diff --git a/pyresample/gradient/__init__.py b/pyresample/gradient/__init__.py index f4de8a4b..bb4fefd5 100644 --- a/pyresample/gradient/__init__.py +++ b/pyresample/gradient/__init__.py @@ -414,14 +414,14 @@ def parallel_gradient_search(data, src_x, src_y, dst_x, dst_y, else: is_pad = False res = dask.delayed(_gradient_resample_data)( - arr.astype(np.float64), + arr, src_x[i], src_y[i], src_gradient_xl[i], src_gradient_xp[i], src_gradient_yl[i], src_gradient_yp[i], dst_x[i], dst_y[i], method=method) res = da.from_delayed(res, (num_bands, ) + dst_x[i].shape, - dtype=np.float64).astype(arr.dtype) + dtype=arr.dtype) if dst_mosaic_locations[i] in chunks: if not is_pad: chunks[dst_mosaic_locations[i]].append(res) diff --git a/pyresample/gradient/_gradient_search.pyx b/pyresample/gradient/_gradient_search.pyx index 838c73b3..51075490 100644 --- a/pyresample/gradient/_gradient_search.pyx +++ b/pyresample/gradient/_gradient_search.pyx @@ -23,18 +23,26 @@ import numpy as np +cimport cython cimport numpy as np +from libc.math cimport fabs, isinf -DTYPE = np.double ctypedef np.double_t DTYPE_t -cimport cython -from libc.math cimport fabs, isinf + +ctypedef fused data_type: + double + float + +ctypedef double float_index +f_index_dtype = float +ctypedef int i_index_type + np.import_array() @cython.boundscheck(False) @cython.wraparound(False) -cdef inline void nn(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, DTYPE_t[:] res) noexcept nogil: +cdef inline void nn(const data_type[:, :, :] data, i_index_type l0, int p0, double dl, double dp, int lmax, int pmax, data_type[:] res) noexcept nogil: cdef int nnl, nnp cdef size_t z_size = res.shape[0] cdef size_t i @@ -54,7 +62,7 @@ cdef inline void nn(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, doub @cython.boundscheck(False) @cython.wraparound(False) -cdef inline void bil(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, DTYPE_t[:] res) noexcept nogil: +cdef inline void bil(const data_type[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, data_type[:] res) noexcept nogil: cdef int l_a, l_b, p_a, p_b cdef double w_l, w_p cdef size_t z_size = res.shape[0] @@ -84,18 +92,18 @@ cdef inline void bil(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, dou @cython.boundscheck(False) @cython.wraparound(False) -cdef inline void indices_xy(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, DTYPE_t[:] res) noexcept nogil: +cdef inline void indices_xy(const data_type[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, data_type[:] res) noexcept nogil: cdef int nnl, nnp cdef size_t z_size = res.shape[0] cdef size_t i res[1] = dl + l0 res[0] = dp + p0 -ctypedef void (*FN)(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, DTYPE_t[:] res) noexcept nogil +ctypedef void (*FN)(const data_type[:, :, :] data, int l0, int p0, double dl, double dp, int lmax, int pmax, data_type[:] res) noexcept nogil @cython.boundscheck(False) @cython.wraparound(False) -cpdef one_step_gradient_search(const DTYPE_t [:, :, :] data, +cpdef one_step_gradient_search(const data_type[:, :, :] data, DTYPE_t [:, :] src_x, DTYPE_t [:, :] src_y, DTYPE_t [:, :] xl, @@ -118,10 +126,14 @@ cpdef one_step_gradient_search(const DTYPE_t [:, :, :] data, cdef size_t y_size = dst_y.shape[0] cdef size_t x_size = dst_x.shape[1] + if data_type is double: + dtype = np.float64 + else: + dtype = np.float32 # output image array --> needs to be (lines, pixels) --> y,x - image = np.full([z_size, y_size, x_size], np.nan, dtype=DTYPE) - cdef DTYPE_t [:, :, :] image_view = image + image = np.full([z_size, y_size, x_size], np.nan, dtype=dtype) + cdef data_type[:, :, :] image_view = image with nogil: one_step_gradient_search_no_gil(data, src_x, src_y, @@ -135,7 +147,7 @@ cpdef one_step_gradient_search(const DTYPE_t [:, :, :] data, @cython.boundscheck(False) @cython.wraparound(False) @cython.cdivision(True) -cdef void one_step_gradient_search_no_gil(const DTYPE_t[:, :, :] data, +cdef void one_step_gradient_search_no_gil(const data_type[:, :, :] data, const DTYPE_t[:, :] src_x, const DTYPE_t[:, :] src_y, const DTYPE_t[:, :] xl, @@ -147,7 +159,7 @@ cdef void one_step_gradient_search_no_gil(const DTYPE_t[:, :, :] data, const size_t x_size, const size_t y_size, FN fun, - DTYPE_t[:, :, :] result_array) noexcept nogil: + data_type[:, :, :] result_array) noexcept nogil: # pixel max ---> data is expected in [lines, pixels] cdef int pmax = src_x.shape[1] - 1 @@ -239,17 +251,17 @@ cpdef one_step_gradient_indices(DTYPE_t [:, :] src_x, cdef size_t x_size = dst_x.shape[1] # output indices arrays --> needs to be (lines, pixels) --> y,x - indices = np.full([2, y_size, x_size], np.nan, dtype=DTYPE) - cdef DTYPE_t [:, :, :] indices_view = indices + indices = np.full([2, y_size, x_size], np.nan, dtype=f_index_dtype) + cdef float_index [:, :, :] indices_view_result = indices # fake_data is not going to be used anyway as we just fill in the indices - cdef DTYPE_t [:, :, :] fake_data = np.full([1, 1, 1], np.nan, dtype=DTYPE) + cdef float_index [:, :, :] fake_data = np.full([1, 1, 1], np.nan, dtype=f_index_dtype) with nogil: - one_step_gradient_search_no_gil(fake_data, - src_x, src_y, - xl, xp, yl, yp, - dst_x, dst_y, - x_size, y_size, - indices_xy, indices_view) + one_step_gradient_search_no_gil[float_index](fake_data, + src_x, src_y, + xl, xp, yl, yp, + dst_x, dst_y, + x_size, y_size, + indices_xy, indices_view_result) return indices