def evaluate_string(self, string, tokens=False, **keywords):
setdefaults(keywords, stress=rcParams['stress'],
diacritics=rcParams['diacritics'], cldf=False)
if not tokens:
tokens = ipa2tokens(string)
score = 1
dist = self.dist['#']
prostring = prosodic_string(tokens, rcParams['art'], cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
if self.classes:
c = tokens2class(tokens, self.model, cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
teststring = list(zip(prostring, c))
else:
teststring = list(zip(prostring, tokens))
scores = []
while len(teststring) > 0:
segment = teststring.pop(0)
freq = dist.count(segment)
allf = len(dist)
s = freq / allf
score = score * s
scores += [s]
dist = self.dist[segment]
score = score * s
scores += [s]
lscore = np.log10(score)
lscore = lscore / len(tokens)
return score, lscore # np.log10(score)
def evaluate_string(self, string, tokens=False, **keywords):
setdefaults(keywords, stress=rcParams['stress'],
diacritics=rcParams['diacritics'], cldf=False)
if not tokens:
tokens = ipa2tokens(string)
score = 1
dist = self.dist['#']
prostring = prosodic_string(tokens, rcParams['art'], cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
if self.classes:
c = tokens2class(tokens, self.model, cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
teststring = list(zip(prostring, c))
else:
teststring = list(zip(prostring, tokens))
scores = []
while len(teststring) > 0:
segment = teststring.pop(0)
freq = dist.count(segment)
allf = len(dist)
s = freq / allf
score = score * s
scores += [s]
dist = self.dist[segment]
score = score * s
scores += [s]
lscore = np.log10(score)
lscore = lscore / len(tokens)
return score, lscore # np.log10(score)
def __init__(self,
words,
tokens=False,
prostrings=[],
classes=False,
class_model=rcParams['model'],
**keywords):
setdefaults(keywords,
stress=rcParams['stress'],
diacritics=rcParams['diacritics'],
cldf=False)
self.model = class_model
self.words = words
self.tokens = []
self.bigrams = []
self.classes = []
# start filling the dictionary
for i, w in enumerate(words):
# check for tokenized string
if not tokens:
tk = ipa2tokens(w, **keywords)
else:
tk = w[:]
self.tokens += [tk]
# create prosodic string
if prostrings:
p = prostrings[i]
else:
print(w, tk)
tt = tokens2class(tk, rcParams['art'])
print(tt)
p = prosodic_string(tk,
rcParams['art'],
cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
# create classes
if classes:
c = tokens2class(tk,
class_model,
cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
bigrams = list(zip(p, c))
self.classes += [c]
else:
# zip the stuff
bigrams = list(zip(p, tk))
# start appending the stuff
self.bigrams += [bigrams]
# init the mother object
MCBasic.__init__(self, self.bigrams)
def __init__(
self,
words,
tokens=False,
prostrings=[],
classes=False,
class_model=rcParams['model'],
**keywords
):
setdefaults(keywords, stress=rcParams['stress'],
diacritics=rcParams['diacritics'], cldf=False)
self.model = class_model
self.words = words
self.tokens = []
self.bigrams = []
self.classes = []
# start filling the dictionary
for i, w in enumerate(words):
# check for tokenized string
if not tokens:
tk = ipa2tokens(w, **keywords)
else:
tk = w[:]
self.tokens += [tk]
# create prosodic string
if prostrings:
p = prostrings[i]
else:
tt = tokens2class(tk, rcParams['art'])
p = prosodic_string(
tk,
rcParams['art'],
cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
# create classes
if classes:
c = tokens2class(tk, class_model, cldf=keywords['cldf'],
diacritics=keywords['diacritics'],
stress=keywords['stress'])
bigrams = list(zip(p, c))
self.classes += [c]
else:
# zip the stuff
bigrams = list(zip(p, tk))
# start appending the stuff
self.bigrams += [bigrams]
# init the mother object
MCBasic.__init__(self, self.bigrams)
def check_tokens(tokens, **keywords):
"""
Function checks whether tokens are given in a consistent input format.
"""
setdefaults(keywords, stress=rcParams['stress'],
diacritics=rcParams['diacritics'], cldf=False)
errors = []
for i, token in enumerate(tokens):
# check for conversion within the articulation-model
cls = token2class(token, rcParams['art'], stress=keywords['stress'],
cldf=keywords['cldf'], diacritics=keywords['diacritics'])
if cls == '0':
errors.append((i, token))
return errors
def check_tokens(tokens, **keywords):
"""
Function checks whether tokens are given in a consistent input format.
"""
setdefaults(keywords, stress=rcParams['stress'],
diacritics=rcParams['diacritics'], cldf=False)
errors = []
for i, token in enumerate(tokens):
# check for conversion within the articulation-model
cls = token2class(token, rcParams['art'], stress=keywords['stress'],
cldf=keywords['cldf'], diacritics=keywords['diacritics'])
if cls == '0':
errors.append((i, token))
return errors
def diff(self, **keywords):
"""
Write all differences between two sets to a file.
Parameters
----------
filename : str (default='eval_psa_diff')
Default
"""
setdefaults(keywords, filename=self.gold.infile)
if not keywords['filename'].endswith('.diff'):
keywords['filename'] = keywords['filename'] + '.diff'
out = []
for i, (a,
b) in enumerate(zip(self.gold.alignments,
self.test.alignments)):
g1, g2, g3 = a
t1, t2, t3 = b
maxL = max([len(g1), len(t1)])
if g1 != t1 or g2 != t2:
taxA, taxB = self.gold.taxa[i]
taxlen = max(len(taxA), len(taxB))
seq_id = self.gold.seq_ids[i]
out.append(
'{0}\n{1}\t{2}\n{3}\t{4}\n{5}\n{1}\t{6}\n{3}\t{7}\n\n'.
format(
seq_id,
taxA,
'\t'.join(g1),
taxB,
'\t'.join(g2),
'{0}\t{1}'.format(
taxlen * ' ',
'\t'.join(['==' for x in range(maxL)])),
'\t'.join(t1),
'\t'.join(t2),
))
log.file_written(keywords['filename'])
write_text_file(keywords['filename'], out)
def diff(self, **keywords):
"""
Write all differences between two sets to a file.
Parameters
----------
filename : str (default='eval_psa_diff')
Default
"""
setdefaults(keywords, filename=self.gold.infile)
if not keywords['filename'].endswith('.diff'):
keywords['filename'] = keywords['filename'] + '.diff'
out = []
for i, (a, b) in enumerate(zip(self.gold.alignments, self.test.alignments)):
g1, g2, g3 = a
t1, t2, t3 = b
maxL = max([len(g1), len(t1)])
if g1 != t1 or g2 != t2:
taxA, taxB = self.gold.taxa[i]
taxlen = max(len(taxA), len(taxB))
seq_id = self.gold.seq_ids[i]
out.append('{0}\n{1}\t{2}\n{3}\t{4}\n{5}\n{1}\t{6}\n{3}\t{7}\n\n'.format(
seq_id,
taxA,
'\t'.join(g1),
taxB,
'\t'.join(g2),
'{0}\t{1}'.format(
taxlen * ' ', '\t'.join(['==' for x in range(maxL)])),
'\t'.join(t1),
'\t'.join(t2),
))
log.file_written(keywords['filename'])
write_text_file(keywords['filename'], out)
def check_tokens(tokens, **keywords):
"""
Function checks whether tokens are given in a consistent input format.
"""
setdefaults(keywords, stress=rcParams['stress'])
errors = []
for i, token in enumerate(tokens):
# check for conversion within the articulation-model
try:
rcParams['art'].converter[token]
except KeyError:
try:
rcParams['art'].converter[token[0]]
except KeyError:
if token[0] in keywords['stress']:
try:
rcParams['art'].converter[token[1]]
except KeyError:
errors.append((i, token))
else:
errors.append((i, token))
return errors
def check_tokens(tokens, **keywords):
"""
Function checks whether tokens are given in a consistent input format.
"""
setdefaults(keywords, stress=rcParams['stress'])
errors = []
for i, token in enumerate(tokens):
# check for conversion within the articulation-model
try:
rcParams['art'].converter[token]
except KeyError:
try:
rcParams['art'].converter[token[0]]
except KeyError:
if token[0] in keywords['stress']:
try:
rcParams['art'].converter[token[1]]
except KeyError:
errors.append((i, token))
else:
errors.append((i, token))
return errors
def align(self, **keywords):
"""
Align a pair of sequences or multiple sequence pairs.
Parameters
----------
gop : int (default=-1)
The gap opening penalty (GOP).
scale : float (default=0.5)
The gap extension penalty (GEP), calculated with help of a scaling
factor.
mode : {"global","local","overlap","dialign"}
The alignment mode, see :evobib:`List2012a` for details.
factor : float (default = 0.3)
The factor by which matches in identical prosodic position are
increased.
restricted_chars : str (default="T\_")
The restricted chars that function as an indicator of syllable or
morpheme breaks for secondary alignment, see :evobib:`List2012c`
for details.
distance : bool (default=False)
If set to *True*, return the distance instead of the similarity
score. Distance is calculated using the formula by
:evobib:`Downey2008`.
model : { None, ~lingpy.data.model.Model }
Specify the sound class model that shall be used for the analysis.
If no model is specified, the default model of :evobib:`List2012a`
will be used.
pprint : bool (default=False)
If set to *True*, the alignments are printed to the screen.
"""
setdefaults(
keywords,
gop=-1,
scale=0.5,
mode='global',
factor=0.3,
restricted_chars='T_',
distance=False,
model=rcParams['sca'],
pprint=False,
transform=rcParams['align_transform'])
if hasattr(self, 'model'):
if keywords['model'] != self.model:
self._set_model(**keywords)
else:
self._set_model(**keywords)
# create the alignments array
self._alignments = calign.align_pairs(
self.classes,
self.weights,
self.prostrings,
keywords['gop'],
keywords['scale'],
keywords['factor'],
self.scoredict,
keywords['mode'],
keywords['restricted_chars'],
distance=1 if keywords['distance'] else 0)
# switch back to alignments
self.alignments = []
for i, (almA, almB, sim) in enumerate(self._alignments):
self.alignments.append((
class2tokens(self.tokens[i][0], almA, local=keywords['mode'] == "local"),
class2tokens(self.tokens[i][1], almB, local=keywords['mode'] == "local"),
sim))
# print the alignments, if this is chosen
as_string(self, pprint=keywords['pprint'])
def psa2html(infile, **kw):
"""
Function converts a PSA-file into colored html-format.
