def test_get_GLS(self):
phy = PhyBo(self.inputfile, output_dir=self.tmp)
# test default scenario
phy.get_GLS()
# check for weight in one of the scenarios
assert phy.gls['w-1-1']['2:1'][1] == 9
assert phy.gls['w-1-1']['8:1'][1] == 2
# test restriction scenario
phy.get_GLS(mode='restriction', force=True)
assert phy.gls['r-3']['12:1'][1] == 3
assert phy.gls['r-3']['8:1'][1] == 2
# test topdown, somehow, the algorithmic ordering leads to unstable
# outputs, this should be fixed, but for testing, it is not unexpected
# right now, which is why I change to the less than construct for the
# moment
phy.get_GLS(mode='topdown', force=True)
assert phy.gls['t-3']['29:3'][1] < 3
assert phy.gls['t-3']['8:1'][1] == 2
glm = list(phy.stats.keys())[0]
phy.get_stats(glm)
def test_get_GLS(self):
phy = PhyBo(self.inputfile, output_dir=self.tmp)
# test default scenario
phy.get_GLS()
# check for weight in one of the scenarios
assert phy.gls['w-1-1']['2:1'][1] == 9
assert phy.gls['w-1-1']['8:1'][1] == 2
# test restriction scenario
phy.get_GLS(mode='restriction', force=True)
assert phy.gls['r-3']['12:1'][1] == 3
assert phy.gls['r-3']['8:1'][1] == 2
# test topdown, somehow, the algorithmic ordering leads to unstable
# outputs, this should be fixed, but for testing, it is not unexpected
# right now, which is why I change to the less than construct for the
# moment
phy.get_GLS(mode='topdown', force=True)
assert phy.gls['t-3']['29:3'][1] < 3
assert phy.gls['t-3']['8:1'][1] == 2
glm = list(phy.stats.keys())[0]
phy.get_stats(glm)
def test_utils(self):
try:
import scipy.stats
except ImportError:
return
with patch('lingpy.compare._phylogeny.utils.sps', new=SPS()):
phy = PhyBo(self.ifile, output_dir=self.tmp.as_posix())
phy.analyze()
get_acs(phy, phy.best_model)
tstats(phy, phy.best_model, return_dists=True)
check_stats([phy.best_model], phy, filename=os.path.join(self.tmp.as_posix(),
'test'), pprint=False)
def test_utils(self):
try:
import scipy.stats
except ImportError:
return
with patch('lingpy.compare._phylogeny.utils.sps', new=SPS()):
phy = PhyBo(self.ifile, output_dir=self.tmp.as_posix())
phy.analyze()
get_acs(phy, phy.best_model)
tstats(phy, phy.best_model, return_dists=True)
check_stats([phy.best_model], phy,
filename=os.path.join(self.tmp.as_posix(), 'test'),
pprint=False)
def construct_mln(tsv_file, newick_tree): tre = PhyBo(str(tsv_file)+'.tsv', degree=175, missing=0, tree=newick_tree) tre.analyze(runs = 'default', plot_dists = True, gpl = 1, push_gains = True, missing_data = 0, homoplasy = 0.05) # get the minimal lateral network. Assume, for now a threshold of 1% of the total number of words to count as a borrowing tre.get_MLN(tre.best_model, method='mr', threshold=10) # plot the MLN tre.plot_MLN(tre.best_model) return(tre.best_model)
def test_utils(mocker, SPS, ifile, tmppath):
try:
import scipy.stats
except ImportError: # pragma: no cover
return
mocker.patch('lingpy.compare._phylogeny.utils.sps', new=SPS)
phy = PhyBo(ifile, output_dir=str(tmppath))
phy.analyze()
get_acs(phy, phy.best_model)
tstats(phy, phy.best_model, return_dists=True)
check_stats([phy.best_model],
phy,
filename=str(tmppath / 'test'),
pprint=False)
def test_plot(self):
phy = PhyBo(self.inputfile, output_dir=self.tmp)
phy.get_GLS()
glm = list(phy.stats.keys())[0]
phy.plot_concept_evolution(glm)
phy.plot_GLS(glm)
phy.plot_ACS(glm)
phy.get_MLN(glm)
phy.graph = defaultdict(lambda: Graph())
phy.plot_MLN(glm)
phy.analyze()
phy.get_IVSD()
phy.get_MSN()
glm = list(phy.geograph.keys())[0]
phy.geograph[glm] = MagicMock(
edges=lambda **kw: [(MagicMock(), MagicMock(), defaultdict(lambda: 2))])
phy._meta['conf'] = dict(linescale=2, groups_colors=defaultdict(lambda: 'a'))
phy.plot_MSN(glm)
def test_plot(self):
phy = PhyBo(self.inputfile, output_dir=self.tmp.as_posix())
phy.get_GLS()
glm = list(phy.stats.keys())[0]
phy.plot_GLS(glm)
phy.plot_ACS(glm)
for method in ['bc', 'td', 'mr']:
phy.get_MLN(glm, method=method)
phy.plot_MLN(glm)
phy.plot_MLN_3d(glm)
phy.analyze(runs=[('weighted', (2, 1))], output_gml=True,
output_plot=True)
phy.get_IVSD()
phy.get_ACS(phy.best_model)
phy.get_MSN(phy.best_model)
phy.get_MSN(phy.best_model, deep_nodes=True)
phy.plot_MSN(phy.best_model, conf=phy.conf)
phy.plot_two_concepts('I', '15:2', '16:2')
edge1, edge2 = list(phy.graph[phy.best_model].edges())[0]
phy.get_edge(phy.best_model, edge1, edge2)
phy.get_edge(phy.best_model, 'Beijing', 'Shanghai')
phy.get_edge(phy.best_model, edge1, edge2, msn=True)
phy.get_PDC('w-2-1', aligned_output=True)
phy.plot_concept_evolution(phy.best_model, concept="I")
print('[i] Carrying out alignment analyses.')
