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zi{G7?Qj6q5=7VPHVt)Vy1Wt6G>O_ zk92Gd!H4b=_}!IDu1<0GT|Q%hGI=8)t9e3dQ54-FBK=j79^DEW@Q_@o<(*ARey}tR zLS6u?Brg60SQ8?>TaaClvzD1h)4X_+r4Gc$j`(41cc>@kqVyc^S`*tYvRBV7cR-4P zqi+aTB(mRwISkJ5W9*Vigbt+83CjrxMBAA=)?1avwV>9%CnmY1UO>hiD;*X0eV3}q zF4`PIUs*tnzwdOAdqyYZk$w~t=fWP{EzGL9V1TkuL)TW0wLP&|4}36$*mHK_LDdqN zjlq*_DaU?VuMARiV9PEQvNae7^o{o6Db>OalR|F@#nj$ou0JnNIK+ zcXraCcZ0YWfrX~9YJr=dNzmaT%swnKd|EIgWFzp?TNp!j#kUj)W0)|U!Fu|&VY=~s zmpk|Thj$pvTuUPwpN2e3Pk{;)_(vd}1DV7ugMdcFwi+IePxxuX*&iKoIX34oC285m zg493h1~rV30su7_Sv3PqtU%NP{n_+KJ_P z2{-^*0=%&76)0r_Q=Q)V&tIR3rhu9pz$2wMtbPM5 z_*XAmw(RwN;NhwlfrG4TK=A-t&j{QgW1}0lCjwaA0LPZAD}j5b1n<`W|7!*8YJUOl zJ$(gSss1|bKCnNtHg0beaCGrm9cUM-)x#EH;C3#axplu@0>@@MfX3{JFah4_1_U00 zkUR~l5yF`m78L@=z+MBVOs)eb6Q;jd^<$QG`8rTr6FA+izDogOJ5jTT;Table length=3\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "713c146c", + "id": "4f6e54c5", "metadata": {}, "source": [ "### 2.2 Cone search\n", @@ -278,14 +278,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "9e4d0a25", + "id": "e82a4645", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=316\n", - "
short_nameres_titleres_description
objectobjectobject
MAST CSMAST ConeSearchAll MAST catalog holdings are available via a ConeSearch endpoint. \\nThis service provides access to all, with an optional non-standard parameter for an individual catalog to query. \\nThe available missions are listed at http://archive.stsci.edu/vo/mast_services.html, \\nand include Hubble (HST) data, Kepler, K2, IUE, HUT, EUVE, FUSE, UIT, WUPPE, BEFS, TUES, IMAPS, High Level Science Products (HLSP), Copernicus, HPOL, VLA First, XMM-OM, and SWIFT.
\n", + "
\n", "\n", "\n", "\n", @@ -354,7 +354,7 @@ }, { "cell_type": "markdown", - "id": "e03722c6", + "id": "1e3ba4c4", "metadata": {}, "source": [ "### 2.3 Image search\n", @@ -365,14 +365,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "2071ad8d", + "id": "21a23d31", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=3\n", - "
ObjIDZoneSeqNoRADECpmRApmDECe_pmRAe_pmDECe_RAe_DECEpochB1MagR1s_gB2MagB2s_gR2MagR2s_gNMagmagB1s_gR1Magdistance
degdegmas / yrmas / yrmas / yrmas / yrarcsecarcsecyrmagmagmagmagmagmagarcsec
int64int32int32float64float64float32float32float32float32float32float32float32float32int32float32int32float32int32float32float32int32float32float32
\n", + "
\n", "\n", "\n", "\n", @@ -402,7 +402,7 @@ }, { "cell_type": "markdown", - "id": "79444893", + "id": "31a0f394", "metadata": {}, "source": [ "#### Search one of the services\n", @@ -416,14 +416,14 @@ { "cell_type": "code", "execution_count": 8, - "id": "151e2955", + "id": "f227abb1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=2\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
\n", + "
\n", "\n", "\n", "\n", @@ -456,7 +456,7 @@ }, { "cell_type": "markdown", - "id": "cc0a457b", + "id": "ef5d2764", "metadata": {}, "source": [ "#### Download an image\n", @@ -468,7 +468,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "1769e3d2", + "id": "b4143642", "metadata": {}, "outputs": [ { @@ -481,7 +481,7 @@ { "data": { "text/plain": [ - "'/tmp/astropy-download-2035-mvp2n0ur'" + "'/tmp/astropy-download-1995-79gj3juv'" ] }, "execution_count": 9, @@ -497,7 +497,7 @@ }, { "cell_type": "markdown", - "id": "1dd04f7a", + "id": "c94c754e", "metadata": {}, "source": [ "### 2.4 Spectral search\n", @@ -512,7 +512,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "4bd35f68", + "id": "c1e7389a", "metadata": {}, "outputs": [ { @@ -525,7 +525,7 @@ { "data": { "text/plain": [ - "'/tmp/astropy-download-2035-cg9frdq8'" + "'/tmp/astropy-download-1995-kk3bpcpi'" ] }, "execution_count": 10, @@ -549,7 +549,7 @@ }, { "cell_type": "markdown", - "id": "34adb982", + "id": "6f30b19c", "metadata": {}, "source": [ "### 2.5 Table search\n", @@ -559,7 +559,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "3b6078ce", + "id": "46126ae4", "metadata": { "tags": [ "output_scroll" @@ -1637,7 +1637,7 @@ }, { "cell_type": "markdown", - "id": "dcc8f300", + "id": "6aca3b4e", "metadata": {}, "source": [ "#### Column Information\n", @@ -1647,7 +1647,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "4ac03ff3", + "id": "fce9154b", "metadata": {}, "outputs": [ { @@ -1693,7 +1693,7 @@ }, { "cell_type": "markdown", - "id": "a7adf0a5", + "id": "091f91d7", "metadata": {}, "source": [ "#### Perform a Query\n", @@ -1703,14 +1703,14 @@ { "cell_type": "code", "execution_count": 13, - "id": "84d76bc1", + "id": "7f307094", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=13\n", - "
filenameidra_j2000dec_j2000urlfilesizemjdmeannaxesnaxisscalecdformatref_frameequinoxcoord_projectioncrpixcrvalctypebandpass_idbandpass_refvaluebandpass_unitbandpass_hilimitbandpass_lolimitprocessingprojectpreviewrepresentativeobject_id
degdegbytedpixdeg / pixdeg / pixyrpixpixmmmm
objectobjectfloat64float64objectint32float64int32objectobjectobjectobjectobjectfloat32str3objectobjectobjectobjectfloat64objectfloat64float64objectobjectobjectobjectobject
\n", + "
\n", "\n", "\n", "\n", @@ -1768,7 +1768,7 @@ }, { "cell_type": "markdown", - "id": "573da87f", + "id": "e20c419c", "metadata": {}, "source": [ "## 3. Astroquery \n", @@ -1796,14 +1796,14 @@ { "cell_type": "code", "execution_count": 14, - "id": "12389f32", + "id": "42093fb6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=41\n", - "
radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
\n", + "
\n", "\n", "\n", "\n", diff --git a/_sources/content/reference_notebooks/catalog_queries.ipynb b/_sources/content/reference_notebooks/catalog_queries.ipynb index e4872a1..f129687 100644 --- a/_sources/content/reference_notebooks/catalog_queries.ipynb +++ b/_sources/content/reference_notebooks/catalog_queries.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "627a307e", + "id": "da3d09b5", "metadata": {}, "source": [ "# Accessing astronomical catalogs\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "d648e26b", + "id": "88acff81", "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "69322d96", + "id": "3e3af761", "metadata": {}, "source": [ "## 1. Simple cone search" @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "16dd3162", + "id": "ccf95dad", "metadata": {}, "source": [ "Starting with a single simple source first:" @@ -72,7 +72,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "471e119a", + "id": "95b2820e", "metadata": {}, "outputs": [ { @@ -91,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "a9672fe2", + "id": "7a20d640", "metadata": {}, "source": [ "Below, we go through the exercise of how we can figure out the most relevant table. But for now, let's assume that we know that we want the CFA redshift catalog refered to as 'zcat'. VO services are listed in a central Registry that can be searched through a [web interface](http://vao.stsci.edu/keyword-search/) or using PyVO's `regsearch`. We use the registry to find the corresponding cone service and then submit our cone search.\n", @@ -108,14 +108,14 @@ { "cell_type": "code", "execution_count": 3, - "id": "3d07c5d7", + "id": "cd5e4811", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=6\n", - "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32str30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
\n", + "
\n", "\n", "\n", "\n", @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "17f78c5f", + "id": "cee332e7", "metadata": {}, "source": [ "Supposing that we want the table with the short_name CFAZ, and we want to retrieve the data for all sources within an arcminute of our specified location:" @@ -160,14 +160,14 @@ { "cell_type": "code", "execution_count": 4, - "id": "a97c7fca", + "id": "7a6cc99f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=2\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://cds.vizier/j/mnras/339/652J/MNRAS/339/652The FLASH Redshift Survey
\n", + "
\n", "\n", "\n", "\n", @@ -202,7 +202,7 @@ }, { "cell_type": "markdown", - "id": "65ec97bd", + "id": "0c8d0a6d", "metadata": {}, "source": [ "The SCS is quite straightforward and returns all of the columns of the given table (which can be anything) for the sources in the region queried." @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "c0db32f9", + "id": "d4291048", "metadata": {}, "source": [ "## 2. Table Access Protocol queries\n", @@ -220,7 +220,7 @@ }, { "cell_type": "markdown", - "id": "42985c4b", + "id": "e006c998", "metadata": {}, "source": [ "### 2.1 TAP services\n", @@ -232,7 +232,7 @@ }, { "cell_type": "markdown", - "id": "66de3e42", + "id": "183bd7ad", "metadata": {}, "source": [ "As before, we use the `vo.regsearch()` for a servicetype 'tap'. There are a lot of TAP services in the registry, but they are listed slightly differently than cone services. The metadata on each catalog is usually published in the registry with its cone service, and then the full TAP service is listed as an \"auxiliary\" service. So to find a TAP service for a given catalog, we need to add the option *includeaux=True*. Alternatively, you can start with a single TAP service and then ask it specifically which tables it serves, but for this use case, that is less efficient.\n", @@ -243,14 +243,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "1e8962a4", + "id": "6a2ce4d1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=20\n", - "
__rownameradecbmagradial_velocityradial_velocity_errorredshiftclassSearch_Offset
degdegkm / skm / s
objectobjectfloat64float64float32int32int16float64int16float64
\n", + "
\n", "\n", "\n", "\n", @@ -314,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "a57ccef8", + "id": "d2da1584", "metadata": {}, "source": [ "There are many tables that mention these keywords. Pick some likely looking ones and look at the descriptions:" @@ -323,7 +323,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "9027b489", + "id": "0b5fafff", "metadata": {}, "outputs": [ { @@ -353,7 +353,7 @@ }, { "cell_type": "markdown", - "id": "835d6eea", + "id": "83bac4ad", "metadata": {}, "source": [ "From the above information, you can choose the table you want and then use the specified TAP service to query it as described below.\n", @@ -363,7 +363,7 @@ }, { "cell_type": "markdown", - "id": "186fbd6a", + "id": "b1a71013", "metadata": {}, "source": [ "You can find out which tables a TAP serves and then look at the tables descriptions. The last line here sends a query directly to the service to ask it for a list of tables. (This can take a minute, since there may be a lot of tables.)" @@ -372,7 +372,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "a45471ac", + "id": "822a7337", "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "markdown", - "id": "8e94fd28", + "id": "df8dd6b2", "metadata": {}, "source": [ "Then let's look for tables matching the terms we're interested in as above." @@ -393,7 +393,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "f3e655d6", + "id": "e980820f", "metadata": {}, "outputs": [ { @@ -459,16 +459,16 @@ " 2MASS Redshift Survey (2MRS) Catalog\n", "\n", "Columns=['\"__row\"', '\"__x_ra_dec\"', '\"__y_ra_dec\"', '\"__z_ra_dec\"', 'alt_name', 'axis_ratio', 'bii', 'dec', 'hmag_0', 'hmag_0_error', 'hmag_0_tot', 'hmag_0_tot_error', 'jmag_0', 'jmag_0_error', 'jmag_0_tot', 'jmag_0_tot_error', 'ks_mag_0', 'ks_mag_0_error', 'ks_mag_0_tot', 'ks_mag_0_tot_error', 'lii', 'log_k20_semimajor_axis', 'log_tot_semimajor_axis', 'morph_type', 'name', 'ra', 'radial_velocity', 'radial_velocity_error', 'radial_velocity_source', 'reddening', 'ref_morph_type', 'ref_radial_velocity', 'xsc_ppc_flags']\n", - "----\n" + "----\n", + "xdeep2\n", + " DEEP2 Galaxy Redshift Survey Fields Chandra Point Source Catalog\n", + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "xdeep2\n", - " DEEP2 Galaxy Redshift Survey Fields Chandra Point Source Catalog\n", - "\n", "Columns=['\"__row\"', '\"__x_ra_dec\"', '\"__y_ra_dec\"', '\"__z_ra_dec\"', 'bayesian_galaxy_prob', 'bii', 'bmag', 'csc_name', 'dec', 'error_radius', 'fb_counts_50pc_eef', 'fb_counts_50pc_eef_error', 'fb_counts_50pc_eef_limit', 'fb_counts_90pc_eef', 'fb_counts_90pc_eef_error', 'fb_counts_90pc_eef_limit', 'fb_flux', 'fb_flux_error', 'fb_flux_limit', 'field_number', 'field_subfield_id', 'flux_ratio', 'flux_ratio_lower', 'flux_ratio_upper', 'hardness_ratio', 'hardness_ratio_lower', 'hardness_ratio_upper', 'hb_counts_50pc_eef', 'hb_counts_50pc_eef_error', 'hb_counts_50pc_eef_limit', 'hb_counts_90pc_eef', 'hb_counts_90pc_eef_error', 'hb_counts_90pc_eef_limit', 'hb_flux', 'hb_flux_error', 'hb_flux_limit', 'imag', 'lii', 'name', 'off_axis', 'off_set', 'opt_dec', 'opt_ra', 'opt_source_number', 'ra', 'radius_50pc_eef', 'radius_90pc_eef', 'redshift', 'rmag', 'sb_counts_50pc_eef', 'sb_counts_50pc_eef_error', 'sb_counts_50pc_eef_limit', 'sb_counts_90pc_eef', 'sb_counts_90pc_eef_error', 'sb_counts_90pc_eef_limit', 'sb_flux', 'sb_flux_error', 'sb_flux_limit']\n", "----\n", "xmmcfrscat\n", @@ -504,7 +504,7 @@ }, { "cell_type": "markdown", - "id": "8924a7d1", + "id": "8dc8aeb7", "metadata": {}, "source": [ "There are a number of tables that appear to be useful table for our goal, including the ZCAT, which contains columns with the information that we need to select a sample of the brightest nearby spiral galaxy candidates.\n", @@ -514,7 +514,7 @@ }, { "cell_type": "markdown", - "id": "9ddfe2ac", + "id": "8de14173", "metadata": {}, "source": [ "### 2.2 Expressing queries in ADQL" @@ -522,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "546c7a92", + "id": "d7e50001", "metadata": {}, "source": [ "The basics of ADQL:\n", @@ -564,7 +564,7 @@ }, { "cell_type": "markdown", - "id": "30e1e111", + "id": "5604bc67", "metadata": {}, "source": [ "### 2.3 A use case\n", @@ -575,7 +575,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "7d416aa7", + "id": "c26c220c", "metadata": {}, "outputs": [], "source": [ @@ -595,14 +595,14 @@ { "cell_type": "code", "execution_count": 10, - "id": "da26e0d5", + "id": "a32fe010", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=3\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://cds.vizier/j/a+a/408/905J/A+A/408/905Very Luminous Galaxies
\n", + "
\n", "\n", "\n", "\n", @@ -635,7 +635,7 @@ }, { "cell_type": "markdown", - "id": "0f4274b3", + "id": "c1e8362c", "metadata": {}, "source": [ "See the __[information on the zcat](https://heasarc.gsfc.nasa.gov/W3Browse/galaxy-catalog/zcat.html)__ for column information. (We will use the 'radial_velocity' column rather than the 'redshift' column.) We note that spiral galaxies have morph_type between 1 - 9." @@ -643,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "718eb3c5", + "id": "848abc69", "metadata": {}, "source": [ "Therefore, we can generalize the query above to complete our exercise and select the brightest (bmag < 14), nearby (radial velocity < 3000), spiral ( morph_type = 1 - 9) galaxies as follows:" @@ -652,7 +652,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ad34da5f", + "id": "e29582d8", "metadata": {}, "outputs": [], "source": [ @@ -665,14 +665,14 @@ { "cell_type": "code", "execution_count": 12, - "id": "bbd5c71f", + "id": "5f2cd5b0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=1120\n", - "
radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
\n", + "
\n", "\n", "\n", "\n", @@ -739,7 +739,7 @@ }, { "cell_type": "markdown", - "id": "1604594e", + "id": "d265bc66", "metadata": {}, "source": [ "### 2.4 TAP examples for a given service\n", @@ -750,7 +750,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "4b73aff2", + "id": "6335f2b3", "metadata": {}, "outputs": [ { @@ -772,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "b9175bb6", + "id": "a0f4e272", "metadata": {}, "source": [ "Above, these examples look like a list of dictionaries. But they are actually a list of objects that can be executed:" @@ -781,7 +781,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "a01a72e5", + "id": "c07f7abf", "metadata": {}, "outputs": [ { @@ -795,7 +795,7 @@ "data": { "text/html": [ "
Table length=2\n", - "
radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
\n", + "
\n", "\n", "\n", "\n", @@ -829,7 +829,7 @@ }, { "cell_type": "markdown", - "id": "513a1b4c", + "id": "d9e97396", "metadata": {}, "source": [ "## 3. Using the TAP to cross-correlate and combine" @@ -837,7 +837,7 @@ }, { "cell_type": "markdown", - "id": "f3b9ab9c", + "id": "3e61f42c", "metadata": {}, "source": [ "### 3.1 Cross-correlating to combine catalogs\n", @@ -852,7 +852,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "8ee8a517", + "id": "0a825759", "metadata": {}, "outputs": [ { @@ -873,7 +873,7 @@ }, { "cell_type": "markdown", - "id": "e4096ba9", + "id": "3c284162", "metadata": {}, "source": [ "The inline method is what PyVO will use. These take a while, i.e. half a minute." @@ -882,14 +882,14 @@ { "cell_type": "code", "execution_count": 16, - "id": "c5b83d9d", + "id": "c708e958", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=14\n", - "
__rowseq_idradecliibiiinstrumentfiltersiteexposurerequested_exposurefits_typestart_timeend_timenamepi_lnamepi_fnamerorindex_idsubj_catproc_revtitleqa_numberaoproposal_numberrollrday_beginrday_endclass__x_ra_dec__y_ra_dec__z_ra_dec
degdegdegdegssdddegdd
objectobjectfloat64float64float64float64objectobjectobjectint32int32objectfloat64float64objectobjectobjectint32objectint16int16objectint32int16int32int16int32int32int16float64float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -952,7 +952,7 @@ }, { "cell_type": "markdown", - "id": "547c8fbe", + "id": "f9e0024d", "metadata": {}, "source": [ "Therefore we now have the Bmag, morphological type and radial velocities for all the sources in our list with a single TAP query." @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "5a620bf8", + "id": "4590db44", "metadata": {}, "source": [ "### 3.2 Cross-correlating with user-defined columns\n", @@ -975,14 +975,14 @@ { "cell_type": "code", "execution_count": 17, - "id": "8c38bf26", + "id": "54dd8354", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=14\n", - "
radecradial_velocitybmagmorph_type
degdeg
float64float64int32float32int16
\n", + "
\n", "\n", "\n", "\n", @@ -1045,7 +1045,7 @@ }, { "cell_type": "markdown", - "id": "ca7d2e9e", + "id": "0da7ed4a", "metadata": {}, "source": [ "Now we construct and run a query that uses the new angDdeg column in every row search. Note, we also don't want to list the original candidates since we know these are in the catalog and we want rather to find any companions. Therefore, we exclude the match if the radial velocities match exactly.\n", @@ -1056,14 +1056,14 @@ { "cell_type": "code", "execution_count": 18, - "id": "7df47b40", + "id": "439bf9bd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=9\n", - "
radecradial_velocitybmagmorph_typeredshiftangDdeg
degdegdeg
float64float64int32float32int16float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "d3fa127b", + "id": "43cfb3ac", "metadata": {}, "source": [ "Therefore, by adding new information to our original data table, we could cross-correlate with the TAP. We find that, in our candidate list, there is one true pair of galaxies." @@ -1129,7 +1129,7 @@ }, { "cell_type": "markdown", - "id": "c730c48c", + "id": "45f3ea37", "metadata": {}, "source": [ "## 4. Synchronous versus asynchronous queries\n", diff --git a/_sources/content/reference_notebooks/image_access.ipynb b/_sources/content/reference_notebooks/image_access.ipynb index daf4867..1bf5543 100644 --- a/_sources/content/reference_notebooks/image_access.ipynb +++ b/_sources/content/reference_notebooks/image_access.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2f2837ad", + "id": "8657e433", "metadata": {}, "source": [ "# Searching for and retrieving images\n", @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "631e41d4", + "id": "41f8552d", "metadata": {}, "source": [ "**\\*Note:** for all of these notebooks, the results depend on real-time queries. Sometimes there are problems, either because a given service has changed, is undergoing maintenance, or the internet connectivity is having problems, etc. Always retry a couple of times, come back later and try again, and only then send us the problem report to investigate." @@ -31,7 +31,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "796eda73", + "id": "19b3b725", "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "markdown", - "id": "b90ea8c1", + "id": "117c1780", "metadata": {}, "source": [ "## 1. Finding SIA resources from the Registry\n", @@ -72,14 +72,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "3b2fdcec", + "id": "3af5ed32", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=18\n", - "
radecra2dec2radial_velocitymorph_typebmag
degdegdegdeg
float64float64float64float64int32int16float32
\n", + "
\n", "\n", "\n", "\n", @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "5d21d833", + "id": "fff86089", "metadata": {}, "source": [ "This returns an astropy table containing information about the services available. We can then specify the service we want by using the corresponding row. We'll repeat the search with additional qualifiers to isolate the row we want (note that in the keyword search the \"%\" character can be used as a wild card):" @@ -148,14 +148,14 @@ { "cell_type": "code", "execution_count": 3, - "id": "c3b22bd8", + "id": "11adf342", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=1\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
\n", + "
\n", "\n", "\n", "\n", @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "9d694a6d", + "id": "8519ce59", "metadata": {}, "source": [ "This shows us that the data we are interested in comes from the HEASARC's SkyView service, but the point of these VO tools is that you don't need to know that ahead of time or indeed to care where it comes from." @@ -189,7 +189,7 @@ }, { "cell_type": "markdown", - "id": "352fb155", + "id": "7ae8d204", "metadata": {}, "source": [ "## 2. Using SIA to retrieve an image\n", @@ -202,22 +202,22 @@ { "cell_type": "code", "execution_count": 4, - "id": "df5ad8af", + "id": "b9a341ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=6\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://nasa.heasarc/skyview/swiftuvotSWIFTUVOTSwift UVOT Combined V Intensity Images
\n", + "
\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName
objectfloat64float64int32objectobjectobjectobjectobjectobject
swiftuvotvint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039557&nofits=1&quicklook=jpeg&return=jpeg1
swiftuvotbint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotbint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039862&nofits=1&quicklook=jpeg&return=jpeg2
swiftuvotuint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590041036&nofits=1&quicklook=jpeg&return=jpeg3
swiftuvotuvw1int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw1int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590041554&nofits=1&quicklook=jpeg&return=jpeg4
swiftuvotuvw2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590042124&nofits=1&quicklook=jpeg&return=jpeg5
swiftuvotuvm2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvm2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590042572&nofits=1&quicklook=jpeg&return=jpeg6
swiftuvotvint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169031&nofits=1&quicklook=jpeg&return=jpeg1
swiftuvotbint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotbint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169488&nofits=1&quicklook=jpeg&return=jpeg2
swiftuvotuint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669170786&nofits=1&quicklook=jpeg&return=jpeg3
swiftuvotuvw1int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw1int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669171610&nofits=1&quicklook=jpeg&return=jpeg4
swiftuvotuvw2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669172139&nofits=1&quicklook=jpeg&return=jpeg5
swiftuvotuvm2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvm2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669172598&nofits=1&quicklook=jpeg&return=jpeg6
" ], "text/plain": [ @@ -247,7 +247,7 @@ }, { "cell_type": "markdown", - "id": "8eccefbd", + "id": "4ed15a25", "metadata": {}, "source": [ "Extract the fields you're interested in, e.g., the URLs of the images made by skyview. Note that specifying as we did SwiftUVOT, we get a number of different images, e.g., UVOT U, V, B, W1, W2, etc. For each survey, there are two URLs, first the FITS IMAGE and second the JPEG. \n", @@ -258,14 +258,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "58366521", + "id": "bfc7c840", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "https://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039557&nofits=1&quicklook=jpeg&return=jpeg\n" + "https://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169031&nofits=1&quicklook=jpeg&return=jpeg\n" ] } ], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "9ffedc78", + "id": "631a4746", "metadata": {}, "source": [ "## 3. Viewing the resulting image" @@ -284,7 +284,7 @@ }, { "cell_type": "markdown", - "id": "3b443a95", + "id": "95237af3", "metadata": {}, "source": [ "### JPG images\n", @@ -295,13 +295,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "315ecf70", + "id": "36c7a827", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -318,7 +318,7 @@ }, { "cell_type": "markdown", - "id": "94a29737", + "id": "cf716522", "metadata": {}, "source": [ "### Fits files\n", @@ -331,14 +331,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "8bea6ee6", + "id": "73ac55df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Filename: /home/runner/.astropy/cache/download/url/de81ae34669ea5e7a0bce916fe722d36/contents\n", + "Filename: /home/runner/.astropy/cache/download/url/2f973d066f0f87a49cad508074d3f4c6/contents\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 111 (300, 300) float32 \n" ] @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "896ef009", + "id": "326895ae", "metadata": {}, "source": [ "#### Using imshow" @@ -364,13 +364,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "e01f8baf", + "id": "d95a5cb4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -394,7 +394,7 @@ }, { "cell_type": "markdown", - "id": "026d0e32", + "id": "5e99184c", "metadata": {}, "source": [ "## 4. Example of data available through multiple services\n", @@ -405,14 +405,14 @@ { "cell_type": "code", "execution_count": 9, - "id": "8c344de5", + "id": "c0d7490e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Table length=2\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -440,7 +440,7 @@ }, { "cell_type": "markdown", - "id": "2e3e3d6d", + "id": "a7bb465b", "metadata": {}, "source": [ "So one of these is served by SDSS's SkyServer and the other by HEASARC's SkyView." @@ -448,7 +448,7 @@ }, { "cell_type": "markdown", - "id": "0054c94f", + "id": "7ad61a7f", "metadata": {}, "source": [ "### Using HEASARC" @@ -457,21 +457,21 @@ { "cell_type": "code", "execution_count": 10, - "id": "b07c89e3", + "id": "03eb2114", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=5\n", - "
ivoidshort_name
objectobject
ivo://nasa.heasarc/skyview/sdssdr7SDSSDR7
\n", + "
\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName
