def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = 'Phonological CorpusTools: Kullback-Leibler CL interface')
parser.add_argument('corpus_file_name', help='Path to corpus file. This can just be the file name if it\'s in the same directory as CorpusTools')
parser.add_argument('seg1', help='First segment')
parser.add_argument('seg2', help='Second segment')
parser.add_argument('side', help='Context to check. Options are \'right\', \'left\' and \'both\'. You can enter just the first letter.')
parser.add_argument('-s', '--sequence_type', default='transcription', help="The attribute of Words to calculate KL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-t', '--type_or_token', default='token', help='Specifies whether entropy is based on type or token frequency.')
parser.add_argument('-c', '--context_type', type=str, default='Canonical', help="How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details.")
parser.add_argument('-o', '--outfile', help='Name of output file (optional)')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS', args.corpus_file_name))
except FileNotFoundError:
corpus_path = args.corpus_file_name
if not os.path.isfile(corpus_path):
corpus_path = os.path.join(os.getcwd(), corpus_path)
corpus = load_binary(corpus_path)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type, args.type_or_token)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type, args.type_or_token)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type, args.type_or_token)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type, args.type_or_token)
results = KullbackLeibler(corpus, args.seg1, args.seg2, args.side, outfile=None)
outfile = args.outfile
if outfile is not None:
if not os.path.isfile(outfile):
outfile = os.path.join(os.getcwd(), outfile)
if not outfile.endswith('.txt'):
outfile += '.txt'
with open(outfile, mode='w', encoding='utf-8-sig') as f:
print('Seg1,Seg2,Seg1 entropy,Seg2 entropy,Possible UR, Spurious UR\n\r',file=f)
print(','.join([str(r) for r in results]), file=f)
print('\n\rContext,Context frequency,{} frequency in context,{} frequency in context\n\r'.format(seg1,seg2), file=f)
for context,result in allC.items():
cfrequency = freq_c[context]/totalC
print('{},{},{},{}\n\r'.format(context,
cfrequency,
result.seg1/result.sum(),
result.seg2/result.sum()),
file=f)
print('Done!')
else:
print(results)
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: mutual information CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-q', '--query', help='bigram or segment pair, as str separated by comma')
group.add_argument('-l', '--all_pairwise_mis', action='store_true', help="Calculate MI for all orders of all pairs of segments")
parser.add_argument('-c', '--context_type', type=str, default='Canonical', help="How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details.")
parser.add_argument('-s', '--sequence_type', default='transcription', help="The attribute of Words to calculate MI over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-w', '--in_word', action='store_true', help="Flag: domain for counting unigrams/bigrams set to the word rather than the unigram/bigram; ignores adjacency and word edges (#)")
parser.add_argument('-e', '--halve_edges', action='store_true', help="Flag: make the number of edge characters (#) equal to the size of the corpus + 1, rather than double the size of the corpus - 1")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS', args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type)
if args.all_pairwise_mis:
result = all_mis(corpus, halve_edges = args.halve_edges, in_word = args.in_word)
else:
query = tuple(args.query.split(','))
if len(query) < 2:
print('Warning! Your queried bigram could not be processed. Please separate the two segments with a comma, as in the call: pct_mutualinfo example.corpus m,a')
result = pointwise_mi(corpus, query, args.halve_edges, args.in_word)
if args.outfile:
with open(args.outfile, 'w') as outfile:
if type(result) != list:
outstr = 'result\t' + '\t'.join([a for a in vars(args)]) + '\n' + str(result) + '\t' + '\t'.join([str(getattr(args, a)) for a in vars(args)])
outfile.write(outstr)
else:
outstr = 'result\tsegments\t' + '\t'.join([a for a in vars(args)]) + '\n'
for element in result:
outstr += str(element[1]) + '\t' + str(element[0]) + '\t' + '\t'.join([str(getattr(args,a)) for a in vars(args)]) + '\n'
outfile.write(outstr)
else:
print('No output file name provided.')
print('The mutual information of the given inputs is {}.'.format(str(result)))
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: phonological search CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
parser.add_argument('sequence',
help=('Sequence to search for, with segment positions separated by commas,'
+' and with sets separated by slashes.'
