pytesmo.grid package

Submodules

pytesmo.grid.grids module

pytesmo.grid.netcdf module

pytesmo.grid.resample module

Created on Mar 25, 2014

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

pytesmo.grid.resample.hamming_window(radius, distances)[source]

Hamming window filter.

Parameters:
  • radius (float32) – Radius of the window.
  • distances (numpy.ndarray) – Array with distances.
Returns:

weights – Distance weights.

Return type:

numpy.ndarray

pytesmo.grid.resample.resample_to_grid(input_data, src_lon, src_lat, target_lon, target_lat, methods='nn', weight_funcs=None, min_neighbours=1, search_rad=18000, neighbours=8, fill_values=None)[source]

resamples data from dictionary of numpy arrays using pyresample to given grid. Searches for the neighbours and then resamples the data to the grid given in togrid if at least min_neighbours neighbours are found

Parameters:
  • input_data (dict of numpy.arrays) –
  • src_lon (numpy.array) – longitudes of the input data
  • src_lat (numpy.array) – src_latitudes of the input data
  • target_lon (numpy.array) – longitudes of the output data
  • target_src_lat (numpy.array) – src_latitudes of the output data
  • methods (string or dict, optional) – method of spatial averaging. this is given to pyresample and can be ‘nn’ : nearest neighbour ‘custom’ : custom weight function has to be supplied in weight_funcs see pyresample documentation for more details can also be a dictionary with a method for each array in input data dict
  • weight_funcs (function or dict of functions, optional) – if method is ‘custom’ a function like func(distance) has to be given can also be a dictionary with a function for each array in input data dict
  • min_neighbours (int, optional) – if given then only points with at least this number of neighbours will be resampled Default : 1
  • search_rad (float, optional) – search radius in meters of neighbour search Default : 18000
  • neighbours (int, optional) – maximum number of neighbours to look for for each input grid point Default : 8
  • fill_values (number or dict, optional) – if given the output array will be filled with this value if no valid resampled value could be computed, if not a masked array will be returned can also be a dict with a fill value for each variable
Returns:

data – resampled data on given grid

Return type:

dict of numpy.arrays

Raises:

ValueError : – if empty dataset is resampled

pytesmo.grid.resample.resample_to_grid_only_valid_return(input_data, src_lon, src_lat, target_lon, target_lat, methods='nn', weight_funcs=None, min_neighbours=1, search_rad=18000, neighbours=8, fill_values=None)[source]

resamples data from dictionary of numpy arrays using pyresample to given grid. Searches for the neighbours and then resamples the data to the grid given in togrid if at least min_neighbours neighbours are found

Parameters:
  • input_data (dict of numpy.arrays) –
  • src_lon (numpy.array) – longitudes of the input data
  • src_lat (numpy.array) – src_latitudes of the input data
  • target_lon (numpy.array) – longitudes of the output data
  • target_src_lat (numpy.array) – src_latitudes of the output data
  • methods (string or dict, optional) – method of spatial averaging. this is given to pyresample and can be ‘nn’ : nearest neighbour ‘custom’ : custom weight function has to be supplied in weight_funcs see pyresample documentation for more details can also be a dictionary with a method for each array in input data dict
  • weight_funcs (function or dict of functions, optional) – if method is ‘custom’ a function like func(distance) has to be given can also be a dictionary with a function for each array in input data dict
  • min_neighbours (int, optional) – if given then only points with at least this number of neighbours will be resampled Default : 1
  • search_rad (float, optional) – search radius in meters of neighbour search Default : 18000
  • neighbours (int, optional) – maximum number of neighbours to look for for each input grid point Default : 8
  • fill_values (number or dict, optional) – if given the output array will be filled with this value if no valid resampled value could be computed, if not a masked array will be returned can also be a dict with a fill value for each variable
Returns:

  • data (dict of numpy.arrays) – resampled data on part of the target grid over which data was found
  • mask (numpy.ndarray) – boolean mask into target grid that specifies where data was resampled

Raises:

ValueError : – if empty dataset is resampled

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