Changeset 9e771a3 in sasmodels for sasmodels/details.py

Timestamp:

Oct 18, 2017 8:51:08 PM (7 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

94f4543

Parents:

9b7b23f

Message:

sort out weights (seems to be correct this time)

File:

• sasmodels/details.py (modified) (2 diffs)

sasmodels/details.py

@@ -234 +234 @@
     # skipping scale and background when building values and weights
     values, dispersity, weights = zip(*mesh[2:npars+2]) if npars else ((), (), ())
-    weights = correct_theta_weights(kernel.info.parameters, values, weights)
+    weights = correct_theta_weights(kernel.info.parameters, dispersity, weights)
     length = np.array([len(w) for w in weights])
     offset = np.cumsum(np.hstack((0, length)))
@@ -247 +247 @@
     return call_details, data, is_magnetic
 
-def correct_theta_weights(parameters, values, weights):
+def correct_theta_weights(parameters, dispersity, weights):
     # type: (ParameterTable, Sequence[np.ndarray], Sequence[np.ndarray]) -> Sequence[np.ndarray]
     """
     If there is a theta parameter, update the weights of that parameter so that
-    the cosine weighting required for polar integration is preserved.  Avoid
-    evaluation strictly at the pole, which would otherwise send the weight to
-    zero.
-
-    Note: values and weights do not include scale and background
-    """
-    # TODO: document code, explaining why scale and background are skipped
-    # given that we don't have scale and background in the list, we
-    # should be calling the variables something other than values and weights
+    the cosine weighting required for polar integration is preserved.
+
+    Avoid evaluation strictly at the pole, which would otherwise send the
+    weight to zero.  This is probably not a problem in practice (if dispersity
+    is +/- 90, then you probably should be using a 1-D model of the circular
+    average).
+
+    Note: scale and background parameters are not include in the tuples for
+    dispersity and weights, so index is parameters.theta_offset, not
+    parameters.theta_offset+2
+
+    Returns updated weights vectors
+    """
+    # TODO: explain in a comment why scale and background are missing
     # Apparently the parameters.theta_offset similarly skips scale and
-    # and background, so the indexing works out.
+    # and background, so the indexing works out, but they are still shipped
+    # to the kernel, so we need to add two there.
     if parameters.theta_offset >= 0:
         index = parameters.theta_offset
-        theta = values[index]
-        theta_weight = np.minimum(abs(cos(radians(theta))), 1e-6)
-        # copy the weights list so we can update it
-        weights = list(weights)
-        weights[index] = theta_weight*np.asarray(weights[index])
-        weights = tuple(weights)
+        theta = dispersity[index]
+        # TODO: modify the dispersity vector to avoid the theta=-90,90,270,...
+        theta_weight = abs(cos(radians(theta)))
+        weights = tuple(theta_weight*v if k == index else v
+                        for k, v in enumerate(weights))
     return weights