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# This file is part of ap_association. 

# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (https://www.lsst.org). 

# See the COPYRIGHT file at the top-level directory of this distribution 

# for details of code ownership. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# This program is distributed in the hope that it will be useful, 

# but WITHOUT ANY WARRANTY; without even the implied warranty of 

# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the GNU General Public License 

# along with this program. If not, see <https://www.gnu.org/licenses/>. 

 

from astropy.stats import median_absolute_deviation 

import numpy as np 

import pandas as pd 

from scipy.stats import skew 

import unittest 

 

from lsst.ap.association import ( 

MeanDiaPosition, MeanDiaPositionConfig, 

HTMIndexDiaPosition, HTMIndexDiaPositionConfig, 

NumDiaSourcesDiaPlugin, NumDiaSourcesDiaPluginConfig, 

SimpleSourceFlagDiaPlugin, SimpleSourceFlagDiaPluginConfig, 

WeightedMeanDiaPsFlux, WeightedMeanDiaPsFluxConfig, 

PercentileDiaPsFlux, PercentileDiaPsFluxConfig, 

SigmaDiaPsFlux, SigmaDiaPsFluxConfig, 

Chi2DiaPsFlux, Chi2DiaPsFluxConfig, 

MadDiaPsFlux, MadDiaPsFluxConfig, 

SkewDiaPsFlux, SkewDiaPsFluxConfig, 

MinMaxDiaPsFlux, MinMaxDiaPsFluxConfig, 

MaxSlopeDiaPsFlux, MaxSlopeDiaPsFluxConfig, 

ErrMeanDiaPsFlux, ErrMeanDiaPsFluxConfig, 

LinearFitDiaPsFlux, LinearFitDiaPsFluxConfig, 

StetsonJDiaPsFlux, StetsonJDiaPsFluxConfig, 

WeightedMeanDiaTotFlux, WeightedMeanDiaTotFluxConfig, 

SigmaDiaTotFlux, SigmaDiaTotFluxConfig) 

import lsst.utils.tests 

 

 

def run_single_plugin(diaObjects, diaSources, plugin): 

pass 

 

 

def run_multi_plugin(diaObjectCat, diaSourceCat, filterName, plugin): 

"""Wrapper for running multi plugins. 

 

Reproduces some of the behavior of `lsst.ap.association.DiaCalcuation.run` 

 

Parameters 

---------- 

diaObjectCat : `pandas.DataFrame` 

Input object catalog to store data into and read from. 

diaSourcesCat : `pandas.DataFrame` 

DiaSource catalog to read data from and groupby on. 

fitlerName : `str` 

String name of the filter to process. 

plugin : `lsst.ap.association.DiaCalculationPlugin` 

Plugin to run. 

""" 

if isinstance(diaObjectCat.index, pd.RangeIndex): 

diaObjectCat.set_index("diaObjectId", inplace=True, drop=False) 

elif diaObjectCat.index.name != "diaObjectId": 

print( 

"Input diaObjectCat is indexed on column(s) incompatible with " 

"this task. Should be indexed on 'diaObjectId'.") 

 

if isinstance(diaSourceCat.index, pd.RangeIndex): 

diaSourceCat.set_index( 

["diaObjectId", "filterName", "diaSourceId"], 

inplace=True, 

drop=False) 

elif (diaSourceCat.index.names != 

["diaObjectId", "filterName", "diaSourceId"]): 

print( 

"Input diaSourceCat is indexed on column(s) incompatible with " 

"this task. Should be indexed on 'multi-index, " 

"['diaObjectId', 'filterName', 'diaSourceId'].") 

 

updatingFilterDiaSources = diaSourceCat.loc[ 

(slice(None), filterName), : 

] 

 

diaSourcesGB = diaSourceCat.groupby(level=0) 

filterDiaSourcesGB = updatingFilterDiaSources.groupby(level=0) 

 

plugin.calculate(diaObjects=diaObjectCat, 

diaSources=diaSourcesGB, 

filterDiaSources=filterDiaSourcesGB, 

filterName=filterName) 

 

 

class TestMeanPosition(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean position calculation. 

