def from_dispersion_spectrum(cls,
dispersion_spectrum,
file_type='observed'):
# type: (BinnedSpectrumWithDispersion, str) -> PHASpectrum
if dispersion_spectrum.is_poisson:
rate_errors = None
else:
rate_errors = dispersion_spectrum.rate_errors
pha = PHAII(instrument_name=dispersion_spectrum.instrument,
telescope_name=dispersion_spectrum.mission,
tstart=dispersion_spectrum.tstart,
telapse=dispersion_spectrum.tstop -
dispersion_spectrum.tstart,
channel=list(range(1,
len(dispersion_spectrum) + 1)),
rate=dispersion_spectrum.rates,
stat_err=rate_errors,
quality=dispersion_spectrum.quality.to_ogip(),
grouping=np.ones(len(dispersion_spectrum)),
exposure=dispersion_spectrum.exposure,
backscale=dispersion_spectrum.scale_factor,
respfile=None,
ancrfile=None,
is_poisson=dispersion_spectrum.is_poisson)
return cls(pha_file_or_instance=pha,
spectrum_number=1,
file_type=file_type,
rsp_file=dispersion_spectrum.response)
def clone(
self,
new_counts=None,
new_count_errors=None,
):
"""
make a new spectrum with new counts and errors and all other
parameters the same
:param new_counts: new counts for the spectrum
:param new_count_errors: new errors from the spectrum
:return: new pha spectrum
"""
if new_counts is None:
new_counts = self.counts
new_count_errors = self.count_errors
if new_count_errors is None:
stat_err = None
else:
stat_err = old_div(new_count_errors, self.exposure)
# create a new PHAII instance
pha = PHAII(
instrument_name=self.instrument,
telescope_name=self.mission,
tstart=0,
telapse=self.exposure,
channel=list(range(1,
len(self) + 1)),
rate=old_div(new_counts, self.exposure),
stat_err=stat_err,
quality=self.quality.to_ogip(),
grouping=self.grouping,
exposure=self.exposure,
backscale=self.scale_factor,
respfile=None,
ancrfile=None,
is_poisson=self.is_poisson,
)
return pha
def _read_pha_or_pha2_file(pha_file_or_instance,
spectrum_number=None,
file_type='observed',
rsp_file=None,
arf_file=None,
treat_as_time_series=False):
"""
A function to extract information from pha and pha2 files. It is kept separate because the same method is
used for reading time series (MUCH faster than building a lot of individual spectra) and single spectra.
:param pha_file_or_instance: either a PHA file name or threeML.plugins.OGIP.pha.PHAII instance
:param spectrum_number: (optional) the spectrum number of the TypeII file to be used
:param file_type: observed or background
:param rsp_file: RMF filename or threeML.plugins.OGIP.response.InstrumentResponse instance
:param arf_file: (optional) and ARF filename
:param treat_as_time_series:
:return:
"""
assert isinstance(pha_file_or_instance, str) or isinstance(
pha_file_or_instance,
PHAII), 'Must provide a FITS file name or PHAII instance'
if isinstance(pha_file_or_instance, str):
ext = os.path.splitext(pha_file_or_instance)[-1]
if '{' in ext:
spectrum_number = int(ext.split('{')[-1].replace('}', ''))
pha_file_or_instance = pha_file_or_instance.split('{')[0]
# Read the data
filename = pha_file_or_instance
# create a FITS_FILE instance
pha_file_or_instance = PHAII.from_fits_file(pha_file_or_instance)
# If this is already a FITS_FILE instance,
elif isinstance(pha_file_or_instance, PHAII):
# we simply create a dummy filename
filename = 'pha_instance'
else:
raise RuntimeError('This is a bug')
file_name = filename
assert file_type.lower() in ['observed', 'background'
], "Unrecognized filetype keyword value"
file_type = file_type.lower()
try:
HDUidx = pha_file_or_instance.index_of("SPECTRUM")
except:
raise RuntimeError("The input file %s is not in PHA format" %
(pha_file_or_instance))
# spectrum_number = spectrum_number
spectrum = pha_file_or_instance[HDUidx]
data = spectrum.data
header = spectrum.header
# We don't support yet the rescaling
if "CORRFILE" in header:
if (header.get("CORRFILE").upper().strip() !=
"NONE") and (header.get("CORRFILE").upper().strip() != ''):
raise RuntimeError("CORRFILE is not yet supported")
