PyObject *computeTimeLagCov(PyArrayObject *i_data, int timeLag)
{
/* Output matrix:
| * * * * * |
| 0 * * * * |
| 0 0 * * * |
| 0 0 0 * * |
| 0 0 0 0 * |
*/
int i, j, k, mCov, nCov, nData, mData;
npy_intp dimCov[2];
double *rowCov, *colData1, *colData2, *colMean1, *colMean2;
ticaC_numpyArrayDim dimData = NULL;
PyObject *o_cov = NULL;
PyArrayObject *cov = NULL;
PyArrayObject *colMeanArray = NULL;
dimData = PyArray_DIMS(i_data);
dimCov[0] = dimData[1];
dimCov[1] = dimData[1];
mCov = dimCov[0];
nCov = dimCov[1];
nData = dimData[0];
mData = dimData[1];
o_cov = PyArray_SimpleNew(TICA_ARRAY_DIMENSION, dimCov, NPY_DOUBLE);
cov = (PyArrayObject*)PyArray_FROM_OTF(o_cov, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
if (NULL == o_cov)
{
return NULL;
}
for (i = 0; i < mCov; i++)
{
rowCov = (double*)PyArray_GETPTR2(cov, i, i);
for (j = i; j < nCov; j++, rowCov++)
{
colData1 = (double*)PyArray_GETPTR2(i_data, TICA_FIRST_ROW, i);
colData2 = (double*)PyArray_GETPTR2(i_data, TICA_FIRST_ROW, j);
colData2 += mData * timeLag;
for (k = 0; k < nData - timeLag; k++, colData1 += mData, colData2 += mData)
{
*rowCov += (*colData1) * (*colData2);
}
}
}
o_cov = (PyObject*)cov;
Py_INCREF(cov);
return o_cov;
}
static PyObject*
calculate(const char sequence[], int s, PyObject* matrix, npy_intp m)
{
npy_intp n = s - m + 1;
npy_intp i, j;
char c;
double score;
int ok;
PyObject* result;
PyArrayObject* array;
float* p;
npy_intp shape = (npy_intp)n;
float nan = 0.0;
nan /= nan;
if ((int)shape!=n)
{
PyErr_SetString(PyExc_ValueError, "integer overflow");
return NULL;
}
result = PyArray_SimpleNew(1, &shape, NPY_FLOAT32);
if (!result)
{
PyErr_SetString(PyExc_MemoryError, "failed to create output data");
return NULL;
}
p = PyArray_DATA((PyArrayObject*)result);
array = (PyArrayObject*)matrix;
for (i = 0; i < n; i++)
{
score = 0.0;
ok = 1;
for (j = 0; j < m; j++)
{
c = sequence[i+j];
switch (c)
{
case 'A':
case 'a':
score += *((double*)PyArray_GETPTR2(array, j, 0)); break;
case 'C':
case 'c':
score += *((double*)PyArray_GETPTR2(array, j, 1)); break;
case 'G':
case 'g':
score += *((double*)PyArray_GETPTR2(array, j, 2)); break;
case 'T':
case 't':
score += *((double*)PyArray_GETPTR2(array, j, 3)); break;
default:
ok = 0;
}
}
if (ok) *p = (float)score;
else *p = nan;
p++;
}
return result;
}
/*==========================================================================*/
PyObject *calculate(PyObject *self, PyObject *args)
{
long i, j;
KD kd;
int nReps=0, bPeriodic=0, bEwald=0;
float fSoft;
int nPos;
PyObject *kdobj, *acc, *pot, *pos;
PARTICLE *testParticles;
PyArg_ParseTuple(args, "OOOOdi", &kdobj, &pos, &acc, &pot, &fSoft, &nPos);
kd = PyCObject_AsVoidPtr(kdobj);
if (kd == NULL) return NULL;
testParticles = (PARTICLE *)malloc(nPos*sizeof(PARTICLE));
assert(testParticles != NULL);
for (i=0; i< nPos; i++) {
for (j=0; j < 3; j++) {
testParticles[i].r[j] =
(float)*((double *)PyArray_GETPTR2(pos, i, j));
testParticles[i].a[j] = 0;
}
testParticles[i].fMass = 0;
testParticles[i].fPot = 0;
testParticles[i].fSoft = fSoft;
testParticles[i].iOrder = i;
}
Py_BEGIN_ALLOW_THREADS
kdGravWalk(kd, nReps, bPeriodic && bEwald, testParticles, nPos);
Py_END_ALLOW_THREADS
for (i=0; i < nPos; i++) {
PyArray_SETITEM(pot, PyArray_GETPTR1(pot,i), PyFloat_FromDouble(testParticles[i].fPot));
for (j=0; j < 3; j++) {
PyArray_SETITEM(acc, PyArray_GETPTR2(acc, i, j),
PyFloat_FromDouble(testParticles[i].a[j]));
}
}
Py_DECREF(kd);
/*
Py_DECREF(pos);
Py_DECREF(pot);
Py_DECREF(acc);
*/
Py_INCREF(Py_None);
return Py_None;
}
static PyObject *
find_all_nearest_pois(PyObject *self, PyObject *args)
{
double radius;
int varind, num, gno, impno;
if (!PyArg_ParseTuple(args, "diiii", &radius, &num, &varind,
&gno, &impno))
return NULL;
std::shared_ptr<MTC::accessibility::Accessibility> sa = sas[gno];
std::vector<std::vector<float> > nodes =
sa->findAllNearestPOIs(radius, num, varind, impno);
npy_intp dims[2];
dims[0] = nodes.size();
dims[1] = num;
PyArrayObject *returnobj = (PyArrayObject *)PyArray_SimpleNew(2, dims,
NPY_FLOAT);
for(int i = 0 ; i < dims[0] ; i++) {
for(int j = 0 ; j < dims[1] ; j++) {
*(npy_float *) PyArray_GETPTR2(returnobj, i, j) = (npy_float) nodes[i][j];
}
}
return PyArray_Return(returnobj);
}
void create_flat_labels( PyArrayObject * label_matrix, int ** flat_labels,
int ** label_lengths )
{
npy_int rows = PyArray_DIMS( label_matrix )[0];
npy_int cols = PyArray_DIMS( label_matrix )[1];
*flat_labels = (int *) calloc( rows * cols, sizeof(int) );
if ( NULL == (*flat_labels) )
return;
*label_lengths = (int *) calloc( rows, sizeof(int) );
if ( NULL == (*label_lengths) )
{
free( *flat_labels );
*flat_labels = NULL;
return;
}
npy_int label_index = 0;
for( npy_int row_idx = 0; row_idx < rows; ++row_idx )
{
npy_int label_length = 0;
for( npy_int col_idx = 0; col_idx < cols; ++col_idx )
{
npy_int label = *( (npy_int *) PyArray_GETPTR2( label_matrix, row_idx, col_idx ) );
if ( label >= 0 ) // negative values are assumed to be padding
{
(*flat_labels)[ label_index++ ] = label;
++label_length;
}
}
(*label_lengths)[ row_idx ] = label_length;
}
}
/**
* Init from a given set of y-values
*
*/
int dtnode :: y_init(PyArrayObject* yinit, int ti)
{
vector<node*> multi = get_multinodes();
// Make sure dimensionality is correct
if(multi.size() != PyArray_DIM(yinit,1))
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"q-init dimensionality mismatch - wrong # q-values");
return BAD;
}
for(uint mi = 0; mi < multi.size(); mi++)
{
multinode* mu = dynamic_cast<multinode*>(ichildren[mi]);
int yval = *((int*)PyArray_GETPTR2(yinit,ti,mi));
//printf("initing w/ yval =%d\n",yval);
// Make sure y-value in valid range [0,Q-1]
if(yval < 0 || yval >= (*mu).num_variants())
{
// ERROR
PyErr_SetString(PyExc_RuntimeError,
"Out-of-range value in q-init");
return BAD;
}
else
(*(mu)).set_y(yval);
}
return OK;
}
/* return the offchip (ovd, length) NumPy array with shape (offcount,2) */
static PyObject* pm_get_offchip(PyObject *self, PyObject *args)
{
PyArrayObject *py_offchip;
npy_intp shape[2];
int i;
long long *tmp;
if (!loaded(0)) {
PyErr_Format(PyExc_ValueError, "pm not Loaded!");
return NULL;
}
shape[0] = offcount;
shape[1] = 2;
if (!PyArg_ParseTuple(args,""))
return NULL;
py_offchip = (PyArrayObject *) PyArray_SimpleNew(2,shape,NPY_LONGLONG);
if (!py_offchip)
goto err_alloc;
for (i = 0; i < offcount; i++) {
tmp = (long long *) PyArray_GETPTR2(py_offchip, i, 0);
if(!tmp)
goto err;
*tmp = (long long) offchip[i].ovd;
tmp = (long long *) PyArray_GETPTR2(py_offchip, i, 1);
if(!tmp)
goto err;
*tmp = (long long) offchip[i].plen;
}
free(offchip);
return (PyObject *) py_offchip;
err:
PyArray_free(py_offchip);
err_alloc:
