LingPy

This documentation is for version 2.0.dev, which is not released yet.

lingpy.compare.borrowing.trebor.TreBor

class lingpy.compare.borrowing.trebor.TreBor(dataset, tree=None, paps='pap', cognates='cogid', verbose=False, tree_calc='neighbor', **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.

cognates : string (default=”cogid”)

Name of the column that stores the general cognate ids.

verbose : bool (default=False)

Handle verbose output.

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

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

Methods

add_entries(entry, source, function[, override]) Add new entry-types to the word list by modifying given ones.
analyze([runs, mixed, verbose, output_gml, ...]) Carry out a full analysis using various parameters.
calculate(data[, taxa, concepts, cognates, ...]) Function calculates specific data.
get_AVSD(glm[, verbose, write]) Function retrieves all paps for ancestor languages in a given tree.
get_CVSD([verbose]) 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([verbose, output_gml, output_plot, tar]) Calculate VSD on the basis of each item.
get_MLN(glm[, threshold, verbose, colormap, ...]) Compute an Minimal Lateral Network for a given model.
get_PDC(glm[, verbose]) Calculate Patchily Distributed Cognates.
get_dict([col, row, entry]) Function returns dictionaries of the cells matched by the indices.
get_entries(entry) Return all entries matching the given entry-type as a two-dimensional list.
get_etymdict([ref, entry, loans]) 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]) Function returns a list of present-absent-patterns of a given word list.
output(fileformat, **keywords) Write wordlist to file.
pickle() Store a dump of the data in a binary file.
plot_MLN([glm, filename, fileformat, ...]) Plot the MLN with help of Matplotlib.
plot_MSN([glm, verbose, filename, ...]) Plot the Minimal Spatial Network.
plot_concepts(concept, cogA, cogB[, labels, ...]) Plot the Minimal Spatial Network.
tokenize([ortho_profile, source, target]) Tokenize the data with help of orthography profiles.

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