diff --git a/.github/workflows/documentation.yml b/.github/workflows/documentation.yml index 79f0e3f..e76389e 100644 --- a/.github/workflows/documentation.yml +++ b/.github/workflows/documentation.yml @@ -18,7 +18,7 @@ jobs: - name: Install dependencies run: | pip install poetry - poetry install --with dev + poetry install --with dev,cloud - name: Sphinx build run: | poetry run sphinx-build docs _build diff --git a/docs/apidoc.rst b/docs/apidoc.rst index 489465e..15ce2e8 100644 --- a/docs/apidoc.rst +++ b/docs/apidoc.rst @@ -15,5 +15,8 @@ API .. autoclass:: lksearch.TESSSearch :members: +.. automodule:: lksearch.CatalogSearch + :members: + .. automodule:: lksearch.config :members: \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 99eb929..38e71d6 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -15,7 +15,7 @@ lksearch :hidden: Quickstart - tutorials/Example_searches + tutorials/index apidoc GetInvolved HowtoCite diff --git a/docs/tutorials/catalog-searches.ipynb b/docs/tutorials/catalog-searches.ipynb new file mode 100644 index 0000000..55161a3 --- /dev/null +++ b/docs/tutorials/catalog-searches.ipynb @@ -0,0 +1,1236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8362de11", + "metadata": {}, + "source": [ + "# How to Search for Catalog Data and IDs with lksearch" + ] + }, + { + "cell_type": "markdown", + "id": "7139b45e-0981-48ce-a7c8-f57c27eacddb", + "metadata": {}, + "source": [ + "`lksearch` enables you to search through the catalogs associated with each mission, and Gaia. You can query:\n", + "\n", + "- The Kepler Input Catalog\n", + "- The Ecliptic Plane Input Catalog\n", + "- The TESS Input Catalog\n", + "- Gaia EDR3.\n", + "\n", + "`lksearch` has three main pieces of functionality - two ways of retrieving data from a catalog, and one for looking up catalog IDs.\n", + "\n", + "These are:\n", + " \n", + "- **A position cone-search** that searches for sources within a given radius from some position using `QueryPosition`\n", + "- **An exact ID lookup**, retrieving catalog rows where the catalog ID matches the search ID using `QueryID`\n", + "- **A way of looking up catalog IDs** given a common name or another catalog ID with `IDLookup`. \n", + "\n", + "`lksearch` also has the ability to crossmatch targets when doing an exact ID lookup between the TIC, KIC, and Gaia EDR3 catalogs because this crossmatch already exists in the TIC. \n", + "\n", + "`lksearch` will propagate the proper motion for you, and can optionally return a SkyCoordinate either directly from the Search or by using the `to_skycoord` property from the results table. \n", + "\n", + "Below are examples of each query." + ] + }, + { + "cell_type": "markdown", + "id": "b35a1450-2a06-4413-8796-e13fba1e74a8", + "metadata": {}, + "source": [ + "## Searching by Position" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9f07ac77", + "metadata": {}, + "outputs": [], + "source": [ + "from lksearch import CatalogSearch\n", + "from astropy.coordinates import SkyCoord\n", + "from astropy.time import Time\n", + "import astropy.units as u" + ] + }, + { + "cell_type": "markdown", + "id": "889a4743-c6ee-4cea-98ad-012aeaec9975", + "metadata": {}, + "source": [ + "Before we search we need a sky position to search. `lksearch` catalog queries use `astropy`'s `SkyCoord` as an input. We can either make one ourselves with an RA and Dec, or we can use the built in `from_name` method." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f98f6097-1f3e-433b-8337-617978b86b9b", + "metadata": {}, + "outputs": [], + "source": [ + "c = SkyCoord.from_name(\"Kepler-10\")" + ] + }, + { + "cell_type": "markdown", + "id": "6a752678-93a8-4848-9365-ff41e54ae094", + "metadata": {}, + "source": [ + "Now we have a position we want to query, we can query each catalog. For example, here is the query for KIC" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fd0abec3-8b85-4812-aafb-ca13a94d306c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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KIC 11904151285.6793850.2412990.01.00.00.010.96154914.467
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" + ], + "text/plain": [ + "\n", + " ID RA Dec Separation Relative_Flux pmRA pmDE Kepmag Teff logg \n", + " mas / yr mas / yr mag K log(cm.s**-2)\n", + " str12 float64 float64 float64 float32 float64 float64 float32 int32 float32 \n", + "------------ ----------------- ----------------- ------------------ ------------- -------- -------- ------- ----- -------------\n", + "KIC 11904151 285.67938 50.241299 0.0 1.0 0.0 0.0 10.961 5491 4.467\n", + "KIC 11904160 285.68076 50.242493 5.345213505938071 0.0066435444 0.0 0.0 16.405 -- --\n", + "KIC 11904158 285.68042 50.245716 16.08046158371309 0.0054200143 0.0 0.0 16.626 -- --\n", + "KIC 11904156 285.6801067536893 50.25001099999198 31.407796176187023 0.0026717787 -4.0 0.0 17.394 -- --\n", + "KIC 11904148 285.67722 50.25901000000001 63.95318581678289 0.017155372 0.0 0.0 15.375 5286 4.640" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(c, epoch=Time.now(), catalog=\"kic\", max_results=5)" + ] + }, + { + "cell_type": "markdown", + "id": "0570848e-1281-4dcc-bc97-aabdef0584a5", + "metadata": {}, + "source": [ + "You can also pass a radius for your query. If you query each catalog directly you can set your query radius in units of pixels, otherwise you can use arcseconds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "37e4bc44-5a6c-4dbd-a4e0-31f0a8c98184", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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KIC 11904151285.6793850.2412990.01.00.00.010.96154914.467
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" + ], + "text/plain": [ + "\n", + " ID RA Dec Separation Relative_Flux pmRA pmDE Kepmag Teff logg \n", + " mas / yr mas / yr mag K log(cm.s**-2)\n", + " str12 float64 float64 float64 float32 float64 float64 float32 int32 float32 \n", + "------------ --------- ----------------- ------------------ ------------- -------- -------- ------- ----- -------------\n", + "KIC 11904151 285.67938 50.241299 0.0 1.0 0.0 0.0 10.961 5491 4.467\n", + "KIC 11904160 285.68076 50.242493 5.345213505938071 0.0066435444 0.0 0.0 16.405 -- --\n", + "KIC 11904165 285.68317 50.241207 8.732390194683243 0.002659501 0.0 0.0 17.399 -- --\n", + "KIC 11904162 285.68124 50.24391599999999 10.348792766445591 0.0015761611 0.0 0.0 17.967 -- --\n", + "KIC 11904158 285.68042 50.245716 16.08046158371309 0.0054200143 0.0 0.0 16.626 -- --" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(c, epoch=Time.now(), radius=30 * u.arcsec, catalog=\"kic\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1b89482-e501-4f55-b307-888074e1b7cd", + "metadata": {}, + "source": [ + "You can query TIC" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "631f9a71-b0f2-4cb7-9ef3-565155c0fe16", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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TIC 377780790285.679222959345650.241592310484634nan915.37714-18.39441.44810.4771.0171.0905706.04.3707
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" + ], + "text/plain": [ + "\n", + " ID RA Dec Separation Relative_Flux pmRA pmDE TESSmag Mass Rad Teff logg \n", + " mas / yr mas / yr mag solMass solRad K log(cm.s**-2)\n", + " str14 float64 float64 float64 float32 float64 float64 float32 float32 float64 float64 float32 \n", + "-------------- ----------------- ------------------ ---------- ------------- --------- --------- ------- ------- -------- ------- -------------\n", + " TIC 377780790 285.6792229593456 50.241592310484634 nan 915.37714 -18.394 41.448 10.477 1.017 1.090 5706.0 4.3707\n", + "TIC 1717218059 nan nan nan 1.0 -2.227 -1.204 17.881 1.070 0.810 5895.0 4.6506" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(c, epoch=Time.now(), radius=20 * u.arcsec, catalog=\"tic\")" + ] + }, + { + "cell_type": "markdown", + "id": "a8e4e95b-5c0f-43bd-84b1-7c427fe482c1", + "metadata": {}, + "source": [ + "EPIC (Note Kepler-10 isn't inside EPIC, because it's outside the ecliptic plane!)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e21a83c5-2975-4fde-a1a0-6d5fb031e95d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "RAJ2000 DEJ2000 pmRA pmDE Plx K2mag logg Teff Rad Mass RA Dec Separation Relative_Flux\n", + "float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 \n", + "------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ------- ---------- -------------" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(c, catalog=\"epic\")" + ] + }, + { + "cell_type": "markdown", + "id": "62473b20-a001-491a-9a2a-0502540cff42", + "metadata": {}, + "source": [ + "And Gaia in the same ways" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2f18882e-a193-4629-9681-b36ea02af6d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDRADecSeparationRelative_FluxpmRApmDEBPmagGmagRPmagTefflogg
mas / yrmas / yrmagmagmagKlog(cm.s**-2)
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Gaia DR3 2132155017099178624285.679351103565250.241408174341810.01.0-18.48341.38211.25350510.92003110.4182635595.84.3544
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" + ], + "text/plain": [ + "\n", + " ID RA Dec Separation ... Gmag RPmag Teff logg \n", + " ... mag mag K log(cm.s**-2)\n", + " str28 float64 float64 float64 ... float64 float64 float64 float64 \n", + "---------------------------- ------------------ ------------------ ------------------ ... --------- --------- ------- -------------\n", + "Gaia DR3 2132155017099178624 285.6793511035652 50.24140817434181 0.0 ... 10.920031 10.418263 5595.8 4.3544\n", + "Gaia DR3 2132155051458917632 285.68021338390014 50.24994176562508 30.78499970152597 ... 17.849485 17.317846 5424.7 4.6774\n", + "Gaia DR3 2132155051458918144 285.6859229038892 50.249918851499665 34.170405504656244 ... 16.553818 15.778805 -- --" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(\n", + " c, epoch=Time.now(), radius=40 * u.arcsecond, catalog=\"gaiadr3\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d20de587-7719-4b39-bc80-ab07a9800c8e", + "metadata": {}, + "source": [ + "Optionally, you can return the output of the search as an `astropy.coordinates.SkyCoord` object" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fb7fc90b-87ad-47a7-841f-402b2ff46867", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryPosition(\n", + " c,\n", + " epoch=Time.now(),\n", + " radius=40 * u.arcsecond,\n", + " catalog=\"gaiadr3\",\n", + " return_skycoord=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a29f3b6f-9ab3-4db5-aa9f-688f93cfc857", + "metadata": {}, + "source": [ + "You can also sub-select from the results table and then convert to a SkyCoordinate" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "502e05d8-f4ff-4089-91b2-e83dd9d0d5b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + " ID RA Dec Separation ... Gmag RPmag Teff logg \n", + " ... mag mag K log(cm.s**-2)\n", + " str28 float64 float64 float64 ... float64 float64 float64 float64 \n", + "---------------------------- ------------------ ------------------ ------------------ ... --------- --------- ------- -------------\n", + "Gaia DR3 2132155017099178624 285.6793511035652 50.241408174341814 0.0 ... 10.920031 10.418263 5595.8 4.3544\n", + "Gaia DR3 2132155051458917632 285.68021338390014 50.24994176562508 30.784999701480263 ... 17.849485 17.317846 5424.7 4.6774\n", + "Gaia DR3 2132155051458918144 285.6859229038892 50.249918851499665 34.17040550463571 ... 16.553818 15.778805 -- --" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = CatalogSearch.QueryPosition(\n", + " c, epoch=Time.now(), radius=40 * u.arcsecond, catalog=\"gaiadr3\"\n", + ")\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "2bcc497d-d927-4baf-a8df-85bd713e9b34", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.to_SkyCoord()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f20bea99-9f9f-4069-8947-9f55c7f2bd5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.to_SkyCoord()" + ] + }, + { + "cell_type": "markdown", + "id": "6f069335-fb49-4301-85cf-a32172e430fe", + "metadata": {}, + "source": [ + "## Searching by Catalog ID" + ] + }, + { + "cell_type": "markdown", + "id": "8a0f27fb-b981-4ca6-b3f1-6ef00a796057", + "metadata": {}, + "source": [ + "For TIC, KIC, EPIC, and Gaia DR3 - if we know the Catalog ID of a source we can use `QueryID` to return the data from that catalog for that id. For example, Kepler-10 above is also known as TIC 377780790. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8f497117-9e06-471f-9420-2334203f64c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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377780790285.6794217923850.24130576642--702-06269519024305+5014286--J190243.03+501429.12132155017099178624--11904151STARtmgaia2tmgaia2-18.3940.04541.4480.046gaia25.36190.0233gaia280.4897343418918.81612807429303.7010258068871.8498786093212.2580.06711.0430.023--------------------9.8890.0229.5630.0259.4960.021AAA-222-111-000-0-09.440.0239.4910.029.4430.0299.4030.48210.91910.00031810.4770.006reredspect5706.0141.04.37070.0854-0.120.11.090.0611.0170.1280.78590.1845DWARF1.134140.03284185.5060.8080.01070.00372230.0101--0.0026680.00280.0046panstarrs0.1190.1380.0610.0610.15710.21190.10420.06660.035980.02970.8050.811bj2018125.2156.8spect11.27130.00100610.42730.0005851ucac4bpbj0.7672930.711508285.6792979640750.241484223590.0204740.02404310237087497
