Borrowing Detection (phylogeny)

class lingpy.compare.phylogeny.PhyBo(dataset, tree=None, paps='pap', ref='cogid', tree_calc='neighbor', output_dir=None, **keywords)

Basic class for calculations using the TreBor method.

Parameters

dataset : string

Name of the dataset that shall be analyzed.

tree : {None, string}

Name of the tree file.

paps : string (default=”pap”)

Name of the column that stores the specific cognate IDs consisting of an arbitrary integer key and a key for the concept.

ref : string (default=”cogid”)

Name of the column that stores the general cognate ids (the “reference” of the analysis).

tree_calc : {‘neighbor’,’upgma’} (default=’neighbor’)

Select the algorithm to be used for the tree calculation if no tree is passed with the file.

missing : int (default=-1)

Specify how missing data should be handled. If set to -1, missing data can account for both presence or absence of a cognate set in the given language. If set to 0, missing data is treated as absence.

degree : int (default=100)

The degree which is chosen for the projection of the tree layout.

Methods

analyze([runs, mixed, output_gml, tar, …])

Carry out a full analysis using various parameters.

get_AVSD(glm, **keywords)

Function retrieves all pap s for ancestor languages in a given tree.

get_CVSD()

Calculate the Contemporary Vocabulary Size Distribution (CVSD).

get_GLS([mode, ratio, restriction, …])

Create gain-loss-scenarios for all non-singleton paps in the data.

get_IVSD([output_gml, output_plot, tar, …])

Calculate VSD on the basis of each item.

get_MLN(glm[, threshold, method])

Compute an Minimal Lateral Network for a given model.

get_MSN([glm, external_edges, deep_nodes])

Plot the Minimal Spatial Network.

get_PDC(glm, **keywords)

Calculate Patchily Distributed Cognates.

get_edge(glm, nodeA, nodeB[, entries, msn])

Return the edge data for a given gain-loss model.

get_stats(glm[, subset, filename])

Calculate basic statistics for a given gain-loss model.

plot_MLN([glm, fileformat, threshold, …])

Plot the MLN with help of Matplotlib.

plot_MSN([glm, fileformat, threshold, …])

Plot a minimal spatial network.

plot_concept_evolution(glm[, concept, …])

Plot the evolution of specific concepts along the reference tree.

plot_two_concepts(concept, cogA, cogB[, …])

Plot the evolution of two concepts in space.

Inherited Methods

get_entries(entry)

Return all entries matching the given entry-type as a two-dimensional list.

add_entries(entry, source, function[, override])

Add new entry-types to the word list by modifying given ones.

calculate(data[, taxa, concepts, ref])

Function calculates specific data.

export(fileformat[, sections, entries, …])

Export the wordlist to specific fileformats.

get_dict([col, row, entry])

Function returns dictionaries of the cells matched by the indices.

get_etymdict([ref, entry, modify_ref])

Return an etymological dictionary representation of the word list.

get_list([row, col, entry, flat])

Function returns lists of rows and columns specified by their name.

get_paps([ref, entry, missing, modify_ref])

Function returns a list of present-absent-patterns of a given word list.

output(fileformat, **keywords)

Write wordlist to file.

renumber(source[, target, override])

Renumber a given set of string identifiers by replacing the ids by integers.