PyErr_Format(PyExc_ValueError, "pm_get_preemption Error");
cleanup();
return NULL;
}
PyObject *addMatrixPiecewise(PyObject *matrix, PyObject *matrix2)
{
int i, j, mCov, nCov;
int dimCov[2];
double *matrixData1, *matrixData2;
double *outMatrixDat;
ticaC_numpyArrayDim dimData = NULL;
PyObject *outMatrix = NULL;
PyArrayObject *outMatrixArray = NULL, *matrixArray, *matrixArray2;
matrixArray = (PyArrayObject*)PyArray_FROM_OTF(matrix, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
matrixArray2 = (PyArrayObject*)PyArray_FROM_OTF(matrix2, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
dimData = PyArray_DIMS(matrixArray);
dimCov[0] = dimData[1];
dimCov[1] = dimData[1];
mCov = dimCov[0];
nCov = dimCov[1];
outMatrix = PyArray_SimpleNew(TICA_ARRAY_DIMENSION, dimCov, NPY_DOUBLE);
outMatrixArray = (PyArrayObject*)PyArray_FROM_OTF(outMatrix, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
if (NULL == outMatrix)
{
return NULL;
}
for (i = 0; i < mCov; i++)
{
for (j = 0; j < nCov; j++)
{
outMatrixDat = (double*)PyArray_GETPTR2(outMatrixArray, i, j);
matrixData1 = (double*)PyArray_GETPTR2(matrixArray, i, j);
matrixData2 = (double*)PyArray_GETPTR2(matrixArray2, i, j);
*outMatrixDat = *matrixData1 + *matrixData2;
}
}
outMatrix = (PyObject*)outMatrixArray;
Py_INCREF(outMatrixArray);
return outMatrix;
}
PyObject *matrixMulti(PyArrayObject *i_data, double tmp)
{
int i, j, mCov, nCov;
npy_intp dimCov[2];
double *rowCov;
ticaC_numpyArrayDim dimData = NULL;
PyObject *o_cov = NULL;
PyArrayObject *cov = NULL;
double *tmp2;
dimData = PyArray_DIMS(i_data);
dimCov[0] = dimData[1];
dimCov[1] = dimData[1];
mCov = dimCov[0];
nCov = dimCov[1];
o_cov = PyArray_SimpleNew(TICA_ARRAY_DIMENSION, dimCov, NPY_DOUBLE);
cov = (PyArrayObject*)PyArray_FROM_OTF(o_cov, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
if (NULL == o_cov)
{
return NULL;
}
for (i = 0; i < mCov; i++)
{
//rowCov = (double*)PyArray_GETPTR2(cov, i, i);
for (j = i; j < nCov; j++)
{
tmp2 = (double*)PyArray_GETPTR2(i_data, i, j);
rowCov = (double*)PyArray_GETPTR2(cov, i, j);
//tmp2 = *rowCov;
*rowCov = *tmp2 * tmp;
}
}
o_cov = (PyObject*)cov;
Py_INCREF(cov);
return o_cov;
}
// get index into counts for given row of obs
int
cptindex(PyArrayObject *obs, int row, int *offsets, int num_parents) {
register int i,ind=0;
for (i=0; i<num_parents; i++) {
ind += *((int*)PyArray_GETPTR2(obs, row, i+1)) * offsets[i];
}
return ind;
}
PyObject *computeFullMatrix(PyArrayObject *matrix)
{
int i, j, mCov, nCov;
npy_intp dimCov[2];
double *colData1;
double *rowCov, *rowCov2;
ticaC_numpyArrayDim dimData = NULL;
PyObject *o_cov = NULL;
PyArrayObject *cov = NULL;
dimData = PyArray_DIMS(matrix);
dimCov[0] = dimData[1];
dimCov[1] = dimData[1];
mCov = dimCov[0];
nCov = dimCov[1];
o_cov = PyArray_SimpleNew(TICA_ARRAY_DIMENSION, dimCov, NPY_DOUBLE);
cov = (PyArrayObject*)PyArray_FROM_OTF(o_cov, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);
if (NULL == o_cov)
{
return NULL;
}
for (i = 0; i < mCov; i++)
{
for (j = i; j < nCov; j++)
{
rowCov = (double*)PyArray_GETPTR2(cov, j, i);
rowCov2 = (double*)PyArray_GETPTR2(cov, i, j);
colData1 = (double*)PyArray_GETPTR2(matrix, i, j);
*rowCov = *colData1;
*rowCov2 = *colData1;
}
}
o_cov = (PyObject*)cov;
Py_INCREF(cov);
return o_cov;
}
/* Adds two (complex) matrices, overwritting the first one. */
static PyObject *
accum (PyObject *self, PyObject *args)
{
int i, j;
PyArrayObject *m0, *m1;
complex double *p0, *p1;
if (!PyArg_ParseTuple(args, "O!O!",
&PyArray_Type, &m0,
&PyArray_Type, &m1))
return NULL;
if (m0->dimensions[0] < m1->dimensions[0]
|| m0->dimensions[1] < m1->dimensions[1]) {
PyErr_SetString(PyExc_ValueError,
"accum: m0 must be able to accomodate m1");
return NULL;
}
if (m0->nd != 2 || m1->nd != 2) {
PyErr_SetString(PyExc_ValueError,
"accum: the arrays must have dimension 2.");
return NULL;
}
for (i = 0; i < m0->dimensions[0]; i++) {
/* We make use here of the fact that the matrices come from a mp
expansion where m <= l. */
for (j = 0; j <= i; j++) {
p0 = (complex double *) PyArray_GETPTR2 (m0, i, j);
p1 = (complex double *) PyArray_GETPTR2 (m1, i, j);
*p0 += *p1;
}
}
Py_RETURN_NONE;
}
//initialization of BallTree object
// argument is a single array of size [D,N]
static int
BallTree_init(BallTreeObject *self, PyObject *args, PyObject *kwds){
//we use goto statements : all variables should be declared up front
PyObject *arg=NULL;
PyObject *arr=NULL;
long int leaf_size=20;
static char *kwlist[] = {"x", "leafsize", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwds, "O|l", kwlist,
&arg,&leaf_size) )
goto fail;
if(leaf_size <= 0){
PyErr_SetString(PyExc_ValueError,
"BallTree : leaf size must be greater than zero");
goto fail;
}
//view this object as an array of doubles,
arr = PyArray_FROM_OTF(arg,NPY_DOUBLE,0);
if(arr==NULL)
goto fail;
//check that it is 2D
if( PyArray_NDIM(arr) != 2)
goto fail;
if(self != NULL){
//create the list of points
self->size = PyArray_DIMS(arr)[0];
self->dim = PyArray_DIMS(arr)[1];
int inc = PyArray_STRIDES(arr)[1]/PyArray_DESCR(arr)->elsize;
self->Points = new std::vector<BallTree_Point*>(self->size);
for(int i=0;i<self->size;i++)
self->Points->at(i) = new BallTree_Point(arr,
(double*)PyArray_GETPTR2(arr,i,0),
inc, PyArray_DIM(arr,1));
self->tree = new BallTree<BallTree_Point>(*(self->Points),
leaf_size);
}
self->data = arr;
//Py_DECREF(arr);
return 0;
fail:
Py_XDECREF(arr);
return -1;
}
static PyObject *
gicdat_vpdistM(PyObject *self, PyObject *args)
{
PyObject *strains;
PyArrayObject *distances;
int i, j;
long n, rshape[2];
float cost, d;
if (!PyArg_ParseTuple(args, "Of", &strains, &cost))
return NULL;
n = (long)PySequence_Length(strains);
rshape[0] = n;
rshape[1] = n;
distances = PyArray_ZEROS(2, rshape, NPY_FLOAT64, 0);
Py_INCREF(distances);
for (i=0;i<n-1;i++) {
for (j=i+1;j<n;j++) {
d = victorDist(PySequence_GetItem(strains, i), PySequence_GetItem(strains, j), cost);
*(double *)PyArray_GETPTR2(distances, i, j) = d;
*(double *)PyArray_GETPTR2(distances, j, i) = d;
}
}
return distances;
}
/*==========================================================================*/
PyObject *treeinit(PyObject *self, PyObject *args)
{
int nBucket, nbodies, nTreeBitsLo=14, nTreebitsHi=18;
int i, j;
KD kd = (KD)malloc(sizeof(struct kdContext));
PyObject *pos; // Nx3 Numpy array of positions
PyObject *mass; // Nx1 Numpy array of masses
double dTheta=0.7 * 2.0/sqrt(3.0);
float fPeriod[3], fTemp, fSoft;
for (i=0; i<3; i++) fPeriod[i] = 1.0;
if (!PyArg_ParseTuple(args, "OOiif", &pos, &mass, &nBucket, &nbodies, &fSoft))
return NULL;
/*nbodies = PyArray_DIM(mass, 0);*/
kdInitialize(kd, nbodies, nBucket, dTheta, nTreeBitsLo, nTreebitsHi, fPeriod);