# See if there is there is a QUALITY==0 in the header
if "QUALITY" in header:
has_quality_column = False
if header["QUALITY"] == 0:
is_all_data_good = True
else:
is_all_data_good = False
else:
if "QUALITY" in data.columns.names:
has_quality_column = True
is_all_data_good = False
else:
has_quality_column = False
is_all_data_good = True
warnings.warn(
'Could not find QUALITY in columns or header of PHA file. This is not a valid OGIP file. Assuming QUALITY =0 (good)'
)
# looking for tstart and tstop
tstart = None
tstop = None
has_tstart = False
has_tstop = False
has_telapse = False
if "TSTART" in header:
has_tstart_column = False
has_tstart = True
else:
if "TSTART" in data.columns.names:
has_tstart_column = True
has_tstart = True
if "TELAPSE" in header:
has_telapse_column = False
has_telapse = True
else:
if "TELAPSE" in data.columns.names:
has_telapse_column = True
has_telapse = True
if "TSTOP" in header:
has_tstop_column = False
has_tstop = True
else:
if "TSTOP" in data.columns.names:
has_tstop_column = True
has_tstop = True
if has_tstop and has_telapse:
warnings.warn(
'Found TSTOP and TELAPSE. This file is invalid. Using TSTOP.')
has_telapse = False
# Determine if this file contains COUNTS or RATES
if "COUNTS" in data.columns.names:
has_rates = False
data_column_name = "COUNTS"
elif "RATE" in data.columns.names:
has_rates = True
data_column_name = "RATE"
else:
raise RuntimeError(
"This file does not contain a RATE nor a COUNTS column. "
"This is not a valid PHA file")
# Determine if this is a PHA I or PHA II
if len(data.field(data_column_name).shape) == 2:
typeII = True
if spectrum_number == None and not treat_as_time_series:
raise RuntimeError(
"This is a PHA Type II file. You have to provide a spectrum number"
)
else:
typeII = False
# Collect information from mandatory keywords
keys = _required_keywords[file_type]
gathered_keywords = {}
for k in keys:
internal_name, keyname = k.split(":")
key_has_been_collected = False
if keyname in header:
if keyname in _required_keyword_types and type(header.get(
keyname)) is not _required_keyword_types[keyname]:
warnings.warn(
"unexpected type of %(keyname)s, expected %(expected_type)s\n found %(found_type)s: %(found_value)s"
% dict(
keyname=keyname,
expected_type=_required_keyword_types[keyname],
found_type=type(header.get(keyname)),
found_value=header.get(keyname),
))
else:
gathered_keywords[internal_name] = header.get(keyname)
# Fix "NONE" in None
if gathered_keywords[internal_name] == "NONE" or \
gathered_keywords[internal_name] == 'none':
gathered_keywords[internal_name] = None
key_has_been_collected = True
# Note that we check again because the content of the column can override the content of the header
if keyname in _might_be_columns[file_type] and typeII:
# Check if there is a column with this name
if keyname in data.columns.names:
# This will set the exposure, among other things
if not treat_as_time_series:
# if we just want a single spectrum
gathered_keywords[internal_name] = data[keyname][
spectrum_number - 1]
else:
# else get all the columns
gathered_keywords[internal_name] = data[keyname]
# Fix "NONE" in None
if gathered_keywords[internal_name] == "NONE" or \
gathered_keywords[internal_name] == 'none':
gathered_keywords[internal_name] = None
key_has_been_collected = True
if not key_has_been_collected:
# The keyword POISSERR is a special case, because even if it is missing,
# it is assumed to be False if there is a STAT_ERR column in the file
if keyname == "POISSERR" and "STAT_ERR" in data.columns.names:
warnings.warn(
"POISSERR is not set. Assuming non-poisson errors as given in the "
"STAT_ERR column")
gathered_keywords['poisserr'] = False
elif keyname == "ANCRFILE":
# Some non-compliant files have no ARF because they don't need one. Don't fail, but issue a
# warning
warnings.warn(
"ANCRFILE is not set. This is not a compliant OGIP file. Assuming no ARF."
