Coverage for python/lsst/afw/image/testUtils.py: 11%

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1# This file is part of afw. 

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/>. 

21 

22# the asserts are automatically imported so unit tests can find them without special imports; 

23# the other functions are hidden unless explicitly asked for 

24__all__ = ["assertImagesAlmostEqual", "assertImagesEqual", "assertMasksEqual", 

25 "assertMaskedImagesAlmostEqual", "assertMaskedImagesEqual"] 

26 

27import numpy as np 

28 

29import lsst.utils.tests 

30from .image import ImageF 

31from .basicUtils import makeMaskedImageFromArrays 

32 

33 

34def makeGaussianNoiseMaskedImage(dimensions, sigma, variance=1.0): 

35 """Make a gaussian noise MaskedImageF 

36 

37 Inputs: 

38 - dimensions: dimensions of output array (cols, rows) 

39 - sigma; sigma of image plane's noise distribution 

40 - variance: constant value for variance plane 

41 """ 

42 npSize = (dimensions[1], dimensions[0]) 

43 image = np.random.normal(loc=0.0, scale=sigma, 

44 size=npSize).astype(np.float32) 

45 mask = np.zeros(npSize, dtype=np.int32) 

46 variance = np.zeros(npSize, dtype=np.float32) + variance 

47 

48 return makeMaskedImageFromArrays(image, mask, variance) 

49 

50 

51def makeRampImage(bbox, start=0, stop=None, imageClass=ImageF): 

52 """!Make an image whose values are a linear ramp 

53 

54 @param[in] bbox bounding box of image (an lsst.geom.Box2I) 

55 @param[in] start starting ramp value, inclusive 

56 @param[in] stop ending ramp value, inclusive; if None, increase by integer values 

57 @param[in] imageClass type of image (e.g. lsst.afw.image.ImageF) 

58 """ 

59 im = imageClass(bbox) 

60 imDim = im.getDimensions() 

61 numPix = imDim[0]*imDim[1] 

62 imArr = im.getArray() 

63 if stop is None: 

64 # increase by integer values 

65 stop = start + numPix - 1 

66 rampArr = np.linspace(start=start, stop=stop, 

67 endpoint=True, num=numPix, dtype=imArr.dtype) 

68 # numpy arrays are transposed w.r.t. afwImage 

69 imArr[:] = np.reshape(rampArr, (imDim[1], imDim[0])) 

70 return im 

71 

72 

73@lsst.utils.tests.inTestCase 

74def assertImagesAlmostEqual(testCase, image0, image1, skipMask=None, 

75 rtol=1.0e-05, atol=1e-08, msg="Images differ"): 

76 """!Assert that two images are almost equal, including non-finite values 

77 

78 @param[in] testCase unittest.TestCase instance the test is part of; 

79 an object supporting one method: fail(self, msgStr) 

80 @param[in] image0 image 0, an lsst.afw.image.Image, lsst.afw.image.Mask, 

81 or transposed numpy array (see warning) 

82 @param[in] image1 image 1, an lsst.afw.image.Image, lsst.afw.image.Mask, 

83 or transposed numpy array (see warning) 

84 @param[in] skipMask mask of pixels to skip, or None to compare all pixels; 

85 an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning); 

86 all non-zero pixels are skipped 

87 @param[in] rtol maximum allowed relative tolerance; more info below 

88 @param[in] atol maximum allowed absolute tolerance; more info below 

89 @param[in] msg exception message prefix; details of the error are appended after ": " 

90 

91 The images are nearly equal if all pixels obey: 

92 |val1 - val0| <= rtol*|val1| + atol 

93 or, for float types, if nan/inf/-inf pixels match. 

94 

95 @warning the comparison equation is not symmetric, so in rare cases the assertion 

96 may give different results depending on which image comes first. 

97 

98 @warning the axes of numpy arrays are transposed with respect to Image and Mask data. 

99 Thus for example if image0 and image1 are both lsst.afw.image.ImageD with dimensions (2, 3) 

100 and skipMask is a numpy array, then skipMask must have shape (3, 2). 

101 

102 @throw self.failureException (usually AssertionError) if any of the following are true 

103 for un-skipped pixels: 

104 - non-finite values differ in any way (e.g. one is "nan" and another is not) 

105 - finite values differ by too much, as defined by atol and rtol 

106 

107 @throw TypeError if the dimensions of image0, image1 and skipMask do not match, 

108 or any are not of a numeric data type. 