msa.align(
method='library',
output = True
)
print('[i] Writing results to wordlist file.')
msa.output('qlc',filename='DOGON_alignments')
print("[i] Plotting the alignments.")
msa.output('html',filename='DOGON')
print("[i] Starting with the borrowing detection.")
from lingpy.compare.phylogeny import PhyBo
tre = PhyBo('DOGON_lexstat.qlc',
ref='lexstatid', degree=180)
tre.analyze(runs='weighted', mode='mixed')
print("[i] Plotting the results.")
# make nice labels for the taxon-names
labels = dict(
[(t,t[:9]) for t in tre.taxa]
)
tre.get_MLN(tre.best_model)
tre.plot_MLN(
tre.best_model,
filename='DOGON',
fileformat='svg',
figsize=(15,7),
labels=labels
def test_plot(self):
phy = PhyBo(self.inputfile, output_dir=self.tmp)
phy.get_GLS()
glm = list(phy.stats.keys())[0]
phy.plot_concept_evolution(glm)
phy.plot_GLS(glm)
phy.plot_ACS(glm)
phy.get_MLN(glm)
phy.graph = defaultdict(lambda: Graph())
phy.plot_MLN(glm)
phy.analyze()
phy.get_IVSD()
phy.get_MSN()
glm = list(phy.geograph.keys())[0]
phy.geograph[glm] = MagicMock(edges=lambda **kw: [(MagicMock(
), MagicMock(), defaultdict(lambda: 2))])
phy._meta['conf'] = dict(linescale=2,
groups_colors=defaultdict(lambda: 'a'))
phy.plot_MSN(glm)
cols=['concepts', 'taxa', 'counterpart', 'tokens', 'lexstatid'])
print('[i] Loading data into the Alignments class.')
msa = Alignments('DOGON_lexstat.qlc', ref='lexstatid')
print('[i] Carrying out alignment analyses.')
msa.align(method='library', output=True)
print('[i] Writing results to wordlist file.')
msa.output('qlc', filename='DOGON_alignments')
print("[i] Plotting the alignments.")
msa.output('html', filename='DOGON')
print("[i] Starting with the borrowing detection.")
from lingpy.compare.phylogeny import PhyBo
tre = PhyBo('DOGON_lexstat.qlc', ref='lexstatid', degree=180)
tre.analyze(runs='weighted', mode='mixed')
print("[i] Plotting the results.")
# make nice labels for the taxon-names
labels = dict([(t, t[:9]) for t in tre.taxa])
tre.get_MLN(tre.best_model)
tre.plot_MLN(tre.best_model,
filename='DOGON',
fileformat='svg',
figsize=(15, 7),
labels=labels)
def setup(self):
self.phy = PhyBo('.../test_data/sagart.qlc')
def test_plot(inputfile, mocker, Bmp, Sp, Plt, tmppath):
mocker.patch('lingpy.compare.phylogeny.mpl', new=mocker.MagicMock())
mocker.patch('lingpy.compare.phylogeny.gls2gml', new=mocker.MagicMock())
mocker.patch('lingpy.compare.phylogeny.plot_tree', new=mocker.MagicMock())
mocker.patch('lingpy.compare.phylogeny.bmp', new=Bmp)
mocker.patch('lingpy.compare.phylogeny.plt', new=Plt)
mocker.patch('lingpy.compare.phylogeny.sp', new=Sp)
phy = PhyBo(inputfile, output_dir=str(tmppath))
phy.get_GLS()
glm = list(phy.stats.keys())[0]
phy.plot_GLS(glm)
phy.plot_ACS(glm)
for method in ['bc', 'td', 'mr']:
phy.get_MLN(glm, method=method)
phy.plot_MLN(glm)
phy.plot_MLN_3d(glm)
phy.analyze(runs=[('weighted', (2, 1))], output_gml=True, output_plot=True)
phy.get_IVSD()
phy.get_ACS(phy.best_model)
phy.get_MSN(phy.best_model)
phy.get_MSN(phy.best_model, deep_nodes=True)
phy.plot_MSN(phy.best_model, conf=phy.conf)
phy.plot_two_concepts('I', '15:2', '16:2')
edge1, edge2 = list(phy.graph[phy.best_model].edges())[0]
phy.get_edge(phy.best_model, edge1, edge2)
phy.get_edge(phy.best_model, 'Beijing', 'Shanghai')
phy.get_edge(phy.best_model, edge1, edge2, msn=True)
phy.get_PDC('w-2-1', aligned_output=True)
phy.plot_concept_evolution(phy.best_model, concept="I")