+' E.g. the input i will return all words with the segment [i], while'
+' the input a/o,t/p,i,n will return all words with [atin], [apin],'
+' [otin], or [opin].'))
parser.add_argument('-s', '--sequence_type', default='transcription',
help="The attribute of Words to search within. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS', args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
split_sequence = [tuple(pos.split('/')) for pos in args.sequence.split(',')]
middle = split_sequence[0]
try:
rhs = split_sequence[1:]
except:
rhs = None
if len(rhs) == 0:
rhs = None
ef = EnvironmentFilter(middle, None, rhs)
results = phonological_search(corpus, [ef], sequence_type=args.sequence_type)
if args.outfile:
with open(args.outfile, 'w') as outfile:
for result in results:
outfile.write(' '.join(getattr(result[0], args.sequence_type))+'\n')
print('Search results written to output file.')
else:
print('No output file name provided.')
print('Your search produced the results below:')
for result in results:
print('{}'.format(result[0]))
print('Total number of results: {}'.format(str(len(results))))
print('Please specify an output file name with -o to save these results.')
def changeFeatureSystem(self):
path = self.changeWidget.path()
if path is None:
self.specifier = None
else:
try:
self.specifier = load_binary(path)
except OSError:
return
self.changeDisplay()
def changeFeatureSystem(self):
path = self.changeWidget.path()
if path is None:
self.specifier = None
else:
try:
self.specifier = load_binary(path)
except OSError:
return
self.changeDisplay()
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):
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 main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: phonological search CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
parser.add_argument('seg_list', help='Segments to search for, separated by commas')
parser.add_argument('-e', '--environments', help='Environments in which to search for the segments, written using _ (underscore) notation and separated by commas')
parser.add_argument('-s', '--sequence_type', default='transcription', help="The attribute of Words to search within. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-o', '--outfile', help='Name of output file')
# fix for argparse's inability to take optional arguments beginning with -
for i, arg in enumerate(sys.argv):
if arg == '-e':
sys.argv[i] = '-e{}'.format(sys.argv[i+1])
sys.argv[i+1] = ''
sys.argv = [arg for arg in sys.argv if arg != '']
args = parser.parse_args()
####
corpus = load_binary(args.corpus_file_name)
segments = args.seg_list.split(',')
if args.environments:
args.environments = re.split(',(?!^|\+|\-|0|\.|1)', args.environments)
results = corpus.phonological_search(segments, envs=args.environments, sequence_type=args.sequence_type)
if args.outfile:
with open(args.outfile, 'w') as outfile:
for result in results:
outfile.write(' '.join(getattr(result[0], args.sequence_type))+'\n')
else:
print('No output file name provided.')
print('Your search produced the results below:')
for result in results:
print('{}'.format(result[0]))
print('Total number of results: {}'.format(str(len(results))))
print('Please specify an output file name with -o to save these results.')
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: phonological search CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
parser.add_argument(
'sequence',
help=
('Sequence to search for, with segment positions separated by commas,'
+ ' and with sets separated by slashes.' +
' E.g. the input i will return all words with the segment [i], while'
+
' the input a/o,t/p,i,n will return all words with [atin], [apin],' +
' [otin], or [opin].'))
parser.add_argument(
'-s',
'--sequence_type',
default='transcription',
help=
"The attribute of Words to search within. Normally this will be the transcription, but it can also be the spelling or a user-specified tier."
)
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(
os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS',
args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
split_sequence = [
tuple(pos.split('/')) for pos in args.sequence.split(',')
]
middle = split_sequence[0]
try:
rhs = split_sequence[1:]
except:
rhs = None
if len(rhs) == 0:
rhs = None
ef = EnvironmentFilter(middle, None, rhs)
results = phonological_search(corpus, [ef],
sequence_type=args.sequence_type)
if args.outfile:
with open(args.outfile, 'w') as outfile:
for result in results:
outfile.write(
' '.join(getattr(result[0], args.sequence_type)) + '\n')
print('Search results written to output file.')
else:
print('No output file name provided.')
print('Your search produced the results below:')
for result in results:
print('{}'.format(result[0]))
print('Total number of results: {}'.format(str(len(results))))
print(
'Please specify an output file name with -o to save these results.'
