Skip to content

GitLab

Commit 61e4b7a3 authored by theos's avatar theos
Browse files
Options

Changed wording of LmSpace, GlSpace and HpSpace to PEP8 convention.

parent 38cd41a8
Hide whitespace changes
Inline Side-by-side
Showing with 149 additions and 143 deletions
__init__.py
View file @ 61e4b7a3
......@@ -60,7 +60,7 @@ from power import PowerSpace,\
## optional submodule `rg`
try:
from rg import RgSpace,\
from rg import RGSpace,\
power_backward_conversion_rg,\
power_forward_conversion_rg
from nifty_paradict import rg_space_paradict
......@@ -69,19 +69,19 @@ except(ImportError):
## optional submodule `lm`
try:
from lm import LmSpace,\
from lm import LMSpace,\
power_backward_conversion_lm,\
power_forward_conversion_lm
from nifty_paradict import lm_space_paradict
try:
from lm import GlSpace
from lm import GLSpace
from nifty_paradict import gl_space_paradict
except(ImportError):
pass
try:
from lm import HpSpace
from lm import HPSpace
from nifty_paradict import hp_space_paradict
except(ImportError):
pass
......
demos/demo_excaliwir.py
View file @ 61e4b7a3
......@@ -256,7 +256,7 @@ class problem(object):
##-----------------------------------------------------------------------------
#
if(__name__=="__main__"):
x = RgSpace((1280), zerocenter=True)
x = RGSpace((1280), zerocenter=True)
p = problem(x, log = False)
about.warnings.off()
## pl.close("all")
......
demos/demo_faraday.py
View file @ 61e4b7a3
......@@ -48,7 +48,7 @@ about.infos.off()
##-----------------------------------------------------------------------------
# (global) Faraday map
m = field(HpSpace(128), val=np.load(os.path.join(get_demo_dir(),
m = field(HPSpace(128), val=np.load(os.path.join(get_demo_dir(),
"demo_faraday_map.npy")))
##-----------------------------------------------------------------------------
......@@ -98,8 +98,8 @@ def run(projection=False, power=False):
m4.plot(title=r"angular quadrupole of $m$ on a Gauss-Legendre grid", **nicely)
# (d) representation on regular grid
y_space = GlSpace(384, nlon=768) # auxiliary gl_space
z_space = RgSpace([768, 384], dist=[1/360, 1/180])
y_space = GLSpace(384, nlon=768) # auxiliary gl_space
z_space = RGSpace([768, 384], dist=[1/360, 1/180])
m5 = m1.transform(y_space)
m5.cast_domain(z_space)
m5.set_val(np.roll(m5.val[::-1, ::-1], y_space.paradict['nlon']//2, axis=1)) # rearrange value array
......
demos/demo_tomography.py
View file @ 61e4b7a3
......@@ -9,7 +9,7 @@ if __name__ == "__main__":
shape = (256, 256)
x_space = RgSpace(shape)
x_space = RGSpace(shape)
k_space = x_space.get_codomain()
power = lambda k: 42/((1+k*shape[0])**3)
......
demos/demo_wf1.py
View file @ 61e4b7a3
......@@ -49,8 +49,8 @@ if __name__ == "__main__":
#shape = [1024, 1024]
#x_space = rg_space(shape)
#y_space = point_space(1280*1280)
x_space = HpSpace(32)
#x_space = GlSpace(800)
x_space = HPSpace(32)
#x_space = GLSpace(800)
k_space = x_space.get_codomain() # get conjugate space
......
demos/demo_wf2.py
View file @ 61e4b7a3
......@@ -55,7 +55,7 @@ from nifty.operators.nifty_minimization import steepest_descent_new
if __name__ == "__main__":
# some signal space; e.g., a two-dimensional regular grid
x_space = RgSpace([256, 256]) # define
x_space = RGSpace([256, 256]) # define
# signal space
k_space = x_space.get_codomain() # get conjugate space
......
demos/demo_wf3.py
View file @ 61e4b7a3
......@@ -39,7 +39,7 @@ if __name__ == "__main__":
# some signal space; e.g., a one-dimensional regular grid
x_space = RgSpace([128,]) #
x_space = RGSpace([128,]) #
# define signal space
k_space = x_space.get_codomain() # get conjugate space
......
lm/__init__.py
View file @ 61e4b7a3
......@@ -38,8 +38,8 @@ except(ImportError):
"INFO: neither libsharp_wrapper_gl nor healpy available.")
