def __init__(self, polar_root_file, reference_time=0., rsp_file=None):
"""
container class that converts raw POLAR root data into useful python
variables
:param polar_root_file: path to polar event file
:param reference_time: reference time of the events (tunix?)
:param rsp_file: path to rsp file
"""
# open the event file
with open_ROOT_file(polar_root_file) as f:
tmp = tree_to_ndarray(f.Get('polar_out'))
# extract the pedestal corrected ADC channels
# which are non-integer and possibly
# less than zero
pha = tmp['Energy']
# non-zero ADC channels are invalid
idx = pha >= 0
pha = pha[idx]
# get the dead time fraction
self._dead_time_fraction = tmp['dead_ratio'][idx]
# get the arrival time, in tunix of the events
self._time = tmp['tunix'][idx] - reference_time
# digitize the ADC channels into bins
# these bins are preliminary
with open_ROOT_file(rsp_file) as f:
matrix = th2_to_arrays(f.Get('rsp'))[-1]
ebounds = th2_to_arrays(f.Get('EM_bounds'))[-1]
mc_low = th2_to_arrays(f.Get('ER_low'))[-1]
mc_high = th2_to_arrays(f.Get('ER_high'))[-1]
mc_energies = np.append(mc_low, mc_high[-1])
# build the POLAR response
self._rsp = InstrumentResponse(matrix=matrix,
ebounds=ebounds,
monte_carlo_energies=mc_energies)
# bin the ADC channels
self._binned_pha = np.digitize(pha, ebounds)
def __init__(self, polar_root_file, reference_time=0., rsp_file=None):
"""
container class that converts raw POLAR root data into useful python
variables
:param polar_root_file: path to polar event file
:param reference_time: reference time of the events (tunix?)
:param rsp_file: path to rsp file
"""
# open the event file
with open_ROOT_file(polar_root_file) as f:
tmp = tree_to_ndarray(f.Get('polar_out'))
# extract the pedestal corrected ADC channels
# which are non-integer and possibly
# less than zero
pha = tmp['Energy']
# non-zero ADC channels are invalid
idx = pha >= 0
pha = pha[idx]
# get the dead time fraction
self._dead_time_fraction = tmp['dead_ratio'][idx]
# get the arrival time, in tunix of the events
self._time = tmp['tunix'][idx] - reference_time
# digitize the ADC channels into bins
# these bins are preliminary
with open_ROOT_file(rsp_file) as f:
matrix = th2_to_arrays(f.Get('rsp'))[-1]
ebounds = th2_to_arrays(f.Get('EM_bounds'))[-1]
mc_low = th2_to_arrays(f.Get('ER_low'))[-1]
mc_high = th2_to_arrays(f.Get('ER_high'))[-1]
mc_energies = np.append(mc_low, mc_high[-1])
# build the POLAR response
self._rsp = InstrumentResponse(matrix=matrix,
ebounds=ebounds,
monte_carlo_energies=mc_energies)
# bin the ADC channels
self._binned_pha = np.digitize(pha, ebounds)
def __init__(self, root_file, run_name):
self._run_name = run_name
# Read the data from the ROOT file
with open_ROOT_file(root_file) as f:
# Read first the TTrees as pandas DataFrame
self._data_on = tree_to_ndarray(
f.Get(run_name + '/data_on')) # type: np.ndarray
self._data_off = tree_to_ndarray(
f.Get(run_name + '/data_off')) # type: np.ndarray
self._tRunSummary = np.squeeze(
tree_to_ndarray(f.Get(run_name +
'/tRunSummary'))) # type: np.ndarray
# Now read the histogram
self._log_recon_energies, \
self._log_mc_energies, \
self._hMigration = th2_to_arrays(f.Get(run_name + "/hMigration"))
# Transform energies to keV (they are in TeV)
self._log_recon_energies += 9
self._log_mc_energies += 9
# Compute bin centers and bin width of the Monte Carlo energy bins
self._dE = (10**self._log_mc_energies[1:] -
10**self._log_mc_energies[:-1])
self._mc_energies_c = (10**self._log_mc_energies[1:] +
10**self._log_mc_energies[:-1]) / 2.0
self._recon_energies_c = (10**self._log_recon_energies[1:] +
10**self._log_recon_energies[:-1]) / 2.0
self._n_chan = self._log_recon_energies.shape[0] - 1
# Remove all nans by substituting them with 0.0
idx = np.isfinite(self._hMigration)
self._hMigration[~idx] = 0.0
# Read the TGraph
tgraph = f.Get(run_name + "/gMeanEffectiveArea")
self._log_eff_area_energies, self._eff_area = tgraph_to_arrays(
tgraph)
# Transform the effective area to cm2 (it is in m2 in the file)
self._eff_area *= 1e8 #This value is for VEGAS, because VEGAS effective area is in cm2
