def read_ptg(filename):
print ""
print "[READ PTG]: BEGIN reading "+filename
ptg = h.mrdfits(filename,hdu=1)
ptg_package = {'ra':ptg[2], 'dec':ptg[1], 'pa':ptg[0], 'hwp':ptg[3]}
print "[READ PTG]: END reading "
return ptg_package
def read_ptg(filename):
print ""
print "[READ PTG]: BEGIN reading " + filename
ptg = h.mrdfits(filename, hdu=1)
ptg_package = {'ra': ptg[2], 'dec': ptg[1], 'pa': ptg[0], 'hwp': ptg[3]}
print "[READ PTG]: END reading "
return ptg_package
def __init__(self, freq, nripple=1, **keywords):
nripple_max = 2
if nripple not in np.arange(1, nripple_max + 1):
raise ValueError('Input nripple is not a positive integer '
'less or equal {}'.format(nripples_max))
self.nripple = nripple
fl = hp.mrdfits(PATH +
'sb_peak_ripple{}_150HGz.fits'.format(nripple))[0]
# fl /= fl.max()
fl = np.sqrt(fl)
if freq == 150e9:
self.fl = fl
else:
ell = np.arange(len(fl)) + 1
spl = splrep(ell * freq / 150e9, fl)
self.fl = splev(ell, spl)
def read_mark_beam_map(self):
beam_kernel = np.array(hp.mrdfits(self.config.input_beam_file))
mark_orig_dim = np.sqrt(beam_kernel.shape[1]) #pixels
beam_kernel = beam_kernel.reshape((4, mark_orig_dim, mark_orig_dim))
mark_fwhm_major = 7.68 #arc-mins
mark_fwhm_minor = 7.68 #arc-mins
mark_resolution = 0.39523370660946627 #arc-mins
new_dim = int(mark_orig_dim * mark_resolution / self.config.scan_resolution)
if new_dim%2 == 0:
new_dim += 1
#print "old resolution :", mark_resolution
#print "new_resolution :", self.config.scan_resolution
old_extent = mark_orig_dim * mark_resolution
new_extent = new_dim * self.config.scan_resolution
#print "old extent :", mark_resolution * 181
#print "new_extent :", new_extent
self.config.fwhm_major = mark_fwhm_major * new_extent / old_extent
self.config.fwhm_minor = mark_fwhm_minor * new_extent / old_extent
#print "old fwhm :", mark_fwhm_major
#print "new fwhm :", self.config.fwhm_major
self.beam_kernel = np.empty((4, new_dim, new_dim))
for i in range(4):
self.beam_kernel[i] = zoom(beam_kernel[i], float(new_dim)/float(mark_orig_dim))
self.beam_kernel[i] = interpolation.rotate(self.beam_kernel[i], angle=self.config.beam_angle, reshape=False)
#print "beam_cutoff required :", self.config.beam_cutoff
beam_extension_required = self.config.fwhm_major * self.config.beam_cutoff #arc-min
#print "extention required :", beam_extension_required
num_pix = int(new_dim * beam_extension_required / new_extent)
if num_pix%2 == 0:
num_pix -= 1
#print "num pix :", num_pix
#print "extension achieved :", num_pix * self.config.scan_resolution
self.config.beam_cutoff = num_pix * self.config.scan_resolution / self.config.fwhm_major
#print "cutoff achieved :", self.config.beam_cutoff
start = new_dim/2 - num_pix/2
stop = new_dim/2 + num_pix/2 + 1
self.beam_kernel = self.beam_kernel[..., start:stop, start:stop]
#print "kernel shape :", self.beam_kernel.shape
self.del_beta = self.config.scan_resolution * np.arange(-num_pix/2 + 1, num_pix/2 + 1)
def __init__(self, freq,
nripple=1,
**keywords):
nripple_max = 2
if nripple not in np.arange(1, nripple_max + 1):
raise ValueError(
'Input nripple is not a positive integer '
'less or equal {}'.format(nripples_max))
self.nripple = nripple
fl = hp.mrdfits(PATH + 'sb_peak_ripple{}_150HGz.fits'.
