From a88b279f564f76d32118b6074e7cd79c72e00ba8 Mon Sep 17 00:00:00 2001 From: Patrick Ogle Date: Fri, 1 Dec 2023 14:39:37 -0500 Subject: [PATCH 1/9] first commit of advanced ifu_optimal notebook --- notebooks/ifu_optimal/ifu_optimal.ipynb | 601 +++++++----------- .../sdssj1652_nirspec_ifu_cubeviz.png | Bin 0 -> 148596 bytes 2 files changed, 238 insertions(+), 363 deletions(-) create mode 100644 notebooks/ifu_optimal/sdssj1652_nirspec_ifu_cubeviz.png diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 8b60f9c26..44675c488 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -4,18 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# IFU Optimal Spectral Extraction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Use case:** optimal spectral extraction; method by [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract).
\n", - "**Data:** JWST simulated NIRSpec IFU data; point sources.
\n", - "**Tools:** jwst, webbpsf, matplotlib, scipy, custom functions.
\n", - "**Cross-intrument:** any spectrograph.
\n", - "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis).
" + "# Advanced: NIRSpec IFU Optimal Point Source Extraction" ] }, { @@ -29,17 +18,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "* _time_ for adding a delay in Cubeviz file upload\n", "* _numpy_ for array math\n", - "* _scipy_ for gaussian smoothing\n", + "* _scipy_ for ndimage shift\n", "* _specutils_ for Spectrum1D data model\n", - "* _jdaviz_ : Cubeviz data visualization tool\n", + "* _jdaviz_ : for data visualization\n", "* _photutils_ to define circular apertures\n", "* _astropy.io_ for reading and writing FITS cubes and images\n", "* _astropy.wcs, units, coordinates_ for defining and reading WCS\n", "* _astropy.stats_ for sigma_clipping\n", "* _astropy.utils_ for downloading files from URLs\n", - "* _matplotlib_ for plotting spectra and images" + "* _matplotlib_ for plotting spectra and images\n", + "* _os_ for file management\n", + "* _astroquery.mast_ to download the data" ] }, { @@ -48,16 +38,21 @@ "metadata": {}, "outputs": [], "source": [ - "import time\n", "import numpy as np\n", "import scipy\n", + "import specutils\n", "from specutils import Spectrum1D\n", - "from jdaviz import Cubeviz\n", - "from photutils import CircularAperture, aperture_photometry \n", + "from specutils.manipulation import spectral_slab\n", + "import jdaviz\n", + "from jdaviz import Cubeviz, Imviz, Specviz\n", + "print(\"jdaviz Version={}\".format(jdaviz.__version__))\n", + "from photutils.aperture import CircularAperture, SkyCircularAperture, aperture_photometry \n", "from astropy.io import fits\n", "from astropy import wcs\n", "from astropy.stats import sigma_clip\n", - "from astropy.utils.data import download_file" + "from astropy.utils.data import download_file\n", + "import os\n", + "from astroquery.mast import Observations" ] }, { @@ -77,19 +72,34 @@ "source": [ "## Introduction\n", "\n", - " This notebook illustrates various extraction methods for a point source in JWST NIRSpec IFU data. First we\n", - "demonstrate a number of regular extraction techniques, including subset extraction with Cubeviz, simple sum over spaxels, cylindrical aperture, and conical aperture photometry. Then we compare optimal extraction using a WebbPSF model PSF to optimal extraction using a reference star PSF. \n" + "This notebook illustrates various extraction methods for a point source in JWST NIRSpec IFU data, utilizing the [Q3D](https://q3d.github.io/) (PID 1335) observation of quasar SDSS J165202.64+172852.3. The extraction techniques include subset extraction with Cubeviz, simple sum over spaxels, cylindrical aperture, conical aperture photometry, and optimal point source extraction using a WebbPSF model PSF. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Read in Simulated NIRSpec IFU Cube\n", - "\n", - "A faint (quasar) point source was simulated using the NIRSpec Instrument Performance Simulator (IPS), then run through the JWST Spec2 pipeline. We will use this for our science dataset.\n", + "## Read in NIRSpec IFU Cube\n", "\n", - "We read in the data both with fits.open and Spectrum1D.read, since the cube handling (slicing) we need to do is not implemented in specutils yet." + "The NIRSpec IFU observation of quasar SDSS J1652+1728 (redshift z=1.9) was taken using the G235H grating with F170LP filter, covering 1.66-3.17 microns at a spectral resolution of R~2700. The IFU spaxels are 0.1\" on a side. \n", + "The level-3 pipeline_processed datacube (s3d.fits, which combines all dithered exposures) is retrieved from MAST \n", + "in the next notebook cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download the data file\n", + "uri = f\"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", + "result = Observations.download_file(uri, base_url='https://mast.stsci.edu/api/v0.1/Download/file')\n", + "if result[0] == 'ERROR':\n", + " raise RuntimeError('Error retrieving file: ' + result[1])\n", + " \n", + "# Construct the local filepath \n", + "filename = os.path.join(os.path.abspath('.'), uri.rsplit('/', 1)[-1])" ] }, { @@ -98,12 +108,11 @@ "metadata": {}, "outputs": [], "source": [ - "# NIRSpec IFU science data cube\n", - "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", - "filename = BoxPath + \"NRS00001-faintQSO-F100LP-G140H-01_1_491_SE_2020-08-25T12h15m00_s3d.fits\"\n", - "\n", "# Open and inspect the file and WCS\n", - "# Load with astropy.fits.open\n", + "# Replace MAST data with custom reprocessed data:\n", + "file_dir = \"/Users/pogle/Desktop/NIRSpec/ifu_optimal/q3d_sdss1652_ifu_rerun/extended_source/\"\n", + "#filename=\"jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", + "#filename= file_dir + 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits'\n", "with fits.open(filename, memmap=False) as hdulist:\n", " sci = hdulist['SCI'].data\n", " err = hdulist['ERR'].data\n", @@ -111,43 +120,23 @@ " hdr = hdulist[1].header\n", " hdulist.info()\n", " print(w)\n", - " \n", - "# Load with Spectrum1D \n", - "spec1d = Spectrum1D.read(filename)\n", - "\n", - "# Wavelengths\n", - "wavelength = np.array(spec1d.spectral_axis.value)\n", - "print(wavelength)\n", "\n", - "# Sum over spaxels\n", - "fnu_sum = np.sum(spec1d.flux, axis=(0, 1))\n", + "# Window the wavelength range to focus on Hbeta-[OIII]\n", + "spec1d = Spectrum1D.read(filename)\n", + "slice_range= range(500,1100,1) \n", + "wavelength = np.array(spec1d.spectral_axis.value)[slice_range[0]:slice_range[-1]+1]\n", "\n", "# List of cube slices for aperture photometry\n", - "data = []\n", - "var = []\n", - "spec1d_len = len(spec1d.spectral_axis.value)\n", - "for idx in range(spec1d_len): \n", - " data.append(sci[idx, :, :])\n", - " var.append(err[idx, :, :]) # variance = err, not variance = err**2. Squaring the err gives noisy results. \n", + "sci_data = []\n", + "sci_var = []\n", + "for idx in slice_range: \n", + " sci_data.append(sci[idx, :, :])\n", + " sci_var.append(err[idx, :, :]) # variance = err, not variance = err**2. Squaring the err gives noisy results.\n", "\n", - "# Window data and variance (and replace NaNs)\n", - "# The existing JWST pipeline window is overgenerous (39x33 instead of the nominal 30x30 pixels)\n", - "data_win = np.nan_to_num(np.array(data)[:, 5:-4, 3:])\n", - "data_var = np.nan_to_num(np.array(var)[:, 5:-4, 3:]) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Note:* Can we fix or suppress this AsdfWarning?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Note:* Is there a way to read in with only Spectrum1D to perform all of our cube operations, not using fits.open()?" + "data = np.nan_to_num(np.array(sci_data))\n", + "var = np.array(sci_var)\n", + "print()\n", + "print(\"Trimmed data shape:\", data.shape)\n" ] }, { @@ -157,102 +146,57 @@ "## Visualize Science Data with Cubeviz" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Note:* Cubeviz incompatible with jupyter_client 6.1.6. Use jupyter_client 5.3.5 instead." - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "cubeviz = Cubeviz()\n", - "cubeviz.app" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### UI Instructions:\n", - "* Load science datacube into Cubeviz using the next code cell below\n", - "* Go to the Hammer-and-Screwdriver icon: Gear icon: Layer in the leftmost image viewer \n", - "* In that tab, change the Linear stretch to 90 percentile to see the faint QSO target at (x,y) ~ (17, 21)\n", - "* Scrubbing through the cube also helps to locate the source\n", - "* Select a circular subset region centered on the source. \n", - "* Note that the region is pixelated and doesn't include fractional pixels\n", - "* Change the collapse method to \"Sum\" in spectrum viewer: Gear icon : Viewer \n", - "* --This \"Sum\" method yields our subset extraction\n", - "* Change the vertical zoom to see the spectral features in the Subset spectrum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Notes*: \n", + "# Launch Cubeviz and load the data cube\n", "\n", - "(1) Image viewer contrast settings change when you click on the side bar to expand/contract jupyter scroll window\n", - "\n", - "(2) Spectrum viewer: Viewer cube collapse method should default to Sum (not Maximum)\n", + "cubeviz = Cubeviz()\n", + "cubeviz.load_data(filename)\n", + "cubeviz.show()\n", "\n", - "(3) Spectrum viewer y scale returns to autoscale when the region is moved, and y-zoom has to be adjusted again\n", + "# Set spectrum display limits\n", + "cubeviz.specviz.x_limits(1.65*u.um,2.4*u.um)\n", + "cubeviz.specviz.y_limits(0.0, 5.0E3)\n", "\n", - "(4) Region selection appears away from cursor after opening hammer-and-screwdriver to change cube viewer contrast" + "#Select slice to visualize\n", + "cubeviz.select_slice(714)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load Cube into Cubeviz" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Data from local directory\n", - "# cubeviz.app.load_data(filename)\n", - "\n", - "# Data from url:\n", - "url = filename\n", - "df = download_file(url)\n", - "time.sleep(2) # Sleep to avoid glue-jupyter timing issue\n", - "cubeviz.app.load_data(df)" + "\"text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*Developer Note:* Spectral cube does not yet recognize JWST NIRSpec IFU datacubes, giving the above warning\n", - "for each FITS extension." + "### UI Instructions:\n", + "* Scrub through the cube to the [OIII] 5007 line (redshifted to ~1.98 microns) using the spectrum-viewer slice tool\n", + "* In the flux-viewer, select one circular subset region centered on the quasar, and a square region to delimit the good area for spectral and background extraction\n", + "* Note that the regions are pixelated and don't include fractional pixels\n", + "* The default collapse method is \"Sum\" in the spectrum viewer (see Plot Options:Line). \"Median\" may also be useful for visualization but will not give an accurate measurement of the total flux.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Export Region from Cubeviz\n", - "Export the region defined by the user in Cubeviz as an astropy CirclePixel Region, which has units of pixels." + "*Developer Note:* There is a jdaviz ticket to export plots from viewers to create static views like the one above of the viz output." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*Developer Note:* cubeviz.app.get_subsets_from_viewer method doesn't work if there are more than 2 datasets selected in the spectrum viewer:\n", - "\n", - "#region1 = cubeviz.app.get_subsets_from_viewer('spectrum-viewer')\n", - "\n", - "#print(region1['Subset1'])\n" + "## Export Source and Good Data Regions from Cubeviz\n", + "Export the region defined by the user in Cubeviz as astropy PixelRegions" ] }, { @@ -262,20 +206,47 @@ "outputs": [], "source": [ "cubeviz_data = cubeviz.app.data_collection[0]\n", + "\n", + "print()\n", + "print('Source Region')\n", "try:\n", - " region1 = cubeviz_data.get_selection_definition(format='astropy-regions')\n", + " region1 = cubeviz_data.get_selection_definition('Subset 1', format='astropy-regions')\n", " print(region1)\n", " region1_exists = True\n", "except Exception:\n", - " print(\"There are no regions selected in the cube viewer.\")\n", - " region1_exists = False" + " print(\"There is no Subset 1 selected in the cube viewer.\")\n", + " region1_exists = False\n", + " \n", + "print()\n", + "print('Good Data Region')\n", + "try:\n", + " region2 = cubeviz_data.get_selection_definition('Subset 2', format='astropy-regions')\n", + " print(region2)\n", + " region2_exists = True\n", + " #help(region2)\n", + " data_xrange=[round(region2.center.x - region2.width/2), round(region2.center.x + region2.width/2)]\n", + " data_yrange=[round(region2.center.y - region2.height/2), round(region2.center.y + region2.height/2)]\n", + " print('Good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n", + " \n", + " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", + " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", + "\n", + "\n", + "except Exception:\n", + " print(\"There is no Subset 2 selected in the cube viewer.\")\n", + " region1_exists = False\n", + " data_xrange=[7,36]\n", + " data_xrange=[6,33]\n", + " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", + " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", + " print('Good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Extract Subset Spectrum in Cubeviz Spectrum Viewer\n", + "## Extract Subset Spectrum and Background in Cubeviz Spectrum Viewer\n", "Retrieve the collapsed spectrum (Subset1) of the user-defined region from the Spectrum Viewer as a Spectrum1D object." ] }, @@ -285,18 +256,30 @@ "metadata": {}, "outputs": [], "source": [ + "subsets = cubeviz.specviz.get_spectra()\n", + "print(subsets.keys())\n", + "\n", + "print('Source')\n", "try:\n", - " spectrum_subset1 = cubeviz.app.get_data_from_viewer('spectrum-viewer')['Subset 1']\n", + " spectrum_subset1 = subsets[[i for i in subsets.keys() if 'Subset 1' in i][0]]\n", " print(spectrum_subset1)\n", "except Exception:\n", - " print(\"There are no subsets selected in the spectrum viewer.\")" + " print(\"There is no Subset 1 selected in the spectrum viewer.\")\n", + " \n", + "print()\n", + "print('Background')\n", + "try:\n", + " spectrum_subset2 = spectrum_subset2 = subsets[[i for i in subsets.keys() if 'Subset 2' in i][0]]\n", + " print(spectrum_subset2)\n", + "except Exception:\n", + " print(\"There is no Subset 2 selected in the spectrum viewer.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*Developer Note:* Can we suppress or fix this glue/core warning?" + "*Developer Note:* The units of the Cubeviz \"Sum\" collapse method need to be multiplied by the pixel area in sr to yield flux units (MJy) instead of surface brightness units (MJy/sr)." ] }, { @@ -308,6 +291,13 @@ "Perform a simple numpy sum over all spaxels in the cube as a rudimentary extraction method. Also sum over wavelength to collapse the cube." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " *Developer Note:* Need to convert all extracted spectra to flux units." + ] + }, { "cell_type": "code", "execution_count": null, @@ -315,17 +305,26 @@ "outputs": [], "source": [ "# Sum over wavelength\n", - "cube_sum = np.sum(data_win, axis=0)\n", + "# Clip data for display purposes\n", + "clip_level = 4E4\n", + "data_clipped = np.clip(good_data,0,clip_level)\n", + "cube_sum = np.sum(data_clipped, axis=0) \n", + "\n", + "# Extraction via sum over spaxels\n", + "fnu_sum = np.sum(good_data, axis=(1, 2))\n", + "fnu_sum_clipped = np.clip(fnu_sum,0,clip_level)\n", + "flux_spaxsum = np.array(fnu_sum) * u.MJy/u.sr\n", + "spec1d_spaxsum = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_spaxsum)\n", "\n", "# Plots\n", - "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) \n", + "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) \n", + "\n", "ax1.plot(wavelength, fnu_sum) \n", - "ax1.set_xlim(0.95, 1.5)\n", "ax1.set_title(\"Spaxel sums\")\n", "ax1.set_xlabel(\"Wavelength (um)\") \n", - "ax1.set_ylabel(\"SCA491 Flux Density\")\n", - "\n", - "ax2.imshow(cube_sum, norm=LogNorm())\n", + "ax1.set_ylabel(\"Flux (MJy/sr)\")\n", + "ax1.set_ylim(0,5E3)\n", + "ax2.imshow(cube_sum , norm=LogNorm(vmin=100,vmax=clip_level),origin='lower')\n", "ax2.set_title(\"Slice sums\")\n", "\n", "plt.show()" @@ -360,9 +359,12 @@ "\n", "cylinder_sum = []\n", "for slice2d in data:\n", - " phot_table = aperture_photometry(slice2d, aperture, wcs=w.celestial, method='exact')\n", + " #phot_table = aperture_photometry(slice2d, aperture, wcs=w.celestial, method='exact')\n", " phot_table = aperture_photometry(slice2d, aperture)\n", - " cylinder_sum.append(phot_table['aperture_sum'][0])" + " cylinder_sum.append(phot_table['aperture_sum'][0])\n", + " \n", + "flux_cylinder = np.array(cylinder_sum) * u.MJy/u.sr\n", + "spec1d_cylinder = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_cylinder)" ] }, { @@ -395,9 +397,13 @@ "for (slice2d, wave) in zip(data, wavelength):\n", " idx = idx + 1\n", " r_cone = r_pix * wave / lambda0\n", + " \n", " aperture_cone = CircularAperture(center_xy, r=r_cone)\n", " phot_table = aperture_photometry(slice2d, aperture_cone, wcs=w.celestial, method='exact')\n", - " cone_sum.append(phot_table['aperture_sum'][0])" + " cone_sum.append(phot_table['aperture_sum'][0])\n", + " \n", + "flux_cone = np.array(cone_sum) * u.MJy/u.sr\n", + "spec1d_cone = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_cone)" ] }, { @@ -416,28 +422,28 @@ "source": [ "f, (ax1) = plt.subplots(1, 1, figsize=(15, 5)) \n", "\n", - "ax1.set_title(\"Non-optimal spectral extractions\")\n", - "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", - "ax1.set_ylabel(\"Flux Density\")\n", - "ax1.set_xlim(0.95, 1.5)\n", - "ax1.set_ylim(0, 0.6)\n", - "ax1.plot(wavelength, np.array(cylinder_sum), label=\"Cylinder\", c='b')\n", - "ax1.plot(wavelength, np.array(cone_sum), label=\"Cone\", c='darkorange', alpha=0.5)\n", + "#ax1.plot(wavelength, flux_spaxsum.value, label=\"All spaxels\", c='k')\n", + "ax1.plot(wavelength, flux_cylinder.value, label=\"Cylinder\", c='b')\n", + "ax1.plot(wavelength, flux_cone.value, label=\"Cone\", c='darkorange', alpha=0.5)\n", "try:\n", - " ax1.plot(wavelength, spectrum_subset1.flux.value, c='r', label=\"Subset1\", alpha=0.4)\n", + " ax1.plot(wavelength, spectrum_subset1.flux.value[slice_range[0]:slice_range[-1]+1], c='r', label=\"Subset1\", alpha=0.4)\n", "except Exception:\n", " print(\"There is no Cubeviz Subset1 spectrum to plot.\")\n", - "ax1.legend()\n", "\n", - "plt.show()" + "ax1.set_title(\"Non-optimal spectral extractions\")\n", + "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", + "ax1.set_ylabel(\"Flux Density\")\n", + "ax1.set_ylim(0,5.0E3)\n", + "ax1.legend()\n", + "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Comparison of the (non-optimal) cylindrical, conical, and Cubeviz subset spectral extractions. \n", - "The conical extraction captures slightly more flux but is noisier than the other spectra at long wavelengths.\n", + "The non-optimal cylindrical, conical, and CubeViz subset spectral extractions are quite similar. \n", + "The conical extraction captures imperceptibly more flux at long wavelengths.\n", "Red-shifted Broad H-beta and narrow [O III] lines are visible in the quasar spectra. " ] }, @@ -453,7 +459,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Caution! The WebbPSF model takes about 10 hr to run. Uncomment the following cell to do so. Otherwise, read in the precomputed WebbPSF model, which covers the full F100LP/G140H wavelength range (blue and red). For other filter/grating combinations, uncomment and run the cell below using the wavelengths from the science data set." + "WebbPSF installation instructions can be found in [ReadTheDocs](https://webbpsf.readthedocs.io/en/latest/).\n", + "\n", + "Caution! The WebbPSF model takes about 10 hr to run. Uncomment the following cell to do so. Otherwise, read in the precomputed WebbPSF model, which covers the full wavelength range of the present NIRSpec G235H dataset. For other filter/grating combinations, uncomment and run the cell below using the wavelengths from the science data set." ] }, { @@ -462,18 +470,17 @@ "metadata": {}, "outputs": [], "source": [ - "'''\n", - "#WebbPSF imports\n", - "%pylab inline\n", - "import webbpsf\n", + "##WebbPSF imports\n", + "#%pylab inline\n", + "#import webbpsf\n", + "#\n", + "##WebbPSF commands used to create PSF model cube\n", + "#ns = webbpsf.NIRSpec()\n", + "#ns.image_mask = \"IFU\" # Sets to 3x3 arcsec square mask\n", "\n", - "#WebbPSF commands used to create PSF model cube\n", - "ns.image_mask = \"IFU\" # Sets to 3x3 arcsec square mask\n", - "ns = webbpsf.NIRSpec()\n", - "wavelengths = wavelength*1.0E-6\n", - "psfcube = ns.calc_datacube(wavelengths, fov_pixels=30, oversample=4, add_distortion=True)\n", - "psfcube.writeto(\"Webbpsf_ifucube.fits\")\n", - "'''" + "#wavelengths = wavelength*1.0E-6\n", + "#psfcube = ns.calc_datacube(wavelengths, fov_pixels=30, oversample=4, add_distortion=True)\n", + "#psfcube.writeto(\"Webbpsf_ifucube.fits\")\n" ] }, { @@ -485,18 +492,22 @@ "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", "psf_filename = BoxPath+\"Webbpsf_ifucube.fits\"\n", "\n", - "# Load with astropy.fits.open\n", + "# Open WebbPSF data cube\n", "with fits.open(psf_filename, memmap=False) as hdulist:\n", " psf_model = hdulist['DET_SAMP'].data\n", " psf_hdr = hdulist['DET_SAMP'].header\n", " hdulist.info() \n", - "print(psf_model.shape)\n", + "\n", + "# Pad PSF model cube with zeros to match the present dataset\n", + "# (Different padding may be needed for your particular dataset)\n", + "print(sci.shape,psf_model.shape)\n", + "psf_model_padded = np.pad(psf_model, ((0,0),(4,5), (6,7)), 'constant')\n", "\n", "# Sum over wavelength\n", - "psf_model_sum = np.sum(psf_model, axis=0)\n", + "psf_model_sum = np.sum(psf_model_padded[slice_range[0]:slice_range[-1]+1], axis=0)\n", "\n", "# Sum over spaxels\n", - "psf_model_fnusum = np.sum(psf_model, axis=(1, 2))" + "psf_model_fnusum = np.sum(psf_model_padded[slice_range[0]:slice_range[-1]+1], axis=(1, 2))" ] }, { @@ -512,14 +523,11 @@ "metadata": {}, "source": [ "## Align Model PSF Cube with Science Data\n", - "Flip, smooth, and shift the model PSF cube to align with the simulated data. Trim the simulated data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Note:* Automate this by finding the (x,y) offset between the Model and simulated PSF peaks. Currently the shift is determined empirically by eye." + "Flip, smooth, and shift the model PSF cube to align with the simulated data. Trim the simulated data. \n", + "\n", + "Important Note 1: this PSF will likely be rotated with respect to your dataset, depending on telescope roll angle. You can either rotate it to match your data or reprocess your data using the ifualign keyword to align the WCS with the instrumental coordinate frame.\n", + "\n", + "Important Note 2: this PSF will likely be shifted with respect to your dataset. It would be beneficial to automatically find the (x,y) offset between the data and simulated PSF peaks. Currently the shift is determined empirically by eye." ] }, { @@ -528,56 +536,42 @@ "metadata": {}, "outputs": [], "source": [ - "# Flip model PSF left-right. For some unknown reason, WebbPSF is flipped with respect to the IPS simulation.\n", - "psf_model_fliplr = psf_model[:, ::-1, :]\n", - "\n", - "# Smooth model\n", - "# EMSM smoothing for G140H grating\n", - "scalerad = 0.046 # sigma (arcsec)\n", - "pixelscale = 0.1\n", - "scalerad_pix = scalerad / pixelscale\n", - "psf_model_smoothed = scipy.ndimage.filters.gaussian_filter(psf_model_fliplr, \n", - " (0.0, scalerad_pix, scalerad_pix), \n", - " order=0, mode='reflect', cval=0.0, \n", - " truncate=10.0)\n", + "# Flip model PSF left-right to match data.\n", + "psf_model_fliplr = psf_model_padded[:, ::-1, :]\n", "\n", "# Empirically (chi-by-eye) determined shift\n", - "shiftx = 1.75 \n", - "shifty = 0.\n", + "shiftx = 1.5 #2 \n", + "shifty = 1.5 #1.5\n", "\n", "# Shift model PSF using linear interpolation\n", - "psf_model_aligned = scipy.ndimage.shift(psf_model_smoothed, (0.0, shiftx, shifty), order=1, \n", + "psf_model_aligned = scipy.ndimage.shift(psf_model_fliplr, (0.0, shiftx, shifty), order=1, \n", " mode='constant', cval=0.0, prefilter=True)\n", "\n", + "good_psf_model = psf_model_aligned[slice_range[0]:slice_range[-1]+1, data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", + "\n", "# Sum over wavelength\n", - "psf_model_sum = np.sum(psf_model_aligned, axis=0)\n", + "psf_model_sum = np.sum(good_psf_model, axis=0)\n", "\n", "# Scale factor for PSF subtraction\n", - "psf_sum_min = np.amin(psf_model_sum)\n", "psf_sum_max = np.amax(psf_model_sum)\n", "scalefactor = np.amax(cube_sum) / psf_sum_max\n", + "print(scalefactor)\n", "\n", "# Plots\n", - "f, ([ax1, ax2, ax3], [ax4, ax5, ax6]) = plt.subplots(2, 3, figsize=(10, 10)) \n", + "f, ([ax1, ax2], [ax3, ax4]) = plt.subplots(2, 2, figsize=(10, 10)) \n", "\n", "ax1.set_title(\"PSF slice sum\")\n", - "ax1.imshow(psf_model_sum, norm=LogNorm())\n", + "ax1.imshow(psf_model_sum, norm=LogNorm(),origin='lower')\n", "\n", "ax2.set_title(\"Science Data slice sum\")\n", - "ax2.imshow(cube_sum, norm=LogNorm()) \n", + "ax2.imshow(cube_sum, norm=LogNorm(),origin='lower') \n", "\n", "ax3.set_title(\"Data / PSF Ratio\")\n", - "ax3.imshow(cube_sum / psf_model_sum, norm=LogNorm())\n", + "ax3.imshow(cube_sum / psf_model_sum, norm=LogNorm(vmin=1,vmax=1E6),origin='lower')\n", "\n", - "ax4.set_title(\"PSF Model integrated flux\")\n", - "ax4.plot(psf_model_fnusum)\n", - "\n", - "ax5.set_title(\"Data - PSF\")\n", - "ax5.imshow(cube_sum - scalefactor * psf_model_sum)\n", - "\n", - "im6 = ax6.imshow(np.log10(np.absolute(cube_sum - scalefactor * psf_model_sum)))\n", - "plt.colorbar(im6)\n", - "ax6.set_title(\"log abs(Data - PSF)\")\n", + "im4 = ax4.imshow(np.log10(np.absolute(cube_sum - 0.75*scalefactor * psf_model_sum)),origin='lower')\n", + "plt.colorbar(im4)\n", + "ax4.set_title(\"log abs(Data - PSF)\")\n", "\n", "plt.show()" ] @@ -586,10 +580,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "_Figure top row_: Comparison of smoothed, aligned WebbPSF PSF (left) to IPS simulation (center). \n", + "_Figure top row_: Comparison of shifted WebbPSF PSF (left) to science data (right). The NIRSpec IFU PSF is signficantly different from the WebbPSF simulation for the telescope. This is partly due to the IFU optics and perhaps partly due to the cube building algorithm. There is also some real extended emission from the QSO host and surrounding galaies.\n", "\n", - "_Figure bottom row_: Integrated WebbPSF model flux (left) decreases with wavelength as PSF expands outside of the FOV. \n", - "Differences (center, right) between the model PSF and IPS-simulated PSF will translate to inaccuracy in the optimally-extracted spectrum." + "_Figure bottom row_: Differences between the model PSF and the observed PSF will result in a sub-optimal extraction, not quite attaining the maximum SNR, but still better than a sum over all spaxels." ] }, { @@ -597,9 +590,9 @@ "metadata": {}, "source": [ "## Optimal Extraction using WebbPSF Model\n", - "Optimal extraction (Horne 1986, PASP, 98, 609) weights the flux contributions to a spectrum by their signal-to-noise ratio (SNR). Dividing the simulated data by the model PSF gives an estimate of the total flux density spectrum in each spaxel. A weighted average of these estimates over all spaxels yields the optimally extracted spectrum over the cube. In the faint source limit, where the noise is background-dominated, optimal extraction inside a 3-sigma radius can increase the effective exposure time by a factor of 1.69 (Horne et al. 1986). In the bright source limit, where the noise is dominated by the Poisson statistics of the source, optimal extraction is formally identical to a straight sum over spaxels for a perfect PSF model. \n", + "Optimal extraction ([Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)) weights the flux contributions to a spectrum by their signal-to-noise ratio (SNR). Dividing the simulated data by the model PSF gives an estimate of the total flux density spectrum in each spaxel. A weighted average of these estimates over all spaxels yields the optimally extracted spectrum over the cube. In the faint source limit, where the noise is background-dominated, optimal extraction inside a 3-sigma radius can increase the effective exposure time by a factor of 1.69 (Horne et al. 1986). In the bright source limit, where the noise is dominated by the Poisson statistics of the source, optimal extraction is formally identical to a straight sum over spaxels for a perfect PSF model. \n", "\n", - "We use the WebbPSF PSF model for this first attempt at optimal extraction." + "We use the precomputed WebbPSF PSF model for optimal extraction here." ] }, { @@ -609,183 +602,72 @@ "outputs": [], "source": [ "# Window PSF model (and replace NaNs)\n", - "profile = np.nan_to_num(psf_model_aligned[0:2059, :, :]) \n", + "good_profile = np.nan_to_num(good_psf_model) \n", + "var_clean = np.nan_to_num(good_var, nan=1E12, posinf=1E12, neginf = 1E12)\n", + "zerovar = np.where(var_clean == 0)\n", + "var_clean[zerovar] = 1E12\n", + "var_clean_sum = np.sum(var_clean, axis=(0))\n", + "snr_clean = np.nan_to_num(good_data/var_clean)\n", "\n", "# Divide data by PSF model\n", - "data_norm = np.nan_to_num(data_win / profile)\n", + "data_norm = np.nan_to_num(good_data / good_profile, posinf=0, neginf = 0)\n", "data_norm_sum = np.sum(data_norm, axis=0) \n", "\n", - "# Mask out bad data using 3-sigma clipping in each slice\n", - "data_norm_clipped = sigma_clip(data_norm, sigma=3.0, maxiters=5, axis=(1, 2))\n", + "# Mask out bad data \n", + "#data_norm_clipped = sigma_clip(data_norm, sigma=3.0, maxiters=5, axis=(1, 2))\n", + "data_norm_clipped = data_norm\n", "data_norm_clipped_sum = np.sum(data_norm_clipped, axis=0) \n", - "badvoxel = np.where(data_norm_clipped == 0)\n", - "data_clean = 1.0 * data_win\n", + "snr_thresh = 1.0\n", + "badvoxel = np.where((data_norm_clipped == 0) | (snr_clean < snr_thresh))\n", + "data_clean = 1.0 * good_data\n", "data_clean[badvoxel] = 0.0\n", + "data_clean_sum = np.sum(data_clean, axis=0) \n", "\n", "# Optimal extraction, using model profile weight and variance cube from the simulated data\n", - "optimal_weight = profile ** 2 / data_var\n", + "optimal_weight = np.nan_to_num(good_profile ** 2 / var_clean, posinf=0, neginf = 0) #Replace nans and infs with 0\n", + "optimal_weight_sum = np.sum(optimal_weight, axis=(0))\n", "optimal_weight_norm = np.sum(optimal_weight, axis=(1, 2))\n", - "spectrum_optimal = np.sum(profile * data_clean / data_var, axis=(1, 2)) / optimal_weight_norm\n", - "\n", - "opt_scalefactor = np.median(np.nan_to_num(cone_sum / spectrum_optimal)) # = 1.33, not ~1.0 because PSF model isn't perfect\n", + "spectrum_optimal = np.sum(good_profile * data_clean / var_clean, axis=(1, 2)) / optimal_weight_norm\n", "\n", "# Plots\n", "f, (ax1) = plt.subplots(1, 1, figsize=(12, 6)) \n", "ax1.set_title(\"Optimal Extraction Comparison\")\n", "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", "ax1.set_ylabel(\"Flux Density\")\n", - "ax1.set_ylim(0, 0.5)\n", + "ax1.set_ylim(0, 5000)\n", "ax1.plot(wavelength, cone_sum, label=\"Conical Extraction\", alpha=0.5)\n", "ax1.plot(wavelength, spectrum_optimal, label=\"Optimal\")\n", "ax1.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The optimally extracted spectrum is less noisy than the aperture extraction, but the flux density is low by a factor of ~1.33 because the PSF model doesn't match the science data perfectly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimal Extraction with (Simulated) Reference Star PSF\n", - "A real (or simulated in this case) IFU observation of a star may be used for the PSF model rather than WebbPSF. We employ a NIRSpec IPS simulated PSF, which matches our data better than the WebbPSF model. We don't have to shift or smooth the PSF model because it was simulated at the same dither/detector position as the data. When using a real observation of a star for the PSF model, make sure it was observed at the same dither positions. It is also beneficial to reduce and extract both simulated datasets in the 'ifualign' detector coordinate system, so that we don't have to rotate the PSF star to match the science data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", - "filename_star = BoxPath + \"NRS00001-brightQSO-F100LP-G140H-01_1_491_SE_2020-08-26T12h15m00_s3d.fits\"\n", - "\n", - "# Open and inspect the file and WCS\n", - "with fits.open(filename_star, memmap=False) as hdulist:\n", - " sci_star = hdulist['SCI'].data\n", - " err_star = hdulist['ERR'].data\n", - " w_star = wcs.WCS(hdulist[1].header)\n", - " hdr_star = hdulist[1].header\n", - " hdulist.info()\n", - " print(w_star)\n", - " \n", - "# Load with Spectrum1D \n", - "spec1d_star = Spectrum1D.read(filename_star)\n", - "\n", - "# Wavelengths\n", - "wavelength_star = np.array(spec1d_star.spectral_axis.value)\n", - "\n", - "# Window reference star to match science data (and replace NaNs)\n", - "ref_star = np.nan_to_num(sci_star[:, 5:-4, 3:])\n", - "\n", - "# Sum over spaxels\n", - "ref_star_fnusum = np.sum(ref_star, axis=(1, 2))\n", - "\n", - "# Normalize PSF star profile to unity. (The flux will still be slightly off. Please see Developer's Note below.)