def setUp(self):
self.path = os.path.dirname( os.path.realpath( __file__ ) )
try:
self.map1 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_W_v4.fits'), (0,1,2))]
self.map2 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_V_v4.fits'), (0,1,2))]
self.mask = hp.read_map(os.path.join(self.path, 'data', 'wmap_temperature_analysis_mask_r9_7yr_v4.fits')).astype(np.bool)
except exceptions.IOError:
warnings.warn("""Missing Wmap test maps from the data folder, please download them from Lambda and copy them in the test/data folder:
http://lambda.gsfc.nasa.gov/data/map/dr4/skymaps/7yr/raw/wmap_band_iqumap_r9_7yr_W_v4.fits
http://lambda.gsfc.nasa.gov/data/map/dr4/skymaps/7yr/raw/wmap_band_iqumap_r9_7yr_V_v4.fits
http://lambda.gsfc.nasa.gov/data/map/dr4/ancillary/masks/wmap_temperature_analysis_mask_r9_7yr_v4.fits
on Mac or Linux you can run the bash script get_wmap_maps.sh from the same folder
""")
for m in chain(self.map1, self.map2):
m.mask = np.logical_not(self.mask)
self.cla = hp.read_cl(os.path.join(self.path, 'data', 'cl_wmap_fortran.fits'))
cls = pyfits.open(os.path.join(self.path, 'data',
'cl_iqu_wmap_fortran.fits'))[1].data
# order of HEALPIX is TB, EB while in healpy is EB, TB
self.cliqu = [cls.field(i) for i in (0,1,2,3,5,4)]
nside = 32
lmax = 64
fwhm_deg = 7.
seed = 12345
np.random.seed(seed)
self.mapiqu = hp.synfast(self.cliqu, nside, lmax=lmax, pixwin=False,
fwhm=np.radians(fwhm_deg), new=False)
def surv_sum_diff(surveydict,ss=1,nside=128,freq=70,mask_ps=True,fwhm=10.0):
"""
function to genreate smoothed difference between chosen survey and sum of the other 5 surveys from andreas interactive destriper/binner. uses common masks smooth to 10 degrees
fwhm is in degrees, if zero or negative don't smooth at all, default 10 degrees
"""
sumlist=surveydict.keys()
sumlist.remove(ss)
nsum=len(sumlist)
m=hp.ma(np.array(surveydict[sumlist[0]]))/nsum
totalmask=m.mask
if mask_ps==True:
psmask = np.logical_not(np.floor(hp.ud_grade(hp.read_map(glob('/project/projectdirs/planck/data/mission/DPC_maps/dx8/lfi/DX8_MASKs/' + 'mask_ps_%dGHz_*.fits' % freq)[0]), nside,order_out='NEST')))
totalmask=m.mask | psmask
for ss in sumlist[1:]:
m1=hp.ma(np.array(surveydict[ss]))
m=m+m1/nsum
totalmask=m1.mask | totalmask
m.mask=totalmask
m.mask |= np.isnan(m)
m1=hp.ma(np.array(surveydict[ss]))
totalmask=totalmask | m1.mask
d=(m1-m)/2.
d=hp.ud_grade(d,nside,order_in='NEST',order_out='RING')
if fwhm>0:
dsm=hp.ma(hp.smoothing(d.filled(),fwhm*np.pi/180.))
dsm.mask=m.mask
if fwhm<=0:
dsm=d
hp.mollview(dsm,min=-1e-5,max=1e-5,title='SS'+np.str(ss)+ ' - sum of others')
return dsm
def setUp(self):
self.lmax = 64
self.path = os.path.dirname( os.path.realpath( __file__ ) )
self.map1 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_W_v4_udgraded32.fits'), (0,1,2))]
self.map2 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_V_v4_udgraded32.fits'), (0,1,2))]
self.mask = hp.read_map(os.path.join(self.path, 'data', 'wmap_temperature_analysis_mask_r9_7yr_v4_udgraded32.fits')).astype(np.bool)
for m in chain(self.map1, self.map2):
m.mask = np.logical_not(self.mask)
self.cla = hp.read_cl(os.path.join(self.path, 'data', 'cl_wmap_band_iqumap_r9_7yr_W_v4_udgraded32_II_lmax64_rmmono_3iter.fits'))
self.cl_fortran_nomask = hp.read_cl(os.path.join(self.path, 'data', 'cl_wmap_band_iqumap_r9_7yr_W_v4_udgraded32_II_lmax64_rmmono_3iter_nomask.fits'))
cls_file = pyfits.open(os.path.join(self.path, 'data',
'cl_wmap_band_iqumap_r9_7yr_W_v4_udgraded32_IQU_lmax64_rmmono_3iter.fits'))
# fix for pyfits to read the file with duplicate column names
for i in range(2, 6):
cls_file[1].header['TTYPE%d' % i] += '-%d' % i
cls = cls_file[1].data
# order of HEALPIX is TB, EB while in healpy is EB, TB
self.cliqu = [np.array(cls.field(i)) for i in (0,1,2,3,5,4)]
nside = 32
lmax = 64
fwhm_deg = 7.
seed = 12345
np.random.seed(seed)
self.mapiqu = hp.synfast(self.cliqu, nside, lmax=lmax, pixwin=False,
fwhm=np.radians(fwhm_deg), new=False)
def read_and_diff_files_fast(f1,f2,nside=256,tmask=None,return_map=False):
#assume tmask input is already degraded
mm1=hp.read_map(f1,[0,1,2],verbose=False)
mm2=hp.read_map(f2,[0,1,2],verbose=False)
mmm1=[]
mmm2=[]
for m1,m2 in zip(mm1,mm2):
m1=hp.ud_grade(hp.ma(m1),nside_out=nside)
m2=hp.ud_grade(hp.ma(m2),nside_out=nside)
tmask=m1.mask | m2.mask | tmask
mmm1.append(m1)
mmm2.append(m2)
diff=[]
for m1,m2 in zip(mmm1,mmm2):
d=m1-m2
d.mask=tmask
diff.append(d)
skyfrac=1-float(tmask.sum())/len(tmask)
cldata=hp.anafast(diff)
cldata_out=[]
for cl in cldata:
cldata_out.append(cl/skyfrac)
if return_map is False:
return cldata_out
if return_map is True:
return cldata_out,diff
def setUp(self):
self.nside = 16
self.m = np.arange(hp.nside2npix(self.nside), dtype=np.double)
self.ma = self.m.copy()
self.ma[3] = hp.UNSEEN
self.ma = hp.ma(self.ma)
self.m2 = self.m.copy()
self.m2[100:] = hp.UNSEEN
self.ma2 = hp.ma(self.m2)
def remove_low_l(map1, mask1):
map1m=hp.ma(map1)
map1m.mask=np.logical_not(mask1)
map2=np.ma.masked_values(map1, UNSEEN)
alm2=hp.map2alm(map2, lmax=LMAX)
fac2=np.array([1.]*(LMAX+1))
fac2[0:lREM]=0.
