{ "cells": [ { "cell_type": "markdown", "id": "1838c7b7-d659-47d3-b831-d318adf2922e", "metadata": {}, "source": [ "## 1.0. Preliminaries" ] }, { "cell_type": "code", "execution_count": 14, "id": "8ab32b86-74df-4bb6-b464-672f6a109f55", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "from astropy.units import UnitsWarning\n", "import warnings\n", "from lsst.rsp import get_tap_service, retrieve_query\n", "\n", "import healpy as hp\n", "import math\n", "import gc" ] }, { "cell_type": "code", "execution_count": 2, "id": "38e9a72f-ef35-4904-b4a5-e84a1039acb9", "metadata": {}, "outputs": [], "source": [ "warnings.simplefilter(\"ignore\", category=UnitsWarning)\n", "pandas.set_option('display.max_rows', 200)" ] }, { "cell_type": "code", "execution_count": 3, "id": "72b7fc50-681b-427d-94f5-792249424076", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "7d7dd2e7-a966-46d2-aba2-07703a1ee13f", "metadata": {}, "source": [ "## 2.0. Query TAP server\n", "\n", "The DP0.2 Documentation contains a list of all DP0.2 catalogs, and also a link to the DP0.2 Schema Browser where users can read about the available tables and their contents.\n", "\n", "Alternatively, the Portal Aspect of the Rubin Science Platform can be used to browse catalog data.\n", "\n", "Below, we show how to browse catalog data from a Notebook using the TAP service." ] }, { "cell_type": "markdown", "id": "261349f3-12d5-4332-bd6d-f4d1fe78c66e", "metadata": {}, "source": [ "To access tables, we will use the TAP service in a similar manner to what we showed in the [Intro to DP0 notebook](https://github.com/rubin-dp0/tutorial-notebooks/blob/main/01_Intro_to_DP0_Notebooks.ipynb), and explored further in the [TAP tutorial notebook](https://github.com/rubin-dp0/tutorial-notebooks/blob/main/02_Intermediate_TAP_Query.ipynb). See those notebooks for more details." ] }, { "cell_type": "code", "execution_count": 4, "id": "5803048d", "metadata": {}, "outputs": [], "source": [ "service = get_tap_service()" ] }, { "cell_type": "markdown", "id": "33d2d224-dab9-4084-af23-5751e5fe76d9", "metadata": {}, "source": [ "### 2.1 Download all SNe from TruthSummary table." ] }, { "cell_type": "code", "execution_count": 5, "id": "0fcde522-cf4c-4516-904f-766ea3ee06d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SELECT * FROM dp02_dc2_catalogs.MatchesTruth WHERE truth_type=3\n", "CPU times: user 6.67 s, sys: 220 ms, total: 6.89 s\n", "Wall time: 54 s\n" ] }, { "data": { "text/html": [ "
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idid_truth_typematch_candidatematch_chisqmatch_countmatch_n_chisq_finitematch_objectIdtruth_type
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418181MS_8920_1868MS_8920_1868_3FalseNaN00<NA>3
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418185 rows × 8 columns

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" ], "text/plain": [ " id id_truth_type match_candidate match_chisq \\\n", "0 MS_10449_1352 MS_10449_1352_3 False NaN \n", "1 MS_10449_1540 MS_10449_1540_3 False NaN \n", "2 MS_10449_1149 MS_10449_1149_3 False NaN \n", "3 MS_10449_1215 MS_10449_1215_3 False NaN \n", "4 MS_10449_1006 MS_10449_1006_3 False NaN \n", "... ... ... ... ... \n", "418180 MS_8920_1824 MS_8920_1824_3 False NaN \n", "418181 MS_8920_1868 MS_8920_1868_3 False NaN \n", "418182 MS_8920_1656 MS_8920_1656_3 False NaN \n", "418183 MS_8920_1687 MS_8920_1687_3 False NaN \n", "418184 MS_8920_2209 MS_8920_2209_3 False NaN \n", "\n", " match_count match_n_chisq_finite match_objectId truth_type \n", "0 0 0 3 \n", "1 0 0 3 \n", "2 0 0 3 \n", "3 0 0 3 \n", "4 0 0 3 \n", "... ... ... ... ... \n", "418180 0 0 3 \n", "418181 0 0 3 \n", "418182 0 0 3 \n", "418183 0 0 3 \n", "418184 0 0 3 \n", "\n", "[418185 rows x 8 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# This cell takes about 1 minute...