[e0a8a3c] | 1 | /** |
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| 2 | This software was developed by the University of Tennessee as part of the |
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| 3 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 4 | project funded by the US National Science Foundation. |
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| 5 | |
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| 6 | If you use DANSE applications to do scientific research that leads to |
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| 7 | publication, we ask that you acknowledge the use of the software with the |
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| 8 | following sentence: |
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| 9 | |
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| 10 | "This work benefited from DANSE software developed under NSF award DMR-0520547." |
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| 11 | |
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| 12 | copyright 2008, University of Tennessee |
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| 13 | */ |
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| 14 | |
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| 15 | /** CSchulz |
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| 16 | * |
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| 17 | * C extension |
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| 18 | * |
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| 19 | * WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY |
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| 20 | * DO NOT MODIFY THIS FILE, MODIFY schulz.h |
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| 21 | * AND RE-RUN THE GENERATOR SCRIPT |
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| 22 | * |
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| 23 | */ |
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| 24 | #define NO_IMPORT_ARRAY |
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| 25 | #define PY_ARRAY_UNIQUE_SYMBOL PyArray_API_sans |
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| 26 | |
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| 27 | extern "C" { |
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| 28 | #include <Python.h> |
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| 29 | #include <arrayobject.h> |
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| 30 | #include "structmember.h" |
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| 31 | #include <stdio.h> |
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| 32 | #include <stdlib.h> |
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| 33 | #include <math.h> |
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| 34 | #include <time.h> |
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| 35 | #include "schulz.h" |
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| 36 | } |
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| 37 | |
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| 38 | #include "models.hh" |
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| 39 | #include "dispersion_visitor.hh" |
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| 40 | |
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| 41 | /// Error object for raised exceptions |
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| 42 | static PyObject * CSchulzError = NULL; |
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| 43 | |
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| 44 | |
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| 45 | // Class definition |
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| 46 | typedef struct { |
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| 47 | PyObject_HEAD |
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| 48 | /// Parameters |
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| 49 | PyObject * params; |
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| 50 | /// Dispersion parameters |
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| 51 | PyObject * dispersion; |
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| 52 | /// Underlying model object |
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| 53 | Schulz * model; |
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| 54 | /// Log for unit testing |
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| 55 | PyObject * log; |
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| 56 | } CSchulz; |
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| 57 | |
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| 58 | |
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| 59 | static void |
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| 60 | CSchulz_dealloc(CSchulz* self) |
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| 61 | { |
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| 62 | self->ob_type->tp_free((PyObject*)self); |
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| 63 | |
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| 64 | |
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| 65 | } |
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| 66 | |
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| 67 | static PyObject * |
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| 68 | CSchulz_new(PyTypeObject *type, PyObject *args, PyObject *kwds) |
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| 69 | { |
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| 70 | CSchulz *self; |
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| 71 | |
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| 72 | self = (CSchulz *)type->tp_alloc(type, 0); |
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| 73 | |
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| 74 | return (PyObject *)self; |
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| 75 | } |
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| 76 | |
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| 77 | static int |
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| 78 | CSchulz_init(CSchulz *self, PyObject *args, PyObject *kwds) |
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| 79 | { |
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| 80 | if (self != NULL) { |
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| 81 | |
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| 82 | // Create parameters |
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| 83 | self->params = PyDict_New(); |
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| 84 | self->dispersion = PyDict_New(); |
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| 85 | self->model = new Schulz(); |
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| 86 | |
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| 87 | // Initialize parameter dictionary |
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| 88 | PyDict_SetItemString(self->params,"scale",Py_BuildValue("d",1.000000)); |
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| 89 | PyDict_SetItemString(self->params,"sigma",Py_BuildValue("d",1.000000)); |
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| 90 | PyDict_SetItemString(self->params,"center",Py_BuildValue("d",0.000000)); |
