Changeset b6f3bbe in sasmodels for sasmodels

Timestamp:

Sep 2, 2016 9:30:49 AM (8 years ago)

Author:

wojciech

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:

fe77343

Parents:

f49675c (diff), e822c97 (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:

Merged with master

Location:

sasmodels

Files:

• __init__.py (modified) (1 diff)
• convert.py (modified) (1 diff)
• kernelcl.py (modified) (1 diff)
• kerneldll.py (modified) (1 diff)
• kernelpy.py (modified) (2 diffs)
• models/core_multi_shell.py (modified) (1 diff)
• models/onion.py (modified) (1 diff)
• models/unified_power_Rg.py (modified) (4 diffs)
• compare.py (modified) (1 diff)
• core.py (modified) (4 diffs)
• modelinfo.py (modified) (1 diff)

sasmodels/__init__.py

@@ -14 +14 @@
 defining new models.
 """
-__version__ = "0.9"
+__version__ = "0.93"
 
 def data_files():

sasmodels/convert.py

@@ -334 +334 @@
             pars['n_shells'] = math.ceil(pars['n_shells'])
             pars['n_steps'] = math.ceil(pars['n_steps'])
-            for k in range(1, 12):
+            for k in range(1, 11):
                 pars['shape%d'%k] = math.trunc(pars['shape%d'%k]+0.5)
-            for k in range(2, 12):
+            for k in range(2, 11):
                 pars['thickness%d_pd_n'%k] = 0
                 pars['interface%d_pd_n'%k] = 0

sasmodels/kernelcl.py

@@ -568 +568 @@
             if v is not None: v.release()
 
-        scale = values[0]/self.result[self.q_input.nq]
+        pd_norm = self.result[self.q_input.nq]
+        scale = values[0]/(pd_norm if pd_norm!=0.0 else 1.0)
         background = values[1]
-        #print("scale",scale,values[0],self.result[self.q_input.nq])
+        #print("scale",scale,values[0],self.result[self.q_input.nq],background)
         return scale*self.result[:self.q_input.nq] + background
         # return self.result[:self.q_input.nq]

sasmodels/kerneldll.py

@@ -389 +389 @@
 
         #print("returned",self.q_input.q, self.result)
-        scale = values[0]/self.result[self.q_input.nq]
+        pd_norm = self.result[self.q_input.nq]
+        scale = values[0]/(pd_norm if pd_norm!=0.0 else 1.0)
         background = values[1]
         #print("scale",scale,background)

sasmodels/kernelpy.py

@@ -187 +187 @@
     n_pars = len(parameters)
     parameters[:] = values[2:n_pars+2]
-    scale, background = values[0], values[1]
     if call_details.num_active == 0:
-        norm = float(form_volume())
-        if norm > 0.0:
-            return (scale/norm)*form() + background
-        else:
-            return np.ones(nq, 'd')*background
+        pd_norm = float(form_volume())
+        scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
+        background = values[1]
+        return scale*form() + background
 
     pd_value = values[2+n_pars:2+n_pars + call_details.num_weights]
@@ -245 +243 @@
             pd_norm += weight * form_volume()
 
-    if pd_norm > 0.0:
-        return (scale/pd_norm)*total + background
-    else:
-        return np.ones(nq, 'd')*background
+    scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
+    background = values[1]
+    return scale*total + background
 
 

sasmodels/models/core_multi_shell.py

@@ -116 +116 @@
 
     # add in the shells
-    for k in range(n):
+    for k in range(int(n)):
         # Left side of each shells
         z.append(z[-1])

sasmodels/models/onion.py

@@ -337 +337 @@
 
     # add in the shells
-    for k in range(n_shells):
+    for k in range(int(n_shells)):
         # Left side of each shells
         z_current = z[-1]

sasmodels/models/unified_power_Rg.py

@@ -8 +8 @@
 has been added which simply calculates
 
-.. math:
+.. math::
 
     I(q) = \text{scale} / q + \text{background}
@@ -16 +16 @@
 (Debye equation), ellipsoidal particles, etc.
 
