPn4avr6+UBq1Pnz4wMDBQqS5CCA4dOoTu3bsrDMxiYmKC48ePo2PHjjh27Bji4uL0JoopG0RGRqJOnTqwtLRU6/Pu7u4oLCyURIimaIaIiAisXbsWU6ZMwbp16xAfHw8PDw/07t1bY4dAVMBSEUaVq00BSxfRv5hAF5U9VVE3etK+ffvg4eGBjh07Vqr9inByctLLRfnMmTNwcnJS+1SMYfDgwUhOTsbNmzdl7iUlJWHt2rUYNmyYJDx/TcDNzQ1GRkY1TsA6d+4cAMgI7YMHD8bBgwdx584drQlZhYWFCA8Px4ABA2qEWWqvXr1QUFCg0FyXifZJBSz1MDMzg4mJid5qsICStcbIyAjnzp0Dh8NR+WBPXkTdBg0aoEGDBiqZCaanp+P69evo37+/1HVra2t07twZx48fV3rdv3PnDmJjY/H555+XW47P5yM2Nlbmuq6jmLLBvXv31DYPBGiodm2QkpKC0aNHo3HjxlizZo3kerNmzfDw4UONtUsFLBVJTEwEl8tlNblvRegi+lerVq2Qmppa6ZdeHeEwKSkJFy5cwJdffgkuV7OPqD5qsIqLi3Hu3Dn07t270t+/b9++EAgEcs0Eg4ODIRQK5YaDr85wuVzUrl27xglY4eHhcHJygq+vr8y9wYMH48CBA7hz5w5atGgBDw8PjZrx/PXXX/jw4UO1Nw9k6NSpE4yNjRWaCZ47dw516tRBrVq1tNyz6gGHw4Gzs7NeHpYxJCQkwM3NDe7u7ujXrx+2b98uZZVSHkxE3R49esjsPQICAnD16lWlg1KFh4dDLBbL9W0eNGgQXrx4gejoaKXqYswDBw4cWGFZRXuJqpyQOCMjA7GxsZUSsJhkw1TA0gxisRhjxoxBdnY2Dhw4IOVy0qxZMzx9+lShZUFloQKWiiQmJkpyF2gLXSRTZAJdqGqPXZqsrCy5p1ZA+ZPqH3/8AbFYLGUKoSkcHR2RlZWl9EKnDW7evImsrKxKmQcyWFhYoFu3bjh27JjUqeSzZ8+wc+dOTJ48GXXq1Kl0O1WNunXr6kUuLG1llFdGaA8ICMCUKVPw8uVLxMfHa9SM58SJEzA1NUXXrl1ZrVdfMTIywmeffYazZ8/K3CssLMRff/1FtVeVxMnJSa81WPHx8ZLN9PTp05GamoojR44o9dmrV68iLi5O7poYEBAAsVisdATA06dPw8XFRW5YcUboUqYuxjywZ8+esLKyqrB8VUoKrSyM/xUbAhYNdKEZNm7ciIiICKxbt04mWq2vry/EYjEeP36skbapgKUiukgyrIvQt8wJ1tChQ1Xe+BFCcPDgwXIDcZQXGW/fvn1o1aqVRgN5MOhjsuHw8HAYGBhIcotUlsGDByMmJkYq6eP8+fNhbGyMhQsXstJGVcPHxwevX7+GSCTSWR+0mVH+1q1bSgntJ0+elLnGthmPWCzGyZMn0bt3b7kJSqsrvXr1wvPnzyXJ4hlu3ryJvLw81pPG1zSqigYLALp27Yp69eph06ZNSn127969MDc3l6spatasGby9vZUyEywsLERERAT69esn96DF1dUVbdq0UcoP6/bt24iPj6/QPJBB3j7G0NBQ75JCq0JlA1wAJXshPp9PNVga4N69e5g7dy78/f0xefJkmfvNmjUDAI2ZCVIBS0USExO1HkFQ26Fvw8LCMGXKFMnfqmz83rx5g759+2L48OFwc3PDsmXLZCZVDoeD1NRU/PjjjzIb3KioKDx8+FAr2itAP3NhhYeHo0OHDmo7zZZl4MCB4HK5kgX45s2bOHbsGL7//nulEzhXN3x8fCAUCnVqnqJN38rw8HDweLwKhXZtmCP/+++/SE5OrjHmgQxMlE7GF47h3LlzMDAwwGeffaaDXlUf9NWfFgBEIhHevn0r2TtwuVxMnToVt2/frjCYVF5eHo4cOYKAgACZtRQoWU8DAgJw6dIlvHv3rty6rly5gpycnHJTn/j7++Pu3bsVbvgPHToEgUCgdBqVsvsYPp8PMzMzDBo0SKnP6yP37t2Dt7e3wgAfysDlciWRBCnskZOTgxEjRsDBwQE7d+6UG8TL29sbZmZmUofPbEIFLBVJSEjQugYL0G7oW0Ubv2+++QZRUVESoai0eZOnpydGjBiBRo0a4dq1a9iwYQNu376NoKAgGeFw586dGDt2LJYuXYpu3bpJmXXs27cPBgYGGD58uMa+X2n0KfpUWFgY3NzcEBUVhQcPHrCmybCzs8Onn34qMRP8/vvv4ejoiFmzZrFSf1VEHyIJatO3UlmhXZG5jouLC2t9OXHiBAwMDFgxga1K+Pj4oFatWjJ+WOfPn0e7du1gYWGho55VD5ydnZGVlaUxf4rKkJKSguLiYok5GACMHj0aZmZm2Lx5c7mfPXXqFD58+FDuoWNAQACKi4tx+vTpcus6ffo0jI2N0aVLF4VlGIFHnjabQSwW49ChQ+jVq5dKB4Gl9zEXLlxARkYGli9frvTn9Y3IyMhKmQcyuLm5UQ0WSzD7UnNzczx//hxjxoyBra2t3LJcLhdNmzbVXKALUsW5e/eu1trKzc0lAMhPP/2ktTZ1AYfDIQAU/jMxMSENGzYkBgYGMvf8/PxIXFycUu38/vvvxMTEhDg4OJC5c+cSDw8PAoAYGxuT0NBQDX/LEmJiYggAsmPHDq20p4jQ0FBiYmIiM87MODx58qRS9Y8aNUqq7rFjx7LR7SpLYmIiAUA2b94s935lx1sZPD095b5fnp6erLaTkJBAAJCVK1dWWFbecwiA2Nvbkzdv3rDSn3r16pHu3bvLXNfGmOuar7/+mpiampLCwkJCCCGpqakEAFm6dKlO+lOdxnzXrl0EAGvPKZvcvn2bACCnTp2SGvOpU6cSgUBAUlNTFX62T58+xN3dnYhEIoVlRCIRcXNzI/3791dYRiwWE3d3dzJw4MAK+1u/fn3StWtXhff/+ecfAoCEhYVVWFd5fPnll0QgEJDnz59Xqp6K0MRznpGRQQCQFStWVLquL774gtSqVYuFXukHuppXKtpHyePrr78mFhYWRCwWq92uIjmEarBUQBc5sHRBeafYoaGh8Pf3x8uXL1FcXCxTJiMjQ+qUrjxGjRqFO3fuwMDAACtXrpSc3Ofn52stP4a+mAhq0lwsLCwMhw8flrp28ODBKp9/pDI4OzvD1NRUpxosbflWMhqT3r17V1hWnjny4sWLUVRUhA4dOuDp06eV6kt0dDSePXtW48wDGXr16oXc3Fxcv34dAHDx4kUAoP5XLMBYI+hjoAvG/Kuse8G0adMgFAqxY8cOuZ9LSUnBuXPnEBgYWG5EWS6Xi4CAAJw/fx4fPnyQWyYqKgrx8fFKmfQNGjQIV65cQWZmptz7hw4dgqGhoUyod1VZvXo1jIyMMH369CqXaPfevXsAIDdYiKq4u7sjMTFRYb5KinKos4/y9fVFdnY2YmJiWO+PVgUsoVCIhQsXolWrVmjfvr3CSQUA7t69i8GDB8PX1xcDBw7EP//8o8WeyqemCFiKNn6rVq1CYGAgFixYgKKiIrmfVdW8qWHDhnLz4GgrP4aRkREsLS11biKoSXOxoKAgGbOZ6pB/pDJwOBzUqVNHpwJWYGAgNm7cKNWnrVu3sm7+Gx4eDjc3NzRu3FjpfpU2R160aBH+/vtvFBUVoVOnTpWyV2eikynrt1Hd6Ny5M/h8vkToPXfuHGxsbFjZpNV0nJ2dAeiHuXdZGPOvsoeP9evXR7du3bBt2za5B5YHDhyASCRSyic5ICAAhYWFOHPmjNz7p06dAofDQd++fSusy9/fHyKRSG5dYrEYhw8fRp8+fWBubl5hXeXh5OSEJUuW4Ny5cyonS9Y1bApYbm5uEAqFSofap8hHnX2UJgNdaFXAWrVqFe7fv489e/YgODgY27Ztk/sCZ2RkYPLkyejVqxdOnTqF3r17Y+rUqRIBRxGaDnnMtK/tIBfaRpmgGmyGXFXk3KmtAAT6kAtLkyFsdZFHrSrg4+Oj81xYTZo0AQCMHz8ehBDWD2+EQiEuXLiAPn36yHXyVZamTZvi2rVrknDj8hJXK8OJEyfQqlWraj+HKsLc3BwdOnRAREQECCE4f/48unXrViOSLWsaffKnLUtCQgIMDQ3l+oJMnz4dCQkJcn2e9u7dCz8/PzRs2LDCNj755BM4OjoqjCZ46tQptGnTRmK1UR4tW7aEq6urXKHn+vXrSEpKUjp6YEVMnToVTZs2xYwZM5Cbm8tKndogMjISXl5eCv17VIGGamcHdfZRjRs3BofD0UigC60JWHl5eTh06BDmz5+Pxo0bo1u3bpgwYQJCQ0NlyjInA5MmTYKHhwcmT54MIyOjciVMbYQ8Zh7+6q7BAioOqsGmeZOu82Pog4AVEhIis8liy1xM1+Orr9StWxevX79WqI3VBkz+jW+++QbGxsY4deoUq/Vfv34dHz58YCWgRN26dXHt2jXY29uje/fumD9/vkoHWomJifj3339rrHkgQ69evRAVFYULFy4gKSmJ5r9iCQcHB3C5XL01EXRzc5N7yNG3b194eXnJBLt48uQJ7t27p3REXR6Ph0GDBiE8PFzGTOrt27e4e/eu0iZ9XC4XAwcOREREhExdhw4dgpGREfr166dUXRVhYGCArVu3Ij4+vkolvWcrwAXw8dCeBrqoHCEhITI5aivaR5mamqJu3bpVW4P19OlTCIVCqQeyRYsWePTokYxq3MrKCh8+fMDZs2dBCMHFixeRm5uLevXqKaxfGyGPExMTYWFhATMzM9bqrKqwGTpeF3m+SqMP4X0HDhwIDocDc3Nz1kPx63p89RUfHx+IRCKN2F4ry5MnT2BkZIRGjRqhR48eOHXqFKu+COHh4eDz+awl9PX09MTVq1dhbW2N5cuXq3SgxQiPVMAqCdc+Z84cAKACFkvweDzY29vrfC6XR0JCgkLfZB6PhylTpuDKlSt49OiR5Pq+ffvA4/EwYsQIpdsJCAhAXl6eTCqAP//8E4BqprmDBg1Cfn4+Lly4ILkmEolw5MgR9O3bl9V9UPv27TFmzBisXbu20n6e2iArKwuvXr1izbSXeTaogFU5AgMD0ahRI/D5fJX2Uc2aNdOIBsug4iLskJaWBktLSxgaGkqu2dnZoaioCJmZmVL5eFq2bIkvv/wSM2fOxHfffQeRSIRly5ahdu3acuuOjo4u1wyKSZpbWZ4+fQo7OzvW6quqFBQUIDo6Gn5+fjh79qzUPXXGxs/PD4sXL8b69euRnJwMJycnzJw5E35+floZaz6fj6SkJJ3+rmfPnkVxcTE2b96MVq1aSa4zfWLGXB10Pb76Cp/PBwBcunRJ5pCnMuOtCrdv34aXlxeeP3+Oli1b4uTJkzhx4gRrSbaPHz+Oli1bsr5wy9P65eXlYc6cOQo3HWFhYfD09AQgf57Q1pjrmoiICHC5XERFRcHAwAAHDhxgTRugKtVtzK2trfHixQu9+06vX79GixYtEB0dLXfMO3ToAENDQyxbtgyLFy+GWCzGb7/9hvbt2yMzM1NhsImyODg4wNLSErt375aaQ/bv3w83NzdwuVylx8bBwQEWFhb47bffULduXQDAnTt3kJycjE8++YT1MR4/fjyOHTuGsWPHYteuXZUyaS4L28/57du3AZQkCWajXkII+Hw+Hj58qHfPrjrocl5JTk5Gr169sHLlSsm1ivri4uKCmJgY/Pvvv5X2K5RC7biEKnL8+HHSoUMHqWtxcXGkbt26JD4+Xup6Tk4OmTRpEtmwYQN5/Pgx2b59O/H19SX379+XqZcJj6iNkMdt2rQh3bp1Y62+qkp1Cu1LCCHLli0jAEh+fr7O+hAQEEAcHR1JcXGx3PvVbcz1gZSUFAKAbNiwQeaetsbb3d2dfPHFF4QQQpKTkwmHwyFLlixhpe43b94QAGTdunWs1FcaRakcOByO3PLv3r0jBgYGZM6cOQrrrAnPuDphhDVJdRvzXr16kZYtW+q6G1KIRCJiYGBA5s2bRwhRPObjxo0jJiYmJDMzk1y+fJkAIPv371e5vbFjxxILCwtSUFBACCnZTxkZGZFvvvlG5bq+/PJLYmNjQ4qKigghJSGtTUxMSE5Ojsp1KcOWLVsIAGJnZ0c4HA7x9PRk5d1g+zlfvXo1AUDS0tJYq9Pb25uMGDGCtfp0ia7mlfT0dAKArF69WqXPnTlzhgAgV69eVatdnYdpNzQ0hFAolLrG/G1sbCx1fdeuXRAKhfj222/RsGFDTJw4ET169MC2bdsU1h8SEiJTD9tmUImJiTXWObs6o+tQ7bm5uQgPD0dAQAB1dtci9vb2sLCw0Fmgi+zsbMTHx6NRo0YASp7Dtm3bsuaHxWiXNZHQV5H/np2dncK+FBcX13jzQG2YstdknJyc9M4HKzU1VSbJsDymTZuGvLw8/Pbbb9i7dy/Mzc0xcOBAldsLCAhAdna2JAXAxYsXUVBQoFbkzkGDBiEzMxPXrl1DcXExjh49in79+sHU1FTlupSBMZFPT0/XmC89G0RGRsLDw0PhfKcO7u7uNMhFJWFMbJs2barS55hIgmybCSolYN24cQMrVqxAYGAgevXqhb59+2LUqFFYtWoV/v33X6UacnR0RHZ2tpSQlZaWBoFAIJMJ/NGjR/Dx8ZG61qhRo3LNXAIDA6WEKTZ9WIAS2+OkpKQaEeCipqFrASs8PBz5+fkYOnSoTtqvqXA4HJ1GEmTMFhgBCyjxkbh7926FEVOVITw8HN7e3hLzHjaR59fH4XCQlpaGSZMmyUQDO3HihESArMnQiJ6axdnZGSkpKXqVT4jZt1R0ONu8eXP4+Phgzpw5+O233yAWi3Hs2DGV2+vWrRssLCwk0QRPnz4NS0tLdOrUSeW6evbsCSMjI5w4cQJXr15Famoqa9ED5bFw4UIZH1R9PICIjIxkPbWCm5sb9cGqJFFRUQBUF7BcXFxgZ2fHeqCLcgWs48ePo2fPnvj++++RkZGBTp06YezYsQgMDETbtm2RkJCA7777Dr17964wh0GDBg3A5/Nx//59ybXIyEg0atRIJuqHg4MDnj17JnXt1atXFUY9YxJp2trayo18VxlSUlIgEomogFUN0XV438OHD8PBwQEdO3bUSfs1GV0KWEwEwdIhmJlTZsYpXV0KCgpw6dKlSodnV4S8IDd79uzB3LlzsXPnTjRv3hx37txBWFgYPDw8cOjQIeTm5mL//v2s96UqQSN6ahYnJycUFxcr7bOkDRQlGS5LWFgYYmNjIRKJAJRYNqijvWESAJ88eRJCoRB//vknevXqJfE5VQVTU1P06NEDJ06cwMGDB2FqaqpUwnJ1qQoHENnZ2Xjx4gVrEQQZaLLhyhMVFQV7e3ulUhGUhsPhoFmzZqwLWAqDXIwcORKurq5YvXp1hdLgrVu3cPDgQRw5ckThZGBsbAx/f38EBwdjxYoVSEtLw+7du7F06VIAJdosc3NzGBkZYdiwYRg+fDh27NiBXr164datWzh27Bi2b99ebj8KCwsBlOTRKiwslAqoUVlqSpLhmoguNVh5eXk4c+YMRo0aRc0DdYCPjw8OHToEoVAIgUCg1bafPHkCQ0NDeHt7S641aNAAtWvXxqlTp/DVV1+pXffVq1eRn5+vEfNAhsDAQLmHWL169cLIkSPRpk0bGBgYSAJi5OTkYNKkSZLP1kRCQkIwadIkKTNBGtGTPZhkw0lJSayab1UGRsCqyEQwKChIxo2C0d6o+r4EBAQgLCwMq1evRkpKSqUSezs6OiIuLg7bt2+HiYkJjh8/rrH318PDA7GxsXKv6wuMkoBtAcvNzQ1FRUVITU2VHPpSVCMqKgpNmzZV61CxWbNm2LJlC4qLi2WUPuqiUIO1aNEirFixQilVW9u2bbF+/XosXry43HI//PADmjRpgtGjR2PRokWYOnWqZAPQoUMHhIeHAyhR723btg1nz57FgAEDsHfvXqxZswbt2rUrt35GwALY10ZQAav6wkSw1IWAxeQsoeaBusHHxwdisRivX7/WetuPHz9G/fr1pQRrDoeDAQMG4NKlS8jJyVG77vDwcElSYG3z2WefISoqCsbGxjLRBvXR3EebsJnegiKLrq0R5BEfHw+BQFChwMem9qZnz57g8/lYsGABAGDu3Llq+TGFhYVJ5SrNy8vTqE+UPNNjgUCgVwcQkZGRAMC6iSAN1V45RCIR/vvvP5XNAxl8fX1RWFiI58+fs9YnhQJWnTp1VK6srN9UWYyNjbFy5Urcv38f//zzD8aNGye59+zZMwwePFjy96effopjx47h/v37OH36NHr27Flh+6VPf9h2dGUELBrkovphZGQEKysrnSzKhw8fhr29vVr28ZTKw/gnsTmpKsuTJ0+k/K8YBgwYgMLCQqn8M6oSHh6OLl26yAT+0RbW1tbIz8+Xe0+fzH10QUVJ3CnqU1qDpS+Ul2S4NGyajx4/flzK1CwhIUEtwSgoKEjmPdbkIUnZAwhDQ0Pw+XxJ7jh9IDIyEq6uriqboVUEI2DRQBfq8fLlS+Tn56stYGki0IVCPdgXX3yhtJpNXyK8lNZgsT3BJiQkgM/nw97entV6KfqBo6Oj1jVYjHngl19+yZpKmqIazKGQtv2wPnz4gLi4OLkCVvv27WFtbY1Tp05h0KBBKtf94sULvHjxAt9++y0bXVWbqmDuQ6le6KMGq7wkw6Vh03w0KChI4svFoI65oS58okqbHj969AjNmzfHvHnzsGPHDo21qQr37t1j3TwQ+Hh4TzVY6qFugAuG+vXrQyAQ4OHDh/jiiy9Y6ZPCXd0nn3zCSgPaRJMCVmJiIpydncHlai2yPUWL6ELAioiIQG5uLjUP1CE2NjawsbHRuoD15MkTANIBLhj4fD769OmDP//8EyKRSGXfPCY8uyad0ZWB+htRtI2ZmRlMTU31SoMVHx+P9u3bV1iOESqCgoIQFxcHDw8PhISEqKXhZEsw0vUhSZMmTTBz5kysWbMGY8eO1fm+9MOHD3j27BlGjBjBet12dnYwNDSkApaaREVFgcfjyV1TlUEgEKBhw4asBrpQKGBNmzaNtUa0RWkTwbdv37Jad2JiIvW/qsY4OTlJRbjUBocPH4adnR0+/fRTrbZLkUYXkQQZAUueBgsoMRMMCwvDrVu3lNqclSY8PBz169eXCp6hC9jcMFIoyuLs7Kw3GiyxWIzExESlNFiA4sAxqsKWYKQPhySLFi3CgQMH8PXXXyMyMlKn1h4PHjwAIUQjGiwOhwM3NzdqIqgmUVFRqFevHoyMjNSuo1mzZoiIiGCtT0o9qbm5uThw4ABevnwpUTsTQiAUCvH06VOcO3eOtQ5VBk1rsJo0acJqnRT9QdsarPz8fJw+fRpffPEFNQ/UMT4+Pvj777+12ubjx49lIgiWhgmrfPLkSZUErNzcXFy5cgVTp05lq6uVgq0NI4WiLE5OTnojYKWlpaGoqEjrvttsCUb6cEhiZmaGjRs3IiAgAJs2bcLMmTO11nZZNBXggsHd3Z1qsNQkKiqq0nkWfX198fvvvyM5OZmVSI5K2bstWLAAO3fuhFAoxJkzZyTOuefOnUP//v0r3Qm2YDRYVlZWrApYhBCJoyqleuLk5ITs7GyFjvlsQ80D9QcfHx/Ex8dr7bcH5EcQLI2FhQU6d+6MU6dOKV1nWFgYatWqhcLCQoSGhuqNbyyFok2cnZ31xkRQ2STDbMNmtEp9CMoyaNAg9OnTBz/++KNONTz37t2Ds7OzJJgK27i7u1MNlhq8f/8eMTExavtfMTCBLtgyE1RKwLp27RrWrl2LtWvXonbt2hgzZgwOHz6MMWPG6CT6liIYDZaXlxerE2x2djZyc3OpiWA1Rtu5sA4fPgxbW1t07txZK+1RFMMEunj16pXW2lQUQbA0AwYMwLNnz2SSrssjLCwMkyZNQlpaGgAgNTVVo+GUKRR9RZ80WMrmwNIE+iAYsQWHw8GmTZtQXFyscw2WJswDGdzc3JCYmCgToIRSPv/99x8A9QNcMOhEwBIKhfDy8gJQshl59OgRAGD48OG4e/cuKx1hg9ICFps+WDQHVvVHmwJWQUEBTp8+jUGDBlHzQD1A25EEy4sgWBrGOuD06dMV1hkUFCRlDgTQnFOUmomzszPev3+vVY20IhgBi1q/VB5vb28sWLAAR44cYdVPRllyc3Px9OlTjQpY7u7uKC4uRmpqqsbaqI5UNoIgg42NDdzd3bUrYNWpUwfXr18HULIZYYSq7OxsmczjuoTpi6enJ9LS0lBcXMxKvVTAqv5oM7zvuXPnkJOTgyFDhmi8LUrFMAKWtrTx5UUQLI2Hhwd8fX2VMhPURThlCkUf0adQ7comGaYox+zZs1GvXj1MnTpVqwJ0WFgYateuDbFYjK1bt2rMMoCGalePhw8fwsrKipWDjGbNmrGWC0spAWv69OlYsmQJDh48iIEDB+Ly5cuYMGECpk+fjo4dO7LSETYorcEihLCmjaACVvVHmxqsw4cPw8bGBl26dNF4W5SKsbS0hIODg9Y0WBVFECzNgAEDcP36daSnpyssk5CQoNCXi+acotQ09EnAYny3aXoXdjA0NMTWrVvx+vVrSdocLy8vjZpCM+bXzN4gLS1NY+bXjCkpFbBUIyoqCs2aNVM6d295+Pr64tmzZygoKKh0XUq99Z07d8bZs2fRtm1bODs7Y//+/fDx8UFgYCB++umnSneCLRgNFmPOyJaZIBWwqj8ODg4ANC9gFRQU4NSpU/D39wefz9doWxTl0Wao9ooiCJZmwIABEIvFCA8Pl3v/zZs36NSpE3g8HgwNDaXu0ZxTlJoIE4BAHwJd0OBY7JOUlAQej4f379+DEILY2FiN+ptq0/yaEbBooAvlEYvFePToUaXNAxmaNWsGkUiEx48fV7oupY9V3Nzc4OnpCaAk4/HcuXMxadIkGBsbV7oTbFFYWAgejycRhNiaYBMSEmBra1up+PoU/cbQ0BDW1tYaP/U8f/48Pnz4QKMH6hnaFLCePHlSbgTB0vj5+cHFxUWumeCzZ8/QsWNHZGVl4dq1a9i1axcrUcMolKqMPmmw4uPjqYDFMkFBQTJBIDTpb6pN82sbGxsYGRlRDZYKxMTEICcnh1UBCwArZoJKedjHx8dj/fr1ePToEYqLi0EIkbp/5cqVSneEDQoLC2FoaAgXFxcA7AlYNMlwzUAbubCOHDkCa2trdO3aVaPtUFTDx8cHv/32G3JycmBmZqbRth4/fowOHTooVZbD4WDAgAHYt28fCgoKJIc8UVFR6N69O4CS+bdp06Zo1aoVFagoNR57e3twuVyda7BUTTJMUQ5t+5uylbRZGTgcDg3VriJsBbhgqF27NkxNTVkJdKGUBisoKAhPnz7FqFGj8M033+Dbb7+V+qcvCIVCGBoawtHRERwOhwpYFJXQtIBVWFiIkydPYuDAgdQ8UM9gAl28fPlSo+0oG0GwNAMGDJAkEAaAu3fv4rPPPgOfz8fVq1dZW1golOoAj8eDg4ODxjRYYWFh8PLyqtD/Jz09HUKhkGqwWEaRYKMpf9OQkBAZ3x5Nml+7ublRDZYKREVFgcPhqLSmlgeXy0XTpk21p8F69OgRQkNDWfsCmqKwsBACgQAGBgawt7dn1QdLU5m7KfqDk5OTJFM724SFhWHmzJnIzs7GmTNnEBYWRrUNekTpUO2+vr4aayc6OhpAxREES9O5c2cIBAIMHToUubm5AABbW1tcu3YNtWrV0kg/KZSqjKaSDTMBDxifHMb/B4DMfK6rJMPVnZCQEKnfANCswOPs7AxCCGxtbZGZmQkPDw+EhIRobP12d3fHX3/9pZG6qyNRUVGoU6cOTE1NWavT19cXf/zxBwghlQqcoZQGy93dHTk5OWo3oi0YE0GAvQlWKBQiNTWVTpI1AE1psMomgdVkFCKKetSpUweA5nNhMY6zqhxWHT16FCKRCDk5OSCEgBCC3Nxc3LhxQ1PdpFCqNJpKNqwo4MH8+fNlyuoyyXB1JjAwENu3b5eMq5WVlUb9Tbds2QJbW1skJCRoJWmzu7s73r59S5MNK8nDhw9Zt+Jo1qwZ3r9/L9c0VBWUErDGjRuHhQsXIiwsDFevXsXNmzel/ukLjIkgALi4uLAiYCUlJYEQQk0EawBOTk748OGDzAJaWWgSWP3HzMwMzs7OGs+FpUoEQQZ5Tt35+fn0+aFQFKApAas8/5/x48fjwoULKC4uRlhYGMaOHQsA8Pf3p4dpLBMYGIi4uDg4OTlh0KBBGhN44uPjceLECUyYMEFrQc7c3NwgEon0IkiLvpOTk4NXr15pRMACKh/oQikTwXnz5gEAli5dKnOPw+FIzF50DWMiCJRosNhwUqMh2msOpXNhsWl6RZPAVg20EUlQlQiCDPT5oVBUw9nZGSkpKRCLxazmoFIU8MDU1BSHDx/G7t27YW5ujvz8fBQXFwMo0WQpMiOkVA5Nz9m//vorCCGYPHmyxtooS+lQ7XTfWT6PHz8GIUQiELFFkyZNwOFw8PDhQ/j7+6tdj1Izz9OnTxX+0xfhCpDWYDk7OyM5ObnSalYqYNUcNJFsODc3VyL0l4UmgdUv6tatq/Rirayje1keP36ssi+rtp26KZSqjpOTE4qLi5GRkcFqvSEhITIBikxMTPDrr78iNTVVYs7LCFcM1GJBM6gyZ6tKYWEhtm/fjv79+0tyq2oDxh2FBrqoGLYjCDKYmprCx8en0hospY928vLy8Mcff2DZsmUIDg7Gvn37kJmZWanG2aa0BsvFxQVisVji96IuVMCqObCdP0UoFGLIkCFSzyUDTQKrf/j4+CAtLQ3v378vtxzjUxcbG6tSoksmgqAqAS6Akk2diYmJ1DX6/FAoitFUsuHAwED4+fmBx+PJ5JszMjLC4MGDkZ+fL/ezVOPMPj4+PkhJSUF2djbrdR85cgRpaWmYOnUq63WXB6PBogJWxURFRcHc3FySo5dNmjVrVmkrOKU1WD169MCOHTuQkpKClJQU7Ny5E3369NF4WGNVKBvkAqj8BJuYmAgjIyPY2NhUun8U/YZNDZZYLMaYMWMQERGB7du3Y/fu3TQJrJ5TOpJgeajrU8do+1XVYDFO3fT5oVCUQ5PJhrOzs9GvXz+FAQ+oxll7KDtnq8OWLVtQt25ddOvWjfW6y8Pa2homJiY0F5YSREVFoUmTJqyaATP4+vrizZs3FR64lodSvQoJCUHHjh1x4cIFbNq0CVu3bsWlS5fQuXNnvTpFLWsiCKDSodoZO9jKhGqkVA0cHBwAVF7AIoTg22+/xf79+7F8+XJMnDgRgYGBiImJ0UoUIop6KLtYq+sTpU4EQQb6/FAoyqMpDVZBQQGePXuGJk2aKCxDNc7aQ1MC1r1793Dz5k1MmTJFI5v38uBwODQXlhIQQhAVFaWxPJCMXxdjhqgOSj05UVFRmDhxIgwMPsbEMDAwwMSJE1lJxsUWZYNcAOxosKh5YM1AIBDAxsam0qeeS5YswebNm/Hdd99h7ty5LPWOomlq164NoOLFmplbylLRCfWTJ09UjiBIoVBUh7FGYFuD9eTJE4jF4nI3dVTjrD2UnbNVZcuWLTAxMcHo0aNZrVdZ3N3dqQarAuLj45GVlaUxAYvJh1kZM0GlBCwHBwe5p7MxMTEwMzNTu3G2KW0iyJgIUAGLogqVzYW1efNmLF68GGPGjMHq1aup5rMKYWxsDHd393IX64KCArkRAJU5oX78+LHKEQQpFIrqmJmZwczMjHUBS1mneqpx1g4mJiZwc3NjVcDKyMjAH3/8gZEjR8LKyoq1elWBarAqRlMBLhiuXLkCLpeL6dOnqxTIqjRKCVjDhg3DggULcPDgQURHRyM6OhoHDhzAjz/+iGHDhqncqKYQCoUSDZahoSFsbW0rJWARQqiAVcNQVcAqHU3O3t4e06dPx4ABA7Bjxw4qXFVBfHx8ys2FNWfOHMTHx+O7776TONbyeDxs3bq1wk2UOhEEKRSKejg7O7NuIvjo0SMYGRlJEpNTdA/bodr37NmDgoICrQe3KA2TbLhsNErKRxgBqzxzXXVhAlmJxWIAUDqQVVmUErDGjx+PYcOGYd26dRg0aBAGDRqELVu2YPz48Tp9CMtSWoMFlEywlfHByszMRGFhoSRsJqX6o0qCyrLR5NLT08HlcuHv7y9lTkupOpS3WJ84cQKbN2/GzJkzsWbNGsTExODMmTMQiUQVaqXUjSBIoVDUQxPJhqOiotCoUSOqhdYj2AzVLhKJsHXrVnTq1EkjG3dlcXNzg1gspsmGyyEqKgq1atWChYUF63WrG8iqLEoJWBwOB9OnT8ft27dx/fp13L17F9euXcPYsWP16pS+dJALoCRUe2VOsBgbWKrBqjmoosGS9xKKxWIEBwdromsULVC3bl28e/cOWVlZUtfj4uIwbtw4tGjRAsuXL5dc79WrFxo0aIB169aBEKKwXnUjCFIoFPXQhAZLk071FPXw8fFBRkYG3r17V+m6IiIi8ObNG50rDmio9orR5LuobiCrsigUsI4cOQKhUCj5f+bfX3/9hYiICKlr+kLZfEOVnWBpDqyah5OTE3JycpCbm1thWbZeQor+wESlio2NlVwrLi7GF198gaKiIhw4cEDqEIfL5WLmzJm4f/8+rly5orDeykQQpFAoqsO2BislJQWpqalUwNIz2IwkuGXLFjg7O2PQoEGVrqsyMOZv7du3V9v/pzrDRPPU1LvIVqoFhXZMW7duRdeuXSEQCLB161aFFXA4HAwZMkSlRjWFPBPBpKQkiMVitUJtUgGr5lE6F1ZF0d48PDykNuKlr1OqJvIErODgYFy/fh1hYWFyfS++/PJLBAUFYd26dejcubPcemkEQQpFuzg7OyM7Oxt5eXkyYdPV4dGjRwA051RPUQ9mzn7+/Dlat26tdj0vX77E2bNnsXjxYvD5fLa6pzJhYWFYsmQJAEglsgdAg6X8H2WieVaGkJAQTJo0ScpCSZ1UCwoFrMuXL8v9/7JkZmYq3ZhQKMTSpUsREREBgUCAMWPGYOLEiTLlRo4ciX///Vfmeps2bbB3795y6y8rYBUXFyMjIwP29vZK95MhMTERHA5HYVhmSvVDFQGLrZeQoj94e3uDy+VKBKzLly8jJCQEY8eOxRdffCH3M8bGxpgyZQqCg4Px7Nkz1KtXT6YMjSBIoWiX0smG2TjY0KRTPUV9mDm7shqsbdu2wcDAQCLM6IqgoCDk5+dLXWP8f6iAVYKmIwgy4xwUFIS4uDh4eHggJCRE5fFXSq3ToEEDuYJUQkICunbtqnRjq1atwv3797Fnzx4EBwdj27ZtOHPmjEy5TZs24Z9//pH8++2332BgYIAxY8YorFskEkEkEkmZCLq4uABQP1R7YmIiHBwcdHqaQdEupRfliggMDMSKFSskf9N8J1UfgUAAT09PxMbGIjU1FYGBgahbty42bdpU7uemTJkCQ0NDrF+/Xu79J0+eUPNACkWLqDKXK0NUVBScnJzUOqylaA5DQ0N4eHioLWCFhYXBw8MD69atg0AgKFehoA2o60HFPHz4EMbGxpI8aJqAjVQLCjVYx48fl/hXEULw9ddfy0RGS0tLg4ODg1IN5eXl4dChQ/jll1/QuHFjNG7cGBMmTEBoaCj69u0rVbZ07gGmbX9/f3Tp0kVh/YWFhQAgo8ECSgQsdSTdhIQEGkGwhlFag6UMjBB/+/btSpknUPSDsLAwJCUl4c2bN/D09ERRUREiIiJgampa7uccHBwwcuRI/P7771i2bBns7Owk93JychAbGytXW0+hUDRD6fWfDWiAC/1F3VDtTCRgxgolLy9P5+Z41PWgYqKiotC4cWO9twhRqMHq2bMn2rVrh3bt2gEA/Pz8JH8z/0aMGIHdu3cr1dDTp08hFArRokULybUWLVrg0aNH5cb6P3PmDF6/fo2ZM2eWWz8TkKNskAsAaodqpzmwah7MgYGyAtbt27chEAjQrFkzTXaLogWYxbagoABAiSMtl8vFf//9p9TnZ86ciYKCAmzbtk3q+pMnTwDQABcUijZhU4NVXFyMJ0+eUAFLT2EErPIiucqDrXDcbBISEiLjM0hdDz5CCMHDhw+rxLuoUINlYmKCadOmASgJ8tC3b18p4SU7O1ul+PNpaWmwtLSU0jDZ2dmhqKgImZmZCjVhv/76K0aMGCF1IiyPijRY6pCYmIgOHTqo9VlK1YTP58PW1lbpRfn27dto3ry51HNHqZrIW2yLioqUtn1v2LAhevfujc2bN2POnDkwMjICQAUsCkUX2NnZgcfjsaLBevHiBQoLC6vEpq4mUrduXbx//x7p6ekqmXDqozkes9ZMmzYNWVlZcHd3x/Lly6nrwf9JTk5GRkZGlTjUViobaocOHfD111+jcePGEk1S79690bhxYyxfvhw2NjYV1pGfny8loAEftU2M9qksd+/exevXr7Fjx45y646OjpZMopmZmZKcMwBgYWGBx48fS11ThoKCAmRmZoLP56v82epOQUFBtR4TKysrvHz5ssLvWFxcjDt37iAgIEDj41Hdx1wfKG+xVXbsAwICcPbsWaxZswYBAQEAgKtXr0IgEKCwsJD+huVAn3HtU93H3MbGBk+fPq30dzx79iwAwNTUtNJ1Vfcx1wXMAeeFCxfQvHlzmfuKxtzJyUmuAO7k5KTT38jPzw8LFy7Ed999h82bN8PHx6dKPTOafMb/+ecfAIClpaXej4lSAtbixYsBQCoce1hYGJYuXYply5Zh3bp1FdZhaGgoI0gxfxsbG8v9zNmzZ9G2bVuJql8RDRo0kASi8PT0RIMGDST3XF1dUVhYKHVNGV69egUAaN68ucqfre5ER0dX6zHx9PREXl5ehd/xwYMHyM/PR58+fTQ+HtV9zPWB8mzflR37+vXr4+eff8aBAwcQFBQEDoeD5ORkNGjQAI0bN2a7y9UK+oxrn+o+5m5ubigoKKj0dwwNDQWPx0Pfvn0rba1Q3cdcFzDxAYRCodyxVTTmq1evxpgxY6TcVExMTLB69Wqd/0YfPnwAUJJrUdd9URVNPuN//vknAKBfv35KKXe0QWRkpNzrSkURvHXrFhYsWCDJLg0AXl5emD9/Pq5du6ZUBxwdHZGdnS0lZKWlpUEgEMDS0lLuZ65evYru3bsrVb88E0GgxExQVR+ssLAwtG/fHgAwd+5cmuSthuHo6KiUieDt27cBlKQPoFR92LB953A4mDVrFh4/fozz588DKDERbNiwIat9pVAoFcPkwqwsjx49Qv369akpuJ7i5eUFHo+ncqALJkosn88Hh8PRq0jAXl5eAICYmBid9kPfiIqKgpubm94IV+WhlIBlamqKhIQEmespKSlKhzBntEz379+XXIuMjESjRo1kohMCJaZ+cXFxaNWqlVL1M4Jb2QnQxcVFpQmWcXRnghykpqZi0qRJVMiqQTg6OioV5OLWrVuws7NDrVq1tNAriqYJDAzE9u3b4enpWanFdsSIEXB2dsa6deskEQSp/xWFon2cnJxYCXJBIwjqN3w+H7Vq1VJZwBKJRIiLi8PEiRMrFY5bE9jb28PY2JgKWP8nLCwMXl5eCA0NRXp6epXYkyslYA0ZMgRBQUE4evQonj59iqdPn+LYsWMICgrCoEGDlGrI2NgY/v7+CA4ORlRUFC5duoTdu3dj1KhRAEq0WUz0LqDEqZR5aZSB0WCV9fNiTrCUjS6jj1FlKNrFyckJubm5yMnJKbfc7du30bZtW3A4HC31jKJpmNwXjx8/VnuxFQgEmDZtGs6fP49Dhw4BoAEuKBRd4OzsjJSUFIhEIrXreP/+PWJjY2mCYT1HnVDt0dHRyMnJ0UsrFA6HAy8vLypg4aPigzHhLygoqBKKD6UErGnTpmHw4MFYu3Yt/P394e/vjzVr1mDYsGGYNWuW0o398MMPaNKkCUaPHo1FixZh6tSp6NOnD4CSQBrh4eGSshkZGTAzMwOXq1QXFWqwnJ2dUVhYiHfv3ilVjz5GlaFoF2VyYWVlZSE6OlovJ2aK7pk8eTL4fL4k99XUqVP1fjGgUKobTk5OEIlEyMjIULuOR48eAQDVYOk56oRq13czfypglVBVFR9KBbngcrmYMWMGZsyYgczMTAgEApiZmancmLGxMVauXImVK1fK3Hv27JnU33369JEIX8qgSIPFJIJNSkpSymaTJnmjlBawFGUKv3PnDgD9nZgpuuXs2bMghEAsFgMoycWn6wSWFEpNg0nVkpycrDAVTEVQAatq4OPjg9zcXCQnJ0t+94q4ffs2rKys4OPjo+HeqYeXl5dECKypFBUVyd2TA/qv+FBOPYSSSeb48eO4fPkyIiIicPjwYYSFhWHBggWa7J/SlBfkAlA+F1ZISIhMVEOa5K1moUyCytu3b4PD4aB169ba6halChEUFCSTQL0qnLhRKNUJZi6vTKCLqKgoWFlZwc3Nja1uUTRA3bp1AUAlM8Hbt2+jdevWSltKaRsvLy9kZmYiOztb113RKIx/FZfLhZeXF8LCwpCdnY1169ahTp06Cj+n74oPpTRYP//8M7Zu3Qo7OztkZGTA0dER6enpEIlE6NGjh6b7qBTlmQgCyk+wgYGBePXqFRYtWgQOhwMPDw+EhITQU+cahDImgrdv30b9+vUVRsCk1GyoqTGFonuUOSyriKioKDRp0oT62uo5jBbqxYsX6NSpU4Xlc3Jy8N9//8Hf31/DPVMfJpJgdfYBZPyrGBPA2NhYjBkzBgYGBigoKECnTp0wZMgQ/PLLL1JmglVB8aGU2H7o0CEEBwfjn3/+gbOzM/bt24cbN27gk08+gaurq6b7qBTlBbkAoFKodsaU4PXr13oVVYaiHZhM8IoELEIIbt26Rc0DKQpRdLKm7ydulP+1d+dhUVbtH8C/I8iiKIooigsoggvIIm6kmNiiYppbRuH25r6kuZvoT1HJMlPTTMMl7ZXcTTS13NLMgBQQMMGEBFwRXNmH5fz+4OJ5GZmBAYdlhu/nuryu5jzbPffQzNxzznMO6ZJX7cESQiAqKorDA7VAq1atYGBggH/++Uet/a9evYr8/Pxq/TleE6ZqV3Z/VW5uLmrVqoUrV67g4sWL+OqrrzQyw29lU6vAevbsGdzd3QEUTLceFhaG+vXrY/bs2fjll18qNEB1qRoiaGJignr16pXpDTY8PBwNGzaElZWVRmMk7VC7dm2Ym5ur/NXz9u3bSElJQY8ePSo5MtIWmlhTi4heTd26dVGvXr1y92AlJCQgNTWVBZYW0NPTQ5s2bdQeIlh4b1N1HuZf3QssZUP7ykrVqI7MzEx06dJFelw4w291m06/JGoVWE2bNsWdO3cAADY2Nrhx4waAgjcvdWfnq2iqhggCZV9sMCwsDC4uLhwSUIOVtBZWdZ95iKqeptbUIqJX8yqLDUdGRgLgBBfaoixTtYeEhMDGxgbm5uYVHFX5Vee1sIpOnS6EQEJCQrmmTtfl0R5qFVgjR47E7NmzcfHiRbz55ps4cOAA/P39sXLlSrRv376iY1SLqiGCQMEbrLpDBHNychAVFYXOnTtrND7SLk2bNlVZYAUHB6NOnTpwcHCo5KhIm2jjL25EuuZVFhsuLLC4jp12sLW1RWxsrDR7a0lCQkKq/Y+k1XktLE1Nne7n51fse7uujPZQq8CaNGkSFi1aBGNjYzg6OmLx4sX49ddfIZPJqk0SSurBsrS0VPsXrOjoaGRnZ8PFxUWj8ZF2sbCwUPmhHBISgi5dukBfX605YoiIqIo0a9bslQqsNm3aoF69ehqOiiqCra0tsrKycO/evRL3u3v3Lu7fv68Vw/yra4GlqYmcvL294eDgAD09PZ0b7aFWgeXj4wMnJydprOp7772Hw4cPY8eOHWjTpk2FBqiu0nqwHjx4oNYCdOHh4QDAHqwaTtUQwezsbISHh1f7X76IiKigB6u8QwQ5wYV2KTqTYEmCg4MBaMcw/+paYGlqaN+zZ89w/fp1zJw5U+dGe6hVYJ05cwZ6enoVHcsrKa3AysjIQGpqaqnnCQsLQ506dartwnNUOZo2bYqMjAykpaUptF+7dg1yuVwr3piJiGq6pk2bIjU1Fenp6WU6LjMzE//88w8LLC2i7lpYISEhMDAwgJOTU2WE9Uqq61pYfn5+xUbxlGdo39GjRyGXy/H+++9rMrxqQa0Ca9y4cVi2bBkuXryI2NhY3LlzR+FfdSCXy2FgYKB0YoqyTNUeHh4OJyenal9QUsUqXAvr5aElhRNcaMPQAiKimq7w87+swwRv3LiB/Px8nV1/SBc1b94cRkZGpU7VHhISAhcXF6W3lFQ3RdfCqk68vb3h7OwsFVm1atXC1q1by9z7tH//flhbW1fr2RzLS+2FhgHg8uXLACAVMUIIyGQyREdHV1B46svOzlbaewUU3IMFFKyFUdKkHPn5+bh27RrGjBlTITGS9ii62HDRlcRDQkLQvHnzarP+GxERqVZ0sWEbGxu1j+MMgtqnVq1aaNu2bYk9WLm5uQgNDcWECRMqMbLyKzpVe3Ur9uVyOfr374///Oc/GD58uLSGqLpSUlJw5swZzJs3Tydn7VZZYF26dAndu3eHgYEBzp07V5kxlUt2drbKXyMKf8EqbRx2XFwcUlNTOcEFKXwoF8UFhomItIe6n/8vi4qKgrGxcZmKMqp6tra2Jf7of/36dWRkZGjNKJTWrVsDqH5rYQkhEBsbi759+2LgwIEwMzPD7t270b9/f7XPceTIEeTl5enk8ECghCGCM2fOxOPHjwEAY8aMQd26daVf7l/+Vx0UDhFURt03WE5wQYWK9mAVSk5Oxr///qs1b8xERDWdqh/LShMZGSnNbkbaw9bWFv/++y/y8vKUbtemCS4AwNzcHHXq1KnQAqs8CwY/fPgQGRkZaNu2LQwNDfHBBx/gp59+wrNnz9S+7v79+2FnZwdnZ+fyB1+NqezBMjMzw9KlS+Hg4IB79+5h69atMDY2VrrvrFmzKixAdZXUg1W/fn0YGxuXeg9WWFgYateuzTUvCI0bN4ZMJlMosP766y8A2vPGTERU05mbm0NPT69MBZYQAhERERg8eHAFRkYVwdbWFnK5HImJiVLvT1EhISEwNzdXuq06qui1sAoXDC5c06pwwWAAJd5PFRcXBwDSLRRjx47F5s2bceDAAbi7u5d63YcPH+LChQvw8fHRyeGBQAkF1ubNm7Fp0yaEhYVBJpPh2rVrqF27drH9qkti5HK5ygJLJpOptRZWeHg4HBwcVPaEUc2hr68Pc3NzhQ/l4OBg6OnpwdXVtQojIyIiddWqVQsWFhZlGiKYlJSElJQU3n+lhYpO1a6qwOrevXu1+e6qjoossEpaMLikAis2NhYApCG0Xbp0QceOHbF79261CqxDhw4hPz9fZ4cHAiUUWO3bt8fmzZsBAH379sWWLVvQsGHDSgusrEqa5AL431pYqgghEBYWxl+sSPLyWlghISHo1KkT6tatW4VRERFRWTRt2rRMPVhRUVEAOMGFNio6Vfvbb7+tsO358+eIiYnBBx98UBWhlZu1tTWCgoIq5NzlXTA4NjYWenp6sLKyAlDQkTF27FgsXLgQ8fHx6NChQ4nH79u3Dw4ODjo9YkzlPVhFZ2E5f/68WsXVzZs3NRNVOZQ0RBAoKLBKGiJ47949pKSk8P4rkjRt2lQqsPLz8/HXX39xeCARkZYp7QfWlxXOIFjdZm2j0jVt2hQmJiZKZxK8cuUKhBBa9zlubW2Np0+f4vnz5xo7Z3p6OqZMmQIhhNLtpS0YHBsbC2tra4WRbaNGjUKtWrVw7NixEo+9c+cOLl++rNO9V0AJBdbKlSsxd+5chIWFlXqS4OBgzJo1C6tWrdJocGVR0hBBAKUOESx8npxBkApZWFhIv3revHkTz58/17o3ZiKimq6sPViRkZFo1qwZzM3NKzAqqggymQxt27ZVuhZW4QQX2rbmkqbXwvrrr7/g4uICf39/eHp6ok6dOgrb1VkwODY2VmEJG6Dge/Zbb72FwMBA5Ofnqzz24MGDAKDzBZbKIYI//PADAgMD4ePjgxcvXqBHjx6wsbFBw4YNkZ+fj6dPn+Kff/5BWFgY6tWrh4kTJ2LYsGGVGbsCdYYIFq7mrmyIV3h4OGQyGYcEkKRwiKAQggsMExFpqWbNmuHRo0fIy8tTa1bAyMhIfhfQYra2ttKs0EWFhISgffv2aNCgQeUH9QqKroX1Kn+Xubm58PPzw8qVK2FpaYnz58+jT58+CAgIgI+PDxISEqCnp4fvvvuuxPuvCqdoV/aD89ixY/Hhhx/i4sWL8PDwUHr8vn370LlzZ+l+OV2lsgcLAN59912cOnUKa9euRZMmTRAUFIQ9e/Zg7969uHr1Klq0aIG1a9fi5MmTVVpcAeoNEQRUT9UeHh6Odu3awcTEpELiI+3TtGlTZGZmIjU1FSEhITA1NUW7du2qOiwiIiqDpk2bIi8vDykpKaXum5ubixs3brDA0mK2tra4ffs2cnJypLbCH0q1cRRK0QKrLIpOv968eXO0b98ey5cvh5eXFyIjI9GnTx8ABbMFxsfHIyAgAHl5edL3ZVWePHmC58+fF+vBAoAhQ4bAxMQEu3btUnrsv//+iytXruh87xVQQg9WUW5ubnBzc6voWF5JSetgAf8rsO7fv6/0jyIsLAy9evWqsPhI+xRdCys4OBhdu3ZFrVol/iZBRETVTOHn/8OHD6X3dVX++ecfyOVyFlhazNbWFnl5eYiPj5d6SeLj45GcnKyVBVZ51sJ6efr1wjkIZsyYgU2bNik9ZtiwYWjYsCG2bduGN954Q+W5C2cQVPZd2tjYGP3798fhw4exefPmYp0W+/fvBwCMHDlS7eeirXTm22JpPViWlpYAlPdgpaSk4M6dO5zgghQUfhDfvn0bUVFRHB5IRKSFChcbVmeiC05wof2KTtVeqHCYvzYWWOVZC0vZ9OsAcPz4cZXHGBkZYfTo0fjpp59K7O0tqcACCnqx0tPTcfjw4WLb9u/fjx49eki9crpMZwqs0ia5KGmIYOFYXU5wQUUVfiifPHkSeXl5WvnGTERU0xW+l6sz0UVkZCT09fXRvn37ig6LKoiyAis4OBjGxsZaWziXtcAq7/TrEyZMgFwux3//+1+V+8TGxkImk6lcrNnFxQVt27bF7t27FdpjYmIQEREBLy+vUqLXDTpTYJU2yUXDhg1haGiodKp2FlikTGEPVmBgIADt/OWLiKimU7cHKyAgABs2bEBubi7atWuHgICAygiPNKxx48YwNTUt1oPl6uqqMK24NilrgaVqmvXSpl/v1KkTunfvju3bt6ucwj0uLg4tWrSAkZGR0u0ymQxjxozBb7/9pjDz4f79+yGTyfDee++p+Sy02ysXWGlpaZqI45WVNkRQJpOpXAsjLCwMVlZWMDMzq8gQScuYm5tDJpMhPj4ebdq0QePGjas6JCIiKqM6deqgfv36JfZgFd6zkpmZCaBgSuxJkyaxyNJCMpkMtra20lTtcrkc4eHhWv0jaVnXwvLz8yvX9OsAMHHiRNy4cUPl4sbKpmh/2ejRowFA6gkTQmDfvn1wd3eXbtnRdWoVWOPGjVPa83PmzBl4enpqPKjyKG2IIKB6scHw8HD2XlEx+vr6UlGlzW/MREQ1XbNmzUossJTds5KRkQEfH5+KDo0qgK2trdSDdfPmTWRnZ2v153hZ18Ly9vbGokWLABQUnFZWVvD39y9x+vVC77//PkxMTLB9+3al29UpsKytrdGnTx/s3r0bQghERUUhJiamxgwPBNQssGrXro133nkHe/fuBQAkJyfj448/xuzZszF48OAKDVBdpQ0RBJQXWKmpqbh16xYnuKBiAgIC8PTpUwDAL7/8wl8yiYi0UEBAAOLj43HgwAFYW1tL7+VCCNy4cQPr1q1T+cW1tHtWqHqytbVFYmIisrOzpYlLdKHAKsswwcL1vuLj4xEfH69WcQUAJiYm+OCDD7B//368ePFCYduLFy+QnJxcaoEFFKyJFRsbiz///BP79+9HrVq1MHz4cLXj13ZqFVjbtm3DsmXLsHHjRnh5eWHgwIFIS0tDYGAg5s2bV9ExlkoIoXYP1ss9cRERERBCsAeLFBQOFylcR+Pp06ccLkJEpGUK38uzs7MBFPQAjB8/Hn379oWVlRXs7e0xd+5c6OsrX7WmtHtWqHqytbVFfn4+/v33X0RERKBZs2Zo2bJlVYdVbuUpsIKCgmBpaVmu5z1hwgRkZGRIHSuF4uLiAKieQbCo4cOHo3bt2ujfvz8+++wzGBgY4MyZM2WORVupfQ+Wq6srOnbsiL///hvp6elwc3ODlZVVRcamttzcXAghSu3BsrS0xLNnz6Qx1sD/JrhgDxYVxeEiRETaT9l7eXZ2Ni5cuICuXbvC398fCQkJ2LVrV7nvWaHqp+hMgpGRkejevTtkMlkVR1V+5ubmqFu3bpkLLDc3t3I9765du8LR0RHbtm1TaC+cot3GxqbUcxw7dgz5+fnSXA1ZWVk16odqtQqsjRs3YuDAgQAKpqzevHkz9u3bh0GDBqm8CU4ZuVyOpUuXomvXrujZs2exF66ouLg4jBkzBk5OTujXrx9+/fVXlfsW/jKlTg8WoDhVa1hYGJo0aVLqytVUs5R3ilMiIqo+SnrPPnz4MCZOnIhWrVrB29sb/v7+sLKyKvM9K1T9FBZYISEhSExM1OrhgUDZ18J6+PAh4uPj4ebmVu7rTZgwAaGhoVJHBFC2AsvHxwd5eXkKbTXph2q1Cqwff/wRvr6+2LFjB1q2bIk+ffrg559/Rq9evTBx4kS1L7ZmzRqEh4fj+++/h6+vL7Zs2YITJ04U2y89PR3/+c9/0LRpUwQGBsLb2xtz586VXtiXyeVyAOoXWEXvwwoPD0fnzp21+pcN0rzyTnFKRETVR1ney729vREfH4/8/Pwy3bNC1Y+ZmRnMzMyk3hJtL7CAsk3VHhwcDADlLrAAYNSoUTA0NFSY7CI2NhZNmzaFiYlJqcfX9B+q1SqwTp48iSFDhii01alTBz4+PsXGZ6qSkZGBAwcOYPHixXBwcMCbb76JCRMmYM+ePcX2PXr0KPT19eHn5wdra2uMGTMGPXv2VKiiiyrswVJniCAA6T6s7Oxs/P3337z/iop5lSlOiYioeuB7ec1lZ2eHhIQEyGQydOnSparDeWVlKbCCgoJQu3btV7r9pWHDhhgxYgQCAgKkYbbqzCBYqKb/UK1WgXX+/HkcOnRI6b+bN2+qdaGYmBjI5XK4urpKba6uroiKikJubq7CviEhIejbt6/CgnDfffedysXJyjpEsLAH6/r168jNzeX9V1QMh4sQEWk/vpfXXHp6egAKJkLr1KmT1t/7U5a1sIKCguDi4qJyMWB1TZw4Ec+fP8ehQ4cAFNy+o87wQIA/biifNucl3377rcLjvLw8PH78GPr6+ujcuTNGjBhR6jmSk5NhamqqUASZm5sjJycHT548QZMmTaT2xMREdOjQAcuXL8fZs2fRuHFjzJw5Ex4eHkrPre4QwUaNGkFfX18qsMLCwgCAPViklLe3Nz+EiYi0HN/La56AgAD89ddf0uPChaMBaO3fQtGZBJ2cnFTul5OTg6tXr0rP91X07t0btra22L59O0aMGIF79+6p3YNVmGcfHx8kJiaiVatW8PPz09r8l5VaBdb58+eLtWVkZGDZsmVqV7KZmZnFhvAVPi4skAqlp6djx44d+PDDD+Hv748//vgD06dPx4EDB+Dg4FDs3NHR0QCAR48eSf+tirm5OaKjoxEdHY3z58/DxMQEWVlZpR5H/8N8VT7mvHIx35WPOa98zHnlY84rx/z586VlVgplZGRg/vz5WjtqSQgBALh06VKJt8Rcv34dmZmZaNWqlUb+1gYPHoyvvvoK/v7+AABjY+MSz1v0b7xz5844deqUwvaa8vevVoGlTJ06dTBjxgx4eXlhypQppe5vaGhYrJAqfGxsbKzQrqenBzs7O8yZMwcA0LFjR4SGhqossFq0aAGgYFaTDh06lBhHq1atkJGRgQ4dOiA+Ph6urq6wt7cvNX76n+jo6FLzTJrFnFcu5rvyMeeVjzmvfMx55Sg6W/TL7dqa/8aNGwMoGEVW0nM4e/YsAGDEiBEaud9p/vz5+Prrr7F161YAwOuvv17i9Wva33hoaKjSdrXXwVImIiIC+fn5au1rYWGBFy9eKBRZycnJMDAwgKmpqcK+TZo0QZs2bRTaWrduXWyR4ELqTnIBFNyH9eDBA+Tl5SEiIoLDA4mIiIh0iC5OsNCoUSO11sIKDg4u9wLDylhYWMDZ2Vmac2Ho0KFafz9bZVCrB+vDDz8sNo15Wloa/vnnH4wfP16tC3Xo0AG1a9dGeHi4NF1maGgo7O3ti62g7uLigkuXLim0xcbGonnz5krPre4kF0BBgXX58mXcvHkTmZmZWttVTERERETF+fn5YdKkSQqLTGv7BAvqroX1KgsMKxMQEICoqCjp8d27d7X+frbKoFYP1muvvQY3NzeFfwMHDsTOnTsxb948tS5kbGyMIUOGwNfXF5GRkTh37hx27tyJMWPGACjozcrKygIAvP/++7h9+za+/PJLJCYmYteuXQgKCsL777+v9NzqTnIBFEzVnpKSIq0RwB4sIiIiIt2hq7NHllZgJSUl4fbt26+0/tXLfHx8pI6MQjVpweDyUqsHa8aMGRq52Kefforly5dj7NixqFu3LqZPnw5PT08AQK9evbB69WoMGzYMlpaW+P7777Fq1Sr88MMPaNmyJTZu3IiOHTsqPW9ZhwgCwKlTp2BkZIT27dtr5LkRERERUfVQOHukLt0TZG1tjcuXL6vcHhQUBADo0aOHxq5Z0xcMLi+VBdaCBQvUPsmaNWvU2s/Y2BhffPEFvvjii2LbXl5Py9nZWZp3vzRlHSIIAKdPn4ajo2Ox4YlERERERNWNtbU1nj17hmfPnqFBgwbFthcuMFx0zdlX1apVKyQkJChtJ9VUDhHU09NT+19VKxwiWJYerBcvXvD+KyIiIiLSCoVrYSkreADNLTBcVE1fMLi8VHbfrF69ujLjeCVl6cGytLSU/pv3XxERERGRNihpsWFNLjBcVE1fMLi8VPZgDRo0CM+fP1doe/LkibTQWXVSlkkuzpw5I/33smXLONUkEREREVV7RQusl0VGRiIzM1OjE1wU8vb2Rnx8PPLz8xEfH8/iSg0qC6xbt24hNzdXoe3NN9/E3bt3KzyoslJ3kouAgACFRZEfPnyISZMmscgiIiIiomqtpLWwCie4qIgCi8quTAsNV8feK0D9IYI+Pj4KayIAnGqSiIiIiKo/mUyG1q1bqyywNLnAML2aMhVY1ZW6QwQ51SQRERERaStVa2EFBQWhR48eGltgmF6NThRY2dnZkMlkpc5oqGpKSU41SURERETVnbICqyIWGKZXU+IiUD///DPq1q0rPc7Pz8epU6dgZmamsN+IESMqJjo1ZWdnw9DQsNSq3c/PD5MmTVIYJsipJomIiIhIGyhbC4v3X1U/KgssS0tL7N69W6GtUaNG2Ldvn0KbTCar8gJLLpertQYWp5okIiIiIm1VdCZBZ2dnABWzwDC9GpUF1vnz5yszjldS2IOlDm9vbxZURERERKR1lBVYwcHBGl9gmF6NTtyDJZfL1S6wiIiIiIi00ctrYeXk5ODKlSscHljN6ESBlZ2drdYQQSIiIiIibWVmZgYTExOpwKrIBYap/HSmwGIPFhERERHpMplMpjCTYOEEFz169KjCqOhlOlFgcYggEREREdUELxdYzZo145JD1YxOFFgcIkhERERENcHLBZabmxsXGK5mdKLAYg8WEREREdUE1tbWeP78OW7evMkFhqspnSiw2INFRERERDVB4UyCe/fuBcAFhqsjnSmw2INFRERERLquaIHFBYarJ50osDhEkIiIiIhqgsIC659//uECw9WUThRYHCJIRERERDXBL7/8Ik1qER0djYCAgCqOiF6mMwUWe7CIiIiISJcFBARg0qRJEEIAAFJTUzFp0iQWWdWMThRYHCJIRERERLrOx8cHGRkZCm0ZGRnw8fGpoohIGZ0osDhEkIiIiIh0XWJiYpnaqWroRIHFHiwiIiIi0nWtWrUqUztVDZ0osNiDRURERES6zs/PD3Xq1FFoq1OnDvz8/KooIlJGJwqsnJwc9mARERERkU7z9vaGv78/rKysIJPJYGVlBX9/f3h7e1d1aFSEflUHoCkssIiIiIhI13l7e7OgquZ0ogcLAIcIEhERERFRlavUAksul2Pp0qXo2rUrevbsiW3btqnc96OPPkK7du0U/p09e1bl/uzBIiIiIiKiqlapQwTXrFmD8PBwfP/993j48CEWLFgAS0tLDBw4sNi+sbGxWL9+Pbp27Sq1mZqaqjw3e7CIiIiIiKiqVVqBlZGRgQMHDmDr1q1wcHCAg4MDJkyYgD179hQrsNLS0pCUlARHR0c0btxYrfOzB4uIiIiIiKpapQ0RjImJgVwuh6urq9Tm6uqKqKgo5ObmKuwbGxsLQ0NDWFpaqn1+FlhERERERFTVKq3ASk5OhqmpqUIhZG5ujpycHDx58kRh39jYWNSvXx+zZ89Gr169MGLECFy8eLHE83OIIBERERERVbVKGyKYmZlZrAgqfCyXyxXa4+LikJ6ejr59+2Lq1Kk4c+YMpkyZgn379sHJyUnp+R89eoTo6OiKCZ4UZGVlMdeVjDmvXMx35WPOKx9zXvmY88rHnFcu5rtApRVYhoaGxQqpwsfGxsYK7fPmzcPUqVNRv359AED79u3x999/l1hgtW3bFh06dKiAyOll0dHRzHUlY84rF/Nd+ZjzysecVz7mvPIx55WrpuU7NDRUaXulDRG0sLDAixcvFIqs5ORkGBgYFJsdUE9PTyquCrVp0waPHj1SeX4OESQiIiIioqpWaQVWhw4dULt2bYSHh0ttoaGhsLe3h76+YkfazJkzsXz5coW26OhotG7dWuX5OckFERERERFVtUorsIyNjTFkyBD4+voiMjIS586dw86dOzFmzBgABb1ZWVlZAIC+ffvi8OHDOH78OOLj47Fx40aEhoZK+yrDHiwiIiIiIqpqlbrQ8Kefforly5dj7NixqFu3LqZPnw5PT08AQK9evbB69WoMGzYMQ4YMQVpaGjZu3IiHDx/Czs4OO3bsQKtWrVSemz1YRERERERU1Sq1wDI2NsYXX3yBL774oti2mzdvKjweNWoURo0apfa5WWAREREREVFVq7QhghWNQwSJiIiIiKiq6UyBxR4sIiIiIiKqaiywiIiIiIiINERnCiwOESQiIiIioqrGAouIiIiIiEhDdKLA0tPTg56eXlWHQURERERENZxOFFi8/4qIiIiIiKoDFlhEREREREQaohMFFu+/IiIiIiKi6kAnCiz2YBERERERUXXAAouIiIiIiEhDdKLA4hBBIiIiIiKqDnSiwGIPFhERERERVQc6UWCxB4uIiIiIiKoDnSiw2INFRERERETVAQssIiIiIiIiDdGJAotDBImIiIiIqDrQiQKLPVhERERERFQd6ESBxR4sIiIiIiKqDnSiwGIPFhERERERVQcssIiIiIiIiDREJwosDhEkIiIiIqLqQCcKLPZgERERERFRdcACi4iIiIiISEN0osDiEEEiIiIiIqoOdKLAYg8WERERERFVBzpRYLEHi4iIiIiIqgP9qg5AE7SxB+vQoUPYv38/0tPTIZfL0bJlS3zyySdwcnIq8bikpCTMmjUL+/btK7ZtxYoVaNiwIT7++ONyx7Vp0yYEBATAwsJCoX3VqlW4cOECnj59ivfee6/c5yciIiIi0mUssKrAunXrcOXKFWzYsAHNmzcHAAQFBWHy5Mk4cuQILC0tVR5rYWGhtLjSJE9PT/zf//1fsfYLFy5U6HWJiIiIiLRdpRZYcrkcK1euxC+//AIDAwOMGzcOEydOLPGYZ8+ewdPTE/PmzcOwYcOU7qPOEMHTwWk4FZRerrjVNcCtLt7uYVLiPikpKdi9ezfOnDmDJk2aSO1ubm5YtGgRMjMzAQB9+/bF119/jU6dOik8btiwIQYNGoTw8HCkpaXBx8cHMTExaNKkCfT09ODq6gqgoKdrxYoVePDgAXJycjBw4EBMmTIFd+/ehbe3N2xsbHDv3j3897//VYhDXariS09Px6xZs3Ds2DE0btwYY8eORY8ePTB9+vQyX4OIiIiISNtUaoG1Zs0ahIeH4/vvv8fDhw+xYMECWFpaYuDAgSqP+eyzz/D48eMSz6tNPVjXrl2DjY2N0qJmyJAhZTrXxo0bYWRkhF9++QVPnz7F0KFDpQJr/vz5GDduHPr27Yvs7GxMnDgRrVq1gqOjIx4+fIivvvoKXbp0UXrekydPIjQ0VHr81ltvYcaMGWrF1KNHD3h5eWHJkiVwcnKCgYEBpk6dWqbnRURERESkrSqtwMrIyMCBAwewdetWODg4wMHBARMmTMCePXtUFlgXL15EZGQkzMzMSjy3OgXW2z1MSu1dqgxCCIXHaWlp8Pb2BlCQowEDBmDOnDlqnSsoKAiLFy+GTCaDmZkZ3nrrLek8V65cwfPnz/H1119LbTExMXB0dIS+vj6cnZ1VnlfVEEF1ffzxx/jwww+xd+9eHD9+HLVq6cRcKkREREREpaq0AismJgZyuVzqYQEAV1dXfPvtt8jNzYW+vmIoaWlpWL58OdasWYO5c+eWeG5tmkXQ0dERt2/fxtOnT9GwYUOYmJggMDAQQMEEE0+fPpX2LVqMyeVypecruo+enh4AID8/H0II7Nu3D8bGxgCAJ0+ewNDQEE+fPoWBgUGxfJeHqvhSU1ORnJwMmUyGhISEUgtkIiIiIiJdUWldC8nJyTA1NVXobTI3N0dOTg6ePHlSbP8vv/wS7u7u6Nq1a6nn1qYhghYWFhgzZgxmzZqF+/fvS+337t1DWFiY1NtjZmaG69evAwBCQkKQnJxc7Fzu7u44dOgQ8vPz8fz5c5w7dw4AYGJiAmdnZ3z//fcAgBcvXuCDDz6QtmtCSfH5+Phg8ODBWL16NebNm4fU1FSNXZeIiIiIqDqrtB6szMzMYj1NhY9f7p3566+/8Ntvv+HEiRNqnfv+/fuIjo7WTKCVoH///jA2Nsa0adOQlZWFjIwM1K1bF+7u7vD09ER0dDRGjhyJrVu3Yvfu3bCxsYGNjQ3i4+Px+PFj5OfnIzo6Gm+//Ta2bNmCN954A6ampmjWrBmSk5MRHR2NKVOmYNu2bfjpp5+Qm5sLd3d32NraIjY2VjpemeTkZKSmpirdXrgtKytLZXynT5/G7du3MXXqVOjr68PBwQGzZs3C/PnzKzqtOi0rK0ur/sa1HfNd+ZjzysecVz7mvPIx55WL+S4gEy/fFFRBTp06heXLlyMkJERqi4uLg6enJ/788080atQIQMELM3jwYMyfP1+6p6h379745JNPlM4iGBoaivz8fLV6ukgzoqOj0aFDh6oOo0ZhzisX8135mPPKx5xXPua88jHnlaum5Ts0NFTh9qdCldaDZWFhgRcvXkAul0s9V8nJyTAwMICpqam0X2RkJBISErBgwQKpLTMzE8uWLcO1a9ewYsWKYufWpiGCRERERESkuyqtwOrQoQNq166N8PBwdO/eHUBB1Wdvb68w4YKjoyNOnz6tcKy3tzfGjh37SutgERERERERVbRKK7CMjY0xZMgQ+Pr64vPPP0dycjJ27tyJlStXAijozapXrx6MjIxgZWWlcGytWrXQqFEjaRjhy9iDRURERERE1UGlLlD06aefolOnThg7diyWLVuG6dOnw9PTEwDQq1cvnDx5slznZYFFRERERETVQaX1YAEFvVhffPEFvvjii2Lbbt68qfK433//vcTzcoggERERERFVB5Xag1VR2INFRERERETVgU4UWOzBIiIiIiKi6oAFlhIBAQGwtrZGrVq1YG1tjYCAAI2ev127dhg0aBDeffddhX93797V6HUqW7t27dC3b1+8vLTapk2b0K5dO0RFRZV4fGpqKsaMGfPKcSxatAju7u7F8puUlIRFixZhx44dr3wNIiIiIiJlKvUerIoik8k0dq6AgABMmjQJGRkZAICEhARMmjQJQMF08Zqye/dumJmZaex81YUQAlevXpUWfhZC4NSpUwprnany/PnzUoswdY0bNw7jx4/XyLmIiIiIiNSlEwWWOn744Qfs3Lmz1P2Cg4ORnZ2t0JaRkYHx48dj27ZtJR770UcfvXIPzE8//YTNmzcjMDAQMpkMw4cPx+TJk9GsWTOsXbsWlpaW+Pfff2FkZITPP/8cNjY2SE1Nha+vL2JiYiCTyeDu7o45c+ZAX18fGzduxJkzZ1C7dm00bNgQq1evRpMmTdCuXTsEBQVJRV7h41u3bsHPzw916tRBeno6Dh8+jD/++ANbtmxBTk4OjIyM8P7776tcpXvw4ME4duyYVGCFhoaibdu2yMrKkvYJCwvD2rVrkZmZiVq1amHGjBnw8PDAp59+iqysLLz77rs4cuQI4uPj4efnh2fPniEvLw+jR4/GiBEjEBISUizG8vRiqsrBxYsXlb4GQ4YMKfM1iIiIiKhmqTEFlrpeLq5Kay+vsWPHolat/43QbNGiBTZv3oyhQ4fijz/+wJdffgm5XI4uXbpgyJAhCAkJwfXr17Fw4UJ06dIFe/fuxfz583HkyBGsWrUKDRo0wPHjx5GTk4OpU6di586dGDRoEHbv3o2goCAYGBhg586diIyMxJtvvllibLdu3cLZs2fRvHlzxMfHY/369fjhhx/QsGFD3Lp1C6NHj0a/fv1Qp06dYse+8847GD16NJYuXQoDAwP89NNPGDp0KK5fvw6goJfq008/xY4dO9CiRQskJSVh5MiRaNeuHVavXo1BgwYhMDAQubm5mDlzJtasWQN7e3ukpqbi/fffR9u2bYvFqMyuXbtw7Ngx6fGoUaPw3nvvqfXaqHoNiIiIiIhKoxMFlrW1Nfz8/EocwjdmzBi1epesra2RkJBQrN3KygoXLlx4lTAVlDRE0NfXF++++y6MjIxw5MgRqb19+/bo0qULAGD48OFYsWIFnj59it9//x179+6FTCaDgYEBvLy8sHv3bkyYMAHt27fH0KFD0bt3b/Tu3Rtubm6lxtasWTOpcLl8+TIePXqEcePGSdtlMhkSExPRvn37Ysc2atQIjo6O+O233/D666/j6tWr8PX1lbZfu3YNycnJmD59usL5bt68CVtbW6ktPj4eiYmJWLx4sdSWlZWFGzduwMbGRiFGZV51iKCq14CIiIiIqCQ6UWBp8j4pPz8/hXuwAKBOnTrw8/N7pfOWxePHj5GdnQ25XI5Hjx6hZcuWAAA9Pb1i++rp6SE/P1/hPrT8/Hzk5uaiVq1a2LNnD6KiohAUFITPPvsM7u7uWLBggcI55HK5wuOiPVP5+flwc3PDhg0bpLbff/9doRh62ZAhQ3Ds2DHI5XL07dsX+vr/+zPLy8uDjY0NDh48KLUlJSXBzMwMSUlJCvvVq1cPgYGBUltKSgrq1auHa9euKe09exUv50DVa0BEREREVBKdmEUQKLhPysfH55XP4+3tDX9/f1hZWUEmk8HKygr+/v4aneCiJDk5OZgzZw5mzZqFGTNmYPbs2cjJyQEAxMTEICYmBgCwf/9+uLi4oH79+ujVqxf27NkDIQTkcjkOHDiA1157DTExMXjnnXdgY2ODyZMnY9y4cdIkEmZmZtJ///zzzyrjcXNzw+XLlxEXFwcAuHjxIj755BOFe6pe9sYbbyA8PBwBAQEYOnSowjZnZ2ckJCTgypUrAIDo6Gj069cPSUlJ0NfXR15eHoQQaN26NYyMjKQC68GDB3jnnXekoYaaoCoHJb0GREREREQl0YkerEKJiYkaOY+3t3eFF1Qv34MFAHPmzEFwcDDMzc2l+4XOnj2L9evX4/XXX4e5uTk2bNiAe/fuwczMDGvWrAEALFmyBKtWrcKgQYOQk5MDd3d3TJkyBQYGBhgwYACGDx+OOnXqwMjICEuWLJGOWbFiBerXr4/XXnsNjRs3Vhpn27ZtsWLFCsyZMwdCCOjr62Px4sWoW7euyudmaGiIvn374saNG7Czs1PYZmZmho0bN2LNmjXIzs6GEAJr1qxBixYtkJeXB0dHRwwcOBABAQH49ttv4efnh+3btyM3NxezZs2Cq6srQkJCyp33olTlYN26dUpfg5d7/oiIiIiIXiYTLy9apGVCQ0Ol+5KsrKwQHx9ftQFVkJCQEKxcubLE3qbKEh0drXIWQaoYzHnlYr4rH3Ne+ZjzysecVz7mvHLVtHyHhobC1dW1WLvODBGs7PukiIiIiIiIXqYTBVZl3ydVFbp3714teq+IiIiIiEg1nbgHS1eHBRIRERERkXbRiR4sIiIiIiKi6oAFFhERERERkYawwCIiIiIiItIQFlhEREREREQawgKLiIiIiIhIQ1hgERERERERaQgLLCIiIiIiIg1hgUVERERERKQhMiGEqOogXkVoaGhVh0BERERERDWQq6trsTatL7CIiIiIiIiqCw4RJCIiIiIi0hAWWERERERERBrCAouIiIiIiEhDtLbAksvlWLp0Kbp27YqePXti27ZtVR2STklMTMSUKVPQtWtX9O7dG59//jmys7MBAPfu3cNHH30EZ2dnDBgwABcvXqziaHWPj48PRo8eLT1mzitGTk4OVq9eje7du6N79+5YtmwZ5HI5AOa8ojx//hzz5s1Dt27d4O7ujrVr1yIvLw8Ac65pcrkc77zzDv7880+prbQcBwcHY9CgQXBycsLo0aORkJBQ2WFrNWU5DwoKwvDhw+Hi4oJ+/frh4MGDCscw5+WnLN9Ftw0cOBCbNm1SaGe+X42ynCclJWHatGlwdnZGnz59EBAQoHBMTcy51hZYa9asQXh4OL7//nv4+vpiy5YtOHHiRFWHpRPkcjmmTJkCAwMD7Nu3D2vXrsXZs2exfv16CCEwbdo0NGjQAIcOHcLQoUMxc+ZM3Llzp6rD1hlBQUE4dOiQ9Jg5rzhr1qzBmTNn8O2332LLli24dOkSNm/ezJxXIF9fXyQlJWHPnj348ssvcfToUXz//ffMuYZlZ2djzpw5uHXrltRWWo4fPHiAqVOnYvDgwTh8+DDMzc0xbdo05OfnV9XT0CrKch4fH4/JkyfjrbfewtGjRzF9+nSsWLEC58+fB8Ccvwpl+S5qy5YtiI2NVWhjvl+Nspzn5+dj6tSpyM7OxuHDhzFv3jysXr0aly9fBlCDcy60UHp6uujUqZO4fPmy1LZ582bh5eVVhVHpjitXrgh7e3uRlpYmtR07dky89tpr4s8//xSdOnUSqamp0raxY8eKdevWVUWoOic9PV288cYbwsvLS4waNUoIIZjzCvL8+XNhb28v/vjjD6nt8OHDYvz48cx5BercubM4c+aM9Hj16tXMuYbdunVLDB48WAwaNEjY2dlJn5Wl5XjDhg0Kn6MZGRnCxcVF4bOWlFOV882bN4uRI0cq7LtkyRLxySefCCGY8/JSle9C0dHRomfPnqJ///5i48aNUjvzXX6qcn7hwgXh4uIinj59Ku27dOlSsWnTJiFEzc25VvZgxcTEQC6XK8w77+rqiqioKOTm5lZhZLqhTZs28Pf3R926daU2mUwGuVyOiIgIdOzYESYmJtI2V1dXXLt2rQoi1T3r169Ht27d0K1bN6mNOa8YoaGhMDIywmuvvSa1DRs2DNu3b2fOK1CDBg1w7NgxZGZmIikpCZcuXYK9vT1zrkFXr15Fz549sX//foX20nIcERGBrl27StuMjY1hb2+P8PDwSolbm6nK+YABA7B06VKFNplMJg25Z87LR1W+ASAvLw+LFy/GvHnz0KBBA4VtzHf5qcp5cHAwunfvrpDrFStWYMaMGQBqbs71qzqA8khOToapqSkMDQ2lNnNzc+Tk5ODJkydo0qRJFUan/czMzBS+dObn52PPnj1wdXVFcnJysfw2atQIDx8+rOwwdU54eDh++eUX/Pzzz9i5c6fUzpxXjMTERDRv3hw///wztm7dioyMDPTv3x+zZ89mzivQsmXLsGDBAnTu3Bn5+fno0aMHPv74Y6xevZo51xAvLy+l7aX9XavanpSUVDGB6hBVOW/durXC45SUFJw4cUL68smcl4+qfAPAjh070LBhQwwZMqRYMcB8l5+qnCcmJsLS0hLr16/H0aNHYWJignHjxuG9994DUHNzrpUFVmZmJgwMDBTaCh8X3qBOmrN69WpER0fj0KFD+P7771G7dm2F7QYGBsjJyami6HSDXC6Hj48PFi9eDFNTU4VtmZmZzHkFSE9Px927d7Fnzx74+voiPT0dvr6+yM3NZc4rUGJiIjp27Ijp06cjLS0NK1euxBdffMGcV4LScqzqs5Wfq5qRkZGBGTNmoEmTJtKXVeZcs27fvo0dO3bg8OHDSrcz35qXnp6OwMBAvP3229i8eTNu3LiBFStWoGHDhnjzzTdrbM61ssAyNDQs9sIUPjY2Nq6KkHSSEAJ+fn7Yu3cvvv76a9ja2sLQ0BBpaWkK+8nlchgZGVVRlLph8+bNsLKywoABA4ptY84rhr6+PtLS0vDll1+iVatWAIAFCxZgwYIFGDp0KHNeARITE/HZZ5/h/PnzaNq0KYCCv++PPvoI7733HnNewUp7L1H12fryMCsqu9TUVEyePBl3797Fjz/+KH1XYc41RwgBHx8fTJ06FS1atFC6D/OteXp6eqhfvz5WrlwJPT09ODg4ICYmBnv37sWbb75ZY3OulQWWhYUFXrx4AblcLlXFycnJMDAwKPbrP5VPfn4+fHx8cPz4caxfvx5vvvkmgILcx8TEKOybkpKCxo0bV0WYOuP48eNITk6Gi4sLgILpw/Py8uDi4oLJkycz5xWgSZMm0NfXl4oroGA4T3Z2Nho3box//vlHYX/m/NVdv34ddevWlYorAHBwcEBeXh5zXglKe/+2sLBAcnJyse22traVFqMuevLkCcaPH4+UlBT88MMPCu85zLnm3L9/H6Ghobhx4wa+/vprAEBWVhaioqIQERGB7du3M98VoEmTJsjPz4eenp7U1rp1awQFBQGouX/jWjnJRYcOHVC7dm2FG+RCQ0Nhb28PfX2trBmrnc8//xzHjx/Hpk2b8Pbbb0vtTk5OiImJQUZGhtQWGhoKZ2fnKohSd/z3v//Fzz//jKNHj+Lo0aN477334ODggKNHjzLnFcTZ2Rm5ubm4efOm1BYXF4e6devC2dmZOa8ATZo0wYsXL/DgwQOpLS4uDkDB5DrMecUq7b3EyckJYWFh0rbMzEzcuHGDr8ErKFz25OnTpwgICECbNm0UtjPnmmNhYYHTp08jMDBQ+izt0KEDvLy84OfnB4D5rgguLi74559/FIZzx8bGonnz5gBqbs61ssAyNjbGkCFD4Ovri8jISJw7dw47d+7EmDFjqjo0nXDt2jXs3r0bM2fOhIODA5KTk6V/3bp1g6WlJRYtWoRbt27B398fERER0s2MVD7NmzeHlZWV9K9+/fowMjKClZUVc15BrK2t8cYbb+DTTz/F9evXcfXqVaxduxYjR46Em5sbc14BnJ2d0aFDB3z66aeIiYnBtWvXsHTpUrz77rvo168fc17BSnsvGT58OCIiIqT1g3x8fGBpaQk3N7cqjlx77dq1C3///TdWr14NY2Nj6bP02bNnAJhzTdLX11f4HLWysoKhoSFMTU1hYWEBgPmuCJ6entDX18eSJUtw+/ZtBAYG4siRI/jwww8B1OCcV/U88eWVkZEhFixYIJydnUXPnj3Fjh07qjoknfH5558LOzs7pf9ycnJEfHy88Pb2Fg4ODsLT01NcunSpqkPWOevWrZPWwRJCMOcVJDU1VSxatEh07txZdOvWTXz22WdCLpcLIZjzivLw4UMxc+ZM0a1bN9GzZ0+xcuVKkZmZKYRgzivCy2sElZbjCxcuiH79+glHR0cxevRokZCQUNkha72iOR86dKjSz9Ki6wIx569G2TpYhby8vBTWwRKC+daEl3MeFxcnxo4dKxwcHISHh4c4cOCAwv41MecyIYSo6iKPiIiIiIhIF2jlEEEiIiIiIqLqiAUWERERERGRhrDAIiIiIiIi0hAWWERERERERBrCAouIiIiIiEhDWGARERERERFpCAssIqrxcnNz8e233+Ktt96Cg4MD3N3dsXTpUjx+/LiqQ1Oqd+/eOHLkiEbP2bdvXxw8eFCj51RHUFAQhg8fDhcXF/Tr169YDMHBwRg0aBCcnJwwevRoJCQkSNvS09OxYsUKuLu7o1u3bpgxYwaSkpKk7XFxcRg3bhxcXFzg4eGB7du3lxpPSdcDgIMHD+KNN96Ai4sLJkyYgPv376v1PBMSEuDo6Ijc3Nxi2wIDA/HBBx9Ij48cOYJ27dqp/Ldo0SIAwLNnzzBz5kx07twZffv2xU8//aRw3qtXr2LYsGFwdnbGu+++iz/++ENlfKXl8smTJ5g9eza6desGDw8P7Nq1q8Tne/jwYbz11ltwdHSEl5cXIiMjFbafPHkSb731FpycnDB16lS1/l+Ty+V455138Oeff6rcPnDgQGzatEmhvbTXlIhI46p6IS4ioqr2+eefC09PT/HHH3+Iu3fviitXroiRI0eKoUOHivz8/KoOrxh3d3dx+PBhjZ7z8ePH0oK/leX27duiU6dOYsuWLSI+Pl4EBgYKBwcHce7cOSGEEPfv3xfOzs7C399f3Lp1S3zyySfC09NT5OXlCSGEWLx4sRg4cKAIDQ0VN2/eFOPHjxfDhg0TeXl5Qi6XCw8PD7Fo0SIRHx8vzp8/L1xcXERgYKDKeEq73pkzZ4SDg4M4duyYiI2NFR999JHCgrElnbdfv37SYu1FBQUFCScnJ4XzZGZmikePHolHjx6Jhw8fCjs7O/Hrr79KbS9evBBCCDF58mQxevRoERMTIw4ePCgcHBxEaGioEEKIlJQU4erqKr777juRkJAgtmzZIhwdHcXdu3eVxlhSLoUQ4oMPPhBDhw4VkZGRIigoSLz++uti165dSs/1xx9/iE6dOokTJ06IhIQE8dlnn4lu3bqJ1NRUIYQQERERolOnTuLw4cMiOjpajBo1Snz00Ucl5jArK0tMnz69xEVlN2zYIOzs7BQWli3tNSUiqggssIioxuvWrZs4deqUQltiYqKws7MT4eHhVRNUCSqiwKoKmzdvFiNHjlRoW7Jkifjkk0+EEAVfmIsWHhkZGcLFxUVcvnxZyOVy4ejoKH7//Xdpe2ExEhsbK+7cuSNmzZqlUDROnz5dLFmyRGU8JV1PCCGGDx8u1q1bJ23/999/hYeHh3j69KnKc545c0b06NFDDBo0qFiBtWnTJuHg4CDeeecdlYVaTk6OsLOzE8HBwQrtCQkJws7OTsTHx0ttixcvFnPnzhVCCHH69Gnh6uqqcEy3bt3EiRMnil2jtFxGRUUJOzs7ERcXJ23/+eefRc+ePZXGfPToUeHv7y89Tk1NFXZ2dlLxN3/+fClOIQqKoJefS1G3bt0SgwcPlnKorMCKjo4WPXv2FP3791cosEp7TYmIKgKHCBIRoWAYUV5envS4ZcuWOHHiBNq3bw8AEELg22+/hbu7O1xdXTF+/HjEx8dL+z99+hRz586Fq6sr3Nzc4OfnJw0He/78OZYuXYrXXnsNnTt3xty5c/Hs2TMAQEhICHr37o39+/ejd+/e6N69O+bPn4+srCzp3Pv27cPrr78OV1dXfPfddwpx37x5E97e3nB2dkbPnj3x+eefKx2GVnitYcOGwdHREX369FE4V9EhgqNHj8bmzZsxfvx4ODk5YdCgQbh48aJazzU1NRULFy6Eq6srevbsiaVLlyItLU1pPAMGDMDSpUsV2mQyGbKzswEAERER6Nq1q7TN2NgY9vb2CA8PhxAC33zzDTp37qxwLFAwVKxFixbYsGEDjIyMIIRAaGgorly5Ajc3N6WxlHa9tLQ0REVFoV+/ftL21q1b4/z582jQoIHKc166dAmzZ8+Gj49PsW1//fUXdu7cibffflvl8SXF2rhxY1hZWUltrq6uuHbtGgCgQYMGSE1NxalTpyCEwNmzZ5Geno527doB+N8wRACl5vLOnTswNTVFmzZtpO3t27dHcnIy7t69CwBo166dNGz13XffxcSJEwEAWVlZ2LVrFxo1agQ7Ozsp9qJ5btasGZo3b47w8HClz/Xq1avo2bMn9u/fr3R7Xl4eFi9ejHnz5hV7LUp6TYmIKgoLLCKq8caMGYO9e/fCw8MDS5YswYkTJ/DixQu0bdsWRkZGAIA9e/YgMDAQa9aswYEDB2BlZYVx48YhMzMTADBjxgzcu3cPu3fvxubNm3H27Fls27ZN2hYdHY2tW7di165duH37NhYsWCBd//Hjxzh58iT8/f3h5+eH06dPS19WL126BD8/P8yePRv79u3DtWvXFO6NmT9/Ptq0aYPjx49jw4YNCAwMxKFDh4o9x7y8PMycORMeHh44efIk/u///g+bN2/GpUuXlObE398fAwcOxJEjR9C6dWv4+PhIBWhJz3Xx4sV4+vQpAgIC8N133+H27dv49NNPlV6jdevWcHBwkB6npKTgxIkT0hfi5ORkNGnSROGYRo0aISkpCQYGBnB3d0fdunWlbT/88AMaNGiAtm3bKhzTu3dvfPjhh9J9XqqUdL3CQuL58+fw9vZGz5498fHHH+PRo0cqzwcAvr6+GDlypNJtP/zwg8KX/7JQFevDhw8BAF26dMGoUaMwe/Zs2NvbY/r06Vi2bBlsbGwAAJ6entI9WaXl0tzcHGlpaQqF8r179wAUFNsA8Mcff8DT01MhnkuXLsHZ2RnffPMNFi9eDBMTEwDAo0ePVOZZGS8vLyxYsADGxsZKt+/YsQMNGzbEkCFD1M6TqmsREWmCflUHQERU1aZPn47WrVvjxx9/xJEjR3Dw4EEYGhpi5syZmDBhAgBg+/btWLJkidQDsnTpUly8eBG//vor7O3tcfXqVZw+fVrqUVi+fDmSk5MRExODv/76CydPnpS+3K5duxYDBgzArVu3ABRMsrF48WK0a9cO7du3h7u7O6KiogAUTKowcOBA6cujn58fXn/9dSn2e/fuoU+fPrC0tETLli2xbds2pT0qqampePbsGRo1aoQWLVqgRYsW2LVrF1q2bKk0J71798awYcMAANOmTcO7776LpKQkpKenq3yuiYmJOHPmDIKDg6UYvvjiC/Tt2xcPHjxAs2bNVL4GGRkZmDFjBpo0aQIvLy8AQGZmJgwMDBT2MzAwgFwuL3b8r7/+ih07dmDVqlWoXbu2wrZvv/0Wjx49wvLly7F69WosWbJEaQwlXa+wuFi+fDnmzp2Lpk2bYt26dZgyZQoOHTqEWrUq9/dKVbHm5ORACIGMjAzcvXsXU6dOxVtvvYXLly/js88+g62tLZydnWFkZCT9ePCyl3Pp5OSEpk2bYvny5Vi+fDnS09PxzTffAABycnIAAI0bNy52nvbt2+Onn37CuXPnsGjRIrRo0QLOzs7IyspS+3Utze3bt7Fjxw4cPny4THkqz7WIiNTFAouICAW/6Ht6euLFixf4888/sX//fnz55ZewtraGm5sbHj58iHnz5il8kc7OzkZ8fDwMDQ1hYmKiMFyrsAg6efIk6tatKxVXANCmTRuYmpoiLi4ODRs2BAC0atVK2m5iYiINuYuLi8N7770nbTMzM0Pz5s2lx3PmzMGqVaukIYYDBw5U6BUq1KBBA0yaNAm+vr7YsmULPDw8MHjwYKVfjAEoFF6FPQ+5ubmIjY1V+Vx/++03CCHg4eFR7Hzx8fEqC6zU1FRMnjwZd+/exY8//ij1VBgaGhb7IiyXy4sVkCdOnMDChQvxn//8B8OHDy92/k6dOgEoGK62cOFCLFiwANOmTUNoaKi0T3h4eInX09cv+LicMGGCNKTvq6++Qs+ePREREYGQkBCFIZfbtm1Dly5dlD5fTVAVq5GREWQyGXbs2AG5XI5Zs2YBADp27IjY2Fhs2bKl2DDTopTl0sDAAJs2bcKcOXPQtWtXmJiYYN68eYiIiJD+NpRp3LgxGjdujA4dOiA8PBz79u2Ds7NzibGXhRACPj4+mDp1Klq0aKF0H3X/hoiINIkFFhHVaDExMTh06JDUq1G/fn30798f/fr1w4gRI/Dnn3+iW7duAIB169YVG35Wr149hIWFqTz/y7+eF8rLy0N+fr70+OVeFyGE0v9+eV9vb294eHjg3LlzuHDhAqZNm4apU6fi448/LnbNuXPnYujQoTh37hx+++03jB49GqtWrVJalLwcT2EcytqLPqc6derg6NGjxbapKuSePHmC8ePHIyUlBT/88INCoWlhYYHk5GSF/VNSUmBrays9PnjwIP7v//4P48aNUxh2mZSUhOvXr+ONN96Q2mxsbJCTk4O0tDT4+fkp3OdW2vUKh5kVvQ+pUaNGMDU1xYMHD+Dl5YUBAwYonKsiWVhYICUlpVishXmOiopSyBMA2NvbY9++fSrPqSqXhcf++uuvSElJQf369ZGYmIhatWrB0tKy2HnCw8NhbGws3b8IAG3btpXuWSwtdnXdv38foaGhuHHjBr7++msABUV0VFQUIiIisH37drX+hoiINI33YBFRjZaXl4f//ve/0uQAhWQyGerVqwczMzPUr18fjRo1QnJyMqysrGBlZYUWLVpg3bp1uHnzJqytrZGWlobExETp+IMHD2LMmDFo06YN0tPTERcXJ22LjY1FWloaWrduXWp8tra20nBBAEhLS8OdO3cAFPSgrVq1CjKZDKNHj8aOHTswY8YMnDx5sth5kpOTsXz5cjRv3hwTJ07Ejz/+iGHDhuHUqVNlyldJz7V169bIyMhAXl6elCcAWL16tdKJLuRyOaZMmSLds1W0eAEAJycnheI1MzMTN27cgLOzMwDgzJkzWLp0KSZNmoSFCxcqHBsXF4ePP/5YYX2lv//+G2ZmZjAzM4OFhYUUY2GcJV2vWbNmsLCwwI0bNxRy+vz5czRv3hwNGjRQOF9Ze2PKytnZWeHeMAAIDQ2Fk5MTAKBJkya4efOmwjFxcXEKBWxRJeXy+fPn+PDDD/H48WOYm5vDwMAA58+fR8eOHZX2YAUEBGDDhg0KbX///bf0+jo5OSn0Hj548AD379+XXld1WVhY4PTp0wgMDMTRo0dx9OhRdOjQAV5eXvDz85OuVdLfEBFRRWCBRUQ1mr29PTw8PDBjxgz89NNPuHPnDqKiorB+/XpER0djxIgRAIBx48bh66+/xtmzZ5GQkABfX1/8+eefaNOmDdq2bYvXXnsNPj4+iImJwdWrV7Flyxb06tULbdq0gYeHBxYuXIjIyEhERkZKs+x16NCh1Pi8vb1x+vRp7Nu3D3FxcViyZIk0y56hoSHCwsKwcuVKxMXF4ebNm/j9999hb29f7DympqY4e/Ys/Pz8kJCQgMjISFy9elXpviUp6bna2NjA3d0dCxYsQEREBGJiYrBw4UI8fvy42EQDALBr1y78/fffWL16NYyNjZGcnIzk5GRphsXhw4cjIiICW7ZsQWxsLHx8fGBpaQk3NzdkZGRg6dKl6NOnD0aNGiUdm5ycDLlcjq5du8LGxgaLFi1CXFwcfvvtN3z11VeYMmWKyudW0vVkMhk++ugjfPPNN7hw4QJiY2OxaNEidOzYEY6OjmXKoSa0bNkSvXr1wsKFCxETE4PDhw/j+PHjGDVqFADg/fffx5UrV7Bt2zbcuXMHBw8exJEjRzB27FgABT09hT07peXS1NQUWVlZ+Pzzz5GYmIhTp07h22+/xbRp06R4kpOTpR5Bb29v/P7779izZw/i4+Oxfv16/P3339K1P/jgA/z88884cOAAbt68iYULF6J3796wtrYG8L/7BUujr6+vUNRaWVnB0NAQpqamUg9iSa8pEVGFqaLp4YmIqo3MzEzx9ddfi379+olOnToJV1dXMWXKFHHr1i1pn9zcXLFhwwbRq1cv4ejoKN5//30REREhbX/06JGYMWOGcHJyEq+99ppYu3atyM3NFUII8fTpUzFnzhzh4uIiunTpIhYuXCiePXsmhBAiODi42PpICxcuVFgn6NixY6Jv377C2dlZfPbZZ2Lw4MHSOljx8fHio48+Ep07dxadO3cWc+fOlc79ssjISOHl5SWcnZ1F9+7dha+vr8jOzhZCCOHh4SEOHDgghBBi1KhRCus93blzR2GdopKe6+PHj8WcOXNE586dhaurq/j444/Fo0ePlMYzdOhQYWdnV+xf0XWLLly4IPr16yccHR3F6NGjRUJCghBCiPPnzys9tug6Sffu3ROTJ08WLi4uolevXmLr1q2lLhyt6nqFvvvuO9GrVy/h5OQkJk+eLJKSkko8XyFlr3OhjRs3lnkdLCEKFhOePHmy6NSpk/Dw8BBHjx4t9lyGDh0qnJ2dxTvvvCN++eUXadvhw4eFnZ2dEEK9XN6+fVuMHj1aODk5ibfffrvYgs12dnYKa7OdPn1aeHp6CgcHBzF8+HBpDaxCR44cEX369BHOzs5i2rRp4vHjx9K2hQsXilGjRinNR0kLDQshhJeXl8I6WIV5KOk1JSLSNJkQLw3uJyIiIiIionLhEEEiIiIiIiINYYFFRERERESkISywiIiIiIiINIQFFhERERERkYawwCIiIiIiItIQFlhEREREREQawgKLiIiIiIhIQ1hgERERERERacj/AwA1V1DNNJzRAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.style.use('seaborn-whitegrid')\n", - "plt.figure(figsize=(12, 5), tight_layout=True)\n", - "plt.plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.object1_flux/max(df_GUIDER.object1_flux), color='royalblue')\n", - "plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec / max(int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec), marker='o', color='k')\n", - "plt.title(\"Guider and Exposure Meter Flux Time Series\", fontsize=14)\n", - "plt.xlabel(\"Seconds since \" + str(guider_header['DATE-BEG']), fontsize=14)\n", - "plt.ylabel(\"Flux (fractional)\", fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.xlim(min(df_GUIDER.timestamp-min(df_GUIDER.timestamp)), max(df_GUIDER.timestamp-min(df_GUIDER.timestamp)))\n", - "plt.legend(['Guider Flux', 'Exposure Meter Flux'], fontsize=12, loc='best') \n", - "plt.show()\n", - "#plt.savefig('guider_xy_times.png',dpi=144)\n" - ] - }, - { - "cell_type": "markdown", - "id": "41a98e41", - "metadata": {}, - "source": [ - "# Show Header for Guider Data" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "ce034a22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "XTENSION= 'IMAGE ' / Image extension \n", - "BITPIX = -32 / array data type \n", - "NAXIS = 2 / number of array dimensions \n", - "NAXIS1 = 640 \n", - "NAXIS2 = 512 \n", - "PCOUNT = 0 / number of parameters \n", - "GCOUNT = 1 / number of groups \n", - "DATE-BEG= '2023-06-11T08:29:30.140' / sequence begin \n", - "DATE-END= '2023-06-11T08:32:22.936' / sequence end \n", - "OUTDIR = '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory \n", - "OFNAME = 'kpfguide_cube_4259.fits' / original output filename \n", - "EXTNAME = 'GUIDER_AVG' / HDU name \n", - "NFRAMES = 17265 / quantity of subframes \n", - "SUBX0 = 274 / [pix] subframe x0 coordinate \n", - "SUBX1 = 402 / [pix] subframe x1 coordinate \n", - "SUBY0 = 198 / [pix] subframe y0 coordinate \n", - "SUBY1 = 326 / [pix] subframe y1 coordinate \n", - "FPS = 100.0 / frames per second \n", - "GAIN = 'Low ' / detector gain setting \n", - "TEMPDET = -40.01 / [deg C] sensor temperature \n", - "EXPOSING= T / continuous exposures active \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/sbin/kpftiptiltd \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpffiuserver.keck.hawaii.edu \n", - "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [kpfguide] \n", - "COMMENT Snapshot timestamp: 2023-06-11T08:30:56.538520Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "INSTRUME= 'KPF ' / Selected instrument \n", - "TELESCOP= 'Keck I ' / Telescope \n", - "RA = '17:20:39.80' / Right ascension \n", - "DEC = '+32:27:40.0' / Declination \n", - "EL = 61.46 / [deg] telescope elevation \n", - "AZ = 57.97 / [deg] telescope azimuth \n", - "PARANG = -108.94 / [deg] parallactic angle \n", - "VA = 118.5 / [deg] vertical angle \n", - "PA = 9.6 / [deg] sky position angle \n", - "MJD-OBS = 60106.354817 / Modified Julian day \n", - "PMFM = 0.0 / PMFM value \n", - "UT = '08:30:56.22' / DCS Universal Time \n", - "SECFOCUS= -2.244 / [mm] Secondary focus \n", - "TELFOCUS= -1.459 / [mm] Telescope focus \n", - "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", - "WCSNAME = 'RA/Dec ' / Descriptive name of this WCS \n", - "CTYPE1 = 'RA---TAN' / Coordinate type of WCS axis 1 \n", - "CTYPE2 = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", - "CNAME1 = 'Right Ascension' / Descriptive name of WCS axis 1 \n", - "CNAME2 = 'Declination' / Descriptive name of WCS axis 2 \n", - "CUNIT1 = 'deg ' / Physical unit of WCS axis 1 \n", - "CUNIT2 = 'deg ' / Physical unit of WCS axis 2 \n", - "RADECSYS= 'ICRS ' / Celestial coordinate system \n", - "CD1_1 = 2.68683E-06 / [deg] RA per pixel in x \n", - "CD1_2 = 1.58855E-05 / [deg] RA per pixel in y \n", - "CD2_1 = 1.58855E-05 / [deg] DEC per pixel in x \n", - "CD2_2 = -2.68683E-06 / [deg] DEC per pixel in y \n", - "CRPIX1 = 337.826 / [pix] X coordinate of center pixel \n", - "CRPIX2 = 262.276 / [pix] Y coordinate of center pixel \n", - "CRVAL1 = 260.165833 / [deg] RA of center pixel \n", - "CRVAL2 = 32.461111 / [deg] DEC of center pixel \n", - "WCSNAMER= 'RA/Dec ' / Descriptive name of this WCS \n", - "CTYPE1R = 'RA---TAN' / Coordinate type of WCS axis 1 \n", - "CTYPE2R = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", - "CNAME1R = 'Right Ascension' / Descriptive name of WCS axis 1 \n", - "CNAME2R = 'Declination' / Descriptive name of WCS axis 2 \n", - "CUNIT1R = 'deg ' / Physical unit of WCS axis 1 \n", - "CUNIT2R = 'deg ' / Physical unit of WCS axis 2 \n", - "RADESYSR= 'ICRS ' / Celestial coordinate system \n", - "CD1_1R = 2.68683E-06 / [deg] RA per pixel in x \n", - "CD1_2R = 1.58855E-05 / [deg] RA per pixel in y \n", - "CD2_1R = 1.58855E-05 / [deg] DEC per pixel in x \n", - "CD2_2R = -2.68683E-06 / [deg] DEC per pixel in y \n", - "CRPIX1R = 337.826 / [pix] X coordinate of center pixel \n", - "CRPIX2R = 262.276 / [pix] Y coordinate of center pixel \n", - "CRVAL1R = 260.165833 / [deg] RA of center pixel \n", - "CRVAL2R = 32.461111 / [deg] DEC of center pixel \n", - "WCSNAMEY= 'Az/El ' / Descriptive name of this WCS \n", - "CTYPE1Y = 'TLON-TAN' / Coordinate type of WCS axis 1 \n", - "CTYPE2Y = 'TLAT-TAN' / Coordinate type of WCS axis 2 \n", - "CNAME1Y = 'Azimuth ' / Descriptive name of WCS axis 1 \n", - "CNAME2Y = 'Elevation' / Descriptive name of WCS axis 2 \n", - "CUNIT1Y = 'deg ' / Physical unit of WCS axis 1 \n", - "CUNIT2Y = 'deg ' / Physical unit of WCS axis 2 \n", - "RADESYSY= 'ICRS ' / Celestial coordinate system \n", - "CD1_1Y = -7.68756E-06 / [deg] Az per pixel in x \n", - "CD1_2Y = -1.41587E-05 / [deg] Az per pixel in y \n", - "CD2_1Y = -1.41587E-05 / [deg] El per pixel in x \n", - "CD2_2Y = 7.68756E-06 / [deg] El per pixel in y \n", - "CRPIX1Y = 337.826 / [pix] X coordinate of center pixel \n", - "CRPIX2Y = 262.276 / [pix] Y coordinate of center pixel \n", - "CRVAL1Y = 57.971911 / [deg] Az of center pixel \n", - "CRVAL2Y = 61.45613 / [deg] El of center pixel \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", - "COMMENT Header generation end time: 2023-06-11T08:32:23.299838Z \n", - "COMMENT ------------------------------------------------------------------------" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "D2['GUIDER_AVG'].header" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.7" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} +{ + "cells": [ + { + "cell_type": "markdown", + "id": "874cf8c2", + "metadata": {}, + "source": [ + "# Import Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "eb18fd32", + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.io import fits\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from astropy.time import Time\n", + "from astropy.table import Table" + ] + }, + { + "cell_type": "markdown", + "id": "b7481710", + "metadata": {}, + "source": [ + "# Define Directories and Filenames\n", + "One needs to have the L0 file KP.20221112.26598.92.fits and the 2D file KP.20221112.26598.92_2D.fits in the data/ directory of the folder from which this notebook is running. These files can be downloaded from Jump or shrek.caltech.edu. Note that as of 11/23/2022, the 2D file will have the name KP.20221112.26598.92.fits and will need to be renamed KP.20221112.26598.92_2D.fits for the code below to work. One can also modify the code below to work with other files or paths." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7f5cf66a", + "metadata": {}, + "outputs": [], + "source": [ + "L0_data_dir = 'data/'\n", + "#ObsID = 'KP.20230615.53679.85' # Spectrum of HD 219134\n", + "ObsID = 'KP.20230611.30570.89' # Spectrum of HD 157214\n", + "#ObsID = 'KP.20221112.26598.92' # Commissioning Spectrum of 51 Pegasi\n", + "L0_file = L0_data_dir + ObsID + '.fits'\n", + "D2_file = L0_data_dir + ObsID + '_2D.fits' # 2D file (which needs to be renamed with '_2D' appended)" + ] + }, + { + "cell_type": "markdown", + "id": "544a5262", + "metadata": {}, + "source": [ + "# Open File and Show Information\n", + "Note that there are exensions for:\n", + " * Header keywords (PRIMARY)\n", + " * Four quadrants of the green CCD (GREEN_AMP1, GREEN_AMP2, GREEN_AMP3, GREEN_AMP4)\n", + " * Two halves of the red CCD (RED_AMP1, RED_AMP2)\n", + " * Spectral time series for the Exposure Meter (EXPMETER_SCI, EXPMETER_SKY)\n", + " * Telemetry (HDU needs to be named! It just has the index 9 here. This was fixed in the L0 Assembler on 11/22/22.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a650ceba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: data/KP.20230611.30570.89.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 407 () \n", + " 1 GREEN_AMP1 1 ImageHDU 13 (2144, 2140) float32 \n", + " 2 GREEN_AMP2 1 ImageHDU 13 (2144, 2140) float32 \n", + " 3 GREEN_AMP3 1 ImageHDU 13 (2144, 2140) float32 \n", + " 4 GREEN_AMP4 1 ImageHDU 13 (2144, 2140) float32 \n", + " 5 RED_AMP1 1 ImageHDU 13 (2094, 4110) float32 \n", + " 6 RED_AMP2 1 ImageHDU 13 (2094, 4110) float32 \n", + " 7 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 8 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 9 guider_avg 1 ImageHDU 102 (640, 512) float32 \n", + " 10 guider_cube_origins 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n", + " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n" + ] + } + ], + "source": [ + "L0 = fits.open(L0_file)\n", + "L0.info()" + ] + }, + { + "cell_type": "markdown", + "id": "1fbd2b16", + "metadata": {}, + "source": [ + "# Primary Extension (header keywords)\n", + "Here is a list of keywords. Later data products (2D, L1, L2 add keywords to this list)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ee65727", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SIMPLE = T / conforms to FITS standard \n", + "BITPIX = 8 / array data type \n", + "NAXIS = 0 / number of array dimensions \n", + "EXTEND = T \n", + "FAVER = '0 ' / Written by FITSAssemble version 1.4 \n", + "TIMESYS = 'UTC ' \n", + "CAMERAS = 4 / Detectors in assembly request \n", + "GREEN = 'YES ' / Was this camera found? \n", + "RED = 'YES ' / Was this camera found? \n", + "CA_HK = 'NO ' / Was this camera found? \n", + "EXPMETER= 'YES ' / Was this camera found? \n", + "GUIDE = 'YES ' / Was this camera found? \n", + "DATE-MID= '2023-06-11T08:30:57.094' / Halfway point of the exposure, unweighted \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT Location: /kroot/rel/default/bin/keyheader \n", + "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", + "COMMENT Hostname: kpfserver \n", + "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [ExposureBegin] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "DATE-OBS= '2023-06-11' / Date dcs1.DATE \n", + "UT = '08:29:30.54' / DCS Universal Time \n", + "DATE-BEG= '2023-06-11T08:29:30.885517' / Start of exposure from kpfexpose \n", + "EXPTIME = 172.0 / Requested exposure time \n", + "PROGNAME= 'C007 ' / Program name from kpfexpose \n", + "OBJECT = '157214 ' / Object \n", + "INSTRUME= 'KPF ' / Instrument name \n", + "DATALVL = '0 ' / Data Product Base Level \n", + "OBSERVAT= 'KECK ' / Observatory name \n", + "OBSMODE = 'SPEC ' / Basic mode of observation \n", + "HEADVER = '125547 ' / Header config version \n", + "CURRINST= 'KPF ' / Selected instrument \n", + "TELESCOP= 'Keck I ' / Telescope \n", + "TARGNAME= '157214 ' / KPF Target name \n", + "DCSNAME = '157214 ' / DCS Target name - may not make any sense \n", + "TARGRA = '17:20:39.80' / [h] DCS Target RA \n", + "TARGDEC = '+32:27:40.0' / [deg] DCS Target Dec \n", + "TARGEPOC= 2000.0 / DCS Target epoch \n", + "TARGEQUI= 2000.0 / DCS Target equinox \n", + "TARGPLAX= 68.59999999999999 / [arcsec] target parallax \n", + "TARGPMDC= 0.0 / [arcsec/yr] target proper motion dec \n", + "TARGPMRA= 0.0 / [s/yr] target proper motion ra \n", + "TARGRADV= -78.43000000000001 / [km/s] target radial velocity \n", + "TARGWAVE= 0.65 / target-wavelength \n", + "TARGFRAM= 'FK5 ' / target-frame \n", + "FULLTARG= '157214 ' / Full Target name from kpfconfig \n", + "GAIAID = 'DR3 1333760419707327744' / GAIA Target name from kpfconfig \n", + "2MASSID = 'J17203955+3228056' / 2MASS Target name from kpfconfig \n", + "GAIAMAG = '5.21 ' / GAIA G band magnitude \n", + "2MASSMAG= '4.16 ' / 2MASS J band magnitude \n", + "TARGTEFF= 5750.0 / Target effective temperature (K) \n", + "RA = '17:20:39.80' / [h] Right ascension \n", + "DEC = '+32:27:40.0' / [deg] Declination \n", + "EQUINOX = 2000.0 / DCS Equinox \n", + "MJD-OBS = 60106.353826 / Modified Julian day \n", + "PMFM = 0.0 / PMFM value \n", + "LFCMODE = 'StandbyHigh' / LFC Operation Mode \n", + "AMPON = 1768.003 / LFC: Amount of time amplifier on \n", + "LFCFO = 250000000.0 / LFC filtered Offset Freq RR Comb counted \n", + "LFCFREF = 250000000.0 / LFC filtered Offset Freq RR Comb setpoint \n", + "LFCFR = 20000000000.0197 / LFC filtered Offset Freq RR Filter counted \n", + "LFCFRREF= 20000000000.0 / LFC filtered Offset Freq RR Filter setpoint \n", + "LFCCEOFR= 5220000000.0 / CEO Filtered Setpoint Freq \n", + "LFCCWFRF= 288005220000000.0 / CW Freq Reference \n", + "LFCCWFRQ= 288005230048093.0 / CW Freq \n", + "LFCCWFER= 10048093.0 / CW Freq Error: Ref-Actual \n", + "LFCCWMDN= 1152021 / CW mode number \n", + "LFCBIACT= 0.033 / Blue cut amp diode current (A) \n", + "LFCBISET= 0.0 / Blue cut amp diode setting (A) \n", + "OCTAGON = 'EtalonFiber' / selected octagon value \n", + "CALMON = 'Out ' / Calibration intensity monitor position \n", + "HATCH = 'Open ' / Status of the hatch on the exterior of the FIU \n", + "FIUMODE = 'Observing' / FIU operating mode \n", + "FOLDNAM = 'Out ' / Named Position of FIU Fold mirror \n", + "FOLDVAL = 24.7 / [mm] Position of FIU Fold mirror \n", + "ADCTRACK= 'On ' / Is the ADC Tracking \n", + "HCLSN = '' / S/N of lamp in use \n", + "CAREQ = 'Yes ' / Ca HK fibers (6,7) requested \n", + "EXPSCREQ= 'Yes ' / ExpM Science fiber (8ab) requested \n", + "FFREQ = 'No ' / Flatfield fiber (10ab) requested \n", + "SICALREQ= 'Yes ' / SimCal fiber (3ab) requested \n", + "VACSCREQ= 'Yes ' / Vac Sci fiber (1b) requested \n", + "VACSKREQ= 'Yes ' / Vac Sci fiber (2b) requested \n", + "THDAYON = '20230610T15:05:07 HST' / ThAr Daily was last turned on at this time \n", + "THDAYTON= 4306.4 / ThAr Daily has been on for this long \n", + "THAUON = '20230503T12:39:28 HST' / ThAr Gold was last turned on at this time \n", + "THAUTON = 1465.7 / ThAr Gold has been on for this long \n", + "UDAYON = '20230610T16:17:58 HST' / UNe Daily was last turned on at this time \n", + "UDAYTON = 3251.2 / UNe Daily has been on for this long \n", + "UAUON = '19691231T14:00:00 HST' / UNe Gold was last turned on at this time \n", + "UAUTON = 0.0 / UNe Gold has been on for this long \n", + "PTHDAY = 9.84700000000000E-06 / Last ThAr Daily calibration power value (Watts)\n", + "PTHDAYT = '20230610T16:11:20 HST' / Time of ThAr Daily last power value \n", + "PTHAU = 0.0 / Last ThAr Gold calibration power value (Watts) \n", + "PTHAUT = '19691231T14:00:00 HST' / Time of ThAr Gold last power value \n", + "PUDAY = 1.328E-05 / Last UNe Daily calibration power value (Watts) \n", + "PUDAYT = '20230610T17:07:06 HST' / Time of UNe Daily last power value \n", + "PUAU = 0.0 / Last UNe Gold calibration power value (Watts) \n", + "PUAUT = '19691231T14:00:00 HST' / Time of UNe Gold last power value \n", + "PLFC = 0.0 / Last LFC calibration power value (Watts) \n", + "PLFCT = '19691231T14:00:00 HST' / Time of LFC last power value \n", + "PETAL = 4.919E-07 / Last Etalon calibration power value (Watts) \n", + "PETALT = '20230610T16:40:09 HST' / Time of Etalon last power value \n", + "PBRB = 9.77900000000000E-06 / Last Broadband calibration power value (Watts) \n", + "PBRBT = '20230610T12:31:09 HST' / Time of Broadband last power value \n", + "PSOL = 0.0 / Last SoCal-CalFib cal. power value (Watts) \n", + "PSOLT = '19691231T14:00:00 HST' / Time of SoCal-CalFib last power value \n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [heartbeats] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:29:30.985518Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "COMMENT Data not available for keyword 'heart_12' \n", + "COMMENT Data not available for keyword 'heart_33' \n", + "COMMENT Data not available for keyword 'heart_34' \n", + "COMMENT Data not available for keyword 'heart_35' \n", + "COMMENT Data not available for keyword 'heart_36' \n", + "COMMENT Any value above other than 'Heartbeat active' indicates \n", + "COMMENT the keyword history daemon was not capturing data from \n", + "COMMENT the affected service as of the snapshot timestamp. \n", + "COMMENT \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT End of keyheader output \n", + "COMMENT Header generation start time: 2023-06-11T08:33:22.741011Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:22.860985Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT Location: /kroot/rel/default/bin/keyheader \n", + "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", + "COMMENT Hostname: kpfserver \n", + "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [ExposureMiddle] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:30:57.093759Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "SRCSHTTR= 'SciSelect,SkySelect' / Source shutters commanded \n", + "TIMSHTTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters commanded \n", + "OTIMSHTR= 'Scrambler,SimulCal,Ca_HK' / Timed shutters open exp. midpoint \n", + "SCISEL = 'open ' / Science Select shutter at exp. midpoint \n", + "SKYSEL = 'open ' / Sky Select Shutter at exp. midpoint \n", + "FFSHTR = 'closed ' / Flat field fiber shutter at exp. midpoint \n", + "SCRAMSHT= 'open ' / Scrambler shutter at exp. midpoint \n", + "SIMCALSH= 'open ' / Simult Cal shutter at exp. midpoint \n", + "TRIGTARG= 'Green,Red,ExpMeter,Guide' / Cameras that were sent triggers \n", + "IMTYPE = 'Object ' / Image Type \n", + "CAL-OBJ = 'EtalonFiber' / Calibration fiber source \n", + "SKY-OBJ = 'Sky ' / Sky fiber source \n", + "SCI-OBJ = 'Target ' / Science fiber source \n", + "CAFBS = 'Yes ' / Ca HK fibers (6,7) on \n", + "EXPSCIFB= 'Yes ' / ExpM Science fiber (8ab) on \n", + "EXPSKYFB= 'Yes ' / ExpM Science fiber (9) on \n", + "FFFB = 'No ' / Flatfield fiber (10ab) on \n", + "FICALFBS= 'No ' / FIU Cal fibers (4,5) on \n", + "SICALFB = 'Yes ' / SimCal fiber (3ab) on \n", + "VACSCFB = 'Yes ' / Vac Sci fiber (1b) on \n", + "VACSKFB = 'Yes ' / Vac Sci fiber (2b) on \n", + "SCIFB = 'Yes ' / Science fiber (1a) on \n", + "SKYFB = 'Yes ' / Sky fiber (2a) on \n", + "CURRBASE= '(335.5, 258.0)' / [pix] Selected pointing origin \n", + "PIXTARG = '(337.826, 262.276)' / [pix] Selected object tip/tilt target \n", + "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / Green MD5 sum for the acf file \n", + "GRACFFLN= 'kpfGreen.20220513-0519.acf' / Green acf file loaded \n", + "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / Red MD5 sum for the acf file \n", + "RDACFFLN= 'kpf_200kHz_2amp_short_oscan.acf' / Red acf file loaded \n", + "AGITSTA = 'Running ' / Agitator status kpfmot.AGITATOR \n", + "THARGD = 'Off ' / Gold ThAr power status \n", + "UNEGD = 'Off ' / Gold UNe power status \n", + "THARDAY = 'Off ' / Daily ThAr power status \n", + "UNEDAY = 'Off ' / Daily UNe power status \n", + "OCTBB = 'Off ' / Octagon broad band power status \n", + "FFSOURCE= 'Off ' / Flat field broad band power status \n", + "SCIFBILL= 'Off ' / Science fiber LED back illuminator power \n", + "SKYFBILL= 'Off ' / Sky fiber LED back illuminator power \n", + "HKFBILL = 'Off ' / Ca H and K fiber LED back illuminator power \n", + "EXPFBILL= 'Off ' / Exposure meter fiber LED back illuminator power\n", + "SSCALFW = 'OD 3.0 ' / Sci/Cal FW Position \n", + "SIMCALFW= 'OD 0.5 ' / Simual Cal FW Position \n", + "FFFW = 'Blank ' / Flatfield FW Position \n", + "OCTAGON = 'EtalonFiber' / selected octagon value \n", + "PRES = 623.7 / [hPa] Pressure at Vaisala kpfmet.PRES \n", + "RELH = 20.291 / Relative humidity at Vaisala kpfmet.RELH \n", + "PONAME = 'KPF ' / DCS Point origin name \n", + "PONAME1 = 'Opt Axis' / DCS Point origin name1 \n", + "DRA = 0.0 / [s/s] DCS Diff RA rate \n", + "DDEC = 0.0 / [asec/s] DCS Diff Dec rate \n", + "RABASE = '17:20:39.80' / DCS RA base \n", + "RAOFF = 0.0 / [asec] DCS RA offset \n", + "DECBASE = '+32:27:40.0' / DCS Dec base \n", + "DECOFF = 0.0 / [asec] DCS Dec offset \n", + "HA = '+22:06:05.70' / DCS Hour angle \n", + "AIRMASS = 1.14 / DCS Airmass \n", + "PARANG = -108.94 / [deg] DCS Parallactic angle astrometric \n", + "PARANTEL= -108.97 / [deg] DCS Parallactic angle telescope \n", + "EL = 61.46 / [deg] DCS Elevation \n", + "AZ = 57.97 / [deg] DCS Azimuth \n", + "LST = '15:26:45.50' / DCS Local sidereal time \n", + "AXESTAT = 'tracking' / DCS axes control status \n", + "TRACKING= 'yes ' / DCS Servos tracking status \n", + "DTRACK = 'disabled' / DCS differential tracking status \n", + "GUIDING = 'true ' / DCS Guiding status \n", + "AUTACTIV= 'yes ' / DCS Guider active \n", + "AUTFWHM = 0.6521940000000001 / [pix] DCS Guider fwhm \n", + "AUTXCENT= 0.2 / [asec] DCS Guider x centroid \n", + "AUTYCENT= 0.4 / [asec] DCS Guider y centroid \n", + "SECFOCUS= -2.244 / [mm] Secondary focus \n", + "TELFOCUS= -1.459 / [mm] Telescope focus \n", + "TUBEFLEX= -13.3 / [arcsec] Telescope tube flexure \n", + "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", + "PRIMTEMP= 3.530288 / [degC] Telescope pri temperature \n", + "SECMTEMP= 2.699448 / [degC] Telescope sec temperature \n", + "DIFFPTDW= 10.930288 / [decC] Diff between pri mirro temp dewpt \n", + "DIFFSTDW= 10.099448 / [decC] Diff between sec mirro temp dewpt \n", + "VIGNETTE= 'false ' / dome vignette (t/f) \n", + "STVIGNE = 'false ' / top shutter vignette (t/f) \n", + "SBVIGNE = 'false ' / bottom shutter vignette (t/f) \n", + "SBELEV = 26.97 / [deg] bottom shutter elevation \n", + "STELEV = 97.62 / [deg] top shutter elevation \n", + "SECSTST = 'INPOS ' / DCS Secondary status string \n", + "SECTHETX= -415.1 / [asec] DCS Secondary theta x \n", + "SECTHETY= 142.9 / [asec] DCS Secondary theta y \n", + "TERTSTST= 'INPOS ' / DCS Tertiary status string \n", + "TERTDEST= 'lnas ' / DCS Tertiary user destination \n", + "TERTPOSN= 'lnas ' / DCS Tertiary user position \n", + "DOMEPOSN= 58.04 / DCS Dome user position \n", + "DOMESTST= 'TRACK ' / DCS Dome status string \n", + "CALOCAL = -28.8 / collimation-azimuth-local \n", + "CELOCAL = 14.6 / collimation-elevation-local \n", + "FOCALSTN= 'lnas (left nasmyth)' / focal-station \n", + "INSTANGL= 312.0 / porg-to-instrument angle \n", + "POXPOS = 0.4 / pointing-origin-x-position \n", + "POYPOS = -0.8 / pointing-origin-y-position \n", + "ROTCALAN= 0.0 / rotator-calibration-angle \n", + "ROTZERO = 0.0 / rotator-zero-angle \n", + "GUIDWAVE= 0.65 / guidestar-wavelength \n", + "TIMEERR = 'ok 2 2 {NTP time correct to within 2 ms}' / resp time serv \n", + "ETAV1C1T= 22.984734 / Etalon v1ch1temp \n", + "ETAV1C2T= 22.702303 / Etalon v1ch2temp \n", + "ETAV1C3T= 22.49995 / Etalon v1ch3temp \n", + "ETAV1C4T= 22.799961 / Etalon v1ch4temp \n", + "ETAV2C3T= 23.026154 / Etalon v2ch3temp \n", + "COMMENT \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT End of keyheader output \n", + "COMMENT Header generation start time: 2023-06-11T08:33:24.085196Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:24.214439Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT Location: /kroot/rel/default/bin/keyheader \n", + "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", + "COMMENT Hostname: kpfserver \n", + "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [ExposureEnd] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:32:23.202000Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "DATE = '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", + "DATE-END= '2023-06-11T08:32:22.898526' / End of exposure from kpfexpose.ENDTIME \n", + "ELAPSED = 172.0 / Actual exposure time from kpfexpose.ELAPSED \n", + "OUTDIR = '/s/sdata1701/kpf1/2023jun10/L0' / Output directory \n", + "OFNAME = 'KP.20230611.30570.89.fits' / Filename of output file \n", + "GREENFN = '/s/sdata1701/kpf1/2023jun10/Green/kpf_63502.fits' \n", + "REDFN = '/s/sdata1701/kpf1/2023jun10/Red/kpf_red_63502.fits' \n", + "EXPMETFN= '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' \n", + "GUIDEFN = '/s/sdata1701/kpf1/2023jun10/CRED2/kpfguide_cube_4259.fits' \n", + "FRAMENO = 63502 \n", + "GRCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd green CAMD_VER \n", + "GREXPTI = 0 / exposure time in msec Kwd green EXPTIME \n", + "GRFILENA= 'kpf_63502.fits' / this filename Kwd green FILENAME \n", + "GRFIRMWA= '/kroot/rel/default/data/kpfgreen/ACF/kpfGreen.20220513-0519.acf' / co\n", + "GRHDRSHI= 0 / number of HDR right-shift bits Kwd green HDRSHI\n", + "GRSHUTT = T / shutter was enabled Kwd green SHUTTEN \n", + "GRTM_ZO = 'GMT ' / time zone Kwd green TM_ZONE \n", + "GRCDS0 = '#eval CDump - 417' / Kwd green CDS0 \n", + "GRCDS1 = '#eval CDump - 1' / Kwd green CDS1 \n", + "GRCDS2 = '#eval PixelT - 417' / Kwd green CDS2 \n", + "GRCDS3 = 950 / Kwd green CDS3 \n", + "GRTEST = 123 / test fitskey from modes file Kwd green TEST \n", + "GRDATE = '2023-06-11T08:33:20.749037' / FITS file write time Kwd green DATE \n", + "GROBSERV= 'Dai,Zhang' / Observer name Kwd green OBSERVER \n", + "GRPROGNA= 'C007 ' / Program name Kwd green PROGNAME \n", + "GRFRAME = 63502 / Kwd green FRAMENO \n", + "GRACF = '20220513-0519' / Last user-chosen ACF key Kwd green ACF \n", + "GRACFFI = 'kpfGreen.20220513-0519.acf' / ACF file from ACF key Kwd green ACFFILE\n", + "GRACFMD5= '6357C1AFA9A0F5A81A9EB130DAA1B46B' / MD5 sum for ACFFILE; 'unknown' if\n", + "GRDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd green DATE-BEG \n", + "GRDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd green DATE-END \n", + "GRELAPS = 172.013 / Shutter-elapsed time Kwd green ELAPSED \n", + "RDCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd red CAMD_VER \n", + "RDEXPTI = 0 / exposure time in msec Kwd red EXPTIME \n", + "RDFILENA= 'kpf_red_63502.fits' / this filename Kwd red FILENAME \n", + "RDFIRMWA= '/kroot/rel/default/data/kpfred/ACF/kpf_200kHz_2amp_short_oscan.acf' \n", + "RDHDRSHI= 0 / number of HDR right-shift bits Kwd red HDRSHIFT\n", + "RDSHUTT = T / shutter was enabled Kwd red SHUTTEN \n", + "RDTM_ZO = 'GMT ' / time zone Kwd red TM_ZONE \n", + "RDCDS0 = '#eval RGsettleT' / Kwd red CDS0 \n", + "RDCDS1 = '#eval CDump - 1' / Kwd red CDS1 \n", + "RDCDS2 = '#eval PixelT' / 2 + SWsettleT Kwd red CDS2 \n", + "RDCDS3 = 950 / Kwd red CDS3 \n", + "RDTEST = 123 / test fitskey from modes file Kwd red TEST \n", + "RDDATE = '2023-06-11T08:33:09.264258' / FITS file write time Kwd red DATE \n", + "RDOBSERV= 'Dai,Zhang' / Observer name Kwd red OBSERVER \n", + "RDPROGNA= 'C007 ' / Program name Kwd red PROGNAME \n", + "RDFRAME = 63502 / Kwd red FRAMENO \n", + "RDACF = '200kHz_2amp_short_oscan' / Last user-chosen ACF key Kwd red ACF \n", + "RDACFFI = 'kpf_200kHz_2amp_short_oscan.acf' / ACF file from ACF key Kwd red ACFF\n", + "RDACFMD5= '46B91FD6D29EEBBDED920738326414BA' / MD5 sum for ACFFILE; 'unknown' if\n", + "RDDATE-B= '2023-06-11T08:29:30.885518' / Shutter-open time Kwd red DATE-BEG \n", + "RDDATE-E= '2023-06-11T08:32:22.898526' / Shutter-close time Kwd red DATE-END \n", + "RDELAPS = 172.013 / Shutter-elapsed time Kwd red ELAPSED \n", + "EMFRAME = 63502 / Frame number from kpf_expmeter Kwd expmeter FRA\n", + "EMFILEN = '/s/sdata1701/kpf1/2023jun10/ExpMeter/kpf_em_63502.fits' / Output file\n", + "EMSEQBEG= 1 / Sequence number of first observation Kwd expmet\n", + "EMSEQEND= 89 / Sequence number of last observation Kwd expmete\n", + "EMDATE-B= '2023-06-11T08:29:30.266' / Date-Beg of first observation Kwd expmeter\n", + "EMDATE-E= '2023-06-11T08:32:23.102' / Date-End of last observation Kwd expmeter \n", + "GCNAXIS1= 640 / Kwd guide NAXIS1 \n", + "GCNAXIS2= 512 / Kwd guide NAXIS2 \n", + "GCDATE-B= '2023-06-11T08:29:30.140' / sequence begin Kwd guide DATE-BEG \n", + "GCDATE-E= '2023-06-11T08:32:22.936' / sequence end Kwd guide DATE-END \n", + "GCOUTDIR= '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory Kwd gu\n", + "GCOFNAME= 'kpfguide_cube_4259.fits' / original output filename Kwd guide OFNAME \n", + "GCEXTNA = 'guider_avg' / HDU name Kwd guide EXTNAME \n", + "GCNFRAM = 17265 / quantity of subframes Kwd guide NFRAMES \n", + "GCSUBX0 = 274 / [pix] subframe x0 coordinate Kwd guide SUBX0 \n", + "GCSUBX1 = 402 / [pix] subframe x1 coordinate Kwd guide SUBX1 \n", + "GCSUBY0 = 198 / [pix] subframe y0 coordinate Kwd guide SUBY0 \n", + "GCSUBY1 = 326 / [pix] subframe y1 coordinate Kwd guide SUBY1 \n", + "GCFPS = 100.0 / frames per second Kwd guide FPS \n", + "GCGAIN = 'Low ' / detector gain setting Kwd guide GAIN \n", + "GCTEMPD = -40.01 / [deg C] sensor temperature Kwd guide TEMPDET \n", + "GCEXPOSI= T / continuous exposures active Kwd guide EXPOSING \n", + "GCINSTRU= 'KPF ' / Selected instrument Kwd guide INSTRUME \n", + "GCTELESC= 'Keck I ' / Telescope Kwd guide TELESCOP \n", + "GCRA = '17:20:39.80' / Right ascension Kwd guide RA \n", + "GCDEC = '+32:27:40.0' / Declination Kwd guide DEC \n", + "GCEL = 61.46 / [deg] telescope elevation Kwd guide EL \n", + "GCAZ = 57.97 / [deg] telescope azimuth Kwd guide AZ \n", + "GCPARANG= -108.94 / [deg] parallactic angle Kwd guide PARANG \n", + "GCVA = 118.5 / [deg] vertical angle Kwd guide VA \n", + "GCPA = 9.6 / [deg] sky position angle Kwd guide PA \n", + "GCMJD-O = 60106.354817 / Modified Julian day Kwd guide MJD-OBS \n", + "GCPMFM = 0.0 / PMFM value Kwd guide PMFM \n", + "GCUT = '08:30:56.22' / DCS Universal Time Kwd guide UT \n", + "GCSECFOC= -2.244 / [mm] Secondary focus Kwd guide SECFOCUS \n", + "GCTELFOC= -1.459 / [mm] Telescope focus Kwd guide TELFOCUS \n", + "GCTUBETE= 3.27 / [degC] Telescope tube temperature Kwd guide TUB\n", + "GCWCSNA = 'RA/Dec ' / Descriptive name of this WCS Kwd guide WCSNAME \n", + "GCCTYPE1= 'RA---TAN' / Coordinate type of WCS axis 1 Kwd guide CTYPE1 \n", + "GCCTYPE2= 'DEC--TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2 \n", + "GCCNAME1= 'Right Ascension' / Descriptive name of WCS axis 1 Kwd guide CNAME1\n", + "GCCNAME2= 'Declination' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", + "GCCUNIT1= 'deg ' / Physical unit of WCS axis 1 Kwd guide CUNIT1 \n", + "GCCUNIT2= 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2 \n", + "GCRADECS= 'ICRS ' / Celestial coordinate system Kwd guide RADECSYS \n", + "GCCD1_1 = 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1 \n", + "GCCD1_2 = 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2 \n", + "GCCD2_1 = 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1 \n", + "GCCD2_2 = -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2 \n", + "GCCRPIX1= 337.826 / [pix] X coordinate of center pixel Kwd guide CR\n", + "GCCRPIX2= 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", + "GCCRVAL1= 260.165833 / [deg] RA of center pixel Kwd guide CRVAL1 \n", + "GCCRVAL2= 32.461111 / [deg] DEC of center pixel Kwd guide CRVAL2 \n", + "GCWCSNAM= 'Az/El ' / Descriptive name of this WCS Kwd guide WCSNAMEY\n", + "GCCTYPE = 'TLAT-TAN' / Coordinate type of WCS axis 2 Kwd guide CTYPE2Y\n", + "GCCNAME = 'Elevation' / Descriptive name of WCS axis 2 Kwd guide CNAME2\n", + "GCCUNIT = 'deg ' / Physical unit of WCS axis 2 Kwd guide CUNIT2Y \n", + "GCRADESY= 'ICRS ' / Celestial coordinate system Kwd guide RADESYSY \n", + "GCCD1_1R= 2.68683E-06 / [deg] RA per pixel in x Kwd guide CD1_1R \n", + "GCCD1_2R= 1.58855E-05 / [deg] RA per pixel in y Kwd guide CD1_2R \n", + "GCCD2_1R= 1.58855E-05 / [deg] DEC per pixel in x Kwd guide CD2_1R \n", + "GCCD2_2R= -2.68683E-06 / [deg] DEC per pixel in y Kwd guide CD2_2R \n", + "GCCRPIX = 262.276 / [pix] Y coordinate of center pixel Kwd guide CR\n", + "GCCRVAL = 61.45613 / [deg] El of center pixel Kwd guide CRVAL2Y \n", + "GCCD1_1Y= -7.68756E-06 / [deg] Az per pixel in x Kwd guide CD1_1Y \n", + "GCCD1_2Y= -1.41587E-05 / [deg] Az per pixel in y Kwd guide CD1_2Y \n", + "GCCD2_1Y= -1.41587E-05 / [deg] El per pixel in x Kwd guide CD2_1Y \n", + "GCCD2_2Y= 7.68756E-06 / [deg] El per pixel in y Kwd guide CD2_2Y \n", + "COMMENT \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT End of keyheader output \n", + "COMMENT Header generation start time: 2023-06-11T08:33:25.372357Z \n", + "COMMENT Header generation end time: 2023-06-11T08:33:25.403001Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT The keywords that begin with GR come from the green detector system. \n", + "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", + "COMMENT The original keyword name follows the Kwd in the comment field \n", + "COMMENT The keywords that begin with RD come from the red detector system. \n", + "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", + "COMMENT The original keyword name follows the Kwd in the comment field \n", + "COMMENT The keywords that begin with EM come from the expmeter detector system. \n", + "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", + "COMMENT The original keyword name follows the Kwd in the comment field \n", + "COMMENT The keywords that begin with GC come from the guide detector system. \n", + "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", + "COMMENT The original keyword name follows the Kwd in the comment field " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "L0['PRIMARY'].header" + ] + }, + { + "cell_type": "markdown", + "id": "d8cb534f", + "metadata": {}, + "source": [ + "# Extract Keywords\n", + "Here we extra the name of the target star and the exposure time and assign them to variables." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "350bcd5e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Star = 157214\n", + "Exposure time = 172.013 sec\n" + ] + } + ], + "source": [ + "starname = L0['PRIMARY'].header['TARGNAME']\n", + "Texp = L0['PRIMARY'].header['GRELAPS'] # actually, this is the green CCD exposure time\n", + "print('Star = ' + starname)\n", + "print('Exposure time = ' + str(Texp) + ' sec')" + ] + }, + { + "cell_type": "markdown", + "id": "a8d9e0fd", + "metadata": {}, + "source": [ + "# Green CCD\n", + "There are four quadrants in the green CCD with separate extensions (when it is read in 4-amplifier mode). Let's plot of them." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2513006d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(tight_layout=True)\n", + "plt.figure(figsize=(6, 6), tight_layout=True)\n", + "plt.imshow(L0['GREEN_AMP4'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['GREEN_AMP4'].data,99))\n", + "plt.colorbar(shrink=0.7)\n", + "plt.grid(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1e33cb61", + "metadata": {}, + "source": [ + "# Red CCD\n", + "There are two halves for the Red CCD (when read out in 2-amplifier mode). Let's plot one of them" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bb4acd97", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(tight_layout=True)\n", + "plt.figure(figsize=(6, 12), tight_layout=True)\n", + "plt.imshow(L0['RED_AMP1'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(L0['RED_AMP1'].data,99))\n", + "plt.colorbar(shrink=0.7)\n", + "plt.grid(False)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0fef6f2b", + "metadata": {}, + "source": [ + "# 2D File\n", + "The 2D file is similar to L0 except that the image segments of the green and red CCDs have been assembled (prescan and overcal removed) and bias has been subtracted. The header information has also been removed." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "355ac676", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: data/KP.20230611.30570.89_2D.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 411 () \n", + " 1 RECEIPT 1 BinTableHDU 29 1R x 10C [26A, 6A, 7A, 40A, 3A, 9A, 1A, 21A, 40A, 4A] \n", + " 2 CONFIG 1 BinTableHDU 13 0R x 2C [D, D] \n", + " 3 GREEN_CCD 1 ImageHDU 13 (4080, 4080) float64 \n", + " 4 GREEN_VAR 1 ImageHDU 8 (0,) \n", + " 5 RED_CCD 1 ImageHDU 11 (4080, 4080) float64 \n", + " 6 RED_VAR 1 ImageHDU 7 (0,) \n", + " 7 CA_HK 1 ImageHDU 23 (0,) \n", + " 8 EXPMETER_SCI 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 9 EXPMETER_SKY 1 BinTableHDU 845 89R x 418C [23A, 23A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D] \n", + " 10 GUIDECAM 1 ImageHDU 7 (0,) \n", + " 11 TELEMETRY 1 BinTableHDU 26 123R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n", + " 12 SOLAR_IRRADIANCE 1 BinTableHDU 9 0R x 0C [] \n", + " 13 GUIDER_AVG 1 ImageHDU 102 (640, 512) float32 \n", + " 14 GUIDER_CUBE_ORIGINS 1 BinTableHDU 87 17265R x 39C [J, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, J, J, D, D, D, D, D, D, D, D] \n" + ] + } + ], + "source": [ + "D2 = fits.open(D2_file)\n", + "D2.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "965868e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "D2 = fits.open(D2_file)\n", + "fig, axs = plt.subplots(1, 2, figsize=(16,10), tight_layout=True)\n", + "im1 = axs[0].imshow(D2['GREEN_CCD'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(D2['GREEN_CCD'].data,99))\n", + "axs[0].set_title('Green CCD')\n", + "fig.colorbar(im1, ax=axs[0], shrink=0.6)\n", + "im2 = axs[1].imshow(D2['RED_CCD'].data, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(D2['RED_CCD'].data,99))\n", + "axs[1].set_title('Red CCD')\n", + "fig.colorbar(im2, ax=axs[1], shrink=0.6)\n", + "axs[0].grid(False)\n", + "axs[1].grid(False)\n", + "plt.subplots_adjust(wspace=0.4)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9cec7fe", + "metadata": {}, + "source": [ + "# Exposure Meter\n", + "Back in the L0 file, there are extensions for the EM SCI and EM SKY fibers. The example below is for EM SCI, but EM SKY is similar. Here we create a Pandas data frame with the spectral time series. Each row is a spectrum for light collected between Date-Beg and Date-End." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "257eb60d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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89 rows × 418 columns

\n", + "
" + ], + "text/plain": [ + " Date-Beg Date-End 872.5 \\\n", + "0 b'2023-06-11T08:29:30.266' b'2023-06-11T08:29:31.403' 65288.482868 \n", + "1 b'2023-06-11T08:29:32.211' b'2023-06-11T08:29:33.348' 145037.293213 \n", + "2 b'2023-06-11T08:29:34.161' b'2023-06-11T08:29:35.291' 102230.586317 \n", + "3 b'2023-06-11T08:29:36.109' b'2023-06-11T08:29:37.246' 124723.655282 \n", + "4 b'2023-06-11T08:29:38.074' b'2023-06-11T08:29:39.204' 158318.758731 \n", + ".. ... ... ... \n", + "84 b'2023-06-11T08:32:14.167' b'2023-06-11T08:32:15.297' 124354.741489 \n", + "85 b'2023-06-11T08:32:16.113' b'2023-06-11T08:32:17.252' 123058.241489 \n", + "86 b'2023-06-11T08:32:18.060' b'2023-06-11T08:32:19.201' 143975.551834 \n", + "87 b'2023-06-11T08:32:20.007' b'2023-06-11T08:32:21.149' 148698.069075 \n", + "88 b'2023-06-11T08:32:21.962' b'2023-06-11T08:32:23.102' 127710.327696 \n", + "\n", + " 869.1 865.8 862.4 859.2 855.9 \\\n", + "0 67002.043378 68390.296032 69528.805586 70656.291786 71491.506223 \n", + "1 149624.853723 151774.106377 153927.615931 156864.102130 159370.316568 \n", + "2 105320.146826 107596.399480 108443.909034 111245.395234 112633.609671 \n", + "3 128458.215792 130836.468446 132898.978000 135887.464199 137469.678637 \n", + "4 162879.319240 165962.571894 168799.081448 171753.567648 174870.782085 \n", + ".. ... ... ... ... ... \n", + "84 127902.301999 129678.554653 132025.064207 134457.550406 136400.764844 \n", + "85 126251.801999 128754.054653 130790.564207 133562.050406 135477.264844 \n", + "86 147620.112344 150533.364997 152976.874552 156002.360751 158174.575189 \n", + "87 153000.629585 155536.882239 158088.391793 161398.877993 164144.092430 \n", + "88 131171.888206 133226.140860 136405.650414 138327.136613 140634.351051 \n", + "\n", + " 852.7 849.6 ... 447.8 447.4 \\\n", + "0 71954.559302 72587.187752 ... 5703.675072 5743.436159 \n", + "1 160914.369646 161398.998096 ... 12800.485416 12607.246504 \n", + "2 113536.662750 114419.291200 ... 8841.778520 8620.539608 \n", + "3 137876.731715 139276.360165 ... 10785.847485 10477.608573 \n", + "4 176089.835164 177499.463614 ... 14599.950934 14220.712021 \n", + ".. ... ... ... ... ... \n", + "84 137870.817922 138140.446372 ... 10249.933692 10183.694780 \n", + "85 136684.317922 137419.946372 ... 10874.433692 10508.194780 \n", + "86 159627.628267 160418.256717 ... 12968.744037 12835.505125 \n", + "87 165108.145508 165504.773959 ... 13531.261278 13506.022366 \n", + "88 141900.404129 142219.032579 ... 11709.519899 11361.280987 \n", + "\n", + " 447.1 446.8 446.4 446.1 445.8 \\\n", + "0 5598.096457 5476.871403 5578.843803 5467.344864 5443.966945 \n", + "1 12358.906801 12037.681748 12279.654147 12212.155209 11889.777290 \n", + "2 8504.199905 8425.974852 8379.947251 8414.448312 8247.070393 \n", + "3 10258.268870 10212.043817 9951.016216 10084.517278 10006.139359 \n", + "4 13647.372319 13832.147266 13758.119665 13724.620726 13457.242807 \n", + ".. ... ... ... ... ... \n", + "84 9927.355077 9671.130024 9683.102423 9695.603485 9860.225566 \n", + "85 10214.855077 10097.630024 10194.602423 10075.103485 9831.725566 \n", + "86 12670.165422 12430.940369 12258.912768 12292.413830 12218.035910 \n", + "87 13153.682663 12955.457610 12900.430010 12634.931071 12658.553152 \n", + "88 11285.941284 10958.716231 10878.688630 10939.189692 10860.811772 \n", + "\n", + " 445.4 445.1 444.8 \n", + "0 5373.393696 5186.548686 4939.809832 \n", + "1 11869.204041 11649.359031 11570.620177 \n", + "2 8242.497145 7905.652134 7840.913281 \n", + "3 9927.566110 9701.721100 9694.982246 \n", + "4 13266.669559 13152.824548 12826.085694 \n", + ".. ... ... ... \n", + "84 9425.652317 9331.807307 9160.068453 \n", + "85 9752.152317 9731.307307 9590.568453 \n", + "86 11989.462662 11920.617651 11626.878798 \n", + "87 12569.979903 12480.134893 12043.396039 \n", + "88 10749.238524 10530.393513 10247.654660 \n", + "\n", + "[89 rows x 418 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_SCI_EM = Table.read(L0_file, format='fits',hdu='EXPMETER_SCI').to_pandas()\n", + "df_SCI_EM" + ] + }, + { + "cell_type": "markdown", + "id": "dea6829e", + "metadata": {}, + "source": [ + "# Convert Exposure Meter Data into Flux Measurements" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "41c889a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/1493378176.py:27: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n", + " int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample)\n" + ] + } + ], + "source": [ + "# Gain of CCD in KPF Exposure Meter\n", + "EM_gain = 1.48424 # e-/ADU\n", + "\n", + "# Define wavelength arrays and disperion at each wavelength (nm per pixel)\n", + "wav_SCI_str = df_SCI_EM.columns[2:] # string (center) wavelengths of each pixel\n", + "wav_SCI = df_SCI_EM.columns[2:].astype(float) # float (center) wavelengths of each pixel\n", + "disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1\n", + "disp_SCI_smooth = np.polyval(np.polyfit(wav_SCI,disp_SCI, deg=6),wav_SCI)\n", + "\n", + "# define normalized flux array (e- / nm / time)\n", + "df_SCI_EM_norm = df_SCI_EM[wav_SCI_str] * EM_gain / disp_SCI_smooth\n", + "\n", + "# define time arrays\n", + "date_beg = np.array(df_SCI_EM[\"Date-Beg\"], dtype=np.datetime64)\n", + "date_end = np.array(df_SCI_EM[\"Date-End\"], dtype=np.datetime64)\n", + "tdur_sec = (date_end-date_beg).astype(float)/1000. # array exposure durations in sec\n", + "time = (date_beg-date_beg[0])/1000 # array of times since beginning in sec\n", + "\n", + "# define indices for wavelength\n", + "ind_550m = np.where((wav_SCI < 550))\n", + "ind_550_650 = np.where((wav_SCI >= 550) & (wav_SCI < 650))\n", + "ind_650_750 = np.where((wav_SCI >= 650) & (wav_SCI < 750))\n", + "ind_750p = np.where((wav_SCI >= 750))\n", + "\n", + "# define flux arrays\n", + "int_SCI_spec = df_SCI_EM_norm[:5].sum(axis=0) / np.sum(tdur_sec[:5]) # flux vs. wavelength per sec (use first five samples)\n", + "int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample)\n", + "int_SCI_flux_550m = df_SCI_EM[wav_SCI_str[np.where((wav_SCI < 550))]].sum(axis=1)\n", + "int_SCI_flux_550_650 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 550) & (wav_SCI < 650))]].sum(axis=1)\n", + "int_SCI_flux_650_750 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 650) & (wav_SCI < 750))]].sum(axis=1)\n", + "int_SCI_flux_750p = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 750))]].sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8c52f9fc", + "metadata": {}, + "source": [ + "# Plot EM SCI Spectrum" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e6d96249", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-whitegrid')\n", + "plt.figure(figsize=(12, 6), tight_layout=True)\n", + "plt.axvspan(440, 550, alpha=0.5, color='b') \n", + "plt.axvspan(550, 650, alpha=0.5, color='g') \n", + "plt.axvspan(650, 750, alpha=0.5, color='orange') \n", + "plt.axvspan(750, 880, alpha=0.5, color='red') \n", + "plt.plot(wav_SCI, int_SCI_spec, marker='o', color='k')\n", + "plt.xlabel(\"Wavelength (nm)\", fontsize=14)\n", + "plt.ylabel(\"Average Exposure Meter Flux (e-/nm/s)\", fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.xlim(445,870)\n", + "plt.legend([\"EM SCI\"], fontsize=12, loc='best') \n", + "plt.show()\n", + "#plt.savefig('expmeter_flux_vs_wavelength.png',dpi=144)" + ] + }, + { + "cell_type": "markdown", + "id": "b813282b", + "metadata": {}, + "source": [ + "# Plot EM SCI Time Series" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "244dfde0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-whitegrid')\n", + "plt.figure(figsize=(12, 6), tight_layout=True)\n", + "plt.plot(time, int_SCI_flux_750p / ((847+4.8/2)-750) / tdur_sec, marker='o', color='r')\n", + "plt.plot(time, int_SCI_flux_650_750 / (750-650) / tdur_sec, marker='o', color='orange')\n", + "plt.plot(time, int_SCI_flux_550_650 / (650-550) / tdur_sec, marker='o', color='g')\n", + "plt.plot(time, int_SCI_flux_550m / (550-(450.1-0.4/2)) / tdur_sec, marker='o', color='b')\n", + "plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec, marker='o', color='k')\n", + "plt.xlabel(\"Time (sec)\", fontsize=14)\n", + "plt.ylabel(\"Exposure Meter Flux (e-/nm/s)\", fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend([\"750-870 nm\",\"650-750 nm\",\"550-650 nm\",\"445-550 nm\",\"445-870 nm\",], fontsize=12, loc='best') \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "426df078", + "metadata": {}, + "source": [ + "# Plot Fraction of Flux Time Series in Each Color Bin\n", + "This is a measure of the ADC performance." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7ed04a87", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-whitegrid')\n", + "plt.figure(figsize=(12, 6), tight_layout=True)\n", + "plt.plot(time, (int_SCI_flux_550m / int_SCI_flux ) , marker='o', color='r')\n", + "plt.plot(time, (int_SCI_flux_550_650 / int_SCI_flux ) , marker='o', color='orange')\n", + "plt.plot(time, (int_SCI_flux_650_750 / int_SCI_flux ) , marker='o', color='g')\n", + "plt.plot(time, (int_SCI_flux_750p / int_SCI_flux ) , marker='o', color='b')\n", + "plt.xlabel(\"Time (sec)\", fontsize=14)\n", + "plt.ylabel(\"Exposure Meter Flux Ratio\", fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.ylim(0,0.4)\n", + "plt.legend([\"750-870 nm / 445-870 nm\",\"650-750 nm / 445-870 nm\",\"550-650 nm / 445-870 nm\",\"445-550 nm / 445-870 nm\"], fontsize=12, loc='best') \n", + "#plt.show()\n", + "plt.savefig('EM_flux_ratio_51peg.png')" + ] + }, + { + "cell_type": "markdown", + "id": "d154a953", + "metadata": {}, + "source": [ + "# Telemetry\n", + "Each KPF L0 file contains telemetry on >100 sensors or state values with average, standard deviation, minimum, and maximum values listed." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "489f8080", + "metadata": {}, + "outputs": [], + "source": [ + "df_telemetry = Table.read(L0_file, format='fits', hdu='TELEMETRY').to_pandas() \n", + "num_columns = ['average', 'stddev', 'min', 'max']\n", + "for column in df_telemetry:\n", + " df_telemetry[column] = df_telemetry[column].str.decode('utf-8')\n", + " df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0\n", + " if column in num_columns:\n", + " df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast=\"float\")\n", + " else:\n", + " df_telemetry[column] = df_telemetry[column].astype(str)\n", + "df_telemetry.set_index(\"keyword\", inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "89f8ef3c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 123 entries, kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS to kpfpower.OUTLET_A1_Amps\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 average 123 non-null float32\n", + " 1 stddev 123 non-null float32\n", + " 2 min 123 non-null float32\n", + " 3 max 123 non-null float32\n", + " 4 units 123 non-null object \n", + " 5 description 123 non-null object \n", + "dtypes: float32(4), object(2)\n", + "memory usage: 4.8+ KB\n" + ] + } + ], + "source": [ + "df_telemetry.info()" + ] + }, + { + "cell_type": "markdown", + "id": "961e0a1a", + "metadata": {}, + "source": [ + "Print the entire table of telemetry." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c0c3069a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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averagestddevminmaxunitsdescription
keyword
kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS1.597510e+011.963010e+021.597500e+011.597600e+01degCBench Bottom Between Cameras C2 c- double degC...
kpfmet.BENCH_BOTTOM_COLLIMATOR1.647870e+012.024890e+021.647700e+011.648000e+01degCBench Bottom Coll C3 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_DCUT1.624350e+011.995990e+021.624300e+011.624400e+01degCBench Bottom D-cut C4 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_ECHELLE1.638640e+012.013550e+021.638600e+011.638700e+01degCBench Bottom Echelle Cam B c- double degC {%.3f}
kpfmet.BENCH_TOP_BETWEEN_CAMERAS1.588580e+011.952040e+021.588500e+011.588700e+01degCBench Top Between Cameras D4 c- double degC {%...
kpfmet.BENCH_TOP_COLL1.657790e+012.037080e+021.657700e+011.657900e+01degCBench Top Coll D5 c- double degC {%.3f}
kpfmet.BENCH_TOP_DCUT1.628050e+012.000540e+021.627900e+011.628100e+01degCBench Top D-cut D3 c- double degC {%.3f}
kpfmet.BENCH_TOP_ECHELLE_CAM1.629190e+012.001940e+021.629100e+011.629300e+01degCBench Top Echelle Cam D1 c- double degC {%.3f}
kpfmet.CALEM_SCMBLR_CHMBR_END1.736520e+012.133830e+021.736400e+011.736600e+01degCCal EM Scrammbler Chamber End C1 c- double deg...
kpfmet.CALEM_SCMBLR_FIBER_END1.721330e+012.115160e+021.721200e+011.721500e+01degCCal EM Scrambler Fiber End D1 c- double degC {...
kpfmet.CAL_BENCH2.020000e+012.482160e+022.020000e+012.020000e+01degCCal_Bench temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_BB_SRC2.050000e+012.519030e+022.050000e+012.050000e+01degCCAL_Bench_BB_Src temperature c- double degC {%...
kpfmet.CAL_BENCH_BOT2.360000e+012.899950e+022.360000e+012.360000e+01degCCal_Bench_Bot temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_ENCL_AIR2.018220e+012.479980e+022.010000e+012.020000e+01degCCal_Bench_Encl_Air temperature c- double degC ...
kpfmet.CAL_BENCH_OCT_MOT1.990000e+012.445300e+021.990000e+011.990000e+01degCCal_Bench_Oct_Mot temperature c- double degC {...
kpfmet.CAL_BENCH_TRANS_STG_MOT2.010000e+012.469880e+022.010000e+012.010000e+01degCCal_Bench_Trans_Stg_Mot temperature c- double ...
kpfmet.CAL_RACK_TOP2.301710e+012.828330e+022.300000e+012.310000e+01degCCal_Rack_Top temperature c- double degC {%.1f}
kpfmet.CHAMBER_EXT_BOTTOM1.570260e+011.929530e+021.570100e+011.570400e+01degCChamber Exterior Bottom B c- double degC {%.3f}
kpfmet.CHAMBER_EXT_TOP-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCChamber Exterior Top C1 c- double degC {%.3f}
kpfmet.CRYOSTAT_G1-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin cryostat green D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_G2-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin cryostat green D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_G31.668370e+012.050090e+021.668300e+011.668500e+01degCWithin cryostat green D4 c- double degC {%.3f}
kpfmet.CRYOSTAT_R11.610210e+011.978620e+021.610000e+011.610300e+01degCWithin Cryostat red D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_R2-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCWithin Cryostat red D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_R31.564080e+011.921940e+021.564000e+011.564200e+01degCWithin Cryostat red D4 c- double degC {%.3f}
kpfmet.ECHELLE_BOTTOM1.641060e+012.016530e+021.641000e+011.641200e+01degCEchelle Bottom D1 c- double degC {%.3f}
kpfmet.ECHELLE_TOP1.628290e+012.000840e+021.628200e+011.628400e+01degCEchelle Top C1 c- double degC {%.3f}
kpfmet.FF_SRC1.957500e+012.405370e+021.950000e+011.960000e+01degCFF_Src temperature c- double degC {%.1f}
kpfmet.GREEN_CAMERA_BOTTOM1.677540e+012.061350e+021.677400e+011.677600e+01degCGreen Camera Bottom C3 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_COLLIMATOR1.682040e+012.066880e+021.681900e+011.682200e+01degCGreen Camera Collimator C4 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_ECHELLE1.671060e+012.053390e+021.670900e+011.671200e+01degCGreen Camera Echelle D5 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_TOP1.661770e+012.041970e+021.661700e+011.661900e+01degCGreen Camera Top C2 c- double degC {%.3f}
kpfmet.GREEN_GRISM_TOP1.639170e+012.014200e+021.639000e+011.639300e+01degCGreen Grism Top C5 c- double degC {%.3f}
kpfmet.GREEN_LN2_FLANGE9.791590e+001.203190e+029.746000e+009.848000e+00degCGreen LN2 Flange A c- double degC {%.3f}
kpfmet.PRIMARY_COLLIMATOR_TOP1.689680e+012.076270e+021.689600e+011.689800e+01degCPrimary Col Top D2 c- double degC {%.3f}
kpfmet.RED_CAMERA_BOTTOM1.630110e+012.003070e+021.630000e+011.630300e+01degCRed Camera Bottom D5 c- double degC {%.3f}
kpfmet.RED_CAMERA_COLLIMATOR1.664290e+012.045070e+021.664200e+011.664400e+01degCRed Camera Coll C3 c- double degC {%.3f}
kpfmet.RED_CAMERA_ECHELLE1.659400e+012.039060e+021.659300e+011.659500e+01degCRed Camera Ech C4 c- double degC {%.3f}
kpfmet.RED_CAMERA_TOP1.654670e+012.033240e+021.654500e+011.654800e+01degCRed Camera Top C5 c- double degC {%.3f}
kpfmet.RED_GRISM_TOP1.636990e+012.011530e+021.636900e+011.637200e+01degCRed Grism Top C2 c- double degC {%.3f}
kpfmet.RED_LN2_FLANGE6.245890e+007.675000e+016.199000e+006.292000e+00degCRed LN2 Flange D1 c- double degC {%.3f}
kpfmet.REFORMATTER1.660480e+012.040380e+021.660400e+011.660600e+01degCReformatter A c- double degC {%.3f}
kpfmet.SCIENCE_CAL_FIBER_STG2.010000e+012.469880e+022.010000e+012.010000e+01degCScience_Cal_Fiber_Stg temperature c- double de...
kpfmet.SCISKY_SCMBLR_CHMBR_END-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCSciSky Scrambler Chamber End A c- double degC ...
kpfmet.SCISKY_SCMBLR_FIBER_END1.747050e+012.146770e+021.746900e+011.747200e+01degCSciSky Scrammbler Fiber End B c- double degC {...
kpfmet.SIMCAL_FIBER_STG2.010000e+012.469880e+022.010000e+012.010000e+01degCSimCal_Fiber_Stg temperature c- double degC {%...
kpfmet.SKYCAL_FIBER_STG1.990000e+012.445300e+021.990000e+011.990000e+01degCSkyCal_Fiber_Stg temperature c- double degC {%...
kpfmet.TEMP1.968480e+012.418860e+021.967300e+011.969700e+01degCVaisala Temperature c- double degC {%.3f}
kpfmet.TH_DAILY2.000000e+012.457590e+022.000000e+012.000000e+01degCTh_daily temperature c- double degC {%.1f}
kpfmet.TH_GOLD1.990000e+012.445300e+021.990000e+011.990000e+01degCTh_gold temperature c- double degC {%.1f}
kpfmet.U_DAILY2.010000e+012.469880e+022.010000e+012.010000e+01degCU_daily temperature c- double degC {%.1f}
kpfmet.U_GOLD1.990000e+012.445300e+021.990000e+011.990000e+01degCU_gold temperature c- double degC {%.1f}
kpfgreen.BPLANE_TEMP3.582320e+014.401930e+023.581200e+013.587500e+01degCBackplane temperature c- double degC {%.3f}
kpfgreen.BRD10_DRVR_T4.050370e+014.977070e+024.050000e+014.056200e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfgreen.BRD11_DRVR_T3.875000e+014.761580e+023.875000e+013.875000e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfgreen.BRD12_LVXBIAS_T3.475000e+014.270060e+023.475000e+013.475000e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfgreen.BRD1_HTRX_T3.184100e+013.912610e+023.181200e+013.187500e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfgreen.BRD2_XVBIAS_T0.000000e+000.000000e+000.000000e+000.000000e+00degCBoard 2 (XV Bias) temperature c- double degC {...