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| 91 | // Initialize dispersion / averaging parameter dict |
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| 92 | DispersionVisitor* visitor = new DispersionVisitor(); |
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| 93 | PyObject * disp_dict; |
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| 94 | |
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| 95 | |
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| 96 | |
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| 97 | // Create empty log |
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| 98 | self->log = PyDict_New(); |
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| 99 | |
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| 100 | |
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| 101 | |
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| 102 | } |
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| 103 | return 0; |
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| 104 | } |
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| 105 | |
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| 106 | static PyMemberDef CSchulz_members[] = { |
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| 107 | {"params", T_OBJECT, offsetof(CSchulz, params), 0, |
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| 108 | "Parameters"}, |
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| 109 | {"dispersion", T_OBJECT, offsetof(CSchulz, dispersion), 0, |
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| 110 | "Dispersion parameters"}, |
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| 111 | {"log", T_OBJECT, offsetof(CSchulz, log), 0, |
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| 112 | "Log"}, |
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| 113 | {NULL} /* Sentinel */ |
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| 114 | }; |
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| 115 | |
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| 116 | /** Read double from PyObject |
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| 117 | @param p PyObject |
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| 118 | @return double |
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| 119 | */ |
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| 120 | double CSchulz_readDouble(PyObject *p) { |
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| 121 | if (PyFloat_Check(p)==1) { |
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| 122 | return (double)(((PyFloatObject *)(p))->ob_fval); |
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| 123 | } else if (PyInt_Check(p)==1) { |
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| 124 | return (double)(((PyIntObject *)(p))->ob_ival); |
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| 125 | } else if (PyLong_Check(p)==1) { |
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| 126 | return (double)PyLong_AsLong(p); |
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| 127 | } else { |
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| 128 | return 0.0; |
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| 129 | } |
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| 130 | } |
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| 131 | /** |
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| 132 | * Function to call to evaluate model |
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| 133 | * @param args: input numpy array q[] |
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| 134 | * @return: numpy array object |
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| 135 | */ |
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| 136 | |
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| 137 | static PyObject *evaluateOneDim(Schulz* model, PyArrayObject *q){ |
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| 138 | PyArrayObject *result; |
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| 139 | |
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| 140 | // Check validity of array q , q must be of dimension 1, an array of double |
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| 141 | if (q->nd != 1 || q->descr->type_num != PyArray_DOUBLE) |
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| 142 | { |
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| 143 | //const char * message= "Invalid array: q->nd=%d,type_num=%d\n",q->nd,q->descr->type_num; |
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| 144 | //PyErr_SetString(PyExc_ValueError , message); |
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| 145 | return NULL; |
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| 146 | } |
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| 147 | result = (PyArrayObject *)PyArray_FromDims(q->nd, (int *)(q->dimensions), |
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| 148 | PyArray_DOUBLE); |
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| 149 | if (result == NULL) { |
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| 150 | const char * message= "Could not create result "; |
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| 151 | PyErr_SetString(PyExc_RuntimeError , message); |
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| 152 | return NULL; |
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| 153 | } |
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| 154 | for (int i = 0; i < q->dimensions[0]; i++){ |
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| 155 | double q_value = *(double *)(q->data + i*q->strides[0]); |
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| 156 | double *result_value = (double *)(result->data + i*result->strides[0]); |
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| 157 | *result_value =(*model)(q_value); |
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| 158 | } |
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| 159 | return PyArray_Return(result); |
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| 160 | } |
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| 161 | |
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| 162 | /** |
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| 163 | * Function to call to evaluate model |
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| 164 | * @param args: input numpy array [x[],y[]] |
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| 165 | * @return: numpy array object |
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| 166 | */ |
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| 167 | static PyObject * evaluateTwoDimXY( Schulz* model, |
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| 168 | PyArrayObject *x, PyArrayObject *y) |
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| 169 | { |
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| 170 | PyArrayObject *result; |
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| 171 | int i,j, x_len, y_len, dims[2]; |
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| 172 | //check validity of input vectors |
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| 173 | if (x->nd != 2 || x->descr->type_num != PyArray_DOUBLE |
