Preprocessing of grids

When resampling is performed repeatedly to the same grid significant execution time can be save by preprocessing grid information.

Preprocessing for grid resampling

Using the function generate_quick_linesample_arrays or generate_nearest_neighbour_linesample_arrays from pyresample.utils arrays containing the rows and cols indices used to calculate the result in image.resample_area_quick or resample_area_nearest_neighbour can be obtained. These can be fed to the method get_array_from_linesample of an ImageContainer object to obtain the resample result.

>>> import numpy
>>> from pyresample import utils, image, geometry
>>> area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
...                                {'a': '6378144.0', 'b': '6356759.0',
...                                 'lat_0': '50.00', 'lat_ts': '50.00',
...                                 'lon_0': '8.00', 'proj': 'stere'},
...                                800, 800,
...                                [-1370912.72, -909968.64,
...                                 1029087.28, 1490031.36])
>>> msg_area = geometry.AreaDefinition('msg_full', 'Full globe MSG image 0 degrees',
...                                'msg_full',
...                                {'a': '6378169.0', 'b': '6356584.0',
...                                 'h': '35785831.0', 'lon_0': '0',
...                                 'proj': 'geos'},
...                                3712, 3712,
...                                [-5568742.4, -5568742.4,
...                                 5568742.4, 5568742.4])
>>> data = numpy.ones((3712, 3712))
>>> msg_con = image.ImageContainer(data, msg_area)
>>> row_indices, col_indices = \
...            utils.generate_nearest_neighbour_linesample_arrays(msg_area, area_def, 50000)
>>> result = msg_con.get_array_from_linesample(row_indices, col_indices)

The numpy arrays returned by generate_*_linesample_arrays can be and used with the ImageContainer.get_array_from_linesample method when the same resampling is to be performed again thus eliminating the need for calculating the reprojection.

Numpy arrays can be saved and loaded using numpy.save and numpy.load.