kpfgreen.BRD3_LVDS_T3.342310e+014.107010e+023.337500e+013.343700e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfgreen.BRD4_DRVR_T3.719820e+014.570890e+023.718700e+013.725000e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfgreen.BRD5_AD_T3.381530e+014.155200e+023.381200e+013.387500e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfgreen.BRD7_HTRX_T3.784640e+014.650540e+023.781200e+013.787500e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfgreen.BRD9_HVXBIAS_T3.627570e+014.457540e+023.625000e+013.631200e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfgreen.CF_BASE_2WT-1.270330e+021.560970e+03-1.270340e+02-1.270310e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfgreen.CF_BASE_T-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfgreen.CF_BASE_TRG-1.275000e+021.566710e+03-1.275000e+02-1.275000e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfgreen.CF_TIP_T-1.280000e+021.572860e+03-1.280010e+02-1.279990e+02degCtip cold finger c- double degC {%.3f}
kpfgreen.CF_TIP_TRG-1.280000e+021.572860e+03-1.280000e+02-1.280000e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfgreen.COL_PRESS4.000000e-084.915170e-074.000000e-084.000000e-08TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.CRYOBODY_T1.899600e+012.334220e+021.899200e+011.900000e+01degCCryo Body Temperature c- double degC {%.3f}
kpfgreen.CRYOBODY_TRG1.900000e+012.334710e+021.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfgreen.CURRTEMP-1.400000e+021.720310e+03-1.400020e+02-1.399970e+02degCCurrent cold head temperature c- double degC {...
kpfgreen.ECH_PRESS1.000000e-111.228790e-101.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.KPF_CCD_T-1.010900e+021.242190e+03-1.010920e+02-1.010890e+02degCSSL Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_T-1.000000e+021.228800e+03-1.000020e+02-9.999800e+01degCSTA Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_TRG-1.000000e+021.228790e+03-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfgreen.TEMPSET-1.400000e+021.720310e+03-1.400000e+02-1.400000e+02degCSet point for the cold head temperature c2 dou...
kpfred.BPLANE_TEMP4.380440e+015.382650e+024.375000e+014.387500e+01degCBackplane temperature c- double degC {%.3f}
kpfred.BRD10_DRVR_T4.811190e+015.911970e+024.806200e+014.818700e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfred.BRD11_DRVR_T4.735680e+015.819170e+024.731200e+014.737500e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfred.BRD12_LVXBIAS_T4.354730e+015.351070e+024.343700e+014.362500e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfred.BRD1_HTRX_T4.136050e+015.082350e+024.131200e+014.137500e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfred.BRD2_XVBIAS_T4.314250e+015.301330e+024.312500e+014.318700e+01degCBoard 2 (XV Bias) temperature c- double degC {...
kpfred.BRD3_LVDS_T4.298010e+015.281370e+024.293700e+014.300000e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfred.BRD4_DRVR_T4.606210e+015.660080e+024.600000e+014.612500e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfred.BRD5_AD_T4.077440e+015.010340e+024.068700e+014.081200e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfred.BRD7_HTRX_T4.536200e+015.574060e+024.531200e+014.543700e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfred.BRD9_HVXBIAS_T4.526700e+015.562380e+024.518700e+014.531200e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfred.CF_BASE_2WT-1.257290e+021.544940e+03-1.257300e+02-1.257260e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfred.CF_BASE_T-2.731500e+023.356450e+03-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfred.CF_BASE_TRG-1.257500e+021.545210e+03-1.257500e+02-1.257500e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfred.CF_TIP_T-1.272500e+021.563640e+03-1.272510e+02-1.272480e+02degCtip cold finger c- double degC {%.3f}
kpfred.CF_TIP_TRG-1.272500e+021.563640e+03-1.272500e+02-1.272500e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfred.COL_PRESS3.715130e-084.565340e-073.700000e-083.800000e-08TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.CRYOBODY_T1.899880e+012.334560e+021.899700e+011.900100e+01degCCryo Body Temperature c- double degC {%.3f}
kpfred.CRYOBODY_TRG1.900000e+012.334710e+021.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfred.CURRTEMP-1.405000e+021.726450e+03-1.405010e+02-1.404980e+02degCCurrent cold head temperature c- double degC {...
kpfred.ECH_PRESS1.000000e-111.228790e-101.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.KPF_CCD_T-1.008710e+021.239500e+03-1.008720e+02-1.008700e+02degCSSL Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_T-1.000000e+021.228800e+03-1.000030e+02-9.999900e+01degCSTA Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_TRG-1.000000e+021.228790e+03-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfred.TEMPSET-1.405000e+021.726460e+03-1.405000e+02-1.405000e+02degCSet point for the cold head temperature c2 dou...
kpfexpose.BENCH_C1.522700e+011.871100e+021.510000e+011.540000e+01degCrtd bench c- double degC {%.1f} { -100.0 .. 10...
kpfexpose.CAMBARREL_C1.602630e+011.969330e+021.580000e+011.620000e+01degCrtd camera barrel c- double degC {%.1f} { -100...
kpfexpose.DET_XTRN_C2.017700e+012.479350e+022.000000e+012.040000e+01degCrtd detector extermal c- double degC {%.1f} { ...
kpfexpose.ECHELLE_C1.376320e+011.691240e+021.350000e+011.400000e+01degCrtd echelle c- double degC {%.1f} { -100.0 .. ...
kpfexpose.ENCLOSURE_C1.863160e+012.289460e+021.850000e+011.880000e+01degCrtd enclosure c- double degC {%.1f} { -100.0 ....
kpfexpose.RACK_AIR_C1.857040e+012.281930e+021.840000e+011.870000e+01degCrtd rack air c- double degC {%.1f} { -100.0 .....
kpfvac.PUMP_TEMP3.900000e+014.792300e+023.900000e+013.900000e+01degCMotor temperature c- double degC {%.2f}
kpf_hk.COOLTARG-6.000000e+017.372760e+02-6.000000e+01-6.000000e+01degCtemperature target c2 int degC
kpf_hk.CURRTEMP-5.978000e+017.345730e+02-5.978000e+01-5.978000e+01degCcurrent temperature c- double degC {%.2f}
kpfgreen.COL_CURR1.100000e-061.351670e-051.100000e-061.100000e-06ACurrent ion pump current c- double A {%.3e}
kpfgreen.ECH_CURR8.910000e-111.094860e-098.910000e-118.910000e-11ACurrent ion pump current c- double A {%.3e}
kpfred.COL_CURR9.986180e-071.227110e-059.800000e-071.000000e-06ACurrent ion pump current c- double A {%.3e}
kpfred.ECH_CURR5.600000e-126.881240e-115.600000e-125.600000e-12ACurrent ion pump current c- double A {%.3e}
kpfcal.IRFLUX9.999000e+031.228670e+059.999000e+039.999000e+03CountsLFC Fiberlock IR Intensity c- int Counts {%d}
kpfcal.VISFLUX2.553950e+013.138880e+022.500000e+012.600000e+01CountsLFC Fiberlock Vis Intensity c- int Counts {%d}
kpfcal.BLUECUTIACT3.326320e-024.111620e-012.300000e-025.300000e-02ABlue cut amplifier 0 measured current c- doubl...
kpfmot.AGITSPD-3.900470e+034.822300e+04-4.699000e+03-1.880000e+03motor_counts/sagit raw velocity c2 int motor counts/s { -750...
kpfmot.AGITTOR-1.102970e+001.590140e+01-2.271000e+001.440000e-01Vagit motor torque c- double V {%.3f}
kpfmot.AGITAMBI_T2.580130e+013.170470e+022.548000e+012.603000e+01degCAgitator ambient temperature c- double degC {%...
kpfmot.AGITMOT_T2.606950e+013.203420e+022.582000e+012.622000e+01degCAgitator motor temperature c- double degC {%.2...
kpfpower.OUTLET_A1_Amps5.070390e+016.235380e+022.700000e+015.300000e+01milliampsOutlet A1 current amperage c- int milliamps
\n", + "
" + ], + "text/plain": [ + " average stddev min \\\n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597510e+01 1.963010e+02 1.597500e+01 \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.647870e+01 2.024890e+02 1.647700e+01 \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.624350e+01 1.995990e+02 1.624300e+01 \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.638640e+01 2.013550e+02 1.638600e+01 \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588580e+01 1.952040e+02 1.588500e+01 \n", + "kpfmet.BENCH_TOP_COLL 1.657790e+01 2.037080e+02 1.657700e+01 \n", + "kpfmet.BENCH_TOP_DCUT 1.628050e+01 2.000540e+02 1.627900e+01 \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629190e+01 2.001940e+02 1.629100e+01 \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736520e+01 2.133830e+02 1.736400e+01 \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.721330e+01 2.115160e+02 1.721200e+01 \n", + "kpfmet.CAL_BENCH 2.020000e+01 2.482160e+02 2.020000e+01 \n", + "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 2.519030e+02 2.050000e+01 \n", + "kpfmet.CAL_BENCH_BOT 2.360000e+01 2.899950e+02 2.360000e+01 \n", + "kpfmet.CAL_BENCH_ENCL_AIR 2.018220e+01 2.479980e+02 2.010000e+01 \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.CAL_RACK_TOP 2.301710e+01 2.828330e+02 2.300000e+01 \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.570260e+01 1.929530e+02 1.570100e+01 \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G3 1.668370e+01 2.050090e+02 1.668300e+01 \n", + "kpfmet.CRYOSTAT_R1 1.610210e+01 1.978620e+02 1.610000e+01 \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_R3 1.564080e+01 1.921940e+02 1.564000e+01 \n", + "kpfmet.ECHELLE_BOTTOM 1.641060e+01 2.016530e+02 1.641000e+01 \n", + "kpfmet.ECHELLE_TOP 1.628290e+01 2.000840e+02 1.628200e+01 \n", + "kpfmet.FF_SRC 1.957500e+01 2.405370e+02 1.950000e+01 \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.677540e+01 2.061350e+02 1.677400e+01 \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682040e+01 2.066880e+02 1.681900e+01 \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.671060e+01 2.053390e+02 1.670900e+01 \n", + "kpfmet.GREEN_CAMERA_TOP 1.661770e+01 2.041970e+02 1.661700e+01 \n", + "kpfmet.GREEN_GRISM_TOP 1.639170e+01 2.014200e+02 1.639000e+01 \n", + "kpfmet.GREEN_LN2_FLANGE 9.791590e+00 1.203190e+02 9.746000e+00 \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689680e+01 2.076270e+02 1.689600e+01 \n", + "kpfmet.RED_CAMERA_BOTTOM 1.630110e+01 2.003070e+02 1.630000e+01 \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.664290e+01 2.045070e+02 1.664200e+01 \n", + "kpfmet.RED_CAMERA_ECHELLE 1.659400e+01 2.039060e+02 1.659300e+01 \n", + "kpfmet.RED_CAMERA_TOP 1.654670e+01 2.033240e+02 1.654500e+01 \n", + "kpfmet.RED_GRISM_TOP 1.636990e+01 2.011530e+02 1.636900e+01 \n", + "kpfmet.RED_LN2_FLANGE 6.245890e+00 7.675000e+01 6.199000e+00 \n", + "kpfmet.REFORMATTER 1.660480e+01 2.040380e+02 1.660400e+01 \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747050e+01 2.146770e+02 1.746900e+01 \n", + "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.TEMP 1.968480e+01 2.418860e+02 1.967300e+01 \n", + "kpfmet.TH_DAILY 2.000000e+01 2.457590e+02 2.000000e+01 \n", + "kpfmet.TH_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfmet.U_DAILY 2.010000e+01 2.469880e+02 2.010000e+01 \n", + "kpfmet.U_GOLD 1.990000e+01 2.445300e+02 1.990000e+01 \n", + "kpfgreen.BPLANE_TEMP 3.582320e+01 4.401930e+02 3.581200e+01 \n", + "kpfgreen.BRD10_DRVR_T 4.050370e+01 4.977070e+02 4.050000e+01 \n", + "kpfgreen.BRD11_DRVR_T 3.875000e+01 4.761580e+02 3.875000e+01 \n", + "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 4.270060e+02 3.475000e+01 \n", + "kpfgreen.BRD1_HTRX_T 3.184100e+01 3.912610e+02 3.181200e+01 \n", + "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "kpfgreen.BRD3_LVDS_T 3.342310e+01 4.107010e+02 3.337500e+01 \n", + "kpfgreen.BRD4_DRVR_T 3.719820e+01 4.570890e+02 3.718700e+01 \n", + "kpfgreen.BRD5_AD_T 3.381530e+01 4.155200e+02 3.381200e+01 \n", + "kpfgreen.BRD7_HTRX_T 3.784640e+01 4.650540e+02 3.781200e+01 \n", + "kpfgreen.BRD9_HVXBIAS_T 3.627570e+01 4.457540e+02 3.625000e+01 \n", + "kpfgreen.CF_BASE_2WT -1.270330e+02 1.560970e+03 -1.270340e+02 \n", + "kpfgreen.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 1.566710e+03 -1.275000e+02 \n", + "kpfgreen.CF_TIP_T -1.280000e+02 1.572860e+03 -1.280010e+02 \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 1.572860e+03 -1.280000e+02 \n", + "kpfgreen.COL_PRESS 4.000000e-08 4.915170e-07 4.000000e-08 \n", + "kpfgreen.CRYOBODY_T 1.899600e+01 2.334220e+02 1.899200e+01 \n", + "kpfgreen.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", + "kpfgreen.CURRTEMP -1.400000e+02 1.720310e+03 -1.400020e+02 \n", + "kpfgreen.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", + "kpfgreen.KPF_CCD_T -1.010900e+02 1.242190e+03 -1.010920e+02 \n", + "kpfgreen.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000020e+02 \n", + "kpfgreen.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", + "kpfgreen.TEMPSET -1.400000e+02 1.720310e+03 -1.400000e+02 \n", + "kpfred.BPLANE_TEMP 4.380440e+01 5.382650e+02 4.375000e+01 \n", + "kpfred.BRD10_DRVR_T 4.811190e+01 5.911970e+02 4.806200e+01 \n", + "kpfred.BRD11_DRVR_T 4.735680e+01 5.819170e+02 4.731200e+01 \n", + "kpfred.BRD12_LVXBIAS_T 4.354730e+01 5.351070e+02 4.343700e+01 \n", + "kpfred.BRD1_HTRX_T 4.136050e+01 5.082350e+02 4.131200e+01 \n", + "kpfred.BRD2_XVBIAS_T 4.314250e+01 5.301330e+02 4.312500e+01 \n", + "kpfred.BRD3_LVDS_T 4.298010e+01 5.281370e+02 4.293700e+01 \n", + "kpfred.BRD4_DRVR_T 4.606210e+01 5.660080e+02 4.600000e+01 \n", + "kpfred.BRD5_AD_T 4.077440e+01 5.010340e+02 4.068700e+01 \n", + "kpfred.BRD7_HTRX_T 4.536200e+01 5.574060e+02 4.531200e+01 \n", + "kpfred.BRD9_HVXBIAS_T 4.526700e+01 5.562380e+02 4.518700e+01 \n", + "kpfred.CF_BASE_2WT -1.257290e+02 1.544940e+03 -1.257300e+02 \n", + "kpfred.CF_BASE_T -2.731500e+02 3.356450e+03 -2.731500e+02 \n", + "kpfred.CF_BASE_TRG -1.257500e+02 1.545210e+03 -1.257500e+02 \n", + "kpfred.CF_TIP_T -1.272500e+02 1.563640e+03 -1.272510e+02 \n", + "kpfred.CF_TIP_TRG -1.272500e+02 1.563640e+03 -1.272500e+02 \n", + "kpfred.COL_PRESS 3.715130e-08 4.565340e-07 3.700000e-08 \n", + "kpfred.CRYOBODY_T 1.899880e+01 2.334560e+02 1.899700e+01 \n", + "kpfred.CRYOBODY_TRG 1.900000e+01 2.334710e+02 1.900000e+01 \n", + "kpfred.CURRTEMP -1.405000e+02 1.726450e+03 -1.405010e+02 \n", + "kpfred.ECH_PRESS 1.000000e-11 1.228790e-10 1.000000e-11 \n", + "kpfred.KPF_CCD_T -1.008710e+02 1.239500e+03 -1.008720e+02 \n", + "kpfred.STA_CCD_T -1.000000e+02 1.228800e+03 -1.000030e+02 \n", + "kpfred.STA_CCD_TRG -1.000000e+02 1.228790e+03 -1.000000e+02 \n", + "kpfred.TEMPSET -1.405000e+02 1.726460e+03 -1.405000e+02 \n", + "kpfexpose.BENCH_C 1.522700e+01 1.871100e+02 1.510000e+01 \n", + "kpfexpose.CAMBARREL_C 1.602630e+01 1.969330e+02 1.580000e+01 \n", + "kpfexpose.DET_XTRN_C 2.017700e+01 2.479350e+02 2.000000e+01 \n", + "kpfexpose.ECHELLE_C 1.376320e+01 1.691240e+02 1.350000e+01 \n", + "kpfexpose.ENCLOSURE_C 1.863160e+01 2.289460e+02 1.850000e+01 \n", + "kpfexpose.RACK_AIR_C 1.857040e+01 2.281930e+02 1.840000e+01 \n", + "kpfvac.PUMP_TEMP 3.900000e+01 4.792300e+02 3.900000e+01 \n", + "kpf_hk.COOLTARG -6.000000e+01 7.372760e+02 -6.000000e+01 \n", + "kpf_hk.CURRTEMP -5.978000e+01 7.345730e+02 -5.978000e+01 \n", + "kpfgreen.COL_CURR 1.100000e-06 1.351670e-05 1.100000e-06 \n", + "kpfgreen.ECH_CURR 8.910000e-11 1.094860e-09 8.910000e-11 \n", + "kpfred.COL_CURR 9.986180e-07 1.227110e-05 9.800000e-07 \n", + "kpfred.ECH_CURR 5.600000e-12 6.881240e-11 5.600000e-12 \n", + "kpfcal.IRFLUX 9.999000e+03 1.228670e+05 9.999000e+03 \n", + "kpfcal.VISFLUX 2.553950e+01 3.138880e+02 2.500000e+01 \n", + "kpfcal.BLUECUTIACT 3.326320e-02 4.111620e-01 2.300000e-02 \n", + "kpfmot.AGITSPD -3.900470e+03 4.822300e+04 -4.699000e+03 \n", + "kpfmot.AGITTOR -1.102970e+00 1.590140e+01 -2.271000e+00 \n", + "kpfmot.AGITAMBI_T 2.580130e+01 3.170470e+02 2.548000e+01 \n", + "kpfmot.AGITMOT_T 2.606950e+01 3.203420e+02 2.582000e+01 \n", + "kpfpower.OUTLET_A1_Amps 5.070390e+01 6.235380e+02 2.700000e+01 \n", + "\n", + " max units \\\n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.597600e+01 degC \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.648000e+01 degC \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.624400e+01 degC \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.638700e+01 degC \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.588700e+01 degC \n", + "kpfmet.BENCH_TOP_COLL 1.657900e+01 degC \n", + "kpfmet.BENCH_TOP_DCUT 1.628100e+01 degC \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.629300e+01 degC \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.736600e+01 degC \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.721500e+01 degC \n", + "kpfmet.CAL_BENCH 2.020000e+01 degC \n", + "kpfmet.CAL_BENCH_BB_SRC 2.050000e+01 degC \n", + "kpfmet.CAL_BENCH_BOT 2.360000e+01 degC \n", + "kpfmet.CAL_BENCH_ENCL_AIR 2.020000e+01 degC \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.990000e+01 degC \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 2.010000e+01 degC \n", + "kpfmet.CAL_RACK_TOP 2.310000e+01 degC \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.570400e+01 degC \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G3 1.668500e+01 degC \n", + "kpfmet.CRYOSTAT_R1 1.610300e+01 degC \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_R3 1.564200e+01 degC \n", + "kpfmet.ECHELLE_BOTTOM 1.641200e+01 degC \n", + "kpfmet.ECHELLE_TOP 1.628400e+01 degC \n", + "kpfmet.FF_SRC 1.960000e+01 degC \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.677600e+01 degC \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.682200e+01 degC \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.671200e+01 degC \n", + "kpfmet.GREEN_CAMERA_TOP 1.661900e+01 degC \n", + "kpfmet.GREEN_GRISM_TOP 1.639300e+01 degC \n", + "kpfmet.GREEN_LN2_FLANGE 9.848000e+00 degC \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.689800e+01 degC \n", + "kpfmet.RED_CAMERA_BOTTOM 1.630300e+01 degC \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.664400e+01 degC \n", + "kpfmet.RED_CAMERA_ECHELLE 1.659500e+01 degC \n", + "kpfmet.RED_CAMERA_TOP 1.654800e+01 degC \n", + "kpfmet.RED_GRISM_TOP 1.637200e+01 degC \n", + "kpfmet.RED_LN2_FLANGE 6.292000e+00 degC \n", + "kpfmet.REFORMATTER 1.660600e+01 degC \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 2.010000e+01 degC \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 degC \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.747200e+01 degC \n", + "kpfmet.SIMCAL_FIBER_STG 2.010000e+01 degC \n", + "kpfmet.SKYCAL_FIBER_STG 1.990000e+01 degC \n", + "kpfmet.TEMP 1.969700e+01 degC \n", + "kpfmet.TH_DAILY 2.000000e+01 degC \n", + "kpfmet.TH_GOLD 1.990000e+01 degC \n", + "kpfmet.U_DAILY 2.010000e+01 degC \n", + "kpfmet.U_GOLD 1.990000e+01 degC \n", + "kpfgreen.BPLANE_TEMP 3.587500e+01 degC \n", + "kpfgreen.BRD10_DRVR_T 4.056200e+01 degC \n", + "kpfgreen.BRD11_DRVR_T 3.875000e+01 degC \n", + "kpfgreen.BRD12_LVXBIAS_T 3.475000e+01 degC \n", + "kpfgreen.BRD1_HTRX_T 3.187500e+01 degC \n", + "kpfgreen.BRD2_XVBIAS_T 0.000000e+00 degC \n", + "kpfgreen.BRD3_LVDS_T 3.343700e+01 degC \n", + "kpfgreen.BRD4_DRVR_T 3.725000e+01 degC \n", + "kpfgreen.BRD5_AD_T 3.387500e+01 degC \n", + "kpfgreen.BRD7_HTRX_T 3.787500e+01 degC \n", + "kpfgreen.BRD9_HVXBIAS_T 3.631200e+01 degC \n", + "kpfgreen.CF_BASE_2WT -1.270310e+02 degC \n", + "kpfgreen.CF_BASE_T -2.731500e+02 degC \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 degC \n", + "kpfgreen.CF_TIP_T -1.279990e+02 degC \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 degC \n", + "kpfgreen.COL_PRESS 4.000000e-08 Torr \n", + "kpfgreen.CRYOBODY_T 1.900000e+01 degC \n", + "kpfgreen.CRYOBODY_TRG 1.900000e+01 degC \n", + "kpfgreen.CURRTEMP -1.399970e+02 degC \n", + "kpfgreen.ECH_PRESS 1.000000e-11 Torr \n", + "kpfgreen.KPF_CCD_T -1.010890e+02 degC \n", + "kpfgreen.STA_CCD_T -9.999800e+01 degC \n", + "kpfgreen.STA_CCD_TRG -1.000000e+02 degC \n", + "kpfgreen.TEMPSET -1.400000e+02 degC \n", + "kpfred.BPLANE_TEMP 4.387500e+01 degC \n", + "kpfred.BRD10_DRVR_T 4.818700e+01 degC \n", + "kpfred.BRD11_DRVR_T 4.737500e+01 degC \n", + "kpfred.BRD12_LVXBIAS_T 4.362500e+01 degC \n", + "kpfred.BRD1_HTRX_T 4.137500e+01 degC \n", + "kpfred.BRD2_XVBIAS_T 4.318700e+01 degC \n", + "kpfred.BRD3_LVDS_T 4.300000e+01 degC \n", + "kpfred.BRD4_DRVR_T 4.612500e+01 degC \n", + "kpfred.BRD5_AD_T 4.081200e+01 degC \n", + "kpfred.BRD7_HTRX_T 4.543700e+01 degC \n", + "kpfred.BRD9_HVXBIAS_T 4.531200e+01 degC \n", + "kpfred.CF_BASE_2WT -1.257260e+02 degC \n", + "kpfred.CF_BASE_T -2.731500e+02 degC \n", + "kpfred.CF_BASE_TRG -1.257500e+02 degC \n", + "kpfred.CF_TIP_T -1.272480e+02 degC \n", + "kpfred.CF_TIP_TRG -1.272500e+02 degC \n", + "kpfred.COL_PRESS 3.800000e-08 Torr \n", + "kpfred.CRYOBODY_T 1.900100e+01 degC \n", + "kpfred.CRYOBODY_TRG 1.900000e+01 degC \n", + "kpfred.CURRTEMP -1.404980e+02 degC \n", + "kpfred.ECH_PRESS 1.000000e-11 Torr \n", + "kpfred.KPF_CCD_T -1.008700e+02 degC \n", + "kpfred.STA_CCD_T -9.999900e+01 degC \n", + "kpfred.STA_CCD_TRG -1.000000e+02 degC \n", + "kpfred.TEMPSET -1.405000e+02 degC \n", + "kpfexpose.BENCH_C 1.540000e+01 degC \n", + "kpfexpose.CAMBARREL_C 1.620000e+01 degC \n", + "kpfexpose.DET_XTRN_C 2.040000e+01 degC \n", + "kpfexpose.ECHELLE_C 1.400000e+01 degC \n", + "kpfexpose.ENCLOSURE_C 1.880000e+01 degC \n", + "kpfexpose.RACK_AIR_C 1.870000e+01 degC \n", + "kpfvac.PUMP_TEMP 3.900000e+01 degC \n", + "kpf_hk.COOLTARG -6.000000e+01 degC \n", + "kpf_hk.CURRTEMP -5.978000e+01 degC \n", + "kpfgreen.COL_CURR 1.100000e-06 A \n", + "kpfgreen.ECH_CURR 8.910000e-11 A \n", + "kpfred.COL_CURR 1.000000e-06 A \n", + "kpfred.ECH_CURR 5.600000e-12 A \n", + "kpfcal.IRFLUX 9.999000e+03 Counts \n", + "kpfcal.VISFLUX 2.600000e+01 Counts \n", + "kpfcal.BLUECUTIACT 5.300000e-02 A \n", + "kpfmot.AGITSPD -1.880000e+03 motor_counts/s \n", + "kpfmot.AGITTOR 1.440000e-01 V \n", + "kpfmot.AGITAMBI_T 2.603000e+01 degC \n", + "kpfmot.AGITMOT_T 2.622000e+01 degC \n", + "kpfpower.OUTLET_A1_Amps 5.300000e+01 milliamps \n", + "\n", + " description \n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS Bench Bottom Between Cameras C2 c- double degC... \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR Bench Bottom Coll C3 c- double degC {%.3f} \n", + "kpfmet.BENCH_BOTTOM_DCUT Bench Bottom D-cut C4 c- double degC {%.3f} \n", + "kpfmet.BENCH_BOTTOM_ECHELLE Bench Bottom Echelle Cam B c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS Bench Top Between Cameras D4 c- double degC {%... \n", + "kpfmet.BENCH_TOP_COLL Bench Top Coll D5 c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_DCUT Bench Top D-cut D3 c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM Bench Top Echelle Cam D1 c- double degC {%.3f} \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END Cal EM Scrammbler Chamber End C1 c- double deg... \n", + "kpfmet.CALEM_SCMBLR_FIBER_END Cal EM Scrambler Fiber End D1 c- double degC {... \n", + "kpfmet.CAL_BENCH Cal_Bench temperature c- double degC {%.1f} \n", + "kpfmet.CAL_BENCH_BB_SRC CAL_Bench_BB_Src temperature c- double degC {%... \n", + "kpfmet.CAL_BENCH_BOT Cal_Bench_Bot temperature c- double degC {%.1f} \n", + "kpfmet.CAL_BENCH_ENCL_AIR Cal_Bench_Encl_Air temperature c- double degC ... \n", + "kpfmet.CAL_BENCH_OCT_MOT Cal_Bench_Oct_Mot temperature c- double degC {... \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT Cal_Bench_Trans_Stg_Mot temperature c- double ... \n", + "kpfmet.CAL_RACK_TOP Cal_Rack_Top temperature c- double degC {%.1f} \n", + "kpfmet.CHAMBER_EXT_BOTTOM Chamber Exterior Bottom B c- double degC {%.3f} \n", + "kpfmet.CHAMBER_EXT_TOP Chamber Exterior Top C1 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G1 Within cryostat green D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G2 Within cryostat green D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G3 Within cryostat green D4 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R1 Within Cryostat red D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R2 Within Cryostat red D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R3 Within Cryostat red D4 c- double degC {%.3f} \n", + "kpfmet.ECHELLE_BOTTOM Echelle Bottom D1 c- double degC {%.3f} \n", + "kpfmet.ECHELLE_TOP Echelle Top C1 c- double degC {%.3f} \n", + "kpfmet.FF_SRC FF_Src temperature c- double degC {%.1f} \n", + "kpfmet.GREEN_CAMERA_BOTTOM Green Camera Bottom C3 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR Green Camera Collimator C4 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_ECHELLE Green Camera Echelle D5 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_TOP Green Camera Top C2 c- double degC {%.3f} \n", + "kpfmet.GREEN_GRISM_TOP Green Grism Top C5 c- double degC {%.3f} \n", + "kpfmet.GREEN_LN2_FLANGE Green LN2 Flange A c- double degC {%.3f} \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP Primary Col Top D2 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_BOTTOM Red Camera Bottom D5 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_COLLIMATOR Red Camera Coll C3 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_ECHELLE Red Camera Ech C4 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_TOP Red Camera Top C5 c- double degC {%.3f} \n", + "kpfmet.RED_GRISM_TOP Red Grism Top C2 c- double degC {%.3f} \n", + "kpfmet.RED_LN2_FLANGE Red LN2 Flange D1 c- double degC {%.3f} \n", + "kpfmet.REFORMATTER Reformatter A c- double degC {%.3f} \n", + "kpfmet.SCIENCE_CAL_FIBER_STG Science_Cal_Fiber_Stg temperature c- double de... \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END SciSky Scrambler Chamber End A c- double degC ... \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END SciSky Scrammbler Fiber End B c- double degC {... \n", + "kpfmet.SIMCAL_FIBER_STG SimCal_Fiber_Stg temperature c- double degC {%... \n", + "kpfmet.SKYCAL_FIBER_STG SkyCal_Fiber_Stg temperature c- double degC {%... \n", + "kpfmet.TEMP Vaisala Temperature c- double degC {%.3f} \n", + "kpfmet.TH_DAILY Th_daily temperature c- double degC {%.1f} \n", + "kpfmet.TH_GOLD Th_gold temperature c- double degC {%.1f} \n", + "kpfmet.U_DAILY U_daily temperature c- double degC {%.1f} \n", + "kpfmet.U_GOLD U_gold temperature c- double degC {%.1f} \n", + "kpfgreen.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", + "kpfgreen.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", + "kpfgreen.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", + "kpfgreen.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", + "kpfgreen.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", + "kpfgreen.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", + "kpfgreen.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", + "kpfgreen.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", + "kpfgreen.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", + "kpfgreen.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", + "kpfgreen.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", + "kpfgreen.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", + "kpfgreen.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", + "kpfgreen.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", + "kpfgreen.CF_TIP_T tip cold finger c- double degC {%.3f} \n", + "kpfgreen.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", + "kpfgreen.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfgreen.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", + "kpfgreen.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", + "kpfgreen.CURRTEMP Current cold head temperature c- double degC {... \n", + "kpfgreen.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfgreen.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", + "kpfgreen.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", + "kpfgreen.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", + "kpfgreen.TEMPSET Set point for the cold head temperature c2 dou... \n", + "kpfred.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", + "kpfred.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", + "kpfred.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", + "kpfred.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", + "kpfred.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", + "kpfred.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", + "kpfred.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", + "kpfred.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", + "kpfred.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", + "kpfred.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", + "kpfred.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", + "kpfred.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", + "kpfred.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", + "kpfred.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", + "kpfred.CF_TIP_T tip cold finger c- double degC {%.3f} \n", + "kpfred.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", + "kpfred.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfred.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", + "kpfred.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", + "kpfred.CURRTEMP Current cold head temperature c- double degC {... \n", + "kpfred.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfred.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", + "kpfred.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", + "kpfred.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", + "kpfred.TEMPSET Set point for the cold head temperature c2 dou... \n", + "kpfexpose.BENCH_C rtd bench c- double degC {%.1f} { -100.0 .. 10... \n", + "kpfexpose.CAMBARREL_C rtd camera barrel c- double degC {%.1f} { -100... \n", + "kpfexpose.DET_XTRN_C rtd detector extermal c- double degC {%.1f} { ... \n", + "kpfexpose.ECHELLE_C rtd echelle c- double degC {%.1f} { -100.0 .. ... \n", + "kpfexpose.ENCLOSURE_C rtd enclosure c- double degC {%.1f} { -100.0 .... \n", + "kpfexpose.RACK_AIR_C rtd rack air c- double degC {%.1f} { -100.0 ..... \n", + "kpfvac.PUMP_TEMP Motor temperature c- double degC {%.2f} \n", + "kpf_hk.COOLTARG temperature target c2 int degC \n", + "kpf_hk.CURRTEMP current temperature c- double degC {%.2f} \n", + "kpfgreen.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfgreen.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfcal.IRFLUX LFC Fiberlock IR Intensity c- int Counts {%d} \n", + "kpfcal.VISFLUX LFC Fiberlock Vis Intensity c- int Counts {%d} \n", + "kpfcal.BLUECUTIACT Blue cut amplifier 0 measured current c- doubl... \n", + "kpfmot.AGITSPD agit raw velocity c2 int motor counts/s { -750... \n", + "kpfmot.AGITTOR agit motor torque c- double V {%.3f} \n", + "kpfmot.AGITAMBI_T Agitator ambient temperature c- double degC {%... \n", + "kpfmot.AGITMOT_T Agitator motor temperature c- double degC {%.2... \n", + "kpfpower.OUTLET_A1_Amps Outlet A1 current amperage c- int milliamps " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n", + " display(df_telemetry)" + ] + }, + { + "cell_type": "markdown", + "id": "d587d5b6", + "metadata": {}, + "source": [ + "Print the average and standard deviation of one keyword. (As of June 21, 2023, there's a problem with the standard deviation. This will probably get fixed soon in the L0 Assembly code.)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "989f592d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Red CCD Temperature = -100.0 +/- 1228.8 degC\n" + ] + } + ], + "source": [ + "print('Red CCD Temperature = ', df_telemetry.at['kpfred.STA_CCD_T', 'average'], '+/-', df_telemetry.at['kpfred.STA_CCD_T', 'stddev'], df_telemetry.at['kpfred.STA_CCD_T', 'units'])" + ] + }, + { + "cell_type": "markdown", + "id": "81db59c1", + "metadata": {}, + "source": [ + "# Guider Image\n", + "The guide camera images are from a CRED-2 camera with 0.056 arcsec pixels in a 950-1200nm passband (approximately z+J band). First, we will fit the image with a Moffat function (assuming a point source)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "584c5e1c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "amplitude = 3228.7076865733984\n", + "seeing = 0.4789702532980194 arcsec\n", + "beta = 2.5682683730057922\n", + "x0 = 337.54450870956305 pixels\n", + "y0 = 262.21891711604087 pixels\n" + ] + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "guider_im = D2['GUIDER_AVG'].data - np.median(D2['GUIDER_AVG'].data)\n", + "guider_header = D2['GUIDER_AVG'].header\n", + "pixel_scale = 0.056 # arcsec per pixel for the CRED-2 imager on the KPF FIU\n", + "\n", + "def moffat_2D(xy, amplitude, x0, y0, alpha, beta):\n", + " x, y = xy\n", + " return amplitude * (1 + ((x - x0) ** 2 + (y - y0) ** 2) / alpha ** 2) ** -beta\n", + "\n", + "x = np.arange(guider_im.shape[1])\n", + "y = np.arange(guider_im.shape[0])\n", + "X, Y = np.meshgrid(x, y)\n", + "x_flat = X.flatten()\n", + "y_flat = Y.flatten()\n", + "image_data_flat = guider_im.flatten()\n", + "p0 = [1, guider_header['CRPIX1'], guider_header['CRPIX2'], 5/0.056, 2.5] # Initial guess for the parameters\n", + "#p0 = [1, guider_im.shape[1] / 2, guider_im.shape[0] / 2, 2/0.056, 2] # Initial guess for the parameters\n", + "popt, pcov = curve_fit(moffat_2D, (x_flat, y_flat), image_data_flat, p0=p0)\n", + "amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit = popt\n", + "print('amplitude = ' + str(amplitude_fit))\n", + "print('seeing = ' + str(alpha_fit*0.056) + ' arcsec')\n", + "print('beta = ' + str(beta_fit))\n", + "print('x0 = ' + str(x0_fit) + ' pixels')\n", + "print('y0 = ' + str(y0_fit) + ' pixels')\n", + "\n", + "image_fit = moffat_2D((X, Y), amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit)\n", + "resid_im = guider_im - image_fit" + ] + }, + { + "cell_type": "markdown", + "id": "ff39df68", + "metadata": {}, + "source": [ + "# Plot Guider Image and Model Fit" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a8fdd21e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:27: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:48: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:67: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24850/3555428779.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.ticker as ticker\n", + "\n", + "# Plot the original image and residuals\n", + "guider_im_zoom = guider_im[255-38:255+38, 320-38:320+38]\n", + "resid_im_zoom = resid_im[255-38:255+38, 320-38:320+38]\n", + "#subx0 = D2['GUIDER_AVG'].header['SUBX0']\n", + "#subx1 = D2['GUIDER_AVG'].header['SUBX1']\n", + "#suby0 = D2['GUIDER_AVG'].header['SUBY0']\n", + "#suby1 = D2['GUIDER_AVG'].header['SUBY1']\n", + "#guider_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", + "#resid_im_zoom = guider_im[subx0:subx1, suby0:suby1]\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5))\n", + "\n", + "# Left panel - full image\n", + "im1 = axs[0].imshow(guider_im, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "axs[1].set_aspect(640/512)\n", + "image_size_pixels = guider_im.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", + "axs[0].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec\n", + "axs[0].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[0].get_xticks()\n", + "yticks = axs[0].get_yticks()\n", + "axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks])\n", + "axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks])\n", + "axs[0].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[0].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[0].set_title('Guider Image - '+str(ObsID)+'\\n' + \n", + " f\"{alpha_fit*pixel_scale:.2f}\" + '\" seeing (z+J-band)\\n' +\n", + " f\"{alpha_fit*pixel_scale*(1100/550)**0.2:.2f}\" + '\" seeing (V-band, scaled)', fontsize=14)\n", + "axs[0].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar1 = plt.colorbar(im1, ax=axs[0], shrink=0.6)\n", + "#cbar1.set_label('Intensity', fontsize=14)\n", + "\n", + "# Middle panel - zoomed image\n", + "im2 = axs[1].imshow(guider_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "axs[1].set_aspect('equal')\n", + "image_size_pixels = guider_im_zoom.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[1].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[1].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[1].get_xticks()\n", + "yticks = axs[1].get_yticks()\n", + "axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "axs[1].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[1].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[1].set_title('Guider Image (zoomed in)', fontsize=14)\n", + "axs[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar2 = plt.colorbar(im2, ax=axs[1], shrink=0.6)\n", + "#cbar2.set_label('Intensity', fontsize=14)\n", + "\n", + "# Right panel - zoomed image of residuals to model\n", + "im2 = axs[2].imshow(resid_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9))\n", + "image_size_pixels = guider_im_zoom.shape\n", + "image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale)\n", + "x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[2].xaxis.set_major_locator(x_tick_locator)\n", + "y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec\n", + "axs[2].yaxis.set_major_locator(y_tick_locator)\n", + "xticks = axs[2].get_xticks()\n", + "yticks = axs[2].get_yticks()\n", + "axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks])\n", + "axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks])\n", + "axs[2].set_xlabel('Arcseconds', fontsize=14)\n", + "axs[2].set_ylabel('Arcseconds', fontsize=14)\n", + "axs[2].set_title('Residuals to Moffat Function Model', fontsize=14)\n", + "axs[2].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar3 = plt.colorbar(im2, ax=axs[2], shrink=0.6)\n", + "#cbar3.set_label('Intensity', fontsize=14)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "#plt.savefig('guider_image.png',dpi=144)\n", + "plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "64e77b06", + "metadata": {}, + "source": [ + "# Examine and Plot Guider Performance Time Series" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "92c05f6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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..................................................................
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0 4927.0 ... \n", + "... ... ... ... ... ... \n", + "17260 459291.0 1 0 8911.0 ... \n", + "17261 458436.0 1 0 8987.0 ... \n", + "17262 457964.0 1 0 9620.0 ... \n", + "17263 458240.0 1 0 10218.0 ... \n", + "17264 460913.0 1 0 8697.0 ... \n", + "\n", + " object3_tnpix object3_npix object3_peak object3_x object3_y \\\n", + "0 0 0 0.0 -999.0 -999.0 \n", + "1 0 0 0.0 -999.0 -999.0 \n", + "2 0 0 0.0 -999.0 -999.0 \n", + "3 0 0 0.0 -999.0 -999.0 \n", + "4 0 0 0.0 -999.0 -999.0 \n", + "... ... ... ... ... ... \n", + "17260 0 0 0.0 -999.0 -999.0 \n", + "17261 0 0 0.0 -999.0 -999.0 \n", + "17262 0 0 0.0 -999.0 -999.0 \n", + "17263 0 0 0.0 -999.0 -999.0 \n", + "17264 0 0 0.0 -999.0 -999.0 \n", + "\n", + " object3_a object3_b object3_theta object3_errxy object3_thresh \n", + "0 0.0 0.0 0.0 0.0 -999.0 \n", + "1 0.0 0.0 0.0 0.0 -999.0 \n", + "2 0.0 0.0 0.0 0.0 -999.0 \n", + "3 0.0 0.0 0.0 0.0 -999.0 \n", + "4 0.0 0.0 0.0 0.0 -999.0 \n", + "... ... ... ... ... ... \n", + "17260 0.0 0.0 0.0 0.0 -999.0 \n", + "17261 0.0 0.0 0.0 0.0 -999.0 \n", + "17262 0.0 0.0 0.0 0.0 -999.0 \n", + "17263 0.0 0.0 0.0 0.0 -999.0 \n", + "17264 0.0 0.0 0.0 0.0 -999.0 \n", + "\n", + "[17265 rows x 39 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_GUIDER = Table.read(L0_file, format='fits',hdu='guider_cube_origins').to_pandas()\n", + "df_GUIDER" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cd1792a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 100)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# Create the figure and subplots\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 4), gridspec_kw={'width_ratios': [2, 1]})\n", + "plt.style.use('seaborn-whitegrid')\n", + "\n", + "# Plot the data\n", + "im1 = axes[1].hist2d(df_GUIDER.command_x*pixel_scale*1000, \n", + " df_GUIDER.command_y*pixel_scale*1000, \n", + " bins=40, cmap='viridis')\n", + "axes[1].set_title(\"Guiding Corrections - RMS-2D = \" + f'{int(np.std((df_GUIDER.command_x*pixel_scale**2+df_GUIDER.command_y*pixel_scale**2)**0.5*1000)*10)/10}'+' mas', fontsize=14)\n", + "axes[1].set_xlabel('x (mas)', fontsize=14)\n", + "axes[1].set_ylabel('y (mas)', fontsize=14)\n", + "axes[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5)\n", + "cbar = plt.colorbar(im1[3])\n", + "cbar.set_label('Samples', fontsize=12)\n", + "\n", + "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_x*pixel_scale*1000, color='royalblue')\n", + "axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.command_y*pixel_scale*1000, color='orange')\n", + "axes[0].set_title(\"Guiding Correction Time Series\", fontsize=14)\n", + "axes[0].set_xlabel(\"Time (sec)\", fontsize=14)\n", + "axes[0].set_ylabel(\"Guiding Correction (mas)\", fontsize=14)\n", + "axes[0].legend(['X - RMS = ' + f'{int(np.std(df_GUIDER.command_x*pixel_scale*1000)*10)/10}' + ' mas', \n", + " 'Y - RMS = ' + f'{int(np.std(df_GUIDER.command_y*pixel_scale*1000)*10)/10}' + ' mas'], \n", + " fontsize=12, loc='best') \n", + "\n", + "# Set the font size of tick mark labels\n", + "axes[0].tick_params(axis='both', which='major', labelsize=14)\n", + "axes[1].tick_params(axis='both', which='major', labelsize=14)\n", + "\n", + "# Display the plots\n", + "plt.show()\n", + "#plt.savefig('guider_corrections.png',dpi=144)" + ] + }, + { + "cell_type": "markdown", + "id": "075ecd3c", + "metadata": {}, + "source": [ + "# Plot Flux Time Series from Guider and Exposure Meter" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a685870f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.style.use('seaborn-whitegrid')\n", + "plt.figure(figsize=(12, 5), tight_layout=True)\n", + "plt.plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.object1_flux/max(df_GUIDER.object1_flux), color='royalblue')\n", + "plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec / max(int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec), marker='o', color='k')\n", + "plt.title(\"Guider and Exposure Meter Flux Time Series\", fontsize=14)\n", + "plt.xlabel(\"Seconds since \" + str(guider_header['DATE-BEG']), fontsize=14)\n", + "plt.ylabel(\"Flux (fractional)\", fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.xlim(min(df_GUIDER.timestamp-min(df_GUIDER.timestamp)), max(df_GUIDER.timestamp-min(df_GUIDER.timestamp)))\n", + "plt.legend(['Guider Flux', 'Exposure Meter Flux'], fontsize=12, loc='best') \n", + "plt.show()\n", + "#plt.savefig('guider_xy_times.png',dpi=144)\n" + ] + }, + { + "cell_type": "markdown", + "id": "921cb59e", + "metadata": {}, + "source": [ + "# Show Header for Guider Data" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d1aebe4a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "XTENSION= 'IMAGE ' / Image extension \n", + "BITPIX = -32 / array data type \n", + "NAXIS = 2 / number of array dimensions \n", + "NAXIS1 = 640 \n", + "NAXIS2 = 512 \n", + "PCOUNT = 0 / number of parameters \n", + "GCOUNT = 1 / number of groups \n", + "DATE-BEG= '2023-06-11T08:29:30.140' / sequence begin \n", + "DATE-END= '2023-06-11T08:32:22.936' / sequence end \n", + "OUTDIR = '/s/sdata1701/kpf1/2023jun10/CRED2' / original output directory \n", + "OFNAME = 'kpfguide_cube_4259.fits' / original output filename \n", + "EXTNAME = 'GUIDER_AVG' / HDU name \n", + "NFRAMES = 17265 / quantity of subframes \n", + "SUBX0 = 274 / [pix] subframe x0 coordinate \n", + "SUBX1 = 402 / [pix] subframe x1 coordinate \n", + "SUBY0 = 198 / [pix] subframe y0 coordinate \n", + "SUBY1 = 326 / [pix] subframe y1 coordinate \n", + "FPS = 100.0 / frames per second \n", + "GAIN = 'Low ' / detector gain setting \n", + "TEMPDET = -40.01 / [deg C] sensor temperature \n", + "EXPOSING= T / continuous exposures active \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT Location: /kroot/rel/default/sbin/kpftiptiltd \n", + "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", + "COMMENT Hostname: kpffiuserver.keck.hawaii.edu \n", + "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT keyheader section [kpfguide] \n", + "COMMENT Snapshot timestamp: 2023-06-11T08:30:56.538520Z \n", + "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", + "COMMENT \n", + "INSTRUME= 'KPF ' / Selected instrument \n", + "TELESCOP= 'Keck I ' / Telescope \n", + "RA = '17:20:39.80' / Right ascension \n", + "DEC = '+32:27:40.0' / Declination \n", + "EL = 61.46 / [deg] telescope elevation \n", + "AZ = 57.97 / [deg] telescope azimuth \n", + "PARANG = -108.94 / [deg] parallactic angle \n", + "VA = 118.5 / [deg] vertical angle \n", + "PA = 9.6 / [deg] sky position angle \n", + "MJD-OBS = 60106.354817 / Modified Julian day \n", + "PMFM = 0.0 / PMFM value \n", + "UT = '08:30:56.22' / DCS Universal Time \n", + "SECFOCUS= -2.244 / [mm] Secondary focus \n", + "TELFOCUS= -1.459 / [mm] Telescope focus \n", + "TUBETEMP= 3.27 / [degC] Telescope tube temperature \n", + "WCSNAME = 'RA/Dec ' / Descriptive name of this WCS \n", + "CTYPE1 = 'RA---TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2 = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1 = 'Right Ascension' / Descriptive name of WCS axis 1 \n", + "CNAME2 = 'Declination' / Descriptive name of WCS axis 2 \n", + "CUNIT1 = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2 = 'deg ' / Physical unit of WCS axis 2 \n", + "RADECSYS= 'ICRS ' / Celestial coordinate system \n", + "CD1_1 = 2.68683E-06 / [deg] RA per pixel in x \n", + "CD1_2 = 1.58855E-05 / [deg] RA per pixel in y \n", + "CD2_1 = 1.58855E-05 / [deg] DEC per pixel in x \n", + "CD2_2 = -2.68683E-06 / [deg] DEC per pixel in y \n", + "CRPIX1 = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2 = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1 = 260.165833 / [deg] RA of center pixel \n", + "CRVAL2 = 32.461111 / [deg] DEC of center pixel \n", + "WCSNAMER= 'RA/Dec ' / Descriptive name of this WCS \n", + "CTYPE1R = 'RA---TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2R = 'DEC--TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1R = 'Right Ascension' / Descriptive name of WCS axis 1 \n", + "CNAME2R = 'Declination' / Descriptive name of WCS axis 2 \n", + "CUNIT1R = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2R = 'deg ' / Physical unit of WCS axis 2 \n", + "RADESYSR= 'ICRS ' / Celestial coordinate system \n", + "CD1_1R = 2.68683E-06 / [deg] RA per pixel in x \n", + "CD1_2R = 1.58855E-05 / [deg] RA per pixel in y \n", + "CD2_1R = 1.58855E-05 / [deg] DEC per pixel in x \n", + "CD2_2R = -2.68683E-06 / [deg] DEC per pixel in y \n", + "CRPIX1R = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2R = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1R = 260.165833 / [deg] RA of center pixel \n", + "CRVAL2R = 32.461111 / [deg] DEC of center pixel \n", + "WCSNAMEY= 'Az/El ' / Descriptive name of this WCS \n", + "CTYPE1Y = 'TLON-TAN' / Coordinate type of WCS axis 1 \n", + "CTYPE2Y = 'TLAT-TAN' / Coordinate type of WCS axis 2 \n", + "CNAME1Y = 'Azimuth ' / Descriptive name of WCS axis 1 \n", + "CNAME2Y = 'Elevation' / Descriptive name of WCS axis 2 \n", + "CUNIT1Y = 'deg ' / Physical unit of WCS axis 1 \n", + "CUNIT2Y = 'deg ' / Physical unit of WCS axis 2 \n", + "RADESYSY= 'ICRS ' / Celestial coordinate system \n", + "CD1_1Y = -7.68756E-06 / [deg] Az per pixel in x \n", + "CD1_2Y = -1.41587E-05 / [deg] Az per pixel in y \n", + "CD2_1Y = -1.41587E-05 / [deg] El per pixel in x \n", + "CD2_2Y = 7.68756E-06 / [deg] El per pixel in y \n", + "CRPIX1Y = 337.826 / [pix] X coordinate of center pixel \n", + "CRPIX2Y = 262.276 / [pix] Y coordinate of center pixel \n", + "CRVAL1Y = 57.971911 / [deg] Az of center pixel \n", + "CRVAL2Y = 61.45613 / [deg] El of center pixel \n", + "COMMENT \n", + "COMMENT ------------------------------------------------------------------------\n", + "COMMENT End of keyheader output \n", + "COMMENT Header generation start time: 2023-06-11T08:32:23.261876Z \n", + "COMMENT Header generation end time: 2023-06-11T08:32:23.299838Z \n", + "COMMENT ------------------------------------------------------------------------" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "D2['GUIDER_AVG'].header" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1527601893f2f36d68114cfdca7982f29e54e4dc Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Wed, 21 Jun 2023 22:12:19 -0700 Subject: [PATCH 143/249] Fix indenting problem --- .../KPF_Data_Tutorial_L2_Time_Series.ipynb | 72 +++++++++---------- 1 file changed, 33 insertions(+), 39 deletions(-) diff --git a/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb b/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb index a7df9ad84..1dd457e02 100644 --- a/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb +++ b/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb @@ -10,10 +10,20 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 1, "id": "bd8e6835", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/howard/opt/anaconda3/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:21: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " int_types = {_np.int, _np.int8, _np.int16, _np.int32, _np.int64, _np.integer}\n" + ] + } + ], "source": [ "from astropy.io import fits\n", "from astropy.time import Time\n", @@ -29,13 +39,13 @@ "id": "5a8d307c", "metadata": {}, "source": [ - "## Define Directories and Filenames\n", + "# Define Directories and Filenames\n", "One needs to have the L2 files kusted bekiw in the data directory of the folder from which this notebook is running. This file can be downloaded from Jump or shrek.caltech.edu. One can also modify the code below to work with other files or paths. The example below is a ~1.5-hour time series on Tau Ceti (HD 10700)." ] }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 2, "id": "932a3b78", "metadata": {}, "outputs": [], @@ -91,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 3, "id": "247746e7", "metadata": {}, "outputs": [ @@ -127,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 4, "id": "e62fb0c0", "metadata": {}, "outputs": [ @@ -448,7 +458,7 @@ "COMMENT The original keyword name follows the Kwd in the comment field " ] }, - "execution_count": 149, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -468,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 5, "id": "313a399c", "metadata": {}, "outputs": [ @@ -493,7 +503,7 @@ "EXTNAME = 'GREEN_CCF' / extension name " ] }, - "execution_count": 150, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -513,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 6, "id": "48bf475a", "metadata": {}, "outputs": [ @@ -556,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 7, "id": "e6593b29", "metadata": {}, "outputs": [ @@ -601,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 8, "id": "2644f5e1", "metadata": {}, "outputs": [ @@ -660,7 +670,7 @@ "EXTNAME = 'RV ' / extension name " ] }, - "execution_count": 153, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -679,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 9, "id": "db786dec", "metadata": {}, "outputs": [ @@ -956,7 +966,7 @@ "[67 rows x 14 columns]" ] }, - "execution_count": 154, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -975,13 +985,13 @@ "id": "82532c02", "metadata": {}, "source": [ - "## Create L3 file with time series RVs\n", + "# Create L3 file with time series RVs\n", "The L3 file is a collection of RVs, state information and telemetry from a series of L2 files. The example below builds the L3 file for the series of Tau Ceti observations and then displays ti." ] }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 10, "id": "82c041b0", "metadata": {}, "outputs": [], @@ -1052,7 +1062,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 11, "id": "090949a0", "metadata": {}, "outputs": [ @@ -2098,7 +2108,7 @@ "[35 rows x 89 columns]" ] }, - "execution_count": 156, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -2118,7 +2128,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 12, "id": "0c8d5049", "metadata": {}, "outputs": [], @@ -2139,7 +2149,7 @@ }, { "cell_type": "code", - "execution_count": 160, + "execution_count": 13, "id": "db842b34", "metadata": {}, "outputs": [ @@ -2199,13 +2209,13 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 14, "id": "a7ea00e4", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -2253,22 +2263,6 @@ "plt.yticks(fontsize=14)\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af3a44ce", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69eb9350", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 96478508a4a6baeae4ee4e121dcf979e9654c4aa Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Wed, 21 Jun 2023 22:16:47 -0700 Subject: [PATCH 144/249] fixed indenting problem --- .../tutorials/KPF_Data_Tutorial_L1.ipynb | 182 ++++++++++-------- 1 file changed, 99 insertions(+), 83 deletions(-) diff --git a/docs/source/tutorials/KPF_Data_Tutorial_L1.ipynb b/docs/source/tutorials/KPF_Data_Tutorial_L1.ipynb index 1076c8809..177ece77e 100644 --- a/docs/source/tutorials/KPF_Data_Tutorial_L1.ipynb +++ b/docs/source/tutorials/KPF_Data_Tutorial_L1.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "596edb6b", + "id": "4d4a902e", "metadata": {}, "source": [ "# Import Packages" @@ -10,14 +10,14 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "bd8e6835", + "execution_count": 1, + "id": "e16c6ff5", "metadata": {}, "outputs": [], "source": [ - "# Import packages\n", "from astropy.io import fits\n", "from astropy.time import Time\n", + "from astropy.table import Table\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 2, "id": "932a3b78", "metadata": {}, "outputs": [], @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "580ce104", "metadata": {}, "outputs": [ @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 4, "id": "f57701ca", "metadata": {}, "outputs": [ @@ -179,10 +179,10 @@ " \n", " \n", " 0\n", - " 2022-11-12T07:32:24.786030\n", + " 2022-12-02T22:34:20.793957\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " SpectralExtraction\n", " 1.0\n", @@ -192,10 +192,10 @@ " \n", " \n", " 1\n", - " 2022-11-12T07:32:25.358923\n", + " 2022-12-02T22:34:21.931624\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -205,10 +205,10 @@ " \n", " \n", " 2\n", - " 2022-11-12T07:32:25.506585\n", + " 2022-12-02T22:34:22.393367\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -218,10 +218,10 @@ " \n", " \n", " 3\n", - " 2022-11-12T07:32:25.631137\n", + " 2022-12-02T22:34:22.867832\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -231,10 +231,10 @@ " \n", " \n", " 4\n", - " 2022-11-12T07:32:25.746821\n", + " 2022-12-02T22:34:23.348420\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -244,10 +244,10 @@ " \n", " \n", " 5\n", - " 2022-11-12T07:32:25.870048\n", + " 2022-12-02T22:34:23.834707\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -257,10 +257,10 @@ " \n", " \n", " 6\n", - " 2022-11-12T07:32:52.996217\n", + " 2022-12-02T22:34:44.166094\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " SpectralExtraction\n", " 1.0\n", @@ -270,10 +270,10 @@ " \n", " \n", " 7\n", - " 2022-11-12T07:32:53.573047\n", + " 2022-12-02T22:34:45.521302\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -283,10 +283,10 @@ " \n", " \n", " 8\n", - " 2022-11-12T07:32:53.711856\n", + " 2022-12-02T22:34:46.070033\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -296,10 +296,10 @@ " \n", " \n", " 9\n", - " 2022-11-12T07:32:53.837845\n", + " 2022-12-02T22:34:46.614998\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -309,10 +309,10 @@ " \n", " \n", " 10\n", - " 2022-11-12T07:32:53.952972\n", + " 2022-12-02T22:34:47.117629\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -322,10 +322,10 @@ " \n", " \n", " 11\n", - " 2022-11-12T07:32:54.068255\n", + " 2022-12-02T22:34:47.600993\n", " v2.0.1\n", " develop\n", - " 261fd92c4d749d8f865873a7c1ae539c6ac01eeb\n", + " 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa\n", " all\n", " ExtCopy\n", " 1.0\n", @@ -339,32 +339,32 @@ ], "text/plain": [ " Time Code_Release Branch_Name \\\n", - "0 2022-11-12T07:32:24.786030 v2.0.1 develop \n", - "1 2022-11-12T07:32:25.358923 v2.0.1 develop \n", - "2 2022-11-12T07:32:25.506585 v2.0.1 develop \n", - "3 2022-11-12T07:32:25.631137 v2.0.1 develop \n", - "4 2022-11-12T07:32:25.746821 v2.0.1 develop \n", - "5 2022-11-12T07:32:25.870048 v2.0.1 develop \n", - "6 2022-11-12T07:32:52.996217 v2.0.1 develop \n", - "7 2022-11-12T07:32:53.573047 v2.0.1 develop \n", - "8 2022-11-12T07:32:53.711856 v2.0.1 develop \n", - "9 2022-11-12T07:32:53.837845 v2.0.1 develop \n", - "10 2022-11-12T07:32:53.952972 v2.0.1 develop \n", - "11 2022-11-12T07:32:54.068255 v2.0.1 develop \n", + "0 2022-12-02T22:34:20.793957 v2.0.1 develop \n", + "1 2022-12-02T22:34:21.931624 v2.0.1 develop \n", + "2 2022-12-02T22:34:22.393367 v2.0.1 develop \n", + "3 2022-12-02T22:34:22.867832 v2.0.1 develop \n", + "4 2022-12-02T22:34:23.348420 v2.0.1 develop \n", + "5 2022-12-02T22:34:23.834707 v2.0.1 develop \n", + "6 2022-12-02T22:34:44.166094 v2.0.1 develop \n", + "7 2022-12-02T22:34:45.521302 v2.0.1 develop \n", + "8 2022-12-02T22:34:46.070033 v2.0.1 develop \n", + "9 2022-12-02T22:34:46.614998 v2.0.1 develop \n", + "10 2022-12-02T22:34:47.117629 v2.0.1 develop \n", + "11 2022-12-02T22:34:47.600993 v2.0.1 develop \n", "\n", " Commit_Hash Chip Module_Name \\\n", - "0 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all SpectralExtraction \n", - "1 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "2 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "3 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "4 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "5 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "6 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all SpectralExtraction \n", - "7 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "8 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "9 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "10 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", - "11 261fd92c4d749d8f865873a7c1ae539c6ac01eeb all ExtCopy \n", + "0 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all SpectralExtraction \n", + "1 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "2 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "3 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "4 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "5 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "6 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all SpectralExtraction \n", + "7 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "8 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "9 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "10 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", + "11 47d149b892ae6cd4980a204fef3ae6f1cb3bb6fa all ExtCopy \n", "\n", " Module_Level Module_Path \\\n", "0 1.0 modules.spectral_extraction.src.spectral_extra... \n", @@ -395,7 +395,7 @@ "11 extension copy from MasterLFCWLS.fits:RED_CAL_... PASS " ] }, - "execution_count": 80, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -423,18 +423,20 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 5, "id": "037d38f6", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -462,18 +464,20 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 6, "id": "db11eaaa", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -498,18 +502,20 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 7, "id": "e9d442ad", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -529,18 +535,20 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 8, "id": "15a1b136", "metadata": {}, "outputs": [ { "data": { - "image/png": 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r1mknwe3evRvDhw9Hhw4dcNNNN2H79u1l2MLQwVUR5BhY4VIECeJS4+ROX1FQGSmC5HcrvlAc8jn2fb8PJ7fwF2cCAmWFS93AWrRoER555BH06tULdevWRZcuXTBt2jTs2rUL27Ztw6ZNmzBnzhy8+eabuP3229GoUSO0a9cOM2fORGJiIt59913VOQOBAGJjY/Hss8+idevWaNeuHW655RZs3LhRsd+GDRvQo0cP5OXlYcWKFZgwYQK6dOmCevXqoU+fPnjuuefw008/4ezZswCkKNqqVavw0Ucf4brrrkODBg3QpUsXzJo1C2fOnMGnn36qasupU6ewfv16PP/888jIyEC7du0wceJE/PTTTygqEtQcAQGB6ODHJ34EAJzbfU53P7LmrYxrwUiizA2skpISPP7449i3b5/mPoWFhbjvvvvQoUMHLFq0CJ07d8YDDzyg6c2rUOCJWFBGvBzBCpMiSBCbGgtPkSci5woV7EtUGSNY5B5o+Xur+HTIp5jVaVakmiQgEBIudQMLkAwdny/oxKpfvz6WLl2KVq1a4euvv0b79u3RtWtXxTExMTF4/fXX8eCDD6rOZ7PZ8NJLL6F9+/YAgL///hvfffedHK0CpHlr3759aN++PWw2G2w2G9avX684T9euXbFs2TKkpaUBABYvXoxBgwahYcOGiv1SUlIwe/Zs3Hzzzaq2JCUlYdasWWjUqJGifX6/H26329wDEhAQuOQRCAQsUcqJYVV4rtDgxKX/VcK1YCRRplJT+/fvx/jx4w1/0GXLlsHlcuHf//437HY7nn76aaxZswbff/89br311jJqbWgolwhWOVMEWWOxIuRg7ViwAw37NkRK3RRT+0cqN05AoLwRLQNr2yfbsOXDLfLnwsJC/J7we1SuRXDZvZehw6gOlo4ZNWoUZs6ciVWrVqFv377o0aMH+vTpg2bNmgGQ2BEdOvDP2apVK8Pzjx8/Ht999x3q1q2LsWPHyts3btyIjh07wuVyweVyYdiwYZg+fTq+/PJL9OnTBz169ECvXr3QtGlT+Zjdu3djzJgx3OsQY45FUlIS+vXrp9j2ySefoEWLFkhNTTVsv4CAgBpvt3kbCdUTcM/P94R0/Pb52/Hjkz/isWOPwe6o+Nk3Po8PL8a8iP5T+qPfs/0M9weAjvd2xOZ3N6PFdS109xMRLAll2gs2bdqEXr164fPPP9fdb9u2bejUqRPsdql5NpsNnTp1wpYtW3SPqxDg5GDJncwW/DuSEazypgjuWrhL8bm8Xyp3gRuLRizCB5d/YPqYimAUCghEApd6BOvhhx/GjBkz0KBBAyxatAiPP/44evfujQ8+kMaDvLw8JCUlhXz+Bx98EJ999hlq1aqFMWPGwO+Xxg5CDyR48cUXMXnyZCQnJ2P+/Pl4+OGH0adPH3z99dfyPnl5eUhOTg65LQDw0UcfYfny5ZgwYUJY5xEQuJRx9q+zOLLmSMjHLx65GPkn88vd4W0WZN348+SfTR+T1igNAFClWRXd/ch6SkSwyhC33367qf3Onj2Lxo0bK7ZVrVoVu3fvjkazIgpeBIv3d6SS/2JTJIpgIBAoN8WWFeNXKD6X90tFjNe8E3nY+flOtB3e1vgYYWAJXCSIloHVYVQHRTQpMzMTGRkZUblWuBgyZAiGDBmC3NxcrFu3Dp9//jleeeUVNGrUCFWqVAlLca958+YAgBkzZqBfv37YuHEjunXrhnXr1mHq1Knyfna7HSNGjMCIESOQlZWFX3/9FXPnzsWECRPQokULtG7dGmlpacjJyQm5LR9++CFefvllTJo0CT179gz5PAICAhIyF2Ui46bQxzUzDubCrEKc/PMkmg5qarhvtBDKOo2srYwidJFe51ZWVMhqlEVFRYiJiVFsi4mJ0eSXZ2ZmlkWzTIFI6+ZmByfwkqISAMCFCxdQkCdJ9Gafz45Iu/M9+UAA2LV9FxwxDhQXF5f582D5uAf2HsCF2Atl2gYaxVlBoYpN8zfB0d5heEzekTz5782rNyOhVujF8SpSfzSD8ugzAtHDyZMn4cp0Rf06FbHfHD58GD/99BPuu+8+eVvDhg3x5JNP4sknn8TSpUtRt25d/PHHH9y2//jjjzh48CAeeOABHDkiebN3794Nr9eLzZs3o1OnToiLC9YdTExMxM6dOxEIBHDq1Cn4/X5kZmZix44d2LVrl8Kp2LJlS0yePBkPPPAAvvnmG9hsNjRu3Bi//fYbevXqpWrLwoULEQgEMHz4cJw4cQJer1fR5gULFuDzzz/Hfffdh06dOlW434KHithnBCo2yrrPfDPmGyAMv9HuXbsRWyVWd5+fhv+E8zvO46Y/byq3ovAl2SXy32af7+kTpwEA58+fN3XMvj37kFwSXoQ+VFSEsaZCGlixsbEqY8rtdismNhoVyYt6uobUARPiggt0p0N6zFWqVEGhSzJGUpJTItLuOk3qIBOZaNaoGeJS4yqEV7lqbFW0yjDOZYgWco4FPcIJMQmmnsc5W1AVp26VuqiVUcvSNem8wpqumqjStEqlqQFREfqMGQQCAXwz+ht0fqAz6veoX97NqbCoU7dOmfyeFbHf+P1+fPfdd7jrrrvQsWNHxXfVq1dH06ZN0bdvX9x2223Iy8vD5ZdfLn9fXFyMZcuWoUuXLsjIyJCjXK1atYLX68XIkSPx6quv4qqrrgIAHDt2DPn5+ejbty/27duHXr16oXXr1vJ3X331FR588EHUrl1b0Y6kpCS0atUKGRkZuPPOO/HEE08gPj5eIVqRlZWF77//HnfffTcyMjKQmZkJp9MpP++PP/4YCxcuxPPPP4/hw4dH+jFGDRWxzwhUbJR1n4lNiA3rek0bN0VybX2jYvHBxQCAls1bIiYpRnffaKHgTLAen9n7/TvlbwDSWtbMMU0aN0G1VtVCa2CYKMt+s3nzZu72CpmJV7NmTVnGluDcuXOoXr16ObXIAjgqgry/SRg5/3Q+Pr/p85ArX8emSJ6SisT7zT1evgUvSRFmwPxzocP684fMx+5vrNFR6Zy6N5q/gb++/MvS8QLGKM4uxraPt2H+NfPLuykVGpUhwTpaaNOmDQYMGICxY8di8eLFOHbsGHbs2IEZM2YgMzMTt9xyC9q3b48RI0Zg7Nix+OKLL3D06FFs2rQJ999/PwoLC/HII4+ozhsXF4ebb74ZL7/8MjZv3owdO3bg8ccfx5VXXonmzZtj3bp1ivyrAQMGoEWLFhg9ejRWrFiB48ePY8uWLXjmmWcQCARw9dVXAwAGDx6M3r17Y/To0Vi2bBmOHTuGtWvX4t5770X16tUxatQoVVtOnDiBadOm4Y477sDAgQNx9uxZ+R+tnCggIGAdPnd475CZ/HrifC1PCl0oufJWqX/lnY9f3qiQM3GHDh2wZcsWOSoQCASwZcsWlUeyIoK8OId/PixvI53SZrOpOuia59dg9+Ld2D7PWp0vV6IL3R/vDle8RAXa+tHWMFseOVjNZ1r97GpMsU2J2PVpqfV9y7TLAdCg25x/Mh+f36gvxKJ3TQDIPpxt6XgBY1SESamiQsuhowdviRfugotP1vu1117Dbbfdhvfeew/XXnstRo8ejb1792L+/PmoVUuKTE+aNAnjxo3D3LlzMXToUDz66KOoUaMGFixYgJo1a3LPO2HCBPTv3x/jxo3DPffcg2bNmsk5V+vXr1dItrtcLsyZMwd9+vTB1KlTMXjwYPzjH/9AcXEx5s2bh4SEIMNh5syZuPXWWzFz5kxcd911mDRpEjp16oSPP/4YiYmJqnasWrUKbrcbn376KXr37q34d/z48Ug+SgGBSw50ZIeGp9CD/6X9D3u+3aN7vBUDrTwNkFBysMjca/bYS32urjAUwbNnzyI5ORlxcXEYPHgwpk+fjhdeeAEjRozAwoULkZ+fjyFDhpR3Mw3BSzCnOyP5m/xfdE6KXMVXibd0HV+JD85Yp8zfXTlhJXr/u3dIbY4UrvjvFVg5YaVlA+uXF34BIKn4VG8dfpQyFA9UuCIX7jzlQjWtYVpY5xNQgwzW7LMWUBr4ZiftD3t+iJN/nsTkwORoNatcEBcXh3HjxmHcuHGa+9hsNowcORIjR47U3Kdbt27Ysye4mIqLi8MzzzyDZ555RrXvypUrVdtSU1MxceJETJw4Ube9MTExeOihh/DQQw9p7nPTTTfhpptuAgDDdgsICEQe53afQ0lOCVZPWo2W17fU3M9MCR53vjSHlae4lpl54s8P/kRS7SS0uLaF4hizhlN5C56VNypMBKt3795YtmwZAImj/t5772HLli0YNmwY/vzzT8yaNSssad0yAyftRk9FkHiQXQnmk9ID/gD8Xj8csQ4448vfRo5NiUXzIc3R7Z/dAIQ+aLzd5u2ItIemCJpFuANdcXax4rMjxlhYQ0CKTq+dulbTa0iD/o3KuzRBRQPtVDAzqRVdKMLJP09K+1soNCkgICBwKeLsX1Layultp3X3s1KCp6wNkPe7vo+FNy+Urk0ZSZ5CD3f/b8d8iwXXLZA/y/VCTTrxLnWKYLmtzmnPIO9z+/btsXjx4rJsUkTAEzagOxmbg0UWilYWjMRb7Yx1yhTB8oSn0IOaHWrC4ZKMivKWPC/OKTbeicHm9/hJiqavyRhYl3po3Cz+/uNvrPz3Shz95ShGLB2huy/drza8vgG9/1W+EduKBHqiNjOpvdv+XfnvktwSxKXyBYQEBAQEBCRpdTMwE8EiKOu10olNJ3Bi0wkAzJxR2o4DPx5AUq0k1GzHp0nLESyzFEERwRKINngdmSzASbTFkoFVuq8j1lFuEp8EPo8Pfq8fzngnbA51DTAj7Ph0R8TbxEZDzHjot87ZGtY1LxxSytILA8scfpv6G4AgZUIPtOEcqULdFwsUThwTz4YWosk9Vr6iNAICAgLlCTMiY3rrGnqNYWX9U5YGFusEVjjiSuMC866ap3C+sZBzsHTWN/R3Zub1ixnCwIo0zFIESai19LOniB+i5YEYZc44Z9SKipoFMfZc8S7YbDbY7DZLi98ts7dEvE0XDiiNHTODWLdHu4V1zcUjldHWSz00bha7F0tqjWb6seIZVw4F/DID3cetjCWAlFsgICAgcKli18JdhvvoRWMUTnQL659orxOy9mZh/w/7ASjL1wDM/Zj0B7MaAjzQc9EXt31hsqUXJ4SBVQZQeJeZJEES9QmVIhiTXD41FAiIseeIleiBAX8Av770q6ljS3JLcGjVoYi3iV0wmhG9YHOm4qqER5kSESxrIO+BHkjOEFD2OW4HVhzAGy3ewDf/902ZXtcs6AlPi0+vhfxT+ZFujoCAgEClAStKxWO96FH/6Pm+IlEE32z5JuYPlsqasHn+9LrUqmiF2QhWUVZo5YcuFggDqwwgd8pAQJWDZXdKP4GVOlbEYHDEOlCrQy006N0ACdUSDI6KDohhGApV8a3Wb0W6OQCkBWZclTh5QDFjYLHCGKFSL5sMagJAGFhWYbV2U7g5c1Yx7+p5OL/vPLZ+uLVMr2sW9GRp1cAS0VYBAYGLFXP6zME3o/UdY+wYmH0oW7WPnhOcnu+tGE1lKdZE6wN4Cj1KdWuTQkfkPunnVZhViMNrDqv2ERAGVuTB6Vtyhwtoh1itvGiygVXqxa/duXbYxfFChRxNC8Egyfs7T7WNDWOH1KYiL6q1qoYrX74SgLmQvbfEq6ioHurz7P6oVAvnUk/utAozESwaLA20LFGSV1Ju19aCgiJo1cAqZ1EaAQEBgWjg+39+j6Nrj2LrR1tRkqs9brNGAU+dWU+dmD63FYrgxwM+Nr1vuKDv8fiG40qjkrNc4TneeDLtX9/9NT7u/7GcxyYMrCCEgRVh6HkCAv6AKkmQvIyWcrCIgVWq2ueIcZSfgVUcpCtagdZL+FqD18JtEjxFHrjiXbIBajaCFZsSG/wc4vOkqZIC+qCfkdUIlt1VdkMXW0TaSKa3PEAb9Fai4eyxAgICAhcL/pj5h/z3yS0nNfczMway8wCNjW9vlP+2QhEsvmBd8ThU0AaTz+NTRrA46xWe440XINi3dB+A4Lwj1j5BCAMr0tDpWwF/QO7k5H/yMoYTwXLEVgADy2IE6/SO6CxSA4EAjv12DFn7suQ2mfHo+0p8Cq9VKLW0gKChKQYZY9ATkVEEi3VcJNdOjkqbePju/u8UnytiDS5eKQg9JNdJliO2IoIlICBwKYPM1zXb11R8pqG3JlCoCBpEsKK5Nig4U4Djvx/nX5cR4qDbEQgEVHMsb14gcx/vHswoDF5qEAZWhKEbwQpwIlilnTikHKyYYASLFB8ua9CKhjSM2vLL879EpT1F56Uwde6xXCTXkRbheSfUVEQWnkKPoqaYz+0LqQAriWCJvBZj0BORkYqgp0BpJJelkXNw5UHltXU8meUFXikIPXiLvWh/V3tp/4usr3q9Xrz99tsYNGgQ2rZtiz59+mDSpEnIyspS7LdkyRIMHz4cl112GXr37o3HH38cR48elb///fff0bJlS3i96t974sSJmDFjhmr7n3/+iZEjRwIACgoKMHXqVPTv3x9t27bFwIEDMXXqVBQUKMtI+P1+zJs3D0OHDkXHjh3Rr18/PPvsszh3LijWs2jRIvTt21f+fPr0aYwdOxadO3dGr1698Morr3DbKSBwqUI1DupM52QMbDSwkbQrx0jQW6PR8xd93XO7z2Hd9HVKAyyK4+2sLrMwu/tsXQOItJEVuWCjeLx5ZPfXkuqvWfrgpQ5hYEUaBhEs2cAiMu2e8A0sEjUpjygWXZOLhhHlMb1FelTaQwaF1re0Rmr9VADm8rqKc4oRmxqLe9bcg4ybM6RzhVBrSUSwzIPur0a1mNwFwXoa7Ua0Q8GZAsty5KGC5e5H4z0rySsxJRXMwlvsxZ5v93BLQejBXeCWI1hs3bjKjunTp2Pp0qV47rnn8MMPP2DGjBnYu3cvxowZIy90pk6dipdeegnDhg3D4sWL8c4776CwsBAjRozAyZPaVCIAeP/99/Hll19yv1u3bh26d5fyMCdMmIBt27Zh2rRpWL58OaZMmYJVq1bhqaeeUhzz6KOPYvbs2RgzZgyWLFmC6dOnY+/evbj77ruRn89XeHz00UfhdruxcOFCvPbaa/juu+/w/vvvW31UAgIXLdj5Yc+3ezT3JfMPSbvgzd/bPtkGgE9PpwUkaGbGN6O/wY9P/Iico8E1SDiO8MNrDmOKbQryT/PHBXIfm99XikCd3nFaYRTZnXaVTDt7z7p1vziUSp4AxqUOYWCVJQLqMCp5Ga28dH//8TcAZQQLKB/POksRHPLWEADGtDxvsTcqtYzI4rfp4KZIqZcCwFwh1ZKcEsSlxaFh34ao172e1MYQnicZfIWBZQx6Ijqx+YTuviSC1fza5nJx6s2zykZJ0J2nLJYYaQNrz7d78L+U/+HL4V8qCgCbwYonVuCzGz7D8Q1BWojRWBIIBBSU2I1vbtTdv7Jh0aJFeOSRR9CrVy/UrVsXXbp0wbRp07Br1y5s27YNmzZtwpw5c/Dmm2/i9ttvR6NGjdCuXTvMnDkTiYmJePddfqHN/Px8jBs3Du+//z5q167N3WfDhg3o0aMH8vLysGLFCkyYMAFdunRBvXr10KdPHzz33HP46aefcPbsWQBSFG3VqlX46KOPcN1116FBgwbo0qULZs2ahTNnzuDTTz/ltqN27dp47rnn0LRpU3Tt2hWDBw/Gxo0X1+8oIBAOWJbDhlc3aO67YvwKAObmb55xkVQrSf6bdswSQSTCrAHCM7DIPRxbd0x3P5a1c3z9ccU92ew2VQSLNYx47azasqq8PwszEu6XGoSBFWGYFbmQc7EY2XYjeIo8WPX0KgBqA6tcIliMiiBZtOlF5AKBAH5/7XfTxe2sgBYAcSW44EpwoTCr0PC44pxixKVKta9INC6UPCzZAyaEAwyh6K8B/XeHVIS/7N7L5G3kWZc1Iv2efXbDZ/LfVqmPWXsl2hvdx40mcPJ9eT2/ssCGDRvg8wV/p/r162Pp0qVo1aoVvv76a7Rv3x5du3ZVHBMTE4PXX38dDz74IPecx48fh9/vx6JFi1C/fn3V94WFhdi3bx/at28vFV232bB+/XrFPl27dsWyZcuQlpYGAFi8eDEGDRqEhg0bKvZLSUnB7NmzcfPNN6uuk5SUhFdffRV16tQBAOzbtw+rVq2SI2cCAgLWxX6AYNkcozpP7PcKgSzKcUjGWNrhfGrrKcvtIpCpiAbLi52f7lQdx4pasDLtZiiCMvOKs14VOVhqhFbsR0AbBhRB8j2rImh2QZ61J5hHQItcAOVLESTUOFJ7Si+CRd8DgSvRpcqzCQXkeZJnowqFa8BT6IEr0aU41srzTKyRiIIzBYhPjwcgBhkzYCmYAV8ANic/rEkognSxxFBrlYWLaL5nVhSoAP6kZkRtlYuDWyzW/MknwIcfBj8XFjZAQpTL7917LzBqlLVjRo0ahZkzZ2LVqlXo27cvevTogT59+qBZs2YAgN27d6NDhw7cY1u1aqV53latWuHNN9/U/H7jxo3o2LEjXC4XXC4Xhg0bhunTp+PLL79Enz590KNHD/Tq1QtNmzaVj9m9ezfGjBnDPV/79u0N7/X222/Hli1b0KZNGzn3S0BAILQ8XT2KYGLNRBSclujUmYsz0frm1vJ39LhNGyZkjKXXQzlHwihFQ+wrg/XF+f3nFZ9jkmKUohZMxIpnNNL34S3xwhnr1M2z4n1Xv6faEXUpQUSwIgzdCBbtMfAxFEGTESy6Bk9qQynHSDYIQlS+CwcsRZAIRejlx/Cod/+68C/571DEJQjY/DSbw2b4bL978Dvkn8yXF+/kXqwM0Em1ktByaEvZwyR4yMZgDRU944LuZ0M/GgoA+PODP5F9ODtq7QOAX//7q2pbNN8zq8YbXWOPwCiCteb5NQCA7CPZlq5VWfDwww9jxowZaNCgARYtWoTHH38cvXv3xgcffAAAyMvLQ1JSksFZrIPQAwlefPFFTJ48GcnJyZg/fz4efvhh9OnTB19//bW8T15eHpKTQ1fEnDx5Mj766COUlJTg8ccfD6f5AgIXFULJ0dWLYNFjMzsH0N99P/b74PlKKYe08zilforldoWMUoPM7zMQtQio1yy0o27pg0vl4wD9HCyFs+8SXweJCFakoadU41V2cMB6BIu8qPf+di9ik6WwtELkIgp5TXqQveGlUTQzESzedzRdye/xW/auy+0pXaSTgc1MBGvze1IuDzFezdwDC7/PD7vDLhtYlS2C9dmNn+HQqkOYkDuhzK7JGlR6hgFtYNVoUwOAxCtfcMMC/GP7P6LSvmPrjsl0XBqRjmAlVE9A4VmJ4mdVWEWe1GiVKgMDa++3ewFAvqZZjBqljCZlZh5FRkaGpXOUFYYMGYIhQ4YgNzcX69atw+eff45XXnkFjRo1QpUqVZCbay3XzQzWrVuHqVOnyp/tdjtGjBiBESNGICsrC7/++ivmzp2LCRMmoEWLFmjdujXS0tKQkxO6R5s8/5deegnDhw/H8ePHUa9evbDvRUCgsiMkpVmdCBE97rPrE61xmzicCcVd69xmQcZ2rbImTQY1wcEfDyLjptJxmWJMsREsVqadzhOjrwUAh1YfkvZj8qwU5+DkYF3qqRIiglWGUNSqCTUHq3TRT+hsQAURuYg1n4NVnM0vrjfolUHSsWHcBxvBsjvsppNKd8yXxBNiEiV1NXpQNELAF4DdaZcHvmgZWEXni7Dz853GO1rEnm/2qMQcog3yWzW+ojEAfePiyJojACRDnp5czuw4o6JDRApatdp+eOyHiP6+CgWqECNYctkDm7GBRTy7dFmCiwW7d+/Giy++KH9OSUnB4MGD8eGHH6Jt27ZYt24d2rZti+3bt3OP/+KLLzBlyhTL1z1//jzOnTuHli1bApAk3mk6YdWqVXHjjTdiwYIFqFatmpyb1a5dO822vP3223jrrbdU27Ozs7Fs2TLFNkJ/vHDhguW2CwhcjGANLHrNpAW9PCLagR2XFqf8ToN9QdZD34z+JniNMIwO+ViNU6Q1TgMAJNZKVB2nKOXBiWiteW6N4hh6HiFrIbaOK3sO+n/2+0sRwsCKMPTobbwIFnkxzS7YSC4KMQKA8hW5WD1pNYDgQGIm+rPwpoXc7ZG4j52f7VScy+60m37JyTFkIKalwY3g9/phc9iCEawoeW4WjVyEr27/CucPRMeoKEsQg4r0GT2K4G9TfwMgRbDsDuWwdW73Od4hYYP16NE4+ttRze+sgn5XVk5Yafq4ktwSHP1V2Q5nrNPQwCLOD3vMxTf8+3w+zJ07F1u3blVst9lsSE5ORnp6Om644Qbs2rULf/zxh2Kf4uJizJ49Gx6PdWrRhg0b0K1bN9lYzsnJwbvvvquSfI+JiUFcXBzS06UyFUOHDsWqVatw+PBhxX5ZWVn45JNPYLerf6OcnBw89thj2LUrKOu/c+dOOBwONG7c2HLbBQQuRrA0Pk+Bx1hggsmRlzcHAvB7/Gg6SMqfZMfY5eOWy38TFWIg6MTirf0AoHYnvhqpfA8eH15r+BoyF2dK5+HkOR355YhsAGlR+Pw+vzqCxVAEWUolr81adVy12iYiWAKRhVmKIFMHyypFUBHBKkeRC/JiyzlYCfo5WKe3n5Y9S3cuv1PxHbmPZQ8vUx1nFn/MlBZNRBHQ7rQj4DX3bIcvHg4Acn0gK6IbZUURJDLe7jx3mRbbjQZIfyV9xgw9zhnrVNEjYpJjNPYOD7Thk9ogVfHdR30/CitXkCAQCCiuc+SXI6aP/WnCT6ptjliH6QhWwBdA06ua6u5b2dCmTRsMGDAAY8eOxeLFi3Hs2DHs2LEDM2bMQGZmJm655Ra0b98eI0aMwNixY/HFF1/g6NGj2LRpE+6//34UFhbikUcesXzddevWKfKvBgwYgBYtWmD06NFYsWIFjh8/ji1btuCZZ55BIBDA1VdfDQAYPHgwevfujdGjR2PZsmU4duwY1q5di3vvvRfVq1fHKI7CR8OGDdGnTx88++yzyMzMxB9//IFnn30WI0eOjEpumYBAZQSZXy77v8vk2pbvXfae7jGJNaXIj0rwoXRuImsDLWdglaZVkFQ7+A7yamaRcyfVSjIcq/NP5iPnaI5swLE0vAuHLuCjfh9h6UPKHCnWqRzwqUUtrIhcyNfV+J++NlvQ+FKGMLAiDLMiF6FSBHUjWOUgckFAPLeyyIWJ/KVmVzdTfCY0w12fWy+4yqJ6m+pSu0yIXBDUvkzyJskUQQsRrIAvAJvDJkdXohUaJ89o3uB5eCn+JRRd0I6yVHSQSYo4C8wo6Pk8PlUEK1qDON2Hu/yji+r7UApRs/C5feqJzexYkKvun844p+FzpCfHej0lb2tlyxnUw2uvvYbbbrsN7733Hq699lqMHj0ae/fuxfz581GrVi0AwKRJkzBu3DjMnTsXQ4cOxaOPPooaNWpgwYIFqFmzpuVrrl+/XiGT7nK5MGfOHPTp0wdTp07F4MGD8Y9//APFxcWYN28eEij5xZkzZ+LWW2/FzJkzcd1112HSpEno1KkTPv74YyQmJvIuh2nTpqFp06a45557MG7cOAwYMABPPPGE5XYLCFysIAZWt3HdUK1VNd19G/VvhDpd6iChmvResg5v2RlI5ioNZ7Yr3mVY9J2MtY4Y884w4oRkjZjsQ9kAKGXC0tNxI1hGMu16BhZbXohDESRt4a1zL1UIkYtIQ2edQi98iMdAT5WFB14ESyFyUc4woggSlR4eQhW2oNGwX0MgEHwmdqf5HCzSdpkiaCEHy+/1K3KwovVbkGdE5GLzT+Yjvkp8xM4fCAQUOUHRhCqCpfM72ew2BPwBpDdLx4WDyjyTSBg6PBjVUfGWeMPus6Rocv8p/fHz5J8BSBNVerN0w2N5k5czzpgiWPfyujj882G0ub0NTm+T8sy8Jd6LJicrLi4O48aNw7hx4zT3sdlsGDlypK60ebdu3bBnzx7ud3PnzlV8XrlSTe1MTU3FxIkTMXHiRN32xsTE4KGHHsJDDz2kuc9NN92Em266Sf6clpaGl19+Wfe8AgKXMkgutyPGoSkKQRDwB+BKdMnOO9bYIHMViWBpzTmOWIfSwKDG4sKsQiRUTZDHbR7b4GzmWcy7ah5G/zoaaY3S5PUeScFgaXjEACPtIg5+dj0Z8AVU1D0/mAiWTh0sOZWFiaDR+6x4YgXa3NYmKDRmskSOWXiKPHDGOctsfRIJiAhWhGE6B8sXUNZOsBDBcsQ6FF788hS5YGEkcsF61+k6CYQiGA58JT7FeewO45c8JikG1VpVk2mOJIJllSJoc0jFRR2xjqhFE2NTYxWfIx3BsmJUmsGm9zZh/w/7ud8dWiUpE5GEYT1DKblOMjqO7gi7w15mESzFeTljeiSM6CX3LgEgKQnKl7Kbm0B475iZHKzUhqlIbZCK5tc0D6uotoCAgIARrNb2i9h1ieBVrANndpzR3TfgD+hS/M1GsOwOZc43PaeR/HBybt5Y/Xbrt5F7PBfzr5kPAFg1UVKxJWs82cAJKFNMCBVRkyLIowQyOVh6ESwyP7ABAfocne7rBAAoOCM5f5PrJEcsglVwpgD/SfgP1k9fb7xzBYIwsMoQbIiWfvmsRLBoeiBQviIXLIwoguSeb/3yVgDAqJWj8OS5J6UvqUewfT5fWcsI3mKvogCtmQiWM86JRgMayZ+JNyjnqHn55IAvIC/8nbHOqBm7RCWIoPgCX5ExVBxdGznxBkCqnzF/8Hzud0Sav1ZHibalNxEXZxfLhhjrjYyWgWWUYxVJo4Q2Gs3mdvHyHM1EsPwevzwhk0hvRXDOCAgIXFw4tOoQXox5EcfWHyvza9OKwmSc04Lf54fNHhSpKsktUXzPRrC05io2akOPxWyNLT2KIBFY2r9cck4SZy+Zn9kIEntuMyIXrMFVt3td5T1T90jyytgIGn2O+KoSk4Y4aePS4iIWwco/lQ8A2Pbxtoicr6wgDKxIw6TIhd/n5yqwGMGd75ZfcoLyFLkAlAIANrsUwdESuSD3TBf1Tagqee/JSwQAi0cuDqktpOK43B4TOVisUUYM1g0zNpi+LqEIAohqBIvtXxEXuijDVByfxwdXgkt+3loRLL/PD3e+W47esTTTqBlYBpNDJN83OmpllvLIi3SZEbnwuX3yMy/vsUNAQKDy4aWElzBv8DzD/Q78eAAAcHj14Si3SA3awDLKMSU51H//8TcA4MenflR8TxxQxLnNjpeN+jdCgz4NpPUGh1oHUEaQL2hgaRlq5Bw120v5oGwOmZGBxRO5UOVgMXWwWCOUvo8W17VQtF3WDuDkm8n3F+uIWASLOATLKxoaKoSBFWHoUgR9wZch4FdSBM1a+u48t0o1rTxFLuKrxqP5dc0V21wJLs0IFrlnurAwQZMrm4TdHhVF0EQEy1uiNLAIiBFoBoQiCEhRgWj9FmykIdIGVllGMnxuH+wuu9wXtH4nUp9LVoYsI4ogPYm2urEVAElp8po3rwEAvN74dfz28m+RuRhlK5k1dmq0q6Ha5ox1GhpoPo9PfuYVQSBHQECgcsFb5MWBHw4Y7kcW/uWhJkfGNGes03ChTyiCtS6T2BQ12irHVqMcLHK8iiJI3TcZc2WKYJxTc6wn66QWN0iGTVIdpTqoloG1c0EpDdEggsWKXiCgnHcCgYC+yAVjTAHBZyLnmLkcEYtgyWuEKOVbRwvCwIo0TESwSBiZ7ixmLf2SvBLEJivzcMpT5IKmxhG4ElyaOVgsZ5hGtVbV0Pvp3mG1x1vsVeVg6RkhhKrJ5n9l3JShouPpgX4OjlhH1AwVv1vZT7zFXpnzHAmUpfQ7iaQYeaeIV5EYwSxFMFpeLTJBj1o1CtVaVsPkwGS0urEVkmsny/v89C+1VLpZ0H2EjkaZ7TveIi9sDhvGHQiKOZihCNIRLOJEsEKHjSTcBW5MsU3Bpnc3lcv1Q8G+7/eh8FxheTdDQKDCg8yJP0/+ucxpyHQEyyivlThIG/RuAACo3VlZn8ooByvgD8Bmt6kpgtQaj8xbxFhJrpuMoqwirjNaLt9DDBuOQQeoDSz6fmhnv0qmnSPbrjCw/IyBxYhbkDmXZ0zSEbpIRbDI71fZmBbCwIogAoEA1r2yTvN7+WVw2TU7sBH0IljlkUdBR24IXPGhRbAAa1EjHtho1PENx3F49WGV8hwBkWJnw+OuRJc1kQuKIliWEaxfX/oV02pOw4VD/PszC3kAK8NIht/jlwws4uXU8E4tuU8Sgjix+QQANTUumhGsWh1rofEAZfFWIuQSLkhkDpDu6YbZN8jXNQNPkQeJ1RNlaWHAeg4WyX/L2ptlqe2RAlHDJIWkKzrcBW58OuRTfHrdp+XdFAGBqOPNVm/is6GfWT6u6HyRpEhLrQ3o8a4sYCUHK+CTDCSyLmHHYKMcLDmHi6EIcmuflhodRNiIzfcCqHk+oD4PYGxgsSIWfq9fRQlk5dQVBpZPHcEKBAJye8i+XKVBKscsUhEsnmphZYAwsCKI4mx9wQHS2exOuyoHy2wdGl4EqzxFLojnhoYeRZA2Mnmgz7XozkWW2+Mr8XEH0/MHznP3X/+qpEqz++vdiu2uRJelOli0oRnNCBYxgIgnLftwNgDg5OaTYZ2XPPdIttvIe+VzS1Q1eVLTiETV6y7Vaur+qFRnKDYlFtVaVcPVr0nFWg+tPBSpJiugJcPO5kCGCjpPseX1LVGlSRUA5t9jX7EPzninoo1Wc7BS6qYACCZVlzVkL2slUd4lkfnz+/jjSTgozomsYI2AQLjI2pOFPUuUpQq00iCOrT+G9zq9h9PbT+Plqi9j49sbFQv/smRHANL4bXdKyoD2GP2lLqH4aeUDqwwsXgSrtA4mPe/RcxoRgSBrPZLPxX0uASj2NYpgbZm9RfU92w6VyBoTfdKNYLEGm0cZraLbIlMEIxjBqqx1GoWBFUHwvP+0B4d0QIfLoerApkUueBGsckxU51EEnfFOY4qgRj0s2sAiNYKswFvi5cq9s0YpAe1toaEXheOeh1ERjFYkyOf2oU7XOnjq3FOK7V/c+kVY541GBMvoXCxFUMsw+OuLvwAA1VtLxaMdLgceznwYHUZ1AADsmG+9nxhh3fR1OPDDAb6Bxah4/vLiL9j0nnWKG3k+N35yI+LT4+XImNmEcE+RB654l6KNZmTaaaEcZ5wTMckxsqFe1iDjQWWpbXL2r7MAtB1EoeL4huOYmjYVmYszI3reyoTNszZjRv0Z5d0MAQPQi/0fHv9BnkOXPrgUp7ackg2y/cv2K9YGVhyWkQDtSOp8f2cAUET7aRAHqUxX14hgOeOcUpSKY/CQCJbCkPEFZMNKllovfV6ErUMbWIR9035ke8W+WhEsrTUfaxCxBe15ES62bBC5ps1uU+VwcSmCjNFld0WuDpYwsAS4nZ0eYBQiF1QIli1Op4eSvBKVgUXqN5RHojqXIhiiyAWgFjCwApJPRVMEO93fSf6OhxptpGTWq6ZfpWyHy3yBYnJuWkXw4E8H8c3ob6zdgAnsX74f3iK+ERkOyG8YSS+jUTSMUATNJrCyi3BSYLn5kOa83cPCj09IKlK8ApVsBGv1pNVY+uBSy9cgz4dEXIkT4JcXfjF3fJEXzninwinhiNVWpiIoyS1BbErQ4VCrYy2cyzxnqe2Rwpy+c6Q/Kod9hY/6fQQgMkXRaZA8Q1Ib7lLEdw98h9zjuabLFAiULfb/sB9Z+7IUc/uGGRtk6vbp7VLRcjI3EYODwArlPhKgDax63erBGe9E4blCnNh0QrUvcZDabFIelZaBReYrVQTLF8zBYimCrLgFWeuRdUreyTx5fzKWx1dTRrvYMd3QwGJyrHwlPlXOFRvBotdIJTklwfVpqQojz5jiqgjSMvQigiUQKfAWlAr5ZYoeR3sIzKjcAFJo3p3n5kZjHLHql74swBW5iHdpy7TriFwA4dU4YhesANDujnaK71TtKX3uKtqly5hqxbaRVhEEgK0fbTXZcnMgz/TMzjOw2WyqRNxwQH7DSFIEjYw1T6FHkYMVilhF/Z71o0o94fUBrRyskjw1l14PxCFCFiS00WMGbHkB0ja9fhsIBHD+wHnFtWKTYzXf12ij8KwkFnHhwIVKNYlG2sAiRsXJTeFRfS8GVKZ+cClh/uD5eLPFm6qx4pvR3+DHfwVlzck6hBgcBGUZwfL7/Phj5h+KNRFh1Zzcon7H6FQHnny6wsCKUa+1Tv55EvuW7lOrCPqC+a7EMKVVBAHg2LpgjTAy/7KiEn4vI1oRSgSLlWnXydHa9sk2ea1GAgCK85F8K842miIoIlgCEYMViiACVDTHZATLV+KD3+vn5oA4YqKX96MHqzlYRhGs1Pqpis9WjEZ2wQoY56eR584adiTKaMabKkcmKRXBaICN8GjRHUKBPGCHEAX1Fnu5ibpG58ram4X0ZulBiqBGBCulfgqaDOJL+Mckx+DCwQs4vOawtUabhFFUmobVPCbWIcBGpvVQdL4Ih1Ydkgt7E7jiXboG1sGfDgIBZRROj9JblljzwpryboJpGKmShYrjG45f0lEsoPIlsl9MyD+Vb6goum6aUsjr7K6zWPdycBsZ90lOEoE7342iC2WT67l7sZRTzVuHsGMmwORQcwwo2sCyu+wqOh0BSxH0e/3yGmThTQsBBOfaupdLhX0TqyfK52HrTNE5WCzFj24XAJUBphCg4FEEadl2j1+hgB1XJU6+R2esUxHBog0nI4qgiGAJRAw8T7qCIsgIPMi8XiqCdWz9MU3FO3J+ngedNyhEG6TT8yiCmjlYGqo3BBk3ZygW01aMRl4Ey8jAYo0jAqO8IBq0eAl7/Uji9zd+V3yOS4uL2LnJYPrrS78ia581Rbn3u76P/6X+T7Xd6LcryStBXHqcYR0sh8uBpJpJ3O+SaiYh+3A2Pu7/cVSoRVwDS6PvWqXAsA4BUueLKPvpYcH1CwCoczed8U5dxwAxAtsMbyNv04s4RxPsNY+sOVLmbaiIyD6SXd5NKFdEyustYB3Ta0/Haw1f091nw6sbdL8/sVGi4NkddoWB8/OzP+Pl9JcVlLhoYelD2pRtngOUZuLYXXZNkQteBIvelwiYKc7LkVAHghEsci7FOYmBFaAiWLSRUfonPceyda4UFEG3CYogNWdUa1UtuD6NdyoMMqKCTbeT/juaEazKRh8WBlYEwaM4aUawQL20VATrw54fYmbTmdzzywYWpyiuM1a7aF00MH/IfHzY+0MAauPEGe/UVhE0ogjabGh9a2v5s5V74j0fIwEQvQgW3V49sBTBo2uPmm6zWZTklmD1M6sBAE2vbgoAGPLWEPn7cChLgYCyJptVcZEzO89wtxtFsMikZkQRpCXwWaQ0SAnuF4UihLx+w8vLAiQPrRXI/ZUyyBv1b2QqknVq6ykA6sUoOZfWxEYmKpoiWF4RrNWTVis+mxX3uChB/VyRio6tnboWr1R/BWd2nSmzyEEkECmv98WKLR9uwc7Pdkb1Gv9LUzrMrCxs9y/fDwD468u/FHUCSe7TkTVHMMU2Bfu+3xeBlvJBqMc0GvZrCIDPQPAWe+GICxZfN8zBKqaoh5SRY3eoc7DY67EUQa6BxdTB2r98P5eOR4/beiIWZkQuFN/7AkHF4niXIrrmcAVzq7i0QUY4LBLRp8o6JggDK4Ko06WOahs3B4ssKC1UGgf4ERoCR4yjTEUu9n+/H8fXHwfAj2CFShEEgmpxgDXKWigUQc0IloUK9Ow5ojEY0OdsfYtkgCZUDVIE6/WoF/K52cW43m/Dgv6dC7OUkxo98ez+RimDD5Ty0512Q4qg3+uHzclfdNKGQjQcDLx+rGXsWTawSp+Por+azKWUJzhm8jJjrALK/u5KiH4E6/Caw/jzgz8V2/548w/Vfvmn8lXHkVIKFQmRpqzQC9hQDayzf53Fq/VeRf5p6Rmu/PdKFJ4rxDtt38GHPT+MSDvLAiKCpY8l/7cEX93xVVSvUZJToujjz9ufj9i5CZ1712e7InZOM7jmjWsA8N9dWlnVyMCKT49X1A0k39XrUU9NEfT5VQI+soEVzxhY1FqHNVbYv8k4SY/b7L5sBEtPpt3n8SmcPHRdLGeckiJod9mBQKljlo6KeZUiHjIzJQLrIZktVUnUZgmEgRVB8Bam9EKG5qYC/AiWHvQiWOUlcgGoFwTOeCc8RR6u18soggUADXo1QI/xPQBYjGCFQBHUimAZ0dZ45yCL2/9b/3+ytyxSCzG6HfSzI5NCOBEI9h6tSFAf//24/PfqZ5URCXrCyPxSLT/t90q8d6M6WHoRLLowdTRyEIsvqGsTRcrAkh0sccr+aqbP8zjwdNu0+i2vv+s5RCKFj/t/jG/HfKvY1vKGlqr92ET4j/t/jBXjV5R5DR0jhJMntGfJHuSd0KZJhWpgrX91PfL+zpNlsmnD/dzu8lGJDAWVKQerOKdYMQZeTGCjWJECyY+KdKkDGkTmnIaWA9Tv88NT6AkaWC6HLkWweuvqKDxXqPqu4+iOKoqg3+tXvc+ygRVrPoLF/k3mJTqSphvBKtGPYLG08oA/AJ/bJ83PMUqRCzoyZSRyQZ5BuBA5WAJc8CiCZBEke7BNylke+OGA6pwEZR3BoqFSEUxwSTKhnIiEmQgWADQa0AgAUHCmwHQ7uBRBYmBpPBujCJYZZTuWIpjeNF3OI4tYkic1kNEL/Id3P4zEmolhRSDoQRqwRjd05wUXxEVZShoSvSgmtUBoyBRBg3w3n8dnysCKRv/niXdoiVyEGsFiHQJm7oP0NS3jWOtZ0qUiCFyJLvg9fk2qZ7RwdtdZJNdNRtOrmsrbtBxNbHS0vBEqHTXgD+CzoZ8F5ek5kBeCXj+O/maebkz6BFmMkOT5yobKRAf67IbPMLv77DJzbh748UCZXAdQju2RRMFpaU4nxXF/f+N3TK89PaLX4NGs2feDgKj40UaUXgQrrkqcgnIrM2diHFKNLLpAry+girrQjna70y7Pk7SDkI1gxabGKtpN8n0V8z6dosXKtBvlYPn8gF95vM/tg8PlgM1uU4pcULLzRjLt9P0aIWtflua7LwwsAS4UFEGmg9IiFwG/sWLd8n8uB8A3OspD5IK+Ng2i0sNb9BuJXBCkN0sHAFw4xBf84CGiKoIWRC54RpoViqEZaJ0npW4KGg9sHFIEqzCrEH6fH8XnlFEaK2F42qjY9fkuxUBIG1jcaGZpZMoo381sBKus+n+kIlhfj/oagLq/mrkPtrYK2zZNA4tDESQOiXfav2Oy5ZHB2b/OIu/vPMRVCYq1kHpQLKKRXxcOzL7X+afz8b+0/8m1gshve+EAM65xcrB+efEXzOk9RyHjrAfy3pI+EUmV0bJEZaIIHt8gRa/o/rD2f2vxWqPXohJRn3fVvIif8/T209jz7Z6In9cslo9bjvxT+REVMGCpxkDwvWLHzJX/XgkA+HuDNPYYGVjOOCeXzmd32mF32E1TBO0OO+LS4lCcXay4BqCOYPm9StEKwjiIVA4WG8EiZYTsTnvQwPIoxTlYI45VEZQjWCYcJll7s/Bmizex5nm+kqwQuRDggqsiyORgkRAs/dLyOhIxAngy7WUtckHDlaiUPSUvIG/Rb4YiCAAxidI9HvjhAFZPXo2S3BL89eVfusdEVEXQgsgFz0iLtIFFC0+wanXuPDfO7z8vF3o0g6ILRXil2itYOWEl3NmSYUCU5eLT1dEmHrwlXqwYv0KxLffv3GA7KQM792iuiv5FpHHNUAS1Ip7RpgjyoCVy8fdGvnFgBLq/mn6PS5vALkbNUgRpI1H+uwznLlpsg373Ft+1GLu/Vufrnc08W6Fobmbf64M/HURJTgnWT18Pv9fPjYgC/Bws8j7zFos8sAvIaJWLiDYqUwSLgO4PKyesRM6RHM3fuqLh3Q7v4rMbPovqNXhrFhaRZCAQGiINLQOLZb6wMuwAZWC5SqNUjFKg1nc+t0+TImiz2xBXJS5I9+MYbWRMZkUoyNigcGKGkYPl9yll2sn3NodNvidZtp0YWByhDPlcsBbByjkmlQY4+is/Yi8iWAJcKBZjpX2El4Pl9/kVkSne4EyK5na8u6Pqu/KqgwUEjSECOfrDmSiJoWAUJSGLg+1zt+OX53/BwpsX4otbv1Akl7KIdB0swGQEixOVi7SBRasxsQVtiWoTTzRAC4TO99eXf8HvltrY7JpmAMwPZpve2aRa/NEDPvnb5rBh99e78W6Hd+XvAv4AEIBC5EJrQaIXwaL7nlWZdC0Yecm0+u6WD7aEtKii+6vNaUPO0RxDqfxQc7BYOiugTXm0guLsYuz7fh/WvLBG8fx+f+N3vBDzgmLfv778SzaW+j7bV3Wuz4d9jim2KQo1zk+HfIq3Mt4Ku52Rgtn3ml7Uzek7B9NqTpO/U+Q8UIuQDa9tkI+RTmKuTeQ3/X7s9/hf6v+iVqsr2ohGBMvv9WOKbQreaP5GRM9Ly2iz2DBDX8783O5zlc4jHyrMRPeLc9T5rqGC9P20RmmqbWz/YsvfaEWwSDTH7lQrBQKQ2Rh0LStPgUdVQJ6M2Ta7TXEtowgW3e6coznwFHpkJ6YrwaWSaVcYVIGAOoLFfmbGo4BfovCTCBYtekGuIStjxzpUxYetRLBoo1Pv+4LTBZXK2BIGVpTBW7ywOVjOWKl2De0B5w3YnkIParStwe2EjlhH1BPVtcBGsPQWeef3nTd1TlbI4+BPBwEAp7ad0jyGplwSGBlYZHBVSc2XnsOM0VoWFEEa7GR184KbAQA129c0fQ4ymNrsQY+bPHCabDNr6AHAwpsXyucmEUySC0LTouhnRgwWulglQdGFIvg9fk1vPN1PiHR5uAjnNwvlHaT7a/ahbADAF7d+oXsMMUqrNK4CAEhvLlFqDemWnL6qFZGzgo8HfoxPh3yKn5/9GXu/24ufJvwET6EHPzz2g6Itfp8fX9z6Bd5u8zaA0tpfGpff9sm2sNsVLZTklpia6BeNWAQAKDxXKKuuEtCLfdpjfuy3Yziw4oDskDNL2aXnhZLckkpFtaNxbL05SqQVEMfH+f3nVWI84YD07SO/qGu4rf3vWs3j9i/fj7cy3sKO+dZKYkQS5/acUxaqLeeFqxY9LBSkN0tH44GN8eD2B+VtZMxj71M2UkpTGxwxHJGLEp+8llDRAGkDi5JpJwZjbLLSwKLrh9odQVEMvRysgC8gUwnp65I5NrlOsm6hYTZixaUIciiENrtNMrB8HIogdU5nnFOVg0XmKFPjkMFYR7dt7f+036uKBmFgRRlcQQpOHSwgmPwJ8Bd5Po9Pk1qXXDcZZ3aeKZdcBVUEKwLGhVax3i9v+1LzGJonLbfFYYfNYdM0sEgtHvZ3IkmyZhJ9o00RzD6crfjc8nql+lr9XvUV1zQDkjtgswVlZa0aWLz9zuw4g7++kKicZOLi5YLwqGo8EKOsZju+8Ug/c1JEN1yEQ7VddOciU/ttfGej/DdPFdSIrpdSNwU2uw03fnwjAODBrQ/iqaynDOmWXIpgBCJYp7YEjdvPbvgMv/3vN3z/z+8VFE5AHaWMTYlFh1EduOesiAYWLchhxZg+tPKQahvtcGD7XN6JPHnBpKc4SIN1vFUWihoAFJwNzn3EKDULn9tnKEZELz6NIkuh4JcXfrG0PxGUWXzXYpw/YM7pqAWfxyezGABg68dbMbPZTN1xvOh8Ed5q9RZejH1R3malP9/21W2W2tjxno6qbcl1kxWfT/xxwtI59ZBzNAfJdZIVxo0mRbDUSCFrK4eLH8Ei6wpWLIM2sGiKIGHUNOjTgHt9m92mkHUn72tsaixXRZDNvff7/LKDmBeRUkSOAkpxDSOKIClUTIzAgD+gpghSyoLOOKfcZr9PUk60UrbGbAQLAFZNXGV4vooCYWBFGXoRLDbiQuqXAHwPtN+jTZWq0aYG/B4/vIVlTxNkF4haxoUVbr1ejpaW8AV5nuyxZuiT7O9Ewvq8KA0LXYpgBAzezbM2y3/ftug21O5UW/G9UZSOByKwcH7/eax9SPIIhWNg0apNnkIPinOKsXvRbsAGJNVK0jyWNWxZygzZj12oE9AeL9bDFyoIRSsUHFqlXkjzsOyhZQDUtfPI/RjlKLrz3Wg/sj3i0iSBCFeCC/Hp8UiuIy1azv51lnsc77nTf1uh6ZzfeV63RlXml5mqxczcK+cqPsemxKLpVU0xauUo1fHlpYqqBzavIVJgz/XN6G9wYqO04Fz28DJT52AXJ1YUWMsbmV+pyziYxVut38L7Xd7X3UclPBAlmD03Pc5tfGujzp7aeLejRLn+efLPmH/NfLm+1JJ7l+DCgQtyXgsPPAEqK6UQCJ2OReMrGnO3d7hH7URhxbFqd6mt2icU5B7PhbfYi+3ztiu2axlY8rMIBNulZ2CxhoMWRZD8xnFV4tB4YGO5ViVZExAjhJyH0PaTaiUFJc+p9QObwxzwBeS2+31+bgRK8dkfdK7RESpyLqsUQTpKpohgkciXI3huIxgaWJU0Gi8MrCiDS+djpMNJBIuOlmhFsLSS/SNRDylUsPQtTQPLgrGhR4vJP8lP+uZFsMhno8Uau9AnBtaa54xpC9GmCCqMDs44Y4XOqAcrBlbAH8Da/wRD9RNyJ8h/J9ZIxFe3f4UjvxyRCjNyJNp5cuHkvIr9DFQniUEBQFGbJBysfiZyFCIjnNjEeG1Lu6FRZK8wqxAJ1dWRwSpNJMqgVjSP11fp6OBXt5svYPrTbT+pRE5oFGcXq/LiTv55UvE5NlV6z8zSFD