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Commit a23a9950 authored by csongor's avatar csongor
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Minor fixes

parent 22cad1dd
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lm/nifty_lm.py
View file @ a23a9950
......@@ -283,17 +283,6 @@ class lm_space(point_space):
mol[self.paradict['lmax'] + 1:] = 2 # redundant: (l,m) and (l,-m)
return mol
def _cast_to_d2o(self, x, dtype=None, hermitianize=True, **kwargs):
casted_x = super(lm_space, self)._cast_to_d2o(x=x,
dtype=dtype,
**kwargs)
complexity_mask = distributed_data_object.iscomplex(casted_x[:self.paradict['lmax']+1])
if distributed_data_object.any(complexity_mask):
about.warnings.cprint("WARNING: Taking the absolute values for " +
"all complex entries where lmax==0")
casted_x[complexity_mask] = distributed_data_object.__abs__(casted_x[complexity_mask])
return casted_x
def _cast_to_np(self, x, dtype=None, hermitianize=True, **kwargs):
casted_x = super(lm_space, self)._cast_to_np(x=x,
dtype=dtype,
......@@ -490,7 +479,7 @@ class lm_space(point_space):
x = hp.synalm(arg['spec'], lmax=lmax, mmax=mmax)
else:
x = gl.synalm(arg['spec'], lmax=lmax, mmax=mmax)
return distributed_data_object(x)
return self.cast(x)
elif arg['random'] == "uni":
x = random.uni(dtype=self.dtype,
......@@ -527,9 +516,9 @@ class lm_space(point_space):
lmax = self.paradict['lmax']
mmax = self.paradict['mmax']
if self.dtype == np.dtype('complex64'):
return distributed_data_object(gl.dotlm_f(x, y, lmax=lmax, mmax=mmax))
return self.cast(gl.dotlm_f(x, y, lmax=lmax, mmax=mmax))
else:
return distributed_data_object(gl.dotlm(x, y, lmax=lmax, mmax=mmax))
return self.cast(gl.dotlm(x, y, lmax=lmax, mmax=mmax))
else:
return self._dotlm(x, y)
......@@ -575,11 +564,11 @@ class lm_space(point_space):
# transform
if self.dtype == np.dtype('complex64'):
Tx = distributed_data_object(gl.alm2map_f(np.array(x), nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax, cl=False))
Tx = gl.alm2map_f(np.array(x), nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax, cl=False)
else:
Tx = distributed_data_object(gl.alm2map(np.array(x), nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax, cl=False))
Tx = gl.alm2map(np.array(x), nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax, cl=False)
# re-weight if discrete
if codomain.discrete:
Tx = codomain.calc_weight(Tx, power=0.5)
......@@ -590,9 +579,9 @@ class lm_space(point_space):
mmax = self.paradict['mmax']
# transform
Tx = distributed_data_object(hp.alm2map(np.array(x).astype(np.complex128), nside, lmax=lmax,
Tx = hp.alm2map(np.array(x).astype(np.complex128), nside, lmax=lmax,
mmax=mmax, pixwin=False, fwhm=0.0, sigma=None,
pol=True, inplace=False))
pol=True, inplace=False)
# re-weight if discrete
if(codomain.discrete):
Tx = codomain.calc_weight(Tx, power=0.5)
......@@ -601,7 +590,7 @@ class lm_space(point_space):
raise ValueError(about._errors.cstring(
"ERROR: unsupported transformation."))
return Tx.copy(dtype=codomain.dtype)
return self.cast(Tx.astype(codomain.dtype))
def calc_smooth(self, x, sigma=0, **kwargs):
"""
......@@ -632,11 +621,11 @@ class lm_space(point_space):
elif sigma < 0:
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
if gc['use_healpy']:
return distributed_data_object(hp.smoothalm(x, fwhm=0.0, sigma=sigma,
return self.cast(hp.smoothalm(x, fwhm=0.0, sigma=sigma,
pol=True, mmax=self.paradict['mmax'],
verbose=False, inplace=False))
else:
return distributed_data_object(gl.smoothalm(x, lmax=self.paradict['lmax'],
return self.cast(gl.smoothalm(x, lmax=self.paradict['lmax'],
mmax=self.paradict['mmax'],
fwhm=0.0, sigma=sigma, overwrite=False))
......@@ -662,17 +651,17 @@ class lm_space(point_space):
# power spectrum
if self.dtype == np.dtype('complex64'):
if gc['use_libsharp']:
return distributed_data_object(gl.anaalm_f(x, lmax=lmax, mmax=mmax))
return self.cast(gl.anaalm_f(x, lmax=lmax, mmax=mmax))
else:
return distributed_data_object(hp.alm2cl(x.copy(dtype=np.complex128), alms2=None,
return self.cast(hp.alm2cl(x.copy(dtype=np.complex128), alms2=None,
lmax=lmax, mmax=mmax, lmax_out=lmax,
nspec=None).copy(dtype=np.float32))
else:
if gc['use_healpy']:
return distributed_data_object(hp.alm2cl(np.array(x), alms2=None, lmax=lmax, mmax=mmax,