"""
util.setdefaults(kw,
template=False,
css=False,
comment='#',
filename=infile[:-4] + '.html',
compact=True)
template = util.read_text_file(kw['template'] or template_path('psa.html'))
css = util.read_text_file(kw['css'] or template_path('psa.css'))
data = []
for line in util.read_text_file(infile, lines=True):
if not line.startswith(kw['comment']):
data.append(line)
seq_ids = []
pairs = []
taxa = []
alignments = []
del data[0]
i = 0
while i <= len(data) - 3:
try:
seq_ids.append(data[i])
datA = data[i + 1].split('\t')
datB = data[i + 2].split('\t')
taxonA = datA[0].strip('.')
taxonB = datB[0].strip('.')
almA = datA[1:]
almB = datB[1:]
taxa.append((taxonA, taxonB))
pairs.append(('.'.join([k for k in almA if k != '-']),
'.'.join([k for k in almB if k != '-'])))
alignments.append(
([str(a) for a in almA], [str(b) for b in almB], 0))
assert len(alignments[-1][0]) == len(alignments[-1][1])
i += 4
except AssertionError:
log.warning("Line {0} of the data is probably miscoded.".format(i +
1))
i += 1
def get_classes(alm):
classes = []
residue = '<div class="residue {1}">{0}</div>'
for j, char in enumerate(alm):
if char == '-':
d = 'dolgo_GAP'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
classes += [residue.format(char, d)]
return ''.join(classes)
out = '<table>\n' # codecs.open(kw['filename'], 'w', 'utf-8')
for i, (a, b, c) in enumerate(alignments):
clsA = get_classes(a)
clsB = get_classes(b)
ids = int(100 * pid(a, b) + 0.5)
out += '<tr class="head">'
out += '<td colspan=2 class="head"><b>Alignment {0}:</b> <i>{1}</i>, PID: {2}</td></tr>'.format(
i + 1, seq_ids[i], ids)
out += '<tr class="psa">'
out += '<td class="taxon">{0}</td>'.format(taxa[i][0])
out += '<td class="psa">{0}</td>'.format(clsA)
out += '</tr>'
out += '<tr class="psa">'
out += '<td class="taxon">{0}</td>'.format(taxa[i][1])
out += '<td class="psa">{0}</td>'.format(clsB)
out += '</tr>'
out += '<tr><td colspan=2></td></tr>'
out += '</table>'
html = template.format(alignments=out, css=css)
if kw['compact']:
html = html.replace('\n', ' ')
html = re.sub(r'\s+', r' ', html)
html = html.replace('> ', '>')
html = html.replace(' >', '>')
util.write_text_file(kw['filename'], html)
def msa2tex(infile, template='', filename='', **keywords):
"""
Convert an MSA to a tabular representation which can easily be used in
LaTeX documents.
"""
util.setdefaults(keywords, pid_mode=1)
# while alm-format can be read from the text-file without problems,
# msa-format should be loaded first (once this is already provided), the
# loss in speed won't matter much, since output of data is not a daily task
# load msa
msa = read_msa(infile)
## load templates
tex = util.read_text_file(template or template_path('msa.tex'))
# calculate pid score, if it is not passed as argument
if 'pid_score' not in keywords:
pid_score = 0
count = 0
for i, seqA in enumerate(msa['alignment']):
for j, seqB in enumerate(msa['alignment']):
if i < j:
pid_score += pid(seqA, seqB, mode=keywords['pid_mode'])
count += 1
pid_score = int(100 * pid_score / count + 0.5)
else:
pid_score = keywords['pid_score']
dataset = msa['dataset']
infile = msa['infile']
seq_id = msa['seq_id']
# determine the length of the longest taxon
taxl = max([len(t) for t in msa['taxa']])
height = len(msa['alignment'])
width = len(msa['alignment'][0])
start = r'\tabular{l' + width * 'c' + '}\n'
start += r'\bf\ttfamily Taxon & \multicolumn{' + str(
width) + r'}{l}{\bf\ttfamily Alignment}\\' + '\n'
# check for swaps in the alignment
if 'swaps' in msa:
swaps = []
for s in msa['swaps']:
swaps.extend(s)
else:
swaps = []
body = start
for i, taxon in enumerate(msa['taxa']):
body += r'\ttfamily ' + taxon.replace('_', r'\_')
for j, char in enumerate(msa['alignment'][i]):
if char != '-':
cls = token2class(char, rcParams['dolgo'])
elif char == '-':
cls = 'X'
if char == '_':
char = r'\#'
if cls == '_':
cls = '2'
if j not in swaps:
body += r'&\cellcolor{col' + cls + r'}' + char
else:
if char != '-':
body += r'&\cellcolor{col' + cls + r'}\color{white}\bf ' + char
else:
body += r'&\cellcolor{col' + cls + r'}\bf ' + char
body += r'\\' + '\n'
body += r'&' + '&'.join([r'\color{white}XXX'
for i in range(width)]) + r'\\' + '\n'
body += r'\endtabular' + '\n'
# create the parameters etc.
w = 1.5 * width + taxl * 0.25
h = 0.5 * height + 1.0
tex = tex.replace('<+WIDTH+>', '{0:2f}'.format(w))
tex = tex.replace('<+HEIGHT+>', '{0:2f}'.format(h))
# create the rput stuff
tex = tex.replace('<+NEWX+>', '{0:.2f}'.format(w / 2.0))
tex = tex.replace('<+NEWY+>', '{0:.2f}'.format((h - 0.5) / 2.0))
# insert the rest
tex = tex.replace('<+CONTENT+>', body)
# write to file
if not filename:
filename = 'lingpy-{0}'
util.write_text_file(filename + '.tex', tex)
def alm2html(infile,
title='',
shorttitle='',
filename='',
colored=False,
main_template='',
table_template='',
dataset='',
confidence=False,
**keywords):
"""
Convert files in ``alm``-format into colored ``html``-format.
Parameters
----------
title : str
Define the title of the output file. If no title is provided, the
default title ``LexStat - Automatic Cognate Judgments`` will be used.
shorttitle : str
Define the shorttitle of the ``html``-page. If no title is provided,
the default title ``LexStat`` will be used.
Notes
-----
The coloring of sound segments with respect to the sound class they belong
to is based on the definitions given in the
``color`` :py:class:`~lingpy.data.model.Model`. It can easily be changed
and adapted.
See also
--------
lingpy.convert.html.msa2html
lingpy.convert.html.msa2tex
"""
util.setdefaults(keywords, json="", labels={})
# open the infile
if not os.path.exists(infile):
infile = infile + '.alm'
data = util.read_text_file(infile)
# create the outfile
if not filename:
filename = rcParams['filename']
# read in the templates
html = util.read_text_file(main_template or template_path('alm2html.html'))
if not table_template:
table_template = template_path(
'alm2html.table.js.html' if confidence else 'alm2html.table.html')
table = util.read_text_file(table_template)
css = util.read_text_file(template_path('alm.css'))
js = util.read_text_file(template_path('alm.js'))
# define a label function for the taxa
label = lambda x: keywords['labels'][x] if x in keywords['labels'] else x
# check for windows-compatibility
data = data.replace(os.linesep, '\n')[:-1]
# split the data into blocks
blocks = data.split('\n\n')
# retrieve the dataset
dataset = dataset or blocks[0]
# create the outstring
tmp_str = ''
for block in blocks[1:]:
lines = block.split('\n')
m = [l.split('\t') for l in lines]
# create colordict for different colors
dc = len(set([l[0] for l in m]))
if colored:
colors = {
a: b
for a, b in zip(
sorted(set([int(l[0]) for l in m])),
colorRange(dc, brightness=400),
)
}
else:
colors = []
white = True
for i in sorted(set([abs(int(l[0])) for l in m])):
if white:
colors.append((i, 'white'))
white = False
else:
colors.append((i, 'gray'))
white = True
colors = dict(colors)
# get the basic item and its id
iName = m[0][2]
iID = m[0][3]
# start writing the stuff to string
tmp_str += table.format(NAME=iName, ID=iID)
# define the basic string for the insertion
bas = ' <tr class="{0}{2} taxon" taxon="{3}">\n{1}'
for tracer, l in enumerate(m):
# check whether the current line is a borrowing
if int(l[0]) < 0:
loan_line = ' loan'
else:
loan_line = ''
# assign the cognate id
tmp = ' <td>{0}</td>\n'.format(l[0])
tmp += ' <td>{0}</td>\n'.format(label(l[1].strip('.')))
# check alignments for confidence scores
ipa_string = ''.join([cell.split('/')[0]
for cell in l[4:]]).replace('-', '')
tmp += ' <td>{0}</td>\n'.format(ipa_string)
tmp += ' <td class="{0}">\n'.format(colors[abs(int(l[0]))])
tmp += ' <table class="{0}">\n'.format(colors[abs(int(l[0]))])
tmp += ' <tr>\n{0} </tr>\n </table>\n </td>\n </tr>\n'
# check whether another entry follows that is also an alignment,
# otherwise, there's no need to display a word as an alignment
cognate_set = False
if tracer < len(m) - 1:
if abs(int(m[tracer + 1][0])) == abs(int(l[0])):
cognate_set = True
if tracer > 0:
if abs(int(m[tracer - 1][0])) == abs(int(l[0])):
cognate_set = True
# fill out html for the cognate sets
if cognate_set:
alm = ''
for char in l[4:]:
# check for confidence scores
if '/' in char:
try:
char, conf, num = char.split('/')
conf = int(conf)
except ValueError:
print(char.split('/'))
raise ValueError("Something is wrong with %s." %
(char))
else:
char, conf, rgb = char, (255, 255, 255), 0.0
if char == '-':
d = 'dolgo_GAP'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
if confidence:
alm += ' '
alm += '<td class="char {1}" confidence={0} '.format(
conf, d)
alm += 'char="{0}" '.format(char)
alm += 'onclick="' + "show('{0}')".format(num) + '" '
alm += 'num="{0}"'.format(num)
alm += '>\n {0}\n </td>\n'.format(char)
else:
alm += ' '
alm += '<td class="char {0}">{1}</td>\n'.format(
d, char)
else:
alm = ' '
alm += '<td class="{0}">--</td>\n'.format(colors[abs(int(
l[0]))])
# format the alignment
try:
tmp = tmp.format(alm)
except ValueError:
raise ValueError("Unknown problem in matchin %s and %s." %
(alm, tmp))
# check for last line, where a new line should be inserted (not the
# fastest solution, but plotting is not a matter of time, and it
# suffices it's current purpose
if tracer < len(m) - 1:
pass
else:
if confidence:
tmp += ' </table>\n'
tmp += ' <tr class="empty"><td colspan="4" class="empty">'
tmp += '<hr class="empty" /></td></tr>\n'
# format the whole string
tmp_str += bas.format(colors[abs(int(l[0]))], tmp, loan_line, l[1])
if not title:
title = "LexStat - Automatic Cognate Judgments"
if not shorttitle:
shorttitle = "LexStat"
# check for json-attribute
if keywords['json']:
keywords['json'] = 'var myjson = ' + json.dumps(keywords['json'],
indent=1)
html = html.format(shorttitle=shorttitle,
title=title,
table=tmp_str,
dataset=dataset,
javascript=js,
css=css,
**keywords)
util.write_text_file(filename + '.html', html)
return
def msa2html(msa, shorttitle='', filename='', template='', **keywords):
"""
Convert files in ``msa``-format into colored ``html``-format.
