Coverage for lst_auto_rta/Ring_Background_Maps_ACADA.py: 0%

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1#!/usr/bin/env python 

2 

3import astropy 

4import gammapy 

5import matplotlib 

6import numpy as np 

7import regions 

8 

9print("gammapy:", gammapy.__version__) 

10print("numpy:", np.__version__) 

11print("astropy", astropy.__version__) 

12print("regions", regions.__version__) 

13print("matplotlib", matplotlib.__version__) 

14 

15import os 

16 

17import astropy.units as u 

18import matplotlib.pyplot as plt 

19import matplotlib.style as style 

20import numpy as np 

21from astropy.coordinates import SkyCoord 

22 

23style.use("tableau-colorblind10") 

24import argparse 

25from pathlib import Path 

26 

27from acceptance_modelisation import RadialAcceptanceMapCreator 

28from gammapy.data import DataStore 

29from gammapy.datasets import ( 

30 Datasets, 

31 MapDataset, 

32 SpectrumDataset, 

33) 

34from gammapy.estimators import ExcessMapEstimator 

35from gammapy.makers import ( 

36 MapDatasetMaker, 

37 ReflectedRegionsBackgroundMaker, 

38 RingBackgroundMaker, 

39 SafeMaskMaker, 

40 SpectrumDatasetMaker, 

41) 

42from gammapy.maps import Map, MapAxis, RegionGeom, WcsGeom 

43from matplotlib.offsetbox import AnchoredText 

44from regions import CircleSkyRegion 

45from scipy.stats import norm 

46 

47parser = argparse.ArgumentParser( 

48 description="Automatic Script for the DL1 check", formatter_class=argparse.ArgumentDefaultsHelpFormatter 

49) 

50parser.add_argument("-d", "--directory", default="/fefs/onsite/pipeline/rta/data/", help="Directory for data") 

51parser.add_argument("-da", "--date", default="20230705", help="Date of the run to check") 

52parser.add_argument("-r", "--run-id", default="13600", help="run id to check") 

53parser.add_argument("-add", "--add-string", default="reco/", help="add a string to the path") 

54parser.add_argument("-RA", "--right-ascension", default="270.19042", help="right-ascension in deg") 

55parser.add_argument("-DEC", "--declination", default="78.46806", help="declination in deg") 

56 

57args = parser.parse_args() 

58config = vars(args) 

59 

60location_data = ( 

61 config["directory"] + config["date"] + "/" + config["run_id"] + "/" + config["add_string"] + "/plots" 

62) # path to DL3 folder 

63source_name = config["run_id"] # e.g., Crab, GRB210807A 

64cut_type = "standard" # e.g., loose, hard, ... 

65filename_output = "{name}_{cut}".format(name=source_name, cut=cut_type) 

66 

67source_position = SkyCoord(ra=config["right_ascension"], dec=config["declination"], unit="deg", frame="icrs") 

68max_offset_run = 5 * u.deg 

69work_directory = location_data 

70path_plot = Path(work_directory + "/") 

71print(work_directory + "/") 

72path_plot.mkdir(exist_ok=True) 

73path_background = Path(work_directory + "/") 

74path_background.mkdir(exist_ok=True) 

75 

76e_min = 0.05 * u.TeV 

77e_max = 10.0 * u.TeV 

78n_bin_per_decade = 10 

79on_radius = 0.2 * u.deg 

80exclusion_radius = 0.35 * u.deg 

81fov_observation = 4.5 * u.deg 

82 

83r_in = 0.5 * u.deg 

84width = 0.4 * u.deg 

85correlation_radius = 0.2 * u.deg 

86 

87n_bin_per_decade_acceptance = 2.5 

88offset_bin_size_acceptance = 0.4 * u.deg 

89 

90data_store = DataStore.from_dir(location_data) 

91data_store.info() 

92 

93obs_ids = data_store.obs_table[source_position.separation(data_store.obs_table.pointing_radec) < max_offset_run][ 

94 "OBS_ID" 

95] 

96obs_collection = data_store.get_observations(obs_ids, required_irf=None) 

97 

98exclude_region = CircleSkyRegion(center=source_position, radius=exclusion_radius) 