objectfloat64float64int32objectobjectobjectobjectobjectobject
sdssdr7g202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7g&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590049681&return=FITS1
sdssdr7i202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7i&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590050327&return=FITS2
sdssdr7u202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7u&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590050781&return=FITS3
sdssdr7r202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7r&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590051292&return=FITS4
sdssdr7z202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7z&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590051848&return=FITS5
sdssdr7g202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7g&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669181003&return=FITS1
sdssdr7i202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7i&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669181557&return=FITS2
sdssdr7u202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7u&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669182092&return=FITS3
sdssdr7r202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7r&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669182627&return=FITS4
sdssdr7z202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7z&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669183120&return=FITS5
" ], "text/plain": [ @@ -501,13 +501,13 @@ { "cell_type": "code", "execution_count": 11, - "id": "f26b426a", + "id": "477916a9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -541,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "d953e313", + "id": "a411df3e", "metadata": {}, "source": [ "### Using SDSS SkyServer" @@ -550,14 +550,14 @@ { "cell_type": "code", "execution_count": 12, - "id": "f022fcb3", + "id": "413dd17c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=30\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -612,14 +612,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table140440800819664-415174').dataTable()\");\n", + " console.log(\"$('#table139638210885904-878382').dataTable()\");\n", " \n", "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n", " \"optionalnum-asc\": astropy_sort_num,\n", " \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n", "});\n", "\n", - " $('#table140440800819664-415174').dataTable({\n", + " $('#table139638210885904-878382').dataTable({\n", " order: [],\n", " pageLength: 5,\n", " lengthMenu: [[5, 10, 25, 50, 100, 500, 1000, -1], [5, 10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -651,13 +651,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "e83cbd93", + "id": "27ab9425", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "7b5f11be", + "id": "7126360d", "metadata": {}, "source": [ "It turns out that SkyView is just getting images by using the SIAP internally to get the data from the SDSS service. The point of the VO protocols is that you don't need to know where the data are coming from. But they can be processed differently." diff --git a/_sources/content/reference_notebooks/spectral_access.ipynb b/_sources/content/reference_notebooks/spectral_access.ipynb index 6fd2355..eed028d 100644 --- a/_sources/content/reference_notebooks/spectral_access.ipynb +++ b/_sources/content/reference_notebooks/spectral_access.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d4494f2a", + "id": "cc157e09", "metadata": {}, "source": [ "# Retrieve spectra using Simple Spectral Access protocol\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "c4fc01ed", + "id": "c0985250", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "4741c0a4", + "id": "9c24d080", "metadata": {}, "source": [ "## Finding available Spectral Access Services\n", @@ -54,14 +54,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "9b411c83", + "id": "abd6e96c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=6\n", - "
idxTitlewidthheightsizeRADECscaleformaturlequinoxnaxesnaxiscrtypecrpixcrvalcdval
0Sloan Digital Sky Survey - Filter u204814893049472.0202.43971584011547.1222952774076[0.000110035211267606]image/fitshttp://das.sdss.org/imaging/3699/41/corr/6/fpC-003699-u6-0100.fit.gz--2[2048 1489]RA--TAN,DEC--TAN[744.5 1024.5][202.439715840115 47.1222952774076][3.57829352076884e-05 0.000157654334590212 0.000107291403325821\\n -2.42761847386219e-05]
1Sloan Digital Sky Survey - Filter g204814893049472.0202.43876345123347.1224982411069[0.000110035211267606]image/fitshttp://das.sdss.org/imaging/3699/41/corr/6/fpC-003699-g6-0100.fit.gz--2[2048 1489]RA--TAN,DEC--TAN[744.5 1024.5][202.438763451233 47.1224982411069][3.57455198349534e-05 0.000157658871914506 0.000107248674325512\\n -2.4298297148212e-05]
\n", + "
\n", "\n", "\n", "\n", @@ -97,7 +97,7 @@ }, { "cell_type": "markdown", - "id": "e64342d9", + "id": "21d9f084", "metadata": {}, "source": [ "We can look at only the Chandra entry:" @@ -106,7 +106,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "547f1caf", + "id": "cc1247f0", "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "badfc940", + "id": "35ae18de", "metadata": {}, "source": [ "## Chandra Spectrum of Delta Ori\n", @@ -138,14 +138,14 @@ { "cell_type": "code", "execution_count": 4, - "id": "b186aa34", + "id": "745faad2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=6\n", - "
ivoidshort_name
objectobject
ivo://nasa.heasarc/chanmasterChandra
\n", + "
\n", "\n", "\n", "\n", @@ -177,14 +177,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table140595577846704-124245').dataTable()\");\n", + " console.log(\"$('#table139805713273456-502366').dataTable()\");\n", " \n", "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n", " \"optionalnum-asc\": astropy_sort_num,\n", " \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n", "});\n", "\n", - " $('#table140595577846704-124245').dataTable({\n", + " $('#table139805713273456-502366').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -212,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "00e2c852", + "id": "58f45ffa", "metadata": {}, "source": [ "Accessing one of the spectra." @@ -221,7 +221,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "cf2f7b94", + "id": "6426f235", "metadata": {}, "outputs": [], "source": [ @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "d415708a", + "id": "9de6d5ab", "metadata": {}, "source": [ "## Simple example of plotting a spectrum" @@ -245,14 +245,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "172699c9", + "id": "5b6e9801", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=12\n", - "
idxobsidstatusnameradectimedetectorgratingexposuretypepipublic_datedatalinkSSA_start_timeSSA_tmidSSA_stop_timeSSA_durationSSA_coord_obsSSA_raSSA_decSSA_fovSSA_titleSSA_referenceSSA_datalengthSSA_datamodelSSA_instrumentSSA_publisherSSA_formatSSA_wavelength_minSSA_wavelength_maxSSA_bandwidthSSA_bandpasscloud_access
degdegdsddddsdegdegdegdegmmmm
0639archivedDELTA ORI83.00125-0.2991751556.1364ACIS-SHETG49680GOCassinelli5203711157:chandra.obs.misc51556.136400463----49680.0--83.00125-0.299170.81acisf00639N004_pha2.fitshttps://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N004_pha2.fits.gz12Spectrum-1.0ACIS-SHEASARCapplication/fits1.2398e-106.1992e-096.07522e-093.16159e-09{"aws":{"bucket_name":"nasa-heasarc","region":"us-east-1","policy":"open","key":"chandra/data/byobsid/9/639/primary/acisf00639N004_pha2.fits.gz"}}
\n", + "
\n", "\n", "\n", "\n", @@ -301,7 +301,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "b08a7848", + "id": "d55f1e89", "metadata": {}, "outputs": [ { @@ -338,7 +338,7 @@ }, { "cell_type": "markdown", - "id": "7e0b1eda", + "id": "b67c8220", "metadata": {}, "source": [ "This can then be analyzed in your favorite spectral analysis tool, e.g., [pyXspec](https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/index.html). (For the winter 2018 AAS workshop, we demonstrated this in a [notebook](https://github.com/NASA-NAVO/aas_workshop_2018/blob/master/heasarc/heasarc_Spectral_Access.md) that you can consult for how to use pyXspec, but the pyXspec documentation will have more information.)" @@ -347,7 +347,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ba68afa8", + "id": "0712c231", "metadata": {}, "outputs": [], "source": [] diff --git a/_sources/content/reference_notebooks/ucds_unified_content_descriptors.ipynb b/_sources/content/reference_notebooks/ucds_unified_content_descriptors.ipynb index 1cf002d..e341c02 100644 --- a/_sources/content/reference_notebooks/ucds_unified_content_descriptors.ipynb +++ b/_sources/content/reference_notebooks/ucds_unified_content_descriptors.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f3fae6ba", + "id": "cd6ae915", "metadata": {}, "source": [ "# UCDs: working with heterogeneous tables\n", @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "910ee03c", + "id": "e884e551", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "markdown", - "id": "b0e2b417", + "id": "2f1cbef6", "metadata": {}, "source": [ "Let's look at some tables in a little more detail. Let's find the Hubble Source Catalog version 3 (HSCv3), assuming there's only one at MAST." @@ -44,7 +44,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d831a916", + "id": "43f67742", "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "1ae27b36", + "id": "7267f16a", "metadata": {}, "source": [ "Now let's see what tables are provided by this service for HSCv3. Note that this is another query to the service:" @@ -77,23 +77,15 @@ { "cell_type": "code", "execution_count": 3, - "id": "20bac7c3", + "id": "b3bd5c2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "32 tables:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "32 tables:\n", "tap_schema.schemas - description of schemas in this dataset\n", - "----\n", - "tap_schema.tables - description of tables in this dataset\n", "----\n" ] }, @@ -101,6 +93,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "tap_schema.tables - description of tables in this dataset\n", + "----\n", "tap_schema.columns - description of columns in this dataset\n", "----\n", "tap_schema.keys - description of foreign keys in this dataset\n", @@ -114,13 +108,7 @@ "output_type": "stream", "text": [ "dbo.DetailedCatalog - None\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.SumMagAper2Cat - None\n", "----\n" ] @@ -132,18 +120,10 @@ "dbo.SumMagAutoCat - None\n", "----\n", "dbo.Catalog_ACS_SourceExtractor - None\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.Catalog_WFC3_SourceExtractor - None\n", "----\n", "dbo.Catalog_WFPC2_SourceExtractor - None\n", - "----\n", - "dbo.Catalog_Image_MetaData - None\n", "----\n" ] }, @@ -151,9 +131,13 @@ "name": "stdout", "output_type": "stream", "text": [ + "dbo.Catalog_Image_MetaData - None\n", + "----\n", "dbo.CloseMatch - None\n", "----\n", "dbo.ClosestMatch - None\n", + "----\n", + "dbo.GroupMembers - None\n", "----\n" ] }, @@ -161,19 +145,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "dbo.GroupMembers - None\n", - "----\n", "dbo.Groups - None\n", "----\n", "dbo.HCVdetailedView - None\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.HCVsummaryView - None\n", + "----\n", + "dbo.HLAscience - None\n", "----\n" ] }, @@ -181,19 +159,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "dbo.HLAscience - None\n", - "----\n", "dbo.ImageMembers - None\n", "----\n", "dbo.Images - None\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.ProductMembers - None\n", + "----\n", + "dbo.ProperMotionsView - None\n", "----\n" ] }, @@ -201,8 +173,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "dbo.ProperMotionsView - None\n", - "----\n", "dbo.SourceLinks - None\n", "----\n", "dbo.SourcePositionsView - None\n", @@ -245,7 +215,7 @@ }, { "cell_type": "markdown", - "id": "819d4c37", + "id": "757dd39b", "metadata": {}, "source": [ "Let's look at the first 10 columns of the DetailedCatalog table. Again, note that calling the columns attribute sends another query to the service to ask for the columns." @@ -254,7 +224,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "38322e96", + "id": "b427a231", "metadata": {}, "outputs": [ { @@ -307,7 +277,7 @@ }, { "cell_type": "markdown", - "id": "bb0e41cc", + "id": "da0a8b8a", "metadata": {}, "source": [ "The PyVO method to get the columns will automatically fetch all the meta-data about those columns. It's up to the service provider to set them correctly, of course, but in this case, we see that the column named \"MatchRA\" is identified with the UCD \"pos.eq.ra\". \n", @@ -318,7 +288,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "dbaf4537", + "id": "085c9ebf", "metadata": {}, "outputs": [ { @@ -336,7 +306,7 @@ }, { "cell_type": "markdown", - "id": "c4875ddd", + "id": "e2b1afe3", "metadata": {}, "source": [ "But a more general approach is to check for the correct UCD. It also has the further advantage that it can be used to label columns that should be used for certain purposes when there are multiple possibilities. For instance, this table has MatchRA and SourceRA. Let's check the UCD: \n", @@ -347,7 +317,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "dc37cc94", + "id": "16b2f739", "metadata": {}, "outputs": [ { @@ -369,7 +339,7 @@ }, { "cell_type": "markdown", - "id": "3cd684c8", + "id": "4c3b5135", "metadata": {}, "source": [ "What that shows you is that though there are two columns in this table that give RA information, only one has the 'pos.eq.ra' UCD. The documentation for this ought to explain the usage of these columns, and the UCD should not be used as a substitute for understanding the table. But it can be a useful tool." @@ -377,7 +347,7 @@ }, { "cell_type": "markdown", - "id": "29370c94", + "id": "aa9087c7", "metadata": {}, "source": [ "In particular, you can use the UCDs to look for catalogs that might have the information you're interested in. Then you can code the same query to work for different tables (with different column names) in a loop. This sends a bunch of queries but doesn't take too long, a minute maybe. (One is particularly slow.)" @@ -386,7 +356,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "1ddc5988", + "id": "36bc5996", "metadata": {}, "outputs": [ { @@ -534,7 +504,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Found 2097555 results from dbo.ssa\n", + " Found 2097627 results from dbo.ssa\n", "\n", " Table ivoa.ObsCore has the right columns. Counting rows matching my time.\n" ] @@ -543,7 +513,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Found 4042090 results from ivoa.ObsCore\n", + " Found 4042162 results from ivoa.ObsCore\n", "\n", "Looking at service from ivo://jvo/subaru/sxds/v1.0\n" ] @@ -615,7 +585,7 @@ }, { "cell_type": "markdown", - "id": "317a6c8e", + "id": "ef1f3d82", "metadata": {}, "source": [ "You can also use UCDs to look at the results. Above, we collected just the first 10 rows of the four columns we're interested in from every catalog that had them. But these tables still have their original column names. So the UCDs will still be useful, and PyVO provides a simple routine to convert from UCD to column (field) name. \n", @@ -628,7 +598,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "1639d928", + "id": "d23c9915", "metadata": {}, "outputs": [ { @@ -659,7 +629,7 @@ }, { "cell_type": "markdown", - "id": "9af15dd8", + "id": "3f88173e", "metadata": {}, "source": [ "Lastly, if you have a table of results from a TAP query (and if that service includes the UCDs), then you can get data based on UCDs with the getbyucd() method, which simply gets the corresponding element using fieldname_with_ucd():" @@ -668,22 +638,22 @@ { "cell_type": "code", "execution_count": 9, - "id": "5e2c2bdd", + "id": "2db46d10", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[20.276399612426758,\n", - " 22.451000213623047,\n", - " 25.335100173950195,\n", - " 22.437999725341797,\n", - " 24.32939910888672,\n", - " 21.936500549316406,\n", - " 25.478700637817383,\n", - " 21.499900817871094,\n", - " 19.24180030822754,\n", - " 24.578399658203125]" + "[nan,\n", + " 20.231000900268555,\n", + " 20.659799575805664,\n", + " 20.813199996948242,\n", + " 21.020200729370117,\n", + " 21.049100875854492,\n", + " 20.983800888061523,\n", + " 21.05500030517578,\n", + " 21.337299346923828,\n", + " 18.671100616455078]" ] }, "execution_count": 9, @@ -698,7 +668,7 @@ }, { "cell_type": "markdown", - "id": "ab18e456", + "id": "eb439458", "metadata": {}, "source": [ "Note that we can see earlier in this notebook, when we looked at this table's contents, that there are two phot.mag fields in this table, MagAper2 and MagAuto. The getbyucd() and fieldname_with_ucd() routines do not currently allow you to handle multiple columns with the same UCD. The code can help you find what you want, but it depends on the meta data the service defines, and you still must look at the detailed information for each catalog you use to understand what it contains." @@ -707,7 +677,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5875ce75", + "id": "c184ad8b", "metadata": {}, "outputs": [], "source": [] diff --git a/_sources/content/reference_notebooks/votables.ipynb b/_sources/content/reference_notebooks/votables.ipynb index 8ade998..8ac2ee5 100644 --- a/_sources/content/reference_notebooks/votables.ipynb +++ b/_sources/content/reference_notebooks/votables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "765a7d0f", + "id": "47411314", "metadata": {}, "source": [ "# Creating a VO Table from a CSV file" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "ee1adf1d", + "id": "e5601e02", "metadata": {}, "source": [ "There are several ways of doing this, and there are a few object layers here, which can be confusing:\n", @@ -26,7 +26,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "339a15c2", + "id": "65a07331", "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "markdown", - "id": "8f67b838", + "id": "1b68c1ba", "metadata": {}, "source": [ "## Create a table with only two columns starting from an astropy Table" @@ -47,7 +47,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "eaf9ab70", + "id": "a7d3214a", "metadata": {}, "outputs": [ { @@ -115,7 +115,7 @@ }, { "cell_type": "markdown", - "id": "934cb36c", + "id": "5c10db67", "metadata": {}, "source": [ "## Then convert this to a VOTableFile object which contains a nested set of *resources* and *tables* (in this case, only one of each)" @@ -124,7 +124,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "fc830942", + "id": "d00f978f", "metadata": {}, "outputs": [ { @@ -172,7 +172,7 @@ { "cell_type": "code", "execution_count": null, - "id": "571e9586", + "id": "f0df227a", "metadata": {}, "outputs": [], "source": [] diff --git a/_sources/content/use_case_notebooks/candidate_list_exercise.ipynb b/_sources/content/use_case_notebooks/candidate_list_exercise.ipynb index 2493ee4..cdb1ed3 100644 --- a/_sources/content/use_case_notebooks/candidate_list_exercise.ipynb +++ b/_sources/content/use_case_notebooks/candidate_list_exercise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4eac9e18", + "id": "392d3801", "metadata": {}, "source": [ "# Science User Case - Inspecting a Candidate List\n", @@ -16,7 +16,7 @@ }, { "cell_type": "markdown", - "id": "d11bbdcc", + "id": "df776480", "metadata": {}, "source": [ "## 1. Import the Python modules we'll be using." @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "29db884b", + "id": "fd3f9b9d", "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "d9ca403f", + "id": "32d0768c", "metadata": {}, "source": [ "The next cell prepares the notebook to display our visualizations." @@ -62,7 +62,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2aad5962", + "id": "9ac297a7", "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "markdown", - "id": "269439e1", + "id": "e58c40ca", "metadata": {}, "source": [ "## 2. Search NED for objects in this paper.\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "9746e8e1", + "id": "025b9484", "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "11738824", + "id": "a3adcddf", "metadata": {}, "source": [ "## 3. Filter the NED results.\n", @@ -103,14 +103,14 @@ { "cell_type": "code", "execution_count": null, - "id": "7375bd5f", + "id": "cb4518b5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "44886207", + "id": "1a5d1eda", "metadata": {}, "source": [ "## 4. Search the NAVO Registry for image resources.\n", @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c5150925", + "id": "eb0c9b73", "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "348005c4", + "id": "226b56fd", "metadata": {}, "source": [ "## 5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images.\n", @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "ddee4909", + "id": "dec52ff8", "metadata": {}, "outputs": [], "source": [ @@ -152,7 +152,7 @@ }, { "cell_type": "markdown", - "id": "eff11004", + "id": "7281e530", "metadata": {}, "source": [ "## 6. Choose the AllWISE image service that you are interested in." @@ -161,14 +161,14 @@ { "cell_type": "code", "execution_count": null, - "id": "d8e08a9a", + "id": "d9066e9d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "09c2650d", + "id": "d3efc7c9", "metadata": {}, "source": [ "## 7. Choose one of the galaxies in the NED list.\n", @@ -178,7 +178,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "ac8b2f1a", + "id": "d818aae1", "metadata": {}, "outputs": [], "source": [ @@ -188,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "cb2d556c", + "id": "4bef2255", "metadata": {}, "source": [ "## 8. Search for a list of AllWISE images that cover this galaxy.\n", @@ -199,7 +199,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "3f8f14d7", + "id": "29231069", "metadata": {}, "outputs": [], "source": [ @@ -209,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "795dfe1e", + "id": "48c321f0", "metadata": {}, "source": [ "## 9. Use the .to_table() method to view the results as an Astropy table." @@ -218,14 +218,14 @@ { "cell_type": "code", "execution_count": null, - "id": "337ee615", + "id": "8546a4ca", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "222f492f", + "id": "26027cf3", "metadata": {}, "source": [ "## 10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)\n", @@ -238,14 +238,14 @@ { "cell_type": "code", "execution_count": null, - "id": "ba53609a", + "id": "2e052158", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "4312969d", + "id": "c26185af", "metadata": {}, "source": [ "## 11. Visualize this AllWISE image.\n", @@ -255,7 +255,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "60a034e6", + "id": "e797579d", "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "a9df421a", + "id": "2a6fef5d", "metadata": {}, "source": [ "## 12. Plot a cutout of the AllWISE image, centered on your position.\n", @@ -278,7 +278,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "e8b5ced6", + "id": "3cc4c101", "metadata": {}, "outputs": [], "source": [ @@ -287,7 +287,7 @@ }, { "cell_type": "markdown", - "id": "89af2c4d", + "id": "a5637bb6", "metadata": {}, "source": [ "## 13. Try visualizing a cutout of a GALEX image that covers your position.\n", @@ -298,7 +298,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "dcffc6d2", + "id": "b465d72a", "metadata": { "tags": [ "output_scroll" @@ -313,7 +313,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "2c80e29b", + "id": "472dd932", "metadata": {}, "outputs": [], "source": [ @@ -324,7 +324,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "93593181", + "id": "b414c09d", "metadata": {}, "outputs": [], "source": [ @@ -335,7 +335,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "71ebf2b1", + "id": "afe0d34d", "metadata": {}, "outputs": [], "source": [ @@ -346,7 +346,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "f84a72bd", + "id": "c0aa018b", "metadata": {}, "outputs": [], "source": [ @@ -356,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "9e9c391e", + "id": "146bf023", "metadata": {}, "source": [ "## 14. Try visualizing a cutout of an SDSS image that covers your position.\n", @@ -370,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "7032bd3c", + "id": "b9b6b61d", "metadata": {}, "source": [ "(As a workaround to a bug in the SDSS service, pass `format=''` as an argument to the search() function when using the SDSS service.)" @@ -379,7 +379,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "ff5b1f55", + "id": "d46f8889", "metadata": {}, "outputs": [], "source": [ @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "1b88e86f", + "id": "d61d0c43", "metadata": {}, "outputs": [], "source": [ @@ -399,7 +399,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "0a08e6c7", + "id": "267190e5", "metadata": {}, "outputs": [], "source": [ @@ -409,7 +409,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "2ae02a8a", + "id": "14f0e00e", "metadata": {}, "outputs": [], "source": [ @@ -420,7 +420,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "16adce3a", + "id": "f5d7848b", "metadata": {}, "outputs": [], "source": [ @@ -429,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "214046a3", + "id": "30c6320b", "metadata": {}, "source": [ "## 15. Try looping over all positions and plotting multiwavelength cutouts.\n", @@ -438,7 +438,7 @@ }, { "cell_type": "markdown", - "id": "d48b6a85", + "id": "88bd17f7", "metadata": {}, "source": [ "Warning: this takes a long time to run! You can limit it to the first three galaxies only, for example, for testing." @@ -447,7 +447,7 @@ { "cell_type": "code", "execution_count": null, - "id": "73a28d57", + "id": "eb379890", "metadata": {}, "outputs": [], "source": [] diff --git a/_sources/content/use_case_notebooks/candidate_list_solution.ipynb b/_sources/content/use_case_notebooks/candidate_list_solution.ipynb index d1ac484..9050675 100644 --- a/_sources/content/use_case_notebooks/candidate_list_solution.ipynb +++ b/_sources/content/use_case_notebooks/candidate_list_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "43c8da38", + "id": "27ba461b", "metadata": {}, "source": [ "# Science User Case - Inspecting a Candidate List\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "64b6a194", + "id": "b01adb6a", "metadata": {}, "source": [ "## 1. Import the Python modules we'll be using." @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "4565e2b6", + "id": "6e7ba4b9", "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "markdown", - "id": "e6c6d170", + "id": "25185783", "metadata": {}, "source": [ "The next cell prepares the notebook to display our visualizations." @@ -60,7 +60,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "2d0c4ebc", + "id": "9515867c", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "58bc91d0", + "id": "4ba1be36", "metadata": {}, "source": [ "## 2. Search NED for objects in this paper.\n", @@ -80,7 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "9669c742", + "id": "2a1ab9b7", "metadata": { "tags": [ "output_scroll" @@ -91,7 +91,7 @@ "data": { "text/html": [ "Table length=62\n", - "
SPEC_NUMTG_MTG_PARTTG_SRCIDXYCHANNELCOUNTSSTAT_ERRBACKGROUND_UPBACKGROUND_DOWNBIN_LOBIN_HI
int16int16int16int16float32float32int16[8192]int16[8192]float32[8192]int16[8192]int16[8192]float64[8192]float64[8192]