109 """ 

110 errStr = imagesDiffer( 

111 image0, image1, skipMask=skipMask, rtol=rtol, atol=atol) 

112 if errStr: 

113 testCase.fail(f"{msg}: {errStr}") 

114 

115 

116@lsst.utils.tests.inTestCase 

117def assertImagesEqual(*args, **kwds): 

118 """!Assert that two images are exactly equal, including non-finite values. 

119 

120 All arguments are forwarded to assertAnglesAlmostEqual aside from atol and rtol, 

121 which are set to zero. 

122 """ 

123 return assertImagesAlmostEqual(*args, atol=0, rtol=0, **kwds) 

124 

125 

126@lsst.utils.tests.inTestCase 

127def assertMasksEqual(testCase, mask0, mask1, skipMask=None, msg="Masks differ"): 

128 """!Assert that two masks are equal 

129 

130 @param[in] testCase unittest.TestCase instance the test is part of; 

131 an object supporting one method: fail(self, msgStr) 

132 @param[in] mask0 mask 0, an lsst.afw.image.Mask, lsst.afw.image.Image, 

133 or transposed numpy array (see warning) 

134 @param[in] mask1 mask 1, an lsst.afw.image.Mask, lsst.afw.image.Image, 

135 or transposed numpy array (see warning) 

136 @param[in] skipMask mask of pixels to skip, or None to compare all pixels; 

137 an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning); 

138 all non-zero pixels are skipped 

139 @param[in] msg exception message prefix; details of the error are appended after ": " 

140 

141 @warning the axes of numpy arrays are transposed with respect to Mask and Image. 

142 Thus for example if mask0 and mask1 are both lsst.afw.image.Mask with dimensions (2, 3) 

143 and skipMask is a numpy array, then skipMask must have shape (3, 2). 

144 

145 @throw self.failureException (usually AssertionError) if any any un-skipped pixels differ 

146 

147 @throw TypeError if the dimensions of mask0, mask1 and skipMask do not match, 

148 or any are not of a numeric data type. 

149 """ 

150 errStr = imagesDiffer(mask0, mask1, skipMask=skipMask, rtol=0, atol=0) 

151 if errStr: 

152 testCase.fail(f"{msg}: {errStr}") 

153 

154 

155@lsst.utils.tests.inTestCase 

156def assertMaskedImagesAlmostEqual( 

157 testCase, maskedImage0, maskedImage1, 

158 doImage=True, doMask=True, doVariance=True, skipMask=None, 

159 rtol=1.0e-05, atol=1e-08, msg="Masked images differ", 

160): 

161 """!Assert that two masked images are nearly equal, including non-finite values 

162 

163 @param[in] testCase unittest.TestCase instance the test is part of; 

164 an object supporting one method: fail(self, msgStr) 

165 @param[in] maskedImage0 masked image 0 (an lsst.afw.image.MaskedImage or 

166 collection of three transposed numpy arrays: image, mask, variance) 

167 @param[in] maskedImage1 masked image 1 (an lsst.afw.image.MaskedImage or 

168 collection of three transposed numpy arrays: image, mask, variance) 

169 @param[in] doImage compare image planes if True 

170 @param[in] doMask compare mask planes if True 

171 @param[in] doVariance compare variance planes if True 

172 @param[in] skipMask mask of pixels to skip, or None to compare all pixels; 

173 an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array; 

174 all non-zero pixels are skipped 

175 @param[in] rtol maximum allowed relative tolerance; more info below 

176 @param[in] atol maximum allowed absolute tolerance; more info below 

177 @param[in] msg exception message prefix; details of the error are appended after ": " 

178 

179 The mask planes must match exactly. The image and variance planes are nearly equal if all pixels obey: 

180 |val1 - val0| <= rtol*|val1| + atol 

181 or, for float types, if nan/inf/-inf pixels match. 

182 

183 @warning the comparison equation is not symmetric, so in rare cases the assertion 

184 may give different results depending on which masked image comes first. 

185 

186 @warning the axes of numpy arrays are transposed with respect to MaskedImage data. 

187 Thus for example if maskedImage0 and maskedImage1 are both lsst.afw.image.MaskedImageD 

188 with dimensions (2, 3) and skipMask is a numpy array, then skipMask must have shape (3, 2). 

189 

190 @throw self.failureException (usually AssertionError) if any of the following are true 

191 for un-skipped pixels: 

192 - non-finite image or variance values differ in any way (e.g. one is "nan" and another is not) 

193 - finite values differ by too much, as defined by atol and rtol 

194 - mask pixels differ at all 

195 

196 @throw TypeError if the dimensions of maskedImage0, maskedImage1 and skipMask do not match, 

197 either image or variance plane is not of a numeric data type, 

198 either mask plane is not of an integer type (unsigned or signed), 

199 or skipMask is not of a numeric data type. 

