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b/_sources/content/reference_notebooks/basic_reference.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "01cce4f3", + "id": "28052493", "metadata": {}, "source": [ "# Basic Reference" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "310b4762", + "id": "c0ee67c7", "metadata": {}, "source": [ "## 0. Setup\n", @@ -20,7 +20,7 @@ }, { "cell_type": "markdown", - "id": "b5373ad3", + "id": "3b88031a", "metadata": {}, "source": [ "## 1. Overview\n", @@ -51,7 +51,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a7991a72", + "id": "f9b4dd0a", "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "markdown", - "id": "b8fc39a8", + "id": "ec4345b2", "metadata": {}, "source": [ "### 2.1 Look Up Services in VO Registry\n", @@ -84,7 +84,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "4c8f23c1", + "id": "44ecfa42", "metadata": {}, "outputs": [ { @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "1f59677f", + "id": "9f268cc7", "metadata": {}, "source": [ "#### 2.1.1 Use different arguments/values to modify the simple example\n", @@ -155,7 +155,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "96206beb", + "id": "a8e9b581", "metadata": {}, "outputs": [ { @@ -180,7 +180,7 @@ }, { "cell_type": "markdown", - "id": "84aead08", + "id": "a62e8bd2", "metadata": {}, "source": [ "##### Filtering results\n", @@ -191,7 +191,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "0656f316", + "id": "96f6dccd", "metadata": {}, "outputs": [ { @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "ed0ed26c", + "id": "1f9270ca", "metadata": {}, "source": [ "##### Using astropy\n", @@ -221,7 +221,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "a050d9d1", + "id": "cdfba631", "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ "data": { "text/html": [ "

