diff --git a/lm/nifty_lm.py b/lm/nifty_lm.py
index 2f6d761310a4ae96108e5342efbc63d7644ec0dd..9b44b9780deef96a0cd3e69936178fc989b4ca74 100644
--- a/lm/nifty_lm.py
+++ b/lm/nifty_lm.py
@@ -50,14 +50,16 @@ from nifty.nifty_paradict import lm_space_paradict,\
hp_space_paradict
from nifty.nifty_power_indices import lm_power_indices
+from nifty.nifty_mpi_data import distributed_data_object
+
from nifty.nifty_random import random
gl = gdi.get('libsharp_wrapper_gl')
hp = gdi.get('healpy')
-LM_DISTRIBUTION_STRATEGIES = []
-GL_DISTRIBUTION_STRATEGIES = []
-HP_DISTRIBUTION_STRATEGIES = []
+LM_DISTRIBUTION_STRATEGIES = ['not']
+GL_DISTRIBUTION_STRATEGIES = ['not']
+HP_DISTRIBUTION_STRATEGIES = ['not']
class lm_space(point_space):
@@ -120,7 +122,7 @@ class lm_space(point_space):
"""
def __init__(self, lmax, mmax=None, dtype=np.dtype('complex128'),
- datamodel='np', comm=gc['default_comm']):
+ datamodel='not', comm=gc['default_comm']):
"""
Sets the attributes for an lm_space class instance.
@@ -167,9 +169,9 @@ class lm_space(point_space):
self.dtype = dtype
# set datamodel
- if datamodel not in ['np']:
+ if datamodel not in ['not']:
about.warnings.cprint("WARNING: datamodel set to default.")
- self.datamodel = 'np'
+ self.datamodel = 'not'
else:
self.datamodel = datamodel
@@ -283,7 +285,15 @@ class lm_space(point_space):
return mol
def _cast_to_d2o(self, x, dtype=None, hermitianize=True, **kwargs):
- raise NotImplementedError
+ 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,
@@ -911,7 +921,7 @@ class gl_space(point_space):
"""
def __init__(self, nlat, nlon=None, dtype=np.dtype('float64'),
- datamodel='np', comm=gc['default_comm']):
+ datamodel='not', comm=gc['default_comm']):
"""
Sets the attributes for a gl_space class instance.
@@ -952,9 +962,9 @@ class gl_space(point_space):
self.dtype = dtype
# set datamodel
- if datamodel not in ['np']:
+ if datamodel not in ['not']:
about.warnings.cprint("WARNING: datamodel set to default.")
- self.datamodel = 'np'
+ self.datamodel = 'not'
else:
self.datamodel = datamodel
@@ -1034,6 +1044,18 @@ class gl_space(point_space):
mol = self.cast(1, dtype=np.float)
return self.calc_weight(mol, power=1)
+ #TODO: Check with Theo!
+ def _cast_to_d2o(self, x, dtype=None, hermitianize=True, **kwargs):
+ casted_x = super(gl_space, self)._cast_to_d2o(x=x,
+ dtype=dtype,
+ **kwargs)
+ complexity_mask = distributed_data_object.iscomplex(casted_x[:self.paradict['nlat']+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
+
# TODO: Extend to binning/log
def enforce_power(self, spec, size=None, kindex=None):
if kindex is None:
@@ -1209,22 +1231,22 @@ class gl_space(point_space):
y : numpy.ndarray
Weighted array.
"""
- x = self.cast(x)
+ x = self._cast_to_np(x)
# weight
nlat = self.paradict['nlat']
nlon = self.paradict['nlon']
if self.dtype == np.dtype('float32'):
- return gl.weight_f(x,
+ return distributed_data_object(gl.weight_f(x,
np.array(self.distances),
p=np.float32(power),
nlat=nlat, nlon=nlon,
- overwrite=False)
+ overwrite=False))
else:
- return gl.weight(x,
+ return distributed_data_object(gl.weight(x,
np.array(self.distances),
p=np.float32(power),
nlat=nlat, nlon=nlon,
- overwrite=False)
+ overwrite=False))
def get_weight(self, power=1):
# TODO: Check if this function is compatible to the rest of nifty
@@ -1554,7 +1576,7 @@ class hp_space(point_space):
An array with one element containing the pixel size.
"""
- def __init__(self, nside, datamodel='np', comm=gc['default_comm']):
+ def __init__(self, nside, datamodel='not', comm=gc['default_comm']):
"""
Sets the attributes for a hp_space class instance.
@@ -1588,9 +1610,9 @@ class hp_space(point_space):
self.dtype = np.dtype('float64')
# set datamodel
- if datamodel not in ['np']:
+ if datamodel not in ['not']:
about.warnings.cprint("WARNING: datamodel set to default.")
- self.datamodel = 'np'
+ self.datamodel = 'not'
else:
self.datamodel = datamodel
diff --git a/nifty_power_indices.py b/nifty_power_indices.py
index 01682124ce2d1e4a3e5c6daf2f0d665d2b63064a..ded40b1ee4ff46dbb3e0dde7cac25c81ba148a89 100644
--- a/nifty_power_indices.py
+++ b/nifty_power_indices.py
@@ -672,7 +672,7 @@ class lm_power_indices(power_indices):
-------
nkdict : distributed_data_object
"""
- if self.datamodel == 'np':
+ if self.datamodel == 'not':
if 'healpy' in gdi: # default
nkdict = hp.Alm.getlm(self.lmax, i=None)[0]
else:
diff --git a/test/test_nifty_spaces.py b/test/test_nifty_spaces.py
index b22ebbc31157a2e55aab8feda4a1ed2ffb7eb103..efc378564f1baaee568d363b073db891ec4017e3 100644
--- a/test/test_nifty_spaces.py
+++ b/test/test_nifty_spaces.py
@@ -82,9 +82,9 @@ all_hp_datatypes = [np.dtype('float64')]
DATAMODELS = {}
DATAMODELS['point_space'] = ['np'] + POINT_DISTRIBUTION_STRATEGIES
DATAMODELS['rg_space'] = ['np'] + RG_DISTRIBUTION_STRATEGIES
-DATAMODELS['lm_space'] = ['np'] + LM_DISTRIBUTION_STRATEGIES
-DATAMODELS['gl_space'] = ['np'] + GL_DISTRIBUTION_STRATEGIES
-DATAMODELS['hp_space'] = ['np'] + HP_DISTRIBUTION_STRATEGIES
+DATAMODELS['lm_space'] = [] + LM_DISTRIBUTION_STRATEGIES
+DATAMODELS['gl_space'] = [] + GL_DISTRIBUTION_STRATEGIES
+DATAMODELS['hp_space'] = [] + HP_DISTRIBUTION_STRATEGIES
###############################################################################
@@ -296,6 +296,7 @@ class Test_Common_Point_Like_Space_Interface(unittest.TestCase):
assert(isinstance(s.get_dof(split=True), tuple))
assert(isinstance(s.get_meta_volume(), np.float))
+ print(s.get_meta_volume(split=True), type(s.cast(1)))
assert(isinstance(s.get_meta_volume(split=True), type(s.cast(1))))
assert_almost_equal(
s.get_meta_volume(), s.get_meta_volume(split=True).sum(), 2)