source: sasview/sansmodels/src/sans/models/c_models/classTemplate.txt @ c52f66f

/**
        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
 */

/** [PYTHONCLASS]
 *
 * C extension 
 *
 * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
 *          DO NOT MODIFY THIS FILE, MODIFY [INCLUDE_FILE]
 *          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 "[INCLUDE_FILE]"
}

#include "models.hh"
#include "dispersion_visitor.hh"

/// Error object for raised exceptions
static PyObject * [PYTHONCLASS]Error = NULL;


// Class definition
typedef struct {
    PyObject_HEAD
    /// Parameters
    PyObject * params;
    /// Dispersion parameters
    PyObject * dispersion;
    /// Underlying model object
    [CMODEL] * model;
    /// Log for unit testing
    PyObject * log;
} [PYTHONCLASS];


static void
[PYTHONCLASS]_dealloc([PYTHONCLASS]* self)
{
    Py_DECREF(self->params);
    Py_DECREF(self->dispersion);
    Py_DECREF(self->log);
    delete self->model;
    self->ob_type->tp_free((PyObject*)self);
    [NUMERICAL_DEALLOC]
}

static PyObject *
[PYTHONCLASS]_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
{
    [PYTHONCLASS] *self;
    
    self = ([PYTHONCLASS] *)type->tp_alloc(type, 0);
   
    return (PyObject *)self;
}

static int
[PYTHONCLASS]_init([PYTHONCLASS] *self, PyObject *args, PyObject *kwds)
{
    if (self != NULL) {
        
        // Create parameters
        self->params = PyDict_New();
        self->dispersion = PyDict_New();
        self->model = new [CMODEL]();
        
        [INITDICTIONARY]
         
        // Create empty log
        self->log = PyDict_New();
        
        [NUMERICAL_INIT]
    }
    return 0;
}

static PyMemberDef [PYTHONCLASS]_members[] = {
    {"params", T_OBJECT, offsetof([PYTHONCLASS], params), 0,
     "Parameters"},
        {"dispersion", T_OBJECT, offsetof([PYTHONCLASS], dispersion), 0,
          "Dispersion parameters"},     
    {"log", T_OBJECT, offsetof([PYTHONCLASS], log), 0,
     "Log"},
    {NULL}  /* Sentinel */
};

/** Read double from PyObject
    @param p PyObject
    @return double
*/
double [PYTHONCLASS]_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([CMODEL]* 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( [CMODEL]* 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([PYTHONCLASS]Error, 
                   "[PYTHONCLASS].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([PYTHONCLASS] *self, PyObject *args){
        PyObject *qx, *qy;
        PyArrayObject * pars;
        int npars ,mpars;
        
        // Get parameters
        
        [READDICTIONARY]
        
        // Get input and determine whether we have to supply a 1D or 2D return value.
        if ( !PyArg_ParseTuple(args,"O",&pars) ) {
            PyErr_SetString([PYTHONCLASS]Error, 
                "[PYTHONCLASS].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([PYTHONCLASS]Error, 
                   "[PYTHONCLASS].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([PYTHONCLASS]Error, 
                        "[PYTHONCLASS].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([PYTHONCLASS]Error, 
                   "[PYTHONCLASS].evalDistribution expect 2 numpy arrays in list.");
                return NULL;
             }
        }
        PyErr_SetString([PYTHONCLASS]Error, 
                   "[PYTHONCLASS].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([PYTHONCLASS] *self, PyObject *args) {
        double q_value, phi_value;
        PyObject* pars;
        int npars;
        
        // Get parameters
        
        [READDICTIONARY]
        
        // Get input and determine whether we have to supply a 1D or 2D return value.
        if ( !PyArg_ParseTuple(args,"O",&pars) ) {
            PyErr_SetString([PYTHONCLASS]Error, 
                "[PYTHONCLASS].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([PYTHONCLASS]Error, 
                        "[PYTHONCLASS].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 = [PYTHONCLASS]_readDouble(PyList_GET_ITEM(pars,0));
            phi_value = [PYTHONCLASS]_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 = [PYTHONCLASS]_readDouble(pars);               
                
                return Py_BuildValue("d",(*(self->model))(q_value));
        }       
}
/**
 * Function to call to calculate_ER
 * @return: effective radius value 
 */
static PyObject * calculate_ER([PYTHONCLASS] *self) {

        PyObject* pars;
        int npars;
        
        // Get parameters
        
        [READDICTIONARY]
                
        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([PYTHONCLASS] *self, PyObject *args) {
        double qx_value, qy_value;
        PyObject* pars;
        int npars;
        
        // Get parameters
        
        [READDICTIONARY]
        
        // Get input and determine whether we have to supply a 1D or 2D return value.
        if ( !PyArg_ParseTuple(args,"O",&pars) ) {
            PyErr_SetString([PYTHONCLASS]Error, 
                "[PYTHONCLASS].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([PYTHONCLASS]Error, 
                        "[PYTHONCLASS].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 = [PYTHONCLASS]_readDouble(PyList_GET_ITEM(pars,0));
            qy_value = [PYTHONCLASS]_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 = [PYTHONCLASS]_readDouble(pars);              
                
                return Py_BuildValue("d",(*(self->model))(qx_value));
        }       
}

static PyObject * reset([PYTHONCLASS] *self, PyObject *args) {
    [NUMERICAL_RESET]
    return Py_BuildValue("d",0.0);
}

static PyObject * set_dispersion([PYTHONCLASS] *self, PyObject *args) {
        PyObject * disp;
        const char * par_name;

        if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) {
            PyErr_SetString([PYTHONCLASS]Error,
                "[PYTHONCLASS].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
        [SET_DISPERSION] {
            PyErr_SetString([PYTHONCLASS]Error,
                "[PYTHONCLASS].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 [PYTHONCLASS]_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 [PYTHONCLASS]Type = {
    PyObject_HEAD_INIT(NULL)
    0,                         /*ob_size*/
    "[PYTHONCLASS]",             /*tp_name*/
    sizeof([PYTHONCLASS]),             /*tp_basicsize*/
    0,                         /*tp_itemsize*/
    (destructor)[PYTHONCLASS]_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*/
    "[PYTHONCLASS] objects",           /* tp_doc */
    0,                         /* tp_traverse */
    0,                         /* tp_clear */
    0,                         /* tp_richcompare */
    0,                         /* tp_weaklistoffset */
    0,                         /* tp_iter */
    0,                         /* tp_iternext */
    [PYTHONCLASS]_methods,             /* tp_methods */
    [PYTHONCLASS]_members,             /* tp_members */
    0,                         /* tp_getset */
    0,                         /* tp_base */
    0,                         /* tp_dict */
    0,                         /* tp_descr_get */
    0,                         /* tp_descr_set */
    0,                         /* tp_dictoffset */
    (initproc)[PYTHONCLASS]_init,      /* tp_init */
    0,                         /* tp_alloc */
    [PYTHONCLASS]_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 add[PYTHONCLASS](PyObject *module) {
        PyObject *d;
        
    if (PyType_Ready(&[PYTHONCLASS]Type) < 0)
        return;

    Py_INCREF(&[PYTHONCLASS]Type);
    PyModule_AddObject(module, "[PYTHONCLASS]", (PyObject *)&[PYTHONCLASS]Type);
    
    d = PyModule_GetDict(module);
    [PYTHONCLASS]Error = PyErr_NewException("[PYTHONCLASS].error", NULL, NULL);
    PyDict_SetItemString(d, "[PYTHONCLASS]Error", [PYTHONCLASS]Error);
}