Coverage for lst_auto_rta/Ring_Background_Maps.py: 0%
149 statements
« prev ^ index » next coverage.py v7.6.4, created at 2024-11-03 14:47 +0000
1#!/usr/bin/env python
3import astropy
4import gammapy
5import matplotlib
6import numpy as np
7import regions
9print("gammapy:", gammapy.__version__)
10print("numpy:", np.__version__)
11print("astropy", astropy.__version__)
12print("regions", regions.__version__)
13print("matplotlib", matplotlib.__version__)
15import os
17import astropy.units as u
18import matplotlib.pyplot as plt
19import matplotlib.style as style
20import numpy as np
21from astropy.coordinates import SkyCoord
23style.use("tableau-colorblind10")
24import argparse
25from pathlib import Path
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.estimators.utils import find_peaks
36from gammapy.makers import (
37 MapDatasetMaker,
38 ReflectedRegionsBackgroundMaker,
39 RingBackgroundMaker,
40 SafeMaskMaker,
41 SpectrumDatasetMaker,
42)
43from gammapy.maps import Map, MapAxis, RegionGeom, WcsGeom
44from matplotlib.offsetbox import AnchoredText
45from regions import CircleSkyRegion
46from scipy.stats import norm
48parser = argparse.ArgumentParser(
49 description="Automatic Script for the DL1 check", formatter_class=argparse.ArgumentDefaultsHelpFormatter
50)
51parser.add_argument("-d", "--directory", default="/fefs/onsite/pipeline/rta/data/", help="Directory for data")
52parser.add_argument("-da", "--date", default="20230705", help="Date of the run to check")
53parser.add_argument("-r", "--run-id", default="13600", help="run id to check")
54parser.add_argument("-add", "--add-string", default="", help="add a string to the path")
55parser.add_argument("-RA", "--right-ascension", default="270.19042", help="right-ascension in deg")
56parser.add_argument("-DEC", "--declination", default="78.46806", help="declination in deg")
58args = parser.parse_args()
59config = vars(args)
61location_data = (
62 config["directory"] + config["date"] + "/" + config["run_id"] + "/" + config["add_string"] + "/DL3"
63) # path to DL3 folder
64source_name = config["run_id"] # e.g., Crab, GRB210807A
65cut_type = "standard" # e.g., loose, hard, ...
66filename_output = "{name}_{cut}".format(name=source_name, cut=cut_type)
68source_position = SkyCoord(ra=config["right_ascension"], dec=config["declination"], unit="deg", frame="icrs")
69max_offset_run = 5 * u.deg
70work_directory = location_data
71path_plot = Path(work_directory + "/../plots")
72print(work_directory + "/../plots")
73path_plot.mkdir(exist_ok=True)
74path_background = Path(work_directory + "/../plots")
75path_background.mkdir(exist_ok=True)
77e_min = 0.05 * u.TeV
78e_max = 10.0 * u.TeV
79n_bin_per_decade = 10
80on_radius = 0.2 * u.deg
81exclusion_radius = 0.0 * u.deg
82fov_observation = 4.5 * u.deg
84r_in = 0.5 * u.deg
85width = 0.4 * u.deg
86correlation_radius = 0.2 * u.deg
88n_bin_per_decade_acceptance = 2.5
89offset_bin_size_acceptance = 0.4 * u.deg
91data_store = DataStore.from_dir(location_data)
92data_store.info()
94obs_ids = data_store.obs_table[source_position.separation(data_store.obs_table.pointing_radec) < max_offset_run][
95 "OBS_ID"
96]
97obs_collection = data_store.get_observations(obs_ids, required_irf=None)
99exclude_region = CircleSkyRegion(center=source_position, radius=exclusion_radius)
101n_bin_energy = int((np.log10(e_max.to_value(u.TeV)) - np.log10(e_min.to_value(u.TeV))) * n_bin_per_decade)
102energy_axis = MapAxis.from_edges(
103 np.logspace(np.log10(e_min.to_value(u.TeV)), np.log10(e_max.to_value(u.TeV)), n_bin_energy + 1),
104 unit="TeV",
105 name="energy",
106 interp="log",
107)
108maximal_run_separation = np.max(
109 source_position.separation(data_store.obs_table[np.isin(data_store.obs_table["OBS_ID"], obs_ids)].pointing_radec)
110)
111geom = WcsGeom.create(
112 skydir=source_position,
113 width=((maximal_run_separation + fov_observation) * 1.5, (maximal_run_separation + fov_observation) * 1.5),
114 binsz=0.02,
115 frame="icrs",
116 axes=[energy_axis],
117)
119geom_image = geom.to_image()
120exclusion_mask = ~geom_image.region_mask([exclude_region])
122stacked = MapDataset.create(geom=geom, name=source_name + "_stacked")
123unstacked = Datasets()
124maker = MapDatasetMaker(selection=["counts"])
125maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=fov_observation)
