Index: sansmodels/src/sans/models/c_models/CGaussian.cpp
===================================================================
--- sansmodels/src/sans/models/c_models/CGaussian.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
+++ sansmodels/src/sans/models/c_models/CGaussian.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
@@ -0,0 +1,530 @@
+/**
+	This software was developed by the University of Tennessee as part of the
+	Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
+	project funded by the US National Science Foundation.
+
+	If you use DANSE applications to do scientific research that leads to
+	publication, we ask that you acknowledge the use of the software with the
+	following sentence:
+
+	"This work benefited from DANSE software developed under NSF award DMR-0520547."
+
+	copyright 2008, University of Tennessee
+ */
+
+/** CGaussian
+ *
+ * C extension 
+ *
+ * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
+ *          DO NOT MODIFY THIS FILE, MODIFY gaussian.h
+ *          AND RE-RUN THE GENERATOR SCRIPT
+ *
+ */
+#define NO_IMPORT_ARRAY
+#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API_sans
+ 
+extern "C" {
+#include <Python.h>
+#include <arrayobject.h>
+#include "structmember.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h>
+#include "gaussian.h"
+}
+
+#include "models.hh"
+#include "dispersion_visitor.hh"
+
+/// Error object for raised exceptions
+static PyObject * CGaussianError = NULL;
+
+
+// Class definition
+typedef struct {
+    PyObject_HEAD
+    /// Parameters
+    PyObject * params;
+    /// Dispersion parameters
+    PyObject * dispersion;
+    /// Underlying model object
+    Gaussian * model;
+    /// Log for unit testing
+    PyObject * log;
+} CGaussian;
+
+
+static void
+CGaussian_dealloc(CGaussian* self)
+{
+    Py_DECREF(self->params);
+    Py_DECREF(self->dispersion);
+    Py_DECREF(self->log);
+    delete self->model;
+    self->ob_type->tp_free((PyObject*)self);
+    
+
+}
+
+static PyObject *
+CGaussian_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CGaussian *self;
+    
+    self = (CGaussian *)type->tp_alloc(type, 0);
+   
+    return (PyObject *)self;
+}
+
+static int
+CGaussian_init(CGaussian *self, PyObject *args, PyObject *kwds)
+{
+    if (self != NULL) {
+    	
+    	// Create parameters
+        self->params = PyDict_New();
+        self->dispersion = PyDict_New();
+        self->model = new Gaussian();
+        
+        // Initialize parameter dictionary
+        PyDict_SetItemString(self->params,"scale",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"sigma",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"center",Py_BuildValue("d",0.000000000000));
+        // Initialize dispersion / averaging parameter dict
+        DispersionVisitor* visitor = new DispersionVisitor();
+        PyObject * disp_dict;
+
+
+         
+        // Create empty log
+        self->log = PyDict_New();
+        
+        
+
+    }
+    return 0;
+}
+
+static PyMemberDef CGaussian_members[] = {
+    {"params", T_OBJECT, offsetof(CGaussian, params), 0,
+     "Parameters"},
+	{"dispersion", T_OBJECT, offsetof(CGaussian, dispersion), 0,
+	  "Dispersion parameters"},     
+    {"log", T_OBJECT, offsetof(CGaussian, log), 0,
+     "Log"},
+    {NULL}  /* Sentinel */
+};
+
+/** Read double from PyObject
+    @param p PyObject
+    @return double
+*/
+double CGaussian_readDouble(PyObject *p) {
+    if (PyFloat_Check(p)==1) {
+        return (double)(((PyFloatObject *)(p))->ob_fval);
+    } else if (PyInt_Check(p)==1) {
+        return (double)(((PyIntObject *)(p))->ob_ival);
+    } else if (PyLong_Check(p)==1) {
+        return (double)PyLong_AsLong(p);
+    } else {
+        return 0.0;
+    }
+}
+/**
+ * Function to call to evaluate model
+ * @param args: input numpy array q[] 
+ * @return: numpy array object 
+ */
+ 
+static PyObject *evaluateOneDim(Gaussian* model, PyArrayObject *q){
+    PyArrayObject *result;
+   
+    // Check validity of array q , q must be of dimension 1, an array of double
+    if (q->nd != 1 || q->descr->type_num != PyArray_DOUBLE)
+    {
+        //const char * message= "Invalid array: q->nd=%d,type_num=%d\n",q->nd,q->descr->type_num;
+        //PyErr_SetString(PyExc_ValueError , message);
+        return NULL;
+    }
+    result = (PyArrayObject *)PyArray_FromDims(q->nd, (int *)(q->dimensions), 
+										  PyArray_DOUBLE);
+	if (result == NULL) {
+        const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+		return NULL;
+	}
+	 for (int i = 0; i < q->dimensions[0]; i++){
+      double q_value  = *(double *)(q->data + i*q->strides[0]);
+      double *result_value = (double *)(result->data + i*result->strides[0]);
+      *result_value =(*model)(q_value);
+	}
+    return PyArray_Return(result); 
+ }
+
+ /**
+ * Function to call to evaluate model
+ * @param args: input numpy array  [x[],y[]]
+ * @return: numpy array object 
+ */
+ static PyObject * evaluateTwoDimXY( Gaussian* model, 
+                              PyArrayObject *x, PyArrayObject *y)
+ {
+    PyArrayObject *result;
+    int i,j, x_len, y_len, dims[1];
+    //check validity of input vectors
+    if (x->nd != 1 || x->descr->type_num != PyArray_DOUBLE
+        || y->nd != 1 || y->descr->type_num != PyArray_DOUBLE
+        || y->dimensions[0] != x->dimensions[0]){
+        const char * message= "evaluateTwoDimXY  expect 2 numpy arrays";
+        PyErr_SetString(PyExc_ValueError , message); 
+        return NULL;
+    }
+   
+	if (PyArray_Check(x) && PyArray_Check(y)) {
+		
+	    x_len = dims[0]= x->dimensions[0];
+        y_len = dims[0]= y->dimensions[0];
+	    
+	    // Make a new double matrix of same dims
+        result=(PyArrayObject *) PyArray_FromDims(1,dims,NPY_DOUBLE);
+        if (result == NULL){
+	    const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+	    return NULL;
+	    }
+       
+        /* Do the calculation. */
+        for ( i=0; i< x_len; i++) {
+            double x_value = *(double *)(x->data + i*x->strides[0]);
+  		    double y_value = *(double *)(y->data + i*y->strides[0]);
+  			double *result_value = (double *)(result->data +
+  			      i*result->strides[0]);
+  			*result_value = (*model)(x_value, y_value);
+        }           
+        return PyArray_Return(result); 
+        
+        }else{
+		    PyErr_SetString(CGaussianError, 
+                   "CGaussian.evaluateTwoDimXY couldn't run.");
+	        return NULL;
+		}      	
+}
+/**
+ *  evalDistribution function evaluate a model function with input vector
+ *  @param args: input q as vector or [qx, qy] where qx, qy are vectors
+ *
+ */ 
+static PyObject * evalDistribution(CGaussian *self, PyObject *args){
+	PyObject *qx, *qy;
+	PyArrayObject * pars;
+	int npars ,mpars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CGaussianError, 
+	    	"CGaussian.evalDistribution expects a q value.");
+		return NULL;
+	}
+    // Check params
+	
+    if(PyArray_Check(pars)==1) {
+		
+	    // Length of list should 1 or 2
+	    npars = pars->nd; 
+	    if(npars==1) {
+	        // input is a numpy array
+	        if (PyArray_Check(pars)) {
+		        return evaluateOneDim(self->model, (PyArrayObject*)pars); 