Table length=3\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "108c6e21", + "id": "259d8614", "metadata": {}, "source": [ "### 2.2 Cone search\n", @@ -287,14 +287,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "9133cbf5", + "id": "e1bda0fc", "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", @@ -361,7 +361,7 @@ }, { "cell_type": "markdown", - "id": "0c3546e7", + "id": "af94e663", "metadata": {}, "source": [ "### 2.3 Image search\n", @@ -374,14 +374,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "67c65d83", + "id": "1ea7e1fc", "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", @@ -411,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "adf0d51d", + "id": "26c0c032", "metadata": {}, "source": [ "#### Search one of the services\n", @@ -426,14 +426,14 @@ { "cell_type": "code", "execution_count": 8, - "id": "5555e305", + "id": "1768408f", "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", @@ -466,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "26a83ca3", + "id": "4ca8e76c", "metadata": {}, "source": [ "#### Download an image\n", @@ -479,7 +479,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "30c3b512", + "id": "870e91ca", "metadata": {}, "outputs": [ { @@ -492,7 +492,7 @@ { "data": { "text/plain": [ - "'/tmp/astropy-download-1925-h1c1apau'" + "'/tmp/astropy-download-1791-dtkmx6ih'" ] }, "execution_count": 9, @@ -508,7 +508,7 @@ }, { "cell_type": "markdown", - "id": "b65f78b1", + "id": "2553bd98", "metadata": {}, "source": [ "### 2.4 Spectral search\n", @@ -525,7 +525,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ee6a9997", + "id": "f71b704b", "metadata": {}, "outputs": [ { @@ -538,7 +538,7 @@ { "data": { "text/plain": [ - "'/tmp/astropy-download-1925-ac3w7j8m'" + "'/tmp/astropy-download-1791-_mypbvfn'" ] }, "execution_count": 10, @@ -562,7 +562,7 @@ }, { "cell_type": "markdown", - "id": "9e9e9ddb", + "id": "cdbf87dd", "metadata": {}, "source": [ "### 2.5 Table search\n", @@ -573,7 +573,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "bf5b4e21", + "id": "5b62b897", "metadata": { "tags": [ "output_scroll" @@ -1491,7 +1491,13 @@ "uc7c151mhz - 7C Catalog 151-MHz Survey Final Unified Source Catalog\n", "ugc - Uppsala General Catalog of Galaxies\n", "uhuru4 - Uhuru Fourth (4U) Catalog\n", - "uit - Ultraviolet Imaging Telescope Near-UV Bright Objects Catalog\n", + "uit - Ultraviolet Imaging Telescope Near-UV Bright Objects Catalog\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "uitmaster - Ultraviolet Imaging Telescope Master Catalog\n", "ulxngcat - Ultraluminous X-Ray Sources in Nearby Galaxies Catalog\n", "ulxrbcat - Ultraluminous X-Ray Sources in External Galaxies Catalog\n", @@ -1499,13 +1505,7 @@ "uvotbscat - UVOT Bright Star Catalog\n", "uvqs - UV-Bright Quasar Survey (UVQS) DR1 Catalog\n", "uzc - Updated Zwicky Catalog\n", - "vela5b - Vela 5B All-Sky Monitor Lightcurves\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "vela5b - Vela 5B All-Sky Monitor Lightcurves\n", "verimaster - VERITAS Source Catalog\n", "veroncat - Veron Catalog of Quasars & AGN, 13th Edition\n", "vla23901p4 - VLA A2390 Cluster of Galaxies 1.4-GHz Source Catalog\n", @@ -1655,7 +1655,7 @@ }, { "cell_type": "markdown", - "id": "30db9571", + "id": "61778c84", "metadata": {}, "source": [ "#### Column Information\n", @@ -1666,7 +1666,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "40d493f8", + "id": "ede09700", "metadata": {}, "outputs": [ { @@ -1712,7 +1712,7 @@ }, { "cell_type": "markdown", - "id": "def54531", + "id": "4d4981d5", "metadata": {}, "source": [ "#### Perform a Query\n", @@ -1723,14 +1723,14 @@ { "cell_type": "code", "execution_count": 13, - "id": "c6d2d6d0", + "id": "2e8debc4", "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", @@ -1788,7 +1788,7 @@ }, { "cell_type": "markdown", - "id": "dc090f3c", + "id": "36772d85", "metadata": {}, "source": [ "## 3. Astroquery\n", @@ -1818,14 +1818,14 @@ { "cell_type": "code", "execution_count": 14, - "id": "4bed7653", + "id": "d6cc79fc", "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 70e5ef6..3a0d008 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": "ed6a15bc", + "id": "efce112b", "metadata": {}, "source": [ "# Catalog Queries\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "95dc9a02", + "id": "be3d4dd1", "metadata": {}, "outputs": [], "source": [ @@ -55,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "220b5729", + "id": "222210cb", "metadata": {}, "source": [ "## 1. Simple cone search" @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "99984188", + "id": "a8510c89", "metadata": {}, "source": [ "Starting with a single simple source first:" @@ -72,7 +72,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "97bc6891", + "id": "59068524", "metadata": {}, "outputs": [ { @@ -91,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "aaa34d79", + "id": "9c55ea2b", "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", @@ -109,14 +109,14 @@ { "cell_type": "code", "execution_count": 3, - "id": "1fd7a9f6", + "id": "96fc8555", "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", @@ -152,7 +152,7 @@ }, { "cell_type": "markdown", - "id": "967d526a", + "id": "b1c117ed", "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:" @@ -161,14 +161,14 @@ { "cell_type": "code", "execution_count": 4, - "id": "bdeba201", + "id": "2c9dcbd7", "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", @@ -203,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "94045583", + "id": "6077a0e3", "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." @@ -211,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "50466f77", + "id": "3f676628", "metadata": {}, "source": [ "## 2. Table Access Protocol queries\n", @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "1e639fd8", + "id": "be4bd022", "metadata": {}, "source": [ "### 2.1 TAP services\n", @@ -233,7 +233,7 @@ }, { "cell_type": "markdown", - "id": "fe928e35", + "id": "319c18f3", "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", @@ -244,14 +244,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "109fb555", + "id": "3e823458", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=20\n", - "
__rownameradecbmagradial_velocityradial_velocity_errorredshiftclassSearch_Offset
degdegkm / skm / s
objectobjectfloat64float64float32int32int16float64int16float64
\n", + "
\n", "\n", "\n", "\n", @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "fbb46362", + "id": "976134fe", "metadata": {}, "source": [ "There are many tables that mention these keywords. Pick some likely looking ones and look at the descriptions:" @@ -324,7 +324,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "bf9e6324", + "id": "40331820", "metadata": {}, "outputs": [ { @@ -354,7 +354,7 @@ }, { "cell_type": "markdown", - "id": "e27d8d17", + "id": "29f23d95", "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", @@ -364,7 +364,7 @@ }, { "cell_type": "markdown", - "id": "d8a18eb1", + "id": "a89df36e", "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.)" @@ -373,7 +373,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "296cbdb4", + "id": "bf9d0b15", "metadata": {}, "outputs": [], "source": [ @@ -385,7 +385,7 @@ }, { "cell_type": "markdown", - "id": "82de3fbc", + "id": "e42e1ae7", "metadata": {}, "source": [ "Then let's look for tables matching the terms we're interested in as above." @@ -394,7 +394,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "ada05c3e", + "id": "566e026f", "metadata": {}, "outputs": [ { @@ -506,7 +506,7 @@ }, { "cell_type": "markdown", - "id": "3974ea41", + "id": "65214be3", "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", @@ -516,7 +516,7 @@ }, { "cell_type": "markdown", - "id": "2b1e86ec", + "id": "cb2f9aad", "metadata": {}, "source": [ "### 2.2 Expressing queries in ADQL" @@ -524,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "d722a8db", + "id": "6270053b", "metadata": {}, "source": [ "The basics of ADQL:\n", @@ -566,7 +566,7 @@ }, { "cell_type": "markdown", - "id": "cfc7a460", + "id": "521d0037", "metadata": {}, "source": [ "### 2.3 A use case\n", @@ -577,7 +577,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9861e788", + "id": "18fd0eb8", "metadata": {}, "outputs": [], "source": [ @@ -597,14 +597,14 @@ { "cell_type": "code", "execution_count": 10, - "id": "f028c9f7", + "id": "88a056aa", "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", @@ -637,7 +637,7 @@ }, { "cell_type": "markdown", - "id": "ae0ea118", + "id": "792f5c0b", "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." @@ -645,7 +645,7 @@ }, { "cell_type": "markdown", - "id": "6ffa970a", + "id": "912cffc1", "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:" @@ -654,7 +654,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "e140d9c8", + "id": "c7e1112a", "metadata": {}, "outputs": [], "source": [ @@ -667,14 +667,14 @@ { "cell_type": "code", "execution_count": 12, - "id": "dd88aa0c", + "id": "a5913ab1", "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": "a860f0b4", + "id": "335a332f", "metadata": {}, "source": [ "### 2.4 TAP examples for a given service\n", @@ -750,7 +750,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "da60d352", + "id": "9aad008d", "metadata": {}, "outputs": [ { @@ -772,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "5fc633c5", + "id": "e18d0f1c", "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": "10451926", + "id": "1e030728", "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": "3acab44a", + "id": "dbcae058", "metadata": {}, "source": [ "## 3. Using the TAP to cross-correlate and combine" @@ -837,7 +837,7 @@ }, { "cell_type": "markdown", - "id": "4270ca02", + "id": "fbea0045", "metadata": {}, "source": [ "### 3.1 Cross-correlating to combine catalogs\n", @@ -852,7 +852,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "891d49ed", + "id": "38bfeb01", "metadata": {}, "outputs": [ { @@ -873,7 +873,7 @@ }, { "cell_type": "markdown", - "id": "abf0edda", + "id": "9222ca19", "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": "90b3b89f", + "id": "23d9b7bd", "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": "512e882b", + "id": "40d3efe8", "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": "10ba066a", + "id": "43dabf99", "metadata": {}, "source": [ "### 3.2 Cross-correlating with user-defined columns\n", @@ -975,14 +975,14 @@ { "cell_type": "code", "execution_count": 17, - "id": "faa92a9f", + "id": "781d1698", "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": "732276f6", + "id": "884ae219", "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." @@ -1054,14 +1054,14 @@ { "cell_type": "code", "execution_count": 18, - "id": "97d4877f", + "id": "785036b6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=9\n", - "
radecradial_velocitybmagmorph_typeredshiftangDdeg
degdegdeg
float64float64int32float32int16float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -1111,7 +1111,7 @@ }, { "cell_type": "markdown", - "id": "6bab5721", + "id": "5ba0d443", "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." @@ -1119,7 +1119,7 @@ }, { "cell_type": "markdown", - "id": "fc770946", + "id": "13785e75", "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 85acba6..3bf2bf3 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": "43a4ecb5", + "id": "8b437fbe", "metadata": {}, "source": [ "# Image Access\n", @@ -19,7 +19,7 @@ }, { "cell_type": "markdown", - "id": "1844d582", + "id": "76b1b502", "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." @@ -28,7 +28,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0a3f5cf5", + "id": "c8cc7e23", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "50288c38", + "id": "62fc3361", "metadata": {}, "source": [ "## 1. Finding SIA resources from the Registry\n", @@ -69,14 +69,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "c5a787ab", + "id": "c96c3549", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=18\n", - "
radecra2dec2radial_velocitymorph_typebmag
degdegdegdeg
float64float64float64float64int32int16float32
\n", + "
\n", "\n", "\n", "\n", @@ -136,7 +136,7 @@ }, { "cell_type": "markdown", - "id": "5fc2c657", + "id": "3510d13f", "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):" @@ -145,14 +145,14 @@ { "cell_type": "code", "execution_count": 3, - "id": "7b8dd61c", + "id": "343b4d68", "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", @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "ce5d39a2", + "id": "91c73b39", "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." @@ -186,7 +186,7 @@ }, { "cell_type": "markdown", - "id": "c51cb485", + "id": "26e14378", "metadata": {}, "source": [ "## 2. Using SIA to retrieve an image\n", @@ -199,22 +199,22 @@ { "cell_type": "code", "execution_count": 4, - "id": "0537bbc3", + "id": "fdd76593", "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=skv1734651634490&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=skv1734651634986&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=skv1734651636170&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=skv1734651636990&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=skv1734651637662&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=skv1734651638096&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=skv1734724357376&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=skv1734724357660&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=skv1734724358799&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=skv1734724359238&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=skv1734724359699&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=skv1734724360111&nofits=1&quicklook=jpeg&return=jpeg6
" ], "text/plain": [ @@ -244,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "ee615595", + "id": "a9d95dfb", "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", @@ -255,14 +255,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "69fad898", + "id": "0b6f596a", "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=skv1734651634490&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=skv1734724357376&nofits=1&quicklook=jpeg&return=jpeg\n" ] } ], @@ -273,7 +273,7 @@ }, { "cell_type": "markdown", - "id": "a8ee5922", + "id": "599835a5", "metadata": {}, "source": [ "## 3. Viewing the resulting image" @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "8d91743a", + "id": "70d67f56", "metadata": {}, "source": [ "### JPG images\n", @@ -292,13 +292,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "7eb162bf", + "id": "0a88ad4d", "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -315,7 +315,7 @@ }, { "cell_type": "markdown", - "id": "24faad36", + "id": "d33cd922", "metadata": {}, "source": [ "### Fits files\n", @@ -328,14 +328,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "9d8649c7", + "id": "e70a93c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Filename: /home/runner/.astropy/cache/download/url/ada1b9e305d642dd2464d3bc873391ab/contents\n", + "Filename: /home/runner/.astropy/cache/download/url/b76bb70fb1e0cd40080fb49ab7343f6e/contents\n", "No. Name Ver Type Cards Dimensions Format\n", " 0 PRIMARY 1 PrimaryHDU 111 (300, 300) float32 \n" ] @@ -352,7 +352,7 @@ }, { "cell_type": "markdown", - "id": "6e032864", + "id": "e1248b98", "metadata": {}, "source": [ "#### Using imshow" @@ -361,13 +361,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "b3bf22d3", + "id": "0dcf05be", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, diff --git a/_sources/content/reference_notebooks/spectral_access.ipynb b/_sources/content/reference_notebooks/spectral_access.ipynb index 4b1ee21..2bd6d04 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": "f5aeff89", + "id": "cf8de4ed", "metadata": {}, "source": [ "# Spectral Access\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "48caf8b8", + "id": "764a81cb", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "5809d452", + "id": "2935bf12", "metadata": {}, "source": [ "## Finding available Spectral Access Services\n", @@ -54,14 +54,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "2309bc4c", + "id": "ddb38d20", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=7\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "d65e4e81", + "id": "048c5597", "metadata": {}, "source": [ "We can look at only the Chandra entry:" @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "cf6bfd0f", + "id": "901268e5", "metadata": {}, "outputs": [ { @@ -129,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "8b7002cc", + "id": "af33554b", "metadata": {}, "source": [ "## Chandra Spectrum of Delta Ori\n", @@ -140,7 +140,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "a2de5b56", + "id": "a04a0c91", "metadata": {}, "outputs": [ { @@ -158,7 +158,7 @@ "data": { "text/html": [ "Table length=6\n", - "
ivoidshort_name
objectobject
ivo://nasa.heasarc/chanmasterChandra
\n", + "
\n", "\n", "\n", "\n", @@ -190,14 +190,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table140695442389744-133963').dataTable()\");\n", + " console.log(\"$('#table139978445357504-97907').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", - " $('#table140695442389744-133963').dataTable({\n", + " $('#table139978445357504-97907').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "c1b96312", + "id": "bb56e342", "metadata": {}, "source": [ "Accessing one of the spectra." @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d5f5c828", + "id": "0401662d", "metadata": {}, "outputs": [], "source": [ @@ -249,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "44e8c606", + "id": "59817a7f", "metadata": {}, "source": [ "## Simple example of plotting a spectrum" @@ -258,14 +258,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "582651f8", + "id": "7e1e06a1", "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-SHETG49680GOCassinelli5203711755:chandra.obs.misc51556.136400463----49680.0--83.00125-0.299170.81acisf00639N005_pha2.fitshttps://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N005_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/acisf00639N005_pha2.fits.gz"}}
\n", + "
\n", "\n", "\n", "\n", @@ -314,7 +314,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "8ed17a06", + "id": "f62e4970", "metadata": {}, "outputs": [ { @@ -351,7 +351,7 @@ }, { "cell_type": "markdown", - "id": "e02ff76f", + "id": "ef6e0e62", "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.)" @@ -360,7 +360,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29d16d92", + "id": "56a8a437", "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 f800c30..3c853f2 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": "0e26b754", + "id": "81744ce5", "metadata": {}, "source": [ "# UCDs (Unified Content Descriptors)\n", @@ -21,7 +21,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f4d841eb", + "id": "44987a7c", "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "markdown", - "id": "1905a0b2", + "id": "24f4ad7f", "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": "ab56fdc1", + "id": "b4c71946", "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "0cf2d3c3", + "id": "5d9a4ebb", "metadata": {}, "source": [ "Now let's see what tables are provided by this service for HSCv3. Note that this is another query to the service:" @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "d88b1c2c", + "id": "75c970b2", "metadata": {}, "outputs": [ { @@ -94,13 +94,7 @@ "tap_schema.schemas - description of schemas in this dataset\n", "----\n", "tap_schema.tables - description of tables in this dataset\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "tap_schema.columns - description of columns in this dataset\n", "----\n", "tap_schema.keys - description of foreign keys in this dataset\n", @@ -114,8 +108,6 @@ "tap_schema.key_columns - description of foreign key columns in this dataset\n", "----\n", "dbo.detailedcatalog - Detailed list of source catalog parameters\n", - "----\n", - "dbo.hcvdetailedview - Detailed list of Hubble Catalog of Variables parameters\n", "----\n" ] }, @@ -123,16 +115,12 @@ "name": "stdout", "output_type": "stream", "text": [ + "dbo.hcvdetailedview - Detailed list of Hubble Catalog of Variables parameters\n", + "----\n", "dbo.hcvsummaryview - Summary list of Hubble Catalog of Variables parameters\n", "----\n", "dbo.propermotionsview - List of proper motion information\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.sourcepositionsview - List of source position information\n", "----\n" ] @@ -142,13 +130,7 @@ "output_type": "stream", "text": [ "dbo.summagaper2catview - Summary list of source catalog with Aper2 magnitudes\n", - "----\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "----\n", "dbo.summagautocatview - Summary list of source catalog with MagAuto magnitudes\n", "----\n", "dbo.catalog_image_metadata - Summary list of Image processing metadata\n", @@ -165,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "fc929f34", + "id": "493b0eea", "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." @@ -174,7 +156,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "fc888aaf", + "id": "e5656f60", "metadata": {}, "outputs": [ { @@ -227,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "e72de3e7", + "id": "6c7334bc", "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", @@ -238,7 +220,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "29adb554", + "id": "a5bdb9b6", "metadata": {}, "outputs": [ { @@ -256,7 +238,7 @@ }, { "cell_type": "markdown", - "id": "2d9277cc", + "id": "5f20a48f", "metadata": {}, "source": [ "Since that guessing doesn't give a unique answer, the more general and reliable 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", @@ -267,7 +249,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "7a83130f", + "id": "bc6f0c74", "metadata": {}, "outputs": [ { @@ -289,7 +271,7 @@ }, { "cell_type": "markdown", - "id": "17e9316f", + "id": "dd17868d", "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." @@ -297,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "952d1695", + "id": "e2fafec6", "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.)" @@ -306,7 +288,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c1e2822a", + "id": "81d705d4", "metadata": {}, "outputs": [], "source": [ @@ -350,7 +332,7 @@ }, { "cell_type": "markdown", - "id": "5de93b62", + "id": "b43db573", "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", @@ -363,7 +345,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "83785497", + "id": "fd3f05b2", "metadata": {}, "outputs": [], "source": [ @@ -384,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "a1272026", + "id": "7c6e1d19", "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():" @@ -393,7 +375,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "fafc10dd", + "id": "68cfd723", "metadata": {}, "outputs": [ { @@ -423,7 +405,7 @@ }, { "cell_type": "markdown", - "id": "9ad3bea2", + "id": "b8770edc", "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." @@ -432,7 +414,7 @@ { "cell_type": "code", "execution_count": null, - "id": "89eaa2f6", + "id": "853e334d", "metadata": {}, "outputs": [], "source": [] diff --git a/_sources/content/reference_notebooks/votables.ipynb b/_sources/content/reference_notebooks/votables.ipynb index 6b7e473..66cac3a 100644 --- a/_sources/content/reference_notebooks/votables.ipynb +++ b/_sources/content/reference_notebooks/votables.