Parameters
----------
msa : dict
A dictionary object that contains all the information of an MSA object.
shorttitle : str
Define the shorttitle of the ``html``-page. If no title is provided,
the default title ``SCA`` will be used.
filename : str (default="")
Define the name of the output file. If no name is defined, the name of
the input file will be taken as a default.
template : str (default="")
The path to the template file. If no name is defined, the basic
template will be used. The basic template currently used can be found
under ``lingpy/data/templates/msa2html.html``.
Examples
--------
Load the libary.
>>> from lingpy import *
Load an ``msq``-file from the test-sets.
>>> msa = MSA('harry.msq')
Align the data progressively and carry out a check for swapped sites.
>>> msa.prog_align()
>>> msa.swap_check()
>>> print(msa)
w o l - d e m o r t
w a l - d e m a r -
v - l a d i m i r -
Save the data to the file ``harry.msa``.
>>> msa.output('msa',filename='harry')
Save the ``msa``-object as ``html``.
>>> msa.output('html',filename='harry')
Notes
-----
The coloring of sound segments with respect to the sound class they belong
to is based on the definitions given in the ``color``
:py:class:`~lingpy.data.model.Model`. It can easily be changed and adapted.
See also
--------
lingpy.convert.html.alm2html
"""
util.setdefaults(
keywords,
pid_mode=1,
stress=rcParams['stress'],
css=False,
js=False,
compact=False,
class_sort=True,
write_to_file=True,
)
# while alm-format can be read from the text-file without problems,
# msa-format should be loaded first (once this is already provided), the
# loss in speed won't matter much, since output of data is not a daily task
# load templates
template = template or template_path('msa2html.html')
if template == 'js':
template = template_path('msa2html.js.html')
html = util.read_text_file(template)
css = util.read_text_file(keywords['css'] or template_path('msa.css'))
js = util.read_text_file(keywords['js'] or template_path('msa.js'))
# treat the msa-object as a file and try to load the file if this is the
# case
if isinstance(msa, string_types):
msa = read_msa(msa, **keywords)
else:
raise ValueError('[!] No filename specified.')
# load dataset, etc.
dataset = msa['dataset']
# calculate pid score, if it is not passed as argument
if 'pid_score' not in keywords:
pid_score = 0
count = 0
for i, seqA in enumerate(msa['alignment']):
for j, seqB in enumerate(msa['alignment']):
if i < j:
pid_score += pid(seqA, seqB, mode=keywords['pid_mode'])
count += 1
pid_score = int(100 * pid_score / count + 0.5)
else:
pid_score = keywords['pid_score']
infile = msa['infile']
seq_id = msa['seq_id']
# define the titles etc.
if not shorttitle:
shorttitle = 'SCA'
# determine the length of the longest taxon
taxl = max([len(t) for t in msa['taxa']])
# format css file
css = css.replace('TAXON_LENGTH', str(taxl * 10))
out = ''
tr = '<tr class="msa" unique="{1}" taxon={2} sequence={3}>{0}</tr>\n'
td_taxon = '<td class="taxon">{0}</td>'
perc = int(80 / len(msa['alignment'][0]) + 0.5)
td_residue = '<td class="residue {1}">{0}</td>'
td_swap = '<td class="residue swap {1}">{0}</td>'
td_unaligned = '<td class="residue noalign {1}">{0}</td>'
# check for swaps in the alignment
if 'swaps' in msa:
swaps = []
for s in msa['swaps']:
swaps.extend(s)
else:
swaps = []
# check for
local = ['*'] * len(msa['alignment'][0])
if 'local' in msa:
local = ['.'] * len(msa['alignment'][0])
for i in msa['local']:
local[i] = '*'
# get two sorting schemas for the sequences
if keywords['class_sort']:
classes = [
tokens2class(ipa2tokens(seq), rcParams['asjp'])
for seq in msa['seqs']
]
seqs = dict([
(a[1], b) for a, b in zip(
sorted(
zip(classes, msa['seqs']),
key=lambda x: x[0] # list(zip(x[0],x[1]))
),
range(1,
len(msa['seqs']) + 1))
])
else:
seqs = dict(zip(sorted(msa['seqs']), range(1, len(msa['seqs']) + 1)))
taxa = dict(zip(sorted(msa['taxa']), range(1, len(msa['taxa']) + 1)))
# set up a list to store unique alignments
alignments = []
# start iteration
for i, taxon in enumerate(msa['taxa']):
tmp = ''
tmp += td_taxon.format(taxon)
# append alignment to alignments
alignment = ''.join(msa['alignment'][i])
sequence = msa['seqs'][i]
if alignment in alignments:
unique = 'false'
else:
unique = 'true'
alignments += [alignment]
for j, char in enumerate(msa['alignment'][i]):
if char == '-':
d = 'dolgo_GAP'
c = '#bbbbbb'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
c = token2class(char, rcParams['_color'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
if j in swaps:
tmp += td_swap.format(char, d)
elif local[j] != '*':
tmp += td_unaligned.format(char, d)
else:
tmp += td_residue.format(char, d)
out += tr.format(tmp, unique, taxa[taxon], seqs[sequence])
html = html.format(
table=out,
dataset=dataset,
pid=pid_score,
file=infile,
sequence=seq_id,
shorttitle=shorttitle,
width=len(msa['alignment'][0]),
table_width='{0}'.format(len(msa['alignment'][0]) * 50 + 8 * taxl),
taxa=len(msa['alignment']),
uniseqs=len(set(msa['seqs'])),
css=css,
js=js)
if not filename:
filename = rcParams['filename']
if not filename.endswith('.html'):
filename = filename + '.html'
if keywords['compact']:
html = html.replace('\n', ' ')
html = re.sub(r'\s+', r' ', html)
html = html.replace('> ', '>')
html = html.replace(' >', '>')
if keywords['write_to_file']:
# check, whether the outfile already exists
util.write_text_file(filename, html)
else:
return html
def calculate_data(
wordlist,
data,
taxa='taxa',
concepts='concepts',
ref='cogid',
**keywords):
"""
Manipulate a wordlist object by adding different kinds of data.
Parameters
----------
data : str
The type of data that shall be calculated. Currently supports
* "tree": calculate a reference tree based on shared cognates
* "dst": get distances between taxa based on shared cognates
* "cluster": cluster the taxa into groups using different methods
"""
logger = log.get_logger()
util.setdefaults(
keywords,
distances=False,
tree_calc="upgma",
cluster="upgma",
force=False,
threshold=0.5,
cluster_method='upgma')
# get taxa for current calculation
these_taxa = eval('wordlist.' + taxa)
# calculate distances
if data in ['distances', 'dst']:
wordlist._meta['distances'] = wl2dst(
wordlist, taxa, concepts, ref, **keywords)
elif data in ['diversity', 'div']:
etd = wordlist.get_etymdict(ref=ref)
wordlist._meta['diversity'] = \
(len(etd) - wordlist.height) / (len(wordlist) - wordlist.height)
elif data in ['tre', 'tree', 'nwk']:
if 'distances' not in wordlist._meta:
wordlist._meta['distances'] = \
wl2dst(wordlist, taxa, concepts, ref, **keywords)
distances = wordlist._meta['distances']
if 'tree' in wordlist._meta and not keywords['force']:
logger.warn(
"Reference tree has already been calculated, "
"force overwrite by "
"setting 'force' to 'True'.")
return
wordlist._meta['tree'] = clustering.matrix2tree(
distances, these_taxa, keywords['tree_calc'],
keywords['distances'])
elif data in ['groups', 'cluster']:
if 'distances' not in wordlist._meta:
distances = wl2dst(wordlist, taxa, concepts, ref, **keywords)
else:
distances = wordlist._meta['distances']
if 'groups' in wordlist._meta and not keywords['force']:
logger.warn(
"Distance matrix has already been calculated, "
"force overwrite by "
"setting 'force' to 'True'.")
return
wordlist._meta['groups'] = clustering.matrix2groups(
keywords['threshold'], distances, these_taxa,
keywords['cluster_method'])
log.info("Successfully calculated {0}.".format(data))
def _export(
self,
fileformat,
sections=None,
entries=None,
entry_sep='',
item_sep='',
template='',
exclude=None,
entry_start='',
entry_close='',
**keywords):
"""
Export a wordlist to various file formats.
