def test_region(self):
pos = SkyCoord(81, 21, unit='deg', frame='icrs')
radius = Angle(1, 'deg')
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(self.events.radec)
table = self.events.table[idx]
assert_allclose(table[4]['RA'], 81, rtol=1)
assert_allclose(table[2]['DEC'], 21, rtol=1)
assert len(table) == 5
def test_EventList_region():
filename = gammapy_extra.filename("test_datasets/unbundled/hess/run_0023037_hard_eventlist.fits.gz")
event_list = EventList.read(filename, hdu="EVENTS")
pos = SkyCoord(81, 21, unit="deg", frame="icrs")
radius = Angle(1, "deg")
circ = CircleSkyRegion(pos, radius)
idx = circ.contains(event_list.radec)
filtered_list = event_list[idx]
assert_allclose(filtered_list[4]["RA"], 81, rtol=1)
assert_allclose(filtered_list[2]["DEC"], 21, rtol=1)
assert len(filtered_list) == 5
def exclusion_mask(geom):
"""Example mask for testing."""
pos = SkyCoord(83.633, 22.014, unit="deg", frame="icrs")
region = CircleSkyRegion(pos, Angle(0.3, "deg"))
exclusion = WcsNDMap.from_geom(geom)
exclusion.data = geom.region_mask([region], inside=False)
return exclusion
def test_lookup_max_region(self):
center = SkyCoord(0, 0, unit='deg', frame='galactic')
circle = CircleSkyRegion(center, radius=Quantity(1, 'deg'))
pos, value = self.image.lookup_max(circle)
assert value == 15
assert_allclose((359.93, -0.01),
(pos.galactic.l.deg, pos.galactic.b.deg))
def spectrum_dataset_crab_fine():
e_true = MapAxis.from_edges(np.logspace(-2, 2.5, 109) * u.TeV, name="energy_true")
e_reco = MapAxis.from_edges(np.logspace(-2, 2, 73) * u.TeV, name="energy")
pos = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
return SpectrumDataset.create(e_reco, e_true, region=region)
def test_select_mask(models_gauss):
center_sky = SkyCoord("0d", "0d")
circle = CircleSkyRegion(center=center_sky, radius=1 * u.deg)
axis = MapAxis.from_energy_edges(np.logspace(-1, 1, 3), unit="TeV")
geom = WcsGeom.create(skydir=center_sky,
width=(5, 4),
axes=[axis],
binsz=0.02)
mask = geom.region_mask([circle])
contribute = models_gauss.select_mask(mask,
margin=None,
use_evaluation_region=True)
assert contribute.names == ["source-1", "source-2"]
inside = models_gauss.select_mask(mask,
margin=None,
use_evaluation_region=False)
assert inside.names == ["source-1"]
contribute_margin = models_gauss.select_mask(mask,
margin=0.1 * u.deg,
use_evaluation_region=True)
assert contribute_margin.names == ["source-1", "source-2", "source-3"]
def test_to_spectrum_dataset(sky_model, geom, geom_etrue):
dataset_ref = get_map_dataset(sky_model, geom, geom_etrue, edisp=True)
dataset_ref.counts = dataset_ref.background_model.map * 0.0
dataset_ref.counts.data[1, 50, 50] = 1
dataset_ref.counts.data[1, 60, 50] = 1
gti = GTI.create([0 * u.s], [1 * u.h],
reference_time="2010-01-01T00:00:00")
dataset_ref.gti = gti
on_region = CircleSkyRegion(center=geom.center_skydir, radius=0.05 * u.deg)
spectrum_dataset = dataset_ref.to_spectrum_dataset(on_region)
spectrum_dataset_corrected = dataset_ref.to_spectrum_dataset(
on_region, containment_correction=True)
assert np.sum(spectrum_dataset.counts.data) == 1
assert spectrum_dataset.data_shape == (2, 1, 1)
assert spectrum_dataset.background.geom.axes[0].nbin == 2
assert spectrum_dataset.aeff.energy.nbin == 3
assert spectrum_dataset.aeff.data.data.unit == "m2"
assert spectrum_dataset.edisp.e_reco.nbin == 2
assert spectrum_dataset.edisp.e_true.nbin == 3
assert spectrum_dataset_corrected.aeff.data.data.unit == "m2"
assert_allclose(spectrum_dataset.aeff.data.data.value[1],
853023.423047,
rtol=1e-5)
assert_allclose(spectrum_dataset_corrected.aeff.data.data.value[1],
559476.3357,
rtol=1e-5)
def setup(self):
from regions import CircleSkyRegion
kwargs = {}
# Define energy range
kwargs['emin'] = 1 * u.TeV
