diff --git a/demos/getting_started_density.py b/demos/getting_started_density.py
index 1b6ee997fc964c3ac8e3c0ec909cea4d3b2bd4a9..2bb54f1bbcfb27b54614c4fff943f22e347f39dc 100644
--- a/demos/getting_started_density.py
+++ b/demos/getting_started_density.py
@@ -30,62 +30,6 @@ import numpy as np
import nifty7 as ift
-
-def density_estimator(domain, pad=1.0, cf_fluctuations=None, cf_azm_uniform=None):
- cf_azm_uniform_sane_default = (1e-4, 1.0)
- cf_fluctuations_sane_default = {
- "scale": (0.5, 0.3),
- "cutoff": (4.0, 3.0),
- "loglogslope": (-6.0, 3.0)
- }
-
- domain = ift.DomainTuple.make(domain)
- dom_scaling = 1. + np.broadcast_to(pad, (len(domain.axes), ))
- if cf_fluctuations is None:
- cf_fluctuations = cf_fluctuations_sane_default
- if cf_azm_uniform is None:
- cf_azm_uniform = cf_azm_uniform_sane_default
-
- domain_padded = []
- for d_scl, d in zip(dom_scaling, domain):
- if not isinstance(d, ift.RGSpace) or d.harmonic:
- te = [f"unexpected domain encountered in `domain`: {domain}"]
- te += "expected a non-harmonic `ift.RGSpace`"
- raise TypeError("\n".join(te))
- shape_padded = tuple((d_scl * np.array(d.shape)).astype(int))
- domain_padded.append(ift.RGSpace(shape_padded, distances=d.distances))
- domain_padded = ift.DomainTuple.make(domain_padded)
-
- # Set up the signal model
- azm_offset_mean = 0.0 # The zero-mode should be inferred only from the data
- cfmaker = ift.CorrelatedFieldMaker("")
- for i, d in enumerate(domain_padded):
- if isinstance(cf_fluctuations, (list, tuple)):
- cf_fl = cf_fluctuations[i]
- else:
- cf_fl = cf_fluctuations
- cfmaker.add_fluctuations_matern(d, **cf_fl, prefix=f"ax{i}")
- scalar_domain = ift.DomainTuple.scalar_domain()
- uniform = ift.UniformOperator(scalar_domain, *cf_azm_uniform)
- azm = uniform.ducktape("zeromode")
- cfmaker.set_amplitude_total_offset(azm_offset_mean, azm)
- correlated_field = cfmaker.finalize(0).clip(-10., 10.)
- normalized_amplitudes = cfmaker.get_normalized_amplitudes()
-
- domain_shape = tuple(d.shape for d in domain)
- slc = ift.SliceOperator(correlated_field.target, domain_shape)
- signal = ift.exp(slc @ correlated_field)
-
- model_operators = {
- "correlated_field": correlated_field,
- "select_subset": slc,
- "amplitude_total_offset": azm,
- "normalized_amplitudes": normalized_amplitudes
- }
-
- return signal, model_operators
-
-
if __name__ == "__main__":
filename = "getting_started_density_{}.png"
ift.random.push_sseq_from_seed(42)
@@ -97,7 +41,7 @@ if __name__ == "__main__":
sp2 = ift.RGSpace(npix2)
position_space = ift.DomainTuple.make((sp1, sp2))
- signal, ops = density_estimator(position_space)
+ signal, ops = ift.density_estimator(position_space)
correlated_field = ops["correlated_field"]
data_space = signal.target
diff --git a/src/sugar.py b/src/sugar.py
index 2653545680dc133315cb15364d551d2063b2bf05..4f5791f4f18f4a749a80ac17c1aaa7f9a8100af3 100644
--- a/src/sugar.py
+++ b/src/sugar.py
@@ -32,14 +32,15 @@ from .operators.diagonal_operator import DiagonalOperator
from .operators.distributors import PowerDistributor
from .operators.operator import Operator
from .operators.sampling_enabler import SamplingDtypeSetter