)
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(
description='Phonological CorpusTools: Kullback-Leibler CL interface')
parser.add_argument(
'corpus_file_name',
help=
'Path to corpus file. This can just be the file name if it\'s in the same directory as CorpusTools'
)
parser.add_argument('seg1', help='First segment')
parser.add_argument('seg2', help='Second segment')
parser.add_argument(
'side',
help=
'Context to check. Options are \'right\', \'left\' and \'both\'. You can enter just the first letter.'
)
parser.add_argument(
'-s',
'--sequence_type',
default='transcription',
help=
"The attribute of Words to calculate KL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier."
)
parser.add_argument(
'-t',
'--type_or_token',
default='token',
help='Specifies whether entropy is based on type or token frequency.')
parser.add_argument(
'-c',
'--context_type',
type=str,
default='Canonical',
help=
"How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details."
)
parser.add_argument('-o',
'--outfile',
help='Name of output file (optional)')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(
os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS',
args.corpus_file_name))
except FileNotFoundError:
corpus_path = args.corpus_file_name
if not os.path.isfile(corpus_path):
corpus_path = os.path.join(os.getcwd(), corpus_path)
corpus = load_binary(corpus_path)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type,
args.type_or_token)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type,
args.type_or_token)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type,
args.type_or_token)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type,
args.type_or_token)
results = KullbackLeibler(corpus,
args.seg1,
args.seg2,
args.side,
outfile=None)
outfile = args.outfile
if outfile is not None:
if not os.path.isfile(outfile):
outfile = os.path.join(os.getcwd(), outfile)
if not outfile.endswith('.txt'):
outfile += '.txt'
with open(outfile, mode='w', encoding='utf-8-sig') as f:
print(
'Seg1,Seg2,Seg1 entropy,Seg2 entropy,Possible UR, Spurious UR\n\r',
file=f)
print(','.join([str(r) for r in results]), file=f)
print(
'\n\rContext,Context frequency,{} frequency in context,{} frequency in context\n\r'
.format(seg1, seg2),
file=f)
for context, result in allC.items():
cfrequency = freq_c[context] / totalC
print('{},{},{},{}\n\r'.format(context, cfrequency,
result.seg1 / result.sum(),
result.seg2 / result.sum()),
file=f)
print('Done!')
else:
print(results)
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: functional load CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-p', '--pairs_file_name_or_segment', help='Name of file with segment pairs (or target segment if relative_fl is True)')
group.add_argument('-l', '--all_pairwise_fls', action='store_true', help="Calculate FL for all pairs of segments")
parser.add_argument('-c', '--context_type', type=str, default='Canonical', help="How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details.")
parser.add_argument('-a', '--algorithm', default='minpair', help='Algorithm to use for calculating functional load: "minpair" for minimal pair count or "deltah" for change in entropy. Defaults to minpair.')
parser.add_argument('-f', '--frequency_cutoff', type=float, default=0, help='Minimum frequency of words to consider as possible minimal pairs or contributing to lexicon entropy.')
parser.add_argument('-r', '--relative_count', type=check_bool, default=True, help='For minimal pair FL: whether or not to divide the number of minimal pairs by the number of possible minimal pairs (words with either segment in the proper environment). Defaults to True; pass -r False to set as False.')
parser.add_argument('-d', '--distinguish_homophones', action='store_true', help="For minimal pair FL: if False, then you'll count sock~shock (sock=clothing) and sock~shock (sock=punch) as just one minimal pair; but if True, you'll overcount alternative spellings of the same word, e.g. axel~actual and axle~actual. False is the value used by Wedel et al.")
parser.add_argument('-t', '--type_or_token', default='token', help='For change in entropy FL: specifies whether entropy is based on type or token frequency.')