\n", - "ref_star_norm = []\n", - "for idx, norm in zip(range(len(wavelength_star)), ref_star_fnusum):\n", - " ref_star_norm.append(ref_star[idx] / norm)\n", - "profile_star = np.array(ref_star_norm)\n", - " \n", - "# Sum over spaxels \n", - "profile_star_fnusum = np.sum(profile_star, axis=(1, 2))\n", - "\n", - "# Sum over wavelength\n", - "profile_star_sum = np.sum(profile_star, axis=0)\n", - "\n", - "# Scale factor for PSF subtraction\n", - "profile_star_sum_max = np.amax(profile_star_sum)\n", - "star_scalefactor = np.amax(cube_sum) / profile_star_sum_max\n", - "\n", - "# Make slight adjustment to scale factor\n", - "star_scalefactor = 0.175\n", - "\n", - "# Plots\n", - "f, ([ax1, ax2, ax3], [ax4, ax5, ax6]) = plt.subplots(2, 3, figsize=(10, 10)) \n", - "\n", - "ax1.imshow(profile_star_sum, norm=LogNorm())\n", - "ax1.set_title(\"PSF Star Slice sum\")\n", - "\n", - "ax2.imshow(cube_sum, norm=LogNorm()) \n", - "ax2.set_title(\"Science Data Slice sum\")\n", - "\n", - "ax3.imshow(cube_sum / profile_star_sum, norm=LogNorm())\n", - "ax3.set_title(\"Data/Star_PSF Ratio\")\n", - "\n", - "star_model_ratio = profile_star_sum / psf_model_sum\n", - "ax4.imshow(star_model_ratio, norm=LogNorm())\n", - "ax4.set_title(\"Star PSF/WebbPSF\")\n", - "\n", - "ax5.imshow(cube_sum - star_scalefactor * profile_star_sum)\n", - "ax5.set_title(\"Data - Star PSF\")\n", - "\n", - "ax6.imshow(np.log10(np.absolute(cube_sum - star_scalefactor * profile_star_sum)))\n", - "ax6.set_title(\"log abs(Data - Star PSF)\")\n", - "\n", "plt.show()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "_Figure top row_: Comparison of PSF star and science data. Bottom left: ratio of PSF star to WebbPSF model shows \n", - "significant differences that can affect the quality of the optimal extraction. Bottom right:\n", - "Difference of PSF star from science data shows they are well matched, with a scale factor of 0.175. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Developer Note:* It would be good to renormalize the PSF profile to account for the fraction of flux lost outside of the detector. Otherwise the extracted flux will be low by a factor of roughly 0.972 to 0.980." - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Mask out bad data using 3-sigma clipping in each slice\n", - "data_norm = np.nan_to_num(data_win / profile_star)\n", - "data_norm_clipped = sigma_clip(data_norm, sigma=3.0, maxiters=5, axis=(1, 2))\n", - "data_norm_clipped_sum = np.sum(data_norm_clipped, axis=0) \n", - "badvoxel = np.where(data_norm_clipped == 0)[0]\n", - "data_clean = 1.0 * data_win\n", - "data_clean[badvoxel] = 0.0\n", - "\n", - "# Optimal extraction, using model profile weight and variance cube from the simulated data\n", - "optimal_weight = profile_star**2 / data_var\n", - "optimal_weight_norm = np.sum(optimal_weight, axis=(1, 2))\n", - "spectrum_optimal_star = np.sum(profile_star * data_clean / data_var, axis=(1, 2)) / optimal_weight_norm\n", + "from jdaviz import Specviz\n", + "specviz = Specviz()\n", "\n", - "# Plots\n", - "f, (ax1) = plt.subplots(1, 1, figsize=(12, 6)) \n", - "ax1.set_title(\"Optimal Extraction Comparison\")\n", - "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", - "ax1.set_ylabel(\"Flux Density\")\n", - "ax1.set_ylim(0, 0.5)\n", + "flux_opt = spectrum_optimal * u.MJy/u.sr\n", + "spec1d_opt = Spectrum1D(spectral_axis=wavelength * u.um, flux=flux_opt)\n", "\n", - "ax1.plot(wavelength, cone_sum, label=\"Conical Extraction\", alpha=0.5)\n", - "ax1.plot(wavelength, spectrum_optimal * opt_scalefactor, label=\"1.3 * Optimal with WebbPSF model\", alpha=0.5)\n", - "ax1.plot(wavelength, spectrum_optimal_star, label=\"Optimal with ref. star\")\n", - "ax1.legend()\n", + "#specviz.load_data(spec1d_spaxsum, data_label=\"collapse spec\")\n", + "specviz.load_data(spec1d_opt, data_label=\"optimal spec\")\n", + "specviz.load_data(spec1d_cone, data_label=\"cone spec\")\n", + "specviz.show()\n", "\n", - "plt.show()" + "# set spectrum display limits\n", + "#specviz.x_limits()\n", + "specviz.y_limits(0.0, clip_level/7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The optimal extraction with the perfectly matched PSF star is less noisy than that achieved with WebbPSF, and unlike the latter, doesn't need to be rescaled. The scaling can be off if the PSF of the reference star is not a good match to the PSF of the science data." + "The optimally extracted spectrum is less noisy than the aperture extraction and incorporates fewer bad pixels and cosmic ray events. The OIII line profile is different because extended emission is downweighted with respect to the unresolved quasar nucleus." ] }, { @@ -801,13 +683,6 @@ "source": [ "Notebook created by Patrick Ogle and James Davies." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -826,7 +701,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/notebooks/ifu_optimal/sdssj1652_nirspec_ifu_cubeviz.png b/notebooks/ifu_optimal/sdssj1652_nirspec_ifu_cubeviz.png new file mode 100644 index 0000000000000000000000000000000000000000..e758192101e05d9974a58277d952732a0adadf0a GIT binary patch literal 148596 zcmdqIgS1K0ijgxYo2SKW^HVHTxO@Pr;Uzh zcOxValkcgLKA7usznkkF8X^MRnmso&L_&S# zSe?}_yE!|1D?zLxkJW?_&OxBAj?+X$KqosM-n@*3^!tsWF7;`*{E{~?$XzM`!L!U^ znxIC5$18G5DEi{6O6(T|1b@oFwm76Pz4+y|syeoE$b4ueV+c{Vh#Q>ICv|qLA`xYA 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Sum over wavelength\n", "# Clip data for display purposes\n", @@ -354,9 +516,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aperture: CircularAperture\n", + "positions: [21.49135399, 20.60216331]\n", + "r: 4.742252588272101\n" + ] + } + ], "source": [ "# IFU pixel scale\n", "pixelscale = 0.1 # arcsec/pixel\n", @@ -398,9 +570,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference wavelength: 1.858197960886173\n" + ] + } + ], "source": [ "# Reference wavelength for expanding aperture\n", "lambda0 = wavelength[0]\n", @@ -430,9 +610,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "f, (ax1) = plt.subplots(1, 1, figsize=(15, 5)) \n", "\n", @@ -480,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -499,9 +690,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: /Users/pogle/.astropy/cache/download/url/4931b72fec159f8a439e8c80c181370f/contents\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 OVERSAMP 1 PrimaryHDU 105786 (120, 120, 3915) float64 \n", + " 1 DET_SAMP 1 ImageHDU 105788 (30, 30, 3915) float64 \n", + " 2 OVERDIST 1 ImageHDU 105831 (120, 120, 3915) float64 \n", + " 3 DET_DIST 1 ImageHDU 105832 (30, 30, 3915) float64 \n", + "(3814, 39, 43) (3915, 30, 30)\n" + ] + } + ], "source": [ "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", "psf_filename = BoxPath+\"Webbpsf_ifucube.fits\"\n", @@ -546,9 +751,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1376.820805778524\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Flip model PSF left-right to match data.\n", "psf_model_fliplr = psf_model_padded[:, ::-1, :]\n", @@ -611,9 +834,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Window PSF model (and replace NaNs)\n", "good_profile = np.nan_to_num(good_psf_model) \n", @@ -657,9 +891,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-01 15:27:02,402 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", + " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", + "\n", + "2023-12-01 15:27:02,752 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", + " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab46c7d73cd34a9f94082f60d5c211c0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='specviz', docs_link='https://jdaviz.readthedocs.io/en/latest/specviz/index.html', events=[…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from jdaviz import Specviz\n", "specviz = Specviz()\n", diff --git a/notebooks/ifu_optimal/pre-requirements.txt b/notebooks/ifu_optimal/pre-requirements.txt deleted file mode 100644 index dc9fd9ab0..000000000 --- a/notebooks/ifu_optimal/pre-requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -astropy-helpers>=2.0.11 - diff --git a/notebooks/ifu_optimal/requirements.txt b/notebooks/ifu_optimal/requirements.txt index d338557ed..db7e542e8 100644 --- a/notebooks/ifu_optimal/requirements.txt +++ b/notebooks/ifu_optimal/requirements.txt @@ -1,17 +1,3 @@ -notebook>=6.1.5 -jupyter_client>=5.3.5 -numpy>=1.19.2 -scipy>=1.10.0 -specutils>=1.1.1 -glue-astronomy>=0.1 -glue-core>=1.0.0 -glue-jupyter>=0.2.1 -glue-vispy-viewers>=1.0.1 -jdaviz>=1.0.3 -regions>=0.7 -photutils>=1.0.1 -astropy>=4.2 -matplotlib>=3.3.2 -ipyvuetify>=1.6.2 -ipyvue>=1.5.0 -radio-beam>=0.3.6 +numpy==1.19.2 +scipy==1.5.2 +jdaviz==1.0.3 From 81090b41cb8b47ccd6210361c32fb68c2100fbb3 Mon Sep 17 00:00:00 2001 From: Patrick Ogle Date: Wed, 6 Dec 2023 12:29:39 -0500 Subject: [PATCH 4/9] Updated Developer Notes, TOC --- notebooks/ifu_optimal/ifu_optimal.ipynb | 475 ++++++------------------ notebooks/ifu_optimal/requirements.txt | 6 +- 2 files changed, 108 insertions(+), 373 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index a174f8164..6853236f0 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -7,6 +7,13 @@ "# NIRSpec IFU Optimal Point Source Extraction" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook illustrates various extraction methods for a point source in JWST NIRSpec IFU data, utilizing the [Q3D](https://q3d.github.io/) (PID 1335) observation of quasar SDSS J165202.64+172852.3. The extraction techniques include subset extraction with Cubeviz, simple sum over spaxels, cylindrical aperture, conical aperture photometry, and optimal point source extraction using a WebbPSF model PSF. " + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -26,7 +33,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Imports " + "## Table of Contents\n", + "1. Imports\n", + "2. Read in NIRSpec IFU Cube\n", + "3. Visualize Science Data with Cubeviz\n", + "4. Export Source and Good Data Regions from Cubeviz\n", + "5. Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer\n", + "6. Extract Spectrum by Sum over Spaxels\n", + "7. Extract Spectrum in Constant Radius Circular Aperture (Cylinder)\n", + "8. Extract Spectrum in Linearly Expanding Circular Aperture (Cone)\n", + "9. Plot and Compare Non-optimal Spectral Extractions\n", + "10. WebbPSF Model for Optimal Extraction\n", + "11. Align Model PSF Cube with Science Data\n", + "12. Optimal Extraction Using WebbPSF Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Imports " ] }, { @@ -49,24 +75,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "**WARNING**: LOCAL JWST PRD VERSION PRDOPSSOC-059 CANNOT BE CHECKED AGAINST ONLINE VERSION\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jdaviz Version=3.8.1.dev1+gc9daae85\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "import scipy\n", @@ -79,6 +90,7 @@ "from photutils.aperture import CircularAperture, SkyCircularAperture, aperture_photometry \n", "from astropy.io import fits\n", "from astropy import wcs\n", + "from astropy import units as u\n", "from astropy.stats import sigma_clip\n", "from astropy.utils.data import download_file\n", "import os\n", @@ -87,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -100,16 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Introduction\n", - "\n", - "This notebook illustrates various extraction methods for a point source in JWST NIRSpec IFU data, utilizing the [Q3D](https://q3d.github.io/) (PID 1335) observation of quasar SDSS J165202.64+172852.3. The extraction techniques include subset extraction with Cubeviz, simple sum over spaxels, cylindrical aperture, conical aperture photometry, and optimal point source extraction using a WebbPSF model PSF. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read in NIRSpec IFU Cube\n", + "## 2. Read in NIRSpec IFU Cube\n", "\n", "The NIRSpec IFU observation of quasar SDSS J1652+1728 (redshift z=1.9) was taken using the G235H grating with F170LP filter, covering 1.66-3.17 microns at a spectral resolution of R~2700. The IFU spaxels are 0.1\" on a side. \n", "The level-3 pipeline_processed datacube (s3d.fits, which combines all dithered exposures) is retrieved from MAST \n", @@ -118,17 +121,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits to /Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits ... [Done]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Download the data file\n", "uri = f\"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", @@ -145,62 +140,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: /Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 PRIMARY 1 PrimaryHDU 367 () \n", - " 1 SCI 1 ImageHDU 92 (43, 39, 3814) float32 \n", - " 2 ERR 1 ImageHDU 12 (43, 39, 3814) float32 \n", - " 3 DQ 1 ImageHDU 12 (43, 39, 3814) int32 (rescales to uint32) \n", - " 4 WMAP 1 ImageHDU 10 (43, 39, 3814) float32 \n", - " 5 HDRTAB 1 BinTableHDU 828 18R x 409C [23A, 5A, 3A, 44A, 7A, 13A, 6A, 7A, 6A, 7A, 13A, 4A, L, D, D, D, D, 32A, 49A, 129A, 19A, 3A, D, 43A, D, 10A, 12A, 23A, 23A, 26A, 11A, 5A, 3A, 3A, 2A, 1A, 2A, 1A, L, 24A, 17A, 2A, 26A, 20A, 27A, 10A, K, L, L, L, L, 17A, 14A, 5A, D, D, D, D, D, D, D, D, D, 8A, 7A, 4A, D, D, 6A, D, D, 5A, D, D, K, D, D, D, D, D, D, D, 4A, 3A, D, D, D, D, D, D, D, D, D, K, 5A, 7A, D, D, D, D, D, D, D, D, D, 7A, D, D, K, K, D, D, K, K, D, D, K, K, K, K, K, D, D, D, D, D, D, D, D, K, K, L, L, K, K, K, K, D, D, L, D, D, 4A, K, K, K, K, K, K, D, D, D, D, 7A, D, D, K, K, K, D, D, D, 5A, D, D, D, D, D, D, D, D, D, D, D, D, D, 7A, 10A, D, D, D, D, D, D, D, D, D, D, D, D, D, 12A, 12A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, K, 27A, 27A, 10A, D, D, D, D, D, D, D, 9A, 27A, D, D, D, D, D, D, D, 7A, 14A, 34A, D, D, 36A, 40A, D, 34A, 39A, 3A, D, D, 3A, 3A, 35A, 35A, 35A, 34A, 33A, D, D, 34A, 37A, 37A, 39A, D, D, 39A, 34A, 33A, D, 33A, 38A, D, 36A, D, 39A, 36A, 3A, D, D, D, D, 40A, D, D, D, 3A, D, 39A, D, D, D, D, D, D, D, 45A, D, D, D, D, D, 8A, D, D, 7A, D, D, 8A, 8A, D, D, D, D, D, 8A, 7A, 8A, 8A, D, 8A, 7A, 8A, D, D, 8A, D, D, 8A, 8A, 8A, D, 8A, 7A, 8A, 8A, D, 7A, D, D, 8A, D, D, 8A, D, 7A, 8A, D, D, D, D, D, D, D, D, 6A, D, D, D, D, 4A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, 121A, D, D, K, K, D, D, K, D, D, D, D] \n", - " 6 ASDF 1 BinTableHDU 11 1R x 1C [39124B] \n", - "WCS Keywords\n", - "\n", - "Number of WCS axes: 3\n", - "CTYPE : 'RA---TAN' 'DEC--TAN' 'WAVE' \n", - "CRVAL : -106.98898425200001 17.481222214 1.6601979666156693e-06 \n", - "CRPIX : 22.0 20.0 1.0 \n", - "PC1_1 PC1_2 PC1_3 : -1.0 0.0 0.0 \n", - "PC2_1 PC2_2 PC2_3 : 0.0 1.0 0.0 \n", - "PC3_1 PC3_2 PC3_3 : 0.0 0.0 1.0 \n", - "CDELT : 2.77777781916989e-05 2.77777781916989e-05 3.95999988541007e-10 \n", - "NAXIS : 43 39 3814\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-01 15:25:25,832 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/astronomy/coordinates/extensions/coordinates-1.0.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", - " warnings.warn(msg, AsdfWarning)\n", - "\n", - "2023-12-01 15:25:25,833 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.15.0), but older package (asdf==2.14.3) is installed.\n", - " warnings.warn(msg, AsdfWarning)\n", - "\n", - "2023-12-01 15:25:25,834 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", - " warnings.warn(msg, AsdfWarning)\n", - "\n", - "2023-12-01 15:25:25,835 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", - " warnings.warn(msg, AsdfWarning)\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Trimmed data shape: (600, 39, 43)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Open and inspect the file and WCS\n", "with fits.open(filename, memmap=False) as hdulist:\n", @@ -221,7 +163,7 @@ "sci_var = []\n", "for idx in slice_range: \n", " sci_data.append(sci[idx, :, :])\n", - " sci_var.append(err[idx, :, :]) # variance = err, not variance = err**2. Squaring the err gives noisy results.\n", + " sci_var.append(err[idx, :, :]) \n", "\n", "data = np.nan_to_num(np.array(sci_data))\n", "var = np.array(sci_var)\n", @@ -230,49 +172,11 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Visualize Science Data with Cubeviz" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-01 15:25:31,947 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", - " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", - "\n", - "2023-12-01 15:25:32,440 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", - " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "cce2fc62d7bd4ed79ac7539768938ccf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='cubeviz', docs_link='https://jdaviz.readthedocs.io/en/latest/cubeviz/index.html', events=[…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Launch Cubeviz and load the data cube\n", - "\n", "cubeviz = Cubeviz()\n", "cubeviz.load_data(filename)\n", "cubeviz.show()\n", @@ -289,60 +193,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"text" + "## 3. Visualize Science Data with Cubeviz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### UI Instructions:\n", - "* Scrub through the cube to the [OIII] 5007 line (redshifted to ~1.98 microns) using the spectrum-viewer slice tool\n", - "* In the flux-viewer, select one circular subset region centered on the quasar, and a square region to delimit the good area for spectral and background extraction\n", - "* Note that the regions are pixelated and don't include fractional pixels\n", - "* The default collapse method is \"Sum\" in the spectrum viewer (see Plot Options:Line). \"Median\" may also be useful for visualization but will not give an accurate measurement of the total flux.)" + "\"text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "*Developer Note:* There is a jdaviz ticket to export plots from viewers to create static views like the one above of the viz output." + "### UI Instructions:\n", + "* Scrub through the cube to a line-free region using the spectrum-viewer slice tool.\n", + "* In the flux-viewer, select one circular subset region centered on the quasar, and a square region to delimit the good area for spectral and background extraction\n", + "* Note that the regions are pixelated and don't include fractional pixels\n", + "* The default collapse method is \"Sum\" in the spectrum viewer (see Plot Options:Line). \"Median\" may also be useful for visualization but will not give an accurate measurement of the total flux.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Export Source and Good Data Regions from Cubeviz\n", + "## 4. Export Source and Good Data Regions from Cubeviz\n", "Export the region defined by the user in Cubeviz as astropy PixelRegions" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Source Region\n", - "Region: CirclePixelRegion\n", - "center: PixCoord(x=21.49135398864746, y=20.602163314819336)\n", - "radius: 4.742252588272101\n", - "\n", - "Good Data Region\n", - "Region: RectanglePixelRegion\n", - "center: PixCoord(x=21.491354228349095, y=19.16824705901036)\n", - "width: 29.82544951538412\n", - "height: 28.56360357434864\n", - "angle: 0.0 rad\n", - "Good data (xmin,xmax), (ymin,ymax): [7, 36] [5, 33]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "cubeviz_data = cubeviz.app.data_collection[0]\n", "\n", @@ -351,9 +235,16 @@ "try:\n", " region1 = cubeviz_data.get_selection_definition('Subset 1', format='astropy-regions')\n", " print(region1)\n", + " center_xy = [region1.center.x, region1.center.y] \n", + " r_pix = region1.radius\n", " region1_exists = True\n", "except Exception:\n", " print(\"There is no Subset 1 selected in the cube viewer.\")\n", + " center_xy = [17.1, 20.]\n", + " r_pix = 5.92\n", + " print(\"Using default pixel center and radius:\")\n", + " print(\"Center pixel:\", center_xy)\n", + " print(\"Radius (pixels):\", r_pix)\n", " region1_exists = False\n", " \n", "print()\n", @@ -362,68 +253,40 @@ " region2 = cubeviz_data.get_selection_definition('Subset 2', format='astropy-regions')\n", " print(region2)\n", " region2_exists = True\n", - " #help(region2)\n", " data_xrange=[round(region2.center.x - region2.width/2), round(region2.center.x + region2.width/2)]\n", " data_yrange=[round(region2.center.y - region2.height/2), round(region2.center.y + region2.height/2)]\n", " print('Good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n", - " \n", " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", "\n", - "\n", "except Exception:\n", " print(\"There is no Subset 2 selected in the cube viewer.\")\n", " region1_exists = False\n", " data_xrange=[7,36]\n", - " data_xrange=[6,33]\n", + " data_yrange=[6,33]\n", " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", - " print('Good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n" + " print('Using default good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Extract Subset Spectrum and Background in Cubeviz Spectrum Viewer\n", + "## 5. Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer\n", "Retrieve the collapsed spectrum (Subset1) of the user-defined region from the Spectrum Viewer as a Spectrum1D object." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-01 15:26:10,357 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/jdaviz/configs/specviz/helper.py:131: UserWarning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift keyword option to True or False.\n", - " warnings.warn(\"Applying the value from the redshift \"\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "dict_keys(['jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI]', 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI] (Subset 1)', 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI] (Subset 2)'])\n", - "Source\n", - "Spectrum1D (length=3814)\n", - "flux: [ 465.19 MJy / sr, ..., nan MJy / sr ], mean=nan MJy / sr\n", - "spectral axis: [ 1.6602 um, ..., 3.1701 um ], mean=2.4152 um\n", - "\n", - "Background\n", - "Spectrum1D (length=3814)\n", - "flux: [ 963.26 MJy / sr, ..., nan MJy / sr ], mean=nan MJy / sr\n", - "spectral axis: [ 1.6602 um, ..., 3.1701 um ], mean=2.4152 um\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "subsets = cubeviz.specviz.get_spectra()\n", "print(subsets.keys())\n", "\n", + "print()\n", "print('Source')\n", "try:\n", " spectrum_subset1 = subsets[[i for i in subsets.keys() if 'Subset 1' in i][0]]\n", @@ -444,41 +307,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "*Developer Note:* The units of the Cubeviz \"Sum\" collapse method need to be multiplied by the pixel area in sr to yield flux units (MJy) instead of surface brightness units (MJy/sr)." + "*Developer Note:* The units of the Cubeviz \"Sum\" collapse method (and all other spectra below) need to be multiplied by the pixel area in sr to yield flux units (MJy) instead of surface brightness units (MJy/sr)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Extract Spectrum by Sum Over Spaxels\n", + "## 6. Extract Spectrum by Sum Over Spaxels\n", "\n", "Perform a simple numpy sum over all spaxels in the cube as a rudimentary extraction method. Also sum over wavelength to collapse the cube." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " *Developer Note:* Need to convert all extracted spectra to flux units." - ] - }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Sum over wavelength\n", "# Clip data for display purposes\n", @@ -510,42 +355,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Extract Spectrum in Constant Radius Circular Aperture (Cylinder)\n", + "## 7. Extract Spectrum in Constant Radius Circular Aperture (Cylinder)\n", "This method is appropriate for an extended source." ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aperture: CircularAperture\n", - "positions: [21.49135399, 20.60216331]\n", - "r: 4.742252588272101\n" - ] - } - ], - "source": [ - "# IFU pixel scale\n", - "pixelscale = 0.1 # arcsec/pixel\n", - "\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "# CircularAperture uses xy pixels\n", - "center_xy = [17.1, 20.]\n", - "r_pix = 5.92\n", - "if region1_exists:\n", - " center_xy = [region1.center.x, region1.center.y] \n", - " r_pix = region1.radius\n", - "\n", "aperture = CircularAperture(center_xy, r=r_pix)\n", "print(aperture)\n", "\n", "cylinder_sum = []\n", "for slice2d in data:\n", - " #phot_table = aperture_photometry(slice2d, aperture, wcs=w.celestial, method='exact')\n", " phot_table = aperture_photometry(slice2d, aperture)\n", " cylinder_sum.append(phot_table['aperture_sum'][0])\n", " \n", @@ -564,23 +389,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Extract Spectrum in Linearly Expanding Circular Aperture (Cone)\n", + "## 8. Extract Spectrum in Linearly Expanding Circular Aperture (Cone)\n", "This method is appropriate for a point source PSF with width proportional to wavelength" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reference wavelength: 1.858197960886173\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Reference wavelength for expanding aperture\n", "lambda0 = wavelength[0]\n", @@ -604,26 +421,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot and Compare Non-optimal Spectral Extractions\n", + "## 9. Plot and Compare Non-optimal Spectral Extractions\n", "Compare spectra extracted in cylinder, cone, Cubeviz subset." ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ERJ0Nl5oSERFR1AYPHozLLrsMzz//PKqrq033nX322QCAxx9/3HS7lmU0bdq0mPaVl5eH0047zfRPW/J29tlno7q6Gn//+9/1x/v9fjz11FMoKirCpEmTTNt6++23sXv3bv37L774Ap9//jnOOuss/TYt+FBbWxvTOOOhZQQZs+A+//xzrFy5Mq7tHTx40PR9Tk4ORo0aBSEEfD6f6b558+aZvn/qqacAQD8WF110EdxuNx544AHT+LTxavsaN24cBg4ciMcff7zNMTP+XDzH1erjc/HFF2P37t144YUX2tzX0tJiWmp51113YceOHXj55Zfxhz/8AQMGDMCMGTPg8Xj0x1j1nOrq6jB//nzT48444wwUFxdjzpw5aG1tNd0XelyjWXbbq1cvjB07Fi+//LJpvN988w0++OAD/e82FocOHWpzm9Zp1niciIiIiBlvREREFKNf/vKXeOWVV7Bx40aMHj1av/2oo47CjBkz8Mc//hG1tbWYNGkSvvjiC7z88su44IILcMopp1g2hp/+9Kd4/vnnMXPmTKxatQoDBgzAP/7xD3z66ad4/PHHUVxcbHr8kCFDcOKJJ+K6666Dx+PB448/jm7duuHOO+/UHzN+/HgAwI033oipU6fC7XbjRz/6kWVjNjrnnHPw1ltv4cILL8S0adNQVVWF5557DqNGjUJjY2PM2zvjjDNQUVGBiRMnory8HBs2bMDTTz+NadOmtTkWVVVVOO+883DmmWdi5cqVePXVV/HjH/9YzyYcPHgwfvvb3+Luu+/G9u3bccEFF6C4uBhVVVX45z//iZ/+9Ke4/fbb4XK58Oyzz+Lcc8/F2LFjceWVV6JXr1747rvv8O2332Lx4sUA4juuVh+fyy+/HK+//jquvfZaLF26FBMnTkQgEMB3332H119/HYsXL8YxxxyDjz/+GM888wzuu+8+jBs3DgAwf/58TJ48Gb/61a/w2GOPAVCCTG63G48++ijq6uqQm5uLKVOmtFsL7owzzkBOTg7OPfdc/OxnP0NjYyNeeOEF9OzZ09SwpKSkBHPnzsWsWbNw7LHH4sc//jG6dOmCtWvXorm5GS+//LJ+XP/+97/j1ltvxbHHHouioiKce+65Yff9u9/9DmeddRYqKytx9dVXo6WlBU899RRKS0tx//33x3w8H3zwQfznP//BtGnT0L9/f9TU1OCZZ55Bnz59cOKJJ8a8PSIiooxmRytVIiIicr758+cLAOLLL79sc9+MGTMEADF69GjT7T6fTzzwwANi4MCBIjs7W/Tt21fcfffdorW11fS4/v37i2nTprXZ7qRJk8SkSZOiGt++ffvElVdeKbp37y5ycnLEkUceKebPn296TFVVlQAgfve734nf//73om/fviI3N1ecdNJJYu3atabH+v1+ccMNN4gePXoISZKEcZoEQNx333369/fdd58AIPbv39/muBQWFoZ9XsZjJcuyePjhh0X//v1Fbm6uOProo8XChQvFjBkzRP/+/U0/G7rvcJ5//nlx8skni27duonc3FwxePBgcccdd4i6uro2Y16/fr344Q9/KIqLi0WXLl3E9ddfL1paWtps88033xQnnniiKCwsFIWFhWLEiBFi9uzZYuPGjabHffLJJ+L0008XxcXForCwUIwZM0Y89dRTHR5X42sTyurjI4QQXq9XPProo2L06NEiNzdXdOnSRYwfP1488MADoq6uTtTX14v+/fuLcePGCZ/PZ/rZW265RbhcLrFy5Ur9thdeeEEMGjRIuN1uAUAsXbpUCBH5d1sIIf71r3+JMWPGiLy8PDFgwADx6KOPij/96U8CgKiqqmrz2BNOOEHk5+eLkpIScdxxx4m//vWv+v2NjY3ixz/+sSgrKxMA9OOiHdfQv4WPPvpITJw4Ud/eueeeK9avX296TKTfa+29QBvjkiVLxPnnny969+4tcnJyRO/evcWll14qNm3a1N5LQERE1ClJQoSsISAiIiLKENu3b8fAgQPxu9/9Drfffrvdw7HV/fffjwceeAD79+9v03WViIiIiJKDNd6IiIiIiIiIiIiSgIE3IiIiIiIiIiKiJGDgjYiIiIiIiIiIKAlsDbzdf//9kCTJ9G/EiBH6/a2trZg9eza6deuGoqIiTJ8+Hfv27TNtY8eOHZg2bRoKCgrQs2dP3HHHHfD7/abHLFu2DOPGjUNubi6GDBmCBQsWpOLpERERkc0GDBgAIUSnr+8GKPMuIQTruxERERGlkO0Zb6NHj8bevXv1f5988ol+3y233IJ///vfeOONN7B8+XLs2bMHF110kX5/IBDAtGnT4PV6sWLFCrz88stYsGABfv3rX+uPqaqqwrRp03DKKadgzZo1uPnmmzFr1iy9xT0REREREREREVEy2NrV9P7778fbb7+NNWvWtLmvrq4OPXr0wGuvvYYf/vCHAIDvvvsOI0eOxMqVKzFhwgS89957OOecc7Bnzx6Ul5cDAJ577jncdddd2L9/P3JycnDXXXdh0aJF+Oabb/Rt/+hHP0JtbS3ef//9lDxPIiIiIiIiIiLqfLLsHsDmzZvRu3dv5OXlobKyEnPmzEG/fv2watUq+Hw+nHbaafpjR4wYgX79+umBt5UrV+LII4/Ug24AMHXqVFx33XX49ttvcfTRR2PlypWmbWiPufnmmyOOyePxwOPx6N/LsoxDhw6hW7dukCTJuidPRERERERERERpRwiBhoYG9O7dGy5X5AWltgbejj/+eCxYsADDhw/H3r178cADD+Ckk07CN998g+rqauTk5KCsrMz0M+Xl5aiurgYAVFdXm4Ju2v3afe09pr6+Hi0tLcjPz28zrjlz5uCBBx6w6mkSEREREREREVEG2rlzJ/r06RPxflsDb2eddZb+9ZgxY3D88cejf//+eP3118MGxFLl7rvvxq233qp/X1dXh379+mHnzp0oKSmxbVxE5Bxvn34jcuuq4XfnIyvQgqwzp+P0By+xZNvVSz/DV3f9AQDQo28hjnlzviXbTaZPnvwQDa++gNYeg3DhokcAAO8ddzEA4Ni/P4fuA7vaOTyiTqtm1Vqsuu4h1Pq6wO2SUeyuw5FP3os+E8bYPTQiInKgt8+4Bbm1uwEAAhLO/uLvcW3nf6+uwP4nH0drcW9cuORxbHp+Kb546u9wDy3ApS9eA3QbaeWwKQrv//AuiB1VwAXX4Kx7Tm9z/5I/fATv3/4IudcATHvnMRtGmH7q6+vRt29fFBcXt/s425eaGpWVlWHYsGHYsmULTj/9dHi9XtTW1pqy3vbt24eKigoAQEVFBb744gvTNrSup8bHhHZC3bdvH0pKSiIG93Jzc5Gbm9vm9pKSEgbeiAgAUODOQa47G/6sHGTBj6zsfMveHxoLClDgzgYAFGZlpcX7TkFuHgLubLiycvXxas+hqLAoLZ4DUSZqylfeT7xyLlxugQJXM4ry+TdJREThFbqzkaPO4QSkuM8XhfmFaHJnw5WVg5KSEhQXFCLflQN3Vg5KiosAnodSriArB8KdDeQWhH1dC/MKkeXORsCdy3lCjDoqSWZ7V1OjxsZGbN26Fb169cL48eORnZ2NJUuW6Pdv3LgRO3bsQGVlJQCgsrIS69atQ01Njf6YDz/8ECUlJRg1apT+GOM2tMdo2yAichrZ2PNGlu0bSDxYB5PIUeSA8n4iJCn49ynS7H2FiIhSxth5UYKFfRj1c1DoXihV9Fl6hPk6p/HJY2vg7fbbb8fy5cuxfft2rFixAhdeeCHcbjcuvfRSlJaW4uqrr8att96KpUuXYtWqVbjyyitRWVmJCRMmAADOOOMMjBo1CpdffjnWrl2LxYsX495778Xs2bP1jLVrr70W27Ztw5133onvvvsOzzzzDF5//XXccsstdj51Ikpz+kREO0PJFk4gDJ+JbWw8HZN2h5kmz4EoEwkteG+YTctWvl8REVGGSc45QjkNSUnbPkWDx94uti413bVrFy699FIcPHgQPXr0wIknnojPPvsMPXr0AADMnTsXLpcL06dPh8fjwdSpU/HMM8/oP+92u7Fw4UJcd911qKysRGFhIWbMmIEHH3xQf8zAgQOxaNEi3HLLLXjiiSfQp08fvPjii5g6dWrKny8RZQ4tliT0D7PWnciEIctNSpfMFAbXiBxJBLT3EAnatW7BwBsREUVi1ZwudDumjDeyk4T2U9v4ElnP1sDb3/72t3bvz8vLw7x58zBv3ryIj+nfvz/efffddrczefJkfPXVV3GNMVpCCPj9fgQCgaTuh+LndruRlZXV4fprotioH2StTHgzbCtdMt70AyC1TaROm+dAlIG0IJuQJP1CAQNvREQULSELSK7YPz/pU0P1e/NKU56H7KHNCcIvfNRfK4beLOeo5grpyuv1Yu/evWhubrZ7KNSBgoIC9OrVCzk5OXYPhdJc8ISUhJpJsmmtqXXbTTEBiSduIptpNd4AFyR1HbtIl0xaIiJKOavmbqHbkVySMq3l1NA+wlD3NdzdHWTCUfwYeEuQLMuoqqqC2+1G7969kZOTw4wqBxJCwOv1Yv/+/aiqqsLQoUPhcjmqtwilm5C4W1K2DaRPEfR2AoTMeCOyj3HpupBcgABEgH+TREQUXui0Ld6MN/3ntcmyXp2F5yC7aMHQDuMVfIksx8BbgrxeL2RZRt++fVFQUGD3cKgd+fn5yM7Oxvfffw+v14u8vDy7h0RpzXzFyNKlpgFjjbf0OPOJZAYiiShu+qpS4yQ7Pd5WiIjIBqGZalZdQGVyigNoNaojTNj5EiUPU34swuyp9MDXiaynLTW1sLmC8WsB89JTx2t7xk6T2CFRZgoYupqqM2pjcN8SvhbA22jtNomIyBbhMt6sYO5HxsmhHbSSExEDbIy8JQ2jEERECUnCCco44xFIi8hV2KuhPHkT2U6r5yYMgTdLl38LAXzzErBmHoNvREQZoE3GW6KBN1N3BYkxNxvphz5CcwVKHh5xIqJ4hHTxtPKDrHGCIwsAchp0Sw63nE2TVhl7RJlFb64guaB3Ybayq2nAC7QcAPwtwIF11m2XiIgcId4pbujPBbuaMvJmF62ETcQmCvrNfI2sxsAbJWzy5Mm4+eab9e8HDBiAxx9/PKFtLlu2DJIkoba2NqHtECVdEgrFmj4UC6RHg4UwYxSCGW9EdhMBw7ISvQmzhe8pPkOW2/611m2XiIjsIUIz3qw5Z+gNGtJkNUdmMicOtMW5e7Iw8Eaorq7GDTfcgEGDBiE3Nxd9+/bFueeeiyVLlsS1vS+//BI//elPLR4lkVNZX+PNeJVJAOmVMWbIeONKUyL76dm4hsibpbF8XxPw+VZg2Qagbg/QVG3hxomIyG6WTXHVVaacHtondBlxRIyLWo5dTTu57du3Y+LEiSgrK8Pvfvc7HHnkkfD5fFi8eDFmz56N7777LuZt9ujRIwkjjZ3X60VOTo7dw6AMpZ+4JMPVO4sYC5+LNFlq2t6kjBc1iewjAsH3KpGMCwUttVj9Xi1aW4HjBhxGVt81wMAzrds+ERGlqdBzjRT2VrKBK3z4U+JS06RhxlsSCAE0NdnzL9a59M9//nNIkoQvvvgC06dPx7BhwzB69Gjceuut+Oyzz3DVVVfhnHPOMf2Mz+dDz5498dJLL4XdZuhSU0mS8OKLL+LCCy9EQUEBhg4din/961+mn3n33XcxbNgw5Ofn45RTTsH27dvbbPeTTz7BSSedhPz8fPTt2xc33ngjmpqaTPv9zW9+gyuuuAIlJSXMuqMUSUZzBcOXabzUlIjsJ8uGei5aV1MLa7x5DhzE7t1AdU02dq88CBzeZNm2iYjIDiFLTRPuhK2ce6QklGeh2Ej6sY/Y1hQAw27JwMBbEjQ3A0VF9vxrbo5+nIcOHcL777+P2bNno7CwsM39ZWVlmDVrFt5//33s3btXv33hwoVobm7GJZdcEvW+HnjgAVx88cX4+uuvcfbZZ+MnP/kJDh06BADYuXMnLrroIpx77rlYs2YNZs2ahV/84hemn9+6dSvOPPNMTJ8+HV9//TX+/ve/45NPPsH1119vetz//d//4aijjsJXX32FX/3qV9EfDKIYaectkYxCsYYJiZLxlkZBrTDrSxOfsBFRvExLTSXrM97qdinn8tqWUuz7tgGoO2zZtomIyAYhp4j4TxnC8P/BGm/mWymVgktNueA31Rh468S2bNkCIQRGjBgR8TEnnHAChg8fjldeeUW/bf78+fh//+//oaioKOp9zZw5E5deeimGDBmChx9+GI2Njfjiiy8AAM8++ywGDx6M3//+9xg+fDh+8pOfYObMmaafnzNnDn7yk5/g5ptvxtChQ3HCCSfgySefxJ///Ge0trbqj5syZQpuu+02DB48GIMHD456fESxSupS0zRsrhDuNB6xYxIRpY4e+A5O+axsrtCwWwm8NXqLUFMDtHy3Jy3es4iIKJKQjLdE25oamm0JIUFizM02+qF3tR8G4gzeeqzxlgQFBUBjY8ePS9a+oxXtm+isWbPwxz/+EXfeeSf27duH9957Dx9//HFM4xozZoz+dWFhIUpKSlBTUwMA2LBhA44//njT4ysrK03fr127Fl9//TX+8pe/mMYvyzKqqqowcuRIAMAxxxwT07iI4qZHmvQ2gdZtWg7NeHN+jbf2Ao+cXxHZRwhjV1M128DCjLeWGiXDrd5TCiGA3V8cxpBZHiA737J9EBFR6oQGxkTc5QnMP2fOeCM7SFDmBBEvjuvLgVMzns6EgbckkCQgzMpNxxk6dCgkSeqwgcIVV1yBX/ziF1i5ciVWrFiBgQMH4qSTToppX9nZ2abvJUmCHMMV98bGRvzsZz/DjTfe2Oa+fv366V+HWzJLlFzJqPEW0tU0HbJHtDpSxvbkEnjiJrKZ0P82DV1NA9b9YXoO1gEAdss/wBHyN9izzYsh/hYG3oiI0pZFGW/61tQab2rgTRKCdd7soi/YidBcIYVD6WwYeOvEunbtiqlTp2LevHm48cYb2wStamtrUVZWhm7duuGCCy7A/PnzsXLlSlx55ZWWjmPkyJFtmi189tlnpu/HjRuH9evXY8iQIZbumyh+5kBT/FcDw2y5TcZbGgTeNGHO2FYeGyKKjdASZiUpeCHbwg88/tp6AEDpwHJ4m7LRUt8K+Fss2z4REdkr0Yw3oQV5JOX/GHOzT7Q13iReObcca7x1cvPmzUMgEMBxxx2HN998E5s3b8aGDRvw5JNPmpZ7zpo1Cy+//DI2bNiAGTNmWDqGa6+9Fps3b8Ydd9yBjRs34rXXXsOCBQtMj7nrrruwYsUKXH/99VizZg02b96Md955p01zBaLUCc3DT86m02WpafjmErxuRmQ3bakpJBe0aZ9lSbRCIFCvZLwVD+oFXyAbPh8gmust2gEREaVcSGQs3sCb1kFTmw1yVugcwtX+q8Gwm/UYeOvkBg0ahNWrV+OUU07BbbfdhiOOOAKnn346lixZgmeffVZ/3GmnnYZevXph6tSp6N27t6Vj6NevH9588028/fbbOOqoo/Dcc8/h4YcfNj1mzJgxWL58OTZt2oSTTjoJRx99NH79619bPhaiWGmp2lZ2NRWhXU3TaKlpzPcRUVLpXYUlKZh1YFUWrb8V/kYvAKB8RA945DwIAbQcarBm+0RElPZCl5qSfaQOPlNEWIFKFuBSU0KvXr3w9NNP4+mnn474mKamJhw+fBhXX311m/uWLVtm+n779u2m78MtaamtrTV9f8455+Ccc84x3Ra6pPXYY4/FBx98EHGMofslSqrQ5goWko1/MwJpFbiKVDOCiOyhZSpIEqxfauprhL/RB28gGz8YVoIqodR1a6ypR8FIa3ZBRESpFXqGiPuUof6gPmXWAm/KVeU4N0rW6KirKV8fqzHjjdolyzJqamrwm9/8BmVlZTjvvPPsHhKRQ2iTCUOhWItIhmyUdGmu0N4HeSvrSRFRbPTmCpD09ysrA2+BJh88vlz06F8AOVdprd58gBlvRETpygXzvFPEnSWtnmtE8KIsY252Uw++K3wYSK9dnarhdCLMeKN27dixAwMHDkSfPn2wYMECZGXxV4YIQJuMNyuDS6aENxlpUeMtyDC5snEURKQw13hT368syqIV9YfgaRXwBHLRs38+RG4hEABaDzLwRkSUrkKntPFPcc0/qC+K4ATRNh0mCnDhStIwikLtGjBgALNViMKQJK0ft3qDhX8mphpvQFpkvOnLCQwnbJ67iewnAiHvVYBly9fr9xyEEEBjoAjlvd0Q+YVAI9Ba22jJ9omIyH7xZ7ypJHONN/MCVLIDS8OkHpeaEhHFQY+NSdrbqIUZb4YPxUpX0zQIvOnansgZvCeyT7DGm0t/v5It+pus33UIAODLKUFuLiAVFCnf1zHwRkSUtkK7miaa8aZdlZUkAFIiG6SEKcde7iAMxJfIegy8ERHFRcsikUzfWrPp0OYKzl9qGvYEzatpRA6gZaOqH3gAy2bUjXsPK5srLAUAuIvUwFtDkyXbJyIi+8VfniCkuYIWf0t4RBQvrWlCpA6zfG2Sh4E3IqKEaB9krctKM2aIyQLpcdlJff7hYm3MeCOyjwiof5suKfj3aVEWbev+WmXbJV0AAO7iYmXzjQy8ERGlq9COlnEH3kLnf5JxqSk5GQNw1mPgjYgoHqHNFazctHFjAulR400lDKcVwdM2ke30rqaSZGgGY822vYfrAQDZXZSMt+wSJeNNbmHgjYiIFEJdampqrsCLsjbRjnuEMBA7YCQNA29ERHHQU7WTEVsyZKOkT3MF5T/hM95SOxQiCtIyTiVI6nJTQ8OFBPlqle6lud2UjLecLiXK9pubLdk+ERHZwaq2pubJoeSS2FjBZnpX0wifX9h0IXkYeCMiSoCW1dVhe+5YtmlsriAjLWq8cRJF5Ez6+4mhxpuw6O9VblCaKBRUKBlv+d2UwBs8relxwYCIiNoIndLGXTIk0o9xymg7IXUQBuJrZDkG3oiIEqGeuCzN6grprZAOKWPmD/ch4i7KS0SJEmoGrZBcwb9PK/4m5QDkphYAQHGFkvFW0F0JwAVa/YDfk/g+iIgo5drUeItzHqpvR5simgr6c25oh+CKnQjNFZjwljQMvBGqq6txww03YNCgQcjNzUXfvn1x7rnnYsmSJXYPjcjBQlO1Lcx4M0xwhIBlhdBTj2dvIrtpQXEl4U35m5SteE/xt8Df4ocQQEmFkulW0iMfAdmNQEsA8Lckvg8iIrJdvNd/gwtLgzXelK8ZdLNPOxfKKamy7B4A2Wv79u2YOHEiysrK8Lvf/Q5HHnkkfD4fFi9ejNmzZ+O7776ze4hEjqRPQrRUbSuXmoYs0RKBQPqEsFjjjchZ5ODVbUtrJvtbEGjxwydno7SiAABQ2jMXuwPZyPe2MvBGRJSm2pRPifNiTfBCcrDGW/A+Xp61g/baig7yrzh1tx4z3pJBCCDgtedfjJ9wf/7zn0OSJHzxxReYPn06hg0bhtGjR+PWW2/FZ599BgDYsWMHzj//fBQVFaGkpAQXX3wx9u3bp2/j/vvvx9ixY/HKK69gwIABKC0txY9+9CM0NDToj5FlGXPmzMHAgQORn5+Po446Cv/4xz+sOd5ENmgzWUjSUlPAouyUZJPNk6vQL4nIHlog37jUVFix1NTTAL9Hhtefgy698gAAZeW58AWy4fMCorEu8X0QEVHKhZ4h4r+AGr6VgiR4UdY+HWS8Sdp/+AJZjRlvySD7gM8ftmffx98DuHOieuihQ4fw/vvv46GHHkJhYWGb+8vKyiDLsh50W758Ofx+P2bPno1LLrkEy5Yt0x+7detWvP3221i4cCEOHz6Miy++GI888ggeeughAMCcOXPw6quv4rnnnsPQoUPxn//8B5dddhl69OiBSZMmWfLUiVLLfOKKu/Bs2E2btxXwC7it23qStNMlKR0Ch0SZSvvzc0lAQP0DtaDxQcvBOsgy4A3koFsvZd7RtbsLrXIeStCA5oMNKOyV8G6IiCjlQmq8BRI9Z4TLeGNgxxahK3ba4FXzZGHgrRPbsmULhBAYMWJExMcsWbIE69atQ1VVFfr27QsA+POf/4zRo0fjyy+/xLHHHgtAychZsGABiouLAQCXX345lixZgoceeggejwcPP/wwPvroI1RWVgIABg0ahE8++QTPP/88A2+U1iRL124pQuNUiU94Usk4qeLJm8huWsabJEmWZrzV71Uy2lpRAPXUj/x8wCMpy04b99Wh8IiEd0NERClm1ewtmDUVrPEGodR4Y9zNHpKkfqbo6EXm62M5Bt6SwZWtZJ7Zte8oRXOlYcOGDejbt68edAOAUaNGoaysDBs2bNADbwMGDNCDbgDQq1cv1NTUAFACfM3NzTj99NNN2/Z6vTj66KOjHi+RE3XYjjseIZE3ORCwfh8WC/t2wrgbke1MHYe1CwUWTKib9imBNzm3SN+sJAFyjhJ4az7UmPhOiIgo9US738a9IVNXUwZ2bBbh8wubLiQNA2/JIElRL/e009ChQyFJkiUNFLKzzQE/SZL0ulSNjcrke9GiRfjBD35gelxubm7C+yayg1acVP8ca+kEwrwx2Z8GsxNt6VqYEzavahLZKEzgzYolPs0H65XN5xeZbhd5hYAPaDnYEO7HiIjI8UKWmiaYJa3nven1w7jU1DYhn18oddhcoRPr2rUrpk6dinnz5qGpqanN/bW1tRg5ciR27tyJnTt36revX78etbW1GDVqVFT7GTVqFHJzc7Fjxw4MGTLE9M+YSUeUTvTpgnrmatMBKpFth6wsDaTpUlOe04nsJwxBcUlfapr4e4rnkBJ4kwpCasTmKd976pjxRkSUEeINvOkdNINp0cJ8F6WYXiCng8gbXx7rMeOtk5s3bx4mTpyI4447Dg8++CDGjBkDv9+PDz/8EM8++yzWr1+PI488Ej/5yU/w+OOPw+/34+c//zkmTZqEY445Jqp9FBcX4/bbb8ctt9wCWZZx4oknoq6uDp9++ilKSkowY8aMJD9LoiRQz0jaicvSpqYhsxHhS5+lpuHO47yqSWQjWbu6LQXfryz4k/TWKoE1V1GJ6XZXUQHQAHjrmhPfCRER2SBkHhrnSUOfGxprvIXZPqWQHgwNn38VzErka2Q1Bt46uUGDBmH16tV46KGHcNttt2Hv3r3o0aMHxo8fj2effRaSJOGdd97BDTfcgJNPPhkulwtnnnkmnnrqqZj285vf/AY9evTAnDlzsG3bNpSVlWHcuHG45x6bauERJUiSzAVjLe3cGTLBkS0ohJ50YSZlgjlvRLbT/zQlF/QC1xZ0NfXXK4E3d5F5qalb7ZLuq2fgjYgoLYXWeIs38KZd+FFjPOaupnFtkhKlHniXK/wcnS9L8jDwRujVqxeefvppPP3002Hv79evH955552IP3///ffj/vvvN91288034+abb9a/lyQJN910E2666SYrhkxkvw7bcSew6dDAWzotNQ1X4y0dAodEGUpfVuqS9LcrK4L5gUalhlt2Wanp9pySfACAr6k14X0QEVHqhWY7xRt4i3SqkQRXQ9hHC7xF8yiyEmu8ERHFRbuKJ5m+t2bTIROedAq8GTHhjch+auBNiYlb19VUNCkZbTldzUtNc4qVwFugmYE3IqJOLaQOiTJntu48RLHTD3uEyJv2ucbK2tWkYOCNiMgKyYu7pUVzhfauXPKqJpF9TEtNtZS3RJeaCgG0KEtN87uaM97y1Iw3udWb2D6IiMgeoReA4+6tEFKWJXgP42420QJqUkcpb2Q5HnEiokQkox93yIdi4Xd+4E0v1mpaesuUNyK7aV1NJZcUTDRIdKmp7IPcogTWCsrLTHdlFyqBN/g8ie2DiIicIc5zhgh2V1D+Y6zxxjIkqSeEniiQjI8v1D4G3oiI4hLsFKh8m8SlplY2bkiysF1NObkiso8hKK69XyXcW8HfgkBLALLsQmm5ublCdpEaePMy442IKBPEn/Gm/Fc790iScpsErjS1m8sdYalpisfRmTDwRkSUAD3Dy8LAW+iHYjmNMt5MJ2yevYlspwe+JckQGU/wPcXbhIDHD28gB2W98k135RbnKbvzM/BGRJSeQua0cV4ADl54NdZ4C78LSgWhZyFKEbqaMhUueRh4IyKKg5TMVO107GoqTP8JuY+zKyLb6M0VJP39KtEsVH9DHfw+wBvIRrcf5JnuyykuUB/k498+EVEGiPutXAvytFlqKlj/1w7GYx4h8MawW/Iw8EZEFBdz5M3K6UPoZCQ9lmq2jUQKwdM3kd30pT4uyZChm9g2G/bWAgBa5Tx06WaeSuaXKYE32RcAZF9iOyIiopSTQk4S8QbJ2vyYBAg1tMO4m03U4+7qKHOAL5DlGHgjIkqE1hXIyjpsoRlvabDUNDji4Imc2epEDqC9NxmWmiaaadCwrx4A4M8uRFaW+b78UmXpqfDKQIDLTYmI0k7IKSLeC8D6skatxpt6uyQY17GHobkCu5qmHI84EVE8kjljkEMz3pwfeGuvSxInV0T2Cf3go9yY2HtKU02dspm8ojb3FZRmIyC7IQcE4GdnUyKi9BM6cUu0qylrvDmCEPphl9wRlpryonnSMPBGCVm2bBkkSUJtba3dQyGyRzKaK4R+nxY13rSsGsNN9oyEiIy0P0SXS7/CnWhXU8/BwwCAQF5Jm/sKy7Lhl90IBAC5pTmxHRERke0SrfGmYY03mwnRptNsOw9O+nA6GwbeOrn9+/fjuuuuQ79+/ZCbm4uKigpMnToVn376qd1D002ePBk333xzm9tvvPFGjB8/Hrm5uRg7dmzKx0UEtNMVKAGhk5G0aK6gC5fylk7jJ8oswrjUNHhrQtv0Hz4EAJALurS5r7DEDb+srD9tqWtJaD9ERGS/uJeaaqcfda4sSQCEBAkM69jCMB+X3OHDQILtFZImq+OHUCabPn06vF4vXn75ZQwaNAj79u3DkiVLcPDgQbuHFpWrrroKn3/+Ob7++mu7h0JkndClpmkVeDPiyZvIfupSU5cEoV0oSHD5ulynBN5EUdc29+XnA17koBDNaK1rQWFCeyIiolSTQrPR4m6uEJLxZlwVwchb6olgjbeIJd641jRpmPGWLH6/Pf9iUFtbi//+97949NFHccopp6B///447rjjcPfdd+O8887D9u3bIUkS1qxZY/oZSZKwbNky07Y+/fRTjBkzBnl5eZgwYQK++eYb/b7vv/8e5557Lrp06YLCwkKMHj0a7777rn7/N998g7POOgtFRUUoLy/H5ZdfjgMHDgAAZs6cieXLl+OJJ56AJEmQJAnbt28HADz55JOYPXs2Bg0aFNtrQ2QBKfTMZekMIv1qvLV3NZSTKyIbGTLeJCu6yQkBUa9cnHN1LW9zt8sFyO5cAEBLLZeaEhGlmzYlT+JeaqqnvKn/YeTNVsYab2yukHLMeEsGvx/405/s2fdVV6FNi7EIioqKUFRUhLfffhsTJkxAbm5u3Lu944478MQTT6CiogL33HMPzj33XGzatAnZ2dmYPXs2vF4v/vOf/6CwsBDr169HUZFSkLm2thZTpkzBrFmzMHfuXLS0tOCuu+7CxRdfjI8//hhPPPEENm3ahCOOOAIPPvggAKBHjx5xj5PIKgJqPpd2ZcjKGm+hS03jTPFPqeCZXL+JF82IHEAONlcQ2h9lIu8p/mb4DitLSIv79wr7EOHOBgB4Glrj3w8REdlCu7gshLY8NL4LwPpFWb2dqbplBt1sEjzuySiVQ+1j4K0Ty8rKwoIFC3DNNdfgueeew7hx4zBp0iT86Ec/wpgxY2La1n333YfTTz8dAPDyyy+jT58++Oc//4mLL74YO3bswPTp03HkkUcCgClD7emnn8bRRx+Nhx9+WL/tT3/6E/r27YtNmzZh2LBhyMnJQUFBASoqKix41kQW0QNNUsgNFmw6ZH6TVktNw5Z4S6PxE2Uc7YOPC3BZ8H7lqYO31oNWXx56DikLv8fsPMAHeBuY8UZElLaUqFvccTL9x0KuxEpg7M0WshxsrhAh8MaL5snDwFsyZGUpmWd27TsG06dPx7Rp0/Df//4Xn332Gd577z089thjePHFFzF58uSot1NZWal/3bVrVwwfPhwbNmwAoDRBuO666/DBBx/gtNNOw/Tp0/XA3tq1a7F06VI9A85o69atGDZsWEzPhxyiqUlZb5Sfb/dIkka7Gqh1BbJyAiHBHKhKh8CbUMuxmsu38+xNZDctY1ZySfrfZEJZtC2H4GvwocVXhOEjisM/JjsH8AGeRk/8+yEiotQzTGgFXJAQiP8Cqjp/1TtoSlIyFopQ1IThWlwHS035+liOi3uTJSvLnn9xyMvLw+mnn45f/epXWLFiBWbOnIn77rsPLvUP0rjszefzxbz9WbNmYdu2bbj88suxbt06HHPMMXjqqacAAI2NjTj33HOxZs0a07/Nmzfj5JNPjuv5kM1kGfjHP4A33+wcZ1VmvAEIFuIVYS6VdYZfAyKnkgxLffTOcgm8X7XsrobXCzT6C9FvePiLKyJbKV3hb2JXUyKidGLlnE3/DBl2bsjJYcoZa7xFSm1LwucaUjDwRm2MGjUKTU1Nei21vXv36vcZGy0YffbZZ/rXhw8fxqZNmzBy5Ej9tr59++Laa6/FW2+9hdtuuw0vvPACAGDcuHH49ttvMWDAAAwZMsT0r7BQ6YWWk5ODQCBg9dOkZPF6AY8HaG7uFBEX/cRl5UQlZGNpUeONJ2giRxJqBq3kckPLSY2zXA8A4NDWagBAk7sbysoiPChHCbz5mpnxRkSUVoxz90RXdQTXmhq2pyxf5bTRBoaTf6SEt4gBOUoYA2+d2MGDBzFlyhS8+uqr+Prrr1FVVYU33ngDjz32GM4//3zk5+djwoQJeOSRR7BhwwYsX74c9957b9htPfjgg1iyZAm++eYbzJw5E927d8cFF1wAALj55puxePFiVFVVYfXq1Vi6dKkelJs9ezYOHTqESy+9FF9++SW2bt2KxYsX48orr9SDbQMGDMDnn3+O7du348CBA5DVdOctW7ZgzZo1qK6uRktLi54t5/V6k3/wKDoZHXgzZ3glkkHSdtMh20qDGmkieAktzJ0pHQoRGanvH8JlrPEW/3tK3fZ9yvZKu0d8jCs3DwAQaGbGGxFROgmbiZZoV1NjPTGJNd5sI4L1+iR3+2EgvjzWY423TqyoqAjHH3885s6di61bt8Ln86Fv37645pprcM899wBQGh1cffXVGD9+PIYPH47HHnsMZ5xxRpttPfLII7jpppuwefNmjB07Fv/+97+Rk5MDAAgEApg9ezZ27dqFkpISnHnmmZg7dy4AoHfv3vj0009x11134YwzzoDH40H//v1x5pln6ktdb7/9dsyYMQOjRo1CS0sLqqqqMGDAAMyaNQvLly/Xx3D00UcDgH4/2cR4Js3ks6r63FzWJ7y1OW5yGgTewh0A1ngjsp8ktBo7bj3pQAQSWGq69wAAwNWtZ+R95qkZby3MeCMiSiemabwkKclpca68CHY1VYM8kqTPDLnU1AZaWRgBuNyco6caA2+dWG5uLubMmYM5c+ZEfMzIkSOxYsUK023GN8rJkyfr359zzjlht6HVc4tk6NCheOuttyLeP2zYMKxcubLN7cuWLWt3u2STThJ4009XWq62lQlvIROcRD4kp4652YTyjU1DISKd1qxFuFzBDz8J8B84CADI6xW507ikZryJltaE90dERKljrivctt53TNsS2tww0VGRNYT6PynyUtPUDqhT4VJTIkqeDA68BalLTRMpmtRGaOAtDTLe2hHvlVIisoD23iQZlprGm0Xr98B3uAEAUNQ/cuDNpXa0DniY8UZElE5MU/dEW5Cq8z9hrPGmrjPtFB8RnEaWg0tNXe2H2CS+PpZjxhsRWauTZLwF+3FLxu8s3bT+bRotNWXCG5HDCGNzBf3G+LblqYWv1gtvIBs9hnSL+LAsdampaGXgjYgonYSv8RbvLFc7/wRzffT2Cpn8EcHBBACIyBlveoyUVd4sx4w3IrJWpwm8qfSlptY9Vy1DTAjtSmM6BN7U5Wxhwm2s40FkH0k2Zrypy4bifUtpqoG33otmbwF+MLwo4sPcBUrGm/Ay8EZElE6MqxRkfalpvNvSvjJ2NVXv49ww9fS5ensZb7xsniwMvBERJaCjVO1E6BOetKjxpjIV8uDJm8h2QukQDpfL8PcZ33uKZ9c2eL3AoZau6DeiIOLjsovUGm9cakpElFaMgTcpwXOG3tzHFHGQoNUaoxSTZf24R8x4U/HlsR4DbxZh1D498HVKAeMxToclkgkKzkks7a4AwBB4S4PjqD37sKE2/t0R2UibZbuD5XrifE+p3bQFAHBQ9EaXrpED69mFSsYbfN649kNERDYxztn0c0Z88zhZW8Fh6GqqbzPe8VFC1HYXERteJDOhoLNj4C1B2dnZAIDm5mabR0LR0F4n7XWjJOgMQRbTpER7G7Vwqam6qUAaBd6kMJ2rBNtYEdlO0jPe3MH3q3jeruQAmjdvAwA0lgxut0udlvEmMfBGRJRWTBVj1ChZolN7U/1fLfbWGT4vOI5yzOX2arxR0jimucIjjzyCu+++GzfddBMef/xxAEBraytuu+02/O1vf4PH48HUqVPxzDPPoLy8XP+5HTt24LrrrsPSpUtRVFSEGTNmYM6cOcjKCj61ZcuW4dZbb8W3336Lvn374t5778XMmTMtGbfb7UZZWRlqamoAAAUFBYa0XHIKIQSam5tRU1ODsrIyuN3ujn+IEpehJ1VT3E1KQvlRY403KT26gmrHRA6X85ahvwdE6UBf6iMZlprG8zfZvA91W+vhDWSjaOTQdh+aU1wALwDJz8AbEVE6EeZJrnpbnBeAZe38Y6zxZk0wj+KgdTWVuNTUDo4IvH355Zd4/vnnMWbMGNPtt9xyCxYtWoQ33ngDpaWluP7663HRRRfh008/BQAEAgFMmzYNFRUVWLFiBfbu3YsrrrgC2dnZePjhhwEAVVVVmDZtGq699lr85S9/wZIlSzBr1iz06tULU6dOtWT8FRUVAKAH38i5ysrK9NeLkqQzNFcIk4afjLam2lLTdFqyy+sORE4TLOiiJ7zF897csAsN3zeitqULhp3U/nk0ryQfjYCy1FQIvjEQEaUJ8+khsXWh2rlGSK7QO+LbICVGCAihZDJGPC2zq2nS2B54a2xsxE9+8hO88MIL+O1vf6vfXldXh5deegmvvfYapkyZAgCYP38+Ro4cic8++wwTJkzABx98gPXr1+Ojjz5CeXk5xo4di9/85je46667cP/99yMnJwfPPfccBg4ciN///vcAgJEjR+KTTz7B3LlzLQu8SZKEXr16oWfPnvD5fJZsk6yXnZ3NTLdU6ASBN9OHVq1LoJXbVzPcAnCr36dD4E2rGhHmnsz8NSBKC1rGG1xuBD9Exf5HGdizBQ0H/TjU0g1nn9Gj3cfmlSqNF2S/DMg+wJ0T8/6IiCj1THPORLKkAX1yrMfdTF1N49skJSK4ooZLTVPP9sDb7NmzMW3aNJx22mmmwNuqVavg8/lw2mmn6beNGDEC/fr1w8qVKzFhwgSsXLkSRx55pGnp6dSpU3Hdddfh22+/xdFHH42VK1eatqE95uabb444Jo/HA4+hE1d9fX1Uz8XtdjOwQ2SUoWfV0I5PAoYPt1buR6/xlgbHUbuqaSgdymX3RA5gCLxJLj3lLebN1PzvOwQCwB4Mwsgj2p/r5JdozRUCQMDLwBsRUZowXT/XlprGO8XVg3jmKI8ENryzhRzsJsspeurZGuv829/+htWrV2POnDlt7quurkZOTg7KyspMt5eXl6O6ulp/jDHopt2v3dfeY+rr69HS0hJ2XHPmzEFpaan+r2/fvnE9P6JOqVNkvBm+kRJLww9HC+LJIn2aK2jCncjTafxEmUZvruDOMrZhjm0jvmYc/nYXACB7yMgOr5QXdslBQHbD7wfga41tX0REZBsRMM7ZtHpsca81VbbiMtZ4M91FKSagBFQjnseT8LmGFLYF3nbu3ImbbroJf/nLX5CXl2fXMMK6++67UVdXp//buXOn3UMiSh+dIPCGMEtNrTxDaQluaVXjTXv6pkkVL6cR2c1l6Gqqz6djfUup34G675vQ0FqEvsf36/DhhaVZ8MtuCAH4mtj1nYgoXYS9uBz3tsKkVyktTRnYsYN68heinRpvlDS2Bd5WrVqFmpoajBs3DllZWcjKysLy5cvx5JNPIisrC+Xl5fB6vaitrTX93L59+/Ti+BUVFdi3b1+b+7X72ntMSUkJ8vPzw44tNzcXJSUlpn9EFIcMDbyFdjUNvS1RktBqvKXPUtOwV0N5UieynZZx6srOMhTaiTHydmgrmnY342BzN4yeUt7hwwuLXfCLbABAc2341QVERORwCdd4Cwm8SVKwJ1mGfkZwtmDAkzXeUs+2Q37qqadi3bp1WLNmjf7vmGOOwU9+8hP96+zsbCxZskT/mY0bN2LHjh2orKwEAFRWVmLdunWmbqIffvghSkpKMGrUKP0xxm1oj9G2QUQW6wQZb6anpabPS5J1z1XbkrbUFGkQeAsWjQhzTzoMnyhTBZSMN3e2O7jcJ8Y/yrp169HSAuxp7YNjJhV2+PicHMAHpa5bax0Db0RE6SJcQCzRpabm9CoJEhPe7KF3NXVFDLwFXyq+QlazrblCcXExjjjiCNNthYWF6Natm3771VdfjVtvvRVdu3ZFSUkJbrjhBlRWVmLChAkAgDPOOAOjRo3C5ZdfjsceewzV1dW49957MXv2bOTm5gIArr32Wjz99NO48847cdVVV+Hjjz/G66+/jkWLFqX2CRN1Fp0g8GZcp5WMjDdtaamM9KvxZoy8MeGNyGZC6O8n7pws+BFH7ZaAFwfWbgMAtPQcgWgWAUgSILKUwFsLA29ERGnDuMpCJDrHVc8/kjHjLc5So2QBWVZqvKG9VcScvSeL7V1N2zN37ly4XC5Mnz4dHo8HU6dOxTPPPKPf73a7sXDhQlx33XWorKxEYWEhZsyYgQcffFB/zMCBA7Fo0SLccssteOKJJ9CnTx+8+OKLmDp1qh1PiahzydDAW7ilpsmYQOjNFZLQMdVqYS9q6vc5f/xEmUkAAeWP05WdBUlrRhpLML9hF2q3NqDFm4/u4wZH/WNyVi4QALwNDLwREaULY3ablGCtYX1LxuYKao23DP2I4HB6QWYuNbWBowJvy5YtM32fl5eHefPmYd68eRF/pn///nj33Xfb3e7kyZPx1VdfWTFEIupIJziTmjo+JaG5AvQab+qn5DRYaqrVpRPG5gq8akZkLzkAWX3/yMpxwxfP32TDDjTuaMShlgoMP7nj+m4akZXDwBsRUZoJl/EW9xRX25bUNsrDGm820JeaRs54Y0Zi8jDWSUTW6gxLTQ307HkLn6s2GUmnjLegcClvqR8FEUFZFq9+8HHnZEFyxd6wxbt9PRoOBbC/uTvGT+0e/a6zlZIfnkZPDAMmIiI7mc4P+lLTOOehQltqar5Za2xKKaZdJG+nxhslDw85ESVPhgberGy13t4OZPUKoZQGNd6CyesGTHgjspeQIQLBwJv2NylF+4nH14wD//sOQgB73CMwcHD000YpW6nx5m9ixhsRUbowT90Tq/GmlyExLDVlQpWNTDWqwz8kGR9rSMHAGxFZqxNkvJnqX2gZJMms8ZYGS031+h/hTtgZ+ntA5Hyy/v6RlZOlf/iJeonPoe9waHMD6lpLUDxqaEwTckltcuVrZsYbEVHaMF7sTbTDpV6GRA05mGq8cW6Yatp8QIh2arypJ/qoL9BR1Bh4IyJrdYrAm+Eb/cxlYVaa1tVUKDXe0iLwpglpGU9ENhJK4E0WErJy3dCmfVG/oxxcj/qqBuyt74XBJ0Rf3w0IBt78DLwREaWnRGu8iZCupsp3CQ2J4qd9nJCljpsrpNEnj7TBwBsRJU+GBt7MbU2TsHn1v1rGW3ocR2WMYbuaOn+lLFFmEjIQEBBCQlaOS69vHdXydV8L/FXfoG6PB7vq+2DstB/EtGspRwm8BVoZeCMiShfalFP5rzqpi7fEW4S5oSTAyI4N9OZwQmpnqSkDo8nCwBsRWatTZLwFn5crGcUq1G0FtLfotIpcSWG/JCIbyAEIWQm8Zee69KXxUb03H/oOe/53GAcbS3CgaASOPSE7pl278pTAm8zAGxFR2hBhGiKIOCe5kjZ9dQWXmupJdJn5EcHRZMMKGjZXSD0eciKyVqcIvAW/1j7IWloLQZ30aDUxRBo0Vwh2V2gbbWMdDyKbqEtNBVzIyglO+aL6izy8EdVrDqO6vheGnzkw5km6S11qKjwMvBERpQs9480ws413HidCl5pKwavVnBumnvZxQiByxhslDwNvRJQ8mXpSNUfe2tyU+PbV/0hKjTekRY23MGMUPKsT2UvWM96UpaZRphrIAQR2bcDhqhZUN5bj5Mv7x7xnd36esikG3oiI0oapgZgeJ4u3uYL6X5dkuEnS+itQqmk1pNFec4XUDaezYeCNiKzVGTLeDIEwKRlrTbXj5kqjpabhMt548iayl5ABWUAWLmWpqfb32VEWbeMu7P2yBo0tOdidPRInnpYX866z1KWmwuuN+WeJiMgmWudLuKA35Im7uYL6g4aMt0T7NVD8ZMO5P1LGmwDXAicLA29EZK3OEHgzdTVV225bGXfTt51GGW/qQQl7Hs/Q3wMix5MDkGXoGW9wRzntq92K6rV12N/UHUNPG4CsrNh37S5g4I2IKN1oF5clCcFJXZwXgLXsOWPBfinkPkod4zLijspH8Nq59Rh4IyKKkWmlqdYmMBkZb1I6dTVVsWM8kXME/BBCWVaSnevSFvh0/J5yYCPqqhqxv7EnjpveN65dZxXmK1/4uNSUiChdGFd1iIQncmrgzdBcQb89jaa2mULrairY1dQWDLwRkbWMH+jSoSlAPIxX/vSUeeubK2gZbyIdlprq2FyByCkCXh8AGLqaqne09zfpa0btus1obhT4vnkATjqnNK59Zxeqy1P9vrh+noiI7COAhDuQSvp8NvT29LqmnCn0+bjUcVdTvjzWY+CNiKzVCc6kxqfo0ruaWrkDfePKttMg8KYfElP7eV41I7KT3+MHEAy8iWiawdRVoXptHepbi5E7fChK44u7IVcLvPm41JSIKF0EL5Ya5nBxljzRpq96trUkBS9Yd4LPC04jAup/o1hqStbjISei5MnQk6rpabmltrdZtR8t4y0darxp2Y1hUtQz9NeAyPF8HmWWLaA0V3C5o2gGU/89Dm2sx8Hm7hh+ap+4951dpATeJGa8ERGlDfOcLdE5bkizMLAKiZ1k9YVsf6lpCgfUyTDwRkTW6gTNFYxLTZNyflK3r9fESKvjyDM2kVNoGW8B4UZ2NqD9fUrtvKcE9m5G3a5WHGzqhuMv+kHc+84rUWq8SQFvmr2HERF1YoYLqSLYCSG+bYXpaqqchwRPCzbQa7xFk/HGF8hyDLwRkbU6QeDN1FwhCctB9e2rGW/psNRUE/ZKWabW+iNyuIAWeIMLWVmA5NKyFyK8N/tbUfPlFvj9wG7XMIw/ITfufeeWFCj78svB9S1ERORowc6XQDDjLc75vKxdSDbcpq42zdCPCI4WXEETTcYbXyCrMfBGRMmToWfVcIE3K5+pFJKanxZ1MMKNkfnqRLbye5XAGySXUlqno+yFhl3Yv7ERTd5CdBs7CFlZ8e87r0RZaiq8ASDA5aZEROlAa+glAYl3V1BJkqGraaJZdBQ/9ZjL7QTeuHIleRh4IyJrdYKMN3PkreOlW3Fv3q1kvMVb1NYW4c7kaTR8okyiB960zNyO1pY07ED9tkYcauqKoSdVJLTvgtJcyLILcgAQPk9C2yIiotQIZrwlHoDRM6xYyd8RZHWpKa+L24N/BURkrU4QeDM2O9CWblm7AzXBX11qmh7HMR3GSNS5yF61uYL2XqJnL4Rf/i3XbEFDdSsOtXTF0WeWJ7TvgpIsBIQbAoCvqSWhbRERUWoEp5zG7LT4SoZoKzj0qbKk9jcVgrNGGxg+vUR8TFI+1xAABt6IKJnSImAUB1PGm36jlTtQ/qNlvKVDjTe9bES4rqZpMH6iDOT3aUtN1XqR2oQ63NuVHMDB1RuV+m7yQBx1fF5C+y4sdsEvlLWqzbUMvBERpQW9IQKghQoS7a0AQzBHa6+QsZ8RHEwEgjXeKPUYeCMia3WGjDfj05KS8DYqaxlv1tTWSAktS88UeOOJnchObTPe2umU3FSN/Rvq4PXnIG/o0ITquwFAdjbgRzYAwFPfnNjGiIgoNQwZb1LoTTFvS73w6jLWeNMaNsS7UYqXLBuDqpRqDLwRkbU6xZnUkKydxCKxkjt9lppKkvPHSNTZBHxqN1G9xpvybdiGLU17ULutEbWtpRg0sbcl+xdZSuCttb7Vku0REVFyiXB1heOsNSyFvSgLACIdpraZRz3oor2kAQblkoaBNyJKHjkzlxgaP7QmpUCptn1X+iw1Fe0uNU3tWIhIEfAq3US1jDftw48UJn9BHN6Bxl3NqG3pgqOmJtZYQd+mOwcA4Glk4I2IKC0YJm1Caqc8QXQbA2AIvBnmiJwbpl7YoGobjLwlCwNvRGStTnAmNXV8sqjVesgeAAAudzvLwhxGhMte57mbyFYBv7bMRw28uSPX66n9dhM8rQI13gocM7nIkv3L7lwAgI+BNyKitKAFZ4QpSBbfBWB9bhhSsF9dbBrXNil+IoauplIafPZINwy8EZG1OkGNN6O26fMW0I5bGi011SdQ4bohpcX4iTKP7FWaK0hafR31406bCXXAi4Pf7AAAeCuGoaDAmv2LbLXGW6PHmg0SEVFSmcrvJ1qPTQvYGTPeWOPNNlqNN9FOCCgpK3kIAANvRJRMmXpWNSyh1UvPWvhU9UmPmp0ipcFSU435MPAUQ2QnrcabcCudEiS3NqMOeU9p2ou67U1o8eWh2xH9rRtAtpLx5m9mV1MionQg1DmuMCxbCFeeIMqtKT9vaK6gdzTN0I8I6YHRNTvwUxERWasTZLyZ6pnp76LWBcckratpOmW8tdcoKX3ihkQZRdabK5hrvLV5S2nYjaadTahtKcPgCT0s27+UrdR48zUz442IKB2Yzg8JZqfp2dXG1RB6hZY0mNtmGG2paTQRIL461mPgjYis1ZkCb5CCVwSTkvHmDu7P8cdSu6rZdnJFRPaQfepSU61epCv8UlP5wHY07feitqUMR06xLvCGXC3jjYE3IqK0ELy6DEOULL5NaStNw9R4c/qsNhMFmyu0M0GXOnwExYmBNyJKHscHi+JkXGqql62w7rlqS0u11HzlMDr8WBobTrS5y+FjJ8pQst+c8Rb87GP+mzywdgv8AWCf3BcjxuRYtn9XjhJ4k1sZeCMiSgd6Jpqhf1gCRd4AAK4wXU05NUy94GvbcViNL4/1GHgjImt1qoy30OCYtVzZ2lJTmIJ9TsYrZETOoWW8aY1a9Do7xvcrOYBDG/YoD+s/GFlZ1u1fylWCeIEWdjUlIkoLhgup2qki0aWmwhUMvCkxH5GpHxEcLdhcob3ZOmfyycLAGxFZqzME3vT/Skk5PelXpIxBPYc3WNAbQIRtapqZvwdETqfXeNOaK+jdTQ1/k55a1G9vhD/gRvcxFjZWAODKUzLeAh5mvBERpQNtziZJwSldvPM47edchgyrYOI154YpJwdf20j05ES+PpZj4I2IkidT37QNafhIOA0/7A4AAC71w7IAHJ/xFix7ZzitsCc5ka3kgBJ40xu1hHu/ajmIpj3NaPIVYmilhfXdALjUGm+yx2vpdomIKElEMCtKSGGypOPYFlzGkIPEGm82EXo2I0NAduBRJyJrdYaMN9nwvFzWv41qV5lcWYaMNzlg+X6s1G6r+Qz9PSByOi3jTcpSa7zpS02Df5Oifh9aDvvQ5CnCyBO6WLp/V16esg9mvBERpYWw2W1xrrqQ9MZb6rnHmEZHKSe0i/jtXBjnNfPkYeCNiKzVCQJvhvSuYEq2hdfu9J5DWcaupg7PeAtXr1Xw7E1kJxEw13iDq+3f5OHNOxAIALWBrhg0wrrGCgCQVZCvjMPLjDcionRguricaBRGhCxtNATeWIYk9URAbd7Wbok3zt2ThYE3IqKYBScLknaCsnD+EMx4MwTeHL7UVGc8YfPcTWQr4TMvNQ2WeAu+YR36bjcAwFdSjhxr427IyleWmsLLjDcionQgDBeXw9wY17akMBd9IDPwlmrGGtXRP5qswsAbEVmrE2S8Cb3+BWAommTd9tX/Sm5DbQ2HZ7y191pn6K8BkeNpNd5cbsMyH8D0R9m0o1p5THkfy/efXaguNfX5LN82ERFZTzJ+oZ4z4s1Ok6BmWLldhtsUgoG31Au7PCUEM96ShoE3IrJWJwq8SYDhg6x122+T8QY4P/CmY8YbkVMIv/qhJ0vrahpyoSDgg7fmIACgYEDyAm/wcakpEVE6MGa8JXxpWZgvURuDOpn5CcHZ5IB+ad/WcXRWDLwRUfJkbOBN/UKSgk08pSR0Nc1yQQg15ub4Y9lOi/K0CRoSZRbhV5uyqEtNtQLXWnAfrYfQUtMKbyAbPYaVW77/nCIl8Cb5GXgjIkoHWgF+U5ne+CNvyn/cYZorOH5em3lEaM29dh+c3LF0Rgy8EZG1OkHGm0ZASkqNN+2wubMkpZ074PiupvrzN9Xx4BU1IlupzRVc2VqNN3XZkHZ/60F4D7WiyVuIvmOs7WgKALlq4A1+LjUlIkoHphpv2hw3zmWhwZWNxpBDYstXKX7a8l7RTgiIK02Th4E3IrJWJwi8mTs+aTdat30tG8XtVk6O6dDVNKjtGTtDfw2IHE+oNd60DsnajFp7j/Ht243WRhmNniIMPrrE8v3nFCuBN1fAyzcCIqJ0oM43JQBCq/EW5yTXpWVY6atDDHPEdJnWZhBJa9TGrqa2YOCNiJInUz9oGZ+WpP3HwuYKWuDNJUEISdmfw7uaBi+Q8oRN5BRa4M2l1XjTC1wrf7EHNuyEAFAr9UDvPtZPCfNLCpS9+QOAcHjWLhER6RM6YQqSxZnxppUhcQe3FVwokqGfERxMfxm51tQWDLwRkbU6QcZbkKSnzyfjmbqyJMiQ0iLjTdKukIY7l7NzFZEt2jRX0DvUKffXbd4JAAh0/UFSYuZ5pVpzBRkIcLkpEZHT6UtAhXFOF+c8Tq8pZuysbT4PUeroK3baOeFLvICeNAy8EZG1OkHgTQSCQTApCRlv2gTH7TbWeHN24E1/+obzteDJm8hespbxptV4U26WIAAh0LprDwAgq1e/pOy+oCwPsuxCQAZkT2tS9kFERNYJBmcALVQQ73Re0peats14k3hRNuViaq5AlmPgjYiSJ0MDbzop2CXQ0iKx2lLTLJcSeBNw/rFs5yTu9KETZSp9qWm2ucYbhABaD6O1phEB2YXCQUkKvBW7ERDKvlvqW5KyDyIiSgZXwj2ygmVIjBlv2n2cHKZaNM0VKHl41InIWp0h403Pwk/OJSO9uUJWcB/C8Rlv6lU0wzHhBTUim0Wq8SYE0FyD1n0taPQUo+9R3ZOy+/xCF3yysu/W2uak7IOIiKyjX0iWTDfGtS2tDInLHWZGmKGfERwtmqWmKRpKZ8TAGxFZqzOcSPUgE4JdApOw1FRyufSrUlrmilPpL3u4k3ln+J0gciAhmzPejEvj5UO70HzYh3pPMYZP6JKU/btcQMCVCwBorWfgjYjI6YwrOESi9dj0OI928pH0yA6nhqkX01JTvj6WY+CNiJLH6VlacTJOSvSTVxJOUFmGjLdAwNnHUpLaHgCmshPZTH3fcGuBN3fwb/LgN9sQCAAHAz0xdHRO8oaQpQTePLVNSdsHERFZJHgl1TDHjW8Oqi8ndYVZDcHATuqJjjPeNNYmFBDAwBsRWa0TXMIyLTVNQuRNGGq8yerbtOx3duBNf9ldhtMK89WJ7KXXeMsGYOgsJwQObdihPKR7P6h3J4WcrXQ2ba1j4I2IyPFMzbIk820x0kqn6EtNjQGfOIN5FL9gjTd2NbUDA29EZK1OUOMtmDsfPEFZ+Uy15DG3GxBCzXhzeODNcEja3pWxvwdEDqdmHetLTbOUCFuWy4eW75WOpjn9ByZ1CCI7HwDgqeNSUyIipzMFZxK8uKx3NZWMF2WtnzdTdKJZair4+iQNA29ElDwZGnARhhboWot0ydLnqma8ZUuQtfoaAacfy3Yib0RkC60piytHaXAgckqxu643JCHQVN0MXyAb3Uf3Te4gcpWMN28DA29ERE4XrrdC3NtS/6vNlSEZlq/KTp/XZiC9uUI7ISDO45OGgTcislYnyHgLPi0pSScoNfDmlvR0cDltJihsrkDkFJLsB2Co8eaS8PWeMWj0FKG1phUNrcUYdEy35I4hTwm8+Rh4IyJyPkONNz1KFuccVL8obShDwhpv9pHV10NwOaktGHgjImt1gsCbeamp9qWFz1Wbp2S5AHWpqex3dldTKVz6Os/rRPaSteYKSsabywX4RTZWf380duztis0Hh2BkZVlShyDlKUtNfY0MvBEROZ8h5S3hZYdq1rWr7YRQpM0F5QyizgkYd7NHlt0DIKIMlqmBN625gpD0dG1rn6qysSw3IEta4M3hNd5U4U7mrPFGZBMt8KYuNc0qzocfWTjUWISVjRPgLeyK3n2Sew3Wla8E3gJNLUndDxERJc7UQEwTZ5BMCCV+Zwy8tVfYn5IrOB2PfN5PSkIBAWDGGxFZrRNkvBm7cesnKMn6jDfJ7dKbK8hyegTejJE3iZMrInvJSqasFngbMDwX3WddiO/RHwBQMPQHSb/y7S4oAAAEWhh4IyJyOmEs8pbgulA9eOMOhhz04v0Z+hnB0UwdaynVmPFGRNbqBIE3rQW6QLBTUzIy3iQJEOr1EeF39rEMe4U09E4iSilJDdhn5QSXmj72Qhd8OnMqnn6sGVfNzk/6GLIKlX3ILa1J3xcRESVINkZntCBZHNsRIngh2WW8KGu4n1JKa7jUfnMF9T6+PJZj4I2IkifjT6rB5gpWXjzSrhC63C5AnawEHL/UNFzdCF5SI7KVUDPecs3TvYkTgYnvFKRkCFlFhcpQPMx4IyJyPEPGWzBIFs8c1BB4M9X/lcz7odRRX8f2Zues/5Y8XGpKpKmtBQ4etHsU6a8TnEiFYdmnlGAafvjtBzeuZbw5fqmpfoE0TAHd1I6EiADlvVjPeHPbNoycYiXAJzwe28ZARETRMS4BFe1lRkWzLeOFZA3jbrbRP1+EaXZBycfAG5Fm4ULgnXcAv9/ukaQ345nU6cGiOBlrvAWv3Fm6B3X7EiStFobTM97C1LgTPK8T2UfI+pKh0Iy3VMotUTLeJGa8ERE5XjDwJiWWnGZcamoKvDHyZhfttU00oErx4VEn0jQ3K0E3r9fukVCaEJAMdSusn0C4XJJehNbpGW+SHowME21jy3ii1BMyREDtkJybbdswcsuUjDfJx4w3IiLHCzdliy/yFgy8GW5N3qyZOqIF3qJbaspXyGoMvBEBygnF2wh46ngFJlGdorlC8EspCRlvknbcXMG2qbLDM95E2MAbU96IbCNkfZKtNVewQ36XIgCA5GvN3HMCEVGmMJYOSbTglwi31FTdJi/Kpp6sT9btHUcnxcAbEQAIgT2rVmLHZ58Cfl6VT0gnCLwZn5aW8SZZemXI2Mpd7Wrq9Iw3KcxlTSKyj5ABNeMtO8++wFtBVyXwJnwBQGYpByIiRzMsNQ3eFMcc17DUFIbAm7aSI0M/IjiaCNvtIhQn8snCrqZEABAIYM2qVgQCQPHBVnQptXtAGSJDz6pC1mokBFutW7wHAIDL5dIDb+mS8SbCncwz89eAyNlEQP+7tLO5QlG3AqXPg1cG/K2A275lr0RE1D5h/K9WCyzOpaYC2jXktnNDay9YUzREFBlvTIZLHma8EUHJJgoElK89zc4OcDheJ8h4M0aSkthbAca4nnB4Sr4U5lzOkzeRfYTfrwS8hITsXPume8Xdc+GXsyDLgK+h0bZxEBFRFNQVFqaVpnFnvAWbhek4ObSPrL0eHT80Yz/C2YiBNyIAAV8w2BbwB2wcSQboBIE3vZ4ZgicvK6/caduSXC6985AccPqxjFywVQgGs4lSze9VlnUK4bI38Fbmhk9WstwaDzXZNg4iIuqYPts0RmfinYJqJYvD1Hhz+gXlTKQvGWZXU1vwqBMBCPiCwTbh+ABHGsnQwJup/oVLS8O3fvPKprUJSnoEhI1LTQXrRBDZxteqBN7sznjLygICrlwAQPPBZtvGQUREHRPB2iHGKm/xbEn/yuUObinYNDNDPyM4meg4401iRmLSMPBGBCBgqJ/l9FpajtcJMt60yYSQpKS03daz54xdTR0eEBbtZryldixEBPg9asYb7A28AYCcrQTeWg5xqSkRkaMZlnVoqy7imsYZl5q6wmS8cXKYeupH3LD1mEOwBp/1bJ2JPfvssxgzZgxKSkpQUlKCyspKvPfee/r9ra2tmD17Nrp164aioiJMnz4d+/btM21jx44dmDZtGgoKCtCzZ0/ccccd8PvNXbOWLVuGcePGITc3F0OGDMGCBQtS8fQojciG5aVygIG3hHSGwJvxaYUpGGvZ9l0uPaPO6V1Ng3XpjHU87BkKEQE+jw8AIAsXsnPs/WOUs/MBAK11XGpKRORkesIbpMRqvBnrISdjrkwxk/XSL+2mvKVkLJ1RzIG3+fPno7nZmqUCffr0wSOPPIJVq1bhf//7H6ZMmYLzzz8f3377LQDglltuwb///W+88cYbWL58Ofbs2YOLLrpI//lAIIBp06bB6/VixYoVePnll7FgwQL8+te/1h9TVVWFadOm4ZRTTsGaNWtw8803Y9asWVi8eLElz4EygznjLT2W9KWFDA28GZ9WMmq8aZekXC4J2tt02mRihu1qmpm/B0ROFvAo5zIhueCye31DTh4AwMPAGxGRo0nG5Yh6B7H45nEi7EVZ9es0mdZmFL3EG4Nrdoh5KvaLX/wCFRUVuPrqq7FixYqEdn7uuefi7LPPxtChQzFs2DA89NBDKCoqwmeffYa6ujq89NJL+MMf/oApU6Zg/PjxmD9/PlasWIHPPvsMAPDBBx9g/fr1ePXVVzF27FicddZZ+M1vfoN58+bB6/UCAJ577jkMHDgQv//97zFy5Ehcf/31+OEPf4i5c+cmNHbKLKbAGzPeEtMpMt60jk9ScDKRhOcqSVIwo87pGW+mVqwauz/tE3VeenMFyW3zSACRqwbeGlpsHgkREbUnOJ1NNDgTXGpqrPEWZkeUKsYa1R09NLkj6ZRi/lS0e/duvPzyyzhw4AAmT56MESNG4NFHH0V1dXVCAwkEAvjb3/6GpqYmVFZWYtWqVfD5fDjttNP0x4wYMQL9+vXDypUrAQArV67EkUceifLycv0xU6dORX19vZ41t3LlStM2tMdo2wjH4/Ggvr7e9I8yW4BLTa3TCU6kehq+kJKTkm1sv54mNd6k9k7mneB3gshp/GpzBeGyP/AmqYE3XwObKxAROZlWe00Ym5rG04FUiGBX06wwNd6sDu3UbgN2Luecsx162Zp2PrtwpWnyxBx4y8rKwoUXXoh33nkHO3fuxDXXXIO//OUv6NevH8477zy88847kGPIzFi3bh2KioqQm5uLa6+9Fv/85z8xatQoVFdXIycnB2VlZabHl5eX60G+6upqU9BNu1+7r73H1NfXo6Ul/JXXOXPmoLS0VP/Xt2/fqJ8PpSfBrqbJkaEnP2OMSUpCITNti5Jb0lt+O73Gm35IwqwmIKLU83vV85pkf+apK68AAOBvYsYbEZGjBXsr6P+fSIxMiAhVSOIJ5rVn3T+BlW8Cjbut3W4mCTdZDyWZ/kMWSmg2Vl5ejhNPPBGVlZVwuVxYt24dZsyYgcGDB2PZsmVRbWP48OFYs2YNPv/8c1x33XWYMWMG1q9fn8iwEnb33Xejrq5O/7dz505bx0PJF/AZM95Y4y0hnWCpqdCWmiIJwSUhgjUxXK70yXjTupqyrSmRIwS0paa2F3gDpHyluULAohrBRESUHNocV0guvdxJPNlpSuacgIBkXmqapBItB97fgB3/rIKo2dfxgzstw4qaDrCrqfXimo3t27cP//d//4fRo0dj8uTJqK+vx8KFC1FVVYXdu3fj4osvxowZM6LaVk5ODoYMGYLx48djzpw5OOqoo/DEE0+goqICXq8XtbW1bfZdUVEBAKioqGjT5VT7vqPHlJSUIF+dCIbKzc3VO61q/yizBQxBDcGlponpBIE3jYChBptVJyghzBektEmP0wPCYZ5+Zr/6RM6mBd7s76wAZBUq8y25udXmkRARUbuMDcQS2IwcEMrPtynLogXzrPXZMh/Wfg1s/MZj8ZYzh55l2E5zBTZeSJ6YZ2Pnnnsu+vbtiwULFuCaa67B7t278de//lWvo1ZYWIjbbrst7iwxWZbh8Xgwfvx4ZGdnY8mSJfp9GzduxI4dO1BZWQkAqKysxLp161BTU6M/5sMPP0RJSQlGjRqlP8a4De0x2jaIAHNQI5Au3SPTQaYG3gzPSz9BWfZUgxtyudJnqanOcMLWv8rQXwMiJwv4lMCb5IAab+5CZampHKHEBxEROYQIzjeFGjCTROxzUCGM81nDHfo24xteJD6fssGmOodfqLaTGnhjaM0eWbH+QM+ePbF8+fJ2A1c9evRAVVVVh9u6++67cdZZZ6Ffv35oaGjAa6+9hmXLlmHx4sUoLS3F1VdfjVtvvRVdu3ZFSUkJbrjhBlRWVmLChAkAgDPOOAOjRo3C5ZdfjsceewzV1dW49957MXv2bOTm5gIArr32Wjz99NO48847cdVVV+Hjjz/G66+/jkWLFsX61CmDycZgW7oEOJyqM2S8mWq8aV9al/Gmb8rtMgTenH4sIy81dfrIiTJRQK3x5oSuptlFSuBNeBh4IyJyMlOvLK0RQhwTOVmGuorDvNQ0uNLU2tmhFhw0NswjM31NTRSZ8Jn6Ec5OMWe8TZo0CePGjWtzu9frxZ///GcAyrrh/v37d7itmpoaXHHFFRg+fDhOPfVUfPnll1i8eDFOP/10AMDcuXNxzjnnYPr06Tj55JNRUVGBt956S/95t9uNhQsXwu12o7KyEpdddhmuuOIKPPjgg/pjBg4ciEWLFuHDDz/EUUcdhd///vd48cUXMXXq1FifOmUwY5ZbwM93moR0psAbJEhui5dxhS411SY9abIEWhhOK4LdFYhso9d4c9sfeMspKVS+8HCpKRFRenAltHJBloXeoMG0fFEv3m/xZwStGysDbxEFL+K3s9Q0NUPplGLOeLvyyitx5plnomfPnqbbGxoacOWVV+KKK66IelsvvfRSu/fn5eVh3rx5mDdvXsTH9O/fH++++26725k8eTK++uqrqMdFnY9seJMWMt+wLZOhgTfjVTrJ6vWUptR8l35VSnb6sdQaQoTLeHP62IkykNY0SHJAV9OckgL4AUheBt6IiJxMm7Mp137jr2NsXKkRbqmp5TNDNeNNZsmgdmgZbzE8liwT82xMCBG2E8auXbtQWlpqyaCIUs0UeEuTzCLH6gQZb+Y0fFfIjQlv3RzE0jPenH4swy015XUzIrsEvD4AgHBAjbf8MjXjzcui10REjqZON4VkqKcSZ8YbICBEaMF+dV5r8cctSZ2Hy05vRmanKDLeEPUjKFZRZ7wdffTRkCQJkiTh1FNPRVZW8EcDgQCqqqpw5plnJmWQRMkmG4IasuMDHA4nBBDwKRGYDA28mSJvesq8RWQ5OL9xufQLHU5vriC1m/KW2rEQESCrGW+wejl8HPK6FgEAXH6P8v7JZehERM4Ubu4eV3MFZW4cqcabBIvnteoYudQ0Mv3TS3s13tjVNGmiDrxdcMEFAIA1a9Zg6tSpKCoq0u/LycnBgAEDMH36dMsHSJQKghlv1pEDwM5lgMsNDD3L7tEkidD/Xy8Sa/3m1a6m6g4c/nsZjEW2reNBMapZAzTsAgadHcyoJIqRttQUDsh4K+haDAAQHj8Q8ABZeTaPiIiIwjGWBxFag694tmPIrjJODUUCDRvao2W88XNcO7SL+O1d/ErWUmCKPvB23333AQAGDBiASy65BHl5nDRR5pANb9KCKcqJCfiAQCsQQMZmvAlDcpeWkWZdkVhDcwWXpF+Vkh2e8aYJeyq3ej1BptvytvLfLkOBrsNtHQqlL9mnNFeAK+ZyvpYr7l4AWXYh4JMhvC2QGHgjInImwyQ3keunxs9Wxoy35FyxVsthwVw+iEJo9ft4YdwWMV9KnzFjBoNulHGM9QB4pSRBxiBLhjaq0OYkAlLYmpcJMQTYJJcUvNooOzyIqc3TTNM0ntljZqqRmJl/P5QaTlpqWtojB345CwJA84F6u4dDREQRmK6ZaxeX47iQHpwrhwTe9PutnddKUM55rPEWWXB1SuR5AYNyyRPVZdCuXbti06ZN6N69O7p06dLuB81Dhw5ZNjiiVJH9hhpvTg9wOJ0x8JahmU6S8XkZ6iQIWYQUkI2DCDZXcLldwVoLDg8IaxMeZLU9mWdo4mNyBAzF57Py7RsHpT3h9ypfuLPtHQiAwmIXvCIXOfCi8UAjCvvZPSIiIgrLtKxDuyn2iZye8SaksMGceIJ57dICfQ6fL9tKNry2HeHc3XJRBd7mzp2L4uJi/WvLMzyIbMaMNwuZMnYy81gan6LxKp4ckOFOuJ6SYampBEiSttTU4WdA9bV2GRrv8FQRB19T8GvWd6MEuHyNAACfVGDzSJT3gkB2HoAGNB1ssHs4REQUkbHzZfwTOe3zVJuMN20lh+U13tT9MeOtHR0vNdXvkxz+uSMNRRV4mzFjhv71zJkzkzUWItsYg20iQ5dHpkwnyHgzRsbc2YYOzz4Z7mzrCplLLkPGm9NrvKnjC/v8M/X3IBl8zcGvedwoAW6/EuDySUUdPDI1RHY+4ANaDjZ1/GAiIrKHIe6mJ9vEk/FmuGBsWg2ifWn5UlMZAoDs59wpEhFLxhtZLubL6atXr8a6dev079955x1ccMEFuOeee+D1ei0dHFGqGN+kRYAR/oQYT6RODxbFLfi83NnBt9GA15/wlo1BYJcLwSuDDj+Wkp7xFgy8CdZ4i52fgTeyhrtJKf3hye1m80gUIldZOt1a12jzSIiIKBJtWamk/x/iW3YoGzPngqRkBN6E0KfmXLnUHvW1dbUXAlLuY8Kb9WIOvP3sZz/Dpk2bAADbtm3DJZdcgoKCArzxxhu48847LR8gUSoYO+DIfMNOjDFA5PBgUbyCcwUJbkOgKWDBVTZjgE0y1HhzfOBNzRR15xiWmto1mHRmXGrK4niUAL9acze3oqfNI1GIPCXw5qljxhsRkVPpTRGk4FLThGq8Kalzhnu0bcY/xjA702ODIpD4RfBMpWe8tVOPmslwyRNz4G3Tpk0YO3YsAOCNN97ApEmT8Nprr2HBggV48803rR4fUWoYghpWd9npdDpZjTdTxpsv8WXKxm1LkqRflXL876U6Ple45gqpHks641JTsoIcgFyndA8t6Vdu82AUUp5Sa85Xz8AbEZFjiXCZagksNZVgjuYkIbIjm0oGcdbZEcbW7BFz4E0IAVkNUnz00Uc4++yzAQB9+/bFgQMHrB0dUYqYl5qyxltiMj/jLdjxyVzTzJKMN0OATSlGq14ZdHrNCi3jzZAByDN7HIxLTRmypHh5G+Cr80KWXeg+pIfdowEASPlq4K2huYNHEhGRbcxXgOPfjPoZoM0WEqgbF0nAZ/gcZ8FF8Iwla80VOn5dOQO1XsyBt2OOOQa//e1v8corr2D58uWYNm0aAKCqqgrl5c64qkoUK+MyPl4pSZDpRJqhx1J/Wi5z4M2Ck71sqDEouQwZbw4/li414OrKCddcIcWDSWempaYOD7aSc7Uchq/RhxZ/HnoPL7Z7NAAAd6ESeAs0MfBGRORY+sVlKaF6bNrnKRGy1FSPu1k4OZQNSRNskheZdnFftLPUVIuUSpy8Wy7mwNvjjz+O1atX4/rrr8cvf/lLDBkyBADwj3/8AyeccILlAyRKBdMbNmu8JcaU5SZnZp2qkKw07VtjrcC4N234/ZMkqB0WADi86Yc2wTJ2eRVSzKcYYsYbWaBh5z74/UCTvxB9h+bZPRwAgLuwEAAgNzPwRkTkVMaVF/o8Lo7piOgocy5ZGW9cudQOLeON83M7ZHX8ELMxY8aYuppqfve738HtDpPpQJQGTJ1MmfGWGFONN/X7DKvUaeitoP7XBUC2JvCmXY0SktrVVN2Jw5ftal1NjTXvdPybih5rvJEFDmzZBwBoze4CNd5lu5wSZSCihYE3IiLHk6TEmpqqJVLafAQIprxZxljqhSuX2mGsuxdRZn1mc5KYA28ar9eLmpoavd6bpl+/fgkPiijVjBlvMrvhJMjwniBExme8AcoVQUnIltR4095SBZS5iUtbapougTd2NU0Ml5qSBeq/rwEAiJKuNo8kKLukSLkW09pi91CIiCgCU2sFbSIXxxw0GAAL6Wqqfi1ZmvFmXLnEjLfI1Iw3djW1RcyBt02bNuHqq6/GihUrTLcLISBJEgL8Zac0ZCpcn4FxopTqZEtNAShnKQHIVnQ1VY+fgFpbw2V9EdqkUINEWTltTytOr0/nKMalpk5/zcmxmvYcBAC4u3a3eSRBeaWFaAEgeRh4IyJyLGO8rL1aYNFup81lWMl8twVMF74Zi4hIxNBcgVN368UceLvyyiuRlZWFhQsXolevXtG9cEQOJxvrGfANOzGmpaaZmfEWnEtokwclKy1gQX1A4xVClwt6jTdHZ7wJoWe8udjVNH6yHwh4g98z443i5N2vBN5yyh0UeOtahBYA8HrsHgoREUWiXQA21QGLfS6vLTUVobECNZhn5ZJQ4Q+uVuJS0yi0V+ONsZ2kiTnwtmbNGqxatQojRoxIxniI7GHqhsM37ISEdjXNxMBb6FU8LQBnRVdTQxcoSUKwq6mDj6NsCDgau7wGH+DcsTuKL7T2FY8bxSdw+DAAoPAHPW0eSVBB1yIcBuDytWZk7U8iokxgnnnEX49NRLh4mIx3fnONN160jEh9TRh3s0fMLS1GjRqFAwcOJGMsRLYxdpJk4C1BphNtZgbetOeoPTPtqqAVNd6MATZTV1MHTySMtTWyco3Xc3j2jomxvhvAjDeKjxwA6usAAGUDe9k8mKDCHiUAANkTktlJRETOYZxv6lGYODLeIi411TZp3ecDmUtNo9PRaxL+wWSRmANvjz76KO68804sW7YMBw8eRH19vekfUTqSTRlvfMNOTOYvNdXo8xH1Cyu6msKQ8WbsaurkgLDfE0zxD1vjLYN/ByzlD8l4Y+CN4uFtgL/BC1l2ocfQHnaPRlfSowCykBDwCQgvO5sSETmRNmWTJCCRDqR6/ezQFKokdDU1zr/5OS4ybT6uraYJjxfNkyXmpaannXYaAODUU0813c7mCpTORMBQ483BAY60YLxSlqEBl9ClplrGm2xBjTdZNme8udzOr/FmDrwZl5ry5B0TLjUlC3j374W3RUaLvxB9RxbZPRxdac9c+APZyMnyoulAA4r6dbF7SEREFJELEtSZSBzzeX3eGiHwZmXjrYDfsC0Hz5dtp0dVIz+ES02TJ+bA29KlS5MxDiJ7ycYrJXzDTkhojbcMPJ5tWqDrGW9WNFdQtiELrcab2nbdwcfRH2mpKU/eseFSU7LAwfVbAACH/T3QoyJMzUWb5BdI8CIXOfCicX8DivrZPSIiIgolwRCcSSQKEymRQV+9auFS0wA/x0VFy3hrr8gbJU3MgbdJkyYlYxxEtpKZ8WYd0Zky3tTvtYw3C5aaBg+fttRU3baDD6WW8SYE4M4OnswdPGRnarPUlEeQYnd43WYAQFNxP0dduZYkQM7OA0QDmg422j0cIiIKQ5t6CEiJ9FbQlzWK0CBPEpaaCjZXiIo+rWxncqBd8CfrxRXu/O9//4vLLrsMJ5xwAnbv3g0AeOWVV/DJJ59YOjiilBEsymkZU7BAzsjggT4p0U5clgbetIkKTF1NJQfXrAhoz1uSTOfy4JeZ9zuQFKFLTZnxRnGo/64KAJA9cKjNI2lLzs4HALQcYuCNiMiR9KwoBAM0cQSzgkkN5kCO/p2Fnw8ChlIvEj/HRSRB7WoaVTI85+5Wiznw9uabb2Lq1KnIz8/H6tWr4fF4AAB1dXV4+OGHLR8gUSoIU4oy32gS0hm6mqonIz1b3sIab/rhEurW1StPTv61lL1qxlukM7mDx+4ogdaQGxh4oxj5muDdtQ8A0H38SJsH05bIVQJvrbVNHTySiIjsoBfgN/x/nBtC2E3oGW9WdjU1fI7LyM8d1tA+40pRvK7Me7NezIG33/72t3juuefwwgsvIDs7W7994sSJWL16taWDI0oVY3MFZpkkyHjCE8jMwJuWPq9NSyys8aYH77S4m9Z5yMGp8wGfMjYRqXMVRSf0vScT/3YoqcThHWiubkGjpwgjTulr93DaUgNvnlpmvBEROZJhjqsv7IjjCqreLKzN3FDdTbzjC7cvP1cuxaSdrqYS5+5JE3PgbePGjTj55JPb3F5aWora2lorxkSUckJmxptlREgQMwODB6E1EqzMeDNsXPmP2tXUycdRb64QIePNwUN3Fv3qsPaaOzfYSs60b9UG+HzAAX9PHHFcgd3DaUPKV8bkq2fGGxGRoylrTQHEN4/Taq2J0NypJBT2F8ZSLw6+UG077YVsJ/BGyRPzUa+oqMCWLVva3P7JJ59g0KBBlgyKKNVMwTYH19JKC0KGxwN4vEDGLjUNfU7qclDh9ye+aTkkm07btoMnErJX+ZsJLaDLa2Yx0gJtegAzA/92KKmqv1AaK/h6DEJurs2DCUMPvDU2d/BIIiKyhfEzUbAgW+zbEeEz3vQsOgsTHUwXvnnRMiJhrN/X4WOTPJhOKObA2zXXXIObbroJn3/+OSRJwp49e/CXv/wFt99+O6677rpkjJEo+ZjxZhm/X2DZcuA//wFkkeGBNy1dXg2UCH/iz1VLzQ/2bVCbKzj4OPp9Wo23CKcUToKiFBJ443GjWAiBpi1KY4W8ocNsHkx47kIl8CY3MfBGRORE+mxTkhLqQBp5FYheITn2jUbal7GrqRWrTzKUFEXgjStNkycr1h/4xS9+AVmWceqpp6K5uRknn3wycnNzcfvtt+OGG25IxhiJkk421HgT/LCbEE+LDK9X+VoOCLgcHDCKV+gz0mu8WZAt2TbjzaXe7tzfS1kNvIUuH4gYiKPwtPee1gDQ0Aj0du5rTg7kqYV39wHIsgs9j3NeYwUAyCosBAAEmhl4IyJyJEOmmpRAIwQ9kSF0Lmh9bwVTkE/i57iIgi9tx/PzeOr6UftiDrxJkoRf/vKXuOOOO7BlyxY0NjZi1KhRKCoqSsb4iFLD+CbNjLeEyIbjJwcyO+NN6wqUlBpvIRlvTj6OweYKkTLeUjiYdKa9D32wFsLXAqnvBHvHQ2nFt/s7tOz3oLa1C447pbfdwwkrq0SdK7awxhsRkSMZ5ptSAktNI3UXlRLIoovEOP9mAkVkkraywsW0NjvEHHgDlD+k+vp6lJeXY9SoUVaPiSj1jNlEDs4sSgfGpbqynKGBN53WelSr8Zb4746e2aZOTCS387uaCrWDlHCFNFfgeT026mTx8y9daDgETDylHvlH2TwmShs7lq1DQAZq3AMwYkyO3cMJK6e0RJn2tzLjjYjIyZRpaALNFbTVRKFBHv1b6+a1xlUhEmt1R6TFJF3tBd4YlEuamNYBVVdX44orrkCXLl1QXl6Onj17okuXLrjqqquwb9++ZI2RKOlkwxu2k5f0pQPjVadMzXhrUwdQz3izYKmpumkRGtRz8HEM6EtNI52snTt2RxEy4PVjy1YXWlqArzbEdW2MOqmDqzYAAKSBoxxboyW/W7HyhafF3oEQEVFY2nzT3I00niJv2s+EBt6sz3gzdjVlre72qK9te0tNnTqByABRz+rr6+txwgknoLGxEVdeeSVGjBgBIQTWr1+Pv/71r/jkk0+wevVqLjml9GQMmPANOyEiEJLxlsGEdm5ST2BWFHTVAr/apl1u5y81lX0RMt6Y8hYbIeBr9EEWymuulsMi6pi3Ea3bdkMIoMeEsXaPJqLCHqVoAuBqbVbe0zjBJyJyFr2cCiASyH6StQBeyPu8UPN+rJzVGmt1M+MtMq25ApPa7BF14O2JJ56A2+3Gt99+ix49epjuu/feezFx4kQ8+eSTuOeeeywfJFHSGQJEzHhLjCyMgTfZ0QGjuAVbPin/cVlX40379dMmKlotDMnBv5da4C1isdZM/B1IChlNB4OBt9wc577m5Cz+vZvQtKcFDZ4SHDVtgN3Diai4ohQ1AITXC8g+wO3MJbFERJ2VvvJCcgUvn8Yzj9MuJIcE3vRvLZwbCr/f8A3nTpHoH3fbibzxeljyRL3UdNGiRbjnnnvaBN0AoGfPnrj77rvx73//29LBEaWKKS2ZQYKEiE6w1LRNXQot4GThUlMtqKdlvAkHL9cM6Blv4U8pGfkrkAxCRvMhH2ShZA4al04QtWfn8q/hDwD7RD8ceWye3cOJqKS8CLLsQsAjQ3jYYIGIyHG0jDcJ+oXluDajfh4QEVY/SBZODgN+1uqOhnbMpWhS3jh3t1zUf02bNm3CCSecEPH+E044ARs3brRkUEQpZ0xL5ht2QrQgptC+zsCoi6HTukLPeLPguYZmzbnVnTj491L2R8h4k0z/oY4IGc2HfZDVNcwyA28UpQNffqN8MWAU3KErvh2ka+88eAPZEAJoqqm1ezhERNSORDLe9ApvoZNAKf6GDRExgSJKoR9g2jLdxWNpqagDb/X19SgrK4t4f1lZGerr660YE1HKmTPenBvgSAda8ElAytiMtzYNELRJRMAf4Sdi2ba5GK1LrZvm5MMY7GoaekpRj0uKx5O2hAxPnQ+yrL7mFixdpk7AU4/WrTsgBFB27Fi7R9Ou/AIJXikfAFBfzTkjEZHzGIIzWrmTeAJv2oX4kIuyUhKahsmGBAqJn+Mi0o65K4FMRopf1EddCNHuiyRJkqO77hG1y/Amzd/jxOg18iRJqfeWgcdT0icl6g1acMyCjDc9CCyZ68c5OSCsp/jzRJ4YIaO13q/XeGPGG0XDt/MbNO5qQW1rGcaeN8Tu4bRLkgA5pwAA0FDDwBsRkdOYSp5I8V9A1S8epmDZgzAsNZUE506RSG2W7LSVjOYXpIi6uYIQAsOGDWtTINF4P1HaklkbwCrBpaaZn/Gm0a7eyZbUeDNv3JUlKRXlHHwcteYKaNPVVHuAc8fuKEKGt94HWSgZQZYsXaaM9/3iL+EPAHulIbisMt/u4XRI5BUAzUDLAQbeiIgcR7vQ65ISaoQQrE0cEjvQFotYmfFmWCEgOOdsh1bjLfIj2FwheaIOvM2fPz+Z4yCyF1OULaMXU5UkyBla4y34nNSrQuoZzIqTfWhqvl43zcHHUVtqGhp448k7VgK+ejZXSBl/KxDwALmldo8kfkLGof8p9d3cI45Oi6RTkV8INAOthxrsHgoREYWyaLqprwJp09U0CRlVwpjxxs9xkegJb9E0VyDLRR14mzFjRjLHQWQrYUp4c26AIx1oGVuZ3FxBv2KkXbXTPu3KFmS8hWRcal1NnTyR0JdERvzUn4m/A0kgZPiafAioS00ZeEsiXxOw9o+ArxEYd2P6Bt8a9qBpywH4Atnof8Z4u0cTnfxCAICnloE3IiLHMc7bgylv8W8nQuMtKz8fyMaupg6eL9tO71gb5VU6IXgV3UJpcG2UKPkEr5RYJrg8TlKCSJkYeFN/RfRn5tICJYn/7gRLa6gnOjXw5uTl/MKvNpVocyLnyTomPh98zQHIslrjjcvek0PIwPrXUf3XL7D9ua+AvevsHlHc6tauRuMhP/Y1l+PEH/ayezhRcRcWAQB8dY02j4SIiEKZF4hK5htjEWkOk0AsLxJjMyp+jmuH9lmCS01twcAbEQDJkKnE2gCJ0TPeJEk55zo4YBSvNlUrtMCbFRlvIXW93G4tm865Ewm5g+YKGfgrkBT+Rg98PkNhW2a8JcfuFah+Yzn+t7gR6/7nw/bF/7N7RHHb/tHXAIC6LiPR6wfpMaVzFxcDAPwNDLwRETmOPo93GYIwsU/k9Glr6FJTPaRn3eTQ1AWek852xJjxxhUrlkqPWRpRkpmCbbxSEj8hzM0VMjSIaQwuAsGlppYEbUO7mupLTZ17LPUab+6Q5gq8bBaTxgMeANCXmrK5QnIc+nAJ1i48iO8P9QUAHPzy2/R835cDaFq/GQBQMm6MzYOJXnapEngTTQy8ERE5jr4cEcELqnG1NdW6mobMBdX6YlZOa43NzZjx1g599W97ISDO3ZOFgTciwPyhy8EBDscTwhCUUuuVOThTK3HqyUkLvFna1VRSNy2F3O48+oQn0omck6CoNB1UAm/aUlNY8PtEIQJebHh7I7w+YEXjFPgC2WjcfABo2G33yGLXsAfNO2vhDWRj8GlH2D2aqOWUKYE3ubnZ5pEQEVFEkpTQqlBj6ZnQ7ca/1QgEa7xFh11N7RRz4K21tTXifXv37k1oMES2MQQ1eKUkMZ0h463NXMHCrqb6MdMaN6hZZI7OeFOXREohGW+CV81