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9b225a89", + "id": "5fbaf1ae", "metadata": {}, "source": [ "# VO Tables\n", @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "9f7b924c", + "id": "a06b0888", "metadata": {}, "source": [ "There are several ways of doing this, and there are a few object layers here, which can be confusing:\n", @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "54082ecc", + "id": "839d68ad", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "edb5e1c2", + "id": "cb2772d2", "metadata": {}, "source": [ "## Create a table with only two columns starting from an astropy Table" @@ -50,7 +50,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "f15fe06e", + "id": "58d71821", "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "afc3f3a2", + "id": "96d2005e", "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)" @@ -127,7 +127,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8e74e696", + "id": "8f1f91c5", "metadata": {}, "outputs": [ { @@ -175,7 +175,7 @@ { "cell_type": "code", "execution_count": null, - "id": "101691cd", + "id": "7a741e11", "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 94d2610..f469f6d 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": "70d54024", + "id": "9237b30f", "metadata": {}, "source": [ "# Candidate List Exercise\n", @@ -16,7 +16,7 @@ }, { "cell_type": "markdown", - "id": "700300d9", + "id": "7227e4d9", "metadata": {}, "source": [ "## 1. Import the Python modules we'll be using" @@ -25,7 +25,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "7af556cc", + "id": "d292dab5", "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "e2d2aed2", + "id": "88e7736f", "metadata": {}, "source": [ "The next cell prepares the notebook to display our visualizations." @@ -62,7 +62,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "0da4592f", + "id": "2ad687a1", "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "markdown", - "id": "55b54d32", + "id": "bec9fe71", "metadata": {}, "source": [ "## 2. Search NED for objects in this paper\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "2f66ffd7", + "id": "6550c3b8", "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "markdown", - "id": "bc3aa1e4", + "id": "3b170225", "metadata": {}, "source": [ "## 3. Filter the NED results\n", @@ -103,14 +103,14 @@ { "cell_type": "code", "execution_count": null, - "id": "53297852", + "id": "6a798126", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "e3cc53ec", + "id": "7983b067", "metadata": {}, "source": [ "## 4. Search the NAVO Registry for image resources\n", @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7914a342", + "id": "6c1ba2e5", "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "markdown", - "id": "0bf457b2", + "id": "ee26cd82", "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": "137ee970", + "id": "be2d8e22", "metadata": {}, "outputs": [], "source": [ @@ -152,7 +152,7 @@ }, { "cell_type": "markdown", - "id": "8b6be167", + "id": "bd988dbf", "metadata": {}, "source": [ "## 6. Choose the AllWISE image service that you are interested in" @@ -161,14 +161,14 @@ { "cell_type": "code", "execution_count": null, - "id": "9e7e629a", + "id": "e35773e0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "9ca48a63", + "id": "cd3c33a6", "metadata": {}, "source": [ "## 7. Choose one of the galaxies in the NED list\n", @@ -179,7 +179,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "68d5b1e7", + "id": "4112fd22", "metadata": {}, "outputs": [], "source": [ @@ -189,7 +189,7 @@ }, { "cell_type": "markdown", - "id": "d2e7654e", + "id": "2cd9af99", "metadata": {}, "source": [ "## 8. Search for a list of AllWISE images that cover this galaxy\n", @@ -200,7 +200,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "ecbe9ebb", + "id": "830da715", "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "41c6d15c", + "id": "97c5900e", "metadata": {}, "source": [ "## 9. Use the .to_table() method to view the results as an Astropy table" @@ -219,14 +219,14 @@ { "cell_type": "code", "execution_count": null, - "id": "0df76eda", + "id": "b0903e24", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "06290913", + "id": "1b201a46", "metadata": {}, "source": [ "## 10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)\n", @@ -240,14 +240,14 @@ { "cell_type": "code", "execution_count": null, - "id": "5c2b4349", + "id": "748d0bc1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "6dae3eb9", + "id": "15045d28", "metadata": {}, "source": [ "## 11. Visualize this AllWISE image\n", @@ -258,7 +258,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4a48af2f", + "id": "48e502cb", "metadata": {}, "outputs": [], "source": [ @@ -270,7 +270,7 @@ }, { "cell_type": "markdown", - "id": "a3d70fae", + "id": "72fdf4a7", "metadata": {}, "source": [ "## 12. Plot a cutout of the AllWISE image, centered on your position\n", @@ -281,7 +281,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "e2b30136", + "id": "8dd40032", "metadata": {}, "outputs": [], "source": [ @@ -290,7 +290,7 @@ }, { "cell_type": "markdown", - "id": "b8a44963", + "id": "f716538c", "metadata": {}, "source": [ "## 13. Try visualizing a cutout of a GALEX image that covers your position\n", @@ -301,7 +301,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "49a4b20c", + "id": "3a671202", "metadata": { "tags": [ "output_scroll" @@ -316,7 +316,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "51e0a68e", + "id": "64d66502", "metadata": {}, "outputs": [], "source": [ @@ -327,7 +327,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "beea9605", + "id": "3f08b724", "metadata": {}, "outputs": [], "source": [ @@ -338,7 +338,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "1852a8fb", + "id": "434fb2ab", "metadata": {}, "outputs": [], "source": [ @@ -349,7 +349,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "cc21ca21", + "id": "67159fe4", "metadata": {}, "outputs": [], "source": [ @@ -359,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "2f1c9200", + "id": "c1cb0f83", "metadata": {}, "source": [ "## 14. Try visualizing a cutout of an SDSS image that covers your position\n", @@ -374,7 +374,7 @@ }, { "cell_type": "markdown", - "id": "5cca302d", + "id": "2adb11b9", "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.)" @@ -383,7 +383,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "d581d847", + "id": "e3bf799a", "metadata": {}, "outputs": [], "source": [ @@ -393,7 +393,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "73bcbc05", + "id": "9f4ad4a8", "metadata": {}, "outputs": [], "source": [ @@ -403,7 +403,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "95bf53a3", + "id": "ea87ad37", "metadata": {}, "outputs": [], "source": [ @@ -413,7 +413,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "9f1c1357", + "id": "c2fff69e", "metadata": {}, "outputs": [], "source": [ @@ -424,7 +424,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "75197661", + "id": "302c1273", "metadata": {}, "outputs": [], "source": [ @@ -433,7 +433,7 @@ }, { "cell_type": "markdown", - "id": "c505b92d", + "id": "3c7fb41e", "metadata": {}, "source": [ "## 15. Try looping over all positions and plotting multiwavelength cutouts\n", @@ -443,7 +443,7 @@ }, { "cell_type": "markdown", - "id": "ae8bacf6", + "id": "320ebb7b", "metadata": {}, "source": [ "Warning: this takes a long time to run! You can limit it to the first three galaxies only, for example, for testing." @@ -452,7 +452,7 @@ { "cell_type": "code", "execution_count": null, - "id": "04017cac", + "id": "6794490c", "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 b16b477..61b3fa4 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": "d2de594f", + "id": "8298ac8c", "metadata": {}, "source": [ "# Candidate List Solution\n", @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "ccbc77fd", + "id": "d6a5cefe", "metadata": {}, "source": [ "## 1. Import the Python modules we'll be using" @@ -23,7 +23,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f37bbcaa", + "id": "352b76f6", "metadata": {}, "outputs": [], "source": [ @@ -51,7 +51,7 @@ }, { "cell_type": "markdown", - "id": "a2380f60", + "id": "065a6799", "metadata": {}, "source": [ "The next cell prepares the notebook to display our visualizations." @@ -60,7 +60,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a58b4ad0", + "id": "febf7c01", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "e561a870", + "id": "1b1ee3fb", "metadata": {}, "source": [ "## 2. Search NED for objects in this paper\n", @@ -80,7 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "8a5a3e96", + "id": "dc95b2a7", "metadata": { "tags": [ "output_scroll" @@ -102,7 +102,7 @@ "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]
1-3114094.91384132.0761 .. 81920 .. 01.8660254 .. 1.86602540 .. 00 .. 07.159166666667378 .. 0.33333333333333337.160000000000712 .. 0.33416666666666667
\n", + "
\n", "\n", "\n", "\n", @@ -190,14 +190,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table140017426249712-851424').dataTable()\");\n", + " console.log(\"$('#table140669518281360-197907').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", - " $('#table140017426249712-851424').dataTable({\n", + " $('#table140669518281360-197907').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -223,7 +223,7 @@ }, { "cell_type": "markdown", - "id": "d6c6a935", + "id": "039c9a6b", "metadata": {}, "source": [ "## 3. Filter the NED results\n", @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c2bf35f2", + "id": "6f01bf10", "metadata": {}, "outputs": [ { @@ -308,7 +308,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "9df0330f", + "id": "d7249cd3", "metadata": { "tags": [ "output_scroll" @@ -330,7 +330,7 @@ "data": { "text/html": [ "Table length=53\n", - "
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506389100
\n", + "
\n", "\n", "\n", "\n", @@ -409,14 +409,14 @@ " datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'\n", "}});\n", "require([\"datatables\"], function(){\n", - " console.log(\"$('#table140016557343360-902580').dataTable()\");\n", + " console.log(\"$('#table140668565441216-164848').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", - " $('#table140016557343360-902580').dataTable({\n", + " $('#table140668565441216-164848').dataTable({\n", " order: [],\n", " pageLength: 50,\n", " lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],\n", @@ -444,7 +444,7 @@ }, { "cell_type": "markdown", - "id": "cd10e43d", + "id": "f1cbc854", "metadata": {}, "source": [ "## 4. Search the NAVO Registry for image resources\n", @@ -455,7 +455,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "3c4c346f", + "id": "6455192e", "metadata": {}, "outputs": [ { @@ -469,7 +469,7 @@ "data": { "text/html": [ "
Table length=321\n", - "
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506389100
\n", + "
\n", "\n", "\n", "\n", @@ -536,7 +536,7 @@ }, { "cell_type": "markdown", - "id": "fcd1de36", + "id": "5da70b5c", "metadata": {}, "source": [ "## 5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images\n", @@ -547,7 +547,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "72f9c1a2", + "id": "fc2d55c7", "metadata": {}, "outputs": [ { @@ -561,7 +561,7 @@ "data": { "text/html": [ "
Table length=1\n", - "
ivoidshort_nameres_title
objectobjectobject
ivo://3crsnapshots/sia3CRSnap.sia3CRSnapshots Simple Image Access Service
\n", + "
\n", "\n", "\n", "\n", @@ -590,7 +590,7 @@ }, { "cell_type": "markdown", - "id": "75df6c38", + "id": "3bb27980", "metadata": {}, "source": [ "## 6. Choose the AllWISE image service that you are interested in" @@ -599,7 +599,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "76f6bb4a", + "id": "8bb64bad", "metadata": {}, "outputs": [ { @@ -620,7 +620,7 @@ }, { "cell_type": "markdown", - "id": "72e74e5f", + "id": "2f056b2f", "metadata": {}, "source": [ "## 7. Choose one of the galaxies in the NED list" @@ -629,7 +629,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "0e98a107", + "id": "f0ec545a", "metadata": {}, "outputs": [], "source": [ @@ -641,7 +641,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ad0456f6", + "id": "1263b400", "metadata": {}, "outputs": [ { @@ -661,7 +661,7 @@ }, { "cell_type": "markdown", - "id": "6bc4551f", + "id": "5347ca19", "metadata": {}, "source": [ "## 8. Search for a list of AllWISE images that cover this galaxy\n", @@ -672,7 +672,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "c34a2188", + "id": "7cbfc170", "metadata": {}, "outputs": [ { @@ -701,7 +701,7 @@ }, { "cell_type": "markdown", - "id": "bdeda523", + "id": "0576022b", "metadata": {}, "source": [ "## 9. Use the .to_table() method to view the results as an Astropy table" @@ -710,14 +710,14 @@ { "cell_type": "code", "execution_count": 12, - "id": "5ec3c1cc", + "id": "bac02bd3", "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", @@ -751,7 +751,7 @@ }, { "cell_type": "markdown", - "id": "53a3ccb2", + "id": "ef36de7f", "metadata": {}, "source": [ "## 10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)\n", @@ -765,7 +765,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "12aa2c31", + "id": "c8807500", "metadata": {}, "outputs": [ { @@ -785,7 +785,7 @@ }, { "cell_type": "markdown", - "id": "c116af72", + "id": "ce4e03c9", "metadata": {}, "source": [ "## 11. Visualize this AllWISE image" @@ -794,7 +794,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "ee492596", + "id": "20950d1d", "metadata": {}, "outputs": [], "source": [ @@ -810,13 +810,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "f50681c7", + "id": "b9559652", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -845,7 +845,7 @@ }, { "cell_type": "markdown", - "id": "53188dae", + "id": "05d64a6e", "metadata": {}, "source": [ "## 12. Plot a cutout of the AllWISE image, centered on your position\n", @@ -856,13 +856,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "a1ce39d4", + "id": "5e07ba63", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, @@ -894,7 +894,7 @@ }, { "cell_type": "markdown", - "id": "3649858b", + "id": "433f95d3", "metadata": {}, "source": [ "## 13. Try visualizing a cutout of a GALEX image that covers your position\n", @@ -905,7 +905,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "fae2e25c", + "id": "0769e214", "metadata": {}, "outputs": [ { @@ -919,7 +919,7 @@ "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_losia_bp_unitmagzpmagzpuncunc_urlcov_urlcoadd_id
degdegpixdegdeg / pixdeg / pix
objectobjectobjectint32objectfloat64float64int32[2]float64[2]float64[2]objectfloat64[2]float64[4]objectfloat64float64float64objectfloat64float64objectobjectobject
\n", + "
\n", "\n", "\n", "\n", @@ -951,7 +951,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "17e31e95", + "id": "389c8af1", "metadata": {}, "outputs": [], "source": [ @@ -961,7 +961,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "d72196d5", + "id": "6d0a1d95", "metadata": {}, "outputs": [], "source": [ @@ -971,14 +971,14 @@ { "cell_type": "code", "execution_count": 20, - "id": "66390102", + "id": "b0826b36", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AIS_270_sg14-nd-int.fits.gz NUV\n" + "AIS_270_0001_sg14-nd-int.fits.gz NUV\n" ] } ], @@ -995,7 +995,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "d7d0bea6", + "id": "9513084f", "metadata": {}, "outputs": [], "source": [ @@ -1008,7 +1008,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "8101ab1a", + "id": "e54f3d9b", "metadata": {}, "outputs": [ { @@ -1016,9 +1016,9 @@ "output_type": "stream", "text": [ "Min: 0.0\n", - "Max: 7.1870303\n", - "Mean: 0.0014934327\n", - "Stdev: 0.012639926\n" + "Max: 7.6572003\n", + "Mean: 0.0014543837\n", + "Stdev: 0.013401416\n" ] } ], @@ -1033,13 +1033,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "a70b9d73", + "id": "686464b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1048,7 +1048,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1067,13 +1067,13 @@ { "cell_type": "code", "execution_count": 24, - "id": "1c5a583d", + "id": "7dbd6acb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -1082,7 +1082,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1103,7 +1103,7 @@ }, { "cell_type": "markdown", - "id": "85857e66", + "id": "16d4f26d", "metadata": {}, "source": [ "## 14. Try visualizing a cutout of an SDSS image that covers your position\n", @@ -1118,14 +1118,14 @@ { "cell_type": "code", "execution_count": 25, - "id": "2981ad09", + "id": "d80b611d", "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", @@ -1192,7 +1192,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "f8dc7567", + "id": "ef2d9f32", "metadata": {}, "outputs": [ { @@ -1216,7 +1216,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "f9311f3e", + "id": "39e3a2ff", "metadata": {}, "outputs": [ { @@ -1238,7 +1238,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "2dee76d7", + "id": "5c5029c0", "metadata": {}, "outputs": [ { @@ -1259,7 +1259,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "5721d13d", + "id": "ecac3341", "metadata": {}, "outputs": [], "source": [ @@ -1272,7 +1272,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "243f5870", + "id": "59c5c649", "metadata": {}, "outputs": [ { @@ -1285,7 +1285,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 30, @@ -1318,7 +1318,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "620a3107", + "id": "87a2e25d", "metadata": {}, "outputs": [ { @@ -1331,7 +1331,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 31, @@ -1361,7 +1361,7 @@ }, { "cell_type": "markdown", - "id": "b5089b1d", + "id": "8fd36b42", "metadata": {}, "source": [ "## 15. Try looping over all positions and plotting multiwavelength cutouts" @@ -1369,7 +1369,7 @@ }, { "cell_type": "markdown", - "id": "05219b6c", + "id": "72a3580b", "metadata": {}, "source": [ "Warning: this cell takes a long time to run! We limit it to the first three galaxies only." @@ -1378,7 +1378,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "6aae69d3", + "id": "e2adade4", "metadata": {}, "outputs": [ { @@ -1404,7 +1404,7 @@ }, { "data": { - "image/png": 