From 30d2749ab96b6213c81f326c6c9c8a4b7c63651b Mon Sep 17 00:00:00 2001 From: Allen Downey Date: Thu, 19 Nov 2020 14:13:47 -0500 Subject: [PATCH] Update documentation --- .buildinfo | 2 +- 01_query.html | 6 +- 02_coords.html | 6 +- 03_motion.html | 6 +- 04_select.html | 6 +- 05_join.html | 31 +++-- 06_photo.html | 6 +- 07_plot.html | 6 +- README.html | 2 +- _sources/05_join.ipynb | 18 +-- _sources/pilot_day4.ipynb | 264 ++++++++++++++++++++++++++++++++++++-- genindex.html | 2 +- index.html | 2 +- last_resort.html | 6 +- pilot_day1.html | 6 +- pilot_day3.html | 6 +- pilot_day4.html | 168 ++++++++++++++++++++++-- search.html | 2 +- searchindex.js | 2 +- test_setup.html | 6 +- 20 files changed, 470 insertions(+), 83 deletions(-) diff --git a/.buildinfo b/.buildinfo index f6e3cda..1e3c018 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 7b7ae02bbec54b0f50ef711ddeec201f +config: 02470608dd8b418f3fd2223c86d95c0f tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/01_query.html b/01_query.html index dfd6465..68b56d2 100644 --- a/01_query.html +++ b/01_query.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
@@ -211,7 +211,7 @@ aria-label="Launch interactive content">
- diff --git a/02_coords.html b/02_coords.html index 98d514b..bb3e713 100644 --- a/02_coords.html +++ b/02_coords.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
@@ -211,7 +211,7 @@ aria-label="Launch interactive content">
- diff --git a/03_motion.html b/03_motion.html index ab3a6bc..ee94651 100644 --- a/03_motion.html +++ b/03_motion.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
@@ -211,7 +211,7 @@ aria-label="Launch interactive content">
- diff --git a/04_select.html b/04_select.html index 83ab596..2c9b698 100644 --- a/04_select.html +++ b/04_select.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
@@ -211,7 +211,7 @@ aria-label="Launch interactive content">
- diff --git a/05_join.html b/05_join.html index 7330c09..5f3f8be 100644 --- a/05_join.html +++ b/05_join.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
@@ -211,7 +211,7 @@ aria-label="Launch interactive content">
- @@ -253,6 +253,11 @@ Getting photometry data +
  • + + Joining tables + +
  • Preparing a table for uploading @@ -377,9 +382,11 @@

    Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.

    In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy. This kind of cross matching is not always possible, because a star might appear in one catalog and not the other. And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.

    Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.

    -

    This document describes the cross matching process. Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical.

    -

    So the hard part of cross-matching has been done for us. However, using the results is a little tricky.

    -

    But, it is also an opportunity to learn about one of the most important tools for working with databases: “joining” tables.

    +

    This document describes the cross matching process. Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of observations most likely to be the same star.

    +
    • +
    +

    Joining tables

    +

    So the hard part of cross-matching has been done for us. Using the results is a little tricky, but it gives us a chance to learn about one of the most important tools for working with databases: “joining” tables.

    In general, a “join” is an operation where you match up records from one table with records from another table using as a “key” a piece of information that is common to both tables, usually some kind of ID code.

    In this example:

      @@ -425,8 +432,8 @@

      To write the file, we can use Table.write with format='votable', as described here.

      -
      table = candidate_table[['source_id']]
-table.write('candidate_df.xml', format='votable', overwrite=True)
+
      table_id = candidate_table[['source_id']]
+table_id.write('candidate_df.xml', format='votable', overwrite=True)
      @@ -481,8 +488,8 @@ The size of the file is about 750 KB, so that’s not too bad.

      Run these cells to find out.

