/
simulate.py
550 lines (446 loc) · 17.9 KB
/
simulate.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Simulate observations"""
import numpy as np
import astropy.units as u
from astropy.coordinates import SkyCoord, SkyOffsetFrame
from astropy.table import Table
from astropy.time import Time
import gammapy
from gammapy.data import EventList, observatory_locations
from gammapy.maps import MapAxis, MapCoord, RegionNDMap, TimeMapAxis
from gammapy.modeling.models import (
ConstantSpectralModel,
ConstantTemporalModel,
PointSpatialModel,
)
from gammapy.utils.random import get_random_state
__all__ = ["MapDatasetEventSampler"]
class MapDatasetEventSampler:
"""Sample events from a map dataset
Parameters
----------
random_state : {int, 'random-seed', 'global-rng', `~numpy.random.RandomState`}
Defines random number generator initialisation.
Passed to `~gammapy.utils.random.get_random_state`.
oversample_energy_factor: {int}
Defines an oversampling factor for the energies; it is used only when sampling
an energy-dependent time-varying source.
t_delta : `~astropy.units.Quantity`
Time interval used to sample the time-dependent source.
"""
def __init__(
self, random_state="random-seed", oversample_energy_factor=10, t_delta=0.5 * u.s
):
self.random_state = get_random_state(random_state)
self.oversample_energy_factor = oversample_energy_factor
self.t_delta = t_delta
def _make_table(self, coords, time_ref):
"""Create a table for sampled events.
Parameters
----------
coords : `~gammapy.maps.MapCoord`
Coordinates of the sampled events.
time_ref : `~astropy.time.Time`
reference time of the event list.
Returns
-------
table : `~astropy.table.Table`
Table of the sampled events.
"""
table = Table()
try:
energy = coords["energy_true"]
except KeyError:
energy = coords["energy"]
table["TIME"] = (coords["time"] - time_ref).to("s")
table["ENERGY_TRUE"] = energy
table["RA_TRUE"] = coords.skycoord.icrs.ra.to("deg")
table["DEC_TRUE"] = coords.skycoord.icrs.dec.to("deg")
return table
def _evaluate_timevar_source(
self,
dataset,
model,
):
"""Calculate Npred for a given `dataset.model` by evaluating
it on a region geometry.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
model : `~gammapy.modeling.models.SkyModel`
Sky model intance.
Returns
-------
npred : `~gammapy.maps.RegionNDMap`
Npred map.
"""
energy_true = dataset.edisp.edisp_map.geom.axes["energy_true"]
energy_new = energy_true.upsample(self.oversample_energy_factor)
position = model.spatial_model.position
region_exposure = dataset.exposure.to_region_nd_map(position)
time_axis_eval = TimeMapAxis.from_gti_bounds(
gti=dataset.gti, t_delta=self.t_delta
)
time_min, time_max = time_axis_eval.time_bounds
time_axis = MapAxis.from_bounds(
time_min.mjd * u.d,
time_max.mjd * u.d,
nbin=time_axis_eval.nbin,
name="time",
)
temp_eval = model.temporal_model.evaluate(
time_axis_eval.time_mid, energy=energy_new.center
)
norm_parameters = model.spectral_model.parameters.norm_parameters
norm = norm_parameters[0].quantity
if temp_eval.unit.is_equivalent(norm.unit):
flux_diff = temp_eval.to(norm.unit)
else:
flux_diff = temp_eval * norm
flux_inte = flux_diff * energy_new.bin_width[:, None]
flux_pred = RegionNDMap.create(
region=position,
axes=[time_axis, energy_new],
data=np.array(flux_inte),
unit=flux_inte.unit,
)
mapcoord = flux_pred.geom.get_coord()
mapcoord["energy_true"] = energy_true.center[:, None, None, None]
flux_values = flux_pred.interp_by_coord(mapcoord) * flux_pred.unit
data = flux_values * region_exposure.quantity[:, None, :, :]
data /= time_axis.nbin / self.oversample_energy_factor
npred = RegionNDMap.create(
region=position,
axes=[time_axis, energy_true],
data=data.to_value(""),
)
return npred
def _sample_coord_time_energy(self, dataset, model):
"""Sample model components of a source with time-dependent spectrum.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
model : `~gammapy.modeling.models.SkyModel`
Sky model instance.