kwargs['emax'] = 10 * u.TeV
# Define reference image
wcs_spec = {'nxpix': 21,
'nypix': 21,
'xref': 83.633083,
'yref': 22.0145,
'coordsys': 'CEL',
'proj': 'CAR',
'binsz': 0.5}
kwargs['reference'] = SkyImage.empty(**wcs_spec)
# Define background estimator
r_in = 0.3 * u.deg
width = 0.2 * u.deg
kwargs['background_estimator'] = RingBackgroundEstimator(r_in=r_in, width=width)
# Defien exclusion mask
center = SkyCoord(83.633083, 22.0145, frame='icrs', unit='deg')
circle = CircleSkyRegion(center, radius=Angle(0.2, 'deg'))
kwargs['exclusion_mask'] = kwargs['reference'].region_mask(circle)
kwargs['exclusion_mask'].data = ~kwargs['exclusion_mask'].data
self.estimator = IACTBasicImageEstimator(**kwargs)
# setup data store and get list of observations
data_store = DataStore.from_dir('$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2/')
self.observations = data_store.obs_list([23523, 23526, 23559, 23592])
def test_get_spectrum():
axis = MapAxis.from_bounds(1, 10, nbin=3, unit="TeV", name="energy")
geom = WcsGeom.create(
skydir=(0, 0), width=(2.5, 2.5), binsz=0.5, axes=[axis], frame="galactic"
)
m = Map.from_geom(geom)
m.data += 1
center = SkyCoord(0, 0, frame="galactic", unit="deg")
region = CircleSkyRegion(center=center, radius=1 * u.deg)
spec = m.get_spectrum(region=region)
assert_allclose(spec.data.squeeze(), [13.0, 13.0, 13.0])
spec = m.get_spectrum(region=region, func=np.mean)
assert_allclose(spec.data.squeeze(), [1.0, 1.0, 1.0])
spec = m.get_spectrum()
assert isinstance(spec.geom.region, RectangleSkyRegion)
region = PointSkyRegion(center)
spec = m.get_spectrum(region=region)
assert_allclose(spec.data.squeeze(), [1.0, 1.0, 1.0])
def test_get_spectrum_weights():
axis = MapAxis.from_bounds(1, 10, nbin=3, unit="TeV", name="energy")
geom = WcsGeom.create(skydir=(0, 0),
width=(2.5, 2.5),
binsz=0.5,
axes=[axis],
frame="galactic")
m_int = Map.from_geom(geom, dtype="int")
m_int.data += 1
weights = Map.from_geom(geom, dtype="bool")
weights.data[:, 2, 2] = True
bad_weights = Map.from_geom(geom.to_image(), dtype="bool")
center = SkyCoord(0, 0, frame="galactic", unit="deg")
region = CircleSkyRegion(center=center, radius=1 * u.deg)
spec_int = m_int.get_spectrum(region=region, weights=weights)
assert spec_int.data.dtype == np.dtype("int")
assert_allclose(spec_int.data.squeeze(), [1, 1, 1])
with pytest.raises(ValueError):
m_int.get_spectrum(region=region, weights=bad_weights)
def test_get_spectrum_type():
axis = MapAxis.from_bounds(1, 10, nbin=3, unit="TeV", name="energy")
geom = WcsGeom.create(skydir=(0, 0),
width=(2.5, 2.5),
binsz=0.5,
axes=[axis],
frame="galactic")
m_int = Map.from_geom(geom, dtype="int")
m_int.data += 1
m_bool = Map.from_geom(geom, dtype="bool")
m_bool.data += True
center = SkyCoord(0, 0, frame="galactic", unit="deg")
region = CircleSkyRegion(center=center, radius=1 * u.deg)
spec_int = m_int.get_spectrum(region=region)
assert spec_int.data.dtype == np.dtype("int")
assert_allclose(spec_int.data.squeeze(), [13, 13, 13])
spec_bool = m_bool.get_spectrum(region=region, func=np.any)
assert spec_bool.data.dtype == np.dtype("bool")
assert_allclose(spec_bool.data.squeeze(), [1, 1, 1])
def test_regions_sky_has_wcs(self):
from regions import (CircleSkyRegion, RectanglePixelRegion,
CirclePixelRegion, EllipsePixelRegion)
# Mimic interactive region (before)
self.imviz._apply_interactive_region('bqplot:circle', (1.5, 2.5),
(3.6, 4.6))
sky = SkyCoord(ra=337.5202808, dec=-20.833333059999998, unit='deg')
my_reg_sky = CircleSkyRegion(sky, Angle(0.5, u.arcsec))
self.imviz.load_static_regions({'my_reg_sky_1': my_reg_sky})
# Mimic interactive regions (after)
self.imviz._apply_interactive_region('bqplot:ellipse', (-2, 0),
(5, 4.5))
self.imviz._apply_interactive_region('bqplot:rectangle', (0, 0),
(10, 10))
