def create_master_bias(bias_files):
"""
Creates a master bias
"""
bias_frames = []
if len(bias_files) > 0:
for bias_file in glob.glob('*{0}*.f*t*'.format(bias_files)):
fits = pf.open(bias_file, memmap=False)
try:
bias_frames.append(float(fits[0].header['BZERO']) + float(fits[0].header['BSCALE']) * fits[0].data)
except KeyError:
bias_frames.append(fits[0].data)
fits.close()
if len(bias_frames) > 0:
if master_bias_method == 'median':
master_bias = np.median(bias_frames, 0)
elif master_bias_method == 'mean':
master_bias = np.mean(bias_frames, 0)
else:
master_bias = np.median(bias_frames, 0)
else:
master_bias = 0.0
return master_bias
def create_master_flat(flat_files):
"""
Creates a master flat
"""
flat_frames = []
if len(str(flat_files)) > 0:
for flat_file in glob.glob('*{0}*.f*t*'.format(flat_files)):
fits = pf.open(flat_file, memmap=False)
try:
flat_frame = float(fits[0].header['BZERO']) + float(fits[0].header['BSCALE']) * fits[0].data
except KeyError:
flat_frame = fits[0].data
flat_frames.append(flat_frame - master_bias - im_details.exposure_time * master_dark) #####NEEED WAY OF ACTUALLY GETTING EXP TIME FROM FIT HEADER, DO THIS WHEN DEFINING IM_DETAILS
fits.close()
if len(flat_frames) > 0:
if master_flat_method == 'median':
flat_frames = [ff / np.median(ff) for ff in flat_frames]
master_flat = np.median(flat_frames, 0)
elif master_flat_method == 'mean':
master_flat = np.mean(flat_frames, 0)
else:
flat_frames = [ff / np.median(ff) for ff in flat_frames]
master_flat = np.median(flat_frames, 0)
master_flat = master_flat / np.median(master_flat)
else:
master_flat = 1.0
return master_flat
def create_master_dark(dark_files):
"""
Creates a master dark
"""
dark_frames = []
if len(str(dark_files)) > 0:
for dark_file in glob.glob('*{0}*.f*t*'.format(dark_files)):
fits = pf.open(dark_file, memmap=False)
try:
dark_frame = float(fits[0].header['BZERO']) + float(fits[0].header['BSCALE']) * fits[0].data
except KeyError:
dark_frame = fits[0].data
dark_frames.append((dark_frame - master_bias) /im_details.exposure_time)
fits.close()
if len(dark_frames) > 0:
if master_dark_method == 'median':
master_dark = np.median(dark_frames, 0)
elif master_dark_method == 'mean':
master_dark = np.mean(dark_frames, 0)
else:
master_dark = np.median(dark_frames, 0)
else:
master_dark = 0.0
return master_dark
def _renormalize_weight(self):
"""Renormalizes the weight image of the stack."""
fits = astropy.io.fits.open(self._coadd_weightpath)
image = fits[0].data
image[image > 0.] = 1.
fits[0].data = image
fits.writeto(self._coadd_weightpath, clobber=True)
fits.close()
def find_point_sources(image,
seeing_pix=4,
threshold_sigma=3,
size_lim=5,
out_file=None,
plot=False):
"""sourceInIm takes a direct image and find all sources above some detection
threshold in it.
Used by find_sources_in_direct_image
Inputs:
image: an array representing the direct image
seeing_pix: seeing size in pixel
threshold_sigma: detection threshold in sigmas away from the standard deviation in background fluctuation
size_lim: the limit of stddev, in pixel, over which we don't accept the fit as a source
out_file: If defined then a .reg file for ds9 is created in the XY format.