alm2n=hp.sphtfunc.almxfl(alm2, fac2, inplace=False)
map2new=hp.ma(hp.alm2map(alm2n, 2048))
map2new.mask=np.logical_not(mask1)
return map2new
def plot_figure(metadata):
try:
allmap = hp.ma(hp.read_map(os.path.join(root_folder, metadata["file_name"]), (0,1,2)))
except exceptions.IndexError:
allmap = [hp.ma(hp.read_map(os.path.join(root_folder, metadata["file_name"])))]
for comp, m in zip("IQU", allmap):
if comp in "QU":
plot_range = 20
else:
plot_range = 20
if len(allmap) == 1: #only T, single ch
plot_range = 20
is_single_channel = isinstance(metadata["channel"], basestring) and len(metadata["channel"])==6
if is_single_channel:
if int(metadata["channel"][3:5]) < 24: # 70GHz
plot_range = 20
test_type = metadata["base_file_name"].split("/")[0]
if isinstance(metadata["channel"], list):
metadata["channel"] = "_".join(metadata["channel"])
if metadata["channel"].find("_") < 0:
try:
if int(metadata["channel"]) > 70:
plot_range = 5
if int(metadata["channel"]) >= 353:
if comp in "QU":
plot_range = 100
else:
plot_range = 30
if int(metadata["channel"]) >= 545:
if comp in "QU":
plot_range = 1e6
else:
plot_range = 500
if int(metadata["channel"]) >= 857:
plot_range = 1e5
if comp in "QU":
plot_range = 1e6
if test_type == "surveydiff" and int(metadata["channel"]) >= 545 and comp == "Q":
plot_range *= 1e2
except exceptions.ValueError:
pass
fig = plt.figure(figsize=(9, 6), dpi=100)
matplotlib.rcParams.update({'font.size': 14})
hp.mollview(m * 1e6, min=-plot_range, max=plot_range, unit="uK", title=metadata["title"] + " %s" % comp, xsize=900, hold=True)
plt.savefig(os.path.join(out_folder, metadata["file_name"].replace(".fits", "_%s.jpg" % comp)), dpi=100)
plt.close()
fig = plt.figure(figsize=(9, 6), dpi=20)
fig.add_axes([0.01, 0.01, 0.98, 0.98])
matplotlib.rcParams.update({'font.size': 30})
hp.mollview(m * 1e6, min=-plot_range, max=plot_range, cbar=True, title="", xsize=180, hold=True)
plt.savefig(os.path.join(out_folder, metadata["file_name"].replace(".fits", "_%s_thumb.jpg" % comp)), dpi=20)
plt.close()
def to_healpix(self, n_side=32):
""" Convert beam pattern to a healpix
Returns
-------
hmap: np.array
Numpy array representing healpix map. Array is in observer frame,
i.e. zenith aligned with equatorial coordinate system.
"""
beam_azel = self.to_azel()
el = np.linspace(0, np.pi, beam_azel.shape[1])
az = np.linspace(-np.pi, np.pi, beam_azel.shape[0])[:, None]
# Generate beam pattern with n_side = 32 (for gridding reasons)
hmap = hp.ma(np.zeros(hp.nside2npix(32)))
pix = hp.ang2pix(32, el, az)
hmap[pix] = beam_azel
# Upsample if required
if n_side != 32:
hmap = hp.ud_grade(hmap, nside_out=n_side)
# Apply mask
n_pix = hp.nside2npix(n_side)
theta, phi = hp.pix2ang(n_side, np.arange(n_pix))
mask1 = phi + np.pi/2 > 2 * np.pi
mask2 = phi < np.pi / 2
hmap.mask = np.invert(np.logical_or(mask1, mask2))
return hmap
def __getMaskedPixels(self, pixels):
pixels_masked = healpy.ma(pixels)
if (self.Configuration.isConfigured(ChartHealpixOptions.MASK)):
pixels_masked.mask = healpy.mask_bad(pixels, badval = self.__getMaskedValue())
return pixels_masked.filled(fill_value = numpy.nan)
def qubin(pix, NSIDE, q_channel, u_channel, pa):
npix = hp.nside2npix(NSIDE)
q_channel_w, u_channel_w = get_qu_weights(pa)
#create M
M = {}
for c1,c2 in itertools.combinations_with_replacement('QU',2):
print (c1,c2)
M[(c1,c2)] = pix2map(pix, NSIDE, q_channel_w[c1] * q_channel_w[c2] + u_channel_w[c1] * u_channel_w[c2], dividebyhits=False)[1].filled()
#invert M
invM = np.array([
[[ M[('Q','Q')][x], M[('Q','U')][x] ],
[ M[('Q','U')][x], M[('U','U')][x] ]] for x in range(npix) ])
for x,blockM in enumerate(invM):
if blockM[0,0] != 0:
invM[x] = np.linalg.inv(blockM)
summap = hp.ma(np.hstack([
pix2map(pix, NSIDE, q_channel * q_channel_w[c] + u_channel * u_channel_w[c], dividebyhits=False)[1].filled()[:,None] for c in 'QU'
]))
summap.mask = summap == 0
return M, invM, summap, weightmap(summap, invM)
def test_smoothcombine():
nside = 256
freq = 30
m = hp.ma(np.random.standard_normal(hp.nside2npix(nside)))
m.mask = np.zeros(len(m), dtype=np.bool)
Reader = reader.Readers[os.environ["NULLTESTS_ENV"]]
mapreader = Reader(os.environ["DDX9_LFI"], nside=nside, baseline_length=None)
ps_mask, gal_mask = mapreader.read_masks(freq)
# ps_mask = False
# gal_mask = False
smooth_combine(
[(m, 1)],
[(np.ones_like(m), 1)],
spectra=True,
smooth_mask=ps_mask,
spectra_mask=gal_mask,
metadata={"file_type": ""},
)
# check chi-square
metadata = json.load(open("out_map.json", "r"))
assert np.abs(metadata["map_unsm_chi2"] - 1) < 0.01
assert np.abs(metadata["map_chi2"] - 1) < 0.1
# check spectrum
cl = hp.read_cl("out_cl.fits")
realization_wn = cl[200:].mean()
assert np.abs(realization_wn - metadata["whitenoise_cl"]) < 1e-5
def mask_map(sky_map, binary_mask=None, pol=True, ret_mask=False, fill_zeros=False):
if binary_mask is None:
binary_mask = np.logical_not(np.isnan(sky_map[0] if pol else sky_map))
if fill_zeros:
if pol:
sky_map[0][np.isnan(sky_map[0])] = 0.0
sky_map[1][np.isnan(sky_map[1])] = 0.0
sky_map[2][np.isnan(sky_map[2])] = 0.0
else:
sky_map[np.isnan(sky_map)] = 0.0
sky_map_masked = hp.ma(sky_map)
if pol:
sky_map_masked[0].mask = np.logical_not(binary_mask)
sky_map_masked[1].mask = np.logical_not(binary_mask)
sky_map_masked[2].mask = np.logical_not(binary_mask)
else:
sky_map_masked.mask = np.logical_not(binary_mask)
if ret_mask:
return sky_map_masked, binary_mask
else:
return sky_map_masked
def get_hem_Cls(skymap, direction, LMAX=256, deg=90.):
"""
from the given healpix skymap, return Cls for two hemispheres defined by the
direction given, useful to study the possible scale dependence of power modulation
direction should be a unit vector
"""
# generate hemispherical mask
NPIX=len(skymap)
NSIDE=hp.npix2nside(NPIX)
maskp=np.array([0.]*NPIX)
disc=hp.query_disc(nside=NSIDE, vec=direction, radius=0.0174532925*deg)
maskp[disc]=1.
#skymap=hp.remove_monopole(skymap)
map1=hp.ma(skymap)
map1.mask=maskp
Clsp=hp.anafast(map1, lmax=LMAX)
if (deg<90.):
maskm=np.array([0.]*NPIX)
disc=hp.query_disc(nside=NSIDE, vec=-direction, radius=0.0174532925*deg)
maskm[disc]=1.