Returns
-------
table : `~astropy.table.Table`
Table of sampled events.
"""
if not isinstance(model.spatial_model, PointSpatialModel):
raise TypeError(
f"Event sampler expects PointSpatialModel for a time varying source. Got {model.spatial_model} instead."
)
if not isinstance(model.spectral_model, ConstantSpectralModel):
raise TypeError(
f"Event sampler expects ConstantSpectralModel for a time varying source. Got {model.spectral_model} instead."
)
npred = self._evaluate_timevar_source(dataset, model=model)
data = npred.data[np.isfinite(npred.data)]
n_events = self.random_state.poisson(np.sum(data))
coords = npred.sample_coord(n_events=n_events, random_state=self.random_state)
coords["time"] = Time(coords["time"], format="mjd", scale="tt")
table = self._make_table(coords, dataset.gti.time_ref)
return table
def _sample_coord_time(self, npred, temporal_model, gti):
"""Sample model components of a time-varying source.
Parameters
----------
npred : `~gammapy.dataset.MapDataset`
Npred map.
temporal_model : `~gammapy.modeling.models\
temporal model of the source.
gti : `~gammapy.data.GTI`
GTI of the dataset
Returns
-------
table : `~astropy.table.Table`
Table of sampled events.
"""
data = npred.data[np.isfinite(npred.data)]
n_events = self.random_state.poisson(np.sum(data))
coords = npred.sample_coord(n_events=n_events, random_state=self.random_state)
time_start, time_stop, time_ref = (gti.time_start, gti.time_stop, gti.time_ref)
coords["time"] = temporal_model.sample_time(
n_events=n_events,
t_min=time_start,
t_max=time_stop,
random_state=self.random_state,
t_delta=self.t_delta,
)
table = self._make_table(coords, time_ref)
return table
def sample_sources(self, dataset):
"""Sample source model components.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
Returns
-------
events : `~gammapy.data.EventList`
Event list
"""
events_all = []
for idx, evaluator in enumerate(dataset.evaluators.values()):
if evaluator.needs_update:
evaluator.update(
dataset.exposure,
dataset.psf,
dataset.edisp,
dataset._geom,
dataset.mask,
)
if evaluator.model.temporal_model is None:
temporal_model = ConstantTemporalModel()
else:
temporal_model = evaluator.model.temporal_model
if temporal_model.is_energy_dependent:
table = self._sample_coord_time_energy(dataset, evaluator.model)
else:
flux = evaluator.compute_flux()
npred = evaluator.apply_exposure(flux)
table = self._sample_coord_time(npred, temporal_model, dataset.gti)
if len(table) == 0:
mcid = table.Column(name="MC_ID", length=0, dtype=int)
table.add_column(mcid)
table["MC_ID"] = idx + 1
table.meta["MID{:05d}".format(idx + 1)] = idx + 1
table.meta["MMN{:05d}".format(idx + 1)] = evaluator.model.name
events_all.append(EventList(table))
return EventList.from_stack(events_all)
def sample_background(self, dataset):
"""Sample background
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset
Returns
-------
events : `gammapy.data.EventList`
Background events
"""
background = dataset.npred_background()
temporal_model = ConstantTemporalModel()
table = self._sample_coord_time(background, temporal_model, dataset.gti)
table["MC_ID"] = 0
table["ENERGY"] = table["ENERGY_TRUE"]
table["RA"] = table["RA_TRUE"]
table["DEC"] = table["DEC_TRUE"]
table.meta["MID{:05d}".format(0)] = 0
table.meta["MMN{:05d}".format(0)] = dataset.background_model.name
return EventList(table)
def sample_edisp(self, edisp_map, events):
"""Sample energy dispersion map.