parser.add_argument('-e', '--relative_fl', action='store_true', help="If True, calculate the relative FL of a single segment by averaging across the functional loads of it and all other segments.")
parser.add_argument('-s', '--sequence_type', default='transcription', help="The attribute of Words to calculate FL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-q', '--environment_lhs', default=None, help="Left hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni.")
parser.add_argument('-w', '--environment_rhs', default=None, help="Right hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni.")
parser.add_argument('-n', '--prevent_normalization', action='store_true', help="For deltah entropy: prevents normalization of the entropy difference by the pre-neutralization entropy. To replicate the Surendran \& Niyogi metric, do NOT use this flag.")
parser.add_argument('-x', '--separate_pairs', action='store_true', help="If present, calculate FL for each pair in the pairs file separately.")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
# Parse paths
try:
home = os.path.expanduser('~')
corpus = load_binary(os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS', args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
# Create corpus context
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
# Create environment filters
if not args.environment_lhs and not args.environment_rhs:
environment_filters = []
else:
if args.environment_lhs:
split_lhs = [tuple(pos.split('/')) for pos in args.environment_lhs.split(',')]
else:
split_lhs = None
if args.environment_rhs:
split_rhs = [tuple(pos.split('/')) for pos in args.environment_rhs.split(',')]
else:
split_rhs = None
environment_filters = [EnvironmentFilter([], split_lhs, split_rhs)]
# Initialize results
overall_result = None
detailed_results = {}
keys_label = ''
values_label = 'functional load'
# Determine which function to call
if args.all_pairwise_fls:
results = all_pairwise_fls(corpus, relative_fl=args.relative_fl, algorithm=args.algorithm, relative_count=args.relative_count,
distinguish_homophones=args.distinguish_homophones, environment_filters=environment_filters, prevent_normalization=args.prevent_normalization)
for pair, fl in results:
detailed_results[pair] = fl
keys_label = 'segment pair'
values_label = 'functional load'
else:
if args.relative_fl != True:
try:
with open(args.pairs_file_name_or_segment) as segpairs_or_segment_file:
segpairs_or_segment = [line for line in csv.reader(segpairs_or_segment_file, delimiter='\t') if len(line) > 0]
except FileNotFoundError:
raise FileNotFoundError("Did not find the segment pairs file even though 'relative_fl' is set to false. If calculating the relative FL of a single segement, please set 'relative_fl' to True. Otherwise, specify correct filename.")
else:
segpairs_or_segment = args.pairs_file_name_or_segment
if args.algorithm == 'minpair':
if args.relative_fl:
results = relative_minpair_fl(corpus, segpairs_or_segment, relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filters=environment_filters)
overall_result = results[0]
detailed_results = results[1]
keys_label = 'segment pair'
else:
if args.separate_pairs:
for pair in segpairs_or_segment:
pair = tuple(pair)
detailed_results[pair] = minpair_fl(corpus, [pair], relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filters=environment_filters)[0]
keys_label = 'segment pair'
else:
results = minpair_fl(corpus, segpairs_or_segment, relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filters=environment_filters)
overall_result = results[0]
detailed_results = {mp: '' for mp in results[1]}
keys_label = 'minimal pair (all listed regardless of distinguish_homophones value)'
elif args.algorithm == 'deltah':
if args.relative_fl:
results = relative_deltah_fl(corpus, segpairs_or_segment, environment_filters=environment_filters, prevent_normalization=args.prevent_normalization)
overall_result = results[0]
detailed_results = results[1]
keys_label = 'segment pair'
else:
if args.separate_pairs:
for pair in segpairs_or_segment:
pair = tuple(pair)
detailed_results[pair] = (deltah_fl(corpus, [pair], environment_filters=environment_filters, prevent_normalization=args.prevent_normalization))
keys_label = 'segment pair'
else:
overall_result = deltah_fl(corpus, segpairs_or_segment, environment_filters=environment_filters, prevent_normalization=args.prevent_normalization)
else:
raise Exception('-a / --algorithm must be set to either \'minpair\' or \'deltah\'.')