)
gathered_keywords['ancrfile'] = None
elif keyname == "FILTER":
# Some non-compliant files have no FILTER because they don't need one. Don't fail, but issue a
# warning
warnings.warn(
"FILTER is not set. This is not a compliant OGIP file. Assuming no FILTER."
)
gathered_keywords['filter'] = None
else:
raise RuntimeError(
"Keyword %s not found. File %s is not a proper PHA "
"file" % (keyname, filename))
is_poisson = gathered_keywords['poisserr']
exposure = gathered_keywords['exposure']
# now we need to get the response file so that we can extract the EBOUNDS
if file_type == 'observed':
if rsp_file is None:
# this means it should be specified in the header
rsp_file = gathered_keywords['respfile']
if arf_file is None:
arf_file = gathered_keywords['ancrfile']
# Read in the response
if isinstance(rsp_file, str) or isinstance(rsp_file, str):
rsp = OGIPResponse(rsp_file, arf_file=arf_file)
else:
# assume a fully formed OGIPResponse
rsp = rsp_file
if file_type == 'background':
# we need the rsp ebounds from response to build the histogram
assert isinstance(
rsp_file, InstrumentResponse
), 'You must supply and OGIPResponse to extract the energy bounds'
rsp = rsp_file
# Now get the data (counts or rates) and their errors. If counts, transform them in rates
if typeII:
# PHA II file
if has_rates:
if not treat_as_time_series:
rates = data.field(data_column_name)[spectrum_number - 1, :]
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")[spectrum_number -
1, :]
else:
rates = data.field(data_column_name)
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")
else:
if not treat_as_time_series:
rates = old_div(
data.field(data_column_name)[spectrum_number - 1, :],
exposure)
rate_errors = None
if not is_poisson:
rate_errors = old_div(
data.field("STAT_ERR")[spectrum_number - 1, :],
exposure)
else:
rates = old_div(data.field(data_column_name),
np.atleast_2d(exposure).T)
rate_errors = None
if not is_poisson:
rate_errors = old_div(data.field("STAT_ERR"),
np.atleast_2d(exposure).T)
if "SYS_ERR" in data.columns.names:
if not treat_as_time_series:
sys_errors = data.field("SYS_ERR")[spectrum_number - 1, :]
else:
sys_errors = data.field("SYS_ERR")
else:
sys_errors = np.zeros(rates.shape)
if has_quality_column:
if not treat_as_time_series:
try:
quality = data.field("QUALITY")[spectrum_number - 1, :]
except (IndexError):
# GBM CSPEC files do not follow OGIP conventions and instead
# list simply QUALITY=0 for each spectrum
# so we have to read them differently
quality_element = data.field("QUALITY")[spectrum_number -
1]
warnings.warn(
'The QUALITY column has the wrong shape. This PHAII file does not follow OGIP standards'
)
if quality_element == 0:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
else:
# we need to be careful again because the QUALITY column is not always the correct shape
quality_element = data.field("QUALITY")
if quality_element.shape == rates.shape:
# This is the proper way for the quality to be stored
quality = quality_element
else:
quality = np.zeros_like(rates, dtype=int)
for i, q in enumerate(quality_element):
if q != 0:
quality[i, :] = 5
else:
if is_all_data_good:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
if has_tstart:
if has_tstart_column:
if not treat_as_time_series:
tstart = data.field("TSTART")[spectrum_number - 1]
else:
tstart = data.field("TSTART")
if has_tstop:
if has_tstop_column:
if not treat_as_time_series:
tstop = data.field("TSTOP")[spectrum_number - 1]
else:
tstop = data.field("TSTOP")
if has_telapse:
if has_telapse_column:
if not treat_as_time_series:
tstop = tstart + data.field("TELAPSE")[spectrum_number - 1]
else:
tstop = tstart + data.field("TELAPSE")
elif typeII == False:
assert not treat_as_time_series, 'This is not a PHAII file but you specified to treat it as a time series'
# PHA 1 file
if has_rates:
rates = data.field(data_column_name)
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")
else:
rates = old_div(data.field(data_column_name), exposure)
rate_errors = None
if not is_poisson:
rate_errors = old_div(data.field("STAT_ERR"), exposure)
if "SYS_ERR" in data.columns.names:
sys_errors = data.field("SYS_ERR")
else:
sys_errors = np.zeros(rates.shape)
if has_quality_column:
quality = data.field("QUALITY")
else:
if is_all_data_good:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
# read start and stop times if needed
if has_tstart:
if has_tstart_column:
tstart = data.field("TSTART")
else:
tstart = header['TSTART']
if has_tstop:
if has_tstop_column:
tstop = data.field("TSTOP")
else:
tstop = header['TSTOP']
if has_telapse:
if has_telapse_column:
tstop = tstart + data.field("TELAPSE")
else:
tstop = tstart + header['TELAPSE']
# Now that we have read it, some safety checks
assert rates.shape[0] == gathered_keywords['detchans'], \
"The data column (RATES or COUNTS) has a different number of entries than the " \
"DETCHANS declared in the header"
quality = Quality.from_ogip(quality)
if not treat_as_time_series:
counts = rates * exposure
if not is_poisson:
count_errors = rate_errors * exposure
else:
count_errors = None
else:
exposure = np.atleast_2d(exposure).T
counts = rates * exposure
if not is_poisson:
count_errors = rate_errors * exposure
else:
count_errors = None
out = collections.OrderedDict(counts=counts,
count_errors=count_errors,
rates=rates,
rate_errors=rate_errors,
sys_errors=sys_errors,
exposure=exposure,
is_poisson=is_poisson,
rsp=rsp,
gathered_keywords=gathered_keywords,
quality=quality,
file_name=file_name,
tstart=tstart,
tstop=tstop)
return out
def from_dispersion_spectrum(cls,
dispersion_spectrum,
file_type="observed",
response=None):
# type: (BinnedSpectrumWithDispersion, str) -> PHASpectrum
if dispersion_spectrum.is_poisson:
rate_errors = None
else:
rate_errors = dispersion_spectrum.rate_errors
if dispersion_spectrum.tstart is None:
tstart = 0
else:
tstart = dispersion_spectrum.tstart
if dispersion_spectrum.tstop is None:
telapse = dispersion_spectrum.exposure
else:
telapse = dispersion_spectrum.tstop - tstart
pha = PHAII(
instrument_name=dispersion_spectrum.instrument,
telescope_name=dispersion_spectrum.mission,
tstart=tstart, # TODO: add this in so that we have proper time!
telapse=telapse,
channel=list(range(1,
len(dispersion_spectrum) + 1)),
rate=dispersion_spectrum.rates,
stat_err=rate_errors,
quality=dispersion_spectrum.quality.to_ogip(),
grouping=np.ones(len(dispersion_spectrum)),
exposure=dispersion_spectrum.exposure,
backscale=dispersion_spectrum.scale_factor,
respfile=None,
ancrfile=None,
is_poisson=dispersion_spectrum.is_poisson,
)
if file_type == "background":
if response is None:
log.error(
"passed a background file but no response to extract energy spectra."
)
raise AssertionError()
else:
response = dispersion_spectrum.response
return cls(pha_file_or_instance=pha,
spectrum_number=1,
file_type=file_type,
rsp_file=response)
def clone(self,
new_counts=None,
new_count_errors=None,
new_exposure=None,
new_scale_factor=None):
"""
make a new spectrum with new counts and errors and all other
parameters the same
:param new_exposure: the new exposure for the clone
:param new_scale_factor: the new scale factor for the clone
:param new_counts: new counts for the spectrum
:param new_count_errors: new errors from the spectrum
:return: new pha spectrum
"""
if new_exposure is None:
new_exposure = self.exposure
if new_counts is None:
new_counts = self.counts
new_count_errors = self.count_errors
if new_count_errors is None:
stat_err = None
else:
stat_err = new_count_errors / new_exposure
if self._tstart is None:
tstart = 0
else:
tstart = self._tstart
if self._tstop is None:
telapse = new_exposure
else:
telapse = self._tstop - tstart
if new_scale_factor is None:
new_scale_factor = self.scale_factor
# create a new PHAII instance
pha = PHAII(instrument_name=self.instrument,
telescope_name=self.mission,
tstart=tstart,
telapse=telapse,
channel=range(1,
len(self) + 1),
rate=new_counts / self.exposure,
stat_err=stat_err,
quality=self.quality.to_ogip(),
grouping=self.grouping,
exposure=new_exposure,
backscale=new_scale_factor,
respfile=None,
ancrfile=None,
is_poisson=self.is_poisson)
return pha
def _read_pha_or_pha2_file(pha_file_or_instance, spectrum_number=None, file_type='observed',
rsp_file=None, arf_file=None, treat_as_time_series=False):
"""
A function to extract information from pha and pha2 files. It is kept separate because the same method is
used for reading time series (MUCH faster than building a lot of individual spectra) and single spectra.