"""
if not sections:
if fileformat == 'txt':
sections = dict(
h1=('concept', '\n# Concept: {0}\n'),
h2=('cogid', '## Cognate-ID: {0}\n'))
elif fileformat == 'tex':
sections = dict(
h1=('concept', r'\section{{Concept: ``{0}"}}' + '\n'),
h2=('cogid', r'\subsection{{Cognate Set: ``{0}"}}' + '\n'))
elif fileformat == 'html':
sections = dict(
h1=('concept', '<h1>Concept: {0}</h1>'),
h2=('cogid', '<h2>Cognate Set: {0}</h2>'))
if not entries:
if fileformat == 'txt':
entries = [('language', '{0} '), ('ipa', '{0}\n')]
elif fileformat == 'tex':
entries = [('language', '{0} '), ('ipa', '[{0}]' + '\n')]
elif fileformat == 'html':
entries = [('language', '{0} '), ('ipa', '[{0}]\n')]
util.setdefaults(keywords, filename=rcParams['filename'])
# get the temporary dictionary
out = wl2dict(self, sections, entries, exclude)
# assign the output string
out_string = ''
# iterate over the dictionary and start to fill the string
for key in sorted(out, key=lambda x: str(x).lower()):
# write key to file
out_string += key[1]
# reassign tmp
tmp = out[key]
# set the pointer and the index
pointer = {0: [tmp, sorted(tmp.keys())]}
while True:
idx = max(pointer.keys())
# check for type of current point
if isinstance(tmp, dict):
if pointer[idx][1]:
next_key = pointer[idx][1].pop()
out_string += next_key[1]
tmp = pointer[idx][0][next_key]
if isinstance(tmp, dict):
pointer[idx + 1] = [tmp, sorted(tmp.keys())]
else:
pointer[idx + 1] = [tmp, tmp]
else:
del pointer[idx]
if idx == 0:
break
else:
tmp_strings = []
for line in sorted(tmp):
tmp_strings += [item_sep.join(line)]
out_string += entry_start + entry_sep.join(tmp_strings) + entry_close
tmp = pointer[idx - 1][0]
del pointer[idx]
if fileformat == 'tex':
out_string = out_string.replace('_', r'\_')
tmpl = util.read_text_file(template) if template else '{0}'
_write_file(keywords['filename'], tmpl.format(out_string), fileformat)
def _output(self, fileformat, **keywords):
"""
Internal function that eases its modification by daughter classes.
"""
# check for stamp attribute
keywords["stamp"] = getattr(self, '_stamp', '')
# add the default parameters, they will be checked against the keywords
util.setdefaults(
keywords,
cols=False,
distances=False,
entries=("concept", "counterpart"),
entry='concept',
fileformat=fileformat,
filename=rcParams['filename'],
formatter='concept',
modify_ref=False,
meta=self._meta,
missing=0,
prettify='false',
ignore='all',
ref='cogid',
rows=False,
subset=False, # setup a subset of the data,
taxa='taxa',
threshold=0.6, # threshold for flat clustering
tree_calc='neighbor')
if fileformat in ['triple', 'triples', 'triples.tsv']:
return tsv2triple(self, keywords['filename'] + '.' + fileformat)
if fileformat in ['paps.nex', 'paps.csv']:
paps = self.get_paps(
ref=keywords['ref'], entry=keywords['entry'], missing=keywords['missing'])
kw = dict(filename=keywords['filename'] + '.paps')
if fileformat == 'paps.nex':
kw['missing'] = keywords['missing']
return pap2nex(self.cols, paps, **kw)
return pap2csv(self.cols, paps, **kw)
# simple printing of taxa
if fileformat == 'taxa':
assert hasattr(self, 'taxa')
return util.write_text_file(keywords['filename'] + '.taxa', self.cols)
# csv-output
if fileformat in ['csv', 'qlc', 'tsv']:
# get the header line
header = sorted(
[s for s in set(self._alias.values()) if s in self._header],
key=lambda x: self._header[x])
header = [h.upper() for h in header]
self._meta.setdefault('taxa', self.cols)
# get the data, in case a subset is chosen
if not keywords['subset']:
# write stuff to file
return wl2qlc(header, self._data, **keywords)
cols, rows = keywords['cols'], keywords['rows']
if not isinstance(cols, (list, tuple, bool)):
raise ValueError("[i] Argument 'cols' should be list or tuple.")
if not isinstance(rows, (dict, bool)):
raise ValueError("[i] Argument 'rows' should be a dictionary.")
# check for chosen header
if cols:
# get indices for header
indices = [self._header[x] for x in cols]
header = [c.upper() for c in cols]
else:
indices = [r for r in range(len(self.header))]
if rows:
stmts = []
for key, value in rows.items():
if key == 'ID':
stmts += ["key " + value]
else:
idx = self._header[key]
stmts += ["line[{0}] ".format(idx) + value]
log.debug("calculated what should be excluded")
# get the data
out = {}
for key, line in self._data.items():
log.debug(key)
if rows:
if eval(" and ".join(stmts)):
out[key] = [line[i] for i in indices]
else:
out[key] = [line[i] for i in indices]
log.debug("passing data to wl2qlc")
return wl2qlc(header, out, **keywords)
# output dst-format (phylip)
if fileformat == 'dst':
# check for distances as keyword
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self, **keywords)
out = matrix2dst(self._meta['distances'], self.taxa,
stamp=keywords['stamp'], taxlen=keywords.get('taxlen', 0))
return _write_file(keywords['filename'], out, fileformat)
# output tre-format (newick)
if fileformat in ['tre', 'nwk']: # ,'cluster','groups']:
if 'tree' not in self._meta:
# check for distances
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self)
# we look up a function to calculate a tree in the cluster module:
tree = getattr(cluster, keywords['tree_calc'])(
self._meta['distances'], self.cols, distances=keywords['distances'])
else:
tree = self._meta['tree']
return _write_file(keywords['filename'], '{0}'.format(tree), fileformat)
if fileformat in ['cluster', 'groups']:
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self) # check for keywords
if 'groups' not in self._meta:
self._meta['groups'] = cluster.matrix2groups(
keywords['threshold'], self._meta['distances'], self.taxa)
lines = []
for taxon, group in sorted(self._meta['groups'].items(), key=lambda x: x[0]):
lines.append('{0}\t{1}'.format(taxon, group))
return _write_file(keywords['filename'], lines, fileformat)
if fileformat in ['starling', 'star.csv']:
# make lambda inline for data-check
l = lambda x: ['-' if x == 0 else x][0]
lines = []
if 'cognates' not in keywords:
lines.append('ID\tConcept\t' + '\t'.join(self.taxa))
for i, concept in enumerate(self.concepts):
for line in self.get_list(row=concept, entry=keywords['entry']):
lines.append(
str(i + 1) + '\t' + concept + '\t' + '\t'.join(
[l(t) for t in line]))
else:
lines.append(
'ID\tConcept\t' + '\t'.join(
['{0}\t COG'.format(t) for t in self.taxa]))
for i, concept in enumerate(self.concepts):
cogs = self.get_list(row=concept, entry=keywords['cognates'])
for j, line in enumerate(
self.get_list(row=concept, entry=keywords['entry'])):
part = '\t'.join(
'{0}\t{1}'.format(l(a), b) for a, b in zip(line, cogs[j]))
lines.append(util.tabjoin(i + 1, concept, part))
return _write_file(
keywords['filename'], lines, 'starling_' + keywords['entry'] + '.csv')
if fileformat == 'multistate.nex':
if not keywords['filename'].endswith('.multistate.nex'):
keywords['filename'] += '.multistate.nex'
matrix = wl2multistate(self, keywords['ref'], keywords['missing'])
return multistate2nex(self.taxa, matrix, keywords['filename'])
if fileformat == 'separated':
if not os.path.isdir(keywords['filename']):
os.mkdir(keywords['filename'])
for l in self.cols:
lines = [''] if 'ignore_keys' in keywords else ['ID\t']
lines[0] += '\t'.join(x.upper() for x in keywords['entries'])
for key in self.get_list(col=l, flat=True):
line = [] if 'ignore_keys' in keywords else [key]
for entry in keywords['entries']:
tmp = self[key, entry]
if isinstance(tmp, list):
tmp = ' '.join([str(x) for x in tmp])
line += [tmp]
lines.append('\t'.join('{0}'.format(x) for x in line))
_write_file('{0}/{1}'.format(keywords['filename'], l), lines, 'tsv')
def msa2tex(
infile,
template='',
filename='',
**keywords
):
"""
Convert an MSA to a tabular representation which can easily be used in
LaTeX documents.
"""
util.setdefaults(keywords, pid_mode=1)
# while alm-format can be read from the text-file without problems,
# msa-format should be loaded first (once this is already provided), the
# loss in speed won't matter much, since output of data is not a daily task
# load msa
msa = read_msa(infile)
## load templates
tex = util.read_text_file(template or template_path('msa.tex'))
# calculate pid score, if it is not passed as argument
if 'pid_score' not in keywords:
pid_score = 0
count = 0
for i, seqA in enumerate(msa['alignment']):
for j, seqB in enumerate(msa['alignment']):
if i < j:
pid_score += pid(seqA, seqB, mode=keywords['pid_mode'])
count += 1
pid_score = int(100 * pid_score / count + 0.5)
else:
pid_score = keywords['pid_score']
dataset = msa['dataset']
infile = msa['infile']
seq_id = msa['seq_id']
# determine the length of the longest taxon
taxl = max([len(t) for t in msa['taxa']])
height = len(msa['alignment'])
width = len(msa['alignment'][0])
start = r'\tabular{l' + width * 'c' + '}\n'
start += r'\bf\ttfamily Taxon & \multicolumn{' + str(
width) + r'}{l}{\bf\ttfamily Alignment}\\' + '\n'
# check for swaps in the alignment
if 'swaps' in msa:
swaps = []
for s in msa['swaps']:
swaps.extend(s)
else:
swaps = []
body = start
for i, taxon in enumerate(msa['taxa']):
body += r'\ttfamily ' + taxon.replace('_', r'\_')
for j, char in enumerate(msa['alignment'][i]):
if char != '-':
cls = token2class(char, rcParams['dolgo'])
elif char == '-':
cls = 'X'
if char == '_':
char = r'\#'
if cls == '_':
cls = '2'
if j not in swaps:
body += r'&\cellcolor{col' + cls + r'}' + char
else:
if char != '-':
body += r'&\cellcolor{col' + cls + r'}\color{white}\bf ' + char
else:
body += r'&\cellcolor{col' + cls + r'}\bf ' + char
body += r'\\' + '\n'
body += r'&' + '&'.join([r'\color{white}XXX' for i in range(width)]) + r'\\' + '\n'
body += r'\endtabular' + '\n'
# create the parameters etc.
w = 1.5 * width + taxl * 0.25
h = 0.5 * height + 1.0
tex = tex.replace('<+WIDTH+>', '{0:2f}'.format(w))
tex = tex.replace('<+HEIGHT+>', '{0:2f}'.format(h))
# create the rput stuff
tex = tex.replace('<+NEWX+>', '{0:.2f}'.format(w / 2.0))
tex = tex.replace('<+NEWY+>', '{0:.2f}'.format((h - 0.5) / 2.0))
# insert the rest
tex = tex.replace('<+CONTENT+>', body)
# write to file
if not filename:
filename = 'lingpy-{0}'
util.write_text_file(filename + '.tex', tex)
def alm2html(
infile,
title='',
shorttitle='',
filename='',
colored=False,
main_template='',
table_template='',
dataset='',
confidence=False,
**keywords
):
"""
Convert files in ``alm``-format into colored ``html``-format.