\n", "\n", "query = \"\"\"SELECT * FROM dp02_dc2_catalogs.MatchesTruth WHERE truth_type=3\"\"\"\n", "\n", "print(query)\n", "\n", "results = service.search(query)\n", "df_mt3 = results.to_table().to_pandas()\n", "del results\n", "\n", "df_mt3" ] }, { "cell_type": "markdown", "id": "8a236b31-2271-45f4-b05e-936ec9e864fb", "metadata": {}, "source": [ "Note that this is the same number of entries that Akshita found." ] }, { "cell_type": "markdown", "id": "1924b0ca-44a8-413c-b9cc-22a356d14469", "metadata": {}, "source": [ "### 2.2 Download all SNe from TruthSummary table within an RA/DEC box of within a box 62.0\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
cosmodc2_hpcosmodc2_iddecflux_gflux_g_noMWflux_iflux_i_noMWflux_rflux_r_noMWflux_u...flux_z_noMWhost_galaxyidid_truth_typeis_pointsourceis_variablemag_rraredshifttruth_type
0-1-1-37.0002590.00.00.00.00.00.00.0...0.082332hl_ms_82332hl_ms_82332_311NaN62.4379561.3776103
1-1-1-37.0465160.00.00.00.00.00.00.0...0.019758hl_ms_19758hl_ms_19758_311NaN62.4397390.7469063
2-1-1-37.0419320.00.00.00.00.00.00.0...0.07938033031MS_9814_1119MS_9814_1119_311NaN62.4455750.5723403
3-1-1-37.0232060.00.00.00.00.00.00.0...0.07938864126MS_9814_2658MS_9814_2658_311NaN62.4549930.8069763
4-1-1-37.0575000.00.00.00.00.00.00.0...0.07938862386MS_9814_2477MS_9814_2477_311NaN62.4612440.7813363
..................................................................
259-1-1-37.0095320.00.00.00.00.00.00.0...0.07938517422MS_9814_2054MS_9814_2054_311NaN62.4210230.7346603
260-1-1-37.0086730.00.00.00.00.00.00.0...0.07937580820MS_9814_174MS_9814_174_311NaN62.0487200.2900503
261-1-1-37.0056570.00.00.00.00.00.00.0...0.07937581805MS_9814_121MS_9814_121_311NaN62.1933140.2789443
262-1-1-37.0055820.00.00.00.00.00.00.0...0.07937875757MS_9814_693MS_9814_693_311NaN62.1042070.4976133
263-1-1-37.0009540.00.00.00.00.00.00.0...0.02957hl_ms_2957hl_ms_2957_311NaN62.1358550.3877403
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264 rows × 24 columns

\n", "" ], "text/plain": [ " cosmodc2_hp cosmodc2_id dec flux_g flux_g_noMW flux_i \\\n", "0 -1 -1 -37.000259 0.0 0.0 0.0 \n", "1 -1 -1 -37.046516 0.0 0.0 0.0 \n", "2 -1 -1 -37.041932 0.0 0.0 0.0 \n", "3 -1 -1 -37.023206 0.0 0.0 0.0 \n", "4 -1 -1 -37.057500 0.0 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "259 -1 -1 -37.009532 0.0 0.0 0.0 \n", "260 -1 -1 -37.008673 0.0 0.0 0.0 \n", "261 -1 -1 -37.005657 0.0 0.0 0.0 \n", "262 -1 -1 -37.005582 0.0 0.0 0.0 \n", "263 -1 -1 -37.000954 0.0 0.0 0.0 \n", "\n", " flux_i_noMW flux_r flux_r_noMW flux_u ... flux_z_noMW host_galaxy \\\n", "0 0.0 0.0 0.0 0.0 ... 0.0 82332 \n", "1 0.0 0.0 0.0 0.0 ... 0.0 19758 \n", "2 0.0 0.0 0.0 0.0 ... 0.0 7938033031 \n", "3 0.0 0.0 0.0 0.0 ... 0.0 7938864126 \n", "4 0.0 0.0 0.0 0.0 ... 0.0 7938862386 \n", ".. ... ... ... ... ... ... ... \n", "259 0.0 0.0 0.0 0.0 ... 0.0 7938517422 \n", "260 0.0 0.0 0.0 0.0 ... 0.0 7937580820 \n", "261 0.0 0.0 0.0 0.0 ... 0.0 7937581805 \n", "262 0.0 0.0 0.0 0.0 ... 0.0 7937875757 \n", "263 0.0 0.0 0.0 0.0 ... 