if args.outfile:
with open(args.outfile, 'w') as outfile:
outstr = '{}\t{}\n'.format(keys_label, values_label)
if overall_result:
outstr += 'OVERALL\t{}\n'.format(overall_result)
for key in detailed_results:
outstr += '{}\t{}\n'.format(key, detailed_results[key])
outfile.write(outstr)
else:
if overall_result:
easy_result = overall_result
else:
easy_result = detailed_results
print('No output file name provided.')
print('The functional load of the given inputs is {}.'.format(str(easy_result)))
def setUpClass(cls):
corpus = load_binary(r'C:\Users\Scott\Documents\GitHub\CorpusTools\corpustools\lemurian.corpus')
inventory = InventoryModel(corpus.inventory, copy_mode=True)
cls.dialog = FLDialog(main, None, corpus, inventory, False)
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: mutual information CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'-q',
'--query',
help='bigram or segment pair, as str separated by comma')
group.add_argument(
'-l',
'--all_pairwise_mis',
action='store_true',
help="Calculate MI for all orders of all pairs of segments")
parser.add_argument(
'-c',
'--context_type',
type=str,
default='Canonical',
help=
"How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details."
)
parser.add_argument(
'-s',
'--sequence_type',
default='transcription',
help=
"The attribute of Words to calculate MI over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier."
)
parser.add_argument(
'-w',
'--in_word',
action='store_true',
help=
"Flag: domain for counting unigrams/bigrams set to the word rather than the unigram/bigram; ignores adjacency and word edges (#)"
)
parser.add_argument(
'-e',
'--halve_edges',
action='store_true',
help=
"Flag: make the number of edge characters (#) equal to the size of the corpus + 1, rather than double the size of the corpus - 1"
)
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
try:
home = os.path.expanduser('~')
corpus = load_binary(
os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS',
args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type)
if args.all_pairwise_mis:
result = all_mis(corpus,
halve_edges=args.halve_edges,
in_word=args.in_word)
else:
query = tuple(args.query.split(','))
if len(query) < 2:
print(
'Warning! Your queried bigram could not be processed. Please separate the two segments with a comma, as in the call: pct_mutualinfo example.corpus m,a'
)
result = pointwise_mi(corpus, query, args.halve_edges, args.in_word)
if args.outfile:
with open(args.outfile, 'w') as outfile:
if type(result) != list:
outstr = 'result\t' + '\t'.join([
a for a in vars(args)
]) + '\n' + str(result) + '\t' + '\t'.join(
[str(getattr(args, a)) for a in vars(args)])
outfile.write(outstr)
else:
outstr = 'result\tsegments\t' + '\t'.join(
[a for a in vars(args)]) + '\n'
for element in result:
outstr += str(element[1]) + '\t' + str(
element[0]) + '\t' + '\t'.join(
[str(getattr(args, a)) for a in vars(args)]) + '\n'
outfile.write(outstr)
else:
print('No output file name provided.')
print('The mutual information of the given inputs is {}.'.format(
str(result)))
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: functional load CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'-p',
'--pairs_file_name_or_segment',
help=
'Name of file with segment pairs (or target segment if relative_fl is True)'
)
group.add_argument('-l',
'--all_pairwise_fls',
action='store_true',
help="Calculate FL for all pairs of segments")
parser.add_argument(
'-c',
'--context_type',
type=str,
default='Canonical',
help=
"How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details."
)
parser.add_argument(
'-a',
'--algorithm',
default='minpair',
help=
'Algorithm to use for calculating functional load: "minpair" for minimal pair count or "deltah" for change in entropy. Defaults to minpair.'
)
parser.add_argument(
'-f',
'--frequency_cutoff',
type=float,
default=0,
help=
'Minimum frequency of words to consider as possible minimal pairs or contributing to lexicon entropy.'
)
parser.add_argument(
'-r',
'--relative_count',
type=check_bool,
default=True,
help=
'For minimal pair FL: whether or not to divide the number of minimal pairs by the number of possible minimal pairs (words with either segment).'