/*
** Allocate particles.
*/
kd->pStorePRIVATE = (PARTICLE *)malloc(nbodies*kd->iParticleSize);
assert(kd->pStorePRIVATE != NULL);
Py_BEGIN_ALLOW_THREADS
for (i=0; i < nbodies; i++)
{
for (j=0; j < 3; j++) {
fTemp = (float)*((double *)PyArray_GETPTR2(pos, i, j));
kd->pStorePRIVATE[i].r[j] = fTemp;
kd->pStorePRIVATE[i].a[j] = 0;
}
fTemp = (float)*((double *)PyArray_GETPTR1(mass, i));
kd->pStorePRIVATE[i].fMass = fTemp;
kd->pStorePRIVATE[i].fPot = 0;
kd->pStorePRIVATE[i].fSoft = fSoft;
}
kdTreeBuild(kd, nBucket);
Py_END_ALLOW_THREADS
return PyCObject_FromVoidPtr((void *)kd, NULL);
}
static PyObject *mandelbrot(PyObject *self, PyObject *args) {
PyArrayObject *result;
PyArrayObject *x_coords;
PyArrayObject *y_coords;
unsigned int size;
int x,y;
if(!PyArg_ParseTuple(args, "O!(O!O!)I", &PyArray_Type, &result, &PyArray_Type, &x_coords, &PyArray_Type, &y_coords, &size))
return NULL;
#pragma omp parallel for private(y)
for(x = 0; x < size; x++) {
for(y = 0; y < size; y++) {
*(char *)(PyArray_GETPTR2(result, y, x)) = solve(*(float *)(PyArray_GETPTR1(x_coords, x)), *(float *)(PyArray_GETPTR1(y_coords, y)));
}
}
return Py_BuildValue("i",1);
}
/**
* Returns a V3D object from the Python object given
* to the converter
* @returns A newly constructed V3D object converted
* from the PyObject.
*/
Kernel::Matrix<double> PyObjectToMatrix::operator()() {
if (m_alreadyMatrix) {
return extract<Kernel::Matrix<double>>(m_obj)();
}
PyArrayObject *ndarray = (PyArrayObject *)PyArray_View(
(PyArrayObject *)m_obj.ptr(), PyArray_DescrFromType(NPY_DOUBLE),
&PyArray_Type);
const auto shape = PyArray_DIMS(ndarray);
npy_intp nx(shape[0]), ny(shape[1]);
Kernel::Matrix<double> matrix(nx, ny);
for (npy_intp i = 0; i < nx; i++) {
auto row = matrix[i];
for (npy_intp j = 0; j < ny; j++) {
row[j] = *((double *)PyArray_GETPTR2(ndarray, i, j));
}
}
return matrix;
}
static PyObject* spherematch_kdtree_build(PyObject* self, PyObject* args) {
int N, D;
int i,j;
int Nleaf, treeoptions, treetype;
kdtree_t* kd;
double* data;
PyArrayObject* x;
if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &x))
return NULL;
if (PyArray_NDIM(x) != 2) {
PyErr_SetString(PyExc_ValueError, "array must be two-dimensional");
return NULL;
}
if (PyArray_TYPE(x) != PyArray_DOUBLE) {
PyErr_SetString(PyExc_ValueError, "array must contain doubles");
return NULL;
}
N = (int)PyArray_DIM(x, 0);
D = (int)PyArray_DIM(x, 1);
if (D > 10) {
PyErr_SetString(PyExc_ValueError, "maximum dimensionality is 10: maybe you need to transpose your array?");
return NULL;
}
data = malloc(N * D * sizeof(double));
for (i=0; i<N; i++) {
for (j=0; j<D; j++) {
double* pd = PyArray_GETPTR2(x, i, j);
data[i*D + j] = *pd;
}
}
Nleaf = 16;
treetype = KDTT_DOUBLE;
//treeoptions = KD_BUILD_SPLIT;
treeoptions = KD_BUILD_BBOX;
kd = kdtree_build(NULL, data, N, D, Nleaf,
treetype, treeoptions);
return Py_BuildValue("k", kd);
}
static PyObject *
gicdat_vpdistS(PyObject *self, PyObject *args)
{
PyObject *itrains, *jtrains;
PyArrayObject *distances;
int i, j;
long rshape[2];
float cost, d;
if (!PyArg_ParseTuple(args, "OOf", &itrains, &jtrains, &cost))
return NULL;
rshape[0] = (long)PySequence_Length(itrains);
rshape[1] = (long)PySequence_Length(jtrains);
distances = PyArray_ZEROS(2, rshape, NPY_FLOAT64, 0);
Py_INCREF(distances);
for (i=0;i<rshape[0];i++) {
for (j=0;j<rshape[1];j++) {
d = victorDist(PySequence_GetItem(itrains, i), PySequence_GetItem(jtrains, j), cost);
*(double *)PyArray_GETPTR2(distances, i, j) = d;
}
}
return distances;
}
static PyObject * cKeo_linear_interpolate(PyObject *self, PyObject *args){
PyObject *keo_arr, *data_list;
int strip_width,max_gap;
npy_intp keo_arr_dims[2], data_list_dims[1];
int keo_arr_num_dim, data_list_num_dim;
long int x,y,k;
int start_pix, end_pix, offset;
double gradient;
//parse the arguments passed to the function by Python - no increase in ref count
if(!PyArg_ParseTuple(args, "OOii", &keo_arr, &data_list, &strip_width, &max_gap)){ //no increase to the object's reference count
PyErr_SetString(PyExc_ValueError,"Invalid parameters");
return NULL;
}
//check that we have been passed array objects
if (!PyArray_Check(keo_arr)){
PyErr_SetString(PyExc_TypeError,"Invalid type for keo_arr argument. Expecting Numpy array.");
return NULL;
}
if (!PyArray_Check(data_list)){
PyErr_SetString(PyExc_TypeError,"Invalid type for data_list argument. Expecting Numpy array.");
return NULL;
}
//check that keo_arr is two dimensional
keo_arr_num_dim = PyArray_NDIM(keo_arr);
if (keo_arr_num_dim != 2){
PyErr_SetString(PyExc_ValueError,"Keogram array must be two dimensional");
return NULL;
}
//check that data_list is 1D
data_list_num_dim = PyArray_NDIM(data_list);
if (data_list_num_dim != 1){
PyErr_SetString(PyExc_ValueError,"Data list array must be one dimensional");
return NULL;
}
//check that both are well behaved
if (! PyArray_ISBEHAVED(keo_arr)){
PyErr_SetString(PyExc_ValueError,"Keogram array must be well behaved");
return NULL;
}
if (! PyArray_ISBEHAVED(data_list)){
PyErr_SetString(PyExc_ValueError,"Data list array must be well behaved");
return NULL;
}
//get the dimensions of the keogram and data_list
keo_arr_dims[0] = PyArray_DIM(keo_arr, 0);
keo_arr_dims[1] = PyArray_DIM(keo_arr, 1);
data_list_dims[0] = PyArray_DIM(data_list, 0);
//do the interpolation
for(k=0;k<data_list_dims[0]-1;k++){
start_pix = *((int*)PyArray_GETPTR1(data_list,(int)k))+(strip_width/2);
end_pix = *((int*)PyArray_GETPTR1(data_list,((int)k)+1))-(strip_width/2);
//check that any interpolation is actually needed
if (start_pix == end_pix){
continue;
}
//check for missing data entries
if (end_pix-start_pix > max_gap){
continue; //don't interpolate over large gaps in the data.