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| 174 | || y->nd != 2 || y->descr->type_num != PyArray_DOUBLE |
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| 175 | || y->dimensions[1] != x->dimensions[0]){ |
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| 176 | const char * message= "evaluateTwoDimXY expect 2 numpy arrays"; |
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| 177 | PyErr_SetString(PyExc_ValueError , message); |
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| 178 | return NULL; |
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| 179 | } |
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| 180 | |
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| 181 | if (PyArray_Check(x) && PyArray_Check(y)) { |
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| 182 | |
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[870f131] | 183 | x_len = dims[1]= x->dimensions[0]; |
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| 184 | y_len = dims[0]= y->dimensions[1]; |
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[e0a8a3c] | 185 | |
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| 186 | // Make a new double matrix of same dims |
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| 187 | result=(PyArrayObject *) PyArray_FromDims(2,dims,NPY_DOUBLE); |
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| 188 | if (result == NULL){ |
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| 189 | const char * message= "Could not create result "; |
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| 190 | PyErr_SetString(PyExc_RuntimeError , message); |
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| 191 | return NULL; |
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| 192 | } |
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| 193 | |
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| 194 | /* Do the calculation. */ |
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| 195 | for ( j=0; j< y_len; j++) { |
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| 196 | for ( i=0; i< x_len; i++) { |
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[870f131] | 197 | double x_value = *(double *)(x->data + i*x->strides[0]); |
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| 198 | double y_value = *(double *)(y->data + j*y->strides[1]); |
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[e0a8a3c] | 199 | double *result_value = (double *)(result->data + |
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[870f131] | 200 | j*result->strides[0] + i*result->strides[1]); |
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[e0a8a3c] | 201 | *result_value = (*model)(x_value, y_value); |
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| 202 | } |
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| 203 | } |
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| 204 | return PyArray_Return(result); |
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| 205 | |
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| 206 | }else{ |
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| 207 | PyErr_SetString(CSchulzError, |
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| 208 | "CSchulz.evaluateTwoDimXY couldn't run."); |
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| 209 | return NULL; |
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| 210 | } |
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| 211 | } |
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| 212 | /** |
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| 213 | * evalDistribution function evaluate a model function with input vector |
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| 214 | * @param args: input q as vector or [qx, qy] where qx, qy are vectors |
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| 215 | * |
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| 216 | */ |
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| 217 | static PyObject * evalDistribution(CSchulz *self, PyObject *args){ |
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| 218 | PyObject *qx, *qy; |
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| 219 | PyArrayObject * pars; |
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| 220 | int npars ,mpars; |
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| 221 | |
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| 222 | // Get parameters |
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| 223 | |
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| 224 | // Reader parameter dictionary |
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| 225 | self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") ); |
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| 226 | self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") ); |
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| 227 | self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") ); |
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| 228 | // Read in dispersion parameters |
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| 229 | PyObject* disp_dict; |
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| 230 | DispersionVisitor* visitor = new DispersionVisitor(); |
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| 231 | |
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| 232 | |
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| 233 | // Get input and determine whether we have to supply a 1D or 2D return value. |
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| 234 | if ( !PyArg_ParseTuple(args,"O",&pars) ) { |
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| 235 | PyErr_SetString(CSchulzError, |
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| 236 | "CSchulz.evalDistribution expects a q value."); |
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| 237 | return NULL; |
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| 238 | } |
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| 239 | // Check params |
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| 240 | |
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| 241 | if(PyArray_Check(pars)==1) { |
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| 242 | |
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| 243 | // Length of list should 1 or 2 |
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| 244 | npars = pars->nd; |
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| 245 | if(npars==1) { |
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| 246 | // input is a numpy array |
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| 247 | if (PyArray_Check(pars)) { |
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| 248 | return evaluateOneDim(self->model, (PyArrayObject*)pars); |
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| 249 | } |
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| 250 | }else{ |
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| 251 | PyErr_SetString(CSchulzError, |
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| 252 | "CSchulz.evalDistribution expect numpy array of one dimension."); |
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| 253 | return NULL; |
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| 254 | } |