      -
      table = candidate_table[['source_id']]
-type(table)
+
      table_id = candidate_table[['source_id']]
+type(table_id)
      @@ -783,8 +790,8 @@ dtype: int64 
      # Solution
 
-table = results1[['source_id', 'original_ext_source_id']]
-table.write('external.xml', format='votable', overwrite=True)
+table_ext = results1[['source_id', 'original_ext_source_id']]
+table_ext.write('external.xml', format='votable', overwrite=True)
      diff --git a/06_photo.html b/06_photo.html index bd01567..b37b19f 100644 --- a/06_photo.html +++ b/06_photo.html @@ -190,7 +190,7 @@ aria-label="Connect with source repository">
      @@ -211,7 +211,7 @@ aria-label="Launch interactive content">
      - diff --git a/07_plot.html b/07_plot.html index eec4ec5..26d715a 100644 --- a/07_plot.html +++ b/07_plot.html @@ -189,7 +189,7 @@ aria-label="Connect with source repository">
      @@ -210,7 +210,7 @@ aria-label="Launch interactive content">
      - diff --git a/README.html b/README.html index 182795c..dae4e63 100644 --- a/README.html +++ b/README.html @@ -189,7 +189,7 @@ aria-label="Connect with source repository">
      diff --git a/_sources/05_join.ipynb b/_sources/05_join.ipynb index a95ea7e..0a4ce13 100644 --- a/_sources/05_join.ipynb +++ b/_sources/05_join.ipynb @@ -184,16 +184,16 @@ "\n", "Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.\n", "\n", - "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html). Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical." + "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html). Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of observations most likely to be the same star." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So the hard part of cross-matching has been done for us. However, using the results is a little tricky.\n", + "## Joining tables\n", "\n", - "But, it is also an opportunity to learn about one of the most important tools for working with databases: \"joining\" tables.\n", + "So the hard part of cross-matching has been done for us. Using the results is a little tricky, but it gives us a chance to learn about one of the most important tools for working with databases: \"joining\" tables.\n", "\n", "In general, a \"join\" is an operation where you match up records from one table with records from another table using as a \"key\" a piece of information that is common to both tables, usually some kind of ID code.\n", "\n", @@ -285,8 +285,8 @@ "metadata": {}, "outputs": [], "source": [ - "table = candidate_table[['source_id']]\n", - "table.write('candidate_df.xml', format='votable', overwrite=True)" + "table_id = candidate_table[['source_id']]\n", + "table_id.write('candidate_df.xml', format='votable', overwrite=True)" ] }, { @@ -395,8 +395,8 @@ } ], "source": [ - "table = candidate_table[['source_id']]\n", - "type(table)" + "table_id = candidate_table[['source_id']]\n", + "type(table_id)" ] }, { @@ -952,8 +952,8 @@ "source": [ "# Solution\n", "\n", - "table = results1[['source_id', 'original_ext_source_id']]\n", - "table.write('external.xml', format='votable', overwrite=True)" + "table_ext = results1[['source_id', 'original_ext_source_id']]\n", + "table_ext.write('external.xml', format='votable', overwrite=True)" ] }, { diff --git a/_sources/pilot_day4.ipynb b/_sources/pilot_day4.ipynb index 7d41f4b..6c6e1a9 100644 --- a/_sources/pilot_day4.ipynb +++ b/_sources/pilot_day4.ipynb @@ -14,32 +14,268 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
