Changeset 9890053 in sasmodels for sasmodels/core.py

Timestamp:

Mar 3, 2015 2:31:38 PM (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:

59c4c4e

Parents:

49f92c1

Message:

add smoke tests for ER/VR; check that smoke test results are valid floats

File:

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

sasmodels/core.py

@@ -53 +53 @@
     return v,w/np.sum(w)
 
+def dispersion_mesh(pars):
+    """
+    Create a mesh grid of dispersion parameters and weights.
+
+    Returns [p1,p2,...],w where pj is a vector of values for parameter j
+    and w is a vector containing the products for weights for each
+    parameter set in the vector.
+    """
+    values, weights = zip(*pars)
+    if len(values) > 1:
+        values = [v.flatten() for v in np.meshgrid(*values)]
+        weights = np.vstack([v.flatten() for v in np.meshgrid(*weights)])
+        weights = np.prod(weights, axis=0)
+    return values, weights
+
 def call_kernel(kernel, pars, cutoff=1e-5):
     fixed_pars = [pars.get(name, kernel.info['defaults'][name])
@@ -59 +74 @@
     return kernel(fixed_pars, pd_pars, cutoff=cutoff)
 
+def call_ER(kernel, pars):
+    ER = kernel.info.get('ER', None)
+    if ER is None:
+        return 1.0
+    else:
+        vol_pars = [get_weights(kernel, pars, name)
+                    for name in kernel.info['partype']['volume']]
+        values, weights = dispersion_mesh(vol_pars)
+        fv = ER(*values)
+        #print values[0].shape, weights.shape, fv.shape
+        return np.sum(weights*fv) / np.sum(weights)
+
+def call_VR(kernel, pars):
+    VR = kernel.info.get('VR', None)
+    if VR is None:
+        return 1.0
+    else:
+        vol_pars = [get_weights(kernel, pars, name)
+                    for name in kernel.info['partype']['volume']]
+        values, weights = dispersion_mesh(vol_pars)
+        whole,part = VR(*values)
+        return np.sum(weights*part)/np.sum(weights*whole)
+