# Geographic filtering

The module pyresample.geo_filter contains classes to filter geo data

## GridFilter

Allows for filtering of data based on a geographic mask. The filtering uses a bucket sampling approach.

The following example shows how to select data falling in the upper left and lower right quadrant of a full globe Plate Carrée projection using an 8x8 filter mask

    >>> import numpy as np
    >>> from pyresample import geometry, geo_filter
    >>> lons = np.array([-170, -30, 30, 170])
    >>> lats = np.array([20, -40, 50, -80])
    >>> swath_def = geometry.SwathDefinition(lons, lats)
    >>> data = np.array([1, 2, 3, 4])
    >>> filter_area = geometry.AreaDefinition('test', 'test', 'test',
    ...         {'proj' : 'eqc', 'lon_0' : 0.0, 'lat_0' : 0.0},
    ...           8, 8,
    ...          (-20037508.34, -10018754.17, 20037508.34, 10018754.17)
    ...             )
    >>> filter = np.array([[1, 1, 1, 1, 0, 0, 0, 0],
    ...         [1, 1, 1, 1, 0, 0, 0, 0],
    ...         [1, 1, 1, 1, 0, 0, 0, 0],
    ...         [1, 1, 1, 1, 0, 0, 0, 0],
    ...         [0, 0, 0, 0, 1, 1, 1, 1],
    ...         [0, 0, 0, 0, 1, 1, 1, 1],
    ...         [0, 0, 0, 0, 1, 1, 1, 1],
    ...         [0, 0, 0, 0, 1, 1, 1, 1],
    ...         ])
    >>> grid_filter = geo_filter.GridFilter(filter_area, filter)
    >>> swath_def_filtered, data_filtered = grid_filter.filter(swath_def, data)

Input swath_def and data must match as described in Resampling of swath data.

The returned data will always have a 1D geometry_def and if multiple channels are present the filtered data will have the shape (number_of_points, channels).