podpac.algorithm.Convolution¶

class podpac.algorithm.Convolution(**kwargs)[source]¶

Bases: podpac.core.algorithm.algorithm.UnaryAlgorithm

Compute a general convolution over a source node.

This node automatically resizes the requested coordinates to avoid edge effects.

source¶

Source node on which convolution will be performed.

Type: podpac.Node

kernel¶

The convolution kernel. This kernel must include the dimensions of source node outputs. The dimensions for this array are labelled by kernel_dims. Any dimensions not in the soucr nodes outputs will be summed over.

Type: np.ndarray, optional

kernel_dims¶

A list of the dimensions for the kernel axes. The dimensions in this list must match the coordinates in the source, or contain additional dimensions, and the order does not need to match. Any extra dimensions are summed out.

Type: list, optional

kernel_type¶

If kernel is not defined, kernel_type will create a kernel based on the inputs, and it will have the same number of axes as kernel_ndim. The format for the created kernels is ‘<kernel_type>, <kernel_size>, <kernel_params>’. Any kernel defined in scipy.signal as well as mean can be used. For example: kernel_type = ‘mean, 8’ or kernel_type = ‘gaussian,16,8’ are both valid. Note: These kernels are automatically normalized such that kernel.sum() == 1

Type: str, optional

Alternative Constructors

`from_definition`(definition)	Create podpac Node from a dictionary definition.
`from_json`(s)	Create podpac Node from a JSON definition.

Methods

`__init__`(**kwargs)	Do not overwrite me
`create_output_array`(coords[, data, attrs])	Initialize an output data array
`eval`(coordinates, **kwargs)	Evaluate the node at the given coordinates.
`eval_group`(group)	Evaluate the node for each of the coordinates in the group.
`find_coordinates`()	Get the available coordinates for the inputs to the Node.
`from_url`(url)	Create podpac Node from a WMS/WCS request.
`get_cache`(key[, coordinates])	Get cached data for this node.
`has_cache`(key[, coordinates])	Check for cached data for this node.
`init`()	Overwrite this method if a node needs to do any additional initialization after the standard initialization.
`load`(path)	Create podpac Node from file.
`put_cache`(data, key[, coordinates, expires, …])	Cache data for this node.
`rem_cache`(key[, coordinates, mode])	Clear cached data for this node.
`save`(path)	Write node to file.
`trait_defaults`(names, *metadata)	Return a trait’s default value or a dictionary of them
`trait_has_value`(name)	Returns True if the specified trait has a value.
`trait_is_defined`(name)
`trait_values`(**metadata)	A `dict` of trait names and their values.

Attributes

`attrs`	List of node attributes
`base_ref`	Default reference/name in node definitions
`cache_ctrl`	A trait whose value must be an instance of a specified class.
`cache_output`	A boolean (True, False) trait.
`definition`
`dtype`	A trait which allows any value.
`force_eval`	A boolean (True, False) trait.
`hash`	hash for this node, used in caching and to determine equality.
`inputs`
`json`	Definition for this node in JSON format.
`json_pretty`	Definition for this node in JSON format, with indentation suitable for display.
`kernel`	A coercing numpy array trait.
`kernel_dims`	An instance of a Python list.
`output`	A trait for unicode strings.
`outputs`	An instance of a Python list.
`source`
`style`	A trait whose value must be an instance of a specified class.
`units`	A trait for unicode strings.

Members

__init__(**kwargs)¶: Do not overwrite me

kernel: A coercing numpy array trait.

kernel_dims: An instance of a Python list.