format(nripple))[0]
# fl /= fl.max()
fl = np.sqrt(fl)
if freq == 150e9:
self.fl = fl
else:
ell = np.arange(len(fl)) + 1
spl = splrep(ell * freq / 150e9, fl)
self.fl = splev(ell, spl)
def make_minimasks(refmap, nside, radius, nside_pixcen, dir_out):
sfrac = radius2skyfrac(radius)
print 'Making mini masks for',sfrac,'sky fraction'
# Read available pixels
m = _hp.mrdfits(refmap)
mappix = m[0]
# Get theta, phi coordinates
pix = _np.array(range(_hp.nside2npix(nside)))
theta, phi = _hp.pix2ang(nside, pix)
# Make minimasks
mask = _np.zeros(_hp.nside2npix(nside))
mask[mappix] = 1.0
mask_pixcen = _hp.ud_grade(mask, nside_pixcen)
pixcen = _np.where(mask_pixcen > 0)[0]
n = 0
for i,p0 in enumerate(pixcen):
if i%100 == 0:
print i,'/',len(pixcen)
theta0, phi0 = _hp.pix2ang(nside_pixcen, p0)
d = gcdist(theta, phi, theta0, phi0)
p = _np.where(d < radius)[0]
if len(_np.where(mask[p] == 0)[0]) > 0:
continue
# p = _np.where( (d < radius) & (mask > 0) )[0]
# x = float(len(p))/float(_hp.nside2npix(nside))
# # Check if entire circle is contained in daniel's map
# if x < sfrac:
# continue
# Make minimask
m = _np.zeros(_hp.nside2npix(nside))
m[p] = _np.cos(_np.pi*d[p]/(2*radius))
out = dir_out+'/mask_n'+str(nside_pixcen)+'_'+str(p0)+'.fits'
_hp.write_map(out,m)
print 'Wrote',out
n = n + 1
print 'Found',n,'good pixel centers'
return
#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* LMAX = 1500 NSIDE = 1024 FWHM_arcmin = 8.0 FWHM = np.deg2rad(FWHM_arcmin/60.0) SIGMA = FWHM/2.35482 ell = np.arange(LMAX + 1) norm = 2*np.pi beam_attenuation = np.exp(-ell*(ell + 1)*SIGMA**2) #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* #Loading input spectra and masks #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* cl = hp.mrdfits(output_folder + "cls/" + tag + "/cellpure_sky_map_mask1_0_0.fits") ps = hp.mrdfits(output_folder + "cls/" + tag + "/pseudopure_sky_map_mask1_0_0.fits") input_spectra = np.load(input_spectra_folder + spectra_name)[...,:LMAX + 1] binary_mask = hp.read_map(binary_mask_folder + "mask_ebex.fits") apodised_mask = hp.read_map(output_folder + "mask/" + tag + "/apodized_mask_I_" + tag + ".fits") #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* #Calculate the normalisation due to partial sky and other factors #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* sky_frac = float(np.sum(binary_mask))/hp.nside2npix(NSIDE) norm_apodised = float(np.sum(apodised_mask**2))/hp.nside2npix(NSIDE) #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#* #Plotting sub routine