99 

100n_bin_energy = int((np.log10(e_max.to_value(u.TeV)) - np.log10(e_min.to_value(u.TeV))) * n_bin_per_decade) 

101energy_axis = MapAxis.from_edges( 

102 np.logspace(np.log10(e_min.to_value(u.TeV)), np.log10(e_max.to_value(u.TeV)), n_bin_energy + 1), 

103 unit="TeV", 

104 name="energy", 

105 interp="log", 

106) 

107maximal_run_separation = np.max( 

108 source_position.separation(data_store.obs_table[np.isin(data_store.obs_table["OBS_ID"], obs_ids)].pointing_radec) 

109) 

110geom = WcsGeom.create( 

111 skydir=source_position, 

112 width=((maximal_run_separation + fov_observation) * 1.5, (maximal_run_separation + fov_observation) * 1.5), 

113 binsz=0.02, 

114 frame="icrs", 

115 axes=[energy_axis], 

116) 

117 

118geom_image = geom.to_image() 

119exclusion_mask = ~geom_image.region_mask([exclude_region]) 

120 

121stacked = MapDataset.create(geom=geom, name=source_name + "_stacked") 

122unstacked = Datasets() 

123maker = MapDatasetMaker(selection=["counts"]) 

124maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=fov_observation) 

125 

126for obs in obs_collection: 

127 cutout = stacked.cutout(obs.pointing_radec, width="6.5 deg") 

128 dataset = maker.run(cutout, obs) 

129 dataset = maker_safe_mask.run(dataset, obs) 

130 stacked.stack(dataset) 

131 unstacked.append(dataset) 

132 

133n_bin_energy_acceptance = int( 

134 (np.log10(e_max.to_value(u.TeV)) - np.log10(e_min.to_value(u.TeV))) * n_bin_per_decade_acceptance 

135) 

136energyAxisAcceptance = MapAxis.from_edges( 

137 np.logspace(np.log10(e_min.to_value(u.TeV)), np.log10(e_max.to_value(u.TeV)), 1 + n_bin_energy_acceptance), 

138 unit="TeV", 

139 name="energy", 

140 interp="log", 

141) 

142n_bin_offset_acceptance = int(fov_observation.to_value(u.deg) / offset_bin_size_acceptance.to_value(u.deg)) 

143offsetAxisAcceptance = MapAxis.from_edges( 

144 np.linspace(0.0, fov_observation.to_value(u.deg), 1 + n_bin_offset_acceptance), 

145 unit="deg", 

146 name="offset", 

147 interp="lin", 

148) 

149 

150background_creator = RadialAcceptanceMapCreator( 

151 energyAxisAcceptance, 

152 offsetAxisAcceptance, 

153 exclude_regions=[ 

154 exclude_region, 

155 ], 

156 oversample_map=10, 

157) 

158background = background_creator.create_radial_acceptance_map_per_observation(obs_collection) 

159# background[list(background.keys())[0]].peek() 

160for obs_id in background.keys(): 

161 hdu_background = background[obs_id].to_table_hdu() 

162 hdu_background.writeto( 

163 os.path.join(path_background, filename_output + "_" + str(obs_id).zfill(5) + "_background.fits"), 

164 overwrite=True, 

165 ) 

166 

167data_store.hdu_table.remove_rows(data_store.hdu_table["HDU_TYPE"] == "bkg") 

168 

169for obs_id in np.unique(data_store.hdu_table["OBS_ID"]): 

170 data_store.hdu_table.add_row( 

171 { 

172 "OBS_ID": obs_id, 

173 "HDU_TYPE": "bkg", 

174 "HDU_CLASS": "bkg_2d", 

175 "FILE_DIR": "", 

176 "FILE_NAME": os.path.join(path_background, filename_output + "_" + str(obs_id) + "_background.fits"), 

177 "HDU_NAME": "BACKGROUND", 

178 "SIZE": hdu_background.size, 

179 } 

180 ) 

181 

182data_store.hdu_table = data_store.hdu_table.copy() 

183obs_collection = data_store.get_observations(obs_ids, required_irf=None) 

184 

185stacked = MapDataset.create(geom=geom) 