+		    }
+		}else{
+		    PyErr_SetString(CGaussianError, 
+                   "CGaussian.evalDistribution expect numpy array of one dimension.");
+	        return NULL;
+		}
+    }else if( PyList_Check(pars)==1) {
+    	// Length of list should be 2 for I(qx,qy)
+	    mpars = PyList_GET_SIZE(pars); 
+	    if(mpars!=2) {
+	    	PyErr_SetString(CGaussianError, 
+	    		"CGaussian.evalDistribution expects a list of dimension 2.");
+	    	return NULL;
+	    }
+	     qx = PyList_GET_ITEM(pars,0);
+	     qy = PyList_GET_ITEM(pars,1);
+	     if (PyArray_Check(qx) && PyArray_Check(qy)) {
+	         return evaluateTwoDimXY(self->model, (PyArrayObject*)qx,
+		           (PyArrayObject*)qy);
+		 }else{
+		    PyErr_SetString(CGaussianError, 
+                   "CGaussian.evalDistribution expect 2 numpy arrays in list.");
+	        return NULL;
+	     }
+	}
+	PyErr_SetString(CGaussianError, 
+                   "CGaussian.evalDistribution couln't be run.");
+	return NULL;
+	
+}
+
+/**
+ * Function to call to evaluate model
+ * @param args: input q or [q,phi]
+ * @return: function value
+ */
+static PyObject * run(CGaussian *self, PyObject *args) {
+	double q_value, phi_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CGaussianError, 
+	    	"CGaussian.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(q,phi)
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CGaussianError, 
+	    		"CGaussian.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the q and phi values at which
+	    // to evaluate I(q,phi)
+	    q_value = CGaussian_readDouble(PyList_GET_ITEM(pars,0));
+	    phi_value = CGaussian_readDouble(PyList_GET_ITEM(pars,1));
+	    // Skip zero
+	    if (q_value==0) {
+	    	return Py_BuildValue("d",0.0);
+	    }
+		return Py_BuildValue("d",(*(self->model)).evaluate_rphi(q_value,phi_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		q_value = CGaussian_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(q_value));
+	}	
+}
+/**
+ * Function to call to calculate_ER
+ * @return: effective radius value 
+ */
+static PyObject * calculate_ER(CGaussian *self) {
+
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+		
+	return Py_BuildValue("d",(*(self->model)).calculate_ER());
+
+}
+/**
+ * Function to call to evaluate model in cartesian coordinates
+ * @param args: input q or [qx, qy]]
+ * @return: function value
+ */
+static PyObject * runXY(CGaussian *self, PyObject *args) {
+	double qx_value, qy_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CGaussianError, 
+	    	"CGaussian.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(qx, qy))
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CGaussianError, 
+	    		"CGaussian.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the qx and qy values at which
+	    // to evaluate I(qx,qy)
+	    qx_value = CGaussian_readDouble(PyList_GET_ITEM(pars,0));
+	    qy_value = CGaussian_readDouble(PyList_GET_ITEM(pars,1));
+	    return Py_BuildValue("d",(*(self->model))(qx_value,qy_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		qx_value = CGaussian_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(qx_value));
+	}	
+}
+
+static PyObject * reset(CGaussian *self, PyObject *args) {
+    
+
+    return Py_BuildValue("d",0.0);
+}
+
+static PyObject * set_dispersion(CGaussian *self, PyObject *args) {
+	PyObject * disp;
+	const char * par_name;
+
+	if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) {
+	    PyErr_SetString(CGaussianError,
+	    	"CGaussian.set_dispersion expects a DispersionModel object.");
+		return NULL;
+	}
+	void *temp = PyCObject_AsVoidPtr(disp);
+	DispersionModel * dispersion = static_cast<DispersionModel *>(temp);
+
+
+	// Ugliness necessary to go from python to C
+	    // TODO: refactor this
+ {
+	    PyErr_SetString(CGaussianError,
+	    	"CGaussian.set_dispersion expects a valid parameter name.");
+		return NULL;
+	}
+
+	DispersionVisitor* visitor = new DispersionVisitor();
+	PyObject * disp_dict = PyDict_New();
+	dispersion->accept_as_source(visitor, dispersion, disp_dict);
+	PyDict_SetItemString(self->dispersion, par_name, disp_dict);
+    return Py_BuildValue("i",1);
+}
+
+
+static PyMethodDef CGaussian_methods[] = {
+    {"run",      (PyCFunction)run     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+    {"runXY",      (PyCFunction)runXY     , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy"},
+    {"calculate_ER",      (PyCFunction)calculate_ER     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+      
+    {"evalDistribution",  (PyCFunction)evalDistribution , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy vector "},
+    {"reset",    (PyCFunction)reset   , METH_VARARGS,
+      "Reset pair correlation"},
+    {"set_dispersion",      (PyCFunction)set_dispersion     , METH_VARARGS,
+      "Set the dispersion model for a given parameter"},
+   {NULL}
+};
+
+static PyTypeObject CGaussianType = {
+    PyObject_HEAD_INIT(NULL)
+    0,                         /*ob_size*/
+    "CGaussian",             /*tp_name*/
+    sizeof(CGaussian),             /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor)CGaussian_dealloc, /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+    "CGaussian objects",           /* tp_doc */
+    0,		               /* tp_traverse */
+    0,		               /* tp_clear */
+    0,		               /* tp_richcompare */
+    0,		               /* tp_weaklistoffset */
+    0,		               /* tp_iter */
+    0,		               /* tp_iternext */
+    CGaussian_methods,             /* tp_methods */
+    CGaussian_members,             /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    (initproc)CGaussian_init,      /* tp_init */
+    0,                         /* tp_alloc */
+    CGaussian_new,                 /* tp_new */
+};
+
+
+//static PyMethodDef module_methods[] = {
+//    {NULL} 
+//};
+
+/**
+ * Function used to add the model class to a module
+ * @param module: module to add the class to
+ */ 
+void addCGaussian(PyObject *module) {
+	PyObject *d;
+	
+    if (PyType_Ready(&CGaussianType) < 0)
+        return;
+
+    Py_INCREF(&CGaussianType);
+    PyModule_AddObject(module, "CGaussian", (PyObject *)&CGaussianType);
+    
+    d = PyModule_GetDict(module);
+    CGaussianError = PyErr_NewException("CGaussian.error", NULL, NULL);
+    PyDict_SetItemString(d, "CGaussianError", CGaussianError);
+}
+
Index: sansmodels/src/sans/models/c_models/CLogNormal.cpp
===================================================================
--- sansmodels/src/sans/models/c_models/CLogNormal.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
+++ sansmodels/src/sans/models/c_models/CLogNormal.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
@@ -0,0 +1,530 @@
+/**
+	This software was developed by the University of Tennessee as part of the
+	Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
+	project funded by the US National Science Foundation.
+
+	If you use DANSE applications to do scientific research that leads to
+	publication, we ask that you acknowledge the use of the software with the
+	following sentence:
+
+	"This work benefited from DANSE software developed under NSF award DMR-0520547."