0.0 2957 \n", "\n", " id id_truth_type is_pointsource is_variable mag_r \\\n", "0 hl_ms_82332 hl_ms_82332_3 1 1 NaN \n", "1 hl_ms_19758 hl_ms_19758_3 1 1 NaN \n", "2 MS_9814_1119 MS_9814_1119_3 1 1 NaN \n", "3 MS_9814_2658 MS_9814_2658_3 1 1 NaN \n", "4 MS_9814_2477 MS_9814_2477_3 1 1 NaN \n", ".. ... ... ... ... ... \n", "259 MS_9814_2054 MS_9814_2054_3 1 1 NaN \n", "260 MS_9814_174 MS_9814_174_3 1 1 NaN \n", "261 MS_9814_121 MS_9814_121_3 1 1 NaN \n", "262 MS_9814_693 MS_9814_693_3 1 1 NaN \n", "263 hl_ms_2957 hl_ms_2957_3 1 1 NaN \n", "\n", " ra redshift truth_type \n", "0 62.437956 1.377610 3 \n", "1 62.439739 0.746906 3 \n", "2 62.445575 0.572340 3 \n", "3 62.454993 0.806976 3 \n", "4 62.461244 0.781336 3 \n", ".. ... ... ... \n", "259 62.421023 0.734660 3 \n", "260 62.048720 0.290050 3 \n", "261 62.193314 0.278944 3 \n", "262 62.104207 0.497613 3 \n", "263 62.135855 0.387740 3 \n", "\n", "[264 rows x 24 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# This cell takes about 1.5 minutes...\n", "\n", "query = \"\"\"SELECT * FROM dp02_dc2_catalogs.TruthSummary \n", " WHERE \n", " truth_type=3 \n", " AND \n", " ra BETWEEN 62.0 AND 62.5\n", " AND\n", " dec BETWEEN -37.5 AND -37.0\n", " \"\"\"\n", "\n", "print(query)\n", "\n", "results = service.search(query)\n", "df_ts3 = results.to_table().to_pandas()\n", "del results\n", "\n", "df_ts3" ] }, { "cell_type": "markdown", "id": "c2310b3d-ea45-4094-ae11-885272e62aa9", "metadata": {}, "source": [ "### 2.3 Download all SNe from DiaObject table within an RA/DEC box of within a box 62.0\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
radecldiaObjectidrPSFluxMax
062.080433-37.33528815679563577667168591135.428626
162.167144-37.1842041567956357766718341NaN
262.038234-37.3607911567956357766718331-510.689848
362.179867-37.1660551567956357766718218NaN
462.089113-37.3420711567956357766716858NaN
...............
4530662.132071-37.0005911651281746966088669NaN
4530762.131649-37.0017381651281746966088668NaN
4530862.010890-37.0002471651281746966089411NaN
4530962.152797-37.00173316512817469660892202517.761732
4531062.169666-37.0017461651281746966089166NaN
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45311 rows × 4 columns

\n", "" ], "text/plain": [ " ra decl diaObjectid rPSFluxMax\n", "0 62.080433 -37.335288 1567956357766716859 1135.428626\n", "1 62.167144 -37.184204 1567956357766718341 NaN\n", "2 62.038234 -37.360791 1567956357766718331 -510.689848\n", "3 62.179867 -37.166055 1567956357766718218 NaN\n", "4 62.089113 -37.342071 1567956357766716858 NaN\n", "... ... ... ... ...\n", "45306 62.132071 -37.000591 1651281746966088669 NaN\n", "45307 62.131649 -37.001738 1651281746966088668 NaN\n", "45308 62.010890 -37.000247 1651281746966089411 NaN\n", "45309 62.152797 -37.001733 1651281746966089220 2517.761732\n", "45310 62.169666 -37.001746 1651281746966089166 NaN\n", "\n", "[45311 rows x 4 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "# This cell takes about half a minute...\n", "\n", "query = \"\"\"SELECT ra, decl, diaObjectid, rPSFluxMax FROM dp02_dc2_catalogs.DiaObject\n", " WHERE \n", " ra BETWEEN 62.0 AND 62.5\n", " AND\n", " decl BETWEEN -37.5 AND -37.0\n", " \"\"\"\n", "\n", "print(query)\n", "\n", "results = service.search(query)\n", "df_do = results.to_table().to_pandas()\n", "del results\n", "\n", "df_do" ] }, { "cell_type": "markdown", "id": "d0be2694-be51-4185-b3d4-2425460d82bd", "metadata": {}, "source": [ "## 3.0 Matching the DiaObject table with the TruthSummary table" ] }, { "cell_type": "markdown", "id": "36daa033-0a87-4dce-a9e0-94c09673e776", "metadata": {}, "source": [ "Unfortunately, at the current time, there is no equivalent of the **MatchesTruth** table for the Dia tables. The **MatchesTruth** table only has matches to the **Objects** table. (The `match_objectid` column in the **MatchesTruth** table holds the `objectid` from the corresponding entry in the **Objects** table.)