)
parser.add_argument(
'-d',
'--distinguish_homophones',
action='store_true',
help=
"For minimal pair FL: if False, then you'll count sock~shock (sock=clothing) and sock~shock (sock=punch) as just one minimal pair; but if True, you'll overcount alternative spellings of the same word, e.g. axel~actual and axle~actual. False is the value used by Wedel et al."
)
parser.add_argument(
'-t',
'--type_or_token',
default='token',
help=
'For change in entropy FL: specifies whether entropy is based on type or token frequency.'
)
parser.add_argument(
'-e',
'--relative_fl',
action='store_true',
help=
"If True, calculate the relative FL of a single segment by averaging across the functional loads of it and all other segments."
)
parser.add_argument(
'-s',
'--sequence_type',
default='transcription',
help=
"The attribute of Words to calculate FL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier."
)
parser.add_argument(
'-q',
'--environment_lhs',
default=None,
help=
"Left hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni."
)
parser.add_argument(
'-w',
'--environment_rhs',
default=None,
help=
"Right hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni."
)
parser.add_argument(
'-x',
'--separate_pairs',
action='store_true',
help=
"If present, calculate FL for each pair in the pairs file separately.")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
corpus = load_binary(args.corpus_file_name)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
if not args.environment_lhs and not args.environment_rhs:
environment_filter = None
else:
if args.environment_lhs:
split_lhs = [
tuple(pos.split('/'))
for pos in args.environment_lhs.split(',')
]
else:
split_lhs = None
if args.environment_rhs:
split_rhs = [
tuple(pos.split('/'))
for pos in args.environment_rhs.split(',')
]
else:
split_rhs = None
environment_filter = EnvironmentFilter([], split_lhs, split_rhs)
if args.all_pairwise_fls:
result = all_pairwise_fls(
corpus,
relative_fl=args.relative_fl,
algorithm=args.algorithm,
relative_count=args.relative_count,
distinguish_homophones=args.distinguish_homophones,
environment_filter=environment_filter)
else:
if args.relative_fl != True:
try:
with open(args.pairs_file_name_or_segment
) as segpairs_or_segment_file:
segpairs_or_segment = [
line for line in csv.reader(segpairs_or_segment_file,
delimiter='\t')
if len(line) > 0
]
except FileNotFoundError:
raise FileNotFoundError(
"Did not find the segment pairs file even though 'relative_fl' is set to false. If calculating the relative FL of a single segement, please set 'relative_fl' to True. Otherwise, specify correct filename."
)
else:
segpairs_or_segment = args.pairs_file_name_or_segment
if args.algorithm == 'minpair':
if args.relative_fl:
result = relative_minpair_fl(
corpus,
segpairs_or_segment,
relative_count=bool(args.relative_count),
distinguish_homophones=args.distinguish_homophones,
environment_filter=environment_filter)
else:
if args.separate_pairs:
result = []
for pair in segpairs_or_segment:
result.append(
minpair_fl(corpus, [pair],
relative_count=bool(
args.relative_count),
distinguish_homophones=args.
distinguish_homophones,
environment_filter=environment_filter))
else:
result = minpair_fl(
corpus,
segpairs_or_segment,
relative_count=bool(args.relative_count),
distinguish_homophones=args.distinguish_homophones,
environment_filter=environment_filter)
elif args.algorithm == 'deltah':
if args.relative_fl:
result = relative_deltah_fl(
corpus,
segpairs_or_segment,
environment_filter=environment_filter)
else:
if args.separate_pairs:
result = []
for pair in segpairs_or_segment:
result.append(
deltah_fl(corpus, [pair],
environment_filter=environment_filter))
else:
result = deltah_fl(corpus,
segpairs_or_segment,
environment_filter=environment_filter)
else:
raise Exception(
'-a / --algorithm must be set to either \'minpair\' or \'deltah\'.'
