Coverage for tests/test_photometryMapping.py: 26%
188 statements
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« prev ^ index » next coverage.py v7.3.1, created at 2023-09-21 19:51 +0000
1# This file is part of jointcal.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import numpy as np
24import abc
25import unittest
26import lsst.utils.tests
28import lsst.geom
29import lsst.jointcal
32CHEBYSHEV_T = [ 32 ↛ exitline 32 didn't jump to the function exit
33 lambda x: 1,
34 lambda x: x,
35 lambda x: 2*x**2 - 1,
36 lambda x: (4*x**2 - 3)*x,
37 lambda x: (8*x**2 - 8)*x**2 + 1,
38 lambda x: ((16*x**2 - 20)*x**2 + 5)*x,
39]
42class PhotometryMappingTestBase:
43 def setUp(self):
44 self.value = 5.0
45 self.valueErr = 2.0
47 baseStar0 = lsst.jointcal.BaseStar(0, 0, 1, 2)
48 self.star0 = lsst.jointcal.MeasuredStar(baseStar0)
49 baseStar1 = lsst.jointcal.BaseStar(1, 2, 3, 4)
50 self.star1 = lsst.jointcal.MeasuredStar(baseStar1)
51 self.star1.setXFocal(2)
52 self.star1.setYFocal(3)
55class PhotometryMappingTestCase(PhotometryMappingTestBase, lsst.utils.tests.TestCase):
56 def setUp(self):
57 super(PhotometryMappingTestCase, self).setUp()
58 self.scale = 3
59 transform = lsst.jointcal.FluxTransformSpatiallyInvariant(self.scale)
60 self.mapping = lsst.jointcal.PhotometryMapping(transform)
62 def test_getNpar(self):
63 result = self.mapping.getNpar()
64 self.assertEqual(result, 1)
66 def _test_offsetParams(self, delta, expect):
67 self.mapping.offsetParams(delta)
68 self.assertFloatsAlmostEqual(expect, self.mapping.getTransform().getParameters())
70 def test_transform(self):
71 result = self.mapping.transform(self.star0, self.value)
72 self.assertEqual(result, self.value*self.scale)
74 def test_offsetParams(self):
75 """Test offsetting; note that offsetParams offsets by `-delta`."""
76 delta = np.array([0.0])
77 self._test_offsetParams(delta, np.array([self.scale]))
78 delta -= 1
79 self._test_offsetParams(delta, self.scale-delta)
81 def test_computeParameterDerivatives(self):
82 """Test that the derivative of a spatially invariant transform is always the same."""
83 result = self.mapping.computeParameterDerivatives(self.star0, self.value)
84 self.assertEqual(self.value, result)
85 result = self.mapping.computeParameterDerivatives(self.star1, self.value)
86 self.assertEqual(self.value, result)
87 transform = lsst.jointcal.FluxTransformSpatiallyInvariant(1000.0)
88 mapping = lsst.jointcal.PhotometryMapping(transform)
89 result = mapping.computeParameterDerivatives(self.star0, self.value)
90 self.assertEqual(self.value, result)
92 def test_getMappingIndices(self):
93 """A mapping with one invariant transform has one index"""
94 self.mapping.setIndex(5)
95 result = self.mapping.getMappingIndices()
96 self.assertEqual(result, [5])
99class ChipVisitPhotometryMappingTestCase(PhotometryMappingTestBase, abc.ABC):
100 def setUp(self):
101 super().setUp()
102 self.bbox = lsst.geom.Box2D(lsst.geom.Point2D(-5, -6), lsst.geom.Point2D(7, 8))
103 self.order = 1
104 self.coefficients = np.array([[5, 2], [3, 0]], dtype=float)
105 self.chipScale = 2
106 self.visitScale = 3
107 self.chipIndex = 5
108 self.visitIndex = 1000
110 def _initMappings(self, InvariantTransform, ChebyTransform, ChipVisitMapping):
111 """Initialize self.mappingInvariants and self.mappingCheby.