i0npIDbwJ6wK5ZCZqd6BpZyNavPk455FTsa+pJxoO+NG0fo3dQ4vZobXr0NIgY39LTxx3Vje7hxO1/K5K4A2tDLwRETmaevE3rs9GImQFB7Rvk5HxFtyYS3DuFImkZzNyfm6HmANv48aNw5o1a9rc/uabb2LMmPRZ6kBkJBmysljjLQFCGE6kUuZ2NdXT59X/aFlpVgRKRDBwCQQz3px8BU+vrRFyCY3n9di01HoBBANvGRu4tlHNF1/D4xHY7yvHBVd2wYGiURAAdi75yu6hxWzXcqUpRGPXYejSNX3+2Aq6Kx1kJQ8Db0RETmP6HJRQU9PwgTd97mzl5wPD/NvJK0TspucgutqZMxhfLx5LS8UceJs8eTImTJiARx99FADQ1NSEmTNn4vLLL8c999xj+QCJUoFdTS0ihN6JUUgZHHjTaXXY1EwvK5bValejtMCb1lzBwcdRBNSupqE13rT7nTt05xAC3jot8KYcR2a8WW/Pyg0AAE+PocjPB/LHHg0AqF+3Jb1+UYVA04aNAID80UfaPJjYFPUsAQBI3lbAgoY0RERkIT1gZgwTxH5+DDY8CB/ksfKMawwWSiLTP3skQPuc0k7gjRfNkycr1h945plnMG3aNMyaNQsLFy7E3r17UVRUhC+++AJHHJE+NUaITIwZb3yzToh2+JSuphl68gt5TsHmCokH3touNXV+jTdtctV+sVZqn0Brk3IcZaFlUPIigKWEQPPGrQCA/NGjAQAjzhqFwFoJDVV1gK8RyCm2c4TRa6pGy/eH4A+40W9yegXeSnqXAQDkVj/gbwFyiuwdEBER6QTUKagUvAgc/5YQJs1H3aaFWf1ywPg5DspFHXfMYY6Mpy81bS/jzUDIAlL4a+oUh7g+JZ111lm46KKL8Omnn2LHjh149NFHGXSj9MbmCtYwdTVVg0UZeDyFXiNBvcHCwJsIWWqaFjXetMBbhIy3TPwdsJyQ4fcpxymgZryBgTdrNdegdddh+ANuDJg0CgBw3Bld0OLLR3OjDE/1HpsHGL36dWvQVBvA/pYeOG5aT7uHE5MuFXnwBbIhB4DWw/V2D4eIiAy0lT9K8C3+tabBLLSQcEMyUqpCVivJfmZTh6MvNWV3BVvEHHjbunUrKisrsXDhQixevBh33nknzjvvPNx5553w+XzJGCNR8hmvumR0F84kM3Y1zeAab4aEduX/tStHVi411VexpsNSU3WC407B5CpTCRlCLQ7sF9nKTVxqaqn6jRvQXOvHodauGH+mEqzqUe5Co6sMALBvQ/oE3nYtWQUAOFg0CuW90ivTtKRrFjxyLgCgfm+dzaMhIiIj48qVhBohCPMKDo0+Z7ZwXhu6QsDv42e5cKLJeOPUPXlinq2NHTsWAwcOxNq1a3H66afjt7/9LZYuXYq33noLxx13XDLGSJR0Eoxv0M4NcKQDYzHVTM14CyW5tJpcFmS8qcEXoV6NkrK0wJtzJxFagChixht1TMjBbNGsHPUm577m6ej75esBAPVFg9Czl5pJKgFyUXcAwOEtaRJ483tQt/Y7AED+uGNsHkzsXC5Azs4HADTuZ+CNiMhRgmlRehRGiuOzUbB0SqRwQ3KaKwBAwOu3btsZJbaupp3gI1xKxRx4e+aZZ/C3v/0NZWVl+m0nnHACvvrqK4wbN87KsRGljGDGmzWE0ONDSsabnJnv2vqkRP2vlullyVJT8/fp0VxBq/HGwFvcDBlvUpZal4TvRZaq/2YLACBnyDDT7VJ3Jfut8ft9KR9TPMSBTWjYVo8mbyGOvCC96rtpArmFAICm/Q02j4SIiEwkrckX9MBbXFNQbQ4TGuPR6gFbOK0N7QLv93HFQDjBvhntZbwx5S1ZYg68XX755WFvLy4uxksvvZTwgIjsILHGmzVMS02Ruc0V5JCsNAtrvOmRS8nc1TSeq40pE2mpqcrJjSGcI1gfEdnaUlMG3izj98C3azcAoNv4Uaa7sssrAACefTUpH1Y8dn38JTytAru9/TFxWpndw4mLyCsAALQeYI03IiJH0ReuSPqyw3jqDAvDdozirxrXjpD5d8DLwFt4alDVzeCaHWJu9/HnP/854n2SJEUMzBE5muENW3Lwkr50oC+XkyQnr45MkOFqIAzdPC3paqp9pTVX0GphOPdg6ktNQzPe2OU0emrGmywkuLKZ8WY1+fAONFc3o8Wbj5GnDjTdV9SvAlgFBA4etGl0MRAC+/67BgDgHzQWeXn2Didu+UVAHZsrEBE5jqE2W0LJT3qztZCNqFNDK5uGhV74DjDjLSy9xlvUS0154dxKMQfebrrpJtP3Pp8Pzc3NyMnJQUFBAQNvlJ4Mbyx8k0mAIeMNkCCEnJHBg9DfEMnlCnZxTXjj5tT8tGiuoL7GUha7msZNyBAyIIQESQ28scabdXavXI+AHzgQKMdFxxWa7isb8gO0AJDqDgFyAHDykumWg2jauBuy7MIPphxr92ji5iooBgB467jUlIjIUYyfiRJqrqCVITFfhA2GfKwNvBlDSQF2NQ1PfSFd7QTe2luGSomJOR3h8OHDpn+NjY3YuHEjTjzxRPz1r39NxhiJko9LTa1hXGoqhdTOyyRy8GogAEhacMyCLpQi5GqUFsyy8sqg5bTAW0hzBZ66Y6A2VxBCgiuHS02tVv3lRgBAoHwQskIuOfYe3QsB2QVfgxdorU394GLQ8O1XaNjnxf6W7pj4owF2Dydu7lIl8BZg4I2IyFG02aaQXMGVHVIcS00jXTxUy7RYOq1ts9SUzRXaIyKUhmn7QAd/9khDlqwDGjp0KB555JE22XBEaUNwqalVhDAsxMzYrqYh3RW0gJMVzRX02J2kbtr6tuuW07uaRjilOHnsTqEF3uCCO4dLTa3WsmUbACB/+PA29/UfVYgmfxH8PqBu265UDy0mVYtXQQA4VDwSA4fGvGjBMbLVBl1yA7uaEhE5ib4cETAEyeKv8dZmvWoCdeMi7itkviT7OX8KT8t4i/LRnL5byrICPFlZWdizZ49VmyNKKdObP99l4ie0AvFKKwA5QwNvwaekZqVpzRWsCNoaMgYBYxaZc4+jkJXAmytkqalzR+xAQgZkAQFAylUy3hh4s4inHr5d+yELCb0qR7W5u7AQaM3tCgCo+W5vqkcXPTmAhq/WAwCKxo+3eTCJye1WBgCQGxvtHQgREZmYAmZ6gCaOGV1IIzJNsL6YlYG30K6mnD+FE+xqGjkExKamyRPz5dJ//etfpu+FENi7dy+efvppTJw40bKBEaUUl5pawxRok5STbiYfz9ClphYsrQ1eVVS26TJmvAnhzDOiGiByRVpqmsG/ApZRmysIISErx7oMSgJat61FyyEv6lrLcMppvcM+Ri7pDtRtR+025wbe5APb0FhVC48/ByMvONru4SSkoGepUlevpcnuoRARkYmxuYJ6gTmOeZx2MbrNtDWRunERSCHzJeHjUtNwtFVdrihT3jL5I5wdYg68XXDBBabvJUlCjx49MGXKFPz+97+3alxEqWXKeOOH3UTIQsnaEZIaRMrEd+2Q56RdOZKEdTXetKiVK8tYC0MATqycpma8IWSpqXBikNCp9BpvwaWmrPFmje8X/QcCwG5pFPoMygn7GFe3nkAd0LxzX2oHF4PvP/oSXo/AXn9/nHtWmd3DSUhxRRcl8Nba5NwLCkREnZHhArqUUHMFbT4butTU+vd7beWFxs/mCu3jWlNbxBx4k3kFnjKQZKrxxjeZuAkByFBPqhLkjM94C1lqasX7Y8g23FlaV1P1vmgLoqaQFiAKXWqajM5VmUsoS02FBHeeutSUFwES56nHwRXfAgByjjs54nw/r3cFsA3w7j+QwsHFpnr5VwCAwKAxyM21eTAJKundBTUA5FYf4G8FsvPtHhIRERlICS41jVjjrc0DLBBa483HwFtYWv2+9paasqtp0jjvExyRDYy1AeIpIEoqIfTUcqF+n4mBt9CnpHUetSJQEpyoqFl0aqBNWLT9pBDhl5oKdcaWeb8BSSBkyLKADAlZeWyuYJXWTV+idksDDjV1xcSrj4r4uOIBFQCAwOHDqRpabHzNaN1UBQDodcpxNg8mcd36FcMXyELAK8PfWG/3cIiISGOctLniX2qqd7wPCbwFS74lr8abzBpv7Wp/qanhsrkFJXQoKKqMt1tvvTXqDf7hD3+IezBEdpHArqZWMXY1FULOyMCbFmjSTk0ul4U13rRt6EtNlWCW0DLenEibXIVmvPGiWfS05gpCgpvNFSyz9a3/IBAAduWMwRWnFkV8XNdBFTgMwNVQq7wWkrOuSzZ+9zWaajyoay3Fif9vqN3DSVjX8mx45Vxku/2o3X0Y3buU2z0kIiICghd5jZO4OCJvwfls6FJTw0oOi4SuOAlwqWl4xo61lHJRzSy/+uqrqP6tWbMmpp3PmTMHxx57LIqLi9GzZ09ccMEF2Lhxo+kxra2tmD17Nrp164aioiJMnz4d+/aZa7Ds2LED06ZNQ0FBAXr27Ik77rgDfr+5qOKyZcswbtw45ObmYsiQIViwYEFMY6UMx66m1hBCzRhU39IzfKmpCMlKsybjzdCcAoDLbajx5tSgcISuprrM/RWwjiHwpme8OfX1ThcNu3D48w2QZRdKJ09qNxBcPqIcsuyCv9EL0VKXujFGadt7/4MAcKBgKAYMzbZ7OAnLygJ82UogtG53rb2DISIinWnKpp0442quoHU1Dcl4s76paZukCZnNFSJQA29Z7Gpqh6gy3pYuXZqUnS9fvhyzZ8/GscceC7/fj3vuuQdnnHEG1q9fj8LCQgDALbfcgkWLFuGNN95AaWkprr/+elx00UX49NNPAQCBQADTpk1DRUUFVqxYgb179+KKK65AdnY2Hn74YQBAVVUVpk2bhmuvvRZ/+ctfsGTJEsyaNQu9evXC1KlTk/LcKL2YaryBH3bjpgfelBOtQIYG3tosNbUu4y20GG2bGm9OpGW8ZYcE3njyjp7WXAESsrUabwGZhecTcHDJv1C7owU76vtjys+OaPexfYYV4H/+AhS5GlG3fS/KRnVJ0SijIATq16wDAOSOHmvvWCwk5xYCHqBpn0OX9xIRdUaGeWgitXq1mtmhS02Dm7RuTttmqWkgAz97WEF7bdnV1BZRN1fYtm0bBg4cGPmPJw7vv/++6fsFCxagZ8+eWLVqFU4++WTU1dXhpZdewmuvvYYpU6YAAObPn4+RI0fis88+w4QJE/DBBx9g/fr1+Oijj1BeXo6xY8fiN7/5De666y7cf//9yMnJwXPPPYeBAwfqXVdHjhyJTz75BHPnzmXgjRTMeLOOLPTTc8Z2NTW0WgcMRUqt6GoautQ0HWq8yeFrvOmcOm4nEbK6TFtCVp5hebFTO9k6XfMBbHvtEwgB7O13JkaPz2v34fn5QGtuGYpEI/ZvqkbZqFEpGmgUmvejtWofArIL/c44xu7RWKegCPAAzTW1do+EiIg0huu/kmRYdRHrZiJ2NbW+lINgxltUJPXFbae3QohM/Axnn6gP+9ChQ7F//379+0suuaTNks9E1dUpyzu6du0KAFi1ahV8Ph9OO+00/TEjRoxAv379sHLlSgDAypUrceSRR6K8PFgfZOrUqaivr8e3336rP8a4De0x2jZCeTwe1NfXm/5RZjNluWVkoChFhBp0k5S39kwtyhl8XmrRWW2JpRU13vSvlLdnd3bwbdqSrqnJoC01bdNxlQGjqAkZCChLTbPzc5SbAoJByzgdXr4INesbsKehF8769eSofkYuUuYetVXWzm0Stf/zVWipD+BAa0+ccH4vu4djncISAEDrIc6xiIicQr94LrkS7GoaPuNN65pp5ScEKbSrKWu8hafFQtnV1BbRxztDghHvvvsumpqaLBuILMu4+eabMXHiRBxxhLIkpLq6Gjk5OSgrKzM9try8HNXV1fpjjEE37X7tvvYeU19fj5aWljZjmTNnDkpLS/V/ffv2teQ5koMZfr+51DQBhqWmkKSM7WoaekrSmytYESTRJg9axpvhBCgHHPq7qWW8ZUedRE0hRCCgBKuFC9lqjbfMzRhNspZD2PrqR5AFsLPnGTjxzMhNFYxcXbsDAJr37O/gkam1Y8kqAEBD91Ho0s1ZTR8S4S5VAm++WgbeiIicQjI2ENNqGcczFwmEz3jTvo1rmxG0XWrq0Pmy7TpY/qvcGXw0p6CWcswMbvbs2fjmm2/wt7/9ze6h4O6770ZdXZ3+b+fOnXYPiZKNS02toy41VSu9ObcuWQL03xBt8qAtsbTguYZOHtw5weWbAYe2R9cnaaE13ihqfq+yLEIICdkFWldTZrzFo37Fv1HzdS32NZRj8j1nRF0iL7tHDwCAp+ZAEkcXo4APLd9sAAAUjs+gZaYAskuUwFugjoE3IiKnMPb4SqC3AgTCB96Mj7BMSKkXwYy38NQX1+WOPDFiWeHkiTrwJklS21RRi16Z66+/HgsXLsTSpUvRp08f/faKigp4vV7U1taaHr9v3z5UVFTojwld8qp939FjSkpKkJ+f32Y8ubm5KCkpMf2jzGZqrsDAW/xMXU0lJQ6ViYGDkKWmrixtZmLBc9U3rbw9u1yALJTtO/YKnjrhcUeo8ZapS46t5Pcox1CGhBw18Kb8OvHYxaT5ALb8eQkCMrC17AycemH05+/83j0BAPLhQ8kaXcz8ezei4ft6tHjzMWb6UXYPx1I5XUsBAIHGRptHQkREQYbmColkp2kXo0OXLroSiOZF3Jd5YwEG3iKIIuONkibqdUFCCMycORO5ubkAgNbWVlx77bV691HNW2+9FfXOhRC44YYb8M9//hPLli3DwIEDTfePHz8e2dnZWLJkCaZPnw4A2LhxI3bs2IHKykoAQGVlJR566CHU1NSgZ09l0vzhhx+ipKQEo9TiyJWVlXj33XdN2/7www/1bVAnJ4L5WcHvKS5C6Fe4lGVzyMiMt+CJS/lOy3gLbWce15a1mhjq9263UnAfEAj4nXkspUhLTZNQQDdTBXzKJFEIJfDmhxqwzMTAdbIIGY2f/BXVXx1GdX05Tn709Jiu3JYOqEALANQ5p8vmtvc/h98H7MEgTJ+SWRcB87uXwQdAambgjYjIaRKOzQjzRergdpMQ9AmZKwmHzpdtp8VU29RkjvBwXji3VNSBtxkzZpi+v+yyyxLe+ezZs/Haa6/hnXfeQXFxsV6TrbS0FPn5+SgtLcXVV1+NW2+9FV27dkVJSQluuOEGVFZWYsKECQCAM844A6NGjcLll1+Oxx57DNXV1bj33nsxe/ZsPUh47bXX4umnn8add96Jq666Ch9//DFef/11LFq0KOHnQBkgJDDEGm+JETIgJCgNFmRkaODNTO88aknGmxq4VK8IulxKMAYSHBt4015jd3Zo5yobxpKmtKWmgIS8AjeaAS41jdWOZdj44qdo9Wbh2/yzMevSLjH9ePdhvbATABobAb8HyMpNyjCjJgQOfvIVAMA1/GhEahqcrgrLy1ALQGpl4I2IyDGMATM9Wy2e5grqVkIz3qSQB1iBzRWi1HHGG6fuyRN14G3+/PmW7/zZZ58FAEyePLnNvmbOnAkAmDt3LlwuF6ZPnw6Px4OpU6fimWee0R/rdruxcOFCXHfddaisrERhYSFmzJiBBx98UH/MwIEDsWjRItxyyy144okn0KdPH7z44ouYOnWq5c+J0k+b5XvMeIufXgxe6WoqC+gdLzNJ8FdEW2pqYVdTffKgbNvtBoR6ghQBhx5LNTjkyopwSuGfVIe0paYB4UJ2nhuykNTsRx68qNTvwI4//RV7vm3C6r3H45wFsWW7AcAPRnTB1kA20OqD50ANcitsbqzUfADNW3ZDll3oN/V4e8eSBCU/6IpaAGhtBWQ/4GJzFiIi2+lxt7ZlpmLbjjI3FKGrHxIpHBdpVyGf3RxbmsVOQhi6mkb7unIOaiVbZzmhfyTh5OXlYd68eZg3b17Ex/Tv37/NUtJQkydPxldffRXzGCnztS1YzzeZuJkCBZL6bSae/LTOo8qJS0vZtmKpqU7vaur8jDdJD7yZU3IMfZFSOp50JPvUjDe3G1k5LkBIXGoaLV8LGj/4Ezb8qxo7D/dF9oU/wWkXFse8me4VWWhGKXJwAPs37kUfmwNv+z//DM2H/ahpqcBFP+pn61iSoUufMmwXEgItfghvM6S8zFpKS0SUlvRUNWN3hXhqvKmF/EMT3rSLyfGOL4zQ+bfs90d4ZCenHnRXVjtLTdnVNGlYgIc6vYA3zJuzUzOL0oAQeh8jJWYgMvBYCu0/asabtgbMgjNUMFvekPGmvlULv0PPgNpS05wIzRVSOZY05fdqwVwX3FmS8rslg7OeKIjv3sbaP67D4cZCrCqYjrueG9jxD4UhSYC/UFmeemjrvg4enXw7Fn4KAKjtORbdemTedK17nzz4AtkQAmja55yGFkREpM5CE8h40z8LhG5DjcRZerE6dFvMeAtDGJIZ2dXUDpk3kyOKUbgsIn7WjZMQ6rlPgpCkjG2uYLwYCAS7mloxidAaFWgnRUkKBvicmvGGCBlvbSZbFJHeXEFyISvHpawIYMZbx1oO4rvnF+PgLi9W7DsZt7xxMvLy4t+cKOkGAGjYWW3RAOPUcgiNa7dAFhJ6njrJ3rEkSUGRCx5JadB1eCcDb0REjmJIeIsv402dz7qSH24IbQIgO3W+bCfD5zGXu535OTPekoaBN+r0tA+8ptvCZcFRx5RIm/6tnKGBt9AzkV7bzMKupiZaWr5Dj6UWcMzKCV+9IK429J2MvtTU5VYz3lzqHxCPXXsav/0CVf85iJrGnjjqrotwxNHZCW1P6tELAODdtduK4cXt4BcrUF/twf6mnjhl1nBbx5JMvlxleWnj7gM2j4SIiBTBq8uJ1HiLVFJK0mq+WTi90eahslDGK3PlUnj6Sxvl68r5u6UYeKNOL1wWkcz2yXETQulqKqC9XzszWGQJSWuuYF2NN22iIgxXCPWMt4BDfy+1jLeQpaZMeIue36tOEl1axpvaXIEZb5EJgW1vLkUgAOwtPBpX3VOR8CYLByq11PzVexPeViKq3vkEAHC4fBz6DU4smOhkokANvO1lxhsRkRMEs8ckSFq13nimn+p2Qgv5699ZOaWVtcCbMg8VXGralpCDh9wdOQTEuXvyMPBGnV74jDdeKYmLoaspICnnwU5w1UlrrhCa6h4PLfBmOu9pGW8ObY+uLY91Z0Wo8cYrZh2StfchtxvubBf0viQMvEXWuBuH/lcFf8CNirMmWTJZ7HHEIOWLQweUTpt2aD2MxjXqMtPTTrZnDCkilZQBAFr2HbZ3IEREpApmvAntvCrFMxfRW2iab9W/tbLGm3rRWtICb1y51Iaxq2mUEyYrPtdQEANv1OmFW77HjLc46YE3NestmPaWUUIDSW4t482KSYShjbtOnbQ4tT26lunnzg4NvPGyWbT0paaShKxsCUK4QroEU6gDK5ejYU8rdjf8AGdcZ81yzIHH9oYvkA1PnRf+Q/bUeTu06ks07PPgYHN3TMngZaYAkFWmNLPwHmDGGxGRIxjrGEvxZ7zpQRspNONNvZhs5fRGW2rqUkqeCKeuELGToflduzXeOHdPGgbeqNPTCnAKwxtNuCw4ik6wq6mUuV1N9X7casDNwq6m4a4uaU0KHB94i1DjjbGjjunNFVzBjDewuUJkB77Fnn99DACo7T0B/YfmWLLZfsPyUC+6QAhgz5oqS7YZq20LvwAA1PUYjb6DMneZKQDkdO8KAPDX1dk8EiIiUhiWmiYSg9FWcIRsQ1t6aml4R5t/u5T5OGu8hSFkQ3M4djW1AwNv1OlpH3hlwynAqQEOxxNCfVOXgjXeHNoQICEhgSQt08ua1ujqNoxdoLTAm995ESxhaAAQGnhLpChvZ6NdAJBcbjXjTekKzDolYTTsxqF/z8eeT2qw7eBADJ5u3XJMlwvwlSkNFmrWfW/ZdqMW8KLp6/UAgC6VE1K//xTLL1e6yIr6WnsHQkREimDcLTiPi+PCsr46JKSraXBqaN2cVpt/C7e21JSBtzYMr2H7GW9hf4QswMAbdXoB9YOtVhcACN9wgaJgWhonqd9m4LEMqcPmyrIw4y3MJgS0GnLOO5b6EkkAWW2Wmmp45u5IwB+s8ZaV49IzcHnVNoQQaPj0H1j93GbsPFiODaUX4IKb+lu6C3dFbwBAw5adlm43Gq3bv0XTjgY0eQsw7tKxKd9/qhX36Q4AkJobbB4JEREBgCEtSg+8SXHM4yR9rhya8qZ9b93cMNiYTJ2HOnC+7Ah6YmA7ISBeNE8aBt6o0xPaslLJpZ8CmPEWPyGEUt9NUhsaZeLJL2Su4MpSJyZWBBm1LlCmGm9axpvzjqXf0IgkK9ec8cZwW/RE6FJTtaKx38MCwSa1W/HNHz9DYz2wWj4T9y+djPx8a3dROKAvAMC7N/WdTTe/vRIBGajJHoYjJxSmfP+p1qV/D+WL5hbA77F3MEREBEkyzEO15l5xTei0KE/4wJuV2VT6/NutzEP5OS4MQ1dTLjW1BwNv1Onp2W0upRMnAMgO7R7peHrGm2GpnJyJx1JrIKH8vritrPGmbyJM4M2BEwm/NxgY0ppMaHjyjp625F1yu/SlpgA7LJsIgX3vvoUDGxux7fAgXP3aWajobf00puuogQAA6cC+1K6zEAKHP18DAMgde0yn+PvpMagM/oAbvpYAAo21dg+HiKjTMyS8tQ2axbKdSM0VtBpvSWiuoNV4A1cLtGVIhIh+qSkvoVuJgTfq9IJBNim4pM+BAY60YOhqGpC1AqeZdywN5S8AAK5stcmCFRlv+uTBcJujA2/tZbzF3w2rsxHa+5BW4009dn42egmqq8KWv69CQHajdsQ5OGZScjLC+o7vD1l2wV/bDLnxYFL2EY58aDtatu6DL5CFYRdWpmy/durZNxctgQIAwOHt+20eDRERafN4YWiuEM9S00iBN8Mj4hhcpE2pY3ZrXU2dN1+2mzGGJrUTUDW9XAy8WYqBN+r0ZL3Gm0vPYAr4+IYdLyGUU2kASuAtI+vlBXO1AQDubDXgZEngLTSsB0BybkDYuBQyK8dc4y0ZBXQzlexXjqPkdplqvPG9KGj3ovdxaGszttcNwMW/OyFp+xl8VDEa/KUIBIB9X29J2n5CVb23At5WGXt9/XDCeT1Ttl87ZWUB/txiAMCh7w/YPBoiIjLPQhNqa6psIULGm5VTQ0nL5nKp8/H/3959x8dRXgsf/81s0666bMu23LuNGzYuGAOGUEwJoYXqBAKEzqUlhPCG0FIgcCmBUELoufTQmymOTccVd9mWu2yrWV3bd+Z5/9iiXUm2Vb0q5/u5uVi7szPPrrQ7z545zzndscxNG6mEjDep8ZYMEngTPZ6KC7zFMovkA7t1ohlvmlaf8dYdM3aiAbbIuUmPLLFszRXBxruOXiGM72oa/k9nzHiL1p1Tqv51iKrPeJPA24HEuppaLOg69UtNu+P7pzUCdex8/3sAPBNOYML0di7sFsdmA0/GQACKl27qsOMkUIqSRUsAMEZNw+E4OIftDExXFgC1uw5edqEQQoh9iDUq0OL6ILSiuUJ0Ptsgu6oj4jrRRg71Nd5k7tSQadb/Dpv7O5Dpe/uSwJvo8WIZWeFiBkDnLGLfJcR1NQ2p8MmvE8aK2l2sqyntl5WWeE7svBlvRqSrqdIsjU7kcs2s+eprTYZfx2iXZTMgzRUA3Gu+pTK/igpPNkffcGSHH88yaAQAVesPUsabpxTvhkIMU2fg3I7L5uuMtIxMADwlFUkeiRBCiCgNEi8Ct5AickGx0eQwelG2Hee0kX0payTjTQJvjcR/h2h4oTyeJLx1HAm8iR4vlkUUt9S0MwY4ugSl6peaqm6c8RYTWWoadwJra4ZSfcZb/GE6byZmtMab2s/kTC6YHVisxlu0UYcefj1DIXn1UCabXv4Uw4BdrunMPiWzww+ZOWksAMbO7Qflkm/p94vxVAYp9fbj6AuHdvjxOhM9KxsA/97KJI9ECCFEbNKm620qGRKLq+kN5ocdEdhpkPGmOuF8OdmiL4lSLWmu0IED6oEk8CZ6PDOSsUNc2+zOuKSvS1Bm5ENaiwXeumeNt8QzkcVWn/HW5uerGi81jQa1OuM8woi9fxqfTuSqWfNFA29aLPAW7WoqGW+qaBUVP2wjYNgY9POTDsrf1dCjxmCaOsGSKpSn4zOxdn6yGIDavpPo3ddygK27F3tODgChyqrkDkQIIUSsUZhG2+qxafuo8RabL7ZnjTfqy3VA3EVsERMfjGz+PEpex/YkgTfR40XXvCtNi50MZKlpK5lGfcZbpMab0Q2DmPVxt2hzhbjAW1sz3oguOYw7K0b+HcuK6kQMfzTjbT8dkuSS2QEpIxp4iywr1qKTx+73/mkRZVLw/Jt4qkMU1I3n9BtHHJTDTjiyF1WhbEIBRfGy/I49WNCNe214SWuvOT2jm2k8Z99eAKia6iSPRAghRIxGfXZaK6ZxqkE95Nhu21A3bj8HC+9blpruU6yZIFqjJMR4++t4KtpGAm+ix1PRQIlW30lQAm+tFFlqCmBEarx1y9cylobf/oE3mny5IoG3ThiEMaLBQK1xls7+gnEiUTSoGus0Fe2wHOjZk0ezaDWFn+QTMGw4T/k5mdkHZ9qS4tTwZw8BYNf3Gzr0WO5Nq6nd7aHal8mseYd06LE6o9QB4cAbbgm8CSFE8sUmubGgWauah8Uq+SSetxtlwLWH6JePaOCtPevHdROxpaZoB8h4q79TMgfblwTeRI8Xq5ul6bElfcrs2V92Wy261FTTCEVrvHXCLK22UtH0+cjP7VnjLXbVLu6m6KSlM9YejD7f/dZ4k/P2AcU6cMWWmkYyRrt1jcQDUCab/vU6nsoQBe4J/OIvEw/q4W3DRgJQm7+lQ4+z+f0fUAr2usYwelJKhx6rM8oa3AcAzesGI5jk0QghRM8Wm7NpoEW/F7VuT+HdNIzy6G3Z574OFVlqarWFf5aMt0biL97vL+NNdBx52UWPpyIZWUrT67NMpKB565hmbKlprKtpN34toxld7VnjLRrUS6zxFvm7NDrfaxkNrDYVeJN8txYwopPGyBLT2LL3njt5NHavYtenGwgaNtJPP4defQ7ulCX70DEAmLt3gNlBtfaUSdWyNQC4ps7okXUR+wzPIWRYCLlDmJ6qZA9HCCF6uGjArP6WVmW8xWoWJ57Yoj+2ZxkSrcFS0864QiTZzMh3CKX2n/HWE+chB4sE3kSPF8s00eubK8gHditFo27U13hT3XCpacPJQnxb7jZ3cY2mdcfXWIieBTvh32WsOcl+L591voBhZxP9HIoG3qKvZ4/NeFMmG/75Ot7qEJu8E5l3z/iDPoSRx4zFF0whUFpLYOfaDjmGUbYN79a9BAwbh5wzvUOO0dn1H+HCY6SiFFRsLkr2cIQQomeLZbxpbVsWGg2G7bNmWHsG3iIXL22RjLdOOF9OurhUxuZmvMmKlfYlgTfR4ykjLsNIkxpvbaLMSNyovqup2Q1PfkolXg3UNGL1AdvcTKKpk1w0+6mLLTVVcopptmiAWrOEr9ZqPbzRS6hwFXs+D2e7ZZ19Llk5B/9vadyMDAoZh2HAxlc/75BjbHl/EcGAotgYyuEn5XTIMTo7mw0CKdkA7N1cnOTRCCFET1cfoKnvatqGjLeGNd6iP7dnUCdyLD1a401KBjVS30ywBUtNJfLWruRbkejxYku54gJvSmoDtE58V9No4K1bZ+zEZ6VFAiWBti1JU02l5kdrbHTGpabR328TZ/EO6VzVTamGGW+RJgvd+/2zD0qR/+Rr+GoNCnyTmHdXchoOWCzgOuonAFR+swKCnvY9gGlQtuB7AEJjj4jVhO6JVHo46FizozTJIxFCiB4ufhoa/V7Umt3sa+7X3tEHpYiOULfLUtN9ib4mB1xqKl1NO4wE3kSPV99cQYtrriAf2K2jIv+ngyXa1bSDaiMlUxNzCTPycdrmGm+xbLrGS007Y/agER+4Fq0W/cyp72oa/XvqeYG34M6VFC3YSNCw0eu880jPSN4kcM61M6n0ZVGzo46qFd+3674Dheuo2VCKL+hg4sXHtOu+uxo9pzcA7j1lSR6JEEL0bLEprl4foNFpxfwzmoVmaVDjrfGR2iQ+wBet8aZ1wvlysqm4rqb7zXiL+/4h183bl3xTEj2eigsc1Bc0l0+aVlEKU4GptPrMnWA3DLw11akpGhxra7akiqaCx+07evWpE6bO77fGm1w0a75Yxlsk7akTd7LtUEqx7vHX8LsNNgcnc8EfxyZ1OOOmOinNmoZSsPnVT9t1FrrhtQWEAopdjGPOmb3bbb9dkSM33Nk0WCqBNyGESCYtEmTT0OpXXLTi1KfF5rMNlppG/9FO51Mz7nuGbg/XeJMEisZUdKkp+894Ex1HAm+ix4t9OOt6LJDSGTOLugTTiGS81QfeunWNqvgTV3sFbZvKeCNae7DzBYRVqOmJVVi0NsjBG09XFV1qaok26uihzRUCG/5LycJwbbfcC88nNS35s8P+p51IyLBQubgAszS/fXYa9FL11VIAXEceg8VygO27OdeAXADMyookj0QIIXq4yJxNafUBmrYsNW3YoEGztG/4wQjWf8/QbJGLl6obf/dopWhX0wPNqiQo13Ek8CZ6PBULZmixLCOp8dZaCgUopde39O6Or2V0UhKfMB8N2rZTV9OE8150ktIJA8JGdCmx3sMjB23UsMZbbNl7dw5cN6AKF7Py9n8R8JjkB2Zy/h/GJHtIAPz0hjFs8R2CrybEhn+92S5X6auXLKRmRy3VvkyOvOrwdhhl15Y5rB8AWm2lrG0RQojOQKuv96W16nO56cCbaufITnyJF6s92tW0G373aKNYxlsLXv/oY0T7kMCb6PHMyIez0vW4IvY958tue4p23VRoWOzduLlCtG15wrmrfWpyRec2Kq64qRbNeOuEJ8BYRuN+Am/7LLAr6kWCqtGrtdErwm1eutxVVO1g5e//TskGN/nlhzDlT1eQkpLsQYVlZuukzD0bw9Qp+WwVqnJb23Zohtj0/PsoBYWZRzJ+Rmr7DLQL6zOmP0qBWeuDUDs3sRBCCNF8sUy1Ni5YiF5IblCsv92XmsbNu/VY4E3mnQ3VL7/df+BNMt46jgTeRI8XDbJpenxXU/nAbo1QJN3bRMMSCSCoblgcPq6Ma/1tkYlF259vNOMtPpsu2vSj8/1dqkjGW5NLTeXk3XyhBs0V9GjgugdcBDCCFPz9cXatrGVH9WAG3PobTpmXnexRJTj3rkPZ7hlFXXmQgpfebtO+vBsXs3f5HrwBJ+Ov/Fk7jbBrGzguE38ohVDAxFMsnU2FECJ56uehbQrCRANrDZeaam3JomssPuPN4ogE3mSpaSOxi/ct+qV2vu8dXZkE3kSPV9/VtD7jzZSMt1aJ1g9QSkOzRTKgumFXU436q4H1IhlvbfzbiQXX4jPeOvES6OiVRm2/LZLkxH1AkczbaMA6WuOtJ2S81XzzHlve24AvmIL/lKuZd1NusofUSG5/C+qo0wEo+mQJeMtbtyMjwIan3yIUUGyxTOeUy/LacZRdV2aOBa8lA4DSDUVJHo0QQvRcsXiYptUHydowj2s4P4xmwLXXzDC+Fq5uk66m+xLfXOGA26rE/4r2IYE30ePFaijpWn3Gm9QGaJX6paY6Frs14bZupYkTUTTjre3NJOprDsbonTfjLZbi30TgrWFdD7Fv0SUAejRgbYnWeOvmn0WBWtY8+BZBv2KV9WSue2xCske0T6ffPpM9dXnU7PKy5+OPW7UP95J3Kf16C95gCoPmnUG0ia0AIzUHgMqtJUkeiRBC9GCxxCgtbploK+af0bIsDZaatqljQxOaynhTkvHWSHPL1cjUveNI4E30eLF6blp8jbfOF+DoCqJBJxMtdtXJNFWnbArQFrErQQlnp3aqDxitrRH/6RwLCHe+17F5Nd4O0mC6ssjfjR6JxGjRpabdMXAdZ9cHH1OxpY693t6c9a8LcDiSPaJ9Gz7WTvXI4wDY8cYCCPlatoO9G1hz/9sEvCYrQnM56+aRHTDKrkvLCgfe6nbJUlMhhEgWjchFdE1Hs0TmJK2ZyMWWmjY6QHSDVo2voegFYFNpWCP1pbvb9472EM0ClJWmySOBN9Hjxa6KaPFdTeUDuzWMuKWm0cCbMhV0u+VyTXQe1aPNFdq2tLZ+bhOf8dZ5A2+x5a9NZLy1d+eqbk2FX0fdFn4dY8uLu3PGmxFg+6vzAagccQJTZzuTPKADO/yGE6nxpVOZX07Nkk+b/0BPKQWPPEbZJjcFFaM54+nzcaXK+yOetVcfAPzFZUkeiRBC9Fy6Cs9jNZsNa4o9/G/VirltNO7WcKlp5Mqy1k5BnfoLlBp6pDO8JhlvjUQT3lSLwj8SeWtPEngTPV79UlM99mXX7IQBjq4gGrA00bHaIzWqumXgLUw11QAh1MaTVOwKYf3Hs9YFmis0nfEWfn10mQAdWOQzJ7pEG0v3z3ir+H4hVRv24g6kcsTvTk32cJrlqNOyKHD9BMOAjU++A76qAz8oUMue5x+h4L2dlLtz0M74NYcfn9bhY+1qUvL6AhCqkMCbEEIkTSgIgDXFjs0ZXrqpma0JvEVrFjcMvEXubvUAE5nRGm+ahm6NHEvmnY3EygHJRfGkkcCb6PFi9dx0S9xSU/nAbo1YnQUtnPGmiASLulsgM9ZqPe7kFQ3atjXIqJpIBY/Wx+iEr2OsK7Cl8Ylcc6WH/+GrPZhD6pJUbKlp5GptN6/xZu74gYKHnkcp2JVzJIefkJnsITWLpsHsP55Nmbs3JT+WU/Lh602vpTYC4K8GfzUVrz/ImmfWUONLZd2Ay/ifx8Yd/IF3AWlDBgKgV5XJ+nQhhEgWIxxks6bYsaeG6z9ohtHyz+Wm5rMQm9NGl7S2VWypqaZjscpS032JXrxvXtgtsupCzsXtSgJvoseLZRFpOipWxF4+sFvDjNXG09F0HYUWXsrb3V7PJvofxDritrG5Qnw3qUb77oS1B+ubKzTOeEvJDddsUrXVB3NIXVNkgmqJNFfQuvFnUWjbEpbe+DClG91sqRrB5N9cmOwhtcgJ52azY9BZmCYUPPsF5prXwQjWb+CvITT/Xjwv3kbN07ey/O/Lqapz8p11HnfMP1oaKuxD/0OHYJo6RqW7eZmEQggh2l8s8GbD7rLHbg75Aq3aXcPmClq7N1eIBpQ0NFlquk+xrqaS8ZY0Mv0TPV60RpUW39VUMt5aJbYsTtPQLTpK08ONKrrpUtOEyFu7Zbw1LkYbm6R0wiDM/mq8pfXvjR/Q3TUHd1BdkRmt8Za41LTbfRa5S/jx9scp3eRhY9VYBv3h95xwbk6yR9Vi5z92Iq8ft4IpLOeHW95kys27cM6+EGypVL//D5Y9sAyPO7xtnT+NL80LuevLU0lPT+64O7NR07JYG0wjQ6+hvGAnvSZlJ3tIQgjR42iRwJvNZSclrb7jkd8TwOpsQQek2OqQBvPD9l5qGm3spumxi5edcb6cbE3WkBYHlQTeRI8XS73V9fo6XfJ53Spm3FJTi0VHaVq4mGc3OwE2lXpdX+Otnbqaxk1UlKXzZj9Fl0JqlsYZb1lDe1MOWLw14eclV9n2SYvVeGuw1LQ7Ba2NIJsefJjilRWU1PVlxF23cublvZI9qlYZd6iDCfddz5e3/JvZxpe4b/+K0XM34Mq2seY/e6ioSuH9HadhKguhwWN45JvD6Jcnf//740rT8blyyTBqKPpxG70mTU72kIQQoseJBd5S7Dhc1tj0ze8OkNqSU3Z0rmxpusab1k6ht9g8SdPRrNHGDZ1vvpxsqhVdTWWlafuSwJvo8VSs2GRcoKM7fdk9iMy4Zbu6RcfUtPAHfbd7PZtoka5Hl4O27bk2tdRUj2ZidsKJRKwRSRMZb72G9WELoAIBQm4P1rTUgzu4riTyOlojzRU0vftlvO1d9CFb3lqHL5hC9U+u4deX9072kNrk55dnM3TcFTx1zhCmuz/G984elIIafwY/+E7hjtUX4HDqDBzY5NtDNEH1yYPizVRt3JnsoQghRI+kRUon2FwOrA4LJhYsGAQ8/pbtKHYhucH+ozXe2mupabTWsKZhjWa8ScSosej3Xcl4SxoJvAkRvQKga/WZRZ0wwNEVRJsraLqGbtHCNd66Y3OFCK2JOmyqjXXYYsVmm2qu0AlrvO0v4633oDSCyoZNC1K+vYy+EyTwtk+RpaYWWyRCY+1mjV48e9nw8BuEQrAx7SRufXpqskfULqYdmcIjBafz95sms/DVHzGtTjKG5/KbdyYyaoxE21oqZeBAKAbvzj3JHooQQvRMkfmILVLfTelWUAZBd8tqvEVXh+yrxltHLDXVIkVUNdXdLvi3ndlUDel9kdhch5DAm+jx6rua6kT7jXSbL7sHmYpfamqN1HhT3a/GW5MX0tor4y32EjYR1OuEAUxlmOHzcxOBN4sFQvZ0bMEKKnaU03fC0IM9vK5BKbTIjCi61FTvThlvSrHpiX9Rua2OIncepz97PjZbsgfVftLS4A//Gop6eqispm6jjNFDYRmYZcXJHooQQvQ8SqGb4aWm9ljgzQIGBDwtC7xFl5JqDVO+Y80V2if0Fm3ypaFhjVy0lIy3xmIrVJoRVVNoaCjpatrO5HKs6PFia951S6wOQWzJpGgRUyUuNe2uGW+aaqIzkNZeQdv617B+19FMzM73dxkNXDeaWEWYzgwAanaVH7QxdT0qVmsy2lyhO9V4c6/8Lzve+AHD1PHNuZiJh3fPzEcJurVd3pShAKiKKgh6kjoWIYToaZRpxubsNle4kYLSw/OSoLeFXU2jGW8Nbq4/V7ZT4C2yGsTUdLTYUtPu9b2jPSiziTI5B6B1wu8dXZkE3kSPF1saGJ8K3R2yTJKgcVfTSOCtGwQPmqI10dW0zYGSpmpixDLeOuEJMNbVtHHGG4BKywTAUySBt31SZiyoanM0DLx18c+imt2svutZfG6TNaGj+dWjs5I9ItGJjZzRB0/ASdBjULtzV7KHI4QQPUrQW1/HLSU9HHgzLeEU9VBLA2/RjLcGzRWiSQ7tdq0qFM7Q0zQt1tVU62YX/NtF5DtE82q8yZXEjiCBNyHilprGCprLB3arxJaa6joWa7i5gqm6X8Zbk+GvyN+O2caupk01V4jWx+iUf5eRIrxN1XgD0DOzAPCVVRysEXU9yoxdALBEmytYo59FXThoHahl/Z/vZ+/mWnbXDeT4x68gI1Mmc2Lfsnpb8Tj6ALB72dYkj0YIIXoWX119cM2RGl5qGi0l0uLAm2q8ggPafaVprLmC0nUs1uhctBPOl5Ms9uuQrqZJI4E30eNFs4g0Xa/PWuqMAY4uwKiv3InFonXbGm+xQGJCVlo7/e2oJlLBo8s4O9nfpVG+m7SdSwAIOPs0uY0tOwuAUGX1wRpW16PM2OeQNZrxFv2dd9WMNyPAtsceYOvHW6nzp2KefT2zTkxP9qhEF2Dm5AFQkb89uQMRQogeJlAXzngzlYYjNVKMNbrU1NfSwFv4P/tqrqC121LT+tU21kidXE2ZEjVqIFbjTZPwT7LIKy96vNhSLl2PLTftlEv6ugAzLuMtWuONbljjLSphMqF3ZI23ThgQ9lWx/PcP4CnzUhwcyKFXnd7kZo7e2QAYNVUHcXBdjDJjSwCs0Yw3SxfOvjWCFL34MBte+hFf0E7+iKu58oFxyR6V6CL0gcMB8G3dnOSRCCFEzxJwhwNvCkvsmq+KLjVtYeCtvrlC+42vKdHVNiquqykKqfPWUCu+20rssn1JV1PR42kq2lxB79TdI7uCWKOKWFdTDVOZ3S/jrYmrdNEreGYb/3ZiGZha46Bea06abaZM8JSBvwpSssFTya63PqDo4yWUbvbiCznIuvYWRk90NPlwZ7/euAFqaw7mqLuWuMCbzRH+XeuRzlwq1MXeO6ZB8f/9ndWPfYPXb2V5xi+58705TTW9FaJJvaeOh1VgFhaGl7JbulELXCGE6MT8kc6lZvznriUcLjB8wRbtK2FFUZz2bhhmGPWZXNaU+rmo4Q9gcTrb5RjdQX1zBVlrmiwSeBM9Xn1XU70+g0kCb60S7SyEpmOxhGu8dceupk1RevsESmKp9wkNU5OT8ebftJgtjz1L7fZK/NUBbOl2ArVBasrDz7HI3Y/Kmb/kd9cP3+c+Mgf2wg3oXgm87ZMyUSq8tMNiixQdjtYp6WLvnfKvPmXV37/E47Ww2H4Bt312OikpyR6V6EoOmTuSFU+noJV58BdvxzFgVLKHJIQQPUK0c2m0kymAimSRhQItC7zFmis0WGra7nX7oxf3NQ1nhgOlwrElX62XVAm8xUS/Q6hmBN40jUjWoATe2pME3kSPFx94U1qkQH4X+7LbWUSXWWq6hm7VUeigjO6X8RYrZRd3Fa+96rDFLkjFR96iyw4P0glQmZR99h/W/uVV6spDhAwLnmAaTpsH0NjsHo938imc8NvDmTXHvt+LZ9lDe7MHsPjqwpldUluiESMQ7sillBbLeIstNe1Kk56gh/xHXiPgh7X2E7l14TlkZUszBdEywye4WKDnkaK2sn3RGsbMk8CbEEIcDAF3JPBmqQ8R6NZoxlsLl5pG5y+NMt60xPvbqP6iv4Yz3YqpLFg0A2+1l9TcdjlEt1D/ch94XqZUeKuuNAXtCiTwJnq8WBZRfMabIZ80rRFf4FS32ghpWmQZXXcLZDbRACEatG1jjTcVrUkRd4UwNmc5GPUqgl62PP4oBS9+SzCgyPdOxXbGpQyZkMneHXVomsbFV+XRt1/zAiq5I3qxVoUnRrV7ykkf0HQThp4s6A8HppXSsNojS00tevivrAsFrbe89AqVBZVU+rM55dlfktNLgm6i5TQNzAEjoXgrZcs3MmZeskckhBA9Q7SOW3zgTVnDy05Nf+sy3vRGzRXa9wKsGV1pounoOpiaDQsGvhpfux6nq6v/jtacrWX+1hEk8CZ6PC2a8WbRO2cR+y4klp2jW9CtNhQaWnfOeIs7MdUHbTsg402PLjvsuICwKt1E4X/eo+yb1RStqiJkWlhjO4VfzL+EYaOjtT6yWrzftCwbAT0Vh3JTvn2vBN6aEIpmvKHX13izWTDoOp9FoaKN7HjxEwDKJ53LYXOkg6lovYwJ46D4M/xbthBbNySEEKJDhbyR5gpxgTctEngzWrrUtKn5LHEf5+00pY2/6A9gWGzYTB8BtwTeErQg443oUlPRriTwJno8FRd4iy2Dk044rWKG6jsY6VYbpqaFl5p2keBBs0UCjAmnLr29GnPE1rHGbtE6MuPNDFH23ivkP/ou1aXhSVWtP43t4y/nN6/+BJer7YcwnOngcVO1vQxmS3fLhkK+uKWm9vDvXbfoGNA13jtBNytufRB3eYBtvjFc8OhJyR6R6OKGHDOBvZ/phPbsBV8lOHOSPSQhhOj2Yp1L45sr2CIZb4GWLTWtvxjfdOBNo33mN9EyNyraIM9qhwD4JeMtQZPN2w70GAm+tSsptiNENIii6+1Xp6uHMs24dG+bDaXp6HTDjLeohAYIkRN+G5+rUvXBy4b7Nts5401V72Htb25h2Z1vUl0aZLNnLMsH34Tz9qe47d32CboB+Fz9Aahb+m377LCbCQbDfzNm3FLT+uYKnfy9YwTY9NBDlCwvosafwdDbbqbfQLmmJ9pm8nG5VAWyCXgMylasTvZwhBCiRwhFOpequMCbHgu8tSzjTY8E1houNW0YiGsrFZ0nRefhkbEH3P52PU5XF2uuIMtIk0Zmx6LHi6/xFo12tHeAo6dQsbibHl5q2l0z3mLqT15Kb5/GHNp+Gjdo7ZXxphSV385n7V9epGqnG3/Izo/Z53PJ22cxcoylfY4Rx/mT0+CNH3F/t5hQRRHWnP7tfoyuzAzUB6yjFyL1SOBNdeZ6k0aArf/4Xza/vISgYaV0+uXM+3VeskcluoG0DJ2qzHHk+L+h8PPF9Jl9TLKHJIQQ3Z7hD2e1aQlLTcP/NoMtC7zVT2ebDry1VzaVii01jct4AwJ1kvEWrzUZb7LetH1JxpsQqr6raezkIEtNW6V+maUWq/EGCoItS0/v7Joqk9B+Nd7quzPF9q1Faw+2wwnQV82Ge+5hyf88QdVON7u9gyj/+X3c8+05HRJ0A/jpb6exxxxOwG2w6rHXO+QYXVmsxltc5y/d2smXvRsBNj/6ABue+R5fwMrq3hdx9fPHJHtUohtJnT4TgNoVayEkmQtCCNHRYs0VrHGBN1s4kKVaXONtH11NY0tN20e0q2msaUNkvEGPBN7iqQb/bdZjJO7WriTwJoRZH3iLZRl12wytjhUtcKrpGlZ7uMabaQJGSzshdW5afWpf/W3tVOMt2tU0/gKhZrFE72z5DkO+2HJF78YVfD/vegreWIbXZ2GV7WSOePVBrvzLKCwdE3MDwJWq4TjhTAAqPv8G5a7ouIN1QSF/JPCmxQfeoktNO+FnkRFg88P3s/G5H/AFrazOvYTff3EmKSnJHpjoTqacNwV3IBX39ioCu/KTPRwhhOj2op1Low0VAHRbNOMt1KJ9afGlfOJvj81v2yeqEyvxEs2kiwXe5IJNAjMxM1AcfPLKCxHXXCFah0C1NWuph4plZOkWXKkWTPRwebdg9zr5KZ8XAIvLWX9jrMZbOxWLTehq2rpuu/6ta1n9q0tZd+1VbH34fr67+G72bqigwp/DjqNv55bF1zBppvPAO2oHp91+FCXBPHyVflY/+cZBOWZXYYTilrtHRANvWmcLvJkhNj/6IBtfWIw/aGVN/0u57fOfSdBNtLspx2RSpI/CMKDg3W+SPRwhhOj2jEjgLdpQAcDiCP9bhVq61DT8naBB3K0+M63dl5qG58rRDL2QBN4SxMooNSPVUOrAdQwJvIkeLxrM0C0WlCWa8Sa5ta0Rey11SE3TCJp2jBAQ6l5LTfG6AbBnZ9Tf1k4Zb7EuT3p9CppuaUWNN281y3/zADt+rGXrV8Wse+ZrPLUmG4xpDHno79zwz2k4HG0aaov0yrWgzQlnvZW+twDlqT54B+/kQv5oBmXc79wWzXLsRM0VTIPtTz/Kpue/IxCysD7vUn4//7SD+nckeg5dB/vkGQBULl7Z+RuNCCFEFxftXKrFBd40e/jfWgtrvMVqFjfMeIteTG7lGBuKXfSPBPQ0e3hSYnhlqWm82KKZlnQ1le/D7UoCb0JEU6EtWn06dGetq9TJRZeaouukpUFA2QgZdLvAm+bzAODMSa+/LdYRt21fDlOoBSCkpcUdsIU13owgq26/j/JNFVQGs/nBchZb64azasCvueTrOzjmZ1ltGmNrnX7PcVQYffFV+Fj5+JtJGUNnFFu+ETc5tUSbK3SWSY8yKfz3U+Q/tZBAUGdt719wyycSdBMda+xZM/AFU6jbVIZZsi7ZwxFCiG4t2rk0YampPZxBZoZattQ09v2qQXOFWI03pdqliFj9UtNo4C0yXr9kvCVokBm4Py3qvyCaTQJvQkQzlCyWWPqzdDVtHRVXSDUtjW6b8ab5w4E3V6/GGW9typZUCmcoXP/M7+pbfzxLCwLCRpD1d/2Jwk/XEjIs+M/4DX9edQkXLP07f/j0dHr3Sd7ZtHd/GxwTyXp79zNMT03SxtKZGJGupio+yzHSXEHrDFk+ZojiN//Fukc+JeDXWZV2LjfPP1uCbqLDHXlGLjv8own6TLb857NkD0cIIbo1Fclq05pYakoLl5oSW2raIPDWsMtpGykjsVxHNPBm+CTwFi/6Ha0lr74mXU3blQTehIgWs9c1sETqA0jGW6uoWGchLZzxZtoxTDAC3SvwpgfD6eupuZn1t7XHUtOQB7MqvIy115hBsZvrGzfs/wSoavfy403/jy1v/0gwZCF/zLVcdt9kNA1yczvHFazT7plLpZlLoMrLSulwCtR3NY3/BVlsnSTjLVDHticeYOW9H+H3w0r7z7jxswtITesEf0yi23OkaGgzjgegbOEK8MsSdSGE6CixjLe4wJs1JRzIammjtGjQpnGgLfJzu9V4i5brCO9XTwlfFTT93eu7R1tFv5+oZjRXiOZRSFfT9iWBNyGinypxGW9trdPVU6m4DrGpqRAwwyfugKf7dDU1A0H0UPgqWnq/9q3xZlQU46018AVTGDYlJ3a7ZolOUvZ9Bqxc+SNfnf0/7FqwgaBhY/OU67n17RMaFbVNtpxcK/zk5wCUvfcZZvl28PfszDczGJ40xjIbqa/xlrSLAIE63IvfYcU1N7Duqe/weC0ss/ycaz67hMzsTvZHJbq1o6+Zzl53L6oKanGv/TbZwxFCiG4rmvGmO+yx2/RYxltrl5o2mDO0d423uNU2ALpDAm9NaVEQTa6tdgiZPQsRzXiz6HE13iTE3xqxjDeLjtMJQRU+cXenwFt1cX2QKDuvPvAW+9sxWr80sGTdLpSCarIZNDRu2eEBarzVbcpn2bV/pWZ3HaWBflSe+1d++8pPsFia3DzpTrvrBMrNfgSqfXx71o0sPvtSdr/2Qo8tnh6t8abimiskLePNDOFZ8SE/3nATX135PLu/L6XCk8XaIVfx++9/SZ++Mm0QB9e0Y9LY5ZqGYcLW17+Q87MQQnSQaODNYrfGbrNGgnCa0dLAW/g/mrafGm/tEH6L1ZeOJE9Ex6sCstQ0QSxA2YLmCnK6bVfWA28iRDcX+cDWLHq7dabsqWIZb5qGpkVaeofA340CbzXF4aVOId2BwxX3EWppe6CkdP0eAMyM3olBs/2sEQ1WVbLkqj/hq/JRwlBmvnwfE2ektnoMB0N2HyucdBHeDx8jUGxg3+un/K9vUbFyI4dccx6WQZNiE6iewAxG60zGZbzFOtkexGCkr4rN//wHm19aStBnUunJZrPjKKb8/gxu/3WfTpc9KXoGTYN+Pz2e4PsLKF+8HSq3QM7IZA9LCCG6n0gdt2hDBQBLZKmpamngjej3q4aBt8h3rVYOsRGjfrUNgO5MwaQ+iCjCYrXwWpTOJpG39iSBN9Hj6WbkJGOzoVs0DKTGW2vVLzWN1Mqz28ELQW/3SfeuKw1nvBk2Z8LtuiVSzaINQduabUUAWPrkNnm/pjU+Aa58fQnuklrqyOKwp/7U6YNuUb944Cg+OuxQPDUh1r0ynwl732D7B2sp+mIdfY4+hCkP3AMW+4F31A2EQtGOXHFZjgdrqalpgKZjlG7kx9sfpui7PQQMG2tCJzDpN2dx59V5nTZzUvQcp904mldfGYbNUkDhB58x6GIJvAkhRHtTkcCbJW6pqTUlvNRUb2HgLVrjTbfoDe+IHKx1Y2woliwRCbzZXA78SMZbQ2ofGYhNkZWmHSOp16+/+uorTjvtNPLy8tA0jXfffTfhfqUUd9xxB/3798fpdHL88cdTUFCQsE1FRQXz5s0jIyODrKwsLrvsMurq6hK2Wb16NUcddRQpKSkMGjSI+++/v6OfmuhCtGD4g9mWkRrrKpj0guZdVH2dhfDrqNvCJ+6Qt/tcdfKU1wJg2l2Jd8T+dlofKPHtKQMgZUD/Ju9vKuW7bm+4w6qlf3+mHJXV6mMfbFYrnP6LdC64Jpv/t+gCSk+5m3W+adTVWdn16Tp+vPuhHpPjbjbR1dQaWebRYZ9FIR8VX7zOyquvYNVVl/HNL/5I0Xd7qPJmsvWQ67l19bWcdZ0E3UTn0C9Pxz/5BAB2v/8tBL1JHpEQQnRDkTpu8TXebM7WNVeITeEaLjXV4yJv7TDPi2VyRY5jdaWEfwx2n4v+7SL6/aQZUTUV2Uj1kHn4wZLUwJvb7Wby5Mk8/vjjTd5///338+ijj/LUU0+xePFiUlNTmTt3Lj6fL7bNvHnzWLduHZ9//jkffvghX331FVdccUXs/pqaGk488USGDBnC8uXLeeCBB7jrrrt4+umnO/z5ia4hGnhLyUytb3ktGW+tEg0SRJekRVPVg77uc/LzRgJvpCQG3rR2+Nsx9oYDb5kjBiTuez8nyaAnckXP7mj1cZPNaoWrHprETevvZM+s6zBNnT1vf8uG++9HVe9J9vA6nBmZ6DbdXKEdl5oqRXDzD2x/7D6WXXYNP9z8fxR+U8rOb8qo2eNja91oXDf8lVvePganS653is7l6BuPptqXScWmGmqWf5Ps4QghRPcTyXizOuq7mkYDb1oLaxjHMt4a1KloRsJVy6jEpabRwBuh7vPdoz2o+pS35A6kB0vqUtOTTz6Zk08+ucn7lFI88sgj3H777Zx++ukAvPTSS/Tt25d3332X888/n/z8fObPn8/SpUuZNm0aAI899hinnHIK//u//0teXh4vv/wygUCA5557Drvdzvjx41m5ciUPPfRQQoBO9FzRwJszy1VfV0oy3lpFNayzEOksFPK1tC5E5+WvDC811RoG3qKpQa3NeFMmWnUFALmHDGxw575PkmbkQkT0te7KHA64+pmf8MjcXYza8yYFL31DySeLyTt5BiMvORctd3iyh9ghYl1N4+raWRzhiaMFA0I+sKa07SBBLztefp5NT36Kry78N1rnT2NTxinYBg9F03XO+ONhjJvSxuMI0UFmHZ/Kl5mHk+n/lI3/ns/0w09I9pCEEKJ7iS41TWmc8aap1nY1bZDxFqth29pBNjhMqMFS09RIxluo+6y2aQ/7ykBsmgTnOkKnrfG2bds2iouLOf7442O3ZWZmMnPmTL7//nvOP/98vv/+e7KysmJBN4Djjz8eXddZvHgxZ555Jt9//z1HH3009rgikXPnzuVvf/sblZWVZGdnNzq23+/H769fF15TU9NoG9FNKIVuhK+IuHql1hcAlYy3VmnY0tsaOVmHulFL72B1eCm7lppYSy2WrdTKwJt/716C7iCmqTPksLzEO/dTDyMUy3jrHvXQLBa48p1f8vQ1Q7F/+x+GlG2j5qVvKfrgO3JPnsmYay9Gy2oYmOziIldllaX+lKynpOINOHFZ/eAuhsyhLd+vUphF69j22vuUL1lP6dpqDFMj3zeN4KiZTPz5VO74da5c/BRdgqbB0PNOxnzhc2pWbEFVFqJlD0r2sIQQovuI1HGzxgfeXK3rahpLsGpY4i36Vat1I2x8HDNxqak9NXwhWpOMt0T1kbeWP0S0i04beCsuLgagb9++Cbf37ds3dl9xcTG5uYlFyK1WKzk5OQnbDBs2rNE+ovc1FXi79957ufvuu9vniYhOLeTxx4JsqTlpaO1Qp6snq19qGqmzEKkRYfi7T8ZbqLYODdBT0xJuj/7ttDbwtntlIQAeSwa5AxoG0SLNKpq4PGj6/eHxdIOMt6i0dI2b/300uwuP5PU7l8JXbzGyIp/qV35gz6crGPLLuYy46AJwpIdrjvgrw+9jewbYXI13WLMTgh7IGd0pu6XagiUAeFT9+Sitl4NqXyZOezHundtJnTi0+Tus2kHtj4vZ+cUKShZuwF0dzqir8aWzY9i5XP36z8ju1fleByEO5NSrhvPSP4cypGYrW9/+lBGX/TrZQxJCiO6jicCbIxp4M41wJKaZV+uiS001a4OlptEGbErRHuG3+sZu4Xm4Iz2S8dbCmnTdXcMApTj4Om3gLZluu+02br755tjPNTU1DBokV1W7o9q94ewlBaT3dtVnLUmIv1WUGan/YEnMeDMC3efkZ7rrsADW9IYZb9FCpK0LvJWvzA/vP7P3Ps+JTf1Zmj4/FsCS0v2WCA4YpHPzczMpKZnJq3euwPb5Swwu38L6Rz5gz3++IHXiKALbdxIoqwMU9hwn/U6axdBzfwZZQ8KTi73rYNN/AAWuXBhyImR3ro6I9rpw0LVW9Yvd1meAHXfaQKCY7V9tYPzEYw68IyNI0QevU/CPd6guCl/pNUwLGzxTUNNO5LDzJnDhOVky5xJdVkamhjHpWCjYSvH8bxnxq4vBYjvwA4UQQhxQNKvN5oqr8RbJIEMpzFAI3da8z1wtutRUa1jjLTJfbutgI5Rphi9PR757pEjgrUmxZl3NmQTKPLFDdNrAW79+4S8gJSUl9O9f3+GvpKSEQw89NLZNaWlpwuNCoRAVFRWxx/fr14+SkpKEbaI/R7dpyOFw4OhG2SNi39zl4cCboduxO/T6OgRS461VVKzMQqTOgjP8PupOgTfD7cEC2DPTE26vr/HW8r+d7a+9ReVrb4T3P2Bs4w32cwKMtkvXU7rvZ1bfvnDjU1Mp3j2ZV6//lKyVb8Cucqp2rQYgaNgwlY5jbx1lmz5n+//9l5xZY8idNhRP6TbWfF6FryJIZuY2+gxbxYgzjiJr2pHg7AW2tIN/9S/+inGgDnfBbpQC1/jxCZs5Ro2CLcuoWLll//szQ9Qu+5yNT79H6eLdGCbsqBnM3owJ9DnhKK68azzZOTKLEt3DYZcfy+4bX8GycS/e/G9xTjgm2UMSQohuIRZ4i894S62fX/rdAZxZzb3YkdhwLSaa8dbqUTY8TDTjLbzHlAxn+OdQKHxfJ1zpkBSt6K0geSjtq9MG3oYNG0a/fv1YsGBBLNBWU1PD4sWLufrqqwGYNWsWVVVVLF++nMMOOwyA//73v5imycyZM2Pb/OEPfyAYDGKLROg///xzxowZ0+QyU9GzeCrcAJiWSBp1rOCnLDVtlWgac+TkZ4t0FjJDRvi+RmffLsgX/puxZzUMvEWLVrSg65MRZPVfHmX3fxYRMmBrygzOvPeSFg3H9IebK1id3TfwFtVvgIWb3jqFjWtPYMGjS6hbs5aUkaMYdPQkLBYL6z5YQcqKjxlqbqb2o/Xs+Gg9AHuqBrCu5DBG9d5EYOs2iha+jiv1DeyZdmxZTqz9+9P/6Cn0P2YmWs7QjpukKcXm51+k/KsfGHPLtWSNn0igZCvuHXXU+jOYdvaIhM37zhwHWyCwfReYIdCbPmXnP/4vtj/zMaEQeAJOCnLP4sK3z2b4GMkEEt3P7FMy+evNM5lkLGLDYy8z5bFpYE878AOFEELsVzRLzOaqn1M6UuuDcP46P86s1EaPa1q0uUKDjLf4LUzV5imXMlV4n5GlpimZztjths+Pxels2wG6CbMVNd4k8ta+khp4q6urY/PmzbGft23bxsqVK8nJyWHw4MHceOON/PnPf2bUqFEMGzaMP/7xj+Tl5XHGGWcAMG7cOE466SQuv/xynnrqKYLBINdddx3nn38+eXnh4uQXXnghd999N5dddhm33nora9eu5e9//zsPP/xwMp6y6GR8VR4ATFskLVlv23LBni5aP8ASCWDao0tNQwYYRrcIvGm+8N+Ms1fDwFvk47SZGW+mu4Zvr7qTqhWbUQoK+pzKJW9dQU7vpl6j+HoYDQTDSwqtru4feIsaM8HGmKdnA7MT77j0OEpLfsI7D22k5NOvoLQUFdDof8w0LjutP9u2HsXK91bRr+JrMvw1OGu96Lv9sK6KPV/kk+J6ldShOdiGDCJ1UD+yh/fHV17N3i8XY+udw+Q7b0ZL6x0+lhEMXzZsKhgWqIOaQsgaDtb6JRobnvwXW5/8AMOE2hvu45g3H2LLp6sxQooqvT+Tj85M2M2Ek0ay5iU7enEd/tLdOPoNaXSomo1r2fHipwSDsMZyIof85hz+cGU/WU4qui1Ng1HXXkjVIz+if1dM3kev0/fMy5I9LCGE6PoiJWOiDRUA7E4Lpqmj6yYBj39fj2wsMmfVLQ1rvEV/VuEFAG0aMLExRy/6O9Pr58O+Oj+pEngLi3w/UdLVNGmSGnhbtmwZxx57bOznaF21iy++mBdeeIHf/e53uN1urrjiCqqqqjjyyCOZP38+KXG1jF5++WWuu+46jjvuOHRd5+yzz+bRRx+N3Z+Zmclnn33Gtddey2GHHUbv3r254447uOKKKw7eExWdlq86HERRtsiHdLRAvkT4WyVWPyByUo12FjKiGW/dgOb3AuDqnRgk0VvQETdUWcrXl/yRuoI9+A0He4+8iuufPJ59lc3Y7zkyEA28ycQCILevxpV/Gwt/G4tS4bdydI43BTjr7sls2/ILtq/3ULSxmsqte6hbuw7nzpUMCBXiW18O68ub2PNuqtbfROroARjFJZgeL5rVRr9Tj6LP+KEULd9M3xkTSD9kEkuvvY26/N1kjcwk76dH0u+cS9n88ltsfeoDQga4g6lQVMMP//MnVOSXbhs3vlFcetTUdL615GI3d7F5wXrGz2sQePPX8OMdjxP0Guy0HsrNP1yH0yWTJdH9nfM//bn7tbPIrHqe/Mfn02f6ZPSB0w78QCGEEE1TCt0MLzW1xy0v1TRQugUwCXiaXzomOhtpOIeN/7k9vm5Fv3tEmys4M2wYpgWLbuCt9pLaJ6vtB+kGokklWovWmnbQYHqopAbejjnmGNR+3nGapnHPPfdwzz337HObnJwcXnnllf0eZ9KkSXz99detHqfovvw10cBbOJirS423NmnY1TR61cmMZrx1A5Zg+G8mLbdBxtv+OuIqhVG0mcoN29j9wwb2fvwlnsoAbiMV59W3c9UNE5p38CbOlSrSLt2W2v2aK7SVpjUdtBw2QmfYiDQ4LQ0YAEzH7YZvPihn66er8BbuQu0txlJTAhqEBh9K78KFsL2M2u1VCfvau+GD2L83/2s+6YPSqdxSg2HquH+sYs/KDxn4QwHFXxdgGLClz88YetaxeJ/4f/DjjugFWgYee2iT49cHD4PCXVR++z2cOQNcvUCZ+FcvYM2Dr1Czdi/uoItD/3yNBN1Ej6FpcNGzp/Kf45cyTlvLij88xrRH74bMockemhBCdEnKNGMXye1xGW8Apm7FYgYJegLN3JmKRdUadjWNTsw02inwZiTWeNN1MDUbFgx8Nb62H6C7UK0o8ibaVaet8SbEwRCsDQdRNEdkqWmsq2n3yM466KKvW7SzUKoNEw3T6B4Zb4Y/iBYKX+1L79sw463p+oDBvUUsv+tJqr75kWDchcLi0ECG/uEWTrho+AGPGztFNjFB0YISeGsPqakw9/xecP5PYreZZnieYrHAioU/ZeHd74PFgmvYUFJye1OxfgspK+djw0+NpQ+jzLVUbqnBG3Ky58hbqMzfwaEV/0fh5xsB2OGYxq/fv4z0TJ2X9v6RupcfoY+zFH/IwZxzxzc5rozJ46Hwayq//JGvT7+CPsdOwe5QbH1jGd46k3JPDrXHXM75Z/Zv8vFCdFfDxzkY+f+uZ/d9d8GyPay77++M/9O9YJXPQiGEaKmgt34ZqSMtsXyJsljBpGVLTSMa1XhLyHhre+RNiy41jSsWZ1hs2Ewf/lpvm/ffXcS+nkjgLWkk8CZ6tIaBt1jGmwTeWiV6Ao12NXW6bHg1HWV0j4y3qqJqIByMyR6QkXhn5Iqe3VdO9colZI4ZyeZ3v2TrYy/jr/Zjmjq7AgMJunrhPGIOZ951DLn9Lc078H7OkZoRDrw50uTLZnuLnytOPTabqcde3GCLsZSXn4rfp+jdR+O567+j6ovPGXTh6dx4z6EYxnQevTCDgT/+iwr6c8obt5CeGd7pRX+ayHdHPsynN79C+oTh/HyAnaYccd0JPPdVCb2KvkftLqbq5cUAhAwLPwaOY+afLuZXv5BGQaJnOuva/jy67n+o++zPbP9oM33mvEPuSRcke1hCCNHl+Orqs9lSGgTeovVsg97mLTWND6g1XNqoxdV8a9elppb6ObWy2iEA/lrJeItSrch4U7ICrF1J4E30aCF3JPCWEqmPFSmQLyXeWim61DRS78yZZsOjdZ+Mt5riGgAMSwo2R2LQzN6nX7hIbHUVX8/7E44U8PvCSWrFDCHrV1dx5W8mYG3Fp+7+CqFGM/Ac6RJ4S4ZevSAaGb3qqSNQ6ojYnMZigRtfO5HPXp/OYYemMnxsYnDtiLkZHLHuqv3uv+8gO7d9fymbN1zER//7I4Ev3sNlVOA+8hf85slZZGbJlUvRs133jwn8deapTPa8wYa/v0ufw49EyxqU7GEJIUSXEqgLZ7OZpo7dlThZNS3RwFvzlppGl39CXA3kiISMt3YI7FgJZ7UpPW6OZbVBAAJ1EniLiXwPa9asMbqRfCFuVxJ4Ez2a4fWhAXqk4000463hckHRPMo00ai/6uRKs7EXDWWGIBRK7uDagac0nPFm2ho3Mphx/mTe2nYPxe9+TJ/qNYRML6ayUDzqNM55+pf06d/6j9v9nf/0SI03R7o0V+gMmioiPPf8tmekjRxr5YZnplNXN529pSZDh3f9DsFCtAddh5//82wWnLmYwTt3kP/g3znkj3eCPf3ADxZCCAFAwB0JvOmWRs2eVCTwZviaF3iLv9beaKmpXj9RarIucgtptRUoQM/Ord+vNdy4SgJv9WIxzuZkvEm8rUNI4E30aKbXiwXQox0hYzXe5BOnNZRS4cBb5KSamm7D1LRwp2+j6wfe3HvDGW+mw9XoPosFzr19Ctw+hd2FJns2e0hJtXDWjA4MiCmFboQz3pxZjcckup+0NEhLk6CbEPHGTnWx6LTLqJv/N7a/t5HMYY8x4KLfgNVx4AcLIYQgEMlmU3rj8IAWuS3U3MDb/jLeGkb12igaeMseUV/rVlnD2W9BjwTeorRI5E014/VXzcuLEy0kgTfRo5k+HxbAlhYOjlgs0Yy3rl+PLCkadDVNzbChND2c7BZqeUHWZDJrytn1xXfoNgu6xYqnrIbKt/8LgGoi8BZvwCCdAYPSOmBUiQHh8NXJ8G0pmZLxJoTouX79yBTuPuqXTK54jjVPLMY16HWyT7go2cMSQoguIZrxFs1uSxC5LeRv56WmbcxzUEEf1NYBkDt+QP0dtnDgLeTtWt89OpKKrOZqVthT4m4dQgJvokdTvvCVEGtqNOMtvERSEt5aJ/qhHl1qmpqqY6BjqnC3JFsyB9cCRd8sZc1tD+GvqGt0n2Hq+MfMTsKooGHgzVNd360pNVsCb0KInstqhd9+fDL3zyxlpudtVv7tA44eNwHLwKnJHpoQQnR60cYJZhMZb9ECxYavec0VzLjabfvtamq0balp1fYizKCJP2Rn0CFxZT1s4WznUCu6sHZbkS+3+6sbvY+HiHYigTfRs/nDgTdbWjiDSWq8tZFKzHhLS9cImeGPGW9N5w+8KZ+XFfc9R8m7n2IEFVVmDtUqBx0DZbOjDRjGIZefyeln5R3cge3jJOmtCgfeTCzYUuTjXAjRs2Vm6/zqP+fz7imbGautZsXdTzH9f/8fZA5N9tCEEKJTizZOUJbGs/XobYa/mV1N4wJvumV/Nd7aFtkpyd8DgN+ejTOtvumZZpeMt4aiXU0bdpkVB498UxM9WyAceHNkRANv4ZODkhB/q0RPoLo1/KFut0MoEm7z1fnJSNrIDkApdr73GQWPvYynuBKAPZmHceKztzBsXGqSBwexnO8Gf5a+mvDfr6nbWtIdXAghuq1Rk5yM/N2VlD14J3xXxJq77mPi7TdAr3HJHpoQQnRaRjRIZbE0vjPSrMBs5lLT+Bpv8YG28M/1gbi2ft2q3FocPl56r8RjRAJvpq+LBN6Ual7TgzYdI/LfZhwntoV8H25XEngTPZoWDH8gRwNvmjV8MpCMt1aK1g/QwydtTQOsdgiAt7Z5J+sOYYQoWvgtpau24q+sJVjrRvmDDDhxOlkjh7D87qfwbtgGQA2ZGHMv4ZL7jsNuP8B+D5Z9nCN9tZEW6pbOMlAhhEi+M68fzEPLriO08Cn4bDehugc49M83oeWOh5APbNKMRggh4gWjjROayHjTbJEab4HmNUozjfiMt46r8eYuLAkfo1fvhNt1e3ipqdHMQGHSKAXrXoRANRxyEaRkH/gxrT1UCzrIRpsrSNitfUngTfRo0cCbMyec1aRFAkYS4W+daMabFn+SjRRk9dUexKtOSrHp9fkU/ucTNDRUZTme4upGm1V+vRSLBQwDAqaN0hFzOfavv2TspE76pUxr0FwhEngzrZ19Ea8QQhxcN714GI9ffzM733oMviskeNMD9JuUja/cTd8jDyHz8GMhZ4wE4YQQAghF6rc1tdRUa2HGW3yQp9FSU63p7VrDX1IKgC03N+F23RHJePN38oy3qi1Qsz387/X/hgmXgr0jmrPFfbVt566yovkk8CZ6NIsRXqrnzA4H3nSbZLy1SbTGW1zgTbPZwAt+dzuf/Iwg1FVQtbmQzR8upW7TVozKKrBZUXYnvvyChPipx3BRkXUIZmomemoq+H3k7PgGFx6KUicx+c5rOPvUAfs8XDJFJykN4m74I4E3rJLxJoQQ8TQNrntsHC/2voUdT/8vrNpJyapwKYFNH5eQNfB7MkdlkD1xGP1njcMyYjak9kvyqIUQIjli2WFNdTW1RQJvwWbWeAtEVmSoJpaaxgXi2lrjzSwvByBtUGLgzeIMZ7ypzr7UtOiHyD808FXAxtfDwbeOWHYa/W7bkn1LIkq7ksCb6LFUyEAzwieQ1EjgLaVPDgA2Xw3KMBNODqIZGnQ1BdAiLb0DnuadrKP8O3aQf/+zZIwfwbCLzkRLC1eIU8EAKx54iaoPPyboDhLcR9a7qTRKhs7FNXI4mt3OrMuPZPgYR8I2W/N/zeYlxVxy4bDonKJLCUaDmTYJvAkhRFMuvnMYb/e+nQV3voNKzUJzpdNv7zcM9O0ic1cp2xeWsvrxxfSd+iGH3n0Vln5jwZICVseBdy6EEN1EKLLUVLM2Dg9EM95UoHlz+bIfVwNQGeqDxZ64v4TmCm2M62g1FSgga0TihfPoUlMz0ImXmnr2QtVmQINDfgEb34DaQti7FvpMbPfDJTXupkzQ5Du1BN5Ej+Wu9MT+ndY7nNY7ZOpANqGjG0EqNhfRa0znzIDqrFQs463+w1WPRLQC7v2c/AwDKsugdzjbQPn9fH3VvXh37oZFP1Lw4gekHXcc2WMHU/jvtwnsCaeWm6aO20ylOnMktrETSR3cH9PjJrB7N3nHHMblV03e7wlm+Dgnw8cNa+OzPhiafhJBdzhjU0nGmxBC7NNZ1/bnrGuvif28fuVcvn5tNxu+24xZsI7htjUYS0upu+wBsoelYnXZGHz2saQdeiRYU8GaAhYHhLxghiAlS75ECCG6FTPasbSJ8iV6JHjW3Iy3XZ8vASA4eFKjlY3ttdRUBX1otbUooO8heQn3WZwODIBQJwm8+SrDUSxnOMEDpWD3V+F/Z4+GrBEwYDbs/C/sXADpA8Hwgyu36XONUuCvBjMAzj7Ni6ZFo2gH69xlhqBmBxQtgaoC6D0Rhs7t0eUdJPAmeiz33jogXEDSlRm+MuJKt+F39sLpLWPXih0SeGupSMq4JW6pqR4pyBrw+EGZbH37M9xFFYw6/QhSBg2ldvsuFl/7V4K7CnGMGcvgs45l94LleHfuxosLj5ZOr7oSKt77mIr3wvv0KRfeY3/JtKtOpt8ACzk5B/2ZHlTR82nDK08hTzjw1nm6QAghROd3yKF2Djl0GDAM0zyB954uZv2f/sFIYz1Vu6vQdZPtC18jc8j72NJt2F1WLE4LQY+BGTRx9U0l85Dh5B17OFrOUHBkgD2j47vSCdGdKBNMo8li/s1+PIQDCUYQzGDHfalXCgK1oNvA5mz+44xg4+enTCjPD9f2yhwGOWM7RSBf91cBkRIxDe+LzDOblfFmBAnmrwOg11EzG9+v6fhDdhzWAOa692HAL1v1/Cu2FWEaCn/IzsBDEr8IWF3hwJsedIf/xvQmOrVC+PcarAN3CXhKwr+btAHhoFfdHnDvAU8FpPYOf8abBuhWsKWGg2OZw8I/1+yEHQugtiR8rTytT3g/KKjZRc3qfAAyJo6GvlPDF3HKVoe/N+UdER5L/1lQvAzcFfg/vQ9/XYi0Qdno/Q+B7DGQNTL8t7T7O4yCRfj3VqOUwtk3G73f6PBYAjVQtzsclAPoNT4c1NMsWHETpJmnqcg2roovoagyHAB05Sa+v6q3h2vU+SvB6gzfnz0m/Jx3fI6xczXlBTXs+KoM945aMkYVMO6c5ThzM8PLmSf+GuzpzfpddxcSeBM9lrvCDYBhcSSkPZvZueAtY+/6ncARSRpdFxVdamqNy3izh0/goT3b+fzMG/EVhLuHFj79KrYMJyGfQcgbviIVWreB/HUbYo/VzrmGs248ivl/+y/ej97EFqzDP34OR//xPEZMzjxYz6oTaPosGfJGA2+yJEoIIVpD1+HMq/qx9rA/8N7DWwkFTDzr1jCiZiE13lpsFg82PYimhUsYKKVh0Svh412sf+JrUvu7cOTY6T11KINPOwat/5Twl7Nd34MrG/IOD38xE6IrM41wcKTht3alwgEKTYfUvokBFKWgdifU7g4vq0vJDH85VwbU7KT8m2/xlNXiyHKRc+ghWEccCUYAPMWoqt0YdXVYnKloWf0gY1A469QMga8Co2wLJd+swPD6sKc7CNb5CflMcg8fi2vsdHD1AW85lG0KX5xMyQzvOxgAZYOsAdBnAlRsgPL14bGnDwZfeThYZnOFA3m+SqjaiWf7Dip31GJ12eg799Rw4GDPKhg6B3InNfmSGdu/pfTDN+g9ZSy2EdMADdxFuDesouDDbVQW1JAxLJ2hPxmE3W5B1yEly4XF5QRnavjzQ7NgFm0hWFeLPSsLZYYI1Lhx9MlBS+sdHqcRgOrd4EiDvlOg1yH1wT6lwF8V/gzaV5BDKVTRUlw7v8MD1Gp5jTaJdjVVoQAEPeHAVPR/lhRw9o4Ftyry1+MvqcMTcDL7wsmN9mWx6aw1j2OK+Smr/vYBs9N9OIYfFv7bCvkjY82AzKHhbON4njKo3QXWFGqWfQdAwJGNw5kYuLO4wiuZUn1FhL55EOvww8OBq6A7/HoBVG3BLFpH1Za9VO30UrPLi2kqMge7CHkNqre7ce/xEKwNktI7BUeWjZA7BAqsKRbSBjrpPTYLe3oKZev2svvbMgJ1ITQNbGlWHL1TQIFvr4/acgOAzNzt9Jq4lKzBLnZ9t5cffxzMztpPsWe6yJvaD2eVlf6VP+LzQsi04nSEcGWvwJpqJXVgOn2mDWLH/AIqNtRgmDoKDavFwJW5jMyR6eFAZFUA3WHBmmrDlvojtjQbtjQrqSXVVEGzIm81/kxydA8bH5tP6SffktY3hfS8FLJG9CFl8lxUwM/Wf75I+cZafDVBbC4rrjwXQ4/qhcLK2hcKqN7lwxNwUFzbn7K6MYwtyado+Sps9vD3xPHPnkfeRAm8CdEjeKvCS03NBnVcbP3zYM86PDt2JWNYXUqoYi+b3lzEwGMPI2P0sNhSU0tcXrkl0lnI+0O4gGjAtFNhG0huaBtGZbj4aqV9AKnnXUrZZwuxlBWiWay4Zs7mgjvnoGlw7r3HE7zneEIhcLbgQmN3ozXormB4wzXedAm8CSFEm0yY7mTCK+MBUGoiX334U7YsraCm1Id7r4dArYeUrFQsVo3aLYVYd6xiUN0WUqvcpNiq2LP0Rza9vJrMURmYIUXVhirs6TaGnTmOYb/6FeSMCh/IVxXOdPFVg88HuaPCmQqSLSc6QtAL3rJw0ErTwgE0R1Y4a0spCPnCWTI1OzAL14Iy0OwOyrdV4i7zkNY/A93w4Nm5m9S8bLKmzQwH19yVmP4Axcs2Ubq0EN2i0Xt8DqmDemPLTMOR5qJs/S52LdxG7c46ApV+bC4rzv5OdKuOp9hLXVn9MkCbbR2ZI+aDUvgrA3jLA4TCsQqc6RbSh6RhcegYfhN/bRBPkZdgoHEBKv2VnaQP+JqUXg58lQHcuz1YLGBNs6IFDYyQIhQCR6qF3jNyqd3loW5rNSm5LrKGp+GrDGD4DXSrjuE3CNYE8FcG8AcijQI0yHp/FykOjZqdbvofvZFDHnikcXA95GfJHS+yd3kRNttWMod+hQJ85X48lUF8QTultX3pt6uYPd+VJTzUYgG7Daw2DaVreGpNTDN87OjKB6sVUnIcWJwWzICJv9yHxWEhe/wickb1ImPMoaT3MvFXFFKycg82l4VBJ85AHzIrnAFVswOqNqOqS6hanc+2z3eyd20Vm/eOYMyNJzd+XW12FJBZs44t999KMKAwfAaGN4TVruHKsZMxMo/scUMofHsTANXpoxk4qnEGoqbBOS9fzOs/q+Mw7Ru+uukTcsZ/jwIMv4mrj4PMvBSyhzhJGz8FbeTJ4eX9gVp2Pvu/lK8tw5aiU7q6CgC3a3CjY1gHjmV9yTiGB7ey4KZvyBm7hoyhqQSqg4R8Biio3eWmrshHKATuQBo1voxwvThnBYZpocqbRZV3MJ5gKqnb3dgtAUylo+sGTquX3LRSUhaEf3dKwc6qweyoGBp+nVJqSHfUYCodXyiF7XXDQGkMK9nCoMJCMlNK2bJ3OOtLh4cHXOGheNtWIAWH5QTqzDRCmp0BKYX0TSuhX3oJadtLKfymlJBhYW3JZDZWj0GhM9BZSG5qGTm7K/AFHVR4BuEOpOGw+hmUVYjLXomGQtMUpkqlzhze6PVqaM/oX5H/xVf0KSsjo6Ca9JQKUu0eNDaTe9hGNMOkeGUVe6rzqPJmYbcGyHFWULgoH90CZTU5rCmZSYk5iAGHD2b8yYP58slVpBbko2smmqaYlNaTEijCJPAmeixfZTjjTdkTr6a4hg4ktByCxcXJGFbnoxS4q8CVCbqOUVvNpne/oeiLJQTWrCLoN9jxz1fIOfk4HN5wxzgtbqlpNPAGUOEaycQ/3cxZcwexdmkdRWt3Eaj1cOr5E+nV1wa/n7HPYdhsdMkGCO1iX0tNfX506tumCyGEaDtNgzmnZTDntIx9bHEoHs9pfP1BFbvWVrFp5R5CSxYxpK4Ab+1eNA1KanPJ9NTgfnY1RQtvZ+R506jbvZeq/CJqttURqA1ihCCtfwoD5o5j6M/moOdNBlfvxoczQ1CeT9265WgWC6kTj4DM4QcO1tUUwt41kDsF0vq3+XURDQQ9UJEPKTmQMTTh96HKCyh5+wUyxgzFdeiJkDGkeftUKhwUqdyMd/sm6nZXgq7T55jjw0vU9rVkrmor1d9+gHdvNa5+vUkfNRKsDna8/h6Vmyqw2TTQNUy0cBKUSkM3fOhGADNk4t3jpqbI3yjA05ArayUqaOLzmLFtgoYV0Njx9V5gU+JLZFgpc/fB7U/FafOSWuTGMC14g7kU1g2lNnMU1royRqWspp+3GG8ohRpvX6r9GfhCKaRY/WRUVpNZVo1FC2IqHU8ggxr/AHYbowim9UW561DONHQVYoBvOb1rykm1uwmEUiipG4KpNFKsfkKmFVNp2C0B+mWUUPdZEUHDyo7KkaSV1pFWUEudP42gYcdu9WMoC96Akxp/BiXevgQzB9A3sJ4JgVUEDRsOq4PAgp0M2bCE1EMSV8iUfruA8h+LqPOnsacsj8yKKpTS8QT7UOrOpa7voUz65Wh++LiAwNYCAjhQSuHSPbg0DzZLEJfNi1UPUenLpiqYSbqlNrwPw0m6tQ7nLg92SxClNGp8GWSk1DC4fCd7fihP+D2Ypo6um2x4s5C+0xaRNyOXouVlVOZX4d3rJxQK/562Vowm+/JrOeXyxmV2Aq7+WJSGu7CO9a/UYZgWgoaVkGnFYQ1gswSBHVit38ce45w6fZ9/5uNnpHLKi1fy73N7McS2gT4le2P71HWTDMde0hx1pKRupM/Ezxhx2dm4S3az5l/5uH0OfEEnnmAvttWMZMptZzfa/1Hn5lGw5mo+e3I5g0pXMai8kIzlpfiCzvAxNEWNP5c91XkUGUNIHZxLnyk5aDqsWbsXm8vGgNm5jD+iD/1GpbPp+3KqS3yk93WhWzSqS3x8910R3i3b0IwQpKYz+ZeHcdH5gzCCJoVrqijaWIPFpjMoN4XLz+9HKASf/HMnqz7ZTt2WPfSZNpx5148nNdPKztVVbFhUjCsnhcPOGMTEWWmkpsK6FX62r65h704P6z9ZgdqyHnPQWM566wRuOzp8ftqzy2TZRyWs/byIlAw7A0akE3QHcO/1UlLpxVfhJVDtxVNrEFBWfnnRvr9rRd323hFs3zSNL1/eReGqCvK3VuHZXc5QfmTs0nwsukl+6ViCx1/EsMN6Ub3HzaqFuzDXryTV5qZuyEx+/fXRjJ/qiNX4m/e7AWzdfBLuygC+mgC9Bva8Wm+aUtIn9kBqamrIzMykurqajIx9TcJEV/PdEwsof/wRfDlDOOfrf8RuX/LyUkr+eg8hVzZnLn0piSNMEqWo3byVgg+XUrEiH3PHZoyqGvTsbCwDBxPcsI6Qr76VaK2ZTrpeW/+zlsns1//OkPG9AFj45GJ2PvZPtClHc84/f4kzbR+TRrFPXz74CTXPPYEvdwTnLHwkdvu7Fz2MZfl/Cc2Yy5nPX5e8AQohRA9XW6NY9EYpG+ZvQhkGh104ieVvb8Wy6FVGZhegRzKWTaVR5c2izp+Gpin6pxdhtRjY7BoZY7KY+o8HSOndN2Hf6x97nJI3v6CuPLyMqddwF3nHjmDQacejp6ZByAMBd3gpWf8Z4eVZe9dCwTso00DTtHCXvKxRYE8LL7VK7QeOzHDGU8gbzoKKRlwk++7A9q6j8pOX2f3dHlJzHeTNGoRj5GTIGIwyDZbe+g9KlpVhsUK/Q7MZc9WppM48u3FmlDLDy+d8FWD4Kfn8c3Z8tIrarbV4ausLz6flpTB47giGzJ2Btf9osLrwrv4v5UvWYFjtFP1QTNmaqtj2jlQLFpcVT5kfX9CBpik0wn9/KTZ/whACITu1/jSK6/rjNZw4ND91WgZ6ehqqthbDtOBxDSS9bht5qXsIGlb8hgPDtODWc7BMmknAtFK3rgCbrxIXdTgsfgKaE23yERx65kiGHZbDngI3O1aGi8yn9XIw97KB9BtoxTDgm4+qWfHhHpyZdvoMTWXMzCwGjEjB6zZZtWAvG74qxTTB7rKSM8jFkAkZzJyblXBBVilYu9TL6gVl7N1aQ2qOg2lnDMQ0oWynF0eaDVe2A1eGlSWvb2Pr+8tI6debGZdMpuC7Moryq+g1NJ3UXikEvAYp6TayB7gYOjmTMYc6SUuD/BVe3rhrPZrNgjP/E8Zq35F73Dhm/v1v4Rpw7mLwV/H1b56halURm9JOZfJvz6Pgh3IcKRq9B7uYfHwfBg+3JtTv1TQIhaCqKvK/0gDVZQFCAZORh6bRp69OVaXCZtdIS4Otaz3sXFONpyqAbrMwYnoOO9bUsOKtLWjF60j1bCXk0an2ZOHNHYelspjh9jUMzCzEZgkRMiwU1eRR7c+gNDSAQN/RzL58Ahfe0nRt6x35Hh4/978YXj+63Y7NacfqsmFxpeCpM/CXlGHbu40cy14smoHPSOH0t29k8rH7L8JcXaX4/N/FrJtfiDPDhsNloXRzDRVbq7CWb2dq7jJ6p5bjcOnoKVbce0MsDp0NA0aQOzKTc24fw4Ch+74q7/fD56/uZeW726ncXo0rN42UrBQwTfqNyeTwswcwdrIDSxf5amIYJHWsC14r44MbP8Hm2cusv5zHWf+T+Pey6js3O9fVcsql/brMa9oemhsrksBbM0jgrXtaeN8H1P37aXz9RnPOggdjt+/ZuJflZ10CwNzFb2JPS9nXLjo9o7YaT1EZoFCmCp/dlaJiexk7F63G8PjIGDmQjEG90NHYtWglvrWrCZZV7LeFdJWWS2D4ofQ/aQ6HnzuBt695EcvqrwiNn81P/nwhI8YlXsVI9omiq/vyoU+oefYJ/H1G8PNFj8Ruf/vce7Gt+w6O/hmnPXl58gYohBCiSasXe3nlus9xbF+BN3UgqZMmMe64QYyckYOma3z2yCo8Xy5gUsYS7JYgA2//f0y5YFbCPt455lqsZTvZUT0Ew7AwKHMnNksIR4qG1a5hBk0MA6wuK/2PGUzW6Fwqt5awbj3kb89h5OAqcjNDODxuLIEAmmmSPshFr0kDqdpcjL86SMbgHGzpTvzlFdizM8meMJKMMaPDtbJCvnANp/TIl6zuEpwL+aB8HXiqwAhB9pBwZlp88XylwnWhbKmx5xyqKmX5pTdQurEOdyAVu8WP3RrCkWbB1tuFrimqt9ZRXNsPf8jO4KydpKRZGPazUdhdFqxpTtJHDKZ8UynV67biLarFXx1A+ULUVRoYpoU9Nf0p8fTFbelNtl7C6Kx8Umx+rFZI621Hc1io2eXFMOqHua1yOLuNUTiD5fR1FeOyedjhHoFx2FyU1Y4yTKy6SYrVS7q1ipDFRciSgWlNIXtwOof/fCB9vctXSQAAMSBJREFU8mx4PYoBAzVsNjDN8L4tFigpMlny8V6y+joYfEgaqRkWMjNpFPwKBKCuDlJSIDX14P06D7a3HtiE5bnf4UiFCRcMJlgXpK7Ej7vMT3l+DbW+VAY/+CizT8896GMzTaitDf/JZmSAz6v46OndfPf0KtieD4PHMPUXE5l0Yj/GHprSLitKAgHYvD5AxS4P6dlWJs9Oa9P+3G746PlS1j/2JuPVQlJsfvJrDuXChX9g4Miu+92sqwsEoLbapFef5DcE6SyaGyuSpaaixwrWhWu80WCpaf9RvQhZUrAaPnYu287IY8by2W3/wreniBMf+w0pGZ1jFqFqy1j7zIcUf/gVSrOQfuQsUnpl4ausJVhVi3frNoxtWzBC+2/VXbcQ9jS4zVAWyl3D0UeMo/f0SQyYMZqC97/Hs2UbfY89nHOvmIojpX7S/es3LiEUugTrPj5RJOjWRvv4gqMC4fooulNqvAkhRGc0aaaTSUt/Rm3tz0hLa/xxPuXV6QSD03ln9uXgLiYUbHzVy4x0Epxw2/lkTp3G+w+sJ/TDFwyxbsJQOkHDRtCw0zetGM+7W4GtKAW7y0ewu7Q/nnVO+qfvISc1hFUzUeikbypl+39Lw/tXGrpW1uCoa0hJs5DS14npDeHs5WDsTeeiAj6KPvkSW69sek0cjiPVgdWhY7XpaJm9IXNweBmsbgEjgHfHWip++AG7w6TXhOEEq6qp21NO9oQxaKlZlHy3lLrCUrRQgN5TRpM55XCCxZtw7ykmc8xYNN1K9dp1pOT2xjFqejgAZobCWX5Bd32hd2WAbkf5Pez+4lt85bX0O2ISKelOqjYX4q3yYoQgZ2R/0gdk4y6qYNsHi9m7tJiAx8A0wZVjJ3VwGo7+WaQO7EfmsL5UrinAU1jKgF/Mo9esnwBQsLiU0o11+EIOvku7GndJLbm1q+mfVkx6cQ0Oi5+9nqFkXX49A0b34rs73mCK+RkbXt0Q9/oui/0raFip86cTMi1UBbKoHHMqh/+/qZx3cja9emsUbgnw2v9bScXXP5CnCsisqcZp81DmHsQm7XBsDguOnExOeWA2N5yajbtO8e27ZezJr+aSXw1m8KiWzhHiOtPHfbfu21/ntMv2H0TSNHA4wv/r7k67fhRPPjOR4d6VrHxuG6bSqPOn4Q6k4gvlUDXoKC5IQtANwr+3zLgSWilOjbNvGMjZNwwkEDgVewdUKLHbwx2jObR9dp6aCudel0vVhVdz/xnj8fy4huPuP1OCbklmtyNBt1aSwJvodta++DkoxfhfHIdm3XfEJ+QOF/bXUxI/wDVdI5iei7VqJyVrdhAKBPC9/z4a8Oll9/Gz1+9G09v+gbP9g28o+mEd/Y8Yz5ATpqPZHZStKmDNE+8SKNwJfh8Zsw/nsBvOwtErGwDTXcfm/3xB6edf4d+8GU9t/QTd/+a7TR7Ha6ag0AlvGa7tEdRTCPYbiZaaDmVF4K1DNwKE+g0j+4gZTD7vcEaOTwwwTj36lP0+n30F3UTHUYHwchGrUyYhQgjRmaXvp3mbzbbv0l0AhMKBt8zcFKbMSmHK21Px+aayZEEtKIUrw4rDZeH717ez/vVPUYEQZq+hTDnJwdkzTHZu60tR+QT8Gbn4XJkEgjqr/7sJc8cm9IHDcPbvg3/bZoxAED2jF1SXkVG3ld41e3GVeTFMOzl7Kii/5jlUyMQIKWAnsCphmA4HOLIduKaOwT7nZ2x94t/oOwvRIx3PLRZiGVo2+2dY7Dq+OiP2eO3FNaT2+xhPmQ8zpLA7P0LTwO8xseiQNuBV7Bk2lBkuUm/4DDRLeN6mWTR0XSNQF4rtM//f+WhAw1Bm/G01vnQqPDnomiLHXU5aaQVQAWxNeEyhexmnRgJvRiQ4qqel8OjyOeEgZ+EpbFtWzq61VRRt9TL8iH6ccUU46HLkT6/gqcsnUbV8LSGLE81TS4Z/NwFnNtZRh5AzYTj9x2SSmuPg6EPTGTkuMf1o0Ag7t7w+A8OYwYpvvWxbVUPFHi+HnZjL1ce4GgVzU9M0TvxFLpCcoE9PYXdojL71Gj776zcYhg6ONDJHZtN3dCaDDsnkovM65+vfEUG3jpSVo/PXr47B6z2mRzdYE12ffFUWnYLyeNi+cA21u8oZecphuAb1PfCDmrD08Y8pfuJJALY99yZ6/0FowCFXnsHQn0wKz/gCAUrWFRJc8jU6QGrjVGitdz+o2knNph2UfvRZ7I2ir1/JJxf9hdHnHY/FbqVqawmV67YSLK8AiwUVDGL6/eGaHZqGpoHFlcrg045k8PRhFK/ZRWXBLiq++xHfpvCVz73vfsh6hw1y81C7d2Ka9VPE8rfeZcG774XvU6BXFBMKhCeUSkFRcBDWo07AlarwLl8SflBKKlpaGrY+fRh5+pFMPmFo7IpldHJmsUgWWlcSV6458Y5gOOPN6pLAmxBCdFdaJPDmSK3/xpySAkefmhjNmzhtDDwwptFK0MY9/wBGAvEX1BKLw1dVmCydX86eDTU40u2sfvZtxrkX4Q2mUWCdg2EobFV7sBBCR6FrBhn2GnLdZdiLV6M+Xo0V8AScFAZGUetNobelFK+RgttMZ4CzELslQIlvAJ7s4RgG9K9bQR//Xiq8eZR7c+ibWoqmmRR7+tHLUUFusARdC2AonUDITsBwEQg5CJpWlNKx6CE0TVHkH0Iwoy85deuxYFJm5qFcaVg1E0ddEam6m1ozDU/2cMZdcjzHnJqHMhX5i0rYsGw33p27MEv3YKkuIzetjDznLvye+gBhtBRH9CXWNBg4WGfg4D5wVp9Gr3RGls7v3jwi4TX2esO/w5as2LVYYPrRTqYfLdGHzuLkS/tz8qXnJHsYPYIE3URXJ4G3HsxTWsOWRfloSjH8qNEE3V5KNxaTM7QXGbkZFC7fjiPNwYDZ40DTqN60ix3fF1CybCO+devQPLWo9CysAwcx8PgZHHLGNCxpLpTXR9ma7ZRv2E11YRl1ZdU4crLoM2EII46bgNWVwvb/rmbPiq24dxQR2FIAJYUoI1ywf+djGmrgSCbc/Es8e2vY8eE32LIycOXl4ikswr97D1pFCcpqR+83ADMQQPl9uMaPx/35J2iAgY6lvBizPNyZdM0NKyiYNI3glk3grcE0DHQFPksa4y75WaPXxjF0CGxegrHoI6ymiaEs+KacTOrKDzF+XEL+j0ua/TqHgIKVyyhodLuFiuxxpFZuJ9VfB4U7ANjbayKps45EBQIEvpxPln83FO0GwASq9N64Rx5J/5OP5/zzhsSlkp/V/F++6B6C4Yw3W6oE3oQQotuKzI8c6c1bv9ce5deycnROuLAPEA4kea7+H968+1j6jsnhwssGomnha5l+f7jmj88HhZu8bJ6fT+jjJ3EFizH7H8IRf7qaQYcPIRjS2JrvJ6efncwsjeULa6gp83PWT3uRkRW+Orh2sZsVHxUxdXYvxs/O4seFVRgGnHd8NtvyfSz/sAhvTRCUov+YDAbkpRAKKkIBk6DfJBRUWO06F5zZm4xMjaI9CtOEvAFa7DVxu6F8r6J3Hw1Xg6Z6kw4bCAwEZgLh5/fJDc+gFu5qUPdWtfl1liCCEEL0LBJ466HeOvo6bOU70SKTh2372XZ5Wi80ZYC7KuF2BVBXQahoK9uXfsmO+22orFy0qlKUEUzY1gtUvQ6b79DBakMFEzsqAbitGQTtqWR6itAKC1h70x2x+wzA1/DYgBEJrAF4dhagAe7socx44i5WPvMpRjCEb1M+WcVr8a1cnHC8yuzRHPnYrQyf0jgV/Kjbfs4Hq/LJLlsLgH/8bM77vyuZ/7/jKP1sEbaiTSiLFcOZgcodhK1vP1Amus2G1eVEs+hggqkUnq3bcWxeis30UWvNwczoDX3yGHvxqZx5+nB8XsWPHxWw69s15EwYzq8unRKbzBnGGaz9tohtX6zCYrPSd+pITjxuSEJ9NdH9qX3M7rVIxpsE3oQQovvSInOqlLTkFc5ypelc/MCkhNssFnC5iAWw+vVzMv3oqZh/eJTK9TvoNXVULL3eZoMxk+rHP2tu4wLUE2amMmHmyNjPR56WHfv3xOkpTJw+rEVj7p/X+NyZmgqpqc2bQ1kiy1gVQNxqhP12nxJCCCGaIIG3Hsqw2rGj8KVkodBI8VVhajp+exq2oAeLMvDZUrGHfFjrysOP0Sx4XH0gN9yVK2vUQGp3lVL941qs29bgClRD+W4U4NNT8Lp6o9KysaRloNVVYqvYjTNQDUE/ft2Jr9dQLDl9cI4azuDjpjBhzlBsdp21X+1k1QMvkbFtCUHdjm/0LDCCmFWVWHr3xTFkEFljhhCoqqOmYAfWNBeYJv4l36KFAsx69DaGTurF8EcvBCAYMPnPLW/iy19P1pFHMPrU6bjS7AwZlca+SrVl93PxiwV/5aP7/0v1qnWc8LdfoWlw8i1Hwy1Ht/j19rgVPp8iO0dvdIU0xakx6+ej4eejGz3OYoHJR/dn8tH9W3xM0X1E/2RUw6WmoXDgrSt33hVCCBGnwce8aYJuhECHlIyuUbFeT3XSa/rYZA+jXWiRSVv8ryUad1PIRVAhhBDNI4G3HurQe/6HtNwM+o/qBUpRuqsOV4aD9Cw73rog1RV++g5MZW+RmxWvfo81zcnE06aQO6Cpjp6nEgqarPy0gJKVW+g3bQwTjhmGIyUxqmUaioKle6gpqmT8CWNxpTX95zdxzmAmzrmd7RuqcaXbyB3ganK7xi5o8labXeeCv5/XzH3U0y0ap912HHBcix/bkCtVw9XMK6xCNFes7o8E3oQQolvy1obQ9XBzAmeGfNYfdHpk7mbuv0O8EEIIsT8SeOuhRh8Zl66vafQdXF+g15lmw5kW7qjUZ0Aac397wgH3Z7XpTPvpGPjpmH1uo1s0xhw+ABjQrDEOHZt54I2E6AG0Bv+N3R7JeHOky5cxIYTojjzV9YU2UuSz/qDTooG3JleaygVVIYQQzbOPhXZCCCE6m4ZlZXQzkvGWKVWahRCiO/LVhmvi6jrodvsBthbtTYvF3aSumxBCiNaTwJsQQnR2TTRXMA0T3Qx3unNmNnc5thBCiK7EVxPOeNOsFtBk2n7QRV/zpporSMKbEEKIZpIzuBBCdHZNBN48Vd7Yv12S8SaEEN2SryZcUsC0Wps8F4iOFV1qqprqriCEEEI0kwTehBCiC/LGBd6cXaTTnRBCiJbx1UVqvFlsyR1IDxXrahoXbIv9U+KgQgghmkkCb0II0QWZRn2HNd1mSeJIhBBCtJeGuVSBunCNN6zSDy0ZoitNNSVdTYUQQrSeBN6EEKKTi64ukom/EEJ0cw0+5wOe8FJTrJLxlgxNLTWVEm9CCCFaSgJvQgjR6cn0XggheqKgO5LxZpPAWzJosbamCUXeIv9fzs1CCCGaRwJvQgjR2UlBbSGE6N728Tkf9IQDb5pkvCVHZK2pSkh5S9JYhBBCdFkSeBNCiC5C5vpCCNGzBL3hpaaaZLwlRbTGGwlLTeVsLIQQomUk8CaEEEIIIUQy7SOWE/JGMt4c9oM4GBEVrfFGU8E2SUYXQgjRTBJ4E0KITi7WXCG5wxBCCNHBGsZ3DF844023SVfTZIitAJalpkIIIdpAAm9CCNFVyPIWIYToUYxIxpsuGW/JoUdqvElTcSGEEG0ggTchhBBCCCE6IcMfBGSpabLULzWtj7xFa7xp0vhICCFEM0ngTQghOjuZ2wshRI9k+MIZbxYJvCVF/VLTxvcpOTkLIYRoJgm8CSFEFyYTfyGE6Ab28VFuBsI13qwO6WqaDLreOPIWrfogZ18hhBDNJYE3IYTo7GQ5ixBC9EgqEF5qanFKxltSRANvZnzKm9RbFUII0TISeBNCiE6uPu4Wf8VdJv5CCNHdNPxoj2W8SeAtKaJ13OLPuXL6FUII0VISeBNCCCGEEKIzCoYz3qwpEnhLhlhzBSGEEKINJPAmhBCdnkz8hRCiRwqGM95sLgm8JUM08JaQZR79p5yahRBCNJME3oQQoquQ5S1CCNGjqEjGmz1VAm/JEC31oDWx1FSaGwkhhGguCbwJIURnJ80VhBCiR9AaXmEJhQNvNpcjCaMRuiX8Vampum5yZhZCCNFcEngTQohOTuJuQgjRze3jg16LBN7saRJ4SwYV/b0oM+5GST8XQgjRMhJ4E0KILkMm+0II0aMYkcCb1HhLCj1W4y3uxsgPSi6KCSGEaCYJvAkhRCcndWSEEKJniA/wBINgMcOBN4dkvCVFrMYbjWu8CSGEEM0lgTchhOjkZKmpEEL0PB4P2LRw4C0lXQJvyaBFa7yZjaNtcmoWQgjRXBJ4E0KIrkIuswshRI/hcSusmgGAXZorJIUW/aYUd/qVM7EQQoiWksCbEEJ0cnJVXQghurkmCoZ5akLomoHFAppDAm/JoEUjb/EXvqI13uTsLIQQopkk8CaEEF2ETPGFEKJ70+LiO97aELpmYrEANgm8JUOsqWkTNd7knCyEEKK5JPAmhBBdhCxvEUKIbqqJKI63zsCim+hWDXTbwR+TiNV4SzwBRyNvEnoTQgjRPBJ4E0KITi621CWOlHsTQojuJ/6j3VcXznjTbDro1qSNqSeLdTVVUuRNCCFE60ngTQghugqZ7AshRI/hrQmiawa6VQJvyaJbwpG3hItdci4WQgjRQhJ4E0IIIYQQopPxuQNoGuiS8ZY8seWkZhP3HdSRCCGE6MIk8CaEEJ2dTO6FEKLHCbj9gATekkmLBt7i4m5Kaj0IIYRoIQm8CSFEl9HUFXeJygkhRHcUrIsLvGmWJI+mZ4ouNW16famcf4UQQjSPBN6EEKKz02VyL4QQPUN9gCfgCQfeNKtNLrIkiaZHu5rW/14k4U0IIURLSeBNCCGEEEKIJGoqryoUCbxhlWWmyRJrKi7dFYQQQrSBBN6EEKKrkLm+EEL0GIY3kvFmtyV5JD2XFsk4Twi7mXIyFkII0TISeBNCiE5OVhgJIUTPo5nhwJtpkcBbskQDb02uL5VzsxBCiGbqUYG3xx9/nKFDh5KSksLMmTNZsmRJsockhBBCCCFEI1YzAICBBN6SRY9c+dLia7zF/iWRNyGEEM3TYwJvr7/+OjfffDN33nknK1asYPLkycydO5fS0tJkD00IIZpJlrcIIURPYYkE3kxdAm/JolkaLzUVQgghWqrHBN4eeughLr/8ci655BIOOeQQnnrqKVwuF88991yyhyaEEPulyVpTIYTocXQtBICpSXOFZNEska9K8XXdJAonhBCihXrEmTwQCLB8+XJuu+222G26rnP88cfz/fffN9re7/fj9/tjP1dXVwNQU1PT8YMVQogG6rxuPEYQfygQ+xyqravFYwRRmi6fTUII0cV5QgFCRpA6d13sM90XqMVmBDH8QfmcTxJ35PzrU/7Y7yB6m2EE5PcihBA9XPQ8oJqqBRpHUwfaohvYs2cPAwYM4LvvvmPWrFmx23/3u9/x5Zdfsnjx4oTt77rrLu6+++6DPUwhhBBCCCGEEEII0YUUFhYycODAfd7fIzLeWuq2227j5ptvjv1smiYVFRX06tWr1Uu+ampqGDRoEIWFhWRkZLTXUIUQ+yHvOyGSQ957QiSHvPeESA557wmRHMl+7ymlqK2tJS8vb7/b9YjAW+/evbFYLJSUlCTcXlJSQr9+/Rpt73A4cDgcCbdlZWW1y1gyMjLkw1iIg0zed0Ikh7z3hEgOee8JkRzy3hMiOZL53svMzDzgNj2iuYLdbuewww5jwYIFsdtM02TBggUJS0+FEEIIIYQQQgghhGgvPSLjDeDmm2/m4osvZtq0acyYMYNHHnkEt9vNJZdckuyhCSGEEEIIIYQQQohuqMcE3s477zzKysq44447KC4u5tBDD2X+/Pn07dv3oBzf4XBw5513NlrCKoToOPK+EyI55L0nRHLIe0+I5JD3nhDJ0VXeez2iq6kQQgghhBBCCCGEEAdbj6jxJoQQQgghhBBCCCHEwSaBNyGEEEIIIYQQQgghOoAE3oQQQgghhBBCCCGE6AASeBNCCCGEEEIIIYQQogNI4K0VvvrqK0477TTy8vLQNI133333gI95+eWXmTx5Mi6Xi/79+3PppZdSXl4eu/+FF15A07SE/6WkpHTgsxCi62nNe+/xxx9n3LhxOJ1OxowZw0svvdRomzfffJOxY8eSkpLCxIkT+fjjjztg9EJ0TR3xvpNznhAHdu+99zJ9+nTS09PJzc3ljDPOYOPGjQd83IHOaUop7rjjDvr374/T6eT444+noKCgo56GEF1KR73vfvWrXzU675100kkd9TSE6HJa895bt24dZ599NkOHDkXTNB555JEmt3v88ccZOnQoKSkpzJw5kyVLlnTAM9g/Cby1gtvtZvLkyTz++OPN2v7bb7/loosu4rLLLmPdunW8+eabLFmyhMsvvzxhu4yMDIqKimL/27FjR0cMX4guq6XvvSeffJLbbruNu+66i3Xr1nH33Xdz7bXX8sEHH8S2+e6777jgggu47LLL+PHHHznjjDM444wzWLt2bUc9DSG6lI5434Gc84Q4kC+//JJrr72WH374gc8//5xgMMiJJ56I2+3e52Oac067//77efTRR3nqqadYvHgxqampzJ07F5/PdzCelhCdWke97wBOOumkhPPeq6++2tFPR4guozXvPY/Hw/Dhw7nvvvvo169fk9u8/vrr3Hzzzdx5552sWLGCyZMnM3fuXEpLSzvqqTRNiTYB1DvvvLPfbR544AE1fPjwhNseffRRNWDAgNjPzz//vMrMzOyAEQrRPTXnvTdr1iz129/+NuG2m2++Wc2ePTv287nnnqtOPfXUhG1mzpyprrzyynYbqxDdRXu97+ScJ0TLlZaWKkB9+eWX+9zmQOc00zRVv3791AMPPBC7v6qqSjkcDvXqq692zMCF6MLa432nlFIXX3yxOv300ztqmEJ0O81578UbMmSIevjhhxvdPmPGDHXttdfGfjYMQ+Xl5al77723vYbaLJLxdhDMmjWLwsJCPv74Y5RSlJSU8J///IdTTjklYbu6ujqGDBnCoEGDOP3001m3bl2SRixE9+D3+xstX3M6nSxZsoRgMAjA999/z/HHH5+wzdy5c/n+++8P2jiF6E6a874DOecJ0VLV1dUA5OTk7HObA53Ttm3bRnFxccI2mZmZzJw5U857QjShPd53UYsWLSI3N5cxY8Zw9dVXJ5QdEkIkas5770ACgQDLly9PeH/qus7xxx9/0M95Eng7CGbPns3LL7/Meeedh91up1+/fmRmZiYs2xkzZgzPPfcc7733Hv/3f/+HaZocccQR7Nq1K4kjF6Jrmzt3Ls888wzLly9HKcWyZct45plnCAaD7N27F4Di4mL69u2b8Li+fftSXFycjCEL0eU1530n5zwhWsY0TW688UZmz57NhAkT9rndgc5p0f/KeU+IA2uv9x2El5m+9NJLLFiwgL/97W98+eWXnHzyyRiG0WHjF6Krau5770D27t2LYRid4pxnPahH66HWr1/PDTfcwB133MHcuXMpKirilltu4aqrruLZZ58Fwllxs2bNij3miCOOYNy4cfzzn//kT3/6U7KGLkSX9sc//pHi4mIOP/xwlFL07duXiy++mPvvvx9dl+sOQnSE5rzv5JwnRMtce+21rF27lm+++SbZQxGix2jP9935558f+/fEiROZNGkSI0aMYNGiRRx33HFt3r8Q3Ul3POfJN8+D4N5772X27NnccsstTJo0iblz5/LEE0/w3HPPUVRU1ORjbDYbU6ZMYfPmzQd5tEJ0H06nk+eeew6Px8P27dvZuXMnQ4cOJT09nT59+gDQr18/SkpKEh5XUlKyzwKdQoj9a877riE55wmxb9dddx0ffvghCxcuZODAgfvd9kDntOh/5bwnxP615/uuKcOHD6d3795y3hOigZa89w6kd+/eWCyWTnHOk8DbQeDxeBpl11gsFiDc0r0phmGwZs0a+vfv3+HjE6K7s9lsDBw4EIvFwmuvvcZPf/rThMybBQsWJGz/+eefJ2TjCCFabn/vu4bknCdEY0oprrvuOt555x3++9//MmzYsAM+5kDntGHDhtGvX7+EbWpqali8eLGc94SgY953Tdm1axfl5eVy3hMiojXvvQOx2+0cdthhCe9P0zRZsGDBQT/nyVLTVqirq0u4OrFt2zZWrlxJTk4OgwcP5rbbbmP37t289NJLAJx22mlcfvnlPPnkk7GlpjfeeCMzZswgLy8PgHvuuYfDDz+ckSNHUlVVxQMPPMCOHTv49a9/nZTnKERn1NL33qZNm1iyZAkzZ86ksrKShx56iLVr1/Liiy/G9nHDDTcwZ84cHnzwQU499VRee+01li1bxtNPP33Qn58QnVFHvO/knCfEgV177bW88sorvPfee6Snp8fq0WRmZuJ0OgG46KKLGDBgAPfeey9w4HOapmnceOON/PnPf2bUqFEMGzaMP/7xj+Tl5XHGGWck5XkK0Zl0xPuurq6Ou+++m7PPPpt+/fqxZcsWfve73zFy5Ejmzp2bnCcqRCfTmvdeIBBg/fr1sX/v3r2blStXkpaWxsiRIwG4+eabufjii5k2bRozZszgkUcewe12c8kllxzcJ3hQe6h2EwsXLlRAo/9dfPHFSqlwu+g5c+YkPObRRx9VhxxyiHI6nap///5q3rx5ateuXbH7b7zxRjV48GBlt9tV37591SmnnKJWrFhxEJ+VEJ1fS99769evV4ceeqhyOp0qIyNDnX766WrDhg2N9vvGG2+o0aNHK7vdrsaPH68++uijg/SMhOj8OuJ9J+c8IQ6sqfcdoJ5//vnYNnPmzIm9F6MOdE4zTVP98Y9/VH379lUOh0Mdd9xxauPGjQfhGQnR+XXE+87j8agTTzxR9enTR9lsNjVkyBB1+eWXq+Li4oP0rITo/Frz3tu2bVuTj2kYi3nsscdi884ZM2aoH3744eA8qTiaUvtY6yiEEEIIIYQQQgghhGg1qfEmhBBCCCGEEEIIIUQHkMCbEEIIIYQQQgghhBAdQAJvQgghhBBCCCGEEEJ0AAm8CSGEEEIIIYQQQgjRASTwJoQQQgghhBBCCCFEB5DAmxBCCCGEEEIIIYQQHUACb0IIIYQQQgghhBBCdAAJvAkhhBBCCCGEEEII0QEk8CaEEEKITm/o0KE88sgjyR5Gu+mqz+eFF14gKysr2cOIae3rWF5eTm5uLtu3b2/T8Y855hhuvPHGNu2jo82fP59DDz0U0zSTPRQhhBCiR5LAmxBCCCGSprCwkEsvvZS8vDzsdjtDhgzhhhtuoLy8PNlDS5r58+ejaRrFxcUJt/fv35+hQ4cm3LZ9+3Y0TWPBggUHcYQHX3sH/P7yl79w+umnN3o9W+rtt9/mT3/6U/sMqoOcdNJJ2Gw2Xn755WQPRQghhOiRJPAmhBBCiKTYunUr06ZNo6CggFdffZXNmzfz1FNPsWDBAmbNmkVFRUXSxmYYRtIyhI488kisViuLFi2K3Zafn4/X66WysjIhS2vhwoU4HA5mz5598AfaRXk8Hp599lkuu+yyNu8rJyeH9PT0Vj1WKUUoFGrzGJrjV7/6FY8++uhBOZYQQgghEkngTQghhBBJce2112K32/nss8+YM2cOgwcP5uSTT+aLL75g9+7d/OEPf0jYvra2lgsuuIDU1FQGDBjA448/HrtPKcVdd93F4MGDcTgc5OXlcf3118fu9/v9/Pa3v2XAgAGkpqYyc+bMhMBWNKPq/fff55BDDsHhcPDMM8+QkpJCVVVVwjhuuOEGfvKTn8R+/uabbzjqqKNwOp0MGjSI66+/HrfbHbu/tLSU0047DafTybBhww6YeZSWlsb06dMTxrdo0SKOPPJIZs+e3ej2ww8/nJSUFObPn8+RRx5JVlYWvXr14qc//SlbtmyJbXvEEUdw6623JhyrrKwMm83GV1991azXqSnvvfceU6dOJSUlheHDh3P33XcnBJQ0TeOZZ57hzDPPxOVyMWrUKN5///2Efbz//vuMGjWKlJQUjj32WF588UU0TaOqqopFixZxySWXUF1djaZpaJrGXXfdFXusx+Ph0ksvJT09ncGDB/P000/vd7wff/wxDoeDww8/POF11DSNTz/9lClTpuB0OvnJT35CaWkpn3zyCePGjSMjI4MLL7wQj8cTe1zDpaZ+v59bb72VQYMG4XA4GDlyJM8++2zCMT755BMOO+wwHA4H33zzDX6/n+uvv57c3FxSUlI48sgjWbp0aaOxLViwgGnTpuFyuTjiiCPYuHFjbJtVq1Zx7LHHkp6eTkZGBocddhjLli2L3X/aaaexbNmyhL8HIYQQQhwcEngTQgghxEFXUVHBp59+yjXXXIPT6Uy4r1+/fsybN4/XX38dpVTs9gceeIDJkyfz448/8vvf/54bbriBzz//HIC33nqLhx9+mH/+858UFBTw7rvvMnHixNhjr7vuOr7//ntee+01Vq9ezTnnnMNJJ51EQUFBbBuPx8Pf/vY3nnnmGdatW8e8efPIysrirbfeim1jGAavv/468+bNA2DLli2cdNJJnH322axevZrXX3+db775huuuuy72mF/96lcUFhaycOFC/vOf//DEE09QWlq639fn2GOPZeHChbGfFy5cyDHHHMOcOXMSbl+0aBHHHnssAG63m5tvvplly5axYMECdF3nzDPPjGXuzZs3j9deey3hNX399dfJy8vjqKOOavbrFO/rr7/moosu4oYbbmD9+vX885//5IUXXuAvf/lLwnZ333035557LqtXr+aUU05h3rx5sYzGbdu28fOf/5wzzjiDVatWceWVVyYEXY844ggeeeQRMjIyKCoqoqioiN/+9rex+x988EGmTZvGjz/+yDXXXMPVV1+dEJRqasyHHXZYk/fddddd/OMf/+C7776jsLCQc889l0ceeYRXXnmFjz76iM8++4zHHntsn/u+6KKLePXVV3n00UfJz8/nn//8J2lpaQnb/P73v+e+++4jPz+fSZMm8bvf/Y633nqLF198kRUrVjBy5Ejmzp3bKOPzD3/4Aw8++CDLli3DarVy6aWXxu6bN28eAwcOZOnSpSxfvpzf//732Gy22P2DBw+mb9++fP311/scuxBCCCE6iBJCCCGEOMh++OEHBah33nmnyfsfeughBaiSkhKllFJDhgxRJ510UsI25513njr55JOVUko9+OCDavTo0SoQCDTa144dO5TFYlG7d+9OuP24445Tt912m1JKqeeff14BauXKlQnb3HDDDeonP/lJ7OdPP/1UORwOVVlZqZRS6rLLLlNXXHFFwmO+/vprpeu68nq9auPGjQpQS5Ysid2fn5+vAPXwww/v49VR6vPPP1eA2rNnj1JKqdzcXLVkyRL13XffqSFDhiillNqyZYsC1JdfftnkPsrKyhSg1qxZo5RSqrS0VFmtVvXVV1/Ftpk1a5a69dZbW/Q6ZWZmJtz317/+NWH7f//736p///6xnwF1++23x36uq6tTgPrkk0+UUkrdeuutasKECQn7+MMf/qCA2Ovc8LhRQ4YMUb/4xS9iP5umqXJzc9WTTz7Z5GuilFKnn366uvTSSxNuW7hwoQLUF198Ebvt3nvvVYDasmVL7LYrr7xSzZ07N/bznDlz1A033KCUUrHf9eeff97kcaPHePfddxNeC5vNpl5++eXYbYFAQOXl5an7779/n2P76KOPFKC8Xq9SSqn09HT1wgsv7PM5K6XUlClT1F133bXfbYQQQgjR/iTjTQghhBBJo+Kyrw5k1qxZjX7Oz88H4JxzzsHr9TJ8+HAuv/xy3nnnndhyxzVr1mAYBqNHjyYtLS32vy+//DJh6Z3dbmfSpEkJx5g3bx6LFi1iz549ALz88suceuqpsUL/q1at4oUXXkjY79y5czFNk23btpGfn4/Vak3IsBo7duwBGwUcccQR2O12Fi1axPr16/F6vUydOpVp06ZRVlbGtm3bWLRoEU6nM7ZksqCggAsuuIDhw4eTkZERaxywc+dOAPr06cOJJ54YW+q6bds2vv/++1j2XnNfp3irVq3innvuSdj+8ssvp6ioKGFJZvzrmpqaSkZGRizrb+PGjUyfPj1hvzNmzNjv6xMvft+aptGvX7/9ZhR6vV5SUlIOuK++ffvicrkYPnx4wm372vfKlSuxWCzMmTNnv+OdNm1a7N9btmwhGAwm1Oiz2WzMmDEj9rfd1Nj69+8PEBvLzTffzK9//WuOP/547rvvviZ/X06nM+F3IoQQQoiDw5rsAQghhBCi5xk5ciSappGfn8+ZZ57Z6P78/Hyys7Pp06dPs/Y3aNAgNm7cyBdffMHnn3/ONddcwwMPPMCXX35JXV0dFouF5cuXY7FYEh4XvwzQ6XSiaVrC/dOnT2fEiBG89tprXH311bzzzju88MILsfvr6uq48sorE+rJRQ0ePJhNmzY1a/wNuVwuZsyYwcKFC6moqODII4/EYrFgsVg44ogjWLhwIQsXLmT27NnY7XYgXMdryJAh/Otf/yIvLw/TNJkwYQKBQCC233nz5nH99dfz2GOP8corrzBx4sTYktzmvk7x6urquPvuuznrrLMa3Rcf3Ipf9gjhAFl7Na9o6b579+5NZWXlAfelaVqL9t1wyfS+pKamNmu7A40NiI3lrrvu4sILL+Sjjz7ik08+4c477+S1115LeG9VVFQ0+/0khBBCiPYjgTchhBBCHHS9evXihBNO4IknnuCmm25KCFoUFxfz8ssvc9FFFyUEwn744YeEffzwww+MGzcu9rPT6eS0007jtNNO49prr2Xs2LGsWbOGKVOmYBgGpaWlsVpmLTFv3jxefvllBg4ciK7rnHrqqbH7pk6dyvr16xk5cmSTjx07diyhUIjly5fHsro2btzYqGFDU4499lhee+01KisrOeaYY2K3H3300SxatIgvv/ySq666CoDy8nI2btzIv/71r9hz/Oabbxrt8/TTT+eKK65g/vz5vPLKK1x00UWx+1rzOk2dOpWNGzfu8/k3x5gxY/j4448TbotvLgDhbETDMFp9jHhTpkzh//7v/9plX/EmTpyIaZp8+eWXHH/88c16zIgRI7Db7Xz77bcMGTIEgGAwyNKlSxOaNjTH6NGjGT16NDfddBMXXHABzz//fCzw5vP52LJlC1OmTGnRPoUQQgjRdrLUVAghhBBJ8Y9//AO/38/cuXP56quvKCwsZP78+ZxwwgkMGDCgUYH+b7/9lvvvv59Nmzbx+OOP8+abb3LDDTcA4a6kzz77LGvXrmXr1q383//9H06nkyFDhjB69GjmzZvHRRddxNtvv822bdtYsmQJ9957Lx999NEBxzlv3jxWrFjBX/7yF37+85/jcDhi991666189913XHfddaxcuZKCggLee++9WHOFMWPGcNJJJ3HllVeyePFili9fzq9//etmZUcde+yxFBQU8OmnnyYsX5wzZw7vvvsuhYWFscYK2dnZ9OrVi6effprNmzfz3//+l5tvvrnRPlNTUznjjDP44x//SH5+PhdccEHsvta8TnfccQcvvfQSd999N+vWrSM/P5/XXnuN22+//YDPL+rKK69kw4YN3HrrrWzatIk33ngjllUYDbwOHTqUuro6FixYwN69e9u0ZHLu3LmsW7dun1lvrTV06FAuvvhiLr30Ut59993YcuA33nhjn49JTU3l6quv5pZbbmH+/PmsX7+eyy+/HI/Hw2WXXdas43q9Xq677joWLVrEjh07+Pbbb1m6dGlCUPqHH37A4XA0Wq4thBBCiI4ngTchhBBCJMWoUaNYtmwZw4cP59xzz2XEiBFcccUVHHvssXz//ffk5OQkbP+b3/yGZcuWMWXKFP785z/z0EMPMXfuXACysrL417/+xezZs5k0aRJffPEFH3zwAb169QLg+eef56KLLuI3v/kNY8aM4YwzzmDp0qUMHjz4gOMcOXIkM2bMYPXq1bF6aFGTJk3iyy+/ZNOmTRx11FFMmTKFO+64g7y8vNg2zz//PHl5ecyZM4ezzjqLK664gtzc3AMed9asWTgcDpRSCTXiZs6cSTAYJC0tLZZFp+s6r732GsuXL2fChAncdNNNPPDAA03ud968eaxatYqjjjqq0fNv6es0d+5cPvzwQz777DOmT5/O4YcfzsMPPxzL3mqOYcOG8Z///Ie3336bSZMm8eSTT8a6mkaDnEcccQRXXXUV5513Hn369OH+++9v9v4bmjhxIlOnTt1vQKy1nnzySX7+859zzTXXMHbsWC6//HLcbvd+H3Pfffdx9tln88tf/pKpU6eyefNmPv30U7Kzs5t1TIvFQnl5ORdddBGjR4/m3HPP5eSTT+buu++ObfPqq68yb948XC5Xm56fEEIIIVpOUy2paiyEEEIIIUQH+8tf/sJTTz1FYWFhh+z/o48+4pZbbmHt2rXoeve+Dr13717GjBnDsmXLGDZsWLKHI4QQQvQ4UuNNCCGEEEIk1RNPPMH06dPp1asX3377LQ888EBsuW5HOPXUUykoKGD37t0MGjSow47TGWzfvp0nnnhCgm5CCCFEkkjGmxBCCCGESKqbbrqJ119/nYqKCgYPHswvf/lLbrvtNqxWuUYshBBCiK5NAm9CCCGEEEIIIYQQQnSA7l3UQgghhBBCCCGEEEKIJJHAmxBCCCGEEEIIIYQQHUACb0IIIYQQQgghhBBCdAAJvAkhhBBCCCGEEEII0QEk8CaEEEIIIYQQQgghRAeQwJsQQgghhBBCCCGEEB1AAm9CCCGEEEIIIYQQQnQACbwJIYQQQgghhBBCCNEB/j9J/IeXU4g18QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "f, (ax1) = plt.subplots(1, 1, figsize=(15, 5)) \n", "\n", @@ -648,7 +454,7 @@ "metadata": {}, "source": [ "The non-optimal cylindrical, conical, and CubeViz subset spectral extractions are quite similar. \n", - "The conical extraction captures imperceptibly more flux at long wavelengths.\n", + "The conical extraction captures more flux at long wavelengths.\n", "Red-shifted Broad H-beta and narrow [O III] lines are visible in the quasar spectra. " ] }, @@ -656,7 +462,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## WebbPSF Model PSF for Optimal Extraction\n", + "## 10. WebbPSF Model PSF for Optimal Extraction\n", "Generate PSF model cube using WebbPSF for NIRSpec IFU, or read in precomputed PSF model cube." ] }, @@ -671,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -690,23 +496,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: /Users/pogle/.astropy/cache/download/url/4931b72fec159f8a439e8c80c181370f/contents\n", - "No. Name Ver Type Cards Dimensions Format\n", - " 0 OVERSAMP 1 PrimaryHDU 105786 (120, 120, 3915) float64 \n", - " 1 DET_SAMP 1 ImageHDU 105788 (30, 30, 3915) float64 \n", - " 2 OVERDIST 1 ImageHDU 105831 (120, 120, 3915) float64 \n", - " 3 DET_DIST 1 ImageHDU 105832 (30, 30, 3915) float64 \n", - "(3814, 39, 43) (3915, 30, 30)\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", "psf_filename = BoxPath+\"Webbpsf_ifucube.fits\"\n", @@ -741,7 +533,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Align Model PSF Cube with Science Data\n", + "## 11. Align Model PSF Cube with Science Data\n", "Flip, smooth, and shift the model PSF cube to align with the simulated data. Trim the simulated data. \n", "\n", "Important Note 1: this PSF will likely be rotated with respect to your dataset, depending on telescope roll angle. You can either rotate it to match your data or reprocess your data using the ifualign keyword to align the WCS with the instrumental coordinate frame.\n", @@ -751,27 +543,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1376.820805778524\n" - ] - }, - { - "data": { - "image/png": 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7POvtVzweV69evXTbbbft8O/bf/Csq+NcPB7Xcccdt9MrBA444ADT+Lb36aefSpK6du0qaWtBMGjQIHXr1k233Xab2rVrp1AopBdeeEG33367r7MoN998s6677jqdd955uummm9S8eXMFAgGNGzeu1uEzMzM1b948zZ49W88//7xeeukl/fOf/9Sxxx6rV155ZafLNRlVbXnooYdUUFBQ4++13WVtwIABWrp0qZ5++mm98sor+vvf/67bb79d9957ry644AJJW/vNHa3zbYu9bdFv7r0obJCSqoqhVatW1cn4QqGQRowYoREjRigej+vSSy/V3/72N1133XWJjqQ2Xbp00csvv6zvv/9+p2dtunTpIuecOnXqlHRH16ZNG1166aW69NJLtXbtWh166KH6wx/+kChsmjVrpo0bN9YY7uuvv96tMzsAUF+2P6Z36dJF8Xhc//nPf3TIIYf4Hk+XLl300UcfadCgQXXyhPtWrVopNzc3UVjsarqbN2/e7S/adiQWi2n69Olq0qSJjjzySEnSs88+q4qKCj3zzDPVzh7s6LKsnS2Hxx9/XMccc4zuv//+au9v3LhRLVu2rLVdgUBAgwYN0qBBg3Tbbbfp5ptv1m9/+1vNnj3b93Lws56rLhXMz89Pevk2b95cY8aM0ZgxY7R582YNGDBAEyZMSBQ2zZo1S1x+uK2vv/46qemh4fAbG+zV3njjDUUikRrvv/DCC5KkAw88cLensWHDhmr/DgQCiVPUltsbjxw5Us65Gg8Nlf73bc2pp56qYDCoiRMn1vgGxzlXoy3bisViNa6bzs/PV2FhYbV2dunSRQsWLKh2G9LnnnvO1+2kAaA+zZ49e4ffXm9/TD/55JMVCAR044031jh7sKtvv8844wx99913O/wN5pYtW1RaWmpqbyAQ0Mknn6xnn322xm37t23LGWecofnz5+vll1+ukdm4cWPSv02MxWK6/PLL9fnnn+vyyy9PXHZVdWZg22VRXFysqVOn1hhHVlbWDr/sCgaDNZbljBkzfP0eZEe3MK4qTCz9pp/1PHToUOXm5urmm2/e4eeBdevW7XIa2/er2dnZ6tq1a41+84svvqg2ro8++iipu7yhYXHGBnu1W265RQsXLtSpp56aKDbef/99Pfjgg2revLnGjRu329O44IIL9P333+vYY49V27Zt9fXXX+vuu+/WIYccooMOOsj3eI455hidc845uuuuu7R48WINGzZM8Xhcb7zxho455hiNHTtWXbp00e9//3uNHz9ey5cv18knn6ycnBwtW7ZMM2fO1EUXXaSrrrpqh+PftGmT2rZtq9NOO00HH3ywsrOz9eqrr+rdd9/VX/7yl2rz8/jjj2vYsGE644wztHTpUj388MPVfiALAA3hsssuU1lZmU455RR169ZNlZWVevvtt/XPf/5THTt21JgxYyRtveTqt7/9rW666SYdddRROvXUUxUOh/Xuu++qsLBQkyZN2uH4zznnHP3rX//SxRdfrNmzZ+uII45QLBbTF198oX/96196+eWXfV/+XOXmm2/WK6+8oqOPPjpxC+lVq1ZpxowZevPNN9W0aVNdffXVeuaZZ3TCCSdo9OjR6tOnj0pLS/XJJ5/o8ccf1/Lly2s9C1JcXKyHH35YklRWVqYlS5boySef1NKlS3XmmWfqpptuSmSHDBmSuNLgF7/4hTZv3qz77rtP+fn5Na5k6NOnj6ZMmaLf//736tq1q/Lz83XsscfqhBNO0I033qgxY8bo8MMP1yeffKJHHnnE15n9G2+8UfPmzdPxxx+vDh06aO3atbrnnnvUtm3bxFklP/ys59zcXE2ZMkXnnHOODj30UJ155plq1aqVvvnmGz3//PM64ogj9Ne//nWn0+jevbsGDhyoPn36qHnz5nrvvfcSj02oct555+m2227T0KFDdf7552vt2rW699571aNHD5WUlPieH+wF9vh92AD3v9s97+j2mdt66623XFFRkevZs6fLy8tz6enprn379m706NFu6dKl1bK13S5zZx5//HE3ZMgQl5+f70KhkGvfvr37xS9+4VatWpXI+Lnds3PORaNR96c//cl169bNhUIh16pVKzd8+HC3cOHCarknnnjCHXnkkS4rK8tlZWW5bt26uaKiIrdo0aKdtrOiosJdffXV7uCDD3Y5OTkuKyvLHXzwwe6ee+6pkf3LX/7i9ttvPxcOh90RRxzh3nvvvZ3e7nnGjBnVht3Zuqm6Teeubu8JALvy4osvuvPOO89169bNZWdnu1Ao5Lp27eouu+wyt2bNmhr5Bx54wP3f//2fC4fDrlmzZu7oo492s2bNSvx9R7fpraysdLfccovr0aNHYrg+ffq4iRMnuuLi4kRO0g5vn7+jW+Z//fXX7txzz3WtWrVy4XDYde7c2RUVFbmKiopEZtOmTW78+PGua9euLhQKuZYtW7rDDz/c/fnPf65xO+vtbf+4guzsbLf//vu7n//85+6VV17Z4TDPPPOM6927t8vIyHAdO3Z0t9xyi3vggQecJLds2bJEbvXq1e744493OTk5TlJieZWXl7tf/epXrk2bNi4zM9MdccQRbv78+Tu99fG2XnvtNXfSSSe5wsJCFwqFXGFhoTvrrLOq3e7az+2eq9S2np3b2mcNHTrU5eXluYyMDNelSxc3evRo99577+2yrb///e/dYYcd5po2beoyMzNdt27d3B/+8Ica6+Thhx92nTt3dqFQyB1yyCHu5Zdf3untnv/0pz/VaJulP0X98ZzjF00AAAAAUhu/sQEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkvL3uAZ3xeFwrV65UTk6OPM9r6OYAQKPinNOmTZtUWFioQIDvvqrQNwFAw7D0S3tdYbNy5Uq1a9euoZsBAI3aihUr1LZt24Zuxl6DvgkAGpaffmmvK2xycnIkSUelnaw0L93XMMG2BaZpxDMzTHmXaV9MsVDQPEx9cmm2b17jIfs3tZXZtnmOZNmmUdHU9i1pLGyKK1huy2dstD/bNr00bsp7cds0vJgpLi9av8/njWXYtyNn3HXiIdt24er7y3bjIg1W2tdBIGIbJq3c/3YXjZbrnbl/TByLsVXV8hjQ5DTffZOL2nZIF4ma8l66vW/ygrZ90kuzTcPFbPPsBY07vLE9e4LXJNOUd9lNbBMwbhfxpsbxS/rq1CxTPr3Eth01/49tu8iZv9yUd1vKTHkl8Wx6F7HNQ6BLe1N+yVW29RYvDpny6S1tyygWse9rHe+zfcYJvP+lr1zURfRG9Clf/dJed4SoOsWf5qX7L2wCtk+w8aAt74L+2rEtLy3FCxtjXpLi6bZ5thZPwbDxE6m1sLF+IA3ZD4xplfVc2BhXm+fVb2Hjpdd/YRNLNxY29X11lXGRpiXRwQaME0mL2rY7SVxutZ3qfZO/DxTOs30gdcZl7nlJFDaebQezTsMZv12xtkeBve5jizzjZxBn/AyiuLFvDdq+vJWkQIZtmGCF7UCalm7bLtICtg/t1n3NfKBOYhoB43oONDGut0rbMgo2sfUDLonCJi3NWNj4/JxfxU+/xAXUAAAAAFIehQ0AAACAlEdhAwAAACDlUdgAAAAASHkUNgAAAABSHoUNAAAAgJRHYQMAAAAg5VHYAAAAAEh5e9+Trv7LRSP+nxBufcBd0PoEe/vDNiub2h6cFDc+ZDBgfGK89UnlydjbHnxofdBj1Pow6EpbXpK8mPEBfL53gv8yLiPzdmR7xpqiGfaHPFofuCnrIrI+xNQ4z0HjvmYdvyQFKo3rzfBg2EASD/NsVAIB30/CtT7k1HyUjtk3Hmd96G+W7Yn0njM+BLDCdiD1ItYHMUpKt33U8UK2/ts6flkffmt84Hc02/5QcVdQYZvGfrb1nPmaKS5XXm4bIGA8sEcitrwkBYyfHT9fbMp3HWX80BK05QOZtgeArjq7hykvSUsusm1Hbfb7P1+5aKRcmjnDV5YzNgAAAABSHoUNAAAAgJRHYQMAAAAg5VHYAAAAAEh5FDYAAAAAUh6FDQAAAICUR2EDAAAAIOVR2AAAAABIeRQ2AAAAAFIehQ0AAACAlEdhAwAAACDlpTV0A3Ym0CRTAS9UPyOPOVPcpdnrv3jIM+UjTWz5uLFNgahtnoOVpvhWzjYNL24bvWcbveLptrwzruaocZ1JUsC4XK3z7MVs+Xi6bR6ccZbjSRxhXNA4gLFN1u0uEDWO37gOPON+I0mBiG0mApX+GxWIGmegkfGCAXmev4NFvNy4scVty94ZRy9JXppxp4wYD1r5LU3xxRfmm/LxVvbOyUVsB/fcz2ydx36vrDflVWGbBy9iOwiFVtqPKe0eaWbKF3exLaO0xYtNeRdPYuO2SDd+QJC5q1GgSRNT/qu/tTXlB3f60pSf92gPUz6tzL4duXJbB+63f7X0w5yxAQAAAJDyKGwAAAAApDxTYTNp0iT17dtXOTk5ys/P18knn6xFixZVywwcOFCe51V7XXzxxXXaaAAAqtA3AQAkY2Ezd+5cFRUVacGCBZo1a5YikYiGDBmi0tLSarkLL7xQq1atSrxuvfXWOm00AABV6JsAAJLx5gEvvfRStX9PmzZN+fn5WrhwoQYMGJB4v0mTJiooKKibFgIAsAv0TQAAaTd/Y1NcXCxJat68ebX3H3nkEbVs2VI9e/bU+PHjVVZWtjuTAQDAN/omAGickr7dczwe17hx43TEEUeoZ8+eifd/9rOfqUOHDiosLNTHH3+sa665RosWLdKTTz65w/FUVFSooqIi8e+SkpJkmwQAaOTomwCg8Uq6sCkqKtKnn36qN998s9r7F110UeL/e/XqpTZt2mjQoEFaunSpunTpUmM8kyZN0sSJE5NtBgAACfRNANB4JXUp2tixY/Xcc89p9uzZatt21w8U6tevnyRpyZIlO/z7+PHjVVxcnHitWLEimSYBABo5+iYAaNxMZ2ycc7rssss0c+ZMzZkzR506dap1mA8//FCS1KZNmx3+PRwOKxwOW