pass ## import nothing
else:
from lm_space import LmSpace ## import lm & hp
from hp_space import HpSpace
from lm_space import LMSpace ## import lm & hp
from hp_space import HPSpace
## TODO: change about
else:
try:
......@@ -49,13 +49,13 @@ else:
about._errors.cprint(
"ERROR: installed healpy version is older than 1.8.1!"))
except(ImportError):
from gl_space import GlSpace ## import lm & gl
from lm_space import LmSpace
from gl_space import GLSpace ## import lm & gl
from lm_space import LMSpace
else:
from gl_space import GlSpace ##import all
from lm_space import LmSpace
from hp_space import HpSpace
from gl_space import GLSpace ##import all
from lm_space import LMSpace
from hp_space import HPSpace
from nifty.lm.nifty_power_conversion_lm import power_backward_conversion_lm,\
power_forward_conversion_lm
power_forward_conversion_lm
lm/gl_space.py
View file @ 61e4b7a3
......@@ -7,6 +7,8 @@ from matplotlib.ticker import LogFormatter as lf
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
from nifty.lm import LMSpace
from nifty.space import Space
from nifty.config import about,\
nifty_configuration as gc,\
......@@ -18,7 +20,8 @@ gl = gdi.get('libsharp_wrapper_gl')
GL_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
class GlSpace(Space):
class GLSpace(Space):
"""
.. __
.. / /
......@@ -129,9 +132,9 @@ class GlSpace(Space):
self.paradict['nlon'] = x[1]
def copy(self):
return GlSpace(nlat=self.paradict['nlat'],
nlon=self.paradict['nlon'],
dtype=self.dtype)
return GLSpace(nlat=self.paradict['nlat'],
nlon=self.paradict['nlon'],
dtype=self.dtype)
@property
def shape(self):
......@@ -205,7 +208,7 @@ class GlSpace(Space):
if not isinstance(codomain, Space):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
if isinstance(codomain, LmSpace):
if isinstance(codomain, LMSpace):
nlat = self.paradict['nlat']
nlon = self.paradict['nlon']
lmax = codomain.paradict['lmax']
......@@ -233,9 +236,9 @@ class GlSpace(Space):
mmax = nlat-1
# lmax,mmax = nlat-1,nlat-1
if self.dtype == np.dtype('float32'):
return LmSpace(lmax=lmax, mmax=mmax, dtype=np.complex64)
return LMSpace(lmax=lmax, mmax=mmax, dtype=np.complex64)
else:
return LmSpace(lmax=lmax, mmax=mmax, dtype=np.complex128)
return LMSpace(lmax=lmax, mmax=mmax, dtype=np.complex128)
def get_random_values(self, **kwargs):
"""
......@@ -308,7 +311,7 @@ class GlSpace(Space):
sample = self.calc_weight(sample, power=0.5)
else:
sample = super(GlSpace, self).get_random_values(**arg)
sample = super(GLSpace, self).get_random_values(**arg)
# elif(arg['random'] == "uni"):
......@@ -404,7 +407,7 @@ class GlSpace(Space):
raise ValueError(about._errors.cstring(
"ERROR: unsupported codomain."))
if isinstance(codomain, LmSpace):
if isinstance(codomain, LMSpace):
# weight if discrete
if self.discrete:
......
lm/hp_space.py
View file @ 61e4b7a3
......@@ -38,6 +38,8 @@ import pylab as pl
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
from nifty.lm import LMSpace
from nifty.space import Space
from nifty.field import Field
......@@ -51,7 +53,8 @@ hp = gdi.get('healpy')
HP_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
class HpSpace(Space):
class HPSpace(Space):
"""
.. __
.. / /
......@@ -148,7 +151,7 @@ class HpSpace(Space):
self.paradict['nside'] = x[0]
def copy(self):
return HpSpace(nside=self.paradict['nside'])
return HPSpace(nside=self.paradict['nside'])
@property
def shape(self):
......@@ -221,7 +224,7 @@ class HpSpace(Space):
if not isinstance(codomain, Space):
raise TypeError(about._errors.cstring("ERROR: invalid input."))
if isinstance(codomain, LmSpace):
if isinstance(codomain, LMSpace):
nside = self.paradict['nside']
lmax = codomain.paradict['lmax']
mmax = codomain.paradict['mmax']
......@@ -244,7 +247,7 @@ class HpSpace(Space):
"""
lmax = 3*self.paradict['nside'] - 1
mmax = lmax
return LmSpace(lmax=lmax, mmax=mmax, dtype=np.dtype('complex128'))
return LMSpace(lmax=lmax, mmax=mmax, dtype=np.dtype('complex128'))
def get_random_values(self, **kwargs):
"""
......@@ -312,7 +315,7 @@ class HpSpace(Space):
sample = self.calc_weight(sample, power=0.5)
else:
sample = super(HpSpace, self).get_random_values(**arg)
sample = super(HPSpace, self).get_random_values(**arg)
# elif arg['random'] == "uni":
......@@ -369,7 +372,7 @@ class HpSpace(Space):
np_x = x.get_full_data()
if isinstance(codomain, LmSpace):
if isinstance(codomain, LMSpace):
# weight if discrete
if self.discrete:
x = self.calc_weight(x, power=-0.5)
......