" + ], + "text/plain": [ + "\n", + " TIC RAJ2000 DEJ2000 HIP TYC UCAC4 _2MASS ... DEOdeg e_RAOdeg e_DEOdeg RadFl WDFl ID \n", + " deg deg ... deg mas mas \n", + " int64 float64 float64 int32 object object object ... float64 float64 float64 int16 int16 int32 \n", + "--------- --------------- -------------- ----- ------ ---------- ---------------- ... -------------- -------- -------- ----- ----- ---------\n", + "377780790 285.67942179238 50.24130576642 -- 702-062695 19024305+5014286 ... 50.24148422359 0.020474 0.024043 1 0 237087497" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"TIC 377780790\")" + ] + }, + { + "cell_type": "markdown", + "id": "d3c8cec5-9925-49e1-bea8-3253bf669013", + "metadata": {}, + "source": [ + "... and KIC 11904151" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a48bdf93-3ff3-43d6-befd-62390a536ac2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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degdegmas / yrmas / yrmasmagmagmagmagmagmagmagmagmagmagmagKlog(cm.s**-2)log(Sun)magmagRsun
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11904151285.6793850.2412990.00.0----11.38810.9210.77810.729--11.1919.8889.5639.49610.9611065107578J19024305+50142861065107578--117326143-105815200SCP56254914.467-0.8120.0310.0950.98
" + ], + "text/plain": [ + "\n", + " KIC RAJ2000 DEJ2000 pmRA pmDE Plx umag gmag ... aq fv Teff logg __Fe_H_ E_B-V_ Av R_ \n", + " deg deg mas / yr mas / yr mas mag mag ... K log(cm.s**-2) log(Sun) mag mag Rsun \n", + " int32 float64 float64 float64 float64 float64 float32 float32 ... int16 int16 int32 float32 float32 float32 float32 float32\n", + "-------- --------- --------- -------- -------- ------- ------- ------- ... ----- ----- ----- ------------- -------- ------- ------- -------\n", + "11904151 285.67938 50.241299 0.0 0.0 -- -- 11.388 ... 6 2 5491 4.467 -0.812 0.031 0.095 0.98" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"KIC 11904151\")" + ] + }, + { + "cell_type": "markdown", + "id": "582ac919-9e81-489a-86cb-fd841c8bc028", + "metadata": {}, + "source": [ + "... and Gaia DR3 2132155017099178624" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6a6968f1-b8e9-400d-a7cf-273b982a6a26", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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degdegmasmasmasmasmas / yrmas / yrmas / yrmas / yrmas / yrmas1 / um1 / um1 / ummasdegdegmagmagmagmagmagmagmagmagmagmagkm / skm / sdkm / sKlog(cm.s**-2)km / skm / smagmagdegdegdegdegKKKlog(cm.s**-2)log(cm.s**-2)log(cm.s**-2)pcpcpcmagmagmagmagmagmagmagmagmagmasmasmasmasmasmasmasmasmasmasmasmasdegdegmasmas
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Gaia DR3 2132155017099178624285.6792940153450.24148992495163614806892137676821321550170991786247127205410.00970.01055.36980.0103521.152145.322-18.4830.01241.3820.013-0.01390.0622-0.0681-0.17020.0472-0.1583-0.201-0.0394-0.1374-0.06913603603564-2.6955715.40110.08711.4633393101.551--------------41260.018997451810.0057575256124.68958000.899----------------0355807113.6601899.634568100.739710.9200310.00275939430541.4769106057224.459261918.127411.2535050.00284739539327.9908645798152.087173546.176810.4182630.0037921.202040000.8352410.3334740.501767-98.440.2411611460.55437-1.87652580.960951912.8372.435750.04.5-0.25222------10.2390540.0116811364.86836NOT_AVAILABLE80.4898821911918.81626758615303.7009956576571.850079083000111001100.00.00.9996615595.85594.45598.14.35444.35294.3558-0.2251-0.2294-0.2204185.2251184.8608185.60030.0010.00020.00250.00081e-040.00210.00041e-040.0011MARCS--------------------------------------------------URAT1-7022705030.0371424018090563910J190243.03+501429.10.064949865694524310------------N2E90004310.039655884409048410--------19024305+50142860.070933842983035910--------285.6794224542750.241306006410.1994790.217694-0.0523
" + ], + "text/plain": [ + "\n", + " DR3Name RA_ICRS DE_ICRS SolID ... DEJ2000 e_RAJ2000 e_DEJ2000 RADEcorJ2000\n", + " deg deg ... deg mas mas \n", + " object float64 float64 int64 ... float64 float64 float64 float32 \n", + "---------------------------- --------------- -------------- ------------------- ... -------------- --------- --------- ------------\n", + "Gaia DR3 2132155017099178624 285.67929401534 50.24148992495 1636148068921376768 ... 50.24130600641 0.199479 0.217694 -0.0523" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"Gaia DR3 2132155017099178624\")" + ] + }, + { + "cell_type": "markdown", + "id": "da1850e6-7967-4814-92da-2c0927867a7e", + "metadata": {}, + "source": [ + "The TIC catalog has been cross-matched with the GAIA DR3 and KIC catalogs. `lksearch` can use this cross-matching to retrieve TIC/KIC/GAIA catalog info for any given TIC/KIC/GaiaDR3 source ID. The EPIC catalog is not included in this cross-matching. \n", + "\n", + "For Example, if we want the TIC info for Kepler-10, but only have the GAIA DR3Name above, we can get that like so:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "068e7e8a-a634-4d19-9c3c-edf2312fd8d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
TICRAJ2000DEJ2000HIPTYCUCAC4_2MASSobjIDWISEAGAIAAPASSKICS_GRefr_PospmRAe_pmRApmDEe_pmDEr_pmPlxe_Plxr_PlxGLONGLATELONELATBmage_BmagVmage_Vmagumage_umaggmage_gmagrmage_rmagimage_imagzmage_zmagJmage_JmagHmage_HmagKmage_Kmagq_2MASSW1mage_W1magW2mage_W2magW3mage_W3magW4mage_W4magGmage_GmagTmage_Tmagf_TmagFlagTeffs_Teffloggs_logg__M_H_e__M_H_Rads_RadMasss_Massrhos_rhoLClassLums_LumDists_DistE_B-V_s_E_B-V_NcontRcontDispm_TICPriore_E_B-V_E_E_B-V_f_E_B-V_e_MassE_Masse_RadE_Rade_rhoE_rhoe_loggE_logge_LumE_Lume_DistE_Distr_Diste_TeffE_Teffr_TeffBPmage_BPmagRPmage_RPmagq_Gaiar_Vmagr_BmagCliste_RAJ2000e_DEJ2000RAOdegDEOdege_RAOdege_DEOdegRadFlWDFlID
degdegmas / yrmas / yrmas / yrmas / yrmasmasdegdegdegdegmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagKKlog(cm.s**-2)log(cm.s**-2)log(Sun)log(Sun)RsunRsunMsunMsunSunSunLsunLsunpcpcmagmagmagmagMsunMsunRsunRsunSunSunlog(cm.s**-2)log(cm.s**-2)LsunLsunpcpcKKmagmagmagmagmasmasdegdegmasmas
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377780790285.6794217923850.24130576642--702-06269519024305+5014286--J190243.03+501429.12132155017099178624--11904151STARtmgaia2tmgaia2-18.3940.04541.4480.046gaia25.36190.0233gaia280.4897343418918.81612807429303.7010258068871.8498786093212.2580.06711.0430.023--------------------9.8890.0229.5630.0259.4960.021AAA-222-111-000-0-09.440.0239.4910.029.4430.0299.4030.48210.91910.00031810.4770.006reredspect5706.0141.04.37070.0854-0.120.11.090.0611.0170.1280.78590.1845DWARF1.134140.03284185.5060.8080.01070.00372230.0101--0.0026680.00280.0046panstarrs0.1190.1380.0610.0610.15710.21190.10420.06660.035980.02970.8050.811bj2018125.2156.8spect11.27130.00100610.42730.0005851ucac4bpbj0.7672930.711508285.6792979640750.241484223590.0204740.02404310237087497
" + ], + "text/plain": [ + "\n", + " TIC RAJ2000 DEJ2000 HIP TYC UCAC4 _2MASS ... DEOdeg e_RAOdeg e_DEOdeg RadFl WDFl ID \n", + " deg deg ... deg mas mas \n", + " int64 float64 float64 int32 object object object ... float64 float64 float64 int16 int16 int32 \n", + "--------- --------------- -------------- ----- ------ ---------- ---------------- ... -------------- -------- -------- ----- ----- ---------\n", + "377780790 285.67942179238 50.24130576642 -- 702-062695 19024305+5014286 ... 50.24148422359 0.020474 0.024043 1 0 237087497" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"Gaia DR3 2132155017099178624\", catalog=\"tic\")" + ] + }, + { + "cell_type": "markdown", + "id": "2d613dbe-1b6a-4ce2-8104-2050e22800eb", + "metadata": {}, + "source": [ + "We can also supply an integer ID, but if so we MUST specify what catalog to search" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5c1b473b-cdda-4f4f-8f54-0d3ba79df22a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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degdegmasmasmasmasmas / yrmas / yrmas / yrmas / yrmas / yrmas1 / um1 / um1 / ummasdegdegmagmagmagmagmagmagmagmagmagmagkm / skm / sdkm / sKlog(cm.s**-2)km / skm / smagmagdegdegdegdegKKKlog(cm.s**-2)log(cm.s**-2)log(cm.s**-2)pcpcpcmagmagmagmagmagmagmagmagmagmasmasmasmasmasmasmasmasmasmasmasmasdegdegmasmas
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Gaia DR3 2132155017099178624285.6792940153450.24148992495163614806892137676821321550170991786247127205410.00970.01055.36980.0103521.152145.322-18.4830.01241.3820.013-0.01390.0622-0.0681-0.17020.0472-0.1583-0.201-0.0394-0.1374-0.06913603603564-2.6955715.40110.08711.4633393101.551--------------41260.018997451810.0057575256124.68958000.899----------------0355807113.6601899.634568100.739710.9200310.00275939430541.4769106057224.459261918.127411.2535050.00284739539327.9908645798152.087173546.176810.4182630.0037921.202040000.8352410.3334740.501767-98.440.2411611460.55437-1.87652580.960951912.8372.435750.04.5-0.25222------10.2390540.0116811364.86836NOT_AVAILABLE80.4898821911918.81626758615303.7009956576571.850079083000111001100.00.00.9996615595.85594.45598.14.35444.35294.3558-0.2251-0.2294-0.2204185.2251184.8608185.60030.0010.00020.00250.00081e-040.00210.00041e-040.0011MARCS--------------------------------------------------URAT1-7022705030.0371424018090563910J190243.03+501429.10.064949865694524310------------N2E90004310.039655884409048410--------19024305+50142860.070933842983035910--------285.6794224542750.241306006410.1994790.217694-0.0523