200 """ 

201 maskedImageArrList0 = maskedImage0.getArrays() if hasattr( 

202 maskedImage0, "getArrays") else maskedImage0 

203 maskedImageArrList1 = maskedImage1.getArrays() if hasattr( 

204 maskedImage1, "getArrays") else maskedImage1 

205 

206 for arrList, arg, name in ( 

207 (maskedImageArrList0, maskedImage0, "maskedImage0"), 

208 (maskedImageArrList1, maskedImage1, "maskedImage1"), 

209 ): 

210 try: 

211 assert len(arrList) == 3 

212 # check that array shapes are all identical 

213 # check that image and variance are float or int of some kind 

214 # and mask is int of some kind 

215 for i in (0, 2): 

216 assert arrList[i].shape == arrList[1].shape 

217 assert arrList[i].dtype.kind in ("b", "i", "u", "f", "c") 

218 assert arrList[1].dtype.kind in ("b", "i", "u") 

219 except Exception: 

220 raise TypeError(f"{name}={arg!r} is not a supported type") 

221 

222 errStrList = [] 

223 for ind, (doPlane, planeName) in enumerate(((doImage, "image"), 

224 (doMask, "mask"), 

225 (doVariance, "variance"))): 

226 if not doPlane: 

227 continue 

228 

229 if planeName == "mask": 

230 errStr = imagesDiffer(maskedImageArrList0[ind], maskedImageArrList1[ind], skipMask=skipMask, 

231 rtol=0, atol=0) 

232 if errStr: 

233 errStrList.append(errStr) 

234 else: 

235 errStr = imagesDiffer(maskedImageArrList0[ind], maskedImageArrList1[ind], 

236 skipMask=skipMask, rtol=rtol, atol=atol) 

237 if errStr: 

238 errStrList.append(f"{planeName} planes differ: {errStr}") 

239 

240 if errStrList: 

241 errStr = "; ".join(errStrList) 

242 testCase.fail(f"{msg}: {errStr}") 

243 

244 

245@lsst.utils.tests.inTestCase 

246def assertMaskedImagesEqual(*args, **kwds): 

247 """!Assert that two masked images are exactly equal, including non-finite values. 

248 

249 All arguments are forwarded to assertMaskedImagesAlmostEqual aside from atol and rtol, 

250 which are set to zero. 

251 """ 

252 return assertMaskedImagesAlmostEqual(*args, atol=0, rtol=0, **kwds) 

253 

254 

255def imagesDiffer(image0, image1, skipMask=None, rtol=1.0e-05, atol=1e-08): 

256 """!Compare the pixels of two image or mask arrays; return True if close, False otherwise 

257 

258 @param[in] image0 image 0, an lsst.afw.image.Image, lsst.afw.image.Mask, 

259 or transposed numpy array (see warning) 

260 @param[in] image1 image 1, an lsst.afw.image.Image, lsst.afw.image.Mask, 

261 or transposed numpy array (see warning) 

262 @param[in] skipMask mask of pixels to skip, or None to compare all pixels; 

263 an lsst.afw.image.Mask, lsst.afw.image.Image, or transposed numpy array (see warning); 

264 all non-zero pixels are skipped 

265 @param[in] rtol maximum allowed relative tolerance; more info below 

266 @param[in] atol maximum allowed absolute tolerance; more info below 

267 

268 The images are nearly equal if all pixels obey: 

269 |val1 - val0| <= rtol*|val1| + atol 

270 or, for float types, if nan/inf/-inf pixels match. 

271 

272 @warning the comparison equation is not symmetric, so in rare cases the assertion 

273 may give different results depending on which image comes first. 

274 

275 @warning the axes of numpy arrays are transposed with respect to Image and Mask data. 

276 Thus for example if image0 and image1 are both lsst.afw.image.ImageD with dimensions (2, 3) 

277 and skipMask is a numpy array, then skipMask must have shape (3, 2). 

278 

279 @return a string which is non-empty if the images differ 

280 

281 @throw TypeError if the dimensions of image0, image1 and skipMask do not match, 

282 or any are not of a numeric data type. 