1-3114102.8154131.8281 .. 81920 .. 01.8660254 .. 1.86602540 .. 00 .. 07.159166666667378 .. 0.33333333333333337.160000000000712 .. 0.33416666666666667
\n", + "
\n", "\n", "\n", "\n", @@ -179,14 +179,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table139768327148864-990385').dataTable()\");\n", + " console.log(\"$('#table140598048756336-464591').dataTable()\");\n", " \n", "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n", " \"optionalnum-asc\": astropy_sort_num,\n", " \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n", "});\n", "\n", - " $('#table139768327148864-990385').dataTable({\n", + " $('#table140598048756336-464591').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -212,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "fa5e61a5", + "id": "c97ba559", "metadata": {}, "source": [ "## 3. Filter the NED results.\n", @@ -223,7 +223,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "1f793654", + "id": "a38a4387", "metadata": {}, "outputs": [ { @@ -299,7 +299,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "efe2e397", + "id": "12d83082", "metadata": { "tags": [ "output_scroll" @@ -310,7 +310,7 @@ "data": { "text/html": [ "Table length=53\n", - "
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506387100
\n", + "
\n", "\n", "\n", "\n", @@ -389,14 +389,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table139767330728416-593505').dataTable()\");\n", + " console.log(\"$('#table140597305452976-997102').dataTable()\");\n", " \n", "jQuery.extend( jQuery.fn.dataTableExt.oSort, {\n", " \"optionalnum-asc\": astropy_sort_num,\n", " \"optionalnum-desc\": function (a,b) { return -astropy_sort_num(a, b); }\n", "});\n", "\n", - " $('#table139767330728416-593505').dataTable({\n", + " $('#table140597305452976-997102').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -424,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "fcd07ae5", + "id": "c26688b8", "metadata": {}, "source": [ "## 4. Search the NAVO Registry for image resources.\n", @@ -435,7 +435,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "0a860b5a", + "id": "1b7c8515", "metadata": {}, "outputs": [ { @@ -449,7 +449,7 @@ "data": { "text/html": [ "
Table length=263\n", - "
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506387100
\n", + "
\n", "\n", "\n", "\n", @@ -518,7 +518,7 @@ }, { "cell_type": "markdown", - "id": "0fe6ef3d", + "id": "b53dd7fe", "metadata": {}, "source": [ "## 5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images.\n", @@ -529,7 +529,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "6b6775fc", + "id": "76d48a8d", "metadata": {}, "outputs": [ { @@ -543,7 +543,7 @@ "data": { "text/html": [ "
Table length=1\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://3crsnapshots/sia3CRSnap.sia3CRSnapshots Simple Image Access Service
\n", + "
\n", "\n", "\n", "\n", @@ -572,7 +572,7 @@ }, { "cell_type": "markdown", - "id": "d06f6675", + "id": "e8ffc88e", "metadata": {}, "source": [ "## 6. Choose the AllWISE image service that you are interested in." @@ -581,13 +581,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "5d05e344", + "id": "7c4c47b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "f11535ba", + "id": "3f2357b3", "metadata": {}, "source": [ "## 7. Choose one of the galaxies in the NED list." @@ -611,7 +611,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "df31af2f", + "id": "4e9acfec", "metadata": {}, "outputs": [], "source": [ @@ -623,7 +623,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "bc11b93f", + "id": "5b01fabe", "metadata": {}, "outputs": [ { @@ -643,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "a3692620", + "id": "0bed9f3d", "metadata": {}, "source": [ "## 8. Search for a list of AllWISE images that cover this galaxy.\n", @@ -654,7 +654,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "bd30aadf", + "id": "5caad7c4", "metadata": {}, "outputs": [ { @@ -683,7 +683,7 @@ }, { "cell_type": "markdown", - "id": "d3f308bd", + "id": "5a60b076", "metadata": {}, "source": [ "## 9. Use the .to_table() method to view the results as an Astropy table." @@ -692,14 +692,14 @@ { "cell_type": "code", "execution_count": 12, - "id": "6e3464a2", + "id": "7d7eff95", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=4\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://irsa.ipac/wise/images/allwise/l3aAllWISE L3aAllWISE Atlas (L3a) Coadd Images
\n", + "
\n", "\n", "\n", "\n", @@ -733,7 +733,7 @@ }, { "cell_type": "markdown", - "id": "741dedfd", + "id": "b6be9d4f", "metadata": {}, "source": [ "## 10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)\n", @@ -746,7 +746,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "b8ae79dc", + "id": "2766c4cb", "metadata": {}, "outputs": [ { @@ -766,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "f2b6fb10", + "id": "232c9cda", "metadata": {}, "source": [ "## 11. Visualize this AllWISE image." @@ -775,7 +775,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "85047bbe", + "id": "5011dff5", "metadata": {}, "outputs": [], "source": [ @@ -791,13 +791,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "adfd7da4", + "id": "590105e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -826,7 +826,7 @@ }, { "cell_type": "markdown", - "id": "df4ca146", + "id": "e93b9e7e", "metadata": {}, "source": [ "## 12. Plot a cutout of the AllWISE image, centered on your position.\n", @@ -837,13 +837,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "83df48d4", + "id": "e2ed4407", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -875,7 +875,7 @@ }, { "cell_type": "markdown", - "id": "2b17735a", + "id": "220ec24b", "metadata": {}, "source": [ "## 13. Try visualizing a cutout of a GALEX image that covers your position.\n", @@ -886,7 +886,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "0b908d42", + "id": "a04ff7b7", "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ "data": { "text/html": [ "
Table length=3\n", - "
sia_titlesia_urlcloud_accesssia_naxessia_fmtsia_rasia_decsia_naxissia_crpixsia_crvalsia_projsia_scalesia_cdsia_bp_idsia_bp_refsia_bp_hisia_bp_lomagzpmagzpuncunc_urlcov_urlcoadd_id
degdegpixdegdeg / pixdeg / pix
objectobjectobjectint32objectfloat64float64objectobjectobjectobjectobjectobjectobjectfloat64float64float64float64float64objectobjectobject
\n", + "
\n", "\n", "\n", "\n", @@ -932,7 +932,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "9184299d", + "id": "f350553f", "metadata": {}, "outputs": [], "source": [ @@ -942,7 +942,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "8c1cc463", + "id": "bf665a86", "metadata": {}, "outputs": [], "source": [ @@ -952,14 +952,14 @@ { "cell_type": "code", "execution_count": 20, - "id": "9848daca", + "id": "8f2b106e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AIS_270_0004_sg14-nd-int.fits.gz NUV\n" + "AIS_270_0001_sg14-nd-int.fits.gz NUV\n" ] } ], @@ -976,7 +976,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "9d977ab7", + "id": "429e6d96", "metadata": {}, "outputs": [], "source": [ @@ -989,7 +989,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "5ddb715f", + "id": "1764ce2e", "metadata": {}, "outputs": [ { @@ -997,9 +997,9 @@ "output_type": "stream", "text": [ "Min: 0.0\n", - "Max: 8.866534\n", - "Mean: 0.0014750387\n", - "Stdev: 0.013614772\n" + "Max: 7.6572003\n", + "Mean: 0.0014543837\n", + "Stdev: 0.013401416\n" ] } ], @@ -1014,13 +1014,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "a2882e3f", + "id": "c9b4e1d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1029,7 +1029,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1048,13 +1048,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "8cae7f8f", + "id": "119c9a56", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -1063,7 +1063,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1084,7 +1084,7 @@ }, { "cell_type": "markdown", - "id": "43d8466d", + "id": "f33bb7d4", "metadata": {}, "source": [ "## 14. Try visualizing a cutout of an SDSS image that covers your position.\n", @@ -1098,14 +1098,14 @@ { "cell_type": "code", "execution_count": 25, - "id": "3be8130a", + "id": "42a18e15", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=21\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
\n", + "
\n", "\n", "\n", "\n", @@ -1172,7 +1172,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "b581e7d7", + "id": "b2645473", "metadata": {}, "outputs": [ { @@ -1196,7 +1196,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "ff762071", + "id": "20c7acdd", "metadata": {}, "outputs": [ { @@ -1218,7 +1218,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "34869ea3", + "id": "c1dfd03d", "metadata": {}, "outputs": [ { @@ -1239,7 +1239,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "5639a542", + "id": "63cc977e", "metadata": {}, "outputs": [], "source": [ @@ -1252,7 +1252,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "3deddedf", + "id": "5a54266f", "metadata": {}, "outputs": [ { @@ -1265,7 +1265,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 30, @@ -1298,7 +1298,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "6f59d5e9", + "id": "abe35885", "metadata": {}, "outputs": [ { @@ -1311,7 +1311,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 31, @@ -1341,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "32395148", + "id": "3a1cb1e3", "metadata": {}, "source": [ "## 15. Try looping over all positions and plotting multiwavelength cutouts." @@ -1349,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "a15494a0", + "id": "73fd0971", "metadata": {}, "source": [ "Warning: this cell takes a long time to run! We limit it to the first three galaxies only." @@ -1358,7 +1358,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "0a1dbe55", + "id": "ce8da72a", "metadata": {}, "outputs": [ { @@ -1384,7 +1384,7 @@ }, { "data": { - "image/png": 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YO3cuRo0ahVWrVmHjxo2yd4nca8yYMVi4cCESExPRu3dvfPXVV2jUqBEuX76Md955R+89Wfc+/m3mzJl6/3ZyckJwcDD69u2Ljh074vjx45g8ebLuUYJlLly4gP79+yMlJQULFixA//794ebmhtOnT+Ott97Cm2++ie3bt+P333+HWq0GALi6uuLNN9/EzJkzMXnyZNjY2OCZZ56BWq3G5s2bMXHiRNjZ2aG4uFj2MXV32717N1599VUAwOeff44OHToY1V70/9577z1cunQJP/74Y4Ue4RcYGIjFixfrTWvUqBEaNWqEXr164bHHHsOOHTuwadMmPPXUU5VcayIiIiKqTvjOIiIiIiJ6ILVv3x5Dhw7F0KFD8corr+CDDz7Ali1bcOHCBTRv3lw3X9m7WQDg1q1bRpWdnJwMAOXu2ADuDAJ07doVn332Gf7880+kpqbiwIED6NmzJwDgxx9/xNq1a81ZNZOVrWtlrOe9Bg0ahJYtW+LKlStYtGiR6ZW0Ijc3N7Ru3RrR0dF4/fXX8eeff2L69OlG5/v7++O3335DcHAwDh48iDZt2sDFxQUtWrTAgQMH9O4Y8fLyMqpMe3t7fPDBBwCA7du3o7i4WBcrKSlBVFQULl++jP/9738YPXo0atSoATc3N3Tu3Bk7duxAQEAAdu7ciZ9++kmv3KlTp+K1115DQUEBxo8fj6CgIPj7++OVV15Bq1atdHdVKdXz4MGDePLJJ1FUVISpU6di0qRJRq2XMS5cuIBhw4aV+1uyZInZZZcNAMq9Owr4/3c7ubm5WbTMvXv3Yv78+ejXr59R700zVps2bXSPydyyZUullUtERERE1RPvLCIiIiKiB9LIkSMxbNgwxflatmwJlUoFSZJw9OhR4TtMgDuPZCt730h4eLhi+Wq1Gh06dMDvv/+ONm3a4NSpU9i4cSOeeeYZo9ajMoWHh+PmzZs4evSoUfMfO3ZMl6dEpVLh008/Re/evTFz5kyj2l7Op59+igsXLlQ4b86cOXqDf+YYPnw4FixYgA0bNmDOnDlG57Vp0waXLl3Chg0bcPz4ceTn56Nu3boYNGgQ8vPzAdx5TFmdOnWMLrPs0XOFhYW4ffu27s6To0ePIjY2Fvb29hgwYEC5PE9PT/Tt2xfLli3Drl279O60U6vVWLhwIV5//XVs3rwZcXFxcHFxQZcuXRAZGYkXX3wRANC0aVPZeh0+fBj9+vVDbm4uPvjgA0ybNs3odTJGUlISfvzxR4OxkSNHmlV22WMO4+LiZOf577//9OY1tsyMjAxkZ2cbvCPNUJkbN26EJEmIi4tDly5d9ObPyMgAAMTHx+tic+fO1b1zTUnDhg2xceNG3Lx506j5iYiIiIjkcLCIiIiIiB5qXl5e6NixI/bv34+ffvpJcRDn559/BnDnLoJ7L+yKqNVqdOvWDadOncLt27fNqbLJnnzySWzatAk7d+5EYmKi8HFXSUlJ2LFjBwDgiSeeMKr8Xr16oXv37ti9eze+/PJL3WPPKmr79u3Yt29fhfOmTZtWaYNFZY8pLLu7qiIcHR3x/PPP4/nnn9ebvnTpUgBA165dK9Q2qampuv+/ewCibKDDyclJtjx3d3cAQFpamsF406ZNyw0ISZKEQ4cOAYDujrh7HTlyBH369EF2djbef/99zJo1y8i1MV6XLl2MfhRiRbVq1QoAcP78eRQUFMDBwUEvnp+fj/Pnz+vNq+SRRx7RvQvqxIkT6Nq1a7l5Tpw4IVvmuXPnZMsuKCjQfSbKBpCMUdZ3jH2UIhERERGRHD6GjoiIiIgeeu+//z4AYOvWrdi0aZPsfP/995/uovgbb7yh9ygpYy5ql13cr1GjhjnVNdkLL7yA0NBQFBcX44033pCtsyRJGDduHIqLixEaGlpu0EPks88+g0qlwpdffomUlBST6rl3715IklThP2PvADHG7t27AQANGjSolPJKSkrwzTffALjTdypi1apVAO7cJeLi4qKbHhwcDABIT0/HpUuXDOaW3UVWu3Zto5e3Zs0axMXFISIiwuBdZceOHUPv3r11A0Uff/yx0WVXFREREQgICEBhYSF+/fXXcvFff/0VRUVFCAoKQtu2bY0qU6PRIDIyEgCwYsWKcvEbN27g8OHDAICnn35aN33u3LmyfXrPnj0A7rxrrWyasYPUubm5usfPtWnTxqgcIiIiIiI5HCwiIiIioode7969MXbsWADA4MGDsXz58nIDKUePHkXXrl2Rnp6ORx99FFOnTtWLf/DBBxg7dizOnj1brvySkhIsWrQI69atA3Dn/T7WoNFosHLlSmg0Gqxfvx4vvPCC3l0rwJ2Bh6FDh2Lt2rV68xsrPDwczzzzDLKzsyvl3TKWsmLFCpw8ebLcdEmSsH79ekyZMgUAMGrUqHLzdO/eHWFhYdiwYUO52IkTJ1BSUqI3LS0tDYMHD8bff/+NwYMHo3fv3nrxPXv26AbI7lZUVIRPP/0U8+bNAwC8+eabevGIiAjdgNHIkSP1Bue0Wi0+/fRTxMTEALjTr++WkJCgeyTa3bZu3YpRo0bB3t4e33//vcH169WrF7Kysio8UFSrVi2oVCosX77c6BxLsbGxwbvvvgsAePfdd3WPlgSAa9eu4b333gMATJ48GTY2+l+L58+fj7CwMIPvF3rvvfegUqmwbNkybN++XTc9Ly8PI0aMQGlpKaKiohAWFlYp67F48WKD2/HatWt48sknkZiYCA8PD907qIiIiIiITMXH0BERERFRtfDNN9/AyckJX3zxBYYPH44pU6agdevWsLe3xz///KMbBOrduzdWr14Ne3t7vfy8vDzMnz8f8+fPR3BwMJo3bw4PDw+kpqbizJkzSEpKAnDn4rPco73uh4iICOzcuRPPPPMMVq5ciQ0bNqB9+/bw9/dHcnIyDh48iIKCAvj5+WHNmjWIiIio8DI+/vhjbNiwAXl5eRZYg8qxY8cOvPDCC6hRowaaNWum21YXLlzAjRs3AABjxowxOFh05coV3LhxA5mZmeViI0eORHx8PJo1awZ/f3/cvn0bhw8fRm5uLh5//HH88MMP5XLOnDmDiRMnwt/fHy1atIC3tzdSUlJw9uxZ3Lp1CwDw1ltvYcSIEXp5dnZ2+Omnn9C/f3/s378f9erVQ9u2beHq6oozZ87gypUrAO7cOdexY0e93GPHjmHAgAFo3rw5ateuDTs7O5w9exYXLlyAi4sLNm7ciGbNmpWra69evZCZmQkPDw/Ex8fLvpvqvffeKzcgotVqdfWuTEuWLNEbmExMTAQAvPrqq7rHrwUGBpYb3Bs7diz279+PDRs2oEmTJujRowcAYNeuXcjLy8PAgQPx+uuvl1ve7du3cfHiRQQEBJSLtWrVCl9++SUmTZqEfv36oXPnzvDz88OBAweQmJiIRx55xOAgnKkWLFiA1157DY0bN0aDBg2g0Whw7do1/PXXXygsLIS3tzfWr19v8PGMprbbb7/9hpkzZ+r+HRsbC+DOYyDnz5+vm37kyJFKW08iIiIisj4OFhERERFRtaBSqfDpp5/ipZdewsKFC7F7927s2rULxcXF8PPzw3PPPYeXXnpJ95ipe3344YeIiIjA7t27cfLkSZw+fRopKSmwt7dHzZo10a9fP4wcOdKkwZfK1qlTJ1y5cgWLFi3C5s2bcebMGWRkZMDDwwOtW7dG//798dprr5n8npN69erhlVdewYIFCyq55pXnlVdegbu7Ow4fPoxTp04hNTUVdnZ2qFGjBoYOHYqRI0eiQ4cOFS535MiR+PXXX3Hu3DkcOHAA7u7u6NChA4YPH47nnnvOYE7nzp0xevRonDx5EmfPnkVaWhpsbGwQFBSE3r1749VXX8Vjjz1mMLdbt274+++/8dVXX2H37t04ePAgSkpK4Ovri6effhqjR482ODjZpEkTDBkyBDExMdi5cydKS0sREhKCiRMn4s0339TdsXSv9PR0AHfem/Pjjz/KtsOwYcP0BouSk5Nx8+ZN+Pr6Gv0OLGPdvHlT97i9u5UNYgBAaGhoubharca6devwv//9D0uWLNE9erBx48YYMWIERo0aBZVKVeH6TJw4EU2bNsWXX36JY8eOITc3FyEhIZg8eTImT55cqe8PGjduHP744w+cPXsWe/fuRVZWFlxcXNCsWTP069cPr7/+Ovz8/AzmmtpuKSkpBvOuXLmiG6AkIiIiooePSrLUG0WJiIiIiIioWoiOjsaLL76Ib775BuPGjbN2dYiIiIiIqII4WERERERERERmGTp0KA4cOIALFy5U6B1YRERERERUNXCwiIiIiIiIiIiIiIiIqBqzsXYFiIiIiIiIiIiIiIiIyHo4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuNgERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsY4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuNgERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsY4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuNgERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIHmDDhg1DrVq1FOe7fv06VCoVli9fbvE6AcClS5fQq1cvuLu7Q6VSYePGjVi+fDlUKhWuX79+X+pARET6atWqhWHDhlm7GpXKnGPL3r17oVKpsHfv3kqtk0qlwrRp0yqtvC5duqBJkyaVVh5Zzr2fMUv1MSIiqr4qcu7zMJ77EZFlcbCI6AGxYMECqFQqtG3b1mBcpVLhjTfe0JuWkpKC8ePHIywsDI6OjvDz80ObNm3w7rvvIicnx+hlX7hwAe+88w5atGgBV1dXBAYGIjIyEidOnCg3b61ataBSqQz+1a9fv9z8S5cuRcOGDeHg4ID69etj3rx5RtfLUlavXo0XX3wR9evXh0qlQpcuXYzK+/jjj6FSqR66Czp5eXmYNm1ahS50DB06FH///Tc+/vhj/Pzzz3j00UcNzrdgwYL7NoBFRPSw+vvvvzFw4ECEhobCwcEBwcHB6NmzZ5U4plZE2YX1sj97e3v4+/ujS5cu+OSTT5CSkmLxOqxYsQJz5861+HKAyjtPqwwPSx+ypLIf3hj6a9eundHl3M8+RkT0oFqzZg1UKhU2bNhQLta8eXOoVCrs2bOnXCwkJASPPfaY7t87duzAiBEj0KRJE6jVaqN+aHmvgoICfP3112jbti3c3d3h4OCABg0a4I033sC///5b4fKqqs2bN6NVq1ZwcHBASEgIpk6dipKSknLzZWRkYNSoUfD19YWzszO6du2KU6dOCcu+cuUKHBwcoFKpDF5HAoBdu3ahW7ducHd3h6urK8LDw7F69epKWbcyx44dw+uvv47w8HDY2dlBpVIJ5zfmelVFr4HdL2WDiob+kpKS9OY19RoYPZxsrV0BIjJOdHQ0atWqhWPHjuHy5cuoV6+ecP60tDQ8+uijyMrKwssvv4ywsDCkpqbi7NmzWLhwIUaPHg0XFxejlr1kyRIsXboUUVFReP3115GZmYlFixahXbt22L59O3r06KGbd+7cueUucNy4cQNTpkxBr1699KYvWrQIr732GqKiojBp0iQcOHAA48aNQ15eHt59910jW6byLVy4ECdPnkTr1q2RmppqVM7NmzfxySefwNnZ2cK1M01oaCjy8/NhZ2dX4dy8vDxMnz4dAIw6acjPz0dMTAw++OADvQHMl156CYMGDYK9vb1u2oIFC+Dj48NfOxERmejw4cPo2rUrQkJC8MorryAgIAD//fcfjhw5gm+++QZjx461dhUrbNy4cWjdujVKS0uRkpKCw4cPY+rUqfjqq6+wZs0adOvWTTevoWOLsTp16oT8/HxoNBrdtBUrVuDcuXOYMGFCZayKrMo8TzPXw9iHLGnw4MHo16+f3jRfX18AwMWLF2FjI/495v3qY0RED7IOHToAAA4ePIinn35aNz0rKwvnzp2Dra0tDh06hK5du+pi//33H/777z8MGjRIN23FihVYvXo1WrVqhaCgoArX4/bt2+jTpw9OnjyJxx9/HM8//zxcXFxw8eJFrFq1CosXL0ZRUZEZa1o1/P7773jqqafQpUsXzJs3D3///TdmzZqF5ORkLFy4UDefVqtFZGQkzpw5g7fffhs+Pj5YsGABunTpgpMnT8oOjkycOBG2trYoLCw0GF+2bBlGjBiBnj174pNPPoFarcbFixfx33//Vep6btu2DUuWLEGzZs1Qp04d4WCfsderKnINzBpmzJiB2rVr603z8PDQ+7cp18DoISYRUZV39epVCYC0fv16ydfXV5o2bVq5eQBIY8aM0f37888/lwBIhw4dKjdvZmamlJ+fb/TyT5w4IWVnZ+tNu337tuTr6yu1b99eMX/mzJnl6pKXlyd5e3tLkZGRevO+8MILkrOzs5SWlmZ0/SpbXFycVFpaKkmSJDVu3Fjq3LmzYs5zzz0ndevWTercubPUuHFjC9fw/w0dOlQKDQ216DJSUlIkANLUqVONmv/GjRsSAOmLL75QnNfY9iUiIsP69esn+fr6Sunp6eVit27d0vt3aGioNHTo0PtTMRPs2bNHAiCtXbu2XOyvv/6S/Pz8JA8PDykhIcFidYiMjDT7uGrMMbMi52mWPreoSB8yR05OTqWVZQ3Xrl0z+vymTFmf3rNnj25aZfSxe5WWllbo3F6r1Up5eXmVWgciospWu3ZtqU2bNnrTtm/fLqlUKmnw4MFS79699WIrVqyQAEibNm3STYuPj5eKiookSTJt/xsZGSnZ2NhI69atKxcrKCiQ3nzzzQqVVxmWLVsmAZCuXbumOK+x536NGjWSmjdvLhUXF+umffDBB5JKpZL++ecf3bTVq1eXO1dLTk6WPDw8pMGDBxsse/v27ZJGo5GmTJkiAZCOHz+uF7927Zrk6OgojRs3TrGe5kpKStId/8aMGSPJXRY393qVoWtg91tZP7m3vQ0x5RoYPbz4GDqiB0B0dDQ8PT0RGRmJgQMHIjo6WjHnypUrUKvVBh+L4ebmBgcHB6OXHx4eXu7Xrd7e3ujYsSP++ecfxfwVK1agdu3aereD79mzB6mpqXj99df15h0zZgxyc3Px22+/GV2/4uJiTJ8+HfXr14eDgwO8vb3RoUMH7Ny5U2++tWvXolGjRnBwcECTJk2wYcMGg+/8qVmzpuKvQu+2f/9+rFu3rsKPFCl7nMmcOXOwePFi1K1bF/b29mjdujWOHz9ebv6NGzeiSZMmevWv6LLufuTbsGHD4OLigvj4eDz11FNwcXGBr68v3nrrLZSWluryyn4tO336dN1ty3LvYpg2bRpCQ0MBAG+//TZUKpWufe99tnKtWrVw/vx57Nu3T1du2Z1Lxm5TQ86ePYvOnTvD0dERNWrUwKxZs7Bs2TK9ZU+bNk32lmze5URED5IrV66gcePG5X4hCAB+fn6K+VevXsUzzzwDLy8vODk5oV27duWOwUVFRfjoo48QHh4Od3d3ODs7o2PHjuUe/1LR41pFNG/eHHPnzkVGRgbmz5+vm27ouf1arRbTpk1DUFAQnJyc0LVrV8TGxiq+T6ZLly747bffcOPGDd0xoewYZmwbGMvc87QdO3bAyckJgwcPxv/+9z+oVCqcPn263Hxlv86Nj48X1sWYPiR6/+G95wZlx9nY2Fg8//zz8PT0RIcOHTBnzhyoVCrcuHGjXBmTJ0+GRqNBenq6btrRo0fRp08fuLu7w8nJCZ07d8ahQ4d08T179sg+pmjFihVQqVSIiYmRXffKpvRuCFEfA4DCwkJMnToV9erVg729PWrWrIl33nmn3C+xyx4/HR0djcaNG8Pe3h7bt28X1uvxxx/HH3/8gUcffRSOjo5YtGiRuatLRGRRHTp0wOnTp5Gfn6+bdujQITRu3Bh9+/bFkSNHoNVq9WIqlQrt27fXTQsKCjLp6RrAnWPQb7/9hhEjRiAqKqpc3N7eHnPmzNGb9ueff6Jjx45wdnaGh4cHnnzyyXLXTG7cuIHXX38djzzyCBwdHeHt7Y1nnnnG4DuIzp8/j27duul9t717nctIkoRZs2ahRo0aunOf8+fPG7WesbGxiI2NxahRo2Br+/8PoHr99dchSRLWrVunm7Zu3Tr4+/tjwIABumm+vr549tlnsWnTpnLHq+LiYowfPx7jx49H3bp1DS7/+++/R2lpKWbMmAEAyMnJgSRJ5ea7+zzzu+++Q506deDk5IRevXrhv//+gyRJmDlzJmrUqAFHR0c8+eSTSEtL0yvD398fjo6Oim1i7vUqQ9fAjLFp0yZERkYiKCgI9vb2qFu3LmbOnKm7PnO3o0ePol+/fvD09ISzszOaNWuGb775xmC52dnZBssoU9FrYPRw42PoiB4A0dHRGDBgADQaDQYPHoyFCxfi+PHjaN26tWxOaGgoSktL8fPPP2Po0KEWqVdSUhJ8fHyE85w+fRr//PMPPvjgg3LTAZR7l014eDhsbGxw+vRpvPjii0bVY9q0aZg9ezZGjhyJNm3aICsrCydOnMCpU6fQs2dPAMBvv/2G5557Dk2bNsXs2bORnp6OESNGIDg42NjVNai0tBRjx47FyJEj0bRpU5PKWLFiBbKzs/Hqq69CpVLh888/x4ABA3D16lXdie2OHTsQFRWFRo0aYfbs2UhNTcXw4cNRo0YNs+vfu3dvtG3bFnPmzMGuXbvw5Zdfom7duhg9ejR8fX11j8N5+umndSeFzZo1M1jegAED4OHhgYkTJ+oe0yL3GJ25c+di7NixcHFx0fUPf39/AMZtU0Pi4+PRtWtXqFQqTJ48Gc7OzliyZEm5xxMNGDCg3KMcT548iblz5xp1cZWIqKoIDQ1FTEwMzp07V+F35t26dQuPPfYY8vLyMG7cOHh7e+PHH3/EE088gXXr1uke+5KVlYUlS5Zg8ODBeOWVV5CdnY2lS5eid+/eOHbsGFq0aKFXrjHHNVMMHDgQI0aMwI4dO/Dxxx/Lzjd58mR8/vnn6N+/P3r37o0zZ86gd+/eKCgoEJb/wQcfIDMzEzdv3sTXX38NALpjWEXbQIk552lbt27FwIED8dxzz+GHH35AXl4exo8fj+joaLRs2VJv3ujoaHTp0kV4vmNOH1LyzDPPoH79+vjkk08gSRIef/xxvPPOO1izZg3efvttvXnXrFmDXr16wdPTE8CdC259+/ZFeHg4pk6dChsbGyxbtgzdunXDgQMH0KZNG3Tp0gU1a9ZEdHS03mOKyta9bt26iIiIqNR1ysvLw+3bt/Wmubu7G9W3RX1Mq9XiiSeewMGDBzFq1Cg0bNgQf//9N77++mv8+++/2Lhxo15Zf/75J9asWYM33ngDPj4+iu/huHjxIgYPHoxXX30Vr7zyCh555BHjV5qIyAo6dOiAn3/+GUePHtX9qPDQoUN47LHH8NhjjyEzMxPnzp3TfTc9dOgQwsLC4O3tXSnL37x5M4A7j7w1xq5du9C3b1/UqVMH06ZNQ35+PubNm4f27dvj1KlTuv308ePHcfjwYQwaNAg1atTA9evXsXDhQnTp0gWxsbFwcnICcOd6S9euXVFSUoL33nsPzs7OWLx4scHBjo8++gizZs1Cv3790K9fP5w6dQq9evUy6hF5ctdmgoKCUKNGDb0fo5w+fRqtWrUqN7DQpk0bLF68GP/++6/edZG5c+ciPT0dU6ZMwfr162XbLSwsDNu2bcPbb7+N+Ph4eHp6YsyYMZg+fXq5ZUVHR6OoqAhjx45FWloaPv/8czz77LPo1q0b9u7di3fffReXL1/GvHnz8NZbb+GHH35QbANj28SY61Vy18CMsXz5cri4uGDSpElwcXHBn3/+iY8++ghZWVn44osvdPPt3LkTjz/+OAIDAzF+/HgEBATgn3/+wdatWzF+/Hi9Mrt27YqcnBxoNBr07t0bX375pVXfpUQPAKve10REik6cOCEBkHbu3ClJ0p3HRtSoUUMaP3683ny45zF0SUlJkq+vrwRACgsLk1577TVpxYoVUkZGRqXUa//+/ZJKpZI+/PBD4XxvvvmmBECKjY3Vmz5mzBhJrVYbzPH19ZUGDRpkdF2aN29e7vbgezVt2lSqUaOG3uP09u7dKwEQ3oqudAvu/PnzJXd3dyk5OVmSpIo9KqbscSbe3t56tzFv2rRJAiBt2bJFN61FixZSYGCg3vbbsWOHYv3vXdayZct004YOHSoBkGbMmKE3b8uWLaXw8HDdvyv6GDq5x7QYul1ern2N2aaGjB07VlKpVNLp06d101JTUyUvLy/hrfopKSlSSEiI1LRp0wf+MTlEVL3s2LFDUqvVklqtliIiIqR33nlH+uOPP3SPXLnbvY8imTBhggRAOnDggG5adna2VLt2balWrVq6x1GUlJRIhYWFemWlp6dL/v7+0ssvv6ybVpHjmiGix9CVad68ueTp6an7973HlqSkJMnW1lZ66qmn9PKmTZsmAdBb/4o8IszYNpAk4x5DV5HztLvPLX799VfJzs5OeuWVV3TbR5IkafDgwVJQUJDetFOnTpU79htibB8ydC4ht85Tp06VABh8JE1ERITeeYYkSdKxY8ckANJPP/0kSdKd89369etLvXv3lrRarW6+vLw8qXbt2lLPnj110yZPnizZ29vrtV1ycrJka2tr9LmLMcrW39BfWR+69zNWkT72888/SzY2NnqfR0mSpO+//77co2wASDY2NtL58+eNqntoaKgEQNq+fbvR60tEZG3nz5+XAEgzZ86UJEmSiouLJWdnZ+nHH3+UJEmS/P39pe+++06SJEnKysqS1Gq19Morr8iWV9HH0D399NMSAIOPaTWkRYsWkp+fn5SamqqbdubMGcnGxkYaMmSIbpqhx4DGxMToHQcl6f/P044ePaqblpycLLm7u+ud+yQnJ0sajUaKjIzUO2a+//775c59DPniiy8kAFJcXFy5WOvWraV27drp/u3s7FzuvEeSJOm3334rd5xJTEyUXF1dpUWLFkmSJP9YNDc3N8nT01Oyt7eXPvzwQ2ndunXS888/LwGQ3nvvPd18ZcdhX19fvWP+5MmTJQDlHqM3ePBgSaPRSAUFBQbXW/QYOnOuV8ldAzOGob7x6quvSk5OTrr1KCkpkWrXri2FhoaW65t3b//Vq1dLw4YNk3788Udpw4YN0pQpUyQnJyfJx8fH4LYuw8fQEe8xI6rioqOj4e/vr3txo0qlwnPPPYdVq1YJbyP19/fHmTNn8NprryE9PR3ff/89nn/+efj5+WHmzJkGb+s1VnJyMp5//nnUrl0b77zzjux8Wq0Wq1atQsuWLdGwYUO92L0vlL6bg4OD3q3mSjw8PHD+/HlcunTJYDwhIQF///03hgwZoneXS+fOnU2+GwgAUlNT8dFHH+HDDz/UParNFM8995zuV7QA0LFjRwB3Hg0EAImJifjrr78wdOhQuLu76+br2bMnGjVqZPJyy7z22mt6/+7YsaNu2daitE3lbN++HREREXq/8Pby8sILL7wgm1NaWorBgwcjOzsbGzZsgLOzs6nVJiK673r27ImYmBg88cQTOHPmDD7//HP07t0bwcHBul/Eytm2bRvatGmje4k0cOcuh1GjRuH69euIjY0FAKjVat0xW6vVIi0tDSUlJXj00Udx6tSpcuUqHdfM4eLiguzsbNn47t27UVJSUu6xIWPHjjVruRVtAyWmnKetXLkSzz33HF599VUsWrRI75e2Q4YMQUJCgt5j8aKjo+Ho6Gjw0Tl3M6cPKbn3HAO40z9OnjyJK1eu6KatXr0a9vb2ePLJJwEAf/31Fy5duoTnn38eqampuH37Nm7fvo3c3Fx0794d+/fv1z2GZ8iQISgsLNR7TM7q1atRUlJi9F3qFTFq1Cjs3LlT76958+Zml7t27Vo0bNgQYWFhuvW9ffs2unXrBgDlHnnYuXPnCp0H1q5dG7179za7nkRE90vDhg3h7e2NgwcPAgDOnDmD3Nxc3aO9HnvsMd2jSWNiYlBaWqp3TmOurKwsAICrq6vivGXf2YcNGwYvLy/d9GbNmqFnz57Ytm2bbtrddwYVFxcjNTUV9erVg4eHh945xbZt29CuXTu0adNGN83X17fcd9tdu3bp7rRRqVS66RMmTDBqPcuuvdz7NA6g/LWZ/Px82fnuLgsA3n33XdSpUwcjR44ULj8nJwfp6emYPn06ZsyYgaioKERHR6NPnz745ptvyp33PfPMM3rXRdq2bQsAePHFF/Ueo9e2bVsUFRUJH8Urx9TrVaJrYMa4u29kZ2fj9u3b6NixI/Ly8nDhwgUAd+5cunbtGiZMmFDuEcJ3b/9nn30Wy5Ytw5AhQ/DUU09h5syZ+OOPP5Camiq8Q5+Ig0VEVVhpaSlWrVqFrl274tq1a7h8+TIuX76Mtm3b4tatW9i9e7cwPzAwEAsXLkRiYiIuXryIb7/9Fr6+vvjoo4+wdOlSk+qUm5uLxx9/HNnZ2di0aZPsI8YAYN++fYiPjzd4od7R0VH2luiCggKjniNbZsaMGcjIyECDBg3QtGlTvP322zh79qwuXvZc/HsfOyY3zVhTpkyBl5eX4gWotLQ0JCUl6f4yMzP14iEhIXr/LrvAVvbM/rL6G7pV+N5HiKSkpOgtKycnR1g3BweHcgNdnp6eeu8LsAalbSrnxo0bFd7OU6ZMwZ9//okVK1bIPkeZiKgqa926NdavX4/09HQcO3YMkydPRnZ2NgYOHKgb8DHkxo0bBh9FVfbl9u73yvz4449o1qyZ7j1yvr6++O2338od0wDl45o5cnJyhBdt5I75Xl5eegNYpqhIGxijIudp165dw4svvoioqCjMmzdP72IAcGfAJzAwUPdeS61Wi5UrV+LJJ5806iKXqX1ISe3atctNe+aZZ2BjY4PVq1cDuPOehbVr16Jv375wc3MDAN2PRYYOHQpfX1+9vyVLlqCwsFDX7mFhYWjdurXeOz2jo6PRrl074fG/tLRU75wpKSnJqMf11K9fHz169ND7M7dvAXfW+fz58+XWt0GDBgDu/FjrbobaVqSi8xMRWZtKpcJjjz2mezfRoUOH4Ofnp9u33z1YVPbfyhwsKjsmiX6kUqbs/EPuvKrsBw/AnYGIjz76CDVr1oS9vT18fHzg6+uLjIwMvXOKGzduGHUNQO56ga+vr1HHp7JrL/e+bwgof23G0dFRdr67yzpy5Ah+/vlnfP3114rvwinLGTx4sN70wYMHIz8/v9w7Ge89zywbOKpZs6bB6aacf5p6vUp0DcwY58+fx9NPPw13d3e4ubnB19dX98OXsr5R9mMbUx4d3KFDB7Rt2xa7du0yqX5UPXCwiKgK+/PPP5GYmIhVq1ahfv36ur9nn30WAPS+FIuoVCo0aNAAY8eOxf79+2FjY2N07t2KioowYMAAnD17Fps2bVI8OEVHR8PGxqbcQR+4c4GktLS03BffoqIipKamIigoyOh6derUCVeuXMEPP/yAJk2aYMmSJWjVqhWWLFlidBkVdenSJSxevBjjxo1DQkICrl+/juvXr6OgoADFxcW4fv267mWKAwYMQGBgoO7v3mfIqtVqg8sw5e6v1q1b6y3r3hdu3ktu2dZ2v7bpxo0b8dlnn2HGjBno06dPpZZNRHS/aTQatG7dGp988gkWLlyI4uJirF271uxyf/nlFwwbNgx169bF0qVLsX37duzcuRPdunUz+JLlyjyu3a24uBj//vuvWT/0MFVF26AijDlPCwwMxGOPPYZt27bhxIkT5cpQq9V4/vnn8euvv6KgoAB79uxBQkJChe+sEfWheweoyojudDd0MSUoKAgdO3bEmjVrANy5oBQXF4fnnntON09Zm37xxRfl7uIp+7v7B0tDhgzBvn37cPPmTVy5cgVHjhxRXPf//vtP75wpMDAQhw8fFuZYklarRdOmTWXX99675SrywypT5iciqgo6dOiAzMxM/P3337r3FZV57LHHcOPGDcTHx+PgwYMICgpCnTp1Km3ZYWFhAIC///670soE7tzt/PHHH+PZZ5/FmjVrsGPHDuzcuRPe3t5mn1OYIjAwEMCdu6PulZiYqHdtJjAwUHY+ALp533nnHXTs2BG1a9fWXSspe99fYmIi4uLidLllOWXvLy5T9i7hewd75M4zK/P809TrVaJrYEoyMjLQuXNnnDlzBjNmzMCWLVuwc+dOfPbZZwBQaX2jZs2aumtVRIbYKs9CRNYSHR0NPz8/fPfdd+Vi69evx4YNG/D9999X6MtfnTp14OnpafAAL6LVajFkyBDs3r0ba9asQefOnYXzFxYW4tdff0WXLl0MHkjLHhN24sQJ9OvXTzf9xIkT0Gq1FX5RtJeXF4YPH47hw4cjJycHnTp1wrRp0zBy5EiEhoYCAC5fvlwuz9A0Y8THx0Or1WLcuHEYN25cuXjt2rUxfvx4zJ07F19++aXeCU5FBsIA6Opv6JFsFy9e1Pt3dHS03i3RlXGyLHdxqDKIyhZtUzmhoaFGb+d///0XQ4cOxVNPPYX333/ftBUgIqqiyl7IKzreh4aGljuOANA95qLs+LNu3TrUqVMH69ev19tvT506tTKrrGjdunXIz88XPkrr7mP+3XdSpKamGvXLUrnj0v1qA7nzNAcHB2zduhXdunVDnz59sG/fPjRu3FhvniFDhuDLL7/Eli1b8Pvvv8PX19esx47d24fKfp2ckZGhN9/dd6AZ67nnnsPrr7+OixcvYvXq1XByckL//v118bI7fd3c3NCjRw/F8gYNGoRJkyZh5cqVyM/Ph52dnd7gkyEBAQHYuXOn3rTKeJycErk+VrduXZw5cwbdu3e36LkXEdGDpOxOoYMHD+LQoUN6j1YLDw+Hvb099u7di6NHj+pdV6gM/fv3x+zZs/HLL7/oHqkrp+z8Q+68ysfHR/e483Xr1mHo0KH48ssvdfMUFBSUO76GhoYadQ3g7usFd3//T0lJMerc5+5rM3c/8i4hIQE3b97EqFGj9OY9cOAAtFqt3h1DR48ehZOTk+5u2Li4ONy4ccPgXa1PPPEE3N3ddesbHh6OS5cuIT4+Xq/+CQkJAGDWI/9NZcr1KqVrYEr27t2L1NRUrF+/Hp06ddJNv3btmt58ZedI586dM+oc6V5Xr161SpvSg4N3FhFVUfn5+Vi/fj0ef/xxDBw4sNzfG2+8gezsbNlnyR89elR3m/Pdjh07htTUVIO3R4uMHTsWq1evxoIFCzBgwADF+bdt24aMjAzZ22+7desGLy8vLFy4UG/6woUL4eTkhMjISKPrlpqaqvdvFxcX1KtXT3d7dFBQEJo0aYKffvpJ77Fs+/btM/lXQk2aNMGGDRvK/TVu3BghISHYsGEDRowYAeDOyc/djyqp6HuGAgMD0aJFC/z44496t6Xv3Lmz3KNh2rdvr7esyhgscnJyAlD+4hBw51boCxcumPwIHmdnZ4PlKm1TuWX37t0bMTEx+Ouvv3TT0tLSyv1COycnB08//TSCg4Px448/8qIMET2w9uzZY/AXk2XPxhcd7/v164djx44hJiZGNy03NxeLFy9GrVq1dMersl9q3r2co0eP6uVZ2pkzZzBhwgR4enpizJgxsvN1794dtra25c4v5s+fb9RynJ2dDR7TKrsNTDlPc3d3xx9//AE/Pz/07NlT750/wJ33IjRr1gxLlizBr7/+ikGDBuk9u1+OsX3Izc0NPj4+2L9/v958CxYsUFzGvaKioqBWq7Fy5UqsXbsWjz/+uN47A8PDw1G3bl3MmTPH4CN1U1JS9P7t4+ODvn374pdfftG958DHx0dYBwcHB4s8Tk6JXB979tlnER8fj//973/lYvn5+Qb7CxHRw+7RRx+Fg4MDoqOjER8fr3dnkb29PVq1aoXvvvsOubm5lfoIOgCIiIhAnz59sGTJEmzcuLFcvKioCG+99RYA/e/sd3+/PXfuHHbs2KE34KBWq8sdd+fNm1fuTt1+/frhyJEjOHbsmG5aSkpKue+2PXr0gJ2dHebNm6dX7ty5c41az8aNGyMsLAyLFy/Wq8PChQuhUqkwcOBA3bSBAwfi1q1bWL9+vW7a7du3sXbtWvTv31/3PqPFixeXu1ZS9vj+OXPm6K1D2Y877n4Er1arxbJly+Dl5YXw8HCj1qMymXK9SukamBJD55pFRUXlzrNatWqF2rVrY+7cueWupdyde++5UlkdT548yaeqkBDvLCKqojZv3ozs7Gw88cQTBuPt2rWDr68voqOjDf5y8ueff0Z0dDSefvpphIeHQ6PR4J9//sEPP/wABweHCt1JMXfuXCxYsAARERFwcnLCL7/8ohd/+umn9b7gA3fucLG3t5d9qbKjoyNmzpyJMWPG4JlnnkHv3r1x4MAB/PLLL/j444/1XgqppFGjRujSpQvCw8Ph5eWFEydOYN26dXjjjTd083zyySd48skn0b59ewwfPhzp6emYP38+mjRpUu4ixP79+3UXQlJSUpCbm4tZs2YBuPN4tE6dOsHHxwdPPfWUwbYCYDBmjtmzZyMyMhIdOnTAyy+/jLS0NMybNw+NGzdWfC+RuRwdHdGoUSOsXr0aDRo0gJeXF5o0aaIbMBs+fDiWLVuGYcOGVbjs8PBwLFy4ELNmzUK9evXg5+eHbt26GbVNDS37nXfewS+//IKePXti7NixcHZ2xpIlSxASEoK0tDTdoND06dMRGxuLKVOmYNOmTXp1qlu3LiIiIkxuLyKi+2ns2LHIy8vD008/jbCwMBQVFeHw4cNYvXo1atWqheHDh8vmvvfee1i5ciX69u2LcePGwcvLCz/++COuXbuGX3/9VfeL0ccffxzr16/H008/jcjISFy7dg3ff/89GjVqZJFj0IEDB1BQUIDS0lKkpqbi0KFD2Lx5M9zd3bFhwwYEBATI5vr7+2P8+PH48ssv8cQTT6BPnz44c+YMfv/9d/j4+Cj+OCA8PByrV6/GpEmT0Lp1a7i4uKB///6V3gamnqf5+Phg586d6NChA3r06IGDBw8iODhYFx8yZIjuwpWxj6CrSB8aOXIkPv30U4wcORKPPvoo9u/fj3///bfC6+/n54euXbviq6++QnZ2drlzWRsbGyxZsgR9+/ZF48aNMXz4cAQHByM+Ph579uyBm5sbtmzZopczZMgQ3QWtmTNnVrhO94tcH3vppZewZs0avPbaa9izZw/at2+P0tJSXLhwAWvWrMEff/yhu9uLiKi6KHs86oEDB2Bvb19u4OCxxx7T3aFjaLDo7Nmzuh/YXr58GZmZmbrv9s2bN9e7q9WQn376Cb169cKAAQPQv39/dO/eHc7Ozrh06RJWrVqFxMRE3aPfv/jiC/Tt2xcREREYMWIE8vPzMW/ePLi7u2PatGm6Mh9//HH8/PPPcHd3R6NGjRATE4Ndu3bB29tbb9nvvPMOfv75Z/Tp0wfjx4+Hs7MzFi9ejNDQUL33+fr6+uKtt97C7Nmz8fjjj6Nfv344ffq07tzHGF988QWeeOIJ9OrVC4MGDcK5c+cwf/58jBw5UvcuS+DOYFG7du0wfPhwxMbGwsfHBwsWLEBpaSmmT5+um69Xr17lllE2sNG5c2e949mTTz6J7t27Y/bs2bh9+zaaN2+OjRs34uDBg1i0aJFuAKoy3LhxAz///DMA6B7rW9YfQkND8dJLLwEw7XqV0jUwJY899hg8PT0xdOhQjBs3DiqVCj///HO5gUUbGxssXLgQ/fv3R4sWLTB8+HAEBgbiwoULOH/+PP744w9deS1btsSjjz4Kd3d3nDp1Cj/88ANq1qxZ7jzTmGtgVI1IRFQl9e/fX3JwcJByc3Nl5xk2bJhkZ2cn3b59WwIgjRkzRhc7e/as9Pbbb0utWrWSvLy8JFtbWykwMFB65plnpFOnTlWoLkOHDpUAyP5du3ZNb/7MzEzJwcFBGjBggGLZixcvlh555BFJo9FIdevWlb7++mtJq9VWqH6zZs2S2rRpI3l4eEiOjo5SWFiY9PHHH0tFRUV6861atUoKCwuT7O3tpSZNmkibN2+WoqKipLCwML35pk6dKruuU6dOFdalc+fOUuPGjY2q97Vr1yQA0hdffFEuZmhZv/76q9SwYUPJ3t5eatSokbR+/Xpp6NChUmhoqNHLWrZsmW7a0KFDJWdn53Lzlq3/3Q4fPiyFh4dLGo1Gr27Lli0rV67cepXNe3d/SUpKkiIjIyVXV1cJgNS5c2dJkozbpoaWLUmSdPr0aaljx46Svb29VKNGDWn27NnSt99+KwGQkpKSdOsut42HDh2q2J5ERFXF77//Lr388stSWFiY5OLiImk0GqlevXrS2LFjpVu3bunNGxoaWm4fd+XKFWngwIGSh4eH5ODgILVp00baunWr3jxarVb65JNPpNDQUMne3l5q2bKltHXr1nLHoIoe1+61Z88evf2xnZ2d5OvrK3Xq1En6+OOPpeTk5HI5ho4tJSUl0ocffigFBARIjo6OUrdu3aR//vlH8vb2ll577bVyy9uzZ49uWk5OjvT8889LHh4eEgDd+hnbBsaua0XO0wydW1y+fFkKDAyUGjZsKKWkpOimJyYmSmq1WmrQoIFw+XerSB/Ky8uTRowYIbm7u0uurq7Ss88+KyUnJ5db57Jzibvrdq///e9/EgDJ1dVVys/PNzjP6dOnpQEDBkje3t6Svb29FBoaKj377LPS7t27y81bWFgoeXp6Su7u7rLlmUPUv8vc+xmrSB+TJEkqKiqSPvvsM6lx48aSvb295OnpKYWHh0vTp0+XMjMzdfPde96vJDQ0VIqMjDR6fiKiqmTy5MkSAOmxxx4rF1u/fr3uWFJSUlIuXnaeYM73vry8PGnOnDlS69atdcfJ+vXrS2PHjpUuX76sN++uXbuk9u3bS46OjpKbm5vUv39/KTY2Vm+e9PR0afjw4ZKPj4/k4uIi9e7dW7pw4YLB87SzZ89KnTt3lhwcHKTg4GBp5syZ0tKlS8ud+5SWlkrTp0+XAgMDJUdHR6lLly7SuXPnDJYpZ8OGDVKLFi1036OnTJlS7pqKJElSWlqaNGLECMnb21tycnKSOnfuLB0/flyx/LJtYWje7Oxsafz48VJAQICk0Wikpk2bSr/88ovePHLH4bJj7dq1axWXd++55t1/Zdci7mbs9aqKXAMTOXTokNSuXTvJ0dFRCgoKkt555x3pjz/+KHcuIUmSdPDgQalnz56Sq6ur5OzsLDVr1kyaN2+eLv7BBx9ILVq0kNzd3SU7OzspJCREGj16tO6ayN3MuQZGDx+VJJn5plkiogdYixYt4OvrW+6Z9fRwmTBhAhYtWoScnBzZF18SEdHDKyMjA56enpg1axY++OADa1fHYm7fvo3AwEB89NFH+PDDD61dnfuqpKQEQUFB6N+/v96jbIiIiIiIyDh8ZxERVQvFxcUoKSnRm7Z3716cOXMGXbp0sU6lyCLy8/P1/p2amoqff/4ZHTp04EAREVE1cO9xAPj/x8Q+7Mf85cuXo7S0VPcYlepk48aNSElJwZAhQ6xdFSIiIiKiBxLvLCKqxvLz8w2+YPduXl5e0Gg096lG+iqzftevX0ePHj3w4osvIigoCBcuXMD3338Pd3d3nDt3rtzzgenB1aJFC3Tp0gUNGzbErVu3sHTpUiQkJGD37t181i4RUTWwfPlyLF++HP369YOLiwsOHjyIlStXolevXrrnuD9s/vzzT8TGxuLDDz9E165d9V48/bA7evQozp49i5kzZ8LHxwenTp2ydpWIiIiIdFJSUlBaWiob12g0FXpvN5El8c4iQl5eHl577TWLvKD4YfOwtdXq1asRGBgo/Dt8+LBJZVdGW1Vm/Tw9PREeHo4lS5Zg7NixWL58OSIjI3Hw4EGrDxQ9bP3KUoxtp379+mHbtm2YOHEiPvvsM4SEhOD333+vVgNF7FNEVUteXp61q1CtNGvWDLa2tvj8888xYcIEHDhwAOPHj8evv/5q7apZzIwZMzBp0iS0aNEC8+bNA1B9+t3ChQsxevRo+Pn54aeffrJ2daq16tLniB4W/M5QOdiO5nvY27B169bC61oDBgyolOU87O14v1T3duSdRYSEhAQEBwfjxo0bCAkJsXZ1qrSHra0SExNx/vx54Tzh4eHw9PSscNllbXX9+nWEhoZWufpVJZXRVtUB28l4bCuiqiMnJweenp5YtWoVoqKirF0dqibY7+h+Y58jevDwO0PlYDua72Fvw0OHDhl8THKZsh83m+thb8f7pbq3o621K0BE1lP2K4aqqqrXj4iISImLiwseeeQRTJo0iRdQTZCTkwMXFxdrV+OBw35HFWXuZ419joiIyLD27dtbuwpERuNj6AhardbaVXhgsK2MV9ZWKpXKyjWp+thWxmE7GY9tRVS1LFq0CHFxcfj999+tXZUHxqFDh9CkSRN4enry0VYmYr8jY1TmZ419jujBwu8MlYPtaD62YeVgO1aO6t6OvLPoIaHVapGQkABXV9cKd+bs7GwAQG5uLrKysixRvYcG28p4bCvjsa2Mw3YyHtvq/0mShOzsbAQFBcHGhr+RsTRzzkceZk2bNkXdunXx+uuv48yZM9auTpW2b98+TJw4EVeuXEFwcDB++OEHlJSUVPt9mSnY70jEEp819jnDeC5yf/FcxHj8zlA52I7mYxtWDrZj5XgY27Ei5yJ8Z9FD4ubNm6hZs6a1q0FERFQl/ffff6hRo4a1q/HQ4/kIERGRYTwXuT94LkJERGSYMecivLPoIeHq6grgzkZ3c3O7r8t2d3cXxjMzM+9TTaoOUZsotQfbk4io8mRlZaFmzZq64yRZVlk7Dxs2DBqNxuA8paWlsvlyOWWCgoJkY6mpqcJcpccrFRQUyMYaNWokzC379Zkh77zzjjB38uTJwriovTw9PYW59vb2srH09HRhrqitASA5OVk2prQdRdvC19dXmJuSkiKMi7ajk5OTMFekqKhIGLe1FX+tKi4ulo2JtjEAODg4yMZE6wuY9/gMpXPS+Ph42ZjShVqleovaW6l/eXt7y8a6dOkizN21a5cwLur3jo6OwlxR/ObNm8Jcpb4r+qynpaWZXC8fHx9hrlLZSn3b1FzR+hqzXNEvapXKFpHrt0VFRViyZAnPRe6Tsnbevn07nJ2dDc6Tm5srm5+TkyMsXxRX2rcp9U3R/k3pOo+Xl5dszMPDQ5gr105lRMchpeOf6PNmyTu/lF4hIIorbaeSkhJhXHTMVzqfMOfVB2q1WhgX9S+lY6toO5vTB5Ti5uRasn8plS2Km1sv0T0fSveDmJOr1DctVS8llswVrbO5rykR9QNLfGays7NRv359o85FOFj0kCjrZG5ubvd9sEhJVauPtZnbHmxPIqKK42NI7o+ydtZoNCYNFildpBNdrFDKVfpiLzrhFy0XEF8UUDpum3PRU6leorLNaWtzy7bUOgHi7WjORWClfYg5F0qU+qao3kpfVM155JVSe4kuLCltR6Uv56L2VrqgJVq2i4uLybmA5T5TSuukVLYobk7Z5n4elfq2yIM4WGTOxUOqPGXt7OzsrPiZN8ScQQalbazUN0X9T2nQWDTgo9QO5uwb7ezshLkP4mCR0r7LkoNF5gyyK52LmDNYJNrOHCyqWJyDRRWPWyv3YRosMma5ujIU5yAiIiIiIiIiIiIiIqKHFgeLiIiIiIiIiIiIiIiIqjEOFhEREREREREREREREVVjHCwiIiIiIiIiIiIiIiKqxsRvIaOHiuglVtZ6mZi5zFknpZd6VdUXrBEREVV1Li4usi9p7tq1q2zenj17hOUmJyfLxnJycoS5Hh4ewriI6EXFAHDr1i3Z2KRJk4S5Si8y9vX1lY25uroKc+Pj42VjwcHBwlylda5Ro4ZsLC4uTpgrelG2aBsbw9PTUzaWnp4uzHV0dDR5uUr9T/TS8nr16glzExMTZWMFBQXCXFF7AOK+K+p7gLj/KfXrwMBAk+vl5uYmzBX1oTVr1ghz8/PzhXHRS83r1q0rzBX1vzp16ghz09LShHHRC9OV+oCo7+bl5Qlza9WqJYyL+m6TJk2EuWfPnpWN1a5dW5irRNR3s7OzhbmiderevbvB6bm5uViwYIFxlaNKU1paKrsvEr2YXGn/JYor5Yr2IYD4xeRKuaK4Uq65L2oXEa2T0jUZa12zMefl8gCgVqtlY3Z2dmaVbepyleLmrLO521EUt1YuYLlrc5a85qdUtiheVa/tmku0f1Pa95mzv1dizmfdlGvhFdm+VfLOovXr16NXr17w9vaGSqXCX3/9VW6egoICjBkzBt7e3nBxcUFUVFS5LxWbN29GgwYN8Mgjj2Dr1q16sbi4OERGRsLJyQl+fn54++239Q6g69evR8+ePeHr6ws3NzdERETgjz/+0Ctj2LBhmDZtmu7fkiTho48+QmBgIBwdHdGjRw9cunRJLyctLQ0vvPAC3Nzc4OHhgREjRuidoO/du1d30n1v+URERERERERERERERJWtSg4W5ebmokOHDvjss89k55k4cSK2bNmCtWvXYt++fUhISMCAAQN08cLCQowZMwYLFizA/PnzMXr0aN0vrkpLSxEZGYmioiIcPnwYP/74I5YvX46PPvpIl79//3707NkT27Ztw8mTJ9G1a1f0798fp0+flq3T559/jm+//Rbff/89jh49CmdnZ/Tu3VvvF38vvPACzp8/j507d2Lr1q3Yv38/Ro0aZU5zERERERERERERERERmaxKPobupZdeAgBcv37dYDwzMxNLly7FihUr0K1bNwDAsmXL0LBhQxw5cgTt2rVDYWEh1Go1WrRoAQCwtbVFYWEhNBoNduzYgdjYWOzatQv+/v5o0aIFZs6ciXfffRfTpk2DRqPB3Llz9Zb5ySefYNOmTdiyZQtatmxZrk6SJGHu3LmYMmUKnnzySQDATz/9BH9/f2zcuBGDBg3CP//8g+3bt+P48eN49NFHAQDz5s1Dv379MGfOHAQFBVVC6xERERERERERERERERmvSt5ZpOTkyZMoLi5Gjx49dNPCwsIQEhKCmJgYAHeeZT18+HAEBgYiKCgIo0eP1j2bOCYmBk2bNoW/v78uv3fv3sjKysL58+cNLlOr1SI7OxteXl4G49euXUNSUpJendzd3dG2bVtdnWJiYuDh4aEbKAKAHj16wMbGBkePHjWxNYiIiIiIiIiIiIiIiExXJe8sUpKUlASNRlPuRcX+/v5ISkrS/Xvq1KmYMGECbGxs9F5imZSUpDdQVJZbFjNkzpw5yMnJwbPPPqubtnz5cr0y7y7HUJ2SkpLg5+enF7e1tYWXl5duni5duujuqLq7/Hvl5OQgNTVV799EREREREREREREREQVZfU7i6Kjo+Hi4qL7O3DgQKWW7+7urjdQZIoVK1Zg+vTpWLNmTbnBHmt56623UKtWLd1fkyZNrF0lIiKiKis+Ph43b95EQkICEhISkJeXZ+0qERERERERERFVGVa/s+iJJ55A27Ztdf8ODg5WzAkICEBRUREyMjL07i66desWAgICjMo/duyY3rRbt27pYndbtWoVRo4cibVr1+o9Ys5QmWXlBAYG6pVb9t6kgIAAJCcn6+WVlJQgLS3NqHrf7auvvsK0adMA/P8j8sLCwoQ5kiRVaBkPuwe1PVQqlWzsQV0nIiJLa9Sokd6/X3vtNSxcuNBKtXn41a5dG46OjgZj27Ztk8279w7te5WUlMjGsrKyhLlubm7CuNyjhgEgNjZWmCs6j7u3793r5MmTwnhpaalsTO79nmW0Wq1sLDExUZjr4+MjjP/zzz+yMVGdAQjf05meni7MVZKfny8bU+pftrbyX43uPYe/171PPLhXQUGBbOzGjRvCXBE7OzthXLROAGBvby8bu/spBoa4u7vLxjIzM4W5Tk5OwnjNmjVNrpc5T1xQ2o6iz3NhYaEwNy0tzZQqAVDejqJ+r/R5FO0blbaTt7e3MF72HdsQpf2qaJ2VPjNyxx9jKPXd27dvy8Z27txpcLpS3yDLkCRJ9vux6Du10udNtN8VnacAgI2N+PfaorLVarUwV7RO5hJdZ1Bariiu1B5KZVfV6x/mrLPSPltEqWxR37Zk/zKnj1gr11yW7JuispWWK4qLvjcYw5x1ttZ1TqV1Fn0ezW0vEaXPsqVZfbDI1dW1wnf+hIeHw87ODrt370ZUVBQA4OLFi4iLi0NERIRifkREBD7++GMkJyfr7hTauXMn3Nzc9L4ArFy5Ei+//DJWrVqFyMhIYZm1a9dGQEAAdu/erRscysrKwtGjRzF69GjdcjMyMnDy5EmEh4cDAP78809otVq9ATNjODk56Z28K10gISIiqs5iY2Ph7OysO9lUusBFRERERERERFSdWH2wyJC0tDTExcUhISEBwJ2BIODOrzYDAgLg7u6OESNGYNKkSfDy8oKbmxvGjh2LiIgItGvXTrH8Xr16oVGjRnjppZfw+eefIykpCVOmTMGYMWN0v65bsWIFhg4dim+++QZt27bVvVPI0dHR4K/oVCoVJkyYgFmzZqF+/fqoXbs2PvzwQwQFBeGpp54CADRs2BB9+vTBK6+8gu+//x7FxcV44403MGjQIOEvLImIiMg8wcHBineWEBERERERERFVV1Z/Z5EhmzdvRsuWLXV38wwaNAgtW7bE999/r5vn66+/xuOPP46oqCh06tQJAQEBWL9+vVHlq9VqbN26FWq1GhEREXjxxRcxZMgQzJgxQzfP4sWLUVJSgjFjxiAwMFD3N378eNly33nnHYwdOxajRo1C69atkZOTg+3bt8PBwUE3T3R0NMLCwtC9e3f069cPHTp0wOLFiyvaRERERERERERERERERJWiSt5ZNGzYMAwbNkw4j4ODA7777jt89913Ji0jNDRU+Mz8vXv3VrhMlUqFGTNm6A063cvLywsrVqyocNlERERERERERERERESWUCXvLCIiIiIiIiIiIiIiIqL7g4NFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKqxKvnOInqwqFQqYVySJIst25JlW4pSexERET3okpKS4ODgYDDm6Ogom5eWliYst7CwUDbm5+cnzPX19RXGL168KBuTW5cyWq1WNvbvv/8Kc+3t7YXxzMxM2ZioPQAgPDxcNnbp0iVhbkFBgTAeGhoqGwsICBDmnjt3TjZmZ2cnzA0KChLGr169KhtT6l+ida5Tp44wV0lJSYlJMXPdvHlTGLe1lf86mJubK8wV9XtnZ2dhbnx8vDB+5coV2ZjS51EUV+rXStsiNjbW5NycnBzZmL+/vzA3IyNDGC8tLZWN1atXT5h7/vx52Zhofw0o798aNWokGxPtB5SI+h6g3HdF+z+NRiPMDQkJkY3FxcUZnF5UVCQskyxDpVLJfvcW7fuUjsuiaxBqtVqYq9R3RfVycnIS5orqLSoXUL5GYalrGOYu11L1Mvc6k6heNjbi3+yLtpVSvZTaQ9Q/lfquqN5K62TOdjRnG5vbP0T55vQRpVxz4tbKBarutU5rtZe19l+VgXcWERERERERERERERERVWMcLCIiIiIiIiIiIiIiIqrGOFhERERERERERERERERUjXGwiIiIiIiIiIiIiIiIqBrjYBEREREREREREREREVE1xsEiIiIiIiIiIiIiIiKiaszW2hWgyuXu7m5SniRJlVwT61OpVMK4OetsTtkPY1sTERHd7ebNm9BoNAZjISEhsnk5OTnCcrVarWzMyclJmHvp0iVhPCMjQzbm5eUlzBUpKioSxj08PITxW7duycZ8fHyEuUePHpWNOTo6CnNdXFyE8bS0NNnYjRs3hLl2dnayMQcHB2FucnKyMO7m5iYbU1onUb1F6wuI+49SXK1WC3ObN28uG7t69aowV+lzIfruUFhYKMxNSUmRjSm1h7e3tzDeqFEj2VhsbKwwt7S0VDYm6h+A8jqLFBcXC+Oiz7pSeymxt7eXjSnVy9nZWTam1O+V9o3nzp2TjdnYiH+3Kupfffv2FeZu3LhRGBctW2md/v33X9lYgwYNDE4vKCgQlkmWoVKpZL+3i45DSt/1bW3lL6MpHcOUrgWYU7bouC53TlZG6TgkorRO5lwbUdoWIkr7GHMolW3OOovOdc1tL1FcaZ1EcaVcc+plTq4Sc3LNYc5nBjCvj1jrWqUlr8+a254i5uxHlNbZnM+UpfHOIiIiIiIiIiIiIiIiomqMg0VERERERERERERERETVGAeLiIiIiIiIiIiIiIiIqjEOFhEREREREREREREREVVjHCwiIiIiIiIiIiIiIiKqxjhYREREREREREREREREVI1xsIiIiIiIiIiIiIiIiKgas7V2BahyZWZmws3NzdrVqBIkSXogyyYiInrQ2drawtbW8GlmQkKCbJ6/v7+w3MDAQNnYjRs3FOsk4uzsLBvLzc0V5rq6usrGnJychLlxcXHCuFarlY0ptZe9vb1sTLS+AHDr1i1hXNSednZ2wlwR0foaw8ZG/rdwWVlZwlxRvVNSUoS53t7ewnidOnVkY1evXhXmJicny8aKi4uFuUpE57RK20L0eczJyRHmuri4COOpqamyMaV1btiwoUnlAkB+fr4wLqLU7zMyMmRjPj4+wtyioiKTy1bqXw4ODrIxd3d3YW7dunWF8cOHD5u0XADw9fWVjR07dsyseqWlpcnGsrOzhbmi/b3cZ7WwsFBYJlmGWq2GWq2WjclROl8QHVuVrhOoVCphXFQvjUZjcr2UcpXWWXRsVSJqE6X2UoqbUy9zcpWYs86iepl7HUrU/5TaQ5Sr1K8tWbZS3FKUlmvOtjLnc2HuZ8oclirbkutkTt81p18rxa3d73lnERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsY4WERERERERERERERERFSN2Vq7AlS53N3dTcqTJEkYV6lUJucqEZWtxNxli1hynYmIiB5marUaarXaYMzf318279atW8Jy7e3tZWMFBQXCXB8fH2G8Z8+esrGjR48Kc21t5U+pk5OThblKnJycZGNubm7C3JKSEtlYTk6OMNfOzk4Y9/Lyko0lJCQIc0XbSulcVqvVCuPp6emyMbk+WcbR0VEYF1Gql1KbiBQWFsrGRP0DUG7P7Oxs2Vh+fr4w18ZG/neHojoDyufSonw/Pz9h7unTp2VjSv1aiaiPKPUfZ2dnk2IA8O+//5pcL6U+INqPKPXbP//8UxgXrVdpaakwNyMjQxgXycrKEsZFx5IaNWoIcz08PGRjV69eNThdaV3JMkTnIqLjgdL1CVFcKVe031Sql9L+S3QuIooZUy9LXbNROnYqLVdUtjnbUak9LHn9TBS35LU3c/qAOW1tbtnmLLeqMrc9TWXJ65xKn3VzmPN5VOr35rBkvzcltyL9hncWERERERERERERERERVWMcLCIiIiIiIiIiIiIiIqrGOFhERERERERERERERERUjXGwiIiIiIiIiIiIiIiIqBrjYBEREREREREREREREVE1xsEiIiIiIiIiIiIiIiKiaszW2hWgypWZmQk3N7cK56lUKmFckiSL5Jpbtog5uYBl15mIiOhhVlpaitLSUoOxhIQE2bygoCBhucnJybKxkJAQYW5aWpowfubMGdnYlClThLmzZs2Sjbm7uwtz5drJGOfOnTM5197eXhivX7++MB4XFycbc3BwMKlOAGBnZyeM29iIf+tWXFxsUgwAvL29ZWNXr14V5ir1v8TERNmYVqsV5orWuaSkRJh748YNYdzf31825uPjI8zNzs6WjSm1dUFBgTDu7OwsG7t586Yw18nJyeTlKhFtq1u3bglzRd/RlD6PSkTt3aJFC2Huvn37ZGNKnzdbW/HlBNH+Tal/FRYWysaU9uf16tUTxkX9ICcnR5hbo0YN2VhYWJjB6fn5+cIyyTLs7e1lP1uivq3U7y2VCwBqtVo2pvR5E+UqLdeSHsRrI+ZeS7IWpXqL4tZcZ0td9zO371mr75qzHS3JnGu7Sue6liRqL3P2jeZuJ0ttR7lyK7I83llERERERERERERERERUjXGwiIiIiIiIiIiIiIiIqBrjYBEREREREREREREREVE1xsEiIiIiIiIiIiIiIiKiaoyDRURERERERERERERERNUYB4uIiIiIiIiIiO6TnJwca1eBiIiIqBwOFhERERERERERWdihQ4fQpEkTeHp6Ii8vz9rVISIiItJja+0K0INPkqQqWbYl62UOlUplVn5VXS8iIqIyrq6usLe3NxirX7++bF5iYqKw3A4dOsjGDh48KMx1cXERxrOysmRjkyZNEuaKjs2FhYXCXB8fH2Fco9HIxi5fvizMdXd3l41lZmYKc+Pi4oRxkdLSUpNzi4uLhfFbt24J476+vrIxNzc3Ye7NmzdlYyUlJcLcCxcuCONNmjSRjf3333/CXA8PD9mYqM4A4OzsLIwnJSXJxpT6pqhve3p6mlUv0XYW9WtA3LfNWSdAfDeI0oV/Ozs7YVxEaf9VUFAgG9u+fbswt0GDBrIx0X4RAPz9/YVxUXsqtXXNmjWFcRFRewBASkqKbExpOxYVFcnGbG0NX14pW9dWrVrhypUrqFmzJlatWgUnJyfhssg8dnZ2ssdQGxv5302LYubmqtVqk8s2J9dcovMcc69vVEXmXnMR5ZtTtlJbP4zbwpK0Wq21q2CQ0nYUfdbNWSelvmlO3Jxcc/u9KG6tXHOZUnZFcjhYRERERERERERkQaWlpdi2bRv69u1r7aoQERERGcTBIiIiIiIiIiIiCzpz5oziHY5ERERE1sR3FhEREREREREREREREVVjHCwiIiIiusf69evRq1cveHt7Q6VS4a+//io3T0FBAcaMGQNvb2+4uLggKiqq3Hs2Nm/ejAYNGuCRRx7B1q1b9WJxcXGIjIyEk5MT/Pz88Pbbb+u9F2X9+vXo2bMnfH194ebmhoiICPzxxx96ZQwbNgzTpk3T+7dKpdL769Onj15OrVq1ys3z6aef6uJ79+5FrVq1DJZPRERERERERA8nDhYRERER3SM3NxcdOnTAZ599JjvPxIkTsWXLFqxduxb79u1DQkICBgwYoIsXFhZizJgxWLBgAebPn4/Ro0frXoxdWlqKyMhIFBUV4fDhw/jxxx+xfPlyfPTRR7r8/fv3o2fPnti2bRtOnjyJrl27on///jh9+rSw7n369EFiYqLub+XKleXmmTFjht48Y8eOrWgTEREREREREdFDhO8sIiIiIrrHSy+9BAC4fv26wXhmZiaWLl2KFStWoFu3bgCAZcuWoWHDhjhy5AjatWuHwsJCqNVqtGjRAgBga2uLwsJCaDQa7NixA7Gxsdi1axf8/f3RokULzJw5E++++y6mTZsGjUaDuXPn6i3zk08+waZNm7Blyxa0bNlStu729vYICAgQrp+rq6viPERERERERERUfXCw6CHj7u4uG5MkyaQYAKhUKovkGpNvatnmlGtJVbVeRERkvJMnT6K4uBg9evTQTQsLC0NISAhiYmLQrl07uLm5Yfjw4QgMDIRKpcKsWbPg6uoKAIiJiUHTpk3h7++vy+/duzdGjx6N8+fPGxwM0mq1yM7OhpeXl7Bue/fuhZ+fHzw9PdGtWzfMmjUL3t7eevN8+umnmDlzJkJCQvD8889j4sSJsLWtvNPC/Px8aLVag7GTJ0/K5oWEhAjLTU9Pl435+fkJc+Pi4oRxe3t72ZidnZ0wt7CwUDZWp04dYW5BQYEwnpqaKhtzcnIS5jo7O5sUA4D//vvP5LLNqdfdj2I0xMHBQRjPzs4WxkXq1asnG/P09BTmHj161OTlOjo6CuO5ubmyMbVaLcxV6l+iz/3t27eFuaLvHfn5+cJcpbLLBuENEe1DAPE6K/WfrKwsYVzUt4OCgoS5os+yjY34gR85OTnCuGgfdPdxxpDk5GTZmNLnsexuWTmifiCqMyDeJ2dkZAhzRZ8ZQNx3RTFA3Hfl+oDS55Asw97eXrYfia4jKF2/EH1elT7L5sSVcpXqLWLuNR1Tc80pF7DctRVzy62q13zMbW85lrwmaE6u3HcRY3ItyVrXSM0tW4m1Po9K6yyKm7NftdTnydyyK2M7cLCIiIiIqIKSkpKg0Wjg4eGhN93f3x9JSUm6f0+dOhUTJkyAjY2NbqCoLP/eC3hl/747/25z5sxBTk4Onn32Wd205cuX683Tp08fDBgwALVr18aVK1fw/vvvo2/fvoiJidFdVB43bhxatWoFLy8vHD58GJMnT0ZiYiK++uorAECXLl10d1TdWz4RERERERERPZw4WERERETVWnR0NF599VXdv3///Xd07Nix0spX+pWyMVasWIHp06dj06ZNwjtoBg0apPv/pk2bolmzZqhbty727t2L7t27AwAmTZqkm6dZs2bQaDR49dVXMXv2bOEvuYmIiIiIiIjo4SW+34qIiIjoIffEE0/gr7/+0v09+uijijkBAQEoKioq9yicW7duGfUuoICAANy6datcblnsbqtWrcLIkSOxZs0avcfeGaNOnTrw8fHB5cuXZedp27YtSkpKZN/PZKxt27ahefPmZpVBRERERERERNbBwSIiIiKq1lxdXVGvXj3dn9L7QwAgPDwcdnZ22L17t27axYsXERcXh4iICMX8iIgI/P3333rviti5cyfc3NzQqFEj3bSVK1di+PDhWLlyJSIjIyu4ZsDNmzeRmpqKwMBA2Xn++usv2NjYKL7zR866desQEhKCyMjISn3vERERERERERHdP/xGT0RERHSPtLQ0xMXFISEhAcCdgSDgzl0/AQEBcHd3x4gRIzBp0iR4eXnBzc0NY8eORUREBNq1a6dYfq9evdCoUSO89NJL+Pzzz5GUlIQpU6ZgzJgxukfBrVixAkOHDsU333yDtm3b6t5l5OjoaPDRdjk5OZg+fTqioqIQEBCAK1eu4J133kG9evXQu3dvAEBMTAyOHj2Krl27wtXVFTExMZg4cSJefPFFeHp6Vrid8vLyMHjwYDRs2BCrV69G48aNK+Wxe0RERERERER0f/HOIiIiIqJ7bN68GS1bttTdzTNo0CC0bNkS33//vW6er7/+Go8//jiioqLQqVMnBAQEYP369UaVr1arsXXrVqjVakRERODFF1/EkCFDMGPGDN08ixcvRklJCcaMGYPAwEDd3/jx42XLPHv2LJ544gk0aNAAI0aMQHh4OA4cOKAbgLK3t8eqVavQuXNnNG7cGB9//DEmTpyIxYsXm9ROTk5OSE9Px9mzZ426o4qIiIiIiIiIqibeWURERER0j2HDhmHYsGHCeRwcHPDdd9/hu+++M2kZoaGh2LZtm2x87969FSrP0dERf/zxh3CeVq1a4ciRIxUqV4mLi0ullkdERERERERE9x8Hix4ymZmZcHNzs3Y1HnqSJFm7CkRERFVWnz594OzsbDDm6+srm7d9+3ZhuTdv3pSN3bhxQ5jr4OAgjJeUlMjGPDw8hLmFhYXCuEjZow5N4e/vL4xnZmbKxpTOZZTe3SV6x1V6erowV6SgoEAYd3V1Fca1Wq1srOwOOzmXL1+WjSmdXwcHBwvjJ0+elI2FhoYKc0X9S6lvxsfHC+O1a9eWjYn6j1K97OzshLlK70j7999/ZWO5ubnCXNEAtlLfVGrP7t27y8b27NkjzBW1l9LnzcfHRxhPSUmRjeXk5AhzzRnwt7ERP6ikuLhYNqbU1qL9fY0aNYS5SvGioiLZmNKxRK1Wy8bS0tIMTjfnGEGms7Ozg0ajqXCeSqUyOa70mVAqW5SvlGst5rSXNdfJnGs6onMNc1XV7WxOe1XVtq6qLNkHqmr/Mqde1txnV0Vyda7IuvAxdERERERERERERERERNUYB4uIiIiIiIiIiIiIiIiqMQ4WERERERERERERERERVWMcLCIiIiIiIiIiIiIiIqrGOFhERERERERERERERERUjXGwiIiIiIiIiIiIiIiIqBrjYBEREREREREREREREVE1ZmvtCtD9o1KpTM6VJMnkckW55qqq9TKnrZVYst5ERESVYc+ePbC3tzcYy8rKks1zdHQ0eZl+fn7CeGpqqsllx8fHC+MuLi6ysYKCAmGunZ2dMO7s7CwbKywsFOaKzhnktk8ZDw8PYTwtLU02prTOou1cXFwszC0pKRHGRevl4OAgzBX1TVtb8demixcvCuPu7u6yMRsb8e/3srOzZWNKfUCpbFF7ZmRkCHPVarVszMnJSZir9Hn09vaWjSn1AVEfUmqv0tJSYXzbtm2yMaXPslarlY2JtrEx9RJ991D6LIs+M0p9QGlbiPYFSvsg0ec1Pz9fmHvjxg2T66W0DzJln8zvb9ZhZ2en+Lk0RGl7iT5vStcBlPbJpi7XXFW1bEtdwzI311qfaUtuJ0sSHf8A8z4XVdWDuK2U6mxu3FTm7ldFcWutkzVUZF0evk8kERERERERERERERERGY2DRURERERERERERERERNUYB4uIiIiIiIiIiIiIiIiqMQ4WERERERERERERERERVWMcLCIiIiIiIiIiIiIiIqrGOFhERERERERERERERERUjdlauwJUudzd3WVjkiSZXK5KpbJIuZZkzXpV1TYhIiK6H5ycnODg4GAwlpOTI5tnb28vLFej0cjGMjMzhbmurq7CuK2t/GlxYWGhMLe0tNSkGADY2Ih/u5WRkSGMi6jVatmYnZ2dMFepPUVlu7i4CHNF7RkYGCjMTU9PF8YLCgpkY7dv3xbmiraFUm6rVq2E8TNnzsjGvLy8hLmhoaGyMaX+VVRUJIw7OTmZtFwASExMlI0p9VvRdxYAyMrKko2J+h4AeHh4yMby8vKEuSUlJSaXnZ+fL8ytUaOGbEy0XwSAa9euCePBwcGyMTc3N2FuXFycbCwkJESYq7QdRftVpbauVauWbKy4uFiYe+PGDWFc9LmRO3YZw9HR0eB0pf08WYZarZbdX4i+ryt9lxddGxHFjGHJsh9E1rquYu5yLVVvc/uApeplzmcGALRarcm55rSJJT9Tlrr+akzc1Fxzlys61lmyPZSOsaK4Uq4lr4WL8i3RNytSJs9aiIiIiIiIiIiIiIiIqjEOFhEREREREREREREREVVjHCwiIiIiIiIiIiIiIiKqxjhYREREREREREREREREVI1xsIiIiIiIiIiIqApZv349evXqBW9vb6hUKvz111/l5ikoKMCYMWPg7e0NFxcXREVF4datW3rzbN68GQ0aNMAjjzyCrVu36sXi4uIQGRkJJycn+Pn54e2330ZJSYleHXr27AlfX1+4ubkhIiICf/zxh14Zw4YNw7Rp0wAAxcXFePfdd9G0aVM4OzsjKCgIQ4YMQUJCgl5OWloaXnjhBbi5ucHDwwMjRoxATk6OLr53717UqlWrXPlERERkWRwsIiIiIiIiIiKqQnJzc9GhQwd89tlnsvNMnDgRW7Zswdq1a7Fv3z4kJCRgwIABunhhYSHGjBmDBQsWYP78+Rg9ejSKiooAAKWlpYiMjERRUREOHz6MH3/8EcuXL8dHH32ky9+/fz969uyJbdu24eTJk+jatSv69++P06dPG6xPXl4eTp06hQ8//BCnTp3C+vXrcfHiRTzxxBN6873wwgs4f/48du7cia1bt2L//v0YNWqUOc1FRERElcDW2hUgIiIiIiIiIqL/99JLLwEArl+/bjCemZmJpUuXYsWKFejWrRsAYNmyZWjYsCGOHDmCdu3aobCwEGq1Gi1atAAA2NraorCwEBqNBjt27EBsbCx27doFf39/tGjRAjNnzsS7776LadOmQaPRYO7cuXrL/OSTT7Bp0yZs2bIFLVu2LFcnd3d37Ny5U2/a/Pnz0aZNG8TFxSEkJAT//PMPtm/fjuPHj+PRRx8FAMybNw/9+vXDnDlzEBQUZEarERERkTk4WERGkSTJ5FyVSmVy2ebkmktp2SKWrBcREVFVp9VqUVpaajBWp04d2byCggJhuampqbKx4uJiYa6Xl5cw7uzsLIyL3L59WzaWn58vzC0sLBTGPTw8TKkSgDuP+ZHj6ekpzBWtEwD4+/vLxhITE4W5Tk5OsrFr164Jc11dXU2Ox8fHC3P9/PxkY1qtVph79uxZYdzHx0c2ptTWjo6OwriIqA+YW7aDg4PJuUrn2aJ9gdJnXW7fA9y560FE1DcBICMjQzZmZ2cnzBVtC1GdASA0NFQY79mzp2wsOjpamCta53sfZ3Yvpf5lYyP/IBOl/iPa39/9mDRDlPar5nxXE/VNuXVSqo+pTp48ieLiYvTo0UM3LSwsDCEhIYiJiUG7du3g5uaG4cOHIzAwECqVCrNmzdLtJ2NiYtC0aVO9/Xnv3r0xevRonD9/3uBgkFarRXZ2tuIx9W6ZmZlQqVS641lMTAw8PDx0A0UA0KNHD9jY2ODo0aN4+umnK9oUBqlUKrO+01uCJetjrXWtqtc+zKmXUq4l11lUtrnLtVQfMfe6nSjfWtciH1aiNhEds41hqb6rtB2V6v0g9ntr42AREREREREREdEDJCkpCRqNptyPCvz9/ZGUlKT799SpUzFhwgTY2NjoDagnJSWVG/gv+/fd+XebM2cOcnJy8Oyzz+qmLV++XLaOBQUFePfddzF48GC4ubnpyr53cN7W1hZeXl665Xbp0kV3R5WofADIysrSDS7a2NjovfuIiIiIKobvLCIiIiIiIiIispLo6Gi4uLjo/g4cOFCp5bu7uyvemalkxYoVmD59OtasWSO8E7NMcXExnn32WUiShIULF5q1bJF33nkHderUQZ06dVCrVi00adLEYssiIiJ6kMXHx+PmzZtISEhAQkKCwbvuOVhERERERERERGQlTzzxBP766y/d392PaJMTEBCAoqKico9HvHXrFgICAozKv/dRg2X/vjd/1apVGDlyJNasWaP32Ds5ZQNFN27cwM6dO3V3FZWVnZycrDd/SUkJ0tLSjKr3vb766iskJiYiMTER8fHxuHDhQoXLICIiqg4aNWqEmjVrIjg4GMHBwXjzzTfLzcPH0BERERERERERWYmrq2uF7/wJDw+HnZ0ddu/ejaioKADAxYsXERcXh4iICMX8iIgIfPzxx0hOTtbdKVQ2sNOoUSPdfCtXrsTLL7+MVatWITIyUrHcsoGiS5cuYc+ePfD29i633IyMDJw8eRLh4eEAgD///BNarRZt27Y1ev3LODk56b17Kysrq8JlEBERVQexsbFwdnbWvVPp3mM0wMEiIiIiIiIiIqIqJS0tDXFxcUhISABwZyAIuHNnTkBAANzd3TFixAhMmjQJXl5ecHNzw9ixYxEREYF27doplt+rVy80atQIL730Ej7//HMkJSVhypQpGDNmDOzt7QHcefTc0KFD8c0336Bt27a6dwo5OjrC3d29XJnFxcUYOHAgTp06ha1bt6K0tFSX4+XlBY1Gg4YNG6JPnz545ZVX8P3336O4uBhvvPEGBg0ahKCgoEppOyIiIiovODhY725fQ/gYOiIiIiIiIiKiKmTz5s1o2bKl7m6eQYMGoWXLlvj+++9183z99dd4/PHHERUVhU6dOiEgIADr1683qny1Wo2tW7dCrVYjIiICL774IoYMGYIZM2bo5lm8eDFKSkowZswYBAYG6v7Gjx9vsMz4+Hhs3rwZN2/eRIsWLfRyDh8+rJsvOjoaYWFh6N69O/r164cOHTpg8eLFpjQTERERVSKVVHbfET3QsrKy4O7ujszMTMURQkNUKpUwLuom5uQak29O2Q8ic9uTiIj+n7nHR6qYsvZ+77334ODgYHCewsJC2fy6desKy9+7d69szNnZWZjbsGFDYfz69euyMQ8PD2Fubm6uMC4iag/gzq+05bi4uAhzc3JyZGOGXmZ6N6X3Rmi1WtlYfn6+MLe0tFQ2ptTWt2/fFsZFioqKhHHROim1dUFBgTAuWmfRNgbEbSKqMwDd3QGm5NvYiH9XqFarZWP3vkPlXkrrXNHHcd2tpKTEpJgxyxV9XgMDA4W5V65ckY3J7S+NWS4g/n5g6PEidxN991D63qHUnqGhobIxpc+yaD/i6OgozK1fv74wnpKSIhu791069xLtC+T2uYWFhViwYAHPRe6TsnORxMRE2fa21Hdqc65tKOUrlW1OrhJRe1nz+oSl6lVVr7mYux1Fx3VzyxaxZHtast+LWPI6pzmfdXMorZM5cXNyzW0Pc/q9Of3L3PasbFlZWfD29jbqXIR3FhEREREREREREREREVVjHCwiIiIiIiIiIiIiIiKqxjhYREREREREREREREREVI1xsKgSzZ49G61bt4arqyv8/Pzw1FNP4eLFi3rzFBQUYMyYMfD29oaLiwuioqJw69YtXfz69eu65x5OmzYNw4YNu5+rQERERERERERERERE1QwHiyrRvn37MGbMGBw5cgQ7d+5EcXExevXqpffS44kTJ2LLli1Yu3Yt9u3bh4SEBAwYMMCKtSYiIiIiIiIiIiIiourM1toVeJhs375d79/Lly+Hn58fTp48iU6dOiEzMxNLly7FihUr0K1bNwDAsmXL0LBhQxw5cgTt2rWzRrWJiIiIiIiIiIiIiKga451FFpSZmQkA8PLyAgCcPHkSxcXF6NGjh26esLAwhISEICYmxip1JCIiIiIiIiIiIiKi6o13FlmIVqvFhAkT0L59ezRp0gQAkJSUBI1GAw8PD715/f39kZSUBACoVasWJEkCcOedRfdL2TLvd25l5FtD2XulTKG0vkpx0bIfxLYkIqKHT1paGjQajcGYnZ2dbN6VK1eE5QYHB8vGMjIyhLk3btwQxkX1SktLE+aq1WqT6+Xu7i6MBwUFmVwvHx8f2VhoaKgw9/jx48K4qL1sbMS/RxPFldrL3t5eGHd2dpaNXb9+XZhb9gMvQwoKCoS5rq6uwrioj2RnZ5ucW1JSIsxVcvfjsu+ltE5l318McXNzE+aK1gkA8vLyZGNFRUXC3Hu/a93t9u3bwlytViuMiyh9HkXn6aI6A8qfqcTERNmYaL8JALGxscK4iIODgzAuahOlfdC1a9dkY1lZWeKKKahRo4bJZSt9Xg3hdzTr0Gq1Zn2m5ZhzLUAp15yyLcla9bLkZ0dUtjnXZCzJmtfezLkOVR33gaJ1tmT/seT+SWk7WmJ/a2mWvLZrLXLrVJF15WCRhYwZMwbnzp3DwYMHLVJ+Tk4OUlNT9f5NREREhsXHxyMrK0t3wc3DwwNOTk5WrhURERERERERUdXAwSILeOONN7B161bs379f71dLAQEBKCoqQkZGht6vx27duoWAgIAKLeOtt97CokWLKqvKRERED7VGjRrp/fu1117DwoULrVQbIiIiIiIiIqKqhYNFlUiSJIwdOxYbNmzA3r17Ubt2bb14eHg47OzssHv3bkRFRQEALl68iLi4OERERFRoWV999ZXuMXVarRbZ2dkICwurlPUgIiJ62MTGxsLZ2Vl3u7i3t7eVa0REREREREREVHVwsKgSjRkzBitWrMCmTZvg6uqqe463u7s7HB0d4e7ujhEjRmDSpEnw8vKCm5sbxo4di4iICLRr165Cy3JyctJ7fI65z20mIiJ6mAUHByu+Q4OIiIiIiIiIqLriYFElKnucTZcuXfSmL1u2DMOGDQMAfP3117CxsUFUVBQKCwvRu3dvLFiw4D7XlIiIiIiIiIiIiIiI6A4OFlWiskfbiDg4OOC7777Dd999dx9qREREREREREREREREJGZj7QoQERERERERERERERGR9fDOompEpVKZnGvMXVNVjTnrC4jX2Zrt8SBuCyIiql769u0LZ2dng7GTJ0/K5mVkZAjLFcW9vLyEuTVq1BDGT58+LRsLDAwU5hYVFcnGPDw8hLnFxcXCuEhpaakwnpycbFIMAOzt7YVxR0dH2ZjSuzTVarVsLD09XZjr5+cnjIvWy9fXV5ibmZkpG3NxcRHmpqSkCOOidbaxEf9+Lzs7WzamtE6iXADIz8+Xjcl9hsv4+/vLxlJTU4W5Sv3LwcFBNib6vCmpWbOmybmAeB+k9Fl2dXU1eblKn9fCwkLZ2MWLF4W5JSUlsrGgoCBhbkJCgjD+yCOPyMZiY2OFuaL+JeofAHD27FlhXPS5adSokTD33LlzsjG5fZ85fZZMp9VqodVqDcZE1wqUriNYKleJJetlDqWyrXX9Qmm5D+J1FXPrLPd5ACzbd83ZFpbsu0os+XkVUWovS7WJueWKzmdFfc9c1uwjIpbcB1l6nXlnERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsZsrV0BqhokSTI5V6VSWaxscygtV6neREREZJqtW7dCo9EYjDk4OMjm9e3bV1jugQMHZGPZ2dnC3Js3bwrjnp6esrGcnBxhrigeEhIizE1JSRHGRe2VmZkpzHVycpKNubi4CHOV2kspX0StVsvGlM7PlNa5oKBANlZaWirMtbWV/2qUnp4uzHV3dxfGPTw8ZGOXLl0S5vr7+8vGiouLhbn5+fnCeFFRkcllZ2RkyMZE/RYA7OzshPG8vDzZmLe3tzBXtC8oLCwU5iptR1HfLSkpEeaK9hOifmuMrKwsk8sW7Se0Wq0wV2k7X716VTam9HkUrZPScuvUqSOMx8fHy8YOHz4szBV9HuUo9TuyDK1WK9uHbWzkfzdtzesEomUr1ctSueYy53qQUq5oH2XNdbYWpfYSxatqH1Cql+izbG7Z1movJZZqT2tdu7UmS+6fqlp7VqQ+vLOIiIiIiIiIiIiIiIioGuNgERERERERERERERERUTXGwSIiIiIiIiIiIiIiIqJqjINFRERERERERERERERE1RgHi4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsZsrV0Bun8kSbJ2FSqdSqWydhWIiIjoHmq1Gmq12mBMdD6yc+dOYbl+fn4m16mkpMTkuNI5lJeXl2wsLy9PmGtvby+Mx8bGysaaNGkizL148aJsLCsrS5hbWloqjIvqnZubK8wtKioSxkXs7OxMzrWxEf9OLj8/Xzam1AcyMzOF8YyMDNmY0jqlpqaaXC8XFxdh3NHRUTYmag9A3J5K5+g5OTnCuKgPFRQUCHM9PDxkY1qtVpir1DednZ1lY0p9QJRbXFwszFX6vIq2hdLnUaPRyMaUtpOtrfhyQmFhoWxMqd+L9quJiYnCXDc3N2E8ODhYNib6rAKQPbYB8v1Lqd/Rw0Np3/egXr8wp96iXHOvUYn2fUplm5NryWtrorKV9iXWrLellqt07iYq29zPoyiuVK8HkVJ7mLMdzdmHWLJfW+uzbElyy63IucjD17uJiIiIiIiIiIiIiIjIaBwsIiIiIiIiIiIiIiIiqsY4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuNgERERERERERERERERUTXGwSIiIiIiIiIiIgvat2+ftatAREREJGRr7QrQg0+SJLPyVSqVyWWbu2wiIiKqfB4eHrC3tzcYy8nJkc0LDg4WlhsfHy8b8/PzE+ampaUJ4yKicxUAyM/Pl415eXkJc1NSUoRxZ2dn2djVq1eFua6urrKxgoICYa6np6cwLqLVaoXx0tJSk8tOTU0Vxj08PGRj2dnZwlxz6qXUR4qKimRjNjbi3+85ODjIxuQ+Z2UyMjJMLltpO4qWrbRcpXr7+/vLxkT7EADIzMyUjWk0GmGu0udCrVYL4yKFhYUm59rZ2Qnjou9ESussyk1KShLmKu13HR0dhXGRK1euyMaU9k83btwQxkX53t7ewtxbt27JxmxtDV9eKdu3TJo0Cf379xeWT5VHpVLJ7puV9tnWYk69zLk2Yq32sORylY6tomOcJa8zKZUtqpe551eWWmdLtpfSdhQdl5VylfqfKF9pnUVlm9vvRflK6yxizjoZk28t1qqXNfcjhijtP+7GO4uIiIiIiIiIiCzo8uXLiImJsXY1iIiIiGRxsIiIiIiIiIiIyIKCg4MxatQoa1eDiIiISBYHi4iIiIiIiIiILGj27Nm4cOGC4qMUiYiIiKyF7ywiIiIiIiIiIrKgJ598EpmZmXBycrJ2VYiIiIgM4p1FREREREREREQWxoEiIiIiqso4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuM7i8jqJEmSjalUKpNziYiIyDoKCgpkj9F5eXmyeTdu3BCWGxQUJBvz9PQU5l69elUYF7G3txfGAwMDTS5b6ZFEomUXFxcLczMyMkzOtbOzE8aLioqEcRFXV1fZWFpamjBX6dwwPT1dNqa0HW1s5H9Hp3TOWVpaKoyLODs7C+Oi7SiqM6C8Hc0pOz8/Xzam1L/M2Y5KuaK+6ejoKMxV2s6ispXqlZOTIxvz9fU1ORcA3NzcZGPZ2dnCXFH/8/LyEubevn1bGBdRKlu0rbRarTBXtI9RkpiYKIzn5ubKxuQ+b4WFhSbXh0ynVquhVqsNxkSfV6XPslLcHJa6vmFuuVW1vcxhzjoptacorrT/Ep1PlJSUCHOV4qKyleolWidz2gMQt7fSuYitrfxlbbnPv7Fli/LNyVXqX0pliyhtR3PKrqrM7X8i1tp/WWKdlPrG3R6+XkJERERERERERERERERG42ARERERERERERERERFRNcbBIiIiIiIiIiIiIiIiomqMg0VERERERERERERERETVGAeLiIiIiIiIiIiIiIiIqjEOFhEREREREREREREREVVjHCwiIiIiIiIiIiIiIiKqxmytXQEilUolG5MkySrLVWLJehERET3oiouLYWNj+DdJNWrUkM0rKioSlhsbGysbc3Z2FuaGhIQI4yUlJbKxhIQEYe6VK1dkY97e3sJcd3d3YfzGjRvCuIhGo5GN2dqKvwYUFhaaHC8uLhZXzAxqtVoYt7Ozk41ptVphblZWlmxMqX+J+g8gPnfMzc01uWyl7ah0zipqz9LSUmGuiGg7AMrrLLf/AAAvLy9hrmg7FxQUCHOViNpLVGcA8Pf3l43l5OQIc/38/ITx9PR02ZhoPwCI661UL6X+17x5c9nYhQsXhLk1a9aUjSn1H6X9pmgf5erqKsx95JFHKlyuuf2OTKNWq2U/s6JrAeZcJ1BiznUEpVxzylY6PoraRGnfZ8m2rqrXZUT1Ujq2ivZPSufJSnFR2Ur1EvURpVyl7STqB0rnfaJjnNK5iFJcdIwz5/zL3HM3S13LVCrXkvWy1nVhJea0l7XI1bki7cg7i4iIiIiIiIiIiIiIiKoxDhYRERERERERERERERFVYxwsIiIiIiIiIiIiIiIiqsY4WERERERERERERERERFSNcbCIiIiIiIiIiIiIiIioGuNgERERERERERHRfZKTk2PtKhARERGVY2vtChBJkmSRclUqlVWWS0RERPISEhJkY8XFxcJcW1v5U9fCwkJhbnJysjBeUFAgG7OzsxPm2tvby8aysrJMzgXEbeLs7CzMFdVb6UKlqK0BwN/fXzaWkpIizC0tLZWNKbW10nYWtZeNjfh3cqL2LCoqMqteomUrbQutVmtSDADc3NyE8fT0dNmYo6OjMLekpEQ25uTkJMzVaDQml52amirMFW1Hpc9j/fr1hfHMzExhXCQ7O1s2Vrt2bWHuzZs3hXG1Wm1SnQDAwcFBNqa0f1Lq9xcuXJCNKe2/Ll68KBtT2k/4+fkJ46LPs2j/BAAZGRkVzr27nQ4dOoRXX30VFy9eRGZmpuJnhUxna2ureCwzheg6gtI1BnPiSvt7Udzcax+iY5jSsdWcXHOu6ZjT1kq5SttCdAwTxQDx/ik/P1+YqxQXnesqneeIzq+Uzt+V2kt0DFM6DomOYUrnMUpli+JKfUTUd5XaQ+lzYSmWvEb6MF6fNacPGJNvTRwsIiIiIiIiIiKyoFatWuHKlSuoWbMmVq1axYEiIiIiqnI4WEREREREREREZEGlpaXYtm0b+vbta+2qEBERERnEwSIiIiIiIiIiIgs6c+aM4qMhiYiIiKzJOg9CJCIiIiIiIiIiIiIioiqBg0VERERED4lp06ZBpVLp/YWFheniBQUFGDNmDLy9veHi4oKoqCjcunVLF79+/bruZZzTpk3DsGHD7vcqEBEREREREZEVcLCIiIiI6CHSuHFjJCYm6v4OHjyoi02cOBFbtmzB2rVrsW/fPiQkJGDAgAGVsty8vLxKKYeIiIiIiIiI7j++s4iIiIjoIWJra4uAgIBy0zMzM7F06VKsWLEC3bp1AwAsW7YMDRs2xJEjR9CuXTuTl5mTkwNPT0/88MMPJpdBRERERERERNbDwaKHjLu7u2xMkiSTyy17JE1ll1uVidZZycPaJkREVPVdunQJQUFBcHBwQEREBGbPno2QkBCcPHkSxcXF6NGjh27esLAwhISEICYmxqzBIhcXFzzyyCN4//33AQB+fn5wcHAwOG9WVpZsOU5OTsLlpKSkmFxHpTufatSoYXJuRkaGbEyr1QpzMzMzhXG5dgSAoqIiYa6dnZ1srKSkRJirVLYov6CgQJirVqtlY7a24q8nSnFR2UrrXFhYaHKuUlx0XqnUR2xs5B8GUVxcLMxNT08XxjUajWxMqd+LtkV+fr4wt7S0VBgXtYm9vb0wV9QmdevWFebGx8cL4y4uLrIxpXUS9c3Lly8Lc0V9EwDq168vG0tMTBTmitYpJydHmOvh4SGMi7Zjbm6uMFd0PPDy8hLmitoaAGrVqiUbS01NFeZevHhRNia3vkr7U7IMGxsb4f5TjtJ3eVHcnFxAvB9R2seYk6tEdAxT+ryJjhVKuaZsP2OJ9k9Kx2Wl9hSdEyjtz0XHT6X9ZnZ2tslxpf29aNlK66TUnqLzVdExCgBcXV1lY25ubsJcpbJFn1el64Wivq3U7x/U64kPar0t5UFuDw4WERERET0k2rZti+XLl+ORRx5BYmIipk+fjo4dO+LcuXNISkqCRqMpd3HP398fSUlJAO5cQCs7sZ02bVqFlr1o0SJ06NChMlaDiIiIiIiIiO4zDhYRERERPST69u2r+/9mzZqhbdu2CA0NxZo1a+Do6GjRZbdv3x5169bFlStXLLocIiIiIiIiIqp8lrunk4iIiIisysPDAw0aNMDly5cREBCAoqL/a+/O46uqzv2Pf0/mOSEJhHlUBGSo4IRaa9VKq231yq/XWnqr1mqtYMHZ3tqWtt6rnVBbcah1uO1VsVqtrTOlihMoggOKIjKTQAKEzPM5+/dHSi6B7Gcl5+TkJDmf9+vlq+U8edZee+21915nr7P3bjrkkWmlpaUdvuOoq5599lnnIywAAAAAAEDvxGQRAABAP1VTU6ONGzdqyJAhmjFjhpKTk7Vs2bK2+Pr167Vt2zbNnDkz7GU8/vjjGjlypM466yzzueEAAAAAAKD3YrIIAACgn7jmmmu0fPlybdmyRW+88Yb+7d/+TYmJiTr//POVm5uriy++WFdddZVeeuklrV69WhdddJFmzpyp448/Pqzl1dXV6fzzz1deXp7eeOMNrV69upvXCAAAAAAA9ATeWQQAANBP7NixQ+eff7727t2rgQMH6qSTTtLKlSs1cOBASdKtt96qhIQEzZ49W42NjZo1a5buvPPOsJeXkZGhffv2KSsrS5JUVVXVLesBAAAAAAB6FpNF/UxlZaVycnK6vVzP83xjgUAg7FyXSMp2LTea9QYAIBaWLFlixtPS0rR48WItXry425a5f6LoQPX19QqFQh3+fXV1tW9ZRUVF5rJKSkp8Y5MnTzZzt23bFnbZLi0tLb6xzMzMsMuVpIaGBt9YSkqKmZuamuob62i7HSgpyf6acPC7rw7kGotaY6y6ujoz1yUxMTGsmGS3ZzAYNHObmprMuN/+ILnHnFY8LS3NzLX6T2fiFqs9XeNs18SytV7JyclmrtVeW7ZsMXNd+5QVr6ysDLte+yf0/Vj7m2Qfv1x9pLS01Dfm2petY58k8z12U6ZMMXPXrl3rG3Pty7t37zbjO3bs8I251vmEE07wjfndXZuQwANdYiEQCPgei6L1Xd9VrnUucMVd+1tzc3PYua56W33YNV6IJutcE8m2cLWXK26NCVznXWsc5DrP7N2714zv2bPHN1ZWVmbmlpeX+8Zc7yx1jaGssXJBQYGZO2jQoLBinamX1b9c5yFrrBLNa42u8VckIql3LK+vRrNNLLFaZ7/17Uo7MGoBAAAAAAAAAACIY0wWAQAAAAAAAAAAxDEmiwAAAAAAAAAAAOIYk0UAAAAAAAAAAABxjMkiAAAAAAAAAACAOMZkEQAAAAAAAAAAQBxjsggAAAAAAAAAACCOJcW6AugdAoGAGfc8L6xYtMsGAAC9T0tLixITEzuMJST4/1Zp69atZrmpqam+sS1btpi5+/btM+MDBw70jVVUVJi5mZmZYS83IyPDjGdnZ/vGgsGgmVteXu4bc42xCgoKzHhaWppvLBQKmbnWspOS7K8nTU1NZrylpcU35hqTWtu5oaEh7OVKdr93bUdLbW2tGW9ubjbjVpu42staJ1f/snIl+R4/JPs4INn7o6utXe1ltberXo2NjWGVK7nbKz093Tfm2h8tkbZXcnKyb2zNmjVm7rhx43xj1vpKUl1dnRm32tO1Tu+//75vzG87uo5b6F1cx69IrlG4cq391XWesfquq1+76mUdk13nCus44jq2ueIW17HPqlekxz7reF9fX2/mVldX+8ascZ0klZSUmHFrnO0aR2/fvj3sernaMy8vzzc2cuRIM3fMmDG+sUjGZpJ9DnOd8614JOPkaIrmsS8SrmNMrMTyerXVJt1RL+4sAgAAAAAAAAAAiGNMFgEAAAAAAAAAAMQxJosAAAAAAAAAAADiGJNFAAAAAAAAAAAAcSysyaKbb75Z999//yGf33///frFL34RcaUAAAAsjEUAAEAsrVy5UmvXrj3k8/Xr1+vdd9/t+QoBAABEKKzJonvuuUcTJkw45PMjjzxSd999d8SVAgAAsDAWAQAAsfTee+8pPz//kM8HDBigjz76KAY1AgAAiExYk0W7du3SkCFDDvl84MCB2rlzZ8SVAgAAsDAWAQAAsVRbW6vMzMxDPk9LS1N9fX0MagQAABCZpHCSRowYoddff11jxoxp9/nrr7+uoUOHdkvF0LM8zws7NxAIRFS2lR9JvVyiWTYAILoYi/Ru9fX1CoVCHcYSExN989LS0sxyGxoafGOjR482c2tqasx4UpL/sDg1NdXMtS4KDhgwwMxtbGw0483Nzb6xhAT7d18pKSm+saamJjPXb/vtZ22LYDBo5lp9wNUerrKtfKstXXHXuNFVL0sk28I1Dnets5Xv6l+RjKVd+5RVb9dFeKs9XcuNpD2Tk5PNXOsYU1tba+amp6ebcSvfdVztaLJjv5aWFjN35MiRZry4uNg35movK9e1v7nGAVY/qKysNHMP3C9ycnK0c+fOtruL9u8Tu3btUkZGRtu/+d4ZG57n+bZ9b90m1vHedV6OJNfVHtb5IJZtaS3bVS+rTVzHPte51RqL1NXVmblVVVW+sd27d5u527ZtM+Mffvihb6yjR2oeaMeOHWY8WkpKSsy41dbWOFiyz3+SlJWV5Rtz9QGrD7nOYZHsr5Fcf3VxlR3N68rRykXHwposuuSSS7RgwQI1Nzfr1FNPlSQtW7ZM1113na6++upurSAAAMDBGIsAAIBYmjZtmpYtW6ZgMKhRo0apurpaJSUleuuttzRlypRYVw8AAKDLwposuvbaa7V3715dfvnlbb/cSktL0/XXX68f/OAH3VpBAACAgzEWAQAAsXTssceqvr5eS5cubfuleGJioqZOnarPfOYzsa0cAABAGMKaLAoEAvrFL36hH/3oR/roo4+Unp6uww8/3HlbPwAAQHdgLAIAAGIpEAjolFNO0QknnKC9e/eqpqZGubm55qM2AQAAejP7QdQOu3btUnl5ucaNG6fU1NRe+9xXAADQPzEWAQAAsVRbW6v6+nrl5OQoMTGRsQgAAOizwpos2rt3r0477TSNHz9eZ555pnbu3ClJuvjii3lPAAAAiDrGIgAAIJbq6+u1ZMkS/f73v9fjjz/e9uL6V199VStXroxx7QAAALourMmiK6+8UsnJydq2bZsyMjLaPj/vvPP0/PPPd1vlAAAAOsJYBAAAxNKyZcuUkJCg733ve0pOTm77fOzYsdqxY0cMawYAABCesN5Z9OKLL+qFF17Q8OHD231++OGHa+vWrd1SMfQdrtvsA4FA2PmR5AIA+i/GIr1bMBhUS0tLh7GGhgbfvKQke2hqnff3/6Lbj7VcSb717Yz9LzbvSG1tbUTLtd59YS3XFXeNofbt22fGD7wwerC0tDQz17WtLK53gVjrHAqFwl5uU1OTGXe1p5XvKttqL1eui7XPufbHlJQU31h6erqZ6+q71neAhAT7945WrlVnSWpubg477mov6xjk6puu/mW1tys3Ly/PN1ZcXGzm5ubmmvEtW7b4xvLz881c69joOm66+pd1HHH1kQOP6Zs3b9ZXv/rVtkfPZWZmKjs7W0OGDFFtba2ys7MlSY2NjWaZiA7P88K6XhDJNYZoXp9wXRuJ5LjpYu0zkRyTo3m9x5VrHXddxxDXMcg6N9fX15u5lZWVvrGysjIz1/Ud6MMPP/SN9dYJ7l27dpnxDRs2+MaKiorM3MGDB5tx69jt6gNW/4r0nG/lu/apcMuVIquXSzSPX+i6sFq8tra23a949ysvL+fF0gAAIOoYiwAAgFhqbm7ucJKwoaHBObEdT2655RYFAgEtWLCg7bOGhgbNnTtXBQUFysrK0uzZs1VaWtoW37JlS9vFw4ULF+rCCy/s4VoDABCfwpos+uxnP6s//vGPbf8OBAIKhUL65S9/qc9//vPdVjkAAICOMBYBAACxNHToUH388cdt/w4EAvI8T2+//fYhdz7Hq1WrVumee+7R1KlT231+5ZVX6u9//7see+wxLV++XCUlJTr33HNjVEsAALBfWI+h++Uvf6nTTjtNb7/9tpqamnTdddfpww8/VHl5uV5//fXuriMAAEA7jEUAAEAsnXDCCfrrX/+qsrIyBYNBvfrqq9q7d68aGhp03nnnxbp6MVdTU6M5c+bo3nvv1U033dT2eWVlpe677z49/PDDOvXUUyVJDzzwgCZOnKiVK1fq+OOPj1WVAQCIe2HdWTR58mR98sknOumkk3T22WertrZW5557rt555x2NGzeuu+sIAADQDmMRAAAQSwUFBfrmN7+poUOHauzYsWpubtZhhx2mOXPmmO+Cihdz587VWWedpdNPP73d56tXr1Zzc3O7zydMmKCRI0dqxYoVPV1NAN0gQ9JnJS2Q9EdJz0paKulpSXdJ+o6koyTxgE6g9wvrziKp9WWWP/zhD7uzLgAAAJ3GWAQAAMRSamqqjj76aElScnJyjGvTeyxZskRr1qzRqlWrDont2rVLKSkph0yoFRUVtb3MfvTo0W0vU1+4cGG0qwsgTJMlXS7pPyRlSaqX9J6kYknNktIknSjpErVOFBVL+r2keyXtjEF9Abh1erLo/fff73ShBz+PNh7dcsst+sEPfqD58+frtttuk9T6Eserr75aS5YsUWNjo2bNmqU777xTRUVFklpf4jhmzBh5nqeFCxdqy5YtevDBB2O3EgAA9CKMRQAAQCzt2bPHN5aU1P7yysCBA6NdnV5p+/btmj9/vpYuXaq0tLSoL6+qqkp79+6VJCUkJKimpibqywTi3RC13jF0tqQSSb+W9ISkdZKCHfx9uqTpkr4p6VpJN0r63b/+t74H6gug8zo9WfSZz3ym7YWNgUCg7fP9v/Y48LNgsKNDQ/ywXuL4zDPP6LHHHlNubq7mzZunc889l3crAADQCYxFAABALC1ZsqTTY5EFCxb0dPV6hdWrV6usrEzTp09v+ywYDOqVV17RHXfcoRdeeEFNTU2qqKhod3dRaWmpBg8e3OXlXXfddbrnnnu6o+oAOuGbkn4rqUHSNyQ9JqnFkVMv6fV//Xe9Wu9G+omkL0u6UBIPoAR6RnFxsaqqqpSQ0Ppmory8PGVkZLT7m05PFm3evLnt/7/zzju65pprdO2112rmzJmSpBUrVug3v/mNfvnLX3ZH3fus/voSxwMHvV21f+AM9FuVldJrr0lvvy2tXi3t2CE1NkopKdLAgdKMGa3/nXSSFMYXIACtGIv0HQMHDvT9NfGmTZt881x3hFl3l5WVlZm5mZmZZry2tjYqufsH4n4GDRpkxhsbG31jlZWVYdcrJycn7FxXPDc318yN5JfmrnW2Jopdk8gNDQ2+sVAoZObW19u/i7WWbS1Xkvkr+fr6eo3xPJ0cCukzoZA+43ka5HlKkdQYCGh7IKB3AwG9k5CglxIStOegMb21LVzb6eC7KA7U1NRk5rr2Cyvuaq/09HTfmFVnSWpubjbjVj/Yf2eDH2u/cPUf1/epffv2+cYKCgrM3N27d4ddr7Vr15rxSLaj1f927Nhh5kbCVa9LL7207f+XlZXp5Zdf1jHHHKOhQ4cqKytL27dv1+uvv64zzjhD+fn5nSqzvznttNMO6RsXXXSRJkyYoOuvv14jRoxQcnKyli1bptmzZ0uS1q9fr23btrWN6bpi0aJFbY+qC4VCqq6u1oQJE+R5XlSuRUTz+oa1z7iOm9bxzZXrkpjo/6YZ13HVyo3kOpOLaztZcVeuazxhnUsiOeeXl5ebucXFxWZ827ZtZrwzfiHpOkl/kjRfkv/Zx1+VpFskPSnpfknL1ToB9ecwytr/6MqOuMaM1hhbsrdzNI8DrjGntWxXrrXPuXIjqZervSI5RrmOI9E8zkSy3FhdZ580aVK7f1922WW666672n3W6cmiUaNGtf3/r33ta/rtb3+rM888s+2zqVOnasSIEfrRj36kc845J8wq930HvsTxwMki10sce/tkEYAOvPuudOed0kMPSXV1Un5+66TQccdJqalSc3PrxNGf/iTdcouUmCidc450+eXS5z8vxeikBfRVjEUAoFWi52lWc7MuamrS6Z6nkKRPAgG9EwjotYQEtUhKlTTO8zQ7GNSVwaCaJD2RkKB7k5L0RiDAOAQIw4GTfn/729902mmnaezYsZJaJ+EHDx6s3NxcLVu27JALMvEiOztbkydPbvdZZmamCgoK2j6/+OKLddVVVyk/P185OTm64oorNHPmzLCui2RkZLT7VXRVVVVkKwCgQ7+SdI2k76v1EXKRWi/pZLVOGD0iKSTp8W4oF4C/devWKTMzs23CqaMfFXV6suhAa9eu1ZgxYw75fMyYMVq3bl04RfYLvMQRiBNlZdK8edJjj0nDhkk33CDNmSONGeN/4WXXLukvf2mdXDrtNOnEE6X775fGj+/ZugP9BGMRAPFqckuL7qyr05RQSCsDAX07KUl/S0hQTQdjkP2/Whzkefp6MKhLgkF9valJzyYkaF5ycli/CAbQas+ePR3eMZaXl+e82zXe3XrrrUpISNDs2bPbvc8ZQO/0HbVOFF0h6Y5uLDeo1sfQJUp6SNKnkt7txvIBtDds2DDnEybCus9r4sSJuvnmm9s9XqCpqUk333yzJk6cGE6Rfd7+lzg+9NBDPfISx5qaGm3durXtv+3bt0d9mQAkPfGEdOSR0j//Kf3xj9KWLdKPfiSNHWv/QnfwYGnuXOmDD6QXXmidPJo2TbrtNolHNQJdxlgEQLxJ8DxdX1+vf9bUKCDp9KwsnZKSoocTEzucKDpQWSCg3yYlaWpKir6enKyjQiGtbmzUvzseFwfAX35+vlauXNnuUUUtLS165ZVXNHDgwBjWrPd5+eWXddttt7X9Oy0tTYsXL1Z5eblqa2v1xBNPhPW+IgDRN1LSIkn3qnsnivbzJF0kaZ2kByUlR2EZADovrDuL7r77bn3lK1/R8OHD254t//777ysQCOjvf/97t1awr+jplzhec801vMQR6Gm/+Y10zTXSuedKd90lOd4z0aFAQDrjDOn996Uf/lC68krpww+lu+9ufUwdgE7p6likMy9yBIDeKtnzdHddnc5ubtavU1P1m7Q0NQcCrY+87QIvENBTiYl6JSFBv2lu1j11dRoRCuk3PfBjN6C/OeOMM/TEE0/o7rvvVmFhoZKSklRaWipJ+uY3vxnj2gFA97hXre8mujqKy2hW6x1GqyT9UNLCKC4LgC2syaJjjz1WmzZt0kMPPaSPP/5YknTeeefpG9/4hvMFwP1Vb3mJI4Aoue221omi//xP6aabIn/Of0aGdOut0lFHSRdd1FrePffw/gCgk7o6FunMixwBoDdK+NdE0VnNzbogI0PPpKREXOa+QEDfTk7WxkBANzY0KCjpNiaMgC4ZMmSILrnkEn300Ufau3evUlNTNWXKFE2dOlUp3bCfAkCszZR0hqSvSqqO8rLeU+sdTFdJ+k0PLA9Ax8KaLJJaX1B46aWXdmdd+jRe4gj0Y0uXtt4BdP310n/9V/eW/a1vtT6G7sILWx9vN39+95YP9GNdGYt05kWOANAbLWhs1NndOFHUJhDQb9LSlOx5+klDgz5ITNQ/knn4C9AVKSkpmjZtmiQ53wEAAH3N5ZI2SHq6h5b3O7XewfRNSfysD4iNsCeLOrJz5041Nzdr5MiR3Vlsv9GXX+LoxeidKrFaLtCmqkq6+GLptNOkm2+OzjIuuEB6+23pBz+QzjpLOuyw6CwHiAN+Y5HOvMgR3Wf37t2+v6q2Hr/7xhtvmOVmZWX5xlzvjExKsoe91mMJ9z9WyM+Bjxg+WEtLi5m7bds2M25NbBYWFpq5oVDIN9bY2Gjmun4Vb8Xr6urMXGt8F4jwDlur7CbH+3msZR/4TpKuLleSmo3HxVnbYmJLi65vaNBvEhP1l1BIamjodG5nLZQ0PSFBt9XWanpqqqr+1Q6ufcrq265cV3z/I0M74toWicZjfcvLy83c1NTUsOOu/XHXrl2+MWtfldz7RbIxyVdWVmbmWsdk1zq5DB061De2adMmMzc7O9s35nofkKuPWO3t6gNW3929e7ckqba2VqFQqG0dumM/Rdd5nhfW9YRIrkG49lVX3Dr2Wfu5KzfS6ypW2dYx15Ubqd56vciql2tcaB0vXOOr7v4ReYGkr0n6gVrfK9QTiiU9Jel76txkkTW+cvWPSPbXSPf1SLjGDBarXq5yI41brG3l2o6uY0wk2zHccqMtnGV3Jadbj9qnnnqqxowZ051F9mm8xBHoB66/Xtq3T/rDH6L7iLhbbpGGDJG+/e3WO40AhIWxCID+IuB5ur26Wp8GAvovx2RnZAsKaG5ysrIl/bfjIhYAtyeeeEIPPvhgrKsBABE5SVKqpD/38HIflTRFkv2zAADR0q3fOv74xz86Z7oBoM/Ytat1kui//1saPTq6y8rMlO64QzrzTOm116TPfja6ywP6KcYiAPqLzzU36zMtLfpCSoqaovzrxe3/mpC6qaVFNyUlaRfvUATCdsYZZ5i/dgeAvmCGpF1qvdunJ60+YPnP9/CyAXTzZNExxxzTncUBQGz94Q9ScrJ0ySU9s7xZs6Tx46U772SyCAgTYxEA/cVF9fX6MDFRr/XQxM3/JCbqxy0tuigY1M3RvJMJ6OeKiopiXQUAiNh0/d/ETU/aJGmfmCwCYiV6Dw8FgL7M86Tf/16aM0cy3kXRrRISpMsuk/7yF2nPnp5ZJgAA6HWKgkGd0dSk+9PTo/sY3ANUBgJakpioi1taeCQuAABxboikrTFa9jZJvLQDiI2wfjI2YMCATr8YyfUSUQDolTZtkrZvl849t2eXe+650lVXSStWSF/5Ss8uG+hDOjsW6a0vvgUAyzEtLUqU9HxqqtSD7xF6OiFBFweDGuV5quyxpQJ9069+9at2/2bMAaA/SZHUFKNlN/5r+QB6XliTRT/60Y900003adasWZo5c6YkacWKFXrhhRf0ox/9SPn5+d1aSQDocav/dcP1jBk9u9yRI6XCwtblM1kE+OrsWKS+vl7f+973YllVAOiyac3NKklI0O6Enn0QxDv/Wt50z9NLPbpkoO/57Gc/q1dffVXjxo3T8OHDVV9fr127dmnr1q069thjlZaWJklqbm7Wyy+/HNvKAkAXNUlKjdGyU9U6YQSg54U1WfT666/rZz/7mebNm9f22fe//33dcccd+sc//qG//vWv3VU/AIiNNWuk4cOlQYN6drmBQOsE1Zo1PbtcoI/p7FikqqqKySIAfc7Ulha9H4P3BpUGAiqR9JlQiMkiwGH79u065ZRT2t6XWFnZej/ee++9p+3bt+vLX/6yJKmxsZHJIgB9Tomk0TFYbuBfy300BssGEOZk0QsvvKBf/OIXh3z+xS9+UTfccEPElULv09nHDnaE2/HRJ+3ZIw0bFptlDxsmrVsXm2UDfQRjkd4tKSlJycnJHcYmTZrkm/fJJ5+Y5TY1+T8MY/9FOj+5ublmfMeOHb6x1FT7d5V7jPfMhUIhMzfP8V68YDDoG6upqTFzrWVnZWWZua51ttrbqrMkpaT4P1jE9Qhr15i0oaHBN5bkmHyx6u0az7ri1rboKHeA5+m9xMSIx9Gu9kpMTDzks10JCSoMBJx912rPBMcdUS2OR+u5tpWlq23dFVa9i4uLzVzrGGQd2yS7X0tSZmamb8za3yT7OOI6Tvgd5/erq6sLu17WdnT1r4EDB4Zdr9raWjP3wG21ceNGnXzyyW2f7T9uHn744XrjjTecx1FEl+d5UbkWEcm1EVfftcp25XZ0PO8uVr0iaY9IRbLsSNratdxotUk45/RIrJYUi5/cjZOU+6/lu1jjaNc5bP/dn36sc5yrra0+FM19JpLxqmvcF0ncVS/XPheJSMqO1baK5XFVksJqsYKCAj311FOHfP7UU0+poKAg4koBQMw1N0uOL8BRk5wsOS4aAPGOsQiA/izJ89Rzbypqr1lh/qIQiDPp6enasGHDIZ9/+umnSk9Pj0GNAKD7rJY0SNLIHl7u0QcsH0DPC+t7wE9/+lN95zvf0csvv6zjjjtOkvTmm2/q+eef17333tutFQSAmEhNlRy/4oyahobW5QPwxVgEQH/WGAgoLUZ356eJ9wQAnXHSSSfpueee07Zt2zR06FB5nqedO3dq8+bNmjVrVqyrBwAReU1Sg6SvS/plDy7365LelbS3B5cJ4P+ENVl04YUXauLEifrtb3+rJ554QpI0ceJEvfbaa20XbACgTxsxQnr8ccnzWt8j1JM+/VQa2dO/3wH6FsYiAPqzHQkJGut4nF80BDxPY0MhPRbFxxkB/cWUKVNUUFCg1atX65NPPpHneSooKND555+voUOHxrp6ABCRfZKWSLpM0q8l2Q8h6x4jJX1ZsXn8HYBWYT9h4LjjjtNDDz3UnXUBgN5jxgxp3z5pyxZpzJieW24wKL3zjnTOOT23TKCPYiwCoL96LylJV9XXK9DDdxcd7nnKlvQuk0VApwwdOrRtYsj1PgcA6GvulHShpH+T9JceWN58STWSHu6BZQHoWNhvedq4caNuvPFGfeMb31BZWZkk6bnnntOHH37YbZUDgJiZMaP1f996q2eX+9FHUl3d/y0fgC/GIgD6q3eTkpTleTqih+8uOvpfy3s3ii8aBvqTffv26ZVXXtHf/vY31dbWSpI2bdqkPXv2xLhmABC5VZKelvQ7SQOivKxj1DpZ9AtJtVFeFgB/YX0LWL58uaZMmaI333xTf/nLX1RTUyNJeu+99/STn/ykWysIADFRVCQddZT0v//bs8v905+kvDyJx2gBJsYiAPqzt5OTVREI6GuNPfv2oG+0tGhFQoIqevoRvEAftG3bNt1///0qKSnRJ598oubmZknS7t279frrr8e4dgDQPb4rKV3S7VFcRqqkByS9o559PxKAQ4U1WXTDDTfopptu0tKlS5WSktL2+amnnqqVK1d2W+UAIKYuv1x65pnWR9H1hIYG6f77pQsvlDIyemaZQB/FWARAf1YfCOiR1FTNaWhQag89im58KKRTQiHdm5zcI8sD+rrly5frs5/9rL7+9a8r8YBHN44cOVIlJSUxrBkAdJ8SSVdI+g9J10ah/ERJD0kap9ZH3vX8GxsBHCisdxatXbtWDz986BMkBw0axO3WMZabm+sb8yL4ohlJbiCCXyZGslwgYuefL11zjfTrX0t33BH95T3wgLRnj3TZZdFfFtDHMRbp3VpaWpTg8xir1157zTcvPz/fLDcrK8s31ui4A6OpqcmM+9W3M9LS0nxjdXV1YZcryXxJemlpqZlrrZPr3Rr19fVmPGg8Hi3ZMdmQnp7uGztw8rery3WV7Vpnqw+51snFGtO2tLR0+PlD2dn6XkODLk1I0L2pqR3+TSTjbEnKOODHKddWVWlPIKAXs7OVEQiY/VqSMjMzfWOJjnce+a1zZ8puaGgwc/c/CqwjVv/oDKv/udpr/x2wHSkoKAi7TpLdJpFsC9d2iuQ44jrGWNtq5MiRZu4nn3xixq390ep7klRdXd32/8vKynTCCSeorKxMoVBIeXl5ys/PVyAQUENDQ9s5zdVnER2e5/lua+vYGelx1eLaH62+Gck4JVKxupYUSa6rvay4K9e1HZOS/C+1usY51rnEGgdL7nG0VbbrOPW/kg5X610/iZJuMf+681LUOlF0tlrfi9TVh4mPHj3aNzZw4EAzNycnx4xb7WVtY8nuQ65jTDSPQdY+5drfXOf8aO3rvfW6cKT1srZzNK7Bd6VfhXW2ycvL086dOw/5/J133tGwYcPCKRIAep/MTOnHP5buvFN69dXoLmvbNun666Vvf1s64ojoLgvoBxiLAOjvNiUl6U8ZGfpxfb1GRPndRZ9vatL5jY26OTNTTTyCDuiUlJSUDn8QUFJS4rwICQB9zU/+9d/Nkv4iaVCE5U2T9KakL0s6V63vRgIQe2FNFn3961/X9ddfr127dikQCCgUCun111/XNddco29961vdXUcAiJ3586UTTmidxDF+qRqRUEi65BIpN1datCg6ywD6GcYiAOLBz7OztS8Q0B319UqI0i8rc0MhLaqu1svJyfqT4+4YAP9nzJgxWr16terq6trGIlu2bNGzzz6ro446KtbVA4Bu9zNJsyWdJGmdpO9I6urIoVDSzyWtUutF6RMk/b0b6wggMmFNFv33f/+3JkyYoBEjRqimpkaTJk3SySefrBNOOEE33nhjd9cRAGInMbH1PUK7dknnnit194umPU/6/velpUulP/yhdcIIgBNjEQDxoCYhQXMzMnRCS4tura9XoJsnjDI8Tw9VVirD83RVdrbEXUVAp82YMUO5ubn685//rObmZt122236/e9/r5EjR+rUU0+NdfUAICqekHSkpBcl3Stph6RfSTpekt8DRAdIOkPSn/7199eo9Q6loyW9E+X6AuiasN5ZlJKSonvvvVc//vGPtXbtWtXU1Oioo47S4Ycf3t31A4DYGz9e+tvfpC99SfrKV6QnnpAczwvulGBQmjdPuvtu6d57pVmzIi8TiBOMRQDEi1eTkzUvI0N31tUpUdKV6elq7oZJnQGepz9XVGhSMKj/l5urHY53MABoLzExUSeeeKKmTZumffv2aeDAgRo6dKgKCwtjXTUAiKo9kr4h6UeSLpN0kVongIJqveOoWFKzWu86Gidp7L/yPpX0Q0kPSCrv2SoD6KSwJov2GzFihEaMGNFddQGA3uvzn5eee0766leladNa7zb63OfCL++jj6SLLpJWrZLuu6/1MXcAuoyxCIB48GhKioKS7qyr09SWFl2ekaEPHC9XtnwpGNQdTU1KDQQ0OzdX7yQnd19lgTiTlZWlrKwsjR8/PtZVAYAetVHStZL+U9JkSTMkTZc0UFKKpApJT0p6W9JqtU4WReehugC6S1iPofPz1FNP6Y9//GN3FgkAvcfnPy+tWSMNHSqdcor0ve9Jn37atTJKS6WFC6WjjpL27ZNee42JIqAbMRYB0F89npKi07OylCDpnzU1+ll9vYaHQl0qY2oopAeamvSXpia9l5CgUwYMYKII6Gbr1q3TmjVrYl0NAOgxzWp9nNwfJF0u6WuSzpb072q942iJpA1iogjoCyK6s+hg119/vTZs2MCLpeNMwPEYDC9KL+MFYuLww6Xly6Xf/U762c9aHyF3xhnSnDnSsce2PrIu4YB5eM+Ttm6VVq+WHn9c+stfWt+D9P3vSz/9qZTu91RfAOFgLNI7JCYmKsnnroeioiLfvB07dpjlZmRk+MaSHRe8a2trzfjQoUN9YyUlJWautU7FxcVmbkNDgxkvLS31jQWDQTN35MiRYS93z549Zry6uto3lpKSYuZWVFT4xlzjSpeQMXlSV1dn5iYaj2FLSLB/Y+faFmlp/q9/bmlpMXNTU1Pb/v82Sf/mebq8okLfrqrS5Z6nf6Sl6bm0NL2fkqJPk5IUOqAN0wMBTWhq0rSmJp1TV6ejm5q0MzFRV+Xn6/GMDCUmJSnTZ7npjjGK1SZ++/9+VltL9nY8sD06YvU/q1xJam5uNuOZmX6t5e4jeXl5vrGmpiYz17UtrP7l6ptWe7nautHxLk+rbw8bNizssl3H5IKCAjNuHWdcx0bL/vV97rnntHfvXk2dOrXd5+g9rD4QyXko0nNYJNdOIl22xaqX67hq5brWN5JrTa5jshV3naNc5zjruOo6nufk5PjGXI+4HDVqlBnft2+fb+zdd981c2N1Xe/II4804xMmTPCNuc4zuY73RVvbyjXWjWRM6er30dzXY7Vcq+xI26u36q3He6mbJ4s+/vjj7iwOAHqnhARp/nzp0kulP/9Zuusu6YILWmNZWdKYMVJqqtTcLO3YIe3d2xo74gjpl79s/dsBA2JXf6AfYywCoL9rDgR0+4ABujc3V1+uqtKc2lrd9q8JuLpAQGUJCWoOBJTqeRoSDCpZrb/4XZmaqksKCvSP9HQF++gXa6AvmD9/fqyrAAAAEJZunSwCgLiSnt468XPBBa2PlFuzpvUOou3bpcZGKSVFGjRImjGj9b/Bg2NdYwAA0E/UJSToocxMPZSZqexQSJObmzW5qUkFoZCSJTVJ2pmcrLXJyfo4JUWNTBABAAAAMIQ1WfT8888rKytLJ510kiRp8eLFuvfeezVp0iQtXrxYA/jFPIB4M2CAdNpprf8BiDrGIgDwf6oTErQiNVUrDnpsmOvRHQDCt2PHDiUnJ7c9ivTNN9/U22+/rYEDB+orX/mK83FTAAAAvU1Y3x6uvfZaVVVVSZLWrl2rq6++WmeeeaY2b96sq666qlsrCAAAcDDGIgAAIJZWrVrV9r6p8vJyPf/88xo/frwqKir03HPPxbh2AAAAXRfWnUWbN2/WpEmTJEl/+ctf9OUvf1n//d//rTVr1ujMM8/s1goCAAAcjLEIAACIpZqaGuXl5UmStm7dqvHjx+sLX/iCSkpK9Kc//Sm2lQMAAAhDWHcWpaSkqK6uTpL0j3/8Q2eccYYkKT8/v+1XvgAAANHCWAQAAMRSQkKCgsGgJKmkpESHHXaYJCk9PV2NjY2xrBoAAEBYwrqz6KSTTtJVV12lE088UW+99ZYeffRRSdInn3yi4cOHd2sFAQAADsZYBAAAxFJRUZHeeustDRo0SHv27NERRxwhSdq7d69ycnJiXDsAAICuC2uy6I477tDll1+uxx9/XHfddZeGDRsmSXruuef0xS9+sVsriK6prKzsVwPTQCBgxj3P66GaAAB6E8YivVt+fr7S0tI6jPl9LkmDBg0yy83OzvaN7b/TzE9Cgn1DfWlpqW/MNbaycpOS7OF2amqqGW9oaPCNZWVlmbkbNmzwjVltKbU+Xsmy/9FLHSkvLzdzU1JSfGOusV19fb0Zd21ni7Ut9r+XxE9mZqYZt+4yyM3NNXOtvm3tT51h5bvqZbVJenq6mRvJXReJiYlm3Fq2tT9J7vYMhUK+MVfftPqXa51cd8xa/a+5udnMtfqXa3/af1eNH2tb7Nq1y8y1lu3qm7W1tWGXbR3bJGnKlClt/3/cuHFasmSJysrKdP755yszM1PBYFDr16/XuHHj2trH1U6IjkAg4LyeEG650RJJ2VZupNdVIrnuYh03I2Wtl2u51nHAdexzje2scY5rvGAd3wYPHmzmusYqFtdYt6SkxDfmGjO6xgT7v8d1ZP8dm+HEhwwZYua6jvcZGRm+seTkZDPX6iOuc76r/0Uy1rX2C9dxwrVc6zgRSdmu5brKjuYxO1bCOSZ3JSesyaKRI0fq6aefPuTzW2+9NZziAAAAuoSxCAAAiKX8/Hxdfvnlbf8uKyuTJH60AgAA+qywJouk1l/H/PWvf9VHH30kSTryyCP11a9+1TlDCgAA0B0YiwAAgFgKhUJ67733tGvXLtXU1GjQoEE64ogjIvrlNwAAQKyENVn06aef6swzz1RxcXHbc3lvvvlmjRgxQs8884zGjRvXrZUEAAA4EGMRAAAQS2VlZbrzzjtVUVGhoqIiNTc367XXXlNOTo7mzJmj/Pz8WFcRAACgS8KaLPr+97+vcePGaeXKlW0DoL179+qb3/ymvv/97+uZZ57p1koCAAAciLEIAACIpccee0yFhYW69tprlZmZqbKyMtXV1emJJ57Qc889pzlz5sS6igAAAF0S1mTR8uXL212ckaSCggLdcsstOvHEE7utcgAAAB1hLAIAAGJpw4YNbRNF+2VkZOj000/XfffdF8OaAQAAhCesB+mmpqaqurr6kM9ramqUkpIScaUAAAAsjEUAAEAsJSUlqaGh4ZDPm5qaeH8iAADok8K6s+jLX/6yLr30Ut1333069thjJUlvvvmmLrvsMn31q1/t1gqi9/M8r0+WDQDouxiL9G7V1dVqamrqMFZTU+ObV1tba5ZbWVnpGwuFQmZuY2OjGU9PT/eNuV5U3tHFwv2ys7PNXNcFxWAw6BtraWkxcwOBgG/M1R5Dhgwx4xUVFb6xjIwMM9ditaUUWXu56mWVbZXbGdYktmtbWO898dvP9ktOTjbjVr+3+o9kr1NqaqqZ69pfXfkWq01c/Scpyf56bO1zubm5Zq7V/zr68cOBXNvRWmdXrrUdIz2uWt/lXPujtU7WuaAzrH7gOg9t37697f+PHTtW//M//6MzzzxTQ4cOVUNDg3bt2qWlS5dq7NixbfV0tRN6nnV8cx37XHFLJNc3IqlXpOsUresyrmNMJCJZZ9e4z3WusM5hrmNfXl6eb8zVXq56H3gX5MEGDRpk5u7bt8835jrGuc7pAwYM8I0VFRWZuYMHD/aNDRw40MzNyckx49YYyfXjRKuPuMYirngkx6BIyo3kOODqm1Y80vW16h1P17O7Up+wJot++9vf6oILLtDMmTPbBp/Nzc06++yzdfvtt4dTJAAAQKcxFgEAALH0hS98QU8//bT++Mc/tl3cCwaDGjdunE455ZTYVg4AACAMYU0W5eXl6amnntKnn36qdevWSZImTZqkww47rFsrBwAA0BHGIgAAIJbS0tL0//7f/1N5ebn27t2rpqYmFRQUmHcGAAAA9GZhTRZJ0n333adbb71VGzZskCQdfvjhWrBggb7zne90W+UAAAD8MBYBAACx9N577+mtt95qe0RTXl6ejjrqKE2ZMiXGNQMAAOi6sCaLfvzjH2vRokW64oorNHPmTEnSihUrdOWVV2rbtm362c9+1q2VBAAAOBBjEQAAEEuvvPKK3nrrLR199NEaNmyYmpqatHPnTi1fvlzV1dU64YQTYl1FAACALglrsuiuu+7Svffeq/PPP7/ts69+9auaOnWqrrjiCi7QAACAqGIsAgAAYmnNmjX60pe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" ] diff --git a/_sources/content/use_case_notebooks/hr_diagram_exercise.ipynb b/_sources/content/use_case_notebooks/hr_diagram_exercise.ipynb index 32bcb5f..26be62d 100644 --- a/_sources/content/use_case_notebooks/hr_diagram_exercise.ipynb +++ b/_sources/content/use_case_notebooks/hr_diagram_exercise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b9c617f2", + "id": "bbe872e5", "metadata": {}, "source": [ "# Creating a stellar color-magnitude (or Hertzsprung-Russell) diagram\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e8f01685", + "id": "9d6cc8c1", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "97591b63", + "id": "1ef3cac9", "metadata": {}, "source": [ "## Step 1: Find appropriate catalogs\n", @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "80257f67", + "id": "62a1be9d", "metadata": {}, "source": [ "### DATA DISCOVERY STEPS: \n", @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f151e6d8", + "id": "f7e31352", "metadata": { "tags": [ "output_scroll" @@ -83,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "c3a536b8", + "id": "11709b12", "metadata": {}, "source": [ "Note: The includeaux=True includes auxiliary services. \n", @@ -93,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "a52e5bd8", + "id": "42136002", "metadata": {}, "source": [ "#### Next, we need to find which of these has the columns of interest, i.e. magnitudes in two bands to create the color-magnitude diagram. \n", @@ -104,7 +104,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "d12cae44", + "id": "f6b791ef", "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "16ece9fb", + "id": "bde99934", "metadata": {}, "source": [ "Note: the '%' serves as a wild card when searching by UCD\n", @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "2607bbfd", + "id": "87e29793", "metadata": {}, "source": [ "So using this we can reduce the matched tables to ones that are a bit more catered to our experiment. Note, that there is redundancy in some resources since these are available via multiple services and/or publishers. Therefore a bit more cleaning can be done to provide only the unique matches." @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a626086c", + "id": "e119e933", "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "07718e66", + "id": "fecbd534", "metadata": {}, "outputs": [], "source": [ @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "16d00726", + "id": "09c60b9c", "metadata": {}, "source": [ "We can read more information about the results we found. For each resource element (i.e. row in the table above), there are useful attributes, which are [described here]( https://pyvo.readthedocs.io/en/latest/api/pyvo.registry.regtap.RegistryResource.html#pyvo.registry.regtap.RegistryResource)" @@ -165,7 +165,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "3ac41058", + "id": "34525afd", "metadata": { "tags": [ "output_scroll" @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "83dc6586", + "id": "03ba8f6c", "metadata": {}, "source": [ " RESULT: Based on these, the second one (by Eichhorn et al) looks like a good start. \n", @@ -192,7 +192,7 @@ }, { "cell_type": "markdown", - "id": "42f025c6", + "id": "9b0fec23", "metadata": { "tags": [ "output_scroll" @@ -219,7 +219,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "2a8abae3", + "id": "dc83533c", "metadata": {}, "outputs": [], "source": [ @@ -228,7 +228,7 @@ }, { "cell_type": "markdown", - "id": "6cf6a6a2", + "id": "03b8a35f", "metadata": {}, "source": [ "First, Try using bibcode:" @@ -237,7 +237,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "94800cfa", + "id": "62112a2d", "metadata": {}, "outputs": [], "source": [ @@ -255,7 +255,7 @@ }, { "cell_type": "markdown", - "id": "275d1f7a", + "id": "509ac44e", "metadata": {}, "source": [ "Note that the URL is a generic TAP url for Vizier. All of its tables can be accessed by that same TAP services. It'll be in the ADQL query itself that you specify the table name. We'll see this below." @@ -263,7 +263,7 @@ }, { "cell_type": "markdown", - "id": "95c6c79e", + "id": "de80f626", "metadata": {}, "source": [ "Next, try using Author name:" @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "ad0668fe", + "id": "2433b787", "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "38f02560", + "id": "aed4b363", "metadata": {}, "source": [ "These examples provide a few ways to access the information of interest. \n", @@ -303,7 +303,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "43721bcd", + "id": "a7819a2d", "metadata": { "tags": [ "output_scroll" @@ -316,7 +316,7 @@ }, { "cell_type": "markdown", - "id": "9199f3d3", + "id": "6a0c615f", "metadata": {}, "source": [ "## Step 2: Acquire the relevant data and make a plot!\n", @@ -326,7 +326,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "3830b49f", + "id": "eacb62c4", "metadata": {}, "outputs": [], "source": [ @@ -336,7 +336,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "c00a9e8a", + "id": "b435d0cb", "metadata": {}, "outputs": [], "source": [ @@ -346,7 +346,7 @@ }, { "cell_type": "markdown", - "id": "f48ce963", + "id": "f15b963d", "metadata": {}, "source": [ "We can access the column data as array using the .getcolumn(colname) attribute, where the colname is given in the table above. In particular the \"CI\" is the color index and \"Ptm\" is the photovisual magnitude. See [here](https://vizier.u-strasbg.fr/viz-bin/VizieR?-source=I/90) for details about the columns." @@ -355,7 +355,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "4a03c041", + "id": "f0d1ec62", "metadata": {}, "outputs": [], "source": [ @@ -365,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "1aabd47c", + "id": "7f79f1ae", "metadata": {}, "source": [ "### Plotting... \n", @@ -375,7 +375,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "1f62e27e", + "id": "e9661cb5", "metadata": {}, "outputs": [ { @@ -409,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "b96a82c4", + "id": "de639fad", "metadata": {}, "source": [ "## Step 3. Compare with other color-magnitude diagrams for Pleiades:\n", @@ -424,7 +424,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "68476d80", + "id": "d3dbadf7", "metadata": {}, "outputs": [], "source": [ @@ -437,7 +437,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "7b543d86", + "id": "19249bed", "metadata": {}, "outputs": [], "source": [ @@ -448,7 +448,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "d3e9b286", + "id": "56322774", "metadata": {}, "outputs": [ { @@ -485,7 +485,7 @@ }, { "cell_type": "markdown", - "id": "6e6397db", + "id": "abe87382", "metadata": {}, "source": [ "## BONUS: Step 4: The CMD as a distance indicator! \n", @@ -496,7 +496,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "a54abe34", + "id": "f34ac7a0", "metadata": {}, "outputs": [], "source": [ @@ -514,7 +514,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "36b45cb7", + "id": "86880d5b", "metadata": {}, "outputs": [], "source": [ @@ -528,7 +528,7 @@ }, { "cell_type": "markdown", - "id": "1a847b00", + "id": "3eb0be32", "metadata": {}, "source": [ "True distance to Pleaides is 136.2 pc (https://en.wikipedia.org/wiki/Pleiades ). Not bad!" diff --git a/_sources/content/use_case_notebooks/hr_diagram_solution.ipynb b/_sources/content/use_case_notebooks/hr_diagram_solution.ipynb index 1add81e..4273375 100644 --- a/_sources/content/use_case_notebooks/hr_diagram_solution.ipynb +++ b/_sources/content/use_case_notebooks/hr_diagram_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "af043746", + "id": "4bea9350", "metadata": {}, "source": [ "# Creating a stellar color-magnitude (or Hertzsprung-Russell) diagram\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "e5e1878d", + "id": "b5f728ca", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "3d501138", + "id": "fbf93081", "metadata": {}, "source": [ "## Step 1: Find appropriate catalogs\n", @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "1c1c75bb", + "id": "f9d1d511", "metadata": {}, "source": [ "### DATA DISCOVERY STEPS: \n", @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "11e4567c", + "id": "a3fcfa30", "metadata": { "tags": [ "output_scroll" @@ -82,7 +82,7 @@ "data": { "text/html": [ "
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ivoidshort_nameres_titlesource_value
objectobjectobjectobject
ivo://mast.stsci/siap/al218VLA.AL218VLA-A Array AL218 Texas Survey Source Snapshots (AL218)
\n", + "
\n", "\n", "\n", "\n", @@ -149,7 +149,7 @@ }, { "cell_type": "markdown", - "id": "da35f8e1", + "id": "f5be5c07", "metadata": {}, "source": [ "Note: The includeaux=True includes auxiliary services. \n", @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "27af47cf", + "id": "63d9a468", "metadata": {}, "source": [ "#### Next, we need to find which of these has the columns of interest, i.e. magnitudes in two bands to create the color-magnitude diagram. \n", @@ -170,7 +170,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "e6121613", + "id": "21488e74", "metadata": { "tags": [ "output_scroll" @@ -193,7 +193,7 @@ }, { "cell_type": "markdown", - "id": "47807bc3", + "id": "b98e5616", "metadata": {}, "source": [ "Note: the '%' serves as a wild card when searching by UCD\n", @@ -203,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "7f1c5cf7", + "id": "5c40d44e", "metadata": { "tags": [ "output_scroll" @@ -216,7 +216,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "b07773e3", + "id": "f073d259", "metadata": { "tags": [ "output_scroll" @@ -296,7 +296,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "cad81ef6", + "id": "861f1e6c", "metadata": { "tags": [ "output_scroll" @@ -321,7 +321,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "10107030", + "id": "3242713a", "metadata": { "tags": [ "output_scroll" @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "073ad8ab", + "id": "3dff4905", "metadata": { "tags": [ "output_scroll" @@ -423,7 +423,7 @@ }, { "cell_type": "markdown", - "id": "6df9f501", + "id": "cf4a3c18", "metadata": { "tags": [ "output_scroll" @@ -435,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "f6929115", + "id": "2a3400ed", "metadata": {}, "source": [ "We can read more information about the results we found. For each resource element (i.e. row in the table above), there are useful attributes, which are [described here]( https://pyvo.readthedocs.io/en/latest/api/pyvo.registry.regtap.RegistryResource.html#pyvo.registry.regtap.RegistryResource)" @@ -444,7 +444,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "358e2e71", + "id": "5274512c", "metadata": { "tags": [ "output_scroll" @@ -835,7 +835,7 @@ }, { "cell_type": "markdown", - "id": "e3a004a7", + "id": "a56bfba5", "metadata": { "tags": [ "output_scroll" @@ -853,7 +853,7 @@ }, { "cell_type": "markdown", - "id": "a16dce0c", + "id": "30dc1b24", "metadata": { "tags": [ "output_scroll" @@ -880,7 +880,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "d9e2d20a", + "id": "988bf34c", "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ }, { "cell_type": "markdown", - "id": "c81c8b56", + "id": "d2e2fb12", "metadata": {}, "source": [ "First, Try using bibcode:" @@ -909,7 +909,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "836c3136", + "id": "9d14c697", "metadata": {}, "outputs": [ { @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "435de4fb", + "id": "af536487", "metadata": {}, "source": [ "Note that the URL is a generic TAP url for Vizier. All of its tables can be accessed by that same TAP services. It'll be in the ADQL query itself that you specify the table name. We'll see this below." @@ -946,7 +946,7 @@ }, { "cell_type": "markdown", - "id": "ee76c078", + "id": "0b10ace8", "metadata": {}, "source": [ "Next, try using Author name:" @@ -955,7 +955,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "6893b56a", + "id": "43cb765c", "metadata": { "tags": [ "output_scroll" @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "a69d15c1", + "id": "d12f7b0f", "metadata": {}, "source": [ "In the code above, the record is a Registry Resource. You can access the attribute, \"creators\", from the resource, which is relevant for our example here since this is a direct way to get the author names. The other attributes, \"access_url\" and \"reference_url\", provides two types of URLs. The former can be used to access the service resource (as described above) and the latter points to a human-readable document describing this resource. \n", @@ -1000,7 +1000,7 @@ }, { "cell_type": "markdown", - "id": "9f93934b", + "id": "b49721b5", "metadata": {}, "source": [ "These examples provide a few ways to access the information of interest. \n", @@ -1015,7 +1015,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "1fdfb04e", + "id": "8f03a070", "metadata": { "tags": [ "output_scroll" @@ -1037,7 +1037,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "74a70a20", + "id": "803a3bca", "metadata": { "tags": [ "output_scroll" @@ -1079,7 +1079,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "373a2afb", + "id": "48f0a175", "metadata": { "tags": [ "output_scroll" @@ -3785,7 +3785,7 @@ }, { "cell_type": "markdown", - "id": "33c84089", + "id": "673ba1c4", "metadata": { "tags": [ "output_scroll" @@ -3799,7 +3799,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "456665b4", + "id": "a965146b", "metadata": { "tags": [ "output_scroll" @@ -3832,7 +3832,7 @@ }, { "cell_type": "markdown", - "id": "a7ed1c42", + "id": "1066b440", "metadata": { "tags": [ "output_scroll" @@ -3847,7 +3847,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "734d09f5", + "id": "741218ed", "metadata": { "tags": [ "output_scroll" @@ -3873,7 +3873,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "7d6f48de", + "id": "d6526bed", "metadata": { "tags": [ "output_scroll" @@ -3891,7 +3891,7 @@ "data": { "text/html": [ "