# Check interactive regions. We do not check if the translation is correct,
# that check hopefully is already done in glue-astronomy.
# Apparently, static region ate up one number...
subsets = self.imviz.get_interactive_regions()
assert list(subsets.keys()) == ['Subset 1', 'Subset 3',
'Subset 4'], subsets
assert isinstance(subsets['Subset 1'], CirclePixelRegion)
assert isinstance(subsets['Subset 3'], EllipsePixelRegion)
assert isinstance(subsets['Subset 4'], RectanglePixelRegion)
# Check static region
self.verify_region_loaded('my_reg_sky_1')
def create_data(input_dir, dataset_config, exclusion_map=None):
telescope = dataset_config['telescope']
on_region_radius = dataset_config['on_radius']
energy_bins = dataset_config['e_reco_bins']
containment = dataset_config['containment_correction']
ds = DataStore.from_dir(os.path.join(input_dir, telescope))
observations = ds.get_observations(ds.obs_table['OBS_ID'].data)
t_obs = sum([o.observation_live_time_duration for o in observations])
# from IPython import embed; embed()
print(f'Total obstime for {telescope} is {t_obs.to("h")}')
source_position = dataset_config['source_position']
on_region = CircleSkyRegion(center=source_position,
radius=on_region_radius)
print('Estimating Background')
bkg_estimate = ReflectedRegionsBackgroundEstimator(
observations=observations,
on_region=on_region,
exclusion_mask=exclusion_map)
bkg_estimate.run()
print('Extracting Count Spectra')
extract = SpectrumExtraction(
observations=observations,
bkg_estimate=bkg_estimate.result,
e_true=energy_bins,
e_reco=energy_bins,
containment_correction=containment,
use_recommended_erange=False, # TODO this might have to be checked.
)
extract.run()
return extract
def test_filter_events(observation):
custom_filter = {
"type": "custom",
"opts": {
"parameter": "ENERGY",
"band": Quantity([0.8 * u.TeV, 10.0 * u.TeV])
},
}
target_position = SkyCoord(ra=229.2, dec=-58.3, unit="deg", frame="icrs")
region_radius = Angle("0.2 deg")
region = CircleSkyRegion(center=target_position, radius=region_radius)
circular_region_filter = {
"type": "circular_region",
"opts": {
"region": region
}
}
time_filter = Time([53090.12, 53090.13], format="mjd", scale="tt")
obs_filter = ObservationFilter(
event_filters=[custom_filter, circular_region_filter],
time_filter=time_filter)
events = observation.events
filtered_events = obs_filter.filter_events(events)
assert np.all((filtered_events.energy >= 0.8 * u.TeV)
& (filtered_events.energy < 10.0 * u.TeV))
assert np.all((filtered_events.time >= time_filter[0])
& (filtered_events.time < time_filter[1]))
assert np.all(
region.center.separation(filtered_events.radec) < region_radius)
def run(dataset, correlation_radius, sigma, negative):
estimator_excess = ExcessMapEstimator(
correlation_radius=correlation_radius,
n_sigma=1,
n_sigma_ul=3,
selection_optional=None,
energy_edges=energy_edges,
)
result = estimator_excess.run(dataset)
sources = find_peaks(
result["sqrt_ts"],
threshold=sigma,
min_distance=correlation_radius,
)
if negative is True:
result["sqrt_ts"].data = -result["sqrt_ts"].data
sources.vstack(
find_peaks(
result["sqrt_ts"],
threshold=sigma,
min_distance=correlation_radius,
)
)
regions = []
for source in sources:
skydir = SkyCoord(source["ra"], source["dec"], unit="deg", frame="icrs")
if dataset.counts.geom.to_image().contains(skydir):
regions.append(CircleSkyRegion(skydir, 0.2 * u.deg))
