pedarProbe.node.PedarNode

class pedarProbe.node.PedarNode

Bases: DynamicNode

Derived from DynamicNode to provide pedar data analysis feature.

In this project, pedarProbe.analyse provides functionalities for data analysis and pedarProbe.export provides functionalities for result export. PedarNode conveys a bunch of short-cut functions to facilitate the usability.

Note

Class Attributes

self.attributes dict

Dictionary of the analysed attributes, for example:

self.attributes['sensor_peak']  # peak pressure
self.attributes['pti']  # pressure-time integral

Warning

The specific attribute is only available after calling the corresponding analysis method.

Attention

In the returned node tree of restructuring (restructure()), all nodes’ self.attributes will be erased. This design is based on the fact that different layer layouts usually leads to different analysis results, therefore there is no reason for keeping old analysis results.

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)
add_branch(branch_node)	Add branch to the node.
branch_names()	Return a list of branch nodes' names.
branches()	Return a list of branch nodes objects.
change_loc_map(start_level, layout)	Change loc_map with a restructured layer layout representation.
clean_copy()	Create and return a deep copy of the node only with its major attributes, including name, loc, and level.
clear()
collect_layer(layer, nodes)	In the node tree starting from this node, recursively collect all nodes of a specific layer.
collect_leaf(nodes)	Recursively collect all leaf nodes starting from this node.
copy()
fromkeys([value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
heatmap([attr_name, range, is_export, ...])	Generate, plot, and export the heatmap for an attribute.
is_layer(layer)	Judgment of whether the node belongs to a specific layer.
is_leaf()	Judgment of whether the node is a leaf node or not.
items()
keys()
layer_layout()	Get the layer layout representation of the node tree starting from this node.
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised
popitem()	2-tuple; but raise KeyError if D is empty.
print()	Recursively print the structure of the node tree starting from this node.
restructure([layout])	Return the restructured the node tree from this node.
sensor_peak([is_export, export_layer, ...])	Analyse the peak pressure of each sensor in the leaf node level, and then average up layer by layer up to the this node.
sensor_pti([is_export, export_layer, ...])	Analyse the pressure-time integral (PTI) of each sensor in the leaf node level, and then average up layer by layer up to the this node.
set_source(source_node)	Set the source node of the node.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
setup(*args, **kwargs)	Compared with setup() of the base class DynamicNode, initialisation of the self.attributes is added.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

setup(*args, **kwargs)

Compared with setup() of the base class DynamicNode, initialisation of the self.attributes is added.

sensor_peak(is_export=False, export_layer: str = 'root', export_folder='output', save_suffix: str = '')

Analyse the peak pressure of each sensor in the leaf node level, and then average up layer by layer up to the this node. Then for each node under this node, the peak pressure analysis result can be accessed with self.attributes['sensor_peak'].

Parameters:

• is_export – export the analysed result as a local file or not.

• export_layer – if export as local file, the name of the layer to export.

• export_folder – the folder of the exported file.

• save_suffix –

  the suffix added to the default export file name sensor_peak.

  Tip

  A specific suffix can avoid exported file be override by future export.

Example

n1.sensor_peak(
    is_export=True,
    export_layer='condition',
    export_folder='output',
    save_suffix='_1213'  # export file name: sensor_peak_1213.xlsx
)

sensor_pti(is_export=False, export_layer: str = 'root', export_folder='output', save_suffix: str = '')

Analyse the pressure-time integral (PTI) of each sensor in the leaf node level, and then average up layer by layer up to the this node. Then for each node under this node, the PTI analysis result can be accessed with self.attributes['sensor_pti'].

Parameters:

• is_export – export the analysed result as a local file or not.

• export_layer – if export as local file, the name of the layer to export.

• export_folder – the folder of the exported file.

• save_suffix –

  the suffix added to the default export file name sensor_pti.

  Tip

  A specific suffix can avoid exported file be override by future export.

Example

n1.sensor_pti(
    is_export=True,
    export_layer='condition',
    export_folder='output',
    save_suffix='_1213'  # export file name: sensor_pti_1213.xlsx
)

heatmap(attr_name: str = 'sensor_peak', range: Union[str, tuple] = 'static', is_export: bool = False, export_folder: str = 'output', save_suffix: str = '') → FootHeatmap

Generate, plot, and export the heatmap for an attribute.

Parameters:

• attr_name – name of the attribute, same as its keyword in self.attributes.

• is_export – export the analysed result as a local file or not.

• export_layer – if export as local file, the name of the layer to export.

• export_folder – the folder of the exported file.

• save_suffix –

  the suffix added to the default export file name foot_heatmap.

  Tip

  A specific suffix can avoid exported file be override by future export.

Returns:

heatmap object that can be further used or manipulated.

Return type:

pedarProbe.export.FootHeatmap

Example

n1.heatmap(
    is_export=True,
    export_folder='output',
    save_suffix='_1213'  # export file name: foot_heatmap_1213.png
)