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Sourcecode: python-biopython version File versions

KDTreemodule.c

#include <Python.h>
#include <numpy/arrayobject.h>
#include "KDTree.h"

typedef struct {
    PyObject_HEAD 
    struct Neighbor neighbor;
} PyNeighbor;

static int
PyNeighbor_init(PyNeighbor *self, PyObject *args, PyObject *kwds)
{
    long int index1, index2;
    float radius = 0.0;
    static char *kwlist[] = {"index1", "index2", "radius", NULL};

    if (! PyArg_ParseTupleAndKeywords(args, kwds, "ii|d", kwlist, 
                                      &index1, &index2, &radius))
        return -1;
    self->neighbor.index1 = index1;
    self->neighbor.index2 = index2;
    self->neighbor.radius = radius;

    return 0;
}

static PyObject*
PyNeighbor_repr(PyNeighbor* self)
{
    char string[64];
    sprintf(string, "(%ld, %ld): %g",
            self->neighbor.index1, self->neighbor.index2, self->neighbor.radius);
    return PyString_FromString(string);
}

static char PyNeighbor_index1__doc__[] =
"index of the first neighbor";

static PyObject*
PyNeighbor_getindex1(PyNeighbor* self, void* closure)
{
    return PyInt_FromLong(self->neighbor.index1);
}

static int
PyNeighbor_setindex1(PyNeighbor* self, PyObject* value, void* closure)
{
    long index1 = PyInt_AsLong(value);
    if (PyErr_Occurred()) return -1;
    self->neighbor.index1 = index1;
    return 0;
}

static char PyNeighbor_index2__doc__[] =
"index of the second neighbor";

static PyObject*
PyNeighbor_getindex2(PyNeighbor* self, void* closure)
{
    return PyInt_FromLong(self->neighbor.index2);
}

static int
PyNeighbor_setindex2(PyNeighbor* self, PyObject* value, void* closure)
{
    long index2 = PyInt_AsLong(value);
    if (PyErr_Occurred()) return -1;
    self->neighbor.index2 = index2;
    return 0;
}

static PyObject*
PyNeighbor_getradius(PyNeighbor* self, void* closure)
{
    float value = self->neighbor.radius;
    return PyFloat_FromDouble((double)value);
}

static int
PyNeighbor_setradius(PyNeighbor* self, PyObject* value, void* closure)
{ const double radius = PyFloat_AsDouble(value);
  if (PyErr_Occurred()) return -1;
  self->neighbor.radius = (float)radius;
  return 0;
}

static char PyNeighbor_radius__doc__[] =
"the radius\n";

static PyGetSetDef PyNeighbor_getset[] = {
    {"index1", (getter)PyNeighbor_getindex1, (setter)PyNeighbor_setindex1, PyNeighbor_index1__doc__, NULL},
    {"index2", (getter)PyNeighbor_getindex2, (setter)PyNeighbor_setindex2, PyNeighbor_index2__doc__, NULL},
    {"radius", (getter)PyNeighbor_getradius, (setter)PyNeighbor_setradius, PyNeighbor_radius__doc__, NULL},
    {NULL}  /* Sentinel */
};

static char PyNeighbor_doc[] =
"A neighbor pair; members are index1, index2, and radius.\n";

static PyTypeObject PyNeighborType = {
    PyObject_HEAD_INIT(NULL)
    0,                         /* ob_size*/
    "KDTree.Neighbor",         /* tp_name*/
    sizeof(PyNeighbor),        /* tp_basicsize*/
    0,                         /* tp_itemsize*/
    0,                         /* tp_dealloc*/
    0,                         /* tp_print*/
    0,                         /* tp_getattr*/
    0,                         /* tp_setattr*/
    0,                         /* tp_compare*/
    (reprfunc)PyNeighbor_repr, /* 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,        /* tp_flags*/
    PyNeighbor_doc,            /* tp_doc */
    0,                         /* tp_traverse */
    0,                         /* tp_clear */
    0,                         /* tp_richcompare */
    0,                         /* tp_weaklistoffset */
    0,                         /* tp_iter */
    0,                         /* tp_iternext */
    0,                         /* tp_methods */
    0,                         /* tp_members */
    PyNeighbor_getset,         /* tp_getset */
    0,                         /* tp_base */
    0,                         /* tp_dict */
    0,                         /* tp_descr_get */
    0,                         /* tp_descr_set */
    0,                         /* tp_dictoffset */
    (initproc)PyNeighbor_init, /* tp_init */
};

typedef struct {
    PyObject_HEAD
    struct KDTree* tree;
} PyTree;