+from .operators.selection_operators import SliceOperator
from .operators.scaling_operator import ScalingOperator
from .plot import Plot
__all__ = ['PS_field', 'power_analyze', 'create_power_operator',
- 'create_harmonic_smoothing_operator', 'from_random',
- 'full', 'makeField',
- 'is_fieldlike', 'is_linearization', 'is_operator',
- 'makeDomain', 'get_signal_variance', 'makeOp', 'domain_union',
+ 'density_estimator', 'create_harmonic_smoothing_operator',
+ 'from_random', 'full', 'makeField', 'is_fieldlike',
+ 'is_linearization', 'is_operator', 'makeDomain',
+ 'get_signal_variance', 'makeOp', 'domain_union',
'get_default_codomain', 'single_plot', 'exec_time',
'calculate_position'] + list(pointwise.ptw_dict.keys())
@@ -214,6 +215,66 @@ def create_power_operator(domain, power_spectrum, space=None):
return DiagonalOperator(field, domain, space)
+def density_estimator(domain, pad=1.0, cf_fluctuations=None,
+ cf_azm_uniform=None):
+ from .domains.rg_space import RGSpace
+ from .library.correlated_fields import CorrelatedFieldMaker
+ from .library.special_distributions import UniformOperator
+
+ cf_azm_uniform_sane_default = (1e-4, 1.0)
+ cf_fluctuations_sane_default = {
+ "scale": (0.5, 0.3),
+ "cutoff": (4.0, 3.0),
+ "loglogslope": (-6.0, 3.0)
+ }
+
+ domain = DomainTuple.make(domain)
+ dom_scaling = 1. + np.broadcast_to(pad, (len(domain.axes), ))
+ if cf_fluctuations is None:
+ cf_fluctuations = cf_fluctuations_sane_default
+ if cf_azm_uniform is None:
+ cf_azm_uniform = cf_azm_uniform_sane_default
+
+ domain_padded = []
+ for d_scl, d in zip(dom_scaling, domain):
+ if not isinstance(d, RGSpace) or d.harmonic:
+ te = [f"unexpected domain encountered in `domain`: {domain}"]
+ te += "expected a non-harmonic `RGSpace`"
+ raise TypeError("\n".join(te))
+ shape_padded = tuple((d_scl * np.array(d.shape)).astype(int))
+ domain_padded.append(RGSpace(shape_padded, distances=d.distances))
+ domain_padded = DomainTuple.make(domain_padded)
+
+ # Set up the signal model
+ azm_offset_mean = 0.0 # The zero-mode should be inferred only from the data
+ cfmaker = CorrelatedFieldMaker("")
+ for i, d in enumerate(domain_padded):
+ if isinstance(cf_fluctuations, (list, tuple)):
+ cf_fl = cf_fluctuations[i]
+ else:
+ cf_fl = cf_fluctuations
+ cfmaker.add_fluctuations_matern(d, **cf_fl, prefix=f"ax{i}")
+ scalar_domain = DomainTuple.scalar_domain()
+ uniform = UniformOperator(scalar_domain, *cf_azm_uniform)
+ azm = uniform.ducktape("zeromode")
+ cfmaker.set_amplitude_total_offset(azm_offset_mean, azm)
+ correlated_field = cfmaker.finalize(0).clip(-10., 10.)
+ normalized_amplitudes = cfmaker.get_normalized_amplitudes()
+
+ domain_shape = tuple(d.shape for d in domain)
+ slc = SliceOperator(correlated_field.target, domain_shape)
+ signal = (slc @ correlated_field).exp()
+
+ model_operators = {
+ "correlated_field": correlated_field,
+ "select_subset": slc,
+ "amplitude_total_offset": azm,
+ "normalized_amplitudes": normalized_amplitudes
+ }
+
+ return signal, model_operators
+
+
def create_harmonic_smoothing_operator(domain, space, sigma):
"""Creates an operator which smoothes a subspace of a harmonic domain.