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from __future__ import print_function 

from builtins import zip 

from builtins import range 

import matplotlib 

matplotlib.use("Agg") 

import numpy as np 

import unittest 

import lsst.sims.maf.metrics as metrics 

import lsst.utils.tests 

 

 

class TestCadenceMetrics(unittest.TestCase): 

 

def testPhaseGapMetric(self): 

""" 

Test the phase gap metric 

""" 

data = np.zeros(10, dtype=list(zip(['observationStartMJD'], [float]))) 

data['observationStartMJD'] += np.arange(10)*.25 

 

pgm = metrics.PhaseGapMetric(nPeriods=1, periodMin=0.5, periodMax=0.5) 

metricVal = pgm.run(data) 

 

meanGap = pgm.reduceMeanGap(metricVal) 

medianGap = pgm.reduceMedianGap(metricVal) 

worstPeriod = pgm.reduceWorstPeriod(metricVal) 

largestGap = pgm.reduceLargestGap(metricVal) 

 

self.assertEqual(meanGap, 0.5) 

self.assertEqual(medianGap, 0.5) 

self.assertEqual(worstPeriod, 0.5) 

self.assertEqual(largestGap, 0.5) 

 

pgm = metrics.PhaseGapMetric(nPeriods=2, periodMin=0.25, periodMax=0.5) 

metricVal = pgm.run(data) 

 

meanGap = pgm.reduceMeanGap(metricVal) 

medianGap = pgm.reduceMedianGap(metricVal) 

worstPeriod = pgm.reduceWorstPeriod(metricVal) 

largestGap = pgm.reduceLargestGap(metricVal) 

 

self.assertEqual(meanGap, 0.75) 

self.assertEqual(medianGap, 0.75) 

self.assertEqual(worstPeriod, 0.25) 

self.assertEqual(largestGap, 1.) 

 

def testTemplateExists(self): 

""" 

Test the TemplateExistsMetric. 

""" 

names = ['finSeeing', 'observationStartMJD'] 

types = [float, float] 

data = np.zeros(10, dtype=list(zip(names, types))) 

data['finSeeing'] = [2., 2., 3., 1., 1., 1., 0.5, 1., 0.4, 1.] 

data['observationStartMJD'] = np.arange(10) 

slicePoint = {'sid': 0} 

# so here we have 4 images w/o good previous templates 

metric = metrics.TemplateExistsMetric(seeingCol='finSeeing') 

result = metric.run(data, slicePoint) 

self.assertEqual(result, 6./10.) 

 

def testUniformityMetric(self): 

names = ['observationStartMJD'] 

types = [float] 

data = np.zeros(100, dtype=list(zip(names, types))) 

metric = metrics.UniformityMetric() 

result1 = metric.run(data) 

# If all the observations are on the 1st day, should be 1 

self.assertEqual(result1, 1) 

data['observationStartMJD'] = data['observationStartMJD']+365.25*10 

slicePoint = {'sid': 0} 

result2 = metric.run(data, slicePoint) 

# All on last day should also be 1 

self.assertEqual(result1, 1) 

# Make a perfectly uniform dist 

data['observationStartMJD'] = np.arange(0., 365.25*10, 365.25*10/100) 

result3 = metric.run(data, slicePoint) 

# Result should be zero for uniform 

np.testing.assert_almost_equal(result3, 0.) 

# A single obseravtion should give a result of 1 

data = np.zeros(1, dtype=list(zip(names, types))) 

result4 = metric.run(data, slicePoint) 

self.assertEqual(result4, 1) 

 

def testTGapMetric(self): 

names = ['observationStartMJD'] 

types = [float] 

data = np.zeros(100, dtype=list(zip(names, types))) 

# All 1-day gaps 

data['observationStartMJD'] = np.arange(100) 

 

metric = metrics.TgapsMetric(bins=np.arange(1, 100, 1)) 

result1 = metric.run(data) 

# By default, should all be in first bin 

self.assertEqual(result1[0], data.size-1) 

self.assertEqual(np.sum(result1), data.size-1) 

data['observationStartMJD'] = np.arange(0, 200, 2) 

result2 = metric.run(data) 

self.assertEqual(result2[1], data.size-1) 

self.assertEqual(np.sum(result2), data.size-1) 

 

data = np.zeros(4, dtype=list(zip(names, types))) 

data['observationStartMJD'] = [10, 20, 30, 40] 

metric = metrics.TgapsMetric(allGaps=True, bins=np.arange(1, 100, 10)) 

result3 = metric.run(data) 

self.assertEqual(result3[1], 2) 

Ngaps = np.math.factorial(data.size-1) 

self.assertEqual(np.sum(result3), Ngaps) 

 

def testNightGapMetric(self): 

names = ['night'] 

types = [float] 

data = np.zeros(100, dtype=list(zip(names, types))) 

# All 1-day gaps 

data['night'] = np.arange(100) 

 

metric = metrics.NightgapsMetric(bins=np.arange(1, 100, 1)) 

result1 = metric.run(data) 

# By default, should all be in first bin 

self.assertEqual(result1[0], data.size-1) 

self.assertEqual(np.sum(result1), data.size-1) 

data['night'] = np.arange(0, 200, 2) 

result2 = metric.run(data) 

self.assertEqual(result2[1], data.size-1) 

self.assertEqual(np.sum(result2), data.size-1) 

 

data = np.zeros(4, dtype=list(zip(names, types))) 

data['night'] = [10, 20, 30, 40] 

metric = metrics.NightgapsMetric(allGaps=True, bins=np.arange(1, 100, 10)) 

result3 = metric.run(data) 

self.assertEqual(result3[1], 2) 