112 Call after setUp().
114 Parameters
115 ----------
116 InvariantTransform : `PhotometryTransformSpatiallyInvariant`-type
117 The PhotometryTransformSpatiallyInvariant-derived class to construct
118 invariant transforms for.
119 ChebyTransform : `PhotometryTransform`-type
120 The PhotometryTransformChebyshev-derived class to construct
121 2d transforms for.
122 ChipVisitMapping : `PhotometryMapping`-type
123 The PhotometryMapping-derived class to construct for both mappings.
124 """
125 # self.mappingInvariants has two trivial transforms in it, to serve
126 # as a simpler test of functionality.
127 chipTransform = InvariantTransform(self.chipScale)
128 chipMapping = lsst.jointcal.PhotometryMapping(chipTransform)
129 chipMapping.setIndex(self.chipIndex)
130 visitTransform = InvariantTransform(self.visitScale)
131 visitMapping = lsst.jointcal.PhotometryMapping(visitTransform)
132 visitMapping.setIndex(self.visitIndex)
133 self.mappingInvariants = ChipVisitMapping(chipMapping, visitMapping)
134 self.mappingInvariants.setWhatToFit(True, True) # default to fitting both
136 # self.mappingCheby is a more realistic mapping, with two components:
137 # spatially-invariant per chip and a chebyshev per visit.
138 # Need a new chipMapping, as it stores shared_ptr to the transform.
139 chipTransform = InvariantTransform(self.chipScale)
140 chipMapping = lsst.jointcal.PhotometryMapping(chipTransform)
141 chipMapping.setIndex(self.chipIndex)
142 visitTransform2 = ChebyTransform(self.coefficients, self.bbox)
143 visitMapping2 = lsst.jointcal.PhotometryMapping(visitTransform2)
144 visitMapping2.setIndex(self.visitIndex)
145 self.mappingCheby = ChipVisitMapping(chipMapping, visitMapping2)
146 self.mappingCheby.setWhatToFit(True, True) # default to fitting both
148 def test_getNpar(self):
149 result = self.mappingInvariants.getNpar()
150 self.assertEqual(result, 2)
151 # order 1 implies 3 parameters, plus one for the chip mapping
152 result = self.mappingCheby.getNpar()
153 self.assertEqual(result, 4)
155 def _evaluate_chebyshev(self, x, y):
156 """Evaluate the chebyshev defined by self.coefficients at (x,y)"""
157 # sx, sy: transform from self.bbox range to [-1, -1]
158 cx = (self.bbox.getMinX() + self.bbox.getMaxX())/2.0
159 cy = (self.bbox.getMinY() + self.bbox.getMaxY())/2.0
160 sx = 2.0 / self.bbox.getWidth()
161 sy = 2.0 / self.bbox.getHeight()
162 result = 0
163 for j in range(self.order+1):
164 Ty = CHEBYSHEV_T[j](sy*(y - cy))
165 for i in range(0, self.order-j+1):
166 Tx = CHEBYSHEV_T[i](sx*(x - cx))
167 result += self.coefficients[j, i]*Tx*Ty
168 return result
170 def _computeChebyshevDerivative(self, star):
171 """Return the derivatives w.r.t. the Chebyshev components."""
172 cx = (self.bbox.getMinX() + self.bbox.getMaxX())/2.0
173 cy = (self.bbox.getMinY() + self.bbox.getMaxY())/2.0
174 sx = 2.0 / self.bbox.getWidth()
175 sy = 2.0 / self.bbox.getHeight()
176 Tx = np.array([CHEBYSHEV_T[i](sx*(star.getXFocal() - cx))
177 for i in range(self.order+1)], dtype=float)
178 Ty = np.array([CHEBYSHEV_T[i](sy*(star.getYFocal() - cy))
179 for i in range(self.order+1)], dtype=float)
180 expect = []
181 for j in range(len(Ty)):
182 for i in range(0, self.order-j+1):
183 expect.append(Ty[j]*Tx[i])
184 return np.array(expect)
186 @abc.abstractmethod
187 def _computeVisitDerivative(self, star):
188 """Return the derivative w.r.t. the chebyshev visit component."""