:param pha_file_or_instance: either a PHA file name or threeML.plugins.OGIP.pha.PHAII instance
:param spectrum_number: (optional) the spectrum number of the TypeII file to be used
:param file_type: observed or background
:param rsp_file: RMF filename or threeML.plugins.OGIP.response.InstrumentResponse instance
:param arf_file: (optional) and ARF filename
:param treat_as_time_series:
:return:
"""
assert isinstance(pha_file_or_instance, str) or isinstance(pha_file_or_instance,
PHAII), 'Must provide a FITS file name or PHAII instance'
if isinstance(pha_file_or_instance, str):
ext = os.path.splitext(pha_file_or_instance)[-1]
if '{' in ext:
spectrum_number = int(ext.split('{')[-1].replace('}', ''))
pha_file_or_instance = pha_file_or_instance.split('{')[0]
# Read the data
filename = pha_file_or_instance
# create a FITS_FILE instance
pha_file_or_instance = PHAII.from_fits_file(pha_file_or_instance)
# If this is already a FITS_FILE instance,
elif isinstance(pha_file_or_instance, PHAII):
# we simply create a dummy filename
filename = 'pha_instance'
else:
raise RuntimeError('This is a bug')
file_name = filename
assert file_type.lower() in ['observed', 'background'], "Unrecognized filetype keyword value"
file_type = file_type.lower()
try:
HDUidx = pha_file_or_instance.index_of("SPECTRUM")
except:
raise RuntimeError("The input file %s is not in PHA format" % (pha_file_or_instance))
# spectrum_number = spectrum_number
spectrum = pha_file_or_instance[HDUidx]
data = spectrum.data
header = spectrum.header
# We don't support yet the rescaling
if "CORRFILE" in header:
if (header.get("CORRFILE").upper().strip() != "NONE") and (header.get("CORRFILE").upper().strip() != ''):
raise RuntimeError("CORRFILE is not yet supported")
# See if there is there is a QUALITY==0 in the header
if "QUALITY" in header:
has_quality_column = False
if header["QUALITY"] == 0:
is_all_data_good = True
else:
is_all_data_good = False
else:
if "QUALITY" in data.columns.names:
has_quality_column = True
is_all_data_good = False
else:
has_quality_column = False
is_all_data_good = True
warnings.warn(
'Could not find QUALITY in columns or header of PHA file. This is not a valid OGIP file. Assuming QUALITY =0 (good)')
# looking for tstart and tstop
tstart = None
tstop = None
has_tstart = False
has_tstop = False
has_telapse = False
if "TSTART" in header:
has_tstart_column = False
has_tstart = True
else:
if "TSTART" in data.columns.names:
has_tstart_column = True
has_tstart = True
if "TELAPSE" in header:
has_telapse_column = False
has_telapse = True
else:
if "TELAPSE" in data.columns.names:
has_telapse_column = True
has_telapse = True
if "TSTOP" in header:
has_tstop_column = False
has_tstop = True
else:
if "TSTOP" in data.columns.names:
has_tstop_column = True
has_tstop = True
if has_tstop and has_telapse:
warnings.warn('Found TSTOP and TELAPSE. This file is invalid. Using TSTOP.')