"""
threshold = threshold_sigma * np.sqrt(np.var(np.nan_to_num(image)))
#get all cutouts
cutouts = pointFinder(image, seeing_pix, threshold)
#list to collect results
all_results = []
#loop through cutouts, fit gaussian to get position of sources
for i in cutouts:
#make cutouts from indices, then run fit_gaussian_to_cutout
if np.logical_and(
len(np.ravel(image[i])) < 200,
len(np.ravel(image[i])) >
10): #a legit cutout shouldn't be large
if plot:
plt.imshow(image[i], interpolation='none')
res = fit_gaussian_to_cutout(image[i], seeing_pix)
#get x y
x = (i[1].start) + res[0].x_mean.value
y = (i[0].start) + res[0].y_mean.value
x_stddev = res[0].x_stddev.value
y_stddev = res[0].y_stddev.value
#filter out bad fits. stddev should be of some reasonable number
if x_stddev < size_lim and y_stddev < size_lim:
all_results += [(y, x, y_stddev, x_stddev)]
else:
None
#print(i, ' is invalid.' )
#return cutouts
if out_file != None:
f = open(out_file, mode='w')
for i in all_results:
f.write(str(i[1] + 1) + ' ' + str(i[0] + 1) + '\n')
f.close()
return all_results
def _parse_fits(uri):
fits = pyfits.open(uri, memmap=False)
#print(fits.info())
if len(fits) > 1:
dat = fits[1].data
#print(dat)
else:
#print(fits[0].data[0])
dat=[]
dat = fits[0].data[0]
#dat[1] = fits[0].data[0]
fits.close()
return dat.tolist()
def filter_list(fullframepath, imdrizzlepath):
"""
Returns a dictionary containig a file list for each filter/grism in the dataset
INPUTS
-------
fullframepath : string
path to full frame data
imdrizzlepath :string
path where image files will be copied to
will be created if does not exist
"""
#Get a list of all fullframe files
files = glob.glob(os.path.join(fullframepath, "*f_flt.fits"))
#Dictionary to store file lists
filters = {}
#Populate filter dictionary with file lists
for file in files:
fits = pyfits.open(file)
filter = fits[0].header["FILTER"]
fits.close()
if filter not in filters:
filters[filter] = []
filters[filter].append(file)
#For each filter write the file list to a text file with extension .lis
#Place images into a directory to be drizzled (create directory if it doesn't exist)
if not os.path.exists(imdrizzlepath):
os.makedirs(imdrizzlepath)
for filter in filters:
f = open(os.path.join(fullframepath, filter + ".lis"),
'w') #Create a list file for that filter
for file in filters[filter]:
(path, fname) = os.path.split(file)
f.write(fname + "\n")
if filter[0].lower() == 'f': #This is an image, not a spectrum
shutil.copy(file, os.path.join(imdrizzlepath,
fname)) #copy to imdrizzlepath
f.close()
if filter[0].lower(
) == 'f': #If this is an image, then copy the list file to imdrizzlepath
shutil.copy(os.path.join(fullframepath, filter + '.lis'),
os.path.join(imdrizzlepath, filter + '.lis'))
return filters
def _parse_fits_metadata(uri,metadata):
fits = pyfits.open(uri, memmap=False)
if len(fits)>1:
header = fits[1].header
#doc = {'ra':header['RA'],'dec':header['DEC'], 'filename':header['TITLE']}
print(header['TITLE'],header['RA'],header['DEC'], sep=',', file= metadata)
else:
header = fits[0].header
#doc = {'ra': header['RA'],'dec': header['DEC'],'filename': header['FILENAME']}
print(header['FILENAME'],header['RA'],header['DEC'], sep=',', file= metadata)
fits.close()
#if 'folder' in json_dict:
#folder = json_dict['folder'
#es.index(index='adass', doc_type='doc', id=header['TITLE'], body=doc)
#print(header, file = metadata)
return header
def _createStarFromFITS(self, fits):
DB_NAME_END = "_name"
DB_IDENT_SEP = "_id_"
prim_hdu = fits[0].header
ra = prim_hdu.get(self.FITS_RA)
dec = prim_hdu.get(self.FITS_DEC)
ra_unit = prim_hdu.get(self.FITS_RA_UNIT)
dec_unit = prim_hdu.get(self.FITS_DEC_UNIT)
star = Star(name=prim_hdu.get(self.FITS_NAME),
coo=(ra, dec, (ra_unit, dec_unit)),
starClass=prim_hdu.get(self.FITS_CLASS))
ident = {}
more = {}
for db_name_key in list(prim_hdu.keys()):
if db_name_key.endswith(DB_NAME_END):
db_name = db_name_key[:-len(DB_NAME_END)]
ident[db_name] = {}
ident[db_name]["name"] = prim_hdu[db_name_key]
elif DB_IDENT_SEP in db_name_key:
db_name, ident_key = db_name_key.split(DB_IDENT_SEP)
if not ident[db_name].get("db_ident"):
ident[db_name]["db_ident"] = {}
ident[db_name]["db_ident"][ident_key] = prim_hdu[db_name_key]
elif db_name_key not in [
"SIMPLE", "BITPIX", "NAXIS", "EXTEND", self.FITS_RA,
self.FITS_DEC, self.FITS_RA_UNIT, self.FITS_DEC_UNIT,
self.FITS_NAME, self.FITS_CLASS
]:
more[db_name_key.lower()] = prim_hdu[db_name_key]
star.ident = ident
star.more = more
for lc_hdu in fits[1:]:
star.putLightCurve(self._createLcFromFits(lc_hdu))
fits.close()
return star
def populate_database_from_fits_file(
db: BundleDB, os_filepath: str, fits_product_lidvid: str
) -> None:
file_basename = basename(os_filepath)
try:
fits = astropy.io.fits.open(os_filepath)
try:
db.create_fits_file(
os_filepath, file_basename, fits_product_lidvid, len(fits)
)
_populate_hdus_associations_and_cards(
db, fits, file_basename, fits_product_lidvid
)
finally:
fits.close()
except OSError as e:
db.create_bad_fits_file(os_filepath, file_basename, fits_product_lidvid, str(e))
def testExposure(self):
"""Test that we load the Wcs from the binary table instead of headers when possible."""