      " + "astropy.table.table.Table" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import matplotlib.pyplot as plt\n", + "from astropy.table import Table\n", "\n", - "x = candidate_df['phi1']\n", - "y = candidate_df['phi2']\n", + "candidate_table = Table.from_pandas(candidate_df)\n", + "type(candidate_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "table = candidate_table[['source_id']]\n", + "table.write('candidate_df.xml', format='votable', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"SELECT *\n", + "FROM gaiadr2.panstarrs1_best_neighbour as best\n", + "JOIN tap_upload.candidate_df as candidate_df\n", + "ON best.source_id = candidate_df.source_id\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "from astroquery.gaia import Gaia\n", "\n", - "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", - "\n", - "plt.xlabel('ra (degree GD1)')\n", - "plt.ylabel('dec (degree GD1)');" + "job = Gaia.launch_job_async(query=query, \n", + " upload_resource='candidate_df.xml', \n", + " upload_table_name='candidate_df')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=3724\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", + "
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      " + ], + "text/plain": [ + "\n", + " source_id original_ext_source_id ... source_id_2 \n", + " ... \n", + " int64 int64 ... int64 \n", + "------------------ ---------------------- ... ------------------\n", + "635860218726658176 130911385187671349 ... 635860218726658176\n", + "635674126383965568 130831388428488720 ... 635674126383965568\n", + "635535454774983040 130631378377657369 ... 635535454774983040\n", + "635497276810313600 130811380445631930 ... 635497276810313600\n", + "635614168640132864 130571395922140135 ... 635614168640132864\n", + "635598607974369792 130341392091279513 ... 635598607974369792\n", + "635737661835496576 131001399333502136 ... 635737661835496576\n", + "635850945892748672 132011398654934147 ... 635850945892748672\n", + "635600532119713664 130421392285893623 ... 635600532119713664\n", + " ... ... ... ...\n", + "612241781249124608 129751343755995561 ... 612241781249124608\n", + "612332147361443072 130141341458538777 ... 612332147361443072\n", + "612426744016802432 130521346852465656 ... 612426744016802432\n", + "612331739340341760 130111341217793839 ... 612331739340341760\n", + "612282738058264960 129741340445933519 ... 612282738058264960\n", + "612386332668697600 130351354570219774 ... 612386332668697600\n", + "612296172717818624 129691338006168780 ... 612296172717818624\n", + "612250375480101760 129741346475897464 ... 612250375480101760\n", + "612394926899159168 130581355199751795 ... 612394926899159168\n", + "612256418500423168 129931349075297310 ... 612256418500423168" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = job.get_results()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "table_ext = results[['source_id', 'original_ext_source_id']]\n", + "table_ext.write('external.xml', format='votable', overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "query2 = \"\"\"SELECT\n", + "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n", + "FROM gaiadr2.panstarrs1_original_valid as ps\n", + "JOIN tap_upload.external as external\n", + "ON ps.obj_id = external.original_ext_source_id\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "job2 = Gaia.launch_job_async(query=query2, \n", + " upload_resource='external.xml', \n", + " upload_table_name='external')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=3724\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", + "
      source_idg_mean_psf_magi_mean_psf_mag
      mag
      int64float64float64
      63586021872665817617.897800445556617.5174007415771
      63567412638396556819.287300109863317.6781005859375
      63553545477498304016.923799514770516.478099822998
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      63585094589274867216.517499923706116.0450000762939
