def plot_observations_positions(ax=None, **kwargs):
from gammapy.data import ObservationTable
from gammapy.spectrum import SpectrumExtraction
kwargs.setdefault('marker', 'x')
kwargs.setdefault('s', 150)
ax = plot_exclusion_mask() if ax is None else ax
obs_table = ObservationTable.read(SpectrumExtraction.OBSTABLE_FILE)
ra = obs_table['RA_PNT']
dec = obs_table['DEC_PNT']
ax.scatter(ra, dec, transform=ax.get_transform('icrs'), **kwargs)
def get_observations(self):
"""Fetch observations from the data store according to criteria defined in the configuration."""
self.config.validate()
log.info("Fetching observations.")
datastore_path = make_path(self.settings["observations"]["datastore"])
if datastore_path.is_file():
self.datastore = DataStore().from_file(datastore_path)
elif datastore_path.is_dir():
self.datastore = DataStore().from_dir(datastore_path)
else:
raise FileNotFoundError(f"Datastore {datastore_path} not found.")
ids = []
selection = dict()
for criteria in self.settings["observations"]["filters"]:
selected_obs = ObservationTable()
# TODO: Reduce significantly the code.
# This block would be handled by datastore.obs_table.select_observations
selection["type"] = criteria["filter_type"]
for key, val in criteria.items():
if key in ["lon", "lat", "radius", "border"]:
val = Angle(val)
selection[key] = val
if selection["type"] == "angle_box":
selection["type"] = "par_box"
selection["value_range"] = Angle(criteria["value_range"])
if selection["type"] == "sky_circle" or selection["type"].endswith(
"_box"):
selected_obs = self.datastore.obs_table.select_observations(
selection)
if selection["type"] == "par_value":
mask = (self.datastore.obs_table[criteria["variable"]] ==
criteria["value_param"])
selected_obs = self.datastore.obs_table[mask]
if selection["type"] == "ids":
obs_list = self.datastore.get_observations(criteria["obs_ids"])
selected_obs["OBS_ID"] = [obs.obs_id for obs in obs_list.list]
if selection["type"] == "all":
obs_list = self.datastore.get_observations()
selected_obs["OBS_ID"] = [obs.obs_id for obs in obs_list.list]
if len(selected_obs):
if "exclude" in criteria and criteria["exclude"]:
exclude = selected_obs["OBS_ID"].tolist()
selection = np.isin(ids, exclude)
ids = list(np.array(ids)[~selection])
else:
ids.extend(selected_obs["OBS_ID"].tolist())
self.observations = self.datastore.get_observations(ids,
skip_missing=True)
for obs in self.observations.list:
log.info(obs)
from astropy.tests.helper import assert_quantity_allclose
from gammapy.data import ObservationTable
from gammapy.datasets import gammapy_extra
from gammapy.spectrum import SpectrumFit, SpectrumObservationGrouping, group_obs_table
obs_table_file = gammapy_extra.filename(
'datasets/hess-crab4_pha/observation_table.fits')
obs_table = ObservationTable.read(obs_table_file)
fit = SpectrumFit.from_observation_table(obs_table)
fit.model = 'PL'
fit.energy_threshold_low = '1 TeV'
fit.energy_threshold_high = '10 TeV'
fit.run(method='sherpa')
#Use each obs in one group
obs_table1 = group_obs_table(obs_table, eff_range=[90, 95], n_eff_bin=5)
obs_table1.write('grouped_table1_debug.fits', overwrite=True)
grouping = SpectrumObservationGrouping(obs_table1)
grouping.run()
fit_band2 = SpectrumFit.from_observation_table(grouping.stacked_obs_table)
fit_band2.model = 'PL'
fit_band2.energy_threshold_low = '1 TeV'
fit_band2.energy_threshold_high = '10 TeV'
fit_band2.run(method='sherpa')
assert_quantity_allclose(fit.result.parameters["index"],
# Setup the logger
import logging
logging.basicConfig()
log = logging.getLogger('gammapy.spectrum')
log.setLevel(logging.WARNING)
datastore = DataStore.from_dir($HESS_DATA)
datastore = DataStore.from_dir("$HESS_DATA")
datastore
datastore.obs
print datastore.obs
print datastore.obs_list
print datastore.obs_table
print datastore.DEFAULT_NAME
print datastore.DEFAULT_OBS_TABLE
from gammapy.data import DataStore, ObservationTable
ObservationTable.select_observations(datastore)
get_ipython().magic(u'pinfo ObservationTable.select_observations')
get_ipython().magic(u'pinfo datastore.obs')
datastore.obs(22222)
EXCLUSION_FILE = '$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits'
allsky_mask = SkyImage.read(EXCLUSION_FILE)
exclusion_mask = allsky_mask.cutout(
position=on_region.center,
size=Angle('6 deg'),
)
obs_id = data_store.obs_table['OBS_ID'].data
obs_id = datastore.obs_table['OBS_ID'].data
obs_id
print("Use observations {}".format(obs_id))
datastore.obs_table['OBS_ID'].data
datastore.obs_table['OBS_ID']
from astropy.tests.helper import assert_quantity_allclose
from gammapy.data import ObservationTable
from gammapy.datasets import gammapy_extra
from gammapy.spectrum import SpectrumFit, SpectrumGrouping, group_obs_table
obs_table_file = gammapy_extra.filename(
'datasets/hess-crab4_pha/observation_table.fits')
obs_table = ObservationTable.read(obs_table_file)
fit = SpectrumFit.from_observation_table(obs_table)
fit.model = 'PL'
fit.energy_threshold_low = '1 TeV'
fit.energy_threshold_high = '10 TeV'
fit.run(method='sherpa')
#Use each obs in one group
obs_table1 = group_obs_table(obs_table, eff_range=[90, 95], n_eff_bin=5)
obs_table1.write('grouped_table1_debug.fits', overwrite=True)
grouping = SpectrumGrouping(obs_table1)
grouping.run()
fit_band2 = SpectrumFit.from_observation_table(grouping.stacked_obs_table)
fit_band2.model = 'PL'
fit_band2.energy_threshold_low = '1 TeV'
fit_band2.energy_threshold_high = '10 TeV'
fit_band2.run(method='sherpa')
assert_quantity_allclose(fit.result.parameters["index"],
from gammapy.data import ObservationTable
intable = ObservationTable.read('observation_table.fits')
for row in intable:
infile = row['PHAFILE']
phafile = infile.split('/')[-1]
outfile = '$GAMMAPY_EXTRA/datasets/hess-crab4_pha/ogip_data/{}'.format(phafile)
row['PHAFILE'] = outfile
intable.write('observation_table.fits', overwrite=True)