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from builtins import str 

from builtins import zip 

import matplotlib 

matplotlib.use("Agg") 

import numpy as np 

import warnings 

import unittest 

from lsst.sims.maf.slicers.oneDSlicer import OneDSlicer 

from lsst.sims.maf.slicers.uniSlicer import UniSlicer 

import lsst.utils.tests 

 

 

def makeDataValues(size=100, min=0., max=1., random=-1): 

"""Generate a simple array of numbers, evenly arranged between min/max, but (optional) random order.""" 

datavalues = np.arange(0, size, dtype='float') 

datavalues *= (float(max) - float(min)) / (datavalues.max() - datavalues.min()) 

datavalues += min 

if random > 0: 

rng = np.random.RandomState(random) 

randorder = rng.rand(size) 

randind = np.argsort(randorder) 

datavalues = datavalues[randind] 

datavalues = np.array(list(zip(datavalues)), dtype=[('testdata', 'float')]) 

return datavalues 

 

 

class TestOneDSlicerSetup(unittest.TestCase): 

 

def setUp(self): 

self.testslicer = OneDSlicer(sliceColName='testdata') 

 

def tearDown(self): 

del self.testslicer 

self.testslicer = None 

 

def testSlicertype(self): 

"""Test instantiation of slicer sets slicer type as expected.""" 

self.assertEqual(self.testslicer.slicerName, self.testslicer.__class__.__name__) 

self.assertEqual(self.testslicer.slicerName, 'OneDSlicer') 

 

def testSetupSlicerBins(self): 

"""Test setting up slicer using defined bins.""" 

dvmin = 0 

dvmax = 1 

nvalues = 1000 

bins = np.arange(dvmin, dvmax, 0.1) 

dv = makeDataValues(nvalues, dvmin, dvmax, random=4) 

# Used right bins? 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=bins) 

self.testslicer.setupSlicer(dv) 

np.testing.assert_equal(self.testslicer.bins, bins) 

self.assertEqual(self.testslicer.nslice, len(bins)-1) 

 

def testSetupSlicerNbins(self): 

"""Test setting up slicer using bins as integer.""" 

for nvalues in (100, 1000, 10000): 

for nbins in (5, 25, 75): 

dvmin = 0 

dvmax = 1 

dv = makeDataValues(nvalues, dvmin, dvmax, random=-1) 

# Right number of bins? 

# expect two more 'bins' to accomodate padding on left/right 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=nbins) 

self.testslicer.setupSlicer(dv) 

self.assertEqual(self.testslicer.nslice, nbins) 

# Bins of the right size? 

bindiff = np.diff(self.testslicer.bins) 

expectedbindiff = (dvmax - dvmin) / float(nbins) 

np.testing.assert_allclose(bindiff, expectedbindiff) 

 

def testSetupSlicerNbinsZeros(self): 

"""Test what happens if give slicer test data that is all single-value.""" 

dv = np.zeros(100, float) 

dv = np.array(list(zip(dv)), dtype=[('testdata', 'float')]) 

nbins = 10 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=nbins) 

with warnings.catch_warnings(record=True) as w: 

warnings.simplefilter("always") 

self.testslicer.setupSlicer(dv) 

self.assertIn("creasing binMax", str(w[-1].message)) 

self.assertEqual(self.testslicer.nslice, nbins) 

 

def testSetupSlicerEquivalent(self): 

"""Test setting up slicer using defined bins and nbins is equal where expected.""" 

dvmin = 0 

dvmax = 1 

for nbins in (20, 50, 100, 105): 

bins = makeDataValues(nbins+1, dvmin, dvmax, random=-1) 

bins = bins['testdata'] 

for nvalues in (100, 1000, 10000): 

dv = makeDataValues(nvalues, dvmin, dvmax, random=11) 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=bins) 

self.testslicer.setupSlicer(dv) 

np.testing.assert_allclose(self.testslicer.bins, bins) 