has_telapse = False
# Determine if this file contains COUNTS or RATES
if "COUNTS" in data.columns.names:
has_rates = False
data_column_name = "COUNTS"
elif "RATE" in data.columns.names:
has_rates = True
data_column_name = "RATE"
else:
raise RuntimeError("This file does not contain a RATE nor a COUNTS column. "
"This is not a valid PHA file")
# Determine if this is a PHA I or PHA II
if len(data.field(data_column_name).shape) == 2:
typeII = True
if spectrum_number == None and not treat_as_time_series:
raise RuntimeError("This is a PHA Type II file. You have to provide a spectrum number")
else:
typeII = False
# Collect information from mandatory keywords
keys = _required_keywords[file_type]
gathered_keywords = {}
for k in keys:
internal_name, keyname = k.split(":")
key_has_been_collected = False
if keyname in header:
if keyname in _required_keyword_types and type(header.get(keyname)) is not _required_keyword_types[keyname]:
warnings.warn("unexpected type of %(keyname)s, expected %(expected_type)s\n found %(found_type)s: %(found_value)s"%
dict(
keyname=keyname,
expected_type=_required_keyword_types[keyname],
found_type=type(header.get(keyname)),
found_value=header.get(keyname),
))
else:
gathered_keywords[internal_name] = header.get(keyname)
# Fix "NONE" in None
if gathered_keywords[internal_name] == "NONE" or \
gathered_keywords[internal_name] == 'none':
gathered_keywords[internal_name] = None
key_has_been_collected = True
# Note that we check again because the content of the column can override the content of the header
if keyname in _might_be_columns[file_type] and typeII:
# Check if there is a column with this name
if keyname in data.columns.names:
# This will set the exposure, among other things
if not treat_as_time_series:
# if we just want a single spectrum
gathered_keywords[internal_name] = data[keyname][spectrum_number - 1]
else:
# else get all the columns
gathered_keywords[internal_name] = data[keyname]
# Fix "NONE" in None
if gathered_keywords[internal_name] == "NONE" or \
gathered_keywords[internal_name] == 'none':
gathered_keywords[internal_name] = None
key_has_been_collected = True
if not key_has_been_collected:
# The keyword POISSERR is a special case, because even if it is missing,
# it is assumed to be False if there is a STAT_ERR column in the file
if keyname == "POISSERR" and "STAT_ERR" in data.columns.names:
warnings.warn("POISSERR is not set. Assuming non-poisson errors as given in the "
"STAT_ERR column")
gathered_keywords['poisserr'] = False
elif keyname == "ANCRFILE":
# Some non-compliant files have no ARF because they don't need one. Don't fail, but issue a
# warning
warnings.warn("ANCRFILE is not set. This is not a compliant OGIP file. Assuming no ARF.")
gathered_keywords['ancrfile'] = None
elif keyname == "FILTER":
# Some non-compliant files have no FILTER because they don't need one. Don't fail, but issue a
# warning
warnings.warn("FILTER is not set. This is not a compliant OGIP file. Assuming no FILTER.")
