Coverage for tests/test_diff_matched_tract_catalog.py: 20%

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1# This file is part of pipe_tasks. 

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 

23import unittest 

24import lsst.utils.tests 

25 

26import lsst.afw.geom as afwGeom 

27from lsst.meas.astrom import ConvertCatalogCoordinatesConfig 

28from lsst.pipe.tasks.diff_matched_tract_catalog import ( 

29 DiffMatchedTractCatalogConfig, DiffMatchedTractCatalogTask, MatchedCatalogFluxesConfig, 

30) 

31 

32import numpy as np 

33import os 

34import pandas as pd 

35 

36 

37def _error_format(column): 

38 return f'{column}Err' 

39 

40 

41ROOT = os.path.dirname(__file__) 

42filename_diff_matched = os.path.join(ROOT, "data", "test_diff_matched.txt") 

43 

44 

45class DiffMatchedTractCatalogTaskTestCase(lsst.utils.tests.TestCase): 

46 """DiffMatchedTractCatalogTask test case.""" 

47 def setUp(self): 

48 ra = np.array([-5.1, -2.2, 0., 3.1, -3.2, 2.01, -4.1]) 

49 dec = np.array([-4.15, 1.15, 0, 2.15, -7.15, -3.05, 5.7]) 

50 mag_g = np.array([23., 24., 25., 25.5, 26., 24.7, 23.3]) 

51 mag_r = mag_g + [0.5, -0.2, -0.8, -0.5, -1.5, 0.8, -0.4] 

52 

53 coord_format = ConvertCatalogCoordinatesConfig 

54 zeropoint = coord_format.mag_zeropoint_ref.default 

55 fluxes = tuple(10**(-0.4*(mag - zeropoint)) for mag in (mag_g, mag_r)) 

56 # Percent error in measurement 

57 err_flux = np.array((0.02, 0.015, -0.035, 0.02, -0.04, 0.06, 0.01)) 

58 # Absolute error 

59 eps_coord = np.array((2.3, 0.6, -1.7, 3.6, -2.4, 55.0, -40.8)) 

60 err_coord = np.full_like(eps_coord, 0.02) 

61 eps_coord *= err_coord 

62 extended_ref = [True, False, True, False, True, False, True] 

63 extended_target = [False, False, True, True, True, False, True] 

64 flags = np.ones_like(eps_coord, dtype=bool) 

65 

66 bands = ['g', 'r'] 

67 

68 columns_flux = [f'flux_{band}' for band in bands] 

69 columns_flux_err = [_error_format(column) for column in columns_flux] 

70 

71 column_ra_ref = coord_format.column_ref_coord1.default 

72 column_dec_ref = coord_format.column_ref_coord2.default 

73 column_ra_target = coord_format.column_target_coord1.default 

74 column_dec_target = coord_format.column_target_coord2.default 

75 

76 column_ra_target_err, column_dec_target_err = [ 

77 _error_format(col) for col in (column_ra_target, column_dec_target) 

78 ] 

79 

80 n_points = len(ra) 

81 n_unmatched = 2 

82 n_matched = n_points - n_unmatched 

83 # Reorder some indices to make arbitrary differences 

84 idx_ref = np.empty(n_points, dtype=int) 

85 idx_ref[:n_matched] = np.arange(n_matched)[::-1] 

86 idx_ref[n_matched:] = np.arange(n_matched, n_points) 

87 data_ref = { 

88 column_ra_ref: ra[idx_ref], 

89 column_dec_ref: dec[idx_ref], 

90 columns_flux[0]: fluxes[0][idx_ref], 

91 columns_flux[1]: fluxes[1][idx_ref], 

92 DiffMatchedTractCatalogConfig.column_ref_extended.default: extended_ref, 

93 } 

94 self.catalog_ref = pd.DataFrame(data=data_ref) 