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lS5g3bx5GjRqFNWvWYPPmzbJ3l9wvPDwcixYtQkJCAkJDQ/HVV1+hUaNGuHLlCiZNmmTwXq77Hyk3c+ZMg78dHBxQrVo19O7dG506dcLx48cxefJk/eMJdS5evIh+/fohOTkZCxcuRL9+/eDi4oLTp0/j3XffxTvvvIMdO3bg999/h7W1NQDA2dkZ77zzDmbOnInJkyfDysoKgwYNgrW1NX799VdMmDABtra2KCoqkn303b3+/PNPvPnmmwCAL774Ah07djSrvej/fPDBB7h8+TJWrFhRqscCVq1aFUuWLDH4rFGjRmjUqBF69uyJ9u3bY+fOndiyZQuef/75cq41ERERET1J+I4kIiIiInrsdOjQAWFhYQgLC8Mbb7yBKVOm4LfffsPFixfRvHlz/XS6d8EAwO3bt80qOykpCQCM7vQA7g4QdO3aFZ9//jn++usvpKSkYP/+/Xj66acBACtWrMAvv/xiyaKVmW5Zy2M57/fyyy+jZcuWuHr1KhYvXlz2SlYiFxcXtG7dGqtWrcJbb72Fv/76CzNmzDA739fXF9u2bUO1atVw4MABtGnTBk5OTmjRogX2799vcKeJh4eHWWVqNBpMmTIFALBjxw4UFRXpY8XFxRgwYACuXLmC77//HmPGjIG/vz9cXFzQpUsX7Ny5E1WqVEF0dDR+/PFHg3KnT5+O0aNHIz8/H2+//Tb8/Pzg6+uLN954A61atdLfjaVUzwMHDuC5555DYWEhpk+fjokTJ5q1XOa4ePEihg0bZvRv6dKlFpetGxyUe1cV8H/vknJxcanQMvfs2YMFCxagT58+Zr2nzVxt2rTRP3rzt99+K7dyiYiIiOjJxDuSiIiIiOixM3LkSAwbNkxxupYtW0KlUkGSJBw9elT4zhTg7mPedO83CQoKUizf2toaHTt2xO+//442bdrg1KlT2Lx5MwYNGmTWcpSnoKAg3Lx5E0ePHjVr+mPHjunzlKhUKnz22WcIDQ3FzJkzzWp7OZ999hkuXrxY6rwvv/zSYGDQEsOHD8fChQuxadMmfPnll2bntWnTBpcvX8amTZtw/Phx5OXloXbt2nj55ZeRl5cH4O6jz2rVqmV2mbrH2RUUFODOnTv6O1aOHj2KmJgYaDQa9O/f3yjP3d0dvXv3xvLly7Fr1y6DO/Ssra2xaNEivPXWW/j1118RFxcHJycnhISEoG/fvnj11VcBAE2bNpWt16FDh9CnTx/k5ORgypQpiIyMNHuZzJGYmIgVK1aYjI0cOdKisnWPToyLi5Od5r///jOY1twy09PTkZWVZfJONlNlbt68GZIkIS4uDiEhIQbTp6enAwDi4+P1sXnz5unf8aakYcOG2Lx5M27evGnW9EREREREcjiQRERERERPLA8PD3Tq1An79u3Djz/+qDjAs3LlSgB37z64/6KviLW1Nbp164ZTp07hzp07llS5zJ577jls2bIF0dHRSEhIED5CKzExETt37gQAPPvss2aV37NnT3Tv3h1//vkn5s6dq3+UWmnt2LEDe/fuLXVeZGRkuQ0k6R59qLsrqzTs7e0xZMgQDBkyxODzH374AQDQtWvXUrVNSkqK/v/vHZzQDYI4ODjIlufq6goASE1NNRlv2rSp0WCRJEk4ePAgAOjvpLvfkSNH0KtXL2RlZeHDDz/EJ598YubSmC8kJMTsxyuWVqtWrQAAFy5cQH5+Puzs7AzieXl5uHDhgsG0SurXr69/99SJEyfQtWtXo2lOnDghW+b58+dly87Pz9dvE7rBJXPo+o65j2ckIiIiIpLDR9sRERER0RPtww8/BABs3boVW7ZskZ3uv//+018wHzt2rMHjqcy54K278O/v729JdcvslVdeQUBAAIqKijB27FjZOkuShHHjxqGoqAgBAQFGAyIin3/+OVQqFebOnYvk5OQy1XPPnj2QJKnU/8y9c8Qcf/75JwCgXr165VJecXExvvnmGwB3+05prFmzBsDdu0ucnJz0n1erVg0AkJaWhsuXL5vM1d19FhgYaPb81q1bh7i4OAQHB5u8G+3YsWMIDQ3VDyLNmjXL7LIfFsHBwahSpQoKCgqwYcMGo/iGDRtQWFgIPz8/tG3b1qwy1Wo1+vbtCwBYvXq1UfzGjRs4dOgQAOCFF17Qfz5v3jzZPr17924Ad9/tpvvM3AHsnJwc/SPt2rRpY1YOEREREZEcDiQRERER0RMtNDQUERERAIDBgwcjKirKaJDl6NGj6Nq1K9LS0vDUU09h+vTpBvEpU6YgIiIC586dMyq/uLgYixcvxvr16wHcfZ9QZVCr1fj555+hVquxceNGvPLKKwZ3uwB3ByXCwsLwyy+/GExvrqCgIAwaNAhZWVnl8i6birJ69WqcPHnS6HNJkrBx40ZMnToVADBq1Cijabp3744GDRpg06ZNRrETJ06guLjY4LPU1FQMHjwYf//9NwYPHozQ0FCD+O7du/WDZ/cqLCzEZ599hvnz5wMA3nnnHYN4cHCwfjBp5MiRBgN3Wq0Wn332GQ4fPgzgbr++161bt/SPWbvX1q1bMWrUKGg0Gnz33Xcml69nz57IzMws9SBSzZo1oVKpEBUVZXZORbGyssL7778PAHj//ff1j6sEgNjYWHzwwQcAgMmTJ8PKyvAr84IFC9CgQQOT7zP64IMPoFKpsHz5cuzYsUP/eW5uLkaMGIGSkhIMGDAADRo0KJflWLJkicn1GBsbi+eeew4JCQlwc3PTv/OKiIiIiKis+Gg7IiIiInriffPNN3BwcMCcOXMwfPhwTJ06Fa1bt4ZGo8E///yjHyAKDQ3F2rVrodFoDPJzc3OxYMECLFiwANWqVUPz5s3h5uaGlJQUnD17FomJiQDuXpiWe1zYgxAcHIzo6GgMGjQIP//8MzZt2oQOHTrA19cXSUlJOHDgAPLz8+Hj44N169YhODi41POYNWsWNm3ahNzc3ApYgvKxc+dOvPLKK/D390ezZs306+rixYu4ceMGACA8PNzkQNLVq1dx48YNZGRkGMVGjhyJ+Ph4NGvWDL6+vrhz5w4OHTqEnJwcPPPMM1i2bJlRztmzZzFhwgT4+vqiRYsW8PT0RHJyMs6dO4fbt28DAN59912MGDHCIM/W1hY//vgj+vXrh3379qFOnTpo27YtnJ2dcfbsWVy9ehXA3TvuOnXqZJB77Ngx9O/fH82bN0dgYCBsbW1x7tw5XLx4EU5OTti8eTOaNWtmVNeePXsiIyMDbm5uiI+Pl30X1gcffGA0WKLVavX1Lk9Lly41GLRMSEgAALz55pv6R7pVrVrVaOAvIiIC+/btw6ZNm9CkSRP06NEDALBr1y7k5uZi4MCBeOutt4zmd+fOHVy6dAlVqlQxirVq1Qpz587FxIkT0adPH3Tp0gU+Pj7Yv38/EhISUL9+fZMDdGW1cOFCjB49Go0bN0a9evWgVqsRGxuLM2fOoKCgAJ6enti4caPJRz6Wtd22bduGmTNn6v+OiYkBcPfRkgsWLNB/fuTIkXJbTiIiIiKqfBxIIiIiIqInnkqlwmeffYbXXnsNixYtwp9//oldu3ahqKgIPj4+eOmll/Daa6/pH111v48++gjBwcH4888/cfLkSZw+fRrJycnQaDSoXr06+vTpg5EjR5ZpYKa8de7cGVevXsXixYvx66+/4uzZs0hPT4ebmxtat26Nfv36YfTo0WV+r0qdOnXwxhtvYOHCheVc8/LzxhtvwNXVFYcOHcKpU6eQkpICW1tb+Pv7IywsDCNHjkTHjh1LXe7IkSOxYcMGnD9/Hvv374erqys6duyI4cOH46WXXjKZ06VLF4wZMwYnT57EuXPnkJqaCisrK/j5+SE0NBRvvvkm2rdvbzK3W7du+Pvvv/HVV1/hzz//xIEDB1BcXAxvb2+88MILGDNmjMmByyZNmmDo0KE4fPgwoqOjUVJSgho1amDChAl455139Hc63S8tLQ3A3ff0rFixQrYdhg0bZjCQlJSUhJs3b8Lb29vsd26Z6+bNm/pH+N1LN8ABAAEBAUZxa2trrF+/Ht9//z2WLl2qf5xh48aNMWLECIwaNQoqlarU9ZkwYQKaNm2KuXPn4tixY8jJyUGNGjUwefJkTJ48uVzfVzRu3Dj88ccfOHfuHPbs2YPMzEw4OTmhWbNm6NOnD9566y34+PiYzC1ruyUnJ5vMu3r1qn7wkoiIiIgePyqpot5gSkRERERERE+8VatW4dVXX8U333yDcePGVXZ1iIiIiIiolDiQRERERERERBUmLCwM+/fvx8WLF0v1zi0iIiIiIno4cCCJiIiIiIiIiIiIiIiITLKq7AoQERERERERERERERHRw4kDSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUkcSCIiIiIiIiIiIiIiIiKTOJBEREREREREREREREREJnEgiYiIiIiIiIiIiIiIiEziQBIRERERERERERERERGZxIEkIiIiIiIiIiIiIiIiMokDSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUkcSCIiIiIiIiIiIiIiIiKTOJBEREREREREREREREREJnEgiYiIiIiIiIiIiIiIiEziQBIRERERERERERERERGZxIEkIiIiIiIiIiIiIiIiMokDSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUkcSCIiIiIiIiIiIiIiIiKTOJBEREREREREREREREREJnEgiYjoMTVs2DDUrFlTcbrr169DpVIhKiqqwusEAJcvX0bPnj3h6uoKlUqFzZs3IyoqCiqVCtevX38gdSAiIkM1a9bEsGHDKrsa5cqSY8uePXugUqmwZ8+ecq2TSqVCZGRkuZUXEhKCJk2alFt5VHHu38Yqqo8REdGTqzTnPo/juR8RVSwOJBE9BhYuXAiVSoW2bduajKtUKowdO9bgs+TkZLz99tto0KAB7O3t4ePjgzZt2uD9999Hdna22fO+ePEiJk2ahBYtWsDZ2RlVq1ZF3759ceLECaNpa9asCZVKZfJf3bp1jab/4Ycf0LBhQ9jZ2aFu3bqYP3++2fWqCCkpKZgzZw46d+4Mb29vuLm5oV27dli7dq1i7qxZs6BSqR67iz25ubmIjIws1UWQsLAw/P3335g1axZWrlyJp556yuR0CxcufGCDW0REj6u///4bAwcOREBAAOzs7FCtWjU8/fTTlX5MLS3dRXfdP41GA19fX4SEhODTTz9FcnJyhddh9erVmDdvXoXPByi/87Ty8Lj0oYqk+1GOqX/t2rUzu5wH2ceIiB5V69atg0qlwqZNm4xizZs3h0qlwu7du41iNWrUQPv27fV/79y5EyNGjECTJk1gbW1t1o8w75efn4+vv/4abdu2haurK+zs7FCvXj2MHTsW//77b6nLe1j9+uuvaNWqFezs7FCjRg1Mnz4dxcXFRtOlp6dj1KhR8Pb2hqOjI7p27YpTp04Jy7569Srs7OygUqlMXkcCgF27dqFbt25wdXWFs7MzgoKCzLoOUxrHjh3DW2+9haCgINja2kKlUgmnN+d6VWmvgT0ougFHU/8SExMNpl27di1effVV1K1bFyqVCiEhIZVTaXoo2FR2BYjIcqtWrULNmjVx7NgxXLlyBXXq1BFOn5qaiqeeegqZmZl4/fXX0aBBA6SkpODcuXNYtGgRxowZAycnJ7PmvXTpUvzwww8YMGAA3nrrLWRkZGDx4sVo164dduzYgR49euinnTdvntHFjxs3bmDq1Kno2bOnweeLFy/G6NGjMWDAAEycOBH79+/HuHHjkJubi/fff9/Mlilfhw8fxpQpU9CnTx9MnToVNjY22LBhA15++WXExMRgxowZJvNu3ryJTz/9FI6Ojg+4xuYJCAhAXl4ebG1tS52bm5urX25zTijy8vL07Xjv4OZrr72Gl19+GRqNRv/ZwoUL4eXlxV9JERGV0aFDh9C1a1fUqFEDb7zxBqpUqYL//vsPR44cwTfffIOIiIjKrmKpjRs3Dq1bt0ZJSQmSk5Nx6NAhTJ8+HV999RXWrVuHbt266ac1dWwxV+fOnZGXlwe1Wq3/bPXq1Th//jzGjx9fHosiqzzP0yz1OPahijR48GD06dPH4DNvb28AwKVLl2BlJf4d54PqY0REj7KOHTsCAA4cOIAXXnhB/3lmZibOnz8PGxsbHDx4EF27dtXH/vvvP/z33394+eWX9Z+tXr0aa9euRatWreDn51fqety5cwe9evXCyZMn8cwzz2DIkCFwcnLCpUuXsGbNGixZsgSFhYUWLOnD4ffff8fzzz+PkJAQzJ8/H3///Tc++eQTJCUlYdGiRfrptFot+vbti7Nnz+K9996Dl5cXFi5ciJCQEJw8eVJ24GTChAmwsbFBQUGByfjy5csxYsQIPP300/j0009hbW2NS5cu4b///ivX5dy+fTuWLl2KZs2aoVatWsKBQHOvV5XmGlhl+PjjjxEYGGjwmZubm8HfixYtwsmTJ9G6dWukpKQ8wNrRQ0kiokfatWvXJADSxo0bJW9vbykyMtJoGgBSeHi4/u8vvvhCAiAdPHjQaNqMjAwpLy/P7PmfOHFCysrKMvjszp07kre3t9ShQwfF/JkzZxrVJTc3V/L09JT69u1rMO0rr7wiOTo6SqmpqWbXrzxdu3ZNun79usFnWq1W6tatm6TRaKTs7GyTeS+99JLUrVs3qUuXLlLjxo0fRFUlSZKksLAwKSAgoELnkZycLAGQpk+fbtb0N27ckABIc+bMUZy2cePGUpcuXSyrIBHRE6xPnz6St7e3lJaWZhS7ffu2wd8BAQFSWFjYg6lYGezevVsCIP3yyy9GsTNnzkg+Pj6Sm5ubdOvWrQqrQ9++fS0+rppzzCzNeVpFn1uUpg9ZQu4c6lERGxtr9vmNjq5P7969W/9ZefSx+5WUlJTq3F6r1Uq5ubnlWgciovIWGBgotWnTxuCzHTt2SCqVSho8eLAUGhpqEFu9erUEQNqyZYv+s/j4eKmwsFCSpLLtf/v27StZWVlJ69evN4rl5+dL77zzTqnKKw/Lly+XAEixsbGK05p77teoUSOpefPmUlFRkf6zKVOmSCqVSvrnn3/0n61du9boXC0pKUlyc3OTBg8ebLLsHTt2SGq1Wpo6daoEQDp+/LhBPDY2VrK3t5fGjRunWE9LJSYm6o9/4eHhktwlc0uvV5m6Bvag6frJ/e1tSlxcnFRSUiJJEq/RkCTx0XZEj7hVq1bB3d0dffv2xcCBA7Fq1SrFnKtXr8La2trkozZcXFxgZ2dn9vyDgoKMfhXr6emJTp064Z9//lHMX716NQIDAw1uMd+9ezdSUlLw1ltvGUwbHh6OnJwcbNu2zez6FRUVYcaMGahbty7s7Ozg6emJjh07Ijo62mC6X375BY0aNYKdnR2aNGmCTZs2Gb1jKDAwEAEBAQZ5KpUKzz//PAoKCnDt2jWj+e/btw/r168v9WNKdI9I+fLLL7FkyRLUrl0bGo0GrVu3xvHjx42m37x5M5o0aWJQ/9LO697HyA0bNgxOTk6Ij4/H888/DycnJ3h7e+Pdd99FSUmJPk/3K9sZM2bob4WWe/dDZGSkvv3ee+89qFQqffve/yznmjVr4sKFC9i7d6++XN0dT+auU1POnTuHLl26wN7eHv7+/vjkk0+wfPlyg3lHRkbK3ubNu6OI6FFy9epVNG7c2OiXhQDg4+OjmH/t2jUMGjQIHh4ecHBwQLt27YyOwYWFhZg2bRqCgoLg6uoKR0dHdOrUyeiRMqU9rpVG8+bNMW/ePKSnp2PBggX6z029J0Cr1SIyMhJ+fn5wcHBA165dERMTo/j+mpCQEGzbtg03btzQHxN0xzBz28Bclp6n7dy5Ew4ODhg8eDC+//57qFQqnD592mg63a964+PjhXUxpw+J3rd4/7mB7jgbExODIUOGwN3dHR07dsSXX34JlUqFGzduGJUxefJkqNVqpKWl6T87evQoevXqBVdXVzg4OKBLly44ePCgPr57927ZRx+tXr0aKpUKhw8fll328qb0LgpRHwOAgoICTJ8+HXXq1IFGo0H16tUxadIko19w6x5pvWrVKjRu3BgajQY7duwQ1uuZZ57BH3/8gaeeegr29vZYvHixpYtLRFShOnbsiNOnTyMvL0//2cGDB9G4cWP07t0bR44cgVarNYipVCp06NBB/5mfn1+ZnsoB3D0Gbdu2DSNGjMCAAQOM4hqNBl9++aXBZ3/99Rc6deoER0dHuLm54bnnnjO6ZnLjxg289dZbqF+/Puzt7eHp6YlBgwaZfOfRhQsX0K1bN4Pvtvcus44kSfjkk0/g7++vP/e5cOGCWcsZExODmJgYjBo1CjY2//dQq7feeguSJGH9+vX6z9avXw9fX1/0799f/5m3tzdefPFFbNmyxeh4VVRUhLfffhtvv/02ateubXL+3333HUpKSvDxxx8DALKzsyFJktF0955nfvvtt6hVqxYcHBzQs2dP/Pfff5AkCTNnzoS/vz/s7e3x3HPPITU11aAMX19f2NvbK7aJpderTF0DM8eWLVvQt29f+Pn5QaPRoHbt2pg5c6b++sy9jh49ij59+sDd3R2Ojo5o1qwZvvnmG5PlZmVlmSxDp3r16op3VNOTg4+2I3rErVq1Cv3794darcbgwYOxaNEiHD9+HK1bt5bNCQgIQElJCVauXImwsLAKqVdiYiK8vLyE05w+fRr//PMPpkyZYvQ5AKN35wQFBcHKygqnT5/Gq6++alY9IiMjMXv2bIwcORJt2rRBZmYmTpw4gVOnTuHpp58GAGzbtg0vvfQSmjZtitmzZyMtLQ0jRoxAtWrVzF5WAEbLW1JSgoiICIwcORJNmzY1q6z7rV69GllZWXjzzTehUqnwxRdfoH///rh27Zr+pHfnzp0YMGAAGjVqhNmzZyMlJQXDhw+Hv79/meZ5b/1DQ0PRtm1bfPnll9i1axfmzp2L2rVrY8yYMfD29tY/YueFF17QnzA2a9bMZHn9+/eHm5sbJkyYoH/0i9yjeebNm4eIiAg4OTnp+4evry8A89apKfHx8ejatStUKhUmT54MR0dHLF261OiRR/379zd6POTJkycxb948sy68EhE9LAICAnD48GGcP3++1O/ou337Ntq3b4/c3FyMGzcOnp6eWLFiBZ599lmsX79e/yiZzMxMLF26FIMHD8Ybb7yBrKws/PDDDwgNDcWxY8fQokULg3LNOa6VxcCBAzFixAjs3LkTs2bNkp1u8uTJ+OKLL9CvXz+Ehobi7NmzCA0NRX5+vrD8KVOmICMjAzdv3sTXX38NAPpjWGnbQIkl52lbt27FwIED8dJLL2HZsmXIzc3F22+/jVWrVqFly5YG065atQohISHC8x1L+pCSQYMGoW7duvj0008hSRKeeeYZTJo0CevWrcN7771nMO26devQs2dPuLu7A7h7Ma53794ICgrC9OnTYWVlheXLl6Nbt27Yv38/2rRpg5CQEFSvXh2rVq0yePSRbtlr166N4ODgcl2m3Nxc3Llzx+AzV1dXs/q2qI9ptVo8++yzOHDgAEaNGoWGDRvi77//xtdff41///0XmzdvNijrr7/+wrp16zB27Fh4eXkpvvfj0qVLGDx4MN5880288cYbqF+/vvkLTURUCTp27IiVK1fi6NGj+h8cHjx4EO3bt0f79u2RkZGB8+fP67+bHjx4EA0aNICnp2e5zP/XX38FcPcxuubYtWsXevfujVq1aiEyMhJ5eXmYP38+OnTogFOnTun308ePH8ehQ4fw8ssvw9/fH9evX8eiRYsQEhKCmJgYODg4ALh7DaJr164oLi7GBx98AEdHRyxZssTkQMi0adPwySefoE+fPujTpw9OnTqFnj17mvXYPblrM35+fvD39zf4ocrp06fRqlUro0GHNm3aYMmSJfj3338NrovMmzcPaWlpmDp1KjZu3Cjbbg0aNMD27dvx3nvvIT4+Hu7u7ggPD8eMGTOM5rVq1SoUFhYiIiICqamp+OKLL/Diiy+iW7du2LNnD95//31cuXIF8+fPx7vvvotly5YptoG5bWLO9Sq5a2DmiIqKgpOTEyZOnAgnJyf89ddfmDZtGjIzMzFnzhz9dNHR0XjmmWdQtWpVvP3226hSpQr++ecfbN26FW+//bZBmV27dkV2djbUajVCQ0Mxd+7cSn13Ez0CKvV+KCKyyIkTJyQAUnR0tCRJdx9F4e/vL7399tsG0+G+R9slJiZK3t7eEgCpQYMG0ujRo6XVq1dL6enp5VKvffv2SSqVSvroo4+E073zzjsSACkmJsbg8/DwcMna2tpkjre3t/Tyyy+bXZfmzZsb3XJ8v6ZNm0r+/v4Gj+jbs2ePBEDx9vaUlBTJx8dH6tSpk1FswYIFkqurq5SUlCRJUukeP6N7RIqnp6fBrdFbtmyRAEi//fab/rMWLVpIVatWNVh/O3fuNKv+985r+fLl+s/CwsIkANLHH39sMG3Lli2loKAg/d+lfbSd3KNfTN2CL3fbtDnr1JSIiAhJpVJJp0+f1n+WkpIieXh4CG//T05OlmrUqCE1bdr0kX/0DhE9WXbu3ClZW1tL1tbWUnBwsDRp0iTpjz/+0D/G5V73P95k/PjxEgBp//79+s+ysrKkwMBAqWbNmvpHXBQXF0sFBQUGZaWlpUm+vr7S66+/rv+sNMc1U0SPttNp3ry55O7urv/7/mNLYmKiZGNjIz3//PMGeZGRkRIAg+UvzWPHzG0DSTLv0XalOU+799xiw4YNkq2trfTGG2/o148kSdLgwYMlPz8/g89OnTpldOw3xdw+ZOpcQm6Zp0+fLgEw+Zib4OBgg/MMSZKkY8eOSQCkH3/8UZKku+e7devWlUJDQyWtVqufLjc3VwoMDJSefvpp/WeTJ0+WNBqNQdslJSVJNjY2Zp+7mEO3/Kb+6frQ/dtYafrYypUrJSsrK4PtUZIk6bvvvjN6PA4AycrKSrpw4YJZdQ8ICJAASDt27DB7eYmIKtuFCxckANLMmTMlSZKkoqIiydHRUVqxYoUkSZLk6+srffvtt5IkSVJmZqZkbW0tvfHGG7LllfbRdi+88IIEwOSjX01p0aKF5OPjI6WkpOg/O3v2rGRlZSUNHTpU/5mpR4sePnzY4DgoSf93nnb06FH9Z0lJSZKrq6vBuU9SUpKkVqulvn37GhwzP/zwQ6NzH1PmzJkjAZDi4uKMYq1bt5batWun/9vR0dHovEeSJGnbtm1Gx5mEhATJ2dlZWrx4sSRJ8o9ac3Fxkdzd3SWNRiN99NFH0vr166UhQ4ZIAKQPPvhAP53uOOzt7W1wzJ88ebIEwOjRfIMHD5bUarWUn59vcrlFj7az5HqV3DUwc5jqG2+++abk4OCgX47i4mIpMDBQCggIMOqb967/tWvXSsOGDZNWrFghbdq0SZo6dark4OAgeXl5mVzXOny0HfHeNKJH2KpVq+Dr66t/iaRKpcJLL72ENWvWCG9N9fX1xdmzZzF69GikpaXhu+++w5AhQ+Dj44OZM2eavFXYXElJSRgyZAgCAwMxadIk2em0Wi3WrFmDli1bomHDhgax+19ufS87OzuD29eVuLm54cKFC7h8+bLJ+K1bt/D3339j6NChBnfHdOnSRfEuIq1Wi1deeQXp6emYP3++QSwlJQXTpk3DRx99pH/8W1m89NJL+l/fAkCnTp0AQP8YvYSEBJw5cwZhYWFwdXXVT/f000+jUaNGZZ6vzujRow3+7tSpk8lH+D1ISutUzo4dOxAcHGzwy3APDw+88sorsjklJSUYPHgwsrKysGnTJjg6Opa12kRED9zTTz+Nw4cP49lnn8XZs2fxxRdfIDQ0FNWqVdP/klbO9u3b0aZNG/0LrYG7d0eMGjUK169fR0xMDADA2tpaf8zWarVITU1FcXExnnrqKZw6dcqoXKXjmiWcnJyQlZUlG//zzz9RXFxs9CiSiIgIi+Zb2jZQUpbztJ9//hkvvfQS3nzzTSxevNjgF7pDhw7FrVu3DB61t2rVKtjb25t8HM+9LOlDSu4/xwDu9o+TJ0/i6tWr+s/Wrl0LjUaD5557DgBw5swZXL58GUOGDEFKSgru3LmDO3fuICcnB927d8e+ffv0j/YZOnQoCgoKDB69s3btWhQXF5t9d3tpjBo1CtHR0Qb/mjdvbnG5v/zyCxo2bIgGDRrol/fOnTvo1q0bABg9RrFLly6lOg8MDAxEaGioxfUkInpQGjZsCE9PTxw4cAAAcPbsWeTk5OgfF9a+fXv9404PHz6MkpISg3MaS2VmZgIAnJ2dFafVfWcfNmwYPDw89J83a9YMTz/9NLZv367/7N47ioqKipCSkoI6derAzc3N4Jxi+/btaNeuHdq0aaP/zNvb2+i77a5du/R36KhUKv3n48ePN2s5ddde7n+KB2B8bSYvL092unvLAoD3338ftWrVwsiRI4Xzz87ORlpaGmbMmIGPP/4YAwYMwKpVq9CrVy988803Rud9gwYNMrgu0rZtWwDAq6++avBovrZt26KwsFD4eF85Zb1eJboGZo57+0ZWVhbu3LmDTp06ITc3FxcvXgRw946n2NhYjB8/3uixxPeu/xdffBHLly/H0KFD8fzzz2PmzJn4448/kJKSIryzn4gDSUSPqJKSEqxZswZdu3ZFbGwsrly5gitXrqBt27a4ffs2/vzzT2F+1apVsWjRIiQkJODSpUv43//+B29vb0ybNg0//PBDmeqUk5ODZ555BllZWdiyZYvsY8sAYO/evYiPjzd5Ed/e3l72Nuv8/Hyznlur8/HHHyM9PR316tVD06ZN8d577+HcuXP6uO45/Pc/ykzus3tFRERgx44dWLp0qdFFgqlTp8LDw0Px4lRqaioSExP1/zIyMgziNWrUMPhbd/FN944AXf1N3X58/2NJkpOTDeaVnZ0trJudnZ3RIJi7u7vB+wkqg9I6lXPjxo1Sr+epU6fir7/+wurVq2Wf20xE9DBr3bo1Nm7ciLS0NBw7dgyTJ09GVlYWBg4cqB8MMuXGjRsmH2+l++J773tsVqxYgWbNmunfW+ft7Y1t27YZHdMA5eOaJbKzs4UXdOSO+R4eHgaDW2VRmjYwR2nO02JjY/Hqq69iwIABmD9/vsGFAuDuYFDVqlX179HUarX4+eef8dxzz5l1AaysfUhJYGCg0WeDBg2ClZUV1q5dC+Duex1++eUX9O7dGy4uLgCg/yFJWFgYvL29Df4tXboUBQUF+nZv0KABWrdubfAO0VWrVqFdu3bC439JSYnBOVNiYqJZjwCqW7cuevToYfDP0r4F3F3mCxcuGC1vvXr1ANz9Ide9TLWtSGmnJyKqbCqVCu3bt9e/C+ngwYPw8fHR79vvHUjS/bc8B5J0xyTRD1h0dOcfcudVuh9DAHcHKaZNm4bq1atDo9HAy8sL3t7eSE9PNzinuHHjhlnXAOSuF3h7e5t1fNJde7n//UaA8bUZe3t72enuLevIkSNYuXIlvv76a8V37+hyBg8ebPD54MGDkZeXZ/QOyPvPM3WDStWrVzf5eVnOP8t6vUp0DcwcFy5cwAsvvABXV1e4uLjA29tb/6MYXd/Q/RCnLI8j7tixI9q2bYtdu3aVqX70ZOBAEtEj6q+//kJCQgLWrFmDunXr6v+9+OKLAGDwhVlEpVKhXr16iIiIwL59+2BlZWV27r0KCwvRv39/nDt3Dlu2bFE8cK1atQpWVlZGJwTA3YsnJSUlRl+KCwsLkZKSAj8/P7Pr1blzZ1y9ehXLli1DkyZNsHTpUrRq1QpLly41uwxTZsyYgYULF+Kzzz4zei7y5cuXsWTJEowbNw63bt3C9evXcf36deTn56OoqAjXr1/Xv9ixf//+qFq1qv7f/c+stba2Njn/stw11rp1a4N53f/yz/vJzbuyVdQ6vd/mzZvx+eef4+OPP0avXr3KtWwiogdNrVajdevW+PTTT7Fo0SIUFRXhl19+sbjcn376CcOGDUPt2rXxww8/YMeOHYiOjka3bt1MvvC5PI9r9yoqKsK///6r+COQilDaNigNc87Tqlativbt22P79u04ceKEURnW1tYYMmQINmzYgPz8fOzevRu3bt0q9R05oj50/+CVjugOeVMXWvz8/NCpUyesW7cOwN2LTXFxcXjppZf00+jadM6cOUZ3/+j+3ftjpqFDh2Lv3r24efMmrl69iiNHjigu+3///WdwzlS1alUcOnRImFORtFotmjZtKru8999lV5ofXZVleiKih0HHjh2RkZGBv//+W/9+JJ327dvjxo0biI+Px4EDB+Dn54datWqV27wbNGgAAPj777/LrUzg7o9VZ82ahRdffBHr1q3Dzp07ER0dDU9PT4vPKcqiatWqAO7eVXW/hIQEg2szVatWlZ0OgH7aSZMmoVOnTggMDNRfK9G9XzAhIQFxcXH6XF2O7n3JOrp3F98/ECR3nlme559lvV4lugamJD09HV26dMHZs2fx8ccf47fffkN0dDQ+//xzACi3vlG9enX9tSoiU2yUJyGih9GqVavg4+ODb7/91ii2ceNGbNq0Cd99912pvhjWqlUL7u7uJg/+IlqtFkOHDsWff/6JdevWoUuXLsLpCwoKsGHDBoSEhJg8yOoePXbixAn06dNH//mJEyeg1WpL/dJqDw8PDB8+HMOHD0d2djY6d+6MyMhIjBw5EgEBAQCAK1euGOWZ+gwAvv32W0RGRmL8+PF4//33jeLx8fHQarUYN24cxo0bZxQPDAzE22+/jXnz5mHu3LkGJz+lGSQDoK+/qce8Xbp0yeDvVatWGdxmXR4n0nIXjsqDqGzROpUTEBBg9nr+999/ERYWhueffx4ffvhh2RaAiOghpXs5sOh4HxAQYHQcAaB/dIbu+LN+/XrUqlULGzduNNhvT58+vTyrrGj9+vXIy8sTPp7r3mP+vXdgpKSkmPWLVLnj0oNqA7nzNDs7O2zduhXdunVDr169sHfvXjRu3NhgmqFDh2Lu3Ln47bff8Pvvv8Pb29uiR5nd34d0v2pOT083mO7eO9fM9dJLL+Gtt97CpUuXsHbtWjg4OKBfv376uO4OYRcXF/To0UOxvJdffhkTJ07Ezz//jLy8PNja2hoMTJlSpUoVREdHG3xWHo+oUyLXx2rXro2zZ8+ie/fuFXruRUT0KNHdYXTgwAEcPHjQ4HFtQUFB0Gg02LNnD44ePWpwXaE89OvXD7Nnz8ZPP/2kf0yvHN35h9x5lZeXl/4R6uvXr0dYWBjmzp2rnyY/P9/o+BoQEGDWNYB7rxfc+/0/OTnZrHOfe6/N3PsYvVu3buHmzZsYNWqUwbT79++HVqs1uNPo6NGjcHBw0N9FGxcXhxs3bpi8G/bZZ5+Fq6urfnmDgoJw+fJlxMfHG9T/1q1bAGDRawTKqizXq5SugSnZs2cPUlJSsHHjRnTu3Fn/eWxsrMF0unOk8+fPm3WOdL9r165VSpvSo4N3JBE9gvLy8rBx40Y888wzGDhwoNG/sWPHIisrS/bZ9UePHtXfOn2vY8eOISUlxeQt1yIRERFYu3YtFi5ciP79+ytOv337dqSnp8ve0tutWzd4eHhg0aJFBp8vWrQIDg4O6Nu3r9l1S0lJMfjbyckJderU0d9y7efnhyZNmuDHH380eNTb3r17Tf66aO3atRg3bhxeeeUVfPXVVybn2aRJE2zatMnoX+PGjVGjRg1s2rQJI0aMAHD3xOjex5+U9r1GVatWRYsWLbBixQqDW92jo6ONHjfToUMHg3mVx0CSg4MDAOMLR8Dd26svXrxY5sf6ODo6mixXaZ3KzTs0NBSHDx/GmTNn9J+lpqYa/bI7OzsbL7zwAqpVq4YVK1bwgg0RPbJ2795t8peWumfxi473ffr0wbFjx3D48GH9Zzk5OViyZAlq1qypP17pfuF573yOHj1qkFfRzp49i/Hjx8Pd3R3h4eGy03Xv3h02NjZG5xcLFiwwaz6Ojo4mj2nl3QZlOU9zdXXFH3/8AR8fHzz99NMG7xgC7r6HoVmzZli6dCk2bNiAl19+2eBdAXLM7UMuLi7w8vLCvn37DKZbuHCh4jzuN2DAAFhbW+Pnn3/GL7/8gmeeecbgHYVBQUGoXbs2vvzyS5OP6U1OTjb428vLC71798ZPP/2kf6+Cl5eXsA52dnYV8og6JXJ97MUXX0R8fDy+//57o1heXp7J/kJE9Lh76qmnYGdnh1WrViE+Pt7gjiSNRoNWrVrh22+/RU5OTrk+1g4AgoOD0atXLyxduhSbN282ihcWFuLdd98FYPid/d7vt+fPn8fOnTsNBiOsra2Njrvz5883usO3T58+OHLkCI4dO6b/LDk52ei7bY8ePWBra4v58+cblDtv3jyzlrNx48Zo0KABlixZYlCHRYsWQaVSYeDAgfrPBg4ciNu3b2Pjxo36z+7cuYNffvkF/fr1078/acmSJUbXSnSvBPjyyy8NlkH3w497H+ur1WqxfPlyeHh4ICgoyKzlKE9luV6ldA1MialzzcLCQqPzrFatWiEwMBDz5s0zupZyb+7950q6Op48eZJPYyEh3pFE9Aj69ddfkZWVhWeffdZkvF27dvD29saqVatM/uJy5cqVWLVqFV544QUEBQVBrVbjn3/+wbJly2BnZ1eqOzDmzZuHhQsXIjg4GA4ODvjpp58M4i+88ILBl3/g7p0xGo1G9gXP9vb2mDlzJsLDwzFo0CCEhoZi//79+OmnnzBr1iyDF1QqadSoEUJCQhAUFAQPDw+cOHEC69evx9ixY/XTfPrpp3juuefQoUMHDB8+HGlpaViwYAGaNGlicIHi2LFjGDp0KDw9PdG9e3ejk7T27dujVq1a8PLywvPPP2+yrQCYjFli9uzZ6Nu3Lzp27IjXX38dqampmD9/Pho3bqz4HiRL2dvbo1GjRli7di3q1asHDw8PNGnSRD+YNnz4cCxfvhzDhg0rddlBQUFYtGgRPvnkE9SpUwc+Pj7o1q2bWevU1LwnTZqEn376CU8//TQiIiLg6OiIpUuXokaNGkhNTdUPGM2YMQMxMTGYOnUqtmzZYlCn2rVrIzg4uMztRUT0IEVERCA3NxcvvPACGjRogMLCQhw6dAhr165FzZo1MXz4cNncDz74AD///DN69+6NcePGwcPDAytWrEBsbCw2bNig/6XpM888g40bN+KFF15A3759ERsbi++++w6NGjWqkGPQ/v37kZ+fj5KSEqSkpODgwYP49ddf4erqik2bNqFKlSqyub6+vnj77bcxd+5cPPvss+jVqxfOnj2L33//HV5eXoo/HAgKCsLatWsxceJEtG7dGk5OTujXr1+5t0FZz9O8vLwQHR2Njh07okePHjhw4ACqVaumjw8dOlR/Ucvcx9qVpg+NHDkSn332GUaOHImnnnoK+/btw7///lvq5ffx8UHXrl3x1VdfISsry+hc1srKCkuXLkXv3r3RuHFjDB8+HNWqVUN8fDx2794NFxcX/PbbbwY5Q4cO1V/smjlzZqnr9KDI9bHXXnsN69atw+jRo7F792506NABJSUluHjxItatW4c//vhDf5cYEdGTQvfI1f3790Oj0RgNKrRv315/Z4+pgaRz587pf3x75coVZGRk4JNPPgFw9y7Ue++GNeXHH39Ez5490b9/f/Tr1w/du3eHo6MjLl++jDVr1iAhIUH/OPk5c+agd+/eCA4OxogRI5CXl4f58+fD1dUVkZGR+jKfeeYZrFy5Eq6urmjUqBEOHz6MXbt2wdPT02DekyZNwsqVK9GrVy+8/fbbcHR0xJIlSxAQEGDw/mBvb2+8++67mD17Np555hn06dMHp0+f1p/7mGPOnDl49tln0bNnT7z88ss4f/48FixYgJEjR+rfnQncHUhq164dhg8fjpiYGHh5eWHhwoUoKSnBjBkz9NP17NnTaB66QY8uXboYHM+ee+45dO/eHbNnz8adO3fQvHlzbN68GQcOHMDixYv1g1Pl4caNG1i5ciUA6B8VrOsPAQEB+lcalOV6ldI1MCXt27eHu7s7wsLCMG7cOKhUKqxcudJo0NHKygqLFi1Cv3790KJFCwwfPhxVq1bFxYsXceHCBfzxxx/68lq2bImnnnoKrq6uOHXqFJYtW4bq1asbnWfu27dP/0Oh5ORk5OTk6Nulc+fOBndI0RNAIqJHTr9+/SQ7OzspJydHdpphw4ZJtra20p07dyQAUnh4uD527tw56b333pNatWoleXh4SDY2NlLVqlWlQYMGSadOnSpVXcLCwiQAsv9iY2MNps/IyJDs7Oyk/v37K5a9ZMkSqX79+pJarZZq164tff3115JWqy1V/T755BOpTZs2kpubm2Rvby81aNBAmjVrllRYWGgw3Zo1a6QGDRpIGo1GatKkifTrr79KAwYMkBo0aKCfZvny5cJlXb58ubAuXbp0kRo3bmxWvWNjYyUA0pw5c4xiAKTp06cbfLZhwwapYcOGkkajkRo1aiRt3LhRCgsLkwICAsye1731DwsLkxwdHY2mnT59unT/oePQoUNSUFCQpFarDeqma697y5VbLt209/aXxMREqW/fvpKzs7MEQOrSpYskSeatU1PzliRJOn36tNSpUydJo9FI/v7+0uzZs6X//e9/EgApMTFRv+xy6zgsLEyxPYmIHha///679Prrr0sNGjSQnJycJLVaLdWpU0eKiIiQbt++bTBtQECA0T7u6tWr0sCBAyU3NzfJzs5OatOmjbR161aDabRarfTpp59KAQEBkkajkVq2bClt3brV6BhU2uPa/Xbv3m2wP7a1tZW8vb2lzp07S7NmzZKSkpKMckwdW4qLi6WPPvpIqlKlimRvby9169ZN+ueffyRPT09p9OjRRvPbvXu3/rPs7GxpyJAhkpubmwRAv3zmtoG5y1qa8zRT5xZXrlyRqlatKjVs2FBKTk7Wf56QkCBZW1tL9erVE87/XqXpQ7m5udKIESMkV1dXydnZWXrxxRelpKQko2XWnUvcW7f7ff/99xIAydnZWcrLyzM5zenTp6X+/ftLnp6ekkajkQICAqQXX3xR+vPPP42mLSgokNzd3SVXV1fZ8iwh6t86929jpeljkiRJhYWF0ueffy41btxY0mg0kru7uxQUFCTNmDFDysjI0E93/3m/koCAAKlv375mT09E9DCZPHmyBEBq3769UWzjxo36Y0lxcbFRXPT93tzvfbm5udKXX34ptW7dWn+crFu3rhQRESFduXLFYNpdu3ZJHTp0kOzt7SUXFxepX79+UkxMjME0aWlp0vDhwyUvLy/JyclJCg0NlS5evGjyPO3cuXNSly5dJDs7O6latWrSzJkzpR9++MHo3KekpESaMWOGVLVqVcne3l4KCQmRzp8/b7JMOZs2bZJatGih/x49depUo2sqkiRJqamp0ogRIyRPT0/JwcFB6tKli3T8+HHF8nXrwtS0WVlZ0ttvvy1VqVJFUqvVUtOmTaWffvrJYBq547DuWPvLL78ozu/+c817/+muRdzL3OtVpbkGJnLw4EGpXbt2kr29veTn5ydNmjRJ+uOPP4zOJSRJkg4cOCA9/fTTkrOzs+To6Cg1a9ZMmj9/vj4+ZcoUqUWLFpKrq6tka2sr1ahRQxozZoz+msi9dOdtpv4pndPS40clSRa+2ZaI6DHVokULeHt7Gz0jnx4v48ePx+LFi5GdnS37Ek4iInp8paenw93dHZ988gmmTJlS2dWpMHfu3EHVqlUxbdo0fPTRR5VdnQequLgYfn5+6Nevn8HjcYiIiIiIyDx8RxIRPfGKiopQXFxs8NmePXtw9uxZhISEVE6lqELk5eUZ/J2SkoKVK1eiY8eOHEQiInoC3H8cAP7v0bOP+zE/KioKJSUl+kezPEk2b96M5ORkDB06tLKrQkRERET0SOIdSURkUl5ensmX/d7Lw8MDarX6AdXIUHnW7/r16+jRowdeffVV+Pn54eLFi/juu+/g6uqK8+fPGz2PmB5dLVq0QEhICBo2bIjbt2/jhx9+wK1bt/Dnn3/y2b5ERE+AqKgoREVFoU+fPnBycsKBAwfw888/o2fPnvrnxj9u/vrrL8TExOCjjz5C165dDV6C/bg7evQozp07h5kzZ8LLywunTp2q7CoRERER6SUnJ6OkpEQ2rlarS/WecKKKxDuSSFZ2djbGjBlTIS9Kftw8jm21du1aVK1aVfjv0KFDpS63vNqqPOvn7u6OoKAgLF26FBEREYiKikLfvn1x4MCBSh1Eehz7VUUoTTv16dMH27dvx4QJE/D555+jRo0a+P3335+YQaTs7GyMHj2afYroIZKbm1vZVXiiNGvWDDY2Nvjiiy8wfvx47N+/H2+//TY2bNhQ2VWrMB9//DEmTpyIFi1aYP78+U9Un1u0aBHGjBkDHx8f/Pjjj5VdnSfak9TviB4Hubm5/N5gIbZh+Xjc27F169bC61r9+/cvl/k87u34ILANeUcSCdy8eRPVq1fHf//9B39//8quzkPtcWyrhIQEXLhwQThNUFAQ3N3dS1VuebVVRdXvYfI49quKwHYyH9uK6OGSnZ0Nd3d3rFmzBgMGDKjs6tATgH2OKgP7HdGj59atW6hWrRquX7+OgICAyq7OI4ltWD4e93Y8ePCgyUcv6+h++Gypx70dHwS2IWBT2RUgooeT7tcPD6uHvX5ERERKnJycUL9+fUycOJEXV8sgOzsbTk5OlV2NRwr7HJWFpdsa+x0REZFpHTp0qOwqEJmNj7YjIiIiIqokixcvRlxcHH7//ffKrsoj4+DBg2jSpAnc3d35uKwyYJ8jc5XntsZ+R/Ro0Wq1AACVSlXJNXl0sQ3LB9uxfLAdLcc25B1JTwStVotbt27B2dm5VJ09KytL/9/MzMyKqt5jgW1lPraV+dhW5mE7mY9tZUiSJGRlZcHPzw9WVvxtTUUq67nIk6Bp06aoXbs23nrrLZw9e7ayq/NQ27t3LyZMmICrV6+iWrVqWLZsGYqLi7k/KyX2OVJSEdsa+51pPBd5cHguUjq67w05OTk8zpYR27B8sB3LB9vRco9rG5bmXITvSHoC6N6JQURERMb4zqiKx3MRIiIieTwXqXg8FyEiIpJnzrkI70h6Ajg7OwO42yFcXFwquTZEREQPh8zMTFSvXl1/nKSKo2vjYcOGQa1Wm5ympKRENl8uR8fPz082lpKSIsxVelxTfn6+bKxRo0bCXN2v1kyZNGmSMHfy5MnCuKi93N3dhbkajUY2lpaWJswVtTUAJCUlycaU1qNoXXh7ewtzk5OThXHRenRwcBDmihQWFgrjNjbir1tFRUWyMdE6BgA7OzvZmGh5AcseyeHq6iqMx8fHy8aULuIq1VvU3kr9y9PTUzYWEhIizN21a5cwLur39vb2wlxR/ObNm8Jcpb4r2tZTU1PLXC8vLy9hrlLZSn27rLmi5TVnvqJf4SqVLSLqt4WFhVi6dCnPRR4AXRvv2LEDjo6OJqfJycmRzc/OzhaWL4or7duU+qZo/6Z0jcfDw0M25ubmJsyVaycd0XFI6fgn2t4q8o4x3aOpyhJXWk/FxcXCuOiYr3Q+oVRvEWtra2Fc1L+Ujq2i9WxJH1CKW5Jbkf1LqWxR3NJ6ie4TUbqHxJJcpb5ZUfVSUpG5omW2ZFsFxP2goraZrKws1K1b16xzEQ4kPQF0ndDFxYUDSURERPfh400qnq6N1Wp1mQaSlC7giS5kKOUqfekXfRkQzRcQXzBQOiez5IKoUr1EZVvS1paWXVHLBIjXoyUXiJX2H5ZcRFHqm6J6K32JteQRWkrtJbropLQelb64i9pb6WKXaN5OTk5lzgUqbptSWialskVxS8q2dHtU6tsij+JAkjnnGTwXqXi6NnZ0dFTc5k2xZABCaf0q9U1R/1MaUBYNBim1gyX7RltbW2HuoziQpLTvqsiBJEsG4JXORSwZSBKtZw4klS7OgaTSxysr93EbSDJn3vpyFKcgIiIiIiIiIiIiIiKiJxIHkoiIiIiIiIiIiIiIiMgkDiQRERERERERERERERGRSRxIIiIiIiIiIiIiIiIiIpPEbz57yMyaNQvbtm3DmTNnoFarkZ6ebjRNXFwcxowZg927d8PJyQlhYWGYPXu2wUveZsyYge+//x41a9bEsmXLUK9ePX1sz549mDhxIi5cuIDq1atj6tSpGDZsmD4+e/ZsbNy4ERcvXoS9vT3at2+Pzz//HPXr19dPU7NmTURFRSEkJAQAkJqaioiICPz222+wsrLCgAED8M033xi8uPDcuXMIDw/H8ePH4e3tjYiICEyaNEkfj4yMxPXr1xEVFWVUPhEREdGjwMnJSfaF0V27dpXN2717t7DcpKQk2Vh2drYw183NTRgXEb00GQBu374tG5s4caIwV+mlyt7e3rIxZ2dnYW58fLxsrFq1asJcpWX29/eXjcXFxQlzRS/tFq1jc7i7u8vG0tLShLn29vZlnq9S/xO9QL1OnTrC3ISEBNlYfn6+MFfUHoC474r6HiDuf0r9umrVqmWul4uLizBX1IfWrVsnzM3LyxPGRS9Yr127tjBX1P9q1aolzE1NTRXGRS9vV+oDor6bm5srzK1Zs6YwLuq7TZo0EeaeO3dONhYYGCjMVSLqu1lZWcJc0TJ1795dNpaTk4OFCxcqV47KTUlJiey+SPSSdKX9lyiulCvahwDil6Qr5YriSrmWvjReRLRMSi98V4pLklSmOinlWvKiewCwtraWjdna2lpUdlnnqxS3ZJktXY+ieGXlApb1r8oo15yyRfGKrJcSpXVhCdH+TWnfZ8n+Xokl27qovcprHT9SdyQVFhZi0KBBGDNmjMl4SUkJ+vbti8LCQhw6dAgrVqxAVFQUpk2bpp/m4MGD2LZtG7Zs2YIhQ4Zg7Nix+lhsbCz69u2Lrl274syZMxg/fjxGjhyJP/74Qz/N3r17ER4ejiNHjiA6OhpFRUXo2bMncnJyZOv9yiuv4MKFC4iOjsbWrVuxb98+jBo1Sh/PzMxEz549ERAQgJMnT2LOnDmIjIzEkiVLLGkuIiIiIiIiIiIiIiIiizxSdyTNmDEDABAVFWUyvnPnTsTExGDXrl3w9fVFixYtMHPmTLz//vuIjIyEWq1GWloa/Pz80KxZMxQXFxuU9d133yEwMBBz584FADRs2BAHDhzA119/jdDQUADAjh07DOYZFRUFHx8fnDx5Ep07dzaq0z///IMdO3bg+PHjeOqppwAA8+fPR58+ffDll1/Cz88Pq1atQmFhIZYtWwa1Wo3GjRvjzJkz+OqrrwwGnIiIiIiIiIiIiIiIiB6kR+qOJCWHDx9G06ZN4evrq/8sNDQUmZmZuHDhgv7v/Px8ODg4oFevXpg9e7ZBfo8ePQzKDA0NxeHDh2XnmZGRAQDw8PCQrZObm5t+EAkAevToASsrKxw9elQ/TefOnaFWqw3me+nSJcVHbRAREREREREREREREVWUR+qOJCWJiYkGg0gA9H8nJiYCuPvc0R07diApKQlubm4Ggzdy+ZmZmcjLyzN6PrpWq8X48ePRoUMHg2c5X79+3aBMHx8fgzwbGxt4eHjo65SYmGj0POd76+3u7o7IyEiT5RMREREREREREREREVWUSh9I+uCDD/D5558Lp/nnn3/QoEGDcp3v/YM7ZREeHo7z58/jwIED5VCj8pOdnW3w4lWlF4MSERE9yeLj45GZman/283NDU5OTpVYIyIiIiIiIiKih0elDyS98847GDZsmHCaWrVqmVVWlSpVcOzYMYPPbt++rY+Zk6+b/t58FxcXo7uRxo4di61bt2Lfvn3w9/cXlpmUlGTwWXFxMVJTU/V1kpuvufW+33vvvYfvvvuu1HlERERPokaNGhn8PXr0aCxatKiSakNERERERERE9HCp9IEkb29veHt7l0tZwcHBmDVrFpKSkvR3HEVHR8PFxcXoIpFc/vbt2w0+i46ORnBwsP5vSZIQERGBTZs2Yc+ePUaPpDNVZnp6Ok6ePImgoCAAwF9//QWtVou2bdvqp5kyZQqKiopga2urn2/9+vXh7u5ufgP8f3PmzMGUKVP0f2dlZZm1/ERERE+imJgYODs76/92c3OrvMo85gIDA41+nKNz/znYve5/9PD9iouLZWP33m1miouLizAu9x5M4G7fERH9IEjp3OzkyZPCeElJiWxM6THIWq1WNpaQkCDM9fLyEsb/+ecf2ZiozgDg5+cnG7P0vaF5eXmyMaX+ZWMj/5Xp/h+M3U9pf5Kfny8bu3HjhjBXRPedQo5omQBAo9HIxlJSUoS5rq6usjHd+2XlODg4COPVq1cvc72ys7OFcRGl9SjangsKCoS59z5JorSU1qOo3yttj6J9o9J68vT0FMbv/xHlvZT2q6JlVtpm5I4/5lDqu3fu3JGNRUdHy8aU+geVP0mSIEmSyZhKpZLNU9reRPtd0XkKAFhZiV9fLirb2tpamCtaJkvJtaM58xXFldpDqWxRvSqTJcustM8WUSpb1Lcrsn9Z0kcqK9dSFdk3RWUrzVcUF31vMIclyyxaFxXZlkrLLNoeLW0vEaVt+UGo9IGk0oiLi0Nqairi4uJQUlKCM2fOAADq1KkDJycn9OzZE40aNcJrr72GL774AomJiZg6dSrCw8OFX4Z0Ro8ejQULFmDSpEl4/fXX8ddff2HdunXYtm2bfprw8HCsXr0aW7ZsgbOzs/49R66uriZPTBs2bIhevXrhjTfewHfffYeioiKMHTsWL7/8sv7L8pAhQzBjxgyMGDEC77//Ps6fP49vvvkGX3/9dZnaycnJyeCRPEoXUIiIiJ5k1apVUxxMICIiIiIiIiJ6UlX+UFYpTJs2DS1btsT06dORnZ2Nli1bomXLljhx4gSAuyPlW7duhbW1NYKDg/Hqq69i6NCh+Pjjj80qPzAwENu2bUN0dDSaN2+OuXPnYunSpQgNDdVPs2jRImRkZCAkJARVq1bV/1u7dq1suatWrUKDBg3QvXt39OnTBx07dsSSJUv0cVdXV+zcuROxsbEICgrCO++8g2nTpmHUqFFlbCkiIiIiIiIiIiIiIiLLPVJ3JEVFRSEqKko4TUBAgPDRKEpCQkJw+vRp2XhZbp3z8PDA6tWrhdM0a9YM+/fvL3XZREREREREREREREREFeWRuiOJiIiIiIiIiIiIiIiIHhwOJBEREREREREREREREZFJHEgiIiIiIiIiIiIiIiIikziQRERERERERERERERERCbZVHYFiIiIiOjJkJiYCDs7O5Mxe3t