Parameters
----------
title : str
Define the title of the output file. If no title is provided, the
default title ``LexStat - Automatic Cognate Judgments`` will be used.
shorttitle : str
Define the shorttitle of the ``html``-page. If no title is provided,
the default title ``LexStat`` will be used.
Notes
-----
The coloring of sound segments with respect to the sound class they belong
to is based on the definitions given in the
``color`` :py:class:`~lingpy.data.model.Model`. It can easily be changed
and adapted.
See also
--------
lingpy.convert.html.msa2html
lingpy.convert.html.msa2tex
"""
util.setdefaults(keywords, json="", labels={})
# open the infile
if not os.path.exists(infile):
infile = infile + '.alm'
data = util.read_text_file(infile)
# create the outfile
if not filename:
filename = rcParams['filename']
# read in the templates
html = util.read_text_file(main_template or template_path('alm2html.html'))
if not table_template:
table_template = template_path(
'alm2html.table.js.html' if confidence else 'alm2html.table.html')
table = util.read_text_file(table_template)
css = util.read_text_file(template_path('alm.css'))
js = util.read_text_file(template_path('alm.js'))
# define a label function for the taxa
label = lambda x: keywords['labels'][x] if x in keywords['labels'] else x
# check for windows-compatibility
data = data.replace(os.linesep, '\n')[:-1]
# split the data into blocks
blocks = data.split('\n\n')
# retrieve the dataset
dataset = dataset or blocks[0]
# create the outstring
tmp_str = ''
for block in blocks[1:]:
lines = block.split('\n')
m = [l.split('\t') for l in lines]
# create colordict for different colors
dc = len(set([l[0] for l in m]))
if colored:
colors = {a: b for a, b in zip(
sorted(set([int(l[0]) for l in m])),
colorRange(dc, brightness=400),
)}
else:
colors = []
white = True
for i in sorted(set([abs(int(l[0])) for l in m])):
if white:
colors.append((i, 'white'))
white = False
else:
colors.append((i, 'gray'))
white = True
colors = dict(colors)
# get the basic item and its id
iName = m[0][2]
iID = m[0][3]
# start writing the stuff to string
tmp_str += table.format(NAME=iName, ID=iID)
# define the basic string for the insertion
bas = ' <tr class="{0}{2} taxon" taxon="{3}">\n{1}'
for tracer, l in enumerate(m):
# check whether the current line is a borrowing
if int(l[0]) < 0:
loan_line = ' loan'
else:
loan_line = ''
# assign the cognate id
tmp = ' <td>{0}</td>\n'.format(l[0])
tmp += ' <td>{0}</td>\n'.format(label(l[1].strip('.')))
# check alignments for confidence scores
ipa_string = ''.join([cell.split('/')[0] for cell in
l[4:]]).replace('-', '')
tmp += ' <td>{0}</td>\n'.format(ipa_string)
tmp += ' <td class="{0}">\n'.format(colors[abs(int(l[0]))])
tmp += ' <table class="{0}">\n'.format(colors[abs(int(l[0]))])
tmp += ' <tr>\n{0} </tr>\n </table>\n </td>\n </tr>\n'
# check whether another entry follows that is also an alignment,
# otherwise, there's no need to display a word as an alignment
cognate_set = False
if tracer < len(m) - 1:
if abs(int(m[tracer + 1][0])) == abs(int(l[0])):
cognate_set = True
if tracer > 0:
if abs(int(m[tracer - 1][0])) == abs(int(l[0])):
cognate_set = True
# fill out html for the cognate sets
if cognate_set:
alm = ''
for char in l[4:]:
# check for confidence scores
if '/' in char:
try:
char, conf, num = char.split('/')
conf = int(conf)
except ValueError:
print(char.split('/'))
raise ValueError("Something is wrong with %s." % (char))
else:
char, conf, rgb = char, (255, 255, 255), 0.0
if char == '-':
d = 'dolgo_GAP'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
if confidence:
alm += ' '
alm += '<td class="char {1}" confidence={0} '.format(
conf,
d
)
alm += 'char="{0}" '.format(char)
alm += 'onclick="' + "show('{0}')".format(num) + '" '
alm += 'num="{0}"'.format(num)
alm += '>\n {0}\n </td>\n'.format(char)
else:
alm += ' '
alm += '<td class="char {0}">{1}</td>\n'.format(d, char)
else:
alm = ' '
alm += '<td class="{0}">--</td>\n'.format(colors[abs(int(l[0]))])
# format the alignment
try:
tmp = tmp.format(alm)
except ValueError:
raise ValueError("Unknown problem in matchin %s and %s." % (alm, tmp))
# check for last line, where a new line should be inserted (not the
# fastest solution, but plotting is not a matter of time, and it
# suffices it's current purpose
if tracer < len(m) - 1:
pass
else:
if confidence:
tmp += ' </table>\n'
tmp += ' <tr class="empty"><td colspan="4" class="empty">'
tmp += '<hr class="empty" /></td></tr>\n'
# format the whole string
tmp_str += bas.format(
colors[abs(int(l[0]))],
tmp,
loan_line,
l[1]
)
if not title:
title = "LexStat - Automatic Cognate Judgments"
if not shorttitle:
shorttitle = "LexStat"
# check for json-attribute
if keywords['json']:
keywords['json'] = 'var myjson = ' + json.dumps(keywords['json'],
indent=1)
html = html.format(
shorttitle=shorttitle,
title=title,
table=tmp_str,
dataset=dataset,
javascript=js,
css=css,
**keywords
)
util.write_text_file(filename + '.html', html)
return
def msa2html(
msa,
shorttitle='',
filename='',
template='',
**keywords
):
"""
Convert files in ``msa``-format into colored ``html``-format.
Parameters
----------
msa : dict
A dictionary object that contains all the information of an MSA object.
shorttitle : str
Define the shorttitle of the ``html``-page. If no title is provided,
the default title ``SCA`` will be used.
filename : str (default="")
Define the name of the output file. If no name is defined, the name of
the input file will be taken as a default.
template : str (default="")
The path to the template file. If no name is defined, the basic
template will be used. The basic template currently used can be found
under ``lingpy/data/templates/msa2html.html``.
Examples
--------
Load the libary.
>>> from lingpy import *
Load an ``msq``-file from the test-sets.
>>> msa = MSA('harry.msq')
Align the data progressively and carry out a check for swapped sites.
>>> msa.prog_align()
>>> msa.swap_check()
>>> print(msa)
w o l - d e m o r t
w a l - d e m a r -
v - l a d i m i r -
Save the data to the file ``harry.msa``.
>>> msa.output('msa',filename='harry')
Save the ``msa``-object as ``html``.
>>> msa.output('html',filename='harry')
Notes
-----
The coloring of sound segments with respect to the sound class they belong
to is based on the definitions given in the ``color``
:py:class:`~lingpy.data.model.Model`. It can easily be changed and adapted.
See also
--------
lingpy.convert.html.alm2html
"""
util.setdefaults(
keywords,
pid_mode=1,
stress=rcParams['stress'],
css=False,
js=False,
compact=False,
class_sort=True,
write_to_file=True,
)
# while alm-format can be read from the text-file without problems,
# msa-format should be loaded first (once this is already provided), the
# loss in speed won't matter much, since output of data is not a daily task
# load templates
template = template or template_path('msa2html.html')
if template == 'js':
template = template_path('msa2html.js.html')
html = util.read_text_file(template)
css = util.read_text_file(keywords['css'] or template_path('msa.css'))
js = util.read_text_file(keywords['js'] or template_path('msa.js'))
# treat the msa-object as a file and try to load the file if this is the
# case
if isinstance(msa, string_types):
msa = read_msa(msa, **keywords)
else:
raise ValueError('[!] No filename specified.')
# load dataset, etc.
dataset = msa['dataset']
# calculate pid score, if it is not passed as argument
if 'pid_score' not in keywords:
pid_score = 0
count = 0
for i, seqA in enumerate(msa['alignment']):
for j, seqB in enumerate(msa['alignment']):
if i < j:
pid_score += pid(seqA, seqB, mode=keywords['pid_mode'])
count += 1
pid_score = int(100 * pid_score / count + 0.5)
else:
pid_score = keywords['pid_score']
infile = msa['infile']
seq_id = msa['seq_id']
# define the titles etc.
if not shorttitle:
shorttitle = 'SCA'
# determine the length of the longest taxon
taxl = max([len(t) for t in msa['taxa']])
# format css file
css = css.replace('TAXON_LENGTH', str(taxl * 10))
out = ''
tr = '<tr class="msa" unique="{1}" taxon={2} sequence={3}>{0}</tr>\n'
td_taxon = '<td class="taxon">{0}</td>'
perc = int(80 / len(msa['alignment'][0]) + 0.5)
td_residue = '<td class="residue {1}">{0}</td>'
td_swap = '<td class="residue swap {1}">{0}</td>'
td_unaligned = '<td class="residue noalign {1}">{0}</td>'
# check for swaps in the alignment
if 'swaps' in msa:
swaps = []
for s in msa['swaps']:
swaps.extend(s)
else:
swaps = []
# check for
local = ['*'] * len(msa['alignment'][0])
if 'local' in msa:
local = ['.'] * len(msa['alignment'][0])
for i in msa['local']:
local[i] = '*'
# get two sorting schemas for the sequences
if keywords['class_sort']:
classes = [tokens2class(ipa2tokens(seq), rcParams['asjp']) for seq in msa['seqs']]
seqs = dict(
[(a[1], b) for a, b in zip(
sorted(
zip(classes, msa['seqs']),
key=lambda x: x[0] # list(zip(x[0],x[1]))
),
range(1, len(msa['seqs']) + 1)
)]
)
else:
seqs = dict(zip(sorted(msa['seqs']), range(1, len(msa['seqs']) + 1)))
taxa = dict(zip(sorted(msa['taxa']), range(1, len(msa['taxa']) + 1)))
# set up a list to store unique alignments
alignments = []
# start iteration
for i, taxon in enumerate(msa['taxa']):
tmp = ''
tmp += td_taxon.format(taxon)
# append alignment to alignments
alignment = ''.join(msa['alignment'][i])
sequence = msa['seqs'][i]
if alignment in alignments:
unique = 'false'
else:
unique = 'true'
alignments += [alignment]
for j, char in enumerate(msa['alignment'][i]):
if char == '-':
d = 'dolgo_GAP'
c = '#bbbbbb'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
c = token2class(char, rcParams['_color'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
if j in swaps:
tmp += td_swap.format(char, d)
elif local[j] != '*':
tmp += td_unaligned.format(char, d)
else:
tmp += td_residue.format(char, d)
out += tr.format(tmp, unique, taxa[taxon], seqs[sequence])
html = html.format(
table=out,
dataset=dataset,
pid=pid_score,
file=infile,
sequence=seq_id,
shorttitle=shorttitle,
width=len(msa['alignment'][0]),
table_width='{0}'.format(len(msa['alignment'][0]) * 50 + 8 * taxl),
taxa=len(msa['alignment']),
uniseqs=len(set(msa['seqs'])),
css=css,
js=js
)
if not filename:
filename = rcParams['filename']
if not filename.endswith('.html'):
filename = filename + '.html'
if keywords['compact']:
html = html.replace('\n', ' ')
html = re.sub(r'\s+', r' ', html)
html = html.replace('> ', '>')
html = html.replace(' >', '>')
if keywords['write_to_file']:
# check, whether the outfile already exists
util.write_text_file(filename, html)
else:
return html
def _list2msa(msa_lines, ids=False, header=True, normalize=False, **keywords):
"""
Function retrieves a dictionary from a list of MSA strings.