      63560053211971366420.450599670410219.5177001953125
      .........
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      61233214736144307221.384899139404320.3076000213623
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      61233173934034176021.865699768066419.5223007202148
      61228273805826496022.515199661254919.9743995666504
      61238633266869760019.379299163818417.9923000335693
      61229617271781862417.494400024414116.926700592041
      61225037548010176015.333000183105514.6280002593994
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      61225641850042316820.871599197387719.9612007141113
      " + ], + "text/plain": [ + "\n", + " source_id g_mean_psf_mag i_mean_psf_mag \n", + " mag \n", + " int64 float64 float64 \n", + "------------------ ---------------- ----------------\n", + "635860218726658176 17.8978004455566 17.5174007415771\n", + "635674126383965568 19.2873001098633 17.6781005859375\n", + "635535454774983040 16.9237995147705 16.478099822998\n", + "635497276810313600 19.9242000579834 18.3339996337891\n", + "635614168640132864 16.1515998840332 14.6662998199463\n", + "635598607974369792 16.5223999023438 16.1375007629395\n", + "635737661835496576 14.5032997131348 13.9849004745483\n", + "635850945892748672 16.5174999237061 16.0450000762939\n", + "635600532119713664 20.4505996704102 19.5177001953125\n", + " ... ... ...\n", + "612241781249124608 20.2343997955322 18.6518001556396\n", + "612332147361443072 21.3848991394043 20.3076000213623\n", + "612426744016802432 17.8281002044678 17.4281005859375\n", + "612331739340341760 21.8656997680664 19.5223007202148\n", + "612282738058264960 22.5151996612549 19.9743995666504\n", + "612386332668697600 19.3792991638184 17.9923000335693\n", + "612296172717818624 17.4944000244141 16.926700592041\n", + "612250375480101760 15.3330001831055 14.6280002593994\n", + "612394926899159168 16.4414005279541 15.8212003707886\n", + "612256418500423168 20.8715991973877 19.9612007141113" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results2 = job2.get_results()\n", + "results2" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_photo.fits'\n", + "results2.write(filename, overwrite=True)" ] }, { diff --git a/genindex.html b/genindex.html index 1892aea..316613f 100644 --- a/genindex.html +++ b/genindex.html @@ -176,7 +176,7 @@ aria-label="Connect with source repository">
      diff --git a/index.html b/index.html index 9faa9b9..4b8337a 100644 --- a/index.html +++ b/index.html @@ -188,7 +188,7 @@ aria-label="Connect with source repository">
      diff --git a/last_resort.html b/last_resort.html index b708665..f76419a 100644 --- a/last_resort.html +++ b/last_resort.html @@ -188,7 +188,7 @@ aria-label="Connect with source repository">
      @@ -209,7 +209,7 @@ aria-label="Launch interactive content">
      - diff --git a/pilot_day1.html b/pilot_day1.html index 3d3e86a..5991fc4 100644 --- a/pilot_day1.html +++ b/pilot_day1.html @@ -188,7 +188,7 @@ aria-label="Connect with source repository">
      @@ -209,7 +209,7 @@ aria-label="Launch interactive content">
      - diff --git a/pilot_day3.html b/pilot_day3.html index 6ca7bb6..211d0fb 100644 --- a/pilot_day3.html +++ b/pilot_day3.html @@ -188,7 +188,7 @@ aria-label="Connect with source repository">
      @@ -209,7 +209,7 @@ aria-label="Launch interactive content">
      - diff --git a/pilot_day4.html b/pilot_day4.html index c83f2bd..ab3ea91 100644 --- a/pilot_day4.html +++ b/pilot_day4.html @@ -188,7 +188,7 @@ aria-label="Connect with source repository">
      @@ -209,7 +209,7 @@ aria-label="Launch interactive content">
      - @@ -259,20 +259,164 @@
      -
      import matplotlib.pyplot as plt
+
      from astropy.table import Table
 