map1.mask=maskm
else:
map1.mask=np.logical_not(maskp)
Clsm=hp.anafast(map1, lmax=LMAX)
return [Clsp, Clsm]
def setUp(self):
self.path = os.path.dirname( os.path.realpath( __file__ ) )
try:
self.map1 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_W_v4.fits'), (0,1,2))]
self.map2 = [hp.ma(m) for m in hp.read_map(os.path.join(self.path, 'data', 'wmap_band_iqumap_r9_7yr_V_v4.fits'), (0,1,2))]
self.mask = hp.read_map(os.path.join(self.path, 'data', 'wmap_temperature_analysis_mask_r9_7yr_v4.fits')).astype(np.bool)
except exceptions.IOError:
warnings.warn("""Missing Wmap test maps from the data folder, please download them from Lambda and copy them in the test/data folder:
http://lambda.gsfc.nasa.gov/data/map/dr4/skymaps/7yr/raw/wmap_band_iqumap_r9_7yr_W_v4.fits
http://lambda.gsfc.nasa.gov/data/map/dr4/skymaps/7yr/raw/wmap_band_iqumap_r9_7yr_V_v4.fits
http://lambda.gsfc.nasa.gov/data/map/dr4/ancillary/masks/wmap_temperature_analysis_mask_r9_7yr_v4.fits
on Mac or Linux you can run the bash script get_wmap_maps.sh from the same folder
""")
raise
for m in chain(self.map1, self.map2):
m.mask = np.logical_not(self.mask)
self.cla = hp.read_cl(os.path.join(self.path, 'data', 'cl_wmap_fortran.fits'))
def pix2map(pix, nside, tod=None):
"""Pixel array to hitmap, if TOD with same lenght of PIX is provided,
it is binned to a map"""
#TODO test case
pix = pix.astype(np.int)
ids = np.bincount(pix, weights=None)
hitmap = np.ones(hp.nside2npix(nside)+1) * hp.UNSEEN
hitmap[:len(ids)] = ids
hitmap = hp.ma(hitmap[:-1])
if tod is None:
return hitmap
else:
ids_binned = np.bincount(pix, weights=tod)
binned = np.ones(hp.nside2npix(nside)+1) * hp.UNSEEN
binned[:len(ids_binned)] = ids_binned
binned = hp.ma(binned[:-1])/hitmap
return hitmap, binned
def test_smoothing_masked(self):
smoothed = hp.smoothing(self.map1, fwhm=np.radians(10), lmax=self.lmax, regression=False)
smoothed_f90 = hp.ma(hp.read_map(os.path.join(self.path, 'data',
'wmap_band_iqumap_r9_7yr_W_v4_udgraded32_masked_smoothed10deg_fortran.fits'), (0,1,2)))
# fortran does not restore the mask
for mm in smoothed_f90:
mm.mask = smoothed[0].mask
for i in range(3):
np.testing.assert_array_almost_equal(smoothed[i].filled(), smoothed_f90[i].filled(), decimal=6)
def load_CTBCORE_data(self):
self.CTB_en_bins, self.CTB_count_maps, self.CTB_exposure_maps, self.CTB_psc_masks_temp = CTB.get_CTB(self.CTB_dir, self.nside, self.CTB_en_min, self.CTB_en_max,is_p8 = self.is_p8, eventclass = self.eventclass, eventtype = self.eventtype, newstyle = self.newstyle)
self.CTB_map_um=np.sum(self.CTB_count_maps,axis=0)
self.CTB_count_psc_maps = [hp.ma(self.CTB_count_map) for self.CTB_count_map in self.CTB_count_maps]
for i in range(len(self.CTB_count_psc_maps)):
self.CTB_count_psc_maps[i].mask = self.CTB_psc_masks_temp[i]
self.total_exposure_map = np.mean(self.CTB_exposure_maps,axis=0)
self.NPIX_total = np.size(self.total_exposure_map)
def fetch_wmap_temperatures(masked=False, data_home=None,
download_if_missing=True):
"""Loader for WMAP temperature map data
Parameters
----------
masked : optional, default=False
If True, then return the foreground-masked healpix array of data
If False, then return the raw temperature array
data_home : optional, default=None
Specify another download and cache folder for the datasets. By default
all scikit learn data is stored in '~/astroML_data' subfolders.
download_if_missing : optional, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
-------
data : np.ndarray or np.ma.MaskedArray
record array containing (masked) temperature data
"""
# because of a bug in healpy, pylab must be imported before healpy is
# or else a segmentation fault can result.
import pylab
import healpy as hp
data_home = get_data_home(data_home)
if not os.path.exists(data_home):
os.makedirs(data_home)
data_file = os.path.join(data_home, os.path.basename(DATA_URL))
mask_file = os.path.join(data_home, os.path.basename(MASK_URL))
if not os.path.exists(data_file):
if not download_if_missing:
raise IOError('data not present on disk. '
'set download_if_missing=True to download')
data_buffer = download_with_progress_bar(DATA_URL)
open(data_file, 'wb').write(data_buffer)
data = hp.read_map(data_file)
if masked:
if not os.path.exists(mask_file):
if not download_if_missing:
raise IOError('mask data not present on disk. '
'set download_if_missing=True to download')
mask_buffer = download_with_progress_bar(MASK_URL)
open(mask_file, 'w').write(mask_buffer)
mask = hp.read_map(mask_file)
data = hp.ma(data)
data.mask = np.logical_not(mask) # WMAP mask has 0=bad. We need 1=bad
return data
def read_and_diff_files_fast_hitweight(f1,f2,fh1,fh2,fhfull,nside=256,tmask=None,return_map=False,remove_monopole=True):
#assume tmask is already degraded, remove_monopole=True: remove from differences before anafast
#monopole is removed from I,Q U maps after masking
mm1=hp.read_map(f1,[0,1,2],verbose=False)
mm2=hp.read_map(f2,[0,1,2],verbose=False)
h1=hp.read_map(fh1,[3],verbose=False)
h2=hp.read_map(fh2,[3],verbose=False)
hit_full=hp.read_map(fhfull,[3],verbose=False)
nsh=hp.npix2nside(len(h1))
if nsh != nside:
h1=hp.ud_grade(h1,nside_out=nside,power=-2)
h2=hp.ud_grade(h2,nside_out=nside,power=-2)
hit_full=hp.ud_grade(hit_full,nside_out=nside,power=-2)
mmm1=[]
mmm2=[]
for m1,m2 in zip(mm1,mm2):
m1=hp.ud_grade(hp.ma(m1),nside_out=nside)
m2=hp.ud_grade(hp.ma(m2),nside_out=nside)
tmask=m1.mask | m2.mask | tmask
mmm1.append(m1)
mmm2.append(m2)
whit = np.sqrt(hit_full*(1./h1+1./h2)) # m1[3] and m2[3] are the hit counts of the two maps to be nulled
diff=[]
for m1,m2 in zip(mmm1,mmm2):
d=(m1-m2)/whit
d.mask=tmask
if remove_monopole:
d=hp.remove_monopole(d)
diff.append(d)
skyfrac=1-float(tmask.sum())/len(tmask)
cldata=hp.anafast(diff)
cldata_out=[]
for cl in cldata:
cldata_out.append(cl/skyfrac)
if return_map is False:
return cldata_out
if return_map is True:
return cldata_out,diff
def Cl_from_map_mask(mask_path, data_path):
mask = hp.read_map(mask_path)
map = hp.read_map(data_path)
map_masked = hp.ma(map)
map_masked.mask = np.logical_not(mask)
Cl = hp.anafast(map_masked.filled(), lmax=LMAX)
ell = np.arange(len(Cl))
return ell, Cl
def apply_mask(parameters,ar,mask_badval,mask_unseen):
mask = sp.zeros_like(ar)
if mask_badval:
mask[ar == parameters.badval] = 1
if mask_unseen:
mask[ar == parameters.unseen] = 1
ar_masked = hp.ma(ar)
ar_masked.mask = mask
return ar_masked, mask
def map_masked(mask_path, data_path):
mask = hp.read_map(mask_path)
map = hp.read_map(data_path)
map_masked = hp.ma(map)
map_masked.mask = np.logical_not(mask)
plt.figure()
hp.mollview(map_masked.filled())
plt.savefig("/home/yhwu/pic/map_masked.png", dpi=1000)
plt.clf()
def LoadPlanck(filemap,
filemask,
nside,
do_plots=False,
filt_plank_lmin=0,
pess=True):
print("...reading Planck map & mask...")
planck_mask = hp.read_map(filemask, verbose=False)
planck_map = hp.read_map(filemap, verbose=False)
print("...done...")
print("...degrading Planck map & mask to N_side = %d ..." % nside)
planck_mask = hp.ud_grade(planck_mask, nside, pess=pess)
planck_mask[
planck_mask < 1.] = 0. # Pessimistc cut, retains only M(n) = 1 pixels
planck_map = hp.ud_grade(planck_map, nside, pess=pess)
if do_plots:
planck_map = hp.ma(planck_map)
planck_map.mask = np.logical_not(planck_mask)
hp.mollview(planck_map, title='Planck 2015 No filtering')
hp.graticule()
# plt.tight_layout()
plt.savefig('figs/planck.pdf') #, bbox_inches='tight')
plt.close()
if filt_plank_lmin != 0:
planck_map_alm = hp.map2alm(planck_map)
filt = np.ones(hp.Alm.getlmax(len(planck_map_alm)))
filt[:filt_plank_lmin + 1] = 0.
planck_map_alm = hp.almxfl(planck_map_alm, filt)
planck_map = hp.alm2map(planck_map_alm, nside)
if do_plots:
planck_map = hp.ma(planck_map)
planck_map.mask = np.logical_not(planck_mask)
hp.mollview(planck_map,
title=r'Planck 2015 $\ell_{\rm min}^{\rm filt} = %d$' %
filt_plank_lmin)
hp.graticule()
plt.savefig('figs/planck_filt.pdf')
plt.close()
print("...done...")