}
for (y=0;y<keo_arr_dims[1];y++){
offset = keo_arr_dims[1]*y;
//gradient = (keo_data[(end_pix*y_stride)+(y*x_stride)] - keo_data[(start_pix*y_stride)+(y*x_stride)])/(double)(end_pix-start_pix);
gradient = (*((int*)PyArray_GETPTR2(keo_arr,end_pix,y))-*((int*)PyArray_GETPTR2(keo_arr,start_pix,y)))/(double)(end_pix-start_pix);
for(x=start_pix+1;x<end_pix;x++){
//keo_data[(x*y_stride)+(y*x_stride)] = keo_data[(start_pix*y_stride)+(y*x_stride)] + (x-start_pix)*gradient;
*((int*)PyArray_GETPTR2(keo_arr,x,y)) = *((int*)PyArray_GETPTR2(keo_arr,start_pix,y))+ (x-start_pix)*gradient;
}
}
}
Py_RETURN_NONE;
}
PyObject *computeColMeans( PyObject *i_funGetData
, PyObject *i_funHasMoreData
, double *i_chunkSize )
{
int i, j, nRow, nCol;
int numRow = 0;
double *col, *sumCol;
double factor = 0;
int whileIter = 0;
int colMeansDim[2];
void *ptrBuffer;
ticaC_numpyArrayDim dimChunk = NULL;
PyObject *o_colMeans = NULL;
PyArrayObject *dataChunk = NULL;
//PyArrayObject *colMeans = NULL;
double *colMeans;
while (call_funHasMoreData( i_funHasMoreData ))
{
whileIter++;
//free(dataChunk);
dataChunk = call_funGetData( i_funGetData, *i_chunkSize );
//return (PyObject*)dataChunk;
dimChunk = PyArray_DIMS( dataChunk );
numRow += dimChunk[0];
nRow = dimChunk[0];
nCol = dimChunk[1];
if (1 == whileIter)
{
colMeansDim[0] = 1;
colMeansDim[1] = dimChunk[1];
/*o_colMeans initialized as row vector*/
//colMeans = (PyArrayObject*)PyArray_FROM_OTF( o_colMeans, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY );
colMeans = (double *)malloc( sizeof( double ) * colMeansDim[0] * colMeansDim[1] );
memset( colMeans, 0, sizeof( double ) * colMeansDim[0] * colMeansDim[1] );
//for (i = 0; i < nCol; i++)
//{
// sumCol = (double*)PyArray_GETPTR2( colMeans, TICA_FIRST_ROW, i );
// *sumCol = 0;
//}
}
for (i = 0; i < nCol; i++)
{
col = (double*)PyArray_GETPTR2( dataChunk, TICA_FIRST_ROW, i );
//sumCol = (double*)PyArray_GETPTR2( colMeans, TICA_FIRST_ROW, i );
sumCol = (double*)TICA_GET_POINTER2D( colMeans, i, TICA_FIRST_COL, colMeansDim );
for (j = 0; j < nRow; j++, col += nCol)
{
*sumCol += *col;
}
//PyArray_free(sumCol);
//PyArray_free( col );
//free(col);
//free(sumCol);
}
//PyArray_free( col );
//PyArray_free( sumCol );
}
factor = 1.0 / (double)numRow;
for (i = 0; i < colMeansDim[1]; i++)
{
sumCol = (double*)TICA_GET_POINTER2D( colMeans, i, TICA_FIRST_COL, colMeansDim );
*sumCol *= factor;
}
o_colMeans = PyArray_SimpleNew( TICA_ARRAY_DIMENSION, colMeansDim, NPY_DOUBLE );
if (NULL == o_colMeans)
{
return NULL;
}
ptrBuffer = PyArray_DATA( (PyArrayObject*)o_colMeans );
memcpy( ptrBuffer, colMeans, sizeof( double ) * colMeansDim[0] * colMeansDim[1] );
free(colMeans);
//o_colMeans = (PyObject*)colMeans;
Py_INCREF( o_colMeans );
return o_colMeans;
}
//Wrapper for Brute-Force neighbor search
static PyObject*
BallTree_knn_brute(PyObject *self, PyObject *args, PyObject *kwds){
long int k = 1;
std::vector<BallTree_Point*> Points;
PyObject *arg1 = NULL;
PyObject *arg2 = NULL;
PyObject *arr1 = NULL;
PyObject *arr2 = NULL;
PyObject *nbrs = NULL;
long int* nbrs_data;
PyArrayIterObject *arr2_iter = NULL;
PyArrayIterObject *nbrs_iter = NULL;
static char *kwlist[] = {"x", "pt", "k", NULL};
npy_intp* dim;
int nd, pt_size, pt_inc;
long int N;
long int D;
//parse arguments. If k is not provided, the default is 1
if(!PyArg_ParseTupleAndKeywords(args,kwds,"OO|l",kwlist,
&arg1,&arg2,&k))
goto fail;
//First array should be a 2D array of doubles
arr1 = PyArray_FROM_OTF(arg1,NPY_DOUBLE,0);
if(arr1==NULL)
goto fail;
if( PyArray_NDIM(arr1) != 2){
PyErr_SetString(PyExc_ValueError,
"x must be two dimensions");
goto fail;
}
//Second array should be a 1D array of doubles
arr2 = PyArray_FROM_OTF(arg2,NPY_DOUBLE,0);
if(arr2==NULL)
goto fail;
nd = PyArray_NDIM(arr2);
if(nd == 0){
PyErr_SetString(PyExc_ValueError,
"pt cannot be zero-sized array");
goto fail;
}
pt_size = PyArray_DIM(arr2,nd-1);
//Check that dimensions match
N = PyArray_DIMS(arr1)[0];
D = PyArray_DIMS(arr1)[1];
if( pt_size != D ){
PyErr_SetString(PyExc_ValueError,
"pt must be same dimension as x");
goto fail;
}
//check the value of k
if(k<1){
PyErr_SetString(PyExc_ValueError,
"k must be a positive integer");
goto fail;
}
if(k>N){
PyErr_SetString(PyExc_ValueError,
"k must be less than the number of points");
goto fail;
}
//create a neighbors array and distance array
dim = new npy_intp[nd];
for(int i=0; i<nd-1;i++)
dim[i] = PyArray_DIM(arr2,i);
dim[nd-1] = k;
nbrs = (PyObject*)PyArray_SimpleNew(nd,dim,PyArray_LONG);
delete[] dim;
if(nbrs==NULL)
goto fail;
//create iterators to cycle through points
nd-=1;
arr2_iter = (PyArrayIterObject*)PyArray_IterAllButAxis(arr2,&nd);
nbrs_iter = (PyArrayIterObject*)PyArray_IterAllButAxis(nbrs,&nd);
nd+=1;
if( arr2_iter==NULL ||
nbrs_iter==NULL ||
(arr2_iter->size != nbrs_iter->size) ){
PyErr_SetString(PyExc_ValueError,
"failure constructing iterators");
goto fail;
}
pt_inc = PyArray_STRIDES(arr2)[nd-1] / PyArray_DESCR(arr2)->elsize;
if(PyArray_STRIDES(nbrs)[nd-1] != PyArray_DESCR(nbrs)->elsize ){
PyErr_SetString(PyExc_ValueError,
"nbrs not allocated as a C-array");
goto fail;
}
//create the list of points
pt_inc = PyArray_STRIDES(arr1)[1]/PyArray_DESCR(arr1)->elsize;
Points.resize(N);
for(int i=0;i<N;i++)
Points[i] = new BallTree_Point(arr1,
(double*)PyArray_GETPTR2(arr1,i,0),
pt_inc, PyArray_DIM(arr1,1));
//iterate through points and determine neighbors
//warning: if nbrs is not a C-array, or if we're not iterating
// over the last dimension, this may cause a seg fault.
