diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..390585a --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +notebooks/.ipynb_checkpoints/ +alercereaduser.json +alerceuser.json + diff --git a/README.md b/README.md index 55ea509..4fadbc2 100644 --- a/README.md +++ b/README.md @@ -1 +1,21 @@ -# usecases + + +# ALeRCE use cases + +A collection of jupyter notebooks and scripts on how to access the ALeRCE database, API, etc. + +## Context + +The ALeRCE broker is a Chilean-led initiative to build a community broker for LSST and other large etendue survey telescopes which produce public streams of alerts. ALeRCE is developed by many institutions in Chile and in the U.S., taking advantage of the large network of collaborators which we have established around data science and astronomy during the last decade. The main motivation of ALeRCE is to facilitate the follow-up and the exploration of the LSST and other observatories alert streams. + +## Scientific aim + +ALeRCE aims to facilitate the study of stationary (non--moving) variable and transient objects. We will do this by providing real-time filtered streams of aggregated, annotated and classified alerts, but also by providing alert exploration and analysis tools that can help researchers look for patterns and outliers within large populations of events. We also aim to provide forecasting tools which can help with the optimization of follow--up resources. + +## This repository + +In this repository we show how to access the ALeRCE ZTF database, focused on different science cases. You can find several introductory jupyter notebooks in the [notebooks](https://github.com/alercebroker/usecases/tree/master/notebooks) directory, which connect to the database, query some tables and does some processing and visualization of the data. If you would like to beta test this please request a json credentials file to francisco dot forster at gmail dot com. + +## How can I contribute? + +The success of ALeRCE depends on being able to build a community of users which can connect LSST with the different follow up resources. Therefore, we are very happy to receive and publish contributed notebooks in this repository showing how your science case can benefit from having a real-time access to an annotated and classified database of ZTF alerts. Please feel free to join this repository and add your science cases! diff --git a/example_data/agn_catalog.pickle b/example_data/agn_catalog.pickle new file mode 100644 index 0000000..1e8b1c4 Binary files /dev/null and b/example_data/agn_catalog.pickle differ diff --git a/example_data/catalina.pkl b/example_data/catalina.pkl new file mode 100644 index 0000000..7971054 Binary files /dev/null and b/example_data/catalina.pkl differ diff --git a/notebooks/ALeRCE_ZTF_SupernovaUseCase.ipynb b/notebooks/ALeRCE_ZTF_SupernovaUseCase.ipynb new file mode 100644 index 0000000..93cf733 --- /dev/null +++ b/notebooks/ALeRCE_ZTF_SupernovaUseCase.ipynb @@ -0,0 +1,3299 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supernova use case notebook\n", + "\n", + "ALeRCE starter notebook for supernova science.\n", + "\n", + "You will need to install psycopg2 and astroquery.\n", + "\n", + "In this notebook you will connect to the ALeRCE database, download some objects with probability greater than 0.7 of being supernova, get selected light curves and plot, as well as querying NED for galaxy crossmatches." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load libraries\n", + "\n", + "*External dependencies*:\n", + "\n", + "psycopg2: pip install psycopg2-binary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:02.405315Z", + "start_time": "2019-06-02T15:47:01.582902Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "from astroquery.ned import Ned # pip install astroquery\n", + "import astropy.units as u\n", + "from astropy import coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:02.410611Z", + "start_time": "2019-06-02T15:47:02.406966Z" + } + }, + "outputs": [], + "source": [ + "import ipyaladin as ipyal # see installation instructions here: https://github.com/cds-astro/ipyaladin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get credentials (not in github repository)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:02.424364Z", + "start_time": "2019-06-02T15:47:02.411808Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "credentials_file = \"../alercereaduser.json\"\n", + "with open(credentials_file) as jsonfile:\n", + " params = json.load(jsonfile)[\"params\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to DB" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:04.433387Z", + "start_time": "2019-06-02T15:47:02.425927Z" + } + }, + "outputs": [], + "source": [ + "conn = psycopg2.connect(dbname=params['dbname'], user=params['user'], host=params['host'], password=params['password'])\n", + "cur = conn.cursor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show all the available tables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:05.253769Z", + "start_time": "2019-06-02T15:47:04.438603Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('class',), ('asassn',), ('crtsnorth',), ('crtssouth',), ('detections',), ('objects',), ('probabilities',), ('xmatch',), ('features',), ('linear',), ('tns',), ('magref',), ('non_detections',), ('tmp',)]\n" + ] + } + ], + "source": [ + "query = \"select tablename from pg_tables where schemaname='public';\"\n", + "\n", + "cur.execute(query)\n", + "tables = cur.fetchall()\n", + "\n", + "print(tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For each table, show column names and column types" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:15.735695Z", + "start_time": "2019-06-02T15:47:05.258914Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0classidinteger
1classnamecharacter varying
0asassnASAS-SN Nametext
1asassnOther Namestext
2asassnLCIDinteger
3asassnradouble precision
4asassndecdouble precision
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6asassnAmplitudedouble precision
7asassnPerioddouble precision
8asassnTypetext
9asassnUrltext
10asassnReferencetext
11asassnDistdouble precision
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13asassnParallax Errordouble precision
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15asassnBpmagdouble precision
16asassnRpmagdouble precision
17asassnJmagdouble precision
18asassnHmagdouble precision
19asassnKmagdouble precision
20asassnW1magdouble precision
21asassnW2magdouble precision
22asassnW3magdouble precision
23asassnW4magdouble precision
24asassnBP-RRdouble precision
25asassnJ-Kdouble precision
26asassnW1-W2double precision
27asassnW3-W4double precision
28asassnSllk Statisticdouble precision
29asassnRF Regression Scoredouble precision
30asassnClassification Probabilitydouble precision
31asassnEpoch (HJD)double precision
0crtsnorthCatalina_Surveys_IDtext
1crtsnorthNumerical_IDbigint
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3crtsnorthPeriod_(days)double precision
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5crtsnorthNumber_Obsinteger
6crtsnorthVar_Typeinteger
7crtsnorthradouble precision
8crtsnorthdecdouble precision
0crtssouthSSS_IDtext
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3crtssouthdecdouble precision
4crtssouthPerioddouble precision
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6crtssouthNptsinteger
7crtssouthV_ampdouble precision
8crtssouthTypeinteger
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4detectionsfidsmallint
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6detectionsmagpsfdouble precision
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8detectionssigmapsfdouble precision
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10detectionsradouble precision
11detectionsdecdouble precision
12detectionssigmaradouble precision
13detectionssigmadecdouble precision
14detectionsisdiffpossmallint
15detectionsdistpsnr1double precision
16detectionssgscore1double precision
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24detectionssigmapsf_corrdouble precision
25detectionssigmagap_corrdouble precision
0objectsidinteger
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6objectsmedian_magap_rdouble precision
7objectsmax_magap_gdouble precision
8objectsmax_magap_rdouble precision
9objectsmin_magap_gdouble precision
10objectsmin_magap_rdouble precision
11objectssigma_magap_gdouble precision
12objectssigma_magap_rdouble precision
13objectslast_magap_gdouble precision
14objectslast_magap_rdouble precision
15objectsfirst_magap_gdouble precision
16objectsfirst_magap_rdouble precision
17objectsmean_magpsf_gdouble precision
18objectsmean_magpsf_rdouble precision
19objectsmedian_magpsf_gdouble precision
20objectsmedian_magpsf_rdouble precision
21objectsmax_magpsf_gdouble precision
22objectsmax_magpsf_rdouble precision
23objectsmin_magpsf_gdouble precision
24objectsmin_magpsf_rdouble precision
25objectssigma_magpsf_gdouble precision
26objectssigma_magpsf_rdouble precision
27objectslast_magpsf_gdouble precision
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29objectsfirst_magpsf_gdouble precision
30objectsfirst_magpsf_rdouble precision
31objectsmeanradouble precision
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35objectsdeltajddouble precision
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6probabilitiesrrl_probdouble precision
7probabilitiessne_probdouble precision
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3xmatchobject_idinteger
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6xmatchclasscharacter varying
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2featuresandersondarling_1double precision
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6featurescar_mean_1double precision
7featurescar_tau_1double precision
8featurescon_1double precision
9featureseta_e_1double precision
10featuresgskew_1double precision
11featuresmaxslope_1double precision
12featuresmean_1double precision
13featuresmeanvariance_1double precision
14featuresmedianabsdev_1double precision
15featuresmedianbrp_1double precision
16featurespairslopetrend_1double precision
17featurespercentamplitude_1double precision
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19featuresperiodls_1double precision
20featuresperiod_fit_1double precision
21featurespsi_cs_1double precision
22featurespsi_eta_1double precision
23featuresrcs_1double precision
24featuresskew_1double precision
25featuressmallkurtosis_1double precision
26featuresstd_1double precision
27featuresstetsonk_1double precision
28featuresfreq1_harmonics_amplitude_0_1double precision
29featuresfreq1_harmonics_rel_phase_0_1double precision
30featuresfreq1_harmonics_amplitude_1_1double precision
31featuresfreq1_harmonics_rel_phase_1_1double precision
32featuresfreq1_harmonics_amplitude_2_1double precision
33featuresfreq1_harmonics_rel_phase_2_1double precision
34featuresfreq1_harmonics_amplitude_3_1double precision
35featuresfreq1_harmonics_rel_phase_3_1double precision
36featuresfreq2_harmonics_amplitude_0_1double precision
37featuresfreq2_harmonics_rel_phase_0_1double precision
38featuresfreq2_harmonics_amplitude_1_1double precision
39featuresfreq2_harmonics_rel_phase_1_1double precision
40featuresfreq2_harmonics_amplitude_2_1double precision
41featuresfreq2_harmonics_rel_phase_2_1double precision
42featuresfreq2_harmonics_amplitude_3_1double precision
43featuresfreq2_harmonics_rel_phase_3_1double precision
44featuresfreq3_harmonics_amplitude_0_1double precision
45featuresfreq3_harmonics_rel_phase_0_1double precision
46featuresfreq3_harmonics_amplitude_1_1double precision
47featuresfreq3_harmonics_rel_phase_1_1double precision
48featuresfreq3_harmonics_amplitude_2_1double precision
49featuresfreq3_harmonics_rel_phase_2_1double precision
50featuresfreq3_harmonics_amplitude_3_1double precision
51featuresfreq3_harmonics_rel_phase_3_1double precision
52featuresn_samples_2double precision
53featuresamplitude_2double precision
54featuresandersondarling_2double precision
55featuresautocor_length_2double precision
56featuresbeyond1std_2double precision
57featurescar_sigma_2double precision
58featurescar_mean_2double precision
59featurescar_tau_2double precision
60featurescon_2double precision
61featureseta_e_2double precision
62featuresgskew_2double precision
63featuresmaxslope_2double precision
64featuresmean_2double precision
65featuresmeanvariance_2double precision
66featuresmedianabsdev_2double precision
67featuresmedianbrp_2double precision
68featurespairslopetrend_2double precision
69featurespercentamplitude_2double precision
70featuresq31_2double precision
71featuresperiodls_2double precision
72featuresperiod_fit_2double precision
73featurespsi_cs_2double precision
74featurespsi_eta_2double precision
75featuresrcs_2double precision
76featuresskew_2double precision
77featuressmallkurtosis_2double precision
78featuresstd_2double precision
79featuresstetsonk_2double precision
80featuresfreq1_harmonics_amplitude_0_2double precision
81featuresfreq1_harmonics_rel_phase_0_2double precision
82featuresfreq1_harmonics_amplitude_1_2double precision
83featuresfreq1_harmonics_rel_phase_1_2double precision
84featuresfreq1_harmonics_amplitude_2_2double precision
85featuresfreq1_harmonics_rel_phase_2_2double precision
86featuresfreq1_harmonics_amplitude_3_2double precision
87featuresfreq1_harmonics_rel_phase_3_2double precision
88featuresfreq2_harmonics_amplitude_0_2double precision
89featuresfreq2_harmonics_rel_phase_0_2double precision
90featuresfreq2_harmonics_amplitude_1_2double precision
91featuresfreq2_harmonics_rel_phase_1_2double precision
92featuresfreq2_harmonics_amplitude_2_2double precision
93featuresfreq2_harmonics_rel_phase_2_2double precision
94featuresfreq2_harmonics_amplitude_3_2double precision
95featuresfreq2_harmonics_rel_phase_3_2double precision
96featuresfreq3_harmonics_amplitude_0_2double precision
97featuresfreq3_harmonics_rel_phase_0_2double precision
98featuresfreq3_harmonics_amplitude_1_2double precision
99featuresfreq3_harmonics_rel_phase_1_2double precision
100featuresfreq3_harmonics_amplitude_2_2double precision
101featuresfreq3_harmonics_rel_phase_2_2double precision
102featuresfreq3_harmonics_amplitude_3_2double precision
103featuresfreq3_harmonics_rel_phase_3_2double precision
104featuresgal_bdouble precision
105featuresgal_ldouble precision
106featuresobject_idinteger
107featuresn_samples_1double precision
0linearLINEARobjectIDbigint
1linearLCtypeinteger
2linearPdouble precision
3linearAdouble precision
4linearmmeddouble precision
5linearstdevdouble precision
6linearrmsdouble precision
7linearLchi2pdfdouble precision
8linearnObsinteger
9linearskewdouble precision
10linearkurtdouble precision
11linearLRdouble precision
12linearCUFinteger
13lineart2integer
14lineart3integer
15linearradouble precision
16lineardecdouble precision
17linearoTypeinteger
18linearnSinteger
19linearrExtdouble precision
20linearudouble precision
21lineargdouble precision
22linearrdouble precision
23linearidouble precision
24linearzdouble precision
25linearuErrdouble precision
26lineargErrdouble precision
27linearrErrdouble precision
28lineariErrdouble precision
29linearzErrdouble precision
0tnsNametext
1tnsRA_origtext
2tnsDEC_origtext
3tnsObj. Typetext
4tnsRedshiftdouble precision
5tnsHost Nametext
6tnsHost Redshiftdouble precision
7tnsDiscovering Group/stext
8tnsClassifying Group/stext
9tnsAssociated Group/stext
10tnsDisc. Internal Nametext
11tnsDisc. Instrument/stext
12tnsClass. Instrument/stext
13tnsTNS ATinteger
14tnsPublicinteger
15tnsEnd Prop. Perioddate
16tnsDiscovery Magdouble precision
17tnsDiscovery Mag Filtertext
18tnsDiscovery Date (UT)timestamp without time zone
19tnsSendertext
20tnsExt. catalog/stext
21tnsradouble precision
22tnsdecdouble precision
23tnsaitoff_xdouble precision
24tnsaitoff_ydouble precision
0magrefobject_idinteger
1magrefoidtext
2magreffidinteger
3magrefrcidinteger
4magreffieldinteger
5magrefmagrefdouble precision
6magrefsigmagrefdouble precision
7magrefcorrectedboolean
0non_detectionsoidtext
1non_detectionsmjddouble precision
2non_detectionsdiffmaglimdouble precision
3non_detectionsfidsmallint
4non_detectionsobject_idinteger
\n", + "
" + ], + "text/plain": [ + " table name \\\n", + "0 class id \n", + "1 class name \n", + "0 asassn ASAS-SN Name \n", + "1 asassn Other Names \n", + "2 asassn LCID \n", + "3 asassn ra \n", + "4 asassn dec \n", + "5 asassn Mean VMag \n", + "6 asassn Amplitude \n", + "7 asassn Period \n", + "8 asassn Type \n", + "9 asassn Url \n", + "10 asassn Reference \n", + "11 asassn Dist \n", + "12 asassn Parallax \n", + "13 asassn Parallax Error \n", + "14 asassn Gmag \n", + "15 asassn Bpmag \n", + "16 asassn Rpmag \n", + "17 asassn Jmag \n", + "18 asassn Hmag \n", + "19 asassn Kmag \n", + "20 asassn W1mag \n", + "21 asassn W2mag \n", + "22 asassn W3mag \n", + "23 asassn W4mag \n", + "24 asassn BP-RR \n", + "25 asassn J-K \n", + "26 asassn W1-W2 \n", + "27 asassn W3-W4 \n", + "28 asassn Sllk Statistic \n", + "29 asassn RF Regression Score \n", + "30 asassn Classification Probability \n", + "31 asassn Epoch (HJD) \n", + "0 crtsnorth Catalina_Surveys_ID \n", + "1 crtsnorth Numerical_ID \n", + "2 crtsnorth V_(mag) \n", + "3 crtsnorth Period_(days) \n", + "4 crtsnorth Amplitude \n", + "5 crtsnorth Number_Obs \n", + "6 crtsnorth Var_Type \n", + "7 crtsnorth ra \n", + "8 crtsnorth dec \n", + "0 crtssouth SSS_ID \n", + "1 crtssouth Numerical_ID \n", + "2 crtssouth ra \n", + "3 crtssouth dec \n", + "4 crtssouth Period \n", + "5 crtssouth V_CSS \n", + "6 crtssouth Npts \n", + "7 crtssouth V_amp \n", + "8 crtssouth Type \n", + "0 detections object_id \n", + "1 detections oid \n", + "2 detections candid \n", + "3 detections mjd \n", + "4 detections fid \n", + "5 detections diffmaglim \n", + "6 detections magpsf \n", + "7 detections magap \n", + "8 detections sigmapsf \n", + "9 detections sigmagap \n", + "10 detections ra \n", + "11 detections dec \n", + "12 detections sigmara \n", + "13 detections sigmadec \n", + "14 detections isdiffpos \n", + "15 detections distpsnr1 \n", + "16 detections sgscore1 \n", + "17 detections field \n", + "18 detections rcid \n", + "19 detections magnr \n", + "20 detections sigmagnr \n", + "21 detections rb \n", + "22 detections magpsf_corr \n", + "23 detections magap_corr \n", + "24 detections sigmapsf_corr \n", + "25 detections sigmagap_corr \n", + "0 objects id \n", + "1 objects oid \n", + "2 objects nobs \n", + "3 objects mean_magap_g \n", + "4 objects mean_magap_r \n", + "5 objects median_magap_g \n", + "6 objects median_magap_r \n", + "7 objects max_magap_g \n", + "8 objects max_magap_r \n", + "9 objects min_magap_g \n", + "10 objects min_magap_r \n", + "11 objects sigma_magap_g \n", + "12 objects sigma_magap_r \n", + "13 objects last_magap_g \n", + "14 objects last_magap_r \n", + "15 objects first_magap_g \n", + "16 objects first_magap_r \n", + "17 objects mean_magpsf_g \n", + "18 objects mean_magpsf_r \n", + "19 objects median_magpsf_g \n", + "20 objects median_magpsf_r \n", + "21 objects max_magpsf_g \n", + "22 objects max_magpsf_r \n", + "23 objects min_magpsf_g \n", + "24 objects min_magpsf_r \n", + "25 objects sigma_magpsf_g \n", + "26 objects sigma_magpsf_r \n", + "27 objects last_magpsf_g \n", + "28 objects last_magpsf_r \n", + "29 objects first_magpsf_g \n", + "30 objects first_magpsf_r \n", + "31 objects meanra \n", + "32 objects meandec \n", + "33 objects sigmara \n", + "34 objects sigmadec \n", + "35 objects deltajd \n", + "36 objects lastmjd \n", + "37 objects firstmjd \n", + "38 objects period \n", + "39 objects catalogid \n", + "40 objects classxmatch \n", + "41 objects classrf \n", + "42 objects pclassrf \n", + "0 probabilities oid \n", + "1 probabilities classifierid \n", + "2 probabilities ceph_prob \n", + "3 probabilities dsct_prob \n", + "4 probabilities eb_prob \n", + "5 probabilities lpv_prob \n", + "6 probabilities rrl_prob \n", + "7 probabilities sne_prob \n", + "8 probabilities other_prob \n", + "9 probabilities object_id \n", + "0 xmatch oid \n", + "1 xmatch catalogid \n", + "2 xmatch cid \n", + "3 xmatch object_id \n", + "4 xmatch dist \n", + "5 xmatch period \n", + "6 xmatch class \n", + "0 features oid \n", + "1 features amplitude_1 \n", + "2 features andersondarling_1 \n", + "3 features autocor_length_1 \n", + "4 features beyond1std_1 \n", + "5 features car_sigma_1 \n", + "6 features car_mean_1 \n", + "7 features car_tau_1 \n", + "8 features con_1 \n", + "9 features eta_e_1 \n", + "10 features gskew_1 \n", + "11 features maxslope_1 \n", + "12 features mean_1 \n", + "13 features meanvariance_1 \n", + "14 features medianabsdev_1 \n", + "15 features medianbrp_1 \n", + "16 features pairslopetrend_1 \n", + "17 features percentamplitude_1 \n", + "18 features q31_1 \n", + "19 features periodls_1 \n", + "20 features period_fit_1 \n", + "21 features psi_cs_1 \n", + "22 features psi_eta_1 \n", + "23 features rcs_1 \n", + "24 features skew_1 \n", + "25 features smallkurtosis_1 \n", + "26 features std_1 \n", + "27 features stetsonk_1 \n", + "28 features freq1_harmonics_amplitude_0_1 \n", + "29 features freq1_harmonics_rel_phase_0_1 \n", + "30 features freq1_harmonics_amplitude_1_1 \n", + "31 features freq1_harmonics_rel_phase_1_1 \n", + "32 features freq1_harmonics_amplitude_2_1 \n", + "33 features freq1_harmonics_rel_phase_2_1 \n", + "34 features freq1_harmonics_amplitude_3_1 \n", + "35 features freq1_harmonics_rel_phase_3_1 \n", + "36 features freq2_harmonics_amplitude_0_1 \n", + "37 features freq2_harmonics_rel_phase_0_1 \n", + "38 features freq2_harmonics_amplitude_1_1 \n", + "39 features freq2_harmonics_rel_phase_1_1 \n", + "40 features freq2_harmonics_amplitude_2_1 \n", + "41 features freq2_harmonics_rel_phase_2_1 \n", + "42 features freq2_harmonics_amplitude_3_1 \n", + "43 features freq2_harmonics_rel_phase_3_1 \n", + "44 features freq3_harmonics_amplitude_0_1 \n", + "45 features freq3_harmonics_rel_phase_0_1 \n", + "46 features freq3_harmonics_amplitude_1_1 \n", + "47 features freq3_harmonics_rel_phase_1_1 \n", + "48 features freq3_harmonics_amplitude_2_1 \n", + "49 features freq3_harmonics_rel_phase_2_1 \n", + "50 features freq3_harmonics_amplitude_3_1 \n", + "51 features freq3_harmonics_rel_phase_3_1 \n", + "52 features n_samples_2 \n", + "53 features amplitude_2 \n", + "54 features andersondarling_2 \n", + "55 features autocor_length_2 \n", + "56 features beyond1std_2 \n", + "57 features car_sigma_2 \n", + "58 features car_mean_2 \n", + "59 features car_tau_2 \n", + "60 features con_2 \n", + "61 features eta_e_2 \n", + "62 features gskew_2 \n", + "63 features maxslope_2 \n", + "64 features mean_2 \n", + "65 features meanvariance_2 \n", + "66 features medianabsdev_2 \n", + "67 features medianbrp_2 \n", + "68 features pairslopetrend_2 \n", + "69 features percentamplitude_2 \n", + "70 features q31_2 \n", + "71 features periodls_2 \n", + "72 features period_fit_2 \n", + "73 features psi_cs_2 \n", + "74 features psi_eta_2 \n", + "75 features rcs_2 \n", + "76 features skew_2 \n", + "77 features smallkurtosis_2 \n", + "78 features std_2 \n", + "79 features stetsonk_2 \n", + "80 features freq1_harmonics_amplitude_0_2 \n", + "81 features freq1_harmonics_rel_phase_0_2 \n", + "82 features freq1_harmonics_amplitude_1_2 \n", + "83 features freq1_harmonics_rel_phase_1_2 \n", + "84 features freq1_harmonics_amplitude_2_2 \n", + "85 features freq1_harmonics_rel_phase_2_2 \n", + "86 features freq1_harmonics_amplitude_3_2 \n", + "87 features freq1_harmonics_rel_phase_3_2 \n", + "88 features freq2_harmonics_amplitude_0_2 \n", + "89 features freq2_harmonics_rel_phase_0_2 \n", + "90 features freq2_harmonics_amplitude_1_2 \n", + "91 features freq2_harmonics_rel_phase_1_2 \n", + "92 features freq2_harmonics_amplitude_2_2 \n", + "93 features freq2_harmonics_rel_phase_2_2 \n", + "94 features freq2_harmonics_amplitude_3_2 \n", + "95 features freq2_harmonics_rel_phase_3_2 \n", + "96 features freq3_harmonics_amplitude_0_2 \n", + "97 features freq3_harmonics_rel_phase_0_2 \n", + "98 features freq3_harmonics_amplitude_1_2 \n", + "99 features freq3_harmonics_rel_phase_1_2 \n", + "100 features freq3_harmonics_amplitude_2_2 \n", + "101 features freq3_harmonics_rel_phase_2_2 \n", + "102 features freq3_harmonics_amplitude_3_2 \n", + "103 features freq3_harmonics_rel_phase_3_2 \n", + "104 features gal_b \n", + "105 features gal_l \n", + "106 features object_id \n", + "107 features n_samples_1 \n", + "0 linear LINEARobjectID \n", + "1 linear LCtype \n", + "2 linear P \n", + "3 linear A \n", + "4 linear mmed \n", + "5 linear stdev \n", + "6 linear rms \n", + "7 linear Lchi2pdf \n", + "8 linear nObs \n", + "9 linear skew \n", + "10 linear kurt \n", + "11 linear LR \n", + "12 linear CUF \n", + "13 linear t2 \n", + "14 linear t3 \n", + "15 linear ra \n", + "16 linear dec \n", + "17 linear oType \n", + "18 linear nS \n", + "19 linear rExt \n", + "20 linear u \n", + "21 linear g \n", + "22 linear r \n", + "23 linear i \n", + "24 linear z \n", + "25 linear uErr \n", + "26 linear gErr \n", + "27 linear rErr \n", + "28 linear iErr \n", + "29 linear zErr \n", + "0 tns Name \n", + "1 tns RA_orig \n", + "2 tns DEC_orig \n", + "3 tns Obj. Type \n", + "4 tns Redshift \n", + "5 tns Host Name \n", + "6 tns Host Redshift \n", + "7 tns Discovering Group/s \n", + "8 tns Classifying Group/s \n", + "9 tns Associated Group/s \n", + "10 tns Disc. Internal Name \n", + "11 tns Disc. Instrument/s \n", + "12 tns Class. Instrument/s \n", + "13 tns TNS AT \n", + "14 tns Public \n", + "15 tns End Prop. Period \n", + "16 tns Discovery Mag \n", + "17 tns Discovery Mag Filter \n", + "18 tns Discovery Date (UT) \n", + "19 tns Sender \n", + "20 tns Ext. catalog/s \n", + "21 tns ra \n", + "22 tns dec \n", + "23 tns aitoff_x \n", + "24 tns aitoff_y \n", + "0 magref object_id \n", + "1 magref oid \n", + "2 magref fid \n", + "3 magref rcid \n", + "4 magref field \n", + "5 magref magref \n", + "6 magref sigmagref \n", + "7 magref corrected \n", + "0 non_detections oid \n", + "1 non_detections mjd \n", + "2 non_detections diffmaglim \n", + "3 non_detections fid \n", + "4 non_detections object_id \n", + "\n", + " dtype \n", + "0 integer \n", + "1 character varying \n", + "0 text \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 integer \n", + "6 integer \n", + "7 double precision \n", + "8 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 integer \n", + "7 double precision \n", + "8 integer \n", + "0 integer \n", + "1 text \n", + "2 bigint \n", + "3 double precision \n", + "4 smallint \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 smallint \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 integer \n", + "40 integer \n", + "41 integer \n", + "42 double precision \n", + "0 text \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 integer \n", + "0 character varying \n", + "1 character varying \n", + "2 character varying \n", + "3 integer \n", + "4 double precision \n", + "5 double precision \n", + "6 character varying \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 double precision \n", + "40 double precision \n", + "41 double precision \n", + "42 double precision \n", + "43 double precision \n", + "44 double precision \n", + "45 double precision \n", + "46 double precision \n", + "47 double precision \n", + "48 double precision \n", + "49 double precision \n", + "50 double precision \n", + "51 double precision \n", + "52 double precision \n", + "53 double precision \n", + "54 double precision \n", + "55 double precision \n", + "56 double precision \n", + "57 double precision \n", + "58 double precision \n", + "59 double precision \n", + "60 double precision \n", + "61 double precision \n", + "62 double precision \n", + "63 double precision \n", + "64 double precision \n", + "65 double precision \n", + "66 double precision \n", + "67 double precision \n", + "68 double precision \n", + "69 double precision \n", + "70 double precision \n", + "71 double precision \n", + "72 double precision \n", + "73 double precision \n", + "74 double precision \n", + "75 double precision \n", + "76 double precision \n", + "77 double precision \n", + "78 double precision \n", + "79 double precision \n", + "80 double precision \n", + "81 double precision \n", + "82 double precision \n", + "83 double precision \n", + "84 double precision \n", + "85 double precision \n", + "86 double precision \n", + "87 double precision \n", + "88 double precision \n", + "89 double precision \n", + "90 double precision \n", + "91 double precision \n", + "92 double precision \n", + "93 double precision \n", + "94 double precision \n", + "95 double precision \n", + "96 double precision \n", + "97 double precision \n", + "98 double precision \n", + "99 double precision \n", + "100 double precision \n", + "101 double precision \n", + "102 double precision \n", + "103 double precision \n", + "104 double precision \n", + "105 double precision \n", + "106 integer \n", + "107 double precision \n", + "0 bigint \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 integer \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 integer \n", + "13 integer \n", + "14 integer \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "0 text \n", + "1 text \n", + "2 text \n", + "3 text \n", + "4 double precision \n", + "5 text \n", + "6 double precision \n", + "7 text \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 text \n", + "12 text \n", + "13 integer \n", + "14 integer \n", + "15 date \n", + "16 double precision \n", + "17 text \n", + "18 timestamp without time zone \n", + "19 text \n", + "20 text \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 integer \n", + "4 integer \n", + "5 double precision \n", + "6 double precision \n", + "7 boolean \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 smallint \n", + "4 integer " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dftab = pd.DataFrame()\n", + "for tab in tables:\n", + " cols = pd.DataFrame()\n", + " query = \"select column_name from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " results = cur.fetchall()\n", + " if len(results) > 0:\n", + " cols[\"table\"] = [tab[0] for i in results]\n", + " cols[\"name\"] = [res[0] for res in results]\n", + " query = \"select data_type from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " cols[\"dtype\"] = [dt[0] for dt in cur.fetchall()]\n", + " dftab = pd.concat([dftab, cols])\n", + "pd.options.display.max_rows = 999\n", + "display(dftab)\n", + "pd.options.display.max_rows = 101" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to query data more easily" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:15.739915Z", + "start_time": "2019-06-02T15:47:15.736883Z" + } + }, + "outputs": [], + "source": [ + "def sql_query(query):\n", + " cur.execute(query)\n", + " result = cur.fetchall()\n", + " \n", + " # Extract the column names\n", + " col_names = []\n", + " for elt in cur.description:\n", + " col_names.append(elt[0])\n", + "\n", + " #Convert to dataframe\n", + " df = pd.DataFrame(np.array(result), columns = col_names)\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query SN data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:16.561752Z", + "start_time": "2019-06-02T15:47:15.741779Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "select probabilities.oid, probabilities.other_prob, objects.meanra, \n", + "objects.meandec, objects.nobs, objects.mean_magpsf_g, objects.mean_magpsf_r,\n", + "objects.min_magpsf_g, objects.min_magpsf_r, objects.classxmatch\n", + "\n", + "from probabilities \n", + "\n", + "inner join objects\n", + "on probabilities.oid=objects.oid\n", + "\n", + "where probabilities.sne_prob>0.7 and objects.classxmatch=6\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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other_probmeanrameandecnobsmean_magpsf_gmean_magpsf_rmin_magpsf_gmin_magpsf_rclassxmatch