return dataset.counts.geom.to_image().region_mask(regions=regions, inside=False), result
def spec_extraction():
data_store = DataStore.from_dir("$GAMMAPY_DATA/hess-dl3-dr1/")
obs_ids = [23523, 23526]
observations = data_store.get_observations(obs_ids)
target_position = SkyCoord(ra=83.63308, dec=22.01450, unit="deg")
on_region_radius = Angle("0.11 deg")
on_region = CircleSkyRegion(center=target_position,
radius=on_region_radius)
bkg_estimator = ReflectedRegionsBackgroundEstimator(
on_region=on_region, observations=observations)
bkg_estimator.run()
e_reco = EnergyBounds.equal_log_spacing(0.2, 100, 50,
unit="TeV") # fine binning
e_true = EnergyBounds.equal_log_spacing(0.05, 100, 200, unit="TeV")
extraction = SpectrumExtraction(
observations=observations,
bkg_estimate=bkg_estimator.result,
containment_correction=False,
e_reco=e_reco,
e_true=e_true,
)
extraction.run()
extraction.compute_energy_threshold(method_lo="area_max",
area_percent_lo=10.0)
return extraction
def spectrum_dataset_gc():
e_reco = np.logspace(0, 2, 5) * u.TeV
e_true = np.logspace(-0.5, 2, 11) * u.TeV
pos = SkyCoord(0.0, 0.0, unit="deg", frame="galactic")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
return SpectrumDataset.create(e_reco, e_true, region=region)
def __init__(self, lambda_true: u.quantity.Quantity, index_true: float,
normalization_true: u.quantity.Quantity,
livetime: u.quantity.Quantity, pointing_galactic: dict,
e_reco_binning: dict, on_region_radius: str, irf_file: str):
normalization_true = normalization_true.to("cm-2 s-1 TeV-1")
self.lambda_true = lambda_true
self.index_true = index_true
self.normalization_true = normalization_true
pointing_l = pointing_galactic["pointing_l"]
pointing_b = pointing_galactic["pointing_b"]
pointing = SkyCoord(pointing_l,
pointing_b,
unit="deg",
frame="galactic")
e_reco_min = u.Quantity(e_reco_binning["e_reco_min"]).to("TeV").value
e_reco_max = u.Quantity(e_reco_binning["e_reco_max"]).to("TeV").value
n_e_reco = e_reco_binning["n_e_reco"]
self.energy_axis = np.logspace(np.log10(e_reco_min),
np.log10(e_reco_max), n_e_reco) * u.TeV
on_region_radius = Angle(on_region_radius)
self.on_region = CircleSkyRegion(center=pointing,
radius=on_region_radius)
irfs = load_cta_irfs(irf_file)
self.obs = Observation.create(pointing=pointing,
livetime=livetime,
irfs=irfs)
def create_stacked_dataset(analysis):
# source_pos = SkyCoord.from_name("MSH 15-52")
source_pos = SkyCoord(228.32, -59.08, unit="deg")
# get the geom that we use
geom = analysis.datasets[0].counts.geom
energy_axis = analysis.datasets[0].counts.geom.axes["energy"]
geom_image = geom.to_image().to_cube([energy_axis.squash()])
# Make the exclusion mask
regions = CircleSkyRegion(center=source_pos, radius=0.3 * u.deg)
exclusion_mask = geom_image.region_mask([regions], inside=False)
ring_maker = RingBackgroundMaker(r_in="0.5 deg",
width="0.3 deg",
exclusion_mask=exclusion_mask)
# Creation of the MapDatasetOnOff
energy_axis_true = analysis.datasets[0].exposure.geom.axes["energy_true"]
stacked_on_off = MapDatasetOnOff.create(geom=geom_image,
energy_axis_true=energy_axis_true,
name="stacked")
for dataset in analysis.datasets:
# Ring extracting makes sense only for 2D analysis
dataset_on_off = ring_maker.run(dataset.to_image())
stacked_on_off.stack(dataset_on_off)
return stacked_on_off
