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Merge pull request #455 from mraspaud/feature-nogil-gradient
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Closes undefined
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@djhoese
djhoese authored Nov 7, 2022
2 parents 87a803a + 8ba117b commit 5f53ecd
Showing 1 changed file with 41 additions and 38 deletions.
79 changes: 41 additions & 38 deletions pyresample/gradient/_gradient_search.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -94,15 +94,15 @@ ctypedef void (*FN)(const DTYPE_t[:, :, :] data, int l0, int p0, double dl, doub

@cython.boundscheck(False)
@cython.wraparound(False)
cpdef one_step_gradient_search(np.ndarray[DTYPE_t, ndim=3] data,
np.ndarray[DTYPE_t, ndim=2] src_x,
np.ndarray[DTYPE_t, ndim=2] src_y,
np.ndarray[DTYPE_t, ndim=2] xl,
np.ndarray[DTYPE_t, ndim=2] xp,
np.ndarray[DTYPE_t, ndim=2] yl,
np.ndarray[DTYPE_t, ndim=2] yp,
np.ndarray[DTYPE_t, ndim=2] dst_x,
np.ndarray[DTYPE_t, ndim=2] dst_y,
cpdef one_step_gradient_search(const DTYPE_t [:, :, :] data,
DTYPE_t [:, :] src_x,
DTYPE_t [:, :] src_y,
DTYPE_t [:, :] xl,
DTYPE_t [:, :] xp,
DTYPE_t [:, :] yl,
DTYPE_t [:, :] yp,
DTYPE_t [:, :] dst_x,
DTYPE_t [:, :] dst_y,
method='bilinear'):
"""Gradient search, simple case variant."""
cdef FN fun
Expand All @@ -119,16 +119,17 @@ cpdef one_step_gradient_search(np.ndarray[DTYPE_t, ndim=3] data,
# output image array --> needs to be (lines, pixels) --> y,x
cdef np.ndarray[DTYPE_t, ndim = 3] image = np.full([z_size, y_size, x_size], np.nan, dtype=DTYPE)
cdef np.ndarray[size_t, ndim = 1] elements = np.arange(x_size, dtype=np.uintp)
one_step_gradient_search_no_gil(data,
src_x, src_y,
xl, xp, yl, yp,
dst_x, dst_y,
x_size, y_size,
fun, image,
elements)
image = np.full([z_size, y_size, x_size], np.nan, dtype=DTYPE)
cdef DTYPE_t [:, :, :] image_view = image
cdef size_t [:] elements = np.arange(x_size, dtype=np.uintp)
with nogil:
one_step_gradient_search_no_gil(data,
src_x, src_y,
xl, xp, yl, yp,
dst_x, dst_y,
x_size, y_size,
fun, image_view,
elements)
# return the output image
return image
Expand Down Expand Up @@ -213,14 +214,14 @@ cdef void one_step_gradient_search_no_gil(const DTYPE_t[:, :, :] data,
@cython.boundscheck(False)
@cython.wraparound(False)
cpdef one_step_gradient_indices(np.ndarray[DTYPE_t, ndim=2] src_x,
np.ndarray[DTYPE_t, ndim=2] src_y,
np.ndarray[DTYPE_t, ndim=2] xl,
np.ndarray[DTYPE_t, ndim=2] xp,
np.ndarray[DTYPE_t, ndim=2] yl,
np.ndarray[DTYPE_t, ndim=2] yp,
np.ndarray[DTYPE_t, ndim=2] dst_x,
np.ndarray[DTYPE_t, ndim=2] dst_y):
cpdef one_step_gradient_indices(DTYPE_t [:, :] src_x,
DTYPE_t [:, :] src_y,
DTYPE_t [:, :] xl,
DTYPE_t [:, :] xp,
DTYPE_t [:, :] yl,
DTYPE_t [:, :] yp,
DTYPE_t [:, :] dst_x,
DTYPE_t [:, :] dst_y):
"""Gradient search, simple case variant, returning float indices.

This is appropriate for monotonous gradients only, i.e. not modis or viirs in satellite projection.
Expand All @@ -232,17 +233,19 @@ cpdef one_step_gradient_indices(np.ndarray[DTYPE_t, ndim=2] src_x,
cdef size_t x_size = dst_x.shape[1]

# output indices arrays --> needs to be (lines, pixels) --> y,x
cdef np.ndarray[DTYPE_t, ndim = 3] indices = np.full([2, y_size, x_size], np.nan, dtype=DTYPE)
cdef np.ndarray[size_t, ndim = 1] elements = np.arange(x_size, dtype=np.uintp)
indices = np.full([2, y_size, x_size], np.nan, dtype=DTYPE)
cdef DTYPE_t [:, :, :] indices_view = indices
cdef size_t [:] elements = np.arange(x_size, dtype=np.uintp)

# fake_data is not going to be used anyway as we just fill in the indices
cdef np.ndarray[DTYPE_t, ndim = 3] fake_data = np.full([1, 1, 1], np.nan, dtype=DTYPE)

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,
elements)
cdef DTYPE_t [:, :, :] fake_data = np.full([1, 1, 1], np.nan, dtype=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,
elements)
return indices

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