while(arr2_iter->index < arr2_iter->size){
BallTree_Point Query_Point(arr2,
(double*)PyArray_ITER_DATA(arr2_iter),
pt_inc,pt_size);
nbrs_data = (long int*)(PyArray_ITER_DATA(nbrs_iter));
BruteForceNeighbors(Points, Query_Point,
k, nbrs_data );
PyArray_ITER_NEXT(arr2_iter);
PyArray_ITER_NEXT(nbrs_iter);
}
for(int i=0;i<N;i++)
delete Points[i];
//if only one neighbor is requested, then resize the neighbors array
if(k==1){
PyArray_Dims dims;
dims.ptr = PyArray_DIMS(arr2);
dims.len = PyArray_NDIM(arr2)-1;
//PyArray_Resize returns None - this needs to be picked
// up and dereferenced.
PyObject *NoneObj = PyArray_Resize( (PyArrayObject*)nbrs, &dims,
0, NPY_ANYORDER );
if (NoneObj == NULL){
goto fail;
}
Py_DECREF(NoneObj);
}
return nbrs;
fail:
Py_XDECREF(arr1);
Py_XDECREF(arr2);
Py_XDECREF(nbrs);
Py_XDECREF(arr2_iter);
Py_XDECREF(nbrs_iter);
return NULL;
}
PyObject* radonShiftCorrelate(PyObject *self, PyObject *args) {
/*
** Inputs:
** (1) 2D numpy array of first Radon transform (using doubles)
** (2) 2D numpy array of second Radon transform (using doubles)
** (3) interger of search radius (in pixels)
** Modifies:
** nothing
** Outputs:
** 1D numpy array containing correlation values of angle (y-axis) and shift (x-axis)
*/
Py_Initialize();
/* Simple test loop */
/*struct item *headtwo = NULL;
int rad;
for(rad=0; rad<8; rad++) {
struct item *headtwo = NULL;
headtwo = getAnglesList(rad, headtwo);
fprintf(stderr, "radius %d, num angles %d\n", rad, list_length(headtwo));
//if (rad < 3) print_list(headtwo);
delete_list(headtwo);
}
fprintf(stderr, "Finish test radii\n");*/
/* End simple test loop */
/* Parse tuples separately since args will differ between C fcns */
int radius;
PyArrayObject *radonone, *radontwo;
//fprintf(stderr, "Start variable read\n");
if (!PyArg_ParseTuple(args, "OOi", &radonone, &radontwo, &radius))
return NULL;
if (radonone == NULL) return NULL;
if (radontwo == NULL) return NULL;
/* Create list of angles to check, number of angles is based on radius */
struct item *head = NULL;
head = getAnglesList(radius, head);
int numangles = list_length(head);
//fprintf(stderr, "Finish get angles, radius %d, num angles %d\n", radius, list_length(head));
/* dimensions error checking */
if (radonone->dimensions[0] != radontwo->dimensions[0]
|| radonone->dimensions[1] != radontwo->dimensions[1]) {
fprintf(stderr, "\n%s: Radon arrays are of different dimensions (%d,%d) vs. (%d,%d)\n\n",
__FILE__,
radonone->dimensions[0], radonone->dimensions[1],
radontwo->dimensions[0], radontwo->dimensions[1]);
return NULL;
}
/* Get the dimensions of the input */
int numrows = radonone->dimensions[0];
int numcols = radonone->dimensions[1];
//fprintf(stderr, "Dimensions: %d rows by %d cols\n", numrows, numcols);
if (2*radius > numcols) {
fprintf(stderr, "\n%s: Shift diameter %d is larger than Radon array dimensions (%d,%d))\n\n",
__FILE__, radius*2,
radontwo->dimensions[0], radontwo->dimensions[1]);
return NULL;
}
/*
double test;
test = *((double *)PyArray_GETPTR2(radonone, 0, 0));
fprintf(stderr, "Array 1 test value at (0,0) = %.3f\n", test);
test = *((double *)PyArray_GETPTR2(radontwo, 0, 0));
fprintf(stderr, "Array 2 test value at (0,0) = %.3f\n", test);
*/
/* Determine the dimensions for the output */
npy_intp outdims[2] = {numrows, numangles};
/* Make a new double matrix of desired dimensions */
//fprintf(stderr, "Creating output array of dimensions %d and %d\n", numrows, numangles);
import_array(); // this is required to use PyArray_New() and PyArray_SimpleNew()
PyArrayObject *output;
//output = (PyArrayObject *) PyArray_New(&PyArray_Type, 2, outdims, NPY_DOUBLE, NULL, NULL, 0, 0, NULL);
output = (PyArrayObject *) PyArray_SimpleNew(2, outdims, NPY_DOUBLE);
//fprintf(stderr, "Finish create output array\n");
/* Do the calculation. */
int row;
#pragma omp parallel for
for (row=0; row<numrows; row++) {
struct item *current;
int anglecol=0;
for(current=head; current!=NULL; current=current->next) {
/* calculation of cross correlation */
double rawshift = radius*sin(current->angle);
int shift;
if (rawshift > 0) {
shift = (int) (radius*sin(current->angle) + 0.5);
} else {
shift = (int) (radius*sin(current->angle) - 0.5);
}
//fprintf(stderr, "raw = %.3f; shift = %d\n", rawshift, shift);
int i,j,ishift,jshift;
double onevalue, twovalue;
double outputsum = 0.0;
//fprintf(stderr, "Position %d, %d\n", row, anglecol);
for (i=0; i<numrows; i++) {
for (j=0; j<numcols; j++) {
ishift = i+row;
if (ishift >= numrows) ishift -= numrows; // wrap overflow values
if (ishift < 0) ishift += numrows; // wrap negative values
jshift = j+shift;
if (jshift >= numcols) jshift -= numcols; // wrap overflow values
if (jshift < 0) jshift += numcols; // wrap negative values
//fprintf(stderr, "%d, %d <-", ishift, jshift);
onevalue = *((double *)PyArray_GETPTR2(radonone, ishift, jshift));
//fprintf(stderr, "-> %d, %d\n", i, j);
twovalue = *((double *)PyArray_GETPTR2(radontwo, i, j));
outputsum += onevalue * twovalue;
}}
outputsum /= (numrows*numcols);
*(double *) PyArray_GETPTR2(output, row, anglecol) = outputsum;
/* interation variables */
anglecol++;
} }
/* clean up any memory leaks */
delete_list(head);
return PyArray_Return(output);
}
PyObject* PackOut( IData *GS, double *ICs,
int state, double *stats, double hlast, int idid ) {
int i, j;
int numEvents = 0;
/* Python objects to be returned at end of integration */
PyObject *OutObj = NULL; /* Overall PyTuple output object */
PyObject *TimeOut = NULL; /* Trajectory times */
PyObject *PointsOut = NULL; /* Trajectory points */
PyObject *StatsOut = NULL; /* */
PyObject *EventPointsOutTuple = NULL;
PyObject *EventTimesOutTuple = NULL;
PyObject *AuxOut = NULL;
assert(GS);
if( state == FAILURE ) {
Py_INCREF(Py_None);
return Py_None;
}
_init_numpy();
EventPointsOutTuple = PyTuple_New(GS->nEvents);
assert(EventPointsOutTuple);
EventTimesOutTuple = PyTuple_New(GS->nEvents);
assert(EventTimesOutTuple);
/* Copy out points */
npy_intp p_dims[2] = {GS->phaseDim, GS->pointsIdx};
PointsOut = PyArray_SimpleNew(2, p_dims, NPY_DOUBLE);
assert(PointsOut);
/* WARNING -- modified the order of the dimensions here! */
for(i = 0; i < GS->pointsIdx; i++) {
for(j = 0; j < GS->phaseDim; j++) {
*((double *) PyArray_GETPTR2((PyArrayObject *)PointsOut, j, i)) = GS->gPoints[i][j];
}
}
/* Copy out times */
npy_intp t_dims[1] = {GS->timeIdx};
TimeOut = PyArray_SimpleNew(1, t_dims, NPY_DOUBLE);
assert(TimeOut);
for( i = 0; i < GS->timeIdx; i++ ) {
*((double *) PyArray_GETPTR1((PyArrayObject *)TimeOut, i)) = GS->gTimeV[i];
}
/* Copy out auxilliary points */
npy_intp au_dims[2] = {GS->nAuxVars, GS->pointsIdx};
if( GS->nAuxVars > 0 && GS->calcAux == 1 ) {
/* WARNING: The order of the dimensions
is switched here! */
AuxOut = PyArray_SimpleNew(2, au_dims, NPY_DOUBLE);
assert(AuxOut);
for( i = 0; i < GS->pointsIdx; i++ ) {
for( j = 0; j < GS->nAuxVars; j++ ) {
*((double *) PyArray_GETPTR2((PyArrayObject *)AuxOut, j, i)) = GS->gAuxPoints[i][j];
}
}
}
/* Allocate and copy out integration stats */
/* Number of stats different between Radau, Dopri */
npy_intp s_dims[1] = {7};
StatsOut = PyArray_SimpleNewFromData(1, s_dims, NPY_DOUBLE, stats);
assert(StatsOut);
/* Setup the tuple for the event points and times (separate from trajectory).
