podpac.algorithm.Convolution

class podpac.algorithm.Convolution(**kwargs: Any)[source]

Bases: 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 source nodes outputs will be summed over.

Type:: np.ndarray, optional

kernel_dims

A list of the dimensions for the kernel axes. If the dimensions in this list do not match the coordinates in the source, then any extra dimensions in the kernel are removed by adding all the values over that axis dimensions in the source are not convolved with any kernel.

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_dims. 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_name_params`(name[, params])	Create podpac Node from a WMS/WCS request.
`from_url`(url)	Create podpac Node from a WMS/WCS request.
`get_bounds`([crs])	Get the full available coordinate bounds for the Node.
`get_cache`(key[, coordinates])	Get cached data for this node.
`get_ui_spec`([help_as_html])	Get spec of node attributes for building a ui
`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.
`probe`([lat, lon, time, alt, crs])	Evaluates every part of a node / pipeline at a point and records which nodes are actively being used.
`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`	An enum whose value must be in a given sequence.
`force_eval`	A boolean (True, False) trait.
`hash`
`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.
`kernel_type`	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.

kernel_type: An instance of a Python list.