Table length=502\n", - "
indexshort_nametitledescriptioninterfaces
int64str16str55str4800str7
0I/163US Naval Observatory Pleiades CatalogThis catalog is a special subset of the Eichhorn et al. (1970) Pleiades catalog (see <I/90>) updated to B1950.0 positions and with proper motions added. It was prepared for the purpose of predicting occultations of Pleiades stars by the Moon, but is useful for general applications because it contains many faint stars not present in the current series of large astrometric catalogs.tap#aux
\n", + "
\n", "\n", "\n", "\n", @@ -3959,7 +3959,7 @@ }, { "cell_type": "markdown", - "id": "7de389a7", + "id": "c0a94005", "metadata": {}, "source": [ "We can access the column data as array using the .getcolumn(colname) attribute, where the colname is given in the table above. In particular the \"CI\" is the color index and \"Ptm\" is the photovisual magnitude. See [here](https://vizier.u-strasbg.fr/viz-bin/VizieR?-source=I/90) for details about the columns." @@ -3968,7 +3968,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "b82c9e89", + "id": "e052aa7b", "metadata": {}, "outputs": [], "source": [ @@ -3978,7 +3978,7 @@ }, { "cell_type": "markdown", - "id": "8d8ed80d", + "id": "a22dc5e1", "metadata": {}, "source": [ "### Plotting... \n", @@ -3988,13 +3988,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "3966a065", + "id": "3bbb1b61", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 19, @@ -4022,7 +4022,7 @@ }, { "cell_type": "markdown", - "id": "8ebdf714", + "id": "bc956de4", "metadata": {}, "source": [ "## Step 3. Compare with other color-magnitude diagrams for Pleiades:\n", @@ -4037,7 +4037,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "c5c35dcc", + "id": "44dd1444", "metadata": {}, "outputs": [ { @@ -4076,7 +4076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_2326/2801309271.py:11: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/tmp/ipykernel_2360/2801309271.py:11: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " ind = int(match[0])\n" ] } @@ -4100,7 +4100,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "1d8de5d6", + "id": "c0f27746", "metadata": { "tags": [ "output_scroll" @@ -4118,7 +4118,7 @@ "data": { "text/html": [ "
Table length=270\n", - "
recnoHertzsprungCIPtmRAB1900e_RAsDEB1900e_DEsrmsRArmsDErpmRArpmDEDrpmRADrpmDEDRADDE
magmagdegmsdegmasmas / yrmas / yrmas / yrmas / yrarcsecarcsec
int32int16float64float64float64float64float64int16float64float64float64float64float64float64float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -4204,13 +4204,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "122deea8", + "id": "feb8bdd1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 22, @@ -4241,7 +4241,7 @@ }, { "cell_type": "markdown", - "id": "ec4042e9", + "id": "ab5b8bf3", "metadata": {}, "source": [ "## BONUS: Step 4: The CMD as a distance indicator! \n", @@ -4252,13 +4252,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "c5f541e3", + "id": "e0104e03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 23, @@ -4294,7 +4294,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "246bb852", + "id": "1f315d13", "metadata": {}, "outputs": [ { @@ -4317,7 +4317,7 @@ }, { "cell_type": "markdown", - "id": "5322790e", + "id": "f5fd9f30", "metadata": {}, "source": [ "True distance to Pleaides is 136.2 pc ( https://en.wikipedia.org/wiki/Pleiades ). Not bad!" diff --git a/_sources/content/use_case_notebooks/proposal_prep_exercise.ipynb b/_sources/content/use_case_notebooks/proposal_prep_exercise.ipynb index 4e303e3..9086e96 100644 --- a/_sources/content/use_case_notebooks/proposal_prep_exercise.ipynb +++ b/_sources/content/use_case_notebooks/proposal_prep_exercise.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "2572ad6f", + "id": "6c65c4ca", "metadata": {}, "source": [ "# Preparing a proposal\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "83ad04d8", + "id": "cdbcb594", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "66c9128b", + "id": "c198a173", "metadata": {}, "source": [ "## Step 1: Find out what the previously quoted Chandra 2-10 keV flux of the central source is for NGC 1365. \n", @@ -51,7 +51,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "8e467375", + "id": "04160ad8", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "17da7935", + "id": "873c5f26", "metadata": {}, "source": [ "Hint: The Chansngcat ( https://heasarc.gsfc.nasa.gov/W3Browse/chandra/chansngcat.html ) table is likely the best table. Create a table with ra, dec, exposure time, and flux (and flux errors) from the public.chansngcat catalog for Chandra observations matched within 0.1 degree." @@ -78,7 +78,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "777c5fcd", + "id": "739cac4b", "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7eb1c506", + "id": "69e3d7b0", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "6a2d87ee", + "id": "d8d8b1f1", "metadata": {}, "source": [ "## Step 2: Make Images \n", @@ -118,7 +118,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "996d444e", + "id": "ea78c997", "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "879a78d8", + "id": "76350271", "metadata": {}, "source": [ "The keyword search for 'galex' returned a bunch of things that may have mentioned it, but let's just use the ones that have GALEX as their short name:" @@ -136,7 +136,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "65711be2", + "id": "5715a568", "metadata": {}, "outputs": [], "source": [ @@ -145,7 +145,7 @@ }, { "cell_type": "markdown", - "id": "da9bf215", + "id": "93a5edf0", "metadata": {}, "source": [ "Though using the result as an Astropy Table makes it easier to look at the contents, to call the service itself, we cannot use the row of that table. You have to use the entry in the service result list itself. So use the table to browse, but select the list of services itself using the properties that have been defined as attributes such as short_name and ivoid:" @@ -154,7 +154,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e0f37c10", + "id": "6d61b40d", "metadata": {}, "outputs": [], "source": [ @@ -163,7 +163,7 @@ }, { "cell_type": "markdown", - "id": "a2c18974", + "id": "eb36d63a", "metadata": {}, "source": [ "Hint: Next create a UV image for the source" @@ -172,7 +172,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "932d3e1e", + "id": "52999f8f", "metadata": {}, "outputs": [], "source": [ @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "11ebb795", + "id": "73e2bad3", "metadata": {}, "outputs": [], "source": [ @@ -197,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "86212cbd", + "id": "c2d67222", "metadata": {}, "source": [ "Hint: Repeat steps for X-ray image. (Note: Ideally, we would find an image in the Chandra 'cxc' catalog)" @@ -206,14 +206,14 @@ { "cell_type": "code", "execution_count": null, - "id": "061c636b", + "id": "b7781c30", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "879a165b", + "id": "00be96b3", "metadata": {}, "source": [ "## Step 3: Make a spectrum \n", @@ -225,7 +225,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "0d7de461", + "id": "26419122", "metadata": {}, "outputs": [], "source": [ @@ -234,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "e4959849", + "id": "ff05396c", "metadata": {}, "source": [ "Hint 2: Take a look at what data exist for our candidate, NGC 1365." @@ -243,14 +243,14 @@ { "cell_type": "code", "execution_count": null, - "id": "0d0001d8", + "id": "596be514", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "e1bdbb64", + "id": "475caaae", "metadata": {}, "source": [ "Hint 3: Download the data to make a spectrum. Note: you might end here and use Xspec to plot and model the spectrum. Or ... you can also try to take a quick look at the spectrum." @@ -259,7 +259,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "da271eec", + "id": "c3acb078", "metadata": {}, "outputs": [], "source": [ @@ -269,7 +269,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "73b4c393", + "id": "e838c2b0", "metadata": {}, "outputs": [], "source": [ @@ -278,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "be530bf5", + "id": "9f5f88b9", "metadata": {}, "source": [ "Extension: Making a \"quick look\" spectrum. For our purposes, the 1st order of the HEG grating data would be sufficient." @@ -287,7 +287,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "38e0f31d", + "id": "5134e528", "metadata": {}, "outputs": [], "source": [ @@ -296,7 +296,7 @@ }, { "cell_type": "markdown", - "id": "d7a8bcce", + "id": "41a26862", "metadata": {}, "source": [ "This can then be analyzed in your favorite spectral analysis tool, e.g., [pyXspec](https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/index.html). (For the winter 2018 AAS workshop, we demonstrated this in a [notebook](https://github.com/NASA-NAVO/aas_workshop_2018/blob/master/heasarc/heasarc_Spectral_Access.md) that you can consult for how to use pyXspec, but the pyXspec documentation will have more information.)" @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "cbee2a54", + "id": "f69bbbfc", "metadata": {}, "source": [ "Congratulations! You have completed this notebook exercise." diff --git a/_sources/content/use_case_notebooks/proposal_prep_solution.ipynb b/_sources/content/use_case_notebooks/proposal_prep_solution.ipynb index ac060e3..b1324f3 100644 --- a/_sources/content/use_case_notebooks/proposal_prep_solution.ipynb +++ b/_sources/content/use_case_notebooks/proposal_prep_solution.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "c9d92cc7", + "id": "f0de3685", "metadata": {}, "source": [ "# Preparing a proposal\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "2f397903", + "id": "dfce12e5", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "41ed6067", + "id": "f35c951e", "metadata": {}, "source": [ "## Step 1: Find out what the previously quoted Chandra 2-10 keV flux of the central source is for NGC 1365. \n", @@ -51,7 +51,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "7ce6f46d", + "id": "36dca3d9", "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "2499679a", + "id": "cbcda96b", "metadata": {}, "source": [ "Hint: The [Chansngcat](https://heasarc.gsfc.nasa.gov/W3Browse/chandra/chansngcat.html) table is likely the best table. Create a table with ra, dec, exposure time, and flux (and flux errors) from the public.chansngcat catalog for Chandra observations matched within 0.1 degree." @@ -72,7 +72,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "a57b3b30", + "id": "ef133f7a", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "55a4c061", + "id": "fadda3c4", "metadata": {}, "outputs": [], "source": [ @@ -107,14 +107,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "7710f52b", + "id": "a18f4933", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=1\n", - "
recnoHIIVmagB-VxposyposDistMultRemMassMassAMassBMassCMassD
magmagarcminarcminarcminMsunMsunMsunMsunMsun
int32int32float64float64float64float64float64int16objectfloat64float64float64float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "7cef7def", + "id": "fa50edf1", "metadata": {}, "source": [ "## Step 2: Make Images: \n", @@ -163,14 +163,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "8565d72d", + "id": "52d81102", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=3\n", - "
radecexposurefluxflux_lowerflux_upper
degdegserg/s/cm^2erg/s/cm^2erg/s/cm^2
float64float64float64float64float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -201,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "93ce0785", + "id": "ae1b6130", "metadata": {}, "source": [ "The keyword search for 'galex' returned a bunch of things that may have mentioned it, but let's just use the ones that have GALEX as their short name:" @@ -210,14 +210,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "6077b515", + "id": "a3966670", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=3\n", - "
ivoidshort_name
objectobject
ivo://archive.stsci.edu/sia/galexGALEX
\n", + "
\n", "\n", "\n", "\n", @@ -248,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "3a6aa1b2", + "id": "2f330e73", "metadata": {}, "source": [ "Though using the result as an Astropy Table makes it easier to look at the contents, to call the service itself, we cannot use the row of that table. You have to use the entry in the service result list itself. So use the table to browse, but select the list of services itself using the properties that have been defined as attributes such as short_name and ivoid:" @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "f44bcbc2", + "id": "ec50d6a3", "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ }, { "cell_type": "markdown", - "id": "9130edd7", + "id": "1da99ce4", "metadata": {}, "source": [ "Hint: Next create a UV image for the source" @@ -276,14 +276,14 @@ { "cell_type": "code", "execution_count": 9, - "id": "de1bf533", + "id": "4e50cb13", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=809\n", - "
ivoidshort_name
objectobject
ivo://archive.stsci.edu/sia/galexGALEX
\n", + "
\n", "\n", "\n", "\n", @@ -297,6 +297,7 @@ "\n", "\n", "\n", + "\n", "\n", "\n", "\n", @@ -305,8 +306,7 @@ "\n", "\n", "\n", - "\n", - "\n", + "\n", "
productTypeimageFormatcontentLengthnamecollectioninsnamemetaReleasedataReleasetrgposRAtrgPosDecs_regionposition_naxesposition_naxisposition_scalecrpixcrvalcdmatrixcoordFrameprojectionposition_ctype1position_ctype2position_cunit1position_cunit2timBoundsSTCStime_bounds_cval1time_bounds_cval2time_bounds_centertimExposureenergy_bandpassNameenergy_bounds_cval1energy_bounds_cval2energy_bounds_centerenergy_unitspublisherDIDaccessURLcloud_access
objectobjectint32objectobjectobjectobjectobjectfloat64float64objectint32objectobjectobjectobjectobjectobjectstr3objectobjectobjectobjectobjectfloat64float64float64float64objectfloat64float64float64objectobjectobjectobject
CATALOGimage/fits12795448FORNAX_MOS07-xd-mcat.fits.gzGALEXGALEX5/11/2010 12:14:57 AM5/11/2010 12:14:57 AM54.0331936068986-36.367881838288CIRCLE ICRS 54.03319361 -36.36788184 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0332 -36.3679][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54105.698090 55148.07208354105.698090277855148.072083333354626.88508680563241.6NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337728220725248https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-xd-mcat.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-xd-mcat.fits.gz"}}
AUXILIARYimage/fits6466083FORNAX_MOS07-nd-cnt.fits.gzGALEXGALEX5/11/2010 12:14:57 AM5/11/2010 12:14:57 AM54.0331936068986-36.367881838288CIRCLE ICRS 54.03319361 -36.36788184 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0332 -36.3679][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54105.698090 55148.07208354105.698090277855148.072083333354626.88508680563241.6NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337728220725248https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-nd-cnt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-nd-cnt.fits.gz"}}
AUXILIARYimage/fits2512896FORNAX_MOS07-nd-skybg.fits.gzGALEXGALEX5/11/2010 12:14:57 AM5/11/2010 12:14:57 AM54.0331936068986-36.367881838288CIRCLE ICRS 54.03319361 -36.36788184 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0332 -36.3679][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54105.698090 55148.07208354105.698090277855148.072083333354626.88508680563241.6NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337728220725248https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-nd-skybg.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-nd-skybg.fits.gz"}}
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INFOimage/fits33054AIS_423_0002_sg49-scst.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-scst.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-scst.fits.gz"}}
INFOimage/fits9178AIS_423_0002_sg49-asprta.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-asprta.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-asprta.fits.gz"}}
AUXILIARYimage/fits2203AIS_423_0002_sg49-fd-flag_tbl.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-fd-flag_tbl.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-fd-flag_tbl.fits.gz"}}
PREVIEWimage/jpeg618132AIS_423_0002_sg49-xd-int_2color_medium_annot.jpgGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_medium_annot.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/qa/AIS_423_0002_sg49-xd-int_2color_medium_annot.jpg"}}
AUXILIARYimage/fits11794AIS_423_0002_sg49-fd-flags.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-fd-flags.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-fd-flags.fits.gz"}}
INFOimage/fits13483AIS_423_0002_sg49-rtastar.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-rtastar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-rtastar.fits.gz"}}
INFOimage/fits8757AIS_423_0002_sg49-aspraw.fits.gzGALEXGALEX6/16/2010 2:52:42 AM6/16/2010 2:52:42 AM53.9452301043047-36.1117650864775CIRCLE ICRS 53.94523010 -36.11176509 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.9452 -36.1118][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54460.401042 54460.40215354460.401041666754460.402152777854460.401597222296.0FUV1.34e-071.806e-071.573e-07metersivo://archive.stsci.edu/GALEX?6385798660088659968https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-aspraw.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/02-vsn/50423-AIS_423/d/00-visits/0002-img/07-try/AIS_423_0002_sg49-aspraw.fits.gz"}}
AUXILIARYimage/fits10482447FORNAX_MOS07_0002-nd-rrhr.fits.gzGALEXGALEX4/28/2010 3:21:44 PM4/28/2010 3:21:44 PM54.0359149055626-36.3362710522186CIRCLE ICRS 54.03591491 -36.33627105 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0359 -36.3363][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54110.286030 54110.29637754110.286030092654110.296377314854110.2912037037894.45NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337590815326208https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0002-img/07-try/FORNAX_MOS07_0002-nd-rrhr.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0002-img/07-try/FORNAX_MOS07_0002-nd-rrhr.fits.gz"}}
AUXILIARYimage/fits207665FORNAX_MOS07_0002-nd-objmask.fits.gzGALEXGALEX4/28/2010 3:21:44 PM4/28/2010 3:21:44 PM54.0359149055626-36.3362710522186CIRCLE ICRS 54.03591491 -36.33627105 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0359 -36.3363][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54110.286030 54110.29637754110.286030092654110.296377314854110.2912037037894.45NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337590815326208https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0002-img/07-try/FORNAX_MOS07_0002-nd-objmask.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0002-img/07-try/FORNAX_MOS07_0002-nd-objmask.fits.gz"}}
AUXILIARYimage/fits187477FORNAX_MOS07_0003-nd-objmask.fits.gzGALEXGALEX4/29/2010 7:42:02 AM4/29/2010 7:42:02 AM54.057710750028-36.3903391229071CIRCLE ICRS 54.05771075 -36.39033912 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0577 -36.3903][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 55147.926562 55147.93417855147.926562555147.934178240755147.9303703704658.35NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337590848880640https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0003-img/07-try/FORNAX_MOS07_0003-nd-objmask.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/00-visits/0003-img/07-try/FORNAX_MOS07_0003-nd-objmask.fits.gz"}}
" ], "text/plain": [ @@ -326,6 +326,7 @@ " AUXILIARY ...\n", " ... ...\n", " INFO ...\n", + " INFO ...\n", " AUXILIARY ...\n", " PREVIEW ...\n", " AUXILIARY ...\n", @@ -333,7 +334,6 @@ " AUXILIARY ...\n", " INFO ...\n", " INFO ...\n", - " AUXILIARY ...\n", " AUXILIARY ..." ] }, @@ -351,18 +351,18 @@ { "cell_type": "code", "execution_count": 10, - "id": "b641e342", + "id": "9739befc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=2\n", - "\n", + "
\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName
objectfloat64float64int32objectobjectobjectobjectobjectobject
galexnear53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexnear&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702590169822&return=FITS1
galexfar53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexfar&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702590170003&return=FITS2
galexnear53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexnear&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702669334356&return=FITS1
galexfar53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexfar&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702669334562&return=FITS2
" ], "text/plain": [ @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "37b5f4aa", + "id": "529952f1", "metadata": {}, "outputs": [ { @@ -414,13 +414,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "68054acd", + "id": "8aa2b3c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -447,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "bf5001de", + "id": "0924ca0c", "metadata": {}, "source": [ "Hint: Repeat steps for X-ray image. (Note: Ideally, we would find an image in the Chandra 'cxc' catalog)" @@ -456,7 +456,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "8f05a463", + "id": "f32c284d", "metadata": {}, "outputs": [ { @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "413f744e", + "id": "507e03cc", "metadata": {}, "outputs": [ { @@ -504,7 +504,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "8a324887", + "id": "bcd716c3", "metadata": {}, "outputs": [ { @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "af3fb140", + "id": "01e9e3c5", "metadata": {}, "source": [ "## Step 3: Make a spectrum: \n", @@ -555,14 +555,14 @@ { "cell_type": "code", "execution_count": 16, - "id": "3e952152", + "id": "62d32ee4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=6\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -599,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "2e44cfb0", + "id": "1738c553", "metadata": {}, "source": [ "Hint 2: Take a look at what data exist for our candidate, NGC 1365." @@ -608,14 +608,14 @@ { "cell_type": "code", "execution_count": 17, - "id": "efb831b1", + "id": "2567487f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=15\n", - "
short_nameivoidwaveband
objectobjectobject
Chandraivo://nasa.heasarc/chanmasterx-ray
\n", + "
\n", "\n", "\n", "\n", @@ -671,7 +671,7 @@ }, { "cell_type": "markdown", - "id": "421de7e5", + "id": "32a7b642", "metadata": {}, "source": [ "Hint 3: Download the data to make a spectrum. Note: you might end here and use Xspec to plot and model the spectrum. Or ... you can also try to take a quick look at the spectrum." @@ -680,7 +680,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "9698aaa5", + "id": "75e2698c", "metadata": {}, "outputs": [ { @@ -720,7 +720,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "b3d87118", + "id": "ac7632aa", "metadata": {}, "outputs": [], "source": [ @@ -737,7 +737,7 @@ }, { "cell_type": "markdown", - "id": "e1681765", + "id": "dca0bafe", "metadata": {}, "source": [ "Extension: Making a \"quick look\" spectrum. For our purposes, the 1st order of the HEG grating data would be sufficient." @@ -746,7 +746,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "a8d9b966", + "id": "2730c44d", "metadata": {}, "outputs": [ { @@ -777,7 +777,7 @@ }, { "cell_type": "markdown", - "id": "0c74bd41", + "id": "e2dc7a5a", "metadata": {}, "source": [ "This can then be analyzed in your favorite spectral analysis tool, e.g., [pyXspec](https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/index.html). (For the winter 2018 AAS workshop, we demonstrated this in a [notebook](https://github.com/NASA-NAVO/aas_workshop_2018/blob/master/heasarc/heasarc_Spectral_Access.md) that you can consult for how to use pyXspec, but the pyXspec documentation will have more information.)" @@ -785,7 +785,7 @@ }, { "cell_type": "markdown", - "id": "e4181100", + "id": "5f4ad3db", "metadata": {}, "source": [ "Congratulations! You have completed this notebook exercise." diff --git a/content/reference_notebooks/basic_reference.html b/content/reference_notebooks/basic_reference.html index 3301e46..e48a2a1 100644 --- a/content/reference_notebooks/basic_reference.html +++ b/content/reference_notebooks/basic_reference.html @@ -682,7 +682,7 @@
Using astropy
Table length=3 -
obsidstatusnameradectimedetectorgratingexposuretypepipublic_datedatalinkSSA_start_timeSSA_tmidSSA_stop_timeSSA_durationSSA_coord_obsSSA_raSSA_decSSA_fovSSA_titleSSA_referenceSSA_datalengthSSA_datamodelSSA_instrumentSSA_publisherSSA_formatSSA_wavelength_minSSA_wavelength_maxSSA_bandwidthSSA_bandpasscloud_access
degdegdsddddsdegdegdegdegmmmm
objectobjectobjectfloat64float64float64objectobjectfloat64objectobjectint32objectfloat64float64float64float64float64float64float64float64objectobjectobjectobjectobjectobjectobjectfloat64float64float64float64object
+
@@ -710,7 +710,7 @@