Ngaps = np.math.factorial(data.size-1) 

self.assertEqual(np.sum(result3), Ngaps) 

 

data = np.zeros(6, dtype=list(zip(names, types))) 

data['night'] = [1, 1, 2, 3, 3, 5] 

metric = metrics.NightgapsMetric(bins=np.arange(0, 5, 1)) 

result4 = metric.run(data) 

self.assertEqual(result4[0], 0) 

self.assertEqual(result4[1], 2) 

self.assertEqual(result4[2], 1) 

 

def testNVisitsPerNightMetric(self): 

names = ['night'] 

types = [float] 

data = np.zeros(100, dtype=list(zip(names, types))) 

# One visit per night. 

data['night'] = np.arange(100) 

 

bins = np.arange(0, 5, 1) 

metric = metrics.NVisitsPerNightMetric(bins=bins) 

result = metric.run(data) 

# All nights have one visit. 

expected_result = np.zeros(len(bins) - 1, dtype=int) 

expected_result[1] = len(data) 

np.testing.assert_array_equal(result, expected_result) 

 

data['night'] = np.floor(np.arange(0, 100) / 2) 

result = metric.run(data) 

expected_result = np.zeros(len(bins) - 1, dtype=int) 

expected_result[2] = len(data) / 2 

np.testing.assert_array_equal(result, expected_result) 

 

def testRapidRevisitMetric(self): 

data = np.zeros(100, dtype=list(zip(['observationStartMJD'], [float]))) 

# Uniformly distribute time _differences_ between 0 and 100 

dtimes = np.arange(100) 

data['observationStartMJD'] = dtimes.cumsum() 

# Set up "rapid revisit" metric to look for visits between 5 and 25 

metric = metrics.RapidRevisitMetric(dTmin=5, dTmax=55, minNvisits=50) 

result = metric.run(data) 

# This should be uniform. 

self.assertLess(result, 0.1) 

self.assertGreaterEqual(result, 0) 

# Set up non-uniform distribution of time differences 

dtimes = np.zeros(100) + 5 

data['observationStartMJD'] = dtimes.cumsum() 

result = metric.run(data) 

self.assertGreaterEqual(result, 0.5) 

dtimes = np.zeros(100) + 15 

data['observationStartMJD'] = dtimes.cumsum() 

result = metric.run(data) 

self.assertGreaterEqual(result, 0.5) 

# Let's see how much dmax/result can vary 

resmin = 1 

resmax = 0 

rng = np.random.RandomState(88123100) 

for i in range(10000): 

dtimes = rng.rand(100) 

data['observationStartMJD'] = dtimes.cumsum() 

metric = metrics.RapidRevisitMetric(dTmin=0.1, dTmax=0.8, minNvisits=50) 

result = metric.run(data) 

resmin = np.min([resmin, result]) 

resmax = np.max([resmax, result]) 

print("RapidRevisit .. range", resmin, resmax) 

 

def testNRevisitsMetric(self): 

data = np.zeros(100, dtype=list(zip(['observationStartMJD'], [float]))) 

dtimes = np.arange(100)/24./60. 

data['observationStartMJD'] = dtimes.cumsum() 

metric = metrics.NRevisitsMetric(dT=50.) 

result = metric.run(data) 

self.assertEqual(result, 50) 

metric = metrics.NRevisitsMetric(dT=50., normed=True) 

result = metric.run(data) 

self.assertEqual(result, 0.5) 

 

def testTransientMetric(self): 

names = ['observationStartMJD', 'fiveSigmaDepth', 'filter'] 

types = [float, float, '<U1'] 

 

ndata = 100 

dataSlice = np.zeros(ndata, dtype=list(zip(names, types))) 

dataSlice['observationStartMJD'] = np.arange(ndata) 

dataSlice['fiveSigmaDepth'] = 25 

dataSlice['filter'] = 'g' 

 

metric = metrics.TransientMetric(surveyDuration=ndata/365.25) 

 

# Should detect everything 

self.assertEqual(metric.run(dataSlice), 1.) 

 

# Double to survey duration, should now only detect half 

metric = metrics.TransientMetric(surveyDuration=ndata/365.25*2) 

self.assertEqual(metric.run(dataSlice), 0.5) 

 

# Set half of the m5 of the observations very bright, so kill another half. 

dataSlice['fiveSigmaDepth'][0:50] = 20 

self.assertEqual(metric.run(dataSlice), 0.25) 

 

dataSlice['fiveSigmaDepth'] = 25 

# Demand lots of early observations 

metric = metrics.TransientMetric(peakTime=.5, nPrePeak=3, surveyDuration=ndata/365.25) 

self.assertEqual(metric.run(dataSlice), 0.) 

 

# Demand a reasonable number of early observations 

metric = metrics.TransientMetric(peakTime=2, nPrePeak=2, surveyDuration=ndata/365.25) 

self.assertEqual(metric.run(dataSlice), 1.) 

 

# Demand multiple filters 

metric = metrics.TransientMetric(nFilters=2, surveyDuration=ndata/365.25) 

self.assertEqual(metric.run(dataSlice), 0.) 

 

dataSlice['filter'] = ['r', 'g']*50 

self.assertEqual(metric.run(dataSlice), 1.) 

 

# Demad too many observation per light curve 

metric = metrics.TransientMetric(nPerLC=20, surveyDuration=ndata/365.25) 

self.assertEqual(metric.run(dataSlice), 0.) 

 

# Test both filter and number of LC samples 

metric = metrics.TransientMetric(nFilters=2, nPerLC=3, surveyDuration=ndata/365.25) 

self.assertEqual(metric.run(dataSlice), 1.) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

264 ↛ 265line 264 didn't jump to line 265, because the condition on line 264 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()