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 get_scx_scz_at_time(time, file):
"""
Read a downloaded FERMI weekly pointing file and extract scx, scz for a
single time.
Parameters
----------
time : `datetime.datetime`
A datetime object or other time format understood by the parse_time function.
file : `str`
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
download_weekly_pointing_file function).
"""
time = parse_time(time)
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data['START']:
timesinutc.append(met_to_utc(tim))
ind = np.searchsorted(timesinutc, time)
scx_radec = (Longitude(hdulist[1].data['RA_SCX'][ind] * u.deg),
Latitude(hdulist[1].data['DEC_SCX'][ind] * u.deg))
scz_radec = (Longitude(hdulist[1].data['RA_SCZ'][ind] * u.deg),
Latitude(hdulist[1].data['DEC_SCZ'][ind] * u.deg))
return scx_radec, scz_radec, timesinutc[ind]
def get_scx_scz_in_timerange(timerange, file):
"""
Read a downloaded FERMI weekly pointing file and extract scx, scz for a
timerange.
Parameters
----------
timerange : `sunpy.time.TimeRange`
A SunPy TimeRange object.
file : `str`
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
download_weekly_pointing_file function).
"""
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data['START']:
timesinutc.append(met_to_utc(tim))
startind = np.searchsorted(timesinutc, timerange.start)
endind = np.searchsorted(timesinutc, timerange.end)
scx_radec = []
scz_radec = []
for i in range(startind, endind):
scx_radec.append((Longitude(hdulist[1].data['RA_SCX'][i] * u.deg),
Latitude(hdulist[1].data['DEC_SCX'][i] * u.deg)))
scz_radec.append((Longitude(hdulist[1].data['RA_SCZ'][i] * u.deg),
Latitude(hdulist[1].data['DEC_SCZ'][i] * u.deg)))
return scx_radec, scz_radec, timesinutc[startind:endind]
def get_scx_scz_at_time(time, file):
"""
read a downloaded FERMI weekly pointing file and extract scx, scz for a single time.
Parameters
----------
time : `datetime.datetime`
A datetime object or other time format understood by the parse_time function.
file : str
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
download_weekly_pointing_file function).
"""
time = parse_time(time)
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data['START']:
timesinutc.append(met_to_utc(tim))
ind = np.searchsorted(timesinutc, time)
scx_radec = (Longitude(hdulist[1].data['RA_SCX'][ind]*u.deg),
Latitude(hdulist[1].data['DEC_SCX'][ind]*u.deg))
scz_radec = (Longitude(hdulist[1].data['RA_SCZ'][ind]*u.deg),
Latitude(hdulist[1].data['DEC_SCZ'][ind]*u.deg))
return scx_radec, scz_radec, timesinutc[ind]
def get_scx_scz_in_timerange(timerange, file):
"""
read a downloaded FERMI weekly pointing file and extract scx, scz for a timerange.
Parameters
----------
date : `datetime.datetime`
A datetime object or other date format understood by the parse_time function.
file : str
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
download_weekly_pointing_file function).
"""
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data['START']:
timesinutc.append(met_to_utc(tim))
startind = np.searchsorted(timesinutc, timerange.start)
endind = np.searchsorted(timesinutc, timerange.end)
scx_radec = []
scz_radec = []
for i in range(startind, endind):
scx_radec.append((Longitude(hdulist[1].data['RA_SCX'][i]*u.deg),
Latitude(hdulist[1].data['DEC_SCX'][i]*u.deg)))
scz_radec.append((Longitude(hdulist[1].data['RA_SCZ'][i]*u.deg),
Latitude(hdulist[1].data['DEC_SCZ'][i]*u.deg)))
return scx_radec, scz_radec, timesinutc[startind:endind]
def get_scx_scz_at_time(time, file):
"""
Read a downloaded FERMI weekly pointing file and extract "scx", "scz" for a
single time.
Parameters
----------
time : {parse_time_types}
A time specified as a parse_time-compatible
time string, number, or a datetime object.
file : `str`
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
`~sunpy.instr.fermi.download_weekly_pointing_file` function).
Returns
-------
`tuple`, `tuple`, `list`:
The pointing coordinates as a `~astropy.coordinates.Longitude` in a `tuple`
and it's time.
"""
time = parse_time(time)
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data['START']:
timesinutc.append(met_to_utc(tim))
ind = np.searchsorted(timesinutc, time)
scx_radec = (Longitude(hdulist[1].data['RA_SCX'][ind] * u.deg),
Latitude(hdulist[1].data['DEC_SCX'][ind] * u.deg))
scz_radec = (Longitude(hdulist[1].data['RA_SCZ'][ind] * u.deg),
Latitude(hdulist[1].data['DEC_SCZ'][ind] * u.deg))
return scx_radec, scz_radec, timesinutc[ind]
def get_scx_scz_in_timerange(timerange, file):
"""
Read a downloaded FERMI weekly pointing file and extract scx, scz for a
timerange.
Parameters
----------
timerange : `sunpy.time.TimeRange`
A SunPy TimeRange object.
file : `str`
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
download_weekly_pointing_file function).