return planck_map, planck_mask
def compute_cl(map1, weight1, mask1, lmax=None):
nside = hp.get_nside(map1)
t1 = time()
log = '! ---- computing cl ------ \n'
if lmax is None:
lmax = 3 * nside - 1
log += 'lmax is calculated from the input map(nside = %d) %d! \n' % (
nside, lmax)
#
# will use gauss-leg points to do the integral
x, w = np.polynomial.legendre.leggauss(lmax + 1)
log += 'generated the leg gauss points ... \n'
#
# map
mapmasked = hp.ma(map1 * weight1)
mapmasked.mask = np.logical_not(mask1)
clmap = hp.anafast(mapmasked.filled(), lmax=lmax)
ximap = cl2xi(clmap, x)
log += 'computed the map cl .... \n'
#
# weight
maskmasked = hp.ma(weight1)
maskmasked.mask = np.logical_not(mask1)
clmask = hp.anafast(maskmasked.filled(), lmax=lmax)
ximask = cl2xi(clmask, x)
log += 'computed the weight cl .... \n'
#
# correct for the mask
xifinal = ximap / ximask
clfinal = xi2cl(x, w, xifinal, lmax)
log += 'correct for the "mask" (xi_map / xi_weight) .... \n'
# update
output = {}
output['attr'] = {'nside': nside, 'lmax': lmax}
output['cl'] = (np.arange(clfinal.size), clfinal)
output['cl_u'] = clmap
output['xi_mask'] = ximask
output['xi'] = (np.rad2deg(np.arccos(x)), xifinal)
log += 'Finished C_l in %.2f [sec]' % (time() - t1)
print(log)
return output
def cmplx_map_2_full_alm(residuals, lmax):
"""
Get the full set of alm for a complex healpix map. NaN will be treated as
a mask. Healpy UNSEEN may be buggy for imaginary components.
"""
l, m = hp.Alm.getlm(lmax)
# healpix only calculates m >= 0
pos_lm_idx = pd.MultiIndex.from_arrays((l, m), names=['l', 'm'])
neg_lm_idx = pd.MultiIndex.from_arrays((l, -m), names=['l', 'm'])
# mask for healpix masked array
mask = np.isnan(residuals)
# FIXME: add healpy UNSEEN pixels to the mask?
# healpix assumes a real map, so split into real & imaginary parts
real_map = hp.ma(np.real(residuals))
real_map.mask = mask
real_alm = hp.map2alm(real_map, lmax=lmax)
imag_map = hp.ma(np.imag(residuals))
imag_map.mask = mask
imag_alm = hp.map2alm(imag_map, lmax=lmax)
alm_real_pos = pd.Series(data=real_alm, index=pos_lm_idx)
# al,-m = (-1)^m(al,m)*
sign = (-1)**m
alm_real_neg = pd.Series(data=np.conj(real_alm) * sign, index=neg_lm_idx)
alm_real_neg.drop([0], level='m', inplace=True)
alm_real = pd.concat([alm_real_pos, alm_real_neg])
alm_real.sort_index(level='l', inplace=True)
alm_imag_pos = pd.Series(data=imag_alm, index=pos_lm_idx)
alm_imag_neg = pd.Series(data=np.conj(imag_alm) * sign, index=neg_lm_idx)
alm_imag_neg.drop([0], level='m', inplace=True)
alm_imag = pd.concat([alm_imag_pos, alm_imag_neg])
alm_imag.sort_index(level='l', inplace=True)
# spherical harmonics are a complete basis: can just add real & imaginary parts
alm = alm_real + 1j * alm_imag
return alm
def alm2map_masked(mask_path,data_path):
mask=hp.read_map(mask_path)
alm=hp.read_alm(data_path)
map=hp.sphtfunc.alm2map(alm,nside)
map_masked=hp.ma(map)
map_masked.mask=np.logical_not(mask)
plt.figure()
hp.mollview(map_masked)
plt.savefig("/home/yhwu/pic/alm2map_masked.png",dpi=1000)
plt.clf()
def surv_diff(surveydict,ss1=1,ss2=2,nside=128,freq=70,mask_ps=True,fwhm=10.0):
"""
function to make differences among 5 surveys from andreas interactive destriper/binner. uses common masks smooth to 10 degrees
fwhm is in degrees, if zero or negative don't smooth at all, default 10 degrees
"""
m1=hp.ma(np.array(surveydict[ss1]))
m2=hp.ma(np.array(surveydict[ss2]))
totalmask=m1.mask|m2.mask
if mask_ps==True:
psmask = np.logical_not(np.floor(hp.ud_grade(hp.read_map(glob('/project/projectdirs/planck/data/mission/DPC_maps/dx8/lfi/DX8_MASKs/' + 'mask_ps_%GHz_*.fits' % freq)[0]), nside,order_out='NEST')))
totalmask=m1.mask | m2.mask | psmask
dif=(m1-m2)/2.
dif.mask=totalmask
dif.mask |= np.isnan(dif)
dif=hp.ud_grade(dif,nside,order_in='NEST',order_out='RING')
if fwhm>0:
difsm=hp.ma(hp.smoothing(dif.filled(),fwhm*np.pi/180.))
difsm.mask=dif.mask
if fwhm<=0:
difsm=dif
return difsm
def local_mean_map(map1, mask1, deg):
""" return the local mean map for map1 with mask mask1
"""
mp1=hp.ma(map1)
mp1.mask=np.logical_not(mask1)
NSIDE1=hp.npix2nside(len(map1))
NSIDE2=nside(deg)
NPIX2=hp.nside2npix(NSIDE2)
mp2=np.array([0.]*NPIX2)
for pix2 in range(0, NPIX2):
disc1=hp.query_disc(nside=NSIDE1, vec=hp.pix2vec(NSIDE2, pix2), radius=deg2rad(deg))
mp2[pix2]=np.mean(mp1[disc1])
return mp2
def test_smoothing_masked(self):
smoothed = hp.smoothing(self.map1, fwhm=np.radians(10), lmax=self.lmax)
smoothed_f90 = hp.ma(
hp.read_map(
os.path.join(
self.path, 'data',
'wmap_band_iqumap_r9_7yr_W_v4_udgraded32_masked_smoothed10deg_fortran.fits'
), (0, 1, 2)))
# fortran does not restore the mask
smoothed_f90.mask = smoothed.mask
np.testing.assert_array_almost_equal(smoothed.filled(),
smoothed_f90.filled(),
decimal=6)
def __init__(self, zmean=default_zmean, zsigma=default_zsigma, star_file=default_star_file, options=None):
if options is not None:
self.zmean=options.star_zmean
self.zsigma=options.star_zsigma
self.star_file=options.star_file
else:
self.zmean=zmean
self.zsigma=zsigma
self.star_file=star_file
table=aptable.Table.read(self.star_file)
self.smap = hp.ma(table['values'].data)
self.smap.mask = table['mask'].data
def plot_map(nside, pix, v, vmin=None, vmax=None, cmap='jet', title=None, save_path=None,
xsize=None, dpi=None, show=True, timing=True, nest=False):
if xsize is None:
xsize = default_xsize[nside]
if dpi is None:
dpi = default_dpi[nside]
npix = hp.nside2npix(nside)
v = np.array(v)
# Density map
map_values = np.zeros(npix, dtype=v.dtype)
hp_mask = np.zeros(npix, dtype=bool)
map_values[pix] = v
hp_mask[pix] = True
mplot = hp.ma(map_values)
mplot.mask = ~hp_mask
# Galactic plane
org = 120
tmpn = 1000
cs = SkyCoord(l=np.linspace(0, 360, tmpn) * units.deg, b=np.zeros(tmpn) * units.deg, frame="galactic")
ras, decs = cs.icrs.ra.degree, cs.icrs.dec.degree
ras = np.remainder(ras + 360 - org, 360) # shift ra values
ras[ras > 180] -= 360 # scale conversion to [-180, 180]
ii = ras.argsort()
