pedarProbe.node.DataNode

class pedarProbe.node.DataNode

Bases: PedarNode

Derived from PedarNode which is actually the leaf node of the node tree, storing the raw data prepared for analysis.

Note

Class Attributes

self.df pandas.core.frame.DataFrame

Pandas data frame that stores the sensor value of within a selected stance. Columns of the self.df is the sensor ID, from 0 ~ 98 belongs to the left foot and form 99 ~ 197 belongs to the right foot. It can be accessed with:

self.df.columns

Rows of the self.df is the time value, . It can be accessed with:

self.df.index

To access a data item with specific sensor id and time:

id = 188
time = 1.58
self.df[id][time]

self.start float

the start time of the selected stance.

self.end float

the end time of the selected stance.

__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(df, start, end, *args, **kwargs)	Compared with setup() of the base class PedarNode, initialisations of the self.df, self.start, and self.end are 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(df: DataFrame, start: float, end: float, *args, **kwargs)

Compared with setup() of the base class PedarNode, initialisations of the self.df, self.start, and self.end are added.