oid
ZTF18acfwmqj0.0735.443577356521731.71408010869572318.69817406790618.762015700340318.189662933349618.14527893066416
ZTF18acbvnen0.168105.93517172941259.54483905882351718.679380893707318.37003729078517.473255157470717.42863464355476
ZTF18abjdjge0.094224.69367535882444.67069321764711719.288319451468319.473921203613319.006845474243218.98139953613286
ZTF18abltaxf0.15223.65467884736869.64843981578951918.924228858947818.867419560750318.247535705566418.39544868469246
ZTF18abaxlpi0.152276.559048245.01020524444442718.89670583936919.12119399176718.309412002563518.37510299682626
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" + ], + "text/plain": [ + " other_prob meanra meandec nobs \\\n", + "oid \n", + "ZTF18acfwmqj 0.07 35.4435773565217 31.7140801086957 23 \n", + "ZTF18acbvnen 0.168 105.935171729412 59.5448390588235 17 \n", + "ZTF18abjdjge 0.094 224.693675358824 44.6706932176471 17 \n", + "ZTF18abltaxf 0.15 223.654678847368 69.6484398157895 19 \n", + "ZTF18abaxlpi 0.152 276.5590482 45.0102052444444 27 \n", + "\n", + " mean_magpsf_g mean_magpsf_r min_magpsf_g \\\n", + "oid \n", + "ZTF18acfwmqj 18.698174067906 18.7620157003403 18.1896629333496 \n", + "ZTF18acbvnen 18.6793808937073 18.370037290785 17.4732551574707 \n", + "ZTF18abjdjge 19.2883194514683 19.4739212036133 19.0068454742432 \n", + "ZTF18abltaxf 18.9242288589478 18.8674195607503 18.2475357055664 \n", + "ZTF18abaxlpi 18.896705839369 19.121193991767 18.3094120025635 \n", + "\n", + " min_magpsf_r classxmatch \n", + "oid \n", + "ZTF18acfwmqj 18.1452789306641 6 \n", + "ZTF18acbvnen 17.4286346435547 6 \n", + "ZTF18abjdjge 18.9813995361328 6 \n", + "ZTF18abltaxf 18.3954486846924 6 \n", + "ZTF18abaxlpi 18.3751029968262 6 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classmapper = {\"Other\": 0, \"Ceph\": 1, \"DSCT\": 2, \"EB\": 3, \"LPV\": 4, \"RRL\": 5, \"SNe\": 6}\n", + "\n", + "query='''\n", + "select probabilities.oid, probabilities.other_prob, objects.meanra, \n", + "objects.meandec, objects.nobs, objects.mean_magpsf_g, objects.mean_magpsf_r,\n", + "objects.min_magpsf_g, objects.min_magpsf_r, objects.classxmatch\n", + "\n", + "from probabilities \n", + "\n", + "inner join objects\n", + "on probabilities.oid=objects.oid\n", + "\n", + "where probabilities.sne_prob>0.7 and objects.classxmatch=%s\n", + "''' % classmapper[\"SNe\"]\n", + "\n", + "print(query)\n", + "\n", + "\n", + "SNe = sql_query(query)\n", + "\n", + "# set oid as the pandas index\n", + "SNe.set_index('oid', inplace=True)\n", + "\n", + "SNe.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert columns to numeric values" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:16.572910Z", + "start_time": "2019-06-02T15:47:16.564844Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "other_prob\n", + "meanra\n", + "meandec\n", + "nobs\n", + "mean_magpsf_g\n", + "mean_magpsf_r\n", + "min_magpsf_g\n", + "min_magpsf_r\n", + "classxmatch\n" + ] + } + ], + "source": [ + "for col in list(SNe):\n", + " if col != 'oid':\n", + " print(col)\n", + " SNe[col] = pd.to_numeric(SNe[col], errors = 'ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create function to plot SN light curve" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:16.586460Z", + "start_time": "2019-06-02T15:47:16.575583Z" + } + }, + "outputs": [], + "source": [ + "def plotLC(oid, SN_det, SN_nondet):\n", + " fig, ax = plt.subplots(figsize = (14, 7))\n", + " labels = {1: 'g', 2: 'r'}\n", + " colors = {1: 'g', 2: 'r'}\n", + " for fid in [1, 2]:\n", + " mask = SN_det.fid == fid\n", + " if np.sum(mask) > 0: \n", + " ax.errorbar(SN_det[mask].mjd, SN_det[mask].magpsf_corr, \n", + " yerr = SN_det[mask].sigmapsf_corr, c = colors[fid], marker = 'o', label = labels[fid])\n", + " mask = (SN_nondet.fid == fid) & (SN_nondet.diffmaglim > -900)\n", + " if np.sum(mask) > 0: \n", + " ax.scatter(SN_nondet[mask].mjd, SN_nondet[mask].diffmaglim, c = colors[fid], alpha = 0.5,\n", + " marker = 'v', label = \"lim.mag. %s\" % labels[fid])\n", + " ax.set_title(oid)\n", + " ax.set_xlabel(\"MJD\")\n", + " ax.set_ylabel(\"Magnitude\")\n", + " ax.legend()\n", + " ax.set_ylim(ax.get_ylim()[::-1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create function to get data and plot SN light curve" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:16.595971Z", + "start_time": "2019-06-02T15:47:16.588045Z" + } + }, + "outputs": [], + "source": [ + "def getSNdata(oid, doplot = False, doNED = False):\n", + " # query detections\n", + " query=\"select oid, ra, dec, fid, mjd, magpsf_corr, sigmapsf_corr from detections where oid='%s'\" % oid\n", + " SN_det = sql_query(query)\n", + " # to numeric\n", + " for col in list(SN_det):\n", + " if col != 'oid':\n", + " SN_det[col] = pd.to_numeric(SN_det[col], errors = 'ignore')\n", + " # sort by jd\n", + " SN_det.sort_values(by=['mjd'], inplace=True)\n", + " \n", + " # query non detections\n", + " query=\"select oid, fid, mjd, diffmaglim from non_detections where oid='%s'\" % oid\n", + " SN_nondet = sql_query(query)\n", + " # to numeric\n", + " for col in list(SN_nondet):\n", + " if col != 'oid':\n", + " SN_nondet[col] = pd.to_numeric(SN_nondet[col], errors = 'ignore')\n", + " # sort by jd\n", + " SN_nondet.sort_values(by=['mjd'], inplace=True)\n", + " \n", + " # find NED galaxies\n", + " if doNED:\n", + " co = coordinates.SkyCoord(ra=SNe.meanra[oid], dec=SNe.meandec[oid], unit=(u.deg, u.deg), frame='fk4')\n", + " result_table = Ned.query_region(co, radius=0.01 * u.deg, equinox='J2000.0')\n", + " display(result_table)\n", + " \n", + " if doplot:\n", + " plotLC(oid, SN_det, SN_nondet)\n", + " \n", + " # return data\n", + " return SN_det, SN_nondet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T03:02:47.774585Z", + "start_time": "2019-05-28T03:02:47.769536Z" + } + }, + "source": [ + "### Get the brightest SN" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:16.602634Z", + "start_time": "2019-06-02T15:47:16.599041Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18abcpmwh\n" + ] + } + ], + "source": [ + "seloid = SNe.min_magpsf_g.idxmin()\n", + "print(seloid)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Do one SN with NED and Aladin" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:50:57.936679Z", + "start_time": "2019-06-02T15:50:56.861396Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18abcpmwh\n" + ] + }, + { + "data": { + "text/html": [ + "Table masked=True length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32bytes30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
1SDSS J130127.18+370242.9195.3632637.04526*----23.9g0.5260051040
2SDSS J130128.69+370325.7195.3695837.05716*----24.3g0.5870051040
3SDSS J130128.72+370232.0195.3696837.04224G----21.5g0.39300151040
4SDSS J130130.53+370256.6195.3772437.04907G----22.2g0.18100151040
5SDSS J130131.03+370310.1195.3793237.05283G----20.8g0.39700151040
" + ], + "text/plain": [ + "\n", + " No. Object Name RA ... Diameter Points Associations\n", + " degrees ... \n", + "int32 bytes30 float64 ... int32 int32 \n", + "----- ------------------------ ---------- ... --------------- ------------\n", + " 1 SDSS J130127.18+370242.9 195.36326 ... 4 0\n", + " 2 SDSS J130128.69+370325.7 195.36958 ... 4 0\n", + " 3 SDSS J130128.72+370232.0 195.36968 ... 4 0\n", + " 4 SDSS J130130.53+370256.6 195.37724 ... 4 0\n", + " 5 SDSS J130131.03+370310.1 195.37932 ... 4 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fe18bf375f3403b96cf7503ce1d4087", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Aladin(fov=0.04, options=['allow_full_zoomout', 'coo_frame', 'fov', 'full_screen', 'log', 'overlay_survey', 'o…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#seloid = np.random.choice(SNe.index)\n", + "print(seloid)\n", + "getSNdata(seloid, doplot = True, doNED = True);\n", + "aladin= ipyal.Aladin(target='%s %s' % (SNe.meanra[seloid], SNe.meandec[seloid]), fov=0.04, survey='P/SDSS9/color')\n", + "aladin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Try some more SNe" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:47:18.917729Z", + "start_time": "2019-06-02T15:47:17.557404Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for idx, oid in enumerate(SNe.index):\n", + " if np.mod(idx, 100) == 0:\n", + " getSNdata(oid, doplot = True, doNED = False);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/ALeRCE_ZTF_VariablesUseCase.ipynb b/notebooks/ALeRCE_ZTF_VariablesUseCase.ipynb new file mode 100644 index 0000000..ed199b0 --- /dev/null +++ b/notebooks/ALeRCE_ZTF_VariablesUseCase.ipynb @@ -0,0 +1,3187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Variable stars use case notebook\n", + "\n", + "ALeRCE starter notebook for variable star science.\n", + "\n", + "You will need to install psycopg2 and astroquery.\n", + "\n", + "In this notebook you will connect to the ALeRCE database, download some objects with probability greater than 0.7 of being Eclipsing variable, RR Lyrae, or some other class, get selected light curves and plot including doing period folding, as well as querying GAIA for crossmatches" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load libraries\n", + "\n", + "*External dependencies*:\n", + "\n", + "psycopg2: pip install psycopg2-binary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:16.871351Z", + "start_time": "2019-06-02T15:52:15.752486Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "import astropy.units as u\n", + "from astropy import coordinates\n", + "import P4J # pip install P4J\n", + "import GPy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:16.876546Z", + "start_time": "2019-06-02T15:52:16.872987Z" + } + }, + "outputs": [], + "source": [ + "import ipyaladin as ipyal # see installation instructions here: https://github.com/cds-astro/ipyaladin" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get credentials (not in github repository)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:16.893133Z", + "start_time": "2019-06-02T15:52:16.878379Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "credentials_file = \"../alercereaduser.json\"\n", + "with open(credentials_file) as jsonfile:\n", + " params = json.load(jsonfile)[\"params\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to DB" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:19.016412Z", + "start_time": "2019-06-02T15:52:16.894691Z" + } + }, + "outputs": [], + "source": [ + "conn = psycopg2.connect(dbname=params['dbname'], user=params['user'], host=params['host'], password=params['password'])\n", + "cur = conn.cursor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show all the available tables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:19.633868Z", + "start_time": "2019-06-02T15:52:19.018468Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('class',), ('asassn',), ('crtsnorth',), ('crtssouth',), ('detections',), ('objects',), ('probabilities',), ('xmatch',), ('features',), ('linear',), ('tns',), ('magref',), ('non_detections',), ('tmp',)]\n" + ] + } + ], + "source": [ + "query = \"select tablename from pg_tables where schemaname='public';\"\n", + "\n", + "cur.execute(query)\n", + "tables = cur.fetchall()\n", + "\n", + "print(tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For each table, show column names and column types" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:30.141398Z", + "start_time": "2019-06-02T15:52:19.638041Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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tablenamedtype
0classidinteger
1classnamecharacter varying
0asassnASAS-SN Nametext
1asassnOther Namestext
2asassnLCIDinteger
3asassnradouble precision
4asassndecdouble precision
5asassnMean VMagdouble precision
6asassnAmplitudedouble precision
7asassnPerioddouble precision
8asassnTypetext
9asassnUrltext
10asassnReferencetext
11asassnDistdouble precision
12asassnParallaxdouble precision
13asassnParallax Errordouble precision
14asassnGmagdouble precision
15asassnBpmagdouble precision
16asassnRpmagdouble precision
17asassnJmagdouble precision
18asassnHmagdouble precision
19asassnKmagdouble precision
20asassnW1magdouble precision
21asassnW2magdouble precision
22asassnW3magdouble precision
23asassnW4magdouble precision
24asassnBP-RRdouble precision
25asassnJ-Kdouble precision
26asassnW1-W2double precision
27asassnW3-W4double precision
28asassnSllk Statisticdouble precision
29asassnRF Regression Scoredouble precision
30asassnClassification Probabilitydouble precision
31asassnEpoch (HJD)double precision
0crtsnorthCatalina_Surveys_IDtext
1crtsnorthNumerical_IDbigint
2crtsnorthV_(mag)double precision
3crtsnorthPeriod_(days)double precision
4crtsnorthAmplitudedouble precision
5crtsnorthNumber_Obsinteger
6crtsnorthVar_Typeinteger
7crtsnorthradouble precision
8crtsnorthdecdouble precision
0crtssouthSSS_IDtext
1crtssouthNumerical_IDbigint
2crtssouthradouble precision
3crtssouthdecdouble precision
4crtssouthPerioddouble precision
5crtssouthV_CSSdouble precision
6crtssouthNptsinteger
7crtssouthV_ampdouble precision
8crtssouthTypeinteger
0detectionsobject_idinteger
1detectionsoidtext
2detectionscandidbigint
3detectionsmjddouble precision
4detectionsfidsmallint
5detectionsdiffmaglimdouble precision
6detectionsmagpsfdouble precision
7detectionsmagapdouble precision
8detectionssigmapsfdouble precision
9detectionssigmagapdouble precision
10detectionsradouble precision
11detectionsdecdouble precision
12detectionssigmaradouble precision
13detectionssigmadecdouble precision
14detectionsisdiffpossmallint
15detectionsdistpsnr1double precision
16detectionssgscore1double precision
17detectionsfieldinteger
18detectionsrcidinteger
19detectionsmagnrdouble precision
20detectionssigmagnrdouble precision
21detectionsrbdouble precision
22detectionsmagpsf_corrdouble precision
23detectionsmagap_corrdouble precision
24detectionssigmapsf_corrdouble precision
25detectionssigmagap_corrdouble precision
0objectsidinteger
1objectsoidtext
2objectsnobsinteger
3objectsmean_magap_gdouble precision
4objectsmean_magap_rdouble precision
5objectsmedian_magap_gdouble precision
6objectsmedian_magap_rdouble precision
7objectsmax_magap_gdouble precision
8objectsmax_magap_rdouble precision
9objectsmin_magap_gdouble precision
10objectsmin_magap_rdouble precision
11objectssigma_magap_gdouble precision
12objectssigma_magap_rdouble precision
13objectslast_magap_gdouble precision
14objectslast_magap_rdouble precision
15objectsfirst_magap_gdouble precision
16objectsfirst_magap_rdouble precision
17objectsmean_magpsf_gdouble precision
18objectsmean_magpsf_rdouble precision
19objectsmedian_magpsf_gdouble precision
20objectsmedian_magpsf_rdouble precision
21objectsmax_magpsf_gdouble precision
22objectsmax_magpsf_rdouble precision
23objectsmin_magpsf_gdouble precision
24objectsmin_magpsf_rdouble precision
25objectssigma_magpsf_gdouble precision
26objectssigma_magpsf_rdouble precision
27objectslast_magpsf_gdouble precision
28objectslast_magpsf_rdouble precision
29objectsfirst_magpsf_gdouble precision
30objectsfirst_magpsf_rdouble precision
31objectsmeanradouble precision
32objectsmeandecdouble precision
33objectssigmaradouble precision
34objectssigmadecdouble precision
35objectsdeltajddouble precision
36objectslastmjddouble precision
37objectsfirstmjddouble precision
38objectsperioddouble precision
39objectscatalogidinteger
40objectsclassxmatchinteger
41objectsclassrfinteger
42objectspclassrfdouble precision
0probabilitiesoidtext
1probabilitiesclassifieridinteger
2probabilitiesceph_probdouble precision
3probabilitiesdsct_probdouble precision
4probabilitieseb_probdouble precision
5probabilitieslpv_probdouble precision
6probabilitiesrrl_probdouble precision
7probabilitiessne_probdouble precision
8probabilitiesother_probdouble precision
9probabilitiesobject_idinteger
0xmatchoidcharacter varying
1xmatchcatalogidcharacter varying
2xmatchcidcharacter varying
3xmatchobject_idinteger
4xmatchdistdouble precision
5xmatchperioddouble precision
6xmatchclasscharacter varying
0featuresoidtext
1featuresamplitude_1double precision
2featuresandersondarling_1double precision
3featuresautocor_length_1double precision
4featuresbeyond1std_1double precision
5featurescar_sigma_1double precision
6featurescar_mean_1double precision
7featurescar_tau_1double precision
8featurescon_1double precision
9featureseta_e_1double precision
10featuresgskew_1double precision
11featuresmaxslope_1double precision
12featuresmean_1double precision
13featuresmeanvariance_1double precision
14featuresmedianabsdev_1double precision
15featuresmedianbrp_1double precision
16featurespairslopetrend_1double precision
17featurespercentamplitude_1double precision
18featuresq31_1double precision
19featuresperiodls_1double precision
20featuresperiod_fit_1double precision
21featurespsi_cs_1double precision
22featurespsi_eta_1double precision
23featuresrcs_1double precision
24featuresskew_1double precision
25featuressmallkurtosis_1double precision
26featuresstd_1double precision
27featuresstetsonk_1double precision
28featuresfreq1_harmonics_amplitude_0_1double precision
29featuresfreq1_harmonics_rel_phase_0_1double precision
30featuresfreq1_harmonics_amplitude_1_1double precision
31featuresfreq1_harmonics_rel_phase_1_1double precision
32featuresfreq1_harmonics_amplitude_2_1double precision
33featuresfreq1_harmonics_rel_phase_2_1double precision
34featuresfreq1_harmonics_amplitude_3_1double precision
35featuresfreq1_harmonics_rel_phase_3_1double precision
36featuresfreq2_harmonics_amplitude_0_1double precision
37featuresfreq2_harmonics_rel_phase_0_1double precision
38featuresfreq2_harmonics_amplitude_1_1double precision
39featuresfreq2_harmonics_rel_phase_1_1double precision
40featuresfreq2_harmonics_amplitude_2_1double precision
41featuresfreq2_harmonics_rel_phase_2_1double precision
42featuresfreq2_harmonics_amplitude_3_1double precision
43featuresfreq2_harmonics_rel_phase_3_1double precision
44featuresfreq3_harmonics_amplitude_0_1double precision
45featuresfreq3_harmonics_rel_phase_0_1double precision
46featuresfreq3_harmonics_amplitude_1_1double precision
47featuresfreq3_harmonics_rel_phase_1_1double precision
48featuresfreq3_harmonics_amplitude_2_1double precision
49featuresfreq3_harmonics_rel_phase_2_1double precision
50featuresfreq3_harmonics_amplitude_3_1double precision
51featuresfreq3_harmonics_rel_phase_3_1double precision
52featuresn_samples_2double precision
53featuresamplitude_2double precision
54featuresandersondarling_2double precision
55featuresautocor_length_2double precision
56featuresbeyond1std_2double precision
57featurescar_sigma_2double precision
58featurescar_mean_2double precision
59featurescar_tau_2double precision
60featurescon_2double precision
61featureseta_e_2double precision
62featuresgskew_2double precision
63featuresmaxslope_2double precision
64featuresmean_2double precision
65featuresmeanvariance_2double precision
66featuresmedianabsdev_2double precision
67featuresmedianbrp_2double precision
68featurespairslopetrend_2double precision
69featurespercentamplitude_2double precision
70featuresq31_2double precision
71featuresperiodls_2double precision
72featuresperiod_fit_2double precision
73featurespsi_cs_2double precision
74featurespsi_eta_2double precision
75featuresrcs_2double precision
76featuresskew_2double precision
77featuressmallkurtosis_2double precision
78featuresstd_2double precision
79featuresstetsonk_2double precision
80featuresfreq1_harmonics_amplitude_0_2double precision
81featuresfreq1_harmonics_rel_phase_0_2double precision
82featuresfreq1_harmonics_amplitude_1_2double precision
83featuresfreq1_harmonics_rel_phase_1_2double precision
84featuresfreq1_harmonics_amplitude_2_2double precision
85featuresfreq1_harmonics_rel_phase_2_2double precision
86featuresfreq1_harmonics_amplitude_3_2double precision
87featuresfreq1_harmonics_rel_phase_3_2double precision
88featuresfreq2_harmonics_amplitude_0_2double precision
89featuresfreq2_harmonics_rel_phase_0_2double precision
90featuresfreq2_harmonics_amplitude_1_2double precision
91featuresfreq2_harmonics_rel_phase_1_2double precision
92featuresfreq2_harmonics_amplitude_2_2double precision
93featuresfreq2_harmonics_rel_phase_2_2double precision
94featuresfreq2_harmonics_amplitude_3_2double precision
95featuresfreq2_harmonics_rel_phase_3_2double precision
96featuresfreq3_harmonics_amplitude_0_2double precision
97featuresfreq3_harmonics_rel_phase_0_2double precision
98featuresfreq3_harmonics_amplitude_1_2double precision
99featuresfreq3_harmonics_rel_phase_1_2double precision
100featuresfreq3_harmonics_amplitude_2_2double precision
101featuresfreq3_harmonics_rel_phase_2_2double precision
102featuresfreq3_harmonics_amplitude_3_2double precision
103featuresfreq3_harmonics_rel_phase_3_2double precision
104featuresgal_bdouble precision
105featuresgal_ldouble precision
106featuresobject_idinteger
107featuresn_samples_1double precision
0linearLINEARobjectIDbigint
1linearLCtypeinteger
2linearPdouble precision
3linearAdouble precision
4linearmmeddouble precision
5linearstdevdouble precision
6linearrmsdouble precision
7linearLchi2pdfdouble precision
8linearnObsinteger
9linearskewdouble precision
10linearkurtdouble precision
11linearLRdouble precision
12linearCUFinteger
13lineart2integer
14lineart3integer
15linearradouble precision
16lineardecdouble precision
17linearoTypeinteger
18linearnSinteger
19linearrExtdouble precision
20linearudouble precision
21lineargdouble precision
22linearrdouble precision
23linearidouble precision
24linearzdouble precision
25linearuErrdouble precision
26lineargErrdouble precision
27linearrErrdouble precision
28lineariErrdouble precision
29linearzErrdouble precision
0tnsNametext
1tnsRA_origtext
2tnsDEC_origtext
3tnsObj. Typetext
4tnsRedshiftdouble precision
5tnsHost Nametext
6tnsHost Redshiftdouble precision
7tnsDiscovering Group/stext
8tnsClassifying Group/stext
9tnsAssociated Group/stext
10tnsDisc. Internal Nametext
11tnsDisc. Instrument/stext
12tnsClass. Instrument/stext
13tnsTNS ATinteger
14tnsPublicinteger
15tnsEnd Prop. Perioddate
16tnsDiscovery Magdouble precision
17tnsDiscovery Mag Filtertext
18tnsDiscovery Date (UT)timestamp without time zone
19tnsSendertext
20tnsExt. catalog/stext
21tnsradouble precision
22tnsdecdouble precision
23tnsaitoff_xdouble precision
24tnsaitoff_ydouble precision
0magrefobject_idinteger
1magrefoidtext
2magreffidinteger
3magrefrcidinteger
4magreffieldinteger
5magrefmagrefdouble precision
6magrefsigmagrefdouble precision
7magrefcorrectedboolean
0non_detectionsoidtext
1non_detectionsmjddouble precision
2non_detectionsdiffmaglimdouble precision
3non_detectionsfidsmallint
4non_detectionsobject_idinteger