72bzU1FRhuQUFBbIxHx8fYa63t7cwfunSJdmY3LLoaLVa2di///4rzNVoNMJ4RkaGbEzUHgAQFBQkG7t8+bIwNz8/XxgPCAiQjVWpUkWYe/78edmYra2tMNfPz08Yv3btmmxMqX+JlrlWrVrCXCXFxcVlilnq5s2bwriNjfzXxJycHGGuqN87OjoKc+Pj44Xxq1evysaUtkdRXKlfK62LmJiYMudmZ2fLxnx9fYW56enpwnhJSYlsrE6dOsLcCxcuyMZE+2tAef/WqFEj2ZhoP6BE1PcA5b4r2v+p1Wphbo0aNWRjcXFxsrHCwkJhuVT+VCoVVCqVyZho36d0XJYkSTZmbW0tzFXqu6J6OTg4CHNF9RaVC0C2ncyNl5Wl862oeonWsTlE9bKyEv/OX7SulOql1B6i/qnUd0X1VlomS9ajJevY0v4hyrekjyjlWhKvrFyg4rZHS1VWe1XW/qu88I4kIiIiIiIiIiIiIiIiMokDSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUkcSCIiIiIiIiIiIiIiIiKTOJBEREREREREREREREREJtlUdgWIiIiI6Mlw8+ZNqNVqk7EaNWrI5mVnZwvL1Wq1sjEHBwdh7uXLl4Xx9PR02ZiHh4cwV6SwsFAYd3NzE8Zv374tG/Py8hLmHj16VDZmb28vzHVychLGU1NTZWM3btwQ5tra2srG7OzshLlJSUnCuIuLi2xMaZlE9RYtLyDuP0pxa2trYW7z5s1lY9euXRPmKm0Xrq6usrGCggJhbnJysmxMqT08PT2F8UaNGsnGYmJihLklJSWyMVH/AJSXWaSoqEgYF23rSu2lRKPRyMaU6uXo6CgbU+r3SvvG8+fPy8asrMS/dRX1r969ewtzN2/eLIyL5q20TP/++69srF69erKx/Px8YblU/lQqFVQqlcmY6Dgkl6NjYyN/eU3pGCZJUoWVLTquy52T6Sgdh0SUlkkUV8pVWhciSvsYSyiVbckyi851LW0vUVxpmURxpVxL6mVJrhJLci1hyTYDWNZHLOmbllBqa0vmbWl7iliyH1FaZku2qQeh8mtAREREREREREREREREDyUOJBEREREREREREREREZFJHEgiIiIiIiIiIiIiIiIikziQRERERERERERERERERCZxIImIiIiIiIiIiIiIiIhM4kASERERERERERERERERmcSBJCIiIiIiIiIiIiIiIjLJprIrQERERERPBhsbG9jYmD79vHXrlmyer6+vsNyqVavKxm7cuKFYJxFHR0fZWE5OjjDX2dlZNubg4CDMjYuLE8a1Wq1sTKm9NBqNbEy0vABw+/ZtYVzUnra2tsJcEdHymsPKSv73c5mZmcJcUb2Tk5OFuZ6ensJ4rVq1ZGPXrl0T5iYlJcnGioqKhLlKJEmSjSmtC9H2mJ2dLcx1cnISxlNSUmRjSsvcsGHDMpULAHl5ecK4iFK/T09Pl415eXkJcwsLC8tctlL/srOzk425uroKc2vXri2MHzp0qEzzBQBvb2/Z2LFjxyyqV2pqqmwsKytLmCva34u21YKCAmG5VP6sra1hbW0tG5OjdL4gOraK9qkAoFKphHFRvdRqdZnrpZSrtMyiY6sSUZsotZdS3JJ6WZKrxJJlFtVLKVeJqP8ptYcoV6lfV2TZSvGKojRfS9aVJduFpduUJSqq7IpcJkv6riX9Win+MPR73pFEREREREREREREREREJnEgiYiIiIiIiIiIiIiIiEziQBIRERERERERERERERGZxIEkIiIiIiIiIiIiIiIiMokDSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUk2lV0BIiIiInoyWFtbw9ra2mTM19dXNu/27dvCcjUajWwsPz9fmOvl5SWMP/3007Kxo0ePCnNtbORPtZOSkoS5ShwcHGRjLi4uwtzi4mLZWHZ2tjDX1tZWGPfw8JCN3bp1S5grWleurq7CXK1WK4ynpaXJxuT6pI69vb0wLqJUL6U2ESkoKJCNifoHoNyeWVlZsrG8vDxhrpWV/G8VRXUGAEmShHFRvo+PjzD39OnTsjGlfq1E1EeU+o+jo2OZYgDw77//lrleSn1AtB9R6rd//fWXMC5arpKSEmFuenq6MC6SmZkpjIuOJf7+/sJcNzc32di1a9dkY0rLS+VPdC4iOh6oVCphuaK4Uq5ov6lUL6X9l+hcRBQzp15KyyUi2t8rHTuV5isq25L1qNQeSscwUdlKuaK4Uq4SS5bZkn5fkduUJfN9WFnanmVlaf8SUdrWLWHJ9qjU7y1Rkf2+rLml6Tu8I4mIiIiIiIiIiIiIiIhM4kASERERERERERERERERmcSBJCIiIiIiIiIiIiIiIjKJA0lERERERERERERERERkEgeSiIiIiIiIiIiIiIiIyCQOJBEREREREREREREREZFJNpVdASIiIiJ6MpSUlKCkpMRk7NatW7J5fn5+wnKTkpJkYzVq1BDmpqamCuNnz56VjU2dOlWY+8knn8jGXF1dhbly7WSO8+fPlzlXo9EI43Xr1hXG4+LiZGN2dnZlqhMA2NraCuNWVuLfxxUVFZUpBgCenp6ysWvXrglzlfpfQkKCbEyr1QpzRctcXFwszL1x44Yw7uvrKxvz8vIS5mZlZcnGlNo6Pz9fGHd0dJSN3bx5U5jr4OBQ5vkqEa2r27dvC3NdXFxkY0rboxJRe7do0UKYu3fvXtmY0vZmYyO+zCDavyn1r4KCAtmY0v68Tp06wrioH2RnZwtz/f39ZWMNGjSQjeXl5QnLpfKn0Whkty1R31bq9xWVCwDW1tayMaXtTZSrNN+KJElSpc27rFQqVWVXoUyU6i2KV+YyWzJvUa6lfa+y+q4l67EiKbWHKK50rluRRO1lyb7R0vVUUeuxvLZz3pFEREREREREREREREREJnEgiYiIiIiIiIiIiIiIiEziQBIRERERERERERERERGZxIEkIiIiIiIiIiIiIiIiMokDSURERERERERERERERGQSB5KIiIiIiIiIiB4C2dnZlV0FIiIiIiMcSCIiIiIiIiIiqkQHDx5EkyZN4O7ujtzc3MquDhEREZEBm8quABERERE9GZydnaHRaEzG6tatK5uXkJAgLLdjx46ysQMHDghznZychPHMzEzZ2MSJE4W5kiTJxgoKCoS5Xl5ewrharZaNXblyRZjr6uoqG8vIyBDmxsXFCeMiJSUlZc4tKioSxm/fvi2Me3t7y8ZcXFyEuTdv3pSNFRcXC3MvXrwojDdp0kQ29t9//wlz3dzcZGOiOgOAo6OjMJ6YmCgbU+qbor7t7u5uUb1E61nUrwFx37ZkmQDxHSRKAwK2trbCuIjS/is/P182tmPHDmFuvXr1ZGOi/SIA+Pr6CuOi9lRq6+rVqwvjIqL2AIDk5GTZmNJ6LCwslI3Z2MhfdtEtb6tWrXD16lVUr14da9asgYODg3B+VHa2trayx1ArK/nfWotiluZaW1uXuWxLci0lOs9RqVQVNt/KIlpeS/MtKVuprR/HdVGRtFptZVfBJKX1KNrWLVkmpb5pSdySXEv7vSheWbmWKmvZpcnjQBIRERERERERUSUpKSnB9u3b0bt378quChEREZFJHEgiIiIiIiIiIqokZ8+eVbw7koiIiKgy8R1JREREREREREREREREZBIHkoiIiIjMNGvWLLRv3x4ODg6y7yeJi4tD37594eDgAB8fH7z33ntG71GZMWMG/P390bFjR/z7778GsT179qBVq1bQaDSoU6cOoqKiDOKzZ89G69at4ezsDB8fHzz//PO4dOmSwTQ1a9bEnj17DP5WqVQG/z777DN9/Pr160ZxlUqFI0eO6KeJjIzEsGHDTJZPRERERERERI8vDiQRERERmamwsBCDBg3CmDFjTMZLSkrQt29fFBYW4tChQ1ixYgWioqIwbdo0/TQHDx7Etm3bsGXLFgwZMgRjx47Vx2JjY9G3b1907doVZ86cwfjx4zFy5Ej88ccf+mn27t2L8PBwHDlyBNHR0SgqKkLPnj2Rk5MjrPvHH3+MhIQE/b+IiAijaXbt2mUwTVBQUGmbiIiIiIiIiIgeM3xHEhEREZGZZsyYAQBGdwnp7Ny5EzExMdi1axd8fX3RokULzJw5E++//z4iIyOhVquRlpYGPz8/NGvWDMXFxQZlfffddwgMDMTcuXMBAA0bNsSBAwfw9ddfIzQ0FACwY8cOg3lGRUXBx8cHJ0+eROfOnWXr7uzsjCpVqgiXz9PTU3EaIiIiIiIiInqycCCJiIiIqJwcPnwYTZs2ha+vr/6z0NBQjBkzBhcuXEDLli0RGhqKBQsWwMHBAU5OTli/fr1Bfo8ePQzKDA0Nxfjx42XnmZGRAQDw8PAQ1u2zzz7DzJkzUaNGDQwZMgQTJkyAjY3hqeCzzz6L/Px81KtXD5MmTcKzzz5r7qKbJS8vD1qt1mTs5MmTsnk1atQQlpuWliYb8/HxEebGxcUJ4xqNRjZma2srzC0oKJCN1apVS5ibn58vjKekpMjGHBwchLmOjo5ligHAf//9V+ayLanX/Y+HvJ+dnZ0wnpWVJYyL1KlTRzbm7u4uzD169GiZ52tvby+Mi+5CtLa2FuYq9a/79w33unPnjjDX1dVVNpaXlyfMVSq7W7dusjHRPgQQL7NS/8nMzBTGRX3bz89PmCvalq2sxA8Qyc7OFsZF+6B7j1OmJCUlycaUtsfCwkJhXNQPRHUGxPvk9PR0Ya7SnbuiviuKAeK+K+oDStsilT+NRiPbj1QqlWyeKAaIt1elbdmSuFKuUr1FJEmqsLItaWslSvWurHIrql6WsrS95Vjaf0T5luTKfRcxJ7ciWbJMlVm2ksraHpWWWRS3ZL9aUduTpWWX13rgQBIRERFROUlMTDS6OKf7OzExEcDdwYcdO3YgKSkJbm5uUKvVivmZmZnIy8szurCs1Woxfvx4dOjQAU2aNNF/fv36dYPpxo0bh1atWsHDwwOHDh3C5MmTkZCQgK+++goA4OTkhLlz56JDhw6wsrLChg0b8Pzzz2Pz5s36waTIyEjZ8omIiIiIiIjo8cWBJCIiInqiTZ8+HfPmzRNO888//6BBgwblOl+lO2XMER4ejvPnz+PAgQPC6SZOnKj//2bNmkGtVuPNN9/E7NmzodFo4OXlZTBN69atcevWLcyZM6fc70oiIiIiIiIiokcLB5KIiIjoiRYREYE333xTOI3SY8h0qlSpgmPHjhl8dvv2bX3MnHzd9Pfmu7i4GN2NNHbsWGzduhX79u2Dv7+/WfXTadu2LYqLi3H9+nXUr19fdpro6OhSlWvK9u3bER4ebnE5RERERERERFQ5OJBERERETzQvLy+4uLiUS1nBwcGYNWsWkpKS9HccRUdHw8XFBY0aNTIrf/v27QafRUdHIzg4WP+3JEmIiIjApk2bsGfPHgQGBpa6nmfOnIGVlZXwrqgzZ86gatWqpS5bZ/369Zg4cSL+++8/4TtmiIiIiIiIiOjhxoEkIiIiIjPFxcUhNTUVcXFxKCkpwZkzZwAAderUgZOTE3r27IlGjRrhtddewxdffIHExERMnToV4eHhwheE64wePRoLFizApEmT8Prrr+Ovv/7CunXrsG3bNv004eHhWL16NbZs2QJnZ2f9u5dcXV2N7loCgMOHD+Po0aPo2rUrnJ2dcfjwYUyYMAGvvvoq3N3dAQArVqyAWq1Gy5YtAQAbN27EsmXLsHTp0jK1U25uLgYPHoyGDRti7dq1aNy4seKLyomIiIiIiIjo4cSBJCIiIiIzTZs2DStWrND/rRt42b17N0JCQmBtbY2tW7dizJgxCA4OhqOjI8LCwvDxxx+bVX5gYCC2bduGCRMm4JtvvoG/vz+WLl2K0NBQ/TSLFi0CAISEhBjkLl++HMOGDTMqU6PRYM2aNYiMjERBQQECAwMxYcIEg3ciAcDMmTNx48YN2NjYoEGDBli7di0GDhxoVr3v5+DggLS0NDg5OQEAMjMzy1QOEREREREREVU+DiQRERERmSkqKgpRUVHCaQICAoweT1caISEhOH36tGxckqRSldeqVSscOXJEOE1YWBjCwsJKVa4S3SASERERERERET3aOJBERERERA9Er1694OjoaDLm7e0tm7djxw5huTdv3pSN3bhxQ5hrZ2cnjBcXF8vG3NzchLkFBQXCuMitW7fKnOvr6yuMZ2RkyMaUBipNPT7xXqL3bqWlpQlzRfLz84VxZ2dnYVyr1crGlB47eeXKFdmY0vvVqlWrJoyfPHlSNhYQECDMFfUvpb4ZHx8vjIvevSbqP0r1srW1FeaK+g8A/Pvvv7KxnJwcYa5ocFupbyq1Z/fu3WVju3fvFuaK2ktpe/Py8hLGk5OTZWPZ2dnCXEt+DGBlZSWMFxUVycaU2lq0v/f39xfmKsULCwtlY0rHEmtra9lYamqqbMyS4wSVja2tLdRqdanzVCpVmeNK24RS2aJ8pdzKYkl7VeYylfYHW/cSnWtY6mFdz5a018Pa1g+riuwDD2v/sqRelbnPfhiV1z5X3DJERERERERERERERET0xOJAEhEREREREREREREREZnEgSQiIiIiIiIiIiIiIiIyiQNJREREREREREREREREZBIHkoiIiIiIiIiIiIiIiMgkDiQRERERERERERERERGRSRxIIiIiIiIiIiIiIiIiIpNsKrsCRERERPRk2L17NzQajclYZmambJ69vX2Z5+nj4yOMp6SklLns+Ph4YdzJyUk2lp+fL8y1tbUVxh0dHWVjBQUFwlxJkmRjcutHx83NTRhPTU2VjSkts2g9FxUVCXOLi4uFcdFy2dnZCXNFfdPGRvx16tKlS8K4q6urbMzKSvybv6ysLNmYUh9QKlvUnunp6cJca2tr2ZiDg4MwV2l79PT0lI0p9QFRH1Jqr5KSEmF8+/btsjGlbVmr1crGROvYnHqpVCrZmNK2LNpmlPqA0roQ7QuU9kGi7TUvL0+Ye+PGjTLXS2kfVNZ9smh/TBXD1tZWcbs0RWldibY3UQxQ3ieXdb6WeljLtiTXkm1OKbeytueKXE8VSXT8AyzbLh5Wj+K6UqqzpfGysnS/KopX1jJVltIsz+O3VRIREREREREREREREVG54EASERERERERERERERERmcSBJCIiIiIiIiIiIiIiIjKJA0lERERERERERERERERkEgeSiIiIiIiIiIiIiIiIyCQOJBEREREREREREREREZFJNpVdASIiIiJ6Mjg4OMDOzs5kLDs7WzZPo9EIy1Wr1bKxjIwMYa6zs7MwbmMjf7pcUFAgzC0pKSlTDACsrMS/90pPTxfGRaytrWVjtra2wlyl9hSV7eTkJMwVtWfVqlWFuWlpacJ4fn6+bOzOnTvCXNG6UMpt1aqVMH727FnZmIeHhzA3ICBANqbUvwoLC4VxBweHMs0XABISEmRjSv3W1dVVGM/MzJSNifoeALi5ucnGcnNzhbnFxcVlLjsvL0+Y6+/vLxsT7RcBIDY2VhivVq2abMzFxUWYGxcXJxurUaOGMFdpPYr2q0ptXbNmTdlYUVGRMPfGjRvCuGi7kTt2mcPe3l42prSvp/JnbW0tu7+QJEk2TxQDAJVKVaaYOSqy7EeR0rp4WOdbUfW2tA9UVL0s2WYAQKvVljnXkjapyG3KkraurGW2dL6i41xFtofS8VUUV8oVzbsi9xMV1TdLUy7PWoiIiIiIiIiIiIiIiMgkDiQRERERERERERERERGRSRxIIiIiIiIiIiIiIiIiIpM4kEREREREREREREREREQmcSCJiIiIiIiIiOgRMGvWLLRv3x4ODg5wc3MzOU1cXBz69u0LBwcH+Pj44L333kNxcbHBNDNmzIC/vz86duyIf//91yC2Z88etGrVChqNBnXq1EFUVJRBfPbs2WjdujWcnZ3h4+OD559/HpcuXTKYpmbNmtizZw8A4Pr16xgxYgQCAwNhb2+P2rVrY/r06SgsLDTIOXfuHDp16gQ7OztUr14dX3zxhUE8MjISw4YNMyqfiIiIKh4HkoiIiIiIiIiIHgGFhYUYNGgQxowZYzJeUlKCvn37orCwEIcOHcKKFSsQFRWFadOm6ac5ePAgtm3bhi1btmDIkCEYO3asPhYbG4u+ffuia9euOHPmDMaPH4+RI0fijz/+0E+zd+9ehIeH48iRI4iOjkZRURF69uyJnJwck3W6ePEitFotFi9ejAsXLuDrr7/Gd999hw8//FA/TWZmJnr27ImAgACcPHkSc+bMQWRkJJYsWWJpkxEREVE5sKnsChARERERERERkbIZM2YAgNFdQjo7d+5ETEwMdu3aBV9fX7Ro0QIzZ87E+++/j8jISKjVaqSlpcHPzw/NmjVDcXGxQVnfffcdAgMDMXfuXABAw4YNceDAAXz99dcIDQ0FAOzYscNgnlFRUfDx8cHJkyfRuXNnozr16tULvXr10v9dq1YtXLp0CYsWLcKXX34JAFi1ahUKCwuxbNkyqNVqNG7cGGfOnMFXX32FUaNGlbm9iIiIqHxwIImIiIiIHgitVouSkhKTsVq1asnm5efnC8tNSUmRjRUVFQlzPTw8hHFHR0dhXOTOnTuysby8PGFuQUGBMC73OCNzpKamysbc3d2FuaJlAgBfX1/ZWEJCgjDXwcFBNhYbGyvMdXZ2LnM8Pj5emOvj4yMb02q1wtxz584J415eXrIxpba2t7cXxkVEfcDSsu3s7Mqcq1KphHHRvkBpW5fb9wBAbm6uMFfUNwEgPT1dNmZrayvMFa0LUZ0BICAgQBh/+umnZWOrVq0S5oqW+fbt28Jcpf5lZSX/YBSl/iPa39//6Lb7Ke1XJUkSxkVEfVO0TEp1KovDhw+jadOmBvvj0NBQjBkzBhcuXEDLli0RGhqKBQsWwMHBAU5OTli/fr1Bfo8ePQzKDA0Nxfjx42XnmZGRAUD5mHp/zr3THz58GJ07d4ZarTaY7+eff460tDTF45O5VCqV4r7mQavI+lTWslqyPVUkS+qllFuRyywq29L5VlQfUSpXqd6ifEuW2dJ6PY5EbSI6Zpujovqu0npUqvej2O8fBhxIIiIiIiIiIiJ6DCQmJhoN6uv+TkxMBHB3gHPHjh1ISkqCm5ubweCNXH5mZiby8vKMBpq1Wi3Gjx+PDh06oEmTJvrPr1+/LlvHK1euYP78+fq7kXTzDQwMlK23u7s7IiMjzSofuPuoPN3Ao5WVFbKzs4XTExERkRjfkUREREREREREVEmmT5+uv1NG7t/FixfLfb4+Pj4Gg0hlER4ejvPnz2PNmjVmTR8fH49evXph0KBBeOONNyyat8ikSZNQq1Yt1KpVCzVr1jQY5CIiIiJD8fHxuHnzJm7duoVbt26ZvGOfdyQREREREREREVWSiIgIvPnmm8JpRI+AvVeVKlVw7Ngxg890jyOsUqWKWfn3P77w9u3bcHFxMbobaezYsdi6dSv27dsHf39/xbJv3bqFrl27on379liyZIlZ8zW33vf76quv9HcwabVaZGVloUGDBqUuh4iI6EnQqFEjg79Hjx6NRYsWGXzGgSQiIiIiIiIiokri5eUFFxeXcikrODgYs2bNQlJSkv4db9HR0XBxcTG6SCSXv337doPPoqOjERwcrP9bkiRERERg06ZN2LNnj9Ej6UyJj49H165dERQUhOXLlxu9vyI4OBhTpkxBUVGR/t1i0dHRqF+/fpnej+Tg4GDwrq/MzMxSl0FERPSkiImJgaOjo/49Tp6enkbT8NF2RERERERERESPgLi4OJw5cwZxcXEoKSnBmTNncObMGf07gHr27IlGjRrhtddew9mzZ/HHH39g6tSpCA8Ph0ajUSx/9OjRuHbtGiZNmoSLFy9i4cKFWLduHSZMmKCfJjw8HD/99BNWr14NZ2dnJCYmIjExEXl5eSbLjI+PR0hICGrUqIEvv/wSycnJ+hydIUOGQK1WY8SIEbhw4QLWrl2Lb775BhMnTrSwxYiIiEhJtWrVUKNGDQQEBCAgIABOTk5G0/COJCIiIiIiIiKiR8C0adOwYsUK/d8tW7YEAOzevRshISGwtrbG1q1bMWbMGAQHB8PR0RFhYWH4+OOPzSo/MDAQ27Ztw4QJE/DNN9/A398fS5cuRWhoqH4a3aNuQkJCDHKXL1+OYcOGGZUZHR2NK1eu4MqVK0aPwNP98tnV1RU7d+5EeHg4goKC4OXlhWnTpmHUqFFm1ZuIiIgqlkrSHbXpsZWZmQlXV1dkZGSU2+3yREREjzoeHx8cXVt/8MEHsLOzMzlNQUGBbH7t2rWF5e/Zs0c25ujoKMxt2LChMH79+nXZmJubmzA3JydHGBcRtQcAFBUVycZM/XrsXrpfrZti6qWq91J6T4VWq5WNyf1SXaekpEQ2ptTWd+7cEcZFCgsLhXHRMim1dX5+vjAuWmbROgbEbSKqMwDFuxJE+fc/jup+1tbWsrH09HRhrtIyOzs7C+MixcXFZYqZM1/R9lq1alVh7tWrV2VjcvtLc+YL/N8FelNMPa7kXiqVqkzlAsrtGRAQIBtT2pZF+5H7399zv7p16wrjycnJsrGkpCRhrmhfINrnFhQUYOHChTwXeQB05yIJCQmybV1Rl8dE25Ol+UplW5KrRNRelXmpsaLq9bBePrV0PYqO65aWLVKR7VmR/V5EaZksmbcl27ollJbJkrgluZa2hyX93pL+ZWl7VoTMzEx4enqadS7CR9sRERERERERERERERGRSRxIIiIiIiIiIiIiIiIiIpM4kPSAzJ49G61bt4azszN8fHzw/PPP49KlSwbT5OfnIzw8HJ6ennBycsKAAQNw+/Ztffz69ev6W+QiIyNNPnuYiIiIiIiIiIiIiIiovHAg6QHZu3cvwsPDceTIEURHR6OoqAg9e/Y0eHb+hAkT8Ntvv+GXX37B3r17cevWLfTv378Sa01ERERERERERERERE8ym8quwJNix44dBn9HRUXBx8cHJ0+eROfOnZGRkYEffvgBq1evRrdu3QAAy5cvR8OGDXHkyBG0a9euMqpNRERERERERERERERPMN6RVEkyMjIAAB4eHgCAkydPoqioCD169NBP06BBA9SoUQOHDx+ulDoSEREREREREREREdGTjXckVQKtVovx48ejQ4cOaNKkCQAgMTERarUabm5uBtP6+voiMTERAFCzZk1IkgTg7juSiIiIiIiIiIiIiIiIKhIHkipBeHg4zp8/jwMHDlRI+dnZ2UhNTdX/nZWVVSHzISIiehzEx8cjMzNT/7ebmxucnJwqsUaPr9TUVKjVapMxW1tb2byrV68Ky61WrZpsLD09XZh748YNYVxUr3vPt0yxtrYuc71cXV2FcT8/vzLXy8vLSzYWEBAgzD1+/LgwLmovKyvxwxBEcaX20mg0wrijo6Ns7Pr168Jc3RMETMnPzxfmOjs7C+OiPqJ0Di/KLS4uFuYqufc9rvdTWibdj+BMcXFxEeaKlgkAcnNzZWOFhYXC3Pt/sHevO3fuCHO1Wq0wLqK0Pep+KGiKqM6A8jaVkJAgGxPtNwEgJiZGGBexs7MTxkVtorQPio2NlY3deywvC39//zKXXdbv3KL1TxVDq9VatE3LUalUFZZrSdkVqbLqVZHbjahspfk+qu1hSb5omZXKfRL3f6Jlrsj+U5H7J6X1WBH724pmSXs9rP1atEylWV4OJD1gY8eOxdatW7Fv3z6DE9UqVaqgsLAQ6enpBl8Ybt++jSpVqpRqHu+99x6+++678qoyERHRY61Ro0YGf48ePRqLFi2qpNoQERERERERET1cOJD0gEiShIiICGzatAl79uxBYGCgQTwoKAi2trb4888/MWDAAADApUuXEBcXh+Dg4FLNa86cOZgyZYr+76ysLKOLZERERHRXTEyMwS/slX4BTkRERERERET0JOFA0gMSHh6O1atXY8uWLXB2dtY/8sHV1RX29vZwdXXFiBEjMHHiRHh4eMDFxQUREREIDg5Gu3btSjUvJycng0fyWHqLPxER0eOsWrVqio9bIiIiIiIiIiJ6UnEg6QHRPSInJCTE4PPly5dj2LBhAICvv/4aVlZWGDBgAAoKChAaGoqFCxc+4JoSERERERERERERERHdxYGkB8Scl23Z2dnh22+/xbfffvsAakRERERERERERERERCRmVdkVICIiIiIiIiIiIiIioocTB5KIiIiIiIiIiIiIiIjIJD7ajoiIiIgeiN69e8PR0dFk7OTJk7J56enpwnJFcQ8PD2Guv7+/MH769GnZWNWqVYW5hYWFsjE3NzdhblFRkTAuUlJSIownJSWVKQYAGo1GGLe3t5eNZWZmCnOtra1lY2lpacJcHx8fYVy0XN7e3sLcjIwM2ZiTk5MwNzk5WRgXLbOVlfg3f1lZWbIxpWUS5QJAXl6ebExuG9bx9fWVjaWkpAhzlfqXnZ2dbEy0vSmpXr16mXMB8T5IaVt2dnYu83yVtteCggLZ2KVLl4S5xcXFsjE/Pz9h7q1bt4Tx+vXry8ZiYmKEuaL+JeofAHDu3DlhXLTdNGrUSJh7/vx52Zho32dJv6Wy0Wq10Gq1JmMqlUo2TxSryFwlFVkvSyiVbc7rHyqC0nwrq16WsLTOctsDULF915J1UZF9V0lFbq8iSu1VUW1iabmi81lR37NUZfYRkYrcBz2IZeYdSURERERERERERERERGQSB5KIiIiIiIiIiIiIiIjIJA4kERERERERERERERERkUkcSCIiIiIiIiIiIiIiIiKTOJBEREREREREREREREREJnEgiYiIiIiIiIiIiIiIiEyyqewKEBEREdGTYevWrVCr1SZjdnZ2snm9e/cWlrt//37ZWFZWljD35s2bwri7u7tsLDs7W5griteoUUOYm5ycLIyL2isjI0OY6+DgIBtzcnIS5iq1l1K+iLW1tWxMpVIJc5WWOT8/XzZWUlIizLWxkf/KlJaWJsx1dXUVxt3c3GRjly9fFub6+vrKxoqKioS5eXl5wnhhYWGZy05PT5eNifotANja2grjubm5sjFPT09hrmhfUFBQIMxVWo+ivltcXCzMFe0nRP3WHJmZmWUuW7Sf0Gq1wlyl9Xzt2jXZmNL2KFompfnWqlVLGI+Pj5eNHTp0SJgr2h5FlPoelT+tVivbh62s5H9rrXQcqkiieSvVq6JyLSVJUoXlivZRlbnMlUWpvUTxh7UPKNVLtC1bWnZltZeSimpPS8p9VFXk/ulhbM/S1Il3JBEREREREREREREREZFJHEgiIiIiIiIiIiIiIiIikziQRERERERERERERERERCZxIImIiIiIiIiIiIiIiIhM4kASERERERERERERERERmcSBJCIiIiIiIiIiIiIiIjKJA0lERERERERERERERERkkk1lV4CIiIiIngzW1tawtrY2GZMkSTYvOjpaWK6Pj0+Z61RcXFzmuKjOAODh4SEby83NFeZqNBphPCYmRjbWpEkTYe6lS5dkY5mZmcLckpISYVxU75ycHGFuYWGhMC5ia2tb5lwrK/Fv6/Ly8mRjSn0gIyNDGE9PT5eNKS1TSkpKmevl5OQkjNvb28vGRO0BiNtTpVIJc7Ozs4VxUR/Kz88X5rq5ucnGtFqtMFepbzo6OsrGlPqAKLeoqEiYq7S9itaF0vaoVqtlY0rrycZGfJmhoKBANqbU70X71YSEBGGui4uLMF6tWjXZmGhbBSB7bAPE/Uup79HjQWnfpxR/WFlSb1Gu0jFMiWjfp1S2JbmW1rusZSvtRyqz3hU1X6VzN1HZlm6PorhSvR5FSu1hyXq0ZB9Skf26srblimTJPuRej18PJyIiIiIiIiIiIiIionLBgSQiIiIiIiIiIiIiIiIyiQNJREREREREREREREREZBIHkoiIiIiIiIiIiIiIiMgkDiQRERERERERERERERGRSRxIIiIiIiIiIiKqJHv37q3sKhAREREJ2VR2BYiIiIjoyeDm5gaNRmMylp2dLZtXrVo1Ybnx8fGyMR8fH2FuamqqMC6iUqmE8by8PNmYh4eHMDc5OVkYd3R0lI1du3ZNmOvs7Cwby8/PF+a6u7sL4yJarVYYLykpKXPZKSkpwribm5tsLCsrS5hrSb2U+khhYaFszMpK/Js/Ozs72ZjcdqaTnp5e5rKV1qNo3krzVaq3r6+vbEy0DwGAjIwM2ZharRbmKm0X1tbWwrhIQUFBmXNtbW2FcUmSZGNKyyzKTUxMFOYq7Xft7e2FcZGrV6/KxpT2Tzdu3BDGRfmenp7C3Nu3b8vGbGzkL7vo9i8TJ05Ev379hPOg8qFSqWT3zUr77MpiSb1E23JFztcSFTlfpWOr6BhnSVsqUSpbVC9Lz68qapkrsr2U1qPouKyUq9T/RPlKyywq29J+L8pXWmYRS5bJnPzKUln1qsz9iBylfci9eEcSEREREREREVEluXLlCg4fPlzZ1SAiIiKSxYEkIiIiIiIiIqJKUq1aNYwaNaqyq0FEREQkiwNJRERERERERESVZPbs2bh48aLiIxqJiIiIKgvfkUREREREREREVEmee+45ZGRkwMHBobKrQkRERGQS70giIiIiIiIiIqpEHEQiIiKihxkHkoiIiIiIiIiIiIiIiMgkDiQRERERERERERERERGRSXxHEhERERE9EPn5+ZAkyWQsNzdXNu/GjRvCcv38/GR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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 286fe67..5d3c44f 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": "617123a7", + "id": "10d05e1d", "metadata": {}, "source": [ "# HR (Hertzsprung-Russell) Diagram Exercise\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "747a3caf", + "id": "368ee79e", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "916aa8cd", + "id": "dac9ca1e", "metadata": {}, "source": [ "## Step 1: Find appropriate catalogs\n", @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "840c5a8c", + "id": "f57ed242", "metadata": {}, "source": [ "### DATA DISCOVERY STEPS\n", @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "c30cfcff", + "id": "7d081cdd", "metadata": { "tags": [ "output_scroll" @@ -83,7 +83,7 @@ }, { "cell_type": "markdown", - "id": "b8b3fd21", + "id": "695fcbc7", "metadata": {}, "source": [ "Note: The includeaux=True includes auxiliary services.\n", @@ -93,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "2d1f2b4e", + "id": "7630b5d9", "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": "7ff9efbd", + "id": "8e9a5c27", "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "markdown", - "id": "c7e204a1", + "id": "ef0005c5", "metadata": {}, "source": [ "Note: the '%' serves as a wild card when searching by UCD\n", @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "08f5cdf1", + "id": "088fb6ed", "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": "397bcef3", + "id": "975b098f", "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "c77cda40", + "id": "11bb9f1f", "metadata": {}, "outputs": [], "source": [ @@ -156,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "433428ef", + "id": "73014af2", "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": "5cc39089", + "id": "815e5d36", "metadata": { "tags": [ "output_scroll" @@ -178,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "a159b9e0", + "id": "20eda4da", "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": "6c2d353f", + "id": "5ed80d70", "metadata": { "tags": [ "output_scroll" @@ -219,7 +219,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "30085d46", + "id": "bdd1c2f7", "metadata": {}, "outputs": [], "source": [ @@ -228,7 +228,7 @@ }, { "cell_type": "markdown", - "id": "e17f7d90", + "id": "793bdf3e", "metadata": {}, "source": [ "First, Try using bibcode:" @@ -237,7 +237,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "1f6a5d03", + "id": "aa4e8673", "metadata": {}, "outputs": [], "source": [ @@ -255,7 +255,7 @@ }, { "cell_type": "markdown", - "id": "4c6247aa", + "id": "72d18bc3", "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": "4a279634", + "id": "bdb2e294", "metadata": {}, "source": [ "Next, try using Author name:" @@ -272,7 +272,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "dac97e68", + "id": "7434abb8", "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "70df4293", + "id": "4e9acc45", "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": "e1966bf8", + "id": "426f7463", "metadata": { "tags": [ "output_scroll" @@ -316,7 +316,7 @@ }, { "cell_type": "markdown", - "id": "fbf17719", + "id": "3f20418f", "metadata": {}, "source": [ "## Step 2: Acquire the relevant data and make a plot\n", @@ -327,7 +327,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "30149374", + "id": "aec24727", "metadata": {}, "outputs": [], "source": [ @@ -337,7 +337,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "c1112bc7", + "id": "4ee6979a", "metadata": {}, "outputs": [], "source": [ @@ -347,7 +347,7 @@ }, { "cell_type": "markdown", - "id": "ad2e496a", + "id": "252c75e7", "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." @@ -356,7 +356,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "11b258ab", + "id": "a4e6f8c2", "metadata": {}, "outputs": [], "source": [ @@ -366,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "3aa833bf", + "id": "4357d1af", "metadata": {}, "source": [ "### Plotting\n", @@ -377,7 +377,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "bf6f584a", + "id": "0c4c618a", "metadata": {}, "outputs": [ { @@ -411,7 +411,7 @@ }, { "cell_type": "markdown", - "id": "3c7a215d", + "id": "7e58af9f", "metadata": {}, "source": [ "## Step 3. Compare with other color-magnitude diagrams for Pleiades\n", @@ -426,7 +426,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "1ef25e79", + "id": "e4a58b96", "metadata": {}, "outputs": [], "source": [ @@ -439,7 +439,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "f06aaa41", + "id": "611da61c", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "42818ff5", + "id": "5e76a664", "metadata": {}, "outputs": [ { @@ -487,7 +487,7 @@ }, { "cell_type": "markdown", - "id": "182e6f74", + "id": "cb5fd570", "metadata": {}, "source": [ "## BONUS: Step 4: The CMD as a distance indicator\n", @@ -498,7 +498,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "3581da16", + "id": "c6219660", "metadata": {}, "outputs": [], "source": [ @@ -516,7 +516,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "2ed6a042", + "id": "303f285a", "metadata": {}, "outputs": [], "source": [ @@ -530,7 +530,7 @@ }, { "cell_type": "markdown", - "id": "16973232", + "id": "02ae86c5", "metadata": {}, "source": [ "True distance to Pleaides is 136.2 pc ( ). 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 78f5586..c281b93 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": "cdb3b7fa", + "id": "4bfe5a28", "metadata": {}, "source": [ "# HR (Hertzsprung-Russell) Diagram Solution\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "3afdf418", + "id": "86ffe331", "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "markdown", - "id": "f11a8774", + "id": "ba1be120", "metadata": {}, "source": [ "## Step 1: Find appropriate catalogs\n", @@ -53,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "7c23f605", + "id": "f1e378a8", "metadata": {}, "source": [ "### DATA DISCOVERY STEPS\n", @@ -64,7 +64,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "d66657bf", + "id": "fffb9faa", "metadata": { "tags": [ "output_scroll" @@ -82,7 +82,7 @@ "data": { "text/html": [ "
Table length=145\n", - "
ivoidshort_nameres_titlesource_value
objectobjectobjectobject
ivo://mast.stsci/siap/al218VLA.AL218VLA-A Array AL218 Texas Survey Source Snapshots (AL218)
\n", + "
