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# This file is part of jointcal. 

# 

# 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/>. 

 

import numpy as np 

 

import abc 

import unittest 

import lsst.utils.tests 

 

import lsst.afw.geom 

import lsst.jointcal.photometryMappings 

import lsst.jointcal.photometryTransform 

import lsst.jointcal.star 

 

 

34 ↛ exit,   34 ↛ exit,   34 ↛ exit,   34 ↛ exit,   34 ↛ exit,   34 ↛ exit6 missed branches: 1) line 40 didn't finish the lambda on line 40, 2) line 39 didn't finish the lambda on line 39, 3) line 38 didn't finish the lambda on line 38, 4) line 37 didn't finish the lambda on line 37, 5) line 36 didn't finish the lambda on line 36, 6) line 35 didn't finish the lambda on line 35CHEBYSHEV_T = [ 

lambda x: 1, 

lambda x: x, 

lambda x: 2*x**2 - 1, 

lambda x: (4*x**2 - 3)*x, 

lambda x: (8*x**2 - 8)*x**2 + 1, 

lambda x: ((16*x**2 - 20)*x**2 + 5)*x, 

] 

 

 

class PhotometryMappingTestBase: 

def setUp(self): 

self.value = 5.0 

self.valueErr = 2.0 

 

baseStar0 = lsst.jointcal.star.BaseStar(0, 0, 1, 2) 

self.star0 = lsst.jointcal.star.MeasuredStar(baseStar0) 

baseStar1 = lsst.jointcal.star.BaseStar(1, 2, 3, 4) 

self.star1 = lsst.jointcal.star.MeasuredStar(baseStar1) 

self.star1.setXFocal(2) 

self.star1.setYFocal(3) 

 

 

class PhotometryMappingTestCase(PhotometryMappingTestBase, lsst.utils.tests.TestCase): 

def setUp(self): 

super(PhotometryMappingTestCase, self).setUp() 

self.scale = 3 

transform = lsst.jointcal.photometryTransform.FluxTransformSpatiallyInvariant(self.scale) 

self.mapping = lsst.jointcal.photometryMappings.PhotometryMapping(transform) 

 

def test_getNpar(self): 

result = self.mapping.getNpar() 

self.assertEqual(result, 1) 

 

def _test_offsetParams(self, delta, expect): 

self.mapping.offsetParams(delta) 

self.assertFloatsAlmostEqual(expect, self.mapping.getTransform().getParameters()) 

 

def test_transform(self): 

result = self.mapping.transform(self.star0, self.value) 

self.assertEqual(result, self.value*self.scale) 

 

def test_offsetParams(self): 

"""Test offsetting; note that offsetParams offsets by `-delta`.""" 

delta = np.array([0.0]) 

self._test_offsetParams(delta, np.array([self.scale])) 

delta -= 1 

self._test_offsetParams(delta, self.scale-delta) 

 

def test_computeParameterDerivatives(self): 

"""Test that the derivative of a spatially invariant transform is always the same.""" 

result = self.mapping.computeParameterDerivatives(self.star0, self.value) 

self.assertEqual(self.value, result) 

result = self.mapping.computeParameterDerivatives(self.star1, self.value) 

self.assertEqual(self.value, result) 

transform = lsst.jointcal.FluxTransformSpatiallyInvariant(1000.0) 

mapping = lsst.jointcal.PhotometryMapping(transform) 

result = mapping.computeParameterDerivatives(self.star0, self.value) 

self.assertEqual(self.value, result) 

 

def test_getMappingIndices(self): 

"""A mapping with one invariant transform has one index""" 

self.mapping.setIndex(5) 

result = self.mapping.getMappingIndices() 

self.assertEqual(result, [5]) 

 

 

class ChipVisitPhotometryMappingTestCase(PhotometryMappingTestBase, abc.ABC): 

def setUp(self): 

super().setUp() 

self.bbox = lsst.afw.geom.Box2D(lsst.afw.geom.Point2D(-5, -6), lsst.afw.geom.Point2D(7, 8)) 

self.order = 1 

self.coefficients = np.array([[5, 2], [3, 0]], dtype=float) 

self.chipScale = 2 

self.visitScale = 3 

self.chipIndex = 5 

self.visitIndex = 1000 

 

def _initMappings(self, InvariantTransform, ChebyTransform, ChipVisitMapping): 

"""Initialize self.mappingInvariants and self.mappingCheby. 

