Changeset 8dc02d8b in sasview for sansmodels/src/sans/models

Timestamp:

Jan 20, 2011 4:10:08 PM (14 years ago)

Author:

Jae Cho <jhjcho@…>

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.1.1, release-4.1.2, release-4.2.2, release_4.0.1, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

75df58b

Parents:

943cacb

Message:

added rectangular function for poly-dispersion

Location:

sansmodels/src/sans/models

Files:

• c_models/c_models.cpp (modified) (2 diffs)
• c_models/dispersion_visitor.cpp (modified) (2 diffs)
• c_models/dispersion_visitor.hh (modified) (2 diffs)
• c_models/parameters.cpp (modified) (2 diffs)
• c_models/parameters.hh (modified) (1 diff)
• dispersion_models.py (modified) (2 diffs)

sansmodels/src/sans/models/c_models/c_models.cpp

@@ -124 +124 @@
 }
 
+
+/**
+ * Delete a rectangle dispersion model object
+ */
+void del_rectangle_dispersion(void *ptr){
+        RectangleDispersion * disp = static_cast<RectangleDispersion *>(ptr);
+        delete disp;
+        return;
+}
+
+/**
+ * Create a rectangle dispersion model as a python object
+ */
+PyObject * new_rectangle_dispersion(PyObject *, PyObject *args) {
+        RectangleDispersion *disp = new RectangleDispersion();
+        return PyCObject_FromVoidPtr(disp, del_rectangle_dispersion);
+}
+
+
 /**
  * Delete a schulz dispersion model object
@@ -203 +222 @@
         {"new_gaussian_model",   (PyCFunction)new_gaussian_dispersion, METH_VARARGS,
                   "Create a new GaussianDispersion object"},
+        {"new_rectangle_model",   (PyCFunction)new_rectangle_dispersion, METH_VARARGS,
+                  "Create a new RectangleDispersion object"},
     {"new_lognormal_model",   (PyCFunction)new_lognormal_dispersion, METH_VARARGS,
                   "Create a new LogNormalDispersion object"},

sansmodels/src/sans/models/c_models/dispersion_visitor.cpp

@@ -25 +25 @@
 
         PyDict_SetItemString(dict, "type",  Py_BuildValue("s", "gaussian"));
+    PyDict_SetItemString(dict, "npts",  Py_BuildValue("i", disp->npts));
+    PyDict_SetItemString(dict, "width", Py_BuildValue("d", disp->width));
+    PyDict_SetItemString(dict, "nsigmas", Py_BuildValue("d", disp->nsigmas));
+#endif
+}
+
+void DispersionVisitor:: rectangle_to_dict(void* dispersion, void* dictionary) {
+#ifndef __MODELS_STANDALONE__
+        RectangleDispersion * disp = (RectangleDispersion*)dispersion;
+        PyObject * dict = (PyObject*)dictionary;
+
+        PyDict_SetItemString(dict, "type",  Py_BuildValue("s", "rectangle"));
     PyDict_SetItemString(dict, "npts",  Py_BuildValue("i", disp->npts));
     PyDict_SetItemString(dict, "width", Py_BuildValue("d", disp->width));
@@ -86 +98 @@
 }
 
+void DispersionVisitor:: rectangle_from_dict(void* dispersion, void* dictionary) {
+#ifndef __MODELS_STANDALONE__
+        RectangleDispersion * disp = (RectangleDispersion*)dispersion;
+        PyObject * dict = (PyObject*)dictionary;
+
+        disp->npts    = PyInt_AsLong( PyDict_GetItemString(dict, "npts") );
+        disp->width   = PyFloat_AsDouble( PyDict_GetItemString(dict, "width") );
+        disp->nsigmas = PyFloat_AsDouble( PyDict_GetItemString(dict, "nsigmas") );
+#endif
+}
+
 void DispersionVisitor:: lognormal_from_dict(void* dispersion, void* dictionary) {
 #ifndef __MODELS_STANDALONE__

sansmodels/src/sans/models/c_models/dispersion_visitor.hh

@@ -19 +19 @@
         void dispersion_to_dict(void *, void *);
         void gaussian_to_dict(void *, void *);
+        void rectangle_to_dict(void *, void *);
         void lognormal_to_dict(void *, void *);
         void schulz_to_dict(void*, void *);
@@ -25 +26 @@
         void dispersion_from_dict(void*, void *);
         void gaussian_from_dict(void*, void *);
+        void rectangle_from_dict(void*, void *);
         void lognormal_from_dict(void*, void *);
         void schulz_from_dict(void*, void *);

