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#!/usr/bin/env python 

 

# 

# LSST Data Management System 

# 

# Copyright 2008-2017 AURA/LSST. 

# 

# This product includes software developed by the 

# LSST Project (http://www.lsst.org/). 

# 

# 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 LSST License Statement and 

# the GNU General Public License along with this program. If not, 

# see <https://www.lsstcorp.org/LegalNotices/>. 

# 

"""Test cases for cp_pipe.""" 

 

from __future__ import absolute_import, division, print_function 

import unittest 

import numpy as np 

import copy 

 

import lsst.utils 

import lsst.utils.tests 

 

import lsst.cp.pipe as cpPipe 

import lsst.ip.isr.isrMock as isrMock 

from lsst.cp.pipe.ptc import PhotonTransferCurveDataset 

 

 

class MeasurePhotonTransferCurveTaskTestCase(lsst.utils.tests.TestCase): 

"""A test case for the PTC task.""" 

 

def setUp(self): 

self.defaultConfig = cpPipe.ptc.MeasurePhotonTransferCurveTask.ConfigClass() 

self.defaultConfig.isr.doFlat = False 

self.defaultConfig.isr.doFringe = False 

self.defaultConfig.isr.doCrosstalk = False 

self.defaultConfig.isr.doAddDistortionModel = False 

self.defaultConfig.isr.doUseOpticsTransmission = False 

self.defaultConfig.isr.doUseFilterTransmission = False 

self.defaultConfig.isr.doUseSensorTransmission = False 

self.defaultConfig.isr.doUseAtmosphereTransmission = False 

self.defaultConfig.isr.doAttachTransmissionCurve = False 

 

self.defaultTask = cpPipe.ptc.MeasurePhotonTransferCurveTask(config=self.defaultConfig) 

 

self.flatMean = 2000 

self.readNoiseAdu = 10 

mockImageConfig = isrMock.IsrMock.ConfigClass() 

 

# flatDrop is not really relevant as we replace the data 

# but good to note it in case we change how this image is made 

mockImageConfig.flatDrop = 0.99999 

mockImageConfig.isTrimmed = True 

 

self.flatExp1 = isrMock.FlatMock(config=mockImageConfig).run() 

self.flatExp2 = self.flatExp1.clone() 

(shapeY, shapeX) = self.flatExp1.getDimensions() 

 

self.flatWidth = np.sqrt(self.flatMean) + self.readNoiseAdu 

 

self.rng1 = np.random.RandomState(1984) 

flatData1 = self.rng1.normal(self.flatMean, self.flatWidth, (shapeX, shapeY)) 

self.rng2 = np.random.RandomState(666) 

flatData2 = self.rng2.normal(self.flatMean, self.flatWidth, (shapeX, shapeY)) 

 

self.flatExp1.image.array[:] = flatData1 

self.flatExp2.image.array[:] = flatData2 

 

# create fake PTC data to see if fit works, for one amp ('amp') 

flux = 1000 # ADU/sec 

timeVec = np.arange(1., 201.) 

muVec = flux*timeVec # implies that signal-chain non-linearity is zero 

self.gain = 1.5 # e-/ADU 

self.c1 = 1./self.gain 

self.noiseSq = 5*self.gain # 7.5 (e-)^2 

self.a00 = -1.2e-6 

self.c2 = -1.5e-6 

self.c3 = -4.7e-12 # tuned so that it turns over for 200k mean 

 

self.ampNames = [amp.getName() for amp in self.flatExp1.getDetector().getAmplifiers()] 

self.dataset = PhotonTransferCurveDataset(self.ampNames) # pack raw data for fitting 

 

for ampName in self.ampNames: # just the expTimes and means here - vars vary per function 

self.dataset.rawExpTimes[ampName] = timeVec 

self.dataset.rawMeans[ampName] = muVec 

 

def test_ptcFitQuad(self): 

localDataset = copy.copy(self.dataset) 

for ampName in self.ampNames: 

localDataset.rawVars[ampName] = [self.noiseSq + self.c1*mu + self.c2*mu**2 for 

mu in localDataset.rawMeans[ampName]] 

 

config = copy.copy(self.defaultConfig) 

config.polynomialFitDegree = 2 

task = cpPipe.ptc.MeasurePhotonTransferCurveTask(config=config) 

