def map2alm(m,lmax=None,mmax=None,iter=1,use_weights=False,regression=True):
"""Computes the alm of an Healpix map.
Input:
- m: a ndarray (not polarized) or a list of 3 ndarray (polarized)
Parameters:
- lmax : maximum l of the power spectrum. Default: 3*nside-1
- mmax : maximum m of the alm. Default: lmax
- iter : number of iteration (default: 1)
- use_weights: whether to use ring weights or not. Default: False.
- regression: if True, subtract map average before computing alm. Default: True.
Return:
- alm as one ndarray or a tuple of 3 ndarrays
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3*nside-1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath+'/'+weightfile):
raise IOError('File not found : '+datapath+'/'+weightfile)
alm = sphtlib._map2alm(m,lmax=lmax,mmax=mmax,cl=False,
iter=iter,
use_weights=use_weights,data_path=datapath,regression=regression)
return alm
def anafast(m,lmax=None,mmax=None,iter=1,alm=False, use_weights=False, regression=True):
"""Computes the power spectrum of an Healpix map.
Input:
- m : either an array representing a map, or a list of 3 arrays
representing I, Q, U maps
Parameters:
- lmax : maximum l of the power spectrum (default: 3*nside-1)
- mmax : maximum m of the alm (default: lmax)
- iter : number of iteration (default: 1)
- alm : (boolean) whether to return alm or not (if True, both are
returned in a tuple)
- regression : if True, map average is removed before computing alm. Default: True.
Return:
- if alm==False: return cl or a list of cl's (TT,TE,EE,BB)
- if alm==True: return a tuple with cl or a list of cl's and alm
or a list of alm's
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3*nside-1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath+'/'+weightfile):
raise IOError('File not found : '+datapath+'/'+weightfile)
clout,almout = sphtlib._map2alm(m,lmax=lmax,mmax=mmax,iter=iter,cl=True,
use_weights=use_weights,data_path=datapath,regression=regression)
if alm:
return (clout,almout)
else:
return clout
def anafast(m,
lmax=None,
mmax=None,
iter=1,
alm=False,
use_weights=False,
regression=True):
"""Computes the power spectrum of an Healpix map.
Parameters
----------
m : float, array-like shape (Npix,) or (3, Npix)
Either an array representing a map, or a sequence of 3 arrays
representing I, Q, U maps
lmax : int, scalar, optional
Maximum l of the power spectrum (default: 3*nside-1)
mmax : int, scalar, optional
Maximum m of the alm (default: lmax)
iter : int, scalar, optional
Number of iteration (default: 1)
alm : bool, scalar, optional
If True, returns both cl and alm, otherwise only cl is returned
regression : bool, scalar, optional
If True, map average is removed before computing alm. Default: True.
Returns
-------
res : array or sequence of arrays
If *alm* is False, returns cl or a list of cl (TT, EE, BB, TE for
polarized input map)
Otherwise, returns a tuple (cl, alm), where cl is as above and
alm is the spherical harmonic transform or a list of almT, almE, almB
for polarized input
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3 * nside - 1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath + '/' + weightfile):
raise IOError('File not found : ' + datapath + '/' + weightfile)
# Replace UNSEEN pixels with zeros
m[m == UNSEEN] = 0
clout, almout = sphtlib._map2alm(m,
lmax=lmax,
mmax=mmax,
iter=iter,
cl=True,
use_weights=use_weights,
data_path=datapath,
regression=regression)
if alm:
return (clout, almout)
else:
return clout
def map2alm(m,
lmax=None,
mmax=None,
iter=1,
use_weights=False,
regression=True):
"""Computes the alm of an Healpix map.
Parameters
----------
m : array-like, shape (Npix,) or (3, Npix)
The input map or a list of 3 input maps (polariztion).
lmax : int, scalar, optional
Maximum l of the power spectrum. Default: 3*nside-1
mmax : int, scalar, optional
Maximum m of the alm. Default: lmax
iter : int, scalar, optional
Number of iteration (default: 1)
use_weights: bool, scalar, optional
If True, use the ring weighting. Default: False.
regression: bool, scalar, optional
If True, subtract map average before computing alm. Default: True.
Returns
-------
alm : array or tuple of array
alm or a tuple of 3 alm (almT, almE, almB) if polarized input.
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3 * nside - 1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Replace UNSEEN pixels with zeros
m[m == UNSEEN] = 0
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath + '/' + weightfile):
raise IOError('File not found : ' + datapath + '/' + weightfile)
alm = sphtlib._map2alm(m,
lmax=lmax,
mmax=mmax,
cl=False,
iter=iter,
use_weights=use_weights,
data_path=datapath,
regression=regression)
return alm
def anafast(m,lmax=None,mmax=None,iter=1,alm=False, use_weights=False, regression=True):
"""Computes the power spectrum of an Healpix map.