127for obs in obs_collection:
128 cutout = stacked.cutout(obs.pointing_radec, width="6.5 deg")
129 dataset = maker.run(cutout, obs)
130 dataset = maker_safe_mask.run(dataset, obs)
131 stacked.stack(dataset)
132 unstacked.append(dataset)
134n_bin_energy_acceptance = int(
135 (np.log10(e_max.to_value(u.TeV)) - np.log10(e_min.to_value(u.TeV))) * n_bin_per_decade_acceptance
136)
137energyAxisAcceptance = MapAxis.from_edges(
138 np.logspace(np.log10(e_min.to_value(u.TeV)), np.log10(e_max.to_value(u.TeV)), 1 + n_bin_energy_acceptance),
139 unit="TeV",
140 name="energy",
141 interp="log",
142)
143n_bin_offset_acceptance = int(fov_observation.to_value(u.deg) / offset_bin_size_acceptance.to_value(u.deg))
144offsetAxisAcceptance = MapAxis.from_edges(
145 np.linspace(0.0, fov_observation.to_value(u.deg), 1 + n_bin_offset_acceptance),
146 unit="deg",
147 name="offset",
148 interp="lin",
149)
151background_creator = RadialAcceptanceMapCreator(
152 energyAxisAcceptance,
153 offsetAxisAcceptance,
154 exclude_regions=[
155 exclude_region,
156 ],
157 oversample_map=10,
158)
159background = background_creator.create_radial_acceptance_map_per_observation(obs_collection)
160# background[list(background.keys())[0]].peek()
161for obs_id in background.keys():
162 hdu_background = background[obs_id].to_table_hdu()
163 hdu_background.writeto(
164 os.path.join(path_background, filename_output + "_" + str(obs_id) + "_background.fits"), overwrite=True
165 )
167data_store.hdu_table.remove_rows(data_store.hdu_table["HDU_TYPE"] == "bkg")
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 )
182data_store.hdu_table = data_store.hdu_table.copy()
183obs_collection = data_store.get_observations(obs_ids, required_irf=None)
185stacked = MapDataset.create(geom=geom)
186unstacked = Datasets()
187maker = MapDatasetMaker(selection=["counts", "background"])
188maker_safe_mask = SafeMaskMaker(methods=["offset-max"], offset_max=fov_observation)
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)
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)
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]
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)
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)
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)
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()
237sources = find_peaks(
238 lima_maps["sqrt_ts"].get_image_by_idx((0,)),
239 threshold=7,
240 min_distance="0.2 deg",
241)
242print(sources)
243# now = dt.datetime.now()
244# timestamp_str = now.strftime("%Y-%m-%d %H:%M:%S")
245# ax1.text(0.02, 0.98, timestamp_str, transform=ax1.transAxes,
246# fontsize=11, fontweight='bold', va='top', ha='left')
247# ax2.text(0.02, 0.98, timestamp_str, transform=ax2.transAxes,
248# fontsize=11, fontweight='bold', va='top', ha='left')
249if len(sources) > 0:
250 ax2.scatter(
251 sources["ra"],
252 sources["dec"],
253 transform=plt.gca().get_transform("icrs"),
254 color="none",
255 edgecolor="white",
256 marker="o",
257 s=300,
258 lw=1.5,
259 )
262ax1.set_title("Excess map")
263lima_maps["npred_excess"].plot(ax=ax1, add_cbar=True)
264ax1.scatter(
265 source_position.ra,
266 source_position.dec,
267 transform=ax1.get_transform("world"),
268 marker="+",
269 c="red",
270 label=filename_output,
271 s=[300],
272 linewidths=3,
273)
274ax1.legend()
276ax3.set_title("Significance distribution")
277ax3.hist(significance_all, density=True, alpha=0.5, color="red", label="All bins", bins=bins)
278ax3.hist(significance_background, density=True, alpha=0.5, color="blue", label="Background bins", bins=bins)
280ax3.plot(x, p, lw=2, color="black")
281ax3.legend()
282ax3.set_xlabel("Significance")
283ax3.set_yscale("log")
284ax3.set_ylim(1e-5, 1)
285xmin, xmax = np.min(significance_all), np.max(significance_all)
286ax3.set_xlim(xmin, xmax)
288text = text = r"$\mu$ = {:.2f}" f"\n" r"$\sigma$ = {:.2f}".format(mu, std)
289box_prop = dict(boxstyle="Round", facecolor="white", alpha=0.5)
290text_prop = dict(fontsize="x-large", bbox=box_prop)
291# txt = AnchoredText(text, loc=2, transform=ax3.transAxes, prop=text_prop, frameon=False)
292txt = AnchoredText(text, loc=2, prop=text_prop, frameon=False)
293ax3.add_artist(txt)
295plt.savefig(os.path.join(path_plot, "{}__sky_map.png".format(filename_output)), dpi=300)
296print(f"Fit results: mu = {mu:.2f}, std = {std:.2f}")