\n", "\n", "Until there is a equivalent match id for Dia objects (or Dia sources), we must do the match ourselves. An imperfect -- but still pretty good - means of matching is to match healpixel values for the RAs/DECs of the entries in the Dia table and the **TruthSummary** table.\n", "\n", "In the following, we will do this for a subsamples of the **DiaObjects** and the **TruthSummary** tables we downloaded above (i.e., for a box 62.0this link:" ] }, { "cell_type": "code", "execution_count": 8, "id": "826e580e-e46e-4f86-af2e-448bb83c317b", "metadata": {}, "outputs": [], "source": [ "# Healpix tools.\n", "# From Sahar Allam (2016.07.08)\n", "\n", "##################################\n", "def radec2thetaphi(ra, dec):\n", " import numpy as np\n", " return (90-dec)*np.pi/180., ra*np.pi/180.\n", "\n", "##################################\n", "#DESDM uses nside=128, nest=True\n", "#Alex Drlica Wagner's healpixelated Gaia DR2 on des40 uses nside=32, nest=False\n", "def getipix(nside,ra,dec,nest=True):\n", " import healpy as hp\n", " theta, phi = radec2thetaphi(ra, dec)\n", " ipix = hp.pixelfunc.ang2pix(nside, theta, phi, nest)\n", " return ipix\n", "\n", "##################################\n" ] }, { "cell_type": "markdown", "id": "c50dc374-776a-4555-82df-92ceed9fd58d", "metadata": {}, "source": [ "To do a suitable match, we want to chose a healpixel area that is pretty small. A healpixel of `NSIDES` = 2^17 seems not unreasonable:" ] }, { "cell_type": "code", "execution_count": 9, "id": "82505edb-fcc8-472a-8aff-55e11e5091a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "131072 2.593337450489216 1.6103842555394088\n" ] } ], "source": [ "nside=2**17\n", "# Area in sq arcsec:\n", "area = 3600.*3600.*hp.pixelfunc.nside2pixarea(nside, degrees=True)\n", "# A rough measure of length in arcsec: \n", "length = math.sqrt(area)\n", "print(nside, area, length)" ] }, { "cell_type": "markdown", "id": "5da7fdaf-3636-41dc-86bd-ceb88c653c3b", "metadata": {}, "source": [ "Let's add an \"`NSIDES`=2^17\" healpixel column to our **TruthSummary** DataFrame:" ] }, { "cell_type": "code", "execution_count": 10, "id": "8f03718e-cfc2-450e-8dcf-b25062b702e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cosmodc2_hpcosmodc2_iddecflux_gflux_g_noMWflux_iflux_i_noMWflux_rflux_r_noMWflux_u...host_galaxyidid_truth_typeis_pointsourceis_variablemag_rraredshifttruth_typeHPX_2_17
0-1-1-37.0002590.00.00.00.00.00.00.0...82332hl_ms_82332hl_ms_82332_311NaN62.4379561.3776103144409004876
1-1-1-37.0465160.00.00.00.00.00.00.0...19758hl_ms_19758hl_ms_19758_311NaN62.4397390.7469063144408279889
2-1-1-37.0419320.00.00.00.00.00.00.0...7938033031MS_9814_1119MS_9814_1119_311NaN62.4455750.5723403144408280312
3-1-1-37.0232060.00.00.00.00.00.00.0...7938864126MS_9814_2658MS_9814_2658_311NaN62.4549930.8069763144408980376
4-1-1-37.0575000.00.00.00.00.00.00.0...7938862386MS_9814_2477MS_9814_2477_311NaN62.4612440.7813363144408278108
..................................................................
