Coverage for temperature/tests/test_robertson1968.py: 100%

1"""Define the unit tests for the :mod:`colour.temperature.robertson1968` module.""" 

2 

3from __future__ import annotations 

4 

5from itertools import product 

6 

7import numpy as np 

8 

9from colour.constants import TOLERANCE_ABSOLUTE_TESTS 

10from colour.temperature import ( 

11 CCT_to_mired, 

12 CCT_to_uv_Robertson1968, 

13 mired_to_CCT, 

14 uv_to_CCT_Robertson1968, 

15) 

16from colour.utilities import ignore_numpy_errors 

17 

18__author__ = "Colour Developers" 

19__copyright__ = "Copyright 2013 Colour Developers" 

20__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause" 

21__maintainer__ = "Colour Developers" 

22__email__ = "colour-developers@colour-science.org" 

23__status__ = "Production" 

24 

25__all__ = [ 

26 "TestMired_to_CCT", 

27 "TestCCT_to_mired", 

28 "TestUv_to_CCT_Robertson1968", 

29 "TestCCT_to_uv_Robertson1968", 

30] 

31 

32TEMPERATURE_DUV_TO_UV: dict = { 

33 (2000, -0.0500): np.array([0.309448284638118, 0.309263824757947]), 

34 (2000, -0.0250): np.array([0.307249142319059, 0.334166912378974]), 

35 (2000, 0.0000): np.array([0.305050000000000, 0.359070000000000]), 

36 (2000, 0.0250): np.array([0.302850857680941, 0.383973087621026]), 

37 (2000, 0.0500): np.array([0.300651715361882, 0.408876175242053]), 

38 (4500, -0.0500): np.array([0.249455133366328, 0.290111562671684]), 

39 (4500, -0.0250): np.array([0.233393122238719, 0.309269114669175]), 

40 (4500, 0.0000): np.array([0.217331111111111, 0.328426666666667]), 

41 (4500, 0.0250): np.array([0.201269099983503, 0.347584218664158]), 

42 (4500, 0.0500): np.array([0.185207088855895, 0.366741770661649]), 

43 (7000, -0.0500): np.array([0.239763542240142, 0.279200871249525]), 

44 (7000, -0.0250): np.array([0.218977485405785, 0.293091149910477]), 

45 (7000, 0.0000): np.array([0.198191428571429, 0.306981428571429]), 

46 (7000, 0.0250): np.array([0.177405371737072, 0.320871707232380]), 

47 (7000, 0.0500): np.array([0.156619314902715, 0.334761985893332]), 

48 (9500, -0.0500): np.array([0.235948844766320, 0.272619554367689]), 

49 (9500, -0.0250): np.array([0.213587053962107, 0.283797671920686]), 

50 (9500, 0.0000): np.array([0.191225263157895, 0.294975789473684]), 

51 (9500, 0.0250): np.array([0.168863472353682, 0.306153907026682]), 

52 (9500, 0.0500): np.array([0.146501681549470, 0.317332024579680]), 

53 (12000, -0.0500): np.array([0.233956908310164, 0.268393952067210]), 

54 (12000, -0.0250): np.array([0.210911787488415, 0.278085309366938]), 

55 (12000, 0.0000): np.array([0.187866666666667, 0.287776666666667]), 

56 (12000, 0.0250): np.array([0.164821545844918, 0.297468023966395]), 

57 (12000, 0.0500): np.array([0.141776425023169, 0.307159381266124]), 

58 (14500, -0.0500): np.array([0.232785809380768, 0.265479540863524]), 

59 (14500, -0.0250): np.array([0.209387387449005, 0.274283735949003]), 

60 (14500, 0.0000): np.array([0.185988965517241, 0.283087931034483]), 

61 (14500, 0.0250): np.array([0.162590543585478, 0.291892126119962]), 

62 (14500, 0.0500): np.array([0.139192121653715, 0.300696321205442]), 

63 (17000, -0.0500): np.array([0.232028466821727, 0.263383405240889]), 

64 (17000, -0.0250): np.array([0.208421880469687, 0.271613173208680]), 

65 (17000, 0.0000): np.array([0.184815294117647, 0.279842941176471]), 

66 (17000, 0.0250): np.array([0.161208707765607, 0.288072709144261]), 

67 (17000, 0.0500): np.array([0.137602121413567, 0.296302477112052]), 

68 (19500, -0.0500): np.array([0.231498602829451, 0.261824985205592]), 

69 (19500, -0.0250): np.array([0.207757250132674, 0.269657492602796]), 

70 (19500, 0.0000): np.array([0.184015897435897, 0.277490000000000]), 

71 (19500, 0.0250): np.array([0.160274544739121, 0.285322507397204]), 