Parameters
----------
m : float, array-like shape (Npix,) or (3, Npix)
Either an array representing a map, or a sequence of 3 arrays
representing I, Q, U maps
lmax : int, scalar, optional
Maximum l of the power spectrum (default: 3*nside-1)
mmax : int, scalar, optional
Maximum m of the alm (default: lmax)
iter : int, scalar, optional
Number of iteration (default: 1)
alm : bool, scalar, optional
If True, returns both cl and alm, otherwise only cl is returned
regression : bool, scalar, optional
If True, map average is removed before computing alm. Default: True.
Returns
-------
res : array or sequence of arrays
If *alm* is False, returns cl or a list of cl (TT, EE, BB, TE for
polarized input map)
Otherwise, returns a tuple (cl, alm), where cl is as above and
alm is the spherical harmonic transform or a list of almT, almE, almB
for polarized input
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3*nside-1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath+'/'+weightfile):
raise IOError('File not found : '+datapath+'/'+weightfile)
# Replace UNSEEN pixels with zeros
m[m == UNSEEN] = 0
clout,almout = sphtlib._map2alm(m,lmax=lmax,mmax=mmax,iter=iter,cl=True,
use_weights=use_weights,data_path=datapath,
regression=regression)
if alm:
return (clout,almout)
else:
return clout
def anafast(m,
lmax=None,
mmax=None,
iter=1,
alm=False,
use_weights=False,
regression=True):
"""Computes the power spectrum of an Healpix map.
Input:
- m : either an array representing a map, or a list of 3 arrays
representing I, Q, U maps
Parameters:
- lmax : maximum l of the power spectrum (default: 3*nside-1)
- mmax : maximum m of the alm (default: lmax)
- iter : number of iteration (default: 1)
- alm : (boolean) whether to return alm or not (if True, both are
returned in a tuple)
- regression : if True, map average is removed before computing alm. Default: True.
Return:
- if alm==False: return cl or a list of cl's (TT,EE,BB,TE)
- if alm==True: return a tuple with cl or a list of cl's and alm
or a list of alm's
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3 * nside - 1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath + '/' + weightfile):
raise IOError('File not found : ' + datapath + '/' + weightfile)
clout, almout = sphtlib._map2alm(m,
lmax=lmax,
mmax=mmax,
iter=iter,
cl=True,
use_weights=use_weights,
data_path=datapath,
regression=regression)
if alm:
return (clout, almout)
else:
return clout
def map2alm(m,lmax=None,mmax=None,iter=1,use_weights=False,regression=True):
"""Computes the alm of an Healpix map.
Parameters
----------
m : array-like, shape (Npix,) or (3, Npix)
The input map or a list of 3 input maps (polariztion).
lmax : int, scalar, optional
Maximum l of the power spectrum. Default: 3*nside-1
mmax : int, scalar, optional
Maximum m of the alm. Default: lmax
iter : int, scalar, optional
Number of iteration (default: 1)
use_weights: bool, scalar, optional
If True, use the ring weighting. Default: False.
regression: bool, scalar, optional
If True, subtract map average before computing alm. Default: True.
Returns
-------
alm : array or tuple of array
alm or a tuple of 3 alm (almT, almE, almB) if polarized input.
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3*nside-1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Replace UNSEEN pixels with zeros
m[m == UNSEEN] = 0
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath+'/'+weightfile):
raise IOError('File not found : '+datapath+'/'+weightfile)
alm = sphtlib._map2alm(m,lmax=lmax,mmax=mmax,cl=False,
iter=iter,
use_weights=use_weights,data_path=datapath,
regression=regression)
return alm
def map2alm(m,
lmax=None,
mmax=None,
iter=1,
use_weights=False,
regression=True):
"""Computes the alm of an Healpix map.
Input:
- m: a ndarray (not polarized) or a list of 3 ndarray (polarized)
Parameters:
- lmax : maximum l of the power spectrum. Default: 3*nside-1
- mmax : maximum m of the alm. Default: lmax
- iter : number of iteration (default: 1)
- use_weights: whether to use ring weights or not. Default: False.
- regression: if True, subtract map average before computing alm. Default: True.
Return:
- alm as one ndarray or a tuple of 3 ndarrays
"""
datapath = DATAPATH #os.path.dirname(__file__)+'/data'
nside = _get_nside(m)
if lmax is None:
lmax = 3 * nside - 1
if mmax is None or mmax < 0 or mmax > lmax:
mmax = lmax
# Check the presence of weights file
if use_weights:
weightfile = 'weight_ring_n%05d.fits' % (nside)
if not os.path.isfile(datapath + '/' + weightfile):
raise IOError('File not found : ' + datapath + '/' + weightfile)
alm = sphtlib._map2alm(m,
lmax=lmax,
mmax=mmax,
cl=False,
iter=iter,
use_weights=use_weights,
data_path=datapath,
regression=regression)
return alm