"""
setdefaults(keywords, seq_id='-', dataset='-', input_file='dummy')
d = dict(ID=[], taxa=[], alignment=[], seqs=[], infile=keywords['input_file'])
if header:
start = 2
d['dataset'] = msa_lines[0]
d['seq_id'] = msa_lines[1]
else:
start = 0
d['dataset'] = keywords['dataset']
d['seq_id'] = keywords['seq_id']
for i, line in enumerate(msa_lines[start:]):
idx = 1 if ids else 0
# check for specific id
if line[0] in ['0', 'LOCAL', 'CROSSED', 'SWAPS', 'MERGE', 'COMPLEX', ]:
if line[idx] == 'LOCAL':
d['local'] = []
for j, x in enumerate(line[idx + 1:]):
if x == '*':
d['local'] += [j]
elif line[idx] in ['CROSSED', 'SWAPS']:
d['swaps'] = []
swapline = [x for x in line[idx + 1:]]
j = 0
while swapline:
x = swapline.pop(0)
if x == '+':
d['swaps'] += [(j, j + 1, j + 2)]
swapline.pop(0)
swapline.pop(0)
j += 2
else:
pass
j += 1
elif line[idx] in ['COMPLEX', 'MERGE']:
d['merge'] = {}
mergeline = [x for x in line[idx + 1:]]
k = 0
merge = False
for j, m in enumerate(mergeline):
if m == '<':
merge = True
if m == '>':
merge = False
d['merge'][j] = k
if not merge:
k += 1
else:
d[line[idx].lower()] = line[idx + 1:]
elif line[0] not in ['LOCAL', 'SWAPS', 'MERGE', 'COMPLEX', '0']:
if ids:
try:
d['ID'] += [int(line[0])]
except ValueError:
d['ID'] += [line[0]]
else:
d["ID"] += [i]
d["taxa"] += [line[idx].rstrip('.')]
d["seqs"] += [' '.join([l for l in line[idx + 1:] if l != '-'])]
d["alignment"] += [line[idx + 1:]]
# normalize the alignment if the option is chosen
if normalize:
d['alignment'] = normalize_alignment(d['alignment'])
return d
def _output(self, fileformat, **keywords):
"""
Internal function that eases its modification by daughter classes.
"""
# check for stamp attribute
keywords["stamp"] = getattr(self, '_stamp', '')
# add the default parameters, they will be checked against the keywords
util.setdefaults(
keywords,
cols=False,
distances=False,
entries=("concept", "counterpart"),
entry='concept',
fileformat=fileformat,
filename=rcParams['filename'],
formatter='concept',
modify_ref=False,
meta=self._meta,
missing=0,
prettify='false',
ignore='all',
ref='cogid',
rows=False,
subset=False, # setup a subset of the data,
taxa='taxa',
threshold=0.6, # threshold for flat clustering
tree_calc='neighbor')
if fileformat in ['triple', 'triples', 'triples.tsv']:
return tsv2triple(self, keywords['filename'] + '.' + fileformat)
if fileformat in ['paps.nex', 'paps.csv']:
paps = self.get_paps(
ref=keywords['ref'], entry=keywords['entry'], missing=keywords['missing'])
kw = dict(filename=keywords['filename'] + '.paps')
if fileformat == 'paps.nex':
kw['missing'] = keywords['missing']
return pap2nex(self.cols, paps, **kw)
return pap2csv(self.cols, paps, **kw)
# simple printing of taxa
if fileformat == 'taxa':
assert hasattr(self, 'taxa')
return util.write_text_file(keywords['filename'] + '.taxa', self.cols)
# csv-output
if fileformat in ['csv', 'qlc', 'tsv']:
# get the header line
header = sorted(
[s for s in set(self._alias.values()) if s in self._header],
key=lambda x: self._header[x])
header = [h.upper() for h in header]
self._meta.setdefault('taxa', self.cols)
# get the data, in case a subset is chosen
if not keywords['subset']:
# write stuff to file
return wl2qlc(header, self._data, **keywords)
cols, rows = keywords['cols'], keywords['rows']
if not isinstance(cols, (list, tuple, bool)):
raise ValueError("[i] Argument 'cols' should be list or tuple.")
if not isinstance(rows, (dict, bool)):
raise ValueError("[i] Argument 'rows' should be a dictionary.")
# check for chosen header
if cols:
# get indices for header
indices = [self._header[x] for x in cols]
header = [c.upper() for c in cols]
else:
indices = [r for r in range(len(self.header))]
if rows:
stmts = []
for key, value in rows.items():
if key == 'ID':
stmts += ["key " + value]
else:
idx = self._header[key]
stmts += ["line[{0}] ".format(idx) + value]
log.debug("calculated what should be excluded")
# get the data
out = {}
for key, line in self._data.items():
log.debug(key)
if rows:
if eval(" and ".join(stmts)):
out[key] = [line[i] for i in indices]
else:
out[key] = [line[i] for i in indices]
log.debug("passing data to wl2qlc")
return wl2qlc(header, out, **keywords)
# output dst-format (phylip)
if fileformat == 'dst':
# check for distances as keyword
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self, **keywords)
out = matrix2dst(self._meta['distances'], self.taxa,
stamp=keywords['stamp'], taxlen=keywords.get('taxlen', 0))
return _write_file(keywords['filename'], out, fileformat)
# output tre-format (newick)
if fileformat in ['tre', 'nwk']: # ,'cluster','groups']:
if 'tree' not in self._meta:
# check for distances
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self)
# we look up a function to calculate a tree in the cluster module:
tree = getattr(cluster, keywords['tree_calc'])(
self._meta['distances'], self.cols, distances=keywords['distances'])
else:
tree = self._meta['tree']
return _write_file(keywords['filename'], '{0}'.format(tree), fileformat)
if fileformat in ['cluster', 'groups']:
if 'distances' not in self._meta:
self._meta['distances'] = wl2dst(self) # check for keywords
if 'groups' not in self._meta:
self._meta['groups'] = cluster.matrix2groups(
keywords['threshold'], self._meta['distances'], self.taxa)
lines = []
for taxon, group in sorted(self._meta['groups'].items(), key=lambda x: x[0]):
lines.append('{0}\t{1}'.format(taxon, group))
return _write_file(keywords['filename'], lines, fileformat)
if fileformat in ['starling', 'star.csv']:
# make lambda inline for data-check
l = lambda x: ['-' if x == 0 else x][0]
lines = []
if 'cognates' not in keywords:
lines.append('ID\tConcept\t' + '\t'.join(self.taxa))
for i, concept in enumerate(self.concepts):
for line in self.get_list(row=concept, entry=keywords['entry']):
lines.append(
str(i + 1) + '\t' + concept + '\t' + '\t'.join(
[l(t) for t in line]))
else:
lines.append(
'ID\tConcept\t' + '\t'.join(
['{0}\t COG'.format(t) for t in self.taxa]))
for i, concept in enumerate(self.concepts):
cogs = self.get_list(row=concept, entry=keywords['cognates'])
for j, line in enumerate(
self.get_list(row=concept, entry=keywords['entry'])):
part = '\t'.join(
'{0}\t{1}'.format(l(a), b) for a, b in zip(line, cogs[j]))
lines.append(util.tabjoin(i + 1, concept, part))
return _write_file(
keywords['filename'], lines, 'starling_' + keywords['entry'] + '.csv')
if fileformat == 'multistate.nex':
if not keywords['filename'].endswith('.multistate.nex'):
keywords['filename'] += '.multistate.nex'
matrix = wl2multistate(self, keywords['ref'], keywords['missing'])
return multistate2nex(self.taxa, matrix, keywords['filename'])
if fileformat == 'separated':
if not os.path.isdir(keywords['filename']):
os.mkdir(keywords['filename'])
for l in self.cols:
lines = [''] if 'ignore_keys' in keywords else ['ID\t']
lines[0] += '\t'.join(x.upper() for x in keywords['entries'])
for key in self.get_list(col=l, flat=True):
line = [] if 'ignore_keys' in keywords else [key]
for entry in keywords['entries']:
tmp = self[key, entry]
if isinstance(tmp, list):
tmp = ' '.join([str(x) for x in tmp])
line += [tmp]
lines.append('\t'.join('{0}'.format(x) for x in line))
_write_file('{0}/{1}'.format(keywords['filename'], l), lines, 'tsv')
def wl2qlc(header, data, filename='', formatter='concept', **keywords):
"""
Write the basic data of a wordlist to file.