\n", + "" + ], + "text/plain": [ + " table name \\\n", + "0 class id \n", + "1 class name \n", + "0 asassn ASAS-SN Name \n", + "1 asassn Other Names \n", + "2 asassn LCID \n", + "3 asassn ra \n", + "4 asassn dec \n", + "5 asassn Mean VMag \n", + "6 asassn Amplitude \n", + "7 asassn Period \n", + "8 asassn Type \n", + "9 asassn Url \n", + "10 asassn Reference \n", + "11 asassn Dist \n", + "12 asassn Parallax \n", + "13 asassn Parallax Error \n", + "14 asassn Gmag \n", + "15 asassn Bpmag \n", + "16 asassn Rpmag \n", + "17 asassn Jmag \n", + "18 asassn Hmag \n", + "19 asassn Kmag \n", + "20 asassn W1mag \n", + "21 asassn W2mag \n", + "22 asassn W3mag \n", + "23 asassn W4mag \n", + "24 asassn BP-RR \n", + "25 asassn J-K \n", + "26 asassn W1-W2 \n", + "27 asassn W3-W4 \n", + "28 asassn Sllk Statistic \n", + "29 asassn RF Regression Score \n", + "30 asassn Classification Probability \n", + "31 asassn Epoch (HJD) \n", + "0 crtsnorth Catalina_Surveys_ID \n", + "1 crtsnorth Numerical_ID \n", + "2 crtsnorth V_(mag) \n", + "3 crtsnorth Period_(days) \n", + "4 crtsnorth Amplitude \n", + "5 crtsnorth Number_Obs \n", + "6 crtsnorth Var_Type \n", + "7 crtsnorth ra \n", + "8 crtsnorth dec \n", + "0 crtssouth SSS_ID \n", + "1 crtssouth Numerical_ID \n", + "2 crtssouth ra \n", + "3 crtssouth dec \n", + "4 crtssouth Period \n", + "5 crtssouth V_CSS \n", + "6 crtssouth Npts \n", + "7 crtssouth V_amp \n", + "8 crtssouth Type \n", + "0 detections object_id \n", + "1 detections oid \n", + "2 detections candid \n", + "3 detections mjd \n", + "4 detections fid \n", + "5 detections diffmaglim \n", + "6 detections magpsf \n", + "7 detections magap \n", + "8 detections sigmapsf \n", + "9 detections sigmagap \n", + "10 detections ra \n", + "11 detections dec \n", + "12 detections sigmara \n", + "13 detections sigmadec \n", + "14 detections isdiffpos \n", + "15 detections distpsnr1 \n", + "16 detections sgscore1 \n", + "17 detections field \n", + "18 detections rcid \n", + "19 detections magnr \n", + "20 detections sigmagnr \n", + "21 detections rb \n", + "22 detections magpsf_corr \n", + "23 detections magap_corr \n", + "24 detections sigmapsf_corr \n", + "25 detections sigmagap_corr \n", + "0 objects id \n", + "1 objects oid \n", + "2 objects nobs \n", + "3 objects mean_magap_g \n", + "4 objects mean_magap_r \n", + "5 objects median_magap_g \n", + "6 objects median_magap_r \n", + "7 objects max_magap_g \n", + "8 objects max_magap_r \n", + "9 objects min_magap_g \n", + "10 objects min_magap_r \n", + "11 objects sigma_magap_g \n", + "12 objects sigma_magap_r \n", + "13 objects last_magap_g \n", + "14 objects last_magap_r \n", + "15 objects first_magap_g \n", + "16 objects first_magap_r \n", + "17 objects mean_magpsf_g \n", + "18 objects mean_magpsf_r \n", + "19 objects median_magpsf_g \n", + "20 objects median_magpsf_r \n", + "21 objects max_magpsf_g \n", + "22 objects max_magpsf_r \n", + "23 objects min_magpsf_g \n", + "24 objects min_magpsf_r \n", + "25 objects sigma_magpsf_g \n", + "26 objects sigma_magpsf_r \n", + "27 objects last_magpsf_g \n", + "28 objects last_magpsf_r \n", + "29 objects first_magpsf_g \n", + "30 objects first_magpsf_r \n", + "31 objects meanra \n", + "32 objects meandec \n", + "33 objects sigmara \n", + "34 objects sigmadec \n", + "35 objects deltajd \n", + "36 objects lastmjd \n", + "37 objects firstmjd \n", + "38 objects period \n", + "39 objects catalogid \n", + "40 objects classxmatch \n", + "41 objects classrf \n", + "42 objects pclassrf \n", + "0 probabilities oid \n", + "1 probabilities classifierid \n", + "2 probabilities ceph_prob \n", + "3 probabilities dsct_prob \n", + "4 probabilities eb_prob \n", + "5 probabilities lpv_prob \n", + "6 probabilities rrl_prob \n", + "7 probabilities sne_prob \n", + "8 probabilities other_prob \n", + "9 probabilities object_id \n", + "0 xmatch oid \n", + "1 xmatch catalogid \n", + "2 xmatch cid \n", + "3 xmatch object_id \n", + "4 xmatch dist \n", + "5 xmatch period \n", + "6 xmatch class \n", + "0 features oid \n", + "1 features amplitude_1 \n", + "2 features andersondarling_1 \n", + "3 features autocor_length_1 \n", + "4 features beyond1std_1 \n", + "5 features car_sigma_1 \n", + "6 features car_mean_1 \n", + "7 features car_tau_1 \n", + "8 features con_1 \n", + "9 features eta_e_1 \n", + "10 features gskew_1 \n", + "11 features maxslope_1 \n", + "12 features mean_1 \n", + "13 features meanvariance_1 \n", + "14 features medianabsdev_1 \n", + "15 features medianbrp_1 \n", + "16 features pairslopetrend_1 \n", + "17 features percentamplitude_1 \n", + "18 features q31_1 \n", + "19 features periodls_1 \n", + "20 features period_fit_1 \n", + "21 features psi_cs_1 \n", + "22 features psi_eta_1 \n", + "23 features rcs_1 \n", + "24 features skew_1 \n", + "25 features smallkurtosis_1 \n", + "26 features std_1 \n", + "27 features stetsonk_1 \n", + "28 features freq1_harmonics_amplitude_0_1 \n", + "29 features freq1_harmonics_rel_phase_0_1 \n", + "30 features freq1_harmonics_amplitude_1_1 \n", + "31 features freq1_harmonics_rel_phase_1_1 \n", + "32 features freq1_harmonics_amplitude_2_1 \n", + "33 features freq1_harmonics_rel_phase_2_1 \n", + "34 features freq1_harmonics_amplitude_3_1 \n", + "35 features freq1_harmonics_rel_phase_3_1 \n", + "36 features freq2_harmonics_amplitude_0_1 \n", + "37 features freq2_harmonics_rel_phase_0_1 \n", + "38 features freq2_harmonics_amplitude_1_1 \n", + "39 features freq2_harmonics_rel_phase_1_1 \n", + "40 features freq2_harmonics_amplitude_2_1 \n", + "41 features freq2_harmonics_rel_phase_2_1 \n", + "42 features freq2_harmonics_amplitude_3_1 \n", + "43 features freq2_harmonics_rel_phase_3_1 \n", + "44 features freq3_harmonics_amplitude_0_1 \n", + "45 features freq3_harmonics_rel_phase_0_1 \n", + "46 features freq3_harmonics_amplitude_1_1 \n", + "47 features freq3_harmonics_rel_phase_1_1 \n", + "48 features freq3_harmonics_amplitude_2_1 \n", + "49 features freq3_harmonics_rel_phase_2_1 \n", + "50 features freq3_harmonics_amplitude_3_1 \n", + "51 features freq3_harmonics_rel_phase_3_1 \n", + "52 features n_samples_2 \n", + "53 features amplitude_2 \n", + "54 features andersondarling_2 \n", + "55 features autocor_length_2 \n", + "56 features beyond1std_2 \n", + "57 features car_sigma_2 \n", + "58 features car_mean_2 \n", + "59 features car_tau_2 \n", + "60 features con_2 \n", + "61 features eta_e_2 \n", + "62 features gskew_2 \n", + "63 features maxslope_2 \n", + "64 features mean_2 \n", + "65 features meanvariance_2 \n", + "66 features medianabsdev_2 \n", + "67 features medianbrp_2 \n", + "68 features pairslopetrend_2 \n", + "69 features percentamplitude_2 \n", + "70 features q31_2 \n", + "71 features periodls_2 \n", + "72 features period_fit_2 \n", + "73 features psi_cs_2 \n", + "74 features psi_eta_2 \n", + "75 features rcs_2 \n", + "76 features skew_2 \n", + "77 features smallkurtosis_2 \n", + "78 features std_2 \n", + "79 features stetsonk_2 \n", + "80 features freq1_harmonics_amplitude_0_2 \n", + "81 features freq1_harmonics_rel_phase_0_2 \n", + "82 features freq1_harmonics_amplitude_1_2 \n", + "83 features freq1_harmonics_rel_phase_1_2 \n", + "84 features freq1_harmonics_amplitude_2_2 \n", + "85 features freq1_harmonics_rel_phase_2_2 \n", + "86 features freq1_harmonics_amplitude_3_2 \n", + "87 features freq1_harmonics_rel_phase_3_2 \n", + "88 features freq2_harmonics_amplitude_0_2 \n", + "89 features freq2_harmonics_rel_phase_0_2 \n", + "90 features freq2_harmonics_amplitude_1_2 \n", + "91 features freq2_harmonics_rel_phase_1_2 \n", + "92 features freq2_harmonics_amplitude_2_2 \n", + "93 features freq2_harmonics_rel_phase_2_2 \n", + "94 features freq2_harmonics_amplitude_3_2 \n", + "95 features freq2_harmonics_rel_phase_3_2 \n", + "96 features freq3_harmonics_amplitude_0_2 \n", + "97 features freq3_harmonics_rel_phase_0_2 \n", + "98 features freq3_harmonics_amplitude_1_2 \n", + "99 features freq3_harmonics_rel_phase_1_2 \n", + "100 features freq3_harmonics_amplitude_2_2 \n", + "101 features freq3_harmonics_rel_phase_2_2 \n", + "102 features freq3_harmonics_amplitude_3_2 \n", + "103 features freq3_harmonics_rel_phase_3_2 \n", + "104 features gal_b \n", + "105 features gal_l \n", + "106 features object_id \n", + "107 features n_samples_1 \n", + "0 linear LINEARobjectID \n", + "1 linear LCtype \n", + "2 linear P \n", + "3 linear A \n", + "4 linear mmed \n", + "5 linear stdev \n", + "6 linear rms \n", + "7 linear Lchi2pdf \n", + "8 linear nObs \n", + "9 linear skew \n", + "10 linear kurt \n", + "11 linear LR \n", + "12 linear CUF \n", + "13 linear t2 \n", + "14 linear t3 \n", + "15 linear ra \n", + "16 linear dec \n", + "17 linear oType \n", + "18 linear nS \n", + "19 linear rExt \n", + "20 linear u \n", + "21 linear g \n", + "22 linear r \n", + "23 linear i \n", + "24 linear z \n", + "25 linear uErr \n", + "26 linear gErr \n", + "27 linear rErr \n", + "28 linear iErr \n", + "29 linear zErr \n", + "0 tns Name \n", + "1 tns RA_orig \n", + "2 tns DEC_orig \n", + "3 tns Obj. Type \n", + "4 tns Redshift \n", + "5 tns Host Name \n", + "6 tns Host Redshift \n", + "7 tns Discovering Group/s \n", + "8 tns Classifying Group/s \n", + "9 tns Associated Group/s \n", + "10 tns Disc. Internal Name \n", + "11 tns Disc. Instrument/s \n", + "12 tns Class. Instrument/s \n", + "13 tns TNS AT \n", + "14 tns Public \n", + "15 tns End Prop. Period \n", + "16 tns Discovery Mag \n", + "17 tns Discovery Mag Filter \n", + "18 tns Discovery Date (UT) \n", + "19 tns Sender \n", + "20 tns Ext. catalog/s \n", + "21 tns ra \n", + "22 tns dec \n", + "23 tns aitoff_x \n", + "24 tns aitoff_y \n", + "0 magref object_id \n", + "1 magref oid \n", + "2 magref fid \n", + "3 magref rcid \n", + "4 magref field \n", + "5 magref magref \n", + "6 magref sigmagref \n", + "7 magref corrected \n", + "0 non_detections oid \n", + "1 non_detections mjd \n", + "2 non_detections diffmaglim \n", + "3 non_detections fid \n", + "4 non_detections object_id \n", + "\n", + " dtype \n", + "0 integer \n", + "1 character varying \n", + "0 text \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 integer \n", + "6 integer \n", + "7 double precision \n", + "8 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 integer \n", + "7 double precision \n", + "8 integer \n", + "0 integer \n", + "1 text \n", + "2 bigint \n", + "3 double precision \n", + "4 smallint \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 smallint \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 integer \n", + "40 integer \n", + "41 integer \n", + "42 double precision \n", + "0 text \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 integer \n", + "0 character varying \n", + "1 character varying \n", + "2 character varying \n", + "3 integer \n", + "4 double precision \n", + "5 double precision \n", + "6 character varying \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 double precision \n", + "40 double precision \n", + "41 double precision \n", + "42 double precision \n", + "43 double precision \n", + "44 double precision \n", + "45 double precision \n", + "46 double precision \n", + "47 double precision \n", + "48 double precision \n", + "49 double precision \n", + "50 double precision \n", + "51 double precision \n", + "52 double precision \n", + "53 double precision \n", + "54 double precision \n", + "55 double precision \n", + "56 double precision \n", + "57 double precision \n", + "58 double precision \n", + "59 double precision \n", + "60 double precision \n", + "61 double precision \n", + "62 double precision \n", + "63 double precision \n", + "64 double precision \n", + "65 double precision \n", + "66 double precision \n", + "67 double precision \n", + "68 double precision \n", + "69 double precision \n", + "70 double precision \n", + "71 double precision \n", + "72 double precision \n", + "73 double precision \n", + "74 double precision \n", + "75 double precision \n", + "76 double precision \n", + "77 double precision \n", + "78 double precision \n", + "79 double precision \n", + "80 double precision \n", + "81 double precision \n", + "82 double precision \n", + "83 double precision \n", + "84 double precision \n", + "85 double precision \n", + "86 double precision \n", + "87 double precision \n", + "88 double precision \n", + "89 double precision \n", + "90 double precision \n", + "91 double precision \n", + "92 double precision \n", + "93 double precision \n", + "94 double precision \n", + "95 double precision \n", + "96 double precision \n", + "97 double precision \n", + "98 double precision \n", + "99 double precision \n", + "100 double precision \n", + "101 double precision \n", + "102 double precision \n", + "103 double precision \n", + "104 double precision \n", + "105 double precision \n", + "106 integer \n", + "107 double precision \n", + "0 bigint \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 integer \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 integer \n", + "13 integer \n", + "14 integer \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "0 text \n", + "1 text \n", + "2 text \n", + "3 text \n", + "4 double precision \n", + "5 text \n", + "6 double precision \n", + "7 text \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 text \n", + "12 text \n", + "13 integer \n", + "14 integer \n", + "15 date \n", + "16 double precision \n", + "17 text \n", + "18 timestamp without time zone \n", + "19 text \n", + "20 text \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 integer \n", + "4 integer \n", + "5 double precision \n", + "6 double precision \n", + "7 boolean \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 smallint \n", + "4 integer " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dftab = pd.DataFrame()\n", + "for tab in tables:\n", + " cols = pd.DataFrame()\n", + " query = \"select column_name from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " results = cur.fetchall()\n", + " if len(results) > 0:\n", + " cols[\"table\"] = [tab[0] for i in results]\n", + " cols[\"name\"] = [res[0] for res in results]\n", + " query = \"select data_type from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " cols[\"dtype\"] = [dt[0] for dt in cur.fetchall()]\n", + " dftab = pd.concat([dftab, cols])\n", + "pd.options.display.max_rows = 999\n", + "display(dftab)\n", + "pd.options.display.max_rows = 101" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classes and their numerical values" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:30.145527Z", + "start_time": "2019-06-02T15:52:30.142975Z" + } + }, + "outputs": [], + "source": [ + "classmapper = {\"Other\": 0, \"Ceph\": 1, \"DSCT\": 2, \"EB\": 3, \"LPV\": 4, \"RRL\": 5, \"SNe\": 6}\n", + "\n", + "# if class_str in ['TDE', 'AGN', 'Pulsating-Other', 'CV', 'Novae', 'Periodic-Other']:\n", + "# numeric_label = 0 #Other\n", + "# elif class_str == 'Ceph':\n", + "# numeric_label = 1\n", + "# elif class_str == 'DSCT':\n", + "# numeric_label = 2\t\n", + "# elif class_str in ['EBSD/D', 'EBC']:\n", + "# numeric_label = 3\n", + "# elif class_str =='LPV':\n", + "# numeric_label = 4\n", + "# elif class_str == 'RRL':\n", + "# numeric_label = 5\n", + "# elif class_str in ['SNeIIb', 'SNeIa', 'SNeIIn', 'SNeIb/c', 'SNeII', 'SNeIa-sub']:\n", + "# numeric_label = 6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to query data more easily" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:30.157328Z", + "start_time": "2019-06-02T15:52:30.147137Z" + } + }, + "outputs": [], + "source": [ + "def sql_query(query):\n", + " cur.execute(query)\n", + " result = cur.fetchall()\n", + " \n", + " # Extract the column names\n", + " col_names = []\n", + " for elt in cur.description:\n", + " col_names.append(elt[0])\n", + "\n", + " #Convert to dataframe\n", + " df = pd.DataFrame(np.array(result), columns = col_names)\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query SN data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:33.213775Z", + "start_time": "2019-06-02T15:52:30.158911Z" + } + }, + "outputs": [], + "source": [ + "query='''\n", + "select probabilities.oid, probabilities.other_prob, objects.meanra, \n", + "objects.meandec, objects.nobs, objects.mean_magpsf_g, objects.mean_magpsf_r,\n", + "objects.min_magpsf_g, objects.min_magpsf_r, objects.sigma_magpsf_g, objects.sigma_magpsf_r, \n", + "objects.classxmatch, objects.period\n", + "\n", + "from probabilities \n", + "\n", + "inner join objects\n", + "on probabilities.oid=objects.oid\n", + "\n", + "where probabilities.rrl_prob>0.5 and objects.classxmatch=%s\n", + "''' % classmapper[\"RRL\"]\n", + "\n", + "RRL = sql_query(query)\n", + "\n", + "RRL.head()\n", + "\n", + "# use oid as the pandas index\n", + "RRL.set_index('oid', inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert columns to numeric values" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:33.226661Z", + "start_time": "2019-06-02T15:52:33.215238Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "other_prob\n", + "meanra\n", + "meandec\n", + "nobs\n", + "mean_magpsf_g\n", + "mean_magpsf_r\n", + "min_magpsf_g\n", + "min_magpsf_r\n", + "sigma_magpsf_g\n", + "sigma_magpsf_r\n", + "classxmatch\n", + "period\n" + ] + } + ], + "source": [ + "for col in list(RRL):\n", + " if col != 'oid':\n", + " print(col)\n", + " RRL[col] = pd.to_numeric(RRL[col], errors = 'ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to get period" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:33.234073Z", + "start_time": "2019-06-02T15:52:33.228646Z" + } + }, + "outputs": [], + "source": [ + "# for the moment use the g band\n", + "def doperiod(LCdet):\n", + "\n", + " objperiod = {}\n", + " freq = {}\n", + " per = {}\n", + " for fid in [1, 2]:\n", + " \n", + " maskdet = LCdet.fid == fid\n", + " \n", + " my_per = P4J.periodogram(method='QMIEU')\n", + " my_per.set_data(np.array(LCdet[maskdet].mjd), np.array(LCdet[maskdet].magpsf_corr),\n", + " np.array(LCdet[maskdet].sigmapsf_corr))\n", + " my_per.frequency_grid_evaluation(fmin=0.0, fmax=5.0, fresolution=1e-3)\n", + " my_per.finetune_best_frequencies(fresolution=1e-5, n_local_optima=1)#10) \n", + " freq[fid], per[fid] = my_per.get_periodogram()\n", + " fbest, pbest = my_per.get_best_frequencies()\n", + " objperiod[fid] = 1. / fbest\n", + " print(fid, objperiod[fid])\n", + "\n", + " period = objperiod[1]\n", + " \n", + " return period, freq, per" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:33.242234Z", + "start_time": "2019-06-02T15:52:33.235788Z" + } + }, + "outputs": [], + "source": [ + "def plotLC(oid, LCdet, LCnondet, dofold, period=None, freq=None, per=None):\n", + "\n", + " fig, ax = plt.subplots(figsize = (12, 6))\n", + " labels = {1: 'g', 2: 'r'}\n", + " colors = {1: 'g', 2: 'r'}\n", + " for idx, fid in enumerate([1, 2]):\n", + " maskdet = LCdet.fid == fid\n", + " masknondet = (LCnondet.fid == fid) & (LCnondet.diffmaglim > -900)\n", + " \n", + " if dofold:\n", + " phase = np.mod(LCdet[maskdet].mjd, period) / period\n", + " ax.errorbar(phase, LCdet[maskdet].magpsf_corr, \n", + " yerr = LCdet[maskdet].sigmapsf_corr, c = colors[fid], marker = 'o', label = labels[fid], lw = 0)\n", + " else:\n", + " ax.errorbar(LCdet[maskdet].mjd, LCdet[maskdet].magpsf_corr, \n", + " yerr = LCdet[maskdet].sigmapsf_corr, c = colors[fid], marker = 'o', label = labels[fid])\n", + " ax.scatter(LCnondet[masknondet].mjd, LCnondet[masknondet].diffmaglim, c = colors[fid], alpha = 0.5,\n", + " marker = 'v', label = \"lim.mag. %s\" % labels[fid])\n", + " if dofold:\n", + " ax.set_title(\"%s (period: %.3f days\" % (oid, period))\n", + " ax.set_xlabel(\"phase\")\n", + " else:\n", + " ax.set_title(oid)\n", + " ax.set_xlabel(\"MJD\")\n", + " ax.set_ylabel(\"Magnitude\")\n", + " ax.legend()\n", + " ax.set_ylim(ax.get_ylim()[::-1])\n", + " \n", + " if dofold:\n", + " fig, ax = plt.subplots(ncols = 2, figsize = (12, 4))\n", + " for idx, fid in enumerate([1, 2]):\n", + " ax[idx].plot(freq[fid], per[fid])\n", + " ax[idx].set_title(\"Periodogram %s\" % labels[fid])\n", + " ax[idx].set_xlabel(\"frequency [1/days]\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to get data, fold and plot SN light curve" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:33.249835Z", + "start_time": "2019-06-02T15:52:33.243957Z" + } + }, + "outputs": [], + "source": [ + "def getLCdata(oid, doplot=False, dofold=False):\n", + "\n", + " # query detections\n", + " query = \"select oid, ra, dec, fid, mjd, magpsf_corr, sigmapsf_corr from detections where oid='%s'\" % oid\n", + " LCdet = sql_query(query)\n", + " # to numeric\n", + " for col in list(LCdet):\n", + " if col != 'oid':\n", + " LCdet[col] = pd.to_numeric(LCdet[col], errors = 'ignore')\n", + " # sort by jd\n", + " LCdet.sort_values(by=[\"mjd\"], inplace=True)\n", + " \n", + " # query non detections\n", + " query = \"select oid, fid, mjd, diffmaglim from non_detections where oid='%s'\" % oid\n", + " LCnondet = sql_query(query)\n", + " # to numeric\n", + " for col in list(LCnondet):\n", + " if col != 'oid':\n", + " LCnondet[col] = pd.to_numeric(LCnondet[col], errors = 'ignore')\n", + " # sort by jd\n", + " LCnondet.sort_values(by=[\"mjd\"], inplace=True)\n", + "\n", + " if dofold:\n", + " # get period\n", + " period, freq, per = doperiod(LCdet)\n", + " else:\n", + " period = None; freq = None; per = None \n", + " \n", + " if doplot:\n", + " plotLC(oid, LCdet, LCnondet, dofold, period=period, freq=freq, per=per)\n", + " \n", + " # return data\n", + " return LCdet, LCnondet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T03:02:47.774585Z", + "start_time": "2019-05-28T03:02:47.769536Z" + } + }, + "source": [ + "### Get the brightest RRL and plot folded light curve" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:34.610244Z", + "start_time": "2019-06-02T15:52:33.253087Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18aagrhuq\n", + "1 [0.6303024]\n", + "2 [0.6304137]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seloid = RRL.min_magpsf_g.idxmin()\n", + "print(seloid)\n", + "getLCdata(seloid, doplot = True, dofold = True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select a random light curve with period and reference location" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "ExecuteTime": { + "end_time": "2019-06-02T15:52:35.857272Z", + "start_time": "2019-06-02T15:52:34.611540Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 [7.227]\n", + "2 [0.5362649]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c9f83517cf00468ab7e153076eb4af42", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Aladin(fov=0.04, options=['allow_full_zoomout', 'coo_frame', 'fov', 'full_screen', 'log', 'overlay_survey', 'o…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seloid = np.random.choice(RRL.index)\n", + "getLCdata(seloid, doplot = True, dofold = True);\n", + "aladin= ipyal.Aladin(target='%s %s' % (RRL.meanra[seloid], RRL.meandec[seloid]), fov=0.04, survey='P/SDSS9/color')\n", + "aladin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/ALeRCE_ZTF_XMatch_inspection.ipynb b/notebooks/ALeRCE_ZTF_XMatch_inspection.ipynb new file mode 100644 index 0000000..9d6c071 --- /dev/null +++ b/notebooks/ALeRCE_ZTF_XMatch_inspection.ipynb @@ -0,0 +1,3190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crossmatch inspection notebook\n", + "\n", + "You will need to install psycopg2 and astroquery.\n", + "\n", + "This notebook looks for all the objects that have a crossmatch label, which can be useful for understanding training set representativity and detecting wrong crossmatches\n", + "\n", + "Please tell us if you find a source with a wrong xmatch\n", + "https://docs.google.com/forms/d/e/1FAIpQLScKfzBV5SjZHCyxkHhiLmfb8cs9Qx2MxxK6RbIErexXIbeRGQ/viewform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load libraries\n", + "\n", + "*External dependencies*:\n", + "\n", + "psycopg2: pip install psycopg2-binary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.735549Z", + "start_time": "2019-05-28T15:13:28.953131Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "from astroquery.ned import Ned # pip install astroquery\n", + "import astropy.units as u\n", + "from astropy import coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "XMATCH_TABLE = [\n", + " 'OTHER',\n", + " 'CEPH',\n", + " 'DSCT',\n", + " 'EB',\n", + " 'LPV',\n", + " 'RRL',\n", + " 'SNE'\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get credentials (not in github repository)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.740792Z", + "start_time": "2019-05-28T15:13:29.737396Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "credentials_file = \"../alerceuser.json\"\n", + "with open(credentials_file) as jsonfile:\n", + " params = json.load(jsonfile)[\"params\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to DB" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.901099Z", + "start_time": "2019-05-28T15:13:29.742114Z" + } + }, + "outputs": [], + "source": [ + "conn = psycopg2.connect(dbname=params['dbname'], user=params['user'], host=params['host'], password=params['password'])\n", + "cur = conn.cursor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show all the available tables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.979839Z", + "start_time": "2019-05-28T15:13:29.905675Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('class',), ('asassn',), ('crtsnorth',), ('crtssouth',), ('detections',), ('objects',), ('insert_tmp',), ('tmp_oid',), ('probabilities',), ('xmatch',), ('features',), ('linear',), ('tns',), ('magref',), ('non_detections',), ('tmp',)]\n" + ] + } + ], + "source": [ + "query = \"select tablename from pg_tables where schemaname='public';\"\n", + "\n", + "cur.execute(query)\n", + "tables = cur.fetchall()\n", + "\n", + "print(tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For each table, show column names and column types" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:30.632181Z", + "start_time": "2019-05-28T15:13:29.984815Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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tablenamedtype
0classidinteger
1classnamecharacter varying
0asassnASAS-SN Nametext
1asassnOther Namestext
2asassnLCIDinteger
3asassnradouble precision
4asassndecdouble precision
5asassnMean VMagdouble precision
6asassnAmplitudedouble precision
7asassnPerioddouble precision
8asassnTypetext
9asassnUrltext
10asassnReferencetext
11asassnDistdouble precision
12asassnParallaxdouble precision
13asassnParallax Errordouble precision
14asassnGmagdouble precision
15asassnBpmagdouble precision
16asassnRpmagdouble precision
17asassnJmagdouble precision
18asassnHmagdouble precision
19asassnKmagdouble precision
20asassnW1magdouble precision
21asassnW2magdouble precision
22asassnW3magdouble precision
23asassnW4magdouble precision
24asassnBP-RRdouble precision
25asassnJ-Kdouble precision
26asassnW1-W2double precision
27asassnW3-W4double precision
28asassnSllk Statisticdouble precision
29asassnRF Regression Scoredouble precision
30asassnClassification Probabilitydouble precision
31asassnEpoch (HJD)double precision
0crtsnorthCatalina_Surveys_IDtext
1crtsnorthNumerical_IDbigint
2crtsnorthV_(mag)double precision
3crtsnorthPeriod_(days)double precision
4crtsnorthAmplitudedouble precision
5crtsnorthNumber_Obsinteger
6crtsnorthVar_Typeinteger
7crtsnorthradouble precision
8crtsnorthdecdouble precision
0crtssouthSSS_IDtext
1crtssouthNumerical_IDbigint
2crtssouthradouble precision
3crtssouthdecdouble precision
4crtssouthPerioddouble precision
5crtssouthV_CSSdouble precision
6crtssouthNptsinteger
7crtssouthV_ampdouble precision
8crtssouthTypeinteger
0detectionsobject_idinteger
1detectionsoidtext
2detectionscandidbigint
3detectionsmjddouble precision
4detectionsfidsmallint
5detectionsdiffmaglimdouble precision
6detectionsmagpsfdouble precision
7detectionsmagapdouble precision
8detectionssigmapsfdouble precision
9detectionssigmagapdouble precision
10detectionsradouble precision
11detectionsdecdouble precision
12detectionssigmaradouble precision
13detectionssigmadecdouble precision
14detectionsisdiffpossmallint
15detectionsdistpsnr1double precision
16detectionssgscore1double precision
17detectionsfieldinteger
18detectionsrcidinteger
19detectionsmagnrdouble precision
20detectionssigmagnrdouble precision
21detectionsrbdouble precision
22detectionsmagpsf_corrdouble precision
23detectionsmagap_corrdouble precision
24detectionssigmapsf_corrdouble precision
25detectionssigmagap_corrdouble precision
0objectsidinteger
1objectsoidtext
2objectsnobsinteger
3objectsmean_magap_gdouble precision
4objectsmean_magap_rdouble precision
5objectsmedian_magap_gdouble precision
6objectsmedian_magap_rdouble precision
7objectsmax_magap_gdouble precision
8objectsmax_magap_rdouble precision
9objectsmin_magap_gdouble precision
10objectsmin_magap_rdouble precision
11objectssigma_magap_gdouble precision
12objectssigma_magap_rdouble precision
13objectslast_magap_gdouble precision
14objectslast_magap_rdouble precision
15objectsfirst_magap_gdouble precision
16objectsfirst_magap_rdouble precision
17objectsmean_magpsf_gdouble precision
18objectsmean_magpsf_rdouble precision
19objectsmedian_magpsf_gdouble precision
20objectsmedian_magpsf_rdouble precision
21objectsmax_magpsf_gdouble precision
22objectsmax_magpsf_rdouble precision
23objectsmin_magpsf_gdouble precision
24objectsmin_magpsf_rdouble precision
25objectssigma_magpsf_gdouble precision
26objectssigma_magpsf_rdouble precision
27objectslast_magpsf_gdouble precision
28objectslast_magpsf_rdouble precision
29objectsfirst_magpsf_gdouble precision
30objectsfirst_magpsf_rdouble precision
31objectsmeanradouble precision
32objectsmeandecdouble precision
33objectssigmaradouble precision
34objectssigmadecdouble precision
35objectsdeltajddouble precision
36objectslastmjddouble precision
37objectsfirstmjddouble precision
38objectsperioddouble precision
39objectscatalogidinteger
40objectsclassxmatchinteger
41objectsclassrfinteger
42objectspclassrfdouble precision
0insert_tmpoidtext
0probabilitiesoidtext
1probabilitiesclassifieridinteger
2probabilitiesceph_probdouble precision
3probabilitiesdsct_probdouble precision
4probabilitieseb_probdouble precision
5probabilitieslpv_probdouble precision
6probabilitiesrrl_probdouble precision
7probabilitiessne_probdouble precision
8probabilitiesother_probdouble precision
9probabilitiesobject_idinteger
0xmatchoidcharacter varying
1xmatchcatalogidcharacter varying
2xmatchcidcharacter varying
3xmatchobject_idinteger
4xmatchdistdouble precision
5xmatchperioddouble precision
6xmatchclasscharacter varying
0featuresoidtext
1featuresamplitude_1double precision
2featuresandersondarling_1double precision
3featuresautocor_length_1double precision
4featuresbeyond1std_1double precision
5featurescar_sigma_1double precision
6featurescar_mean_1double precision
7featurescar_tau_1double precision
8featurescon_1double precision
9featureseta_e_1double precision
