def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
corpus = kwargs.pop('corpus')
sequence_type = kwargs.pop('sequence_type')
type_token = kwargs.pop('type_token')
rounding = kwargs.pop('rounding')
with cm(corpus, sequence_type, type_token) as c:
try:
# if len(kwargs['segs']) == 1:
# seg = kwargs['segs'][0]
# seg = c.inventory[seg]
# result = informativity.get_informativity(c, seg, sequence_type,
# rounding=rounding,stop_check= kwargs['stop_check'], call_back=kwargs['call_back'])
# try:
# result.pop('Rounding')
# self.results.append(result)
# except AttributeError:
# self.stopped = True #result is None if user cancelled
# else:
results = informativity.get_multiple_informativity(
c,
kwargs['segs'],
sequence_type,
type_or_token=kwargs['type_or_token'],
rounding=rounding,
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
try:
for result in results:
result.pop('Rounding')
self.results.append(result)
except (TypeError, AttributeError):
self.stopped = True #result is None if user cancelled
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == RestrictedContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == RestrictedContextWidget.frequent_value:
cm = MostFrequentVariantContext
corpus = kwargs['corpusModel'].corpus
st = kwargs['sequence_type']
tt = kwargs['type_token']
att = kwargs.get('attribute', None)
ft = kwargs['frequency_cutoff']
log_count = kwargs['log_count']
with cm(corpus,
st,
tt,
attribute=att,
frequency_threshold=ft,
log_count=log_count) as c:
try:
if 'query' in kwargs:
for q in kwargs['query']:
res = phonotactic_probability(
c,
q,
algorithm=kwargs['algorithm'],
probability_type=kwargs['probability_type'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append([q, res])
else:
end = kwargs['corpusModel'].beginAddColumn(att)
phonotactic_probability_all_words(
c,
algorithm=kwargs['algorithm'],
probability_type=kwargs['probability_type'],
#num_cores = kwargs['num_cores'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
end = kwargs['corpusModel'].endAddColumn(end)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'],
kwargs['sequence_type'],
kwargs['type_token'],
frequency_threshold=kwargs['frequency_cutoff']) as c:
try:
envs = kwargs.pop('envs', None)
for pair in kwargs['segment_pairs']:
ordered_pair = pair
if envs is not None:
for env in envs:
env.middle = set(pair)
res = calc_prod(c,
envs,
kwargs['strict'],
ordered_pair=ordered_pair,
all_info=True,
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
else:
res = calc_prod_all_envs(
c,
pair[0],
pair[1],
all_info=True,
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
textType = self.kwargs.pop('text_type')
isDirectory = self.kwargs.pop('isDirectory')
logging.info('Importing {} corpus named {}'.format(
textType, self.kwargs['corpus_name']))
logging.info('Path: '.format(self.kwargs['path']))
log_annotation_types(self.kwargs['annotation_types'])
try:
if textType == 'spelling':
if isDirectory:
corpus = load_directory_spelling(**self.kwargs)
else:
corpus = load_discourse_spelling(**self.kwargs)
elif textType == 'transcription':
if isDirectory:
corpus = load_directory_transcription(**self.kwargs)
else:
corpus = load_discourse_transcription(**self.kwargs)
elif textType == 'ilg':
if isDirectory:
corpus = load_directory_ilg(**self.kwargs)
else:
corpus = load_discourse_ilg(**self.kwargs)
elif textType == 'textgrid':
if isDirectory:
corpus = load_directory_textgrid(**self.kwargs)
else:
corpus = load_discourse_textgrid(**self.kwargs)
elif textType == 'csv':
corpus = load_corpus_csv(**self.kwargs)
elif textType in ['buckeye', 'timit']:
self.kwargs['dialect'] = textType
if isDirectory:
corpus = load_directory_multiple_files(**self.kwargs)
else:
corpus = load_discourse_multiple_files(**self.kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
time.sleep(0.1)
self.finishedCancelling.emit()
return
self.dataReady.emit(corpus)
def do_check(*args, **kwargs):
self = args[0]
try:
function(*args, **kwargs)
except PCTError as e:
if not hasattr(self, 'handleError'):
raise
self.handleError(e)
except Exception as e:
if not hasattr(self, 'handleError'):
raise
e = PCTPythonError(e)
self.handleError(e)
def run(self):
time.sleep(0.1)
try:
system = load_feature_matrix_csv(**self.kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
time.sleep(0.1)
self.finishedCancelling.emit()
return
self.dataReady.emit(system)
def run(self):
time.sleep(0.1)
if self.stopCheck():
return
try:
self.results = load_binary(self.kwargs['path'])
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopCheck():
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'],
kwargs['sequence_type'],
kwargs['type_token'],
frequency_threshold=kwargs['frequency_cutoff']) as c:
try:
envs = kwargs.pop('envs', None)
if envs is not None: # if env is set, c(orpus context) is 'extracted'
context_output_path = kwargs.pop(
'context_output_path'
) # context_output_path for env context export
c = mi_env_filter(c, envs, context_output_path)
kwargs['in_word'] = False
for pair in kwargs['segment_pairs']:
res = pointwise_mi(c,
pair,
halve_edges=kwargs['halve_edges'],
in_word=kwargs['in_word'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
try:
self.results = phonological_search(**kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == RestrictedContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == RestrictedContextWidget.frequent_value:
cm = MostFrequentVariantContext
corpus = kwargs.pop('corpus')
st = kwargs.pop('sequence_type')
tt = kwargs.pop('type_token')
ft = kwargs.pop('frequency_cutoff')
with cm(corpus, st, tt, frequency_threshold=ft) as c:
try:
for pair in kwargs['segment_pairs']:
res = calc_freq_of_alt(
c,
pair[0],
pair[1],
kwargs['algorithm'],
min_rel=kwargs['min_rel'],
max_rel=kwargs['max_rel'],
min_pairs_okay=kwargs['include_minimal_pairs'],
from_gui=True,
phono_align=kwargs['phono_align'],
output_filename=kwargs['output_filename'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
if 'envs' not in kwargs or not kwargs['envs']:
return #user clicked "search" without actually entering a single environment
try:
self.results = phonological_search(**kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'],
kwargs['sequence_type'],
kwargs['type_token'],
frequency_threshold=kwargs['frequency_cutoff']) as c:
try:
for pair in kwargs['segment_pairs']:
res = KullbackLeibler(c,
pair[0],
pair[1],
outfile=None,
side=kwargs['side'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs['corpus'], kwargs['sequence_type'],
kwargs['type_token']) as c:
try:
for pair in kwargs['segment_pairs']:
res = pointwise_mi(c,
pair,
halve_edges=kwargs['halve_edges'],
in_word=kwargs['in_word'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if self.stopped:
break
self.results.append(res)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if kwargs.pop('algorithm') == 'min_pairs':
func = FL.minpair_fl
rel_func = FL.relative_minpair_fl
else:
func = FL.deltah_fl
rel_func = FL.relative_deltah_fl
if context == ContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == ContextWidget.frequent_value:
cm = MostFrequentVariantContext
elif context == ContextWidget.separate_value:
cm = SeparatedTokensVariantContext
elif context == ContextWidget.relative_value:
cm = WeightedVariantContext
with cm(kwargs.pop('corpus'),
kwargs.pop('sequence_type'),
kwargs.pop('type_token'),
frequency_threshold=kwargs.pop('frequency_cutoff')) as c:
try:
pairs = kwargs.pop('segment_pairs')
output_filename = kwargs.pop('output_filename', None)
if output_filename is not None:
to_output = []
outf = open(output_filename,
mode='w',
encoding='utf-8-sig',
newline='')
save_minimal_pairs(outf, [], write_header=True)
else:
outf = None
for pair in pairs:
if len(pair) == 1:
res = rel_func(c, pair[0], **kwargs)
#output_filename = outf, **kwargs)
else:
if isinstance(pair[0], (list, tuple)):
in_list = list(zip(pair[0], pair[1]))
else:
in_list = [pair]
res = func(c, in_list, **kwargs)
if output_filename is not None:
to_output.append((pair, res[1]))
if self.stopped:
break
self.results.append(res)
if output_filename is not None:
save_minimal_pairs(outf, to_output)
outf.close()
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == RestrictedContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == RestrictedContextWidget.frequent_value:
cm = MostFrequentVariantContext
corpus = kwargs['corpusModel'].corpus
st = kwargs['sequence_type']
tt = kwargs['type_token']
att = kwargs.get('attribute', None)
ft = kwargs['frequency_cutoff']
output = list()
with cm(corpus, st, tt, attribute=att, frequency_threshold=ft) as c:
try:
tierdict = defaultdict(list)
# Create a dict with sequence_type keys for constant-time lookup
for entry in c:
w = getattr(entry, kwargs['sequence_type'])
key = str(w)
tierdict[key].append(entry)
if 'query' in kwargs: #this will be true when searching for a single word (in the corpus or not)
last_value_removed = None
last_key_removed = None
for q in kwargs['query']:
q = ensure_query_is_word(q, c, c.sequence_type,
kwargs['tier_type'])
#the following code for adding/removing keys is to ensure that homophones are counted later in
#the ND algorithm (if the user wants to), but that words are not considered their own neighbours
#however, we only do this when comparing inside a corpus. when using a list of external words
#we don't want to do this, since it's possible for the external list to contain words that
#are in the corpus, and removing them gives the wrong ND value in this case
if kwargs['in_corpus']:
if last_value_removed:
tierdict[last_key_removed].append(
last_value_removed)
w = getattr(q, kwargs['sequence_type'])
last_key_removed = str(w)
#last_value_removed = tierdict[last_key_removed].pop()
for i, item in enumerate(
tierdict[last_key_removed]):
if str(item) == str(q):
last_value_removed = tierdict[
last_key_removed].pop(i)
break
#now we call the actual ND algorithms
if kwargs['algorithm'] != 'substitution':
res = neighborhood_density(
c,
q,
tierdict,
algorithm=kwargs['algorithm'],
max_distance=kwargs['max_distance'],
force_quadratic=kwargs['force_quadratic'],
collapse_homophones=kwargs[
'collapse_homophones'],
file_type=kwargs['file_type'],
tier_type=kwargs['tier_type'],
sequence_type=kwargs['sequence_type'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
else:
res = find_mutation_minpairs(
c,
q,
tier_type=kwargs['tier_type'],
collapse_homophones=kwargs[
'collapse_homophones'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if 'output_filename' in kwargs and kwargs[
'output_filename'] is not None:
print_neighden_results(kwargs['output_filename'],
res[1],
kwargs['output_format'])
if self.stopped:
break
if kwargs['file_list'] is not None:
output.append(','.join([
str(q),
str(res[0]), ','.join([str(r) for r in res[1]])
]))
self.results.append([q, res[0]])
else: #this will be the case if searching the entire corpus
end = kwargs['corpusModel'].beginAddColumn(att)
if kwargs['algorithm'] != 'substitution':
results = neighborhood_density_all_words(
c,
tierdict,
tier_type=kwargs['tier_type'],
algorithm=kwargs['algorithm'],
output_format=kwargs['output_format'],
max_distance=kwargs['max_distance'],
num_cores=kwargs['num_cores'],
call_back=kwargs['call_back'],
stop_check=kwargs['stop_check'],
settable_attr=kwargs['attribute'],
collapse_homophones=kwargs['collapse_homophones'])
else:
results = find_mutation_minpairs_all_words(
c,
tierdict,
tier_type=kwargs['tier_type'],
collapse_homophones=kwargs['collapse_homophones'],
num_cores=kwargs['num_cores'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
end = kwargs['corpusModel'].endAddColumn(end)
if 'output_filename' in kwargs and kwargs[
'output_filename'] is not None:
print_all_neighden_results(kwargs['output_filename'],
results)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
if output and 'output_filename' in kwargs:
with open(kwargs['output_filename'], encoding='utf-8',
mode='w') as outf:
print('Word,Density,Neighbors', file=outf)