Call after setUp(). 

 

Parameters 

---------- 

InvariantTransform : `PhotometryTransformSpatiallyInvariant`-type 

The PhotometryTransformSpatiallyInvariant-derived class to construct 

invariant transforms for. 

ChebyTransform : `PhotometryTransform`-type 

The PhotometryTransformChebyshev-derived class to construct 

2d transforms for. 

ChipVisitMapping : `PhotometryMapping`-type 

The PhotometryMapping-derived class to construct for both mappings. 

""" 

# self.mappingInvariants has two trivial transforms in it, to serve 

# as a simpler test of functionality. 

chipTransform = InvariantTransform(self.chipScale) 

chipMapping = lsst.jointcal.PhotometryMapping(chipTransform) 

chipMapping.setIndex(self.chipIndex) 

visitTransform = InvariantTransform(self.visitScale) 

visitMapping = lsst.jointcal.PhotometryMapping(visitTransform) 

visitMapping.setIndex(self.visitIndex) 

self.mappingInvariants = ChipVisitMapping(chipMapping, visitMapping) 

self.mappingInvariants.setWhatToFit(True, True) # default to fitting both 

 

# self.mappingCheby is a more realistic mapping, with two components: 

# spatially-invariant per chip and a chebyshev per visit. 

# Need a new chipMapping, as it stores shared_ptr to the transform. 

chipTransform = InvariantTransform(self.chipScale) 

chipMapping = lsst.jointcal.PhotometryMapping(chipTransform) 

chipMapping.setIndex(self.chipIndex) 

visitTransform2 = ChebyTransform(self.coefficients, self.bbox) 

visitMapping2 = lsst.jointcal.PhotometryMapping(visitTransform2) 

visitMapping2.setIndex(self.visitIndex) 

self.mappingCheby = ChipVisitMapping(chipMapping, visitMapping2) 

self.mappingCheby.setWhatToFit(True, True) # default to fitting both 

 

def test_getNpar(self): 

result = self.mappingInvariants.getNpar() 

self.assertEqual(result, 2) 

# order 1 implies 3 parameters, plus one for the chip mapping 

result = self.mappingCheby.getNpar() 

self.assertEqual(result, 4) 

 

def _evaluate_chebyshev(self, x, y): 

"""Evaluate the chebyshev defined by self.coefficients at (x,y)""" 

# sx, sy: transform from self.bbox range to [-1, -1] 

cx = (self.bbox.getMinX() + self.bbox.getMaxX())/2.0 

cy = (self.bbox.getMinY() + self.bbox.getMaxY())/2.0 

sx = 2.0 / self.bbox.getWidth() 

sy = 2.0 / self.bbox.getHeight() 

result = 0 

for j in range(self.order+1): 

Ty = CHEBYSHEV_T[j](sy*(y - cy)) 

for i in range(0, self.order-j+1): 

Tx = CHEBYSHEV_T[i](sx*(x - cx)) 

result += self.coefficients[j, i]*Tx*Ty 

return result 

 

def _computeChebyshevDerivative(self, star): 

"""Return the derivatives w.r.t. the Chebyshev components.""" 

cx = (self.bbox.getMinX() + self.bbox.getMaxX())/2.0 

cy = (self.bbox.getMinY() + self.bbox.getMaxY())/2.0 

sx = 2.0 / self.bbox.getWidth() 

sy = 2.0 / self.bbox.getHeight() 

