pyemma.coordinates.cluster_uniform_time

pyemma.coordinates.cluster_uniform_time(data=None, k=100, stride=1, metric='euclidean')

Uniform time clustering

If given data, performs a clustering that selects data points uniformly in time and then assigns the data using a Voronoi discretization. Returns a UniformTimeClustering object that can be used to extract the discretized data sequences, or to assign other data points to the same partition. If data is not given, an empty UniformTimeClustering will be created that still needs to be parametrized, e.g. in a pipeline().

Parameters:
  • data (ndarray (T, d) or list of ndarray (T_i, d) or a reader created by source function) – input data, if available in memory
  • k (int) – the number of cluster centers
  • stride (int, optional, default = 1) – If set to 1, all input data will be used for estimation. Note that this could cause this calculation to be very slow for large data sets. Since molecular dynamics data is usually correlated at short timescales, it is often sufficient to estimate transformations at a longer stride. Note that the stride option in the get_output() function of the returned object is independent, so you can parametrize at a long stride, and still map all frames through the transformer.
Returns:

uniformTime – Object for uniform time clustering. It holds discrete trajectories and cluster center information.
Return type:

a UniformTimeClustering clustering object