""" 

n_sources = 10 

objId = 0 

 

plug = MeanDiaPosition(MeanDiaPositionConfig(), 

"ap_meanPosition", 

None) 

 

# Test expected means in RA. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame(data={"ra": np.linspace(-1, 1, n_sources), 

"decl": np.zeros(n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources), 

"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, 

dtype=int)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

 

self.assertAlmostEqual(diaObjects.loc[objId, "ra"], 0.0) 

self.assertAlmostEqual(diaObjects.loc[objId, "decl"], 0.0) 

self.assertEqual(diaObjects.loc[objId, "radecTai"], 10) 

 

# Test expected means in DEC. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame(data={"ra": np.zeros(n_sources), 

"decl": np.linspace(-1, 1, n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources), 

"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, 

dtype=int)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

 

self.assertAlmostEqual(diaObjects.loc[objId, "ra"], 0.0) 

self.assertAlmostEqual(diaObjects.loc[objId, "decl"], 0.0) 

self.assertEqual(diaObjects.loc[objId, "radecTai"], 10) 

 

# Test failure mode RA is nan. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame(data={"ra": np.full(n_sources, np.nan), 

"decl": np.zeros(n_sources), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources), 

"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, 

dtype=int)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

 

self.assertTrue(np.isnan(diaObjects.loc[objId, "ra"])) 

self.assertTrue(np.isnan(diaObjects.loc[objId, "decl"])) 

self.assertTrue(np.isnan(diaObjects.loc[objId, "radecTai"])) 

 

# Test failure mode DEC is nan. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame(data={"ra": np.zeros(n_sources), 

"decl": np.full(n_sources, np.nan), 

"midPointTai": np.linspace(0, 

n_sources, 

n_sources), 

"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, 

dtype=int)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

 

self.assertTrue(np.isnan(diaObjects.loc[objId, "ra"])) 

self.assertTrue(np.isnan(diaObjects.loc[objId, "decl"])) 

self.assertTrue(np.isnan(diaObjects.loc[objId, "radecTai"])) 

 

 

class TestHTMIndexPosition(unittest.TestCase): 

 

def testCalculate(self): 

"""Test HTMPixel assignment calculation. 

""" 

# Test expected pixelId at RA, DEC = 0 

objId = 0 

n_sources = 10 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaObjects.loc[objId, "ra"] = 0. 

diaObjects.loc[objId, "decl"] = 0. 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int)}) 

plug = HTMIndexDiaPosition(HTMIndexDiaPositionConfig(), 

"ap_HTMIndex", 

None) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "pixelId"], 131072) 

 

# Test expected pixelId at some value of RA and DEC. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaObjects.loc[objId, "ra"] = 45.37 

diaObjects.loc[objId, "decl"] = 13.67 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "pixelId"], 260033) 

 

 

class TestNDiaSourcesDiaPlugin(unittest.TestCase): 

 

def testCalculate(self): 

"""Test that the number of DiaSources is correct. 

""" 

 

for n_sources in [1, 8, 10]: 

# Test expected number of sources per object. 

objId = 0 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int)}) 

plug = NumDiaSourcesDiaPlugin(NumDiaSourcesDiaPluginConfig(), 

"ap_nDiaSources", 

None) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

 

self.assertEqual(n_sources, diaObjects.at[objId, "nDiaSources"]) 

 

 

class TestSimpleSourceFlagDiaPlugin(unittest.TestCase): 

 

def testCalculate(self): 

"""Test that DiaObject flags are set. 