Parameters
----------
edisp_map : `~gammapy.irf.EDispMap`
Energy dispersion map
events : `~gammapy.data.EventList`
Event list with the true energies
Returns
-------
events : `~gammapy.data.EventList`
Event list with reconstructed energy column.
"""
coord = MapCoord(
{
"lon": events.table["RA_TRUE"].quantity,
"lat": events.table["DEC_TRUE"].quantity,
"energy_true": events.table["ENERGY_TRUE"].quantity,
},
frame="icrs",
)
coords_reco = edisp_map.sample_coord(coord, self.random_state)
events.table["ENERGY"] = coords_reco["energy"]
return events
def sample_psf(self, psf_map, events):
"""Sample psf map.
Parameters
----------
psf_map : `~gammapy.irf.PSFMap`
PSF map.
events : `~gammapy.data.EventList`
Event list.
Returns
-------
events : `~gammapy.data.EventList`
Event list with reconstructed position columns.
"""
coord = MapCoord(
{
"lon": events.table["RA_TRUE"].quantity,
"lat": events.table["DEC_TRUE"].quantity,
"energy_true": events.table["ENERGY_TRUE"].quantity,
},
frame="icrs",
)
coords_reco = psf_map.sample_coord(coord, self.random_state)
events.table["RA"] = coords_reco["lon"] * u.deg
events.table["DEC"] = coords_reco["lat"] * u.deg
return events
@staticmethod
def event_det_coords(observation, events):
"""Add columns of detector coordinates (DETX-DETY) to the event list.
Parameters
----------
observation : `~gammapy.data.Observation`
In memory observation.
events : `~gammapy.data.EventList`
Event list.
Returns
-------
events : `~gammapy.data.EventList`
Event list with columns of event detector coordinates.
"""
sky_coord = SkyCoord(events.table["RA"], events.table["DEC"], frame="icrs")
frame = SkyOffsetFrame(origin=observation.get_pointing_icrs(observation.tmid))
pseudo_fov_coord = sky_coord.transform_to(frame)
events.table["DETX"] = pseudo_fov_coord.lon
events.table["DETY"] = pseudo_fov_coord.lat
return events
@staticmethod
def event_list_meta(dataset, observation):
"""Event list meta info.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset.
observation : `~gammapy.data.Observation`
In memory observation.
Returns
-------
meta : dict
Meta dictionary.
"""
# See: https://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html#mandatory-header-keywords # noqa: E501
meta = {}
meta["HDUCLAS1"] = "EVENTS"
meta["EXTNAME"] = "EVENTS"
meta[
"HDUDOC"
] = "https://github.com/open-gamma-ray-astro/gamma-astro-data-formats"
meta["HDUVERS"] = "0.2"
meta["HDUCLASS"] = "GADF"
meta["OBS_ID"] = observation.obs_id
meta["TSTART"] = (observation.tstart - dataset.gti.time_ref).to_value("s")
meta["TSTOP"] = (observation.tstop - dataset.gti.time_ref).to_value("s")
meta["ONTIME"] = observation.observation_time_duration.to("s").value
meta["LIVETIME"] = observation.observation_live_time_duration.to("s").value
meta["DEADC"] = 1 - observation.observation_dead_time_fraction
fixed_icrs = observation.pointing.fixed_icrs
meta["RA_PNT"] = fixed_icrs.ra.deg
meta["DEC_PNT"] = fixed_icrs.dec.deg
meta["EQUINOX"] = "J2000"
meta["RADECSYS"] = "icrs"
meta["CREATOR"] = "Gammapy {}".format(gammapy.__version__)
meta["EUNIT"] = "TeV"
meta["EVTVER"] = ""
meta["OBSERVER"] = "Gammapy user"
meta["DSTYP1"] = "TIME"
meta["DSUNI1"] = "s"
meta["DSVAL1"] = "TABLE"
meta["DSREF1"] = ":GTI"
meta["DSTYP2"] = "ENERGY"
meta["DSUNI2"] = "TeV"
meta[
"DSVAL2"
] = f'{dataset._geom.axes["energy"].edges.min().value}:{dataset._geom.axes["energy"].edges.max().value}' # noqa: E501