def test_reflected_bkg_maker_no_off(reflected_bkg_maker, observations, caplog):
pos = SkyCoord(83.6333313, 21.51444435, unit="deg", frame="icrs")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
maker = SpectrumDatasetMaker(selection=["counts"])
datasets = []
e_reco = MapAxis.from_edges(np.logspace(0, 2, 5) * u.TeV, name="energy")
e_true = MapAxis.from_edges(np.logspace(-0.5, 2, 11) * u.TeV,
name="energy_true")
geom = RegionGeom.create(region=region, axes=[e_reco])
dataset_empty = SpectrumDataset.create(geom=geom, energy_axis_true=e_true)
for obs in observations:
dataset = maker.run(dataset_empty, obs)
dataset_on_off = reflected_bkg_maker.run(dataset, obs)
datasets.append(dataset_on_off)
assert datasets[0].counts_off is None
assert_allclose(datasets[0].acceptance_off, 0)
assert_allclose(datasets[0].mask_safe.data, False)
assert "WARNING" in [record.levelname for record in caplog.records]
message1 = f"ReflectedRegionsBackgroundMaker failed. " \
f"No OFF region found outside exclusion mask for {datasets[0].name}."
message2 = f"ReflectedRegionsBackgroundMaker failed. " \
f"Setting {datasets[0].name} mask to False."
assert message1 in [record.message for record in caplog.records]
assert message2 in [record.message for record in caplog.records]
def test_mapdatasetonoff_to_spectrum_dataset(images):
e_reco = MapAxis.from_bounds(0.1,
10.0,
1,
name="energy",
unit=u.TeV,
interp="log")
new_images = dict()
for key, image in images.items():
new_images[key] = Map.from_geom(image.geom.to_cube([e_reco]),
data=image.data[np.newaxis, :, :])
dataset = get_map_dataset_onoff(new_images)
gti = GTI.create([0 * u.s], [1 * u.h],
reference_time="2010-01-01T00:00:00")
dataset.gti = gti
on_region = CircleSkyRegion(center=dataset.counts.geom.center_skydir,
radius=0.1 * u.deg)
spectrum_dataset = dataset.to_spectrum_dataset(on_region)
assert spectrum_dataset.counts.data[0] == 8
assert spectrum_dataset.data_shape == (1, 1, 1)
assert spectrum_dataset.counts_off.data[0] == 33914
assert_allclose(spectrum_dataset.alpha.data[0], 0.0002143, atol=1e-7)
excess_map = new_images["counts"] - new_images["background"]
excess_true = excess_map.get_spectrum(on_region, np.sum).data[0]
excess = spectrum_dataset.excess.data[0]
assert_allclose(excess, excess_true, atol=1e-6)
assert spectrum_dataset.name != dataset.name
def test_targetsummary(data_manager):
pos = SkyCoord(83.63 * u.deg, 22.01 * u.deg, frame='icrs')
on_size = 0.3 * u.deg
on_region = CircleSkyRegion(pos, on_size)
target = Target(on_region, name='Test Target', obs_id=[23523, 23592])
assert 'Target' in str(target)
def spectrum_dataset_gc():
e_reco = MapAxis.from_edges(np.logspace(0, 2, 5) * u.TeV, name="energy")
e_true = MapAxis.from_edges(np.logspace(-0.5, 2, 11) * u.TeV, name="energy_true")
pos = SkyCoord(0.0, 0.0, unit="deg", frame="galactic")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
return SpectrumDataset.create(e_reco, e_true, region=region)
def spectrum_dataset_crab_fine():
e_true = np.logspace(-2, 2.5, 109) * u.TeV
e_reco = np.logspace(-2, 2, 73) * u.TeV
pos = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
radius = Angle(0.11, "deg")
region = CircleSkyRegion(pos, radius)
return SpectrumDataset.create(e_reco, e_true, region=region)
def build_exclusion_mask_from_catalog(geom, catalog="gamma-cat", min_radius="0.3 deg"):
"""Build exclusion mask from a given geometry and input catalog.