Tx = np.array([CHEBYSHEV_T[i](sx*(star.getXFocal() - cx)) 

for i in range(self.order+1)], dtype=float) 

Ty = np.array([CHEBYSHEV_T[i](sy*(star.getYFocal() - cy)) 

for i in range(self.order+1)], dtype=float) 

expect = [] 

for j in range(len(Ty)): 

for i in range(0, self.order-j+1): 

expect.append(Ty[j]*Tx[i]) 

return np.array(expect) 

 

@abc.abstractmethod 

def _computeVisitDerivative(self, star): 

"""Return the derivative w.r.t. the chebyshev visit component.""" 

pass 

 

@abc.abstractmethod 

def _computeChipDerivative(self, star): 

"""Return the derivative w.r.t. the chip component.""" 

pass 

 

def test_getMappingIndices(self): 

"""There are npar indices in a constrained mapping.""" 

expect = [self.chipIndex, self.visitIndex] 

result = self.mappingInvariants.getMappingIndices() 

self.assertEqual(result, expect) 

 

# npar - 1 because the chip mapping has the 1st parameter 

expect = [self.chipIndex, ] + list(range(self.visitIndex, 

self.visitIndex + self.mappingCheby.getNpar() - 1)) 

result = self.mappingCheby.getMappingIndices() 

self.assertEqual(result, expect) 

 

def _test_transform_mappingInvariants(self, star, expect): 

result = self.mappingInvariants.transform(star, self.value) 

self.assertEqual(result, expect) 

 

def _test_transform_mappingCheby(self, star, expect): 

result = self.mappingCheby.transform(star, self.value) 

self.assertEqual(result, expect) 

 

def _test_computeParameterDerivatives(self, star, expectInvariant): 

"""Test self.mappingInvariants and self.mappingCheby transforming star. 

expectCheby is calculated from _computeChipDerivative and 

_computeChebyshevDerivative. 

""" 

result = self.mappingInvariants.computeParameterDerivatives(star, self.value) 

self.assertFloatsAlmostEqual(result, expectInvariant) 

 

# the chip derivative is a single number 

expectCheby = [self._computeChipDerivative(self.star1)] 

# the Chebyshev Derivatives are a list, so we have to use extend 

expectCheby.extend(self._computeVisitDerivative(self.star1)) 

expectCheby = np.array(expectCheby) 

result = self.mappingCheby.computeParameterDerivatives(star, self.value) 

self.assertFloatsAlmostEqual(result, expectCheby) 

 

def _test_setWhatToFit(self, fittingChips, fittingVisits, nPar, indices, derivatives): 

""" 

Parameters 

---------- 

fittingChips : `bool` 

Are we fitting the chip component? 

Passed to ``self.mappingCheby.setWhatToFit()``. 

fittingVisits : `bool` 

Are we fitting the visit component? 

Passed to ``self.mappingCheby.setWhatToFit()``. 

nPar : `int` 

Expected result from ``self.mappingCheby.getNpar()``. 

indices : `list` 

Expected result from ``self.mappingCheby.getMappingIndices()``. 

derivatives : `list` 

Expected result from ``self.mappingCheby.computeParameterDerivatives()``. 

""" 

self.mappingCheby.setWhatToFit(fittingChips, fittingVisits) 

self.assertEqual(self.mappingCheby.getNpar(), nPar) 

self.assertEqual(self.mappingCheby.getMappingIndices(), indices) 

result = self.mappingCheby.computeParameterDerivatives(self.star1, self.value) 

self.assertFloatsAlmostEqual(result, derivatives) 

 

def test_setWhatToFit(self): 

"""Test that mapping methods behave correctly when chip and/or visit 

fitting is disabled. 

 

The "fit both" case (True, True) is tested by all of the above tests. 