Must remember to keep the reference count for Py_None up to date*/
for( i = 0; i < GS->nEvents; i++ ) {
PyTuple_SetItem(EventPointsOutTuple, i, Py_None);
Py_INCREF(Py_None);
PyTuple_SetItem(EventTimesOutTuple, i, Py_None);
Py_INCREF(Py_None);
}
/* Count the number of events for which something was caught */
numEvents = 0;
for( i = 0; i < GS->haveActive; i++ ) {
if( GS->gCheckableEventCounts[i] > 0 ) {
numEvents++;
}
}
/* Only allocate memory for events if something was caught */
if( numEvents > 0 ) {
/* Lower reference count for Py_None from INCREFs in initialization of
OutTuples above. Decrease by 2*numevents) */
for( i = 0; i < numEvents; i++ ) {
Py_DECREF(Py_None);
Py_DECREF(Py_None);
}
for( i = 0; i < GS->haveActive; i++ ) {
if( GS->gCheckableEventCounts[i] > 0 ) {
int k, l;
/* Get the number of points caught for this event, and which one (in list
of all active and nonactive events) it is */
int EvtCt = GS->gCheckableEventCounts[i], EvtIdx = GS->gCheckableEvents[i];
npy_intp et_dims[1] = {EvtCt};
npy_intp ep_dims[2] = {GS->phaseDim, EvtCt};
/* Copy the event times, points into a python array */
PyObject *e_times = PyArray_SimpleNewFromData(1, et_dims, NPY_DOUBLE, GS->gEventTimes[i]);
assert(e_times);
PyObject *e_points = PyArray_SimpleNew(2, ep_dims, NPY_DOUBLE);
assert(e_points);
for( k = 0; k < EvtCt; k++ ) {
for( l = 0; l < GS->phaseDim; l++ ) {
*((double *) PyArray_GETPTR2((PyArrayObject *)e_points, l, k)) = GS->gEventPoints[i][l][k];
}
}
/* The returned python tuple has slots for all events, not just active
ones, which is why we insert into the tuple at position EvtIdx */
PyTuple_SetItem(EventPointsOutTuple, EvtIdx, e_points);
PyTuple_SetItem(EventTimesOutTuple, EvtIdx, e_times);
}
}
}
/* Pack points, times, stats, events, etc. into a 7 tuple */
OutObj = PyTuple_New(8);
assert(OutObj);
PyTuple_SetItem(OutObj, 0, (PyObject *)TimeOut);
PyTuple_SetItem(OutObj, 1, (PyObject *)PointsOut);
if( GS->nAuxVars > 0 && GS->calcAux == 1) {
PyTuple_SetItem(OutObj, 2, (PyObject *)AuxOut);
}
else {
PyTuple_SetItem(OutObj, 2, Py_None);
Py_INCREF(Py_None);
}
PyTuple_SetItem(OutObj, 3, (PyObject *)StatsOut);
PyTuple_SetItem(OutObj, 4, PyFloat_FromDouble(hlast));
PyTuple_SetItem(OutObj, 5, PyFloat_FromDouble((double)idid));
PyTuple_SetItem(OutObj, 6, EventTimesOutTuple);
PyTuple_SetItem(OutObj, 7, EventPointsOutTuple);
return OutObj;
}
static PyObject *
_fasttrips_find_pathset(PyObject *self, PyObject *args)
{
PyArrayObject *pyo;
fasttrips::PathSpecification path_spec;
int hyperpath_i, outbound_i, trace_i;
char *user_class, *purpose, *access_mode, *transit_mode, *egress_mode;
if (!PyArg_ParseTuple(args, "iiiisssssiiidi", &path_spec.iteration_, &path_spec.passenger_id_, &path_spec.path_id_, &hyperpath_i,
&user_class, &purpose, &access_mode, &transit_mode, &egress_mode,
&path_spec.origin_taz_id_, &path_spec.destination_taz_id_,
&outbound_i, &path_spec.preferred_time_, &trace_i)) {
return NULL;
}
path_spec.hyperpath_ = (hyperpath_i != 0);
path_spec.outbound_ = (outbound_i != 0);
path_spec.trace_ = (trace_i != 0);
path_spec.user_class_ = user_class;
path_spec.purpose_ = purpose;
path_spec.access_mode_ = access_mode;
path_spec.transit_mode_= transit_mode;
path_spec.egress_mode_ = egress_mode;
fasttrips::PathSet pathset;
fasttrips::PerformanceInfo perf_info = { 0, 0, 0, 0, 0, 0};
pathfinder.findPathSet(path_spec, pathset, perf_info);
// count links
int num_links = 0;
for (fasttrips::PathSet::const_iterator psi=pathset.begin(); psi != pathset.end(); ++psi) {
num_links += (int)psi->first.size();
}
// package for returning. We'll separate ints and doubles.