""" 

objId = 0 

n_sources = 10 

 

# Test expected flags, no flags set. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"flags": np.zeros(n_sources, dtype=np.uint64)}) 

plug = SimpleSourceFlagDiaPlugin(SimpleSourceFlagDiaPluginConfig(), 

"ap_diaObjectFlag", 

None) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "flags"], 0) 

 

# Test expected flags, all flags set. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"flags": np.ones(n_sources, dtype=np.uint64)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "flags"], 1) 

 

# Test expected flags, random flags. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"flags": np.random.randint(0, 2 ** 16, size=n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "flags"], 1) 

 

# Test expected flags, one flag set. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

flag_array = np.zeros(n_sources, dtype=np.uint64) 

flag_array[4] = 256 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["g"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"flags": flag_array}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertEqual(diaObjects.at[objId, "flags"], 1) 

 

 

class TestWeightedMeanDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean value calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected mean. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": np.linspace(-1, 1, n_sources), 

"psFluxErr": np.ones(n_sources)}) 

 

plug = WeightedMeanDiaPsFlux(WeightedMeanDiaPsFluxConfig(), 

"ap_meanFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

 

self.assertAlmostEqual(diaObjects.loc[objId, "uPSFluxMean"], 0.0) 

self.assertAlmostEqual(diaObjects.loc[objId, "uPSFluxMeanErr"], 

np.sqrt(1 / n_sources)) 

self.assertEqual(diaObjects.loc[objId, "uPSFluxNdata"], n_sources) 

 

# Test expected mean with a nan value. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

fluxes = np.linspace(-1, 1, n_sources) 

fluxes[4] = np.nan 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxMean"], 

np.nanmean(fluxes)) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxMeanErr"], 

np.sqrt(1 / (n_sources - 1))) 

self.assertEqual(diaObjects.loc[objId, "rPSFluxNdata"], n_sources - 1) 

 

 

class TestPercentileDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux percentile calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected percentile values. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = PercentileDiaPsFlux(PercentileDiaPsFluxConfig(), 

"ap_percentileFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

for pTile, testVal in zip(plug.config.percentiles, 

np.nanpercentile( 

fluxes, 

plug.config.percentiles)): 

self.assertAlmostEqual( 

diaObjects.at[objId, "uPSFluxPercentile{:02d}".format(pTile)], 

testVal) 

 

# Test expected percentile values with a nan value. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

fluxes[4] = np.nan 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

for pTile, testVal in zip(plug.config.percentiles, 

np.nanpercentile( 

fluxes, 

plug.config.percentiles)): 

self.assertAlmostEqual( 

diaObjects.at[objId, "rPSFluxPercentile{:02d}".format(pTile)], 

testVal) 

 

 

class TestSigmaDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux scatter calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected sigma scatter of fluxes. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = SigmaDiaPsFlux(SigmaDiaPsFluxConfig(), 

"ap_sigmaFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "uPSFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

# test one input, returns nan. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": 1 * [objId], 

"filterName": 1 * ["g"], 

"diaSourceId": [0], 

"psFlux": [fluxes[0]], 

"psFluxErr": [1.]}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertTrue(np.isnan(diaObjects.at[objId, "gPSFluxSigma"])) 

 

# Test expected sigma scatter of fluxes with a nan value. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

 

class TestChi2DiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux chi2 calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected chi^2 value. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId], 

"uPSFluxMean": [0.0]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = Chi2DiaPsFlux(Chi2DiaPsFluxConfig(), 

"ap_chi2Flux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual( 

diaObjects.loc[objId, "uPSFluxChi2"], 

np.nansum(((diaSources["psFlux"] - 

np.nanmean(diaSources["psFlux"])) / 

diaSources["psFluxErr"]) ** 2)) 

 

# Test expected chi^2 value with a nan value set. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId], 

"rPSFluxMean": [np.nanmean(fluxes)]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual( 

diaObjects.loc[objId, "rPSFluxChi2"], 

np.nansum(((diaSources["psFlux"] - 

np.nanmean(diaSources["psFlux"])) / 

diaSources["psFluxErr"]) ** 2)) 

 

 

class TestMadDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux median absolute deviation calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected MAD value. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = MadDiaPsFlux(MadDiaPsFluxConfig(), 

"ap_madFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "uPSFluxMAD"], 

median_absolute_deviation(fluxes, 

ignore_nan=True)) 

 

# Test expected MAD value with a nan set. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxMAD"], 

median_absolute_deviation(fluxes, 

ignore_nan=True)) 

 

