def test_convenience():
info('m')
warning('m')
debug('m')
error('m')
deprecated('o', 'n')
missing_module('m')
file_written('f')
def test_convenience():
info('m')
warning('m')
debug('m')
error('m')
deprecated('o', 'n')
missing_module('m')
file_written('f')
def test_convenience(self):
from lingpy.log import info, warn, debug, error, deprecated, missing_module, file_written
info('m')
warn('m')
debug('m')
error('m')
deprecated('o', 'n')
missing_module('m')
file_written('f')
def test_convenience(self):
from lingpy.log import info, warn, debug, error, deprecated, missing_module, file_written
info('m')
warn('m')
debug('m')
error('m')
deprecated('o', 'n')
missing_module('m')
file_written('f')
from __future__ import unicode_literals, print_function, division
from lingpy import log
import numpy as np
import networkx as nx
try:
import matplotlib.patches as mplPatches
except ImportError:
log.missing_module('matplotlib')
from .convex_hull import convex_hull
def perp(a):
"""
code for intersection taken from
http://stackoverflow.com/questions/3252194/numpy-and-line-intersections
"""
b = np.empty_like(a)
b[0] = -a[1]
b[1] = a[0]
return b
# line segment a given by endpoints a1, a2
# line segment b given by endpoints b1, b2
# return
def seg_intersect(nA, nB):
a1 = np.array(nA[0], dtype='float')
a2 = np.array(nA[1], dtype='float')
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import types
from ...settings import *
from lingpy import log
try:
import regex as re
if not isinstance(re, types.ModuleType):
# this is the case when creating the docs!
raise ImportError
except ImportError:
import re
log.missing_module('regex')
import sys
import os
import codecs
# data for sampa2ipa (Peter Kleiwegs implementation)
path = os.path.split(
os.path.abspath(
__file__
)
)[0]
f = codecs.open(path + '/sampa.csv','r','utf-8')
xsdata = []
_xsKeys = [' ']
"""
Module offers methods to handle colexification patterns in wordlist objects.
"""
from collections import defaultdict
from lingpy import log
from lingpy.util import combinations2, dotjoin, join
try:
import networkx as nx
except ImportError:
log.missing_module('networkx')
try:
import community
except ImportError:
log.missing_module('community')
def _get_colexifications(wordlist, entry='ipa', concept='concept', family='family'):
"""
Helper function computes colexifications for a given set of languages in a
wordlist.
"""
if family not in wordlist.header:
family = 'doculect'
taxa = wordlist.cols
colexifications = []
for taxon in taxa:
log.info('Analyzing taxon {0}...'.format(taxon))
Notes
-----
This code has been taken from: http://www.scipy.org/Cookbook/Finding_Convex_Hull
Author: Angus McMorland
Date: 2007-08-16
"""
from lingpy import log
import numpy as n
try:
import pylab as p
except ImportError:
log.missing_module('matplotlib')
def _angle_to_point(point, centre):
'''calculate angle in 2-D between points and x axis'''
delta = point - centre
res = n.arctan(delta[1] / delta[0])
if delta[0] < 0:
res += n.pi
return res
def _draw_triangle(p1, p2, p3, **kwargs):
tmp = n.vstack((p1, p2, p3))
x, y = [x[0] for x in zip(tmp.transpose())]
p.fill(x, y, **kwargs)
def fuzzy(threshold, matrix, taxa, method='upgma', revert=False):
"""
Create fuzzy cluster of a given distance matrix.
Parameters
----------
threshold : float
The threshold that shall be used for the basic clustering of the data.
matrix : list
A two-dimensional list containing the distances.
taxa : list
An list containing the names of all taxa corresponding to the distances
in the matrix.
method : { "upgma", "single", "complete" } (default="upgma")
Select the method for the flat cluster analysis.
distances : bool
If set to "False", only the topology of the tree will be returned.
revert : bool (default=False)
Specify whether a reverted dictionary should be returned.
