Changeset 2773c66 in sasmodels for sasmodels/sasview_model.py

Timestamp:

Sep 8, 2018 8:29:05 AM (6 years ago)

Author:

Torin Cooper-Bennun <torin.cooper-bennun@…>

Branches:

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

Children:

ba51e00

Parents:

b763f9d (diff), c88f983 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.

Message:

Merge branch 'beta_approx' into beta_approx_new_R_eff

File:

• sasmodels/sasview_model.py (modified) (7 diffs)

sasmodels/sasview_model.py

@@ -543 +543 @@
             # pylint: disable=unpacking-non-sequence
             q, phi = x
-            return self.calculate_Iq([q*math.cos(phi)], [q*math.sin(phi)])[0]
-        else:
-            return self.calculate_Iq([x])[0]
+            result, _ = self.calculate_Iq([q*math.cos(phi)], [q*math.sin(phi)])
+            return result[0]
+        else:
+            result, _ = self.calculate_Iq([x])
+            return result[0]
 
 
@@ -560 +562 @@
         """
         if isinstance(x, (list, tuple)):
-            return self.calculate_Iq([x[0]], [x[1]])[0]
-        else:
-            return self.calculate_Iq([x])[0]
+            result, _ = self.calculate_Iq([x[0]], [x[1]])
+            return result[0]
+        else:
+            result, _ = self.calculate_Iq([x])
+            return result[0]
 
     def evalDistribution(self, qdist):
@@ -596 +600 @@
             # Check whether we have a list of ndarrays [qx,qy]
             qx, qy = qdist
-            return self.calculate_Iq(qx, qy)
+            result, _ = self.calculate_Iq(qx, qy)
+            return result
 
         elif isinstance(qdist, np.ndarray):
             # We have a simple 1D distribution of q-values
-            return self.calculate_Iq(qdist)
+            result, _ = self.calculate_Iq(qdist)
+            return result
 
         else:
@@ -640 +646 @@
         if composition and composition[0] == 'product': # only P*S for now
             with calculation_lock:
-                self._calculate_Iq(qx)
-                # for compatibility with sasview 4.3
-                results = self._intermediate_results()
-                return results["P(Q)"], results["S(Q)"]
+                _, lazy_results = self._calculate_Iq(qx)
+                # for compatibility with sasview 4.x
+                results = lazy_results()
+                pq_data = results.get("P(Q)")
+                sq_data = results.get("S(Q)")
+                return pq_data, sq_data
         else:
             return None
 
-    def calculate_Iq(self, qx, qy=None):
-        # type: (Sequence[float], Optional[Sequence[float]]) -> np.ndarray
+    def calculate_Iq(self,
+                     qx,     # type: Sequence[float]
+                     qy=None # type: Optional[Sequence[float]]
+                     ):
+        # type: (...) -> Tuple[np.ndarray, Callable[[], collections.OrderedDict[str, np.ndarray]]]
         """
         Calculate Iq for one set of q with the current parameters.
@@ -656 +667 @@
         This should NOT be used for fitting since it copies the *q* vectors
         to the card for each evaluation.
+
+        The returned tuple contains the scattering intensity followed by a
+        callable which returns a dictionary of intermediate data from
+        ProductKernel.
         """
         ## uncomment the following when trying to debug the uncoordinated calls
@@ -688 +703 @@
         result = calculator(call_details, values, cutoff=self.cutoff,
                             magnetic=is_magnetic)
+        lazy_results = getattr(calculator, 'results',
+                               lambda: collections.OrderedDict())
         #print("result", result)
-        self._intermediate_results = getattr(calculator, 'results', None)
+
         calculator.release()
         #self._model.release()
-        return result
+
+        return result, lazy_results
 
     def calculate_ER(self):
@@ -881 +899 @@
     q = np.linspace(-0.35, 0.35, 500)
     qx, qy = np.meshgrid(q, q)
-    result = model.calculate_Iq(qx.flatten(), qy.flatten())
+    result, _ = model.calculate_Iq(qx.flatten(), qy.flatten())
     result = result.reshape(qx.shape)