def __interp2_thread(rx, ry, data, zx, zy, pmap, weight, nx, ny, mask):
"""
internal routine: 2D interpolation thread for parallel interpolation
"""
data = np.ma.fix_invalid(data, copy=False)
# Convolve the water over the land
ksize = 2 * np.round(
np.sqrt((nx / np.ma.median(np.ma.diff(rx)))**2 +
(ny / np.ma.median(np.ma.diff(ry.T)))**2)) + 1
if ksize < _ksize_range[0]:
warn("nx or ny values are too small for stable OA, {:f}".format(ksize))
ksize = _ksize_range[0]
elif ksize > _ksize_range[1]:
warn("nx or ny values are too large for stable OA, {:f}".format(ksize))
ksize = _ksize_range[1]
data = seapy.convolve_mask(data, ksize=ksize, copy=False)
# Interpolate the field and return the result
with timeout(minutes=30):
res, pm = seapy.oasurf(rx, ry, data, zx, zy, pmap, weight, nx, ny)
return np.ma.masked_where(np.logical_or(mask == 0,
np.abs(res) > 9e4),
res,
copy=False)
def __interp2_thread(rx, ry, data, zx, zy, pmap, weight, nx, ny, mask):
"""
internal routine: 2D interpolation thread for parallel interpolation
"""
data = np.ma.fix_invalid(data, copy=False)
# Convolve the water over the land
ksize = 2 * np.round(np.sqrt((nx / np.median(np.diff(rx)))**2 +
(ny / np.median(np.diff(ry.T)))**2)) + 1
if ksize < _ksize_range[0]:
warn("nx or ny values are too small for stable OA, {:f}".format(ksize))
ksize = _ksize_range[0]
elif ksize > _ksize_range[1]:
warn("nx or ny values are too large for stable OA, {:f}".format(ksize))
ksize = _ksize_range[1]
data = seapy.convolve_mask(data, ksize=ksize, copy=False)
# Interpolate the field and return the result
with timeout(minutes=30):
res, pm = seapy.oasurf(rx, ry, data, zx, zy, pmap, weight, nx, ny)
return np.ma.masked_where(np.logical_or(mask == 0, np.abs(res) > 9e4), res,
copy=False)
def __interp3_thread(rx, ry, rz, data, zx, zy, zz, pmap,
weight, nx, ny, mask, up_factor=1.0, down_factor=1.0):
"""
internal routine: 3D interpolation thread for parallel interpolation
"""
# Make the mask 3D
mask = seapy.adddim(mask, zz.shape[0])
data = np.ma.fix_invalid(data, copy=False)
# To avoid extrapolation, we are going to convolve ocean over the land
# and add a new top and bottom layer that replicates the data of the
# existing current and top. 1) iteratively convolve until we have
# filled most of the points, 2) Determine which way the
# depth goes and add/subtract new layers, and 3) fill in masked values
# from the layer above/below.
gradsrc = (rz[0, 1, 1] - rz[-1, 1, 1]) > 0
# Convolve the water over the land
ksize = 2 * np.round(np.sqrt((nx / np.median(np.diff(rx)))**2 +
(ny / np.median(np.diff(ry.T)))**2)) + 1
if ksize < _ksize_range[0]:
warn("nx or ny values are too small for stable OA, {:f}".format(ksize))
ksize = _ksize_range[0]
elif ksize > _ksize_range[1]:
warn("nx or ny values are too large for stable OA, {:f}".format(ksize))
ksize = _ksize_range[1]
# Iterate at most 5 times, but we will hopefully break out before that by
# checking if we have filled at least 40% of the bottom to be like
# the surface
bot = -1 if gradsrc else 0
top = 0 if gradsrc else -1
topmask = np.maximum(1, np.ma.count_masked(data[top, :, :]))
if np.ma.count_masked(data[bot, :, :]) > 0:
for iter in range(5):
# Check if we have most everything by checking the bottom
data = seapy.convolve_mask(data, ksize=ksize + iter, copy=False)
if topmask / np.maximum(1, np.ma.count_masked(data[bot, :, :])) > 0.4:
break
# Now fill vertically
nrz = np.zeros((data.shape[0] + 2, data.shape[1], data.shape[2]))
nrz[1:-1, :, :] = rz
nrz[bot, :, :] = rz[bot, :, :] - 5000
nrz[top, :, :] = 1
if not gradsrc:
# The first level is the bottom
# factor = down_factor
levs = np.arange(data.shape[0], 0, -1) - 1
else:
# The first level is the top
# factor = up_factor
levs = np.arange(0, data.shape[0])
# Fill in missing values where we have them from the shallower layer
for k in levs[1:]:
if np.ma.count_masked(data[k, :, :]) == 0:
continue
idx = np.nonzero(np.logical_xor(data.mask[k, :, :],
data.mask[k - 1, :, :]))
data.mask[k, idx[0], idx[1]] = data.mask[k - 1, idx[0], idx[1]]
data[k, idx[0], idx[1]] = data[k - 1, idx[0], idx[1]] * down_factor
# Add upper and lower boundaries
ndat = np.zeros((data.shape[0] + 2, data.shape[1], data.shape[2]))
ndat[bot, :, :] = data[bot, :, :].filled(np.nan) * down_factor
ndat[1:-1, :, :] = data.filled(np.nan)
ndat[top, :, :] = data[top, :, :].filled(np.nan) * up_factor
# Interpolate the field and return the result
with timeout(minutes=30):
if gradsrc:
res, pm = seapy.oavol(rx, ry, nrz[::-1, :, :], ndat[::-1, :, :],
zx, zy, zz, pmap, weight, nx, ny)
else:
res, pm = seapy.oavol(rx, ry, nrz, ndat, zx, zy, zz,
pmap, weight, nx, ny)
return np.ma.masked_where(np.logical_or(mask == 0, np.abs(res) > 9e4), res,
copy=False)
def __interp3_thread(rx, ry, rz, data, zx, zy, zz, pmap,
weight, nx, ny, mask, up_factor=1.0, down_factor=1.0):
"""
internal routine: 3D interpolation thread for parallel interpolation
"""
# Make the mask 3D
mask = seapy.adddim(mask, zz.shape[0])
data = np.ma.fix_invalid(data, copy=False)
# To avoid extrapolation, we are going to convolve ocean over the land
# and add a new top and bottom layer that replicates the data of the
# existing current and top. 1) iteratively convolve until we have
# filled most of the points, 2) Determine which way the
# depth goes and add/subtract new layers, and 3) fill in masked values
# from the layer above/below.
gradsrc = (rz[0, 1, 1] - rz[-1, 1, 1]) > 0
# Convolve the water over the land
ksize = 2 * np.round(np.sqrt((nx / np.median(np.diff(rx)))**2 +
(ny / np.median(np.diff(ry.T)))**2)) + 1
if ksize < _ksize_range[0]:
warn("nx or ny values are too small for stable OA, {:f}".format(ksize))
ksize = _ksize_range[0]
elif ksize > _ksize_range[1]:
warn("nx or ny values are too large for stable OA, {:f}".format(ksize))
ksize = _ksize_range[1]
# Iterate at most 5 times, but we will hopefully break out before that by
# checking if we have filled at least 40% of the bottom to be like
# the surface
bot = -1 if gradsrc else 0
top = 0 if gradsrc else -1
topmask = np.maximum(1, np.ma.count_masked(data[top, :, :]))
if np.ma.count_masked(data[bot, :, :]) > 0:
for iter in range(5):
# Check if we have most everything by checking the bottom
data = seapy.convolve_mask(data, ksize=ksize + iter, copy=False)
if topmask / np.maximum(1, np.ma.count_masked(data[bot, :, :])) > 0.4:
break
# Now fill vertically
nrz = np.zeros((data.shape[0] + 2, data.shape[1], data.shape[2]))
nrz[1:-1, :, :] = rz
nrz[bot, :, :] = rz[bot, :, :] - 5000
nrz[top, :, :] = 1
if not gradsrc:
# The first level is the bottom
# factor = down_factor
levs = np.arange(data.shape[0], 0, -1) - 1
else:
# The first level is the top
# factor = up_factor
levs = np.arange(0, data.shape[0])
# Fill in missing values where we have them from the shallower layer
for k in levs[1:]:
if np.ma.count_masked(data[k, :, :]) == 0:
continue
idx = np.nonzero(np.logical_xor(data.mask[k, :, :],
data.mask[k - 1, :, :]))
data.mask[k, idx[0], idx[1]] = data.mask[k - 1, idx[0], idx[1]]
data[k, idx[0], idx[1]] = data[k - 1, idx[0], idx[1]] * down_factor
# Add upper and lower boundaries
ndat = np.zeros((data.shape[0] + 2, data.shape[1], data.shape[2]))
ndat[bot, :, :] = data[bot, :, :].filled(np.nan) * down_factor
ndat[1:-1, :, :] = data.filled(np.nan)
ndat[top, :, :] = data[top, :, :].filled(np.nan) * up_factor
# Interpolate the field and return the result
with timeout(minutes=30):
if gradsrc:
res, pm = seapy.oavol(rx, ry, nrz[::-1, :, :], ndat[::-1, :, :],
zx, zy, zz, pmap, weight, nx, ny)
else:
res, pm = seapy.oavol(rx, ry, nrz, ndat, zx, zy, zz,
pmap, weight, nx, ny)
return np.ma.masked_where(np.logical_or(mask == 0, np.abs(res) > 9e4), res,
copy=False)