ras, decs = ras[ii], decs[ii]
if timing:
time_start = time.time()
projview(mplot, min=vmin, max=vmax,
rot=(120, 0, 0), cmap=cmap, xsize=xsize,
graticule=True, graticule_labels=True, projection_type="mollweide", nest=nest,
title=title,
xlabel='RA (deg)', ylabel='Dec (deg)',
custom_xtick_labels=[r'$240\degree$', r'$180\degree$', r'$120\degree$', r'$60\degree$', r'$0\degree$'],
fontsize=fontsize_dict)
newprojplot(theta=np.radians(90-decs), phi=np.radians(ras), color='k', lw=1)
if save_path is not None:
plt.savefig(save_path, bbox_inches="tight", dpi=dpi)
if show:
plt.show()
else:
plt.close()
if timing:
print('Done!', time.strftime("%H:%M:%S", time.gmtime(time.time() - time_start)))
def updateMask(self, idx_mask):
"""Set the mask used to analyze the data
Parameters
----------
idx_mask : `np.ndarray`
Masks
"""
self.idx_mask = idx_mask
self.f_sky = 1. - len(idx_mask[0]) / float(Defaults.NPIXEL)
countsmap = self.countsmap_fullsky.copy() + 0. # +0. to convert to float array
for i in range(Defaults.NEbin):
countsmap[i][idx_mask] = hp.UNSEEN
self.countsmap = hp.ma(countsmap)
def test_single(cl, mask, rseed, lmin=0, lmax=100, nside=256):
print_message('Single test with my code')
print_message('Mask coverage = %f' % (np.average(mask)))
#mask = np.logical_not(hp.ud_grade(mask, nside_out = nside))
mask = hp.ud_grade(mask, nside_out=nside)
print_message('Mask coverage for nside %d = %f' %
(nside, np.average(mask)))
np.random.seed(rseed)
st = time.time()
m = hp.synfast(cl, nside=nside, new=True)
mm = hp.ma(m)
mm.mask = np.logical_not(mask)
hp.mollview(mm.filled()[0])
print_debug('Time for synfast:', time.time() - st)
st = time.time()
cl_ana = hp.anafast(m)
print_debug('Time for get anafast:', time.time() - st)
st = time.time()
Wl = hp.anafast(mask)
Mll = coupling_matrix(Wl, lmin=lmin, lmax=lmax)
Mlli = np.linalg.pinv(Mll)
print_debug('Time for getting Mll:', time.time() - st)
st = time.time()
biased = hp.anafast(mm.filled())
print_debug('Time for getting biased spectrum:', time.time() - st)
st = time.time()
Cl_tmp = np.concatenate(biased[:4, lmin:lmax], 0)
unbiased = np.dot(Mlli, Cl_tmp).reshape(4, lmax - lmin)
print_debug('Time for get unbiased spectra:', time.time() - st)
st = time.time()
cl_ana = hp.anafast(m)
print_debug('Time for get anafast:', time.time() - st)
plt.figure()
plt.loglog(np.abs(cl2dl(cl[:3].T)))
plt.loglog(np.abs(cl2dl(cl_ana[:3].T)), '*-', lw=0.5)
plt.loglog(np.abs(cl2dl(biased[:3].T)), 'x-', lw=0.5)
plt.loglog(np.abs(cl2dl(unbiased[:3])).T, '+-', lw=0.5)
plt.ylim(1e-6, 1e4)
plt.xlabel = 'multipole moment ($l$)'
plt.ylabel = '$D_l (K^2)'
plt.title("Coupling matrix")
return biased, unbiased
def plot_HPcart(ra, dec, nside, title, norm=None, border=1.):
plt.clf()
# from matplotlib import rc
# rc('font',**{'family':'sans-serif','sans-serif':['DejaVu'], 'size': 8})
# rc('font',**{'size': 8})
# rc('text', usetex=True)
ramin = np.min(ra)
ramax = np.max(ra)
decmin = np.min(dec)
decmax = np.max(dec)
aspect = (decmax - decmin) / ((ramax - ramin) * np.cos (np.deg2rad((decmin + decmax) / 2.)))
ipix = hp.ang2pix(nside, (90-dec)/180*np.pi, ra/180*np.pi, nest=True)
HPX = np.bincount(ipix, minlength=hp.nside2npix(nside))
pixarea = hp.pixelfunc.nside2pixarea(nside, degrees=True)
ext = [ramax + border, ramin - border, decmin - border, decmax + border]
HPX = HPX / (3600. * pixarea)
mask = np.zeros(hp.nside2npix(nside), dtype=np.bool)
mask[HPX == 0.] = 1
m = hp.ma(HPX)
m.mask = mask
d_op = hp.visufunc.cartview(map=m, lonra=[ramin - border, ramax + border],
latra=[decmin - border, decmax + border], flip='astro', format='%.3g', cmap=cmap,
nest=True, norm=norm, return_projected_map=True)
fig = plt.figure(figsize = [6.4, 6.4 * aspect])
ax1 = fig.add_subplot(111)
ax1.set_facecolor('dimgray')
if norm==None:
plt.imshow(d_op, extent=ext, aspect='auto', origin='lower', interpolation=None, vmin=np.min(m),
vmax=np.max(m), cmap=cmap)
else:
plt.imshow(d_op, extent=ext, aspect='auto', origin='lower', interpolation=None, vmin=np.log10(np.min(m)),
vmax=np.log10(np.max(m)), cmap=cmap)
# plt.title('$\mathrm{%s}$' % title.replace('_', '\ '))
plt.xlabel('ra')
plt.ylabel('dec')
plt.ylim([decmin - border, decmax + border])
plt.xlim([ramax + border, ramin - border])
plt.grid(c='grey', lw=0.25, alpha=0.5)
cbaxes = fig.add_axes([0.90, 0.11, 0.015, 0.77])
if norm==None:
cb = plt.colorbar(cax=cbaxes, label=r'$\mathrm{density\ (objects\ per\ sq.\ arcmin)}$')
plt.savefig('linear.png', bbox_inches='tight')
else:
cb = plt.colorbar(cax=cbaxes, label=r'$\mathrm{log(density\ [objects\ per\ sq.\ arcmin])}$')
plt.savefig('%slogscale.png' % title, bbox_inches='tight')
plt.close()
return
def get_gain(
self,
el,
az,
n_pix=4,
nside_exp_grid=10,
nside_exp_syn=10,
lmax=4,
mmax=4,
interpolate="default",
):
if self.recalculate:
self.recalculate = False
self.values = np.array(self.values)
self.distances = np.array(self.distances)
self.i_map, self.pixel_dict = gen_interpolation_map(
self.points, self.values, self.antenna_num, nside_exp_grid)
if interpolate == "increasing_neighborhood":
self.interp_gain = lambda el, az: hp_interpolator(
self.i_map, el, az, n_pix)
# self.interp_gain(el, az)
np.power(2, nside_exp_syn)
self.norm_map = self.i_map
else:
alms = hp.sphtfunc.map2alm(hp.ma(self.i_map),
lmax=lmax,
mmax=mmax,
iter=10,
pol=False)
print(hp.Alm.getlmax(len(alms), mmax=mmax))
norm_map = hp.sphtfunc.alm2map(
alms,
np.power(2, nside_exp_syn),
lmax=lmax,
mmax=mmax,
pixwin=False,
pol=False,
)
norm_map_min = norm_map.min()
norm_map = norm_map - norm_map_min
norm_map_max = norm_map.max()
norm_map = norm_map / norm_map_max
self.norm_map = norm_map
# self.interp_gain = lambda el, az: (hp_spheric_harmonics(alms, el, az, mmax=mmax)-norm_map_min)/norm_map_max
self.interp_gain = lambda el, az: self.norm_map[
hp.pixelfunc.ang2pix(np.power(2, nside_exp_syn), np.pi / 2
- el.to_rad(), az.to_rad())]
return self.interp_gain(el, az)
def get_lfi_dx11_mask(nside,masktype='int',ps=True):
"""