72 (19500, 0.0500): np.array([0.136533192042344, 0.293155014794408]), 

73 (22000, -0.0500): np.array([0.231114936519607, 0.260621561540512]), 

74 (22000, -0.0250): np.array([0.207281559168895, 0.268169417133892]), 

75 (22000, 0.0000): np.array([0.183448181818182, 0.275717272727273]), 

76 (22000, 0.0250): np.array([0.159614804467469, 0.283265128320653]), 

77 (22000, 0.0500): np.array([0.135781427116756, 0.290812983914034]), 

78 (24500, -0.0500): np.array([0.230812633988541, 0.259664771591227]), 

79 (24500, -0.0250): np.array([0.206910092504474, 0.266990651101736]), 

80 (24500, 0.0000): np.array([0.183007551020408, 0.274316530612245]), 

81 (24500, 0.0250): np.array([0.159105009536342, 0.281642410122754]), 

82 (24500, 0.0500): np.array([0.135202468052276, 0.288968289633262]), 

83 (27000, -0.0500): np.array([0.230583187091274, 0.258895100975109]), 

84 (27000, -0.0250): np.array([0.206630667619711, 0.266055883820888]), 

85 (27000, 0.0000): np.array([0.182678148148148, 0.273216666666667]), 

86 (27000, 0.0250): np.array([0.158725628676585, 0.280377449512446]), 

87 (27000, 0.0500): np.array([0.134773109205022, 0.287538232358225]), 

88 (29500, -0.0500): np.array([0.230395500499851, 0.258258464459758]), 

89 (29500, -0.0250): np.array([0.206403428216027, 0.265285588162082]), 

90 (29500, 0.0000): np.array([0.182411355932203, 0.272312711864407]), 

91 (29500, 0.0250): np.array([0.158419283648380, 0.279339835566731]), 

92 (29500, 0.0500): np.array([0.134427211364556, 0.286366959269056]), 

93 (32000, -0.0500): np.array([0.230235978654155, 0.257721638699323]), 

94 (32000, -0.0250): np.array([0.206211114327078, 0.264635819349661]), 

95 (32000, 0.0000): np.array([0.182186250000000, 0.271550000000000]), 

96 (32000, 0.0250): np.array([0.158161385672923, 0.278464180650339]), 

97 (32000, 0.0500): np.array([0.134136521345845, 0.285378361300677]), 

98 (34500, -0.0500): np.array([0.230105666168844, 0.257266749246258]), 

99 (34500, -0.0250): np.array([0.206054789606161, 0.264089896362260]), 

100 (34500, 0.0000): np.array([0.182003913043478, 0.270913043478261]), 

101 (34500, 0.0250): np.array([0.157953036480796, 0.277736190594262]), 

102 (34500, 0.0500): np.array([0.133902159918113, 0.284559337710263]), 

103 (37000, -0.0500): np.array([0.229999835834901, 0.256877639136083]), 

104 (37000, -0.0250): np.array([0.205928431430964, 0.263628008757231]), 

105 (37000, 0.0000): np.array([0.181857027027027, 0.270378378378378]), 

106 (37000, 0.0250): np.array([0.157785622623090, 0.277128747999526]), 

107 (37000, 0.0500): np.array([0.133714218219153, 0.283879117620674]), 

108 (39500, -0.0500): np.array([0.229907042065651, 0.256537886918202]), 

109 (39500, -0.0250): np.array([0.205817888121433, 0.263224639661633]), 

110 (39500, 0.0000): np.array([0.181728734177215, 0.269911392405063]), 

111 (39500, 0.0250): np.array([0.157639580232997, 0.276598145148494]), 

112 (39500, 0.0500): np.array([0.133550426288780, 0.283284897891924]), 

113 (42000, -0.0500): np.array([0.229825016678223, 0.256238659086365]), 

114 (42000, -0.0250): np.array([0.205720365481969, 0.262869329543182]), 

115 (42000, 0.0000): np.array([0.181615714285714, 0.269500000000000]), 

116 (42000, 0.0250): np.array([0.157511063089460, 0.276130670456817]), 

117 (42000, 0.0500): np.array([0.133406411893206, 0.282761340913635]), 

118 (44500, -0.0500): np.array([0.229751988653572, 0.255973111790163]), 

119 (44500, -0.0250): np.array([0.205633690955999, 0.262553971625419]), 

120 (44500, 0.0000): np.array([0.181515393258427, 0.269134831460674]), 

121 (44500, 0.0250): np.array([0.157397095560855, 0.275715691295930]), 

122 (44500, 0.0500): np.array([0.133278797863282, 0.282296551131185]), 

123 (47000, -0.0500): np.array([0.229686555034366, 0.255735860408829]), 