"""
util.setdefaults(keywords,
ignore=['taxa', 'doculects', 'msa'],
fileformat='qlc',
prettify=True)
if keywords['ignore'] == 'all':
keywords['ignore'] = [
'taxa', 'scorer', 'meta', 'distances', 'doculects', 'msa', 'json'
]
formatter = formatter.upper()
if not filename:
filename = rcParams['filename']
# create output string
out = '# Wordlist\n' if keywords['prettify'] else ''
# write meta to file
meta = keywords.get("meta", {})
kvpairs = {}
jsonpairs = {}
msapairs = {}
trees = {}
distances = ''
taxa = ''
scorer = ''
for k, v in meta.items():
# simple key-value-pairs
if isinstance(v, (str, int)) or k == "tree":
kvpairs[k] = v
elif k == 'msa' and k not in keywords['ignore']:
# go a level deeper, checking for keys
for ref in v:
if ref not in msapairs:
msapairs[ref] = {}
for a, b in v[ref].items():
msapairs[ref][a] = b
elif k == 'distances':
distances = matrix2dst(v, meta['taxa'])
elif k in ['taxa', 'doculect', 'taxon', 'doculects']:
# we need to find a better solution here, since it is not nice to
# have taxa written to json again and again
pass
elif k == 'trees' and k not in keywords['ignore']:
trees = ''
for key, value in v.items():
trees += '<tre id="{0}">\n{1}\n</tre>\n'.format(key, value)
elif k == 'scorer' and k not in keywords['ignore']:
for key, value in v.items():
scorer += '<{2} id="{0}">\n{1}</{2}>\n\n'.format(
key, scorer2str(value), k)
else:
# check whether serialization works
try:
json.dumps(v)
jsonpairs[k] = v
except TypeError:
pass
if kvpairs and 'meta' not in keywords['ignore']:
out += '\n# META\n' if keywords['prettify'] else ''
for k, v in sorted(kvpairs.items(), key=lambda x: x[0]):
out += '@{0}:{1}\n'.format(k, v)
if taxa and keywords['taxa']:
out += '\n# TAXA\n<taxa>\n' + taxa + '\n</taxa>\n'
if jsonpairs and 'json' not in keywords['ignore']:
out += "@json: " + json.dumps(jsonpairs) + '\n'
if msapairs and 'msa' not in keywords['ignore']:
for ref in msapairs:
out += "\n# MSA reference: {0}\n".format(ref)
for k, v in msapairs[ref].items():
if 'consensus' in v:
out += '#\n<msa '
out += 'id="{0}" ref="{1}" consensus="{2}">\n'.format(
k, ref, ' '.join(v['consensus']))
else:
out += '#\n<msa id="{0}" ref="{1}">\n'.format(k, ref)
outs = msa2str(v, wordlist=True)
out += outs
out += "</msa>\n"
if distances and 'distances' not in keywords['ignore']:
out += '\n# DISTANCES\n<dst>\n'
out += distances + '</dst>\n'
if trees:
out += '\n# TREES\n' + trees
if scorer and 'scorer' not in keywords['ignore']:
out += '\n# SCORER\n' + scorer
out += '\n# DATA\n' if keywords['prettify'] else ''
out += 'ID\t' + '\t'.join(header) + '\n'
# check for gloss in header to create nice output format
if formatter in header:
idx = header.index(formatter)
formatter = None
sorted_data = sorted(data.keys(), key=lambda x: data[x][idx])
elif len(formatter.split(',')) == 2:
idxA, idxB = formatter.split(',')
idxA = header.index(idxA)
idxB = header.index(idxB)
idx = idxA
formatter = None
sorted_data = sorted(data.keys(),
key=lambda x: (data[x][idxA], data[x][idxB]))
else:
idx = False
formatter = ''
sorted_data = sorted(data.keys())
for key in sorted_data:
# get the line
line = data[key]
# check for formatter
if idx in range(len(line)):
if line[idx] != formatter:
out += '#\n' if keywords['prettify'] else ''
formatter = line[idx]
# add the key
out += str(key)
# add the rest of the values
for value in line:
if type(value) == list:
try:
out += '\t' + ' '.join(value)
except:
out += '\t' + ' '.join([str(v) for v in value])
elif type(value) == int:
out += '\t' + str(value)
elif type(value) == float:
out += '\t{0:.4f}'.format(value)
elif value is None:
out += '\t'
else:
out += '\t{:}'.format(value)
out += '\n'
util.write_text_file(filename + '.' + keywords['fileformat'],
out + keywords.get('stamp', ''),
normalize="NFC")
return
def psa2html(infile, **kw):
"""
Function converts a PSA-file into colored html-format.
"""
util.setdefaults(
kw,
template=False,
css=False,
comment='#',
filename=infile[:-4]+'.html',
compact=True)
template = util.read_text_file(kw['template'] or template_path('psa.html'))
css = util.read_text_file(kw['css'] or template_path('psa.css'))
data = []
for line in util.read_text_file(infile, lines=True):
if not line.startswith(kw['comment']):
data.append(line)
seq_ids = []
pairs = []
taxa = []
alignments = []
del data[0]
i = 0
while i <= len(data) - 3:
try:
seq_ids.append(data[i])
datA = data[i + 1].split('\t')
datB = data[i + 2].split('\t')
taxonA = datA[0].strip('.')
taxonB = datB[0].strip('.')
almA = datA[1:]
almB = datB[1:]
taxa.append((taxonA, taxonB))
pairs.append(
(
'.'.join([k for k in almA if k != '-']),
'.'.join([k for k in almB if k != '-'])
)
)
alignments.append(
(
[str(a) for a in almA],
[str(b) for b in almB],
0)
)
assert len(alignments[-1][0]) == len(alignments[-1][1])
i += 4
except AssertionError:
log.warning("Line {0} of the data is probably miscoded.".format(i + 1))
i += 1
def get_classes(alm):
classes = []
residue = '<div class="residue {1}">{0}</div>'
for j, char in enumerate(alm):
if char == '-':
d = 'dolgo_GAP'
else:
d = 'dolgo_' + token2class(char, rcParams['dolgo'])
# bad check for three classes named differently
if d == 'dolgo__':
d = 'dolgo_X'
elif d == 'dolgo_1':
d = 'dolgo_TONE'
elif d == 'dolgo_0':
d = 'dolgo_ERROR'
classes += [residue.format(char, d)]
return ''.join(classes)
out = '<table>\n' # codecs.open(kw['filename'], 'w', 'utf-8')
for i, (a, b, c) in enumerate(alignments):
clsA = get_classes(a)
clsB = get_classes(b)
ids = int(100 * pid(a, b) + 0.5)
out += '<tr class="head">'
out += '<td colspan=2 class="head"><b>Alignment {0}:</b> <i>{1}</i>, PID: {2}</td></tr>'.format(
i + 1,
seq_ids[i],
ids
)
out += '<tr class="psa">'
out += '<td class="taxon">{0}</td>'.format(taxa[i][0])
out += '<td class="psa">{0}</td>'.format(clsA)
out += '</tr>'
out += '<tr class="psa">'
out += '<td class="taxon">{0}</td>'.format(taxa[i][1])
out += '<td class="psa">{0}</td>'.format(clsB)
out += '</tr>'
out += '<tr><td colspan=2></td></tr>'
out += '</table>'
html = template.format(alignments=out, css=css)
if kw['compact']:
html = html.replace('\n', ' ')
html = re.sub(r'\s+', r' ', html)
html = html.replace('> ', '>')
html = html.replace(' >', '>')
util.write_text_file(kw['filename'], html)
def wl2qlc(
header,
data,
filename='',
formatter='concept',
**keywords):
"""
Write the basic data of a wordlist to file.
"""
util.setdefaults(
keywords,
ignore=['taxa', 'doculects', 'msa'],
fileformat='qlc',
prettify=True)
if keywords['ignore'] == 'all':
keywords['ignore'] = [
'taxa', 'scorer', 'meta', 'distances', 'doculects', 'msa', 'json']
formatter = formatter.upper()
if not filename:
filename = rcParams['filename']
# create output string
out = '# Wordlist\n' if keywords['prettify'] else ''
# write meta to file
meta = keywords.get("meta", {})
kvpairs = {}
jsonpairs = {}
msapairs = {}
trees = {}
distances = ''
taxa = ''
scorer = ''
for k, v in meta.items():
# simple key-value-pairs
if isinstance(v, (text_type, int)) or k == "tree":
kvpairs[k] = v
elif k == 'msa' and k not in keywords['ignore']:
# go a level deeper, checking for keys
for ref in v:
if ref not in msapairs:
msapairs[ref] = {}
for a, b in v[ref].items():
msapairs[ref][a] = b
elif k == 'distances':
distances = matrix2dst(v, meta['taxa'])
elif k in ['taxa', 'doculect', 'taxon', 'doculects']:
# we need to find a better solution here, since it is not nice to
# have taxa written to json again and again
pass
elif k == 'trees' and k not in keywords['ignore']:
trees = ''
for key, value in v.items():
trees += '<tre id="{0}">\n{1}\n</tre>\n'.format(key, value)
elif k == 'scorer' and k not in keywords['ignore']:
for key, value in v.items():
scorer += '<{2} id="{0}">\n{1}</{2}>\n\n'.format(
key, scorer2str(value), k)
else:
# check whether serialization works
try:
json.dumps(v)
jsonpairs[k] = v
except TypeError:
pass
if kvpairs and 'meta' not in keywords['ignore']:
out += '\n# META\n' if keywords['prettify'] else ''
for k, v in sorted(kvpairs.items(), key=lambda x: x[0]):
out += '@{0}:{1}\n'.format(k, v)
if taxa and keywords['taxa']:
out += '\n# TAXA\n<taxa>\n' + taxa + '\n</taxa>\n'
if jsonpairs and 'json' not in keywords['ignore']:
out += "@json: " + json.dumps(jsonpairs) + '\n'
if msapairs and 'msa' not in keywords['ignore']:
for ref in msapairs:
out += "\n# MSA reference: {0}\n".format(ref)
for k, v in msapairs[ref].items():
if 'consensus' in v:
out += '#\n<msa '
out += 'id="{0}" ref="{1}" consensus="{2}">\n'.format(
k, ref, ' '.join(v['consensus']))
else:
out += '#\n<msa id="{0}" ref="{1}">\n'.format(k, ref)
outs = msa2str(v, wordlist=True)
out += outs
out += "</msa>\n"
if distances and 'distances' not in keywords['ignore']:
out += '\n# DISTANCES\n<dst>\n'
out += distances + '</dst>\n'
if trees:
out += '\n# TREES\n' + trees
if scorer and 'scorer' not in keywords['ignore']:
out += '\n# SCORER\n' + scorer
out += '\n# DATA\n' if keywords['prettify'] else ''
out += 'ID\t' + '\t'.join(header) + '\n'
# check for gloss in header to create nice output format
if formatter in header:
idx = header.index(formatter)
formatter = None
sorted_data = sorted(data.keys(), key=lambda x: data[x][idx])
elif len(formatter.split(',')) == 2:
idxA, idxB = formatter.split(',')
idxA = header.index(idxA)
idxB = header.index(idxB)
idx = idxA
formatter = None
sorted_data = sorted(data.keys(), key=lambda x: (
data[x][idxA], data[x][idxB]))
else:
idx = False
formatter = ''
sorted_data = sorted(data.keys())
for key in sorted_data:
# get the line
line = data[key]
# check for formatter
if idx in range(len(line)):
if line[idx] != formatter:
out += '#\n' if keywords['prettify'] else ''
formatter = line[idx]
# add the key
out += text_type(key)
# add the rest of the values
for value in line:
if type(value) == list:
try:
out += '\t' + ' '.join(value)
except:
out += '\t' + ' '.join([text_type(v) for v in value])
elif type(value) == int:
out += '\t' + text_type(value)
elif type(value) == float:
out += '\t{0:.4f}'.format(value)
elif value is None:
out += '\t'
else:
out += '\t{:}'.format(value)
out += '\n'
util.write_text_file(
filename + '.' + keywords['fileformat'],
out + keywords.get('stamp', ''),
normalize="NFC")
return
def _list2msa(msa_lines, ids=False, header=True, normalize=False, **keywords):
"""
Function retrieves a dictionary from a list of MSA strings.
