pyresample.data_reduce module

Reduce data sets based on geographical information.

pyresample.data_reduce.get_valid_index_from_cartesian_grid(cart_grid, lons, lats, radius_of_influence)

Calculate relevant data indices using coarse data reduction of swath data by comparison with cartesian grid.

Parameters:

• chart_grid (numpy array) – Grid of area cartesian coordinates

• lons (numpy array) – Swath lons

• lats (numpy array) – Swath lats

• data (numpy array) – Swath data

• radius_of_influence (float) – Cut off distance in meters

Returns:

valid_index – Boolean array of same size as lons and lats indicating relevant indices

Return type:

numpy array

pyresample.data_reduce.get_valid_index_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, radius_of_influence)

Find relevant indices from grid boundaries using the winding number theorem.

pyresample.data_reduce.get_valid_index_from_lonlat_grid(grid_lons, grid_lats, lons, lats, radius_of_influence)

Calculate relevant data indices using coarse data reduction of swath data by comparison with lon lat grid.

Parameters:

• chart_grid (numpy array) – Grid of area cartesian coordinates

• lons (numpy array) – Swath lons

• lats (numpy array) – Swath lats

• data (numpy array) – Swath data

• radius_of_influence (float) – Cut off distance in meters

Returns:

valid_index – Boolean array of same size as lon and lat indicating relevant indices

Return type:

numpy array

pyresample.data_reduce.swath_from_cartesian_grid(cart_grid, lons, lats, data, radius_of_influence)

Make coarse data reduction of swath data by comparison with cartesian grid.

Parameters:

• chart_grid (numpy array) – Grid of area cartesian coordinates

• lons (numpy array) – Swath lons

• lats (numpy array) – Swath lats

• data (numpy array) – Swath data

• radius_of_influence (float) – Cut off distance in meters

Returns:

(lons, lats, data) – Reduced swath data and coordinate set

Return type:

list of numpy arrays

pyresample.data_reduce.swath_from_lonlat_boundaries(boundary_lons, boundary_lats, lons, lats, data, radius_of_influence)

Make coarse data reduction of swath data by comparison with lon lat boundary.

Parameters:

• boundary_lons (numpy array) – Grid of area lons

• boundary_lats (numpy array) – Grid of area lats

• lons (numpy array) – Swath lons

• lats (numpy array) – Swath lats

• data (numpy array) – Swath data

• radius_of_influence (float) – Cut off distance in meters

Returns:

(lons, lats, data) – Reduced swath data and coordinate set

Return type:

list of numpy arrays

pyresample.data_reduce.swath_from_lonlat_grid(grid_lons, grid_lats, lons, lats, data, radius_of_influence)

Make coarse data reduction of swath data by comparison with lon lat grid.

Parameters:

• grid_lons (numpy array) – Grid of area lons

• grid_lats (numpy array) – Grid of area lats

• lons (numpy array) – Swath lons

• lats (numpy array) – Swath lats

• data (numpy array) – Swath data

• radius_of_influence (float) – Cut off distance in meters

Returns:

(lons, lats, data) – Reduced swath data and coordinate set

Return type:

list of numpy arrays