API Reference

TemporalMapper

temporal_mapper.TemporalMapper(time, data, clusterer)

The TemporalMapper object stores all the data and preprocessing associated to the temporal mapper graph, with semantic data data and timestamps time. Clustering is performed using an sklearn clusterer clusterer, which can optionally support weighted points.

Attributes:

G: networkx.classes.Digraph(Graph)

The temporal graph itself.

density: ndarray

The f-density rho for each data point.

Parameters:

time: ndarray

time array (1 dim)

data: ndarray

data array (n dim)

clusterer: sklearn clusterer

the clusterer to use for the slice-wise clustering, optionally accepts sample_weights

N_checkpoints: int

number of time-points at which to cluster

checkpoints: arraylike

array of time-points at which to cluster

overlap: float

A float in (0,1) which specifies the g parameter (see README)

neighbours: float

The number of nearest neighbours to use in the density computation.

show_outliers: bool

If true, include unclustered points in the graph

slice_method: str

One of 'time' or 'data'. If time, generates N_checkpoints evenly spaced in time. If data, generates N_checkpoints such that there are equal amounts of data between the points.

rate_sensitivity: float

A positive float, or -1. The rate parameter is raised to this parameter, so higher numbers means that the algorithm is more sensitive to changes in rate. If rate_sensivity == -1, then the rate parameter is taken log2.

kernel: function

A function with signature f(t0, t, density, binwidth, epsilon=0.01, params=None). Two options are included in weighted_clustering.py, weighted_clustering.square and weighted_clustering.gaussian.

kernel_parameters: tuple or None,

Passed to kernel as params kwarg.

verbose: bool

Does what you expect.

Methods:

build():

Run the fuzzy mapper algorithm to construct the temporal graph.

get_vertex_data(str node):

Returns the index of elements of data which are in vertex node.

Utilities

temporal_mapper.utilities.time_semantic_plot(TemporalGraph, semantic_axis)

Create a time-semantic plot of the graph TemporalGraph.G.

Parameters:

TemporalMapper: temporal_mapper.TemporalMapper

The temporal graph object to plot.

semantic_axis: ndarray

Array of shape (n_samples,) with the 1D semantic data to use in the plot.

ax: matplotlib.axes (optional, default=None)

Matplotlib axis to draw on

vertices: list (optional, default=None)

List of nodes in TG.G to include in the plot.

label_edges: bool (optional, default=False)

If true, include text labels of the edge weight on top of edges.

edge_scaling: float (optional, default = 1)

Scales the thickness of edges, larger is thicker.

bundle: bool (optional, default=True)

If true, bundle the edges of the graph using datashader's hammer_bundle function.

print_params: bool (optional, default=False)

If true, print extra information about the parameters of TemporalMapper on the plot.

Returns: matplotlib.axes

temporal_mapper.utilities.centroid_datamap(TemporalGraph)

Plot the temporal graph in 2d with vertices at their cluster centroids.

Parameters:

TemporalMapper: temporal_mapper.TemporalMapper

The temporal graph object to plot.

ax: matplotlib.axes (optional, default=None)

Matplotlib axis to draw on

node_colouring: 'desaturate' or 'override' (optional, default='desaturate')

Determines how to incorporate temporal information in the color. The desaturate option will take the semantic colouring from datamapplot and desaturate points that are further back in time. The override option will throw away the semantic colouring and colour points only based on their time value.

vertices: list (optional, default=None)

List of nodes in TG.G to include in the plot.

label_edges: bool (optional, default=False)

If true, include text labels of the edge weight on top of edges.

edge_scaling: float (optional, default = 1)

Scales the thickness of edges, larger is thicker.

bundle: bool (optional, default=True)

If true, bundle the edges of the graph using datashader's hammer_bundle function.

print_params: bool (optional, default=False)

If true, print extra information about the parameters of TemporalMapper on the plot.

Returns: matplotlib.axes