npy_intp dims_int[2];
dims_int[0] = num_links;
dims_int[1] = 7; // path_num, stop_id, deparr_mode_, trip_id_, stop_succpred_, seq_, seq_succpred_
PyArrayObject *ret_int = (PyArrayObject *)PyArray_SimpleNew(2, dims_int, NPY_INT32);
npy_intp dims_double[2];
dims_double[0] = num_links;
dims_double[1] = 5; // label_, deparr_time_, link_time_, cost_, arrdep_time_
PyArrayObject *ret_double = (PyArrayObject *)PyArray_SimpleNew(2, dims_double, NPY_DOUBLE);
// costs and probability
npy_intp dims_paths[2];
dims_paths[0] = pathset.size();
dims_paths[1] = 2;
PyArrayObject *ret_paths = (PyArrayObject*)PyArray_SimpleNew(2, dims_paths, NPY_DOUBLE);
int ind = 0;
int path_num = 0;
for (fasttrips::PathSet::const_iterator psi=pathset.begin(); psi != pathset.end(); ++psi) {
const fasttrips::Path& path = psi->first;
*(npy_double*)PyArray_GETPTR2(ret_paths, path_num, 0) = path.cost();
*(npy_double*)PyArray_GETPTR2(ret_paths, path_num, 1) = psi->second.probability_;
for (int link_num = 0; link_num < path.size(); ++link_num) {
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 0) = path_num;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 1) = path[link_num].first;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 2) = path[link_num].second.deparr_mode_;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 3) = path[link_num].second.trip_id_;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 4) = path[link_num].second.stop_succpred_;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 5) = path[link_num].second.seq_;
*(npy_int32*)PyArray_GETPTR2(ret_int, ind, 6) = path[link_num].second.seq_succpred_;
*(npy_double*)PyArray_GETPTR2(ret_double, ind, 0) = 0.0; // TODO: label
*(npy_double*)PyArray_GETPTR2(ret_double, ind, 1) = path[link_num].second.deparr_time_;
*(npy_double*)PyArray_GETPTR2(ret_double, ind, 2) = path[link_num].second.link_time_;
*(npy_double*)PyArray_GETPTR2(ret_double, ind, 3) = path[link_num].second.cost_;
*(npy_double*)PyArray_GETPTR2(ret_double, ind, 4) = path[link_num].second.arrdep_time_;
ind += 1;
}
path_num += 1;
}
PyObject *returnobj = Py_BuildValue("(OOOiiiillll)",ret_int,ret_double,ret_paths, pathfinder.processNumber(),
perf_info.label_iterations_, perf_info.num_labeled_stops_, perf_info.max_process_count_,
perf_info.milliseconds_labeling_, perf_info.milliseconds_enumerating_,
perf_info.workingset_bytes_, perf_info.privateusage_bytes_);
return returnobj;
}
static PyObject * uberseg_direct(PyObject* self, PyObject* args) {
int dmin=0,d=0,x=0,y=0,k=0,imin=0;
PyObject* seg = NULL;
PyObject* weight = NULL;
int Nx;
int Ny;
int object_number;
PyObject* obj_inds_x = NULL;
PyObject* obj_inds_y = NULL;
int Ninds;
int *ptrx,*ptry,*ptrs;
float *ptrw;
int dx;
if (!PyArg_ParseTuple(args, (char*)"OOiiiOOi", &seg, &weight, &Nx, &Ny, &object_number, &obj_inds_x, &obj_inds_y, &Ninds)) {
return NULL;
}
for(x=0;x<Nx;++x) {
for(y=0;y<Ny;++y) {
//shortcuts
ptrs = (int*)PyArray_GETPTR2(seg,x,y);
if(*ptrs == object_number)
continue;
if(*ptrs > 0 && *ptrs != object_number) {
ptrw = (float*)PyArray_GETPTR2(weight,x,y);
*ptrw = 0.0;
continue;
}
//must do direct search
imin = -1;
for(k=0;k<Ninds;++k) {
ptrx = (int*)PyArray_GETPTR1(obj_inds_x,k);
ptry = (int*)PyArray_GETPTR1(obj_inds_y,k);
dx = x-(*ptrx);
d = dx*dx;
dx = y-(*ptry);
d += dx*dx;
if(d < dmin || imin == -1) {
dmin = d;
imin = k;
}
}
if(imin == -1) {
continue;
}
ptrx = (int*)PyArray_GETPTR1(obj_inds_x,imin);
ptry = (int*)PyArray_GETPTR1(obj_inds_y,imin);
ptrs = (int*)PyArray_GETPTR2(seg,*ptrx,*ptry);
if(*ptrs != object_number) {
ptrw = (float*)PyArray_GETPTR2(weight,x,y);
*ptrw = 0.0;
}
}
}
Py_INCREF(Py_None);
return Py_None;
}
static PyObject * uberseg_tree(PyObject* self, PyObject* args) {
int x=0,y=0,k=0,segmin=0;
float dmin=0,r=0,dx=0,d=0;
float pos[2],fac=0;
PyObject* seg = NULL;
PyObject* weight = NULL;
int Nx;
int Ny;
int object_number;
PyObject* obj_inds_x = NULL;
PyObject* obj_inds_y = NULL;
int Ninds;
int *ptrx,*ptry,*ptrs;
float *ptrw;
struct mytype *obj = NULL;
struct fast3tree *tree = NULL;
struct fast3tree_results *res = NULL;
if (!PyArg_ParseTuple(args, (char*)"OOiiiOOi", &seg, &weight, &Nx, &Ny, &object_number, &obj_inds_x, &obj_inds_y, &Ninds)) {
return NULL;
}
obj = (struct mytype *)malloc(sizeof(struct mytype)*Ninds);
if(obj == NULL) exit(1);
for(k=0;k<Ninds;++k) {
ptrx = (int*)PyArray_GETPTR1(obj_inds_x,k);
ptry = (int*)PyArray_GETPTR1(obj_inds_y,k);
ptrs = (int*)PyArray_GETPTR2(seg,*ptrx,*ptry);
obj[k].idx = k;
obj[k].seg = *ptrs;
obj[k].pos[0] = (float)(*ptrx);
obj[k].pos[1] = (float)(*ptry);
}
tree = fast3tree_init(Ninds,obj);
if(tree == NULL) exit(1);
res = fast3tree_results_init();
if(res == NULL) exit(1);
float maxr = 1.1*sqrt(Nx*Nx+Ny*Ny);
for(x=0;x<Nx;++x) {
for(y=0;y<Ny;++y) {
//shortcuts
ptrs = (int*)PyArray_GETPTR2(seg,x,y);
if(*ptrs == object_number)
continue;
if(*ptrs > 0 && *ptrs != object_number) {
ptrw = (float*)PyArray_GETPTR2(weight,x,y);
*ptrw = 0.0;
continue;
}
//must do search
pos[0] = (float)x;
pos[1] = (float)y;
r = fast3tree_find_next_closest_distance(tree,res,pos);
fac = 1.0/1.1;
do {
fac *= 1.1;
fast3tree_results_clear(res);
fast3tree_find_sphere(tree,res,pos,r*fac);
} while(res->num_points == 0 && r*fac <= maxr);
segmin = -1;
for(k=0;k<res->num_points;++k) {
dx = res->points[k]->pos[0]-x;
d = dx*dx;
dx = res->points[k]->pos[1]-y;
d += dx*dx;
if(segmin == -1 || d < dmin) {
segmin = res->points[k]->seg;
dmin = d;
}
}
if(segmin == -1) {
continue;
}
if(segmin != object_number) {
ptrw = (float*)PyArray_GETPTR2(weight,x,y);
*ptrw = 0.0;
}
}
}
free(obj);
fast3tree_free(&tree);
fast3tree_results_free(res);
Py_INCREF(Py_None);
return Py_None;
}
static PyObject * shift(PyObject * self,
PyObject * args, PyObject * kwds) {
PyArrayObject * pos, * arowvectors, *abox, *amin, *amax;
static char * kwlist[] = {"POS", "ROWVECTORS", "BOX", "MIN", "MAX", NULL};
if(!PyArg_ParseTupleAndKeywords(args, kwds, "O!O!O!O!O!", kwlist,
&PyArray_Type, &pos,
&PyArray_Type, &arowvectors,
&PyArray_Type, &abox,
&PyArray_Type, &amin,
&PyArray_Type, &amax))
return NULL;
int D = PyArray_DIMS(pos)[1];
npy_intp length = PyArray_DIMS(pos)[0];
int i,j;
npy_intp p;
amin = (PyArrayObject *) PyArray_Cast(amin, NPY_INT);
amax = (PyArrayObject *) PyArray_Cast(amax, NPY_INT);
abox = (PyArrayObject *) PyArray_Cast(abox, NPY_FLOAT);
arowvectors = (PyArrayObject *) PyArray_Cast(arowvectors, NPY_FLOAT);
