API

import superexacttestpy as stest

Tools: tl

Here you will find the documentation of the supertest function.

tl.supertest(data, n[, names, degree, ...])

Calculate the supertest for a given data set

Here you will find all the useful functions to perform the superexacttest.

tl.len_data(data)	Get the length of all the sub list of data
tl.log_choose(n, k)	Approximation of log-binomial coefficient nCk
tl.dmv_hyper(x, nl, set_len, n, logp)	Calculate the probability density of x elements in all of the subsets
tl.dhyper(x, w, b, n, logP)	Probability to getting x white balls out of n draws from an urn with w white balls and b black balls
tl.dp_sets(x, data, n[, logp])	Calculate the probability density of the intersection
tl.intersect(data)	Get intersection on multiple input set
tl.dmv_hyper_logval(x, nl, set_len, n, logval)	Get the distribution of the multiset intersection test
tl.cpsets_sim(x, set_len, n[, lower_tail, ...])	Function to simulate the intersection probability on multisets
tl.cpsets(x, data, n[, lower_tail, logp, ...])	Compute the distribution function of the multiset intersection test.
tl.mset(data, n[, lower_tail, logp])	A wrapper to call cpsets
tl.mk_barcode_degree(n, degree)	Generate a barcode of length n with a given degree
tl.inc_intersect(x, degree)	Calculate the number of intersections for a given degree for all barcodes
tl.intersect_elements(x)	Create a dataframe with the barcode for all the possible intersections
tl.exclusive_intersect0(x)	Calculate the number of exclusive intersections for all barcodes :type x: list :param x: List of sets :type x: list
tl.exc2inc_intersect(df)	Calculate the number of intersections (exclusive plus inclusive) for all barcodes
tl.enumerate_intersect_sizes(x[, degree])	Get a table with the number of intersections for each barcode of a given degree
tl.formating(barcode, names[, collapse])	Format a barcode to a string
tl.supertest(data, n[, names, degree, ...])	Calculate the supertest for a given data set
tl.decode(barcode, name)	Decode a barcode

Plotting

You will find the function to plot the results

pl.color_map_color(value[, cmap_name, vmin, ...])	Create a color map from a given value
pl.plot(data, n, name[, degree, sort_by, ...])	Plot the results of the superexact test