259-1-1-37.0095320.00.00.00.00.00.00.0...7938517422MS_9814_2054MS_9814_2054_311NaN62.4210230.7346603144408305593
260-1-1-37.0086730.00.00.00.00.00.00.0...7937580820MS_9814_174MS_9814_174_311NaN62.0487200.2900503144408572904
261-1-1-37.0056570.00.00.00.00.00.00.0...7937581805MS_9814_121MS_9814_121_311NaN62.1933140.2789443144408724735
262-1-1-37.0055820.00.00.00.00.00.00.0...7937875757MS_9814_693MS_9814_693_311NaN62.1042070.4976133144408741459
263-1-1-37.0009540.00.00.00.00.00.00.0...2957hl_ms_2957hl_ms_2957_311NaN62.1358550.3877403144408722563
\n", "

264 rows × 25 columns

\n", "
" ], "text/plain": [ " cosmodc2_hp cosmodc2_id dec flux_g flux_g_noMW flux_i \\\n", "0 -1 -1 -37.000259 0.0 0.0 0.0 \n", "1 -1 -1 -37.046516 0.0 0.0 0.0 \n", "2 -1 -1 -37.041932 0.0 0.0 0.0 \n", "3 -1 -1 -37.023206 0.0 0.0 0.0 \n", "4 -1 -1 -37.057500 0.0 0.0 0.0 \n", ".. ... ... ... ... ... ... \n", "259 -1 -1 -37.009532 0.0 0.0 0.0 \n", "260 -1 -1 -37.008673 0.0 0.0 0.0 \n", "261 -1 -1 -37.005657 0.0 0.0 0.0 \n", "262 -1 -1 -37.005582 0.0 0.0 0.0 \n", "263 -1 -1 -37.000954 0.0 0.0 0.0 \n", "\n", " flux_i_noMW flux_r flux_r_noMW flux_u ... host_galaxy id \\\n", "0 0.0 0.0 0.0 0.0 ... 82332 hl_ms_82332 \n", "1 0.0 0.0 0.0 0.0 ... 19758 hl_ms_19758 \n", "2 0.0 0.0 0.0 0.0 ... 7938033031 MS_9814_1119 \n", "3 0.0 0.0 0.0 0.0 ... 7938864126 MS_9814_2658 \n", "4 0.0 0.0 0.0 0.0 ... 7938862386 MS_9814_2477 \n", ".. ... ... ... ... ... ... ... \n", "259 0.0 0.0 0.0 0.0 ... 7938517422 MS_9814_2054 \n", "260 0.0 0.0 0.0 0.0 ... 7937580820 MS_9814_174 \n", "261 0.0 0.0 0.0 0.0 ... 7937581805 MS_9814_121 \n", "262 0.0 0.0 0.0 0.0 ... 7937875757 MS_9814_693 \n", "263 0.0 0.0 0.0 0.0 ... 2957 hl_ms_2957 \n", "\n", " id_truth_type is_pointsource is_variable mag_r ra redshift \\\n", "0 hl_ms_82332_3 1 1 NaN 62.437956 1.377610 \n", "1 hl_ms_19758_3 1 1 NaN 62.439739 0.746906 \n", "2 MS_9814_1119_3 1 1 NaN 62.445575 0.572340 \n", "3 MS_9814_2658_3 1 1 NaN 62.454993 0.806976 \n", "4 MS_9814_2477_3 1 1 NaN 62.461244 0.781336 \n", ".. ... ... ... ... ... ... \n", "259 MS_9814_2054_3 1 1 NaN 62.421023 0.734660 \n", "260 MS_9814_174_3 1 1 NaN 62.048720 0.290050 \n", "261 MS_9814_121_3 1 1 NaN 62.193314 0.278944 \n", "262 MS_9814_693_3 1 1 NaN 62.104207 0.497613 \n", "263 hl_ms_2957_3 1 1 NaN 62.135855 0.387740 \n", "\n", " truth_type HPX_2_17 \n", "0 3 144409004876 \n", "1 3 144408279889 \n", "2 3 144408280312 \n", "3 3 144408980376 \n", "4 3 144408278108 \n", ".. ... ... \n", "259 3 144408305593 \n", "260 3 144408572904 \n", "261 3 144408724735 \n", "262 3 144408741459 \n", "263 3 144408722563 \n", "\n", "[264 rows x 25 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_ts3.loc[:,'HPX_2_17'] = getipix(nside, df_ts3.loc[:,'ra'].values, df_ts3.loc[:,'dec'].values)\n", "df_ts3" ] }, { "cell_type": "markdown", "id": "7e48d78f-f2d3-44bf-bbbc-d84f0195c4c9", "metadata": {}, "source": [ "Likewise, let's add an \"`NSIDES`=2^17\" healpixel column to our **DiaObjects** DataFrame:" ] }, { "cell_type": "code", "execution_count": 12, "id": "7fbeb38d-7a11-4136-87d6-d3bab8282fee", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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radecldiaObjectidrPSFluxMaxHPX_2_17
062.080433-37.33528815679563577667168591135.428626144407968279
162.167144-37.1842041567956357766718341NaN144408404242
262.038234-37.3607911567956357766718331-510.689848144405517493
362.179867-37.1660551567956357766718218NaN144408405375
462.089113-37.3420711567956357766716858NaN144407966415
..................