self.addSipMetadata()
wcsIn = lsst.afw.geom.makeSkyWcs(self.metadata)
dim = lsst.afw.geom.Extent2I(20, 30)
expIn = lsst.afw.image.ExposureF(dim)
expIn.setWcs(wcsIn)
with lsst.utils.tests.getTempFilePath(".fits") as fileName:
expIn.writeFits(fileName)
# Manually mess up the headers, so we'd know if we were loading the Wcs from that;
# when there is a WCS in the header and a WCS in the FITS table, we should use the
# latter, because the former might just be an approximation.
fits = astropy.io.fits.open(fileName)
fits[1].header.remove("CTYPE1")
fits[1].header.remove("CTYPE2")
fits.writeto(fileName, overwrite=True)
fits.close()
# now load it using afw
expOut = lsst.afw.image.ExposureF(fileName)
wcsOut = expOut.getWcs()
self.assertEqual(wcsIn, wcsOut)
def testExposure(self):
"""Test that we load the Wcs from the binary table instead of headers when possible."""
self.addSipMetadata()
wcsIn = lsst.afw.geom.makeSkyWcs(self.metadata)
dim = lsst.geom.Extent2I(20, 30)
expIn = lsst.afw.image.ExposureF(dim)
expIn.setWcs(wcsIn)
with lsst.utils.tests.getTempFilePath(".fits") as fileName:
expIn.writeFits(fileName)
# Manually mess up the headers, so we'd know if we were loading the Wcs from that;
# when there is a WCS in the header and a WCS in the FITS table, we should use the
# latter, because the former might just be an approximation.
fits = astropy.io.fits.open(fileName)
fits[1].header.remove("CTYPE1")
fits[1].header.remove("CTYPE2")
fits.writeto(fileName, overwrite=True)
fits.close()
# now load it using afw
expOut = lsst.afw.image.ExposureF(fileName)
wcsOut = expOut.getWcs()
self.assertEqual(wcsIn, wcsOut)
def _make_temp_images(self):
"""Make pixel coverage and variance images."""
cov_paths, var_paths = [], []
for sigma_path, weight_path in zip(self._paths, self._weight_paths):
coverage_path = ".".join((os.path.splitext(sigma_path)[0],
"coverage.fits"))
var_path = ".".join((os.path.splitext(sigma_path)[0],
"var.fits"))
fits = astropy.io.fits.open(sigma_path)
wfits = astropy.io.fits.open(weight_path)
# Make a coverage image from the weightmap
wfits[0].data[wfits[0].data > 0.] = 1.
wfits[0].data[wfits[0].data <= 0.] = 0.
wfits.writeto(coverage_path, clobber=True)
# Make a variance map
fits[0].data = fits[0].data ** 2.
fits[0].data[wfits[0].data == 0.] = 0. # FIXME NaNs propagate bad?