" + ], + "text/plain": [ + "\n", + " DR3Name RA_ICRS DE_ICRS SolID ... DEJ2000 e_RAJ2000 e_DEJ2000 RADEcorJ2000\n", + " deg deg ... deg mas mas \n", + " object float64 float64 int64 ... float64 float64 float64 float32 \n", + "---------------------------- --------------- -------------- ------------------- ... -------------- --------- --------- ------------\n", + "Gaia DR3 2132155017099178624 285.67929401534 50.24148992495 1636148068921376768 ... 50.24130600641 0.199479 0.217694 -0.0523" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"2132155017099178624\", catalog=\"gaiadr3\")" + ] + }, + { + "cell_type": "markdown", + "id": "6a0f83b9-74ad-424d-8a0e-a412ba5044f1", + "metadata": {}, + "source": [ + "If we specify an integer and wish to use our limited cross-match, we can do that by specifying both the `input_catalog` and `catalog` parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d1209696-e696-4162-84e6-d1061f49f152", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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" + ], + "text/plain": [ + "\n", + " TIC RAJ2000 DEJ2000 HIP TYC UCAC4 _2MASS ... DEOdeg e_RAOdeg e_DEOdeg RadFl WDFl ID \n", + " deg deg ... deg mas mas \n", + " int64 float64 float64 int32 object object object ... float64 float64 float64 int16 int16 int32 \n", + "--------- --------------- -------------- ----- ------ ---------- ---------------- ... -------------- -------- -------- ----- ----- ---------\n", + "377780790 285.67942179238 50.24130576642 -- 702-062695 19024305+5014286 ... 50.24148422359 0.020474 0.024043 1 0 237087497" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"2132155017099178624\", catalog=\"tic\", input_catalog=\"gaiadr3\")" + ] + }, + { + "cell_type": "markdown", + "id": "97df2dd7-f970-496a-ad3e-6cc30ca0bd47", + "metadata": {}, + "source": [ + "We can also search for multiple IDS from a single catalog and get a table of all of the resulting catalog entries" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5ff097ae-9182-4075-b9d0-1fa500c673f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + " TIC RAJ2000 DEJ2000 HIP TYC UCAC4 ... DEOdeg e_RAOdeg e_DEOdeg RadFl WDFl ID \n", + " deg deg ... deg mas mas \n", + " int64 float64 float64 int32 object object ... float64 float64 float64 int16 int16 int32 \n", + "--------- --------------- -------------- ----- ------------ ---------- ... -------------- -------- -------- ----- ----- ---------\n", + " 27916356 296.83724132289 48.23993319637 -- 692-068460 ... 48.23996948867 0.011393 0.014386 1 0 338972619\n", + " 28230919 297.70936440263 48.08086037921 97657 3561-02092-1 691-070316 ... 48.08186352485 0.019271 0.024141 1 0 347873767\n", + "120571842 285.57397671045 38.40088256878 -- 643-065464 ... 38.400819222 0.013008 0.016279 1 0 236288245\n", + "121660904 288.58150969662 41.08982373933 -- 656-070649 ... 41.0897335401 0.021624 0.02059 1 0 259926978\n", + "123233041 281.28812071329 42.4510809768 -- 663-070081 ... 42.4510975153 0.011065 0.014161 1 0 214866427\n", + "268823307 299.40703534003 44.03505264692 -- 671-080900 ... 44.0350387042 0.018613 0.016314 1 0 363112972\n", + "350814622 285.61540891557 50.13575104429 -- 701-064857 ... 50.1357711589 0.019169 0.024925 1 0 237044605\n", + "377780790 285.67942179238 50.24130576642 -- 702-062695 ... 50.24148422359 0.020474 0.024043 1 0 237087497\n", + "399860444 286.80849132167 49.31641413759 -- 3549-02811-1 697-064165 ... 49.31642110842 0.019726 0.022829 1 0 245833148\n", + "424865156 292.24730755483 47.96950451412 -- 3547-01402-1 690-068273 ... 47.96954287694 0.017649 0.01983 1 0 290881018" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Kepler Objects 1-10\n", + "idlist = [\n", + " \"TIC 399860444\",\n", + " \"TIC 424865156\",\n", + " \"TIC 28230919\",\n", + " \"TIC 350814622\",\n", + " \"TIC 268823307\",\n", + " \"TIC 27916356\",\n", + " \"TIC 121660904\",\n", + " \"TIC 123233041\",\n", + " \"TIC 120571842\",\n", + " \"TIC 377780790\",\n", + "]\n", + "CatalogSearch.QueryID(idlist, catalog=\"tic\")" + ] + }, + { + "cell_type": "markdown", + "id": "f9a35a25-cde5-4810-87e9-2c83a7249481", + "metadata": {}, + "source": [ + "Large queries (over one thousand objects) will be performed asynchronously and may take some time. " + ] + }, + { + "cell_type": "markdown", + "id": "6cde6883-77d6-4fa4-854f-8bff08743144", + "metadata": {}, + "source": [ + "Like `QueryPosition`, These results can also be returned as `astropy SkyCoord` objects. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "57f90e43-e171-4e32-a2f9-80eac85b0ccd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"TIC 377780790\", return_skycoord=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ea580933-9961-4c47-aca6-1d4a037c802d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = CatalogSearch.QueryID(\"TIC 377780790\")\n", + "result.to_SkyCoord()" + ] + }, + { + "cell_type": "markdown", + "id": "b5b2bcc0-a0a7-4167-976c-9ddaf2abfde9", + "metadata": {}, + "source": [ + "## Looking up Catalog IDs" + ] + }, + { + "cell_type": "markdown", + "id": "6a171db2-9c22-4e01-bd26-9509c5bb4a1c", + "metadata": {}, + "source": [ + "We have also included a limited way of looking up catalog IDs using other known IDs. This functionality wraps the `astroquery.simbad.Simbad.query_objectids` functionality. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "7299501b-112f-419d-af24-3553a3586365", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Table length=14\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
id
object
LAMOST J190243.11+501428.7
AP J19024305+5014286
Gaia DR3 2132155017099178624
TIC 377780790
SPOCS 4268
Gaia DR2 2132155017099178624
Kepler-10
UCAC3 281-142262
2MASS J19024305+5014286
PPMX J190243.0+501428
USNO-B1.0 1402-00324696
GSC 03549-00354
KOI-72
KIC 11904151
" + ], + "text/plain": [ + "\n", + " id \n", + " object \n", + "----------------------------\n", + " LAMOST J190243.11+501428.7\n", + " AP J19024305+5014286\n", + "Gaia DR3 2132155017099178624\n", + " TIC 377780790\n", + " SPOCS 4268\n", + "Gaia DR2 2132155017099178624\n", + " Kepler-10\n", + " UCAC3 281-142262\n", + " 2MASS J19024305+5014286\n", + " PPMX J190243.0+501428\n", + " USNO-B1.0 1402-00324696\n", + " GSC 03549-00354\n", + " KOI-72\n", + " KIC 11904151" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.IDLookup(\"Kepler 10\")" + ] + }, + { + "cell_type": "markdown", + "id": "600430bf-96d1-4f13-ba4b-6cc17b575f10", + "metadata": {}, + "source": [ + "It is based off of joining IDs across Simbad tables, and is useful but not comprehensive as it relies upon published works to do the ID crossmatching. That means that you may run into situations where not all assosciations have been made, eg. for this EPIC ID:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f8755caf-b19b-4972-ad97-afbe7bb09890", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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id
object
Gaia DR3 3796414192429498880
LBQS 1145+0145
WD 1145+017
SDSS J114833.62+012859.4
EPIC 201563164
HE 1145+0145
Gaia DR2 3796414192429498880
" + ], + "text/plain": [ + "\n", + " id \n", + " object \n", + "----------------------------\n", + "Gaia DR3 3796414192429498880\n", + " LBQS 1145+0145\n", + " WD 1145+017\n", + " SDSS J114833.62+012859.4\n", + " EPIC 201563164\n", + " HE 1145+0145\n", + "Gaia DR2 3796414192429498880" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.IDLookup(\"EPIC 201563164\")" + ] + }, + { + "cell_type": "markdown", + "id": "8203f0ee-83e3-4781-bd4d-70bbd6806b91", + "metadata": {}, + "source": [ + "We can see above that this EPIC ID has a Gaia DR3 ID assosciated with it, and we know that GAIA DR3 and TIC are crossmatched, but the TIC ID is not reported. To retrieve the TIC ID we can use the GAIA DR3 ID above and QueryID" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "833a1cd8-4f09-4c59-bca4-fc20e6b3eb5e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
CatalogResult length=1\n", + "
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degdegmas / yrmas / yrmas / yrmas / yrmasmasdegdegdegdegmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagmagKKlog(cm.s**-2)log(cm.s**-2)log(Sun)log(Sun)RsunRsunMsunMsunSunSunLsunLsunpcpcmagmagmagmagMsunMsunRsunRsunSunSunlog(cm.s**-2)log(cm.s**-2)LsunLsunpcpcKKmagmagmagmagmasmasdegdegmasmas
int64float64float64int32objectobjectobjectint64objectint64int32int32objectobjectobjectfloat64float32float64float32objectfloat64float32objectfloat64float64float64float64float32float32float32float32float32float64float32float64float32float64float32float64float32float64float32float32float32float32float32float32objectfloat32float32float32float32float32float32float32float32float64float64float32float32objectobjectfloat32float32float32float32float32float32float64float32float32float32float64float64objectfloat64float64float64float64float32float32int32float64objectint64float64float32float32objectfloat32float32float32float32float64float64float32float32float64float64float64float64objectfloat32float32objectfloat64float64float64float64int16objectobjectobjectfloat64float64float64float64float64float64int16int16int32
902906874177.140124015041.48317268714----3796414192429498880----STARgaia2gaia2-43.710.242-4.1020.134gaia27.05850.1234gaia2269.8500485668560.25223135972176.786469757970.22389926894----17.190.046------------------------------------------------17.16710.00169717.330.012rered----------------------------141.1662.47850.01460.0094--------0.00660.0122panstarrs--------------------2.4362.521bj2018----17.03930.01208317.23420.0096131gaia23.7598262.071664177.139935758291.483155026790.1158320.070643-11144272311
" + ], + "text/plain": [ + "\n", + " TIC RAJ2000 DEJ2000 HIP TYC UCAC4 _2MASS ... RAOdeg DEOdeg e_RAOdeg e_DEOdeg RadFl WDFl ID \n", + " deg deg ... deg deg mas mas \n", + " int64 float64 float64 int32 object object object ... float64 float64 float64 float64 int16 int16 int32 \n", + "--------- --------------- ------------- ----- ------ ------ ------ ... --------------- ------------- -------- -------- ----- ----- ---------\n", + "902906874 177.14012401504 1.48317268714 -- ... 177.13993575829 1.48315502679 0.115832 0.070643 -1 1 144272311" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.QueryID(\"Gaia DR3 3796414192429498880\", catalog=\"tic\")" + ] + }, + { + "cell_type": "markdown", + "id": "497d2223-6f8e-477b-b693-a8582c32f41a", + "metadata": {}, + "source": [ + "`lksearch` also alows you to input a list of strings, which will then perform sequential ID searches for each item in the list and return a list of the resulting tables: " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "dc0d422f-fa40-4f36-8017-3cbc9bc03f19", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Throttling query limit to Simbad's: max 5/s\n" + ] + }, + { + "data": { + "text/plain": [ + "[\n", + " id \n", + " object \n", + " ----------------------------\n", + " LAMOST J190243.11+501428.7\n", + " AP J19024305+5014286\n", + " Gaia DR3 2132155017099178624\n", + " TIC 377780790\n", + " SPOCS 4268\n", + " Gaia DR2 2132155017099178624\n", + " Kepler-10\n", + " UCAC3 281-142262\n", + " 2MASS J19024305+5014286\n", + " PPMX J190243.0+501428\n", + " USNO-B1.0 1402-00324696\n", + " GSC 03549-00354\n", + " KOI-72\n", + " KIC 11904151,\n", + "
\n", + " id \n", + " object \n", + " ----------------------------\n", + " LAMOST J191618.16+514526.7\n", + " Gaia DR3 2133476355197071616\n", + " TIC 299096355\n", + " Gaia DR2 2133476355197071616\n", + " 2MASS J19161817+5145267\n", + " UCAC3 284-140854\n", + " GSC 03554-01147\n", + " KIC 12644769\n", + " Kepler-16\n", + " NAME Kepler-16 (AB)\n", + " KOI-1611]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.IDLookup([\"Kepler 10\", \"Kepler 16\"])" + ] + }, + { + "cell_type": "markdown", + "id": "99a4d45d-d05e-4497-8e56-e8109f9cb6c2", + "metadata": {}, + "source": [ + "### Matching ID Results to Catalogs\n", + "`lksearch` has the ability to parse the id lists returned by `QueryID` to look for ids which contain the string contained in the optional parameter `match`. This can be usefull for only returning the catalog results which interest you. \n", + "\n", + "Just like the search input can be a list, so can the match criteria, as so: " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "b05efbea-5edb-471c-ba7c-96af0a24a78a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Throttling query limit to Simbad's: max 5/s\n" + ] + }, + { + "data": { + "text/html": [ + "
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searchtickic
str9str13str12
Kepler 10TIC 377780790KIC 11904151
Kepler 16TIC 299096355KIC 12644769
