Module reference
If the GUI does not offer enough flexibility, you may always write your own Python code. Example scripts can be produced with the GUI menu item File→Generate standalone Python script. The module reference below documents the relevant functions and data structures in the mapper module. (To be completed...!)

mapper. mapper (pcd, filt, cover, cutoff, mask=None, cluster=<mapper._mapper.single_linkage instance>, point_labels=None, metricpar={}, simple=False, filter_info=None, verbose=True)
Mapper algorithm
Parameters: 
 pcd (
numpy.ndarray((N,n), dtype=float) or
numpy.ndarray((N*(N1)/2), dtype=float) ) – input data, point cloud in \(R^n\), or compressed distance
matrix for N points
 filt (
numpy.ndarray((N, comp), dtype=float) or
numpy.ndarray(N, dtype=float) ) – filter function with comp components
 cover (iterator) – Class for the cover of the filter range. See Cover methods.
 cutoff (function or
None ) – Cutoff function for the partial clustering tree. See
section:cluster_cutoff.
 mask (Anything that can be used for indexing of NumPy arrays, e.g. a Boolean
array of size N.) – (Mainly for the GUI) A mask to choose a subset of the input points
 cluster (See section:clustering_function) – Clustering function.
 point_labels (
numpy.ndarray(N) ) – Labels for the input points (optional). If this is
None, the points are labeled 0,1,...,N−1.
 metricpar (dict) – If the input data is in vector form, these are the parameters
that are given to the
scipy.spatial.distance.pdist function. If the input data
is a compressed distance matrix, this argument is ignored.
 simple (bool) – (to be documented by example) If
True , then intersections are only
considered for adjacent cover patches in the 1dimensional variant. In particular,
the output simplicial complex is a graph without higherdimensional simplices.
 filter_info – (For the GUI) Filter info to be stored in the output
 verbose (bool) – Print status message?

Returns:  Mapper output data structure

Return type:  mapper_output instance