fits.writeto(var_path, clobber=True)
fits.close()
wfits.close()
cov_paths.append(coverage_path)
var_paths.append(var_path)
return cov_paths, var_paths
def fits(zl,pzl,zs,pzs,filename):
try:
os.remove(filename)
print 'Removing file ',filename
except OSError:
pass
fits=fio.FITS(filename,'rw')
hdr={'extname':'NZ_POSITION','NZDATA':True}
out=np.empty(np.shape(zl),dtype=[('Z_MID','f8')])
out['Z_MID']=zl
fits.write(out,header=hdr)
fits[-1].insert_column('Z_LOW',np.abs(zl-(zl[1]-zl[0])/2.))
fits[-1].insert_column('Z_HIGH',zl+(zl[1]-zl[0])/2.)
if 'notomo' in filename:
fits[-1].insert_column('BIN1',pzl)
else:
for i in range(len(pzl)):
fits[-1].insert_column('BIN'+str(i+1),pzl[i,:])
hdr={'extname':'NZ_SHEAR','NZDATA':True}
out=np.empty(np.shape(zs),dtype=[('Z_MID','f8')])
out['Z_MID']=zs
fits.write(out,header=hdr)
fits[-1].insert_column('Z_LOW',np.abs(zs-(zs[1]-zs[0])/2.))
fits[-1].insert_column('Z_HIGH',zs+(zs[1]-zs[0])/2.)
if 'notomo' in filename:
fits[-1].insert_column('BIN1',pzs)
else:
for i in range(len(pzs)):
fits[-1].insert_column('BIN'+str(i+1),pzs[i,:])
fits.close()
return
def plot_diff(diff_path, median, sigma, plot_path):
"""Plot histogram of the difference image."""
fits = astropy.io.fits.open(diff_path)
pixels = fits[0].data
pixels = pixels[np.isfinite(pixels)].ravel()
fig = Figure(figsize=(3.5, 3.5))
canvas = FigureCanvas(fig)
gs = gridspec.GridSpec(1, 1, left=0.15, right=0.95, bottom=0.15, top=0.95,
wspace=None, hspace=None, width_ratios=None, height_ratios=None)
ax = fig.add_subplot(gs[0])
ax.hist(pixels, 1000, histtype='stepfilled',
edgecolor='None', facecolor='dodgerblue')
ax.axvline(median, ls='-', c='k', lw=2)
ax.axvline(median - sigma, ls='--', c='k', lw=1)
ax.axvline(median + sigma, ls='--', c='k', lw=1)
ax.text(0.1, 0.9, r"$%.2f \pm %.2f$" % (median, sigma),
ha='left', va='top',
transform=ax.transAxes)
ax.set_xlim(median - 3 * sigma, median + 3 * sigma)
gs.tight_layout(fig, pad=1.08, h_pad=None, w_pad=None, rect=None)
canvas.print_figure(plot_path + ".pdf", format="pdf")
fits.close()
def reduce_observations(self, observation_files):
"""
Reduces the observations
Args
observation_files (str)
- string containing identifying name of observation files
"""
# observation_files = glob.glob('../*{0}*.f*t*'.format(observation_files))
observation_files = Frame.objects.filter(campaign_id=1,observation_type='S')
observation_files.sort()
percent = 0
lt0 = time.time()
for counter, science_file in enumerate(observation_files):
# correct it with master bias_files, master dark_files and master flat_files
fits = pf.open(science_file, memmap=False)
try:
data_frame = float(fits[0].header['BZERO']) + float(fits[0].header['BSCALE']) * fits[0].data
except KeyError:
data_frame = fits[0].data
fits[0].data = (data_frame - master_bias - fits[0].header[exposure_time_key] * master_dark) / master_flat ###RENAME EVERY TIME EXPOSURE TIME KEY IS USED TO IM_DETAILS.EXPOSURE
fits[0].header.set('BZERO', 0.0)
fits[0].header.set('BSCALE', 1.0)
norm, floor, mean, std = fit_distribution1d_gaussian(fits[0].data, binning=fits[0].data.size / bin_to)
if np.isnan(norm):
mean = np.mean(fits[0].data)
std = np.std(fits[0].data)
julian_date = (ephem.julian_date(float(ephem.Date(local_time))) +
im_details.exposure / (2.0 * 60.0 * 60.0 * 24.0))
ra_target, dec_target = target_ra_dec.split()
heliocentric_julian_date = jd_to_hjd(ra_target, dec_target, julian_date)
self.testx.append(heliocentric_julian_date)
self.testy.append(mean)
self.testz.append(std)
fits[0].header.set(mean_key, mean)
fits[0].header.set(std_key, std)
# write the new fits file
if observation_date_key == observation_time_key:
local_time = fits[0].header[observation_date_key]
local_time = '{0}_'.format(local_time.replace('-', '_').replace('T', '_').replace(':', '_'))