lm/lm_space.py
View file @ 61e4b7a3
......@@ -10,8 +10,8 @@ from matplotlib.ticker import LogFormatter as lf
from d2o import STRATEGIES as DISTRIBUTION_STRATEGIES
from nifty.space import Space
from hp_space import HpSpace
from gl_space import GlSpace
from hp_space import HPSpace
from gl_space import GLSpace
from nifty.config import about,\
nifty_configuration as gc,\
dependency_injector as gdi
......@@ -25,7 +25,7 @@ hp = gdi.get('healpy')
LM_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
class LmSpace(Space):
class LMSpace(Space):
"""
.. __
.. / /
......@@ -168,9 +168,9 @@ class LmSpace(Space):
return tuple(sorted(temp))
def copy(self):
return LmSpace(lmax=self.paradict['lmax'],
mmax=self.paradict['mmax'],
dtype=self.dtype)
return LMSpace(lmax=self.paradict['lmax'],
mmax=self.paradict['mmax'],
dtype=self.dtype)
@property
def shape(self):
......@@ -294,7 +294,7 @@ class LmSpace(Space):
raise TypeError(about._errors.cstring(
"ERROR: The given codomain must be a nifty lm_space."))
elif isinstance(codomain, GlSpace):
elif isinstance(codomain, GLSpace):
# lmax==mmax
# nlat==lmax+1
# nlon==2*lmax+1
......@@ -303,7 +303,7 @@ class LmSpace(Space):
(codomain.paradict['nlon'] == 2*self.paradict['lmax']+1)):
return True
elif isinstance(codomain, HpSpace):
elif isinstance(codomain, HPSpace):
# lmax==mmax
# 3*nside-1==lmax
if ((self.paradict['lmax'] == self.paradict['mmax']) and
......@@ -353,11 +353,11 @@ class LmSpace(Space):
raise NotImplementedError
nlat = self.paradict['lmax'] + 1
nlon = self.paradict['lmax'] * 2 + 1
return GlSpace(nlat=nlat, nlon=nlon, dtype=new_dtype)
return GLSpace(nlat=nlat, nlon=nlon, dtype=new_dtype)
elif coname == 'hp' or (coname is None and not gc['lm2gl']):
nside = (self.paradict['lmax']+1) // 3
return HpSpace(nside=nside)
return HPSpace(nside=nside)
else:
raise ValueError(about._errors.cstring(
......@@ -434,7 +434,7 @@ class LmSpace(Space):
sample = gl.synalm(arg['spec'], lmax=lmax, mmax=mmax)
else:
sample = super(LmSpace, self).get_random_values(**arg)
sample = super(LMSpace, self).get_random_values(**arg)
# elif arg['random'] == "uni":
# x = random.uni(dtype=self.dtype,
......@@ -528,7 +528,7 @@ class LmSpace(Space):
np_x = x.get_full_data()
if isinstance(codomain, GlSpace):
if isinstance(codomain, GLSpace):
nlat = codomain.paradict['nlat']
nlon = codomain.paradict['nlon']
lmax = self.paradict['lmax']
......@@ -543,7 +543,7 @@ class LmSpace(Space):
lmax=lmax, mmax=mmax, cl=False)
Tx = codomain.cast(np_Tx)
elif isinstance(codomain, HpSpace):
elif isinstance(codomain, HPSpace):
nside = codomain.paradict['nside']
lmax = self.paradict['lmax']
mmax = self.paradict['mmax']
......
lm/nifty_power_conversion_lm.py
View file @ 61e4b7a3
......@@ -27,7 +27,7 @@ from nifty.field import Field
from nifty.nifty_simple_math import sqrt, exp, log
def power_backward_conversion_lm(k_space,p,mean=None):
def power_backward_conversion_lm(k_space, p, mean=None):
"""
This function is designed to convert a theoretical/statistical power
spectrum of a log-normal field to the theoretical power spectrum of
......