189 pass
191 @abc.abstractmethod
192 def _computeChipDerivative(self, star):
193 """Return the derivative w.r.t. the chip component."""
194 pass
196 def test_getMappingIndices(self):
197 """There are npar indices in a constrained mapping."""
198 expect = [self.chipIndex, self.visitIndex]
199 result = self.mappingInvariants.getMappingIndices()
200 self.assertEqual(result, expect)
202 # npar - 1 because the chip mapping has the 1st parameter
203 expect = [self.chipIndex, ] + list(range(self.visitIndex,
204 self.visitIndex + self.mappingCheby.getNpar() - 1))
205 result = self.mappingCheby.getMappingIndices()
206 self.assertEqual(result, expect)
208 def _test_transform_mappingInvariants(self, star, expect):
209 result = self.mappingInvariants.transform(star, self.value)
210 self.assertEqual(result, expect)
212 def _test_transform_mappingCheby(self, star, expect):
213 result = self.mappingCheby.transform(star, self.value)
214 self.assertEqual(result, expect)
216 def _test_computeParameterDerivatives(self, star, expectInvariant):
217 """Test self.mappingInvariants and self.mappingCheby transforming star.
218 expectCheby is calculated from _computeChipDerivative and
219 _computeChebyshevDerivative.
220 """
221 result = self.mappingInvariants.computeParameterDerivatives(star, self.value)
222 self.assertFloatsAlmostEqual(result, expectInvariant)
224 # the chip derivative is a single number
225 expectCheby = [self._computeChipDerivative(self.star1)]
226 # the Chebyshev Derivatives are a list, so we have to use extend
227 expectCheby.extend(self._computeVisitDerivative(self.star1))
228 expectCheby = np.array(expectCheby)
229 result = self.mappingCheby.computeParameterDerivatives(star, self.value)
230 self.assertFloatsAlmostEqual(result, expectCheby)
232 def _test_setWhatToFit(self, fittingChips, fittingVisits, nPar, indices, derivatives):
233 """
234 Parameters
235 ----------
236 fittingChips : `bool`
237 Are we fitting the chip component?
238 Passed to ``self.mappingCheby.setWhatToFit()``.
239 fittingVisits : `bool`
240 Are we fitting the visit component?
241 Passed to ``self.mappingCheby.setWhatToFit()``.
242 nPar : `int`
243 Expected result from ``self.mappingCheby.getNpar()``.
244 indices : `list`
245 Expected result from ``self.mappingCheby.getMappingIndices()``.
246 derivatives : `list`
247 Expected result from ``self.mappingCheby.computeParameterDerivatives()``.
248 """
249 self.mappingCheby.setWhatToFit(fittingChips, fittingVisits)
250 self.assertEqual(self.mappingCheby.getNpar(), nPar)
251 self.assertEqual(self.mappingCheby.getMappingIndices(), indices)
252 result = self.mappingCheby.computeParameterDerivatives(self.star1, self.value)
253 self.assertFloatsAlmostEqual(result, derivatives)
255 def test_setWhatToFit(self):
256 """Test that mapping methods behave correctly when chip and/or visit
257 fitting is disabled.
259 The "fit both" case (True, True) is tested by all of the above tests.
260 """
261 # Using mappingCheby so getNpar() will distinguish chips (1 param) from visits (3 params).