else:
local_time = '{0}_{1}_'.format(fits[0].header[observation_date_key].split('T')[0].replace('-', '_'),
fits[0].header[observation_time_key].replace(':', '_'))
try:
hdu = pf.CompImageHDU(header=fits[0].header, data=fits[0].data)
except:
hdu = pf.ImageHDU(header=fits[0].header, data=fits[0].data)
hdu.writeto('{0}{1}{2}{3}{4}'.format(reduction_directory,
os.sep, reduction_prefix, local_time, science_file.split(os.sep)[-1]))
if counter == 0:
ax.cla()
ax.imshow(fits[0].data[::2, ::2], origin='lower', cmap=cm.Greys_r,
vmin=fits[0].header[mean_key] + frame_low_std * fits[0].header[std_key],
vmax=fits[0].header[mean_key] + frame_upper_std * fits[0].header[std_key])
ax.axis('off')
canvas.show()
fits.close()
def _resize_resampled_images(self, target_fits_path, resampled_paths):
"""Ensures that the CRPIX, CRVAL and NAXIS1/2 values of the resampled
images match the target, and crops/adds padding if not. Swarp *should*
do this properly, but sometimes does not.
"""
target_fits = astropy.io.fits.open(target_fits_path)
rNAXIS1 = target_fits[0].header['NAXIS1']
rNAXIS2 = target_fits[0].header['NAXIS2']
rCRPIX1 = target_fits[0].header['CRPIX1']
rCRPIX2 = target_fits[0].header['CRPIX2']
for path in resampled_paths:
touched = False # toggled True if image modified
print "path", path
fits = astropy.io.fits.open(path)
image = fits[0].data
print "orig shape", image.shape
# x-axis
if rCRPIX1 > fits[0].header['CRPIX1']:
# pad from left
print "CRPIX1 conflict %i %i" \
% (rCRPIX1, fits[0].header['CRPIX1'])
dx = rCRPIX1 - fits[0].header['CRPIX1']
print "Pad left by %i" % dx
pad = np.ones((image.shape[0], dx)) * np.nan
image = np.hstack((pad, image))
print image.shape
touched = True
elif rCRPIX1 < fits[0].header['CRPIX1']:
# trim from left
print "CRPIX1 conflict %i %i" \
% (rCRPIX1, fits[0].header['CRPIX1'])
dx = fits[0].header['CRPIX1'] - rCRPIX1
print "Trim left by %i" % dx
image = image[:, dx:]
print image.shape
touched = True
if rNAXIS1 > image.shape[1]:
# pad to the right
print "NAXIS1 conflict %i %i" % (rNAXIS1, image.shape[1])
dx = rNAXIS1 - image.shape[1]
print "Pad from right by %i" % dx
pad = np.ones((image.shape[0], dx)) * np.nan
image = np.hstack((image, pad))
print image.shape
touched = True
elif rNAXIS1 < image.shape[1]:
# trim from right
print "NAXIS1 conflict %i %i" % (rNAXIS1, image.shape[1])
dx = image.shape[1] - rNAXIS1
print "Trim from right by %i" % dx
image = image[:, :-dx]
print image.shape
touched = True
# y-axis
crpix2 = fits[0].header['CRPIX2']
if rCRPIX2 > crpix2:
# Pad from bottom (low index in image array)
print "pad from bottom"
dx = rCRPIX2 - crpix2
pad = np.ones((dx, image.shape[1])) * np.nan
image = np.vstack((pad, image))
touched = True
elif rCRPIX2 < crpix2:
# Trim from bottom (low index in image array)
print "trim from bottom"
dx = crpix2 - rCRPIX2
image = image[dx:, :]
touched = True
if rNAXIS2 > image.shape[0]:
# Pad from top (high index in image array)
print "pad from top"
dx = rNAXIS2 - image.shape[0]
pad = np.ones((dx, image.shape[1])) * np.nan
image = np.vstack((image, pad))
touched = True
elif rNAXIS2 < image.shape[0]:
# Trim from top (high index in image array)
print "trim from top"
dx = rNAXIS2 - image.shape[0]
image = image[:-dx, :]
touched = True
if touched:
fits[0].data = image
print "image.shape", image.shape
fits[0].header.set('NAXIS1', image.shape[1])
fits[0].header.set('NAXIS2', image.shape[0])
fits[0].header.set('CRPIX1', rCRPIX1)
fits[0].header.set('CRPIX2', rCRPIX2)
fits.writeto(path, clobber=True)
fits.close()
target_fits.close()
def sigma_l2fits(
filename,
nchains,
burnin,
path,
outname,
save=True,
):
"""
Converts c3-h5 dataset to fits for c1 BR and GBR estimator analysis.