+
+	copyright 2008, University of Tennessee
+ */
+
+/** CLogNormal
+ *
+ * C extension 
+ *
+ * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
+ *          DO NOT MODIFY THIS FILE, MODIFY logNormal.h
+ *          AND RE-RUN THE GENERATOR SCRIPT
+ *
+ */
+#define NO_IMPORT_ARRAY
+#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API_sans
+ 
+extern "C" {
+#include <Python.h>
+#include <arrayobject.h>
+#include "structmember.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h>
+#include "logNormal.h"
+}
+
+#include "models.hh"
+#include "dispersion_visitor.hh"
+
+/// Error object for raised exceptions
+static PyObject * CLogNormalError = NULL;
+
+
+// Class definition
+typedef struct {
+    PyObject_HEAD
+    /// Parameters
+    PyObject * params;
+    /// Dispersion parameters
+    PyObject * dispersion;
+    /// Underlying model object
+    LogNormal * model;
+    /// Log for unit testing
+    PyObject * log;
+} CLogNormal;
+
+
+static void
+CLogNormal_dealloc(CLogNormal* self)
+{
+    Py_DECREF(self->params);
+    Py_DECREF(self->dispersion);
+    Py_DECREF(self->log);
+    delete self->model;
+    self->ob_type->tp_free((PyObject*)self);
+    
+
+}
+
+static PyObject *
+CLogNormal_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CLogNormal *self;
+    
+    self = (CLogNormal *)type->tp_alloc(type, 0);
+   
+    return (PyObject *)self;
+}
+
+static int
+CLogNormal_init(CLogNormal *self, PyObject *args, PyObject *kwds)
+{
+    if (self != NULL) {
+    	
+    	// Create parameters
+        self->params = PyDict_New();
+        self->dispersion = PyDict_New();
+        self->model = new LogNormal();
+        
+        // Initialize parameter dictionary
+        PyDict_SetItemString(self->params,"scale",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"sigma",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"center",Py_BuildValue("d",0.000000000000));
+        // Initialize dispersion / averaging parameter dict
+        DispersionVisitor* visitor = new DispersionVisitor();
+        PyObject * disp_dict;
+
+
+         
+        // Create empty log
+        self->log = PyDict_New();
+        
+        
+
+    }
+    return 0;
+}
+
+static PyMemberDef CLogNormal_members[] = {
+    {"params", T_OBJECT, offsetof(CLogNormal, params), 0,
+     "Parameters"},
+	{"dispersion", T_OBJECT, offsetof(CLogNormal, dispersion), 0,
+	  "Dispersion parameters"},     
+    {"log", T_OBJECT, offsetof(CLogNormal, log), 0,
+     "Log"},
+    {NULL}  /* Sentinel */
+};
+
+/** Read double from PyObject
+    @param p PyObject
+    @return double
+*/
+double CLogNormal_readDouble(PyObject *p) {
+    if (PyFloat_Check(p)==1) {
+        return (double)(((PyFloatObject *)(p))->ob_fval);
+    } else if (PyInt_Check(p)==1) {
+        return (double)(((PyIntObject *)(p))->ob_ival);
+    } else if (PyLong_Check(p)==1) {
+        return (double)PyLong_AsLong(p);
+    } else {
+        return 0.0;
+    }
+}
+/**
+ * Function to call to evaluate model
+ * @param args: input numpy array q[] 
+ * @return: numpy array object 
+ */
+ 
+static PyObject *evaluateOneDim(LogNormal* model, PyArrayObject *q){
+    PyArrayObject *result;
+   
+    // Check validity of array q , q must be of dimension 1, an array of double
+    if (q->nd != 1 || q->descr->type_num != PyArray_DOUBLE)
+    {
+        //const char * message= "Invalid array: q->nd=%d,type_num=%d\n",q->nd,q->descr->type_num;
+        //PyErr_SetString(PyExc_ValueError , message);
+        return NULL;
+    }
+    result = (PyArrayObject *)PyArray_FromDims(q->nd, (int *)(q->dimensions), 
+										  PyArray_DOUBLE);
+	if (result == NULL) {
+        const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+		return NULL;
+	}
+	 for (int i = 0; i < q->dimensions[0]; i++){
+      double q_value  = *(double *)(q->data + i*q->strides[0]);
+      double *result_value = (double *)(result->data + i*result->strides[0]);
+      *result_value =(*model)(q_value);
+	}
+    return PyArray_Return(result); 
+ }
+
+ /**
+ * Function to call to evaluate model
+ * @param args: input numpy array  [x[],y[]]
+ * @return: numpy array object 
+ */
+ static PyObject * evaluateTwoDimXY( LogNormal* model, 
+                              PyArrayObject *x, PyArrayObject *y)
+ {
+    PyArrayObject *result;
+    int i,j, x_len, y_len, dims[1];
+    //check validity of input vectors
+    if (x->nd != 1 || x->descr->type_num != PyArray_DOUBLE
+        || y->nd != 1 || y->descr->type_num != PyArray_DOUBLE
+        || y->dimensions[0] != x->dimensions[0]){
+        const char * message= "evaluateTwoDimXY  expect 2 numpy arrays";
+        PyErr_SetString(PyExc_ValueError , message); 
+        return NULL;
+    }
+   
+	if (PyArray_Check(x) && PyArray_Check(y)) {
+		
+	    x_len = dims[0]= x->dimensions[0];
+        y_len = dims[0]= y->dimensions[0];
+	    
+	    // Make a new double matrix of same dims
+        result=(PyArrayObject *) PyArray_FromDims(1,dims,NPY_DOUBLE);
+        if (result == NULL){
+	    const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+	    return NULL;
+	    }
+       
+        /* Do the calculation. */
+        for ( i=0; i< x_len; i++) {
+            double x_value = *(double *)(x->data + i*x->strides[0]);
+  		    double y_value = *(double *)(y->data + i*y->strides[0]);
+  			double *result_value = (double *)(result->data +
+  			      i*result->strides[0]);
+  			*result_value = (*model)(x_value, y_value);
+        }           
+        return PyArray_Return(result); 
+        
+        }else{
+		    PyErr_SetString(CLogNormalError, 
+                   "CLogNormal.evaluateTwoDimXY couldn't run.");
+	        return NULL;
+		}      	
+}
+/**
+ *  evalDistribution function evaluate a model function with input vector
+ *  @param args: input q as vector or [qx, qy] where qx, qy are vectors
+ *
+ */ 
+static PyObject * evalDistribution(CLogNormal *self, PyObject *args){
+	PyObject *qx, *qy;
+	PyArrayObject * pars;
+	int npars ,mpars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLogNormalError, 
+	    	"CLogNormal.evalDistribution expects a q value.");
+		return NULL;
+	}
+    // Check params
+	
+    if(PyArray_Check(pars)==1) {
+		
+	    // Length of list should 1 or 2
+	    npars = pars->nd; 
+	    if(npars==1) {
+	        // input is a numpy array
+	        if (PyArray_Check(pars)) {
+		        return evaluateOneDim(self->model, (PyArrayObject*)pars); 
+		    }
+		}else{
+		    PyErr_SetString(CLogNormalError, 
+                   "CLogNormal.evalDistribution expect numpy array of one dimension.");
+	        return NULL;
+		}
+    }else if( PyList_Check(pars)==1) {
+    	// Length of list should be 2 for I(qx,qy)
+	    mpars = PyList_GET_SIZE(pars); 
+	    if(mpars!=2) {
+	    	PyErr_SetString(CLogNormalError, 
+	    		"CLogNormal.evalDistribution expects a list of dimension 2.");