283 """ 

284 errStrList = [] 

285 imageArr0 = image0.getArray() if hasattr(image0, "getArray") else image0 

286 imageArr1 = image1.getArray() if hasattr(image1, "getArray") else image1 

287 skipMaskArr = skipMask.getArray() if hasattr(skipMask, "getArray") else skipMask 

288 

289 # check the inputs 

290 arrArgNameList = [ 

291 (imageArr0, image0, "image0"), 

292 (imageArr1, image1, "image1"), 

293 ] 

294 if skipMask is not None: 

295 arrArgNameList.append((skipMaskArr, skipMask, "skipMask")) 

296 for i, (arr, arg, name) in enumerate(arrArgNameList): 

297 try: 

298 assert arr.dtype.kind in ("b", "i", "u", "f", "c") 

299 except Exception: 

300 raise TypeError(f"{name!r}={arg!r} is not a supported type") 

301 if i != 0: 

302 if arr.shape != imageArr0.shape: 

303 raise TypeError(f"{name} shape = {arr.shape} != {imageArr0.shape} = image0 shape") 

304 

305 # np.allclose mis-handled unsigned ints in numpy 1.8 

306 # and subtraction doesn't give the desired answer in any case 

307 # so cast unsigned arrays into int64 (there may be a simple 

308 # way to safely use a smaller data type but I've not found it) 

309 if imageArr0.dtype.kind == "u": 

310 imageArr0 = imageArr0.astype( 

311 np.promote_types(imageArr0.dtype, np.int8)) 

312 if imageArr1.dtype.kind == "u": 

313 imageArr1 = imageArr1.astype( 

314 np.promote_types(imageArr1.dtype, np.int8)) 

315 

316 if skipMaskArr is not None: 

317 skipMaskArr = np.array(skipMaskArr, dtype=bool) 

318 maskedArr0 = np.ma.array(imageArr0, copy=False, mask=skipMaskArr) 

319 maskedArr1 = np.ma.array(imageArr1, copy=False, mask=skipMaskArr) 

320 filledArr0 = maskedArr0.filled(0.0) 

321 filledArr1 = maskedArr1.filled(0.0) 

322 else: 

323 skipMaskArr = None 

324 filledArr0 = imageArr0 

325 filledArr1 = imageArr1 

326 

327 try: 

328 np.array([np.nan], dtype=imageArr0.dtype) 

329 np.array([np.nan], dtype=imageArr1.dtype) 

330 except Exception: 

331 # one or both images does not support non-finite values (nan, etc.) 

332 # so just use value comparison 

333 valSkipMaskArr = skipMaskArr 

334 else: 

335 # both images support non-finite values, of which numpy has exactly three: nan, +inf and -inf; 

336 # compare those individually in order to give useful diagnostic output 

337 nan0 = np.isnan(filledArr0) 

338 nan1 = np.isnan(filledArr1) 

339 if np.any(nan0 != nan1): 

340 errStrList.append("NaNs differ") 

341 

342 posinf0 = np.isposinf(filledArr0) 

343 posinf1 = np.isposinf(filledArr1) 

344 if np.any(posinf0 != posinf1): 

345 errStrList.append("+infs differ") 

346 

347 neginf0 = np.isneginf(filledArr0) 

348 neginf1 = np.isneginf(filledArr1) 

349 if np.any(neginf0 != neginf1): 

350 errStrList.append("-infs differ") 

351 

352 valSkipMaskArr = nan0 | nan1 | posinf0 | posinf1 | neginf0 | neginf1 

353 if skipMaskArr is not None: 

354 valSkipMaskArr |= skipMaskArr 

355 

356 # compare values that should be comparable (are finite and not masked) 

357 valMaskedArr1 = np.ma.array(imageArr0, copy=False, mask=valSkipMaskArr) 

358 valMaskedArr2 = np.ma.array(imageArr1, copy=False, mask=valSkipMaskArr) 

359 valFilledArr1 = valMaskedArr1.filled(0.0) 

360 valFilledArr2 = valMaskedArr2.filled(0.0) 

361 

362 if not np.allclose(valFilledArr1, valFilledArr2, rtol=rtol, atol=atol): 

363 errArr = np.abs(valFilledArr1 - valFilledArr2) 

364 maxErr = errArr.max() 

365 maxPosInd = np.where(errArr == maxErr) 

366 maxPosTuple = (maxPosInd[1][0], maxPosInd[0][0]) 

367 errStr = f"maxDiff={maxErr} at position {maxPosTuple}; " \ 

368 f"value={valFilledArr1[maxPosInd][0]} vs. {valFilledArr2[maxPosInd][0]}" 

369 errStrList.insert(0, errStr) 

370 

371 return "; ".join(errStrList)