The Block class

To write a new Block subclass, we need to write the following:

  1. the __init__ that validates the arguments when constructing the block

  2. the get_sources_and_requests that processes the request

  3. the process that processes the data

  4. a number of attributes such as extent and period

About the 2-step data processing

The get_sources_and_requests method of any block is called recursively from get_compute_graph and feeds the request from the block to its sources. It does so by returning a list of (source, request) tuples. During the data evaluation each of these 2-tuples will be converted to a single data object which is supplied to the process function.

First, an example in words. We construct a View add = RasterFileSource('path/to/geotiff') + 2.4 and ask it the following:

  • give me a 256x256 raster at location (138000, 480000)

We do that by calling get_data, which calls get_compute_graph, which calls get_sources_and_requests on each block instance recursively.

First add.get_sources_and_requests would respond with the following:

  • I will need a 256x256 raster at location (138000, 480000) from RasterFileSource('/path/to/geotiff')

  • I will need 2.4

Then, on recursion, the RasterFileSource.get_sources_and_requests would respond:

  • I will give you the 256x256 raster at location (138000, 480000)

These small subtasks get summarized in a compute graph, which is returned by get_compute_graph. Then get_data feeds that compute graph to dask.

Dask will evaluate this graph by calling the process methods on each block:

  1. A raster is loaded using RasterFileSource.process

  2. This, together with the number 2.4, is given to Add.process

  3. The resulting raster is presented to the user.

Implementation example

As an example, we use a simplified Dilate block, which adds a buffer of 1 pixel around all pixels of given value:

class Dilate(RasterBlock):
    def __init__(self, source, value):
        assert isinstance(source, RasterBlock):
        value = float(value)
        super().__init__(source, value)

    def source(self):
        return self.args[0]

    def value(self):
        return self.args[1]

    def get_sources_and_requests(self, **request):
        new_request = expand_request_pixels(request, radius=1)
        return [(, new_request), (self.value, None)]

    def process(data, values=None):
        # handle empty data cases
        if data is None or values is None or 'values' not in data:
            return data
        # perform the dilation
        original = data['values']
        dilated = original.copy()
        dilated[ndimage.binary_dilation(original == value)] = value
        dilated = dilated[:, 1:-1, 1:-1]
        return {'values': dilated, 'no_data_value': data['no_data_value']}

    def extent(self):
        return self.source.extent

    def period(self):
        return self.source.period

In this example, we see all the essentials of a Block implementation.

  • The __init__ checks the types of the provided arguments and calls the super().__init__ that further initializes the block.

  • The get_sources_and_requests expands the request with 1 pixel, so that dilation will have no edge effects. It returns two (source, request) tuples.

  • The process (static)method takes the amount arguments equal to the length of the list that get_sources_and_requests produces. It does the actual work and returns a data response.

  • Some attributes like extent and period need manual specification, as they might change through the block.

  • The class derives from RasterBlock, which sets the type of block, and through that its request/response schema and its required attributes.

Block types specification

A block type sets three things:

  1. the response schema: e.g. “RasterBlock.process returns a dictionary with a numpy array and a no data value”

  2. the request schema: e.g. “RasterBlock.get_sources_and_requests expects a dictionary with the fields ‘mode’, ‘bbox’, ‘projection’, ‘height’, ‘width’”

  3. the attributes to be implemented on each block

This is not enforced at the code level, it is up to the developer to stick to this specification. The specification is written down in the type baseclass RasterBlock() or GeometryBlock().

API specification

Module containing the core graphs.

class dask_geomodeling.core.graphs.Block(*args)

A class that generates dask-like compute graphs for given requests.

Arguments (args) are always stored in self.args. If a request is passed into the Block using the get_data or (the lazy version) get_compute_graph method, the Block figures out what args are actually necessary to evaluate the request, and what requests need to be sent to those args. This happens in the method get_sources_and_requests.

After the requests have been evaluated, the data comes back and is passed into the process method.

classmethod deserialize(val, validate=False)

Deserialize this block from a dict containing version, graph and name

static from_import_path(path)

Deserialize the Block by importing it from given path.

classmethod from_json(val, **kwargs)

Construct a graph from a json stream.

get_compute_graph(cached_compute_graph=None, **request)

Lazy version of get_data, returns a compute graph dict, that can be evaluated with compute (or dask’s get function).

The dictionary has keys in the form name_token and values in the form tuple(process, *args), where args are the precise arguments that need to be passed to process, with the exception that args may reference to other keys in the dictionary.


Directly evaluate the request and return the data.


Generate a graph that defines this Block and its dependencies in a dictionary.

The dictionary has keys in the form name_token and values in the form tuple(Block class, *args), where args are the precise arguments that were used to construct the Block, with the exception that args may also reference other keys in the dictionary.

If serialize == True, the Block classes will be replaced by their corresponding import paths.

classmethod get_import_path()

Serialize the Block by returning its import path.


Adapt the request and/or select the sources to be computed. The request is allowed to differ per source.

This function should return an iterable of (source, request). For sources that are no Block instance, the request is ignored.

Exceptions raised here will be raised before actual computation starts. (at .get_compute_graph(request)).

static process(data)

Overridden to modify data from sources in unlimited ways.

Default implementation passes single-source unaltered data.


Serialize this block into a dict containing version, graph and name


Dump the graph to a json stream.

property token

Generates a unique and deterministic representation of this object

dask_geomodeling.core.graphs.compute(graph, name, *args, **kwargs)

Compute a graph ({name: [func, arg1, arg2, …]}) using the configured scheduler. See dask.config.

dask_geomodeling.core.graphs.construct(graph, name, validate=True)

Construct a Block with dependent Blocks from a graph and endpoint name.