/s9aHp9kUD9ERv9L6ZiSB5S7zYt2yfrGBGw6y4BQHbZyIV0S0L0G1vcmUTS8deOHDBUOBHsfiM4mKNdfLQct7KBvE3r5y4Ei/Fv2TqWqe3nYa3xCtTEYuzi3Fs3TH5XvXynnjzoZVSH9yoO7QdYY36NVJtK84uVsxtJdmRibjmneRHfMlajDWCa7StAQBocUMLAKXrBVbkgmIPsXM7SxEk+Ux0cVybwyZ/JtFOeXtpfyQGVHx6vLxWUtTbKv3NiBiV3xekCKoofj5lYWC5KLANchv9Pr8iTyrgDyiMUNpQUuRbUXWwZIpgrFNBaySRL97zBoB109fh1//8Gmwv6Qca00B501dDhTCwogzewoEncgEoBzge5cHv9Wt6tkkeVHlEsFiDRsu4iJTHV8t7JtfYYtoTmxJrSPVjDTqS22JGIjnaFEFF/QsOjSOUCBaPticXPjRR+0tLThuQBtQzuyQKjDPOKUdZFPswcuHE86kyyg0MrJR6KXjs2GOo3bm2Wvo6BLARtP7P9zd13JUvXxn2tQFKbUtnPvEUeeAp8PB/w3htBU+ATxGse3ld+e8jv6pzSXiIBP3MEetA1RZVVe3Rw7F1x8q16DC9WPAWe6WFCUeowF3gxv9SjQu1TqsxDZ9e+ykO/3w47Lbx3rPKAvr3r9KsiuZ+f//xd0iLLRUdKsI4+edJTLFNwfePfK/Yvvfbvdz9tcQt1v5nLbzFXtP3+PNzP8t/22w2xfX1DCzeO8TLI+PBGefUfF+tCKsUXyhG3t9BYyhSDAQtaNXBSm+ajqTaScgYlgGgVEWQmUsD3oDs3CZ06L9/l+ZAliIIlI6zxGag8phY0BEsYlS54l3yHEwzCliVZL9XnyKoGOsDUEWkAr6AUukvoCxPE/BJhhK9P6AUr1BQBCkVQZKaIZ+bwY9P/Kik+pXusv/7/VxDXxhYAlyYFbkAlIsi3sLc79GWqy7XCFYZG1haHkitCFZ8lXhNb2581Xgk1VZT2GhZWaOXm2cEkN8pIiIX1OV5k5BssFugVBFeOI3Y1Fi4ElzIOaJNLSHQS0T3e4OeMUeMg7vwY43Splc3lY9lzwXoL8BT6qUgsXpiRFSo6PfnuveuQ79J/bj7sX2m6aCmaHNbG6TUTwnr+n2fkVT1al1WS3MfQiXhGVh6NegAiiKosRDyFHhMTWY/TQiqWq58eqXh/jw8svcRJFSV7sFKHphmVKAMoKgtU+LD9NrT8bz9eVw4qDTu518zX/McrBhFJJB7PNdUtB0A9n63V67LVVFAO6e0mA6H1xzGB90+CKosBgL44fEfTJ0/GhEs3ti+Z4myntTXd3+NP976Q2VQndkRLOrNc2j98ZakCLtlzhbd8g+0gfLZ0M8UEWLawHqt4WtY/+p6nNl1RtNJsXjkYpXzkufQfXj3w6jVsRbq96qPO767Q/Ed/Zwb9JbmmB7je2i2n06LiNS7QJyQV/z3CsV2PZl2WumPRxG8cOiCPKaSmmF7v5OMZzaCBTCUPRtkI4UFHcEi/cCV4ArmM3mVDh0g6JBnKYI8mXVAWouoRCsYA4s4imgnq8IgYyJi8jVoiiBVByvUHCyAT5kVBpYAF7yFjCqCVdrJjQwsPZELsrDyFZV93oJVA+uaN64J63pahpqmgZWubWDFJMYoEte55zWI6OhRBCNRu4ceXHi/v81mgyPWmkw/r06IM9aJtEZpppLqVzyupoYRxSZ64LbZbCqZWkBNEdTsM1QRRz3Q3PdwYNZIYw2CQCCApDpJyD2Wq1pk6aHVsFaKz7U71YYrwaVbN4bQRkKJYJHEZRapDVLlv81IKhMjD9BXS9MDTTOyEpU6vz88UYBQsGPBDpzbc06xWNj/w34UnpV+C5ruAijrubQcqhSlaT+yve612t7R1nL7Fly/wLQDa8H1CwxzlqKNQ6sP4ZeXfpGNJfr91roPMi6R3LSCMwWmBSv0ynuEin3L9qm2EcVUGt+P/V6VE0mrB+5dsle1gCSG05J7l+CDyyWjct30dap8Vr2FJ6HnBgIB5BzNwYrxK/BO23fwYa8PNecKdtzo/mh3xefbvroNaQ3TEJsci3vX3osW17ZQfE87nu766S6MXjsaV027SrONCmnxCCnukvGr8UBlPpiWgcWmXvBUBI/+elQWIWNZAHReK72NVuoltDxAOW5zI1gJLpXkOaCsmwpINSfJNpYiSEeX7E6JIujOd8PuoiJY/oBC6S/gD8jGm8/tk8Uqii8U49TWU6oIFm10sREsUj+LvQcWxNFDj6u8EiXCwBLggksRZAsNcyiCvHwSvQiWJUnMCMOqgWWm6CAPXf7RRXEeFnQxQBrJdZM1E369xV5NPjl7Xi3oUQQX3rxQ91gzoJPVO/1fJ+4+rgRX2DL9NocNdpc9ZGEO+XenuN2w8X9vliKombdnIoJF2h6JCbokh/Ki6rBdVOIcvgASa0iLq8+GGhftrJZRDdVaVcPNn96s+s7n8enmz2QfyQagzD8jsDukumJ6ESzes/zn4X/Kf5vpR5GIlJP8KyCohGkGH/X9KOxrW8WiEYvwVqu3FInhPzwajJ44Yh2avxn7O7W8sSV3P4KMmzIUnxsNaGTYPqMSBSn1wousRhqfDPwEq59ZjR8ek56hGQOLLa5qhY7GiqsQ+H1+bPlwS0hjB+8YLfo6rRDMIudojkJVFFBHsH947Af8+MSPqmejGK+YNhBDgzee60VYadDiRcO/Hq7qmzRG/zoaV/wnGDVyxjrRoFeQKfHPw//EU1lPAQCqt6kOQFm7MFIGFjknO+/IERXOukShPMyhCNKgZdbp87EUQUUOFlUOpe7ldWVxIzqCtX2uJMrhjFcq8hHIEazStv7++u+KNrEy7AonZgDIPZaLKo2ryAaW3xdcT5K6lHJEyxOkCBIGCi+CRVME6Rwsu8MeDBzoOH7f7/I+ptim4Mcnfgy2nbOGFQaWABc86gsr0y7TeqjBZt7V81TH+Tw+zYWmTEmLokISAUt3MGtgkRfNSCGNgFUWa3+X5Pk1MrDY86c2SEXu8VzuMZ4ij+akKJ/XgHqnpyIIaPPtzYIMvLSHjEVMUgw8+eYMLK2InN1p59IjzIKewGgjl62TBqi9gIYGlkGfYYs/hopQI1hpjdJQ+zLzKljeIi/qdK3DNe79Hj+2ztmqeSyhcKY1TuN+H5MUoxmFCvgC3DGJzkE0MrCWPrzUUpSOh5s/u1lZr87lUHi/q2VUC+v80UL24Wwu5XXT25vwctWXuQqndAR39K+jkd4sXfcabGTSqjF7+bjLVdvYMboigaUb7/p8F3KP52L/D/vx2dDP5PHzt//9BiDoiGQXYp8P+9zytbd8uAVL/m8JNrxuXTmUN65r5cEtfWipQomNxZkdZxQ0Uy2xGXbs5tFlSRQta18W3AVurrOFZ5gR1OlSBzd/djP6Te6Hyx+W+pLNbkOroa00jwEkJwmh/PKQ1jAN8enxeNb/LK5+9WoAyrEm0hEsrfqcuz7fpdju9yhz243mQGK4sWsuUohY3kbnYFEiF7SYBB3BIlFOR6xD3sYT1SEOeUWxY59fLo9CrsHKspOIlSmKoDtIEazetjocMQ5uDpaeiiB5TqzQEQ9037e77CjJLcGcvnOw9aOt8v1URggDK8owE8EiEzCdX6Mp066x0CTnDHii3xHZzm7WwCIUj6Sa6pwnHmj1uQa9GxiKOfg9foV6DYEz3qkIZ9OIRARLjyIImBtgzEBLoQmQqI65x3NNGUfFF6R+xi7Q7U67VAPEhMgFD7T3ju4TPNoMKxcebgSLnqjCAb3w0FOyJOIMt355K544/QQSqiUoIjJGRrWn0KP7e+qBVXNiEZcWp6nI5fdpl3ogYOWAWWx6e5NhG2NTYtHyBu1IDW8hSu8/4IUBhtcoDxSeK1T8zix4jhxaZZX2CPNw+SOXy5FQAqPfg8XgGYNV28w6tcoDH/b8EJveUfapw2sO46s7vsKeJXuwZsoarjw+O1bs/lpNezYCiQqwOXShgi0mTRDwB/Dl7V/KdadYp97m9zZjZtOZim08RwfrOOHl5CZUT4Azzolfnv8Fr9Z9lVuDTQ/NhjRD2+Ft0f+5/pbYJqYLYpdS2oHoGFjknOz4St4BQjEl4FEE9ebAm+bfBACo2V5SX6XnKEL9PLDigDoHy6c2sOgIltxOqpCxot8zFEFaRTbgC+DsriANlc6PcrgcsqohLXJBM01UFEFPkCJI70/OJ1+DbIt1KKJudqddro9qdfyyO+34480/cPTXo/hm9DfytSojKu6oe5HATA4WmbCNkrfZyADvnGURwWKNP3bBphWKP/bbMcAG1GhXw9R1UuoGaS0p9VMMDSw21E8ge2WYSJTf54ff49c0sEjEzCi3iVdbiH4mZmppGZ0b0KdWxiTF4OBPB/Fi7IuG+SyFWRL9lDV0Cb2MPF+f28flQ7PRC0IjoSmCpD/abDY0G9wMABQCEGZzsJbcu0TxvRbKOoI1bO4w9JnYB61ubCUviOl306gtZiKnWuAZ9DSyD2WrasDQ7dKSRB/womTUGEWw6HwtLdiddl2Zcd7vec0b18i1YipS3RPWWNYzkE5uPqkb4bc77Eitn4qHdj3EPf6amdcgsabSwDL6Pdjr2ew2Va5ORYpg8UQbDq8+rPicWCNRdsysmbIGLzhfkL8j47iWHLcVkEK0ZgrKs7AS6a97eV1FhCG9qX4UEwCm1Zym3si8FjznnSveJTvPSnJKLNPUFY7C0nHcisPjqlevwnXvXae7D5mT6cV3pAwsLQeU1hyiimBxCg3TIPMYr9BwlwelNIbE6omKHCxa5EIrgkVAFyumKcmsKBoZh1pc3wIBfwAN+zWUj1fJtJdGsFiRC8W6kaII+tw+pYpgQK0iqCo0TMnW2512eX6jx6+9S/diim2K5rMFpGgurTBYklciDCwBPsyoCJJBnkQWtKAnciFHsLzR74jkRWo/sj16PtVT7SnSWCznHM1BUs0k0xGshGoJuPHjGwGUiiWUPidiILDwufl1wrQMM2KIEIomi+qtJZ74n+//qdtO8rvFVwlG3OjBPJTJm4BWVtJTY6LpVT+M11fWogsa0rA5bIoE36UPLcWbLd5UPe+co8p8Nrk4LmVY095MR4wDrW9trdhmNgeLeGyNVOZ4nsBQQEew2PpuNBKqJWDgiwMV7aKdKUZ5bN4ir2a/a3t7WzlCxoPZqB4PWhRBAKjbta7i/Jrn0IjOdR3bVf7bGe/UlfLnCXQ4XEHZdr38NyCyRX714C3xYvWzyrpoWr8bIOXKPG9/XrGt85jO8t/EuK3euroivwUA+j0nKVbS4whgHAHf9vE21baO93YEANTqWAvVMqqZji6UBT64/APDfWx2m2be7LF1x7DhtQ3c8+z6Yhd2fr7TdFuIh1yvLIIWrDATaFELv89vKnptRmwm+3C2apvP41P1IStQFCG32TA5MBl9nu5j+vgej/VA5/s76+4TzQiW1vio9Q7wIlisaAQN2pHIXi+9eanhbKNytWxKkQsSGQKU8xaZw2lnod/rD0aVmAiWnGuWGAO/zy87YBv0aqAp085GpGiVwECgVP3PKeVhyxEvmzKCpaUiKOdgeQMKRUX6OS64bgH3mdI4skZZLuCTgZ9ERaSmLCAMrCiD5y2mc7DsTrvMFS7OLpaTvYk3goYeRTCisuAGIC9S7c61MWjqINXApZmDVeS17LWXF602yXPujHPi/D6+iphWBIsMUGwkihhYWhEsEoIndS+0UHC2QLE/oBxUwolg0dGonuN7au6X1ihN/jvnsL7MOknGZz3lzlingiJ48MeDANQTPfsbkggOTREkC2BSH6pGuxrIOZojT6hmc7AIjIpgRjqC1e2f3dDujnaWjqUprXoUE79X4q5r9Tvag8kDT1SFRp9n+kgTPMcQ0qMIGqk++b1+ZO3NQu4xfj7jkDeGyO+aM86Jq6Zrq4fV6sCXob9q+lXo+WRPtLqxFVre0BJJtZMwYukIXDfrOtzzyz3yfrl/89sQKXiLvXi17qv4bOhn+PVFpUKgVbodTfmjjVt2rKLzXWgYRbB4huyJPyQa1Kmtp1CjTQ2c3n4af87+U85pqKhod6f0zhVnF+vmnhFxDBZf3vYlvrr9K7kOHw/cCGwI9qce9elfF/6l+Z23yBvS9QiMIsi+Eh/iqoReE02LJaOFNsPbWJ7XSRSmLA0sGvT8zK4bZIcsM4aTgsQsQ0chckErFTJ1sMiYTv/+dASrSpMqaHxFYxVFkMwTrEy7TIVMdEkRKypfmRW5IBRB0hb4EYxQlc43AX9Aom+W5qDRFEEEgvMCnXsmqwjGO2UDkESwzKgImsGJTSewetJq4x0rIISBFWEQdRgCLkWQysGyOYKRGUAaeBpf0Vhbpl1j0CiPCJZRNE1lYJnId2Ihe4FKX3Q9yXVNAytG6QGi2wNoKz9ddu9lAIBqrfUT7gvPFsLmsCm8hrQxF04Ei9zrDR/eoLuflclJpghSEawJ+RPgSnApEnxpDxwN2oN/7bvX4q6f7gKg/N3zT+ej7R1t0eGuDgCC9EYyaZGBWZWDpRH5MVJBszsjmINlA65+9WrLEaK0hmnoMEq6X73fw8z7o3u8AUXQleACAnxxFj2KoJGRO3fQXLzZ8k3NdgHAHd9KdXFik2NVMslmkFA1AYNeHgSHy4Hbv7kd40+MR/MhzdF5TGdFVI/Io0cDB348gM2zNiPvRB4O/HBA9b2VfjH0o6EAIHu26WdPS90D2hFTd4FbN6ePF0mhaXikvd/e920wYlNBQUpmfHHrF2Gd552272h+l3M0B1NsUxRFta1G+AKBgEJFkkVcWhwGvjSQ+91/k/+LoqwiecFuFbQzjYdTW0+FFcEyU6aDxi2f3YKJhRMtHSMbCaV9N1IOMsCcgUXTl1nHNZvCQebB1re2BqB2RHENLDoCZoNC5OL4huNyGQc6gkUbJjRFUK6TSkQumAiWK8GlzIeKcSgEKBwxDi5FkESwZIMuILXV7rJLdbAoiiB9nzJFkIqSxSbHwlPkQSAQkO/DSh2sixXCwIowxmwco/jMVRGk5CsdLgfi0+MV32lxgM3ItJdFBIssgo2MPbYtniKPLr2GB1YZMC4tTlMBye/x6+dgsRTBIv0Ils0m5TIYUTUKzhYgoWqCwpimI09bPtyie7wePrtRkvw2W+zYDHgRLJKQSmTaN769UU7YZ6NHhN6SUC0BXR7ogrSGadKx1IBakluiEANgaZrsJKjlNazaoiraDG9juGCIlEx7cXYxYlNiLUlA06jbvZRmp0MRNDKQjOiOfq9fTpzmgfw+vMiHHkVQb0I8+edJHP75sGabyKKD0H/pYtMAVHS4UECr8WlFNPXk7c1i3lXzsPyfyzW/t+Lh73h3RwDB94s3rnd+oDMa9mso78Mi4Avo9ideJKVay6BTSG+heX7/eayfsT5spVOzMLqO3GfKoDm03L+3xIsDP6qNaR68JV4VDRRQGz68Yu4EBWcKNFVAjeiDZsa52DRtIRYuqKHEDDUxXLA5WI7Y0MWVWBgVUweUjg4eRRDQnqvYcZJXaDjgD6hzsDhCW4o6WJRhQkewZIpgMUMRLGQMrNIxwhnrVIpcxDiAABQRKdngclCfSymCJE2AFsUgzwlQUwRtdpvEwip16hEnXqQiWJUZwsCKMrgqgsRDUiLlVKkMLI5MqM/jQ0luiXHUqAy8BeRF0zT2IkgRJDlIZHEVmxrLVU4CjCNYVimCQNDg0EPRuSJVTgntEd0+dzv2L9+vew4tXDggqVsZGlgWfndSKZ0kyda/JliDyJXgQt6JPCx7eJm87Zt7lF5v0gdv/fJWxXZ6QHXnuRWRWXbSkimCDjv3ewJ3gdtUvkKkCg2X5JQgLjV8eo3eYsGI4mfkzdUzkgB9A0urDha5LtmHxccDPta8HqDOpYMt+Hd683Q0H9Jc3vfq167WPZcWXPEujPxhJAD+vR348QBervoyDqwILpQ/6P6BLN998s+T3JwVqwhFkY/QwHmL117/6oV7fr5Hd0GoRxOkI5UPZz4MQKpZRKA3Niz/53KseHyFqhButOAtMJCcN/BrGEVvLIG61q7Pd2HeVfPkWlt6WHiTUjSCvMdjNimdqw37qGn+BD6PT/MdHrlipO712Tl08Otq1Uh67DWDTmM6yX8ToZloghgN615eB0AyCiIZwbI77bpRSXpO54lc0PtoKd7uW7pP8T1tjBDKHVBaB6s0gsWyCngRLJqNwYtgscYp6Q+yM9pphzvPrVQR1BC5IBLyfp8f+5fvx4WDF2QHv5wrZlM+M7bQsM1hU8w5bASrsgpURALCwIoy9CiC3hKvJGeZHCPvRwwsdlH/21SpBsjeb/dyryNLZ5YBRZAkIbJiBwSRjGCR/CViYMWlxWkqvfncfBEQujo5DSORC8AcdcFb7FUZAc2vbY6BLw1EakOJL79hhvU6KzQMhQeo393I2Mo+lA2A6ptUl0msmagQ1gAkSgOvLewkTvqgp9ADb7FXEbXQ8gqSSUvLwPIUerh1tFjYHZHLwdKT4TZsR2n/e63Ba5rtMYpgGUnO7/xsp+69GkWwtAw7PY8jT5SCd2y1VtXQ/fHuuO2r24ILWIq/f8vnt6D7P7vrnksPhNbKy885tk6qp0RTv/7+/W9ZvntW51l4vfHrALSFcsyAdixNLDJHjepwt0Qd5RkIPAfCPw/9U/FZL9+H9JU6XeqgWispcpVcWypu3Pb2tti5QFv0geSPajmtwsHCWxaqCui6c4P3ce+6e1XHGFH1bvvqtsg0DnzHmpnnsG/ZPsXnm+bfhKotqiIuLQ5t72ircGC0uK4F9xzeIq+mo4MuTP2vbHUuF+vY5OVkWZFXB4Br374W/875Nx7Z/4hMjY8mWEaCM86J4gvFyFycGfa59ZxIV70q5YXSDjDNHCy3ks5Ozkne18yvMuXrke/lMVQjB4t18vIiWDaHlPPk9/oV8x9P5IIwnoDgeuavLyW1yvP7pVx1u4vKwXLoUwSLsoqCESwtiiAl006cffSaj+T5yoWYBUVQIFrQLTRc4pM9LWSwd8Q4uJXECS+68Bx/YWCUPxFJnN4hefnoicBMW0LJwSKcfFIjJy41znIES0umndCM9NrkcKmNXdV1OeqOdocdfZ7uIz+jcL04Rr8r/T0xoIxAFjM0bYetwcODVgTGGe9ETHKMrPijF8FiJy1NA6vAXL2oSOZgRSKCBQCHVvPrz9B5hTwYGfVGv69uBEtH5EJvDGl1E7/IKHF8yKpYdhuunn41qjavyleNDEH5kAa5NzbiUpxdjJObTwIA1r2yDp5Cj6YRtXfpXrxS7RUcXnM4pDbQ77qWscqi85jOmFg0UabT0uBRA9MapeFf2f/C9R9cD0A/giUbxExTnvU/i5sX3Mw/iPhWSvtiNFQZM7/KxLKHlim20WMpT+5ezzCo2b6mSphHD0Z0RK6IRgjM4LbD22LsnrGwO+y4+dObMckzSf5u+NfDMXrtaO5xWn2HGMcAuGMRO9e0uK6FrBpJIKvZcdCwbzCy9uS5J/HU+adgd9gRmxKL9KbpZaI4aXfa0Wxws+C6p9QJykYHQ4GegUXmN7ofsvO3EUUwPj0eMckxclSe7OeIcWjnYJUaNWwJFW4Eq3TNmH86H95iL6o2l3JPeSIXjhiH/AxZJwxRNyYUQW4EixK5SKqVhHYj2snrT4UoBijmElVomDi15fumcrDoZ2EWZkqAVCYIAyvKMIpgkQUZCfM6Yhw4suYIzu8/r8gz4EUbaMhVs/MjU9RWD8RLSuobsYgkRbB+j/qYHJiMOp0l8ZDY1FjNHCyfJwoUQRMRLN3cuAgZvkaDFO2R05I0DQQCeCnhJfkzMVQVv4nGZfxeP6bVmoYdC3ZoRmBsNhuSayfLnjNFBMulnLTMUAT9XmkA18pNUVw7UjlYEYpgAdo0HSOKYLiS8+R5aVIEQ8jBYhejD2x9ANf/fD1GrRoltZmzKKvSpAq6PNQFt39zOzo/2BlxVeJM18DTAumrqyetVtD9fnjsBzm67yvx4as7vsIr1V7hnoMUStaTkdcD/a7bnXZN2toV/7tC2XaNcUZrTIxLjZPp43qS4GTsJ2UlCMhvcs+ae1THKNTOINWeiRSIF5uGp9ADT6FHEWnnGVN6/WPwzMHccbblUH5Ba57IS//n+2ueH5Cey1cjvsJrDV/T3c8s7A675jig9R4645zoM7EPej7JV41lj7M77Rj8mpIm2OK6Ftyoc73u9VCzQ035c0LVhLAEMcKBK9EVnINjle+AVaENGlaFfNj5W07h8DAGFvXcq7euLm8n46wz3qmZg0Vk2nkGFjeChWD/JU4lliLoLfHC7rIHDaxS+rFMZSy9D02KYGmOlRzBsgHOBKfcVlnGvXQckXPvqRwsd4GUCiA7a/3hiVxosaIqK4SBFWXoFhouCaoCEpqaPcYuDy5acsg8OGIksYySrNAlwc3CyBsdSYogi7g06xEs4vEhyj0ERiqCQFBRRw969cnClc8ntTFqd6qtu5+Z82+fu12xUG5xfQvU7VYXbR5uEzyPxmBYcLYABacL8P3Y77HmuTUA+L+/I8YhCw3oRrC0RC4oA4tO4jWCK94lJdiGGcUKN4JFLxS0+oQhRTBMRa1QKYJ6zgBWVKJWh1qIrxEf/I05p7TZbbj2rWtRvXV1NLu6GZ7Kekr2xoYKui8UnS/C6smrsW76Ouz6YpdiP7YYNg1C8aK9rj88/gNObT2FE5tPGLZBEcGy2VR0PgC4ZeEt6PVUL8NzAdr9AND/LQkIDfnad67lft+wb0Pc+gWTL8kYWMv/uRxvNH/DVHv1EAgE8ILrBUVhYEBSzvtP4n8U72dCVaUBcPfPdyO1firajeCXR2jUrxH3nWp6dVPu/rwC8X0n9pUdhDzYbDbsXLBTtdDze/0h5+/RbaZVhnljVY8npFqHA18ciEEvDzJ/DWosfuzYY3DFuxRlDQBg/Knx+L/1/6db368scWhlMMJPRxv3frcXr9Z9NeS8Zb0IFi9H1iiCxZYUIcg/LSkRegqlovE2G0WnY3OwSkUuVAaWXTuCJasGkjIzJIIVE/xsd9rl7ecyzwGQipXT7XfEOFQy7QqDi8i0+wJy5Il8pimCbAQr4AvAk+9BTFKMIgCgJ9MeSv5qZcaldbflAMMcrNIOR1MEr31Xmiit0jYSqiWgJDt6Blb24WxMqzlNrkNldaHmLbYewWIRmxoLn9unGqgAY5ELtpaCkYogALnonh60ChwDwdpIZqlELJpd3QyuBJeC1sGDmQU5iSwRJNdOxn0b7kNiXYp2oxE4IVLzReeLZHoW755sDptMTaA91FoUQb0cLLrOhxFI1InNHzOL+UPmY4ptStgRLPqetfqNociFTg6WGbU3sijn5e3oRbD0crC0ahKR8csMrSgS1CPawHLGOfHL87/gxyd+5EYrzLan8FwhNszYgHlXz1NFcojxQoO3SOjyUBfF5za3tonI/ZJo5Ie9PuTW0zt/4Lyc36nnvGLp3GS8oN+38/vPh60mqPU7kMUmiWDZnXa4Elxofm1z3PbVbZgcmIxG/RoBUNYTZMEb36tnVFfUoNNri81u4xpewR3Um4qzizGri5S/x46hWjlWNMj7lt48Xa4LCIROyyT9r2b7mpiQPwFA0HCo06WOXNKCjRCSiJZRPmVZgXaUZu0Jsi5+f/13AMpSA1agK+RD1JY92hEsI2cgIOV2nt52Gn6vX1E0XjMHy8GnCHJVBKkyPkBwfSLnYFGFhx0uh5wjvXvxbm77tWTayWcSwaINI3Z/+jnQOVjufLfUzyjKsZbIBa10eKlAGFjRBmfAZnOwACVFMKWuNEAaFVdlYXfZoypysW3uNhScKcCf7/8pXc+oJhezUPMWWc/BYkF4+7wolpahw9IPCMxESBwuhymKoJZn5po3JG9Sk0FNdM/Bwp3vRnF2MbZ+tNWw0CgAU7LGdP6CmagQgZZyI2+hbnfa5X0VFEGmGr2ZCBYx5Mzk7ZB+EWrF9/3fS97SkpyS8Aws6p61Ip9mIlhaFMF1r6wzbINe1MNbov0O6imR0gZWo/6N5L/lBW/00zaU1wOwbGwwvyeciB9Z9NhddoUDaJJ3Eu5dey+GvDVEsw0E174VjB7pGQhWQb+nPKW/v383twhVjU+l3Yt1SFilpl44eAFTbFNk9T1d4wXB34moHI74bgQybspQ7MM6JVPqp8h/88Z3UgiVhVZbeM45gu/Hfq/atnjUYpzeJt0fG+Ec+J+Bmufige47XAPLxOPv/EBnAFLfkMtrOO24++e7ZZVNQG1gkfHGTJ5teeLgTwfDOl43gsWZZ1QiFwYqgoprlQpXkOP1crD8Pr+pHCxyHZkSGOdUfKZTHuxOO3r9S4qU17pMKt4uG5FuitJXKjTE1sGijT/ifGNFMLRUBP0+v2xg0RFx9j7IfBKNPM+KDmFgRRm6hYYpg4B4QBwxDnmSpxc1hItfv1d9aCFSUtUsso9kS4u10lOzAgW8dgB8imC4ESxC3+IpCWpFsLQiBWYMLLMUQa0IFvEWWv1dZvecjalVppre/9p3rkXza4NS2Gd2nVE8/xObTigWD1bEBjrd1wlFF9T1hbglCJxBgRa2gDYQnCTM5GD98cYfAIJS9XogBtbs7rMN99WD3+sPiyKoiGBpLPpl6ohODpbf6+dGE37610+GbSD9mVBGaOjlQcqceU67FQsDqtmk35dFYjx7ncOrD4d3LrsNgUAArzV4DYC0kCH32f6u9rA77Eipl4KuD3XFncvv5LaBBjEUbvzoRlPXv/LlKzXpcASK6G0YQzu7uIlLi0MgEFCUCAH0ywvwsGuhRM3cPn87AH3jBQA8ucHCslpg58x/bP8H/nlYomFyHVk2fmT3xMYT3O16C72Tf55Ubcs5EqQLfnX7V/LfQ+cMRc12NVX7ayKgrOVGn1fehdNe2sAEgu83fS5AolDSv6dW7qqVOm7lCbNiTSz0DCzZWCntp3Iukp7Ihd56h4hHkLwnvTpYHJl2ku9Et5ucQ45A0RRBqvyFt1hiQJH+wKoM0pQ+liJIKIyyqmCp5DqhCBIDkUcRZHOwXIkutciFQy1yYTQ2AJLq6cUEYWBFGXo5WEDwZSELREeMQza26A5JKB5D3lR6UxXnNZB3DhWvN3od8wbPkwcMMiDoeeAB5ULNXeCG3+PnKkdZgV4ES6vQsFZYmlCo9EQUzIpcaNYnK/UAWV24WE06T6mXghHfjZAXbO+0fQcvuIJ5EDsW7FDsr1UEkzfBBwIBmfZHgzfh0NvoaA47sZmJYJHcii4PKulXPFiVJdaDlegeC1JoFzBBEdRQESQGHqFlcqFjz5D2//LCL6rv9PIgtSLP5Dj50lS7ZSOxjCJYoYKn4umIcShUWQvOFMg1366ceqVi32ZXU4I+Gvc6bO4w3P/n/YqaX3ro9WQv3DT/Jt196L4YTgFYWpkOkMbPb8d8q8qDsupllstoJEvUbTZqmtYoDWd2BceyX+6X+qTeIp/uXzd+fCPi0uJk9UWecasVwVp480LsmL9Dtd0snfTCIcmxw0bCEmskou3tbdHxno6mzpNSPwV2px0DXxqomP+8xV6MWjlKsV/Xf3RVHf/okUcxIW+C/Llet3roN7kfbvz4Rt3r0nPhuAPj5L/Jb1y3W11T7S8vbJ2zNaTj9AwsMvaR8YxX09MMRVC+ls8P+INrIa0cLFbk4uoZUi1AWuSiKKtIUaCXRxGkJdGJ1D9rkNEOfNigoPzZHXapqDOlEkjWjYpCx3SdLHJPJIJFKIK+gKzya0bkgrxHl/3fZbj+/eu5v0+zIUrhNB71tzJBGFhRBm9CoF9U8jfxRtGym/SihrzkuoIMziBF0O/1Y/P7myNmcNECESSyZiWClX9SSghlJ3qrkHNtOEqCWhEsUjuHxV9fSPUi9J6pWZl2vQWDmXPoofvj5usGsYYe+f0TqytpIUPnDDV9zoAvYJoiSN+nIgeLpQiayMEi+5jJwYpkBCWcJPD49HjU7ylFmUOtg0WinqRGEQ+XP3K55nd6BqJeHqRM6eBFsIq8smFBP2vyu9W+TF+EpbzBMxz8Pr/CwKINWi1asR5cCa6IPwd6QZ53Uq2sRhYtN35yo+55qjSpgqcLnsb1H1wvOz62zN6ioqEXnCnAud3qyKcWtn64FYD0zrwY+yIWXLdA8X324Wy80/Yd1XFmI1g8uXFWktxms2l6xxfftRib39+s2Fa1hVJohT0fwfJxy3Fs3TFFfhAg9Zu4dPOOQle8C5M8k9DmtjYKg9butKPxwMaYWDwRE4sm4rGjjyG9mfp+bTabYiy12W3o/1x/zXmNRf2e9VGlSRX5M3lnjfJ6o40BLwww3IcuGm4WuhEshh3EKuPRf6sMLM54TecqATo5WETkovR9Jc+e5D+Rd+6PmX+oRC5k9kepqEWVxtRv6XIoKIU2h01hcMkRKaqdJFJPi1wEfAGFqAVXRdCrfFbEmKLl6QkVUVUTDEHnar0e9dDpvk6K59j4isZIrJmoYiVU9iLFwsCKMnheatoIIJ2VTHqOGAeS60pGCJ1Qa0TLI98R7/Pvb/yO7+7/Dpvf26y5vxkoOjjT1zUpTqXVv+moDaGYxVWJXgRLz8BqeUNLlZzyiY0Sn15PxSvcCJbZc9AgnlMCK5QOth2Zi6RiiOwCxIo0r9/n5+YD8n5/+jenjToVNcOkTDtgjs7IEyMIFeFSaEjenVbU0kjkgrwjbzTTVnXTm3j0HAZ0QjYLvRwsT5FHlgFvdk3QyxibHIt7frkHty2KXAHYcNFqmLpmF28Bvnzcchxff1y1HYBuHl5Z0SEBJc2Wl4NFcqhaXGsstuBKcKHT/3VCpzHBxQ37XOYOmou3Mt4yHXHPPyU5zja9s0nVxsv+7zLN43QNLOq94OULsWOXzW7j/uYEv78miSYQI+PO7+9UfN/qRv6xe7/biw97fajaToqrhgLa+TF8sZSH5ox1hp2brIXxJ8fjrh/vUmxre3tbXDn1SvR/rn9UrmkWdKRXHlOYV4sVFTEDPQOLvE/EucqLYJFaTGd2SpFXLRVBQG1g0TRrRQ6WQ0kRJE48EsEiioT0ddh9iQHlSghS8hQRrJKgQQUEDSxixKkMLB8/gqUwyFiKoE1ZzkN+1noiF6XPj0TgyW9AR2BH/TQKT5x6QhWlJ8cO/ci8Q7giQRhY0QbnCbN1VICgt9/usCOpZhJiU2IVMu1mFpt0sVXimeXlzphF9pFs/PJSkGbE0sf0Jhk2aqM3SFkBeU48RS1S9I6H+PR41cIxpX6KIR0sXJl2QFrwWqnrMbPJTFUbzII1Dr687UsASkW5CXkTtH8Hzrqd0AVY8J4dvfiknQtsoq4ZiqCe55BF1eZVEZMco0qYDwU8I90KeEpVNIwiWGbqfunl49hsNjQe2Bh1L1dTgDxFHjjiNGq26eVgFXlRs11NjD81Ht0fU0ZUG/ZpGFbeWqTQ88meaHlDS0UuIoGWUMyS/1vC3R7qAjqa4FFGiYFF03GNoCiyykT2SF6QlRIhdDsI2t7RFkVZ2nOP3phGxpD2I9sjvak6otP/uf7oN7kfxu4Zi8sfuRx1utbRpcuxdY0SaySiastgFKvl9S1x3azrNI9n4S32hqwKS0NPLj5SSKqVpBqn7U47ej3VKywqdCRAz8f9JvdDx9EdVWNfKMIIegYWcbIm1EhQnJ8e85NqJiGlXoocudRbe5HcJdngocZ+2ngjeU/s9YjxQ/8WKpGLWIoiSObLUqPL7qIog6WiF8TYkQ0sl102+Gx2G5yxTvhKfDJl0OawBSN5rEw7QxFU5Fb5OUWFA9oiF8R4Imu4DqM6qJ5n30l9FZ/JsVq15Co6ouM2EZChJ3IBBBfEZIIkC4GYpBjFotjMYjPcAqUsvhz+pUKlattH26iLaeeQAJBfagIr0Qg9sAo/NLQiWABgc6qfTVxqnKIuCQ9mZNr1Cg0DUs2gUDxxBOFEsAjoQqVmDLZO93dCwakC7FmyR5ZwZcEzBLRknmmaA8ChCLrUBpZVozy9WXpElIrCNbDIZJC1T6Pgs0EEywwl0og64Up0yfXIaJiJYHFl2kuphXSOWUXC8K+Ho+UNLWGz2bg1sEwpcZpEuFLmoULLwHLGOS2NEWyRVR6+vf9bjPpplGLbniV7UK97PW5Uie0zbW5rg2UPL1PtR8AznAiI8dN0ML++VUxSjBx9ITV/oHP7RAGQzr0au3ssdizYIbMY6nY1n4/kLfaGZYCPPzU+KmJUlQ1ETh6Qxv+k2knwFHmQ3ixdni9DGc/3fKNdAw9QsmjO7pIirqwD1BHrUBUa5o3XdKSH3AcgGfN0mRG7Q1LyY40mEtmiaZtEnIhEhekIFjHEHDEOOQeLztmi6XqygeWUnMTEwHIluODOd8vGk91hl9tFDC4FRZAysOgcMYWBxcnBYkUuyBqENiYnFk1U5DiyudTyGqCS1s+qnK2uRODmYPFELko9wGRR5Ep0wZMvdcija4/it//9ptifh0irCLLJwHTxRaNFL3mpCYy89mbBeiNp+DzaBpbdoabpeYuNZePNyLQbRbBiU2NNKegAwM7Pd6q2WYpgadw/bWCZMSASqibg9m9uR1LtJIkKQHkbr3z5Sjx2/DH+gaXdj6UHGYlc2Ow2hQIhbx8jOGIcETGwwh3M0xqlwRHrQMFpfg6VkUiMmQiW0SLfGevkylSHmoMVCQXQSOGun+5SUfhaDW0lj7U8GnIkDSxWva2swDOY3Xluy+1RjMsaYxtdBDb3eC6OrTuGz4Z+hu8fCSqR0uwI1fO1ab9HCdUTVOqFNNqPbI/Rv442VFhkcddPd6F6m+oqShwB+z60u6Mdrn5VEhuwStELJ4KVVDNJVZfsUgSdj21zSJGVgC+gyIs0kv0PBc5YpzxPkKhKjbY1FPvQc4leeoaKIkhFsGiDgnxPcr/kCBQjUNFjfA/Z6CN573LfDAT7HTHQHC6HImeLpQg6XA6ZTUQibYm1EpF3Mk+Rg0ULZKgogg7KYGPUAVljihQatjlt0nhso2TaPepooTPOqWA/sHOimdSYiozK2epKBKMIFnkha3eSkqOJ9zomKUZ++ef0mSPnHOkN7FwDi/nIkzfXgp4ym5GhpEURDJdaEWoEi5cHZcbAYg1FHowiWLT8sxFoGWACK95pXoRh29xtigWQXg4JjwbKUgS7jesm12rTOp5VKWRFLtgcLEBtIFk1sLSMCqsIN4IFSItwrbbsXy7V3OLlEQKRiWCxfS4QCGDv0r3wuX0h5WDpRb7KGk2uaIJ/Z/9b/swu5HmL90gaWJ3v7xyxc5nBsHnDAPDzUUpySywbWLRwRvbhbO4+9Ps7o/4MOReJFto48ssR+W/WsWGz2TTHLaOx0GazoUHvBpZz3Zpc0QQP7XwITa5sonleLVg1sCoihbQyw+60y8q59LjoK/Hhm97f4OcpP0fsWo4YhzwPkfUZKy7icDnUIhfUPNTzqZ7Sdz6/wsCiI1iEgUQbWGQcoiNYQPD9oVUF6fYSsJR6OoKFAPgRLJddjizZ7DbEJsfCW+QN1sGycwwsX1BFkKYg0iIadLSKl4MFqGXo2efIgl0vk3VCZSktwKJMRwm3241Jkyaha9eu6NWrF95//33NfdevX49hw4bhsssuwx133IHt27eXYUsjB67IBScHq8mVTXD7ktvR5+k+ACQvNk+WV5eWRxkRvMnk2PpjmJo2Fb+/8buptutNOmYiWNGgCJLFFJuDFQgEjA0sXwgGlstYoMIwB8uCgUWj94TeACCLnpgBoV1UaRpUGtr20TYF3VQPrW9pDQCy99hmt2Hrh1vx8+Sf5X30+iAx6NkBkQzcWhEsQMPAMqCi0ohUBCsSgznhufNAlLG0IkLh5mABpRQX6vpb52yVFd60jDMycbKS/IFAoEJFsFg0v0aZc8UTcImEgXX/n/dj1MpRSK0fOUEVM2h/Z3tc9n+XqfKcgNLC2BYNrAFTjNXbeNcClOqKunR1uw0DXuRfJ5LGrhXoRX3LMoIloIbdYUfvp3urtvvcPpScL8Ga59aYOk8gIEVdeOciIDLlAFXbiVmXOGIcqkLDdH8nBhktZw4oI1g5R3Jgc9iQVCspmFdVrCwWzJNYZ/uiwsAq3Z+883aXXemkjFUaWDZH0EAin2XKX6nBZXfYgwYnoyJocwQdJYQaS0vRa+ZgEdl6upAyR7GRhcrA8guKoGm8/PLL2LJlC+bMmYMpU6bgnXfewdKlS1X7HThwAGPGjEHPnj3x1VdfYeDAgbjnnntw6tSpsmxuZMAZh3ky7YCUbEsXHOZFTvS8cHoUQb/Pj29GSzVelo9bbq7pOgtbo0Wv3aXMXYoURZC87OyAu2P+DiCg/SKS4q00zEaw9HKwAgFpgI1UBIvGgOcH4JaFt1gqvkcMrIAvIA/CNodNQRHUQ7WW1TA5MFmmTHCLCZv4DdnfwWazwRHrCIpcMDlYAN/AstJfWKMiVEQigqXXlkb9GwEAWt/cmvs9HcHSVCI0iGDRzxoA/v7jb529JZDfYt0r6xRKlj63DwhYX4RGG/f9cR8AKUGeRrgUQbo2EY3al9VG44GNLbQwcnDGO7lKnjlHcxS5LGZgRtzA7/Fj9bOrMaP+DMX27MPZ8rPULQFiUxu+8ldlqMJoFiKCVb6wOWxcVoRVh5nf41eJRrCgI1hajl+7K0hX5+UC00IPRCwCgKLupbvADVeCC85YZzCCVfoO0yqCQJDZYXfY5Rp8La5rIbdXvm7p/iQPUhHBgiRSI8uqeyTjR46qlfiUohXeYE4VbeDJrJXS+yJzORF3MczB8ikjWCqKoJ4DU2NoEBEsAxQWFmLhwoV4+umn0bZtW1x55ZW47777MG/ePNW+n376Kdq0aYMnn3wSTZo0wZgxY3DZZZdh/vz5ZdXciMGo0LBWx2ENFDOwO4J1sFhseneTqp6HEfSMKKNaQQ6X0kCMFEWQfnb0BL/4rsUAoCm7HDJF0EBF0IxXJlQDy+60o82tbSwtSIhnrXan2nKUr/BsIdwFbtTpWgePHn3UWht4Cwmd5hDjgBeppJ8Db9JiDayDPx20JG9Pc+vDQSQMLGes9m9eeK4Qznin5vtFU/G0DOPqbapbuj5t7GlR3Oh+psiDKD1PRaEIEtTtWheTA5NlejUBr52/v64ftb/tq9vwyP5H8Mi+R8rNiNKDM84p528QBAIBXDh0AWlN0iJ+Pb/Xj19e+AW5x5Vqguf3n8eiOxfJ+2jBZrNpj60Vz76yrKgnIliRhd1p564p2P5nBGLA6I1V9DzB1nYioB3cPCOMzkOiKYJAMD2CJ9/uKfQoaHZsBMvmsMnlTYgTjUcRJPMsnYPFts3n8SkNpBKv2sBiRS7oHKzSyJxWBEsvB0vOrbYYwdJ6D0UOlgF2794Nt9uNzp2Dk3vnzp2xY8cOeL3KiePYsWPo2LGjYlurVq2wdevWMmhpZGGYg6XRcVgDxQzsTrvKs01oEVl7rRlXgL6BZShvHiWKIL0I3PjWRtX3vERwcl06uuf3SuFtUxEsnYWEGa+MM94JT6HHkvpY2zvMR61oJNVKwuhfR+OaN6/Blf+TPGGntp6CO9+NtEZplulNvD6gZ/ARzxovwkLT5ni0C0eMA3538FkThS+zcMQ4IpKDFQk6AhtBorHxrY2qxTIN+pmz1E6bw4bGVzRG93/qF592xmlTFM0k2NO/H2lrRaUI6uGmT28CIBnresi4KQPpTdO5hV4rAlzxLql2TSCAP976A9+M/gYlOSXwFHhCoixODkzW/V5v7jn882EA+gYWbNL72LCfuphtOEXXw4LO8GvVqWKWthxJtL29LTrcrZa2vhhgd9i5xb0zv8q0dB5Zuc8ggqVVLoSAdlDxVATpPCTWwCJOWZo6SItc8CJSiggSZRDJbbMpr0ueFRvBoq9FVP9ohV6egaWIYDmCMu2EIshGsKzkYBFjTe9Z04hLjcP9m+9Xba+sFMEymzHPnj2L1NRUxMYG+eLVqlWDx+PB+fPnUaNGUMWlatWqKjrgiRMncOGCsgArQWamtZewLMFr84FDwerkuQW53PYXFBegKL9I9Z3eveYV5MHn8SEzMxPnsiSpz7NnzyIzMxO5hUpPkJlnll+Yr/ldwBHQPUdJUQlO7zst73P8iBRZOnzkMLLjsg2vbQZr/rMGKYOUtAK3381t1/kL5+H3+uXvvIXS4HU+97zufWTnZsPr9mru486RFsBnz5/V3CfXnYuAL4Adm3bAlaRvmDpipcrszf7ZLPR+XRUoyC5A+vXpaPt3W+ycuRP5Z/KR7EnmnrO4uFjzWh6/OoKi1y63S3oeB7YfgK01w6d2BXDuxDlkZmbi9ElJOnnPvj3yhOOFFxfOXbDU52nkF+ejuED7Xszi6PGjyE2y5jllUVxUjFNLT6naQkddzbTzrz//QmpuqnxswBdAQkYCdu/ZrXvc+Wypv//25W9Ib5OOnNygAqiZ6x7cexB5KZKgQf5xaRw4e0HZx/X6TXmjaseqSG+fjlNn+LTyq7+5Gj8M/UH+HOp9lNX9X8iT5pGdW3fi+7GSkl+D/5OEKM5ln4t4O7IPZWt+5/NKc8zuX7X74PHjx+HN9KLDSx1wpPcR1ffl0W/8fn/Errvvl32ofpN+FDnSaP2sRCmuqO9cONh/cD/i84O5kx0ndMTW/25V7GNqzXK0dKzK1p6PS3wlKMkuQWZmprwuOXjoIE67T8v7FPuKkZ+dj8zMTPx9WKJXHzl6RJ4XTp48CQDYt3cf8nLz4C6h1h0O4Nzpc1I0JyD1uTPnJAGPrFNZsLls8r5nzkrbjx6SFAOPHjsqj7t5F6T/jxw5Iqsge7weZGZmoqBEUqjNL8rHnn1KWfqjx6RzuYvdsCfacfqsdF8BXwDZOdnwnJPmdK/bi+ycbBQVF6GkQGK7nDpzCgVFBSgqLILP60N2Tjb+PiHdf2FuIXwBH/YfkESa/j72NwL+ALIuZMH3t2SgHTxwEH6PH+ezpXVVAAFknc1CZmYmjuyTxoGDh5XPWgUqhTa+VjyKThXhyLEjaHVfK+z+QBpzzPSFijA/lZmBVVRUhJgYZfI2+ex2K7201157Le6//3788MMPuOKKK7B27VqsWrUKtWrV4p47IyP84qLRQtVqVbEP+xTbWma0lP9Or5bObf+u9F0oPlas+k7vXvfX2o/T7tPIyMjAyWrSAFC9enXpc62T2I3ghNiqVStD6tmfcX9qfpdYJVG3LQuPLUT+sXx5H/8OaWHZtHlT1MiooXmcFcTExqjakJSSxG3XmVpn8Bf+QssWLWF32FGYJVGg6jSoo3sff9f6Gwd8BzT3KTgjDXR16mmfp7h1MbZjOxpUbyAXOuQhEJAqvfed1BcdukXGU5nbNBc7sRMlF0pQL6Met42ZmZmabV/pWKnapve8znc9j0NfHkKtmrVU+62tuRYxfuk3O5MuTSyt27aWPWJrktYgMU7dr8y+3/ur78c5/7mwx4NmrZqhekZ4i6eFuxcCAFILU1Gnc7DWGk3b02vnmI1j8H7X9xF7LhYZ10v7Ee9srXrqZ8tiT6E06W7+12aMOzAO+6oExyAzz6durbpomiHVITobkGSDGzRtoDhWr9+UNzK2SO3a/Y3aCPjHjn+gRtsa+AFBAyvU+yir+89rmocd2IEmdYMKec2aNgOgP/ZEA558D1o0a4GFby3U3KdBwwZy/8nIzsCC6xbg6Nqj8vfl0W/sdrvudf+x4x94p907ps5VrU61Ctv3KxVsAALSmohWwG3YsiG2YqtiVzPP+4xPmlfYsYrGprRNcOe5kZGRgcIa0jqgRUYLRQ5YZs1MFB0pQkZGBgJ/SRGYps2bomZGTQCAd4s0jjdp3AR74vfA6XXK1/su9jukJqfCZrPhhOsEMjIykFtbMswSYhIQEx9ct+TXzccWbEHNatJ5GzdtjLrt6+IrfIUYe4y8bY1rDfxeP+IS45CRkYE91fbgKI4iLT0Nrdsoc3kbNW4kPVq/DXEJcajboC7+hLSWS6+ajrRaadKOPmn9iQtArk9qX936dZGbkgvfeR8KA4WoWq0qGjVthF/wCxwBB+yxdrTr2g5LsRQpTul51axdEzUaSGu6Ro0awe/zo0atGsjIyMB3Md8hLTUNGRkZWPiiNF60bN1SIdGvB5fThSIUoVmLZqh+W3XZwDLTF8pyftq8eTN3e5nF3WJjY1WGFPkcH69Uferduzcee+wxPPnkk2jXrh3eeOMN3HnnnUhMVBc4rOjgUgQpWpRW6JOXg3XL57foXssZ74SvmE/tYI0pI5pGIBCQ5aRpJNWWBkGrSYeRogjSaHZNM9W2jvd05O5Lq/sAwYVuuDLthPOtdx4ii6wly01AREiM8tusgKZKpDa0TiWic3HMoPtj3XH1a1ej032dVN/Fp8ejKEuicPo9aoVANgerSpMqaHen+Vo4tDpUOIhkQi1dOw4wn7Rdu1NtJNZMxJE1ailsM3Qm8p6R95VQOMxSm2h6oZk+XlHB/pb3/X6fLODydMHTGDZvGAa/Pjikc2uNNdEAEe6gxxC9AqjRhpbKoAyqSXGpcRj962gM/3p4dBsVJthaSLpqZ06RgxUJsDlKBDFJMbjif1dYPp+ZHCxfiQ/HN0iRK/IOseOEM8EZFHPh5WBRQg90oWFyLpKDJavpaVAEyffn90klGJLrJMNmk5T/CEWQlkon+8t1tFx2taO89CMRiaLvjUsRtNuCIhtUDhaPImh32BGbEgtHrEMuzqwqKhwIPqvCs4VywWT6+VhFZaUIllmra9asidzcXIWRdfbsWcTExCA1Vb3wGzNmDDZv3oxffvkFX30l1QaqV69eWTU3cuCMwwq+roUcLL26VIC0mNYysNicGJ4iFY38k3x6YFItacFmtFDs/IAykT5SKoIAcPs3twNQypc745zo+WRPtB/ZnnsM4SyTQUs2sAzySpxxTiCgfb9kkNGTUifXNsqp2/bxNsX+kQBtYFlN5AasG8QOlwPd/9mdawTEV42XI4eeQg9c8S7F5MAaWN4SryVjM1I5WJEQuSAoyVEuRM3mVdrsNqQ3TVcYaOTezPSP/lP6AwjeC3nOZicq+jmayWuoqGD7b80ONeW/XQkutL+zPbqN62b5vJMDkzF0ztCw22cWpBgnz8AK12lVo20N9J7QGx1GmY+as/lXbO0xHjuCHFORc/kaDWgk/60335JcU4HwQMt500htkKrqU/mntVMWCIgDT88ZROq3kfITgNqpGZcah6ILRbJKMMDPnWfFLICgc5wrclHkUZY6KD1P/inp3khkxxHjkNcotIHFFhrmvfu0cAZtIJHjaQVEInJBSgLFJMUE62CVGoi0yAW5vt1pVxll5Jp0O3kwM149sPUBPLz7YfmzUBE0QEZGBlwuF7Zs2SJv27x5M9q0aQOnU/kyLF26FM8//zxcLheqV6+OQCCAn3/+Gd26WZ8IyxtckQCHsYHFq79U6zI+RZLAFe+SK3azYM9lJFtMFsIsiFiGkTc7Pj1ecW+RUhEEgBbXS/KlJBoV8AfgLfbqJ7bGBhM9AcrAMlisEqlzLY8toQjyCvzK145RFtk1gpEEtxUoDKwQVOAiObDFV42XRUh4dZVYA8vn9lkyNvVqT+mBfd4RNbCYfkPub8jbQwyPdSW4lEqAFiJYhO5yePVhaQNJkjaY3Eh/oZ+jO0+afGOTrdVbqghgn1UknRdlCfK70I6xSBlYVVtWxRX/uQIJNRI096nbra7iM82AuP/P+zHuwDj0fbavvI0375E5oPkQvnx7tGFGZIgeI/UYI/2e7af5nYB5yAaIUxnpSayeqFpjTK81XfdcBWcLMP8aSWlazzFH5M8DvoDkALOpx7bEmonwFnnhznfLSoYxyUGDm/QTT5FHVU6ECGORQr70fXmLlE5DYsTnn8yHM94ZjFS57IpiyPJ2pzKCxZufyRqLtItVP6TfTSJyQRCbHBuUaSeFhpkIFgBF7Sxa5EKrrhgNM06+Wh1qoVrLaob7VXSUmYEVHx+PG2+8EVOmTMH27duxcuVKfPjhhxg1Sqo5cvbsWRQXS965Jk2aYOHChVi6dCmOHj2KZ555BkVFRRg2bFhZNTdi4E00zjinrORlRqY9rkocuo7tashbJZMwT6GMNbp4RYxpEE8Qr+0dRnXAgBf0i1USA5FMapGkCNpsNoV8ukwLSNRXDgKCC0ezFEEjA4ssevXOI1/bJD0sEjQ3gtjU4MQRCsWrze1tItaWhKoJKM4uht/nh7fIqzL4VAZWic9yBMvv9Vs2UFnnQyTpCCwtVO4vJhb6bC0t8ncoFFI5gmUQQX5o10MAlBEsIvdPLzAqC2gHQ2WW1ibvLs/gDpcVQOaivs/0RdexXbn7sNEE+p1Ja5iGuLQ4DJgyIFiTi/OoE6pLBly1VtFfOIUaba3bPWhINh3UFE2ubMLdr7Ia6hUNsgFC1VEC+AV3jTCtxjT5b705oEEfSRzG5/ahOKcYscmxqnWavJYq9qIwqxCOWIdi/UWcg94iL9x5bsXYKLOP/NT9OYJ1sOi+Q9YXeSfyFBFTh8uhTxEkhYp1IljkuloUQbIvvb8r0SWpCJaqLDtiHfI16AiWzWGTxyJeBIscw6tJWFmjUaGgTImNEyZMQLt27XD33Xdj8uTJePjhhzFkiOTJ7d27N5YtWwZAina9+OKLePXVVzF06FCcPn0aH330ERIStD1sFRU8qoTNZkNizWChOB5oiiAte6kH0plJVIUGu4g8v/+87rm0Ilx2hx03fnyjYa0Y8hKR60aSIkjOTwxQMxQmliJodrFqaGCVGEcVrBpYeoaiVcRXCS6MQqHmXPm/K/HA1gci0hZXogsIAHl/52HrnK0qjzJtYAUCUlTSSjSJjVKaBUsrjKQEMyuzbqYWCAFbP42002qEzefxyQsOo3uTnyEdwcqvvBEs0uerZVTDs95ny7k1oYMsNunfhTjSQnVa3b7kdjTo3UAu5xCXGochbwwx1f9pqqvi+iTXjzPvNR7QGH3e64P+z/UPqb1WcP/m+3HtO9cqN5rwu/Sd2BeDpg3Ck+eeBGBMpRcID+1GSjm27Jhmd/ENLLOlTvTmAHo+9rn5TjyyTvF7/fB7/CqjgI5gleSWyBRe0naWIqiVg0Xm+sKsQoXDUYsiyBpahFqoaDtNZaTqYJFz6UWwiLFEru2IcSjunY5gsbWzAGp+K21D+7vayzno9PMxC+KMoftCSn1rhdXLE2VqYMXHx2Pq1KnYsmUL1q5di3vvvVf+bs+ePbjpppvkzzfeeCNWrlyJLVu24IMPPkDDhup6GpUBRpOVGZEL3gvOQ9UWVQEAWfvUNa/oGlBAMHdIC1q5ImYHOJWoRJG5iJFZBPwBFOdI0QFSjNWo9gXAoQiajGCtfFqtpkefLxIGFqE+th0eWg0sHujBLRSKoN1hD6nODg/kOfz0758ASIYW+z15RkXni+Bz+0yrDdHnt2xglfbNmu1r4voPrlcYpaGCvPfH1x3HVyO+Clazt0DzYw0sK9EvGqQeCgA0G6wWhlFck3FEAEGKYGWMYJFxkx3/KhvIIlBhcBeHZ2C1vL4lRv86WjVujtk4RrVvfHo8bv7sZjnCtXtxUJ2Re32Naa92n9plUjS0Wqtq6PJgF8vH2ew29BzfEwlVJWcumb/6T+mPsXvHRrKJAgCufetaPHX+KdV46HA5uHOz2cLzaQ3TNL+j54mAL8B1+pI+KhfTZdZpighWvlvhFCXOcZ6B5Sn0KAw6ch1PAbNdgyJIxvFj644BAHZ9vkvRrta3tlYYTDaHzZAiqIh4lRpcsrBRrFOVw0X+5+VgKWp3AbK8PA0j9Woat35xK0YsHYHEGokyZbDf5MpDz62c0hyVCJoGVul8rzXZOOOcwWiLx2fK6pfzJzgLTLaTF1/QV7QjE4sqUmVynSIXtys11IouSJRDmrIWDrzFXmz5QMrnIxGsmETtBSDZZ/MsSU5TFgwwaWDt/16tqAhE1sByxjpRLaOaqUKwZkErB4Zq3EaKMkcW71rRUdrAIsaXTDmycH6rQhdkoXr5uMvR6f/U6oehgCzGjq49ip0LdspiFVaEKlQGlkWKYI8nekjHeYK5bEbCDLyFvEwRNBDZqYiQ1b4sFPmuiOBRBGUKUYSpj7U71Zb/vn/z/ej2z24Y8uYQtB3eVvYo//Svn+R9eHNYeRTi5SFcY444RpoPaY6qzatGokkCFOxOO9ehpRXB8rl93BxzFsTZzAOZj/NP5ctKeSzoPCafx6fqRzRDh42CWRK5KN3uLnAr7pce+2kjiPxPxuKeT/VUtCvgD6giWAqKoM0gguUwGcFy2hUOHllYg87LKv3fzO+lhbi0ODlnM7VBKiZ5JkVsji4LCAMryjCk5GhEppxxTgR8AXgKPQj4AqYWZLKyDcfLQ3dym8MmGzxaIIbRkLeG4I7v7pC3tx1hLrrCRrCKLxQjLi0uYhRBGmYogtlHsgEA2z6SlPqsilwAfAOJUMAiYWD5PL6I85Npb1Go6l2xybEYNjf8/Ee5oDAnR5B8T56RTEtLMW+QhxzBKu0LoUT4tJDeNB2pDYLGLYmgEMcGS5vgwZXgQkluiUzvsxL9AiBHHknCdVyVOEMjm7wPK/8djNi689xwxDoqJXc+vWk6ujzUBcMXV2yJcCOQ3+XMrjPytnApgnro/lh3XDn1StTuVBuDXxss91eec4RrYFnwUkcT4TImWMqTQNlAKwdrTp85eMH5QljnJuP9vKvnGUaw/D4+RZBea/k8PsWYTCJYtPGmKdNOqRHS90vPRTyK4LXvXIshbw2R6b0EiTUTVTlYWiqC8rnZCBZjYPEiWCqKoEMjguUMRrCqtqyqUOkMBZXtPaxcra2M0ApgBdTSnzTIy0Y832ZkYWnecPBCUGzrN7kf4lLjVPLRLOhcEXoxOGCKvrgFARvBytqTZSkaYQWb3pXqLOgZWJePvVz6/5HL4XP78PfvUnVysxEsAPh749+q73984kcAkTGw/B41FSGSCMeA0JK/twLi5Tv711nu9/YYu8rAspKPxssfMoNo1Xmiz0feP6KiyIoG8FCjbQ24890hRb8Aysnh9WsuJFTHcMYjd767UuZfAdKC4dq3rkWNNpEpbl5eIH1p3cvr5G2H1xwGELm8VhpXv3o1ej3VS7WdjqARcJ2IFcO+Ct/AYsSZUhum4rL7Lgu7XQL60KIIntpyKvyTl/bN/FP5klIeJ4LF5mCx8zLpDzlHc+Ap8OBc5rngd7wcLErkgo520demx3WFOA+HIphcOxldH+qqcmRc9cpVqoiUnsiFyuAq/SxL07vs3AiWgiLo0ha5oA0sQFKHvJQgDKwoI9QcLLIYJpEXIoqhey7Kq8K+eAFfAKkNU9H/uf6ISYqR85a0QAwjh8uhSOA0O2GxEaycozmolhE59agOd3eQIwRbZktUQT0Di3DqbQ4bVj69Er9N/Q2AeZELQK28SHtzK2oEi0Z5158hEwiRvWXBi2BZoaXJ9cZCjGBF08CSqY+luY9mHCYJ1aQ+u+S+JdI5TAiq0KDHA7NCOTx4i73l3ncudfD65vpp6wGUrVfX6B0h805FiWCFW7RdFmcqfcaPHn4UN7x/Q9jtEtCHzWELWanxuveuM73v9nnbuQJWdA4Wb14mRsyeJXsAAPuW7lMcS9Rs2RwsBMClCALKd4seb3kqglpwJbhUBZEVnw0ogjzKIK/AMhvBomtv0e20OWxAQKIuRtuBXBFxad1tOSCcHCwAKDwn1aMy40GmBwUW+5btQ84RyRPuSnQZyrTTESwir0s+m4GcD1MczCOLZH0hIgNPUx/1oh1kkPK5fTi1NegFM1ow0EYbS4+hFXz0nouc12KQGxTtASjSBoRVGP3+4RpYMgUxxBysaBpYniIPPIUerH52NWKSY0w5TEi+4qGVhwBQFEGTi0aFgaWRa8BD3cvrIr1ZuvzZ547suytgHXq/eaTyWs3AqCiznOtWMeyrsN/pplc3BRB0dghEF1e/djViU2PlUiwAkFArAVdOvdLgyCAa9W+k+z1r/PNK0hABrd3f7NaNYPEcCTyRC9ow4lEEAeU7ThthPIqgHhQ1uSyqCHJFMFzK8wEckYvSc/BysADJUXHh4IWI1visDBAGVpSh5ckzSxEkORtmPMh6OViFZ4OFg2OSYvDXl3/h7TZvayYg0hEs2ttulo5CjB1ilER6kWZ3SnWw6IKXJEqlBbKAp41VIy8Z/fuxBhZNl9Hz2OpFsM7tPgd3gRveYi+OrTsWNRolEH6OUds72lrKiWJhZBiEbWCVnv/k5pOW2iUbWBGO0vz9R5BSuvY/a/HZ0M9QklMCd57blIeffdZWqYz0eGCWIggAVZpUUSyQhYFV/tBbWJXl4t8Z60T11tUN96soIhf0uxKK0MnV06/Go0ceFQZWGaH7P7vj39n/BkA5jBFQOHkBtRPt5JbgmB8JRxlxwq6fvp4b/ZcL/nLGRZki6ONEsABtiiDVbtqoSaiWIF/PyjqQnJ+lCKqiY4xBxhpYiuMNIlissin5P/twNgBg54Kdhu2/mCB4H1FGyCIXpS8BEaMwM2jQyjd6IIvWs3+dhTvfraAAEtARLJvNhju+u0NXmUd1jVJFPyICEelFmsMlLcZpI8dIfY8s4OkBxspgzIozkAXvrV/eanhdQMrXanJlE9TqUAuAFDZ/K+MtNL2qKXweqSZHWuM00+2xinCjYzd/enNYx5dVBGvJ/y3BZfeaz5OIdAkBHgiVxAqSaiYpPpP6dmZ57HQCtRWKoN1lx/l9wTp5wsAqf2gZ5I5YB1Lqlm1dGK0cSqDiUQQV73QIznO7064QqxEoOxBHaHq7dJUj9P0u7+MfO/4hfz7400H5b8OxykTXJOsfEo3SErkgtUdvXhCcG8kx2Uey5f6nZWDRYzJ9j+R6zngnYhJj5P3M5CSrKH5MBIueP+wOu34Ey2EyglV6zOpJqwEE52/SbiPG1MUKEcGKMsKlCJIIlpnoAy+CFQgEVJ47hZy5xqRDR7AAoMW1LSzJ1BJqHR3BiiT9ze60oySnBFOrTDV9DFnA05O/GbrVE6efAAD8/vrviu3EuDOK6tCD83sd30PAH8COBTvk4w+sOIDDqw8DiO4iv7wXPUbRQkeMQ+Jqe/3SgGyzFnWjn/NfX/0l5y8aIVoUwXCRVCtoYOUez8WZ7WfgjHfKk7oR2AiWWYrgvmVSPkHeSSlfTBhYFRcjvhtRMZW1KoZ9VeHeaQHziEuLk8oE/Lebavw5s/OMxlHKcZMHM/MguV5McowuRZAYGbRzl0Sw3HlupDVKA6A0MBQUQZo6GKeUegeC8x+5nl4pGrZtgDoHCzaOgAarOsioCtod9mABcUJ5ZGTa2bmFUCxpWfdLEZfmXZclNN5l8sJpLfDDogj6/IrrEg99v+ekAm10VECroHDusVzAFnrUQ6YIlopp+D3+iC7SyAtMMOAFY3VDOUJCPRsztClCkWQHdbORD9bIfqPFG1g0YhE2vr1RvfNFTFE2QxEEgFfrvQp3gRuuBJclqhFNd/3ili8wu/tsU8cRw7msFmNmJxv6fTn400HsWbIHrW9ubdpQpmu1+H1+0xRBoh5H1AuFgVUxcOf3d6q2WVHZLBOQhVhFjGAJVDrU7lQbzgSnYV7tT0/9pPs9jfiqxgquvf/dGwDQaUwn3QgWrxYdLdNO+l/u30FhJ0V+lYaKIBlv5QhY6X5m3neW8qerIsjJwWILE9P3a0QRZI8jzn1SGNkMvfhigjCwogytBSLhpNbtWpf7PfFcXDh0QfFZD1yZdgSNOZKj5EoKnovOYVK071A2qrWsFrKqHWkvodFFfJHGNDuptr7XCpAGLb/bz01qNULHezuqrhFq/aQLB6Tf9OwuNdVGy+C9GGCGIggABacLJOU6i4sjVtyFFiHh7h8IYNsn23B8w3EA+iqUkUQoymbfjP4GReeLFIWjjRCqiiCpVUIoicLAqhhoNriZapsZj3ak0XF0R8N9KkwOVohKdAIVC7zyAKGi9S2tDfchrBS7w24qgqUwakojWPSYq8WaUVAEOTlYZAwmazoz77vCYGLrYDEGVmxqrOKzSjWw9DuyDjSiCLLHEVn9ZQ8tAwD0fFJZGPlihzCwogyjiUbLMCAv25E1RxSf9aAlciHXzyk9B/2SauVr+b3+sNSpFPSkQEBapEVQgpwV5zBzbhLB2r98v+XrxSbHaopchOol3frRVtW2UIy/ygJ2sVO/Z33FZ3oRH8qi3ij3kMX2udvx9d1fy5/ZnKdoIRxjxco7SSZWn9tniSJI+jPJh/OVRLd8gIB5sMn+ZeUUoHHFf66Q/9asj1cx7CsRwbpIEMnf0WazofHAxvr7UPnseiIXR9ceVewPSOOuz+OTyuUwhYYB5TyokGaPU+dgkTlNduZqRLC6ju0q0xTpthpFsOKrxKspgS5OBMuljmDRz4KdW4hBSYww3nO4FCAMrChg3MFx8t9aVInanWsD0KaosQOKFYog68mnBSvYc2lFTMKtycQWOQXCW1iyYBfTZqiMjliH5RpJBM54p8rAikaBWp6XOlxc8+Y16Dupb8TPaxW05+7mBTerKE/04Ov3+C33vzpd6qi2+b1+/P3H31wFMdq4InLMkYTWZBKWgWVBxVHOgyzyWKIIykXCS9+VogtFpvO+BKKLZlcrx4fyoAjS7/GwucO4+1QYiqCo33ZRoO3tbTH49cFo2LdhRM5ntF6g0y24dbDYiA3V3+U6WL5A0NihdqfHf9rZrYhsMe0jY7GWQ2XIG0Pw+N+PS8eyOVVO7QiWM86pogiyBhnAj2DR98uugViKIFmDCgNLIGxUaVxF/lurQ92z5h6MPzle8xyKF8lmblHGjWAF1IIVtAGmF8EKR5SCVjCT6/dEkmbErJfNnNsR4zCkjWnBleCS7oUySK3Ie0/yTEKTK5sY7tfyhpYhtU8Plz98OQY8b5yjFm3Qv1Gzwc1UxsKFgxfkv0PJ2Uuskaio3wQAL7hewAfdPsDur3frHmumzpxVaBnePcb3iPg5eaCFZqxQBMlzJ5NiwZkCU4WRBcoAzHRSHhRBQonWXexWkHUUvWgNRaZdoGLA7rCj27huCgGLKbYp+OqOr2SlZcD8+Gg0tyjo1RyKoM2mNFzodcHRX46i+EIxCs8VBo0dquuZoggyYzWpH2VFTZr8rYhIMYWG7S67oUFG9qPPzUawNJVt2VeugowLZQVhYEUZWgZWTGKMrtpNct2gKo3NbjPlEZSLvTFRKTaCRSeMauVg+T3mF2Q80AOUXN07giqCcelKj7oZrr0jxiHn21gFWazSUu1W5L3tTrtiIrgUQYur8PoWEUQBpD4cSn/RKuBrRL1kRVMiAV6/SG2Yil5P9jJ9juZDmis+W4nqyQZWgccSRZA8912f78LxDcdRklMiDKwKAnY+KQ+KoDPOidFrR+OOb+/Q3KeieKoVJUiEfVXpwY5hOz/biZfTX5Y/3/yZuVIiRgYW6b9k/cKtd0XNYfQ66vT206p9tOTYpYupt7PrPSssINqIMqIIsvQ+rcLC8n3oRLDobVWaSEEGdg48+ae1GpWVHcLAijZCnGfsDjvaDG8DQE3507yU3QZHvAMlOSWKF5REqeTETIompxXBCpciSHOI5QhaBCNY3R/trvhshioTzvXJQoZeiFvNwWKjK5ca6CgRb7Hff0p/+e/8U/kh9T+t35hXKJRMAoAkgx5p8PqFlbpeADDwPwMVn604PUif3fnZTmsUwdJnuG/ZPszuISkxCgOrYoAsjlre0BIDXhhQbvLHDXo10KWrVhSKYP/n+qPVsFbl3QyBSMFgKcQ6pLRgNLfYbJK6nt/r1xRc0opg8fbpfH/n4LU15ij6GqyDgkSwzK5h5EgTx4BiDSwj1UEg+Lzo89LnoPcBgKZXSZT7q2dcrWjXpSbXfmndbTkgHE9exk0Z1q5lsyG+WjzyTyppcCxFkH6htAaGSFEESQFdILIGVtXmVXHFf4PJ1mYpgiGjdGD/9r5v5U1WDSyraoMXI8iinxdxTKyeKBf1PPrr0ZB+r2bX8HPYeP28Rrsa8t/R8LjT1NGWQyXqp1VVs3D6DDGK9nyzxxpFkLP4EAZWxQDppy2ub4G+z5R/XiUL2bCqGPYVYlNicdX0q8q7GQIRAjE0tGDWKUfPLR1GdeDuY3faEfAF4C0xNrBoDHl7iPw3MUgUAhasimxAvZ2dj0hxbzYPXAu0rDrt7DAVwdKhCMoRLYfawOI9D9axWR4R9/KEMLCiDLkzhzDhxKcb12tgEVMlBkXnlXQoliLYf0p/VG1RVfEdi7ApgpTIhWxgRViJjI7EEQl6PYRjYJFrHVhxQN7mKfJwa0BoweYMdoK2d7RVfX/5I5eH3L7KgqcLnsbkwGRNg4YIhwChUUp7PtETg14ZpNq++d3Nqm1EAhcwyCcJEaTIJAD5fbPaB9mJ3WiBQcMV78Ll46Q+ZYUiyGujFvVSoGzR/7n+aHZNM7S5rU15N0UXFSWCBVSstgiEh0iJNdFF6AfPHMzdh4hVmIlgNR4QVCVMqZvC3YdAy8lGX2PbJ9u4++QeM8e0IOsVUkdVNmxsSuPN4XIo62I5bIrxXyuCxVIEAXPz9aX2LgoDK8ogHSqUjmWmIB4LR5xDsUgF1BGshKoJuG7WdQCg2pc+JiwVQUrkghhxka6lQy82q7WqZrg/e/1r3rjG9LV46oPeYq8llSp6sG13Zzv575R6KRjy9hBcM9N8ey5W0DkTofQ/m82Gnk/0VE2Ih1YdUiW5H18fzMdjqQyRwM0LgvkAxOlhlSLI9i8rBhYAOfnYW+w17QjgTZQiglUxkFIvBXcuu9OSmmSZoiKun0rbdKnRky5G1Ghbw3gnE8jakyX/rcVAsTuCBhavdiHpTwnVElRRIb1zm6EIsiwkArP3T9JKtn0sGWokp59LEWQiVrxImpHIBWBuvrY6f1V2iBEnyiAdNBQKUigRLEesQ1WUj41gAUBybemF03qRI6YiGCWRC/oaZkF7jup1r4fLx5qPGPHqD1kthksPSnQ+1mPHHkPXf3Q1fZ6LGb3+HRSACEdimVeYsiS3RHP/aBQkTayeiJErRmLMpjHYMnsLAODc7nOWzhFOBAsI0k48hR7LKoI0hIElYAZNBklKqRVJsU92cpqM4ApUbNy77t6wz0E7V7UMAxLB8pX4dCNYbL+ix1maWVOzfU3pehqF5uk5KC6NXxbDbI4ZC+KQsdltaoGKWGXuF/sZMJZpB8yt7yrSuFAWEAZWlFEuBlYRY2AxIhdA0KNxZtcZ7nnCpQjSSaJRkWmHdQOLHtjYQsVG6HRfJwDKekm+Yp+lHBm6D1iNZFwqaDs8SJ0khRNDAbcmFkWHJQN9aoNUTQ5+JNB0UFPU6VxHntD1lEN5YPuJ1X5LJktPocf0AtNms2HYvGB9I0eMQ/RXAVMY8uYQ3PXjXajRJjKRhohARLAuKtTvUd94JwPQDiOttZndaYe32IuAP6BvYLE1sahxlueY1XLm0cbHDbNvUHzX++neYdW8kw0jJoLliHEoKYE2JUWQXJM1JvUiWGwedLsRQbaOiGAJRBTh5GCFsqhxxBpTBIFg/ZTf/vcb16ugJU1qBXanVNE8GiqCAFCnq3oRrQdFTRSLL7rdYUerYa2QtTdLfl5WI1i0al151K+pDKD7SDh5P/f9cZ9qG62YSYytzg90xo0f3xjydcyCFBa36mix2WwKOlioESx3gds0RRBQTooxyTGXHHdeIDQ4XA5T9f7KEqTvCgNLgMCMs8nmsMGd7wbAN4qyD2cDgGpcDZUiSI/tbATripeuwNP5Txu2WQuygcTUwUqsmaiKqNGfSQSOFbngRrA0qILVWlPpG5eWfSUMrGjhmjevQe+ne8uGVSgRrFAWNY44TgSLQxGkMb32dNW24pxirvfFUltiHPC5ffCVREfkoskV1iZyerALxZNSt1tdZB/Kxvrp6wFYN7C6jesm/x2TLBlYA18aqLX7JQm6j5oRLtEC792hlQSjFVXVAulvodCU/p3zbwyaJgl3pNZPtXQs6Z9WKIIA8/wusUlR4OICcYgJA0uAwExfsDvtcm1GvXlejyJIzy/EyUbmfhZ0OZ56PeoZts8KCGXelehSrEVjU2JVxiPdZuLkNyPTTgub0di5YKf896UWwYp84oEAAODyh6X8nm1zpSRDK95jGs44JxoNaGR6f3uMXZV/wotgAUD3x7pjw4wNKDhdgAsHL8gRFk+RB74SH+KrWKco0nDnufH7a7+jQa8GAICE6qEvmLVw5dQrTUc66JfbbG0xGkRy9Mcnf0Szwc0si1wokksddkwOTLbchosd9MI+rgqfh24W96y5B1l7s/DtGElanx74oxVV1ULtTrXRoE8DDH6Nr1hlhB6P9UC97vXkd8ksyOTpLfKGnIPCqpIKCFQmkLE+1DlY4OKDWQPLXVAawdIzsBhnHt3PaOPlmpnXoN2IdqjWki/IRTu0I13ShYzhybWTlaqBNpsqgkW3mXxH1k7kOfAogiTHed+yfYrz8aj5lwqEgRVlyEmCIS7kJhZNtLS/3WlX5GkEAgHDCBYQ9HAEAgH8J+E/ALQTLa2CFHG16n03g15P9TLeqRTb526X/zZbT0IBamxwF7hRklsiqH5RRLgGfsO+DdGwb0O+gVVStgaWK96F0b+MDvl4m91m2bgClEIhVj34A/8zEKueXmX5mgICFQlkPhQRrIsXLa5vgT5P9zG9PzEQ9Fg6doc9SBHUMbDYtYRWBCsmKUaXPtuofyPdNocDwtiISYpRsalUESzK4CLtJwYaccIrKIKlz7IoS9qn+bVKIQ76GfByoy9miBEnyiDeDS3lmIhfr1RYgs75It56doIhQhdA8AUkvGIAYSVV0ijMKoTNbouYwRYqaK+QnqKcFmjD9cSmEzi27pioDxRFaFEpQgXtSSO02PzTfBXNiwV0HqdVDz6ReBcQqMwgjhWhInjx4IkzT+DJc0/Kn2//5nbU626eVkfWQnqFb8/vP48TG08A0F+/0fUU6XMbHUcwbO4w9HyqZ1TzXMnaxZXgUs8DzGVpg4h1QKrqYVGiGWSd2eXBLopj2o9qL//d+ubWId5B5YQwsKIM8rLFJpdN3RIiLUqDfGYpgl0f6oqqLaUCqMTAOvnnSfn7SL3whWcLEZcWF1IeWiRB1yUKpcYYTSv8fuz3AIR8dTQRab52ztEc1btxft/5iF6jooEed6wuMInXtm63uhFtk4BAWYI4GawswAUqNhKrJypydK2uVchYaJaKZyXXmh5nzZT/aD+yPQZNHaTaXi2jGlrfGp5BcuuXtyraYXfZVdR7dp5VyLST50qUOBmxC9qYJOsj1mjt+UTPcG6hUkNQBKMM0iEj7Y3XghzBoqBFEXTFuzDkzSGYO2guNr27CXUvr6vIt4gUfargTEHY+TSRQMO+DfFU1lPYPGuzotCvWfAW/MLAih5CocTpYf418wEAD+16CIk1E1FwugC9J/SO6DUqGhQRrBDLGoQjly8gUN5IqZuCMRvHRKxIrUDFwYilI2TnsBWQNZFeBIuGpXqXGhRBq3j4r4dDPpYgrWEaACmK9OuLvyIuNU5ljLL56HptVsm1c+YU9lldygq0IoIVZRBLv6zqyNgcNumFIe9MADi08hAAvoofeZm2ztmKwz8flnOxOo7uiFbDWoXVFiL1XHCmIOx8mkghPj0evf/dO6R8MB5fOxrCHZc6ej7ZEz2f6hmxd2bE0hGKz2+3eRsFpwvQcmjLilWvJwpIqZ8i/201gkXGi1AEYQQEKhLqdKljaZEsUDnQfEhztLrR+jqF5E3pGVht7wjWZNTrO6zin1WKYDRBWEMDpgzAk2eflIW6aLC1FXnzrpzqUjonEHVFXi47z0BrcmWTSzIH8tK74zIG6eDkRQ5lMLAC0onplyZzUab0HUfkglZ1+XjAx7KBdc3Ma8KWVa/bXaIWVZQIVrhof2d7tLi+hWKb1byyB7Y8gMeOPRbJZl10GPTyIC5lIlRoibuUN2W1LBCbHCvXi7OagyWPJUxEXEBAQKAyo0a7Gkhvno6rpl+luU/huUL5b72SISN/GKn4rKiDZYIiGE3QuVI84woA6nZVUsD11jTk3rL2ZKm+I4YVz5C668e7MMkzyVyjLyJY+vUPHDiAAwcOwOfzoXHjxmjVKrrGwsUAeREXAMafGh911TLyQpFFEW1A8To+G14n6mqR8PYRA63gTAFqX1Y77POVN2x2G4YvHo4XnC/I28xSDAhqdawV6WYJGEDLUXCpGA6pDVJxYuMJyx5EeSzxXRrPSUBA4NJATGIMHtn7iP5OVOA+rVGa6utRq0bBV+JT5dfTjruyUqnVghknYkq9FMVnvbUfW/OKRkK1BOSdyLskI1VaMLWKzsnJwb/+9S/8/PPPSE1Nhc/nQ0FBAbp06YK3334bycmCo68F0sED/gCSaiZF/XreQikCdXKzJFZBG1C8hSYxqAiOrz8Om8MWkZdEro+QU4LYtLIR+Yg27A47qjSpggsHLwCwbmAJlD20qIZEVvZiB5kwrVIERQRLQEDgUgXtnOYZHY0HNDY8h15pnLKAFmuhw6gOOPDjAevnc6nFLQjIWlcYWEGYehIvvPACzp49i++//x6///47Nm3ahG+//RZFRUX473//G+02VmqUtRf4zB9nAAAHfzwIQCowyraFBqustG/ZvoiFtenBpaLkYEUCdAhd8PorPrQEZo6tO1bGLSkfkFpYlimCDmFgCQgIXJqgRa2s0MlpUYfyXvdotfvGj2/E+BPjLZxI+k+OYPGMKGYfAZMRrNWrV+Pjjz9G48ZBi71Zs2Z49tlnMWbMmKg17mJAnc5S/oOVgrhhgVkLeYqCSYg8NRceL9db4lVtCwV0xOxiyMEioIu30gasQMWEVgTryqlXlnFLygfhRrCEyIWAgMClBpKPHipY6l1ZotWNrbD76926hZRpDJs3DFUaV5E/X3bf/7N31WFyVNn3tI9PZJIQdxlCIBDc3SXIYovtLraLL7Is/JDALiwssMgu7u4OwQIE94QQMglxdxtt798fr27Vq1fvlXT3WFLn++brnuqyrq56754r526LjQs3WtYjm84uguX3mzPgimCVlMiN40AggEzGu0Tm5oTSbqW4Lnddmx1PlBJ3QwDGnjEWUx+fauyjSAYVn3/c3p6cYoLvndF7XOevLdvUEauSTzJt5vRoZ9QvqgcA/PbWb9j/ZveksucYprC46+Wbbx8THz58bJ6gMgCvoIyJIQcMKebpeMJh9x+GXa/YFZW93ZXvbP37rU3/H/nQkab/yTlvF8HSHfi+P06Hq1jevvvuixtuuAHz58/Xl82bNw833ngj9tlnn1Y7OR/eIRKsX575xXGbIx46olXOha9P2pQiWJSb/fuJv0dJ9abzvTZVxCpjefU921RAUrrb/nFbT9uVdmXOoZFHjmyN0/Lhw4ePDguSIveK0q6luGDOBTj8/sOLfEbuUdGrAv136V/8HVMaoCTdnNcb8MHgimBdfvnliMViOOSQQ7D99ttj++23x2GHHYZu3brhmms2P+nFjozKwXKPxSH3HKLcJhgO4ozJZ+j/dxvWrSjnwqfSbUoRLBpA/FzjzoOjnzpaf997XG8MP2x4O55N24IcAhSR8uHDhw8f9uDLK7yi29Bu7a4g2Cqw4U69t2PZPL7wlwFXKYJVVVV46qmnMHPmTMybNw+xWAxDhgwx1WT56BgYe+VYLHp7Ecp7lqNpVZO+vKa2xna7/rv2x4A9BqDb0G7Y+4a9i3IuPAHZlCJYQw4YggWfLJBKt/romODrD49/5Xi9w/1mAW1SlNVg+vDhw4cPK8acNAbTnp7W3qfRMeBi6jjq8aOw44U7orKPrypOUBKsxYsXo1+/fggEAli8mKltlZeXY8yYMaZ1AKB//1YIRfrIC+Ey9pPy5Apw9ioEw0H84bM/tNp5bUoRrN3/tjvGnDxm8zLSNyFsbsqPutywz698+PDhwxWOfPRIn2AJ4KXrAXNmVLQ8ioF7DGzrU+rQUFoaBxxwAL788kt0794dBxxwgNT7mcvlEAgEUFdX16on6cM9VFLM7eG95s9lU4pgBYIBn1x1YmxuBKv/rv2x4JMFqOrbfqpWPnz48NGZoGpQvzlCtB+JaFX0bv3erp0ZSktj0qRJ6Nq1q/7eR+dAICwnUu3Ry4bvsaVScvPho60QjASRTWWL1uets2DvCXtjzMljUDPKPk3Yhw8fPnz4EDHm92Ow9LulumOZBED8eit7KC2Nvn376u///ve/47///S+qqswe0HXr1uGss87CK6+80npn6MMTVJGq/ru2fRpnIBjAtZlrkU6kPTc59eGj2DjzmzPxy3O/IBTbvDyTwVAQPbbs0d6n4cOHDx8+OiF2vGBHbH/u9rpwR2l3VvJRsYUfwbKDkmBNnjwZU6dOBQB8//33uPfee1Faaq6jWbRoEZYsWdKqJ+ijcBx050GeOpEXE4FgwNQ3yoeP9kLv7XrrSkc+fPjw4cOHD2cEAgGTKuJh9x2GEUeMQO9t/fnUDkqCNXToUDzyyCPI5XLI5XKYOnUqIhHDUA4EAigrK8Mtt9zSJifqI3/sfNHO7X0KPnz48OHDhw8fnQojjhjR3qfQ4VBSXYIxJ22+vSXdQkmw+vXrhyeffBIASxG8+uqrUVHhhwM7A7Y+dWtMe8pXv/Hhw4cPHz58+MgH12avbe9T8NGJ4ara++abb0YqlcLSpUuRzTKxhFwuh2QyiRkzZuDII49s1ZP04cOHDx8+fPjw4aOt4PcO9FEIXBGsDz/8EP/3f/+H+vp6y2e9evXyCZYPHz58+PDhw4cPHz58AHAl7Xb77bfjoIMOwjvvvIOqqio8//zzuP/++9G3b19ccMEFrX2OPjxir+v2Qv9d+2P4YcOxz437tPfp+PDhw4cPHz58+PCx2cBVBGvJkiV48MEHMWDAAGy11VZYvXo19t9/f4RCIfzrX//Cscce29rn6cMDug3thj9++cf2Pg0fPnz48OHDhw8fPjY7uIpgVVVVoaWlBQAwePBg1NXV6e99mXYfPnz48OHDhw8fPnz4YHBFsPbZZx9MmDABs2fPxk477YQ33ngD06ZNw7PPPotevXq19jn68OHDhw8fPnz48LFZ4L77gNdey3/7n38GVq0q3vn48A5XKYJXXXUVbr75Zvz666846qij8P777+OEE05AWVkZbr311tY+Rx8+LBgxAigpAab5avQ+fPjw4cOHj00If/kLe83l8tt+7FigRw+fZLUnXBGs8vJy/OMf/9D/v+2223D99dejpKREl2334aMtMXs2e21qAsrL3W3z/ffAf/8LPPYYEHQVu/Xhw4cPHz58+Oh8WL26vc9g84atmdnU1ISPP/4YkydPRlNTk+mziooKfPbZZzjssMNa9QR9+LCDcFva4vDDgSef9D06Pnz48OHDhw8fPloPygjWDz/8gPPOOw8bN24EANTU1ODRRx/FiBEjsGzZMlx//fX47LPPsP3227fZyfrwAQDptPHeC8Ei+EFXHz58+PDhw4cPH60FZQTr1ltvxZgxYzB58mR89dVX2G677fDPf/4T3377LY444gjMnDkTt912G55++um2PF8fPvDTT8b75mZ323zxhRG5iseLf04+fPjw4cOHDx/tjXzrtnwUF8oI1uzZs00qgf/85z+xyy674NJLL8Xhhx+Oyy+/HBUVFW12oj58EA46yHi/bBkwerTzNvtw/Za1jgM+fPjw4cOHDx+bFDKZ9j4DH4BNBKulpQU9e/bU/6+srEQkEsEJJ5yACRMm+OTKR7vh0EON91OnutuGTyskgQy3qK8Hvv7a2zY+fPjw4cOHDx9tjVSqvc/AB+CyDxaPI444ojXOw4cP1ygrA7p0Ye/z8dSccoq39Y85Bth1V/fpiD58+PDhw4ePzReHHgqMGtU+x+5oBGuXXZgdtblBmSIYCAQQCAQsy0OhUKuekA8fTmhqAqqrgQ0b8htIjj3W2/qff85eGxsZufPhozPgjTeAvn0BX4fIhw8fPtoWEye27fG+/ZaN9aFQxyNY33zT3mfQPlASrFwuhwkTJiAWi+nLUqkUbrnlFpQJVqbfbNhHW4IIFmBO/XOL3r3dr5tKAckke9/QAHBZsz58dGiMH89e/YJnHz58+Nh08e23wM47A9dfD1x3XccjWJsrlATr6KOPtizz0wN9dARQc+FwOL+BJJFwvy5fr9XQ4P1YPny0Bxob2/sMfPjw4WPzRKFOLa/bL1vGXqdMYa8+weoYUBKsm2++uS3Pw4cP12huZgQrnQZuvhm44QZGttzCi0w7nxG7eDEwdqz7bX34aC/cdFN7n4GP1sS11wI33sh6+kky+X348NGOKFTFz2tmDtkp1OPTJ1gdA55FLnz4aG9QBIuwcqXzNgMHGu+9ECxe0n3mTPfb+fDRnli7tr3PwEdr4sYb2Ws+KdI+fPhoXbz4YmHbe32ug5olT8TOJ1gdAz7B8tHpIBIsN9Gr7t2Zqs+QIcDjj7tPE+QJFjUq9tFx0dAAzJ/f3mfR/vDFWDYPeEl3bgtksx3vnHz4aGtcf31h2+cbwdqwgb36BKtjwCdYPjodRILlNBjNnw/89BMjS/PmsWVuU6h4afbbbjMEL1oDDQ3AmDFGHrUP79h7b0aiN3eQR3Obbdr3PHy0LjoamTnzTKCkpL3PwoeP9kWhz0C+EayvvmKvHYlgUdri5oiCCVajX03to43R1GT20DvlO//jH+z1k0+MZatXuzsWH8ECgFtucbddPvj8c2D6dODqq1vvGJs6fvqpvc+gY6Cpib3mM7l9950fre0s6GgE67HH2KuvXOnDBwONxV7glSCJ3ZMWL2avnAi4LebOZbXsrfHczphR/H12FrgiWGeccQaWkUwJhw8//BCHHnpo0U/Khw8VcjnvESxZ6za3HiKxufCcOe62ywd+sXrxsLkbeOT3yqcWa6edgNra4p6Pj9aBl3rSYiGbBfbYA3jrLfU6hRb5+/DRmcHPPw8/7H37fCNYBEoV7NPH3fZHHMHk3VvDQcnbUJtbNMsVwYpEIjj88MPx3HPPAQBWr16NCy64AJdccgmOPPJI1wdLJpO45pprsMMOO2C33XbDQw89pFz3hx9+wDHHHIOxY8fiqKOOwhdffOH6OD42XSQS7CH1QrD4Gi0K3bsdwMQIVmuCCBYNzpMmsWU//9x257CpYHM38IhgLVvmzRu6Zg17XbfOTy/sDGiPCFZTE/DFF/YN2ztSipIPH20NnmCNHOl9exqH3UJ0InslaBs3stf99vO2nRvwZRWb27zsimA99NBDuO6663D33XfjxBNPxGGHHYbGxka88cYbuOyyy1wf7NZbb8WUKVPw2GOPYcKECbjvvvvwzjvvWNZbu3Ytzj33XBx88MF48803ccghh+C8887D0qVL3X8zH5skyBtSXg4cdBB77/TQ0mDXvTtQWsreuzUA6HjkIWpND4xIsF59lb36vgXvaM1auc4APi3FixHeo4fxfto01rxSjOL6aF/wUavnn2/749PYKRtDaQzb3J8/H5s3Bgww3vM2w5gxgBuT+cQTvR1PzNjwSmRofSJaxQT//Tc31VPXNVjjxo3DlltuiV9//RVNTU3YZZddMJDXvnZAc3MzXnzxRVx11VXYaqutsP/+++PMM8/E008/bVn3Jy1OefbZZ2PAgAE499xzUVJSgp99V/5mDzIcy8tZQTXg/NBSvdYppxg5yW4e9Hgc+N//2PtJk9hrdbW383WLhQuBgw9m72mwpEFPDP/7cMbmbuDxpbFevaE8vv0WOPDAws/HR/HA/7YrVrT98e2eLcoW8CNYPjZnbLWV8Z4nO9OnA7ff7rx9XZ3x3o1TVyRUXomMuP6yZazevBip9vxY4EewJLj77rtx2GGHAQDeffdd/O9//8Pzzz+PI444Al9//bWrA82cORPJZBLjxo3Tl40bNw6//PIL0sKv26VLFzQ0NGDixInI5XL46KOP0NTUhJH5xFp9bFIg721JiTGZOz203bqx13/9y1uK4LXXAr/+yt7vthvLZ26tlJxBg6zL6HvJash8WMELM7z1FiPDDQ3tdz7tCd4IHzy4sH19+WVh2/soLvi05faoNfQJlo/NBakUcNFFwMsve9uOty/yIRVDhxrv3USVRBJGx3c7PojneOKJwJVXGvZPIeCvhR/BkuDZZ5/FhAkT8Mgjj6B///7Ye++98fbbb2P33XfHWWed5epAq1evRnV1NWKcrElNTQ1SqRTWrVtnWnf77bfHKaecgksuuQSjR4/Geeedh+uuuw5D+bvOx2YJnmAR8XB6aONxlroSixkGgBtBCb6BcSTCImGtkS4lDoJr17Lze+kl9r9PsNxh+XLj/aWXAvX1wKxZ7Xc+7Yl8lKt8dA7wKYLt8Tu7IVibewTZx6aBaBS4+27gd7/zth1PWPIhWLxZXV/PRCsCAeDdd52Px/9PtkUuBzzwgCF+IUK0oYjUFYMQ8c6WzY1guWjRyqJW3SgMoKGsrAxXX321a5GLlpYWRKNR0zL6PymMxs3NzViyZAn+/Oc/44ADDsCXX36Jm266CcOHD8fYsWMt+67j46mbOeLx+CZ9PWbOLAEwGKtWLUYolAMwAHPmzEd5uVpOa9mynojFumLmzFlIpYYAiKG+vh51dfY1fQ0NvQF0AcDusVBoMFauTKGubkmxvg4AoKUlAGCU/v+PP7JXGuRWrFiGurpWSI7WsKncM9OmlQIYBABobMwCCGLx4nkoL28fLetEIoBAIAdh2GsTbNw4HPzw7v73lcsHyrbfVO6bzoZZs2IAWLO3FSsaij4eybByZRgvvtgF5523BvPmRQEwZ+crr8zDllsaz1cgMAJACHV1c9DcbA1j+feMD69o33vGGA+9nMOaNb0AMJt54cIlqKtr0MhOrat9rVjRHUBPAMDMmXOwbl0YwCD8/e8tGDx4gWX9BQvKAbDCrxkz6rB0aTcAvZBKJVFXNxc//1yCc88djNdeq8d//mO1ezKZkaB4S11dHRKJwQBKMG/ePMRihc2f8+dXAugHALjjjhU49dT1Be3PLTrCWOOKYH388ce2n48ZM8ZxH7FYzEKk6P9SUh7Q8MgjjyCZTOKiiy4CAGy55ZaYM2cO7rvvPjzwwAOWfdf6msI66urqNunrQcHOYcP668v69x9sKytdXs4iXrW1tbqxW1ZWhdraKttjde9uvK+trUWXLkAoVFL06+vUc6h79z7o3bsPunTJb/+vvMLyvr/8Uh6521TuGV5Cv6WFTRYjRw5pN8lxutYbNwJV9rdaUZFMGs8JoXv3WvTsmf8+ZffHpnLfdDbwaa/BYGWb/AannsocP+ef3wP9+hnLBwwwP1+RCHsdNGgYRo2CBf4948MrOso94+UcKiuN91ts0Q+1tcA117jfF297DBo0TM/EmDatVLotP/cNH16rbx+JRFFbW6tndySTcruHr/Oura3Va9WHDCl8/pw+3Xg/d+4WqK3dorAdukRb3jc/kldcgCuCde+995r+z2QyWLt2LcLhMLbbbjscd9xxjvvo1asX6uvrkUwm9cjV6tWrEY1GUS0oB/zyyy8YPny4adno0aPxfHtIJvnoUOBTBCnc7BSCj8eN2isyet2EqkVxibKy1pFtd8qx/vOf2V++9Rb0eKbThgGUD+6/n/XJePDB/PfRmqivty7rCEW11dVtWysjay55wQXACy+03Tn4aD3QGBgItF2KIBH2hgZ70RRRCdWHj80RshRBUgV2A7GGS4s1KMHXYCUS1hosJ8Gs1nxe+RTB9ujb157IO4LV3NzsqS6qtrYWkUgEU6ZMwU477QSAsb7Ro0cjHDafRs+ePTFLKJ6YO3cuBvDalz42S5DIRCxmDAoXXGDfK4onWAQ3BEsUtCgrax3VLkmnglbBsGFMrTBf/PnP7LWjEiwZUW2vYvsDDmif4/LYZhvjuXCjfsk/E5ddBtx2W+ucl4/CQE6e7t3bjmARcbrySmCXXYzlO+7IxkkxDdYnWD42Z8gIlhc1V357N7aKSLBoe0oao8/dKhK7qVF3C34OHjGiePvtDMhbALqsrAznn38+nnjiCVfrl5aWYvz48ZgwYQKmTZuGSZMm4dFHH8Vpp50GgEWz4hq9PeGEE/D999/joYcewuLFi/HSSy/h1Vdfxemnn57v6frYRECkh1cRnDbNeRsiWG6FMQCrt6WtRC5aC4sWtc1xcjkma9/WRpaMYLVHUe2sWcBHH7X9cQmVlUBNDTBxorFs662dt6MoxWGHWVMafYO544DGpZqatiNYO+/MXj/9FLj5ZvNn6yUlFa3ZL9CHj7YCtXgRqlgckU4btgaRHS+Ktl5FMsT1ad5btYq9dyJY1IqjNVpy8ARrc5tHCuqw8/PPPyPrYST9+9//jjFjxuD000/Hddddh/POOw+HHnooAGD33XfHu5pEytZbb4377rsPEydOxJFHHoknn3wSt912G3bhXWc+NkuQcRGLARUV7rehtKnXX2evQ4Y4b0dG6TffsNeystYxaHhJ7U0BTz4J7L8/8NRTbXvc+nqWAllTYyxrjwiWrPakLVFTw3qq9e5tLHOj6kbOi/HjjWgl4auvinZ6PvJEJsM8y+PHs/+7d2+7JtB2EVDZveUTLB9tiZ9/bp12EkRcxAwYN9uJPTc1U9fUzN3puPz2duCfN55gZbPs+XQiWLRcrNMtRor95izT7ipF8OSTT0ZAiBk2Njbit99+w5/+9CfXBystLcUtt9yCW265xfKZmBK41157Ya+99nK9bx+bB/gUwfJyd9vwKYIjRjDD040BQB59at3WpYta5rQQbNzYetExEbJ0yWKDhHuWtL64mQkbNzJDkB+qNrcBHWApZORxbW5m95YbgkVkVCSpgC/73hEg/obV1W1X02A3Xv71r0ZLCcLm5qn20T74/nuWpkoo9n1HBGP9ejY+uq1hTqdZ2mxzs7EPyrhx4xguJIKVTltruOj5VbV8ofXF+dKXaS8MrgjWrrvualkWjUZx5ZVX+lElH20KPkWQV+qxg0gqwmF3Dzp1XKdBqUsXFiXJZIrXm2rxYmDqVGDgQOaZ5tNvKiuL3yi3oqL1BzleiKQtQQRr7lxjWVtHsIp1vEAAOOccJiziFUSqAKM2xivBEuE2d99H60E0tLbYomMQrJdfthqfnTGC9cMPbLzdZ5/2PhMfbvHii627f/6ZmzoV2GEH99tRBIv2Qc+EG0dqoREscXsnkQtRMIyclMWIYNG8UlrqEywpzj///NY+Dx8+XEGWIti3r/02iYQ5xSUU8jZw0GBDMukbNphlVPPF558De+7J3p96KnDTTezvySeBe+4BPvgAENrPFQwafFuzeTFJwpKR3xbYsAFYvdqaynTZZWxibCsUI9JDE9IDD+RHsBIJY3IPhdifKNgiA01+Ycms4BOs9oc4ZvXty8bDXK64RekyOBGmP/6RpQR3ZhVBMp4747lvjli61CrEc/DBwHvvFWf/2az5Xth3X/cOTzuCJasVTiaZg0KmclxIDRb97zWCRd+7mBEsn2BxuOKKK1zv5NZbby3Kyfjw4QQ+RRBgtT5ORm08DvTqZfzvNoI1aBBMPV+6dmWvGzcWh2D98ovxnieJp53G/loLyaT3ol0voO/V2kYfD/ptDj7YvNxOXbI1UIzJnVeqnD8fGDzY2/aiHH806o5giRGsBx8Ezj6bve+MEYlNDeKYVVLCDKFUyqriV2w4/f5PPw3cd5/79X34KBR33mld9v77xdv/F1+Y//dSK51OW2uw6JmIx81ZBi0t7P3VVwP/+Adb5jWCxVfdiATLSwSrGCmCK1eyWi6RLJaUtJ+qb3tB6ZcMhUKu/3z4aCuIBMsNWco3RTAeh6nJHh/BKgZ4AtKnj3yd/fc33ufjWd1uO+uy1h7kiCC0Rs8wGfimusOGtc0xZaivL84Ev3Sp8X7KFG/b5nJsMuWjUJWV8h5hIuiZIIJ11lnAH/7A3rshaD5aF6Inm8a0tnjO3BAmvh2mHwXy0dpo7TYSgiyAJ2QyhtNDjGABZqcwiXM88IB5e/79fvux9yo12BkzzOuLESxKEfeaIuiVYM2YwVKX+da5fgRLgptFLVYfPjoAEgkW5iYDMhx2DqHzMu0A297Ng15fb045I4IlkyXOB/xgpyJYr74KnHIK8Oab+TUKJgOdVOUee6z1CRblmLeVwhkfTRTlxQHg449Zekdr47jjgA8/LHw/y5YZ7+++Gzj6aPfRQFkdVY8e7nqw0LY8ObvoInbP+ASr/SGOcxSFjsfd9TlT4f772W9+5pnqdbJZZjjZ9QF8/31DhWxTiGDNm8eyGPz02M0ThdQQOxEs3v644Qb2yo/RYgSLSgXcZJ7wESv63y3BEu0irzVYJHA1aRJw3nnsfSrFbK5IZPMjWMqh44gjjsBGIVl03bp1yPmuKR/tiH/+0/zQu41gUcTL7TapFCMIvMFe7AgWP9jJiAHAog+7787e85ENN+DzxWMxo8bAK8Hic7idkEoZxvjVV3s7Tj4QJwBZ3Rd5/1obIrniRVAXL3a/H55gTZ4MnHyy+23FKBTAJmc+yqeCjJzRc9NWYgo+1JClCAKF/zZ//jOLVtohm2U1r/x9LJJ2UlsFOn8Ea+FCYOhQ4Npr2/tMfHhFscg9OT1POcX7tnyKoIxg8XMwkRIeYgSL/qfXVIo9swsXyrdVRbBk9bX8fgtNEZTNISSA4zZzaFOCkmDNnj0baeFq7L///ljS1trLPnxokBWYOkWjcjkWieKlUbt2BdautT8WpVTJIlitkSJoJwgxciR7veceb/v/4x+N95GIMejtsos7g5sQDgMSIVEp2ipqReBTIwB2Hd0qPbU2+DYCAwa43040XJ9/3v22sgkuGnVHqmXkjIx4P4LV/uCNrssuKx7BcoNsljmE+vVj49D++1vrUPnnrrNFsERCSOPjc8+1/bn4cEY2C4wZI/+sWGMVjYfbb+99Wz6CJabf8csAeVRJpQJIr199BTz8MHD66fJtxRosSiNW1WrKzlE8TzegujieyKXT7H+fYDnAj14VDw8+CCxY0LrHyOVYSNmrYd5RIYvgOD20K1cyo59vLNyvn3OPJiJYfGSJhBRag2DZ9fSiBoVuGysT5s833r/1lmE4L1gAvPaat319+6279draEL/uOvP/paXAd9+xe5++4/77s/Pir0db4Iwz8ttOJqnutommLM0vEnFHsGTbkhfWJ1jtD974+ec/i0+w7BqDZzKGIXj++fJUWD7hpTOZClOmWI1cEjQQl592GnDIIW1zXj7UuOoqs0gUj2ITLLf9NnmQUm8w6BzBOuww9soTOTGCRduKYhWyZ18WwaL7WZViqEoRnDxZvr4KZCf4ESwGP7u4HbB+PetxQ4ZzayEeZ38XX9y6x2kr0GDyn/8Yy5xqsIiU8WqAlZXOyoNkLPARrIoKNrAVi2Dxg43dIB4Os4HRa3SIN5QTCfOgx1+PYsJNv6ViQiSK/HcePx7Ye292TmeeyUh2sRvmPv44I8pr1gBjx5o/Gz2a1T95hYwMua3tkkWh3BIs2badnWDlcqzges6c9j6TwkG/z7/+xTzRZCzlK3LxxhvAs88a/592mtrpRxEsOzz3HLBqlbH+jBnMeOzo6aVff21dNm0aexVTqp56qngy4D7yx9NPqz975ZXiHIPGzHwIFtVL8y1hVASLIsE//GDenn8vRrD+9jf2KnMUy/pgUfaPqpZXRbDuuku+PsC+z7XXmlPaCfxzM2UKs5nWrmX10JsTfILVDiBvAq/sdeedxY800UPVmpLcbQky8ihlDnBOEVy5kr3yMu1uDE5ZBCsYZISrWASLN1qdolPl5d7JAT/IBYNmw7m1Crfb2xAXRU2jUUawJk5k/xdTcS2XM1T2Fi5khIqwYgWw5Zb59TGT3Zu8mqWbbfMhWJtiDdaPP7Ji6+HD2/tMCgcZTYMGsVdKK/YiH81j/Hjg9783L3vhBfaaTJqjUG4IFo9cDthxR+Ddd+UEpiNBrH/9+msWpQOAmTPb/nx8OMNOvNpOrMULCiVY4bCaYDn1ucpkjHFYVoNFz5RMdCadVkewVM5oMUXQzTz5/ffAjTfKywf4seLLL9l4MH9+551H8oVto+G3334b5dzdlc1mMXHiRHQTrIbjeH1WH44gQ5mvu7nkEvZ62mmFKULxIIJViBpORwJFR/g8YqewMyn+8besG4NTFsECWB1WsQgWP9g4RToqKrwbUjzB+u47YNEi43+3oXqvwhr0G5HiGE00rY0dd2TfUTQCYzFG+mh5MRUU337beF9XZ3j/L7/cIPT5EFlZzZXb30uW5pdOA7/+mt+2nT2C5fX+7cgg44d+H4pCexFQccLGjewZjsXMfXlUBOutt9hnRx1lXp7NGvPcaacBv/3WcR19Yv2r23pTH+2HYs0p69axOV5G2Pj+TV7B1x05RbBUBCsWY+ul09YUQadtxQgY2YJOBIte3WSikANGJrRhp3rr1Bj9p5+YEz0fYtvRoLxN+/TpgyeeeMK0rHv37nhemP0DgYBPsDyCbnbZDbRwobrXQb7H2VQIltgDC2DX0I540Db8Ncg3ggUwglUsmXbeaHWaMPKJYPGRiHHjzHVUX3/trpbAayohfafBgxnBise9147lAzL+VBEsWl5MDxqlQwHAqaey19NOA/i+6/xExzeXVCGVMqKuPNwSHJoY+WfkzTfZ69Sp1jRGHrIUQTIS2lq8pFhoy2bXrQ36feherqlhr/mMR6qo0n/+Axx4IHv/2GPOBOvww+X74aNfS5awtK181NjaAk73yPnns55Lm8o82tmxejWT0C8Uzc0sPe/UU4Enn7R+Lovou0U6zZ5TPsPGTQSLnjNR5l1G0njstx+TRqf1xf2TLajaXiRYW23FIk52zgZxPOCfebtnihw4MtTXM1vlyCNZCnNnh9Ks+3hzS5ZsQ5DxLjO2BGX8ohyno3oOvUJGsHr0YNeMCindbEMEy86Toopgde3aOimCTsgngiWe+7HHAnfcAcydy0L71H+jmCADn47d1gRLFsFKJu2LgvPF9OnWZZWV5v/5iS6RcCZYvLG8115GkfGf/mRWhXTangRZeDiRJJVBMXBgcQwaH4VBjDDSmLZ6tfd9qeqI4nGjZxw/fnhNEexMKoJOEcD//Y951H1xi46BHXeULx81yltKJ42HTz0F/Pvf5jICwHjeqLebF1Czdz5FkCdSqgjWypWsXyVFsAB5DZaI3r3N+xNrsGjeU20vk4EHrPMZD348yOXM34nsKpnYTSKhJlhkE331lfq4nQl+DVY7gLwJrU2wNrUIlixFkApEVbLrNLDwD7QonyoD/Q6yCFZ7EKx8Ilg0yA0cyF579SqOV8hOIYxSskjSvpg1T3bnce657JV6hhFKStg5FDuC9cwzhiQtDzEqzV9vN783n5qRT7E2yUvLar9UEr0EWYogwJwYxbrn2xpuGix3FtDzTw4L+j3/9S/v+7J7hukzfn7KpwaLRzEacLcWLrrIeZ36+k2jjm9TAC/EUl0NLF/OxtnrrzeWp9Ospt3tnHnhhdZlZB907cqimF4chaoaLLJDeDIik2wXZd5VKYIE/nmT1WA5ETQxgkXnZycgxjunMxnz/Eaf0X4uuMD4zG4epO02lXHbJ1jtAIosyVIEeeGLQrGpESxZNIqMAJXhrEoRBOzTBOvr2XritWsNgvXbb87r5hPBSibZIP/dd8ayYuSu23mnZ89mrzvtxF5bs6iVhCsA4KCD2CQj9puqqmJkudgRrJtuki8XnSZjxgCPPMLeeyVY+dRi2hEsJ+lsWYog/V/M2rW2wsSJLPLnFk1NwHHHyVWxOgLo+SevciHpj25k1H/8kR1j9Wr2zNsJC4jqg+IYIUvB6kzYlFJNNyVs3MjqfY880jxX33QTI02kticDf4/Kngc+ol9e7m0MVNVgyZy7ssiW2KjYiSDx508pgjTnuYmAiQRL1ReLB+9wSaXk8xst4+dlu3mwM7V3cAOfYBUZH3/MivTsIKYI8jdxMWWuNzUVQQrp8wMpDViq66ZKEQTsB8yWFnYccWItNsHq1cudZ3TePODnn4FZs9zvP5kEtt3WnOJQDIJlpwpGBKZPH/bamhEsnmCp8uSrq9lzQPdHPM7qSj76qLBjiw2OCbKotBehCP4+DodZiuCWW7J0QTeQEawrrnA+/m+/Ae+/z97LCFZby+8XA1984W39F15gUcOrr26d8ykURLCKkXLrtq8dwCTunSJYFCUnFDMToyNAzGTw0fHA358079hFQniJc5nzgCdYVMvrlgDwESwiKw0NRuo2b3s89pjxnsZZPkUwH4KVyRh2kmz7piZm29x/v3G+gNEr0k0Ei0cqZXZeEnnl60ap/tduHnJ7vM4Cn2AVGfvtx4r07EAPPz1AvPFSTEOGCMnnnxdvn+0JmeS6k/GaSLCBjh98SfXmgw/Ux6IcahFdurDBqRgefTeiBwQy6D/5xP3+UylrWhhvPOfrLXrpJfVn8bjh8aP/WwuimqQMFAVavpy9vvwycM01wAEHtM45yaLFdI86OV4A6/O/555MaMQtUV23jk2cfPSLGlnajS0jRxo9xcRrGQiwnPjOJrGryvNXgYysjlo/RPdAMTISvKTshULAp596c5bImhbzqptthQcesI9iunVYtVZbCx/FA0+SaKyzm+P4FgWy9YgchMNsrsnl3Ku58iIXRBrWrjVsF5X9wBMbvgbLS4ogRbDsario7vCOO8zfFWDEk87js8/U31EMDPA2GJ0vnVcwCFx5JXtfCMF64on821K0B/xhox1ANzNFR3jDp5ipOJ3NIHLC8uXMcOQjck4RrHjcamj9+CN7Pf549bFUBIs8ULyH9o03jGihF6xb575P0ssvs1cvqn7JpJVg8d8p3+iS3aRFkT8ijsVu7Mtjyy2N9yqCRbVghHvvbbXTASC/NnT/nXSSs/FO9/FbbxnLSkvdP8vNzWx93iCke8BtzZ8YwSJ1qs6W5iUSkaYme6EPvv6hI4LuHZm3vTWNjj//md2XU6e630YWOTjiiKKdkmucey7w6KPyz557jgkjuMF55xXvnHy0DninEtlRduMtH8FyShGUOcNVyGbZ3N6lC3Pm0rgZjxuRUJ7QBAJG2i9PsGQqgl4IFh/BElP+aEyn4/LnE4m4I5JiaqMsgkXnFQi4y+Sw+72+/ho44wzgL39xPreOAp9gtQPEm5cnVcWMYHXW3jUqLF9uVssBnAe+RMJqaPEheRXIAyWCDHZSa5s9mzXsdKPwJsILwRoyhL16IeDJpNVY5olIvtLbdgQrHmfXmzx1skaIxQLfmNspgtVWkE1MPMF3MoS//JK98r9baal7Miwj1V4JlkoMIx+54vaE+D0qKux7q3T0CBadF0+eSfFvzhxv+5KRHVV7EDeRV8AstqFKI3722a4dRvJ/woT2PgMfhYK/l3bdFTj9dPae5gY7Z4kTiRBTBAF39llDAxtrt9jCvJwXuaD2BwDbN6XUqyJYIsHaYw/zvnlFZBK5kG1PYwjVfVOtNH8tREVAFfj5V6zBov3xBMvNPGT3e9HcSdkonQE+wWoHiDefKoKVzRY22fMehc5YpM4jmWRRHDEa5fTQyiRBhw9nqZy77aY+XiZjT7DmzmWvdI1fftn7Nd640T0B8DLAE2TGNn8t8jV07O7JeJwRgv792f+U011srFrFatIAVmfWWQiWk4jNxRezV156m5QQ3UD2m1M00e0+VESqs4nleE2B9RLBuuIK4J13vJ9TIZARLKqv8/osp9PGMwqwVKCff1b3tXIDO0EBwj/+sQUuvzz/YxQTXupZfXQMiM80n80SCFijG3YZFE72EQlFBIPe5l+VWBBPsKZNM5bz9VJ8DRYfwRJTBLffnr1SRk0uZ8yBFPGyq+EiRxPNj2IvLlG0RoZnnjHeiwSLInV8iiB9R7t5qKNmD+QLn2C1A+y6ZvPvt9/eaCbpFQ0NZjWsjuI1zBeUMkVGNSGfFEHazm6wpCJVEUS6jj7a/D/grQcHwAYatzVY+RAsWQ0Wf7x80/fsCFYiwY5ZWcmUg+rq5OtdfLHRnDcfXHWV8f7zz9UqX3aNElsDTgTLqfifDIattjIvc5siKOsHR5Opm9/75pvVn3W2OhQnD3UgwOrxCPQsu5nk//3vwshIPpARLBK88JoiGI+b043JI17od9plF+syMRV70aLCjtFWeOklYMqU9j4LHzweftj+c3E+dftcyMaKVMrac85NFgCNH6KDNpu1pqzT+jTuy1IEZREoUUSCP56bGiw6TjLJ9pXLGQ6XTMZ83ehYxx1nOHREiCmCNNfQtoGAIba1apV8H/z5ET75xNqGwm0dXEdAJ5syNw2ICi2qCNaUKebGo17QqxcrCCS0Zi1MW0DWOBVwJ3Ih87w7ESxVBIsMdhJJ4H8vLzUKACNYbhUeixXB4o1vN6R7zz2ty379Vb1+Om0co7paPcHddRfw9NPOx1eBdzzYRVZaS0GT+q/x2GoreX44T7Ccokj77MPUlkhxCSg8RZCM8LPOUkcUKZ2F+onJ0JkmNkDdH+qXXwxjgE/V8UKwvGLaNNYgtZBaKRnBIk+xV9W+lhZ5E1FKGeKvC+HTT533K6txFI3ejlwbvN9+xvt4XP6cA370q73Ai7NQSxAe4j2tet7EiFWPHvJ1aC7zMv/aESzRjiFyQ3MYL9MeChky7yJBolc+G4qPYDnVYPEEiz6T9ejij/HKK8yxJIMockF2K58iSOUQdirM4ti7777A3/9uXuZmHOoo8AlWO0C82VtD5EI0yDp7BIseflK9IdDkrfp+qq7hbgiWLIJVXc0G47592f9UMwN4J8MkSOAGXgb4ZcuYDLnM2BaP7wZbbcUKdR96iP1vp0rJR/7cEAOvtSMEIg3xuH1/ntaCLP3s1VflHkr+/nN6vhMJq0FKKYJuUt7sIliAIfDCI5czauVk5091DZ0tzZgk60VsvTVwzjnsfTTKyP7WWxtR0NaowbrsMuD7783jhVfICBbVpHqNnlOtpIixY9nvLJOqd9MqQBZJFu/njty0+pprWLP0E08EjjlGnXrsVhwDYL+9Xe2fD2dkMiyC8s037P9wGBg2zLqeeK+pCDLvcO7eXe6gnT3bWK9YBIvmApq/aF1ZBItUCGUqguKrjGDZRbAoipRIGPYofUc6B16Qg1dclpHWhgbDcVJeLidY4v5l8FMEfRQMuqFffJG9ylIEiy1Q0dkjWPTgiYXYNFDdfrt8OzuCZXeNVSIXABvEiZzw6lJePPxz5rBByW2KIBnNkyc7r7vDDizCtmCB+xx0FbJZFto/9VTgzDPZMj66IsINweKJgpseYKrzArwJLzz/vPFeNjl7QSbD5M95gqiKpHkhWDJSXFrKvq+b+8tO5AKwV8tS4cYb2Wtni2ARXn8duOgi8zJS9iovZ+mqv/wCXHstW9aak3whDWvpvHiCRQYknbsbNDWxaLvK+ULPLx/tvOsud/sWn4GddgIGDzYvmzfP3b6KCbf1eLkcExx47jk2NttJ/butwbv99s7v4GxvLFnCIigkL64aa8WSClVbk6++Mt6XlbF6IvEe4ZVc6T5wI9pkR7CCQVb6QZkhIsHia7C8RrBoLiRCZVeD9dxz7JUnWKJgGF3jdNrsGFq71vqd//53w56qqTHsDr4Gi87PybGtQmdsQuwTrHaAaKjICBafp+pUGG8H8kJ09gFeNWhRSsD338u3U3lq800RBNhg+OSTwN57m5d78fATsXAbwSIi9sILzuvytXeylDAyNt2knWUyZoNur73sm27yxFRFsIpxL/K53W5BhcFA4eIX6TTrHTV0KFBby5YVi2CJRh15v92kl8mUI3nIrj0RbVk6KOCuMXdHxlFHWY18Av/8TZ/OXlURrBUrWKTorLPMy++/3zkSWwzjQBbByoewUf3iu+/arzdokPHebW2W+FxNnmwItxDaQ93WrUOFr30E7Mfn1hLw8WHFffeZ/1eNtYEA633mBOoN+NBDBmmz69NGx+NTSFXgbZWzzmJR5lSKLU8m2fMrEh4xRVCMYIkiFHxNVi7nPoIljm2yCBbZRfz2/PhFx+PRo4cxj1RXG+/5edpNFLCjKrjmC59gtRI++kj9Gd3QZDjxhouYIwsAp5zi7dj8zd8W/YjaAiqCRREswGhEzEMVwYrF8hO5AIxrKkaT8vHwuyVYJSWMTB55pLf982IQBDIQ3USwRKI5eTJTHFPdT/x1KymRH4NXyMsX2SwbtN0YmF98ATz7LCNDH3wAHHhg4UYef12efRa4+255Hj9gzrvPJ4JFRqubOhuZsAlgeGxlhJd+oxNOkO+Tfs/OFMESDQBqcyCCd0YQRC/q9dcDJ5/MDKXaWnOhfSrF+kTtthswcSIz0GW/MZ8qky9kBCsfUPqOF2PGbaSdTzF96SU2zorjqFsHy7Jl3mvLVJBFzcaMMf+fy1kjIHb1nV5VNTujB76j4JVXzP/bXXvxd7XDzjsb71UpxQCbOwCzvaECb6sEAuw5I9J3221suZjypyJYFMHix14+IkX/O9Vgqfpo2REsPoLFg/p88TjoIGNOra423stSBN1GsMTjFjJ2thd8gtVKOOAAucEPWGU8+RtOVBgEgB9+8Hbsb7813lOEZ1ONYPGTt8xwL6QGSxXBUglu5GOAujVcAOaF9erhoYmBB5E6txEs2XVQkSSeYPGplDxOO835uE6gdAs32G031uQXYM9lt26F95vjr8vYscAFF6jX5esv3NRgFUKwVHV31JTZLoKlSofqjBEs8Vxlz4EKohEyYYKRUiOCDIlVq4BDD2UCMBTR5NEWBMvrPW2X/ibC7ThVUgJcfjmbg447Tr6OW6LRt6+3Wiev4CN0Ktj9Xl6uH2Dck//4h7zGzSuWLLGq6m5qaGoCnn++iyVCbOeY9PK78OvakTaKgP7pT+blt95q7YHJ2yrBILvfGxrYslyOLVMRLHqGaR6VRbBEgkURJrsIFtkmlOq3zTbsNZEw9mUXweKRzVrn8GzWTLBoTuHHPboebmuw+CBFZ41s+QSrFaGqL7GTaedVZAhei4Jvu81470WiuSODHjxZVIn/viLylWlXiVwA1v0ddhgbOFozggWw8/F6DNn+aTB3W4MlI1iq+4m/bmVl8vVEkYx8PLteCJaISMRotJgv7Ai4HZyMYNn9SlEBtxEsWYqgHamm+0BlYNDveemlzsfvKKDrfOut7HXAAPfbepnMZZHQuXOBRx6R1xMUg2Cp7jsy4pxA5xCLsbQoEg6wg5dx6tZbmWIij5deMv8vU4CTneOKFa3XWNRLrZ0sAupW2ZNw1FFMpfOaa4CbbvK2rQz9+9vXw24K2Htv4IYbeluW292PvINJbPYrgk9pdZpba2qs5OBvfwMee8y8TCRYfEofICdYMpGLcNhag0Wf2UWw7GqwACOtEHBfgyV+P0qr5PfJpwjKIliAO7uLwM9jIsnsLPAJVitCNeHZESxZBMtLIT9gNhBU0pudDXQ9ZMYFeZdkD26+Mu12IhfPPmv+v6XFID/ffMN+92yWKR7K6mb443r5bSMR7wRLRi69RrB4IkOpDiTQIoKPYFVUyAnW7rub/89HtrkQgvXUU+zVjWEpQy6nJp5OcHoOZc2n6X83jhZVBCsSYeebTwSrtaTuWxP0jNG18PIdvEzkqlTTM880G16tVYPFw2utbizGnEMkzW4Hr3OQCDHqP2GC+2379AEOPth+nYMPdiavr79u/t8tkZ45U55Fcs453n7X995zVx+0uWPhQibaksups3fsRF34cUz1+xxxBHul3kyAfGzmo9FONgOBt1UoRZC/13iCRevKZNrtIlj8/mgfbmqwADbH0PZua7B4yJ6bTMYYC6uqrDVYNGZFIu4JFv87ptMsTbuzwSdY7QCvBOvYY73tny/o5pvVdWaoUgQB+xQmuxRBXv5UxKRJ6msmGsBEsBoaWKPN8eNZAfmll8q9/ryR68WYyyeCJfvuXiJYYqRm9Gj2esMN8vXnzTMmtfJyRrDESW7bbc3/59MbqBCCRci3iSilm3khWNQfyI5g5XJyguUlgqUSuQgEWERRRqopn16V+trZGgwDhoADjX/8b6US8yB4iWDZkXS+bUNrpgi+/DJ7tetPx0M0oOwgqrbmCzEb4JlnvG3//vvm/xsbzeMKfb5hgzo6Rs3hCfzYKzp9eIwcyZ6N/fe3ftYegh3FxuTJ8lrE9sIxxzDlUrvWA3Y2EX9fq4z5YNB6b4tjc9euZlELO4LFi0/x2TaUIqgiWKoIFk+wZBEoLymC4vaNjXKCZRfB4p81PgJGczlFsIJB5lgtRgRLFIfKtydse6ITTp2dHzyJyuWMG468Ffw6ADPIZ8wA/vtfd/vnjQk3vQc6A1qDYAHyh50GBbuO4ytXAg8+aJxbOGwQp48/Ns5Xlt7Cn6cb7zEhHPb+O6oEPgB3EayWFvM+7BQEW1pYSs+kSez/8nJzbjYhkTAXkxdEsJIbvG+sId8CelIPU6WQynDooezV7veLx9lzL15jIj5uatdUIhcAm8RlESyqFe3Vy3n/rYmXXmLftRgCJKeeyt7LrgUVtvMF7uL2BCeyZWfo0ViVyxWnOaaKYFEDT7f1OE4poTy++6446eWFRsB4LF7Maovvvtv62S67ACNGuLuH+N9ZVISV4eWXrc3R3VwbWU1eayOXA265xZrKJWLRIvbdd9uNyc7bNRpvK5Ahne89w6cDr18vzyTiU6lJ3EfWYJcf4+3IwYknGu9lKYI8QfFSg0VO1XTaOF9ViiDfKD2dNsY+mUiGXQSL5giquxSvC/99aOyheT4WM8SteLXBQlMEO2N6IOATrHYBf7Nns8YNV1YmJ1hvvulcSM+Dj0xsKimC+RIsuxosQP6w06T55z+rz6dnT6PJZyDABkLem00eKVmUiI754IPMO+oWH3/MilSXLHG/jey7B4NsuVMEa+FCFpGaNctYRqIpMojXX1X/R810yfPutnaERzYLBAMp4OWuwOqvnDfgQBEMO9UoN/ASwXIjFEGfib+ZqlmmDHYy7SrRESKasibDhAsuUEe4ioVLLmERCDvHhhssWmS8t2u0zX+fJ54w3vOTeSEtMkj2+ZdfjGXFiGCJ+6CCdTcKZ4BBPtwQrFjMmxCPCjvsUPg+4nHgzjuN9K5XX7WuQ1GPkhLg3/+2318mY0SLxeiWDNXVwO9/b17mRLASieIpIRLWrTPfAzInwOzZwJVXqoVGCMcfz14XLGBS/A880P7GLM2PTuTQLWQ9MnmC9b//sddrrlGvA7hPEZSpCPLOEZlMu5sIFk+Y+N8olTLEM4JBa4Sa1ieVWxXBovGA+lzRvCOrwSLQHCgSLP68ADPBspsD+XuZP46oItlZ4BOsdgB/w6bTxg2nIlgtLcY6bnK+ecN5c08RVHn0xXA4D6p1cTIoab+BADuHujrr/u0IVr4euv791Z+tWWP+X5UGVFLiHMGaNo29UlogYB/BEid6FcGiOiE6t3wiFtksEIT2g6/70dO2ZPjmQ+z456/YBEvMpedxxBFqqXFjBy1INW9ENCIPu6j6ktEyuzolyp3Pp17OLXgPbCGYO9d4b0eweGcBH2XwEsGywyefMGcIH6EtlGDJ0jXdyB/zoPvMLSErBuj+79HDnadPJdRyySVGpM4pgnzFFfafZ7MsxSmXA7bbztVpWeBEsA46SJ1+l++9JWZFrF9v7clFY6rT+Yl9v/ht2wt0/GIRLJntw5MnehUdbiLBcqofIshUBHmC5UamnWrAKYJlR7AoXZbsELGmVkbQZASLxn+yIVQES1WDRT0ceduHbzQMeItg8cc5+2z1Nh0ZPsFqB4gEyy6CJUat3AzKfgTLgKqflZ1RQh5Hp2a0dFyKYMlgR7DsjL98cckl5v9V51Va6mwskyfrnHOMZXYEi36jfv3Yq10EKxYzd573CmZsknvMm9pEZSVLI8mndQFvfLQlwaqudnaurJp8KxYsrUY0Ke+AqopguSFY0Sj7HUtLjdTYYoMm4ULHqoULjfftSbAA5nThn7PWJFhurxvVG/FRu7bAvHnAG2+4687rpj6MHED5Il8lUB529XyBgLVXIo98nRUi+aypYc4XmfCB0/eTyeB7VUcsNujcTz+9uPsTl9E4Kxv3cjl2fxQawQrWT0c2mzNFn2UpgqLDl8oO3ESwNm40Eyz6/cSUQhnB4gUwiOSR45EcMDKCRXX+pNJImVgkpiQeh8a9SMSewBdz7O0I8AlWO8ANwSL1nBNPNKfuuPHubooEy874VBmvFJqPLHkKmPckMPt+/TM7gkUDlFNqDD/4iudFUuSySZTOszUIltt0lLIy+9qn8nLgD39g7+2+Jw+6N6+80tgHYD0OESzaVz73JksR1BjH6i88b79oEfDkk96Pyxsf+RAsJ+VKQH2PO12nvf50JgCgrFS+olMEyy5ljL8HnniCiRSsXVugdSqArud++xlKjyIyGSZgYXcd+cik+IzxRIonWKpojtdJvrTUqLcD7A2K44/3lnapIlj027ip83ruOeC665ijxC4S3hoYPBjo0sVdeNJNpgZ52vNVaCwGwRIzBrxg4sT8tlM5pGRNWqdNAx5/XL0v2f2dr/hPsVBsUR1VZgsfwTr8cLP4kmwsdiJYxx1nFqAItSxEYPm7yKYTlhRBkWCFw0ZWCZE7vgYrmzXbcqoIVjRqjOciQeNVAen4S5caDlAimuSEo/9FkYuFC4Fx49j7O+5gr+vXG5kpPMESUwQXLWKpvXzaNA+fYPkoGPwDT6FVgBmk9GD/7W/sNRg0CwK4uen4CX1zSBFUkSV98GqeCXxzOvD9nx23AdwZnDwCAauhRPncsoHktdfYazGkm0Vsv7279Xr1UjfCBsyRDtFjqiqEFovwiaCKUZPiRbC0Ay5UdIF1Aa+/Qb4Ey02UoVCCNXNhXwDAkIFyi94ughWL2Rs2PFGZMwc45RTgggv62Z+QR9D1XLxYLehx661MWpyMheXLrb+h6t6tr2fGI4lb7Luv8znRPX3rre5S6lIpc18+Ph2I3x/ARD289DhUESwyXj780HkfJHUcjxcWTSsEbp4bt8ZVIABceGF+55FvqwU3UI0rAwYY46dTfZQKqvFSdNwSxEa4PGTnmY/jqZhQjUPnnw/cc485Qu0Gd9xhdXSK6X9ERPjPAatz0S6698orwNSpnK2CFgSDWeRyhjNnhx3kMu3hsKG6y5OuUMg4JtVQbdzIzo+uExEgimDR+MfLvPMETYyA/d//sVdxvqb/k0nz+scdx/YZDBpZPrfeapRj2BEscrZuvTXwlaR0mj+OKCjTGeETrHaAKoJVWmodPBsbDaUWwF0E6803jffhMLu5v/66eDnN7YF8UgTp/3DIOiORwSjzLuspUyU5oHmp8pz4wcMuvSqXA+66iw1wixcbPTy8/h5ujIGmJnacI48EHn1Uvd4WW7gXExCjADU1cuNM/I142Vge5OkqKIKVySCYzSPHT8DzzwNXXeV+fX6C9aIi6Oa76vdrngSLEArKBwm7CJZTnyj+HqAo6bJlRZSGg7vrScYAwJRV+/SxqqvyBIs/78pK9mwcfTSrjTriCPb/n/6kPh4ZQtXV8kLrO+9kaYD/+Q/7/7TTzKnFyaT5dyvEK3vrrc5pZTNmqD/79lvzebUX3DxvXq6TW3VdGrMaGlgdl9jjzy0++cR5HdXv1K8fsNde3o/JQxUR5cdZt70WZdd5jz3yO69iQfWb3H03I1lemoYTeGN99mzgp5/MolQiwSI7jL92n3xifoZkKC3l5sFgFgHkkM0GdUGsRx6RR7BCIYNg8aQrHDZqwwYNYq/r1rHflwgQrS/WYIVC7E/MmBH7aJEokBjBonKAeFzuvKb9E+wiWLLf9KOPrMv486LWJp0ZPsFqB8gIFim7iUZUPG42fpwmnilTzJYSFVm+9x6Tr+2syIdg6QNVUE2wZIaGLmO85nXg9X7Aup+k5+SWYK1aBVx8MTsWPzl4UYcD3PW5ocbKb7xhpPjJHC6UeAAAyK5JREFU4KYGiyBO0JGItbcHYPwvEizRaVBoBCseBx54MISVG7cwFs6+z9tONJx8MnDzze7XzzeCRXV6rRnBIujiHwLsIlhOBItPlyUDL5stXghkyRJrhEhMoVq50nzPUZT/gQfMxpKKYPHoy4J9iMeBhx82f8Z79WncCQbl12i77Vgdy8UXs2jUAw8w0kdRgEMOMY8xsvG7mGSHF6QR4bWfYmuBiM706cwZOG6c1QnTGulBJBs9fjxT5f3pp/wiWG5aa4h1p5ddBjz0ECPpbjMjVFCp2/LXkB/b7RwXsghWMWT5C4Eqzb2QiCufNvzxx+yVaowBa49JO2eXHUwpgqEsgsEssrmgfj+Xl7MxicZQ3q6h8ZmfB0Ihg2BRBCuZZH+Ugs+n8PEpgkSAxJpvsf8nfdfS1G8AgOYm9iERrJYWq61ABIv/TdzUYPGQjXubQlogD59gtQNkBIs8+vQZyacefLC5caJTBGvOHBYHpkGY9xy0pgJYa6MQghUJWQ1ONymCpQ2fsTcNc6TnxBOsbt2MmapHlTk0tMUWkIJ69biFm3oNupec4LZgl9bloSJOvDEKqFXhCq3BktY9fP8XIOueqYl1BrJ+ZTLwBMtNIT4PJ5JULIIVC8vzWFSNht0QLNnna9aEXfdessPjj7N6oO++My8/9FBW70VtAsTIB6lB/vor63+UzTIFQYomAfnVOfIRAj7tdfhw67q77Wa8r642fjuq50ilDBlofn88ii3jrYLX+7W1QGnTY8YwsvPTT1ZFz2yW/d5u5NO94KSTDAMbyI9gudlGdGR07w6ceSabCwpN1/9RIZrK75ef6+3EfGT3I6/C2R5oDRVDqhVSQYxgEcnk7SeyqezSyvn6qFAwg2Agi1wuYBpHKiqMVDnerqE5mRcoCYeNcyEHVCZjtDoBrCmCYgSLriefIigTRCldwpyUzY1sA0prjMfNv0nPnvL6RV5ano5j12C9vt7aFLw1yibaEz7BagfIZNpFglVZyTyh4bBZ5tWJ4cfj7E6muq32yrMnNDUV1kuGYCJYi14GPtxDfxodI1iSFEE7mXa9yDOijYJBuZVGIfujjgK6d2M/zN2nXYBf/jXG6euw3Xp8+vhUURXcEqxIhKUGrF7tbl0eKmIkkmAngpVvBEtJNFLubzSR9F58sbvteIJSnp0DzHKZnwT2fT/8UH3+hRKs3+/DQj6nHCZnPapGw2KUXAaV4MvYsfbbuYFd7dAZZxhkxS7lFWC1jcOGmZd5IVjULJQn8HxUVrxnTjxR/QzzzpDPPrPujx933nnH+dycxn03RKSYzX4LAV93RgaVTKAoFAKefZalEjn1tHKLF14w/59PiqCbqIb4nPG/35Zbej+mG/Dj7D/+4W4bmUH73/+ae8l1RojZOk69I0WCdc897PWhh4xlFPWme/XZZ6374euVQsEsAoEcsrmQiWBVVhoOBRnBEmuwRIJETnkZweJTAkkSXoxgKQlWlE1uzS3MaCSCRRGsoUPZPo49Vq7OTDWibgnWPfdYfyefYPkoGG4iWKQiI8IpgpVIsJ+U0hD4CaRXrwJOOg+89x7zujjJnbuByfj84ndMOU5TjyukBktGsKg2qUezJp6QlYd6Bg5kaQZ//Svwu6PZ4LTj0O9QEilyqLBpMbDhV4TDrK+KXYqKW4L1k5b1KDZXlEGWIgio+2O4JVj5RrCUHtlFL7reh0go3DZv5glW5qe/Az9eoF5ZQH09q/24+mr557K8f0I0ap0YRZTFWrBFl+WI/Xox8P4u1s8VEaxXX3VOP3UiYIWAGk6rQMpaTpCJBnghWM88A0yYwAgW3WOicAsPO0+7yhlC+3uRu1XtUnndHAtw56Rob2cbQSbswX8/SuULBtk8tt9+LMXuppuKfy75RLCCQXP9qiyK60Swdt7ZnciKF5x+OjPcg0FDhZiguj9U44lbgiYik2HiLe2d6vXzz94EZESCRa1G+HTMyG//AmDMV2LTafpMj0AFswgG2IUQU41pHOZ7YsZi5ggW1WDJIlB2BIv2KaYIqiJY9H1KIuxAzc1ssKP9ZzKMYMVizHFEIhn07Bx1FHM2i3VZTgSLEAgY6Zrtfd8UGz7BagekE80Ih9mdpyJYqv5NTgSrRfM+0MPE39itTrCm/wN4lh3wl19Y/UGxIE0R/PIEAMWvwVqxAiiPNaKipMl0HBm6dWPXeL896xF/PIadhrUCwXpjAPDuVgAMVSAV3BIsmnzcEAvK9Sa4TRFUrUfnmG8ES0mwOJVIJ4iEwS3Jo8lu772Bo8a9wf7x6HZTpdXZRbDIYWKX5ptKB4x02LXfAHFzeJLq7vhJzK0RwkuaFxtuUlXzTavyQrCCQaPuwKgzMz4DWDofea7tSI+KkP7hD0wUQ0wPfvZZ+wJ6+t1VacVuxI86CsEaIwnwiwQLsJJaL5L2bpGviiBfRyWTxxfHKHGIKCsz3/ePP85EVwrBe+8Bt98uH44mTJBvoxq6Hnoov1TB++9n5Q0PP8zut/vyK43VkW9PrpISoLrxbelnsudAJFg0bowfry3IJBHJMtldca7gHWamCFQorRMsGr9EmXbK7qmutqYIUgRLRpDSaeMe9BrBEmuw6NkrjbFBplmwIbNZQ3UxFAIy9YuQSaX0Z6eqih1bRrDsHFQ8iKz6ESwfBSPduBIlYZZ+RgQrEnFHsJwYfiIRNA3+bTqpTtPCIbkstt66uLuWEqwsG0kKqcGSGUmNjUB1mcfCiMRqxCJsJIuGnS1GVZ8fC9LmimOxGFeEW4Ll1mCdPt07wXKKYBWqIijWa+SDSMT8bFD6hxPoXO+6C4iGtX9y3qx/1fel5TKjjwx2O4MjnRLu9eQG0+fkkeT3QenHfDNpGVorrckt8u3Z5LUGSxxLRAOhqsoQp8mnVmT1avm1/P3vDfl4GYhg7bqr/HM3z1BdnfM6bQGZ0T1zpvFeZZR5FRxwg3x7LvFz7N/+Bmyzjflz8d4Q520yplMpNg794Q+sprDQHlQqIqWq2xINWr5PnJhu6wbU+uM3ppeAK67wvg8+7ThvQZDlHwKTzYyVaplkRrxIsERSgnSjPraKz1p1NVMHpM/0eTCQQmUpm6yozpJID90PtJwIViJhjWAR+BTBTEZOsIJB8zziJYJVGtUIVjObGGn/fMQqFEwhs2wS7n8wopcXBIPCOjYRLNU8+/777NUnWD4KRjoT1qMc5E0IhdjkLiNYFK4GnD2V8XgApaXy0Cz/vqWFqacFAsBjjxX4hUR4EBtwCynBijB3s6qJa74pgs3NRj4yAKDbDs4nOHEsex1wvCtS6zptMsFJHS1+DeGw/T2wfr07r6zooVdBNsGpiJHbFEGqOcw3giUWxuoYoI40ihCVH93UtwGSiRcAMu4ilipHAIHkkWX1TvQ72BGsJWt7IZnmTixrPi8ZSSOyeuSR6v0ChoKVDG/LHcV5QRX1VvVsc+pN5bXuSOxXJjP27eo3Wwv0vKoMzs7U51B2f/ORORXB2mcf4/3DDwOTJxf/3NwiEjEaqicSrKEvD3Fc3W478/9EsETiwyvbycDL8LtRlSWoyGk2y8bCSZNYuvjdd7vfp91xChGqsFPCdI24tQfJSSexV7KN+DFNJFiWKKoNwYpGjaisSLBqKlhBJ6WUUgSL0mB1xeISdQSLIEaweMKVywGBXAqhlnlIpbL6sfgaLlUNFkEnWC12BCuDTNZsYBBhdEOwvv+eKRyr4KcI+vCGZwPsLpt2PdA4DwCQzloJVjBojWDRjcoPeqSopUI8HjRNYCqC9emnhirXX/7i+VvZw6NH3w6NjcwLRt6nUAhAj93ZP30O1ZfxnhuCPrDwESXtiXdSESyNcFZo5VD3J1xjdkOrfi/XnnVeuKF+pmOK4KefsmaHTqDB08nzLTtPFTFyqyLIR2xV5/DFF+o+YUrDIuciT4qD6HBwA7GnCABgwy9As3OuJZE4p2suRgwBdxGsT6fviiXruFCPQPxkjZ+JYDmlANpFDwpNbeLhVjSDPO3/+pf9evlGsGhckBn7W7FsXZx3nrd9FwIyxFRKgE6GiazheUfCuHFG6qyKYA0dChx4IHt/+unqtiPffMP6JblBIdHwf/7T/D/vfaf3jz7KHELiMxKNsnF6F6FU0knp9/PPjfdOUWVelZMfq5uamHjMmjVGrdu++wI33MDq3XhcfbW3DAMaJ2icyieDpijNnyUHfust9kq/DZ/aGYuZr71FXjzdpNtsYvpnJGIeNwyClTSEI7RtiGDRebhREeTPETAID0+wACDQvBCh1Eokm5v07d1EsAhk81ANFk/ISBMgFMxZCFYw24hMOu2KYPXubd8Hzo9g+fCOhtnA9AnA58cB2Yw0giUjWPRw8RPNAQfYHyqRMEeweEydaiznPUzFzm3PZopHsG65hSlI/fe/7CFlD6r2JdLGSBeJWCdLCr93KdvAnRybLey80C0tQgQr62WGMVuqqsiI63SXFPelAiHbCJaXwYlC8jIDlN+PLALgNkUw3xqs6dNZNEfVUHLFCu6fkMY8Yj2UYiQq8NfxuefcRST0ouQwd5E+3A14vT/w0T7AezsCObm1S8Rp/Xr7Y+RLsABgRG+O0WfcR7Dc1FgVk0ip8Ic/OCs67r+/0RzYKYKVL8EaMoQZoKLTAGApgrkccIL7gGnB4D3dMlCKkqy+CQAWLiz+ORUTH31kkGu7uo2XX2bRIjF9ihCPMxEgIsGAvSiQW3EbGcTzkxnoo0bJU+1URNmJYIm84bTT5OtVVwM7cIkXGzcy8vT11yxa/cQTTMiCIliq/d90k1wtTwWSH6eITfsZzOyLXHP6K/qSLl3YK6mE8r9Ljx5sLKTrb4lgbZyBPl1ZPjWv6gywMYaPfPMRLIp6JZM5fX+0TyIuAJszSeRCVBEkiCmC9D8Ja4SCGYSCGaTSIf1YbmqwCCVaBCubDZj6WWWzhsM/lGtCNme+8UOLnkamea0tweKfFTsC7UewfHhHmozkHJBpcUWweBVBLyHzeDyoTBEEgLPOMvZPWL6ceRX4/iD54pNf98ZJpxSv4QoNDs3N3ISa0RZy9UnduzPDnAcV8JvqqXJswHNKESyLcW4qTwTLbPHRoC7CdTf6NE+wwrYRLPJoytSNRIwYwYQaZB5cvuu9zEBVESeaiHkVI8CaekEFs+GlTEpN9JDyaTAy0CR492kXQifbkaqCCBZgk3rIQY9ghSX3xKpPgXXfA+lG6bYUQfrtN6sMMm+IlC9/EGheavqcxBfsWh7UVK7BfqMnGQvWfi89Pu+BpfcqGXYeb74J7Lmn83qFYOhQeyI3YQKTdb/4YnafnnoqcPjh6vW91nDw9/txx1nTXlsTqrECcE4RHDqUK8iX4MEH2avbyE57giLUMoJVWWmQSPHzsWMNo5OM5xtusK9tK2ZqJb8vp+J+FfF3cqCITYCfeII9lyJef938/1dfsfl9112NeT4aNSJYdvASwaKm7RQtamxU9C20gUjKzj9/dR7poMzwmXDAcdh+WzbIUVp+Q4ORkkcgJzM5ZS0RrC+OQ2UJmyybm8wnOHCgKoKV0MsTEtqcxcuYE3EB2JwqSxG0i2DxNVjxOFAaSyIUzCCZYj+o2AfLKUUwHEojHDLq2kUiGAoBwdRqcwSrabHeTNlO5IK3Q+3GUj+C5cM75j3OXsPlQKaZpQhG5QSLBjM+gjVsGHDRRcbu7HpUUA0WQSRY5OkUc6Q/+4x5uHj52Xyw702f4MWXrQTLTdqaiOZm4wHN5bQHM5dlRiwAZAxLcexYa8PO+g1s4+qyjcDWN7KFWWeCFY9zhkxpH28EK2ImWDIv69ix8qalUqTMDCgWU+e3U6H4/Pnuds334+DBN92VGX30nWprzWId1Mxx5UoAiXUIrWQNjngiQ5NHNApEfmDu17feMkb7eNw5MhCPAztvvQgXHHyvkRZYBILlJqqoR7CCNq7m3+S9sXgSM2+e+TOe9HSddzHwmbmxEfW1s5OqTqYjiIW5m2PKpaYZiwwM3uBxioyIuPde4LDD3K2bDyhdSSUCQ9cpFGKOhGDQMOYAlhbF379eiREfsZ082b0KVj7o0QN48knjf9VzPX++UeNj1yxYTHPiQYY534S5PXHJJfLluRyw447svVN6mXjP8mpu++3HiPiVV7adQce3G1ApIRI++EC+XEaw+vYF/v539v6vf7V+LnNIuHHglZRYI1iFQHUtZQTQDqLx/5e/rPHu2FnPitsCASBUP8203x9+sD4n9FzRMyhzUBNZWrfG/KB266aIYC16Qt8mmWQHp3IGOh+7FEGnGiyRYJVEEwgGspYIluicURKsxCI94iY2DOZrsJoSXIpF41y9Luvnn9nc4iTTrppn+fXJIdTZ4ROstsBybTQNlQHZFFKZiJ7v6iZFEDBPrESSZKAaLKeJQzWZu/Hi5wOZlK0TysuB224z/g+FAKzhtIy5CFZ5uTU3uqWFjV6lkRYg0oUtdEGwkkkgVlYKdB0LlA/Uo16uIESwZHjuORf7SaxlPyIfwUqsQXm51Yspwq0xqCJYPFF16v/D9wkhtLQAmHw4Qt+zynWeyJADIRIxJqzPPjMO4kZ4IpEASqJpIBBkBBhg190jwRInGTeeWiJG0ZxNh+af5Y2ueIIlHpuXSy+LtQAJs9uXCNbEierDJtNRq4LlAiMcOWoUeyWFL8AwNNz2uRo9uriiFvx14B1HQ4bI11dK9GvYdlvWEHjyZG+pTQQxslAsgvXNN9Zl3buzpp2Elha54TNkCHD22ey9HRHu0UPeODyXY8Rz3LjWUeLLByQQIYIfj52ueVmZWUpcbLa8//4wpTrJUEyC9cc/Gu9lnnseS5fKl3/yiTVtcdky51rD++83/0/PTzarfra/+spdBOuss4B337VfB1DLunuVWi/Kb1JnGA6Pn3sGACMF9ZNPrKvHouwHEwkWf22o3cvvTzM/hNGokTKeTGQN0rT+a30b2q9diqBXFUH+/3gciEVSLEWQIljBHEKhnJRgycoMwqG0TrAiESCEuGn9UAhYtr4P3p3KedhyaZPwxVdfORMs1fP45ZfGc9Onj3ydzgafYLUFIpVYvn4LbGzuCuTSrlIEEwmzN5U3/uwmyWQygJIS5wZvKoK1++6Gt8wLdtkF2PMGdRy/GLm1lgeTI1hlZVbjK5lgo0g0nASC2sUUCJbsOiSbmxBJLgYCESAQ9pgiyLw77z/8Bj77jC1audLsVXfsR9a0CHilBvjqFOAbrgvpjJtRVuZMsI46yt2pqgiWU0NP/l4UxQAAjRit+UYnUHzqDJ8PHgqaR3kxXUc1EMfjQCyaAhAE9p8M7PIku+4eCZaIJUucva0kOBP9eFv7FedYXXCU5gdYjQgitddeSQVa5geGCJYdpARro6EIQgSPN2K9RrAIp5zibX0V6DcfN84sxS6eD0XwVcbaK6+wyCeNjXvuaaiGeQHfXuKkkwojWHwUTlYHRHUXPJyMUbtauZoaRtTF5+jll1mqlkqquz3Qs6d8uReCBZgj7KrxojXTO8VoLs1BThEsFZ5/Xu1csIOqzUIgoO6dNmmSPIJ17LHWqJibqPWJJ8qXe03DpGu3//7etlNhVJ9Z2HrAz7b3QWz6hQCs85ksgiUiEgEiy7R09+VfG6Rp2BlcBMtag1VIBEtKsBKzTCmCwYVPIVQ/FYkEOzYf8ZLZY6FgxhzB+vJo03mGQsD6JsPrucMOALIZhALmqJZTDRb/nhdVSSaN9ckp0FF69+ULn2C1MiZN3xeBQ35An/OXo8sRL6BhYwYZRYpgJGIYr83N5mJ3fuJxIlhuCrvtimn5qJETVq9mnrtvvgE+n6WO47tphMlD5sWqrwdQd4uxwCXBikUSBsHSolGhEHt4pRGsjcs0UhZm28VXAcveV59siqu50SJYB+62QJfd7tmTedUJjtGCRi3Hb6HVBZ/LsXtG5qkmyNJIZFARLIKqpoMnWPz9Sjjj9ByAnE6g+N9+t93Ya0mJdfAUm96qiCSLYKVYBKt8IDD4VCAYBTLetIHJgXHddez1kEMYOXWSSgaAaMBBfuw7q8XDe5nFCY6Kw3fYjlyo5hX46Jfs2chkgGwuJOnBZgzxsqjtZZex15ISbcdrf7DuXIJHHgFGjiy8oTb9xqJBJxIPIpiqKOMxxzDjtFDwLTGWLDGa/+ZDsEQSuno18NprBtGhxqA8nCJ0PEkXQeOKOLa3VlZCoZg71ypvztdfujGu3PwuQwURWFIiBAqPloj3I92DThEsOwXKVIrJut9zj/n8+FrsZ55Rby9Gu8R6LB6yCNbLL7NGwyKciJKqablXyfZcjkVj33nH23ZOeP11JuwhQyzNwufKCFa4QkmwACCaYOJCqZYmcw2WFsFKaiSHf+Z5giWrwXJSEQyH2fmxXqo5xMJxhIIZ5HLspguteAvBQFYnWKLqoIhIKIVI2CBYgRXvWY7H45JLAOTSiEUSyGTZh3vu6VyDxYO/93jiN2oUGytV7Tk6C3yC1cq4872LTf8vXqJ1ydZqJXiCFY0aD11Tk5lguY1gpdMBi/LbwoXmqEkuBz26IoPbNJJMhpEHN320vEawlIPyEq6JggPBSsTZQaO9tjMIliaQEQgYA5oIPRIQ0AhWfR3w6cFAWuFeTnCFa6RqZyNVb1dHwbaVWJFaZIxSNezSRL2kCLa0qI3Wu+6SL5ddM9rHsccCwVWs9krWN4QMKJlH/+uvzf/zNXg8WDpEGghw1mkw6jmCRU0PxSa2qigCCUzccmIe3TPBJGoJomGnS+k2ada3RHKetpdNkHohc++dgR256Flynf7WLi02EgEw/yng/R3Mz5gC0Shw5pkumKgDKF324YfNy8Vn5Pe/Z156p1SpYuLzz4ELmWM77xTB3/0OOPhg9r6mhjktaF+yfTpFp+0iWHTNTCqb6LjKXEOGWFUPeUEKN41u3USnSHGSUEw1THHspCiBUwTr0kvt9ztlCrv3ZGPn4MHAySert/3DH8z/i7XJPFQ1WLIsC6foqpiiSfAqhZ/LMdEJrwqgdpi2iHWCvuYa+eexCBtAxf6Q+rXJZRDuMU66bS4HRMAmh2Sm1CBYiHMRLEMFWRXBisUMRUBAHcGiSE9Li5H1lErlEAmnTFkhoXAIoWAGiXjGtL1qvudTBMNh7VwDGWTSWaxcaX3WysvZdaFsLICNd04pgjz45+PVV83rjx/PiLadg6CjwydYrQzyJhBIRIIe6GzWIFi8jLJIsK6/3nhvN6mk08xY4m/UAQOYMtO4cew4d9zBvAMquE2p4Gs5VKBB12uawEcfuVgpYyVYvOFKYfnoiJOZAQ6YhDFkpAwAUpkII1jlg8yy6wp1OAQ4Rhvtwl5tCJajZ1ZMSQyEgeF/BkIl+M8d7DsNGmTdLBSyVyITQUIbdXVGPhZ//XhCwEMcoD/7zFi2774AkhsAQB94ZdFSsfEmYG52SzUaskmdFfRqESxCKOaZYD3zDKsh4yWdVccEjMnXpDApIsgxg6w6bCvrDQYAsblahFYi9U5pcrJnKZlg60diYWDYWcA+7wPRbiaCRVFbZa1Z82L2uuZrxQpmDB9eQDdRDRQxuuUW83K6D3v3ZmIFgwezCCAfCW4tyFLp8q1devFFa92cndHhFMGya6lBBpSomifea/vtx3oLdnQceqjzOm6Ir3idzzvPiKQXmj4oPos0hzt57p3aCxConQYPp+/s5V7NZNw7D5zuTVVq4z/+wQxvEZ9+Cmy/vdnh8+yzwAsvtI6ojB3I4a2MYOWyCISMeZ5/piMRIBpkFyeVjRoRKE5FMJmyOlZIrIKWEaGk+UdVg0VR36++MoTRUinm0OQJVjAURiiYQVyrRRd7Xw4dZJ7owsG0ngFBxw0FM8ik09JIUnk5gGzKRLBiMW8Ei8dDD8kdE25aiHRU+ASrlSH2DKDULrqReYJFD0A8rkmFc2lBvXsbKUZ2KWZiBItu7O7dmQGbzTKDxQ5uJx038qsvstRkx8FZhKOXsdd+pghWLMYeTl5BL5nIIhRMIxQOG4bmT4brsKpKLnu9fEMf9vsMPJGp0xFUBIuPNoS0Hy1rtYJ//FFeYGuBmOoWigEIApk4hoHdBLLr2aePvVSzCCJYa9caozhvXMp6YAHQUx8Je+1l9rpRZCkcyiAczrkqchaNfooq9ehhjXYlElnEghthGr7yiGDtuitTxRONHVUUQf+OQQnDKdOYQowrlsqqCYgYobU0xZZEsFTy+ACQSbP1I/RT9j4QqBoFJNaZ1lNFbQFo9xlc1xyOGJHA7rub65a8gs5F3Ef37uz1gguKV4vhFjLyrxJcyQek8EYtM3iI9x4fRd51V3tDhT7jU1zTaWv7io8+shLa9oRMAMltE2evhvicOew60XUtlGCJ49YxxzBlSKfavR49DJJ37rns9ZxzWANlHi+8YN3WyVgVx20ZSSPcc486wnXrreb/t9jCvoefLFJKYyuvsEg45xw238ybZzTdpRYjBdfe9D7Y9O/vd3tasSKDmwhWLmA4z3g7Y9gwIBTU+l2lwkbaXyDO9cGyEiy+tgkwCBaNAXYRLIC1XKC+mESwggHjRwgFmWx7IsUMS7Ivafs/nbwMg3vM084ph2AwZ4pgseVZZFJppNPANtuYr1kkAiDT4opgqZ4DsRZT5pjwCZYPJT745UDT/2QYR0MGwSIvkl0EC2CDN2Bfz0QES5ZbTg8wL7ggg5uGq4A7SfdwmHld7fr35IUuYxjB0r7oTz+xxXzdQyKeMwQuqK/QSqPZl4xg/aCVoLz47fFAzc5AMyfppCRYnMWri2lYL+J227HeU7ZY+CKT1+YRjAEpNguWzbkWgNUQS6WYYaUiRTJQtKu+3hjFVXn0PMaNsxqHdB3ZoGuwv9LSrGPzTMD6fejeb2lhCmg84vUbUZKYAaS4k82DYBnnaP6/UfEz6wRLlos/XLMIY5wlnlF/cfEZ0yNYEZrhrd/FjmClk2nTOgAsESzAhmC91g+YfR97H3DvAu/Vy3t9JQ8i36KoRbdu7J5Qqc21NWQR43xBjYqJRLz3HmumDlgdJ3zT5a++st+vzJFxzjly49Yzfr4GeDYArJ/mvK5HiCm6qmUyeCVYVI/F174UAlk0+PTTjXQ+u/ObNImNbXfeCVx1FXD77UxkioesrtBrBMuu0bIdZGJJdo4GGcHincQlJcBf/mL8T+d5xRXsGvDnXVfn6VSt6GpmA5ceervt6uTYUsu0Z5ENGIOU6AAIII1oOIFUOmxOEaQaLI5giTVY9L3JPqMxQIxg0ec0n1ZV8SmCAWsEK7HC9L8YwQoGcqipZF5yshtMBKu0DyNoG5abjk8IhQBkWow5C1aC5RTJ/a/Q0UQW8bKrO+3o8AlWK4OK/wj08FAxoSxFsKGBPQRi80+xV4MMIsHib1R6iMQcfRFuPcZiR3PCK3e9gVWrjHNwElOQQfTkPfMMMPsNTn2D5NCn/R8A41i8Ny6ZzBoEiwbc7jvqn8sIFk2Mq+t7MmO5kisESCksb4pWbfNPrYq1BMh41KYFgJblwJcnAI1Ck6RQDChhSfHlMcZEREPs+OPZsvwIljEMuK3XEGVU6ffasAFA0nBzlsSyuuEnI/03n8AsaJ5gzZljdS7wSKRjpkEdACNY8ZWmqKZbiMa9LIK1eLEReZBGsCjiFOLYmoRgbb89e3WMYGWsIUqabGVGXTql9VkJcw98zAPBallq3Hd1t0pWkIPv3ZcP7FQMy8raT0VKrEHxknrrFQcdZESFnWqw7CBzYj3xhPGeHHR54VdNHYBXNQWAxgXAc2Fgwy9571pGctzW35DR6vX34YUECoGKCFJKv939G4sxJbZYDPjnP9mYJzqjZM4LJ4IlzgFe5gQeqt9g++3ZPHHPPex843E278rGd94Jm0gA991n/E/X/q23DHXWokFIsd520FT1uulmS4qgiRjkckAui2jM5sJnmQIfT7CCSBqNhhMB1xEsvtcfH92l5r90jxABSyaBbNZKsHhVQMBagyWLWJn+7zoWoUAGzU1J0/np+9cIFi+s5DVFUCRPsojXiBHybTsDfILVxmhpYXcQ3ci5nDVFkFI8RCOTCJjKww5ongwFwaIHRFSgy+VYaH6nnVjRsdsBWUWwjtn7J/ToYfyfD8ESDf1DDwWGZe9h/xz+mxFNmsVGZhog+O1S8WZ2nUt7AwO0JPBee+ufywgW7Wf0EC3MN5arqneKYFVqI0EwT4KVEEQDRmlygMEosCUjIiUD9gJgNcSoEFQ5+f58NfNAf3Uq8PnvgORGfcBtaQmiZ0+WzkmGh0rggsCrrQFGlCmVAvCj0RW7tMSIYFkiL8EY+ndfbPk+AweqCVYuB8RTJaa0BLYv7eaetJ91Iwe4IVhTphjvTRGssv7Ars8aBCvMnXjWSrBeeYW9igRLl6+nyUqSpkfPpTxFUBbB6mqbImgbeZLUgMkQiXivr+SRr0x8a4O/jiYjoGEue45Wf1nU45EzzWsqNQ9KNdtnH2MZ/cZ77GHcewWhSrB2Fr3E7v35T+a9ywEDjObCBLf3VCTCmieT2qNb8OICheDee4Frr1V/7jXC5iYa7OR0EL9Tvt9RVYrw449MnObCC4EbbmC/XZcu3sVUfuE4edH7s7kcvwAAP11qSRHM5fj0QLavQf0a0K1CIeyTYwIRyVTQIE3ZpEnkQlaDxRMsmo/5CBZ/X4dC5sbBkQhbhxyYoshFMJg1zVUlUe1cGth3CAaynGqg2S5lv0cQoWAGLS1B0/kZ+weQiRdEsETIIl6dWardJ1htDDIo6EamCFYoZAxoKoIVibD0ErsIlNigmL85aX+yHiRPP82k1ktLmYE5daqzZ1pFsJAys5bKSm8pgqmUuYcMoEUNmpmUKqqGGwpymmFL0vK8BG863sSuc/kgtiBSbVIClBEsioC9fbcmjRhxIXJBBItSq8Kl+REsMeIxVMvDC8bYPruNQzAUUopzADYGGinDLXgaWPwyMO8xfSBcuzaM1auZiAL95mKdlQi+qSaPXA5A1Uj9fz6CRYa9LiMfKkVFjF1TPvIYDqsn93QayOWCusdRBxGstR4tLViPJSNYyn4oB30LDDrJmNB77AF01XpkaWIfPIhIqCJYlu/FwTZFUOaRj3Zljaq5ekCeYNk+3yvcqMyYe/flA74vWkcCSfc/+SQbC3Ws+pS9zhY6uxYIGpu//NIY/0Unx//+Z7+Pnj3ZWPvJJ8zhUswmuhiudRSvFAgWCaIEC2PIYq8mWWNmFS6+2LuXm55nnTwvfAGI2/S+UKB3bxatuvde++O4hRuCpSJt1EhXdUxeXdBNKxYxg4YH1WJt3GgQJTKOhw9nTtxPP2U1lCrwv1nxjWj7m98k3d+8yBBkWrOAbW2Sr6eCuhjOP8Cc03b88dqbTDOi4SRSaZgJlp4iGJCmCKbT1ggWL3LBj4sUsRIjWDrBkkSw+GyLkuRMAEBqAesUHwzk9M8jEY1gcTLtCAQRDGbREg+Zzk/ffxEiWCLy7R/XUbGJfI3OA/3h0Iw0mcgFiUfIBrhevexrn+xqsCiqtHKlunHgzz8DH3wAbLstcPXV9t+FL6aedNW+AIAhvRZa0pKqqrxFsPheKIRwRrsoFdrIGNSsTY1gDRhgDTen403sOpdq+WwhM/GprrYSrLVr2IXr00ti6KoIFhmwdE6hUrWkux1EUYTK4cCws4E9X2f/a02Pm5tZvr5UAVFlNMd6mP8PhE0DIcEkVmGDYFAuqJFOw7jeAEpL0vo9T8Y0pdoBWXQtZzP1Io0733EHe1XdLxS9tSj5hWLWlV3CDcHir7UpRZBUJknRMBQDttcmYYnBpkrztUSwAGCpuRGMPcHKautwDz4pGnLRsEjESrBuvF5yr650o8bCJt2WlvyN+WJFEoqNs85i3+nUU5mCoQHNUggU94RprL/zThYRaGw0118BTPHSCfTcHH208SwBxSBb2vfm600b5wNLNDnasFNzP4e9CwYYReOKBfEZ33VXRqIfewxaavaJwOfH5rXvQED923g1FCnV8e9/V6+j2ufHH6ubSafT5lYIJLBhBzsxLZmIBy376SfWkmCvvdQNpQH2G7QaHCJYppYguYw+D61fwtQrTPL1enZCGSpLzROT7giOr2IRrGSAI1gpTkXQfYogL3LxxCOGzUERLN1JL0awBJGLYMAcwYptYPXnqRQ1Pc7qjv6ykqy+Dzo2AloEK24XwWrR9QQAZsN6EbkQQXObOB54jVB3FPgEq42hE6yglWDRgEYESqaeEovZ12BlMuoUQT5tT9WLiR94f/7Z5ovAnC++7+hPkJv7JOY+daqlhqiqyr4XhwjZ94vmNIK1zU3aEu3W5SZ8MSKVTqYRCuXMxIeLEsnOa93qFlSUNCBa2d16Em4jWAKRcw0xghUMATs+AFTXav9HTN/3xBOtu1AKlIjkLRjmPGnsJqEmxoC7lA2ZtzWVApBqYNETsLQEmgD0KA3de9k0ulUwMr5gAVtE9/y++8qPSQItfbqK4dPiDWWOBIuPYFFK4KhLmJd/xIVAiWZVJKyeEPrufNsFgLs2fG3Z5MNN69jVYGXSOdM6ACxRXkAewaosl/yQM9w1nOrfn3mrg0HjN/SCjkqwlCDnUdRGLz0P8M60efOsaeDTp7vzAt9zj/H+gQeM904CGY4gg5VXtyRZf8DkVMkH225rvH/9deCAAwranQVr15qf4WCQPYO9esGorW1ZWtyDwntk5vTT2e82YYL3fXbtKlfABNjzxRu5js3uYYwlFBnjQU5gfryRkS5xrOLVQlu3P5vao9Cvn9kWQi6D8lgTYpE41qxnF8YUwaJ7P1aDLmUbTPvSozrZFItgpZwjWCLBUopcrP8GZVOMhmdiiqCbCBZfg1WyiDn+GuvZsljUSBHsUz1f3wftG0te1wiWPIIFAMgkbSNY8zRT0O454FWtKRoqri+2Uuks8AlWGyORYHeOmCLIEyySAJX1fIlE1FEKJnVqbTRM4AcVVUrOq68a751Sf0wFuQNOAIacxibauNmw7NuXiQS49aKKBOuoo4BgVpsESdyitzYD9zBkl7p2NcvIZjJAmBtwRPGJqipro92N65rZIFo2wFg4RpvtnEQuFETONfhtuu1g/TwQNqkgvvWWdRVlBEuUfg+ETakKBC8ES3p/5LLAuh904lEaTVhSBPV7L5dBdy2nnYxzikKK6bG0LdUP9qj0ns7jFpddBpxwgnmZMoJFM0GkEtjhXiBSYRCs78627Ju+uxih069NKMlImgR2jYLTmlfSRFQcCBalGldW5J/jx48pc+d6375Yam5tBhrbQoVFbESIzrR5Zh8Vqqvd7YdX95w923hfuDGr7YBvP2EaDwszJfhohtvv6gWlpTakgpxWQWHiTK7XG9O7QS4HjBxpXuY1ghUKAWefbV/bWIz0KZ7QT5qkXm/NGnmj1xtvZK/8eCOrnxHnI97u4B10/L1aFEgiWP/+yzMAJP3VclkEAkBNxRqsmfsrAEUEKxBClyGSeRkAQqWIhFNIJrJmghVhFyiT0QhWLodgtllbZi9yEV72gpkwheQpgrxwmqkGS4hgERFa08BaiXStThgRrBjbqSh6EQpk0JKwIVi5tC3Boki0OGe9/75xD/GiamS7ivd4vkIt7Q2fYLUx6OGgB0EnWKk1KAkw7yhNrrJGr3YEi5a7IViqCBafZicqxYloaQFKSnKY9q+xQKWWuiepP+rXj0UF3KYJig/j2WfDkK2mVLBeWiV3d6Orpkiw0pkAwiGeYJVaCBZgPq+WphRKoy1AjItgbfV/AAKtH8GiKNPeE4H9JSlamgFw7n73WT4aM4a98jL1JmyYKuwrrHeWz2SMGdHpHuJB0tI8/nqhdo0qmPpiScsUS4ogT7AoNUOMYAGszweBfiPal7XZb3EKTui5oP5tBGnUeNg58p1Q2qCEZKuMI5NMe4W8Y6edimgmo6UIhrjroCBY9BuTI2fLkRJngFhrowA/6VKrBC8olppbm4FUKm0aieeDQMAsiy2mcJmMm6VvA1PknYJV43rBkEWw+PEwJ5mUvj+fiep4RGsQLFtQCm1AGPRe7gZ8IemQa4NZs8z/F0KGVM9EIfskG4Anm3b1tt2729di8edIaY38+R1sbkdlcsra9dQqGLksa1Pxu43AgV8DVSNRXsLmZFU0pXvlWp18mCNYNL+H0LVK4TgNhhENJZFKZkwpgpGo4TkKBsFqn79nxXCOKoJLnjf3tZKkCEYi7muw2OdprG9i0feqirROqKKyGixAq8Fi/4hjSy4HIJcxpyEKBEuFAw8E/u//rMvpuOJv1GkccAJ8gtXGiGsRLLopmYpgDsH136H0yz0BGATLawSLN45lKYKVlcZDHI0y6fMrhHmaX1/0xlm+Sxw47OA4xvT/WVnnBBjRCLcKWaIBWV8PIwITFJ5yTlJaVCt8cdKOmLl0uLEgbI4s0UTOpxW2NGdYwSufAhQIAsgB02+Qn7A+QRdIsMgjXDnMrEannwfb/4FjPtAXUR1cnz5MBfLooyX7ja+yevRms/yhXA545ZUu+mIvEazRo4GTTzYvq6JoSA0jvqXRFksESx+oc2mURBMoi7VICdYddxgeeZKbpn2VRvK4vi6gSr/ln7mWFFknipmaf4gk8tWUJsNHFRIJIBDIsQkyKrcw7QiW9HdzmSJYktNSowafDuz0CFPdDLkTLeAN/yuuACZPdrWZjkwGOtHvFKBr6bIZsxfYGc4mI3fyEUCdxLuRL1Z9ATSrFIs00PiRiQNNWmogL2YkaayO2f9jojouQfVHbU+waG7hbmb6vkvf9LQrapRLaI37upB9zpnDolK8I9VprO/O+RrF2m0ZCeTv4z33NH82fTo79h//6K6PZv7IsXk7UsXmokAYFIW1XD9tnKypXIO1jezLmiJYSa2OINIFXSoVBCubRiScEghWEmFugAyFAMy6C8FgVj+GTEVQF7kIpS0RLC8pgrxKIL+/poSWXZJdoNuh0cpu+j5o3wC0FEH2TzSSwyHbvKvvjwgWDxXBcpu9RGnNIpnrNPODAJ9gFRFubqLmZnbJTSmCmSyCgSxKAysBMG9+165ybyTvgRYhiz6IcpdUdBqLMeP4llus+zlHc8w7NRxuaQFKIxopKNcqwYMlQGKNyaAnb5nbHi90XAohb7MNjEnQRswgFnNQSFNEsPhas5aWHItgRbu4O1nA6FlEE3SohBVOe0Vcy9kq6SX/XItg8SF56h+STNooscnSFdd+A2TNg2NTkzeCBUi8mzTgRpiVVBpt0Ym13uspCtP9UVnaaKSrcQQrEjHSBy69lDWB1mW9o3mkYNpA1gCadwgsXGi8X7WRKrdd5F3NfdSyaJrWq5VX4WxuBgJgqSomQ4+DPcGSpAgG7QkWvUbqrtHWjwBD/8jSbl02bRbvOV5YgcecOcAXX1iX8wZGpwBdyyJHsAB7glVRIVk45QpGjgrB6q+Aj/YA3tnSYUXtXp9zP/DGAGZ0mghW4YSTnrc2J1g0JzQvBjZMNy/zCDGDoFhqaC+9BByraXCsW2e/rh2qqhhh4gmWk/EaDLLm8oB17HEiWDI0NDBxkVYjWKlGYPa95rT4QEgtfKHNqzUVa+QRLKq7jHVDlyqFBy6XRjSURDKZEwiWYYwFgwDSjXpUilQElTVYAsEKBLypCGayYXMEq+/eCIfSaMyyHiuxBXfqdmgoSDLt5nYfoWAGLQkiWBm8dsnRGFizgPveZhtCFLnQV3OwjcXxrbNGrET4BKuIcEOwmprZ3WdWEcwhGMyaevuoulcnEmpFFVkESwSlCNilktx/vyGmccklwL8U9e7xOFASoYNqT8hcTapowbOW9XV5bgfQIH7ssewhra2FOoLFgRcAaW6SDKahUpNRQNeYV8OLxwPsdwjZ5EWIqJ+p7VBLq1r6JpOUb1zgfh8A0LyURa54aXgemjeVF0IYNYq9JpOKtL7EWqB5CXs/ZgKwL1dRmtpgWpUnWG5zni1y7TTgRquBQaeisqQBjY3sZiTvVDQKk8c7Eoyjvp5tJxI2nnD9+isQ38jySix9sNIeG60J+Ogjq0OhvNyQxuYbZO47WquDqx7jvOOw1TK+8072ytdY3HEHkM1pM1MwCvTc27KdbYqgriLILVREsGh7Gi+iCa0AQq9FiVlr9hQQx5HffpOcW4ZJN4upSJkMUFfXySZTMtJaIYLlOSWs7t/A91a5PSfHmAlT/8ZeUw4qROKEkmkxpwXmUsDqr1EIumpJA6q5r9VAiq+J1cC7Y1jEP4+G5YC14XExPO8jRgDHHQcMY1nXempvW4K+h9giJh+CRdiwoaBTUmPWneyVnxM2TENu/TT5+pozsKbSIFimCBb1p4x2Q1cVwVrzNYtgpTjp9WwKoYjhgQoGAWTTOsFS1WDpKoLBtClFEGDr8nae2AeLXz+ZiZjS98KrJiIWTqC+mU2yJZE4AgH2XL/2Aas5/2UJS6+g784iWMwYiEVSiEWS6FHF6p9ZBCuNXM64yXv0AEItC9j3SxtjhlMNKD+/qkD7UPXI7IjwCVYR4YZgNTRpBItXEaQIVtTwmqkab1IKjsyQef994zxU/QfEJsMqkKf7zjvVkrEtLUBpTJvNqQ8KDWrcBEUpeG+6zLbga3X0wdplBEsnWBsl9VLBKLBxum48yq5xSzzIfgfxOEPPYjndMrQsZ3U3YtRr/RTp6kr8drfniZ0GHWkEa+UnwCs1wIdaQUflCLPal6RPEw3uln1l08BnRwNzHzMt3mUX4zouWQKuIDgM9NoLFSWNFoIVCsFs9IeTSCbZcyEa7Lxna948IL7sBwAawep9kPFh952M97/eBK8IheSkkldiA4DcMwHU9p0JHDodGO5GS9o6s5CTQ2kIp+qB1Vpkot6o/vYcwZIQLF5pU49gkdIUEcbGeUDTfFckQhxfZs60rvPaa8Z7fqKdMIEV0MeLG4xsXbRiiqCKYNn2Rmq2Kt+JhDUatZE55j3QGyX9MfT1hPs4sRqYeqXx/09/BT7c1dJawAu++IJJibdZQfuGX1nT6E+FQqENv8jH4SVvWBp3ixAJVjAIYPHrrB4tDyxcCHz/PXt/++157aIooEbQYoN5mdqhW1LppTemJzhEH63nlwMqR6BX9UqsbazB+vVCo2FdObQbqiokz72WCcIaDYcMZcBsEsFICYIB9nkwCKB5kan+XkawvtR6mIeCGVNECjA/2+EvxyO8/gtTBCsSNtZPpqOmCFY4lEYsksDGRjaRlETiJnIEAL8sYnJ9lF0RDGS5CBbbV0Crd87l2Henfey1RwKBABCawu71TJMRosx+cSrwoSFIJsINKQ8EWMbODz84r9tR4BOsIsINwWpsYjerJYIVyCIcyugPkJOMqszIOu009sqH3sXBZIkWzPjawdnY0GB42lWIx4GSKCUEayc8TuuOWdZXX8+rghUNvKawsV0ESzN2+PSn5noJweqmadhqA6bs99rYEEVlSYP1OOEydVpQfLWhHAcAuz3PXhOto3QXChiD6MSJ7FVKsOY8aP4/GDHqxACmksUhl2NRor59JS0C1v0ILHkd+PaPlujGsmXMEOjbF6aCYJQNQEVJI5qbgyYjccUKmK5lMm2cuEh6eW/23LlAPG1437DPe8aH/Y8FhpzB3v/s0MDNA/hebyZUb2moRtohZY2sydQAB/RP48AxmoekaYFxfZqN3ES6NrJU23Xr2IPepYozACQEq2dPNj6sXm1EH/WU01EXs9c1mqa30G5BBrHNAW8cP/YY8M475ka9d93FCOaf/sT69nQ6tGKKoEiwolGzhLEUksitOOaPH28YyBbwBOud0cA6lVKJMIh/qmikWIDU+bBh7L5oM8yzpu8C0PoYCg9Zy0rgs/GOvbJEYapAAMDnR7N6tDzA93cspKG3DNJ6XQX+8x9G0iljokND4QgVyYSOTBwIxTBye1avvWyZKkWwO8IRYPn9g8zbZ5mHiDUaDplSBBEu1W09XaadSxGUESyCmCIIsFRr/fON3yAciJsIVmnMuEm2qF5hkmmPhFKIhpKob2QTSSyS0M/l5CO4HUOLJke7IhxMozke074f29f47V8HoImg5TLYZiCrsbjyr2wyCMVZU8tMyrATcvFVwOovoYLbqOcFF3SSe1CDT7CKCCeCVV7SgsZmZpSZarCalus3eklJTnvN//ihkPO52CkDuT1WPM5kuAEYEaweWrSE83iSUIHb5pFLlzJPjalXBdWEyOpTtMnQFMGSEazyQexVSxPcZRfzx9kssGBZNQb3XGg1noMxdV1KaqOeZgAAqNF2/J1CZU4Gul4jL1Gvo8nF773lp3jicfYDP/OMdgopCcES07yCEXODVCFFEGAGM1/UrIOX3p9xq+mjrl255sGcpC1CJYyswkwKjjySW2/A8Viyrr/+mRjB6sbNlcuXA/FMFwBAyc5C3mogAJTL1fcKgdI54OSmPVDzYAgkFpATrEwmh37dNO9H3yONDziiP2QIey5+/dV6uKXL2O/arzcfDqJeccaX6N6dqXc9+aSxViSUAmp2NZolb6sJKLhIE6ypMf9fVcXS/gBG4A4/HPjnP43P//pXVmT/6KOF1ZK0G1oxRVA0MsaNM0sYy+EcLrB1cIniFKqouxjB4ntg8SD5ej7SE19je34dDjKCRc6yRvteBNXVZuGfYtVgAeyZKSZeftmmrYeAaJSR9Hzk/mneH+FOmBQ9e+Zn/xiQGz857VmxDN2ZOBAsQSzGPkgmxRRBimB1BQIhbFG93Lo92DhqJlgpIFRmJVicyEU8bnxXce4LB9P6ugCANeYwdCTEGhnTbxIOpVFaYjzPg3suMKUIhoIZU3lBSbcBut25/VZsfh/eZ55xLqJCoFYOcuUR/8Kat87QHKoZdKtYj9wzARy8P7O5QgF2DEpbB2zILeia2H7cadGmXyuZTOKaa67BDjvsgN122w0PPfSQdL1TTz0VI0eOtPydRiGaDgonUtOlohGNzczFa5JpTzZzDx3biVMEyy6tJpVSpwgSnAjW2WfLVQwJRGT0CBapjpHULefhpZzZ+++3PyZhzRpmuJkeOjLIZVEDSQRr7RpJXg2RIM0zLzZJXLoUSCQjGLKFxHgIRpnBKfuRM3Gz6lo+PXLIYCmpUa+jKQsGAsCh+67UFy9cqIhgiV72YMR8/SQpgm++KSk+XvYesEJdu2VCliNYwQgqStigSwIHxxyjpdGQYddtnGlzcWLlyd7HHxsqnLF+knQDhfqeF1x7rfn/bNZFFEGGmp2Brtu6JljNzQFDer779sCeWj4tl+4Si7FxoUWSAUPLysu4+14SwSIBAd5QioaTwK6c2lv1aO3YzrKfBx/MPNsrVrCmw2vXAls66SVoMKmKrvuJ/a36zL3klBtsnAk0LSp8P4tfAxrm5p8iWD+LRX3S6vSlgQPN/7uqTeu5l+Mqdk1rLWOESpyHCJakNhB9uIbYa75hr2u/M5aJLSI6CuzuM6pNA9g1+UYL96qIJYerrjLeF9NwPOOM4u0LYOdmucdm3QP8olDLhUO6qgL33MPGBbdO3SVLCkwflClaUnYDJDZRNgGEYojG2I+VSmTMKYKJtWzuDcWYE0oQdiCCFQ0nkUyHkUoB4XCOPVuhUt2ZLkawMhk2BtJ1EefvYDBnjmB9sLPp83AobZFh5yNYtA4dOxjMIRY2EyzaP6UxXnkcq6GPxcCUEbkIGEWwgsEculdq8xo/fmhzf0g7J2odAgDZnPblFXWaludkiTf1zo6KNiVYt956K6ZMmYLHHnsMEyZMwH333Yd33rHmbN9zzz344osv9L/HH38c4XAYZxR7hCkyZOP1Xw8xEqcrSlsQT5hFLnI5dvNRXiupDMpqrADDO+ZEsJzgpNzVpYu1oJUHHb+U1NyIYJABX4CHlx90dOiDmuSW1T7jI1hLlkrWo/5Cqz63fJROs2bIADCw50rL56wmKyfP784mCidYX2nyU2ttEow5L3IkYFinP/zAUgcsv2lAWJDLmZdJjH9A8rt/egjw2z3m/SjPkSNYgbBOsEjeV+/ITusJ10qcZMT7YMKdW2q7l0Qy+Shinka6aIxms8zTmxcUUU/6jnyab3NLAGVRjnGUa1E9wZMuNiC99FJmCNx2D1MICId52VB5DRZgJneRUMqcVkMtAlzWA+64I9CrF+ttQlDJ3fPglRnx3jj299