Changeset 3c56da87 in sasmodels for sasmodels/sasview_model.py

Timestamp:

Mar 5, 2015 12:55:38 AM (9 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, release_v0.94, release_v0.95, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

3a45c2c

Parents:

b89f519

Message:

lint cleanup

File:

• sasmodels/sasview_model.py (modified) (6 diffs)

sasmodels/sasview_model.py

@@ -69 +69 @@
         self.dispersion = dict()
         partype = model.info['partype']
-        for name, units, default, limits, ptype, description in model.info['parameters']:
+        for name, units, default, limits, _, _ in model.info['parameters']:
             self.params[name] = default
             self.details[name] = [units] + limits
@@ -120 +120 @@
 
 
+    # pylint: disable=no-self-use
     def getProfile(self):
         """
@@ -213 +214 @@
         """
         if isinstance(x, (list, tuple)):
+            # pylint: disable=unpacking-non-sequence
             q, phi = x
             return self.calculate_Iq([q * math.cos(phi)],
@@ -263 +265 @@
 
 
-        :param qdist: ndarray of scalar q-values or list [qx,qy] where qx,qy are 1D ndarrays
+        :param qdist: ndarray of scalar q-values or list [qx,qy]
+        where qx,qy are 1D ndarrays
         """
         if isinstance(qdist, (list, tuple)):
@@ -279 +282 @@
 
         else:
-            raise TypeError("evalDistribution expects q or [qx, qy], not %r" % type(qdist))
+            raise TypeError("evalDistribution expects q or [qx, qy], not %r"
+                            % type(qdist))
 
     def calculate_Iq(self, *args):
@@ -382 +386 @@
         limits = self._model.info['limits']
         dis = self.dispersion[par]
-        v, w = weights.get_weights(
+        value, weight = weights.get_weights(
             dis['type'], dis['npts'], dis['width'], dis['nsigmas'],
             self.params[par], limits[par], par in relative)
-        return v, w / w.max()
-
+        return value, weight / np.sum(weight)
+