 

def testSetupSlicerLimits(self): 

"""Test setting up slicer using binMin/Max.""" 

binMin = 0 

binMax = 1 

nbins = 10 

dvmin = -.5 

dvmax = 1.5 

dv = makeDataValues(1000, dvmin, dvmax, random=342) 

self.testslicer = OneDSlicer(sliceColName='testdata', 

binMin=binMin, binMax=binMax, bins=nbins) 

self.testslicer.setupSlicer(dv) 

self.assertAlmostEqual(self.testslicer.bins.min(), binMin) 

self.assertAlmostEqual(self.testslicer.bins.max(), binMax) 

 

def testSetupSlicerBinsize(self): 

"""Test setting up slicer using binsize.""" 

dvmin = 0 

dvmax = 1 

dv = makeDataValues(1000, dvmin, dvmax, random=8977) 

# Test basic use. 

binsize = 0.5 

self.testslicer = OneDSlicer(sliceColName='testdata', binsize=binsize) 

self.testslicer.setupSlicer(dv) 

# When binsize is specified, oneDslicer adds an extra bin to first/last spots. 

self.assertEqual(self.testslicer.nslice, (dvmax-dvmin)/binsize+2) 

# Test that warning works. 

with warnings.catch_warnings(record=True) as w: 

warnings.simplefilter("always") 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=200, binsize=binsize) 

self.testslicer.setupSlicer(dv) 

# Verify some things 

self.assertIn("binsize", str(w[-1].message)) 

 

def testSetupSlicerFreedman(self): 

"""Test that setting up the slicer using bins=None works.""" 

dvmin = 0 

dvmax = 1 

dv = makeDataValues(1000, dvmin, dvmax, random=2234) 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=None) 

self.testslicer.setupSlicer(dv) 

# How many bins do you expect from optimal binsize? 

from lsst.sims.maf.utils import optimalBins 

bins = optimalBins(dv['testdata']) 

np.testing.assert_equal(self.testslicer.nslice, bins) 

 

 

class TestOneDSlicerIteration(unittest.TestCase): 

 

def setUp(self): 

self.testslicer = OneDSlicer(sliceColName='testdata') 

dvmin = 0 

dvmax = 1 

nvalues = 1000 

self.bins = np.arange(dvmin, dvmax, 0.01) 

dv = makeDataValues(nvalues, dvmin, dvmax, random=5678) 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=self.bins) 

self.testslicer.setupSlicer(dv) 

 

def tearDown(self): 

del self.testslicer 

self.testslicer = None 

 

def testIteration(self): 

"""Test iteration.""" 

for i, (s, b) in enumerate(zip(self.testslicer, self.bins)): 

self.assertEqual(s['slicePoint']['sid'], i) 

self.assertEqual(s['slicePoint']['binLeft'], b) 

 

def testGetItem(self): 

"""Test that can return an individual indexed values of the slicer.""" 

for i in ([0, 10, 20]): 

self.assertEqual(self.testslicer[i]['slicePoint']['sid'], i) 

self.assertEqual(self.testslicer[i]['slicePoint']['binLeft'], self.bins[i]) 

 

 

class TestOneDSlicerEqual(unittest.TestCase): 

 

def setUp(self): 

self.testslicer = OneDSlicer(sliceColName='testdata') 

 

def tearDown(self): 

del self.testslicer 

self.testslicer = None 

 

def testEquivalence(self): 

"""Test equals method.""" 