Returns
-------
cluster : dict
A dictionary with cluster-IDs as keys and a list as value, containing
the taxa that are assigned to a given cluster-ID.
Examples
--------
The function is automatically imported along with LingPy.
>>> from lingpy import *
from lingpy.algorithm import squareform
Create a list of arbitrary taxa.
>>> taxa = ['German','Swedish','Icelandic','English','Dutch']
Create an arbitrary distance matrix.
>>> matrix = squareform([0.5,0.67,0.8,0.2,0.4,0.7,0.6,0.8,0.8,0.3])
>>> matrix
[[0.0, 0.5, 0.67, 0.8, 0.2],
[0.5, 0.0, 0.4, 0.7, 0.6],
[0.67, 0.4, 0.0, 0.8, 0.8],
[0.8, 0.7, 0.8, 0.0, 0.3],
[0.2, 0.6, 0.8, 0.3, 0.0]]
Carry out the fuzzy flat cluster analysis.
>>> fuzzy(0.5,matrix,taxa)
{1: ['Swedish', 'Icelandic'], 2: ['Dutch', 'German'], 3: ['Dutch', 'English']}
Notes
-----
This is a very simple fuzzy clustering algorithm. It basically does nothing
else than removing taxa successively from the matrix, flat-clustering the
remaining taxa with the corresponding threshold, and then returning a
combined "consensus" cluster in which taxa may be assigned to multiple
clusters.
See also
--------
link_clustering
"""
if nx is None:
log.missing_module('networkx')
return
g = nx.Graph()
for taxon in taxa:
g.add_node(taxon)
for idx, taxon in enumerate(taxa):
new_matrix = []
for i, line in enumerate(matrix):
for j, cell in enumerate(line):
if i < j and i != idx and j != idx:
new_matrix += [cell]
new_matrix = misc.squareform(new_matrix)
clusters = cluster.flat_cluster(
method, threshold, new_matrix, [t for t in taxa if t != taxon])
for clr in clusters:
for i, tA in enumerate(clusters[clr]):
for j, tB in enumerate(clusters[clr]):
if i < j:
try:
g.edge[tA][tB]['weight'] += 1
except:
g.add_edge(tA, tB, weight=1)
out = {i + 1: c for i, c in enumerate(nx.find_cliques(g))}
if revert:
new_out = defaultdict(list)
for key, val in out.items():
for v in val:
new_out[v].append(key)
return new_out
return out
"""
Utility functions for borrowing detection with the PhyBo class.
"""
from lingpy import log
from lingpy.util import write_text_file, as_string
try:
import scipy.stats as sps
except ImportError:
log.missing_module('scipy')
def tstats(wordlist,
glm='',
network=False,
acs=False,
tree=False,
singletons=True,
return_dists=False):
"""
Calculate transmission statistics for a given MLN.
"""
# check for attributes
# return if no glm and no network is given
if not glm and not network:
raise ValueError(
"You must specify at least one network or a gain-loss model.")
# check for acs and network
if glm:
"""
Conversion routines for the GML format.
"""
from lingpy import log
from lingpy import util
import networkx as nx
try:
import igraph as ig
except:
log.missing_module('igraph')
import numpy as np
from lingpy.thirdparty import cogent as cg
def networkx2igraph(graph):
"""Helper function converts networkx graph to igraph graph object."""
newgraph = ig.Graph(directed=graph.is_directed())
nodes = {}
for i, (node, data) in enumerate(graph.nodes(data=True)):
data = {a: b for a, b in data.items()}
newgraph.add_vertex(i,
Name=node,
**{
a: b
for a, b in data.items()
if a not in [b'Name', b'name', 'Name', 'name']
})
# *-* coding: utf-8 *-*
"""
Utility functions for borrowing detection with the PhyBo class.
"""
from __future__ import unicode_literals, division, print_function
from lingpy import log
try:
import scipy.stats as sps
except:
log.missing_module('scipy')
def tstats(
wordlist,
glm='',
network=False,
acs=False,
tree=False,
singletons=True,
return_dists=False
):
"""
Calculate transmission statistics for a given MLN.