 

class TestSkewDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux skew calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected skew value. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = SkewDiaPsFlux(SkewDiaPsFluxConfig(), 

"ap_skewFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual( 

diaObjects.loc[objId, "uPSFluxSkew"], 

skew(fluxes, bias=False, nan_policy="omit")) 

 

# Test expected skew value with a nan set. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

# Skew returns a named tuple when called on an array 

# with nan values. 

self.assertAlmostEqual( 

diaObjects.at[objId, "rPSFluxSkew"], 

skew(fluxes, bias=False, nan_policy="omit").data) 

 

 

class TestMinMaxDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux min/max calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test expected MinMax fluxes. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

 

plug = MinMaxDiaPsFlux(MinMaxDiaPsFluxConfig(), 

"ap_minMaxFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertEqual(diaObjects.loc[objId, "uPSFluxMin"], -1) 

self.assertEqual(diaObjects.loc[objId, "uPSFluxMax"], 1) 

 

# Test expected MinMax fluxes with a nan set. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertEqual(diaObjects.loc[objId, "rPSFluxMin"], -1) 

self.assertEqual(diaObjects.loc[objId, "rPSFluxMax"], 1) 

 

 

class TestMaxSlopeDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux maximum slope. 

""" 

n_sources = 10 

objId = 0 

 

# Test max slope value. 

fluxes = np.linspace(-1, 1, n_sources) 

times = np.concatenate([np.linspace(0, 1, n_sources)[:-1], [1 - 1/90]]) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources), 

"midPointTai": times}) 

 

plug = MaxSlopeDiaPsFlux(MaxSlopeDiaPsFluxConfig(), 

"ap_maxSlopeFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "uPSFluxMaxSlope"], 2 + 2/9) 

 

# Test max slope value returns nan on 1 input. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": 1 * [objId], 

"filterName": 1 * ["g"], 

"diaSourceId": np.arange(1, dtype=int), 

"psFlux": fluxes[0], 

"psFluxErr": np.ones(1), 

"midPointTai": times[0]}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertTrue(np.isnan(diaObjects.at[objId, "gPSFluxMaxSlope"])) 

 

# Test max slope value inputing nan values. 

fluxes[4] = np.nan 

times[7] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": np.ones(n_sources), 

"midPointTai": times}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxMaxSlope"], 2 + 2 / 9) 

 

 

class TestErrMeanDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test error mean calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test mean of the errors. 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.linspace(1, 2, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors}) 

 

plug = ErrMeanDiaPsFlux(ErrMeanDiaPsFluxConfig(), 

"ap_errMeanFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "uPSFluxErrMean"], 

np.nanmean(errors)) 

 

# Test mean of the errors with input nan value. 

errors[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxErrMean"], 

np.nanmean(errors)) 

 

 

class TestLinearFitDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test a linear fit to flux vs time. 

""" 

n_sources = 10 

objId = 0 

 

# Test best fit linear model. 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.linspace(1, 2, n_sources) 

times = np.linspace(0, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

 

plug = LinearFitDiaPsFlux(LinearFitDiaPsFluxConfig(), 

"ap_LinearFit", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.loc[objId, "uPSFluxLinearSlope"], 

2.) 

self.assertAlmostEqual(diaObjects.loc[objId, "uPSFluxLinearIntercept"], 

-1.) 

 

# Test best fit linear model with input nans. 

fluxes[7] = np.nan 

errors[4] = np.nan 

times[2] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors, 

"midPointTai": times}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.loc[objId, "rPSFluxLinearSlope"], 2.) 

self.assertAlmostEqual(diaObjects.loc[objId, "rPSFluxLinearIntercept"], 

-1.) 

 

 

class TestStetsonJDiaPsFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test the stetsonJ statistic. 