""" 

# Using mappingCheby so getNpar() will distinguish chips (1 param) from visits (3 params). 

 

# fit nothing means 0 parameters and no indices 

self._test_setWhatToFit(False, False, 0, [], []) 

 

# fit just chips means 1 parameter and one index [self.chipIndex] 

self._test_setWhatToFit(True, False, 1, [self.chipIndex], 

np.array([self._computeChipDerivative(self.star1)])) 

 

# fit just visits means 3 parameters (order 1) and 3 indices starting at self.visitIndex 

self._test_setWhatToFit(False, True, 3, list(range(self.visitIndex, self.visitIndex+3)), 

np.array([self._computeVisitDerivative(self.star1)])) 

 

 

class ChipVisitFluxMappingTestCase(ChipVisitPhotometryMappingTestCase, lsst.utils.tests.TestCase): 

def setUp(self): 

super().setUp() 

self._initMappings(lsst.jointcal.FluxTransformSpatiallyInvariant, 

lsst.jointcal.FluxTransformChebyshev, 

lsst.jointcal.ChipVisitFluxMapping) 

 

def _computeVisitDerivative(self, star): 

return self._computeChebyshevDerivative(star) * self.value * self.chipScale 

 

def _computeChipDerivative(self, star): 

return self.value * self._evaluate_chebyshev(star.getXFocal(), star.getYFocal()) 

 

def test_transform(self): 

expect = self.value * self.chipScale * self.visitScale 

self._test_transform_mappingInvariants(self.star0, expect) 

# The doubly-spatially invariant mapping should be independent of star position. 

self._test_transform_mappingInvariants(self.star1, expect) 

 

expect = self.value * self.chipScale * self._evaluate_chebyshev(self.star0.getXFocal(), 

self.star0.getYFocal()) 

self._test_transform_mappingCheby(self.star0, expect) 

expect = self.value * self.chipScale * self._evaluate_chebyshev(self.star1.getXFocal(), 

self.star1.getYFocal()) 

self._test_transform_mappingCheby(self.star1, expect) 

 

def test_computeParameterDerivatives(self): 

expectInvariant = np.array([self.value*self.visitScale, self.value*self.chipScale]) 

self._test_computeParameterDerivatives(self.star1, expectInvariant) 

 

 

class ChipVisitMagnitudeMappingTestCase(ChipVisitPhotometryMappingTestCase, lsst.utils.tests.TestCase): 

def setUp(self): 

super().setUp() 

self._initMappings(lsst.jointcal.MagnitudeTransformSpatiallyInvariant, 

lsst.jointcal.MagnitudeTransformChebyshev, 

lsst.jointcal.ChipVisitMagnitudeMapping) 

 

def _computeVisitDerivative(self, star): 

return self._computeChebyshevDerivative(star) 

 

def _computeChipDerivative(self, star): 

# Magnitude chip derivative is always identically 1: 

# d(M(m))/d(m0)=1 where M(m) = m + m0 

return 1.0 

 

def test_transform(self): 

expect = self.value + self.chipScale + self.visitScale 

self._test_transform_mappingInvariants(self.star0, expect) 

# The doubly-spatially invariant mapping should be independent of star position. 

self._test_transform_mappingInvariants(self.star1, expect) 

 

expect = self.value + self.chipScale + self._evaluate_chebyshev(self.star0.getXFocal(), 

self.star0.getYFocal()) 

self._test_transform_mappingCheby(self.star0, expect) 

 

expect = self.value + self.chipScale + self._evaluate_chebyshev(self.star1.getXFocal(), 

self.star1.getYFocal()) 

self._test_transform_mappingCheby(self.star1, expect) 

 

def test_computeParameterDerivatives(self): 

# the parameter derivative of a spatially invariant magnitude transform is always 1. 

expectInvariant = np.array([1.0, 1.0]) 

self._test_computeParameterDerivatives(self.star1, expectInvariant) 

 

 

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

pass 

 

 

def setup_module(module): 

lsst.utils.tests.init() 

 

 

351 ↛ 352line 351 didn't jump to line 352, because the condition on line 351 was never trueif __name__ == "__main__": 

lsst.utils.tests.init() 

unittest.main()