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# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import division
import numpy as np
cimport numpy as np
DTYPE = np.float
ctypedef np.float_t DTYPE_t
cdef extern from "numpy/npy_math.h" nogil:
bint npy_isnan(double x)
cimport cython
@cython.boundscheck(False) # turn off bounds-checking for entire function
def convolve1d_boundary_wrap(np.ndarray[DTYPE_t, ndim=1] f,
np.ndarray[DTYPE_t, ndim=1] g):
if g.shape[0] % 2 != 1:
raise ValueError("Convolution kernel must have odd dimensions")
assert f.dtype == DTYPE and g.dtype == DTYPE
cdef int nx = f.shape[0]
cdef int nkx = g.shape[0]
cdef int wkx = nkx // 2
cdef np.ndarray[DTYPE_t, ndim=1] fixed = np.empty([nx], dtype=DTYPE)
cdef np.ndarray[DTYPE_t, ndim=1] conv = np.empty([nx], dtype=DTYPE)
cdef unsigned int i, iii
cdef int ii
cdef int iimin, iimax
cdef DTYPE_t top, bot, ker, val
# release the GIL
with nogil:
# Need a first pass to replace NaN values with value convolved from
# neighboring values
for i in range(nx):
if npy_isnan(f[i]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
for ii in range(iimin, iimax):
iii = ii % nx
val = f[iii]
if not npy_isnan(val):
ker = g[<unsigned int>(wkx + ii - i)]
top += val * ker
bot += ker
if bot != 0.:
fixed[i] = top / bot
else:
fixed[i] = f[i]
else:
fixed[i] = f[i]
# Now run the proper convolution
for i in range(nx):
if not npy_isnan(fixed[i]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
for ii in range(iimin, iimax):
iii = ii % nx
val = fixed[iii]
ker = g[<unsigned int>(wkx + ii - i)]
if not npy_isnan(val):
top += val * ker
bot += ker
if bot != 0:
conv[i] = top / bot
else:
conv[i] = fixed[i]
else:
conv[i] = fixed[i]
# GIL acquired again here
return conv
@cython.boundscheck(False) # turn off bounds-checking for entire function
def convolve2d_boundary_wrap(np.ndarray[DTYPE_t, ndim=2] f,
np.ndarray[DTYPE_t, ndim=2] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
raise ValueError("Convolution kernel must have odd dimensions")
assert f.dtype == DTYPE and g.dtype == DTYPE
cdef int nx = f.shape[0]
cdef int ny = f.shape[1]
cdef int nkx = g.shape[0]
cdef int nky = g.shape[1]
cdef int wkx = nkx // 2
cdef int wky = nky // 2
cdef np.ndarray[DTYPE_t, ndim=2] fixed = np.empty([nx, ny], dtype=DTYPE)
cdef np.ndarray[DTYPE_t, ndim=2] conv = np.empty([nx, ny], dtype=DTYPE)
cdef unsigned int i, j, iii, jjj
cdef int ii, jj
cdef int iimin, iimax, jjmin, jjmax
cdef DTYPE_t top, bot, ker, val
# release the GIL
with nogil:
# Need a first pass to replace NaN values with value convolved from
# neighboring values
for i in range(nx):
for j in range(ny):
if npy_isnan(f[i, j]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
jjmin = j - wky
jjmax = j + wky + 1
for ii in range(iimin, iimax):
for jj in range(jjmin, jjmax):
iii = ii % nx
jjj = jj % ny
val = f[iii, jjj]
if not npy_isnan(val):
ker = g[<unsigned int>(wkx + ii - i),
<unsigned int>(wky + jj - j)]
top += val * ker
bot += ker
if bot != 0.:
fixed[i, j] = top / bot
else:
fixed[i, j] = f[i, j]
else:
fixed[i, j] = f[i, j]
# Now run the proper convolution
for i in range(nx):
for j in range(ny):
if not npy_isnan(fixed[i, j]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
jjmin = j - wky
jjmax = j + wky + 1
for ii in range(iimin, iimax):
for jj in range(jjmin, jjmax):
iii = ii % nx
jjj = jj % ny
val = fixed[iii, jjj]
ker = g[<unsigned int>(wkx + ii - i),
<unsigned int>(wky + jj - j)]
if not npy_isnan(val):
top += val * ker
bot += ker
if bot != 0:
conv[i, j] = top / bot
else:
conv[i, j] = fixed[i, j]
else:
conv[i, j] = fixed[i, j]
# GIl acquired again here
return conv
@cython.boundscheck(False) # turn off bounds-checking for entire function
def convolve3d_boundary_wrap(np.ndarray[DTYPE_t, ndim=3] f,
np.ndarray[DTYPE_t, ndim=3] g):
if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1 or g.shape[2] % 2 != 1:
raise ValueError("Convolution kernel must have odd dimensions")
assert f.dtype == DTYPE and g.dtype == DTYPE
cdef int nx = f.shape[0]
cdef int ny = f.shape[1]
cdef int nz = f.shape[2]
cdef int nkx = g.shape[0]
cdef int nky = g.shape[1]
cdef int nkz = g.shape[2]
cdef int wkx = nkx // 2
cdef int wky = nky // 2
cdef int wkz = nkz // 2
cdef np.ndarray[DTYPE_t, ndim=3] fixed = np.empty([nx, ny, nz], dtype=DTYPE)
cdef np.ndarray[DTYPE_t, ndim=3] conv = np.empty([nx, ny, nz], dtype=DTYPE)
cdef unsigned int i, j, k, iii, jjj, kkk
cdef int ii, jj, kk
cdef int iimin, iimax, jjmin, jjmax, kkmin, kkmax
cdef DTYPE_t top, bot, ker, val
# release the GIL
with nogil:
# Need a first pass to replace NaN values with value convolved from
# neighboring values
for i in range(nx):
for j in range(ny):
for k in range(nz):
if npy_isnan(f[i, j, k]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
jjmin = j - wky
jjmax = j + wky + 1
kkmin = k - wkz
kkmax = k + wkz + 1
for ii in range(iimin, iimax):
for jj in range(jjmin, jjmax):
for kk in range(kkmin, kkmax):
iii = ii % nx
jjj = jj % ny
kkk = kk % nz
val = f[iii, jjj, kkk]
if not npy_isnan(val):
ker = g[<unsigned int>(wkx + ii - i),
<unsigned int>(wky + jj - j),
<unsigned int>(wkz + kk - k)]
top += val * ker
bot += ker
if bot != 0.:
fixed[i, j, k] = top / bot
else:
fixed[i, j, k] = f[i, j, k]
else:
fixed[i, j, k] = f[i, j, k]
# Now run the proper convolution
for i in range(nx):
for j in range(ny):
for k in range(nz):
if not npy_isnan(fixed[i, j, k]):
top = 0.
bot = 0.
iimin = i - wkx
iimax = i + wkx + 1
jjmin = j - wky
jjmax = j + wky + 1
kkmin = k - wkz
kkmax = k + wkz + 1
for ii in range(iimin, iimax):
for jj in range(jjmin, jjmax):
for kk in range(kkmin, kkmax):
iii = ii % nx
jjj = jj % ny
kkk = kk % nz
val = fixed[iii, jjj, kkk]
ker = g[<unsigned int>(wkx + ii - i),
<unsigned int>(wky + jj - j),
<unsigned int>(wkz + kk - k)]
if not npy_isnan(val):
top += val * ker
bot += ker
if bot != 0:
conv[i, j, k] = top / bot
else:
conv[i, j, k] = fixed[i, j, k]
else:
conv[i, j, k] = fixed[i, j, k]
# GIL acquired again here
return conv
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