hypertools.DataGeometry

class hypertools.DataGeometry(fig=None, ax=None, line_ani=None, data=None, xform_data=None, reduce=None, align=None, normalize=None, semantic=None, vectorizer=None, corpus=None, kwargs=None, version='0.8.1', dtype=None)[source]

Hypertools data object class

A DataGeometry object contains the data, figure handles and transform functions used to create a plot. Note: this class should not be called directly, but is used by the hyp.plot function to create a plot object.

Parameters:
figmatplotlib.Figure

The matplotlib figure handle for the plot

axmatplotlib.Axes

The matplotlib axes handle for the plot

line_animatplotlib.animation.FuncAnimation

The matplotlib animation handle (if the plot is an animation)

datalist

A list of numpy arrays representing the raw data

xform_datalist

A list of numpy arrays representing the transformed data

reducedict

A dictionary containing the reduction model and parameters

aligndict

A dictionary containing align model and parameters

normalizestr

A string representing the kind of normalization

kwargsdict

A dictionary containing all kwargs passed to the plot function

versionstr

The version of the software used to create the class instance

Methods

get_data()

Return a copy of the data

get_formatted_data()

Return a formatted copy of the data

plot([data])

Plot the data

save(fname[, compression])

Save method for the data geometry object

transform([data])

Return transformed data, or transform new data using the same model parameters

__init__(fig=None, ax=None, line_ani=None, data=None, xform_data=None, reduce=None, align=None, normalize=None, semantic=None, vectorizer=None, corpus=None, kwargs=None, version='0.8.1', dtype=None)[source]

Methods

__init__([fig, ax, line_ani, data, ...])

get_data()

Return a copy of the data

get_formatted_data()

Return a formatted copy of the data

plot([data])

Plot the data

save(fname[, compression])

Save method for the data geometry object

transform([data])

Return transformed data, or transform new data using the same model parameters