-The empirical fit function is (eq 9'):
+The empirical fit function is:
 
-.. math:
+.. math::
 
     I(q) = \text{background}
-    + \Sum_{i=1}^N \left[
-        G_i \exp\left(-\frac{q^2R_{gi}^2}{3}\right)
-       + B_i \exp\left(-\frac{q^2R_{g(i+1)}^2}{3}\right)
-             \left(\frac{1}{q_i^*}\right)^{P_i}
+    + \sum_{i=1}^N \Bigl[
+        G_i \exp\Bigl(-\frac{q^2R_{gi}^2}{3}\Bigr)
+       + B_i \exp\Bigl(-\frac{q^2R_{g(i+1)}^2}{3}\Bigr)
+             \Bigl(\frac{1}{q_i^*}\Bigr)^{P_i} \Bigr]
 
 where
 
-.. math:
+.. math::
 
     q_i^* = \frac{q}{\operatorname{erf}^3(q R_{gi}/\sqrt{6}}
@@ -45 +45 @@
 where the $q$ vector is defined as
 
-.. math:
+.. math::
 
     q = \sqrt{q_x^2 + q_y^2}
@@ -66 +66 @@
 
 category = "shape-independent"
+name = "unified_power_Rg"
+title = "Unified Power Rg"
+description = """
+        The Beaucage model employs the empirical multiple level unified
+        Exponential/Power-law fit method developed by G. Beaucage. Four functions
+        are included so that 1, 2, 3, or 4 levels can be used.
+        """
 
 # pylint: disable=bad-whitespace, line-too-long

sasmodels/compare.py

@@ -563 +563 @@
     elif dtype.endswith('!'):
         return eval_ctypes(model_info, data, dtype=dtype[:-1], cutoff=cutoff)
+    elif not model_info.opencl:
+        return eval_ctypes(model_info, data, dtype=dtype, cutoff=cutoff)
     else:
         return eval_opencl(model_info, data, dtype=dtype, cutoff=cutoff)

sasmodels/core.py

@@ -46 +46 @@
 #    build_model
 
-KINDS = ("all", "py", "c", "double", "single", "1d", "2d",
+KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d",
          "nonmagnetic", "magnetic")
 def list_models(kind=None):
@@ -60 +60 @@
         * single: models which support single precision
         * double: models which require double precision
+        * opencl: controls if OpenCL is supperessed
         * 1d: models which are 1D only, or 2D using abs(q)
         * 2d: models which can be 2D
@@ -85 +86 @@
         return True
     elif kind == "single" and info.single:
+        return True
+    elif kind == "opencl" and info.opencl:
         return True
     elif kind == "2d" and any(p.type == 'orientation' for p in pars):
@@ -215 +218 @@
     """
     # Assign default platform, overriding ocl with dll if OpenCL is unavailable
+    # If opencl=False OpenCL is switched off
+    # Finally one can force OpenCL calculation even if HAVE_OPENCL=False
     if platform is None:
         platform = "ocl"
-    if platform == "ocl" and not HAVE_OPENCL:
+    if platform == "ocl" and not HAVE_OPENCL or not model_info.opencl:
         platform = "dll"
+    #if  model_info.opencl and not HAVE_OPENCL:
+    #    platform = "ocl"
 
     # Check if type indicates dll regardless of which platform is given

sasmodels/modelinfo.py

@@ -731 +731 @@
     info.category = getattr(kernel_module, 'category', None)
     info.single = getattr(kernel_module, 'single', True)
+    info.opencl = getattr(kernel_module, 'opencl', True)
     info.structure_factor = getattr(kernel_module, 'structure_factor', False)
     info.profile_axes = getattr(kernel_module, 'profile_axes', ['x', 'y'])