# In[10]:
# First calculate the covariance matrix by the definition, i.e.
#
# C_ij = < \delta T_i \delta T_j > = 1/4pi \sum^{N_pix} p =
# ( T_i(p) - mean(T_i) ) * ( T_j(p) - mean(T_j) )
#
#
# Larson, Weiland, Hinshaw, Bennett,
# http://arxiv.org/pdf/1409.7718.pdf
#
# In[11]:
tempmap = hp.mrdfits("camb_nside4.fits") #type() = list
# In[12]:
tempdata = tempmap[0] #len()=192, type()=numpy.ndarray
# In[13]:
# First calculate the mean
#
# numpy.mean
#
# numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False)
# Compute the arithmetic mean along the specified axis.
mapa[0][:] = mapa[0][:] * 1e6
hp.write_map('new_map.fits',mapa, coord='G', column_names=list(['30GHz','44GHz','70GHz']), column_units=list(['microK','K','K']))
mapa2 = hp.read_map('new_map.fits', field={0,1,2}, h=True)
#%%
"""
STEP 6: Reading/Writing generic data of/into an fits file
"""
#####################################
data = hp.mrdfits('COM_PCCS_030_R2.04.fits')
# OU AINDA:
from astropy.io import fits
hdulist = fits.open('COM_PCCS_030_R2.04.fits')
hdulist.info()
data = hp.mrdfits(hdulist, hdu=1)
hdulist.writeto('new_table.fits')
#####################################
len(data)
hp.mwrfits('new_table.fits', data)
def main():
usage = "usage: %(prog)s [options]"
description = "Make output summary plots of all-sky fitting"
parser = argparse.ArgumentParser(usage, description=description)
parser.add_argument('-i',
"--input",
type=str,
default="mcube_baseline",
help="Input file prefix")
parser.add_argument('-c',
"--ccube",
type=str,
default="ccube.fits",
help="Input file prefix")
args = parser.parse_args(sys.argv[1:])
fontsize = 9
matplotlib.rcParams.update({'font.size': fontsize})
#pdf_pages = PdfPages("%s.pdf"%(args.input))
ccube = hp.mrdfits(args.ccube, hdu=1)[1:]
ebins = hp.mrdfits(args.ccube, hdu=2)
eranges = np.vstack([ebins[1], ebins[2]]).T / 1e6 # keV to GeV
ecents = np.sqrt(ebins[1] * ebins[2])
ewidths = ebins[2] - ebins[1]
nebins = len(eranges)
counts_spectrum = np.zeros((nebins))
idx = 0
sys.stdout.write("Making plots: ")
region_spectra = {}
for i in range(NCOMP):
mcube_file = "%s_%02i.fits" % (args.input, i)
mcube = hp.mrdfits(mcube_file, hdu=1)[1:]
for j in range(len(mcube)):
sys.stdout.write('.')
sys.stdout.flush()
counts_cast = hp.ud_grade(ccube[idx], NSIDE, power=-2)
model_cast = hp.ud_grade(mcube[j], NSIDE, power=-2)
fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(14, 4))
figure_model(model_cast, eranges[idx])
figure_frac_resid(counts_cast, model_cast, eranges[idx])
figure_signif(counts_cast, model_cast, eranges[idx])
#pdf_pages.savefig(fig,bbox_inches='tight')
fig.savefig('fig_%s_maps_%02i_%02i.png' % (args.input, i, j),
bbox_inches='tight')
for regName, regPixels in zip(REGIONS['regionNames'],
REGIONS['regionPixels']):
if not region_spectra.has_key(regName):
region_spectra[regName] = dict(counts=np.zeros((nebins)),
model=np.zeros((nebins)))
region_counts_spectra = region_spectra[regName]['counts']
region_model_spectra = region_spectra[regName]['model']
if regPixels is None:
region_counts_spectra[idx] = counts_cast.sum()
region_model_spectra[idx] = model_cast.sum()
else:
region_counts_spectra[idx] = counts_cast[regPixels].sum()
region_model_spectra[idx] = model_cast[regPixels].sum()
idx += 1
fig_region_spectra = figure_region_spectra(REGIONS['regionNames'],
region_spectra, ecents, ewidths)
fig_region_resid = figure_region_resid(REGIONS['regionNames'],
region_spectra, ecents, ewidths)
fig_region_spectra.savefig('fig_%s_region_spectra.png' % (args.input),
bbox_inches='tight')
fig_region_resid.savefig('fig_%s_region_resid.png' % (args.input),
bbox_inches='tight')
#
# Larson, Weiland, Hinshaw, Bennett,
# http://arxiv.org/pdf/1409.7718.pdf
#
# In[51]:
temp1 = "camb_nside2.fits" #CAMB simulated maps associated with scalar C_l values above
temp2 = "camb_nside4.fits"
temp3 = "camb_nside8.fits"
# In[52]:
tempmap1 = hp.mrdfits("camb_nside2.fits") #type() = list
tempmap2 = hp.mrdfits("camb_nside4.fits")
tempmap3 = hp.mrdfits("camb_nside8.fits")
# In[53]:
tempdata1 = tempmap1[0] #len()=48, type()=numpy.ndarray
tempdata2 = tempmap2[0] #len()=192
tempdata3 = tempmap3[0] #len()=768
# In[54]:
# First calculate the mean
#
def read_FPDB_fits(filename):
fpdb = h.mrdfits(filename, hdu=1)
return fpdb
def my_mwrfits(filename,
data,
colnames=None,
keys=None,
bintable=False,
ftype=None,
extnames=None,
origin=None,
dets=None):
"""Write columns to a fits file in a table extension.