now using masks suggested by AZa on 1/27/2015, common mask, should already have PS
apo=true is apodized, masktype='pol' is polarized mask, masktype='int' intensity mask
"""
maskdir='/global/homes/p/peterm/masks/'
f=maskdir+'dx11_v2_common_%s_mask_010a_1024.fits' %masktype
tmask=hp.ma(hp.read_map(f))
tmask=degrade_mask(tmask,nside_out=nside)
tmask=np.logical_not(tmask)
if ps:
fpsmask30='/global/project/projectdirs/planck/data/mission/DPC_maps/dx9_delta/lfi/MASKs/mask_ps_30GHz_beam33amin_nside2048.00_DX9_nonblind_holesize3.fits'
psmask30 = np.logical_not(np.floor(hp.ud_grade(hp.read_map(fpsmask30), nside)))
tmask=psmask30.astype(np.bool)|tmask.astype(np.bool)
return tmask
def local_mean_map(map1, mask1, deg):
""" return the local mean map for map1 with mask mask1
"""
mp1 = hp.ma(map1)
mp1.mask = np.logical_not(mask1)
NSIDE1 = hp.npix2nside(len(map1))
NSIDE2 = nside(deg)
NPIX2 = hp.nside2npix(NSIDE2)
mp2 = np.array([0.] * NPIX2)
for pix2 in range(0, NPIX2):
disc1 = hp.query_disc(nside=NSIDE1,
vec=hp.pix2vec(NSIDE2, pix2),
radius=deg2rad(deg))
mp2[pix2] = np.mean(mp1[disc1])
return mp2
def read_and_diff_2_files_fast(f1,f2,f3,nside=256,tmask=None,return_map=False):
#assume tmask is already degraded- this version subtracts (f2+f3)/2 from f1
mm1=hp.read_map(f1,[0,1,2],verbose=False)
mm2=hp.read_map(f2,[0,1,2],verbose=False)
mm3=hp.read_map(f3,[0,1,2],verbose=False)
mmm1=[]
mmm2=[]
mmm3=[]
for m1,m2,m3 in zip(mm1,mm2,mm3):
m1=hp.ud_grade(hp.ma(m1),nside_out=nside)
m2=hp.ud_grade(hp.ma(m2),nside_out=nside)
m3=hp.ud_grade(hp.ma(m3),nside_out=nside)
tmask=m1.mask | m2.mask | m3.mask | tmask
mmm1.append(m1)
mmm2.append(m2)
mmm3.append(m3)
diff=[]
for m1,m2,m3 in zip(mmm1,mmm2,mmm3):
d=m1-(m2+m3)/2
d.mask=tmask
diff.append(d)
skyfrac=1-float(tmask.sum())/len(tmask)
cldata=hp.anafast(diff)
cldata_out=[]
for cl in cldata:
cldata_out.append(cl/skyfrac)
if return_map is False:
return cldata_out
if return_map is True:
return cldata_out,diff
def mask_map(self, binary_mask):
"""
MASKS THE GIVEN SKY MAP WITH THE BINARY MASK.
FOR EXAMPLE, PASSING A SINGLE BINARY MASK FOR A SET OF I,Q,U SKY MAPS, WILL APPLY THE SAME MASK TO ALL THE THREE.
IN GENERAL, THE MASKS ARE ASSIGNED IN A CYCLIC MANNER. FOR EXAMPLE IF SKY_MAP HAS 4 MAPS AND BINARY_MASK HAS 2 MASKS, THEN:
binary_mask[0] -> sky_map[0]
binary_mask[1] -> sky_map[1]
binary_mask[0] -> sky_map[2]
binary_mask[1] -> sky_map[3]
"""
# CHECKING THAT THE SKY MAP AND MASK HAVE THE SAME RESOLUTION
assert self.nside == binary_mask.nside, "nside of sky map and mask does not match.\nnside of sky map : {}\nnside of mask : {}".format(self.nside, binary_mask.nside)
self.sky_map = hp.ma(self.sky_map)
self.sky_map.mask = np.logical_not(binary_mask.sky_map)
def mkForeCl(**kwargs):
"""
"""
get_var_from_file(kwargs['config'])
logger.info('Starting Cl analysis...')
in_label = data.IN_LABEL
out_label = data.OUT_LABEL
binning_label = data.BINNING_LABEL
from GRATools.utils.gFTools import get_cl_param
cl_param_file = os.path.join(
GRATOOLS_OUT, '%s_%s_parameters.txt' % (in_label, binning_label))
_emin, _emax, _emean, _f, _ferr, _cn, _fsky = get_cl_param(cl_param_file)
cl_txt = open(os.path.join(GRATOOLS_OUT, '%s_%s_forecls.txt' \
%(out_label, binning_label)), 'w')
for i, (emin, emax) in enumerate(zip(_emin, _emax)):
mask_file = data.MASK_FILE
if type(mask_file) == list:
mask_file = mask_file[i]
mask = hp.read_map(mask_file)
logger.info('Considering bin %.2f - %.2f ...' % (emin, emax))
cl_txt.write('ENERGY\t %.2f %.2f %.2f\n' % (emin, emax, _emean[i]))
l_max = 1000
_l = np.arange(l_max)
flux_map_name = in_label + '_fore_%i-%i.fits' % (emin, emax)
flux_map = hp.read_map(os.path.join(GRATOOLS_OUT_FORE, flux_map_name))
flux_map_masked = hp.ma(flux_map)
flux_map_masked.mask = np.logical_not(mask)
fsky = 1.-(len(np.where(flux_map_masked.filled() == hp.UNSEEN)[0])/\
float(len(flux_map)))
if kwargs['show'] == True:
hp.mollview(flux_map_masked.filled(),
title='f$_{sky}$ = %.3f' % fsky,
min=1e-7,
max=1e-4,
norm='log')
plt.show()
print 'fsky = ', fsky
nside = hp.npix2nside(len(flux_map))
wpix = hp.sphtfunc.pixwin(nside)[:l_max]
_cl = hp.sphtfunc.anafast(flux_map_masked.filled(), lmax=l_max-1, \
iter=5)
_cl_fit = hp.sphtfunc.anafast(flux_map_masked.filled(), iter=4)
cl_txt.write('Cl\t%s\n'%str(list(_cl)).replace('[',''). \
replace(']','').replace(', ', ' '))
cl_txt.close()
logger.info('Created %s'%(os.path.join(GRATOOLS_OUT, '%s_%s_forecls.txt' \
%(out_label, binning_label))))
def local_variance_map(map1, mask1, deg):
""" return the local variance map for map1 with mask mask1, given the error tolerance tol for mask2
"""
mp1=hp.ma(map1)
mp1.mask=np.logical_not(mask1)
NSIDE1=hp.npix2nside(len(map1))
NSIDE2=nside(deg)
NPIX2=hp.nside2npix(NSIDE2)
#mask2=np.round(hp.ud_grade(mask1, nside_out=NSIDE2)+tol)
mp2=np.array([0.]*NPIX2)
for pix2 in range(0, NPIX2):
disc1=hp.query_disc(nside=NSIDE1, vec=hp.pix2vec(NSIDE2, pix2), radius=deg2rad(deg))
mp2[pix2]=np.var(mp1[disc1])
#varmp2.mask=np.logical_not(mask2)
return mp2
def __init__(self,
zmean=default_zmean,
zsigma=default_zsigma,
star_file=default_star_file,
options=None):
if options is not None:
self.zmean = options.star_zmean
self.zsigma = options.star_zsigma
self.star_file = options.star_file
else:
self.zmean = zmean
self.zsigma = zsigma
self.star_file = star_file
table = aptable.Table.read(self.star_file)
self.smap = hp.ma(table['values'].data)
self.smap.mask = table['mask'].data
def test_smoothing_masked(self):
smoothed = hp.smoothing(self.map1, fwhm=np.radians(10), lmax=self.lmax)
smoothed_f90 = hp.ma(
hp.read_map(
os.path.join(
self.path,
"data",
"wmap_band_iqumap_r9_7yr_W_v4_udgraded32_masked_smoothed10deg_fortran.fits",
),
(0, 1, 2),
)
)
# fortran does not restore the mask
smoothed_f90.mask = smoothed.mask
np.testing.assert_array_almost_equal(
smoothed.filled(), smoothed_f90.filled(), decimal=6
)
def mkForeCl(**kwargs):
"""
"""
get_var_from_file(kwargs['config'])
logger.info('Starting Cl analysis...')