Parameters:
-----------
geom : `~gammapy.maps.WcsGeom`
the input WCS geometry
catalog : str or `~gammapy.catalog.SourceCatalog`
the input catalog to use. Default is 'gamma-cat'.
min_radius : `~astropy.coordinates.Angle`
the minimum radius of the circular exclusion radius around a source. Default is 0.3 deg.
"""
if catalog is None:
catalog = "gamma-cat"
if isinstance(catalog, str):
try:
catalog = CATALOG_REGISTRY.get_cls(catalog)()
except KeyError:
raise ValueError(f"Unknown catalog name. Available catalogs are :\n{CATALOG_REGISTRY}")
exclusion_regions = []
min_radius = Angle(min_radius)
is_in_geom = geom.contains(catalog.positions)
src_in_geom = catalog[is_in_geom].to_models()
for src in src_in_geom.to_regions():
if not isinstance(src, PointSkyRegion):
radius += src.width
exclusion_regions.append(CircleSkyRegion(src.center, min_radius))
return ~geom.region_mask(exclusion_regions)
def exclusion_mask():
"""Example mask for testing."""
pos = SkyCoord(83.63, 22.01, unit="deg", frame="icrs")
exclusion_region = CircleSkyRegion(pos, Angle(0.3, "deg"))
geom = WcsGeom.create(skydir=pos, binsz=0.02, width=10.0)
mask = geom.region_mask([exclusion_region], inside=False)
return WcsNDMap(geom, data=mask)
def test_compute_flux_spatial():
center = SkyCoord("0 deg", "0 deg", frame="galactic")
region = CircleSkyRegion(center=center, radius=0.1 * u.deg)
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(".1 TeV",
"10 TeV",
nbin=nbin,
name="energy_true")
spectral_model = ConstantSpectralModel()
spatial_model = PointSpatialModel(lon_0=0 * u.deg,
lat_0=0 * u.deg,
frame="galactic")
models = SkyModel(spectral_model=spectral_model,
spatial_model=spatial_model)
model = Models(models)
exposure_region = RegionNDMap.create(region,
axes=[energy_axis_true],
binsz_wcs="0.01deg")
exposure_region.data += 1.0
exposure_region.unit = "m2 s"
geom = RegionGeom(region, axes=[energy_axis_true], binsz_wcs="0.01deg")
psf = PSFKernel.from_gauss(geom.to_wcs_geom(), sigma="0.1 deg")
evaluator = MapEvaluator(model=model[0], exposure=exposure_region, psf=psf)
flux = evaluator.compute_flux_spatial()
g = Gauss2DPDF(0.1)
reference = g.containment_fraction(0.1)
assert_allclose(flux.value, reference, rtol=0.003)
def test_compute_flux_spatial_no_psf():
# check that spatial integration is not performed in the absence of a psf
center = SkyCoord("0 deg", "0 deg", frame="galactic")
region = CircleSkyRegion(center=center, radius=0.1 * u.deg)
nbin = 2
energy_axis_true = MapAxis.from_energy_bounds(".1 TeV",
"10 TeV",
nbin=nbin,
name="energy_true")
spectral_model = ConstantSpectralModel()
spatial_model = GaussianSpatialModel(lon_0=0 * u.deg,
lat_0=0 * u.deg,
frame="galactic",
sigma="0.1 deg")
models = SkyModel(spectral_model=spectral_model,
spatial_model=spatial_model)
model = Models(models)
exposure_region = RegionNDMap.create(region, axes=[energy_axis_true])
exposure_region.data += 1.0
exposure_region.unit = "m2 s"
evaluator = MapEvaluator(model=model[0], exposure=exposure_region)
flux = evaluator.compute_flux_spatial()
assert_allclose(flux, 1.0)
def target():
"""An example Target for tests."""