"""
# check for attributes
# return if no glm and no network is given
if not glm and not network:
raise ValueError("You must specify at least one network or a gain-loss model.")
try:
from .cython import cluster as cluster
except ImportError:
from .cython import _cluster as cluster
from lingpy.thirdparty import linkcomm as lc
from lingpy.thirdparty import cogent as cg
from lingpy import log
from lingpy import util
try: # pragma: no cover
import networkx as nx
except ImportError:
nx = None
log.missing_module('networkx')
def flat_upgma(threshold, matrix, taxa=None, revert=False):
"""
Carry out a flat cluster analysis based on the UPGMA algorithm \
(:evobib:`Sokal1958`).
Parameters
----------
threshold : float
The threshold which terminates the algorithm.
matrix : list
A two-dimensional list containing the distances.
from __future__ import unicode_literals, print_function, division
from lingpy import log
import numpy as np
try:
import matplotlib.patches as mplPatches
except:
log.missing_module('matplotlib')
try:
import networkx as nx
except:
log.missing_module('networkx')
from .convex_hull import convex_hull
def perp(a):
"""
code for intersection taken from
http://stackoverflow.com/questions/3252194/numpy-and-line-intersections
"""
b = np.empty_like(a)
b[0] = -a[1]
b[1] = a[0]
return b
# line segment a given by endpoints a1, a2
# email : [email protected] # created : 2013-09-25 11:47 # modified : 2013-11-22 15:43 """ Utility functions for borrowing detection with the PhyBo class. """ __author__ = "Johann-Mattis List" __date__ = "2013-11-22" from lingpy import log try: import networkx as nx except: log.missing_module('networkx') try: import scipy.stats as sps except: log.missing_module('scipy') def tstats(wordlist, glm='', network=False, acs=False, tree=False, singletons=True, return_dists=False): """ Calculate transmission statistics for a given MLN.
"""
Conversion routines for the GML format.
"""
from __future__ import unicode_literals, print_function, division
from lingpy import log
from lingpy import util
try:
import networkx as nx
except:
log.missing_module('networkx')
try:
import igraph as ig
except:
log.missing_module('igraph')
import numpy as np
from lingpy.thirdparty import cogent as cg
def networkx2igraph(graph):
"""Helper function converts networkx graph to igraph graph object."""
newgraph = ig.Graph(directed=graph.is_directed())
nodes = {}
for i,(node, data) in enumerate(graph.nodes(data=True)):
newgraph.add_vertex(i,Name=node, **dict([(a,b) for a,b in data.items()
if a not in ['Name','name']]))
nodes[node] = i
# imports
from ...settings import rcParams
from lingpy import log
import numpy as np
try:
import matplotlib.pyplot as plt
import matplotlib.patches as mplPatches
except:
log.missing_module('matplotlib')
try:
import networkx as nx
except:
log.missing_module('networkx')
from .convex_hull import *
def perp(a):
"""
code for intersection taken from http://stackoverflow.com/questions/3252194/numpy-and-line-intersections
"""
b = np.empty_like(a)
b[0] = -a[1]
b[1] = a[0]
return b
# line segment a given by endpoints a1, a2
"""
Adapting specific cluster algorithms from scikit-learn to LingPy.
"""
from lingpy import log
try:
from sklearn import cluster
except ImportError:
log.missing_module('sklearn')
import numpy as np
def dbscan(
threshold,
matrix,
taxa,
revert=False,
min_samples=1):
"""
Compute DBSCAN cluster analysis.
"""
if not taxa:
taxa = list(range(1, len(matrix) + 1))
core_samples, labels = cluster.dbscan(
matrix, eps=threshold, min_samples=min_samples, metric='precomputed')
# change to our internal cluster style
idx = max(labels) + 1
if idx == 0:
idx += 1