10featuresgskew_1double precision
11featuresmaxslope_1double precision
12featuresmean_1double precision
13featuresmeanvariance_1double precision
14featuresmedianabsdev_1double precision
15featuresmedianbrp_1double precision
16featurespairslopetrend_1double precision
17featurespercentamplitude_1double precision
18featuresq31_1double precision
19featuresperiodls_1double precision
20featuresperiod_fit_1double precision
21featurespsi_cs_1double precision
22featurespsi_eta_1double precision
23featuresrcs_1double precision
24featuresskew_1double precision
25featuressmallkurtosis_1double precision
26featuresstd_1double precision
27featuresstetsonk_1double precision
28featuresfreq1_harmonics_amplitude_0_1double precision
29featuresfreq1_harmonics_rel_phase_0_1double precision
30featuresfreq1_harmonics_amplitude_1_1double precision
31featuresfreq1_harmonics_rel_phase_1_1double precision
32featuresfreq1_harmonics_amplitude_2_1double precision
33featuresfreq1_harmonics_rel_phase_2_1double precision
34featuresfreq1_harmonics_amplitude_3_1double precision
35featuresfreq1_harmonics_rel_phase_3_1double precision
36featuresfreq2_harmonics_amplitude_0_1double precision
37featuresfreq2_harmonics_rel_phase_0_1double precision
38featuresfreq2_harmonics_amplitude_1_1double precision
39featuresfreq2_harmonics_rel_phase_1_1double precision
40featuresfreq2_harmonics_amplitude_2_1double precision
41featuresfreq2_harmonics_rel_phase_2_1double precision
42featuresfreq2_harmonics_amplitude_3_1double precision
43featuresfreq2_harmonics_rel_phase_3_1double precision
44featuresfreq3_harmonics_amplitude_0_1double precision
45featuresfreq3_harmonics_rel_phase_0_1double precision
46featuresfreq3_harmonics_amplitude_1_1double precision
47featuresfreq3_harmonics_rel_phase_1_1double precision
48featuresfreq3_harmonics_amplitude_2_1double precision
49featuresfreq3_harmonics_rel_phase_2_1double precision
50featuresfreq3_harmonics_amplitude_3_1double precision
51featuresfreq3_harmonics_rel_phase_3_1double precision
52featuresn_samples_2double precision
53featuresamplitude_2double precision
54featuresandersondarling_2double precision
55featuresautocor_length_2double precision
56featuresbeyond1std_2double precision
57featurescar_sigma_2double precision
58featurescar_mean_2double precision
59featurescar_tau_2double precision
60featurescon_2double precision
61featureseta_e_2double precision
62featuresgskew_2double precision
63featuresmaxslope_2double precision
64featuresmean_2double precision
65featuresmeanvariance_2double precision
66featuresmedianabsdev_2double precision
67featuresmedianbrp_2double precision
68featurespairslopetrend_2double precision
69featurespercentamplitude_2double precision
70featuresq31_2double precision
71featuresperiodls_2double precision
72featuresperiod_fit_2double precision
73featurespsi_cs_2double precision
74featurespsi_eta_2double precision
75featuresrcs_2double precision
76featuresskew_2double precision
77featuressmallkurtosis_2double precision
78featuresstd_2double precision
79featuresstetsonk_2double precision
80featuresfreq1_harmonics_amplitude_0_2double precision
81featuresfreq1_harmonics_rel_phase_0_2double precision
82featuresfreq1_harmonics_amplitude_1_2double precision
83featuresfreq1_harmonics_rel_phase_1_2double precision
84featuresfreq1_harmonics_amplitude_2_2double precision
85featuresfreq1_harmonics_rel_phase_2_2double precision
86featuresfreq1_harmonics_amplitude_3_2double precision
87featuresfreq1_harmonics_rel_phase_3_2double precision
88featuresfreq2_harmonics_amplitude_0_2double precision
89featuresfreq2_harmonics_rel_phase_0_2double precision
90featuresfreq2_harmonics_amplitude_1_2double precision
91featuresfreq2_harmonics_rel_phase_1_2double precision
92featuresfreq2_harmonics_amplitude_2_2double precision
93featuresfreq2_harmonics_rel_phase_2_2double precision
94featuresfreq2_harmonics_amplitude_3_2double precision
95featuresfreq2_harmonics_rel_phase_3_2double precision
96featuresfreq3_harmonics_amplitude_0_2double precision
97featuresfreq3_harmonics_rel_phase_0_2double precision
98featuresfreq3_harmonics_amplitude_1_2double precision
99featuresfreq3_harmonics_rel_phase_1_2double precision
100featuresfreq3_harmonics_amplitude_2_2double precision
101featuresfreq3_harmonics_rel_phase_2_2double precision
102featuresfreq3_harmonics_amplitude_3_2double precision
103featuresfreq3_harmonics_rel_phase_3_2double precision
104featuresgal_bdouble precision
105featuresgal_ldouble precision
106featuresobject_idinteger
107featuresn_samples_1double precision
0linearLINEARobjectIDbigint
1linearLCtypeinteger
2linearPdouble precision
3linearAdouble precision
4linearmmeddouble precision
5linearstdevdouble precision
6linearrmsdouble precision
7linearLchi2pdfdouble precision
8linearnObsinteger
9linearskewdouble precision
10linearkurtdouble precision
11linearLRdouble precision
12linearCUFinteger
13lineart2integer
14lineart3integer
15linearradouble precision
16lineardecdouble precision
17linearoTypeinteger
18linearnSinteger
19linearrExtdouble precision
20linearudouble precision
21lineargdouble precision
22linearrdouble precision
23linearidouble precision
24linearzdouble precision
25linearuErrdouble precision
26lineargErrdouble precision
27linearrErrdouble precision
28lineariErrdouble precision
29linearzErrdouble precision
0tnsNametext
1tnsRA_origtext
2tnsDEC_origtext
3tnsObj. Typetext
4tnsRedshiftdouble precision
5tnsHost Nametext
6tnsHost Redshiftdouble precision
7tnsDiscovering Group/stext
8tnsClassifying Group/stext
9tnsAssociated Group/stext
10tnsDisc. Internal Nametext
11tnsDisc. Instrument/stext
12tnsClass. Instrument/stext
13tnsTNS ATinteger
14tnsPublicinteger
15tnsEnd Prop. Perioddate
16tnsDiscovery Magdouble precision
17tnsDiscovery Mag Filtertext
18tnsDiscovery Date (UT)timestamp without time zone
19tnsSendertext
20tnsExt. catalog/stext
21tnsradouble precision
22tnsdecdouble precision
23tnsaitoff_xdouble precision
24tnsaitoff_ydouble precision
0magrefobject_idinteger
1magrefoidtext
2magreffidinteger
3magrefrcidinteger
4magreffieldinteger
5magrefmagrefdouble precision
6magrefsigmagrefdouble precision
7magrefcorrectedboolean
0non_detectionsoidtext
1non_detectionsmjddouble precision
2non_detectionsdiffmaglimdouble precision
3non_detectionsfidsmallint
4non_detectionsobject_idinteger
\n", + "
" + ], + "text/plain": [ + " table name \\\n", + "0 class id \n", + "1 class name \n", + "0 asassn ASAS-SN Name \n", + "1 asassn Other Names \n", + "2 asassn LCID \n", + "3 asassn ra \n", + "4 asassn dec \n", + "5 asassn Mean VMag \n", + "6 asassn Amplitude \n", + "7 asassn Period \n", + "8 asassn Type \n", + "9 asassn Url \n", + "10 asassn Reference \n", + "11 asassn Dist \n", + "12 asassn Parallax \n", + "13 asassn Parallax Error \n", + "14 asassn Gmag \n", + "15 asassn Bpmag \n", + "16 asassn Rpmag \n", + "17 asassn Jmag \n", + "18 asassn Hmag \n", + "19 asassn Kmag \n", + "20 asassn W1mag \n", + "21 asassn W2mag \n", + "22 asassn W3mag \n", + "23 asassn W4mag \n", + "24 asassn BP-RR \n", + "25 asassn J-K \n", + "26 asassn W1-W2 \n", + "27 asassn W3-W4 \n", + "28 asassn Sllk Statistic \n", + "29 asassn RF Regression Score \n", + "30 asassn Classification Probability \n", + "31 asassn Epoch (HJD) \n", + "0 crtsnorth Catalina_Surveys_ID \n", + "1 crtsnorth Numerical_ID \n", + "2 crtsnorth V_(mag) \n", + "3 crtsnorth Period_(days) \n", + "4 crtsnorth Amplitude \n", + "5 crtsnorth Number_Obs \n", + "6 crtsnorth Var_Type \n", + "7 crtsnorth ra \n", + "8 crtsnorth dec \n", + "0 crtssouth SSS_ID \n", + "1 crtssouth Numerical_ID \n", + "2 crtssouth ra \n", + "3 crtssouth dec \n", + "4 crtssouth Period \n", + "5 crtssouth V_CSS \n", + "6 crtssouth Npts \n", + "7 crtssouth V_amp \n", + "8 crtssouth Type \n", + "0 detections object_id \n", + "1 detections oid \n", + "2 detections candid \n", + "3 detections mjd \n", + "4 detections fid \n", + "5 detections diffmaglim \n", + "6 detections magpsf \n", + "7 detections magap \n", + "8 detections sigmapsf \n", + "9 detections sigmagap \n", + "10 detections ra \n", + "11 detections dec \n", + "12 detections sigmara \n", + "13 detections sigmadec \n", + "14 detections isdiffpos \n", + "15 detections distpsnr1 \n", + "16 detections sgscore1 \n", + "17 detections field \n", + "18 detections rcid \n", + "19 detections magnr \n", + "20 detections sigmagnr \n", + "21 detections rb \n", + "22 detections magpsf_corr \n", + "23 detections magap_corr \n", + "24 detections sigmapsf_corr \n", + "25 detections sigmagap_corr \n", + "0 objects id \n", + "1 objects oid \n", + "2 objects nobs \n", + "3 objects mean_magap_g \n", + "4 objects mean_magap_r \n", + "5 objects median_magap_g \n", + "6 objects median_magap_r \n", + "7 objects max_magap_g \n", + "8 objects max_magap_r \n", + "9 objects min_magap_g \n", + "10 objects min_magap_r \n", + "11 objects sigma_magap_g \n", + "12 objects sigma_magap_r \n", + "13 objects last_magap_g \n", + "14 objects last_magap_r \n", + "15 objects first_magap_g \n", + "16 objects first_magap_r \n", + "17 objects mean_magpsf_g \n", + "18 objects mean_magpsf_r \n", + "19 objects median_magpsf_g \n", + "20 objects median_magpsf_r \n", + "21 objects max_magpsf_g \n", + "22 objects max_magpsf_r \n", + "23 objects min_magpsf_g \n", + "24 objects min_magpsf_r \n", + "25 objects sigma_magpsf_g \n", + "26 objects sigma_magpsf_r \n", + "27 objects last_magpsf_g \n", + "28 objects last_magpsf_r \n", + "29 objects first_magpsf_g \n", + "30 objects first_magpsf_r \n", + "31 objects meanra \n", + "32 objects meandec \n", + "33 objects sigmara \n", + "34 objects sigmadec \n", + "35 objects deltajd \n", + "36 objects lastmjd \n", + "37 objects firstmjd \n", + "38 objects period \n", + "39 objects catalogid \n", + "40 objects classxmatch \n", + "41 objects classrf \n", + "42 objects pclassrf \n", + "0 insert_tmp oid \n", + "0 probabilities oid \n", + "1 probabilities classifierid \n", + "2 probabilities ceph_prob \n", + "3 probabilities dsct_prob \n", + "4 probabilities eb_prob \n", + "5 probabilities lpv_prob \n", + "6 probabilities rrl_prob \n", + "7 probabilities sne_prob \n", + "8 probabilities other_prob \n", + "9 probabilities object_id \n", + "0 xmatch oid \n", + "1 xmatch catalogid \n", + "2 xmatch cid \n", + "3 xmatch object_id \n", + "4 xmatch dist \n", + "5 xmatch period \n", + "6 xmatch class \n", + "0 features oid \n", + "1 features amplitude_1 \n", + "2 features andersondarling_1 \n", + "3 features autocor_length_1 \n", + "4 features beyond1std_1 \n", + "5 features car_sigma_1 \n", + "6 features car_mean_1 \n", + "7 features car_tau_1 \n", + "8 features con_1 \n", + "9 features eta_e_1 \n", + "10 features gskew_1 \n", + "11 features maxslope_1 \n", + "12 features mean_1 \n", + "13 features meanvariance_1 \n", + "14 features medianabsdev_1 \n", + "15 features medianbrp_1 \n", + "16 features pairslopetrend_1 \n", + "17 features percentamplitude_1 \n", + "18 features q31_1 \n", + "19 features periodls_1 \n", + "20 features period_fit_1 \n", + "21 features psi_cs_1 \n", + "22 features psi_eta_1 \n", + "23 features rcs_1 \n", + "24 features skew_1 \n", + "25 features smallkurtosis_1 \n", + "26 features std_1 \n", + "27 features stetsonk_1 \n", + "28 features freq1_harmonics_amplitude_0_1 \n", + "29 features freq1_harmonics_rel_phase_0_1 \n", + "30 features freq1_harmonics_amplitude_1_1 \n", + "31 features freq1_harmonics_rel_phase_1_1 \n", + "32 features freq1_harmonics_amplitude_2_1 \n", + "33 features freq1_harmonics_rel_phase_2_1 \n", + "34 features freq1_harmonics_amplitude_3_1 \n", + "35 features freq1_harmonics_rel_phase_3_1 \n", + "36 features freq2_harmonics_amplitude_0_1 \n", + "37 features freq2_harmonics_rel_phase_0_1 \n", + "38 features freq2_harmonics_amplitude_1_1 \n", + "39 features freq2_harmonics_rel_phase_1_1 \n", + "40 features freq2_harmonics_amplitude_2_1 \n", + "41 features freq2_harmonics_rel_phase_2_1 \n", + "42 features freq2_harmonics_amplitude_3_1 \n", + "43 features freq2_harmonics_rel_phase_3_1 \n", + "44 features freq3_harmonics_amplitude_0_1 \n", + "45 features freq3_harmonics_rel_phase_0_1 \n", + "46 features freq3_harmonics_amplitude_1_1 \n", + "47 features freq3_harmonics_rel_phase_1_1 \n", + "48 features freq3_harmonics_amplitude_2_1 \n", + "49 features freq3_harmonics_rel_phase_2_1 \n", + "50 features freq3_harmonics_amplitude_3_1 \n", + "51 features freq3_harmonics_rel_phase_3_1 \n", + "52 features n_samples_2 \n", + "53 features amplitude_2 \n", + "54 features andersondarling_2 \n", + "55 features autocor_length_2 \n", + "56 features beyond1std_2 \n", + "57 features car_sigma_2 \n", + "58 features car_mean_2 \n", + "59 features car_tau_2 \n", + "60 features con_2 \n", + "61 features eta_e_2 \n", + "62 features gskew_2 \n", + "63 features maxslope_2 \n", + "64 features mean_2 \n", + "65 features meanvariance_2 \n", + "66 features medianabsdev_2 \n", + "67 features medianbrp_2 \n", + "68 features pairslopetrend_2 \n", + "69 features percentamplitude_2 \n", + "70 features q31_2 \n", + "71 features periodls_2 \n", + "72 features period_fit_2 \n", + "73 features psi_cs_2 \n", + "74 features psi_eta_2 \n", + "75 features rcs_2 \n", + "76 features skew_2 \n", + "77 features smallkurtosis_2 \n", + "78 features std_2 \n", + "79 features stetsonk_2 \n", + "80 features freq1_harmonics_amplitude_0_2 \n", + "81 features freq1_harmonics_rel_phase_0_2 \n", + "82 features freq1_harmonics_amplitude_1_2 \n", + "83 features freq1_harmonics_rel_phase_1_2 \n", + "84 features freq1_harmonics_amplitude_2_2 \n", + "85 features freq1_harmonics_rel_phase_2_2 \n", + "86 features freq1_harmonics_amplitude_3_2 \n", + "87 features freq1_harmonics_rel_phase_3_2 \n", + "88 features freq2_harmonics_amplitude_0_2 \n", + "89 features freq2_harmonics_rel_phase_0_2 \n", + "90 features freq2_harmonics_amplitude_1_2 \n", + "91 features freq2_harmonics_rel_phase_1_2 \n", + "92 features freq2_harmonics_amplitude_2_2 \n", + "93 features freq2_harmonics_rel_phase_2_2 \n", + "94 features freq2_harmonics_amplitude_3_2 \n", + "95 features freq2_harmonics_rel_phase_3_2 \n", + "96 features freq3_harmonics_amplitude_0_2 \n", + "97 features freq3_harmonics_rel_phase_0_2 \n", + "98 features freq3_harmonics_amplitude_1_2 \n", + "99 features freq3_harmonics_rel_phase_1_2 \n", + "100 features freq3_harmonics_amplitude_2_2 \n", + "101 features freq3_harmonics_rel_phase_2_2 \n", + "102 features freq3_harmonics_amplitude_3_2 \n", + "103 features freq3_harmonics_rel_phase_3_2 \n", + "104 features gal_b \n", + "105 features gal_l \n", + "106 features object_id \n", + "107 features n_samples_1 \n", + "0 linear LINEARobjectID \n", + "1 linear LCtype \n", + "2 linear P \n", + "3 linear A \n", + "4 linear mmed \n", + "5 linear stdev \n", + "6 linear rms \n", + "7 linear Lchi2pdf \n", + "8 linear nObs \n", + "9 linear skew \n", + "10 linear kurt \n", + "11 linear LR \n", + "12 linear CUF \n", + "13 linear t2 \n", + "14 linear t3 \n", + "15 linear ra \n", + "16 linear dec \n", + "17 linear oType \n", + "18 linear nS \n", + "19 linear rExt \n", + "20 linear u \n", + "21 linear g \n", + "22 linear r \n", + "23 linear i \n", + "24 linear z \n", + "25 linear uErr \n", + "26 linear gErr \n", + "27 linear rErr \n", + "28 linear iErr \n", + "29 linear zErr \n", + "0 tns Name \n", + "1 tns RA_orig \n", + "2 tns DEC_orig \n", + "3 tns Obj. Type \n", + "4 tns Redshift \n", + "5 tns Host Name \n", + "6 tns Host Redshift \n", + "7 tns Discovering Group/s \n", + "8 tns Classifying Group/s \n", + "9 tns Associated Group/s \n", + "10 tns Disc. Internal Name \n", + "11 tns Disc. Instrument/s \n", + "12 tns Class. Instrument/s \n", + "13 tns TNS AT \n", + "14 tns Public \n", + "15 tns End Prop. Period \n", + "16 tns Discovery Mag \n", + "17 tns Discovery Mag Filter \n", + "18 tns Discovery Date (UT) \n", + "19 tns Sender \n", + "20 tns Ext. catalog/s \n", + "21 tns ra \n", + "22 tns dec \n", + "23 tns aitoff_x \n", + "24 tns aitoff_y \n", + "0 magref object_id \n", + "1 magref oid \n", + "2 magref fid \n", + "3 magref rcid \n", + "4 magref field \n", + "5 magref magref \n", + "6 magref sigmagref \n", + "7 magref corrected \n", + "0 non_detections oid \n", + "1 non_detections mjd \n", + "2 non_detections diffmaglim \n", + "3 non_detections fid \n", + "4 non_detections object_id \n", + "\n", + " dtype \n", + "0 integer \n", + "1 character varying \n", + "0 text \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 integer \n", + "6 integer \n", + "7 double precision \n", + "8 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 integer \n", + "7 double precision \n", + "8 integer \n", + "0 integer \n", + "1 text \n", + "2 bigint \n", + "3 double precision \n", + "4 smallint \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 smallint \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 integer \n", + "40 integer \n", + "41 integer \n", + "42 double precision \n", + "0 text \n", + "0 text \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 integer \n", + "0 character varying \n", + "1 character varying \n", + "2 character varying \n", + "3 integer \n", + "4 double precision \n", + "5 double precision \n", + "6 character varying \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 double precision \n", + "40 double precision \n", + "41 double precision \n", + "42 double precision \n", + "43 double precision \n", + "44 double precision \n", + "45 double precision \n", + "46 double precision \n", + "47 double precision \n", + "48 double precision \n", + "49 double precision \n", + "50 double precision \n", + "51 double precision \n", + "52 double precision \n", + "53 double precision \n", + "54 double precision \n", + "55 double precision \n", + "56 double precision \n", + "57 double precision \n", + "58 double precision \n", + "59 double precision \n", + "60 double precision \n", + "61 double precision \n", + "62 double precision \n", + "63 double precision \n", + "64 double precision \n", + "65 double precision \n", + "66 double precision \n", + "67 double precision \n", + "68 double precision \n", + "69 double precision \n", + "70 double precision \n", + "71 double precision \n", + "72 double precision \n", + "73 double precision \n", + "74 double precision \n", + "75 double precision \n", + "76 double precision \n", + "77 double precision \n", + "78 double precision \n", + "79 double precision \n", + "80 double precision \n", + "81 double precision \n", + "82 double precision \n", + "83 double precision \n", + "84 double precision \n", + "85 double precision \n", + "86 double precision \n", + "87 double precision \n", + "88 double precision \n", + "89 double precision \n", + "90 double precision \n", + "91 double precision \n", + "92 double precision \n", + "93 double precision \n", + "94 double precision \n", + "95 double precision \n", + "96 double precision \n", + "97 double precision \n", + "98 double precision \n", + "99 double precision \n", + "100 double precision \n", + "101 double precision \n", + "102 double precision \n", + "103 double precision \n", + "104 double precision \n", + "105 double precision \n", + "106 integer \n", + "107 double precision \n", + "0 bigint \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 integer \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 integer \n", + "13 integer \n", + "14 integer \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "0 text \n", + "1 text \n", + "2 text \n", + "3 text \n", + "4 double precision \n", + "5 text \n", + "6 double precision \n", + "7 text \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 text \n", + "12 text \n", + "13 integer \n", + "14 integer \n", + "15 date \n", + "16 double precision \n", + "17 text \n", + "18 timestamp without time zone \n", + "19 text \n", + "20 text \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 integer \n", + "4 integer \n", + "5 double precision \n", + "6 double precision \n", + "7 boolean \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 smallint \n", + "4 integer " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dftab = pd.DataFrame()\n", + "for tab in tables:\n", + " cols = pd.DataFrame()\n", + " query = \"select column_name from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " results = cur.fetchall()\n", + " if len(results) > 0:\n", + " cols[\"table\"] = [tab[0] for i in results]\n", + " cols[\"name\"] = [res[0] for res in results]\n", + " query = \"select data_type from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " cols[\"dtype\"] = [dt[0] for dt in cur.fetchall()]\n", + " dftab = pd.concat([dftab, cols])\n", + "pd.options.display.max_rows = 999\n", + "display(dftab)\n", + "pd.options.display.max_rows = 101" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to query data more easily" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:30.636730Z", + "start_time": "2019-05-28T15:13:30.633746Z" + } + }, + "outputs": [], + "source": [ + "def sql_query(query):\n", + " cur.execute(query)\n", + " result = cur.fetchall()\n", + " \n", + " # Extract the column names\n", + " col_names = []\n", + " for elt in cur.description:\n", + " col_names.append(elt[0])\n", + "\n", + " #Convert to dataframe\n", + " df = pd.DataFrame(np.array(result), columns = col_names)\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query objects with xmatch label and computed probabilities (more than 5 detections on each band)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "query='''\n", + "select objects.oid, objects.classxmatch, \n", + "objects.nobs, objects.meanra, \n", + "objects.meandec, features.periodls_1, features.periodls_2,\n", + "probabilities.other_prob,\n", + "probabilities.ceph_prob,\n", + "probabilities.dsct_prob,\n", + "probabilities.eb_prob,\n", + "probabilities.lpv_prob,\n", + "probabilities.rrl_prob,\n", + "probabilities.sne_prob\n", + "\n", + "from objects \n", + " inner join features on objects.oid=features.oid\n", + " inner join probabilities on objects.oid=probabilities.oid\n", + " \n", + "\n", + "where objects.classxmatch is not null\n", + "'''\n", + "xmatched_sources = sql_query(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "xmatched_sources = xmatched_sources.astype(\n", + " {'%s_prob' % c.lower(): float for c in XMATCH_TABLE})" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OTHER 781\n", + "CEPH 374\n", + "DSCT 490\n", + "EB 19843\n", + "LPV 2014\n", + "RRL 12392\n", + "SNE 471\n", + " oid classxmatch nobs meanra meandec \\\n", + "36360 ZTF18abbkgnr 5 32 250.390694340625 21.15503005625 \n", + "36361 ZTF18abeepdd 3 23 270.25343843913 4.13102815217391 \n", + "36362 ZTF18absuouj 5 19 308.293048226316 -18.6195545210526 \n", + "36363 ZTF18ablrvfx 3 29 281.754208893103 -5.85609522758621 \n", + "36364 ZTF18acwzaxs 5 20 215.49762625 -13.646848375 \n", + "\n", + " periodls_1 periodls_2 other_prob ceph_prob dsct_prob \\\n", + "36360 0.337837833483956 0.337860665695153 0.032 0.020 0.606 \n", + "36361 0.226449281035151 1.51400456496685 0.110 0.144 0.130 \n", + "36362 0.212125068048067 1.09433136216638 0.096 0.162 0.320 \n", + "36363 0.632191181014538 0.52996980629327 0.032 0.070 0.164 \n", + "36364 0.322611868364207 0.594601019948949 0.070 0.106 0.486 \n", + "\n", + " eb_prob lpv_prob rrl_prob sne_prob \n", + "36360 0.106 0.006 0.212 0.018 \n", + "36361 0.432 0.014 0.138 0.032 \n", + "36362 0.090 0.016 0.286 0.030 \n", + "36363 0.478 0.010 0.240 0.006 \n", + "36364 0.128 0.022 0.178 0.010 \n" + ] + } + ], + "source": [ + "count = xmatched_sources.groupby('classxmatch').count()\n", + "for i in range(7):\n", + " print(XMATCH_TABLE[i], count.iloc[i]['oid'])\n", + "print(xmatched_sources.tail())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize object" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def get_lc_data(oid, doplot = False, doNED = False):\n", + " # detections\n", + " query=\"select oid, ra, dec, fid, mjd, magpsf_corr, sigmapsf_corr from detections where oid='%s'\" % oid\n", + " detections = sql_query(query)\n", + " for col in list(detections):\n", + " if col != 'oid':\n", + " detections[col] = pd.to_numeric(detections[col], errors = 'ignore')\n", + " \n", + " # non detections\n", + " query=\"select oid, fid, mjd, diffmaglim from non_detections where oid='%s'\" % oid\n", + " non_detections = sql_query(query)\n", + " for col in list(non_detections):\n", + " if col != 'oid':\n", + " non_detections[col] = pd.to_numeric(non_detections[col], errors = 'ignore')\n", + " \n", + " # sort by date\n", + " detections.sort_values(by='mjd', inplace=True)\n", + " non_detections.sort_values(by='mjd', inplace=True)\n", + " \n", + " # find NED galaxies\n", + " if doNED:\n", + " co = coordinates.SkyCoord(ra=xmatched_sources.meanra[xmatched_sources.oid == oid], \n", + " dec=xmatched_sources.meandec[xmatched_sources.oid == oid], \n", + " unit=(u.deg, u.deg), frame='fk4')\n", + " result_table = Ned.query_region(co, radius=0.01 * u.deg, equinox='J2000.0')\n", + " display(result_table)\n", + " \n", + " # plot\n", + " if doplot:\n", + " classxmatch = xmatched_sources['classxmatch'][xmatched_sources.oid == oid].values[0]\n", + " classxmatch = XMATCH_TABLE[int(classxmatch)]\n", + " fig, ax = plt.subplots(figsize = (14, 7))\n", + " labels = {1: 'g', 2: 'r'}\n", + " colors = {1: 'g', 2: 'r'}\n", + " for fid in [1, 2]:\n", + " mask = detections.fid == fid\n", + " ax.errorbar(detections[mask].mjd, detections[mask].magpsf_corr, \n", + " yerr = detections[mask].sigmapsf_corr, c = colors[fid], marker = 'o', label = labels[fid])\n", + " mask = (non_detections.fid == fid) & (non_detections.diffmaglim > -900)\n", + " ax.scatter(non_detections[mask].mjd, non_detections[mask].diffmaglim, c = colors[fid], alpha = 0.5,\n", + " marker = 'v', label = \"lim.mag. %s\" % labels[fid])\n", + " ax.set_title('oid: %s, xmatch class: %s' % (oid, classxmatch))\n", + " ax.set_xlabel(\"MJD\")\n", + " ax.set_ylabel(\"Magnitude\")\n", + " ax.legend()\n", + " ax.set_ylim(ax.get_ylim()[::-1])\n", + " \n", + " period_1 = float(xmatched_sources['periodls_1'][xmatched_sources.oid == oid].values[0])\n", + " period_2 = float(xmatched_sources['periodls_2'][xmatched_sources.oid == oid].values[0])\n", + " \n", + " period_mean = (period_1 + period_2) / 2\n", + " diff = abs(period_1-period_2)/period_mean\n", + " if diff < 0.1:\n", + " fig, ax = plt.subplots(figsize=(14, 7))\n", + " for fid in [1, 2]:\n", + " mask = detections.fid == fid\n", + " ax.errorbar((detections[mask].mjd % period_mean)/period_mean, detections[mask].magpsf_corr,\n", + " yerr = detections[mask].sigmapsf_corr, c = colors[fid], marker = 'o', \n", + " label = labels[fid], linestyle='')\n", + " ax.set_title('oid: %s, xmatch class: %s, folded with period %.3f' % (oid, classxmatch, period_mean))\n", + " ax.set_xlabel(\"MJD\")\n", + " ax.set_ylabel(\"Magnitude\")\n", + " ax.legend()\n", + " ax.set_ylim(ax.get_ylim()[::-1])\n", + " \n", + " \n", + " # return data\n", + " return detections, non_detections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Objects from a given class according to xmatch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "object_class = 'SNE'\n", + "subset = xmatched_sources[xmatched_sources.classxmatch == str(XMATCH_TABLE.index(object_class))]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18aaxsioa\n" + ] + }, + { + "data": { + "text/html": [ + "Table masked=True length=9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32bytes30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
1SDSS J165105.90+452417.1252.7745845.40477G----22.3g0.46800151040
2SDSS J165106.21+452407.9252.7758845.40222G----20.8g0.5300151040
3SDSS J165106.27+452421.8252.7761645.40608G----22.9g0.3700151040
4SDSS J165106.92+452411.1252.7788645.4031G----22.2g0.41500151040
5SDSS J165107.42+452456.6252.7809345.41573*----18.7g0.4180051030
6SDSS J165108.07+452407.3252.7836545.40203G----22.0g0.42200151040
7SDSS J165109.20+452418.9252.7883645.40526*----22.3g0.2970051040
8SDSS J165109.46+452450.0252.7894345.4139G----21.2g0.37400151040
9SDSS J165109.62+452418.1252.7900945.40504*----21.6g0.3570051040
