# 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*(N-1)/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 1-dimensional variant. In particular, the output simplicial complex is a graph without higher-dimensional simplices. filter_info – (For the GUI) Filter info to be stored in the output verbose (bool) – Print status message? Mapper output data structure mapper_output instance

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Custom data processing in the GUI