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1from lsst.afw.table import (SchemaMapper, Field, 

2 MultiMatch, SimpleRecord, 

3 SourceCatalog, updateSourceCoords) 

4 

5import numpy as np 

6from astropy.table import join, Table 

7 

8__all__ = ("match_catalogs", "ellipticity_from_cat", "ellipticity", "make_matched_photom") 

9 

10 

11def match_catalogs(inputs, photoCalibs, astromCalibs, vIds, matchRadius, 

12 apply_external_wcs=False, logger=None): 

13 schema = inputs[0].schema 

14 mapper = SchemaMapper(schema) 

15 mapper.addMinimalSchema(schema) 

16 mapper.addOutputField(Field[float]('base_PsfFlux_snr', 

17 'PSF flux SNR')) 

18 mapper.addOutputField(Field[float]('base_PsfFlux_mag', 

19 'PSF magnitude')) 

20 mapper.addOutputField(Field[float]('base_PsfFlux_magErr', 

21 'PSF magnitude uncertainty')) 

22 # Needed because addOutputField(... 'slot_ModelFlux_mag') will add a field with that literal name 

23 aliasMap = schema.getAliasMap() 

24 # Possibly not needed since base_GaussianFlux is the default, but this ought to be safe 

25 modelName = aliasMap['slot_ModelFlux'] if 'slot_ModelFlux' in aliasMap.keys() else 'base_GaussianFlux' 

26 mapper.addOutputField(Field[float](f'{modelName}_mag', 

27 'Model magnitude')) 

28 mapper.addOutputField(Field[float](f'{modelName}_magErr', 

29 'Model magnitude uncertainty')) 

30 mapper.addOutputField(Field[float](f'{modelName}_snr', 

31 'Model flux snr')) 

32 mapper.addOutputField(Field[float]('e1', 

33 'Source Ellipticity 1')) 

34 mapper.addOutputField(Field[float]('e2', 

35 'Source Ellipticity 1')) 

36 mapper.addOutputField(Field[float]('psf_e1', 

37 'PSF Ellipticity 1')) 

38 mapper.addOutputField(Field[float]('psf_e2', 

39 'PSF Ellipticity 1')) 

40 mapper.addOutputField(Field[np.int32]('filt', 

41 'filter code')) 

42 newSchema = mapper.getOutputSchema() 

43 newSchema.setAliasMap(schema.getAliasMap()) 

44 

45 # Create an object that matches multiple catalogs with same schema 

46 mmatch = MultiMatch(newSchema, 

47 dataIdFormat={'visit': np.int32, 'detector': np.int32}, 

48 radius=matchRadius, 

49 RecordClass=SimpleRecord) 

50 

51 # create the new extended source catalog 

52 srcVis = SourceCatalog(newSchema) 

53 

54 filter_dict = {'u': 1, 'g': 2, 'r': 3, 'i': 4, 'z': 5, 'y': 6, 

55 'HSC-U': 1, 'HSC-G': 2, 'HSC-R': 3, 'HSC-I': 4, 'HSC-Z': 5, 'HSC-Y': 6} 

56 

57 # Sort by visit, detector, then filter 

58 vislist = [v['visit'] for v in vIds] 

59 ccdlist = [v['detector'] for v in vIds] 

60 filtlist = [v['band'] for v in vIds] 

61 tab_vids = Table([vislist, ccdlist, filtlist], names=['vis', 'ccd', 'filt']) 

62 sortinds = np.argsort(tab_vids, order=('vis', 'ccd', 'filt')) 

63 

64 for ind in sortinds: 

65 oldSrc = inputs[ind] 

66 photoCalib = photoCalibs[ind] 

67 wcs = astromCalibs[ind] 

68 vId = vIds[ind] 

69 

70 if logger: 

71 logger.debug(f"{len(oldSrc)} sources in ccd {vId['detector']} visit {vId['visit']}") 