" + ], + "text/plain": [ + "\n", + " search tic kic \n", + " str9 str13 str12 \n", + "--------- ------------- ------------\n", + "Kepler 10 TIC 377780790 KIC 11904151\n", + "Kepler 16 TIC 299096355 KIC 12644769" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CatalogSearch.IDLookup([\"Kepler 10\", \"Kepler 16\"], match=[\"tic\", \"kic\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b422d41-82bb-40af-a08d-8c7722869a96", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/cloud-data-searches.ipynb b/docs/tutorials/cloud-data-searches.ipynb new file mode 100644 index 0000000..90d7402 --- /dev/null +++ b/docs/tutorials/cloud-data-searches.ipynb @@ -0,0 +1,438 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e0dbfed3-0284-4144-9c0c-4ec132aa66f8", + "metadata": {}, + "source": [ + "# lksearch Configuration and working with Cloud Data\n", + "lksearch has a `config` class and configuration file that can be used to configure the default behaviour of the package including how lksearch treats cloud data and where (or if) local files are cached. " + ] + }, + { + "cell_type": "markdown", + "id": "80410c2f-64ac-4aa9-9c0a-77d65a448f49", + "metadata": {}, + "source": [ + "## lksearch File Download and Cache\n", + "The `lksearch` file cache is a directory where files are downloaded to. This directory also serves as a cache directory, and if a file matching the name of the file to be downloaded exists we treat this as a cached file and by default do not overwrite the current file on disk. \n", + "\n", + "The default file download and cache directory is located at:\n", + "`~/.lksearch/cache`\n", + "\n", + "This can be verified using the get_cache_dir convenience function in the config sub-module, e.g.:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "24045f63-26b7-48dc-938e-f4e6eaf436ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/tapritc2/.lksearch/cache'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from lksearch import config as lkconfig\n", + "\n", + "lkconfig.get_cache_dir()" + ] + }, + { + "cell_type": "markdown", + "id": "71100a2a-d8bc-4788-b5a2-316c21c765b1", + "metadata": {}, + "source": [ + "### Clearing the Cache & Corrupted Files\n", + "If you wish to delete an individual file that you downloaded (for example, if you are concerned that a previously downloaded file is corrupted), the easiest way to do that is using the `Local Path` information in the manifest returned by the `.download()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd4060e3-bab0-406f-a76f-d4e4fe6f35ea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading products: 100%|████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:01<00:00, 1.94it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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COMPLETE None None" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from lksearch import K2Search\n", + "\n", + "##First lets download a few files\n", + "manifest = K2Search(\"K2-18\").HLSPs.timeseries.download()\n", + "manifest" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6ce478f4-f0e4-4853-a2e4-b34a90d1c26d", + "metadata": {}, + "outputs": [], + "source": [ + "# The manifest returned by download() is a pandas DataFrame\n", + "# We will access the first local path using iloc as so\n", + "os.remove(manifest.iloc[0][\"Local Path\"])" + ] + }, + { + "cell_type": "markdown", + "id": "2de81d86-b2e2-4698-8522-7d3da13daff8", + "metadata": {}, + "source": [ + "If you want to clear *everything* from your cache, you can use the `config.clearcache()` function to completely empty your cache of downloaded files. by default this will run in \"test\" mode and print what you will be deleting. To confirm deletion, run with `test=False` optional parameter. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "bc7a176f-ae42-49fe-a0cd-ce3444ee61e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running in test mode, rerun with test=False to clear cache\n", + "removing /Users/tapritc2/.lksearch/cache/mastDownload/TESS\n", + "removing /Users/tapritc2/.lksearch/cache/mastDownload/K2\n", + "removing /Users/tapritc2/.lksearch/cache/mastDownload/Kepler\n", + "removing /Users/tapritc2/.lksearch/cache/mastDownload/TESSCut\n", + "removing /Users/tapritc2/.lksearch/cache/mastDownload/HLSP\n" + ] + } + ], + "source": [ + "lkconfig.clearcache()" + ] + }, + { + "cell_type": "markdown", + "id": "0e5adafe-cbb4-4423-b5f0-164071077119", + "metadata": {}, + "source": [ + "**Passing `test=False` will then fully delete the above directories** \n", + "\n", + "e.g. `lkconfig.clearcache(test=False)`" + ] + }, + { + "cell_type": "markdown", + "id": "e5782847-4de5-40b0-aab5-78d75d0a0c8e", + "metadata": {}, + "source": [ + "### lksearch Configuration and Configuration File\n", + "lksearch has a number of configuration parameters, these are contained in the `~lksearch.Conf` [class](https://lightkurve.github.io/lksearch/apidoc.html#lksearch.Conf). One can modify these parameters for a given python session by updating the values in the Conf class. To modify these configuration parameters default values, lksearch also has an optional configuration file that is built on-top of `~astropy.config` using `~astropy.config.ConfigNamespace`. This file does not exist by default, but a default version can be created using the `config.create_config_file` helper function. Modifications to the values in this file will then update the default `~lksearch.Conf` values. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6662b57f-70d9-4a1f-82f4-79ea56c3d7c8", + "metadata": {}, + "outputs": [], + "source": [ + "lkconfig.create_config_file(overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "id": "36de9527-3ba9-492c-8687-6a48d23b9e5f", + "metadata": {}, + "source": [ + "This file can be found in the below location. To edit this, please see the astropy.config documentation. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1d114c6a-bf53-4212-94c0-8309a3a12895", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/tapritc2/.lksearch/config'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lkconfig.get_config_dir()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "54d1962b-a74c-407f-9d69-3e048b239a7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/Users/tapritc2/.lksearch/config/lksearch.cfg'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lkconfig.get_config_file()" + ] + }, + { + "cell_type": "markdown", + "id": "72533d4f-c8af-4085-9f9c-50f01955f118", + "metadata": {}, + "source": [ + "## lksearch Cloud Configuration\n", + "`lksearch` has three configuration parameters that are particularly relevant to cloud-based science platforms. These are:\n", + " - `CLOUD_ONLY`: Only Download cloud based data. If `False`, will download all data. If `True`, will only download data located on a cloud (Amazon S3) bucket\n", + " - `PREFER_CLOUD`: Prefer Cloud-based data product retrieval where available\n", + " - `DOWNLOAD_CLOUD`: Download cloud based data. If `False`, download() will return a pointer to the cloud based datainstead of downloading it - intended usage for cloud-based science platforms (e.g. TiKE)\n", + "\n", + "`CLOUD_ONLY` governs whether or not non-cloud based data will be possible to be downloaded. Many science files have both a cloud-based location (typically on Amazon S3) and a MAST archive location. By default this is `False`, and all products will be downloaded regardless of whether the file is available via cloud-hosting or MAST archive hosting. If `CLOUD_ONLY` is `True`, only files available for download on a cloud-based platform will be retrieved. This configuration parameter is passed through to the `~astroquery.mast` parameter of the same name. \n", + "\n", + "`PREFER_CLOUD` governs the default download behaviour in the event that a data product is available from both a cloud-based location and a MAST-hosted archive location. If `True` (default), then `lksearch` will preferentially download files from the cloud-host rather than the MAST-hosted Archive. This configuration parameter is passed through to the `~astroquery.mast` parameter of the same name. \n", + "\n", + "`DOWNLOAD_CLOUD` governs whether files that are hosted on the cloud are downloaded locally. If this value is `True` (default), cloud-hosted files are downloaded normally. If `False`, then files hosted on a cloud based platform are not downloaded, and a URI containing the path to the desired file on the cloud-host is returned instead of the local path to the file. This path can then be used to read the file remotely (see `~astropy.io.fits` [working with remote and cloud hosted files](https://docs.astropy.org/en/stable/io/fits/#working-with-remote-and-cloud-hosted-files:~:text=with%20large%20files-,Working%20with%20remote%20and%20cloud%2Dhosted%20files,-Unsigned%20integers) for more information). This ability may be most relevant when using `lksearch` on a cloud-based science platform where the remote read is very rapid and short-term local storage comparatively expensive. \n", + "\n", + "Using this `DOWNLOAD_CLOUD` functionality, we can find a cloud-hosted file and read it directly into memory like so:\"" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ff63034a-75de-4bf4-bc74-931e472d9af3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading products: 100%|███████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 584.16it/s]\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Local Path Status \\\n", + "0 s3://stpubdata/tess/public/tid/s0014/0000/0001... COMPLETE \n", + "\n", + " Message URL \n", + "0 Link to S3 bucket for remote read None " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First, lets update our configuration to not download a cloud-hosted file\n", + "from lksearch import Conf, TESSSearch\n", + "\n", + "Conf.DOWNLOAD_CLOUD = False\n", + "\n", + "# Now, lets find some data. We use this target earlier in the tutorial.\n", + "toi = TESSSearch(\"TOI 1161\")\n", + "\n", + "# What happens when we try to download it in our updated configuration?\n", + "cloud_result = toi.timeseries.mission_products[0].download()\n", + "cloud_result" + ] + }, + { + "cell_type": "markdown", + "id": "7bea0002-f5bf-4dcc-bb13-ac93da0d7585", + "metadata": {}, + "source": [ + "As we can see above, instead of downloading the above file we have instead returned an amazon S3 URI for its cloud hosted location. If we want to access the file, we can do it using the remote-read capabilities of `~astropy.io.fits`. \n", + "\n", + "(Note: to do this you will need to install `fsspec` and `s3fs`.)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6f4f92ad-e754-4e58-abf2-54d6fab0fd08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'file': >, 'filemode': 'readonly', 'hdrLoc': 0, 'datLoc': 5760, 'datSpan': 0}\n", + "{'file': >, 'filemode': 'readonly', 'hdrLoc': 5760, 'datLoc': 20160, 'datSpan': 1935360}\n", + "{'file': >, 'filemode': 'readonly', 'hdrLoc': 1955520, 'datLoc': 1961280, 'datSpan': 2880}\n" + ] + } + ], + "source": [ + "import astropy.io.fits as fits\n", + "\n", + "with fits.open(\n", + " cloud_result[\"Local Path\"].values[0], use_fsspec=True, fsspec_kwargs={\"anon\": True}\n", + ") as hdu:\n", + " for item in hdu:\n", + " print(item.fileinfo())" + ] + }, + { + "cell_type": "markdown", + "id": "8d43578c-b414-4ea3-aa8e-1da55e5f548f", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "726de463-2a7f-4a51-88b6-f7abe59b3082", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/tutorials/Example_searches.ipynb b/docs/tutorials/data-searches.ipynb similarity index 92% rename from docs/tutorials/Example_searches.ipynb rename to docs/tutorials/data-searches.ipynb index 69c5900..192f07f 100644 --- a/docs/tutorials/Example_searches.ipynb +++ b/docs/tutorials/data-searches.ipynb @@ -5,7 +5,7 @@ "id": "8362de11", "metadata": {}, "source": [ - "# Tutorial" + "# How to Search for TESS/Kepler/K2 Mission Data with lksearch" ] }, { @@ -4559,319 +4559,6 @@ "manifest = K2_HLSPs.timeseries.download()\n", "manifest" ] - }, - { - "cell_type": "markdown", - "id": "a42fd1bd-f70a-4db9-b483-3a39f4787d4c", - "metadata": {}, - "source": [ - "## Configuration and Caching\n", - "\n", - "`lksearch` has a default file download location that serves as the file cache, and an optional configuration file that can be created and used to overwrite the default values" - ] - }, - { - "cell_type": "markdown", - "id": "03f52bd0-c5d3-4974-9b3a-dfd0c3d1a248", - "metadata": {}, - "source": [ - "### lksearch File Download and Cache\n", - "The `lksearch` file cache is a directory where files are downloaded to. This directory also serves as a cache directory, and if a file matching the name of the file to be downloaded exists we treat this as a cached file and by default do not overwrite the current file on disk. \n", - "\n", - "The default file