5oBAEACfRMAQDIWNkVFRZo+fbqefvpp5eTkaPXq1ZKkvLw8ZWZmaunSpZo+fbp+8pOfqEWLFvr44491xRVXaMCAAerdu3e9zAAAoHGjbwIASMbCZsqUKZK2PuhsW1OnTtXo0aMVCoX06quv6o477lBpaanatWunkSNH6ne/+12dNRgAgG3RNwEApCQuRduVdu3aae7cubvVoCpeVpa8QMhX1jXJsI086NnaEo3bxi8pENn1strBVEzpmPEKiUiW7edUgai1/VIgYpsHFzRPwsY4C/F02wCRbNv8SpIXtQ0TrLS1yXnGNtlnod7H76yzYNw9rcvUyjOOPpBEewIx4zCWeP0unnqxJ/smBYJbXz54GcYDdcC48ceTWFnGabhI1Db+TH/9dpV464raQ9to3arYlJekzeW29RALN7VNwHrcTbfdt8mFbcs00izTlJek77unm/IZG4x9U5sWpry3wrbd/WbhbFP+D8tOMOUlKXiG7fbwbku5Kd/q0Sam/NwufUx5GT9zFczbYBtAUutHVpny34zt5SsXq/Df+N16jg0AAAAA7A0obAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKS+toRuwU9lNpGDYVzTeJGQatfOMbfGsA0iBiDPmreO35ePptnwkyz7PMg7ixeyTMI0/bsu7oC0fzbKtY0ny4raFFN9iXKjGryqs+4J1mSYjELXlvQpj3jj+QNS4L9dzXpICFbadJ1Duf6YDsXreMVOd5yXVJ/jx3eV9TPnNnYwbs6Rgqe1AV/imbXvInrvIlD/otzmmvGuSYcpLUtOw7cDlVaw1T8Mi3sTfZ5sqkWa2eS5tY+zwJW3uZFvPkSzbdpT9bZYpn/mDLb8ummvKl1Ta1oEkXbNgjin/wMojTfmN328y5dueu8KUV8T2wTEetR9fvDRbWdH2lnd85aIuosU+x8kZGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDy0hq6ATvjmoTlgmFf2UhuyDbuoGfL2+JbhzGWjGkVcdsAnm0CceOajmbaZzqWYRzA2eJezDh64zzH020NcmnGGZBk/S4hnm4bezLbqkWw0jYBL2qfhnU9e862HjzjrhastI0/YMwrnsR2ZN13Iv4XqhczroDGJr+F5LNvCmzYaBr15s62HaZlO9v4Jamk1Hag/uGAbFN+xcldTflwVqUpHwgkc9y1qay0LaOMj5qY8m3e3mLKR3KDpnyo1HiQk9T5cdu2F1q30TaByogpnjm93JRfUtHalG9+gW0dSNL+b60z5Sd1mGnKb2pn+yw7MT7AlHfWvjLs7zhXbRoVFaZ8IMPnsdR5UpnPcZpaAAAAAAB7IQobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKS2voBuxMNDdDSsvwla3Mtc2Gs5ZznjEvyQVsA8WDtvF7zpYPRG158zKS5Izz4KzLNWSb6WimbfTxjLhtgJAxLykatg0TTzMuVCPPPgsmAc++8wRitrwzT8O2HVn3ZfPOmQTP2aZhyVvH3dhs7pKrtHR/fVP22u9N4+48w7bxrz20pSkvSR3ml5ny5S1tncfmrrbOI5Jm/BiSTH8csw3kttjalGZbpIrkWD+z2NofiNj34XjItt4qCrJN+eAW23YU8IqNeWPf2iLXlJekC8ZfYcoP+808U75DeL0p72WEbfmI8fNE0P75wzMO4/z2N4YPmJyxAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACkvraEbsDNbWoWUlh7yla3MsdVn8WAyLbIJxGx559ny8XTj+I0lrOdseUnyosYBjOshkm3Lx7KNKyEcN8UDIeP4JXkB24KNhWxt8spsu7Szjd68oXouiQ1JxmnEbdMwb9vWeTDGk9rXIsZtL25Y0ZZsI5Tz7gqlBfz1TcpuYhp3NMt2UIyFTfH/TsN2jMh5f6Up3/0jW2fjwj6XZZWAsbOUpICtTbEc24KNZtk65C0tjR24cZYrjJ+JJCkQM643z9aoJmtt+Y1XtzXlXw53MOXTghWmvCRdMfFRU75HaLUpf/mSn5ry4fgmU15B44eumP0zjjMO42X429cs/TxnbAAAAACkPAobAAAAACnPVNhMmjRJffv2VU5OjvLz83XyySdr0aJF1TLl5eUqKipSixYtlJ2drZEjR2rNmjV12mgAAKrQNwEAJGNhM3fuXBUVFWnBggWaNWuWIpGIhgwZotLS0kTmiiuu0LPPPqsZM2Zo7ty5WrlypU499dQ6bzgAABJ9EwBgK9OvCF966aVq/542bZry8/O1cOFCDRgwQMXFxbr//vs1ffp0HXvssZKkqVOn6qCDDtKCBQv04x//uO5aDgCA6JsAAFvt1m9siouLJUnNmzeXJC1cuFCRSESDBw9OZLp166b27dtr/vz5OxxHRUWFSkpKqr0AAEgWfRMANE5JFzbxeFzjxo3TEUccoZ49e0qSVq9erVAopKZNm1bLtm7dWqtX7/i2d5MmTVJeXl7i1a5du2SbBABo5OibAKDxSrqwKSoq0qeffqrHHntstxowfvx4FRcXJ14rVqzYrfEBABov+iYAaLySekDn2LFj9dxzz2nevHlq2/Z/D1EqKChQZWWlNm7cWO2bsTVr1qigoGCH4wqHwwqHk3jKGAAA26BvAoDGzXTGxjmnsWPHaubMmXr99dfVqVOnan/v06eP0tPT9dprryXeW7Rokb755hv179+/bloMAMA26JsAAJLxjE1RUZGmT5+up59+Wjk5OYlrk/Py8pSZmam8vDydf/75uvLKK9W8eXPl5ubqsssuU//+/bnrDACgXtA3AQAkY2EzZcoUSdLAgQOrvT916lSNHj1aknT77bcrEAho5MiRqqio0NChQ3XPPfeYGxZP9xRP93xloxm2cccy/I33f3nb+JPhxW35WMg4AWeLW9sjSfGQbSLRTNv4Y1kxU97LtOXDWZWmfHp61JSXpPSgrU0u27atVubZri6trLDloxtsO0M8Zv8ZXyzdlk8L2pZRPM22nfo9DlXxymw7jxc37pySXNC2XOMZ/g8Y8Zi9PQ1tT/ZN8rytLz/KK0yjzv5wpSmf9WUSnZMzrt/0pK5Y982FbeN3YeMBIgnx9KApbz3ubmpv23/LWxqPWca+WJKarLK1KWDbtBUusY0/VGybh2C5rT8OVNr77z/cc7YpX9LNNo2DfrfUlDeL2T5/KGDr+yTJk23f8Zo08ZeLB6Xv/Y3TtDc6HwfEjIwMTZ48WZMnT7aMGgCApNA3AQCk3XyODQAAAADsDShsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApL62hG7AzwUqnoHO+soGoZxp3zN9od4szloxx45qIZdpmwgVt41fcmJcUa2JrU7xJzJQPNIma8uGMiCnfLLvMlM8Ll5vykpSdXmHKV8ZsG8b6LVmm/JZ02/h/qLDl4xXpprwkxcPGfJpt/7fuC9bxx8LWnd8Wl6R40NamYLn/fS0Wte2XjY2LxuQCPpdR3LYsbWtV8jbbjlmSJOM+70LGfTjNtoPFsmw7fKyJ/WNLLGTbJ+Mh25qIG48pgUpb3jN+ZrF+/pCk0ra2bTVYbptIWRvbQnJBW18WsHX3KnzT2NFIKnz1e1O+7dNbbBPIamLLx4ydR9T2GcoZ80mJ+Fxxcf9t4YwNAAAAgJRHYQMAAAAg5VHYAAAAAEh5FDYAAAAAUh6FDQAAAICUR2EDAAAAIOVR2AAAAABIeRQ2AAAAAFIehQ0AAACAlEdhAwAAACDlUdgAAAAASHlpDd2AnUkriyktLeYrm57hmcYdiNraEi+15SUpnm7LR7Js8yDPlo9mOlPeJVHyxkNxU94z5jMyK0353CblpvwBTdeZ8qe0WGjKS1Kv0FpT/vt4yJS/d+0xpvzCNW1NeXn1vx1Zh4mHbflo3LbveDHbPMdC1vEb931JChgXUsD/NGLRvbZb2CtEDiiUS8vwlQ0tWVWvbXFRY2cmyTP2HQoZOzPj+IOlFaa8C9r3l8pc2zZtPQYZD4uKNrHl1aHMFB/WdZFxAlKHjA2m/KrKPFM+KFt/nxaw5b/b0tSU/2BTd1NeklpHs035jG9t86CY8TNUhe0zkXXfNB8rJCkYtOVj/j7nW9rOGRsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8tIaugE7E/6hQmlBz1fWBfzlqsQybPVcPN02fkmKhm3DeDFnyscqjeOP2vLRJrb2JDMNF7PlAwFbm7JDlaZ8XvoWU75L+gZTXpI6pWeb8k1jZaZ8YcZGU/7zUGtTPpJbbspv3mI/xEQCtv3TGb+eSfd5XPnf+I35oCmuaNg4gKT0srgpH4j6z0Yj9vY0JukbSpUW9LdAXSRiGreXZttfPM/eN7mcJqb8qmNbmfJbWtuO0/GQKa60zfZ5bvalbX+xKmtpOwiVdTDskJKO6rjMlL+s1eumvCQdFLJtF5M3tjPl/7xgqCmfudS2YWSttG13rdbY9k1JCn1v6/9k3T/TjMfeiLHzixn3g7Bx55QkZ/zs6Dvvf1lyxgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkvLSGbsDOBDaWKRCM+cqGQkHTuCNx22zHMu31nxf3THkXME7Dc8bx29ojWfNSPN02D9Fw3JSPRGzreVNF2JRfuSXPlF8aaWHKS1ITb60p/308ZMpvqMw25a3Sfe6TCWFjXpJty5aiQeO26tm2U+u+E8m25YMVprgkKVZimwfLNGKVfN+1K4vHNFcgI8NXNp7ZzDTu0HrbMa7TU5tM+WSUFdj2yMpWxn0+zdYPxML2jy2xdFs+YJyFWKYtb/XlD/mm/HM5vczTWJHxjSm/vLylKZ/TvNSU3xS1HUfLC2zHre+Nnye2svWv8dyoKe+V1vNH8qDxc2O6bd/cOpBtvXkZ/na2+JZy6WJ/46QHAwAAAJDyKGwAAAAApDxzYTNv3jyNGDFChYWF8jxPTz31VLW/jx49Wp7nVXsNGzasrtoLAEA19EsAACmJwqa0tFQHH3ywJk+evNPMsGHDtGrVqsTr0Ucf3a1GAgCwM/RLAAApiZsHDB8+XMOHD99lJhwOq6CgIOlGAQDgF/0SAECqp9/YzJkzR/n5+TrwwAN1ySWXaMOGDTvNVlRUqKSkpNoLAIC6ZOmXJPomAEhFdV7YDBs2TA8++KBee+013XLLLZo7d66GDx+uWGzHt3SbNGmS8vLyEq927drVdZMAAI2YtV+S6JsAIBXV+U2zzzzzzMT/9+rVS71791aXLl00Z84cDRo0qEZ+/PjxuvLKKxP/LikpoQMBANQZa78k0TcBQCqq99s9d+7cWS1bttSSJUt2+PdwOKzc3NxqLwAA6ktt/ZJE3wQAqajeC5tvv/1WGzZsUJs2bep7UgAA1Ip+CQD2TeZL0TZv3lztW65ly5bpww8/VPPmzdW8eXNNnDhRI0eOVEFBgZYuXapf//rX6tq1q4YOHVqnDQcAQKJfAgBsZS5s3nvvPR1zzDGJf1ddgzxq1ChNmTJFH3/8sf7xj39o48aNKiws1JAhQ3TTTTcpHA6bpuNFo/LiQV/ZQOXOfwC6I2llninvgrZ8MtIqnCnvOVubvKht/DKOX5LiYdsw8TJ/67dKRSDDlF8fsW3eceM8/z02wJSXpFbhzaZ8xNlOqn6zqbkpv2FTlikfqTQeMiL2k8JezLjt1fOmHQ8Zxx80Nihg39eiUdswMcOuE6uo/+NdXdtT/ZIkuTQnl+5vHbd413aMa/XOD6Z8YL0tL0nybOu3699s09h4uO13SJFRtvFH8+3HlC1ftzDlMzbY9uFghSluPsZtLrdtpw8vOcyUl6Qnw4eY8nnhclM+N8O2kNIK4qZ8LG7bLiJR274pSfG4bb3lZtmW0caSJqZ83DjPVoGAbR1I9jYFgv4+v8fjEd/jNBc2AwcOlHM73+lffvll6ygBAEga/RIAQNoDv7EBAAAAgPpGYQMAAAAg5VHYAAAAAEh5FDYAAAAAUh6FDQAAAICUR2EDAAAAIOVR2AAAAABIeRQ2AAAAAFIehQ0AAACAlEdhAwAAACDlUdgAAAAASHlpDd2AnXFbKuQCzlfW29LENO5gNJ5Mk0yiTYKmvBc1TiDbVpPG0j3jBOyC5bZ8WtDWpqhnW6axoL/tp0pZRciUX+tlm/KStMG4rUZitnneVJZhyldsSTfl3Rbjdl1h/+4kYBzGut0FK437gm0zkiK28Xsx4/glOetiNTTJ2VZxo3PAzUuVFrAdK3zzjNtmWhJduDNu0EHbxlbW0pbfUmo7ZoVD1s5SKiu0zfOmzrbPCC7Tlk/fYFtv3vI8U74ix3rQkiqM+Y3Rev5M0WuTKR56O8eUz/8yYspLUvpm27bnRWz9ffMK+7ZtEc0Jm/KhdcbOVZK3xbglVVT6ikXjFVrmc5ScsQEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApL62hG7BTcSfJNXQrJEmBiqh9mHRbzRgP2cYfD9ry8ozxuHH8kgIR6wD2aZhU2CZQVpJhypdvMa40SZ5n26YDQduKiEVtG4Yrsx0CAuW2ZRqoNG54koJbbMOkbbGO35YPxGx5JbHv1DcvbtjuKveO4+5ey/O2vvxwxmVpzDvr+CV5AeOBN2g7pmzuaDzGOdv+XloWNuUlKfcrWz5jo61NxZ3STfm4cRbSN9mWafom2/glydg1yRkHsH6miJbkmPI539oO1Gll1gO7FCypNA9j4cVsbXLGfdMqnmnbriXJC9r2HS/N3zy4mP/jFmdsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMpLa+gG7JSLb3354MX95RLinq0pydR/tkmYBSO2vAva8pFsW16S4tatybjaApW2hepFbDPtttjWczzkTHlJcgHjMGHrQrKN34tZl6ktHzBup5IUiNryceO2HTDm00psyzS9zL5dWFn3Z4tYpP7bn8q8tDR5AX8HO+fqd1l66enmYVxm2JTf8OPWpnx4g+0Ykf6NrbNpPb/YlJckL1ZpysdDth0se4UtX5lrW2+RnHrc4f/Li9m21Xi6tS+wjT+4au87Dpk/axqXqReL2fJlFaZ8uLjUlFcSxy8XrKfzJYZlzxkbAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPLSGroBO+Xc1pcfsbht3OlBW1PS7PWf88yDmMTSbfm4dU37XPS7IxCxLaRAzDZ+L2rLx9Nt7YmnJ7GQjJtSLGQbwBnb5MVs8xysNMWVVprEjmBcrM64bZvzxnUWiNlmIK3MePySFIha17P/fDQasTanUXGxuJzzuc4C9dwR+O0jtxHPa2LK/3B8qSlf2LzElC+tDJnyi/rkmPKS1GRx2JTPXW7bJzcX2g4SMVtzFDTukk2XGDtL2Y4RybAe59LLjB34HmD9LBgo22LKe8bPspt65ZvyK4ab4pJn3yZyP7d9ON1v1gZ/QcP2yRkbAAAAACnPXNjMmzdPI0aMUGFhoTzP01NPPVXt7845XX/99WrTpo0yMzM1ePBgLV68uK7aCwBANfRLAAApicKmtLRUBx98sCZPnrzDv99666266667dO+99+qdd95RVlaWhg4dqvLy8t1uLAAA26NfAgBISfzGZvjw4Ro+fMcX6jnndMcdd+h3v/udTjrpJEnSgw8+qNatW+upp57SmWeeuXutBQBgO/RLAACpjn9js2zZMq1evVqDBw9OvJeXl6d+/fpp/vz5dTkpAABqRb8EAI1Hnd4VbfXq1ZKk1q1bV3u/devWib9tr6KiQhUVFYl/l5TY7qgCAMDOJNMvSfRNAJCKGvyuaJMmTVJeXl7i1a5du4ZuEgCgkaNvAoDUU6eFTUFBgSRpzZo11d5fs2ZN4m/bGz9+vIqLixOvFStW1GWTAACNWDL9kkTfBACpqE4Lm06dOqmgoECvvfZa4r2SkhK988476t+//w6HCYfDys3NrfYCAKAuJNMvSfRNAJCKzL+x2bx5s5YsWZL497Jly/Thhx+qefPmat++vcaNG6ff//732n///dWpUyddd911Kiws1Mknn1yX7QYAQBL9EgBgK3Nh89577+mYY45J/PvKK6+UJI0aNUrTpk3Tr3/9a5WWluqiiy7Sxo0bdeSRR+qll15SRkZG3bUaAID/ol8CAEiS55xzDd2IbZWUlCgvL0+Dmp6jNC/kb6A2+aZpxLPCpnwsM92Ul6RItq1mjGXargqszLblo5mmuKKZnm0ASS5ozFsvhLQ2KW7MG9sTs28W5mlEM227Z8yY94zLKG2zbQYyNtjGL0le1JaP23ZnyXjEC5bbBkgvs40/4/uYbQBJaWW2Yby4/3mIRsv1xhs3qri4mMuvtlHVNw3se63S0vwVRIt/bjvwZn1jO4gWvG3c2CSlbaqoPbSNPv/41JRvnV6/d4/7pqK5eZjnv+phyrtPbdt9eWHElPdCxgNvqe3zRJs5ttFLUuY62zxEm9i2Vesxy7qdehXG42jQ/hlHnm0Yr9LYmUVs+Y2H2j77rhpqG38gZO+b3Eafn9v/a7/Xas9IUjRSrn8/e52vfqnB74oGAAAAALuLwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPLSGroBOxUIbn354NL95arEQ7a8S/NMeUnmkjEWsk0jbpsFOessOGNeUiBiy3tx4wSMbfJitgFcwLaQgknsPbGwLe9FbW3yjMsobpwHL2bMR215SUovM663Lbbxx0O2vDPuy9ZlGjfu+5KkMls8EPG/swWi1h2zcVlydoYCmRm+sl4T20GxvLltY4uHjR2BpMCKzab8U48dZcofetKnpnzU2Jl9/OxBprwkdXxmvW2A2Fpb3th3WD+zyDMeI6x5SfEM24HLMx4nvLi1c7LFrZ+5vHLjBxZJLt22jFzA1igvzbZdNHt3tSmft6iJKb9iaFNTXpLKW9lWnN/+JhD1v/1wxgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACkvraEbsDNeRlheIOwrGw8GjSP3TPF4ur3+cwHbNJwtLmedZWfLByuMA0jy4rZ8IGqehIm1PZJtnuPGdbB1EsYV7W8XSEjbbBt/tIlxPRt3Bet2KkmBiK1NacZtNRo27v9ptnwgat93zIybUXBzhe+si1UaG9O4HPD3TUoL+ltG3hbbsvQqI8k0ySZmOzB2eHSFKb/hsaamvEuzHSQ6xFea8pK9PzaL2eIuI2TKFx+UY8pv3N/+mSXo/xAhScr7yrYdrRxm6/D3ez7DlM9ZXGzKyyXxGafcuJBC6aa4M36W9WQ7Xnir1pvy7f++2pSvT9G4/2MpZ2wAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyktr6AbsVFpQCgR9Zm31mQt4pnw8zZaXpFi4nqfhbHEvassHjOOXJC9mnEbUOhPGeNyWDxiXUSzdlt86EVvci9lmOpplG38gWr/bnUvmCGNsUrDC1qi0LbYNw3q8MO+bcfvO5qxNMhwjncf3Xbv09UrJC/nLerYV5dLrv0v20o0HLuMxyEUitvEbtzfz/ihJxn3MS/P52SNJwc1lpnyzsgpTPme5sSOQFIjYjouBUlubspeHTfm09ZtMeTnbOnZJrOPKts1M+eUjfB4n/mu/ObZ1kPVFpSlv3Q/MeUlyxg9efvd/w/qlBwMAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApL62hG7BTcSfJ+Yum2+qzWNiWj4c8U16S4mn2YSwCUWve37JM5GO28Uvyu7oSvLhxACPrMgqWx23jz9gD3wtkWAewbXeebZYVtx4xjOOXJOfV776TVmbbuL2IcSaM7bcevyQpYGyTF/O/r1myjVI87n/HCQbrtSleerp5mFjbVrYBnG17CK7+wTb68nJTPpljiprlmeL/uaalKR/ItHU2aV/bDuxd7lpqyqeXbDblJUkh47Zk3LaDMduKcwFjXxYxfmjx7B9/t7SyLaNg2zLb+FtkmfJZabZ14BnzSfUEkUpb3vncLvzmxBkbAAAAAPuAOi9sJkyYIM/zqr26detW15MBAMA3+iYA2PfVy6VoPXr00Kuvvvq/iaTtvVe8AQAaB/omANi31ctRPS0tTQUFBfUxagAAkkLfBAD7tnr5jc3ixYtVWFiozp076+yzz9Y333yz02xFRYVKSkqqvQAAqGv0TQCwb6vzwqZfv36aNm2aXnrpJU2ZMkXLli3TUUcdpU2bNu0wP2nSJOXl5SVe7dq1q+smAQAaOfomANj31XlhM3z4cJ1++unq3bu3hg4dqhdeeEEbN27Uv/71rx3mx48fr+Li4sRrxYoVdd0kAEAjR98EAPu+ev/lZNOmTXXAAQdoyZIlO/x7OBxWOByu72YAAJBA3wQA+556f47N5s2btXTpUrVp06a+JwUAgC/0TQCw76nzwuaqq67S3LlztXz5cr399ts65ZRTFAwGddZZZ9X1pAAA8IW+CQD2fXV+Kdq3336rs846Sxs2bFCrVq105JFHasGCBWrVqlVdTwoAAF/omwBg31fnhc1jjz1WJ+NxoXS5YLq/bLrtxFPcmg96pnwyAlFnynu2uLyYbYBAzDb+ZAQixnmOG2faGA9E4qa8uT2SPGc9SVq/V4tat6NYRv3vC3HjUSmeVr9tClbYdgavImLKu2DQlJdk3iy8LZX+szH/2VRSV32TnJPvg4t13RqPKc7Zj0GB0grbAJGoKe6itrx1nr0M+++evhjb2jZAwDYP8S22g1ZaqfGYFTN2yIEk+o1oPU8jzbgvWOc5Zuy/txj3A0lN/73Sln/PtowWFTUx5dcdlWfKB39oYcofOHmVKS9J8bVl5mH8cM7/Plnvv7EBAAAAgPpGYQMAAAAg5VHYAAAAAEh5FDYAAAAAUh6FDQAAAICUR2EDAAAAIOVR2AAAAABIeRQ2AAAAAFIehQ0AAACAlEdhAwAAACDlUdgAAAAASHlpDd2AnQqHpGDIVzSWbqvPXJqXTItMvLgx75wpH4jaxh+I2MZvbY8kyThIsNy2kAKVMVPepdm2C+s8u2gS25FnGyY9YF1vprgiAVt7YsbtWkksIhe05QNR475TaZyJZPYFg8DmLfaBjNuRaR7i1pXcuLhITM7zdwB2EeuB2rhey2xxSQpUVNgHqkexbh1M+cXnZNonkhUxxZu+GzblC1/4zpSPr1lnyiszwzb+/Ga28Usq7pZnyjf9cL0pv/ic5qZ8+x/ZlmnAs/X3ab/KMeUl6avTbMuoSa8fTHm3zBS396/ONsB3I/YzTkAqOaDAlG/yrb8OP1ZRLt32qK8sZ2wAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyktr6AbUiYBnyzvj6GPGASQpahsmYMx71vFHjOOP2+fZc8Y2VcRs+bJKU96lB015edbtKIllFAsZp2GbBy9u+67CBax5U1zxoHGZSvLitry1TYFK23bnRY0NMsa9mHEASYpEbXnDturFK4yNaWRcXH5Xsosa15PxGOQFjcc4SfEt5eZhLKxt2twu0zYB2+4rSXLltjYVd7NNpPiANqa8y8w35WU8jHpb7N9ZB1ratouKpq1M+Wgz276wujjHlA+l2dZZ6W/sH3/zsjeY8mlBW5vS97eNv6Q0wzb+/9g+f1QOKDHlJal3q3Wm/Jou2b5ysdIK6TZ/4+SMDQAAAICUR2EDAAAAIOVR2AAAAABIeRQ2AAAAAFIehQ0AAACAlEdhAwAAACDlUdgAAAAASHkUNgAAAABSHoUNAAAAgJRHYQMAAAAg5VHYAAAAAEh5FDYAAAAAUl5aQzdgZ7zvi+UFQr6yoZBtNuIZtrwLeqa8JHkxZ8oHKqO28UfjtnwkZsorZhv/1mFs0/AqIqa8K68w5QNpQVNeAWOdb5xfSQpmZpjy6ZlhUz6Wbcun5/jbx6pEcmzLNJ5m33cCUdu+E95o244CP2w25bWp1JaP29ofLzWOX1J8yxbbAM5/m6LOtjwbGy8ckuf522+8tPrtYp1hvSYYt08vaDsuemHjMStsO0a4UBJ9U5ptGBc3zrN1NcSNx0Xj+JusNPZ9ktrf8LUpX3lwJ1O+rMDW9zV7JduUT99s64/Drez7pgtkmfLBUtt2l72+0pRv+cV3pry22Nax96htHUjS4nO72Kbhc9uOVZT7HidnbAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKS2voBuzME1/+Wbm5uQ3dDABoVEpKSpSXl9fQzdhrLf9VTwUyMnxlC+bHTOPOXFVqyntffmPKS1J882ZT3sVs338GArZ8s6c/M+Wbzqg05SXJRaKmfCAjbMrHe3Yx5YPFW2zjX77ClFfc2fKSnHGeQ5/Z2tT5i/r9Ht052zxX/iPbPI28cLkpv2x9C1M+PC/HlN/vS9syNW8VaUHrEIqHbHkv7nO8PnMSZ2wAAAAA7APqrbCZPHmyOnbsqIyMDPXr10///ve/62tSAADUin4JAPZt9VLY/POf/9SVV16pG264Qe+//74OPvhgDR06VGvXrq2PyQEAsEv0SwCw76uXwua2227ThRdeqDFjxqh79+6699571aRJEz3wwAP1MTkAAHaJfgkA9n11XthUVlZq4cKFGjx48P8mEgho8ODBmj9/fo18RUWFSkpKqr0AAKgr1n5Jom8CgFRU54XN+vXrFYvF1Lp162rvt27dWqtXr66RnzRpkvLy8hKvdu3a1XWTAACNmLVfkuibACAVNfhd0caPH6/i4uLEa8UK420NAQCoY/RNAJB66vw5Ni1btlQwGNSaNWuqvb9mzRoVFBTUyIfDYYXDtvunAwDgl7VfkuibACAV1fkZm1AopD59+ui1115LvBePx/Xaa6+pf//+dT05AAB2iX4JABqHOj9jI0lXXnmlRo0apR/96Ec67LDDdMcdd6i0tFRjxoypj8kBALBL9EsAsO+rl8Lmpz/9qdatW6frr79eq1ev1iGHHKKXXnqpxg83d8Q5J0ncgQYAGkDVsbfqWLyv2J1+Sfrf8ohXlPueZjQSM7UxGqsw5T1XacpLUtxFjEPYLuwIOM82ehe0xc3tl5yLmvLWeYhH/W8TkuSM69m8zpLYdz3jPHtx23bhefX7k27r8SpaalsHkhSN2oaJldm2i1hFuikfjdv2f2c8Xnhx+zKKldvm2fO52qqOu37Ws+f2st7r22+/5e4zANDAVqxYobZt2zZ0M/Ya9E0A0LD89Et7XWETj8e1cuVK5eTkyPP+9w1CSUmJ2rVrpxUrVig3N7cBW7jnMM/M876osc2vlFrz7JzTpk2bVFhYqECgwW+cudegb/of5nnfn+fGNr8S87w3z7OlX6qXS9F2RyAQ2GU1lpubu1cv/PrAPDcOjW2eG9v8Sqkzz3l5eQ3dhL0OfVNNzPO+r7HNr8Q876389kt8HQcAAAAg5VHYAAAAAEh5KVPYhMNh3XDDDY3qgWnMc+PQ2Oa5sc2v1DjnubFojOuWed73Nbb5lZjnfcVed/MAAAAAALBKmTM2AAAAALAzFDYAAAAAUh6FDQAAAICUR2EDAAAAIOWlTGEzefJkdezYURkZGerXr5/+/e9/N3ST6s2ECRPkeV61V7du3Rq6WXVq3rx5GjFihAoLC+V5np566qlqf3fO6frrr1ebNm2UmZmpwYMHa/HixQ3T2DpQ2/yOHj26xjofNmxYwzS2jkyaNEl9+/ZVTk6O8vPzdfLJJ2vRokXVMuXl5SoqKlKLFi2UnZ2tkSNHas2aNQ3U4t3jZ34HDhxYYz1ffPHFDdRi1AX6pn2nb2ps/ZLU+PqmxtYvSY2vb0qJwuaf//ynrrzySt1www16//33dfDBB2vo0KFau3ZtQzet3vTo0UOrVq1KvN58882GblKdKi0t1cEHH6zJkyfv8O+33nqr7rrrLt1777165513lJWVpaFDh6q8vHwPt7Ru1Da/kjRs2LBq6/zRRx/dgy2se3PnzlVRUZEWLFigWbNmKRKJaMiQISotLU1krrjiCj377LOaMWOG5s6dq5UrV+rUU09twFYnz8/8StKFF15YbT3feuutDdRi7C76pn2rb2ps/ZLU+PqmxtYvSY2wb3Ip4LDDDnNFRUWJf8diMVdYWOgmTZrUgK2qPzfccIM7+OCDG7oZe4wkN3PmzMS/4/G4KygocH/6058S723cuNGFw2H36KOPNkAL69b28+ucc6NGjXInnXRSg7RnT1m7dq2T5ObOneuc27pO09PT3YwZMxKZzz//3Ely8+fPb6hm1pnt59c5544++mj3y1/+suEahTpF37Tvamz9knONs29qbP2Sc/t+37TXn7GprKzUwoULNXjw4MR7gUBAgwcP1vz58xuwZfVr8eLFKiwsVOfOnXX22Wfrm2++aegm7THLli3T6tWrq63zvLw89evXb59e53PmzFF+fr4OPPBAXXLJJdqwYUNDN6lOFRcXS5KaN28uSVq4cKEikUi19dytWze1b99+n1jP289vlUceeUQtW7ZUz549NX78eJWVlTVE87Cb6JsaV9/UWPslad/umxpbvyTt+31TWkM3oDbr169XLBZT69atq73funVrffHFFw3UqvrVr18/TZs2TQceeKBWrVqliRMn6qijjtKnn36qnJychm5evVu9erUk7XCdV/1tXzNs2DCdeuqp6tSpk5YuXaprr71Ww4cP1/z58xUMBhu6ebstHo9r3LhxOuKII9SzZ09JW9dzKBRS06ZNq2X3hfW8o/mVpJ/97Gfq0KGDCgsL9fHHH+uaa67RokWL9OSTTzZga5EM+qbG1Tc1xn5J2rf7psbWL0mNo2/a6wubxmj48OGJ/+/du7f69eunDh066F//+pfOP//8BmwZ6suZZ56Z+P9evXqpd+/e6tKli+bMmaNBgwY1YMvqRlFRkT799NN96nr8XdnZ/F500UWJ/+/Vq5fatGmjQYMGaenSperSpcuebiZgQt/U+OzLfVNj65ekxtE37fWXorVs2VLBYLDGHSnWrFmjgoKCBmrVntW0aVMdcMABWrJkSUM3ZY+oWq+NeZ137txZLVu23CfW+dixY/Xcc89p9uzZatu2beL9goICVVZWauPGjdXyqb6edza/O9KvXz9J2ifWc2ND39S4+ib6pa32lb6psfVLUuPpm/b6wiYUCqlPnz567bXXEu/F43G99tpr6t+/fwO2bM/ZvHmzli5dqjZt2jR0U/aITp06qaCgoNo6Lykp0TvvvNNo1vm3336rDRs2pPQ6d85p7Nixmjlzpl5//XV16tSp2t/79Omj9PT0aut50aJF+uabb1JyPdc2vzvy4YcfSlJKr+fGir6pcfVN9EtbpXrf1Nj6JakR9k0Ne+8Cfx577DEXDofdtGnT3H/+8x930UUXuaZNm7rVq1c3dNPqxa9+9Ss3Z84ct2zZMvfWW2+5wYMHu5YtW7q1a9c2dNPqzKZNm9wHH3zgPvjgAyfJ3Xbbbe6DDz5wX3/9tXPOuT/+8Y+uadOm7umnn3Yff/yxO+mkk1ynTp3cli1bGrjlydnV/G7atMldddVVbv78+W7ZsmXu1VdfdYceeqjbf//9XXl5eUM3PWmXXHKJy8vLc3PmzHGrVq1KvMrKyhKZiy++2LVv3969/vrr7r333nP9+/d3/fv3b8BWJ6+2+V2yZIm78cYb3XvvveeWLVvmnn76ade5c2c3YMCABm45kkXftG/1TY2tX3Ku8fVNja1fcq7x9U0pUdg459zdd9/t2rdv70KhkDvssMPcggULGrpJ9eanP/2pa9OmjQuFQm6//fZzP/3pT92SJUsaull1avbs2U5SjdeoUaOcc1tvrXnddde51q1bu3A47AYNGuQWLVrUsI3eDbua37KyMjdkyBDXqlUrl56e7jp06OAuvPDClP9wtKP5leSmTp2ayGzZssVdeumlrlmzZq5JkybulFNOcatWrWq4Ru+G2ub3m2++cQMGDHDNmzd34XDYde3a1V199dWuuLi4YRuO3ULftO/0TY2tX3Ku8fVNja1fcq7x9U2ec87V/XkgAAAAANhz9vrf2AAAAABAbShsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAAAAAKQ8ChsAAAAAKY/CBgAAAEDKo7ABAAAAkPIobAAAAACkPAobAAAAACmPwgYAAABAyqOwAQAAAJDyKGwAAAAApDwKGwAAAAApj8IGAAAAQMqjsAEAAACQ8ihsAPgybdo0eZ6n5cuXN3RTAAAAaqCwQZ2q+vBb9crIyFBhYaGGDh2qu+66S5s2bUp63G+//bYmTJigjRs31l2Dt/HDDz8oLS1N//rXv3aaGT16dLX5y83N1cEHH6y//OUvqqioqJZ98803NXz4cO23337KyMhQ+/btNWLECE2fPr1abtvxbfsqKCjYZXvnzJlTLR8MBpWfn6/TTjtNn3/+edLL4eabb9ZTTz2V9PAAAAANIa2hG4B904033qhOnTopEolo9erVmjNnjsaNG6fbbrtNzzzzjHr37m0e59tvv62JEydq9OjRatq0aZ23+eWXX5bneRoyZMguc+FwWH//+98lSRs3btQTTzyhq666Su+++64ee+wxSdKMGTP005/+VIcccoh++ctfqlmzZlq2bJnmzZun++67Tz/72c+qjfO4447TueeeW+29zMxMX+2+/PLL1bdvX0UiEX388ce69957NWfOHH366ae1Fkc7cvPNN+u0007TySefXO39c845R2eeeabC4bB5nAAAAPWNwgb1Yvjw4frRj36U+Pf48eP1+uuv64QTTtCJJ56ozz//3PcH9z3lhRde0BFHHFFr0ZSWlqaf//zniX9feuml6tevn/75z3/qtttuU2FhoSZMmKDu3btrwYIFCoVC1YZfu3ZtjXEecMAB1cZpcdRRR+m0005L/PvAAw/UJZdcogcffFC//vWvkxrnjgSDQQWDwTobHwAAQF3iUjTsMccee6yuu+46ff3113r44YcT73/88ccaPXq0OnfurIyMDBUUFOi8887Thg0bEpkJEybo6quvliR16tQpcflV1e89pk6dqmOPPVb5+fkKh8Pq3r27pkyZ4rtt8XhcL730ko4//njzfAUCAQ0cOFCSEu1ZunSp+vbtW6OokaT8/HzzNCyOOuqoRBu29ec//1mHH364WrRooczMTPXp00ePP/54tYzneSotLdU//vGPxDIePXq0pJ3/xuaee+5Rjx49FA6HVVhYqKKionq7XBAAAGBnOGODPeqcc87Rtddeq1deeUUXXnihJGnWrFn66quvNGbMGBUUFOizzz7T//t//0+fffaZFixYIM/zdOqpp+rLL7/Uo48+qttvv10tW7aUJLVq1UqSNGXKFPXo0UMnnnii0tLS9Oyzz+rSSy9VPB5XUVFRre169913tW7dOv3kJz9Jar6qiogWLVpIkjp06KDXXntN3377rdq2bVvr8OXl5Vq/fn2193JycpK67Kuq8GjWrFm19++8806deOKJOvvss1VZWanHHntMp59+up577rlEQffQQw/pggsu0GGHHaaLLrpIktSlS5edTmvChAmaOHGiBg8erEsuuUSLFi3SlClT9O677+qtt95Senq6uf0AAABJcUAdmjp1qpPk3n333Z1m8vLy3P/93/8l/l1WVlYj8+ijjzpJbt68eYn3/vSnPzlJbtmyZTXyOxrH0KFDXefOnX21+7rrrnMdOnSoNTdq1CiXlZXl1q1b59atW+eWLFnibr75Zud5nuvdu3cid//99ztJLhQKuWOOOcZdd9117o033nCxWKzGOCXt8DV16tRdtmX27NlOknvggQfcunXr3MqVK91LL73kunbt6jzPc//+97+r5bdfRpWVla5nz57u2GOPrfZ+VlaWGzVqVI3pVa3bquW/du1aFwqF3JAhQ6rN11//+tdEuwAAAPYUzthgj8vOzq52d7Rtf2tTXl6uzZs368c//rEk6f33309cWrUr246juLhYkUhERx99tF5++WUVFxcrLy9vl8O/8MILvi9DKy0tTZwpqnL44YfroYceSvz7vPPO03777afbbrtNs2fP1uzZs3XTTTepc+fOeuihh3T44YdXG/6kk07S2LFjq73Xo0cPX+0577zzqv27VatWeuihh9S3b99q72+7jH744QfFYjEdddRRevTRR31NZ3uvvvqqKisrNW7cOAUC/7uq9cILL9S1116r559/XmPGjElq3AAAAFYUNtjjNm/eXO13Jt9//70mTpyoxx57rMYP64uLi32N86233tINN9yg+fPnq6ysrMY4dlXYrF69Wu+//75uvPFGX9PKyMjQs88+K2nrHdI6deq0w8vNhg4dqqFDh6qsrEwLFy7UP//5T91777064YQT9MUXX1RbBm3bttXgwYN9TX97119/vY466iht3rxZM2fO1GOPPVat0Kjy3HPP6fe//70+/PDDarem9jwvqel+/fXXkrberGBboVBInTt3TvwdAABgT6CwwR717bffqri4WF27dk28d8YZZ+jtt9/W1VdfrUMOOUTZ2dmKx+MaNmyY4vF4reNcunSpBg0apG7duum2225Tu3btFAqF9MILL+j222+vdRwvvviiMjIydMwxx/iah2AwaCpCmjRpoqOOOkpHHXWUWrZsqYkTJ+rFF1/UqFGjfI9jV3r16pVoz8knn6yysjJdeOGFOvLII9WuXTtJ0htvvKETTzxRAwYM0D333KM2bdooPT1dU6dOrfFcHQAAgFREYYM9qupyraFDh0raeknUa6+9pokTJ+r6669P5BYvXlxj2J2dWXj22WdVUVGhZ555Ru3bt0+8P3v2bF9tev7553XMMcfskdtPV90Ce9WqVfU2jT/+8Y+aOXOm/vCHP+jee++VJD3xxBPKyMjQyy+/XO2GBFOnTq0xvN8zOB06dJAkLVq0SJ07d068X1lZqWXLliV9BgoAACAZ3O4Ze8zrr7+um266SZ06ddLZZ58tSYnnojjnqmXvuOOOGsNnZWVJUo1bCe9oHMXFxTv80L69SCSiWbNmJXWb51157bXXdvj+Cy+8IKnm5Vt1qUuXLho5cqSmTZum1atXS9q6jDzPUywWS+SWL1+up556qsbwWVlZvm7XPHjwYIVCId11113Vlv3999+v4uLiOl+mAAAAu8IZG9SLF198UV988YWi0ajWrFmj119/XbNmzVKHDh30zDPPKCMjQ5KUm5urAQMG6NZbb1UkEtF+++2nV155RcuWLasxzj59+kiSfvvb3+rMM89Uenq6RowYoSFDhigUCmnEiBH6xS9+oc2bN+u+++5Tfn5+rWdG3nzzTZWUlNT5h/CTTjpJnTp10ogRI9SlSxeVlpbq1Vdf1bPPPqu+fftqxIgRdTq97V199dX617/+pTvuuEN//OMfdfzxx+u2227TsGHD9LOf/Uxr167V5MmT1bVrV3388cfVhu3Tp49effXVxMNGO3XqpH79+tWYRqtWrTR+/HhNnDhRw4YN04knnqhFixbpnnvuUd++fZN+4CgAAEBSGviubNjHVN0SuOoVCoVcQUGBO+6449ydd97pSkpKagzz7bffulNOOcU1bdrU5eXludNPP92tXLnSSXI33HBDtexNN93k9ttvPxcIBKrdeviZZ55xvXv3dhkZGa5jx47ulltucQ888MBObw9d5aqrrnLdu3f3PX9Vt3uuzaOPPurOPPNM16VLF5eZmekyMjJc9+7d3W9/+9say0CSKyoq8t2GKlW3e54xY8YO/z5w4ECXm5vrNm7c6Jzbegvq/fff34XDYdetWzc3depUd8MNN7jtDwNffPGFGzBggMvMzHSSErd+3v52z1X++te/um7durn09HTXunVrd8kll7gffvjBPD8AAAC7w3Nuu2uAgEake/fuOuGEE3Trrbc2dFMAAACwG7gUDY1WZWWlfvrTn+qMM85o6KYAAADUmfLyclVWVu7WOEKhUOKnA6mCMzYAAADAPqK8vFydOmRr9dpY7eFdKCgo0LJly1KquOGMDQAAALCPqKys1Oq1MX29sKNyc5K7AXLJprg69FmuyspKChsAAAAADSc7x1N2jr9n020vruSGa2gUNgAAAMA+JubiiiX5g5OYi9dtY/aQva6wicfjWrlypXJycnw/AR0AUDecc9q0aZMKCwsVCPAMZwBIVXE5xZVcZZPscA1trytsVq5cqXbt2jV0MwCgUVuxYoXatm3b0M0AAMC3va6wycnJkSTdNe9gZWYHfQ3zYWl70zR+qGxiypfFQ6a8JFXG/LW9SsCzVcZxZzubFXW29kRi9m9qi8szTflN5WFTvqIs3ZR3xutD09Jsdw8paF5iykvS+E4vmPLFcdu2+u/Szqb8Jz8UmvIVMdshoyySxL4TtW2r2eEKU/6W/Z8w5dfGckz5jmk/mPJNAvbT/TeuHGbKryzN9Z2NllXq32f9v8SxGACQmuKKK9kLypIfsmHtdYVN1eVnmdlBNfFZ2IQ92wfeUKXtw1YkZv9w5vaywsYzFjbW9ktSMGgrVIIBWz4g23pwxmUUSLcVNmlZtg/UkpSVYyww47a8dV9Iq7Stg5ixsAka9zVJChoLmzTjzVqyjXeI2WzcF7LTbOPPSuJqr/Qs23JNk209S+JSYABIcTHnFEvyqS7JDtfQ9rrCBgAAAMDuaYy/seGXoQAAAABSHmdsAAAAgH1MXE6xRnbGhsIGAAAA2MdwKRoAAAAApCDO2AAAAAD7GO6KBgAAACDlxf/7SnbYVERhAwAAAOxjYrtx84Bkh2toe21h08SrUJOAvwfjpXu2BytGnPEBfRH7w+3Wl2WZ8mnGp49b88Eknm5uZX1oaDBoa5MXND7EtMK2nqNx2+5QkcRDTHMC5aZ86+BmU/7/bRxgyq8rtW2nwYBtHZRV2B4YKklR4wM6S8ts++dP519kylu303A4YspfuP/bprwkTW7/oin/cMkBvrNbNkdlbxEAAA2PmwcAAIBdmjZtmjzP0/Llyxu6KTvVsWNHnXDCCXU6zksvvVTHHXdcnY4TNW3YsEFZWVl64YUXGrop+5SY271XKqKwAQAA2M6yZcv097//Xddee23iveXLl8vzvMQrPT1dLVu21OGHH65rr71W33zzTdLTW7lypSZMmKAPP/ywDlqfnO3nLxgMqn379jrllFNqtGvz5s264YYb1LNnT2VlZalFixY65JBD9Mtf/lIrV65M5CZMmFBtnNu+7r33XklSixYtdMEFF+i6667bk7O7z4vv5isV7bWXogEAADSUO++8U506ddIxxxxT429nnXWWfvKTnygej+uHH37Qu+++qzvuuEN33nmn7r//fp155pnm6a1cuVITJ05Ux44ddcghh9TBHCSvav5isZg+//xzTZkyRS+++KIWLFigQw45RJFIRAMGDNAXX3yhUaNG6bLLLtPmzZv12Wefafr06TrllFNUWFhYbZxTpkxRdnZ2tff69euX+P+LL75Yd911l15//XUde+yxe2Q+93VxeYrJ9jOBbYdNRRQ2AAAA24hEInrkkUd08cUX7/Dvhx56qH7+859Xe+/rr7/WkCFDNGrUKB100EE6+OCD90RT68X283fEEUfoxBNP1JQpU/S3v/1NTz31lD744AM98sgj+tnPflZt2PLyclVWVtYY52mnnaaWLVvudJoHHXSQevbsqWnTplHYIGlcigYAAJJyzz33qEePHgqHwyosLFRRUZE2btxYIzd58mR17txZmZmZOuyww/TGG29o4MCBGjhwYK3TmDp1qo499ljl5+crHA6re/fumjJlyk7zr7zyig455BBlZGSoe/fuevLJJ6v9PRKJaOLEidp///2VkZGhFi1a6Mgjj9SsWbMSmTfffFPr16/X4MGDfS+LDh06aNq0aaqsrNStt96aeP/777/XVVddpV69eik7O1u5ubkaPny4Pvroo0Rmzpw56tu3ryRpzJgxiUu1pk2bJkl64403dPrpp6t9+/YKh8Nq166drrjiCm3ZssV3+3ZHVaGxbNkySdLSpUslbS14tpeRkaHc3NykpnPcccfp2WeflUvRZ6jsbeJu916piMIGAACYTZgwQUVFRSosLNRf/vIXjRw5Un/72980ZMgQRSL/uzvglClTNHbsWLVt21a33nqrjjrqKJ188sn69ttvfU1nypQp6tChg6699lr95S9/Ubt27XTppZdq8uTJNbKLFy/WT3/6Uw0fPlyTJk1SWlqaTj/99GpFy4QJEzRx4kQdc8wx+utf/6rf/va3at++vd5///1E5u2335bnefq///s/0zLp37+/unTpUm16X331lZ566imdcMIJuu2223T11Vfrk08+0dFHH534LcpBBx2kG2+8UZJ00UUX6aGHHtJDDz2kAQO23ulyxowZKisr0yWXXKK7775bQ4cO1d13361zzz3X1L5kVRUyLVq0kLS1iJOkBx980HcR8v3332v9+vWJ1w8//FAj06dPH23cuFGfffZZHbW8cYv991K0ZF+piEvRAACAybp16zRp0iQNGTJEL774ogKBrd+TduvWTWPHjtXDDz+sMWPGqLKyUtddd5369u2r119/XWlpWz929O7dW6NHj1bbtm1rndbcuXOVmZmZ+PfYsWM1bNgw3XbbbSoqKqqW/fLLL/XEE0/o1FNPlSSdf/756tatm6655prE3c2ef/55/eQnP9H/+3//b6fT/OKLL9S8efOkzjz07NlTTz/9tEpKSpSbm6tevXrpyy+/TCwjSTrnnHPUrVs33X///bruuuvUunVrDR8+XNdff7369+9f4zK3W265pdoyuOiii9S1a9fEDQvat29vbueulJWVaf369YrFYvriiy90xRVXSJJOP/10SdLJJ5+sAw88UNdff73uv/9+HXPMMTrqqKN0wgknKD8/f4fjPPDAA6v9u0OHDjXuste5c2dJ0n/+8x/17NmzTuepMdqdAiVVCxvO2AAAAJNXX31VlZWVGjduXLUP7BdeeKFyc3P1/PPPS5Lee+89bdiwQRdeeGGiqJGks88+W82aNfM1rW0/0BcXF2v9+vU6+uij9dVXX6m4uLhatrCwUKecckri37m5uTr33HP1wQcfaPXq1ZKkpk2b6rPPPtPixYt3Os0NGzb4bt/2qn4gv2nTJklSOBxOLKNYLKYNGzYoOztbBx54YLWzRLuy7TIoLS3V+vXrdfjhh8s5pw8++CCpdu7KDTfcoFatWqmgoEADBw7U0qVLdcsttyQKxszMTL3zzju6+uqrJW29Hfj555+vNm3a6LLLLlNFRUWNcT7xxBOaNWtW4vXII4/UyFQt8/Xr19f5PKFx4IwNAAAw+frrryXV/BY+FAqpc+fOib9X/bdr167VcmlpaerYsaOvab311lu64YYbNH/+fJWVlVX7W3FxsfLy8hL/7tq1qzyv+jfNBxyw9QG1y5cvV0FBgW688UaddNJJOuCAA9SzZ08NGzZM55xzjnr37l1tuGR/57F589YHK+fk5EiS4vG47rzzTt1zzz1atmyZYrH/PVS86tKu2nzzzTe6/vrr9cwzz9S4hGv74m5bsVhM69atq/Ze8+bNFQqFdjm9iy66SKeffroCgYCaNm2a+B3VtvLy8nTrrbfq1ltv1ddff63XXntNf/7zn/XXv/5VeXl5+v3vf18tP2DAgF3ePED63zLffh0iOXHnmR+evu2wqYgzNgAAYK+0dOlSDRo0SOvXr9dtt92m559/XrNmzUpcGhWP25+2MWDAAC1dulQPPPCAevbsqb///e869NBD9fe//z2RadGixQ5/A+LHp59+qvz8/MRlbDfffLOuvPJKDRgwQA8//LBefvllzZo1Sz169PDV/lgspuOOO07PP/+8rrnmGj311FOaNWtW4sYCuxrHihUr1KZNm2qvt99+u9Zp7r///ho8eLCOPfZYHXrooTWKmu116NBB5513nt566y01bdp0h2dj/Kha5rUVQPCH39gAAADUourH44sWLUr8LkKSKisrtWzZssTdxKpyS5YsqfY8mGg0quXLl9c4S7K9Z599VhUVFXrmmWeq/Y5k9uzZO8wvWbJEzrlq3/h/+eWXklTtDFHz5s01ZswYjRkzRps3b9aAAQM0YcIEXXDBBZK2/lbokUceqXFGqDbz58/X0qVLq/1G5vHHH9cxxxyj+++/v1p248aN1T7A7+wsxSeffKIvv/xS//jHP6rdLGDbGxTsTEFBQY1cfd6GulmzZurSpYs+/fTTpIavuuvaQQcdVJfNQiOy1xY2m12GYnF/zYsZTzwFPdvp5YqYfTFVRoOmfGls16eFtxdKi5ryVx9Q+wFwW73CK2sPbSfibOthZsmhpvxjX/Qx5WMVtmWqmO3biXXf239U+ttlp9Qe2ka3vDWm/Nff264Jj8Xq96RtPInxx+PGYYz789/7PWjKD8y0fSN82/edaw9tI5lvxeLGS2TSvVjtof+KGrJovAYPHqxQKKS77rpLw4YNS3wov//++1VcXKzjjz9ekvSjH/1ILVq00H333acxY8YkfmfzyCOP+DojEgxu7Uu3vSysuLhYU6dO3WF+5cqVmjlzZuK3ICUlJXrwwQd1yCGHqKCgQNLW389sewlYdna2unbtqhUrViTe69+/v5xzWrhwoe9nqnz99dcaPXq0QqFQ4rcnVfOw/WVtM2bM0HfffVftEr2srCxJqnG77B0tA+ec7rzzzlrblJGRYbpltV8fffSR9ttvvxpnVr7++mv95z//qXGJol8LFy5UXl6eevToURfNbPRiCpg/I/9v2NS01xY2AABg79SqVSuNHz9eEydO1LBhw3TiiSdq0aJFuueee9S3b9/EGYtQKKQJEybosssu07HHHqszzjhDy5cv17Rp09SlS5daf0sxZMgQhUIhjRgxQr/4xS+0efNm3XfffcrPz9eqVatq5A844ACdf/75evfdd9W6dWs98MADWrNmTbVCqHv37ho4cKD69Omj5s2b67333tPjjz+usWPHJjJHHnmkWrRooVdffXWHhc3777+vhx9+WPF4XBs3btS7776rJ554Qp7n6aGHHqp2JuqEE07QjTfeqDFjxujwww/XJ598okceeaTamS5J6tKli5o2bap7771XOTk5ysrKUr9+/dStWzd16dJFV111lb777jvl5ubqiSeeSPpSubowa9Ys3XDDDTrxxBP14x//WNnZ2frqq6/0wAMPqKKiQhMmTEh6vCNGjOA3NnXE7cZvbFyK/saGwgYAAJhNmDBBrVq10l//+lddccUVat68uS666CLdfPPNSk9PT+TGjh0r55z+8pe/6KqrrtLBBx+sZ555RpdffrkyMjJ2OY0DDzxQjz/+uH73u9/pqquuUkFBgS655BK1atVK5513Xo38/vvvr7vvvltXX321Fi1apE6dOumf//ynhg4dmshcfvnleuaZZ/TKK6+ooqJCHTp00O9///tqZ1lCoZDOPvtszZgxQzfffHON6Tz66KN69NFHlZaWptzcXO2///4aN26cLr744hq3Xr722mtVWlqq6dOn65///KcOPfRQPf/88/rNb35TLZeenq5//OMfGj9+vC6++GJFo1FNnTpVo0eP1rPPPqvLL79ckyZNUkZGhk455RSNHTu2Xi8r25WRI0dq06ZNeuWVV/T666/r+++/V7NmzXTYYYfpV7/6VbXLDv364osv9Omnn+qOO+6o+wY3Uo3xds+eM9z2Y9KkSXryySf1xRdfKDMzU4cffrhuueWWaqccBw4cqLlz51Yb7he/+IXuvfdeX9MoKSlRXl6e/vZ+H2Vm+6u7Pi5r53cWJElrK3JM+W9Lm5rykrShtIkpH4nZLl3jUrTaVf6w6w5ze57xUrRA00pTXpK6tllrylsvRZu13Hb6n0vRalffl6IFPPuPn0fl/seUn7G5a+2h/9qyOaorf/S2iouLk356OFCbeDyuVq1a6dRTT9V9993X0M3Zoa+++krdunXTiy++qEGDBjV0c/Z548aN07x587Rw4ULO2Oymqs/Sr3zSQVk5yfXzpZviGtLr65TrC0xzO3fuXBUVFWnBggWaNWuWIpGIhgwZotLS0mq5Cy+8UKtWrUq8br311jptNAAASA3l5eU1fmPy4IMP6vvvv9fAgQMbplE+dO7cWeeff77++Mc/NnRT9nkbNmzQ3//+d/3+97+nqKlDMRfYrVcqMl2K9tJLL1X797Rp05Sfn6+FCxdqwIABifebNGmS+JEeAABovBYsWKArrrhCp59+ulq0aKH3339f999/v3r27Jl4kv3easqUKQ3dhEahRYsWief/oO7E5Sme5M0D4kruOU4NbbfKsaqHQjVv3rza+4888ohatmypnj17avz48TUeqAUAABqHjh07ql27drrrrrt02WWX6emnn9a5556r1157rdYHRQJIHs+xMYjH4xo3bpyOOOII9ezZM/H+z372M3Xo0EGFhYX6+OOPdc0112jRokV68skndzieiooKVVRUJP5dUlKSbJMAAMBepmPHjnrmmWcauhkAGoGkC5uioiJ9+umnevPNN6u9f9FFFyX+v1evXmrTpo0GDRqkpUuXqkuXLjXGM2nSJE2cODHZZgAAAADYzu78ViZmfF7a3iKpuR07dqyee+45zZ49W23btt1ltl+/fpK2Pg14R8aPH6/i4uLEa9sHZAEAAACw2/obm+Rfqch0xsY5p8suu0wzZ87UnDlz1KlTp1qH+fDDDyVJbdq02eHfw+GwwuGwpRkAAOxR8XhcK1euVE5ODndtAlAvnHPatGmTCgsLFQjs/l3J4goo1shuHmAqbIqKijR9+nQ9/fTTysnJ0erVqyVJeXl5yszM1NKlSzV9+nT95Cc/UYsWLfTxxx/riiuu0IABA6o9hRcAgFSycuVKtWtne2YaACRjxYoVtV4RhR0zFTZVtz3c/r7zVU/GDYVCevXVV3XHHXeotLRU7dq108iRI/W73/2uzhoMAMCelpOz9cHOHX91vQLhXT/8188zV/0+V9b5PTnkM+cCPibsd6J+Z8LvPPgYnRf3NzK/s+BCdfetdDTL38N2D+69zFfu+/LaH/S9Yk3zWjOSlP6tvytjvIiPBed3tft89nCkqb/giQPerTWzxucD2Dtkfu8rVx5P95Wb+e/aHyCesab2j9zxinJ9dceNiePN7mqMv7ExX4q2K+3atdPcuXN3q0FVNsaaqCLmr3mlUdulbAHj6bXMtIgpL0nh9KgpX17pb+epEvWCprxVZA88mMnvASNpQdt6dsYHwMdj9stR8jM3mfLfljU15a1XyFSW29bBV4MfMOU/3OaOh36dOu8S2wDGbfXWb4aZ8nObLzflW6bb1vHc7w8w5SXp1XUHmfJ/6viE7+zmqHFHaCSqLj8LhDMUzKiDwsbnYva9ee/FhU1djq6uC5t4HRY2gUx/KzU9y98trtOCtX+2CWTueltM5DL8fU4KBOuwsIn5y8Uy/C23cHbt/VV6mr9lG27ir+9zPj+n+FkPwbD/j9x1dblrXAGeYwMAAAAAqSbp2z0DAAAA2DvFnKeY7+tZaw6biihsAAAAgH1MbDfuihZL0UvRKGwAAACAfUzcBRRP8jfT8RS9eQC/sQEAAACQ8jhjAwAAAOxjuBQNAADsnOdqvy9xHd2qVfJ/W2i/4n56fZ+XoPi99bJXhx+Q4mkN8Owcv88cSvcX7JS1wVeuW86aWjNff1nga1yBSr/33PYR8XkbZ7+rPWOtvw/ez/3r8Non6fMz/Ls+n3QQOuQHX7mT+r1fa+bZz3vVmomXlfuanl9xJX8TgFS98T+FDQAAALCP2b3n2KTmr1VSs9UAAAAAsA3O2AAAAAD7mJgLKJbkXdGSHa6hUdgAAAAA+5i4PMV9/+Cs5rCpaK8tbNZHchSO+Pt1V0k0bBr3NW1eNuU7pQVNeUlaFvX767qtblt9nCn/xvLOpvzvFp5kyiuJH5t5fn9hmWTeGdvkpdt++uaMV2YG0+w/rctOqzTl0+v6l8PbcRHbPFe4iCn/TbSZKS9JXx33gCm/NLLZlC91tsPe2R+cZ8qH0qKmfE7Ytk1IUvOMUlP+Dyt/4jsbKa2U9KCxRQAANLy9trABAAAAkBwuRQMAAACQ8nbvOTYUNgAAAAD2AnHnKZ7sc2ySHK6hpWY5BgAAAGCv88c//lGe52ncuHF7fNqcsQEAwC+v9nur+Lovit97p/jN+fxyNRCtPRht4m+ivX682FeufZa/p7dXxmv/SPLO6va+xlXyeQtfudDG2pdHWpmvUckF/N1o6PEP+vjK5bao/SYhoQ3+vp/2/N7PqAG+pI+H/OV+etqcWjNHZ3/ha1xXf36ar1x5xN/H5FIfN7Fy8doXrvUmSbWJ78alaMk+oPPdd9/V3/72N/Xu3Tup4XcXZ2wAAClt0qRJ6tu3r3JycpSfn6+TTz5ZixYtqpYZOHCgPM+r9rr44osbqMUAUP/iLrBbL6vNmzfr7LPP1n333admzex3Ra0LFDYAgJQ2d+5cFRUVacGCBZo1a5YikYiGDBmi0tLq33hfeOGFWrVqVeJ16623NlCLAaD+xeTt1kuSSkpKqr0qKip2Or2ioiIdf/zxGjx48J6axRq4FA0AkNJeeumlav+eNm2a8vPztXDhQg0YMCDxfpMmTVRQULCnmwcAKatdu3bV/n3DDTdowoQJNXKPPfaY3n//fb377rt7qGU7RmEDANinFBcXS5KaN29e7f1HHnlEDz/8sAoKCjRixAhdd911atKkyQ7HUVFRUe2byZKSkvprMADUg2QvKasaVpJWrFih3NzcxPvhcM3fE61YsUK//OUvNWvWLGVkZCTX2DpCYQMA2GfE43GNGzdORxxxhHr27Jl4/2c/+5k6dOigwsJCffzxx7rmmmu0aNEiPfnkkzscz6RJkzRx4sQ91WwAqHMxKXFJWTLDSlJubm61wmZHFi5cqLVr1+rQQw/93/CxmObNm6e//vWvqqioUDDo7+Yau4vCBgCwzygqKtKnn36qN998s9r7F110UeL/e/XqpTZt2mjQoEFaunSpunTpUmM848eP15VXXpn4d0lJSY1LMgBgb1YXZ2z8GDRokD755JNq740ZM0bdunXTNddcs8eKGmkvLmxCXlRhz1+VWVKZaRp3q2DclG8SsI1fkrqmR0z5o5v6u0VhlUv7vW7Kb4rbTg0uj7Q05SVpTSTPlI8424b+3g8dTPmPv97PlFfQ731Vt4puSreNX9KL7/cyD2PhRW0HsN8PftyUXxTxe8/QrX75+tmmvCSdOOI+U75LerZ5GhahtKgxb1tG6UFbPhlZaTv/sef2KoOV9diS+jV27Fg999xzmjdvntq2bbvLbL9+/SRJS5Ys2WFhEw6Hd3jJBQCgupycnGpnyCUpKytLLVq0qPF+fdtrCxsAAPxwzumyyy7TzJkzNWfOHHXq1KnWYT788ENJUps2beq5dQDQMGIuoFiSZ2ySHa6hUdgAAFJaUVGRpk+frqefflo5OTlavXq1JCkvL0+ZmZlaunSppk+frp/85Cdq0aKFPv74Y11xxRUaMGCA/SFyAdX6oIQ6/TxgO5FcKz8Paoyn+Ztop6wNvnKdM9f5yhVHd3wjh219kGY8E18LPw+HjPm8yCOW4W+5tSoo9pUrKa39Sotolr9phir8XQHzwSV31pr55yZ/Xwb88aEzfOUCPk8S/+PDH9eaebFVd1/jCgb8rdSyZbv+bUmVNz4+uNZMYb/VtWaipRVa4WuK/jh5iif5Gxu3m09rnTNnzm4NnywKGwBASpsyZYqkrQ/h3NbUqVM1evRohUIhvfrqq7rjjjtUWlqqdu3aaeTIkfrd737XAK0FANQXChsAQEpzbtffWrdr105z587dQ60BgL0Dl6IBAAAASHlx5ynukrukLNnhGhqFDQAAALCPiSmgWG0/CtzFsKkoNVsNAAAAANvgjA0AAACwj+FSNAAAAAApL66A4klenJXscA2NwgYAAADYx8Scp1iSZ16SHa6hpWY5BgAAAADb2GvP2OQEK5QR9PGIZEnrt2SZxn3liuNN+SObLjblJemldT1N+dzQFlP+gNZrTHmr76PZ5mHWR2zDlERrf6rytlaX5pjyLmKs2435QHkS3wsYnyIez/T5yOskx78mkmfKd0z39wTxBJ9PMN9W/49GmvJzej9myoe9dFM+YPzSKujZ5jk94O84t62MYNQ2Dc//duSM7ccO+FiGTX+81teoujXzlyuu9Hc8/XBZu1oz4aX+xvXcC/185ZzPTxou6CNkPCTWJppR+7qKZPvbJ1yLSl+5A5r5O45+F6r9+Lz8+0xf49JGf/3Vpnjt87Ap7m+a8y7+k6/c61sKfeWWVeTXmpk24zhf4/J7mAv7i8nPI1++f6ug1kysotznFP3hNzYAAAAAUp5zAcWTfNCmS9EHdKZmqwEAAABgG5yxAQAAAPYxMXmKKcmbByQ5XEOjsAEAAAD2MXGX/G9l4in6c0sKGwAAAGAfE9+N39gkO1xDS81WAwAAAMA2OGMDAAAA7GPi8hRP8rcyyQ7X0ChsAAAAgH1MzHmKJfkbm2SHa2gUNgAAAMA+pjH+xobCBgAAn6JZToFanlYfLKv9m85uzdb6mt6hOd/4yq2PZvvKrWxV+9PsD+vxia9xtUgv9ZULenFfuZiPD1Jl8ZCvcc1b3cVXbu2iVrVm4mF/7fd+8Ne2d+Yd5CuXtqX27Shns69R6caLH/SVWxOrfR1MeXCEr3EVXb7CV+6M7GJfuYqs9bVmpnnH+RqX7xt++f1s72MTcUEfmdSsJfYqFDYAAADAPiYuL/nbPfMbm7r1+LeHKC0r7Cu7udxfrsrGjExTfk2k9m+4tlcZ91Gab+OD1W1N+fO+G2XKe57thuRbSm3LVJJcxPZVgxe0tclVGr/KMN6D3YvadmIXsN/k3ToNL2Y8sBibNPnVIab83ZmDbBNI4rg4q9cjpnzYyzDlf4iVmfLWe/kHjPtaMsLBqCmflVbhO5uWFrE2BwCwF3K7cfMAR2EDAAAAYG8Qd7txxiZFbx7A1XwAAAAAUp6psJk0aZL69u2rnJwc5efn6+STT9aiRYuqZcrLy1VUVKQWLVooOztbI0eO1Jo1a+q00QAAAAB2ruquaMm+UpGp1XPnzlVRUZEWLFigWbNmKRKJaMiQISot/d+dUa644go9++yzmjFjhubOnauVK1fq1FNPrfOGAwAAANixqkvRkn2lItNvbF566aVq/542bZry8/O1cOFCDRgwQMXFxbr//vs1ffp0HXvssZKkqVOn6qCDDtKCBQv04x//uO5aDgAAAAD/tVvnmYqLt957vHnz5pKkhQsXKhKJaPDgwYlMt27d1L59e82fP3+H46ioqFBJSUm1FwAAAIDkxf97V7RkX6ko6cImHo9r3LhxOuKII9SzZ09J0urVqxUKhdS0adNq2datW2v16tU7HM+kSZOUl5eXeLVr1y7ZJgEAAAAQl6KZFBUV6dNPP9Wbb765Ww0YP368rrzyysS/S0pKKG4AAHsl17RSLnPX3wl6m2t/Dticj7v5mt7nbVv7yq35urmvnJcZqzVT0dLfR4PNMfvzznYl4uPR7KVRf9MsrQj5yjkfz1MLlPv7DrjFR/4+CLaa852v3JeX7FdrJhDZ8x8+Izn+ntXVdfYYX7nHD7/XV655sPZnbPlZn1uD/pab3+fVeXX0O/tkno+3K43xds9JFTZjx47Vc889p3nz5qlt2/89WLKgoECVlZXauHFjtbM2a9asUUFBwQ7HFQ6HFQ7X7cERAAAAQONiqjGdcxo7dqxmzpyp119/XZ06dar29z59+ig9PV2vvfZa4r1Fixbpm2++Uf/+/eumxQAAAAB2iUvRalFUVKTp06fr6aefVk5OTuJ3M3l5ecrMzFReXp7OP/98XXnllWrevLlyc3N12WWXqX///twRDQAAANhDuBStFlOmTJEkDRw4sNr7U6dO1ejRoyVJt99+uwKBgEaOHKmKigoNHTpU99xzj7lhkVia4lF/zduyxd+1tFU+37Tjy+J25j/LC035PcFVGi/oTI+b4gFjXpKc8dJQV1779dS7w8uM2gbwce15NclcCvuDbVsNbLEdWNJKbdtFwLiIKprZ8i5kX0gnfXGGKX964UJT/s7PjjXlQ+nGhaTarwPfVlrAvq9F4rZ9Jxr3v11YsgCAvZeTkr67Wd3+2mfPMRU2zscn14yMDE2ePFmTJ09OulEAAAAAYJH0XdEAAAAA7J24FA0AAABAyqOwAQAAAJDyKGwAAMBOpX8XViBj189ec356Vp83aAkFfd7UJM3fT33TVtZ+A5PZKw/xNS4fz9OUJHk+Z8HzsUh8PxPU87c8XJPaJxr0eVOWjQf6iskFan/wpiTFw7XPgwv4+/B552Vn+cp9O7D2jdfvMyTfHvBXX7n8YJav3LQSHw9v9/th3Of24TPma7q+Hh7KvVt2G4UNAAAAsI9pjGdsqA0BAClt0qRJ6tu3r3JycpSfn6+TTz5ZixYtqpYpLy9XUVGRWrRooezsbI0cOVJr1qxpoBYDQP1zztutVyqisAEApLS5c+eqqKhICxYs0KxZsxSJRDRkyBCVlpYmMldccYWeffZZzZgxQ3PnztXKlSt16qmnNmCrAQB1jUvRAAAp7aWXXqr272nTpik/P18LFy7UgAEDVFxcrPvvv1/Tp0/XscdufUDr1KlTddBBB2nBggX68Y9/3BDNBoB6FZeX9AM6kx2uoXHGBgCwTykuLpYkNW/eXJK0cOFCRSIRDR48OJHp1q2b2rdvr/nz5+9wHBUVFSopKan2AoBUUvUbm2RfqYjCBgCwz4jH4xo3bpyOOOII9ezZU5K0evVqhUIhNW3atFq2devWWr169Q7HM2nSJOXl5SVe7dr5uCMTAOxF+I0NAAAprKioSJ9++qkee+yx3RrP+PHjVVxcnHitWLGijloIAKgve+1vbNKDUaWl+btJvuf3pur/Za1CXWQvrP+ixko67vOBA1Vx29i3itqWkxexzUOg0paPR9JNeXOZn8xCsg5jXM2VLX0+MOK/XMjWoFBuhSlfuan2Z2Zs76ulrU35P68ZYsqHwhFT3sp6+r4iaj8Mb3SZprylTZGKSmtz9hpjx47Vc889p3nz5qlt27aJ9wsKClRZWamNGzdWO2uzZs0aFRQU7HBc4XBY4bDfh6YAwN6H2z0DAJBinHMaO3asZs6cqddff12dOnWq9vc+ffooPT1dr732WuK9RYsW6ZtvvlH//v33dHMBYI9ojJei7bVnbAAA8KOoqEjTp0/X008/rZycnMTvZvLy8pSZmam8vDydf/75uvLKK9W8eXPl5ubqsssuU//+/evljmgBHye9wt/4Oxu05rsdn1Hant9zo56Pk7R+z45Hs/xdLREP+8sFfJzFD/o8oRjJ9pfLKiitNdOya+0ZSUoP+jtjvna1v99rebHal1tl9zJf4/r6AH/fY7eYXfvHwopm/raPYR+c5yvXp/W3vnJvvHSwr5wvfj+0ez6vCPKRi/v4xO0nY+F244wNhQ0AAA1gypQpkqSBAwdWe3/q1KkaPXq0JOn2229XIBDQyJEjVVFRoaFDh+qee+7Zwy0FANQnChsAQEpzrvZvSzMyMjR58mRNnjx5D7QIABqek+Tj8LjTYVMRhQ0AAACwj4nLk8cDOgEAAAAgtXDGBgAAANjH7M7dzbh5AAAAAIC9Qtx58hrZc2wobAAAAIB9jHO7cfOAFL17AL+xAQAAAJDyKGwAAACAfUzVb2ySfVlMmTJFvXv3Vm5urnJzc9W/f3+9+OKL9TRnO7fXXor2fy2+Uyg73Vf2rUgn07gr0v2Nt0o0w17/xWO2YWJRWz7uBU15RYzzYM0nwYvZdppguS1vvTw0nuHjkdzbCBjXmSQFfT7Ru4r1THC4vb8nZFfZP3+dKd8qvNmUf8/nE7a3takk05R3ttWmmHHfDAZtE4jGbeOvjBv3ZUme36dh/9fmqL+n3EtSJJqa11XvMU617pjWY9uu+D3uOZ+bkZ9cIOpvXJ7PXcP/09Rr367Tt/jbvzLW+ltuZV52rZmV+fZ9dFeiXWO+cq5J7bmQz+NTq2abfOUyflb7yt/wnza+xhV+v4Wv3Ly05r5yLr3uro3yfQitw8ux/Owvfvcpv/bkzQPatm2rP/7xj9p///3lnNM//vEPnXTSSfrggw/Uo0ePpNqQjL22sAEAAACQnD1584ARI0ZU+/cf/vAHTZkyRQsWLKCwAQAAANCwSkpKqv07HA4rHN71VQCxWEwzZsxQaWmp+vfvX5/Nq4Hf2AAAAAD7mKq7oiX7kqR27dopLy8v8Zo0adJOp/fJJ58oOztb4XBYF198sWbOnKnu3bvvobndijM2AAAAwD5ma4GS7G9stv53xYoVys3NTby/q7M1Bx54oD788EMVFxfr8ccf16hRozR37tw9WtxQ2AAAAAD7mLq4eUDVXc78CIVC6tq1qySpT58+evfdd3XnnXfqb3/7W1JtSAaXogEAAACoU/F4XBUVFXt0mpyxAQAAAPYxPu5Ov8thLcaPH6/hw4erffv22rRpk6ZPn645c+bo5ZdfTrIFyaGwAQAAAPYxe/I5NmvXrtW5556rVatWKS8vT71799bLL7+s4447LqnpJ4vCBgAAAEDS7r///oZugiQKGwAAfKtsEVMgc9dPhA9t8PGkep/XebiAv29NXcDnCH38sjbu85NBsMxf2+I+FockxUO1z0POcn/zuf6YSl+59BWhWjPte/zga1wr3mrrK3fHWf/wlVsdbVprpmP6Ol/jygr4+53Dhlh2rZmbIz/xNa61X7XwlUvb5O/n3n5OIHh+9yu/JyP8Xo/lZxb8jCu5kyu7nuaeuhZtL0FhAwAAAOxrduNSNP/V395lry1sMoIRhX1+y9O8yRbTuLdEoqZ8ZcxnQ7YR8Pu1wX8d3PI7Uz47aLvLxMZIE1P+m9JmprwkLVne2pR3EdtOE8swxRXP2vW3qtsLNLFtF/E0++7j0m03IkwrseVjS2r/tm1b//kmy5T3osZ1lhk35SXJi9mmEQ/bphFpYlymMVs+GrUdLypC9u0oK+zv2+gqaQH/yyhakZqdGQCgum0ftJnMsKmI2z0DAAAASHl77RkbAAAAAMnZk3dF21tQ2AAAAAD7Gucl/1sZChsAAAAAewN+YwMAAAAAKYgzNgAAAMC+hufYAACAnQlsCSjgarnYoQ4fxOfS/H26iIX95bK6FteaKcjZ5Gtc6UF/t9QvqfB3r/4Vy1rVmik+wN/t1NO/q/3Bm5LUtn/tj1o4e793fI3rqFGP+sqVOn8fvfZL21hr5i8rh/oa17/nH+grF8upfZ02WZ7ua1z7T3rbV+7b8Yf7ykVyat/GjU/aqJWzP+1jr9IYbx7ApWgAAAAAUh5nbAAAAIB9UYpeUpYsChsAAABgH9MYL0WjsAEAAAD2Ndw8YO9xY/4nys3x9xOgWP77pnEvrPT3g8cqf/5umCkvSeUxfz+uq9Iza6UpnxcsNeWLY1mmfGYwYspL0tdZzUz5SMTfD0qrxHz+iDYhFDfFzd9OxJL4NsPWJPn8jWlCNNu2bSvD1iAvYFsHWXlbTHlJykiPmvItmtj2hez0ClM+P2OzKZ/m2dZBQajElJekDuH1pny79A2+s6Wb4jrB2iAAAPYCe21hAwAAACBZnnzfgnGHw6YeChsAAABgX9MIL0Uz3+553rx5GjFihAoLC+V5np566qlqfx89erQ8z6v2GjbMfikXAAAAgCS53XylIHNhU1paqoMPPliTJ0/eaWbYsGFatWpV4vXoo/4eWgUAAAAAyTBfijZ8+HANHz58l5lwOKyCgoKkGwUAwN7o0B8tUXrWrp9qn5NW+w0qVpQ29TW9Lz9r6yvnV0HOploz7bN/8DWu9IC/G2WsT/N385pvm9R+A5pI1Od1/zn+boCTmVZ7bllFK1/jqvR5t5dVlU195TICtbetLLrrbbFK977LfeUqY8FaM6Fu/tZ72nB/y+3IjA985Yojmb5yfqR5/m6ck8yNlHYmy8dxoXJzRH+bUGeTlJy39ZXssCnIfMbGjzlz5ig/P18HHnigLrnkEm3YsPM78lRUVKikpKTaCwAAv7hEGgBqcm73XqmozgubYcOG6cEHH9Rrr72mW265RXPnztXw4cMVi+24wp80aZLy8vISr3bt2tV1kwAA+zAukQaAHWiEv7Gp87uinXnmmYn/79Wrl3r37q0uXbpozpw5GjRoUI38+PHjdeWVVyb+XVJSQnEDAPCNS6QBAFI9XYq2rc6dO6tly5ZasmTJDv8eDoeVm5tb7QUAQF2yXCINAPuEqt/YJPtKQfX+HJtvv/1WGzZsUJs2bep7UgAA1DBs2DCdeuqp6tSpk5YuXaprr71Ww4cP1/z58xUM7vjH0hUVFaqo+N+Pffn9J4BU47mtr2SHTUXmwmbz5s3Vzr4sW7ZMH374oZo3b67mzZtr4sSJGjlypAoKCrR06VL9+te/VteuXTV06NA6bTgAAH5YL5GWtv7+c+LEiXuqiQCAOmAubN577z0dc8wxiX9X/T5m1KhRmjJlij7++GP94x//+P/t3X9wlFWe7/FPdyfd+UHSMSTkhwQIKKAiMIsQuSKDC0tgqlgZ2ClkvDPoeLXGCe5iynEWr4I4VqUuOzVDucvI1tYqzq3BX3MVV51lakSBdQdwwckwrMpAxCEMJPzQJCQh3en0c/+AZIgE+pxOJ/0j71fVU5V0vn2e79NPd6dPP+d8j5qamlRaWqr58+frhz/8oXw+X+yy/pL/6LA7jF98PtMqviWYYRUvSR2hdKv4//vZDKv48dector/36W/tIq/3tdgFS9JtXnXWsWf7IxcVvJSTpfdyMnMnA679i0vu9q1fnEfIbtjCF8TtIrPyrKLz82yO4qMtJBVfHa6XT6SlJVmd58CX5tVvNtyRqRJic5LhcKW51j2l/s7wnbvLw0hv3Fse8islGsyu3SI9JU6Nsz/BJD0+lMEYKhcsZkzZ46cq9SA+9WvftWvhAAAGEgmQ6R9Pt+AfiEHAANuCK5jM+BzbAAAGEgMkQaAPnDFBgCA5DKYQ6S97pDS3Vcfbvif9eUR2wkcH2a0v7T22H5revSDyMPp6kaZrRj/d1PfNYrrzDQbdnwkryBiTJOTbdRWms9s2Oyw9MhDTTvDZvkfPl9kFOc2nJXtT2uPGDN7+GGjtjodu6HfV2M6FNZ0mG1BWqtR3Mlg5CG1XTEu9pvuMhuaa/L4mrQV8HQa7Q9XRscGAJDUGCINAH3gig0AAACApEfHBgAAAEDSG4LFA2I7GBEAAAAA4oArNgAAAECKcTkXtmjvm4zo2AAAAACpZgjOsWEoGgAAAICkR8cGAAAAQNJjKBoAAACQYlzqxxybmGYyeBK2Y/PPTWXKCJmlV9dhtkpyt07H7kJVnu+8VbwktbrDVvEZaXarzV6ffcoq/vNwhlV8VxRP6cnDT1jFj8n93Cq+pdPuGI6cjryK9aU6GsxWtO4Pl93TQo7LbrXo9g67l/SIXLMVn7sda8y3ig+3mq1Q3S9euwfV5bZ8l7f8r+BOt8snHLRfEdzlsduHEzZ/PYfPd0iqtUtoCNm34wa5MyK8Fxk83Kav1FhXXDVpLzu7w6itz0Nm75lZnoBR3Ch/U8SYicPN/vflpJsdw6fnIv+f2Pvf44zacvnMVql32gzPflrk955hhW1GTS0Z+zujuF8cmRoxJvBZjlFb7qDZkzds+HAY/f80fL10ZZq9h6adM/u8aJKbuzNycl0dHZLeMtqnEco9AwAAAEDySdgrNgAAAACiNASrotGxAQAAAFINHRsAAAAAyW4oLtDJHBsAAAAASY8rNgAAAECqYSgaAAAAgKRHxwYAAABAshuKc2zo2AAAYMqlyIsAmnwgiPXad6btGcysLb/GbPHksOFOz3SaLeh45GzkxTLbz/mM2hpR2GIU5/VEXlTTZbgIsHPe7COVO2A2vdnTHPnxbQuaPba/CE81ims/HXnR1bQuwydbjD8YO57IDTqG6x076WbJhbLN4tyhyDFhg/Wqw7YLSOMydGwAAACAVOO4LmzR3jcJ0bEBAAAAUg1zbBJHmiuk9AHqLHosBw7mpAWs9xEK21XSbne8VvE3ZP7JKr7LsrL3ua5Mq3hJ+qw13yq+7lTkYQeXyvB1WsW7LS/pOobDDXqE7J+gLttvQCzfWFxBu/Ncf8runIUDhtf5u0VzWd3yPh5f5KEkl0pLNxgzcGl8mt3zIssXtIoPdNq/DXdZvr+Ew+bPuy6XXf4AACSKhO3YAAAAAIgOxQMAAAAAJD+GogEAAABIev24YpOsHRu7gdoAAAAAkIC4YgMAAACkmiE4FI0rNgAAAECqcfq5WaipqdH06dOVk5OjESNGaPHixTp06FCMDsQcV2wAADDkuC9sVw+K4Q4Nv34MpxmukD6yPWJMY/swo7a2fjHZKK71qN8ozt0ZuSy5afX1L+oLjeI6cyM36GRaLgUQqT3Dc+oYVNf3f2RWgv/c+VyznWZHLp8f9po91zyGSyK4DVdycNyR23MZVv93By2XLogFk4cjxsucDGZVtJ07d6qqqkrTp09XKBTSY489pvnz5+ujjz5SdnZ2dElEgY4NAAAAgKht27at1++bN2/WiBEjtH//fs2ePXvQ8qBjAwAAAOAyLS0tvX73+Xzy+XwR79fc3CxJys+3Wwi8v5hjAwAAAKSaGMyxKSsrk9/v79lqamoi7jYcDmvVqlW67bbbNGnSpFgf1VVxxQYAAABIMbGYY1NfX6/c3D/P0TK5WlNVVaWDBw/q/fffj27n/ZCwHZsMd6cy3GZnw+cOWbUdduxmZ3W57Gdzed2GM9gu+ovhdVbxp0OGEwEv+q/WsVbxb9XZ97A7g3ZPp67zdpP3gl9kWMUr3W7Cp8trF++47C94mk4a7ebqsnvuuQwm316qqyXdKl6+gX1MJckJWx5D0PZBtXveZfoMZ7Ze5PXYvfZt4yUpFLY7ZpfFf7YuBWzTAQCkqNzc3F4dm0hWrlypt956S7t27dLIkSMHMLO+JWzHBgAAAEA/DNJ6NI7j6KGHHtLrr7+uHTt2qLy8fHB2/CV0bAAAAIBUM4gLdFZVVWnLli164403lJOTo4aGBkmS3+9XZmZmlEnYo3gAAAAAgKg9++yzam5u1pw5c1RSUtKzvfzyy4OaB1dsAAAAgBQzmAt0Os4gjXmLgI4NAACm+jO0YwCFfWZJ3X3D/ogxo7xnjdpqD0eujiRJh0eOMIr78ExZxJiTZ/xGbeXntRnFZaZHLg4S7DIrONJ4Is8ozvTpE8qIHNk2yuxjXPo5w6Is7sgDecJesyMIp5vt0zQ3Jy3yfoOjgkZtzb3hE6M4t8usAM6+xsjP3aYjkddzCXfF+M1lEIeiJQqGogEAktquXbu0aNEilZaWyuVyaevWrb3+7jiO1qxZo5KSEmVmZmrevHk6fPhwfJIFgEHSfcUm2i0Z0bEBACS1trY2TZkyRRs3buzz7+vXr9czzzyjTZs2ae/evcrOzlZlZaU6OjoGOVMAwEBiKBoAIKktXLhQCxcu7PNvjuNow4YNevzxx3XnnXdKkn72s5+pqKhIW7du1V133TWYqQLA4GEoGgAAqePo0aNqaGjQvHnzem7z+/2qqKjQ7t27r3i/QCCglpaWXhsAJBWnn1sSomMDAEhZ3WspFBUV9bq9qKio5299qampkd/v79nKyiJPDgaARMIcGwAAoNWrV6u5ublnq6+vj3dKAIAIUmKOTbqryyr+vNKt4oNh+4cpJ91uUurf5v/OKr7L8hrhiaxDVvHjMk5ZxUvSji8mWMV/fLooctAlrsk6bxWf57OLbw5mWMUfP32NVbwkdYXsvktwugxLdHbHnzcrS9rNFbZs3ypa8njtXpuSFLY85vB5u9en5SGr7Nomq/inRr1hFV/giVxu9sv2BYqt4t84+xXj2GB6UJELAieP4uILj1VjY6NKSkp6bm9sbNTUqVOveD+fzyefz6ycMQAkJObYAACQOsrLy1VcXKzt27f33NbS0qK9e/dq5syZccwMAAYYc2wiY70AAEAiaW1tVW1trWprayVdKBhQW1urY8eOyeVyadWqVXr66af1b//2b/r973+vb3/72yotLdXixYvjmjcADKShOMfGeoxV93oB3/nOd7RkyZLL/t69XsALL7yg8vJyPfHEE6qsrNRHH32kjAy7oT4AAESyb98+3XHHHT2/V1dXS5JWrFihzZs369FHH1VbW5seeOABNTU1adasWdq2bVtU/5M6/V1yZ159iOXtX4m8qvnhpkKj/TX8cbhRnCtg9j3lX+f+NmJMjtt+eOTVTM/81ChuhPdcxJj30sYbtXVd7mmjOJ87FDGmqTPTqK1Tp3ON4mQ4ZNgx+ITmDpiNrc0+afYptdUT+XkUyg4bteV4zPYZyjIKk8tgt+50s9wyPUGjOJPnhyTdVHDlQiTd3v9iWMQY57xZXrgy644N6wUAABLJnDlz5DhX/hDlcrn01FNP6amnnhrErAAgzphj0z/RrhcAAAAAIHYYitZP0awXEAgEFAgEen5nETQAAAAAtuJeFY1F0AAAAIAYoypa/1y6XsClGhsbe/72ZSyCBgAAAMQYHZv+iWa9AJ/Pp9zc3F4bAAAAgOi5+rklI+s5Nq2trTpy5EjP793rBeTn52vUqFE96wVcf/31PeWeWS8AAAAAwECy7tgM5noBAAAAAKIwBMs9W3dsBmu9gInek8r2mY2Uuyf3lFXbdZ2tVvHPf9H3MLqr+azdbFG1bsPcA9vx83vt4vPch6z3kePpsIrP9NxoFT8m86xVvD+t3Sr+VNBuGOTbbYaril2ird1nFR+2HC1q/T4UsrzYbBmfln71hQz7vE+ExQ+/rMNwEbhuto9R2LE75qaw2WJ+3coNF5S71KyMxshBl/gsx3zuYocrpJdsExpC5kz9WN5hV39DzU07H7Gdpiyz948Gd75RnOe82fP0bw9FXk/uieveNmor6JgtNPl/6hYYxZ36XVHkIEPHdK1RnJMW+R3B9C3A9J3C8GGTy2Cd1K4Ms3e08wVm2ZksqunqMn1AzHILe83i3Ab/f9zHzD5LvX1qmlHciIlmC71eO6w5clDY4HEzibHQn7LNlHsGAAAAkBiG4BWbuJd7BgAAAID+4ooNAAAAkIqS9MpLtOjYAAAAAClmKM6xYSgaAAAAgKTHFRsAAAAg1QzB4gF0bAAAAIAUMxSHotGxAQAAAFLNELxiwxwbAAAAAEmPKzYAABja+V83yZ159dXN3QYrxrsMVlGXzP9Jm65Xfrq2KGLM3/72O0ZtObFdgN7sIGK7MLvCBg+wp7DDqK1RIz43imvvTDeKO3UmN2JM5cyPjNp6+/c3G8X5jhvkZng+XV1mJ8tJM2swbBDnDhu+rlrN4j7fN8IsTpHj0g12Ge4IG+3PFEPRAAAAACS/ITgULWE7NtN86cr1DcxIOb87xl/59OFMR7ZV/MamMqv4/+X/1Cr+866AVfyak5VW8ZK067NxVvEuy68D9rjGWMWnp4es4h3Trx8v6uqyf356PHbfxrgtH6NOr2VOlscst10+tudYkjK9Bl93XyLD8jyfD5p9W9rtyKkCq/hH25ZaxX9v3E6reEn6aqbd6z/d1WUcG7KIBQAksCHYsWGODQAAAICkl7BXbAAAAABEhzk2AAAAAJLfEByKRscGAAAASDEux5HLia6HEu394o05NgAAAACSHldsAAAAgFTDUDQAAAAAyY7iAQAA4IrSW1zyBPu/FppjOhDc9MNFDAeWuzsNV4w3fRhi+AnJ8cSsKUmS+1zkg+jqzDBq69OmYqM4V8jsgXMHIp/Ufz/7FaO2shrNniAmpyqcZnY+O/PM1sSaPqnOKG7fkTERY5zTXqO2ZLr8meFT1+RxM2oqSTsTiYSODQAAAJBqGIoGAAAAINkxFA0AAABA8huCV2wo9wwAAAAg6SXsFZvXWnOV5TKbJVjbNtqq7ZMBv1V8c9Bs4uCluoxnhl7wwtFbreJ/2jHbKt5WOGzf501LM5so2BPvCVvvw0aWL2gVP85/1ir+xmEnreIl6Y8d+VbxB86WWsUvuPFjq/gtn9xiFR9s8VnFh0L2zyO35fVvf8Z5q/jz6elW8a0Bw8moFwU67d5W//no7VbxkvSLzGlW8aOyvzCODbbZvW4AAImJoWgAAAAAkt8QHIpGxwYAAABIQcl65SVazLEBAAAAkPS4YgMAAACkGse5sEV73yRExwYAkPKefPJJrVu3rtdtEyZM0CeffGLXkOO6sF1NzJYhlxyzGjoK5RgWYwlHXvU+vdWsKVcocls2ugzq9PzVHb81aisnrcMo7vdNkQu01P3GrECRO2h4sgzHypg8RVwG51OSQtlmTzh30KA9w/zdwzqN4m7IaTCK+3125HMV/NysOIzjMXvcXIYvK2dgayFFjeIBAACkqJtuuknvvPNOz+9pafwLBIBUwrs6AGBISEtLU3FxcbzTAIDBMQSrolE8AAAwJBw+fFilpaUaO3as7r77bh07dizeKQHAgHGF+7clI67YAABSXkVFhTZv3qwJEybo5MmTWrdunW6//XYdPHhQOTk5l8UHAgEFAoGe31taWgYzXQDovyF4xYaODQAg5S1cuLDn58mTJ6uiokKjR4/WK6+8ovvuu++y+JqamsuKDQAAEhtD0QAAQ05eXp7Gjx+vI0eO9Pn31atXq7m5uWerr68f5AwBoH+6q6JFuyWjhL1i84eOEvnSzMr2He/Is2q7o8us3W7NwUyreElq77TbR6DT7lSEQoZlJS9yWT5D3W77Z7TLsvKnx203gNOJVGL1S2wf0z+1+a3iz3RkW8VL0tl2u/u0dXit4rd8cotVfPALg/qq/RAO2393kp/ZbhX/wzFbreJfaZphFf/fzSVW8W2dducsGiHLx7UzbP5+YRObzFpbW1VXV6dvfetbff7d5/PJ5/MNclYAEENDcB0brtgAAFLeI488op07d+qzzz7Tb37zG33961+Xx+PR8uXL450aAAyIwbxis2vXLi1atEilpaVyuVzaunXrgBxTJAl7xQYAgFg5fvy4li9frrNnz6qwsFCzZs3Snj17VFhYGPN9WV5cvirTykRGCytKcnUZxMX4i9qw4SeN6bdFXizVbZjcuZDZ1ejWoMFVOdPHw/SrYtP2TE6Vx6wx4xEPocgh7oBhW8fMzsGWP802ipvyPw5HjCkec86orYaOywuG9OV3u683ivOcj9GL3nDB1UTU1tamKVOm6Dvf+Y6WLFkStzzo2AAAUt5LL70U7xQAYHANYlW0hQsX9irSEi90bAAAAIAU058iAN33+3Kp+0Sff8gcGwAAACDVdBcPiHaTVFZWJr/f37PV1NTE+aCujis2AAAAAC5TX1+v3Nzcnt8T+WqNRMcGAAAASDmxGIqWm5vbq2OT6OjYAAAAAKlmEIsHJAo6NgAAAACi1traqiNHjvT8fvToUdXW1io/P1+jRo0atDzo2AAAAAApJhZD0Uzt27dPd9xxR8/v1dXVkqQVK1Zo8+bN0SURBTo2AAAAQKoJOxe2aO9rYc6cOXKc+I9fS9iOjdsVlsdwyeWg6bLGF7V22lV06ArbV8X2+zqs4tPdhstLXzTQT500y3wkKcvbaRX/TxNetIr3u+3aP9Q53Cr+J3/8K6v49k6vVXw00tK6rOKNV5e+yJ1t95iGgx6r+GjWUM73tVvFT0i3e67elHncKr4tZPd+8Xkwyyq+o8v+bdht+VWa23T5esvYocgVvrBdNaYr8jM/2m9Rr8Qdit2K5aZvI8ECs/enWX/xsVFcvjfya/+9erOV4Nv/kGcUl9ZucLBpZicrlGUW5y40/HxwMiNiyKRbjho1tbjot0ZxbkV+/T/19t8YtXXdV+qN4uaPMHt+fNA8xijORNjwSZ4z8XOjuEmFJyPG1DUXRIwJtQX0qdEeDQ3BOTasYwMAAAAg6cW8Y/Pkk0/K5XL12iZOnBjr3QAAAAC4Apf+PM/Geot38lEakKFoN910k955550/7yQtYUe8AQAAAKnHcS5s0d43CQ1IjyMtLU3FxcUD0TQAAACACAazKlqiGJA5NocPH1ZpaanGjh2ru+++W8eOHbtibCAQUEtLS68NAAAAAGzEvGNTUVGhzZs3a9u2bXr22Wd19OhR3X777Tp37lyf8TU1NfL7/T1bWVlZrFMCAAAAhhann1sSinnHZuHChfrGN76hyZMnq7KyUr/85S/V1NSkV155pc/41atXq7m5uWerrzcrDwgAAACgby7H6deWjAZ8Vn9eXp7Gjx+vI0eO9Pl3n88nn89unQgAAAAAVxG+uEV73yQ04OvYtLa2qq6uTiUlJQO9KwAAAABDVMyv2DzyyCNatGiRRo8erRMnTmjt2rXyeDxavnx5rHcFAMDgizBCw9U1OGn03mkMmzJsy+UPGsWFHbPvUNtCkUdvTCn6k1FbocIGo7ic9I6IMR7D8lAtnRlGcaMyzVazfzNzUsSYwoxWo7ZOBK8xistwd0aMuWvufxq1VZDe99zqL0s3fMHcllcXMebzULZRW/tOm83nXjLmd0Zx4zNORow57c+NGHO+NaR9Rns0058hZQxFu+j48eNavny5zp49q8LCQs2aNUt79uxRYWFhrHcFAAAAoC/9KQKQnP2a2HdsXnrppZi00xLKVCCUbhQb7PJYtR3osjvsgGX7khR27L4+6wzbjQpM9wzsV4KZ6SHr+/xo/KtW8fmeyN8MXWqgx03aPqbRnAOP23bQqt38szTr9u0EwnbP63CX/dfIB07ZDVv9+vlvWMU/f/2LVvHfzj1jFW/r+w1fsb7PmeAwq/hhnoBxbMDydQkAQKIY8OIBAAAAAAaZ41zYor1vEqJjAwAAAKQYl3Nhi/a+yYiODQAAAJBqhuAVmwEv9wwAAAAAA40rNgAAAECKcYUvbNHeNxnRsQEAAABSzRAcikbHBgAAAEg1rGMDAACuxHFLTqSlzUxmr8b6Q4Npewa5hT1mjXnSzNby8rnN1kVLd0duL+SYTQ0OG5Z0Chu0FzJcv+toS75R3H+9e4NRXLAw8uP2ac5wo7be/cN4o7hwIPK6fenDgkZtuQyXMQt1mq0VGG6NvLZh+udmbbkMl6F74cAdRnElX2mIGNNlsL5hqC0g6T+M9om+0bEBAAAAUozLceSKckhZtPeLNzo2AAAAQKoZgnNsKPcMAAAAIOkl7BWbcyGfAiGvUWxzMNOq7bagWbvdAp32D1PAcslWl2W813Bsc7fr805bxT9e+u9W8ZJU6DYcUHvRzo5iq/g3z061im/syLGKz/B0WsUXZ5yzipeksozPreLfO202LrrbPdf+xip+eka9VfzpLrvX2j377rGKl6RvXfeBVfz38+us4lvDifW29/3C963vM8KTbRUfcMyf2y3nwnrWNiEAQOJxJEVbtjk5L9gkbscGAAAAQHSYYwMAAAAg+TnqxxybmGYyaJhjAwAAACDpccUGAAAASDVDsCoaHRsAAAyV3/ZHpWX7rhrzyR9LIjd0zuzfr/cLs4EV7k6z4i2OwURil+HKiq4/mBWx+M0nk43iTCY5R1wc9aJggVmBHVcg8rF6WwwHtxh+DnQZLoCafjbyc6T+82uN2jJ82GSSWvX/MCsulOdpN4o7EigyinvtsykRY1qbzBZJlWn9JcNTn+6J3KDHYIFOt0E7VsKS7Oo69b5vEmIoGgBgSNi4caPGjBmjjIwMVVRU6IMP7CrwAUAy6S4eEO2WjOjYAABS3ssvv6zq6mqtXbtWH374oaZMmaLKykqdOnUq3qkBAGKEjg0AIOX9+Mc/1v333697771XN954ozZt2qSsrCw999xz8U4NAAZG9xybaLckRMcGAJDSgsGg9u/fr3nz5vXc5na7NW/ePO3evbvP+wQCAbW0tPTaACCp0LEBACC1nDlzRl1dXSoq6j1JuaioSA0NDX3ep6amRn6/v2crKysbjFQBIHbo2AAAgNWrV6u5ublnq6+vj3dKAIAIErbcc9hxK+yY9bs6u0wLGXa3bVf7zu22r3nncdv1dE1KBV7Kaxk/PfePVvH5UXR5s9zpVvGLsuyGdizOft8qPhWUpH9hFf8XGcet4ruirgNpZv1X/p/1fa5PP2N5jyyr6GHuDMv27RwPtVrFj0wbNkCZ/JnPZf7a9LmStMbnVRQUFMjj8aixsbHX7Y2NjSouLu7zPj6fTz7f1cs6A0BCo9wzAACpxev1atq0adq+fXvPbeFwWNu3b9fMmTPjmBkADJyhWO45Ya/YAAAQK9XV1VqxYoVuueUWzZgxQxs2bFBbW5vuvfdeo/s7F//Jh9qDEWPD5zsiN3je7N9vV4fZ949OyHCBToMww8ESksvsg48rbPiVsUFzprmFzxsu0BmMnJvpOTBdoNMxXKDTMXmKxPqiu0Fq51tDRk2ZjizpCHQaxXW1ByLGhDsMXnuSXGa7VNjwnIbaDHIzePF1v784SdqpSAR0bAAAKW/ZsmU6ffq01qxZo4aGBk2dOlXbtm27rKDAlZw7d06StPMbzw9kmkDCe2RdvPb8q3jtOKLPYtzeuXPn5Pf7+99Qf4oAJGnnio4NAGBIWLlypVauXBnVfUtLS1VfX6+cnBy5XBe+eW1paVFZWZnq6+uVm5sby1QHTbIfQ7LnLyX/MSR7/lLiHIPjODp37pxKS0tj02DYMb6y2ud9kxAdGwAAInC73Ro5cmSff8vNzU3aD3Tdkv0Ykj1/KfmPIdnzlxLjGGJypabbELxiQ/EAAAAAAEmPKzYAAABAyunPQpvJecWGjg0AAFHw+Xxau3ZtUq93k+zHkOz5S8l/DMmev5Qax9CnITgUzeUkWE25lpYW+f1+/c93l8s7zGt0n0NNI6z20RGy6891mZaqvESiLdC5pLTWKn557kdW8ZKU4zY7X93SZLewqsc19EZO/qylwCr+1ky7hVhtfd5lt7jlqa4c633YLtB5g9dugc6BlogLdNpoORfWNeM/VXNzc9zHmgMA7HV/lp43eqXS3NF11kLhgN754z8l3f+CofdJEQAAAEDKYSgaAAAAkGqc8IUt2vsmITo2AAAAQKoZgnNsErZjc132KWVkm6UXcuxG1J3rtBtvGAzbP0xpLrs5MFlpnVbxuekdVvHTMz+1ii/wZFvFw8y60zdaxZf7TlnFZ1kuxPViyxSr+CPtdvPZ8tLPW8VL0gHPKKv4a9LarOKvTf/CKr6h025NgU7H7v1itPe0Vbwk/XV2u/V9AABIdcyxAQDA0saNGzVmzBhlZGSooqJCH3zwQbxTMvbkk0/K5XL12iZOnBjvtK5q165dWrRokUpLS+VyubR169Zef3ccR2vWrFFJSYkyMzM1b948HT58OD7J9iFS/vfcc89l52TBggXxSfYKampqNH36dOXk5GjEiBFavHixDh061Cumo6NDVVVVGj58uIYNG6alS5eqsbExThn3ZpL/nDlzLjsP3/3ud+OUcQyEnf5tSYiODQAAFl5++WVVV1dr7dq1+vDDDzVlyhRVVlbq1Cm7K6zxdNNNN+nkyZM92/vvvx/vlK6qra1NU6ZM0caNG/v8+/r16/XMM89o06ZN2rt3r7Kzs1VZWamODrvRDQMlUv6StGDBgl7n5MUXXxzEDCPbuXOnqqqqtGfPHv36179WZ2en5s+fr7a2P181f/jhh/Xmm2/q1Vdf1c6dO3XixAktWbIkjln/mUn+knT//ff3Og/r16+PU8Yx0D0ULdotCSXsUDQAABLRj3/8Y91///269957JUmbNm3S22+/reeee05///d/H+fszKSlpam4uDjeaRhbuHChFi5c2OffHMfRhg0b9Pjjj+vOO++UJP3sZz9TUVGRtm7dqrvuumswU+3T1fLv5vP5EvqcbNu2rdfvmzdv1ogRI7R//37Nnj1bzc3N+td//Vdt2bJFf/mXfylJev7553XDDTdoz549uvXWW+ORdo9I+XfLyspK6PNgxVE/5tjENJNBwxUbAAAMBYNB7d+/X/Pmzeu5ze12a968edq9e3ccM7Nz+PBhlZaWauzYsbr77rt17NixeKcUtaNHj6qhoaHXOfH7/aqoqEiqc7Jjxw6NGDFCEyZM0IMPPqizZ8/GO6Wram5uliTl5+dLkvbv36/Ozs5e52HixIkaNWpUQp6HL+ff7ec//7kKCgo0adIkrV69Wu3tzGlMJlyxAQDA0JkzZ9TV1aWioqJetxcVFemTTz6JU1Z2KioqtHnzZk2YMEEnT57UunXrdPvtt+vgwYPKybFfVDfeGhoaJKnPc9L9t0S3YMECLVmyROXl5aqrq9Njjz2mhQsXavfu3fJ47BazHgzhcFirVq3SbbfdpkmTJkm6cB68Xq/y8vJ6xSbieegrf0n65je/qdGjR6u0tFQHDhzQD37wAx06dEivvfZaHLPtB6qiAQCAVHbpkKjJkyeroqJCo0eP1iuvvKL77rsvjpkNXZcOl7v55ps1efJkjRs3Tjt27NDcuXPjmFnfqqqqdPDgwYSfm3UlV8r/gQce6Pn55ptvVklJiebOnau6ujqNGzdusNPsv3BYUpTr0YSTcx0bhqIBAGCooKBAHo/nskpPjY2NSTsuPy8vT+PHj9eRI0finUpUuh/3VDonY8eOVUFBQUKek5UrV+qtt97Se++9p5EjR/bcXlxcrGAwqKampl7xiXYerpR/XyoqKiQpIc+DkTgUD4h3xUg6NgAAGPJ6vZo2bZq2b9/ec1s4HNb27ds1c+bMOGYWvdbWVtXV1amkpCTeqUSlvLxcxcXFvc5JS0uL9u7dm7Tn5Pjx4zp79mxCnRPHcbRy5Uq9/vrrevfdd1VeXt7r79OmTVN6enqv83Do0CEdO3YsIc5DpPz7UltbK0kJdR4SWSJUjGQoGgAAFqqrq7VixQrdcsstmjFjhjZs2KC2traeKmmJ7pFHHtGiRYs0evRonThxQmvXrpXH49Hy5cvjndoVtba29vrW/OjRo6qtrVV+fr5GjRqlVatW6emnn9b111+v8vJyPfHEEyotLdXixYvjl/QlrpZ/fn6+1q1bp6VLl6q4uFh1dXV69NFHdd1116mysjKOWfdWVVWlLVu26I033lBOTk7PvBm/36/MzEz5/X7dd999qq6uVn5+vnJzc/XQQw9p5syZca+IJkXOv66uTlu2bNHXvvY1DR8+XAcOHNDDDz+s2bNna/LkyXHOPkqDPMcmESpG0rEBAMDCsmXLdPr0aa1Zs0YNDQ2aOnWqtm3bdtnk9UR1/PhxLV++XGfPnlVhYaFmzZqlPXv2qLCwMN6pXdG+fft0xx139PxeXV0tSVqxYoU2b96sRx99VG1tbXrggQfU1NSkWbNmadu2bcrIyIhXyr1cLf9nn31WBw4c0AsvvKCmpiaVlpZq/vz5+uEPfyifzxevlC/z7LPPSrqwiOWlnn/+ed1zzz2SpJ/85Cdyu91aunSpAoGAKisr9dOf/nSQM+1bpPy9Xq/eeeedni8qysrKtHTpUj3++ONxyDZGwo6irttsuUBnd8XI1atX99wWj4qRdGwAALC0cuVKrVy5Mt5pROWll16KdwrW5syZI+cq3yC7XC499dRTeuqppwYxK3OR8v/Vr341iNlE52r5d8vIyNDGjRuvuhBpvETKv6ysTDt37hykbAaH44TlONEVAei+X0tLS6/bfT5fnx3uRKkYmbAdm4cnvqbc3Nx4pwHE1LrEmT8pSfpBvBNAwnFntUjyxzsNAEACKCsr6/X72rVr9eSTT8YnGQMJ27EBAAAAECXHsR5S1uu+kurr63tdaLjS8MhEqRg5YFXR4l3uDQAAABiyYlDuOTc3t9d2pY5NolSMHJCOTSKUewMAAAAwOKqrq/Uv//IveuGFF/Txxx/rwQcfHPSKkQPSsbm03NuNN96oTZs2KSsrS88999xA7A4AAADApcLh/m2Wli1bph/96Edas2aNpk6dqtra2kGvGBnzOTa25d4CgYACgUDP71+uvgAAAADAktOPcs9Rrn8T74qRMb9ic7Vyb92LIV2qpqZGfr+/Z/ty9QUAAAAAdpxwuF9bMhqw4gGmVq9erebm5p6tvr4+3ikBAAAASDIxH4pmW+7tSgv9AAAAAIhSHIaixVvMr9gkSrk3AAAAYMgKO/3bktCALNBZXV2tFStW6JZbbtGMGTO0YcOGQS/3BgAAAAxZjiMpyrkySXrFZkA6NsuWLdPp06e1Zs0aNTQ0aOrUqcbl3pyLDyTV0QBg8HW/9zpJ+k8NADB0uZwE++91/PhxKqMBQJzV19dr5MiR8U4DAGCppaVFfr9fd6T9jdJc6VG1EXI69V7oF2publZubm6MMxw4A3LFpj9KS0tVX1+vnJwcuVyunttbWlpUVlam+vr6pHqA+4Nj5phT0VA7Xim5jtlxHJ07d06lpaXxTgUA0B9OWNEPRUvOcs8J17Fxu91X/ZYwNzc34T8YxBrHPDQMtWMeascrJc8x+/3+eKcAAOgnJ+zIcUU3MCvBBnQZi/s6NgAAAADQXwl3xQYAAABA/4ScQNRDykLqjHE2gyNpOjY+n09r164dUot5csxDw1A75qF2vNLQPGYAQHx4vV4VFxfr/YZf9qud4uJieb3eGGU1OBKuKhoAAACA6HV0dCgYDParDa/Xq4yMjBhlNDjo2AAAAABIehQPAAAAAJD06NgAAAAASHp0bAAAAAAkvaTp2GzcuFFjxoxRRkaGKioq9MEHH8Q7pQHz5JNPyuVy9domTpwY77RiateuXVq0aJFKS0vlcrm0devWXn93HEdr1qxRSUmJMjMzNW/ePB0+fDg+ycZApOO95557LjvnCxYsiE+yMVJTU6Pp06crJydHI0aM0OLFi3Xo0KFeMR0dHaqqqtLw4cM1bNgwLV26VI2NjXHKuH9MjnfOnDmXnefvfve7ccoYAIDUkhQdm5dfflnV1dVau3atPvzwQ02ZMkWVlZU6depUvFMbMDfddJNOnjzZs73//vvxTimm2traNGXKFG3cuLHPv69fv17PPPOMNm3apL179yo7O1uVlZXq6OgY5ExjI9LxStKCBQt6nfMXX3xxEDOMvZ07d6qqqkp79uzRr3/9a3V2dmr+/Plqa2vriXn44Yf15ptv6tVXX9XOnTt14sQJLVmyJI5ZR8/keCXp/vvv73We169fH6eMAQBIMU4SmDFjhlNVVdXze1dXl1NaWurU1NTEMauBs3btWmfKlCnxTmPQSHJef/31nt/D4bBTXFzs/MM//EPPbU1NTY7P53NefPHFOGQYW18+XsdxnBUrVjh33nlnXPIZLKdOnXIkOTt37nQc58I5TU9Pd1599dWemI8//tiR5OzevTteacbMl4/XcRznq1/9qvN3f/d38UsKAIAUlvBXbILBoPbv36958+b13OZ2uzVv3jzt3r07jpkNrMOHD6u0tFRjx47V3XffrWPHjsU7pUFz9OhRNTQ09Drnfr9fFRUVKX3Od+zYoREjRmjChAl68MEHdfbs2XinFFPNzc2SpPz8fEnS/v371dnZ2es8T5w4UaNGjUqJ8/zl4+3285//XAUFBZo0aZJWr16t9vb2eKQHAEDKSYt3ApGcOXNGXV1dKioq6nV7UVGRPvnkkzhlNbAqKiq0efNmTZgwQSdPntS6det0++236+DBg8rJyYl3egOuoaFBkvo8591/SzULFizQkiVLVF5errq6Oj322GNauHChdu/eLY/HE+/0+i0cDmvVqlW67bbbNGnSJEkXzrPX61VeXl6v2FQ4z30dryR985vf1OjRo1VaWqoDBw7oBz/4gQ4dOqTXXnstjtkCAJAaEr5jMxQtXLiw5+fJkyeroqJCo0eP1iuvvKL77rsvjplhoNx11109P998882aPHmyxo0bpx07dmju3LlxzCw2qqqqdPDgwZSbK3YlVzreBx54oOfnm2++WSUlJZo7d67q6uo0bty4wU4TAICUkvBD0QoKCuTxeC6rlNTY2Kji4uI4ZTW48vLyNH78eB05ciTeqQyK7vM6lM/52LFjVVBQkBLnfOXKlXrrrbf03nvvaeTIkT23FxcXKxgMqqmpqVd8sp/nKx1vXyoqKiQpJc4zAADxlvAdG6/Xq2nTpmn79u09t4XDYW3fvl0zZ86MY2aDp7W1VXV1dSopKYl3KoOivLxcxcXFvc55S0uL9u7dO2TO+fHjx3X27NmkPueO42jlypV6/fXX9e6776q8vLzX36dNm6b09PRe5/nQoUM6duxYUp7nSMfbl9raWklK6vMMAECiSIqhaNXV1VqxYoVuueUWzZgxQxs2bFBbW5vuvffeeKc2IB555BEtWrRIo0eP1okTJ7R27Vp5PB4tX7483qnFTGtra69vqY8ePara2lrl5+dr1KhRWrVqlZ5++mldf/31Ki8v1xNPPKHS0lItXrw4fkn3w9WONz8/X+vWrdPSpUtVXFysuro6Pfroo7ruuutUWVkZx6z7p6qqSlu2bNEbb7yhnJycnnkzfr9fmZmZ8vv9uu+++1RdXa38/Hzl5ubqoYce0syZM3XrrbfGOXt7kY63rq5OW7Zs0de+9jUNHz5cBw4c0MMPP6zZs2dr8uTJcc4eAIAUEO+ybKb+8R//0Rk1apTj9XqdGTNmOHv27Il3SgNm2bJlTklJieP1ep1rr73WWbZsmXPkyJF4pxVT7733niPpsm3FihWO41wo+fzEE084RUVFjs/nc+bOnescOnQovkn3w9WOt7293Zk/f75TWFjopKenO6NHj3buv/9+p6GhId5p90tfxyvJef7553tizp8/73zve99zrrnmGicrK8v5+te/7pw8eTJ+SfdDpOM9duyYM3v2bCc/P9/x+XzOdddd53z/+993mpub45s4AAApwuU4jjOYHSkAAAAAiLWEn2MDAAAAAJHQsQEAAACQ9OjYAAAAAEh6dGwAAAAAJD06NgAAAACSHh0bAAAAAEmPjg0AAACApEfHBgAAAEDSo2MDAAAAIOnRsQEAAACQ9OjYAAAAAEh6dGwAAAAAJL3/D9Yk3e0N1E02AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Flip model PSF left-right to match data.\n", "psf_model_fliplr = psf_model_padded[:, ::-1, :]\n", @@ -792,7 +566,6 @@ "# Scale factor for PSF subtraction\n", "psf_sum_max = np.amax(psf_model_sum)\n", "scalefactor = np.amax(cube_sum) / psf_sum_max\n", - "print(scalefactor)\n", "\n", "# Plots\n", "f, ([ax1, ax2], [ax3, ax4]) = plt.subplots(2, 2, figsize=(10, 10)) \n", @@ -826,7 +599,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Optimal Extraction using WebbPSF Model\n", + "## 12. Optimal Extraction using WebbPSF Model\n", "Optimal extraction ([Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)) weights the flux contributions to a spectrum by their signal-to-noise ratio (SNR). Dividing the simulated data by the model PSF gives an estimate of the total flux density spectrum in each spaxel. A weighted average of these estimates over all spaxels yields the optimally extracted spectrum over the cube. In the faint source limit, where the noise is background-dominated, optimal extraction inside a 3-sigma radius can increase the effective exposure time by a factor of 1.69 (Horne et al. 1986). In the bright source limit, where the noise is dominated by the Poisson statistics of the source, optimal extraction is formally identical to a straight sum over spaxels for a perfect PSF model. \n", "\n", "We use the precomputed WebbPSF PSF model for optimal extraction here." @@ -834,20 +607,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Window PSF model (and replace NaNs)\n", "good_profile = np.nan_to_num(good_psf_model) \n", @@ -891,36 +653,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-12-01 15:27:02,402 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", - " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", - "\n", - "2023-12-01 15:27:02,752 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", - " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ab46c7d73cd34a9f94082f60d5c211c0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Application(config='specviz', docs_link='https://jdaviz.readthedocs.io/en/latest/specviz/index.html', events=[…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from jdaviz import Specviz\n", "specviz = Specviz()\n", @@ -956,7 +691,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notebook created by Patrick Ogle and James Davies." + "Notebook created by Patrick Ogle and James Davies. Last update: December 2023." ] } ], @@ -976,7 +711,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/notebooks/ifu_optimal/requirements.txt b/notebooks/ifu_optimal/requirements.txt index db7e542e8..fc4aff4ff 100644 --- a/notebooks/ifu_optimal/requirements.txt +++ b/notebooks/ifu_optimal/requirements.txt @@ -1,3 +1,3 @@ -numpy==1.19.2 -scipy==1.5.2 -jdaviz==1.0.3 +numpy>=1.26.2 +scipy>=1.11.4 +jdaviz>=3.8.0 From c93c020d7b0a8cc5e20edd1960902e57cf258711 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Thu, 7 Dec 2023 14:26:44 -0500 Subject: [PATCH 5/9] Fix style errors --- notebooks/ifu_optimal/ifu_optimal.ipynb | 281 ++++++++++++++---------- 1 file changed, 163 insertions(+), 118 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 6853236f0..e06bb26f3 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -127,15 +127,17 @@ "source": [ "# Download the data file\n", "uri = f\"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", - "result = Observations.download_file(uri, base_url='https://mast.stsci.edu/api/v0.1/Download/file')\n", - "if result[0] == 'ERROR':\n", - " raise RuntimeError('Error retrieving file: ' + result[1])\n", - " \n", - "# Construct the local filepath \n", - "filename = os.path.join(os.path.abspath('.'), uri.rsplit('/', 1)[-1])\n", + "result = Observations.download_file(\n", + " uri, base_url=\"https://mast.stsci.edu/api/v0.1/Download/file\"\n", + ")\n", + "if result[0] == \"ERROR\":\n", + " raise RuntimeError(\"Error retrieving file: \" + result[1])\n", "\n", - "#Optionally Replace MAST data with custom reprocessed data in the current directory\n", - "#filename=\"./jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"" + "# Construct the local filepath\n", + "filename = os.path.join(os.path.abspath(\".\"), uri.rsplit(\"/\", 1)[-1])\n", + "\n", + "# Optionally Replace MAST data with custom reprocessed data in the current directory\n", + "# filename=\"./jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"" ] }, { @@ -146,8 +148,8 @@ "source": [ "# Open and inspect the file and WCS\n", "with fits.open(filename, memmap=False) as hdulist:\n", - " sci = hdulist['SCI'].data\n", - " err = hdulist['ERR'].data\n", + " sci = hdulist[\"SCI\"].data\n", + " err = hdulist[\"ERR\"].data\n", " w = wcs.WCS(hdulist[1].header)\n", " hdr = hdulist[1].header\n", " hdulist.info()\n", @@ -155,20 +157,20 @@ "\n", "# Window the wavelength range to focus on Hbeta-[OIII]\n", "spec1d = Spectrum1D.read(filename)\n", - "slice_range= range(500,1100,1) \n", - "wavelength = np.array(spec1d.spectral_axis.value)[slice_range[0]:slice_range[-1]+1]\n", + "slice_range = range(500, 1100, 1)\n", + "wavelength = np.array(spec1d.spectral_axis.value)[slice_range[0] : slice_range[-1] + 1]\n", "\n", "# List of cube slices for aperture photometry\n", "sci_data = []\n", "sci_var = []\n", - "for idx in slice_range: \n", + "for idx in slice_range:\n", " sci_data.append(sci[idx, :, :])\n", - " sci_var.append(err[idx, :, :]) \n", + " sci_var.append(err[idx, :, :])\n", "\n", "data = np.nan_to_num(np.array(sci_data))\n", "var = np.array(sci_var)\n", "print()\n", - "print(\"Trimmed data shape:\", data.shape)\n" + "print(\"Trimmed data shape:\", data.shape)" ] }, { @@ -182,10 +184,10 @@ "cubeviz.show()\n", "\n", "# Set spectrum display limits\n", - "cubeviz.specviz.x_limits(1.65*u.um,2.4*u.um)\n", - "cubeviz.specviz.y_limits(0.0, 5.0E3)\n", + "cubeviz.specviz.x_limits(1.65 * u.um, 2.4 * u.um)\n", + "cubeviz.specviz.y_limits(0.0, 5.0e3)\n", "\n", - "#Select slice to visualize\n", + "# Select slice to visualize\n", "cubeviz.select_slice(714)" ] }, @@ -231,42 +233,56 @@ "cubeviz_data = cubeviz.app.data_collection[0]\n", "\n", "print()\n", - "print('Source Region')\n", + "print(\"Source Region\")\n", "try:\n", - " region1 = cubeviz_data.get_selection_definition('Subset 1', format='astropy-regions')\n", + " region1 = cubeviz_data.get_selection_definition(\n", + " \"Subset 1\", format=\"astropy-regions\"\n", + " )\n", " print(region1)\n", - " center_xy = [region1.center.x, region1.center.y] \n", + " center_xy = [region1.center.x, region1.center.y]\n", " r_pix = region1.radius\n", " region1_exists = True\n", "except Exception:\n", " print(\"There is no Subset 1 selected in the cube viewer.\")\n", - " center_xy = [17.1, 20.]\n", + " center_xy = [17.1, 20.0]\n", " r_pix = 5.92\n", " print(\"Using default pixel center and radius:\")\n", " print(\"Center pixel:\", center_xy)\n", - " print(\"Radius (pixels):\", r_pix)\n", + " print(\"Radius (pixels):\", r_pix)\n", " region1_exists = False\n", - " \n", + "\n", "print()\n", - "print('Good Data Region')\n", + "print(\"Good Data Region\")\n", "try:\n", - " region2 = cubeviz_data.get_selection_definition('Subset 2', format='astropy-regions')\n", + " region2 = cubeviz_data.get_selection_definition(\n", + " \"Subset 2\", format=\"astropy-regions\"\n", + " )\n", " print(region2)\n", " region2_exists = True\n", - " data_xrange=[round(region2.center.x - region2.width/2), round(region2.center.x + region2.width/2)]\n", - " data_yrange=[round(region2.center.y - region2.height/2), round(region2.center.y + region2.height/2)]\n", - " print('Good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n", - " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", - " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", + " data_xrange = [\n", + " round(region2.center.x - region2.width / 2),\n", + " round(region2.center.x + region2.width / 2),\n", + " ]\n", + " data_yrange = [\n", + " round(region2.center.y - region2.height / 2),\n", + " round(region2.center.y + region2.height / 2),\n", + " ]\n", + " print(\"Good data (xmin,xmax), (ymin,ymax):\", data_xrange, data_yrange)\n", + " good_data = np.nan_to_num(\n", + " data[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " )\n", + " good_var = var[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", "\n", "except Exception:\n", " print(\"There is no Subset 2 selected in the cube viewer.\")\n", " region1_exists = False\n", - " data_xrange=[7,36]\n", - " data_yrange=[6,33]\n", - " good_data = np.nan_to_num(data[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]])\n", - " good_var = var[:,data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", - " print('Using default good data (xmin,xmax), (ymin,ymax):',data_xrange, data_yrange)\n" + " data_xrange = [7, 36]\n", + " data_yrange = [6, 33]\n", + " good_data = np.nan_to_num(\n", + " data[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " )\n", + " good_var = var[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " print(\"Using default good data (xmin,xmax), (ymin,ymax):\", data_xrange, data_yrange)" ] }, { @@ -287,17 +303,19 @@ "print(subsets.keys())\n", "\n", "print()\n", - "print('Source')\n", + "print(\"Source\")\n", "try:\n", - " spectrum_subset1 = subsets[[i for i in subsets.keys() if 'Subset 1' in i][0]]\n", + " spectrum_subset1 = subsets[[i for i in subsets.keys() if \"Subset 1\" in i][0]]\n", " print(spectrum_subset1)\n", "except Exception:\n", " print(\"There is no Subset 1 selected in the spectrum viewer.\")\n", - " \n", + "\n", "print()\n", - "print('Background')\n", + "print(\"Background\")\n", "try:\n", - " spectrum_subset2 = spectrum_subset2 = subsets[[i for i in subsets.keys() if 'Subset 2' in i][0]]\n", + " spectrum_subset2 = spectrum_subset2 = subsets[\n", + " [i for i in subsets.keys() if \"Subset 2\" in i][0]\n", + " ]\n", " print(spectrum_subset2)\n", "except Exception:\n", " print(\"There is no Subset 2 selected in the spectrum viewer.\")" @@ -327,25 +345,25 @@ "source": [ "# Sum over wavelength\n", "# Clip data for display purposes\n", - "clip_level = 4E4\n", - "data_clipped = np.clip(good_data,0,clip_level)\n", - "cube_sum = np.sum(data_clipped, axis=0) \n", + "clip_level = 4e4\n", + "data_clipped = np.clip(good_data, 0, clip_level)\n", + "cube_sum = np.sum(data_clipped, axis=0)\n", "\n", "# Extraction via sum over spaxels\n", "fnu_sum = np.sum(good_data, axis=(1, 2))\n", - "fnu_sum_clipped = np.clip(fnu_sum,0,clip_level)\n", - "flux_spaxsum = np.array(fnu_sum) * u.MJy/u.sr\n", - "spec1d_spaxsum = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_spaxsum)\n", + "fnu_sum_clipped = np.clip(fnu_sum, 0, clip_level)\n", + "flux_spaxsum = np.array(fnu_sum) * u.MJy / u.sr\n", + "spec1d_spaxsum = Spectrum1D(spectral_axis=wavelength * u.um, flux=flux_spaxsum)\n", "\n", "# Plots\n", - "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) \n", + "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))\n", "\n", - "ax1.plot(wavelength, fnu_sum) \n", + "ax1.plot(wavelength, fnu_sum)\n", "ax1.set_title(\"Spaxel sums\")\n", - "ax1.set_xlabel(\"Wavelength (um)\") \n", + "ax1.set_xlabel(\"Wavelength (um)\")\n", "ax1.set_ylabel(\"Flux (MJy/sr)\")\n", - "ax1.set_ylim(0,5E3)\n", - "ax2.imshow(cube_sum , norm=LogNorm(vmin=100,vmax=clip_level),origin='lower')\n", + "ax1.set_ylim(0, 5e3)\n", + "ax2.imshow(cube_sum, norm=LogNorm(vmin=100, vmax=clip_level), origin=\"lower\")\n", "ax2.set_title(\"Slice sums\")\n", "\n", "plt.show()" @@ -372,10 +390,10 @@ "cylinder_sum = []\n", "for slice2d in data:\n", " phot_table = aperture_photometry(slice2d, aperture)\n", - " cylinder_sum.append(phot_table['aperture_sum'][0])\n", - " \n", - "flux_cylinder = np.array(cylinder_sum) * u.MJy/u.sr\n", - "spec1d_cylinder = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_cylinder)" + " cylinder_sum.append(phot_table[\"aperture_sum\"][0])\n", + "\n", + "flux_cylinder = np.array(cylinder_sum) * u.MJy / u.sr\n", + "spec1d_cylinder = Spectrum1D(spectral_axis=wavelength * u.um, flux=flux_cylinder)" ] }, { @@ -401,20 +419,22 @@ "source": [ "# Reference wavelength for expanding aperture\n", "lambda0 = wavelength[0]\n", - "print('Reference wavelength:', lambda0)\n", + "print(\"Reference wavelength:\", lambda0)\n", "\n", "cone_sum = []\n", "idx = -1\n", "for (slice2d, wave) in zip(data, wavelength):\n", " idx = idx + 1\n", " r_cone = r_pix * wave / lambda0\n", - " \n", + "\n", " aperture_cone = CircularAperture(center_xy, r=r_cone)\n", - " phot_table = aperture_photometry(slice2d, aperture_cone, wcs=w.celestial, method='exact')\n", - " cone_sum.append(phot_table['aperture_sum'][0])\n", - " \n", - "flux_cone = np.array(cone_sum) * u.MJy/u.sr\n", - "spec1d_cone = Spectrum1D(spectral_axis=wavelength*u.um, flux=flux_cone)" + " phot_table = aperture_photometry(\n", + " slice2d, aperture_cone, wcs=w.celestial, method=\"exact\"\n", + " )\n", + " cone_sum.append(phot_table[\"aperture_sum\"][0])\n", + "\n", + "flux_cone = np.array(cone_sum) * u.MJy / u.sr\n", + "spec1d_cone = Spectrum1D(spectral_axis=wavelength * u.um, flux=flux_cone)" ] }, { @@ -431,22 +451,28 @@ "metadata": {}, "outputs": [], "source": [ - "f, (ax1) = plt.subplots(1, 1, figsize=(15, 5)) \n", + "f, (ax1) = plt.subplots(1, 1, figsize=(15, 5))\n", "\n", - "#ax1.plot(wavelength, flux_spaxsum.value, label=\"All spaxels\", c='k')\n", - "ax1.plot(wavelength, flux_cylinder.value, label=\"Cylinder\", c='b')\n", - "ax1.plot(wavelength, flux_cone.value, label=\"Cone\", c='darkorange', alpha=0.5)\n", + "# ax1.plot(wavelength, flux_spaxsum.value, label=\"All spaxels\", c='k')\n", + "ax1.plot(wavelength, flux_cylinder.value, label=\"Cylinder\", c=\"b\")\n", + "ax1.plot(wavelength, flux_cone.value, label=\"Cone\", c=\"darkorange\", alpha=0.5)\n", "try:\n", - " ax1.plot(wavelength, spectrum_subset1.flux.value[slice_range[0]:slice_range[-1]+1], c='r', label=\"Subset1\", alpha=0.4)\n", + " ax1.plot(\n", + " wavelength,\n", + " spectrum_subset1.flux.value[slice_range[0] : slice_range[-1] + 1],\n", + " c=\"r\",\n", + " label=\"Subset1\",\n", + " alpha=0.4,\n", + " )\n", "except Exception:\n", " print(\"There is no Cubeviz Subset1 spectrum to plot.