 

task.fitPtcAndNl(localDataset, ptcFitType='POLYNOMIAL') 

 

for ampName in self.ampNames: 

self.assertAlmostEqual(self.gain, localDataset.gain[ampName]) 

self.assertAlmostEqual(np.sqrt(self.noiseSq)*self.gain, localDataset.noise[ampName]) 

# Linearity residual should be zero 

self.assertTrue(localDataset.nonLinearityResiduals[ampName].all() == 0) 

 

def test_ptcFitCubic(self): 

localDataset = copy.copy(self.dataset) 

for ampName in self.ampNames: 

localDataset.rawVars[ampName] = [self.noiseSq + self.c1*mu + self.c2*mu**2 + self.c3*mu**3 for 

mu in localDataset.rawMeans[ampName]] 

 

config = copy.copy(self.defaultConfig) 

config.polynomialFitDegree = 3 

 

task = cpPipe.ptc.MeasurePhotonTransferCurveTask(config=config) 

task.fitPtcAndNl(localDataset, ptcFitType='POLYNOMIAL') 

 

for ampName in self.ampNames: 

self.assertAlmostEqual(self.gain, localDataset.gain[ampName]) 

self.assertAlmostEqual(np.sqrt(self.noiseSq)*self.gain, localDataset.noise[ampName]) 

# Linearity residual should be zero 

self.assertTrue(localDataset.nonLinearityResiduals[ampName].all() == 0) 

 

def test_ptcFitAstier(self): 

localDataset = copy.copy(self.dataset) 

g = self.gain # next line is too long without this shorthand! 

for ampName in self.ampNames: 

localDataset.rawVars[ampName] = [(0.5/(self.a00*g**2)*(np.exp(2*self.a00*mu*g)-1) + 

self.noiseSq/(g*g)) for mu in localDataset.rawMeans[ampName]] 

 

config = copy.copy(self.defaultConfig) 

task = cpPipe.ptc.MeasurePhotonTransferCurveTask(config=config) 

 

task.fitPtcAndNl(localDataset, ptcFitType='ASTIERAPPROXIMATION') 

 

for ampName in self.ampNames: 

self.assertAlmostEqual(self.gain, localDataset.gain[ampName]) 

# noise already comes out of the fit in electrons with Astier 

self.assertAlmostEqual(np.sqrt(self.noiseSq), localDataset.noise[ampName]) 

# Linearity residual should be zero 

self.assertTrue(localDataset.nonLinearityResiduals[ampName].all() == 0) 

 

def test_meanVarMeasurement(self): 

task = self.defaultTask 

mu, varDiff = task.measureMeanVarPair(self.flatExp1, self.flatExp2) 

 

self.assertLess(self.flatWidth - np.sqrt(varDiff), 1) 

self.assertLess(self.flatMean - mu, 1) 

 

def test_getInitialGoodPoints(self): 

xs = [1, 2, 3, 4, 5, 6] 

ys = [2*x for x in xs] 

points = self.defaultTask._getInitialGoodPoints(xs, ys, 0.25) 

assert np.all(points) == np.all(np.array([True for x in xs])) 

 

ys[-1] = 30 

points = self.defaultTask._getInitialGoodPoints(xs, ys, 0.25) 

assert np.all(points) == np.all(np.array([True, True, True, True, False])) 

 

 

class MeasurePhotonTransferCurveDatasetTestCase(lsst.utils.tests.TestCase): 

def setUp(self): 

self.ptcData = PhotonTransferCurveDataset(['C00', 'C01']) 

self.ptcData.inputVisitPairs = {'C00': [(123, 234), (345, 456), (567, 678)], 

'C01': [(123, 234), (345, 456), (567, 678)]} 

 

def test_generalBehaviour(self): 

test = PhotonTransferCurveDataset(['C00', 'C01']) 

test.inputVisitPairs = {'C00': [(123, 234), (345, 456), (567, 678)], 

'C01': [(123, 234), (345, 456), (567, 678)]} 

 

with self.assertRaises(AttributeError): 

test.newItem = 1 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

194 ↛ 195line 194 didn't jump to line 195, because the condition on line 194 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()