Multivariate Pattern Analysis in Python

# mvpa2.clfs.stats.plot_distribution_matches

`mvpa2.clfs.stats.``plot_distribution_matches`(data, matches, nbins=31, nbest=5, expand_tails=8, legend=2, plot_cdf=True, p=None, tail='both')

  Plot best matching distributions

  Parameters:

  data : np.ndarray

  > Data which was used to obtain the matches

  matches : list of tuples

  > Sorted matches as provided by match_distribution

  nbins : int

  > Number of bins in the histogram

  nbest : int

  > Number of top matches to plot

  expand_tails : int

  > How many bins away to add to parametrized distributions plots

  legend : int

  > Either to provide legend and statistics in the legend. 1 – just lists distributions. 2 – adds distance measure 3 – tp/fp/fn in the case if p is provided

  plot_cdf : bool

  > Either to plot cdf for data using non-parametric distribution

  p : float or None

  > If not None, visualize null-hypothesis testing (given p). Bars in the histogram which fall under given p are colored in red. False positives and false negatives are marked as triangle up and down symbols correspondingly

  tail : (‘left’, ‘right’, ‘any’, ‘both’)

  > If p is not None, the choise of tail for null-hypothesis testing

  Returns: | histogram list of lines