Changeset 6edb74a in sasmodels

Timestamp:

Feb 19, 2015 3:59:14 PM (10 years ago)

Author:

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, fd1c792

Parents:

16bc3fc

Message:

support for pure python models without polydispersity

Location:

sasmodels

Files:

• kernelpy.py (modified) (3 diffs)
• models/broad_peak.py (modified) (2 diffs)

sasmodels/kernelpy.py

@@ -87 +87 @@
         self.pd_pars = pd_pars
 
-    def __call__(self, fixed, pd, cutoff):
+    def __call__(self, fixed, pd, cutoff=1e-5):
         #print "fixed",fixed
         #print "pd", pd
@@ -144 +144 @@
     ##########################################################
 
-    # weight vector, to be populated by polydispersity loops
     weight = np.empty(len(pd), 'd')
-
-    # identify which pd parameters are volume parameters
-    vol_weight_index = np.array([(index in vol_index) for index in pd_index])
-
-    # Sort parameters in decreasing order of pd length
-    Npd = np.array([len(pdi[0]) for pdi in pd], 'i')
-    order = np.argsort(Npd)[::-1]
-    stride = np.cumprod(Npd[order])
-    pd = [pd[index] for index in order]
-    pd_index = pd_index[order]
-    vol_weight_index = vol_weight_index[order]
-
-    fast_value = pd[0][0]
-    fast_weight = pd[0][1]
+    if weight.size > 0:
+        # weight vector, to be populated by polydispersity loops
+
+        # identify which pd parameters are volume parameters
+        vol_weight_index = np.array([(index in vol_index) for index in pd_index])
+
+        # Sort parameters in decreasing order of pd length
+        Npd = np.array([len(pdi[0]) for pdi in pd], 'i')
+        order = np.argsort(Npd)[::-1]
+        stride = np.cumprod(Npd[order])
+        pd = [pd[index] for index in order]
+        pd_index = pd_index[order]
+        vol_weight_index = vol_weight_index[order]
+
+        fast_value = pd[0][0]
+        fast_weight = pd[0][1]
+    else:
+        stride = np.array([1])
+        vol_weight_index = slice(None, None)
 
     ret = np.zeros_like(args[0])
@@ -169 +173 @@
         fast_index = k%stride[0]
         if fast_index == 0:  # bottom loop complete ... check all other loops
-            for i,index, in enumerate(k%stride):
-                args[pd_index[i]] = pd[i][0][index]
-                weight[i] = pd[i][1][index]
+            if weight.size > 0:
+                for i,index, in enumerate(k%stride):
+                    args[pd_index[i]] = pd[i][0][index]
+                    weight[i] = pd[i][1][index]
         else:
             args[pd_index[0]] = fast_value[fast_index]

sasmodels/models/broad_peak.py

@@ -51 +51 @@
 
 import numpy as np
-from numpy import pi, inf, sin, cos, sqrt, exp, log
+from numpy import pi, inf, sin, cos, sqrt, exp, log, fabs
 
 name = "broad_peak"
@@ -92 +92 @@
 def Iq(q, porod_scale, porod_exp, lorentz_scale, lorentz_length, peak_pos, lorentz_exp):
     inten = porod_scale/pow(q,porod_exp) + lorentz_scale/(1.0 \
-        + pow((math.fabs(q-peak_pos)*lorentz_length),lorentz_exp))
+        + pow((fabs(q-peak_pos)*lorentz_length),lorentz_exp))
     return inten