EnsembleLikelihood ignores np.nans in data and extracts data if an ensemble was given as data.

imagine/likelihoods/ensemble_likelihood/ensemble_likelihood.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from nifty import DiagonalOperator
+from nifty import DiagonalOperator, FieldArray, Field
 
 from imagine.likelihoods.likelihood import Likelihood
 from imagine.create_ring_profile import create_ring_profile
@@ -12,13 +12,24 @@ class EnsembleLikelihood(Likelihood):
     def __init__(self, observable_name,  measured_data,
                  data_covariance_operator, profile=None):
         self.observable_name = observable_name
-        self.measured_data = measured_data
+        self.measured_data = self._strip_data(measured_data)
         self.data_covariance_operator = data_covariance_operator
+        self.data_covariance_includes_profile = False
+
         if profile is None:
             profile = create_ring_profile(
                             self.measured_data.val.get_full_data())
         self.profile = profile
 
+    def _strip_data(self, data):
+        # if the first element in the domain tuple is a FieldArray we must
+        # extract the data
+        if isinstance(data.domain[0], FieldArray):
+            stripped_data = Field(domain=data.domain[1:],
+                                  val=data.val.get_full_data()[0],
+                                  distribution_strategy='not')
+        return stripped_data
+
     def __call__(self, observable):
         field = observable[self.observable_name]
         return self._process_simple_field(field,
@@ -80,6 +91,8 @@ class EnsembleLikelihood(Likelihood):
                             "DiagonalOperator.")
 
         A_bare_diagonal = data_covariance_operator.diagonal(bare=True)
+        if not self.data_covariance_includes_profile:
+            A_bare_diagonal *= (profile**2)
         A_bare_diagonal.val += rho*mu
         A = DiagonalOperator(
                     domain=data_covariance_operator.domain,
@@ -97,6 +110,10 @@ class EnsembleLikelihood(Likelihood):
         middle = np.linalg.inv(middle)
         c = measured_data - obs_mean
 
+        # If the data was incomplete, i.e. contains np.NANs, set those values
+        # to zero.
+        np.nan_to_num(c, copy=False)
+
         # assuming that A == A^dagger, this can be shortend
         # a_c = A.inverse_times(c)
         # u_a_c = a_c.dot(U, spaces=1)