HistMST

class HistMST

A class for binning the default MST statistics which are then stored in a dictionary.

setup([uselog=False, use_sqrt_s=True, usenorm=True, d_min=0.5, d_max=6.5, num_d_bins=6, l_min=0., l_max=None, num_l_bins=100, b_min=0., b_max=None, num_b_bins=100, s_min=0., s_max=1., num_s_bins=50, logl_min=None, logl_max=None, logb_min=None, logb_max=None])

Setups bin sizes for the MST statistics.

Parameters

  • uselog (bool) – Determines whether to use log bins for l and b.

  • use_sqrt_s (bool) – Determines whether to use the sqrt(1-s) projection of s or just s itself.

  • usenorm (bool) – Determines whether to normalise the histograms.

  • d_min (float) – Minimum for degree bins (use half integer values).

  • d_max (float) – Maximum for degree bins (use half integer values).

  • num_d_bins (int) – Number of bins for the distribution of degree, this should be equal to d_max - d_min.

  • l_min (float) – Minimum for edge length bins.

  • l_max (float) – Maximum for edge length bins.

  • num_l_bins (int) – Number of bins for the distribution of edge lengths.

  • b_min (float) – Minimum for branch length bins.

  • b_max (float) – Maximum for branch length bins.

  • num_b_bins (int) – Number of bins for the distribution of branch lengths.

  • s_min (float) – Minimum for branch shape bins.

  • s_max (float) – Maximum for branch shape bins.

  • num_s_bins (int) – Number of bins for the distribution of branch shapes.

  • logl_min (float) – Minimum of edge lengths in log to base 10.

  • logl_max (float) – Maximum of edge lengths in log to base 10.

  • logb_min (float) – Minimum of branch lengths in log to base 10.

  • logb_max (float) – Maximum of branch lengths in log to base 10.

get_hist(d, l, b, s)

Bins the MST distribution which is returned as a dictionary.

Parameters

  • d (array) – Distribution of degree.

  • l (array) – Distribution of edge length.

  • b (array) – Distribution of branch length.

  • s (array) – Distribution of branch shape.

Returns

mst_hist (dict): Dictionary of MST binned histograms.

start_group()

Begins group mode for calculating the mean and standard deviation of the MST from different realisations of data points coming from the same model.

end_group()

Ends group mode for calculating the mean and standard deviation of the MST.

Returns

mst_hist (dict): Dictionary of the mean and standard deviation of the MST binned histograms.

clean()

Resets HistMST variables.