""" 

n_sources = 10 

objId = 0 

 

# Test stetsonJ calculation. 

fluxes = np.linspace(-1, 1, n_sources) 

errors = np.ones(n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId], 

"uPSFluxMean": [np.nanmean(fluxes)]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors}) 

 

plug = StetsonJDiaPsFlux(StetsonJDiaPsFluxConfig(), 

"ap_StetsonJ", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

# Expected StetsonJ for the values created. Confirmed using Cesimum's 

# implementation. http://github.com/cesium-ml/cesium 

self.assertAlmostEqual(diaObjects.loc[objId, "uPSFluxStetsonJ"], 

-0.5958393936080928) 

 

# Test stetsonJ calculation returns nan on single input. 

diaObjects = pd.DataFrame({"diaObjectId": [objId], 

"gPSFluxMean": [np.nanmean(fluxes)]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": 1 * [objId], 

"filterName": 1 * ["g"], 

"diaSourceId": np.arange(1, dtype=int), 

"psFlux": fluxes[0], 

"psFluxErr": errors[0]}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertTrue(np.isnan(diaObjects.at[objId, "gPSFluxStetsonJ"])) 

 

# Test stetsonJ calculation returns when nans are input. 

fluxes[7] = np.nan 

errors[4] = np.nan 

nonNanMask = np.logical_and(~np.isnan(fluxes), 

~np.isnan(errors)) 

diaObjects = pd.DataFrame( 

{"diaObjectId": [objId], 

"rPSFluxMean": [np.average(fluxes[nonNanMask], 

weights=errors[nonNanMask])]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"psFlux": fluxes, 

"psFluxErr": errors}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rPSFluxStetsonJ"], 

-0.5412797916187173) 

 

 

class TestWeightedMeanDiaTotFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test mean value calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test test mean on totFlux. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

 

plug = WeightedMeanDiaTotFlux(WeightedMeanDiaTotFluxConfig(), 

"ap_meanTotFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

 

self.assertAlmostEqual(diaObjects.at[objId, "uTOTFluxMean"], 0.0) 

self.assertAlmostEqual(diaObjects.at[objId, "uTOTFluxMeanErr"], 

np.sqrt(1 / n_sources)) 

 

# Test test mean on totFlux with input nans 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

 

self.assertAlmostEqual(diaObjects.at[objId, "rTOTFluxMean"], 

np.nanmean(fluxes)) 

self.assertAlmostEqual(diaObjects.at[objId, "rTOTFluxMeanErr"], 

np.sqrt(1 / (n_sources - 1))) 

 

 

class TestSigmaDiaTotFlux(unittest.TestCase): 

 

def testCalculate(self): 

"""Test flux scatter calculation. 

""" 

n_sources = 10 

objId = 0 

 

# Test test scatter on totFlux. 

fluxes = np.linspace(-1, 1, n_sources) 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["u"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

 

plug = SigmaDiaTotFlux(SigmaDiaTotFluxConfig(), 

"ap_sigmaTotFlux", 

None) 

run_multi_plugin(diaObjects, diaSources, "u", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "uTOTFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

# Test test scatter on totFlux returns nan on 1 input. 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": 1 * [objId], 

"filterName": 1 * ["g"], 

"diaSourceId": np.arange(1, dtype=int), 

"totFlux": fluxes[0], 

"totFluxErr": np.ones(1)}) 

run_multi_plugin(diaObjects, diaSources, "g", plug) 

self.assertTrue(np.isnan(diaObjects.at[objId, "gTOTFluxSigma"])) 

 

# Test test scatter on totFlux takes input nans. 

fluxes[4] = np.nan 

diaObjects = pd.DataFrame({"diaObjectId": [objId]}) 

diaSources = pd.DataFrame( 

data={"diaObjectId": n_sources * [objId], 

"filterName": n_sources * ["r"], 

"diaSourceId": np.arange(n_sources, dtype=int), 

"totFlux": fluxes, 

"totFluxErr": np.ones(n_sources)}) 

run_multi_plugin(diaObjects, diaSources, "r", plug) 

self.assertAlmostEqual(diaObjects.at[objId, "rTOTFluxSigma"], 

np.nanstd(fluxes, ddof=1)) 

 

 

class MemoryTester(lsst.utils.tests.MemoryTestCase): 

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

910 ↛ 911line 910 didn't jump to line 911, because the condition on line 910 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()