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.

Members

__init__(**kwargs)

Do not overwrite me

reduce(x)[source]

Computes the standard deviation across dimension(s)

Parameters

x (UnitsDataArray) – Source data.

Returns

Standard deviation of the source data over dims

Return type

UnitsDataArray

reduce_chunked(xs, output)[source]

Computes the standard deviation across a chunk

Parameters

xs (iterable) – Iterable of sources

Returns

Standard deviation of the source data over dims

Return type

UnitsDataArray