Commit 92e7fe7d authored by Martin Reinecke's avatar Martin Reinecke

big simplifications; power_synthesize() -> draw_sample()

parent f0696a82
Pipeline #25248 passed with stages
in 9 minutes and 57 seconds
...@@ -229,14 +229,13 @@ ...@@ -229,14 +229,13 @@
"s_space = ift.RGSpace(N_pixels)\n", "s_space = ift.RGSpace(N_pixels)\n",
"h_space = s_space.get_default_codomain()\n", "h_space = s_space.get_default_codomain()\n",
"HT = ift.HarmonicTransformOperator(h_space, target=s_space)\n", "HT = ift.HarmonicTransformOperator(h_space, target=s_space)\n",
"p_space = ift.PowerSpace(h_space)\n",
"\n", "\n",
"# Operators\n", "# Operators\n",
"Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n", "Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
"R = HT #*ift.create_harmonic_smoothing_operator((h_space,), 0, 0.02)\n", "R = HT #*ift.create_harmonic_smoothing_operator((h_space,), 0, 0.02)\n",
"\n", "\n",
"# Fields and data\n", "# Fields and data\n",
"sh = ift.power_synthesize(ift.PS_field(p_space, pow_spec),real_signal=True)\n", "sh = Sh.draw_sample()\n",
"noiseless_data=R(sh)\n", "noiseless_data=R(sh)\n",
"noise_amplitude = np.sqrt(0.2)\n", "noise_amplitude = np.sqrt(0.2)\n",
"N = ift.ScalingOperator(noise_amplitude**2, s_space)\n", "N = ift.ScalingOperator(noise_amplitude**2, s_space)\n",
...@@ -394,7 +393,7 @@ ...@@ -394,7 +393,7 @@
"# R is defined below\n", "# R is defined below\n",
"\n", "\n",
"# Fields\n", "# Fields\n",
"sh = ift.power_synthesize(ift.PS_field(p_space,pow_spec),real_signal=True)\n", "sh = Sh.draw_sample()\n",
"s = HT(sh)\n", "s = HT(sh)\n",
"n = ift.Field.from_random(domain=s_space, random_type='normal',\n", "n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
" std=noise_amplitude, mean=0)" " std=noise_amplitude, mean=0)"
...@@ -565,14 +564,13 @@ ...@@ -565,14 +564,13 @@
"source": [ "source": [
"h_space = s_space.get_default_codomain()\n", "h_space = s_space.get_default_codomain()\n",
"HT = ift.HarmonicTransformOperator(h_space,s_space)\n", "HT = ift.HarmonicTransformOperator(h_space,s_space)\n",
"p_space = ift.PowerSpace(h_space)\n",
"\n", "\n",
"# Operators\n", "# Operators\n",
"Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n", "Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)\n",
"N = ift.ScalingOperator(sigma2,s_space)\n", "N = ift.ScalingOperator(sigma2,s_space)\n",
"\n", "\n",
"# Fields and data\n", "# Fields and data\n",
"sh = ift.power_synthesize(ift.PS_field(p_space,pow_spec),real_signal=True)\n", "sh = Sh.draw_sample()\n",
"n = ift.Field.from_random(domain=s_space, random_type='normal',\n", "n = ift.Field.from_random(domain=s_space, random_type='normal',\n",
" std=np.sqrt(sigma2), mean=0)\n", " std=np.sqrt(sigma2), mean=0)\n",
"\n", "\n",
......
...@@ -31,21 +31,21 @@ if __name__ == "__main__": ...@@ -31,21 +31,21 @@ if __name__ == "__main__":
p_space = ift.PowerSpace(h_space, p_space = ift.PowerSpace(h_space,
binbounds=ift.PowerSpace.useful_binbounds( binbounds=ift.PowerSpace.useful_binbounds(
h_space, logarithmic=True)) h_space, logarithmic=True))
s_spec = ift.Field.full(p_space, 1e-5)
# Choosing the prior correlation structure and defining # Choosing the prior correlation structure and defining
# correlation operator # correlation operator
p = ift.PS_field(p_space, p_spec) p = ift.PS_field(p_space, p_spec)
log_p = ift.log(p) log_p = ift.log(p)
S = ift.create_power_operator(h_space, power_spectrum=s_spec) S = ift.create_power_operator(h_space, power_spectrum=lambda k: 1e-5)
# Drawing a sample sh from the prior distribution in harmonic space # Drawing a sample sh from the prior distribution in harmonic space
sh = ift.power_synthesize(s_spec) sh = S.draw_sample()
# Choosing the measurement instrument # Choosing the measurement instrument
# Instrument = SmoothingOperator(s_space, sigma=0.01) # Instrument = SmoothingOperator(s_space, sigma=0.01)
mask = np.ones(s_space.shape) mask = np.ones(s_space.shape)
#mask[6000:8000] = 0. # mask[6000:8000] = 0.