# Note that two OneD slicers will be considered equal if they are both the same kind of 

# slicer AND have the same bins. 

# Set up self.. 

dvmin = 0 

dvmax = 1 

nvalues = 1000 

bins = np.arange(dvmin, dvmax, 0.01) 

dv = makeDataValues(nvalues, dvmin, dvmax, random=32499) 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=bins) 

self.testslicer.setupSlicer(dv) 

# Set up another slicer to match (same bins, although not the same data). 

dv2 = makeDataValues(nvalues+100, dvmin, dvmax, random=334) 

testslicer2 = OneDSlicer(sliceColName='testdata', bins=bins) 

testslicer2.setupSlicer(dv2) 

self.assertTrue(self.testslicer == testslicer2) 

self.assertFalse(self.testslicer != testslicer2) 

# Set up another slicer that should not match (different bins) 

dv2 = makeDataValues(nvalues, dvmin+1, dvmax+1, random=445) 

testslicer2 = OneDSlicer(sliceColName='testdata', bins=len(bins)) 

testslicer2.setupSlicer(dv2) 

self.assertTrue(self.testslicer != testslicer2) 

self.assertFalse(self.testslicer == testslicer2) 

# Set up a different kind of slicer that should not match. 

dv2 = makeDataValues(100, 0, 1, random=12) 

testslicer2 = UniSlicer() 

testslicer2.setupSlicer(dv2) 

self.assertTrue(self.testslicer != testslicer2) 

self.assertFalse(self.testslicer == testslicer2) 

# Get another oneDslicer that is not set up, and check equivalence. 

testslicer2 = OneDSlicer(sliceColName='testdata') 

self.assertTrue(self.testslicer != testslicer2) 

self.assertFalse(self.testslicer == testslicer2) 

testslicer2 = OneDSlicer(sliceColName='testdata', binMin=0, binMax=1, binsize=0.5) 

testslicer3 = OneDSlicer(sliceColName='testdata', binMin=0, binMax=1, binsize=0.5) 

self.assertTrue(testslicer2 == testslicer3) 

self.assertFalse(testslicer2 != testslicer3) 

testslicer3 = OneDSlicer(sliceColName='testdata', binMin=0, binMax=1) 

self.assertFalse(testslicer2 == testslicer3) 

self.assertTrue(testslicer2 != testslicer3) 

usebins = np.arange(0, 1, 0.1) 

testslicer2 = OneDSlicer(sliceColName='testdata', bins=usebins) 

testslicer3 = OneDSlicer(sliceColName='testdata', bins=usebins) 

self.assertTrue(testslicer2 == testslicer3) 

self.assertFalse(testslicer2 != testslicer3) 

testslicer3 = OneDSlicer(sliceColName='testdata', bins=usebins+1) 

self.assertFalse(testslicer2 == testslicer3) 

self.assertTrue(testslicer2 != testslicer3) 

 

 

class TestOneDSlicerSlicing(unittest.TestCase): 

 

longMessage = True 

 

def setUp(self): 

self.testslicer = OneDSlicer(sliceColName='testdata') 

 

def tearDown(self): 

del self.testslicer 

self.testslicer = None 

 

def testSlicing(self): 

"""Test slicing.""" 

dvmin = 0 

dvmax = 1 

nbins = 100 

# Test that testbinner raises appropriate error before it's set up (first time) 

self.assertRaises(NotImplementedError, self.testslicer._sliceSimData, 0) 

for nvalues in (1000, 10000, 100000): 

dv = makeDataValues(nvalues, dvmin, dvmax, random=560) 

self.testslicer = OneDSlicer(sliceColName='testdata', bins=nbins) 

self.testslicer.setupSlicer(dv) 

sum = 0 

for i, s in enumerate(self.testslicer): 

idxs = s['idxs'] 

dataslice = dv['testdata'][idxs] 

sum += len(idxs) 

258 ↛ 261line 258 didn't jump to line 261, because the condition on line 258 was never false if len(dataslice) > 0: 

self.assertEqual(len(dataslice), nvalues/float(nbins)) 

else: 

self.assertGreater(len(dataslice), 0, 

msg='Data in test case expected to always be > 0 len after slicing') 

self.assertTrue(sum, nvalues) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

274 ↛ 275line 274 didn't jump to line 275, because the condition on line 274 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()