FewMLnXR3XFd6pBd4Y6LyeEz4/Bnh3K2uK4Ae7sToeSSqoCT9eBCx7F1il1rEX+8K4IlguSLAeXZZBJFgLnmXfZ97jwopZoKoWGC0pyN2b63g++15WN7hsorHs4wOAHy4AGttBncEWijHis6PMv9PXZzDRIpfgnUQmY37Og8CkfT2dIY82kar+8ULgl+uUH++8s/IjJcJhcyYCDzGl8ptvjP5OeUOWvhvraU6PJyTWsVrl1V8iEmXjZTKeYvXlNDUl1xnjYyCkJFiRUAqpRBqrVgE9e1A6UalOgohE8M51/jgyFWAxRZAHRbD4/ykDil8GGGMJH8GKhVt0x75RJ8bVQufS5kbFQW4+IPuOvxbadQ8F2GfSCNZ3Z0q/i4VgfXaUdL3OhjYjWM3NzXjxxRdx1VVXYauttsL++++PM888E08/be2T0aVLF/To0QM9evRATU0Nbr/9dowfPx777pv/4NQWWCmzy7kHpLwkjoRWzM+nCKYyEVO/AkBd+/GXv7DXQgmWU6i/b1/1Z9ms0SSwNLSeeXWobxRFsGReJJcwDW4EIhdBjiAM/7P2GbvGpaXMK5RMGjUpd556kbF+dS2Tk5fUlqTTRj1J10qJ0ULE6ocLJJ/FzTVb+RCsJs3alPW/IsSMWSoaNAw6SsH46ith/YDweGdahBqsDd7PE4CtPDkfaQxGUB4zG+m68aE3jjaHrFwbEbJUUV7Ao0gpXLlcAZN9MCyd7MUIViYDJBIhM8EiFcu0+V4U++CRLPqq1VptJ29DSGTaiWDxYjdlsRazciUpH3oUXOGf2YLqqzZKciA7AjJxYOkb7D3dX1Sv9rOka6YJlK6pNpiiUaCx0Wiw7dToHQAjds1LbR0K0vs33QxMuRyoEgoa6rX8zm/+YF6e0/oK8Q3YVXh7BBPs4fHbf4E3Pabw/nAB8FzI8gwUD4prJkapsgm4ScUk8DLzJqP5u3OYMd/RsPITqViKDKecAtx2m/E/KT+6gWzds882/7/TTtZ1PEP2jAXDyMmeEZ1I5xCNst84lUyjqYlTq0uuM+570WkJ6ASrKVGOWctHYeHCLLbopZ1DuBRrG1lWiqwGyy6CBQBdyjYov6Y0glVqnptN5C5caYpgxSJx3T7NaZlTyQwbxClF8OvZuxrrz7rG2DEpiPLPZi4N5HIIJdjzwxMsPYKliOD7KYIFYubMmUgmkxhHDRUAjBs3Dr/88gvSNtWb77zzDubNm4dLLrGpTekg6N/fuoz3AJTFDILFqwimM2FTKBZQG5pkxMjShMgbtP/+6hTBl16Cflw7bLON+rMTT2T1IACwVf8ZzDtEkSt6LaAIXE6wJBGsbtubjkXCDE1NRs3POfsKMnDRLlKvbzoNNCxkBd5VwTmWz/UasyWvWz8TI1g8CXQbSdE94DbrDz4DGHACACAaMAgWRQssv6lFWrnZTLoUqX6HHupwrrYRLE7kIhhBr2qz1+EX6rtLv4EdobSDjGBtsT9QpXXHdtnHyQlr15qbVwNQ9ykTEYxIiZ5IsOhZLos1A2XaIBLWZt2vzY11wmGDYInpOoFAFiGXEazlWibYtoN+Avb/zHxfeIxgEfg2DOI9dOqpRg8+R2RdhL9aAw1zWPRm2Xvm5fz9Tr+n+LuSzHmqHpi0n9WJ06LJcql68WgoL2dKWffcY7QIsMXG6cDr/YDngqbfawzXQUBKsGbdBdTdBix+1cVBACDL7pGu27lcvwj47b/seq34qHX273ZsDmrZCy7B1yVK26Fk1SQ7LzQtBha+kN+2uRyLqr3eD5j/jKtNLrzQeD/Fg1Dus9bOLerejflg0ctM2VLm3A2EcfIuT2OHHXK47DJuOTc2RqLMdknGU2hsFAiWHuGXmMya83XEFiztaNGioEGwuHYvwSCAUIkpRbClxZ5gDelpHkeuOdpI3wwH05YIVmlMIFjc58ilTD00w4Ek5q4cCgBYvIJ92WSWGV7RSM5CVKP1nAeXxj9eyCubBlZ9pjWOz5pTBLPadcvI55RNlWC1WQeS1atXo7q6GjFuxKmpqUEqlcK6devQUzH7PvDAAzjppJNQI1ZTc6ijquoOgJ2GbsS3c404Oh/Bioab9RuNbvwVK1YhLTSEY8iirm6WZf8rV0YBDMWsWUtRV2dOVB42rD/Ky0MYMmQB3nqrB4AarFmzCnV1RjisT58QSkqG4fjjF6GuTl0PsGZNCYDB+v/8NX7ppVr9fY+yn5FAGeZpn4eTyzEcwPKlC7Ahwf8utZb9yDB3bhQTJw5FIJBDXZ2h+dxt5XL0AjDzt9nIhdhgUL12JfoAmDN7FlKxZjQ0VAPog59+mo3lK+oRDLCiTv6YA5M5ZNNrsVhbdvLJvfDss93w66+zsHT2DADbobK0wXKegcChGIVrsa76MKwUPhvSUo84EljGLacrVFf3ixHVs8HITAJBAPUb1mKpzTUqLTkKg/ACli6aAYDqkDIAQkink6irM4qw+29cDV6IcdGqOJria9Gl37XoveQGTc6cUyDQcMklc1BXRykAOdQKn69bv9ZyDQglzXMwGMDiJcuQKCnFzsO+MX1eW7scdXUbEGuegSEAFi9fh17VK7FyYy/07p1CXZ2E3FrOAPht7gJkwtbePV0rj8EW9Tdj1szpyIbzq8maPDmMvfYarv9PghCREJucVnY/HetcjDn9m5MIZRqwQFh3yZIYgCGYN28J6uoasHZtCMAIlMeaMHvIU0jX1SGY3giNKgr34lCsXduMurrlePzxbgAMshcOprFk6TI0NLP1KzYsQ38AC+bOQEs5m73XrSsFMAhz5jQDKMPX1++CWSs/RXatcYxwcgWGA2iadgcWtYxVfr94PG46N0bwrb8VAKTT6xAIVAJwfhbWrVmhvL+8wngOnfdXueED9APQMOXfWLKRSyvMJq3fasUHWPrV7dAD/fOfwJzSE1Ha9DP6rvwYGz+/CMsGMTGY0sYpGLSBKW0tXrwAjQ3O57LffkzNjG9GLX4nEfOmfYREKVMSeOKJALbbjkWnZs2yHq9m5RL0sCw1g79m/eo3IpJMYv7MmZbj19XVKc9JhHjPqNBt5WP6nb14yVJX18wreq1fC0XmmgkNzQmUplO6sTTz158QTq1FKtbPZit2RebMqYNYkjhrxk/IhmSdo93AOo8O/XV/RJPLUNe0pTwNzgbR+HwMpX84Z878H19GvGy043k0NtZBvCPtf1/zukuXrga4O1G2rdt7pnbK7wAAa3qdiRoAK/tcqo/TNWvWokflOrx/4/FY0XKDLsRTsXElyC++sZGl5ZxxVgl69GpBNJpDXd1CDGlYgURsCJbW1aHnirngY7h1dXUobZyJQQAuPuRO3DHxUgDAsmVsX2s2GM7cVCqOXDaNZIaNxS+/vBr19V2QSDSirm6FxvfN1ycQAN77x1n4sp7tt1u5IR4TDObMBCucQiBotulyWtZLLpdFLpPSUwT32acBTY0bsd2gnzDx50Oxag3bT4PWp7WpyZpCFQoZ5Ky5qR4L6+owpHk9wsEyhLLNmD9/DsLJFegPZvduoDQnGAIjmWQjfpP8lkuXVgAwRygKtevd3jetiTYjWC0tLYgKFJ3+Tyoawvzwww+YN2+eUgyDUFvrdnhvfSzbYM7V5olTSdTwgtGDUVPTE6nMKv3/E/b/Di98tCMCgaD0e1GfjcrKvqitNefxRaNAZWUzamtrsdNOrIfPTjv1RG2tmbwyj/kg2+8hehRU13ho7yaEUl2Nz1u6Ar8CvRdfj957GLncf/oT8MgjwKhRtbZpYB98wF5zuYD5mLkaYBkwatSWhnd//k/AImDYkEFA1QgM12zi3r2HY+WaxcjmQggEhHNfWgWs+gy1IwYDoRLsthvzrA0ZMhKfag1Xu5WvQ3/Z951RgW7de6Cb+NmsHGLdeqKaX6559mpHDHUXpZk/DNjwC6qGj0fVcJv7eUMa+A0Y0NswDaiHVFVV1Pxdl0UBLvoyYCdNzrF2AvAs84S1vDgS57//JR55xHBg7LjjMEMePd0CTDWfQrcuXazXgLC2EZgF9B8wCKgeBQjj20kn9cawYb2BVWvZeoNG4rmLz8K+E97EyJER18/yiFFbG82teYT6AUuBkcMGA6UuI00Chg+XLz9196cAAL22/SN6UaTMDiurgXjc8p3IcX7hhf2QShlpk2XRZgyv3RaIVDFBFS3ax29fWgokElHU1naxpAmnMlH06z8A6K+tv3QuMB8Y9NvJwMnsoBTlXLKkDNFwArFIEiNHbWVOEUz2Bn4Fyht/QO3QPkrxkLq6Ost3u/12Vhcmol+/bq6VwbpVBNX3lxdwkuOu7qtVq4H5QGU0YV4/3QT8bF2974LLTP8Pm3GI/r46+Quqk08D2/wTmPcdoLUz69+vr/H75AtF1GBIv65AT+u+pd89UQXY1NgCQO2okUzgoawvsKocaC5j+xKOX1tbC/T6CXjPObpVUlKC2th3QPcdWKsDFab8W3/bv2yl1m2+yFjrLo24srwMaDEcpaOWXcT61J2YNmcrSLDllrWW8XPkkL5AmZv8TysmT2bzvOk3ncJYeO3IEbZ9IqX49j/SxYNn/Q4YvxgosyORwOjR5t9l/p2DMKjXq0CXrYHnI8C4u4GRRlr93LksMkQCWuXlBrliUSPr7ywbZ6TQ7suabl2BNaXotfdthvupOQqsALqufRldD3rJ2GbJHEALEm2/JfuNV6wqxYpVTMCntrYWmJ1DrGtPVNXWAovMTu/a2lpgxVJgNtCzylCHqqxg42lNT+P65XIlCOQymLZoawDA//7XA9XVQJ8+XVFbq861PGj76Tho1zDwFiyNh3n7MhpOoku1+fcvK2X3ZyAQQAAZPUWwqqoS5WUlGFDD7NVYlA3OuSAb63tv0cVyHqXRLPPlAiiLhdl3/y0AoBxINGPwwP5ASwSYzwhWWdiwd4f1Yo7TULZZOqfMk3QEKdSud33fFAE//vijdHmbBeZisZiFSNH/pQrJvIkTJ2LnnXfGFnZydh0Mi9eaK4pNnbRDxiBtShHMRlmKYMUQ3H3x4wDUKYJk+MpUdlIpIBJhN/UZZwCffgqcdFJeX8NSYP2zZmDMFVRqQ2g0hcFVHjRKKVQ2V9WgzNqg1Bo+B1pIgaK+WY2NwItvS/I1AaZSBrD0GBjfM50G5i3pip5VK1Feosj5D5Xo+dYmZIUaLB5uUjcS61hzSwAYdrb9unp9jCFDTymCu+0mnldS7dGMsBupJJZGlCP+Aweae0/pqU08JL13dEz/h7ZOE0uRE6D319JTBMsQCrGb3VVRP4DyWKOatNIxFakIbqA6jwf+dA4w5nojDdEJDimCAFPMvEtrHVde0gRo0VlpCiSYmMl77zGngFyohhvSJffk4MHMebJqFYz7nCdXgPnaekzX49OHeFRUuP99Mf9J4McipITzfVfq3HRptcraA7BvwKtC00IWIW5ZIRSCa++XvsMa0HpBNs1SGFUQxqY997TZ14x/2Xyo4ZcJLHWsaaF2TWyO3W1b5/0BbID/5gxg4lh36wPAdE15JpsGVkzS22wUDJoLnLB8opECChhNwF0oCqJ5iXWZx9RbHnvu0oidY39TzEN51J3ajWWvK+ZQGwzqsRCYfqPxGwk9CYcMYYI4y5YBo0cDf/6zkTpc7sIP6QrZtHXeS2jkRxxX6XkcMwE1XcytXfQ+nJmEMZZueQUA4L0X6vDZZ9znAGIRw7gJEBMJG7YR62GVw6Fj3wUA/P73XDlEJs4aX8uQS+vnGQiYDSSTCEU4i9JS4XPhMlAEKxRi++XrwQAgkdZ6ZAmphgAQGniE9qbUqMHKJo2a82walEobCmZMKexb9ee+2/d/tuz7u+8si8yIr2Fj3+z7HFbsWGgzgtWrVy/U19ebSNbq1asRjUZRXS33kH722Wc44IAD2uoUWwV8imDERLbMNVjhqgFAqBSxoI3xCpavGwzKCVYyqcmDghG0vfbKX3lILLC+VWt9ZFGYSzebRR0UzVdl0tQyqAkWqdPxBmTY9BkN0KY+QVsoGsloxZlUn5BOAw1NUXQtlyvrAWAES2ZwZhIWsQYd37ronLlhmvHe6QcjgpVqtCgz3S3UlSObYjVnMujGdxCHHmrcTLvuKqz31jDrtnaKaUs1edWWZdLUSH0SJQMhVILdtpyKv570Ph57TL1bHpOu2k99nYgQTrve3c48IBzKONbQmBBwJljBoEGwaqobDI84//0koiyTJgHbayWIV1zBH5PbrssYiCgrM4IB5dFGucw3T4w9Gm0qElVebq4L4lFVak31xKw72esHuwFzH/F0Djp4Y2oKF2369HCrWE1yA1e3x13DdAvwvqpLrwsk1hiNSgHj/pl8OGtA6wVOdYWCwul778lTDF1julbv0bICaJoHW3EblwhmNSPWs8z9bEb2Pt4fmFwkhbFIlfM6dlg/1XkdGUmZeTuwWlQkcokZNwN1twJzHmYtCJa8YXyWV91zfmqr8+fL+/EBYHXKc+x7svTuDUyfzhw+P/zA2oMUDS3LrGIUVDsoNiGma9b/aASGmYVd9KhKlmvDot0zB+29BnvsIeyjy9bGxjRHSiKAw7dg4eyRQ+qRTGo1WN/8iamUyrDuB33cWNNgLpUxiVxEciitNn8/k6osgJIIm3eDQQBZnmDlAASQSDO7IBZly/99MjdudtEK82t2NurcskmDROYy0AlWIINMJoeuXYELzubqtACpY4KXYejBRQJ1NC1gr3Metn7WgdFmBKu2thaRSARTuKrIH3/8EaNHj0ZYMiuvW7cOixYtwg477NBWp9gqcIpg5XJAKhNmxChUitIQMzaOUswhgYC53xMP1n+hOOct9tt59lmmkmg5bqbZ5KVRed6p9M5JvtlEsH69ifuAJnfultUjWOxakvH+6iucIbCjIHJB0AZMPoIVT4a1AUhhvAdjcs+hqCIowskoV/WekYEIVqbJ8KBpsBRUZ1NApIt8P/Q7BYIYOtT4UW3rvrtqXmoFiTZtXDFU/33+sBfrkFlSwnkFdTGMCELhEG4//2n0s89G0dG7i831GqZJKgZD+Rd+w3AoWGCjAmeBg4ogYJ5YKssV+5Z47AMB43JvvTX/Cfd8KAzIRVoW85J1/W1lwwEAcxTPjwvst5/xfsEC1gfvgw/Y8efMYY2+Z09ZiMV3Kzzlcx5mKn3fatK+DXOBlQ7ny0MVfVv2DhNQIGTiwMtdgR8lCqF20Vo32DCdqfURlr8PvMk5LZa9Byx9192+xHupSkh/WWdOUykttcpg54X4ShbFEwmFG4JSe4XJARdKS8i0G7w9gp0HAKz6NL99iBCjN0NdOMN4JCSGoIYZ387DG389Uv7hnAeAD8V0A5eg+WfjdODz8cBn443PiLSmGtyPfU5z09xHzCROw6BBRr87qRgLtQBx4eHt3x844gjH1dxj0QtGmwvCqIvZq9B30dRWhJSQNaxZo73hI1jkfOKdHUQ2+nEOE/q8zNojgQTN6tczJ29pKYCVk0zrdK00R9Mow8UkWiH8H41kUVpqJpaU0UQwEaxchhPcCACBEHp0YdHVvr3Zfl/57lhjYz2LKGLuB0jPNzc+UQQrmwWC6Q3m7yIRibr4YuN9QEr6HRq7dlC0GcEqLS3F+PHjMWHCBEybNg2TJk3Co48+qjcPXr16NeJcUcHs2bMRiUQwePBg1S47BULBDPp2ZWkCkTB3A4aY0gqLYIVYJCVUimiwAQsX2ndtF3vhEH78EVi9unhldSUlZq/zqFHA1KnCSukWc4pguIx5i0rMdV95ESw+vUAWwRJSBIlglZeywW2LLsuBUsFqp4dbI4U8wUokgyyEvvtLkEKWIpjLWVUEAWBHrm7QKSWECNZBTnFy7RwCQSDVCEXgl9vvUmv6F4EmClHKnYeodlW9FYuKLHoJyEgYPt9Xq9+RugEW1Ypj99mH37dmDGhy7l482rwSkgVUdzXvceDLE/NL7wJw+eWK9NpaSYGRCvWzgIbZlu8mEqxYDCiNJTBupJB/S5BEAgMBI63DVNvE/56KdgEWVUQ7TL+RvcZXSSNpdujfHzhEK0saP56lBh1wAFs+dChrKzBs4EZUlSlO6LuzzP+/NQyYtLf7E8hIBhs+DWfVF+x7vaBdJ7pXTJO4iwm9xx7AkD/IP6v7t/n/eY8Bjdzv/OkhwOTDnI8BmO+j2iuA3geaP6ffyg162uUPClijiLYcvRw4do38M8K2txiNqwEEcu2kEEmY+yhT3QPMY3b5QKD3Qd729d05So9U7dC1OHLcW9LPCkJCu95zHmDjCw8ycF+qYmPfhukudugQwfr2TDOJk+Avf2EOk2Uv/M5YqM8d7WQQ1+xi/j8QBPocZk155wmWAD1VPsNFsMgxyRMs2ke4FOcdoDlu6LeIVOKSQ+4wn0oACIdSmPEb8zaWlcFwHmjoUtkClHLeEe3ZF/ti8erTkUjOKtMeNo4JGARLTxEMcimCwTAuOuEjPHPJRTh175cBAN/M4a8jtcqJGO/5CFY2rT8PjGDlGMESc9nXfGV5bnpwiju6pDsPfX2fYCnx97//HWPGjMHpp5+O6667Dueddx4O1fR8d999d7z7ruHJW7t2LSoqKhDs5PqNoUAGc/8zFM2PlZoiWMFQEMFgDpkMkM5E2IMQKgUyLRgwQCHvqiEcthIs6mtTV+eyktwl+PPYsMEsxQzAGsECgAG/Y4ZL3Jh8804RpAVUA8AbP5RnnTUTrBdeZtfghfNPAMSGtPtpfUi0prQ0AKVSQDweRElpGBhwLKQIlViNNr2fk/CDDeMa6qUFb5SIlmVs3yQ7b4dAgEWxfv0HSt4x0gUsqX1NC9mgvX4K22/tFebPiSjaeaETgvEULjNqxfgIAEH0sIeiQLdxSMVYAZ7pniWvXyDCogSrrc2fCX/4AyuKvvxCpmxUVeqhBqOAflhS4VLBy2mL9Vq0XmjayhMskly/8Nh3EIgKZJhEAHLW7+CKYInevhS7D03Nmw80qzwq8foA4M2hzutxuOce4I03WL0XH80ywW09yg9cP7t3RgMJRaNAHmIEa94T5jScj/YAVn4q2ZC7bnZpVzT+dN0G2O4O+Trrf5IvF5HLsbosOwlvXn66rK+0xtER8x4Hln/orVffjFvky8Nl6r5YfQ4F9teeae4ZDBRLgn/6P9kck8sB85+Wk2kR6RaWsv2RltuVTTOH0ck54KgF+Y0Vnx0FNC6QHMthzAfya4Y+/0n1Z9m02QH47hhGKG3PwWXa5/P24hlDhwK9KznHgf7cBIA3hwMf7e3uOMVCqMy6LFxmSaN1JFjZDPsuNL/rBIu7V2gfoVKMHThV+9wgWP868Urzjkt6IhJK4d0PGMGSSRDkcjA71jQ7Q4zumCNYOfTpZf5+7lMEAQRCCEeCOHnPVxH89XoAwN2ncVF9vRdp2Bin+BqsnLUGK5OB1YZvWQ4sfM7yncePZ6/dKrSUanJ4pRqBONWm+ARLidLSUtxyyy2YMmUKvvjiC/zxj3/UP5s1axaOOeYY/f9DDz0U33zjcvLvwAgEcohFkiiNxk01WMFgAMFATjewwmEA4VJpjyYdyfXAlychHM5amglv2MBeTzjBpoYoDzimHIo1WIAx6WaNwZ6ImhawVILmnIoSzatNA3UuYx0EFSmChG7bn249QEyzmrUBy0SwEkGUxGwMnGDM9J0AGBNa0IbYqmqWcjng+/OAJa8BJb3dh7+1NEEaLAHguOO4z+t/M3uzD/6eeZJ59NSMDIEwmJxN4mTET1qyVDlZ+mQgjEcnMk/7hx9yy+l3DYbZgCsT09Dw6KOsb9Ot185D82OlrDGuW+RjhGqg9Kpjd5uEpkfLgO4ea3HGakIC35mFS3inRSLB/kojDUb6p769Fv2QRAuzWaMPllmdTzGkr/gIeKkSWPWZuX9WjaKzJ6WD6gf0bhhXVLBoO++dtIDGO1UEiMA3rt04g6XaOUE0ur85w7rOjJvt9yFtWqoZWRVDWNR529vUtY5usfAFVpclNug1nQs36Jf1s6Zjd98ZjvjmD8AnB7q7fvniwG+Bvd8BemptJDhv/5BZxyk28ohp/wd8fRqr9/n6VOCTg4D4akZSVePtZ1rePTV1zwliCJE82josfQuYIolqu0klLqBXpHx/KeC3e83LfnW4v93WYLnpK8iP/7/dw15TG4DGOc6pyMWGbNwPRKw9smwIVnU1jHFPj2BJUgR1giWZH8MVlh6nGHiSKQujTMIFczmw55vSgBeyJmI5gWCYVAQjGb1Zsv55hP5nv3NJ1JwiOKI369214+gF7BoEQuwaaY7XcYO5tGMxRTCX08gnESzjnl/d0AOLV3ZhEayg5B6jmioOf/sbe9UdqCQQM3Gs8ez6KYI+ePCqL9GwOZwbDOb0lDlKEVR1ugbAFFQWPo8wGi0RLCJYe+/twnPmAWJ/Z0vTSlkESx+EjO9LRuUXX9gf7wGt5OOHG7Vojm4kZa3pbAqRC/2YPSQSneSJ0vZLxt/y5UA8EUKJRD3H2FaSIqgPwBIP32CN4KnqODLNwOx7WeqVF9leCcGiuhoAwNsjnYUBdn5cawbMjLSrrmKLTWmHItlfw6myxSQdZGQES0Vw6N7gDRxZg0geqQaURiXHsIPTPm1Aioe9um3QSJ3H4XIoF8XcaPR0i0RYdAcw0vXKwhKCFZKko2h45BEjgkXPVmVJvXUCorQn8mRv/FWPIn9/+zFQoj8XxRWV69ItwLqfULHxY2DRK+p9uAHdM1WjvG0nXisZlr3jvI7UMcJ9X/7+KdfS1Uu1ZzWXZXLj9OxLREVc4ystH7V5qXod/lxK+xoCJXu9zdRH137DVPZUcDS2NeTrlBh5MTMyawRHhGxcKAaWv294tldNBl7vy0iqjEgDwIoPzf+LanN9nDqsKyCrv3UTGXo+yuoKi4VsWkL2HM7Di2iP07qiQ64tsfBF8/+86iMhELIKLNgQrMpKGPN70C6CpT2X4TJdBr17tRaZD5VaOUG4Ahubu+j/qglWhDlvAGWzbYpgsSbzsNRHRxQpgpkMO+8dh36Pmf8eiQuP/5g9C8EwuyY992Zf1yQLL6QI0jUgYslFsHK5IN79citks0AoKLlvBHVJAGjSLpkudx+MMocJn1LtR7B8qLBFVyPlqqwkiVAwqxOscDigpwiqwX6uSHa9hWBRT7fKSg9F+C5w/PHm/8XIGQsRCyNEwEqw3HRsX73akIEnz4o+OdtFsJIbAIiefCBWIgm/0QCpkYehWtbTggVAPBmxTc2UEixODc+CwVq4LqUgvUluEhDVjewQZwMQn3JqIb5OCGsRGe38R2n2LRUuA7B6gtf9YLwPCWwWMFLh+HRElbGmR7C4z50MMRJ7OFjec0J+nPxTBOl+aklqHjq7ejUZ+PQpQSCgr9bCbqN2C5RFNlr7ekny/XkJfTFFMIeA9RzJaKRrFyzRo8RbDlyoPneVBz6XYxG598ah/7zzgS+sEYlnngH+K8kglSJf49tNbzlK0bSDU2SOvw7kVIhRSE7wzB74rfHeKSKngh254Y26sr6sBuvYNUDfw4xU348VqqmAs8TxVtcBe73lHH0Zv5T1SBIx7j/ACZKUz22szcwBsBoibew2QZU6d+DX1mV8Chxdn4Y51vVkqZe5lNkg5S3hY9cCW10rPw8RknQn18SlmLLTv0yQnIdDhEo8T1EEgoeTs6qPy1rC1oAg8CKNYi54ij3v/P1hQ7DGj4d1freNYJXiuJ1exg2XzsR1535q3o4HX1sFmxTBYNRyXnzECjDqkRmJCVjk6UNaiuA2o5ldSQQwmYT+e47s8xsCuaQWwdKEmcpZo/VQP67Ok56hoBbBomsgqcEKBjLoUraeRbACimfh2zOBaUav1D33BC48cykePlNzTAZCzGHCY8PPwDybNNkOBp9gtTKyWeMSD+5l9MQoK0kjEs7oSn16BMuOYGkPdziURjptHjgpglVsguUKYoqgTQTLDrxqoT7XkQGUk0SwyGj87kzzNnRaERnBijEDSWtCSqp2zc2MYNk2Qw3GrAYZRdhknnASmFBFsHgvW1ghRiHdboP+tmn+F/j3v4FrVbaASkURMH2f3/8eePVV1pdEh+jt2+NV7hwkqaiUbsWnHQYiqCyTGF16DRb3GzkZ23Qdvcgry8Q4XIImvnhSu3nzSU/4nXaPNsw2LabU1Fe1S1oW2WCNyhDB+ng/vVapK3dpxQhWLheAZUgn54d+bXP4y1+0Y5bajBWqCFE2yVS6bHDyycB559muYoDOi14HHO+OyDoZsLxhqTLwAcvvAsD8O/NpXHRNdOEYwXgNl7L60x0fAraTN3B1hB3B4s+lROsNSSTeTsXUad/lg4G93gG2vh7oe7jztS3r49iA1oRBimaMb4+S1/WJx6fvWtYPGC7cWLzTh8C3vSBYIhc5eb8kQqwbsLWEsLiGgtiItXpexxQ7cRItjcyEpvkO46p2njTW7Pm6etVv/wR8d67684oiCZLVz2ZtFLxE15JCTaadg4OPEpPjS0KwTjoJxvzuRuQiVIrSaBzXXPgrymLaNQ+EreNZxRDTv9GI9V7RI1iWiJQgmKQRrBzVpwvrZ8Ge+ZpubDuKYKVSMDtSshzByqb1z4KRctPetIVmgkXXhmuFcdDW72N4v6VaiqB2HDGFee4jrBWEZgtEIsBdN85C765aVPojyb2eiQNT/2Zd3kHhE6xWBp8zO264oWhWGkshEs6gmfqtRgLyIkwemjEcDqWRalxn+uhJjdRXVRXeq8QzVCmCufwJlg4TwRIGwUpN7lgi+wkAlZWS2zsQALqO1Wt+KDx/+eXAglUDUFJiM+FJUwTJwyX5gkSaJis0aHn5bTdF0QTqRwGgLLQGl13GyZ+L6GvTM4b7PsEgcPTRWm42AMx/ylogXTHEEEX4/i+seHnRy9r5cz8eb3wFwyjXaqZu4cvA6N7gJwSxvo1HLstqLgBvBGvq5e761UjQX1P6HdRDi/SU52FARCqZZP1Gc9MYijhSSmyP8qVqggXoKlO8sqFegxVgxkVOFIEBuCaQ5BjI4X//A7KTDlW2VAAAjDhfLvH/QklBwiEW0G9O/epGXuguXdCpJoTEM6pH2+9P+tzxBEu7yLu9YAixUFqajNTs/iITuBHHxDEujXW734Su++4vW1sluCJYin33ORToy6XHqVo7tAaS64DX+jFFR4IYQdNrbXP5py9+Ixjcyz9ghFXc386PAfsKqYT5QEYOtroO6C7UPKabgBcrgRUfu9tvWX/7cUh2H9j1DdPPU7vnJYqlOhY8bd+2wQshssPnxzARpY117rdpERp09rbpn8o/BxRBVN1Xeo21C5ELPZKT1Or7Qmw85kl89WiLHVMasTog//Pn+6QRLFLkHdGbKUiaFXXZsabfMhqfP0k1W+x7BQPsHKnGPJmE2WGTTWiKviHwjY1DYe74eg1WmL2na0A2a/NiEGEPh9JIpcNaBEu7Pjtxyso8eDvITe1ifIXzOh0EPsFqZfCSk2VRo6alrDSNSCiNlhbthgwHjQiWKqy/jqW8hINppJNmI+cdrdygrSJYRx0F7L+P9mCJKYKSCNbGjXDEunWShZR7nMvAcrtWb8k8uDXWniJn7v0QqrqovJM1ukKeqCA6c6FN45hwudUgs0sRFFO+RPBkmhr0usG4u4z3TgNS1KZwOxRTq299fRqwfKJ5WSDMJnhC4xzgW01Kmzd4B/3eeB+M4P0bz8Vf/8pIrA6KYAUjxvdR1R9m02ahCC/RvtVfAp/ml7qy556sWev1l2opJdvbCBDYIdbdFHUErPddr4qFVkl9/rnS7rMJnJ1O6cWxj5kgRS4XsBpItE9KmdHGlkAuaW+shqLAVteoP+dRiGFFz0/lcKbm1mM3YISkH5UIJ5JH0eERF9g7GRyPQ5HWkPE9Sd1K5lQhiNd2jMt0M4rWyMALw4jgx5/4auvngDVaUz0a2OMVltrH4yCFuBSfAukVO9nUhLYsNZT9APOY1v9YFhUE2L0si07JIBrn4ridbpRHsIacoW5ObwdRDVN8Jo5bD4y5ziqkMfs+di4fq2Q2BZCjcbs75Z/3P4b1XuLbpKz+TL4u2yEYuaJIVgTY+11LGps72NUvSwqNlLuhrBCXLWfW/mCut3Qat/hns582NmiR4MFbLDGvS03C7VIEaYzQnVlEsKgAihvsA0HLPVdRZh3LjtvjY3YsgWAN7cVqKM7djzVyJsKl7RwIhjG63wzsvtVUAEBOO1ZAS9MrL2PPVjwOgWAJKYIUwQoJBCsQ1MZCLoLVVUsr7bWPPr+EozGk0mxbvQZLFS3mz6PYwi/tDJ9gtTJY2g4DL0oQi2URDmV0ghWJQuJtFqA97JFQCum02bCmGpqysvw6s9thxAjrstdfBz58bQH7R/TWSmqwTD2QFLBIjQNGk85clnlXRESqpSl4E467Tu2x5QiWiM9/sjYG1BHtYu71BFhTCHjwREBGmnmC1VcR5ZKeB5cn5jQgyc6LIFNFtEMgZCNawd2zJin9CLYeOAO33y4EV/RGw2FDNEB1Lr9MMIt22H0n1XnniYMOAqJRzXPuRaKdR6TaXG8H1hPK9H/lYmsEiyfoGkHia+2oKJjGlfHjXrf+PmSw601Rtckul3KOBoRdSnnLCsrdQvcQc8/q8HOd5eOdIljkFY1Us3HDU/RREsHijT1K8XF6Zvtr9WlHzmevh05zrlOxSxkjI15mrPJkb5micbGYqhQqYwa5eB+IDXgBoMvWVvEKL/DSoJvu0bG3Anu8zAr9j1nFxl9SAHRC/Uzz/4NOsZ5PLu3eiCcQ+aq9zLz8k0PMPdb4FMG+R7JzDwScn6mJ44BpdmQ8x35HVTre0rfZ78mn7dulCOay2j3HEaw+hwBD/qjexm5fKmSarVEmFXS7wSF9cuMMJsDz/g7Gst9tBLa+wX47/n4PREwNuwPi8/fVyeyVagVpnJrK1RnXaV3peYKV5e4tjVj0rVmjzUVmW2DkMIljMZuURrAO3uZ9fHbNHrjoIOaU1FMEc1r2AtleWs9CikCVRNk1JYLV0gLzfZFJcAQro/+WpggWsgCC7P7LZaA/p+FyFvWu3sqIfGXrdQdgPM7dWzK82gtYrdVXehonOj58gtXK4AlWLGw8SIFgBJFwGi3NQgQLUKcJasZ9OJRGOmk2rPv2VRCUImDaNKPGywRSmXNRg1VRwXpoiUp/BIt4BoG8l+t+kNfThCulBa1l0Wb1Ax3rwYxCiRc8HLaZJKJd2aDED0xZIYXAtDPOQJZ53Pn97Pa8+rgieAW/AlTyqK9XIJtgE9XMO9Xrdt8JKB9kvabpeibVP+UK6WbKJsL0mwXChmdL9V0skTSPdQuFSmjn0vapM26OL0Swtt7aaMILaMpJIsHihRw0J0IgAPzvf2zRSq03ZSySwJJ7+uLxc8+wOhVKtLYE1DeKfousC4Jll67GQ9YHyC10JSrh+XH6zVbbyJGunwpM2pe9p3RSsUbDLm2QJ/A5LoK1h6aYWDEUOGohMOZ6+3Pc4yUWlasYxP7vMgbY+23gkJ/V29g9z/TMyFJkNdUvADa/m/Dc1LiQddc3LbCBvRfDSWwqHwwBJZqwiBv1SIClma38lBXmZ5KwRFeIYDl9r+3/Z/6/l+YpFEWJVn/OeqxRqh+RjTETgF24onxyJqmw/if7htFEiESnprGClWDZIgcgaDgAaUwY/XeX2wvnxoNUdAmvuYyK8WOUChvrgHe3MS/rso19+nivfa3nKaogi844IvRZwRHEnxvJidOYtX6qJYK1/uO/YdaT5wIIWrJGgjkZwUpZI1halG2PUV8g2I9NHmYJ+IDRO1E7xmF7LcRlh/0bd1/HahUrytl3b24GszNpjskmOBXBrD7uBXmCtXGGkfrIkTAEgsY8r20XDqUxZ+VwAMDN922tnX83w54TMUuLohdiz3RA+ASrlcGnCMbCnEEdDJtTBCMcwZKlSmXiepNXWYpgOu2iZ1WeiMUE+W79oFRA5lyDBTDRgBZFBuRdd1mXsX1kgSVvAGu/lYtFRCqlyytKGtWGBk3WEo9aZbnNAx7WBm8+Z9guRZCPuMk87jyRVk6YEjhFsNymdwRjQCaOYEa7fjP+pV73oG9Y2phokOSywIvlwPwn2P8jLzJ/TikHIupnMKWiYAii3L4FXqJ7APN88yIDXlIKZcgKamNeEamWRnmO4L5WSTQhIVjc/5xzgcQ3iGgFA1n07baMtYEQ73ki/qLRkkk4EyiZGpsMvAfZK+i5sKQ2cobSsHOs2/12jzW9NVUPfHs28NXvjTx92k+PPczrHvS9WSVu5yeM97xxqhv7Ye73yAHlA7yrShLs7iW7iLQu8iK5n7tuze1f8buK50sS0G6w3e3u15VBu47ra05k6aAyvFJjWlcaed7xfvfHnPMQMGlv4IWYdWyZebs5yqDCiL8wkkwYeTEw6q/snpTV/uqS0prx2e8oc5p2qATY+z35sRa/Kl9ugkaIeu4D1F4u+TjL5l+VIZtqAD7/nSH0QISNiCON7+FyKCNI7++sMISFiX2Xx62r1M+S71OGd7cCNkyXf/bJwdZnxel5HHkxe+XvhXSzKSIc0IqQt9pK2JbGURkhH3QqS8mksWb2veZ7KxhGl4oGlJc0s3PsY3jWolEA3xvKUjsP0yI5mWb2HPO2TYxL+8y0AOWDuAgWwFIEo8bnAMIlMfz75CvQo3oDAKCinP1GiUSOzQU0pvEpgoAW0QoiFOLuAeplRunSIsHKcQQrKLk/ItWsPYwMNA/4ESwfXmCKYHEpgghoESxKEYxF7CNYnx+r37xlsWasWW826FuTYImYTmMenaeooCeJYAHMMMxm5dEqvkbr9D0eN/7JpoDPxqtPRhHBCocy6gG3Whs9NcLK4/rL5quPRV52nizZpQgCwIATtG0kaZ/59g0JlRneQXGSyWZY415X+ynRzku7Tm5qacLl9hMZNXImqCJYmbiRviY0jLaAN7bdGIVbXg6Mutj4v5AaoSVvAbPuMpNqr4hUS+Wot9C+/uHbvqWtJxjOwQjQ72j2nnNWiLK+oSA3KakIFhk/dM/Fl9vX+wBA74PtPycUMilmk9oELRjTvPHKk2W+V5HYnHrmf4C5Dxk1UoBh3O70MBO3AVhUIVLBIrKEml2M9/w15Guw6L53kr52hDYnyBwhdh5cShF0kqgnj7rlsGKKoA3BrhhmJqU1BaZHaPdIjq9lE5FYC3x9uuFMkBEsL1G3td8ZkU7xHl33I7BxuvvI3J5vMHGRcBkjm7FuwBGzjagIQXcuaveIbKxU1e59fqx8OQ+qgwmGgG1vtX6eaWbOijHXsyirvly7pgufAxa/DPxyPe0QQJApxB463Uw4T87Kx9u13xoRcfHc9G0Vz8jbLgRs+Mj64tfk68jGHCeCFZDMc0IEi1IEX6H2flQzRymmsuyJXIY5X3knkViDRSl1mvBFWQmzBT/8EKb+bF9etxvST4WYTROMMCJN4H+bNCNg0Sj3XQIB49rROK/blGzsKC1j5x+g+YCcj3qKoPbMaYQryBOsml3YmCVGsMBHsNjvQv25TAgE1WI8tC+/BsuHEpKJl49gBfhBIRhBJJTiVAQjRn62zPDm8uq3HTgF0+eavWdtQbC+/JLJSo8erS3QZTqFh0ZBsFZrtdevv27ddzcu4+LWk7h0M34fu79k3VCIYH3xBfD6JVrhqipyQdLGQuQr+UQEF/7ZxpAOygiWTYogYPQVWfu99bN8ewAFAsA2N7H34kTzy3XW9VUIxYRzcEFE+GPLID4D5NkSkeVEFpxSBPkJsVZspOkCBfTCwmdH5r8tIdqFPdNCiitJrm+gppN83ywCSUVn1QTL1GdETPsTn83Geex6tixnvZTs0GsvtaFULFCtgYhAkNUCVQxl4yJFmPh1ybmRzbD0ad1o5EDkvHQLoJvWvJzIG1/jVsqNp6aia74Gi4yNQq+Jtr0oeADY36t2kR0eP11sXbZ+qrlXEFd3IsWRs4EDOHGEfNX7CNo5ZwOlZpEeEfOfNBoFq74nRSKc0Cjpd2Q5L5eTZr8jgQECAYpUcj3RaH90nTjjM59j2o6FnNG75d+t0dnGOez5KR/AauwAljL5xYl6D0WjOaxG2CIVQJfRsGDUJfLzmHk7sPh1ybkVAW6ujzRN3IlgkSNPHcHadRtGSqnJvE7KeGfE0DPNzhGq5SP7rarWTLCCEU34wtpqprfgYwkGc0wUIlXPxrogR3p4h3CGlUBEw/x9bY1gGVlRzNDMad81SMITES6CFQwbJI4IVpBd51BII+IlW1hrsAJBdt9nU/o58k6/PUby44jimV6qORn9FEEfSkgGGD6CRTfkNsPmsxTBcBotLezzcDRieRikGH4eulTGkUhGTJGgtiBYu+7KpLx1ENEQyYVE5AIAvtHq1s+VtNLg+/t0Kd9g/LP0DeP9AGtTU0TMEazddgOO2usXpmSnUvGjaJNAcCLhtH0Otz54cdEoXaZdEcEiD+rUK62f5RvBAoyBcBU3eDUuAH7lev4MPNlhH9QHSxsMZc04AdaQ2LSdnciE8AyoIlh8DZBs4uNRaNqAqPDV1iBDWkgTpCbXe43SUi+IAPCQPEsWghXkCZYqgqUhud64793Ws8iwNyek0HOv/PeTlaQ1Eg6dDhyhNRyne4Rf95MDgZe7A8+HgZcVjbr553mo1sCyt9Y8k//+vDNm8SusJvGHC8w1WGTYFmpI6qk1IRY52Od94HAtdWrK5Sy1TbqdS4Ilq/MRW0UcLOkfZYd8esDxGPonoPZyrN3iHNYY+ZhV6nWXayl0qu+5zT9YFLLrdu6PrxrbCkn9BSRRQer6Tb+x5Lop2oqYkFyviJTmzMccexMwUlDd5KPtRB6WvcP6180jRwVF2gTCJkIVFar7N/D50cJC7TsfX+B4y6dUKom95LyobYsKsnkmvsIUwXrg/97ATzftaBCfqlFWVclMgjmoSLiIJ1NdtmaOIVOKYAxomMuOq11PsgsjCx+Wn2uqnvuNJPduuoURrBL+GeEiWNRkXbcp2W8ycvB67F37CR6+R3v+VCmCWmYLpQgy25IicIoUwWyKyesDqOg3Vj+rG44TRFuEPmAm+CmCPtSwTrymUGkugzkPHoTP7rmOpQiGUjrBisSiziIXABAIoLKMfd7ABWDSabPCWJuAPPIWo05OsAYNYq/rJT1qSXFm6VKwWhL9GA5RHlmKYLrBnigJBGvPPYHKcu272G6XR4ogyQPL0nqo1u7wmdbPnEAD4YJnjGW8clb3nYBdn7bfR6gEyGURIK95aoPVg7TfJ1ZFN1kNCEE0PqlxoQg+cqHXYKm8ttqg+7s81eoKSe8rBqj4WSBY/fsDCxYA15/2GDD4NLmwg0QWWOx5Zo5gKZ5FQiZhOHDcFsIf/AOwy1NAv/HGMkq3E87NM1QRLIDdF2TgUd0h7zluXmzIKKsQ5p7nmp000QlNgY0nWDJD+Lf/wtQQW1+nwAgWb5j0P5oRPt445FsSSLdTEA9qKi5rRiumDXqp+SwGQiXAtrciG9LGwViNWZhDBtX3DJcz2XtVuqCsCfLcBxXHKJRgCeeo15LYRLAqh7GeWHZ4tae8JlZGiChLQgZxrKao3szbgQXPwULYZFDVzQDmsZ0k5Au9t/jUZRUBlj2vA0+0369IsOpnMTXhLmOMQ5cGsO1AzvkgG58WPMVeZ92prSMoBmbTMAkjNc5lIiiSXp6RmdcZipTj+DYgOfm4SPL8WipolG8wGgha51rdpmRjfrS0BJ/8377YZTuBYPEqgtz/Ue0UcjkYETiVyAUXfe/VyxgjzUIcsG/34KcI+lBC8Dj167YY5x/4X+7zLIb2moeqyrQmcpFCcwv7CUwRLNvIRgCVpexhEQlWW9Vg6dBTBBVGnZDuohSygNb4DkJDYjfNMyNVjFDxhn2q3hPBmjwZ2Pjtncb+VJBFsJxSBEecz1577W39LBtng5VMFtkJMuNj3mPG+9I+zl5n7ZyDWe5+Ez2n4QrrfuzuT5FgyVIEczlWH0ETEA3qThEs18pYGqjHSSEEq5sm4DD8L/nvgyJYkjqsgQOBMJrVBF2iWjVYUGg212AJg0AgYL43s3FD/dOtIdRtHDD4FGDP1xhBOSljJjpuCNas/wJfnWZeVj+bFYS7ISx9DmG9lLa52d05E1QpKYC7CB4vclE0SIzvQNAqv75+GvDWSKM1hP58KKbtYWez537h86wROKHOg5hFWyEQAPZ6w2EdB/Nku/8AB3xpXS7WgQI2dWn5t3AAAPQ+yPw/19Cb7V/R7H6YgkTzmPcYi2i+wn8fCSGqGAIcr8h6oXpjGb462TmCBQBb2PToeruWXdvVXwFLXnPelxvwxELvuSRmQQjXoHK4sxiSft2054/6xfGR0EAYQM6Yx+zUVgOcY5DmsqAm6sSTLr3WM6ufdyCg1d6HUkZ6nCj+IlOurdTSHtJNQCiKaAk7Ri4XZPdVFddTJ1RizCtao3qQg4PmIlMEK2yMl1lGsHp0Y/dzv75Z6L1ISWpeJ0NcBKvLGKDf0di61jBOLfVYJZLnE2CiY34Ey4cSgiF5y4l/Q2mUi8DovTciegRLDxVHw2qC9S0/GAfQqyvz2s5/xejF0S4EayHrFu42gtVdUmJCoAhWNArDs9mNG/hUsspiI9VMgg0WtkSJCBYnm5+uZwOHXUNE+p6rPjWWTdFqglQGMhErMVcfYBEsr6RBPxfJj73oReP9jgqPLQ/tnIN8/ym+Qzsgj7yV2tXuuEgRnH0ve105ib3qhbUOESyvxtAer7Gmk+lGdYqQHVZ+CqzTaue2v8f79gT9uVZEYzNxNUGXOCt69zZHq038V0YEeC92Jm4Ym16af/IQDTynpr8A8OMFhueX8J2WsicrmLccMwAM/SOrc9jtBXfn6QQyLuyks+OaYAzfS6vv4YUdt3oMI+xiTalIeKffADT8Bkzclv2v14PZPAck/DH7PiCxjr1SL8F8sNOjwL4f5b+9HZwcaE7PeygK9NjVapju8pR8fYClcA043vi/UINu8ClmGXZyvvHefRmcmtADQMNsRo4Taw1FT0kdDwB1f63hknx8E1xEsOycC41zgNf7Ax/uZsh48zj8N2vj8Lo7DDl76fG4333KZaidOgZ4PmrUIC16BWgSxKgOq3N2KIoRLJlozAathcK6n9hrNiUnOoBxPF5lVm/Uy6UN9j+GHYNLESREwilDWVEkcjJiR/tMNwHBGOvRCKBvj7UAAmz/VMsb7WrsY97j5u9K2RR8DZYpRTAJBEMIRwJ469LD8cl7G2H0Ig0a3xsw12ClGoFwBXrWGE63cCjNammdUD/Lj2D5sEGq0f5zMl61fgMRrgt3OMz1tiDDv2Ul0LSYKWMRAgGMHcIeyFm/NujRhnYhWMvfZ68iwVLUYNmdHxGs2HtD2aBQs4s5UqSqe6Daia80lR8iWnbeaUr14w1einrZDdJkCE79m/UzpTSy/Fqw4xdAsOwmvSFnqL1EPLTrEOAjWNmks3w8RYZkEOu1AhHNscBFKdZrk5iuQukyguVVFjsQYA0QAdavyy2Wvg0seBaYxCk45SvJDUjT/EzIxm0iWNq235sjaCedpDiW7DzL+hvvM3Fgo9YQVebpzweFpAjmdTxFI3bCUYuc+w0BRuSdDK/DfgVGXGhe51uNBAbCrJ/VsWuAUXkIrfAIhoAd/gdUCcSAb36eSRrnRb147NLOZHilu+W+8Yyhf7CPYBQCpzYBbh0qoshD5Qh15ObQn82S88XwmA880RDe+P5cNr7pv5ViPolUAUe7bLwLAO9o38dNxImH07g16y7nRuGFRG+rhgPb3cGeK3JQTLkU+Ji7p+Y/w2oeyfHD/+78vP+u1orgC0kttp3TgaA78rTfPCMhWEvfZq+LNCdOfZ2lh6EO2o9J0CJs9IPSa7BIYY9PEdQc63z6nMVRLfx/4Lecwy0NpiLI/mWNhLX7Qpfar7Dug74r1Y/pEayElpJNKYJEuEI4fLt3MGBA2ohg0bXm61PpO6YbgUgFyjizIZmOstYGjshaHaFiv7lOBp9gFRP0wGoIBHLCYJFhnuhgBAhGmPdCg6kPFhmer20BvDHAfIxIFbpUsHSAcx99ALlpTGWsXQgWwWUEyw6UIhhJzjMU5tb/ZKygIlh07GVvs+PJBk0RNOksfM5YtuIj535JpLLEK3BVjQRJr8rPz8a4zsaLQ7B4WWrA6jFUQU8R5BsnJ83y1yHJdQwE1IXaIvmS1VdZ+pfYyLR/8yfWeDPfVB5SylOlCMkw+QjWS6lYkDWnJORyzJHgRLAEUATrgT+dbW54K7tOvFpdfKUR0ei6rf15OyAR0wymxBpgzXd57CHPdKLygcZ7UQ2vz6FAeX8mGnGsQ2Qsqnl6h57FXqu3BHrspjhV7brGujt7youBmbeZI9JA/pHcjopAANj3QyON2vK5y+859l/A8POM/0NRJsGuAl+jVQzlu2CECW8QJo4FvtHaaNgRHF65cq+37Y/Bpx4W4uxpDwTDwPZ3qaNs1ENxg+b4sfvdZX2xjnOow9TPQxs0ST1YFsEiwpFuNhyqKxQRXJqvxBqsXNpwpAPAqs/ZvLrma33O3WE4+x6mflGiU1gc+2t2NC8LxXSCxe4LgWAR8eFBtaxEqkWZdl1FUGvfQmNO8xIwkYug8fvwESwqBUizCFY4YtyjVaX17sbM2fdbbYBt/+28XQdGJ3tSOzgEtbJcLiDc4FkjpzcQRiRoGN2RaJAVuQeCLDVAhS5jUF5mbJeewWq82pdgKULbEsnhq68GgtxdN3Mm8NprLIIVDSfYc0hhedPDpZoIucjIhmnGbyAjBiIafjPepxsMRUAVuoxhxipfTxWusObh87Ajm+kWtWHtBH6STTUAM24x/ndrOMtSBH++CmhaZPyvIqrHrHBXI6eTC45gihEpuxqseY+a1/UKSmdsXma/XmvC5nlg90VOfS0VaXw0sSbTUXORu8w44dNJmxdzk6uLZ8QG87Z8B/j/9s48PIoq+/vfXrIHSNghAmEnG0mAgOwIyhZZxBGDgoAwzusyzriOMgOIM44j8nNc0FHEZRxHR1CWEURQUVERlBgQ2fcwrGGHrCRd7x+3b9Wt6qru6u7qznY+z5Onu6urqm+nb926555zvqfDdGZgrevjf65boN4DUcThurXq93gJgYh4VqvIGxHxwK3lQPqflG1GCweW5mCZQLfAqg+Ri9pIy+v1RSkA898zuhmQs9D3fnpYlfNhdP2aHbeScn3vc3Gndw9WsntRyCg83t/8RY63emn+YFQ/Tq5PZWIBYb8m9D31D4rR4ItGqeyed2Ide61nYHFF1MjGSu6jFp6zxUOvPSTZrzKji287s1E51r04tHLug9j4RF9WYJ6jNbD0xlNxHLJHscgn5U33dtHA0vx2PHxQDhF0fwc9FUGbQ4kEqLigeOC4PL+8AOQ2sKrKmHPAGa9qV9o1O2HK1Chc4nk9BlseopohA8tK9EIExQvCVaVceO46WByn08YGxviOSkiIHm1+BZswYaooZd6usjIIqxlhRuuF8WJUOByAy6WMtSkpwIQJQEVZJaKc7sHmykF2jpSHFTlfbeiZLjZFIc3s5JE3pKoMaHOz930BZmCJMvpVXsK7APeKj11/ch1UiKAwsNocigx8t4fMr7DbdUIEC5cAO4TVWG/fjSdqe5t88ps9D0kAlFwr7lnxlYMl7uMvvP3BhLF5y+czgzcj25fMv93JflNNP+EerKtVEZ5J4VqMfp9AjXsRsVhv6Sn/jjWawJjhxr3Mc8cn5y2GMgGOxEz/zuOIVF8vvjxY4UJvYmEmVDZ9rn59Lc6vzptf8Q8X/LpvMUxRSQP8X1QZuExRZDOLZQaW0Rhoobez8EN49WBd+zYwahsw9qD++0aGrC8iGgJDv2Ae4cEfe9/X22/WfrL69elv3E804hPe2KvJhc3SUVo0whnHFh+vHGCv9QysXm5DPbq5IgShzWfu/Sp75GOfq1K5Xm0RioqgXp9we/EaZd6Ovp016ryRmutWLzxOHBfsUbA52GewwsFaD5ZdZ/HbvRAgL7K5jTpuJPJxjnu0uJiRVKnkkPE8NR7qz3OwZOGMODgjNOOl2TmJRxSL3nFhiCCwCDKwrKRRN9htyoDNQgTFi8ylhAjanIhwKKsXcod0xPiUaYfdiakD3wYAVFRFoqICOHsWaGGivIalJPYAWt/oOah6yTviXrYqzX3t7y84lZDJspNKLZToZsCIH5mCmB5xQghlZYn+oKkHF82oKmMTw/Iz5upSOWPV+1WVwXtdKCgJoFqqgggRFFmbozzXFpb1hp4HC1BLX3sbGHkRXH6j0avjxFcXRQl5PqmRDSwfOVhA4EWZZWl9H3k73hh/NPBjAeV62DbL8z2eZ+jNG+iMZ31OCGd69FFgWL9jmDrwn+qbqN7vZWRgWRHqFiNIKnNBCLNoc2f8oWFnIGMu+w6TXMCwLwI/l4jRRDnYekl+I/w2TfuyR5eJFf7uTwA3GXhrUx5hURJmV/zDxTXuYt7OWLU31pcEv5Y2N3kPKdIzMEKtWuaPkehVPAisiLyOUIKM3QkkdvcsWM6v/2By6VoOZR5hXwIv/ZcYv9dNY/zyfGk5RJyPbxYVLNbD2UApnSJHuwhRAnze4LqqLMrFatI0+D687+gVFeZpDlr4d+10F/PEi+HOWiEsvcUicTHNEQWVAIfNhwerda5yP9TmYMn7CyIXKo9WpZBDxj2RglKmPUJZMHPGw+HQ9lHNvablDexRW8heXGSNTPQ8rtfCwMraVBNkYFlJZCIOv5CMDs3ZCgkLERRuzJIQImiPUIlcRETyiuExLHSsaCM86MQVgWzI6cDUza5WRqDIrTYadgPLVa4/ofeSd8TFLL76yvOwc1cMZAab9DKWlG55vbKKVVksGFg+FNJ43H/lFWDP39nzQ17UpziOWLUHy+Ulf4ZjVGy3qtQ4Lj1QfCWOi7hvFK2OamqyaHO6jIhMZAPe0HVA3lXPelmAcvPa/5qyTb6xuf9vPGk2mMLLRnDDpSZ4sC7t9iwiXuXDgwUoN3Th2KQk4PO3V6Fx/Hnfv3kojQNxsv/5YP/UGq0SxwhHTlS4PVg8LwXwnHj6mrTr9aXGOf6t9oeThAymVthnsfq39COH1xx6/SSEk3kAfk2xRuZ7rxEEuIVQ/OzvYqjs+GOaeksW461vGl1D2hBBo1BCK3CVsxIh/LloVADqcG7e/7SGklaNUCtoIV1lHiK+gJg0VjhW+Kz4TmohL+04xj+nsbCAqvFgNU1k98yJ130HTw+WQz0W2IVoBy7cEaExsEQVQZtdnUMtKTLzbJumDpbsFYuFM1K76K55PeQT/dICYs3L8f/z/J90vkctRV/DIQPLYto0+R96dxQSvsULSqpyr0AxtRanXRS5cF9MZzcDJ9cx2VMt/IKrOI9IJ5ucVFRGovQyu8jigkup8J+qcv3Jnc3mLgDsqU60z51edo8vcathXmRctbQa6W5PiVDjx8c/Q05mvaJ8BzOrmc4Y5TMA3yGCAFtR1BNZqCr17f3yxqAVOhv9uPlGNw/8szld7mWFW+1CDQ2RVu6VqoTuyjbu9ej+pPt1a7awwEMPrESvdpk3ApFz99kG4aaolSTnnjVTBlax5g33Dc6nGpuOgTVqq/djzKI9946nzB8bCoPaCvQ8CeHIwRqyRn/72c3AB7Hm1TRtds8+MfKHwPMYw0HH6Ww8EifX3kKGA0JnbAy5B8uP8TimBRMyGPIp0Pdd/X1c5f7/jlz0KCIBiG3NPH2hwquBpXmP12bi22WD2j2uWVWOQeT01+7PPq1f40r02PAFIA+VZIdyDkAtaGGPZK8rLiiF41MfU469tEt57ohSzyW08P/LDRtYaK+2LY4oJDa6ivPvdsef7/w3PAws2NURMjancryeB0ulIqgpPCyGCPJrlOeWlZ50G5Z8fIqA06mdC2iuA7tTf3GZhwhGNHQvkmsNtdoTHgiQgRVSbDZJvTrCJ1M6HixRdcUQOelRkvO3rlZFoKKYXSxhz8FyVRiHpEU1YXVYNDR0OwNOn/Zxbq06mDe4l6SyRInr9mlgcbnS8yzxHwASs3x/VmUJcGq9OnfLl9hDTBJw9EOgVBNCVVUWnAfLTG0Jb+jV5hK5QceL6i96HiR7BJMIb3sLe+2IZrHeOv0laBxCzPnOZ32vimtz5ayQiRVvihUaA0v2YHkLEeTlGzQGlmTSwNLzYAXbdzhaw2P/q76PKT7iToj2MrmoTkZu8dwWjgKY3sRyqkrdExyTnjTRYB/6WXDtCiuCgRXKmjiD3EWOQ7GgosLHff2mE2ylXqT1CCDZoA5DVbnvc2pJ/xMLo+X3Gu2injZMyxeZTwHZ/6f/XsvhxsdpJ8e73QWw5bwf91goSUxqv/VI/9rlD+Vn9GtcifmyvjxYPz3AHi/tFowxd4igGP4vjr9iKQZ7lHdhIP45jmjFWBPH+qoywB6BhNhzsNtd+iGCRh4sHiYpvi+qDhYfYiJgdsHghDZE0M3l/RrPmlOez8bF8vupScOo/Kz6niL2me5/8dy/hkMGVgiRJJvGg8UnRBGA3YkIu7KqHmHGwOLEtVd5sMqL2UQlyts833UV2POitWEXlVeMi5VGJnqNob/oo/SGPKCYgU9Ad/4N2OsOf/ClIshXbj7tqRThu/afhrvLnC9gj5d2s0K0lVc8J81aeNy6qM537BMmJRxosVfAdxikL3zlfzXrG9z5AXfOYJQ6h6qq3NMovXJQKVxtJfxztj0ObH0UOOIlRwAAyorUr699M/g2iMa+1pN2fDV79ObJNPJgieOJN/S8L/6EknpDa7yVngDK3JOIM5s9JxCSBKxMBr6dCFxyq3j6kqgONzEtgeu/BlqNYsZW8hRzdbWCxWYzzp8C2O9v1nshXtstrw+uXeFEVdYkCANLT7BIJQzkh7CCWcbpiFP5+r1iWiqlJMwc56oIbBVfPCa6KdDv3/6fg5M2i9Va1MOfBcOLO4E9L0GefJfzsdctBx7RELBH4kLjcfoRLUM+9aPRGjbPNPBg2VgflK4qi20e46vwv+SLgv9boezL62DJoXriIrvQpwOpAyces+8VJSRREkUuuIftpHocEA0sHj2gyuFyeEZSyB49XsfL7mlc2yM053HC5o5msduEPC0jWgj1Jq8c0Hxv93dqewuQ/kfjc9RQyMAKNbqJjqxD6opctL1V/zwtbwB6uFd87JGyIMTVqghZSdCrB2vPS0D+74C9r/j7DfSRXCyO1yhh2hGtKywgRoBc9Wbr+aNwxg0NsfaJL+NDrxCxL1lnEZsd2P0ce35+q/d9eZK6uGL/tVuWNxgjSddA8uPm681rkr3A7+Z4/RyxLxjl7gFqQ0xcXQ509UpbVNrX/5sb6PLxQRqxAFss4HkP2gUOrv7orb87fBlYJj1YqlVGi+Rv9SYBZSfZRGLdtcAXQ4E9goQ2//7HPmbhMgndzUlUh5vmg4DrPmGiC/3e0Q9/DQVctUuPq5f992DxZPLaQtNrlefBhAj2ew+YoFW11CivAtZ6JkXBJb3P9JfR2z0Ne1cFLJm2Jd/GBEF4WQN/iUxkEulN+gTXjvz7lXqXBW4RDNFYyCvHiXZPqyfhADBgKfP0+UvyFPZ45ntg38v6i8D2CGYwbRjvfq0ZX8XQZu3xPERQlGk3WjQQ74Ed7vRshy8Dq/O97HVVBSCqCF5xq0gWH1YvZGoNLG3+mc2powitEyLIfwsuSNPpLo3QkgMRkaztQ3pz76yX60BUpjzzvTqKhKtHt7/D+PgaDBlYIcRTRdCN3enpwXJ3SEOVnp7PKzkzNgci3eGFJRWx5gwsvpLsrzKT4fkuswmekYFlIOzgEhYM39M4LI4tDHCVWG8SrGdAiUTovG+mrhOnqlzJMfPlbRNDGI3eCwQ9A8uf1U3tqlKmkD9j5Yq9I1rtuXFVeP6vueS7mKNU6e6zPZ4PfPVK+//1taIc30l57oy3bvWfK0IZCTt4M3Z5/+IyuDJ+5mA1ShO2WRTLbtMx3n56QDFoz+UD+ULh63Oa8LtWIQwDqq1kPaO//eCb/htY4a7fFSw2uyKW08pLuJkvHJHsfqlSNhX7PBdWCLHIRTACMwnpitotp/xM8JELnJSHgbTHAzvWZmOiKRnzlG2BFoXlBoFc8sFAiv76b9h8Y2Ix0PZXgX2WmZpeNqda6Em7GCUqp/I5AB+Dbe4QQalSWZRRpYkIcyJxAeGUjgqqLwOr068VD5aq0LBwnM2mHgtkA6uM7Sfej3Q9WELIJFcRlBcn3Kvljhh1u+xOxMbZse3p7vjPgv8q7TBCjPC4ekl9TcYnsxBWX+qVNRQysEKN3gBr5x4sIQeLG1hGkyWVq9eJ6Eg2gfls+w0oL2bxtF5DBHn4lVU3FC7JaWhgRepOJkUPVplGebtlo5PsSZsJ/rVF+z/r/4HvCWREgu/z6NFnMXusKlMSRX0ZZtoQL9FLY7UHS+97mUU0LqwKIQPY/0f0YOkVWOYr7aKBxQ0Kf8JFtWj7QaUPYQVx/yFrLDREjJU1Wbu0AhYCUU3Zoxi/D5j3YPFJdijC3OxCGAwfC85uMRaw0Ir3RPvIA6yPNHCrZOkuBpi8ZfOQ09pYqLNpHzapapLje19fGC202UPgwdIj2Fpz/XTCpnn/qAmIRb/9rUGmpfgwm1cYFVNuPoDVcAvmnpk+1/c+HrWjNK8jE9m1GddOWTgc9F9lX22IoEp5TzCquJohAKTqGLp617841idmCp8neLC08xFxLJAjOkrZ+cXarTaHek7RcYayEF15BWxBzy7kzBUrx6nk3pkDoXvb7Yjd/QDf6Pld6gFkYIUavRVEmzsHy6msZsghgoYGljBQt5+KgV2ZmIPd5kJFCbtIvHqwLrvl+6y6ofgysAxqP4kG1llN6pLdLrEQSW7EmEU7CRYHfSOidTTtzRgVce3ZY1WpEvLnK6+N3xC+c4d/lgmhK8GoCOq1t9OvAz+fuCpopYEV0VDtfbl60dNo4rVbKnQMLG+FU82QJtSfcpUB+14Fzmgk5SWJGTn8hjPiB3ZDtwq+cmpkYHmrgcMNrAs/q7ebzcHiRpCzgff9AoHfbG0OpYBtRAPgyiFzxwcrgV8X4ZOc6JaehY/1xi3dc7jHlVAKRdQKhBuOOGHli0lWr4wP/x7ocr/yOthxNKaFp0fTn1qHocZfAzL1MZZz2dHgPlX0HbwWUw4WM9ePh4Gl8xtKLibWI4tFRCn7uioUtWhA/V1EyXbRmEm+3fMzdD1YOoWDXRVgxg/3YGnmQ7IHSxS5KGHnFxfC7E717+mIA5zu8fnqRRayb7MrixOX9ijHieO4zeE5r6nJCqYhpH5+63CiN/mxO8FUBJWJeYQ/Hqy2NyNmGpvcz1ryNC6dYxeJrgfr0L+BK4eFDRZ5sLgLPYgQwdWrdY4b8J/ACmGKA7a20KIeYjJu0lhW9NTMjYv/DlVlihJb19/5OEZYcZNcTNaUExHEpFfPuxLMzVccBK28icckqWXqK857etoi3b+Z6MHitTqC8WAB6uKlVaXAj3cD6zQCHtvnAUvigYKH2OvE7OA+U4vdwMCyOdlKYWJ3z2M4/Pvv0obgcAPLx2+lp2ZlFXqJ3CVHgXUmczOcZGB5wMcmZ5yn8I6ZxSOASbsDwPFPrGtXrURUPBPGy/hk4OazQNffW/txTa8F0mcLH2nBFCv1USB9jvLaysWvYPHXw5/1NMu5FHPtRL4eY+zBsgIzHl3torhWaVCEh/3z89ojAEjsO/DxVqzbJIZUpv5Bec7vt22ERU4eIaNqi+b/IivMlirvFWvEVuTxXzSwLrMFRXE80Xqw7BHMmHLEKB4sm8Mz1cHmUHsV7U4dL6OP37Ozr7o9tRMysELAjCFvAAD6dd5o7MGyOVUGlt2hk8vQYqjyXBMOJl5ne/azi7ORdqG/qhz4fjJLNOdc+AWWcHIde4zvoP++QYigaGBtFFTA5908x2Nfv+D/nybXmg+Lib2GPXa8k9WaMHMz5Cs1FefZoJjQHWgz3vsx4mDjuqr2YPE2BEp0S9/7BIKV3o7IRODcj0xVDnB7sDSdNUrHwLIiRBBQXztbfqu/z4FF6tdWGyN6BhYPJeFeUSOM+qVZDxb3jundsINFFtAI8P9VG0PYQg3vK854T+/tNWM99yeMEfNctJPTqMbWhQCLRDe1/pyiJzMcJQP8oVE60HGmf8d49XyF0IOl/b3FIr4c7ZjkLW+Lh3bza1Yl9qCXHiJ4pcTflO87cKmyzUwhdn5vqyyGoREje9ecnsZ5dDMlhUFP1h1QhDu4iqBKzMXGtqnyvpw6v6+P66yO3gfIwAoB16d/AenfNrRrVmiQg+XpwZI7qHgBNO0nHGN8kT+xmN1027TRvMEnq6WC9O9xPbdRINhYjSBd5SQYerCMirTndPgxuObwC9pbLRkt3D3uj5ES754MFx9k4WS+xDQAtQfLVaEYWD2eA64Zb/6z9Rh3iFVFBwJTDBu8Gmeaz1AkhnlbYy3M1+GD5zr3qqWeyIVeiCBPIA42jEz8jYwmJ9oaZVYjG1jua+LgP4HP3CGIvuTyxeNFuIHlazLCxXG4R9BK9DxY/qCjNFrvkRUv49lkfdwRRezAbP5N8yEhaVqto/JydbfAGsSxXczhrQnkbgf6vO7fMd7GvGMfI2w5O3pjp9Zj5c2DxdMEZANLLfbg/bNtSrSOnqFv5nfm9+uKi/D4n3EFPlGAQ+8+wr+f1jASvXKuq0DpMfY6oqFyv5ZDxIX/o83huUDrszh6mFRawwwZWKFG14NlZx4s51X1NkCtbpcheHVMrLQ5tR/Fk+JDsRp06J/eE/PtkZ5FW6EYWG01dtnIzCBqWgDKKo0/K5J8ddOfoqvOWHZzqDjPbt5mQvzEVSvRg9X57uA9JY5ooPUoVqySF8/0h6TRKEp6SDGUb9jAapzEBOlZE9GuTqkSgN04otnNQvRgcQM4WKn0qBCsKPuLXMDSvSq5aZqS5GwmabtdHkuqFpEFa3xc39wL4u16DRT+vfgYk/GEueP4xKJRquVNqvU06MweO9/NHuPaAn3/xbwEZg0sUfq4PqOqwxbGRPuR+ew3swrxvqatpVcr8fFbhDJnRxTR0hWS8CFyIcL7lypEkJ/bhNHwq3PGhZ69hY1zuKLhlf1KH+GRT8mT1e1wROobWPIY7mD/j1j3Sr1N8GBd/IXNXQ69435PE7kgfle7k31Wkz6KsJKveVlS3fTMk4EVanTrYDl0PFjuDshzEiIa+nSbTpyoPE9O1rlIZdUxTee2aoDWK5AoI3nGAgN49FGgRQsgV1P6JuhIDVl+1I/vNmQNmxD6G9LhjGOT1cor/ofSuSpYwWcgeIUpkdgk/4o8GtG4J3DtW7C07o+2H+sVeATc/1chvpuv4Jnx8HjDrDBAKDHKwQLMGZA8TEOF4MEad5gVxdWDh1iGwsCSfxv3+CMqFWpr14h0nMFyYBKzrG9TbSc2iU26kicp25r0Yl4Cs9dlRDyrHaOnQlefEKXDQxEOaETjHkD7ydaek8uY1wXBAC4OYUgIf6uBHwE3ugUaxCghjnbR09s8jNfu4vMAcV+eVwyw4rwDfBS512K0+JQ+B2g/lT0XUwzOfM8eG/dgj3IxYXd/sblzqrT9RzSwAHXOFsDuPTy8n8MNLH7v0oYIAmzRWxbR8PF7thjs/f1aSh24Ums4uh4sh0cOlgwPhzIhp36rUJN4Z/5Jzx24gSUWuAUUBcBA4W6oZC83kAvb2aMm9CorCzh5Evh//0/nGH8HIBEecuaPOEOzvkCGCdlWLc44Fh5YcUG/npY3ys+yivX+1Nyq7WivASMDS1ucWjawgjREoxorN1QzDNWpSRIs3MA6vQH4WOOFMGMY2yM8jTM5RNDGvFuimIeI6MG6cS8rYGoV/GbM29IoXXnv1JfKc9H4S76dLWz4U9ib8J++/1QbafWRtrcAuTvcL2q5VPSIH4F2k4D0P1V3S4KnmcawGaxJXeDhaKGiYRdg1FZWy0uLR4igiWkyN3TEe524sJfyIOuLZhi8Wl2TUkv3eUDft9lzXY+U+34phxi62y8bTFoZd76de6Nsmu0RnmqkfF/+HfUMrJgkZa5ZFxYFAqB+futwohcC5q6grW9guVdCGqawxw53KtXHNfQSaijGnP2v5w7aujmcYIsN84met4lv8m3sURWiodC9O3DRnWIzLO1zIOUR8wOQHh2mA9eMU6vwhApHLHD4X+rcNrPwHCNvA2hdQ8+DpZsAHKWOO+fPrVDNaqgxapp4UblrMST4z9PCv0PhEqVkAseMB0uv7IGr3FxycKRgYDXszAqYWoXWwDIaExr3VBaPsp9VF5ckCMI30U2B/u+ZU8mt6cR3AG4VxvrG2UygihPIvdVfEjMNcuSFMXWcZxSOioZdgYRMT6MECDyvKGm0urSIN/TujXwM5ouV3LjRyxMDhDxa3l6tgRXpmbsse6n44qAmBwvQpE/U8sWNACEDK9ToJkgy1ZXoCJ0kRmcsqxMxxL2ic+0bQL93dE+tymP6UcclZGRglQdpYJnxLPBwBr2QqIu7AFcVGjYEpG9vw+ezbgheZCC6KTBoBZCQFtx5zCB+70YmJ6s8dOpnt8esJoSthQvxpiNJ7ir3JjxYrnK2LRShPd7ESUKx2ubNSDQTAunQUeUsKwKiTBTq5ZL4vDCklcgeY7eBJU7++HfmOQFceMFMmwnCKrgxz4VCiJqBShAiyrcab7gQ702xWuUwDWWn9b03QHi8Nnr3la6/ZVEC3R5St0M0mES48cS3X9rNHs9vZY8Xd8ADrXqsNgcL0CyikoFFhAKjHCybE/HRBnHISblMPtNf1mhq9xgZWMGqiZnJjdGqpnGuHARWpwLbHgcKHgGOvM+216aCmOLAyT2Nvuj1Ens8/RV7rE8r+OIiA88D0vNgOTQerKtXrA2l5Ndi7DWKAMupr4D3wjD4e1vNNCNyoefBKjdpYPG+1uFO3/v6CzdUr3Enjse1Ba7/Gph4BRi+CWjcSxFc6P8eC9UMRT0ugjAirh3Lt+2rv1BJVBPiwpkjSp2/2Xai5/7hQmUk+bg3VJz3YmCFQRlPNJa6/5k9RjQE+r2rhGAbGVi8BhYvh6K9l5zeYPy5/HtypV+7zv/AX2NzwIe+96ll0J0u1OgWGmYhgnFRwa8ob8+/gJiv3bkX57fC3kYw2viFoyVYmVeXCQ+WUVI/956d/AI4/5Oy3Ui/vUYiDBZmJ4taT2awyni1CbFaPFdQNPJgiSIl5wtCs+rsjFc+5wuNEEOoPIvebtRmZOh5HPy5AhZOA7BEcTMqljYbMLE4NHl/zljgpuNqpUZ+426cDYwUyi844zxDNQkiHLQeWd0tILxhj1RP8KszhF6rjOpzf2EOoAoRDLMHq/Vo/X3KTrPHqObqY7h3jt+fuRiSswFTSBYFYrRw4+mSO7dZZWTykkN+erC0eXl1APJghRovIheGHiw/SO+RgI4Zihs7umSn8qbLwJAK1sCqdF+Q9gAMLL5af+WA5oBaZGCJA6cJMRIAQEKG+rUZr0VdYc/zyvMd7htn4VLP/exRSp/9eQ5wZqNxIetgaNgVuLyX/XZiJXuAeV/CjSkDy309fdpD2VZZYt5Qd8ZaqwwpEtPKXC4YQRCEHjanOmxbW2A7nMhjmcnpsV54HBB+A8soDJ17mXiZD7lwMm+fJn92xGamPtrmJi8frJmv+QoRNBPmH9OKlaYIRaRFNUEGVqjRnXiwOlgNY/QFIPwmUlHjsrsEb4FWsjznH+zxyH+Ya/vUV4F9XjAeLH4h8otepo4bWPHJSu0foH4ZWLFCsiCP5760y3M/0YP1izvcQejbltFqFOv/X1ynDoNIyGDGV7gxI/WvHUdOrgfObrZW6p8gCKI6sNnUirzVGUYsh7h5MQrEOnM1JUTQKEKB78NDxeXCw+55jJyD5T6+UYqx+ij3ennk1uuIXKh+Q5OmRs4rTHegjkAGVqjRrZzNQgTbNj0KAGja8KznPv7Qa6Fyapcgye7SGFgx7rpVx1cD393OJphGYYTeMCNywSeEVVpp6SrPfYHaFSIoGlhG0th6iIVi61OIYP//KM95KBkvoiqiVREErM1V4/2/1Q3sURtjrpUKtppmA/S3mw0R5LiqgPXD2HOzBj5BEERNRvRg6YqDhQmu9OttbBUjUmwGIYLhmF6Ln+cw8GBlL2BzNR7+znPntQagmcW6QcvZo3bxXjwXF1Xy14NVB6EcrFBjKHLBOqRrWVv3PtqQOT+IVZJDZQ/W7r8DJ9YqRXEBdb7GZXfsbMkxJfbWLKYMLAMPlpGBVZs8WHzgHPYVEONHzo5406hPHqzGgvgKj/fupKN66YhmFeO3Pq5sszL07PqvWG2mmGvU2wcsZQp3/hac9pcbvvEU1IjvZO7mc1UIJ5YEsYvIagylIQiCsApnDfFgFX3HHg3nKjD2VIXbgyXeO4xCBNvdyv44Ma3cx/ICxA5mTBp5wJr1V/4nPC3EQ7Zd+K7c0CMVQfJghRwvOVgAYHOVwWZhboRsYP30IHuM76S8KRpYPKH0yn7/P8SMgcXlm7UV241Whbise20gxS1/alRp3YjS/ynP65MHyxENTCxhA/jpb9zb9Op3uPvMTqH4o7/Gvzfi2gEdpnl+dmTj0BtXnGYD1a/5iqAvKoVwYlFNULy+CYIgais1xYNlBqO8q3DnYImYrRfpdEdM8O/AHx0GBlavl5Xn8nfSLIjrirmRgUUGVqjR9WDZlc7nKrd0pcNReUHzWeIKh2AQ8crjl30YWOe3eYbvmTGw4jux988XKNsuH2Ay7SL9PwAGLqtdhXevGQfcJvkvpS+Gv5mpfVSXcMYADTopcvx6q2ViP2vkrmfWcWZo2jPhlPI8nHlMQ9cB6XPY8y73mS/6KxpVh99TnodjlZQgCCIU9H4d6HwPey5GdYTbOPEXQw9WmFUERcxGezg0OVhy0WCD+6C4MM+P4aklbX7l3q5jQIW7JlgNpH5+63DipQ4WADbp1vNy+cuNewHY0Ozky8Cylsr289uEtjiBdpOYcXX1MttW8DBw4jP9c/7vv8CaLKVWFafKhIqgI5LJgpYL+WWf9QO+maDer91EplZjtHpSl+CDUqtR6oTe+oIqBERntU00IqrKgXa3WevBEolurjwPp4HliAa6/R5oeQOQ+pj541xCnTixqHiolAEJgiBCTaeZQI7bQyJOwmt6zo7onREVD+1hDhEUMTuPlBc3hRBB1XYNPHdZ3LfSPX9MzGKPupFJwoJpPc0VphysUGNoYLkHEFeFNRdiw85AYjarLVUmrM5HJjDFNP65jiig5H/sj/PlcOaR0XLWXcPm8j71dn7+KB9hVVGNgYpzwnGn1e83H+L9+DqH+3987VvV24zqwpeBJRabrrxsrsaTFXiLtQ8FkYnMk+UPRoW4yYNFEERdYUJR9S+22uy+DQLRmIlto789XF6bcUeYIrTZvG5uSJkNEVRt1xi+8v1HZ/5YKBYOrp8GFnmwQo1eLLHowQKsCxfjqwnqDxOeOvW9TkauZW6YaS+q4iNMPCOqiff2RCQI59AweBUw9HPvx9dV/BHGqEuIggy68eLCIF12KnwGViik4K3GqC5MdEv97QRBELWN6KbhG/eNGLjC9z6i8SQaNuFWEQSAuLZAhzvM788NJv4dZK+bF69hA3eBeO1CH3cU6KpA1ybhstBABlaoMcrBEg0sqxTl9PI5VDlYTv3Byyih9Fw+e9w+V7mASo4BVy+wSakvN74zXlEw1OKIqn/hTSN+BIZ+Ud2tqD4S3WqCjmh9kQvtIK1V+7OanFeBtD8CDTqG9nOsIGMuEK9ppz0SaD+letpDEARRFzETMi7O2cQF8nCrCAaCHAronr8N+i9LHRHD5rXw/wlPD+Hh7Tx0vem1LG96xI/KMTX1+4cRMrBCjQ+ZdgDWKcrp1tMRPVgOoGlfz12MJFHLzyjPt/wWOP4psOIalmRvZpUpwouBVR9VZZr0AloOre5WVB+8f7a7zWAHjYEVm6S/m1V0/g2Q+ZfQfoZVOGOZ11fkpuP1NnmYIAgiJBjOWTR0uJM9igaW6OGpqXlk3IPF29q0D9D/Pe/3kqQx7DHSHbXUfiqbT7adyF5HNAByf2FzHI44r6xNdU4thO7OocbIwBI7n1XFVH2FGtqc+omMRiGCVy8qz/e9rORkuSoApwkDyxnnKdPOCXfeC1H9yLHfBjeeGI1BFZkY2vbUNhp1Azr9RnntK0SXIAiC8A/upYlr730/boiJucVi3U9txEFNoWgjezzyH/PHdH8SGFcIxLnzzRp1AyZVstx/I8iDRQZWyNEzerQ5WFaFCOoaWBoPlt7kNtogJ6i8SHmuNcJ0vWXa9sQZrwa5DJL2ibqLrwE3629A5tPKa6O8o/rM2c3V3QKCIIi6C/fwtLnJ+36lx9ljrBDKLubBV2exZG9c2MoexfmdL2x2xbgyfYz4/cmDRYQC3ULDdvXFZ5VMtF6ooTYHS08dR6/I7/E16tfOBupz+RMiKEnsT5xgkwerHsIHWQMPljMGSBOky3nFeUKBh/hmPFGtzSAIgqiTXDMe6LUQ6O4jfNxVzh5rU/0uIHyS6bqia/WLWtAbajl6q/ZaD5a3elL+4NTxYDW/TvhcJ+RJbuvRYoM8jzujWSmPaKDez4wHyxnH4nyvHACOfKAxquqnbGe9hkv2+wov7fYQqxUW6hys2kj2AmDIGiZ6QRAEQViLzQ50uVd/PiXCwwGN6kfVVBqZLG4fLJlPhedzajBkYIUaIwNL5cGy6ALVTlxHb1fXXLI5gNi27Hmz/sp2UcyCE91Mc+54tQerstR3exzu3LKPOwMbJ6nfIw9W/aPVCPbYcbr3/XosAK77JPTtqY04Y4HWI6u7FQRBEPUb2cDSKzlSg8lyh+E3Sgvt56gisyhEkLCCYV+qX+vF4WpVBK1aAREL/3a5j8m2OzUSoondgdydiswmwPI6/vcxUHFB2ab1UGmLCp/53nd7IuI9t3FBD8rBqn8kZrGC1gkZ1d0SgiAIggicfu8xefOGXdXbec2omoo83wyj0UMqgoQltBiifq3rwdLUwbIqB4sbQfEdgF4v6X8uADRK8YwV3jAW+DAROP0te73DvcrRcjh7dF0FTgo1nHot9N0eh446Iq96TgozBEEQBEHURhIzmby5dhF99DbglsvV0yYzeC0ObDGtc0P/GTUYMrBCjZkcLMtCBGNwvO1TwNDPDdpioi7D+a3s8dIu9tjvXSB5ClByFDi1XtkvpqXvc+l5sPq8AaTPZomkBEEQBEEQdQVHtP7cp8bA54H106sUTmqojmQtJ74jE3ZoNsBcDpaFSZIXm9yE1vE+6jd4IzJB/doRDcS1A0qPqbeb8UBp63s5ooFm/dgfQRAEQRAEEUbCaWDVb2OODKxQMHwTUHwIaJKjhNXZ7Io8ps2hDtGzyoNlBdqaXPYoZmBppT3NGIVadcTsBcG1jSAIgiAIgggMOUQwDErONjKwCKuJbsr+ACUU0OYQDCxNZGZNkvn85mb1a0ckENVEvS0mCWiUauJkARhlBEEQBEEQRAgwkSpCWAIZWKGGh9LZnACuqrdxrBK5MKLNzWqFQM4N3wKX9zNxjK9vND5eG+o3vtBcPleMpo5RbZMzJQiCIAiCqCvw/PnO91ZvO+oBZGCFGm5MOWKAqlL1tpbXAyc/D71nZ+CH+tub9VfqYY3ZD3zcSX8/hyZs0Gy18vhkZlhKbkl2XjeCIAiCIAiCCC8RDVm5lLAQRsXCGgipCIYa0cBSNrofItyPNeBn0Na9EtF6sPxBDC+sKgn8PARBEARBEETtwEykUx2mBszs6zjceBLDAHmn48aXVBXeNumhFbcwei/rb/6dt6pMeF7q37EEQRAEQRBELYY8WEQo4MaTnlIgl2rnIXTViTMOGHdEvY3H6EY0UrZ1ud+/86oMrPLA2kYQBEEQBEEQtQQysEINN7C0kuWAojBYEzxYABDXVnl+mwTkLGTPIxsr2/0Vqsh6hj22uw3o+tvg2kcQBEEQBEHUfNLnsDI/LYZUd0uqBRK5CDWyB0vHwMr6G1BxHmg1Mrxt8karkcCJT9XbHIJRZTdRYFik2+/YH0EQBEEQBFE/aNwDGHe4ultRbZCBFWpkD5bOv7pBR2DY5+Ftjy8Gf1wzQhYJgiAIgiAIohYS1hDBiooKzJ49Gzk5Oejfvz9ef/11w30PHDiAO+64A5mZmRgxYgTWrl0bxpZaCM9fatC1etthFrtT39s2/li9XokgCIIgCIIgCDOE1cCaP38+CgoK8NZbb2HevHn4xz/+gdWrV3vsV1xcjOnTp6Nly5ZYuXIlbr/9djz00EPYv39/OJtrDY2zgcGrgF4vVndLgiO2NYulJQiCIAiCIAjCkLCFCJaUlGDJkiV49dVXkZ6ejvT0dMycORPvvvsucnNzVfuuWLECTqcTTz31FCIiIpCcnIzvvvsOBQUF6NTJoBhuTSYp1/c+BEEQBEEQBEHUesJmYO3evRsVFRXo2bOnvK1nz5545ZVXUFlZCadTacrmzZsxdOhQREREyNtee+21cDU1dDQf4r2gL0EQBEEQBEEQtZqwhQgWFRWhUaNGiIpS6kE1bdoUV69exblz51T7FhYWokmTJnjiiScwYMAA3HTTTfjyyy/D1dTQcf2XwOCV1d0KgiAIgiAIgiBCRNg8WKWlpYiMVNdQ4q8rKipU24uLi/HGG2/gtttuw6JFi/Dtt9/i3nvvxZIlS5Cenu5x7l27doWu4bWMsrIy+n8QfkF9hggE6jeEv1CfIfyF+gwRCDWh34TNwIqKivIwpPjrmJgY1XaHw4EuXbrgwQcfBACkpqYiPz/f0MBKSUkJUatrH7t27aL/B+EX1GeIQKB+Q/gL9RnCX6jPEIEQzn6Tn5+vuz1sIYItWrTApUuXVEZWUVERIiMj0ahRI9W+zZs3R4cOHVTb2rdvj+PHj4elrQRBEARBEARBEIEQNgMrJSUFERERKCgokLfl5+cjLS1NJXABANnZ2di5c6dq2/79+5GUlBSWthIEQRAEQRAEQQRC2AysmJgYjB8/HvPmzcPPP/+ML774Am+++SbuuOMOAMybVVZWBgC49dZbcejQITz77LMoLCzE22+/je+//x633npruJpLEARBEARBEAThN2EtNPz4448jIyMDU6dOxdy5c3Hvvfdi9OjRAIABAwbgk08+AQC0bt0ab731FjZv3ozc3FwsWbIEL774IlJTU8PZXIIgCIIgCIIgCL8Im8gFwLxYzzzzDJ555hmP9/bs2aN6nZWVhQ8//DBcTSMIgiAIgiAIggiasHqwCIIgCIIgCIIg6jJkYBEEQRAEQRAEQVgEGVgEQRAEQRAEQRAWQQYWQRAEQRAEQRCERZCBRRAEQRAEQRAEYRFkYBEEQRAEQRAEQVgEGVgEQRAEQRAEQRAWQQYWQRAEQRAEQRCERdgkSZKquxHBkJ+fX91NIAiCIAiCIAiiHtKzZ0+PbbXewCIIgiAIgiAIgqgpUIggQRAEQRAEQRCERZCBRRAEQRAEQRAEYRFkYBEEQRAEQRAEQVgEGVg1lIsXL+Lhhx9G7969MXDgQCxYsABVVVWqfSoqKpCbm4uXXnpJtf2zzz7D6NGjkZ2djby8PGzfvl31/r/+9S8MGjQI2dnZePzxx1FSUhLy70OEHm99Zvbs2ejatavq7+2335aP3bRpE8aMGYPMzExMmTIFR44cUZ2b+kzdJZh+884772D48OHIzs7Grbfeip9++kl1buo3dZNg+gznyJEjyMzMxObNm1Xbqc/UXYLpNwUFBbj55pvRvXt3jBs3Dj/88IPq3NRv6ibB9JlqnwtLRI3kgQcekCZPnizt2bNH+v7776X+/ftLr7/+umqf559/XurSpYv04osvytv27t0rZWRkSB9++KF05MgRad68eVLfvn2lK1euSJIkSWvXrpV69Oghff7559LPP/8s5ebmSrNnzw7rdyNCg7c+k5eXJy1evFg6ffq0/FdSUiJJkiQdP35cysrKkhYtWiTt27dP+v3vfy+NHj1aqqqqkiSJ+kxdJ9B+s2LFCqlHjx7SmjVrpMOHD0sLFiyQsrOzpZMnT0qSRP2mLhNon+G4XC5p8uTJUpcuXaRNmzbJ26nP1G2CvUfNnz9fHmt69eolnT17VpIk6jd1mUD7TE2YC5OBVUPp0aOH9Nlnn8mvn376aWnGjBny6127dkn9+/eXRo4cqTKw3nrrLWns2LHy68uXL0tdunSRtm7dKkmSJN12223Sc889J7//448/Sunp6XKnI2ov3vpM7969VRMZkeeff17Ky8uTX5eUlEjZ2dnSd999J0kS9Zm6TqD9ZurUqdKCBQtU24YPHy699957kiRRv6nLBNpnOP/+97+lSZMmeRhY1GfqNoH2m2eeeUZ1j3K5XNL48eOlDRs2SJJE/aYuE2ifqQlzYQoRrKEkJCTgv//9L0pLS3Hq1Cl88803SEtLAwBUVVVh1qxZePjhh5GQkOBx3MGDB7Flyxa4XC4sW7YM8fHxaNeuHaqqqrB9+3bk5OTI+2dlZaGqqgq7du0K59cjQoBRnykqKsKFCxfQvn173eO2bdum6hMxMTFIS0tDQUEB9Zl6QKD95ve//z0mTpzosb28vJz6TR0n0D4DACdOnMDChQvx5z//WbWd+kzdJ9B+s2nTJowYMUJ+bbPZsHz5cgwcOJD6TR0n0D5TE+bCZGDVUObOnYsffvgBPXr0wKBBg9C0aVP89re/BQC88cYbSExMxPjx4z2OGz16NK677jrcfvvtSE9Px/z58/H3v/8dCQkJuHTpEsrLy9G8eXN5f6fTiYSEBJw8eTJcX40IEUZ9Zv/+/XA6nXjhhRcwcOBAjB07FsuWLZOPKyoqUvUJAGjSpAlOnTpFfaYeEGi/ycrKQps2beTXGzZswOHDh5GTk0P9po4TaJ8BgDlz5mDq1Klo166dajv1mbpPoP3m6NGjiI2NxUMPPYR+/fohLy8PW7duBUD9pq4TaJ+pCXNhMrBqKIWFhUhNTcW7776LRYsW4dixY3jmmWdw6NAhvPHGG5g3b57ucRcvXsSZM2cwa9YsLF26FFOmTMEjjzyCwsJClJWVAQAiIyNVx0RGRqKioiLk34kILUZ95uDBgwCAbt264fXXX8evfvUrzJkzB2vWrAEAlJaWGvYJ6jN1n0D7jcihQ4fwhz/8AePHj0daWhr1mzpOoH1mxYoVOH36NGbMmOFxTuozdZ9A+01xcTH+7//+DxkZGVi8eDEyMzMxffp0nDx5kvpNHSfQPlMT5sJOy85EWEZhYSH++te/Yv369WjZsiUAICoqCtOnT8e3336Lu+++G9dcc43usQsWLECHDh0wdepUAEBaWhq2b9+Ot99+G/fddx8AeHSgiooKREdHh/AbEaHGqM/ceeed2LBhA3Jzc+Vw0m7duuHIkSN4//33MWrUKERFRen2iYSEBERFRcmvte9Tn6n9BNNvOHv37sWdd96JDh064Mknn5TPAVC/qYsE2mdycnLwzDPPYNGiRXA6naisrFSdl/pM3SaYscbhcGDQoEGYNm0aACA1NRUbN27EypUrccsttwCgflMXCabP1IS5MHmwaiC//PIL4uLi5A4FAOnp6XC5XDh48CCef/55ZGdnIzs7G1u3bsVrr72GmTNnAgC2b9+Ozp07q86XlpaGo0ePyhPmM2fOyO9VVlbiwoULHiFiRO3CqM9UVVXh+PHjHrl6HTp0wKlTpwAALVq0QFFRker9M2fOoFmzZtRn6jjB9BuAjTdTpkxB+/btsWjRInmSTP2m7hJon/nmm29w/vx53HHHHcjOzpbzH37961/j1VdfpT5TxwlmrGnevDk6dOigej85OVk+jvpN3SSYPlMT5sJkYNVAmjdvjkuXLuHEiRPytgMHDgAAPvzwQ6xcuRIrVqzAihUrkJKSgry8PDz11FPysXv27FGd78CBA2jbti3sdjsyMjKQn58vv7d161Y4HA6kpKSE4ZsRocJbn/nPf/6D3/zmN6r9d+3aJd+wMjMzVfWLSktLsXPnTmRlZVGfqeME02+OHj2KmTNnIjU1FYsWLUJcXJy8H/WbukugfeaGG27A2rVr5XvXRx99BAD4y1/+gry8POozdZxgxpqsrCzs3LlTfk+SJBw8eBBJSUnUb+owwfSZGjEXtkyPkLCMq1evSuPGjZOmTp0q7dq1SyooKJDGjBkjPfLIIx775uXlqWTa16xZI6Wlpcna/6+99pqUlpYm7d+/X5IkSVq1apWUlZUlrV27Vvr555+lG2+8UZo7d264vhoRIrz1mc2bN0vdunWT/vnPf0pHjhyR3n33XSktLU368ccfJUmSpKNHj0oZGRnSK6+8Iu3bt0964IEHpNzcXLkOFvWZuksw/WbGjBnSkCFDpMLCQlUdEi5zS/2mbhJMn9GeRyvTTn2m7hJMv9m2bZuUlpYmvfXWW9Lhw4el+fPnq2ruUb+pmwTTZ2rCXJgMrBrKyZMnpfvvv1/q3bu31L9/f+nPf/6zVFpa6rGf1sCSJElatmyZNHr0aCkrK0u6+eabpR9++EH1/muvvSb17dtX6tmzp/TYY4/pnpeofXjrM6tXr5Zyc3Ol9PR0adSoUdLatWtVx3711VfSiBEjpO7du0tTpkyRjhw5onqf+kzdJZB+w2uK6P2JtUWo39RNghlrOHoGliRRn6nLBNNvvvzyS/n9CRMmSPn5+ar3qd/UTYLpM9U9F7ZJkiRZ5w8jCIIgCIIgCIKov1AOFkEQBEEQBEEQhEWQgUUQBEEQBEEQBGERZGARBEEQBEEQBEFYBBlYBEEQBEEQBEEQFkEGFkEQBEEQBEEQhEWQgUUQBEEQBEEQBGERZGARBEEQhvz617/Go48+qtr29ddfo2vXrnjqqadU25csWYI+ffoglNU/hg4diqVLl4bs/CKffvopioqKAAAvvfQSJk2a5Pc5pkyZgj179qi2rVq1Cl27dsWbb75pSTs5FRUVGD9+PM6ePWvpeQmCIAj/IAOLIAiCMCQnJwfbtm1Tbdu0aROaN2+OTZs2qbZv3boVvXr1gs1mC2cTQ8KxY8fwu9/9DiUlJQGfY+XKlWjWrBm6du2q2r5q1Sq0a9cOy5cvD7aZKiIjIzF58mQ8++yzlp6XIAiC8A8ysAiCIAhDevXqhSNHjuDSpUvyts2bN2PGjBnYt28fzp07J2/funUrevfuXR3NtJxgvXCSJOEf//gHbrvtNtX2ixcv4ttvv8V9992HvXv3YufOnUF9jpaxY8di/fr1OHbsmKXnJQiCIMxDBhZBEARhSEZGBqKiorB9+3YAwKVLl7B7926MGTMGbdu2lb1Yly9fxsGDB9GnTx8AwEcffYRRo0YhPT0dffr0wdy5c1FZWYkDBw6ga9euOHz4sPwZp0+fRkpKCvbt2wcA+OCDDzBs2DBkZ2dj0qRJ+Pnnn3XbJkkSXnnlFQwcOBA9e/bEjBkzVOft2rUrVqxYgTFjxiA7OxtTpkxBYWGh/P4vv/yCiRMnonv37sjLy8MLL7yAKVOmAACGDRsGABg+fDiWLVsGAKisrMRf/vIX9OzZE3379sXixYsN/2+bNm3CxYsX0aNHD9X2tWvXIjIyEqNHj0ZycrJ8bs6UKVPw8ssvY8aMGcjMzMSYMWPw9ddfy++fP38e9913H7KzszFs2DC8//77Kg9ZZGQk+vXrhw8++MCwbQRBEERoIQOLIAiCMCQiIgKZmZlymOAPP/yA9u3bo0mTJujdu7dsYG3duhWNGjVC165dsWXLFsybNw8PPPAA1q5di3nz5mHZsmVYt24dOnbsiG7dumHdunXyZ6xbtw6dOnVC586dsX79erzwwgt4/PHHsXz5cgwaNAhTp07F6dOnPdr27rvvYuXKlZg/fz6WLFmCdu3aYdq0aSgtLZX3WbhwIWbNmoV33nkHZ86cwXPPPQeAGYQzZ85ESkoKli9fjhtvvBGLFi2Sj+N5Xh988AFGjx4NALKht3z5cvzmN7/Bs88+65FfxdmwYQP69u0Lu119m/34448xaNAgOJ1ODBs2DKtWrcLVq1dV+yxatAi5ublYtmwZ2rdvjz/+8Y+oqqoCADz44IM4c+YM3nvvPcyZMwcvv/yyx2f3798fGzZs0G0XQRAEEXrIwCIIgiC8kpOTIxsXmzZtkr1Uffr0webNmwEA27Ztk/OvoqOj8dRTT2H48OFISkrCyJEjkZqaiv379wMAcnNzVQbWp59+Khsxixcvxl133YXrr78eycnJuPvuu5Genq4rbLF48WI8/PDD6Nu3Lzp27IjZs2fD4XBg7dq18j5Tp05F3759kZGRgUmTJsmeuE8++QTR0dGYPXs2OnbsiMmTJ2PEiBHycY0bNwYAJCYmIjo6GgDQrFkzzJo1C23btsW0adPQsGFDQwPrl19+QYcOHVTbTp06hS1btuD6668HwLxj58+fV3moAGDQoEGYMGECOnbsiHvuuQdFRUU4deoUDh06hI0bN+Lpp59GSkoKBg8ejPvuu8/jszt27Ig9e/agoqJCt20EQRBEaCEDiyAIgvBKr169ZMNk8+bNsoHVu3dvHD58GGfPnkVBQYGcf5Weno6UlBS8+OKLuP/++zFixAhs27ZN9sLk5uZix44dOHHiBIqKipCfn4/c3FwAwIEDB/Dcc88hOztb/vvpp59UoX8AUFxcjJMnT+Lhhx+W9+vRowdOnDih2rdt27by8/j4eFRWVgIA9uzZg5SUFDidTvn9rKwsr/+HpKQklUeqQYMGKC8v19337NmzSExMVG375JNP4HA4MHjwYABAZmYmmjdvjhUrVqj2a9OmjarNAAtP3LNnD+Lj49G+fXuvbU5ISIDL5cL58+e9fh+CIAgiNDh970IQBEHUZ7KysnDx4kXs2LED+/fvR05ODgCgRYsWSE5ORn5+PrZv345HHnkEAPDNN9/gnnvuwfjx4zFw4EDce++9mDdvnny+pKQkZGZmYt26dXA6nUhNTZUNoaqqKvzhD3/AgAEDVG2IjY1VvebG2nPPPYdOnTqp3mvQoIH8PCIiQvUeF69wOBweQha+hC204X7esNlsHufj4YDcQAUAl8uFr776CufOnZO9Zto287Y5nU5T4hsulwsA+44EQRBE+CEDiyAIgvBKTEwM0tLS8P7776Nz586yIQCwMMG1a9fCZrPJYgtLly7FTTfdhCeffBIA874UFhbKhhkAjB49GuvXr4fNZpO9VwDQvn17nDx5Eu3atZO3zZ07F71791bt17BhQzRp0gRFRUWyIEVVVRUefPBB5OXloW/fvl6/U+fOnfH555+jqqpKNkR27Nghvx+s1HzTpk1VHqTDhw/jl19+weOPP45+/frJ20+cOIG77roLq1evlgU2jOjYsSOKi4tx+PBhJCcnA2ChiFouXLgAu93u4UEjCIIgwgOFCBIEQRA+6dWrF1avXq3yvgAsTPCLL75Q1b9KSEhAQUEBdu/ejX379uGxxx5DUVGRKido1KhRKCgowJYtWzBq1Ch5+/Tp0/Gvf/0Ly5cvR2FhIRYuXIiPPvrII58JAKZNm4YXXngBn3/+OY4cOYJ58+Zh48aNuvtqyc3NRUlJCf7617/i4MGDWLp0KT755BP5fe4x2717N4qLi/37ZwFITU1V5WetWrUKDRs2xKRJk9ClSxf5b/DgwcjOzjZVE6t9+/YYMGAA/vSnP2H37t3YuHEjXnzxRY/9du/ejZSUFPJgEQRBVBNkYBEEQRA+ycnJQUlJia6BVVpaqqp/dd9996F58+bIy8vD9OnTERERgdtvv11V86lZs2bIzs5GRkYGWrVqJW8fPXo0HnroISxcuBC5ubn47LPP8PLLLyMlJcWjTTNmzEBeXh7mzZuHsWPHYu/evXjjjTfQokULn98nLi4Or776KvLz8zF27FgsW7YMY8aMQWRkJAAmbjFhwgQ89NBD+PDDD/3+fw0aNAgFBQVyuN7q1atx4403IioqymPfSZMmYceOHdi7d6/P8z799NOIi4vDxIkTMWfOHEyYMMEjpDA/Px9Dhgzxu80EQRCENdikYKspEgRBEEQt4+jRozh16hR69eolb5s3bx5KS0vxt7/9Lejzu1wujBo1Ck888YTPcEWzlJaWYuPGjRg0aJBsVK1ZswbPPvss1q9fD4CJfwwePBgrV65EUlKSJZ9LEARB+Ad5sAiCIIh6x5UrVzBt2jR8+umnOHbsGNatW4eVK1di5MiRlpzfbrfjrrvuwvvvv2/J+QAgKioKs2bNwsKFC3H06FEUFBTg5ZdfVsnLr1y5EkOHDiXjiiAIohohDxZBEARRL1m6dClef/11nDhxAq1bt8bMmTNxyy23WHZ+SZIwefJkzJ49G926dbPknFu2bMH8+fNlyfaxY8figQceQGRkJCoqKnDzzTfjzTffRLNmzSz5PIIgCMJ/yMAiCIIgCIIgCIKwCAoRJAiCIAiCIAiCsAgysAiCIAiCIAiCICyCDCyCIAiCIAiCIAiLIAOLIAiCIAiCIAjCIsjAIgiCIAiCIAiCsIj/D8C80mzmJnFlAAAAAElFTkSuQmCC\n", + "image/png": 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JA0uQTOhczLPRg1VQUIClS5di/vz56NevHwCgWbNmuPDCCwEo+aGjRo1CixYtsGzZMrjdbvXYjh07YsyYMYbnvueee9S/c3Nz8Z///AedOnXCzp070bZtW/W9mTNn4tprrwUAzJo1C0OGDNEcm5eXh+HDh6uvX331VSxZsgSrVq1C165d1e3NmzfHsGHDUFTEL7sxffp0zeu33noLM2bMwLx584SBJRA44OSuk6goqkD98+pHdby/1I/Tf5xG7da149yyxFBeWI49P+5Bm8FtHB9r6sEKz2PP1NSQeJFsD1YWgI0APgz/M0WSpBoA5gP4EcCFANoCeB9AMYCXE9bKBMATudCECAbiEyJI8GZ6UXykOC7nipbj246rf1eGB23uQ3NRVlCG6z+8PtVNEQgSCl0PL945WPMemYdD+Yfiek4rGpzfAJe/ernt/bOyspCVlYVZs2ahV69eSE9P17yfn5+PTZs2Yfr06RrjipCTk2PrOsXFxZg6dSrOOecc5ObmqttlWcasWbPw4YfKMNegQQMsXLgQO3fuRIsWLbjnmj59Ovr3768xrggulws1atSw1aaKigqUlZWhZs2atvYXCAQKr7d4HQAwTh4X1fFf3/I1fvvfb/hn2T/h8VX+ALEf/u8HrH5zNe5ceScad2vs6FizyAgy5lSGeV8qSWqApCzL38qy/Lgsy18CsDPq3wIgE8DtsixvlGX5KwAvAPiLdIa6RLg5WIi/B8uT4Ul5iCCdA1YZHI6r3liF9R+tx5FNR1LdFIEgodDlGs7GEEGPx4P3338f06ZNQ05ODi6++GI8+uijWLlyJQBg27ZtAIB27dpFdf6JEyeqRtzcuXPxww8/wOfzqe+vWbMGJSUluOSSSwAA48aNQ+3atdGyZUu0bt0aI0eOxIcffgi/P9JHb9u2Ler20IwdOxZZWVm45pprYj6XQHA2UnqyNKrjfvvfbwDiN49LNEWHFK/4yV0nHR9r5sEqP63ki3rSK7+RmUgq+6e/GMBSWZbpu/07AE8DyAWwi95ZkqS7AdwNAOecc06SmmgPYmDwciMkSVK3x0tS2ZPuSXmIIE1lmuS91eEtxytUJcdKkFknM0EtEgjiSyJDBJ14klLJ0KFDMXjwYCxduhQrVqzAvHnz8PLLL+OZZ55By5YtYzr3LbfcggEDBuDgwYN46aWXMHz4cCxbtgyZmUofMXPmTAwePBgejzLENmzYECtWrMDGjRuxZMkSLF++HPfccw9eeeUV9bh4LEK99tprmDRpEhYsWGDb4yUQCLS83uJ1PHbysaiPt5NLv+HjDdj6zVYM/WRo1NeJlVg8TGZzuhM7TgAAcprlRH3+qkBll/hoAOAws+0w9Z4GWZbfkWW5qyzLXevWrZvwxjkifC/SD55qVMlyxIMVw2SINs48GZ6UqgiWFZRpXqfamxYrS59bmuomCDj4S/0oLyxPdTMqHXSIYGVa3Eg26enpGDBgAP79739j+fLlGD16NJ544gk1nI+n/meH7OxstG7dGpdccgm+/PJLbN26FV999ZX6/owZM9T8K5oOHTrg/vvvx/Tp0zF//nzk5+fj888/BwC0adMm6vYAinE1duxYfPvtt+jWrVvU5xEIznbY+YtT7Hiwvr7la2z8dGNM14kVNRAsiiHCzBlQuL8QAFC9UfVomlVlqOwGFqD/6SWD7WcERiIXZBJE37SFBwodnZtetfZmeFNq1BTsKdC8PrH9RGoaEidO7TkVddgAAMx/bH7Kc+KqIhPzJuL5Gs+nuhmVjrNd5MKIvLw8BAIBnHvuucjLy8OLL76IYFC/2lxQUGD7nLIsQ5ZlVUZ9x44d2L59Oy6/3NzTl5eXBwCqeMWIESOwYMECrFmzRrdvKBTC6dOnddsJEyZMwD//+U/MmTMHvXr1st12gUAQf1Jdg9Q2MSTamI0r5PO7vGeCiZE4KvunPwS9p6pe+H/Ws1WpIeEfBbsLItt4IYLhbRs/3YgJjSdg34p9tq9BVq27/6U7POkeVBRWpGxyRWJwCRWFFY5DYHZ8v0N3nlSx5astGF9rfNTHLx+/HIvGLYpjiwSA9nkSRKBzsM7GQsPHjx9H3759MW3aNKxfvx67du3CF198gfHjx6Nfv37Izs7G1KlTsWPHDvTs2ROzZ8/Gjh07sGHDBowfPx79+/fnnnf79u144YUXsHbtWuzduxfLly/H8OHD4fP51NpUM2fORL9+/ZCVlaUeN2bMGDz99NNYtmwZ9uzZg59//hm33XYbMjMzMXDgQADAI488gt69e2PAgAF4/fXXkZ+fj127duHrr79Gr1698Msvv3Db9OKLL+If//gHpkyZgjZt2uDQoUM4dOgQTp06FedvVSAQHPzloGXUxJmSg0WwOzej97NTaFiIXFRuVgDoLUkSLQE1AMABALtT0qJoCd+LdNgeT+SC3LS/z/odAHByh/3kQ7JqXatlLXgyPAgFQvjk6k9iana0+Esi3rNLx10KwFmo0ql9pzBt0DTMuH1GvJsGANj5w86EnJeG7YDSc9IN9hQI4ks0IYLrpqyrMgIwWVlZ6N69O1577TVceumlaN++PR5//HGMGDECn332GQCgW7duWLt2Ldq1a4d7770X7dq1w+DBg7Fq1Sr897//5Z7X5/Nh8eLFuOKKK9CqVSvceOONqF69OlasWIEGDZS1QF544IABA7By5UrccMMNaNOmDa6/XlEynT9/Ptq0aaOe+/vvv8c//vEPTJ48GRdffDEuuOACjB8/Hrfffjt69OjBbdObb74Jv9+PG2+8EQ0bNlT/Pfzww3H5LgUCgYK/1I93uryDL2/80nS/M8WDZSdEkJ7L0WOJ2bhCPv/ZbmAluw5WFoBW4ZcuAOdIknQ+gBOyLO+VJOk5AN1kWe4X3udjAOMAvC9J0n8AtAHwDwBPnml1sHjN5dbBCm8rO6nEADuZlJNJldvnVtVbts/dHl2DY8RfrDyUDTo3UNsSCoTg8tiz6ctPKStEv834Daf3n0aNxvFN2Lbj+YhVcKSiqELzuk7bOjGdT2DM3p/24pxelUvYJpU4DRGUZRmzRs8CEL1EcWXC5/Ph2WefxbPPPmu6X+vWrTF16lTTfei+u2nTppg7d67hvseOHcOKFSvUvCrCkCFDMGTIEFvtfuyxx/DYY8YJ9u+//77m9e7duy3PKxAIYqf0uJImYFWmojJ7sLb8bwtaX9Fao/C37dttOG/Eebp9dy/ejQ8u+wC3L7oduX1ytWktwoNlSbI9WF0BrAv/ywDwZPjvp8LvNwSgyjvJsnwKiseqEYA1AN6EUv9qQvKaHCc49yK9AsB6sEj+lJPwHjKp8vg8KZfHJKsewz8frhpVTj4LvQK09p218W0cgLRqadZtqIhtFSrWRNmzlelXTMfzOc7yqqb2Np8kn23w1ErN+P7R7xPZnLOGEydOYMKECao3SyAQVB3++PkPAEDRQX7Rb4ITD1a8lKPtcHDdQXw+5HN8+8C3ACIG0IbpG7j7716yGwCwa6Ei2E231bTQMBl/zig3SPxJ6ixcluXFMEmrk2V5FGfbBgCXJK5VyYHrweKFCIYnQ0Ri3YlQBe3BSuZDy4MYWJ4MT1QG1u8zf49re9gOz1vNa3nMrx/+GtM1y05pDaxU/yZnAnJIxvZ5qfG6ViV4izdm/Dzh50Q256yhTZs2asifQCCoWpgtutJzPCcerFAgBHeavth5Iig5VgJAySMDoJuNH918FBPbT8Q96+5Bg/MbqAYYmbtoQgRN5jNkvnW2CyxV9hysKo068ZH0HixiLDmpZUV7sGL1vsQKabc30xuVgbXkySVxbQ/pWJzgydCvPzjpMFgJ1rO9s7HDgn8ssL3vmRLnngroZ40OF7TDGRZ9LRAIBDFTesJaJdi0L6W6Tau5jsYYS+K84H+3/g8AULCrgPv+798oC9sbPlY8Wi53eO4W1JcRMouMIAYmKbx8tiIMrGTBuRfVm1Xm38CAMwOL9mClWhKctNvji3iwCg/ak51PhOqZumITxo4BmpalDyOkxQOs+OnZnzSvhQfLmvUfrbe97+k/jGWrE03QH8TJnSdxZGPlFIWg+xGrenjsfZnqvkMgEAiSTdFh87A/wHz8p/tRq8U/XvRSoni50cuY+7CSN1p8WOnbiTozMaAIJLWEzN/IZ1I9WUGbIYJiMRmAMLCSBm9VmJuDFd5GjBInxYJpD9bFf74YAFC/Y/3oGhwj5AF1+9w4vvU4AFgq7wBKSOTT3qfj3h6SnEqw48LndaaxeAaFgWWNE+9JKr20M++Yiddbvo63znurUv6udN9Cq0DxYBdxUlmgXCAQCOLN8a3Hsfenvab7qIp6YdhFWcB8wVuTn2Qxv3CaIxsLRQeLsOr1VZptpK2+HJ9mOwlVZA1E8t3YFbmojGNiKhAGVrIwud9kWdaFCEpu5YZ25MGqiHiwss/JRosBLeDNtM41SgSBsgBcXhdcbpf6UFolhgL6sDpyrlghuWw3fH0DAHuTc144gNNwKwC4+r2rASS+I60SOPiKiFKl+jqJhbXppODio5XP40MP4FYGFvv+2Vg3SyAQVE2KjxTjv23/i6m9p5r2bazX5eiWo7p9zMZ/niq04b7U+zxDLlH0elxbhJxVdSbtMlJ75sm0h4IhTLt8GjZ8EhkTNcbmWTyeCAMrSVitzJMbkjykZMXAkcgFMbDCqxCpzMUKlAfg8SnuZic5WEWH9EbY8vHLY29PeFW+VqtaAOzl73A9WA5CBAlZDZSCo2JVxxonHiy22GPhAXshqPHm8PrKV/OcHuwdG1givEMgEFQRXqr/kvr3wn8tNNyPXQAl8xcas/F/x/c7Ivs5CBH8sN+HpvvGEzbtgXzmVle00rTL0MCi5jDk7+Ijxdjx3Q58e/+33P3isUB+piIMrGRhMW9UVVqIoRW+0aPyYHkVA8ud5o7K4xIPAmUBNZ5XFe6wYdQY5X9s+3ZbTO05vV/J1/HVUFzi0XqwojFYye8hDCxrnHxHrEFVctS5kEk0nNhxQvPajmc22dCTBat7VniwBALB2cDRTXqvFIEde3g1SOk5Abv/oXWR2lhOQgSTCdtmspjGqgSyuVlk4VPjpQv/TdIveMYXkNzIksqGMLAqA3Lkxib/E2PEST4E68Fy+9yp82CVBeD2uTXtstOprHx1JXf79u9ik+5e8dIKAEBWfcWbxOZk8eCtVkVjsJIEUeEZsAGtxGTxfX094mvN62QtJqx8TXuPpmoRwwwnSdTEwOp0eycAIpRVIBBUTcw8UGS86XJPFwDgplfQx7PGCl1Ul15MDgVDqqiEui2BBtbHV32MOffNUa9Nw/bt6mew2eXTtT3JsUShOaNmhv68EB4sQRKwGyKoerD8MXiw0iIerGhC2uJBsDyoerDSs5WVIKvix2bei3hVBPeke1CtXjWc2nfKct/ywnJAAi584EJ1W1TfZ7jpwoNlDf2cOC3UzA5iiYJ9JlNdEoGHk3wAYmClVU+ztb9AIBCciZjWsQqPz6Q8C2+83vTZJvVv3QIgNUWh+9B5j8zD89nPa64dSx97au8p01qR2+Zsw5q31gDQf172M6kL+wZzE1UTIJyysnvRbt2xJccVA4v2+NGG3NksmiQMrGRhIXLB5mDFFCJIG1gp9GARg+qSfyl1otte09b0GLNcEVbhxykNL2iI5n2bAwBqNKmB0/usJb7LCsrgq+7DlW9ciRHfjgAQ3WSatF0YWNbQ35GV6hNh1JJRAIBPr/k0EU3SkWgDq7ywHEufXYrns593PBD/+MyPeD77eUeFhslz56uuhM/uWbrHYYsrJ0ePHsV9992H3Nxc+Hw+1K9fH/369cP8+fPVfXbs2IHRo0ejadOm8Pl8yM3NxbBhw7B8eSTvU5IkfPllRAH1nXfewWWXXYacnBxIkoTdu3dzr19RUYHs7Gz8+qtSsPy9995D586dkZWVhezsbHTs2BFjx47VHfPiiy+ic+fOqFatGmrVqoXu3btj0qRJKC9XFhBGjRqFq666Sj3miy++QNeuXZGTk4Nq1arh/PPPxwcffBDz9ycQVDV2L95tWO8q//18APqapDR0aRDWG0QXC6ZDBIkgEn1sLAbW2+e/jelXTLfcTw7JurGJNQrVEEEDBwAbQlizZU3N+QGgorACgNbjJzxYCuYuBUHcMPVgyXoPFnEx2w3XqSiuwOy7ZwOoPCGCJEnUm+FFvQ71LL0/ZmpssRY/DQVD6gp9jSY1ULCnwPKY8lPl6qoM+SyxhAiK0Ctr6PvVqBgioeEFDSGHZGTWyUxwq7Sw93G8n7Hnazyv/l1RXKF6gO2waOwiAM48WKT95D797pHv0P3h7ravWVkZOnQoSkpKMHnyZLRq1QpHjhzBkiVLcPy4UjZizZo16NevH9q1a4eJEyciLy8PxcXFmDNnDh588EGsXbuWe96SkhIMHDgQ1157Lf785z8bXn/RokWoWbMmOnXqhClTpuChhx7CK6+8gn79+qGiogKbNm3CihUr1P0rKiowaNAgrFu3Dk899RR69+6NnJwcrF69GhMmTEDbtm3Rp08f3XVq166NsWPH4txzz4XX68Xs2bMxevRo1K1bF1deeWVsX6JAUMUoPlKMjFoZuu1rJipen/ICZSHDakGUfb9m84jxQYcIkrkDnYsUS75w2UklsiMUDOlypWj8pX7bHizVAcCsY5Oxgy0jRK4PKGMUEPH8sdcRBpYg8dgVuSDSl37jVRQe+1fuV/+mPVipyg+hQwQBZXXDSs3svW7vGb4Xa9hSsCKoik140j22Qv0qCitU1R21PkQUk+k67eoAEB4sO9DhBFaiKIGyAOqcWyfp3yu7CpjIRYxoz00/L1bfo/p+fKJwKwUFBQVYunQp5s+fj379+gEAmjVrhgsvVMJ9ZVnGqFGj0KJFCyxbtgxud2T1uWPHjhgzZozhuR955BEAioFmxsyZM3HttdcCAGbNmoUhQ4bgnnvuUd/Py8vD8OHD1devvvoqlixZglWrVqFr167q9ubNm2PYsGEoKuKLqfTt21fz+uGHH8YHH3yApUuXCgNLIGCwSlVweRWjhTeu1M2ri6ObFaGMfcv3oeXAlup7mrwrqv8lcwd6/vPzKz9H0XItgdKAThWQZvWbq3HeLedF9i8P6HOw2BBB5iPrDCyOkAX5XPT3qpFpP4tzz4WBVQkwCxG0e3OSOFggspKQUpl2KkSQtMnKwCLJkhc9cpFO7MJOYWAzQv6Q2tG5PC5Lg63wYCG2fL0FjS5sBAARwQ4HOVgt+rdARXEFqtWtprThLO5oosEytK3UD2+mF9XqV1O3lRWUcdWf4oW/xI8tX23RbEvkMxbtfU/fp1bf49pJiqfGbhjuI48A+flRNStqzj8fePVV+/tnZWUhKysLs2bNQq9evZCerr0n8vPzsWnTJkyfPl1jXBFycnJiaq8sy5g1axY+/FCRYG7QoAEWLlyInTt3okWLFtxjpk+fjv79+2uMK4LL5UKNGjVsXXfhwoX4/fff8cwzz8T0GQSCsxFSg5RnYNF9/Z4f92gMLLqv3r9qP7reozzHxGCj5z+tB7fm1vx0QkVxhamBVXigUJv35Q+pn4m0yShEUFUNDI8dP7/yMwZNGKQtNEwE2cLXoKXdNYbYWRy5I3KwkkSiQwRJ0dWbZ9+suqTdaW7NQ5VMaBVBIOzBsinXSdz3jbs1xoX3KyvOJLwvWoIVQdXAktySpbEzbdA0AEqYIKCEOQLOJEdDwZDS6ZyhIhdr31mb9BpPREYfsDYuguVBuH1uVKtbDdUbVwcAvFDzhYS1TQ7JeLbas9x2JIpojTf6PrUysHZ8F67fUoU8WB6PB++//z6mTZuGnJwcXHzxxXj00UexcqWycLNtm1L2oV27dgm5/po1a1BSUoJLLlHyT8eNG4fatWujZcuWaN26NUaOHIkPP/wQfn/kd9q2bVvU7Tl16hSysrKQlpaGwYMH4/XXX8cVV1wRl88iEFQlLBc6iTPHwsAiRor6HuXByp+Sr/5N5mMVRRXqNjpfK1qs5ob1OtTTGljBkKEsO3nN5qfRYwfrAWOdAfQCnfBgKQgPVrIws6841j6ZXNq9OUkcbKOujdRtalibP8gtmpdI6BwsQDFQrDxYBDqueODLA7H6zdWaiXc0BCuCcKUp57XjwTqy4QgA4PhWJV/DW00xsOhO0go5KMPldkGSJEguKWEGlizLgBw/pUXC7HuUnL5x8ri4nteMxhc1RtHBIhzdfNQytK3wQKHqJaVX8g7lH0KD8xvEvW3Ew8ry6we/YsD4AXG7jreaV10widbA+u3r39S/7YbX2r1/nHiSUsnQoUMxePBgLF26FCtWrMC8efPw8ssv45lnnkHLli2tTxADM2fOxODBg+HxKPdnw4YNsWLFCmzcuBFLlizB8uXLcc899+CVV17BsmXLkJmZGVOeafXq1ZGfn4+ioiL88MMP+Mtf/oLc3Fw1PFIgOBvh9X1Whgl5Dq0MrOoNq2uvZbAgSCKK/ljxB9oMbmN4bqdYnaNavWqa9spBWVcOiA0N/Om5nzTnoD9T6fFSrQeLNdKo/kt4sBSEBytJmA2edCfAFhp26sFKqxaZaEYT1hYPZFnGkY1HdDlYZnKd9INLT/Q8Pg8klxTTZwj6gyg6VKTxYDl96MkEnnzPdggFQmq4geRyfk27zLpzFp5yP5WQcyebkD+E9JrpihFs4sHaMV/xupAC1LRRPv1Ka4WlaCg6zM+BKT5SjANrD8TlGrIsaxYijv1+zPaxtEF6KD9c9FKyH2YYq1JnZSQ9PR0DBgzAv//9byxfvhyjR4/GE088gdzcXADAli1bzE8QJTNmzFDzr2g6dOiA+++/H9OnT8f8+fORn5+Pzz//HADQpk2bqNvjcrnQqlUrnH/++fjrX/+K4cOH49ln9d5WgeBsgpeDbim6YOHBatqzKQDoxJV2zt/JPR2Jfln6zFJ1Gz3faTGAHzKsNickY+27ay0FK1hCwZCm7w8FtdFMsizrPFq6cwRMjifOAM45hAdLQRhYycLkWWBvYiAyWXLqwaKlMmMRZoiFDR8rsqR0jLEn0zwH643Wb6h/125bG4B2JWnl6yujXuFd/qIiuXxw7UEA9jxYLMRwJd+zHdQQQYSNugR5sEg4QqxKizTxPJcTSCiny2v+G5HfkigNEkMWQMJysIzkfQHg3a7vOro3jAiWBzV9xRfDvrB97IzbZ+i2edI9tu/180YoCdEk77AqkpeXh0AggHPPPRd5eXl48cUXEQzq+8eCgoKor7Fjxw5s374dl19+uWVbAKjiFSNGjMCCBQu44hmhUAinT1uXlqD3J7LuAsHZCm/u83ant02PIdEyRgYWeZ89928zlKiBGk1rIG94nrqdVtcjkHNn1M7Q5C7xWDd1HWbfPRsrXlmh2U7OsWvRLjwpPalTRpaDsi5nivVA8bxPNJrFf8oDRl+fV0tLs5/wYAkSjakHi73pZTmiIujAg+XN9Oq8P0DyDayig/qVfm+GeQ4WmSjfOONG1GgcTuamPnpFYQUO/xpdPtDJXScBADm5OQAUb4ddw7XvM4pClyfDA0jRhQgCSGiIIGHbt9uwcOzCuJwrVcVmg35F7dHtdZuGCLLfJe3Bati5YULaRvLxACXXkaX4iHGZAbuwixBOnt2Nn+iTpj0++wZWZp1M1O9UXxf6ciZy/Phx9O3bF9OmTcP69euxa9cufPHFFxg/fjz69euH7OxsTJ06FTt27EDPnj0xe/Zs7NixAxs2bMD48ePRv39/w3MfOnQI+fn52Lp1KwBg8+bNyM/Px4kTJwAo4YH9+vVDVlaWesyYMWPw9NNPY9myZdizZw9+/vln3HbbbcjMzMTAgQMBKOqEvXv3xoABA/D6668jPz8fu3btwtdff41evXrhl19+4bbnmWeewYIFC7Bz505s2bIFL7/8Mj766COMHDkyXl+nQHBGQiJfBr81GMO/HG66b6fbOgEAOo7sCMDYwCLRLEbjkyfdo5m38dIzyLlJnrwZhQcKAUTqTbHn+HmCokh4aN0hzfuhYEiXM8V6mQw9WOGXbHSVpvwHk4N17Ldjmn3Z/c5GRA5WJYB+wNhVAiceLJInRCAerGRLtfNkUO3ItANAdtNsw89slZNjBMnHGfiSMpFx4sFSQ/wkSfkMUYYIujyuqNtvl0+u+gQAcNlTl8WcjxWramO0aDxYJm0gq36dR3dWNlAfl7diGA/KT0cMLJ6XLB4GNAkJrNE0UgxbDslR/55WnkDNvh4XPD5Pyko7xJOsrCx0794dr732GrZv347y8nI0btwYI0aMUIv7duvWDWvXrsWzzz6Le++9F0eOHEGDBg3QrVs3/Pe//zU899tvv40nn3xSfT148GAAwNSpUzFq1CjMmDFDZ9wMGDAAU6ZMwdtvv41jx46hVq1a6NKlC+bPn482bZS8DJ/Ph++//x6vvvoqJk+ejMceewzp6elo27Yt7rjjDvTo0YPbnqKiIowZMwZ//PEHMjIycO655+LDDz/EzTfrFwEEgrMJskDlTnOb1owClLG+RpMa6tiiqxklK4V7STSL0eKXN8PLjUrSnCtIGVhM/1x2qgyH1h1Cs0ubQZIkNbWCHddI+8oKlLpYbG0vngeL9Szp6mAx52YNLJ4Hi1zj5I6Tuvfoz3o2IgysZGEzRFAOyTq3rB38xX5N/hVA5WAl2YPFM7A8GR6E/CGEAiFTl7gn3aMaIroOI8oJLIm5JkqEdnKwmvdtjl0Ld6HrvRHJ5LSstKhDBD0+e7W34kF5Ybmj4rQ8EmUMhoIhHFhzAE0uaqJ7T5ZlHP71MGq1qmVpkJJ7+so3lTo/9Opdorxv5YXmIVfx+H2n9JgCAOj3XD/8b+T/lPPGIFJjx7D3pHtQvXF1pGWlwZ3mTnrOZiLw+Xx49tlnLfOQWrdujalTp5ruw0YfPPHEE3jiiSe4+x47dgwrVqxQ86oIQ4YMwZAhQ2y1+7HHHsNjjz1muM/777+vef3cc8/hueeeszy3QHC2QRaL3D63JoycB1nIIotZ7HyDLPh5s7ya1ywurzZChh6Pdi3aheaXNVfPzYsweCFHUcK9+t2rccGdF6ihhySXi24vfX5W1ZD1YMkh2bYHi/VOqefjGE68uVTpyVKk56SjrKAsbuPxytdXwlfDh/NHnR+X8yUDESKYJOyGCIaCIW3BOpseLH+x39CDlewJE8+AIrlhRmGCJHG0bl5d1O9YH1e8cQWun3a9Zp+jm49GlRtEPj+ZpNrxYKVlpaHB+Q00hkpatTRHHiw6RNDtS1zR5+xm2ZrXpNJ7LCTKg7X8peWY3H0y9v60V/ceCb+UJAlurxtywPi3LisogzfTyzU8EtV2zUIFp2nx/H3pXMpYFkhcHpfp9wgoqoUtBymqem6fO2W186oCJ06cwIQJE9CgQfxVLAWCM5VVb65Sc7OTiRMPlhyUIbkjBpZRQXkrD5bL7dJGIVHjEQnz04QIGsxFiHDSsS1KVIM308tV6uPJpJPPw84tdQaXQQ6WKmBhEiLIXp+ef5YeL1XrU8YrRHDew/Mw846ZcTlXshAGVrKw68EKytqQQZtem4riCr0HK0UiFyR8qtfjvdRtah0pgzDB9Ox0VWJekiR0e6CbWqCXMOtPs7DqjVWO20MmvmSFx+W2NrDYOl4AcHLnSWyYbn+QoEMEE+nBqt26tua1kzwxIxLlwTr+myJ7T+Tvach9f84l58DlNfe8lBWUwZfNl+5PlAfL6veL53NGG46xnNftNR7ACSF/CG6vW71uVQgRTBVt2rTBgw8+mOpmCAQJp+R4Cb65+xtbtSHnPjAXX9/ydRJapYU2sKw8WKFgSDGOwnOuhY9r85lVA8sgB6tpz6bIbpatW8Cl/yaLz8TocPuM+2c2LNztc3PznNSaqRwvlC4kkMmNUo2f8OaaLWtqzsXORbl1sMhnoWp7hQIhNZLpbA4RFAZWkjDzvLCrCtGECFYUVuiK8aZK5IKQNyyipENW5I2k2oP+oKWaDqDUknAKMZbICo/LYy1yESgLcEMdnUA6bCDsGUiSJzEeE+RojHw7kHuUZwSqg6HXbSnTXn6aHwZZvXH1xBlY1HNEVudqtqiJbg91AwCs/2g9/vjZ+f1JoL9nejJg9/mtm1dXt82Ot5Yuwp3M+1QgEJy5LBy7EL+8+wvWT1uf6qYYwkavmCGHZI3a74ntJ7TnYg0spl92p7mRfU42JLekmV/Qhhib32UmQkTmKzVbKEZPKBDSeOFYI0iWZa2HKyjrQvx0gmpECdBGiKDOI8aUEmKvRcaUs1nkQhhYycKGB4tM/KMJESwvLIevunZFP1UiF6TNdGeghggaeLBC/pAuhpgwZsMY3bmdECwPaowlyS0BsrnhECjXG1i9x/ZW1ABthinKQVmTg5Wo34E9bzwmyPQ9GE8D3cyrSq7pTrNWEdzy1RbuwOSr4UuYgUW+54YXNETt1rVx7/p7cf9v96sLCavfXI3JF0+O+vy00UmvXtr9/qs3UtT/Br0ySN1my8DyB9Vnz+PzoPSksRx9oik/XR4XNcZkEQqFUlbSQCBIJaRfclI/74+V0S9ARQPtwTKbgwHhEEGXpIpiXfTwRdxzkVA4XV2q8PG6EEGOB0uVaa+VgeKj/P6OjAGZdTPV8/BqTamGTkiO1D8Ex6AK6nOw2BBB8pmMQgQ1HjC/1ghjF2VJVITwYAkSzuH1xhLjdJKiHJJ1qoJ24HmwUiVyQdpMr8ITBRwjA4vIc/Oo2z6yMl+jaQ3H7QmUBzQrWGRA2L9qv+ExFYUVulWvtGppkEOybQMmFIyECCYyt4U979p31mLqJeaJ+1bQ92A8DUPS4fPCNcjncHldpup3gfIA5JCsW2EEFEM+UeGNxNNz1+q7AAD1z6sPt9etSz6OFlqlUHJJOPe6c9Xr2iFQFkDuZbmo37G+us1K5IIMsuTZy2mRg8L9hbqaX8kyIo5vPY5Te08l5Vrx4NAvh3By50nrHSsZwigU2MVf6sec++eg7BST2xvunp0onE7tFdu45BTawKJD2HjQESek1hXvXN4MpRwOG2Ehh5Sca12IILUfWcgic6TqTapzy9oAke9VDSv0WxtYdBFlXkgfGzKoilwEbRpY1PGm+wZl4cGCMLCShlnujurx8bi4bl07lBeW6w2sFIlckAef68EyiNc282DRK2S7FuzSTETtECwLavKpyATus+s/4+5fdqoMRzcfxe+zftdsJ5/BrpJgsnKw2PP++sGv2LtULyLh6JzUpJzutGNFXUzgJBxrBkOvcX0QXnhh9caK96aisMLUcI4F4gllJxQkZCRW6MWHWi1rocOIDsp1bRpY/lI/vBlezb1uJdNOfmfy7NVtpyxmlBwvUffxer0oLU2dV6uyEw9RmWRTWloKrzc+CwOCqs0v7/6CNRPX4Menf9RsJ+M82x/+Put3HPr1EH56/if877b/ad5Ldn1FWkXQysAiIYKAMgax/S5rrOk8WESFkAkRNPNgpWWl6dJCCKTuIhkrQwF9iB99/pM7TxqqBALKHEtzvBwxmMh21WjiyLSzHjEydtDhhGThhg4RFB4sQUohN7Hb644pB8soRDDZHizVS0F1vFYiF6FAyNCDRXN4/WHMGTPHUXvYcD/yt1FbjEQiSGiAnXpegDZEMJEqgoHyAJr2aIohH2tloPM/yI/6nLRxE0/DUB0AOIue5JokB8vI80IWKy77z2Xqtvs334+/H/87jm89jtLj8TcGjm4+ipWvr+QO0qx65/5V+6PKxSKG7NXvXY2c3BzVg2o3ZC5QGoAnw6Npo1WIILmXiUAOKY1AX7NevXrYv38/SkpKhOeDIhRKzGTRX+bH4Q2HE+KJlWUZJSUl2L9/P+rVqxf388cDWZbx439+RNEh/sq+ILnw8nGASF9+eMNhjdf502s/xaTzJ+GH//sB6z9KbX4WbRTVaGIe/UJC/Mj+7POnUSTkiDCFgiHDEEGy6MVKwJN+lyw803OE6k2qa44J+oNcDxaZr8z60yydjDrrcTIywMjYSz4jNweL9Yj5OftQ0u1k0e5s9mCJOlhJgDfBoWsxsTlY6v6SvZszWBFUCuAZiFwkOweLFyJoS+TCwIPF4jRHI1iurSPU9tq2WP3majS6sBF3f9IJdbipg2a7U4OVDjnw+DzYv2o/Nn62ER1u7GBxpDNKT5Si4QUNUadtHc32dZPX4fzbz4/qnBoPVhzvH144gXpNZgAz8mAteXIJgEiFeyAS0tGwS0McXHswpuK8POY9Mg+QgZJjJbr3WA/Wexe9BwAYJ49zdA3yPVerpwhokOf5w74f2jqXv9QPT7pHc69bGVjEG0y+v3odlEn3wV8O4pye5wAAatRQJiYHDhyA32+/TIFT5JCMU8eUidqpLZU/TLD4aLFqoMazvSXHS1BRVIGDpQd1i2bxwOv1on79+urvWtnYv2o/Fv1rEfYu3YuR3420PkCQNGRZxuk/TiO7abY6N/l5ws/4ecLPGCePcxxdkmjI4qA7zY2c3By0uboNtn6zFQfWHkCjLtrxnx6vXV4XQhV8mXZTD5Y77MFiPD++6j6UlJfoRCXoeZGvuk+VcQciObVkHmWUg0W3QyeYRnucKoKGMu2sYMW+ZfvU7WSuyuZwqeqFQe02l0dJdREeLGFgJQVeiJXkkjTWPkDlYIVvdo/PY+vmJAVQK5sHiw4Ds8rBoqWirbCjNkjDSq63HKDU/DE0sMLfeYuBLbjXtRvmQIcIun1ulJ8qx1c3fRVXA+vE9hMoPlyMDdM34Lr3r9O8F8vvnigPltFqKKDNwXJ73Yb3SvXG1VF6ohQZNTN073W4uQMOrj2IiqIKbhx9tBxce9DwPbpmFQ0J2bOLqngV9rA6ze0KlIU9WD7Gg2WhxghEDCxi3LFe3Bo1aiR8Qv7L5F/w/Z3fA3BunKaCJ/OeVP+OZ3u/ufsb/PLuL7hq0lVod3e7uJ33TIHcr06KugsSBwnRl2UZa95ag2/v/xZ3/3K3TiSqYHcB3un6TiqaaMiRTUcAUIrK4T52/t/m4/aFt2v2JXWwAHshgmy/emD1AdRsWRP1O9bXhQiquVck9C78Plmw2796P9oMbqPNoSIL1aQul0EOFr0AaiRiQfYz9GARAyscoUA8kqGAEuoXKA1wQw7pz0KfR6gIKogQwSTAM7Bo44P2YEGm1NR8bls3J1n1qMwiF1YqgnZl2sm+TuApAqZlpRl+LzwDEYBqANotZMuqCCaCgt0F6t/s9xfL754wDxYntptAPkv1RtVN62Cdd8t5AIAej/bQvUcWGU7/cToezVUxq6FitDBwaN0h7nYjyPdM7hUnA1OgPIDiI8VKDpaDEMFf3vsFAJBZR1GqcvvcgGTsaU4kp/dFfrPKNlFLJmSSM/ue2TqxkbMBEYZaeSG5vXuX7kXxYW0kyWvNX0tIeHYsLP3PUgDUYnN4TOGNjXTUg1WIoNurNcDIPXtyx0ldiCAderj6v6vVawFA7mW5mtf0OdV+O3yqoF/vgQKMS6rocqYqgoYy7Ua1tIL+SPSPUR0sXtggLXJxNnuwhIGVBLgeLGrCRudgAZGHjPZglZ4sNZzokvhddiU91SIXmhysGEQuAGDoJ0PVv8sKnCWVsyGCQLgDNfheeAYiEIUHi6mDpZ4/jhMIuvAgS7w8WCteXuHo2EB5AOPrjMfmLzfr3lNDEjhGatFhJeciu2m2qedFrSGSoTdaSQ7RxPYTseET+0WhreCJchAkl8TNKXMaoqiGs4TvlSYXNVH+v7iJ5bGTL54MOSjDk6ENEbQqNFz4hxJm2bRHU6XNkgRPusdW8dB4Q0I/AXOPYZWH6h7OJEXFeGEkniBIDuWny/Gk9CRWvrFS9x4Zo+c9PA+7F+92fO7sZtl4p8s7WDNpTazNtKRgT4H6N5kLsZ4sGs147XXmwaL7WF2IYCCk7rvx040AIvc4iRgg+9NzPNarZBQiSGPmweLlYOlC9plTBsuD6jhLDDbJLWkisHhFleUQlYOVZGGTyoQwsJKAbQ9W+IakH2TyQIyvNR4f9f/I9PyslybZhYZDwRBmj5mN49uOA2BUBG2IXJgZWHQ+VP3z6hvux4MNEQTMZdONPFixhAjSCdvxWtE5uO4g5j44F4BSRR4Abpl7i/p+jcbRh3TRq3cbP9no6Niig0UoPV6K7/7ynf68JImW8x2qobIel2kdLI3HlyG7Wbb6984FOx212wyryR7PADMSSzGC9WC5PC7Uzatr63ck3jJPujORC18NH7LPydYc483wpsSDxXI2D8yEeBkZa99di81fbT6jvlMn9ZXORrbP247T++PrqQciua3zHpqHPUv3qNsPrDpgO0+a5oVaL2heH/zlIObcOwc/v/ozVr6uN+LixeqJq9W/Sf9GFqsyauvDy4MVkTxwXgigTuSCmj/Qf5NcegIRv6AhYx2Zs5HjacOP9Sod+uWQzkPFoik0zAim8XKwjEIEa7asqR5D5m7E4+VyuzQFmY2UBUWIoDCwkgNvddvMg0WtZNM3596f+NLbRtXKk11oeP+q/Vj79lqseEnxeGjqYIU7EjORC7s5WJn1Mh21i62DBfBjrAmGHixiANsMUaRDBPct36duj9ckh57AX/rvSwEArS5vpW6r3bZ21Oe2GwbJg9QFokO+COReZSV/AW25AjN5cbKdN/kkHiyj96PFMnyVcymnBhbrwSJ/O1kg0cm0W9TBohcBCJ4Mj22lzGj5/ZvfNQtPPOGaeX+ep3kth2Rs/HTjGWUkxIpZaKoZhzccxqo3V6mvZ989G18M+wJPe5/G9nnb49W8xEDmgMK+MkSWZUy/Yjomd4++sLnZuQnvX/K+2p/88fMfyJ+S7/h8dBkDevz/7s/fYd7D83iHxB3yGXr/szcAoFFXff51RVFEidkqBystK00Tnq8xsHiFhpl7WQ7JgMQxsOgQQX/EGwQoC4a0gcULH2ZVBHUiFxYeLFWEIxjJ7yIeLLK/5FI8WNwQQUosQw0tFCGCgkRSq2Ut3TaNB4tMLBkPll2RCyMPFnu+ZEN/RskVDj0yEbmwWh27Y+kdAJyHPAbK9DlYZgZWPDxYshxZ7QGAEXNGqGpz8ZogEolXANzvLpZJciwS0f+79X+G72kScplQSdVwckumIYIhfwguj4u7wk2HycZzBdyy1hXnMY3VgwUo96mTBRJ3mtuRiiAdFkPwZnoTamD9sfIPfHrNp/j+0e/VbTx1xn0/7dO83jp7K766+SsseXqJbt8zlZM7T+p+X/q5iHaR4O1Ob2PuA3O5722dszWqcyaLMzEHSw7JqthUMiB9Y7xzTQF96NneH2OrqUjjVAE4FogiKhBZbCaFgHnhdRVFFWo/z3qoAK2BVTevrkbFlsxJrpx4pS5EUA7KurGI5HuxQmR0X8B6sKrVq6YxmHjfpVWIIJuDpROdCu9O18VSPVhhg01yh6XoebWyKOELIdMuDKyUQQ+cbMgTXRyPTVTksW/FPnV/zTUkydSQiDdsJ8KuvppN3Ox4sM7pdQ6qN6oeVQ6WLkQwCg+WE5EL9hzn9DwHlz2t1G2Kl4FF3xf0d3fjjBsBxCZUwBqxTiY99MDD/t6aEAhW5paECLqtQwSNPEoaAyuOHqxoFAkdG1hlkeee4PT5lWVZY2xbFRrmfZeedA+2fL3F9jWdQiYG9AowmZzeNOsmdRsbxkP6xSMbjiSsbcmkvLAcr7d8Hd/c9Y32DepRUxXcQrIzozd8jjPRWCGcSSGCP/zzBzxf4/mkGVnxDH9mYfsbkjeUKALlAYQCIcy5f05ccw7p/l+z2EuFtxGCFUGc3HFSHVOtRC4y62ai9GSp7j2Pz6MvNMwJESTb2Dx5Mw9WnXPraNrN6w/MCg0Hy41l2uWgrAsvJO0hi9Nkf5fbpc3B4oQIyiFZnZMID5Yg6dCTd3IDGolcWHlsFj6+UHM8jZmYQ9xhxkN2ZdyTYZw8T7wSVtRqVQsntp9w1KxAOScHK8EeLDrcLZrj7aCpEE9Nqs+99lzU61AvJqGC4qPa1THeip8Rdc6N1OMixj+BDgtjDRASSiG5JNM6WGb5eokysKLBqYFFnmPa2+rYwGJWS608WLQ0MeHIhiMI+UPY/l1iQsnogtKERWMX6bYd33pccxy5d4xk8Ss7gfIA5tw/R/XWkc+zbc427Y7Uo0b6kfl/n49nqz3Lzec144wMpzwDQwRJQd3yUxEDa/t32/HViK8SsoL/8eCP437OwxsOo+xUWdJFsQoPFGLvsr1YM3ENZoyaEbfzGn0OyaU3sPI/yAcAbPpsEwD+vEkTIlgtTZEul7Xqfy5v2ENmI0RQcknqwh0pl8HLwVK9SozIhb/Y3MAKBUP2Cw0HQ5p+hw4dJHOnUDCk1iuljVSjEEGXx2W7lisAvNnuTbzd6W1b+54pCAMrRdCTd3Kjkgk4m4Nld1DlrWg4zeGIJ+xEyCx53krkgpCek46Kogps+mITSo6X4OTOk5aGhNMQQSsVQTvhc7Q3hj0+XpOeHd/t0J2bUFZQht/+95uj337ngp14UnoShQcKUXykWPN72M3JKthToFE2ZIVZ6N9KZ2CxhR5NcrCMjHF6gk7nY8WKVZgez1Pg1NNKYurZEEEnvyE7mNkKETT4LlkZ5nhBnh/6/iJerbp5ddVtp/edxqy7ZqmvyYTCk+7RiMacKWz+YjPWTFyD+X+fD4DKdzBZvCD9CJHTd7poEksuZao4I0OKOD/h9MunY+MnGzU5SJWZtzu+jQ/7fZiS+QLp9+Np3P364a/c7TwDi52X8CIoaAOLzA3YHCaXxwWXO9LnyrIiNKETuQinD3gzvPCke9S+X6MiyHiwdAYWz6NNG0mcQsNGMu2Q9cYZ+XyqJyokK/VK09yaHCw2RFBVASWhhDY9WMd+O4bD6w/b2vdMQRhYKYK3um6Ug0VXR+cNxg06NwAQUZKjcZrDES88GR7dZzSaNFcUVWhcyma4fW4c/vUwvrzhS3x5w5d4veXr+PqWr02P4cm0e3we5x4sB7KjdD6RenycDSxa1poNpyGx+XuX2Y+fX/WGkhS/f9V+BEoD8GZ4MeDFAQDst/m13NdM36cXC17LfU0TTkgbTmbiDGYGFk00ildGRDPpWPrM0qhyDtgQwQOrD9gOD2MHM6tCw6GAPgdLJQYPQnlhOfYt34cTOyLeZlmWMef+Odi1cBcAZRJTfrocRzcfRZOLm6BavWrIPidbc551763Dgv9bgFVvrlLvnXWT1+Hlhi879mSnGrpuzdyH5mJKzynKaxODQlWJI/MgB55kAHj3wnex+St9uYTKDLlfExUiOGPUDEy6YFJcz2kWirl1duXOeQMi9+DBtQeTbuAGyyPqfU49tGak56Rzt9PGAYEd3yxVBN3a/CLawKJDBANlAchBWZMvDWhrbtGRPRplQiYHKxTQeqR+eu4nzX3Xbkg70xwsjUGFcFggx/ukuWZQWyQ5WKGkcmhysKgQSNoIJGqD8b6fzqQC5MLAShE8dSijHCxaPZA30fWke9CifwvuRCmZIYL0w8p2KIDxavrXIxUDiY5pNoI2lMhE7bf//WZ6TLAivjlYdibbyQgRpGG9QXXaKWF6RBXJDqSzJgOQy+OKS5vptgVKA5qB7/CGyIoVHa5WsLMA5afKcfAXfT2ksoIyWwZWPHN1LJ8hg/nVqX3Ocwroz7brB+UeX/B/C4zbRhmi7PdilYPFCxEkxDLBfb7G85jScwreaPUGCnYX4OTOk5CDMtZMXINf3vlFbduH/T/ExPYTcWTjEfiy+ffqsueXYe4Dc3WTL6JWmSro9toxfJa/tByAsvK86o1VOLlDaX9FYQUWjl0YORc1afr4yo9RdKiIW1TUDkc3H8UXw75wdEyqIYsJpH+PN79+8CsOrTuEBf8wfqacQsYMntE/846Zhsdtn7cdT0pPavrBZCLLMoL+oKZ/S3bOjL/Ur97fvP4+WkgO552r7tRsp40DAmsE8OYH6ryM58GiFXCpEEHivWRzeOniw7SXh/4deB4sdpyn2+jL8ZnmYMmy3uDS5IoxwhyyLAMyNLlURIyMzsGSg3JEDdEf8ZJJLmceLLucSWGEwsBKEVxjiJFpJx6skuMRhS3eZClYEdTUsaHJrJ2JIxuTkxROT/S81fR5EkYG1p4flVobdibxrKFkRSgY4nrHzAzPNW8pRRBZDxxRGLKTW5PoEEG2/kn1RtU1rwe9MgiAM8/Lju/DIYdSxEsUjzY/V/059e9AWQCZdSMy+/Qkng5X27VImVytfXet7nzHfjuGuu3q6razxDPEzTJE0GCCvXz8clvnV797aL8Tcq+VHjNefFC9WxJw0UMXAQDGbBiDh3c9HFOIYLQeLHY1/7Xmr+H1lq/rvkOX14UDqw8AAPYt22fpwT62RVtUO9WhZOdee67698F11hNDYvDzBESWPrM08oK5lY5vO65+p4mWz68MrHk78UVoAWDZC8vidi7iqbaKpmAh98I7F7wTs/fGX+pXi7UDwLLxy7D6rdUmRwBrJ63Ff9L+oynKbve5Oqf3OY7ax5NGB5Qx4Zs7v+G+FwuhihBqtqiJxhc21mznhQiy46SZiiDJswL4HiyXO1IHi3immvVppjmXHIosbNFeHtLf0x402sCi54IA4/EL6WXaeR4sMq9hPVyaUL8g5YmiPVj+oGpg0u0iBhbtZZPcUkI8WGRh6kxAGFgpghsiSHJ8iCs67MGiw4x4YVNmEucNOjdIWr4C/YASaU8ao8meUR0vJyx5ii/dTLv1acw8WEQ1iY3LZhNSzVBXcRIUIjhrdCQ3ZcS3I1CrlbYUAPkunYSHkt/hjxV/YO3ba1W5dCA22XbC6f2nUXiwEFn1syIbqceADlcjgwtPHj3kDxmGf9A4zYEy4sCaA9yaXnbY9PkmW/tNGzQNAHDudedy3zcLdyST7sETB6t5j/U61ENObg4yamWgrKCMK4MOmIcIOh3Ifpv5Gwr2FBh6Wdh+KFCivTdJG/927G/c49mcilSrUyXLwPvuke/U/LPv/qwv3l3VIF5bp+S/n483895MqbCHmrtiU72R7BcKhDRGjp1jCOumrgOg9CEvN3hZ3b7gsQX49r5vTc9FxjrawLH7XDmNjGHzoAmB0gCObj7q6Fx2WD9tPdfLbZaDReZlRiqCJK+I9Jm8HCyNTHv4MvXa10PDLg3R+srWyuYQ34NFDKisBlmaor3kGuychH5NG0XkGppnQdYuqJl6sCjji87BIiGCmhysIGVg+SOq1+R7SnU/nUqEgZUieGE5bGFgd5obkLUeE97gYZaTklY9jas2kwjoiRXP02RkYJHPa6eoppFRtHjcYtM28QwsK+ODNaTSqiuTfZKrZAYrvU//HY8JAD24VW9YXfc+W1/DCT899xMAxZPipM306ikAXPKvSzSvX2nyCiADdTtEvE+0t4YuzEy+a55inN0cLF4hxmgwC/GJN7/NYMJdw1+PHQOL5zUmnj6jXDBeiCAJfVs8bjGObzvOO4zLZ9d9hncvfNfwnnuj1Rua12z4JJG4zqxtr5D450M/163oJhN64mCYx0b2tZk7dfCXgyg8WKjbRtizdI+t86RaQTMVzL53No5tOZaQ2lBO0U2EjbxTHOU2AFgxYQW+fZBvHLELGLP+NAvlp8uxd2kklSD//Xxb7eSFkNtdOLCz0GgHVrglnnlYPOjwNnVbuA9se01bAPwF2JA/pBobZH+uB4sKEVSfe0kbmrj6zdUoPV6qnovsX1FYAU+6B95qXvV3JvuFAhHjpUX/Fsisk6n57nhS66FgRMGQeLBojxQ95upCBBkPVigYUhfzXW4XEIrsq/FgEZELl7EHq/REKRb8Y8GZqXLqAGFgpQjegMyKXBAjhe5wuB4gkxpSadXSFBGJJNRDodt2+Fd9TLmRgaW6lG3kfDg1GIw8WN4sr6XhyX5n5Ds+ueMkCvYUmB+b4BBBesLGNdZ92nDTaCg8UOioze90eUfzmhhJLHXa1uFuDwVC6me5feHtAKIzsB7Y+gA63d4JhQcLY5KqJ9Ahtqyn0IiLHrkouosxPyXJZTQLnyP3Me+78mQoA59heQROiODti25X/7br/SZGQMnREtveTnpCCAAXPezsOwtWBA0918nAaPWXRQ7JmHrJVMvzvX/p+3inyzvYOX+n8TVt5mAZ5bOdSZgtuJUXlusm4motoTh426Ol+HAxPrnmEyx+crFm+7Zvt3H314wxUmTb93/9Hqv/yw/v432+yRdP1ry2uyjEmxfYHTPOG3merf1oarXW95+L/71Y8zpekQdG8DxYZH5w/bTrARiECPojYhwk/Jyo3rEhgnJIMXbovGaeuAYATUghuQatYkhy00OBkHqdzLqZcPvcmmeAFbEgMu2qt03WGlTEACOfnRbmoo052iAjSr+siqA63/AHNSGCRnO+7/78HZa9sEy/oAh+HuOZijCwUoRpiCATMqdxA3MGWLMQQW81r+LaTYLQhdXAZvSw1WxZEwBQv1P9+LfJwMDKqJlh6OHocFMHAED74e0Nz8vW6GFJdIigZqWK44KPxYNFQ6TOiw5aT7QL92tX3ut1qMfdr3pjyuNGhwhSMu1EpMOpxxYAareujaY9mgIy4iqR3Lxvc9z/2/3c91oObKl5fU7Pc5DbJ9exVHy1utU0rwe+PBCAPseOhtzHvLBJsrJoWh6BWeyp3bq2+reuRpMBtHG98J8LTfbk0+ySZuj/XH/Hx6VSApt+7gJlAcweM1stc0Aza/Qs7Fu2jz1cB8lFNcOO8XB0y1Hb38vvs35PicqsHdgVeZrnazyvued2/hAxSpMtM862ces3W/HzKz9rtn0+9HNsn6evK3dyeySEjYzt9Fh9dIsSOkfKktD70dAhdk95ntK8R8aiosNF+KDvByg6XIRDvx5C2akybv8696G5um28xZvej/dGz3/01Cl/0lz6xKXq34PfHswVA2KFLcpOxf5Mm3nheIWGA2UBSC4psqDFURGkF2R/n/k7AKhGMBsiCGi9QJIkca8LaD1qxEtG14Ek56bzm9xpbshB2TpEMBhetJSgSrHTIX9yUI7MFfyReafmWsz+klvSGKmaEMGAvRBBs4Lcb7R+w/C9Mw1hYKUIroqgkQer1NqDZVV4NRnSllarq0YGFpmcnnez9YqYkZhH+xv5xhCdmEqTUSsD/hI/PxxBUlbazCbxViFBZiGCbBHfaKA7Ld5v6zQHy0jtrnYbZbJt5bHjJd+3vqI1Wl/ZGjWa1tBsz6zDF7mQA5HVNTUXy+B+lzzm3k42hCMekNVJs+sRQsEQmvZsitITpY7U0NhSC+eNUJ4JM3GXU3uV34430SG5kEbiCPSKJoHOe4tGCICIxDghWvGGVBhY+1fvx4kdJzT31pGNR7D2bUWQZc6YOZr96XCtc3pphQE63d7J9Fq5l+VqXtsJn5yYN9H0fWLE7122F59e+6lalysVVBRVYO9Pe/HD4z9w+2KzMYU2Kuh6e2bHJEIk5FD+Id02dqEEAKZfMV1nfNNG9aFflfPQ30NZQRlKjpfgyxu+xIt1XsTK11di95Ldpu1hJ7TkM695aw12L9qN1W+uxqTzJ2HawGncyS9vMS27mbZv+dOyP0GSJPR/rj8e3v0w7lx5p+4YQFnIvGXeLXhw24Oo176eunBGw0YFxKN2G/nMpMwIjZHIBT23cKe5dXWnfnn3F1QUKmMtXesJoMZ7Kj+LLt5LPFi875sOo+N5sGgDi/ztTlPy8/ev3q+ehzboSBuJB0uSlM8cKAtoQwSpkEH13GTeSaeqQIk4UD1Ybr6KIFsHi96Phlzrj5//0L3HIxkRWIlAGFgpghsiyMi0k0kyHd7DC/WhY4NZyPZkxLpaXcNQ5KIiqPVqmFC7bW3N65otFO+XkeFl5MHKaqgILbBqfIBi0Bol5BKs8sXMQgQ/vvJj02PtQIdRNL1YX/+MGNZ2JxQFuwu429XwAYtBz8iQy2qUpVnVArQTeE2YVTASIqiGVEThwQLi5y3khfBwr8c8z3JQVg3JD/t9aHmdnNwcuH1uXPfBddrzksHP5PsnBlaNJjV071mGCFJhmTTXTL7Gss3xpPFFWqWvfs/1s3Xc1tlbcWRTclRSCe91ew9vtHpDM3FgjSojarXRTiZJDUMj2lzVRvO69eDWpvvz+jMWEj1BREVO7XZeSiBefDXiK0ztPRU/PfcTVv1Xn9vK89hZ5f6YebA+ufoTw/f2LN0TVUgxr481Gj/McgbXvr0W+5bv0xhYbq9b04fNe3gePrvuM0ftU5XpwhNn8nr/qv3Yt9zas0rTuFtjPLzrYSVCIIwkSZrPO/rn0ZEDJKDVoFaqETVk2hCM/G4kWl3RCoASzcCqEcZjrkI+I08kiWfosFFAqqKzUS1GplYmm4MFMAaPpDXsGl3YSP0OaC/Pxo83ovhwscaDRQtpkOsSD9aceyP9DitqIQcjHiwylp7ae0qdM9HGGu81uQ9zcnMAKIvSdjxYaoigS1IMRc7zSD7bipdX4Om0p/HdXyzEe85M+0oYWKmCmzdDjKEK5eYjNy3dgc++e7buOLMJp50JWryINkQwWG4sM8/S67Femtc9/tYDtdvWNhxUSZvY85PVfjI5pQmU2TCwLPLFzEIEAWfqfjzI6m3rwa254aZE8MCuwImRIWY31NAoMZkMHvT9pzGwmAGB9fgZGVhWkt6sylO0GIXWsbDPc932dS0nz5rrlAXQ6bZOuqRz8n2Yhd0VHixEes10rnKnVWkBOiyTpsH59touyzKelJ60ta8ZJBSS0OXuLprXrBeUJlllKFj2r9rPncD9Put3PCk9qSmyTKDDOP+07E+ocy4/H5FAavkQrBZM2NDl80edr9uHPFP0RChV7F8ZWYEvOlSk88avn7YewYogZo6eqfbV0y6fZnrO9y56z/A9I29y4YFCvH/J+5g5Kj6CNkb3q5EYE+HIpiMaxc+Vr62MOuSRLCKSPEqyYEuHL5rdT80ubYaBLw/EFW9coY5310y5Rp1w05D+r1brWmhyURN0ubeLbh9AUeJtObAlbppxE/527G/wpHt06QvxMLDUvFSO8M+pPaewftp6zTY2j91yYZHYTUw9LDpEMBQIaXKwaJELWkWQDh0k/bTL49KF/BHjRc1tYoslB0NaNUjKg0VKrwCRvF51bsQswqvRL2UR8TFPhgeyHMnB0hQapmTa6TpYLrcL3mpe7j2mkYT3h3QhtQCwbso6LPzXQt33cCYhDKwUYZaDRSbfRBac9lbQRYcJZiGCZ4IHq+hwke1cFdaQ7HJPF9OaVkYeLNLJ8AwDf6mfO1nlndcIbogg9RuRUINooFdZjULWyOezG89OlIpY1LBVC+OZPX7kdyMBRMIf6O/LyMBi84HMDKxkebDo78/MqK5WXwkJ6vlYT4z6cRQadGqguaetJsb+Ej83z8EqFBVQBiijEgfp2cqEvvwUf9WfFyII6EOCjLBrwN8y7xbT99n2syGROc1yDI9NlRJV8ZFi03IBvNAx+nO6fW7Tfq/DzR20JQ1gfR/R90ujCxuh/wv6vDbVwAql3sCi++UVL6/Ac1nPad6fc+8cHMo/hPwp+Xi12auoKKrAHyvshRU5gUwK7eTBsfDGgj1L+Ocp2FWAmaNn4vns57nvz757Nt467y319fpp66MWfSB5m+9c8A4WjVvkWFX1msnX4OK/XIxuD3SzfQzpS1TjwaDPdKe5kVk7Ex6fB4HyAM69PlKeIi4erLChzutTAb2qZ7BCO4eyWlgc/NZgAEDzfs0BaMf7jR8r0vd7l+3V5mBRwhCsTDtrLLncLl3kBwkRJCF67PfELibSOVh0NIg6NjIKy7oQQWJghaNJIEPjwVI/C6siSBmbaVlp3BQGO7/xrNGzsPQ/S7F64mphYAmcYaoiGDYWiBKUVZ6BWYhgPOsYWRFNDlbQH8SBNQcMBRF4kJU5QOm4zGpaGdbBMlHZM/NgDZwwUHNeI8xCBAHzJE8r6IHSKFSRfN4lTyzBzNHWq7JGgy870FQUVXA7THpSAERyt8hvzhpYpBAyW9zQyoN1au8plJ8qx6F1+skrTbxysDSGick8tP9z/dHp9k7o9X+90Ky3UlSSHtytnj9/qV8N53MKHVrJQgyA5S/yCx4bhQjalUq3u7per7395xtQFgjuyb9HfW1myKRS6tesXaSwMA09iXO5XWh8YWPcNOsm7vFDPx6qGu4EJ8qn3gyvJt+RoBpYcmoNrFAwxJVUr1ZP+5np54IuWh5PyAQuGulxJ2VQWgxsgfwp+ep17ERuvN3xbcdtArSiUT8+9aO2mLUN6LGr0yglVzCrQRZ3XzL2k/77oocuQo2mNdBuaDvTa7h9yuIo66mJFaPyLGb70/uy+fAsxItHnh3aeOkwQhHJ8qR7dJLlhh4sjmw8WyiYGFykFpcclNH1vq4AlNBNOSSrc6OM2hnq8SQHS20js2jKGlisB4uoBmpUBN0GIYL+kOa78GZqPVihQAjj64zH7sW7ud8r4b3uES/00U1HhYElcAZX5IIqNCy5JHWln8h0GmHmwUpmiCB5sDrd1gnXTr1W3xbOhLmsoAxlJ8vQsEtD29d5aPtDmtdpWWmGq3yqgcUYoGYiEIGygOFkN7dPLgBg0b8WmbaRtIeWS6aNh1g8WPRnNSpMS5M/Jd+yfg7JDWBDnlgD67nqz+Glei9ZN5LUbwoPBBoDq1qaKmxCT2jYyT7vftk2V1G1s1ppToQHy8xrllErA9e9f53qMQK0eRhmzx+pL2LkNc3tk6vLU6Ax8kLRbTDKsTMKEQSAznd2NlUvBIwnINUbV9dMxjLr2jPYaBp0ioQpmoXsJkMhFVAMkl/e+0WzzWiFHACWPLlEFz5Jh+yRe73NYG2eFQD0faYvAL2xYeXBonMy3GlKQVCSL5M3PA9A5fFg/fifH7nb2ZptZn3l/lX78dn1+pykEztOcHO6jPj5VSVEKZpkeicCUsvHaxc6SJRKIuAJbTiB7ot7PNoDY8vHGi68qN6P8Dhbp20d/Hnvn7k1GmmIB4s1JGKFF0HCQv/W7CI1q6zHwoYG0tcj4dUut8tQ5MLMg+VJ96jb6Jwr1YNFihmHI0OqN6quepTOvf5cZNbJRHpOupqT5fKEQwSD2u9E58FiXtMhgkQkQ5ODFdSHCNJCHLQhSNizdI9htAwNHTq8dtLaSlHbLhqEgZUieIMaecAD5QHFvRoO8yo/Va6qSZFCeARS28DKg5WMVV7SGV3++uXc2H/ehJnkuJhNVFjYfWu3rW1YENXQg2USAmAmckEmway0LAtRCqQHuXh5sOgJZafbzFXICFYy66UnSuHL9uk+txpiShkIdoQz6NW5UDCkKULsTnMrKo1eF45uiiiB0TU7AP79YnclK145WMSD1fCChrjq7ascHWvXg0WeASOjni5cyYMntU7TvF9zTVI6jZlxRsJUDK8bDGHlGyu57/V9pi/+evCv6muzleTbfrjN8L3Lnr4Mty64FR1v7QgAGDFnBNrf0B4P73pY3YfOWUkUX974Jeb/fT6+uesbzXY7Ba9pajavGTnWHQmnYhfcev1DyTVlDSyryfyx346pf+9csBMAVCGD2m1rw5PuwYqXV+DIxiOY+0BYjjtFEYL7f95vuU+L/i0wpecUw/ffu+g9bi2dN1q9gbkPzsWO+TtstWXlq/z72A5mHqw///Fnw/dkWUZ6TWMPaKzw8gOdQM8pSKSIESTnzEoVU3eNsAeLDRWPFdbg40Ev7rGL1FYhguq8ildomIw9IWORCyMPVvVG1XHeyPN0Hiy3zx0xmCj582BZEG6fW92fGFRqLS46pM+v/U6MPFhGIYJGOVhyUFbHrkB5QGPIsYWGo13MOVOl26OLSRE4pmGXhji4NjIp5w3MtFtackuaDtKb6UWjro10EzWrlRorNZx4YtUW3oSZ5BNZ5TyZUa1uNZSeKIUsy7qYb6NQgWhDBOucW0dRRrq8lWmbSo4qkz565Z42DmLxYBHjbcScEfYPsujXSo+XIrN2psbwe6zgMctQCZahnwzFie0nVEU78psTD8rda+8GoNyX3kyv5rxsbpXL4zL0/PAMeJp4e7Cu++A6S28OS0bNDHS8tSPWf7Te1IPFJhuz8OLtacggaoQ302u4amgUIghEEq2Njnva+7TxNZnn2Sx/rW77uobvXTL2EvXvcfI4AEDrK7VKek5zS5xwdMtRHN18FJs+38R93yhygAcR8shqmIWig0Wa710XIhSeiLD3RCwy40WHitRJEx3SG01YXDwwEl4h1O9UP6qcKJppA6ep9w2PJ6UnccNXN6iv7RS7Z5k1epbhezUa10Dvsb2x9D/68Lyn3E+hRpMaqNehXlRCLU0ubmKaj2b1/bKw47MTAy2rfhbGlo919DwAiger7FSZMh6HazUly4O16o1Vav8SrGBELjgLi75snzrusOqBmjpYJGwwyBe5qCiuwNFNR9XFRdqDpRpI4b6XDturKKxAsDyoGi5Edp14vGiPlSakz8MPETR6bRYiyMvBCgVD6r0SKA3ojE26b4t1wfNMQ3iwksTda+7WvOatOKvGULnysNMJ0O40NzfXSI19tgoRTIbIBbNComuLiQfLSrXPDF+2D3JQ5q4kRuPBssqHye2Tazl4FR8pBiRofkPamGNVjJww/fLpAIw9HgQnq0WlJ0o1be39z95Iz06HJCmKRcVHi7H6rdWW52naoykuGXuJOlEhHSyZxNE5K+z9zOYScYs9hieYl792uWk72Pok0cIL9XRCi/4tAJgvcKhqbkaGDicJmobNXWNh4+DZY42ua2bYTbpgkuH1APPnue+zfTWv7eZJsOQ0zwFgLEEfKA/EHLs/MW8ivhj2heH7VmqWNBf/5WIAEQEanvRw+xvbo+EFxuHSTvJ9WE+/kSImWezZ8f0Oy1DieGLVhxpJPMebz4d+rnm958c9tkIF5ZCMV3Nf1W1nhUuadG9icALFQ+4kPJ7Gqn/fvWi3o/OxcwieAp8Z7jS3YwNVzcEKyurEPlUhglYeLHoxyihEkNR+ArQeLFrkgiy+EmgPFu2BokUu1ILxZQE19A4Iz1V8HvXcckBrAIX8EYPLSuRCXehjPVjhQsWqaAaVT0YiqLwZXri8LvhL/Dpjkx67LOXYqxjCwEoRXBVBb0RF0OVxcQ0sdsJJVtgtQwSTmIMVjQcr2gR/IDJp5+VhGRlYljlYJhNEI3U7mpJjJcisnakxpOmOZsP0Ddj+3XbTc1hhpBxHsGtgybKM7XO3KwV8wwMkndvlyfBgzcQ1+Pa+b9VtbE5PiwGKIcEWuyUdMZnEpVWPrIrqDCzGg8VbUCCGglVIKRvCES0kRJDOrXKCnRxIMggbhflZ3W9WIYKmBpZZiQeT0ESegAON2fNMwt8AaHJNnTJm/Rj4avgQKOEbDs+kP4OZd0QEXpa/vByvNnsVgCLLfXjD4aiuS+N0xR6gSihwfpPBEwerXl4eTjxYj+x9BADQdYySCG9krLQcpORDThs0De9f8r7t88eKpYEVpeHN4sRI85f48f6l7+OXd3+x3Hf+3+fj1J5ImQ8S7nfnKm3RXV6OHYFMmHmw52Fh+3dW8t9p+CGbLxWNN88pW7/ZikP5hxAoD2jyeGLFjoFFh98G/fpCw4D23qHDqcn/2+Zs012P/C5yUNbmYIXHQvbz8TxYvBBBQFmkJSITgLLg4kn3REIEw4ttwYogygrKIiVNJL3Hio2cIAswqpq1gYqg6tGi6nxJbgneTC8qiiu03wUlhgEAh3+Nvc89kxAGVoowFbkoV+KB6Q7SyINFcgJO7jzJvU4yQwRJjQ+jiT3XgxV+iGMJESSTX54kOfm+2IlQtCGCQLj4o1Xh3dKAzghoenFT9B7bW1UgMqttZAer/A+7xgWR/qc7P/rcbB4IEKmtQvD4PNy6T+T+I2FcdJ0n9n5mc7C4BlaxH+40t7VMOxPCES1lp8oAKfp8BvL532z3pmXRSqPnhqcyRfPb/37Dsd+PGb5vVIsE0H/nNFY5WGaQ5/n2Rbej1+Pa2nX0xO3fwX878gLRpGWlIathlqnR8euHv6p/z390Pk7tPYVgRRATGk9Q1dkqiiqi9nTR351ZKBrNebecBwA6hUCA7zW4a81d6t92BRWu++A6VZCg+yPdAZgI4iR4Hr1/9X580PcD3WIW+SxGtcDYPpve7+ZvbtYVnTdi3ZR1TpoLADix/YTlPqzgyZVvXom06mmo0aSG7rtu2pOfAxmsCBo+f/Ri1c2zb9a9zxpArGKk00XVtte1xd9P/B1/PfRXPLTjIesD4kD9jorSYcHuAnVMnvfwvJjPa2Zg3b7odgBag5IVueCVJ6EXo8j8YPs8ZZGUzjsyzcEK6g0sQw9WMKSTmw+UBTTX8Jf44fa51bkVOf7E9hPY9cOuiMEmRa5h5ME6ukUJWSSLikYqgqp4RShiALrcLk0b6O/ibAsLpBEGVoowCxEkHiy3160aYu40N1xel27CSWK3jXJ6kilyYYVpiGAUHixSgyIaD5ZRiKCVohugdL5WBitP2VFySej7dF91UIn1N7E8nurXjOogsfup6n9U23nKUez3aRSmRpTkjm89rtzTVH0jNgSIPYeRB8tO6Ao5TzxELnw1fFEn59Lf4++zfufuYxkiaOLBImEuZpMpqxBBU49zlB5A8jvn9slFv2f6RXUOO3gzvTi586Tm+5FlGXuX7VVfB/1BFB2O5DzRKnVlp8rwXPXnsPiJxVFd3yz3zYgej/bAYycfQ43GNXTv8bw2jbo0wl8O/AW9Hu+lCALY+E1og7F2m9r4Z+k/cd6I8/j7JngCNGv0LOxetFsjwMG2kQdbS4r2fre5qg2O/84XNmIxCiE1xcbPyi7OnXfzefi/0/8Hj8+DG76+Af8O/Vt97/aFt2PEt/ycWSsFUACof1593fvs+HL9tOs1C0HBiqAaRmvFfZvvw4AXBiCjZgay6mehZoua1gfFgcuevgwAUHy4WI3IIAWlY8HMwCLFu+kx3E4dLDpSwJvhRXpOuqruSu4FotwJGOdgsWOamQfr9D5FPa92a2UxIVAe0IQh+kuUBUdPugeBsoAumoHOyWI9VuS1+rnDjyOJRiFzM1XkgipcTEL/6AVs4uViPVhOxhBeEeszGWFgpQizQsMkBwuIdLLuNDe2z92Og78cdDQxJx1uMhKZm/ZsinN6GctJm4YIOszBGiePw63zbwUQyY/hGRGsUo7alrCbnV1VJas2sYYI2qlNFmvYppP7gFU/IxQfKcb7l76vvibfIS0fvH+VXu3L5XVh+3fb8XLDl9W4a96iAanLcWLbCaRVT9OsuvJCBNkcLHYwqiiusKU4Ga8crPJT5VGHBwLa0F2jdqurn2aeJINByk5OjjfTqwy+nHOYiVxYiWvQ3LXmLjy8+2E1rJkXvjryu5Ho8bceABTRnwvuusDWuc0IBUI4sOYA5v05suq9Z8keTO01VX39Xrf38HKDl1VDgi4uThao1n8UXU4k+5sZTUxpL54kSYb1s4zCsqo3rI6Mmsp3aydMkJXFJ/0ZT3WU/Y3jtRhHVMfUPOHwd0W82WaLFnnD83SiMlb9Jc/TDvANSFI/yAjJJeHHZ37UyezT8IrUq8dLkq6vM+pHDGvYZaej7TVtcflrl3MXldjxJadZDvo9F1nMqNW6FoZ/MZx77iHTh2he121X17EiZjwgn4uW+iavywrKTL9jM8wMLN74G/QzIheMgaWGcVPna9C5gWqUqKHrGV7THCw5JOvmHFwPFuMNImMHEbmgw/jc3oiBxarCamTaDUQt2M+qRveE504amfaQrBp4RLADUOaZZB/1Om69B6vN1cbhsgB/kfxMRhhYKYIbIsjkYAGRUBt3mlt9WOm6WKQTN0rKJRPc4sPF3PfjiRySTT1RpjLtUYQIks8ejQcLUAbe/Cn52vaUWXvU7IQImtUmi9fkn0jjxgKbpD30k6FoObClqgJohMvtwnePfIeiQ0XY/NVm7Jy/k3tPk++99ESpJjyQvOc0RDBQEoiIBJhA7qdoVddkWUngLTtVFrXABQCNx86o3XY8WEZeBl5YLIsaXsIROTANEfQodVxYTwOvr2nUpRFymuWo79Gfm9ByYEsMGD8AAHDX6rtw1SRnsvc8iOd++7fbsXvxbnx2/We6FfBD+dqi1PRzRwwxerK/4pUV2Dp7q67+YK3WtXTXZ38zXmjVkOlD0PfpvrrtTiG/o9k9TQQVWKVFwtXvXa0zbFnD+2nv01j4r9jClwHgh8d/wNOep1XPVaA8gN9m/Ibxtcdjz9I96mIOG37b6vJWGDJ9CO799V7NdtIXXPLvS8DDKIyXt7DQol8L9H+hv2HbJUnCorH6WoeyLBuG41tC3Sq0Ic57/oZ+OhQAcNPMm3DRQxfx+w7qfCR8ktwb9TvWx5X/vRKNujTChfdfqDlsnDzO0JuZbOjPRfcZFcUVeKHmC6YS/WaYGViqQiAtDc+KXDAqgrzzSZKkhsr7S/1weV26HCxNoeGw14c1GskCGjFgaM8PTzqdGC5AZK54ZOMRHN96XH3dcWRH1GhaQ5mHGIlcMFL2xPBj62KpIYJyJESQhDuSPMq0rLTIPiYiF7Va6fvQaDj06yHrnSoBwsBKEbxOlc3BAiITfXeaWy0+aaQGxYPEZZNCsolg+cvL8dHAjyyT7c1ysGISubCRg2XkTSo6VKSZMNpRNYzVg0V+k2gS5AFlolqzRU1NTZ1ooe+l3Mtyce5152LkdyMtE5zlkKxKus+4bQYAg3s6vK2soEwT4gNEJ3Jh14NFjCI7BgiPZS8sw1Oup1B4oDAmDxY96TMKibISuTDzJNmR+zebmJvKtJM8NmaCyoZG9f5nb/Vv8lmsBArYFf5oIW1Ly0rDx1d9jN9m/IbT+0+bHsMN76Wa8v1fvscnV3+iyzPkhmlxJnAX//VizevzRpwXl2K+5Hd8p8s7XCN3wycb8MfPf6B+p/qG363b61YX3Qi8xSKerLhTVv9XqzrqL/Fj9+LdAICDaw+q8vyD3x6MIdOHoO21bfHv0L9xy9xb4Pa6dUV41ZAmk5DWwW8N1m3nLU5ILn30gnYH6ng54vmcNnAaXm/5OnYt2qXZnWd8G9GkexNVsh/gL6yw9wtvwYL8xgNeHIB71t0DIOLFu/D+C9X7JZaxNdHQnjna6114oBCAdb1JI0w9WJz8KiuRC1olkLBr4S6c2H4C5YXlSs51eFGPzsHiiVywBhYtUEHaTDw/qnQ6URFkQgQDZQFNPcniI8Wqh4tIs7My7ayBqX4f4X6d/A6qB4uIXIQiC3Kq5DxlYBGlQTrXixW5sPJI2g25rgwpL3YQBlaKsFNoGNCGCGY3U5JenbjNXW6tezgRzH90PnbO32mqSAbwV8TjUQcrWg8WgQ4ttKNSF20OFqH/88rK6bnXGySdG1B6shRlp8qw4/sdmjySWKANSXZiaEYoGNKFnfK+f3I/lJ7keLDCEr30Oa1CBLd+s9XW6hW5J5xKFRNWv6lMDgt2FcTkwaI/c7QiF2a5ULRKnhFGBhZZITVaTGCliAlsTovmvgnvGi8FOCtIX3J4/WE1XNIqh4NnUKirzpThQt+bty64FddMvgYDJwzU3KM8JbyBLw1Uv9N4GFYEMhkt3F/IFedZO2ktAGulLnb8MAofdcqsu2Zhau9IaCZ7X/mL/Zqkf0+6B7mX5aJRl0Y4b8R5uGnGTRrDkO0/67WvB4Bv6JJz8iZovM8muSTTcfSnZ39S/yZtXjRukVq8eddCrYF1zXvXGJ6LB/18cKMhGJuQZzBXa6AYU+k56ZoQ0Jtm3qTxUtJja9tr2zpqZ6KhPVj0/WJWW8wOph4sTn6Vrg4Wa2AFjc/nL/EjUB5Qj9HkYHFELow8WLzcJXJdjYogJXJBRC96PtZT/dwaWfVA2DMnwVDkgo6col+T93Uy7ZQMPOvB4uVg0eOH6aIG7Je9iGe/mkiEgZUizEIE6Tha8tC609yRsCdqglOzpeLFMKudEosamBGyLOsmumz8r64dHMGNeNTB8qR74E5z83OwbBhYdIdHJqFmYWixerB8NXzK7+8wt/zVZq/ihZwXANgrIjn659FqyFB6TjoOrDmgeX/ps0s1n91JTpgclHXek8Pr9RM7WmyC9WB5fB5Nh2vHg6WczLp9ZPU72twaQllBWdw8WEbfry2RC4Nj//jZuNAogRhYZFWYYKXgaSSQw3rQ6ZVnYqBYlRCIF7yBdu3ba02P4T275Dd4Lfc1dRt5xmq1roXcPrlIz0nHxX++GHetiqj6Fewq4F6jw4gOAIAhHw/hvs/S56k+6PZQN9N96EUf3vNvN7Jh+1xteYiSoyVcg80p695bpyqSAvpFKn9JxMA6vvV4RELaANaouOiRizBmwxhDNUR1pZ3h6MajOk+25JJsf2ZyvxQfiixq0R6+Qa8MQrNLmlmehw7np71ztNQ7wSjkn/QRObk56nNLf8+SS0Lba9pqvjt6bG1ysb4ml5GKYzKgPVi0l46IO0SLmYGleoNKtWMfbdCzXi6z85HcKl2NrHCdKLIP8eaw953qweKo7/E8WLRMO8nXJ+IQaiFiKlxPJ3LB1r3yao1J9n06RFAVuSA5WEUmOViUGiLB6pmzu8ibjBIC8UAYWCnCLEQQiNz0ZKLv9rlVVz/dMRD514sevsj4WjYMAqesnbQWk86fhJ0/7FS3mYUbkXaQ/QilJ0rh8rhshX2Z4cv2cT0oIX/IcGVT3Ydqj10PlmUOVoWxBwtQvgun0vm0QWNmUBOaXNQEo1eMRp12dVBWUIZ3L3wXb7R5Q32flYlnk8rNsCtrTd/TJIyBQBJz1XNa5GCRwar9De2tr2sSqmqL8O0SCoTgyYzeWKClk608WEZt9tXwoexUmWnx006368UL1OuGB7VPr/tUs91KwdMoRJD1YGkWL8JNjDb81SnRrGTyfgdyn9LeL+KNufqdqzW/Df3sGX3OwRMH4578e9Dhxg622nTpvy7FFa9dYboPvejDk2svPFio28aDvVc2frpR04/HCsldZO+rokNF2L9SEcxZM3ENjv9+3JGwQu6luajXoZ76mi0LQYQEWDZ9vgnf3v+tdl+rEEGKHd/vAKD3/NVuWxutr2ytyuBbUbd9XaRVT8NlT12mETkJlAdw06ybNPu1HNhSd/w4eRz+tOxPAIDT+0+j37P9cOm4Sy37Q/I71GxZExc9qJ8njPx+pK32JwJ6nKUXZeh+//g2e2qRNGYGEW+h2krkghZuYCGGBXmPm4Ml6XOwbvzfjeo5NWIwlMgFWfhRw/bKg4pMOkknqQiqniIgEv2kyqpzQgTVeWQZ47GqCGmupXqwKBVBNgeLRA14M718D5ZL68EKlgdRu21tXPb0Zbj0iUt13yVbpLvd0Ha6fejvuLIjDKwUwRUEoOswhB8gsgJu5MEiRo2ZRU8bWIUHC5H/QX7M7T+6WZks0wVHj24+asuDRU9wig4VIatBVswPTHpOuqEHy6rCPD2B3Ld8HwALA8uGccR22Cx2hDLM6HxnZ9v70u04se1EZDvj1WvcrbHtc9oNKaLvB