in_label = data.IN_LABEL
out_label = data.OUT_LABEL
binning_label = data.BINNING_LABEL
from GRATools.utils.gFTools import get_cl_param
cl_param_file = os.path.join(GRATOOLS_OUT, '%s_%s_parameters.txt'
%(in_label, binning_label))
_emin, _emax, _emean, _f, _ferr, _cn, _fsky = get_cl_param(cl_param_file)
cl_txt = open(os.path.join(GRATOOLS_OUT, '%s_%s_forecls.txt' \
%(out_label, binning_label)), 'w')
for i, (emin, emax) in enumerate(zip(_emin, _emax)):
mask_file = data.MASK_FILE
if type(mask_file) == list:
mask_file = mask_file[i]
mask = hp.read_map(mask_file)
logger.info('Considering bin %.2f - %.2f ...'%(emin, emax))
cl_txt.write('ENERGY\t %.2f %.2f %.2f\n'%(emin, emax, _emean[i]))
l_max= 1000
_l = np.arange(l_max)
flux_map_name = in_label+'_fore_%i-%i.fits'%(emin, emax)
flux_map = hp.read_map(os.path.join(GRATOOLS_OUT_FORE, flux_map_name))
flux_map_masked = hp.ma(flux_map)
flux_map_masked.mask = np.logical_not(mask)
fsky = 1.-(len(np.where(flux_map_masked.filled() == hp.UNSEEN)[0])/\
float(len(flux_map)))
if kwargs['show'] == True:
hp.mollview(flux_map_masked.filled(), title='f$_{sky}$ = %.3f'%fsky,
min=1e-7, max=1e-4, norm='log')
plt.show()
print 'fsky = ', fsky
nside = hp.npix2nside(len(flux_map))
wpix = hp.sphtfunc.pixwin(nside)[:l_max]
_cl = hp.sphtfunc.anafast(flux_map_masked.filled(), lmax=l_max-1, \
iter=5)
_cl_fit = hp.sphtfunc.anafast(flux_map_masked.filled(), iter=4)
cl_txt.write('Cl\t%s\n'%str(list(_cl)).replace('[',''). \
replace(']','').replace(', ', ' '))
cl_txt.close()
logger.info('Created %s'%(os.path.join(GRATOOLS_OUT, '%s_%s_forecls.txt' \
%(out_label, binning_label))))
def local_variance_map(map1, mask1, deg):
""" return the local variance map for map1 with mask mask1, given the error tolerance tol for mask2
"""
mp1 = hp.ma(map1)
mp1.mask = np.logical_not(mask1)
NSIDE1 = hp.npix2nside(len(map1))
NSIDE2 = nside(deg)
NPIX2 = hp.nside2npix(NSIDE2)
#mask2=np.round(hp.ud_grade(mask1, nside_out=NSIDE2)+tol)
mp2 = np.array([0.] * NPIX2)
for pix2 in range(0, NPIX2):
disc1 = hp.query_disc(nside=NSIDE1,
vec=hp.pix2vec(NSIDE2, pix2),
radius=deg2rad(deg))
mp2[pix2] = np.var(mp1[disc1])
#varmp2.mask=np.logical_not(mask2)
return mp2
def local_power_spectrum(map1, mask1, deg, LMAX=256):
""" return the local power spectrum for each disk given by the radius [deg]
"""
mp1=hp.ma(map1)
mp1.mask=np.logical_not(mask1)
NSIDE1=hp.npix2nside(len(map1))
NSIDE2=nside(deg)
NPIX2=hp.nside2npix(NSIDE2)
NPIX1=hp.nside2npix(NSIDE1)
mp2=[0]*NPIX2
for pix2 in range(0, NPIX2):
newmask=np.array([0.]*NPIX1)
disc1=hp.query_disc(nside=NSIDE1, vec=hp.pix2vec(NSIDE2, pix2), radius=deg2rad(deg))
newmask[disc1]=1.
newmask=newmask*mask1
mp1.mask=np.logical_not(mask1)
mp2[pix2]=hp.anafast(mp1, lmax=LMAX)
return mp2
def subtract_mono_di(map_in, mask_in, nside):
map_masked = hp.ma(map_in)
map_masked.mask = (mask_in < 1)
mono, dipole = hp.pixelfunc.fit_dipole(map_masked)
print(mono, dipole)
m = map_in.copy()
npix = hp.nside2npix(nside)
bunchsize = npix // 24
bad = hp.UNSEEN
for ibunch in range(npix // bunchsize):
ipix = np.arange(ibunch * bunchsize, (ibunch + 1) * bunchsize)
ipix = ipix[(np.isfinite(m.flat[ipix]))]
x, y, z = hp.pix2vec(nside, ipix, False)
m.flat[ipix] -= dipole[0] * x
m.flat[ipix] -= dipole[1] * y
m.flat[ipix] -= dipole[2] * z
m.flat[ipix] -= mono
return m
def rotate_map(Map, rotator, mask = None):
"""
Map is map in system A
rotator is rotator from system B to A
mask is a mask in system B
returns new map in system B
"""
npix = Map.shape[0]
nside = hp.npix2nside(npix)
thetas, phis = hp.pix2ang(nside, np.arange(npix))
rts, rps = rotator.I(thetas, phis)
vals = hp.pixelfunc.get_interp_val(Map, rts, rps)
nm = vals
if mask == None:
return nm
else:
nm = hp.ma(nm)
nm.mask = mask
return nm
def rotate_map(Map, rotator, mask=None):
"""
Map is map in system A
rotator is rotator from system B to A
mask is a mask in system B
returns new map in system B
"""
npix = Map.shape[0]
nside = hp.npix2nside(npix)
thetas, phis = hp.pix2ang(nside, np.arange(npix))
rts, rps = rotator.I(thetas, phis)
vals = hp.pixelfunc.get_interp_val(Map, rts, rps)
nm = vals
if mask == None:
return nm
else:
nm = hp.ma(nm)
nm.mask = mask
return nm
def changeSides(map, nSides):
"""
Take a HEALPix map upsample or downsample the map to make it have
'nSides' sides and conserve flux.
"""
nSidesOld = hp.pixelfunc.npix2nside(map.size)
if getattr(map, 'mask', None) is not None:
map2 = hp.pixelfunc.ud_grade(map.data, nSides)
mask2 = hp.pixelfunc.ud_grade(map.mask.astype(np.float64), nSides)
map2 = hp.ma(map2)
map2.mask = np.where(mask2 >= 0.005, 1, 0).astype(np.bool)
else:
map2 = hp.pixelfunc.ud_grade(map, nSides)
return map2
def generate(self, freq):
""" Generate the observed sky for the observer, based on the GSM.
Parameters
----------
freq: float
Frequency of map to generate, in units of MHz (default).
Returns
-------
observed_sky: np.array
Numpy array representing the healpix image, centered on zenith,
with below the horizon masked.