2.2 Cone search
Table length=316 -

short_nameres_titleres_description
objectobjectobject
MAST CSMAST ConeSearchAll MAST catalog holdings are available via a ConeSearch endpoint. \nThis service provides access to all, with an optional non-standard parameter for an individual catalog to query. \nThe available missions are listed at http://archive.stsci.edu/vo/mast_services.html, \nand include Hubble (HST) data, Kepler, K2, IUE, HUT, EUVE, FUSE, UIT, WUPPE, BEFS, TUES, IMAPS, High Level Science Products (HLSP), Copernicus, HPOL, VLA First, XMM-OM, and SWIFT.
+
@@ -751,7 +751,7 @@

Find an image service
Table length=3 -

ObjIDZoneSeqNoRADECpmRApmDECe_pmRAe_pmDECe_RAe_DECEpochB1MagR1s_gB2MagB2s_gR2MagR2s_gNMagmagB1s_gR1Magdistance
degdegmas / yrmas / yrmas / yrmas / yrarcsecarcsecyrmagmagmagmagmagmagarcsec
int64int32int32float64float64float32float32float32float32float32float32float32float32int32float32int32float32int32float32float32int32float32float32
+
@@ -777,7 +777,7 @@

Search one of the services
Table length=2 -

ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
+
@@ -802,7 +802,7 @@

Download an image
image/fits
-
'/tmp/astropy-download-2035-mvp2n0ur'
+
'/tmp/astropy-download-1995-79gj3juv'
@@ -838,7 +838,7 @@

2.4 Spectral search
Title: acisf00639N004_pha2.fits, Format: application/fits

-
'/tmp/astropy-download-2035-cg9frdq8'
+
'/tmp/astropy-download-1995-kk3bpcpi'
@@ -1971,7 +1971,7 @@

Perform a Query
Table length=13 -

filenameidra_j2000dec_j2000urlfilesizemjdmeannaxesnaxisscalecdformatref_frameequinoxcoord_projectioncrpixcrvalctypebandpass_idbandpass_refvaluebandpass_unitbandpass_hilimitbandpass_lolimitprocessingprojectpreviewrepresentativeobject_id
degdegbytedpixdeg / pixdeg / pixyrpixpixmmmm
objectobjectfloat64float64objectint32float64int32objectobjectobjectobjectobjectfloat32str3objectobjectobjectobjectfloat64objectfloat64float64objectobjectobjectobjectobject
+
@@ -2030,7 +2030,7 @@

3.1 NED
Table length=41 -

radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
+
diff --git a/content/reference_notebooks/catalog_queries.html b/content/reference_notebooks/catalog_queries.html index fa50912..94382e9 100644 --- a/content/reference_notebooks/catalog_queries.html +++ b/content/reference_notebooks/catalog_queries.html @@ -553,7 +553,7 @@

1. Simple cone search
Table length=6 -

No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32str30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
+
@@ -579,7 +579,7 @@

1. Simple cone search
Table length=2 -

ivoidshort_nameres_title
objectobjectobject
ivo://cds.vizier/j/mnras/339/652J/MNRAS/339/652The FLASH Redshift Survey
+
@@ -607,7 +607,7 @@

2.1 TAP services
Table length=20 -

__rownameradecbmagradial_velocityradial_velocity_errorredshiftclassSearch_Offset
degdegkm / skm / s
objectobjectfloat64float64float32int32int16float64int16float64
+
@@ -742,12 +742,11 @@

2.1 TAP services
xdeep2
-    DEEP2 Galaxy Redshift Survey Fields Chandra Point Source Catalog
-
-Columns=['"__row"', '"__x_ra_dec"', '"__y_ra_dec"', '"__z_ra_dec"', 'bayesian_galaxy_prob', 'bii', 'bmag', 'csc_name', 'dec', 'error_radius', 'fb_counts_50pc_eef', 'fb_counts_50pc_eef_error', 'fb_counts_50pc_eef_limit', 'fb_counts_90pc_eef', 'fb_counts_90pc_eef_error', 'fb_counts_90pc_eef_limit', 'fb_flux', 'fb_flux_error', 'fb_flux_limit', 'field_number', 'field_subfield_id', 'flux_ratio', 'flux_ratio_lower', 'flux_ratio_upper', 'hardness_ratio', 'hardness_ratio_lower', 'hardness_ratio_upper', 'hb_counts_50pc_eef', 'hb_counts_50pc_eef_error', 'hb_counts_50pc_eef_limit', 'hb_counts_90pc_eef', 'hb_counts_90pc_eef_error', 'hb_counts_90pc_eef_limit', 'hb_flux', 'hb_flux_error', 'hb_flux_limit', 'imag', 'lii', 'name', 'off_axis', 'off_set', 'opt_dec', 'opt_ra', 'opt_source_number', 'ra', 'radius_50pc_eef', 'radius_90pc_eef', 'redshift', 'rmag', 'sb_counts_50pc_eef', 'sb_counts_50pc_eef_error', 'sb_counts_50pc_eef_limit', 'sb_counts_90pc_eef', 'sb_counts_90pc_eef_error', 'sb_counts_90pc_eef_limit', 'sb_flux', 'sb_flux_error', 'sb_flux_limit']
+
Columns=['"__row"', '"__x_ra_dec"', '"__y_ra_dec"', '"__z_ra_dec"', 'bayesian_galaxy_prob', 'bii', 'bmag', 'csc_name', 'dec', 'error_radius', 'fb_counts_50pc_eef', 'fb_counts_50pc_eef_error', 'fb_counts_50pc_eef_limit', 'fb_counts_90pc_eef', 'fb_counts_90pc_eef_error', 'fb_counts_90pc_eef_limit', 'fb_flux', 'fb_flux_error', 'fb_flux_limit', 'field_number', 'field_subfield_id', 'flux_ratio', 'flux_ratio_lower', 'flux_ratio_upper', 'hardness_ratio', 'hardness_ratio_lower', 'hardness_ratio_upper', 'hb_counts_50pc_eef', 'hb_counts_50pc_eef_error', 'hb_counts_50pc_eef_limit', 'hb_counts_90pc_eef', 'hb_counts_90pc_eef_error', 'hb_counts_90pc_eef_limit', 'hb_flux', 'hb_flux_error', 'hb_flux_limit', 'imag', 'lii', 'name', 'off_axis', 'off_set', 'opt_dec', 'opt_ra', 'opt_source_number', 'ra', 'radius_50pc_eef', 'radius_90pc_eef', 'redshift', 'rmag', 'sb_counts_50pc_eef', 'sb_counts_50pc_eef_error', 'sb_counts_50pc_eef_limit', 'sb_counts_90pc_eef', 'sb_counts_90pc_eef_error', 'sb_counts_90pc_eef_limit', 'sb_flux', 'sb_flux_error', 'sb_flux_limit']
 ----
 xmmcfrscat
     XMM-Newton/Canada-France Redshift Survey Fields X-Ray Sources
@@ -839,7 +838,7 @@ 
2.3 A use case
 
Table length=3
-

ivoidshort_nameres_title
objectobjectobject
ivo://cds.vizier/j/a+a/408/905J/A+A/408/905Very Luminous Galaxies
+
@@ -870,7 +869,7 @@

2.3 A use case
Table length=1120 -

radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
+
@@ -931,7 +930,7 @@

2.4 TAP examples for a given service
Table length=2 -

radecradial_velocityradial_velocity_errorbmagmorph_type
degdegkm / skm / s
float64float64int32int16float32int16
+
@@ -980,7 +979,7 @@

3.1 Cross-correlating to combine catalogs
Table length=14 -

__rowseq_idradecliibiiinstrumentfiltersiteexposurerequested_exposurefits_typestart_timeend_timenamepi_lnamepi_fnamerorindex_idsubj_catproc_revtitleqa_numberaoproposal_numberrollrday_beginrday_endclass__x_ra_dec__y_ra_dec__z_ra_dec
degdegdegdegssdddegdd
objectobjectfloat64float64float64float64objectobjectobjectint32int32objectfloat64float64objectobjectobjectint32objectint16int16objectint32int16int32int16int32int32int16float64float64float64
+
@@ -1025,7 +1024,7 @@

3.2 Cross-correlating with user-defined columns
Table length=14 -

radecradial_velocitybmagmorph_type
degdeg
float64float64int32float32int16
+
@@ -1070,7 +1069,7 @@

3.2 Cross-correlating with user-defined columns
Table length=9 -

radecradial_velocitybmagmorph_typeredshiftangDdeg
degdegdeg
float64float64int32float32int16float64float64
+
diff --git a/content/reference_notebooks/image_access.html b/content/reference_notebooks/image_access.html index d2f5b2c..bb3638d 100644 --- a/content/reference_notebooks/image_access.html +++ b/content/reference_notebooks/image_access.html @@ -531,7 +531,7 @@

1. Finding SIA resources from the Registry
Table length=18 -

radecra2dec2radial_velocitymorph_typebmag
degdegdegdeg
float64float64float64float64int32int16float32
+
@@ -564,7 +564,7 @@

1. Finding SIA resources from the Registry
Table length=1 -

ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
+
@@ -587,15 +587,15 @@

2. Using SIA to retrieve an image
Table length=6 -

ivoidshort_nameres_title
objectobjectobject
ivo://nasa.heasarc/skyview/swiftuvotSWIFTUVOTSwift UVOT Combined V Intensity Images
+
- - - - - - + + + + + +
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName
objectfloat64float64int32objectobjectobjectobjectobjectobject
swiftuvotvint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039557&nofits=1&quicklook=jpeg&return=jpeg1
swiftuvotbint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotbint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039862&nofits=1&quicklook=jpeg&return=jpeg2
swiftuvotuint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590041036&nofits=1&quicklook=jpeg&return=jpeg3
swiftuvotuvw1int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw1int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590041554&nofits=1&quicklook=jpeg&return=jpeg4
swiftuvotuvw2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590042124&nofits=1&quicklook=jpeg&return=jpeg5
swiftuvotuvm2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvm2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590042572&nofits=1&quicklook=jpeg&return=jpeg6
swiftuvotvint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169031&nofits=1&quicklook=jpeg&return=jpeg1
swiftuvotbint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotbint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169488&nofits=1&quicklook=jpeg&return=jpeg2
swiftuvotuint202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669170786&nofits=1&quicklook=jpeg&return=jpeg3
swiftuvotuvw1int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw1int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669171610&nofits=1&quicklook=jpeg&return=jpeg4
swiftuvotuvw2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvw2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669172139&nofits=1&quicklook=jpeg&return=jpeg5
swiftuvotuvm2int202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/jpegFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotuvm2int&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669172598&nofits=1&quicklook=jpeg&return=jpeg6

Extract the fields you’re interested in, e.g., the URLs of the images made by skyview. Note that specifying as we did SwiftUVOT, we get a number of different images, e.g., UVOT U, V, B, W1, W2, etc. For each survey, there are two URLs, first the FITS IMAGE and second the JPEG.

