podpac.algorithm.StandardDeviation¶

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

Bases: podpac.core.algorithm.stats.Variance

Computes the standard deviation across dimension(s)

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
`dims_axes`(output)	Finds the indices for the dimensions that will be reduced.
`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.
`iteroutputs`(coordinates, _selector)	Generator for the chunks of the output
`load`(path)	Create podpac Node from file.
`put_cache`(data, key[, coordinates, expires, …])	Cache data for this node.
`reduce`(x)	Computes the standard deviation across dimension(s)
`reduce_chunked`(xs, output)	Computes the standard deviation across a chunk
`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.
`chunk_size`	Size of chunks for parallel processing or large arrays that do not fit in memory
`definition`
`dims`	An instance of a Python list.
`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.
`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.

reduce(x)[source]¶

Computes the standard deviation across dimension(s)

reduce_chunked(xs, output)[source]¶

Computes the standard deviation across a chunk