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| 255 | }else if( PyList_Check(pars)==1) { |
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| 256 | // Length of list should be 2 for I(qx,qy) |
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| 257 | mpars = PyList_GET_SIZE(pars); |
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| 258 | if(mpars!=2) { |
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| 259 | PyErr_SetString(CSchulzError, |
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| 260 | "CSchulz.evalDistribution expects a list of dimension 2."); |
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| 261 | return NULL; |
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| 262 | } |
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| 263 | qx = PyList_GET_ITEM(pars,0); |
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| 264 | qy = PyList_GET_ITEM(pars,1); |
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| 265 | if (PyArray_Check(qx) && PyArray_Check(qy)) { |
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| 266 | return evaluateTwoDimXY(self->model, (PyArrayObject*)qx, |
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| 267 | (PyArrayObject*)qy); |
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| 268 | }else{ |
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| 269 | PyErr_SetString(CSchulzError, |
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| 270 | "CSchulz.evalDistribution expect 2 numpy arrays in list."); |
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| 271 | return NULL; |
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| 272 | } |
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| 273 | } |
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| 274 | PyErr_SetString(CSchulzError, |
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| 275 | "CSchulz.evalDistribution couln't be run."); |
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| 276 | return NULL; |
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| 277 | |
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| 278 | } |
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| 279 | |
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| 280 | /** |
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| 281 | * Function to call to evaluate model |
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| 282 | * @param args: input q or [q,phi] |
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| 283 | * @return: function value |
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| 284 | */ |
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| 285 | static PyObject * run(CSchulz *self, PyObject *args) { |
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| 286 | double q_value, phi_value; |
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| 287 | PyObject* pars; |
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| 288 | int npars; |
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| 289 | |
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| 290 | // Get parameters |
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| 291 | |
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| 292 | // Reader parameter dictionary |
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| 293 | self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") ); |
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| 294 | self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") ); |
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| 295 | self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") ); |
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| 296 | // Read in dispersion parameters |
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| 297 | PyObject* disp_dict; |
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| 298 | DispersionVisitor* visitor = new DispersionVisitor(); |
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| 299 | |
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| 300 | |
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| 301 | // Get input and determine whether we have to supply a 1D or 2D return value. |
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| 302 | if ( !PyArg_ParseTuple(args,"O",&pars) ) { |
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| 303 | PyErr_SetString(CSchulzError, |
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| 304 | "CSchulz.run expects a q value."); |
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| 305 | return NULL; |
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| 306 | } |
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| 307 | |
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| 308 | // Check params |
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| 309 | if( PyList_Check(pars)==1) { |
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| 310 | |
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| 311 | // Length of list should be 2 for I(q,phi) |
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| 312 | npars = PyList_GET_SIZE(pars); |
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| 313 | if(npars!=2) { |
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| 314 | PyErr_SetString(CSchulzError, |
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| 315 | "CSchulz.run expects a double or a list of dimension 2."); |
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| 316 | return NULL; |
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| 317 | } |
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| 318 | // We have a vector q, get the q and phi values at which |
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| 319 | // to evaluate I(q,phi) |
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| 320 | q_value = CSchulz_readDouble(PyList_GET_ITEM(pars,0)); |
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| 321 | phi_value = CSchulz_readDouble(PyList_GET_ITEM(pars,1)); |
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| 322 | // Skip zero |
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| 323 | if (q_value==0) { |
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| 324 | return Py_BuildValue("d",0.0); |
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| 325 | } |
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| 326 | return Py_BuildValue("d",(*(self->model)).evaluate_rphi(q_value,phi_value)); |
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| 327 | |
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| 328 | } else { |
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| 329 | |
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| 330 | // We have a scalar q, we will evaluate I(q) |
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| 331 | q_value = CSchulz_readDouble(pars); |
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| 332 | |
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| 333 | return Py_BuildValue("d",(*(self->model))(q_value)); |
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| 334 | } |
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| 335 | } |
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| 336 | |
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| 337 | /** |
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| 338 | * Function to call to evaluate model in cartesian coordinates |