# Transform energies to keV
self._log_eff_area_energies += 9
# Now use the effective area provided in the file to renormalize the migration matrix appropriately
self._renorm_hMigration()
# Exposure is tOn*(1-tDeadtimeFrac)
self._exposure = float(1 -
self._tRunSummary['DeadTimeFracOn']) * float(
self._tRunSummary['tOn'])
# Members for generating OGIP equivalents
self._mission = "VERITAS"
self._instrument = "VERITAS"
# Now bin the counts
self._counts, _ = self._bin_counts_log(self._data_on['Erec'] * 1e9,
self._log_recon_energies)
# Now bin the background counts
self._bkg_counts, _ = self._bin_counts_log(
self._data_off['Erec'] * 1e9, self._log_recon_energies)
print(
"Read a %s x %s matrix, spectrum has %s bins, eff. area has %s elements"
% (self._hMigration.shape[0], self._hMigration.shape[1],
self._counts.shape[0], self._eff_area.shape[0]))
# Read in the background renormalization (ratio between source and background region)
self._bkg_renorm = float(self._tRunSummary['OffNorm'])
self._start_energy = np.log10(175E6) #175 GeV in keV
self._end_energy = np.log10(18E9) #18 TeV in keV
self._first_chan = (
np.abs(self._log_recon_energies -
self._start_energy)).argmin() #Values used by Giacomo 61
self._last_chan = (
np.abs(self._log_recon_energies -
self._end_energy)).argmin() #Values used by Giacomo 110
def __init__(self, root_file, run_name):
self._run_name = run_name
# Read the data from the ROOT file
with open_ROOT_file(root_file) as f:
# Read first the TTrees as pandas DataFrame
self._data_on = tree_to_ndarray(f.Get(run_name+'/data_on')) # type: np.ndarray
self._data_off = tree_to_ndarray(f.Get(run_name+'/data_off')) # type: np.ndarray
self._tRunSummary = np.squeeze(tree_to_ndarray(f.Get(run_name+'/tRunSummary'))) # type: np.ndarray
# Now read the histogram
self._log_recon_energies, \
self._log_mc_energies, \
self._hMigration = th2_to_arrays(f.Get(run_name + "/hMigration"))
# Transform energies to keV (they are in TeV)
self._log_recon_energies += 9
self._log_mc_energies += 9
# Compute bin centers and bin width of the Monte Carlo energy bins
self._dE = (10 ** self._log_mc_energies[1:] - 10 ** self._log_mc_energies[:-1])
self._mc_energies_c = (10 ** self._log_mc_energies[1:] + 10 ** self._log_mc_energies[:-1]) / 2.0
self._recon_energies_c = (10 ** self._log_recon_energies[1:] + 10 ** self._log_recon_energies[:-1]) / 2.0
self._n_chan = self._log_recon_energies.shape[0] - 1
# Remove all nans by substituting them with 0.0
idx = np.isfinite(self._hMigration)
self._hMigration[~idx] = 0.0
# Read the TGraph
tgraph = f.Get(run_name + "/gMeanEffectiveArea")
self._log_eff_area_energies, self._eff_area = tgraph_to_arrays(tgraph)
# Transform the effective area to cm2 (it is in m2 in the file)
self._eff_area *= 1e8 #This value is for VEGAS, because VEGAS effective area is in cm2
# Transform energies to keV
self._log_eff_area_energies += 9
# Now use the effective area provided in the file to renormalize the migration matrix appropriately
self._renorm_hMigration()
# Exposure is tOn*(1-tDeadtimeFrac)
self._exposure = float(1 - self._tRunSummary['DeadTimeFracOn']) * float(self._tRunSummary['tOn'])
# Members for generating OGIP equivalents
self._mission = "VERITAS"
self._instrument = "VERITAS"
# Now bin the counts
self._counts, _ = self._bin_counts_log(self._data_on['Erec'] * 1e9, self._log_recon_energies)
# Now bin the background counts
self._bkg_counts, _ = self._bin_counts_log(self._data_off['Erec'] * 1e9, self._log_recon_energies)
print("Read a %s x %s matrix, spectrum has %s bins, eff. area has %s elements" %
(self._hMigration.shape[0], self._hMigration.shape[1], self._counts.shape[0], self._eff_area.shape[0]))
# Read in the background renormalization (ratio between source and background region)
self._bkg_renorm = float(self._tRunSummary['OffNorm'])
self._start_energy = np.log10(175E6)#175 GeV in keV
self._end_energy = np.log10(18E9)#18 TeV in keV
self._first_chan = (np.abs(self._log_recon_energies-self._start_energy)).argmin()
self._last_chan = (np.abs(self._log_recon_energies-self._end_energy)).argmin()