mask[30:70,30:70] = 0. mask[30:70, 30:70] = 0.
mask = ift.Field.from_global_data(s_space, mask) mask = ift.Field.from_global_data(s_space, mask)
MaskOperator = ift.DiagonalOperator(mask) MaskOperator = ift.DiagonalOperator(mask)
...@@ -69,7 +69,7 @@ if __name__ == "__main__": ...@@ -69,7 +69,7 @@ if __name__ == "__main__":
# Creating the mock data # Creating the mock data
d = noiseless_data + n d = noiseless_data + n
m0 = ift.power_synthesize(ift.Field.full(p_space, 1e-7)) m0 = ift.Field.full(h_space, 1e-7)
t0 = ift.Field.full(p_space, -4.) t0 = ift.Field.full(p_space, -4.)
power0 = Distributor.times(ift.exp(0.5 * t0)) power0 = Distributor.times(ift.exp(0.5 * t0))
...@@ -120,5 +120,5 @@ if __name__ == "__main__": ...@@ -120,5 +120,5 @@ if __name__ == "__main__":
ift.plot(nonlinearity(HT(power0*m0)), title="Reconstructed sky", ift.plot(nonlinearity(HT(power0*m0)), title="Reconstructed sky",
name="reconstructed_sky.png", zmin=zmin, zmax=zmax, **plotdict) name="reconstructed_sky.png", zmin=zmin, zmax=zmax, **plotdict)
ymin = np.min(p.to_global_data()) ymin = np.min(p.to_global_data())
ift.plot([ift.exp(t0),p], title="Power spectra", ift.plot([ift.exp(t0), p], title="Power spectra",
name="ps.png", ymin=ymin, **plotdict) name="ps.png", ymin=ymin, **plotdict)
...@@ -33,15 +33,14 @@ if __name__ == "__main__": ...@@ -33,15 +33,14 @@ if __name__ == "__main__":
p_space = ift.PowerSpace(h_space, p_space = ift.PowerSpace(h_space,
binbounds=ift.PowerSpace.useful_binbounds( binbounds=ift.PowerSpace.useful_binbounds(
h_space, logarithmic=True)) h_space, logarithmic=True))
s_spec = ift.Field.full(p_space, 1.)
# Choosing the prior correlation structure and defining # Choosing the prior correlation structure and defining
# correlation operator # correlation operator
p = ift.PS_field(p_space, p_spec) p = ift.PS_field(p_space, p_spec)
log_p = ift.log(p) log_p = ift.log(p)
S = ift.create_power_operator(h_space, power_spectrum=s_spec) S = ift.create_power_operator(h_space, lambda k: 1.)
# Drawing a sample sh from the prior distribution in harmonic space # Drawing a sample sh from the prior distribution in harmonic space
sh = ift.power_synthesize(s_spec) sh = S.draw_sample()
# Choosing the measurement instrument # Choosing the measurement instrument
# Instrument = SmoothingOperator(s_space, sigma=0.01) # Instrument = SmoothingOperator(s_space, sigma=0.01)
...@@ -70,7 +69,7 @@ if __name__ == "__main__": ...@@ -70,7 +69,7 @@ if __name__ == "__main__":
# Creating the mock data # Creating the mock data
d = noiseless_data + n d = noiseless_data + n
m0 = ift.power_synthesize(ift.Field.full(p_space, 1e-7)) m0 = ift.Field.full(h_space, 1e-7)
t0 = ift.Field.full(p_space, -4.) t0 = ift.Field.full(p_space, -4.)