+	    	return NULL;
+	    }
+	     qx = PyList_GET_ITEM(pars,0);
+	     qy = PyList_GET_ITEM(pars,1);
+	     if (PyArray_Check(qx) && PyArray_Check(qy)) {
+	         return evaluateTwoDimXY(self->model, (PyArrayObject*)qx,
+		           (PyArrayObject*)qy);
+		 }else{
+		    PyErr_SetString(CLogNormalError, 
+                   "CLogNormal.evalDistribution expect 2 numpy arrays in list.");
+	        return NULL;
+	     }
+	}
+	PyErr_SetString(CLogNormalError, 
+                   "CLogNormal.evalDistribution couln't be run.");
+	return NULL;
+	
+}
+
+/**
+ * Function to call to evaluate model
+ * @param args: input q or [q,phi]
+ * @return: function value
+ */
+static PyObject * run(CLogNormal *self, PyObject *args) {
+	double q_value, phi_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLogNormalError, 
+	    	"CLogNormal.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(q,phi)
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CLogNormalError, 
+	    		"CLogNormal.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the q and phi values at which
+	    // to evaluate I(q,phi)
+	    q_value = CLogNormal_readDouble(PyList_GET_ITEM(pars,0));
+	    phi_value = CLogNormal_readDouble(PyList_GET_ITEM(pars,1));
+	    // Skip zero
+	    if (q_value==0) {
+	    	return Py_BuildValue("d",0.0);
+	    }
+		return Py_BuildValue("d",(*(self->model)).evaluate_rphi(q_value,phi_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		q_value = CLogNormal_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(q_value));
+	}	
+}
+/**
+ * Function to call to calculate_ER
+ * @return: effective radius value 
+ */
+static PyObject * calculate_ER(CLogNormal *self) {
+
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+		
+	return Py_BuildValue("d",(*(self->model)).calculate_ER());
+
+}
+/**
+ * Function to call to evaluate model in cartesian coordinates
+ * @param args: input q or [qx, qy]]
+ * @return: function value
+ */
+static PyObject * runXY(CLogNormal *self, PyObject *args) {
+	double qx_value, qy_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLogNormalError, 
+	    	"CLogNormal.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(qx, qy))
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CLogNormalError, 
+	    		"CLogNormal.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the qx and qy values at which
+	    // to evaluate I(qx,qy)
+	    qx_value = CLogNormal_readDouble(PyList_GET_ITEM(pars,0));
+	    qy_value = CLogNormal_readDouble(PyList_GET_ITEM(pars,1));
+	    return Py_BuildValue("d",(*(self->model))(qx_value,qy_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		qx_value = CLogNormal_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(qx_value));
+	}	
+}
+
+static PyObject * reset(CLogNormal *self, PyObject *args) {
+    
+
+    return Py_BuildValue("d",0.0);
+}
+
+static PyObject * set_dispersion(CLogNormal *self, PyObject *args) {
+	PyObject * disp;
+	const char * par_name;
+
+	if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) {
+	    PyErr_SetString(CLogNormalError,
+	    	"CLogNormal.set_dispersion expects a DispersionModel object.");
+		return NULL;
+	}
+	void *temp = PyCObject_AsVoidPtr(disp);
+	DispersionModel * dispersion = static_cast<DispersionModel *>(temp);
+
+
+	// Ugliness necessary to go from python to C
+	    // TODO: refactor this
+ {
+	    PyErr_SetString(CLogNormalError,
+	    	"CLogNormal.set_dispersion expects a valid parameter name.");
+		return NULL;
+	}
+
+	DispersionVisitor* visitor = new DispersionVisitor();
+	PyObject * disp_dict = PyDict_New();
+	dispersion->accept_as_source(visitor, dispersion, disp_dict);
+	PyDict_SetItemString(self->dispersion, par_name, disp_dict);
+    return Py_BuildValue("i",1);
+}
+
+
+static PyMethodDef CLogNormal_methods[] = {
+    {"run",      (PyCFunction)run     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+    {"runXY",      (PyCFunction)runXY     , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy"},
+    {"calculate_ER",      (PyCFunction)calculate_ER     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+      
+    {"evalDistribution",  (PyCFunction)evalDistribution , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy vector "},
+    {"reset",    (PyCFunction)reset   , METH_VARARGS,
+      "Reset pair correlation"},
+    {"set_dispersion",      (PyCFunction)set_dispersion     , METH_VARARGS,
+      "Set the dispersion model for a given parameter"},
+   {NULL}
+};
+
+static PyTypeObject CLogNormalType = {
+    PyObject_HEAD_INIT(NULL)
+    0,                         /*ob_size*/
+    "CLogNormal",             /*tp_name*/
+    sizeof(CLogNormal),             /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor)CLogNormal_dealloc, /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+    "CLogNormal objects",           /* tp_doc */
+    0,		               /* tp_traverse */
+    0,		               /* tp_clear */
+    0,		               /* tp_richcompare */
+    0,		               /* tp_weaklistoffset */
+    0,		               /* tp_iter */
+    0,		               /* tp_iternext */
+    CLogNormal_methods,             /* tp_methods */
+    CLogNormal_members,             /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    (initproc)CLogNormal_init,      /* tp_init */
+    0,                         /* tp_alloc */
+    CLogNormal_new,                 /* tp_new */
+};
+
+
+//static PyMethodDef module_methods[] = {
+//    {NULL} 
+//};
+
+/**
+ * Function used to add the model class to a module
+ * @param module: module to add the class to
+ */ 
+void addCLogNormal(PyObject *module) {
+	PyObject *d;
+	
+    if (PyType_Ready(&CLogNormalType) < 0)
+        return;
+
+    Py_INCREF(&CLogNormalType);
+    PyModule_AddObject(module, "CLogNormal", (PyObject *)&CLogNormalType);
+    
+    d = PyModule_GetDict(module);
+    CLogNormalError = PyErr_NewException("CLogNormal.error", NULL, NULL);
+    PyDict_SetItemString(d, "CLogNormalError", CLogNormalError);
+}
+
Index: sansmodels/src/sans/models/c_models/CLorentzian.cpp
===================================================================
--- sansmodels/src/sans/models/c_models/CLorentzian.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
+++ sansmodels/src/sans/models/c_models/CLorentzian.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
@@ -0,0 +1,530 @@
+/**
+	This software was developed by the University of Tennessee as part of the
+	Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
+	project funded by the US National Science Foundation.
+
+	If you use DANSE applications to do scientific research that leads to
+	publication, we ask that you acknowledge the use of the software with the
+	following sentence:
+
+	"This work benefited from DANSE software developed under NSF award DMR-0520547."