center = SkyCoord(83.63, 22.01, unit='deg', frame='icrs')
radius = Angle('0.11 deg')
on_region = CircleSkyRegion(center, radius)
target = Target(on_region)
return target
def get_map_dataset(sky_model, geom, geom_etrue, edisp=True, **kwargs):
"""Returns a MapDatasets"""
# define background model
m = Map.from_geom(geom)
m.quantity = 0.2 * np.ones(m.data.shape)
background_model = BackgroundModel(m)
psf = get_psf(geom_etrue)
exposure = get_exposure(geom_etrue)
if edisp:
# define energy dispersion
e_true = geom_etrue.get_axis_by_name("energy").edges
e_reco = geom.get_axis_by_name("energy").edges
edisp = EnergyDispersion.from_diagonal_response(e_true=e_true,
e_reco=e_reco)
else:
edisp = None
# define fit mask
center = sky_model.spatial_model.position
circle = CircleSkyRegion(center=center, radius=1 * u.deg)
mask_fit = background_model.map.geom.region_mask([circle])
mask_fit = Map.from_geom(geom, data=mask_fit)
return MapDataset(model=sky_model,
exposure=exposure,
background_model=background_model,
psf=psf,
edisp=edisp,
mask_fit=mask_fit,
**kwargs)
def spec_extraction():
data_store = DataStore.from_dir(
'$GAMMAPY_EXTRA/datasets/hess-crab4-hd-hap-prod2/')
obs_ids = [23523, 23526]
obs_list = data_store.obs_list(obs_ids)
target_position = SkyCoord(ra=83.63308, dec=22.01450, unit='deg')
on_region_radius = Angle('0.11 deg')
on_region = CircleSkyRegion(center=target_position,
radius=on_region_radius)
target = Target(on_region=on_region, name='Crab', tag='ana_crab')
exclusion_file = '$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits'
allsky_mask = SkyImage.read(exclusion_file)
exclusion_mask = allsky_mask.cutout(
position=target.on_region.center,
size=Angle('6 deg'),
)
bkg_estimator = ReflectedRegionsBackgroundEstimator(
on_region=on_region, obs_list=obs_list, exclusion_mask=exclusion_mask)
bkg_estimator.run()
e_reco = EnergyBounds.equal_log_spacing(0.2, 100, 50,
unit='TeV') # fine binning
e_true = EnergyBounds.equal_log_spacing(0.05, 100, 200, unit='TeV')
extraction = SpectrumExtraction(obs_list=obs_list,
bkg_estimate=bkg_estimator.result,
containment_correction=False,
e_reco=e_reco,
e_true=e_true)
extraction.run()
extraction.compute_energy_threshold(method_lo='area_max',
area_percent_lo=10.0)
return extraction
def test_exclusion_region(tmp_path):
config = get_example_config("1d")
analysis = Analysis(config)
exclusion_region = CircleSkyRegion(center=SkyCoord("85d 23d"),
radius=1 * u.deg)
exclusion_mask = Map.create(npix=(150, 150),
binsz=0.05,
skydir=SkyCoord("83d 22d"))
mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)
exclusion_mask.data = mask.astype(int)
filename = tmp_path / "exclusion.fits"
exclusion_mask.write(filename)
config.datasets.background.method = "reflected"
config.datasets.background.exclusion = filename
analysis.get_observations()
analysis.get_datasets()
assert len(analysis.datasets) == 2
config = get_example_config("3d")
analysis = Analysis(config)
analysis.get_observations()
analysis.get_datasets()
geom = analysis.datasets[0]._geom
exclusion = WcsNDMap.from_geom(geom)
exclusion.data = geom.region_mask([exclusion_region],
inside=False).astype(int)
filename = tmp_path / "exclusion3d.fits"
exclusion.write(filename)
config.datasets.background.exclusion = filename
analysis.get_datasets()
assert len(analysis.datasets) == 1
from astropy.coordinates import SkyCoord, Angle
from regions import (
make_example_dataset,
CircleSkyRegion,
)
import matplotlib.pyplot as plt
config = dict(crpix=(18, 9), cdelt=(-10, 10), shape=(18, 36))
dataset = make_example_dataset(data='simulated', config=config)
wcs = dataset.wcs
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
ax.imshow(dataset.image.data, cmap='gray', vmin=0, vmax=1,
interpolation='nearest', origin='lower')
for source in dataset.source_table:
# Plot a sky circle around each source
center = SkyCoord(source['GLON'], source['GLAT'], unit='deg', frame='galactic')
radius = Angle(20, 'deg')
region = CircleSkyRegion(center=center, radius=radius)
pix_region = region.to_pixel(wcs=wcs)
pix_region.plot(ax=ax, edgecolor='yellow', facecolor='yellow', alpha=0.5, lw=3)
plt.show()
# load example dataset to get skymap
config = dict(crval=(0, 0),
crpix=(180, 90),
cdelt=(-1, 1),
shape=(180, 360))
dataset = make_example_dataset(data='simulated', config=config)
wcs = dataset.wcs
# remove sources
dataset.image.data = np.zeros_like(dataset.image.data)
# define 2 sky circles
circle1 = CircleSkyRegion(
center=SkyCoord(20, 0, unit='deg', frame='galactic'),
radius=Angle('30 deg')
)
circle2 = CircleSkyRegion(
center=SkyCoord(50, 45, unit='deg', frame='galactic'),
radius=Angle('30 deg'),
)
# define skycoords
lon = np.arange(-180, 181, 10)
lat = np.arange(-90, 91, 10)
coords = np.array(np.meshgrid(lon, lat)).T.reshape(-1, 2)
skycoords = SkyCoord(coords, unit='deg', frame='galactic')
# get events in AND and XOR
compound_and = circle1 & circle2
"""Example how to compute and plot reflected regions."""