263 # fit nothing means 0 parameters and no indices
264 self._test_setWhatToFit(False, False, 0, [], [])
266 # fit just chips means 1 parameter and one index [self.chipIndex]
267 self._test_setWhatToFit(True, False, 1, [self.chipIndex],
268 np.array([self._computeChipDerivative(self.star1)]))
270 # fit just visits means 3 parameters (order 1) and 3 indices starting at self.visitIndex
271 self._test_setWhatToFit(False, True, 3, list(range(self.visitIndex, self.visitIndex+3)),
272 np.array([self._computeVisitDerivative(self.star1)]))
275class ChipVisitFluxMappingTestCase(ChipVisitPhotometryMappingTestCase, lsst.utils.tests.TestCase):
276 def setUp(self):
277 super().setUp()
278 self._initMappings(lsst.jointcal.FluxTransformSpatiallyInvariant,
279 lsst.jointcal.FluxTransformChebyshev,
280 lsst.jointcal.ChipVisitFluxMapping)
282 def _computeVisitDerivative(self, star):
283 return self._computeChebyshevDerivative(star) * self.value * self.chipScale
285 def _computeChipDerivative(self, star):
286 return self.value * self._evaluate_chebyshev(star.getXFocal(), star.getYFocal())
288 def test_transform(self):
289 expect = self.value * self.chipScale * self.visitScale
290 self._test_transform_mappingInvariants(self.star0, expect)
291 # The doubly-spatially invariant mapping should be independent of star position.
292 self._test_transform_mappingInvariants(self.star1, expect)
294 expect = self.value * self.chipScale * self._evaluate_chebyshev(self.star0.getXFocal(),
295 self.star0.getYFocal())
296 self._test_transform_mappingCheby(self.star0, expect)
297 expect = self.value * self.chipScale * self._evaluate_chebyshev(self.star1.getXFocal(),
298 self.star1.getYFocal())
299 self._test_transform_mappingCheby(self.star1, expect)
301 def test_computeParameterDerivatives(self):
302 expectInvariant = np.array([self.value*self.visitScale, self.value*self.chipScale])
303 self._test_computeParameterDerivatives(self.star1, expectInvariant)
306class ChipVisitMagnitudeMappingTestCase(ChipVisitPhotometryMappingTestCase, lsst.utils.tests.TestCase):
307 def setUp(self):
308 super().setUp()
309 self._initMappings(lsst.jointcal.MagnitudeTransformSpatiallyInvariant,
310 lsst.jointcal.MagnitudeTransformChebyshev,
311 lsst.jointcal.ChipVisitMagnitudeMapping)
313 def _computeVisitDerivative(self, star):
314 return self._computeChebyshevDerivative(star)
316 def _computeChipDerivative(self, star):
317 # Magnitude chip derivative is always identically 1:
318 # d(M(m))/d(m0)=1 where M(m) = m + m0
319 return 1.0
321 def test_transform(self):
322 expect = self.value + self.chipScale + self.visitScale
323 self._test_transform_mappingInvariants(self.star0, expect)
324 # The doubly-spatially invariant mapping should be independent of star position.
325 self._test_transform_mappingInvariants(self.star1, expect)
327 expect = self.value + self.chipScale + self._evaluate_chebyshev(self.star0.getXFocal(),
328 self.star0.getYFocal())
329 self._test_transform_mappingCheby(self.star0, expect)
331 expect = self.value + self.chipScale + self._evaluate_chebyshev(self.star1.getXFocal(),
332 self.star1.getYFocal())
333 self._test_transform_mappingCheby(self.star1, expect)
335 def test_computeParameterDerivatives(self):
336 # the parameter derivative of a spatially invariant magnitude transform is always 1.
337 expectInvariant = np.array([1.0, 1.0])
338 self._test_computeParameterDerivatives(self.star1, expectInvariant)
341class MemoryTester(lsst.utils.tests.MemoryTestCase):
342 pass
345def setup_module(module):
346 lsst.utils.tests.init()
349if __name__ == "__main__": 349 ↛ 350line 349 didn't jump to line 350, because the condition on line 349 was never true
350 lsst.utils.tests.init()
351 unittest.main()