+
+	copyright 2008, University of Tennessee
+ */
+
+/** CLorentzian
+ *
+ * C extension 
+ *
+ * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
+ *          DO NOT MODIFY THIS FILE, MODIFY lorentzian.h
+ *          AND RE-RUN THE GENERATOR SCRIPT
+ *
+ */
+#define NO_IMPORT_ARRAY
+#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API_sans
+ 
+extern "C" {
+#include <Python.h>
+#include <arrayobject.h>
+#include "structmember.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h>
+#include "lorentzian.h"
+}
+
+#include "models.hh"
+#include "dispersion_visitor.hh"
+
+/// Error object for raised exceptions
+static PyObject * CLorentzianError = NULL;
+
+
+// Class definition
+typedef struct {
+    PyObject_HEAD
+    /// Parameters
+    PyObject * params;
+    /// Dispersion parameters
+    PyObject * dispersion;
+    /// Underlying model object
+    Lorentzian * model;
+    /// Log for unit testing
+    PyObject * log;
+} CLorentzian;
+
+
+static void
+CLorentzian_dealloc(CLorentzian* self)
+{
+    Py_DECREF(self->params);
+    Py_DECREF(self->dispersion);
+    Py_DECREF(self->log);
+    delete self->model;
+    self->ob_type->tp_free((PyObject*)self);
+    
+
+}
+
+static PyObject *
+CLorentzian_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CLorentzian *self;
+    
+    self = (CLorentzian *)type->tp_alloc(type, 0);
+   
+    return (PyObject *)self;
+}
+
+static int
+CLorentzian_init(CLorentzian *self, PyObject *args, PyObject *kwds)
+{
+    if (self != NULL) {
+    	
+    	// Create parameters
+        self->params = PyDict_New();
+        self->dispersion = PyDict_New();
+        self->model = new Lorentzian();
+        
+        // Initialize parameter dictionary
+        PyDict_SetItemString(self->params,"scale",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"center",Py_BuildValue("d",0.000000000000));
+        PyDict_SetItemString(self->params,"gamma",Py_BuildValue("d",1.000000000000));
+        // Initialize dispersion / averaging parameter dict
+        DispersionVisitor* visitor = new DispersionVisitor();
+        PyObject * disp_dict;
+
+
+         
+        // Create empty log
+        self->log = PyDict_New();
+        
+        
+
+    }
+    return 0;
+}
+
+static PyMemberDef CLorentzian_members[] = {
+    {"params", T_OBJECT, offsetof(CLorentzian, params), 0,
+     "Parameters"},
+	{"dispersion", T_OBJECT, offsetof(CLorentzian, dispersion), 0,
+	  "Dispersion parameters"},     
+    {"log", T_OBJECT, offsetof(CLorentzian, log), 0,
+     "Log"},
+    {NULL}  /* Sentinel */
+};
+
+/** Read double from PyObject
+    @param p PyObject
+    @return double
+*/
+double CLorentzian_readDouble(PyObject *p) {
+    if (PyFloat_Check(p)==1) {
+        return (double)(((PyFloatObject *)(p))->ob_fval);
+    } else if (PyInt_Check(p)==1) {
+        return (double)(((PyIntObject *)(p))->ob_ival);
+    } else if (PyLong_Check(p)==1) {
+        return (double)PyLong_AsLong(p);
+    } else {
+        return 0.0;
+    }
+}
+/**
+ * Function to call to evaluate model
+ * @param args: input numpy array q[] 
+ * @return: numpy array object 
+ */
+ 
+static PyObject *evaluateOneDim(Lorentzian* model, PyArrayObject *q){
+    PyArrayObject *result;
+   
+    // Check validity of array q , q must be of dimension 1, an array of double
+    if (q->nd != 1 || q->descr->type_num != PyArray_DOUBLE)
+    {
+        //const char * message= "Invalid array: q->nd=%d,type_num=%d\n",q->nd,q->descr->type_num;
+        //PyErr_SetString(PyExc_ValueError , message);
+        return NULL;
+    }
+    result = (PyArrayObject *)PyArray_FromDims(q->nd, (int *)(q->dimensions), 
+										  PyArray_DOUBLE);
+	if (result == NULL) {
+        const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+		return NULL;
+	}
+	 for (int i = 0; i < q->dimensions[0]; i++){
+      double q_value  = *(double *)(q->data + i*q->strides[0]);
+      double *result_value = (double *)(result->data + i*result->strides[0]);
+      *result_value =(*model)(q_value);
+	}
+    return PyArray_Return(result); 
+ }
+
+ /**
+ * Function to call to evaluate model
+ * @param args: input numpy array  [x[],y[]]
+ * @return: numpy array object 
+ */
+ static PyObject * evaluateTwoDimXY( Lorentzian* model, 
+                              PyArrayObject *x, PyArrayObject *y)
+ {
+    PyArrayObject *result;
+    int i,j, x_len, y_len, dims[1];
+    //check validity of input vectors
+    if (x->nd != 1 || x->descr->type_num != PyArray_DOUBLE
+        || y->nd != 1 || y->descr->type_num != PyArray_DOUBLE
+        || y->dimensions[0] != x->dimensions[0]){
+        const char * message= "evaluateTwoDimXY  expect 2 numpy arrays";
+        PyErr_SetString(PyExc_ValueError , message); 
+        return NULL;
+    }
+   
+	if (PyArray_Check(x) && PyArray_Check(y)) {
+		
+	    x_len = dims[0]= x->dimensions[0];
+        y_len = dims[0]= y->dimensions[0];
+	    
+	    // Make a new double matrix of same dims
+        result=(PyArrayObject *) PyArray_FromDims(1,dims,NPY_DOUBLE);
+        if (result == NULL){
+	    const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+	    return NULL;
+	    }
+       
+        /* Do the calculation. */
+        for ( i=0; i< x_len; i++) {
+            double x_value = *(double *)(x->data + i*x->strides[0]);
+  		    double y_value = *(double *)(y->data + i*y->strides[0]);
+  			double *result_value = (double *)(result->data +
+  			      i*result->strides[0]);
+  			*result_value = (*model)(x_value, y_value);
+        }           
+        return PyArray_Return(result); 
+        
+        }else{
+		    PyErr_SetString(CLorentzianError, 
+                   "CLorentzian.evaluateTwoDimXY couldn't run.");
+	        return NULL;
+		}      	
+}
+/**
+ *  evalDistribution function evaluate a model function with input vector
+ *  @param args: input q as vector or [qx, qy] where qx, qy are vectors
+ *
+ */ 
+static PyObject * evalDistribution(CLorentzian *self, PyObject *args){
+	PyObject *qx, *qy;
+	PyArrayObject * pars;
+	int npars ,mpars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    self->model->gamma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "gamma") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLorentzianError, 
+	    	"CLorentzian.evalDistribution expects a q value.");
+		return NULL;
+	}
+    // Check params
+	
+    if(PyArray_Check(pars)==1) {
+		
+	    // Length of list should 1 or 2
+	    npars = pars->nd; 
+	    if(npars==1) {
+	        // input is a numpy array
+	        if (PyArray_Check(pars)) {
+		        return evaluateOneDim(self->model, (PyArrayObject*)pars); 
+		    }
+		}else{
+		    PyErr_SetString(CLorentzianError, 
+                   "CLorentzian.evalDistribution expect numpy array of one dimension.");
+	        return NULL;
+		}
+    }else if( PyList_Check(pars)==1) {
+    	// Length of list should be 2 for I(qx,qy)
+	    mpars = PyList_GET_SIZE(pars); 
+	    if(mpars!=2) {
+	    	PyErr_SetString(CLorentzianError, 
+	    		"CLorentzian.evalDistribution expects a list of dimension 2.");
+	    	return NULL;
+	    }
+	     qx = PyList_GET_ITEM(pars,0);
+	     qy = PyList_GET_ITEM(pars,1);