download and cache directory is located at:\n", - "`~/.lksearch/cache`\n", - "\n", - "This can be verified using the get_cache_dir convenience function in the config sub-module, e.g.:" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "5ebda13a-0051-48c7-8272-e6aa3040f4b2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/tapritc2/.lksearch/cache'" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from lksearch import config as lkconfig\n", - "\n", - "lkconfig.get_cache_dir()" - ] - }, - { - "cell_type": "markdown", - "id": "e70e062e-d0f8-45e0-b01a-06cec8da54e5", - "metadata": {}, - "source": [ - "#### Clearing the Cache & Corrupted Files\n", - "If you wish to delete an individual file that you downloaded (for example, if you are concerned that a previously downloaded file is corrupted), the easiest way to do that is using the `Local Path` information in the manifest returned by the `.download()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "6e6625e2-c1c2-40fb-8fd2-fa3633cf3722", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "\n", - "# The manifest returned by download() is a pandas DataFrame\n", - "# We will access the first local path using iloc as so\n", - "os.remove(manifest.iloc[0][\"Local Path\"])" - ] - }, - { - "cell_type": "markdown", - "id": "7ffa2d24-eba6-48fd-9f59-12a348dcb08d", - "metadata": {}, - "source": [ - "If you want to clear *everything* from your cache, you can use the `config.clearcache()` function to completely empty your cache of downloaded files. by default this will run in \"test\" mode and print what you will be deleting. To confirm deletion, run with `test=False` optional parameter. " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "1345bf88-74be-4aea-96c9-512ab31b349b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running in test mode, rerun with test=False to clear cache\n", - "removing /Users/tapritc2/.lksearch/cache/mastDownload/TESS\n", - "removing /Users/tapritc2/.lksearch/cache/mastDownload/K2\n", - "removing /Users/tapritc2/.lksearch/cache/mastDownload/Kepler\n", - "removing /Users/tapritc2/.lksearch/cache/mastDownload/TESSCut\n", - "removing /Users/tapritc2/.lksearch/cache/mastDownload/HLSP\n" - ] - } - ], - "source": [ - "lkconfig.clearcache()" - ] - }, - { - "cell_type": "markdown", - "id": "6291a748-1e67-41b1-8049-57aabe2b744b", - "metadata": {}, - "source": [ - "**Passing `test=False` will then fully delete the above directories** \n", - "\n", - "e.g. `lkconfig.clearcache(test=False)`" - ] - }, - { - "cell_type": "markdown", - "id": "9adc419e-91b3-41bc-bc40-d37b5c628ebe", - "metadata": {}, - "source": [ - "### lksearch Configuration and Configuration File\n", - "lksearch has a number of configuration parameters, these are contained in the `~lksearch.Conf` [class](https://lightkurve.github.io/lksearch/apidoc.html#lksearch.Conf). One can modify these parameters for a given python session by updating the values in the Conf class. To modify these configuration parameters default values, lksearch also has an optional configuration file that is built on-top of `~astropy.config` using `~astropy.config.ConfigNamespace`. This file does not exist by default, but a default version can be created using the `config.create_config_file` helper function. Modifications to the values in this file will then update the default `~lksearch.Conf` values. " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "345bb5e5-7b13-4ee8-9a08-c85de036ace4", - "metadata": {}, - "outputs": [], - "source": [ - "lkconfig.create_config_file(overwrite=True)" - ] - }, - { - "cell_type": "markdown", - "id": "1561451f-d3c0-4df9-bdb9-343b63e0f136", - "metadata": {}, - "source": [ - "This file can be found in the below location. To edit this, please see the astropy.config documentation. " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "be7bc33c-eb6c-4ce7-81f8-d98076581cc4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/tapritc2/.lksearch/config'" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lkconfig.get_config_dir()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "341857e6-aa78-4cc6-b68d-79fca23f7e63", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/Users/tapritc2/.lksearch/config/lksearch.cfg'" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lkconfig.get_config_file()" - ] - }, - { - "cell_type": "markdown", - "id": "edd120c1-cb63-462a-b997-f83f3da3c3ea", - "metadata": {}, - "source": [ - "#### lksearch Cloud Configuration\n", - "lksearch has three configuration parameters that are particularly relevant to cloud-based science platforms. These are:\n", - " - CLOUD_ONLY: Only Download cloud based data. If False, will download all data. If True, will only download data located on a cloud (Amazon S3) bucket\n", - " - PREFER_CLOUD: Prefer Cloud-based data product retrieval where available\n", - " - DDOWNLOAD_CLOUD: Download cloud based data.If False, download() will return a pointer to the cloud based datainstead of downloading it - intended usage for cloud-based science platforms (e.g. TIKE)\n", - "\n", - "CLOUD_ONLY governs whether or not non-cloud based data will be possible to be downloaded. Many science files have both a cloud-based location (typically on Amazon S3) and a MAST archive location. By default this is False, and all products will be downloaded regardless of whether the file is available via cloud-hosting or MAST archive hosting. If CLOUD_ONLY is True, only files available for download on a cloud-based platform will be retrieved. This configuration parameter is passed through to the `~astroquery.mast` parameter of the same name. \n", - "\n", - "PREFER_CLOUD governs the default download behaviour in the event that a data product is available from both a cloud-based location and a MAST-hosted archive location. If True (default), then lksearch will preferentially download files from the cloud-host rather than the MAST-hosted Archive. This configuration parameter is passed through to the `~astroquery.mast` parameter of the same name. \n", - "\n", - "DOWNLOAD_CLOUD governs whether files that are hosted on the cloud are downloaded locally. If this value is True (default), cloud-hosted files are downloaded normally. If False, then files hosted on a cloud based platform are not downloaded, and a URI containing the path to the desired file on the cloud-host is returned instead of the local path to the file. This path can then be used to read the file remotely (see `~astropy.io.fits` [working with remote and cloud hosted files](https://docs.astropy.org/en/stable/io/fits/#working-with-remote-and-cloud-hosted-files:~:text=with%20large%20files-,Working%20with%20remote%20and%20cloud%2Dhosted%20files,-Unsigned%20integers) for more information). This ability may be most relevant when using lksearch on a cloud-based science platform where the remote read is very rapid and short-term local storage comparatively expensive. \n", - "\n", - "Using this DOWNLOAD_CLOUD functionality, we can find a cloud-hosted file and read it directly into memory like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "745b341d-2c8a-4de4-8050-02c039f4e1ee", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "pipeline products: 100%|███████████████████████████████████████████| 1/1 [00:00<00:00, 395.88it/s]\n" - ] - }, - { - "data": { - "text/html": [ - "
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We use this target earlier in the tutorial.\n", - "toi = TESSSearch(\"TOI 1161\")\n", - "\n", - "# What happens when we try to download it in our updated configuration?\n", - "cloud_result = toi.timeseries.mission_products[0].download()\n", - "cloud_result" - ] - }, - { - "cell_type": "markdown", - "id": "7027025a-7883-4c05-9659-ceda59ed79b4", - "metadata": {}, - "source": [ - "As we can see above, instead of downloading the above file we have instead returned an amazon S3 URI for its cloud hosted location. If we want to access the file, we can do it using the remote-read capabilities of `~astropy.io.fits`. " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "244ccba1-3d75-427d-ae06-93abbbb5c12d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'file': >, 'filemode': 'readonly', 'hdrLoc': 0, 'datLoc': 5760, 'datSpan': 0}\n", - "{'file': >, 'filemode': 'readonly', 'hdrLoc': 5760, 'datLoc': 20160, 'datSpan': 1935360}\n", - "{'file': >, 'filemode': 'readonly', 'hdrLoc': 1955520, 'datLoc': 1961280, 'datSpan': 2880}\n" - ] - } - ], - "source": [ - "import astropy.io.fits as fits\n", - "\n", - "with fits.open(\n", - " cloud_result[\"Local Path\"].values[0], use_fsspec=True, fsspec_kwargs={\"anon\": True}\n", - ") as hdu:\n", - " for item in hdu:\n", - " print(item.fileinfo())" - ] } ], "metadata": { diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst new file mode 100644 index 0000000..67f1753 --- /dev/null +++ b/docs/tutorials/index.rst @@ -0,0 +1,17 @@ +.. _tutorials: + +Tutorials +========= + +1. Getting started with lksearch +-------------------------------- + +This set of tutorials will help you get started with lksearch. Click below to learn how to access data, access catalogs, and configure lksearch to use the cloud. + + +.. toctree:: + :maxdepth: 2 + + data-searches.ipynb + catalog-searches.ipynb + cloud-data-searches.ipynb diff --git a/poetry.lock b/poetry.lock index f821a25..cdc0e99 100644 --- a/poetry.lock +++ b/poetry.lock @@ -18,6 +18,151 @@ pygments = ">=1.5" dev = ["pillow", "pkginfo (>=1.10)", "playwright", "pre-commit", "setuptools", "twine (>=5.0)"] tests = ["hypothesis", "pytest"] +[[package]] +name = "aiobotocore" +version = "2.13.1" +description = "Async client for aws services using botocore and aiohttp" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiobotocore-2.13.1-py3-none-any.whl", hash = "sha256:1bef121b99841ee3cc788e4ed97c332ba32353b1f00e886d1beb3aae95520858"}, + {file = "aiobotocore-2.13.1.tar.gz", hash = "sha256:134f9606c2f91abde38cbc61c3241113e26ff244633e0c31abb7e09da3581c9b"}, +] + +[package.dependencies] +aiohttp = ">=3.9.2,<4.0.0" +aioitertools = 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["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "517f3bfc9d310007ab5c43cf2825dc9a0f100f63629840b5c8a636a61caed676" +content-hash = "ee6a938188cb4a6b11f825f2f68ceb451bf4ded746df79f6b358531638ab7101" diff --git a/pyproject.toml b/pyproject.toml index 466683e..1ccf43f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "lksearch" -version = "1.0.1" +version = "1.1.1" description = "A helpful little package to search for TESS/Kepler/K2 data" authors = ["TESS Science Support Center "] license = "MIT" @@ -17,6 +17,14 @@ tesswcs = "^1.1.2" tqdm = "^4.50.0" boto3 = "^1.34.25" + +[tool.poetry.group.cloud] +optional = true + +[tool.poetry.group.cloud.dependencies] +fsspec = "^2024.6.1" +s3fs = "^2024.6.1" + [tool.poetry.group.dev] optional = true diff --git a/src/lksearch/CatalogSearch.py b/src/lksearch/CatalogSearch.py new file mode 100644 index 0000000..6596371 --- /dev/null +++ b/src/lksearch/CatalogSearch.py @@ -0,0 +1,616 @@ +"""Catalog class to search various catalogs for missions""" + +from typing import Union +import numpy as np + +from astropy.coordinates import Angle, SkyCoord, Distance +from astropy.table import Table +from astropy.time import Time +import astropy.units as u + +from astroquery.vizier import Vizier +from astroquery.utils.tap.core import TapPlus +from astroquery.mast import MastClass +from astroquery.simbad import Simbad + +import pandas as pd + +import warnings +import time +import json + +from . import log +from . import PACKAGEDIR + + +# This is a lits of VizieR catalogs and their input parameters to be used in the +# query_skycatalog function +def _load_cat_config(): + with open(f"{PACKAGEDIR}/data/catalog_config.json", "r") as j: + cat_dict = json.loads(j.read()) + for key in cat_dict.keys(): + cat_dict[key]["equinox"] = Time( + cat_dict[key]["equinox"], format="jyear", scale="tt" + ) + return cat_dict + + +_Catalog_Dictionary = _load_cat_config() + +# Connect to the Vizier TAP server here so that we