int min[3] = {0}, max[3] = {0};
float box[3];
float rowvectors[3][3];
for(i = 0; i < D; i++) {
min[i] = *((int*)PyArray_GETPTR1(amin, i));
max[i] = *((int*)PyArray_GETPTR1(amax, i));
box[i] = *((float*)PyArray_GETPTR1(abox, i));
for(j = 0; j < D; j++) {
rowvectors[i][j] = *((float*)PyArray_GETPTR2(arowvectors, i, j));
}
}
#if 0
printf("box = %f %f %f\n", box[0], box[1], box[2]);
printf("min = %d %d %d\n", min[0], min[1], min[2]);
printf("max = %d %d %d\n", max[0], max[1], max[2]);
printf("rowvectors = %f %f %f\n", rowvectors[0][0], rowvectors[0][1], rowvectors[0][2]);
printf("rowvectors = %f %f %f\n", rowvectors[1][0], rowvectors[1][1], rowvectors[1][2]);
printf("rowvectors = %f %f %f\n", rowvectors[2][0], rowvectors[2][1], rowvectors[2][2]);
printf("D = %d\n", D);
#endif
int I[3] = {0,0,0};
int failed_count = 0;
#pragma omp parallel for private(I) reduction(+: failed_count)
for(p = 0; p < length; p++) {
int i;
float * ppos[3];
float shifted[3];
float original[3];
for(i = 0; i < D; i++) {
ppos[i] = (float *) PyArray_GETPTR2(pos, p, i);
original[i] = *(ppos[i]);
}
tryshift(original, shifted, rowvectors, I, D);
if(!inbox(shifted, box, D)) {
int newI[3];
for(newI[0] = min[0]; newI[0] <= max[0]; newI[0]++)
for(newI[1] = min[1]; newI[1] <= max[1]; newI[1]++)
for(newI[2] = min[2]; newI[2] <= max[2]; newI[2]++) {
tryshift(original, shifted, rowvectors, newI, D);
if(inbox(shifted, box, D)) {
for(i = 0; i < D; i++) {
I[i] = newI[i];
}
goto out_of_here;
}
}
out_of_here:;
}
if(!inbox(shifted, box, D)) {
printf("%f %f %f to ", original[0], original[1], original[2]);
printf("%f %f %f faild\n", shifted[0], shifted[1], shifted[2]);
for(i = 0; i < D; i++) {
shifted[i] = original[i];
}
failed_count++;
}
for(i = 0; i < D; i++) {
*(ppos[i]) = shifted[i];
}
}
#if 0
printf("failed = %d\n", failed_count);
#endif
Py_DECREF(abox);
Py_DECREF(amax);
Py_DECREF(amin);
Py_DECREF(arowvectors);
Py_RETURN_NONE;
}
Py::Object
_path_module::point_in_path_collection(const Py::Tuple& args)
{
args.verify_length(9);
//segments, trans, clipbox, colors, linewidths, antialiaseds
double x = Py::Float(args[0]);
double y = Py::Float(args[1]);
double radius = Py::Float(args[2]);
agg::trans_affine master_transform = py_to_agg_transformation_matrix(args[3].ptr());
Py::SeqBase<Py::Object> paths = args[4];
Py::SeqBase<Py::Object> transforms_obj = args[5];
Py::SeqBase<Py::Object> offsets_obj = args[6];
agg::trans_affine offset_trans = py_to_agg_transformation_matrix(args[7].ptr());
bool filled = Py::Boolean(args[8]);
PyArrayObject* offsets = (PyArrayObject*)PyArray_FromObject(
offsets_obj.ptr(), PyArray_DOUBLE, 0, 2);
if (!offsets ||
(PyArray_NDIM(offsets) == 2 && PyArray_DIM(offsets, 1) != 2) ||
(PyArray_NDIM(offsets) == 1 && PyArray_DIM(offsets, 0) != 0))
{
Py_XDECREF(offsets);
throw Py::ValueError("Offsets array must be Nx2");
}
size_t Npaths = paths.length();
size_t Noffsets = offsets->dimensions[0];
size_t N = std::max(Npaths, Noffsets);
size_t Ntransforms = std::min(transforms_obj.length(), N);
size_t i;
// Convert all of the transforms up front
typedef std::vector<agg::trans_affine> transforms_t;
transforms_t transforms;
transforms.reserve(Ntransforms);
for (i = 0; i < Ntransforms; ++i)
{
agg::trans_affine trans = py_to_agg_transformation_matrix
(transforms_obj[i].ptr(), false);
trans *= master_transform;
transforms.push_back(trans);
}
Py::List result;
agg::trans_affine trans;
for (i = 0; i < N; ++i)
{
PathIterator path(paths[i % Npaths]);
if (Ntransforms)
{
trans = transforms[i % Ntransforms];
}
else
{
trans = master_transform;
}
if (Noffsets)
{
double xo = *(double*)PyArray_GETPTR2(offsets, i % Noffsets, 0);
double yo = *(double*)PyArray_GETPTR2(offsets, i % Noffsets, 1);
offset_trans.transform(&xo, &yo);
trans *= agg::trans_affine_translation(xo, yo);
}
if (filled)
{
if (::point_in_path(x, y, path, trans))
result.append(Py::Int((int)i));
}
else
{
if (::point_on_path(x, y, radius, path, trans))
result.append(Py::Int((int)i));
}
}
return result;
}
Py::Object
_path_module::get_path_collection_extents(const Py::Tuple& args)
{
args.verify_length(5);
//segments, trans, clipbox, colors, linewidths, antialiaseds
agg::trans_affine master_transform = py_to_agg_transformation_matrix(args[0].ptr());
Py::SeqBase<Py::Object> paths = args[1];
Py::SeqBase<Py::Object> transforms_obj = args[2];
Py::Object offsets_obj = args[3];
agg::trans_affine offset_trans = py_to_agg_transformation_matrix(args[4].ptr(), false);
PyArrayObject* offsets = NULL;
double x0, y0, x1, y1, xm, ym;
try
{
offsets = (PyArrayObject*)PyArray_FromObject(
offsets_obj.ptr(), PyArray_DOUBLE, 0, 2);
if (!offsets ||
(PyArray_NDIM(offsets) == 2 && PyArray_DIM(offsets, 1) != 2) ||
(PyArray_NDIM(offsets) == 1 && PyArray_DIM(offsets, 0) != 0))
{
throw Py::ValueError("Offsets array must be Nx2");
}
size_t Npaths = paths.length();
size_t Noffsets = offsets->dimensions[0];
size_t N = std::max(Npaths, Noffsets);
size_t Ntransforms = std::min(transforms_obj.length(), N);
size_t i;
// Convert all of the transforms up front
typedef std::vector<agg::trans_affine> transforms_t;
transforms_t transforms;
transforms.reserve(Ntransforms);
for (i = 0; i < Ntransforms; ++i)
{
agg::trans_affine trans = py_to_agg_transformation_matrix
(transforms_obj[i].ptr(), false);
trans *= master_transform;
transforms.push_back(trans);
}
// The offset each of those and collect the mins/maxs
x0 = std::numeric_limits<double>::infinity();
y0 = std::numeric_limits<double>::infinity();
x1 = -std::numeric_limits<double>::infinity();
y1 = -std::numeric_limits<double>::infinity();
xm = std::numeric_limits<double>::infinity();
ym = std::numeric_limits<double>::infinity();
agg::trans_affine trans;
for (i = 0; i < N; ++i)
{
PathIterator path(paths[i % Npaths]);
if (Ntransforms)
{
trans = transforms[i % Ntransforms];
}
else
{
trans = master_transform;
}
if (Noffsets)
{
double xo = *(double*)PyArray_GETPTR2(offsets, i % Noffsets, 0);
double yo = *(double*)PyArray_GETPTR2(offsets, i % Noffsets, 1);
offset_trans.transform(&xo, &yo);
trans *= agg::trans_affine_translation(xo, yo);
}
::get_path_extents(path, trans, &x0, &y0, &x1, &y1, &xm, &ym);
}
}
catch (...)
{
Py_XDECREF(offsets);
throw;
}
Py_XDECREF(offsets);
Py::Tuple result(4);
result[0] = Py::Float(x0);
result[1] = Py::Float(y0);
result[2] = Py::Float(x1);
result[3] = Py::Float(y1);
return result;
}