ex. c3pp sigma-l2fits chains_v1/chain 5 10 cmb_sigma_l_GBRlike.fits
If "chain_c0001.h5", filename is cut to "chain" and will look in same directory for "chain_c*****.h5".
See comm_like_tools for further information about BR and GBR post processing
"""
click.echo("{:-^48}".format("Formatting sigma_l data to fits file"))
import h5py
if filename.endswith(".h5"):
filename = filename.rsplit("_", 1)[0]
temp = np.zeros(nchains)
for nc in range(1, nchains + 1):
with h5py.File(
filename + "_c" + str(nc).zfill(4) + ".h5",
"r",
) as f:
groups = list(f.keys())
temp[nc - 1] = len(groups)
nsamples_max = int(max(temp[:]))
click.echo(
f"Largest chain has {nsamples_max} samples, using burnin {burnin}\n")
for nc in range(1, nchains + 1):
fn = filename + "_c" + str(nc).zfill(4) + ".h5"
with h5py.File(
fn,
"r",
) as f:
click.echo(f"Reading {fn}")
groups = list(f.keys())
nsamples = len(groups)
if nc == 1:
dset = np.zeros((
nsamples_max + 1,
1,
len(f[groups[0] + "/" + path]),
len(f[groups[0] + "/" + path][0]),
))
nspec = len(f[groups[0] + "/" + path])
lmax = len(f[groups[0] + "/" + path][0]) - 1
else:
dset = np.append(
dset,
np.zeros((
nsamples_max + 1,
1,
nspec,
lmax + 1,
)),
axis=1,
)
click.echo(
f"Dataset: {path} \n# samples: {nsamples} \n# spectra: {nspec} \nlmax: {lmax}"
)
for i in range(nsamples):
for j in range(nspec):
dset[i + 1, nc - 1,
j, :] = np.asarray(f[groups[i] + "/" + path][j][:])
click.echo("")
# Optimize with jit?
ell = np.arange(lmax + 1)
for nc in range(1, nchains + 1):
for i in range(1, nsamples_max + 1):
for j in range(nspec):
dset[i, nc - 1,
j, :] = dset[i, nc - 1,
j, :] * ell[:] * (ell[:] + 1.0) / 2.0 / np.pi
dset[0, :, :, :] = nsamples - burnin
if save:
click.echo(f"Dumping fits file: {outname}")
dset = np.asarray(dset, dtype="f4")
from astropy.io import fits
head = fits.Header()
head["FUNCNAME"] = ("Gibbs sampled power spectra",
"Full function name")
head["LMAX"] = (lmax, "Maximum multipole moment")
head["NUMSAMP"] = (nsamples_max, "Number of samples")
head["NUMCHAIN"] = (nchains, "Number of independent chains")
head["NUMSPEC"] = (nspec, "Number of power spectra")
fits.writeto(outname, dset, head, overwrite=True)
# FITSIO Saving Deprecated (Use astropy)
if False:
import fitsio
fits = fitsio.FITS(
outname,
mode="rw",
clobber=True,
verbose=True,
)
h_dict = [
{
"name": "FUNCNAME",
"value": "Gibbs sampled power spectra",
"comment": "Full function name",
},
{
"name": "LMAX",
"value": lmax,
"comment": "Maximum multipole moment",
},
{
"name": "NUMSAMP",
"value": nsamples_max,
"comment": "Number of samples",
},
{
"name": "NUMCHAIN",
"value": nchains,
"comment": "Number of independent chains",
},
{
"name": "NUMSPEC",
"value": nspec,
"comment": "Number of power spectra",
},
]
fits.write(
dset[:, :, :, :],
header=h_dict,
clobber=True,
)
fits.close()
return dset