+	     if (PyArray_Check(qx) && PyArray_Check(qy)) {
+	         return evaluateTwoDimXY(self->model, (PyArrayObject*)qx,
+		           (PyArrayObject*)qy);
+		 }else{
+		    PyErr_SetString(CLorentzianError, 
+                   "CLorentzian.evalDistribution expect 2 numpy arrays in list.");
+	        return NULL;
+	     }
+	}
+	PyErr_SetString(CLorentzianError, 
+                   "CLorentzian.evalDistribution couln't be run.");
+	return NULL;
+	
+}
+
+/**
+ * Function to call to evaluate model
+ * @param args: input q or [q,phi]
+ * @return: function value
+ */
+static PyObject * run(CLorentzian *self, PyObject *args) {
+	double q_value, phi_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    self->model->gamma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "gamma") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLorentzianError, 
+	    	"CLorentzian.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(q,phi)
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CLorentzianError, 
+	    		"CLorentzian.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the q and phi values at which
+	    // to evaluate I(q,phi)
+	    q_value = CLorentzian_readDouble(PyList_GET_ITEM(pars,0));
+	    phi_value = CLorentzian_readDouble(PyList_GET_ITEM(pars,1));
+	    // Skip zero
+	    if (q_value==0) {
+	    	return Py_BuildValue("d",0.0);
+	    }
+		return Py_BuildValue("d",(*(self->model)).evaluate_rphi(q_value,phi_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		q_value = CLorentzian_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(q_value));
+	}	
+}
+/**
+ * Function to call to calculate_ER
+ * @return: effective radius value 
+ */
+static PyObject * calculate_ER(CLorentzian *self) {
+
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    self->model->gamma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "gamma") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+		
+	return Py_BuildValue("d",(*(self->model)).calculate_ER());
+
+}
+/**
+ * Function to call to evaluate model in cartesian coordinates
+ * @param args: input q or [qx, qy]]
+ * @return: function value
+ */
+static PyObject * runXY(CLorentzian *self, PyObject *args) {
+	double qx_value, qy_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    self->model->gamma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "gamma") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CLorentzianError, 
+	    	"CLorentzian.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(qx, qy))
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CLorentzianError, 
+	    		"CLorentzian.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the qx and qy values at which
+	    // to evaluate I(qx,qy)
+	    qx_value = CLorentzian_readDouble(PyList_GET_ITEM(pars,0));
+	    qy_value = CLorentzian_readDouble(PyList_GET_ITEM(pars,1));
+	    return Py_BuildValue("d",(*(self->model))(qx_value,qy_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		qx_value = CLorentzian_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(qx_value));
+	}	
+}
+
+static PyObject * reset(CLorentzian *self, PyObject *args) {
+    
+
+    return Py_BuildValue("d",0.0);
+}
+
+static PyObject * set_dispersion(CLorentzian *self, PyObject *args) {
+	PyObject * disp;
+	const char * par_name;
+
+	if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) {
+	    PyErr_SetString(CLorentzianError,
+	    	"CLorentzian.set_dispersion expects a DispersionModel object.");
+		return NULL;
+	}
+	void *temp = PyCObject_AsVoidPtr(disp);
+	DispersionModel * dispersion = static_cast<DispersionModel *>(temp);
+
+
+	// Ugliness necessary to go from python to C
+	    // TODO: refactor this
+ {
+	    PyErr_SetString(CLorentzianError,
+	    	"CLorentzian.set_dispersion expects a valid parameter name.");
+		return NULL;
+	}
+
+	DispersionVisitor* visitor = new DispersionVisitor();
+	PyObject * disp_dict = PyDict_New();
+	dispersion->accept_as_source(visitor, dispersion, disp_dict);
+	PyDict_SetItemString(self->dispersion, par_name, disp_dict);
+    return Py_BuildValue("i",1);
+}
+
+
+static PyMethodDef CLorentzian_methods[] = {
+    {"run",      (PyCFunction)run     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+    {"runXY",      (PyCFunction)runXY     , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy"},
+    {"calculate_ER",      (PyCFunction)calculate_ER     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+      
+    {"evalDistribution",  (PyCFunction)evalDistribution , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy vector "},
+    {"reset",    (PyCFunction)reset   , METH_VARARGS,
+      "Reset pair correlation"},
+    {"set_dispersion",      (PyCFunction)set_dispersion     , METH_VARARGS,
+      "Set the dispersion model for a given parameter"},
+   {NULL}
+};
+
+static PyTypeObject CLorentzianType = {
+    PyObject_HEAD_INIT(NULL)
+    0,                         /*ob_size*/
+    "CLorentzian",             /*tp_name*/
+    sizeof(CLorentzian),             /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor)CLorentzian_dealloc, /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+    "CLorentzian objects",           /* tp_doc */
+    0,		               /* tp_traverse */
+    0,		               /* tp_clear */
+    0,		               /* tp_richcompare */
+    0,		               /* tp_weaklistoffset */
+    0,		               /* tp_iter */
+    0,		               /* tp_iternext */
+    CLorentzian_methods,             /* tp_methods */
+    CLorentzian_members,             /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    (initproc)CLorentzian_init,      /* tp_init */
+    0,                         /* tp_alloc */
+    CLorentzian_new,                 /* tp_new */
+};
+
+
+//static PyMethodDef module_methods[] = {
+//    {NULL} 
+//};
+
+/**
+ * Function used to add the model class to a module
+ * @param module: module to add the class to
+ */ 
+void addCLorentzian(PyObject *module) {
+	PyObject *d;
+	
+    if (PyType_Ready(&CLorentzianType) < 0)
+        return;
+
+    Py_INCREF(&CLorentzianType);
+    PyModule_AddObject(module, "CLorentzian", (PyObject *)&CLorentzianType);
+    
+    d = PyModule_GetDict(module);
+    CLorentzianError = PyErr_NewException("CLorentzian.error", NULL, NULL);
+    PyDict_SetItemString(d, "CLorentzianError", CLorentzianError);
+}
+
Index: sansmodels/src/sans/models/c_models/CSchulz.cpp
===================================================================
--- sansmodels/src/sans/models/c_models/CSchulz.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
+++ sansmodels/src/sans/models/c_models/CSchulz.cpp	(revision c7a7e1b43ffc376e5c441f8a90c5291a702ff4b3)
@@ -0,0 +1,530 @@
+/**
+	This software was developed by the University of Tennessee as part of the
+	Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
+	project funded by the US National Science Foundation.
+
+	If you use DANSE applications to do scientific research that leads to
+	publication, we ask that you acknowledge the use of the software with the
+	following sentence:
+
+	"This work benefited from DANSE software developed under NSF award DMR-0520547."