power0 = Distributor.times(ift.exp(0.5 * t0)) power0 = Distributor.times(ift.exp(0.5 * t0))
...@@ -116,4 +115,4 @@ if __name__ == "__main__": ...@@ -116,4 +115,4 @@ if __name__ == "__main__":
ift.plot(nonlinearity(HT(power0*m0)), ift.plot(nonlinearity(HT(power0*m0)),
name="reconstructed_sky.png", **plotdict) name="reconstructed_sky.png", **plotdict)
ift.plot(MeasurementOperator.adjoint_times(d), name="data.png", **plotdict) ift.plot(MeasurementOperator.adjoint_times(d), name="data.png", **plotdict)
ift.plot([ift.exp(t0),p], name="ps.png") ift.plot([ift.exp(t0), p], name="ps.png")
...@@ -20,43 +20,28 @@ if __name__ == "__main__": ...@@ -20,43 +20,28 @@ if __name__ == "__main__":
a = 4 * correlation_length_1 * field_variance_1**2 a = 4 * correlation_length_1 * field_variance_1**2
return a / (1 + k * correlation_length_1) ** 4. return a / (1 + k * correlation_length_1) ** 4.
def power_spectrum_2(k): # note: field_variance**2 = a*k_0/4.
a = 4 * correlation_length_2 * field_variance_2**2
return a / (1 + k * correlation_length_2) ** 2.5
signal_space_1 = ift.RGSpace([N_pixels_1], distances=L_1/N_pixels_1) signal_space_1 = ift.RGSpace([N_pixels_1], distances=L_1/N_pixels_1)
harmonic_space_1 = signal_space_1.get_default_codomain() harmonic_space_1 = signal_space_1.get_default_codomain()
signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2) signal_space_2 = ift.RGSpace([N_pixels_2], distances=L_2/N_pixels_2)
harmonic_space_2 = signal_space_2.get_default_codomain() harmonic_space_2 = signal_space_2.get_default_codomain()
signal_domain = ift.DomainTuple.make((signal_space_1, signal_space_2)) signal_domain = ift.DomainTuple.make((signal_space_1, signal_space_2))
mid_domain = ift.DomainTuple.make((signal_space_1, harmonic_space_2))
harmonic_domain = ift.DomainTuple.make((harmonic_space_1, harmonic_domain = ift.DomainTuple.make((harmonic_space_1,
harmonic_space_2)) harmonic_space_2))
ht_1 = ift.HarmonicTransformOperator(harmonic_domain, space=0) ht_1 = ift.HarmonicTransformOperator(harmonic_domain, space=0)
power_space_1 = ift.PowerSpace(harmonic_space_1) ht_2 = ift.HarmonicTransformOperator(ht_1.target, space=1)
mock_power_1 = ift.PS_field(power_space_1, power_spectrum_1)
def power_spectrum_2(k): # note: field_variance**2 = a*k_0/4.
a = 4 * correlation_length_2 * field_variance_2**2
return a / (1 + k * correlation_length_2) ** 2.5
ht_2 = ift.HarmonicTransformOperator(mid_domain, space=1)
power_space_2 = ift.PowerSpace(harmonic_space_2)
mock_power_2 = ift.PS_field(power_space_2, power_spectrum_2)
ht = ht_2*ht_1 ht = ht_2*ht_1
mock_power = ift.Field.from_global_data( S = (ift.create_power_operator(harmonic_domain, power_spectrum_1, 0) *
(power_space_1, power_space_2), ift.create_power_operator(harmonic_domain, power_spectrum_2, 1))
np.outer(mock_power_1.to_global_data(),
mock_power_2.to_global_data()))
diagonal = ift.power_synthesize_nonrandom(mock_power, spaces=(0, 1))**2
S = ift.DiagonalOperator(diagonal)
np.random.seed(10) np.random.seed(10)
mock_signal = ift.power_synthesize(mock_power, real_signal=True) mock_signal = S.draw_sample()
# Setting up a exemplary response # Setting up a exemplary response
N1_10 = int(N_pixels_1/10) N1_10 = int(N_pixels_1/10)
......
...@@ -20,15 +20,11 @@ if __name__ == "__main__": ...@@ -20,15 +20,11 @@ if __name__ == "__main__":
signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels) signal_space = ift.RGSpace([N_pixels, N_pixels], distances=L/N_pixels)
harmonic_space = signal_space.get_default_codomain() harmonic_space = signal_space.get_default_codomain()
ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space) ht = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(
harmonic_space, binbounds=ift.PowerSpace.useful_binbounds(
harmonic_space, logarithmic=True))
# Creating the mock signal # Creating the mock signal
S = ift.create_power_operator(harmonic_space, S = ift.create_power_operator(harmonic_space,
power_spectrum=power_spectrum) power_spectrum=power_spectrum)
mock_power = ift.PS_field(power_space, power_spectrum) mock_signal = S.draw_sample()
mock_signal = ift.power_synthesize(mock_power, real_signal=True)
# Setting up an exemplary response # Setting up an exemplary response
mask = np.ones(signal_space.shape) mask = np.ones(signal_space.shape)
......