\")\n", "\n", "ax1.set_title(\"Non-optimal spectral extractions\")\n", - "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", + "ax1.set_xlabel(\"Observed Wavelength (microns)\")\n", "ax1.set_ylabel(\"Flux Density\")\n", - "ax1.set_ylim(0,5.0E3)\n", + "ax1.set_ylim(0, 5.0e3)\n", "ax1.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -481,17 +507,17 @@ "metadata": {}, "outputs": [], "source": [ - "##WebbPSF imports\n", - "#%pylab inline\n", - "#import webbpsf\n", + "# #WebbPSF imports\n", + "# %pylab inline\n", + "# import webbpsf\n", "#\n", - "##WebbPSF commands used to create PSF model cube\n", - "#ns = webbpsf.NIRSpec()\n", - "#ns.image_mask = \"IFU\" # Sets to 3x3 arcsec square mask\n", + "# #WebbPSF commands used to create PSF model cube\n", + "# ns = webbpsf.NIRSpec()\n", + "# ns.image_mask = \"IFU\" # Sets to 3x3 arcsec square mask\n", "\n", - "#wavelengths = wavelength*1.0E-6\n", - "#psfcube = ns.calc_datacube(wavelengths, fov_pixels=30, oversample=4, add_distortion=True)\n", - "#psfcube.writeto(\"Webbpsf_ifucube.fits\")\n" + "# wavelengths = wavelength*1.0E-6\n", + "# psfcube = ns.calc_datacube(wavelengths, fov_pixels=30, oversample=4, add_distortion=True)\n", + "# psfcube.writeto(\"Webbpsf_ifucube.fits\")" ] }, { @@ -501,24 +527,26 @@ "outputs": [], "source": [ "BoxPath = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n", - "psf_filename = BoxPath+\"Webbpsf_ifucube.fits\"\n", + "psf_filename = BoxPath + \"Webbpsf_ifucube.fits\"\n", "\n", "# Open WebbPSF data cube\n", "with fits.open(psf_filename, memmap=False) as hdulist:\n", - " psf_model = hdulist['DET_SAMP'].data\n", - " psf_hdr = hdulist['DET_SAMP'].header\n", - " hdulist.info() \n", + " psf_model = hdulist[\"DET_SAMP\"].data\n", + " psf_hdr = hdulist[\"DET_SAMP\"].header\n", + " hdulist.info()\n", "\n", "# Pad PSF model cube with zeros to match the present dataset\n", "# (Different padding may be needed for your particular dataset)\n", - "print(sci.shape,psf_model.shape)\n", - "psf_model_padded = np.pad(psf_model, ((0,0),(4,5), (6,7)), 'constant')\n", + "print(sci.shape, psf_model.shape)\n", + "psf_model_padded = np.pad(psf_model, ((0, 0), (4, 5), (6, 7)), \"constant\")\n", "\n", "# Sum over wavelength\n", - "psf_model_sum = np.sum(psf_model_padded[slice_range[0]:slice_range[-1]+1], axis=0)\n", + "psf_model_sum = np.sum(psf_model_padded[slice_range[0] : slice_range[-1] + 1], axis=0)\n", "\n", "# Sum over spaxels\n", - "psf_model_fnusum = np.sum(psf_model_padded[slice_range[0]:slice_range[-1]+1], axis=(1, 2))" + "psf_model_fnusum = np.sum(\n", + " psf_model_padded[slice_range[0] : slice_range[-1] + 1], axis=(1, 2)\n", + ")" ] }, { @@ -551,14 +579,24 @@ "psf_model_fliplr = psf_model_padded[:, ::-1, :]\n", "\n", "# Empirically (chi-by-eye) determined shift\n", - "shiftx = 1.5 #2 \n", - "shifty = 1.5 #1.5\n", + "shiftx = 1.5 # 2\n", + "shifty = 1.5 # 1.5\n", "\n", "# Shift model PSF using linear interpolation\n", - "psf_model_aligned = scipy.ndimage.shift(psf_model_fliplr, (0.0, shiftx, shifty), order=1, \n", - " mode='constant', cval=0.0, prefilter=True)\n", - "\n", - "good_psf_model = psf_model_aligned[slice_range[0]:slice_range[-1]+1, data_yrange[0]:data_yrange[1],data_xrange[0]:data_xrange[1]]\n", + "psf_model_aligned = scipy.ndimage.shift(\n", + " psf_model_fliplr,\n", + " (0.0, shiftx, shifty),\n", + " order=1,\n", + " mode=\"constant\",\n", + " cval=0.0,\n", + " prefilter=True,\n", + ")\n", + "\n", + "good_psf_model = psf_model_aligned[\n", + " slice_range[0] : slice_range[-1] + 1,\n", + " data_yrange[0] : data_yrange[1],\n", + " data_xrange[0] : data_xrange[1],\n", + "]\n", "\n", "# Sum over wavelength\n", "psf_model_sum = np.sum(good_psf_model, axis=0)\n", @@ -568,18 +606,20 @@ "scalefactor = np.amax(cube_sum) / psf_sum_max\n", "\n", "# Plots\n", - "f, ([ax1, ax2], [ax3, ax4]) = plt.subplots(2, 2, figsize=(10, 10)) \n", + "f, ([ax1, ax2], [ax3, ax4]) = plt.subplots(2, 2, figsize=(10, 10))\n", "\n", "ax1.set_title(\"PSF slice sum\")\n", - "ax1.imshow(psf_model_sum, norm=LogNorm(),origin='lower')\n", + "ax1.imshow(psf_model_sum, norm=LogNorm(), origin=\"lower\")\n", "\n", "ax2.set_title(\"Science Data slice sum\")\n", - "ax2.imshow(cube_sum, norm=LogNorm(),origin='lower') \n", + "ax2.imshow(cube_sum, norm=LogNorm(), origin=\"lower\")\n", "\n", "ax3.set_title(\"Data / PSF Ratio\")\n", - "ax3.imshow(cube_sum / psf_model_sum, norm=LogNorm(vmin=1,vmax=1E6),origin='lower')\n", + "ax3.imshow(cube_sum / psf_model_sum, norm=LogNorm(vmin=1, vmax=1e6), origin=\"lower\")\n", "\n", - "im4 = ax4.imshow(np.log10(np.absolute(cube_sum - 0.75*scalefactor * psf_model_sum)),origin='lower')\n", + "im4 = ax4.imshow(\n", + " np.log10(np.absolute(cube_sum - 0.75 * scalefactor * psf_model_sum)), origin=\"lower\"\n", + ")\n", "plt.colorbar(im4)\n", "ax4.set_title(\"log abs(Data - PSF)\")\n", "\n", @@ -612,37 +652,41 @@ "outputs": [], "source": [ "# Window PSF model (and replace NaNs)\n", - "good_profile = np.nan_to_num(good_psf_model) \n", - "var_clean = np.nan_to_num(good_var, nan=1E12, posinf=1E12, neginf = 1E12)\n", + "good_profile = np.nan_to_num(good_psf_model)\n", + "var_clean = np.nan_to_num(good_var, nan=1e12, posinf=1e12, neginf=1e12)\n", "zerovar = np.where(var_clean == 0)\n", - "var_clean[zerovar] = 1E12\n", + "var_clean[zerovar] = 1e12\n", "var_clean_sum = np.sum(var_clean, axis=(0))\n", - "snr_clean = np.nan_to_num(good_data/var_clean)\n", + "snr_clean = np.nan_to_num(good_data / var_clean)\n", "\n", "# Divide data by PSF model\n", - "data_norm = np.nan_to_num(good_data / good_profile, posinf=0, neginf = 0)\n", - "data_norm_sum = np.sum(data_norm, axis=0) \n", + "data_norm = np.nan_to_num(good_data / good_profile, posinf=0, neginf=0)\n", + "data_norm_sum = np.sum(data_norm, axis=0)\n", "\n", - "# Mask out bad data \n", - "#data_norm_clipped = sigma_clip(data_norm, sigma=3.0, maxiters=5, axis=(1, 2))\n", + "# Mask out bad data\n", + "# data_norm_clipped = sigma_clip(data_norm, sigma=3.0, maxiters=5, axis=(1, 2))\n", "data_norm_clipped = data_norm\n", - "data_norm_clipped_sum = np.sum(data_norm_clipped, axis=0) \n", + "data_norm_clipped_sum = np.sum(data_norm_clipped, axis=0)\n", "snr_thresh = 1.0\n", "badvoxel = np.where((data_norm_clipped == 0) | (snr_clean < snr_thresh))\n", "data_clean = 1.0 * good_data\n", "data_clean[badvoxel] = 0.0\n", - "data_clean_sum = np.sum(data_clean, axis=0) \n", + "data_clean_sum = np.sum(data_clean, axis=0)\n", "\n", "# Optimal extraction, using model profile weight and variance cube from the simulated data\n", - "optimal_weight = np.nan_to_num(good_profile ** 2 / var_clean, posinf=0, neginf = 0) #Replace nans and infs with 0\n", + "optimal_weight = np.nan_to_num(\n", + " good_profile**2 / var_clean, posinf=0, neginf=0\n", + ") # Replace nans and infs with 0\n", "optimal_weight_sum = np.sum(optimal_weight, axis=(0))\n", "optimal_weight_norm = np.sum(optimal_weight, axis=(1, 2))\n", - "spectrum_optimal = np.sum(good_profile * data_clean / var_clean, axis=(1, 2)) / optimal_weight_norm\n", + "spectrum_optimal = (\n", + " np.sum(good_profile * data_clean / var_clean, axis=(1, 2)) / optimal_weight_norm\n", + ")\n", "\n", "# Plots\n", - "f, (ax1) = plt.subplots(1, 1, figsize=(12, 6)) \n", + "f, (ax1) = plt.subplots(1, 1, figsize=(12, 6))\n", "ax1.set_title(\"Optimal Extraction Comparison\")\n", - "ax1.set_xlabel(\"Observed Wavelength (microns)\") \n", + "ax1.set_xlabel(\"Observed Wavelength (microns)\")\n", "ax1.set_ylabel(\"Flux Density\")\n", "ax1.set_ylim(0, 5000)\n", "ax1.plot(wavelength, cone_sum, label=\"Conical Extraction\", alpha=0.5)\n", @@ -658,19 +702,20 @@ "outputs": [], "source": [ "from jdaviz import Specviz\n", + "\n", "specviz = Specviz()\n", "\n", - "flux_opt = spectrum_optimal * u.MJy/u.sr\n", + "flux_opt = spectrum_optimal * u.MJy / u.sr\n", "spec1d_opt = Spectrum1D(spectral_axis=wavelength * u.um, flux=flux_opt)\n", "\n", - "#specviz.load_data(spec1d_spaxsum, data_label=\"collapse spec\")\n", + "# specviz.load_data(spec1d_spaxsum, data_label=\"collapse spec\")\n", "specviz.load_data(spec1d_opt, data_label=\"optimal spec\")\n", "specviz.load_data(spec1d_cone, data_label=\"cone spec\")\n", "specviz.show()\n", "\n", "# set spectrum display limits\n", - "#specviz.x_limits()\n", - "specviz.y_limits(0.0, clip_level/7)" + "# specviz.x_limits()\n", + "specviz.y_limits(0.0, clip_level / 7)" ] }, { @@ -711,7 +756,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.11.6" } }, "nbformat": 4, From 0ce8c03c80d356b9b9daa3d05aa49eb10a7c93ae Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Thu, 7 Dec 2023 14:36:30 -0500 Subject: [PATCH 6/9] Fix remaining style errors --- notebooks/ifu_optimal/ifu_optimal.ipynb | 32 +++++++++++-------------- 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index e06bb26f3..23b4248e8 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -81,18 +81,14 @@ "source": [ "import numpy as np\n", "import scipy\n", - "import specutils\n", "from specutils import Spectrum1D\n", - "from specutils.manipulation import spectral_slab\n", "import jdaviz\n", - "from jdaviz import Cubeviz, Imviz, Specviz\n", + "from jdaviz import Cubeviz, Specviz\n", "print(\"jdaviz Version={}\".format(jdaviz.__version__))\n", - "from photutils.aperture import CircularAperture, SkyCircularAperture, aperture_photometry \n", + "from photutils.aperture import CircularAperture, aperture_photometry \n", "from astropy.io import fits\n", "from astropy import wcs\n", "from astropy import units as u\n", - "from astropy.stats import sigma_clip\n", - "from astropy.utils.data import download_file\n", "import os\n", "from astroquery.mast import Observations" ] @@ -126,7 +122,7 @@ "outputs": [], "source": [ "# Download the data file\n", - "uri = f\"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", + "uri = \"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", "result = Observations.download_file(\n", " uri, base_url=\"https://mast.stsci.edu/api/v0.1/Download/file\"\n", ")\n", @@ -158,7 +154,7 @@ "# Window the wavelength range to focus on Hbeta-[OIII]\n", "spec1d = Spectrum1D.read(filename)\n", "slice_range = range(500, 1100, 1)\n", - "wavelength = np.array(spec1d.spectral_axis.value)[slice_range[0] : slice_range[-1] + 1]\n", + "wavelength = np.array(spec1d.spectral_axis.value)[slice_range[0]: slice_range[-1] + 1]\n", "\n", "# List of cube slices for aperture photometry\n", "sci_data = []\n", @@ -269,9 +265,9 @@ " ]\n", " print(\"Good data (xmin,xmax), (ymin,ymax):\", data_xrange, data_yrange)\n", " good_data = np.nan_to_num(\n", - " data[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " data[:, data_yrange[0]: data_yrange[1], data_xrange[0]: data_xrange[1]]\n", " )\n", - " good_var = var[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " good_var = var[:, data_yrange[0]: data_yrange[1], data_xrange[0]: data_xrange[1]]\n", "\n", "except Exception:\n", " print(\"There is no Subset 2 selected in the cube viewer.\")\n", @@ -279,9 +275,9 @@ " data_xrange = [7, 36]\n", " data_yrange = [6, 33]\n", " good_data = np.nan_to_num(\n", - " data[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " data[:, data_yrange[0]: data_yrange[1], data_xrange[0]: data_xrange[1]]\n", " )\n", - " good_var = var[:, data_yrange[0] : data_yrange[1], data_xrange[0] : data_xrange[1]]\n", + " good_var = var[:, data_yrange[0]: data_yrange[1], data_xrange[0]: data_xrange[1]]\n", " print(\"Using default good data (xmin,xmax), (ymin,ymax):\", data_xrange, data_yrange)" ] }, @@ -459,7 +455,7 @@ "try:\n", " ax1.plot(\n", " wavelength,\n", - " spectrum_subset1.flux.value[slice_range[0] : slice_range[-1] + 1],\n", + " spectrum_subset1.flux.value[slice_range[0]: slice_range[-1] + 1],\n", " c=\"r\",\n", " label=\"Subset1\",\n", " alpha=0.4,\n", @@ -541,11 +537,11 @@ "psf_model_padded = np.pad(psf_model, ((0, 0), (4, 5), (6, 7)), \"constant\")\n", "\n", "# Sum over wavelength\n", - "psf_model_sum = np.sum(psf_model_padded[slice_range[0] : slice_range[-1] + 1], axis=0)\n", + "psf_model_sum = np.sum(psf_model_padded[slice_range[0]: slice_range[-1] + 1], axis=0)\n", "\n", "# Sum over spaxels\n", "psf_model_fnusum = np.sum(\n", - " psf_model_padded[slice_range[0] : slice_range[-1] + 1], axis=(1, 2)\n", + " psf_model_padded[slice_range[0]: slice_range[-1] + 1], axis=(1, 2)\n", ")" ] }, @@ -593,9 +589,9 @@ ")\n", "\n", "good_psf_model = psf_model_aligned[\n", - " slice_range[0] : slice_range[-1] + 1,\n", - " data_yrange[0] : data_yrange[1],\n", - " data_xrange[0] : data_xrange[1],\n", + " slice_range[0]: slice_range[-1] + 1,\n", + " data_yrange[0]: data_yrange[1],\n", + " data_xrange[0]: data_xrange[1],\n", "]\n", "\n", "# Sum over wavelength\n", From 057f9d5ea70bd07789994c4cfcd4ca0b06f3b72f Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Fri, 8 Dec 2023 19:40:35 -0500 Subject: [PATCH 7/9] Improve notebook navigation with internal links --- notebooks/ifu_optimal/ifu_optimal.ipynb | 50 ++++++++++++------------- 1 file changed, 24 insertions(+), 26 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 23b4248e8..819117b58 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -34,25 +34,25 @@ "metadata": {}, "source": [ "## Table of Contents\n", - "1. Imports\n", - "2. Read in NIRSpec IFU Cube\n", - "3. Visualize Science Data with Cubeviz\n", - "4. Export Source and Good Data Regions from Cubeviz\n", - "5. Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer\n", - "6. Extract Spectrum by Sum over Spaxels\n", - "7. Extract Spectrum in Constant Radius Circular Aperture (Cylinder)\n", - "8. Extract Spectrum in Linearly Expanding Circular Aperture (Cone)\n", - "9. Plot and Compare Non-optimal Spectral Extractions\n", - "10. WebbPSF Model for Optimal Extraction\n", - "11. Align Model PSF Cube with Science Data\n", - "12. Optimal Extraction Using WebbPSF Model" + "1. [Imports](#imports)\n", + "2. [Read in NIRSpec IFU Cube](#read)\n", + "3. [Visualize Science Data with Cubeviz](#visualize)\n", + "4. [Export Source and Good Data Regions from Cubeviz](#source)\n", + "5. [Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer](#viewer)\n", + "6. [Extract Spectrum by Sum over Spaxels](#sum)\n", + "7. [Extract Spectrum in Constant Radius Circular Aperture (Cylinder)](#cylinder)\n", + "8. [Extract Spectrum in Linearly Expanding Circular Aperture (Cone)](#cone)\n", + "9. [Plot and Compare Non-optimal Spectral Extractions](#plot)\n", + "10. [WebbPSF Model for Optimal Extraction](#webbpsf)\n", + "11. [Align Model PSF Cube with Science Data](#align)\n", + "12. [Optimal Extraction Using WebbPSF Model](#optimal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Imports " + "## 1. Imports " ] }, { @@ -108,7 +108,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Read in NIRSpec IFU Cube\n", + "## 2. Read in NIRSpec IFU Cube \n", "\n", "The NIRSpec IFU observation of quasar SDSS J1652+1728 (redshift z=1.9) was taken using the G235H grating with F170LP filter, covering 1.66-3.17 microns at a spectral resolution of R~2700. The IFU spaxels are 0.1\" on a side. \n", "The level-3 pipeline_processed datacube (s3d.fits, which combines all dithered exposures) is retrieved from MAST \n", @@ -191,7 +191,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Visualize Science Data with Cubeviz" + "## 3. Visualize Science Data with Cubeviz " ] }, { @@ -216,7 +216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Export Source and Good Data Regions from Cubeviz\n", + "## 4. Export Source and Good Data Regions from Cubeviz \n", "Export the region defined by the user in Cubeviz as astropy PixelRegions" ] }, @@ -285,7 +285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer\n", + "## 5. Extract Subset Spectrum and Background from Cubeviz Spectrum Viewer \n", "Retrieve the collapsed spectrum (Subset1) of the user-defined region from the Spectrum Viewer as a Spectrum1D object." ] }, @@ -328,7 +328,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Extract Spectrum by Sum Over Spaxels\n", + "## 6. Extract Spectrum by Sum Over Spaxels \n", "\n", "Perform a simple numpy sum over all spaxels in the cube as a rudimentary extraction method. Also sum over wavelength to collapse the cube." ] @@ -369,7 +369,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 7. Extract Spectrum in Constant Radius Circular Aperture (Cylinder)\n", + "## 7. Extract Spectrum in Constant Radius Circular Aperture (Cylinder) \n", "This method is appropriate for an extended source." ] }, @@ -403,7 +403,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 8. Extract Spectrum in Linearly Expanding Circular Aperture (Cone)\n", + "## 8. Extract Spectrum in Linearly Expanding Circular Aperture (Cone) \n", "This method is appropriate for a point source PSF with width proportional to wavelength" ] }, @@ -437,7 +437,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 9. Plot and Compare Non-optimal Spectral Extractions\n", + "## 9. Plot and Compare Non-optimal Spectral Extractions \n", "Compare spectra extracted in cylinder, cone, Cubeviz subset." ] }, @@ -484,7 +484,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 10. WebbPSF Model PSF for Optimal Extraction\n", + "## 10. WebbPSF Model PSF for Optimal Extraction \n", "Generate PSF model cube using WebbPSF for NIRSpec IFU, or read in precomputed PSF model cube." ] }, @@ -557,7 +557,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 11. Align Model PSF Cube with Science Data\n", + "## 11. Align Model PSF Cube with Science Data \n", "Flip, smooth, and shift the model PSF cube to align with the simulated data. Trim the simulated data. \n", "\n", "Important Note 1: this PSF will likely be rotated with respect to your dataset, depending on telescope roll angle. You can either rotate it to match your data or reprocess your data using the ifualign keyword to align the WCS with the instrumental coordinate frame.\n", @@ -635,7 +635,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 12. Optimal Extraction using WebbPSF Model\n", + "## 12. Optimal Extraction using WebbPSF Model \n", "Optimal extraction ([Horne 1986, PASP, 98, 609](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract)) weights the flux contributions to a spectrum by their signal-to-noise ratio (SNR). Dividing the simulated data by the model PSF gives an estimate of the total flux density spectrum in each spaxel. A weighted average of these estimates over all spaxels yields the optimally extracted spectrum over the cube. In the faint source limit, where the noise is background-dominated, optimal extraction inside a 3-sigma radius can increase the effective exposure time by a factor of 1.69 (Horne et al. 1986). In the bright source limit, where the noise is dominated by the Poisson statistics of the source, optimal extraction is formally identical to a straight sum over spaxels for a perfect PSF model. \n", "\n", "We use the precomputed WebbPSF PSF model for optimal extraction here." @@ -697,8 +697,6 @@ "metadata": {}, "outputs": [], "source": [ - "from jdaviz import Specviz\n", - "\n", "specviz = Specviz()\n", "\n", "flux_opt = spectrum_optimal * u.MJy / u.sr\n", From 476c167f1cd2e80e1f9f70912c3efd2e6fb6f6ae Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Fri, 8 Dec 2023 19:44:56 -0500 Subject: [PATCH 8/9] Improve image description with update alt text --- notebooks/ifu_optimal/ifu_optimal.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 819117b58..31ae21c47 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -198,7 +198,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"text" + "\"Cubeviz," ] }, { From 7b063c05aadd6fa6d7dfbbd2aa40ae16035aeb00 Mon Sep 17 00:00:00 2001 From: Hatice Karatay Date: Fri, 8 Dec 2023 20:17:32 -0500 Subject: [PATCH 9/9] Consolidate print statements --- notebooks/ifu_optimal/ifu_optimal.ipynb | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 31ae21c47..e27edc23b 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -165,8 +165,7 @@ "\n", "data = np.nan_to_num(np.array(sci_data))\n", "var = np.array(sci_var)\n", - "print()\n", - "print(\"Trimmed data shape:\", data.shape)" + "print(\"\\nTrimmed data shape:\", data.shape)" ] }, { @@ -228,8 +227,7 @@ "source": [ "cubeviz_data = cubeviz.app.data_collection[0]\n", "\n", - "print()\n", - "print(\"Source Region\")\n", + "print(\"\\nSource Region\")\n", "try:\n", " region1 = cubeviz_data.get_selection_definition(\n", " \"Subset 1\", format=\"astropy-regions\"\n", @@ -246,9 +244,8 @@ " print(\"Center pixel:\", center_xy)\n", " print(\"Radius (pixels):\", r_pix)\n", " region1_exists = False\n", - "\n", - "print()\n", - "print(\"Good Data Region\")\n", + " \n", + "print(\"\\nGood Data Region\")\n", "try:\n", " region2 = cubeviz_data.get_selection_definition(\n", " \"Subset 2\", format=\"astropy-regions\"\n", @@ -298,16 +295,14 @@ "subsets = cubeviz.specviz.get_spectra()\n", "print(subsets.keys())\n", "\n", - "print()\n", - "print(\"Source\")\n", + "print(\"\\nSource\")\n", "try:\n", " spectrum_subset1 = subsets[[i for i in subsets.keys() if \"Subset 1\" in i][0]]\n", " print(spectrum_subset1)\n", "except Exception:\n", " print(\"There is no Subset 1 selected in the spectrum viewer.\")\n", "\n", - "print()\n", - "print(\"Background\")\n", + "print(\"\\nBackground\")\n", "try:\n", " spectrum_subset2 = spectrum_subset2 = subsets[\n", " [i for i in subsets.keys() if \"Subset 2\" in i][0]\n",

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zhA2){;HLgV3xE}HfFNj?AWPqHI+C*@D|})ga_PHNrDlUD`pDbdnBBlcT|MoUG!y&Z zBgBXZwtoDT%@?VED1|E?>~MmUPu|)eEA=jYo~N$&9KmZ{BMcZfbeaEYTs?#-%w4tK z(pNhFW+7w&uBx&t52hvxk^hwIKO982JSLy-{ea@XF91&gIO1K610J^ii>`pn8e#HR z*oyuCMRNTA+y7&_|F1rdnOWs;439$ir+D}uJNWN^bwNOLRQ~$<`o|_` Date: Fri, 1 Dec 2023 15:20:55 -0500 Subject: [PATCH 2/9] update preamble of advanced ifu_optimal notebook --- notebooks/ifu_optimal/ifu_optimal.ipynb | 26 +++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index 44675c488..e096c6522 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -4,7 +4,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Advanced: NIRSpec IFU Optimal Point Source Extraction" + "# NIRSpec IFU Optimal Point Source Extraction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use case:** optimal spectral extraction; method by [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract).\n", + "\n", + "**Data:** JWST simulated NIRSpec IFU data; point sources.\n", + "\n", + "**Tools:** jwst, webbpsf, matplotlib, scipy, custom functions.\n", + "\n", + "**Cross-intrument:** any spectrograph. \n", + "\n", + "**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis)." ] }, { @@ -99,7 +114,10 @@ " raise RuntimeError('Error retrieving file: ' + result[1])\n", " \n", "# Construct the local filepath \n", - "filename = os.path.join(os.path.abspath('.'), uri.rsplit('/', 1)[-1])" + "filename = os.path.join(os.path.abspath('.'), uri.rsplit('/', 1)[-1])\n", + "\n", + "#Optionally Replace MAST data with custom reprocessed data in the current directory\n", + "#filename=\"./jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"" ] }, { @@ -109,10 +127,6 @@ "outputs": [], "source": [ "# Open and inspect the file and WCS\n", - "# Replace MAST data with custom reprocessed data:\n", - "file_dir = \"/Users/pogle/Desktop/NIRSpec/ifu_optimal/q3d_sdss1652_ifu_rerun/extended_source/\"\n", - "#filename=\"jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", - "#filename= file_dir + 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits'\n", "with fits.open(filename, memmap=False) as hdulist:\n", " sci = hdulist['SCI'].data\n", " err = hdulist['ERR'].data\n", From ae1a646fc8ad660d69765636202b625ce874b278 Mon Sep 17 00:00:00 2001 From: Patrick Ogle Date: Sat, 2 Dec 2023 14:47:16 -0500 Subject: [PATCH 3/9] update requirements --- notebooks/ifu_optimal/ifu_optimal.ipynb | 351 ++++++++++++++++++--- notebooks/ifu_optimal/pre-requirements.txt | 2 - notebooks/ifu_optimal/requirements.txt | 20 +- 3 files changed, 309 insertions(+), 64 deletions(-) delete mode 100644 notebooks/ifu_optimal/pre-requirements.txt diff --git a/notebooks/ifu_optimal/ifu_optimal.ipynb b/notebooks/ifu_optimal/ifu_optimal.ipynb index e096c6522..a174f8164 100644 --- a/notebooks/ifu_optimal/ifu_optimal.ipynb +++ b/notebooks/ifu_optimal/ifu_optimal.ipynb @@ -13,7 +13,7 @@ "source": [ "**Use case:** optimal spectral extraction; method by [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract).\n", "\n", - "**Data:** JWST simulated NIRSpec IFU data; point sources.\n", + "**Data:** JWST NIRSpec IFU data; point sources.\n", "\n", "**Tools:** jwst, webbpsf, matplotlib, scipy, custom functions.\n", "\n", @@ -49,9 +49,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "**WARNING**: LOCAL JWST PRD VERSION PRDOPSSOC-059 CANNOT BE CHECKED AGAINST ONLINE VERSION\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jdaviz Version=3.8.1.dev1+gc9daae85\n" + ] + } + ], "source": [ "import numpy as np\n", "import scipy\n", @@ -72,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -103,9 +118,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits to /Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits ... [Done]\n" + ] + } + ], "source": [ "# Download the data file\n", "uri = f\"mast:jwst/product/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\"\n", @@ -122,9 +145,62 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filename: /Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits\n", + "No. Name Ver Type Cards Dimensions Format\n", + " 0 PRIMARY 1 PrimaryHDU 367 () \n", + " 1 SCI 1 ImageHDU 92 (43, 39, 3814) float32 \n", + " 2 ERR 1 ImageHDU 12 (43, 39, 3814) float32 \n", + " 3 DQ 1 ImageHDU 12 (43, 39, 3814) int32 (rescales to uint32) \n", + " 4 WMAP 1 ImageHDU 10 (43, 39, 3814) float32 \n", + " 5 HDRTAB 1 BinTableHDU 828 18R x 409C [23A, 5A, 3A, 44A, 7A, 13A, 6A, 7A, 6A, 7A, 13A, 4A, L, D, D, D, D, 32A, 49A, 129A, 19A, 3A, D, 43A, D, 10A, 12A, 23A, 23A, 26A, 11A, 5A, 3A, 3A, 2A, 1A, 2A, 1A, L, 24A, 17A, 2A, 26A, 20A, 27A, 10A, K, L, L, L, L, 17A, 14A, 5A, D, D, D, D, D, D, D, D, D, 8A, 7A, 4A, D, D, 6A, D, D, 5A, D, D, K, D, D, D, D, D, D, D, 4A, 3A, D, D, D, D, D, D, D, D, D, K, 5A, 7A, D, D, D, D, D, D, D, D, D, 7A, D, D, K, K, D, D, K, K, D, D, K, K, K, K, K, D, D, D, D, D, D, D, D, K, K, L, L, K, K, K, K, D, D, L, D, D, 4A, K, K, K, K, K, K, D, D, D, D, 7A, D, D, K, K, K, D, D, D, 5A, D, D, D, D, D, D, D, D, D, D, D, D, D, 7A, 10A, D, D, D, D, D, D, D, D, D, D, D, D, D, 12A, 12A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, K, 27A, 27A, 10A, D, D, D, D, D, D, D, 9A, 27A, D, D, D, D, D, D, D, 7A, 14A, 34A, D, D, 36A, 40A, D, 34A, 39A, 3A, D, D, 3A, 3A, 35A, 35A, 35A, 34A, 33A, D, D, 34A, 37A, 37A, 39A, D, D, 39A, 34A, 33A, D, 33A, 38A, D, 36A, D, 39A, 36A, 3A, D, D, D, D, 40A, D, D, D, 3A, D, 39A, D, D, D, D, D, D, D, 45A, D, D, D, D, D, 8A, D, D, 7A, D, D, 8A, 8A, D, D, D, D, D, 8A, 7A, 8A, 8A, D, 8A, 7A, 8A, D, D, 8A, D, D, 8A, 8A, 8A, D, 8A, 7A, 8A, 8A, D, 7A, D, D, 8A, D, D, 8A, D, 7A, 8A, D, D, D, D, D, D, D, D, 6A, D, D, D, D, 4A, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, 121A, D, D, K, K, D, D, K, D, D, D, D] \n", + " 6 ASDF 1 BinTableHDU 11 1R x 1C [39124B] \n", + "WCS Keywords\n", + "\n", + "Number of WCS axes: 3\n", + "CTYPE : 'RA---TAN' 'DEC--TAN' 'WAVE' \n", + "CRVAL : -106.98898425200001 17.481222214 1.6601979666156693e-06 \n", + "CRPIX : 22.0 20.0 1.0 \n", + "PC1_1 PC1_2 PC1_3 : -1.0 0.0 0.0 \n", + "PC2_1 PC2_2 PC2_3 : 0.0 1.0 0.0 \n", + "PC3_1 PC3_2 PC3_3 : 0.0 0.0 1.0 \n", + "CDELT : 2.77777781916989e-05 2.77777781916989e-05 3.95999988541007e-10 \n", + "NAXIS : 43 39 3814\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-01 15:25:25,832 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/astronomy/coordinates/extensions/coordinates-1.0.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", + " warnings.warn(msg, AsdfWarning)\n", + "\n", + "2023-12-01 15:25:25,833 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension class 'asdf.extension.BuiltinExtension' (from package asdf==2.15.0), but older package (asdf==2.14.3) is installed.\n", + " warnings.warn(msg, AsdfWarning)\n", + "\n", + "2023-12-01 15:25:25,834 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/transform/extensions/transform-1.5.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", + " warnings.warn(msg, AsdfWarning)\n", + "\n", + "2023-12-01 15:25:25,835 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/asdf/asdf.py:348: AsdfWarning: File 'file:///Users/pogle/Desktop/jdat/jdat_notebooks/notebooks/ifu_optimal/jw01335-o008_t007_nirspec_g235h-f170lp_s3d.fits' was created with extension URI 'asdf://asdf-format.org/core/extensions/core-1.5.0' (from package asdf-astropy==0.4.0), but older package (asdf-astropy==0.3.0) is installed.\n", + " warnings.warn(msg, AsdfWarning)\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Trimmed data shape: (600, 39, 43)\n" + ] + } + ], "source": [ "# Open and inspect the file and WCS\n", "with fits.open(filename, memmap=False) as hdulist:\n", @@ -162,9 +238,38 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-01 15:25:31,947 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", + " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", + "\n", + "2023-12-01 15:25:32,440 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/glue/viewers/common/qt/__init__.py:3: GlueDeprecationWarning: Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead\n", + " warnings.warn('Importing from glue.viewers.common.qt is deprecated, use glue_qt.viewers.common instead', GlueDeprecationWarning)\n", + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cce2fc62d7bd4ed79ac7539768938ccf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Application(config='cubeviz', docs_link='https://jdaviz.readthedocs.io/en/latest/cubeviz/index.html', events=[…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Launch Cubeviz and load the data cube\n", "\n", @@ -215,9 +320,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Source Region\n", + "Region: CirclePixelRegion\n", + "center: PixCoord(x=21.49135398864746, y=20.602163314819336)\n", + "radius: 4.742252588272101\n", + "\n", + "Good Data Region\n", + "Region: RectanglePixelRegion\n", + "center: PixCoord(x=21.491354228349095, y=19.16824705901036)\n", + "width: 29.82544951538412\n", + "height: 28.56360357434864\n", + "angle: 0.0 rad\n", + "Good data (xmin,xmax), (ymin,ymax): [7, 36] [5, 33]\n" + ] + } + ], "source": [ "cubeviz_data = cubeviz.app.data_collection[0]\n", "\n", @@ -266,9 +391,35 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-12-01 15:26:10,357 - stpipe - WARNING - /Users/pogle/miniconda3/envs/science/lib/python3.10/site-packages/jdaviz/configs/specviz/helper.py:131: UserWarning: Applying the value from the redshift slider to the output spectra. To avoid seeing this warning, explicitly set the apply_slider_redshift keyword option to True or False.\n", + " warnings.warn(\"Applying the value from the redshift \"\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI]', 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI] (Subset 1)', 'jw01335-o008_t007_nirspec_g235h-f170lp_s3d[SCI] (Subset 2)'])\n", + "Source\n", + "Spectrum1D (length=3814)\n", + "flux: [ 465.19 MJy / sr, ..., nan MJy / sr ], mean=nan MJy / sr\n", + "spectral axis: [ 1.6602 um, ..., 3.1701 um ], mean=2.4152 um\n", + "\n", + "Background\n", + "Spectrum1D (length=3814)\n", + "flux: [ 963.26 MJy / sr, ..., nan MJy / sr ], mean=nan MJy / sr\n", + "spectral axis: [ 1.6602 um, ..., 3.1701 um ], mean=2.4152 um\n" + ] + } + ], "source": [ "subsets = cubeviz.specviz.get_spectra()\n", "print(subsets.keys())\n", @@ -314,9 +465,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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