72 

73 # create temporary catalog 

74 tmpCat = SourceCatalog(SourceCatalog(newSchema).table) 

75 tmpCat.extend(oldSrc, mapper=mapper) 

76 

77 filtnum = filter_dict[vId['band']] 

78 tmpCat['filt'] = np.repeat(filtnum, len(oldSrc)) 

79 

80 tmpCat['base_PsfFlux_snr'][:] = tmpCat['base_PsfFlux_instFlux'] \ 

81 / tmpCat['base_PsfFlux_instFluxErr'] 

82 

83 if apply_external_wcs and wcs is not None: 

84 updateSourceCoords(wcs, tmpCat) 

85 

86 photoCalib.instFluxToMagnitude(tmpCat, "base_PsfFlux", "base_PsfFlux") 

87 tmpCat['slot_ModelFlux_snr'][:] = (tmpCat['slot_ModelFlux_instFlux'] 

88 / tmpCat['slot_ModelFlux_instFluxErr']) 

89 photoCalib.instFluxToMagnitude(tmpCat, "slot_ModelFlux", "slot_ModelFlux") 

90 

91 _, psf_e1, psf_e2 = ellipticity_from_cat(oldSrc, slot_shape='slot_PsfShape') 

92 _, star_e1, star_e2 = ellipticity_from_cat(oldSrc, slot_shape='slot_Shape') 

93 tmpCat['e1'][:] = star_e1 

94 tmpCat['e2'][:] = star_e2 

95 tmpCat['psf_e1'][:] = psf_e1 

96 tmpCat['psf_e2'][:] = psf_e2 

97 

98 srcVis.extend(tmpCat, False) 

99 mmatch.add(catalog=tmpCat, dataId=vId) 

100 

101 # Complete the match, returning a catalog that includes 

102 # all matched sources with object IDs that can be used to group them. 

103 matchCat = mmatch.finish() 

104 

105 # Create a mapping object that allows the matches to be manipulated 

106 # as a mapping of object ID to catalog of sources. 

107 

108 # I don't think I can persist a group view, so this may need to be called in a subsequent task 

109 # allMatches = GroupView.build(matchCat) 

110 

111 return srcVis, matchCat 

112 

113 

114def ellipticity_from_cat(cat, slot_shape='slot_Shape'): 

115 """Calculate the ellipticity of the Shapes in a catalog from the 2nd moments. 

116 Parameters 

117 ---------- 

118 cat : `lsst.afw.table.BaseCatalog` 

119 A catalog with 'slot_Shape' defined and '_xx', '_xy', '_yy' 

120 entries for the target of 'slot_Shape'. 

121 E.g., 'slot_shape' defined as 'base_SdssShape' 

122 And 'base_SdssShape_xx', 'base_SdssShape_xy', 'base_SdssShape_yy' defined. 

123 slot_shape : str, optional 

124 Specify what slot shape requested. Intended use is to get the PSF shape 

125 estimates by specifying 'slot_shape=slot_PsfShape' 

126 instead of the default 'slot_shape=slot_Shape'. 

127 Returns 

128 ------- 

129 e, e1, e2 : complex, float, float 

130 Complex ellipticity, real part, imaginary part 

131 """ 

132 i_xx, i_xy, i_yy = cat.get(slot_shape+'_xx'), cat.get(slot_shape+'_xy'), cat.get(slot_shape+'_yy') 

133 return ellipticity(i_xx, i_xy, i_yy) 

134 

135 

136def ellipticity(i_xx, i_xy, i_yy): 

137 """Calculate ellipticity from second moments. 