+
+	copyright 2008, University of Tennessee
+ */
+
+/** CSchulz
+ *
+ * C extension 
+ *
+ * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
+ *          DO NOT MODIFY THIS FILE, MODIFY schulz.h
+ *          AND RE-RUN THE GENERATOR SCRIPT
+ *
+ */
+#define NO_IMPORT_ARRAY
+#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API_sans
+ 
+extern "C" {
+#include <Python.h>
+#include <arrayobject.h>
+#include "structmember.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <time.h>
+#include "schulz.h"
+}
+
+#include "models.hh"
+#include "dispersion_visitor.hh"
+
+/// Error object for raised exceptions
+static PyObject * CSchulzError = NULL;
+
+
+// Class definition
+typedef struct {
+    PyObject_HEAD
+    /// Parameters
+    PyObject * params;
+    /// Dispersion parameters
+    PyObject * dispersion;
+    /// Underlying model object
+    Schulz * model;
+    /// Log for unit testing
+    PyObject * log;
+} CSchulz;
+
+
+static void
+CSchulz_dealloc(CSchulz* self)
+{
+    Py_DECREF(self->params);
+    Py_DECREF(self->dispersion);
+    Py_DECREF(self->log);
+    delete self->model;
+    self->ob_type->tp_free((PyObject*)self);
+    
+
+}
+
+static PyObject *
+CSchulz_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CSchulz *self;
+    
+    self = (CSchulz *)type->tp_alloc(type, 0);
+   
+    return (PyObject *)self;
+}
+
+static int
+CSchulz_init(CSchulz *self, PyObject *args, PyObject *kwds)
+{
+    if (self != NULL) {
+    	
+    	// Create parameters
+        self->params = PyDict_New();
+        self->dispersion = PyDict_New();
+        self->model = new Schulz();
+        
+        // Initialize parameter dictionary
+        PyDict_SetItemString(self->params,"scale",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"sigma",Py_BuildValue("d",1.000000000000));
+        PyDict_SetItemString(self->params,"center",Py_BuildValue("d",0.000000000000));
+        // Initialize dispersion / averaging parameter dict
+        DispersionVisitor* visitor = new DispersionVisitor();
+        PyObject * disp_dict;
+
+
+         
+        // Create empty log
+        self->log = PyDict_New();
+        
+        
+
+    }
+    return 0;
+}
+
+static PyMemberDef CSchulz_members[] = {
+    {"params", T_OBJECT, offsetof(CSchulz, params), 0,
+     "Parameters"},
+	{"dispersion", T_OBJECT, offsetof(CSchulz, dispersion), 0,
+	  "Dispersion parameters"},     
+    {"log", T_OBJECT, offsetof(CSchulz, log), 0,
+     "Log"},
+    {NULL}  /* Sentinel */
+};
+
+/** Read double from PyObject
+    @param p PyObject
+    @return double
+*/
+double CSchulz_readDouble(PyObject *p) {
+    if (PyFloat_Check(p)==1) {
+        return (double)(((PyFloatObject *)(p))->ob_fval);
+    } else if (PyInt_Check(p)==1) {
+        return (double)(((PyIntObject *)(p))->ob_ival);
+    } else if (PyLong_Check(p)==1) {
+        return (double)PyLong_AsLong(p);
+    } else {
+        return 0.0;
+    }
+}
+/**
+ * Function to call to evaluate model
+ * @param args: input numpy array q[] 
+ * @return: numpy array object 
+ */
+ 
+static PyObject *evaluateOneDim(Schulz* model, PyArrayObject *q){
+    PyArrayObject *result;
+   
+    // Check validity of array q , q must be of dimension 1, an array of double
+    if (q->nd != 1 || q->descr->type_num != PyArray_DOUBLE)
+    {
+        //const char * message= "Invalid array: q->nd=%d,type_num=%d\n",q->nd,q->descr->type_num;
+        //PyErr_SetString(PyExc_ValueError , message);
+        return NULL;
+    }
+    result = (PyArrayObject *)PyArray_FromDims(q->nd, (int *)(q->dimensions), 
+										  PyArray_DOUBLE);
+	if (result == NULL) {
+        const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+		return NULL;
+	}
+	 for (int i = 0; i < q->dimensions[0]; i++){
+      double q_value  = *(double *)(q->data + i*q->strides[0]);
+      double *result_value = (double *)(result->data + i*result->strides[0]);
+      *result_value =(*model)(q_value);
+	}
+    return PyArray_Return(result); 
+ }
+
+ /**
+ * Function to call to evaluate model
+ * @param args: input numpy array  [x[],y[]]
+ * @return: numpy array object 
+ */
+ static PyObject * evaluateTwoDimXY( Schulz* model, 
+                              PyArrayObject *x, PyArrayObject *y)
+ {
+    PyArrayObject *result;
+    int i,j, x_len, y_len, dims[1];
+    //check validity of input vectors
+    if (x->nd != 1 || x->descr->type_num != PyArray_DOUBLE
+        || y->nd != 1 || y->descr->type_num != PyArray_DOUBLE
+        || y->dimensions[0] != x->dimensions[0]){
+        const char * message= "evaluateTwoDimXY  expect 2 numpy arrays";
+        PyErr_SetString(PyExc_ValueError , message); 
+        return NULL;
+    }
+   
+	if (PyArray_Check(x) && PyArray_Check(y)) {
+		
+	    x_len = dims[0]= x->dimensions[0];
+        y_len = dims[0]= y->dimensions[0];
+	    
+	    // Make a new double matrix of same dims
+        result=(PyArrayObject *) PyArray_FromDims(1,dims,NPY_DOUBLE);
+        if (result == NULL){
+	    const char * message= "Could not create result ";
+        PyErr_SetString(PyExc_RuntimeError , message);
+	    return NULL;
+	    }
+       
+        /* Do the calculation. */
+        for ( i=0; i< x_len; i++) {
+            double x_value = *(double *)(x->data + i*x->strides[0]);
+  		    double y_value = *(double *)(y->data + i*y->strides[0]);
+  			double *result_value = (double *)(result->data +
+  			      i*result->strides[0]);
+  			*result_value = (*model)(x_value, y_value);
+        }           
+        return PyArray_Return(result); 
+        
+        }else{
+		    PyErr_SetString(CSchulzError, 
+                   "CSchulz.evaluateTwoDimXY couldn't run.");
+	        return NULL;
+		}      	
+}
+/**
+ *  evalDistribution function evaluate a model function with input vector
+ *  @param args: input q as vector or [qx, qy] where qx, qy are vectors
+ *
+ */ 
+static PyObject * evalDistribution(CSchulz *self, PyObject *args){
+	PyObject *qx, *qy;
+	PyArrayObject * pars;
+	int npars ,mpars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CSchulzError, 
+	    	"CSchulz.evalDistribution expects a q value.");
+		return NULL;
+	}
+    // Check params
+	
+    if(PyArray_Check(pars)==1) {
+		
+	    // Length of list should 1 or 2
+	    npars = pars->nd; 
+	    if(npars==1) {
+	        // input is a numpy array
+	        if (PyArray_Check(pars)) {
+		        return evaluateOneDim(self->model, (PyArrayObject*)pars); 
+		    }
+		}else{
+		    PyErr_SetString(CSchulzError, 
+                   "CSchulz.evalDistribution expect numpy array of one dimension.");
+	        return NULL;
+		}
+    }else if( PyList_Check(pars)==1) {
+    	// Length of list should be 2 for I(qx,qy)
+	    mpars = PyList_GET_SIZE(pars); 
+	    if(mpars!=2) {
+	    	PyErr_SetString(CSchulzError, 
+	    		"CSchulz.evalDistribution expects a list of dimension 2.");
+	    	return NULL;
+	    }
+	     qx = PyList_GET_ITEM(pars,0);
+	     qy = PyList_GET_ITEM(pars,1);
+	     if (PyArray_Check(qx) && PyArray_Check(qy)) {
+	         return evaluateTwoDimXY(self->model, (PyArrayObject*)qx,
+		           (PyArrayObject*)qy);
+		 }else{
+		    PyErr_SetString(CSchulzError, 
+                   "CSchulz.evalDistribution expect 2 numpy arrays in list.");
+	        return NULL;
+	     }
+	}
+	PyErr_SetString(CSchulzError, 
+                   "CSchulz.evalDistribution couln't be run.");
+	return NULL;
+	
+}
+
+/**
+ * Function to call to evaluate model
+ * @param args: input q or [q,phi]
+ * @return: function value
+ */
+static PyObject * run(CSchulz *self, PyObject *args) {
+	double q_value, phi_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CSchulzError, 
+	    	"CSchulz.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(q,phi)
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CSchulzError, 
+	    		"CSchulz.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the q and phi values at which
+	    // to evaluate I(q,phi)
+	    q_value = CSchulz_readDouble(PyList_GET_ITEM(pars,0));
+	    phi_value = CSchulz_readDouble(PyList_GET_ITEM(pars,1));
+	    // Skip zero
+	    if (q_value==0) {
+	    	return Py_BuildValue("d",0.0);
+	    }
+		return Py_BuildValue("d",(*(self->model)).evaluate_rphi(q_value,phi_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		q_value = CSchulz_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(q_value));
+	}	
+}
+/**
+ * Function to call to calculate_ER
+ * @return: effective radius value 
+ */
+static PyObject * calculate_ER(CSchulz *self) {
+
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+		
+	return Py_BuildValue("d",(*(self->model)).calculate_ER());
+
+}
+/**
+ * Function to call to evaluate model in cartesian coordinates
+ * @param args: input q or [qx, qy]]
+ * @return: function value
+ */
+static PyObject * runXY(CSchulz *self, PyObject *args) {
+	double qx_value, qy_value;
+	PyObject* pars;
+	int npars;
+	
+	// Get parameters
+	
+	    // Reader parameter dictionary
+    self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") );
+    self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") );
+    self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") );
+    // Read in dispersion parameters
+    PyObject* disp_dict;
+    DispersionVisitor* visitor = new DispersionVisitor();
+
+	
+	// Get input and determine whether we have to supply a 1D or 2D return value.
