Mapchete Process

A Mapchete process has two parts. First, the process itself has to be implemented by creating a python file with an execute(mp) function. The mp object then helps to read data and access the process parameters. You can also ship the process file within a python package and reference from the configuration directly to the python module path instead of the python file path.

Second, a Mapchete process requires a configuration where all the necessary information is collected such as the location to your process Python file, the output location and format, and any user-defined parameters desired. This configuration file has a YAML syntax and has to have a .mapchete file extension.

If you have both ready, you can point either mapchete serve or mapchete execute to your process configuration (.mapchete) file to either view your process output in a browser or batch process a larger area.

mapchete serve my_process.mapchete

Starts a local web server on port 5000 with a simple OpenLayers interface.

mapchete execute my_process.mapchete --zoom 5 10

Executes your process on zoom level 5 to 10.

To access the process parameters, use the dictionary stored in mp.params. To read data, use the<input_file_id>) function.


Open and read data

with"<input_file_id>") as src:
  • <input_file_id>: Input file from mp.params. Can be a raster or vector file or the configuration file from another Mapchete process.

  • resampling: Resampling method to be used when reading raster data.

Opens a reader object, depending on the data source (raster, vector, Mapchete process). This object offers following standard functions:

The data reader object

  • indexes: A list of bands, a single band index or None to check all bands.

Returns bool indicating whether data within this tile is available or not., resampling="nearest")
  • indexes: A list of bands, a single band index or None to read all bands.

For raster files it either returns a masked numpy array of reprojected and resampled data fitting to the current tile.

For vector files it returns a list of GeoJSON-like feature dictionaries intersecting with and clipped to current tile boundaries.

If reading a Mapchete file, either vector or raster data in the form described above is returned.

All input drivers have a similar method interface, i.e. all have a generic .is_empty() and a .read() function implemented. It depends however on the driver which data type is returned.

Modify data

After reading the data you can do whatever you want. For vector data, shapely provides a rich selection of functions to deal with geometries, for raster data, NumPy, SciPy or Pillow are excellent packages for image processing and other desired tasks.

Mapchete also comes with some [common purpose functions]( which allow clipping, calculating a hillshade or extract contour lines from an elevation model.

Write data

return output_data
  • output_data: For raster data either a single or a tuple of numpy array(s). For vector data, a GeoJSON-like iterator of properties-geometry pairs. The write options are specified in the process configuration.


The process file should look like this:

def execute(mp):
    """User defined process."""

    # Reading and writing data works like this:
    with"raster_file") as my_raster_rgb_file:
        if my_raster_rgb_file.is_empty():
            # this ensures a transparent tile instead of a pink error tile is returned
            # when using mapchete serve
            return "empty"
        r, g, b ="bilinear")

    return (r, g, b)


You can also package a process within a python module and register it to the entrypoint mapchete.processes in your packages file. This will show your process when you run mapchete processes from the command line.