" + ], + "text/plain": [ + "\n", + " No. Object Name RA ... Diameter Points Associations\n", + " degrees ... \n", + "int32 bytes30 float64 ... int32 int32 \n", + "----- ------------------------ ---------- ... --------------- ------------\n", + " 1 SDSS J165105.90+452417.1 252.77458 ... 4 0\n", + " 2 SDSS J165106.21+452407.9 252.77588 ... 4 0\n", + " 3 SDSS J165106.27+452421.8 252.77616 ... 4 0\n", + " 4 SDSS J165106.92+452411.1 252.77886 ... 4 0\n", + " 5 SDSS J165107.42+452456.6 252.78093 ... 3 0\n", + " 6 SDSS J165108.07+452407.3 252.78365 ... 4 0\n", + " 7 SDSS J165109.20+452418.9 252.78836 ... 4 0\n", + " 8 SDSS J165109.46+452450.0 252.78943 ... 4 0\n", + " 9 SDSS J165109.62+452418.1 252.79009 ... 4 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "random_oid = subset['oid'].iloc[np.random.randint(len(subset))]\n", + "print(random_oid)\n", + "detections, non_detections = get_lc_data(random_oid, doplot=True, doNED=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choose xmatch label and Random Forest label\n", + "\n", + "Example:\n", + "\n", + "xmatch_label = 'SNE'\n", + "\n", + "predicted_label = 'DSCT'\n", + "\n", + "confidence = 0.4\n", + "\n", + "All objects that are SNe according to crossmatch but were classified as Delta Scuti with a probability greater than 40 %" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36 object(s) found\n" + ] + } + ], + "source": [ + "xmatch_label = 'CEPH'\n", + "predicted_label = 'DSCT'\n", + "confidence = 0.4\n", + "subset = xmatched_sources[\n", + " (xmatched_sources.classxmatch == str(XMATCH_TABLE.index(xmatch_label)))\n", + " & (xmatched_sources['%s_prob' % predicted_label.lower()] > confidence)]\n", + "print('%d object(s) found' % len(subset))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18acckybv\n" + ] + }, + { + "data": { + "text/html": [ + "Table masked=True length=5\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32bytes30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
12MASS J04142668+615354963.6112161.89859IrS----0.5240061000
22MASS J04142674+615404863.6114561.90135IrS----0.450061000
32MASS J04143125+615435763.6302361.90992IrS----0.410061000
42MASS J04143147+615358263.6311661.89951IrS----0.2560061000
52MASS J04143156+615346863.6315161.89634IrS----0.4360061000
" + ], + "text/plain": [ + "\n", + " No. Object Name RA ... Diameter Points Associations\n", + " degrees ... \n", + "int32 bytes30 float64 ... int32 int32 \n", + "----- ----------------------- ---------- ... --------------- ------------\n", + " 1 2MASS J04142668+6153549 63.61121 ... 0 0\n", + " 2 2MASS J04142674+6154048 63.61145 ... 0 0\n", + " 3 2MASS J04143125+6154357 63.63023 ... 0 0\n", + " 4 2MASS J04143147+6153582 63.63116 ... 0 0\n", + " 5 2MASS J04143156+6153468 63.63151 ... 0 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " other_prob ceph_prob dsct_prob eb_prob lpv_prob rrl_prob sne_prob\n", + "30715 0.06 0.334 0.46 0.05 0.006 0.088 0.002\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "random_oid = subset['oid'].iloc[np.random.randint(len(subset))]\n", + "print(random_oid)\n", + "detections, non_detections = get_lc_data(random_oid, doplot=True, doNED=True);\n", + "print(subset[subset.oid == random_oid][['%s_prob' % c.lower() for c in XMATCH_TABLE]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/ALeRCE_ZTF_XMatch_one_stamp_classifier.ipynb b/notebooks/ALeRCE_ZTF_XMatch_one_stamp_classifier.ipynb new file mode 100644 index 0000000..9c8a724 --- /dev/null +++ b/notebooks/ALeRCE_ZTF_XMatch_one_stamp_classifier.ipynb @@ -0,0 +1,3100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crossmatch one stamp classifier\n", + "\n", + "You will need to install psycopg2 and astroquery.\n", + "\n", + "This notebook gets all the objects xmatched as SNe or variable star" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load libraries\n", + "\n", + "*External dependencies*:\n", + "\n", + "psycopg2: pip install psycopg2-binary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.735549Z", + "start_time": "2019-05-28T15:13:28.953131Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "from astroquery.ned import Ned # pip install astroquery\n", + "import astropy.units as u\n", + "from astropy import coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "XMATCH_TABLE = [\n", + " 'OTHER',\n", + " 'CEPH',\n", + " 'DSCT',\n", + " 'EB',\n", + " 'LPV',\n", + " 'RRL',\n", + " 'SNE'\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get credentials (not in github repository)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.740792Z", + "start_time": "2019-05-28T15:13:29.737396Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "credentials_file = \"../alerceuser.json\"\n", + "with open(credentials_file) as jsonfile:\n", + " params = json.load(jsonfile)[\"params\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to DB" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.901099Z", + "start_time": "2019-05-28T15:13:29.742114Z" + } + }, + "outputs": [], + "source": [ + "conn = psycopg2.connect(dbname=params['dbname'], user=params['user'], host=params['host'], password=params['password'])\n", + "cur = conn.cursor()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show all the available tables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:29.979839Z", + "start_time": "2019-05-28T15:13:29.905675Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[('class',), ('asassn',), ('crtsnorth',), ('crtssouth',), ('detections',), ('objects',), ('insert_tmp',), ('tmp_oid',), ('probabilities',), ('xmatch',), ('features',), ('linear',), ('tns',), ('magref',), ('non_detections',), ('tmp',)]\n" + ] + } + ], + "source": [ + "query = \"select tablename from pg_tables where schemaname='public';\"\n", + "\n", + "cur.execute(query)\n", + "tables = cur.fetchall()\n", + "\n", + "print(tables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For each table, show column names and column types" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:30.632181Z", + "start_time": "2019-05-28T15:13:29.984815Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0classidinteger
1classnamecharacter varying
0asassnASAS-SN Nametext
1asassnOther Namestext
2asassnLCIDinteger
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4asassndecdouble precision
5asassnMean VMagdouble precision
6asassnAmplitudedouble precision
7asassnPerioddouble precision
8asassnTypetext
9asassnUrltext
10asassnReferencetext
11asassnDistdouble precision
12asassnParallaxdouble precision
13asassnParallax Errordouble precision
14asassnGmagdouble precision
15asassnBpmagdouble precision
16asassnRpmagdouble precision
17asassnJmagdouble precision
18asassnHmagdouble precision
19asassnKmagdouble precision
20asassnW1magdouble precision
21asassnW2magdouble precision
22asassnW3magdouble precision
23asassnW4magdouble precision
24asassnBP-RRdouble precision
25asassnJ-Kdouble precision
26asassnW1-W2double precision
27asassnW3-W4double precision
28asassnSllk Statisticdouble precision
29asassnRF Regression Scoredouble precision
30asassnClassification Probabilitydouble precision
31asassnEpoch (HJD)double precision
0crtsnorthCatalina_Surveys_IDtext
1crtsnorthNumerical_IDbigint
2crtsnorthV_(mag)double precision
3crtsnorthPeriod_(days)double precision
4crtsnorthAmplitudedouble precision
5crtsnorthNumber_Obsinteger
6crtsnorthVar_Typeinteger
7crtsnorthradouble precision
8crtsnorthdecdouble precision
0crtssouthSSS_IDtext
1crtssouthNumerical_IDbigint
2crtssouthradouble precision
3crtssouthdecdouble precision
4crtssouthPerioddouble precision
5crtssouthV_CSSdouble precision
6crtssouthNptsinteger
7crtssouthV_ampdouble precision
8crtssouthTypeinteger
0detectionsobject_idinteger
1detectionsoidtext
2detectionscandidbigint
3detectionsmjddouble precision
4detectionsfidsmallint
5detectionsdiffmaglimdouble precision
6detectionsmagpsfdouble precision
7detectionsmagapdouble precision
8detectionssigmapsfdouble precision
9detectionssigmagapdouble precision
10detectionsradouble precision
11detectionsdecdouble precision
12detectionssigmaradouble precision
13detectionssigmadecdouble precision
14detectionsisdiffpossmallint
15detectionsdistpsnr1double precision
16detectionssgscore1double precision
17detectionsfieldinteger
18detectionsrcidinteger
19detectionsmagnrdouble precision
20detectionssigmagnrdouble precision
21detectionsrbdouble precision
22detectionsmagpsf_corrdouble precision
23detectionsmagap_corrdouble precision
24detectionssigmapsf_corrdouble precision
25detectionssigmagap_corrdouble precision
0objectsidinteger
1objectsoidtext
2objectsnobsinteger
3objectsmean_magap_gdouble precision
4objectsmean_magap_rdouble precision
5objectsmedian_magap_gdouble precision
6objectsmedian_magap_rdouble precision
7objectsmax_magap_gdouble precision
8objectsmax_magap_rdouble precision
9objectsmin_magap_gdouble precision
10objectsmin_magap_rdouble precision
11objectssigma_magap_gdouble precision
12objectssigma_magap_rdouble precision
13objectslast_magap_gdouble precision
14objectslast_magap_rdouble precision
15objectsfirst_magap_gdouble precision
16objectsfirst_magap_rdouble precision
17objectsmean_magpsf_gdouble precision
18objectsmean_magpsf_rdouble precision
19objectsmedian_magpsf_gdouble precision
20objectsmedian_magpsf_rdouble precision
21objectsmax_magpsf_gdouble precision
22objectsmax_magpsf_rdouble precision
23objectsmin_magpsf_gdouble precision
24objectsmin_magpsf_rdouble precision
25objectssigma_magpsf_gdouble precision
26objectssigma_magpsf_rdouble precision
27objectslast_magpsf_gdouble precision
28objectslast_magpsf_rdouble precision
29objectsfirst_magpsf_gdouble precision
30objectsfirst_magpsf_rdouble precision
31objectsmeanradouble precision
32objectsmeandecdouble precision
33objectssigmaradouble precision
34objectssigmadecdouble precision
35objectsdeltajddouble precision
36objectslastmjddouble precision
37objectsfirstmjddouble precision
38objectsperioddouble precision
39objectscatalogidinteger
40objectsclassxmatchinteger
41objectsclassrfinteger
42objectspclassrfdouble precision
0insert_tmpoidtext
0probabilitiesoidtext
1probabilitiesclassifieridinteger
2probabilitiesceph_probdouble precision
3probabilitiesdsct_probdouble precision
4probabilitieseb_probdouble precision
5probabilitieslpv_probdouble precision
6probabilitiesrrl_probdouble precision
7probabilitiessne_probdouble precision
8probabilitiesother_probdouble precision
9probabilitiesobject_idinteger
0xmatchoidcharacter varying
1xmatchcatalogidcharacter varying
2xmatchcidcharacter varying
3xmatchobject_idinteger
4xmatchdistdouble precision
5xmatchperioddouble precision
6xmatchclasscharacter varying
0featuresoidtext
1featuresamplitude_1double precision
2featuresandersondarling_1double precision
3featuresautocor_length_1double precision
4featuresbeyond1std_1double precision
5featurescar_sigma_1double precision
6featurescar_mean_1double precision
7featurescar_tau_1double precision
8featurescon_1double precision
9featureseta_e_1double precision
10featuresgskew_1double precision
11featuresmaxslope_1double precision
12featuresmean_1double precision
13featuresmeanvariance_1double precision
14featuresmedianabsdev_1double precision
15featuresmedianbrp_1double precision
16featurespairslopetrend_1double precision
17featurespercentamplitude_1double precision
18featuresq31_1double precision
19featuresperiodls_1double precision
20featuresperiod_fit_1double precision
21featurespsi_cs_1double precision
22featurespsi_eta_1double precision
23featuresrcs_1double precision
24featuresskew_1double precision
25featuressmallkurtosis_1double precision
26featuresstd_1double precision
27featuresstetsonk_1double precision
28featuresfreq1_harmonics_amplitude_0_1double precision
29featuresfreq1_harmonics_rel_phase_0_1double precision
30featuresfreq1_harmonics_amplitude_1_1double precision
31featuresfreq1_harmonics_rel_phase_1_1double precision
32featuresfreq1_harmonics_amplitude_2_1double precision
33featuresfreq1_harmonics_rel_phase_2_1double precision
34featuresfreq1_harmonics_amplitude_3_1double precision
35featuresfreq1_harmonics_rel_phase_3_1double precision
36featuresfreq2_harmonics_amplitude_0_1double precision
37featuresfreq2_harmonics_rel_phase_0_1double precision
38featuresfreq2_harmonics_amplitude_1_1double precision
39featuresfreq2_harmonics_rel_phase_1_1double precision
40featuresfreq2_harmonics_amplitude_2_1double precision
41featuresfreq2_harmonics_rel_phase_2_1double precision
42featuresfreq2_harmonics_amplitude_3_1double precision
43featuresfreq2_harmonics_rel_phase_3_1double precision
44featuresfreq3_harmonics_amplitude_0_1double precision
45featuresfreq3_harmonics_rel_phase_0_1double precision
46featuresfreq3_harmonics_amplitude_1_1double precision
47featuresfreq3_harmonics_rel_phase_1_1double precision
48featuresfreq3_harmonics_amplitude_2_1double precision
49featuresfreq3_harmonics_rel_phase_2_1double precision
50featuresfreq3_harmonics_amplitude_3_1double precision
51featuresfreq3_harmonics_rel_phase_3_1double precision
52featuresn_samples_2double precision
53featuresamplitude_2double precision
54featuresandersondarling_2double precision
55featuresautocor_length_2double precision
56featuresbeyond1std_2double precision
57featurescar_sigma_2double precision
58featurescar_mean_2double precision
59featurescar_tau_2double precision
60featurescon_2double precision
61featureseta_e_2double precision
62featuresgskew_2double precision
63featuresmaxslope_2double precision
64featuresmean_2double precision
65featuresmeanvariance_2double precision
66featuresmedianabsdev_2double precision
67featuresmedianbrp_2double precision
68featurespairslopetrend_2double precision
69featurespercentamplitude_2double precision
70featuresq31_2double precision
71featuresperiodls_2double precision
72featuresperiod_fit_2double precision
73featurespsi_cs_2double precision
74featurespsi_eta_2double precision
75featuresrcs_2double precision
76featuresskew_2double precision
77featuressmallkurtosis_2double precision
78featuresstd_2double precision
79featuresstetsonk_2double precision
80featuresfreq1_harmonics_amplitude_0_2double precision
81featuresfreq1_harmonics_rel_phase_0_2double precision
82featuresfreq1_harmonics_amplitude_1_2double precision
83featuresfreq1_harmonics_rel_phase_1_2double precision
84featuresfreq1_harmonics_amplitude_2_2double precision
85featuresfreq1_harmonics_rel_phase_2_2double precision
86featuresfreq1_harmonics_amplitude_3_2double precision
87featuresfreq1_harmonics_rel_phase_3_2double precision
88featuresfreq2_harmonics_amplitude_0_2double precision
89featuresfreq2_harmonics_rel_phase_0_2double precision
90featuresfreq2_harmonics_amplitude_1_2double precision
91featuresfreq2_harmonics_rel_phase_1_2double precision
92featuresfreq2_harmonics_amplitude_2_2double precision
93featuresfreq2_harmonics_rel_phase_2_2double precision
94featuresfreq2_harmonics_amplitude_3_2double precision
95featuresfreq2_harmonics_rel_phase_3_2double precision
96featuresfreq3_harmonics_amplitude_0_2double precision
97featuresfreq3_harmonics_rel_phase_0_2double precision
98featuresfreq3_harmonics_amplitude_1_2double precision
99featuresfreq3_harmonics_rel_phase_1_2double precision
100featuresfreq3_harmonics_amplitude_2_2double precision
101featuresfreq3_harmonics_rel_phase_2_2double precision
102featuresfreq3_harmonics_amplitude_3_2double precision
103featuresfreq3_harmonics_rel_phase_3_2double precision
104featuresgal_bdouble precision
105featuresgal_ldouble precision
106featuresobject_idinteger
107featuresn_samples_1double precision
0linearLINEARobjectIDbigint
1linearLCtypeinteger
2linearPdouble precision
3linearAdouble precision
4linearmmeddouble precision
5linearstdevdouble precision
6linearrmsdouble precision
7linearLchi2pdfdouble precision
8linearnObsinteger
9linearskewdouble precision
10linearkurtdouble precision
11linearLRdouble precision
12linearCUFinteger
13lineart2integer
14lineart3integer
15linearradouble precision
16lineardecdouble precision
17linearoTypeinteger
18linearnSinteger
19linearrExtdouble precision
20linearudouble precision
21lineargdouble precision
22linearrdouble precision
23linearidouble precision
24linearzdouble precision
25linearuErrdouble precision
26lineargErrdouble precision
27linearrErrdouble precision
28lineariErrdouble precision
29linearzErrdouble precision
0tnsNametext
1tnsRA_origtext
2tnsDEC_origtext
3tnsObj. Typetext
4tnsRedshiftdouble precision
5tnsHost Nametext
6tnsHost Redshiftdouble precision
7tnsDiscovering Group/stext
8tnsClassifying Group/stext
9tnsAssociated Group/stext
10tnsDisc. Internal Nametext
11tnsDisc. Instrument/stext
12tnsClass. Instrument/stext
13tnsTNS ATinteger
14tnsPublicinteger
15tnsEnd Prop. Perioddate
16tnsDiscovery Magdouble precision
17tnsDiscovery Mag Filtertext
18tnsDiscovery Date (UT)timestamp without time zone
19tnsSendertext
20tnsExt. catalog/stext
21tnsradouble precision
22tnsdecdouble precision
23tnsaitoff_xdouble precision
24tnsaitoff_ydouble precision
0magrefobject_idinteger
1magrefoidtext
2magreffidinteger
3magrefrcidinteger
4magreffieldinteger
5magrefmagrefdouble precision
6magrefsigmagrefdouble precision
7magrefcorrectedboolean
0non_detectionsoidtext
1non_detectionsmjddouble precision
2non_detectionsdiffmaglimdouble precision
3non_detectionsfidsmallint
4non_detectionsobject_idinteger
\n", + "" + ], + "text/plain": [ + " table name \\\n", + "0 class id \n", + "1 class name \n", + "0 asassn ASAS-SN Name \n", + "1 asassn Other Names \n", + "2 asassn LCID \n", + "3 asassn ra \n", + "4 asassn dec \n", + "5 asassn Mean VMag \n", + "6 asassn Amplitude \n", + "7 asassn Period \n", + "8 asassn Type \n", + "9 asassn Url \n", + "10 asassn Reference \n", + "11 asassn Dist \n", + "12 asassn Parallax \n", + "13 asassn Parallax Error \n", + "14 asassn Gmag \n", + "15 asassn Bpmag \n", + "16 asassn Rpmag \n", + "17 asassn Jmag \n", + "18 asassn Hmag \n", + "19 asassn Kmag \n", + "20 asassn W1mag \n", + "21 asassn W2mag \n", + "22 asassn W3mag \n", + "23 asassn W4mag \n", + "24 asassn BP-RR \n", + "25 asassn J-K \n", + "26 asassn W1-W2 \n", + "27 asassn W3-W4 \n", + "28 asassn Sllk Statistic \n", + "29 asassn RF Regression Score \n", + "30 asassn Classification Probability \n", + "31 asassn Epoch (HJD) \n", + "0 crtsnorth Catalina_Surveys_ID \n", + "1 crtsnorth Numerical_ID \n", + "2 crtsnorth V_(mag) \n", + "3 crtsnorth Period_(days) \n", + "4 crtsnorth Amplitude \n", + "5 crtsnorth Number_Obs \n", + "6 crtsnorth Var_Type \n", + "7 crtsnorth ra \n", + "8 crtsnorth dec \n", + "0 crtssouth SSS_ID \n", + "1 crtssouth Numerical_ID \n", + "2 crtssouth ra \n", + "3 crtssouth dec \n", + "4 crtssouth Period \n", + "5 crtssouth V_CSS \n", + "6 crtssouth Npts \n", + "7 crtssouth V_amp \n", + "8 crtssouth Type \n", + "0 detections object_id \n", + "1 detections oid \n", + "2 detections candid \n", + "3 detections mjd \n", + "4 detections fid \n", + "5 detections diffmaglim \n", + "6 detections magpsf \n", + "7 detections magap \n", + "8 detections sigmapsf \n", + "9 detections sigmagap \n", + "10 detections ra \n", + "11 detections dec \n", + "12 detections sigmara \n", + "13 detections sigmadec \n", + "14 detections isdiffpos \n", + "15 detections distpsnr1 \n", + "16 detections sgscore1 \n", + "17 detections field \n", + "18 detections rcid \n", + "19 detections magnr \n", + "20 detections sigmagnr \n", + "21 detections rb \n", + "22 detections magpsf_corr \n", + "23 detections magap_corr \n", + "24 detections sigmapsf_corr \n", + "25 detections sigmagap_corr \n", + "0 objects id \n", + "1 objects oid \n", + "2 objects nobs \n", + "3 objects mean_magap_g \n", + "4 objects mean_magap_r \n", + "5 objects median_magap_g \n", + "6 objects median_magap_r \n", + "7 objects max_magap_g \n", + "8 objects max_magap_r \n", + "9 objects min_magap_g \n", + "10 objects min_magap_r \n", + "11 objects sigma_magap_g \n", + "12 objects sigma_magap_r \n", + "13 objects last_magap_g \n", + "14 objects last_magap_r \n", + "15 objects first_magap_g \n", + "16 objects first_magap_r \n", + "17 objects mean_magpsf_g \n", + "18 objects mean_magpsf_r \n", + "19 objects median_magpsf_g \n", + "20 objects median_magpsf_r \n", + "21 objects max_magpsf_g \n", + "22 objects max_magpsf_r \n", + "23 objects min_magpsf_g \n", + "24 objects min_magpsf_r \n", + "25 objects sigma_magpsf_g \n", + "26 objects sigma_magpsf_r \n", + "27 objects last_magpsf_g \n", + "28 objects last_magpsf_r \n", + "29 objects first_magpsf_g \n", + "30 objects first_magpsf_r \n", + "31 objects meanra \n", + "32 objects meandec \n", + "33 objects sigmara \n", + "34 objects sigmadec \n", + "35 objects deltajd \n", + "36 objects lastmjd \n", + "37 objects firstmjd \n", + "38 objects period \n", + "39 objects catalogid \n", + "40 objects classxmatch \n", + "41 objects classrf \n", + "42 objects pclassrf \n", + "0 insert_tmp oid \n", + "0 probabilities oid \n", + "1 probabilities classifierid \n", + "2 probabilities ceph_prob \n", + "3 probabilities dsct_prob \n", + "4 probabilities eb_prob \n", + "5 probabilities lpv_prob \n", + "6 probabilities rrl_prob \n", + "7 probabilities sne_prob \n", + "8 probabilities other_prob \n", + "9 probabilities object_id \n", + "0 xmatch oid \n", + "1 xmatch catalogid \n", + "2 xmatch cid \n", + "3 xmatch object_id \n", + "4 xmatch dist \n", + "5 xmatch period \n", + "6 xmatch class \n", + "0 features oid \n", + "1 features amplitude_1 \n", + "2 features andersondarling_1 \n", + "3 features autocor_length_1 \n", + "4 features beyond1std_1 \n", + "5 features car_sigma_1 \n", + "6 features car_mean_1 \n", + "7 features car_tau_1 \n", + "8 features con_1 \n", + "9 features eta_e_1 \n", + "10 features gskew_1 \n", + "11 features maxslope_1 \n", + "12 features mean_1 \n", + "13 features meanvariance_1 \n", + "14 features medianabsdev_1 \n", + "15 features medianbrp_1 \n", + "16 features pairslopetrend_1 \n", + "17 features percentamplitude_1 \n", + "18 features q31_1 \n", + "19 features periodls_1 \n", + "20 features period_fit_1 \n", + "21 features psi_cs_1 \n", + "22 features psi_eta_1 \n", + "23 features rcs_1 \n", + "24 features skew_1 \n", + "25 features smallkurtosis_1 \n", + "26 features std_1 \n", + "27 features stetsonk_1 \n", + "28 features freq1_harmonics_amplitude_0_1 \n", + "29 features freq1_harmonics_rel_phase_0_1 \n", + "30 features freq1_harmonics_amplitude_1_1 \n", + "31 features freq1_harmonics_rel_phase_1_1 \n", + "32 features freq1_harmonics_amplitude_2_1 \n", + "33 features freq1_harmonics_rel_phase_2_1 \n", + "34 features freq1_harmonics_amplitude_3_1 \n", + "35 features freq1_harmonics_rel_phase_3_1 \n", + "36 features freq2_harmonics_amplitude_0_1 \n", + "37 features freq2_harmonics_rel_phase_0_1 \n", + "38 features freq2_harmonics_amplitude_1_1 \n", + "39 features freq2_harmonics_rel_phase_1_1 \n", + "40 features freq2_harmonics_amplitude_2_1 \n", + "41 features freq2_harmonics_rel_phase_2_1 \n", + "42 features freq2_harmonics_amplitude_3_1 \n", + "43 features freq2_harmonics_rel_phase_3_1 \n", + "44 features freq3_harmonics_amplitude_0_1 \n", + "45 features freq3_harmonics_rel_phase_0_1 \n", + "46 features freq3_harmonics_amplitude_1_1 \n", + "47 features freq3_harmonics_rel_phase_1_1 \n", + "48 features freq3_harmonics_amplitude_2_1 \n", + "49 features freq3_harmonics_rel_phase_2_1 \n", + "50 features freq3_harmonics_amplitude_3_1 \n", + "51 features freq3_harmonics_rel_phase_3_1 \n", + "52 features n_samples_2 \n", + "53 features amplitude_2 \n", + "54 features andersondarling_2 \n", + "55 features autocor_length_2 \n", + "56 features beyond1std_2 \n", + "57 features car_sigma_2 \n", + "58 features car_mean_2 \n", + "59 features car_tau_2 \n", + "60 features con_2 \n", + "61 features eta_e_2 \n", + "62 features gskew_2 \n", + "63 features maxslope_2 \n", + "64 features mean_2 \n", + "65 features meanvariance_2 \n", + "66 features medianabsdev_2 \n", + "67 features medianbrp_2 \n", + "68 features pairslopetrend_2 \n", + "69 features percentamplitude_2 \n", + "70 features q31_2 \n", + "71 features periodls_2 \n", + "72 features period_fit_2 \n", + "73 features psi_cs_2 \n", + "74 features psi_eta_2 \n", + "75 features rcs_2 \n", + "76 features skew_2 \n", + "77 features smallkurtosis_2 \n", + "78 features std_2 \n", + "79 features stetsonk_2 \n", + "80 features freq1_harmonics_amplitude_0_2 \n", + "81 features freq1_harmonics_rel_phase_0_2 \n", + "82 features freq1_harmonics_amplitude_1_2 \n", + "83 features freq1_harmonics_rel_phase_1_2 \n", + "84 features freq1_harmonics_amplitude_2_2 \n", + "85 features freq1_harmonics_rel_phase_2_2 \n", + "86 features freq1_harmonics_amplitude_3_2 \n", + "87 features freq1_harmonics_rel_phase_3_2 \n", + "88 features freq2_harmonics_amplitude_0_2 \n", + "89 features freq2_harmonics_rel_phase_0_2 \n", + "90 features freq2_harmonics_amplitude_1_2 \n", + "91 features freq2_harmonics_rel_phase_1_2 \n", + "92 features freq2_harmonics_amplitude_2_2 \n", + "93 features freq2_harmonics_rel_phase_2_2 \n", + "94 features freq2_harmonics_amplitude_3_2 \n", + "95 features freq2_harmonics_rel_phase_3_2 \n", + "96 features freq3_harmonics_amplitude_0_2 \n", + "97 features freq3_harmonics_rel_phase_0_2 \n", + "98 features freq3_harmonics_amplitude_1_2 \n", + "99 features freq3_harmonics_rel_phase_1_2 \n", + "100 features freq3_harmonics_amplitude_2_2 \n", + "101 features freq3_harmonics_rel_phase_2_2 \n", + "102 features freq3_harmonics_amplitude_3_2 \n", + "103 features freq3_harmonics_rel_phase_3_2 \n", + "104 features gal_b \n", + "105 features gal_l \n", + "106 features object_id \n", + "107 features n_samples_1 \n", + "0 linear LINEARobjectID \n", + "1 linear LCtype \n", + "2 linear P \n", + "3 linear A \n", + "4 linear mmed \n", + "5 linear stdev \n", + "6 linear rms \n", + "7 linear Lchi2pdf \n", + "8 linear nObs \n", + "9 linear skew \n", + "10 linear kurt \n", + "11 linear LR \n", + "12 linear CUF \n", + "13 linear t2 \n", + "14 linear t3 \n", + "15 linear ra \n", + "16 linear dec \n", + "17 linear oType \n", + "18 linear nS \n", + "19 linear rExt \n", + "20 linear u \n", + "21 linear g \n", + "22 linear r \n", + "23 linear i \n", + "24 linear z \n", + "25 linear uErr \n", + "26 linear gErr \n", + "27 linear rErr \n", + "28 linear iErr \n", + "29 linear zErr \n", + "0 tns Name \n", + "1 tns RA_orig \n", + "2 tns DEC_orig \n", + "3 tns Obj. Type \n", + "4 tns Redshift \n", + "5 tns Host Name \n", + "6 tns Host Redshift \n", + "7 tns Discovering Group/s \n", + "8 tns Classifying Group/s \n", + "9 tns Associated Group/s \n", + "10 tns Disc. Internal Name \n", + "11 tns Disc. Instrument/s \n", + "12 tns Class. Instrument/s \n", + "13 tns TNS AT \n", + "14 tns Public \n", + "15 tns End Prop. Period \n", + "16 tns Discovery Mag \n", + "17 tns Discovery Mag Filter \n", + "18 tns Discovery Date (UT) \n", + "19 tns Sender \n", + "20 tns Ext. catalog/s \n", + "21 tns ra \n", + "22 tns dec \n", + "23 tns aitoff_x \n", + "24 tns aitoff_y \n", + "0 magref object_id \n", + "1 magref oid \n", + "2 magref fid \n", + "3 magref rcid \n", + "4 magref field \n", + "5 magref magref \n", + "6 magref sigmagref \n", + "7 magref corrected \n", + "0 non_detections oid \n", + "1 non_detections mjd \n", + "2 non_detections diffmaglim \n", + "3 non_detections fid \n", + "4 non_detections object_id \n", + "\n", + " dtype \n", + "0 integer \n", + "1 character varying \n", + "0 text \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 integer \n", + "6 integer \n", + "7 double precision \n", + "8 double precision \n", + "0 text \n", + "1 bigint \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 integer \n", + "7 double precision \n", + "8 integer \n", + "0 integer \n", + "1 text \n", + "2 bigint \n", + "3 double precision \n", + "4 smallint \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 smallint \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 integer \n", + "40 integer \n", + "41 integer \n", + "42 double precision \n", + "0 text \n", + "0 text \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 integer \n", + "0 character varying \n", + "1 character varying \n", + "2 character varying \n", + "3 integer \n", + "4 double precision \n", + "5 double precision \n", + "6 character varying \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 double precision \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 double precision \n", + "13 double precision \n", + "14 double precision \n", + "15 double precision \n", + "16 double precision \n", + "17 double precision \n", + "18 double precision \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "30 double precision \n", + "31 double precision \n", + "32 double precision \n", + "33 double precision \n", + "34 double precision \n", + "35 double precision \n", + "36 double precision \n", + "37 double precision \n", + "38 double precision \n", + "39 double precision \n", + "40 double precision \n", + "41 double precision \n", + "42 double precision \n", + "43 double precision \n", + "44 double precision \n", + "45 double precision \n", + "46 double precision \n", + "47 double precision \n", + "48 double precision \n", + "49 double precision \n", + "50 double precision \n", + "51 double precision \n", + "52 double precision \n", + "53 double precision \n", + "54 double precision \n", + "55 double precision \n", + "56 double precision \n", + "57 double precision \n", + "58 double precision \n", + "59 double precision \n", + "60 double precision \n", + "61 double precision \n", + "62 double precision \n", + "63 double precision \n", + "64 double precision \n", + "65 double precision \n", + "66 double precision \n", + "67 double precision \n", + "68 double precision \n", + "69 double precision \n", + "70 double precision \n", + "71 double precision \n", + "72 double precision \n", + "73 double precision \n", + "74 double precision \n", + "75 double precision \n", + "76 double precision \n", + "77 double precision \n", + "78 double precision \n", + "79 double precision \n", + "80 double precision \n", + "81 double precision \n", + "82 double precision \n", + "83 double precision \n", + "84 double precision \n", + "85 double precision \n", + "86 double precision \n", + "87 double precision \n", + "88 double precision \n", + "89 double precision \n", + "90 double precision \n", + "91 double precision \n", + "92 double precision \n", + "93 double precision \n", + "94 double precision \n", + "95 double precision \n", + "96 double precision \n", + "97 double precision \n", + "98 double precision \n", + "99 double precision \n", + "100 double precision \n", + "101 double precision \n", + "102 double precision \n", + "103 double precision \n", + "104 double precision \n", + "105 double precision \n", + "106 integer \n", + "107 double precision \n", + "0 bigint \n", + "1 integer \n", + "2 double precision \n", + "3 double precision \n", + "4 double precision \n", + "5 double precision \n", + "6 double precision \n", + "7 double precision \n", + "8 integer \n", + "9 double precision \n", + "10 double precision \n", + "11 double precision \n", + "12 integer \n", + "13 integer \n", + "14 integer \n", + "15 double precision \n", + "16 double precision \n", + "17 integer \n", + "18 integer \n", + "19 double precision \n", + "20 double precision \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "25 double precision \n", + "26 double precision \n", + "27 double precision \n", + "28 double precision \n", + "29 double precision \n", + "0 text \n", + "1 text \n", + "2 text \n", + "3 text \n", + "4 double precision \n", + "5 text \n", + "6 double precision \n", + "7 text \n", + "8 text \n", + "9 text \n", + "10 text \n", + "11 text \n", + "12 text \n", + "13 integer \n", + "14 integer \n", + "15 date \n", + "16 double precision \n", + "17 text \n", + "18 timestamp without time zone \n", + "19 text \n", + "20 text \n", + "21 double precision \n", + "22 double precision \n", + "23 double precision \n", + "24 double precision \n", + "0 integer \n", + "1 text \n", + "2 integer \n", + "3 integer \n", + "4 integer \n", + "5 double precision \n", + "6 double precision \n", + "7 boolean \n", + "0 text \n", + "1 double precision \n", + "2 double precision \n", + "3 smallint \n", + "4 integer " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dftab = pd.DataFrame()\n", + "for tab in tables:\n", + " cols = pd.DataFrame()\n", + " query = \"select column_name from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " results = cur.fetchall()\n", + " if len(results) > 0:\n", + " cols[\"table\"] = [tab[0] for i in results]\n", + " cols[\"name\"] = [res[0] for res in results]\n", + " query = \"select data_type from information_schema.columns where table_name = '%s';\" % tab\n", + " cur.execute(query)\n", + " cols[\"dtype\"] = [dt[0] for dt in cur.fetchall()]\n", + " dftab = pd.concat([dftab, cols])\n", + "pd.options.display.max_rows = 999\n", + "display(dftab)\n", + "pd.options.display.max_rows = 101" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Function to query data more easily" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-28T15:13:30.636730Z", + "start_time": "2019-05-28T15:13:30.633746Z" + } + }, + "outputs": [], + "source": [ + "def sql_query(query):\n", + " cur.execute(query)\n", + " result = cur.fetchall()\n", + " \n", + " # Extract the column names\n", + " col_names = []\n", + " for elt in cur.description:\n", + " col_names.append(elt[0])\n", + "\n", + " #Convert to dataframe\n", + " df = pd.DataFrame(np.array(result), columns = col_names)\n", + " return(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query objects with xmatch label" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "query='''\n", + "select oid, classxmatch, \n", + "nobs, meanra, \n", + "meandec\n", + "\n", + "from objects\n", + "\n", + "where objects.classxmatch is not null\n", + "'''\n", + "xmatched_sources = sql_query(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OTHER 2388\n", + "CEPH 780\n", + "DSCT 1018\n", + "EB 44787\n", + "LPV 4475\n", + "RRL 20589\n", + "SNE 1000\n", + " oid classxmatch nobs meanra meandec\n", + "75032 ZTF17aaazqqa 3 1 15.5786863 38.0385364\n", + "75033 ZTF18aabeppx 5 3 253.039347333333 35.1768932\n", + "75034 ZTF18aakzliv 6 7 260.479410457143 29.3093011857143\n", + "75035 ZTF18aaxjigo 3 125 308.9459258216 40.8836436768\n", + "75036 ZTF18abbuwlf 4 122 2.07410858114754 52.7658553262295\n" + ] + } + ], + "source": [ + "count = xmatched_sources.groupby('classxmatch').count()\n", + "for i in range(7):\n", + " print(XMATCH_TABLE[i], count.iloc[i]['oid'])\n", + "print(xmatched_sources.tail())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get oids for SNe and Variable stars" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "We have 1000 SNe and 71649 variable stars\n", + " oid classxmatch nobs meanra meandec\n", + "85 ZTF18abcyilc 6 7 184.048474757143 68.5583956571429\n", + "127 ZTF18aayiahw 6 9 209.808347511111 28.5407553888889\n", + "135 ZTF18acvwagt 6 2 179.98748235 -0.55052565\n", + "143 ZTF18abdiqdh 6 9 184.683486444444 44.7819575666667\n", + "217 ZTF18aahheaj 6 5 195.11048026 18.61935766\n" + ] + } + ], + "source": [ + "SNe = xmatched_sources[xmatched_sources.classxmatch == '6']\n", + "VS = xmatched_sources[(xmatched_sources.classxmatch == '1') \n", + " | (xmatched_sources.classxmatch == '2')\n", + " | (xmatched_sources.classxmatch == '3')\n", + " | (xmatched_sources.classxmatch == '4')\n", + " | (xmatched_sources.classxmatch == '5')]\n", + "print('We have %d SNe and %d variable stars' % (len(SNe), len(VS)))\n", + "print(SNe.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize object" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def get_lc_data(oid, doplot = False, doNED = False):\n", + " # detections\n", + " query=\"select oid, ra, dec, fid, mjd, magpsf_corr, sigmapsf_corr from detections where oid='%s'\" % oid\n", + " detections = sql_query(query)\n", + " for col in list(detections):\n", + " if col != 'oid':\n", + " detections[col] = pd.to_numeric(detections[col], errors = 'ignore')\n", + " \n", + " # non detections\n", + " query=\"select oid, fid, mjd, diffmaglim from non_detections where oid='%s'\" % oid\n", + " non_detections = sql_query(query)\n", + " for col in list(non_detections):\n", + " if col != 'oid':\n", + " non_detections[col] = pd.to_numeric(non_detections[col], errors = 'ignore')\n", + " \n", + " # sort by date\n", + " detections.sort_values(by='mjd', inplace=True)\n", + " non_detections.sort_values(by='mjd', inplace=True)\n", + " \n", + " # find NED galaxies\n", + " if doNED:\n", + " co = coordinates.SkyCoord(ra=xmatched_sources.meanra[xmatched_sources.oid == oid], \n", + " dec=xmatched_sources.meandec[xmatched_sources.oid == oid], \n", + " unit=(u.deg, u.deg), frame='fk4')\n", + " result_table = Ned.query_region(co, radius=0.01 * u.deg, equinox='J2000.0')\n", + " display(result_table)\n", + " \n", + " # plot\n", + " if doplot:\n", + " classxmatch = xmatched_sources['classxmatch'][xmatched_sources.oid == oid].values[0]\n", + " classxmatch = XMATCH_TABLE[int(classxmatch)]\n", + " fig, ax = plt.subplots(figsize = (14, 7))\n", + " labels = {1: 'g', 2: 'r'}\n", + " colors = {1: 'g', 2: 'r'}\n", + " for fid in [1, 2]:\n", + " mask = detections.fid == fid\n", + " ax.errorbar(detections[mask].mjd, detections[mask].magpsf_corr, \n", + " yerr = detections[mask].sigmapsf_corr, c = colors[fid], marker = 'o', label = labels[fid])\n", + " mask = (non_detections.fid == fid) & (non_detections.diffmaglim > -900)\n", + " ax.scatter(non_detections[mask].mjd, non_detections[mask].diffmaglim, c = colors[fid], alpha = 0.5,\n", + " marker = 'v', label = \"lim.mag. %s\" % labels[fid])\n", + " ax.set_title('oid: %s, xmatch class: %s' % (oid, classxmatch))\n", + " ax.set_xlabel(\"MJD\")\n", + " ax.set_ylabel(\"Magnitude\")\n", + " ax.legend()\n", + " ax.set_ylim(ax.get_ylim()[::-1]) \n", + " \n", + " # return data\n", + " return detections, non_detections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw random object" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF19aacwljg\n" + ] + }, + { + "data": { + "text/html": [ + "Table masked=True length=5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32bytes30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
1SDSS J093328.32+192721.3143.3680119.45592G----20.1g0.56700151040
2SDSS J093328.65+192722.3143.3693819.45622G----22.5g0.57700151040
3SDSS J093329.46+192647.8143.3727619.44662G----20.8g0.17400151040
4SDSS J093329.55+192620.5143.3731619.43905G----22.6g0.49400151040
5SDSS J093330.52+192703.6143.377219.45102G----22.9g0.50100151030
" + ], + "text/plain": [ + "\n", + " No. Object Name RA ... Diameter Points Associations\n", + " degrees ... \n", + "int32 bytes30 float64 ... int32 int32 \n", + "----- ------------------------ ---------- ... --------------- ------------\n", + " 1 SDSS J093328.32+192721.3 143.36801 ... 4 0\n", + " 2 SDSS J093328.65+192722.3 143.36938 ... 4 0\n", + " 3 SDSS J093329.46+192647.8 143.37276 ... 4 0\n", + " 4 SDSS J093329.55+192620.5 143.37316 ... 4 0\n", + " 5 SDSS J093330.52+192703.6 143.3772 ... 3 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "random_oid = SNe['oid'].iloc[np.random.randint(len(SNe))]\n", + "print(random_oid)\n", + "detections, non_detections = get_lc_data(random_oid, doplot=True, doNED=True);" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ZTF18aabjrfi\n" + ] + }, + { + "data": { + "text/html": [ + "Table masked=True length=4\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
No.Object NameRADECTypeVelocityRedshiftRedshift FlagMagnitude and FilterSeparationReferencesNotesPhotometry PointsPositionsRedshift PointsDiameter PointsAssociations
degreesdegreeskm / sarcmin
int32bytes30float64float64objectfloat64float64objectobjectfloat64int32int32int32int32int32int32int32
1SDSS J084747.17+420200.5131.9465542.03348G----23.6g0.43400151030
2SDSS J084747.71+420150.4131.9488242.03068G----21.5g0.26100151040
3SDSS J084749.13+420149.9131.9547242.03055G----23.0g0.09300151040
4SDSS J084750.96+420120.4131.9623642.02235G----22.2g0.5400151040
" + ], + "text/plain": [ + "\n", + " No. Object Name RA ... Diameter Points Associations\n", + " degrees ... \n", + "int32 bytes30 float64 ... int32 int32 \n", + "----- ------------------------ ---------- ... --------------- ------------\n", + " 1 SDSS J084747.17+420200.5 131.94655 ... 3 0\n", + " 2 SDSS J084747.71+420150.4 131.94882 ... 4 0\n", + " 3 SDSS J084749.13+420149.9 131.95472 ... 4 0\n", + " 4 SDSS J084750.96+420120.4 131.96236 ... 4 0" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "random_oid = VS['oid'].iloc[np.random.randint(len(VS))]\n", + "print(random_oid)\n", + "detections, non_detections = get_lc_data(random_oid, doplot=True, doNED=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "SNe[['oid', 'classxmatch', 'nobs']].to_pickle('SNe_xmatch.pkl')\n", + "VS[['oid', 'classxmatch', 'nobs']].to_pickle('VS_xmatch.pkl')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Xmatch_example.ipynb b/notebooks/Xmatch_example.ipynb new file mode 100644 index 0000000..88b53f4 --- /dev/null +++ b/notebooks/Xmatch_example.ipynb @@ -0,0 +1,1492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ALeRCE Xmatch service example\n", + "\n", + "A short example for accessing the courrent data aviable on the ALeRCE servers for cross-match, using custom arbirtrary objects" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting alerce_xmatch\n", + " Downloading https://files.pythonhosted.org/packages/f0/a0/12cbd8ef8b859cef79197acf8c5efcd81a5548ba195e582d651e9ca2f10b/alerce_xmatch-0.0.5-py3-none-any.whl\n", + "Installing collected packages: alerce-xmatch\n", + "Successfully installed alerce-xmatch-0.0.5\n" + ] + } + ], + "source": [ + "!pip install alerce_xmatch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For documentation visit GitHub repository: https://github.com/alercebroker/xmatch_client\n", + "\n", + "Or the PyPi project: https://pypi.org/project/alerce-xmatch/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data example" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#input\n", + "columns = ['oid', 'ra', 'dec']\n", + "values = [\n", + " [0, 168.821096816667, 29.6921437083333],\n", + " [1, 164.669160169091, 16.0180675727273],\n", + " [2, 174.65028162381, 20.726187647619],\n", + " [3, 154.4155987, 30.8199994785714]\n", + "]\n", + "df = pd.DataFrame(values, columns=columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using Catalog class" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from alerce_xmatch import Catalog, TargetCatalog, OutputCols\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create Catalog class with test data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Catalog name: My catalog\n", + "Crossmatch take: 12.34043574333191 s\n", + "Result:\n" + ] + }, + { + "data": { + "text/html": [ + "
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1 rows × 28 columns

\n", + "" + ], + "text/plain": [ + " My catalog_dec My catalog_oid My catalog_ra \\\n", + "0 16.018068 1.0 164.66916 \n", + "\n", + " GAIA-DR2_designation GAIA-DR2_ra GAIA-DR2_dec GAIA-DR2_parallax \\\n", + "0 Gaia DR2 3981758871818873600 164.669179 16.018048 0.107938 \n", + "\n", + " GAIA-DR2_pmra GAIA-DR2_pmdec GAIA-DR2_astrometric_pseudo_colour ... \\\n", + "0 -3.091704 -2.052673 1.652324 ... \n", + "\n", + " GAIA-DR2_bp_rp GAIA-DR2_bp_g GAIA-DR2_g_rp GAIA-DR2_radial_velocity \\\n", + "0 0.572648 0.185996 0.386652 NaN \n", + "\n", + " GAIA-DR2_teff_val GAIA-DR2_lum_val GAIA-DR2_classifier_name \\\n", + "0 6898.6665 NaN nTransits:2+ \n", + "\n", + " GAIA-DR2_best_class_name GAIA-DR2_best_class_score separation \n", + "0 RRAB 0.985119 0.093911 \n", + "\n", + "[1 rows x 28 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_catalog = Catalog(df=df, name=\"My catalog\")\n", + "\n", + "## Crossmatch with Gaia (a Catalog in DataBase)\n", + "\n", + "start = time.time()\n", + "\n", + "cross_catalog = my_catalog.crossmatch(TargetCatalog.GAIA)\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Catalog name: {}\".format(cross_catalog.name))\n", + "print(\"Crossmatch take: {} s\".format(end - start))\n", + "print(\"Result:\")\n", + "cross_catalog.df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To much info? Let's do a crossmatch with a catalog in memory (it supposed to be faster) and just see our info" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Crossmatch take: 0.9985611438751221 s\n", + "Result:\n" + ] + }, + { + "data": { + "text/html": [ + "
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separationMy catalog_oidMy catalog_raMy catalog_dec
0None0.0168.82109729.692144
1None3.0154.41559930.819999
2None1.0164.66916016.018068
3None2.0174.65028220.726188
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" + ], + "text/plain": [ + " separation My catalog_oid My catalog_ra My catalog_dec\n", + "0 None 0.0 168.821097 29.692144\n", + "1 None 3.0 154.415599 30.819999\n", + "2 None 1.0 164.669160 16.018068\n", + "3 None 2.0 174.650282 20.726188" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "start = time.time()\n", + "\n", + "cross_catalog = my_catalog.crossmatch(TargetCatalog.ASASSN, output=OutputCols.SOURCE) # Just my info please\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Crossmatch take: {} s\".format(end - start))\n", + "print(\"Result:\")\n", + "cross_catalog.df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I know the ra, dec position, maybe that's info is not neccesary" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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separationZTF_indexZTF_oid
0None1107927ZTF19aadfkyz
1None6233821ZTF19aadfkza
2None9711443ZTF19aaknuwq
3None13445830ZTF18aaxpvha
4None13448367ZTF17aabtute
5None13448374ZTF17aaajmde
6None13845503ZTF18acbwbhp
7None14676185ZTF18aaanyxu
8None15105850ZTF18aabksxe
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" + ], + "text/plain": [ + " separation ZTF_index ZTF_oid\n", + "0 None 1107927 ZTF19aadfkyz\n", + "1 None 6233821 ZTF19aadfkza\n", + "2 None 9711443 ZTF19aaknuwq\n", + "3 None 13445830 ZTF18aaxpvha\n", + "4 None 13448367 ZTF17aabtute\n", + "5 None 13448374 ZTF17aaajmde\n", + "6 None 13845503 ZTF18acbwbhp\n", + "7 None 14676185 ZTF18aaanyxu\n", + "8 None 15105850 ZTF18aabksxe" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cross_catalog = my_catalog.crossmatch(TargetCatalog.ZTF, output=OutputCols.TARGET, # Just yuor info\n", + " radec=False) # Don't show me the position\n", + "cross_catalog.df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using just the DataFrame\n", + "If you don't want to use that annoying Catalog class, use the crossmatch static method instead" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from alerce_xmatch import crossmatch" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ZTF_indexZTF_oidZTF_raZTF_decradecseparationinput_catalog_oidinput_catalog_rainput_catalog_dec
01107927ZTF19aadfkyz154.41595030.819799154.41577430.819899None3.0154.41559930.819999
16233821ZTF19aadfkza154.41616130.819826154.41588030.819913None3.0154.41559930.819999
29711443ZTF19aaknuwq164.66915616.017554164.66915816.017811None1.0164.66916016.018068
313445830ZTF18aaxpvha164.66951816.018026164.66933916.018047None1.0164.66916016.018068
413448367ZTF17aabtute164.66916516.018068164.66916316.018068None1.0164.66916016.018068
513448374ZTF17aaajmde174.65028420.726185174.65028320.726186None2.0174.65028220.726188
613845503ZTF18acbwbhp154.41560930.819991154.41560430.819995None3.0154.41559930.819999
714676185ZTF18aaanyxu168.82100029.692205168.82104829.692174None0.0168.82109729.692144
815105850ZTF18aabksxe168.82110229.692145168.82109929.692144None0.0168.82109729.692144
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" + ], + "text/plain": [ + " ZTF_index ZTF_oid ZTF_ra ZTF_dec ra dec \\\n", + "0 1107927 ZTF19aadfkyz 154.415950 30.819799 154.415774 30.819899 \n", + "1 6233821 ZTF19aadfkza 154.416161 30.819826 154.415880 30.819913 \n", + "2 9711443 ZTF19aaknuwq 164.669156 16.017554 164.669158 16.017811 \n", + "3 13445830 ZTF18aaxpvha 164.669518 16.018026 164.669339 16.018047 \n", + "4 13448367 ZTF17aabtute 164.669165 16.018068 164.669163 16.018068 \n", + "5 13448374 ZTF17aaajmde 174.650284 20.726185 174.650283 20.726186 \n", + "6 13845503 ZTF18acbwbhp 154.415609 30.819991 154.415604 30.819995 \n", + "7 14676185 ZTF18aaanyxu 168.821000 29.692205 168.821048 29.692174 \n", + "8 15105850 ZTF18aabksxe 168.821102 29.692145 168.821099 29.692144 \n", + "\n", + " separation input_catalog_oid input_catalog_ra input_catalog_dec \n", + "0 None 3.0 154.415599 30.819999 \n", + "1 None 3.0 154.415599 30.819999 \n", + "2 None 1.0 164.669160 16.018068 \n", + "3 None 1.0 164.669160 16.018068 \n", + "4 None 1.0 164.669160 16.018068 \n", + "5 None 2.0 174.650282 20.726188 \n", + "6 None 3.0 154.415599 30.819999 \n", + "7 None 0.0 168.821097 29.692144 \n", + "8 None 0.0 168.821097 29.692144 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crossmatch(df, TargetCatalog.ZTF) # df was declared on cell 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also use the crossmatch method with a Catalog object, and the output will be a Catalog as well" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LINEAR_idLINEAR_classLINEAR_periodLINEAR_ALINEAR_mmedLINEAR_stdevLINEAR_rmsLINEAR_Lchi2pdfLINEAR_nObsLINEAR_skew...LINEAR_gErrLINEAR_rErrLINEAR_iErrLINEAR_zErrradecseparationMy catalog_oidMy catalog_raMy catalog_dec
01863652RR Lyr ab0.6356220.8816.680.310.381.671250-0.29...0.010.010.010.01174.65026720.726207None2.0174.65028220.726188
123555046RR Lyr ab0.5143670.7715.620.250.272.268303-0.66...0.010.010.010.01164.66918316.018061None1.0164.66916016.018068
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2 rows × 36 columns

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" + ], + "text/plain": [ + " LINEAR_id LINEAR_class LINEAR_period LINEAR_A LINEAR_mmed LINEAR_stdev \\\n", + "0 1863652 RR Lyr ab 0.635622 0.88 16.68 0.31 \n", + "1 23555046 RR Lyr ab 0.514367 0.77 15.62 0.25 \n", + "\n", + " LINEAR_rms LINEAR_Lchi2pdf LINEAR_nObs LINEAR_skew ... LINEAR_gErr \\\n", + "0 0.38 1.671 250 -0.29 ... 0.01 \n", + "1 0.27 2.268 303 -0.66 ... 0.01 \n", + "\n", + " LINEAR_rErr LINEAR_iErr LINEAR_zErr ra dec separation \\\n", + "0 0.01 0.01 0.01 174.650267 20.726207 None \n", + "1 0.01 0.01 0.01 164.669183 16.018061 None \n", + "\n", + " My catalog_oid My catalog_ra My catalog_dec \n", + "0 2.0 174.650282 20.726188 \n", + "1 1.0 164.669160 16.018068 \n", + "\n", + "[2 rows x 36 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crossmatch(my_catalog, TargetCatalog.LINEAR).df # Remember print just the DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## An example with a bigger catalog\n", + "\n", + "To check a bigger crossmatch, we're gonna use the _catalina.pkl_ dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "with open(\"example_data/catalina.pkl\", \"rb\") as file:\n", + " catalina = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It was found 34757 rows\n", + "Firsts rows:\n" + ] + }, + { + "data": { + "text/html": [ + "
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ASASSN_idASASSN_Other NamesASASSN_LCIDASASSN_raASASSN_decASASSN_Mean VMagASASSN_AmplitudeASASSN_periodASASSN_classASASSN_Url...ASASSN_Sllk StatisticASASSN_RF Regression ScoreASASSN_Classification ProbabilityASASSN_Epoch (HJD)radecseparationCatalina Example_oidCatalina Example_raCatalina Example_dec
0ASASSN-V J204308.90-011533.7CSS_J204308.9-011532175106310.78710-1.2593715.160.380.4294613EB/database/light_curves/283889...0.280.860.9982.457904e+06310.787175-1.259241NoneCSS_J204308.9-011532310.787250-1.259111
1ASASSN-V J013051.79+313851.1AM Psc5115022.7157831.6475213.830.80107.8898995SR/database/light_curves/381090...0.600.701.0002.457588e+0622.71574431.647552NoneCSS_J013051.7+31385122.71570831.647583
2ASASSN-V J234254.97+384328.2CSS_J234255.0+384328131254355.7290338.7245014.830.93216.351107SR/database/light_curves/382465...0.730.661.0002.457309e+06355.72922338.724486NoneCSS_J234255.0+384328355.72941738.724472
3ASASSN-V J165903.31+433053.6V0782 Her56887254.7637943.5149014.191.70228.2831729SR/database/light_curves/382544...0.260.871.0002.458019e+06254.76381243.514881NoneCSS_J165903.3+433053254.76383343.514861
4ASASSN-V J055246.35-024207.0CSS_J055246.3-02420742461088.19312-2.7019415.550.462.1161883EA/database/light_curves/286677...0.320.840.9982.457335e+0688.193122-2.701942NoneCSS_J055246.3-02420788.193125-2.701944
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5 rows × 38 columns

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" + ], + "text/plain": [ + " ASASSN_id ASASSN_Other Names ASASSN_LCID ASASSN_ra \\\n", + "0 ASASSN-V J204308.90-011533.7 CSS_J204308.9-011532 175106 310.78710 \n", + "1 ASASSN-V J013051.79+313851.1 AM Psc 51150 22.71578 \n", + "2 ASASSN-V J234254.97+384328.2 CSS_J234255.0+384328 131254 355.72903 \n", + "3 ASASSN-V J165903.31+433053.6 V0782 Her 56887 254.76379 \n", + "4 ASASSN-V J055246.35-024207.0 CSS_J055246.3-024207 424610 88.19312 \n", + "\n", + " ASASSN_dec ASASSN_Mean VMag ASASSN_Amplitude ASASSN_period ASASSN_class \\\n", + "0 -1.25937 15.16 0.38 0.4294613 EB \n", + "1 31.64752 13.83 0.80 107.8898995 SR \n", + "2 38.72450 14.83 0.93 216.351107 SR \n", + "3 43.51490 14.19 1.70 228.2831729 SR \n", + "4 -2.70194 15.55 0.46 2.1161883 EA \n", + "\n", + " ASASSN_Url ... ASASSN_Sllk Statistic \\\n", + "0 /database/light_curves/283889 ... 0.28 \n", + "1 /database/light_curves/381090 ... 0.60 \n", + "2 /database/light_curves/382465 ... 0.73 \n", + "3 /database/light_curves/382544 ... 0.26 \n", + "4 /database/light_curves/286677 ... 0.32 \n", + "\n", + " ASASSN_RF Regression Score ASASSN_Classification Probability \\\n", + "0 0.86 0.998 \n", + "1 0.70 1.000 \n", + "2 0.66 1.000 \n", + "3 0.87 1.000 \n", + "4 0.84 0.998 \n", + "\n", + " ASASSN_Epoch (HJD) ra dec separation \\\n", + "0 2.457904e+06 310.787175 -1.259241 None \n", + "1 2.457588e+06 22.715744 31.647552 None \n", + "2 2.457309e+06 355.729223 38.724486 None \n", + "3 2.458019e+06 254.763812 43.514881 None \n", + "4 2.457335e+06 88.193122 -2.701942 None \n", + "\n", + " Catalina Example_oid Catalina Example_ra Catalina Example_dec \n", + "0 CSS_J204308.9-011532 310.787250 -1.259111 \n", + "1 CSS_J013051.7+313851 22.715708 31.647583 \n", + "2 CSS_J234255.0+384328 355.729417 38.724472 \n", + "3 CSS_J165903.3+433053 254.763833 43.514861 \n", + "4 CSS_J055246.3-024207 88.193125 -2.701944 \n", + "\n", + "[5 rows x 38 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create catalog:\n", + "catalina_catalog = Catalog(df=catalina, name=\"Catalina Example\")\n", + "\n", + "result = catalina_catalog.crossmatch(TargetCatalog.ASASSN).df\n", + "\n", + "print(\"It was found {} rows\".format(len(result)))\n", + "print(\"Firsts rows:\")\n", + "result.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It was found 45052 rows\n", + "Firsts rows:\n" + ] + }, + { + "data": { + "text/html": [ + "
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ZTF_indexZTF_oidZTF_raZTF_decradecseparationCatalina Example_oidCatalina Example_raCatalina Example_dec
01871ZTF18accduwj131.98449127.225956131.98449527.225950NoneCSS_J084756.2+271333131.98450027.225944
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" + ], + "text/plain": [ + " ZTF_index ZTF_oid ZTF_ra ZTF_dec ra dec \\\n", + "0 1871 ZTF18accduwj 131.984491 27.225956 131.984495 27.225950 \n", + "1 6148 ZTF18actbgye 82.033952 -6.872630 82.033872 -6.872579 \n", + "2 9461 ZTF19aamnvqt 121.353403 6.106200 121.353452 6.105989 \n", + "3 10020 ZTF19aapcpqx 106.680897 40.673811 106.681094 40.673989 \n", + "4 11245 ZTF17aaahmvb 97.973156 49.054054 97.973057 49.054207 \n", + "\n", + " separation Catalina Example_oid Catalina Example_ra Catalina Example_dec \n", + "0 None CSS_J084756.2+271333 131.984500 27.225944 \n", + "1 None CSS_J052808.1-065221 82.033792 -6.872528 \n", + "2 None CSS_J080524.8+060620 121.353500 6.105778 \n", + "3 None CSS_J070643.5+404027 106.681292 40.674167 \n", + "4 None CSS_J063153.5+490315 97.972958 49.054361 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Crossmatch with ZTF\n", + "result = catalina_catalog.crossmatch(TargetCatalog.ZTF).df\n", + "\n", + "print(\"It was found {} rows\".format(len(result)))\n", + "print(\"Firsts rows:\")\n", + "result.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It takes 39.94607448577881 s\n", + "It was found 761 rows\n", + "Firsts rows:\n" + ] + }, + { + "data": { + "text/html": [ + "
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TNS_idTNS_RA_origTNS_DEC_origTNS_classTNS_RedshiftTNS_Host NameTNS_Host RedshiftTNS_Discovering Group/sTNS_Classifying Group/sTNS_Associated Group/s...TNS_raTNS_decTNS_aitoff_xTNS_aitoff_yradecseparationinput_catalog_nameinput_catalog_rainput_catalog_dec
0AT 2018kxt04:36:52.120-52:16:38.64UnknownNaNNaNNaNGaiaAlertsNaNGaiaAlerts...69.217167-52.277400-78.085702-61.32593169.217172-52.277402NoneWISEA J043652.12-521638.669.217177-52.277404
1AT 2018kvz07:44:20.390-69:19:07.10UnknownNaNNaNNaNGaiaAlertsNaNGaiaAlerts...116.084958-69.318639-29.514722-73.858661116.084972-69.318648NoneWISEA J074420.39-691907.1116.084986-69.318657
2AT 2018kvr05:33:02.520-48:25:21.47UnknownNaNNaNNaNGaiaAlertsNaNGaiaAlerts...83.260500-48.422631-74.382066-56.08770083.260424-48.422669NoneJ053302.48-482521.783.260348-48.422708