)
if args.outfile:
with open(args.outfile, 'w') as outfile:
if type(result) != list:
outstr = 'result\t' + '\t'.join([
a for a in vars(args)
]) + '\n' + str(result) + '\t' + '\t'.join(
[str(getattr(args, a)) for a in vars(args)])
outfile.write(outstr)
else:
outstr = 'result\tsegment(s)\t' + '\t'.join(
[a for a in vars(args)]) + '\n'
for element in result:
outstr += str(element[1]) + '\t' + str(
element[0]) + '\t' + '\t'.join(
[str(getattr(args, a)) for a in vars(args)]) + '\n'
outfile.write(outstr)
else:
print('No output file name provided.')
print('The functional load of the given inputs is {}.'.format(
str(result)))
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: functional load CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'-p',
'--pairs_file_name_or_segment',
help=
'Name of file with segment pairs (or target segment if relative_fl is True)'
)
group.add_argument('-l',
'--all_pairwise_fls',
action='store_true',
help="Calculate FL for all pairs of segments")
parser.add_argument(
'-c',
'--context_type',
type=str,
default='Canonical',
help=
"How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details."
)
parser.add_argument(
'-a',
'--algorithm',
default='minpair',
help=
'Algorithm to use for calculating functional load: "minpair" for minimal pair count or "deltah" for change in entropy. Defaults to minpair.'
)
parser.add_argument(
'-f',
'--frequency_cutoff',
type=float,
default=0,
help=
'Minimum frequency of words to consider as possible minimal pairs or contributing to lexicon entropy.'
)
parser.add_argument(
'-r',
'--relative_count',
type=check_bool,
default=True,
help=
'For minimal pair FL: whether or not to divide the number of minimal pairs by the number of possible minimal pairs (words with either segment in the proper environment). Defaults to True; pass -r False to set as False.'
)
parser.add_argument(
'-d',
'--distinguish_homophones',
action='store_true',
help=
"For minimal pair FL: if False, then you'll count sock~shock (sock=clothing) and sock~shock (sock=punch) as just one minimal pair; but if True, you'll overcount alternative spellings of the same word, e.g. axel~actual and axle~actual. False is the value used by Wedel et al."
)
parser.add_argument(
'-t',
'--type_or_token',
default='token',
help=
'For change in entropy FL: specifies whether entropy is based on type or token frequency.'
)
parser.add_argument(
'-e',
'--relative_fl',
action='store_true',
help=
"If True, calculate the relative FL of a single segment by averaging across the functional loads of it and all other segments."
)
parser.add_argument(
'-s',
'--sequence_type',
default='transcription',
help=
"The attribute of Words to calculate FL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier."
)
parser.add_argument(
'-q',
'--environment_lhs',
default=None,
help=
"Left hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni."
)
parser.add_argument(
'-w',
'--environment_rhs',
default=None,
help=
"Right hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni."
)
parser.add_argument(
'-n',
'--prevent_normalization',
action='store_true',
help=
"For deltah entropy: prevents normalization of the entropy difference by the pre-neutralization entropy. To replicate the Surendran \& Niyogi metric, do NOT use this flag."
