minor

geobipy/src/classes/data/datapoint/TdemDataPoint.py

@@ -75,7 +75,7 @@ class TdemDataPoint(EmDataPoint):
     The same is true for the errors, and the predicted data vector.
 
     """
-    __slots__ = ('_loop_pair', '_predicted_primary_field', '_predicted_secondary_field', '_primary_field', '_secondary_field', '_centered_on')
+    __slots__ = ('_loop_pair', '_predicted_primary_field', '_predicted_secondary_field', '_primary_field', '_secondary_field')
 
     def __init__(self, x=0.0, y=0.0, z=0.0, elevation=0.0,
                  primary_field=None, secondary_field=None,
@@ -314,7 +314,6 @@ def __deepcopy__(self, memo={}):
         out._secondary_field = deepcopy(self.secondary_field)
         out._predicted_primary_field = deepcopy(self.predicted_primary_field)
         out._predicted_secondary_field = deepcopy(self.predicted_secondary_field)
-        out._centered_on = deepcopy(self._centered_on)
 
         return out
 
@@ -1029,7 +1028,6 @@ def sensitivity(self, model, ix=None, model_changed=False):
 
         assert isinstance(model, Model), TypeError("Invalid model class for sensitivity matrix [1D]")
         self._sensitivity_matrix = StatArray.StatArray(tdem1dsen(self, model, ix, model_changed), 'Sensitivity', '$\\frac{V}{SAm^{3}}$')
-        self._centered_on = model.values
         return self.sensitivity_matrix
 
     def fm_dlogc(self, model):

geobipy/src/inversion/Inference1D.py

@@ -533,7 +533,6 @@ def accept_reject(self):
         # dprint('\n\niteration', self.iteration)
 
         # dprint('A', self.prng.random())
-        dprint('a', self.datapoint._centered_on)
 
         # print('incoming predicted data', self.datapoint.predictedData)
         # print('incoming model', self.model.values)
@@ -550,7 +549,6 @@ def accept_reject(self):
         # print('sensitivity before perturbing', np.diag(test_datapoint.sensitivity_matrix))
         # try:
         remapped_model, test_model = self.model.perturb(observation, self.low_variance, self.high_variance, self.covariance_scaling)
-        dprint('b', test_datapoint._centered_on)
         # test predicted data and sensitivity are centered on remapped model
         # test variance is centered on the remapped model
         # The data errors have not been perturbed yet.
@@ -599,7 +597,6 @@ def accept_reject(self):
             test_likelihood = test_datapoint.likelihood(log=True)
             observation = test_datapoint
 
-        dprint('c', test_datapoint._centered_on)
         proposal, test_proposal = test_model.proposal_probabilities(remapped_model, observation)
 
         test_posterior = test_prior + test_likelihood