138 Parameters 

139 ---------- 

140 i_xx : float or `numpy.array` 

141 i_xy : float or `numpy.array` 

142 i_yy : float or `numpy.array` 

143 Returns 

144 ------- 

145 e, e1, e2 : (float, float, float) or (numpy.array, numpy.array, numpy.array) 

146 Complex ellipticity, real component, imaginary component 

147 """ 

148 e = (i_xx - i_yy + 2j*i_xy) / (i_xx + i_yy) 

149 e1 = np.real(e) 

150 e2 = np.imag(e) 

151 return e, e1, e2 

152 

153 

154def make_matched_photom(vIds, catalogs, photo_calibs): 

155 # inputs: vIds, catalogs, photo_calibs 

156 

157 # Match all input bands: 

158 bands = list(set([f['band'] for f in vIds])) 

159 

160 # Should probably add an "assert" that requires bands>1... 

161 

162 empty_cat = catalogs[0].copy() 

163 empty_cat.clear() 

164 

165 cat_dict = {} 

166 mags_dict = {} 

167 magerrs_dict = {} 

168 for band in bands: 

169 cat_dict[band] = empty_cat.copy() 

170 mags_dict[band] = [] 

171 magerrs_dict[band] = [] 

172 

173 for i in range(len(catalogs)): 

174 for band in bands: 

175 if (vIds[i]['band'] in band): 

176 cat_dict[band].extend(catalogs[i].copy(deep=True)) 

177 mags = photo_calibs[i].instFluxToMagnitude(catalogs[i], 'base_PsfFlux') 

178 mags_dict[band] = np.append(mags_dict[band], mags[:, 0]) 

179 magerrs_dict[band] = np.append(magerrs_dict[band], mags[:, 1]) 

180 

181 for band in bands: 

182 cat_tmp = cat_dict[band] 

183 if cat_tmp: 

184 if not cat_tmp.isContiguous(): 

185 cat_tmp = cat_tmp.copy(deep=True) 

186 cat_tmp_final = cat_tmp.asAstropy() 

187 cat_tmp_final['base_PsfFlux_mag'] = mags_dict[band] 

188 cat_tmp_final['base_PsfFlux_magErr'] = magerrs_dict[band] 

189 # Put the bandpass name in the column names: 

190 for c in cat_tmp_final.colnames: 

191 if c not in 'id': 

192 cat_tmp_final[c].name = c+'_'+str(band) 

193 # Write the new catalog to the dict of catalogs: 

194 cat_dict[band] = cat_tmp_final 

195 

196 cat_combined = join(cat_dict[bands[1]], cat_dict[bands[0]], keys='id') 

197 if len(bands) > 2: 

198 for i in range(2, len(bands)): 

199 cat_combined = join(cat_combined, cat_dict[bands[i]], keys='id') 

200 

201 qual_cuts = (cat_combined['base_ClassificationExtendedness_value_g'] < 0.5) &\ 

202 (cat_combined['base_PixelFlags_flag_saturated_g'] == False) &\ 

203 (cat_combined['base_PixelFlags_flag_cr_g'] == False) &\ 

204 (cat_combined['base_PixelFlags_flag_bad_g'] == False) &\ 

205 (cat_combined['base_PixelFlags_flag_edge_g'] == False) &\ 

206 (cat_combined['base_ClassificationExtendedness_value_r'] < 0.5) &\ 

207 (cat_combined['base_PixelFlags_flag_saturated_r'] == False) &\ 

208 (cat_combined['base_PixelFlags_flag_cr_r'] == False) &\ 

209 (cat_combined['base_PixelFlags_flag_bad_r'] == False) &\ 

210 (cat_combined['base_PixelFlags_flag_edge_r'] == False) &\ 

211 (cat_combined['base_ClassificationExtendedness_value_i'] < 0.5) &\ 

212 (cat_combined['base_PixelFlags_flag_saturated_i'] == False) &\ 

213 (cat_combined['base_PixelFlags_flag_cr_i'] == False) &\ 

214 (cat_combined['base_PixelFlags_flag_bad_i'] == False) &\ 

215 (cat_combined['base_PixelFlags_flag_edge_i'] == False) # noqa: E712 

216 

217 # Return the astropy table of matched catalogs: 

218 return(cat_combined[qual_cuts])