sansmodels/src/sans/models/c_models/parameters.cpp

@@ -98 +98 @@
         double vary, expo_value;
     vary = x-mean;
-    expo_value = -vary*vary/(2*sigma*sigma);
+    expo_value = -vary*vary/(2.0*sigma*sigma);
     //return 1.0;
     return exp(expo_value);
@@ -130 +130 @@
                           && ((*par).has_max==false || val<(*par).max)  ) {
                                 double _w = gaussian_weight(value, width, val);
+                                weights.insert(weights.end(), WeightPoint(val, _w));
+                        }
+                }
+        }
+}
+
+
+/**
+ * Flat dispersion
+ */
+
+RectangleDispersion :: RectangleDispersion() {
+        npts  = 21;
+        width = 0.0;
+        nsigmas = 1.0;
+};
+
+void RectangleDispersion :: accept_as_source(DispersionVisitor* visitor, void* from, void* to) {
+        visitor->rectangle_to_dict(from, to);
+}
+void RectangleDispersion :: accept_as_destination(DispersionVisitor* visitor, void* from, void* to) {
+        visitor->rectangle_from_dict(from, to);
+}
+
+double rectangle_weight(double mean, double sigma, double x) {
+        double vary, expo_value;
+    double sig = fabs(sigma);
+    if (x>= (mean-sig) && x<(mean+sig)){
+        return 1.0;
+    }
+    else{
+        return 0.0;
+    }
+}
+
+/**
+ * Flat dispersion
+ * @param mean: mean value
+ * @param sigma: half width of top hat function
+ * @param x: value at which the Flat is evaluated
+ * @return: value of the Flat
+ */
+void RectangleDispersion :: operator() (void *param, vector<WeightPoint> &weights){
+        // Check against zero width
+        if (width<=0) {
+                width = 0.0;
+                npts  = 1;
+                nsigmas = 1.0;
+        }
+
+        Parameter* par = (Parameter*)param;
+        double value = (*par)();
+
+        if (npts<2) {
+                weights.insert(weights.end(), WeightPoint(value, 1.0));
+        } else {
+                for(int i=0; i<npts; i++) {
+                        // We cover n(nsigmas) times sigmas on each side of the mean
+                        double val = value + width * (2.0*nsigmas*double(i)/double(npts-1) - nsigmas);
+                        if ( ((*par).has_min==false || val>(*par).min)
+                          && ((*par).has_max==false || val<(*par).max)  ) {
+                                double _w = rectangle_weight(value, width, val);
                                 weights.insert(weights.end(), WeightPoint(val, _w));
                         }

sansmodels/src/sans/models/c_models/parameters.hh

@@ -71 +71 @@
 
         GaussianDispersion();
+        void operator()(void *, vector<WeightPoint>&);
+        void accept_as_source(DispersionVisitor*, void*, void*);
+        void accept_as_destination(DispersionVisitor*, void*, void*);
+};
+
+/**
+ * Flat dispersion model
+ */
+class RectangleDispersion: public DispersionModel {
+public:
+        /// Number of sigmas on each side of the mean
+        double nsigmas;
+
+        RectangleDispersion();
         void operator()(void *, vector<WeightPoint>&);
         void accept_as_source(DispersionVisitor*, void*, void*);

sansmodels/src/sans/models/dispersion_models.py

@@ -78 +78 @@
         message += "  Solution: Use an ArrayDispersion object"
         raise "RuntimeError", message
+        
+class RectangleDispersion(DispersionModel):
+    """
+    Python bridge class for a dispersion model based 
+    on a Gaussian distribution.
+    """
+    def __init__(self):
+        self.cdisp = c_models.new_rectangle_model()
+        
+    def set_weights(self, values, weights):
+        """
+            Set the weights of an array dispersion
+        """
+        message = "set_weights is not available for GaussianDispersion.\n"
+        message += "  Solution: Use an ArrayDispersion object"
+        raise "RuntimeError", message 
     
 class SchulzDispersion(DispersionModel):
@@ -133 +149 @@
         """
         if len(values) != len(weights):
-            raise ValueError, "ArrayDispersion.set_weights: given arrays are of different lengths"
+            raise ValueError, "ArrayDispersion.set_weights: \
+            given arrays are of different lengths"
         
         c_models.set_dispersion_weights(self.cdisp, values, weights)
- 
-models = {"gaussian":GaussianDispersion, "array":ArrayDispersion,
-          "schulz":SchulzDispersion, "lognormal":LogNormalDispersion}       
         
+models = {"gaussian":GaussianDispersion,  "rectangula":RectangleDispersion,
+          "array":ArrayDispersion, "schulz":SchulzDispersion, 
+          "lognormal":LogNormalDispersion}       
+