def _parse_fits(filepath):
"""
This method parses GBM CSPEC data files to create summary lightcurves.
"""
hdulist=fits.open(filepath)
header=OrderedDict(hdulist[0].header)
#these GBM files have three FITS extensions.
#extn1 - this gives the energy range for each of the 128 energy bins
#extn2 - this contains the data, e.g. counts, exposure time, time of observation
#extn3 - eclipse times?
energy_bins=hdulist[1].data
count_data=hdulist[2].data
misc=hdulist[3].data
#rebin the 128 energy channels into some summary ranges
#4-15 keV, 15 - 25 keV, 25-50 keV, 50-100 keV, 100-300 keV, 300-800 keV, 800 - 2000 keV
#put the data in the units of counts/s/keV
summary_counts=_bin_data_for_summary(energy_bins,count_data)
gbm_times=[]
#get the time information in datetime format with the correct MET adjustment
for t in count_data['time']:
gbm_times.append(fermi.met_to_utc(t))
column_labels=['4-15 keV','15-25 keV','25-50 keV','50-100 keV','100-300 keV',
'300-800 keV','800-2000 keV']
return header, pandas.DataFrame(summary_counts, columns=column_labels, index=gbm_times)
def _parse_hdus(cls, hdulist):
header = MetaDict(OrderedDict(hdulist[0].header))
# these GBM files have three FITS extensions.
# extn1 - this gives the energy range for each of the 128 energy bins
# extn2 - this contains the data, e.g. counts, exposure time, time of observation
# extn3 - eclipse times?
energy_bins = hdulist[1].data
count_data = hdulist[2].data
misc = hdulist[3].data
# rebin the 128 energy channels into some summary ranges
# 4-15 keV, 15 - 25 keV, 25-50 keV, 50-100 keV, 100-300 keV, 300-800 keV, 800 - 2000 keV
# put the data in the units of counts/s/keV
summary_counts = _bin_data_for_summary(energy_bins, count_data)
gbm_times = []
# get the time information in datetime format with the correct MET adjustment
for t in count_data['time']:
gbm_times.append(fermi.met_to_utc(t))
column_labels = [
'4-15 keV', '15-25 keV', '25-50 keV', '50-100 keV', '100-300 keV',
'300-800 keV', '800-2000 keV'
]
# Add the units data
units = OrderedDict([('4-15 keV', u.ct / u.s / u.keV),
('15-25 keV', u.ct / u.s / u.keV),
('25-50 keV', u.ct / u.s / u.keV),
('50-100 keV', u.ct / u.s / u.keV),
('100-300 keV', u.ct / u.s / u.keV),
('300-800 keV', u.ct / u.s / u.keV),
('800-2000 keV', u.ct / u.s / u.keV)])
return pd.DataFrame(summary_counts,
columns=column_labels,
index=gbm_times), header, units
def _parse_hdus(cls, hdulist):
header = MetaDict(OrderedDict(hdulist[0].header))
# these GBM files have three FITS extensions.
# extn1 - this gives the energy range for each of the 128 energy bins
# extn2 - this contains the data, e.g. counts, exposure time, time of observation
# extn3 - eclipse times?
energy_bins = hdulist[1].data
count_data = hdulist[2].data
misc = hdulist[3].data
# rebin the 128 energy channels into some summary ranges
# 4-15 keV, 15 - 25 keV, 25-50 keV, 50-100 keV, 100-300 keV, 300-800 keV, 800 - 2000 keV
# put the data in the units of counts/s/keV
summary_counts = _bin_data_for_summary(energy_bins, count_data)
# get the time information in datetime format with the correct MET adjustment
gbm_times = Time([fermi.met_to_utc(t) for t in count_data['time']])
gbm_times.precision = 9
gbm_times = gbm_times.isot.astype('datetime64')
column_labels = ['4-15 keV', '15-25 keV', '25-50 keV', '50-100 keV',
'100-300 keV', '300-800 keV', '800-2000 keV']
# Add the units data
units = OrderedDict([('4-15 keV', u.ct / u.s / u.keV), ('15-25 keV', u.ct / u.s / u.keV),
('25-50 keV', u.ct / u.s / u.keV), ('50-100 keV', u.ct / u.s / u.keV),
('100-300 keV', u.ct / u.s / u.keV), ('300-800 keV', u.ct / u.s / u.keV),
('800-2000 keV', u.ct / u.s / u.keV)])
return pd.DataFrame(summary_counts, columns=column_labels, index=gbm_times), header, units
def _parse_hdus(cls, hdulist):
"""
Parses a GBM CSPEC `astropy.io.fits.HDUList`.
Parameters
----------
filepath : `str`
The path to the file you want to parse.
"""
header = MetaDict(OrderedDict(hdulist[0].header))
# these GBM files have three FITS extensions.
# extn1 - this gives the energy range for each of the 128 energy bins
# extn2 - this contains the data, e.g. counts, exposure time, time of observation
# extn3 - eclipse times?
energy_bins = hdulist[1].data
count_data = hdulist[2].data
# rebin the 128 energy channels into some summary ranges
# 4-15 keV, 15 - 25 keV, 25-50 keV, 50-100 keV, 100-300 keV, 300-800 keV, 800 - 2000 keV
# put the data in the units of counts/s/keV
summary_counts = _bin_data_for_summary(energy_bins, count_data)
# get the time information in datetime format with the correct MET adjustment
gbm_times = Time([fermi.met_to_utc(t) for t in count_data['time']])
gbm_times.precision = 9
gbm_times = gbm_times.isot.astype('datetime64')
column_labels = [
'4-15 keV', '15-25 keV', '25-50 keV', '50-100 keV', '100-300 keV',
'300-800 keV', '800-2000 keV'
]
# Add the units data
units = OrderedDict([('4-15 keV', u.ct / u.s / u.keV),
('15-25 keV', u.ct / u.s / u.keV),
('25-50 keV', u.ct / u.s / u.keV),
('50-100 keV', u.ct / u.s / u.keV),
('100-300 keV', u.ct / u.s / u.keV),
('300-800 keV', u.ct / u.s / u.keV),
('800-2000 keV', u.ct / u.s / u.keV)])
return pd.DataFrame(summary_counts,
columns=column_labels,
index=gbm_times), header, units
def test_met_to_utc():
time = fermi.met_to_utc(500000000)
assert (time - parse_time('2016-11-05T00:53:16.000')) < 1e-7 * u.s
def test_met_to_utc():
time = fermi.met_to_utc(500000000)
assert (time - parse_time('2016-11-05T00:53:16.000')) < 1e-7 * u.s
