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""" 

LingPy package for quantitative tasks in historical linguistics. 

 

Documentation is available in the docstrings. Online documentation is available 

at http://lingpy.org 

 

Subpackages 

----------- 

algorithm --- Basic Algorithms for Sequence Comparison 

align --- Specific Algorithms Alignment Analyses 

basic --- Basic Classes for Language Comparison 

compare --- Basic Modules for Language Comparison 

convert --- Functions for Format Conversion 

data --- Data Handling 

evaluate --- Basic Classes and Functions for Algorithm Evaluation 

read --- Basic Functions for Data Input 

sequence --- Basic Functions for Sequence Modeling 

thirdparty --- Temporary Forks of Third-Party-Modules 

 

""" 

 

__author__ = "Johann-Mattis List, Robert Forkel (with contributions by Simon Greenhill, Steven Moran, Taraka Rama, Johannes Dellert, Frank Nagel, and Peter Bouda)" 

__date__ = "2016-21-04" 

__version__ = "2.6" 

 

# We exempt this module from QA, because it only provides import shortcuts. 

# flake8: noqa 

# import settings 

from lingpy.settings import rc 

 

# general imports 

from lingpy.basic.wordlist import Wordlist, get_wordlist 

from lingpy.basic.tree import Tree 

from lingpy.basic.ops import iter_rows 

 

# import converts 

# from .convert import * 

 

# we don't import align for the moment for safety reasons... 

from lingpy.align import Pairwise, Multiple, SCA, MSA, PSA, Alignments, edit_dist, \ 

pw_align, nw_align, sw_align, we_align, structalign, turchin, \ 

mult_align 

 

# load the sound-class models 

from lingpy.data import Model 

 

# import reading routine 

from lingpy.read.csv import csv2list, csv2dict 

 

# import sequence routines 

from lingpy.sequence.sound_classes import ipa2tokens, tokens2class, prosodic_string, \ 

prosodic_weights, class2tokens, pid, sampa2uni, strip_chars 

 

# import lexstat 

from lingpy.compare.lexstat import LexStat 

 

# import algorithm-stuff 

from lingpy.algorithm.clustering import upgma, neighbor, flat_upgma, flat_cluster, \ 

fuzzy, link_clustering, mcl