for item in output:
print(item, file=outf)
self.dataReady.emit(self.results)
def run(self):
time.sleep(0.1)
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == RestrictedContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == RestrictedContextWidget.frequent_value:
cm = MostFrequentVariantContext
corpus = kwargs['corpusModel'].corpus
st = kwargs['sequence_type']
tt = kwargs['type_token']
att = kwargs.get('attribute', None)
with cm(corpus, st, tt, att) as c:
try:
if 'query' in kwargs:
for q in kwargs['query']:
if kwargs['algorithm'] != 'substitution':
res = neighborhood_density(
c,
q,
algorithm=kwargs['algorithm'],
max_distance=kwargs['max_distance'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
else:
res = find_mutation_minpairs(
c,
q,
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if 'output_filename' in kwargs and kwargs[
'output_filename'] is not None:
print_neighden_results(kwargs['output_filename'],
res[1])
if self.stopped:
break
self.results.append([q, res[0]])
else:
end = kwargs['corpusModel'].beginAddColumn(att)
if kwargs['algorithm'] != 'substitution':
neighborhood_density_all_words(
c,
algorithm=kwargs['algorithm'],
max_distance=kwargs['max_distance'],
num_cores=kwargs['num_cores'],
call_back=kwargs['call_back'],
stop_check=kwargs['stop_check'])
else:
find_mutation_minpairs_all_words(
c,
num_cores=kwargs['num_cores'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
end = kwargs['corpusModel'].endAddColumn(end)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
self.dataReady.emit(self.results)
def run(self):
kwargs = self.kwargs
self.results = []
context = kwargs.pop('context')
if context == RestrictedContextWidget.canonical_value:
cm = CanonicalVariantContext
elif context == RestrictedContextWidget.frequent_value:
cm = MostFrequentVariantContext
corpus = kwargs['corpusModel'].corpus
st = kwargs['sequence_type']
tt = kwargs['type_token']
att = kwargs.get('attribute', None)
ft = kwargs['frequency_cutoff']
output = list()
with cm(corpus, st, tt, attribute=att, frequency_threshold=ft) as c:
try:
tierdict = defaultdict(list)
for entry in c:
w = getattr(entry, kwargs['sequence_type'])
tierdict[str(w)].append(entry)
if 'query' in kwargs:
# Create a dict with sequence_type keys for constaint-time lookup
for q in kwargs['query']:
if kwargs['algorithm'] != 'substitution':
res = neighborhood_density(
c,
q,
tierdict,
algorithm=kwargs['algorithm'],
max_distance=kwargs['max_distance'],
force_quadratic=kwargs['force_quadratic'],
file_type=kwargs['file_type'],
tier_type=kwargs['tier_type'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
else:
res = find_mutation_minpairs(
c,
q,
tier_type=kwargs['tier_type'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
if 'output_filename' in kwargs and kwargs[
'output_filename'] is not None:
print_neighden_results(kwargs['output_filename'],
res[1])
if self.stopped:
break
if kwargs['file_list'] is not None:
output.append(','.join([
q,
str(res[0]), ','.join([str(r) for r in res[1]])
]))
self.results.append([q, res[0]])
else:
end = kwargs['corpusModel'].beginAddColumn(att)
if kwargs['algorithm'] != 'substitution':
neighborhood_density_all_words(
c,
tierdict,
tier_type=kwargs['tier_type'],
algorithm=kwargs['algorithm'],
max_distance=kwargs['max_distance'],
num_cores=kwargs['num_cores'],
call_back=kwargs['call_back'],
stop_check=kwargs['stop_check'],
settable_attr=kwargs['attribute'])
else:
find_mutation_minpairs_all_words(
c,
tier_type=kwargs['tier_type'],
num_cores=kwargs['num_cores'],
stop_check=kwargs['stop_check'],
call_back=kwargs['call_back'])
end = kwargs['corpusModel'].endAddColumn(end)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
self.finishedCancelling.emit()
return
if output and kwargs['file_list']:
with open(kwargs['output_filename'], encoding='utf-8',
mode='w') as outf:
print('Word,Density,Neighbors', file=outf)
for item in output:
print(item, file=outf)
self.dataReady.emit(self.results)