only do this once +VizTap = TapPlus(url="http://TAPVizieR.u-strasbg.fr/TAPVizieR/tap/") + +# TODO Swap this out with a configuration parameter maybe? +# Or None and Raise Exception +# Make this an optional keword argument for debugging/doc +_default_catalog = "tic" + + +# use simbad to get name/ID crossmatches +def IDLookup(search_input: Union[str, list[str]], match: Union[str, list[str]] = None): + """Uses the Simbad name resolver and ids to disambiguate the search_input string or list. + + Parameters + ---------- + search_input: Union[str, list[str]] + A string or list of strings to query simbad for ID disambiguation + + match: Union[str, list[str]] = None + Short name of catalog to parse the simbad id results for. If this is passed the list of ids are not + reported and a column per item in the match list is added and the ids with that match str contained in the id are listed. + + Returns + ------- + result: Table, list[Table] + Results from the `~astroquery.simbad.Simbad` ID query in `~astropy.table.Table` format. + + """ + # match_str = None only usable in bleeding edge astroquery + # match_list = _Catalog_Dictionary.keys() + + # if match_catalog is not None: + # if match_catalog.lower() in match_list: + # match_str = _Catalog_Dictionary[match_catalog.lower()]["SIMBAD_match_like"] + + if isinstance(search_input, list): + result = [] + log.warning("Throttling query limit to Simbad's: max 5/s") + for item in search_input: + result_iter = _IDLookup(item) + time.sleep(0.2) + result.append(result_iter) + else: + result = _IDLookup(search_input) + + if match is not None: + # Make an Empty table with # columns = 1(the search input) + len(match) + # This will provide the ids that match each search input given the match criteria + col = ["search"] + for item in np.atleast_1d(match): + col.append(item) + dt = ["str"] * (len(np.atleast_1d(match)) + 1) + + final_result = Table(names=col, dtype=dt) + + # Iterate through the search inputs, returned ids, and match criteria + i = 0 + for item in np.atleast_1d(search_input): + row = [item] + # make sure we're in a list in the event we have a single result + if not isinstance(result, list): + result = [result] + # For each item in the match terms, see if it is contained in ID + for cat in np.atleast_1d(match): + mcat = cat.strip().replace(" ", "").lower() + cmatch = None + for sid in result[i]["id"]: + id = sid.strip().replace(" ", "").lower() + if mcat in id: + if cmatch is None: + cmatch = sid + else: + cmatch.append(sid) + if cmatch is None: + cmatch = "" + row.append(cmatch) + final_result.add_row(row) + i += 1 + # get rid of our old list of tables result and return the matching results + result = final_result + + return result + + +def _IDLookup(search_item): + # Construct exact ID TAP queries for various surveys + # result_table = Simbad.query_objectids(search_item, criteria = match_str) + result_table = Simbad.query_objectids(search_item) + return result_table + + +def _get_TAP_Query(catalog: str, ID: str, max_results: int = None, id_column=None): + if catalog not in _Catalog_Dictionary.keys(): + raise ValueError(f"{catalog} not found in TAP catalogs list") + if max_results is None: + max_results = len(ID.split(",")) + if id_column is None: + id_column = _Catalog_Dictionary[catalog]["default_id_column"] + + query_model = f""" SELECT TOP {max_results} * + FROM "{_Catalog_Dictionary[catalog]["catalog"]}" + WHERE {id_column} IN({ID}) """ + + return query_model + + +def _parse_search_input(search_input, catalog: str = None): + if isinstance(search_input, SkyCoord): + search_object = search_input + elif isinstance(search_input, tuple): + search_object = SkyCoord(search_input[0], search_input[1], unit="deg") + elif isinstance(search_input, str): + # Try to turn the passed string into a SkyCoord. + try: + search_object = SkyCoord(search_input) + except ValueError: + # If this fails assume we were given an object name + search_object = search_input + # If no catalog is specified, see if we can infer a preffered catalog + if catalog is None: + search_catalog, _ = _match_target_catalog(search_input) + else: + raise TypeError("Cannot Resolve type of search input") + + # If a catalog is passed make this the catalog to search + # Else use a default catalog(tic?) if none is assigned + if isinstance(catalog, str): + search_catalog = catalog + elif catalog is None: + search_catalog = None + return search_object, search_catalog + + +def _match_target_catalog(search_input): + search = search_input.strip().replace(" ", "").lower() + if search_input.isnumeric(): + # If string is purelt numbers, make no assumptions + search_string = search_input + search_catalog = None + elif search[0:3] == "tic": + search_catalog = "tic" + search_string = search[3:] + elif search[0:4] == "tess": + search_catalog = "tic" + search_string = search[4:] + elif search[0:3] == "kic": + search_catalog = "kic" + search_string = search[3:] + elif search[0:4] == "kplr": + search_catalog = "kic" + search_string = search[4:] + elif search[0:4] == "epic": + search_catalog = "epic" + search_string = search[4:] + elif search[0:4] == "ktwo": + search_catalog = "epic" + search_string = search[4:] + elif search[0:7] == "gaiadr3": + search_catalog = "gaiadr3" + search_string = search[7:] + elif search[0:4] == "gaia" and search_input[4:6] != "dr": + search_string = search[4:] + search_catalog = "gaiadr3" + else: + # If we cannot parse a catalog, make no assumptions + search_catalog = (None,) + search_string = search_input + return search_catalog, search_string + + +def _parse_id_list(search_object): + ids = [_parse_id(item)[0] for item in search_object] + id_list = ", ".join(str(id) for id in ids) + return id_list + + +def _parse_id(search_item): + if isinstance(search_item, int): + id = str(search_item) + scat = None + elif isinstance(search_item, str): + scat, id = _match_target_catalog(search_item) + else: + id = None + scat = None + + return id, scat + + +def QueryID( + search_object: Union[str, int, list[str, int]], + catalog: str = None, + input_catalog: str = None, + max_results: int = None, + return_skycoord: bool = False, + epoch: Union[str, Time] = None, +): + """Searches a catalog (TIC, KIC, EPIC, or GAIA DR3) for an exact ID match and + returns the assosciated catalog rows. A limited cross-match between the TIC, KIC, and gaiadr3 + catalogs is possible using the catalog, and input_catalog optional parameters. + + Parameters + ---------- + search_object : Union[str, int, list[str, int]] + A string or integer, or list of strings or integers, that represents + a list of IDs from a single catalog to match. If an integer is supplied the + catalog optional parameter must be specified. + catalog : str, optional + Catalog to search for an ID match to. If no input_catalog is + specified catalog and input_catalog are assumed to be the same. + If search_object is a string and catalog and is None, search_object is + parsed to try and determine the catalog, by default None + input_catalog : str, optional + _description_, by default None + max_results : int, optional + limits the maximum rows to return, by default None + return_skycoord : bool, optional + If true, an `~astropy.coordinates.SkyCoord` objects is returned for each + row in the result table, by default False + epoch : Union[str, Time], optional + If a return_skycoord is True, epoch can be used to specify the epoch for the + returned SkyCoord object, by default None + + Returns + ------- + results_table: Union[Table, SkyCoord, list[SkyCoord]] + `~astropy.table.Table` object containing the rows of the catalog with IDs matching the search_input. + If return_skycoord is set to True, a `~astropy.coordinates.SkyCoord` object or list of `~astropy.coordinates.SkyCoord` objects + is instead returned. + + """ + id_column = None + + if isinstance(search_object, list): + id_list = _parse_id_list(search_object) + else: + id_list, scat = _parse_id(search_object) + # IF we can figure out the soruce catalog from context - + # EG TIC Blah, assume the catalog to search is the catalog detected + # And th + if catalog is None and scat is not None: + catalog = scat + if input_catalog is None and scat is not None: + input_catalog = scat + + # Assuming a 1:1 match. TODO is this a bad assumption? + if max_results is None: + max_results = len(np.atleast_1d(search_object)) + + if catalog is not None and input_catalog is not None: + if catalog != input_catalog: + max_results = max_results * 10 + if input_catalog in np.atleast_1d( + _Catalog_Dictionary[catalog]["crossmatch_catalogs"] + ): + if _Catalog_Dictionary[catalog]["crossmatch_type"] == "tic": + # TIC is is crossmatched with gaiadr3/kic + # If KIC data for a gaia source or vice versa is desired + # search TIC to get KIC/gaia ids then Search KIC /GAIA + source_id_column = _Catalog_Dictionary["tic"][ + "crossmatch_column_id" + ][input_catalog] + new_id_table = _QueryID( + "tic", id_list, max_results, id_column=source_id_column + ) + id_list = ", ".join( + new_id_table[ + _Catalog_Dictionary["tic"]["crossmatch_column_id"][catalog] + ].astype(str) + # .values + ) + if _Catalog_Dictionary[catalog]["crossmatch_type"] == "column": + # TIC is is crossmatched with gaiadr3/kic + # If we want TIC Info for a gaiadr3/KIC source - match appropriate column in TIC + id_column = _Catalog_Dictionary[catalog]["crossmatch_column_id"][ + input_catalog + ] + else: + raise ValueError( + f"{input_catalog} does not have crossmatched IDs with {catalog}. {catalog} can be crossmatched with {_Catalog_Dictionary[catalog]['crossmatch_catalogs']}" + ) + else: + if catalog is None: + catalog = _default_catalog + + results_table = _QueryID(catalog, id_list, max_results, id_column=id_column) + if return_skycoord: + return _table_to_skycoord(results_table, epoch=epoch, catalog=catalog) + else: + return CatalogResult(results_table) + + +def _QueryID(catalog: str, id_list: str, max_results: int, id_column: str = None): + query = _get_TAP_Query( + catalog, id_list, max_results=max_results, id_column=id_column + ) + async_limit = 1e3 + if max_results > async_limit: + # we should chex max_results and if low do a synchronous query, if large async + log.warn( + f"Warning: Queries over {async_limit} will be done asynchronously, and may take some time" + ) + job = VizTap.launch_job_async(query) + job.wait_for_job_end() + results_table = job.get_data() + else: + job = VizTap.launch_job(query) + results_table = job.get_data() + return results_table # .to_pandas() + + +def QueryPosition( + search_input: Union[str, SkyCoord, tuple, list[str, SkyCoord, tuple]], + epoch: Union[str, Time] = None, + catalog: str = "tic", + radius: Union[float, u.Quantity] = u.Quantity(100, "arcsecond"), + magnitude_limit: float = 18.0, + max_results: int = None, + return_skycoord: bool = False, +): + """ + Query a catalog for a single source location, obtain nearby sources + Parameters + ---------- + coord : astropy.coordinates.SkyCoord, string, tuple, or list thereof + Coordinates around which to do a radius query. If passed a string, will first try to resolve string as a coordinate using `~astropy.coordinates.SkyCoord`, if this fails then tries to resolve the string as a name using '~astroquery.mast.MastClass.resolve_object'. + epoch: astropy.time.Time + The time of observation in JD. + catalog: str + The catalog to query, either 'kepler', 'k2', or 'tess', 'gaia' + radius : float or astropy quantity + Radius in arcseconds to query + magnitude_limit : float + A value to limit the results in based on the Tmag/Kepler mag/K2 mag or Gaia G mag. Default, 18. + return_skycoord: bool + Whether to return an astropy.coordinates.SkyCoord object. Default is False. + Returns + ------- + result: Table or astropy.coordinates.SkyCoord + By default returns a pandas dataframe of the sources within radius query, corrected for proper motion. Optionally will return astropy.coordinates.SkyCoord object. + + """ + + coord, search_catalog = _parse_search_input(search_input, catalog=catalog) + + # Check to make