9aDwgsRNPNgkY663nmRSVYyiMUb4830qoOEodS6hQcrs04mguVBU4WlDjcZT+TJKmPJUW0eplV4rqEHiw1xoX4zskIebW0rp0RjSNOfh9QaMluwIKp3PIw+p8fnQYNO+rpwsUBPPgr3642pFv0UeeW/Hvyr6Xku/svFGFsxVrNavOfHPbr9Dq47iOKj9kKR6THp48Ef44WcF3T98bf3f6vxopPFNTvwIhxu++E27QbJOHx+w/QNOLguks/T+KLGOpl8I2/j50M+x4aPN2DT55s02+Wg7Ch82Ffdh/87/X9oObClZuHFneZG26vbYmzFWPzL/y/ct/E+VTGShSyChfwh+Gr40OeJPpbfIenX+z7TVzOukXwtXrmAZKERuaDGI/p3nHT+JMfnNQ0RDHvK6AXLkD8EV5p27AEii1NseB2Nqq4XnsMY5mAxKoKkzyEerPXTlWiLbXO26UQuVA9WOCRQI5LijYQMqoWIWQOLErlgQ8aNRC40KoKMTDvJwdLkdVFhieS7YmXaA2VKfdBLxl6i6c9y++Rq/icYjU3CwBKYYibTDkRWFUhYlTvNrarG0UUQrfKeyHnJTT/9iumYOWomSo7FJnphlNdhJXIBaHM6ygrKHFec55GenW6Yg2U0CbpmihI3T0+4ljy1BIC5geX2um2FCJp6sGzkcdGwEyAnhW/ZdhBVL1rRx0xen4fd2jm00ZA3PE/zHuvBYj2grjSXTgQDME5yjyesxHIskFovhiGClNIUDzIQvdrsVcNrmA045Ldlw4OswnONcrDIcbx78IavbsCdq+6M2SNtF6uBlieHTv8OxGO18dONWDZ+GfccpN4ej2R56gBoCuuyNaUAZYEiJzdHrT1nhCRJcHvdGPgSJbTA+R7fueAd25PbLV9tUf8mIYjknCR3mIfd74+3ks2G7nozvabhbuunRcKFM2tnosejPTTv127DL1wcCoTw9S1f67cHzUWdzKCLTBM1TbfXuoB6NIs95/Q8B389+FedN3X0itEY9vmwlE5W6WvT/RDtoY1GCdY0pC/ct9NFyNl5QmadTKRlpalh7zyRCwIxPMhnoVWC6RwstQ4WqZkVNvSIB4ssWh/+9bAuRFBVEQyHCNKGKVEvVN+nPVj+SF0tMpayqrJqOCRV0wpgPFiS3oOlyxtjVATpgsSEiqIKddy4YHQkT7DZpc0wTh6nK3NBjN68YXno8ffI8yoMLIEpZjlYAMeD5XWrqnH06qVV3hO5FpkkFR1UlLFiSWre+OlGdRBlQ1WcerCs4vDtklY9jZuXQDxYPFRXOGUskfAZNvyExiwnRr2uhQfLm+FF4R/2QnoAaGpVkePtwrbjzXZvAtB+7juW3mH7fEB0HixSwJDgSdfmYLH3Mk9lkD2nGd0eNM9psUusk2i2sCOLVYggyVMwUww0Cx+s37E+0rLS1Hw8gpWCp5EHi3grhn85HI8XP655z1fdh8YX2veExorRQFuzRU10uq0Tbpl3i+49o8WRBY8t4G43y8GLORTVAfTkmih40pSfLtcp79nF6Jlivc6GbeMY6aQtoxaPcnxdlmunXKvbJrkkXP/R9Xhg6wPoPbY3bvjyBjS/rLla/JVFVwvR40L3v0TC+zJrZ+Ke/HtstQcAyk6WRVVoGohM8jNqZaB6Q/uh2dH2RTyju2aLmmg/3DrcOlm4PC7ctUbJb+QVt3eCnbEiq37kOykvLNfJtNdtX1eN+DA7HxGzIPcC3d/TeU6qyEV4zCPPM5mfkfuTKAUGKyJFxcnzRXKs6Hw1OkSQvE97k9xeN9zeSLSIujgeDu9jvXVGOViQocvB0hhYnBwsVuSivLBcFX7yZnox9BOlHIFRDiP5LiW3pF1cODPsK1Re/c4qDm9g1sQAkxyssAcr6A9CcknwZno1hoSVNDoQ/xysr27+Sv2bra9lmoPFrIwAygMb7SBF4/a6uStdrPyqpj0cj1rIH0K1+tXMQy699kQuzAbD+p3q4+SuKOupwJlXxWiQcXpP3LrgVvz49I/Ys2QPNwfr9sW3m16b9Xi4fW5TD1asBlZGbcXzQ4dv2IbaPVYPFplo8rwOgHWIoJ26X1Y5cawxC9gIETTIwaI9WMnyVBlRu21tHN+qzdHocFMHDH5rMNJz0rkhY05zH80wM2wTCc/YToSBBQAH1hxAo66N1NdFh4qwdfZWXHBnZAWaZ6Qf++0YvJleUw+WncW1rAZZhs9gx5EdAUBTZ6xmi5rYu3Svbt9lz+s9lINeHoTOf+qMtZPWIvucbFQr4xcr5lFWEL2BBQB/O/Y3x974ZIXepgJZllWPc7X61WJSyl352koAxvd1s0uaqX186clShPwhNT+Q4PF5IiIXJiqCuhBBap6j5nRX80ZELhgxCRJG16iL8oxd+u9L8dPziorlvmX71LYAyoIxa2y4vW5dXSw2RNDlcan9vcfngcvrUlWA2RBBcm45EBmXVI8Vk4PFerAga78rtg6Wv8SvGTM63NQBba5uYzjGke+SnY8JD5bAFG6IIMeDRTocEtKXlhXx1Kyfvh4rX1vpKERQJU7zgnWT1+muZQS3wF8MYRaa6xoIT4QqQpYGlkbVsCxgqWjo8rg0Vdp5WHmwfDV8tuX2V0xYoW+Dk5VMg77I6USzRb8W6PNkHwBM2JikeDNyL83VHUPfD2yIiifdo8/BMhG5cGpg8eR47RLPEMFq9aohvWa6YViupQfLwIjR3H8WzzNrzAI2RC4sPFixKH/Gi+s/uh5trm6j2dbmmjZqv8lrYyy5jyxsSEuy4NU1LD9drlPqtIuZuhdtwO74fgfe7fYuvrnrG00h5l/f/5V3KPwlftPFqpaD9GIONH/e92fcv+V+031YBk0YhJaDWuLGGTfa2r9e+3q44vUrILkkbr0pM2IZuzJrZzpWKE1mSGqyGDghHKoqR/o6Orc7Gk7/oagQGo0VdP4vybFkPZ/0nMJM5IKIWZA5He3BYkUgQsEQN0RQDsrqAmSL/i3UfNnjvx/X7Es+Ez0m0R6skD9iABHBCZfHBZfXpfb/kltC9YbV1XqBRiGCmjpYYU+UUQ4W6zUj28hnI/AWnq0Um0kb6HmMMLAEpnBDBDk5WMR1SpJiaQPrfyP/pzuOh8bA4tyXoUAIBbsLHLXf7FpW77EhgvHIqXF73VyDwSxEkI6VJtgxsKxCvgBrD5Yn3WNbVvn7v36vb4ODST8vYfrolqOO6qkRyABDT66y6mchb2gef3/qt2U7RY9Pn4MVzxBBshJnVy2Mhm5rPFaNfdV9hpPYdVOURQqjosi0lDENff9ZerB8WmO26FAR8qfmK+85zcGykHdPJunZ6bh51s3oP76/us1M8AOIT01A8ptc8q9LYj6XE0je6Mnteu93RWFF1B6sZS/w88+AiEjK9nnbMW3QNFVCm/4eN3+5OarrWolE1GhSg5tHZ0ZGrQyMnDcS517rrM4g4Nxgikf0hROqogeLjGWyLKNaXb4H8cubvsST0pOOz230+3h8Hp1CYIPztWkB9PjDG3sGvDhAaTeTg0V7sMizQwwsInJB8pNIG+l8Jk2bpUhbCC43EyJIiVyQz0Z7sCSPpAkRlFwSvNW8OpELNkRQVTV0u5TjK7SiF1Y5WDyRC6uFZxb1czM/ozCwDJAk6T5JknZJklQmSdJaSZJ6W+x/gyRJ+ZIklUiStEeSpL8lq62JxCpEkDzIra9ojT8t+xO63tsVgDKwk1URglUnb1UHa/aY2Xit+WtRyaGypCpE0OVxcUOwzOTSo/ZgefXH6a5rUmgY0As82EFyS+g6RrkPrBLZaTLrKsZ5r8d7qduObz2uu4+MuGbKNWh9ZWulDeGO7YthX6jvm4VJmU1YPOkezYoXey+409yaRGHHHixfDB4sV/w8WKQtRgbW8d+U565GE76al9EKH30+q1A11ls4+eLJapK3lYpg0aEizXYrz1cqyG4aCUOzqpESjxDBB357AHf8dAdqtaxlvXMc6XxHZzS7pBk337SsoMyxgXXbwtss9yGhRGTFmxAPQzXR9dKsaovFSjJz8IAzZ2LpBLU/l4379k2fbeJuN6LZpc3Q7JJmhn2BO82tLrzR8ujsPqSv4KkIkjGYRLOQe4FegC3YXQBPhgeZtTM1Ihf0fa+G23EiGdh6qIAyD6Cfc9qDRfbVhAi6wx4sUkg4nFPFfm4yTqrhiJSKIO0BY1UK1TYY1MGiF/+IwWcXNQdLhAhaI0nSjQBeA/AsgM4AlgOYK0kSNytVkqQrAHwM4B0AHQDcB+DPkiQ9kJwWJw67IYIA0LRH08jDyym+SoQrjHB5XIaqb6f2nsK695QV9MkXT7bXeItrGZHIEMHNX25GyB/C7iW71W2H1x/G5i836yaIbFtZGVE7IYKAeaiRWaFhIDoDSw7KuPzVyzF6xWjUa29fqpwYWHSnXnKsRF3BanapeZHMznd0xog5IwAYGNAmc3vT+8Gnjf3mybQD9oo9cs/PJO86Io45WIC+qDJN015KLbumFzflvm/kwdKcz06IILU/7bE2UqQkzyWpB0Wwyt1KBe1vbI867ergug+us9zXiWFgVCOoRpMaOKenM+XNeOHJ8OhqkflL/Cg+UqzKztslp1mO5T6lJ0rxpPQk5j4wV7N954JI7Sw7dfl4xOPZSuX5k+3BqoqQfoT0T2QxLxYCpQHTBSC3z9w7BWhLqfC8S2QOp4pc8HKwSv1KEV5XJG+JjapR62BR7Rj22TAA2jI96v4eRUVQNb4Y9cnDGw5zc7BoDxYt286G0vNUBF2eiIEmuSMhg9wcLJM6WFbzIha1eHP43Op2UWiYy18AvC/L8ruyLG+RZflBAAcBjDHY/1YA38iyPFGW5Z2yLM8B8ByAx6Qz5Rs2wCpE0OgmNAqFM8NM5OLD/h+qf5ceN673Yhcz+fBEhggStn27Tf37qxGKGEfZSX7olZrEH2WIYLI9WNXqVYM7za1Tg7OiaQ9l4k4nm5N6Xz3+1gMj59kvNMmNQTcJTzOT4KdrewB6wRa246eLwNqBTgx2Cr0AEo+8BzMP1rHNx0xzZww9WNTnssoFYsMx1e0ZHtRsyT/W6LmsjB4sSZJw/+b70ek244LLhCVPLjF9//LXL8c/Tv8Dj+x5BC0HmOcIpQJeeDExmJ3mhNkpLk4KhLP3z4zbZqhCPVZ95nkjzuNuT7QBlGiS7cGqihBPEBG1IB7TWGDFFFjcaZH+WFPLid3HxAhTQ6gZkQtJUgyQoD+o3e6OhAjSIX50fhN5nZObAyDyzLEGGb2NeIoIB9ce1Mmqu73uSDkQt9bAIuObUR0sEiJo5cEyqoNFviNyTqvxlB5XyHfHeuyFB4tBkqQ0AF0AsAkl3wPooT8CAOADwM6OSwE0AWC+7F7J4d0g9ANu5EY1EnMwg2dgkZAiuvBsNLAPi6mkcQJDBAnLxy9X/+aF0WjaE4PIBWCdg2Uq057pRbAiaGks06FfFz1ykem+RrS9ui3u/fVenDfiPLWWBFHZqtG0hiOxAm4dEJPwNLMQILo6PcCXaQcihsS3D3wLANi9eLetttKyt06Jp8gFYOzBCgVD2Dp7q6kEOz1RoO9V8r31faYv6nUw92iyIYKE7n/ubrgaaPRcViaRCyeQArwFuwpM9+twUwf4qvsce4OShTfDq/4GU3tPxczRM1F4UJFTr97YvuQ3oPyGD2w1DwjZt2Kf4XvsCr8R1314HXd7wj1MCV6HjaZviZXOd3bGDV/fkPTrJgoS7kYm8Ht/0itAOsVf6jf1sNMefUMPVnrEqOCpCJI5nGrIMHm7IX9IEzpIjBI2RFAVjKBCBFWhCSYvim4D7TFj+2qSD6WKUtDRUW5t4WE2B0stX+OPeO1YkQw2b4ybg+WOGH7ks9nxYD166FGNdw4Aio8Wcw2vyk4yl1/qAHADOMxsPwzAqOjQdwCukyRpoCRJLkmS2gAgZep1MQmSJN0tSdIaSZLWHD16NF7tTghWhYaNbkKXx1mBWvYYdsCJdXWeNfbMkpbJZyKqPUD8QgR52DWwaBd2snKwSPFYI++aep5wp9ekexP0/FtP033NqN+xPtxeNwa8MABtr2mryuvbkQCn4d6XUSpSqvWdiiqUwSCkz8ECIgaWupplYoxo2lqJcrAC5QHs+mGXzhi10zY6RJAWFyHH2vFanNx1Ent+3KMLlzVdEDHyYJUFAOnM8T4M/WQoLrjrAsPPw5YXqEyhjzw8GREP1t6f9iJ/Sr7aD0eT02QVQUAUzXiUnSpDKBjC/lX7DfcBlMnWzd/c7PjaqcDIo8tj2+xt1jvFmWvevQbtrtcXXj5TIeMkLxKC9bBaifkQ/CV+eDJNQgQtvFOAsrBFFjJ4KoKqgRXUj10ur0vvwQr/HygL6HKqWGOFXRxk96fPx3qwyHu0B4s1DM08WOwCsuSS+B4sNkQw7DWjPXG0UJIsy6goqrB85n01fOr8qFbrSI5r90ciNeuEgWUM+4RInG2EdwG8DmAmgAoAPwP4NPyebmYiy/I7six3lWW5a926dePU3MTgJAeLho4LJrS6opXptegBWXdNhwOc1YqdWZI1May+uesbdVu8QwRprCYbPE9UPHKwZFm2XKkhNZp4css05DvLuyEvbt+Tt5pXDcMwq1HDg7SbJtpaQKQTLT1RGkkiNgkRbH+jUhTzsv9cZuv8ccvBioNy18G1BwEoSmw0dgwsl9uFWxfcCgCa+j68wdeIU3uU8Mptc7UTQjPvsdEgFigNwJvhPWPi4Dvc1AFXv3M1d8Hjplk3IffSXE0OUWUKfeThy/bpwqi4CmQ2ieX+ntJjCta+s9bWvm2uaoP7Nt2HRhdGampFs/iRaO5ec7ftfXn9ocAZvIVOAi3MBNi/XwKlAdMQQVpV1dTAKvYb7kPGKjZEkOwXCoSAEGUIkVpVpdoQQSJlTnuM1NpUJiGCtCw8a9wRA4t4sDTRUeEQQbUIMjXOqrlU0KoIsh6szNqZKCsoUxfbiNFGcrAkt6SESlJ5aps+V4RK7CiO8uYUlX3hi0cyDaxjUIwi1ltVD3qvFgBAVngMQBaUkMAGAFaF396dmGYmB97khCfTrtuHU+T2qrevMr0WvRKja4fDlYBdP+wyfd9sRZyEMdErhPEKEazbXm9Qtx6sJMt2ubcL9xheyGI8ZNrV4oImHTwxRK3izSd1ngQgvmpbdLtqNOYr1xlBygXQWK0qPrTzIYzZqE+zzKytnKvkeAnXWDDyYNlJzAdik2mnB3u7q6Z2YAtz250w5F6aC7fPrebOAdDVU7EDqz5p9vwbtc1f6q/0RggPdgI17PNhaHt1WwDA3WvvxgO/P4ArJ17p2KPeon8L9PxH9N5lp6TnpKP8dLlGnMesEKoVsSzchAIhRwVh6+bVxV2r7lJfJ3wlOorTp+eka8YMMxGgaGXxBRHoUDsgsujnyfDoaifa7S/9pX7TcTxYEYS/xA9ZliO5RhwDK1AW0HhljEIEWQOLLITToYPkf3+pX6siGM7NOrH9BLyZXmTWyTQNESTpI0YeLLfXrVEmJCF+dLtp8QnVmCsNqMYZoPVguTxaFcGM2hmQQzJKT5RGvhMqB4tsO7ZZUXZeO2ktjm1R/rYK0Qag5r6S35CdL8dzTE4kSTOwZFmuALAWwADmrQFQ1ATNjg3Ksrw/fI6bAayQZTm2SnQpxipE0MyDxU7srQwUb0ak5oGddphhtfpgNuDUzasLyS1pCvqxynHRcsePd6Bu+7oa4y1QFkBO8xxc9RbfAOVNwO0YWGRyaeQVJEUOjWS36Wtb5dORyYvTAphm0AaW04lyNCGCNZvX5KoektXf0uOl6ndJt401sJwaFLEUGqaPiecqO1vrSg3tsuE1rdG4hkYxlJXVNWPY54oq1R8r/tBsN3v+jdQ3/cXmCeSVFfbeZT9D7Ta1ceGYCx2f99b5t6L/c/2td4wTZBGLXpxxqrBJQ0++hn85HH9a/idHx7OTnW4P2pdGb9glOvXBREPL75OFIB6NujYyfE9gD9bAuuY9pdZbnXPr6PrFhWMXWkZMhAIhXZ4Ty++zfgegKA2TuRE7FpL6a6UnS7keYjq0jq6DRd7jiVwAYQ8WvZAY7pdKjpYgo3aG4nFKs+HBcvNzsGgjieQ1a0oAuV06bxugjCdpWWmaosXkOpo6WlQNr2B5UJMTRnKwVAPrd8Wo2j5vu7oIZGfOec2Ua/DA1gfU+ST7mwsDi88EAKMkSbpTkqR2kiS9BqARgLcBQJKk5yRJ+oHsLElSHUmSxoT3PT+8/3AAjyS53XEn6hyssMgFfcNl1TevieTN9OqNAdm4HWYYGQ3kQbBa0WNDHFnluGjJqJWBRl0bac5tNRHkTcADZQHLCbyV94mE9fG8PWbXNiOe+S70dxIPt3s8QgRV4QRqkNMZWBVBZTXN5v2i5mBFESKYKAOLvWfIua+ceKXlsZ50rVCGkxDBuu0UD++PT/+o2W72XbYf3l79m/aaVxRWwFf9zFu5Z6MCzpQQRxZ1gYcp0g1EZ2DRY02L/i0MywUQrn7vas1rerLzz7J/ov8L/dH22ra2rp3w3yDKeRj9TA1+a7Dhfv2e6xfdBQQqdBgZEHlO07LSdAbWqjdWacKkWYqPFOOTqz8BAGz4eIPhfpeOuxSAcu+WFSiLXmxBazKvKj5SjJM7FbVMMmYBlLx8aUDJuWbmb0Tkgs2Z8pf6NXMMoiBbeKBQ7VdVr5LdHCzm2mwoI6t+aKSSSyTlAa2KoK4OFjEWywIRA0uSdB6s6g2rq+c1qjfGw+PzoHbr2obv0977ykxSDSxZlj+DYhyNBZAPoBeAK2VZ3hPepSEAVhf3NgCrASwD0B5AH1mWV+FMhzOueHwRyWQrDxZx7/Z5qo/lDevJ9KCiWCsMQCbF9INmJ57cKGfolnm34JJ/XYI67eqYHk9WdtR2xFFFkD23v8RvKuLAm4Db8WBZGVhk0mxmqDk1sIzqmEUDLUQSTahX/Y71Na+jXU1Kz06H5JIUA6tEX1uJ/Y5YeVsr4uXBiqdSmJGBZcdIYqX9nXj0jPYxW2DJqJWB/i8onhn6O6goqjCVla+s0JP5Wq1qIbdPbuoaEwNqeQNq0YyXw2gXuv8lZTbu23SfYQ0wupyHt5pX0zd5fB54M7y4/LXLTa8Zj9IHdqjR1FkINIGUtgCU8hg9H+OHgMYjP/Nsh/VgqXLiLok7tzmUf8jwXB/0/UDNczUTZyGh0sGKoCqaxC4O0yJJ5YXhnGWqoDlZqPSX+lF+ulxb/JcshNP1scLPpr9EGyJIPNKFBwvV58+JiqDb69Y8995Mr7Zel0GIoPo5qXvYm+mNiHGE+xe3z61JTaFFMzQGlktvYPV9pi8AoO01bXWiGk5gF2LMUlEqE0kPpJdleSKAiQbvjWJeHwNwcRKalXSMJjZkBcPIaCIPrpMVy2p1q6H4SLE2tJDjwSo9XorSE6WaVRoWo9pNOc1ycNlT1uIDOg9WHFUE2XPbqYUBRCa5shyWUI3VwOJ0ilbXtiKesf709x2N1Pa9v96LSRdMwqF14YEuSttPcknwpCtFU6cNnKa0x8SD5S8xl95liSUHi/5darWqZbJn9OcFIquEdiZqrNS6kxBBIwPdyoOtWYSopmwrLyw/Iz1YNA9uezDVTYga8szSi2axeLDSqqXhug+uQ7V61dS+oW5eXdTN4wtF0Z75pj2achdYrDxTfz/296SE+XS7vxtqNK6B32b8hvUfrbd9XKOujfDQjofUIu1OaxYK7EOiYnr9X1jQwiK6xmxMJzXbzI4HtGOLUR9Ml3HhPV90qkD56XI1SoCcK+gPaowZ2nChF7zIeUpPlKpeMzshgiRcPFAe0BhUQ6YP0eTqGsm0s9+Fui8xBMNRJe40ty7EkPxGgXIDA8sd8UICitFslOtmBlGRPOcSbVF3szlqZaLyaaSeJVhNbMxELsw6BR41W9ZEyB/S5G/wPFgALBOWQ35+zpTdUEPWy1RRVKGRoY4FyS2h5Fhk1aqiuMJSSQiIfGa7E1Zi7Hx9y9fc9+14JewaWC36twAQn+r2BBISAUS/AktPoKINEQQicrmkiDBt8LHf0el9px3V+YnVg5U3LA+3L74drQaZq3TageSarHtvHZ6UnlQnx048WG6fWzPRcxIiyIpbEJr2NA8H4xmpFYVnpgerqkB+E6JwBsRmYAFAp9s6odXl+vu83RC9HPj5o85X6zDtnL8TxUf1Y4bVeOCr4dOFZCUCySWh3ZB2UY0xNVvUVBcSyJjV4+898OjhR+PaxrOdtGppGCePQ+c7OgOgPFgGRrpZf0cbGj3+ZlReVTs20LLiNKpKIKXwR8/LVE9yWUAnC696sIJ6mXZ/qZ/rkfIXR4Q5dCqCXv3+hEPrDmkWTas3rq4LCeTJtNNtpfdlPVgen0e/T/i70uRguSPS7WQbz0h1Mueo3bo2Htz2IPo80QcA0HJQS9PftbIhDKwUYTQAkcmq0UBJ8jB4D7wRqqACrT4YnhOzq4h0nR0eIX8IOc1zcNEjF2mMF7u1uWgvE4l/Tq8Zn4F29X9XAwD2/KhEnNr1YC0etxhApDOz68ECoIYO0DgxsCy9K5JSAyueRV273ts15nM07t5Y/TuWlWi3z63xypipCBYfLlZjuu2eG4g+B6tWm1rIvTTX8bE8bl+o1FoioZCn9ylCKOogZuP31YUI2ghFJaTnpKPdkHaqkme9DvVw7vXnov559U2PI+ema8pVBQ/WmUy8PVhm5N2QBwC44K4LMGbDGNy99m7FaKHqMJESABoqWXpbrKIs5Put2aImqtWrFo8mCQygQwS578syDqw9wH2PNjRIWQ8eZJK/4/sdmgK/mnNRxkHQH1Slx7nvVzDFg8My7ZpCw5TIBVtoGFDGBtLf0lLrbKgkOQ/pBy4Ze4nGOAz5Q1rRCwOZdva7ALTGF+3B0uV0Ecl5yoNFCiYTYQ32OyL1GlnpfStqtaqlXm/kvJEYMJ7Vyau8CAMrRViFUBhZ+d4MRTqUTHjsDByqKgxlYBFDjpV8tyx86w/C7XXj8lcuR97wPHV7tbr2Bh2ysgOEQ+xkIKNmfN29mpAyk5VLdlKrVjK3mOzS3/nx34/r3j/2m6KcEw8PVsgfinu+QjwUCS9/5XLctvA25UUMkT50PRJAe9+z35HT3J9oPVgkzCGewiK+Gj7UbhNJ2iWDOlnQsBPykJ6djqLDlIpguf0QQYCqzQL7oblEUnfB3xeo2yoKK+DNOvNUBAHg1gW34u619uscVUZI/7R78W51m6rQFad8VkLe0Dz0Htsb/Z/vj3od6mnqhRF44XOVrRBorGI+PJluQWKwMrDmPzof73Z9F3+s/EP3Hn3/my38kb59+fjlhosT6rwpXATYMIQwGEJZQZlOGdCo0DArckG3mZYlJ6GDrIFF+u2Hdj6E+zbfh4xaGZr307LStAWRmRwsIxVB9T1KRZDUxdIZaDwPlitSMJn2agHKPJPkw3e4SSu9X5URvUWKMByAwn2CqQerNKBKKBuF/tDQqwjqymL4OsQjcPPsmwFY12WiJ/x0iIpdDwstM3/oVyWHh67WHQtEGcib6cWvH/2KU3tOmRqg5Hup37E+ZFnG8peUagFWBghtHB9cd1D3/sJ/LgRgPvG1O/knBm08icf53GluNO6meLFiDRHc+1NEFUqjhsQzsLLsG1jR5mCpnqE4f+/0M0IGf6I4acfAqtuhLk7vO60urjgJEQSU75Y8e/QqoxnEO0GvGDv9HSoTLfq14BoJZxKkf1r6n6XqtkVjFwGIvwHg8rjQ9+m+pvfnvmVKvgedm1XZFBpj9mAxCmiDXh2Ea6deG3O7BHrU8cTgFiJpAIX7C3Xv0TnYZiGodJ9pGCJIh7dxFjrJ+4fyD0EOyvhtxm+R90xELkL+EDdEENCOEeSelVyS1iNFKfSRvC96rKreqLrOaDLyUrHn14UPEk8UY6DxPFimIYJBZ7oBVYWz55NWMixDBA28FmRV4/R+JcTIjueIvsnV64QneKFgCB1u6qAqw7Fqgyz0hD8azwrtwSJqVNnnZJsdYptmlzRT2zjjthkArAfW5v2aI616GvYu3YvlLyoGlpNwPFb+njY2KqsHK14dnHqeGDxYbp8bJ7afiJzTa2JgFTub2NMqUE5QDZc41h5jz0faVLC7AACQ1dB6oYRMGD647APNOey2k1aCCgVCtrwd5408D4AS+w6EhWAqrIVgBImD990TQz2VqnaafqVy2VdxCxEkE8vuD3fH+aPOj7VZAhMsxynOPUZ+p+b9mpvOjeixmaQIsHMy1Ygo08uw0+07nH8YgLaArpkHC9D22ZoiwdR24nVVQxPDh/P6bXYM0KkImsi006/ZMEhyHtaDRasIqvNBt0vnwWLDLNlzVXWEgZUqjAYgGx4sIGKc2EnepW9y8vCQgnAntp3AiR0n1Ilr6YlS0wkpLXIRjcS3N8OrxvY6SfC3A5mc06tYVgMrEVmgOxy7IVcAdAWc6ddOa3DxCJQF4hqqBsTRwCJJwLHkYDGfzTJE0ImBRY53mIPlNPTOdnuozxYoC+DU3lNY/O/FyKidYVpOgECkaQ+sORBVO6MJESRS5jnNcpTjws9WvO9JgX3MjNtkio8M/XSo5jW7Kl6ZiNXA6jxaEWAgi3iCxNGifwt0va8rrn5HW2/t78f/bvsctJw6D17/xXpdSV/5zV3fKF4ngxBBnsGjqj0HQ1wDi+6zeSGCdBvZhUze+M1+Hl3OlIlMO11bkm2r6n1jQwyNRC4YmXbVaxcIYc+SPbpzVXXOnk9aybCURzaw8lUDK5y7YSe2nL7JVWTgyMYjAIADqw+oE7wFf1+A52o8Z3iuoD+oPiAt+ikKd8M+G2bZBoI306saIfFe0dCEQoax8o4RA4vuoJyszrMGFp2PYBZWY+Zd+WXyLzi17xSO/X4Mh9cfRu22xgX3oiFeHZzklnDBXRfg9sW3R30O1jgwChEM+oMIlgcdqYGR+56sUNolUR4sWjp3aq+peLXZqwC0dYXMYD97RXGFEh/vIESQPBtyUIbksZ4Ekxh8cl87kYYXJAaz/jKZoXm6ovPUpStbiGA0i4E0zfs2xzh5XNyiLQTGuL1uDH5zsCrRTWDHLRKaysViiLPTZ5JonsL9hYrRYBAiSOYLtPgJ8WAFyiIS6rQhZeTB0ohfhK/HpoHwFuN04yiTg8XKrNMiRXSIICvIQRczps/HDRF0KSGCuxbtUpVF6TnZ7zN/V7+bswVhYKWIaFUEiUFFxCjsGAM8w0OWZc2A6E5z64rb8aBXcs4bcR7u/+1+tL/BWK1H1/5qXjV3K94eLNKubXO3qdsadW1kfkzYwKINHTvfKanZoTOwwiGDV71zla22Lnx8IfLfz1e3l50qwzd3foNpA6fhzXPfBIC4q1bFq+6YJEm4+p2r0ax39Ku6rBFjFCJI7plk5P7YFTtJNuzAWnSoCNXqVbNfIsGjDRG0cx9IkrIque1b5ZmK9zMrcI5RzuPlr5sX94037H2nCS2qbB6sGEUuBKmjfsf6SM9J1/XHK15eYXhMk+5NTM9pZ5GRnhOYebDI9msmX6M5f8gfwqm9p1RjxrD2lEEOFjkvKwLGW2Rk+2M2Z4s1kDLqRM5px4PFGmg8DxYJESw/Va6WBCL70XUYhQdLkHCsVvgsQwTDHiw7K3M8AwtyRKSi7TVtAdibvNIeLACo07aO5TE03kxvVDWA7EA+56rXV6nbrIrEEgOLFkKw47no92w/APpO3q7UO93ZzrxjJoqPFOPLG79Ua1QRJUIg9vAWs2unGjshgju/36kKO0RrYK16cxUWP7nY1r4khDVZXhq7A0698+qpf8shGXsW73EkEKPJwQray8EikKLSwsBKPZm1M7nbO97SMantoEWOdFSeLgZA5VssEdjn3l/vxWMnH7MMbafnNxfceYHpvnYWl+iFTXbeA0TmG2TuQI/TtAerxjmKp5cuguwkRJC9d3keLKO2AfwQQbr/1ohcsJ4sToigoQcrLHJBQ3LHaGO1snm3E4kwsFKE0SSXWP5GCjjEoCIeLFshguEHgL75ZVlWJ609/q4UbrOavMohGUc3H1VzQaIhrVqaOjDHO5+D7WRy++RafqZoPVhAJESG7tjJ5NzpiulL9V/Cps83YeXrK7ltrKoc3XxU85oXIrh19lZsnbMVAH9wscPcB+ZiyRNLbO277PllAKAqdSYauwY0XQNs8ZOLcWL7CbQbqi8EawQbImg3Fy+3T65a+00YWKkno1YGRv04Src9XgXb7cJ60ipzDpYwsM58rCbm9Nhpta+dPvecnucAUAoW07nnBGJkkIgH1msUCoQ0/SytemhH5ILMZ9gFX95zzn5eqxBBtg6WzqDyaF/b8WCRul06ZOCn5346K8NrhYGVIowGIKIGRSSwWchAseO7HZrXZhh5sIgniXQ25afMJdqDfiVUq17Heqb7meH2udVJGvk/Xi5jXQdo47zEwDqy4Yi6za7n4qJHLgIQMaoA+x4sI/b/vF+3zakK3pkEK7WrCRGkOnVi7MQ7L4pl7kNzsfGzjQCAOuc6885aYfTMR2Os/PjUjwDsybsTogkRBICGXRvqnllhYKUW3viQ7N+E5OAS6FyRyrZKLQysqs/xrfqalEZ40j22Fqe81byRuohMiCARiyAeLLo/VUUuKMEHNiVD3dcgRJD01Sd3nNRc184iI1to2EzkIr1mus5zRfZnX5PjaYErNkTQiPod65/xJTKcIgysJEIUuQBYhlD4sn3c7eQBJIXt7KwUGhUaZhPW7dTAovePBnqSp4pcxDkHi2Dnu3GnuREsD2LBY5FCqnYHY2KY0nlYsRpYtBACgdT9OBugw594MeuJTJAtPFiIVW+sUj29LQe2jOv5je6JWO5/J95kUtRZlmVHIYLutEjtOrKYkGhDV2AOrw9OtlEjuSTU76SU9+hyTxfcNPMm6s2kNsUSYWBVTWJRsK3euLrlPi63S5UY5y3Yujwu7F8ZXhSl7nmXV6k5SC9kGakI0t40evuB1Ypa7MmdjIFlEJUz+O3BuGXuLWq7CDyZdtImT4YHbq9bl3Nl5sFiz0eHCNIKziy0ZP3ZgjCwksjtiyJqa0Y3Wt9n+qJB5waGgyUdemZXGcnIg+W08Fs8CsWRjgeIf0FXtgMkXj4ziAerZoua6raYDKywyIWd36bnYz1tXafrPV1t7eeEEXNG4MHtD8b9vLEwtmKsobFBBlKn3s5zrztX89pf4se0QdN0oYkAMPue2erfdupSOcXIKIlFuMPJseR+DZQGHIUIur1uyEEZckhWjX079fcEiaUyGA0kZ7TH33poPViVbCJVGb4rQez89dBfcd/m+9TXB9cdVBXrSMqDXewsbLk84dpOHJEL8j6BNi5cbheKDhYp5TCIAULN6eixgC6tQN+nRs+Q0dyi6z1d0eryVur16bYYhQgSg45VDyT781QE6RBB+r206mmqNgAPthzO2YAwsFKE0Y3W+/HeuOeXewyPoz1bdvN8NIWGw5eVZTlSnd7mpJUYRrGE9Gk8WBVBXdG7WCB5Ik5wp7nhL/FrVonsrs7H6sHq/3x/NO/X3HI/VqY1HrS+sjVqtbQvkJAoNDlXJoY2ydtz6u0hYZyEtzq+hR3f78DE9hN1+1YURgboREzIjJ7XoZ8M5W63g5Pvg75fnYQIqmqO/iCKjyiTmXgrWwqcUxkmKxfefyEA6CS1RYigIBFk1c/S5Ke/2/VdvFTvJZzceRIbpm9wdC47C7tkvmLmwSLQC9hrJ60FoIxbqjFCPRK0p4peJKPvU0OhMxsRRDqPlUGIIFvLixW1sBK5oM+RWSdT1QbgsXPBTuxfpU+BqMoIAytFRDsA0ap4dgcN8gBocnlk53WoVIMsBg+W2+uO5IH4Q3HNG4hGfIN3/Vg8WE5FLpwWwa1q2M0hIquTTr2drMQtiWfneajoUI2CXQWOrmMH3n1VN68uGna2H5fe9T6tN9PJYofGwHIQIkiuMaHxBDVsRRhYqacyGFg9/9YT4+Rxuv6uMrSNJr1m9MJMgsoFb8x+veXr6t9283zszD0kt6QYWBVBrmFDz4XIfMponwadG3CvrVEOpEUuDOZZdtutnsetl2lnDSw114r8b1Pkgt5XLGLoEQZWioh2AHK5Xbj0iUsBwDShkIaE85QcjeTymHmwjFzQxDCKJaTP5XGp12UL/MaDgS8PdLQ/z1tlN8eMTFjpVRmnOVg1W9Y0fb+qd1qNL+KLuRCGfzkcALDqDUV636n31OhebjWolW5bs0ujr+cVbVuc3v9tBrfRHu/gWST367qp6wDZfj000sbS46VY8/YaSC7JkbiGIEGEh5D7t9yPh3Y8lNq2sFQu+wqZtTNxT75xZIjgzMGqz7t26rW2zmNnLHF5XJCDMgJlAe5YbBQiSEfTkH263d9N3UbPO4yMrVgMLFZFUBN6KEUMLFaenTWonHiw6N/F6TysqiIMrBQRywofkQcn+T5WZNbNhOSSUHiQUmzjeLDqdVDUAY28a3EJEfS6IIeUfI5EGFjtb7Rf9Bjgd1ZOQwTnPjBX3ebUwKI7UV64YFUvynf9R9ej5cCWeGgnf4JY/7z6mtdO75ecZjnc7ZJHf48nWq2RvidI6KLTz8PeV07uj9zLcgEAW77aohzrIAeLhvQngtRCno3qjatrckgrA5Xx/mjQqYH1ToJKj1W/Rec0mUH3vV3u7WJ4rVAghEBZgDsvoNtCi2Zc9c5VkX0sRC7o+Ra93Uh0zG7uGH19dkFMJ2rB5GCpIYJu+x4seizaOnurZRvPBqr27K0SE8sARJTW6NA0M1xuF9Ky0jQ5JrIs60QrRv04Cm2vaWvo6o5HiCAtuBH0B+OuCmen0zTan2A3fJMXBqiKXERhYNVtX1f3/qglo2yd50wlPTsdI78biZrN+RNEtl6J0/vFnebG48WPo/WVrTXb1723TrvgAGDXwl3q322u1nqK4gEtuKGGVdgUqiGw+zv5PrLqZyG3T6563zpREST4i/0iPLCScPM3N2PkdyPhq+489zTRVLYcLEHVwZvpNRX3seuZpwv/drnLwMByRwwsKw9Woy6N1L9Z1T0WIyPJSdkdMzTXlJT8KM37jIHFerCIoccTubDjwdqzZI9lG88GhIGVKmIYf8hqhF0DC1AmZsS7AkBREWRCBDNqZqDxRY0R8oe453aas8WDHBsKhBCqiG8OFgDNZKPXP3pZt4e5Pq30aAUvRFP1YDlUeASADjd2UP8e/NZgPLzrYUf5OVURNlcqGo+eN9OL7Gb6Ioc7F+zUvN69aLf6d79n+zm+jhW9H+8NQMmj3DZ7GwDnOXisUe/0+3D73Gr9O7sTEXYlVRhYlYOMWhlxLyUQL+wa7wJBNAx6dZDhe3bvPeLRB4z7UbshgmxZHXpc5+WGG0XJ2DGw7Ih50X27HJKR3VQ7/hnJspPjyLyIFyKoy+ny6I2wZBc9r6wIAytFxOLBiib/wZPu0YQUyrLMNZhI+OHp/ad154iLTDsR3PAHExIiqEkYtWEIsvlWmlplFrDKWQBlYNnM46IHAzoRu+u9XZGTm2O7LVUV1gigFaScwJNlZxcRcprnqH8nIsRJckn4v8L/w5gNY1Q1PmLs2IUduJwudnh8HvVz232OO93eSfNaGFgCKyRJQnazbFxw1wWpboqgCtL5T50N37O7cEQkzQFjj5KqIlgeNA0RZK9J960ZtSPzNdJ/G80P7KQn2Om32X3Y87IGFuuxIvurBhfjkbMKERwybYj6N9EMOBsRBlaKiMnAqu3cwPJmeFWFOwAaDxb9sNRoqhhYv8/6XXcOp7LuPNgQwUTmGNk5dywFU7PqZyFveJ4m9jpQFrBdABrQhtLEUg+pKnP+HecDUO7TaCf3wz4bptvG/kYZtTLQ7NJmGPbZMNTN04drxoO0rDR40j0YMl0ZgJyGdxmtRNrF7XOrBpbdld7M2pnoeGtH9bUQuBDY4ZHdj+Dqd65OdTMEVRBJktS5CovdhSN6P6N+mKgIWnmw2L6Ufp1WTT+u2wkRPH/U+Zr3bpl7i1pI2Ar6+nJI1oXsknGDRESQ9hQdLgIAtd6hGu3AerAsQgSzz4mMUz3/Zq/eZ1VEGFgpIpYY9ag8WBmKB4tcVw7JqoFDt4V4ZeY/Ol+X9E8MtFiU7ciDGvKHEuLB0lzLRkcbq0pfg84NULi/EKf/UDx+gbKAo7yaDjdHwgJJR5yIuldnMuQeyaydaXt1kiWrfhbqtKuj2cY+g8GKIDJqZqD9Dc6EUqKB5JY5DaXypHsw4KUB6mtSgNn28T6PWlPMyXdJq0KJ8A+BQJBq6LpTNLYNLKr/M1rcJIWGjQwsEhlh5sGiF3FZDxEL7dmq1UZbp7LV5a00Xjcz6OvzxgjSDrJgeey3YwCA7XO3AwAO/3oYQKRciVMPFv13LIvYZzrCwEoyj+x9BHevvTsmD5bd8DPNMekexUBiCg2znVHtNrXVv//j+4/mPVJEjq0t5LQdAHBq7yn4S/wJkSFvOUjJS7BlYEXxXdKQmhuvNH0FJ3eehL/U2WdqclET9e/0muno+0xfjPpxVExtqmoQAyvWWjZ5w/O0G5hHMNEGPw2J2a9zbh2LPfX0+GsPDP10KLyZXscePXc6lYPlINRXoyIVQ4iwQCAQxANaFp3GTo4SYC9nyOVxIVgeRCgQcjSu0+em5xgDXx6I2m1rG/bb9HEX/+Vi29fTnYc2+DhrcKo6YHi8s4r2cerBYlUHz1aqdpGdSkh202xkN83Gqb2nYjrPlW9eabugHqA85JoOSVZWgNgcDiPPmhyS8fmwzwHENtElndTkHpNRq2Ut1O9U3+II5wz/YjiObj5qK/wq1tUVWuVu81ebESjlr3TZQZIkVQhBEIEMArxQCyf0eaIPLrzvQrzc4GUA+pW9eBe+NqNBpwa4+ZubudL8duhwYweNKIpdPD6POuA68Z7Rk5b1H61Hv2fiLwIiEAgEdjHyYNldvGalzLn7uF3qgpTZYqwm/YI5n0bZ+K4uhoqFAL9+VjSwIYK699kcLIs5i0a0o2a6RvhI9WB59EbX2Y74FlJF+P6PNgfpwvsuRJPuTax3DEOSNQlE5MLu9X+f9TvkoPKgxuLBIueQgzIKDxbqVOLiga+6T+MZMoOES0UL3THJQRmlJ0pFjkqcIWEYB385GNN5JElCVv3I/UY/Dye2n8DJnScNa48kgjZXteFK/ScSeqXWycoiPWkpKyiLa5sEAoHAKUM/HYpmlzZTQ+q7PdgN/zj1D9vH21lgCpQFcGyLEj5nZoSw8wijEEErNAWBY4hysgoRJGJcx35XPhuJTjKCXoj3Zni5xhQ9lzSqy1fV63qynF2fthKhJhlmJmeCxRpYROSCp0LWcaSS0E5Pxvav2q/+bdcFz4NuQ0VhRcqNkVi/f7pDXPSvRdg2Z5tQWTtDoD26X974JQC+uEtVgvbqRrvKmAgJe4FAIHBCq0GtMGrxKHWxuudjPR3NTewsMB385aC6oGRmYLHeNKMQQSMe2vEQHtrxkGYbiSaKJmKI/mx02gfh4BplsfLkjpMArBfNWMPILETQzHC96KGLTK9T1XA8wkqSlCVJkphBxghxGyerSCTPgxXyh7grCtdMuQbtb2gPf7EfsqysfpAkSCC2lRW2dlQs3rB40Lxfc1zy70sARCcuQXek5PsVBlZ8SdQixNp31mLT55vgL/WjycWKx7NWq1oWR53Z0Mnc0dYqajGgRbyaIxAklZaDWqr9vaBqQHJrnS78Op3HOAn9p/tWO2HnNVvU5Hp9rp92Pe5ec7ft6xLoeQkvz9fpM6BLJTERueAt3BFhJjKfPFuwfcdIknQ/gMcANA6//gPAC7IsT0xQ26o05GFNq54caW6eByvoD3IfBrfXjSMbjwAAfnnvF3S5q4umhlYsnNPrHM3rWIULYkWSJFz25GXoM65PVMfz3O/CwIovZAWOJ7UeDWM2jsFbHd7C0U1H8eWNXyKrYRaKDirytFVdYISeJESbfBxLoXGBIJWMnDcy1U0QxJlBEwbh0n9dmpDF6tw+udi9eDcAZ6F+mhysGPK8O97S0XonDrzFs+s/ul4NDazRmC9xb4SZB4uMB0Y1wYDIImkiRM0qM7Y+rSRJjwP4PwAvAfgpvLk3gOclSaohy/LzCWpflYW4jVMVIlhyrAQbpm8w3P/kLsV1PPvu2Sg5VoJAeQANuzTErfNvjakdddrWQfXG1ZFRKwNHNhxJuQeLEK1XjtdRZdbNdHSORl0baarKC7T0ebIPmvZsijaD28TlfGzIBDGuAKB6Q33x6KpE3rA8zBkzB0D0IYJnWxy9QCCovLjcLmTWcTbm0tRoYmxsNO7eWDWwzIyDIR8P0bym5xPJEk6i4fXtJPUDMDb6VNElCRr1QTMPFvleyOfkiY9ccOcFKD5cjJ5/P7tqYtk1J+8FcLcsy59Q236QJGkbgGcBCAPLIfQN2ntsb+Q0y0no9VgDa8+Pe8z3p1YhFj6+EE0uboKMmhlxMYjcXjeKjxQDSL0HK1bSc9LxL/+/8B/ff1RvltOCwXetvisRTasyuL3uuBlXwNmtcERPRKINETybvz+BQFB1+NOyP6FmS74gA6CdB/HCx2u3rY3jvx/HeTefp32D6lpjLQUTDVbXZI2+9je2x6bPNiG3Ty4ApfzMwbUHVUOR7fN5HjpiqLL1UwFlDO/zRB9Hn6EqYPeXrwdgNWf7KgDx19k+CyA3rhyS0ffpvkm4IHB863G1+rlVgVJ28hUoCyCzdvSrRDQurwvFexQDq7J4sGLB5XGhaY+m2PvTXgCp6VAF9omlyHdVQoQICgSCs5mmPZqavk/Pk3hzlfs23Wc5l0pFoV1L2fVw31+tvpLOUKu1YjySz3Llm1dicvfJqJtXV9mfiVrQeLDC851k6QmcSdgdYbcCGMHZPgJA1ZbdShDkBrd6OOPFxk82AgBO7zutXNci2bB+R63dHCwPxq2jcHvdqvv5TPdgEerkOS8YKxCkEseeKFJaQniwBALBWQAd9cNL53C5XZYLTqnIO7LTRw//cjjuXHknAO2CP2CdV0UvUpJ5YSpCISs7dkfKJwD8W5KkBZIkPSlJ0hOSJC0AMBbAuIS1rgpDxC1qtUyNapmVYTf88+Ga16UnSuPWUdCrIVXBgwVoi+CWFyavlpJAEC1OQwR59U4EAoGgqmJlYBlBDJA659aJOlIg0eQNzVNTU5pfpuReNbu0GYBIiZ7abfUS7yxkXijGBT22ZsyyLH8tSdJFAP4M4Cooa5mbAXSTZXldAttXZanVshZumnUTci/NTcn1ScFfI4jrmFB0qChu6njBciVG15PuqTKqMnTnG0vCrSB13PbDbaluQlLhJSObUa9DPRxad6jSThgEAoEgnhQfLlb/dmRAVIIo9AvuvsB26ZncPrl4vORxtfB9nbZ1cPM3N6s5WWaQEEER2aDH9uxWluW1AITGaRxpe3XblF3bqrCcJEkY9OogfPfId+o2EqcbK6SmFpEMrQqQzqVxt8YatR5B5aR+p/o4/OthzbYm3ZukqDWpwWlO5cjvRuLAmgNVZlFEIBAIzCg8UKj+7SR3t3ab2uh8Z2d0f6R7Ippli6snXe1of2JcEdpcZU9YikRCiNxmPYYmpyRJtei/zf4lp6mCeEIk2i9/7XLDfbo/rO0cxANkDAm5bHl5S/E9nQHwijemIhk5lTS6sJGj/avVrYbWV7ROUGsEAoGgcuEv8Ud1nMvtwjXvXoN67evFuUWVDxHRYIzZN3NUkiRydxwDcJTzj2wXVHI639mZu93K23LZfy5T/245sGVc21SVIB4ssbp/ZuDyuHD+qPMBAP1f6I/bF91+1g0UZ9vnFQgEAieIsDcbUOvJba5qg2vfvzZ1balkmM0G+wI4Qf2dHLk7QULofEdnrHtPny5nlbjZ67FeaNG/BZpcdHaFTzml+yPdUXS4CN0e6Jbqpghs4k5XPFZpWWm2Ys0FAoFAcPYw9JOheLXZq6luRqWGDq2/+ZubU9iSyoehgSXL8hLq78VJaY0gYRglaFqFRbk8LmFc2cBXw4fBbw5OdTMEDiAx5/7S6MJABAKBQFB1yT4nO9VNqPSI+lfG2PJ/SpIUpMIF6e21JUnSl20WVDqMajWkIl8ob3geAMCTIcLpBKmD3H+B0qojtmKHEd+OwMCXB6a6GQKBQCAQVFnsznCNZuE+ABVxaosggSSroLEdhn06DOuvWo/GFzVOdVMEZzEt+rXAT8/+dNapB7a+orUQqxAIBAKBbW6Zd4soQeMQUwNLkqS/hP+UAdwrSVIR9bYbQG8AvyWobYI4EijXr9JfNemqFLREqRre6bZOKbm2QEBo3rc5Hjv5GNJz0lPdFIFAIBAIKi2tBrXSvr68lWMl2rMNKw/Wg+H/JQB3AqDDASsA7AZwb/ybJYg3vhr6ONlOtwsjR3B2I4wrgUAgEAicccvcW1LdhEqPqYEly3JzAJAkaRGAIbIsn0xKqwRxh63HkF4zXa3ALRAIBAKBQCDQ8qdlf0JaVlqqmyE4A7E1w5Zl+TLrvQQCgUAgEAgEgqpB0x5NU90EwRmKbReGJEltAAwDcA4AjTkvy/Kf4twugUAgEAgEAoFAIDjjsCvTPhjAegBXA/gTgLYArgRwPYA6CWudIK50HNlR/bvtNW1T2BKBQCAQCAQCgaBqIsmytXy3JElrAXwpy/JzkiQVAugE4ACAjwCskGV5QmKb6ZyuXbvKa9asSXUzKhVBfxDlp8rhL/Ujq34W3GnmRYYFAoFAIBAIBAIBH0mS1sqy3JXdbsuDBcVj9Vn4bz+ATFmWywA8BeCRuLRQkHDcXjcy62Qiu2m2MK4EAoFAIBAIBIIEYNfAKgRA9IwPAiCC+B4ANePdKIFAIBAIBAKB4Gzl55+B48dT3QpBtNg1sFYC6BX+ew6AlyVJGgdgKoAViWiYQOCULVuA8eMBG1GvAoFAIBAIBJWWiy8G+vaN7tgtWwBJAhYsiG+bBPaxqyL4FwBZ4b+fAFAdwFAAW8PvCQQpJy9P+d/lAh591N4xsgx88gkwbBiQJkpdCAQCgUAgSDGhkPL/+vXRHb9okfL/V18B/fvHp00CZ9jyYMmyvFOW5fXhv0tkWR4jy3JHWZaHybK8N7FNFAic8d579vedORO45Rbg6acT1x6BQCAQCAQCuwQCsR1PDDS3SLdPGXZDBLlIkjRMkqSN8WqMQBAL2dnK/1262D+GxDfv3x//9ggEAoFAIBA4JVYDKxhU/nfFNMsXxILlVy9J0l2SJH0hSdLHkiRdFN52qSRJ6wB8CGBZohspENihenXl/+bN7e0fCgFTpih/i7wtgUAgEAgElYF4GVjCg5U6TA0sSZIeBfAmgOYArgWwUJKkvwP4EsAMAOfIsnxPohspEFixaxfwxx/K36dO2Tvmo4+A5cuVv4WBJRAIBAKBoDLg98d2vAgRTD1WIhejAdwry/IUSZL6AFgIYCCA1rIsFyS2aQKBfQoLI38vXmzvmCNHIn8fPuz8muXlgM/n/DiBQCAQCAQCI4gHK9oQPxEimHqsvvpmABYAgCzLi6EUGf6nMK4ElY3iYufH0C74efOcHfv550B6OvDbb86vKxAIBAKBoGrzwQfAO+9EdyyZn0hSdMdXphDBP/4AxoyJ3St3pmFlYKUDKKNeVwA4mrjmCATRQQysBg3sxy7HEuP8+efK/9FKqAoEyWbdOsXrKhAIBILEM2oUcE+USTROPViyHAkLBCqXgXXPPcDbbwM//JDqliQXO3Ww7pUkqYjaf7QkSZra0rIsT4h7ywQCBxADKyfH/ioJbWC1bevsel99pfzv9To7TiBIBfv2ARdcANx9NzBpUqpbIxAIBAIznBpYr78OPPIIcOwYULt25crBIjnutAF4NmBlYO0FcAf1+hCAEcw+MgBhYAlSCm1gHTxo7xha2CI9Pbrrnm0dhuDMZPt25f9Vq1LbDoFAIDgbiFU4iywU2w0RJIrIf/yhGFiVKQeLtOFsmy+ZGliyLOcmqR0CQUwQAys7G/j5Z6WuVePG5sfQHVdFhf1r0R3nUREwKzgD6NtX+b+szHw/wZnHf/8LHDoE/Oc/qW6JQCAgnD4d2/FOc7CIEUMMq8oUIni2GliVwLYVCGKnpOT/2bvucEmK6nt60osb3+YALLssGRZYcs5IliCCigQFRUAJ8hOJEkQUEUVRBBUQkBxFcs55SUvchQ2wOb84qX9/VN+pW9VVHWbmpWXO971v5vV0mp7uqntuOFe8fvWVeP3Od+JtH8fw5CmItRqsGvoTli/v7TOoodo45RTg0kt7+yxqqKEGjjfeqGz7uCmCRKSIxNQIVu+jRrBqWC1AESx6gKNEpHI5IJUCfvAD0Ucrav8sInNA5V6qGrofX7dBPQgNDb19BjXUUEMNqz/iOnl1xCVYegSL5r2+lCJI5/Z1QR+49DXUUDna2oSnhh7ggQPDt/nNb8QgNny4+H+rraIdq6NDvr/llnjphXHR1QWceWZ08leDimefFffFyy/39pn0LrbZRrwedFDvnkcNXw+4LjBsGHD99b19JjXU0DsoV16dEFfSnEjMhReK174UwZo+Xbx+3TIoagSrhtUCbW1AU5MkP2EEa8UKWUtFCoKffBLtWDyCBQDHHx/9POPi5puB3/8eOP/87jvG6gzqb/b00717Hr0Nei7iOgNct7wm3DV8vVEoAEuWCNXKGmr4OoLXapcjeEERrLgZGDTnLV4sXqMSrJdfBj74IN6xouLjj8XrrFnds/++ihrBqmG1QFyCxb1DcaXWeQQLAO66K972cUAGcU2coDz0pTSJ3kSr12gjbkrrs8+K3nK33179c6qh/2P6dOC88/wGJP1fqZJaDTWsDnjwwfjbEMGKmlan9zgkghUV220HbLRRvG3ioq6ue/ff1xDJ7HAc55CAz/6veqdTQw3loa0NaGyUtVhhBKuShqsUwRoyRLz2BPkhQ+WZZ4CRI2u1X1FB1+3rTrDoufjii3jbkUfz298GnniiqqdUQxXQ2wTm4IOFeiGJCxF6+7xqqKEvIUrJgo4lXrdZ3q8zCDYiFrfuacaMeOvXYEdUs+MWx3GudxynkRY4jjPOcZxnAJwW54CO45zkOM7njuN0Oo7zpuM4O4asv7fjOC87jrPKcZzFjuPc7zjO5DjHrGH1B0Ww7rlH/N/YGLx+JaSIIliDB5e/j6jQ87jPOQdYuLCmXhgVtQiWAEWw6DUK2tqAk0+W/++5Z41k9TVQ6g0g6xx6EvRc6bUVNWGZGr7uaGmR7/k8/sMfAv/9b/j2hx8e73hU6qAjKkEjLFwYb/04+Lo5XqKaHVsD2AbANMdxpjqOcwSA9wB0ANg06sG87f4I4NcANgPwEoCHHcdZw7L+BAD3A3jeW38PAA0A/hf1mDV8PUAEa6+9RMpfmNemEoL1+OPi9ec/F69TppS/ryB89RXw4x+ry/pS4Wp/wN13i9evM8FyXRnBilODtXSpf9mee/prEGvoPcyfL9/3RhNpUqXUI+pfN0Oqhhp07LGHfM/tkeuvBw44IPp+os71668vXg87TLwSqYsrlkHbvfpq9Z02X7dxIZLZ4bruuwCmAngBwMsA/g3gAtd1v+G6bpwS6NMB3OC67nWu637ouu4pAOYB+LFl/S0ApAGc7bruZ67rTgNwGYCJjuMMi3HcGlZzdHbKyT6Vik6wrrkm3nFefVX2nNl+e2CHHWSqYLXBGyXTwESe4RrBigZKicvlhEfwww979XR6BR0d8v6ZPh340Y+ibWeLQpx1VnXOq4bKwR1Fem1oT4DuK92Iq0WwalidcO+9wKOPxtuG2yBxo0gcUcsZ9AbD9GzGPTYRrG22ATbcMN62YagRLDs2BbAzgM8AZAFs5TjOgKgbO46TgSBMj2kfPQZgO8tmbwDIAfiB4zhJ73jfB/C667oxS/hqWJ3R2QnU14v3yWT4oEKD1qRJ8eRUuce4oUH8dYdho0cJFi0CNthA1sR8nSMy5eC994QYyddR1UxPC7z22mjb2aJdj+kjeA29Bj72UJSyJ0EG07Jl5uU11NDfsc8+wCGHiNc44ARLJz9RQFGupibx+vnnwI47+p81Ajk1yLahY5EttHIl8OST4cetVF7eBHJ+f93EuqKKXJwH4DmIdL1NIYjSugDeC6uhYhgGIAlAj3gtADDKtIHrul8A2BPArwB0AVgBYGMA+1vO8wTHcd5wHOeNRYsWRTytGlYHdHVJhZo4Eay6unjGAI8cNTaKv+5ImdKNpfvvF9EXOlaNYMUDXc+4ipGrA+LUXXHYJsOvW7PIvgz+G/VU6uazz8p0RBo7Dz5YXadGsGpYXRA3ckXgTl4aM+OIa1FmDEWHL70UeOEF4M47zevrx6Dj0/ZHHinSFruzxsqGESPE62WX9fyxexNRzbQfAzjAdd3TXdfNuq77MYBtAdwGIG7Zsz70OoZl4gPHGQXgHwBuArAlgF0ArAJwh+M4vnN3XffvrutOdV136nDqHlvD1wKcYEWJYJFhQlGvqOAEq6Gh5wiWjlyusjScG24A/v738rfvD+BGHnn9UqneORdANLR2nMrSRcpBuX2sbJHZnj7/GuzojQjWLrsAW28t3tuIVC1FsIavO0wRrF/8Iv729Prss+L1o4/M64dFsN55R/3chu6IYMWtA1tdEJVgbeK6rpIY4rpu3nXdX0BEmKJgMYAC/NGqEfBHtQg/AdDmuu5Zruu+7brucwC+C5GqaEsrrOFriM7O8iJYlRCsxkZBsrqDYIVFHbbZBhg3rvz9H3sscOKJ5W8PCOPqF78A3nijsv10F7jBSZNTJtM75wLIviQ9HUXbzjBSRqlFs93XtQhW3wGPYPVGiqCuHkioRbBq+LrDRLCuvjr+9oWCeJ4++0z8/4c/BK9Pqd1ErPSGxabsF+4Q6Y5n9+vqlIsqcmGtd/JIT5R9ZAG8CT8h2xNCTdCERghSxkH/15Kkaiihq0uSpQULRJ1JkBeVvDj19fE8NnxwqqsTJKs7arBI/S4I8+ZVfpwrryx/21wOuPxyIfbRF7FihX9Zb6UIkuJkT8NGhqKkmNpSSb6uk2VfBBGsQYN6nmCtXAnMnSv///e/5fsawarh6w4+TpYzZvKxO0pEWI9g0TFpjAjaR6XnGoZaBCsAjuM8EPQX43hXAjjGcZwfOI6zvuM4fwQwBsDfvONc5jgOL8N7CMDmjuNc4DjOOo7jbA7gXwDmQJC1GmoAoKYIEoKa8fIarDjgg4/jdF+KYE/VWJ1xRvnbkqcsiqLhwoX+ZqTdDRPB6o0I1vz5wBVX9PxxAXmf77GHaBZMiDLhURmr3rOl3JTDGqoPcu60tPQ8wRo0SP3/6KPl+1qKYA2rCyZMKG87UwSr3O253XHUUcHr65Ermnf1zzl6imD1ZgZJbyCqGbdE+1sJYAKAnSBS/yLBdd3bAfwMwLkApgHYAcC+ruvO8lYZDWAiW/8pAEcBOAjA2wAehVAV3Md13V5IiKihr4KnCEZdHxARrP09yZQo29NA8YMfiNfGRkHuqj0o9YdeQ+QpixIVGjlSlZ3vCRDBHjlSLuuNCNbo0T1/TAIZ4AceCFxyiVwepR8WPSM77tgzTbVriI6vvhIOnnPPFf8PGdK3xoxaBKuGnsbKld2jcMqJTpz7ulCQ8w3tg2oXo8if2wiazaFJTg29dovGBf1zDpMgRzVBdtPXLZIVqeTbdd1jTcsdx/k9hOhEZLiuew0AY/ch13WPMSy7DUJMo4YajHBdNUWQEDRQcII1YIDw7r/1VvixyEt8yinilby4K1cCQ4fGO+8grFgBDBsm63a6E65bXmFrHILVG6AIVkuLjLp83TxoNLk2NgITJwL/+Y9Qk4qiZkWTYToN/PSnwK9+1X3nWUM8PPOM+n9DQ89IIEc1MGsEq4aexJAhsh5w3jxglFGXujxwO6K1VdgLUZDPC6dtLif3QY4qki2Pelw9cyZofVskiwhWb0SwaJ+uK87j66KCXOnXvBbASdU4kRpqKBdkCOoRqKCBgtdgAdGUBwHgO98Rr6RGR1KqtmLvcrB0KfD++yLic/jh1duvDT/8YXnb9ReCxZUD++q5BiGfF5Pqn/8cf1uKYNGETnK5USJY9DykUrXG1n0NuhpmfX3PNBoOI05Uk9WfUwRnzy5fmruGnkdXlzr/VpsgcKJzxx3xtiObRCc5UaLNtqiSjWDpESranv7vLYLlumo07+tUw1spwVq3KmdRQw0VgAx9GsyOOEK8Bj3I5O2liEYqFe3Bp0GKiBl5pGzN/+Li6adFxOX554HNNxcDuusCF14IbLKJqv42caJ1N7Hwj3+Utx0Z6WHS51GM+Wojl5MiIDwtoael6auREkHRtwsvjL+trpZJz0iU34TOPZXqn8R0dQb/PSZPFr9vT0SwwojT+PGCZPXnCNaGG8ZvKltD72GXXdT/p0wBVsXKqwrG/PnyPZUGREGhIO0LPboUVB/OtyfEiWDpkSud3JkyeyqtFwsCzSPk5KsRLA2O4/xJ+7vacZy7IFL3bu/eU6yhhmDoghV77SVewwhWXZ0crKJIuwOyoebaa4vXakewXn1Vvuet3C64QPSxWJe5NHrbSxw1ghVFErza2Htv4Gc/E7+vHtnsSePvJZs+agwQUVyyJH6dDT0D9BvRhB8lRTCfF5ErxwFOPjmecVFD94I7NYYP7zsECxDNUPszwSq3MXcNPY9CAXjlFXXZkiXCUVkNfPll+dtSiiDgjyItWaKuO3OmGGd5E+G4pOdf/1LXtb12dwSrq0udp2oEKxwba38bAMgDOM37q6GGXoOe7kfGR1gNFq/ZipoiuGoVsO228n+KYFWLYHHv1LBh5s/jpHmFHQMAJk0qbz9RI1gzZ5a3/3JRLMoJduxY/+/aE4YoIFK2osjth4FP8nFr8ngUCgCam8VrFA9qLieJWXMzcN118Y5dQ/eBP3MvvthzNVhRCNaRR0a7v2qooVLw1gAc1YrCVFIDbYpg0fPT0aE+S5T+ffvt6vYEPoc1NoYfl2/T0zVY668PNDX590f2Vo1gaXBdd1ftb3fXdb/tuu7fXdf9Gl2uGvoi9BRBMj7CarB4ZCNqiuDKlao8cbUJFi/+NBEsAHj3XWDLLStPPzvrLBERi9tsmUDXK6w+h3vremJw5WIMAwb400W++93uPwcA+NGP4jWWtIHL2990U7xtuVAFIKOiUQwHTrAIp50mSVoNvQf9d6lGDVahAJxwAvDxx/Z1okbNe8qJ0RP48sveSXOuIRy21PxqEaxy50Y6B1sNFl8GyObBvNWCTrAmT/bvw3Zcvn9bLRaHXu9VSXbM55+r/9ciWDXU0I9x+eXilTw7UQiWHsGKSrBWrOheghUWwQKE7Pg224jeUnGNqhkz1P1vuml5RC2fl9crTBGIE6y//CX+seLioovk+0TCn/Jzzz3dfw6AP02FJHoBYM6c6PvhEazzzotXR6anCFJKa5SawXzeH52sq4uWXlhD94IbQNtsU50UwffeE1FKqmENOu7ll8v7O5Px99NbHe4R1xXPwLhxUtyohr4FmypstQgWjZ/l1OQFpQgC5nnXRrAKBX+q39y5QqBKJ/82YqW/mrah99Wsw6oRLAPCmguX2Wi4hhqqDkpdIpl0iqgEPcgrV6ph7KFDxbIwT+XKlcDAgfL/AQOEEV8tkYsoESz+WRxVI0BNB8xkhOH98cdqk9AwvPee2I6IShjB4pPGNcYGDd2HDz8EfvlL8b6npWF10s3vtzXWiL4fPdp04onRt9UjWMmk+IsqcmGKlORyvV//93UHN4AuvbTna7BSKWDnnYGNNgJuvNE/VvVXgqUbtRT9vuuu3jmfGoLRUwRrjz3ibxuUIsj3zcFrl/QIlk6QTjkFuP564H//8x+Xv3KJdH2/pnOphGDNnu1fRnNQLUVQhd5cOOivhirhr3/1h1irDSqo1ItD+zso/SlKBOuzz6RQBSBqdVxXCgrYoEewHEdEsXoyggVIj2olaYJcHc6Wy27Ciy+K1wc810oYceHGVncPrtOnq/8Xi8Dpp4vflj9Xrgt88EH3FuPrkbMjjyxvPyYyFDUCptdgAeI3j3Lf5PN+ghVHhbCG7gM3gHbbTXiHOzoqu59p23feUSPdHGQgJhJirHrvPdFDUDd0+6NQxKpV6nNSKNhryU49Fdhss545rxrM+PhjkdJqQrUIFo2T3DkWFWEEi4/Ba64pXnmWg072dRVAclzqzgybTLtpvwQ9RZD/H0dYySSERPuqRbAYXNc9NupfT57w6owVK4CTThIKaN0J8njEMar7MjbeWJCRjTcW/0cRufjqKyEpTKDmgTzaoiOXE0YMj2ABvUOwKNUr6HzD0NFRnvw2pSWSERZGsLgx3t2d3L/9bftna6whvP0A8NBDwvt+ww3VPf6DD4rf8JNPgH33VT/bfHP1nosK0zWjfkNh0FME6X2U3yGXM6cIAv23xuaBB4D77uvts6gc9LtSvybyDpdDfF1XRHnfe08uswnfcILFoRMs/d7/yU+q/6xVG3qtZqEgnmMTrr4amDat20+phgB89JH9s7PPrs4xKiFY+bx8LsMiWBttJF55hoctqkSvjz8uXsMIlk5owmTaefo/ADz8sH99wgsvqOc8a5Z/HcrAIIJVzZ6hfR2BppHjOJs4jlOr0+ohkNePe/8uvhj44x+rexwyjqJ0E+8PKBREugohLIKVz4u6oJEj5TIyQIMMT/Jm8ggWUF2CxQ0k/TgcNOBXQrBcVzW8o6Z96XVftr4cBD4BrLdetGOUi4ULgz8nQ5CMyTffrO7xDzxQvL7xhkqmFi8WBIuIcRyY7skw5UZ923IJli2C1R9TwFwXOOgg4Jvf7J8RFg4yiEaNEq9Uf1pO/5/ly4HLLgO+/311OY2ffJ82gkVOJ6pJ1fdzzTXAsf3MFfv447LlRw19D0FjYCXy6hz0DJRLsIJELvgYbBKKKhTkc8ZrsPR5Wnd2VRrB0lMEgxyoO+4onCcU5friC/86BxwgXim7pL+NA5UgjDy9DaDkR3cc5yHHcUZ37yl9fUGTPpfhPP980c+nEkNaBxnIqwvBymZVRUAarGxG5MqVwtjixm6lBKtaNVjccA0a2DIZMcFUYij+6EfqgBg1dP/ss+r/UVIEx44V7ynK2F2g67fDDuJVJ39EsGyTTyX44AP5fvp02f/rjDNE8+hywaV7CVEjSCaCtXQp8MQT4duaRC7II9sfCRa/vytV3Ott0Heh32fcOPEaRzyFYHsGOjpEpGbgQLlfMu50g/Coo4R6p6nGkjcyj6uC2ZPQxz/qeUiodgPWGipDmHptVCxcaLevKo1gpdNiDjIJTHBbwzSH6iqEtkbB+liuz236fR1FRZD/H0VJkdLfTbXc5PRcsUK8BkUeAWGT9EbvzO5AGMHSfdM7AVhNzPK+B/IUmh7manlkAGlcVCJB2pfQ1aWmqJCMtI186H2zgGgEiwYIPUVwyJDqRbCiGq6OI+6Tcol3IiHSInnN0qefRtv2scf8+woCEeBhw7rfsKUIIE1M+rkFeRQrBd0fgEhFfPZZES254gq5nE9cUcixTU496n1C6+kpXEFS3ARTBIucMtV0+PQUujs9tSdBxhMZmUTgyxmHDj/cvPxXv5Ly0eRAskWwUinhDNTHRkC9z/UoWV9CGIE69NDwCHkNPYNisXrCIyNHykiwjkoJViolntGwFEFb2h6v4bKpAPLmxIBwHpMCJq3PazPjRrCizJHPPSdeg3p06XOvDRMmABtsEH7M/oBa+l8fAkVITA8zN94qxeqWIqhHsMJ6/dD359v0lRTBOJGB5ubyI1iHHipeb75ZfnfKA4+LKBGsTKZnlM50gqV7ObszgvXqq/5lVNtH4OcT5bfmPbD4pHP66dHOiQzjclITTSIXVIzd082jq4FqNtPsbeiRSbrfFy2Kv69nnjEv//3vpTAMGVk2gkUwGVg9rd5ZLubPD/78/vuBM88E3n+/Z86nBjuuu06NjNJzYEpRjYLWVuCtt/zLaZzgtkJUIZl8Xoz3qVS4yIWNYHFSEiSzDsgoNqCWGuTz6v9RCFbYuekIamJMv80WW6jrfh0QNvS53p++rIZuABnwpkmqmoWBFEXgg0Z/ht40mMQhwrz/1Ypg9RbBqiSCRal6228vDKmoMA2OYQbU7NniWpPSWXeCKxa1tAhVTg49xa1ag/1jj5lJj/4s3323fB/lt+YT4403yvdckCAIS5eK38cUWQiDSeSikkhJb4MXYPd3gkXPPUXrafwL6mFVCTo7VSUz2zPPjTyCvm5fTS+NovKZTnd/mnMN4dBFfhYtEnWvPP3siy+EjHnUsXKnnfzLuArrxReL91GdcjyCxTMmTDXiYRGsIIK1ySbilRM//ozx6JftWPq5xCVYdGzTuErKglttFb4fjtUhJTdKiuDNrN9VPYDran2wugc6weI3q00uthxQFGF18SToKYJhdSK03BTBClLhot/ARLDa26sjXU3nFiUltJwI1ogRoj6JqyxFFUwAzPdM2H30+efAhhv2XK8eQKRQLl4MHHecupyij3TdqjWIX3+9eblOsNZZB/jnP8X7uASLR6HWWivaeS1dKrYrJ4pgShGk56y/pdutXCkNESD8/AsFUcD9yCPde17lgtLJiWB1d7r3ttuKthZhBGuddfzL9DrI+++v7rlVC2EtOgB/RLqG3oE+bjc3iwgJF6765S9FI17+3McFjxTT2Bd1ni8U/CmCnDTFiWCZZNoJEyeKVxvB0lP+6L3rynOoNIJlUywExL5Gj47fQqIv12tGRdi0eyOAryD7Xd0MYA5qfbBi49FHhTR0EPQUQf4gV7PvDBm5/c1IMqFQEN+HG7I0gNkMWFOKIG0TRABsqZWUllANr35Xl5ATHzMmfN233hL31IIF0fefzYr+LZxUxSFYpsHWlBrH0dEh0jbr63tfXIB+K0oHKhREnckZZ1S2Xz0PnmCKEsdR4uPP/cSJonfdmDFmb6sJS5fKBtwEEhwJwi23iH5IOsGKEunti1i6VP0/LIK1YoWQIC63d1l3o7VVEBcai8pptxAXs2eHEyxAykcTrr1W/b8nzrUcRJljg2pMaug56E490/24zTbh++GOaxMB4G0uwuwK07Y6wVq5UmYB0BhaKKhCFVycghMsWwTLdN7ciWyLYF11lfhOS5ao4+GDD4ZH13TQs2MaV+k6nHqq+N8khGFCf1d6BUIIVq0PVvWwzz7A/vsHr0PGpymaUk2DhvardwDvj1i4UAww3HPlOOIa2ibMoBTBoIefBhqdkFBkoRoEq60t/iQeh2Dlcn7BgzjRjXIiPp2dMkWwOyNYUSKyFMEiL/ott4jUviuv7LlzoklTFwsxge7hXXcVr1tvLSKoUYmqiWD98If2cyN897ti4tXvdXpOdKO5r0Of+MPGUy6P3BdBadEUHeqp86TUrKDWDHoKnd78+7LLwuudqo2HHxbjj03G/qWXou1ndalb7u/Q73fT/RiFCP3978Hr8xTBuE3WqQaLE6ylS6WtQmOSnubPiVecGixTBKu+3t/XisZ9UvxcvFjd52uvqeNjFPEr2t50DV1X/D6DBwtHa1T7xmSX3HNP9Ge1L6CflJ9+PUAPAQ0W3UWw6GF48snq7bO3QBP1aK15QCYTL0WQ+jgEeU3o99GFE0wRrBtvNDfdC8PSpdElvcshBdmsn2DFQVxDjvK56+vFwNqdXqkoE2pQb7HugMmjR/fdT38afj3pub/8crksTi1bR4d/QotjKNgIVrX7h3U39N/ho4+Azz4L366vEqxiUR2HokS8q4EddxSvQU6ZsGv2+utC1r0ncf75wrljkoieOVPUokbdD0fctKcaqoMozyXN6UHg4mGmfZabIpjLCYGhlhZBYP76V3GvZLMyzZQTKUA+w3w5VxG0pQjSPcjvRS72xBUF+fY0/vEaMf17A8B554V/X0IQwaLziRoBNI0xhx4a/VntC6gRrD4E/SbvrhTBvmo0lAPKm9cJVl2d/ZqZUgS33DL8WLo0MoEIFim25XLAMccAm24avk8dpoiDDVTvEPXeoJzrSlJ04tbt0WDa0CB+oyh1DuXiwQfle5uHvVyVqXIRRLCAcMJ5yy3iVa8xjBoJNBHqsFQXPlHrNUh9Nb0rDLqD6rDDxPOjpw4SbE09+wp4E1JArQuNY/SXSxCCtuPZBDY8/bQq+NKbuO228rftq/fH6g5+3fU+ga+/Ll4vvDB8P0GRWKB8grV8uTjHESPkMnpmaD9nnileaayhesqwCJZ+z9H/pgiWKR1dt/9yuWDbMw5sBIvGqkoJVn/DavAVVh9EjWAtX16ZbHtfVXEqB2edJV71PhZRIlg8RTCZlAOyjYDqzT0JZLS/8IK6/xUr4qXvASJHO6riW9yiW7qHdIM7TsNG27WxGRpEBOrrhTDDvHnd00PJdVUFtT/+0bxeOf1MomDddc3LwwiWLWWJ8J//iFf+G8eJYJkINUW0bB5ePtbo17GS6GdvwlZzRU6R9naVtNq8xSYceKA/stHdKBb9Bshll4nXOOM7/dak/rf55iLtOixjIohgJZOiiXkYDjss2jl2N845J3wd2xjZ39Uo+yteeUW+1+/VqVOj74faENjA5/w4NVgmW4HGFLI73n5bXZeW01gfJoix7bbq/zxSpDuRg2Tas1n1Pt5++/LbcPAxlCv10nk1NESLLAI1glVDlaH35rERrCFDVM9IHLz/vpAvBYQqVH/HBx+IVxPBCqvB0gUIwhTSbBEsSju75BLxyo9r6q0RBFNKlw3VIlhx5PptBqdt0qFzS6eB9dcXk4ApNevtt8Ug/PLL0c+Fg8v2nnCCP6JJcBz7Z5WAT6R8YggSuQCiq4PyZzWOWIgpgkUk09Y0ld9P3/2u+ll/jWDZDGH6rk1NqjJjnAjWgw9KCeeegolghTVYN4EMovHjxWtDg6iTCBO+CatF+sUv/MtorO5v+NGP7E6hGsHqeXR2qsJKlagE3nxz8Oc8ghUntdpkK9BYomdR0Lr0TJkiWCaCpL+6rhyfdSdyUASLE6yxY4X9YbrfX3lFzJ9EDHk/uPXW8x+HnFec+I0cGa/+ctYsIe40Z070bfoSagSrD0GXugyqwSonhNvZKQqQqWgxqiehP0CXKQ4KRZtSBPn/tmtri2BR7jQ1guXHffdd+zmb0N4evZA6LsGi9WwpY1FgI1i2BqdchYlyz03k4K67xGsU4QcT+PNxzDHB6x5+eHnHCIJJlXHnnYELLvCvy++7sFS/qVOB/fZT6/LiiIWYCBYZ4lOmmAkE/T677upPV+2vXkVbveKll8oo4oIFwhhobY0XwYqL++8XBoctPTEK9BosQEa+44jt0H1ERh/f569/DUyaJFRHdRxySPB+11zTX+/Ym6npcVMhN9hACMoA4vmxEc4wZWCOXG71mnN7C3x+nTOnen3JqL6Qo9wUwSCCpWdR0Lpkw5hqsIIIFs2xrivvU16DBQQTrK4utaGy3geLnGzXXSdeH/AaM3GRtMmTxSufl0wEa9Cg6A6gzk4hQjJzptoDErD3OO1r6KfT5eoJPYLFb3IeNi4X+oS+Ogz26bRZuCCoCW9YBMs2gNoiWImEqOEiLzAnCWEpYDo6OrqHYD32GHDvveK9rbdRFNAkccYZanoFr3/i4KSUJhATwSKj8MILy6troG3+/W+ZOmEDDfy77Rb/ODbw34Cu7+9+Z0735Pdd2G9nIkiVpgjy/01EjRpPh0VS+1OBv63O5pZb1N/on/8UjoAZM7rvXEiw5MMPy9+HKYJF8vtUgxIFdB/RGMrHtrPPFs64X/1K3eb886OlFeuGlCna09Pp6mE1N4R99xUp3+edJ6KTNsfCEUdEd3DtuWf3pSh/XfCtb6lqzKbG1uVgo43M9yI9SzxFsFKCtcYa4pVabVQawTIRrKAaLH1+5RGsTEZ8TsdraZH7pP6NVErxf/8n90Gkq6tL3uNkb/IarCB1Zx0DB8rnVZ9raA7v66gRrD4Eusmpp44pglWJvK0+gLS19S8jyYQJE4QEvo5p04TRbzIgeV0QR9gAalMRBIQxSsYKj6DESSF59llx7KgpgmQo8x4aNuy9N3D88eK9TtKHDZPvwwg8fb7eeiKlitTk+D44OMGiCcREDvg1J09ZHETpz0MgMveTn5j3UQ4KBWGULV4sfxdb81dOsMJqXUwEq9IUwe22k+9NThbyDtqcAxddJF77Y4H/aacJqV8b6N6L2mesHNCYG9XYN8FEsIYPF69xFPqoXQHdk6bn54ADRM8cQpSaJcA/lqy5pn8dntrbnaBrHqWBOyGVEvc6N/RM4NcmCM8+G/3YNZhx552y1jkI556r/m+a13j7gDFjhDz5Eq2rK2VW8BTBadPCj88JFo2/NF4mk2LupOdBj2DxnlJBEShTiqCNYOkEjatpdnXJfWQy4j2t39AQT8G6s1NmPdAcxWuwgko3dAwZYidY/cVurRGsPoQgJRe6yXkqlj4YhIGTjZYWfyi4P4K6pdtgKmKtJILlOGYjpKEBeO45/0Qch2DtsovcVxRQLREv+I0C3Yu67bbAFluI92GpZ7pnjgZTW8jeRLBMx+DXvJx86zgEi66bbvBVotRZKIg0q5YW4akGZEqkDk54wp6/ri5zil/UNIts1h/B4ml/JqJGBMwmFELPW38cOw46KLj2NKxpNuGNN0RDXf1ZPe00f6NdHd1FsMrp0fTTn4pXusdszw/V/I4dGz3izfc1d6647/Tv3NMRrIMP9i+jlFnC1KmyKWoU2NKja6gu4ogj6aIzprGdxrnvfU9mnZx9tnl/iYRMoz3llPDj83ny8MNFHRE5x9vbxf70NGSeIkifBUWwaB0ewaKxXnci6wSN2yV6imCxKMf2hoZ4NkxXl4yG0znwFME4BIsTsxrBqiESLr3U/hndyGQA8RtRr88C/B74MHBjioy/7lB060lQMz8bTPU4NNjEla8OOpYt6lROEXRUQ2nECPE7RulYz8HV9gjUYDmMYHEvHCDP9eSTzetHjWC9+KJ8H0fVUD+vKATr978H7rtPkMoFC2TKQ6UEi877z38W+elcNIGDC9SUE8GiPPYo6cKmxtKATFMzRbDo/reJgdAk3h8L/JcsqUzc54QTxKS/5ZbAXnv5n5errhLLr7pKrGcSEulLBItA94jt2SOnTJyoJTkwzjlH1qnq91QUgrVwYfm1mVGgj12vvy7TvaPAFqmuobqIMz7rTiXTtjT2UWotEDz/TZggXqNkmPBsF8cRzw31k7rlFrE8iGDxiBIQHMEqpwaL37OcYPEIFqVFRhnnaU7r7JRE1Eawojrmbr/dTrD6S/ZEjWD1MM49V4SiTQhSETQRrLhS7XwiobqD/l6HxQ1bDhrMTDLpnZ3iQdcNlCgRLFu0TFcxJJRjhEZNEQTEABrnGPX15jqAIPLDQfcmXbuwczURLP2eKxTUmpcwBTMT4hCs5mYRyQDExEACEpV40/l9OGIE8I1v2NeNE8EyESyawKIoENoaS1MPNdPvbROBIfTHCBYJ0Oy3nz2yGAVB6avcCDjtNPEapSdUOag2wSJCsdFG5s/pOY/jOX76aeCGG4S6KhlKlF5KCCtWLxaF93/vvasnkqF/h0qFW+ISLDr+BhsAu+9e2bEB4SyKm8XQn1AsClXKqP0hTTDN6SZV3aD5rLlZOA10kaRDDvFvxyNYiYT4zWmOoiwYnWDxGixaZopg6dvx54LmoKAarDPPVAmKKYJF80YqFW5f1NfL/fEIFh2TE6w4NVi33VaLYNVQBkidSEeQiqCJYEWtwyBwCWxKi1hdCRZN5LrMNCAefJPxGKUGy+bh1YUVRo4UpKs7I1iAGADjGLo2Y4CWx00RjEOwiNjpUVP9mOUYPHEIlg66F+Km3HLY7sMwhE02JlVJmsCiqMXZGksH9cIy9YnjoGv9hz+EH7+voKlJkN44LQniwvZbnnRSPOn0KDARrEoiKTvtBDz5pFAONIFIfZwowpprAt//vrqM6kAJe+0VvI8f/EBeu3/8I1qNaJgcvL6POMRtww39y+IqxSYSwnD88EPgqafibWvCN78ZLuzTn3HjjTLiXi5M9+2uu4pXSukGwttQmFLc7r3Xb4vRPZVKSTJFpCCRUAmW3geLN/4NklmPI3Khn7ONYFEEizIfTASLRDoIFKGjfekpgnysipMiSPum78ZJVY1g1RAbUWTa+c0+ZEj5x6IHtz+m+XAEGbb19eaH2UawwmTagyJYOpHr6JCD0403Ah9/LPZ7xBHAJ5/4t+cGWBwjMIqHicO277gRLLrmyaQUBCDBC44oBEuPHEWVIOeohGDNmiVe44gDcLS1id+vnMhbEDnO58W+dZVMMnbDItiua08RDPq9aZmt1oZIWU/3fqoEtkheNWFzVv31r2o9W3elCMaJfOuorxeqmjYDk6IH5dzjYQgylv71L/n+xBPF8YPq5H79axGFe+cd+zo6GYlKsLq6zAIHd9zRf1TN+gtyOWDTTYGHH7YLk+y8c/T9mbITdthBvG6/vVym399rrqn22YpKEPg8SQQkagSLq/pFEbkwEaygGizATrDSaRnBSqfN9oU+bu20kzgX2i4oglUJwbr1VvP592XUCFYfQtwUwUmT4u2fq4fRg9uf0nxMCCI96bT5+3V2mr29YRGsO+8MNmr33Ve+J4KVy4k6sPXWExHEO+4QdRw6uKEbx/CKQrD497FFaaJGsPQaLECmR5JHkOOrr+R5ZjLiNwkjWOV4+yshWHSPUAPFuGhuFul6cSJY//2vemwTKAVQJ1j0f1h6Fe/hoiMogrVokUibtF1Lm2JktcH7UVWCxx4TBreJYJ15ZuX7J/z2t/bPTN7XahMsx5EF+HF7bIU5dcaMEamvf/lLvP1GQdB1M+G55+T7Tz8FZs+W/7/xhnidPl3UQtL9w68/rUOIeo+RR/9vf/N/FrVheF/F8uXiWehp0REb5s0TkcETT7QTcNM8aoMtRTCdVp9D3ZZobJQ9noBggsCdrKYUQX1ZUA1WHIJlajSs12Dp87qJYFG0jWqwoqYIbrCBKu0eJnJBZCwKOMH6+GO5vBbBqiE2OIki7zMgbnoTwVq1Skwi3/pWtP1zD6fe1K6/IiiCZSNY5aYIhqXu/fe/MheevEn8+EEkhq9HnrUo6OhQBx4TOGGxDZZRI1hU08bruOhamgz5Qw8Vr/Sdm5r8BIquNyl3VUSwWj8BOg3KAgEoJ2JmQhyCRbUuQc8fyaTrNUNUUMxTW0ww1RgQaCww/d4LF6pCHDqOO068kgJdd6CrS3zv00+vfF977y1eTdeBUtj4eEBy53Hxm9/YPyMy1N4uDfxqEyxAfsfPPou3v7DUqPp68ewfdli8/UZBkHy5KYWQ/46TJ6tqoPQ7nn22IJsXXBAeXeIRrPXWCz/fE04A3n9fXRbFK2+r0y0XrmvOhuB4913RiDcs2j1kiBD/Oe88YTAH3cs9AbqemYw9Uh7n+aEeThy5nCRUVBevE0w9xTqIYN1yi3wfFMHSUwRNNVh6iiAdM5mMlyJIzwqfZ8aMUe95kmlPpeT+g1IEddB3oXk0jGDp56OD5p4zz7T/xrUIVg2xwW9k7hFobDQTrGuvFZMI9c0KAzckaSLq7ymCQXVR5RIsmxcvmzWrEhIcR+7DccTgxBuc0jGD+kD961/xDLwFC0TKzBdf2NeJohQZNYJFUTpevE+DehDBIA+vqQE0Xe+tthLiCxURrGk/Ax7dKta21LiykhQrIB7Bokk7LEUQ8BOD9dePdgxupOiwCY4A4voHCUFkMuJ37I50MQI9IybDqFyYrgMt41LduhBDNUC9o3iUtDsIFpHJuI6z7vwtddC9RWmHQcYSiZNw3HKL6E9kun70e5J64yWXCBIWFJ0uFITo03e+Y29IzeE4/lqsMMfU7NmV9bA04YYbgHXXDV7n3HMFGXzmmWj7/N3vRG2YTa68p8BriGz3cpxshd/9zr8sn5fj8JZbiterr1bXiUOwOEzRKlsNVpwIFkWA+HZ0PqYUQb3soaVF/NkiWOQU5imCYWMJfRc6Bj3fPEWQNxrm52OCKWX30ktVJ0EtglVDbHCyk8/Lm7CpyUyw4oIbz1/nCJat8D8sgrV8uax/sYHvN5UyDwRBfaDCPMk2bLyx/bMoaT1RI1ibby5euZGvD+om0H0bRLDq6uLnaBNKBMspAm2zYm27556iULwS+W6gPIIV9F15/Zp+nK22ksa0Ddm5zyvH4ghKEezoCI/WZjIijcdUd1cNmBSyygG/n00Ei64NJ9eVEJ8gTJsWX5jIBhvBinJfmVCpcyEOKDpCEZ2g39hEvl5/3a/kRiA1Ur7PKEqFmQxw882i7qccmPotEj75xNxkuVLo0avXX/c34bWNIX0dNCcEkahKn1ObHcBBxINQLsGKoiLIo1W8JoqW0fH1sZEyHVzX78TVCVZdnT9FjxMsklGna2OKYOnPK08rpGNkMvI31BsN8/MxQe/zRbj7bvm+FsGqITY4GeAEyxTBKieEXyNYAvqgSQh6+ItFMZCRvL0NegSLg84liGCVW4gfFPWJosAUJYL1wgtiEl9jDbWGjUgnDfQclPb37W+L1zCClU6X50RQCFYZaGioPFWwuyJYpnvVdm9z3Pa7uwAAmZT/goaJXIQRrHRaFB1Pndo93kR6HvL5yupCqAYQCH62uCE3blxlAkI2LFigXm9uJCxcaC/mNyEsRTDquE5y/d0lJ2/CSSeJe4baI8QlWCbQs0sp2nEIZrkKoBymBsaEsChTuc+P3m5jq62AHXdUl9EYsnBh//H6A/K89VRMjkrl9aMQrIojWMjCmf8IioW8L4KlEyUuckHL0mlxbwYRrJUrZYSMnn+dYOkRMP5cUePhVErOK1SDZZqP9e9PPb34OfJURlOKoMlW0K8dJ2b8+gD9x2FQI1h9CLYIFidYl10mXvffP74Hx5Qi+LUkWLkC0iteBu4ZCdzqADnBToJUBGmAimJ4Amq6IIFS0YJ6D3W30pmtvovC+rZc/VdfFZN3sagWlQPAD38oXvfYw79dQ4P4TvS9wghWXNl5gvQOlmdFzJ4t6laiSJ/bUG2CRZ+VS7B+9m8hXTdutJ+hBKUIRo1gEZ56SjSqrqYBR89gNhssP37ZZcFy8fxe4+d87rlS/VLHvvuGP4dRWylwAQfdWOFGzsiRgthFRaEQTLAefDB4+7Y2MUZ9+mk8sYBqgpoOB0XPohKsK68s30FSDYJVCcqtd4oy/9P9dtxxwBNPlHec3kCUsYTS+sqFTp5++lO/AzUuwXIcQXhKBGvVO0i0fQa3a7lSg8UbDdNvRHNwe7vqXEulVPuD3nMCk82aCZbuNDZFsK64wk+weARLn2cWLpTH/tGPxHHzeVkGEESwaCyfMMFvRxBoO91pzMfcciPNPY0awepDsBGshgb52Vtvide6OukBjAquLLU6ybTHVRHMZ/NIJfNSDKFDuLmDIlg0YIX1muERLF1tjeoCFi0Sr11dwJw54v0ZZ4jXagkucOy3n3xPRo0O8mDPm2f+PKjGa511RN0YecM5dOOlocFPMLl3rWoRrEK8sAel1jz8cLzjmpo8RkFPRLAIa6/pL8KjybPcCBYnIHvsAfz739VVUtO/m8ngcl3gl78UQhgzZgAvvijU4zhsKYIXXyzEFUaPFmMpj/KanCOEVEoc8/zzo30PEnkBZKoQoZL0x5deMo8VdK/8+c/B23P5855ShdRxxRXiNUisJSrBOuec8hst26KBYYhCiG3P6HbbCVIIiPupHEQhWPweC+sPpqM3I162Y//85+Led934aZd33aX+P326f/zWn0k926WrK7gFACCcFiWC5RSQcIoouolSWv13v2tOESRxiLY2dexPJmXEZ8wY8Z6iTHTfkjBaWIpgJiPW1Z8rqmXXCZaNUM6YIW0car7+j3/IY+gEi86T20+234+2u/564Qgj2GyTvowawepDMBEs7lXgKBTiNZacM0clWPTgnXxycP54X4ZJMpzDNjjkC0AqwS52IlVaHzBvQ8ZMQ70LzLwByJub3gQRLH1/3/mOSLe7+24pPUwkLCqi3AMdHWJC/93vRE8e23kPGlR+s11b5KlYVH8f0yTGvWvlRrCozqJEsDrLqyj/+9/Fb0dqiWHgRm5fJVjN9eZ7tbGx/AiWSSSm0nopDv0ZNH1XMtAB4bTYYQe/+AD/fqZ0oPp68RsecYToV0f7tKUOkTH+4x/7P7v+emFMvPmmqC/6xz/U1Ls5c+wRrDiYP198X5OCHE+nNPVsIpCcO9B9NWdhoGgB3TfLl/vvoe408leuFGIOX3xRXgQrCmGx9UbbccfKr7vuTDCB329B6XCm79+bku223/3CC6M3VdZJ84UXyvft7UI5kKcQm+YmPYL1yCPiNejaNDbyGqwCHMdF0U2UCPnRR5tTBJuaxD3R2uqPYJHtRqRk5UoxRlL0lwhWOREs2j6VAtKpAnI5t/S9KXVe/z0aGsSyZFLOvSTiQmmNplS/KPaKbVwMq6Xsi6gRrD4EE8GySWXGTSW75hr1fzKQPv9chHn7I+iaxBa5yLoigkXwRo8oEayG7AfAK8cC0/7PekxADChBghjXXy+LNrn08dSp9m1MIK9RECjN6swzg6OedXX29IcwY8AWedJTmUyTWDVqsChSkEh4o/PDmwMrQ/TrGf7zH/FKaluffhptOx4hiWOkUY1eOSIXQFyCZZaRbGwsP4Jl+rxaBtmjj4qGsRzrrqsaQ64LnHWW/J+8qICQXifiy0lGWL3F0UfLSLJtfCWCZXoexo4Vr5tvLrytxx0njCbKOjj6aHsNFiEKoQgSyuC/C9U/hqGaxDgO6L4+4wyRmjxkiF+9rlgMr3stB198IRrM/uY3IsXVRoSCEOW89P1ecw3w0UdC3bBS8shlwW3gY2lQ3Ypp7Aqqk+lu2K5NnNobnWDxdGHTWBWFYBHCxu0SwUoUkXCKcF1HEb6or5fPMS1PpcRy3viXIljUaoBqQ4n0UB2erjirlxwE1WDROaRSQPqz3yPX1VX63nSe+vXi30WHTrB4imBYvz1af3VBjWD1IegEy9SLgIppJ0wA5s6Nvu+VK8W+qCs59yT0leaCceF7yDsXK0+nXeTCVSNYRbFSkEx7KUUwsdw71iLjOdG5OI6UIQaA3x2ldjTlHmSOqN45ApeXtoGIehiC8svDCJatX4aeImharxo1WIRSBCu7FJgfveiARDgIUVPAuLEbN0UpjCRVi2A11ZkVUBoayo9gmT7/1rdkymsl2Gcfv/H4xRfAlClSOVH/fbic+003CWdCZ6faRyuOccafF71ZbSJhlrHfZx/zvrgYAW/zYGqCG0VlMCjyReM7EF2oo7cJFiD7Xd1+u7pOsSiudbW912uvrQooVEvdUYf+fBUKwllQ6TgXFXysDXIAmergnn66+ucTFbZ7PM4zrDs4v/giWJ1Un5tWrRLr8fT4q64Sr0G/HVcBTCZkBIvXYA0YIGuMuB1D6nt6BIvuT71/ISdYPIKlE6ywCBb1BEuncsjlUwrB6uz020S8LxeB3ieT0QnWOef4a61qBKuGbkGUCFYiIZuUxkFHh2jgRg8WJ1jVboAYBtcVEZwo/ZmCoBCsVTOAe4YDH8qGF0EEK51iH7gqwTKRDPLmNWe8WH3CzFiamkTx7S23yBTBH+3+V5y8Z0hRRJmg+qogye6oBGvuXFGbUc7vEpQi2BMRLIKqIhgvB2fCBPk+qnHBDbNlM94SoinFaBar44iUNFtaaBDBiqJmNXn0xxg9+Csklz4PZP3qJaYIluuKCTWMYJkMshde6N7mw4sWAY89Jt6b+tpw/OUv/u8Qxzi78Ub5ntT9uIGkOxyCxmRb1Jj2xw24/zMHxhVErdOMSvh7qw6X/x7U6FU3wGj8aGkR9+a0afHrJE3QjbhyFel4lNBkGOoEixu23/ueeA1rGB4XK1aIc5k3T0ZPgWCCRfU/HEccUd3zigObkR3nd7rtNtlWhEBzlGnsTCbFcenYTz4pXm+9Va6Tdlco+9l1V/9+cjlzBIuXNDQ3S5tCJ1gKQfPICp8jAT/hIoJFzxSdny7zXldnrsEqlaMkcygUU6U+WHV14tg05myzjXil80mlgD/9SbwnZ6+JYNl+t1//GjjqKHVZf5Fgj4IawepDiEKwghrrBoG80nTz8olMH4SqjhXTgS5BTGbNEg/bD38YzZgIAg+tY9Vn4h+Wuhco065EsMToQ13XTYMv5RePXnyBeLP4ZeM5JRLCWDjoIOB73y3i4Kn34pcH/Rp16SqHCWfeCMy6Hcmk8IiaJkhCVIJFiJLbryNqBCuMYFUcwUqw0fmNn8TaNk5NI4EbuxOKXhilEI2hkvFli2ZWRLDcIhrSHdhy7deBt38OPDzFt4opgvXf/4rXF18MPPWyBQXCcP/90dajdLw4iDNuTpkiz4Xq8ei+JWOBF2AH3bO2VGH6fQ84QC4LE6cAomccRP2NeiuCZTK69GdQd9Bsuqk9UlgJyr0GnOhff73/8yCCNXq0qBkMaupdDs4+W4i4jBmjLv/Nb+zExXavlGvsXnONqGddtUqos8YFP0+uxBkHu+/u79NHz6npeaXxge4FIi88DT/9/pnK9qYGzlxmPZWUIhfcQcMbKHMBLUrT57YNVxHUCRb9bkSw6DvQvnWnMaUImuZgIli0f57qp6so8wjWvvuq100nWLwGy3T/PfigyH4g1CJYNXQL8q3zkUqJu9JGsIJU84LQ0SEGDLp5OcHq9lSFhzYEHpmKFSuAtdaSi0lNr1yoKYL+mSCo0bBSg+WlCDqOvQ6JznXEQC/c0Bo+awxs6sC9px2C8S1zIxU0R5ZpdovAK8cAL4q8Nhu5IcQlWKZ0GX7+XIGMYIs8xRG5qERFsHSeKH90Loc00Ln/4hfA8bvfLP4pxOvyaqv/CCJYJjVGdeN25IspeZ+3feFbxSRy8bLnNwhT6OqOuhhA9EgKwyefAOPHx993XMeU3lNKF9W5+GJpPAaRHttx99lHGL26WMIWW9jFaABJ6okM2xDVYdBbBMs0JoYRrO5CuXMgr8/5+GO/YFQQwQJUR8kHH4hr4jiVGZk33QRccIF/+Sef2IU5bMdLJoEPP4x/Dj/5CXDiiSLCY1KXDQM/Hz19OxbeOU/5l7d/0KETLIow8bpoIiD6/cIFNNQIVgHJRAG5Qlpx0HAVQWqNMmiQP4JFNVi63aYTrlzOHMGyESz9PtQJVnu7uLfpPOl8Sv0y372mdM3o+aRjBqUI2u6zO+8M/ry/okaw+hDyne2oT4kRmWqwSEWQR7BMBleYp6m9XSq/AOEEK66kayjaPvd5ZyttFqcQrILf2owewZIr2SIDNFE21cXIn1vyRvR1AVx9dcQVV7Afxy0aSQtHXIJlKm7mxhCvIyEERbC4gZRKmQuJAelMiGvs8EE5my+/kRgnWCNGRNuG7pVddgEc+qKGezEINkL5+uvi1VRkHUqwCp3IFdKlCdME0z5oAv3LXwL2DXuxMhVjl4so0Zl11xUS64SoZC/ueKMbJ9wDTdANnrjQRR0AkdYVRDSJYIVFPmwOA/1+6y2CBfjT0OieJ/QUwSrXqcPHxd//XtR2cehjmT5Pk0Nv5kw1zTQsDZS3RNBTDINSvG12QrEI7Lyz+TNKCysHegQpKviYXlFvyA8uUf697z7xGiWCpWTIAEAhWyot0LdPp2WrDx6BSiYKGNK0DPlCujSv6o2G6bccONBMsPi4pY83vN0OJ1g0ZtHcwQmZKUWwRLBSKsGifl10rvRbXPanNQCIdF26bjyqZSNY660HK7q6agSrhmph8Su+RfliCvVpMbLSQ5BMqkYnJ1h84AubJCmCReCDFp9c7rtPPAwbbQT8738xvo8N7InRJ41KCZaiIpg3EyyjTHuuaIxgAeEEqyFDsfkx/pV0PLmLeN34V8ri4483rx55Iskul+8XPh8YwXJd0dAvThF3OepRNmJkShG0dYZPp8uLYFGNDAC0pzdgB4uX88e951xeOwiKpzDrsYtCtCIZ3fOn46KLxKup3onUnWwTklss4JN562LBCvsXMUWwVq0ShnlY9MP2+TPPRJe4N6EcorLbbtHWixvB0qX0TQSLSEyYQmE1oXuvbbBFzfX0z94kWGF1hDaCRU3b99yzsubg/Djl4r337J/pz6dOKGm+0WsKw3rKXcJ4wx13hJ8jgY/trqsawWPGiDrcpUvVuphq9LeLazjz3yOK8lxU/OAH4tVEYGl8oPlHNq/3Vii0WSNYmYwcE5UIllPA0GZRHkFCLY6jNhrmDeVNIhd83NL7WtH/JYK1+HFln1FFLrq6hKOWvl9bmyRYfHva3zuzNy19pygEi57hcePs482MGfI+2XFH8zr9DTWC1Vt4bFuRSnSrA3x0FQAgX/ATrETCX4NFxOQb35C7u/fe4MNRDRbdwNyY54MFTy2YNauM76WjKGdQ3XiqhGCNGQMceaR4n0yqxyFkMmbjtW1luxqJikCwOjoAxykik6KRKkZ3zrTqar74YvNqkQlWjjMgNzCCRSlMujpXEMolWNVIEbQRtZtuAtZf3/wZT2FpTzEXWSGeS4x7/G1d5nXonkIAwIzrgTdOBToWAG32h4hUJsMidlyFjp9rsWjfdsliMYPOX8EUbLRrYRK5WLUqWk2IrWE1EP3amaAbPlHqAU88Mdq+y00RfP11celMBGvoUODSS6X4Rk+ArpGN5FI9ps1RoQvi9CbBMt2/J54oFXJtBOtvfxOvv/iFuf50wgQx35Tb1y8OdIETkxT/K6+Ie0hPbc1khFOCvg8hjNRQtGSddcLnDa7AuGKF7Kn0ox/JOZiM4LFjhfqk3i6k0jrtuE6zqkWwAJxz0CW+ZXRfcDEjEmmg+Y+rhgIAcq0Y2CB+GD1ST015AT2ClSvZDLwxsN5omIRzokawdIJVSs9b8bbyuS5yYU0R7CwKguedK6kI2ghWa6e4WA0NQKIovHQ5z8ALqsGi725CsWgeY/szVpOv0U/R5Y180y8HigXki6mSGEIUgsVvQr1vjI6gFMHf/lYu5wPh3LmVK/1xb/4996gflSPWQZg3T6YJpVJQCZb3ZdJp0TuHD9auC6xoH4RBjUxVzVUJlk2mvaEuKweKYohVzJFSLVabfHLk65FnDMhJBkawyFsdproGiBoCwEywwuo9bJGnjz5SG1zbeo3QPtJYgXzeT4q+/32xr5de8h+Dn+/QAVzz1QXc6NYjJ1grVvjvVxN0TyEA4MPfAp9cDdw7Crh/LdE+wAAiTtRk2rRfvh4HRbVs9VtdXWKmvmXZoAAAq9JJREFU+tk+V8mFmhPCJHLR2hpN+v/nPweuvNL8WSWS1/weGjMmOKUEEM4KSpH6/veDvd3lRrB++UsRTTRN/o4jPl933Xj7DkNQBDGMYJHDwUa+bT1tegMmZ9bf/y7V+WwEa+xYMZbbopczZ4pnkguMPPpoxacbCdxJEGYw2shDGMGiechxxFhgqosFhOoiN9BPOkkoMp59trjO/Dy5EazPAW+/HXw+HKba6rhjAp+z6+qijUn+nYiL//P9f4dkUp1TaE4aPlwuo6wFUnX1RbCmX4YxQ0QzvvlfqZNdU5Ma8ZYRrFypHCGIYNFvRCmjYREsq6pgXXvpHIAYIhceweIp5UEEq61LTEr19UDyw0vFOktniu8ckCIYBK7gqEu391fUCFZvggrPU41AoT12BOsnTCQtKE0BCE4RBIDLLhOvfGD79a/FwGYzpCKh0IGPvloX65z+CWbMUD8qR13IBF8EyyN1kyeL781TyDo7gWy+DoMblwMtW4uFESNYDRlmmcQhWFoEy2YYbbllxP0pESwnkGAde6x4jSLtP3GieDURrBtuCN42lRKD4rvvqstff10W8QL2CFYqBTiFNqTm3Ixchzq68u1N144MmmuPPwG7bK6FPAyRTRv0fZvInA5jBEtHzi+RDqjPo/788u/c9EAKmP+k8jmRdFvNU7ZLXGQaTwAAc+72HV8nWFF6YAHiOTntNPNn5TRtNWH99cXEfOaZ9nXOPVfcU7mcMDI/+EBGaNZaC9hgA3nv8750UcDHyAsv9KsIVhPrr6/+HyTiEZYiOHq0IKdhUQOSou9NgmWTKE8mxW95550Vtm1IiN/xjDP817i7wIktzac2A9NmSJoI1quvymeLxiba/zHHmHuFbbqpOjZRVspvfiOXFQp+Ke1Krvnhh/uXUSP4qOB2SDIpbAXetyzaTsSNPahxJa49Qy0qpevIiRu9p898EaxP/4q6lHj4cp0qY5w4URXFKRGs3PxSOUJXl0qwaB2uDG2SaY9Sg0X3UWNGJVh6DRado78XpWskWHoae+k7FjKl4yc+v1ZZVgnBItLZU89qd6NGsHoTrxwjXlNNQKFTIVgUxjURLHoYhwyRDRqLxeABSE8R1NPzzjlHvJoyqs44I/5XKyG3Elc8dCY+W+CXEiqnkeGsWaK/BUcyCeBN1pAkL8JupITGvWdEHgbUrwI2vlD8E4FgdXUBdXXeKDHum0rUKxTJcIv15z+P4aXjEaxiNlzwANGMqGRSGN3lpAi+9poozt90U+kB9N1Lzx+OVNtHvsGd0hGQW4WP562LJSsGlO7lWbNUL7QpokqTx94bG1zUMQiWTiyiRDyMESwdD04yLuYESyclvK4klSwAn16jfE6eV72onpD1IlillFYAeOk7yo8ydqwgcjyNqrMznlz9e+8Bjz+uLqtWH5NLhWM0UvQ1lRKT+MSJMqqxyy7CSCeDL27/QJ00Vyt9xSQaMHKkSrJtJPXXvwb+/W/xPuh3am62R0EcRxi8elF/b8A2t2QyUkzn00/D98NVF3UZ964u0W+ukoyJOODPZNg9Y4peA/7fbt48cd9QDRGBj7G2fmuNjcHOu1zOH8EyRT9nzrTvg0NXUgSAJ56Iti1BnztGjhSS9rEwTw5M450HlI/IucDHYJ28mMhxMiEelsceVxlDQ4OajlciWB9eVCJYPILFa7B4nbJNRVA/Rz1FsESwLBEsXWZd/30XLk4hhbbQCJbu1Emn5TV59s2Jpe8WtQ8Wx89/Lq957N+6j6JGsHoTnV41eLIRKOaMIhdBESz6nxBU+9Dersq02yYbW8nKv/9dnsLL/+6Zj3yhQjULhm23lbVXBN93KYhBxpRGVRoo0l1AymM0jGDZZNqzWaAukxcNhuuGxotgeU2J6zLyxzrrLDUCEEpiiwWRSppdAcx/Si6ffTuamsJTOaOKBwwYEEywrrjCvJwbBJR+ofSFcV1gzl1ILnrEGMHKZAAU2vHkB3sAkIXbW22lrmv6nqWUqUwnMMgbmYduIV5jECz9PgorwAeAB7x5O73kyeAVDSIsfHLXSckKPeilpTpSE2sbjAQLANqk9bOGEIJSUnqiNBnm2GgjYIcd1GWVeL+pVcFDDwFbb13ePihSRdufc44w+ihCGxW6uIgu014uTOJBqZQaYbM9z+ecAzzlPf5Bv9OIEWaxEaoF2nBDf1F/b4BaY+jgfYKi4Ec/ku95k2iO7iRYPO2b/3a+KEhEHHKI6oCk8fUNTZhW/048rZkL9VAjZxP+8x+/EWxSUZ04MVqqYCU1mISqOGkWyHlyz40fR0vzYowdK34QKi9QCFZenDjNlb4UQcj2LjfcrHpDEwkgXRCeqlzbSkmwhm9RIi3ZrGtNEdQJlt4Hq3SOlhRBuucaMh1wnCKyWa9MYtU0AHKO1Gu2CB0dSaQ6Z5ZUBOnYyaUvKcfTnTqbbiqEPDja24NrsADgsMPEK4/UP/ZY+c9LX8Vq8jX6H4pFB09+fiz+/NhPcNwfzgPcPHKFdCjB0lN4+IMSJBohIjDyf/2Gp2ZxNhJ19NHRalI4nn4a2O/YnXDj88fE2zAA8+b5lyUT2mjsRbBMBKs0MKW6ROQQUIxwWwQru/gjZIoLxbpOuiyCtfSFK0qepssvV1MvQ43a2XcA034B3DUY+IoVRH32dzQ1mb3V/LeM2pwzjGDpxc8m0PVTIhu55QDEYMwJVj4PvPOO5/3LS8uE1qFoGCGIYNWluoDBGwGHLQMmeRZXIbosHU1ye+whz22NNdQUUx03e62vhkwPadZyh18KcJdd5Hv9uSOC9fQdXhFdMSbBytoIlhTd0PPzXVcY73ENUX3SrcRgp3GIXk0IS6U97jhB0E84QfyfTKr996KCT/6jRlUWweJG55AhsrkwRTscR72OUdIsgyLegwcbSDqEg4pAv3O1Io7lgtLd+PxkEyiKAltj5+4kWPvtp/7/6qvi1WSkR0GxKCKxL7wgfkceTeEtCvRoKE+5tMmu67j9dr8RfMopwqmqRxIqFbuICvq+5TpZAACOfFAdBzhoi/vhOOpAy+fdulcOBOCPYJWe9/Qgtb2Lhsxc0ZE4++XzkmANnMBqsKIRLJuKoOP4U/70FMFMMotkogDXFT9m5uX9lPX1MZ8jiS4lgpVKAcm5/1HW18f6886DQsoAcU3DUgS320686uM8f14mTxYErj+jRrB6CVc+fDr2+L+rcMqNf8a/Ht0PH3/sIG8hWFxuvK1NLXrnD0oQwaI0LD5obLCB+jkQHKWKKtdaKIgHJKp8cqVIffZ7dUEAwSoVa6ayMoKliVxYCRYZq4k00LUIeHIPIGe5KPxCJsSo2FjXaRQsACKkZXkExYQ77hAGnE4+OZExSX2bMHCg/3fmBjM30GwgwkPRiL/8BcAMMfmkknllf+ecIwyG5csB5FqR8LxhtrSloBRB8ewkgMzgEqmNE8EiA4+iZn/+MzBnjvDw2jBhArDXxo9i+ECzkIUC7eHiip36c0fXsIHuCy2CFSYjX4pgNWg/PEnJwz/ZkhJpyYD76mFg+m+DD+SBG+mVECxeeG9DWK+tREKkBVbDCzrG68Ywf758X85+9ZqqBx4Qvzn1GyOxAgJPE7IhiDDU1QVHrXmqeW+mCALCaHdd1Xlz7bXRFCRNsNVDmtQGqwWdDFI/rzCPPKXB2rDjjsD556v1f9wxozek5oTBFsnTsWiRP4KVSgHf/a65YXFPgK7bn/5UwU4c9QHJ5jOYOzdhVbSj+d0awUoPRCptf+jSEJ7JXLGBiVx0yRTBgBqsKCIXlHoH+AkP2TiZVLaUsgfImirqc6X3yVLOP5X3EayEIy4C3d/8/lp7baFgTHVpBIqOBxEsfh04uCPh44+FSEs5ja77CmoEq5fw0VeqPNayZeLOoj5LlBedSIiHqLNT3HyU6keYMkW+t3nJqIiVTzyJhKhPoBquXE7IxQblu0eVVSdhhZ5Ccsnz6oIoBGvAMCDjzbhaDZapR0Y2XycJVtKzxBc8CSy3FL5xgziREd40N8D7FSZFW9S2HbwpMGIXYOC6pdQinWCV4wEeNEgoL3JwQy2KZC6RoKYmcU+edBKAt38OAKhL55DNyoH0mWfEq4hgtYo0P+/cdVEUvm8O+r0yqaz0WtJvFINgkcNBL7ANMkDnzwc2WeNd+woc2rlwA9lfdCxe694jJRs1zDBggHBg2OTSKUUks/GpwDemsR1LSUedYHG1RwDAM/sC0/4vUm5wWP1GtZBICGGLOXP80c3uwPe+519WLoF84QV/3Sk3wPVUuUrEQvJ5QVDmzDF/nsvJtLaoPd+6G7boiM0pZYNtHtSJF6UpVQP6PUHRxbAI1hZbhO/7T3+Szyjfzze/GVw3GrWWcvFicxoXYBasKBcPPmhe/umn/hpimoPiRv4UOKp5+9Kn2wWuTgrO1giWW0RquAyp6D3FMo5YkC3US4KFLmMEK04NFtld/D3J9NN1o9d0KlciRY4j66M6O4tKTZVpjE4li/4Ilrc92YX8nqLnMpFwkfFI1qhR8vsF1WDR76o7iSgSx9cPU5Lty6gRrF4ChXAJ8+d7IV3PiCdFFSJYxaJ46PQI1h//KN/baka4oow+aDz6KLD77mKdXXcNPueoBIuKsG3gD2mcxqK64U8oeWya1hKvGsHiSk0lw3XwmFJkiaurNTeblZ2yuaT4bQauD2RYwr3NgOdkqmUrwEkZJcN32SXiRKinJCbrRLPjlR/jH38XDEMnhvS7RxEJIEydKpQAuU1NqX5R+w3ttptIkSmJVzDUp9vhuvJepd9jjz0A5NtKEdyuLqlsCQA//KF4nTcPuOUWdZ9dXUAmU0Qi4cpJtYwI1rHHihqDI49UJ/agFKp8XlVeCkRAA2L9OVAirYDx3pk0yc59cl6KYCqVAIZsCnzLu6mzdoJljcwERE85rvF0OCqJYA0d6u/TxFEoCDn2ceNUieXugqn9hckBEwXbb69GHwBpYJAzgkN3JvCaKkp/tIHaKpB4kY5sVpzLjTcCv/+9eZ2ehm2c6g4P9siRIlr6ySfV2Z9usN53n0jtC0sr5QSL6p423ti/HrXb4PdIReSDYexY4SiKmipajggSABx4oF/UI5cTqWDfZhnWt9wix/uKvmNaDVluP/lFy4oCFImxily4BaTS0gjiKfBrrgmkk57CYCEtVQDRaVURJFnyqCIX/D3VzVKKcYlgJXMleyiZlLZRV1dC2Z7u1+/sIssNUsmClWBRVguvY+XknuZtchKF1WDR/7bU+Grd272NGsHqJRRd9dKvXOkVJXo3ODVdSyTkjdzaKh4+Xfnm/vvF+ygEi8BvYJ6CGIRq5bCPHClD/3EGa5+H3QM1/ysRrIJKsHhXetnPwRW1VACwQLqVBw0yp0I+/v5e+GzBJGDsAUAds+zyFp1dHnFyHEGwDHVbTz8doddYIaukdgEAEnXAUpGHMuCrqwH492P63cMwfLgYGPm+Tj5ZvAY1oNX7ZN1/v1+QBZARWjJSlVzyfGtpkuvsVCNHkyaJZ+E3vxGpK1xxrbOtA/VJupHKJ1iOIyLCyaRKeoMiWPm8LHxWMMVLrcsw5YIYBKvkCEhTvor/GEHy/NRLLJUml2mjuCZRCRYXUolYx7b//nTsSKsbkU6b66Xuvdfe66c7kUj4xTFsNT7lYPfdRUROT/UC/BEs8g4D0aN3/D7mz2hrq7jfjz46foQI858EVpSZvxcAm+x8kGR9uZg5U0TwJpkFPmNDJ1j33y+ubZhM+7BhIrKzwQYyXfCGG4Djj1fXo3RJrhQYN1XVRmBfflm8/uMf0fYzcKBZEj4KvvpKvQ/puj3wgLB3/v1vKXAEVGhoD1RTEa459qTA1WmszXaJH81HjjWCxYn/qFFAwskjmcgjl0/KlE50GVUE6TUKwaI5lKcIknNpHU+c2USwEglXSRfk+6LrftQuD5c+TzcOVEUuknJ7Got4tJs7J+jamQhWUIqgTuprjYZrqApueE7No+M5tICZYJGcsj4hTpggXsuJYNHyKGk95agI6vjTnwSpIGO9XG8YYeSIAjZby5M2avGS+OfcB8BcdySLPV0ZwWIw1SDRb7No5Qhgk4tUz1jOQrD0dMBkvdXADhxM8m3A7XXAB1o3+mRdKVrTVPhQOU8CpWTEIViUNsRlwufPF69czluHXuSdzwvPuH5O9Z6SIk0ISi55vhX/+/m+pfPgxObHP1bvex5F6Jr3piQiegQrhsiFcp4RCNa994pnwlj43OBZw3XMIipWEMEy3DuBBCvnRbDSrCNpZqiSIsgbYwLaffjU7vJ9+1zreevnw/dXDmwy8QcfLGW7exp63eF3vlO9fSeTolkyGUz5vDQwgxwvYUptdM9y0kLCGkCFY/lTewAPbQjktYe7bTaw/IMKdtxzoHu1Wp5yUzrnAw/IXpVBY/z++4t0/eOPF2Pt5pv7f3si4M+zbPi4RujYsfHWJ5jmj+HDozlldRx5pLgPKRrL93HGGYKUkjIrUKmhrd7kzfXBnsxSBKtTPDx+clxEkl2Mc8/VDyciQNl8qkSaHDer1EHpBIua/hJxotpJ3geLak45QdKbjRsjWImCWo9lkl1Pyx8glXK1CFaxlG5Ix+Nz8PLl3oVJDcDKjoGlc6TzjkqwLr9cLqfj1CJYNVQVHR1qBEtPEQSk10gnWLp0p44wgmUTdtA9WnHS+XQcs9O/4GZbccopghCWQ7B0w+3ii4H5n3wsB84Jx4jXWbcCMBOskuFal5ZGOAMRLG6AKMQiWSeJHBAQwfJOduqfve3qgULMdvYAsPw98/JEHbCu0HlvGiwupj4pUy2clWDNewx460ygawnQIQq46HdZuhR40lMeJyWnoNQtHQopcaWbqqFFuKP1CFYqBaDQiSlrvYOhzUvR3i6NlnXXFefF73v++3R2SXGYUmEz/bYfxsiPZOAk20RivvxSSCkDhghWIiNT+khIBTCSpJtuEq82glUqIC74LbhUyk5mCgUvgpViD3rd0PJSBJ/ey/KB/3yAyiJYHR3x+nD1BPTIvVKH+MVtdidLmcfSG56aEFYLSepcJMzBMXAg8Nto2iXB0AnW/WsC/4vZbEzDXtFuNSPiNJKOmu4eFbb0aZo7ohiMjiMjBFHIS1wjtNzn0hbhvPxyQZgmThTj8XnnBSuuctC4xcevW2/1r1eRoe1GzHkEgNbPWQ2W2M4XTSkWkKkLMJmLQiSCIljJJIBijtVgqX2w6Bg808PUaPiVV8R7nuIXhWAlEq4yN6VSUlKdrntdmkestBRBFsGi/RvvBTeHrpw4EYq0Rq3Bcl215pnmQf13v+giw3H7AWoEq4+AbmAewSoUzBEsnThQ1MEWYQhLEbQJOxx3nMglp4LKKATL5hld1TEAyEnd4IHC4RFZmRDwGxzpNIB3PDfSbk8AzWspn5ukz/Pel0gPGCEuwtgDhWAEOy/XVckKpSYevL13IZrXZjsMiWA55NJpCEwRs0LfZitPZi1ZB6xzIpCsR6N3MW0ebyvBenpv4KPfA3cPA+4VlhgN4L/4haiLeuUVkSM/bBhwxBHBp8pTMRUjuV1W2tenxY1O9zullV14IUrXrKmuFW1t8r7dbDPxSpE0QP2undmkJCJ6BOtL5g6NAU4QTfc0v2+5lxBbXgMc+LkkWA3MbawbpBAqYYA9RbAUwTJsm04HRbDESSeT/EEvk2Dp6akW0H1WriFHtXm2VLHeAk+LUZwMy98DXjoSeP1Hvm0qgUmcRx8nw8QHqBm7qenrJZfYm9LGQoBoT7l41NArPCq+/DJCurWHaqcgHXlksLpl3ONFUXaMu08aTw89VC6Lch/YCNbSpeI+mzlT1K5ecolaS0VpjibjmMY3TrBMzqLKIhkxCNaXD0kVwTbx4BkjWJk6/PpbZ5v34RaQSWWRzSYYwcrKFMGcU5pfeQRHTxHM5eQYyuduniKoNw42iVwkE66SXZFOA8m5YmDIdYoNMin2eUqvweIpgp5ioGlsNpQ+hNVg8RRJ/hnZofq9Hcd50pdQI1h9BF2dnsfZexjjpAi2tIiHx+Y9ooErlTJHsIYOlcYrHyAB4KCDZC+CE04QRnbQ4M8NgTFD5Ak1ZDoUSfNyIljf/Kb6fzYLYK6nLd08QZAYhkRCiDbwYvh8q6gOTTaT3E2DElki4sd7yNB1/+mhBmM9Z/kCVDOT4ASrjAiWnuJGZHC41901PQhNdcKquO8+8y7i1FjQAP6+J464aJEY7Om6BIGLTyhef+bhb0iLyYt74AYN8preeqRkYMNKrFzplmpOrrvOfyxuSHXlG/wpgsnqWekmo40bvwrBGr4D0DhGek/rRwA7e0UHXf7CBVv02Zci2PGVzyMbXIPlrZNms1d6oPIM2gjWHZcaCoIioNIUQd5csy/hwguBq68W5/fII+yDrDdILI+oIhkR9Lzuvrtovrl8uV9inDcpN4HG1+uuExExPmZX7fpy4ZUv/2tfr4dQXx+9HYWOgw7y3twzEnjzZ2XtY/Bgu1BEXKJAv1dQOqqNYJ1wgqyb5fj2t4XoCYnRhO2fECWiTN9v1SqxfiIh0hopsqXDRLBMDtzKIljBebDf/S5ftyBaJdS1YdWnTwAw12AhUYfmetWpWhoTskuQTuWQy8NMsAw1WCaCBUjClExKdVvHiZcimEi44s+LWqVSQGrhQ+JcOryaqQwjVIm80jcxlSzKCFa7i7o6IO1ok6HrGgWY4qQI8s9IYExfX+/31l9QI1h9BB2d4o4ijwMRLF5wTymC+iSSSAgD2EZWwlIEhw+XBqOpiSdvaHnHHcDcgJIMrsB33M7/xOw/jcfFZ83EH4/+aSkNDZAGQJwIFinnEJYsgSQazWv7ZFkBf2PcQodgS6kmzy2dUiNLZMjw8yKC1TLEYM3GimCVQbD0up1hWwH7fQCs+7PS/pvqxY/3v/+pUR6C0eiwTD6m3jgmsYowXHwx+yfv3ZhOEg1p8Z7XYJXSnTxS2tK8BEsWF7BqlSjiNTVU5ffZ4hWD0NJM4VstglUmuGc3jGAVi+y+S3gP6yAvVWrkLsAAr5K+S7uBIQnWZ5+py30iF4V24NGtlHVSKdmCQYckK1x2LKlMhjrBKkUMJxn08Wff6V+moaFBjDH//KdfLSwKeGF3X0JDgzBYfQZtl6c0oamVVeN4hG9/29+u4KOP4hmebW2qKEycmsxA8AjWswfY16sAr71W3f29+aaqTgqIseSuu7x/OhcCH//Rt11U2H6XuNEmUokkNb04x7r2WuEQ0NHYKKJMnKyHNriHXWTkqqvke54enEwKB0DQfUYEgRMsU0psd0awFNEPb1wc1rwYS5aJL+NXESwCyQYMalQ7eJeaO2eXI5PMIpt1jCmCXV1OaA0Wjcnt7V4NV8dcHLXr46Xz0FMEfREsRUVQddpzVcBsTuyoPiNZ7YjsvVqKYIGlCLrIZID0p1pucdGc1hRGsOj/YlG9v447Tr0+hCjtDPoiagSrj4AeGFJxMUWwiNiYwvpBQhVhKYI8wmNLz+FEJUhNUE81HL/5Tjj3/CSGNi8DWmeWltN30ElTHOy5J4QIxJj9rOsMHixSN2iwzGe9waYkd2OOYHGCtXSJ2HjICIMhFaYiSAQrVaUIFgAM2kD+gIlUqf8G4EWC9F2YIo4mdT23aCRYJrn1WKBeYfUjUJ8UF5YmBCGxTscXBx3avBRLl7hYuVJVLiTFJEAlWPMWNWL0YI+86ymCZYKT0kceAZ56Sv1c6a2WZ8eq83IZhm8LHPwlMOF7IooFKGqVpdW92/DKK9XlwtvpIpkoAkO92WXpm8o6ukgFB6kIKimCIQSLSB3JDSt4N7zjaDIpJsuPPgJ23jl0dR9KvWOqpFba7ej0CBZv21AF8Hs+m/V79idPjrYf3rSZ0mwBkdJVNjibN3iuqw2Tw68SbL65SH/maGqqLql/4QV/T6+4BOuAA8Sl3mknVZykkn0S+Hfl4xwnTBw33gicckpwJgQfi8huCQLd02G1ZhU1wTbUYF1z7I8BCKc1V+UkMjZswGIsXmIjWAUgkcLg8eoDWPquTlJEsHKuMYJVLEqCxWuwCgW10TAgCRZe+g4Si54qnQcnWI7jj3ipESyxjI6fTkuCtXSZ2FHLAGnoDG1eqqoILnkaiUSxtP9MBkgXNPlSg7otfb9CQfzOH3wA3HOP+jnVXW29NbDJJsBZZ4n/KYhQE7mooaooFftrESxOsGhiNDUXjUqwTBEs6r8B2IuneT+VoEGRNww8eOp9wEbnidQkQCEjI0eKQe6LL+z7CsN++0FEn3hqoGZYDxki+4cBTL6aPPuJ+lCC1bZK/DjNQwfLhYctF57rsAhWxSmCHmMd901gz5f8nztpOLNldbDpHjD+pibPk1vwESzXLS+CpaDNKwSpH4mG3McAJBFXam5cGcFaukxEZHlq4jPPyPc8WruqLY3BjcvFP1UiWITmZtGsdffd1eX8Ps8WvGOlB6uqgY1j5HIAmHG9b/+29BtxXbyHddzBxnWCxG2MKYJOUjE6yCii5/mDDzzDY7BBA3yNb5lPVEMUj7gN/Y5gdXlR0ypHsLiTpKtLKGhyKMZHx3zxZwD1yNFRbh8vAUawLMaVfxMXmPtAZNEBqinu8fvAllL22d+BL/8XeTfbbw+89Za6rFyD0XHsxKdcgsW3o+d1t92An/7UvP4aawj136DaSNrnJ5+IsTHs+9I9aOo5yRG1N5cR9HtOPrX0jO69zceWdcWBWpqXYElri3Js3mgYThKDR1gK11KNIoLVVVAIVjojJ89EAsCyd5GY99/SMSjqB6gRrFTKBRY+55NaB8T146qCvAZLqgiKL0BRKR7BWrLUQSYDDGiU6Rl1qS41glVYXBLF6OigFEHtB7M4WYhg2TKUtttOZG1QpJbKUAg1glVDVdHZ5aUIKjVYLhKLn0H9/NsBSILFi64JlRAsUwRrzTXVfXASFiR2QQPng397CFtMeEt4d4kAMYLhOGIiXbHCsJM4KHap9TYTjlY+pp41JDv+3kfCPZxMU/e+8BqszjbxpRoGsFy1zCAx4H5+k/m8qpYi6F3sqX8SUREdCfE9fnXo+b6P6LsblblWGiaafGtpoOcTXxyCpUckRaPWhCA+y6aV1P6MKYLeYC0IVtIXwRozRtZKcILV2ZVCfcaiIgjEU5PSYOt5pNQO5Lz7b8A65pX5bJFX7wHKsR+iBUG6uoBM2jvvOvOEHkSwjPVMTiIwgtXWJs6jbqB3PIdtnIpW3PIBU+mO2ldHP+d+Q7AMxd3VApGsfF40/rbi3tHizwDbMxumQBgI/ixFjWB9cSvw3EHAJ3+JtDrJdJcrK142bCJEr50IPGvPkoiCSkQ1bNkDlZC2c88VzeBpvtYj9CbYImmAn5vq3/c3v1H/f/tt0e9r3jwEoqIIFqUITv4JcPhyYK3vIOGIednal2zAYixeNQyAFsEq5sScnqjD4IEWD7NbEBEshWDlkMrIHzCRAPDGT5BYIAq3bDVY7e0iRQ+QpIhHsLq6xH3hI1hJKXIxf5EYs8lpn05L+3LlSgfNzUAaciLNpLJWkQuKYKUS2phnEbohghX0+02caP8d+lqaeLmoEaw+go4O8VPQw+C6QLFQRCK/DA2fCaW8mTOFmptpgqwkRVCPYM2ZA7zzjroP6rUFRItg1SeWezscJCTF4fgIRmOjGgkIgylyh0KXt38PFCWYI8QvyENHE8lv/7YuACZfnWwQhoLnjTXVYHW0dsFxisg0D1aPTbVFJs+nUeSiDNcxCSPwhrXKccQIVihKq5RSSevqhHywcXJ//xL/ske39uV4A/EI1rBh6v/19RDX1yM+1GjYmCLoXbPBjcvR2ZnAokV+cY377hPrX3ABcMUVYllHV4rJtBtELmI0GyaQ08FmAPBnbVmbx46CLB66R6df7vto+nSRxqqkHWaBuoR3E2biE6x8XpxLnBTBbNYbIz71RC4OXw4c7t3jZVzDH/wg3vp9tQbLDs+A64ZUudgkc+UnpbGAYBOmsRo9c+8HbnXMzpcS2Fj39D7AnHvsqxI6vAGJqYkGgRTDyhWsKBvlOMAs4II/QGUeeRs5q4S0XXwxsNVWInIVFTzlVIceadLPTe+TeMYZoo0ItQKxoSKCVVI/pV6AKSQdi5Hk9cTkBEuJYNG+6loweKDFw0wy7bmimiKY1iJYi14okaCgGiwaBylNz3XVFEFjBIulCBJMNVht7Y4QI2mV3ZIzLZMDZdozGcBxXFx8GGsApo19dH5EsKJGILkjFagsE6IvoUaw+gjaiWDpESyniAZP2rqtzW/AEqpZgzVunF+1avRombuezQqPl4lolYovU8tF76dkvXcwF3j/YmXdGTP8E1EQHEcYbffeywigHsEitM4ofR/Ab4Sm0oxgAaXJlYwSkpgFgI72POrTnXDSBrUFwOzFoeasdd7FzS4FVn3qi2CEomOeSG+wRRBahTpC0ZWPMuUxK9EhHSN3Fa/b3iyXrfrUaNjl8+XXYOVyENfHSQG7P1VSYGprExPGnDn+CBZFo5YtMxtZ9Jv+/OdiH11Z1geLJlMewSqDHNx/vygU5wP9ZZcJzyug9hEqFT0nDPdh6Ryo6MCv5UxF0jxqeO21LhYu9yzNMiJY9hRBOSHqNVzifmEGtJMCUk0QzpHymuCZfA9ffimeZWqETeh3ESy6lt0QyYptPP93XWDWf5RFm23GCvAZrDL6zx0sXufebz8Oj2C1zwaeP9S+rg/RWIZewN9j4ATr1vLvecAffauEDNFzMX68eJ6IFJUjJKNDb6IdhKDIZ5ghbUuFvt6fNa2g7J56+Q7gDU9KkZxu2WVIds0yr+/91sMGLMby9iHIdeXVCBalA2eGYvBAy/O+9E1Ppl2rwdIjWJBEJyiCVSIrjj9FkOqi6X9yzhkJlue0TyWLJZXAlasSvpTPRAJIp+UPmUqB1WA5Yn23iHO/eancSCNYFKlMOl0oFNzIvx+1ayGYnn1Ty4m+jhrB6iNobRNPk1KDVRCdtEvpT7BPOu+/L4iHCVTnxFMEOXh6UlCeNQ3sr7wialKoMJFDRrCWRi7+jurlIMJw8MGiMBKAP4JF8B583UtPkATLG/nbZgPwe1IAoLPTFZGXlHbxxx4gz0GHR3wwcD3xOs9r8DLnLv+6QfjkaqV/mA08gkXX00iwVn4K/CcFfPWw+H/EjsDmV5U+NkUPcjlLVCHfDsx90Gdgcln1Y4+FjGCN3BXNA8X1bm0VtQVvvQW88Ya3skdUyYvW3m6+3/kyIhclgkUGICdYcywPRgDGjRPKcdzY/+UvZfE6Efw/fPdnuOiw84Ex+wNbRFAfM7iyqacRv0ddl62XyADNk+iD0uLgFEGt1hDwESzHEfugyTmXAzLFBey4aekcaTUoC0YAV68jjBsnXnVhD3Jq9B+CRRGs6hMs2zWgpqNGGJqS//KX/tX0ekI/AuSto6TbvnEq8CbTkQ+Ry9ZB6ek/+1mszSrD9N/5lTI//F3scyfoKb+OA6BzEbDio9j7Gj8eOPVUoRILyHS+Tz8t69QUVKsfmB5p0ud0WzQyLIOl7AhWB+tZQwTrywd85KMEzwFGarSLFmS1CJbXP7CuBQOaDazBdYFCuxfB0hoNs0m4pCLoyGbGPEOExvTnnwcWLRYLTSmCgEeAvrgBjuMqNVi89xUtA4C0uxSZpEewViZ8pPelD9ZHOi1viNSgNWQEq1PUbJWydkrXTT3WmWeKa5GcdQMKnStUp//031qfJ8dR70UTmV9rLWDDDeO1nelt1AhWH8GqVvEwBUWwgHCvnsljcMwx4pUTLG7n8Yc2yEtFn1GPJF5vQSh5HxNLZHF/CKJ0rad9+whgscssaOAZktYIFo1oTd5svnK6t9y/q/Z2V/wGCW1EGrWHdw6mUN4iEQEgkrn2sfZ1bYjhGV/aKlMIKRKSyxl+z0c2E9dm/mPi/4QamtLtf9cVniNVccnDtF8Azx0IfPWIsviYY0Rh/jvveNFRliLYOFQUEK5aZWgq6haARFrpxWGaYPlkXeoHku4EdrhDKu4l64DRXlfYV44pO8oQNsEfud1/0JDpFA2Gh24WvDJgTCezOQEy1Dy50CEJOynXIZhgdXQmvH3bCRYgfp/Fi8XfXXcBK5axAYS3PdCiI1FBxf6u65eif+YZkc56zz3idyQDst+kCJYiWNVvuGsiWIceKlS3rEj4L5xujLz/vt9b7MO0XwBvnWH50ECwbtUGjU+uBj6+ii0wTDoBaGkR98vRR4evWxWs+AiYdhbwltZcLLvMnDZ4qwNMMzBXBp1gJRIQUcaH1o99eo4D/PGPwEYbxd606rj4YnNfQl2kQCdY5UYjyxa5YP3+eOSUiI3vVix0AokMJu+wEwDg/Xf1CJZHsDJD4TgJXHGU9nx4zsFMKotsPqmlCPojWBQZskWwOGhdwE+w8MqxSCezkmClHdnag9ajCFbKLX22clUC9fUq2dl9yutwUtLAymScUvSsvd2LYOn2iyk9OrccyUQBhVxOJVjT/g9Y+Ix//dL31L6bAe+/H693am+jRrD6ABKJAlrbxZND3vtiESjmOpFIqBGssKZ/QQpRthRBjqAIFhX8U2qPyRlRimBhiRrBGrWHTJfTEIVgtbaKP0Xgw3XFA29KESyqBEs/RpJqsAZ7oTBlQBYgD+HcrzIYPWSe/zgUOTORpuwSkdpFF3qKFzt/60zDt7OAImMjdgld9cz9rijdG/vtJy5NqaaGI681dXLSSoqjPqG5rkjpmjTJcNBFL4rX5w5UFqdSopllKcrICFaiYQiaG9pKv6eCYh5ID4bDPOim+5RHGSkdsj7dCaxxuLrBJqzObEZMxQUPQYIugHxeS/LsNqztNfgw3GcmgjVoYB4/3oMa/rLvxO41qp2cZch6+XJ+PQY3LlM9xxaCtXChFEL5cpkXXgpofRAHdE/+/e9CZv+hh9TPx48XxOHYY+Wy/hPB6r4UQf0anH4669VkhX86pyalBFuKuQ8fXemr6QLgj2CNjFDEs+R18WqIsPUJWJVgi/7xkjD9MvNyDxSlJTgOjOnBfQF33y2dpmE499xotZWVEiwibGW3BymaHUXWCFahE0jWY2iLePA6OwqqIFgpgjUUSCRxxn5X+reHmA9y+aSnDOgCbh7pensEq1BQ26CYCJZJRRCQ26SS+ZIjMJ1JluqcS9swmfY6z2lXKDg+W3LKhI9lRg+Apoau0rau60Wwhggn4q0/ORIvPPRhyXaY86dxePsGT2gr6xGsYtJfthKQdssJVlBktT8pDNYIVh/A4KZVaG0TT4sSwWpfiIRTRDJRRDotnvawgSoo5G5LEeQIimCRwUzeOdO+ZARrIZAZLD9oHK88vIDwygHRmg2T2MCYMWwhGTiOaRQWJ2evwfIsGJKQz/rT8J722hbNmNWIiSNm+M6/FDkzSZ7nO4Aks27pfW654VwtKHi5W+MPsa/jpSlOGjUDj3lBqdZWGckMVQxLpBWju5BVv0tXl7gXfSke+TZrk0Efinmp7uek0FzfgdZW2SuplPbk9RlZkZINdU33GK8PpCbQ9WOn+ldsYGG3MoUIdt3Vv4wToRLBSlpyYAhbXyeaD5MnlMFEsNo7EmjMeL//qD2kVDsz+EiG2yTEsWxFBsMGLFajUJpMOyCcJitWyNqyEra/Tb5f89syRTEEd98tUv/I237kkcBjj8m0pieeMG/HJfj7PMEq5sV1LDdFsNAFLAwuoNENjEjXxHCP68+tqWm3FV0LDQu1B3JBBPm5uV6K7lfRpc57FpZJMTNU1M0SClmZWh0CxwGmTZP/VysVDxD1odXEIYeI1CsFC561k8sI0CP/9fVC6p1fkyA884ywD7bfvswT4M8CjYGTT1bS7RQURalBpk4wmGxXvkQS1RqsFhjNZo9gZVJZZHMpLyol7qtEuh6OR6hMNVjt7fI5DSJYjuMieadcgcgWTwlMZ5KljKfD95urfJ5KQolu1WXU+z6ZyCrlFo31qqpgJoOSM+nI7W7D9lsuK13ncS1fYso6nsx1octOsJZr6mkMyj1TiF833RfR4wTLcZyTHMf53HGcTsdx3nQcZ8eAdS90HMe1/I2wbdcfcPBUWRcysLENbR1eiiCvwXITcBzxEORy8QqETSgWzSmCHEEeI8cRHufp0+X+dJQiWM5CNYLlpHxiEPO99i0nnWQ/JoGiHUqNVKnexmB9eOky3HjlBmypBis1AIAjjQCG+nrx0M/6sglrj5hpJ1imwaDYqa7Pe3VFzet/82feyS+3r6PIJsv9ksx8XIJVzKre3G9/W7zSbwUAWDYNuKMZWGHIETWeY0GmLyXSaG5ow9tvk4Q769LuCiKWS0piZLpUXB2NegTVNxuK5+pZuDNZXo7K3Xf7l/EoTGkCCnOrOQmhqEmkmUEnWLkckMsxguU4wNrHiPdse3pWeVrwQw8BO+wALFqSEefmI1iq5TNokMXBwQVdUk1AIZqxdcghwGmnSecEINKKaFxQ7iMGvjy5/A2RhvXgZODt/4t03Ej46A/AfWuErxeG29LAY9v5UwQXPgd8dl348/3mz4AndgZWTLeuovc+iuTJNxjE+m0Zq35h3qPAjH9pY4z3fsNzDRs4aquC9i/9q3x4RYwT6CHYfq9EGnicWfivHCsk5yOCRwiU32HVjFCCHQRj241qom028OQuwKsxZUAZTOPmKacYiJwFgweLurOyoxWKreHtZNj2pciRD5/fBHQtQtojWLlsvjSuJpMQESwnKRyyjsHe4BGsnIsVK4CBA7z7KtlQsusSCY90sRqsjg7pODdlENG6s2Y5KpliESwAcJwiEun6UsYTHYtqsFIpV0m///Aj9eImkFOydBrrupTGw74UQTevzic0Dha7kHQsBGua1uWbgROsxHP7AEvesK7bX9CjBMtxnCMA/BHArwFsBuAlAA87jmOb9a4AMFr7exbAM67rmtxrfRKmYtRhzYtL7xvrOpHN+VUE84WUr2DxY4uC7p/+JF6DCBa/2W0DV5htwKWkAyNYmK/WYCVSvlSapZ4z/9VXg48JSANNieCVHm52G+/gFSp7PXxokuvsFKlQhCRJpyaSQMuWQMdXvmM2NAjjs1BIYMTAhf4aLBqMvvi3/4QLmrohJ4FRoylU9xIUKWLGT65LDn57e+VHkQgWS6codJnTZRYv5v9E+MGUc5Qpgkik0VTXjtdflx+X0h6KQm0wV5DWpOke42kSFHkxps7y+jKbzH0ITB5/7ljgE1AoDE4GwE+w6BlrrGMPG0XI8n6CxZ/r/fcHXnwRePyF0d5YEkywBg6MEEFONsX2ZvMoY329JFC33WZenyP1qSdlv+pT4MPfBq8cB2+dLqTCq5GqteRVoNWTtaJx7YmdgddOAD7+U/C2K7x8rK7F1lV23VWtUYxUl2ZLdQvdrs1Mhl45Fnj1OOBjJt5C4029wb+ZrAc2ZLVJ940DHtf8p2//3JOCj6HQkGsFZvxTRhGqDsuk987Z6v9z7orVU48bywpBfnCSuFfKRNlpc1FB1znAAXDffcG7mD3bvDzKfXznneHrhIKPs6Xm8/Lgiv2Tby+NbzyC1dYmxq4SwcoMERs6pgiWMFDSyRwWrxyMefOKGD2KZFEbkfOa0ZtELsIiWItWytIKIk2AP4KVThWAZEMpgpXILlI/T7ulFEEAWLBANQKTTk6NYNVlFfszkwHw2bVyg2LOT7iA4AhWRDhwgaWvh6/Yx9HTEazTAdzguu51rut+6LruKQDmAfixaWXXdVtd151PfwDSAHYEYCiz7LsYarDtKDIFAE31HejKeiqCpZxXIF9MKSFaWm4Cpc6F1WCFEaiwolIudqDv69e/Fs0DhwxxUQc9gqUa8vxYUdInjASLCq65R4lqR7yJkDy2vObnb8ediGSKbTNsW6OxU1cHrFwuBsmBDSv9ESyKimjy8wC8ollLwVxc2fAgQraxbDCc65T5oSQuEJpa5KSAThk+KFoIVqmeCjCff5DhwQmWk8Y7n1sKvQsdQLJBIVi8hQDB9J2stYkkYlKGVDshqB9PMhHd4BJk1j/jWAlWhhGslHcjsyJhLtlrQpQIFhEsun4n7fEXYKcH1B2lPIIVQ1GNG4FPPAGFUAMiymWDtU6iUhDh7rQTGwUf/Fr0l7Lhq/+KVz1F8EumP//lf/0RbnoWQoz1vfaS2QJHHmlYQf89Zv4TuGc08FlAwyITntxDkCEbeKS6dM4OMGIndb1kg/+cFr1g3ueXD5iXm/DsAcCrxwNPGvJ1q4KI93WiDkaRDwvGj5fvK2rurME6phey5UvLz3tcEN93zpUOvYC2E9TwHRBR64kTyzusCRWlCBeywLzHzMIzTqoUwTmEZ90z4aB0Rhw811VAaytzsHUtle0yAiJYnbl6LFo5AtOnJzB6JPXKkEZLwrP7SLiiUBC2TRDB2mMjc141lY2UaqySeSBZX6rBSrROVz5PJQo+AQyOhE6w6jv9KYIcxZya8k5zy2NbV06wHNd8nfsZeoxgOY6TAbAFgMe0jx4DsF3E3RwPYDkAQwC676KlBTh6xxuVZTxU3VTXgWJRbTRcLAK5Qrr0cBBskSd6QNsMjmaqmZoyxZ4ieOqp4tVEBjk4weH1Xq4LnHOOiLBtvFERDoqyvgkQ3iPNe08evigeuf/8x398WYPFqyM9q9M7FqUUtrYCba3iy48aNF99eJP1ShPgS702D6kUsHKxGECMBKvFk/Qy1d8UO83iG0D8ovggcjBsG2CbGwAAg5r86WehA5zjABOkXFf9DEMDYnjyq0HnEzSxu6wGK5HCjhtYImCFdiDViLXHy5DK6af7VzNNwAXXYsHs+rh3zuULERBZJRx2mLbCcGuWswonJsGqawf28yxs6oP27nml7fQI1jXXqPtNJfPqgx5AsKZOFc/Gb488Cxh3gLqjdLPYLmYj1h/+0P7ZzgEOfN2pVDXQ4FeM0PA71wq8cw7weISpaembKllaPk28LnxBkIN3WFSn/Stg4bPe+YQb6+uvL057vfUMH5r673XOB147UVl0++0hB1niFUGaPPOAFr0kp1YCWF9L30w2+FRJ7YhIarIrpFOhu0QyojoOkplYEaxQdd4yJeCtuH9NkbpdDj77m3j94FJgrkd+QwxcaguTzwNraDlI//qH/TqF2RgVNZh+52zg6b2BxS/JZeQITaRQl87iq4fPU/tvsecoUy/u3zen1WHVKpZSm10KpKmhvJ1g/Wj3v5UWNTVSjiEjWFqKYFub2o7EdJ9sPuFttAxqxcbrisj7UduJ5qH+CFYeSDCCNVg4MqkPVypVUCJYf7talePTUwQb6rrgMoElv4JzTmYDOEmF1BLBavebJJHgOK5ZHbqfoScjWMMAJAEs0JYvAGASgVbgOE4CwHEAbnJd12jNOY5zguM4bziO88aiRYsqPd+qQulrA1V6c0CDnMC4imC+kPIRLBuoLsUkYdnQIHvM2AjaFVeIdKswTxTffvly+Z6nGY0e6RlIvDlubpVIYWEqahddJF6pzicI//BE4FSCZYhglbzDwpAkD9SqVUBbq1jWVN+mbpOoF6TB8zRTel0+DyxfJIqZBjas9D/w9cOEcAHJgXMUuvyEjBA3mkLRC+vn4ktus7k/hSZSo79hWwP7i/4sO65xG/76a38hqkJqTOcfmMaopggevNUj5vXy7UCyEcccLCWt9IbXgDniud5Ei3YrGXwVRLDWXTdkhShKanQuAQTrnnvEqxLBGjBZ/GMg8TTB0m/8k59oh3OK8KUI5tuEt9rDwIFi+8WLXew35b9oqjfMiN79hReOCPp2PuiEj2P0aPtnelp01QqeyZCK0uybiGjUtLT/sAeka4mIBnx+k/h/6euibhFQhR7KFF4pIchpwD771rci7s9KHhL+dZyEXxY+WSeehaCG23w/hSzQYSnKIzxl6JRcbUQWKfF6whG+fAh4cN1I96dx3i1EIPoWTJ4sRaJK6JxvJt1RMOce+f5TT710ySvA4ztYN6E5oVDQxKcAHD7cyyRZ/j7w2fXKZ88+C/zqV+r667OkBlNz7MggEk7P7e7PSLEtr2xg9NJLVKcuew6bm8WX+tuNo/HvfzOClW+TdakmqXHPabPVxNdKi1askDVYBCJYD00T1+ecc1QRKVukc9Ejp+Hdx54Uu/Mi/ClHHLMUwUrlgUQaDZku71gAEmmkk2L9dDKv1GA1NarjTxI5xWZJII9sXp6Q79xcliKYalLuvWSigKKbNDpIIz0vcIHGgKh6P0FvqAjqbhvHsMyEbwAYD+B62wqu6/7ddd2prutOHW7KLepF/PsFtakHl6LmN71eg5VO5oD0YDzzV00WVANFakz1FFwG9PHHRQNHRe4c4vMpU8K/BzdueW0Z9wh1dcrc4xJmeFmdTC57yJBw2XkdoREsJwHAKX1WXy/OedUq4OabvX2kO0qDrThP7yReEB1fueH6xQzx26w5bJZ5ltSiXyUUOu2GRhT1PZ7isMHZ9vUAaQDn/Ol9Pm8hP6dx35TvvXo5xwF+9L0vlE2+//2AcyPkA1xVs26TalxOGu/OXMu8XqEdSDXBSQV7rkx5/BMn2grVvX11QzPYB8/wGgptZCr4N51L2mgA0cR17bVCtZHS6Rrqi7J2zxANpWfxwgvNketXPtvWnyIIAE/LKnmSev/oowSa6y01PGQgUEpcRKRSdtU6Y181OpyeImhKwY2LTuZwmxdBCa5S8gPIMW/hc8DDm4nnk9+HRFY65qnnFwWFLPDGKQGfx4s2BiKRAt4+C3j3fJSmaiehjqGExjHArnqDOwPcIvDy94B7RwdHhfQ6jHJT4IKwOKh7M0PXYrUZ8bP7A6s+idaE2/Qdy62ZA/Dx9E6cekxA+mol4Eq3i1603ps0DhcK6rz84Bn7o2nVI4KU/G9j4DU1lL3RRsD558uI/aBBsun4HntUqLhIBj+NexnmobNF5Niz3jRIFUMqjV+eyiAAYKzIjzxo74UyVdKzAQY1SjXiVNL7zZkd9OJL4qINHyCuKUWFiGDZ7CEH+dI9RNGvVMen3nHEnFKXygKJNOozWW+bIuCkkPIIVipZUGxNfZxNOKqKINy8kqZuTBGk651s8BEsQCrc/uF4NobfyTKbLNhkjXf9bSJm3qA6AvoBepJgLQZQgD9aNQL+qJYJJwB4yXXdiNJlfRs8gpVOyRuTE6xcMYPUoAlAeiBGDQ7W9KAIlolgZbPy4dh4Y+APfyhfnUeXTaU6Kl6QPe1dkrcxxfrVA9fVhfca4lDSB0wRLPq/SMo6wgvV1gZc9w8xuE0e/YkW9fLOyasL4ARr4UJxjFGDLZ7WZJ2ZMBUDIlj3jTcv51j0vHyfarCvB0jPmmHC9qWzuTkZEeMecC6pDwctLfI/X6rXe+fDh2xET//Mf2GzNd8yf5ZvFwO1k8ZuGz6Jlhaz4WXM0U9bBm2KYAUZo2Vi6tpviAiTocGr9VwMUQeeepHPy2jz0MHsvuLfz5Ba9MQTMvJ64IHsA06wmtfybbfttuzj+lZgK0P9TgW58Jdfbl5OPfVM0Pu44AMvbXXFdGCOX+0zEnj/sfculG0Zlr6tGs0EG8HSm+rGQdsXmtHg3d/3jgHuiSmKO+ceUXNlgxalu/9+VQo/Fj6/Cfjwd4LoloiCIYJF96Vt3FNQBGbf4Z1rDAGV7FKhqnqrA9weRxIxALbUyKhY/m74OvcaQrZtX5R/zJePEY2Lc63CuO9OWWst5ZSw++7C+fbXv6oEqzPn/f53tRi3I6TTos3G7NlC+XSDDaSybNkgg79EtNg9aks5o2d925uAprWUjxaQZVpgqsADRVbBff98S4p9eOO6TzwDUEmeh5P3+jMAYKstWfudD36NlhcsKURFqdh34/PHAACee1cURispgk4KDY1eHVk+ATjJEsFKpwqKSEbC0SJYjtZT1C1g3TGSxJfmqbph8jvTfJZsVMZMIlgUwfrxjqz0IIKDeXDTCv8Y/MqxwPOHhm7bl9BjBMt13SyANwHoAeA9IdQErXAcZwyA/dDPxC2CwCNY9AAAag1WvpAUxn6qASlHPKy2RpFEPEw5rzyCVSl+8xvgbBZQoeaXvKB31AjyahgIljaZZTLRGg0TGlpfkpOJKYIF+GpNiGARho9Mq6p+mleUy193tosBxGf0ERJlRLCA8Jqgdr+qoRVEmPKtOOss9SMlxa5YEAYSrd/2ufxMiZA4eE1mOkS7d2we+JyWulfswqFbyhLKS3jJl5sTJCSRwZO/3AOL5/p7RgF+D+e5B19sJ1hp7wIUs14Eoby6B5PIQCaVjZcnHlKDBagpnS2D2X3FBWMM6Y6OA2y+ufitttqKf8IuVsZv8PCGqHWpLnNUghOsmNePO0SoTxsgIu7f/774/Wn5qFHA7446EzusaxBGWPQi8NCGwPOHyHMIiprq0Cd1uoaPbA68oOXQvXGqTJOqJrLLgDcZ0XcLwCxWINUxL4ZSXsjvoLUDOPDA4Lq3yPjiZvleJ971XtaIiWCN1wwjfh8Zmm9bce8Y4C7vWTC0PCgLekR+jcPjbR9w/ldcPB97bPS4IqRQwqNblU/Y53tpvoVO4LFtgdsNc00xFz3iN2SK/bPcSmDB075nP5MRolZrraU6dUoiRREM6aFDhXO4uRn44ANgyy2jna4V9FzP8wYVPm4OtzTVIlsh1SzS/hlKTdwLLIJlSjun6M0Gsi7RzXlGx0C/qBPVQq1YJvbR2AjgnXOQ6JhZWmeTNViq/hc3W50+5JTPpHJAQhKsrmxCi2Cp97kewXKQ90WwODIZAA1jgcEbe58XWIpgg68GCwDyObXmLBaqkUXQy+jpFMErARzjOM4PHMdZ33GcPwIYA+BvAOA4zmWO4zxp2O44AG0A7ui5U+1eKBGsJI9gSfnOfCEtlGKSDVh75Be46ipzfwnAX4/B0d5ePRWjdFr1Mh1xhOh5w6NQJc8Ij7zs/oz3oWqtx41gNby0K/D2GeIfWwQrkQokWFjru+r62vb8WnZ25OE4RaSHWDxLthRBUwRrxC7yvU48dJBs/FYRfAopGcHaLagciNKTaLK39bFy80oKqXLv6I1yN/UUQdoturzPe3JNbJLh0uZKI99i3iNYwXVT5Emjnirzl4+yE6xkRjTKBYA7B4RLaFtw/fX+yT+ZKADrGAVQzShmVVLrgV9fLkqy1hh/8+vSfjQ4jhgzEgnt9+IuVUOkjROgpW1DzdEq7sAIaBRpAk952XRT9bRuuEHUIOy5p6jnnDkTOHO/35uj67wWJLcSmP8kcEeTEJKIAt3QLOY0YQqv7u/dC4FPrgbeuyDafk3gRh3Hh79T/3/2AOBFVoB67xjgbosHTQf/nUyEQD9WtUB9bNycJOPNk4At/wbsdJ/43+RY2vwPwGHL5f/82gf1+QuLnkZN79PRtUSOwXra7pYxyfVrJ/jTmTyccdKXePzsbmhcRQ/Jw5vI+j4dt2WA22Pm4Juw4Engyd2AT+1FlXvsIVoxnHIKcMiWhjSuagt62EBjY+sMEY1qHCs/S6SA9c4Q8zI/n5KjVtxrjokM8L6W5FTjzjLaR7IRY4fM9ZZ595VhbqI5cPkK8TuahD12nDJT1Hjrx9BQimAlxTPZUCf23ZVNAomkkG9n6xF8pMctAMk6/O24E3HWcS8Ds1VjU8wrReEoBMQzXGTOdEOKYI4Ilq62a7gf3tGnlhrBigfXdW8H8DMA5wKYBmAHAPu6rkt+gtEAFEvWcRwHQj3wFtd1q+Sy6n0knCLe+81GePbcnRTPQiLpwHGKJUMrlQKQbIRT7MBPf+ovJiXYCNaLL4rX67ox9rfbbsBXLODiUt4gj2AN21b8rylBxSVYmVRWTiimPliAF8GSF6KpCfjSa/MyceRnwMZahe3kk8WrpwbHr2VXZx716U44+75tPqFkvdlTVzCoCO7G5FbzYQRrnrheE48PXg+QBOvTv6J+qRSQmDpVW4+u25pHiddRlom/0KGkfCgRrJe/p65LUZFXjjXva77nLxnnuTh3uFNRiVPqqYqe0UYEy1I3tZOnDr2jJ943oGGVnWABanRtbnk53I2NwAknaMsy7fEI1mwvWqH1EePXl9osXHj0jcg02kRS/NfFcUQtRCKhR/gMNVj8U/bxklUtwKg9/Mfj2718jIgg3+rEjvKMGCEa6D5pcKENGqS3YAhA6wzgKe883/oZ0DozcHUA/md03iNq6tP/Nhb7eV8bG+KieRKw34fmzxY8FX0/YQYp/00mHK32HASC5eUJy6aVr65ZzEvC7iSAdU4EGrw0OJOSYN1QdXm+VRqqnfPsxwkzslZ8IJ/vfFv073P3MJmqTZ73b0wD9noVehp7JHz2N2MNbKgjrVxQpLPDcu34GPPmaeHXJQoBeusMoZhpwciRoh9nXdrgGOuYB7x+UvemMgKq80lJe6dlQ8TczK9HUSVYrmvqc8Xm8xLB4hEsIlgNOPvAy7xlRLAGYPpv1ShWJim2vf6fYl+msc91i0A983RafkMZwcoCiRSGDxYR1ZlftigRLF6KAvhTBOEWgEQ9Ttz977j8Z08Ay9RU/ro6CFJVUmousBRBcwQr26XWjZVgaLnAyxIAqNHyfooeF7lwXfca13XXcl23znXdLVzXfY59dozrumtp67uu605wXfeknj7X7oQDFxuN/wA7rf+8YnAmEgkkEm4pbU4QrIbgouVCJ9IL7gPgJ1jUIPb4CHZ6JVihONu9h4nXYCUzQNMavn5ThQJw663BJIuP/Y4DNukG1GBpEaznvLvs29vc5q9pygwCBq5bMhDUCFYR9elOc9oLILy1xhRBQwSLpyWaJmPCVw8DK94T5xOlWI4I1uKX0fCZrI/6/e/ZOl1LpEHZPgs4fBWwy0Pm/RU6FcNbiYjoxmxdWANf78cjj3VmiJ1guZ7RRtfzwyuMezzqKFFP8pe/APeffiDOPfgSmQpoAq9Ni6JwZgGXIn7xgu28nPcYxhjJuT+2jbKY15RRDWVTZoX8XQkbXyheAyJYyaR2SiaRC0Ck0Lz0PeXhqhswRIgU6OCR1+XvyJSoab/0r6uBdn+EJ0D4058iOMpKqolBeJUNZkvfFClSYdAjWK8eD8zQ9JICFNMiYeNfAbv8F2gYaf48aq3Rl/8D/pOQUTUT+G/ZMFaMrxwtWyEQ8x4Twhu3lZnawFsv6M8AP7ddHwWOckVaMk+n/fC38j4OEvhoXjv4PF79gaxfu6NZqA4WC8KQN4nxAMB8j+jmVqjfZcimwLCtyvOcv3Gy+NNRgXppRXiRKX5+fJVQPAxEhBSuYhfwiO61i4jXfywcMrxHXHeAE7gVBkeHyXmnRbDM++2SfS1NGRZE0lKNsscp3X+pZowZoqb8pycdpfxvimC5RU2qfPGLxlOTNVjCQbnOOHGshcsGAE4SSa/uSo9gEQlK0vJinolCyXV3Wk+0lchk4BEsuoYsRTDZYKzByuWKojezPk1SqwqGsWO1BXO8CNrSt4Bbe0OPr3L0z7NeDcBDpkoNVspFwpEEK512xM0bJC087WykXhZpInrfI0qNO9YSYKgWFILlGiJYgLHI/4svxOvfDbX1hGXLtAX0IFtrsPwpgoQ5OYOHHhCGt+flpvWXLQM6O13UpQO8f6YUQdcVKQUmY56uiS2ClV0GPLMvMP8JoMESrtTBiCz1UwO0yNPdw2QxtpMWwhh6yhgJHGjefkWJUDcSDXU9RjCp3ECC5aSBTq+y+JM/G3flOKKeJJEADtziQQxtXuYnI+oW8m0FvTUobXLi+CXYbvLL8Xew/W3yPWt2O2yYTJUkgtWYNhCsRvK4+422v/5VpgjSZPaTPf+sPhtElNKDRK+YL25Wnsfrzr/PfN466Zp2lnm9hS9YU6Ui81DHkSmdNugpUab6Fh25FeHr2KIBBO7poeeYCr4B0fR7YJimfwSQ6tqS1+zrcGOwcRww0GuUte1NQGYoMP2y4Dnj7Z+Hn8eoAL1sJylr4Jo0EkTR5DWPAkbvpW5jQpC0+OBN7Z9x0H238FngjkZRk/SwZdundvcfn9ce1kUc03SY0qSjRIYe3db63JSNtlnq/2Eqqq4LjN6nuufAQY2lu7N5rOuqKdimzJISOWL3XBSCFZoi6O0v2YB8QdxLDmTaIO8/BQB1A9SMCyPBIjKznqcUMfMG5fOMt89SBCuZA5wkhgwQz+XKtgbASaHoReT0/oIUwfrg8g1x47l/Q6mdipYBRNv5UgTBUgRTqorgx/PEOPj5zIJdFTJKNLNYAB7bDtGExvseagSrD2DogOWl9w31eSQSbilVKJVyxM0bVNDb9nnpIdMjWNSripoNVws3qn2TMYMr1ZYIlhYpshT5A8FCFyQff8WvvAmsRLDCVQQBlWC1d1nykBJ1pQd+0CBhTH/6KdDVWSzJnhphShF0PUlVE8Ha1Uvhs6WOZJkhWB9RWYwZ0VyG1aQoCcCueke1HJq3X7l3dKXCsB5dhEle9CyRVmqw1P5aOa9+jpwPEaxyJyGMsHQAweK/TwUEiyR7O7vKVIyhFCrA58GjCHOJYKWWA+kB6vam1BQPDz3kJ1jJREElWKkGYNzBkqgBiuE1akTEvjwz/0Ubi5c7B4uUwSd29ClMkuDGd74TbdfId0RUoWMwienoeO6g8HXCwCMbdN/XW/Tm9/+4/ONQ/WXUe7VuGLDjPcAOdwETvifJpEntkxBF+W69M+yfOSnRaH2j8wSpU85nKLDvu8DWWoTQxrJbv7Afx6hEa8A7THmJno8V06Nty9MdAfWZ2fs1YNh20faz4GnTzsO3W/IKMOs/0Y4RBXqdLBCB6BWjj+WVIKriajmIIpZCqZVc/j+AYE2ZAq/WKBc5RfDALR7AepNa8dOjPCdcMqPMywCQqFMJljE92i2KY/F5g4H2qb46GDJIOFa6smnASeLdzycBANYepwroJD2Cte6YT3D0Ps95KYIp6aBODwbqRyBfFL+ZL0Ww6KUIJtLCtmMEa+IIYRAu/KrVTrAWvQB8dp1yb263HbD/5l6vwMEbi7o/3b6afZf5Hu+DqBGsHkJTnWqY8sbDawyXntPG+hzSqQI6POdjOo0IKYJZOI4I9doIlqlhayWYNCngQzKQ9cnRIlMNWKS3PcyZI14b6r39EnmyRbA658seNFAJVkfWYrRkBgErPyr9O3gw0NoKdHY6qK8L8LCaUgSJoJgMRTKabT1Q+HLdwI6AzSe8jYsu6MT666sS3ArW+Yl5ORFC7/s895zosaQMkFktnDhog2gnRl7hRBpJFr1V9l30Ilil1M+Q4cl1ASSAMfsGr1clgkW95jq6yjQSHAc40POwat5uiuR94pXONCSX+yNYdO4W2WSqwSI7VowxmlGrjyXcSIh9bbyJkUeHFquRvYkTxc+0//4Rd1nslKk4ALDpZeHbxHlOtv5H9HV18EhLs1cqbJBgBlCScsbwHYBvxlAE5Qj6Pfi5OI54vtbwlPpoXIxSm6Zj0onANv8SaX1jDA3US+eWEinPm1zkU14DIIyjsPYShPfOFwTdmELJxoB1vCqBoVOBnbWUt3JFPdyiP4LF0bIlsIc/nSnW/k3Qx6xqKSICwFuG7q5hDYhdVx1vebRdx3sXqY7AOHBiOKe+etQ/3wSh40v1f93BC0j1RZ5+TtfeQLDuvBP++bwUBTOJXDRgreGz8OFTT2DymgvEfeWkFHl0ACKtl8GeIpi23puUhkcEi1LWmxrYb51IYfHKwQCATSYJO5MiV0oNlluQqbKJlJiLM0OAUXshVxB2QSlF0NFSBBMZYPFLwqHhkaUtJrzpHb5OzPGNLL+e8NTuQiDmi1tLi158EXjwHM/bmBkKvHmqf7sXDi9vbOsF1AhWD8HVDB1OsLaYKHPtG+rySCfzJYKVohTBIILVPEGsm8wjn1MH9Ke8dPM6QzClEpgGBIIblCJoSVUwNY/VUe96hkpYBEsDJ1hrjLZEjlq2VvrUNDYCL7wA3P/iVqjPBExOphRB+t9EsFKeMfjaCf7PANULF8Uzb8B5p32B6dNlbzQfxh9sXk4eOo+Q7LgjcAEXUyPBCo70AJSM+E//JiYu8kiZaqi0AnilHxLVYFE9VVBdFSAmSzcfbmDzVITWGcDy8lrp0X1UUSPMpjVF6udSVTSF7v9TPBXvoQ3z7ARrwVNGj7Reg+XC8ZNUH8HKYfoHLuZePdYsTkDY8m/+ZbmVwItH+ZdVAt5vBgDWPiZ8m8D0UA1xJbg5aNxZ5yfSqxyUhvetdmD3p4ShwDEsQs0YEPx7kMG8s6H5M10PG2kIwoa/jHbNy031mmQZ9wBRJ/Sl9n141LBUH1KITqr1+1GPak2/3B/BAkR640ivWDCRCk6XDIKJYK31Pb84TjEPvH9JcG0ux+CN1Ug0x5JX/cte+g7QPjdgh0UozhibEiYgFDbfCa+/NCJqBKtzMfDMPv4WCkFo1wjW7s/41ymNm+y7vutNcp3+XpfDh0M4fQAm026KYFG9VaP8jDIy9Odw+Pa+69Cc86uz7rDeq+JY2rM2arAgSqfuLRRx9QiWk0jhtINvxmNXXgw4yZKt2VDXoazvJ1iUIpjysnDE/5TyKAhWgY1LXoqgk5ZRd++aUEZVNucI57lbANY+DtjWIFzxsqbqTM+8oU6rhLhZDr2EGsHqIfDaGAClvFgAaBkgvTSNDXkRwWr3ChNTEWqwVgm3dyqRR65TNfZNil3VgIlgre2l4u++1WfijU/kwR7BikSwZns1OSs8QupaIh0jVYUaHh274kyLklf9SACuaGTpbfPll0BXrg4fzzV4X0o793KPFVWiCBGsrsXmtA3uyWRRuFBseI58r3sro0rkOgnxG5lEOxY8K5XbdKzpNYl6/ceitoOiMx9dKV65J96bbBzHxY9/DIzm2Q+kIrjuz8T/QQqK2RWijggIVhAE1AjW0jeA/21kXzcATU3ARRcBT9/sec83/0P8nTiOMM61xsw6aRs5cK6fOHCPrPcbPfCAXFQsAgm3Hc4nYuJ1XcdvCKea1ChpMYv11ytg7NCvgr3L65ijZr7UpmoRrEMWAYcuUWucbIhibA/aCBh/SFlR4RIoct68NjDXa3hMSlumaFOqAdTXTQFXEw1ClD56AwypBJTqRY18o2C7W4ANfmE32nWM/kb0fXNMNddVAhDCDM8eoC1kBGW4l6rXtFZ08qjVraBNq5Na+Jw5grX9LcDubPKMatD5CJU29m79T2Crv/mjK2/8BHj3PNFKIupxbEImKz8S98CQzdTlQaIpegRr2Daqgp2OcsU7ojZ1LnjRzChqmIA4f92hR/2azCci39J95dkNe06Vzq9MBoYIVlCKICdY3n3lc0YkfPfb0MF+J+53d77Di2Cp208aKeyrb20tnm9SJBTtfhwgkcSVh38Pe06d5tVgeQQr3Qk4KdSlxfdJctELt+BFp1IyguURrIIrji9TBHkEK6c6grw5nAQ1sllvbqP0w44I0fywaCtQI1g1qChq0p88olWflgZgQ10e6VQe7e1ioE6nE+ERrA7heUkl88h39YyS/QDDPLDeeqKXza9OeEics09dyk+w9vBsdl2cgzCXOd147Q5cl3k3tdu4ZWvPC6NObv93wG/Q2GS55eu8JpmeymEHu9wdXQGGDok38F4uBc3jxZFiF85ENjmRpsL1KGgcJ9/7CFaMAupEnbk5pZ5+waEpQ5aue9Iz9MayLpTeYFyc+R9cc422jZsXnyczngFiIYZuEbhrsPw/FWKURGh4GRXnnQdsNNlLj1njsPJ2kh7s6/2jE6xRg+b7yQBPufVEUtZZRy4qFIBEfjlaXNEbaPiARX7jvn6ESoKKOaYEVYVmeZUQLOqrkqgTaWd1Q8WkvM8bwdtFiWDlVkgivsUfyzw/7zlKpOBLvRy0oX07x5EGwc4Pit9xS3tPIXZA+0dBUXJTalQYhm0HTLnMP2bveK9/3cNXmtUmoyAoKkfQexQNXA84ZKGIPu5wl0hhjLIfwB9116OJpdSoEMKmj+WU8jRgHXX5uxeo/3PC1bI1MPFY8fuHEY0Pfh0caXe9iNNoSxpnZqhQRbSdi21/hPRA4BB/RKeEcu4xAFjyevg6gJwbo/zOb54miOn8x8T/2/wLOLJoSVElp6z2XeGUbIBRLdIBVVcHUScEyAgZ3StzWMsPqr+jYxaz8r7yiXAlfcuoV5V6ql76nRbtuv2UI3Dpt36JDceJ+0OJYDmOjFQW80oEK53sBBJpNGTE2JEAsz9ozldELoqAk0DeaxxdErngBIvm7M09Z6qbAxrHITlwTQDA3K8yaG31ljspu7jXHDbORLFXagSrBg6eEqj/zxVm0hlHSxFMsCiJhdnnlottkznku9Tow/jx3aMgOHo08N//Ar/9rVxWXw9MmAAk3TZzcbIhgnWv91x1WPjjlVfK940ZRh7dAqwy7ekB3vUS15XG0jVaZgNdFklgqiPwyEJk1TNKpeC54oHGD1tm8gJyIr3LI/7PbeDphPp9wo/DiZhxP/UiJWLVDODZgyThM0XBmj3vOXkbdbR6kUyuymXKXwdYPV1Krmfr3aIvD4tgmQhjJSACW04KFiDqdjRVu/32U1dpGbDETxz4pOyJpKy3nlAgHDLESxFMFPDtbW/D9T+/GmcfdJn/HHWvdDEnr2ec+ggbqIi8HJR6qmhEb+gWwdsFTchuEfjoD0D7HJlyWq73vfS7J4FdvELsumHAjndL8RobDp4LHDgTGOsVo63zY1HnFBQFDepdRCI5JufCiJ3YPiJ4gwF76pbp2paZuhwZnAhQ2lK95wBb41Dx/ERNNfvyAZVk6d+n5IUP2d+wrdX/N/YERFq05R9cIurJSv0eve8yYmcRJSQ0rWk/ViELvHNOSPsBL+Jki3IWc/7nOfC+d8NJH0e5Y9+0/zNnSOgghcqg38V1gVl3CBl6Xr+35lH2CZz6/A1gap/5do/0im2cpLxuiQRkP6ZVnnAN7Zv3iZp9p3il57FriSQfjgM4KTxz/XX47IYjxHVmkbk114T5tykJSDD7xklizJB5+OVBl8HxnLt6iiDG7Me2T+HPp/4FwwctxaCmVUAijUYvVTAJ73dwUtIJmaAUQZkySCIXIprXKX97t+hFvRKqsqKbF6UtAFa2evdndoUYf4ds7v+eAPD8Iex7RxizEjWCVQODXoPFI1r1GWZUOymkU3l0dHgpgmlHekVsUSzPGy4iWOrglctFS78rB/vtBxx5pPyfVNZQaDdPwoYaLFLPsRGsPzD7QyFYxRzwvFfUrXt6aZDzjBAS/nAc11eEXwJFsDzJ5+gEa7B45RGJoBRBvmMjwWK/XxwvMSe0egSLG2p8YjGBIlhvneYZJ4/RTtT11voOsL8nCqIXpz8wQfaaAYApv5HvHQvBWuF5a0k5MYhg6UZSmDd1A09WvBoS2gAjJGU+WIYIViol0g8JyUTREJlhwzW7TzbbTLQU+Oc/gYRTRCLh4vgpp4oJUb+R9eeSagWA6FGBILj54L5GQaDnwZRuR0bGuj/1f7b4ZWDOff7lXz0qPKNU9G8j4ltfD0z6kfx/g/+znB8j1rw/0/hDwhU/61pKtbIKhkyxbxOUKkORQlPK41bXyvdBmQ8cVseLIeqR0FOeysS3LPVGrx4nvfBEsHQ0hDiKOJ7aA1jwDHDfmn4P+oKnZCpXENY7XTQiJow9UNwDpvsRAOZ5YgrkmJr6Z2DARPl589p2AZd2T9UpqBk9GbYbnm3+vNDuf56f/6Z83zYb+IKl97pFGM3BzQx1tAAw69Zo0ahJJwJ7vqQu+/wm87ocNIeu/BiYe795nYXPqv2+CEHjGNW+DWZp4pq94uiRrxLhDzCXKU2eUpqnnaXW9jlJ7Lz5Z5g4eo743SafVNq0sRHA+xf790kRrLksD3wsS6GtHwE0jpMiF8m8Fy2nWupOwEniyD1exMLb9kI60QUk0iVV5AQRrFSzHHsdUhH012DVYblYZ5XnOC05uROq47SYRyqhO1DzIiPBVv+trBshglWNuaoHUCNYPQS9BotHsFIJruSVRjqZR7tXg5WuS0sDMm9I/3vl+FJNRTqZQ1urOiHm891HsABVSKEUzcp3mI1eg9GcTAqlxM4ITq3Jo1k+9oe/lUoyqz5VVywp9YkJaonnVG9pDvCuN08UAx/VdzEMHRSQdkkkiqehBaUIAqIeRN+mtG1Eg0gH91rrA5TiMQypxyLZeTJqaHLxpZckpKG1ySX+/fBeM0RCATnh6MYjRXSopiRA0t/3/YZtY16PsOEvRbSAKzdFrUvT0TEP+FjUOJU9yKcHlaLOHCSHf+JunqCETrAGszQ0i1gM769nJCp6dKiYlVFdPX3Kt23EtKCS9z4mSkTPcN4kh77mkWbvv0nm+pl9gBdYGicRrLW+B4xny9c+Dpj0A/n/4CnyPX+GeYpg6bevsD8LPVcmghPkyS20eelDhnuQO3aijCdjAiQemzxSSNFqapZdDdgiYZ/fBNw3XpDIooVg1Q+L3iMLAF4/SdSGmuTpcyvDn2UnIVLuNv6VIFf1w4EDZwAtU4EDPgUGrq+uz4UAxA78+7T1OXwwSKLXA6X0DdsGONyg6JdbKe6P/T8G1j3N//lj2wAvHQXZf6sojPOdHwT2ZsRpvdP9MvyA6FX4aEgz63V/KurN9N/PooKqgM83zx1sXsfW2y7IO1qa19gcQhEsWsWb1/78R+/ZoVT9jc617zeREeMSv4946mmCokJFcQ4sq+PWW8EcmcCMP6yN2X8aLyNYnMTzsbGYA5KN/ggWOTHzHV5NVUY60pw06F5sqvMco+kBjGAxFcGiFsFKeA6R4dvLa0hEnztO3YJq0xKizh9RarC6s59aFVEjWD2EopvU/meXng8mpQiW+DeVTsuJyDRZzvxn6e3G49/DtI/UJondTbAGDAAOPhh4/HHZiBXFrPSicFiM5lwO+M1v/KvrGDuUFUi+d6F8P+F76opkSHkRrMsuA/bZ+m3sO+V/QjbZhHSziCxo9US3nXwEZjxuUL4hmApeg1IEATlg6s0ggfIJVv0IoVgG+A2zV46Jvp+kJztP6SJWgsUMy7H7qR79INgiWLpxnUhZSYQyOa77U7tUtu/Y7EEo9zq/+G2Z+lhuT5fMYBH1034nUlRc3u69adDS+dIDhQEEWMnnF4tYlMSU8qeT/vY5MlrQFCJwcNBsYML3g9cByr+2QRGsCUeL12S9lLrn0Hv4UASBg1IEG0YKeXFARDUdRyWzPBrFjQKeIliqQ6i0Aab3XGmyzcrxjJvloxH8T//qX6Y3ah60vn8dQstUEanewGtMHKbsGQdhaQJ3DrJHsABgn4j1PACw8kPxavKOf/Xf6GnEG58P7KxFVAZM8gkrScXDoJYTldw7LKXPZrgmUqJdwBYsz37+EyIiRE21SSGPRC7G7i9+c4LjiPl1YxZe5/j0r/baLp10xkKUaxM1zYRvojkOAV8Eq2VI1nv1nr/lnsIfr7OcfKr6LBCZov04CTUy6qQ88uGPFK6tTZ1rj/gc41vmesIQGWAoS6tT0mdzQKqRRbByECIX3r1X6JBjVTEn1k+kS7ZoCmRkNms1WClWg8VUBNPkDPLmCU6weASraxGSDcMRG/kOrbY+ADWCVUMQlJosRYo2jXQyh44O8XmqLiMH0CDDZcNzMXLQAqxYpRon+bzXS6ub4DiijorEKgDI0LaOoLQvBNsqRx8+z/zBBmf7G/GVUgRFGs3kycDDf70VzfVt5mgLgUmuU8PXTdd8B4NbAvJ9yWDlBCsoRRAAlooeEXjFUBxXrnEKyEGH15ktmwbMY/Uhes2AjkS9N9gSwWIeTg69gDpqupytBqtkXHufO2m7B58/LxQNjHRsdo7lijHwGqNya1FoYtY8sGt6gZmRgxaIN00T4IONoJpgEq3QCVbHPKkqGGY81w8Dpv7J/BlJWgPl1zjp9wDHppcAe70i7juTYT7zX0Il7VZH/D1j6I3GUwSJRI3zcv8VgsWMg9xysb/pv1XrBJ0qR7DSA4SzYNIJMnXsvQuAlZ9atisg0vRtamj+sKYut4nFeCYMXFemAZvSHONi8qnSyXREWOpC0W5MJdLA+EOBTS+NfmwbGchXqH7pU+v0xgaa1EwEqxKyykUpEmkRoddVBbkIA+GpPdWIUElUSRO50GGrGXv9JDWFjb7v0C2YJL/hmoc5JoIEOUqohGCxOSS7QolgXfTTN3Dld0/Dtw7xMleGTBG1q3zcSWbE+E3EhNIBE0lBxMZ9E6W2I4AYX/KtnsNAE7hoNURVCXofrPlMgbSYA9KDZQQrnfWInfdMdc73zolFsBJpnHqg6Ds1brhnJygRLE/1sKQimJCNhqldjZMQf1SDxVMEFz0HAGjofLN0mpNGemPYAs8BvMNd5u/aNivi747ynZs9jBrB6iXoEayHLvwJPrnlx0AihXQqh3aPYKUz6fAaLADIDMKA+lVY1aYaJ90dwTIiiGDZohIAbjP0Nhw0SNR5Xf+HGeaNjBOXWoMl3q8Ug2TQg8kI1g5eoGvskC+DRRRK3iJDiqApigdIw9pkJNO2uz9jP6YNNBC/wHr9fMDy/CefEm5MJetKudsApBw1H/j2eUOkjnAYeogYUfL8h0WwIqQIrvntYCl327GB8gmWJ+MPIEahngZKmdQI1o47isaWl33vtyJiU2dI2TMQVOtpmCJY+j1Z6JKpx1FSONIDRRG5roLHjYAo5M+EoBRBJ+EXGtDxPybLbIr+8Oe4rkVIwW/qOVw4waoz1FNN+z9Wg5Vkqa4RDQIbSoZmAtjiKlE/tbYXJWydqabaKtsFRHY4wiLLqaZoilz03atR+zD1j+K7AuJ+tNX5AGo03YQd75IkWYfJmbToJf8ygKXKlQnrOQakCI7e2+8c1HHnIGCZyQA3iFKM2Fk7dARHx3vni+dfl2nXEaSYqoyl3v089kA5MJmekTl3h5xYBMeF6XzDrqdOsIp5YPGLSiPcxiYHp33jKiQc7553i/4I82zv/