124 (47000, -0.0250): np.array([0.205556149857609, 0.262272185523563]), 

125 (47000, 0.0000): np.array([0.181425744680851, 0.268808510638298]), 

126 (47000, 0.0250): np.array([0.157295339504093, 0.275344835753032]), 

127 (47000, 0.0500): np.array([0.133164934327336, 0.281881160867767]), 

128 (49500, -0.0500): np.array([0.229627590056716, 0.255522610251793]), 

129 (49500, -0.0250): np.array([0.205486370785934, 0.262018880883472]), 

130 (49500, 0.0000): np.array([0.181345151515151, 0.268515151515151]), 

131 (49500, 0.0250): np.array([0.157203932244369, 0.275011422146831]), 

132 (49500, 0.0500): np.array([0.133062712973587, 0.281507692778510]), 

133} 

134 

135 

136class TestMired_to_CCT: 

137 """ 

138 Define :func:`colour.temperature.robertson1968.mired_to_CCT` 

139 definition unit tests methods. 

140 """ 

141 

142 def test_mired_to_CCT(self) -> None: 

143 """ 

144 Test :func:`colour.temperature.robertson1968.mired_to_CCT` 

145 definition. 

146 """ 

147 

148 np.testing.assert_allclose( 

149 CCT_to_mired(312.5), 3200, atol=TOLERANCE_ABSOLUTE_TESTS 

150 ) 

151 

152 np.testing.assert_allclose( 

153 CCT_to_mired(153.846153846154), 6500, atol=TOLERANCE_ABSOLUTE_TESTS 

154 ) 

155 

156 np.testing.assert_allclose( 

157 CCT_to_mired(66.666666666666667), 

158 15000, 

159 atol=TOLERANCE_ABSOLUTE_TESTS, 

160 ) 

161 

162 def test_n_dimensional_mired_to_CCT(self) -> None: 

163 """ 

164 Test :func:`colour.temperature.robertson1968.mired_to_CCT` 

165 definition n-dimensional arrays support. 

166 """ 

167 

168 mired = 312.5 

169 CCT = mired_to_CCT(mired) 

170 

171 mired = np.tile(mired, (6, 1)) 

172 CCT = np.tile(CCT, (6, 1)) 

173 np.testing.assert_allclose( 

174 mired_to_CCT(mired), CCT, atol=TOLERANCE_ABSOLUTE_TESTS 

175 ) 

176 

177 mired = np.reshape(mired, (2, 3, 1)) 

178 CCT = np.reshape(CCT, (2, 3, 1)) 

179 np.testing.assert_allclose( 

180 mired_to_CCT(mired), CCT, atol=TOLERANCE_ABSOLUTE_TESTS 

181 ) 

182 

183 @ignore_numpy_errors 

184 def test_nan_mired_to_CCT(self) -> None: 

185 """ 

186 Test :func:`colour.temperature.robertson1968.mired_to_CCT` 

187 definition nan support. 

188 """ 

189 

190 cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] 

191 cases = np.array(list(set(product(cases, repeat=2)))) 

192 mired_to_CCT(cases) 

193 

194 

195class TestCCT_to_mired: 

196 """ 

197 Define :func:`colour.temperature.robertson1968.CCT_to_mired` 

198 definition unit tests methods. 

199 """ 

200 

201 def test_CCT_to_mired(self) -> None: 

202 """ 

203 Test :func:`colour.temperature.robertson1968.CCT_to_mired` 

204 definition. 

205 """ 

206 

207 np.testing.assert_allclose( 

208 CCT_to_mired(3200), 312.5, atol=TOLERANCE_ABSOLUTE_TESTS 

209 ) 

210 

211 np.testing.assert_allclose( 

212 CCT_to_mired(6500), 153.846153846154, atol=TOLERANCE_ABSOLUTE_TESTS 

213 ) 

214 

215 np.testing.assert_allclose( 

216 CCT_to_mired(15000), 

217 66.666666666666667, 

218 atol=TOLERANCE_ABSOLUTE_TESTS, 

219 ) 

220 

221 def test_n_dimensional_CCT_to_mired(self) -> None: 

222 """ 

223 Test :func:`colour.temperature.robertson1968.CCT_to_mired` 

224 definition n-dimensional arrays support. 

225 """ 

226 

227 CCT = 3200 

228 mired = CCT_to_mired(CCT) 

229 

230 CCT = np.tile(CCT, (6, 1)) 

231 mired = np.tile(mired, (6, 1)) 

232 np.testing.assert_allclose( 

233 CCT_to_mired(CCT), mired, atol=TOLERANCE_ABSOLUTE_TESTS 

234 ) 

235 

236 CCT = np.reshape(CCT, (2, 3, 1)) 

237 mired = np.reshape(mired, (2, 3, 1)) 

238 np.testing.assert_allclose( 

239 CCT_to_mired(CCT), mired, atol=TOLERANCE_ABSOLUTE_TESTS 

240 ) 

241 

242 @ignore_numpy_errors 

243 def test_nan_CCT_to_mired(self) -> None: 

244 """ 

245 Test :func:`colour.temperature.robertson1968.CCT_to_mired` 

246 definition nan support. 