4530662.132071-37.0005911651281746966088669NaN144408721914
4530762.131649-37.0017381651281746966088668NaN144408721902
4530862.010890-37.0002471651281746966089411NaN144414166096
4530962.152797-37.00173316512817469660892202517.761732144408721217
4531062.169666-37.0017461651281746966089166NaN144408726315
\n", "

45311 rows × 5 columns

\n", "
" ], "text/plain": [ " ra decl diaObjectid rPSFluxMax HPX_2_17\n", "0 62.080433 -37.335288 1567956357766716859 1135.428626 144407968279\n", "1 62.167144 -37.184204 1567956357766718341 NaN 144408404242\n", "2 62.038234 -37.360791 1567956357766718331 -510.689848 144405517493\n", "3 62.179867 -37.166055 1567956357766718218 NaN 144408405375\n", "4 62.089113 -37.342071 1567956357766716858 NaN 144407966415\n", "... ... ... ... ... ...\n", "45306 62.132071 -37.000591 1651281746966088669 NaN 144408721914\n", "45307 62.131649 -37.001738 1651281746966088668 NaN 144408721902\n", "45308 62.010890 -37.000247 1651281746966089411 NaN 144414166096\n", "45309 62.152797 -37.001733 1651281746966089220 2517.761732 144408721217\n", "45310 62.169666 -37.001746 1651281746966089166 NaN 144408726315\n", "\n", "[45311 rows x 5 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_do.loc[:,'HPX_2_17'] = getipix(nside, df_do.loc[:,'ra'].values, df_do.loc[:,'decl'].values)\n", "df_do" ] }, { "cell_type": "markdown", "id": "4f3c7174-068e-4b19-9795-4ea7fbf4b7c0", "metadata": {}, "source": [ "Now, let's match both DataFrames using an inner join on their `HPX_2_17` columns:" ] }, { "cell_type": "code", "execution_count": 15, "id": "f7ac7002-d85d-42d7-b6e7-f12beaa8629d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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80 rows × 29 columns

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redshift truth_type HPX_2_17 ra_y decl \\\n", "0 62.445575 0.572340 3 144408280312 62.445568 -37.041935 \n", "1 62.472898 0.751813 3 144408977709 62.472855 -37.042497 \n", "2 62.483956 0.584056 3 144408973002 62.483939 -37.038764 \n", "3 62.485471 0.562078 3 144408982215 62.485479 -37.015396 \n", "4 62.047917 0.563353 3 144405118588 62.048010 -37.460026 \n", "5 62.192780 0.566719 3 144408046369 62.192496 -37.213724 \n", "6 62.192780 0.566719 3 144408046369 62.192496 -37.213724 \n", "7 62.027592 0.317800 3 144405142566 62.027555 -37.447466 \n", "8 62.027592 0.317800 3 144405142566 62.027559 -37.447336 \n", "9 62.103504 0.339279 3 144408344154 62.103356 -37.239311 \n", "10 62.103504 0.339279 3 144408344154 62.103360 -37.239054 \n", "11 62.103504 0.339279 3 144408344154 62.103484 -37.239204 \n", "12 62.024163 0.399881 3 144408354501 62.024164 -37.249437 \n", "13 62.012834 0.615240 3 144405148100 62.012816 -37.437378 \n", "14 62.005189 0.628250 3 144405135506 62.005197 -37.492241 \n", "15 62.125387 0.502520 3 144408382442 62.125411 -37.230280 \n", "16 62.074809 0.488349 3 144408370199 62.074780 -37.212242 \n", "17 62.074809 0.488349 3 144408370199 62.074780 -37.212242 \n", "18 62.185995 0.653902 3 144408396615 62.186133 -37.199256 \n", "19 62.185995 0.653902 3 144408396615 62.186133 -37.199256 \n", "20 62.034337 0.675599 3 144405519820 62.034246 -37.342822 \n", "21 62.034337 0.675599 3 144405519820 62.034115 -37.342667 \n", "22 62.034337 0.675599 3 144405519820 62.034480 -37.342733 \n", "23 62.034337 0.675599 3 144405519820 62.034317 -37.342555 \n", "24 62.004246 0.750275 3 144405525056 62.004231 -37.347190 \n", "25 62.136690 0.689956 3 144408417655 62.136680 -37.166950 \n", "26 62.136690 0.689956 3 144408417655 62.136680 -37.166950 \n", "27 62.226727 0.894265 3 144408576013 62.226713 -37.166605 \n", "28 62.226727 0.894265 3 144408576013 62.226713 -37.166605 \n", "29 62.176425 0.780161 3 144408399371 62.176361 -37.184700 \n", "30 62.176425 0.780161 3 144408399371 62.176496 -37.184447 \n", "31 62.176425 0.780161 3 144408399371 