power/power_space.py
View file @ 61e4b7a3
......@@ -7,7 +7,7 @@ from nifty.nifty_paradict import power_space_paradict
class PowerSpace(Space):
def __init__(self, dtype=np.dtype('float'), distribution_strategy='fftw',
def __init__(self, distribution_strategy, dtype=np.dtype('float'),
log=False, nbin=None, binbounds=None):
self.dtype = np.dtype(dtype)
self.paradict = power_space_paradict(
......
power/rg_power_space.py
View file @ 61e4b7a3
......@@ -13,13 +13,13 @@ class RGPowerSpace(PowerSpace):
binbounds=None):
self.dtype = np.dtype(dtype)
self.paradict = rg_power_space_paradict(
shape=shape,
dgrid=dgrid,
zerocentered=zerocentered,
distribution_strategy=distribution_strategy,
log=log,
nbin=nbin,
binbounds=binbounds)
shape=shape,
dgrid=dgrid,
zerocentered=zerocentered,
distribution_strategy=distribution_strategy,
log=log,
nbin=nbin,
binbounds=binbounds)
# self.power_indices = RGPowerIndexFactory.get_power_indices(
# **self.paradict.parameters)
rg/__init__.py
View file @ 61e4b7a3
......@@ -20,7 +20,7 @@
## along with this program. If not, see <http://www.gnu.org/licenses/>.
from __future__ import division
from nifty_rg import RgSpace,\
from nifty_rg import RGSpace,\
utilities
from nifty_power_conversion_rg import power_backward_conversion_rg,\
power_forward_conversion_rg
......
rg/nifty_rg.py
View file @ 61e4b7a3
......@@ -59,7 +59,7 @@ MPI = gdi[gc['mpi_module']]
RG_DISTRIBUTION_STRATEGIES = DISTRIBUTION_STRATEGIES['global']
class RgSpace(Space):
class RGSpace(Space):
"""
.. _____ _______
.. / __/ / _ /
......@@ -241,7 +241,7 @@ class RgSpace(Space):
return tuple(sorted(temp))
def copy(self):
return RgSpace(shape=self.paradict['shape'],
return RGSpace(shape=self.paradict['shape'],
complexity=self.paradict['complexity'],
zerocenter=self.paradict['zerocenter'],
distances=self.distances,
......@@ -353,7 +353,7 @@ class RgSpace(Space):
if codomain is None:
return False
if not isinstance(codomain, RgSpace):
if not isinstance(codomain, RGSpace):
raise TypeError(about._errors.cstring(
"ERROR: The given codomain must be a nifty rg_space."))
......@@ -461,7 +461,7 @@ class RgSpace(Space):
complexity = {0: 1, 1: 0, 2: 2}[self.paradict['complexity']]
harmonic = bool(not self.harmonic)
new_space = RgSpace(shape,
new_space = RGSpace(shape,
zerocenter=cozerocenter,
complexity=complexity,
distances=distances,
......@@ -541,7 +541,7 @@ class RgSpace(Space):
# Case 1: uniform distribution over {-1,+1}/{1,i,-1,-i}
if arg['random'] == 'pm1' and not hermitianizeQ:
sample = super(RgSpace, self).get_random_values(**arg)
sample = super(RGSpace, self).get_random_values(**arg)
elif arg['random'] == 'pm1' and hermitianizeQ:
sample = self.get_random_values(random='uni', vmin=-1, vmax=1)
......@@ -579,14 +579,14 @@ class RgSpace(Space):
# Case 2: normal distribution with zero-mean and a given standard
# deviation or variance
elif arg['random'] == 'gau':
sample = super(RgSpace, self).get_random_values(**arg)
sample = super(RGSpace, self).get_random_values(**arg)
if hermitianizeQ:
sample = utilities.hermitianize_gaussian(sample)
# Case 3: uniform distribution
elif arg['random'] == "uni" and not hermitianizeQ:
sample = super(RgSpace, self).get_random_values(**arg)
sample = super(RGSpace, self).get_random_values(**arg)
elif arg['random'] == "uni" and hermitianizeQ:
# For a hermitian uniform sample, generate a gaussian one
......@@ -1614,6 +1614,6 @@ class RgSpace(Space):
fig.canvas.draw()
def __repr__(self):
string = super(RgSpace, self).__repr__()
string = super(RGSpace, self).__repr__()
string += repr(self.fft_machine) + "\n "
return string
test/test_nifty_fft.py
View file @ 61e4b7a3
......@@ -6,7 +6,7 @@ import d2o
class TestFFTWTransform(unittest.TestCase):
def test_comm(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = d2o.distributed_data_object(a)
b.comm = [1, 2, 3] # change comm to something not supported
......@@ -14,14 +14,14 @@ class TestFFTWTransform(unittest.TestCase):
x.fft_machine.transform(b, x, x.get_codomain())