247 """ 

248 

249 cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] 

250 cases = np.array(list(set(product(cases, repeat=2)))) 

251 CCT_to_mired(cases) 

252 

253 

254class TestUv_to_CCT_Robertson1968: 

255 """ 

256 Define :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968` 

257 definition unit tests methods. 

258 """ 

259 

260 def test_uv_to_CCT_Robertson1968(self) -> None: 

261 """ 

262 Test :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968` 

263 definition. 

264 """ 

265 

266 for key, value in TEMPERATURE_DUV_TO_UV.items(): 

267 np.testing.assert_allclose(uv_to_CCT_Robertson1968(value), key, atol=0.25) 

268 

269 def test_n_dimensional_uv_to_CCT_Robertson1968(self) -> None: 

270 """ 

271 Test :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968` 

272 definition n-dimensional arrays support. 

273 """ 

274 

275 uv = np.array([0.1978, 0.3122]) 

276 CCT_D_uv = uv_to_CCT_Robertson1968(uv) 

277 

278 uv = np.tile(uv, (6, 1)) 

279 CCT_D_uv = np.tile(CCT_D_uv, (6, 1)) 

280 np.testing.assert_allclose( 

281 uv_to_CCT_Robertson1968(uv), 

282 CCT_D_uv, 

283 atol=TOLERANCE_ABSOLUTE_TESTS, 

284 ) 

285 

286 uv = np.reshape(uv, (2, 3, 2)) 

287 CCT_D_uv = np.reshape(CCT_D_uv, (2, 3, 2)) 

288 np.testing.assert_allclose( 

289 uv_to_CCT_Robertson1968(uv), 

290 CCT_D_uv, 

291 atol=TOLERANCE_ABSOLUTE_TESTS, 

292 ) 

293 

294 @ignore_numpy_errors 

295 def test_nan_uv_to_CCT_Robertson1968(self) -> None: 

296 """ 

297 Test :func:`colour.temperature.robertson1968.uv_to_CCT_Robertson1968` 

298 definition nan support. 

299 """ 

300 

301 cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] 

302 cases = np.array(list(set(product(cases, repeat=2)))) 

303 uv_to_CCT_Robertson1968(cases) 

304 

305 

306class TestCCT_to_uv_Robertson1968: 

307 """ 

308 Define :func:`colour.temperature.robertson1968.CCT_to_uv_Robertson1968` 

309 definition unit tests methods. 

310 """ 

311 

312 def test_CCT_to_uv_Robertson1968(self) -> None: 

313 """ 

314 Test :func:`colour.temperature.robertson1968.CCT_to_uv_Robertson1968` 

315 definition. 

316 """ 

317 

318 for key, value in TEMPERATURE_DUV_TO_UV.items(): 

319 np.testing.assert_allclose( 

320 CCT_to_uv_Robertson1968(key), 

321 value, 

322 atol=TOLERANCE_ABSOLUTE_TESTS, 

323 ) 

324 

325 def test_n_dimensional_CCT_to_uv_Robertson1968(self) -> None: 

326 """ 

327 Test :func:`colour.temperature.robertson1968.CCT_to_uv_Robertson1968` 

328 definition n-dimensional arrays support. 

329 """ 

330 

331 CCT_D_uv = np.array([4500, 0.0250]) 

332 uv = CCT_to_uv_Robertson1968(CCT_D_uv) 

333 

334 CCT_D_uv = np.tile(CCT_D_uv, (6, 1)) 

335 uv = np.tile(uv, (6, 1)) 

336 np.testing.assert_allclose( 

337 CCT_to_uv_Robertson1968(CCT_D_uv), 

338 uv, 

339 atol=TOLERANCE_ABSOLUTE_TESTS, 

340 ) 

341 

342 CCT_D_uv = np.reshape(CCT_D_uv, (2, 3, 2)) 

343 uv = np.reshape(uv, (2, 3, 2)) 

344 np.testing.assert_allclose( 

345 CCT_to_uv_Robertson1968(CCT_D_uv), 

346 uv, 

347 atol=TOLERANCE_ABSOLUTE_TESTS, 

348 ) 

349 

350 @ignore_numpy_errors 

351 def test_nan_CCT_to_uv_Robertson1968(self) -> None: 

352 """ 

353 Test :func:`colour.temperature.robertson1968.CCT_to_uv_Robertson1968` 

354 definition nan support. 

355 """ 

356 

357 cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan] 

358 cases = np.array(list(set(product(cases, repeat=2)))) 

359 CCT_to_uv_Robertson1968(cases)