...@@ -9,7 +9,7 @@ if __name__ == "__main__": ...@@ -9,7 +9,7 @@ if __name__ == "__main__":
L = 2. L = 2.
# Typical distance over which the field is correlated (in same unit as L) # Typical distance over which the field is correlated (in same unit as L)
correlation_length = 0.1 correlation_length = 0.1
# Variance of field in position space sqrt(<|s_x|^2>) (in unit of s) # Variance of field in position space sqrt(<|s_x|^2>) (in same unit as s)
field_variance = 2. field_variance = 2.
# Smoothing length of response (in same unit as L) # Smoothing length of response (in same unit as L)
response_sigma = 0.01 response_sigma = 0.01
...@@ -29,14 +29,11 @@ if __name__ == "__main__": ...@@ -29,14 +29,11 @@ if __name__ == "__main__":
s_space = ift.RGSpace([N_pixels, N_pixels], distances=pixel_width) s_space = ift.RGSpace([N_pixels, N_pixels], distances=pixel_width)
h_space = s_space.get_default_codomain() h_space = s_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(h_space, s_space) HT = ift.HarmonicTransformOperator(h_space, s_space)
p_space = ift.PowerSpace(h_space)
# Create mock data # Create mock data
Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec) Sh = ift.create_power_operator(h_space, power_spectrum=pow_spec)
sh = Sh.draw_sample()
sp = ift.PS_field(p_space, pow_spec)
sh = ift.power_synthesize(sp, real_signal=True)
R = HT*ift.create_harmonic_smoothing_operator((h_space,), 0, R = HT*ift.create_harmonic_smoothing_operator((h_space,), 0,
response_sigma) response_sigma)
......
...@@ -52,15 +52,13 @@ if __name__ == "__main__": ...@@ -52,15 +52,13 @@ if __name__ == "__main__":
signal_space = ift.RGSpace(shape, distances=L/N_pixels) signal_space = ift.RGSpace(shape, distances=L/N_pixels)
harmonic_space = signal_space.get_default_codomain() harmonic_space = signal_space.get_default_codomain()
HT = ift.HarmonicTransformOperator(harmonic_space, target=signal_space) HT = ift.HarmonicTransformOperator(harmonic_space, target=signal_space)
power_space = ift.PowerSpace(harmonic_space)
# Creating the mock data # Creating the mock data
S = ift.create_power_operator(harmonic_space, S = ift.create_power_operator(harmonic_space,
power_spectrum=power_spectrum) power_spectrum=power_spectrum)
np.random.seed(43) np.random.seed(43)
mock_power = ift.PS_field(power_space, power_spectrum) mock_signal = S.draw_sample()
mock_signal = ift.power_synthesize(mock_power, real_signal=True)
sensitivity = (1./nu.m)**dimensionality/nu.K sensitivity = (1./nu.m)**dimensionality/nu.K
R = ift.GeometryRemover(signal_space) R = ift.GeometryRemover(signal_space)
......
...@@ -13,17 +13,13 @@ if __name__ == "__main__": ...@@ -13,17 +13,13 @@ if __name__ == "__main__":
h_space = s_space.get_default_codomain() h_space = s_space.get_default_codomain()
ht = ift.HarmonicTransformOperator(h_space, s_space) ht = ift.HarmonicTransformOperator(h_space, s_space)
# Set up power space
p_space = ift.PowerSpace(h_space)
# Choose prior correlation structure and define correlation operator # Choose prior correlation structure and define correlation operator
p_spec = (lambda k: (42/(k+1)**3)) p_spec = (lambda k: (42/(k+1)**3))
S = ift.create_power_operator(h_space, power_spectrum=p_spec) S = ift.create_power_operator(h_space, power_spectrum=p_spec)
# Draw sample sh from the prior distribution in harmonic space # Draw sample sh from the prior distribution in harmonic space
sp = ift.PS_field(p_space, p_spec) sh = S.draw_sample()
sh = ift.power_synthesize(sp, real_signal=True)
# Choose measurement instrument # Choose measurement instrument
diag = np.ones(s_space.shape) diag = np.ones(s_space.shape)
......
...@@ -10,7 +10,8 @@ from .operators import * ...@@ -10,7 +10,8 @@ from .operators import *
from .field import Field, sqrt, exp, log from .field import Field, sqrt, exp, log
from .probing.utils import probe_with_posterior_samples, probe_diagonal from .probing.utils import probe_with_posterior_samples, probe_diagonal, \
StatCalculator
from .minimization import * from .minimization import *
......