N/3xg9Xi1a5eXVZx5detpHfYZB+cnOZ4q//hrqKYHqAaFMAiPGyblhJpl1IoztC1AcQaZxO0iNYnpiRk8KP978P7tMHYFCjN/+kmphzK8nSGcUYQ+UtaYpglZQGvRosHsHysiGGT5ZRUFI7LM0v4y3Pq5OIlh5I59APUCNYvYQz9v29/MctYN+pz2Cd8YsAR9RgdXZ6YdxMxAhWqtkjWBnFOdTnCFaA8TVtmn9ZVxcwbhx7uHWsbYgCUQQrrxGsMLU5RrCuuw746IXXMKChNXi7pCGCVarBshguozyjyWQAFLz6tZE7+z8Lg2nQIXUjOl6Y5yfpRbBo8prlsV4+SQ7dwj+5RSUsNOHo0SmKotBAHUTG6VxG7ByP5AzbXr635e+HYY0YjS9tIM+1JnQBAIcdBjSmlkvRFR2GlNRxtnp/07XRI1jFLtl7zaT8acL2t4hi8W/OAw6eA3xzvno/hBl2xYJQSfMEZUr4/EbvHCPIxW91nZAQj5OGpKcR1g+T580/46qXHLwGK47iWhBKUTG2Px4ZJSfRio+A9y6ShMwtBotNkIDEGz8RPexomxn/0FaM+PxUqpwZhPXPsH+26MVwY2rQeqqwB8EUIfrqv+Z9xGllYcKAyer/5HALShFMNajKgibkVgKf/gX4+GrgQ24zFP373PTXwCZMLKE+hGwAIsXvvQsQGsFKNdqFXF75PtDupe9/9bB4DXs+wsaIKI4L0zH2fCFkG41g5VaI6CyvYS7NUTn5qo9JpJxL2SLUwxHw7Jx8ScVPgeF3SyYKYixsHOdvhO5oEazGcbIkopgD6lrE9mLnYszf6Hy2c68XH4tglSJaJISSyMisG4PIxcjBomwil2XRWCJYeg2W56yr3+w0TBgxS34/QI6xjmNWT51zV/TnsFrjbzejf5zlaoaLDjsPk0fz1I8iu/lTSCfl4JNKp+wE6+M/y/fJegxpWoZiMYGFb4iog+v2EsFa+KzReLTVYFEKoylrIJv1elHQgKyTEq7MVNqhJYIVRrASkmA1NADrrumlakWJYC1iAzsV3Ns8w2t+W7zqaXaAR7DKlSA1ef3YsnEHhe8iUWfxGlfoqS/t35Li9tJ3xKvDCFZYBCvuILv+GcAWV4v35USwupYAH3s9lL4ZMWJnAk00JtEaQEx2tnvAcP1+/GPLcUxyyDqJKnRKsZi4KY8No8SE3zASinEWZjwtfFr0+dGL3UlJK0qUZNIPhCLV3q9EP98gQ50bQlbJch7B8oiqrdlrVAzbWnzfDX4hl3GyR8d8ek9hCJOiWliKII9GPrmLSAWd8Q/g1R9YNwnEhKMFmWXqZz0CNy9rBIMw6QRgO03oJCziyVFuWithnR8D6zGiWIxAsABR9xuGz/4OvHkq8PaZ7Jk2EKJESnU48ufIREAJuZXhEaww3DdWOACe9QRT+L5athZpoRwLnrKrAwLwzWXPHSxSded5YhD5dhFV5tjj2fCIbam22JtDKPWVR7DptyOxHq92Sd0PRbC9/ZAkO33mqemV1pv4AxFdMxAsx4FoDt0+13+cZEYdtxrGyDmy0A6kB2D2EhF9Gz5wCUDCJ4TMEGGjFDpFNsqKD8Q+i1lPjKxR7F+vwSrmS4Ic9/7yZJx+xENYb512eQ2dpEwR5BEsEm9KNSOVFNfmf++w1hQEk6jPO+dEj2D1E9QIVk8gLN+4WJAKL14NFiGdSTAVQY9gFbLi781T5D6cJLaYIPoyvHuPSN8pemN7jxIsmgypMJTDYjQ3efaEriSYz4vvkPn8z7JvxPDtEQoiWK/9UC7LrQhvSJtqkPnLAJNCjkCwPrlaLvvSEyKwGYpBdTSFzvKbOOpGAvcCbnWtEPIIA6kI6kNDWG+KNQxyuSY4jjc4W8gT1Q1FURGMmybgJIARXu5nnAhW2yzRF+zuYXKZLkARB7Y0SUDmvtsUKGlbpppXVyfbHQCQvUZMhfv6xFbskoZElIa9NigRrJAUQTovm7BAlAgWIcxpstZ3pXc6Y4lMmbDPm0JtkOPhKeLVSQmiuvdrogdWJahrAb6dVSPWnOBRZJecayS1HZYiqBvL944BXjvBtGK082wcC+w/Pbh/U3eBp+UGYYzWdLduGLDV383rfuMdcd0JlUawHEekX1NN6Bsne3Mhzf2W6zx0M2DXx8yfmfCmV7tmI0SNXCmVZx1M9a9L+OzvIsU7zOkUZseQ8wmA8n0TSZEWuuEvZa3mjH8I0rSM2QmPTAVm/NN/7oAkY0/vDbTNAZ4/zK9WGsXZoUewSgSLOTUojfSNk8Xr8vf8zm2dYHEylTCkCJaEJkIcI/rY56TVTIQN/o+lJxeAZCMKRfGdBjWsAOCoEa/UALEPGkcAMbcUu2S2jJP0qwiWIlgJrDVqHn5/8k1IOCxFEAmZIshrsKgfaKoZi1eJFPfObJ1QaeYtKWzPW6XPYR9DjWD1BEKFC4rSA+KkFIJVajTM93P3UOA+zVBKNmLoADFAnn/XRcDiV0r9n3qUYAUNwpa0r8svF6+DtIyOLs/+qivMBj708vRthicHJ1KrvNqt7ApVLtx4fnVqESmRxMAUQQMZGuY1iLTJ8NqiOIAc9MqBTrD4fTfxh4gEimDpniRbg2bCJItnnKJ1yjECyFNYBKt1JvCa17OonDxskvumlJYouH+taNLJUUG/fyGgD1pYBIuaX3qgKPAXV60JjNnHW2p4FnUp9hUfiojzgMlmUYyoWJMR7I55wc1EXT5Re+C/dRyCFYZt/ikadu/2hEglCwM1Yh66ObDez8zrkAHTsqW5D1W1seBZQwPnAPGHOCi3jrDa+Mbbar88jqj3Q2qgcC5QKluyQfaU0sGVIIHqGHapRlXZ8IGJwEdeWl9QdGj0nvJ9WE1pSQHS0rdKAXv+o2RFUDp4ueBE2OQ82fRSUTPHwQ3/pW8Cr9L3D7AjntgZmPewumzbm6J9x1L6nzf2mggWOSLzrcKe6VoMLH1D24/37JHjkQta8BosuseWvSP2s/LD4Ai9nsas3/stW6rbJ+vUe8tx/NH4ZEYTUqMUQU6weIpgUo7HTlIEAL56GDKLJSGOU8z6I1hEsNLNaKrjdq82zthSQHVHbm84dKqIGsHqCfBBhMC9DLybfCKtNGlLpRLSwG+f7e2vzW/wNo7DoCZxnFc+2xb46MpeIlgB3uuSF0cdPH/g2eYZNpY8/TSwwgsyZFJZOUBwqVTrcdgXfsxLE8mtCFdtIpWbxa+K11Ve1CyoP1D9cOH5H7WXXJZpAYZsZjdegqI4lRAsPoi5LvCBZ7DUDY9uSFEEi0+WC572ydf7MGoPIeNNGLqFeDWp/FEKhQlhNVivHCcn17II1kix744IaUfdhZKEsymCFaJAaYnC0LOTrsvIehDTJOZoHs6594rxiYhFuZjwPeBQ7x55/1fAE7vY1zWleHJnQFyCRap7LVo/tDWOEL91qkHWPQbhoNnA/h/K/221WD1dYP3pX/zLDGpksTBmP/FK0c7expApwOST5f+jvyHfRyU/iSTwjTeBkZ6kbbLe/lv5UgIrldunc+B9irIsyhLxt9rij8GfL3lNyn2HjekkhgBUp3YurJk2R2aIebmeRUJp0rphHVSD1WZo06C3arEhWSeuCzlSTQSLHKP1w+3OcYoUUg22L1qVh9JoeDETV/Fkzqf/dn3ceaomHqJnEZjSlbl4UYITLBe+CJaThE/siBOslCGC5aQY4UoC2SUiHbzIxu3cSuDTa/w1WNllpRTqzpy4Ntf+/Dr/vRo1gkXjVD9FjWD1BOgh5uAPjluUKYKOWoOVziREf5q64aKruQ0tW2FIM6s56lpcigB1p0y7D0H9ROgh1B6ihHcXEiH89FNgt92A4z1nVl26CyXvSctU4MgY9UCDNxGvUQgWgcQxinlg8Mbhnv0hm8En0x7mTbNFaAqd5ddg8V4sxSzwgadwtNnvou8jWSd+QyJIAPDkbtHk43kfJbruNpVHLkDCCTfVCNmuDzeYyjF0HUcQ2Kh9b7oDgRHMEAXKxjGCSAzaQFlcck44iXBjyjRp657TcpAeLA2EJa8GrGioS8lzghVzwFrvZ8DofYCt/w6M9IjUUS6wQ0yPfNN41TBkymIKeloimD8fVCdna8DLEfT5qD2BPZ4Ddrqv4tOrGrgoEO81RV7xqJj8E/E6ag/7Ot3lGbeR3sgOrgjOtZWfQBjTlmMdNBvY/Cpg6+vkMh5pjdqzUMcuDwNb/hXY/5PwdW0ODf3ZedpzTFZCeOPew6N2FzVPgJlgbeDVdo09UN57egbIVK/+nfpqckGLUs+rvHks9n6L9TcdisO21lKMdQJqFAvjEao60H3gOB7BSmgEi+9j7AGSYOXbvQhWShJaUhHkNVmj9vLua0PmgS7Tnl0m5gDHweKVwjE9oLHD/1zYCJZeux/U3LkfoEawegKFLjTXa/1IuFeBCFaCarC4yAWpXDUHG7mOg4GNKzG40RsQilks9ES6RsRwPFUM8nzseK//s5Jh6U+NSiaBgvfMUeTqEa99U12qS9Z2pQeJyWrf96NJmQ/aSFzf3KrwhrS8Y7rrAl89ZEjNMSDVKJXYgGgkyVZjVEkEiw9ij+8o30dJqyyt6wl96J60yITEMyQGe5LZRLQ4XBf4ivXm4teBjmu7Pvy6lhtJoBz0cvGNt8vfFpDP/ozr/Z/RedkUKAFgwDo+p80VVwB1mRxaBixnE6zFSDFN+mULqzAkkrIJZRDCIlgmefkgJOuBXR8W99wu/wW+GSP9MwiOo9YNlJb3cASLX6fxh3pvDCpyOo7oAnZ+yPzZkCnAiB2jN+nuCXBVxErk4IdvJwg2iQqYYGqB0K2IaGpFiUq+d0FwBKtpPLDeT1VSxeXLd38m2rmY9rvOj4CB66iCHiYMDuhPaIpuBdUPh8HUViEImSGy3qxgIFiJtJiHink5Huu131RuEFSDxZUFOWjZ7l7GDK+L1UW7BhrSmjlhStax8cj1ZyjoqbBEsAqUItioOS09FUG9JotELwDtHqUUQe+YRLAYVrbVw5ciSCR/iz8CO7J+bSSSUfp+mvDS+EOkUFU/QI1g9QQGrYe3L90Maw1noW0lglWQObw+guXd/KkG8UAsfcu//40vFK/5Nhy/yz/QVNcKFLKY7wmdjaww+ycWqK7EJtMOGA3nQgG49FLxXp83qJEeAJmmMHjDYCnz3Z/xdtzuqQm64RGsCV7/mVwr0OEZaR0RjLVko2ogFjrDSY0tQpOvJEWQYSmrBbBFQ0xINYpBjmoHCExUIRD7viv6ba37U2D/j4GxhhB/53zRF4TAyQ4N3om0mQRVhWBlKotgmYzuWMf3noOv/uevwwqLYAHCGNAI1pFHAp0vni6aQTplEKywBtRRwSdfUvzSYarBUpxHFShWJuvDe+HEgsGI7Q6p8iDMvkO+p9rIKH2wEkmz9P4ah5fXBqInsMvDwNb/rN7+Ku1TVi3ESefc5w1g10ftn8+5G4ERrDA0jfcrLsZFJU4GkxNGH4/DBDUqOZdEvRhvXFceV3doUYp6qTefTvhJjdC7v3QVQTevKhfzdGM632SdiLxzcqmPXabvxm2rRB2mTBBpzROGfwF/imBCnTMTaakiyGuw+PGUFMEEqykzOMZ8KYLLfWOO+Cm1cXTEjtJWUASSvPFty78JIRrdAbTWd4B1T0Z/QY1g9RAmjZqB727PCtOVSTokRRAAVkwXndkfYalbBPZQppM5ZPMZYOnraPeCKgN6oA67hFINicFApMEmRK1IJ1giRRDewx/xlh25M9A8SYTBSTEujGCR58VUMxe4XaMquV2MEMFKDwK6FvqXR9k2CCYxizg1LdR/yRa5qxtmXk4YvBEw9U/idxo42bzOmH3FK02iJrLTtIYQYNAJCCeuNpnzMCTrKpdlrgR8ss5q1zlM5AIwEiwArOA4Jf83wfQM8ehtJeCpH0/vbVmHFUsTeAQ4TLGyJ0GiKBw9ISW8zk/My2f9B3j1hGgEC/DfR+MPAXa4w7xuX8CYfYCJht6GZSOEYMXpo1YRYoiJDN0CGL2XaMC8oSVFytQHKw7W+jYwfEd1WZQ2HoRKCJZp7PUt83637vh9Zt8uXr+41U6gyAFatDiM6fuTLeNqEaxiTkRzKNK1zQ1yW65gm6wLnsfoOAfNBg7+0n8uyTqcvP/NePtf52KXDV+AMUVQcUqm/DVYCY1gJQyqgkoEi60/7xFRF1i6fq7vWm2y9izzvTp4Y8/YM7T4SA8AhhiyX/oZagSrt6DXYJEHJJFGOiENzlQqwk+Uk4Qgk8oiVxDNhrPevZqJYV9XDNuABEgvToDsbmenIYJFhHO90+KdCxmibZ74Qlg6DFcP6pgX/TiJDNA6Qw5AUVIEm9cWTYA7FqjLKxK5gKpIxc8vKsIKmfcxRFDjglQWyVCle2bLv8p1Bkz2vICanDq/rlGiiyZQiuBXDwOdIeqI3QE+meviITaPKkeqyfPAasZjyZvIUkZM0B+wYduKibYa0MmRiTzzVJNiXoxfvAarL/VC2fYm/7LOBf5l1UZQ3eSM66Ib2PpYMuW3lZ1Xf0NYBGuPZ3rkNEJ/q2+8LSL+HOufAaz9ffP6bkhjYBMOXwkczsZTro6bGQJsH4N4TzzOrq47cN3gbX1qt53+bAVyvnFiYkM+RF5exyqvB+mi56XQkKnPFaX5GT/3xti3vVTJVTMga5Q8kQtKwQPU55DPaYm6YIcuHadpvKi/BXwRLCeZwZRJn0GkCGo1uE5SPTZvNFzs8kQy9BTBtGhNAgCtX8hrAYNjjO9XP2cP2230IQLvVf5szLpVnoeOYdupdeb9ADWC1Vsw3UAJfwSrlCIYBObNJon3fCGFrg5hqNQFZYh1LgbmPR7plCOhVDRqSE0hNb4AgrVwoRS9ILRnvX2lQnre+M5lFfDlA8ATnqcuagTr9R8Dj24p3ts8iBwz/yVeZ90uasVWfRpu/FNOd+tnctmil4SQienaRYWJnMVNEbShZWtVyKJcUBSKjG+Tch6d8/wnzdsC8YvfS/vIiN/nmX1lg2Mb9FSVNb5V3jE5+G+kExBSsgxLEQQMnk+v4JjO2dpHTPtO1ZRF18nRG16vvnyHbGjMRS5e/j5w5wDpCc4MVRtl9jbqh4l+V05KpK81juuZST7VIOTlbSgngjV6H3Nj9tUZphoWjmrUHurYb7p/WRjBGjLFHPG3qfGRMR0H6QEqKdrmn8CaR3rHaYnXpmHAJEHWTPfgRhcGb6sTrPcuNMjpszGCImtbXuN3eGx0HjBil2jnTJhwtHj97NoIESz63BLBAsQ4XGiXvbp4HywufFH6amyMDCslCEsRbP1MpjMS6VaERAwpguRgJKEcfoyEFvGa+Y/gFEG+39I567ata68XBNQsgRn/8PZh+N57vRjwPPRN1AhWb8FW/JhII52URlc6491oQzYz72f49lI1KVlfqlfK5jPIdoh0o8AI1rP7CSWfoL41cUBGr+lBoAc34FiPPAJ8rDnyGjOeIRm3T0/JoPMQRrD0gkpANIKMinwbMPNG8X7hc8HrjvIiTdxIfnx7+3lEhYlgxTGggwb8Xf4X/3xMoPuAiFUpasOO3emlT771M3VbMuDTg9XO8HGQXQYsetE7zvzgdUltimBKGYuLuhZgg7PFe93YeNcj9GEpgoA/TZCiGnEFPLqTYM3wlMxe/5HoC5RbCbzsGTjJBumxfNar1dvzxe4xeitBy5bAkTmRvnbwnHhy1ZWAR6PTg9XP3Ih9sPi1HLGjfb2+iG3+Vfk+JhwN7BWgaBlHACgqBpnS2srsN1bXAuz8oH95pSmCgLiPt/mnkCXf/Mry9rHro36nU1j6te74mX65UKoluK50AjkOsMNdwLdaxXjPa5SGbSeaO8dV9eQCHdRM2BehSoux/7FtLZ+z31N3GHNCQnaeorzHvj93pPEMjtK+QgjW+ENZPbdB5GLxS6pNwFMEoWc8eJ/z9YtZmfLIUwTX+i7QNEH8Xz8SqnS8OP7QARSpC6kXbJlq+I7ab9pX2knERI1g9RZMgwJFsHiKIBGsSZZGsTveIxWRnGSJYOUKaXS1RyBYy98Xr1FkuKMgiGDRwBAwAJ94InDEEeqyA7d4wNu+QsMrVDo96Sc3URTNaECj1K0oICO5oEchUP0IVhwDWo+cUL0UUB0pb34MItqmur31TheveqPi7HKRl3/4MqB5rfKOT/3kgPB7SjcGNr2kvGPqGG2TJ/YQdK+So0BPnywRrIBedGJF8UL1DdVMeSMvJ3+O3KIktDxaritVAtGaAX9d4CSA8V6fnAaN2C+bhkjTN7+/e1qco1KsfYx4HbZd+ftwHGDYVsB2t5o/r6ZzIQiVSPuP3d+/rNBenXNP1gPf/BIYd0B524/aHdjhdv+yINCYZ6uvKnRCSUdLpOTcM9TLLNnybyKiUQ5M/Qf1ZyORErVFNFbqdgAfn/VMCiIkvA+WEvHSBLEIKw3y9yYSzR3NTWtJwuQa+mAV2g0RrIynXJ0V5/UpI3Z6zdaoPSFl51lU0UmKsT49WBBsfk7ed33v+hPx0h9OiNazzfe92fU6ohPYO6jtR99FjWD1FkwytEaZdu9Gsw2mmnciVxD7fePzqch2iAfZmiLoutLAL1cwQAcNNqZ0vqD+PxZMHPmZfDbXrCA9y0kAQzYNX0+v04oieEGKT7wjehiIROUN17+SCJap7iYOMdI9urzYvlrGCB2jFMHq9B871Sg8qzmtvUGcfmZREOYF5r//hueo0seVoORssD0LARMSCY34mj97KYJR70GSB9avcSWg9JdJJ8pleWYMcoMiTp3j1xUjdhKvetPj9tnRIli8tq4S6fPewhEdwB7PVr4fEhvQEdfwKxeVRsr2fl393y2G1zr1JKj/3C4Pyya8NhDBIieTjg9/p0awODKDhAT/Oif6t4sKU+sQU4qg8r8296WahO3VtJYc03b0elpRixGlDxabl4tsDPyCCZ+NZc5MQlgEy3H8ESylvt/112ARGSp0+Pev12xtcrFsEVRyBnoEq9gpo3TcWeZ95zH527HtiOu8TIuQ54xL1bN9ABCO157uPVgl1AhWb8GaIpgq1VEBLEUwCsGadCJ2Wk+kpi1tHYouT0bQGsFaxvr5mFTJykF2mTCAE4aBwQknWEYyWD9ChM8rkV9e44jwdQB/CliUOh9qWlnolN3Ow6JlRKLeOFkMgjyKUInggOm+GmBR8zNur/1uPB2qWsYIkRSqu6FIjG4E1bX4Vfayy+3GUlTs9bJ87ySBhS8Ac01pOK4kWBtfCGz8q8qOy2HqCcdro4KIHBGs1s/V5aUIVkSFRKqJrKQnmA5KERy1uyCkgKhLXCmkhJWC9J4ybvszaBypN4x9YYqegGb49EOClayvjnEVJvtNRLZa2O1JYM2j5P+VOqdapvrTpKrRzqNaoPEsiiz+9neIOsZNLzV//t4FCBRUqBQmR4P+bOj/m7YpdABtX8i5m5oEJ3gfKe/e5ZkqQw0pcYC5MXYYwaL/i10QJM7xOw0VFcG06uzyEayUVgtdL53O1AQ4kQRm/lOk8edXif8Tabmd/rx2LQp3ZO50v/p/T/ca7CbUCFZvwSpykUY6JQlIIknSn5YBmt/MUy7DyKOeBgAccfUdyNoIllsEZvxTleI2paqVg3mP2musyHMSYAB2abbeFUedCWx/u2hwGBe8ZqhpjWjbEFlq2UpMYGMMXiUdNNHx9MCNzgvehiJY2aXienCCVUkhZ8tW6v/jDo5nyOrGdv3w8s/FhgbPw0nNo2ng1utMMi1+ufjc8soJFvf8OkkhgvLcgeo6i14C/pMAXvPuu3EHm50G5cKULkvXfsL3ZSNGE+g3efm76nIiWGEpVWQs0H1WTYJFEaxkvSTOD7FUIO6wmHVb9Y67uoIMk3SzkO3m0JufBm0PoKL+Yv0eAQTrm18FC4qUg1G7AZuzXoLVcCZ84031/55Kb4wCGlOipMiP3Vc02Q3KrPjyv+K1xyKM2tgeFtHiIBVnWieRlteB7DOK8AHABv/H3v/Cvz8+/0chWMlGkXZIKYI6lBTBlNyeIktDWeufhBbBctKSOJba62i0odTXq9F+zmERrKGa82A1cb7VCFYPYr8pDwEA9t7kUXsNlhbBchKGCNZoS38ZJ4HGgTK9bNnSLNJpoF4Ppsy6HXj1eOAD5kEiKfNKseoTu8EWIYKl4+Cp95trNaIgxaIA1Iw5DKRgNnQL4Ij2aHU+pYjMCvHd61qADX8Zcm5scnHzKsGK25meQx+YYlxrAT4kOLIvVjVBkZPPbxZ9rkoRLC31r85EsFb4iVhc8AlkySvmdWiCb/OiRNWqPyOYCBZNymGNjG2RC9dLERy8Ucj23v1FtZvVErgBWA1WvTnt+J1zqnesrwMosyDZ5E9tGv/NePsiR8bXEUHS/w2jq9emQNlvFQRxdHCHX18iWCQ4FDb2RMWyad6bXjJRdfss6FrrrWmcNFPcoxosNi9zp8daBhXbw5hohoms6M75VKPnIDeo9aUHaHWYafW7zLhOtVX0GqwEW59SG52EOh/qQh76+RWziC/yUiNYNcTENuu8CvcWB1tNfN0Spk6KCBYjWKWHkT8UATLBTey+v/z6rdHQAJ/seSly0MV6AD0Xo8mgDZQet66lX1UpjSCm0V+uccsnzcjKZN6DHSf9Ij1AGL2tM0U9SxRCyHPyi1mgw1Oz2+QSYOx+0Y9twqGLgUkniPd6RCsMI3YUqV3rniYIZiWCGzbQfTD7duDd89gEpeWHWlMEK6zBUq695V785Gr1/3JJvvUcNII18ybgGe93D7vm9DxQtLWEiMpiRKwyXl1PVQmWZ8gm6uITgBr8oBTVVJNQFdz3XfnZgHXi7cvWPPzrgGrWGfYmeJpynPYb3Y0x3wCOLFSvLozKF3orkhElRZBQ8JwgNKYH9ITyYdAGIedh+P66/ZRs8JwnhgjWhr9U7xOSaVeOoUms+/pmeZ/TM+Qkgc2vYtsktVdNmr7QGW1eUk8q5vp9E/2zcmx1gLEGK+mLYJUeME4y1jsdmGYILQNo0HjBSlMPvrw2IFQLi17wTsJSKxVa2K/ij987Vbwpm2DF7JsFyGhK/ch429UN89L9cmrkzAY+cBZzMoK1wVmVF6PXtQBbXQtMPjW8D4zvvBJ+pbzNfld5kTYH/36tM+T9on9vShF0Pc9csSAISUUiIIg2aeviJt1GsLxn4RXWUDQKuR++g/96RW4+6z1PlIpC3tZqgGowkvVm9bMgDN64euexuoB6/Iz1VN4Gbyxkqxe9GH2c2PBc4INLAvsPrvbImSbCHsBeLwMrPqje/vjY1ZciWEAZRnQUdFMMYKMLgPcDampjpQiuUtfh64bVDzoJIdphPQ/Db5zU7KH6kazdiDa3DZmi1tcn0v596oSweYJ5/fcvkufMv5cewaKsq+1vA144zHPgrR6EKS5qBKu3YCVYag1WCdQgcOB6gQ97IgG0tABLPGfl/iYbp+RxqbIHjFTBTCo9gDzv9i99Hx1wADBxInDVVXLZkCavXqPcvjjl1OpMPkU0oV3npHjbJRtFSlSxK74xXswBn/xZvK+m0tfgDauzn/XPrM5+CMp3dOzNHlNNwvgv5kT9nqkhcXdh+A7SYQBUP5JnErkgRCGQpV4mDJxg7f2aXW2RHBZE5OKS8CA4jtf2RBtbRuwMLAxQgztsWfXHo9UBw7cDvp1Xa0TWOFT8RcX6p4uG66Z6j68LxuzTO8cdtk11G2cPniLffx2el+6KYG1yofi71QEGG9SFw1QEOaZ5NVVUKsC3pXR4QPQ+5PVOUWCa6wauA7RsDaxxuPifK/DpjgQnpd4niYy/n6hOsPi8YSJkSKj2a0qrvaLPSu1YOsqQaV89CFktRbC3YPJsOEnA0SJYBDKGIqj0HMBaWtx1l2EF8mgsqXJvAcrRNXWkB+SD/vEffR898ADwhz8A27O67aKbEMWhlMoUF+XIeTeMEg0u0zFJUqpJXNfs8vhS3l1LRLpmpZGZ/gI+ODsJmfKgOx1KDYm9+4pS2apBsA6eIyR2beCTyqaXVt87S/tf8DTwkFa3ECWClUgbyJlXgwWI5ri257DUg60N2P1pYPdnop1zJHjH138j/VkkSWNASDtnBndPHczqgErFVTJDgH3fqZ7DpT+iee3gSEF/QSIJ7PMmMPH4+MZ6X8Rw1vza6ADoZhN1v+nmNgD6XBSl7pfmE55yxzNhpvw6umNky7+JPxv2fgVY32uYzAkU1QwTgmqq+LLS+5T6uZP2k03PTi2BlJdpjqTPGsaI19zKbopu9n18Pb91X4BJMtdJ2AkWRUUo737cQSIFzIB9WR1sXdtb/hVyrf5l1UCYAUyek4BUoBdeAM7zBPi2n/yi6DFVroGRzADrn2XukF5tpJqAhc8AS9+IJlXLQf2MNqmiDHhfRtQIFk0cBa1fVjUIVuM4IbFrA59Axh5gX69c0CQ2+w5/ClGUaBn1WuEodEWLgBLByrcDI3cBGmKmwwaeF9Uwat51HtXd7wPRwJLQW9GFGmrojxi6ObD19eHpZ/0Bez4n33cu9JOdaiqcmjBofXNNLx9H93g22PnTPEk0TS7Vy8eowbJhnROj9/rihEhvPu8k1c8TGT/BctIB6xsImd5ri66NXouV5iUaNZGLGnoS1ghWEvVpQ9F503hgx3uB7bzGdDvdB0z1R4IA4ECuOP2IwculP4SEYoW1GCUDOGAwGratOXUvuwxo/QIAcNEFXXBvcTBp1IzKvbebXV6exHtccKPYJz5gAUlxv+Op+Jh63ayOUCYgR0h7Owl7/w66ryiSlahSiuCIndk5/X97dx4nV1Xnffzz6046e0JCVrYEEiAJS1gCISwhIaCIKKDIOmBUHEEfX6ID+vDoo48Pi4MI6jiiA6JsggwjPgLDhGXYIYqELcguJBBCQgJCyEq6c54/zr3pU7dubd23qrqqv+/Xq15Vdesup6pPV93fPef8TuJHJMw6Vs58Q5Uq1uWknJbM1pQughtXlpf1cUuAldHcdznik4wowIqv4IZz3gyb2vm33TaRHl9EeqdNq/O7K1c6Fjor4W/U4EnF192wIjeQyvl9q8Epdni8AVF3wY8s8F37zRLj9lICptYiAVja+m5zohdKcgxWIsmFf1L6fRx2T2XrNwAFWPVSKItgSx8G9y/QwrT9sWWNK4on65045hX/4LHElZC4xSSpvZuZlsppYWhpS09yccc0uHVHeO9ZuKkGY2yyFnaBKjaHUWify/x93FUz61TgPVWy9dZtSh+TmAyw4pbXrLJnjTksKEN0cWHdMt8vf8W9na91tYtqMcVamsrpKpfWgrVhZXnzlm3zMX8/cmbpdSs14WR/H//tjnrWd2kaPAF2+R8wPRhrePSLcPBN2ZdBpJAjH4dPvlZ6Pam9tmH5F7OqMQ9jOZLd2Itp/6BIgFXjCXPjC1kjD/DJqZKSAdSBN+T+Hvcbmfv7ai3pyZRSk1wkugi2VPAZgp+cfsv6zRFgNUEbc4NK7SLo+7YO6tf9K8t/ufcVdnghmnD0lSt8N7m4km96L32j7qZrjrcvNvi2pS0/QxvAujf8/eLfdq8M9RJmDiy3i2Dyc6pGSvSeKGyV7NgI7y4skOwh+nzibiLx3ChDp+Sv212uw7fgPvXNlHJUIVtXsR+QvmVkv2xpgw9e9hck4rlnOtaWl8FyzBw4YW116tt+v4RpF3X+7fqP9DeA6YnU94XGiIlUSzOMW2pWLW09Z6xOsht7yfXDYKLGLVhh+YZMLL5qn0G5AVSfwYkxWH1zE3Mk9w/+tzIMQOMeUYXStafto5foIbW5F0q9gt1SvAWrAtPnTGL0sGCeqw3B40KBVHcDrM0bfNBQ7OQxdXB+4P3nuleGegmvVJWb9jo5Fq23JLkIdayH5fekv9YStGCtW9qZyrycyZ8rEWdhWv1CfheVtAHQ1VZOcpaWvoCDO4I61F7BvGXVCuZb+kD/bkyULSK9y77xUIfE6eiQOl6AqbSbX6EWrFqcYlfS2pPs8pfWQtV3MBxyC3zy1WhBMjlMogUrvqBcrItguWX8yAJ/AbVJLoQowKqXtDFYLb4Fa+iAKszZ0RG0inUkBo7O+JW/X/UovLcI3k1JjFHWMTaUTkCQll469OatXTt2vYVfwuUGWAO3hQHbdj7vLV0EQx3rC78WtmC985fO5eW00lRiTNQ14Y7d/cTHodGzsj1WOcp5f8kfxecv9S3DvTFIF5HGlwxkspyypFLltEIdEUzjEQYTNe8iWOFpfKkAC/wk8Vvmw0oEWP3HpHfpj7usL74xOk64TpllHHkAHP1c05wLKcCql7RB7tEYrAmjlgCw907Pdu8YB9/c+Tgc0J7MzBN/CTx6KtyxZ3pijHJ0rC8zwCpvouGGEn4J7/gP5W8XDuLtLV0EQx++V/i1cAxW+Plm1T877u8/fK/01+fen81xCimUnbCcxC6WuIL4ZDRXWW+sQyLSuLa0gCROR9NO4mslvCBdqHtbnNEZ6pvkopzfw53Pgolf9I/DJGTWWno+NRcEWFvt6Xso5Pxtor/fxqiX1Ii9o313oQWrySjAqpciY7AA3O/H8cRPPtW9Y4TNrO1r/RiTBfN8kovwSneyRaurOjaUnsOnUAvWwO2zKUO9xF+ke14AQ3ctf7vwKk9van2YGWXD3LjSzx+y4+n568Rf/PceXp0yTPoS7H+lT74Q2v178Ol3YMyh6dtl5dCU1trRZR7z/UWdj8OuvfXKuiUi0hVbxiwnTsLrmYb+1as7HxcKksKgKudxrVuwyghe9rscZlzhH8fzVoF/b6Vai8ILkO894+9zughGPXbi5GkT/zF/HY3BkpoqNAYrjvo3b8zgnzOo1O1rYcNyeO0a/3xIkHo0rZvWh3+v/HDldBFs7e8HRbpEs3Oj97ndcZ4PCMafWNl265Z2Pi5ngtlmseOpsOf5sH6Zn48qrUU3rEtdqY+ltLTCpDP8D8GwYLLfPgOgX3Kgb5WMS8wBNfPa8rb7MJhqIWyRrkYCEBGRqumBLVihggFW2C2wUBfBHtKCFQqTNllr6Qu7fQZAv0Qm3WIJLLbMi1VhFsEm1DvfdU9QaB6seHnHxu5/wYQT6K1fnttNcOAOnY93OMHfjw1aCtYUSWW7YZXP/JZUToC11TTY+I4/sQbfXXDJv+dehR89Cz72JBz9UvF99STDJsNJG3ID13KErXm9rXtXGAykBVhh14W4vsz6f9Upy1HPBMet4TQBh94Gu0fjvnb/Hgzaofj6sXCerqe/0/m4Sfqui0gvMelLMOE02O3bucvrOQYrJ2goEMDkZA6sY5r2bk0j0lLmXKNFWhfHn5xYNZFNsBdTgFUvBbsIRpUyixastuFwZJSwYsE/wO1B17XwH2TgNr6LXthVcP6+hYOs+fvC/On5yzvWlz45ja+EbIrm3Fr+3/DIifDW/M51dvu2b5YeunPe5k0nblafdGbvC7D6DO58XKoFa10UYI2ZU52yhFcB33u6OsdI09IHppwLE8+AKd8of7swwHr58s7HvakVVEQaX98hcOC1nb0GJp7h75PTOtRSOa1Q4flZmNq81i1Y3flNLPccc+evJLYLhzYkf7vj+bCC39S37qy4aM1AAVa9pF6x7xyD5ecayOAKwPC90rOSJVvHWtpg5UO5y16/mVTrXk9fvv6t0ima45PqeFLjtMmNCyUdaGbxBIG9Sd8gwEr7IQqDhbgFKwzKqmXjO9U/RqjvEJhxZXnzX8U2t6cvr+e4BRGR7tr/CvjMB/U9D9j++OBJGV0EBwbZgK0OadqPesZP6F7xtmWWb4/v5T4v1rpYjXkjG5QCrHopMg/WFlm0aJjB8Gkph0qciKXNgVWqi2J7YuzWujdKd3HqGwV7aZMNA5zieuc8OrUa89OTtA3vfNy+Lv/1sLvb0j/44KoWVwQrHUdXD4XmiMk6hb2ISC2Z5V58q4fdg27XhcY4hRfAw4uBNU/Tjp9Tc8Q+lW9X7u9p8jMYvFPhdUdXqZdJA9LlznopNAYrp+k1oy5jQ6fAyodzlyWDp80pAVZaELg5mOPp0VNh1i1+TNbGVbBpde5Jc5q4BSLuIphVBsNG9YlXCgebzS4cgzU5pXtc8gJDueOTuurIJ/ykxxNOqe5xsnDQDXDL6NwpD6aeB/1H1q9MIiLNIKeHUaEWrCDoyAmwGii5QxwAHnB1ZRlo084NZ/0RPni5zDFdvYMCrHopkaYdyG7AemvKPAfJf5ChU/O7CKb9E218u/Px0j/AqsfggaM750AodQU92YKVnJOrtxkysd4lqJ/wB2rgdimvJ36cBmxT3fKM2LtzDo+erm0rP0/X3Qd1Lpt2QZ0KIyLSRHJap4p0edv1a/DiT3PHC4cXjeuaqKMM8W/sTp8tb/2tD+h8vM+Pc3s+bffJ7MrVJBRg1UvaP16YRRCyS3qQ1tUvr+na5a+TVsYNK3Kfr32tM7iC0uNI8lqwUlrOpPcplX0SoO+w0uv0JqMOxGd3cjD+lJ5/tVREpBG4AmNckzat9vfhxfCcC4c9fH7PSrowntxBTjbByWdnXZqmo1/keklrnbKW6nQRTO2OmFi2ZbK/cLuUlq81i3OfJ1us+lbYgtXbuwiKV6if+8f/2nnVrFTd6pWiCyNpyWJERKRy8blPMgV50vrl/j7sXTE8GAtV6RxVNVdBCGAtDfB+ehYFWPWSGmAlWrCySrmc1oKV1zoVnaiF2XPSruI8dFxi34l/uFItWK3R+45bsNLGfonEhk2FkTP8Y7V25htzmL+f/vP6lkNEpFkM2h4OfwBm/Kr4evE8luHF6EYKQtTroar06dZLWvAUzoMF6WOnuiItwBo7N/d53IK1QxBghZPgFpI86S01Bqul1b/3ly/3yTGe/nbx9UV2PRu2O86n75Vch94Gx7zuTwhERCQbo2eVHqaxJcBq0NTkmgy4qhRg1Uo8wW6s0LiocHlaF72u6BMEcyMPhM+shnFH5q4TD3IcGkxG/MErpfedDLDK6cbVsd6P23rkhNLrSnObdhFM+0HxdQZP8Nkq1UUwX5+BCq5EROohHlOe1Xj5Whkwzt9XuwVryjnV3X8PpwCrVpKTwKWOi0rMg5VVC1aYHGCfH/sT1eSVi0lnwkntMDBIhf38JbDk3+Hvz3Qu2+4YGLZb5/NkgNV/bPnl2riq/HWlOe12Huz2P+tdChERkcoc8BvY64cwYnru8vEnwVYp84/2FLWaL3HP82tznB5KWQRrZdB4H+hset8/T2uazZsHq4zMauUYOtnfT/sBjNw//fhmflmy6+Ij0aSrpzjfjXDpH/3z0bPh7fth3dLc9QdWkEr7vUXlrysiIiLSUwwYA1PPzV9+0I21L0sl4nFiLiV7dJayOodtUAqwaioY/FhOgJVVF8Gxc2HufTDq4OBYBQZiFvuHCMdkzb0Xbh4Ki76bTRmPfKKzuV1EREREqiA+/6tygNXLqYtgLU04xd/v8X8KB1jV6CIIMGZ2erfEvDIUCLzc5tyU6ma+VS4Ln17lJ3gdunM2+xMRERGRFHELVsr0PJIZBVi1tO+/wPHvwh7fKxBgteQOOsyqBSsLmz+E1S/kLgvHa3WVteYnABERERGR7JlasGpBXQRrqaUV2oZHjwtkEQz1pP6rNwVjsyad6e/btspd52NPVb5f19HVEomIiIhIRRRg1YJasOqlUBfBULVbsPb4Psy9P3/5kQvh2Dd8hpw0cYtTMjXp8DKz5kz5ZrklFBEREZGszLgKxsyBIbuWXle6TAFWvcTBVDhBXbxszGH+PssxWGn2+C6MOTR/+Yh9YOB2sNO8AsFQdNWjdVDXjjvtwq5tJyIiIiJdN3KGT1TW2qATJDcIBVj1EgdTYTfAePzVlpasHvDn2bA8f9nmdn/fJwiwkvNAFKPZw0VERESkSfWAM/heakuANSB/WZyq3bXXtkxpJn4hZWGUeSYMsObML3+fhTIVioiIiIg0OAVY9bKli2DQDTBuwWrpQQHW6FlwbDCZ8NYzYOp5/nGc5GLUQcoEKCIiIiKCsgjWX1o2wQHb+vvWgfmv1UP/MZ2PP/qnzsdtUVDVvr7yfU67CPqNguF7datoIiIiItIDHXQTDN2l3qWoCwVY9RKPY7KUP8E+P/KJJsYeXtsyFdLSB3b6vM86E+o/MnrQhVSfu53X7WKJiIiISA81/oR6l6BuFGDVSzz/U1rChz6DYNIXa1ueUg64Kn/ZiP38vdKui4iIiIgAGoNVPwPG+fuJn69vObqjbRic4mDCSfUuiYiIiIhIj1DzAMvMvmxmr5nZBjNbaGaHlFjfzOxsM3vBzDaa2Vtm9s+1Km/V9BsBJ30Ik/+p3iUREREREZGM1LSLoJmdCPwU+DLwcHT/X2Y21Tn3eoHNLgWOBs4FFgHDgHE1KG71tfStdwlERERERCRDtR6D9Q3gaufcldHzr5rZkcBZQF7WAzPbFfgqsKdz7vngpSerXtJaOvg/NPmuiIiIiEgTqFkXQTNrA/YF7kq8dBdwYIHNjgFeBY40s1fNbLGZXWNmo6tY1Nrb4dOw/bH1LoWIiIiIiHRTLcdgjQRagRWJ5SuAsQW22QkYD5wEzANOAyYDt5lZXtnN7B/N7HEze3zlypVZlVtERERERKQs9cgimJw0yVKWxVqAfsBpzrkHnXMP4YOs/YH98nbs3BXOuenOuemjRo3KsswiIiIiIiIl1TLAWgV0kN9aNZr8Vq3YW0C7c+6lYNnLQDuwQ+YlFBERERER6YaaBVjOuQ+BhcARiZeOAB4tsNkjQB8zmxgs2wmfnGNJ5oUUERERERHphlp3EbwMmGdmZ5jZFDP7KbAN8EsAM/uBmf13sP49wBPAr81sbzPbG/g18Gfg8RqXXUREREREpKiapml3zt1kZlsD38HPZfUscJRzLm6NGgdMDNbfbGZHA/8CPAisB+4GvuGc21zLsouIiIiIiJRizhXKL9HYpk+f7h5/XI1cIiIiIiKSPTNb6JybnlxejyyCIiIiIiIiTUkBloiIiIiISEYUYImIiIiIiGREAZaIiIiIiEhGFGCJiIiIiIhkRAGWiIiIiIhIRhRgiYiIiIiIZEQBloiIiIiISEaadqJhM1sJLKl3OXqQkcCqehdCGobqi1RC9UUqofoilVKdkUrUsr6Md86NSi5s2gBLcpnZ42kzTYukUX2RSqi+SCVUX6RSqjNSiZ5QX9RFUEREREREJCMKsERERERERDKiAKv3uKLeBZCGovoilVB9kUqovkilVGekEnWvLxqDJSIiIiIikhG1YImIiIiIiGREAZaIiIiIiEhGFGCJiIiIiIhkRAFWAzCzVjM738xeM7MN0f0FZtanwPpXmJkzs3MSy8ea2XVmttzM1prZ02Z2amKd4dE670e368xsqyq+PclYOfXFzK6O6kh4+1NiP/3M7GdmtiqqL7ea2XaJdVRfGlwW9cXMRkR15QUzW29mb5jZL8xs68SxVF+aQFbfMcG6Zmbzo3WOT7ymOtPgsqwvZra/md1tZmvM7AMze9TMRgavq740uAzPYep6zpt6gi49zreArwCfBRYBewLXABuB88MVox+n/YBlKfu5FhgBHAOsBI4DrjOzN5xzD0br3ADsAHwMcMCvgOuAT2T7lqSKyq0v9wCnBc8/TOznJ/i6cjLwDnAZcLuZ7euc64jWUX1pfFnUl22AbYFvAs9Fjy8HbgQ+Eqyn+tIcsvqOif0T0FHgNdWZxpdJfTGzGcCdwCXA16PXdwc2BaupvjS+rL5f6nvO65zTrYffgNuBaxLLrgFuTywbD7wJTAEWA+ckXl8DfC6xbEm8XrSdAw4KXj84WrZrvT8H3bKrL8DVyfqTWH8Y/svq1GDZ9sBm4KOqL81zy6K+FNjvUVF9Gar60ly3LOsMMB14Axgd1YXjg9dUZ5rgllV9AR4FLizyuupLE9wyrC91PedVF8HG8DAwx8wmA5jZVOAw4I54hajp9EbgAufc80X2c4KZbW1mLWZ2DDAKfxUAYCa+Qj4abPMIsBY4MMP3I9VVsr5EDjazt83sJTO70sxGB6/tC/QF7ooXOOfeAJ6nsy6ovjSHLOpLmqH4K47roueqL80jkzpjZkPwv1tfcs69nXIc1Znm0O36Ej2eCbxlZg+b2Qoze8jM5gbbq740h6x+k+p6zqsugo3hYmAI8JyZdeD/bhc65y4P1vk+8I5z7hdF9nMC8DtgFdCOP/k52Tn3VPT6WGCli8J4AOecM7O3o9ekMZRTX+YDtwCvAROAC4B7o+5/G/F/7w58XQmtoLMuqL40hyzqS46oD/v5wJXOufZosepL88iqzvwSmO+cS544xVRnmkMW9WWnaL3vA+cCTwKfAe6M1nka1ZdmkdX3S13PeRVgNYYTgdOBU4C/AnsBPzWz15xzV5nZocC8aHkxFwAjgcPxFe5Y4FozmxV9OYFvGk2yAsulZypaXwCcc78L1l9kZgvxTecfx39pFZKsC6ovjS/T+mJmg4Db8N2Vv5k4lupLc+h2nTGz04Bp+C6CxajONL4svmPiHlf/5pz7dfT4STObDZwJnBUtU31pfFn9JtX1nFcBVmO4BPhRUKEWmdl44DzgKmAOMA7fdB5v0wpcbGZnO+e2M7OJwFeBvYKK9bSZHRItPwNYDow2M4sjevM7HIVvuZDGUKq+5HHOLTOzpcDO0aLl+Do0Ej84NDYaeDBYR/Wl8WVRXwAws8F0duM42jm3IXhZ9aV5ZFFn5gJTgTXB7xbATWa2wDl3MKozzSKL+vJWdP9cYtXn8UkKQPWlWXS7vvSEc16NwWoMA8nPsNRB59/vcnyWlb2C2zLgx/gfsXgf8XaF9rMAGIzvlxqbCQwit4+q9Gyl6kse82lut6XzR2whPjPTEcE62+EHhcZ1QfWlOWRRX+LxNPPxgflRzrk1ic1UX5pHFnXm2+T/bgGcg796DaozzSKL+rIYf16za2LVXfAtF6D60iyyqC/1P+ftbpYM3ap/w2dLWYpv+pyATzW5Eri0yDaLCbII4hMWvIxvfdgfmIhPjbsZ+ESw3n/h02IeEFW0RcBt9f4MdMuuvkRfKD+K/r4TgNnRF81SYEiwn1/gu3kdDuwN3Ac8BbSqvjTPLYv6gu8vvwDfnWNnfP/1+Nam+tJct6y+Y1L2m5NFUHWmOW4Z/iadDbyPH3s1Cfhf+AuB01RfmueW0W9S3c956/5B6lZWZRuCn5NoCbAeeBW4COhfZJvF5Kdp3xn4Pb7pcy3wNPDZxDojgOuB1dHtemCren8GumVXX4AB+LlE3sanYl8SfaFtn9hPf+Bn+Dmw1uHH1STXUX1p8FsW9SX6gXMFbrNVX5rrltV3TMp+0wIs1ZkGv2VZX/DjOl+PzmEeAw5XfWmuW4bnMHU957XoACIiIiIiItJNGoMlIiIiIiKSEQVYIiIiIiIiGVGAJSIiIiIikhEFWCIiIiIiIhlRgCUiIiIiIpIRBVgiIiIiIiIZUYAlIiINwcwmmJkzs+n1LguAmc0zszVd3PZQM3vJzFqzLlcZxx5tZivNbLtaH1tEpDdQgCUiIluY2ZlmttbM2oJlbWa2zswWJdbdOQp4Dqt9SWvLzBab2TkZ7vIS4ELnXEfiOG1R8POBmQ3L8HhbOOfeBq4Fvl+N/YuI9HYKsEREJHQvMBDYP1g2A3gf2MXMRgXLZwMbgUdrVromYGYHApOBm1NePhZ4DfgTcEoVi/Eb4FQzG1HFY4iI9EoKsEREZAvn3EvAMmBOsHgOcA/wOD6oCpcvcM5tMLMjzewhM/u7mb1rZnea2ZR4RTNbYGaXhscys6Fmtt7Mjouet5nZxWa2NGpF+4uZfbRYec1sqpn9Z9Ti87aZ3WhmY4PXrzaz283sa2b2ZlS+35jZwGCdQWZ2rZmtMbMVZnZetM3V0ev3A+OBS6IWO5cow1wzezYq831mtmOJj/kU4B7n3LqU174AXIdvYfpCyvtdbGbfMbN/M7PV0Wd1bmKdXczsATPbYGYvmtlR0XubF6/jnHsW/3f+VImyiohIhRRgiYhI0n3kB1j3R7dw+exoXYBBwE/wLV+z8S1etwVdDa8HTjKz8Hfn08B64D+j578BDsUHIHsA10T7mJZWSDMbBzwIPBsd93BgMHBr4jiHALtHr58IHAd8LXj90ui4xwGHAdOibWKfApYC/xcYF91i/YDzgM8DM4GtgF+mlTdRnsdT3s94/Gf3O+AWYLKZ7ZWy/deBRcA+wMXAD81sZrSPFuAPQDtwADAP+F5UzqTH8O9bREQypABLRESS7gNmmlk/M+uPP1G/H3iAKMAys8n4QONeAOfc76Pby865Z4DPATvS2dXwd8AocgO0U4GbnXMfmtlE4GTgBOfcg865V51z/wrcAXypQDnPAp52zn3LOfd8dNzTgf2AMBHGauCsaJ278F3z5kbvYzA+OPqWc+5u59xf8S1Hm+ONnXPvAh3AB8655c655cG++wBfcc49Fh3/R8CcRICXNB54K2X554G7nXMrnXNr8UFWXisWcJdz7l+dc684534GvBK/H+AIYFfgdOfcU865BfiArE/KfpYBE4qUU0REukABloiIJN0H9Me3yMwEVjnn/gY8AkyMuuDNAdYBfwYws4lmdoOZ/c3MVgMr8L8xOwA4594B7sQHVXHr0xx8yxb41hgDnou6s62JMvR9HJhYoJz7ArMS678RvRZu85xzrj14vgwYHazXF9+aQ1TWtfhWsXJsdM69mNh3X3xLViEDgA3hgiggm4fvHhi7Dj9Oqn9i+2cSz8P3MxlY5px7M3j9LwQBY2B9VBYREclQ2hUtERHpxZxzr5rZEnx3NcO3XuGcW2tmC6Pls4GHnXObos1uA97Etza9ie+i9hzQFuz6euAKM/syvrXqDeDh6LUWwOFbnzaRa32BorbguxemZfdbETxO7s/ReYHRgmVd0Z54Hu+n2AXMVcDwxLKP4IPR35rZb4PlrfiulOGyUu+n3PcyAlhZ5roiIlImtWCJiEiaeBxWPP4qdj9+nNJsou6BZrY1MAW4yDl3j3PueWAI+Rfx/hjdH41vyfqtcy4OBp7EBwdjo65v4e1N0j0B7AYsSdnmgzLf5yv4gGVL1sQoAcbuifU+xAc7WXgSmJpY9gV8l8C9ErcrSe8mWMjzwLZmtk2wbDrpv/e74z9DERHJkAIsERFJcx9+7NUMcgOsB4CT8F3S4gQXf8e3ynzRzCaZ2aH4RA85rTvOuQ34IOI7+C6B1wevvYRvpbnazI43s53MbLqZnWNmhTLd/RwYBtxkZjOibQ43syvMbEg5b9I5twb4NXBxlA1wKvArOlvUYouBQ8xsWzMbWc6+i7gTODh+Eu3vk8A1zrlnwxtwFTA7GqNWjruBF4FrzGyamR0AXIb/W2x5P1EQuS8wv5vvRUREEhRgiYhImvvw3fvejsZfxR7Gj9tZDSwEcM5txmfn2xM/dunnwP/Gz5GVdB0+S98TUUtX6HP4TII/BF4AbgdmAUvSCuicWwYchB9fNB/4a3TsjQWOXcg5wEPArfj3/Qw+y184Tuq7wPbA3+h+t7rr8XOK7RY9Pz0q753JFZ1zf8Z3pSyrFSv6WxyHzxr4GD4T44X44Cp8P8cArzvnHuriexARkQKss3eGiIiImFk/fFB3iXPu0lLrd/EY/wyMcs5V0v2vq8eaBjwFTHfOLYyWPQb8xDl3Q7WPLyLS2yjJhYiI9Gpmtjd+DNlj+LFj34rub6riYS8Cvmpmrc65jix3HE3cvBZ4GZ+G/TLgaaLxVmY2GvgP4MYsjysiIp5asEREpFeLAqwr8fNHteNbe86JW3sajZmdjh/ntj1+fNz9wNedcyuKbSciItlQgCUiIiIiIpIRJbkQERERERHJiAIsERERERGRjCjAEhERERERyYgCLBERERERkYwowBIREREREcnI/wf68EspF4zvIQAAAABJRU5ErkJggg==\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -568,18 +576,20 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 9, "id": "24dff58d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -607,17 +617,17 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 10, "id": "54e80b71", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.02531515232658421" + "0.021120196752235643" ] }, - "execution_count": 76, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -636,18 +646,20 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 11, "id": "2382ef19", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -680,18 +692,20 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 12, "id": "b383afbd", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -716,18 +730,20 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 13, "id": "f0f1fd0f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], From 96f6b13c60a9e17ab899902e08c07c25fe51021a Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Wed, 21 Jun 2023 22:18:51 -0700 Subject: [PATCH 145/249] fix indenting problem --- .../KPF_Data_Tutorial_L2_Time_Series.ipynb | 3835 ++++++++--------- 1 file changed, 1691 insertions(+), 2144 deletions(-) diff --git a/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb b/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb index 1dd457e02..109f1c6ef 100644 --- a/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb +++ b/docs/source/tutorials/KPF_Data_Tutorial_L2_Time_Series.ipynb @@ -2,695 +2,76 @@ "cells": [ { "cell_type": "markdown", - "id": "596edb6b", + "id": "55980bd5", "metadata": {}, "source": [ - "# Import Packages" + "# Import Packages\n", + "This notebook demonstrates how to read in 2D KPF files, do basic image manipulations, and measure photometry. It was designed to measure a dark-current-like signal from the ion pumps." ] }, { "cell_type": "code", "execution_count": 1, - "id": "bd8e6835", + "id": "2c65bdc1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/howard/opt/anaconda3/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:21: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n", - "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " int_types = {_np.int, _np.int8, _np.int16, _np.int32, _np.int64, _np.integer}\n" - ] - } - ], + "outputs": [], "source": [ "from astropy.io import fits\n", - "from astropy.time import Time\n", - "from astropy.table import Table\n", - "import pandas as pd\n", "import numpy as np\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "from radvel.utils import timebin # needed for timebinning" + "from datetime import datetime\n", + "from astropy.table import Table" ] }, { "cell_type": "markdown", - "id": "5a8d307c", + "id": "6f7807da", "metadata": {}, "source": [ - "# Define Directories and Filenames\n", - "One needs to have the L2 files kusted bekiw in the data directory of the folder from which this notebook is running. This file can be downloaded from Jump or shrek.caltech.edu. One can also modify the code below to work with other files or paths. The example below is a ~1.5-hour time series on Tau Ceti (HD 10700)." + "# Define Parameters\n", + "Different example exposures can be selected below." ] }, { "cell_type": "code", "execution_count": 2, - "id": "932a3b78", + "id": "80924695", "metadata": {}, "outputs": [], "source": [ - "L2_data_dir = '/Users/howard/temp/kpf/L2/20230110/'\n", + "# Parameters\n", + "ccd = ['green', 'red'] # ['green'] or ['red'] or ['green', 'red']\n", + "data_dir = 'data/'\n", + "out_dir = 'ion_pump_plots/'\n", + "#ObsID = 'KP.20230311.63253.52' # dark frame\n", + "#ObsID = 'KP.20230312.03990.62' # dark frame\n", + "#ObsID = 'KP.20230308.32335.99' # dark frame - before warmup\n", + "#ObsID = 'KP.20230308.32335.99' # dark frame\n", + "#ObsID = 'KP.20230314.03992.75' # dark frame just as ion pump was turned on after warmup\n", + "#ObsID = 'KP.20230314.07774.61' # dark frame next - about 1 hr after ion pump turned on\n", + "ObsID = 'KP.20230314.11989.91' # dark frame next - about 2 hr after ion pump turned on\n", + "ObsID = 'KP.20230317.07770.97' # dark frame with ion pump currents recorded\n", + "#ObsID = 'KP.20230311.29791.13' # bias frame\n", + "#ObsID = 'KP.20230308.29360.64' # bias frame\n", "\n", - "# Time series of observations of 10700 (Tau Ceti) from Jan. 10, 2023\n", - "ObsIDs = ['KP.20230110.16623.72',\n", - " 'KP.20230110.16803.31',\n", - " 'KP.20230110.16986.84',\n", - " 'KP.20230110.17094.84',\n", - " 'KP.20230110.17186.85',\n", - " 'KP.20230110.17279.12',\n", - " 'KP.20230110.17371.41',\n", - " 'KP.20230110.17463.29',\n", - " 'KP.20230110.17580.34',\n", - " 'KP.20230110.17672.60',\n", - " 'KP.20230110.17764.89',\n", - " 'KP.20230110.17856.97',\n", - " 'KP.20230110.17949.22',\n", - " 'KP.20230110.18179.54',\n", - " 'KP.20230110.18271.75',\n", - " 'KP.20230110.18363.65',\n", - " 'KP.20230110.18455.80',\n", - " 'KP.20230110.18547.96',\n", - " 'KP.20230110.18640.14',\n", - " 'KP.20230110.18732.02',\n", - " 'KP.20230110.18823.97',\n", - " 'KP.20230110.18916.31',\n", - " 'KP.20230110.19440.98', # one skipped bad frame above\n", - " 'KP.20230110.19533.16',\n", - " 'KP.20230110.19625.30',\n", - " 'KP.20230110.19717.19',\n", - " 'KP.20230110.19809.15',\n", - " 'KP.20230110.19901.19',\n", - " 'KP.20230110.19993.43',\n", - " 'KP.20230110.20085.43',\n", - " 'KP.20230110.20177.43',\n", - " 'KP.20230110.20377.95',\n", - " 'KP.20230110.20470.20',\n", - " 'KP.20230110.20562.48',\n", - " 'KP.20230110.20654.18',\n", - " ]" + "do_print = False\n", + "do_display = True\n", + "do_save_png = True" ] }, { "cell_type": "markdown", - "id": "a31d1c0e", + "id": "fee850db", "metadata": {}, "source": [ - "# Open File and List Extensions" + "# Read 2D image and telemetry" ] }, { "cell_type": "code", "execution_count": 3, - "id": "247746e7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: /Users/howard/temp/kpf/L2/20230110/KP.20230110.16623.72_L2.fits\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 312 () \n", - " 1 RECEIPT 1 BinTableHDU 29 2R x 10C [26A, 1A, 1A, 1A, 3A, 14A, 1A, 43A, 71A, 4A] \n", - " 2 CONFIG 1 BinTableHDU 13 0R x 2C [D, D] \n", - " 3 TELEMETRY 1 BinTableHDU 26 116R x 7C [35A, 11A, 11A, 8A, 8A, 14A, 67A] \n", - " 4 GREEN_CCF 1 ImageHDU 16 (1608, 35, 3) float64 \n", - " 5 RED_CCF 1 ImageHDU 16 (1608, 32, 3) float64 \n", - " 6 RV 1 BinTableHDU 50 67R x 14C [D, D, D, D, D, K, K, D, D, D, D, 15A, 15A, 15A] \n", - " 7 ACTIVITY 1 BinTableHDU 9 0R x 0C [] \n" - ] - } - ], - "source": [ - "L2 = fits.open(L2_data_dir + ObsIDs[0] + '_L2.fits')\n", - "L2.info()" - ] - }, - { - "cell_type": "markdown", - "id": "1ea6b300", - "metadata": {}, - "source": [ - "# Primary Extension (header keywords)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "e62fb0c0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "SIMPLE = T / conforms to FITS standard \n", - "BITPIX = 8 / array data type \n", - "NAXIS = 0 / number of array dimensions \n", - "EXTEND = T \n", - "FAVER = '0 ' / Written by FITSAssemble version 1.3 \n", - "TIMESYS = 'UTC ' \n", - "CAMERAS = 3 / Detectors in assembly request \n", - "GREEN = 'YES ' / Was this camera found? \n", - "RED = 'YES ' / Was this camera found? \n", - "CA_HK = 'NO ' / Was this camera found? \n", - "EXPMETER= 'YES ' / Was this camera found? \n", - "DATE-MID= '2023-01-10T04:37:19.484' / Halfway point of the exposure, unweighted \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-01-10T04:38:33.225263Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureBegin] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:03.822048Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "DATE-OBS= '2023-01-10' / Date dcs1.DATE \n", - "UT = '04:37:03.57' / DCS Universal Time \n", - "DATE-BEG= '2023-01-10T04:37:03.722048' / Start of exposure from kpfexpose \n", - "EXPTIME = 30.0 / Requested exposure time \n", - "PROGNAME= 'E328 ' / Program name from kpfexpose \n", - "OBSERVER= 'Fulton,Halverson,Howard,Isaacson,Petigura,Walawender,Weiss,Yeh' / Obs\n", - "OBJECT = '10700 ' / Object \n", - "INSTRUME= 'KPF ' / Instrument Name \n", - "DATALVL = '2 ' / Data Product Base Level \n", - "OBSERVAT= 'KECK ' / Observatory name \n", - "OBSMODE = 'SPEC ' / Basic mode of observation \n", - "HEADVER = '122650 ' / Header config version \n", - "CURRINST= 'KPF ' / Selected instrument \n", - "TELESCOP= 'Keck I ' / Telescope \n", - "TARGNAME= '10700 ' / DCS Target name \n", - "TARGRA = '01:44:01.40' / [h] DCS Target RA \n", - "TARGDEC = '-15:55:55.0' / [deg] DCS Target Dec \n", - "TARGEPOC= 2000.0 / DCS Target epoch \n", - "TARGEQUI= 2000.0 / DCS Target equinox \n", - "TARGPLAX= 0.0 / [arcsec] target parallax \n", - "TARGPMDC= 0.0 / [arcsec/yr] target proper motion dec \n", - "TARGPMRA= 0.0 / [s/yr] target proper motion ra \n", - "TARGRADV= -16.597 / [km/s] target radial velocity \n", - "TARGWAVE= 0.65 / target-wavelength \n", - "TARGFRAM= 'FK5 ' / target-frame \n", - "RA = '01:44:01.40' / [h] Right ascension \n", - "DEC = '-15:55:55.0' / [deg] Declination \n", - "EQUINOX = 2000.0 / DCS Equinox \n", - "MJD-OBS = 59954.192402 / Modified Julian day \n", - "PMFM = 0.0 / PMFM value \n", - "OCTAGON = 'LFCFiber' / selected octagon value \n", - "CALMON = 'Out ' / Calibration intensity monitor position \n", - "HATCH = 'Open ' / Status of the hatch on the exterior of the FIU \n", - "FIUMODE = 'None ' / FIU operating mode \n", - "FOLDNAM = 'Out ' / Named Position of FIU Fold mirror \n", - "FOLDVAL = 24.7 / [mm] Position of FIU Fold mirror \n", - "ADCTRACK= 'On ' / Is the ADC Tracking \n", - "HCLSN = '' / S/N of lamp in use \n", - "CAREQ = 'No ' / Ca HK fibers (6,7) requested \n", - "EXPSCREQ= 'Yes ' / ExpM Science fiber (8ab) requested \n", - "FFREQ = 'No ' / Flatfield fiber (10ab) requested \n", - "FICALREQ= 'No ' / FIU Cal fibers (4,5) requested \n", - "SICALREQ= 'Yes ' / SimCal fiber (3ab) requested \n", - "VACSCREQ= 'Yes ' / Vac Sci fiber (1b) requested \n", - "VACSKREQ= 'Yes ' / Vac Sci fiber (2b) requested \n", - "THDAYON = '20230109T14:45:02 HST' / ThAr Daily was last turned on at this time \n", - "THDAYTON= 3173.9 / ThAr Daily has been on for this long \n", - "THAUON = '20221228T10:54:05 HST' / ThAr Gold was last turned on at this time \n", - "THAUTON = 64.90000000000001 / ThAr Gold has been on for this long \n", - "UDAYON = '20230109T15:38:52 HST' / UNe Daily was last turned on at this time \n", - "UDAYTON = 2611.9 / UNe Daily has been on for this long \n", - "UAUON = '19691231T14:00:00 HST' / UNe Gold was last turned on at this time \n", - "UAUTON = 0.0 / UNe Gold has been on for this long \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:03.822048Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_12' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-01-10T04:38:33.225263Z \n", - "COMMENT Header generation end time: 2023-01-10T04:38:33.422198Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-01-10T04:38:34.544577Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureMiddle] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:19.483524Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "SRCSHTTR= 'SciSelect,SkySelect' / Source shutters commanded for exposure \n", - "TIMSHTTR= 'Scrambler,SimulCal' / Timed shutters commanded for exposure \n", - "OTIMSHTR= 'Scrambler,SimulCal' / Timed shutters open at exposure midpoint \n", - "SCISEL = 'open ' / Science Select shutter at exposure midpoint \n", - "SKYSEL = 'open ' / Sky Select Shutter at exposure midpoint \n", - "FFSHTR = 'closed ' / Flat field fiber shutter at exposure midpoint \n", - "SCRAMSHT= 'open ' / Scrambler shutter at exposure midpoint \n", - "SIMCALSH= 'open ' / Simult Cal shutter at exposure midpoint \n", - "TRIGTARG= 'Green,Red,ExpMeter' / Cameras that were sent triggers \n", - "IMTYPE = 'Object ' / Image Type \n", - "CAL-OBJ = 'LFCFiber' / Calibration fiber source \n", - "SKY-OBJ = 'Sky ' / Sky fiber source \n", - "SCI-OBJ = 'Target ' / Science fiber source \n", - "CAFBS = 'No ' / Ca HK fibers (6,7) on \n", - "EXPSCIFB= 'Yes ' / ExpM Science fiber (8ab) on \n", - "EXPSKYFB= 'Yes ' / ExpM Science fiber (9) on \n", - "FFFB = 'No ' / Flatfield fiber (10ab) on \n", - "FICALFBS= 'No ' / FIU Cal fibers (4,5) on \n", - "SICALFB = 'Yes ' / SimCal fiber (3ab) on \n", - "VACSCFB = 'Yes ' / Vac Sci fiber (1b) on \n", - "VACSKFB = 'Yes ' / Vac Sci fiber (2b) on \n", - "SCIFB = 'Yes ' / Science fiber (1a) on \n", - "SKYFB = 'Yes ' / Sky fiber (2a) on \n", - "GRACFMD5= 'unknown ' / Green MD5 sum for the acf file \n", - "GRACFFLN= '-default-' / Green acf file loaded \n", - "RDACFMD5= 'unknown ' / Red MD5 sum for the acf file \n", - "RDACFFLN= '-default-' / Red acf file loaded \n", - "AGITSTA = 'Running ' / Agitator status kpfmot.AGITATOR \n", - "THARGD = 'Off ' / Gold ThAr power status \n", - "UNEGD = 'Off ' / Gold UNe power status \n", - "THARDAY = 'Off ' / Daily ThAr power status \n", - "UNEDAY = 'Off ' / Daily UNe power status \n", - "OCTBB = 'Off ' / Octagon broad band power status \n", - "FFSOURCE= 'Off ' / Flat field broad band power status \n", - "SCIFBILL= 'Off ' / Science fiber LED back illuminator power \n", - "SKYFBILL= 'Off ' / Sky fiber LED back illuminator power \n", - "HKFBILL = 'Off ' / Ca H and K fiber LED back illuminator power \n", - "EXPFBILL= 'Off ' / Exposure meter fiber LED back illuminator power\n", - "SSCALFW = 'OD 3.0 ' / Sci/Cal FW Position \n", - "SIMCALFW= 'OD 0.1 ' / Simual Cal FW Position \n", - "FFFW = 'Blank ' / Flatfield FW Position \n", - "OCTAGON = 'LFCFiber' / selected octagon value \n", - "PRES = 622.947 / [hPa] Pressure at Vaisala kpfmet.PRES \n", - "RELH = 12.901 / Relative humidity at Vaisala kpfmet.RELH \n", - "PONAME = 'KPF ' / DCS Point origin name \n", - "PONAME1 = 'Opt Axis' / DCS Point origin name1 \n", - "DRA = 0.0 / [s/s] DCS Diff RA rate \n", - "DDEC = 0.0 / [asec/s] DCS Diff Dec rate \n", - "RABASE = '01:44:01.40' / DCS RA base \n", - "RAOFF = 0.0 / [asec] DCS RA offset \n", - "DECBASE = '-15:55:55.0' / DCS Dec base \n", - "DECOFF = 0.0 / [asec] DCS Dec offset \n", - "HA = '+23:49:11.92' / DCS Hour angle \n", - "AIRMASS = 1.23 / DCS Airmass \n", - "PARANG = -4.85 / [deg] DCS Parallactic angle astrometric \n", - "PARANTEL= -4.9 / [deg] DCS Parallactic angle telescope \n", - "EL = 54.24 / [deg] DCS Elevation \n", - "AZ = 175.08 / [deg] DCS Azimuth \n", - "LST = '01:33:13.32' / DCS Local sidereal time \n", - "AXESTAT = 'tracking' / DCS axes control status \n", - "TRACKING= 'yes ' / DCS Servos tracking status \n", - "DTRACK = 'disabled' / DCS differential tracking status \n", - "GUIDING = 'true ' / DCS Guiding status \n", - "AUTACTIV= 'yes ' / DCS Guider active \n", - "AUTFWHM = 0.6079830000000001 / [pix] DCS Guider fwhm \n", - "AUTXCENT= 0.0 / [asec] DCS Guider x centroid \n", - "AUTYCENT= 0.0 / [asec] DCS Guider y centroid \n", - "SECFOCUS= -2.11 / [mm] Secondary focus \n", - "TELFOCUS= -1.499 / [mm] Telescope focus \n", - "TUBEFLEX= -13.3 / [arcsec] Telescope tube flexure \n", - "TUBETEMP= 2.39 / [degC] Telescope tube temperature \n", - "PRIMTEMP= 2.042575 / [degC] Telescope pri temperature \n", - "SECMTEMP= 2.169584 / [degC] Telescope sec temperature \n", - "DIFFPTDW= 31.742575 / [decC] Diff between pri mirro temp dewpt \n", - "DIFFSTDW= 31.769584 / [decC] Diff between sec mirro temp dewpt \n", - "VIGNETTE= 'false ' / dome vignette (t/f) \n", - "STVIGNE = 'false ' / top shutter vignette (t/f) \n", - "SBVIGNE = 'false ' / bottom shutter vignette (t/f) \n", - "SBELEV = 23.98 / [deg] bottom shutter elevation \n", - "STELEV = 104.01 / [deg] top shutter elevation \n", - "SECSTST = 'INPOS ' / DCS Secondary status string \n", - "SECTHETX= -462.6 / [asec] DCS Secondary theta x \n", - "SECTHETY= 123.8 / [asec] DCS Secondary theta y \n", - "TERTSTST= 'INPOS ' / DCS Tertiary status string \n", - "TERTDEST= 'lnas ' / DCS Tertiary user destination \n", - "TERTPOSN= 'lnas ' / DCS Tertiary user position \n", - "DOMEPOSN= 174.78 / DCS Dome user position \n", - "DOMESTST= 'TRACK ' / DCS Dome status string \n", - "CALOCAL = -27.4 / collimation-azimuth-local \n", - "CELOCAL = 16.5 / collimation-elevation-local \n", - "FOCALSTN= 'lnas (left nasmyth)' / focal-station \n", - "INSTANGL= 312.0 / porg-to-instrument angle \n", - "POXPOS = 0.4 / pointing-origin-x-position \n", - "POYPOS = -0.8 / pointing-origin-y-position \n", - "ROTCALAN= 0.0 / rotator-calibration-angle \n", - "ROTZERO = 0.0 / rotator-zero-angle \n", - "GUIDWAVE= 0.63 / guidestar-wavelength \n", - "TIMEERR = 'ok 2 1 {NTP time correct to within 1 ms}' / resp from time serv \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:19.483524Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_12' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-01-10T04:38:34.544577Z \n", - "COMMENT Header generation end time: 2023-01-10T04:38:34.820641Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT Location: /kroot/rel/default/bin/keyheader \n", - "COMMENT Configuration: /kroot/rel/default/data/kpfheaders/keyheader.conf \n", - "COMMENT Hostname: kpfserver \n", - "COMMENT Header generation start time: 2023-01-10T04:38:35.952646Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [ExposureEnd] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:35.145000Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "DATE = '2023-01-10T04:37:33.739466' / End of exposure from kpfexpose.ENDTIME \n", - "DATE-END= '2023-01-10T04:37:33.739466' / End of exposure from kpfexpose.ENDTIME \n", - "ELAPSED = 30.0 / Actual exposure time from kpfexpose.ELAPSED \n", - "OUTDIR = '/sdata1701/kpfdev/L0' / Output directory for file in OFNAME \n", - "OFNAME = 'KP.20230110.16623.72.fits' / Filename of output file \n", - "GREENFN = '/s/sdata1701/kpfeng/2023jan09/Green/kpf_23060.fits' \n", - "REDFN = '/s/sdata1701/kpfeng/2023jan09/Red/kpf_red_23060.fits' \n", - "EXPMETFN= '/s/sdata1701/kpfeng/2023jan09/ExpMeter/kpf_em_23060.fits' \n", - "FRAMENO = 23060 \n", - "GRCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd green CAMD_VER \n", - "GREXPTI = 0 / exposure time in msec Kwd green EXPTIME \n", - "GRFILENA= 'kpf_23060.fits' / this filename Kwd green FILENAME \n", - "GRFIRMWA= '/kroot/rel/default/data/kpfgreen/ACF/kpfGreen.20220513-0519.acf' / co\n", - "GRHDRSHI= 0 / number of HDR right-shift bits Kwd green HDRSHI\n", - "GRSHUTT = T / shutter was enabled Kwd green SHUTTEN \n", - "GRTM_ZO = 'GMT ' / time zone Kwd green TM_ZONE \n", - "GRCDS0 = '#eval CDump - 417' / Kwd green CDS0 \n", - "GRCDS1 = '#eval CDump - 1' / Kwd green CDS1 \n", - "GRCDS2 = '#eval PixelT - 417' / Kwd green CDS2 \n", - "GRCDS3 = 950 / Kwd green CDS3 \n", - "GRTEST = 123 / test fitskey from modes file Kwd green TEST \n", - "GRDATE = '2023-01-10T04:38:31.844154' / FITS file write time Kwd green DATE \n", - "GROBSERV= 'Fulton,Halverson,Howard,Isaacson,Petigura,Walawender,Weiss,Yeh' / Obs\n", - "GRPROGNA= 'E328 ' / Program name Kwd green PROGNAME \n", - "GRFRAME = 23060 / Kwd green FRAMENO \n", - "GRDATE-B= '2023-01-10T04:37:03.722048' / Shutter-open time Kwd green DATE-BEG \n", - "GRDATE-E= '2023-01-10T04:37:33.739466' / Shutter-close time Kwd green DATE-END \n", - "GRELAPS = 30.017 / Shutter-elapsed time Kwd green ELAPSED \n", - "RDCAMD_V= 'Nov 9 2022 09:50:15' / camerad build date Kwd red CAMD_VER \n", - "RDEXPTI = 0 / exposure time in msec Kwd red EXPTIME \n", - "RDFILENA= 'kpf_red_23060.fits' / this filename Kwd red FILENAME \n", - "RDFIRMWA= '/kroot/rel/default/data/kpfred/ACF/kpf_200kHz_2amp.acf' / controller \n", - "RDHDRSHI= 0 / number of HDR right-shift bits Kwd red HDRSHIFT\n", - "RDSHUTT = T / shutter was enabled Kwd red SHUTTEN \n", - "RDTM_ZO = 'GMT ' / time zone Kwd red TM_ZONE \n", - "RDCDS0 = '#eval CDump - 160' / Kwd red CDS0 \n", - "RDCDS1 = '#eval CDump - 1' / Kwd red CDS1 \n", - "RDCDS2 = '#eval PixelT - 160' / Kwd red CDS2 \n", - "RDCDS3 = 950 / Kwd red CDS3 \n", - "RDTEST = 123 / test fitskey from modes file Kwd red TEST \n", - "RDDATE = '2023-01-10T04:38:20.934246' / FITS file write time Kwd red DATE \n", - "RDOBSERV= 'Fulton,Halverson,Howard,Isaacson,Petigura,Walawender,Weiss,Yeh' / Obs\n", - "RDPROGNA= 'E328 ' / Program name Kwd red PROGNAME \n", - "RDFRAME = 23060 / Kwd red FRAMENO \n", - "RDDATE-B= '2023-01-10T04:37:03.722048' / Shutter-open time Kwd red DATE-BEG \n", - "RDDATE-E= '2023-01-10T04:37:33.739466' / Shutter-close time Kwd red DATE-END \n", - "RDELAPS = 30.017 / Shutter-elapsed time Kwd red ELAPSED \n", - "EMFRAME = 23060 / Frame number from kpf_expmeter Kwd expmeter FRA\n", - "EMFILEN = '/s/sdata1701/kpfeng/2023jan09/ExpMeter/kpf_em_23060.fits' / Output fi\n", - "EMSEQBEG= 1 / Sequence number of first observation Kwd expmet\n", - "EMSEQEND= 3 / Sequence number of last observation Kwd expmete\n", - "EMDATE-B= '2023-01-10T04:37:03.001' / Date-Beg of first observation Kwd expmeter\n", - "EMDATE-E= '2023-01-10T04:37:35.045' / Date-End of last observation Kwd expmeter \n", - "TARGTEFF= 5349 \n", - "EXTNAME = 'PRIMARY ' / extension name \n", - "ORIGIN = 'astropy.fits' / File Originator \n", - "COMMENT \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT keyheader section [heartbeats] \n", - "COMMENT Snapshot timestamp: 2023-01-10T04:37:35.145000Z \n", - "COMMENT - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \n", - "COMMENT \n", - "COMMENT Data not available for keyword 'heart_12' \n", - "COMMENT Any value above other than 'Heartbeat active' indicates \n", - "COMMENT the keyword history daemon was not capturing data from \n", - "COMMENT the affected service as of the snapshot timestamp. \n", - "COMMENT \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT End of keyheader output \n", - "COMMENT Header generation start time: 2023-01-10T04:38:35.952646Z \n", - "COMMENT Header generation end time: 2023-01-10T04:38:36.040979Z \n", - "COMMENT ------------------------------------------------------------------------\n", - "COMMENT The keywords that begin with GR come from the green detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field \n", - "COMMENT The keywords that begin with RD come from the red detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field \n", - "COMMENT The keywords that begin with EM come from the expmeter detector system. \n", - "COMMENT The keyword and value come from the Primary HDU of the FITS file. \n", - "COMMENT The original keyword name follows the Kwd in the comment field " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L2['PRIMARY'].header" - ] - }, - { - "cell_type": "markdown", - "id": "817c0f47", - "metadata": {}, - "source": [ - "# CCF Extensions\n", - "The header of the GREEN_CCF extesion shows the CCF mask used and the orderlet names associated with the three differente CCFs " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "313a399c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "XTENSION= 'IMAGE ' / Image extension \n", - "BITPIX = -64 / array data type \n", - "NAXIS = 3 / number of array dimensions \n", - "NAXIS1 = 1608 \n", - "NAXIS2 = 35 \n", - "NAXIS3 = 3 \n", - "PCOUNT = 0 / number of parameters \n", - "GCOUNT = 1 / number of groups \n", - "STARTSEG= 0 \n", - "STARTV = -217.597 / km/sec \n", - "STEPV = 0.25 / km/sec \n", - "MASKTYPE= 'G9_espresso' \n", - "CCF1 = 'GREEN_SCI_FLUX1' \n", - "CCF2 = 'GREEN_SCI_FLUX2' \n", - "CCF3 = 'GREEN_SCI_FLUX3' \n", - "EXTNAME = 'GREEN_CCF' / extension name " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L2['GREEN_CCF'].header" - ] - }, - { - "cell_type": "markdown", - "id": "ef3338a1", - "metadata": {}, - "source": [ - "# Plot CCFs\n", - "To plot the cross correlations functions, first the velocity array needs to be defined in terms of parameters in header from the GREEN_CCF extension. The current CCF code in the KPF DRP has a range of +- 100 km/s from the approximate CCF peak. Shown below are the CCFs of the three science slices" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "48bf475a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "CCF_RV = L2['GREEN_CCF'].header['STARTV'] + \\\n", - " np.arange(L2['GREEN_CCF'].header['NAXIS1']) * L2['GREEN_CCF'].header['STEPV']\n", - "o = 18# order number\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[0,o,:], color='g')\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[1,o,:], color='orange')\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[2,o,:], color='blue')\n", - "plt.legend(['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3'], fontsize=14, loc='best') \n", - "plt.xlabel('Doppler Shift (km/s)', fontsize=14)\n", - "plt.ylabel('Cross-Correlation Function', fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ee842c5d", - "metadata": {}, - "source": [ - "Zoom in on the CCF peaks and mark the determined RVs for each slice" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e6593b29", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[0,o,:], '-o', color='g')\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[1,o,:], '-o', color='orange')\n", - "plt.plot(CCF_RV, L2['GREEN_CCF'].data[2,o,:], '-o', color='blue')\n", - "plt.axvline(x=L2['RV'].header['CCD1RV1'], color='g', label='CCD1RV1')\n", - "plt.axvline(x=L2['RV'].header['CCD1RV2'], color='orange', label='CCD1RV2')\n", - "plt.axvline(x=L2['RV'].header['CCD1RV3'], color='blue', label='CCD1RV3')\n", - "plt.legend(['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3'], fontsize=14, loc='best') \n", - "plt.xlim(-26,-6)\n", - "plt.xlabel('Doppler Shift (km/s)', fontsize=14)\n", - "plt.ylabel('Cross-Correlation Function', fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "0e4e92ab", - "metadata": {}, - "source": [ - "# RVs\n", - "The RV extension is below. Note that CCD1RV1, CCD1RV2, CCD1RV3 are the RVs for the three slices on the green CCD, while similar versions for CCD2 are for the Red CCD." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2644f5e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "XTENSION= 'BINTABLE' / binary table extension \n", - "BITPIX = 8 / array data type \n", - "NAXIS = 2 / number of array dimensions \n", - "NAXIS1 = 133 / length of dimension 1 \n", - "NAXIS2 = 67 / length of dimension 2 \n", - "PCOUNT = 0 / number of group parameters \n", - "GCOUNT = 1 / number of groups \n", - "TFIELDS = 14 / number of table fields \n", - "STAR_RV = -16.597 \n", - "CCD1ROW = 0 \n", - "CCD1RV1 = -16.4964486965 / BaryC RV (km/s) \n", - "CCD1RV2 = -16.5914246799 / BaryC RV (km/s) \n", - "CCD1RV3 = -16.6457022282 / BaryC RV (km/s) \n", - "CCD1RV = -16.5853532683 / BaryC RV (km/s) \n", - "CCD1JD = 2459954.69258662 \n", - "CCD2ROW = 35 \n", - "CCD2RV1 = -17.1850137388 / BaryC RV (km/s) \n", - "CCD2RV2 = -16.2775775817 / BaryC RV (km/s) \n", - "CCD2RV3 = -16.9146750402 / BaryC RV (km/s) \n", - "CCD2RV = -16.7288769031 / BaryC RV (km/s) \n", - "CCD2JD = 2459954.69258662 \n", - "TTYPE1 = 'orderlet1' \n", - "TFORM1 = 'D ' \n", - "TTYPE2 = 'orderlet2' \n", - "TFORM2 = 'D ' \n", - "TTYPE3 = 'orderlet3' \n", - "TFORM3 = 'D ' \n", - "TTYPE4 = 's_wavelength' \n", - "TFORM4 = 'D ' \n", - "TTYPE5 = 'e_wavelength' \n", - "TFORM5 = 'D ' \n", - "TTYPE6 = 'segment no.' \n", - "TFORM6 = 'K ' \n", - "TTYPE7 = 'order no.' \n", - "TFORM7 = 'K ' \n", - "TTYPE8 = 'RV ' \n", - "TFORM8 = 'D ' \n", - "TTYPE9 = 'RV error' \n", - "TFORM9 = 'D ' \n", - "TTYPE10 = 'CCFJD ' \n", - "TFORM10 = 'D ' \n", - "TTYPE11 = 'Bary_RVC' \n", - "TFORM11 = 'D ' \n", - "TTYPE12 = 'source1 ' \n", - "TFORM12 = '15A ' \n", - "TTYPE13 = 'source2 ' \n", - "TFORM13 = '15A ' \n", - "TTYPE14 = 'source3 ' \n", - "TFORM14 = '15A ' \n", - "EXTNAME = 'RV ' / extension name " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L2['RV'].header" - ] - }, - { - "cell_type": "markdown", - "id": "0ad8215a", - "metadata": {}, - "source": [ - "Here is the telemetry section, which will be incorporated into the next section." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "db786dec", + "id": "d0e1a726", "metadata": {}, "outputs": [ { @@ -714,1510 +95,1584 @@ " \n", " \n", " \n", - " orderlet1\n", - " orderlet2\n", - " orderlet3\n", - " s_wavelength\n", - " e_wavelength\n", - " segment no.\n", - " order no.\n", - " RV\n", - " RV error\n", - " CCFJD\n", - " Bary_RVC\n", - " source1\n", - " source2\n", - " source3\n", + " average\n", + " stddev\n", + " min\n", + " max\n", + " units\n", + " description\n", " \n", - " \n", - " \n", " \n", - " 0\n", - " -14.667295\n", - " -14.370558\n", - " -14.580347\n", - " 4505.883111\n", - " 4465.447737\n", - " 0\n", - " 0\n", - " -14.522091\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447883\n", - " GREEN_SCI_FLUX1\n", - " GREEN_SCI_FLUX2\n", - " GREEN_SCI_FLUX3\n", - " \n", - " \n", - " 1\n", - " -16.357935\n", - " -16.027360\n", - " -16.238453\n", - " 4538.813663\n", - " 4497.982847\n", - " 1\n", - " 1\n", - " -16.190784\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447883\n", - " GREEN_SCI_FLUX1\n", - " GREEN_SCI_FLUX2\n", - " GREEN_SCI_FLUX3\n", - " \n", - " \n", - " 2\n", - " -15.785628\n", - " -15.401463\n", - " -15.253319\n", - " 4572.585395\n", - " 4531.223766\n", - " 2\n", - " 2\n", - " -15.456391\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447883\n", - " GREEN_SCI_FLUX1\n", - " GREEN_SCI_FLUX2\n", - " GREEN_SCI_FLUX3\n", - " \n", - " \n", - " 3\n", - " -25.820058\n", - " -25.755219\n", - " -25.809710\n", - " 4606.535466\n", - " 4564.890307\n", - " 3\n", - " 3\n", - " -25.791927\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447883\n", - " GREEN_SCI_FLUX1\n", - " GREEN_SCI_FLUX2\n", - " GREEN_SCI_FLUX3\n", - " \n", - " \n", - " 4\n", - " -16.496334\n", - " -16.625476\n", - " -16.675179\n", - " 4641.171952\n", - " 4599.291587\n", - " 4\n", - " 4\n", - " -16.608664\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447883\n", - " GREEN_SCI_FLUX1\n", - " GREEN_SCI_FLUX2\n", - " GREEN_SCI_FLUX3\n", - " \n", - " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " \n", - " \n", - " 62\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 8226.205303\n", - " 8151.111836\n", - " 27\n", - " 27\n", - " 0.000000\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447848\n", - " RED_SCI_FLUX1\n", - " RED_SCI_FLUX2\n", - " RED_SCI_FLUX3\n", - " \n", - " \n", - " 63\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 8337.245583\n", - " 8261.128164\n", - " 28\n", - " 28\n", - " 0.000000\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447851\n", - " RED_SCI_FLUX1\n", - " RED_SCI_FLUX2\n", - " RED_SCI_FLUX3\n", - " \n", - " \n", - " 64\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 8451.799956\n", - " 8374.624137\n", - " 29\n", - " 29\n", - " 0.000000\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447854\n", - " RED_SCI_FLUX1\n", - " RED_SCI_FLUX2\n", - " RED_SCI_FLUX3\n", - " \n", - " \n", - " 65\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 8568.565099\n", - " 8490.321120\n", - " 30\n", - " 30\n", - " 0.000000\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447854\n", - " RED_SCI_FLUX1\n", - " RED_SCI_FLUX2\n", - " RED_SCI_FLUX3\n", - " \n", - " \n", - " 66\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", - " 8689.103743\n", - " 8609.755742\n", - " 31\n", - " 31\n", - " 0.000000\n", - " 0.0\n", - " 2.459955e+06\n", - " -27.447854\n", - " RED_SCI_FLUX1\n", - " RED_SCI_FLUX2\n", - " RED_SCI_FLUX3\n", - " \n", - " \n", - "\n", - "

67 rows × 14 columns

\n", - "" - ], - "text/plain": [ - " orderlet1 orderlet2 orderlet3 s_wavelength e_wavelength segment no. \\\n", - "0 -14.667295 -14.370558 -14.580347 4505.883111 4465.447737 0 \n", - "1 -16.357935 -16.027360 -16.238453 4538.813663 4497.982847 1 \n", - "2 -15.785628 -15.401463 -15.253319 4572.585395 4531.223766 2 \n", - "3 -25.820058 -25.755219 -25.809710 4606.535466 4564.890307 3 \n", - "4 -16.496334 -16.625476 -16.675179 4641.171952 4599.291587 4 \n", - ".. ... ... ... ... ... ... \n", - "62 0.000000 0.000000 0.000000 8226.205303 8151.111836 27 \n", - "63 0.000000 0.000000 0.000000 8337.245583 8261.128164 28 \n", - "64 0.000000 0.000000 0.000000 8451.799956 8374.624137 29 \n", - "65 0.000000 0.000000 0.000000 8568.565099 8490.321120 30 \n", - "66 0.000000 0.000000 0.000000 8689.103743 8609.755742 31 \n", - "\n", - " order no. RV RV error CCFJD Bary_RVC source1 \\\n", - "0 0 -14.522091 0.0 2.459955e+06 -27.447883 GREEN_SCI_FLUX1 \n", - "1 1 -16.190784 0.0 2.459955e+06 -27.447883 GREEN_SCI_FLUX1 \n", - "2 2 -15.456391 0.0 2.459955e+06 -27.447883 GREEN_SCI_FLUX1 \n", - "3 3 -25.791927 0.0 2.459955e+06 -27.447883 GREEN_SCI_FLUX1 \n", - "4 4 -16.608664 0.0 2.459955e+06 -27.447883 GREEN_SCI_FLUX1 \n", - ".. ... ... ... ... ... ... \n", - "62 27 0.000000 0.0 2.459955e+06 -27.447848 RED_SCI_FLUX1 \n", - "63 28 0.000000 0.0 2.459955e+06 -27.447851 RED_SCI_FLUX1 \n", - "64 29 0.000000 0.0 2.459955e+06 -27.447854 RED_SCI_FLUX1 \n", - "65 30 0.000000 0.0 2.459955e+06 -27.447854 RED_SCI_FLUX1 \n", - "66 31 0.000000 0.0 2.459955e+06 -27.447854 RED_SCI_FLUX1 \n", - "\n", - " source2 source3 \n", - "0 GREEN_SCI_FLUX2 GREEN_SCI_FLUX3 \n", - "1 GREEN_SCI_FLUX2 GREEN_SCI_FLUX3 \n", - "2 GREEN_SCI_FLUX2 GREEN_SCI_FLUX3 \n", - "3 GREEN_SCI_FLUX2 GREEN_SCI_FLUX3 \n", - "4 GREEN_SCI_FLUX2 GREEN_SCI_FLUX3 \n", - ".. ... ... \n", - "62 RED_SCI_FLUX2 RED_SCI_FLUX3 \n", - "63 RED_SCI_FLUX2 RED_SCI_FLUX3 \n", - "64 RED_SCI_FLUX2 RED_SCI_FLUX3 \n", - "65 RED_SCI_FLUX2 RED_SCI_FLUX3 \n", - "66 RED_SCI_FLUX2 RED_SCI_FLUX3 \n", - "\n", - "[67 rows x 14 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_rv = Table.read(L2_data_dir + ObsIDs[0] + '_L2.fits', format='fits',hdu='RV').to_pandas()\n", - "str_columns = ['source1', 'source2', 'source3']\n", - "for column in df_rv:\n", - " if column in str_columns:\n", - " df_rv[column] = df_rv[column].str.decode('utf-8')\n", - "df_rv" - ] - }, - { - "cell_type": "markdown", - "id": "82532c02", - "metadata": {}, - "source": [ - "# Create L3 file with time series RVs\n", - "The L3 file is a collection of RVs, state information and telemetry from a series of L2 files. The example below builds the L3 file for the series of Tau Ceti observations and then displays ti." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "82c041b0", - "metadata": {}, - "outputs": [], - "source": [ - "rv_headers = ['JD', \n", - " 'CCD1RV', 'CCD2RV', \n", - " 'CCD1RV1', 'CCD1RV2', 'CCD1RV3', \n", - " 'CCD2RV1', 'CCD2RV2', 'CCD2RV3', \n", - " 'CCD1JD', 'CCD2JD']\n", - "headers = ['DATE-OBS', 'DATE-BEG', 'DATE-MID', 'DATE-END', 'UT', \n", - " 'TEXP',\n", - " 'GREEN', 'RED', 'CA_HK', 'EXPMETER', \n", - " 'TARGNAME', 'TELESCOP',\n", - " 'PROGNAME', 'OBSERVER', 'OBJECT', 'TARGNAME',\n", - " 'TARGRA', 'TARGDEC', 'RA', 'DEC', \n", - " 'OCTAGON', 'FIUMODE', 'ADCTRACK', 'HCLSN',\n", - " 'SRCSHTTR', 'TIMSHTTR', 'OTIMSHTR', \n", - " 'SCISEL', 'SKYSEL', 'FFSHTR', 'SCRAMSHT', 'SIMCALSH',\n", - " 'IMTYPE', 'CAL-OBJ', 'SKY-OBJ', 'SCI-OBJ',\n", - " 'CAFBS', 'EXPSCIFB', 'EXPSKYFB', 'FFFB', 'FICALFBS', 'SICALFB ', 'VACSCFB ', 'VACSKFB ', 'SCIFB', 'SKYFB',\n", - " 'GRACFFLN', 'RDACFFLN',\n", - " 'AGITSTA',\n", - " 'SSCALFW', 'SIMCALFW',\n", - " 'PRES', 'RELH', \n", - " 'RABASE', 'DECBASE','RA', 'DEC', 'EL', 'AZ', 'LST', 'AXESTAT', 'GUIDING', 'AUTACTIV',\n", - " 'AUTFWHM',\n", - " 'SECFOCUS', 'TELFOCUS', 'TUBEFLEX', 'TUBETEMP', 'PRIMTEMP', 'SECMTEMP', 'DIFFPTDW', 'DIFFSTDW', \n", - " 'VIGNETTE', 'STVIGNE', 'SBVIGNE', 'SBELEV', 'STELEV', \n", - " ]\n", - "headers_bool = ['GREEN', 'RED', 'CA_HK', 'EXPMETER'\n", - " 'CAFBS', 'EXPSCIFB', 'EXPSKYFB', 'FFFB', 'FICALFBS', 'SICALFB ', 'VACSCFB ', 'VACSKFB ', 'SCIFB', 'SKYFB',\n", - " 'AUTACTIV',\n", - " 'VIGNETTE', 'STVIGNE', 'SBVIGNE',\n", - " ]\n", - "headers_time = ['DATE-OBS', 'DATE-BEG', 'DATE-MID', 'DATE-END']\n", - "headers_f64 = ['CCD1JD', 'CCD2JD']\n", - "all_columns = ['obsID'] + rv_headers + headers\n", - "\n", - "#To-do: add RV uncertainties, activity metrics, and additional telemetry (temperatures, etc.)\n", - "\n", - "L3 = pd.DataFrame(columns=all_columns, index=range(1, len(ObsIDs)))\n", - "for i in range(len(ObsIDs)):\n", - " L3.at[i,'obsID'] = ObsIDs[i]\n", - " with fits.open(L2_data_dir + ObsIDs[i] + '_L2.fits') as L2:\n", - " for h in headers:\n", - " if h in headers_bool:\n", - " if (L2['PRIMARY'].header[h] == 'YES') or (L2['PRIMARY'].header[h] == 'Yes') or (L2['PRIMARY'].header[h] == 'yes') or (L2['PRIMARY'].header[h] == 'true'):\n", - " L3.at[i,h] = True\n", - " if (L2['PRIMARY'].header[h] == 'NO') or (L2['PRIMARY'].header[h] == 'No') or (L2['PRIMARY'].header[h] == 'no') or (L2['PRIMARY'].header[h] == 'false'):\n", - " L3.at[i,h] = False\n", - " elif h in headers_time:\n", - " L3.at[i,h] = Time(L2['PRIMARY'].header[h], format='isot', scale='utc')\n", - " elif h == 'TEXP':\n", - " (L3.at[0,'DATE-END'] - L3.at[0,'DATE-BEG']).sec\n", - " else:\n", - " L3.at[i,h] = L2['PRIMARY'].header[h]\n", - " for h in rv_headers:\n", - " if h == 'JD':\n", - " if L3.at[i,'GREEN']:\n", - " L3.at[i,h] = np.float64(L2['RV'].header['CCD1JD'])\n", - " if L3.at[i,'RED']:\n", - " L3.at[i,h] = np.float64(L2['RV'].header['CCD2JD'])\n", - " elif h in headers_f64:\n", - " L3.at[i,h] = np.float64(L2['RV'].header[h])\n", - " else:\n", - " L3.at[i,h] = L2['RV'].header[h]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "090949a0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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keywordobsIDJDCCD1RVCCD2RVCCD1RV1CCD1RV2CCD1RV3CCD2RV1CCD2RV2CCD2RV3...TUBETEMPPRIMTEMPSECMTEMPDIFFPTDWDIFFSTDWVIGNETTESTVIGNESBVIGNESBELEVSTELEV
0KP.20230110.16623.722459954.692587-16.585353-16.728877-16.496449-16.591425-16.645702-17.185014-16.277578-16.914675...2.392.0425752.16958431.74257531.769584FalseFalseFalse23.98104.01
1KP.20230110.16803.312459954.694665-16.583159-16.729211-16.493994-16.589117-16.643846-17.184929-16.277798-16.9152...2.382.0199672.06962631.91996731.969626FalseFalseFalse23.98104.01
2KP.20230110.16986.842459954.696782-16.584377-16.728139-16.495132-16.590903-16.644465-17.183856-16.277265-16.913679...2.332.0293191.99178331.92931931.891783FalseFalseFalse23.98104.01
3KP.20230110.17094.842459954.698035-16.581968-16.729253-16.492783-16.588338-16.6422-17.184946-16.278056-16.914516...2.342.0206541.99178331.62065431.491783FalseFalseFalse23.98104.01
4KP.20230110.17186.852459954.699099-16.584495-16.729022-16.495813-16.590788-16.644448-17.18519-16.2776-16.91432...2.322.0260851.98205332.72608532.682053FalseFalseFalse23.98104.01
5KP.20230110.17279.122459954.700168-16.591628-16.72818-16.502983-16.597813-16.651663-17.184077-16.277782-16.914636...2.292.0291221.97851533.02912232.978515FalseFalseFalse23.98104.01
6KP.20230110.17371.412459954.701237-16.590533-16.728859-16.501783-16.596878-16.650447-17.185047-16.278163-16.915102...2.292.0649391.8927132.66493932.49271FalseFalseFalse23.98104.01
7KP.20230110.17463.292459954.702299-16.590101-16.72839-16.501853-16.596208-16.649906-17.184939-16.277577-16.914709...2.292.0297921.87590332.92979232.775903FalseFalseFalse23.98104.01
8KP.20230110.17580.342459954.703654-16.583366-16.72922-16.494517-16.589604-16.643495-17.184119-16.27845-16.914934...2.252.0324341.85555833.33243433.155558FalseFalseFalse23.98104.01
9KP.20230110.17672.602459954.704722-16.586729-16.728562-16.497864-16.592944-16.646888-17.184767-16.277349-16.914209...2.252.0286211.81486732.82862132.514867FalseFalseFalse23.98104.01
10KP.20230110.17764.892459954.70579-16.585602-16.728953-16.49665-16.591723-16.645903-17.184747-16.277705-16.915195...2.222.0423061.79629133.44230633.196291FalseFalseFalse23.98104.01
11KP.20230110.17856.972459954.706855-16.583152-16.730229-16.494366-16.589059-16.643587-17.185683-16.278486-16.916435...2.212.0245311.80602132.92453132.706021FalseFalseFalse23.98104.01
12KP.20230110.17949.222459954.707923-16.58432-16.72906-16.49583-16.590152-16.644617-17.185094-16.277611-16.914655...2.212.0411051.7343732.94110532.63437FalseFalseFalse23.98104.01
13KP.20230110.18179.542459954.710589-16.58178-16.729235-16.492407-16.58814-16.64217-17.185386-16.27781-16.914723...2.162.038051.72552431.5380531.225524FalseFalseFalse23.98104.01
14KP.20230110.18271.752459954.711656-16.58313-16.72954-16.493781-16.589197-16.643775-17.184877-16.278594-16.914729...2.152.0281831.6397232.22818331.83972FalseFalseFalse23.98104.01
15KP.20230110.18363.652459954.712721-16.582045-16.729361-16.493035-16.588146-16.642404-17.184745-16.278591-16.914226...2.152.0209851.59637631.82098531.396376FalseFalseFalse23.98104.01
16KP.20230110.18455.802459954.713787-16.581068-16.730119-16.49181-16.587402-16.641383-17.185744-16.278655-16.915466...2.122.0191121.62202831.61911231.222028FalseFalseFalse23.98104.01
17KP.20230110.18547.962459954.714854-16.581984-16.729576-16.49309-16.588206-16.642114-17.184903-16.278062-16.915427...2.12.0119481.53622432.61194832.136224FalseFalseFalse23.98104.01
18KP.20230110.18640.142459954.71592-16.582457-16.729342-16.493414-16.588736-16.64266-17.183931-16.278837-16.914921...2.112.0229131.5026133.52291333.00261FalseFalseFalse23.98104.01
19KP.20230110.18732.022459954.716984-16.58251-16.729896-16.493812-16.588584-16.642677-17.185192-16.278902-16.915323...2.092.031781.50084134.0317833.600841FalseFalseFalse23.98104.01
20KP.20230110.18823.972459954.718048-16.583076-16.729131-16.494105-16.589444-16.643095-17.185018-16.278327-16.913991...2.082.011271.48314934.5112733.983149FalseFalseFalse23.98104.01
21KP.20230110.18916.312459954.719116-16.58472-16.72971-16.495895-16.590824-16.644947-17.186572-16.278696-16.914599...2.072.031451.4389234.5314533.83892FalseFalseFalse23.98104.01
22KP.20230110.19440.982459954.725189-16.583024-16.729234-16.49392-16.589373-16.643201-17.185251-16.278054-16.914259...2.02.0228591.34515534.32285933.545155FalseFalseFalse23.98104.01
23KP.20230110.19533.162459954.726256-16.583523-16.728935-16.494546-16.58974-16.643728-17.183844-16.278015-16.914699...1.992.0118021.32392534.31180233.723925FalseFalseFalse23.98104.01
24KP.20230110.19625.302459954.727322-16.586753-16.730421-16.497797-16.593093-16.64683-17.186316-16.279511-16.915877...1.972.0234811.31507934.82348134.115079FalseFalseFalse23.98104.01
25KP.20230110.19717.192459954.728386-16.583053-16.728649-16.493576-16.589619-16.643271-17.183901-16.277587-16.914269...1.962.0223011.26465834.82230133.864658FalseFalseFalse23.98104.01
26KP.20230110.19809.152459954.729451-16.584398-16.730675-16.494997-16.590852-16.644669-17.186596-16.278929-16.916308...1.962.0354421.25138934.63544233.751389FalseFalseFalse23.98104.01
27KP.20230110.19901.192459954.730515-16.584161-16.730171-16.494961-16.590387-16.644522-17.186441-16.278959-16.914979...1.942.0273661.24696634.82736633.946966FalseFalseFalse23.98104.01
28KP.20230110.19993.432459954.731584-16.584828-16.729079-16.495445-16.591071-16.645289-17.18519-16.277999-16.914004...1.922.0161921.25138934.71619233.951389FalseFalseFalse23.98104.01
29KP.20230110.20085.432459954.732649-16.588626-16.730867-16.499556-16.594846-16.648878-17.185848-16.280165-16.916673...1.922.0343021.19831435.03430234.198314FalseFalseFalse23.98104.01
30KP.20230110.20177.432459954.733713-16.586157-16.729713-16.49671-16.592678-16.646365-17.185857-16.278541-16.915062...1.912.0286571.20273735.52865734.702737FalseFalseFalse23.98104.01
31KP.20230110.20377.952459954.736033-16.585375-16.729351-16.496212-16.591657-16.645657-17.185538-16.27799-16.914679...1.862.0340391.15320134.83403933.853201FalseFalseFalse23.98104.01
32KP.20230110.20470.202459954.737101-16.584817-16.728998-16.495631-16.59116-16.645047-17.184686-16.277588-16.9144...1.872.0288581.10808734.72885833.808087FalseFalseFalse23.98104.01
33KP.20230110.20562.482459954.738169-16.587568-16.729865-16.498611-16.59389-16.647619-17.186526-16.279577-16.913922...1.872.0355691.10631834.33556933.406318FalseFalseFalse23.98104.01
34KP.20230110.20654.182459954.739231-16.586172-16.729336-16.49782-16.592309-16.646013-17.185386-16.278009-16.914952...1.862.0252931.10543334.22529333.305433FalseFalseFalse23.98104.01kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS1.483580e+015.433410e-041.483400e+011.483700e+01degCBench Bottom Between Cameras C2 c- double degC...
kpfmet.BENCH_BOTTOM_COLLIMATOR1.527060e+015.191650e-041.526900e+011.527200e+01degCBench Bottom Coll C3 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_DCUT1.504410e+015.312330e-041.504300e+011.504600e+01degCBench Bottom D-cut C4 c- double degC {%.3f}
kpfmet.BENCH_BOTTOM_ECHELLE1.517080e+015.221560e-041.516900e+011.517200e+01degCBench Bottom Echelle Cam B c- double degC {%.3f}
kpfmet.BENCH_TOP_BETWEEN_CAMERAS1.476310e+015.260400e-041.476200e+011.476400e+01degCBench Top Between Cameras D4 c- double degC {%...
kpfmet.BENCH_TOP_COLL1.537070e+016.026400e-041.536900e+011.537300e+01degCBench Top Coll D5 c- double degC {%.3f}
kpfmet.BENCH_TOP_DCUT1.508190e+015.166630e-041.508100e+011.508300e+01degCBench Top D-cut D3 c- double degC {%.3f}
kpfmet.BENCH_TOP_ECHELLE_CAM1.510350e+015.589560e-041.510200e+011.510500e+01degCBench Top Echelle Cam D1 c- double degC {%.3f}
kpfmet.CALEM_SCMBLR_CHMBR_END1.557310e+012.691610e-031.556800e+011.558000e+01degCCal EM Scrammbler Chamber End C1 c- double deg...
kpfmet.CALEM_SCMBLR_FIBER_END1.542650e+012.545780e-031.542200e+011.543100e+01degCCal EM Scrambler Fiber End D1 c- double degC {...
kpfmet.CAL_BENCH1.896120e+014.874150e-021.890000e+011.900000e+01degCCal_Bench temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_BB_SRC1.910000e+011.641430e-061.910000e+011.910000e+01degCCAL_Bench_BB_Src temperature c- double degC {%...
kpfmet.CAL_BENCH_BOT2.237250e+014.467490e-022.230000e+012.240000e+01degCCal_Bench_Bot temperature c- double degC {%.1f}
kpfmet.CAL_BENCH_ENCL_AIR1.910000e+011.641430e-061.910000e+011.910000e+01degCCal_Bench_Encl_Air temperature c- double degC ...
kpfmet.CAL_BENCH_OCT_MOT1.910790e+012.497180e-011.870000e+011.970000e+01degCCal_Bench_Oct_Mot temperature c- double degC {...
kpfmet.CAL_BENCH_TRANS_STG_MOT1.917330e+011.005580e-011.900000e+011.940000e+01degCCal_Bench_Trans_Stg_Mot temperature c- double ...
kpfmet.CAL_RACK_TOP2.112580e+014.377600e-022.110000e+012.120000e+01degCCal_Rack_Top temperature c- double degC {%.1f}
kpfmet.CHAMBER_EXT_BOTTOM1.411820e+012.057170e-031.411300e+011.412200e+01degCChamber Exterior Bottom B c- double degC {%.3f}
kpfmet.CHAMBER_EXT_TOP-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCChamber Exterior Top C1 c- double degC {%.3f}
kpfmet.CRYOSTAT_G1-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCWithin cryostat green D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_G2-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCWithin cryostat green D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_G31.693110e+011.211280e-031.692800e+011.693400e+01degCWithin cryostat green D4 c- double degC {%.3f}
kpfmet.CRYOSTAT_R11.619120e+017.576270e-041.618900e+011.619300e+01degCWithin Cryostat red D2 c- double degC {%.3f}
kpfmet.CRYOSTAT_R2-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCWithin Cryostat red D3 c- double degC {%.3f}
kpfmet.CRYOSTAT_R31.573390e+017.458020e-041.573200e+011.573600e+01degCWithin Cryostat red D4 c- double degC {%.3f}
kpfmet.ECHELLE_BOTTOM1.519130e+014.757220e-041.519000e+011.519200e+01degCEchelle Bottom D1 c- double degC {%.3f}
kpfmet.ECHELLE_TOP1.504020e+014.932660e-041.503900e+011.504100e+01degCEchelle Top C1 c- double degC {%.3f}
kpfmet.FF_SRC1.815000e+015.002360e-021.810000e+011.820000e+01degCFF_Src temperature c- double degC {%.1f}
kpfmet.GREEN_CAMERA_BOTTOM1.573110e+017.309630e-041.572900e+011.573300e+01degCGreen Camera Bottom C3 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_COLLIMATOR1.577890e+016.735330e-041.577700e+011.578200e+01degCGreen Camera Collimator C4 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_ECHELLE1.567970e+017.110530e-041.567800e+011.568400e+01degCGreen Camera Echelle D5 c- double degC {%.3f}
kpfmet.GREEN_CAMERA_TOP1.558570e+017.078670e-041.558400e+011.558800e+01degCGreen Camera Top C2 c- double degC {%.3f}
kpfmet.GREEN_GRISM_TOP1.525500e+017.296770e-041.525300e+011.525700e+01degCGreen Grism Top C5 c- double degC {%.3f}
kpfmet.GREEN_LN2_FLANGE7.506140e+004.209020e-027.408000e+007.613000e+00degCGreen LN2 Flange A c- double degC {%.3f}
kpfmet.PRIMARY_COLLIMATOR_TOP1.564220e+014.603570e-041.564100e+011.564300e+01degCPrimary Col Top D2 c- double degC {%.3f}
kpfmet.RED_CAMERA_BOTTOM1.524030e+016.096150e-041.523900e+011.524200e+01degCRed Camera Bottom D5 c- double degC {%.3f}
kpfmet.RED_CAMERA_COLLIMATOR1.558040e+017.093410e-041.557900e+011.558200e+01degCRed Camera Coll C3 c- double degC {%.3f}
kpfmet.RED_CAMERA_ECHELLE1.552650e+016.935470e-041.552500e+011.552800e+01degCRed Camera Ech C4 c- double degC {%.3f}
kpfmet.RED_CAMERA_TOP1.548590e+016.706720e-041.548400e+011.548700e+01degCRed Camera Top C5 c- double degC {%.3f}
kpfmet.RED_GRISM_TOP1.522710e+017.658890e-041.522500e+011.522900e+01degCRed Grism Top C2 c- double degC {%.3f}
kpfmet.RED_LN2_FLANGE5.827260e+002.805880e-025.742000e+005.898000e+00degCRed LN2 Flange D1 c- double degC {%.3f}
kpfmet.REFORMATTER1.534500e+013.928870e-041.534400e+011.534600e+01degCReformatter A c- double degC {%.3f}
kpfmet.SCIENCE_CAL_FIBER_STG1.910000e+011.641430e-061.910000e+011.910000e+01degCScience_Cal_Fiber_Stg temperature c- double de...
kpfmet.SCISKY_SCMBLR_CHMBR_END-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCSciSky Scrambler Chamber End A c- double degC ...
kpfmet.SCISKY_SCMBLR_FIBER_END1.557920e+013.036740e-031.557300e+011.558500e+01degCSciSky Scrammbler Fiber End B c- double degC {...
kpfmet.SIMCAL_FIBER_STG1.880000e+013.755280e-061.880000e+011.880000e+01degCSimCal_Fiber_Stg temperature c- double degC {%...
kpfmet.SKYCAL_FIBER_STG1.890000e+010.000000e+001.890000e+011.890000e+01degCSkyCal_Fiber_Stg temperature c- double degC {%...
kpfmet.TEMP1.789910e+011.242890e-021.786900e+011.792700e+01degCVaisala Temperature c- double degC {%.3f}
kpfmet.TH_DAILY2.296740e+017.216640e-012.170000e+012.390000e+01degCTh_daily temperature c- double degC {%.1f}
kpfmet.TH_GOLD1.890000e+010.000000e+001.890000e+011.890000e+01degCTh_gold temperature c- double degC {%.1f}
kpfmet.U_DAILY2.187710e+012.410030e+001.880000e+012.600000e+01degCU_daily temperature c- double degC {%.1f}
kpfmet.U_GOLD1.860080e+018.666810e-031.860000e+011.870000e+01degCU_gold temperature c- double degC {%.1f}
kpfgreen.BPLANE_TEMP3.604550e+012.960890e-023.600000e+013.612500e+01degCBackplane temperature c- double degC {%.3f}
kpfgreen.BRD10_DRVR_T4.033100e+015.731550e-024.025000e+014.062500e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfgreen.BRD11_DRVR_T3.844200e+011.704040e-023.843700e+013.850000e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfgreen.BRD12_LVXBIAS_T3.439430e+012.871260e-023.437500e+013.443700e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfgreen.BRD1_HTRX_T3.281510e+011.362580e-023.281200e+013.287500e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfgreen.BRD2_XVBIAS_T3.472000e+013.147680e-023.468700e+013.475000e+01degCBoard 2 (XV Bias) temperature c- double degC {...
kpfgreen.BRD3_LVDS_T3.406200e+010.000000e+003.406200e+013.406200e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfgreen.BRD4_DRVR_T3.698330e+012.783710e-023.693700e+013.700000e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfgreen.BRD5_AD_T3.313130e+012.734880e-023.306200e+013.318700e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfgreen.BRD7_HTRX_T3.746470e+013.128340e-023.743700e+013.750000e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfgreen.BRD9_HVXBIAS_T3.606380e+012.197080e-023.600000e+013.612500e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfgreen.CF_BASE_2WT-1.271340e+021.225080e-03-1.271360e+02-1.271310e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfgreen.CF_BASE_T-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfgreen.CF_BASE_TRG-1.275000e+020.000000e+00-1.275000e+02-1.275000e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfgreen.CF_TIP_T-1.280000e+021.025910e-03-1.280020e+02-1.279980e+02degCtip cold finger c- double degC {%.3f}
kpfgreen.CF_TIP_TRG-1.280000e+020.000000e+00-1.280000e+02-1.280000e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfgreen.COL_PRESS1.200000e-070.000000e+001.200000e-071.200000e-07TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.CRYOBODY_T1.899930e+012.107870e-031.899400e+011.900400e+01degCCryo Body Temperature c- double degC {%.3f}
kpfgreen.CRYOBODY_TRG1.900000e+010.000000e+001.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfgreen.CURRTEMP-1.400000e+021.689700e-03-1.400040e+02-1.399960e+02degCCurrent cold head temperature c- double degC {...
kpfgreen.ECH_PRESS1.000000e-111.615340e-181.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfgreen.KPF_CCD_T-1.010510e+021.121940e-03-1.010540e+02-1.010480e+02degCSSL Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_T-1.000000e+021.197860e-03-1.000040e+02-9.999500e+01degCSTA Detector temperature c- double degC {%.3f}
kpfgreen.STA_CCD_TRG-1.000000e+020.000000e+00-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfgreen.TEMPSET-1.400000e+020.000000e+00-1.400000e+02-1.400000e+02degCSet point for the cold head temperature c2 dou...
kpfred.BPLANE_TEMP4.239640e+012.950560e-024.237500e+014.243700e+01degCBackplane temperature c- double degC {%.3f}
kpfred.BRD10_DRVR_T4.657900e+014.034890e-024.650000e+014.675000e+01degCBoard 10 (Driver) temperature c- double degC {...
kpfred.BRD11_DRVR_T4.586460e+012.861250e-024.581200e+014.593700e+01degCBoard 11 (Driver) temperature c- double degC {...
kpfred.BRD12_LVXBIAS_T4.213570e+013.482020e-024.206200e+014.218700e+01degCBoard 12 (LVxBias) temperature c- double degC ...
kpfred.BRD1_HTRX_T3.973850e+013.022770e-023.962500e+013.981200e+01degCBoard 1 (HeaterX) temperature c- double degC {...
kpfred.BRD2_XVBIAS_T4.148590e+012.991190e-024.143700e+014.156200e+01degCBoard 2 (XV Bias) temperature c- double degC {...
kpfred.BRD3_LVDS_T4.128600e+013.060210e-024.125000e+014.131200e+01degCBoard 3 (LVDS) temperature c- double degC {%.3f}
kpfred.BRD4_DRVR_T4.425990e+012.789750e-024.418700e+014.431200e+01degCBoard 4 (Driver) temperature c- double degC {%...
kpfred.BRD5_AD_T3.908470e+014.649120e-023.900000e+013.918700e+01degCBoard 5 (AD) temperature c- double degC {%.3f}
kpfred.BRD7_HTRX_T4.386850e+013.391160e-024.375000e+014.393700e+01degCBoard 7 (HeaterX) temperature c- double degC {...
kpfred.BRD9_HVXBIAS_T4.359600e+013.233490e-024.350000e+014.362500e+01degCBoard 9 (HVxBias) temperature c- double degC {...
kpfred.CF_BASE_2WT-1.257910e+021.258060e-03-1.257950e+02-1.257890e+02degCtip cold finger (2 wire) c- double degC {%.3f}
kpfred.CF_BASE_T-2.731500e+022.871130e-05-2.731500e+02-2.731500e+02degCbase cold finger 2wire temp c- double degC {%.3f}
kpfred.CF_BASE_TRG-1.277500e+020.000000e+00-1.277500e+02-1.277500e+02degCbase cold finger heater 1A, target temp c2 dou...
kpfred.CF_TIP_T-1.271500e+029.500420e-04-1.271520e+02-1.271480e+02degCtip cold finger c- double degC {%.3f}
kpfred.CF_TIP_TRG-1.271500e+022.507640e-05-1.271500e+02-1.271500e+02degCtip cold finger heater 1B, target temp c2 doub...
kpfred.COL_PRESS3.793480e-082.470260e-103.700000e-083.800000e-08TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.CRYOBODY_T1.900020e+011.799820e-031.899500e+011.900500e+01degCCryo Body Temperature c- double degC {%.3f}
kpfred.CRYOBODY_TRG1.900000e+010.000000e+001.900000e+011.900000e+01degCCryo body heater 7B, target temp c2 double deg...
kpfred.CURRTEMP-1.405010e+022.325550e-02-1.405690e+02-1.404480e+02degCCurrent cold head temperature c- double degC {...
kpfred.ECH_PRESS1.000000e-111.615340e-181.000000e-111.000000e-11TorrCurrent ion pump pressure c- double Torr {%.3e}
kpfred.KPF_CCD_T-1.007930e+021.434590e-03-1.007960e+02-1.007890e+02degCSSL Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_T-1.000000e+021.236360e-03-1.000030e+02-9.999600e+01degCSTA Detector temperature c- double degC {%.3f}
kpfred.STA_CCD_TRG-1.000000e+020.000000e+00-1.000000e+02-1.000000e+02degCDetector heater 7A, target temp c2 double degC...
kpfred.TEMPSET-1.405000e+020.000000e+00-1.405000e+02-1.405000e+02degCSet point for the cold head temperature c2 dou...
kpfexpose.BENCH_C1.289100e+017.137450e-021.270000e+011.310000e+01degCrtd bench c- double degC {%.1f} { -100.0 .. 10...
kpfexpose.CAMBARREL_C1.357630e+017.530780e-021.340000e+011.380000e+01degCrtd camera barrel c- double degC {%.1f} { -100...
kpfexpose.DET_XTRN_C1.775630e+017.672690e-021.750000e+011.800000e+01degCrtd detector extermal c- double degC {%.1f} { ...
kpfexpose.ECHELLE_C1.154080e+018.762650e-021.130000e+011.190000e+01degCrtd echelle c- double degC {%.1f} { -100.0 .. ...
kpfexpose.ENCLOSURE_C1.622860e+016.540930e-021.600000e+011.640000e+01degCrtd enclosure c- double degC {%.1f} { -100.0 ....
kpfexpose.RACK_AIR_C1.645330e+016.146600e-021.620000e+011.660000e+01degCrtd rack air c- double degC {%.1f} { -100.0 .....
kpfvac.PUMP_TEMP3.900000e+010.000000e+003.900000e+013.900000e+01degCMotor temperature c- double degC {%.2f}
kpf_hk.COOLTARG-6.000000e+010.000000e+00-6.000000e+01-6.000000e+01degCtemperature target c2 int degC
kpf_hk.CURRTEMP-5.978000e+019.710960e-06-5.978000e+01-5.978000e+01degCcurrent temperature c- double degC {%.2f}
kpfgreen.COL_CURR3.258700e-064.926130e-083.200000e-063.300000e-06TorrCurrent ion pump current c- double A {%.3e}
kpfgreen.ECH_CURR8.868580e-111.890010e-128.350000e-119.330000e-11TorrCurrent ion pump current c- double A {%.3e}
kpfred.COL_CURR9.999620e-076.140020e-109.900000e-071.000000e-06TorrCurrent ion pump current c- double A {%.3e}
kpfred.ECH_CURR5.600000e-120.000000e+005.600000e-125.600000e-12TorrCurrent ion pump current c- double A {%.3e}
kpfcal.IRFLUX4.281660e-014.950470e-010.000000e+001.000000e+00TorrLFC Fiberlock IR Intensity c- int Counts {%d}
kpfcal.VISFLUX-1.847450e+015.071030e-01-2.000000e+01-1.800000e+01TorrLFC Fiberlock Vis Intensity c- int Counts {%d}
kpfcal.BLUECUTIACT0.000000e+000.000000e+000.000000e+000.000000e+00TorrBlue cut amplifier 0 measured current c- doubl...
kpfmot.AGITSPD0.000000e+000.000000e+000.000000e+000.000000e+00motor_counts/sagit raw velocity c2 int motor counts/s { -750...
kpfmot.AGITTOR0.000000e+000.000000e+000.000000e+000.000000e+00Vagit motor torque c- double V {%.3f}
kpfmot.AGITAMBI_T2.572010e+012.185980e-022.564000e+012.579000e+01degCAgitator ambient temperature c- double degC {%...
kpfmot.AGITMOT_T2.590800e+012.002300e-022.584000e+012.596000e+01degCAgitator motor temperature c- double degC {%.2...
