/usr/lib/python2.7/dist-packages/pyresample/grid.py is in python-pyresample 1.2.9-1.
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#
# Copyright (C) 2010, 2014, 2015 Esben S. Nielsen
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Resample image from one projection to another
using nearest neighbour method in cartesian projection coordinate systems"""
from __future__ import absolute_import
import numpy as np
from pyresample import geometry, _spatial_mp
try:
range = xrange
except NameError:
pass
def get_image_from_linesample(row_indices, col_indices, source_image,
fill_value=0):
"""Samples from image based on index arrays.
Parameters
----------
row_indices : numpy array
Row indices. Dimensions must match col_indices
col_indices : numpy array
Col indices. Dimensions must match row_indices
source_image : numpy array
Source image
fill_value : int or None, optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
Returns
-------
image_data : numpy array
Resampled image
"""
# mask out non valid row and col indices
row_mask = (row_indices >= 0) * (row_indices < source_image.shape[0])
col_mask = (col_indices >= 0) * (col_indices < source_image.shape[1])
valid_rows = row_indices * row_mask
valid_cols = col_indices * col_mask
# free memory
del(row_indices)
del(col_indices)
# get valid part of image
target_image = source_image[valid_rows, valid_cols]
# free memory
del(valid_rows)
del(valid_cols)
# create mask for valid data points
valid_data = row_mask * col_mask
if valid_data.ndim != target_image.ndim:
for i in range(target_image.ndim - valid_data.ndim):
valid_data = np.expand_dims(valid_data, axis=valid_data.ndim)
# free memory
del(row_mask)
del(col_mask)
# fill the non valid part of the image
if fill_value is not None:
target_filled = (target_image * valid_data +
(1 - valid_data) * fill_value)
else:
if np.ma.is_masked(target_image):
mask = ((1 - valid_data) | target_image.mask)
else:
mask = (1 - valid_data)
target_filled = np.ma.array(target_image, mask=mask)
return target_filled.astype(target_image.dtype)
def get_linesample(lons, lats, source_area_def, nprocs=1):
"""Returns index row and col arrays for resampling
Parameters
----------
lons : numpy array
Lons. Dimensions must match lats
lats : numpy array
Lats. Dimensions must match lons
source_area_def : object
Source definition as AreaDefinition object
nprocs : int, optional
Number of processor cores to be used
Returns
-------
(row_indices, col_indices) : tuple of numpy arrays
Arrays for resampling area by array indexing
"""
# Proj.4 definition of source area projection
if nprocs > 1:
source_proj = _spatial_mp.Proj_MP(**source_area_def.proj_dict)
else:
source_proj = _spatial_mp.Proj(**source_area_def.proj_dict)
# get cartesian projection values from longitude and latitude
source_x, source_y = source_proj(lons, lats, nprocs=nprocs)
# Find corresponding pixels (element by element conversion of ndarrays)
source_pixel_x = (source_area_def.pixel_offset_x +
source_x / source_area_def.pixel_size_x).astype(np.int32)
source_pixel_y = (source_area_def.pixel_offset_y -
source_y / source_area_def.pixel_size_y).astype(np.int32)
return source_pixel_y, source_pixel_x
def get_image_from_lonlats(lons, lats, source_area_def, source_image_data,
fill_value=0, nprocs=1):
"""Samples from image based on lon lat arrays
using nearest neighbour method in cartesian projection coordinate systems.
Parameters
----------
lons : numpy array
Lons. Dimensions must match lats
lats : numpy array
Lats. Dimensions must match lons
source_area_def : object
Source definition as AreaDefinition object
source_image_data : numpy array
Source image data
fill_value : int or None, optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used
Returns
-------
image_data : numpy array
Resampled image data
"""
source_pixel_y, source_pixel_x = get_linesample(lons, lats,
source_area_def,
nprocs=nprocs)
# Return target image
return get_image_from_linesample(source_pixel_y, source_pixel_x,
source_image_data, fill_value)
def get_resampled_image(target_area_def, source_area_def, source_image_data,
fill_value=0, nprocs=1, segments=None):
"""Resamples image using nearest neighbour method in cartesian
projection coordinate systems.
Parameters
----------
target_area_def : object
Target definition as AreaDefinition object
source_area_def : object
Source definition as AreaDefinition object
source_image_data : numpy array
Source image data
fill_value : {int, None} optional
Set undetermined pixels to this value.
If fill_value is None a masked array is returned
with undetermined pixels masked
nprocs : int, optional
Number of processor cores to be used
segments : {int, None} optional
Number of segments to use when resampling.
If set to None an estimate will be calculated.
Returns
-------
image_data : numpy array
Resampled image data
"""
if not isinstance(target_area_def, geometry.AreaDefinition):
raise TypeError('target_area_def must be of type AreaDefinition')
if not isinstance(source_area_def, geometry.AreaDefinition):
raise TypeError('source_area_def must be of type AreaDefinition')
if not isinstance(source_image_data, (np.ndarray,
np.ma.core.MaskedArray)):
raise TypeError('source_image must be of type ndarray'
' or a masked array.')
# Calculate number of segments if needed
if segments is None:
rows = target_area_def.y_size
cut_off = 500
if rows > cut_off:
segments = int(rows / cut_off)
else:
segments = 1
if segments > 1:
# Iterate through segments
for i, target_slice in enumerate(geometry._get_slice(segments,
target_area_def.shape)):
# Select data from segment with slice
lons, lats = target_area_def.get_lonlats(
nprocs=nprocs, data_slice=target_slice)
# Calculate partial result
next_result = get_image_from_lonlats(lons, lats, source_area_def,
source_image_data,
fill_value, nprocs)
# Build result iteratively
if i == 0:
# First iteration
result = next_result
else:
result = np.row_stack((result, next_result))
return result
else:
# Get lon lat arrays of target area
lons, lats = target_area_def.get_lonlats(nprocs)
# Get target image
return get_image_from_lonlats(lons, lats, source_area_def,
source_image_data, fill_value, nprocs)
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