...@@ -18,8 +18,6 @@ ...@@ -18,8 +18,6 @@
from ..operators.endomorphic_operator import EndomorphicOperator from ..operators.endomorphic_operator import EndomorphicOperator
from ..operators.inversion_enabler import InversionEnabler from ..operators.inversion_enabler import InversionEnabler
from ..field import Field, sqrt
from ..sugar import power_analyze, power_synthesize
import numpy as np import numpy as np
......
...@@ -17,6 +17,7 @@ ...@@ -17,6 +17,7 @@
# and financially supported by the Studienstiftung des deutschen Volkes. # and financially supported by the Studienstiftung des deutschen Volkes.
from .linear_operator import LinearOperator from .linear_operator import LinearOperator
import numpy as np
class ChainOperator(LinearOperator): class ChainOperator(LinearOperator):
...@@ -119,3 +120,9 @@ class ChainOperator(LinearOperator): ...@@ -119,3 +120,9 @@ class ChainOperator(LinearOperator):
for op in t_ops: for op in t_ops:
x = op.apply(x, mode) x = op.apply(x, mode)
return x return x
def draw_sample(self, dtype=np.float64):
sample = self._ops[-1].draw_sample(dtype)
for op in reversed(self._ops[:-1]):
sample = op.process_sample(sample)
return sample
...@@ -133,6 +133,14 @@ class DiagonalOperator(EndomorphicOperator): ...@@ -133,6 +133,14 @@ class DiagonalOperator(EndomorphicOperator):
return DiagonalOperator(self._diagonal.conjugate(), self._domain, return DiagonalOperator(self._diagonal.conjugate(), self._domain,
self._spaces) self._spaces)
def process_sample(self, sample):
if np.issubdtype(self._ldiag.dtype, np.complexfloating):
raise ValueError("cannot draw sample from complex-valued operator")
res = Field.empty_like(sample)
res.local_data[()] = sample.local_data * np.sqrt(self._ldiag)
return res
def draw_sample(self, dtype=np.float64): def draw_sample(self, dtype=np.float64):
if np.issubdtype(self._ldiag.dtype, np.complexfloating): if np.issubdtype(self._ldiag.dtype, np.complexfloating):
raise ValueError("cannot draw sample from complex-valued operator") raise ValueError("cannot draw sample from complex-valued operator")
......
...@@ -83,6 +83,11 @@ class ScalingOperator(EndomorphicOperator): ...@@ -83,6 +83,11 @@ class ScalingOperator(EndomorphicOperator):
return self.TIMES | self.ADJOINT_TIMES return self.TIMES | self.ADJOINT_TIMES
return self._all_ops return self._all_ops
def process_sample(self, sample):
if self._factor.imag != 0. or self._factor.real <= 0.:
raise ValueError("Operator not positive definite")
return sample * np.sqrt(self._factor)
def draw_sample(self, dtype=np.float64): def draw_sample(self, dtype=np.float64):
if self._factor.imag != 0. or self._factor.real <= 0.: if self._factor.imag != 0. or self._factor.real <= 0.:
raise ValueError("Operator not positive definite") raise ValueError("Operator not positive definite")
......