kpfpower.OUTLET_A1_Amps2.698300e+011.293820e-012.600000e+012.700000e+01milliampsOutlet A1 current amperage c- int milliamps
\n", - "

35 rows × 89 columns

\n", "
" ], "text/plain": [ - " obsID JD CCD1RV CCD2RV CCD1RV1 \\\n", - "0 KP.20230110.16623.72 2459954.692587 -16.585353 -16.728877 -16.496449 \n", - "1 KP.20230110.16803.31 2459954.694665 -16.583159 -16.729211 -16.493994 \n", - "2 KP.20230110.16986.84 2459954.696782 -16.584377 -16.728139 -16.495132 \n", - "3 KP.20230110.17094.84 2459954.698035 -16.581968 -16.729253 -16.492783 \n", - "4 KP.20230110.17186.85 2459954.699099 -16.584495 -16.729022 -16.495813 \n", - "5 KP.20230110.17279.12 2459954.700168 -16.591628 -16.72818 -16.502983 \n", - "6 KP.20230110.17371.41 2459954.701237 -16.590533 -16.728859 -16.501783 \n", - "7 KP.20230110.17463.29 2459954.702299 -16.590101 -16.72839 -16.501853 \n", - "8 KP.20230110.17580.34 2459954.703654 -16.583366 -16.72922 -16.494517 \n", - "9 KP.20230110.17672.60 2459954.704722 -16.586729 -16.728562 -16.497864 \n", - "10 KP.20230110.17764.89 2459954.70579 -16.585602 -16.728953 -16.49665 \n", - "11 KP.20230110.17856.97 2459954.706855 -16.583152 -16.730229 -16.494366 \n", - "12 KP.20230110.17949.22 2459954.707923 -16.58432 -16.72906 -16.49583 \n", - "13 KP.20230110.18179.54 2459954.710589 -16.58178 -16.729235 -16.492407 \n", - "14 KP.20230110.18271.75 2459954.711656 -16.58313 -16.72954 -16.493781 \n", - "15 KP.20230110.18363.65 2459954.712721 -16.582045 -16.729361 -16.493035 \n", - "16 KP.20230110.18455.80 2459954.713787 -16.581068 -16.730119 -16.49181 \n", - "17 KP.20230110.18547.96 2459954.714854 -16.581984 -16.729576 -16.49309 \n", - "18 KP.20230110.18640.14 2459954.71592 -16.582457 -16.729342 -16.493414 \n", - "19 KP.20230110.18732.02 2459954.716984 -16.58251 -16.729896 -16.493812 \n", - "20 KP.20230110.18823.97 2459954.718048 -16.583076 -16.729131 -16.494105 \n", - "21 KP.20230110.18916.31 2459954.719116 -16.58472 -16.72971 -16.495895 \n", - "22 KP.20230110.19440.98 2459954.725189 -16.583024 -16.729234 -16.49392 \n", - "23 KP.20230110.19533.16 2459954.726256 -16.583523 -16.728935 -16.494546 \n", - "24 KP.20230110.19625.30 2459954.727322 -16.586753 -16.730421 -16.497797 \n", - "25 KP.20230110.19717.19 2459954.728386 -16.583053 -16.728649 -16.493576 \n", - "26 KP.20230110.19809.15 2459954.729451 -16.584398 -16.730675 -16.494997 \n", - "27 KP.20230110.19901.19 2459954.730515 -16.584161 -16.730171 -16.494961 \n", - "28 KP.20230110.19993.43 2459954.731584 -16.584828 -16.729079 -16.495445 \n", - "29 KP.20230110.20085.43 2459954.732649 -16.588626 -16.730867 -16.499556 \n", - "30 KP.20230110.20177.43 2459954.733713 -16.586157 -16.729713 -16.49671 \n", - "31 KP.20230110.20377.95 2459954.736033 -16.585375 -16.729351 -16.496212 \n", - "32 KP.20230110.20470.20 2459954.737101 -16.584817 -16.728998 -16.495631 \n", - "33 KP.20230110.20562.48 2459954.738169 -16.587568 -16.729865 -16.498611 \n", - "34 KP.20230110.20654.18 2459954.739231 -16.586172 -16.729336 -16.49782 \n", - "\n", - " CCD1RV2 CCD1RV3 CCD2RV1 CCD2RV2 CCD2RV3 ... TUBETEMP \\\n", - "0 -16.591425 -16.645702 -17.185014 -16.277578 -16.914675 ... 2.39 \n", - "1 -16.589117 -16.643846 -17.184929 -16.277798 -16.9152 ... 2.38 \n", - "2 -16.590903 -16.644465 -17.183856 -16.277265 -16.913679 ... 2.33 \n", - "3 -16.588338 -16.6422 -17.184946 -16.278056 -16.914516 ... 2.34 \n", - "4 -16.590788 -16.644448 -17.18519 -16.2776 -16.91432 ... 2.32 \n", - "5 -16.597813 -16.651663 -17.184077 -16.277782 -16.914636 ... 2.29 \n", - "6 -16.596878 -16.650447 -17.185047 -16.278163 -16.915102 ... 2.29 \n", - "7 -16.596208 -16.649906 -17.184939 -16.277577 -16.914709 ... 2.29 \n", - "8 -16.589604 -16.643495 -17.184119 -16.27845 -16.914934 ... 2.25 \n", - "9 -16.592944 -16.646888 -17.184767 -16.277349 -16.914209 ... 2.25 \n", - "10 -16.591723 -16.645903 -17.184747 -16.277705 -16.915195 ... 2.22 \n", - "11 -16.589059 -16.643587 -17.185683 -16.278486 -16.916435 ... 2.21 \n", - "12 -16.590152 -16.644617 -17.185094 -16.277611 -16.914655 ... 2.21 \n", - "13 -16.58814 -16.64217 -17.185386 -16.27781 -16.914723 ... 2.16 \n", - "14 -16.589197 -16.643775 -17.184877 -16.278594 -16.914729 ... 2.15 \n", - "15 -16.588146 -16.642404 -17.184745 -16.278591 -16.914226 ... 2.15 \n", - "16 -16.587402 -16.641383 -17.185744 -16.278655 -16.915466 ... 2.12 \n", - "17 -16.588206 -16.642114 -17.184903 -16.278062 -16.915427 ... 2.1 \n", - "18 -16.588736 -16.64266 -17.183931 -16.278837 -16.914921 ... 2.11 \n", - "19 -16.588584 -16.642677 -17.185192 -16.278902 -16.915323 ... 2.09 \n", - "20 -16.589444 -16.643095 -17.185018 -16.278327 -16.913991 ... 2.08 \n", - "21 -16.590824 -16.644947 -17.186572 -16.278696 -16.914599 ... 2.07 \n", - "22 -16.589373 -16.643201 -17.185251 -16.278054 -16.914259 ... 2.0 \n", - "23 -16.58974 -16.643728 -17.183844 -16.278015 -16.914699 ... 1.99 \n", - "24 -16.593093 -16.64683 -17.186316 -16.279511 -16.915877 ... 1.97 \n", - "25 -16.589619 -16.643271 -17.183901 -16.277587 -16.914269 ... 1.96 \n", - "26 -16.590852 -16.644669 -17.186596 -16.278929 -16.916308 ... 1.96 \n", - "27 -16.590387 -16.644522 -17.186441 -16.278959 -16.914979 ... 1.94 \n", - "28 -16.591071 -16.645289 -17.18519 -16.277999 -16.914004 ... 1.92 \n", - "29 -16.594846 -16.648878 -17.185848 -16.280165 -16.916673 ... 1.92 \n", - "30 -16.592678 -16.646365 -17.185857 -16.278541 -16.915062 ... 1.91 \n", - "31 -16.591657 -16.645657 -17.185538 -16.27799 -16.914679 ... 1.86 \n", - "32 -16.59116 -16.645047 -17.184686 -16.277588 -16.9144 ... 1.87 \n", - "33 -16.59389 -16.647619 -17.186526 -16.279577 -16.913922 ... 1.87 \n", - "34 -16.592309 -16.646013 -17.185386 -16.278009 -16.914952 ... 1.86 \n", - "\n", - " PRIMTEMP SECMTEMP DIFFPTDW DIFFSTDW VIGNETTE STVIGNE SBVIGNE SBELEV \\\n", - "0 2.042575 2.169584 31.742575 31.769584 False False False 23.98 \n", - "1 2.019967 2.069626 31.919967 31.969626 False False False 23.98 \n", - "2 2.029319 1.991783 31.929319 31.891783 False False False 23.98 \n", - "3 2.020654 1.991783 31.620654 31.491783 False False False 23.98 \n", - "4 2.026085 1.982053 32.726085 32.682053 False False False 23.98 \n", - "5 2.029122 1.978515 33.029122 32.978515 False False False 23.98 \n", - "6 2.064939 1.89271 32.664939 32.49271 False False False 23.98 \n", - "7 2.029792 1.875903 32.929792 32.775903 False False False 23.98 \n", - "8 2.032434 1.855558 33.332434 33.155558 False False False 23.98 \n", - "9 2.028621 1.814867 32.828621 32.514867 False False False 23.98 \n", - "10 2.042306 1.796291 33.442306 33.196291 False False False 23.98 \n", - "11 2.024531 1.806021 32.924531 32.706021 False False False 23.98 \n", - "12 2.041105 1.73437 32.941105 32.63437 False False False 23.98 \n", - "13 2.03805 1.725524 31.53805 31.225524 False False False 23.98 \n", - "14 2.028183 1.63972 32.228183 31.83972 False False False 23.98 \n", - "15 2.020985 1.596376 31.820985 31.396376 False False False 23.98 \n", - "16 2.019112 1.622028 31.619112 31.222028 False False False 23.98 \n", - "17 2.011948 1.536224 32.611948 32.136224 False False False 23.98 \n", - "18 2.022913 1.50261 33.522913 33.00261 False False False 23.98 \n", - "19 2.03178 1.500841 34.03178 33.600841 False False False 23.98 \n", - "20 2.01127 1.483149 34.51127 33.983149 False False False 23.98 \n", - "21 2.03145 1.43892 34.53145 33.83892 False False False 23.98 \n", - "22 2.022859 1.345155 34.322859 33.545155 False False False 23.98 \n", - "23 2.011802 1.323925 34.311802 33.723925 False False False 23.98 \n", - "24 2.023481 1.315079 34.823481 34.115079 False False False 23.98 \n", - "25 2.022301 1.264658 34.822301 33.864658 False False False 23.98 \n", - "26 2.035442 1.251389 34.635442 33.751389 False False False 23.98 \n", - "27 2.027366 1.246966 34.827366 33.946966 False False False 23.98 \n", - "28 2.016192 1.251389 34.716192 33.951389 False False False 23.98 \n", - "29 2.034302 1.198314 35.034302 34.198314 False False False 23.98 \n", - "30 2.028657 1.202737 35.528657 34.702737 False False False 23.98 \n", - "31 2.034039 1.153201 34.834039 33.853201 False False False 23.98 \n", - "32 2.028858 1.108087 34.728858 33.808087 False False False 23.98 \n", - "33 2.035569 1.106318 34.335569 33.406318 False False False 23.98 \n", - "34 2.025293 1.105433 34.225293 33.305433 False False False 23.98 \n", + " average stddev min \\\n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.483580e+01 5.433410e-04 1.483400e+01 \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.527060e+01 5.191650e-04 1.526900e+01 \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.504410e+01 5.312330e-04 1.504300e+01 \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.517080e+01 5.221560e-04 1.516900e+01 \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.476310e+01 5.260400e-04 1.476200e+01 \n", + "kpfmet.BENCH_TOP_COLL 1.537070e+01 6.026400e-04 1.536900e+01 \n", + "kpfmet.BENCH_TOP_DCUT 1.508190e+01 5.166630e-04 1.508100e+01 \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.510350e+01 5.589560e-04 1.510200e+01 \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.557310e+01 2.691610e-03 1.556800e+01 \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.542650e+01 2.545780e-03 1.542200e+01 \n", + "kpfmet.CAL_BENCH 1.896120e+01 4.874150e-02 1.890000e+01 \n", + "kpfmet.CAL_BENCH_BB_SRC 1.910000e+01 1.641430e-06 1.910000e+01 \n", + "kpfmet.CAL_BENCH_BOT 2.237250e+01 4.467490e-02 2.230000e+01 \n", + "kpfmet.CAL_BENCH_ENCL_AIR 1.910000e+01 1.641430e-06 1.910000e+01 \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.910790e+01 2.497180e-01 1.870000e+01 \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 1.917330e+01 1.005580e-01 1.900000e+01 \n", + "kpfmet.CAL_RACK_TOP 2.112580e+01 4.377600e-02 2.110000e+01 \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.411820e+01 2.057170e-03 1.411300e+01 \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_G3 1.693110e+01 1.211280e-03 1.692800e+01 \n", + "kpfmet.CRYOSTAT_R1 1.619120e+01 7.576270e-04 1.618900e+01 \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfmet.CRYOSTAT_R3 1.573390e+01 7.458020e-04 1.573200e+01 \n", + "kpfmet.ECHELLE_BOTTOM 1.519130e+01 4.757220e-04 1.519000e+01 \n", + "kpfmet.ECHELLE_TOP 1.504020e+01 4.932660e-04 1.503900e+01 \n", + "kpfmet.FF_SRC 1.815000e+01 5.002360e-02 1.810000e+01 \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.573110e+01 7.309630e-04 1.572900e+01 \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.577890e+01 6.735330e-04 1.577700e+01 \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.567970e+01 7.110530e-04 1.567800e+01 \n", + "kpfmet.GREEN_CAMERA_TOP 1.558570e+01 7.078670e-04 1.558400e+01 \n", + "kpfmet.GREEN_GRISM_TOP 1.525500e+01 7.296770e-04 1.525300e+01 \n", + "kpfmet.GREEN_LN2_FLANGE 7.506140e+00 4.209020e-02 7.408000e+00 \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.564220e+01 4.603570e-04 1.564100e+01 \n", + "kpfmet.RED_CAMERA_BOTTOM 1.524030e+01 6.096150e-04 1.523900e+01 \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.558040e+01 7.093410e-04 1.557900e+01 \n", + "kpfmet.RED_CAMERA_ECHELLE 1.552650e+01 6.935470e-04 1.552500e+01 \n", + "kpfmet.RED_CAMERA_TOP 1.548590e+01 6.706720e-04 1.548400e+01 \n", + "kpfmet.RED_GRISM_TOP 1.522710e+01 7.658890e-04 1.522500e+01 \n", + "kpfmet.RED_LN2_FLANGE 5.827260e+00 2.805880e-02 5.742000e+00 \n", + "kpfmet.REFORMATTER 1.534500e+01 3.928870e-04 1.534400e+01 \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 1.910000e+01 1.641430e-06 1.910000e+01 \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.557920e+01 3.036740e-03 1.557300e+01 \n", + "kpfmet.SIMCAL_FIBER_STG 1.880000e+01 3.755280e-06 1.880000e+01 \n", + "kpfmet.SKYCAL_FIBER_STG 1.890000e+01 0.000000e+00 1.890000e+01 \n", + "kpfmet.TEMP 1.789910e+01 1.242890e-02 1.786900e+01 \n", + "kpfmet.TH_DAILY 2.296740e+01 7.216640e-01 2.170000e+01 \n", + "kpfmet.TH_GOLD 1.890000e+01 0.000000e+00 1.890000e+01 \n", + "kpfmet.U_DAILY 2.187710e+01 2.410030e+00 1.880000e+01 \n", + "kpfmet.U_GOLD 1.860080e+01 8.666810e-03 1.860000e+01 \n", + "kpfgreen.BPLANE_TEMP 3.604550e+01 2.960890e-02 3.600000e+01 \n", + "kpfgreen.BRD10_DRVR_T 4.033100e+01 5.731550e-02 4.025000e+01 \n", + "kpfgreen.BRD11_DRVR_T 3.844200e+01 1.704040e-02 3.843700e+01 \n", + "kpfgreen.BRD12_LVXBIAS_T 3.439430e+01 2.871260e-02 3.437500e+01 \n", + "kpfgreen.BRD1_HTRX_T 3.281510e+01 1.362580e-02 3.281200e+01 \n", + "kpfgreen.BRD2_XVBIAS_T 3.472000e+01 3.147680e-02 3.468700e+01 \n", + "kpfgreen.BRD3_LVDS_T 3.406200e+01 0.000000e+00 3.406200e+01 \n", + "kpfgreen.BRD4_DRVR_T 3.698330e+01 2.783710e-02 3.693700e+01 \n", + "kpfgreen.BRD5_AD_T 3.313130e+01 2.734880e-02 3.306200e+01 \n", + "kpfgreen.BRD7_HTRX_T 3.746470e+01 3.128340e-02 3.743700e+01 \n", + "kpfgreen.BRD9_HVXBIAS_T 3.606380e+01 2.197080e-02 3.600000e+01 \n", + "kpfgreen.CF_BASE_2WT -1.271340e+02 1.225080e-03 -1.271360e+02 \n", + "kpfgreen.CF_BASE_T -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 0.000000e+00 -1.275000e+02 \n", + "kpfgreen.CF_TIP_T -1.280000e+02 1.025910e-03 -1.280020e+02 \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 0.000000e+00 -1.280000e+02 \n", + "kpfgreen.COL_PRESS 1.200000e-07 0.000000e+00 1.200000e-07 \n", + "kpfgreen.CRYOBODY_T 1.899930e+01 2.107870e-03 1.899400e+01 \n", + "kpfgreen.CRYOBODY_TRG 1.900000e+01 0.000000e+00 1.900000e+01 \n", + "kpfgreen.CURRTEMP -1.400000e+02 1.689700e-03 -1.400040e+02 \n", + "kpfgreen.ECH_PRESS 1.000000e-11 1.615340e-18 1.000000e-11 \n", + "kpfgreen.KPF_CCD_T -1.010510e+02 1.121940e-03 -1.010540e+02 \n", + "kpfgreen.STA_CCD_T -1.000000e+02 1.197860e-03 -1.000040e+02 \n", + "kpfgreen.STA_CCD_TRG -1.000000e+02 0.000000e+00 -1.000000e+02 \n", + "kpfgreen.TEMPSET -1.400000e+02 0.000000e+00 -1.400000e+02 \n", + "kpfred.BPLANE_TEMP 4.239640e+01 2.950560e-02 4.237500e+01 \n", + "kpfred.BRD10_DRVR_T 4.657900e+01 4.034890e-02 4.650000e+01 \n", + "kpfred.BRD11_DRVR_T 4.586460e+01 2.861250e-02 4.581200e+01 \n", + "kpfred.BRD12_LVXBIAS_T 4.213570e+01 3.482020e-02 4.206200e+01 \n", + "kpfred.BRD1_HTRX_T 3.973850e+01 3.022770e-02 3.962500e+01 \n", + "kpfred.BRD2_XVBIAS_T 4.148590e+01 2.991190e-02 4.143700e+01 \n", + "kpfred.BRD3_LVDS_T 4.128600e+01 3.060210e-02 4.125000e+01 \n", + "kpfred.BRD4_DRVR_T 4.425990e+01 2.789750e-02 4.418700e+01 \n", + "kpfred.BRD5_AD_T 3.908470e+01 4.649120e-02 3.900000e+01 \n", + "kpfred.BRD7_HTRX_T 4.386850e+01 3.391160e-02 4.375000e+01 \n", + "kpfred.BRD9_HVXBIAS_T 4.359600e+01 3.233490e-02 4.350000e+01 \n", + "kpfred.CF_BASE_2WT -1.257910e+02 1.258060e-03 -1.257950e+02 \n", + "kpfred.CF_BASE_T -2.731500e+02 2.871130e-05 -2.731500e+02 \n", + "kpfred.CF_BASE_TRG -1.277500e+02 0.000000e+00 -1.277500e+02 \n", + "kpfred.CF_TIP_T -1.271500e+02 9.500420e-04 -1.271520e+02 \n", + "kpfred.CF_TIP_TRG -1.271500e+02 2.507640e-05 -1.271500e+02 \n", + "kpfred.COL_PRESS 3.793480e-08 2.470260e-10 3.700000e-08 \n", + "kpfred.CRYOBODY_T 1.900020e+01 1.799820e-03 1.899500e+01 \n", + "kpfred.CRYOBODY_TRG 1.900000e+01 0.000000e+00 1.900000e+01 \n", + "kpfred.CURRTEMP -1.405010e+02 2.325550e-02 -1.405690e+02 \n", + "kpfred.ECH_PRESS 1.000000e-11 1.615340e-18 1.000000e-11 \n", + "kpfred.KPF_CCD_T -1.007930e+02 1.434590e-03 -1.007960e+02 \n", + "kpfred.STA_CCD_T -1.000000e+02 1.236360e-03 -1.000030e+02 \n", + "kpfred.STA_CCD_TRG -1.000000e+02 0.000000e+00 -1.000000e+02 \n", + "kpfred.TEMPSET -1.405000e+02 0.000000e+00 -1.405000e+02 \n", + "kpfexpose.BENCH_C 1.289100e+01 7.137450e-02 1.270000e+01 \n", + "kpfexpose.CAMBARREL_C 1.357630e+01 7.530780e-02 1.340000e+01 \n", + "kpfexpose.DET_XTRN_C 1.775630e+01 7.672690e-02 1.750000e+01 \n", + "kpfexpose.ECHELLE_C 1.154080e+01 8.762650e-02 1.130000e+01 \n", + "kpfexpose.ENCLOSURE_C 1.622860e+01 6.540930e-02 1.600000e+01 \n", + "kpfexpose.RACK_AIR_C 1.645330e+01 6.146600e-02 1.620000e+01 \n", + "kpfvac.PUMP_TEMP 3.900000e+01 0.000000e+00 3.900000e+01 \n", + "kpf_hk.COOLTARG -6.000000e+01 0.000000e+00 -6.000000e+01 \n", + "kpf_hk.CURRTEMP -5.978000e+01 9.710960e-06 -5.978000e+01 \n", + "kpfgreen.COL_CURR 3.258700e-06 4.926130e-08 3.200000e-06 \n", + "kpfgreen.ECH_CURR 8.868580e-11 1.890010e-12 8.350000e-11 \n", + "kpfred.COL_CURR 9.999620e-07 6.140020e-10 9.900000e-07 \n", + "kpfred.ECH_CURR 5.600000e-12 0.000000e+00 5.600000e-12 \n", + "kpfcal.IRFLUX 4.281660e-01 4.950470e-01 0.000000e+00 \n", + "kpfcal.VISFLUX -1.847450e+01 5.071030e-01 -2.000000e+01 \n", + "kpfcal.BLUECUTIACT 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "kpfmot.AGITSPD 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "kpfmot.AGITTOR 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "kpfmot.AGITAMBI_T 2.572010e+01 2.185980e-02 2.564000e+01 \n", + "kpfmot.AGITMOT_T 2.590800e+01 2.002300e-02 2.584000e+01 \n", + "kpfpower.OUTLET_A1_Amps 2.698300e+01 1.293820e-01 2.600000e+01 \n", "\n", - " STELEV \n", - "0 104.01 \n", - "1 104.01 \n", - "2 104.01 \n", - "3 104.01 \n", - "4 104.01 \n", - "5 104.01 \n", - "6 104.01 \n", - "7 104.01 \n", - "8 104.01 \n", - "9 104.01 \n", - "10 104.01 \n", - "11 104.01 \n", - "12 104.01 \n", - "13 104.01 \n", - "14 104.01 \n", - "15 104.01 \n", - "16 104.01 \n", - "17 104.01 \n", - "18 104.01 \n", - "19 104.01 \n", - "20 104.01 \n", - "21 104.01 \n", - "22 104.01 \n", - "23 104.01 \n", - "24 104.01 \n", - "25 104.01 \n", - "26 104.01 \n", - "27 104.01 \n", - "28 104.01 \n", - "29 104.01 \n", - "30 104.01 \n", - "31 104.01 \n", - "32 104.01 \n", - "33 104.01 \n", - "34 104.01 \n", + " max units \\\n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS 1.483700e+01 degC \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR 1.527200e+01 degC \n", + "kpfmet.BENCH_BOTTOM_DCUT 1.504600e+01 degC \n", + "kpfmet.BENCH_BOTTOM_ECHELLE 1.517200e+01 degC \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS 1.476400e+01 degC \n", + "kpfmet.BENCH_TOP_COLL 1.537300e+01 degC \n", + "kpfmet.BENCH_TOP_DCUT 1.508300e+01 degC \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM 1.510500e+01 degC \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END 1.558000e+01 degC \n", + "kpfmet.CALEM_SCMBLR_FIBER_END 1.543100e+01 degC \n", + "kpfmet.CAL_BENCH 1.900000e+01 degC \n", + "kpfmet.CAL_BENCH_BB_SRC 1.910000e+01 degC \n", + "kpfmet.CAL_BENCH_BOT 2.240000e+01 degC \n", + "kpfmet.CAL_BENCH_ENCL_AIR 1.910000e+01 degC \n", + "kpfmet.CAL_BENCH_OCT_MOT 1.970000e+01 degC \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT 1.940000e+01 degC \n", + "kpfmet.CAL_RACK_TOP 2.120000e+01 degC \n", + "kpfmet.CHAMBER_EXT_BOTTOM 1.412200e+01 degC \n", + "kpfmet.CHAMBER_EXT_TOP -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G1 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_G3 1.693400e+01 degC \n", + "kpfmet.CRYOSTAT_R1 1.619300e+01 degC \n", + "kpfmet.CRYOSTAT_R2 -2.731500e+02 degC \n", + "kpfmet.CRYOSTAT_R3 1.573600e+01 degC \n", + "kpfmet.ECHELLE_BOTTOM 1.519200e+01 degC \n", + "kpfmet.ECHELLE_TOP 1.504100e+01 degC \n", + "kpfmet.FF_SRC 1.820000e+01 degC \n", + "kpfmet.GREEN_CAMERA_BOTTOM 1.573300e+01 degC \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR 1.578200e+01 degC \n", + "kpfmet.GREEN_CAMERA_ECHELLE 1.568400e+01 degC \n", + "kpfmet.GREEN_CAMERA_TOP 1.558800e+01 degC \n", + "kpfmet.GREEN_GRISM_TOP 1.525700e+01 degC \n", + "kpfmet.GREEN_LN2_FLANGE 7.613000e+00 degC \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP 1.564300e+01 degC \n", + "kpfmet.RED_CAMERA_BOTTOM 1.524200e+01 degC \n", + "kpfmet.RED_CAMERA_COLLIMATOR 1.558200e+01 degC \n", + "kpfmet.RED_CAMERA_ECHELLE 1.552800e+01 degC \n", + "kpfmet.RED_CAMERA_TOP 1.548700e+01 degC \n", + "kpfmet.RED_GRISM_TOP 1.522900e+01 degC \n", + "kpfmet.RED_LN2_FLANGE 5.898000e+00 degC \n", + "kpfmet.REFORMATTER 1.534600e+01 degC \n", + "kpfmet.SCIENCE_CAL_FIBER_STG 1.910000e+01 degC \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END -2.731500e+02 degC \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END 1.558500e+01 degC \n", + "kpfmet.SIMCAL_FIBER_STG 1.880000e+01 degC \n", + "kpfmet.SKYCAL_FIBER_STG 1.890000e+01 degC \n", + "kpfmet.TEMP 1.792700e+01 degC \n", + "kpfmet.TH_DAILY 2.390000e+01 degC \n", + "kpfmet.TH_GOLD 1.890000e+01 degC \n", + "kpfmet.U_DAILY 2.600000e+01 degC \n", + "kpfmet.U_GOLD 1.870000e+01 degC \n", + "kpfgreen.BPLANE_TEMP 3.612500e+01 degC \n", + "kpfgreen.BRD10_DRVR_T 4.062500e+01 degC \n", + "kpfgreen.BRD11_DRVR_T 3.850000e+01 degC \n", + "kpfgreen.BRD12_LVXBIAS_T 3.443700e+01 degC \n", + "kpfgreen.BRD1_HTRX_T 3.287500e+01 degC \n", + "kpfgreen.BRD2_XVBIAS_T 3.475000e+01 degC \n", + "kpfgreen.BRD3_LVDS_T 3.406200e+01 degC \n", + "kpfgreen.BRD4_DRVR_T 3.700000e+01 degC \n", + "kpfgreen.BRD5_AD_T 3.318700e+01 degC \n", + "kpfgreen.BRD7_HTRX_T 3.750000e+01 degC \n", + "kpfgreen.BRD9_HVXBIAS_T 3.612500e+01 degC \n", + "kpfgreen.CF_BASE_2WT -1.271310e+02 degC \n", + "kpfgreen.CF_BASE_T -2.731500e+02 degC \n", + "kpfgreen.CF_BASE_TRG -1.275000e+02 degC \n", + "kpfgreen.CF_TIP_T -1.279980e+02 degC \n", + "kpfgreen.CF_TIP_TRG -1.280000e+02 degC \n", + "kpfgreen.COL_PRESS 1.200000e-07 Torr \n", + "kpfgreen.CRYOBODY_T 1.900400e+01 degC \n", + "kpfgreen.CRYOBODY_TRG 1.900000e+01 degC \n", + "kpfgreen.CURRTEMP -1.399960e+02 degC \n", + "kpfgreen.ECH_PRESS 1.000000e-11 Torr \n", + "kpfgreen.KPF_CCD_T -1.010480e+02 degC \n", + "kpfgreen.STA_CCD_T -9.999500e+01 degC \n", + "kpfgreen.STA_CCD_TRG -1.000000e+02 degC \n", + "kpfgreen.TEMPSET -1.400000e+02 degC \n", + "kpfred.BPLANE_TEMP 4.243700e+01 degC \n", + "kpfred.BRD10_DRVR_T 4.675000e+01 degC \n", + "kpfred.BRD11_DRVR_T 4.593700e+01 degC \n", + "kpfred.BRD12_LVXBIAS_T 4.218700e+01 degC \n", + "kpfred.BRD1_HTRX_T 3.981200e+01 degC \n", + "kpfred.BRD2_XVBIAS_T 4.156200e+01 degC \n", + "kpfred.BRD3_LVDS_T 4.131200e+01 degC \n", + "kpfred.BRD4_DRVR_T 4.431200e+01 degC \n", + "kpfred.BRD5_AD_T 3.918700e+01 degC \n", + "kpfred.BRD7_HTRX_T 4.393700e+01 degC \n", + "kpfred.BRD9_HVXBIAS_T 4.362500e+01 degC \n", + "kpfred.CF_BASE_2WT -1.257890e+02 degC \n", + "kpfred.CF_BASE_T -2.731500e+02 degC \n", + "kpfred.CF_BASE_TRG -1.277500e+02 degC \n", + "kpfred.CF_TIP_T -1.271480e+02 degC \n", + "kpfred.CF_TIP_TRG -1.271500e+02 degC \n", + "kpfred.COL_PRESS 3.800000e-08 Torr \n", + "kpfred.CRYOBODY_T 1.900500e+01 degC \n", + "kpfred.CRYOBODY_TRG 1.900000e+01 degC \n", + "kpfred.CURRTEMP -1.404480e+02 degC \n", + "kpfred.ECH_PRESS 1.000000e-11 Torr \n", + "kpfred.KPF_CCD_T -1.007890e+02 degC \n", + "kpfred.STA_CCD_T -9.999600e+01 degC \n", + "kpfred.STA_CCD_TRG -1.000000e+02 degC \n", + "kpfred.TEMPSET -1.405000e+02 degC \n", + "kpfexpose.BENCH_C 1.310000e+01 degC \n", + "kpfexpose.CAMBARREL_C 1.380000e+01 degC \n", + "kpfexpose.DET_XTRN_C 1.800000e+01 degC \n", + "kpfexpose.ECHELLE_C 1.190000e+01 degC \n", + "kpfexpose.ENCLOSURE_C 1.640000e+01 degC \n", + "kpfexpose.RACK_AIR_C 1.660000e+01 degC \n", + "kpfvac.PUMP_TEMP 3.900000e+01 degC \n", + "kpf_hk.COOLTARG -6.000000e+01 degC \n", + "kpf_hk.CURRTEMP -5.978000e+01 degC \n", + "kpfgreen.COL_CURR 3.300000e-06 Torr \n", + "kpfgreen.ECH_CURR 9.330000e-11 Torr \n", + "kpfred.COL_CURR 1.000000e-06 Torr \n", + "kpfred.ECH_CURR 5.600000e-12 Torr \n", + "kpfcal.IRFLUX 1.000000e+00 Torr \n", + "kpfcal.VISFLUX -1.800000e+01 Torr \n", + "kpfcal.BLUECUTIACT 0.000000e+00 Torr \n", + "kpfmot.AGITSPD 0.000000e+00 motor_counts/s \n", + "kpfmot.AGITTOR 0.000000e+00 V \n", + "kpfmot.AGITAMBI_T 2.579000e+01 degC \n", + "kpfmot.AGITMOT_T 2.596000e+01 degC \n", + "kpfpower.OUTLET_A1_Amps 2.700000e+01 milliamps \n", "\n", - "[35 rows x 89 columns]" + " description \n", + "keyword \n", + "kpfmet.BENCH_BOTTOM_BETWEEN_CAMERAS Bench Bottom Between Cameras C2 c- double degC... \n", + "kpfmet.BENCH_BOTTOM_COLLIMATOR Bench Bottom Coll C3 c- double degC {%.3f} \n", + "kpfmet.BENCH_BOTTOM_DCUT Bench Bottom D-cut C4 c- double degC {%.3f} \n", + "kpfmet.BENCH_BOTTOM_ECHELLE Bench Bottom Echelle Cam B c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_BETWEEN_CAMERAS Bench Top Between Cameras D4 c- double degC {%... \n", + "kpfmet.BENCH_TOP_COLL Bench Top Coll D5 c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_DCUT Bench Top D-cut D3 c- double degC {%.3f} \n", + "kpfmet.BENCH_TOP_ECHELLE_CAM Bench Top Echelle Cam D1 c- double degC {%.3f} \n", + "kpfmet.CALEM_SCMBLR_CHMBR_END Cal EM Scrammbler Chamber End C1 c- double deg... \n", + "kpfmet.CALEM_SCMBLR_FIBER_END Cal EM Scrambler Fiber End D1 c- double degC {... \n", + "kpfmet.CAL_BENCH Cal_Bench temperature c- double degC {%.1f} \n", + "kpfmet.CAL_BENCH_BB_SRC CAL_Bench_BB_Src temperature c- double degC {%... \n", + "kpfmet.CAL_BENCH_BOT Cal_Bench_Bot temperature c- double degC {%.1f} \n", + "kpfmet.CAL_BENCH_ENCL_AIR Cal_Bench_Encl_Air temperature c- double degC ... \n", + "kpfmet.CAL_BENCH_OCT_MOT Cal_Bench_Oct_Mot temperature c- double degC {... \n", + "kpfmet.CAL_BENCH_TRANS_STG_MOT Cal_Bench_Trans_Stg_Mot temperature c- double ... \n", + "kpfmet.CAL_RACK_TOP Cal_Rack_Top temperature c- double degC {%.1f} \n", + "kpfmet.CHAMBER_EXT_BOTTOM Chamber Exterior Bottom B c- double degC {%.3f} \n", + "kpfmet.CHAMBER_EXT_TOP Chamber Exterior Top C1 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G1 Within cryostat green D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G2 Within cryostat green D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_G3 Within cryostat green D4 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R1 Within Cryostat red D2 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R2 Within Cryostat red D3 c- double degC {%.3f} \n", + "kpfmet.CRYOSTAT_R3 Within Cryostat red D4 c- double degC {%.3f} \n", + "kpfmet.ECHELLE_BOTTOM Echelle Bottom D1 c- double degC {%.3f} \n", + "kpfmet.ECHELLE_TOP Echelle Top C1 c- double degC {%.3f} \n", + "kpfmet.FF_SRC FF_Src temperature c- double degC {%.1f} \n", + "kpfmet.GREEN_CAMERA_BOTTOM Green Camera Bottom C3 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_COLLIMATOR Green Camera Collimator C4 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_ECHELLE Green Camera Echelle D5 c- double degC {%.3f} \n", + "kpfmet.GREEN_CAMERA_TOP Green Camera Top C2 c- double degC {%.3f} \n", + "kpfmet.GREEN_GRISM_TOP Green Grism Top C5 c- double degC {%.3f} \n", + "kpfmet.GREEN_LN2_FLANGE Green LN2 Flange A c- double degC {%.3f} \n", + "kpfmet.PRIMARY_COLLIMATOR_TOP Primary Col Top D2 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_BOTTOM Red Camera Bottom D5 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_COLLIMATOR Red Camera Coll C3 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_ECHELLE Red Camera Ech C4 c- double degC {%.3f} \n", + "kpfmet.RED_CAMERA_TOP Red Camera Top C5 c- double degC {%.3f} \n", + "kpfmet.RED_GRISM_TOP Red Grism Top C2 c- double degC {%.3f} \n", + "kpfmet.RED_LN2_FLANGE Red LN2 Flange D1 c- double degC {%.3f} \n", + "kpfmet.REFORMATTER Reformatter A c- double degC {%.3f} \n", + "kpfmet.SCIENCE_CAL_FIBER_STG Science_Cal_Fiber_Stg temperature c- double de... \n", + "kpfmet.SCISKY_SCMBLR_CHMBR_END SciSky Scrambler Chamber End A c- double degC ... \n", + "kpfmet.SCISKY_SCMBLR_FIBER_END SciSky Scrammbler Fiber End B c- double degC {... \n", + "kpfmet.SIMCAL_FIBER_STG SimCal_Fiber_Stg temperature c- double degC {%... \n", + "kpfmet.SKYCAL_FIBER_STG SkyCal_Fiber_Stg temperature c- double degC {%... \n", + "kpfmet.TEMP Vaisala Temperature c- double degC {%.3f} \n", + "kpfmet.TH_DAILY Th_daily temperature c- double degC {%.1f} \n", + "kpfmet.TH_GOLD Th_gold temperature c- double degC {%.1f} \n", + "kpfmet.U_DAILY U_daily temperature c- double degC {%.1f} \n", + "kpfmet.U_GOLD U_gold temperature c- double degC {%.1f} \n", + "kpfgreen.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", + "kpfgreen.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", + "kpfgreen.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", + "kpfgreen.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", + "kpfgreen.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", + "kpfgreen.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", + "kpfgreen.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", + "kpfgreen.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", + "kpfgreen.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", + "kpfgreen.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", + "kpfgreen.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", + "kpfgreen.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", + "kpfgreen.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", + "kpfgreen.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", + "kpfgreen.CF_TIP_T tip cold finger c- double degC {%.3f} \n", + "kpfgreen.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", + "kpfgreen.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfgreen.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", + "kpfgreen.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", + "kpfgreen.CURRTEMP Current cold head temperature c- double degC {... \n", + "kpfgreen.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfgreen.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", + "kpfgreen.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", + "kpfgreen.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", + "kpfgreen.TEMPSET Set point for the cold head temperature c2 dou... \n", + "kpfred.BPLANE_TEMP Backplane temperature c- double degC {%.3f} \n", + "kpfred.BRD10_DRVR_T Board 10 (Driver) temperature c- double degC {... \n", + "kpfred.BRD11_DRVR_T Board 11 (Driver) temperature c- double degC {... \n", + "kpfred.BRD12_LVXBIAS_T Board 12 (LVxBias) temperature c- double degC ... \n", + "kpfred.BRD1_HTRX_T Board 1 (HeaterX) temperature c- double degC {... \n", + "kpfred.BRD2_XVBIAS_T Board 2 (XV Bias) temperature c- double degC {... \n", + "kpfred.BRD3_LVDS_T Board 3 (LVDS) temperature c- double degC {%.3f} \n", + "kpfred.BRD4_DRVR_T Board 4 (Driver) temperature c- double degC {%... \n", + "kpfred.BRD5_AD_T Board 5 (AD) temperature c- double degC {%.3f} \n", + "kpfred.BRD7_HTRX_T Board 7 (HeaterX) temperature c- double degC {... \n", + "kpfred.BRD9_HVXBIAS_T Board 9 (HVxBias) temperature c- double degC {... \n", + "kpfred.CF_BASE_2WT tip cold finger (2 wire) c- double degC {%.3f} \n", + "kpfred.CF_BASE_T base cold finger 2wire temp c- double degC {%.3f} \n", + "kpfred.CF_BASE_TRG base cold finger heater 1A, target temp c2 dou... \n", + "kpfred.CF_TIP_T tip cold finger c- double degC {%.3f} \n", + "kpfred.CF_TIP_TRG tip cold finger heater 1B, target temp c2 doub... \n", + "kpfred.COL_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfred.CRYOBODY_T Cryo Body Temperature c- double degC {%.3f} \n", + "kpfred.CRYOBODY_TRG Cryo body heater 7B, target temp c2 double deg... \n", + "kpfred.CURRTEMP Current cold head temperature c- double degC {... \n", + "kpfred.ECH_PRESS Current ion pump pressure c- double Torr {%.3e} \n", + "kpfred.KPF_CCD_T SSL Detector temperature c- double degC {%.3f} \n", + "kpfred.STA_CCD_T STA Detector temperature c- double degC {%.3f} \n", + "kpfred.STA_CCD_TRG Detector heater 7A, target temp c2 double degC... \n", + "kpfred.TEMPSET Set point for the cold head temperature c2 dou... \n", + "kpfexpose.BENCH_C rtd bench c- double degC {%.1f} { -100.0 .. 10... \n", + "kpfexpose.CAMBARREL_C rtd camera barrel c- double degC {%.1f} { -100... \n", + "kpfexpose.DET_XTRN_C rtd detector extermal c- double degC {%.1f} { ... \n", + "kpfexpose.ECHELLE_C rtd echelle c- double degC {%.1f} { -100.0 .. ... \n", + "kpfexpose.ENCLOSURE_C rtd enclosure c- double degC {%.1f} { -100.0 .... \n", + "kpfexpose.RACK_AIR_C rtd rack air c- double degC {%.1f} { -100.0 ..... \n", + "kpfvac.PUMP_TEMP Motor temperature c- double degC {%.2f} \n", + "kpf_hk.COOLTARG temperature target c2 int degC \n", + "kpf_hk.CURRTEMP current temperature c- double degC {%.2f} \n", + "kpfgreen.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfgreen.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.COL_CURR Current ion pump current c- double A {%.3e} \n", + "kpfred.ECH_CURR Current ion pump current c- double A {%.3e} \n", + "kpfcal.IRFLUX LFC Fiberlock IR Intensity c- int Counts {%d} \n", + "kpfcal.VISFLUX LFC Fiberlock Vis Intensity c- int Counts {%d} \n", + "kpfcal.BLUECUTIACT Blue cut amplifier 0 measured current c- doubl... \n", + "kpfmot.AGITSPD agit raw velocity c2 int motor counts/s { -750... \n", + "kpfmot.AGITTOR agit motor torque c- double V {%.3f} \n", + "kpfmot.AGITAMBI_T Agitator ambient temperature c- double degC {%... \n", + "kpfmot.AGITMOT_T Agitator motor temperature c- double degC {%.2... \n", + "kpfpower.OUTLET_A1_Amps Outlet A1 current amperage c- int milliamps " ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "L3 = L3.sort_values(by=['JD'])\n", - "L3" - ] - }, - { - "cell_type": "markdown", - "id": "169466e2", - "metadata": {}, - "source": [ - "Bin velocites in a p-mode time (323 seconds)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0c8d5049", - "metadata": {}, - "outputs": [], - "source": [ - "t_bin, CCD2RV_bin, CCD2ERRRV_bin = timebin((L3.JD - min(L3.JD))*24*60,\n", - " (L3.CCD2RV - np.median(L3.CCD2RV))*1000*100,\n", - " (L3.CCD2RV - np.median(L3.CCD2RV))*0+1,\n", - " 323/60)" + "D2_file = data_dir + ObsID + '_2D.fits'\n", + "D2 = fits.open(D2_file)\n", + "exptime = D2['PRIMARY'].header['EXPTIME']\n", + "#D2['PRIMARY'].header\n", + "#D2.info()\n", + "\n", + "# Read telemetry\n", + "df_telemetry = Table.read(D2_file, format='fits', hdu=11).to_pandas() # need to refer to HDU by name\n", + "num_columns = ['average', 'stddev', 'min', 'max']\n", + "for column in df_telemetry:\n", + " df_telemetry[column] = df_telemetry[column].str.decode('utf-8')\n", + " df_telemetry = df_telemetry.replace('-nan', 0)# replace nan with 0\n", + " if column in num_columns:\n", + " df_telemetry[column] = pd.to_numeric(df_telemetry[column], downcast=\"float\")\n", + " else:\n", + " df_telemetry[column] = df_telemetry[column].astype(str)\n", + "df_telemetry.set_index(\"keyword\", inplace=True)\n", + "\n", + "# Print telemetry\n", + "with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n", + " display(df_telemetry)" ] }, { "cell_type": "markdown", - "id": "1d2a5c73", + "id": "32037d93", "metadata": {}, "source": [ - "# Plot KPF RV time series" + "# Define apertures, measure photometry, and plot CCD images" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "db842b34", + "execution_count": 4, + "id": "c4f3d89d", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24992/984263924.py:92: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", + " coll_text = 'Ion Pump (Coll): \\n' + (f'{coll_pressure_torr:.1e}' + ' Torr, ' + f'{coll_current_a*1e6:.1f}' + ' $\\mu$A')*(coll_pressure_torr > 1e-9) + ('Off')*(coll_pressure_torr < 1e-9)\n", + "/var/folders/kk/bxbvpk_j4q371d1wh64wykq00000gn/T/ipykernel_24992/984263924.py:93: DeprecationWarning: In future, it will be an error for 'np.bool_' scalars to be interpreted as an index\n", + " ech_text = 'Ion Pump (Ech): \\n' + (f'{ech_pressure_torr:.1e}' + ' Torr, ' + f'{ech_current_a*1e6:.1f}' + ' $\\mu$A')*(ech_pressure_torr > 1e-9) + ('Off')*(ech_pressure_torr < 1e-9)\n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" - } - ], - "source": [ - "L3old = L3\n", - "L3=L3old.where(L3old.CCD2RV2 < 100)\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot()\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD2RV - np.median(L3.CCD2RV))*1000*100, \n", - " marker=\"o\", color='darkred', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD2RV1 - np.median(L3.CCD2RV1))*1000*100, \n", - " marker=\"<\", color='lightcoral', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD2RV2 - np.median(L3.CCD2RV2))*1000*100, \n", - " marker=\"s\", color='lightcoral', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD2RV3 - np.median(L3.CCD2RV3))*1000*100, \n", - " marker=\">\", color='lightcoral', s=10)\n", - "plt.scatter(t_bin, \n", - " CCD2RV_bin, \n", - " marker=\"o\", color='darkred', s=100)\n", - "plt.legend([\n", - " 'RED (CCD2RV), ' + str(int(np.std(L3.CCD2RV*1000*100))) + ' cm/s RMS',\n", - " 'RED1 (CCD2RV1), ' + str(int(np.std(L3.CCD2RV1*1000*100))) + ' cm/s RMS', \n", - " 'RED2 (CCD2RV2), ' + str(int(np.std(L3.CCD2RV2*1000*100))) + ' cm/s RMS', \n", - " 'RED3 (CCD2RV3), ' + str(int(np.std(L3.CCD2RV3*1000*100))) + ' cm/s RMS',\n", - " ], fontsize=14, loc='best') \n", - "plt.xlabel('Minutes since ' + str(min(L3.JD)), fontsize=14)\n", - "plt.ylabel('RV (cm/s)', fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "920d6ce1", - "metadata": {}, - "source": [ - "Same thinge for Green CCD, which currently has problems with the wavelength solution for certain orders." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "a7ea00e4", - "metadata": {}, - "outputs": [ + }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": { @@ -2227,41 +1682,133 @@ } ], "source": [ - "t_bin, CCD1RV_bin, CCD1ERRRV_bin = timebin((L3.JD - min(L3.JD))*24*60,\n", - " (L3.CCD1RV - np.median(L3.CCD1RV))*1000*100,\n", - " (L3.CCD1RV - np.median(L3.CCD1RV))*0+1,\n", - " 323/60)\n", + "for c in ccd:\n", + " if c == 'green':\n", + " frame = D2['GREEN_CCD'].data\n", + " coll_pressure_torr = df_telemetry.at['kpfgreen.COL_PRESS', 'average']\n", + " ech_pressure_torr = df_telemetry.at['kpfgreen.ECH_PRESS', 'average']\n", + " coll_current_a = df_telemetry.at['kpfgreen.COL_CURR', 'average']\n", + " ech_current_a = df_telemetry.at['kpfgreen.ECH_CURR', 'average']\n", + " png_fn = out_dir + ObsID + '_green.png'\n", + " elif c == 'red':\n", + " frame = D2['RED_CCD'].data\n", + " coll_pressure_torr = df_telemetry.at['kpfred.COL_PRESS', 'average']\n", + " ech_pressure_torr = df_telemetry.at['kpfred.ECH_PRESS', 'average']\n", + " coll_current_a = df_telemetry.at['kpfred.COL_CURR', 'average']\n", + " ech_current_a = df_telemetry.at['kpfred.ECH_CURR', 'average']\n", + " png_fn = out_dir + ObsID + '_red.png'\n", + "\n", + " # Dark frame\n", + " if exptime > 0:\n", + " exptype = 'dark'\n", + " timelabel = ' e$^-$ hr$^{-1}$'\n", + " frame *= (3600./exptime) # convert to e- per hour\n", + " # Bias frame\n", + " else:\n", + " exptype = 'bias'\n", + " timelabel = ' e$^-$'\n", + "\n", + " # Define regions\n", + " reg = {'ref1': {'name': 'Reference Region 1', 'x1': 1690, 'x2': 1990, 'y1': 1690, 'y2': 1990, 'short':'ref1', 'med_elec':0, 'label':''},\n", + " 'ref2': {'name': 'Reference Region 2', 'x1': 1690, 'x2': 1990, 'y1': 2090, 'y2': 2390, 'short':'ref2', 'med_elec':0, 'label':''},\n", + " 'ref3': {'name': 'Reference Region 3', 'x1': 2090, 'x2': 2390, 'y1': 1690, 'y2': 1990, 'short':'ref3', 'med_elec':0, 'label':''},\n", + " 'ref4': {'name': 'Reference Region 4', 'x1': 2090, 'x2': 2390, 'y1': 2090, 'y2': 2390, 'short':'ref4', 'med_elec':0, 'label':''},\n", + " 'ref5': {'name': 'Reference Region 5', 'x1': 80, 'x2': 380, 'y1': 700, 'y2': 1000, 'short':'ref5', 'med_elec':0, 'label':''},\n", + " 'ref6': {'name': 'Reference Region 6', 'x1': 80, 'x2': 380, 'y1': 3080, 'y2': 3380, 'short':'ref6', 'med_elec':0, 'label':''},\n", + " 'amp1': {'name': 'Amplifier Region 1', 'x1': 300, 'x2': 500, 'y1': 5, 'y2': 20, 'short':'amp1', 'med_elec':0, 'label':''},\n", + " 'amp2': {'name': 'Amplifier Region 2', 'x1': 3700, 'x2': 3900, 'y1': 5, 'y2': 20, 'short':'amp2', 'med_elec':0, 'label':''},\n", + " 'coll': {'name': 'Ion Pump (Collimator side)', 'x1': 3700, 'x2': 4000, 'y1': 700, 'y2': 1000, 'short':'coll', 'med_elec':0, 'label':''},\n", + " 'ech': {'name': 'Ion Pump (Echelle side)', 'x1': 3700, 'x2': 4000, 'y1': 3080, 'y2': 3380, 'short':'ech', 'med_elec':0, 'label':''}\n", + " }\n", + " for r in reg.keys():\n", + " current_region = frame[reg[r]['y1']:reg[r]['y2'],reg[r]['x1']:reg[r]['x2']]\n", + " reg[r]['med_elec'] = np.median(current_region)\n", + " if do_print:\n", + " print(reg[r]['name'] + ': ' + str(np.round(reg[r]['med_elec'],1)) + ' e- per hour') \n", + " if do_print:\n", + " print('Ion Pump pressure (Torr) - Collimator side: ' + f'{coll_pressure_torr:.1e}')\n", + " print('Ion Pump pressure (Torr) - Echelle side: ' + f'{ech_pressure_torr:.1e}')\n", + " print('Ion Pump current (A) - Collimator side: ' + f'{coll_current_a:.1e}')\n", + " print('Ion Pump current (A) - Echelle side: ' + f'{ech_current_a:.1e}')\n", "\n", - "L3old = L3\n", - "#L3=L3old.where(L3old.CCD1RV2 < 100)\n", - "plt.figure(figsize=(12, 6), tight_layout=True)\n", - "plt.plot()\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD1RV - np.median(L3.CCD1RV))*1000*100, \n", - " marker=\"o\", color='forestgreen', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD1RV1 - np.median(L3.CCD1RV1))*1000*100, \n", - " marker=\"<\", color='yellowgreen', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD1RV2 - np.median(L3.CCD1RV2))*1000*100, \n", - " marker=\"s\", color='yellowgreen', s=10)\n", - "plt.scatter((L3.JD - min(L3.JD))*24*60, \n", - " (L3.CCD1RV3 - np.median(L3.CCD1RV3))*1000*100, \n", - " marker=\">\", color='yellowgreen', s=10)\n", - "plt.scatter(t_bin, \n", - " CCD1RV_bin, \n", - " marker=\"o\", color='forestgreen', s=100)\n", - "plt.legend([\n", - " 'RED (CCD2RV), ' + str(int(np.std(L3.CCD1RV*1000*100))) + ' cm/s RMS',\n", - " 'RED1 (CCD2RV1), ' + str(int(np.std(L3.CCD1RV1*1000*100))) + ' cm/s RMS', \n", - " 'RED2 (CCD2RV2), ' + str(int(np.std(L3.CCD1RV2*1000*100))) + ' cm/s RMS', \n", - " 'RED3 (CCD2RV3), ' + str(int(np.std(L3.CCD1RV3*1000*100))) + ' cm/s RMS',\n", - " ], fontsize=14, loc='best') \n", - "plt.xlabel('Minutes since ' + str(min(L3.JD)), fontsize=14)\n", - "plt.ylabel('RV (cm/s)', fontsize=14)\n", - "plt.xticks(fontsize=14)\n", - "plt.yticks(fontsize=14)\n", - "plt.show()" + " # Plot\n", + " if do_display or do_save_png:\n", + " from matplotlib.patches import Rectangle\n", + " plt.figure(figsize=(16, 12))\n", + " plt.imshow(frame, \n", + " cmap='viridis', \n", + " origin='lower', \n", + " vmin=np.percentile(frame[300:3780,0:4080],5), \n", + " vmax=np.percentile(frame[300:3780,0:4080],95)\n", + " )\n", + " for r in reg.keys():\n", + " plt.gca().add_patch(Rectangle((reg[r]['x1'],reg[r]['y1']),reg[r]['x2']-reg[r]['x1'],reg[r]['y2']-reg[r]['y1'],linewidth=1,edgecolor='r',facecolor='none')) \n", + " plt.text(((reg[r]['short'] == 'ref3') or \n", + " (reg[r]['short'] == 'ref4') or \n", + " (reg[r]['short'] == 'ref5') or\n", + " (reg[r]['short'] == 'ref6') or \n", + " (reg[r]['short'] == 'amp1'))*(reg[r]['x1'])+\n", + " ((reg[r]['short'] == 'ref1') or \n", + " (reg[r]['short'] == 'ref2') or \n", + " (reg[r]['short'] == 'ech') or \n", + " (reg[r]['short'] == 'coll') or \n", + " (reg[r]['short'] == 'amp2'))*(reg[r]['x2']),\n", + " (((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*(reg[r]['y1']-30)+\n", + " ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*(reg[r]['y2']+30)), \n", + " str(np.round(reg[r]['med_elec'],1)) + timelabel, \n", + " size=16,\n", + " weight='bold',\n", + " color='r', \n", + " ha=(((reg[r]['short'] == 'ref3') or \n", + " (reg[r]['short'] == 'ref4') or \n", + " (reg[r]['short'] == 'ref5') or\n", + " (reg[r]['short'] == 'ref6') or \n", + " (reg[r]['short'] == 'amp1'))*('left')+\n", + " ((reg[r]['short'] == 'ref1') or \n", + " (reg[r]['short'] == 'ref2') or \n", + " (reg[r]['short'] == 'ech') or \n", + " (reg[r]['short'] == 'coll') or \n", + " (reg[r]['short'] == 'amp2'))*('right')),\n", + " va=(((reg[r]['y1'] < 2080) and (reg[r]['y1'] > 100))*('top')+\n", + " ((reg[r]['y1'] > 2080) or (reg[r]['y1'] < 100))*('bottom'))\n", + " )\n", + " now = datetime.now()\n", + " coll_text = 'Ion Pump (Coll): \\n' + (f'{coll_pressure_torr:.1e}' + ' Torr, ' + f'{coll_current_a*1e6:.1f}' + ' $\\mu$A')*(coll_pressure_torr > 1e-9) + ('Off')*(coll_pressure_torr < 1e-9)\n", + " ech_text = 'Ion Pump (Ech): \\n' + (f'{ech_pressure_torr:.1e}' + ' Torr, ' + f'{ech_current_a*1e6:.1f}' + ' $\\mu$A')*(ech_pressure_torr > 1e-9) + ('Off')*(ech_pressure_torr < 1e-9)\n", + " plt.text(4080, -250, now.strftime(\"%m/%d/%Y, %H:%M:%S\"), size=9, ha='right', color='gray')\n", + " plt.text(4220, 500, coll_text, size=12, rotation=90, ha='center')\n", + " plt.text(4220, 3000, ech_text, size=12, rotation=90, ha='center')\n", + " plt.text(3950, 1500, 'Bench Side\\n (blue side of orders)', size=14, rotation=90, ha='center', color='white')\n", + " plt.text( 150, 1500, 'Top Side\\n (red side of orders)', size=14, rotation=90, ha='center', color='white')\n", + " plt.text(2040, 70, 'Collimator Side', size=14, rotation= 0, ha='center', color='white')\n", + " plt.text(2040, 3970, 'Echelle Side', size=14, rotation= 0, ha='center', color='white')\n", + " cbar = plt.colorbar()\n", + " cbar.set_label(timelabel, fontsize=18)\n", + " cbar.ax.tick_params(labelsize=18)\n", + " cbar.ax.tick_params(size=18)\n", + " plt.title(ObsID + ' (' + (c == 'green')*('Green CCD') + (c == 'red')*('Red CCD') + ', ' + str(round(exptime)) + ' sec)', fontsize=18)\n", + " plt.xlabel('Column (pixel number)', fontsize=18)\n", + " plt.ylabel('Row (pixel number)', fontsize=18)\n", + " plt.xticks(fontsize=14)\n", + " plt.yticks(fontsize=14)\n", + " plt.grid(False)\n", + " if do_save_png:\n", + " plt.savefig(png_fn, facecolor='w')\n", + " if do_display:\n", + " plt.show()\n", + "\n", + "# close files\n", + "D2.close()" + ] + }, + { + "cell_type": "markdown", + "id": "625fb7c6", + "metadata": {}, + "source": [ + "Some notes about the above images:\n", + "* There are apertures for the two ion pumps per CCD. Two apertures placed symmetrically on the left edge of the CCDs are for reference. Also for reference are the four apertures in the middle of each chip, one per quadrant (which have separate amplifiers, at least on the green CCD).\n", + "* With the current data processing (see, e.g., KP.20230314.11989.91), there appears to be over-subtraction of bias in a quadrant-dependent way. Also, cosmic rays are not being subtracted, but should." ] } ], From 66ba4948dad11dba5f0d86cb323512311d017be1 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 22 Jun 2023 05:46:16 -0700 Subject: [PATCH 146/249] Renamed RawFiles database table to L0Files. --- docs/source/intro/database_setup.rst | 175 ++++++++++++++------------- 1 file changed, 88 insertions(+), 87 deletions(-) diff --git a/docs/source/intro/database_setup.rst b/docs/source/intro/database_setup.rst index 21553b797..29f656193 100644 --- a/docs/source/intro/database_setup.rst +++ b/docs/source/intro/database_setup.rst @@ -133,14 +133,15 @@ output at the very end:: Here is how to connect to your new database: /data/user/rlaher/test/pg15.2/bin/psql -h localhost -d kpfopsdb -p 6125 -U rlaher -There are currently three database tables in the pipeline operations database: CalFiles, RawFiles, and -Infobits. CalFiles stores a record for each master calibration file (many records -are inserted daily). RawFiles stores a record for each exposure. -Infobits stores definitions of the bit-wise information flags -corresponding to the infobits column in the RawFiles table (the -CalFiles infobits have not been formalized into a database table, +There are currently three database tables in the pipeline operations database: CalFiles, L0Files, and +L0infobits. The CalFiles database table stores a record for each master calibration file (many records +are inserted daily for biases, darks, flats, arclamps, etc.). The +L0Files database table stores a record for each exposure. +The L0infobits database table stores definitions of the bit-wise information flags +corresponding to the infobits column in the L0Files table (the +CalFiles infobits have not yet been formalized into a database table, and are described on :doc:`../api/pipeline/masters`). -These tables are queried by the pipeline for relevant nearest-in-time +These database tables are queried by the pipeline for relevant nearest-in-time master calibration files, and generally for quality-control purposes. Here are the table-column definitions:: @@ -150,98 +151,98 @@ Here are the table-column definitions:: ----------------------------- CREATE TABLE calfiles ( - cid integer NOT NULL, -- Primary key - level smallint NOT NULL, -- Product level (L0, L1, or L2) - caltype character varying(32) NOT NULL, -- FITS-header keyword: IMTYPE in extension 4-6 (lowercase) - "object" character varying(32) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT (lowercase) - contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK - nframes smallint, -- FITS-header keyword: NFRAMES (GREEN CCD) - minmjd double precision, -- FITS-header keyword: MINMJD (GREEN CCD) - maxmjd double precision, -- FITS-header keyword: MAXMJD (GREEN CCD) - infobits integer, -- FITS-header keyword: INFOBITS - startdate date NOT NULL, -- Start date for application of master (for earliest-in-time selection) - enddate date NOT NULL, -- End date for application of master (may not be used) - filename character varying(255) NOT NULL, -- Path and filename of master calibration file. - checksum character varying(32) NOT NULL, -- MD5 checksum - status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good - createdby character varying(30) NOT NULL, -- Script that inserted the record - created timestamp without time zone NOT NULL, -- FITS-header keyword: CREATED (GREEN CCD) in Zulu time - "comment" character varying(255) -- Descriptive comment + cid integer NOT NULL, -- Primary key + level smallint NOT NULL, -- Product level (L0, L1, or L2) + caltype character varying(32) NOT NULL, -- FITS-header keyword: IMTYPE in extension 4-6 (lowercase) + "object" character varying(32) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT (lowercase) + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + nframes smallint, -- FITS-header keyword: NFRAMES (GREEN CCD) + minmjd double precision, -- FITS-header keyword: MINMJD (GREEN CCD) + maxmjd double precision, -- FITS-header keyword: MAXMJD (GREEN CCD) + infobits integer, -- FITS-header keyword: INFOBITS + startdate date NOT NULL, -- Start date for application of master (for earliest-in-time selection) + enddate date NOT NULL, -- End date for application of master (may not be used) + filename character varying(255) NOT NULL, -- Path and filename of master calibration file. + checksum character varying(32) NOT NULL, -- MD5 checksum + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good + createdby character varying(30) NOT NULL, -- Script that inserted the record + created timestamp without time zone NOT NULL, -- FITS-header keyword: CREATED (GREEN CCD) in Zulu time + "comment" character varying(255) -- Descriptive comment ); ----------------------------- - -- TABLE: RawFiles + -- TABLE: L0Files ----------------------------- - CREATE TABLE rawfiles ( - rid integer NOT NULL, -- Primary key - dateobs date NOT NULL, -- FITS-header keyword: DATE-OBS - ut time without time zone NOT NULL, -- FITS-header keyword: UT - datebeg timestamp without time zone NOT NULL, -- FITS-header keyword: DATE-BEG - mjdobs double precision NOT NULL, -- FITS-header keyword: MJD-OBS - exptime real NOT NULL, -- FITS-header keyword: EXPTIME - progname character varying(64) NOT NULL, -- FITS-header keyword: PROGNAME - imtype character varying(64) NOT NULL, -- FITS-header keyword: IMTYPE - sciobj character varying(64) NOT NULL, -- FITS-header keyword: SCI-OBJ - calobj character varying(64) NOT NULL, -- FITS-header keyword: CAL-OBJ - skyobj character varying(64) NOT NULL, -- FITS-header keyword: SKY-OBJ - "object" character varying(64) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT - contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK - infobits bigint DEFAULT 0 NOT NULL, -- Bit-wise information flags - filename character varying(255) NOT NULL, -- Full path and filename - checksum character varying(32) NOT NULL, -- MD5 checksum of entire file - status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good (verify automatically with - -- DATASUM and CHECKSUM keywords, or set this manually later, if necessary) - created timestamp without time zone -- Timestamp of database record INSERT or last UPDATE - DEFAULT now() NOT NULL, - targname character varying(64), -- FITS-header keyword: TARGNAME - gaiaid character varying(64), -- FITS-header keyword: GAIAID - twomassid character varying(64), -- FITS-header keyword: 2MASSID - ra double precision, -- FITS-header keyword: RA converted to decimal - dec double precision, -- FITS-header keyword: DEC converted to decimal - medgreen1 real, -- Median of GREEN_AMP1 image - p16green1 real, -- 16th percentile of GREEN_AMP1 image - p84green1 real, -- 84th percentile of GREEN_AMP1 image - medgreen2 real, -- Median of GREEN_AMP2 image - p16green2 real, -- 16th percentile of GREEN_AMP2 image - p84green2 real, -- 84th percentile of GREEN_AMP2 image - medgreen3 real, -- Median of GREEN_AMP3 image - p16green3 real, -- 16th percentile of GREEN_AMP3 image - p84green3 real, -- 84th percentile of GREEN_AMP3 image - medgreen4 real, -- Median of GREEN_AMP4 image - p16green4 real, -- 16th percentile of GREEN_AMP4 image - p84green4 real, -- 84th percentile of GREEN_AMP4 image - medred1 real, -- Median of RED_AMP1 image - p16red1 real, -- 16th percentile of RED_AMP1 image - p84red1 real, -- 84th percentile of RED_AMP1 image - medred2 real, -- Median of RED_AMP2 image - p16red2 real, -- 16th percentile of RED_AMP2 image - p84red2 real, -- 84th percentile of RED_AMP2 image - medcahk real, -- Median of CA_HK image - p16cahk real, -- 16th percentile of CA_HK image - p84cahk real, -- 84th percentile of CA_HK image - comment character varying(255), -- Reason for status=0, etc. - CONSTRAINT rawfiles_ra_check CHECK (((ra >= 0.0) AND (ra < 360.0))), - CONSTRAINT rawfiles_dec_check CHECK (((dec >= -90.0) AND (dec <= 90.0))) + CREATE TABLE l0files ( + rid integer NOT NULL, -- Primary key + dateobs date NOT NULL, -- FITS-header keyword: DATE-OBS + ut time without time zone NOT NULL, -- FITS-header keyword: UT + datebeg timestamp without time zone NOT NULL, -- FITS-header keyword: DATE-BEG + mjdobs double precision NOT NULL, -- FITS-header keyword: MJD-OBS + exptime real NOT NULL, -- FITS-header keyword: EXPTIME + progname character varying(64) NOT NULL, -- FITS-header keyword: PROGNAME + imtype character varying(64) NOT NULL, -- FITS-header keyword: IMTYPE + sciobj character varying(64) NOT NULL, -- FITS-header keyword: SCI-OBJ + calobj character varying(64) NOT NULL, -- FITS-header keyword: CAL-OBJ + skyobj character varying(64) NOT NULL, -- FITS-header keyword: SKY-OBJ + "object" character varying(64) NOT NULL, -- FITS-header keyword: TARGOBJ or OBJECT + contentbits integer NOT NULL, -- BIT-WISE FLAGS FOR INCLUDING CCDs: BIT0: GREEN; BIT1: RED; BIT2: CA_HK + infobits bigint DEFAULT 0 NOT NULL, -- Bit-wise information flags + filename character varying(255) NOT NULL, -- Full path and filename + checksum character varying(32) NOT NULL, -- MD5 checksum of entire file + status smallint DEFAULT 0 NOT NULL, -- Set to zero if bad and one if good (verify automatically with + -- DATASUM and CHECKSUM keywords, or set this manually later, if necessary) + created timestamp without time zone -- Timestamp of database record INSERT or last UPDATE + DEFAULT now() NOT NULL, + targname character varying(64), -- FITS-header keyword: TARGNAME + gaiaid character varying(64), -- FITS-header keyword: GAIAID + twomassid character varying(64), -- FITS-header keyword: 2MASSID + ra double precision, -- FITS-header keyword: RA converted to decimal + dec double precision, -- FITS-header keyword: DEC converted to decimal + medgreen1 real, -- Median of GREEN_AMP1 image + p16green1 real, -- 16th percentile of GREEN_AMP1 image + p84green1 real, -- 84th percentile of GREEN_AMP1 image + medgreen2 real, -- Median of GREEN_AMP2 image + p16green2 real, -- 16th percentile of GREEN_AMP2 image + p84green2 real, -- 84th percentile of GREEN_AMP2 image + medgreen3 real, -- Median of GREEN_AMP3 image + p16green3 real, -- 16th percentile of GREEN_AMP3 image + p84green3 real, -- 84th percentile of GREEN_AMP3 image + medgreen4 real, -- Median of GREEN_AMP4 image + p16green4 real, -- 16th percentile of GREEN_AMP4 image + p84green4 real, -- 84th percentile of GREEN_AMP4 image + medred1 real, -- Median of RED_AMP1 image + p16red1 real, -- 16th percentile of RED_AMP1 image + p84red1 real, -- 84th percentile of RED_AMP1 image + medred2 real, -- Median of RED_AMP2 image + p16red2 real, -- 16th percentile of RED_AMP2 image + p84red2 real, -- 84th percentile of RED_AMP2 image + medcahk real, -- Median of CA_HK image + p16cahk real, -- 16th percentile of CA_HK image + p84cahk real, -- 84th percentile of CA_HK image + comment character varying(255), -- Reason for status=0, etc. + CONSTRAINT l0files_ra_check CHECK (((ra >= 0.0) AND (ra < 360.0))), + CONSTRAINT l0files_dec_check CHECK (((dec >= -90.0) AND (dec <= 90.0))) ); ----------------------------- - -- TABLE: Infobits + -- TABLE: L0infobits -- - -- Definitions of infobits for RawFiles table only + -- Definitions of infobits for L0Files table only -- (CalFiles infobits have different definitions). ----------------------------- - CREATE TABLE infobits ( - bid integer NOT NULL, -- Primary key - bit smallint NOT NULL, -- Bit number (allowed range is 0-63, inclusive) - param1 real, -- Parameter 1 - param2 real, -- Parameter 2 - param3 real, -- Parameter 3 - created timestamp without time zone -- Creation timestamp of database record - DEFAULT now() NOT NULL, -- Definition of bit and parameter(s) - definition character varying(256) NOT NULL, - CONSTRAINT infobits_bit_check CHECK (((bit >= 0) AND (bit <= 63))) + CREATE TABLE l0infobits ( + bid integer NOT NULL, -- Primary key + bit smallint NOT NULL, -- Bit number (allowed range is 0-63, inclusive) + param1 real, -- Parameter 1 + param2 real, -- Parameter 2 + param3 real, -- Parameter 3 + created timestamp without time zone -- Creation timestamp of database record + DEFAULT now() NOT NULL, + definition character varying(256) NOT NULL, -- Definition of bit and parameter(s) + CONSTRAINT l0infobits_bit_check CHECK (((bit >= 0) AND (bit <= 63))) ); From 05cbc1f6d7b8d412e1f81650deb00b8ff6a4b02b Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 22 Jun 2023 09:57:56 -0700 Subject: [PATCH 147/249] Cronjob scripts for daily registering L0 files in database. --- cronjobs/runL0FilesRegisterInDB.sh | 9 + database/cronjobs/l0files_register_in_db.pl | 304 ++++++++++++++++++++ 2 files changed, 313 insertions(+) create mode 100755 cronjobs/runL0FilesRegisterInDB.sh create mode 100755 database/cronjobs/l0files_register_in_db.pl diff --git a/cronjobs/runL0FilesRegisterInDB.sh b/cronjobs/runL0FilesRegisterInDB.sh new file mode 100755 index 000000000..730977fd1 --- /dev/null +++ b/cronjobs/runL0FilesRegisterInDB.sh @@ -0,0 +1,9 @@ +#! /bin/bash -l + +printenv >& $KPFCRONJOB_LOGS/runL0FilesRegisterInDB.env + +procdate=$(date +\%Y\%m\%d) + +echo "Processing date: $procdate" +$KPFCRONJOB_CODE/database/cronjobs/l0files_register_in_db.pl $procdate >& $KPFCRONJOB_LOGS/l0files_register_in_db_$procdate.out +echo Return value from l0files_register_in_db.pl = $? diff --git a/database/cronjobs/l0files_register_in_db.pl b/database/cronjobs/l0files_register_in_db.pl new file mode 100755 index 000000000..2578023fa --- /dev/null +++ b/database/cronjobs/l0files_register_in_db.pl @@ -0,0 +1,304 @@ +#! /usr/local/bin/perl + +########################################################################## +# Pipeline Perl script to do detached docker run. Can run this script +# in the background so that an open terminal window is not required. +# +# Usage: +# ./l0files_register_in_db 202306?? (file glob) +# +# For the given date on the command line (or file glob that +# covers a range of dates), ingest all L0 files in +# /data/kpf/L0/202306??/KP*fits on the shrek machine. +# +# This script will be run as a daily cronjob. +# It can also be executed in parallel for different inputs. +########################################################################## + +use strict; +use warnings; +use File::Copy; + +select STDERR; $| = 1; select STDOUT; $| = 1; + + +# Log start time. + +my ($startscript, $endscript, @checkpoint, $icheckpoint); + +$startscript = time(); +print "Start time = ", scalar localtime($startscript), "\n"; +print "====================================================\n"; +$icheckpoint = 0; +$checkpoint[$icheckpoint++] = $startscript; + + +# Read KPF-related environment variables. + +# Base directory of L0 files for permanent storage. +# E.g., /data/kpf/L0 +my $l0dir = $ENV{KPFPIPE_L0_BASE_DIR}; + +if (! (defined $l0dir)) { + die "*** Env. var. KPFPIPE_L0_BASE_DIR not set; quitting...\n"; +} + +# Code directory of KPF-Pipeline git repo where the docker run command is executed. +# E.g., /data/user/rlaher/git/KPF-Pipeline +my $codedir = $ENV{KPFCRONJOB_CODE}; + +if (! (defined $codedir)) { + die "*** Env. var. KPFCRONJOB_CODE not set; quitting...\n"; +} + +# Logs directory where log file (STDOUT) from this script goes (see runDailyPipelines.sh). +# Normally this is the code directory of KPF-Pipeline git repo. +# E.g., /data/user/rlaher/git/KPF-Pipeline +my $templogdir = $ENV{KPFCRONJOB_LOGS}; + +if (! (defined $templogdir)) { + die "*** Env. var. KPFCRONJOB_LOGS not set; quitting...\n"; +} + +# Docker container name for this Perl script, a known name so it can be monitored by docker ps command. +# E.g., russkpfl0registerindb +my $containername = 'russkpfl0registerindb'; + +my $trunctime = time() - int(53 * 365.25 * 24 * 3600); # Subtract off number of seconds in 53 years (since 00:00:00 on January 1, 1970, UTC). +$containername .= '_' . $$ . '_' . $trunctime; # Augment container name with unique numbers (process ID and truncated seconds). + + +# Database user for connecting to the database to run this script and insert records into the L0Files table. +# E.g., kpfporuss +my $dbuser = $ENV{KPFDBUSER}; + +if (! (defined $dbuser)) { + die "*** Env. var. KPFDBUSER not set; quitting...\n"; +} + +# Database name of KPF operations database containing the L0Files table. +# E.g., kpfopsdb +my $dbname = $ENV{KPFDBNAME}; + +if (! (defined $dbname)) { + die "*** Env. var. KPFDBNAME not set; quitting...\n"; +} + + +# Initialize fixed parameters and read command-line parameter. + +my $iam = 'l0files_register_in_db.pl'; +my $version = '1.0'; +my $verbose = 1; + +my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. + +if (! (defined $procdate)) { + die "*** Error: Missing command-line parameter YYYYMMDD; quitting...\n"; +} + +my $dockercmdscript = 'jobs/l0registerdbcmd'; # Auto-generates this shell script with multiple commands. +$dockercmdscript .= '_' . $$ . '_' . $trunctime . '.sh'; # Augment with unique numbers (process ID and truncated seconds). +my $containerimage = 'kpf-drp:latest'; +my $recipe = '/code/KPF-Pipeline/recipes/quality_control_exposure.recipe'; +my $inputconfig = $codedir . '/configs/quality_control_exposure.cfg'; +my $config = 'quality_control_exposure'; +$config .= '_' . $$ . '_' . $trunctime . '.cfg'; # Augment with unique numbers (process ID and truncated seconds). + +my ($basedir) = '/data/L0'; +my $configtomodify = $codedir . '/jobs/' . $config; +&modifyConfigFile($basedir, $procdate, $inputconfig, $configtomodify, $iam, $verbose); + + +my ($dbport, $dbpass); +my @op = `cat ~/.pgpass`; +foreach my $op (@op) { + chomp $op; + $op =~ s/^\s+|\s+$//g; # strip blanks. + if (($op =~ /$dbuser/) and ($op =~ /$dbname/)) { + my (@f) = split(/\:/, $op); + $dbport = $f[1]; + $dbpass = $f[4]; + } +} + +my $dbenvfilename = "db"; +$dbenvfilename .= '_' . $$ . '_' . $trunctime . '.env'; # Augment with unique numbers (process ID and truncated seconds). +my $dbenvfile = "$codedir/jobs/" . $dbenvfilename; +my $dbenvfileinside = "/code/KPF-Pipeline/jobs/" . $dbenvfilename; + +`touch $dbenvfile`; +`chmod 600 $dbenvfile`; +open(OUT,">$dbenvfile") or die "Could not open $dbenvfile ($!); quitting...\n"; +print OUT "export DBPASS=\"$dbpass\"\n"; +close(OUT) or die "Could not close $dbenvfile ($!); quitting...\n"; + +my $logdir = $l0dir; +$logdir =~ s/L0/logs/; +$logdir .= '/db'; + + +# Print environment. + +print "iam=$iam\n"; +print "version=$version\n"; +print "procdate=$procdate\n"; +print "dockercmdscript=$dockercmdscript\n"; +print "containerimage=$containerimage\n"; +print "dbenvfilename=$dbenvfilename\n"; +print "recipe=$recipe\n"; +print "inputconfig=$inputconfig\n"; +print "config=$config\n"; +print "configtomodify=$configtomodify\n"; +print "KPFPIPE_L0_BASE_DIR=$l0dir\n"; +print "KPFCRONJOB_LOGS=$templogdir\n"; +print "KPFCRONJOB_CODE=$codedir\n"; +print "dbuser=$dbuser\n"; +print "dbname=$dbname\n"; +print "dbport=$dbport\n"; +print "dbenvfile=$dbenvfile\n"; +print "dbenvfileinside=$dbenvfileinside\n"; +print "Docker container name = $containername\n"; +print "basedir=$basedir\n"; +print "logdir=$logdir\n"; + + +# Change directory to where the Dockerfile is located. + +chdir "$codedir" or die "Couldn't cd to $codedir : $!\n"; + +my $script = "#! /bin/bash\n" . + "source $dbenvfileinside\n" . + "make init\n" . + "export PYTHONUNBUFFERED=1\n" . + "pip install psycopg2-binary\n" . + "git config --global --add safe.directory /code/KPF-Pipeline\n" . + "kpf -r $recipe -c /code/KPF-Pipeline/jobs/$config\n" . + "exit\n"; +my $makescriptcmd = "echo \"$script\" > $dockercmdscript"; +`$makescriptcmd`; +`chmod +x $dockercmdscript`; + +my $dockerruncmd = "docker run -d --name $containername " . + "-v ${codedir}:/code/KPF-Pipeline -v ${l0dir}:${basedir} " . + "--network=host -e DBPORT=$dbport -e DBNAME=$dbname -e DBUSER=$dbuser -e DBSERVER=127.0.0.1 " . + "$containerimage bash ./$dockercmdscript"; +print "Executing $dockerruncmd\n"; +my $opdockerruncmd = `$dockerruncmd`; +print "Output from dockerruncmd: $opdockerruncmd\n"; + + +# Poll to see if done. + +while (1) { + my $cmd = "docker ps | grep $containername"; + my @op = `$cmd`; + my $n = @op; + if ($n == 0) { print "Exiting while loop...\n"; last; } + for (my $i = 0; $i < @op; $i++) { + my $op = $op[$i]; + chomp $op; + print "i=$i: $op\n"; + } + my $timestamp = localtime; + print "[$timestamp] Sleeping 300 seconds...\n"; + sleep(300); +} + +my $dockerrmcmd = "docker rm $containername"; +print "Executing $dockerrmcmd\n"; +my $opdockerrmcmd = `$dockerrmcmd`; +print "Output from dockerrmcmd: $opdockerrmcmd\n"; + + +# Checkpoint + +$checkpoint[$icheckpoint] = time(); +printf "Elapsed time to run recipe (sec.) = %d\n", + $checkpoint[$icheckpoint] - $checkpoint[$icheckpoint-1]; +$icheckpoint++; + + +# Log end time. + +$endscript = time(); +print "====================================================\n"; +print "End time = ", scalar localtime($endscript), "\n"; +print "Elapsed total time (sec.) = ", $endscript - $startscript, "\n"; + +print "Terminating normally...\n"; + + +# Move log file from runtime directory to product directory, assuming +# that the following convention for log-file naming is followed. + +my ($logfileBase) = $iam =~ /(.+)\.pl/; + +my $logfile = $templogdir . '/' . $logfileBase . '_' . $procdate . '.out'; + +if (-e $logfile) { + + if (! (move($logfile, $logdir))) { + print "*** Warning: couldn't move $logfile to $logdir; " . + "quitting...\n"; + exit(64); + } else { + print "Moved $logfile to $logdir\n"; + } +} + + +exit(0); + + +#------------------------------------------ +# Subroutine to edit onfig file to change to the correct observation date. +# + +sub modifyConfigFile { + + my ($basedir, $procdate, $inputconfigfile, $configfile, $iam, $verbose) = @_; + + print "inconfigfile=$inputconfigfile\n"; + print "configfile=$configfile\n"; + + if (! (-e $inputconfigfile)) { + print "*** Error: Input config file does not exist ($inputconfigfile); quitting...\n"; + exit(64); + } + + + # Edit the input config file. + + my $cmd = "cat $inputconfigfile"; + + print "Executing command=[$cmd]\n"; + my (@lines) = `$cmd`; + + if (! open(OUT, ">$configfile") ) { + print "*** Error: Couldn't open $configfile for writing; quitting...\n"; + exit(64); + } + + foreach my $line (@lines) { + + chomp $line; + + ####################################################################### + # Change this line: + # + # lev0_fits_file_glob = /data/L0/20230620/KP.*.fits + ####################################################################### + + if ($line =~ /^lev0_fits_file_glob\s*=/) { + $line = 'lev0_fits_file_glob = ' . $basedir . '/' . $procdate . '/KP.*.fits'; + } + + print OUT "$line\n"; + } + + if (! close(OUT) ) { + print "*** Error: Couldn't close $configfile; quitting...\n"; + exit(64); + } +} From 3c7e72de88bf5ff3e05df36c99b30bdcf526d418 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 22 Jun 2023 09:58:06 -0700 Subject: [PATCH 148/249] Made more changes for parallel execution, and to put temporary files in jobs subdirectory. --- cronjobs/kpfmasters_wls_auto.pl | 5 +++-- cronjobs/kpfmastersruncmd_l0.pl | 8 ++++++-- cronjobs/kpfmastersruncmd_l1.pl | 12 +++++++----- database/cronjobs/kpfmasters_register_in_db.pl | 12 +++++++----- 4 files changed, 23 insertions(+), 14 deletions(-) diff --git a/cronjobs/kpfmasters_wls_auto.pl b/cronjobs/kpfmasters_wls_auto.pl index dbefe8d00..74d8779cd 100755 --- a/cronjobs/kpfmasters_wls_auto.pl +++ b/cronjobs/kpfmasters_wls_auto.pl @@ -93,7 +93,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmasters_wls_auto.pl'; -my $version = '1.2'; +my $version = '1.3'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -101,7 +101,8 @@ die "*** Error: Missing command-line parameter YYYYMMDD; quitting...\n"; } -my $dockercmdscript = 'kpfmasters_wls_auto.sh'; # Auto-generates this shell script with multiple commands. +my $dockercmdscript = 'jobs/kpfmasters_wls_auto'; # Auto-generates this shell script with multiple commands. +$dockercmdscript .= '_' . $$ . '_' . $trunctime . '.sh'; # Augment with unique numbers (process ID and truncated seconds). my $containerimage = 'kpf-drp:latest'; my $recipe = '/code/KPF-Pipeline/recipes/wls_auto.recipe'; my $config = '/code/KPF-Pipeline/configs/wls_auto.cfg'; diff --git a/cronjobs/kpfmastersruncmd_l0.pl b/cronjobs/kpfmastersruncmd_l0.pl index de9e803f7..42966d964 100755 --- a/cronjobs/kpfmastersruncmd_l0.pl +++ b/cronjobs/kpfmastersruncmd_l0.pl @@ -17,6 +17,9 @@ # master-dark subtraction, and master-flattening, as appropriate, are # done by the individidual Frameworks for generation of the master bias, # master dark, master flat, and various master arclamps. +# +# Be sure to make a jobs subdirectory for temporary files: +# mkdir -p $KPFCRONJOB_CODE/jobs ########################################################################## use strict; @@ -96,7 +99,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmastersruncmd_l0.pl'; -my $version = '1.3'; +my $version = '1.4'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -105,7 +108,8 @@ } # These parameters are fixed for this Perl script. -my $dockercmdscript = 'kpfmasterscmd_l0.sh'; # Auto-generates this shell script with multiple commands. +my $dockercmdscript = 'jobs/kpfmasterscmd_l0'; # Auto-generates this shell script with multiple commands. +$dockercmdscript .= '_' . $$ . '_' . $trunctime . '.sh'; # Augment with unique numbers (process ID and truncated seconds). my $containerimage = 'kpf-drp:latest'; my $recipe = '/code/KPF-Pipeline/recipes/kpf_masters_drp.recipe'; my $config = '/code/KPF-Pipeline/configs/kpf_masters_drp.cfg'; diff --git a/cronjobs/kpfmastersruncmd_l1.pl b/cronjobs/kpfmastersruncmd_l1.pl index fa470cedc..91810ee4e 100755 --- a/cronjobs/kpfmastersruncmd_l1.pl +++ b/cronjobs/kpfmastersruncmd_l1.pl @@ -113,7 +113,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmastersruncmd_l1.pl'; -my $version = '1.5'; +my $version = '1.6'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -121,7 +121,8 @@ die "*** Error: Missing command-line parameter YYYYMMDD; quitting...\n"; } -my $dockercmdscript = 'kpfmasterscmd_l1.sh'; # Auto-generates this shell script with multiple commands. +my $dockercmdscript = 'jobs/kpfmasterscmd_l1'; # Auto-generates this shell script with multiple commands. +$dockercmdscript .= '_' . $$ . '_' . $trunctime . '.sh'; # Augment with unique numbers (process ID and truncated seconds). my $containerimage = 'kpf-drp:latest'; my $recipe = '/code/KPF-Pipeline/recipes/kpf_drp.recipe'; my $config = '/code/KPF-Pipeline/configs/kpf_masters_l1.cfg'; @@ -138,9 +139,10 @@ } } -my $dbenvfilename = "db.env"; -my $dbenvfile = "$codedir/" . $dbenvfilename; -my $dbenvfileinside = "/code/KPF-Pipeline/" . $dbenvfilename; +my $dbenvfilename = "db"; +$dbenvfilename .= '_' . $$ . '_' . $trunctime . '.env'; # Augment with unique numbers (process ID and truncated seconds). +my $dbenvfile = "$codedir/jobs/" . $dbenvfilename; +my $dbenvfileinside = "/code/KPF-Pipeline/jobs/" . $dbenvfilename; `touch $dbenvfile`; `chmod 600 $dbenvfile`; diff --git a/database/cronjobs/kpfmasters_register_in_db.pl b/database/cronjobs/kpfmasters_register_in_db.pl index b2416c726..21daa33cf 100755 --- a/database/cronjobs/kpfmasters_register_in_db.pl +++ b/database/cronjobs/kpfmasters_register_in_db.pl @@ -100,7 +100,7 @@ # Initialize fixed parameters and read command-line parameter. my $iam = 'kpfmasters_register_in_db.pl'; -my $version = '1.3'; +my $version = '1.4'; my $procdate = shift @ARGV; # YYYYMMDD command-line parameter. @@ -109,7 +109,8 @@ } my $pythonscript = 'database/scripts/registerCalFilesForDate.py'; -my $dockercmdscript = 'kpfmasters_register_in_db.sh'; # Auto-generates this shell script with multiple commands. +my $dockercmdscript = 'jobs/kpfmasters_register_in_db'; # Auto-generates this shell script with multiple commands. +$dockercmdscript .= '_' . $$ . '_' . $trunctime . '.sh'; # Augment with unique numbers (process ID and truncated seconds). my $containerimage = 'kpf-drp:latest'; my ($pylogfileDir, $pylogfileBase) = $pythonscript =~ /(.+)\/(.+)\.py/; @@ -129,9 +130,10 @@ } } -my $dbenvfilename = "db.env"; -my $dbenvfile = "$codedir/" . $dbenvfilename; -my $dbenvfileinside = "/code/KPF-Pipeline/" . $dbenvfilename; +my $dbenvfilename = "db"; +$dbenvfilename .= '_' . $$ . '_' . $trunctime . '.env'; # Augment with unique numbers (process ID and truncated seconds). +my $dbenvfile = "$codedir/jobs/" . $dbenvfilename; +my $dbenvfileinside = "/code/KPF-Pipeline/jobs/" . $dbenvfilename; `touch $dbenvfile`; `chmod 600 $dbenvfile`; From 33a0d983f7d20d494f0058d1037f1e8ca80fe598 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 22 Jun 2023 14:52:18 -0700 Subject: [PATCH 149/249] Updated text --- docs/source/data_exploration/tutorial_2D_ion_pump.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/data_exploration/tutorial_2D_ion_pump.rst b/docs/source/data_exploration/tutorial_2D_ion_pump.rst index 16b0085ba..654a4f901 100644 --- a/docs/source/data_exploration/tutorial_2D_ion_pump.rst +++ b/docs/source/data_exploration/tutorial_2D_ion_pump.rst @@ -1,5 +1,5 @@ -Manipulating 2D images to diagnose problem with ion pumps -========================================================= +Manipulating 2D images to diagnose a problem with the ion pumps +=============================================================== .. toctree:: :maxdepth: 0 From a07a003e01de5451422dfba8960460bab412de0b Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 22 Jun 2023 14:52:25 -0700 Subject: [PATCH 150/249] Fix the TOC in readthedocs --- docs/source/data_exploration/tutorial_L2.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/data_exploration/tutorial_L2.rst b/docs/source/data_exploration/tutorial_L2.rst index 6f651f5a0..6d4c30056 100644 --- a/docs/source/data_exploration/tutorial_L2.rst +++ b/docs/source/data_exploration/tutorial_L2.rst @@ -1,3 +1,6 @@ +Reading L1 files and examining the data products +================================================ + .. toctree:: :maxdepth: 0 From 0974b5cc57379e0def80ca94f73ba0b30afac485 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Fri, 23 Jun 2023 09:20:20 -0700 Subject: [PATCH 151/249] Fixed logic for empty image extension. --- .../src/quality_control_exposure_framework.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/modules/quality_control_exposure/src/quality_control_exposure_framework.py b/modules/quality_control_exposure/src/quality_control_exposure_framework.py index 4bdc838f2..55b86f786 100644 --- a/modules/quality_control_exposure/src/quality_control_exposure_framework.py +++ b/modules/quality_control_exposure/src/quality_control_exposure_framework.py @@ -211,10 +211,14 @@ def _perform(self): image = np.array(l0_file[ffi]) image_shape = np.shape(image) + len_image_shape = len(image_shape) + image_size = np.size(image) + image_type = type(image) - #self.logger.info('ffi,image_shape = {},{}'.format(ffi,image_shape)) + #self.logger.info('ffi,image_shape,len_image_shape,image_size,image_type = {},{},{},{},{}'.\ + # format(ffi,image_shape,len_image_shape,image_size,image_type)) - if image_shape[0] != 0: + if len_image_shape >= 2: median = np.nanpercentile(image, 50) p16 = np.nanpercentile(image, 16) p84 = np.nanpercentile(image, 84) From 74e2ede42c59ab3b15245624ad3a536d7ebef504 Mon Sep 17 00:00:00 2001 From: Aaron Householder Date: Fri, 23 Jun 2023 13:54:53 -0400 Subject: [PATCH 152/249] Improved peak outlier rejection --- modules/wavelength_cal/src/alg.py | 47 +++++++++++++++++++++++++++---- 1 file changed, 42 insertions(+), 5 deletions(-) diff --git a/modules/wavelength_cal/src/alg.py b/modules/wavelength_cal/src/alg.py index a75fbbc61..6ec829183 100644 --- a/modules/wavelength_cal/src/alg.py +++ b/modules/wavelength_cal/src/alg.py @@ -146,7 +146,6 @@ def run_wavelength_cal( # make a plot of all of the precise new wls minus the rough input wls if self.save_diagnostics_dir is not None and rough_wls is not None: - # don't do this for etalon exposures, where we're either not # deriving a new wls or using drift to do so if self.cal_type != 'Etalon': @@ -649,14 +648,50 @@ def clip_peaks( (np.abs(fitted_peak_pixels - detected_peak_pixels) < 1) ) [0] - # clip peaks with heights less than half of previous peak + # clip peaks that are a factor of 3 greater than the 8 adjacent peaks on both sides new_good_peak_idx = [] + for i in range(len(good_peak_idx)): + peak_idx = good_peak_idx[i] + if peak_idx >= 8 and peak_idx < n_pixels - 8: + peak_flux = detected_peak_heights[peak_idx] + adjacent_fluxes = np.concatenate((detected_peak_heights[peak_idx-9:peak_idx-1], detected_peak_heights[peak_idx+1:peak_idx+9])) + #print(peak_flux) + max_adjacent_flux = np.max(adjacent_fluxes) + #import pdb; pdb.set_trace() + if peak_flux <= 3 * max_adjacent_flux: + new_good_peak_idx.append(peak_idx) + elif peak_idx < 8: + # Handle peaks near the beginning + peak_flux = detected_peak_heights[peak_idx] + adjacent_fluxes = detected_peak_heights[peak_idx+1:peak_idx+9] + max_adjacent_flux = np.max(adjacent_fluxes) + if peak_flux <= 3 * max_adjacent_flux: + new_good_peak_idx.append(peak_idx) + else: + # Handle peaks near the end + peak_flux = detected_peak_heights[peak_idx] + adjacent_fluxes = detected_peak_heights[peak_idx-9:peak_idx-1] + max_adjacent_flux = np.max(adjacent_fluxes) + if peak_flux <= 3 * max_adjacent_flux: + new_good_peak_idx.append(peak_idx) + + good_peak_idx = np.array(new_good_peak_idx) + import pdb;pdb.set_trace() + + # clip peaks with heights less than a third of previous peak + final_good_peak_idx = [] prev_peak_height = 0 for i in range(len(good_peak_idx)): - if detected_peak_heights[good_peak_idx[i]] >= (prev_peak_height / 2): - new_good_peak_idx.append(good_peak_idx[i]) + if detected_peak_heights[good_peak_idx[i]] >= (prev_peak_height / 3): + final_good_peak_idx.append(good_peak_idx[i]) prev_peak_height = detected_peak_heights[good_peak_idx[i]] - good_peak_idx = np.array(new_good_peak_idx) + missed_peak_count = 0 + else: + missed_peak_count += 1 + if missed_peak_count == 5: + print('Warning: Check for outliers. 5 peaks in a row with heights < 1/3 of previous peak.') + + good_peak_idx = np.array(final_good_peak_idx) # # if we know the wavelengths of the peaks (i.e. if dealing with LFC), # # then we can clip peaks with derived wavelengths far from the location @@ -759,6 +794,8 @@ def clip_peaks( order_flux[zoom_section_pixels * i : zoom_section_pixels * (i + 1)] ) ) + ax.set_xlabel('Pixel') + ax.set_ylabel('Counts') plt.tight_layout() plt.savefig('{}/unclipped_peaks_zoom.png'.format(plot_path), dpi=250) From 7ac5524227ec8acb4266640c9ec0bac3e9afc9ea Mon Sep 17 00:00:00 2001 From: Aaron Householder Date: Fri, 23 Jun 2023 14:19:26 -0400 Subject: [PATCH 153/249] remove pdb --- modules/wavelength_cal/src/alg.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/wavelength_cal/src/alg.py b/modules/wavelength_cal/src/alg.py index 6ec829183..83700e34b 100644 --- a/modules/wavelength_cal/src/alg.py +++ b/modules/wavelength_cal/src/alg.py @@ -676,7 +676,6 @@ def clip_peaks( new_good_peak_idx.append(peak_idx) good_peak_idx = np.array(new_good_peak_idx) - import pdb;pdb.set_trace() # clip peaks with heights less than a third of previous peak final_good_peak_idx = [] From 4994528c26748daaa53363e957249fcf4a860136 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 12:19:25 -0700 Subject: [PATCH 154/249] add flip for green chip in 2 amp mode --- modules/Utils/overscan_subtract.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/modules/Utils/overscan_subtract.py b/modules/Utils/overscan_subtract.py index c4256fc83..b971eb3c0 100644 --- a/modules/Utils/overscan_subtract.py +++ b/modules/Utils/overscan_subtract.py @@ -463,6 +463,7 @@ def _perform(self): # Trim the orientation arrays accordingly. # Also, change self.channel_datasec_nrows accordingly. + green2amp = False if self.ffi_exts[0] == 'GREEN_CCD': naxis2 = l0_obj.header['GREEN_AMP1']['NAXIS2'] if naxis2 >= 4000: @@ -477,6 +478,7 @@ def _perform(self): channel_exts.pop(3) channel_exts.pop(2) self.channel_datasec_nrows = 4080 + green2amp = True frames_data = [] for ext in channel_exts: @@ -509,6 +511,8 @@ def _perform(self): single_frame_data = np.array_split(frames_data,len(self.ffi_exts))[frame] full_frame_img = self.run_oscan_subtraction(single_frame_data,channels,channel_keys,channel_rows,channel_cols,channel_exts) + if green2amp: + full_frame_img = np.flipud(full_frame_img) l0_obj[self.ffi_exts[frame]] = full_frame_img l0_obj.header[self.ffi_exts[frame]]['BUNIT'] = ('electrons','Units of image data') From 846f041dea9462293b1b563f0332d65b14d61eee Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:04:39 -0700 Subject: [PATCH 155/249] output exception and traceback to logs --- kpfpipe/pipelines/kpfpipeline.py | 3 ++- kpfpipe/primitives/core.py | 15 ++++++++++++++- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/kpfpipe/pipelines/kpfpipeline.py b/kpfpipe/pipelines/kpfpipeline.py index 3fc03bd75..e50ae8c73 100644 --- a/kpfpipe/pipelines/kpfpipeline.py +++ b/kpfpipe/pipelines/kpfpipeline.py @@ -207,8 +207,9 @@ def start_recipe(self, action, context): self.logger = start_logger(logname, self.configfile, log_path=log_path) self.context.logger = self.logger self.logger.info("*************** Executing recipe {} ***************".format(recipe_file)) - + self._recipe_visitor = KpfPipelineNodeVisitor(pipeline=self, context=context) + self.register_recipe_builtins() ## set up environment try: diff --git a/kpfpipe/primitives/core.py b/kpfpipe/primitives/core.py index 84144b31c..747856b98 100644 --- a/kpfpipe/primitives/core.py +++ b/kpfpipe/primitives/core.py @@ -1,4 +1,6 @@ +import traceback + from keckdrpframework.primitives.base_primitive import BasePrimitive from keckdrpframework.models.arguments import Arguments @@ -19,4 +21,15 @@ def __init__(self, action, context): self.action = action self.context = context - self.logger = self.context.logger \ No newline at end of file + self.logger = self.context.logger + + def apply(self): + try: + if self._pre_condition(): + self.output = self._perform() + if self._post_condition(): + return self.output + except Exception as e: + self.logger.error(f"Failed executing primitive {self.__class__.__name__}: {e}\n{traceback.format_exc()}") + raise(Exception) + return None \ No newline at end of file From 26c7d191dfb26488028bea48366cdc508aca85e6 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:10:51 -0700 Subject: [PATCH 156/249] allow recipe to continue if QLP fails --- kpfpipe/primitives/core.py | 2 +- modules/quicklook/src/quick_prim.py | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/kpfpipe/primitives/core.py b/kpfpipe/primitives/core.py index 747856b98..d186c474b 100644 --- a/kpfpipe/primitives/core.py +++ b/kpfpipe/primitives/core.py @@ -31,5 +31,5 @@ def apply(self): return self.output except Exception as e: self.logger.error(f"Failed executing primitive {self.__class__.__name__}: {e}\n{traceback.format_exc()}") - raise(Exception) + raise(e) return None \ No newline at end of file diff --git a/modules/quicklook/src/quick_prim.py b/modules/quicklook/src/quick_prim.py index 501588dc1..b1e614f2e 100644 --- a/modules/quicklook/src/quick_prim.py +++ b/modules/quicklook/src/quick_prim.py @@ -50,4 +50,8 @@ def __init__(self, self.alg=QuicklookAlg(config=self.config,logger=self.logger) def _perform(self) -> None: - self.alg.qlp_procedures(self.input_file,self.output_dir,self.end_of_night_summary) + try: + self.alg.qlp_procedures(self.input_file,self.output_dir,self.end_of_night_summary) + except: + # allow recipe to continue if QLP fails + pass From 0ba5e5b1d9c0c20a7188f33d1f06bf7e35216e6a Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:16:45 -0700 Subject: [PATCH 157/249] log error from quicklook --- modules/quicklook/src/quick_prim.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/quick_prim.py b/modules/quicklook/src/quick_prim.py index b1e614f2e..563bdceec 100644 --- a/modules/quicklook/src/quick_prim.py +++ b/modules/quicklook/src/quick_prim.py @@ -1,6 +1,7 @@ # Standard dependencies import configparser import numpy as np +import traceback # Pipeline dependencies from kpfpipe.logger import start_logger @@ -52,6 +53,7 @@ def __init__(self, def _perform(self) -> None: try: self.alg.qlp_procedures(self.input_file,self.output_dir,self.end_of_night_summary) - except: + except Exception as e: + self.logger.error(f"Failure in quicklook pipeline: {e}\n{traceback.format_exc()}") # allow recipe to continue if QLP fails pass From b4f1957540ebb2e8248e6b88047a98462774c979 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:33:01 -0700 Subject: [PATCH 158/249] dynamically find the first non-date column in EM data --- .../radial_velocity/src/midpoint_photon_arrival.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modules/radial_velocity/src/midpoint_photon_arrival.py b/modules/radial_velocity/src/midpoint_photon_arrival.py index ec38fddd9..d16263c66 100644 --- a/modules/radial_velocity/src/midpoint_photon_arrival.py +++ b/modules/radial_velocity/src/midpoint_photon_arrival.py @@ -359,9 +359,15 @@ def orderedMidPoint(self): self.segmentMin=orderMin self.segmentMax=orderMax warnings.warn("No segments bounds were provided. The default bounds for WAVE_1 are being asserted. ") - - wav_str = self.df_EM.columns[2:] # string (center) wavelengths of each pixel - wav = self.df_EM.columns[2:].astype(float) # float (center) wavelengths of each pixel + + i = 0 + for col in df_EM.columns: + if col.lower().startswith('date'): + i += 1 + else: + break + wav_str = self.df_EM.columns[i:] # string (center) wavelengths of each pixel + wav = self.df_EM.columns[i:].astype(float) # float (center) wavelengths of each pixel # define disperion at each wavelength (nm per pixel) disp = wav*0+np.gradient(wav,1)*-1 From ae8eeb926314cb6a7085696976f8997f4669db8b Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:36:17 -0700 Subject: [PATCH 159/249] bug --- modules/radial_velocity/src/midpoint_photon_arrival.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/radial_velocity/src/midpoint_photon_arrival.py b/modules/radial_velocity/src/midpoint_photon_arrival.py index d16263c66..e9659cd7c 100644 --- a/modules/radial_velocity/src/midpoint_photon_arrival.py +++ b/modules/radial_velocity/src/midpoint_photon_arrival.py @@ -361,7 +361,7 @@ def orderedMidPoint(self): warnings.warn("No segments bounds were provided. The default bounds for WAVE_1 are being asserted. ") i = 0 - for col in df_EM.columns: + for col in self.df_EM.columns: if col.lower().startswith('date'): i += 1 else: From a5af7ba954506e2e5466aa05c99ccc85dbcc159a Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Fri, 23 Jun 2023 13:43:53 -0700 Subject: [PATCH 160/249] bug --- modules/quicklook/src/alg.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index b8e220605..1893308a9 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -538,10 +538,16 @@ def gaussian_1d_apply(row): df_SKY_EM = dat_SKY.to_pandas() df_SCI_EM = dat_SCI.to_pandas() - wav_SCI_str = df_SCI_EM.columns[2:] - wav_SCI = df_SCI_EM.columns[2:].astype(float) - wav_SKY_str = df_SKY_EM.columns[2:] - wav_SKY = df_SKY_EM.columns[2:].astype(float) + i = 0 + for col in df_SCI_EM.columns: + if col.lower().startswith('date'): + i += 1 + else: + break + wav_SCI_str = df_SCI_EM.columns[i:] + wav_SCI = df_SCI_EM.columns[i:].astype(float) + wav_SKY_str = df_SKY_EM.columns[i:] + wav_SKY = df_SKY_EM.columns[i:].astype(float) disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1 disp_SKY = wav_SKY*0+np.gradient(wav_SKY,1)*-1 From a5e26dffda7230d825760b8d22aa53519f11b413 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 14:11:58 -0700 Subject: [PATCH 161/249] Fixed title --- docs/source/data_exploration/tutorial_L2.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/data_exploration/tutorial_L2.rst b/docs/source/data_exploration/tutorial_L2.rst index 6d4c30056..1944c7ae7 100644 --- a/docs/source/data_exploration/tutorial_L2.rst +++ b/docs/source/data_exploration/tutorial_L2.rst @@ -1,5 +1,5 @@ -Reading L1 files and examining the data products -================================================ +Plotting KPF RV time series +=========================== .. toctree:: :maxdepth: 0 From 1f3c1cb6a8f68c6f630f389ed743ae87b3b8f196 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 23 Jun 2023 16:08:43 -0700 Subject: [PATCH 162/249] showing individual wav soln --- modules/quicklook/src/alg.py | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index b8e220605..3470cb4c8 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -772,18 +772,28 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #print(hdulist1.info()) + wav_green = np.array(hdulist['GREEN_SCI_WAVE1'].data,'d') + wav_red = np.array(hdulist['RE_SCI_WAVE1'].data,'d') flux_green = np.array(hdulist['GREEN_SCI_FLUX1'].data,'d') flux_red = np.array(hdulist['RED_SCI_FLUX1'].data,'d')#hdulist[40].data + wav_green2 = np.array(hdulist['GREEN_SCI_WAVE2'].data,'d') + wav_red2 = np.array(hdulist['RE_SCI_WAVE2'].data,'d') flux_green2 = np.array(hdulist['GREEN_SCI_FLUX2'].data,'d') flux_red2 = np.array(hdulist['RED_SCI_FLUX2'].data,'d')#hdulist[40].data + wav_green3 = np.array(hdulist['GREEN_SCI_WAVE3'].data,'d') + wav_red3 = np.array(hdulist['RE_SCI_WAVE3'].data,'d') flux_green3 = np.array(hdulist['GREEN_SCI_FLUX3'].data,'d') flux_red3 = np.array(hdulist['RED_SCI_FLUX3'].data,'d')#hdulist[40].data + wav_green_cal = np.array(hdulist['GREEN_CAL_WAVE'].data,'d') + wav_red_cal = np.array(hdulist['RE_CAL_WAVE'].data,'d') flux_green_cal = np.array(hdulist['GREEN_CAL_FLUX'].data,'d') flux_red_cal = np.array(hdulist['RED_CAL_FLUX'].data,'d')#hdulist[40].data + wav_green_sky = np.array(hdulist['GREEN_SKY_WAVE'].data,'d') + wav_red_sky = np.array(hdulist['RE_SKY_WAVE'].data,'d') flux_green_sky = np.array(hdulist['GREEN_SKY_FLUX'].data,'d') flux_red_sky = np.array(hdulist['RED_SKY_FLUX'].data,'d')#hdulist[40].data @@ -802,6 +812,10 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): print(np.shape(flux_green),np.shape(flux_green)==(0,),np.shape(flux_red),np.shape(flux_green)) wav = np.concatenate((wav_green,wav_red),axis = 0) + wav2 = np.concatenate((wav_green2,wav_red2),axis = 0) + wav3 = np.concatenate((wav_green3,wav_red3),axis = 0) + wav_cal = np.concatenate((wav_green_cal,wav_red_cal),axis = 0) + wav_sky = np.concatenate((wav_green_sky,wav_red_sky),axis = 0) print('test wave',np.shape(wav)) #print(hdulist1.info()) @@ -865,7 +879,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): j = int(i/n) rgba = cm((i % n)/n*1.) #print(j,rgba) - ax[j].plot(wav[i,:],flux2[i,:], linewidth = 0.3,color = rgba) + ax[j].plot(wav2[i,:],flux2[i,:], linewidth = 0.3,color = rgba) for j in range(int(np.shape(flux2)[0]/n)): low, high = np.nanpercentile(flux2[j*n:(j+1)*n,:],[.1,99.9]) @@ -900,7 +914,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): j = int(i/n) rgba = cm((i % n)/n*1.) #print(j,rgba) - ax[j].plot(wav[i,:],flux3[i,:], linewidth = 0.3,color = rgba) + ax[j].plot(wav3[i,:],flux3[i,:], linewidth = 0.3,color = rgba) for j in range(int(np.shape(flux3)[0]/n)): low, high = np.nanpercentile(flux3[j*n:(j+1)*n,:],[.1,99.9]) @@ -935,7 +949,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): j = int(i/n) rgba = cm((i % n)/n*1.) #print(j,rgba) - ax[j].plot(wav[i,:],flux_cal[i,:], linewidth = 0.3,color = rgba) + ax[j].plot(wav_cal[i,:],flux_cal[i,:], linewidth = 0.3,color = rgba) for j in range(int(np.shape(flux_cal)[0]/n)): low, high = np.nanpercentile(flux_cal[j*n:(j+1)*n,:],[.1,99.9]) @@ -970,7 +984,7 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): j = int(i/n) rgba = cm((i % n)/n*1.) #print(j,rgba) - ax[j].plot(wav[i,:],flux_sky[i,:], linewidth = 0.3,color = rgba) + ax[j].plot(wav_sky[i,:],flux_sky[i,:], linewidth = 0.3,color = rgba) for j in range(int(np.shape(flux_sky)[0]/n)): low, high = np.nanpercentile(flux_sky[j*n:(j+1)*n,:],[.1,99.9]) From d4447e8cd7ea864a1a87b422dc29b780a31e2c3e Mon Sep 17 00:00:00 2001 From: DaiFei Date: Fri, 23 Jun 2023 16:11:10 -0700 Subject: [PATCH 163/249] showing individual wav soln --- modules/quicklook/src/alg.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 3470cb4c8..0e794b6dc 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -773,27 +773,27 @@ def extract_HK_spectrum(data,trace_location,rv_shift,wavesoln ): #print(hdulist1.info()) wav_green = np.array(hdulist['GREEN_SCI_WAVE1'].data,'d') - wav_red = np.array(hdulist['RE_SCI_WAVE1'].data,'d') + wav_red = np.array(hdulist['RED_SCI_WAVE1'].data,'d') flux_green = np.array(hdulist['GREEN_SCI_FLUX1'].data,'d') flux_red = np.array(hdulist['RED_SCI_FLUX1'].data,'d')#hdulist[40].data wav_green2 = np.array(hdulist['GREEN_SCI_WAVE2'].data,'d') - wav_red2 = np.array(hdulist['RE_SCI_WAVE2'].data,'d') + wav_red2 = np.array(hdulist['RED_SCI_WAVE2'].data,'d') flux_green2 = np.array(hdulist['GREEN_SCI_FLUX2'].data,'d') flux_red2 = np.array(hdulist['RED_SCI_FLUX2'].data,'d')#hdulist[40].data wav_green3 = np.array(hdulist['GREEN_SCI_WAVE3'].data,'d') - wav_red3 = np.array(hdulist['RE_SCI_WAVE3'].data,'d') + wav_red3 = np.array(hdulist['RED_SCI_WAVE3'].data,'d') flux_green3 = np.array(hdulist['GREEN_SCI_FLUX3'].data,'d') flux_red3 = np.array(hdulist['RED_SCI_FLUX3'].data,'d')#hdulist[40].data wav_green_cal = np.array(hdulist['GREEN_CAL_WAVE'].data,'d') - wav_red_cal = np.array(hdulist['RE_CAL_WAVE'].data,'d') + wav_red_cal = np.array(hdulist['RED_CAL_WAVE'].data,'d') flux_green_cal = np.array(hdulist['GREEN_CAL_FLUX'].data,'d') flux_red_cal = np.array(hdulist['RED_CAL_FLUX'].data,'d')#hdulist[40].data wav_green_sky = np.array(hdulist['GREEN_SKY_WAVE'].data,'d') - wav_red_sky = np.array(hdulist['RE_SKY_WAVE'].data,'d') + wav_red_sky = np.array(hdulist['RED_SKY_WAVE'].data,'d') flux_green_sky = np.array(hdulist['GREEN_SKY_FLUX'].data,'d') flux_red_sky = np.array(hdulist['RED_SKY_FLUX'].data,'d')#hdulist[40].data From 10de5745013302533d59320bcfe67618f3ef4854 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 20:52:44 -0700 Subject: [PATCH 164/249] Added kpf_slowtouch.sh This script is used to touch a list of KPF L0 files that have names like KP.20230623.12345.67.fits. This is useful to initiate reprocessing using the KPF DRP. The list of L0 files can be provided in multiple ways: 1. As command-line arguments when invoking the script. 2. In the first column of a CSV file specified with the -f option. This is useful for CSV files with a large set of L0 filenames downloaded from Jump. Such files might have double quotes around the L0 filename, which the script will remove when appropriate. 3. All filenames in a directory specified with the -d option. --- scripts/kpf_slowtouch.sh | 175 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 scripts/kpf_slowtouch.sh diff --git a/scripts/kpf_slowtouch.sh b/scripts/kpf_slowtouch.sh new file mode 100644 index 000000000..28147ee93 --- /dev/null +++ b/scripts/kpf_slowtouch.sh @@ -0,0 +1,175 @@ +#!/bin/bash + +read -r -d '' SCRIPT_DOC <<'EOF' +Script name: kpf_slowtouch.sh +Author: Andrew Howard + with assistance from Chat-GPT4 + or maybe the other way around +Date: June 23, 2023 + +This script is used to touch a list of KPF L0 files that have names like +KP.20230623.12345.67.fits. This is useful to initiate reprocessing +using the KPF DRP. The list of L0 files can be provided in multiple ways: + 1. As command-line arguments when invoking the script. + 2. In the first column of a CSV file specified with the -f option. + This is useful for CSV files with a large set of L0 filenames + downloaded from Jump. Such files might have double quotes around + the L0 filename, which the script will remove when appropriate. + 3. All filenames in a directory specified with the -d option. + +Command-line options (all are optional): +-f : The script will read the KPF L0 filenames + from the first column of a CSV with the name . + Useful for lists of L0 files downloaded from Jump. +-d : Adds every file in to the list of L0 files. +-p : Sets the L0 path to . + Default value: /data/kpf/L0 +-s : Sets the interval between file touches. + Default value: 0.2 [sec] +-e : Echo the touch commands instead of executing them. + +Examples: +1. To provide filenames using command line arguments: + ./kpf_slowtouch.sh KP.20230623.12345.67.fits KP.20230623.12345.68.fits +2. To provide filenames using a CSV file: + ./kpf_slowtouch.sh -f filenames.csv +3. To provide files listed in a directory: + ./kpf_slowtouch.sh -d /path/to/directory +4. To change the default L0 path and sleep interval between touches: + ./kpf_slowtouch.sh KP.20230623.12345.67.fits -p /new/path -s 0.5 +5. To echo the touch commands instead of executing them: + ./kpf_slowtouch.sh KP.20230623.12345.67.fits -e +EOF + +# Default values +l0_path="/data/kpf/L0" +sleep_interval=0.2 + +if [[ $# -eq 0 ]]; then + echo "No arguments supplied. Please provide -f filename.csv, -d directory, or KPF filenames as command line arguments." + exit 1 +fi + +# Declare an empty array to store the KPF filenames +declare -a filenames=() + +# Flags to keep track of command line options +csv_processed=0 +l0_path_processed=0 +sleep_interval_processed=0 +directory_processed=0 +echo_mode=0 + +# Process all the arguments +for arg in "$@" +do + # Check for help option + if [[ $arg == "-h" ]] || [[ $arg == "--help" ]]; then + echo "$SCRIPT_DOC" + exit 0 + fi + + # Check for echo option + if [[ $arg == "-e" ]]; then + echo_mode=1 + continue + fi + + # Check if the "-f" option was used + if [[ $arg == "-f" ]]; then + csv_processed=1 + continue + fi + + # Check if the "-p" option was used + if [[ $arg == "-p" ]]; then + l0_path_processed=1 + continue + fi + + # Check if the "-s" option was used + if [[ $arg == "-s" ]]; then + sleep_interval_processed=1 + continue + fi + + # Check if the "-d" option was used + if [[ $arg == "-d" ]]; then + directory_processed=1 + continue + fi + + # Check if the previous argument was "-f", indicating that this argument is CSV filename + if [[ $csv_processed -eq 1 ]]; then + filename=$arg + if [[ ! -f $filename ]]; then + echo "File $filename does not exist." + exit 1 + fi + # Use awk to get the first column of the csv, removing quotes and 'observation_id' strings + # Add the filenames to the array + while IFS= read -r line + do + filenames+=("$line") + done < <(awk -F',' '{ gsub(/"/,""); if ($1 !~ /^observation_id/) print $1 }' "$filename") + csv_processed=0 + continue + fi + + # Check if the previous argument was "-p", indicating that this argument is the l0_path + if [[ $l0_path_processed -eq 1 ]]; then + l0_path=$arg + l0_path_processed=0 + continue + fi + + # Check if the previous argument was "-s", indicating that this argument is the sleep_interval + if [[ $sleep_interval_processed -eq 1 ]]; then + sleep_interval=$arg + sleep_interval_processed=0 + continue + fi + + # Check if the previous argument was "-d", indicating that this argument is a directory + if [[ $directory_processed -eq 1 ]]; then + directory=$arg + if [[ ! -d $directory ]]; then + echo "Directory $directory does not exist." + exit 1 + fi + # Use ls to get the filenames in the directory and add them to the array + while IFS= read -r file + do + filenames+=("$file") + done < <(ls "$directory") + directory_processed=0 + continue + fi + + # The argument is a filename + text=${arg//\"/} # Remove quotes (which are present in KPF L0 lists downloaded from Jump) + if [[ $text != observation_id* ]]; then + filenames+=("$text") + fi +done + +# Check if "-f" or "-d" was used but no following argument was provided +if [[ $csv_processed -eq 1 ]]; then + echo "No filename supplied. Please provide a filename after -f." + exit 1 +fi + +# Iterate over the array of KPF filenames and touch/echo them +for fn in "${filenames[@]}" +do + fn=${fn%.fits} # Remove ".fits" from filename (needed to make the -d and -f options compatible) + yyyymmdd=${fn:3:8} + fullpath=$l0_path/$yyyymmdd/$fn.fits + if [[ $echo_mode -eq 1 ]]; then + echo "touch $fullpath" + else + echo "$(date +%T) touching file: $fullpath" + touch "$fullpath" + sleep $sleep_interval + fi +done From f25cdf137b7fbb4ada9535f44182326b787c00a8 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:01:56 -0700 Subject: [PATCH 165/249] Added LICENSE file --- LICENSE | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..42d14f8bd --- /dev/null +++ b/LICENSE @@ -0,0 +1,29 @@ +Copyright (c) 2023, California Institute of Technology +All rights reserved. + +Redistribution and use in source and binary forms for academic and other +non-commercial purposes with or without modification, are permitted provided +that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this +list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, +this list of conditions and the following disclaimer in the documentation +and/or other materials provided with the distribution. + +* Neither the name of the California Institute of Technology (Caltech) nor +the names of its contributors may be used to endorse or promote products +derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR +CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS +INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN +CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) +ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +POSSIBILITY OF SUCH DAMAGE. From dbcf2bf894785a1e25cebadd3f7c48242f7f84c5 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:06:36 -0700 Subject: [PATCH 166/249] Add WMKO --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 854ad84ec..6ee2703ef 100644 --- a/README.md +++ b/README.md @@ -8,5 +8,5 @@ Facility data reduction pipline for the [Keck Planet Finder](https://exoplanets. Install instructions and documentation is available at [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) -Under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu) and [Caltech Institute of Technology](https://www.caltech.edu). +Under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu), the [Caltech Institute of Technology](https://www.caltech.edu), and the [W. M. KEck Observatory](https://www.keckobservatory.org/). From 72b9f65a9b91c81e18979ed9d3eec21f356c7d21 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:07:08 -0700 Subject: [PATCH 167/249] typo --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6ee2703ef..567fcd74b 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ [![Documentation Status](https://readthedocs.org/projects/kpf-pipeline/badge/?version=latest)](https://kpf-pipeline.readthedocs.io/en/latest/) [![Coverage Status](https://coveralls.io/repos/github/Keck-DataReductionPipelines/KPF-Pipeline/badge.svg?branch=master)](https://coveralls.io/github/Keck-DataReductionPipelines/KPF-Pipeline?branch=master) -Facility data reduction pipline for the [Keck Planet Finder](https://exoplanets.caltech.edu/kpf/) high-resolution optical spectrograph. +Facility data reduction pipeline for the [Keck Planet Finder](https://exoplanets.caltech.edu/kpf/) high-resolution optical spectrograph. Install instructions and documentation is available at [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) -Under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu), the [Caltech Institute of Technology](https://www.caltech.edu), and the [W. M. KEck Observatory](https://www.keckobservatory.org/). +Under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu), the [Caltech Institute of Technology](https://www.caltech.edu), and the [W. M. Keck Observatory](https://www.keckobservatory.org/). From 632818ce5249ae0e1ad444772e83d774a305c283 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:24:33 -0700 Subject: [PATCH 168/249] Add citation information --- README.md | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 567fcd74b..dc43a070d 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,21 @@ -# Data Reduction Pipeline for the Keck Planet Finder +# Data Reduction Pipeline (DRP) for the Keck Planet Finder (KPF) [![Build Status](http://shrek.caltech.edu:4444/buildStatus/icon?job=KPF+CI)](http://shrek.caltech.edu:4444/job/KPF%20CI/) [![Documentation Status](https://readthedocs.org/projects/kpf-pipeline/badge/?version=latest)](https://kpf-pipeline.readthedocs.io/en/latest/) [![Coverage Status](https://coveralls.io/repos/github/Keck-DataReductionPipelines/KPF-Pipeline/badge.svg?branch=master)](https://coveralls.io/github/Keck-DataReductionPipelines/KPF-Pipeline?branch=master) -Facility data reduction pipeline for the [Keck Planet Finder](https://exoplanets.caltech.edu/kpf/) high-resolution optical spectrograph. +The KPF DRP is the facility data reduction pipeline for the [Keck Planet Finder](https://exoplanets.caltech.edu/kpf/) high-resolution optical spectrograph at the W. M. Keck Observatory. It is under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu), the [Caltech Institute of Technology](https://www.caltech.edu), and the [W. M. Keck Observatory](https://www.keckobservatory.org/). -Install instructions and documentation is available at [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) +## Documention -Under active development by the [NASA Exoplanet Science Institute](https://nexsci.caltech.edu), the [Caltech Institute of Technology](https://www.caltech.edu), and the [W. M. Keck Observatory](https://www.keckobservatory.org/). +Install instructions and documentation are available at [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) +## Citation + +If you use the KPF Data Reduction Pipeline in your research, please cite the following publication: + +* Gibson et al. (2020, SPIE): [ADS entry](https://ui.adsabs.harvard.edu/abs/2020SPIE11447E..42G/abstract); BibTeX + +If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgment (using the provided BibTeX entry): + +This research made use of the KPF Data Reduction Pipeline \citep{kpf:gibson2020}. From 7f5c53d02d1fac49d0642099492870f7a8b58a4b Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:27:41 -0700 Subject: [PATCH 169/249] Add bibtex file for KPF citations --- kpf_bibliography.bib | 46 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 kpf_bibliography.bib diff --git a/kpf_bibliography.bib b/kpf_bibliography.bib new file mode 100644 index 000000000..d05f67430 --- /dev/null +++ b/kpf_bibliography.bib @@ -0,0 +1,46 @@ +@INPROCEEDINGS{kpf:gibson2020, + author = {{Gibson}, Steven R. and {Howard}, Andrew W. and {Rider}, Kodi and {Roy}, Arpita and {Edelstein}, Jerry and {Kassis}, Marc and {Grillo}, Jason and {Halverson}, Sam and {Sirk}, Martin M. and {Smith}, Christopher and {Allen}, Steve and {Baker}, Ashley and {Beichman}, Charles and {Berriman}, Bruce and {Brown}, Thomas and {Casey}, Kelleen and {Chin}, Jason and {Coutts}, David and {Cowley}, David and {Deich}, William and {Feger}, Tobias and {Fulton}, Benjamin and {Gers}, Luke and {Gurevich}, Yulia and {Ishikawa}, Yuzo and {James}, Ean and {Jelinsky}, Sharon and {Kaye}, Stephen and {Lanclos}, Kyle and {Li}, Adela and {Lilley}, Scott and {McCarney}, Ben and {Miller}, Tim and {Milner}, Steve and {O'Hanlon}, Timothy J. and {Pember}, Jacob and {Raffanti}, Mike and {Rockosi}, Constance and {Rubenzahl}, Ryan and {Rumph}, Dave and {Sandford}, Dale and {Savage}, Maureen and {Schwab}, Christian and {Seifahrt}, Andreas and {Shaum}, Abby and {Smith}, Roger and {Stuermer}, Julian and {Thorne}, Jim and {Vandenberg}, Adam and {Von Boeckmann}, Tod and {Wang}, Cindy and {Wang}, Qifan and {Weisfeiler}, Marie and {Wilcox}, Mavourneen and {Wishnow}, Edward H. and {Wizinowich}, Peter and {Wold}, Truman and {Wolfenberger}, Anna}, + title = "{Keck Planet Finder: design updates}", + booktitle = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, + year = 2020, + series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, + volume = {11447}, + month = dec, + eid = {1144742}, + pages = {1144742}, + doi = {10.1117/12.2561783}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2020SPIE11447E..42G}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@INPROCEEDINGSkpf:gibson2018, + author = {{Gibson}, Steven R. and {Howard}, Andrew W. and {Roy}, Arpita and {Smith}, Christopher and {Halverson}, Sam and {Edelstein}, Jerry and {Kassis}, Marc and {Wishnow}, Edward H. and {Raffanti}, Michael and {Allen}, Steve and {Chin}, Jason and {Coutts}, David and {Cowley}, David and {Curtis}, Jim and {Deich}, William and {Feger}, Tobias and {Finstad}, Daniel and {Gurevich}, Yulia and {Ishikawa}, Yuzo and {James}, Ean and {Jhoti}, Elisha and {Lanclos}, Kyle and {Lilley}, Scott and {Miller}, Tim and {Milner}, Steve and {Payne}, Tom and {Rider}, Kodi and {Rockosi}, Constance and {Sandford}, Dale and {Schwab}, Christian and {Seifahrt}, Andreas and {Sirk}, Martin M. and {Smith}, Roger and {Stuermer}, Julian and {Weisfeiler}, Marie and {Wilcox}, Mavourneen and {Vandenberg}, Adam and {Wizinowich}, Peter}, + title = "{Keck Planet Finder: preliminary design}", + booktitle = {Ground-based and Airborne Instrumentation for Astronomy VII}, + year = 2018, + editor = {{Evans}, Christopher J. and {Simard}, Luc and {Takami}, Hideki}, + series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, + volume = {10702}, + month = jul, + eid = {107025X}, + pages = {107025X}, + doi = {10.1117/12.2311565}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2018SPIE10702E..5XG}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@INPROCEEDINGSkpf:gibson2016, + author = {{Gibson}, Steven R. and {Howard}, Andrew W. and {Marcy}, Geoffrey W. and {Edelstein}, Jerry and {Wishnow}, Edward H. and {Poppett}, Claire L.}, + title = "{KPF: Keck Planet Finder}", + booktitle = {Ground-based and Airborne Instrumentation for Astronomy VI}, + year = 2016, + editor = {{Evans}, Christopher J. and {Simard}, Luc and {Takami}, Hideki}, + series = {Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series}, + volume = {9908}, + month = aug, + eid = {990870}, + pages = {990870}, + doi = {10.1117/12.2233334}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2016SPIE.9908E..70G}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} From e2b46c98d27269b4ec0f0705d80ab80ce1ef2ea3 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:29:50 -0700 Subject: [PATCH 170/249] Added link to bibtex file --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index dc43a070d..9d0459557 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,8 @@ Install instructions and documentation are available at [ReadTheDocs](https://kp If you use the KPF Data Reduction Pipeline in your research, please cite the following publication: -* Gibson et al. (2020, SPIE): [ADS entry](https://ui.adsabs.harvard.edu/abs/2020SPIE11447E..42G/abstract); BibTeX - +* Gibson et al. (2020, SPIE): [ADS entry](https://ui.adsabs.harvard.edu/abs/2020SPIE11447E..42G/abstract); kpf:gibson2020 in [BibTex file](kpf_bibliography.bib) + If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgment (using the provided BibTeX entry): This research made use of the KPF Data Reduction Pipeline \citep{kpf:gibson2020}. From cd42821e2ee18367a266200527caed24bb794758 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:30:44 -0700 Subject: [PATCH 171/249] Added quotation marks --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9d0459557..88adb7e55 100644 --- a/README.md +++ b/README.md @@ -18,4 +18,4 @@ If you use the KPF Data Reduction Pipeline in your research, please cite the fol If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgment (using the provided BibTeX entry): -This research made use of the KPF Data Reduction Pipeline \citep{kpf:gibson2020}. +"This research made use of the KPF Data Reduction Pipeline \citep{kpf:gibson2020}." From c0a8d176d3387deae7824ff1e74dedd42100e280 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 23 Jun 2023 22:34:51 -0700 Subject: [PATCH 172/249] Minor text updates --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 88adb7e55..388cb5493 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ The KPF DRP is the facility data reduction pipeline for the [Keck Planet Finder] ## Documention -Install instructions and documentation are available at [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) +Install instructions and documentation are available on [ReadTheDocs](https://kpf-pipeline.readthedocs.io/en/latest/) ## Citation @@ -16,6 +16,6 @@ If you use the KPF Data Reduction Pipeline in your research, please cite the fol * Gibson et al. (2020, SPIE): [ADS entry](https://ui.adsabs.harvard.edu/abs/2020SPIE11447E..42G/abstract); kpf:gibson2020 in [BibTex file](kpf_bibliography.bib) -If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgment (using the provided BibTeX entry): +If there is no place to include the relevant citations in the text of the publication, please include the following acknowledgment (in LaTeX using the [provided BibTeX entry](kpf_bibliography.bib)): "This research made use of the KPF Data Reduction Pipeline \citep{kpf:gibson2020}." From b6259d501e6c79083be32d5b6113390e96d63af7 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 10:23:15 -0700 Subject: [PATCH 173/249] manual night summary --- examples/nightly_test.recipe | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index 1d9da21e2..a8c3e0cd2 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,5 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night_list = ['20230603'] +night_list = ['20230625','20230624','20230623','20230622','20230621','20230620','20230619','20230618','20230617','20230616','20230615','20230614','20230613','20230612','20230611','20230610','20230609','20230608','20230607','20230606','20230605','20230604'] for night in night_list: Nightly_summary(night) From a278806440876b6fe84fa529f2e0d3e76c658089 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 10:27:10 -0700 Subject: [PATCH 174/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index adb5ce8cc..e6bc7495d 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -177,11 +177,12 @@ def nightly_procedures(self,night): #plt.savefig(output_dir+'fig/'+exposure_name+'_Histogram_'+ccd_color[i_color]+'.png') plt.savefig(output_dir+'/'+version+'/'+exposure_name+'_'+ccd_color[i_color]+'_histogram.png', dpi=200) plt.close() - hdulist.close() - if master_master_file != 'None':hdulist1.close() + hdulist.close() + if master_master_file != 'None':hdulist1.close() + From 84e85ece01aa31987fa43d7bc2eb598044b07641 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 11:02:18 -0700 Subject: [PATCH 175/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index e6bc7495d..7ae02fd91 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -111,7 +111,7 @@ def nightly_procedures(self,night): #2D image plt.figure(figsize=(5,4)) plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) - plt.imshow(counts, vmin = np.percentile(flatten_counts,1),vmax = np.percentile(flatten_counts,99),interpolation = 'None',origin = 'lower') + plt.imshow(counts, vmin = np.nanpercentile(flatten_counts,1),vmax = np.nanpercentile(flatten_counts,99),interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') plt.title(ccd_color[i_color]+' '+exposure_name, fontsize = 8) @@ -147,12 +147,12 @@ def nightly_procedures(self,night): plt.figure(figsize=(5,4)) plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #pcrint(counts,master_counts) - counts_norm = np.percentile(counts,99) - master_counts_norm = np.percentile(master_counts,99) + counts_norm = np.nanpercentile(counts,99) + master_counts_norm = np.nanpercentile(master_counts,99) if np.shape(counts)!=np.shape(master_counts): continue difference = counts/counts_norm-master_counts/master_counts_norm - plt.imshow(difference, vmin = np.percentile(difference,1),vmax = np.percentile(difference,99), interpolation = 'None',origin = 'lower') + plt.imshow(difference, vmin = np.nanpercentile(difference,1),vmax = np.nanpercentile(difference,99), interpolation = 'None',origin = 'lower') plt.xlabel('x (pixel number)') plt.ylabel('y (pixel number)') plt.title(ccd_color[i_color]+' '+version+'- Master '+version+' '+exposure_name, fontsize =8) @@ -164,10 +164,10 @@ def nightly_procedures(self,night): plt.figure(figsize=(5,4)) plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) - #print(np.percentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.percentile(flatten_counts,0.005),np.percentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.percentile(master_flatten_counts,0.005),np.percentile(master_flatten_counts,99.995))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.005),np.nanpercentile(master_flatten_counts,99.995))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:29:46 -0700 Subject: [PATCH 176/249] manual night summary --- examples/nightly_test.recipe | 2 +- modules/quicklook/src/alg_nightly.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index a8c3e0cd2..d3349726b 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,5 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night_list = ['20230625','20230624','20230623','20230622','20230621','20230620','20230619','20230618','20230617','20230616','20230615','20230614','20230613','20230612','20230611','20230610','20230609','20230608','20230607','20230606','20230605','20230604'] +night_list = ['20230622','20230621','20230620','20230619','20230618','20230617','20230616','20230615','20230614','20230613','20230612','20230611','20230610','20230609','20230608','20230607','20230606','20230605','20230604'] for night in night_list: Nightly_summary(night) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 7ae02fd91..377903785 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,9 +165,9 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.005),np.nanpercentile(flatten_counts,99.995)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.005),np.nanpercentile(master_flatten_counts,99.995))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:32:10 -0700 Subject: [PATCH 177/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 377903785..8e194b0e0 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,9 +165,9 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99)))#[flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanmin(master_flatten_counts),np.nanmax(master_flatten_counts))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:34:07 -0700 Subject: [PATCH 178/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 8e194b0e0..2c63e1180 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,9 +165,14 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanmin(flatten_counts),np.nanmax(flatten_counts)))#np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99) + flatten_counts[flatten_counts == -np.inf] = np.nan + flatten_counts[flatten_counts == np.inf] = np.nan + master_flatten_counts[master_flatten_counts == -np.inf] = np.nan + master_flatten_counts[master_flatten_counts == np.inf] = np.nan + + plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99)))# if master_master_file != 'None': - if len(master_flatten_counts)>1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanmin(master_flatten_counts),np.nanmax(master_flatten_counts))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:34:58 -0700 Subject: [PATCH 179/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 2c63e1180..236fbf9ba 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -169,7 +169,7 @@ def nightly_procedures(self,night): flatten_counts[flatten_counts == np.inf] = np.nan master_flatten_counts[master_flatten_counts == -np.inf] = np.nan master_flatten_counts[master_flatten_counts == np.inf] = np.nan - + print(flatten_counts) plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99)))# if master_master_file != 'None': if len(master_flatten_counts)>1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:35:37 -0700 Subject: [PATCH 180/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 236fbf9ba..222028615 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,11 +165,12 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) + print(flatten_counts) flatten_counts[flatten_counts == -np.inf] = np.nan flatten_counts[flatten_counts == np.inf] = np.nan master_flatten_counts[master_flatten_counts == -np.inf] = np.nan master_flatten_counts[master_flatten_counts == np.inf] = np.nan - print(flatten_counts) + plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99)))# if master_master_file != 'None': if len(master_flatten_counts)>1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts Date: Mon, 26 Jun 2023 11:36:22 -0700 Subject: [PATCH 181/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 222028615..0d608bc85 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,7 +165,7 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) - print(flatten_counts) + print(master_list[i],flatten_counts) flatten_counts[flatten_counts == -np.inf] = np.nan flatten_counts[flatten_counts == np.inf] = np.nan master_flatten_counts[master_flatten_counts == -np.inf] = np.nan From 28790f666c28e91f8aed21472550f093da983f15 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 11:37:33 -0700 Subject: [PATCH 182/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 0d608bc85..60c58e5b4 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -165,7 +165,7 @@ def nightly_procedures(self,night): plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) - print(master_list[i],flatten_counts) + print(master_list[i],np.nanpercentile(flatten_counts,0.01)) flatten_counts[flatten_counts == -np.inf] = np.nan flatten_counts[flatten_counts == np.inf] = np.nan master_flatten_counts[master_flatten_counts == -np.inf] = np.nan From 47ca85946c1b8590df03b8f9551a1c24ce757657 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 11:39:47 -0700 Subject: [PATCH 183/249] manual night summary --- modules/quicklook/src/alg_nightly.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 60c58e5b4..3f40cd636 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -166,15 +166,15 @@ def nightly_procedures(self,night): #print(np.nanpercentile(flatten_counts,99.9),saturation_limit) print(master_list[i],np.nanpercentile(flatten_counts,0.01)) - flatten_counts[flatten_counts == -np.inf] = np.nan - flatten_counts[flatten_counts == np.inf] = np.nan - master_flatten_counts[master_flatten_counts == -np.inf] = np.nan - master_flatten_counts[master_flatten_counts == np.inf] = np.nan - - plt.hist(flatten_counts, bins = 50,alpha =0.5, label = 'Median: ' + '%4.1f; ' % np.nanmedian(flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(flatten_counts),density = False, range = (np.nanpercentile(flatten_counts,0.01),np.nanpercentile(flatten_counts,99.99)))# - if master_master_file != 'None': - if len(master_flatten_counts)>1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts1: plt.hist(master_flatten_counts, bins = 50,alpha =0.5, label = 'Master Median: '+ '%4.1f' % np.nanmedian(master_flatten_counts)+'; Std: ' + '%4.1f' % np.nanstd(master_flatten_counts), histtype='step',density = False, color = 'orange', linewidth = 1 , range = (np.nanpercentile(master_flatten_counts,0.01),np.nanpercentile(master_flatten_counts,99.99))) #[master_flatten_counts Date: Mon, 26 Jun 2023 13:05:05 -0700 Subject: [PATCH 184/249] L0 plots --- modules/quicklook/configs/default.cfg | 1 + modules/quicklook/src/alg.py | 35 +++++++++++++++++++++++++-- 2 files changed, 34 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/configs/default.cfg b/modules/quicklook/configs/default.cfg index 35e7f8399..ef4c06fa8 100644 --- a/modules/quicklook/configs/default.cfg +++ b/modules/quicklook/configs/default.cfg @@ -35,6 +35,7 @@ n_per_row = 8 [IO] +input_prefix_l0_pre =/data/L0/ input_prefix_l0 =/data/2D/ input_prefix_l1 =/data/L1/ input_prefix_l2 =/data/L2/ diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 2a53b2d48..2cc72dfb6 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -127,8 +127,39 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): print('working on',date,exposure_name) - - + #operate on L0 data before image assembly + L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'_L0.fits' + L0 = fits.open(L0_data) + + green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 + plt.figure(tight_layout=True) + plt.figure(figsize=(18, 18), tight_layout=True) + plt.imshow(green_image, cmap='viridis', origin='lower', + vmin=np.percentile(green_image,1), + vmax=np.percentile(green_image,99.5)) + plt.title(ObsID + ' - L0 (no processing) - Green CCD') + plt.xlabel('x') + plt.ylabel('y') + plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') + plt.grid(False) + #plt.show() + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=144,facecolor='white') + plt.close() + + red_image = np.concatenate((L0['RED_AMP1'].data, L0['RED_AMP2'].data), axis=1)/2**16 # flip not needed for Red + plt.figure(tight_layout=True) + plt.figure(figsize=(18, 18), tight_layout=True) + plt.imshow(red_image, cmap='viridis', origin='lower', + vmin=np.percentile(red_image,1), + vmax=np.percentile(red_image,99.5)) + plt.title(ObsID + ' - L0 (no processing) - Red CCD') + plt.xlabel('x') + plt.ylabel('y') + plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') + plt.grid(False) + #plt.show() + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=144,facecolor='white') + plt.close() From 47b2e4b7c7a78978571c14d0047be953df3e26a5 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:07:57 -0700 Subject: [PATCH 185/249] L0 plots --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 2cc72dfb6..a0f7c1b51 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -129,7 +129,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'_L0.fits' - L0 = fits.open(L0_data) + L0 = fits.open(L0_file) green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 plt.figure(tight_layout=True) From 28a72939bd576f198a094fa3c460a9c68ce25105 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:09:41 -0700 Subject: [PATCH 186/249] L0 plots --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index a0f7c1b51..fab8af30a 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -128,7 +128,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly - L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'_L0.fits' + L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 From f1c5e4b57ddf72cb2cd3e7d7a308183e8859d3cf Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:10:49 -0700 Subject: [PATCH 187/249] L0 plots --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index fab8af30a..6599d88b1 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -137,7 +137,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.imshow(green_image, cmap='viridis', origin='lower', vmin=np.percentile(green_image,1), vmax=np.percentile(green_image,99.5)) - plt.title(ObsID + ' - L0 (no processing) - Green CCD') + plt.title(exposure_name + ' - L0 (no processing) - Green CCD') plt.xlabel('x') plt.ylabel('y') plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') @@ -152,7 +152,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.imshow(red_image, cmap='viridis', origin='lower', vmin=np.percentile(red_image,1), vmax=np.percentile(red_image,99.5)) - plt.title(ObsID + ' - L0 (no processing) - Red CCD') + plt.title(exposure_name + ' - L0 (no processing) - Red CCD') plt.xlabel('x') plt.ylabel('y') plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') From ca62d59f5b0e7617ca069a6ec87253f367d34984 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:14:17 -0700 Subject: [PATCH 188/249] L0 plots --- modules/quicklook/src/alg.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 6599d88b1..838f5612b 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -143,6 +143,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() + if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=144,facecolor='white') plt.close() From 545c40b4a7b785d03a59d377800b9abc7fcf86f0 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:21:20 -0700 Subject: [PATCH 189/249] L0 plots --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 838f5612b..4cfaddc01 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 plt.figure(tight_layout=True) - plt.figure(figsize=(18, 18), tight_layout=True) + plt.figure(figsize=(5, 5), tight_layout=True) plt.imshow(green_image, cmap='viridis', origin='lower', vmin=np.percentile(green_image,1), vmax=np.percentile(green_image,99.5)) @@ -149,7 +149,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): red_image = np.concatenate((L0['RED_AMP1'].data, L0['RED_AMP2'].data), axis=1)/2**16 # flip not needed for Red plt.figure(tight_layout=True) - plt.figure(figsize=(18, 18), tight_layout=True) + plt.figure(figsize=(5, 5), tight_layout=True) plt.imshow(red_image, cmap='viridis', origin='lower', vmin=np.percentile(red_image,1), vmax=np.percentile(red_image,99.5)) From 3aba0859e0f9949bb91434909a54e6da3df4f8aa Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 13:23:34 -0700 Subject: [PATCH 190/249] L0 plots --- modules/quicklook/src/alg.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 4cfaddc01..bd0622f6a 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 plt.figure(tight_layout=True) - plt.figure(figsize=(5, 5), tight_layout=True) + plt.figure(figsize=(8, 8), tight_layout=True) plt.imshow(green_image, cmap='viridis', origin='lower', vmin=np.percentile(green_image,1), vmax=np.percentile(green_image,99.5)) @@ -144,12 +144,12 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.grid(False) #plt.show() if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') - plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=144,facecolor='white') + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=500,facecolor='white') plt.close() red_image = np.concatenate((L0['RED_AMP1'].data, L0['RED_AMP2'].data), axis=1)/2**16 # flip not needed for Red plt.figure(tight_layout=True) - plt.figure(figsize=(5, 5), tight_layout=True) + plt.figure(figsize=(8, 8), tight_layout=True) plt.imshow(red_image, cmap='viridis', origin='lower', vmin=np.percentile(red_image,1), vmax=np.percentile(red_image,99.5)) @@ -159,7 +159,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() - plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=144,facecolor='white') + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() From 73d57c7118a4fca50e4f193812a3327ab040e5ac Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 14:08:22 -0700 Subject: [PATCH 191/249] L0 plots --- modules/quicklook/src/alg.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index bd0622f6a..6b876ba95 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -143,8 +143,8 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() - if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') - plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=500,facecolor='white') + if os.path.exists(output_dir+'/'+exposure_name+'/2D/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') + plt.savefig(output_dir+'/'+exposure_name+'/2D/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=500,facecolor='white') plt.close() red_image = np.concatenate((L0['RED_AMP1'].data, L0['RED_AMP2'].data), axis=1)/2**16 # flip not needed for Red @@ -159,9 +159,9 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() - plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') + plt.savefig(output_dir+'/'+exposure_name+'/2D/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() - + L0.close() #read ccd directly From 4018f9f341411656c5c32e48bc79c35afe83384f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 14:10:29 -0700 Subject: [PATCH 192/249] L0 plots --- modules/quicklook/src/alg_nightly.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg_nightly.py b/modules/quicklook/src/alg_nightly.py index 3f40cd636..8074ce473 100644 --- a/modules/quicklook/src/alg_nightly.py +++ b/modules/quicklook/src/alg_nightly.py @@ -193,7 +193,7 @@ def nightly_procedures(self,night): #get all exposures taken on a particular night - + ''' file_list = glob.glob(exposures_dir+night+'/*.fits') date_obs = [] temp = [] @@ -221,3 +221,4 @@ def nightly_procedures(self,night): plt.savefig(output_dir+'/diagnostics/'+night+'_Relative_Humidity_variation.png') plt.close() plt.close('all') + ''' From a2f942dd02fd58934862349789f89e26a6c99c7a Mon Sep 17 00:00:00 2001 From: DaiFei Date: Mon, 26 Jun 2023 14:13:28 -0700 Subject: [PATCH 193/249] L0 plots --- examples/nightly_test.recipe | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/nightly_test.recipe b/examples/nightly_test.recipe index d3349726b..aa66e0d94 100644 --- a/examples/nightly_test.recipe +++ b/examples/nightly_test.recipe @@ -1,5 +1,5 @@ from modules.quicklook.src.nightly_prim import Nightly_summary -night_list = ['20230622','20230621','20230620','20230619','20230618','20230617','20230616','20230615','20230614','20230613','20230612','20230611','20230610','20230609','20230608','20230607','20230606','20230605','20230604'] +night_list = ['20230619','20230618','20230617','20230616','20230615','20230614','20230613','20230612','20230611','20230610','20230609','20230608','20230607','20230606','20230605','20230604'] for night in night_list: Nightly_summary(night) From f8464f54120b2078a66cdd022fe92a9c16df5f2d Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Mon, 26 Jun 2023 15:26:21 -0700 Subject: [PATCH 194/249] Added 4 amplifiers for RED detector. --- static/kpfsim_ccd_orient_red.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 static/kpfsim_ccd_orient_red.txt diff --git a/static/kpfsim_ccd_orient_red.txt b/static/kpfsim_ccd_orient_red.txt new file mode 100644 index 000000000..1e5fe64e6 --- /dev/null +++ b/static/kpfsim_ccd_orient_red.txt @@ -0,0 +1,5 @@ +CHANNEL CHANNEL_KEY CHANNEL_FFI_ROW CHANNEL_FFI_COLUMN CHANNEL_EXT +1 4 1 1 RED_AMP1 +2 1 1 2 RED_AMP2 +3 3 2 1 RED_AMP3 +4 2 2 2 RED_AMP4 From 0485ca473960529c46e882597a80cd8823d1bdac Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Mon, 26 Jun 2023 15:26:28 -0700 Subject: [PATCH 195/249] Upgraded to handle either 2 or 4 amplifiers for RED detector. --- configs/kpf_drp.cfg | 5 +++-- configs/kpf_masters_l1.cfg | 7 +++--- kpfpipe/models/metadata/KPF_definitions.py | 2 ++ modules/Utils/overscan_subtract.py | 26 ++++++++++++++++++++-- 4 files changed, 33 insertions(+), 7 deletions(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index 0d011d802..a08eae574 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -127,11 +127,12 @@ do_bc = True # for L0->2D process [WATCHFOR_L0] # Define numbers of columns and rows in data section of channel images for each detector. +# Assumes 4 amps per detector here. Special logic in overscan_subtract.py handles 2 amps per detector. channel_datasec_ncols_green = 2040 channel_datasec_nrows_green = 2040 channel_datasec_ncols_red = 2040 -channel_datasec_nrows_red = 4080 -channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red_2amp.txt +channel_datasec_nrows_red = 2040 +channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red.txt channel_orientation_ref_path_green = /code/KPF-Pipeline/static/kpfsim_ccd_orient_green.txt do_db_query_for_master_files = True masterbias_path = /data/masters/20230314/kpf_20230314_master_bias.fits diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index d37977a08..52e809c2d 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -125,12 +125,13 @@ hk_wavelength_path = ["masters/kpfMaster_HKwave20220909_sci.csv", "masters/kpfMa # for L0->2D process [WATCHFOR_L0] # Define numbers of columns and rows in data section of channel images for each detector. +# Assumes 4 amps per detector here. Special logic in overscan_subtract.py handles 2 amps per detector. channel_datasec_ncols_green = 2040 channel_datasec_nrows_green = 2040 channel_datasec_ncols_red = 2040 -channel_datasec_nrows_red = 4080 -channel_orientation_ref_path_red = /test_biasflats/kpfsim_ccd_orient_red_2amp.txt -channel_orientation_ref_path_green = /test_biasflats/kpfsim_ccd_orient_green.txt +channel_datasec_nrows_red = 2040 +channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red.txt +channel_orientation_ref_path_green = /code/KPF-Pipeline/static/kpfsim_ccd_orient_green.txt masterbias_path = /data/masters/master_bias_20221022.fits pl_overscan_reg_green = [2040,2140] srl_overscan_reg_green = [2040,2140] diff --git a/kpfpipe/models/metadata/KPF_definitions.py b/kpfpipe/models/metadata/KPF_definitions.py index bbc2b86ea..1ae99dd75 100644 --- a/kpfpipe/models/metadata/KPF_definitions.py +++ b/kpfpipe/models/metadata/KPF_definitions.py @@ -32,6 +32,8 @@ 'RED_AMP1': fits.ImageHDU, 'RED_AMP2': fits.ImageHDU, + 'RED_AMP3': fits.ImageHDU, + 'RED_AMP4': fits.ImageHDU, 'RED_CCD': fits.ImageHDU, 'RED_VAR': fits.ImageHDU, diff --git a/modules/Utils/overscan_subtract.py b/modules/Utils/overscan_subtract.py index b971eb3c0..3a1c1a04b 100644 --- a/modules/Utils/overscan_subtract.py +++ b/modules/Utils/overscan_subtract.py @@ -54,7 +54,7 @@ class OverscanSubtraction(KPF0_Primitive): channel_datasec_ncols (int): Number of columns in data section of channel images for a given detector (default is 2040). channel_datasec_nrows (int): Number of rows in data section of channel images for a given detector - (default is 2040 for GREEN_CCD or otherwise 4080 for RED_CCD). + (default is 2040 for GREEN_CCD and RED_CCD). n_sigma (float): Number of sigmas for overscan-value outlier rejection (default is 2.5). """ @@ -83,7 +83,7 @@ def __init__(self,action,context): try: self.channel_datasec_nrows = self.action.args[10] except: - if self.ffi_exts[0] == 'GREEN_CCD': + if self.ffi_exts[0] == 'GREEN_CCD' or self.ffi_exts[0] == 'RED_CCD': self.channel_datasec_nrows = 2040 else: self.channel_datasec_nrows = 4080 @@ -462,6 +462,8 @@ def _perform(self): # Either case will result in the same size of 2D image (4080x4080 pixels). # Trim the orientation arrays accordingly. # Also, change self.channel_datasec_nrows accordingly. + # + # Similar implementation for RED_CCD, except no flipping the output image. green2amp = False if self.ffi_exts[0] == 'GREEN_CCD': @@ -479,6 +481,20 @@ def _perform(self): channel_exts.pop(2) self.channel_datasec_nrows = 4080 green2amp = True + elif self.ffi_exts[0] == 'RED_CCD': + naxis2 = l0_obj.header['RED_AMP1']['NAXIS2'] + if naxis2 >= 4000: + channels.pop(3) + channels.pop(2) + channel_keys.pop(3) + channel_keys.pop(2) + channel_rows.pop(3) + channel_rows.pop(2) + channel_cols.pop(3) + channel_cols.pop(2) + channel_exts.pop(3) + channel_exts.pop(2) + self.channel_datasec_nrows = 4080 frames_data = [] for ext in channel_exts: @@ -534,6 +550,12 @@ def _perform(self): if naxis2 >= 4000: l0_obj.del_extension('GREEN_AMP3') l0_obj.del_extension('GREEN_AMP4') + elif self.ffi_exts[0] == 'RED_CCD': + self.logger.debug('---->{}._perform(): self.ffi_exts[0],naxis2 = {},{}'.\ + format(self.__class__.__name__,self.ffi_exts[0],naxis2)) + if naxis2 >= 4000: + l0_obj.del_extension('RED_AMP3') + l0_obj.del_extension('RED_AMP4') self.logger.debug('---->{}._perform(): Done with overscan_subtraction.py; returning...'.\ format(self.__class__.