\n", "\n", "\n", "\n", @@ -147,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "00683c35", + "id": "31c951b7", "metadata": {}, "source": [ "Note: The includeaux=True includes auxiliary services.\n", @@ -157,7 +157,7 @@ }, { "cell_type": "markdown", - "id": "41911023", + "id": "a6799b91", "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", @@ -168,7 +168,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "c28c0dfc", + "id": "2dc4c18b", "metadata": { "tags": [ "output_scroll" @@ -191,7 +191,7 @@ }, { "cell_type": "markdown", - "id": "adbcfd59", + "id": "69d3c430", "metadata": {}, "source": [ "Note: the '%' serves as a wild card when searching by UCD\n", @@ -201,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "b13faefe", + "id": "dc8477cc", "metadata": { "tags": [ "output_scroll" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "45d2606a", + "id": "3d1f4c86", "metadata": { "tags": [ "output_scroll" @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "64b33674", + "id": "0ee307a5", "metadata": { "tags": [ "output_scroll" @@ -317,7 +317,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "cb10d4af", + "id": "ea9c362b", "metadata": { "tags": [ "output_scroll" @@ -340,7 +340,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "669e131e", + "id": "8ac2abb5", "metadata": { "tags": [ "output_scroll" @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "5d887a0a", + "id": "c8e03775", "metadata": { "tags": [ "output_scroll" @@ -429,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "b84d6700", + "id": "59602db0", "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)" @@ -438,7 +438,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "29790c86", + "id": "161b4596", "metadata": { "tags": [ "output_scroll" @@ -843,7 +843,7 @@ }, { "cell_type": "markdown", - "id": "b74f762c", + "id": "b42c0413", "metadata": { "tags": [ "output_scroll" @@ -861,7 +861,7 @@ }, { "cell_type": "markdown", - "id": "b8d6b6bf", + "id": "bc3aa270", "metadata": { "tags": [ "output_scroll" @@ -888,7 +888,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "c6913565", + "id": "555c7035", "metadata": {}, "outputs": [ { @@ -908,7 +908,7 @@ }, { "cell_type": "markdown", - "id": "367e1712", + "id": "66f4e6ba", "metadata": {}, "source": [ "First, Try using bibcode:" @@ -917,7 +917,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "0b9f7295", + "id": "fe40c781", "metadata": {}, "outputs": [ { @@ -946,7 +946,7 @@ }, { "cell_type": "markdown", - "id": "78b75248", + "id": "7313b5ed", "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." @@ -954,7 +954,7 @@ }, { "cell_type": "markdown", - "id": "040a151d", + "id": "f3a4f7e3", "metadata": {}, "source": [ "Next, try using Author name:" @@ -963,7 +963,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ee3e3dc3", + "id": "35cd2509", "metadata": { "tags": [ "output_scroll" @@ -993,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "36c82668", + "id": "dcad3438", "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", @@ -1007,7 +1007,7 @@ }, { "cell_type": "markdown", - "id": "9a50691c", + "id": "7cba3ad5", "metadata": {}, "source": [ "These examples provide a few ways to access the information of interest.\n", @@ -1022,7 +1022,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "b27ba8d6", + "id": "4d48c635", "metadata": { "tags": [ "output_scroll" @@ -1044,7 +1044,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "96d05e62", + "id": "f64ea302", "metadata": { "tags": [ "output_scroll" @@ -1085,7 +1085,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "5749319c", + "id": "231182d1", "metadata": { "tags": [ "output_scroll" @@ -3878,7 +3878,7 @@ }, { "cell_type": "markdown", - "id": "40ded79b", + "id": "4b4ee81c", "metadata": { "tags": [ "output_scroll" @@ -3893,7 +3893,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "36749c93", + "id": "ce5bf831", "metadata": { "tags": [ "output_scroll" @@ -3926,7 +3926,7 @@ }, { "cell_type": "markdown", - "id": "b8ed667b", + "id": "7e84956f", "metadata": { "tags": [ "output_scroll" @@ -3941,7 +3941,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "5254382f", + "id": "d67c0798", "metadata": { "tags": [ "output_scroll" @@ -3967,7 +3967,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "a70ba5a0", + "id": "5ffc0af7", "metadata": { "tags": [ "output_scroll" @@ -3985,7 +3985,7 @@ "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", @@ -4051,7 +4051,7 @@ }, { "cell_type": "markdown", - "id": "267202b7", + "id": "d3fef454", "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." @@ -4060,7 +4060,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "20f9d233", + "id": "0caad7d7", "metadata": {}, "outputs": [], "source": [ @@ -4070,7 +4070,7 @@ }, { "cell_type": "markdown", - "id": "168e49b4", + "id": "c7ef5779", "metadata": {}, "source": [ "### Plotting\n", @@ -4081,13 +4081,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "60833c9d", + "id": "2dbbe4dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 19, @@ -4115,7 +4115,7 @@ }, { "cell_type": "markdown", - "id": "e109bbb0", + "id": "472731b0", "metadata": {}, "source": [ "## Step 3. Compare with other color-magnitude diagrams for Pleiades\n", @@ -4130,7 +4130,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "ecddb910", + "id": "0ef08d55", "metadata": {}, "outputs": [ { @@ -4168,7 +4168,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_2202/2733767379.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_2086/2733767379.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" ] } @@ -4192,7 +4192,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "6460ecb2", + "id": "65104109", "metadata": { "tags": [ "output_scroll" @@ -4210,7 +4210,7 @@ "data": { "text/html": [ "
Table length=270\n", - "
recnoHertzsprungCIPtmRAB1900e_RAB1900DEB1900e_DEB1900rmsRArmsDErpmRArpmDEDrpmRADrpmDEDRADDE_RA_icrs_DE_icrs
magmagdegmsdegmasmas / yrmas / yrmas / yrmas / yrarcsecarcsecdegdeg
int32int16float64float64float64float64float64int16float64float64float64float64float64float64float64float64float64float64
\n", + "
\n", "\n", "\n", "\n", @@ -4294,13 +4294,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "3a85f70a", + "id": "569b340d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 22, @@ -4331,7 +4331,7 @@ }, { "cell_type": "markdown", - "id": "677950ad", + "id": "f1694066", "metadata": {}, "source": [ "## BONUS: Step 4: The CMD as a distance indicator\n", @@ -4342,13 +4342,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "b208f01c", + "id": "3364ba43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 23, @@ -4384,7 +4384,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "b8446e3a", + "id": "28005330", "metadata": {}, "outputs": [ { @@ -4407,7 +4407,7 @@ }, { "cell_type": "markdown", - "id": "68d24b94", + "id": "bc270114", "metadata": {}, "source": [ "True distance to Pleaides is 136.2 pc ( ). 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 492c451..4afc1ba 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": "df192872", + "id": "e2ecb70c", "metadata": {}, "source": [ "# Proposal Preparation Exercise\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a7459a9c", + "id": "e40e5250", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "a3251ff6", + "id": "791d4ddd", "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": "a94c490f", + "id": "7172fdee", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "markdown", - "id": "c27c18d5", + "id": "612b2f01", "metadata": {}, "source": [ "Hint: The Chansngcat ( ) 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": "c0d2d279", + "id": "5141bfa5", "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "3363baaa", + "id": "a933568c", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "73114883", + "id": "7854a863", "metadata": {}, "source": [ "## Step 2: Make Images\n", @@ -119,7 +119,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d8549f34", + "id": "472bc507", "metadata": {}, "outputs": [], "source": [ @@ -128,7 +128,7 @@ }, { "cell_type": "markdown", - "id": "cdd2a73a", + "id": "6ad5dabc", "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:" @@ -137,7 +137,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "6c8dfd67", + "id": "95ffad62", "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ }, { "cell_type": "markdown", - "id": "c6cd020d", + "id": "a5d0d941", "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:" @@ -155,7 +155,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "7c620eaa", + "id": "4be0003e", "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "markdown", - "id": "435671b9", + "id": "0756dd2b", "metadata": {}, "source": [ "Hint: Next create a UV image for the source" @@ -173,7 +173,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "c8557e65", + "id": "319f964b", "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "47d95e11", + "id": "86095e81", "metadata": {}, "outputs": [], "source": [ @@ -198,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "9b1659bf", + "id": "b0b4756d", "metadata": {}, "source": [ "Hint: Repeat steps for X-ray image. (Note: Ideally, we would find an image in the Chandra 'cxc' catalog)" @@ -207,14 +207,14 @@ { "cell_type": "code", "execution_count": null, - "id": "789054ec", + "id": "cd220048", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "cb51b393", + "id": "326dd78a", "metadata": {}, "source": [ "## Step 3: Make a spectrum\n", @@ -227,7 +227,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "d8011c35", + "id": "1d1cdf5f", "metadata": {}, "outputs": [], "source": [ @@ -236,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "b697a173", + "id": "6b8ca89c", "metadata": {}, "source": [ "Hint 2: Take a look at what data exist for our candidate, NGC 1365." @@ -245,14 +245,14 @@ { "cell_type": "code", "execution_count": null, - "id": "f7c7587b", + "id": "2fda617f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "dd1f8670", + "id": "2cb46926", "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." @@ -261,7 +261,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "898edc57", + "id": "49350373", "metadata": {}, "outputs": [], "source": [ @@ -271,7 +271,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "ae96ffae", + "id": "114ff21b", "metadata": {}, "outputs": [], "source": [ @@ -280,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "2f1c1adf", + "id": "05ccc7a5", "metadata": {}, "source": [ "Extension: Making a \"quick look\" spectrum. For our purposes, the 1st order of the HEG grating data would be sufficient." @@ -289,7 +289,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "724d00e5", + "id": "a44c9e62", "metadata": {}, "outputs": [], "source": [ @@ -298,7 +298,7 @@ }, { "cell_type": "markdown", - "id": "55cdd3da", + "id": "317e28ed", "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.)" @@ -306,7 +306,7 @@ }, { "cell_type": "markdown", - "id": "9d8d0979", + "id": "df32f0b5", "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 331b2ae..7942b91 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": "88759f6e", + "id": "c3df1297", "metadata": {}, "source": [ "# Proposal Preparation Solution\n", @@ -15,7 +15,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "8ed183bc", + "id": "90bd08fe", "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "markdown", - "id": "bb291930", + "id": "35fcdda3", "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": "8c2c4a56", + "id": "aa039f13", "metadata": {}, "outputs": [], "source": [ @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "ca11d366", + "id": "c6e08ac1", "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": "b3fdf73c", + "id": "e386459d", "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "f3287277", + "id": "277905ff", "metadata": {}, "outputs": [], "source": [ @@ -107,14 +107,14 @@ { "cell_type": "code", "execution_count": 5, - "id": "bd805855", + "id": "b18d6627", "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": "f1f808f1", + "id": "f2d1bb18", "metadata": {}, "source": [ "## Step 2: Make Images\n", @@ -164,14 +164,14 @@ { "cell_type": "code", "execution_count": 6, - "id": "6fdf331a", + "id": "d0ac4cda", "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", @@ -202,7 +202,7 @@ }, { "cell_type": "markdown", - "id": "5deda5c5", + "id": "ad1e055e", "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:" @@ -211,14 +211,14 @@ { "cell_type": "code", "execution_count": 7, - "id": "fce4903b", + "id": "aec5e0bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=3\n", - "
ivoidshort_name
objectobject
ivo://archive.stsci.edu/sia/galexGALEX
\n", + "
\n", "\n", "\n", "\n", @@ -249,7 +249,7 @@ }, { "cell_type": "markdown", - "id": "4b3a3e03", + "id": "bdcd1033", "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:" @@ -258,7 +258,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4a0f9d41", + "id": "782d659e", "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "markdown", - "id": "82c57140", + "id": "e3acb4dc", "metadata": {}, "source": [ "Hint: Next create a UV image for the source" @@ -277,36 +277,36 @@ { "cell_type": "code", "execution_count": 9, - "id": "046a52bc", + "id": "2a17f5ed", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=809\n", - "
ivoidshort_name
objectobject
ivo://archive.stsci.edu/sia/galexGALEX
\n", + "
\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\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
SCIENCEimage/fits16510788NGA_NGC1365-nd-int.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-int.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-int.fits.gz"}}
AUXILIARYimage/fits5423152NGA_NGC1365-nd-cnt.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-cnt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-cnt.fits.gz"}}
AUXILIARYimage/fits2596019NGA_NGC1365-nd-skybg.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-skybg.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-skybg.fits.gz"}}
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AUXILIARYimage/fits22620937NGA_NGC1365-nd-intbgsub.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-intbgsub.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-intbgsub.fits.gz"}}
AUXILIARYimage/fits43097NGA_NGC1365-nd-flags.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-flags.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-flags.fits.gz"}}
AUXILIARYimage/fits25227NGA_NGC1365-nd-flagstar.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-flagstar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-flagstar.fits.gz"}}
THUMBNAILimage/jpeg10198NGA_NGC1365-xd-int_2color_thumb.jpgGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS 53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_thumb.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_thumb.jpg"}}
scienceimage/fits12544983FORNAX_MOS06-xd-mcat.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-xd-mcat.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-xd-mcat.fits.gz"}}
AUXILIARYimage/fits22647591FORNAX_MOS06-nd-intbgsub.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-intbgsub.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-intbgsub.fits.gz"}}
AUXILIARYimage/fits43549FORNAX_MOS06-nd-flags.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flags.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flags.fits.gz"}}
AUXILIARYimage/fits24262FORNAX_MOS06-nd-flagstar.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flagstar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flagstar.fits.gz"}}
THUMBNAILimage/jpeg10251FORNAX_MOS06-xd-int_2color_thumb.jpgGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_thumb.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_thumb.jpg"}}
INFOimage/fits14617FORNAX_MOS06-nd-cat_mch_rtastar.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cat_mch_rtastar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cat_mch_rtastar.fits.gz"}}
PREVIEWimage/jpeg543324FORNAX_MOS06-xd-int_2color_medium_annot.jpgGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_medium_annot.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_medium_annot.jpg"}}
SCIENCEimage/fits17443052FORNAX_MOS06-nd-int.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-int.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-int.fits.gz"}}
AUXILIARYimage/fits5756688FORNAX_MOS06-nd-cnt.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS 53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cnt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cnt.fits.gz"}}
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THUMBNAILimage/jpeg9796AIS_423_0002_sg49-xd-int_2color_thumb.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_thumb.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_thumb.jpg"}}
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/fits54117AIS_423_0002_sg49-fd-exp.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-exp.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-exp.fits.gz"}}
AUXILIARYimage/fits3967636AIS_423_0002_sg49-fd-intbgsub.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-intbgsub.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-intbgsub.fits.gz"}}
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"}}
scienceimage/fits269663AIS_423_0002_sg49_asprefine-nd-cat.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_asprefine-nd-cat.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/qa/AIS_423_0002_sg49_asprefine-nd-cat.fits.gz"}}
AUXILIARYimage/fits339243AIS_423_0002_sg49-nd-fcat.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-fcat.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-nd-fcat.fits.gz"}}
PREVIEWimage/jpeg3487526AIS_423_0002_sg49-xd-int_2color.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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.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.jpg"}}
PREVIEWimage/jpeg663752AIS_423_0002_sg49-xd-int_2color_large.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_large.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_large.jpg"}}
PREVIEWimage/jpeg575892AIS_423_0002_sg49-xd-int_2color_medium.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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.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.jpg"}}
PREVIEWimage/jpeg134331AIS_423_0002_sg49-xd-int_2color_small.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_small.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_small.jpg"}}
INFOimage/fits8737AIS_423_0002_sg49-asp.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-asp.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-asp.fits.gz"}}
AUXILIARYimage/fits64456AIS_423_0002_sg49-nd-cat_mch_flagstar.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-cat_mch_flagstar.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-nd-cat_mch_flagstar.fits.gz"}}
AUXILIARYimage/fits25691AIS_423_0002_sg49-nd-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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-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-nd-flag_tbl.fits.gz"}}
AUXILIARYimage/fits9113FORNAX_MOS07_0002-fd-cat_mch_flagstar.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 54110.29637754110.286030092654110.296377314854110.2912037037894.45FUV1.34e-071.806e-071.573e-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-fd-cat_mch_flagstar.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-fd-cat_mch_flagstar.fits.gz"}}
" ], "text/plain": [ @@ -314,26 +314,26 @@ "productType ...\n", " object ...\n", "----------- ...\n", - " SCIENCE ...\n", - " AUXILIARY ...\n", - " AUXILIARY ...\n", - " AUXILIARY ...\n", " science ...\n", " AUXILIARY ...\n", " AUXILIARY ...\n", " AUXILIARY ...\n", " THUMBNAIL ...\n", - " ... ...\n", - " THUMBNAIL ...\n", - " INFO ...\n", " INFO ...\n", - " AUXILIARY ...\n", " PREVIEW ...\n", + " SCIENCE ...\n", " AUXILIARY ...\n", + " ... ...\n", + " science ...\n", " AUXILIARY ...\n", - " AUXILIARY ...\n", + " PREVIEW ...\n", + " PREVIEW ...\n", + " PREVIEW ...\n", + " PREVIEW ...\n", " INFO ...\n", - " INFO ..." + " AUXILIARY ...\n", + " AUXILIARY ...\n", + " AUXILIARY ..." ] }, "execution_count": 9, @@ -350,18 +350,18 @@ { "cell_type": "code", "execution_count": 10, - "id": "e5612e38", + "id": "eb4d8101", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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=skv1734651752925&return=FITS2
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" ], "text/plain": [ @@ -388,7 +388,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "c4048820", + "id": "e2a8d4ea", "metadata": {}, "outputs": [ { @@ -413,13 +413,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "12dcc6c9", + "id": "b1ba76fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -446,7 +446,7 @@ }, { "cell_type": "markdown", - "id": "f530e444", + "id": "0a765649", "metadata": {}, "source": [ "Hint: Repeat steps for X-ray image. (Note: Ideally, we would find an image in the Chandra 'cxc' catalog)" @@ -455,7 +455,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "7e83973e", + "id": "ac452c01", "metadata": {}, "outputs": [ { @@ -482,7 +482,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "6f7acd9a", + "id": "87088c52", "metadata": {}, "outputs": [ { @@ -504,7 +504,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "97b70804", + "id": "027ee6ba", "metadata": {}, "outputs": [ { @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "f4c08e18", + "id": "ab39eb0b", "metadata": {}, "source": [ "## Step 3: Make a spectrum\n", @@ -556,14 +556,14 @@ { "cell_type": "code", "execution_count": 16, - "id": "79be633b", + "id": "47b727ca", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Table length=7\n", - "\n", + "
\n", "\n", "\n", "\n", @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "85fe77f6", + "id": "d757217f", "metadata": {}, "source": [ "Hint 2: Take a look at what data exist for our candidate, NGC 1365." @@ -611,14 +611,14 @@ { "cell_type": "code", "execution_count": 17, - "id": "40e20b0e", + "id": "1771287b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Chandraivo://nasa.heasarc/chanmasterx-ray
\n", + "
\n", "\n", "\n", "\n", @@ -674,7 +674,7 @@ }, { "cell_type": "markdown", - "id": "9e09f10c", + "id": "f7f579fa", "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." @@ -683,7 +683,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "389b2c49", + "id": "8bf969ac", "metadata": {}, "outputs": [ { @@ -723,7 +723,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "5dbcdff8", + "id": "29a2b6b4", "metadata": {}, "outputs": [], "source": [ @@ -740,7 +740,7 @@ }, { "cell_type": "markdown", - "id": "4e20acda", + "id": "95c85d0c", "metadata": {}, "source": [ "Extension: Making a \"quick look\" spectrum. For our purposes, the 1st order of the HEG grating data would be sufficient." @@ -749,7 +749,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "1bfc2f91", + "id": "4444b80e", "metadata": {}, "outputs": [ { @@ -780,7 +780,7 @@ }, { "cell_type": "markdown", - "id": "02938eb5", + "id": "4d192c28", "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.)" @@ -788,7 +788,7 @@ }, { "cell_type": "markdown", - "id": "30f05916", + "id": "e4eea30f", "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 5d41e26..e627848 100644 --- a/content/reference_notebooks/basic_reference.html +++ b/content/reference_notebooks/basic_reference.html @@ -680,7 +680,7 @@
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
+
@@ -708,7 +708,7 @@

