Changeset 6e7ff6d in sasmodels for sasmodels/generate.py

Timestamp:

Apr 7, 2016 2:01:54 PM (8 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:

3543141

Parents:

8bd7b77

Message:

reenable python models

File:

• sasmodels/generate.py (modified) (4 diffs)

sasmodels/generate.py

@@ -125 +125 @@
 An *model_info* dictionary is constructed from the kernel meta data and
 returned to the caller.
-
-The model evaluator, function call sequence consists of q inputs and the return vector,
-followed by the loop value/weight vector, followed by the values for
-the non-polydisperse parameters, followed by the lengths of the
-polydispersity loops.  To construct the call for 1D models, the
-categories *fixed-1d* and *pd-1d* list the names of the parameters
-of the non-polydisperse and the polydisperse parameters respectively.
-Similarly, *fixed-2d* and *pd-2d* provide parameter names for 2D models.
-The *pd-rel* category is a set of those parameters which give
-polydispersitiy as a portion of the value (so a 10% length dispersity
-would use a polydispersity value of 0.1) rather than absolute
-dispersity such as an angle plus or minus 15 degrees.
-
-The *volume* category lists the volume parameters in order for calls
-to volume within the kernel (used for volume normalization) and for
-calls to ER and VR for effective radius and volume ratio respectively.
-
-The *orientation* and *magnetic* categories list the orientation and
-magnetic parameters.  These are used by the sasview interface.  The
-blank category is for parameters such as scale which don't have any
-other marking.
 
 The doc string at the start of the kernel module will be used to
@@ -552 +531 @@
     @property
     def ctypes(self): return self.details.ctypes
+
     @property
     def pd_par(self): return self._pd_par
+
     @property
     def pd_length(self): return self._pd_length
+
     @property
     def pd_offset(self): return self._pd_offset
+
     @property
     def pd_stride(self): return self._pd_stride
+
     @property
     def pd_coord(self): return self._pd_coord
+
     @property
     def par_coord(self): return self._par_coord
+
     @property
     def par_offset(self): return self._par_offset
+
+    @property
+    def num_active(self): return self.details[-4]
+    @num_active.setter
+    def num_active(self, v): self.details[-4] = v
+
+    @property
+    def total_pd(self): return self.details[-3]
+    @total_pd.setter
+    def total_pd(self, v): self.details[-3] = v
+
     @property
     def num_coord(self): return self.details[-2]
     @num_coord.setter
     def num_coord(self, v): self.details[-2] = v
-    @property
-    def total_pd(self): return self.details[-3]
-    @total_pd.setter
-    def total_pd(self, v): self.details[-3] = v
-    @property
-    def num_active(self): return self.details[-4]
-    @num_active.setter
-    def num_active(self, v): self.details[-4] = v
+
+    @property
+    def theta_par(self): return self.details[-1]
 
     def show(self):
@@ -638 +630 @@
 
 
+def create_vector_Iq(model_info):
+    """
+    Define Iq as a vector function if it exists.
+    """
+    Iq = model_info['Iq']
+    if callable(Iq) and not getattr(Iq, 'vectorized', False):
+        def vector_Iq(q, *args):
+            """
+            Vectorized 1D kernel.
+            """
+            return np.array([Iq(qi, *args) for qi in q])
+        model_info['Iq'] = vector_Iq
+
+def create_vector_Iqxy(model_info):
+    """
+    Define Iqxy as a vector function if it exists, or default it from Iq().
+    """
+    Iq, Iqxy = model_info['Iq'], model_info['Iqxy']
+    if callable(Iqxy) and not getattr(Iqxy, 'vectorized', False):
+        def vector_Iqxy(qx, qy, *args):
+            """
+            Vectorized 2D kernel.
+            """
+            return np.array([Iqxy(qxi, qyi, *args) for qxi, qyi in zip(qx, qy)])
+        model_info['Iqxy'] = vector_Iqxy
+    elif callable(Iq):
+        # Iq is vectorized because create_vector_Iq was already called.
+        def default_Iqxy(qx, qy, *args):
+            """
+            Default 2D kernel.
+            """
+            return Iq(np.sqrt(qx**2 + qy**2), *args)
+        model_info['Iqxy'] = default_Iqxy
+
 def create_default_functions(model_info):
     """
@@ -643 +669 @@
 
     This only works for Iqxy when Iq is written in python. :func:`make_source`
-    performs a similar role for Iq written in C.
-    """
-    if callable(model_info['Iq']) and model_info['Iqxy'] is None:
-        partable = model_info['parameters']
-        if partable.has_2d:
-            raise ValueError("Iqxy model is missing")
-        Iq = model_info['Iq']
-        def Iqxy(qx, qy, **kw):
-            return Iq(np.sqrt(qx**2 + qy**2), **kw)
-        model_info['Iqxy'] = Iqxy
-
+    performs a similar role for Iq written in C.  This also vectorizes
+    any functions that are not already marked as vectorized.
+    """
+    create_vector_Iq(model_info)
+    create_vector_Iqxy(model_info)  # call create_vector_Iq() first
 
 def load_kernel_module(model_name):