"""
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data["START"]:
timesinutc.append(met_to_utc(tim))
startind = np.searchsorted(timesinutc, timerange.start)
endind = np.searchsorted(timesinutc, timerange.end)
scx_radec = []
scz_radec = []
for i in range(startind, endind):
scx_radec.append(
(Longitude(hdulist[1].data["RA_SCX"][i] * u.deg), Latitude(hdulist[1].data["DEC_SCX"][i] * u.deg))
)
scz_radec.append(
(Longitude(hdulist[1].data["RA_SCZ"][i] * u.deg), Latitude(hdulist[1].data["DEC_SCZ"][i] * u.deg))
)
return scx_radec, scz_radec, timesinutc[startind:endind]
def from_fits_file(cls, filename):
r"""
Create a new visibility object from a fits file.
Parameters
----------
filename : `basestring`
The path/filename of the the fits file to read
Returns
-------
`Visibility`
The new visibility object
Raises
------
TypeError
If the fits file is not from a supported instrument
"""
with fits.open(filename) as hdu_list:
primary_header = hdu_list[0].header
if primary_header.get('source') == 'xrayvision':
return Visibility.from_fits(hdu_list)
elif primary_header.get('TELESCOP') == 'RHESSI' and \
primary_header.get('INSTRUME') == 'RHESSI':
return RHESSIVisibility.from_fits_old(hdu_list=hdu_list)
else:
raise TypeError(
"This type of fits visibility file is not supported")
def get_scx_scz_in_timerange(timerange, file):
"""
Read a downloaded FERMI weekly pointing file and extract scx, scz for a
timerange.
Parameters
----------
timerange : `sunpy.time.TimeRange`
A SunPy `~sunpy.time.TimeRange`.
file : `str`
A filepath to a Fermi/LAT weekly pointing file (e.g. as obtained by the
`~sunkit_instruments.fermi.download_weekly_pointing_file` function).
Returns
-------
`list`, `list`, `list`:
The pointing coordinates as a `~astropy.coordinates.Longitude` in a `list`
and it's time.
"""
hdulist = fits.open(file)
timesinutc = []
for tim in hdulist[1].data["START"]:
timesinutc.append(met_to_utc(tim))
startind = np.searchsorted(timesinutc, timerange.start)
endind = np.searchsorted(timesinutc, timerange.end)
scx_radec = []
scz_radec = []
for i in range(startind, endind):
scx_radec.append((
Longitude(hdulist[1].data["RA_SCX"][i] * u.deg),
Latitude(hdulist[1].data["DEC_SCX"][i] * u.deg),
))
scz_radec.append((
Longitude(hdulist[1].data["RA_SCZ"][i] * u.deg),
Latitude(hdulist[1].data["DEC_SCZ"][i] * u.deg),
))
return scx_radec, scz_radec, timesinutc[startind:endind]
def from_fits_file(path):
"""
Creates RHESSIVisibility objects from compatible fits files
Parameters
----------
path: str
Path where the fits file can be found
Examples
--------
Notes
-----
It separates the Visibility data based on the time and energy
ranges.
"""
hudlist = fits.open(path)
for i in hudlist:
if i.name == "VISIBILITY":
# Checking how many data structures we have
data_sort = {}
erange = i.data["erange"]
erange_unique = np.unique(erange, axis=0)
trange = i.data["trange"]
trange_unique = np.unique(trange, axis=0)
def find_erange(e):
for i, j in enumerate(erange_unique):
if np.allclose(j, e):
return i
def find_trange(t):
for i, j in enumerate(trange_unique):
if np.allclose(j, t, rtol=1e-15):
return i
for j, k in enumerate(erange_unique):
data_sort[j] = {}
for j, k in enumerate(trange):
eind = find_erange(erange[j])
tind = find_trange(k)
if tind not in data_sort[eind]:
data_sort[eind][tind] = [j]
else:
data_sort[eind][tind].append(j)
# Creating the RHESSIVisibilities
visibilities = []
for j, k in data_sort.items():
for l, m in k.items():
visibilities.append(
RHESSIVisibility(np.array([]),
np.array([[], []]),
erange=erange_unique[j],
trange=trange_unique[l]))
u = np.take(i.data["u"], m)
v = np.take(i.data["v"], m)
visibilities[-1].uv = np.array([u, v])
if "XYOFFSET" in i.header.values():
visibilities[-1].xyoffset = i.data["xyoffset"][
m[0]]
if "ISC" in i.header.values():
visibilities[-1].isc = np.take(i.data["isc"], m)
if "HARM" in i.header.values():
visibilities[-1].harm = i.data["harm"][m[0]]
if "OBSVIS" in i.header.values():
visibilities[-1].vis = np.take(i.data["obsvis"], m)
if "TOTFLUX" in i.header.values():
visibilities[-1].totflux = np.take(
i.data["totflux"], m)
if "SIGAMP" in i.header.values():
visibilities[-1].sigamp = np.take(
i.data["sigamp"], m)
if "CHI2" in i.header.values():
visibilities[-1].chi2 = np.take(i.data["chi2"], m)
if "TYPE" in i.header.values():
visibilities[-1].type_string = i.data["type"][m[0]]
if "UNITS" in i.header.values():
string = RHESSIVisibility.convert_units_to_tex(
i.data["units"][m[0]])
visibilities[-1].units = string
if "ATTEN_STATE" in i.header.values():
visibilities[-1].atten_state = i.data[
"atten_state"][m[0]]
if "COUNT" in i.header.values():
visibilities[-1].count = np.take(
i.data["count"], m)
return visibilities
return None