3AT 2018kuv05:38:50.360-44:05:08.95UnknownNaNNaNNaNGaiaAlertsNaNGaiaAlerts...84.709833-44.085819-78.434362-51.40191084.709836-44.085818NonePKS 0537-44184.709840-44.085816
4AT 2019nr23:58:02.130-45:55:18.80UnknownNaNNaNNaNGaiaAlertsNaNGaiaAlerts...359.508875-45.921889125.027521-64.559123359.508896-45.921897NonePKS 2355-461359.508916-45.921905
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" + ], + "text/plain": [ + " TNS_id TNS_RA_orig TNS_DEC_orig TNS_class TNS_Redshift \\\n", + "0 AT 2018kxt 04:36:52.120 -52:16:38.64 Unknown NaN \n", + "1 AT 2018kvz 07:44:20.390 -69:19:07.10 Unknown NaN \n", + "2 AT 2018kvr 05:33:02.520 -48:25:21.47 Unknown NaN \n", + "3 AT 2018kuv 05:38:50.360 -44:05:08.95 Unknown NaN \n", + "4 AT 2019nr 23:58:02.130 -45:55:18.80 Unknown NaN \n", + "\n", + " TNS_Host Name TNS_Host Redshift TNS_Discovering Group/s \\\n", + "0 NaN NaN GaiaAlerts \n", + "1 NaN NaN GaiaAlerts \n", + "2 NaN NaN GaiaAlerts \n", + "3 NaN NaN GaiaAlerts \n", + "4 NaN NaN GaiaAlerts \n", + "\n", + " TNS_Classifying Group/s TNS_Associated Group/s ... TNS_ra TNS_dec \\\n", + "0 NaN GaiaAlerts ... 69.217167 -52.277400 \n", + "1 NaN GaiaAlerts ... 116.084958 -69.318639 \n", + "2 NaN GaiaAlerts ... 83.260500 -48.422631 \n", + "3 NaN GaiaAlerts ... 84.709833 -44.085819 \n", + "4 NaN GaiaAlerts ... 359.508875 -45.921889 \n", + "\n", + " TNS_aitoff_x TNS_aitoff_y ra dec separation \\\n", + "0 -78.085702 -61.325931 69.217172 -52.277402 None \n", + "1 -29.514722 -73.858661 116.084972 -69.318648 None \n", + "2 -74.382066 -56.087700 83.260424 -48.422669 None \n", + "3 -78.434362 -51.401910 84.709836 -44.085818 None \n", + "4 125.027521 -64.559123 359.508896 -45.921897 None \n", + "\n", + " input_catalog_name input_catalog_ra input_catalog_dec \n", + "0 WISEA J043652.12-521638.6 69.217177 -52.277404 \n", + "1 WISEA J074420.39-691907.1 116.084986 -69.318657 \n", + "2 J053302.48-482521.7 83.260348 -48.422708 \n", + "3 PKS 0537-441 84.709840 -44.085816 \n", + "4 PKS 2355-461 359.508916 -45.921905 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open(\"../example_data/agn_catalog.pickle\", \"rb\") as file:\n", + " big_df = pickle.load(file)\n", + "\n", + "start = time.time()\n", + "result = crossmatch(big_df, TargetCatalog.TNS)\n", + "end = time.time()\n", + "\n", + "print(\"It takes {} s\".format(end - start))\n", + "print(\"It was found {} rows\".format(len(result)))\n", + "print(\"Firsts rows:\")\n", + "result.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/ZTF_DB_example.ipynb b/notebooks/ZTF_DB_example.ipynb new file mode 100644 index 0000000..822622e --- /dev/null +++ b/notebooks/ZTF_DB_example.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load libraries\n", + "\n", + "*External dependencies*:\n", + "\n", + "psycopg2: pip install psycopg2-binary\n", + "\n", + "*ALeRCE dependencies*:\n", + "\n", + "ztf_pipeline\n", + "xmatches\n", + "VariableCatalogs\n", + "turbo-fats" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-27T23:07:16.359889Z", + "start_time": "2019-05-27T23:07:14.047030Z" + } + }, + "outputs": [], + "source": [ + "import pandas\n", + "import numpy as np\n", + "from ztf_pipeline.db import DBClient" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get credentials (not from github repository!)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-27T23:07:16.364992Z", + "start_time": "2019-05-27T23:07:16.362035Z" + } + }, + "outputs": [], + "source": [ + "import json\n", + "credentials_file = \"../alerceuser.json\"\n", + "with open(credentials_file) as jsonfile:\n", + " params = json.load(jsonfile)[\"params\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DB Client" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-27T23:07:18.862521Z", + "start_time": "2019-05-27T23:07:18.784772Z" + } + }, + "outputs": [], + "source": [ + "#DBClient\n", + "client = DBClient(params)\n", + "client.connect()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Query data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-27T23:07:20.020093Z", + "start_time": "2019-05-27T23:07:19.916986Z" + } + }, + "outputs": [], + "source": [ + "query = \"select * from detections limit 100\"\n", + "client.execute(query)\n", + "result = client.fetchall()\n", + "client.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Convert to dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2019-05-27T23:07:21.231162Z", + "start_time": "2019-05-27T23:07:21.190044Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0.063034\n", + "6 58541.236181 1.0 129.079824 21.351438 17.272770 0.027846\n", + "18 58556.207118 2.0 129.079796 21.351421 14.625766 0.019045\n", + "7 58556.270336 1.0 129.079811 21.351431 14.429075 0.021771\n", + "19 58561.179086 2.0 129.079789 21.351422 16.868532 0.037494\n", + "8 58561.210845 1.0 129.079780 21.351427 17.083556 0.041519\n", + "0 58575.149375 2.0 129.079744 21.351455 17.391183 0.042903" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query = \"select jd, fid, ra, dec, magpsf_corr, sigmapsf_corr from detections where oid='ZTF17aaantxj' \"\n", + "\n", + "df1 = sql_query(query)\n", + "\n", + "df1 = df1.sort_values(['jd'])\n", + "display(df1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the obtained light curve in the different bands:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df1_1 = df1[df1['fid'] == 1]\n", + "df1_2 = df1[df1['fid'] == 2]\n", + "\n", + "plt.errorbar(df1_1['jd'].astype(float) ,df1_1['magpsf_corr'].astype(float),yerr=df1_1['sigmapsf_corr'].astype(float), color='blue',ecolor='blue',label='g')\n", + "plt.errorbar(df1_2['jd'].astype(float) ,df1_2['magpsf_corr'].astype(float),yerr=df1_2['sigmapsf_corr'].astype(float), color='red',ecolor='red',label='r')\n", + "plt.legend()\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's do a more complex query. If we want all the sources from the \"probabilties\" table, with a high probability of being a source classified as \"sne\", having access to the coordinates of the sources and the number of epochs, we can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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oidother_probmeanrameandecnobsmean_magpsf_gmean_magpsf_g
0ZTF18aazjztm0.06260.10163660666755.21463832666671519.187431653340719.1874316533407
1ZTF18abmrzun0.088349.93532787674422.50235034883724320.203398068745920.2033980687459
2ZTF18abucflq0.106290.86947607242.1832641082519.771136522293119.7711365222931
3ZTF18abvglon0.1153.58431825263256.32122696315791920.409187952677420.4091879526774
4ZTF18acbxsge0.1269.80091044285714-5.200707347619052118.139060735702518.1390607357025
5ZTF18acbznkf0.12166.63422154615458.07901581538461319.248380978902219.2483809789022
6ZTF18aahksyi0.1202.43164582857131.89354007428573519.307588430551419.3075884305514
7ZTF18aaisuhc0.106172.21275908523.1769672352019.483301374647419.4833013746474
8ZTF18aceyyly0.116185.49812954722256.53984701388893618.536959768402818.5369597684028
9ZTF18acvgjqv0.114134.07503784074152.54946591851852719.237725257873519.2377252578735
10ZTF18acvilwk0.07240.67699821215.2364577642518.165968736012818.1659687360128
11ZTF18acwyqeg0.114206.58035219166714.18110994166671218.276792208353718.2767922083537
12ZTF18aauqdyb0.05278.56087612105332.91931680526321919.831705638340519.8317056383405
13ZTF18acywbgz0.0887.114277653333316.838010981519.74907847813219.749078478132
14ZTF18adbabsh0.08175.98137772307759.40555008461541319.58267021179219.582670211792
15ZTF18aaxvbjb0.1328.37994386938851.74799620612254920.313176679611220.3131766796112
16ZTF18aayjvve0.082226.06916725635.8150349082518.695333957672118.6953339576721
17ZTF18aaztiva0.078307.65947003529450.90794158235291719.608195478858419.6081954788584
18ZTF18abaxlpi0.04276.55904807826145.01019752318.89670583936918.896705839369
19ZTF18abbodly0.114331.40559440503644.837686674820113918.833769983320118.8337699833201
20ZTF18abbpebf0.084251.54855308333356.3312033252419.67291556464319.672915564643
21ZTF18abckpwp0.09339.99883074285731.7258814224494920.102525574820420.1025255748204
22ZTF18abdayai0.112335.69400102272749.34405704545452219.402367851950919.4023678519509
23ZTF18abdfydj0.036255.59406175217457.75142995652172319.015896813941719.0158968139417
24ZTF18abeyqpr0.068266.05521562777866.71291294444441819.721123377482119.7211233774821
25ZTF18abfwwxl0.112341.40205304117633.64463312941181719.768843968709319.7688439687093
26ZTF18abimhfu0.1240.1422720431.642942541519.420725504557319.4207255045573
27ZTF18ablfqxm0.066356.94909857916753.23055243333332419.032882577164819.0328825771648
28ZTF18ablongw0.032262.67816138888962.83109483333331819.256307601928719.2563076019287
29ZTF18ablpujj0.066336.88796507916711.12601442083332420.03683352470420.036833524704
........................
691ZTF18aaxqfrr0.068180.09982424615440.04618907692311319.406953493754119.4069534937541
692ZTF18aaxvpsw0.064224.97666818235339.07879328235291719.143236637115519.1432366371155
693ZTF18aazembj0.13317.18349307826140.34798499565222319.584658145904519.5846581459045
694ZTF18aazixbw0.056244.74158529556.7168777252018.946598870413618.9465988704136
695ZTF18abcdzyc0.082195.44056105384661.46540510769231318.595841725667318.5958417256673
696ZTF18abdgstm0.042337.14208204705948.72872840588241719.331494437323719.3314944373237
697ZTF18abffyqp0.092252.70869098928645.39789443571432819.112720662897319.1127206628973
698ZTF18abgladq0.052239.70684993548414.96998294516133118.641303380330418.6413033803304
699ZTF18abivonr0.086321.87962708630145.42235039315077319.649017715454119.6490177154541
700ZTF18ablllyw0.088262.51925558479.83860442519.298399734497119.2983997344971
701ZTF18abmenfr0.132346.8452288243.6039062522518.622907911028218.6229079110282
702ZTF18abpvpei0.07825.800387013953552.45547969302334318.901445837581918.9014458375819
703ZTF18abrnktj0.034290.323647143751.50120636251619.502403736114519.5024037361145
704ZTF18abryewg0.1189.1206227539.17037938752418.583827799016818.5838277990168
705ZTF18abryqnn0.084355.19134335882414.10045152941181718.906388812594918.9063888125949
706ZTF18abtgunq0.07438.4455264488889-1.024502191111114518.93344431338118.933444313381
707ZTF18abvfsrv0.05473.019904478333349.5457624556019.197044089988419.1970440899884
708ZTF18acbwghv0.126163.04157014545532.9738236303033318.816083602149318.8160836021493
709ZTF18aagajuk0.098119.7396045528.64244743076922619.988321304321319.9883213043213
710ZTF18acecedi0.088103.38278664545556.51930158636362219.251874576915419.2518745769154
711ZTF18acnmlxn0.14104.7376115125-4.898844856254818.823636707506718.8236367075067
712ZTF18aaqnawz0.076208.07685198148134.46741563333332720.166044553120920.1660445531209
713ZTF18aasdikm0.082232.42587971034535.14758070689652918.847655232747418.8476552327474
714ZTF18aczuooo0.076195.270661326.35105193888891819.114227082994219.1142270829942
715ZTF18adaifep0.1385.30733106-13.224198721518.148640632629418.1486406326294
716ZTF18adasopt0.10282.46802044519.834088692018.86674989972818.866749899728
717ZTF19aaapkgk0.104227.2989518187554.197036668751618.37062358856218.370623588562
718ZTF19aabhmcx0.1153.53673187538.93045590833331219.37639967600519.376399676005
719ZTF19aadgdyk0.088170.91973639473737.55844382631581918.80192895369118.801928953691
720ZTF18aaycrnz0.094258.76298943793144.49776391724142918.846209102206818.8462091022068
\n", + "

721 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " oid other_prob meanra meandec nobs \\\n", + "0 ZTF18aazjztm 0.06 260.101636606667 55.2146383266667 15 \n", + "1 ZTF18abmrzun 0.088 349.935327876744 22.5023503488372 43 \n", + "2 ZTF18abucflq 0.106 290.869476072 42.183264108 25 \n", + "3 ZTF18abvglon 0.1 153.584318252632 56.3212269631579 19 \n", + "4 ZTF18acbxsge 0.126 9.80091044285714 -5.20070734761905 21 \n", + "5 ZTF18acbznkf 0.12 166.634221546154 58.0790158153846 13 \n", + "6 ZTF18aahksyi 0.1 202.431645828571 31.8935400742857 35 \n", + "7 ZTF18aaisuhc 0.106 172.212759085 23.176967235 20 \n", + "8 ZTF18aceyyly 0.116 185.498129547222 56.5398470138889 36 \n", + "9 ZTF18acvgjqv 0.114 134.075037840741 52.5494659185185 27 \n", + "10 ZTF18acvilwk 0.07 240.676998212 15.236457764 25 \n", + "11 ZTF18acwyqeg 0.114 206.580352191667 14.1811099416667 12 \n", + "12 ZTF18aauqdyb 0.05 278.560876121053 32.9193168052632 19 \n", + "13 ZTF18acywbgz 0.08 87.1142776533333 16.83801098 15 \n", + "14 ZTF18adbabsh 0.08 175.981377723077 59.4055500846154 13 \n", + "15 ZTF18aaxvbjb 0.1 328.379943869388 51.7479962061225 49 \n", + "16 ZTF18aayjvve 0.082 226.069167256 35.815034908 25 \n", + "17 ZTF18aaztiva 0.078 307.659470035294 50.9079415823529 17 \n", + "18 ZTF18abaxlpi 0.04 276.559048078261 45.0101975 23 \n", + "19 ZTF18abbodly 0.114 331.405594405036 44.8376866748201 139 \n", + "20 ZTF18abbpebf 0.084 251.548553083333 56.331203325 24 \n", + "21 ZTF18abckpwp 0.09 339.998830742857 31.725881422449 49 \n", + "22 ZTF18abdayai 0.112 335.694001022727 49.3440570454545 22 \n", + "23 ZTF18abdfydj 0.036 255.594061752174 57.7514299565217 23 \n", + "24 ZTF18abeyqpr 0.068 266.055215627778 66.7129129444444 18 \n", + "25 ZTF18abfwwxl 0.112 341.402053041176 33.6446331294118 17 \n", + "26 ZTF18abimhfu 0.1 240.14227204 31.64294254 15 \n", + "27 ZTF18ablfqxm 0.066 356.949098579167 53.2305524333333 24 \n", + "28 ZTF18ablongw 0.032 262.678161388889 62.8310948333333 18 \n", + "29 ZTF18ablpujj 0.066 336.887965079167 11.1260144208333 24 \n", + ".. ... ... ... ... ... \n", + "691 ZTF18aaxqfrr 0.068 180.099824246154 40.0461890769231 13 \n", + "692 ZTF18aaxvpsw 0.064 224.976668182353 39.0787932823529 17 \n", + "693 ZTF18aazembj 0.13 317.183493078261 40.3479849956522 23 \n", + "694 ZTF18aazixbw 0.056 244.741585295 56.716877725 20 \n", + "695 ZTF18abcdzyc 0.082 195.440561053846 61.4654051076923 13 \n", + "696 ZTF18abdgstm 0.042 337.142082047059 48.7287284058824 17 \n", + "697 ZTF18abffyqp 0.092 252.708690989286 45.3978944357143 28 \n", + "698 ZTF18abgladq 0.052 239.706849935484 14.9699829451613 31 \n", + "699 ZTF18abivonr 0.086 321.879627086301 45.4223503931507 73 \n", + "700 ZTF18ablllyw 0.088 262.519255584 79.8386044 25 \n", + "701 ZTF18abmenfr 0.132 346.84522882 43.603906252 25 \n", + "702 ZTF18abpvpei 0.078 25.8003870139535 52.4554796930233 43 \n", + "703 ZTF18abrnktj 0.034 290.32364714375 1.5012063625 16 \n", + "704 ZTF18abryewg 0.118 9.12062275 39.1703793875 24 \n", + "705 ZTF18abryqnn 0.084 355.191343358824 14.1004515294118 17 \n", + "706 ZTF18abtgunq 0.074 38.4455264488889 -1.02450219111111 45 \n", + "707 ZTF18abvfsrv 0.054 73.0199044783333 49.545762455 60 \n", + "708 ZTF18acbwghv 0.126 163.041570145455 32.973823630303 33 \n", + "709 ZTF18aagajuk 0.098 119.73960455 28.6424474307692 26 \n", + "710 ZTF18acecedi 0.088 103.382786645455 56.5193015863636 22 \n", + "711 ZTF18acnmlxn 0.14 104.7376115125 -4.89884485625 48 \n", + "712 ZTF18aaqnawz 0.076 208.076851981481 34.4674156333333 27 \n", + "713 ZTF18aasdikm 0.082 232.425879710345 35.1475807068965 29 \n", + "714 ZTF18aczuooo 0.076 195.2706613 26.3510519388889 18 \n", + "715 ZTF18adaifep 0.13 85.30733106 -13.22419872 15 \n", + "716 ZTF18adasopt 0.102 82.468020445 19.83408869 20 \n", + "717 ZTF19aaapkgk 0.104 227.29895181875 54.19703666875 16 \n", + "718 ZTF19aabhmcx 0.11 53.536731875 38.9304559083333 12 \n", + "719 ZTF19aadgdyk 0.088 170.919736394737 37.5584438263158 19 \n", + "720 ZTF18aaycrnz 0.094 258.762989437931 44.4977639172414 29 \n", + "\n", + " mean_magpsf_g mean_magpsf_g \n", + "0 19.1874316533407 19.1874316533407 \n", + "1 20.2033980687459 20.2033980687459 \n", + "2 19.7711365222931 19.7711365222931 \n", + "3 20.4091879526774 20.4091879526774 \n", + "4 18.1390607357025 18.1390607357025 \n", + "5 19.2483809789022 19.2483809789022 \n", + "6 19.3075884305514 19.3075884305514 \n", + "7 19.4833013746474 19.4833013746474 \n", + "8 18.5369597684028 18.5369597684028 \n", + "9 19.2377252578735 19.2377252578735 \n", + "10 18.1659687360128 18.1659687360128 \n", + "11 18.2767922083537 18.2767922083537 \n", + "12 19.8317056383405 19.8317056383405 \n", + "13 19.749078478132 19.749078478132 \n", + "14 19.582670211792 19.582670211792 \n", + "15 20.3131766796112 20.3131766796112 \n", + "16 18.6953339576721 18.6953339576721 \n", + "17 19.6081954788584 19.6081954788584 \n", + "18 18.896705839369 18.896705839369 \n", + "19 18.8337699833201 18.8337699833201 \n", + "20 19.672915564643 19.672915564643 \n", + "21 20.1025255748204 20.1025255748204 \n", + "22 19.4023678519509 19.4023678519509 \n", + "23 19.0158968139417 19.0158968139417 \n", + "24 19.7211233774821 19.7211233774821 \n", + "25 19.7688439687093 19.7688439687093 \n", + "26 19.4207255045573 19.4207255045573 \n", + "27 19.0328825771648 19.0328825771648 \n", + "28 19.2563076019287 19.2563076019287 \n", + "29 20.036833524704 20.036833524704 \n", + ".. ... ... \n", + "691 19.4069534937541 19.4069534937541 \n", + "692 19.1432366371155 19.1432366371155 \n", + "693 19.5846581459045 19.5846581459045 \n", + "694 18.9465988704136 18.9465988704136 \n", + "695 18.5958417256673 18.5958417256673 \n", + "696 19.3314944373237 19.3314944373237 \n", + "697 19.1127206628973 19.1127206628973 \n", + "698 18.6413033803304 18.6413033803304 \n", + "699 19.6490177154541 19.6490177154541 \n", + "700 19.2983997344971 19.2983997344971 \n", + "701 18.6229079110282 18.6229079110282 \n", + "702 18.9014458375819 18.9014458375819 \n", + "703 19.5024037361145 19.5024037361145 \n", + "704 18.5838277990168 18.5838277990168 \n", + "705 18.9063888125949 18.9063888125949 \n", + "706 18.933444313381 18.933444313381 \n", + "707 19.1970440899884 19.1970440899884 \n", + "708 18.8160836021493 18.8160836021493 \n", + "709 19.9883213043213 19.9883213043213 \n", + "710 19.2518745769154 19.2518745769154 \n", + "711 18.8236367075067 18.8236367075067 \n", + "712 20.1660445531209 20.1660445531209 \n", + "713 18.8476552327474 18.8476552327474 \n", + "714 19.1142270829942 19.1142270829942 \n", + "715 18.1486406326294 18.1486406326294 \n", + "716 18.866749899728 18.866749899728 \n", + "717 18.370623588562 18.370623588562 \n", + "718 19.376399676005 19.376399676005 \n", + "719 18.801928953691 18.801928953691 \n", + "720 18.8462091022068 18.8462091022068 \n", + "\n", + "[721 rows x 7 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query='''\n", + "select probabilities.oid, probabilities.other_prob, objects.meanra, \n", + "objects.meandec, objects.nobs, objects.mean_magpsf_g, objects.mean_magpsf_g\n", + "\n", + "from probabilities \n", + "\n", + "inner join objects\n", + "on probabilities.oid=objects.oid\n", + "\n", + "where probabilities.sne_prob>0.7\n", + "'''\n", + "\n", + "\n", + "df2 = sql_query(query)\n", + "\n", + "display(df2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the coordinates of al these sources:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'dec')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(df2['meanra'].astype(float),df2['meandec'].astype(float),'ro')\n", + "plt.xlabel('ra')\n", + "plt.ylabel('dec')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if we want to obtain the light curve of the source with the largest number of ephocs, we can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ID with maximum number of observations: ZTF18abcfdzu\n" + ] + }, + { + "data": { + "text/html": [ + "
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oidradecfidjdmagpsf_corrsigmapsf_corr
0ZTF18abcfdzu230.217204154.2155942158290.201099500119.84097480773930.182884991168976
33ZTF18abcfdzu230.217218954.2155163258290.262164419.94844245910640.188140884041786
34ZTF18abcfdzu230.21723854.2155212258291.264560199820.13755989074710.210432842373848
1ZTF18abcfdzu230.217088854.2155429158293.201284700119.70846939086910.196374148130417
35ZTF18abcfdzu230.217194954.2155202258293.247013919.68319320678710.145388498902321
36ZTF18abcfdzu230.217124654.2155012258294.241377300119.62574577331540.145378559827805
37ZTF18abcfdzu230.217116454.2154732258297.300925900219.44320106506350.181374505162239
2ZTF18abcfdzu230.21714454.2155562158299.179398099919.49391365051270.142742544412613
3ZTF18abcfdzu230.217154354.2156067158300.220949099919.52098464965820.130015000700951
4ZTF18abcfdzu230.217145854.2155543158302.197939800119.50582122802730.169038847088814
\n", + "
" + ], + "text/plain": [ + " oid ra dec fid jd \\\n", + "0 ZTF18abcfdzu 230.2172041 54.2155942 1 58290.2010995001 \n", + "33 ZTF18abcfdzu 230.2172189 54.2155163 2 58290.2621644 \n", + "34 ZTF18abcfdzu 230.217238 54.2155212 2 58291.2645601998 \n", + "1 ZTF18abcfdzu 230.2170888 54.2155429 1 58293.2012847001 \n", + "35 ZTF18abcfdzu 230.2171949 54.2155202 2 58293.2470139 \n", + "36 ZTF18abcfdzu 230.2171246 54.2155012 2 58294.2413773001 \n", + "37 ZTF18abcfdzu 230.2171164 54.2154732 2 58297.3009259002 \n", + "2 ZTF18abcfdzu 230.217144 54.2155562 1 58299.1793980999 \n", + "3 ZTF18abcfdzu 230.2171543 54.2156067 1 58300.2209490999 \n", + "4 ZTF18abcfdzu 230.2171458 54.2155543 1 58302.1979398001 \n", + "\n", + " magpsf_corr sigmapsf_corr \n", + "0 19.8409748077393 0.182884991168976 \n", + "33 19.9484424591064 0.188140884041786 \n", + "34 20.1375598907471 0.210432842373848 \n", + "1 19.7084693908691 0.196374148130417 \n", + "35 19.6831932067871 0.145388498902321 \n", + "36 19.6257457733154 0.145378559827805 \n", + "37 19.4432010650635 0.181374505162239 \n", + "2 19.4939136505127 0.142742544412613 \n", + "3 19.5209846496582 0.130015000700951 \n", + "4 19.5058212280273 0.169038847088814 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "max_nobs_id = df2[df2.nobs == df2.nobs.max()]['oid'].values[0]\n", + "print(\"ID with maximum number of observations: \",max_nobs_id)\n", + "\n", + "query=\"select oid, ra, dec, fid, jd, magpsf_corr, sigmapsf_corr from detections where oid='\"+max_nobs_id+\"'\"\n", + "\n", + "df3 = sql_query(query)\n", + "\n", + "df3 = df3.sort_values(['jd'])\n", + "#display(df3)\n", + "display(df3.head(n=10))\n", + "\n", + "df3_1 = df3[df3['fid']=='1']\n", + "df3_2 = df3[df3['fid']=='2']\n", + "\n", + "\n", + "plt.errorbar(df3_1['jd'].astype(float) ,df3_1['magpsf_corr'].astype(float),yerr=df3_1['sigmapsf_corr'].astype(float), color='blue',ecolor='blue',label='g')\n", + "plt.errorbar(df3_2['jd'].astype(float) ,df3_2['magpsf_corr'].astype(float),yerr=df3_2['sigmapsf_corr'].astype(float), color='red',ecolor='red',label='r')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's define some variability features. The probability that the source is intrinsically variable ($P_{var}$):\n", + "\n", + "$$\\chi^2=\\sum_{i=1}^{N_{obs}}\\frac{(x_i-\\bar{x})^2}{\\sigma^2_{err,i}}$$\n", + "$$P_{var}=P(\\chi^2)$$ \n", + "\n", + "And the excess variance ($\\sigma_{rms}$), which a measure of the intrinsic variability amplitude:\n", + "\n", + "$$\\sigma^2_{rms}=\\frac{1}{N_{obs}\\bar{x}^2}\\sum^{N_{obs}}_{i=1}[(x_i-\\bar{x})^2-\\sigma^2_{err,i}]$$" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def var_parameters(jd,mag,err):\n", + " \"\"\"function to calculate the probability of a light curve to be variable and the excess variance\"\"\"\n", + " \n", + " from scipy.stats import chi2\n", + " \n", + " mean=np.mean(mag)\n", + " nepochs=float(len(jd))\n", + "\n", + " #P_var\n", + " chi= np.sum( (mag - mean)**2. / err**2. )\n", + " P_var=chi2.cdf(chi,(nepochs-1))\n", + "\n", + " #ex_var\n", + " a=(mag-mean)**2\n", + " ex_var=(np.sum(a-err**2)/((nepochs*(mean**2))))\n", + " \n", + "\n", + " return [P_var,ex_var]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can estimate these variability features for the previous light curve:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0 0.00042510167372398393\n" + ] + } + ], + "source": [ + "P_var, ex_var = var_parameters(df3_2['jd'].astype(float) ,df3_2['magpsf_corr'].astype(float),df3_2['sigmapsf_corr'].astype(float))\n", + "print(P_var, ex_var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, if we want to obtain the newest (last week for instance) alerts produced by ZTF, we can do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "58619.0\n" + ] + }, + { + "data": { + "text/html": [ + "
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candidoidjdfiddiffmaglimmagpsfmagapsigmapsfsigmagapra...fieldrcidmagnrsigmagnrrbmagpsf_corrmagap_corrsigmapsf_corrsigmagap_corrid
94868160122915010000ZTF17aaajffd58622.2118.830417.790418.12770.09284790.1993153.594...7122917.1110.0210.85857116.645816.75190.03512870.0581199None
160868160126115010007ZTF17aaajffz58622.2118.601818.339418.23140.1523570.2407153.295...7126116.9760.0190.87285716.703916.6790.03600020.0588822None
358868159662115015002ZTF17aaajfgj58622.2118.835417.484817.4970.08121380.1137157.014...6682116.1530.0180.91857115.716815.71920.019510.0248221None
495868160122415010001ZTF17aaajfgk58622.2118.808213.106413.12280.02208560.0025157.466...7122412.9670.0110.6212.197212.20430.0127970.00862976None
617868161061115010000ZTF17aaajfgl58622.2118.560518.325318.31030.1646180.3128156.188...7521115.8870.0130.82285715.777815.77630.01965860.0325099None
546868161066315015012ZTF17aaajfhf58622.2118.55415.89415.90050.07376070.0359153.379...7526314.2870.0120.78428613.459113.45980.01328090.0113827None
825868162465815010001ZTF17aaajfia58622.2118.767215.694315.67890.04257680.0228162.898...8185813.8470.0090.8513.052313.05090.01330680.0129156None
164868162462015010004ZTF17aaajfic58622.2118.609917.22317.20580.05976590.0993168.864...8182015.9530.0150.94285715.731215.72680.01974920.0284011None
853868162466115015005ZTF17aaajfil58622.2118.695216.233916.09820.0708910.0355158.809...8186113.7390.0140.75142913.63513.62190.01387020.0126516None
230868160122515010003ZTF17aaajidk58622.2118.807816.787516.64280.06800270.0526156.265...7122515.4070.0140.94285715.138615.10520.01847560.0165929None
363868161060515010000ZTF17aaajidx58622.2118.588518.250618.29520.1841150.2931157.821...752516.9520.0160.86428616.665116.67530.04447810.0670847None
1045868167020715015000ZTF17aaajigg58622.2119.070317.657417.71560.08809720.1067182.721...843715.810.0110.92428615.628115.63690.01646930.0183118None
874868167026115010008ZTF17aaajigj58622.2118.926517.988117.93720.1436720.136167.806...8436115.2440.0120.72571414.788514.78570.01608980.0160214None
1237868167024715010000ZTF17aaajigm58622.2119.087718.353318.48110.1047180.2108174.027...8434716.3220.0130.91428616.166516.18270.0179490.0278382None
713868167024015015004ZTF17aaajigo58622.2119.073515.532415.56870.03871140.0175179.313...8434013.7410.0130.89714313.550213.5560.01256310.0113011None
81868159202515010001ZTF17aaajigy58622.2119.123316.471116.46420.04740090.0342163.324...6692515.5360.0170.91428615.217515.21530.01832620.0151467None
541868167025815015004ZTF17aaajihk58622.2119.065115.278615.27690.02164890.0119175.911...8435816.5710.0350.9214.990314.9890.01849840.0122386None
613868161522715010003ZTF17aaajiic58622.2118.354618.196718.08160.1824760.2823165.389...7132716.780.0150.62142916.519516.49380.04067940.0664125None
1241868167020415010000ZTF17aaajiiu58622.2119.133416.849116.91520.05928110.0511182.777...843414.5830.0170.9214.110214.11530.01415950.0139495None
610868167024115015003ZTF17aaajiix58622.2119.01515.904315.91670.04611260.024176.196...8434113.9950.0150.93428613.822413.82420.01447950.0132843None
543868167024115015004ZTF17aaajiix58622.2119.01515.848116.10940.02627450.0278176.196...8434113.9950.0150.84714313.81413.85020.01332210.0135785None
989868167021315010004ZTF17aaajikw58622.2118.928914.780714.78020.02598570.0082171.165...8431313.8950.0120.85142913.529713.52960.01210310.00926141None
1115868168484315015033ZTF17aaajjao58622.2118.725618.428418.51930.1537340.2763171.362...5234317.1560.0160.86571416.468916.48330.02900520.0447408None
641868167994315015001ZTF17aaajjch58622.2118.64514.974914.98590.02353150.0135180.398...6244314.7740.0180.86428614.019114.02360.01479510.012478None
606868167994315015002ZTF17aaajjcj58622.2118.64517.613917.61760.08992380.1404180.117...6244316.3930.0180.91285716.087616.08850.02589780.0369294None
574868167991215010001ZTF17aaajjcp58622.2118.837918.306218.26570.1832370.2312178.423...6241216.3410.0230.67142914.770414.76880.03063020.0311608None
1185868168483115010002ZTF17aaajjeh58622.2118.771817.237517.46610.07282390.1045169.737...5233115.7260.010.93571415.48515.52680.01656250.0193922None
1245868168483815010004ZTF17aaajjfh58622.2118.759218.236518.39380.145350.2383172.547...5233817.1230.0130.91285716.732316.76960.03854020.0549937None
1218868168482115010002ZTF17aaajjfs58622.2118.747217.256117.11070.07996450.0768173.129...5232115.1340.0120.89714315.224515.20080.01650760.0170503None
1194868168483615010000ZTF17aaajjgp58622.2118.905917.795317.80390.09816620.1251173.577...5233616.2590.0140.95285715.906415.90790.0207420.0246922None
..................................................................
