def __init__(self, title, task_count, cols=100):
self.log = get_level() <= logging.INFO
self.title = title
self.cols = cols
self.step = cols if not task_count else self.cols / task_count
# number of columns we have already written:
self.written = 0
# number of tasks
self.count = 0
def __init__(self, title, task_count, cols=100):
self.log = get_level() <= logging.INFO
self.title = title
self.cols = cols
self.step = cols if not task_count else self.cols / task_count
# number of columns we have already written:
self.written = 0
# number of tasks
self.count = 0
def as_string(obj, pprint=False):
obj = text_type(obj)
if get_level() <= logging.ERROR and pprint:
print(obj)
return obj
def as_string(obj, pprint=False):
obj = text_type(obj)
if get_level() <= logging.INFO and pprint:
print(obj)
return obj
def star2qlc(filename, clean_taxnames=False, debug=False):
"""
Converts a file directly output from starling to LingPy-QLC format.
"""
if not clean_taxnames:
cleant = lambda x: x
else:
cleant = clean_taxnames
data = csv2list(filename)
# check for strange chars in data due to notepad errors
data[0][0] = data[0][0].replace('\ufeff','')
# get the header
header = data[0]
# debugging
if debug:
error = False
print("[i] Header line has length {0}.".format(len(header)))
for line in data[1:]:
if len(line) != len(header):
print("[!] Error for item {0} with length {1}, expected {2}.".format(
'/'.join(line[0:2]),
len(line),
len(header)))
error = True
if error:
print("[!] Errors were found, aborting function call.")
return
else:
print("[i] Everything went fine, carrying on with function call.")
# search for '#' char in header
cognates = False
for h in header:
if '#' in h:
cognates = True
# determine language names in header
taxa = []
for i in range(len(header)-1):
prev = header[i]
post = header[i+1]
if prev in post and '#' in post:
taxa += [prev]
if len(taxa) == 1:
lngIdx = i
if prev == 'Number':
numIdx = i
if prev == 'Word':
wrdIdx = i
if log.get_level() <= logging.INFO:
print('starling, indices',lngIdx,numIdx,wrdIdx)
print('starling, taxa:',taxa)
# start filling in the dictionary
D = {}
idx = 1
cognate_counter = 0
current_concept = ''
cognate_sets = []
for line in data[2:]:
gloss = line[wrdIdx]
gnum = line[numIdx]
# switch to next cognate set if there is a switch in concepts
if current_concept != gloss and len(cognate_sets) != 0:
max_cog = max(cognate_sets)
cognate_counter = max_cog
cognate_sets = []
current_concept = gloss
else:
if debug:
print(gloss,current_concept,cognate_counter)
for i in range(lngIdx,len(header),2):
word = line[i]
if '{' in word:
ipa = word[:word.index('{')].strip()
ortho = word[word.index('{')+1:word.index('}')].strip()
else:
ipa = word
ortho = word
cogid = int(line[i+1])
if cogid != 0 and word:
if cogid > 0:
cogid = cogid + cognate_counter
else:
pass
# append cognate sets, essential for raising the counter
cognate_sets += [int(cogid)]
taxon = cleant(header[i])
D[idx] = [taxon,gloss,gnum,word,ortho,ipa,cogid]
idx += 1
# re-iterate through data and reassign cognate sets with negative ids
for k in D:
cogid = D[k][-1]
if cogid < 0:
cogid = -cognate_counter
cognate_counter += 1
D[k][-1] = cogid
D[0] = ['DOCULECT','CONCEPT','GLOSSID','WORDINSOURCE','ORTHOGRAPHY','IPA','COGID']
return D
def as_string(obj, pprint=False):
obj = str(obj)
if get_level() <= logging.ERROR and pprint:
print(obj)
return obj