95 

96 data_target = { 

97 column_ra_target: ra + eps_coord, 

98 column_dec_target: dec + eps_coord, 

99 column_ra_target_err: err_coord, 

100 column_dec_target_err: err_coord, 

101 columns_flux[0]: fluxes[0]*(1 + err_flux), 

102 columns_flux[1]: fluxes[1]*(1 + err_flux), 

103 _error_format(columns_flux[0]): np.sqrt(fluxes[0]), 

104 _error_format(columns_flux[1]): np.sqrt(fluxes[1]), 

105 DiffMatchedTractCatalogConfig.columns_target_select_true.default[0]: flags, 

106 DiffMatchedTractCatalogConfig.columns_target_select_false.default[0]: ~flags, 

107 DiffMatchedTractCatalogConfig.column_target_extended.default: extended_target, 

108 } 

109 self.catalog_target = pd.DataFrame(data=data_target) 

110 

111 # Make the last two rows unmatched (we set eps_coord very large) 

112 match_row = np.arange(len(ra))[::-1] - n_unmatched 

113 self.catalog_match_ref = pd.DataFrame(data={ 

114 'match_candidate': flags, 

115 'match_row': match_row, 

116 }) 

117 

118 self.catalog_match_target = pd.DataFrame(data={ 

119 'match_candidate': flags, 

120 'match_row': match_row, 

121 }) 

122 

123 self.diff_matched = np.loadtxt(filename_diff_matched) 

124 

125 columns_flux_configs = { 

126 band: MatchedCatalogFluxesConfig( 

127 column_ref_flux=columns_flux[idx], 

128 columns_target_flux=[columns_flux[idx]], 

129 columns_target_flux_err=[columns_flux_err[idx]], 

130 ) 

131 for idx, band in enumerate(bands) 

132 } 

133 

134 self.task = DiffMatchedTractCatalogTask(config=DiffMatchedTractCatalogConfig( 

135 columns_target_coord_err=[column_ra_target_err, column_dec_target_err], 

136 columns_flux=columns_flux_configs, 

137 mag_num_bins=1, 

138 )) 

139 self.wcs = afwGeom.makeSkyWcs(crpix=lsst.geom.Point2D(9000, 9000), 

140 crval=lsst.geom.SpherePoint(180., 0., lsst.geom.degrees), 

141 cdMatrix=afwGeom.makeCdMatrix(scale=0.2*lsst.geom.arcseconds)) 

142 

143 def tearDown(self): 

144 del self.catalog_ref 

145 del self.catalog_target 

146 del self.catalog_match_ref 

147 del self.catalog_match_target 

148 del self.diff_matched 

149 del self.task 

150 del self.wcs 

151 

152 def test_DiffMatchedTractCatalogTask(self): 

153 # These tables will have columns added to them in run 

154 columns_ref, columns_target = (list(x.columns) for x in (self.catalog_ref, self.catalog_target)) 

155 result = self.task.run( 

156 catalog_ref=self.catalog_ref, 

157 catalog_target=self.catalog_target, 

158 catalog_match_ref=self.catalog_match_ref, 

159 catalog_match_target=self.catalog_match_target, 

160 wcs=self.wcs, 

161 ) 

162 columns_result = list(result.cat_matched.columns) 

163 columns_expect = list(columns_target) + ["match_distance", "match_distanceErr"] 

164 prefix = DiffMatchedTractCatalogConfig.column_matched_prefix_ref.default 

165 columns_expect.append(f'{prefix}index') 

166 columns_expect.extend((f'{prefix}{col}' for col in columns_ref)) 

167 self.assertEqual(columns_expect, columns_result) 

168 

169 row = result.diff_matched.iloc[0].values.astype(float) 

170 # Run to re-save reference data. Will be loaded after this test completes. 

171 resave = False 

172 if resave: 

173 np.savetxt(filename_diff_matched, row) 

174 

175 self.assertEqual(len(row), len(self.diff_matched)) 

176 self.assertFloatsAlmostEqual(row, self.diff_matched, rtol=1e-15) 

177 

178 

179class MemoryTester(lsst.utils.tests.MemoryTestCase): 

180 pass 

181 

182 

183def setup_module(module): 

184 lsst.utils.tests.init() 

185 

186 

187if __name__ == "__main__": 187 ↛ 188line 187 didn't jump to line 188, because the condition on line 187 was never true

188 lsst.utils.tests.init() 

189 unittest.main()