62.176361 -37.184700 \n", "32 62.176425 0.780161 3 144408399371 62.176496 -37.184447 \n", "33 62.105562 0.341483 3 144408439027 62.105560 -37.124043 \n", "34 62.077799 0.466509 3 144408518037 62.077788 -37.110492 \n", "35 62.171538 0.312754 3 144408623152 62.171540 -37.060105 \n", "36 62.299676 0.822819 3 144408234513 62.299674 -37.149417 \n", "37 62.450231 0.919968 3 144408164067 62.450317 -37.142674 \n", "38 62.353230 0.307606 3 144408214001 62.353240 -37.141614 \n", "39 62.353230 0.307606 3 144408214001 62.353241 -37.141921 \n", "40 62.353230 0.307606 3 144408214001 62.353209 -37.141776 \n", "41 62.452150 0.474149 3 144408165738 62.452148 -37.134009 \n", "42 62.272238 0.650945 3 144408595850 62.272247 -37.088808 \n", "43 62.497681 0.480296 3 144408168145 62.497669 -37.143347 \n", "44 62.497681 0.480296 3 144408168145 62.497485 -37.143353 \n", "45 62.427570 0.473627 3 144408164998 62.427581 -37.147007 \n", "46 62.490527 0.468090 3 144408129584 62.490533 -37.212426 \n", "47 62.490527 0.468090 3 144408129584 62.490533 -37.212426 \n", "48 62.490527 0.468090 3 144408129584 62.490533 -37.212426 \n", "49 62.396398 0.964176 3 144408064521 62.396431 -37.302028 \n", "50 62.441720 0.301798 3 144406430312 62.441721 -37.485969 \n", "51 62.441720 0.301798 3 144406430312 62.441721 -37.485969 \n", "52 62.418985 0.224556 3 144406463795 62.418987 -37.428685 \n", "53 62.428727 0.224568 3 144406657569 62.428728 -37.336575 \n", "54 62.245446 0.933222 3 144408014014 62.245513 -37.254315 \n", "55 62.309042 0.864500 3 144407906755 62.309068 -37.331739 \n", "56 62.305480 0.335230 3 144407811863 62.305621 -37.446762 \n", "57 62.305480 0.335230 3 144407811863 62.305356 -37.446845 \n", "58 62.269644 0.387474 3 144408005863 62.269822 -37.276959 \n", "59 62.269644 0.387474 3 144408005863 62.270020 -37.276879 \n", "60 62.401146 0.781175 3 144406475676 62.401160 -37.385486 \n", "61 62.254881 0.751594 3 144407991835 62.254715 -37.312056 \n", "62 62.360607 0.911748 3 144408086155 62.360360 -37.287327 \n", "63 62.256482 0.896927 3 144407795266 62.256197 -37.499465 \n", "64 62.230838 0.434530 3 144407998877 62.231118 -37.296764 \n", "65 62.230838 0.434530 3 144407998877 62.230854 -37.297025 \n", "66 62.312380 0.454146 3 144408182936 62.312363 -37.257355 \n", "67 62.429520 0.618687 3 144406657778 62.429521 -37.329135 \n", "68 62.122948 0.342278 3 144408709739 62.122959 -37.051587 \n", "69 62.111003 0.342278 3 144408715824 62.111011 -37.042936 \n", "70 62.274045 0.769000 3 144408608152 62.274029 -37.040674 \n", "71 62.134099 0.310650 3 144408710903 62.133932 -37.035451 \n", "72 62.134099 0.310650 3 144408710903 62.134104 -37.035447 \n", "73 62.182367 0.248282 3 144408625045 62.182583 -37.034453 \n", "74 62.182367 0.248282 3 144408625045 62.182196 -37.034521 \n", "75 62.424241 0.618320 3 144408304643 62.424232 -37.027649 \n", "76 62.157860 0.633101 3 144408714828 62.157878 -37.018053 \n", "77 62.419941 0.734660 3 144408305363 62.419914 -37.014393 \n", "78 62.421023 0.734660 3 144408305593 62.421038 -37.009520 \n", "79 62.048720 0.290050 3 144408572904 62.048707 -37.008681 \n", "\n", " diaObjectid rPSFluxMax \n", "0 1651211378221911496 1656.389908 \n", "1 1651211378221910651 767.806607 \n", "2 1651211378221916223 2272.397393 \n", "3 1651211378221912111 1729.674228 \n", "4 1567894785115556658 NaN \n", "5 1567956357766716978 NaN \n", "6 1651220174314938117 NaN \n", "7 1567894785115555725 NaN \n", "8 1567894785115560713 1508.113946 \n", "9 1567956357766717633 NaN \n", "10 1567956357766716664 -1049.063493 \n", "11 1567956357766711480 1705.895895 \n", "12 1567956357766711508 3665.267373 \n", "13 1567894785115554039 1235.785458 \n", "14 1567894785115556857 1509.608676 \n", "15 1567956357766709341 NaN \n", "16 1567956357766715308 755.632451 \n", "17 1651220174314936272 755.632451 \n", "18 1567956357766710315 