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.
+
@@ -748,7 +748,7 @@

Find an image service
Table length=3 -

ObjIDZoneSeqNoRADECpmRApmDECe_pmRAe_pmDECe_RAe_DECEpochB1MagR1s_gB2MagB2s_gR2MagR2s_gNMagmagB1s_gR1Magdistance
degdegmas / yrmas / yrmas / yrmas / yrarcsecarcsecyrmagmagmagmagmagmagarcsec
int64int32int32float64float64float32float32float32float32float32float32float32float32int32float32int32float32int32float32float32int32float32float32
+
@@ -774,7 +774,7 @@

Search one of the services
Table length=2 -

ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
+
@@ -799,7 +799,7 @@

Download an image
image/fits
-
'/tmp/astropy-download-1925-h1c1apau'
+
'/tmp/astropy-download-1791-dtkmx6ih'
@@ -835,7 +835,7 @@

2.4 Spectral search
Title: acisf00639N005_pha2.fits, Format: application/fits

-
'/tmp/astropy-download-1925-ac3w7j8m'
+
'/tmp/astropy-download-1791-_mypbvfn'
@@ -1762,7 +1762,9 @@

2.5 Table search
uitmaster                      - Ultraviolet Imaging Telescope Master Catalog
 ulxngcat                       - Ultraluminous X-Ray Sources in Nearby Galaxies Catalog
 ulxrbcat                       - Ultraluminous X-Ray Sources in External Galaxies Catalog
 upprscoxmm                     - Upper Sco XMM-Newton X-Ray Point Source Catalog
@@ -1770,9 +1772,7 @@ 
2.5 Table search
verimaster                     - VERITAS Source Catalog
+verimaster                     - VERITAS Source Catalog
 veroncat                       - Veron Catalog of Quasars & AGN, 13th Edition
 vla23901p4                     - VLA A2390 Cluster of Galaxies 1.4-GHz Source Catalog
 vla3701p4                      - VLA A370 Cluster of Galaxies 1.4-GHz Source Catalog
@@ -1972,7 +1972,7 @@ 
Perform a Query
 
Table length=13
-

filenameidra_j2000dec_j2000urlfilesizemjdmeannaxesnaxisscalecdformatref_frameequinoxcoord_projectioncrpixcrvalctypebandpass_idbandpass_refvaluebandpass_unitbandpass_hilimitbandpass_lolimitprocessingprojectpreviewrepresentativeobject_id
degdegbytedpixdeg / pixdeg / pixyrpixpixmmmm
objectobjectfloat64float64objectint32float64int32objectobjectobjectobjectobjectfloat32str3objectobjectobjectobjectfloat64objectfloat64float64objectobjectobjectobjectobject
+
@@ -2031,7 +2031,7 @@

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 75c280c..bf3c34f 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
+
@@ -840,7 +840,7 @@

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
+
@@ -871,7 +871,7 @@

2.3 A use case
Table length=1120 -

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

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

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

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 +1025,7 @@

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

radecradial_velocitybmagmorph_type
degdeg
float64float64int32float32int16
+
@@ -1061,7 +1061,7 @@

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 333d251..1893a5a 100644 --- a/content/reference_notebooks/image_access.html +++ b/content/reference_notebooks/image_access.html @@ -520,7 +520,7 @@

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

radecra2dec2radial_velocitymorph_typebmag
degdegdegdeg
float64float64float64float64int32int16float32
+
@@ -553,7 +553,7 @@

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

ivoidshort_nameres_title
objectobjectobject
ivo://archive.stsci.edu/sia/galexGALEXGalaxy Evolution Explorer (GALEX)
+
@@ -576,15 +576,15 @@

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=skv1734651634490&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=skv1734651634986&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=skv1734651636170&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=skv1734651636990&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=skv1734651637662&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=skv1734651638096&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=skv1734724357376&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=skv1734724357660&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=skv1734724358799&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=skv1734724359238&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=skv1734724359699&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=skv1734724360111&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.

@@ -597,7 +597,7 @@

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=skv1734651634490&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=skv1734724357376&nofits=1&quicklook=jpeg&return=jpeg
@@ -616,7 +616,7 @@

JPG images -

+
@@ -635,7 +635,7 @@

Fits files -
Filename: /home/runner/.astropy/cache/download/url/ada1b9e305d642dd2464d3bc873391ab/contents
+
Filename: /home/runner/.astropy/cache/download/url/b76bb70fb1e0cd40080fb49ab7343f6e/contents
 No.    Name      Ver    Type      Cards   Dimensions   Format
   0  PRIMARY       1 PrimaryHDU     111   (300, 300)   float32   
 

@@ -651,7 +651,7 @@ 
Using imshow
-
<matplotlib.image.AxesImage at 0x7fd0e8f9cd90>
+
<matplotlib.image.AxesImage at 0x7f37c7f5e740>
 

 

 ../../_images/ba9cffa29f71b5eef810ef3ecd08a619c2bb06f6e3711eda9023615ed0435d98.png
diff --git a/content/reference_notebooks/spectral_access.html b/content/reference_notebooks/spectral_access.html
index dd03d2f..8653d9c 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=7
-
+

@@ -546,7 +546,7 @@ 
Chandra Spectrum of Delta OriTable length=6
-

 
ivoidshort_name

 
objectobject

 
ivo://nasa.heasarc/chanmasterChandra

+

@@ -578,14 +578,14 @@ 
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-SHETG49680GOCassinelli5203711755:chandra.obs.misc51556.136400463----49680.0--83.00125-0.299170.81acisf00639N005_pha2.fitshttps://heasarc.gsfc.nasa.gov/FTP/chandra/data/byobsid/9/639/primary/acisf00639N005_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/acisf00639N005_pha2.fits.gz"}}

+

diff --git a/content/reference_notebooks/ucds_unified_content_descriptors.html b/content/reference_notebooks/ucds_unified_content_descriptors.html
index 89bc0ad..44cbcc0 100644
--- a/content/reference_notebooks/ucds_unified_content_descriptors.html
+++ b/content/reference_notebooks/ucds_unified_content_descriptors.html
@@ -501,9 +501,7 @@ 
UCDs (Unified Content Descriptors)
tap_schema.columns             - description of columns in this dataset
+tap_schema.columns             - description of columns in this dataset
 ----
 tap_schema.keys                - description of foreign keys in this dataset
 ----
@@ -513,25 +511,21 @@ 
UCDs (Unified Content Descriptors)
dbo.hcvsummaryview             - Summary list of Hubble Catalog of Variables parameters
+
dbo.hcvdetailedview            - Detailed list of Hubble Catalog of Variables parameters
+----
+dbo.hcvsummaryview             - Summary list of Hubble Catalog of Variables parameters
 ----
 dbo.propermotionsview          - List of proper motion information
 ----
-

-

-
dbo.sourcepositionsview        - List of source position information
+dbo.sourcepositionsview        - List of source position information
 ----
 

 

 
dbo.summagaper2catview         - Summary list of source catalog with Aper2 magnitudes
 ----
-

-

-
dbo.summagautocatview          - Summary list of source catalog with MagAuto magnitudes
+dbo.summagautocatview          - Summary list of source catalog with MagAuto magnitudes
 ----
 dbo.catalog_image_metadata     - Summary list of Image processing metadata
 ----
diff --git a/content/use_case_notebooks/candidate_list_solution.html b/content/use_case_notebooks/candidate_list_solution.html
index c394f71..6c73102 100644
--- a/content/use_case_notebooks/candidate_list_solution.html
+++ b/content/use_case_notebooks/candidate_list_solution.html
@@ -528,7 +528,7 @@ 
2. Search NED for objects in this paperTable 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]

 
1-3114094.91384132.0761 .. 81920 .. 01.8660254 .. 1.86602540 .. 00 .. 07.159166666667378 .. 0.33333333333333337.160000000000712 .. 0.33416666666666667

+

@@ -616,14 +616,14 @@ 
2. Search NED for objects in this paper3. Filter the NED resultsTable length=53
-

 
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations

 
degreesdegreeskm / sarcmin

 
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506389100

+

@@ -770,14 +770,14 @@ 
3. Filter the NED results4. Search the NAVO Registry for image resources
Table length=321
-

 
idxNo.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations

 
degreesdegreeskm / sarcmin

 
01WISEA J001550.14-100242.33.95892-10.04511G52766.00.17601SLS17.5g--1506389100

+

@@ -851,7 +851,7 @@ 
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

+

@@ -934,7 +934,7 @@ 
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

+

@@ -995,7 +995,7 @@ 
11. Visualize this AllWISE image
-
<matplotlib.collections.PathCollection at 0x7f5824b31ed0>
+
<matplotlib.collections.PathCollection at 0x7feff363a6e0>
 

 

 ../../_images/99c3c57cd0e8b4ac127daf7659df26cb47a7566b0ee83d6e59c1dca8217b280b.png
@@ -1020,7 +1020,7 @@ 
12. Plot a cutout of the AllWISE image, centered on your position
-
<matplotlib.collections.PathCollection at 0x7f58248e9c30>
+
<matplotlib.collections.PathCollection at 0x7fefeb3bca90>
 

 

 ../../_images/07fac4eeb5c02470f6d9deef914353d7cfbd875541d034f680f13f6db72d8a7e.png
@@ -1043,7 +1043,7 @@ 
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_losia_bp_unitmagzpmagzpuncunc_urlcov_urlcoadd_id

 
degdegpixdegdeg / pixdeg / pix

 
objectobjectobjectint32objectfloat64float64int32[2]float64[2]float64[2]objectfloat64[2]float64[4]objectfloat64float64float64objectfloat64float64objectobjectobject

+

@@ -1078,7 +1078,7 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position
 
 

-
AIS_270_sg14-nd-int.fits.gz NUV
+
AIS_270_0001_sg14-nd-int.fits.gz NUV
 

 

 

@@ -1105,9 +1105,9 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position
 

 
Min: 0.0
-Max: 7.1870303
-Mean: 0.0014934327
-Stdev: 0.012639926
+Max: 7.6572003
+Mean: 0.0014543837
+Stdev: 0.013401416
 

 

 

@@ -1122,10 +1122,10 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position
 

 

-
<matplotlib.collections.PathCollection at 0x7f58196bef20>
+
<matplotlib.collections.PathCollection at 0x7fefe81b45b0>
 

 

-../../_images/709471c04f3485c1924208d699a17e5a3b302c2f513c426a456b19a23847adf9.png
+../../_images/b65fb31b02404013bf6ee288e3b106b0f1675842da91339de255ee6d2b1f1311.png
 

 

 

@@ -1141,10 +1141,10 @@ 
13. Try visualizing a cutout of a GALEX image that covers your position
 

 

-
<matplotlib.collections.PathCollection at 0x7f58196ed000>
+
<matplotlib.collections.PathCollection at 0x7fefe8024ca0>
 

 

-../../_images/a7fdc1e8c14db6bd7ab9ea9ddd10bbca10ff3b2e3d9567c0e377d80f44adbafc.png
+../../_images/c99310cbe9f7b3b61ed767fcc2e900d3383789bd8d81529b17da637c9ffdb128.png
 

 

 
@@ -1165,7 +1165,7 @@ 
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)

+

@@ -1262,7 +1262,7 @@ 
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 0x7f58196bfb20>
+
<matplotlib.collections.PathCollection at 0x7fefe80fbdc0>
 

 

 ../../_images/c6437eda497b676362e20637ad992737a2bbe1bfb2b33b7b32b18ecb6dc952c5.png
@@ -1284,7 +1284,7 @@ 
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 0x7f5819449120>
+
<matplotlib.collections.PathCollection at 0x7fefdcef8670>
 

 

 ../../_images/cace13656c31942febb4172e15d1c1b59fbd5a36a975dfa024ca98c610680293.png
@@ -1415,9 +1415,9 @@ 
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/f985e497b906730d3361e774d3ed3e3946061539b90c046c70a8ccad428bbeb5.png
+../../_images/a608ae0ad3389440663842747b03b82e593e49dc425983626fa11d3151161f87.png
 ../../_images/7b2e37a1e5f327e7e3dfaa01a4b2cf2182bab2fa7c2f828a05b357552d32f6b1.png
-../../_images/51a58b908a1077d99f17c3c4ca2d07db9f53ca2fc8ab8020abe5b3ac7139d85e.png
+../../_images/c36cd3fbea9e9c795a43b7bb4cafc084faeb657101030e8d9a799ce10811d96b.png
 

 

 
diff --git a/content/use_case_notebooks/hr_diagram_solution.html b/content/use_case_notebooks/hr_diagram_solution.html
index 4d86fff..d2892f1 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=145
-

 
ivoidshort_nameres_titlesource_value

 
objectobjectobjectobject

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

+

@@ -4042,7 +4042,7 @@ 
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

+

@@ -4090,7 +4090,7 @@ 
Plotting
 
 
 

-
[<matplotlib.lines.Line2D at 0x7f6c5b5b8430>]
+
[<matplotlib.lines.Line2D at 0x7fdfb8e84ca0>]
 

 