"""
import numpy as np
import healpy as hp
self.gsm.generate(freq)
sky = self.gsm.generated_map_data
# Get RA and DEC of zenith
ra_rad, dec_rad = self.radec_of(0, np.pi / 2)
ra_deg = ra_rad / np.pi * 180
dec_deg = dec_rad / np.pi * 180
# Apply rotation
hrot = hp.Rotator(rot=[ra_deg, dec_deg],
coord=['G', 'C'],
inv=True)
g0, g1 = hrot(self._theta, self._phi)
n_side = hp.npix2nside(self.gsm.generated_map_data.shape[0])
pix0 = hp.ang2pix(n_side, g0, g1)
sky_rotated = sky[pix0]
# Generate a mask for below horizon
mask1 = self._phi + np.pi / 2 > 2 * np.pi
mask2 = self._phi < np.pi / 2
mask = np.invert(np.logical_or(mask1, mask2))
self.observed_sky = hp.ma(sky_rotated)
self.observed_sky.mask = mask
return self.observed_sky
def klm_2_product(klmname, width, maskname, nsides, lmin, subtract_mf=False, writename=None):
# read in planck alm convergence data
planck_lensing_alm = hp.read_alm(klmname)
lmax = hp.Alm.getlmax(len(planck_lensing_alm))
if subtract_mf:
mf_alm = hp.read_alm('maps/mf_klm.fits')
planck_lensing_alm = planck_lensing_alm - mf_alm
# if you want to smooth with a gaussian
if width > 0:
# transform a gaussian of FWHM=width in real space to harmonic space
k_space_gauss_beam = hp.gauss_beam(fwhm=width.to('radian').value, lmax=lmax)
# if truncating small l modes
if lmin > 0:
# zero out small l modes in k-space filter
k_space_gauss_beam[:lmin] = 0
# smooth in harmonic space
filtered_alm = hp.smoothalm(planck_lensing_alm, beam_window=k_space_gauss_beam)
else:
# if not smoothing with gaussian, just remove small l modes
filtered_alm = zero_modes(planck_lensing_alm, lmin)
planck_lensing_map = hp.sphtfunc.alm2map(filtered_alm, nsides, lmax=lmax)
# mask map
importmask = hp.read_map(maskname)
if nsides < 2048:
mask_lowres_proper = hp.ud_grade(importmask.astype(float), nside_out=1024).astype(float)
finalmask = np.where(mask_lowres_proper == 1., True, False).astype(bool)
else:
finalmask = importmask.astype(np.bool)
# set mask, invert
smoothed_masked_map = hp.ma(planck_lensing_map)
smoothed_masked_map.mask = np.logical_not(finalmask)
if writename:
hp.write_map('%s.fits' % writename, smoothed_masked_map.filled(), overwrite=True, dtype=np.single)
return smoothed_masked_map.filled()
def setUp(self):
try:
self.map = hp.ma(
hp.read_map(
os.path.join('data', 'wmap_band_imap_r9_7yr_W_v4.fits')))
self.mask = hp.read_map(
os.path.join(
'data',
'wmap_temperature_analysis_mask_r9_7yr_v4.fits')).astype(
np.bool)
except exceptions.IOError:
warnings.warn(
"""Missing Wmap test maps from the data folder, please download them from Lambda and copy them in the test/data folder:
http://lambda.gsfc.nasa.gov/data/map/dr4/skymaps/7yr/raw/wmap_band_imap_r9_7yr_W_v4.fits
http://lambda.gsfc.nasa.gov/data/map/dr4/ancillary/masks/wmap_temperature_analysis_mask_r9_7yr_v4.fits
on Mac or Linux you can run the bash script get_wmap_maps.sh from the same folder
""")
raise
self.map.mask = np.logical_not(self.mask)
self.cla = hp.read_cl(os.path.join('data', 'cl_wmap_fortran.fits'))
def sub_mono_di(map_in, mask_in, nside, sub_dipole=True, verbose=False):
"""Subtract monopole and dipole from a healpix map."""
map_masked = hp.ma(map_in)
map_masked.mask = mask_in < 1
mono, dipole = hp.pixelfunc.fit_dipole(map_masked)
if verbose:
print("mono:", mono, ", dipole:", dipole)
m = map_in.copy()
npix = hp.nside2npix(nside)
bunchsize = npix // 24
for ibunch in range(npix // bunchsize): # adapted from healpy
ipix = np.arange(ibunch * bunchsize, (ibunch + 1) * bunchsize)
ipix = ipix[(np.isfinite(m.flat[ipix]))]
x, y, z = hp.pix2vec(nside, ipix, False)
if sub_dipole:
m.flat[ipix] -= dipole[0] * x
m.flat[ipix] -= dipole[1] * y
m.flat[ipix] -= dipole[2] * z
m.flat[ipix] -= mono
return m
def local_power_spectrum(map1, mask1, deg, LMAX=256):
""" return the local power spectrum for each disk given by the radius [deg]
"""
mp1 = hp.ma(map1)
mp1.mask = np.logical_not(mask1)
NSIDE1 = hp.npix2nside(len(map1))
NSIDE2 = nside(deg)
NPIX2 = hp.nside2npix(NSIDE2)
NPIX1 = hp.nside2npix(NSIDE1)
mp2 = [0] * NPIX2
for pix2 in range(0, NPIX2):
newmask = np.array([0.] * NPIX1)
disc1 = hp.query_disc(nside=NSIDE1,
vec=hp.pix2vec(NSIDE2, pix2),
radius=deg2rad(deg))
newmask[disc1] = 1.
newmask = newmask * mask1
mp1.mask = np.logical_not(mask1)
mp2[pix2] = hp.anafast(mp1, lmax=LMAX)
return mp2
def from_array(
cls,
map_array: np.array,
opening_angle: float,
quantity: str,
dir_in: str,
map_file: Optional[str] = None,
) -> "SkyMap":
"""
Initialize class by reading the skymap data from np.ndarray.
Args:
map_filename:
File path with which skymap pd.DataFrame can be loaded.
file_dsc:
Dictionary pointing to a file via {path, root, extension}.
Use when multiple skymaps need to be loaded.
"""
dirs = {"sim": dir_in}
map_array = hp.ma(map_array) # mask out bad values (e.g Nan)
return cls(map_array, opening_angle, quantity, dirs, map_file)
def test_weighting(self, tag):
stokes, mask = tag.split('_')
instrument = 'dict_vary'
components = 'fixedpowerlaw'
nside = 32
sky_tag = '%s__nside_%i__nsidepar_%i__%s__%s__%s' % (
stokes, nside, 0, components, mask, instrument)
data, _, _ = _get_sky(sky_tag)
instrument = _get_instrument(instrument, nside)
components = _get_component(components)
cov = self._get_cov(instrument, sky_tag.split('__')[0])
np.random.seed(0)
data += np.random.normal(size=cov.size).reshape(cov.shape) * cov**0.5
res = weighted_comp_sep(components, instrument, data, cov)
mask = hp.ma(data[0]).mask
chi2s = (res.chi[:, ~mask]**2).sum(axis=0).flatten()
dof = data.shape[0] - 1
_, p_value = kstest(chi2s.flatten(), 'chi2', (dof, ))
assert p_value > 0.10
def Counts2Delta(counts, mask=None):
"""
Converts a number counts Healpix map into a density contrast map.
Note
----
Use only binary mask.
"""
counts = np.asarray(counts)
if mask is not None:
mask = np.asarray(mask)
counts = hp.ma(counts)
counts.mask = np.logical_not(mask)
mean = np.mean(counts)
delta = (counts - mean) / mean
delta = delta.filled(
counts.fill_value) # To avoid pixels with fill_value = 1e+20
return delta
def remove_md(m, remove_dipole, remove_monopole, nside):
if remove_monopole:
dip_mask_name = remove_monopole
if remove_dipole:
dip_mask_name = remove_dipole
# Mask map for dipole estimation
if dip_mask_name == 'auto':
mono, dip = hp.fit_dipole(m, gal_cut=30)
else:
m_masked = hp.ma(m)
m_masked.mask = np.logical_not(
hp.read_map(
dip_mask_name,
verbose=False,
dtype=None,
))
# Fit dipole to masked map
mono, dip = hp.fit_dipole(m_masked)
# Subtract dipole map from data
if remove_dipole:
click.echo(click.style("Removing dipole:", fg="yellow"))
click.echo(click.style("Dipole vector:", fg="green") + f" {dip}")
click.echo(
click.style("Dipole amplitude:", fg="green") +
f" {np.sqrt(np.sum(dip ** 2))}")
# Create dipole template
nside = int(nside)
ray = range(hp.nside2npix(nside))
vecs = hp.pix2vec(nside, ray)
dipole = np.dot(dip, vecs)
m = m - dipole
if remove_monopole:
click.echo(click.style("Removing monopole:", fg="yellow"))
click.echo(click.style("Mono:", fg="green") + f" {mono}")
m = m - mono
return m
def mask_map(sky_map, binary_mask):
"""
Masks the given sky map with the binary mask.
At least with healpy version >= 1.11.0, the number of sky maps does not matter.
For example, passing a single binary mask for a set of I,Q,U sky maps, will apply the same mask for all the three.
Passing a set of masks in the form of an array will assign the masks to the corresponding sky maps.
In general, the masks are assigned in a cyclic manner. For example if sky_map has 4 maps and binary_mask has 2 masks, then:
binary_mask[0] -> sky_map[0]
binary_mask[1] -> sky_map[1]
binary_mask[0] -> sky_map[2]
binary_mask[1] -> sky_map[3]
"""
# Checking that the sky map and mask have the same resolution
assert hp.get_nside(sky_map) == hp.get_nside(
binary_mask
), "nside of sky map and mask does not match.\nnside of sky map : {}\nnside of mask : {}".format(
hp.get_nside(sky_map), hp.get_nside(binary_mask))
sky_map_masked = hp.ma(sky_map)
sky_map_masked.mask = np.logical_not(binary_mask)
return sky_map_masked