NaN \n", "19 1651220174314931224 NaN \n", "20 1567956357766719761 NaN \n", "21 1567956357766713195 NaN \n", "22 1567956357766714681 NaN \n", "23 1567956357766712522 NaN \n", "24 1567956357766713884 1315.820783 \n", "25 1567956357766718487 1617.539000 \n", "26 1651220174314939608 1617.539000 \n", "27 1567956357766714035 -1049.933899 \n", "28 1651220174314934985 -1049.933899 \n", "29 1567956357766712579 -64.993871 \n", "30 1567956357766712036 NaN \n", "31 1651220174314933442 -64.993871 \n", "32 1651220174314932945 NaN \n", "33 1651220174314932090 6245.473956 \n", "34 1651220174314939017 3146.496318 \n", "35 1651220174314933625 6138.951425 \n", "36 1651211378221909625 NaN \n", "37 1651211378221908500 NaN \n", "38 1651211378221908269 -267.911756 \n", "39 1651211378221911211 NaN \n", "40 1651211378221911072 NaN \n", "41 1651211378221914585 1835.754491 \n", "42 1651211378221913853 NaN \n", "43 1651211378221909695 2084.873311 \n", "44 1651211378221908752 -654.915488 \n", "45 1651211378221911225 1849.622773 \n", "46 1568431346789912126 3536.657114 \n", "47 1567947561673692577 3536.657114 \n", "48 1651211378221913368 3536.657114 \n", "49 1567947561673693269 NaN \n", "50 1568369774138759584 1579.606121 \n", "51 1567885989022536358 1579.606121 \n", "52 1567885989022535012 NaN \n", "53 1567947561673690264 3762.987720 \n", "54 1567956357766714677 NaN \n", "55 1567947561673688971 NaN \n", "56 1567885989022532878 -864.031823 \n", "57 1567885989022534981 NaN \n", "58 1567947561673693500 2872.743555 \n", "59 1567947561673690185 NaN \n", "60 1567885989022532930 NaN \n", "61 1567947561673694054 NaN \n", "62 1567947561673691933 NaN \n", "63 1567885989022534242 NaN \n", "64 1567956357766716845 NaN \n", "65 1567956357766714414 -4555.054456 \n", "66 1567947561673695653 3368.038983 \n", "67 1567947561673692750 1054.027100 \n", "68 1651220174314941537 5414.301191 \n", "69 1651220174314930324 1399.221022 \n", "70 1651211378221912780 1328.563624 \n", "71 1651220174314934956 978.972191 \n", "72 1651220174314932295 74.602104 \n", "73 1651220174314935091 NaN \n", "74 1651220174314939765 NaN \n", "75 1651211378221913815 1130.354409 \n", "76 1651220174314937552 1380.554292 \n", "77 1651211378221916279 NaN \n", "78 1651211378221910649 1392.815386 \n", "79 1651220174314931923 1276.575213 \n", "\n", "[80 rows x 29 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged = pd.merge(df_ts3, df_do, how='inner', on=['HPX_2_17','HPX_2_17'])\n", "df_merged" ] }, { "cell_type": "markdown", "id": "09278d75-5803-447c-b77f-8cf6ff1e30e0", "metadata": {}, "source": [ "How do the RA's/DEC's from the two merged tables compare?" ] }, { "cell_type": "code", "execution_count": 16, "id": "453c4230-210c-4949-83b7-52302f854e28", "metadata": {}, "outputs": [], "source": [ "df_merged['diff_ra'] = 3600.*(df_merged['ra_x'] - df_merged['ra_y'])\n", "df_merged['diff_dec'] = 3600.*(df_merged['dec'] - df_merged['decl'])" ] }, { "cell_type": "code", "execution_count": 17, "id": "c91ca2a2-8c39-457f-90fa-6b122c15d88a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_merged.hist('diff_ra')" ] }, { "cell_type": "code", "execution_count": 18, "id": "5d12dd48-785c-4fca-a210-f9bee157e743", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_merged.hist('diff_dec')" ] }, { "cell_type": "markdown", "id": "f0ea3e08-1011-4514-8d9a-727647da38ff", "metadata": {}, "source": [ "Not too bad. Contamination is probably relatively low, and purity is probably relatively high. For better results, an improved sky matching algorithm can be employed." ] }, { "cell_type": "code", "execution_count": null, "id": "bf00d63e-8863-4e38-a157-9d51a77974a9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "LSST", "language": "python", "name": "lsst" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5" } }, "nbformat": 4, "nbformat_minor": 5 }