Streamline

demos/getting_started_density.py

@@ -49,19 +49,16 @@ def density_estimator(domain, pad=1.0, cf_fluctuations=None, cf_azm_uniform=None
     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}\n"
-                "expected a non-harmonic `ift.RGSpace`"
-            )
-            raise TypeError(te)
+            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
-    prefix = "de_"  # density estimator
-    azm_offset_mean = 0.  # The zero-mode should be inferred only from the data
-    cfmaker = ift.CorrelatedFieldMaker(prefix)
+    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]
@@ -126,7 +123,7 @@ if __name__ == "__main__":
         title="Ground Truth",
     )
     plot.add(ift.Field.from_raw(plotting_domain, data.val), title="Data")
-    plot.output(ny=1, nx=3, xsize=10, ysize=10, name=filename.format("setup"))
+    plot.output(ny=1, nx=3, xsize=10, ysize=3, name=filename.format("setup"))
     print("Setup saved as", filename.format("setup"))
 
     # Minimization parameters
@@ -206,6 +203,5 @@ if __name__ == "__main__":
         ift.Field.from_raw(plotting_domain_expanded, ift.sqrt(sc_unsliced.var).val),
         title="Posterior Unsliced Standard Deviation",
     )
-    filename_res = filename.format("results")
-    plot.output(ny=2, nx=2, xsize=15, ysize=15, name=filename_res)
-    print("Saved results as '{}'.".format(filename_res))
+    plot.output(xsize=15, ysize=15, name=filename.format("results"))
+    print("Saved results as", filename.format("results"))