@@ -608,7 +608,7 @@

2. Using SIA to retrieve an image -
https://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590039557&nofits=1&quicklook=jpeg&return=jpeg
+
https://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=swiftuvotvint&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669169031&nofits=1&quicklook=jpeg&return=jpeg
@@ -627,7 +627,7 @@

JPG images -

+
@@ -646,7 +646,7 @@

Fits files -
Filename: /home/runner/.astropy/cache/download/url/de81ae34669ea5e7a0bce916fe722d36/contents
+
Filename: /home/runner/.astropy/cache/download/url/2f973d066f0f87a49cad508074d3f4c6/contents
 No.    Name      Ver    Type      Cards   Dimensions   Format
   0  PRIMARY       1 PrimaryHDU     111   (300, 300)   float32   
 

@@ -662,7 +662,7 @@ 
Using imshow
-
<matplotlib.image.AxesImage at 0x7fbaed372920>
+
<matplotlib.image.AxesImage at 0x7f001422d900>
 

 

 ../../_images/ca874e9ffcbe41ffffb888a6d4d0d8171b8cc41046c416d23d09f5aa6e1c01f0.png
@@ -683,7 +683,7 @@ 
4. Example of data available through multiple services
 

 
Table length=2
-
+

@@ -704,14 +704,14 @@ 
Using HEASARC
 
Table length=5
-

 
ivoidshort_name

 
objectobject

 
ivo://nasa.heasarc/skyview/sdssdr7SDSSDR7

+

-
-
-
-
-
+
+
+
+
+

 
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName

 
objectfloat64float64int32objectobjectobjectobjectobjectobject
sdssdr7g202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7g&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590049681&return=FITS1
sdssdr7i202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7i&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590050327&return=FITS2
sdssdr7u202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7u&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590050781&return=FITS3
sdssdr7r202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7r&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590051292&return=FITS4
sdssdr7z202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7z&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702590051848&return=FITS5
sdssdr7g202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7g&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669181003&return=FITS1
sdssdr7i202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7i&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669181557&return=FITS2
sdssdr7u202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7u&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669182092&return=FITS3
sdssdr7r202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7r&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669182627&return=FITS4
sdssdr7z202.46957547.19525832[300 300][-0.0006666666666666668 0.0006666666666666668]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=202.469575%2C47.1952583&survey=sdssdr7z&pixels=300%2C300&sampler=LI&size=0.20000000000000004%2C0.20000000000000004&projection=Tan&coordinates=J2000.0&requestID=skv1702669183120&return=FITS5

 

 

 

@@ -731,7 +731,7 @@ 
Using HEASARC
-
<matplotlib.image.AxesImage at 0x7fbaed371960>
+
<matplotlib.image.AxesImage at 0x7f001422cca0>
 

 

 ../../_images/2cd87b8bacf966fd9b560b68dd3acc1b2ac23dde0f5c942cb81a6fd71d5a3a6d.png
@@ -754,7 +754,7 @@ 
Using SDSS SkyServer
 

 
Table length=30
-
+

@@ -809,14 +809,14 @@ 
Using SDSS SkyServerUsing SDSS SkyServer
 
 

-
<matplotlib.image.AxesImage at 0x7fbaec068520>
+
<matplotlib.image.AxesImage at 0x7f00140f72b0>
 

 

 ../../_images/de20ccf85cbcbdf5166f18f9f8abc0309753b5ab1b3cd0ef21e6df0d68882fcf.png
diff --git a/content/reference_notebooks/spectral_access.html b/content/reference_notebooks/spectral_access.html
index dfcfd09..2d158e3 100644
--- a/content/reference_notebooks/spectral_access.html
+++ b/content/reference_notebooks/spectral_access.html
@@ -498,7 +498,7 @@ 
Finding available Spectral Access Services
 
Table length=6
-

 
idxTitlewidthheightsizeRADECscaleformaturlequinoxnaxesnaxiscrtypecrpixcrvalcdval

 
0Sloan Digital Sky Survey - Filter u204814893049472.0202.43971584011547.1222952774076[0.000110035211267606]image/fitshttp://das.sdss.org/imaging/3699/41/corr/6/fpC-003699-u6-0100.fit.gz--2[2048 1489]RA--TAN,DEC--TAN[744.5 1024.5][202.439715840115 47.1222952774076][3.57829352076884e-05 0.000157654334590212 0.000107291403325821\n -2.42761847386219e-05]

 
1Sloan Digital Sky Survey - Filter g204814893049472.0202.43876345123347.1224982411069[0.000110035211267606]image/fitshttp://das.sdss.org/imaging/3699/41/corr/6/fpC-003699-g6-0100.fit.gz--2[2048 1489]RA--TAN,DEC--TAN[744.5 1024.5][202.438763451233 47.1224982411069][3.57455198349534e-05 0.000157658871914506 0.000107248674325512\n -2.4298297148212e-05]

+

@@ -538,7 +538,7 @@ 
Chandra Spectrum of Delta Ori
 
Table length=6
-

 
ivoidshort_name

 
objectobject

 
ivo://nasa.heasarc/chanmasterChandra

+

@@ -570,14 +570,14 @@ 
Chandra Spectrum of Delta OriSimple example of plotting a spectrum
 
Table length=12
-

 
idxobsidstatusnameradectimedetectorgratingexposuretypepipublic_datedatalinkSSA_start_timeSSA_tmidSSA_stop_timeSSA_durationSSA_coord_obsSSA_raSSA_decSSA_fovSSA_titleSSA_referenceSSA_datalengthSSA_datamodelSSA_instrumentSSA_publisherSSA_formatSSA_wavelength_minSSA_wavelength_maxSSA_bandwidthSSA_bandpasscloud_access

 
degdegdsddddsdegdegdegdegmmmm

 
0639archivedDELTA ORI83.00125-0.2991751556.1364ACIS-SHETG49680GOCassinelli5203711157:chandra.obs.misc51556.136400463----49680.0--83.00125-0.299170.81acisf00639N004_pha2.fitshttps://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N004_pha2.fits.gz12Spectrum-1.0ACIS-SHEASARCapplication/fits1.2398e-106.1992e-096.07522e-093.16159e-09{"aws":{"bucket_name":"nasa-heasarc","region":"us-east-1","policy":"open","key":"chandra/data/byobsid/9/639/primary/acisf00639N004_pha2.fits.gz"}}

+

diff --git a/content/reference_notebooks/ucds_unified_content_descriptors.html b/content/reference_notebooks/ucds_unified_content_descriptors.html
index ddf958e..ba57f22 100644
--- a/content/reference_notebooks/ucds_unified_content_descriptors.html
+++ b/content/reference_notebooks/ucds_unified_content_descriptors.html
@@ -495,15 +495,13 @@ 
UCDs:  working with heterogeneous tables
 
32 tables:
-

-

-
tap_schema.schemas             - description of schemas in this dataset
-----
-tap_schema.tables              - description of tables in this dataset
+tap_schema.schemas             - description of schemas in this dataset
 ----
 

 

-
tap_schema.columns             - description of columns in this dataset
+
tap_schema.tables              - description of tables in this dataset
+----
+tap_schema.columns             - description of columns in this dataset
 ----
 tap_schema.keys                - description of foreign keys in this dataset
 ----
@@ -513,9 +511,7 @@ 
UCDs:  working with heterogeneous tables
dbo.DetailedCatalog            - None
 ----
-

-

-
dbo.SumMagAper2Cat             - None
+dbo.SumMagAper2Cat             - None
 ----
 

 

@@ -523,49 +519,43 @@ 
UCDs:  working with heterogeneous tables
dbo.Catalog_WFC3_SourceExtractor - None
+dbo.Catalog_WFC3_SourceExtractor - None
 ----
 dbo.Catalog_WFPC2_SourceExtractor - None
 ----
-dbo.Catalog_Image_MetaData     - None
-----
 

 

-
dbo.CloseMatch                 - None
+
dbo.Catalog_Image_MetaData     - None
+----
+dbo.CloseMatch                 - None
 ----
 dbo.ClosestMatch               - None
 ----
+dbo.GroupMembers               - None
+----
 

 

-
dbo.GroupMembers               - None
-----
-dbo.Groups                     - None
+
dbo.Groups                     - None
 ----
 dbo.HCVdetailedView            - None
 ----
-

-

-
dbo.HCVsummaryView             - None
+dbo.HCVsummaryView             - None
+----
+dbo.HLAscience                 - None
 ----
 

 

-
dbo.HLAscience                 - None
-----
-dbo.ImageMembers               - None
+
dbo.ImageMembers               - None
 ----
 dbo.Images                     - None
 ----
-

-

-
dbo.ProductMembers             - None
+dbo.ProductMembers             - None
+----
+dbo.ProperMotionsView          - None
 ----
 

 

-
dbo.ProperMotionsView          - None
-----
-dbo.SourceLinks                - None
+
dbo.SourceLinks                - None
 ----
 dbo.SourcePositionsView        - None
 ----
@@ -792,12 +782,12 @@ 
UCDs:  working with heterogeneous tables
    Found 2097555 results from dbo.ssa
+
    Found 2097627 results from dbo.ssa
 
     Table ivoa.ObsCore has the right columns.  Counting rows matching my time.
 

 

-
    Found 4042090 results from ivoa.ObsCore
+
    Found 4042162 results from ivoa.ObsCore
 
 Looking at service from ivo://jvo/subaru/sxds/v1.0
 

@@ -859,16 +849,16 @@ 
UCDs:  working with heterogeneous tables
-
[20.276399612426758,
- 22.451000213623047,
- 25.335100173950195,
- 22.437999725341797,
- 24.32939910888672,
- 21.936500549316406,
- 25.478700637817383,
- 21.499900817871094,
- 19.24180030822754,
- 24.578399658203125]
+
[nan,
+ 20.231000900268555,
+ 20.659799575805664,
+ 20.813199996948242,
+ 21.020200729370117,
+ 21.049100875854492,
+ 20.983800888061523,
+ 21.05500030517578,
+ 21.337299346923828,
+ 18.671100616455078]
 

 

 

diff --git a/content/use_case_notebooks/candidate_list_solution.html b/content/use_case_notebooks/candidate_list_solution.html
index d8fe57f..33937db 100644
--- a/content/use_case_notebooks/candidate_list_solution.html
+++ b/content/use_case_notebooks/candidate_list_solution.html
@@ -521,7 +521,7 @@ 
2. Search NED for objects in this paper.
 
Table length=62
-

 
SPEC_NUMTG_MTG_PARTTG_SRCIDXYCHANNELCOUNTSSTAT_ERRBACKGROUND_UPBACKGROUND_DOWNBIN_LOBIN_HI

 
int16int16int16int16float32float32int16[8192]int16[8192]float32[8192]int16[8192]int16[8192]float64[8192]float64[8192]

 
1-3114102.8154131.8281 .. 81920 .. 01.8660254 .. 1.86602540 .. 00 .. 07.159166666667378 .. 0.33333333333333337.160000000000712 .. 0.33416666666666667

+

@@ -609,14 +609,14 @@ 
2. Search NED for objects in this paper.3. Filter the NED results.
 
Table length=53
-

 
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations

 
degreesdegreeskm / sarcmin

 
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506387100

+

@@ -757,14 +757,14 @@ 
3. Filter the NED results.4. Search the NAVO Registry for image resources.
Table length=263
-

 
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations

 
degreesdegreeskm / sarcmin

 
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506387100

+

@@ -839,7 +839,7 @@ 
5. Search the NAVO Registry for image resources that will allow you to searc
 
 
 
Table length=1
-

 
ivoidshort_nameres_title

 
objectobjectobject

 
ivo://3crsnapshots/sia3CRSnap.sia3CRSnapshots Simple Image Access Service

+

@@ -856,7 +856,7 @@ 
6. Choose the AllWISE image service that you are interested in.
-
<pyvo.dal.sia.SIAService at 0x7f1e56676920>
+
<pyvo.dal.sia.SIAService at 0x7fdf88c4f550>
 

 

 

@@ -922,7 +922,7 @@ 
9. Use the .to_table() method to view the results as an Astropy table.
 

 
Table length=4
-

 
ivoidshort_nameres_title

 
objectobjectobject

 
ivo://irsa.ipac/wise/images/allwise/l3aAllWISE L3aAllWISE Atlas (L3a) Coadd Images

+

@@ -983,7 +983,7 @@ 
11. Visualize this AllWISE image.
-
<matplotlib.collections.PathCollection at 0x7f1e1dca9c90>
+
<matplotlib.collections.PathCollection at 0x7fdf5c24cfa0>
 

 

 ../../_images/88926761088696a84a5443fc969b1499cae72c2355b10eaaed4b1aa481ff7431.png
@@ -1008,7 +1008,7 @@ 
12. Plot a cutout of the AllWISE image, centered on your position.
-
<matplotlib.collections.PathCollection at 0x7f1e15a76a40>
+
<matplotlib.collections.PathCollection at 0x7fdf46f69ea0>
 

 

 ../../_images/7f7768c40ab8e6c435cff1790189f87b0d5c2c11b0c772dea86aa59fab9a4ef3.png
@@ -1031,7 +1031,7 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position.

 

 
Table length=3
-

 
sia_titlesia_urlcloud_accesssia_naxessia_fmtsia_rasia_decsia_naxissia_crpixsia_crvalsia_projsia_scalesia_cdsia_bp_idsia_bp_refsia_bp_hisia_bp_lomagzpmagzpuncunc_urlcov_urlcoadd_id

 
degdegpixdegdeg / pixdeg / pix

 
objectobjectobjectint32objectfloat64float64objectobjectobjectobjectobjectobjectobjectfloat64float64float64float64float64objectobjectobject

+

@@ -1066,7 +1066,7 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position.
 
 

-
AIS_270_0004_sg14-nd-int.fits.gz NUV
+
AIS_270_0001_sg14-nd-int.fits.gz NUV
 

 

 

@@ -1093,9 +1093,9 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position.
 

 
Min: 0.0
-Max: 8.866534
-Mean: 0.0014750387
-Stdev: 0.013614772
+Max: 7.6572003
+Mean: 0.0014543837
+Stdev: 0.013401416
 

 

 

@@ -1110,10 +1110,10 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position.
 

 

-
<matplotlib.collections.PathCollection at 0x7f1e1487ab00>
+
<matplotlib.collections.PathCollection at 0x7fdf44d81cf0>
 

 

-../../_images/82c8853d15507d826ef603a9542f5bccbb4a9a6adf7a744cf5a10e857f9f3265.png
+../../_images/27a723a61d1a31250443cf8f50ad1a3ad40c8120e16cc9c414010921066a4c4e.png
 

 

 

@@ -1129,10 +1129,10 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position.
 

 

-
<matplotlib.collections.PathCollection at 0x7f1e148bc2e0>
+
<matplotlib.collections.PathCollection at 0x7fdf44db9990>
 

 

-../../_images/2608e978b2819f81b393ed03584877df6c5086c574bbcf36f83b9836ad5fb2c4.png
+../../_images/90e97a47385ed46e81642253ef2a66a4c826896cbd4368e017448531d0d76774.png
 

 

 
@@ -1153,7 +1153,7 @@ 
14. Try visualizing a cutout of an SDSS image that covers your position.
 

 
Table length=21
-

 
ivoidshort_nameres_title

 
objectobjectobject

 
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)

+

@@ -1250,7 +1250,7 @@ 
14. Try visualizing a cutout of an SDSS image that covers your position.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]
 

 
-
<matplotlib.collections.PathCollection at 0x7f1e147ef430>
+
<matplotlib.collections.PathCollection at 0x7fdf44cea7d0>
 

 

 ../../_images/22ac5111c66af718cd6f344643a56ab61cc64a582ce930dd152a50a3c4210738.png
@@ -1272,7 +1272,7 @@ 
14. Try visualizing a cutout of an SDSS image that covers your position.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]
 

 

-
<matplotlib.collections.PathCollection at 0x7f1e14689750>
+
<matplotlib.collections.PathCollection at 0x7fdf44b84a90>
 

 

 ../../_images/92f706abac1dc55aadb8bd6474f78cc5b08f3fabbe6e50329d6e4ff52dd36dc1.png
@@ -1403,8 +1403,8 @@ 
15. Try looping over all positions and plotting multiwavelength cutouts.
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 51465.000000 from DATE-OBS'. [astropy.wcs.wcs]
 

 

-../../_images/07f01920a1de347b66ada205750e076278a021279aaf0fdeaf061309957b456e.png
-../../_images/2825060c519481692d10738219db440cc1fbb9f8484e6c3707ebbf18a1e2cfdd.png
+../../_images/af2e14d50daa1473c836333fe81c590484d9b436861b051ab38979476cc65acd.png
+../../_images/f9160b89da6bb840f69c2e367c97705464bdae31d265af731830c73dfd030a89.png
 ../../_images/9a23ad48383bf3f9c15bd11f56e2761a02e0392bdc649d380834693d7a2ae084.png
 

 

diff --git a/content/use_case_notebooks/hr_diagram_solution.html b/content/use_case_notebooks/hr_diagram_solution.html
index 4ca8be2..47259c8 100644
--- a/content/use_case_notebooks/hr_diagram_solution.html
+++ b/content/use_case_notebooks/hr_diagram_solution.html
@@ -517,7 +517,7 @@ 
DATA DISCOVERY STEPS:
 
 
Table length=137
-

 
ivoidshort_nameres_titlesource_value

 
objectobjectobjectobject

 
ivo://mast.stsci/siap/al218VLA.AL218VLA-A Array AL218 Texas Survey Source Snapshots (AL218)

+

@@ -3945,7 +3945,7 @@ 
Step 2: Acquire the relevant data and make a plot!
 
 
Table length=502
-

 
indexshort_nametitledescriptioninterfaces

 
int64str16str55str4800str7

 
0I/163US Naval Observatory Pleiades CatalogThis catalog is a special subset of the Eichhorn et al. (1970) Pleiades catalog (see <I/90>) updated to B1950.0 positions and with proper motions added. It was prepared for the purpose of predicting occultations of Pleiades stars by the Moon, but is useful for general applications because it contains many faint stars not present in the current series of large astrometric catalogs.tap#aux

+

@@ -3994,7 +3994,7 @@ 
Plotting…
-
[<matplotlib.lines.Line2D at 0x7fa52cee16f0>]
+
[<matplotlib.lines.Line2D at 0x7f44581bc9a0>]
 

 

 ../../_images/ef3c616dc1ad5708524385f3624ba15e414c9ebf75e7d4fa16b6fa2c61b37347.png
@@ -4054,7 +4054,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades:
/tmp/ipykernel_2326/2801309271.py:11: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+
/tmp/ipykernel_2360/2801309271.py:11: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
   ind = int(match[0])
 

 

@@ -4091,7 +4091,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades:
Table length=270
-

 
recnoHertzsprungCIPtmRAB1900e_RAsDEB1900e_DEsrmsRArmsDErpmRArpmDEDrpmRADrpmDEDRADDE

 
magmagdegmsdegmasmas / yrmas / yrmas / yrmas / yrarcsecarcsec

 
int32int16float64float64float64float64float64int16float64float64float64float64float64float64float64float64

+

@@ -4131,7 +4131,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades:
-
[<matplotlib.lines.Line2D at 0x7fa52e0de0e0>]
+
[<matplotlib.lines.Line2D at 0x7f44701e26b0>]
 

 

 ../../_images/b7e29cce03cc9d6c7fcefd20620b3a59ea672629fb904c865480efecfdb4d18d.png
@@ -4159,7 +4159,7 @@ 
BONUS: Step 4: The CMD as a distance indicator!
-
[<matplotlib.lines.Line2D at 0x7fa54c1a1c60>]
+
[<matplotlib.lines.Line2D at 0x7f44701fcf10>]
 

 

 ../../_images/247f91967efccb915462f5ac1d94170e075c4aae836f427efe8aafd0d10014a7.png
diff --git a/content/use_case_notebooks/proposal_prep_solution.html b/content/use_case_notebooks/proposal_prep_solution.html
index 684c8c3..19f2d85 100644
--- a/content/use_case_notebooks/proposal_prep_solution.html
+++ b/content/use_case_notebooks/proposal_prep_solution.html
@@ -546,7 +546,7 @@ 
Step 1: Find out what the previously quoted Chandra 2-10 keV flux of the cen
 

 

 
Table length=1
-

 
recnoHIIVmagB-VxposyposDistMultRemMassMassAMassBMassCMassD

 
magmagarcminarcminarcminMsunMsunMsunMsunMsun

 
int32int32float64float64float64float64float64int16objectfloat64float64float64float64float64

+

@@ -569,7 +569,7 @@ 
Create ultraviolet and X-ray images
 
Table length=3
-

 
radecexposurefluxflux_lowerflux_upper

 
degdegserg/s/cm^2erg/s/cm^2erg/s/cm^2

 
float64float64float64float64float64float64

+

@@ -588,7 +588,7 @@ 
Create ultraviolet and X-ray images
 
Table length=3
-

 
ivoidshort_name

 
objectobject

 
ivo://archive.stsci.edu/sia/galexGALEX

+

@@ -616,7 +616,7 @@ 
Create ultraviolet and X-ray images
 
Table length=809
-

 
ivoidshort_name

 
objectobject

 
ivo://archive.stsci.edu/sia/galexGALEX

+

@@ -630,6 +630,7 @@ 
Create ultraviolet and X-ray images
@@ -653,11 +653,11 @@ 
Create ultraviolet and X-ray images
 
Table length=2
-

 
productTypeimageFormatcontentLengthnamecollectioninsnamemetaReleasedataReleasetrgposRAtrgPosDecs_regionposition_naxesposition_naxisposition_scalecrpixcrvalcdmatrixcoordFrameprojectionposition_ctype1position_ctype2position_cunit1position_cunit2timBoundsSTCStime_bounds_cval1time_bounds_cval2time_bounds_centertimExposureenergy_bandpassNameenergy_bounds_cval1energy_bounds_cval2energy_bounds_centerenergy_unitspublisherDIDaccessURLcloud_access

 
objectobjectint32objectobjectobjectobjectobjectfloat64float64objectint32objectobjectobjectobjectobjectobjectstr3objectobjectobjectobjectobjectfloat64float64float64float64objectfloat64float64float64objectobjectobjectobject

 
CATALOGimage/fits12795448FORNAX_MOS07-xd-mcat.fits.gzGALEXGALEX5/11/2010 12:14:57 AM5/11/2010 12:14:57 AM54.0331936068986-36.367881838288CIRCLE ICRS  54.03319361 -36.36788184 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][54.0332 -36.3679][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54105.698090 55148.07208354105.698090277855148.072083333354626.88508680563241.6NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555337728220725248https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-xd-mcat.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07091-FORNAX_MOS07/d/01-main/0001-img/07-try/FORNAX_MOS07-xd-mcat.fits.gz"}}

+

-
-
+
+

 
SurveyRaDecDimSizeScaleFormatPixFlagsURLLogicalName

 
objectfloat64float64int32objectobjectobjectobjectobjectobject
galexnear53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexnear&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702590169822&return=FITS1
galexfar53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexfar&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702590170003&return=FITS2
galexnear53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexnear&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702669334356&return=FITS1
galexfar53.4019083-36.14065832[300 300][-0.0003333333333333334 0.0003333333333333334]image/fitsFhttps://skyview.gsfc.nasa.gov/cgi-bin/images?position=53.4019083%2C-36.1406583&survey=galexfar&pixels=300%2C300&sampler=LI&size=0.10000000000000002%2C0.10000000000000002&projection=Tan&coordinates=J2000.0&requestID=skv1702669334562&return=FITS2

 

 

 

@@ -689,7 +689,7 @@ 
Create ultraviolet and X-ray images
-
<matplotlib.image.AxesImage at 0x7f937cee2a70>
+
<matplotlib.image.AxesImage at 0x7f36718b5d80>
 

 

 ../../_images/562a1eb47a96225892d2d094c82a4147202e02a94623836882b39f281951770e.png
@@ -771,7 +771,7 @@ 
Find what Chandra spectral observations exist already for this source.
 

 
Table length=6
-
+

@@ -792,7 +792,7 @@ 
Find what Chandra spectral observations exist already for this source.
 

 
Table length=15
-

 
short_nameivoidwaveband

 
objectobjectobject

 
Chandraivo://nasa.heasarc/chanmasterx-ray

+

diff --git a/searchindex.js b/searchindex.js
index 7a6d993..3d7f25d 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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oci:2,unbias:2,unbound:11,unc:9,uncertain:11,uncertainti:11,unclear:11,unconfus:11,uncorrel:11,under:[1,3],underdens:3,underestim:11,undergo:4,understand:[1,6,11],undetect:11,unexplor:11,unidentifi:2,unifi:[2,6],uniform:[2,11],uniformli:2,unimport:[2,4,5,6,8,9,10,11,12,13],unipa:11,uniq_ind:11,uniqu:[2,6,8,9,10,11],unique_ind:11,unistra:11,unit:[2,3,9,11,13],univers:[2,3,11],unknown:[2,6],unknownelementwarn:[2,6],unless:11,unlik:[3,11],unrealist:11,unrel:11,unresolv:11,unscan:11,unscreen:2,unseen:11,unstudi:11,until:[3,11],unus:2,unusu:[2,11],unveil:11,up:[0,3,4,5,6,9,10,11,12,13],updat:[2,11],upgrad:[0,11],upload:[3,6],upload_method:3,uploadmethod:3,upon:11,upper:[2,11],upprscoxmm:2,uppsala:2,upward:[10,11],urgent:2,uri:13,url:[1,2,4,10,11,13],us:[0,6,12,13,14],usag:[3,4,6],usco:11,usdssdr3:9,use_case_notebook:0,user:[0,1,2,11,14],usign:10,usloan:9,usno:2,usual:[1,3],usuda:11,usus:2,ut:11,utf8:1,util:[2,3,4,5,6,8,9,10,11,12,13],utrilla:11,uv:[2,4,9,11,12,13],uv_servic:[4,13],uvbi:11,uvi:6,uvot:[2,4],uvot_servic:4,uvotbscat:2,uvq:2,uzc:[2,3],uzcuzcupd:3,v0:13,v1:[2,6],v2:2,v367:11,v6:2,v:[2,4,10,11],v_0_:11,v_:11,v_rot_:11,vab:[10,11],vaccari:11,vachier:11,vaiana:11,valenti:11,valentini:11,valett:11,valid:[6,11],vall:11,vallei:2,vallenari:11,valu:[0,3,6,8,11],van:11,vanderburg:11,vanleeuwen:11,vao:1,vapp:11,var_flag:3,varadi:11,varela:11,vari:[1,3,11],varia:2,variabilit:2,variabl:[2,3,6,11],variant:11,variat:11,varieti:[2,10,11],variou:[1,6,11],vast:11,vastli:11,vaucouleur:3,vecchiato:11,vector:[2,11],veil:11,vela5b:2,vela:2,velidi:11,veljanoski:11,veloc:[2,3,11],veltz:11,venaticorum:11,venera:2,ventura:11,ver:[2,4],verbos:[2,13],veri:[1,2,3,6,8,11],verifi:[0,8,9,11],verimast:2,verita:2,veron:2,veroncat:2,versi:2,version:[2,3,4,6,11],versu:[11,14],vet:[10,11],vettolani:3,vhsdr1:9,vi:[8,9,11],via:2,viala:11,vicent:11,vicin:11,victor:11,vidal:11,view:[2,3,10,11,14],viewer:0,vigor:11,vii:3,viii:11,viith:2,vine:11,virmo:2,virtanen:11,virtual:[2,4,9,11],visibl:[2,11],visit:[6,13],vista:9,visual:[11,12,14],vitens:11,viz:11,vizcaino:11,vizier:[2,3,10,11],vl:11,vla23901p4:2,vla3701p4:2,vla74mhzdp:2,vla:[1,2,3,9],vlacdfscat:2,vlacomacat:2,vlacos324m:2,vlacos3ghz:2,vlacosmjsc:2,vlacosmo:2,vlacosxoid:2,vlaecdfs1p4:2,vlaecdfscl:2,vlaecdfsoi:2,vlaen20cm:2,vlagbsoph:2,vlagbsori:2,vlagbsper:2,vlagbsser:2,vlagbstau:2,vlagoodsn:2,vlahdf20cm:2,vlalh1400m:2,vlalhn3ghz:2,vlam311p4g:2,vlam31325m:2,vlanep:2,vlaonccat:2,vlasdf20cm:2,vlasdf90cm:2,vlass821p4:2,vlasxdf1p4:2,vlasxdfoid:2,vlaxl325mh:2,vlaxl74mhz:2,vlba:2,vleck:11,vlssr:2,vlt:2,vlulxcat:2,vmag:[3,11],vmag_error:3,vmax:[4,8,9,13],vmin:[4,9,13],vo:[1,3,4,5,6,8,9,10,11,12,13,14],vodataservic:6,vogt:11,volmerang:11,volum:11,vosa:11,vosi:6,voss:11,vot_obj:3,votabl:[3,4,5,7,8,9,10,11,12,13],votablefil:14,votruba:11,voutsina:11,vrba:11,vsa:2,vsini:11,vsn:13,vvd:2,vvds20cm:2,vxposyposdistmultremmassmassamassbmasscmassd:11,w02:6,w13:9,w1:[2,4,14],w24:9,w2:[4,9],w2r:2,w2ragncat:2,w31:9,w3:9,w3brows:12,w40sfrcxo:2,w42:9,w4:9,w:[2,11],wa:[3,10,11],wackerl:2,wai:[1,2,3,6,7,9,10,11],wainscoat:11,wait:[1,3],walk:0,walker:11,wall:3,wallut:11,walmslei:11,walton:11,wam:2,wampler:11,want:[1,3,4,6,10,11],warm:11,warmer:11,warn:[1,2,3,4,5,6,8,9,10,11,12,13],warp:2,warps2:2,washington:2,watanab:11,water:11,watson:11,waveband:[1,2,4,5,11,13],wavelength:[2,6,11],wavelet:[2,11],wb:13,wbhgp20cm:2,wbhgp6cm:2,wbl:2,wblgalaxi:2,wc:[8,9],wd0340:11,wd1cxo:2,wd:2,wdb:11,we:[0,1,2,3,4,5,6,12,13,14],weak:11,weaker:11,web:[2,3],webb:11,webpag:11,weekli:2,weight:11,weiler:11,weingril:11,well:[3,6,11],wenss:2,were:[3,10,11],west:[9,11],westerbork:2,westerlund:2,westphal:2,wever:11,wfau:[3,5,6,9,13],wfc3:6,wfc:2,wfcfwfcfrosat:4,wfcpoint:2,wfpc2:6,wfpc2pcfrac:6,wga:2,wgacat:2,what:[1,2,3,4,5,6,8,9,10,11,14],whatev:6,wheatlei:11,when:[1,3,6,8,10,11],where:[1,2,3,4,6,10,11,12,13],wherea:11,whether:[0,1,11],which:[1,2,3,4,6,8,9,13,14],whippl:3,white:[2,6,11],whole:11,whose:[1,3,11],why:11,wibral:2,wichmann:11,wide:[2,4,11],widefield:2,wider:[10,11],width:11,wielen:11,wiki:[10,11],wikipedia:[10,11],wild:[4,10,11],wilkinson:11,wilson:11,window:[0,2,6,11],wing:2,winter:[5,11,12,13],wire:[1,4],wise:[2,11,14],wisehspcat:2,wish:[2,3,11],wisniewski:11,wit:11,within:[2,3,11,12,13],without:[3,11],wiyn:11,wl:6,wmap:2,wmapcmbfp:2,wmapitnpt:2,wmapptsrc:2,wolf:2,wood:2,woodebcat:2,woollei:2,word:[1,2],work:[0,1,2,3,4,10,11,13,14],workaround:[8,14],workshop:[2,5,12,13,14],worldwid:2,worlei:11,would:[1,3,8,9,10,11,12,13],wrcat:2,wright:[10,11],write:[3,10,11,12,13],writeto:3,wrong:1,wsrt20anep:2,wsrt:2,wsrtgp:2,wtt:11,wupp:2,www:[1,10,11,12],wyrzykowski:11,x3:11,x40:11,x80:11,x:[2,3,5,6,11],x_servic:13,xamin:[2,5,11],xassist:2,xboot:2,xbootesoid:2,xc:2,xcatdb:[6,9],xcopraw:2,xd:13,xdeep2:[2,3],xfl:2,xgp:2,xhdu_list:13,xi:2,xid:2,xim_tabl:13,ximag:6,xl:2,xlabel:[10,11],xlii:11,xlim:13,xm:2,xmatchv2:6,xmdsvvds4:2,xml:[2,3,4,5,6,8,9,10,11,12,13],xmm:[2,3,6,11],xmmao:2,xmmatla:2,xmmbss:2,xmmbssagn:2,xmmcdfs210:2,xmmcdfs510:2,xmm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