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| 339 | * @param args: input q or [qx, qy]] |
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| 340 | * @return: function value |
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| 341 | */ |
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| 342 | static PyObject * runXY(CSchulz *self, PyObject *args) { |
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| 343 | double qx_value, qy_value; |
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| 344 | PyObject* pars; |
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| 345 | int npars; |
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| 346 | |
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| 347 | // Get parameters |
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| 348 | |
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| 349 | // Reader parameter dictionary |
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| 350 | self->model->scale = PyFloat_AsDouble( PyDict_GetItemString(self->params, "scale") ); |
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| 351 | self->model->sigma = PyFloat_AsDouble( PyDict_GetItemString(self->params, "sigma") ); |
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| 352 | self->model->center = PyFloat_AsDouble( PyDict_GetItemString(self->params, "center") ); |
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| 353 | // Read in dispersion parameters |
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| 354 | PyObject* disp_dict; |
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| 355 | DispersionVisitor* visitor = new DispersionVisitor(); |
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| 356 | |
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| 357 | |
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| 358 | // Get input and determine whether we have to supply a 1D or 2D return value. |
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| 359 | if ( !PyArg_ParseTuple(args,"O",&pars) ) { |
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| 360 | PyErr_SetString(CSchulzError, |
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| 361 | "CSchulz.run expects a q value."); |
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| 362 | return NULL; |
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| 363 | } |
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| 364 | |
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| 365 | // Check params |
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| 366 | if( PyList_Check(pars)==1) { |
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| 367 | |
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| 368 | // Length of list should be 2 for I(qx, qy)) |
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| 369 | npars = PyList_GET_SIZE(pars); |
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| 370 | if(npars!=2) { |
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| 371 | PyErr_SetString(CSchulzError, |
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| 372 | "CSchulz.run expects a double or a list of dimension 2."); |
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| 373 | return NULL; |
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| 374 | } |
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| 375 | // We have a vector q, get the qx and qy values at which |
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| 376 | // to evaluate I(qx,qy) |
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| 377 | qx_value = CSchulz_readDouble(PyList_GET_ITEM(pars,0)); |
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| 378 | qy_value = CSchulz_readDouble(PyList_GET_ITEM(pars,1)); |
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| 379 | return Py_BuildValue("d",(*(self->model))(qx_value,qy_value)); |
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| 380 | |
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| 381 | } else { |
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| 382 | |
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| 383 | // We have a scalar q, we will evaluate I(q) |
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| 384 | qx_value = CSchulz_readDouble(pars); |
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| 385 | |
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| 386 | return Py_BuildValue("d",(*(self->model))(qx_value)); |
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| 387 | } |
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| 388 | } |
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| 389 | |
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| 390 | static PyObject * reset(CSchulz *self, PyObject *args) { |
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| 391 | |
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| 392 | |
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| 393 | return Py_BuildValue("d",0.0); |
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| 394 | } |
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| 395 | |
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| 396 | static PyObject * set_dispersion(CSchulz *self, PyObject *args) { |
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| 397 | PyObject * disp; |
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| 398 | const char * par_name; |
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| 399 | |
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| 400 | if ( !PyArg_ParseTuple(args,"sO", &par_name, &disp) ) { |
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| 401 | PyErr_SetString(CSchulzError, |
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| 402 | "CSchulz.set_dispersion expects a DispersionModel object."); |
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| 403 | return NULL; |
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| 404 | } |
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| 405 | void *temp = PyCObject_AsVoidPtr(disp); |
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| 406 | DispersionModel * dispersion = static_cast<DispersionModel *>(temp); |
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| 407 | |
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| 408 | |
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| 409 | // Ugliness necessary to go from python to C |
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| 410 | // TODO: refactor this |
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| 411 | { |
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| 412 | PyErr_SetString(CSchulzError, |
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| 413 | "CSchulz.set_dispersion expects a valid parameter name."); |
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| 414 | return NULL; |
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| 415 | } |
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| 416 | |
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| 417 | DispersionVisitor* visitor = new DispersionVisitor(); |
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| 418 | PyObject * disp_dict = PyDict_New(); |
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| 419 | dispersion->accept_as_source(visitor, dispersion, disp_dict); |
