Clean up the zm handling in the density_estimator

demos/getting_started_density.py

@@ -32,8 +32,9 @@ import nifty7 as ift
 
 
 def density_estimator(
-        domain, exposure=1., pad=1., cf_fluctuations=None
+        domain, exposure=1., pad=1., cf_fluctuations=None, cf_azm_uniform=None
     ):
+    cf_azm_uniform_sane_default = (0., 20.)
     cf_fluctuations_sane_default = {
         "scale": (0.5, 0.3),
         "cutoff": (7.0, 3.0),
@@ -44,6 +45,8 @@ def density_estimator(
     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_uni = cf_azm_uniform_sane_default
 
     domain_padded = []
     for d_scl, d in zip(dom_scaling, domain):
@@ -69,15 +72,16 @@ def density_estimator(
             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, loc=0., scale=20.)
-    zm = uniform.ducktape("zeromode") # defines the zeromode operator with unifrom prior
-    cfmaker.set_amplitude_total_offset(0., zm)
+    uniform = ift.UniformOperator(scalar_domain, *cf_azm_uni)
+    zm = uniform.ducktape("zeromode")
+    zm_offset_mean = 0.  # The zero-mode should be inferred only from the data
+    cfmaker.set_amplitude_total_offset(zm_offset_mean, zm)
     correlated_field = cfmaker.finalize(0)
 
     domain_shape = tuple(d.shape for d in domain)
     slc = ift.SliceOperator(correlated_field.target, domain_shape)
 
-    signal = slc @ ift.exp(correlated_field)
+    signal = ift.exp(slc @ correlated_field)
     # Cache the result of the correlated field to use it several times
     signal_cache = signal.ducktape_left("signal_cache")
     signal_plchr = ift.FieldAdapter(signal.target, "signal_cache")