"""
setdefaults(keywords, seq_id='-', dataset='-', input_file='dummy')
d = dict(ID=[],
taxa=[],
alignment=[],
seqs=[],
infile=keywords['input_file'])
if header:
start = 2
d['dataset'] = msa_lines[0]
d['seq_id'] = msa_lines[1]
else:
start = 0
d['dataset'] = keywords['dataset']
d['seq_id'] = keywords['seq_id']
for i, line in enumerate(msa_lines[start:]):
idx = 1 if ids else 0
# check for specific id
if line[0] in [
'0',
'LOCAL',
'CROSSED',
'SWAPS',
'MERGE',
'COMPLEX',
]:
if line[idx] == 'LOCAL':
d['local'] = []
for j, x in enumerate(line[idx + 1:]):
if x == '*':
d['local'] += [j]
elif line[idx] in ['CROSSED', 'SWAPS']:
d['swaps'] = []
swapline = [x for x in line[idx + 1:]]
j = 0
while swapline:
x = swapline.pop(0)
if x == '+':
d['swaps'] += [(j, j + 1, j + 2)]
swapline.pop(0)
swapline.pop(0)
j += 2
else:
pass
j += 1
elif line[idx] in ['COMPLEX', 'MERGE']:
d['merge'] = {}
mergeline = [x for x in line[idx + 1:]]
k = 0
merge = False
for j, m in enumerate(mergeline):
if m == '<':
merge = True
if m == '>':
merge = False
d['merge'][j] = k
if not merge:
k += 1
else:
d[line[idx].lower()] = line[idx + 1:]
elif line[0] not in ['LOCAL', 'SWAPS', 'MERGE', 'COMPLEX', '0']:
if ids:
try:
d['ID'] += [int(line[0])]
except ValueError:
d['ID'] += [line[0]]
else:
d["ID"] += [i]
d["taxa"] += [line[idx].rstrip('.')]
d["seqs"] += [' '.join([l for l in line[idx + 1:] if l != '-'])]
d["alignment"] += [line[idx + 1:]]
# normalize the alignment if the option is chosen
if normalize:
d['alignment'] = normalize_alignment(d['alignment'])
return d
def calculate_data(wordlist,
data,
taxa='taxa',
concepts='concepts',
ref='cogid',
**keywords):
"""
Manipulate a wordlist object by adding different kinds of data.
Parameters
----------
data : str
The type of data that shall be calculated. Currently supports
* "tree": calculate a reference tree based on shared cognates
* "dst": get distances between taxa based on shared cognates
* "cluster": cluster the taxa into groups using different methods
"""
logger = log.get_logger()
util.setdefaults(keywords,
distances=False,
tree_calc="upgma",
cluster="upgma",
force=False,
threshold=0.5,
cluster_method='upgma')
# get taxa for current calculation
these_taxa = eval('wordlist.' + taxa)
# calculate distances
if data in ['distances', 'dst']:
wordlist._meta['distances'] = wl2dst(wordlist, taxa, concepts, ref,
**keywords)
elif data in ['diversity', 'div']:
etd = wordlist.get_etymdict(ref=ref)
wordlist._meta['diversity'] = \
(len(etd) - wordlist.height) / (len(wordlist) - wordlist.height)
elif data in ['tre', 'tree', 'nwk']:
if 'distances' not in wordlist._meta:
wordlist._meta['distances'] = \
wl2dst(wordlist, taxa, concepts, ref, **keywords)
distances = wordlist._meta['distances']
if 'tree' in wordlist._meta and not keywords['force']:
logger.warning("Reference tree has already been calculated, "
"force overwrite by "
"setting 'force' to 'True'.")
return
wordlist._meta['tree'] = clustering.matrix2tree(
distances, these_taxa, keywords['tree_calc'],
keywords['distances'])
elif data in ['groups', 'cluster']:
if 'distances' not in wordlist._meta:
distances = wl2dst(wordlist, taxa, concepts, ref, **keywords)
else:
distances = wordlist._meta['distances']
if 'groups' in wordlist._meta and not keywords['force']:
logger.warning("Distance matrix has already been calculated, "
"force overwrite by "
"setting 'force' to 'True'.")
return
wordlist._meta['groups'] = clustering.matrix2groups(
keywords['threshold'], distances, these_taxa,
keywords['cluster_method'])
log.info("Successfully calculated {0}.".format(data))
def _export(
self,
fileformat,
sections=None,
entries=None,
entry_sep='',
item_sep='',
template='',
exclude=None,
entry_start='',
entry_close='',
**keywords):
"""
Export a wordlist to various file formats.
"""
if not sections:
if fileformat == 'txt':
sections = dict(
h1=('concept', '\n# Concept: {0}\n'),
h2=('cogid', '## Cognate-ID: {0}\n'))
elif fileformat == 'tex':
sections = dict(
h1=('concept', r'\section{{Concept: ``{0}"}}' + '\n'),
h2=('cogid', r'\subsection{{Cognate Set: ``{0}"}}' + '\n'))
elif fileformat == 'html':
sections = dict(
h1=('concept', '<h1>Concept: {0}</h1>'),
h2=('cogid', '<h2>Cognate Set: {0}</h2>'))
if not entries:
if fileformat == 'txt':
entries = [('language', '{0} '), ('ipa', '{0}\n')]
elif fileformat == 'tex':
entries = [('language', '{0} '), ('ipa', '[{0}]' + '\n')]
elif fileformat == 'html':
entries = [('language', '{0} '), ('ipa', '[{0}]\n')]
util.setdefaults(keywords, filename=rcParams['filename'])
# get the temporary dictionary
out = wl2dict(self, sections, entries, exclude)
# assign the output string
out_string = ''
# iterate over the dictionary and start to fill the string
for key in sorted(out, key=lambda x: str(x).lower()):
# write key to file
out_string += key[1]
# reassign tmp
tmp = out[key]
# set the pointer and the index
pointer = {0: [tmp, sorted(tmp.keys())]}
while True:
idx = max(pointer.keys())
# check for type of current point
if isinstance(tmp, dict):
if pointer[idx][1]:
next_key = pointer[idx][1].pop()
out_string += next_key[1]
tmp = pointer[idx][0][next_key]
if isinstance(tmp, dict):
pointer[idx + 1] = [tmp, sorted(tmp.keys())]
else:
pointer[idx + 1] = [tmp, tmp]
else:
del pointer[idx]
if idx == 0:
break
else:
tmp_strings = []
for line in sorted(tmp):
tmp_strings += [item_sep.join(line)]
out_string += entry_start + entry_sep.join(tmp_strings) + entry_close
tmp = pointer[idx - 1][0]
del pointer[idx]
if fileformat == 'tex':
out_string = out_string.replace('_', r'\_')
tmpl = util.read_text_file(template) if template else '{0}'
_write_file(keywords['filename'], tmpl.format(out_string), fileformat)
def align(self, **keywords):
"""
Align a pair of sequences or multiple sequence pairs.
Parameters
----------
gop : int (default=-1)
The gap opening penalty (GOP).
scale : float (default=0.5)
The gap extension penalty (GEP), calculated with help of a scaling
factor.
mode : {"global","local","overlap","dialign"}
The alignment mode, see :evobib:`List2012a` for details.
factor : float (default = 0.3)
The factor by which matches in identical prosodic position are
increased.
restricted_chars : str (default="T_")
The restricted chars that function as an indicator of syllable or
morpheme breaks for secondary alignment, see :evobib:`List2012c`
for details.
distance : bool (default=False)
If set to *True*, return the distance instead of the similarity
score. Distance is calculated using the formula by
:evobib:`Downey2008`.
model : { None, ~lingpy.data.model.Model }
Specify the sound class model that shall be used for the analysis.
If no model is specified, the default model of :evobib:`List2012a`
will be used.
pprint : bool (default=False)
If set to *True*, the alignments are printed to the screen.
"""
setdefaults(
keywords,
gop=-1,
scale=0.5,
mode='global',
factor=0.3,
restricted_chars='T_',
distance=False,
model=rcParams['sca'],
pprint=False,
transform=rcParams['align_transform'])
if hasattr(self, 'model'):
if keywords['model'] != self.model:
self._set_model(**keywords)
else:
self._set_model(**keywords)
# create the alignments array
self._alignments = calign.align_pairs(
self.classes,
self.weights,
self.prostrings,
keywords['gop'],
keywords['scale'],
keywords['factor'],
self.scoredict,
keywords['mode'],
keywords['restricted_chars'],
distance=1 if keywords['distance'] else 0)
# switch back to alignments
self.alignments = []
for i, (almA, almB, sim) in enumerate(self._alignments):
self.alignments.append((
class2tokens(self.tokens[i][0], almA, local=keywords['mode'] == "local"),
class2tokens(self.tokens[i][1], almB, local=keywords['mode'] == "local"),
sim))
# print the alignments, if this is chosen
as_string(self, pprint=keywords['pprint'])
