correlated_fields.py: No un-normalized amplitudes

Adapt the density_estimator accordingly.

demos/getting_started_density.py

@@ -32,18 +32,21 @@ import nifty7 as ift
 
 
 def density_estimator(
-        domain, exposure=1., pad=1., cf_fluctuations_sane_defaults=None
+        domain, exposure=1., pad=1., cf_fluctuations=None, cf_azm=None
     ):
-    from functools import reduce
+    cf_azm_sane_default = (0., (1e-2, 1e-6))
+    cf_fluctuations_sane_default = {
+        "scale": (1.0, 0.5),
+        "cutoff": (10.0, 5.0),
+        "loglogslope": (-12.0, 6.0)
+    }
 
     domain = ift.DomainTuple.make(domain)
     dom_scaling = 1. + np.broadcast_to(pad, (len(domain.axes), ))
-    if cf_fluctuations_sane_defaults is None:
-        cf_fluctuations_sane_defaults = {
-          "scale": (1.0, 0.5),
-          "cutoff": (10.0, 5.0),
-          "loglogslope": (-12.0, 6.0)
-        }
+    if cf_fluctuations is None:
+        cf_fluctuations = cf_fluctuations_sane_default
+    if cf_azm is None:
+        cf_azm = cf_azm_sane_default
 
     domain_padded = []
     for d_scl, d in zip(dom_scaling, domain):
@@ -63,11 +66,13 @@ def density_estimator(
     prefix = "de"  # density estimator
     cfmaker = ift.CorrelatedFieldMaker(prefix)
     for i, d in enumerate(domain_padded):
-        cfmaker.add_fluctuations_matern(
-            d, **cf_fluctuations_sane_defaults, prefix=f"ax{i}"
-        )
-    # cfmaker.set_amplitude_total_offset(0., (1e-2, 1e-6))
-    correlated_field = cfmaker.finalize(0, normalize=False)
+        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}")
+    cfmaker.set_amplitude_total_offset(*cf_azm)
+    correlated_field = cfmaker.finalize(0)
 
     domain_shape = tuple(d.shape for d in domain)
     slc = ift.SliceOperator(correlated_field.target, domain_shape)

src/library/correlated_fields.py

@@ -676,7 +676,7 @@ class CorrelatedFieldMaker:
                 zm = _Distributor(dofdex, zm.target, UnstructuredDomain(self._total_N)) @ zm
             self._azm = zm
 
-    def finalize(self, prior_info=100, normalize=True):
+    def finalize(self, prior_info=100):
         """Finishes model construction process and returns the constructed
         operator.
 
@@ -707,36 +707,18 @@ class CorrelatedFieldMaker:
                                            self._target_subdomains[i][amp_space],
                                            space=spaces[i]) @ ht
 
-        if normalize:
-            expander = ContractionOperator(hspace, spaces=spaces).adjoint
-            azm = expander @ self.azm
+        expander = ContractionOperator(hspace, spaces=spaces).adjoint
+        azm = expander @ self.azm
 
-            a = list(self.get_normalized_amplitudes())
-        else:
-            a = list(self.fluctuations)
+        a = list(self.get_normalized_amplitudes())
         for ii in range(n_amplitudes):
             co = ContractionOperator(hspace, spaces[:ii] + spaces[ii + 1:])
             pp = a[ii].target[amp_space]
             pd = PowerDistributor(co.target, pp, amp_space)
             a[ii] = co.adjoint @ pd @ a[ii]
         corr = reduce(mul, a)
-        if normalize:
-            xi = ducktape(hspace, None, self._prefix + 'xi')
-            op = ht(azm * corr * xi)
-        else:
-            if self._total_N > 0:
-                re = (
-                    "initializing a correlated field without an amplitude total"
-                    " offset with `total_N` larger than zero is not supported"
-                )
-                raise RuntimeError(re)
-            m = np.zeros(hspace.shape, dtype=bool)
-            m.flat[0] = True
-            mask_zm = MaskOperator(Field(hspace, m))
-
-            xi = ducktape(mask_zm.adjoint.domain, None, self._prefix + "xi")
-            xi = mask_zm.adjoint @ xi
-            op = ht(corr * xi)
+        xi = ducktape(hspace, None, self._prefix + 'xi')
+        op = ht(azm * corr * xi)
 
         if self._offset_mean is not None:
             offset = self._offset_mean