...@@ -28,18 +28,16 @@ from . import dobj, utilities ...@@ -28,18 +28,16 @@ from . import dobj, utilities
__all__ = ['PS_field', __all__ = ['PS_field',
'power_analyze', 'power_analyze',
'power_synthesize',
'power_synthesize_nonrandom',
'create_power_field', 'create_power_field',
'create_power_operator', 'create_power_operator',
'create_harmonic_smoothing_operator'] 'create_harmonic_smoothing_operator']
def PS_field(pspace, func, dtype=None): def PS_field(pspace, func):
if not isinstance(pspace, PowerSpace): if not isinstance(pspace, PowerSpace):
raise TypeError raise TypeError
data = dobj.from_global_data(func(pspace.k_lengths)) data = dobj.from_global_data(func(pspace.k_lengths))
return Field(pspace, val=data, dtype=dtype) return Field(pspace, val=data)
def _single_power_analyze(field, idx, binbounds): def _single_power_analyze(field, idx, binbounds):
...@@ -119,21 +117,21 @@ def power_analyze(field, spaces=None, binbounds=None, ...@@ -119,21 +117,21 @@ def power_analyze(field, spaces=None, binbounds=None,
return parts[0] + 1j*parts[1] if keep_phase_information else parts[0] return parts[0] + 1j*parts[1] if keep_phase_information else parts[0]
def power_synthesize_nonrandom(field, spaces=None): def _power_synthesize_nonrandom(field, spaces=None):
spaces = utilities.parse_spaces(spaces, len(field.domain)) spaces = utilities.parse_spaces(spaces, len(field.domain))
result_domain = list(field.domain) result_domain = list(field.domain)
spec = sqrt(field) amplitude = sqrt(field)
for i in spaces: for i in spaces:
result_domain[i] = field.domain[i].harmonic_partner result_domain[i] = field.domain[i].harmonic_partner
pd = PowerDistributor(result_domain, field.domain[i], i) pd = PowerDistributor(result_domain, field.domain[i], i)
spec = pd(spec) amplitude = pd(amplitude)
return spec return amplitude
def power_synthesize(field, spaces=None, real_power=True, real_signal=True): def _power_synthesize(field, spaces=None, real_power=True, real_signal=True):
"""Returns a sampled field with `field`\**2 as its power spectrum. """Returns a sampled field with `field` as its power spectrum.
This method draws a Gaussian random field in the harmonic partner This method draws a Gaussian random field in the harmonic partner
domain of this field's domains, using this field as power spectrum. domain of this field's domains, using this field as power spectrum.
...@@ -141,7 +139,7 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True): ...@@ -141,7 +139,7 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True):
Parameters Parameters
---------- ----------
field : Field field : Field
The input field containing the square root of the power spectrum The input field containing the power spectrum
spaces : None, int, or tuple of int, optional spaces : None, int, or tuple of int, optional
Specifies the subdomains containing all the PowerSpaces which Specifies the subdomains containing all the PowerSpaces which
should be converted (default : None). should be converted (default : None).
...@@ -161,16 +159,16 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True): ...@@ -161,16 +159,16 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True):
Notes Notes
----- -----
For this the spaces specified by `spaces` must be a PowerSpace. For this the spaces specified by `spaces` must be of type PowerSpace.
This expects this field to be the square root of a power spectrum, i.e. This expects this field to be a power spectrum, i.e.
to have the unit of the field to be sampled. to have the unit of "field to be sampled"**2.
Raises Raises
------ ------
ValueError : If a domain specified by `spaces` is not a PowerSpace. ValueError : If a domain specified by `spaces` is not a PowerSpace.
""" """
spec = power_synthesize_nonrandom(field, spaces) spec = _power_synthesize_nonrandom(field, spaces)
self_real = not np.issubdtype(spec.dtype, np.complexfloating) self_real = not np.issubdtype(spec.dtype, np.complexfloating)
if (not real_power) and self_real: if (not real_power) and self_real:
raise ValueError("can't draw complex realizations from real spectrum") raise ValueError("can't draw complex realizations from real spectrum")
...@@ -190,7 +188,7 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True): ...@@ -190,7 +188,7 @@ def power_synthesize(field, spaces=None, real_power=True, real_signal=True):
return result[0] if real_power else result[0] + 1j*result[1] return result[0] if real_power else result[0] + 1j*result[1]
def create_power_field(domain, power_spectrum, dtype=None): def create_power_field(domain, power_spectrum):
if not callable(power_spectrum): # we have a Field living on a PowerSpace if not callable(power_spectrum): # we have a Field living on a PowerSpace
if not isinstance(power_spectrum, Field): if not isinstance(power_spectrum, Field):
raise TypeError("Field object expected") raise TypeError("Field object expected")
...@@ -199,15 +197,15 @@ def create_power_field(domain, power_spectrum, dtype=None): ...@@ -199,15 +197,15 @@ def create_power_field(domain, power_spectrum, dtype=None):
if not isinstance(power_spectrum.domain[0], PowerSpace): if not isinstance(power_spectrum.domain[0], PowerSpace):
raise TypeError("PowerSpace required") raise TypeError("PowerSpace required")
power_domain = power_spectrum.domain[0] power_domain = power_spectrum.domain[0]
fp = Field(power_domain, val=power_spectrum.val, dtype=dtype) fp = Field(power_domain, val=power_spectrum.val)
else: else:
power_domain = PowerSpace(domain) power_domain = PowerSpace(domain)
fp = PS_field(power_domain, power_spectrum, dtype) fp = PS_field(power_domain, power_spectrum)