meta["DSTYP3"] = "POS(RA,DEC) "
offset_max = np.max(dataset._geom.width).to_value("deg")
meta[
"DSVAL3"
] = f"CIRCLE({fixed_icrs.ra.deg},{fixed_icrs.dec.deg},{offset_max})" # noqa: E501
meta["DSUNI3"] = "deg "
meta["NDSKEYS"] = " 3 "
# get first non background model component
for model in dataset.models:
if model is not dataset.background_model:
break
else:
model = None
if model:
meta["OBJECT"] = model.name
meta["RA_OBJ"] = model.position.icrs.ra.deg
meta["DEC_OBJ"] = model.position.icrs.dec.deg
meta["TELAPSE"] = dataset.gti.time_sum.to("s").value
meta["MJDREFI"] = int(dataset.gti.time_ref.mjd)
meta["MJDREFF"] = dataset.gti.time_ref.mjd % 1
meta["TIMEUNIT"] = "s"
meta["TIMESYS"] = dataset.gti.time_ref.scale
meta["TIMEREF"] = "LOCAL"
meta["DATE-OBS"] = dataset.gti.time_start.isot[0][0:10]
meta["DATE-END"] = dataset.gti.time_stop.isot[0][0:10]
meta["CONV_DEP"] = 0
meta["CONV_RA"] = 0
meta["CONV_DEC"] = 0
meta["NMCIDS"] = len(dataset.models)
# Necessary for DataStore, but they should be ALT and AZ instead!
telescope = observation.aeff.meta["TELESCOP"]
instrument = observation.aeff.meta["INSTRUME"]
if telescope == "CTA":
if instrument == "Southern Array":
loc = observatory_locations["cta_south"]
elif instrument == "Northern Array":
loc = observatory_locations["cta_north"]
else:
loc = observatory_locations["cta_south"]
else:
loc = observatory_locations[telescope.lower()]
# this is not really correct but maybe OK for now
coord_altaz = observation.pointing.get_altaz(dataset.gti.time_start, loc)
meta["ALT_PNT"] = str(coord_altaz.alt.deg[0])
meta["AZ_PNT"] = str(coord_altaz.az.deg[0])
# TO DO: these keywords should be taken from the IRF of the dataset
meta["ORIGIN"] = "Gammapy"
meta["TELESCOP"] = observation.aeff.meta["TELESCOP"]
meta["INSTRUME"] = observation.aeff.meta["INSTRUME"]
meta["N_TELS"] = ""
meta["TELLIST"] = ""
meta["CREATED"] = ""
meta["OBS_MODE"] = ""
meta["EV_CLASS"] = ""
return meta
def run(self, dataset, observation=None):
"""Run the event sampler, applying IRF corrections.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset`
Map dataset
observation : `~gammapy.data.Observation`
In memory observation.
edisp : Bool
It allows to include or exclude the Edisp in the simulation.
Returns
-------
events : `~gammapy.data.EventList`
Event list.
"""
if len(dataset.models) > 1:
events_src = self.sample_sources(dataset)
if len(events_src.table) > 0:
if dataset.psf:
events_src = self.sample_psf(dataset.psf, events_src)
else:
events_src.table["RA"] = events_src.table["RA_TRUE"]
events_src.table["DEC"] = events_src.table["DEC_TRUE"]
if dataset.edisp:
events_src = self.sample_edisp(dataset.edisp, events_src)
else:
events_src.table["ENERGY"] = events_src.table["ENERGY_TRUE"]
if dataset.background:
events_bkg = self.sample_background(dataset)
events = EventList.from_stack([events_bkg, events_src])
else:
events = events_src
if len(dataset.models) == 1 and dataset.background_model is not None:
events_bkg = self.sample_background(dataset)
events = EventList.from_stack([events_bkg])
events = self.event_det_coords(observation, events)
events.table["EVENT_ID"] = np.arange(len(events.table))
events.table.meta.update(self.event_list_meta(dataset, observation))
geom = dataset._geom
selection = geom.contains(events.map_coord(geom))
return events.select_row_subset(selection)