Borrowing Detection (phylogeny)

class, tree=None, paps='pap', ref='cogid', tree_calc='neighbor', output_dir=None, **keywords)

Basic class for calculations using the TreBor method.


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.


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.