static void
PyTree_dealloc(PyTree* self)
{
    KDTree_destroy(self->tree);
    self->ob_type->tp_free((PyObject*)self);
}

static int
PyTree_init(PyTree* self, PyObject* args, PyObject* kwds)
{
    int dim;
    int bucket_size;
    struct KDTree* tree;
   
    if(!PyArg_ParseTuple(args, "ii:KDTree_init" ,&dim, &bucket_size)) return -1;

    if (dim <= 0 || bucket_size <= 0)
    {
        PyErr_SetString(PyExc_ValueError, "Both arguments should be positive");
        return -1;
    }

    tree = KDTree_init(dim, bucket_size);
    if (tree==NULL)
    {
        PyErr_SetString(PyExc_MemoryError, "Insufficient memory for tree");
      return -1;
    }

    self->tree = tree;
    return 0;
}

static PyObject*
PyTree_get_count(PyTree* self)
{
    long count;
    struct KDTree* tree = self->tree;
    PyObject* result;
    count = KDTree_get_count(tree);
    result = PyInt_FromLong(count);
    if (!result)
    {
        PyErr_SetString (PyExc_MemoryError, "Failed to allocate memory for object.");
        return NULL;
    }
    return result;
}

static PyObject*
PyTree_neighbor_get_count(PyTree* self)
{
    long count;
    struct KDTree* tree = self->tree;
    PyObject* result;
    count = KDTree_neighbor_get_count(tree);
    result = PyInt_FromLong(count);
    if (!result)
    {
        PyErr_SetString (PyExc_MemoryError, "Failed to allocate memory for object.");
        return NULL;
    }
    return result;
}

static PyObject*
PyTree_set_data(PyTree* self, PyObject* args)
{
    float* coords;
    long int n, m, i;
    PyObject *obj;
    PyArrayObject *array;
    struct KDTree* tree = self->tree;
    int ok;
    npy_intp rowstride, colstride;
    const char* p;

    if(!PyArg_ParseTuple(args, "O:KDTree_set_data",&obj)) return NULL;

    /* Check if it is an array */
    if (!PyArray_Check(obj))
    {
        PyErr_SetString(PyExc_TypeError, "First argument must be an array.");
        return NULL;
    }
    array=(PyArrayObject *) obj;
    if(PyArray_NDIM(array)!=2)
    {
        PyErr_SetString(PyExc_ValueError, "Array must be two dimensional.");
        return NULL;
    }
    if (PyArray_TYPE(array) == NPY_DOUBLE)
    {
        Py_INCREF(obj);
    }
    else
    {
        /* Cast to type double */
        obj = PyArray_Cast(array, NPY_DOUBLE);
        if (!obj)
        {
            PyErr_SetString(PyExc_ValueError,
                            "coordinates cannot be cast to needed type.");
            return NULL;
        }
        array = (PyArrayObject*) obj;
    }

    n = (long int) PyArray_DIM(array, 0);
    m = (long int) PyArray_DIM(array, 1);

    /* coord_data is deleted by the KDTree object */
    coords= malloc(m*n*sizeof(float));
    if (!coords)
    {
        Py_DECREF(obj);
        PyErr_SetString (PyExc_MemoryError, "Failed to allocate memory for coordinates.");
        return NULL;
    }

    rowstride =  PyArray_STRIDE(array, 0);
    colstride =  PyArray_STRIDE(array, 1);
    p = PyArray_BYTES(array);

    for (i=0; i<n; i++)
    {
        int j;

        for (j=0; j<m; j++)
        {
            coords[i*m+j]=*(double *) (p+i*rowstride+j*colstride);
        }
    } 
    Py_DECREF(obj);

    ok = KDTree_set_data(tree, coords, n);
    if (!ok)
    {
        PyErr_SetString (PyExc_MemoryError, "Failed to allocate memory for nodes.");
        return NULL;
    }

    Py_INCREF(Py_None);
    return Py_None;
}

static PyObject*
PyTree_search_center_radius(PyTree* self, PyObject* args)
{
    PyObject *obj;
    double radius;
    PyArrayObject *array;
    long int n, i;
    float *coords;
    struct KDTree* tree = self->tree;
    int ok;
    npy_intp stride;
    const char* p;

    if(!PyArg_ParseTuple(args, "Od:KDTree_search_center_radius", &obj ,&radius))
        return NULL;

    if(radius <= 0)
    {
        PyErr_SetString(PyExc_ValueError, "Radius must be positive.");
        return NULL;
    }