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| 420 | PyDict_SetItemString(self->dispersion, par_name, disp_dict); |
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| 421 | return Py_BuildValue("i",1); |
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| 422 | } |
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| 423 | |
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| 424 | |
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| 425 | static PyMethodDef CSchulz_methods[] = { |
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| 426 | {"run", (PyCFunction)run , METH_VARARGS, |
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| 427 | "Evaluate the model at a given Q or Q, phi"}, |
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| 428 | {"runXY", (PyCFunction)runXY , METH_VARARGS, |
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| 429 | "Evaluate the model at a given Q or Qx, Qy"}, |
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| 430 | |
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| 431 | {"evalDistribution", (PyCFunction)evalDistribution , METH_VARARGS, |
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| 432 | "Evaluate the model at a given Q or Qx, Qy vector "}, |
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| 433 | {"reset", (PyCFunction)reset , METH_VARARGS, |
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| 434 | "Reset pair correlation"}, |
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| 435 | {"set_dispersion", (PyCFunction)set_dispersion , METH_VARARGS, |
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| 436 | "Set the dispersion model for a given parameter"}, |
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| 437 | {NULL} |
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| 438 | }; |
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| 439 | |
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| 440 | static PyTypeObject CSchulzType = { |
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| 441 | PyObject_HEAD_INIT(NULL) |
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| 442 | 0, /*ob_size*/ |
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| 443 | "CSchulz", /*tp_name*/ |
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| 444 | sizeof(CSchulz), /*tp_basicsize*/ |
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| 445 | 0, /*tp_itemsize*/ |
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| 446 | (destructor)CSchulz_dealloc, /*tp_dealloc*/ |
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| 447 | 0, /*tp_print*/ |
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| 448 | 0, /*tp_getattr*/ |
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| 449 | 0, /*tp_setattr*/ |
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| 450 | 0, /*tp_compare*/ |
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| 451 | 0, /*tp_repr*/ |
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| 452 | 0, /*tp_as_number*/ |
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| 453 | 0, /*tp_as_sequence*/ |
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| 454 | 0, /*tp_as_mapping*/ |
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| 455 | 0, /*tp_hash */ |
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| 456 | 0, /*tp_call*/ |
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| 457 | 0, /*tp_str*/ |
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| 458 | 0, /*tp_getattro*/ |
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| 459 | 0, /*tp_setattro*/ |
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| 460 | 0, /*tp_as_buffer*/ |
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| 461 | Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/ |
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| 462 | "CSchulz objects", /* tp_doc */ |
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| 463 | 0, /* tp_traverse */ |
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| 464 | 0, /* tp_clear */ |
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| 465 | 0, /* tp_richcompare */ |
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| 466 | 0, /* tp_weaklistoffset */ |
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| 467 | 0, /* tp_iter */ |
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| 468 | 0, /* tp_iternext */ |
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| 469 | CSchulz_methods, /* tp_methods */ |
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| 470 | CSchulz_members, /* tp_members */ |
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| 471 | 0, /* tp_getset */ |
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| 472 | 0, /* tp_base */ |
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| 473 | 0, /* tp_dict */ |
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| 474 | 0, /* tp_descr_get */ |
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| 475 | 0, /* tp_descr_set */ |
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| 476 | 0, /* tp_dictoffset */ |
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| 477 | (initproc)CSchulz_init, /* tp_init */ |
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| 478 | 0, /* tp_alloc */ |
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| 479 | CSchulz_new, /* tp_new */ |
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| 480 | }; |
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| 481 | |
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| 482 | |
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| 483 | //static PyMethodDef module_methods[] = { |
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| 484 | // {NULL} |
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| 485 | //}; |
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| 486 | |
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| 487 | /** |
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| 488 | * Function used to add the model class to a module |
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| 489 | * @param module: module to add the class to |
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| 490 | */ |
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| 491 | void addCSchulz(PyObject *module) { |
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| 492 | PyObject *d; |
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| 493 | |
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| 494 | if (PyType_Ready(&CSchulzType) < 0) |
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| 495 | return; |
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| 496 | |
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| 497 | Py_INCREF(&CSchulzType); |
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| 498 | PyModule_AddObject(module, "CSchulz", (PyObject *)&CSchulzType); |
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| 499 | |
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| 500 | d = PyModule_GetDict(module); |
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| 501 | CSchulzError = PyErr_NewException("CSchulz.error", NULL, NULL); |
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| 502 | PyDict_SetItemString(d, "CSchulzError", CSchulzError); |
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| 503 | } |
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| 504 | |
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