-x = candidate_df['phi1']
-y = candidate_df['phi2']
-
-plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)
-
-plt.xlabel('ra (degree GD1)')
-plt.ylabel('dec (degree GD1)');
+candidate_table = Table.from_pandas(candidate_df)
+type(candidate_table)
      -_images/pilot_day4_1_0.png +
      astropy.table.table.Table
+
      +
      +
      +
      +
      +
      +
      table = candidate_table[['source_id']]
+table.write('candidate_df.xml', format='votable', overwrite=True)
+
      +
      +
      +
      +
      +
      +
      query = """SELECT *
+FROM gaiadr2.panstarrs1_best_neighbour as best
+JOIN tap_upload.candidate_df as candidate_df
+ON best.source_id = candidate_df.source_id
+"""
+
      +
      +
      +
      +
      +
      +
      from astroquery.gaia import Gaia
+
+job = Gaia.launch_job_async(query=query, 
+                            upload_resource='candidate_df.xml', 
+                            upload_table_name='candidate_df')
+
      +
      +
      +
      +
      INFO: Query finished. [astroquery.utils.tap.core]
+
      +
      +
      +
      +
      +
      +
      results = job.get_results()
+results
+
      +
      +
      +
      +
      Table length=3724 +
      + + + + + + + + + + + + + + + + + + + + + + + +
      source_idoriginal_ext_source_idangular_distancenumber_of_neighboursnumber_of_matesbest_neighbour_multiplicitygaia_astrometric_paramssource_id_2
      arcsec
      int64int64float64int32int16int16int16int64
      6358602187266581761309113851876713490.0536670358954670841015635860218726658176
      6356741263839655681308313884284887200.0388102681415775161015635674126383965568
      6355354547749830401306313783776573690.0343230288289910761015635535454774983040
      6354972768103136001308113804456319300.047202554132500061015635497276810313600
      6356141686401328641305713959221401350.0203041897099641431015635614168640132864
      6355986079743697921303413920912795130.0365246268534030541015635598607974369792
      6357376618354965761310013993335021360.0366268278207166061015635737661835496576
      6358509458927486721320113986549341470.0211787423933783961015635850945892748672
      6356005321197136641304213922858936230.045188209150430151015635600532119713664
      ........................
      6122417812491246081297513437559955610.042357158300018151015612241781249124608
      6123321473614430721301413414585387770.022652498590129771015612332147361443072
      6124267440168024321305213468524656560.032476530099618431015612426744016802432
      6123317393403417601301113412177938390.0360642408180257351015612331739340341760
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      6123863326686976001303513545702197740.020103160014030861015612386332668697600
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      6122503754801017601297413464758974640.0317837403475309051015612250375480101760
      6123949268991591681305813551997517950.040191748305466981015612394926899159168
      6122564185004231681299313490752973100.0092427896695131561015612256418500423168
      +
      +
      +
      +
      table_ext = results[['source_id', 'original_ext_source_id']]
+table_ext.write('external.xml', format='votable', overwrite=True)
+
      +
      +
      +
      +
      +
      +
      query2 = """SELECT
+external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag
+FROM gaiadr2.panstarrs1_original_valid as ps
+JOIN tap_upload.external as external
+ON ps.obj_id = external.original_ext_source_id
+"""
+
      +
      +
      +
      +
      +
      +
      job2 = Gaia.launch_job_async(query=query2, 
+                       upload_resource='external.xml', 
+                       upload_table_name='external')
+
      +
      +
      +
      +
      INFO: Query finished. [astroquery.utils.tap.core]
+
      +
      +
      +
      +
      +
      +
      results2 = job2.get_results()
+results2
+
      +
      +
      +
      +
      Table length=3724 + + + + + + + + + + + + + + + + + + + + + + + + +
      source_idg_mean_psf_magi_mean_psf_mag
      mag
      int64float64float64
      63586021872665817617.897800445556617.5174007415771
      63567412638396556819.287300109863317.6781005859375
      63553545477498304016.923799514770516.478099822998
      63549727681031360019.924200057983418.3339996337891
      63561416864013286416.151599884033214.6662998199463
      63559860797436979216.522399902343816.1375007629395
      63573766183549657614.503299713134813.9849004745483
      63585094589274867216.517499923706116.0450000762939
      63560053211971366420.450599670410219.5177001953125
      .........
      61224178124912460820.234399795532218.6518001556396
      61233214736144307221.384899139404320.3076000213623
      61242674401680243217.828100204467817.4281005859375
      61233173934034176021.865699768066419.5223007202148
      61228273805826496022.515199661254919.9743995666504
      61238633266869760019.379299163818417.9923000335693
      61229617271781862417.494400024414116.926700592041
      61225037548010176015.333000183105514.6280002593994
      61239492689915916816.441400527954115.8212003707886
      61225641850042316820.871599197387719.9612007141113
      +
      +
      +
      +
      filename = 'gd1_photo.fits'
+results2.write(filename, overwrite=True)
+
      +
      diff --git a/search.html b/search.html index bb20461..9fb2f55 100644 --- a/search.html +++ b/search.html @@ -181,7 +181,7 @@ aria-label="Connect with source repository">
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