    /* Check if it is an array */
    if (!PyArray_Check(obj))
    {
        PyErr_SetString(PyExc_TypeError, "First argument must be an array.");
        return NULL;
    }
    array=(PyArrayObject *) obj;

    if(PyArray_NDIM(array)!=1)
    {
        PyErr_SetString(PyExc_ValueError, "Array must be one dimensional.");
        return NULL;
    }
    if (PyArray_TYPE(array) == NPY_DOUBLE)
    {
        Py_INCREF(obj);
    }
    else
    {
        /* Cast to type double */
        obj = PyArray_Cast(array, NPY_DOUBLE);
        if (!obj)
        {
            PyErr_SetString(PyExc_ValueError,
                            "coordinates cannot be cast to needed type.");
            return NULL;
        }
        array = (PyArrayObject*) obj;
    }

    n = (long int) PyArray_DIM(array, 0);

    /* coord_data is deleted by the KDTree object */
    coords= malloc(n*sizeof(float));
    if (!coords)
    {
        Py_DECREF(obj);
        PyErr_SetString (PyExc_MemoryError, "Failed to allocate memory for coordinates.");
        return NULL;
    }

    stride =  PyArray_STRIDE(array, 0);
    p = PyArray_BYTES(array);
    for (i=0; i<n; i++)
    {
        coords[i]=*(double *) (p+i*stride);
    }
    Py_DECREF(obj);

    ok = KDTree_search_center_radius(tree, coords, radius);

    if (!ok)
    {
        PyErr_SetString (PyExc_MemoryError, "Insufficient memory for calculation.");
        return NULL;
    }

    Py_INCREF(Py_None);
    return Py_None;
}

static PyObject*
PyTree_neighbor_search(PyTree* self, PyObject* args)
{
    int ok;
    double radius;
    struct KDTree* tree = self->tree;
    struct Neighbor* neighbors;
    struct Neighbor* pp, *qq;
    PyObject* list;
    Py_ssize_t i, n;

    if(!PyArg_ParseTuple(args, "d:KDTree_neighbor_search", &radius))
        return NULL;

    if(radius <= 0)
    {
        PyErr_SetString(PyExc_ValueError, "Radius must be positive.");
        return NULL;
    }

    ok = KDTree_neighbor_search(tree, radius, &neighbors);
    if (!ok)
    {
        PyErr_SetString(PyExc_MemoryError,
            "calculation failed due to lack of memory");
        return NULL;
    }

    pp = neighbors;
    n = 0;
    while (pp)
    {
        n+=1;
        pp = pp->next;
    }

    list = PyList_New(n);
    if (list)
    {
        PyNeighbor* p;
        pp = neighbors;
        for (i = 0; i < n; i++)
        {
            p = (PyNeighbor*) PyNeighborType.tp_alloc(&PyNeighborType, 0);
            if(!p)
            {
                PyErr_SetString(PyExc_MemoryError,
                    "could not create node for return value");
                Py_DECREF(list);
                return NULL;
            }
            p->neighbor = *pp;
            PyList_SET_ITEM(list, i, (PyObject*)p);
            qq = pp->next;
            free(pp);
            pp = qq;
        }
    }

    return list;
}

static PyObject*
PyTree_neighbor_simple_search(PyTree* self, PyObject* args)
{
    int ok;
    double radius;
    struct KDTree* tree = self->tree;
    struct Neighbor* neighbors;
    struct Neighbor* pp, *qq;
    PyObject* list;
    Py_ssize_t i, n;

    if(!PyArg_ParseTuple(args, "d:KDTree_neighbor_simple_search", &radius))
        return NULL;

    if(radius <= 0)
    {
        PyErr_SetString(PyExc_ValueError, "Radius must be positive.");
        return NULL;
    }

    ok = KDTree_neighbor_simple_search(tree, radius, &neighbors);
    if (!ok)
    {
        PyErr_SetString(PyExc_MemoryError,
            "calculation failed due to lack of memory");
        return NULL;
    }

    pp = neighbors;
    n = 0;
    while (pp)
    {
        n+=1;
        pp = pp->next;
    }

    list = PyList_New(n);
    if (list)
    {
        PyNeighbor* p;
        pp = neighbors;
        for (i = 0; i < n; i++)
        {
            p = (PyNeighbor*) PyNeighborType.tp_alloc(&PyNeighborType, 0);
            if(!p)
            {
                PyErr_SetString(PyExc_MemoryError,
                    "could not create node for return value");
                Py_DECREF(list);
                return NULL;
            }
            p->neighbor = *pp;
            PyList_SET_ITEM(list, i, (PyObject*)p);
            qq = pp->next;
            free(pp);
            pp = qq;
        }
    }

    return list;
}

static char PyTree_get_indices__doc__[] =
"returns indices of coordinates within radius as a Numpy array\n";

static PyObject *PyTree_get_indices(PyTree *self)
{
      npy_intp length;
      PyArrayObject *array;
      struct KDTree* tree = self->tree;
 
      length=KDTree_get_count(tree);
      
      if (length==0)
      {
            Py_INCREF(Py_None);
            return Py_None;
      }

      array=(PyArrayObject *) PyArray_SimpleNew(1, &length, PyArray_LONG);
      if (!array)
      {
            PyErr_SetString(PyExc_MemoryError,
                        "Insufficient memory for array");
            return NULL;
      }