sure that user input is in the correct format + if not isinstance(coord, SkyCoord): + if isinstance(coord, str): + coord = MastClass().resolve_object(coord) + else: + raise TypeError(f"could not resolve {coord} to SkyCoord") + if epoch is not None: + if not isinstance(epoch, Time): + try: + epoch = Time(epoch, format="jd") + except ValueError: + raise TypeError( + "Must pass an `astropy.time.Time object` or parsable object." + ) + raise TypeError( + "Must pass an `astropy.time.Time object` or parsable object." + ) + if not coord.isscalar: + raise ValueError("must pass one target only.") + + # Here we check to make sure that the radius entered is in arcseconds + # This also means we do not need to specify arcseconds in our catalog query + try: + radius = u.Quantity(radius, "arcsecond") + except u.UnitConversionError: + raise + + # Check to make sure that the catalog provided by the user is valid for this function + if search_catalog.lower() not in _Catalog_Dictionary.keys(): + raise ValueError(f"Can not parse catalog name '{catalog}'") + catalog_meta = _Catalog_Dictionary[search_catalog.lower()] + + # Get the Vizier catalog name + catalog_name = catalog_meta["catalog"] + + # Get the appropriate column names and filters to be applied + filters = Vizier( + columns=catalog_meta["columns"], + column_filters={catalog_meta["column_filters"]: f"<{magnitude_limit}"}, + ) + # The catalog can cut off at 50 - we dont want this to happen + if max_results is not None: + filters.ROW_LIMIT = max_results + else: + filters.ROW_LIMIT = -1 + # Now query the catalog + result = filters.query_region(coord, catalog=catalog_name, radius=Angle(radius)) + if len(result) == 0: + # Make an empty Table + empty_table = pd.DataFrame( + columns=[ + *catalog_meta["columns"], + "ID", + "RA", + "Dec", + "Separation", + "Relative_Flux", + ] + ) + # Make Sure Columns are consistently renamed for the catalog + empty_table = empty_table.rename( + { + i: o + for i, o in zip(catalog_meta["rename_in"], catalog_meta["rename_out"]) + }, + axis=1, + ) + # Make sure we have an index set + empty_table = empty_table.set_index("ID") + empty_table = Table.from_pandas(empty_table) + return empty_table + + result = result[catalog_name] + # Rename the columns so that the output is uniform + result.rename_columns( + catalog_meta["rename_in"], + catalog_meta["rename_out"], + ) + if catalog_meta["prefix"] is not None: + prefix = catalog_meta["prefix"] + result["ID"] = [f"{prefix} {id}" for id in result["ID"]] + if epoch is None: + epoch = catalog_meta["equinox"] + c = _table_to_skycoord( + table=result, + equinox=catalog_meta["equinox"], + epoch=epoch, + catalog=search_catalog, + ) + ref_index = np.argmin(coord.separation(c).arcsecond) + sep = c[ref_index].separation(c) + if return_skycoord: + s = np.argsort(sep.deg) + return c[s] + result["RA"] = c.ra.deg + result["Dec"] = c.dec.deg + result["Separation"] = sep.arcsecond + # Calculate the relative flux + result["Relative_Flux"] = 10 ** ( + ( + result[catalog_meta["default_mag"]] + - result[catalog_meta["default_mag"]][ref_index] + ) + / -2.5 + ) + # Now sort the table based on separation + result.sort(["Separation"]) + # return result + # result = result.to_pandas().set_index("ID") + return CatalogResult(result[_get_return_columns(result.columns)]) + + +def _get_return_columns(columns): + """Convenience function to reorder columns and remove motion columns.""" + downselect_columns = list( + set(columns) + - set( + [ + "ID", + "RA", + "Dec", + "RAJ2000", + "DEJ2000", + "Plx", + "pmRA", + "pmDE", + "Separation", + "Relative_Flux", + ] + ) + ) + downselect_columns = np.hstack( + [ + np.sort([i for i in downselect_columns if i.endswith("mag")]), + np.sort([i for i in downselect_columns if not i.endswith("mag")]), + ] + ) + new_columns = [ + "ID", + "RA", + "Dec", + "Separation", + "Relative_Flux", + "pmRA", + "pmDE", + *downselect_columns, + ] + return new_columns + + +def _table_to_skycoord( + table: Table, equinox: Time = None, epoch: Time = None, catalog=None +) -> SkyCoord: + """ + Convert a table input to astropy.coordinates.SkyCoord object + + Parameters + ---------- + table : astropy.table.Table + astropy.table.Table which contains the coordinates of targets and proper motion values + equinox: astropy.time.Time + The equinox for the catalog + epoch : astropy.time.Time + Desired time of the observation + + Returns + ------- + coords : astropy.coordinates.SkyCoord + SkyCoord object with RA, Dec, equinox, and proper motion parameters. + """ + + if equinox is None and catalog is None: + _, catalog = _parse_id(table[0][0]) + if equinox is None and catalog is not None: + equinox = _Catalog_Dictionary[catalog]["equinox"] + if epoch is None and catalog is not None: + epoch = equinox + + # We need to remove any nan values from our proper motion list + # Doing this will allow objects which do not have proper motion to still be displayed + if "pmRA" in table.keys(): + table["pmRA"] = np.ma.filled(table["pmRA"].astype(float), 0.0) + else: + table["pmRA"] = 0.0 * u.mas / u.yr + + if "pmDE" in table.keys(): + table["pmDE"] = np.ma.filled(table["pmDE"].astype(float), 0.0) + else: + table["pmDE"] = 0.0 * u.mas / u.yr + + # If an object does not have a parallax then we treat it as if the object is an "infinite distance" + # and set the parallax to 1e-7 arcseconds or 10Mpc. + if "Plx" in table.keys(): + table["Plx"] = np.ma.filled(table["Plx"].astype(float), 1e-4) + else: + table["Plx"] = 1e-4 * u.mas + + if "RAJ2000" in table.keys() and "DEJ2000" in table.keys(): + ra = table["RAJ2000"] + dec = table["DEJ2000"] + elif "RA" in table.keys() and "Dec" in table.keys(): + ra = table["RA"] * u.deg + dec = table["Dec"] * u.deg + else: + raise KeyError("RA, DEC Disambiguation failed") + # Suppress warning caused by Astropy as noted in issue 111747 (https://github.com/astropy/astropy/issues/11747) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="negative parallaxes") + + # Get the input data from the table + c = SkyCoord( + ra=ra, + dec=dec, + distance=Distance(parallax=table["Plx"].quantity, allow_negative=True), + pm_ra_cosdec=table["pmRA"], + pm_dec=table["pmDE"], + frame="icrs", + obstime=equinox, + ) + + # Suppress warning caused by Astropy as noted in issue 111747 (https://github.com/astropy/astropy/issues/11747) + if epoch != equinox: + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="ERFA function") + warnings.filterwarnings("ignore", message="invalid value") + c = c.apply_space_motion(new_obstime=epoch) + return c + + +class CatalogResult(Table): + def to_SkyCoord(self, equinox: Time = None, epoch: Time = None): + return _table_to_skycoord(Table(self), equinox=equinox, epoch=epoch) diff --git a/src/lksearch/K2Search.py b/src/lksearch/K2Search.py index ac3b976..adad2a3 100644 --- a/src/lksearch/K2Search.py +++ b/src/lksearch/K2Search.py @@ -1,7 +1,6 @@ import pandas as pd from typing import Union, Optional import re -import logging import numpy as np from astropy import units as u @@ -11,8 +10,6 @@ pd.options.display.max_rows = 10 -log = logging.getLogger(__name__) - class K2Search(MASTSearch): """ diff --git a/src/lksearch/KeplerSearch.py b/src/lksearch/KeplerSearch.py index d2d8896..4e5cc8c 100644 --- a/src/lksearch/KeplerSearch.py +++ b/src/lksearch/KeplerSearch.py @@ -1,7 +1,6 @@ import pandas as pd from typing import Union, Optional import re -import logging import numpy as np from astropy import units as u @@ -12,8 +11,6 @@ pd.options.display.max_rows = 10 -log = logging.getLogger(__name__) - class KeplerSearch(MASTSearch): """ diff --git a/src/lksearch/MASTSearch.py b/src/lksearch/MASTSearch.py index e787125..e2fcecd 100644 --- a/src/lksearch/MASTSearch.py +++ b/src/lksearch/MASTSearch.py @@ -18,12 +18,10 @@ from .utils import SearchError, SearchWarning, suppress_stdout -from . import conf, config +from . import conf, config, log pd.options.display.max_rows = 10 -log = logging.getLogger(__name__) - class MASTSearch(object): """ @@ -91,9 +89,9 @@ def __init__( pipeline = np.atleast_1d(pipeline).tolist() self.search_pipeline = pipeline - if ("kepler" in (m.lower() for m in mission)) & (sequence != None): + if ("kepler" in (m.lower() for m in mission)) & (sequence is not None): log.warning( - f"Sequence not valid when searching for Kepler data. Setting sequence to None" + "Sequence not valid when searching for Kepler data. Setting sequence to None" ) sequence = None @@ -215,7 +213,7 @@ def uris(self): if conf.PREFER_CLOUD: cloud_uris = self.cloud_uris - mask = cloud_uris != None + mask = cloud_uris is not None uris[mask] = cloud_uris[mask] return uris @@ -587,7 +585,7 @@ def _search_obs( if filetype == "ffi": raise SearchError( - f"FFI search not implemented in MASTSearch. Please use TESSSearch." + "FFI search not implemented in MASTSearch. Please use TESSSearch." ) # Ensure mission is a list diff --git a/src/lksearch/TESSSearch.py b/src/lksearch/TESSSearch.py index a272aa4..c69ef62 100644 --- a/src/lksearch/TESSSearch.py +++ b/src/lksearch/TESSSearch.py @@ -2,7 +2,6 @@ import pandas as pd from typing import Union, Optional import re -import logging import os import numpy as np @@ -16,15 +15,13 @@ from copy import deepcopy from .MASTSearch import MASTSearch -from . import conf, config +from . import conf, config, log PREFER_CLOUD = conf.PREFER_CLOUD DOWNLOAD_CLOUD = conf.DOWNLOAD_CLOUD pd.options.display.max_rows = 10 -log = logging.getLogger(__name__) - class TESSSearch(MASTSearch): """ diff --git a/src/lksearch/__init__.py b/src/lksearch/__init__.py index f7ff2fb..f0a660e 100644 --- a/src/lksearch/__init__.py +++ b/src/lksearch/__init__.py @@ -1,9 +1,10 @@ #!/usr/bin/env python from __future__ import absolute_import from . import config as _config -from .version import __version__ +import logging import os +__version__ = "1.1.1" PACKAGEDIR = os.path.abspath(os.path.dirname(__file__)) @@ -78,8 +79,13 @@ class Conf(_config.ConfigNamespace): conf = Conf() +log = logging.getLogger("lksearch") -from .MASTSearch import MASTSearch -from .TESSSearch import TESSSearch -from .KeplerSearch import KeplerSearch -from .K2Search import K2Search +from .MASTSearch import MASTSearch # noqa +from .TESSSearch import TESSSearch # noqa +from .KeplerSearch import KeplerSearch # noqa +from .K2Search import K2Search # noqa + +# from .catalog import * # noqa +# from .CatalogSearch import CatalogSearch # noqa +from . import CatalogSearch # noqa diff --git a/src/lksearch/config/__init__.py b/src/lksearch/config/__init__.py index 0586a8d..05d0fae 100644 --- a/src/lksearch/config/__init__.py +++ b/src/lksearch/config/__init__.py @@ -2,10 +2,12 @@ import warnings import glob import shutil +import logging import astropy.config as _config ROOTNAME = "lksearch" +log = logging.getLogger(ROOTNAME) class ConfigNamespace(_config.ConfigNamespace): diff --git a/src/lksearch/data/catalog_config.json b/src/lksearch/data/catalog_config.json new file mode 100644 index 0000000..d4ec358 --- /dev/null +++ b/src/lksearch/data/catalog_config.json @@ -0,0 +1,140 @@ +{ + "kic": { + "catalog": "V/133/kic", + "columns": [ + "KIC", + "RAJ2000", + "DEJ2000", + "pmRA", + "pmDE", + "Plx", + "kepmag", + "Radius", + "Teff", + "logg" + ], + "column_filters": "kepmag", + "rename_in": [ + "KIC", + "kepmag" + ], + "rename_out": [ + "ID", + "Kepmag" + ], + "equinox": 2000, + "prefix": "KIC", + "default_mag": "Kepmag", + "default_id_column": "KIC", + "crossmatch_catalogs": [ + "tic", + "gaiadr3" + ], + "crossmatch_type": "tic", + "crossmatch_columns": null + }, + "epic": { + "catalog": "IV/34/epic", + "columns": [ + "ID", + "RAJ2000", + "DEJ2000", + "pmRA", + "pmDEC", + "plx", + "Kpmag", + "logg", + "Teff", + "Rad", + "Mass" + ], + "column_filters": "Kpmag", + "rename_in": [ + "Kpmag", + "pmDEC", + "plx" + ], + "rename_out": [ + "K2mag", + "pmDE", + "Plx" + ], + "equinox": 2000, + "prefix": "EPIC", + "default_mag": "K2mag", + "default_id_column": "ID", + "crossmatch_catalogs": null + }, + "tic": { + "catalog": "IV/39/tic82", + "columns": [ + "TIC", + "RAJ2000", + "DEJ2000", + "pmRA", + "pmDE", + "Plx", + "Tmag", + "logg", + "Teff", + "Rad", + "Mass" + ], + "column_filters": "Tmag", + "rename_in": [ + "TIC", + "Tmag" + ], + "rename_out": [ + "ID", + "TESSmag" + ], + "equinox": 2000, + "prefix": "TIC", + "default_mag": "TESSmag", + "default_id_column": "TIC", + "crossmatch_catalogs": [ + "gaiadr3", + "kic" + ], + "crossmatch_type": "column", + "crossmatch_column_id": { + "kic": "KIC", + "gaiadr3": "GAIA", + "tic": "TIC" + } + }, + "gaiadr3": { + "catalog": "I/355/gaiadr3", + "columns": [ + "DR3Name", + "RAJ2000", + "DEJ2000", + "pmRA", + "pmDE", + "Plx", + "Gmag", + "BPmag", + "RPmag", + "logg", + "Teff" + ], + "column_filters": "Gmag", + "rename_in": [ + "DR3Name" + ], + "rename_out": [ + "ID" + ], + "equinox": 2016, + "prefix": null, + "default_mag": "Gmag", + "default_id_column": "Source", + "crossmatch_catalogs": [ + "tic", + "kic" + ], + "crossmatch_type": "tic", + "crossmatch_column_id": null + } +} \ No newline at end of file diff --git a/src/lksearch/version.py b/src/lksearch/version.py deleted file mode 100644 index 3083e9a..0000000 --- a/src/lksearch/version.py +++ /dev/null @@ -1,3 +0,0 @@ -# Store the version number in a separate file -# so that we can read it from setup.py without importing the package -__version__ = "1.0.1" diff --git a/tests/test_catalogs_conesearch.py b/tests/test_catalogs_conesearch.py new file mode 100644 index 0000000..cb96fd3 --- /dev/null +++ b/tests/test_catalogs_conesearch.py @@ -0,0 +1,145 @@ +"""Tests catalog querying""" + +import numpy as np +from astropy.coordinates import SkyCoord +from astropy.table import Table +from astropy.time import Time +import astropy.units as u +from lksearch.CatalogSearch import QueryPosition +import pytest + +# Tests the region around TIC 228760807 which should return a catalog containing 4 objects. +c = SkyCoord(194.10141041659, -27.3905828803397, unit="deg") +epoch = Time(1569.4424277786259 + 2457000, scale="tdb", format="jd") + + +def test_tic(): + catalog = QueryPosition( + c, + epoch=epoch, + catalog="tic", + radius=u.Quantity(80, "arcsecond"), + magnitude_limit=18, + ) + assert len(catalog) == 4 + + # Checks that an astropy Table is returned + assert isinstance(catalog, Table) + + # Test that the proper motion works + + assert np.isclose(catalog[0]["RA"], 194.10075230969787, atol=1e-6) + assert np.isclose(catalog[0]["Dec"], -27.390340343480744, atol=1e-6) + + # Test different epochs + catalog_new = QueryPosition( + c, + epoch=Time(2461041.500, scale="tt", format="jd"), + catalog="tic", + radius=80, + magnitude_limit=18, + ) + + assert np.isclose(catalog_new[0]["RA"], 194.10052070792756, atol=1e-6) + assert np.isclose(catalog_new[0]["Dec"], -27.390254988629433, atol=1e-6) + + +def test_bad_catalog(): + # test the catalog type i.e., simbad is not included in our catalog list. + # Look at other tests to see if this is correct syntax + with pytest.raises(ValueError, match="Can not parse catalog name 'simbad'"): + QueryPosition(c, epoch=epoch, catalog="simbad", radius=80, magnitude_limit=18) + + +def test_gaia_position(): + catalog_gaia = QueryPosition( + c, + epoch=Time(1569.4424277786259 + 2457000, scale="tdb", format="jd"), + catalog="gaiadr3", + radius=80, + magnitude_limit=18, + ) + + assert len(catalog_gaia) == 2 + + catalog_gaia = QueryPosition( + c, + epoch=Time(1569.4424277786259 + 2457000, scale="tdb", format="jd"), + radius=80, + magnitude_limit=18, + catalog="gaiadr3", + ) + + +def test_kic(): + catalog_kepler = QueryPosition( + SkyCoord(285.679391, 50.2413, unit="deg"), + epoch=Time(120.5391465105713 + 2454833, scale="tdb", format="jd"), + catalog="kic", + radius=20, + magnitude_limit=18, + ) + assert len(catalog_kepler) == 5 + + +def test_epic(): + catalog_k2 = QueryPosition( + SkyCoord(172.560465, 7.588391, unit="deg"), + epoch=Time(1975.1781333280233 + 2454833, scale="tdb", format="jd"), + catalog="epic", + radius=20, + magnitude_limit=18, + ) + assert len(catalog_k2) == 1 + + +def test_empty(): + catalog = QueryPosition( + SkyCoord.from_name("Kepler-10"), + Time.now(), + catalog="epic", + radius=20 * u.arcsecond, + magnitude_limit=18, + ) + assert isinstance(catalog, Table) + assert len(catalog) == 0 + + +def test_resolving(): + catalog = QueryPosition("Kepler 10", catalog="tic") + assert np.isclose(catalog["RA"][0], 285.679422) + assert np.isclose(catalog["Dec"][0], 50.241306) + + catalog = QueryPosition("19h02m43.03s +50d14m29.34s", catalog="tic") + assert np.isclose(catalog["RA"][0], 285.679422) + assert np.isclose(catalog["Dec"][0], 50.241306) + + catalog = QueryPosition("285.679422 50.241306", catalog="tic") + assert np.isclose(catalog["RA"][0], 285.679422) + assert np.isclose(catalog["Dec"][0], 50.241306) + + catalog = QueryPosition((285.679422, 50.241306), catalog="tic") + assert np.isclose(catalog["RA"][0], 285.679422) + assert np.isclose(catalog["Dec"][0], 50.241306) + + +def test_skycoord(): + sc = QueryPosition( + c, + epoch=epoch, + catalog="tic", + radius=u.Quantity(80, "arcsecond"), + magnitude_limit=18, + return_skycoord=True, + ) + assert isinstance(sc, SkyCoord) + + sc = QueryPosition( + c, + epoch=epoch, + catalog="tic", + radius=u.Quantity(80, "arcsecond"), + magnitude_limit=18, + ).to_SkyCoord() + + assert isinstance(sc, SkyCoord) diff --git a/tests/test_catalogs_idsearch.py b/tests/test_catalogs_idsearch.py new file mode 100644 index 0000000..442d2ea --- /dev/null +++ b/tests/test_catalogs_idsearch.py @@ -0,0 +1,99 @@ +"""Tests catalog id querying""" + +import numpy as np +from astropy.coordinates import SkyCoord +import pandas as pd +from astropy.time import Time +import astropy.units as u +from lksearch import CatalogSearch +import pytest + + +def test_id_query(): + tic = 299096513 + tic_result = CatalogSearch.QueryID(tic, catalog="tic") + assert len(tic_result) == 1 + assert tic_result["TIC"] == tic + + kic = 12644769 + kic_result = CatalogSearch.QueryID(kic, catalog="kic") + assert len(kic_result) == 1 + assert kic_result["KIC"] == kic + + epic = 201563164 + epic_result = CatalogSearch.QueryID(epic, catalog="epic") + assert len(epic_result) == 1 + assert epic_result["ID"] == epic + + gaia = 2133452475178900736 + gaia_result = CatalogSearch.QueryID(gaia, catalog="gaiadr3") + assert len(gaia_result) == 1 + assert gaia_result["Source"] == gaia + + +def name_disambiguation(string, key, target): + result = CatalogSearch.QueryID(string) + return (len(result) == 1) and result[key][0] == target + + +def test_name_disambiguation(): + tic = 902906874 + assert name_disambiguation(f"TIC {tic}", "TIC", tic) + assert name_disambiguation(f"TIC{tic}", "TIC", tic) + assert name_disambiguation(f"tess{tic}", "TIC", tic) + assert name_disambiguation(f"tess {tic}", "TIC", tic) + + kic = 12644769 + assert name_disambiguation(f"KIC {kic}", "KIC", kic) + assert name_disambiguation(f"KIC{kic}", "KIC", kic) + assert name_disambiguation(f"kplr{kic}", "KIC", kic) + assert name_disambiguation(f"kplr {kic}", "KIC", kic) + + epic = 201563164 + assert name_disambiguation(f"EPIC {epic}", "ID", epic) + assert name_disambiguation(f"EPIC{epic}", "ID", epic) + assert name_disambiguation(f"ktwo{epic}", "ID", epic) + assert name_disambiguation(f"ktwo {epic}", "ID", epic) + + gaiadr3 = 2133452475178900736 + assert name_disambiguation(f"gaiadr3 {gaiadr3 }", "Source", gaiadr3) + assert name_disambiguation(f"gaiadr3{gaiadr3}", "Source", gaiadr3) + assert name_disambiguation(f"GAIA{gaiadr3 }", "Source", gaiadr3) + assert name_disambiguation(f"GAIA {gaiadr3 }", "Source", gaiadr3) + + +def test_lists(): + sources = [2133452475178900736, 3201680999981276544] + assert len(CatalogSearch.QueryID(sources, catalog="gaiadr3")) == 2 + + +def test_crossmatch(): + result = CatalogSearch.QueryID("GAIA 2133452475178900736", catalog="tic") + assert len(result) == 1 + assert result["TIC"] == 299096513 + + sources = [2133452475178900736, 3201680999981276544] + result = CatalogSearch.QueryID(sources, catalog="tic", input_catalog="gaiadr3") + assert len(result) == 2 + assert 299096513 in result["TIC"] + assert 299096514 in result["TIC"] + + result = CatalogSearch.QueryID("TIC 299096513", catalog="gaiadr3") + assert len(result) == 1 + assert result["Source"] == 2133452475178900736 + + result = CatalogSearch.QueryID(f"KIC 12644769", catalog="tic") + assert len(result) == 1 + assert result["TIC"] == 299096355 + + # TODO Add KIC->GAIA vice versa + + +def test_skycoord(): + tic = 299096513 + tic_result = CatalogSearch.QueryID(tic, catalog="tic") + sc = tic_result.to_SkyCoord() + assert isinstance(sc, SkyCoord) + + sc = CatalogSearch.QueryID(tic, catalog="tic", return_skycoord=True) + assert isinstance(sc, SkyCoord) diff --git a/tests/test_search.py b/tests/test_missionsearch.py similarity index 95% rename from tests/test_search.py rename to tests/test_missionsearch.py index e290997..1f34821 100644 --- a/tests/test_search.py +++ b/tests/test_missionsearch.py @@ -6,19 +6,12 @@ from numpy.testing import assert_almost_equal, assert_array_equal import numpy as np -import tempfile -from requests import HTTPError from astropy.coordinates import SkyCoord import astropy.units as u -from astropy.table import Table import pandas as pd -from pathlib import Path -from astropy.utils.data import get_pkg_data_filename - -import shutil from lksearch.utils import SearchError, SearchWarning @@ -438,10 +431,10 @@ def test_split_k2_campaigns(): assert search_c11.table["campaign"][1] == "11b" -def test_FFI_retrieval(): - """Can we find TESS individual FFI's""" - target = "Kepler 16b" - assert len(TESSSearch("Kepler 16b").search_sector_ffis(14)) == 1241 +# MAST is deprecating FFI search and retrieval through astroquery. How should we handle this? +# def test_FFI_retrieval(): +# """Can we find TESS individual FFI's""" +# assert len(TESSSearch("Kepler 16b").search_sector_ffis(14)) == 1241 def test_tesscut(): @@ -467,15 +460,15 @@ class TestMASTSearchFilter: ), ) def test_target_name(self, target_name): - filter_results = self.results.filter_table(target_name=target_name) + self.results.filter_table(target_name=target_name) @pytest.mark.parametrize("limit", (0, 10, 1000)) def test_limit(self, limit): - filter_results = self.results.filter_table(limit=limit) + self.results.filter_table(limit=limit) @pytest.mark.parametrize("filetype", (0, "lightcurve")) def test_filetype(self, filetype): - filter_results = self.results.filter_table(filetype=filetype) + self.results.filter_table(filetype=filetype) @pytest.mark.parametrize( "exptime", @@ -494,33 +487,33 @@ def test_filetype(self, filetype): ), ) def test_exptime(self, exptime): - filter_results = self.results.filter_table(exptime=exptime) + self.results.filter_table(exptime=exptime) @pytest.mark.parametrize("distance", (0, 0.2, (0.2, 0.4))) def test_distance(self, distance): - filter_results = self.results.filter_table(distance=distance) + self.results.filter_table(distance=distance) @pytest.mark.parametrize("year", (0, 2013, (2000, 2020), [2013, 2019])) def test_year(self, year): - filter_results = self.results.filter_table(year=year) + self.results.filter_table(year=year) @pytest.mark.parametrize( "description", (0, "data", ["TPS", "report"], ("TPS", "report")) ) def test_description(self, description): - filter_results = self.results.filter_table(description=description) + self.results.filter_table(description=description) @pytest.mark.parametrize("pipeline", (0, "Kepler", "spoc", ["kepler", "spoc"])) def test_pipeline(self, pipeline): - filter_results = self.results.filter_table(pipeline=pipeline) + self.results.filter_table(pipeline=pipeline) @pytest.mark.parametrize("sequence", (0, 14, [14, 15])) def test_sequence(self, sequence): - filter_results = self.results.filter_table(sequence=sequence) + self.results.filter_table(sequence=sequence) @pytest.mark.parametrize("mission", (0, "Kepler", "Tess", ["Kepler", "Tess"])) def test_mission(self, mission): - filter_results = self.results.filter_table(mission=mission) + self.results.filter_table(mission=mission) def test_combination( self,