1437868179324815010002ZTF19aavhwfz58622.2119.01418.732618.90770.2003570.3031210.114...7574822.8640.2940.70857118.732618.90770.2003570.3031None
1834868180742915015010ZTF19aavhwgr58622.2119.047916.695717.24920.1453770.0627209.237...7582922.6870.3260.29857116.695717.24920.1453770.0627None
1862868180742915010002ZTF19aavhwgs58622.2119.047918.567718.840.1991340.2672210.346...7582921.7740.1320.39142918.567718.840.1991340.2672None
1817868180745115010000ZTF19aavhwgt58622.2118.928418.940918.58040.1914670.2312220.197...7585122.3590.2230.10857118.940918.58040.1914670.2312None
1900868181211115015008ZTF19aavhwgu58622.2118.872517.671918.25410.2163530.1762223.34...7931122.9140.3540.14428617.671918.25410.2163530.1762None
1939868181210015015006ZTF19aavhwgv58622.2118.735216.887517.38150.1843010.09230.483...793022.8470.2880.14857116.887517.38150.1843010.09None
1584868181210015015029ZTF19aavhwgw58622.2118.735217.987518.46450.1575610.2719229.596...793013.8430.0120.30428617.987518.46450.1575610.2719None
1808868181211415015005ZTF19aavhwgx58622.2118.855617.18517.52360.175880.096219.715...7931422.7310.3360.15285717.18517.52360.175880.096None
1585868181213615015011ZTF19aavhwgy58622.2118.807815.809416.30690.09574730.0461226.236...7933612.4610.0120.40428612.412412.430.01221620.0117343None
1821868181213115015008ZTF19aavhwgz58622.2118.900117.091317.81930.1978610.1527219.934...7933113.1670.0110.28142913.138113.15210.01190230.0110471None
1883868181213115015001ZTF19aavhwha58622.2118.900118.42618.1670.2070730.1672221.186...7933121.9880.2230.12857118.42618.1670.2070730.1672None
1923868181215915015014ZTF19aavhwig58622.2118.793316.85917.13890.1435280.0717223.636...7935922.40.2460.13428616.85917.13890.1435280.0717None
1943868181215115010002ZTF19aavhwii58622.2118.70818.71418.48370.2156620.2604231.283...7935122.1050.2510.19285718.71418.48370.2156620.2604None
1812868181215415015017ZTF19aavhwij58622.2118.778316.799217.41020.2103270.0931224.712...7935422.5890.3550.2816.799217.41020.2103270.0931None
1843868183073915010008ZTF19aavhwjm58622.2118.16116.542816.74850.118120.102209.239...8623912.8280.0120.38428612.793112.7990.01220630.0119886None
1449868183073815015024ZTF19aavhwjn58622.2118.088416.106516.46710.1420880.0744206.069...8623822.7630.3320.2116.106516.46710.1420880.0744None
1911868183073615015017ZTF19aavhwjo58622.2118.131916.821517.52770.2109020.188210.246...8623622.0790.1910.25142916.821517.52770.2109020.188None
1549868183073115010003ZTF19aavhwjp58622.2118.019716.597916.92550.1305230.1318194.335...8623113.2170.0120.35857113.169813.18190.01276060.0123516None
1908868183072115015006ZTF19aavhwjq58622.2118.111416.992917.50890.2115370.1876205.49...8622122.6320.3040.26285716.992917.50890.2115370.1876None
1912868183072815015018ZTF19aavhwjr58622.2118.039116.437517.08640.2045810.1388194.857...8622822.7980.3230.1616.437517.08640.2045810.1388None
1913868183071515015010ZTF19aavhwjt58622.2117.853617.158917.58280.1591130.2581195.349...8621513.9350.010.50714313.880613.89790.01228160.0129808None
1846868183070615015008ZTF19aavhwju58622.2118.034116.445917.00530.206770.126205.87...862623.1290.3550.20428616.445917.00530.206770.126None
1928868183070615015013ZTF19aavhwjv58622.2118.034117.942918.24180.2129530.3928206.549...862622.230.1790.57285717.942918.24180.2129530.3928None
1948868183070115015001ZTF19aavhwjw58622.2118.113116.430216.5740.1070170.0875212.361...862113.330.0120.51714313.269313.27660.01275370.0121712None
1284868183076315015022ZTF19aavhwky58622.2118.108815.014215.68030.1973580.0444193.015...8626312.2550.0190.29142912.172712.20970.02275610.0183131None
1285868183075615015009ZTF19aavhwlb58622.2118.113217.427917.56870.2103040.2014199.22...8625622.4040.2660.25428617.427917.56870.2103040.2014None
1533868183075615015040ZTF19aavhwlc58622.2118.113217.338917.58970.2159180.2054200.151...8625621.9040.2070.2317.338917.58970.2159180.2054None
1553868183076115015040ZTF19aavhwld58622.2117.937516.145516.80660.2065940.112187.093...8626122.6610.3290.18857116.145516.80660.2065940.112None
1876868183075715010011ZTF19aavhwlf58622.2118.08517.854518.40290.2002690.4425197.996...8625722.5010.3530.41428617.854518.40290.2002690.4425None
1454868183074915015018ZTF19aavhwlg58622.2117.937717.173317.50850.2076860.2136218.543...8624922.7060.3060.26285717.173317.50850.2076860.2136None
\n", + "

1954 rows × 26 columns

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" + ], + "text/plain": [ + " candid oid jd fid diffmaglim magpsf \\\n", + "94 868160122915010000 ZTF17aaajffd 58622.2 1 18.8304 17.7904 \n", + "160 868160126115010007 ZTF17aaajffz 58622.2 1 18.6018 18.3394 \n", + "358 868159662115015002 ZTF17aaajfgj 58622.2 1 18.8354 17.4848 \n", + "495 868160122415010001 ZTF17aaajfgk 58622.2 1 18.8082 13.1064 \n", + "617 868161061115010000 ZTF17aaajfgl 58622.2 1 18.5605 18.3253 \n", + "546 868161066315015012 ZTF17aaajfhf 58622.2 1 18.554 15.894 \n", + "825 868162465815010001 ZTF17aaajfia 58622.2 1 18.7672 15.6943 \n", + "164 868162462015010004 ZTF17aaajfic 58622.2 1 18.6099 17.223 \n", + "853 868162466115015005 ZTF17aaajfil 58622.2 1 18.6952 16.2339 \n", + "230 868160122515010003 ZTF17aaajidk 58622.2 1 18.8078 16.7875 \n", + "363 868161060515010000 ZTF17aaajidx 58622.2 1 18.5885 18.2506 \n", + "1045 868167020715015000 ZTF17aaajigg 58622.2 1 19.0703 17.6574 \n", + "874 868167026115010008 ZTF17aaajigj 58622.2 1 18.9265 17.9881 \n", + "1237 868167024715010000 ZTF17aaajigm 58622.2 1 19.0877 18.3533 \n", + "713 868167024015015004 ZTF17aaajigo 58622.2 1 19.0735 15.5324 \n", + "81 868159202515010001 ZTF17aaajigy 58622.2 1 19.1233 16.4711 \n", + "541 868167025815015004 ZTF17aaajihk 58622.2 1 19.0651 15.2786 \n", + "613 868161522715010003 ZTF17aaajiic 58622.2 1 18.3546 18.1967 \n", + "1241 868167020415010000 ZTF17aaajiiu 58622.2 1 19.1334 16.8491 \n", + "610 868167024115015003 ZTF17aaajiix 58622.2 1 19.015 15.9043 \n", + "543 868167024115015004 ZTF17aaajiix 58622.2 1 19.015 15.8481 \n", + "989 868167021315010004 ZTF17aaajikw 58622.2 1 18.9289 14.7807 \n", + "1115 868168484315015033 ZTF17aaajjao 58622.2 1 18.7256 18.4284 \n", + "641 868167994315015001 ZTF17aaajjch 58622.2 1 18.645 14.9749 \n", + "606 868167994315015002 ZTF17aaajjcj 58622.2 1 18.645 17.6139 \n", + "574 868167991215010001 ZTF17aaajjcp 58622.2 1 18.8379 18.3062 \n", + "1185 868168483115010002 ZTF17aaajjeh 58622.2 1 18.7718 17.2375 \n", + "1245 868168483815010004 ZTF17aaajjfh 58622.2 1 18.7592 18.2365 \n", + "1218 868168482115010002 ZTF17aaajjfs 58622.2 1 18.7472 17.2561 \n", + "1194 868168483615010000 ZTF17aaajjgp 58622.2 1 18.9059 17.7953 \n", + "... ... ... ... .. ... ... \n", + "1437 868179324815010002 ZTF19aavhwfz 58622.2 1 19.014 18.7326 \n", + "1834 868180742915015010 ZTF19aavhwgr 58622.2 1 19.0479 16.6957 \n", + "1862 868180742915010002 ZTF19aavhwgs 58622.2 1 19.0479 18.5677 \n", + "1817 868180745115010000 ZTF19aavhwgt 58622.2 1 18.9284 18.9409 \n", + "1900 868181211115015008 ZTF19aavhwgu 58622.2 1 18.8725 17.6719 \n", + "1939 868181210015015006 ZTF19aavhwgv 58622.2 1 18.7352 16.8875 \n", + "1584 868181210015015029 ZTF19aavhwgw 58622.2 1 18.7352 17.9875 \n", + "1808 868181211415015005 ZTF19aavhwgx 58622.2 1 18.8556 17.185 \n", + "1585 868181213615015011 ZTF19aavhwgy 58622.2 1 18.8078 15.8094 \n", + "1821 868181213115015008 ZTF19aavhwgz 58622.2 1 18.9001 17.0913 \n", + "1883 868181213115015001 ZTF19aavhwha 58622.2 1 18.9001 18.426 \n", + "1923 868181215915015014 ZTF19aavhwig 58622.2 1 18.7933 16.859 \n", + "1943 868181215115010002 ZTF19aavhwii 58622.2 1 18.708 18.714 \n", + "1812 868181215415015017 ZTF19aavhwij 58622.2 1 18.7783 16.7992 \n", + "1843 868183073915010008 ZTF19aavhwjm 58622.2 1 18.161 16.5428 \n", + "1449 868183073815015024 ZTF19aavhwjn 58622.2 1 18.0884 16.1065 \n", + "1911 868183073615015017 ZTF19aavhwjo 58622.2 1 18.1319 16.8215 \n", + "1549 868183073115010003 ZTF19aavhwjp 58622.2 1 18.0197 16.5979 \n", + "1908 868183072115015006 ZTF19aavhwjq 58622.2 1 18.1114 16.9929 \n", + "1912 868183072815015018 ZTF19aavhwjr 58622.2 1 18.0391 16.4375 \n", + "1913 868183071515015010 ZTF19aavhwjt 58622.2 1 17.8536 17.1589 \n", + "1846 868183070615015008 ZTF19aavhwju 58622.2 1 18.0341 16.4459 \n", + "1928 868183070615015013 ZTF19aavhwjv 58622.2 1 18.0341 17.9429 \n", + "1948 868183070115015001 ZTF19aavhwjw 58622.2 1 18.1131 16.4302 \n", + "1284 868183076315015022 ZTF19aavhwky 58622.2 1 18.1088 15.0142 \n", + "1285 868183075615015009 ZTF19aavhwlb 58622.2 1 18.1132 17.4279 \n", + "1533 868183075615015040 ZTF19aavhwlc 58622.2 1 18.1132 17.3389 \n", + "1553 868183076115015040 ZTF19aavhwld 58622.2 1 17.9375 16.1455 \n", + "1876 868183075715010011 ZTF19aavhwlf 58622.2 1 18.085 17.8545 \n", + "1454 868183074915015018 ZTF19aavhwlg 58622.2 1 17.9377 17.1733 \n", + "\n", + " magap sigmapsf sigmagap ra ... field rcid magnr sigmagnr \\\n", + "94 18.1277 0.0928479 0.1993 153.594 ... 712 29 17.111 0.021 \n", + "160 18.2314 0.152357 0.2407 153.295 ... 712 61 16.976 0.019 \n", + "358 17.497 0.0812138 0.1137 157.014 ... 668 21 16.153 0.018 \n", + "495 13.1228 0.0220856 0.0025 157.466 ... 712 24 12.967 0.011 \n", + "617 18.3103 0.164618 0.3128 156.188 ... 752 11 15.887 0.013 \n", + "546 15.9005 0.0737607 0.0359 153.379 ... 752 63 14.287 0.012 \n", + "825 15.6789 0.0425768 0.0228 162.898 ... 818 58 13.847 0.009 \n", + "164 17.2058 0.0597659 0.0993 168.864 ... 818 20 15.953 0.015 \n", + "853 16.0982 0.070891 0.0355 158.809 ... 818 61 13.739 0.014 \n", + "230 16.6428 0.0680027 0.0526 156.265 ... 712 25 15.407 0.014 \n", + "363 18.2952 0.184115 0.2931 157.821 ... 752 5 16.952 0.016 \n", + "1045 17.7156 0.0880972 0.1067 182.721 ... 843 7 15.81 0.011 \n", + "874 17.9372 0.143672 0.136 167.806 ... 843 61 15.244 0.012 \n", + "1237 18.4811 0.104718 0.2108 174.027 ... 843 47 16.322 0.013 \n", + "713 15.5687 0.0387114 0.0175 179.313 ... 843 40 13.741 0.013 \n", + "81 16.4642 0.0474009 0.0342 163.324 ... 669 25 15.536 0.017 \n", + "541 15.2769 0.0216489 0.0119 175.911 ... 843 58 16.571 0.035 \n", + "613 18.0816 0.182476 0.2823 165.389 ... 713 27 16.78 0.015 \n", + "1241 16.9152 0.0592811 0.0511 182.777 ... 843 4 14.583 0.017 \n", + "610 15.9167 0.0461126 0.024 176.196 ... 843 41 13.995 0.015 \n", + "543 16.1094 0.0262745 0.0278 176.196 ... 843 41 13.995 0.015 \n", + "989 14.7802 0.0259857 0.0082 171.165 ... 843 13 13.895 0.012 \n", + "1115 18.5193 0.153734 0.2763 171.362 ... 523 43 17.156 0.016 \n", + "641 14.9859 0.0235315 0.0135 180.398 ... 624 43 14.774 0.018 \n", + "606 17.6176 0.0899238 0.1404 180.117 ... 624 43 16.393 0.018 \n", + "574 18.2657 0.183237 0.2312 178.423 ... 624 12 16.341 0.023 \n", + "1185 17.4661 0.0728239 0.1045 169.737 ... 523 31 15.726 0.01 \n", + "1245 18.3938 0.14535 0.2383 172.547 ... 523 38 17.123 0.013 \n", + "1218 17.1107 0.0799645 0.0768 173.129 ... 523 21 15.134 0.012 \n", + "1194 17.8039 0.0981662 0.1251 173.577 ... 523 36 16.259 0.014 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "1437 18.9077 0.200357 0.3031 210.114 ... 757 48 22.864 0.294 \n", + "1834 17.2492 0.145377 0.0627 209.237 ... 758 29 22.687 0.326 \n", + "1862 18.84 0.199134 0.2672 210.346 ... 758 29 21.774 0.132 \n", + "1817 18.5804 0.191467 0.2312 220.197 ... 758 51 22.359 0.223 \n", + "1900 18.2541 0.216353 0.1762 223.34 ... 793 11 22.914 0.354 \n", + "1939 17.3815 0.184301 0.09 230.483 ... 793 0 22.847 0.288 \n", + "1584 18.4645 0.157561 0.2719 229.596 ... 793 0 13.843 0.012 \n", + "1808 17.5236 0.17588 0.096 219.715 ... 793 14 22.731 0.336 \n", + "1585 16.3069 0.0957473 0.0461 226.236 ... 793 36 12.461 0.012 \n", + "1821 17.8193 0.197861 0.1527 219.934 ... 793 31 13.167 0.011 \n", + "1883 18.167 0.207073 0.1672 221.186 ... 793 31 21.988 0.223 \n", + "1923 17.1389 0.143528 0.0717 223.636 ... 793 59 22.4 0.246 \n", + "1943 18.4837 0.215662 0.2604 231.283 ... 793 51 22.105 0.251 \n", + "1812 17.4102 0.210327 0.0931 224.712 ... 793 54 22.589 0.355 \n", + "1843 16.7485 0.11812 0.102 209.239 ... 862 39 12.828 0.012 \n", + "1449 16.4671 0.142088 0.0744 206.069 ... 862 38 22.763 0.332 \n", + "1911 17.5277 0.210902 0.188 210.246 ... 862 36 22.079 0.191 \n", + "1549 16.9255 0.130523 0.1318 194.335 ... 862 31 13.217 0.012 \n", + "1908 17.5089 0.211537 0.1876 205.49 ... 862 21 22.632 0.304 \n", + "1912 17.0864 0.204581 0.1388 194.857 ... 862 28 22.798 0.323 \n", + "1913 17.5828 0.159113 0.2581 195.349 ... 862 15 13.935 0.01 \n", + "1846 17.0053 0.20677 0.126 205.87 ... 862 6 23.129 0.355 \n", + "1928 18.2418 0.212953 0.3928 206.549 ... 862 6 22.23 0.179 \n", + "1948 16.574 0.107017 0.0875 212.361 ... 862 1 13.33 0.012 \n", + "1284 15.6803 0.197358 0.0444 193.015 ... 862 63 12.255 0.019 \n", + "1285 17.5687 0.210304 0.2014 199.22 ... 862 56 22.404 0.266 \n", + "1533 17.5897 0.215918 0.2054 200.151 ... 862 56 21.904 0.207 \n", + "1553 16.8066 0.206594 0.112 187.093 ... 862 61 22.661 0.329 \n", + "1876 18.4029 0.200269 0.4425 197.996 ... 862 57 22.501 0.353 \n", + "1454 17.5085 0.207686 0.2136 218.543 ... 862 49 22.706 0.306 \n", + "\n", + " rb magpsf_corr magap_corr sigmapsf_corr sigmagap_corr id \n", + "94 0.858571 16.6458 16.7519 0.0351287 0.0581199 None \n", + "160 0.872857 16.7039 16.679 0.0360002 0.0588822 None \n", + "358 0.918571 15.7168 15.7192 0.01951 0.0248221 None \n", + "495 0.62 12.1972 12.2043 0.012797 0.00862976 None \n", + "617 0.822857 15.7778 15.7763 0.0196586 0.0325099 None \n", + "546 0.784286 13.4591 13.4598 0.0132809 0.0113827 None \n", + "825 0.85 13.0523 13.0509 0.0133068 0.0129156 None \n", + "164 0.942857 15.7312 15.7268 0.0197492 0.0284011 None \n", + "853 0.751429 13.635 13.6219 0.0138702 0.0126516 None \n", + "230 0.942857 15.1386 15.1052 0.0184756 0.0165929 None \n", + "363 0.864286 16.6651 16.6753 0.0444781 0.0670847 None \n", + "1045 0.924286 15.6281 15.6369 0.0164693 0.0183118 None \n", + "874 0.725714 14.7885 14.7857 0.0160898 0.0160214 None \n", + "1237 0.914286 16.1665 16.1827 0.017949 0.0278382 None \n", + "713 0.897143 13.5502 13.556 0.0125631 0.0113011 None \n", + "81 0.914286 15.2175 15.2153 0.0183262 0.0151467 None \n", + "541 0.92 14.9903 14.989 0.0184984 0.0122386 None \n", + "613 0.621429 16.5195 16.4938 0.0406794 0.0664125 None \n", + "1241 0.92 14.1102 14.1153 0.0141595 0.0139495 None \n", + "610 0.934286 13.8224 13.8242 0.0144795 0.0132843 None \n", + "543 0.847143 13.814 13.8502 0.0133221 0.0135785 None \n", + "989 0.851429 13.5297 13.5296 0.0121031 0.00926141 None \n", + "1115 0.865714 16.4689 16.4833 0.0290052 0.0447408 None \n", + "641 0.864286 14.0191 14.0236 0.0147951 0.012478 None \n", + "606 0.912857 16.0876 16.0885 0.0258978 0.0369294 None \n", + "574 0.671429 14.7704 14.7688 0.0306302 0.0311608 None \n", + "1185 0.935714 15.485 15.5268 0.0165625 0.0193922 None \n", + "1245 0.912857 16.7323 16.7696 0.0385402 0.0549937 None \n", + "1218 0.897143 15.2245 15.2008 0.0165076 0.0170503 None \n", + "1194 0.952857 15.9064 15.9079 0.020742 0.0246922 None \n", + "... ... ... ... ... ... ... \n", + "1437 0.708571 18.7326 18.9077 0.200357 0.3031 None \n", + "1834 0.298571 16.6957 17.2492 0.145377 0.0627 None \n", + "1862 0.391429 18.5677 18.84 0.199134 0.2672 None \n", + "1817 0.108571 18.9409 18.5804 0.191467 0.2312 None \n", + "1900 0.144286 17.6719 18.2541 0.216353 0.1762 None \n", + "1939 0.148571 16.8875 17.3815 0.184301 0.09 None \n", + "1584 0.304286 17.9875 18.4645 0.157561 0.2719 None \n", + "1808 0.152857 17.185 17.5236 0.17588 0.096 None \n", + "1585 0.404286 12.4124 12.43 0.0122162 0.0117343 None \n", + "1821 0.281429 13.1381 13.1521 0.0119023 0.0110471 None \n", + "1883 0.128571 18.426 18.167 0.207073 0.1672 None \n", + "1923 0.134286 16.859 17.1389 0.143528 0.0717 None \n", + "1943 0.192857 18.714 18.4837 0.215662 0.2604 None \n", + "1812 0.28 16.7992 17.4102 0.210327 0.0931 None \n", + "1843 0.384286 12.7931 12.799 0.0122063 0.0119886 None \n", + "1449 0.21 16.1065 16.4671 0.142088 0.0744 None \n", + "1911 0.251429 16.8215 17.5277 0.210902 0.188 None \n", + "1549 0.358571 13.1698 13.1819 0.0127606 0.0123516 None \n", + "1908 0.262857 16.9929 17.5089 0.211537 0.1876 None \n", + "1912 0.16 16.4375 17.0864 0.204581 0.1388 None \n", + "1913 0.507143 13.8806 13.8979 0.0122816 0.0129808 None \n", + "1846 0.204286 16.4459 17.0053 0.20677 0.126 None \n", + "1928 0.572857 17.9429 18.2418 0.212953 0.3928 None \n", + "1948 0.517143 13.2693 13.2766 0.0127537 0.0121712 None \n", + "1284 0.291429 12.1727 12.2097 0.0227561 0.0183131 None \n", + "1285 0.254286 17.4279 17.5687 0.210304 0.2014 None \n", + "1533 0.23 17.3389 17.5897 0.215918 0.2054 None \n", + "1553 0.188571 16.1455 16.8066 0.206594 0.112 None \n", + "1876 0.414286 17.8545 18.4029 0.200269 0.4425 None \n", + "1454 0.262857 17.1733 17.5085 0.207686 0.2136 None \n", + "\n", + "[1954 rows x 26 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'dec')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from astropy.time import Time\n", + "\n", + "date = '2019-05-16'\n", + "mjd_date = Time(date).mjd\n", + "print(mjd_date)\n", + "\n", + "query = \"select * from detections where jd>=\"+str(mjd_date)\n", + "\n", + "df4 = sql_query(query)\n", + "df4 = df4.sort_values(['oid'])\n", + "\n", + "#display(df4.head(n=10))\n", + "display(df4)\n", + "\n", + "plt.plot(df4['ra'].astype(float),df4['dec'].astype(float),'ro')\n", + "plt.xlabel('ra')\n", + "plt.ylabel('dec')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}