186unstacked = Datasets() 

187maker = MapDatasetMaker(selection=["counts", "background"]) 

188maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=fov_observation) 

189 

190for obs in obs_collection: 

191 cutout = stacked.cutout(obs.pointing_radec, width="6.5 deg") 

192 dataset = maker.run(cutout, obs) 

193 dataset = maker_safe_mask.run(dataset, obs) 

194 stacked.stack(dataset) 

195 unstacked.append(dataset) 

196 

197ring_bkg_maker = RingBackgroundMaker(r_in=r_in, width=width) # , exclusion_mask=exclusion_mask) 

198stacked_ring = ring_bkg_maker.run(stacked.to_image()) 

199estimator = ExcessMapEstimator(correlation_radius, correlate_off=False) 

200lima_maps = estimator.run(stacked_ring) 

201 

202significance_all = lima_maps["sqrt_ts"].data[np.isfinite(lima_maps["sqrt_ts"].data)] 

203significance_background = lima_maps["sqrt_ts"].data[ 

204 np.logical_and(np.isfinite(lima_maps["sqrt_ts"].data), exclusion_mask.data) 

205] 

206 

207bins = np.linspace( 

208 np.min(significance_all), 

209 np.max(significance_all), 

210 num=int((np.max(significance_all) - np.min(significance_all)) * 3), 

211) 

212 

213# Now, fit the off distribution with a Gaussian 

214mu, std = norm.fit(significance_background) 

215x = np.linspace(-8, 8, 50) 

216p = norm.pdf(x, mu, std) 

217 

218plt.figure(figsize=(8, 21)) 

219ax1 = plt.subplot(3, 1, 1, projection=lima_maps["sqrt_ts"].geom.wcs) 

220ax2 = plt.subplot(3, 1, 2, projection=lima_maps["sqrt_ts"].geom.wcs) 

221ax3 = plt.subplot(3, 1, 3) 

222 

223ax2.set_title("Significance map") 

224lima_maps["sqrt_ts"].plot(ax=ax2, add_cbar=True) 

225ax2.scatter( 

226 source_position.ra, 

227 source_position.dec, 

228 transform=ax2.get_transform("world"), 

229 marker="+", 

230 c="red", 

231 label=filename_output, 

232 s=[300], 

233 linewidths=3, 

234) 

235ax2.legend() 

236 

237ax1.set_title("Excess map") 

238lima_maps["npred_excess"].plot(ax=ax1, add_cbar=True) 

239ax1.scatter( 

240 source_position.ra, 

241 source_position.dec, 

242 transform=ax1.get_transform("world"), 

243 marker="+", 

244 c="red", 

245 label=filename_output, 

246 s=[300], 

247 linewidths=3, 

248) 

249ax1.legend() 

250 

251ax3.set_title("Significance distribution") 

252ax3.hist(significance_all, density=True, alpha=0.5, color="red", label="All bins", bins=bins) 

253ax3.hist(significance_background, density=True, alpha=0.5, color="blue", label="Background bins", bins=bins) 

254 

255ax3.plot(x, p, lw=2, color="black") 

256ax3.legend() 

257ax3.set_xlabel("Significance") 

258ax3.set_yscale("log") 

259ax3.set_ylim(1e-5, 1) 

260xmin, xmax = np.min(significance_all), np.max(significance_all) 

261ax3.set_xlim(xmin, xmax) 

262 

263text = text = r"$\mu$ = {:.2f}" f"\n" r"$\sigma$ = {:.2f}".format(mu, std) 

264box_prop = dict(boxstyle="Round", facecolor="white", alpha=0.5) 

265text_prop = dict(fontsize="x-large", bbox=box_prop) 

266# txt = AnchoredText(text, loc=2, transform=ax3.transAxes, prop=text_prop, frameon=False) 

267txt = AnchoredText(text, loc=2, prop=text_prop, frameon=False) 

268ax3.add_artist(txt) 

269 

270plt.savefig(os.path.join(path_plot, "{}__sky_map.png".format(filename_output)), dpi=300) 

271print(f"Fit results: mu = {mu:.2f}, std = {std:.2f}")