+	if ( !PyArg_ParseTuple(args,"O",&pars) ) {
+	    PyErr_SetString(CSchulzError, 
+	    	"CSchulz.run expects a q value.");
+		return NULL;
+	}
+	  
+	// Check params
+	if( PyList_Check(pars)==1) {
+		
+		// Length of list should be 2 for I(qx, qy))
+	    npars = PyList_GET_SIZE(pars); 
+	    if(npars!=2) {
+	    	PyErr_SetString(CSchulzError, 
+	    		"CSchulz.run expects a double or a list of dimension 2.");
+	    	return NULL;
+	    }
+	    // We have a vector q, get the qx and qy values at which
+	    // to evaluate I(qx,qy)
+	    qx_value = CSchulz_readDouble(PyList_GET_ITEM(pars,0));
+	    qy_value = CSchulz_readDouble(PyList_GET_ITEM(pars,1));
+	    return Py_BuildValue("d",(*(self->model))(qx_value,qy_value));
+
+	} else {
+
+		// We have a scalar q, we will evaluate I(q)
+		qx_value = CSchulz_readDouble(pars);		
+		
+		return Py_BuildValue("d",(*(self->model))(qx_value));
+	}	
+}
+
+static PyObject * reset(CSchulz *self, PyObject *args) {
+    
+
+    return Py_BuildValue("d",0.0);
+}
+
+static PyObject * set_dispersion(CSchulz *self, PyObject *args) {
+	PyObject * disp;
+	const char * par_name;
+
+	if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) {
+	    PyErr_SetString(CSchulzError,
+	    	"CSchulz.set_dispersion expects a DispersionModel object.");
+		return NULL;
+	}
+	void *temp = PyCObject_AsVoidPtr(disp);
+	DispersionModel * dispersion = static_cast<DispersionModel *>(temp);
+
+
+	// Ugliness necessary to go from python to C
+	    // TODO: refactor this
+ {
+	    PyErr_SetString(CSchulzError,
+	    	"CSchulz.set_dispersion expects a valid parameter name.");
+		return NULL;
+	}
+
+	DispersionVisitor* visitor = new DispersionVisitor();
+	PyObject * disp_dict = PyDict_New();
+	dispersion->accept_as_source(visitor, dispersion, disp_dict);
+	PyDict_SetItemString(self->dispersion, par_name, disp_dict);
+    return Py_BuildValue("i",1);
+}
+
+
+static PyMethodDef CSchulz_methods[] = {
+    {"run",      (PyCFunction)run     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+    {"runXY",      (PyCFunction)runXY     , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy"},
+    {"calculate_ER",      (PyCFunction)calculate_ER     , METH_VARARGS,
+      "Evaluate the model at a given Q or Q, phi"},
+      
+    {"evalDistribution",  (PyCFunction)evalDistribution , METH_VARARGS,
+      "Evaluate the model at a given Q or Qx, Qy vector "},
+    {"reset",    (PyCFunction)reset   , METH_VARARGS,
+      "Reset pair correlation"},
+    {"set_dispersion",      (PyCFunction)set_dispersion     , METH_VARARGS,
+      "Set the dispersion model for a given parameter"},
+   {NULL}
+};
+
+static PyTypeObject CSchulzType = {
+    PyObject_HEAD_INIT(NULL)
+    0,                         /*ob_size*/
+    "CSchulz",             /*tp_name*/
+    sizeof(CSchulz),             /*tp_basicsize*/
+    0,                         /*tp_itemsize*/
+    (destructor)CSchulz_dealloc, /*tp_dealloc*/
+    0,                         /*tp_print*/
+    0,                         /*tp_getattr*/
+    0,                         /*tp_setattr*/
+    0,                         /*tp_compare*/
+    0,                         /*tp_repr*/
+    0,                         /*tp_as_number*/
+    0,                         /*tp_as_sequence*/
+    0,                         /*tp_as_mapping*/
+    0,                         /*tp_hash */
+    0,                         /*tp_call*/
+    0,                         /*tp_str*/
+    0,                         /*tp_getattro*/
+    0,                         /*tp_setattro*/
+    0,                         /*tp_as_buffer*/
+    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/
+    "CSchulz objects",           /* tp_doc */
+    0,		               /* tp_traverse */
+    0,		               /* tp_clear */
+    0,		               /* tp_richcompare */
+    0,		               /* tp_weaklistoffset */
+    0,		               /* tp_iter */
+    0,		               /* tp_iternext */
+    CSchulz_methods,             /* tp_methods */
+    CSchulz_members,             /* tp_members */
+    0,                         /* tp_getset */
+    0,                         /* tp_base */
+    0,                         /* tp_dict */
+    0,                         /* tp_descr_get */
+    0,                         /* tp_descr_set */
+    0,                         /* tp_dictoffset */
+    (initproc)CSchulz_init,      /* tp_init */
+    0,                         /* tp_alloc */
+    CSchulz_new,                 /* tp_new */
+};
+
+
+//static PyMethodDef module_methods[] = {
+//    {NULL} 
+//};
+
+/**
+ * Function used to add the model class to a module
+ * @param module: module to add the class to
+ */ 
+void addCSchulz(PyObject *module) {
+	PyObject *d;
+	
+    if (PyType_Ready(&CSchulzType) < 0)
+        return;
+
+    Py_INCREF(&CSchulzType);
+    PyModule_AddObject(module, "CSchulz", (PyObject *)&CSchulzType);
+    
+    d = PyModule_GetDict(module);
+    CSchulzError = PyErr_NewException("CSchulz.error", NULL, NULL);
+    PyDict_SetItemString(d, "CSchulzError", CSchulzError);
+}
+