Parameters
----------
filename : str
The fits file name
data : list of 1D arrays
A list of 1D arrays to write in the table
colnames : list of str
The column names
keys : dict-like
A dictionary with keywords to write in the header
"""
hline = '----------------------------------------------------------------'
if ftype == 'B':
# name = 'WINDOW FUNCTION'
comments = [
'Beam Window Function B(l)',
'Compatible with Healpix (synfast, smoothing, ...) and PolSpice',
'To be squared before applying to power spectrum',
' C_map(l) = C_sky(l) * B(l)^2 '
]
if ftype == 'B_TEB':
# name = 'WINDOW FUNCTIONS'
comments = [
'Beam Window Functions B(l), for T, E and B',
'Compatible with Healpix (synfast, smoothing, ...) and PolSpice',
'To be squared before applying to power spectrum',
' C_TT_map(l) = C_TT_sky(l) * B_T(l)^2 ',
' C_EE_map(l) = C_EE_sky(l) * B_E(l)^2 ',
' C_BB_map(l) = C_BB_sky(l) * B_B(l)^2 '
]
if ftype == 'W':
# name = 'WINDOW FUNCTIONS'
comments = [
'Beam Window Functions W(l) = B(l)^2',
'Applies directly to power spectrum ',
' C_map(l) = C_sky(l) * W(l) ', 'Includes cross-talk terms '
]
# ---- primary header -----
hdu = pyfits.PrimaryHDU(None)
#hdu.name = name
hhu = hdu.header.set
#hhb = hdu.header.add_blank
hhc = hdu.header.add_comment
#hhh = hdu.header.add_history
fdate = datetime.datetime.now().strftime('%Y-%m-%d')
hhu('DATE', fdate, comment=' Creation date (CCYY-MM-DD) of FITS header')
if extnames is not None:
nx = len(extnames)
hhu('NUMEXT', nx, 'Number of extensions')
for xt in range(nx):
hhu('XTNAME%d' % (xt + 1), extnames[xt],
'Name of extension #%d' % (xt + 1))
hhc(hline)
for mycom in comments:
hhc(mycom)
if origin is not None:
for myor in origin:
hhc(myor)
hhc(hline)
if dets is not None:
for id, det in enumerate(dets):
hhu('DET%d' % (id + 1), det, 'Detector (set)')
hdulist = pyfits.HDUList([hdu])
# ---- other HDUs : tables ----
getformat = hp.fitsfunc.getformat
for xt in range(len(data)):
cols = []
for line in range(len(data[xt])):
namei = colnames[xt][line]
array = data[xt][line]
if bintable:
nt = len(array) # total length
repeat = nt # length / cell
fmt = str(repeat) + getformat(array)
array = np.reshape(array, (nt // repeat, repeat))
else:
fmt = getformat(array)
cols.append(pyfits.Column(name=namei, format=fmt, array=array))
if bintable:
tbhdu = pyfits.BinTableHDU.from_columns(cols)
else:
tbhdu = pyfits.TableHDU.from_columns(cols)
if extnames is not None:
tbhdu.name = extnames[xt]
ncols = len(cols)
tbhdu.header['MAX-LPOL'] = (len(data[xt][0]) - 1,
'Maximum L multipole')
tbhdu.header['POLAR'] = ((ncols > 1))
tbhdu.header['BCROSS'] = ((ncols > 4))
tbhdu.header['ASYMCL'] = ((ncols > 6))
tbhdu.header.add_comment(hline)
for mycom in comments:
tbhdu.header.add_comment(mycom)
for myor in origin:
tbhdu.header.add_comment(myor)
tbhdu.header.add_comment(hline)
if type(keys) is dict:
for k, v in list(keys.items()):
tbhdu.header[k] = (v)
hdulist.append(tbhdu)
# write the file
hdulist.writeto(filename, overwrite=True)
# checking out the file
#
try:
# pyfits.info(filename)
p1 = pyfits.getdata(filename)
junk = hp.mrdfits(filename)
print(prefix,
'%s checking out %s%s' % (GREEN_COLOR, filename, NO_COLOR),
flush=True)
except:
raise RuntimeError('Failed to load {}'.format(filename))