      /* copy the data into the Numpy data pointer */
      KDTree_copy_indices(tree, (long int *) PyArray_BYTES(array));
      return PyArray_Return(array);
}

static char PyTree_get_radii__doc__[] =
"returns distances of coordinates within radius as a Numpy array.\n";

static PyObject *PyTree_get_radii(PyTree *self)
{
      npy_intp length;
      PyArrayObject *array;
      struct KDTree* tree = self->tree;
       
      length=KDTree_get_count(tree);

      if (length==0)
      {
            Py_INCREF(Py_None);
            return Py_None;
      }
            
      array=(PyArrayObject *) PyArray_SimpleNew(1, &length, PyArray_FLOAT);
      if (!array)
      {
            PyErr_SetString(PyExc_MemoryError,
                        "Insufficient memory for array");
            return NULL;
      }

      /* copy the data into the Numpy data pointer */
      KDTree_copy_radii(tree, (float *) PyArray_BYTES(array));
      return PyArray_Return(array);
}                                   

static PyMethodDef PyTree_methods[] = {
    {"get_count", (PyCFunction)PyTree_get_count, METH_NOARGS, NULL},
    {"set_data", (PyCFunction)PyTree_set_data, METH_VARARGS, NULL},
    {"search_center_radius", (PyCFunction)PyTree_search_center_radius, METH_VARARGS, NULL},
    {"neighbor_get_count", (PyCFunction)PyTree_neighbor_get_count, METH_NOARGS, NULL},
    {"neighbor_search", (PyCFunction)PyTree_neighbor_search, METH_VARARGS, NULL},
    {"neighbor_simple_search", (PyCFunction)PyTree_neighbor_simple_search, METH_VARARGS, NULL},
    {"get_indices", (PyCFunction)PyTree_get_indices, METH_NOARGS, PyTree_get_indices__doc__},
    {"get_radii", (PyCFunction)PyTree_get_radii, METH_NOARGS, PyTree_get_radii__doc__},
    {NULL}  /* Sentinel */
};

static char PyTree_doc[] = "C KDTree.\n";

static PyTypeObject PyTreeType = {
    PyObject_HEAD_INIT(NULL)
    0,                           /*ob_size*/
    "C KDTree",                  /*tp_name*/
    sizeof(PyTree),              /*tp_basicsize*/
    0,                           /*tp_itemsize*/
    (destructor)PyTree_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,          /*tp_flags*/
    PyTree_doc,                  /* tp_doc */
    0,                             /* tp_traverse */
    0,                             /* tp_clear */
    0,                             /* tp_richcompare */
    0,                             /* tp_weaklistoffset */
    0,                             /* tp_iter */
    0,                             /* tp_iternext */
    PyTree_methods,              /* tp_methods */
    NULL,                        /* tp_members */
    0,                           /* tp_getset */
    0,                           /* tp_base */
    0,                           /* tp_dict */
    0,                           /* tp_descr_get */
    0,                           /* tp_descr_set */
    0,                           /* tp_dictoffset */
    (initproc)PyTree_init,       /* tp_init */
};

/* ========================================================================== */
/* -- Initialization -------------------------------------------------------- */
/* ========================================================================== */

void init_CKDTree(void)
{
  PyObject *m;

  import_array();

  PyTreeType.tp_new = PyType_GenericNew;
  PyNeighborType.tp_new = PyType_GenericNew;
  if (PyType_Ready(&PyTreeType) < 0) return;
  if (PyType_Ready(&PyNeighborType) < 0) return;

  m = Py_InitModule("_CKDTree", NULL);
  if (m==NULL) return;

  Py_INCREF(&PyTreeType);
  Py_INCREF(&PyNeighborType);
  PyModule_AddObject(m, "KDTree", (PyObject*) &PyTreeType);
  PyModule_AddObject(m, "Neighbor", (PyObject*) &PyNeighborType);

  if (PyErr_Occurred()) Py_FatalError("can't initialize module _CKDTree");
}

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