gathered_keywords['filter'] = None
else:
raise RuntimeError("Keyword %s not found. File %s is not a proper PHA "
"file" % (keyname, filename))
is_poisson = gathered_keywords['poisserr']
exposure = gathered_keywords['exposure']
# now we need to get the response file so that we can extract the EBOUNDS
if file_type == 'observed':
if rsp_file is None:
# this means it should be specified in the header
rsp_file = gathered_keywords['respfile']
if arf_file is None:
arf_file = gathered_keywords['ancrfile']
# Read in the response
if isinstance(rsp_file, str) or isinstance(rsp_file, unicode):
rsp = OGIPResponse(rsp_file, arf_file=arf_file)
else:
# assume a fully formed OGIPResponse
rsp = rsp_file
if file_type == 'background':
# we need the rsp ebounds from response to build the histogram
assert isinstance(rsp_file, InstrumentResponse), 'You must supply and OGIPResponse to extract the energy bounds'
rsp = rsp_file
# Now get the data (counts or rates) and their errors. If counts, transform them in rates
if typeII:
# PHA II file
if has_rates:
if not treat_as_time_series:
rates = data.field(data_column_name)[spectrum_number - 1, :]
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")[spectrum_number - 1, :]
else:
rates = data.field(data_column_name)
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")
else:
if not treat_as_time_series:
rates = data.field(data_column_name)[spectrum_number - 1, :] / exposure
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")[spectrum_number - 1, :] / exposure
else:
rates = data.field(data_column_name) / np.atleast_2d(exposure).T
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR") / np.atleast_2d(exposure).T
if "SYS_ERR" in data.columns.names:
if not treat_as_time_series:
sys_errors = data.field("SYS_ERR")[spectrum_number - 1, :]
else:
sys_errors = data.field("SYS_ERR")
else:
sys_errors = np.zeros(rates.shape)
if has_quality_column:
if not treat_as_time_series:
try:
quality = data.field("QUALITY")[spectrum_number - 1, :]
except(IndexError):
# GBM CSPEC files do not follow OGIP conventions and instead
# list simply QUALITY=0 for each spectrum
# so we have to read them differently
quality_element = data.field("QUALITY")[spectrum_number - 1]
warnings.warn(
'The QUALITY column has the wrong shape. This PHAII file does not follow OGIP standards')
if quality_element == 0:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
else:
# we need to be careful again because the QUALITY column is not always the correct shape
quality_element = data.field("QUALITY")
if quality_element.shape == rates.shape:
# This is the proper way for the quality to be stored
quality = quality_element
else:
quality = np.zeros_like(rates, dtype=int)
for i, q in enumerate(quality_element):
if q != 0:
quality[i, :] = 5
else:
if is_all_data_good:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
if has_tstart:
if has_tstart_column:
if not treat_as_time_series:
tstart = data.field("TSTART")[spectrum_number - 1]
else:
tstart = data.field("TSTART")
if has_tstop:
if has_tstop_column:
if not treat_as_time_series:
tstop = data.field("TSTOP")[spectrum_number - 1]
else:
tstop = data.field("TSTOP")
if has_telapse:
if has_telapse_column:
if not treat_as_time_series:
tstop = tstart + data.field("TELAPSE")[spectrum_number - 1]
else:
tstop = tstart + data.field("TELAPSE")
elif typeII == False:
assert not treat_as_time_series, 'This is not a PHAII file but you specified to treat it as a time series'
# PHA 1 file
if has_rates:
rates = data.field(data_column_name)
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR")
else:
rates = data.field(data_column_name) / exposure
rate_errors = None
if not is_poisson:
rate_errors = data.field("STAT_ERR") / exposure
if "SYS_ERR" in data.columns.names:
sys_errors = data.field("SYS_ERR")
else:
sys_errors = np.zeros(rates.shape)
if has_quality_column:
quality = data.field("QUALITY")
else:
if is_all_data_good:
quality = np.zeros_like(rates, dtype=int)
else:
quality = np.zeros_like(rates, dtype=int) + 5
# read start and stop times if needed
if has_tstart:
if has_tstart_column:
tstart = data.field("TSTART")
else:
tstart = header['TSTART']
if has_tstop:
if has_tstop_column:
tstop = data.field("TSTOP")
else:
tstop = header['TSTOP']
if has_telapse:
if has_telapse_column:
tstop = tstart + data.field("TELAPSE")
else:
tstop = tstart + header['TELAPSE']
# Now that we have read it, some safety checks
assert rates.shape[0] == gathered_keywords['detchans'], \
"The data column (RATES or COUNTS) has a different number of entries than the " \
"DETCHANS declared in the header"
quality = Quality.from_ogip(quality)
if not treat_as_time_series:
counts = rates * exposure
if not is_poisson:
count_errors = rate_errors * exposure
else:
count_errors = None
else:
exposure = np.atleast_2d(exposure).T
counts = rates * exposure
if not is_poisson:
count_errors = rate_errors * exposure
else:
count_errors = None
out = collections.OrderedDict(counts=counts, count_errors=count_errors, rates=rates, rate_errors=rate_errors,
sys_errors=sys_errors, exposure=exposure, is_poisson=is_poisson, rsp=rsp,
gathered_keywords=gathered_keywords, quality=quality, file_name=file_name,
tstart=tstart, tstop=tstop )
return out