def test_shapemismatch(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = x.cast(a)
with self.assertRaises(ValueError):
x.fft_machine.transform(b, x, x.get_codomain(), axes=(0, 1, 2))
def test_local_ndarray(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
self.assertTrue(
np.allclose(
......@@ -31,7 +31,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_local_notzero(self):
x = nt.RgSpace(8, fft_module='pyfftw')
x = nt.RGSpace(8, fft_module='pyfftw')
a = np.ones((8, 8))
b = x.cast(a)
self.assertTrue(
......@@ -42,7 +42,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_not(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = d2o.distributed_data_object(a, distribution_strategy='not')
self.assertTrue(
......@@ -53,7 +53,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_axesnone(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = x.cast(a)
self.assertTrue(
......@@ -64,7 +64,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_axesnone_equal(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = d2o.distributed_data_object(a, distribution_strategy='equal')
self.assertTrue(
......@@ -75,7 +75,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_axesall(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = x.cast(a)
self.assertTrue(
......@@ -86,7 +86,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_axesall_equal(self):
x = nt.RgSpace((8, 8), fft_module='pyfftw')
x = nt.RGSpace((8, 8), fft_module='pyfftw')
a = np.ones((8, 8))
b = d2o.distributed_data_object(a, distribution_strategy='equal')
self.assertTrue(
......@@ -97,7 +97,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_zero(self):
x = nt.RgSpace(8, fft_module='pyfftw')
x = nt.RGSpace(8, fft_module='pyfftw')
a = np.ones((8, 8)) + 1j*np.zeros((8, 8))
b = x.cast(a)
self.assertTrue(
......@@ -108,7 +108,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_zero_equal(self):
x = nt.RgSpace(8, fft_module='pyfftw')
x = nt.RGSpace(8, fft_module='pyfftw')
a = np.ones((8, 8)) + 1j*np.zeros((8, 8))
b = d2o.distributed_data_object(a, distribution_strategy='equal')
self.assertTrue(
......@@ -119,7 +119,7 @@ class TestFFTWTransform(unittest.TestCase):
)
def test_mpi_zero_not(self):
x = nt.RgSpace(8, fft_module='pyfftw')
x = nt.RGSpace(8, fft_module='pyfftw')
a = np.ones((8, 8)) + 1j*np.zeros((8, 8))
b = d2o.distributed_data_object(a, distribution_strategy='not')
self.assertTrue(
......
test/test_nifty_field.py
View file @ 61e4b7a3
......@@ -14,7 +14,7 @@ from d2o import distributed_data_object
from nifty import Space, \
point_space
from nifty.rg import RgSpace
from nifty.rg import RGSpace
from nifty.lm import *
......@@ -95,7 +95,7 @@ for param in itertools.product([(1,), (4, 6), (5, 8)],
[False],
DATAMODELS['rg_space'],
fft_modules):
space_list += [[RgSpace(shape=param[0],
space_list += [[RGSpace(shape=param[0],
zerocenter=param[1],
complexity=param[2],
distances=param[3],
......@@ -106,10 +106,10 @@ for param in itertools.product([(1,), (4, 6), (5, 8)],
def generate_space_with_size(name, num):
space_dict = {'space': Space(),
'point_space': point_space(num),
'rg_space': RgSpace((num, num)),
'lm_space': LmSpace(mmax=num+1, lmax=num+1),
'hp_space': HpSpace(num),
'gl_space': GlSpace(nlat=num, nlon=2*num-1),
'rg_space': RGSpace((num, num)),
'lm_space': LMSpace(mmax=num+1, lmax=num+1),
'hp_space': HPSpace(num),
'gl_space': GLSpace(nlat=num, nlon=2*num-1),
}
return space_dict[name]
......@@ -135,7 +135,7 @@ class Test_field_init(unittest.TestCase):
def test_successfull_init_and_attributes(self, shape, zerocenter,
complexity, distances, harmonic,
fft_module, datamodel):
s = RgSpace(shape=shape, zerocenter=zerocenter,
s = RGSpace(shape=shape, zerocenter=zerocenter,
complexity=complexity, distances=distances,
harmonic=harmonic, fft_module=fft_module)
f = Field(domain=(s,), dtype=s.dtype, datamodel=datamodel)
......@@ -171,10 +171,10 @@ class Test_field_multiple_rg_init(unittest.TestCase):
def test_multiple_space_init(self, shape, zerocenter,
complexity, distances, harmonic,
fft_module,