__name__)) From 8e0b93b2ad402a81235bd3695ab754805ab4e697 Mon Sep 17 00:00:00 2001 From: cwang2016 Date: Mon, 26 Jun 2023 16:10:54 -0700 Subject: [PATCH 196/249] feature/docs_modules: - Add high level module description to the KPFPipeline documentation section of "Implemeted Primitives" for modules order trace, spectral extraction, radial velocity, barycentric correction and CaH&K. - Fix the docstrings for modules' primitive and algorithmic implementation. - Update description in the recipe. - add document for order mask --- .../barycentric_correction.rst | 6 + .../primitives/ca_hk/ca_hk_extraction.rst | 12 ++ .../optimal_extraction/optimal_extraction.rst | 30 +++ .../primitives/order_trace/alg_order_mask.rst | 9 + .../primitives/order_trace/order_trace.rst | 38 ++++ .../order_trace/primitive_order_mask.rst | 9 + .../radial_velocity/alg_rv_init.rst | 2 +- .../radial_velocity/radial_velocity.rst | 177 +++++++++++++++++- .../src/barycentric_correction.py | 2 + modules/ca_hk/src/ca_hk_extraction.py | 19 +- modules/order_trace/src/alg.py | 50 +---- modules/order_trace/src/alg_order_mask.py | 15 +- modules/order_trace/src/order_mask.py | 26 +-- modules/order_trace/src/order_trace.py | 17 +- modules/radial_velocity/configs/default.cfg | 25 +++ modules/radial_velocity/src/alg.py | 43 +---- modules/radial_velocity/src/alg_rv_init.py | 20 +- .../radial_velocity/src/radial_velocity.py | 103 +++++++--- .../src/radial_velocity_init.py | 28 ++- .../src/radial_velocity_reweighting.py | 58 ++++-- .../src/radial_velocity_reweighting_ref.py | 29 +-- modules/spectral_extraction/src/alg.py | 1 - modules/spectral_extraction/src/bary_corr.py | 11 +- .../src/order_rectification.py | 2 + .../src/spectral_extraction.py | 1 + recipes/kpf_drp.recipe | 15 +- 26 files changed, 556 insertions(+), 192 deletions(-) create mode 100644 docs/source/primitives/order_trace/alg_order_mask.rst create mode 100644 docs/source/primitives/order_trace/primitive_order_mask.rst diff --git a/docs/source/primitives/barycentric_correction/barycentric_correction.rst b/docs/source/primitives/barycentric_correction/barycentric_correction.rst index 4a1689946..86464514e 100644 --- a/docs/source/primitives/barycentric_correction/barycentric_correction.rst +++ b/docs/source/primitives/barycentric_correction/barycentric_correction.rst @@ -1,6 +1,12 @@ Module Barycentric Correction ============================= +The barycentric correction module utilizes the "barycorrpy" package to calculate the barycentric velocity correction over a specified period of time, typically spanning multiple days. + +To use this module, you need to provide the starting day in Julian data format, specify the period of days for which the correction is required, and provide the observation-related configuration. The module will then calculate the barycentric velocity correction values for each day within the specified period. + +This module outputs a list of barycentric velocity correction values, with each value corresponding to a specific day in the given period. Additionally, the module provides the minimum and maximum redshift values derived from the list of the corrections. + .. toctree:: :maxdepth: 2 diff --git a/docs/source/primitives/ca_hk/ca_hk_extraction.rst b/docs/source/primitives/ca_hk/ca_hk_extraction.rst index ed16e7a81..d257b6b6a 100644 --- a/docs/source/primitives/ca_hk/ca_hk_extraction.rst +++ b/docs/source/primitives/ca_hk/ca_hk_extraction.rst @@ -1,6 +1,18 @@ Module Ca H&K ============== +The purpose of the Ca H&K module is to extract Ca H&K spectra and convert 2D raw image (L0) into 1D spectra (L1) data. + +This module provides the following primitivie to be imported to the recipe: + + * ``CaHKExtraction``: Calcium H&K extraction. + + The following processes are performed, + + - performing flat and bias correction on the H&K science spectrum. + - extracting Ca H&K spectra from the extension CA_HK of L0 data based on the provided position information of all the orders (both science and sky). The extracted spectra from the same column are summed together. In the resulting L1 data, the reduced Ca H&K spectra from the science fiber and the sky fiber are stored in the CA_HK_SCI and CA_HK_SKY extensions, respectively. + - loading the wavelength solution files for science and sky fibers and converting the WLS (wavelength solution) data to extensions CA_HK_SCI_WAVE and CA_HK_SKY_WAVE in L1 data, respectively. + .. toctree:: :maxdepth: 2 diff --git a/docs/source/primitives/optimal_extraction/optimal_extraction.rst b/docs/source/primitives/optimal_extraction/optimal_extraction.rst index b93219995..3704247d9 100644 --- a/docs/source/primitives/optimal_extraction/optimal_extraction.rst +++ b/docs/source/primitives/optimal_extraction/optimal_extraction.rst @@ -1,6 +1,36 @@ Module Spectral Extraction ========================== +The purpose of the spectral extraction module is to convert 2D raw image (L0) into 1D spectra (L1) with high fidelity on photon counts and line shapes. + +This module provides the following primitivies to be imported to the recipe: + + * ``OrderRectification``: spectral order rectification + + Order rectification involves straightening up the spectral order trace, transforming it from a cursive shape to a straight line shape. The original 2D raw image (L0) is converted to be an L0 instance with spectral orders that are not curved. Using the order trace results, the pixels along the normal or vertical direction of each curved order trace are gathered, and then subjected to various weighting methods for further processing. Finally, these pixels are relocated to a 2D rectangular area. + + * ``SpectralExtraction``: spectral order summation extraction or optimal extraction + + The 2D science raw data along each order is converted into 1D data by using either + + - summation extraction method: the pixels along the rectified spectral order in same the column are directly summed up, or + - optimal extraction method: the pixels of the rectified spectral order are weighted and summed up column by column. The weights for this process + are obtained from the corresponding pixels in the rectified flat L0 image. + + Using spectral extraction, the KPF DRP (Data Reduction Pipeline) processes the 2D spectral data from GREEN_CCD and RED_CCD extensions of L0. These data are then transformed into 1D data and stored in [GREEN, RED]_SCI_FLUX[1, 2, 3], [GREEN, RED]_SKY_FLUX, and [GREEN, RED]_CAL_FLUX extensions of L1, corresponding to the science fibers, calibration fiber and the sky fibers, respectively. + + * ``BaryCorrTable``: BARY_CORR table in L1 to include photo-weighted midpoints and barycentric velocity correction per order. + + For KPF DRP, the computation of the BARY_CORR table is based on the EXPMETER_SCI table in L0 data if it exists and the keywords from the primary header: + *IMTYPE, DATE-MID, DATE-BEG, EXPTIME, TARGFRAM, TARGRA, TARGDEC, TARGPMRA, TARGPMDC, TARGPLAX, TARGEPOC, TARGRADV*. + + The resulting BARY_CORR table includes the following columns, + + - **GEOMID_UTC**: geometric midpoint UTC + - **GEOMID_BJD**: geometric midpoint BJD + - **PHOTON_BJD**: weighted-photon midpoint BJD + - **BARYVEL**: barycentric velocity(m/sec) + .. toctree:: :maxdepth: 2 diff --git a/docs/source/primitives/order_trace/alg_order_mask.rst b/docs/source/primitives/order_trace/alg_order_mask.rst new file mode 100644 index 000000000..c01856f54 --- /dev/null +++ b/docs/source/primitives/order_trace/alg_order_mask.rst @@ -0,0 +1,9 @@ +Order Mask Algorithm +==================== + +.. module:: modules + :noindex: + +.. automodule:: modules.order_trace.src.alg_order_mask + :members: + diff --git a/docs/source/primitives/order_trace/order_trace.rst b/docs/source/primitives/order_trace/order_trace.rst index c0192561c..8e6d23105 100644 --- a/docs/source/primitives/order_trace/order_trace.rst +++ b/docs/source/primitives/order_trace/order_trace.rst @@ -1,8 +1,46 @@ Module Order Trace ================== +The purpose of the order trace module is to determine the location and the curvature of the spectral order from fiber flat exposures, the L0 flat files. + +This module provides the following primitives which can be imported to the recipe: + + * ``OrderTrace``: spectral order trace + + This process involves the tracing and fitting of the order maximum, which is approximately located at the center of the spectral order. This tracing and fitting are done using a cubic polynomial (degree 3). Furthermore, the extraction windows above and below the fitted curve are determined to cover the entire spectral order. + + The order trace result is stored in an instance of Pandas DataFrame and optionally a csv file in which each row contains the following numbers to represent the location and the coverage of each spectral order: + + - polynomial coefficients in the order of increasing degree, + + - the extraction windows below and above the fitted polynomial, a.k.a. bottom edge and top edge, + + - the starting and ending horizontal positions of the trace on the 2D image domain. + + + For KPF, the order trace module outputs two csv files for the image of green CCD and red CCD, respectively. + + Here is an example containing the order trace result of the first few spectral orders:: + + ,Coeff0,Coeff1,Coeff2,Coeff3,BottomEdge,TopEdge,X1,X2 + 0,0.5445672380235284,0.004080092310042913,2.0577957001418133e-06,-3.5091410725241624e-10,6.7088,6.4642,86,4079 + 1,15.266582090451204,0.009653957574193602,-2.982172565863953e-07,-5.072604426717458e-11,8.5358,9.4401,146,4079 + 2,34.06984874062919,0.009905479153527023,-4.360738114529129e-07,-3.499266829075321e-11,8.5599,8.8955,44,4079 + 3,49.1875320114883,0.009978684769828283,-4.518502978011858e-07,-3.045119160129834e-11,4.3219,4.4365,0,4079 + 4,64.90185616615426,0.00919113555034767,-2.1738284969730856e-07,-5.283099431400399e-11,9.5635,9.4245,0,4079 + : + + * ``OrderMask``: spectral order mask + + Using the order trace result, order mask produces an L0 instance with the same image size as the + input L0 spectral data. In the generated 2D image of order mask L0, the pixels corresponding to the orderlets are set to 1, while non-orderlet pixels + are set to be 0 by default. Orderlet pixels refers to the piexls that belong to the spectral orders as defined in the order trace result. + This order mask L0 image provides a binary representation where the orderlets are distinguished from the non-orderlet areas. + .. toctree:: :maxdepth: 2 alg_order_trace.rst primitive_order_trace.rst + alg_order_mask.rst + primitive_order_mask.rst diff --git a/docs/source/primitives/order_trace/primitive_order_mask.rst b/docs/source/primitives/order_trace/primitive_order_mask.rst new file mode 100644 index 000000000..ad60a9648 --- /dev/null +++ b/docs/source/primitives/order_trace/primitive_order_mask.rst @@ -0,0 +1,9 @@ +Order Mask Primitive +===================== + +.. module:: modules + :noindex: + +.. automodule:: modules.order_trace.src.order_mask + :members: + diff --git a/docs/source/primitives/radial_velocity/alg_rv_init.rst b/docs/source/primitives/radial_velocity/alg_rv_init.rst index a6f0e325d..b67fbd0af 100644 --- a/docs/source/primitives/radial_velocity/alg_rv_init.rst +++ b/docs/source/primitives/radial_velocity/alg_rv_init.rst @@ -1,5 +1,5 @@ Radial Velocity Init -============================ +==================== .. module:: modules :noindex: diff --git a/docs/source/primitives/radial_velocity/radial_velocity.rst b/docs/source/primitives/radial_velocity/radial_velocity.rst index b91d98a0a..b3afae6c8 100644 --- a/docs/source/primitives/radial_velocity/radial_velocity.rst +++ b/docs/source/primitives/radial_velocity/radial_velocity.rst @@ -1,5 +1,180 @@ Module Radial Veloctiy -========================= +======================= + +The purpose of the radial velocity module is to calculate CCFs and radial velocity from L1 data and produce L2 data with CCFs and RV for each spectral segment. + + +.. note:: + A spectral segment may encompass a wavelength range that spans one or more spectral orders. In current KPF DRP, each spectral segment is considered equivalent to a single spectral order. + +This module provides the following primitives to be imported to the recipe: + + * ``RadialVelocityInit``: initial work based on the target and fiber sources of each observation. + + This initial work involves preparing the velocity steps, mask lines and redshifts per orderlet using information from fiber sources, the target and the observing location, etc. These preparations are essential for computing CCFs and the radial velocity. + + * ``RadialVelocity``: CCF, radial velocity, and optional CCF reweighting. + + The CCF (cross correlation function) uses cross-correlation method on a shifted mask line in redshift space and the spectrum data of one segment to compute CCFs at each velocity step. + + Based on CCFs, the radial velocity is computed for: + + - each spectral segment of each orderlet. + - each spectral segment of the summed CCFs across all science fibers (orderlets). + - each orderlet based on the summed CCFs across all segments. + - each CCD based on the summed CCFs across all segments and all science orderlets. + + In the process of radial velocity, the reweighting operation is selectively applied on CCFs based on whether a reweighting ratio table is specified. Please refer to the description on RadialVelocityReweighting for more details. + + With this primitive, L2 data is produced with the extensions containing CCFs and RVs of all orderlets. + + For KPF DRP, the resulting L2 instance has the following extensions for CCFs and RVs: + + - **GREEN_CCF, RED_CCF**: with 3D image containing original CCFs (not reweighted) for all orderlets of green CCD and red CCD. + - **GREEN_CCF_RW, RED_CCF_RW**: with 3D image containing reweighted CCFs for all orderlets of green CCD and red CCD. + - **RV**: with the header and a table containing radial velocity values and radial velocity errors for all orderlets, as well as their spectral segments of both green CCD and red CCD. + + + The CCF-related extensions in KPF DRP consists of CCFs in 3D data format. These extensions also include the following specific keywords in their headers that provide the relevant information about the CCFs, + + .. list-table:: + + * - **key** + - **usage** + + * - NAXIS + - dimension of the data + + * - NAXIS1 + - total velocity steps + + * - NAXIS2 + - total segments + + * - NAXIS3 + - total orderlets if NAXIS is 3. + + * - STARTSEG + - first segment index of the CCFs + + * - STARTV + - starting velocity for the CCFs + + * - STEPS + - velocity interval for velocity steps + + * - TOTALV + - total velocity steps + + * - TOTALSCI + - total science orderlets + + * - CCF1, CCF2, ..., CCF5 + - related data source (extension) in L1 for each slice in the 3D CCFs + + * - SCI_MASK, CAL_MASK, SKY_MASK + - masks for science fibers, calibration fiber and sky fiber + + + The RV table consists of two parts, with the top part dedicated to the green CCD and the bottom part dedicated to the red CCD, and each row in the RV table corresponds to RV-related data for a specific segment of each CCD. This RV table includes the columns as follows, + + .. list-table:: + + * - **column name** + - **usage** + + * - orderlet1, orderlet2, orderlet3 + - rv values for science orderlets per segment and per CCD. the top part of the table is for GREEN CCD, and the bottom is for RED_CCD + + * - s_wavelength, e_wavelength + - starting and ending wavelength per segment + + * - segment no., order no. + - the associated segment index and order index for each row + + * - RV + - rv values per segment and CCD based on the CCF summation across the science orderlets + + * - RV error + - rv values per segment and CCD based on the unweighted CCF summation across the science orderlets + + * - CAL RV, SKY RV + - rv values per segment and CCD for Cal fiber and Sky fiber + + * - CAL error, SKY error + - rv error per segment and CCD for Cal fiber and Sky fiber based on unweighted CCFs. + + * - CCFJD + - time in Julian Date format per segment and CCD + + * - source[1, 2, 3], source CAL, source SKY + - related data source (extension) in L1 + + * - CCF Weights + - weights for science fibers if reweighting is applied. + + The RV extension includes the following keywords in the header that provide the relevant information for the RVs, (note: the keyword starting with 'CCD1' or 'CCD2' is for green CCD or red CCD), + + .. list-table:: + + * - **key** + - **usage** + + * - STAR_RV + - systematic star radial velocity + + * - CCD[1, 2]ROW + - start row index in the RV table for green CCD or red CCD + + * - CCD[1,2]RV[1,2,3] + - rv per science fiber (SCI1-3) and per CCD based on CCF summation across segments + + * - CCD[1,2]ERV[1,2,3] + - rv error per science fiber (SCI1-3) and per CCD based on CCF summation across segments + + * - CCD[1,2]RV[C,S] + - rv per CAL or SKY fiber and per CCD based on CCF summation across segments + + * - CCD[1,2]ERV[C,S] + - rv error per CAL or SKY fiber and per CCD based on CCF summation across segments + + * - CCD[1,2]RV + - rv per CCD based on CCF summation across segments and science orderlets + + * - CCD[1,2]ERV + - rv error per CCD based on CCF summation across segments and science orderlets + + * - RV[1,2]CORR + - if the science rv value is corrected by the CAL rv value per CCD + + * - CCD[1,2]JD + - exposure time per CCD in Julian date format + + * - RWCCF[1-5] + - if the CCFs for CCFn is reweighted. CCFn is defined in the header of CCF related extensions + + + * ``RadialVelocityReweightingRef``: reweighting ratio table used for CCF reweighting. + + This primitive provides the following 2 methods which either builds or loads the ratio table for CCF reweighting: + + #. To build a ratio table dynamically, start by collecting L2 candidates along with the original CCFs. Identify the observation with the best quality as the 'template' based on the highest count order. This order can be determined by either the highest mean count or the maximum count in CCF. Normalize the peaks of the CCFs by setting the mean value (or the maximum value) of the highest count order to 1. Adjust the mean (or the maximum) values of all other segments proportionally to create a ratio table. + #. To load a table with pre-defined static CCF weights, then the reweighting can be applied to the original (unweighted) CCFs directly inside the RadialVelocity call. + Method 1 builds the reweighting ratio table based on a set of L2. That means the reweighting process may need to wait until the entire set of L2 data is calculated. On the other hand, Method 2 allows for immediate reweighting on CCFs as soon as when each L2 data is initially calculated. + + + KPF DRP employs the second method for CCF reweighting. In this approach, the CCFs are reweighted within the RadialVelocity module when they are initially calculated, and the subsequent RV computation is performed directly on the reweighted CCFs. + + + * ``RadialVelocityReweighting``: CCF reweighting per observation based on the loaded ratio table. + + For KPF DRP, the reweighting ratio table consists of columns representing weights for different masks, such as 'espresso' masks, 'lfc', 'thar', 'etalon', and so on. The specific column selected depends on the fiber source of each orderlet. Additionally, a list of masks enabling the reweighting is one optional setting for this primitive call. The following reweighting methods are provided: + + - **ccf_max, ccf_mean**: Adjust the CCFs in such ways that either the maximum or the mean value among all the orders aligns with the ratios specified in the table. + - **ccf_static**: First, CCFs of each order are normalized by dividing them by the CCF summation. Secondly, the CCFs are reweighted according to the weights specified in the ratio table. + + + KPF DRP applies the method of **ccf_static** at the point. .. toctree:: :maxdepth: 2 diff --git a/modules/barycentric_correction/src/barycentric_correction.py b/modules/barycentric_correction/src/barycentric_correction.py index 708007a75..e8c2c655d 100644 --- a/modules/barycentric_correction/src/barycentric_correction.py +++ b/modules/barycentric_correction/src/barycentric_correction.py @@ -63,6 +63,8 @@ : op_data = BarycentricCorrection(start_time='2458591.5', period=380, bc_corr_path=KPF_TEST_DATA) : + + where `op_data` is dict object wrapped in `Arguments` class object. """ import configparser diff --git a/modules/ca_hk/src/ca_hk_extraction.py b/modules/ca_hk/src/ca_hk_extraction.py index 4e19eb8f7..2a909717f 100644 --- a/modules/ca_hk/src/ca_hk_extraction.py +++ b/modules/ca_hk/src/ca_hk_extraction.py @@ -42,15 +42,20 @@ For the recipe, the order trace event is issued like:: : - hk_data = kpf0_from_fits(hk_file) - output_lev1_hk = CaHKExtraction(hk_data, input_trace_file, fiber_list, None, output_exts=output_exts) + lev0_data = kpf0_from_fits(input_lev0_file) + output_data = kpf1_from_fits(output_lev1_file, data_type = data_type) + : + output_data = CaHKExtraction(lev0_data, + hk_trace_table, # ex. /data/masters/kpfMaster_HKOrderBounds20220909.csv + hk_fiber_list, # ex. ['sci', 'sky'] + output_data, # lev1 containing Ca H&K extraction + output_exts=hk_spec_ext, # ex. ['CA_HK_SCI', 'CA_HK_SKY'] + dark=hk_dark_data, # lev0 containing dark image + wave_files=hk_wavelength_tables) # ex. ['/data/masters/kpfMaster_HKwave20220909_sci.csv', + # '/data/masters/kpfMaster_HKwave20220909_sky.csv'] : - where 'hk_data' is level 0 data (`KPF0`) object containing 'CA_HK' extension, - 'input_trace_file' is the path of the csv file containing the location defintion of the order trace, - 'fiber_list' is a list containing the fibers to be processed, and - 'output_exts' is a list containing the names of the extensions to contain the output. - + where `output_data` is KPF1 object wrapped in `Arguments` class object. """ import configparser diff --git a/modules/order_trace/src/alg.py b/modules/order_trace/src/alg.py index 1f07e64e4..aca375fc5 100644 --- a/modules/order_trace/src/alg.py +++ b/modules/order_trace/src/alg.py @@ -65,7 +65,7 @@ class OrderTraceAlg(ModuleAlgBase): LOWER = 0 name = 'OrderTrace' - def __init__(self, data, poly_degree=None, expected_traces=None, config=None, logger=None): + def __init__(self, data, poly_degree=None, expected_traces = None, config=None, logger=None): if not isinstance(data, np.ndarray): raise TypeError('image data type error, cannot construct object from OrderTraceAlg') if not isinstance(config, ConfigParser): @@ -86,7 +86,7 @@ def __init__(self, data, poly_degree=None, expected_traces=None, config=None, lo self.config_ins = ConfigHandler(config, ins, self.config_param) # section of instrument or 'PARAM' self.poly_degree = poly_degree self.trace_ratio = None - self.expected_traces = expected_traces + self.expected_traces = expected_traces # this is useful for regression test def get_config_value(self, param: str, default): """Get defined value from the config file. @@ -2095,52 +2095,6 @@ def find_all_cluster_widths(self, index_t: np.ndarray, new_x: np.ndarray, new_y: pre_top_edge[x_list] = cluster_points[n, x_list] + cluster_widths[n-1]['top_edge'] bottom_edge.fill(0.0) - # remove the cluster which is not in the vertical peaks, need more work on this - """ - if self.expected_traces is not None: - # self.check_expected_traces(cluster_widths, cluster_points) - check_x_pts = np.array([nx//2], dtype=int) - cluster_check = np.zeros((len(cluster_set)+1, check_x_pts.size), dtype=int) - for x_pt_idx, x_pt in enumerate(check_x_pts): - y_vals = spec[:, x_pt] - peaks, prop = signal.find_peaks(spec[:, x_pt], distance=self.get_trace_vertical_gap()) - y_peaks = y_vals[peaks] - y_peaks_sorted_idx = np.flip(np.argsort(y_peaks)) - y_peaks_sorted = y_peaks[y_peaks_sorted_idx] - last_peak = y_peaks_sorted[self.expected_traces-1] - bottom_mean = np.nanmean(y_peaks_sorted[self.expected_traces:]) - peaks, prop = signal.find_peaks(spec[:, x_pt], distance=self.get_trace_vertical_gap(), - height=(last_peak+bottom_mean)/2) - num_peaks = peaks.size - if x_pt_idx == 0: - peaks_check = np.zeros((1, num_peaks), dtype=int) - for c_idx in cluster_set: - if cluster_points[c_idx, x_pt] == 0: - continue - top_edge = cluster_points[c_idx, x_pt] + cluster_widths[c_idx-1]['top_edge'] - bot_edge = cluster_points[c_idx, x_pt] - cluster_widths[c_idx-1]['bottom_edge'] - for p_idx in range(num_peaks): - if peaks[p_idx] < bot_edge: - continue - if peaks[p_idx] <= top_edge: - if x_pt_idx == 0: - peaks_check[x_pt_idx] = c_idx - cluster_check[c_idx][x_pt_idx] = p_idx+1 - break - - rm_cluster = [] - for c_idx in cluster_set: - s_x = int(coeffs[c_idx, power + 1]) - e_x = int(coeffs[c_idx, power + 2] + 1) - sel_pts_idx = np.where((check_x_pts >= s_x) & (check_x_pts < e_x))[0] - if np.any(cluster_check[c_idx] == 0): - rm_cluster.insert(0, c_idx) - for rm_idx in rm_cluster: - np.delete(coeffs, rm_idx, axis=0) - np.delete(cluster_points, rm_idx, axis=0) - np.delete(cluster_widths, rm_idx-1, axis=0) - max_cluster_no -= 1 - """ return cluster_widths, coeffs def find_cluster_width_by_gaussian(self, cluster_no: int, poly_coeffs: np.ndarray, cluster_points: np.ndarray): diff --git a/modules/order_trace/src/alg_order_mask.py b/modules/order_trace/src/alg_order_mask.py index 235988447..5e24dc121 100644 --- a/modules/order_trace/src/alg_order_mask.py +++ b/modules/order_trace/src/alg_order_mask.py @@ -9,9 +9,10 @@ class OrderMaskAlg(ModuleAlgBase): """ This module defines class 'OrderMaskAlg' and methods to perform order trace mask. A 2D array of the same size as that of the input spectrum data is returned and the pixels that are covered by the - order traces are set to be True, otherwise the pixels are set to be False. The coverage is determined by the order - trace location, the vertical edges and the horizontal range defined in the order trace data plus the origin defined - in the order trace header regarding where the trace position is related to. + order traces are set to be 1 or a specified value, otherwise the pixels are set to be 0 or a specified value. + The coverage is determined by the order trace location, the top and bottom edges and the horizontal range + defined in the order trace data plus the origin defined in the order trace header to indicate where the trace + position is related to. Args: spectrum_data (numpy.ndarray): 2D spectrum raw data. @@ -19,8 +20,9 @@ class OrderMaskAlg(ModuleAlgBase): top/bottom edges and horizontal coverage of the order trace. order_trace_header (dict): dict instance containing order trace info including the origin and polynomial degree to fit the trace. - start_order (int): the index of the first orderlet of the first order. It is based on the assumption that - the index of the first trace in the order_trace_data is counted as 0. + start_order (int): the index of the first orderlet of the first order based on the assumption that + the first few traces may not be indentified by order trace process and the first trace (indexed as 0) + listed in the order_trace_data may not the first orderlet of the first order. A negative number like -n means the first n traces are not included in the order_trace_data. order_mask_data (numpy.ndarray): 2D array to contain the order mask data. Defaults to None. config (configparser.ConfigParser, optional): config context. Defaults to None. @@ -114,7 +116,8 @@ def __init__(self, spectrum_data, order_trace_data, order_trace_header, self.order_edges = None self.order_xrange = None - # orderlet names in flat image, the index in order trace list for the first orderlet of first order + # orderlet names in flat image, + # the index for the first orderlet of first order against the first trace in the order_trace_data self.orderlet_names = orderlet_names self.start_order = start_order self.total_trace_per_order = len(orderlet_names) \ diff --git a/modules/order_trace/src/order_mask.py b/modules/order_trace/src/order_mask.py index 2a1804cc6..2444d2453 100644 --- a/modules/order_trace/src/order_mask.py +++ b/modules/order_trace/src/order_mask.py @@ -1,10 +1,7 @@ # Standard dependencies """ This module defines class OrderMask which inherits from `KPF0_Primitive` and provides methods to perform - the event on order Mask in the recipe. - - Attributes: - OrderNask + the event to produce order mask data in the recipe. Description: * Method `__init__`: @@ -18,22 +15,23 @@ making order mask based on the order trace result. - `action.args[1] (kpfpipe.models.level0.KPF0)`: Instance of `KPF0` to contain order mask result. - `action.args['data_extension']: (str, optional)`: the name of the extension containing data. - - `action.args['trace_file']: (str, optional)`: the name file containing order trace results. + - `action.args['trace_file']: (str, optional)`: the path of the file containing order trace result. - `action.args['orderlet_names'] (str|list, optional)`: Name or list of names of all orderlets included in the order trace result. Defaults to 'SCI1'. - `action.args['orderlets_on_image'] (str|list, optional)`: Name or list of names of the orderlets appear on the L0 image. Defaults to None. - `action.args['start_order'] (int, optional)`: Index of the first orderlet of the first order - assuming the first trace in the order trace file starts with index 0. Defaults to 0. + against the first trace included in the order trace data indexed as 0. Defaults to 0. - `action.args['poly_degree']: (str, optional)`: Polynomial degree for order trace curve fitting. Defaults to 3. - `action.args['origin']: (list, optional)`: Origin of the image where the order trace is related to. Defaults to [0, 0] - `action.args['orderlet_widths']: (dict, optional)`: Orderlet widths to replace the edge widths from the order trace file. Defaults to {} or None. - - `action.args['orderlet_values']: (dict, optional)`: Values for the pixels of each orderlet, like - [1, 2, 3, 4, 5] is for each orderlet, or [3] for all orderlets. Defaults to 1 for all trace - pixels. + - `action.args['orderlet_values']: (dict, optional)`: The values to be assigned to the pixels of + each orderlet of the output, ex. [1, 2, 3, 4, 5] assigns 5 numbers to the pixels of 5 orderlets + respectively. [3] assigns all orderlet pixels the same number, i.e. 3. Defaults to 1 for all + trace pixels. - `action.args['non_orderlet_value']: (number, optional)`: Value for non orderlet pixels. Defaults to 0. @@ -65,11 +63,15 @@ For the recipe, the spectral extraction event is issued like:: : + order_names=['GREEN_SKY_FLUX', 'GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX'] lev0_data = kpf0_from_fits(input_lev0_flat, data_type=data_type) - op_data = OrderMask(lev0_data, NULL, orderlet_names=order_names, - rectification_method=rect_method, - clip_file="/clip/file/folder/fileprefix") + op_data = OrderMask(lev0_data, NULL, + orderlet_names=order_names, + start_order=-1, + trace_file='/data/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv', + data_extension='GREEN_CCD') : + where `op_data` is level 0 data (`KPF0`) object Wrapped in `Arguments` class object. """ diff --git a/modules/order_trace/src/order_trace.py b/modules/order_trace/src/order_trace.py index 7ee19cf58..2f80c3f18 100644 --- a/modules/order_trace/src/order_trace.py +++ b/modules/order_trace/src/order_trace.py @@ -19,15 +19,15 @@ - `action.args['data_col_range'] (list, optional)`: Column coverage of the level 0 data to be processed. If the number is less than 0, it stands for the position relative to the last column of the image. - - `action.args['data_extension'] (string, optional)`: name of the extension with the image. `data` - is the default. + - `action.args['data_extension'] (string, optional)`: name of the extension with the image. Defaults + to "data". - `action.args['result_path'] (string, optional)`: name of the file path or data model extension containing the order trace result. Defaults to None. - `action.args['fitting_poly_degree'] (int, optional)`: Order of polynomial used to fit the trace. - Defaults to None. The value overrides the number defined in the configuration file for the module + Defaults to None. This value overrides the number defined in the configuration file for the module if it is defined. - `action.args['is_output_file'] (boolean, optional)`: if the result is output to a file or data - model extension. Defaults to None. + model extension. Defaults to True meaning output to a file. - `context (keckdrpframework.models.processing_context.ProcessingContext)`: `context.config_path` contains the path of the config file defined for the module of order trace in the master @@ -49,7 +49,7 @@ - `result_path (str)`: File or extension containing the output. - `alg (modules.order_trace.src.alg.OrderTraceAlg)`: Instance of `OrderTraceAlg` which has operation codes for the computation of order trace. - - `poly_degree (int)`: Order of polynimial for order trace fitting. + - `poly_degree (int)`: Order of polynomial for order trace fitting. * Method `__perform`: @@ -62,10 +62,13 @@ : flat_data = kpf0_from_fits(input_flat_file, data_type=data_type) - ot_data = OrderTrace(flat_data, data_row_range=[0, 2000]) + order_result_data = OrderTrace(flat_data, data_extension='GREEN_CCD_STACK', + data_col_range=[0, -1], data_row_range=[0, -1], + result_path="/data/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv", + fitting_poly_degree=3) : - where `flat_data` is level 0 data (`KPF0`) object stored in `Arguments` object. + where `order_result_data` is Panda.Dataframe object wrapped in `Arguments` class object. """ diff --git a/modules/radial_velocity/configs/default.cfg b/modules/radial_velocity/configs/default.cfg index ab1e580d6..631fc2458 100644 --- a/modules/radial_velocity/configs/default.cfg +++ b/modules/radial_velocity/configs/default.cfg @@ -53,6 +53,31 @@ mask_width = 0.5 reweighting_ccf_method = ccf_steps start_vel = -100.0 +## neid related parameters +[KPF] +star_config_file = fits_header +starname = TARGNAME +star_rv = TARGRADV +obslon = -155.474719 +obslat = 19.8260 +obsalt = 4145.0 + +stellarmask_dir = /code/KPF-Pipeline/static/stellarmasks/ +ra = TARGRA +dec = TARGDEC +pmra = TARGPMRA +pmdec = TARGPMDC +parallax = TARGPLAX +epoch = TARGEPOC + +step = 0.25 +step_range = [-402, 402] +mask_width = 0.5 +ccf_engine = c +reweighting_ccf_method = ccf_max + +# km/s/pixel +vel_span_pixel = 0.87 diff --git a/modules/radial_velocity/src/alg.py b/modules/radial_velocity/src/alg.py index da696432a..cbbf1d834 100644 --- a/modules/radial_velocity/src/alg.py +++ b/modules/radial_velocity/src/alg.py @@ -12,13 +12,13 @@ from modules.radial_velocity.src.alg_rv_base import RadialVelocityBase from modules.barycentric_correction.src.alg_barycentric_corr import BarycentricCorrectionAlg from modules.CLib.CCF import CCF_3d_cpython -from modules.Utils.config_parser import ConfigHandler LIGHT_SPEED = const.c.to('km/s').value # light speed in km/s LIGHT_SPEED_M = const.c.value # light speed in m/s SEC_TO_JD = 1.0 / 86400.0 FIT_G = fitting.LevMarLSQFitter() + class RadialVelocityAlg(RadialVelocityBase): """Radial velocity calculation using cross correlation method. @@ -35,11 +35,11 @@ class RadialVelocityAlg(RadialVelocityBase): logger (logging.Logger): Instance of logging.Logger. ccf_engine (string): CCF engine to use, 'c' or 'python'. Defaults to None, reweighting_method (string): reweighting method, ccf_max or ccf_mean, of ccf_steps. Defaults to None. - segment_list (pandas.DataFrame): Table containing segment list containing segment index, start wavelength, + segment_limits (pandas.DataFrame): Table containing segment list containing segment index, start wavelength, and end wavelength. Defaults to None. - order_limits_mask (pandas.DataFrame): Table containing order index and the left and right limits of the + order_limits (pandas.DataFrame): Table containing order index and the left and right limits of the order. Defaults to None. - bary_corr_table (pd.ndarray): table from L1 BARY_CORR table. Defaults to None. + bary_corr_table (pd.ndarray): table from L1 BARY_CORR extension. Defaults to None. start_bary_index (int): starting index in BARY_CORR table. Attributes: @@ -65,15 +65,16 @@ class RadialVelocityAlg(RadialVelocityBase): ccf of the same order. bary_corr_table (pd.ndarray): table from L1 BARY_CORR table. start_bary_index (int): starting index in BARY_CORR table. - + orderlet (str): name of the orderlet associated with `spactrum_data`. + vel_span_pixel (float): velocity span per pixel (km/sec) Raises: AttributeError: The ``Raises`` section is a list of all exceptions that are relevant to the interface. TypeError: If there is type error for `spectrum_data`. TypeEoor: If there is type error for `header`. TypeError: If there is type error for `wave_cal`. Exception: If there is init error for radial velocity calculation. - """ + ROWS_FOR_ANALYSIS = 3 SEGMENT_IDX = 0 SEGMENT_X1 = 1 @@ -703,7 +704,6 @@ def get_mask_line(self): return self.orderlet_mask_line - def cross_correlate_by_mask_shift(self, wave_cal, spectrum, vb): """Cross correlation by the shifted mask line and the spectrum data of one order for each velocity step. @@ -785,35 +785,6 @@ def cross_correlate_by_mask_shift(self, wave_cal, spectrum, vb): new_wave_cal.astype('float64'), new_spec.astype('float64'), new_line_weight.astype('float64'), sn.astype('float64'), self.velocity_loop[c], -v_b) - """ - ccf_pixels = CCF_3d_cpython.calc_ccf_pixels(new_line_start.astype('float64'), - new_line_end.astype('float64'), - new_wave_cal.astype('float64'), - new_spec.astype('float64'), - new_line_weight.astype('float64'), sn.astype('float64'), - self.velocity_loop[c], v_b) - ccf_pixels_c[c, :] = ccf_pixels - """ - """ - sn_p = np.ones(n_pixel) - ccf_python, ccf_pixels_python = self.calc_ccf(v_steps, new_line_start.astype('float64'), - new_line_end.astype('float64'), - x_pixel_wave.astype('float64'), - spectrum.astype('float64'), - new_line_weight.astype('float64'), - sn_p, zb) - ccf_diff_pixel = ccf_pixels_python - ccf_pixels_c - max_pixel_diff = np.amax(abs(ccf_diff_pixel)) - total_pixel_size = np.size(ccf_diff_pixel) - total_pixel_diff = np.size(np.where(ccf_diff_pixel != 0.0)[0]) - - ccf_diff = ccf_python-ccf - max_ccf_diff = np.amax(abs(ccf_diff)) - total_ccf_size = np.size(ccf_diff) - total_ccf_diff = np.size(np.where(ccf_diff != 0.0)[0]) - self.d_print("max diff of ccf in pixels: ", max_pixel_diff, '(', total_pixel_diff, '/', total_pixel_size, ')', info=True) - self.d_print("max diff of ccf in v-steps: ", max_ccf_diff, '(', total_ccf_diff, '/', total_ccf_size, ')', info=True) - """ else: sn_p = np.ones(n_pixel) ccf, ccf_pixels_python = self.calc_ccf(v_steps, new_line_start.astype('float64'), diff --git a/modules/radial_velocity/src/alg_rv_init.py b/modules/radial_velocity/src/alg_rv_init.py index e2f908d18..8de75c2f8 100644 --- a/modules/radial_velocity/src/alg_rv_init.py +++ b/modules/radial_velocity/src/alg_rv_init.py @@ -379,6 +379,7 @@ def init_star_config(self): self.d_print("RadialVelocityAlgInit: mask config file: ", self.mask_path) return self.ret_status('ok') + def init_calculation(self): """ Initial data setup for radial velocity computation based on the setting in `PARAM` section of the configuration file. @@ -398,6 +399,8 @@ def init_calculation(self): * `velocity_steps` * `velocity_loop` * `zb_range` + * `ccf_engine` + * `vel_span_pixel` * `mask_line` * `mask_path` * `reweighting_ccf_method` @@ -629,9 +632,11 @@ def get_redshift_range(self, bc_path=None, jd_time=None, period=None, bc_output= def get_mask_line(self): """ Get mask coverage per mask width and the mask centers read from the mask file. - Returns: dict containing mask line info or dict of dict containg mask line info for each fiber - dict: Mask information, like:: + Returns: + dict containing mask line info for entire observation or dict of dict containing mask line info + for each fiber source, like:: + dict: Mask line for entire observation { 'start' : numpy.ndarray # start points of masks 'end' : numpy.ndarray # end points of masks @@ -641,8 +646,8 @@ def get_mask_line(self): 'bc_corr_end': numpy.ndarray # adjusted end points of masks } - dict of dict: - { 'fiber1': { # mask line info. for fiber1 + dict of dict: mask line information for different fiber source, + { 'fiber1': { # mask line info. for fiber source 1 { 'start' : numpy.ndarray # start points of masks 'end' : numpy.ndarray # end points of masks @@ -653,11 +658,6 @@ def get_mask_line(self): } : } - - Attribute `mask_line` is updated. - - - """ if self.mask_line is None: @@ -723,7 +723,7 @@ def start(self, print_debug=None): { 'status': True|False 'msg': - 'data': # Please see Returns of function collect_init_data + 'data': # Please see Returns of collect_init_data } """ diff --git a/modules/radial_velocity/src/radial_velocity.py b/modules/radial_velocity/src/radial_velocity.py index ea9bc8e14..1812e49fe 100644 --- a/modules/radial_velocity/src/radial_velocity.py +++ b/modules/radial_velocity/src/radial_velocity.py @@ -18,22 +18,41 @@ radial velocity computation. - `action.args[2] (kpfpipe.models.level2.KPF2)`: Instance of `KPF2` containing radial velocity results. If not existing, it is None. - - `action.args[3] (str)`: Extension name associated with the level 1 science data. + - `action.args[3] (str|list)`: Extension name(s) associated with the level 1 science data. - `action.args['area_def'] (list, optional)`: pixel area, [start_y, end_y, start_x, end_x], to be processed. Defaults to None. - `action.args['segment_def'] (str)`: csv file defining segment wavelength information. + Each row in the csv includes items of index, starting and ending wavelength, related order index. + Defaults to None. - `action.args['order_def'] (str)`: csv file defining order limits information. + Each row in the csv includes items of order index, pixels relative to the left and right border. + Defaults to None. - `action.args['ccf_ext'] (str)`: Extension name containing the ccf results. - `action.args['rv_ext'] (str)`: Extension name containing rv results. - - `action.args['input_ref'] (np.ndarray|str|pd.DataFrame, optional)`: Reference for + - `action.args['input_ref'] (np.ndarray|str|pd.DataFrame, optional)`: Reference or weighs for reweighting ccf orders. Defaults to None. - `action.args['reweighting_method'] (str, optional)`: reweighting method. Defaults to None. - `action.args['start_seg'] (int)`: Index of first segment to be processed. Defaults to None. - `action.args['end_seg'] (int)`: Index of last segment to be processed. Defaults to None. - - `action.args['bary_corr'] (pd.DataFrame)`: extension name of bary correction table. Defaults to - 'BARY_CORR'. - - `action.args['start_bary_index'] (int)`: starting index in bary correction table. Defaults to 0. - - `action.args['rv_correction_by_cal'] (bool)`: if using CAL fiber CCF to correct RV + - `action.args['bary_corr'] (pd.DataFrame)`: extension name of barycentric correction table. + Defaults to 'BARY_CORR'. + - `action.args['start_bary_index'] (int)`: starting index in barycentric correction table. + Defaults to 0. + - `action.args['rv_correction_by_cal'] (bool)`: if using CAL fiber CCF to correct RV of SCI fiber. + Defaults to False. + - `action.args['ccf_engine'] (str)`: using Python ('python') or C ('c') version CCF engine. + Defaults to 'c'. + - `action.args['rv_set'] (int)`: the index of ccd per ccd list that L1 data is associated with. + ex. 0 for 'GREEN_CCD' and 1 for 'RED_CCD' in terms of KPF. Defaults to 0. + - `action.args['ins'] (str)`: instrument name. Defaults to None or value of 'INSTRUME' + in the primary header. + - `action.args['reweighting_masks'] (list)`: masks allowing reweighting. Defaults to None to include + all available masks. + - `action.args['exptime'] (float)`: exposure time. Defaults to the value of keyword 'EXPTIME' + in primary header. + - `action.args['obstime'] (float)`: observation time. Defaults to the value of keyword 'DATE-MID' in + primary header for KPF. + - `action.args['vel_span_pixel'] (float)`: velocity span per pixel (km.sec). Defaults to None. - `context (keckdrpframework.models.processing_context.ProcessingContext)`: `context.config_path` contains the path of the config file defined for the module of radial velocity in the master @@ -44,39 +63,73 @@ - `input (kpfpipe.models.level1.KPF1)`: Instance of `KPF1`, assigned by `action.args[0]`. - `rv_init (dict)`: Result from radial velocity init. - `output_level2 (kpfpipe.models.level2.KPF2)`: Instance of `KPF2`, assigned by `action.args[2]`. - - `sci_names (list)`: Name of the order to be processed, assigned by `action.args[2]`. + - `od_names (list)`: Name of the orderlet to be processed, assigned by `action.args[2]`. - `area_def (list)`: Pixel area to be processed. - - `segment_def (np.ndarray)`: segments to be processed. - - `order_def (np.ndarray)`: orders to be processed. + - `segment_limits (np.ndarray)`: table defining segment wavelength information. + - `order_limits (np.ndarray)`: table defining order pixel boundaries. - `start_seg (int)`: Index of first segment to be processed. - `end_seg (int)`: Index of last segment to be processed. - `reweighting_method (str)`: reweighting method associated with `action.args['reweighting_method']`. + - `ccf_ext (str)`: ccf extension name. + - `rv_ext (str)`: rv extension name. + - `rv_set_idx (int)`: ccd index for the ccd that spectral data is associated with. + - `is_cal_cor (bool)`: if using CAL fiber CCF to correct RV of SCI fiber. + - `ins (str)`: instrument name. + - `reweighting_masks (list)`: the masks allowing reweighting. + - `spectrum_data_set (list)`: a list containing spectral data to process. + - `wave_cal_set (list)`: a list containing wavelength data associated with the spectral data in + `spectrum_data_set`. + - `header_set (list)`: a list containing the header associated with the spectral data in + `spectrum_data_set`. + - `total_orderlet (int):` total orderlet in `spectrum_data_set`. + - `is_solar_data (bool):` if the observation is for target solar. + - `ref_ccf (numpy.ndarray)`: Reference or ratio of cross correlation values for scaling the computation + of cross correlation, associated with `action.args['input_ref']`. + - `ccf_engine (str)`: ccf engine, 'python' or 'c', associated with `action.args['ccf_engine']`. + - `reweighted (dict)`: containing key/value as orderlet_name/'T' or 'F' to indicate if the orderlet is + CCF reweighted. - `config_path (str)`: Path of config file for radial velocity. - `config (configparser.ConfigParser)`: Config context. - `logger (logging.Logger)`: Instance of logging.Logger. - - `spectrum_data (numpy.ndarray)`: Reduced 1D data of all orders from optimal extraction, associated - with `action.args[0]`. - - `wave_cal (numpy.ndarray)`: Wavelength calibration data, associated with `action.args[0]`. - - `header (fits.header.Header)`: Fits header of HDU associated with `spectrum_data`. - - `ref_ccf (numpy.ndarray)`: Reference or ratio of cross correlation values for scaling the computation - of cross correlation, associated with `action.args['input_ref']`. - `alg (RadialVelocityAlg)`: Instance of RadialVelocityAlg which has operation codes for the computation of radial velocity. * Method `__perform`: - RadialVelocity returns the result in `Arguments` object which contains the original input - level 1 data object (`KPF1`) plus an extension with the radial velocity result. - (the result will be put into a level 2 data object after level 2 data model is implemented.) + RadialVelocity returns the L2 wrapped in `Arguments` class object. L2 has the extensions containing CCF and + RV values. Usage: - For the recipe, the optimal extraction event is issued like:: + For the recipe, the radial velocity event is issued like:: + - rv_init = RadialVelocityInit() : + data_ext_rv = [['GREEN_SCI_FLUX1', 'GREEN_SCI_FLUX2', 'GREEN_SCI_FLUX3', 'GREEN_CAL_FLUX', 'GREEN_SKY_FLUX'], + ['RED_SCI_FLUX1', 'RED_SCI_FLUX2', 'RED_SCI_FLUX3', 'RED_CAL_FLUX', 'RED_SKY_FLUX']] lev1_data = kpf1_from_fits(input_L1_file, data_type='KPF') - rv_data = RadialVelocity(lev1_data, rv_init, order_name=order_name) + rv_init = RadialVelocityInit(...) + + rv_data = None + for idx in [0, 1]: + ratio_ref = RadialVelocityReweightingRef(...) # load reweighting ratio table for green or red ccd + : + # idx 0 is for green_ccd, 1 is for red_ccd + rv_data = RadialVelocity(lev1_data, rv_init, rv_data, data_ext_rv[idx], + ccf_ext="GREEN_CCD_CCF", # "RED_CCD_CCF" for idx = 1 + rv_ext="RV", + area_def=[0, 34, 500, -500], # [0, 31, 500, -500] for idx = 1 + start_seg=0, + end_seg=34, # 31 for idx = 1 + rv_set=0, # 1 for idx = 1 + ccf_engine='c', + start_bary_index=0, # 35 for idx = 1 + rv_correction_by_cal=False, + reweighting_method='ccf_static', + input_ref=ratio_ref, + reweighting_masks=['espresso']) : + + where `rv_data` is KPF2 object wrapped in `Arguments` class object. """ import configparser @@ -184,6 +237,7 @@ def __init__(self, self.ccf_engine = action.args['ccf_engine'].lower() \ if 'ccf_engine' in args_keys and action.args['ccf_engine'] is not None \ else self.default_args_val['ccf_engine'] + self.area_def = action.args['area_def'] if 'area_def' in args_keys else None self.segment_limits = self.load_csv(action.args['segment_def']) if 'segment_def' in args_keys else None self.order_limits = self.load_csv(action.args['order_def']) if 'order_def' in args_keys else None @@ -194,11 +248,14 @@ def __init__(self, self.rv_ext = action.args['rv_ext'] if 'rv_ext' in args_keys else self.default_args_val['rv_ext'] self.rv_set_idx = action.args['rv_set'] if 'rv_set' in args_keys else self.default_args_val['rv_set'] self.is_cal_cor = action.args['rv_correction_by_cal'] if 'rv_correction_by_cal' in args_keys else False + bary_corr_ext = action.args['bary_corr'] if 'bary_corr' in args_keys else self.default_args_val['bary_corr'] bc_table = getattr(self.input, bary_corr_ext) if hasattr(self.input, bary_corr_ext) else None start_bary_index = action.args['start_bary_index'] \ if 'start_bary_index' in args_keys else self.default_args_val['start_bary_index'] + p_header = self.input.header['PRIMARY'] if self.input is not None else None + if 'ins' in args_keys and action.args['ins']: self.ins = action.args['ins'] elif p_header is not None and 'INSTRUME' in p_header: @@ -280,6 +337,7 @@ def __init__(self, self.rv_init['data'][mod][RadialVelocityAlg.get_fiber_object_in_header(self.ins, sci)][mtype] self.total_orderlet = len(self.spectrum_data_set) + do_rv_corr = False self.is_solar_data = False if p_header is not None: @@ -388,8 +446,7 @@ def _perform(self): perform radial velocity computation by calling method 'compute_rv_by_cc' from RadialVelocityAlg. Returns: - Level 1 data from the input plus an extension with the cross correlation results from all orders. - (this part will be updated after level 2 data model is made.) + Level 2 data including extensions containing CCFs for all CCDs and RVs for all orderlets """ # _, nx, ny = self.alg.get_spectrum() diff --git a/modules/radial_velocity/src/radial_velocity_init.py b/modules/radial_velocity/src/radial_velocity_init.py index 3334c31f8..55b35ffef 100644 --- a/modules/radial_velocity/src/radial_velocity_init.py +++ b/modules/radial_velocity/src/radial_velocity_init.py @@ -15,9 +15,11 @@ Defaults to None. - `action.args['period'] (str | int)`: A period of days for Barycentric correction computation. Default to None. - - `action.args['bc_corr_path'] (str)`: Path of file, a csv file storing a list of Barycentric + - `action.args['l1_data'] (KPF1)`: KPF1 instance. The primary header of this L1 object provides + star configuration information for the initial setting. + - `action.args['bc_corr_path'] (str)`: Path of the csv file storing a list of Barycentric correction related data over a period of time. Default to None. - - `actions.args['bc_corr_output'] (str)`: Path of output file, a csv file. Default to None. + - `actions.args['test_data_path'] (str)`: Path of the star config file. Default to None. - `context (keckdrpframework.models.processing_context.ProcessingContext)`: `context.config_path` contains the path of the config file defined for the module of radial velocity in the master @@ -25,10 +27,12 @@ and following attributes are defined to initialize the object, - - `bc_period`: Period for Barycentric velocity correction calculation. - - `bc_start_jd`: Start time in Julian data format for Barycentric velocity correction calculation. - - `bc_data`: Path of csv file storing barycentric correction related data for a period of time. - - `bc_output_data`: Path of csv output file storing the result from barycentric correction computation. + - `bc_period (float)`: Period for Barycentric velocity correction calculation. + - `bc_start_jd (float)`: Start time in Julian data format for Barycentric velocity correction calculation. + - `bc_data (str)`: Path of csv file storing barycentric correction related data for a period of time. + - `test_data (str)`: Path of the star config file. + - `bc_output_data (str)`: Path of csv output file storing the result from barycentric correction computation. + - `l1_data (KPF1)`: L1 instance containing star configuration information. - `config_path (str)`: Path of config file for radial velocity. - `config (configparser.ConfigParser)`: Config context. - `logger (logging.Logger)`: Instance of logging.Logger. @@ -45,12 +49,18 @@ Usage: For the recipe, the optimal extraction init event is issued like:: - rv_init = RadialVelocityInit() - : lev1_data = kpf1_from_fits(input_L1_file, data_type='KPF') - rv_data = RadialVelocity(lev1_data, rv_init, order_name=order_name) + rv_init = RadialVelocityInit(start_time="2021-03-01", + l1_data=lev1_data, + bc_corr_path='/data/bary/', + test_data_path='/data/masters/' + ) + : + + rv_data = RadialVelocity(lev1_data, rv_init, ...) : + where `rv_data` is dict object wrapped in `Arguments` class object. """ import configparser diff --git a/modules/radial_velocity/src/radial_velocity_reweighting.py b/modules/radial_velocity/src/radial_velocity_reweighting.py index 1285509e5..fff3766a3 100644 --- a/modules/radial_velocity/src/radial_velocity_reweighting.py +++ b/modules/radial_velocity/src/radial_velocity_reweighting.py @@ -1,7 +1,7 @@ # Standard dependencies """ This module defines class `RadialVelocityReweighting` which inherits from `KPF1_Primitive` and provides - methods to perform the event on radial velocity CCF orderes reweighting in the recipe. + methods to perform the event on radial velocity CCF order reweighting in the recipe. Description: * Method `__init__`: @@ -13,14 +13,15 @@ - `action.args[0] (str|KPF2)`: one file or one KPF2 with ccf for reweighting. - `action.args[1] (str)`: Reweighting method. - - `action.args[2] (pandas.DataFrame|np.ndarray)`: The ratio table or reference ccf for reweighting. + - `action.args[2] (str|pandas.DataFrame|np.ndarray)`: The ratio table file or ratio table + for ccf reweighting. - `action.args[3] (int)`: total segment for the ccf data. - `action.args['ccf_ext'] (str)`: The HDU name in fits file for the HDU with ccf data. Defaults to 'CCF'. - `action.args['rv_ext'] (str)`: The HDU name in fits file for the HDU with rv data. Defaults to 'RV'. - `action.args['rv_ext_idx'] (int)`: The set index in the extension containing rv data. - Defaults to 0. + Defaults to 0. For KPF, 0 is for green ccd, and 1 is for red ccd. - 'action.args['ccf_start_index'] (int)`: The segment index that the first row of ccf_data is associated with. @@ -30,7 +31,7 @@ and the following attributes are defined to initialize the object, - - `ccf_data (np.ndarray)`: ccf data. + - `ccf_data (dict)`: ccf data per orderlet. - `reweighting_method (str)`: Reweighting method. - `total_order (int)`: Total order for reweighting. - `ccf_ref (np.ndarray)`: Ratio table or Referece ccf for reweighting. @@ -38,11 +39,30 @@ - `config_path (str)`: Path of config file for radial velocity. - `config (configparser.ConfigParser)`: Config context. - `logger (logging.Logger)`: Instance of logging.Logger. - - `ccf_ext`: name of hdu containing ccf data + - `ccf_ext (str)`: name of hdu containing ccf data + - `rv_ext (str)`: name of hdu containing rv data + - `rv_ext_idx (int)`: the set index per ccd. + - `lev2_obj (KPF2)`: L2 instance. + - `instrument (str)`: instrument. + - `reweighting_mask (list)`: list of masks allowing reweighting. + - `is_solar_data (bool)`: if solar is the observation target. + - `original_ccf_exts (list)`: extensions containing original ccf. + - `rw_ccf_exts (list)`: extensions containing reweighted ccf. + - `ccf_dim (int)`: ccf extension dimension. + - `ccf_ext_header`: header associated with ccf extension. + - `orderlet_ccf (dict)`: index in the ccf extension data per orderlet. + - `sci_orderlets (list)`: list of science orderlets. + - `cal_orderlets (list)`: list of cal orderlets. + - `sky_orderlets (list)`: list of sky orderlets. + - `mask_type (dict)`: mask type per orderlet. + - `totals (int)`: total segment in ccf data. + - `totalv (int)`: total velocity steps in ccf data. + - `total_orderlet (int)`: total orderlet in ccf data. + - `od_names (list)`: names of the orderlets included in the ccf data. * Method `__perform`: - RadialVelocityReweighting returns the result in `Arguments` object containing a level 1 data model + RadialVelocityReweighting returns the result in `Arguments` object containing a level 2 data model object with the reweighted ccf orders. * Note: @@ -50,13 +70,27 @@ It will be refactored into KPF2 style when level2 data model is implemented. Usage: - For the recipe, the optimal extraction event is issued like:: - - : - lev1_data = kpf1_from_fits(input_L1_file, data_type='KPF') - rv_data = RadialVelocityReweighting(lev1_data, , , - , ccf_hdu_index=12, ccf_start_idx=0) + For the recipe, the reweighting event is issued like:: + + area_def = [0, 34, 500, -500] + total_segment = area_def[1] - area_def[0] + 1 + ratio_ref = RadialVelocityReweightingRef(None, reweighting_method, ...) + + lev2_rv = kpf2_from_fits(output_lev2_file, data_type=data_type) + + rv_data = RadialVelocityReweighting(lev2_rv, + reweighting_method, + ratio_ref, + total_segment, + ccf_ext="GREEN_CCF", + rv_ext="RV", + rv_ext_idx=0, # index for green ccd + ccf_start_idx=0, + reweighting_mask=['espresso'] + ) : + + where `rv_data` is KPF2 object wrapped in `Arguments` class object. """ import configparser diff --git a/modules/radial_velocity/src/radial_velocity_reweighting_ref.py b/modules/radial_velocity/src/radial_velocity_reweighting_ref.py index 70dd648e9..e06d9c86a 100644 --- a/modules/radial_velocity/src/radial_velocity_reweighting_ref.py +++ b/modules/radial_velocity/src/radial_velocity_reweighting_ref.py @@ -1,7 +1,7 @@ # Standard dependencies """ This module defines class `RadialVelocityReweightingRef` which inherits from `KPF_Primitive` and provides - methods to perform the event on radial velocity reweighting ratio table creation in the recipe. + methods to perform the event on radial velocity reweighting ratio table creation or loading in the recipe. Description: * Method `__init__`: @@ -11,7 +11,8 @@ - `action (keckdrpframework.models.action.Action)`: `action.args` contains positional arguments and keyword arguments passed by the `RadialVelocityReweightingRef` event issued in the recipe: - - `action.args[0] (list|str)`: List of KPF2 or one single KPF2 for reweighting. + - `action.args[0] (list|str)`: List of KPF2 or one single KPF2 for reweighting. None means to load + a predefined ratio table file. - `action.args[1] (str)`: Reweighting method. - `action.args[2] (int)`: Total order from the ccf data to build the reweighting template. - `action.args['ccf_hdu_name'] (str)`: The HDU name in fits file for the HDU with ccf data. @@ -30,9 +31,10 @@ - `files (List)`: list of KPF2 objects to find the reference template for reweighting. - `reweighting_method (str)`: Reweighting method. - - `total_order (int)`: Total order to build the reweighting reference. + - `total_segment (int)`: Total segments to build the reweighting reference. - `ccf_hdu_name (str)`: name of hdu containing ccf. - `ccf_start_index (int)`: The order index that the first row of ccf_data is associated with. + - `is_ratio_data (bool)`: If the file is a csv file containing the ratio for reweighting. - `ccf_ratio_file (str)`: output file containing ccf ratio. - `config_path (str)`: Path of config file for radial velocity. - `config (configparser.ConfigParser)`: Config context. @@ -41,25 +43,28 @@ * Method `__perform`: RadialVelocityReweightingRef returns the result in `Arguments` object containing an instance of - numpy.ndarray as for the reweighting reference and the ratio result is written into a file in csv - format if `ccf_ratio_file` is set in the action arguments. - - * Note: - The event input tentatively uses ccf data in the format of KPF1 object. It will be refactored into KPF2 - style when level2 data model is implemented. + numpy.ndarray to represent the ratio for the reweighting and the ratio result is written into a file in csv + format if `ccf_ratio_file` is set and `is_ratio_data` is False. Usage: For the recipe, the make reweighting ratio table is issued like:: : - reweighting_ref = RadialVelocityReweightingRef(list_files, 'ccf_max', 116, ccf_hdu_nane='ccf', - ccf_start_index=0, ccf_ratio_file=) + # load the table file with the weighs + reweighting_ref = RadialVelocityReweightingRef(None, 'ccf_max', + 35, # total segment + ccf_hdu_nane='GREEN_CCF', + is_ratio_data=Tue, + ccf_ratio_file= + ccf_start_index=0 + ) : + + where `reweighting_ref` is Pandas DataFrame object wrapped in `Arguments` class object. """ import configparser import numpy as np -from astropy.io import fits import os.path import pandas as pd diff --git a/modules/spectral_extraction/src/alg.py b/modules/spectral_extraction/src/alg.py index 96162c2ae..b12de68a7 100644 --- a/modules/spectral_extraction/src/alg.py +++ b/modules/spectral_extraction/src/alg.py @@ -7,7 +7,6 @@ from modules.Utils.alg_base import ModuleAlgBase import os import json -from astropy.time import Time # Pipeline dependencies # from kpfpipe.logger import start_logger diff --git a/modules/spectral_extraction/src/bary_corr.py b/modules/spectral_extraction/src/bary_corr.py index 2cd859e0b..7e7f6b7f1 100644 --- a/modules/spectral_extraction/src/bary_corr.py +++ b/modules/spectral_extraction/src/bary_corr.py @@ -55,11 +55,16 @@ : lev0_data = kpf0_from_fits(input_lev0_file, data_type=data_type) - lev1_data = kpf1_from_fits(input_lev1_file, data_type=data_type) - new_lev1 = BaryCorrTable(lev0_data, lev1_data, 67, 32, - wls_ext='GREEN_SCI_WAVE1', + output_data = kpf1_from_fits(input_lev1_file, data_type=data_type) + + # for red CCD + output_data = BaryCorrTable(lev0_data, output_data, 67, 32, + start_bary_index=35, + wls_ext='RED_SCI_WAVE1', start_bary_index=35) : + + where `output_data` is KPF1 object wrapped in `Arguments` class object. """ diff --git a/modules/spectral_extraction/src/order_rectification.py b/modules/spectral_extraction/src/order_rectification.py index 31f923642..dbfc28e3c 100644 --- a/modules/spectral_extraction/src/order_rectification.py +++ b/modules/spectral_extraction/src/order_rectification.py @@ -62,6 +62,8 @@ rectification_method=rect_method, clip_file="/clip/file/folder/fileprefix") : + + where `op_data` is KPF0 object wrapped in `Arguments` class object. """ diff --git a/modules/spectral_extraction/src/spectral_extraction.py b/modules/spectral_extraction/src/spectral_extraction.py index 1bfd076c8..ebeb1a50a 100644 --- a/modules/spectral_extraction/src/spectral_extraction.py +++ b/modules/spectral_extraction/src/spectral_extraction.py @@ -99,6 +99,7 @@ trace_file=trace_file, wavecal_fits=input_lev1_file) : + where `op_data` is KPF1 object wrapped in `Arguments` class object. """ diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 0eb46741c..bb77e6310 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -5,7 +5,7 @@ from modules.spectral_extraction.src.spectral_extraction import SpectralExtracti from modules.radial_velocity.src.radial_velocity_init import RadialVelocityInit from modules.radial_velocity.src.radial_velocity import RadialVelocity from modules.radial_velocity.src.radial_velocity_reweighting_ref import RadialVelocityReweightingRef -from modules.src.radial_velocity_reweighting import RadialVelocityReweighting +from modules.radial_velocoty.src.radial_velocity_reweighting import RadialVelocityReweighting from modules.ca_hk.src.ca_hk_extraction import CaHKExtraction from modules.quicklook.src.quick_prim import Quicklook from modules.Utils.string_proc import str_replace @@ -380,7 +380,7 @@ if do_order_trace: if b_lev0_rect: result = to_fits(lev0_flat_rect, output_lev0_flat_rect) -## do spectral extraction including CA-HK extraction +## do spectral extraction including background subtracton and build BARY_CORR and do CA-HK extraction after spectral extraction. # description: do spectrum extraction based on extraction method (summ or optimal) per fiber per ccd and # spectral extraction from CA-HK spectrometer fibers. # copy wavelength solution data from wls sample files to produced L1 data. @@ -391,6 +391,8 @@ if do_order_trace: # output_lev1_file: path of produced L1 file, /data/L1//_L1.fits # lev1_list: a list containing the path of L1 files produced by either spectral extraction or ca_hk extraction. # +# for background extraction: +# order_mask: KPF0 instance, same size as input L0 data, the image distinguishes the orderlet pixels from the non-orderlet pixels. # for spectral extraction: # output_lev0_flat_rect: fits file with rectified L0 flat data # lev0_flat_rect: KPF0 instance for 'output_lev0_flat_rect' @@ -398,13 +400,15 @@ if do_order_trace: # trace_list: list containing order trace result file for each ccd. # [/data/order_trace//_GREEN_CCD.csv (or _RED_CCD.csv)] # b_level1_sp: a boolean to indicate if spectral extraction for each L0 is successful. -# op_data: KPF1 instance containing spectral extraction result which is added into op_data extension by extension +# op_data: KPF1 instance containing spectral extractis_bary_idxon result which is added into op_data extension by extension # per specified extension list (order_names) in each ccd iteration. # wavecal_data: KPF1 instance per wls sample file. # for ca_hk: # hk_dark_data: KPF0 for dark data # hk_bias_data: KPF0 for bias data # primitives: +# OrderMask: os_bary_idxrder mask +# ImageProcessing: perform background subtraction # SpectralExtraction: spectral extraction # ExtCopy: extension copy from the extension of one data model instance to the extension of another # (or the same) data model instance. @@ -573,7 +577,10 @@ if do_spectral_extraction or do_hk or do_bc: # # primitives: # RadialVelocityInit: do rv init on each L1 input. -# RadialVelocity: do radial velocity per ccd per L1 data. +# RadialVelocity: do radial velocity per ccd per L1 data, CCF reweighting is optional based on if a ratio table is provided and +# if producing L2 with reweighted CCF without keeping the unweighted CCF. +# RadialVelocityReweightingRef: build or load CCF reweighting ratio table. +# RadialVelocityReweightin: perform CCF reweighitng # if do_rv or do_rv_reweighting: rv_star_dir = masters_data_dir From cdd49ef9dff906471b858f05531976ddac546b1b Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Tue, 27 Jun 2023 05:58:04 -0700 Subject: [PATCH 197/249] Upgraded to handle either 2 or 4 amplifiers for RED detector. Forgot to change this file earlier. --- configs/kpf_masters_drp.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/configs/kpf_masters_drp.cfg b/configs/kpf_masters_drp.cfg index 9b728b03f..135587567 100644 --- a/configs/kpf_masters_drp.cfg +++ b/configs/kpf_masters_drp.cfg @@ -18,9 +18,9 @@ input_dir = /data/L0 channel_datasec_ncols_green = 2040 channel_datasec_nrows_green = 2040 channel_datasec_ncols_red = 2040 -channel_datasec_nrows_red = 4080 +channel_datasec_nrows_red = 2040 # These two paths will have KPFPIPE_TEST_DATA/ prepended. -channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red_2amp.txt +channel_orientation_ref_path_red = /code/KPF-Pipeline/static/kpfsim_ccd_orient_red.txt channel_orientation_ref_path_green = /code/KPF-Pipeline/static/kpfsim_ccd_orient_green.txt prescan_reg = [0,4] # overscan_method = clippedmean is n_sigma clipping and average over entire post-overscan strip. From 57d8bab968e3db8062b20e9919739ce8258d55e4 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Tue, 27 Jun 2023 09:16:40 -0700 Subject: [PATCH 198/249] L0 plots --- modules/quicklook/src/alg.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 6b876ba95..8dc52dad5 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -143,8 +143,8 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() - if os.path.exists(output_dir+'/'+exposure_name+'/2D/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') - plt.savefig(output_dir+'/'+exposure_name+'/2D/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=500,facecolor='white') + if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png',dpi=500,facecolor='white') plt.close() red_image = np.concatenate((L0['RED_AMP1'].data, L0['RED_AMP2'].data), axis=1)/2**16 # flip not needed for Red @@ -159,7 +159,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.colorbar(shrink=0.7, label=r'ADU / $2^{16}$') plt.grid(False) #plt.show() - plt.savefig(output_dir+'/'+exposure_name+'/2D/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') + plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() L0.close() From 8110b9e775a93116c9a73bf1c9f474e1063ce1ba Mon Sep 17 00:00:00 2001 From: cwang2016 Date: Tue, 27 Jun 2023 12:58:44 -0700 Subject: [PATCH 199/249] fix the bug in unit test file test_bary_corr_recipe.py --- tests/regression/test_bary_corr_recipe.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/regression/test_bary_corr_recipe.py b/tests/regression/test_bary_corr_recipe.py index 492ff19ab..2db440764 100644 --- a/tests/regression/test_bary_corr_recipe.py +++ b/tests/regression/test_bary_corr_recipe.py @@ -21,7 +21,7 @@ lev1_stem, lev1_ext = splitext(short_lev0_file) if lev0_stem_suffix != None and lev0_stem_suffix in lev1_stem: lev1_stem = str_replace(lev1_stem, lev0_stem_suffix, '') -output_extraction = output_dir + config.ARGUMENT.output_extraction + date_dir + 'tmp/' +output_extraction = output_dir + config.ARGUMENT.output_extraction + date_dir #output_lev1_file = output_extraction + lev1_stem + lev1_stem_suffix + '.fits' output_lev1_file = output_extraction + 'KP.20230114.03263.62_L1.fits' @@ -29,7 +29,7 @@ output_data = kpf1_from_fits(output_lev1_file, data_type=data_type) t_order = orders_per_ccd[0] + orders_per_ccd[1] for idx in ccd_idx: - result = BaryCorrTableTest(lev0_data, output_data, t_order, orders_per_ccd[idx], start_bary_index=s_bary_idx[idx], wls_ext=wave_to_ext[idx][0]) + result = BaryCorrTable(lev0_data, output_data, t_order, orders_per_ccd[idx], start_bary_index=s_bary_idx[idx], wls_ext=wave_to_ext[idx][0]) """ From 5fd09ebfe69734b31426c0afe4f539444dae09be Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Tue, 27 Jun 2023 13:29:03 -0700 Subject: [PATCH 200/249] add do bc to masters l1 --- configs/kpf_masters_l1.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index 52e809c2d..e51964889 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -28,7 +28,7 @@ do_hk = True do_wavecopy_in_sp = True do_qlp = False do_bk_subtraction = True -do_bc = False +do_bc = True # the subdirectory containing order trace result, L1 data, L2 data, L2 reweighted data, qlp, bary data output_trace = / From e32b6d89be9122e8ea3884e4922dfa549a555af1 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Wed, 28 Jun 2023 14:29:13 -0700 Subject: [PATCH 201/249] Added new function compute_clip_corr. --- modules/Utils/frame_stacker.py | 45 +++++++++++++++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/modules/Utils/frame_stacker.py b/modules/Utils/frame_stacker.py index a78ff0167..a8682a167 100644 --- a/modules/Utils/frame_stacker.py +++ b/modules/Utils/frame_stacker.py @@ -30,6 +30,47 @@ def __init__(self,frames_data,n_sigma=2.5,logger=None): if self.logger: self.logger.info('Started {}'.format(self.__class__.__name__)) + def compute_clip_corr(self): + + """ + Compute a correction factor to properly reinflate the variance after it is + naturally diminished via data-clipping. Employ a simple Monte Carlo method + and standard normal deviates to simulate the data-clipping and obtain the + correction factor. + """ + + n_sigma = self.n_sigma + + var_trials = [] + for x in range(0,10): + a = np.random.normal(0.0, 1.0, 1000000) + med = np.median(a, axis=0) + p16 = np.percentile(a, 16, axis=0) + p84 = np.percentile(a, 84, axis=0) + sigma = 0.5 * (p84 - p16) + mdmsg = med - n_sigma * sigma + b = np.less(a,mdmsg) + mdpsg = med + n_sigma * sigma + c = np.greater(a,mdpsg) + mask = np.any([b,c],axis=0) + mx = ma.masked_array(a, mask) + var = ma.getdata(mx.var(axis=0)) + var_trials.append(var) + + np_var_trials = np.array(var_trials) + avg_var_trials = np.mean(np_var_trials) + std_var_trials = np.std(np_var_trials) + corr_fact = 1.0 / avg_var_trials + + if self.logger: + self.logger.debug('{}.compute_clip_corr(): avg_var_trials,std_var_trials,corr_fact = {},{},{}'.\ + format(self.__class__.__name__,avg_var_trials,std_var_trials,corr_fact)) + else: + print('---->{}.compute_clip_corr(): avg_var_trials,std_var_trials,corr_fact = {},{},{}'.\ + format(self.__class__.__name__,avg_var_trials,std_var_trials,corr_fact)) + + return corr_fact + def compute(self): """ @@ -39,6 +80,8 @@ def compute(self): Return the data-clipped-mean image. """ + cf = self.compute_clip_corr() + a = self.frames_data n_sigma = self.n_sigma frames_data_shape = np.shape(a) @@ -61,7 +104,7 @@ def compute(self): mask = np.any([b,c],axis=0) mx = ma.masked_array(a, mask) avg = ma.getdata(mx.mean(axis=0)) - var = ma.getdata(mx.var(axis=0)) + var = ma.getdata(mx.var(axis=0)) * cf cnt = ma.getdata(ma.count(mx,axis=0)) unc = np.sqrt(var/cnt) From 2de90bb6c0645cbfe3eba358d3f03e3a2531d22a Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Wed, 28 Jun 2023 14:29:18 -0700 Subject: [PATCH 202/249] Issue manualcal and autocal #621. --- configs/kpf_masters_drp.cfg | 4 + modules/Utils/kpf_fits.py | 23 +++++- modules/Utils/pick_inputs_masters_drp.py | 14 ++-- .../master_bias/src/master_bias_framework.py | 79 ++++++++++++++---- .../master_dark/src/master_dark_framework.py | 71 +++++++++++++--- recipes/kpf_masters_drp.recipe | 82 ++++++++++++++----- 6 files changed, 215 insertions(+), 58 deletions(-) diff --git a/configs/kpf_masters_drp.cfg b/configs/kpf_masters_drp.cfg index 135587567..b5a77513f 100644 --- a/configs/kpf_masters_drp.cfg +++ b/configs/kpf_masters_drp.cfg @@ -38,12 +38,16 @@ output_dir = /data/2D [BIAS] n_sigma = 2.1 lev0_ffi_exts = ['GREEN_CCD','RED_CCD','CA_HK'] +# Leave empty list for bias_objects for recipe to automatically fill in. +bias_objects = [] masterbias_path = /testdata/kpf_master_bias.fits [DARK] exptime_minimum = 300.0 n_sigma = 2.2 lev0_ffi_exts = ['GREEN_CCD','RED_CCD','CA_HK'] +# Leave empty list for dark_objects for recipe to automatically fill in. +dark_objects = [] masterdark_path = /testdata/kpf_master_dark.fits [FLAT] diff --git a/modules/Utils/kpf_fits.py b/modules/Utils/kpf_fits.py index 9e326ba4e..2a7c3b068 100644 --- a/modules/Utils/kpf_fits.py +++ b/modules/Utils/kpf_fits.py @@ -24,8 +24,9 @@ class FitsHeaders: def __init__(self, search_path, header_keywords, header_values, logger=None): self.n_header_keywords = np.size(header_keywords) - if self.n_header_keywords == 1: + if not isinstance(header_keywords, list): header_keywords = [header_keywords] + if not isinstance(header_values, list): header_values = [header_values] self.header_keywords = header_keywords self.header_values = header_values @@ -215,8 +216,11 @@ def get_good_darks(self,exptime_minimum): matched_fits_files = self.match_headers_string_lower() filtered_matched_fits_files = [] + all_dark_objects = [] for fits_file in matched_fits_files: + hdul = fits.open(fits_file) + flag = 'remove' try: @@ -226,6 +230,9 @@ def get_good_darks(self,exptime_minimum): if (val4 >= exptime_minimum): flag = 'keep' filtered_matched_fits_files.append(fits_file) + obj = hdul[0].header['OBJECT'] + if obj not in all_dark_objects: + all_dark_objects.append(obj) if self.logger: self.logger.debug('flag,val4 = {},{}'.\ @@ -247,6 +254,8 @@ def get_good_darks(self,exptime_minimum): self.logger.debug('TypeError: {}; removing {} from list...'.format(err,fits_file)) else: print('---->TypeError: {}; removing {} from list...'.format(err,fits_file)) + + hdul.close() if self.logger: self.logger.debug('FitsHeaders.get_good_darks(): filtered_matched_fits_files = {}'.\ @@ -255,7 +264,7 @@ def get_good_darks(self,exptime_minimum): print('---->FitsHeaders.get_good_darks(): filtered_matched_fits_files = {}'.\ format(filtered_matched_fits_files)) - return filtered_matched_fits_files + return filtered_matched_fits_files,all_dark_objects def get_good_arclamps(self): @@ -323,8 +332,11 @@ def get_good_biases(self): matched_fits_files = self.match_headers_string_lower() filtered_matched_fits_files = [] + all_bias_objects = [] for fits_file in matched_fits_files: + hdul = fits.open(fits_file) + flag = 'remove' try: @@ -334,6 +346,9 @@ def get_good_biases(self): if (val4 <= exptime_maximum): flag = 'keep' filtered_matched_fits_files.append(fits_file) + obj = hdul[0].header['OBJECT'] + if obj not in all_bias_objects: + all_bias_objects.append(obj) if self.logger: self.logger.debug('flag,val4 = {},{}'.\ @@ -355,6 +370,8 @@ def get_good_biases(self): self.logger.debug('TypeError: {}; removing {} from list...'.format(err,fits_file)) else: print('---->TypeError: {}; removing {} from list...'.format(err,fits_file)) + + hdul.close() if self.logger: self.logger.debug('FitsHeaders.get_good_biases(): filtered_matched_fits_files = {}'.\ @@ -363,4 +380,4 @@ def get_good_biases(self): print('---->FitsHeaders.get_good_biases(): filtered_matched_fits_files = {}'.\ format(filtered_matched_fits_files)) - return filtered_matched_fits_files + return filtered_matched_fits_files,all_bias_objects diff --git a/modules/Utils/pick_inputs_masters_drp.py b/modules/Utils/pick_inputs_masters_drp.py index 57cf653d4..be6c25cb2 100644 --- a/modules/Utils/pick_inputs_masters_drp.py +++ b/modules/Utils/pick_inputs_masters_drp.py @@ -26,10 +26,8 @@ def __init__(self, action, context): self.all_fits_files_path = self.action.args[1] self.exptime_minimum = self.action.args[2] - self.exptime_keyword = 'EXPTIME' # Unlikely to be changed. - self.exptime_value_str = '0.0' - self.imtype_keywords = 'IMTYPE' # Unlikely to be changed. + self.bias_imtype_values_str = 'Bias' self.dark_imtype_values_str = 'Dark' self.arclamp_imtype_values_str = 'Arclamp' @@ -63,15 +61,15 @@ def _perform(self): """ - # Filter biasd files with EXPTIME= 0.0. + # Filter bias files with IMTYPE='Bias' and EXPTIME= 0.0. - fh = FitsHeaders(self.all_fits_files_path,self.exptime_keyword,self.exptime_value_str,self.logger) - all_bias_files = fh.match_headers_float_le() + fh = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.bias_imtype_values_str,self.logger) + all_bias_files,all_bias_objects = fh.get_good_biases() # Filter dark files with IMTYPE=‘Dark’ and the specified minimum exposure time. fh2 = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.dark_imtype_values_str,self.logger) - all_dark_files = fh2.get_good_darks(self.exptime_minimum) + all_dark_files,all_dark_objects = fh2.get_good_darks(self.exptime_minimum) # Filter flat files with IMTYPE=‘flatlamp’, but exclude those that either don't have # SCI-OBJ == CAL-OBJ and SKY-OBJ == CALOBJ or those with SCI-OBJ == "" or SCI-OBJ == "None". @@ -84,4 +82,4 @@ def _perform(self): fh4 = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.arclamp_imtype_values_str,self.logger) all_arclamp_files,all_arclamp_objects = fh4.get_good_arclamps() - return Arguments(all_bias_files,all_dark_files,all_flat_files,all_arclamp_files,all_arclamp_objects) + return Arguments(all_bias_files,all_dark_files,all_flat_files,all_arclamp_files,all_bias_objects,all_dark_objects,all_arclamp_objects) diff --git a/modules/master_bias/src/master_bias_framework.py b/modules/master_bias/src/master_bias_framework.py index 455ed6eff..84bba23cc 100644 --- a/modules/master_bias/src/master_bias_framework.py +++ b/modules/master_bias/src/master_bias_framework.py @@ -18,16 +18,18 @@ class MasterBiasFramework(KPF0_Primitive): """ Description: - This class works within the Keck pipeline framework to compute - the master bias by stacking input images for exposures with - EXPTIME <= 0.0 selected from the given path, which can include - many kinds of FITS files, not just biases. + This class works within the Keck pipeline framework to compute the master bias, + for a specific type of bias given by OBJECT in the primary FITS header, + by stacking input images for exposures with IMTYPE.lower() == 'bias' and + EXPTIME <= 0.0 and specified input bias_object. The input FITS files are selected + from the given path, which can include many kinds of FITS files, not just biases. Arguments: data_type (str): Type of data (e.g., KPF). n_sigma (float): Number of sigmas for data-clipping (e.g., 2.1). all_fits_files_path (str , which can include file glob): Location of inputs (e.g., /data/KP*.fits). lev0_ffi_exts (list of str): FITS extensions to stack (e.g., ['GREEN_CCD','RED_CCD']). + bias_object (str): Desired kind of bias (e.g., autocal-bias). masterbias_path (str): Pathname of output master bias (e.g., /testdata/kpf_green_red_bias.fits). Attributes: @@ -35,6 +37,7 @@ class MasterBiasFramework(KPF0_Primitive): n_sigma (float): Number of sigmas for data-clipping (e.g., 2.1). all_fits_files_path (str , which can include file glob): Location of inputs (e.g., /data/KP*.fits). lev0_ffi_exts (list of str): FITS extensions to stack (e.g., ['GREEN_CCD','RED_CCD']). + bias_object (str): Desired kind of bias (e.g., autocal-bias). masterbias_path (str): Pathname of output master bias (e.g., /testdata/kpf_green_red_bias.fits). imtype_keywords (str): FITS keyword for filtering input bias files (fixed as ['IMTYPE','OBJECT']). imtype_values_str (str): Values of FITS keyword (fixed as ['Bias','autocal-bias']). @@ -50,10 +53,11 @@ def __init__(self, action, context): self.n_sigma = self.action.args[1] self.all_fits_files_path = self.action.args[2] self.lev0_ffi_exts = self.action.args[3] - self.masterbias_path = self.action.args[4] + self.bias_object = self.action.args[4] + self.masterbias_path = self.action.args[5] - self.imtype_keywords = ['IMTYPE','OBJECT'] # Unlikely to be changed. - self.imtype_values_str = ['Bias','autocal-bias'] + self.imtype_keywords = 'IMTYPE' # Unlikely to be changed. + self.imtype_values_str = 'Bias' try: self.config_path = context.config_path['master_bias'] @@ -85,14 +89,21 @@ def _perform(self): master_bias_exit_code = 0 master_bias_infobits = 0 + # Filter bias files with IMTYPE=‘Bias’ and EXPTIME = 0.0 for now. Later in this class, exclude + # those FITS-image extensions that don't match the input object specification with OBJECT. + fh = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.imtype_values_str,self.logger) - all_bias_files = fh.get_good_biases() + all_bias_files,all_bias_objects = fh.get_good_biases() mjd_obs_list = [] + bias_object_list = [] for bias_file_path in (all_bias_files): bias_file = KPF0.from_fits(bias_file_path,self.data_type) mjd_obs = float(bias_file.header['PRIMARY']['MJD-OBS']) mjd_obs_list.append(mjd_obs) + header_object = bias_file.header['PRIMARY']['OBJECT'] + bias_object_list.append(header_object) + #self.logger.debug('bias_file_path,exp_time,header_object = {},{},{}'.format(bias_file_path,exp_time,header_object)) tester = KPF0.from_fits(all_bias_files[0]) del_ext_list = [] @@ -100,24 +111,49 @@ def _perform(self): if i != 'GREEN_CCD' and i != 'RED_CCD' and i != 'CA_HK' and i != 'PRIMARY' and i != 'RECEIPT' and i != 'CONFIG': del_ext_list.append(i) master_holder = tester + + mjd_obs_min = {} + mjd_obs_max = {} for ffi in self.lev0_ffi_exts: keep_ffi = 0 - frames_data=[] - for path in all_bias_files: + + frames_data = [] + frames_data_mjdobs = [] + frames_data_path = [] + n_all_bias_files = len(all_bias_files) + for i in range(0, n_all_bias_files): + + mjd_obs = mjd_obs_list[i] + header_object = bias_object_list[i] + + if header_object != self.bias_object: + #self.logger.debug('---->ffi,header_object,self.bias_object = {},{},{}'.format(ffi,header_object,self.bias_object)) + continue + + path = all_bias_files[i] obj = KPF0.from_fits(path) np_obj_ffi = np.array(obj[ffi]) np_obj_ffi_shape = np.shape(np_obj_ffi) n_dims = len(np_obj_ffi_shape) self.logger.debug('path,ffi,n_dims = {},{},{}'.format(path,ffi,n_dims)) if n_dims == 2: # Check if valid data extension - keep_ffi = 1 - frames_data.append(obj[ffi]) + keep_ffi = 1 + frames_data.append(obj[ffi]) + frames_data_mjdobs.append(mjd_obs) + frames_data_path.append(path) if keep_ffi == 0: self.logger.debug('ffi,keep_ffi = {},{}'.format(ffi,keep_ffi)) del_ext_list.append(ffi) break + mjd_obs_min[ffi] = min(frames_data_mjdobs) + mjd_obs_max[ffi] = max(frames_data_mjdobs) + + # + # Stack the frames. + # + frames_data = np.array(frames_data) fs = FrameStacker(frames_data,self.n_sigma) avg,var,cnt,unc = fs.compute() @@ -158,13 +194,28 @@ def _perform(self): master_holder.header['PRIMARY']['IMTYPE'] = ('Bias','Master bias') + # Remove confusing or non-relevant keywords, if existing. + + try: + del master_holder.header['GREEN_CCD']['OSCANV1'] + del master_holder.header['GREEN_CCD']['OSCANV2'] + del master_holder.header['GREEN_CCD']['OSCANV3'] + del master_holder.header['GREEN_CCD']['OSCANV4'] + del master_holder.header['RED_CCD']['OSCANV1'] + del master_holder.header['RED_CCD']['OSCANV2'] + del master_holder.header['RED_CCD']['OSCANV3'] + del master_holder.header['RED_CCD']['OSCANV4'] + + except KeyError as err: + pass + for ffi in self.lev0_ffi_exts: if ffi in del_ext_list: continue master_holder.header[ffi]['BUNIT'] = ('DN','Units of master bias') master_holder.header[ffi]['NFRAMES'] = (len(all_bias_files),'Number of frames in stack') master_holder.header[ffi]['NSIGMA'] = (self.n_sigma,'Number of sigmas for data-clipping') - master_holder.header[ffi]['MINMJD'] = (min(mjd_obs_list),'Minimum MJD of bias observations') - master_holder.header[ffi]['MAXMJD'] = (max(mjd_obs_list),'Maximum MJD of bias observations') + master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of dark observations') + master_holder.header[ffi]['MAXMJD'] = (mjd_obs_max[ffi],'Maximum MJD of dark observations') datetimenow = datetime.now(timezone.utc) createdutc = datetimenow.strftime("%Y-%m-%dT%H:%M:%SZ") master_holder.header[ffi]['CREATED'] = (createdutc,'UTC of master-bias creation') diff --git a/modules/master_dark/src/master_dark_framework.py b/modules/master_dark/src/master_dark_framework.py index 87a591497..70c3dbf9b 100644 --- a/modules/master_dark/src/master_dark_framework.py +++ b/modules/master_dark/src/master_dark_framework.py @@ -54,7 +54,8 @@ class MasterDarkFramework(KPF0_Primitive): """ Description: - This class works within the Keck pipeline framework to compute the master dark + This class works within the Keck pipeline framework to compute the master dark, + for a specific type of dark given by OBJECT in the primary FITS header, by stacking input images for exposures with IMTYPE.lower() == 'dark' and specified minimum EXPTIME, selected from the given path that can include many kinds of FITS files, not just darks. @@ -69,6 +70,7 @@ class MasterDarkFramework(KPF0_Primitive): n_sigma (float): Number of sigmas for data-clipping (e.g., 2.1). all_fits_files_path (str , which can include file glob): Location of inputs (e.g., /data/KP*.fits). lev0_ffi_exts (list of str): FITS extensions to stack (e.g., ['GREEN_CCD','RED_CCD']). + dark_object (str): Desired kind of bias (e.g., autocal-dark). masterbias_path (str): Pathname of input master bias (e.g., /testdata/kpf_master_bias.fits). masterdark_path (str): Pathname of output master dark (e.g., /testdata/kpf_master_dark.fits). @@ -77,6 +79,7 @@ class MasterDarkFramework(KPF0_Primitive): n_sigma (float): Number of sigmas for data-clipping (e.g., 2.1). all_fits_files_path (str , which can include file glob): Location of inputs (e.g., /data/KP*.fits). lev0_ffi_exts (list of str): FITS extensions to stack (e.g., ['GREEN_CCD','RED_CCD']). + dark_object (str): Desired kind of bias (e.g., autocal-dark). masterbias_path (str): Pathname of input master bias (e.g., /testdata/kpf_master_bias.fits). masterdark_path (str): Pathname of output master dark (e.g., /testdata/kpf_master_dark.fits). imtype_keywords (str): FITS keyword for filtering input dark files (fixed as 'IMTYPE'). @@ -95,8 +98,9 @@ def __init__(self, action, context): self.n_sigma = self.action.args[1] self.all_fits_files_path = self.action.args[2] self.lev0_ffi_exts = self.action.args[3] - self.masterbias_path = self.action.args[4] - self.masterdark_path = self.action.args[5] + self.dark_object = self.action.args[4] + self.masterbias_path = self.action.args[5] + self.masterdark_path = self.action.args[6] self.imtype_keywords = 'IMTYPE' # Unlikely to be changed. self.imtype_values_str = 'Dark' @@ -149,13 +153,15 @@ def _perform(self): master_dark_exit_code = 0 master_dark_infobits = 0 - # Filter dark files with IMTYPE=‘Dark’ and the specified exposure time. + # Filter dark files with IMTYPE=‘Dark’ and the specified minimum exposure time. Later in this class, exclude + # those FITS-image extensions that don't match the input object specification with OBJECT. fh = FitsHeaders(self.all_fits_files_path,self.imtype_keywords,self.imtype_values_str,self.logger) - all_dark_files = fh.get_good_darks(self.exptime_minimum) + all_dark_files,all_dark_objects = fh.get_good_darks(self.exptime_minimum) mjd_obs_list = [] exp_time_list = [] + dark_object_list = [] for dark_file_path in (all_dark_files): dark_file = KPF0.from_fits(dark_file_path,self.data_type) mjd_obs = float(dark_file.header['PRIMARY']['MJD-OBS']) @@ -163,6 +169,9 @@ def _perform(self): exp_time = float(dark_file.header['PRIMARY']['EXPTIME']) exp_time_list.append(exp_time) self.logger.debug('dark_file_path,exp_time = {},{}'.format(dark_file_path,exp_time)) + header_object = dark_file.header['PRIMARY']['OBJECT'] + dark_object_list.append(header_object) + #self.logger.debug('dark_file_path,exp_time,header_object = {},{},{}'.format(dark_file_path,exp_time,header_object)) tester = KPF0.from_fits(all_dark_files[0]) del_ext_list = [] @@ -171,21 +180,42 @@ def _perform(self): del_ext_list.append(i) master_holder = tester + mjd_obs_min = {} + mjd_obs_max = {} for ffi in self.lev0_ffi_exts: self.logger.debug('Loading dark data, ffi = {}'.format(ffi)) keep_ffi = 0 - frames_data=[] - for path in all_dark_files: + frames_data = [] + frames_data_exptimes = [] + frames_data_mjdobs = [] + frames_data_path = [] + n_all_dark_files = len(all_dark_files) + for i in range(0, n_all_dark_files): + + exp_time = exp_time_list[i] + mjd_obs = mjd_obs_list[i] + header_object = dark_object_list[i] + + #self.logger.debug('i,fitsfile,ffi,exp_time,dark_object_list[i] = {},{},{},{},{}'.format(i,all_dark_files[i],ffi,exp_time,dark_object_list[i])) + + if header_object != self.dark_object: + #self.logger.debug('---->ffi,header_object,self.dark_object = {},{},{}'.format(ffi,header_object,self.dark_object)) + continue + + path = all_dark_files[i] obj = KPF0.from_fits(path) np_obj_ffi = np.array(obj[ffi]) np_obj_ffi_shape = np.shape(np_obj_ffi) n_dims = len(np_obj_ffi_shape) self.logger.debug('path,ffi,n_dims = {},{},{}'.format(path,ffi,n_dims)) if n_dims == 2: # Check if valid data extension - keep_ffi = 1 - frames_data.append(obj[ffi]) + keep_ffi = 1 + frames_data.append(obj[ffi]) + frames_data_exptimes.append(exp_time) + frames_data_mjdobs.append(mjd_obs) + frames_data_path.append(path) if keep_ffi == 0: self.logger.debug('ffi,keep_ffi = {},{}'.format(ffi,keep_ffi)) @@ -198,13 +228,15 @@ def _perform(self): normalized_frames_data=[] n_frames = (np.shape(frames_data))[0] + mjd_obs_min[ffi] = min(frames_data_mjdobs) + mjd_obs_max[ffi] = max(frames_data_mjdobs) self.logger.debug('Number of frames in stack = {}'.format(n_frames)) for i in range(0, n_frames): single_frame_data = frames_data[i] # Separately normalize debiased images by EXPTIME. - exp_time = exp_time_list[i] + exp_time = frames_data_exptimes[i] self.logger.debug('Normalizing dark image: i,fitsfile,ffi,exp_time = {},{},{},{}'.format(i,all_dark_files[i],ffi,exp_time)) single_normalized_frame_data = single_frame_data / exp_time normalized_frames_data.append(single_normalized_frame_data) @@ -258,14 +290,29 @@ def _perform(self): master_holder.header['PRIMARY']['IMTYPE'] = ('Dark','Master dark') + # Remove confusing or non-relevant keywords, if existing. + + try: + del master_holder.header['GREEN_CCD']['OSCANV1'] + del master_holder.header['GREEN_CCD']['OSCANV2'] + del master_holder.header['GREEN_CCD']['OSCANV3'] + del master_holder.header['GREEN_CCD']['OSCANV4'] + del master_holder.header['RED_CCD']['OSCANV1'] + del master_holder.header['RED_CCD']['OSCANV2'] + del master_holder.header['RED_CCD']['OSCANV3'] + del master_holder.header['RED_CCD']['OSCANV4'] + + except KeyError as err: + pass + for ffi in self.lev0_ffi_exts: if ffi in del_ext_list: continue master_holder.header[ffi]['BUNIT'] = ('DN/sec','Units of master dark') master_holder.header[ffi]['NFRAMES'] = (len(all_dark_files),'Number of frames in input stack') master_holder.header[ffi]['MINEXPTM'] = (self.exptime_minimum,'Minimum exposure time of input darks (seconds)') master_holder.header[ffi]['NSIGMA'] = (self.n_sigma,'Number of sigmas for data-clipping') - master_holder.header[ffi]['MINMJD'] = (min(mjd_obs_list),'Minimum MJD of dark observations') - master_holder.header[ffi]['MAXMJD'] = (max(mjd_obs_list),'Maximum MJD of dark observations') + master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of dark observations') + master_holder.header[ffi]['MAXMJD'] = (mjd_obs_max[ffi],'Maximum MJD of dark observations') datetimenow = datetime.now(timezone.utc) createdutc = datetimenow.strftime("%Y-%m-%dT%H:%M:%SZ") master_holder.header[ffi]['CREATED'] = (createdutc,'UTC of master-dark creation') diff --git a/recipes/kpf_masters_drp.recipe b/recipes/kpf_masters_drp.recipe index 20848db2e..32821745d 100644 --- a/recipes/kpf_masters_drp.recipe +++ b/recipes/kpf_masters_drp.recipe @@ -23,7 +23,13 @@ else: exptime_minimum = config.DARK.exptime_minimum -all_bias_files_list, all_dark_files_list, all_flat_files_list, all_arclamp_files_list, arclamp_objects_auto = PickInputsMastersDRP(data_type,search_string,exptime_minimum) +all_bias_files_list,\ + all_dark_files_list,\ + all_flat_files_list,\ + all_arclamp_files_list,\ + bias_objects_auto,\ + dark_objects_auto,\ + arclamp_objects_auto = PickInputsMastersDRP(data_type,search_string,exptime_minimum) channel_datasec_ncols_green = config.OVERSCAN_SUBTRACTION.channel_datasec_ncols_green channel_datasec_nrows_green = config.OVERSCAN_SUBTRACTION.channel_datasec_nrows_green @@ -104,19 +110,35 @@ for raw_file_path in file_list: if all_bias_files_list: bias_n_sigma = config.BIAS.n_sigma bias_lev0_ffi_exts = config.BIAS.lev0_ffi_exts + + + bias_objects_config = config.BIAS.bias_objects + + if len(bias_objects_config) == 0: + bias_objects = bias_objects_auto + else: + bias_objects = bias_objects_config + masterbias_path = config.BIAS.masterbias_path bias_input_files = overscan_output_dir + "/" + "*_2D.fits" - if date_dir: - new_masterbias_path = str_replace(masterbias_path, '_master_bias.fits', '_' + date_dir + '_master_bias.fits') - else: - new_masterbias_path = masterbias_path + for bias_object in bias_objects: - exit_list_bias = MasterBiasFramework(data_type, - bias_n_sigma, - bias_input_files, - bias_lev0_ffi_exts, - new_masterbias_path) + if date_dir: + new_masterbias_path = str_replace(masterbias_path,\ + '_master_bias.fits',\ + '_' + date_dir + '_master_bias_' + bias_object + '.fits') + else: + new_masterbias_path = str_replace(masterbias_path,\ + '_master_bias.fits',\ + '_master_bias_' + bias_object + '.fits') + + exit_list_bias = MasterBiasFramework(data_type, + bias_n_sigma, + bias_input_files, + bias_lev0_ffi_exts, + bias_object, + new_masterbias_path) @@ -127,20 +149,38 @@ if all_bias_files_list: if all_dark_files_list: dark_n_sigma = config.DARK.n_sigma dark_lev0_ffi_exts = config.DARK.lev0_ffi_exts + + + + + dark_objects_config = config.DARK.dark_objects + + if len(dark_objects_config) == 0: + dark_objects = dark_objects_auto + else: + dark_objects = dark_objects_config + masterdark_path = config.DARK.masterdark_path dark_input_files = overscan_output_dir + "/" + "*_2D.fits" - if date_dir: - new_masterdark_path = str_replace(masterdark_path, '_master_dark.fits', '_' + date_dir + '_master_dark.fits') - else: - new_masterdark_path = masterdark_path - - exit_list_dark = MasterDarkFramework(data_type, - dark_n_sigma, - dark_input_files, - dark_lev0_ffi_exts, - new_masterbias_path, - new_masterdark_path) + for dark_object in dark_objects: + + if date_dir: + new_masterdark_path = str_replace(masterdark_path,\ + '_master_dark.fits',\ + '_' + date_dir + '_master_dark_' + dark_object + '.fits') + else: + new_masterdark_path = str_replace(masterdark_path,\ + '_master_dark.fits',\ + '_master_dark_' + dark_object + '.fits') + + exit_list_dark = MasterDarkFramework(data_type, + dark_n_sigma, + dark_input_files, + dark_lev0_ffi_exts, + dark_object, + new_masterbias_path, + new_masterdark_path) ######################## From 5418cc069b2fd7f1b24715ce934185edf4eb311f Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 29 Jun 2023 05:28:31 -0700 Subject: [PATCH 203/249] Fixed typos. --- modules/master_bias/src/master_bias_framework.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/master_bias/src/master_bias_framework.py b/modules/master_bias/src/master_bias_framework.py index 84bba23cc..c9043c7f1 100644 --- a/modules/master_bias/src/master_bias_framework.py +++ b/modules/master_bias/src/master_bias_framework.py @@ -214,8 +214,8 @@ def _perform(self): master_holder.header[ffi]['BUNIT'] = ('DN','Units of master bias') master_holder.header[ffi]['NFRAMES'] = (len(all_bias_files),'Number of frames in stack') master_holder.header[ffi]['NSIGMA'] = (self.n_sigma,'Number of sigmas for data-clipping') - master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of dark observations') - master_holder.header[ffi]['MAXMJD'] = (mjd_obs_max[ffi],'Maximum MJD of dark observations') + master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of bias observations') + master_holder.header[ffi]['MAXMJD'] = (mjd_obs_max[ffi],'Maximum MJD of bias observations') datetimenow = datetime.now(timezone.utc) createdutc = datetimenow.strftime("%Y-%m-%dT%H:%M:%SZ") master_holder.header[ffi]['CREATED'] = (createdutc,'UTC of master-bias creation') From f30fd9b3cfb7b692ef63512fe065470b20299d78 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 29 Jun 2023 07:40:14 -0700 Subject: [PATCH 204/249] Properly accounted for n_frames. Do not stack if n_frames less than 2. --- modules/master_bias/src/master_bias_framework.py | 15 ++++++++++++++- modules/master_dark/src/master_dark_framework.py | 16 ++++++++++++++-- 2 files changed, 28 insertions(+), 3 deletions(-) diff --git a/modules/master_bias/src/master_bias_framework.py b/modules/master_bias/src/master_bias_framework.py index c9043c7f1..e554c6819 100644 --- a/modules/master_bias/src/master_bias_framework.py +++ b/modules/master_bias/src/master_bias_framework.py @@ -112,6 +112,7 @@ def _perform(self): del_ext_list.append(i) master_holder = tester + n_frames_kept = {} mjd_obs_min = {} mjd_obs_max = {} for ffi in self.lev0_ffi_exts: @@ -147,6 +148,18 @@ def _perform(self): del_ext_list.append(ffi) break + n_frames = (np.shape(frames_data))[0] + self.logger.debug('Number of frames in stack = {}'.format(n_frames)) + + # Exit without making product if headers of FITS files in input list do not contain specified OBJECT, + # or the number of frames to stack is less than 2. In either case, exit_code=7 is returned. + + if n_frames < 2: + master_bias_exit_code = 7 + exit_list = [master_bias_exit_code,master_bias_infobits] + return Arguments(exit_list) + + n_frames_kept[ffi] = n_frames mjd_obs_min[ffi] = min(frames_data_mjdobs) mjd_obs_max[ffi] = max(frames_data_mjdobs) @@ -212,7 +225,7 @@ def _perform(self): for ffi in self.lev0_ffi_exts: if ffi in del_ext_list: continue master_holder.header[ffi]['BUNIT'] = ('DN','Units of master bias') - master_holder.header[ffi]['NFRAMES'] = (len(all_bias_files),'Number of frames in stack') + master_holder.header[ffi]['NFRAMES'] = (n_frames_kept[ffi],'Number of frames in input stack') master_holder.header[ffi]['NSIGMA'] = (self.n_sigma,'Number of sigmas for data-clipping') master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of bias observations') master_holder.header[ffi]['MAXMJD'] = (mjd_obs_max[ffi],'Maximum MJD of bias observations') diff --git a/modules/master_dark/src/master_dark_framework.py b/modules/master_dark/src/master_dark_framework.py index 70c3dbf9b..1286cb5bf 100644 --- a/modules/master_dark/src/master_dark_framework.py +++ b/modules/master_dark/src/master_dark_framework.py @@ -180,6 +180,7 @@ def _perform(self): del_ext_list.append(i) master_holder = tester + n_frames_kept = {} mjd_obs_min = {} mjd_obs_max = {} for ffi in self.lev0_ffi_exts: @@ -228,9 +229,20 @@ def _perform(self): normalized_frames_data=[] n_frames = (np.shape(frames_data))[0] + self.logger.debug('Number of frames in stack = {}'.format(n_frames)) + + # Exit without making product if headers of FITS files in input list do not contain specified OBJECT, + # or the number of frames to stack is less than 2. In either case, exit_code=7 is returned. + + if n_frames < 2: + master_dark_exit_code = 7 + exit_list = [master_dark_exit_code,master_dark_infobits] + return Arguments(exit_list) + + n_frames_kept[ffi] = n_frames mjd_obs_min[ffi] = min(frames_data_mjdobs) mjd_obs_max[ffi] = max(frames_data_mjdobs) - self.logger.debug('Number of frames in stack = {}'.format(n_frames)) + for i in range(0, n_frames): single_frame_data = frames_data[i] @@ -308,7 +320,7 @@ def _perform(self): for ffi in self.lev0_ffi_exts: if ffi in del_ext_list: continue master_holder.header[ffi]['BUNIT'] = ('DN/sec','Units of master dark') - master_holder.header[ffi]['NFRAMES'] = (len(all_dark_files),'Number of frames in input stack') + master_holder.header[ffi]['NFRAMES'] = (n_frames_kept[ffi],'Number of frames in input stack') master_holder.header[ffi]['MINEXPTM'] = (self.exptime_minimum,'Minimum exposure time of input darks (seconds)') master_holder.header[ffi]['NSIGMA'] = (self.n_sigma,'Number of sigmas for data-clipping') master_holder.header[ffi]['MINMJD'] = (mjd_obs_min[ffi],'Minimum MJD of dark observations') From 8ec1d1dd8aa562cdedf9bfca9f0c2aa4f456bc20 Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Thu, 29 Jun 2023 07:54:11 -0700 Subject: [PATCH 205/249] Made changes to handle varying OBJECT values for master bias and dark. --- database/scripts/registerCalFilesForDate.py | 33 +++++++++------------ 1 file changed, 14 insertions(+), 19 deletions(-) diff --git a/database/scripts/registerCalFilesForDate.py b/database/scripts/registerCalFilesForDate.py index 703972707..39e8766bb 100644 --- a/database/scripts/registerCalFilesForDate.py +++ b/database/scripts/registerCalFilesForDate.py @@ -119,7 +119,7 @@ def md5(fname): try: filename_caltype = filename_match.group(1) - #print("-----1-----> fn_caltype =",filename_caltype) + print("-----1-----> fn_caltype =",filename_caltype) filename_match = re.match(r"(.+?)_(.+)", filename_caltype) @@ -127,11 +127,11 @@ def md5(fname): filename_caltype = filename_match.group(1) filename_object = filename_match.group(2) - #print("-------2--------> fn_caltype =",filename_caltype) - #print("-------2--------> fn_object =",filename_object) + print("-------2--------> fn_caltype =",filename_caltype) + print("-------2--------> fn_object =",filename_object) except: - #print("-------2--------> No filename match found") + print("-------2--------> No filename match found") if filename_caltype == 'bias': filename_object = "autocal-bias" @@ -141,27 +141,22 @@ def md5(fname): filename_object = "autocal-flat-all" except: - #print("-----1-----> No filename match found") + print("-----1-----> No filename match found") pass try: - if re.match(r".+_master_bias", fits_file): - filename_caltype = 'bias' - elif re.match(r".+_master_dark", fits_file): - filename_caltype = 'dark' - elif re.match(r".+_master_flat", fits_file): + if re.match(r".+_master_flat", fits_file): filename_caltype = 'flat' - except: - pass - - try: - if filename_caltype == 'bias': - filename_object = "autocal-bias" - elif filename_caltype == 'dark': - filename_object = "autocal-dark" - elif filename_caltype == 'flat': filename_object = "autocal-flat-all" + else: + filename_match = re.match(r".+_master_(.+)_(.+)\.fits", fits_file) + filename_caltype = filename_match.group(1) + filename_object = filename_match.group(2) + + print("-------3--------> fn_caltype =",filename_caltype) + print("-------3--------> fn_object =",filename_object) + except: pass From 42b64e08f30d36e313cc3356fc11c508a10a3789 Mon Sep 17 00:00:00 2001 From: BJ Fulton Date: Thu, 29 Jun 2023 10:02:59 -0700 Subject: [PATCH 206/249] order trace fixes --- configs/kpf_drp.cfg | 3 ++- configs/kpf_masters_l1.cfg | 5 ++++- recipes/kpf_drp.recipe | 31 +++++++++++++++++++++---------- 3 files changed, 27 insertions(+), 12 deletions(-) diff --git a/configs/kpf_drp.cfg b/configs/kpf_drp.cfg index a08eae574..282b066bc 100644 --- a/configs/kpf_drp.cfg +++ b/configs/kpf_drp.cfg @@ -42,7 +42,8 @@ ccd_list = ['GREEN_CCD', 'RED_CCD'] # flat_file: flat file for order trace process. updated upon new flat file. # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe #flat_file = KP.20221107.04689.77 -flat_file = /data/masters/20230411/kpf_20230411_master_flat.fits +flat_file = *_master_flat.fits +order_trace_flat = /data/masters/20230411/kpf_20230411_master_flat.fits order_trace_files = [ '/data/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv', '/data/masters/20230411/kpf_20230411_master_flat_RED_CCD.csv'] fitting_poly_degree = 3 ccd_idx = [0, 1] diff --git a/configs/kpf_masters_l1.cfg b/configs/kpf_masters_l1.cfg index e51964889..b0e524cf9 100644 --- a/configs/kpf_masters_l1.cfg +++ b/configs/kpf_masters_l1.cfg @@ -55,7 +55,10 @@ ccd_list = ['GREEN_CCD', 'RED_CCD'] # flat_file: path or pattern to a flat file to use to find order locations. If specifying pattern with wildcard use relative paths # ccd_idx: index in ccd_list for the ccd to be processed in DRP recipe flat_file = *_master_flat.fits -order_trace_files = ['', ''] +# order_trace_files = ['', ''] +# flat_file = /data/masters/20230411/kpf_20230411_master_flat.fits +order_trace_flat = /data/masters/20230411/kpf_20230411_master_flat.fits +order_trace_files = [ '/data/masters/20230411/kpf_20230411_master_flat_GREEN_CCD.csv', '/data/masters/20230411/kpf_20230411_master_flat_RED_CCD.csv'] fitting_poly_degree = 3 ccd_idx = [0, 1] diff --git a/recipes/kpf_drp.recipe b/recipes/kpf_drp.recipe index 0eb46741c..9aa5807a1 100644 --- a/recipes/kpf_drp.recipe +++ b/recipes/kpf_drp.recipe @@ -5,7 +5,7 @@ from modules.spectral_extraction.src.spectral_extraction import SpectralExtracti from modules.radial_velocity.src.radial_velocity_init import RadialVelocityInit from modules.radial_velocity.src.radial_velocity import RadialVelocity from modules.radial_velocity.src.radial_velocity_reweighting_ref import RadialVelocityReweightingRef -from modules.src.radial_velocity_reweighting import RadialVelocityReweighting +from modules.radial_velocoty.src.radial_velocity_reweighting import RadialVelocityReweighting from modules.ca_hk.src.ca_hk_extraction import CaHKExtraction from modules.quicklook.src.quick_prim import Quicklook from modules.Utils.string_proc import str_replace @@ -128,8 +128,8 @@ date_dir = sel_obsid + '/' output_dir = config.ARGUMENT.output_dir input_2d_dir = config.ARGUMENT.input_dir_root + date_dir -# input_2d_dir_flat = config.ARGUMENT.masters_dir + date_dir_flat -masters_data_dir = output_dir + "masters/" +masters_data_dir = config.ARGUMENT.masters_dir +#masters_data_dir = output_dir + "masters/" ## general purpose variables ccd_list = config.ARGUMENT.ccd_list @@ -209,6 +209,7 @@ origin = [data_col_range[0], data_row_range[0]] lev0_flat_pattern = config.ARGUMENT.flat_file # output_order_trace = output_dir + config.ARGUMENT.output_trace + date_dir_flat # use the full path of ourder trace files defind in config +order_trace_flat = config.ARGUMENT.order_trace_flat output_order_trace = config.ARGUMENT.order_trace_files ## for spectral extraction: input and output @@ -326,6 +327,7 @@ b_all_traces = False lev0_flat_rect = None if do_order_trace: + trace_list = [] flat_data = None if exists(input_flat_file): @@ -338,7 +340,8 @@ if do_order_trace: ccd = ccd_list[idx] # get order trace file name defined in config file # output_lev0_trace_csv = output_order_trace + flat_stem + '_' + ccd + csv_ext - output_lev0_trace_csv = output_order_trace[idx] + # output_lev0_trace_csv = output_order_trace[idx] + output_lev0_trace_csv = str_replace(input_flat_file, fits_ext, '_' + ccd + csv_ext); trace_list = trace_list + [output_lev0_trace_csv] if not exists(output_lev0_trace_csv): order_result_data = OrderTrace(flat_data, data_extension=ccd + '_STACK', @@ -348,7 +351,6 @@ if do_order_trace: b_all_traces = b_all_traces and exists(output_lev0_trace_csv) - # output_lev0_flat_rect = output_order_trace + flat_stem + flat_rect + fits_ext output_lev0_flat_rect = str_replace(input_flat_file, fits_ext, flat_rect+fits_ext) lev0_flat_rect = flat_data @@ -380,7 +382,7 @@ if do_order_trace: if b_lev0_rect: result = to_fits(lev0_flat_rect, output_lev0_flat_rect) -## do spectral extraction including CA-HK extraction +## do spectral extraction including background subtracton and build BARY_CORR and do CA-HK extraction after spectral extraction. # description: do spectrum extraction based on extraction method (summ or optimal) per fiber per ccd and # spectral extraction from CA-HK spectrometer fibers. # copy wavelength solution data from wls sample files to produced L1 data. @@ -391,6 +393,8 @@ if do_order_trace: # output_lev1_file: path of produced L1 file, /data/L1//_L1.fits # lev1_list: a list containing the path of L1 files produced by either spectral extraction or ca_hk extraction. # +# for background extraction: +# order_mask: KPF0 instance, same size as input L0 data, the image distinguishes the orderlet pixels from the non-orderlet pixels. # for spectral extraction: # output_lev0_flat_rect: fits file with rectified L0 flat data # lev0_flat_rect: KPF0 instance for 'output_lev0_flat_rect' @@ -398,13 +402,15 @@ if do_order_trace: # trace_list: list containing order trace result file for each ccd. # [/data/order_trace//_GREEN_CCD.csv (or _RED_CCD.csv)] # b_level1_sp: a boolean to indicate if spectral extraction for each L0 is successful. -# op_data: KPF1 instance containing spectral extraction result which is added into op_data extension by extension +# op_data: KPF1 instance containing spectral extractis_bary_idxon result which is added into op_data extension by extension # per specified extension list (order_names) in each ccd iteration. # wavecal_data: KPF1 instance per wls sample file. # for ca_hk: # hk_dark_data: KPF0 for dark data # hk_bias_data: KPF0 for bias data # primitives: +# OrderMask: os_bary_idxrder mask +# ImageProcessing: perform background subtraction # SpectralExtraction: spectral extraction # ExtCopy: extension copy from the extension of one data model instance to the extension of another # (or the same) data model instance. @@ -415,12 +421,13 @@ lev1_list = [] if do_spectral_extraction or do_hk or do_bc: lev0_flat_rect = None + trace_list = [] # prepare trace files and rectified L0 flat data for spectral extraction if do_spectral_extraction: # existence of flat and trace files # output_lev0_flat_rect = output_order_trace + flat_stem + flat_rect + fits_ext - output_lev0_flat_rect = str_replace(input_flat_file, fits_ext, flat_rect+fits_ext) + output_lev0_flat_rect = str_replace(order_trace_flat, fits_ext, flat_rect+fits_ext) if exists(output_lev0_flat_rect): lev0_flat_rect = kpf0_from_fits(output_lev0_flat_rect, data_type=data_type) @@ -447,7 +454,8 @@ if do_spectral_extraction or do_hk or do_bc: hk_bias_data = None # loop through ccd and L0 to do spectral extraction - for input_lev0_file in find_files(lev0_science_pattern): + lev0_files = find_files(lev0_science_pattern) + for input_lev0_file in lev0_files: # lev0_data is KPF0 instance for input_lev0_file lev0_data = kpf0_from_fits(input_lev0_file, data_type=data_type) _, short_lev0_file = split(input_lev0_file) @@ -573,7 +581,10 @@ if do_spectral_extraction or do_hk or do_bc: # # primitives: # RadialVelocityInit: do rv init on each L1 input. -# RadialVelocity: do radial velocity per ccd per L1 data. +# RadialVelocity: do radial velocity per ccd per L1 data, CCF reweighting is optional based on if a ratio table is provided and +# if producing L2 with reweighted CCF without keeping the unweighted CCF. +# RadialVelocityReweightingRef: build or load CCF reweighting ratio table. +# RadialVelocityReweightin: perform CCF reweighitng # if do_rv or do_rv_reweighting: rv_star_dir = masters_data_dir From e73c30bd62bc9bc641cef615a46a3afbd5ff913c Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 14:52:39 -0700 Subject: [PATCH 207/249] analyze l1 --- modules/Utils/L1_SNR_prim.py | 6 +- modules/Utils/analyze_l1.py | 104 +++++++++++++++++------------------ 2 files changed, 55 insertions(+), 55 deletions(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index 6d316e6d7..eb11cad95 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -53,10 +53,10 @@ def __init__(self, action:Action, context:ProcessingContext) -> None: #Perform def _perform(self) -> None: - L1_file = fits.open(self.data_dir+self.l1_obj.filename) + #L1_file = fits.open(self.data_dir+self.l1_obj.filename) - L1_SNR = AnalyzeL1(L1_file) - L1_SNR.measure_L1_snr(L1_file,snr_percentile=95) + L1_SNR = AnalyzeL1(self.l1_obj) #L1_file + L1_SNR.measure_L1_snr(self.l1_obj,snr_percentile=95)#L1_file #print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) self.l1_obj.header['PRIMARY']['SNRSC452'] = L1_SNR.GREEN_SNR[1,-1] diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index ecbfdd4de..bff625065 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -66,24 +66,24 @@ def measure_L1_snr(self,L1, snr_percentile=95): self.snr_percentile = snr_percentile # Determine the number of orders - norders_green = (L1['GREEN_SKY_WAVE'].data).shape[0] - norders_red = (L1['RED_SKY_WAVE'].data).shape[0] + norders_green = (L1.data['GREEN_SKY_WAVE']).shape[0] + norders_red = (L1.data['RED_SKY_WAVE']).shape[0] orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} norderlets = len(orderlets) # Define SNR arrays (needed for operations below where VAR = 0) - GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'].data - GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'].data - GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'].data - GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'].data - GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'].data - GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'].data - RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'].data - RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'].data - RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'].data - RED_CAL_SNR = 0 * L1['RED_CAL_VAR'].data - RED_SKY_SNR = 0 * L1['RED_SKY_VAR'].data - RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'].data + GREEN_SCI_SNR1 = 0 * L1.data['GREEN_SCI_VAR1'] + GREEN_SCI_SNR2 = 0 * L1.data['GREEN_SCI_VAR2'] + GREEN_SCI_SNR3 = 0 * L1.data['GREEN_SCI_VAR3'] + GREEN_CAL_SNR = 0 * L1.data['GREEN_CAL_VAR'] + GREEN_SKY_SNR = 0 * L1.data['GREEN_SKY_VAR'] + GREEN_SCI_SNR = 0 * L1.data['GREEN_SCI_VAR1'] + RED_SCI_SNR1 = 0 * L1.data['RED_SCI_VAR1'] + RED_SCI_SNR2 = 0 * L1.data['RED_SCI_VAR2'] + RED_SCI_SNR3 = 0 * L1.data['RED_SCI_VAR3'] + RED_CAL_SNR = 0 * L1.data['RED_CAL_VAR'] + RED_SKY_SNR = 0 * L1.data['RED_SKY_VAR'] + RED_SCI_SNR = 0 * L1.data['RED_SCI_VAR1'] # Create Arrays GREEN_SNR = np.zeros((norders_green, norderlets+1)) @@ -92,46 +92,46 @@ def measure_L1_snr(self,L1, snr_percentile=95): RED_SNR_WAV = np.zeros(norders_red) # Compute SNR arrays for each of the orders, orderlets, and CCDs. - GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)), - where=(L1['GREEN_SCI_VAR1'].data!=0)) - GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR2'].data)), - where=(L1['GREEN_SCI_VAR2'].data!=0)) - GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR3'].data)), - where=(L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1'].data+L1['GREEN_SCI_FLUX3'].data+L1['GREEN_SCI_FLUX3'].data, - np.sqrt(abs(L1['GREEN_SCI_VAR1'].data)+abs(L1['GREEN_SCI_VAR2'].data)+abs(L1['GREEN_SCI_VAR3'].data)), - where=(L1['GREEN_SCI_VAR1'].data+L1['GREEN_SCI_VAR2'].data+L1['GREEN_SCI_VAR3'].data!=0)) - GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'].data, - np.sqrt(abs(L1['GREEN_CAL_VAR'].data)), - where=(L1['GREEN_CAL_VAR'].data!=0)) - GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'].data, - np.sqrt(abs(L1['GREEN_SKY_VAR'].data)), - where=(L1['GREEN_SKY_VAR'].data!=0)) - RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)), - where=(L1['RED_SCI_VAR1'].data!=0)) - RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'].data, - np.sqrt(abs(L1['RED_SCI_VAR2'].data)), - where=(L1['RED_SCI_VAR2'].data!=0)) - RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR3'].data)), - where=(L1['RED_SCI_VAR3'].data!=0)) - RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1'].data+L1['RED_SCI_FLUX3'].data+L1['RED_SCI_FLUX3'].data, - np.sqrt(abs(L1['RED_SCI_VAR1'].data)+abs(L1['RED_SCI_VAR2'].data)+abs(L1['RED_SCI_VAR3'].data)), - where=(L1['RED_SCI_VAR1'].data+L1['RED_SCI_VAR2'].data+L1['RED_SCI_VAR3'].data!=0)) - RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'].data, - np.sqrt(abs(L1['RED_CAL_VAR'].data)), - where=(L1['RED_CAL_VAR'].data!=0)) - RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'].data, - np.sqrt(abs(L1['RED_SKY_VAR'].data)), - where=(L1['RED_SKY_VAR'].data!=0)) + GREEN_SCI_SNR1 = np.divide(L1.data['GREEN_SCI_FLUX1'], + np.sqrt(abs(L1.data['GREEN_SCI_VAR1'])), + where=(L1.data['GREEN_SCI_VAR1']!=0)) + GREEN_SCI_SNR2 = np.divide(L1.data['GREEN_SCI_FLUX2'], + np.sqrt(abs(L1.data['GREEN_SCI_VAR2'])), + where=(L1.data['GREEN_SCI_VAR2']!=0)) + GREEN_SCI_SNR3 = np.divide(L1.data['GREEN_SCI_FLUX3'], + np.sqrt(abs(L1.data['GREEN_SCI_VAR3'])), + where=(L1.data['GREEN_SCI_VAR3']!=0)) + GREEN_SCI_SNR = np.divide(L1.data['GREEN_SCI_FLUX1']+L1.data['GREEN_SCI_FLUX3']+L1.data['GREEN_SCI_FLUX3'], + np.sqrt(abs(L1.data['GREEN_SCI_VAR1'])+abs(L1.data['GREEN_SCI_VAR2'])+abs(L1.data['GREEN_SCI_VAR3'])), + where=(L1.data['GREEN_SCI_VAR1']+L1.data['GREEN_SCI_VAR2']+L1.data['GREEN_SCI_VAR3']!=0)) + GREEN_CAL_SNR = np.divide(L1.data['GREEN_CAL_FLUX'], + np.sqrt(abs(L1.data['GREEN_CAL_VAR'])), + where=(L1.data['GREEN_CAL_VAR']!=0)) + GREEN_SKY_SNR = np.divide(L1.data['GREEN_SKY_FLUX'], + np.sqrt(abs(L1.data['GREEN_SKY_VAR'])), + where=(L1.data['GREEN_SKY_VAR']!=0)) + RED_SCI_SNR1 = np.divide(L1.data['RED_SCI_FLUX1'], + np.sqrt(abs(L1.data['RED_SCI_VAR1'])), + where=(L1.data['RED_SCI_VAR1']!=0)) + RED_SCI_SNR2 = np.divide(L1.data['RED_SCI_FLUX2'], + np.sqrt(abs(L1.data['RED_SCI_VAR2'])), + where=(L1.data['RED_SCI_VAR2']!=0)) + RED_SCI_SNR3 = np.divide(L1.data['RED_SCI_FLUX3'], + np.sqrt(abs(L1.data['RED_SCI_VAR3'])), + where=(L1.data['RED_SCI_VAR3']!=0)) + RED_SCI_SNR = np.divide(L1.data['RED_SCI_FLUX1']+L1.data['RED_SCI_FLUX3']+L1.data['RED_SCI_FLUX3'], + np.sqrt(abs(L1.data['RED_SCI_VAR1'])+abs(L1.data['RED_SCI_VAR2'])+abs(L1.data['RED_SCI_VAR3'])), + where=(L1.data['RED_SCI_VAR1']+L1.data['RED_SCI_VAR2']+L1.data['RED_SCI_VAR3']!=0)) + RED_CAL_SNR = np.divide(L1.data['RED_CAL_FLUX'], + np.sqrt(abs(L1.data['RED_CAL_VAR'])), + where=(L1.data['RED_CAL_VAR']!=0)) + RED_SKY_SNR = np.divide(L1.data['RED_SKY_FLUX'], + np.sqrt(abs(L1.data['RED_SKY_VAR'])), + where=(L1.data['RED_SKY_VAR']!=0)) # Compute SNR per order and per orderlet for o in range(norders_green): - GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'].data[o,2040] + GREEN_SNR_WAV[o] = L1.data['GREEN_SCI_WAVE1'][o,2040] GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], snr_percentile) GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], snr_percentile) GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], snr_percentile) @@ -139,7 +139,7 @@ def measure_L1_snr(self,L1, snr_percentile=95): GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], snr_percentile) GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], snr_percentile) for o in range(norders_red): - RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'].data[o,2040] + RED_SNR_WAV[o] = L1.data['RED_SCI_WAVE1'][o,2040] RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], snr_percentile) RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], snr_percentile) RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], snr_percentile) From 08251b16a1bf28ad69998594ee6a63cf38d3fa83 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 14:56:07 -0700 Subject: [PATCH 208/249] analyze l1 --- modules/Utils/L1_SNR_prim.py | 2 +- modules/Utils/analyze_l1.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index eb11cad95..ec4f3549e 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -56,7 +56,7 @@ def _perform(self) -> None: #L1_file = fits.open(self.data_dir+self.l1_obj.filename) L1_SNR = AnalyzeL1(self.l1_obj) #L1_file - L1_SNR.measure_L1_snr(self.l1_obj,snr_percentile=95)#L1_file + L1_SNR.measure_L1_snr(snr_percentile=95)# self.l1_obj,L1_file #print(L1_SNR.GREEN_SNR) #(orders number, orderlet number) self.l1_obj.header['PRIMARY']['SNRSC452'] = L1_SNR.GREEN_SNR[1,-1] diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index bff625065..a7bd08136 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -35,7 +35,7 @@ def __init__(self, L1, logger=None): self.logger = None print('---->AnalyzeL0 class constructor') - def measure_L1_snr(self,L1, snr_percentile=95): + def measure_L1_snr(self, snr_percentile=95): """ Compute the signal-to-noise ratio (SNR) for each spectral order and @@ -153,7 +153,7 @@ def measure_L1_snr(self,L1, snr_percentile=95): self.GREEN_SNR_WAV = GREEN_SNR_WAV self.RED_SNR_WAV = RED_SNR_WAV - def plot_L1_snr(self,L1,ObsID, fig_path=None, show_plot=False): + def plot_L1_snr(self,ObsID, fig_path=None, show_plot=False): """ From cc11b3b51d78811c8a431e776e91e994584491c6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 14:57:44 -0700 Subject: [PATCH 209/249] analyze l1 --- modules/Utils/analyze_l1.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index a7bd08136..f0ac29746 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -62,7 +62,7 @@ def measure_L1_snr(self, snr_percentile=95): Returns: None """ - + L1= self.L1 self.snr_percentile = snr_percentile # Determine the number of orders @@ -154,7 +154,7 @@ def measure_L1_snr(self, snr_percentile=95): self.RED_SNR_WAV = RED_SNR_WAV def plot_L1_snr(self,ObsID, fig_path=None, show_plot=False): - + L1= self.L1 """ Generate a plot of SNR per order as compuated using the compute_l1_snr From e3e99cbca1d6ba24bdd507a83ce153a7b17ad696 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 14:58:43 -0700 Subject: [PATCH 210/249] analyze l1 --- modules/Utils/analyze_l1.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index f0ac29746..e7121552e 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -63,6 +63,7 @@ def measure_L1_snr(self, snr_percentile=95): None """ L1= self.L1 + print('KPF1',L1) self.snr_percentile = snr_percentile # Determine the number of orders From 7fb8132f6797e2b2ca9cd65d9931757125ce4869 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 14:59:58 -0700 Subject: [PATCH 211/249] analyze l1 --- modules/Utils/analyze_l1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index e7121552e..b26e15356 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -63,7 +63,7 @@ def measure_L1_snr(self, snr_percentile=95): None """ L1= self.L1 - print('KPF1',L1) + print('KPF1',L1.info(),L1.__dir__()) self.snr_percentile = snr_percentile # Determine the number of orders From 04fb48c36b95d6f0474dd6ee0860cabfdd65f2d6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 15:00:48 -0700 Subject: [PATCH 212/249] analyze l1 --- modules/Utils/analyze_l1.py | 104 ++++++++++++++++++------------------ 1 file changed, 52 insertions(+), 52 deletions(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index b26e15356..27a71f9ac 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -67,24 +67,24 @@ def measure_L1_snr(self, snr_percentile=95): self.snr_percentile = snr_percentile # Determine the number of orders - norders_green = (L1.data['GREEN_SKY_WAVE']).shape[0] - norders_red = (L1.data['RED_SKY_WAVE']).shape[0] + norders_green = (L1['GREEN_SKY_WAVE']).shape[0] + norders_red = (L1['RED_SKY_WAVE']).shape[0] orderlets = {'CAL','SCI1','SCI2','SCI3','SKY'} norderlets = len(orderlets) # Define SNR arrays (needed for operations below where VAR = 0) - GREEN_SCI_SNR1 = 0 * L1.data['GREEN_SCI_VAR1'] - GREEN_SCI_SNR2 = 0 * L1.data['GREEN_SCI_VAR2'] - GREEN_SCI_SNR3 = 0 * L1.data['GREEN_SCI_VAR3'] - GREEN_CAL_SNR = 0 * L1.data['GREEN_CAL_VAR'] - GREEN_SKY_SNR = 0 * L1.data['GREEN_SKY_VAR'] - GREEN_SCI_SNR = 0 * L1.data['GREEN_SCI_VAR1'] - RED_SCI_SNR1 = 0 * L1.data['RED_SCI_VAR1'] - RED_SCI_SNR2 = 0 * L1.data['RED_SCI_VAR2'] - RED_SCI_SNR3 = 0 * L1.data['RED_SCI_VAR3'] - RED_CAL_SNR = 0 * L1.data['RED_CAL_VAR'] - RED_SKY_SNR = 0 * L1.data['RED_SKY_VAR'] - RED_SCI_SNR = 0 * L1.data['RED_SCI_VAR1'] + GREEN_SCI_SNR1 = 0 * L1['GREEN_SCI_VAR1'] + GREEN_SCI_SNR2 = 0 * L1['GREEN_SCI_VAR2'] + GREEN_SCI_SNR3 = 0 * L1['GREEN_SCI_VAR3'] + GREEN_CAL_SNR = 0 * L1['GREEN_CAL_VAR'] + GREEN_SKY_SNR = 0 * L1['GREEN_SKY_VAR'] + GREEN_SCI_SNR = 0 * L1['GREEN_SCI_VAR1'] + RED_SCI_SNR1 = 0 * L1['RED_SCI_VAR1'] + RED_SCI_SNR2 = 0 * L1['RED_SCI_VAR2'] + RED_SCI_SNR3 = 0 * L1['RED_SCI_VAR3'] + RED_CAL_SNR = 0 * L1['RED_CAL_VAR'] + RED_SKY_SNR = 0 * L1['RED_SKY_VAR'] + RED_SCI_SNR = 0 * L1['RED_SCI_VAR1'] # Create Arrays GREEN_SNR = np.zeros((norders_green, norderlets+1)) @@ -93,46 +93,46 @@ def measure_L1_snr(self, snr_percentile=95): RED_SNR_WAV = np.zeros(norders_red) # Compute SNR arrays for each of the orders, orderlets, and CCDs. - GREEN_SCI_SNR1 = np.divide(L1.data['GREEN_SCI_FLUX1'], - np.sqrt(abs(L1.data['GREEN_SCI_VAR1'])), - where=(L1.data['GREEN_SCI_VAR1']!=0)) - GREEN_SCI_SNR2 = np.divide(L1.data['GREEN_SCI_FLUX2'], - np.sqrt(abs(L1.data['GREEN_SCI_VAR2'])), - where=(L1.data['GREEN_SCI_VAR2']!=0)) - GREEN_SCI_SNR3 = np.divide(L1.data['GREEN_SCI_FLUX3'], - np.sqrt(abs(L1.data['GREEN_SCI_VAR3'])), - where=(L1.data['GREEN_SCI_VAR3']!=0)) - GREEN_SCI_SNR = np.divide(L1.data['GREEN_SCI_FLUX1']+L1.data['GREEN_SCI_FLUX3']+L1.data['GREEN_SCI_FLUX3'], - np.sqrt(abs(L1.data['GREEN_SCI_VAR1'])+abs(L1.data['GREEN_SCI_VAR2'])+abs(L1.data['GREEN_SCI_VAR3'])), - where=(L1.data['GREEN_SCI_VAR1']+L1.data['GREEN_SCI_VAR2']+L1.data['GREEN_SCI_VAR3']!=0)) - GREEN_CAL_SNR = np.divide(L1.data['GREEN_CAL_FLUX'], - np.sqrt(abs(L1.data['GREEN_CAL_VAR'])), - where=(L1.data['GREEN_CAL_VAR']!=0)) - GREEN_SKY_SNR = np.divide(L1.data['GREEN_SKY_FLUX'], - np.sqrt(abs(L1.data['GREEN_SKY_VAR'])), - where=(L1.data['GREEN_SKY_VAR']!=0)) - RED_SCI_SNR1 = np.divide(L1.data['RED_SCI_FLUX1'], - np.sqrt(abs(L1.data['RED_SCI_VAR1'])), - where=(L1.data['RED_SCI_VAR1']!=0)) - RED_SCI_SNR2 = np.divide(L1.data['RED_SCI_FLUX2'], - np.sqrt(abs(L1.data['RED_SCI_VAR2'])), - where=(L1.data['RED_SCI_VAR2']!=0)) - RED_SCI_SNR3 = np.divide(L1.data['RED_SCI_FLUX3'], - np.sqrt(abs(L1.data['RED_SCI_VAR3'])), - where=(L1.data['RED_SCI_VAR3']!=0)) - RED_SCI_SNR = np.divide(L1.data['RED_SCI_FLUX1']+L1.data['RED_SCI_FLUX3']+L1.data['RED_SCI_FLUX3'], - np.sqrt(abs(L1.data['RED_SCI_VAR1'])+abs(L1.data['RED_SCI_VAR2'])+abs(L1.data['RED_SCI_VAR3'])), - where=(L1.data['RED_SCI_VAR1']+L1.data['RED_SCI_VAR2']+L1.data['RED_SCI_VAR3']!=0)) - RED_CAL_SNR = np.divide(L1.data['RED_CAL_FLUX'], - np.sqrt(abs(L1.data['RED_CAL_VAR'])), - where=(L1.data['RED_CAL_VAR']!=0)) - RED_SKY_SNR = np.divide(L1.data['RED_SKY_FLUX'], - np.sqrt(abs(L1.data['RED_SKY_VAR'])), - where=(L1.data['RED_SKY_VAR']!=0)) + GREEN_SCI_SNR1 = np.divide(L1['GREEN_SCI_FLUX1'], + np.sqrt(abs(L1['GREEN_SCI_VAR1'])), + where=(L1['GREEN_SCI_VAR1']!=0)) + GREEN_SCI_SNR2 = np.divide(L1['GREEN_SCI_FLUX2'], + np.sqrt(abs(L1['GREEN_SCI_VAR2'])), + where=(L1['GREEN_SCI_VAR2']!=0)) + GREEN_SCI_SNR3 = np.divide(L1['GREEN_SCI_FLUX3'], + np.sqrt(abs(L1['GREEN_SCI_VAR3'])), + where=(L1['GREEN_SCI_VAR3']!=0)) + GREEN_SCI_SNR = np.divide(L1['GREEN_SCI_FLUX1']+L1['GREEN_SCI_FLUX3']+L1['GREEN_SCI_FLUX3'], + np.sqrt(abs(L1['GREEN_SCI_VAR1'])+abs(L1['GREEN_SCI_VAR2'])+abs(L1['GREEN_SCI_VAR3'])), + where=(L1['GREEN_SCI_VAR1']+L1['GREEN_SCI_VAR2']+L1['GREEN_SCI_VAR3']!=0)) + GREEN_CAL_SNR = np.divide(L1['GREEN_CAL_FLUX'], + np.sqrt(abs(L1['GREEN_CAL_VAR'])), + where=(L1['GREEN_CAL_VAR']!=0)) + GREEN_SKY_SNR = np.divide(L1['GREEN_SKY_FLUX'], + np.sqrt(abs(L1['GREEN_SKY_VAR'])), + where=(L1['GREEN_SKY_VAR']!=0)) + RED_SCI_SNR1 = np.divide(L1['RED_SCI_FLUX1'], + np.sqrt(abs(L1['RED_SCI_VAR1'])), + where=(L1['RED_SCI_VAR1']!=0)) + RED_SCI_SNR2 = np.divide(L1['RED_SCI_FLUX2'], + np.sqrt(abs(L1['RED_SCI_VAR2'])), + where=(L1['RED_SCI_VAR2']!=0)) + RED_SCI_SNR3 = np.divide(L1['RED_SCI_FLUX3'], + np.sqrt(abs(L1['RED_SCI_VAR3'])), + where=(L1['RED_SCI_VAR3']!=0)) + RED_SCI_SNR = np.divide(L1['RED_SCI_FLUX1']+L1['RED_SCI_FLUX3']+L1['RED_SCI_FLUX3'], + np.sqrt(abs(L1['RED_SCI_VAR1'])+abs(L1['RED_SCI_VAR2'])+abs(L1['RED_SCI_VAR3'])), + where=(L1['RED_SCI_VAR1']+L1['RED_SCI_VAR2']+L1['RED_SCI_VAR3']!=0)) + RED_CAL_SNR = np.divide(L1['RED_CAL_FLUX'], + np.sqrt(abs(L1['RED_CAL_VAR'])), + where=(L1['RED_CAL_VAR']!=0)) + RED_SKY_SNR = np.divide(L1['RED_SKY_FLUX'], + np.sqrt(abs(L1['RED_SKY_VAR'])), + where=(L1['RED_SKY_VAR']!=0)) # Compute SNR per order and per orderlet for o in range(norders_green): - GREEN_SNR_WAV[o] = L1.data['GREEN_SCI_WAVE1'][o,2040] + GREEN_SNR_WAV[o] = L1['GREEN_SCI_WAVE1'][o,2040] GREEN_SNR[o,0] = np.percentile(GREEN_CAL_SNR[o], snr_percentile) GREEN_SNR[o,1] = np.percentile(GREEN_SCI_SNR1[o], snr_percentile) GREEN_SNR[o,2] = np.percentile(GREEN_SCI_SNR2[o], snr_percentile) @@ -140,7 +140,7 @@ def measure_L1_snr(self, snr_percentile=95): GREEN_SNR[o,4] = np.percentile(GREEN_SKY_SNR[o], snr_percentile) GREEN_SNR[o,5] = np.percentile(GREEN_SCI_SNR[o], snr_percentile) for o in range(norders_red): - RED_SNR_WAV[o] = L1.data['RED_SCI_WAVE1'][o,2040] + RED_SNR_WAV[o] = L1['RED_SCI_WAVE1'][o,2040] RED_SNR[o,0] = np.percentile(RED_CAL_SNR[o], snr_percentile) RED_SNR[o,1] = np.percentile(RED_SCI_SNR1[o], snr_percentile) RED_SNR[o,2] = np.percentile(RED_SCI_SNR2[o], snr_percentile) From f0c2d41c029db0d1d41e301a1e9fa362f440b5ba Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 15:01:12 -0700 Subject: [PATCH 213/249] analyze l1 --- modules/Utils/analyze_l1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index 27a71f9ac..bbd2bb650 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -63,7 +63,7 @@ def measure_L1_snr(self, snr_percentile=95): None """ L1= self.L1 - print('KPF1',L1.info(),L1.__dir__()) + #print('KPF1',L1.info(),L1.__dir__()) self.snr_percentile = snr_percentile # Determine the number of orders From a3b56d79a6d9de9276db74ba0d0f395e4841af97 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 15:01:48 -0700 Subject: [PATCH 214/249] analyze l1 --- modules/Utils/L1_SNR_prim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index ec4f3549e..dce1824fa 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -95,7 +95,7 @@ def _perform(self) -> None: self.l1_obj.to_fits(self.data_dir+self.l1_obj.filename) - #print(self.l1_obj.header) + print(self.l1_obj.header) #print(L1_SNR.RED_SNR) From 31bf11818354b71506aac9f4da99de5b7ee308b3 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 15:07:29 -0700 Subject: [PATCH 215/249] analyze l1 --- modules/Utils/L1_SNR_prim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/L1_SNR_prim.py b/modules/Utils/L1_SNR_prim.py index dce1824fa..ec4f3549e 100644 --- a/modules/Utils/L1_SNR_prim.py +++ b/modules/Utils/L1_SNR_prim.py @@ -95,7 +95,7 @@ def _perform(self) -> None: self.l1_obj.to_fits(self.data_dir+self.l1_obj.filename) - print(self.l1_obj.header) + #print(self.l1_obj.header) #print(L1_SNR.RED_SNR) From 3e0dd9240c887a47a6a91f24e7d01d16eca5db17 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 15:51:17 -0700 Subject: [PATCH 216/249] Added plot_L0_stitched_image function --- modules/Utils/analyze_l0.py | 104 ++++++++++++++++++++++++++++++++++-- 1 file changed, 100 insertions(+), 4 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 74c31febc..5068a0145 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -1,13 +1,12 @@ import numpy as np +import matplotlib.pyplot as plt class AnalyzeL0: """ Description: - This class [will] contain functions to analyze L0 images (storing - them as attributes) and functions to plot the results. - Currently, this class is a placeholder for future functions to be - added. See analyze_l1.py for examples. + This class contains functions to analyze L0 images (storing them + as attributes) and functions to plot the results. Arguments: L0 - an L0 object @@ -24,3 +23,100 @@ def __init__(self, L0, logger=None): else: self.logger = None print('---->AnalyzeL0 class constructor') + + + def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): + + """ + + Generate a plot of the stitched L0 image. + The image will be divided by 2^16, if appropriate. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + if chip == 'green' or chip == 'red': + if chip == 'green': + CHIP = 'GREEN' + if chip == 'red': + CHIP = 'RED' + # Determine the number of regions in image + regions = 0 + amp1_present = False + amp2_present = False + amp3_present = False + amp4_present = False + for hdu in L0: + if hdu.name == CHIP + '_AMP1': + amp1_present = True + if hdu.name == CHIP + '_AMP2': + amp2_present = True + if hdu.name == CHIP + '_AMP3': + amp3_present = True + if hdu.name == CHIP + '_AMP4': + amp4_present = True + if amp1_present: + regions = 1 + if amp1_present & amp2_present: + regions = 2 + if amp1_present & amp2_present & amp3_present & amp4_present: + regions = 4 + + # Assemble image + if regions == 1: + self.logger.debug("The code currently doesn't support single amplifier mode because this requires knowing which amplifer is used to get the proper orientation.") + return + if regions == 2: + if chip == 'green': + image = np.flipud(np.concatenate((L0[CHIP + '_AMP1'].data, L0[CHIP + '_AMP2'].data), axis=1)) + if chip == 'red': + image = np.concatenate((L0[CHIP + '_AMP1'].data, L0[CHIP + '_AMP2'].data), axis=1) + if regions == 4: + image_bot = np.concatenate((L0[CHIP + '_AMP1'].data, L0[CHIP + '_AMP2'].data), axis=1) + image_top = np.concatenate((L0[CHIP + '_AMP3'].data, L0[CHIP + '_AMP4'].data), axis=1) + image = np.concatenate((image_bot, image_top), axis=0) + + # Determine if image needs to be divided by 2^16 + if np.nanmedian(image) > 400*2**16: + twotosixteen = True + image /= 2**16 + else: + twotosixteen = False + else: + self.logger.debug('plot_L0_stitched_image: need to set chip="green" or "red"') + return + + plt.figure(tight_layout=True) + plt.figure(figsize=(12, 12), tight_layout=True) + plt.imshow(image, cmap='viridis', origin='lower', + vmin=np.percentile(image,1), + vmax=np.percentile(image,99.5)) + if chip == 'green': + plt.title(ObsID + ' - L0 (no processing) - Green CCD', fontsize=14) + if chip == 'red': + plt.title(ObsID + ' - L0 (no processing) - Red CCD', fontsize=14) + plt.xlabel('x (pixel number)', fontsize=14) + plt.ylabel('y (pixel number)', fontsize=14) + cbar_label = 'ADU' + if twotosixteen: + cbar_label = cbar_label + r' / $2^{16}$' + cbar = plt.colorbar(shrink=0.7, label=cbar_label) + cbar.ax.yaxis.label.set_size(14) + cbar.ax.tick_params(labelsize=12) + plt.grid(False) + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=144, facecolor='w') + if show_plot == True: + plt.show() + plt.close() From 016d5bf85b02a8e26ec13449015a40689b7125d5 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 15:54:11 -0700 Subject: [PATCH 217/249] minor text update --- modules/Utils/analyze_l0.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 5068a0145..5564e3d1f 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -12,7 +12,7 @@ class AnalyzeL0: L0 - an L0 object Attributes: - TBD + None so far """ def __init__(self, L0, logger=None): From d7ce1893adaa279907a3b4eb1792e2b456ecfeea Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 16:04:01 -0700 Subject: [PATCH 218/249] Create analyze_2d (placeholder for now) --- modules/Utils/analyze_2d | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 modules/Utils/analyze_2d diff --git a/modules/Utils/analyze_2d b/modules/Utils/analyze_2d new file mode 100644 index 000000000..6ffb9951d --- /dev/null +++ b/modules/Utils/analyze_2d @@ -0,0 +1,28 @@ +import numpy as np +import matplotlib.pyplot as plt + +class Analyze2D: + + """ + Description: + This class contains functions to analyze 2D images (storing them + as attributes) and functions to plot the results. + It is only a placeholder for now. + + Arguments: + 2D - a 2D object + + Attributes: + TBD + """ + + def __init__(self, D2, logger=None): + self.D2 = D2 # use D2 instead of 2D because variable names can't start with a number + if logger: + self.logger = logger + self.logger.debug('Analyze2D class constructor') + else: + self.logger = None + print('---->Analyze2D class constructor') + + From 1620fdfed15a8e111a9da5b8077643dbd8b479d9 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 16:04:28 -0700 Subject: [PATCH 219/249] Rename analyze_2d to analyze_2d.py --- modules/Utils/{analyze_2d => analyze_2d.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename modules/Utils/{analyze_2d => analyze_2d.py} (100%) diff --git a/modules/Utils/analyze_2d b/modules/Utils/analyze_2d.py similarity index 100% rename from modules/Utils/analyze_2d rename to modules/Utils/analyze_2d.py From ce76f7142b82c2baded969a6e79bee8ea14552e6 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 16:25:01 -0700 Subject: [PATCH 220/249] Added placeholder functions to make existing plots in QLP --- modules/Utils/analyze_2d.py | 88 ++++++++++++++++++++++++++++++++++++- 1 file changed, 87 insertions(+), 1 deletion(-) diff --git a/modules/Utils/analyze_2d.py b/modules/Utils/analyze_2d.py index 6ffb9951d..f738b7e4c 100644 --- a/modules/Utils/analyze_2d.py +++ b/modules/Utils/analyze_2d.py @@ -25,4 +25,90 @@ def __init__(self, D2, logger=None): self.logger = None print('---->Analyze2D class constructor') - + def plot_2D_image(self, chip=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a plot of the stitched L0 image. + The image will be divided by 2^16, if appropriate. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + def plot_2D_order_trace_image(self, chip=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a plot of the stitched L0 image. + The image will be divided by 2^16, if appropriate. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + def plot_2D_image_histogram(self, chip=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a plot of the stitched L0 image. + The image will be divided by 2^16, if appropriate. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + def plot_2D_column_cut(self, chip=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a plot of the stitched L0 image. + The image will be divided by 2^16, if appropriate. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ From c5af347727f6f73239e77bb1ae1ad8a15850b58f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:27:52 -0700 Subject: [PATCH 221/249] L0 updates --- modules/quicklook/src/alg.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 8dc52dad5..183b39bd2 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -13,6 +13,7 @@ from astropy import modeling from astropy.time import Time from datetime import datetime +from modules.Utils.analyze_l1 import AnalyzeL0 class QuicklookAlg: @@ -128,6 +129,12 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly + L0_obj = AnalyzeL0(kpf0_file) + if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') + L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) + L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) + + ''' L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) @@ -162,6 +169,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() L0.close() + ''' #read ccd directly From 342ea36b7af565adfb80fe31ada7a1e30fb323c5 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:32:54 -0700 Subject: [PATCH 222/249] L0 updates --- modules/quicklook/src/alg.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 183b39bd2..93caab3a0 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -129,13 +129,16 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly - L0_obj = AnalyzeL0(kpf0_file) + L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' + L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') + L0_obj = AnalyzeL0(L0_kpf) + print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) ''' - L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' + L0 = fits.open(L0_file) green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 From 01ce9041e1936e721ef4a5bf5de06713044c5725 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:34:42 -0700 Subject: [PATCH 223/249] L0 updates --- modules/quicklook/src/alg.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 93caab3a0..bc05dd80e 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -129,6 +129,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly + ''' L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') L0_obj = AnalyzeL0(L0_kpf) @@ -137,7 +138,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) - ''' + L0 = fits.open(L0_file) From 15e589ebbd46886339b82b5613c30e08215b5d9f Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 16:35:44 -0700 Subject: [PATCH 224/249] Added placeholder functions for plots and analysis already on the QLP --- modules/Utils/analyze_l1.py | 62 +++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) diff --git a/modules/Utils/analyze_l1.py b/modules/Utils/analyze_l1.py index bbd2bb650..80107b802 100644 --- a/modules/Utils/analyze_l1.py +++ b/modules/Utils/analyze_l1.py @@ -217,3 +217,65 @@ def plot_L1_snr(self,ObsID, fig_path=None, show_plot=False): if show_plot == True: plt.show() plt.close() + + def plot_1D_spectrum(self, chip=None, orderlet=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a rainbow-colored plot L1 spectrum. One must select an orderlet. + + Args: + chip (string) - "green" or "red" + orderlet (string) - "CAL", "SCI1", "SCI2", "SCI3", "SKY" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + def plot_1D_spectrum_single_order(self, chip=None, fig_path=None, show_plot=False): + + """ + + TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Generate a plot of a single order of the L1 spectrum showing all orderlets. + + Args: + chip (string) - "green" or "red" + fig_path (string) - set to the path for the file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + + def measure_orderlet_flux_ratio(self): + + """ + TO-DO: MOVE THE ANALYSIS CODE FROM THE QLP HERE. + THIS IS A PLACEHOLDER. + + Compute the flux ratios of SCI2/SCI1, SCI3/SCI1, CAL/SCI1, SKY/SCI1. + + Args: + None + + Attributes: + To be defined. + + Returns: + None + """ From ce7ccf3f1f54845382481ff28bf8cfa949a11666 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:36:29 -0700 Subject: [PATCH 225/249] L0 updates --- modules/quicklook/src/alg.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index bc05dd80e..2d5c58bc1 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -137,9 +137,9 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) + ''' - - + L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 @@ -173,7 +173,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() L0.close() - ''' + #read ccd directly From 3e63c8cd862f53e9cfafaa45c9680ca5de72bdb1 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:37:56 -0700 Subject: [PATCH 226/249] L0 updates --- modules/quicklook/src/alg.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 2d5c58bc1..8dc52dad5 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -13,7 +13,6 @@ from astropy import modeling from astropy.time import Time from datetime import datetime -from modules.Utils.analyze_l1 import AnalyzeL0 class QuicklookAlg: @@ -129,16 +128,6 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #operate on L0 data before image assembly - ''' - L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') - L0_obj = AnalyzeL0(L0_kpf) - print(L0_obj,L0_obj.info()) - if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') - L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) - L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) - ''' - L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) @@ -175,7 +164,6 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0.close() - #read ccd directly L0_data = self.config['IO']['input_prefix_l0']+date+'/'+exposure_name+'_2D.fits' hdulist = fits.open(L0_data) From 29a1b2a7661ef39cdd546c3a259d618e9996b7b4 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:39:27 -0700 Subject: [PATCH 227/249] L0 updates --- modules/quicklook/src/alg.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 8dc52dad5..38870e8ba 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -131,6 +131,15 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) + L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' + L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') + L0_obj = AnalyzeL0(L0_kpf) + print(L0_obj,L0_obj.info()) + if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') + L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) + L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) + + ''' green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 plt.figure(tight_layout=True) plt.figure(figsize=(8, 8), tight_layout=True) @@ -162,7 +171,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): plt.savefig(output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png',dpi=500,facecolor='white') plt.close() L0.close() - + ''' #read ccd directly L0_data = self.config['IO']['input_prefix_l0']+date+'/'+exposure_name+'_2D.fits' From 0bb86cd43a17ab904111e1b78234c573be3fa86f Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:41:01 -0700 Subject: [PATCH 228/249] L0 updates --- modules/quicklook/src/alg.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 38870e8ba..dbed9cb22 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -13,6 +13,7 @@ from astropy import modeling from astropy.time import Time from datetime import datetime +from modules.Utils.analyze_l0 import AnalyzeL0 class QuicklookAlg: @@ -132,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') + L0_kpf = KPF0.kpf0_from_fits(L0_file,data_type='KPF') L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From cae10e314e5bf47734012a9eadc098ecf4e6d8fe Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:43:49 -0700 Subject: [PATCH 229/249] L0 updates --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index dbed9cb22..4fd3b6886 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -14,7 +14,7 @@ from astropy.time import Time from datetime import datetime from modules.Utils.analyze_l0 import AnalyzeL0 - +from kpfpipe.pipelines.fits_primitives import kpf0_from_fits class QuicklookAlg: """ From c334e08f627f20e87426247ff760e471b469a73d Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:44:18 -0700 Subject: [PATCH 230/249] L0 updates --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 4fd3b6886..84a4a357e 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = KPF0.kpf0_from_fits(L0_file,data_type='KPF') + L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From bc7c160b85cb953c998a7063ac548c70ef8270c5 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:45:06 -0700 Subject: [PATCH 231/249] L0 updates --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 84a4a357e..30f0ebe47 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') + L0_kpf = kpf0_from_fits(L0_file) L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From 8ab895b426d38c16bdfff5ce9f1b27d11712ced6 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:47:17 -0700 Subject: [PATCH 232/249] L0 updates --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 30f0ebe47..5ee641594 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -14,7 +14,7 @@ from astropy.time import Time from datetime import datetime from modules.Utils.analyze_l0 import AnalyzeL0 -from kpfpipe.pipelines.fits_primitives import kpf0_from_fits +import kpfpipe.pipelines.fits_primitives class QuicklookAlg: """ @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = kpf0_from_fits(L0_file) + L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From 3e8d6df062a6e249c2e17de7d2ff2123d5427816 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:48:13 -0700 Subject: [PATCH 233/249] L0 updates --- modules/quicklook/src/alg.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 5ee641594..18c3c0fc4 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -14,7 +14,7 @@ from astropy.time import Time from datetime import datetime from modules.Utils.analyze_l0 import AnalyzeL0 -import kpfpipe.pipelines.fits_primitives +import kpfpipe.pipelines.fits_primitives as fits_primitives class QuicklookAlg: """ @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = kpf0_from_fits(L0_file,data_type='KPF') + L0_kpf = fits_primitives.kpf0_from_fits(L0_file,data_type='KPF') L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From 43c44fc894637d9f12e02d8edec0e0a8e496ea26 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:49:03 -0700 Subject: [PATCH 234/249] L0 updates --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 18c3c0fc4..2db5b32e7 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -133,7 +133,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0 = fits.open(L0_file) L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = fits_primitives.kpf0_from_fits(L0_file,data_type='KPF') + L0_kpf = fits_primitives.kpf0_from_fits(L0_file) L0_obj = AnalyzeL0(L0_kpf) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') From 15eeb2c542d58d4ca5e8c9bb205d8ed57225ad0b Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:50:30 -0700 Subject: [PATCH 235/249] L0 updates --- modules/quicklook/src/alg.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 2db5b32e7..0a2a341af 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -132,9 +132,9 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' L0 = fits.open(L0_file) - L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' - L0_kpf = fits_primitives.kpf0_from_fits(L0_file) - L0_obj = AnalyzeL0(L0_kpf) + #L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' + #L0_kpf = fits_primitives.kpf0_from_fits(L0_file) + L0_obj = AnalyzeL0(L0) print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) From c09fe4ee98386d6a2059d68c8fc26248149d0e55 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:50:58 -0700 Subject: [PATCH 236/249] L0 updates --- modules/quicklook/src/alg.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index 0a2a341af..f4d9377b6 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -135,7 +135,7 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): #L0_file = self.config['IO']['input_prefix_l0_pre']+date+'/'+exposure_name+'.fits' #L0_kpf = fits_primitives.kpf0_from_fits(L0_file) L0_obj = AnalyzeL0(L0) - print(L0_obj,L0_obj.info()) + #print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) From 852c402ab5616f91b82c93ea64dddf555db81e43 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:54:06 -0700 Subject: [PATCH 237/249] L0 updates --- modules/Utils/analyze_l0.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 5564e3d1f..0e8bd4a6f 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -24,22 +24,22 @@ def __init__(self, L0, logger=None): self.logger = None print('---->AnalyzeL0 class constructor') - + def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): """ - - Generate a plot of the stitched L0 image. + + Generate a plot of the stitched L0 image. The image will be divided by 2^16, if appropriate. Args: chip (string) - "green" or "red" - fig_path (string) - set to the path for a SNR vs. wavelength file + fig_path (string) - set to the path for a SNR vs. wavelength file to be generated. show_plot (boolean) - show the plot in the current environment. Returns: - PNG plot in fig_path or shows the plot it the current environment + PNG plot in fig_path or shows the plot it the current environment (e.g., in a Jupyter Notebook). """ @@ -55,7 +55,7 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): amp2_present = False amp3_present = False amp4_present = False - for hdu in L0: + for hdu in self.L0: if hdu.name == CHIP + '_AMP1': amp1_present = True if hdu.name == CHIP + '_AMP2': @@ -70,7 +70,7 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): regions = 2 if amp1_present & amp2_present & amp3_present & amp4_present: regions = 4 - + # Assemble image if regions == 1: self.logger.debug("The code currently doesn't support single amplifier mode because this requires knowing which amplifer is used to get the proper orientation.") @@ -84,7 +84,7 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): image_bot = np.concatenate((L0[CHIP + '_AMP1'].data, L0[CHIP + '_AMP2'].data), axis=1) image_top = np.concatenate((L0[CHIP + '_AMP3'].data, L0[CHIP + '_AMP4'].data), axis=1) image = np.concatenate((image_bot, image_top), axis=0) - + # Determine if image needs to be divided by 2^16 if np.nanmedian(image) > 400*2**16: twotosixteen = True @@ -94,11 +94,11 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): else: self.logger.debug('plot_L0_stitched_image: need to set chip="green" or "red"') return - + plt.figure(tight_layout=True) plt.figure(figsize=(12, 12), tight_layout=True) - plt.imshow(image, cmap='viridis', origin='lower', - vmin=np.percentile(image,1), + plt.imshow(image, cmap='viridis', origin='lower', + vmin=np.percentile(image,1), vmax=np.percentile(image,99.5)) if chip == 'green': plt.title(ObsID + ' - L0 (no processing) - Green CCD', fontsize=14) @@ -113,7 +113,7 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): cbar.ax.yaxis.label.set_size(14) cbar.ax.tick_params(labelsize=12) plt.grid(False) - + # Display the plot if fig_path != None: plt.savefig(fig_path, dpi=144, facecolor='w') From 69b08a5d495a1aa14fc28fdd86514b09f2b04632 Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:55:03 -0700 Subject: [PATCH 238/249] L0 updates --- modules/Utils/analyze_l0.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 0e8bd4a6f..392d11afb 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -26,7 +26,7 @@ def __init__(self, L0, logger=None): def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): - + L0 = self.L0 """ Generate a plot of the stitched L0 image. @@ -55,7 +55,7 @@ def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): amp2_present = False amp3_present = False amp4_present = False - for hdu in self.L0: + for hdu in L0: if hdu.name == CHIP + '_AMP1': amp1_present = True if hdu.name == CHIP + '_AMP2': From 335143a1aa4462d9006ccfbc94ebcff60cd4c0be Mon Sep 17 00:00:00 2001 From: DaiFei Date: Thu, 29 Jun 2023 16:56:49 -0700 Subject: [PATCH 239/249] L0 updates --- modules/Utils/analyze_l0.py | 2 +- modules/quicklook/src/alg.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 392d11afb..961781a6a 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -25,7 +25,7 @@ def __init__(self, L0, logger=None): print('---->AnalyzeL0 class constructor') - def plot_L0_stitched_image(self, chip=None, fig_path=None, show_plot=False): + def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=False): L0 = self.L0 """ diff --git a/modules/quicklook/src/alg.py b/modules/quicklook/src/alg.py index f4d9377b6..2be91e552 100644 --- a/modules/quicklook/src/alg.py +++ b/modules/quicklook/src/alg.py @@ -137,8 +137,8 @@ def qlp_procedures(self,kpf0_file,output_dir,end_of_night_summary): L0_obj = AnalyzeL0(L0) #print(L0_obj,L0_obj.info()) if os.path.exists(output_dir+'/'+exposure_name+'/L0/') == False: os.makedirs(output_dir+'/'+exposure_name+'/L0/') - L0_obj.plot_L0_stitched_image(chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) - L0_obj.plot_L0_stitched_image(chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) + L0_obj.plot_L0_stitched_image(exposure_name,chip='green', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_GREEN_L0_zoomable.png', show_plot=False) + L0_obj.plot_L0_stitched_image(exposure_name,chip='red', fig_path=output_dir+'/'+exposure_name+'/L0/'+exposure_name+'_RED_L0_zoomable.png', show_plot=False) ''' green_image = np.flipud(np.concatenate((L0['GREEN_AMP1'].data, L0['GREEN_AMP2'].data), axis=1))/2**16 From cfaa527096f6c969ec8c3d167db73a3b8cb394bc Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 18:12:02 -0700 Subject: [PATCH 240/249] Create analyze_guider.py --- modules/Utils/analyze_guider.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 modules/Utils/analyze_guider.py diff --git a/modules/Utils/analyze_guider.py b/modules/Utils/analyze_guider.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/modules/Utils/analyze_guider.py @@ -0,0 +1 @@ + From 6bf9a6eb430f991f5c2cf7ad32854f450806889b Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 18:13:10 -0700 Subject: [PATCH 241/249] Added three plotting functions for guider images --- modules/Utils/analyze_guider.py | 299 ++++++++++++++++++++++++++++++++ 1 file changed, 299 insertions(+) diff --git a/modules/Utils/analyze_guider.py b/modules/Utils/analyze_guider.py index 8b1378917..b74ccfedd 100644 --- a/modules/Utils/analyze_guider.py +++ b/modules/Utils/analyze_guider.py @@ -1 +1,300 @@ +import numpy as np +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +from scipy.optimize import curve_fit +from astropy.table import Table +from astropy.time import Time +class AnalyzeGuider: + + """ + Description: + This class contains functions to analyze the Guider extensions in + L0 or 2D images (storing them as attributes) and functions to plot the results. + + Arguments: + L0 - an L0 object (or a 2D object -- to be tested) + + Attributes: + None so far + """ + + def __init__(self, L0, logger=None): + self.L0 = L0 + if logger: + self.logger = logger + self.logger.debug('AnalyzeGuider class constructor') + else: + self.logger = None + print('---->AnalyzeGuider class constructor') + + def measure_seeing(self): + + """ + Compute the seeing from a stacked guider image. + + Args: + None + + Attributes: + guider_image - stacked guider image + image_fit - image of the Moffat function fit of the guider image; + this has the same dimensions as guider_image + amplitude - amplitude of the fitted Moffat function + seeing_550nm - seeing value scaled with wavelength^(1/5) + seeing - seeing value at the guide camera wavelengths (950-1200 nm); + equal to the alpha parameter of the fitted Moffat function + beta - beta parameter of the fitted Moffat function + x0 - x-coordinate of the centroid of the fitted Moffat function + y0 - x-coordinate of the centroid of the fitted Moffat function + + Returns: + None + """ + + guider_im = L0['GUIDER_AVG'].data - np.median(L0['GUIDER_AVG'].data) + guider_header = L0['GUIDER_AVG'].header + if 'TTGAIN' in L0['GUIDER_AVG'].header: + tiptilt_gain = L0['GUIDER_AVG'].header['TTGAIN'] + else: + tiptilt_gain = 0.3 + + def moffat_2D(xy, amplitude, x0, y0, alpha, beta): + x, y = xy + return amplitude * (1 + ((x - x0) ** 2 + (y - y0) ** 2) / alpha ** 2) ** -beta + + x = np.arange(guider_im.shape[1]) + y = np.arange(guider_im.shape[0]) + X, Y = np.meshgrid(x, y) + x_flat = X.flatten() + y_flat = Y.flatten() + image_data_flat = guider_im.flatten() + p0 = [1, guider_header['CRPIX1'], guider_header['CRPIX2'], 5/0.056, 2.5] # Initial guess for the parameters + #p0 = [1, guider_im.shape[1] / 2, guider_im.shape[0] / 2, 2/0.056, 2] # Initial guess for the parameters + popt, pcov = curve_fit(moffat_2D, (x_flat, y_flat), image_data_flat, p0=p0) + amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit = popt + alpha_fit = abs(alpha_fit) + #print('amplitude = ' + str(amplitude_fit)) + #print('seeing = ' + str(alpha_fit*0.056) + ' arcsec') + #print('beta = ' + str(beta_fit)) + #print('x0 = ' + str(x0_fit) + ' pixels') + #print('y0 = ' + str(y0_fit) + ' pixels') + + self.guider_image = guider_im + self.image_fit = moffat_2D((X, Y), amplitude_fit, x0_fit, y0_fit, alpha_fit, beta_fit) + #resid_im = guider_im - image_fit + + self.amplitude = amplitude_fit + self.seeing = alpha_fit + self.seeing_550nm = self.seeing*(((1200+950)/2)/550)**0.2 # seeing scales with wavelength^0.2 + self.beta = beta_fit + self.x0 = x0_fit + self.y0 = y0_fit + + def plot_guider_image(self, fig_path=None, show_plot=False): + + """ + Generate a three-panel plot of the guider image (full view), the zoomed-in + guider image, and residuals of the guider image with a fitted Moffat function. + Note that the function AnalyzeGuider.measure_seeing must be run first. + + Args: + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] + pixel_scale = 0.056 # arcsec per pixel for the CRED-2 imager on the KPF FIU + + # Plot the original image and residuals + guider_im_zoom = self.guider_image[255-38:255+38, 320-38:320+38] + resid_im = self.guider_image - self.image_fit + resid_im_zoom = resid_im[255-38:255+38, 320-38:320+38] + + fig, axs = plt.subplots(1, 3, figsize=(15, 5)) + + # Left panel - full image + im1 = axs[0].imshow(self.guider_image, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9)) + axs[1].set_aspect(640/512) + image_size_pixels = self.guider_image.shape + image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale) + x_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec + axs[0].xaxis.set_major_locator(x_tick_locator) + y_tick_locator = ticker.MultipleLocator(5/(pixel_scale-0.001)) # Set tick every 5 arcsec + axs[0].yaxis.set_major_locator(y_tick_locator) + xticks = axs[0].get_xticks() + yticks = axs[0].get_yticks() + axs[0].set_xticklabels([f'{int(x * pixel_scale)}' for x in xticks]) + axs[0].set_yticklabels([f'{int(y * pixel_scale)}' for y in yticks]) + axs[0].set_xlabel('Arcseconds', fontsize=12) + axs[0].set_ylabel('Arcseconds', fontsize=12) + axs[0].set_title(str(ObsID)+' - ' + starname + '\n' + + "seeing: " + f"{self.seeing*pixel_scale:.2f}" + '" (z+J)'+ r' $\rightarrow$ ' + + f"{self.seeing_550nm*pixel_scale:.2f}" + '" (V, scaled)', fontsize=12) + axs[0].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5) + #cbar1 = plt.colorbar(im1, ax=axs[0], shrink=0.5) + #cbar1.set_label('Intensity', fontsize=12) + + # Middle panel - zoomed image + im2 = axs[1].imshow(guider_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9)) + axs[1].set_aspect('equal') + image_size_pixels = guider_im_zoom.shape + image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale) + x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec + axs[1].xaxis.set_major_locator(x_tick_locator) + y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec + axs[1].yaxis.set_major_locator(y_tick_locator) + xticks = axs[1].get_xticks() + yticks = axs[1].get_yticks() + axs[1].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks]) + axs[1].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks]) + axs[1].set_xlabel('Arcseconds', fontsize=12) + axs[1].set_ylabel('Arcseconds', fontsize=12) + axs[1].set_title('Guider Image (zoomed in)', fontsize=12) + axs[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5) + cbar2 = plt.colorbar(im2, ax=axs[1], shrink=0.7) + #cbar2.set_label('Intensity', fontsize=12) + + # Right panel - zoomed image of residuals to model + im2 = axs[2].imshow(resid_im_zoom, cmap='viridis', origin='lower', vmin=0, vmax=np.percentile(guider_im_zoom,99.9)) + image_size_pixels = guider_im_zoom.shape + image_size_arcsec = (image_size_pixels[1] * pixel_scale, image_size_pixels[0] * pixel_scale) + x_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec + axs[2].xaxis.set_major_locator(x_tick_locator) + y_tick_locator = ticker.MultipleLocator(0.5/(pixel_scale-0.001)) # Set tick every 0.5 arcsec + axs[2].yaxis.set_major_locator(y_tick_locator) + xticks = axs[2].get_xticks() + yticks = axs[2].get_yticks() + axs[2].set_xticklabels([f'{int(x * pixel_scale*10)/10}' for x in xticks]) + axs[2].set_yticklabels([f'{int(y * pixel_scale*10)/10}' for y in yticks]) + axs[2].set_xlabel('Arcseconds', fontsize=12) + axs[2].set_ylabel('Arcseconds', fontsize=12) + axs[2].set_title('Residuals to Moffat Function Model', fontsize=12) + axs[2].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5) + cbar3 = plt.colorbar(im2, ax=axs[2], shrink=0.7) + #cbar3.set_label('Intensity', fontsize=12) + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=144, facecolor='w') + if show_plot == True: + plt.show() + plt.close() + + def plot_guider_error_time_series(self, fig_path=None, show_plot=False): + + """ + Generate a two-panel plot of the guider time series errors as 1) a time series + and 2) as a 2-D histogram. + + Args: + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + df_GUIDER = Table.read(L0, format='fits',hdu='guider_cube_origins').to_pandas() + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] + if 'TTGAIN' in L0['GUIDER_AVG'].header: + tiptilt_gain = L0['GUIDER_AVG'].header['TTGAIN'] + else: + tiptilt_gain = 0.3 + pixel_scale = 0.056 # arcsec per pixel for the CRED-2 imager on the KPF FIU + + if np.sqrt(df_GUIDER.shape[0]) < 60: + hist_bins = 25 + else: + hist_bins = 40 + + # Create the figure and subplots + fig, axes = plt.subplots(1, 2, figsize=(16, 4), gridspec_kw={'width_ratios': [2, 1]}) + plt.style.use('seaborn-whitegrid') + + x_mas = df_GUIDER.command_x/tiptilt_gain*pixel_scale*1000 + y_mas = df_GUIDER.command_y/tiptilt_gain*pixel_scale*1000 + r_mas = (x_mas**2+y_mas**2)**0.5 + + # Plot the data + im1 = axes[1].hist2d(x_mas, y_mas, bins=hist_bins, cmap='viridis') + axes[1].set_title(r'Guiding Errors - $\langle\,\left|\mathrm{r}\right|\,\rangle$ = ' + f'{int(np.average(np.absolute(r_mas))*10)/10}'+' mas', fontsize=14) + axes[1].set_xlabel('x (mas)', fontsize=14) + axes[1].set_ylabel('y (mas)', fontsize=14) + axes[1].grid(True, linestyle='solid', linewidth=0.5, alpha=0.5) + cbar = plt.colorbar(im1[3]) + cbar.set_label('Samples', fontsize=12) + + axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), x_mas, color='royalblue') + axes[0].plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), y_mas, color='orange') + axes[0].set_title("Guiding Error Time Series: " + str(ObsID)+' - ' + starname, fontsize=14) + axes[0].set_xlabel("Time (sec)", fontsize=14) + axes[0].set_ylabel("Guiding Error (mas)", fontsize=14) + axes[0].legend([r'$\langle\,\left|\mathrm{x}\right|\,\rangle$ = ' + f'{int(np.average(np.absolute(x_mas))*10)/10}' + ' mas', + r'$\langle\,\left|\mathrm{y}\right|\,\rangle$ = ' + f'{int(np.average(np.absolute(y_mas))*10)/10}' + ' mas'], + fontsize=12, loc='best') + + # Set the font size of tick mark labels + axes[0].tick_params(axis='both', which='major', labelsize=14) + axes[1].tick_params(axis='both', which='major', labelsize=14) + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=144, facecolor='w') + if show_plot == True: + plt.show() + plt.close() + + def plot_guider_flux_time_series(self, fig_path=None, show_plot=False): + + """ + Generate a two-panel plot of the guider time series errors as 1) a time series + and 2) as a 2-D histogram. + + To-do: compute the flux in a octagonal aperture centered on the guiding origin + + Args: + fig_path (string) - set to the path for a SNR vs. wavelength file + to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it the current environment + (e.g., in a Jupyter Notebook). + + """ + + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] + guider_header = L0['GUIDER_AVG'].header + + # Construct plots + plt.style.use('seaborn-whitegrid') + plt.figure(figsize=(12, 5), tight_layout=True) + plt.plot(df_GUIDER.timestamp-min(df_GUIDER.timestamp), df_GUIDER.object1_flux/np.nanpercentile(df_GUIDER.object1_flux, 95), color='royalblue') + #plt.plot(time, int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec / max(int_SCI_flux / ((847+4.8/2)-(450.1-0.4/2)) / tdur_sec), marker='o', color='k') + plt.title("Guiding Error Time Series: " + str(ObsID)+' - ' + starname, fontsize=14) + plt.xlabel("Seconds since " + str(guider_header['DATE-BEG']), fontsize=14) + plt.ylabel("Flux (fractional)", fontsize=14) + plt.xticks(fontsize=14) + plt.yticks(fontsize=14) + plt.xlim(min(df_GUIDER.timestamp-min(df_GUIDER.timestamp)), max(df_GUIDER.timestamp-min(df_GUIDER.timestamp))) + plt.legend(['Guider Flux', 'Exposure Meter Flux'], fontsize=12, loc='best') + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=144, facecolor='w') + if show_plot == True: + plt.show() + plt.close() From 4b2a5974c51cbff5eca2de030b411a0e7abc9eff Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 21:31:57 -0700 Subject: [PATCH 242/249] Added plot_2D_image function --- modules/Utils/analyze_2d.py | 47 +++++++++++++++++++++++++++---------- 1 file changed, 35 insertions(+), 12 deletions(-) diff --git a/modules/Utils/analyze_2d.py b/modules/Utils/analyze_2d.py index f738b7e4c..a921843b4 100644 --- a/modules/Utils/analyze_2d.py +++ b/modules/Utils/analyze_2d.py @@ -7,7 +7,7 @@ class Analyze2D: Description: This class contains functions to analyze 2D images (storing them as attributes) and functions to plot the results. - It is only a placeholder for now. + Some of the functions need to be filled in Arguments: 2D - a 2D object @@ -28,17 +28,11 @@ def __init__(self, D2, logger=None): def plot_2D_image(self, chip=None, fig_path=None, show_plot=False): """ - - TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. - THIS IS A PLACEHOLDER. - - Generate a plot of the stitched L0 image. - The image will be divided by 2^16, if appropriate. + Generate a plot of the a 2D image. Args: chip (string) - "green" or "red" - fig_path (string) - set to the path for the file - to be generated. + fig_path (string) - set to the path for the file to be generated. show_plot (boolean) - show the plot in the current environment. Returns: @@ -46,11 +40,42 @@ def plot_2D_image(self, chip=None, fig_path=None, show_plot=False): (e.g., in a Jupyter Notebook). """ + starname = D2['PRIMARY'].header['TARGNAME'] + ObsID = D2['PRIMARY'].header['OFNAME'] + + if chip == 'green' or chip == 'red': + if chip == 'green': + CHIP = 'GREEN' + chip_title = 'Green' + if chip == 'red': + CHIP = 'RED' + chip_title = 'Red' + image = D2[CHIP + '_CCD'].data + + # Generate 2D image + plt.figure(figsize=(10,8), tight_layout=True) + #plt.subplots_adjust(left=0.15, bottom=0.15, right=0.9, top=0.9) + plt.imshow(image, vmin = np.percentile(image,1), + vmax = np.percentile(image,99.5), + interpolation = 'None', + origin = 'lower') + plt.title('2D - ' + chip_title + ' CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) + plt.xlabel('x (pixel number)', fontsize=14) + plt.ylabel('y (pixel number)', fontsize=14) + cbar = plt.colorbar(label = 'Counts (e-)') + cbar.ax.yaxis.label.set_size(14) + cbar.ax.tick_params(labelsize=12) + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=1200, facecolor='w') + if show_plot == True: + plt.show() + plt.close() def plot_2D_order_trace_image(self, chip=None, fig_path=None, show_plot=False): """ - TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. THIS IS A PLACEHOLDER. @@ -72,7 +97,6 @@ def plot_2D_order_trace_image(self, chip=None, fig_path=None, show_plot=False): def plot_2D_image_histogram(self, chip=None, fig_path=None, show_plot=False): """ - TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. THIS IS A PLACEHOLDER. @@ -94,7 +118,6 @@ def plot_2D_image_histogram(self, chip=None, fig_path=None, show_plot=False): def plot_2D_column_cut(self, chip=None, fig_path=None, show_plot=False): """ - TO-DO: MOVE THE PLOTTING CODE FROM THE QLP HERE. THIS IS A PLACEHOLDER. From 3ea67fcd862167fea042f1d1211720d4ce3e8c80 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 21:34:47 -0700 Subject: [PATCH 243/249] Update titles --- modules/Utils/analyze_l0.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 961781a6a..6bde3609a 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -101,9 +101,9 @@ def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=Fals vmin=np.percentile(image,1), vmax=np.percentile(image,99.5)) if chip == 'green': - plt.title(ObsID + ' - L0 (no processing) - Green CCD', fontsize=14) + plt.title('L0 (no processing) - Green CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) if chip == 'red': - plt.title(ObsID + ' - L0 (no processing) - Red CCD', fontsize=14) + plt.title('L0 (no processing) - Red CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) plt.xlabel('x (pixel number)', fontsize=14) plt.ylabel('y (pixel number)', fontsize=14) cbar_label = 'ADU' From aa934110b52d4eba8de1537ab8e4a81179d7325e Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 22:02:56 -0700 Subject: [PATCH 244/249] Updated image size --- modules/Utils/analyze_l0.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/modules/Utils/analyze_l0.py b/modules/Utils/analyze_l0.py index 6bde3609a..3b21cf08e 100644 --- a/modules/Utils/analyze_l0.py +++ b/modules/Utils/analyze_l0.py @@ -28,7 +28,6 @@ def __init__(self, L0, logger=None): def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=False): L0 = self.L0 """ - Generate a plot of the stitched L0 image. The image will be divided by 2^16, if appropriate. @@ -41,9 +40,11 @@ def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=Fals Returns: PNG plot in fig_path or shows the plot it the current environment (e.g., in a Jupyter Notebook). - """ + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] # better to use header keywords than pass in ObsID + if chip == 'green' or chip == 'red': if chip == 'green': CHIP = 'GREEN' @@ -96,7 +97,7 @@ def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=Fals return plt.figure(tight_layout=True) - plt.figure(figsize=(12, 12), tight_layout=True) + plt.figure(figsize=(10, 8), tight_layout=True) plt.imshow(image, cmap='viridis', origin='lower', vmin=np.percentile(image,1), vmax=np.percentile(image,99.5)) @@ -104,8 +105,8 @@ def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=Fals plt.title('L0 (no processing) - Green CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) if chip == 'red': plt.title('L0 (no processing) - Red CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) - plt.xlabel('x (pixel number)', fontsize=14) - plt.ylabel('y (pixel number)', fontsize=14) + plt.xlabel('Column (pixel number)', fontsize=14) + plt.ylabel('Row (pixel number)', fontsize=14) cbar_label = 'ADU' if twotosixteen: cbar_label = cbar_label + r' / $2^{16}$' @@ -116,7 +117,7 @@ def plot_L0_stitched_image(self, ObsID, chip=None, fig_path=None, show_plot=Fals # Display the plot if fig_path != None: - plt.savefig(fig_path, dpi=144, facecolor='w') + plt.savefig(fig_path, dpi=1200, facecolor='w') if show_plot == True: plt.show() plt.close() From f3f2402cc662451a55a1fde5b9edf51ff4cd24b5 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Thu, 29 Jun 2023 22:03:23 -0700 Subject: [PATCH 245/249] Updated axes labels --- modules/Utils/analyze_2d.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Utils/analyze_2d.py b/modules/Utils/analyze_2d.py index a921843b4..a1ae99061 100644 --- a/modules/Utils/analyze_2d.py +++ b/modules/Utils/analyze_2d.py @@ -60,8 +60,8 @@ def plot_2D_image(self, chip=None, fig_path=None, show_plot=False): interpolation = 'None', origin = 'lower') plt.title('2D - ' + chip_title + ' CCD: ' + str(ObsID) + ' - ' + starname, fontsize=14) - plt.xlabel('x (pixel number)', fontsize=14) - plt.ylabel('y (pixel number)', fontsize=14) + plt.xlabel('Column (pixel number)', fontsize=14) + plt.ylabel('Row (pixel number)', fontsize=14) cbar = plt.colorbar(label = 'Counts (e-)') cbar.ax.yaxis.label.set_size(14) cbar.ax.tick_params(labelsize=12) From eec64aa60544afd2d1392eebbb61516fbc3fc229 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 30 Jun 2023 00:00:54 -0700 Subject: [PATCH 246/249] Create analyze_em.py --- modules/Utils/analyze_em.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 modules/Utils/analyze_em.py diff --git a/modules/Utils/analyze_em.py b/modules/Utils/analyze_em.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/modules/Utils/analyze_em.py @@ -0,0 +1 @@ + From 7d5bf80ffa9196b19a9165edd0d70194813db4d1 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 30 Jun 2023 00:01:30 -0700 Subject: [PATCH 247/249] Added plotting functions for exposure meter --- modules/Utils/analyze_em.py | 204 ++++++++++++++++++++++++++++++++++++ 1 file changed, 204 insertions(+) diff --git a/modules/Utils/analyze_em.py b/modules/Utils/analyze_em.py index 8b1378917..84242dd1b 100644 --- a/modules/Utils/analyze_em.py +++ b/modules/Utils/analyze_em.py @@ -1 +1,205 @@ +import numpy as np +import matplotlib.pyplot as plt +from astropy.table import Table +class AnalyzeEM: + + """ + Description: + This class contains functions to analyze EM data + (storing them as attributes) and functions to plot the results. + Some of the functions need to be filled in. + + Arguments: + L0 - an L0 object + + Attributes: + TBD + """ + + def __init__(self, L0, logger=None): + self.L0 = L0 + if logger: + self.logger = logger + self.logger.debug('AnalyzeEM class constructor') + else: + self.logger = None + print('---->AnalyzeEM class constructor') + + def plot_EM_time_series(self, fig_path=None, show_plot=False): + + """ + Generate a time series plot of the Exposure Meter spectra. + + Args: + fig_path (string) - set to the path for the file to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + EM_gain = 1.48424 #np.float(self.config['EM']['gain']) + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] + + # Read data tables + dat_SKY = Table.read(L0, format='fits',hdu='EXPMETER_SKY') + dat_SCI = Table.read(L0, format='fits',hdu='EXPMETER_SCI') + df_SKY_EM = dat_SKY.to_pandas() + df_SCI_EM = dat_SCI.to_pandas() + i = 0 + for col in df_SCI_EM.columns: + if col.lower().startswith('date'): + i += 1 + else: + break + wav_SCI_str = df_SCI_EM.columns[i:] + wav_SCI = df_SCI_EM.columns[i:].astype(float) + wav_SKY_str = df_SKY_EM.columns[i:] + wav_SKY = df_SKY_EM.columns[i:].astype(float) + + # Define time arrays + date_end = np.array(df_SCI_EM["Date-End"], dtype=np.datetime64) + if 'Date-Beg-Corr' in df_SCI_EM.columns: + date_beg = np.array(df_SCI_EM["Date-Beg-Corr"], dtype=np.datetime64) + date_end = np.array(df_SCI_EM["Date-End-Corr"], dtype=np.datetime64) + else: + date_beg = np.array(df_SCI_EM["Date-Beg"], dtype=np.datetime64) + date_end = np.array(df_SCI_EM["Date-End"], dtype=np.datetime64) + tdur_sec = (date_end-date_beg).astype(float)/1000. # exposure duration in sec + time_em = (date_beg-date_beg[0]).astype(float)/1000. # seconds since beginning + + # Define flux arrays + ind_550m = np.where((wav_SCI < 550)) + ind_550_650 = np.where((wav_SCI >= 550) & (wav_SCI < 650)) + ind_650_750 = np.where((wav_SCI >= 650) & (wav_SCI < 750)) + ind_750p = np.where((wav_SCI >= 750)) + int_SCI_flux = df_SCI_EM.sum(axis=1) # flux (ADU) vs. time (per sample) + int_SCI_flux_550m = df_SCI_EM[wav_SCI_str[np.where((wav_SCI < 550))]].sum(axis=1) + int_SCI_flux_550_650 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 550) & (wav_SCI < 650))]].sum(axis=1) + int_SCI_flux_650_750 = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 650) & (wav_SCI < 750))]].sum(axis=1) + int_SCI_flux_750p = df_SCI_EM[wav_SCI_str[np.where((wav_SCI >= 750))]].sum(axis=1) + int_SKY_flux = df_SKY_EM.sum(axis=1) # flux (ADU) vs. time (per sample) + int_SKY_flux_550m = df_SKY_EM[wav_SKY_str[np.where((wav_SKY < 550))]].sum(axis=1) + int_SKY_flux_550_650 = df_SKY_EM[wav_SKY_str[np.where((wav_SKY >= 550) & (wav_SKY < 650))]].sum(axis=1) + int_SKY_flux_650_750 = df_SKY_EM[wav_SKY_str[np.where((wav_SKY >= 650) & (wav_SKY < 750))]].sum(axis=1) + int_SKY_flux_750p = df_SKY_EM[wav_SKY_str[np.where((wav_SKY >= 750))]].sum(axis=1) + + # Plot time series + plt.figure(figsize=(10, 4), tight_layout=True) + total_duration = (date_end[-1]-date_beg[0]).astype(float)/1000. + plt.plot(time_em, int_SCI_flux_750p / (870-750) / tdur_sec, marker='o', color='r', label = '750-870 nm') + plt.plot(time_em, int_SCI_flux_650_750 / (750-650) / tdur_sec, marker='o', color='orange', label = '650-750 nm') + plt.plot(time_em, int_SCI_flux_550_650 / (650-550) / tdur_sec, marker='o', color='g', label = '550-650 nm') + plt.plot(time_em, int_SCI_flux_550m / (550-445) / tdur_sec, marker='o', color='b', label = '445-550 nm') + plt.plot(time_em, int_SCI_flux / (870-445) / tdur_sec, marker='o', color='k', label = 'SCI 445-870 nm') + plt.plot(time_em, int_SKY_flux_750p / (870-750) / tdur_sec,':', marker='o', color='r') + plt.plot(time_em, int_SKY_flux_650_750 / (750-650) / tdur_sec,':', marker='o', color='orange') + plt.plot(time_em, int_SKY_flux_550_650 / (650-550) / tdur_sec,':', marker='o', color='g') + plt.plot(time_em, int_SKY_flux_550m / (550-445) / tdur_sec,':', marker='o', color='b') + plt.plot(time_em, int_SKY_flux / (870-445) / tdur_sec,':', marker='o', color='k', label = 'SKY 445-870 nm') + plt.title('Exposure Meter Time Series: ' + str(ObsID) + ' - ' + starname, fontsize=14) + plt.xlabel("Time (sec)",fontsize=14) + plt.ylabel("Exposure Meter Flux (e-/nm/s)",fontsize=14) + plt.yscale('log') + plt.xlim([-total_duration*0.03,total_duration*1.18]) + plt.xticks(fontsize=12) + plt.yticks(fontsize=12) + plt.legend(fontsize=12, loc='best') + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=200, facecolor='w') + if show_plot == True: + plt.show() + plt.close() + + def plot_EM_spectrum(self, fig_path=None, show_plot=False): + + """ + Generate spectra from the Exposure Meter of the SCI and SKY fibers. + + Args: + fig_path (string) - set to the path for the file to be generated. + show_plot (boolean) - show the plot in the current environment. + + Returns: + PNG plot in fig_path or shows the plot it in the current environment + (e.g., in a Jupyter Notebook). + + """ + + EM_gain = 1.48424 #np.float(self.config['EM']['gain']) + starname = L0['PRIMARY'].header['TARGNAME'] + ObsID = L0['PRIMARY'].header['OFNAME'] + + # Read data tables + dat_SKY = Table.read(L0, format='fits',hdu='EXPMETER_SKY') + dat_SCI = Table.read(L0, format='fits',hdu='EXPMETER_SCI') + df_SKY_EM = dat_SKY.to_pandas() + df_SCI_EM = dat_SCI.to_pandas() + i = 0 + for col in df_SCI_EM.columns: + if col.lower().startswith('date'): + i += 1 + else: + break + wav_SCI_str = df_SCI_EM.columns[i:] + wav_SCI = df_SCI_EM.columns[i:].astype(float) + wav_SKY_str = df_SKY_EM.columns[i:] + wav_SKY = df_SKY_EM.columns[i:].astype(float) + + # Define dispersion + disp_SCI = wav_SCI*0+np.gradient(wav_SCI,1)*-1 + disp_SKY = wav_SKY*0+np.gradient(wav_SKY,1)*-1 + disp_SCI_smooth = np.polyval(np.polyfit(wav_SCI,disp_SCI, deg=6),wav_SCI) + disp_SKY_smooth = np.polyval(np.polyfit(wav_SKY,disp_SKY, deg=6),wav_SKY) + df_SCI_EM_norm = df_SCI_EM[wav_SCI_str] * EM_gain /disp_SCI_smooth + df_SKY_EM_norm = df_SKY_EM[wav_SCI_str] * EM_gain /disp_SKY_smooth + + # Define time arrays + date_end = np.array(df_SCI_EM["Date-End"], dtype=np.datetime64) + if 'Date-Beg-Corr' in df_SCI_EM.columns: + date_beg = np.array(df_SCI_EM["Date-Beg-Corr"], dtype=np.datetime64) + date_end = np.array(df_SCI_EM["Date-End-Corr"], dtype=np.datetime64) + else: + date_beg = np.array(df_SCI_EM["Date-Beg"], dtype=np.datetime64) + date_end = np.array(df_SCI_EM["Date-End"], dtype=np.datetime64) + tdur_sec = (date_end-date_beg).astype(float)/1000. # exposure duration in sec + time_em = (date_beg-date_beg[0]).astype(float)/1000. # seconds since beginning + int_SCI_spec = df_SCI_EM_norm[:5].sum(axis=0) / np.sum(tdur_sec[:5]) # flux vs. wavelength per sec (use first five samples) + int_SKY_spec = df_SKY_EM_norm[:5].sum(axis=0) / np.sum(tdur_sec[:5]) # flux vs. wavelength per sec (use first five samples) + + # Plot spectra + plt.figure(figsize=(10, 4), tight_layout=True) + fig, ax1 = plt.subplots(figsize=(12, 6), tight_layout=True) + plt.axvspan(445, 550, alpha=0.5, color='b') + plt.axvspan(550, 650, alpha=0.5, color='g') + plt.axvspan(650, 750, alpha=0.5, color='orange') + plt.axvspan(750, 870, alpha=0.5, color='red') + plt.xticks(fontsize=14) + plt.yticks(fontsize=14) + plt.xlim(445,870) + lns1 = ax1.plot(wav_SCI, int_SCI_spec, marker='.', color='k', label ='SCI',zorder = 1) + ax2 = ax1.twinx() + lns2 = ax2.plot(wav_SKY, int_SKY_spec, marker='.', color='brown', label = 'SKY',zorder = 0, alpha = 0.5) + ax1.set_ylim(0,np.percentile(int_SCI_spec,99.9)*1.1) + ax2.set_ylim(0,np.percentile(int_SKY_spec,99.9)*1.1) + plt.title('Exposure Meter Spectrum: ' + str(ObsID) + ' - ' + starname, fontsize=14) + plt.yticks(fontsize=14, color='brown') + ax1.set_xlabel("Wavelength (nm)",fontsize=14) + ax1.set_ylabel("SCI Exposure Meter Flux (e-/nm/s)",fontsize=14) + ax2.set_ylabel("SKY Exposure Meter Flux (e-/nm/s)",fontsize=14, color='brown') + lns = lns1+lns2 + labs = [l.get_label() for l in lns] + ax1.legend(lns, labs, loc=0,fontsize=14) + + # Display the plot + if fig_path != None: + plt.savefig(fig_path, dpi=200, facecolor='w') + if show_plot == True: + plt.show() + plt.close() From 9717ff34460c4fdc17ad47614dff4e42a4a39f46 Mon Sep 17 00:00:00 2001 From: Andrew Howard Date: Fri, 30 Jun 2023 00:03:42 -0700 Subject: [PATCH 248/249] minor --- modules/Utils/analyze_em.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Utils/analyze_em.py b/modules/Utils/analyze_em.py index 84242dd1b..ed4890323 100644 --- a/modules/Utils/analyze_em.py +++ b/modules/Utils/analyze_em.py @@ -6,7 +6,7 @@ class AnalyzeEM: """ Description: - This class contains functions to analyze EM data + This class contains functions to analyze Exposure Meter data (storing them as attributes) and functions to plot the results. Some of the functions need to be filled in. From 5e8e16011f7b127d94311e4c9f0ac8231896b39f Mon Sep 17 00:00:00 2001 From: Russ Laher Date: Fri, 30 Jun 2023 08:55:29 -0700 Subject: [PATCH 249/249] Modified to handle missing CA_HK extension in inputs. --- modules/master_dark/src/master_dark_framework.py | 13 +++++++++++-- modules/master_flat/src/master_flat_framework.py | 13 +++++++++++-- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/modules/master_dark/src/master_dark_framework.py b/modules/master_dark/src/master_dark_framework.py index 1286cb5bf..dadafc621 100644 --- a/modules/master_dark/src/master_dark_framework.py +++ b/modules/master_dark/src/master_dark_framework.py @@ -218,14 +218,23 @@ def _perform(self): frames_data_mjdobs.append(mjd_obs) frames_data_path.append(path) + np_frames_data = np.array(frames_data) + np_bias_data = np.array(master_bias_data[ffi]) + + self.logger.debug('ffi,np.shape(np_frames_data),np.shape(np_bias_data) = {},{},{}'.format(ffi,np.shape(np_frames_data),np.shape(np_bias_data))) + + if len(np.shape(np_bias_data)) != 2: + self.logger.debug('Master bias missing for ffi = {}'.format(ffi)) + keep_ffi = 0 + if keep_ffi == 0: self.logger.debug('ffi,keep_ffi = {},{}'.format(ffi,keep_ffi)) del_ext_list.append(ffi) break - frames_data = np.array(frames_data) - np.array(master_bias_data[ffi]) # Subtract master bias. + frames_data = np_frames_data - np_bias_data # Subtract master bias. - self.logger.debug('Subtracting master bias from dark data...') + self.logger.debug('Subtracted master bias from dark data...') normalized_frames_data=[] n_frames = (np.shape(frames_data))[0] diff --git a/modules/master_flat/src/master_flat_framework.py b/modules/master_flat/src/master_flat_framework.py index 84a49c485..fb204b1cf 100644 --- a/modules/master_flat/src/master_flat_framework.py +++ b/modules/master_flat/src/master_flat_framework.py @@ -253,14 +253,23 @@ def _perform(self): frames_data_path.append(path) self.logger.debug('Keeping flat image: i,fitsfile,ffi,mjd_obs,exp_time = {},{},{},{},{}'.format(i,all_flat_files[i],ffi,mjd_obs,exp_time)) + np_frames_data = np.array(frames_data) + np_bias_data = np.array(master_bias_data[ffi]) + + self.logger.debug('ffi,np.shape(np_frames_data),np.shape(np_bias_data) = {},{},{}'.format(ffi,np.shape(np_frames_data),np.shape(np_bias_data))) + + if len(np.shape(np_bias_data)) != 2: + self.logger.debug('Master bias missing for ffi = {}'.format(ffi)) + keep_ffi = 0 + if keep_ffi == 0: self.logger.debug('ffi,keep_ffi = {},{}'.format(ffi,keep_ffi)) del_ext_list.append(ffi) break - frames_data = np.array(frames_data) - np.array(master_bias_data[ffi]) # Subtract master bias. + frames_data = np_frames_data - np_bias_data # Subtract master bias. - self.logger.debug('Subtracting master bias from flat data...') + self.logger.debug('Subtracted master bias from flat data...') normalized_frames_data=[] n_frames = (np.shape(frames_data))[0]