from astropy.coordinates import SkyCoord, Angle
from regions import CircleSkyRegion
from gammapy.image import SkyMask
from gammapy.background import find_reflected_regions
mask = SkyMask.empty(name='Exclusion Mask', nxpix=801, nypix=701, binsz=0.01,
coordsys='CEL', xref=83.2, yref=22.7)
mask.fill_random_circles(n=8, min_rad=30, max_rad=80)
pos = SkyCoord(80.2, 23.5, unit='deg')
radius = Angle(0.4, 'deg')
test_region = CircleSkyRegion(pos, radius)
center = SkyCoord(82.8, 22.5, unit='deg')
regions = find_reflected_regions(test_region, center, mask)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 5), dpi=80)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=mask.wcs)
mask.plot(ax, fig)
for reg in regions:
reg.plot(ax, facecolor='red')
test_region.plot(ax, facecolor='blue')
plt.show()
from regions import (
make_example_dataset,
CircleSkyRegion,
)
import matplotlib.pyplot as plt
config = dict(crpix=(18, 9), cdelt=(-10, 10), shape=(18, 36))
dataset = make_example_dataset(data='simulated', config=config)
wcs = dataset.wcs
fig = plt.figure()
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=wcs)
ax.set_xlim(-0.5, dataset.config['shape'][1] - 0.5)
ax.set_ylim(-0.5, dataset.config['shape'][0] - 0.5)
ax.imshow(dataset.image.data, cmap='gray', vmin=0, vmax=1,
interpolation='nearest', origin='lower')
for source in dataset.source_table:
# Plot a sky circle around each source
center = SkyCoord(source['GLON'], source['GLAT'], unit='deg', frame='galactic')
radius = Angle(20, 'deg')
region = CircleSkyRegion(center=center, radius=radius)
pix_region = region.to_pixel(wcs=wcs)
pix_region.plot(ax, edgecolor='green', facecolor='none', alpha=0.8, lw=3)
region.plot(ax, edgecolor='yellow', facecolor='none', alpha=0.8, lw=3)
plt.show()
"""Example how to compute and plot reflected regions."""
from astropy.coordinates import SkyCoord, Angle
from regions import CircleSkyRegion
from gammapy.image import SkyMask
from gammapy.background import find_reflected_regions
mask = SkyMask.empty(name='Exclusion Mask', nxpix=801, nypix=701, binsz=0.01,
coordsys='CEL', xref=83.2, yref=22.7)
mask.fill_random_circles(n=8, min_rad=30, max_rad=80)
pos = SkyCoord(80.2, 23.5, unit='deg')
radius = Angle(0.4, 'deg')
test_region = CircleSkyRegion(pos, radius)
center = SkyCoord(82.8, 22.5, unit='deg')
regions = find_reflected_regions(test_region, center, mask)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 5), dpi=80)
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=mask.wcs)
mask.plot(ax, fig)
for reg in regions:
reg.to_pixel(mask.wcs).plot(ax, facecolor='red')
test_region.to_pixel(mask.wcs).plot(ax, facecolor='blue')
plt.show()