podpac.algorithm.StandardDeviation

class podpac.algorithm.StandardDeviation(**kwargs: Any)

Bases: 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_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.
iteroutputs(coordinates, _selector)	Generator for the chunks of the output
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.
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

DimsTrait
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
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.
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)

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)

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