 ../../_images/b42395cf23e56c1d04597a950797687a5b0c98304e196110b7139ca953872f16.png
@@ -4149,7 +4149,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades
/tmp/ipykernel_2202/2733767379.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_2086/2733767379.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])
 

 

@@ -4186,7 +4186,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades
Table length=270
-

 
recnoHertzsprungCIPtmRAB1900e_RAB1900DEB1900e_DEB1900rmsRArmsDErpmRArpmDEDrpmRADrpmDEDRADDE_RA_icrs_DE_icrs

 
magmagdegmsdegmasmas / yrmas / yrmas / yrmas / yrarcsecarcsecdegdeg

 
int32int16float64float64float64float64float64int16float64float64float64float64float64float64float64float64float64float64

+

@@ -4225,7 +4225,7 @@ 
Step 3. Compare with other color-magnitude diagrams for Pleiades
-
[<matplotlib.lines.Line2D at 0x7f6c66a61870>]
+
[<matplotlib.lines.Line2D at 0x7fdfc426d990>]
 

 

 ../../_images/e81e8d079b9354b932c4acff90f479f836ce48f4ef7dce824b71fafb6e3da0a8.png
@@ -4253,7 +4253,7 @@ 
BONUS: Step 4: The CMD as a distance indicator
-
[<matplotlib.lines.Line2D at 0x7f6c66939810>]
+
[<matplotlib.lines.Line2D at 0x7fdfc40f20b0>]
 

 

 ../../_images/aff688f7fffbd0cff7c2686632192a7fcd4db2b045dd014d666c6b994f3dcc34.png
diff --git a/content/use_case_notebooks/proposal_prep_solution.html b/content/use_case_notebooks/proposal_prep_solution.html
index 5d3bf1f..115f54e 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

+

@@ -616,29 +616,29 @@ 
Create ultraviolet and X-ray images
 
Table length=809
-

 
ivoidshort_name

 
objectobject

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

+

-
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
+
-
-
-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
+
+
+

 
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
SCIENCEimage/fits16510788NGA_NGC1365-nd-int.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-int.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-int.fits.gz"}}
AUXILIARYimage/fits5423152NGA_NGC1365-nd-cnt.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-cnt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-cnt.fits.gz"}}
AUXILIARYimage/fits2596019NGA_NGC1365-nd-skybg.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-skybg.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-skybg.fits.gz"}}
AUXILIARYimage/fits24130405NGA_NGC1365-nd-wt.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-wt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-wt.fits.gz"}}
scienceimage/fits11561386NGA_NGC1365-xd-mcat.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-xd-mcat.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-xd-mcat.fits.gz"}}
AUXILIARYimage/fits22620937NGA_NGC1365-nd-intbgsub.fits.gzGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-intbgsub.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/NGA_NGC1365-nd-intbgsub.fits.gz"}}
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THUMBNAILimage/jpeg10198NGA_NGC1365-xd-int_2color_thumb.jpgGALEXGALEX5/11/2010 1:38:21 AM5/11/2010 1:38:21 AM53.3543381647092-36.1370016988435CIRCLE ICRS  53.35433816 -36.13700170 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.3543 -36.137][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 53320.004630 53328.50803253320.004629629653328.508032407453324.25633101852677.95NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2483772715392040960https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_thumb.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/05057-NGA_NGC1365/d/01-main/0001-img/07-try/qa/NGA_NGC1365-xd-int_2color_thumb.jpg"}}
scienceimage/fits12544983FORNAX_MOS06-xd-mcat.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-xd-mcat.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-xd-mcat.fits.gz"}}
AUXILIARYimage/fits22647591FORNAX_MOS06-nd-intbgsub.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-intbgsub.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-intbgsub.fits.gz"}}
AUXILIARYimage/fits43549FORNAX_MOS06-nd-flags.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flags.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flags.fits.gz"}}
AUXILIARYimage/fits24262FORNAX_MOS06-nd-flagstar.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flagstar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-flagstar.fits.gz"}}
THUMBNAILimage/jpeg10251FORNAX_MOS06-xd-int_2color_thumb.jpgGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_thumb.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_thumb.jpg"}}
INFOimage/fits14617FORNAX_MOS06-nd-cat_mch_rtastar.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cat_mch_rtastar.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cat_mch_rtastar.fits.gz"}}
PREVIEWimage/jpeg543324FORNAX_MOS06-xd-int_2color_medium_annot.jpgGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_medium_annot.jpg{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/qa/FORNAX_MOS06-xd-int_2color_medium_annot.jpg"}}
SCIENCEimage/fits17443052FORNAX_MOS06-nd-int.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-int.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-int.fits.gz"}}
AUXILIARYimage/fits5756688FORNAX_MOS06-nd-cnt.fits.gzGALEXGALEX5/11/2010 12:15:31 AM5/11/2010 12:15:31 AM53.0653359237686-36.3609134371405CIRCLE ICRS  53.06533592 -36.36091344 0.6252[3840 3840][-0.00041666667 0.00041666667][1920.5 1920.5][53.0653 -36.3609][-0.000416667 -0.0 -0.0 0.000416667]ICRSTANRA---TANDEC--TANdegdegRANGE 54106.382801 54117.28317154106.382800925954117.283171296354111.83298611113445.05NUV1.693e-073.007e-072.35e-07metersivo://archive.stsci.edu/GALEX?2555302543848636416https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:GALEX/url/data/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cnt.fits.gz{"aws": {"bucket_name":"stpubdata","region":"us-east-1","access":"open","key":"galex/GR6/pipe/01-vsn/07090-FORNAX_MOS06/d/01-main/0001-img/07-try/FORNAX_MOS06-nd-cnt.fits.gz"}}

 
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THUMBNAILimage/jpeg9796AIS_423_0002_sg49-xd-int_2color_thumb.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_thumb.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_thumb.jpg"}}
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/fits54117AIS_423_0002_sg49-fd-exp.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-exp.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-exp.fits.gz"}}
AUXILIARYimage/fits3967636AIS_423_0002_sg49-fd-intbgsub.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-intbgsub.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-intbgsub.fits.gz"}}
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"}}
scienceimage/fits269663AIS_423_0002_sg49_asprefine-nd-cat.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_asprefine-nd-cat.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/qa/AIS_423_0002_sg49_asprefine-nd-cat.fits.gz"}}
AUXILIARYimage/fits339243AIS_423_0002_sg49-nd-fcat.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-fcat.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-nd-fcat.fits.gz"}}
PREVIEWimage/jpeg3487526AIS_423_0002_sg49-xd-int_2color.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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.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.jpg"}}
PREVIEWimage/jpeg663752AIS_423_0002_sg49-xd-int_2color_large.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_large.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_large.jpg"}}
PREVIEWimage/jpeg575892AIS_423_0002_sg49-xd-int_2color_medium.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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.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.jpg"}}
PREVIEWimage/jpeg134331AIS_423_0002_sg49-xd-int_2color_small.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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_small.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_small.jpg"}}
INFOimage/fits8737AIS_423_0002_sg49-asp.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-asp.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-asp.fits.gz"}}
AUXILIARYimage/fits64456AIS_423_0002_sg49-nd-cat_mch_flagstar.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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-cat_mch_flagstar.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-nd-cat_mch_flagstar.fits.gz"}}
AUXILIARYimage/fits25691AIS_423_0002_sg49-nd-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 54460.401030 54460.40222254460.401030092654460.402222222254460.4016261574103.0NUV1.693e-073.007e-072.35e-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-nd-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-nd-flag_tbl.fits.gz"}}
AUXILIARYimage/fits9113FORNAX_MOS07_0002-fd-cat_mch_flagstar.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 54110.29637754110.286030092654110.296377314854110.2912037037894.45FUV1.34e-071.806e-071.573e-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-fd-cat_mch_flagstar.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-fd-cat_mch_flagstar.fits.gz"}}

 

 

 

@@ -652,11 +652,11 @@ 
Create ultraviolet and X-ray images
 
Table length=2
-
+

-
-
+
+

 
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=skv1734651752648&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=skv1734651752925&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=skv1734724494538&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=skv1734724494738&return=FITS2

 

 

 

@@ -688,7 +688,7 @@ 
Create ultraviolet and X-ray images
-
<matplotlib.image.AxesImage at 0x7f67cd14bb80>
+
<matplotlib.image.AxesImage at 0x7fc1037787f0>
 

 

 ../../_images/1979cc91924887400ac07d0500b0876bc3e1e33ac2eee690c5c4c8c19bd17795.png
@@ -771,7 +771,7 @@ 
Find what Chandra spectral observations exist already for this source
 

 
Table length=7
-
+

@@ -793,7 +793,7 @@ 
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 6a14c9b..67bb73c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"0. (Only for Windows) Install WSL": [[0, "only-for-windows-install-wsl"]], "0. Setup": [[2, "setup"]], "1. Finding SIA resources from the Registry": [[4, "finding-sia-resources-from-the-registry"]], "1. Import the Python modules we\u2019ll be using": [[8, "import-the-python-modules-we-ll-be-using"], [9, "import-the-python-modules-we-ll-be-using"]], "1. Install Miniconda (if needed)": [[0, "install-miniconda-if-needed"]], "1. Overview": [[2, "overview"]], "1. Simple cone search": [[3, "simple-cone-search"]], "10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)": [[8, "from-the-result-in-8-select-the-first-record-for-an-image-taken-in-wise-band-w1-3-6-micron"], [9, "from-the-result-in-8-select-the-first-record-for-an-image-taken-in-wise-band-w1-3-6-micron"]], "11. Visualize this AllWISE image": [[8, "visualize-this-allwise-image"], [9, "visualize-this-allwise-image"]], "12. Plot a cutout of the AllWISE image, centered on your position": [[8, "plot-a-cutout-of-the-allwise-image-centered-on-your-position"], [9, "plot-a-cutout-of-the-allwise-image-centered-on-your-position"]], "13. Try visualizing a cutout of a GALEX image that covers your position": [[8, "try-visualizing-a-cutout-of-a-galex-image-that-covers-your-position"], [9, "try-visualizing-a-cutout-of-a-galex-image-that-covers-your-position"]], "14. Try visualizing a cutout of an SDSS image that covers your position": [[8, "try-visualizing-a-cutout-of-an-sdss-image-that-covers-your-position"], [9, "try-visualizing-a-cutout-of-an-sdss-image-that-covers-your-position"]], "15. Try looping over all positions and plotting multiwavelength cutouts": [[8, "try-looping-over-all-positions-and-plotting-multiwavelength-cutouts"], [9, "try-looping-over-all-positions-and-plotting-multiwavelength-cutouts"]], "2. Search NED for objects in this paper": [[8, "search-ned-for-objects-in-this-paper"], [9, "search-ned-for-objects-in-this-paper"]], "2. Table Access Protocol queries": [[3, "table-access-protocol-queries"]], "2. Update conda version": [[0, "update-conda-version"]], "2. Using SIA to retrieve an image": [[4, "using-sia-to-retrieve-an-image"]], "2. VO Services": [[2, "vo-services"]], "2.0 Import Necessary Packages": [[2, "import-necessary-packages"]], "2.1 Look Up Services in VO Registry": [[2, "look-up-services-in-vo-registry"]], "2.1 TAP services": [[3, "tap-services"]], "2.1.1 Use different arguments/values to modify the simple example": [[2, "use-different-arguments-values-to-modify-the-simple-example"]], "2.1.2 Inspect the results": [[2, "inspect-the-results"]], "2.2 Cone search": [[2, "cone-search"]], "2.2 Expressing queries in ADQL": [[3, "expressing-queries-in-adql"]], "2.3 A use case": [[3, "a-use-case"]], "2.3 Image search": [[2, "image-search"]], "2.4 Spectral search": [[2, "spectral-search"]], "2.4 TAP examples for a given service": [[3, "tap-examples-for-a-given-service"]], "2.5 Table search": [[2, "table-search"]], "3. Astroquery": [[2, "astroquery"]], "3. Filter the NED results": [[8, "filter-the-ned-results"], [9, "filter-the-ned-results"]], "3. Install git (if needed)": [[0, "install-git-if-needed"]], "3. Using the TAP to cross-correlate and combine": [[3, "using-the-tap-to-cross-correlate-and-combine"]], "3. Viewing the resulting image": [[4, "viewing-the-resulting-image"]], "3.1 Cross-correlating to combine catalogs": [[3, "cross-correlating-to-combine-catalogs"]], "3.1 NED": [[2, "ned"]], "3.2 Cross-correlating with user-defined columns": [[3, "cross-correlating-with-user-defined-columns"]], "4. Clone This Repository": [[0, "clone-this-repository"]], "4. Search the NAVO Registry for image resources": [[8, "search-the-navo-registry-for-image-resources"], [9, "search-the-navo-registry-for-image-resources"]], "4. Synchronous versus asynchronous queries": [[3, "synchronous-versus-asynchronous-queries"]], "5. Create a conda environment for the workshop": [[0, "create-a-conda-environment-for-the-workshop"]], "5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images": [[8, "search-the-navo-registry-for-image-resources-that-will-allow-you-to-search-for-allwise-images"], [9, "search-the-navo-registry-for-image-resources-that-will-allow-you-to-search-for-allwise-images"]], "6. Check Installation": [[0, "check-installation"]], "6. Choose the AllWISE image service that you are interested in": [[8, "choose-the-allwise-image-service-that-you-are-interested-in"], [9, "choose-the-allwise-image-service-that-you-are-interested-in"]], "7. Choose one of the galaxies in the NED list": [[8, "choose-one-of-the-galaxies-in-the-ned-list"], [9, "choose-one-of-the-galaxies-in-the-ned-list"]], "7. Starting Jupyterlab": [[0, "starting-jupyterlab"]], "8. Handling Notebooks in MyST-Markdown format": [[0, "handling-notebooks-in-myst-markdown-format"]], "8. Search for a list of AllWISE images that cover this galaxy": [[8, "search-for-a-list-of-allwise-images-that-cover-this-galaxy"], [9, "search-for-a-list-of-allwise-images-that-cover-this-galaxy"]], "9. 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+Search.setIndex({"alltitles": {"0. (Only for Windows) Install WSL": [[0, "only-for-windows-install-wsl"]], "0. Setup": [[2, "setup"]], "1. Finding SIA resources from the Registry": [[4, "finding-sia-resources-from-the-registry"]], "1. Import the Python modules we\u2019ll be using": [[8, "import-the-python-modules-we-ll-be-using"], [9, "import-the-python-modules-we-ll-be-using"]], "1. Install Miniconda (if needed)": [[0, "install-miniconda-if-needed"]], "1. Overview": [[2, "overview"]], "1. Simple cone search": [[3, "simple-cone-search"]], "10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)": [[8, "from-the-result-in-8-select-the-first-record-for-an-image-taken-in-wise-band-w1-3-6-micron"], [9, "from-the-result-in-8-select-the-first-record-for-an-image-taken-in-wise-band-w1-3-6-micron"]], "11. Visualize this AllWISE image": [[8, "visualize-this-allwise-image"], [9, "visualize-this-allwise-image"]], "12. Plot a cutout of the AllWISE image, centered on your position": [[8, "plot-a-cutout-of-the-allwise-image-centered-on-your-position"], [9, "plot-a-cutout-of-the-allwise-image-centered-on-your-position"]], "13. Try visualizing a cutout of a GALEX image that covers your position": [[8, "try-visualizing-a-cutout-of-a-galex-image-that-covers-your-position"], [9, "try-visualizing-a-cutout-of-a-galex-image-that-covers-your-position"]], "14. Try visualizing a cutout of an SDSS image that covers your position": [[8, "try-visualizing-a-cutout-of-an-sdss-image-that-covers-your-position"], [9, "try-visualizing-a-cutout-of-an-sdss-image-that-covers-your-position"]], "15. Try looping over all positions and plotting multiwavelength cutouts": [[8, "try-looping-over-all-positions-and-plotting-multiwavelength-cutouts"], [9, "try-looping-over-all-positions-and-plotting-multiwavelength-cutouts"]], "2. Search NED for objects in this paper": [[8, "search-ned-for-objects-in-this-paper"], [9, "search-ned-for-objects-in-this-paper"]], "2. Table Access Protocol queries": [[3, "table-access-protocol-queries"]], "2. Update conda version": [[0, "update-conda-version"]], "2. Using SIA to retrieve an image": [[4, "using-sia-to-retrieve-an-image"]], "2. VO Services": [[2, "vo-services"]], "2.0 Import Necessary Packages": [[2, "import-necessary-packages"]], "2.1 Look Up Services in VO Registry": [[2, "look-up-services-in-vo-registry"]], "2.1 TAP services": [[3, "tap-services"]], "2.1.1 Use different arguments/values to modify the simple example": [[2, "use-different-arguments-values-to-modify-the-simple-example"]], "2.1.2 Inspect the results": [[2, "inspect-the-results"]], "2.2 Cone search": [[2, "cone-search"]], "2.2 Expressing queries in ADQL": [[3, "expressing-queries-in-adql"]], "2.3 A use case": [[3, "a-use-case"]], "2.3 Image search": [[2, "image-search"]], "2.4 Spectral search": [[2, "spectral-search"]], "2.4 TAP examples for a given service": [[3, "tap-examples-for-a-given-service"]], "2.5 Table search": [[2, "table-search"]], "3. Astroquery": [[2, "astroquery"]], "3. Filter the NED results": [[8, "filter-the-ned-results"], [9, "filter-the-ned-results"]], "3. Install git (if needed)": [[0, "install-git-if-needed"]], "3. Using the TAP to cross-correlate and combine": [[3, "using-the-tap-to-cross-correlate-and-combine"]], "3. Viewing the resulting image": [[4, "viewing-the-resulting-image"]], "3.1 Cross-correlating to combine catalogs": [[3, "cross-correlating-to-combine-catalogs"]], "3.1 NED": [[2, "ned"]], "3.2 Cross-correlating with user-defined columns": [[3, "cross-correlating-with-user-defined-columns"]], "4. Clone This Repository": [[0, "clone-this-repository"]], "4. Search the NAVO Registry for image resources": [[8, "search-the-navo-registry-for-image-resources"], [9, "search-the-navo-registry-for-image-resources"]], "4. Synchronous versus asynchronous queries": [[3, "synchronous-versus-asynchronous-queries"]], "5. Create a conda environment for the workshop": [[0, "create-a-conda-environment-for-the-workshop"]], "5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images": [[8, "search-the-navo-registry-for-image-resources-that-will-allow-you-to-search-for-allwise-images"], [9, "search-the-navo-registry-for-image-resources-that-will-allow-you-to-search-for-allwise-images"]], "6. Check Installation": [[0, "check-installation"]], "6. Choose the AllWISE image service that you are interested in": [[8, "choose-the-allwise-image-service-that-you-are-interested-in"], [9, "choose-the-allwise-image-service-that-you-are-interested-in"]], "7. Choose one of the galaxies in the NED list": [[8, "choose-one-of-the-galaxies-in-the-ned-list"], [9, "choose-one-of-the-galaxies-in-the-ned-list"]], "7. Starting Jupyterlab": [[0, "starting-jupyterlab"]], "8. Handling Notebooks in MyST-Markdown format": [[0, "handling-notebooks-in-myst-markdown-format"]], "8. Search for a list of AllWISE images that cover this galaxy": [[8, "search-for-a-list-of-allwise-images-that-cover-this-galaxy"], [9, "search-for-a-list-of-allwise-images-that-cover-this-galaxy"]], "9. Use the .to_table() method to view the results as an Astropy table": [[8, "use-the-to-table-method-to-view-the-results-as-an-astropy-table"], [9, "use-the-to-table-method-to-view-the-results-as-an-astropy-table"]], "Additional Resources": [[0, "additional-resources"], [14, "additional-resources"]], "Alternative Method: Use ADS to search for appropriate paper and access data via NED": [[10, "alternative-method-use-ads-to-search-for-appropriate-paper-and-access-data-via-ned"], [11, "alternative-method-use-ads-to-search-for-appropriate-paper-and-access-data-via-ned"]], "Asynchronous TAP queries": [[1, "asynchronous-tap-queries"]], "At this point, you can proceed to Step 2": [[10, "at-this-point-you-can-proceed-to-step-2"], [11, "at-this-point-you-can-proceed-to-step-2"]], "BONUS: Step 4: The CMD as a distance indicator": [[10, "bonus-step-4-the-cmd-as-a-distance-indicator"], [11, "bonus-step-4-the-cmd-as-a-distance-indicator"]], "Basic Reference": [[2, null]], "Candidate List Exercise": [[8, null]], "Candidate List Solution": [[9, null]], "Catalog Queries": [[3, null]], "Chandra Spectrum of Delta Ori": [[5, "chandra-spectrum-of-delta-ori"]], "Column Information": [[2, "column-information"]], "Configuring the Workshop Environment": [[0, null]], "Create a VO Table from an Astropy Table": [[7, "create-a-vo-table-from-an-astropy-table"]], "Create a table with only two columns starting from an astropy Table": [[7, "create-a-table-with-only-two-columns-starting-from-an-astropy-table"]], "Create ultraviolet and X-ray images": [[12, "create-ultraviolet-and-x-ray-images"], [13, "create-ultraviolet-and-x-ray-images"]], "DATA DISCOVERY STEPS": [[10, "data-discovery-steps"], [11, "data-discovery-steps"]], "Download an image": [[2, "download-an-image"]], "Filtering results": [[2, "filtering-results"]], "Find an image service": [[2, "find-an-image-service"]], "Find what Chandra spectral observations exist already for this source": [[12, "find-what-chandra-spectral-observations-exist-already-for-this-source"], [13, "find-what-chandra-spectral-observations-exist-already-for-this-source"]], "Finding available Spectral Access Services": [[5, "finding-available-spectral-access-services"]], "Fits files": [[4, "fits-files"]], "Galex service from STScI doesn\u2019t take format specification:": [[1, "galex-service-from-stsci-doesn-t-take-format-specification"]], "Geometric functions in TAP services": [[1, "geometric-functions-in-tap-services"]], "HR (Hertzsprung-Russell) Diagram Exercise": [[10, null]], "HR (Hertzsprung-Russell) Diagram Solution": [[11, null]], "Image Access": [[4, null]], "Indexing and slicing registry results": [[1, "indexing-and-slicing-registry-results"]], "JPG images": [[4, "jpg-images"]], "Known issues and workarounds": [[1, null]], "NASA-NAVO notebooks": [[14, null]], "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": [[10, "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"], [11, "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"]], "Perform a Query": [[2, "perform-a-query"]], "Plotting": [[10, "plotting"], [11, "plotting"]], "Proposal Preparation Exercise": [[12, null]], "Proposal Preparation Solution": [[13, null]], "PyVO regsearch() update:": [[1, "pyvo-regsearch-update"]], "Reference Notebooks": [[14, "reference-notebooks"]], "Search one of the services": [[2, "search-one-of-the-services"]], "Simple example of plotting a spectrum": [[5, "simple-example-of-plotting-a-spectrum"]], "Some services do not like PyVO\u2019s specification of some parameters": [[1, "some-services-do-not-like-pyvo-s-specification-of-some-parameters"]], "Spectral Access": [[5, null]], "Step 1: Find appropriate catalogs": [[10, "step-1-find-appropriate-catalogs"], [11, "step-1-find-appropriate-catalogs"]], "Step 1: Find out what the previously quoted Chandra 2-10 keV flux of the central source is for NGC 1365": [[12, "step-1-find-out-what-the-previously-quoted-chandra-2-10-kev-flux-of-the-central-source-is-for-ngc-1365"], [13, "step-1-find-out-what-the-previously-quoted-chandra-2-10-kev-flux-of-the-central-source-is-for-ngc-1365"]], "Step 2: Acquire the relevant data and make a plot": [[10, "step-2-acquire-the-relevant-data-and-make-a-plot"], [11, "step-2-acquire-the-relevant-data-and-make-a-plot"]], "Step 2: Make Images": [[12, "step-2-make-images"], [13, "step-2-make-images"]], "Step 3. 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