return self.cast(hp.alm2cl(np.array(x), alms2=None, lmax=lmax, mmax=mmax,
lmax_out=lmax, nspec=None))
else:
return distributed_data_object(gl.anaalm(x, lmax=lmax, mmax=mmax))
return self.cast(gl.anaalm(x, lmax=lmax, mmax=mmax))
def get_plot(self, x, title="", vmin=None, vmax=None, power=True,
norm=None, cmap=None, cbar=True, other=None, legend=False,
......@@ -967,7 +956,7 @@ class gl_space(point_space):
self.discrete = False
self.harmonic = False
self.distances = tuple(distributed_data_object(gl.vol(self.paradict['nlat'],
self.distances = tuple(self.cast(gl.vol(self.paradict['nlat'],
nlon=self.paradict['nlon']
).astype(np.float)))
self.comm = self._parse_comm(comm)
......@@ -1177,11 +1166,11 @@ class gl_space(point_space):
nlon = self.paradict['nlon']
lmax = nlat - 1
if self.dtype == np.dtype('float32'):
x = distributed_data_object(gl.synfast_f(arg['spec'],
x = self.cast(gl.synfast_f(arg['spec'],
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=lmax, alm=False))
else:
x = distributed_data_object(gl.synfast(arg['spec'],
x = self.cast(gl.synfast(arg['spec'],
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=lmax, alm=False))
# weight if discrete
......@@ -1221,13 +1210,13 @@ class gl_space(point_space):
nlat = self.paradict['nlat']
nlon = self.paradict['nlon']
if self.dtype == np.dtype('float32'):
return distributed_data_object(gl.weight_f(x,
return self.cast(gl.weight_f(x,
np.array(self.distances),
p=np.float32(power),
nlat=nlat, nlon=nlon,
overwrite=False))
else:
return distributed_data_object(gl.weight(x,
return self.cast(gl.weight(x,
np.array(self.distances),
p=np.float32(power),
nlat=nlat, nlon=nlon,
......@@ -1280,11 +1269,11 @@ class gl_space(point_space):
mmax = codomain.paradict['mmax']
if self.dtype == np.dtype('float32'):
Tx = distributed_data_object(gl.map2alm_f(np.array(x),
Tx = self.cast(gl.map2alm_f(np.array(x),
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax))
else:
Tx = distributed_data_object(gl.map2alm(np.array(x),
Tx = self.cast(gl.map2alm(np.array(x),
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax))
else:
......@@ -1323,7 +1312,7 @@ class gl_space(point_space):
raise ValueError(about._errors.cstring("ERROR: invalid sigma."))
# smooth
nlat = self.paradict['nlat']
return distributed_data_object(gl.smoothmap(x,
return self.cast(gl.smoothmap(x,
nlat=nlat, nlon=self.paradict['nlon'],
lmax=nlat - 1, mmax=nlat - 1,
fwhm=0.0, sigma=sigma))
......@@ -1353,12 +1342,12 @@ class gl_space(point_space):
lmax = nlat - 1
mmax = nlat - 1
if self.dtype == np.dtype('float32'):
return distributed_data_object(gl.anafast_f(np.array(x),
return self.cast(gl.anafast_f(np.array(x),
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax,
alm=False))
else:
return distributed_data_object(gl.anafast(np.array(x),
return self.cast(gl.anafast(np.array(x),
nlat=nlat, nlon=nlon,
lmax=lmax, mmax=mmax,
alm=False))
......@@ -1792,7 +1781,7 @@ class hp_space(point_space):
elif arg['random'] == "syn":
nside = self.paradict['nside']
lmax = 3*nside-1
x = distributed_data_object(hp.synfast(arg['spec'], nside, lmax=lmax, mmax=lmax, alm=False,
x = self.cast(hp.synfast(arg['spec'], nside, lmax=lmax, mmax=lmax, alm=False,
pol=True, pixwin=False, fwhm=0.0, sigma=None))
# weight if discrete
if self.discrete:
......@@ -1849,7 +1838,7 @@ class hp_space(point_space):
if self.discrete:
x = self.calc_weight(x, power=-0.5)
# transform
Tx = distributed_data_object(hp.map2alm(x.copy(dtype=np.float64),
Tx = self.cast(hp.map2alm(x.copy(dtype=np.float64),
lmax=codomain.paradict['lmax'],
mmax=codomain.paradict['mmax'],
iter=niter, pol=True, use_weights=False,
......@@ -1901,7 +1890,7 @@ class hp_space(point_space):
# smooth
lmax = 3*nside-1
mmax = lmax
return distributed_data_object(hp.smoothing(x, fwhm=0.0, sigma=sigma, pol=True,
return self.cast(hp.smoothing(x, fwhm=0.0, sigma=sigma, pol=True,
iter=niter, lmax=lmax, mmax=mmax,
use_weights=False, datapath=None))
......@@ -1935,7 +1924,7 @@ class hp_space(point_space):
lmax = 3*nside-1
mmax = lmax
# power spectrum
return distributed_data_object(hp.anafast(x, map2=None, nspec=None, lmax=lmax, mmax=mmax,
return self.cast(hp.anafast(x, map2=None, nspec=None, lmax=lmax, mmax=mmax,
iter=niter, alm=False, pol=True, use_weights=False,
datapath=None))
......
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