)
parser.add_argument(
'-x',
'--separate_pairs',
action='store_true',
help=
"If present, calculate FL for each pair in the pairs file separately.")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
# Parse paths
try:
home = os.path.expanduser('~')
corpus = load_binary(
os.path.join(home, 'Documents', 'PCT', 'CorpusTools', 'CORPUS',
args.corpus_file_name))
except FileNotFoundError:
corpus = load_binary(args.corpus_file_name)
# Create corpus context
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(
corpus,
args.sequence_type,
args.type_or_token,
frequency_threshold=args.frequency_cutoff)
# Create environment filters
if not args.environment_lhs and not args.environment_rhs:
environment_filters = []
else:
if args.environment_lhs:
split_lhs = [
tuple(pos.split('/'))
for pos in args.environment_lhs.split(',')
]
else:
split_lhs = None
if args.environment_rhs:
split_rhs = [
tuple(pos.split('/'))
for pos in args.environment_rhs.split(',')
]
else:
split_rhs = None
environment_filters = [EnvironmentFilter([], split_lhs, split_rhs)]
# Initialize results
overall_result = None
detailed_results = {}
keys_label = ''
values_label = 'functional load'
# Determine which function to call
if args.all_pairwise_fls:
results = all_pairwise_fls(
corpus,
relative_fl=args.relative_fl,
algorithm=args.algorithm,
relative_count=args.relative_count,
distinguish_homophones=args.distinguish_homophones,
environment_filters=environment_filters,
prevent_normalization=args.prevent_normalization)
for pair, fl in results:
detailed_results[pair] = fl
keys_label = 'segment pair'
values_label = 'functional load'
else:
if args.relative_fl != True:
try:
with open(args.pairs_file_name_or_segment
) as segpairs_or_segment_file:
segpairs_or_segment = [
line for line in csv.reader(segpairs_or_segment_file,
delimiter='\t')
if len(line) > 0
]
except FileNotFoundError:
raise FileNotFoundError(
"Did not find the segment pairs file even though 'relative_fl' is set to false. If calculating the relative FL of a single segement, please set 'relative_fl' to True. Otherwise, specify correct filename."
)
else:
segpairs_or_segment = args.pairs_file_name_or_segment
if args.algorithm == 'minpair':
if args.relative_fl:
results = relative_minpair_fl(
corpus,
segpairs_or_segment,
relative_count=bool(args.relative_count),
distinguish_homophones=args.distinguish_homophones,
environment_filters=environment_filters)
overall_result = results[0]
detailed_results = results[1]
keys_label = 'segment pair'
else:
if args.separate_pairs:
for pair in segpairs_or_segment:
pair = tuple(pair)
detailed_results[pair] = minpair_fl(
corpus, [pair],
relative_count=bool(args.relative_count),
distinguish_homophones=args.distinguish_homophones,
environment_filters=environment_filters)[0]
keys_label = 'segment pair'
else:
results = minpair_fl(
corpus,
segpairs_or_segment,
relative_count=bool(args.relative_count),
distinguish_homophones=args.distinguish_homophones,
environment_filters=environment_filters)
overall_result = results[0]
detailed_results = {mp: '' for mp in results[1]}
keys_label = 'minimal pair (all listed regardless of distinguish_homophones value)'
elif args.algorithm == 'deltah':
if args.relative_fl:
results = relative_deltah_fl(
corpus,
segpairs_or_segment,
environment_filters=environment_filters,
prevent_normalization=args.prevent_normalization)
overall_result = results[0]
detailed_results = results[1]
keys_label = 'segment pair'
else:
if args.separate_pairs:
for pair in segpairs_or_segment:
pair = tuple(pair)
detailed_results[pair] = (deltah_fl(
corpus, [pair],
environment_filters=environment_filters,
prevent_normalization=args.prevent_normalization))
keys_label = 'segment pair'
else:
overall_result = deltah_fl(
corpus,
segpairs_or_segment,
environment_filters=environment_filters,
prevent_normalization=args.prevent_normalization)
else:
raise Exception(
'-a / --algorithm must be set to either \'minpair\' or \'deltah\'.'
)
if args.outfile:
with open(args.outfile, 'w') as outfile:
outstr = '{}\t{}\n'.format(keys_label, values_label)
if overall_result:
outstr += 'OVERALL\t{}\n'.format(overall_result)
for key in detailed_results:
outstr += '{}\t{}\n'.format(key, detailed_results[key])
outfile.write(outstr)
else:
if overall_result:
easy_result = overall_result
else:
easy_result = detailed_results
print('No output file name provided.')
print('The functional load of the given inputs is {}.'.format(
str(easy_result)))
def main():
#### Parse command-line arguments
parser = argparse.ArgumentParser(description = \
'Phonological CorpusTools: functional load CL interface')
parser.add_argument('corpus_file_name', help='Name of corpus file')
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-p', '--pairs_file_name_or_segment', help='Name of file with segment pairs (or target segment if relative_fl is True)')
group.add_argument('-l', '--all_pairwise_fls', action='store_true', help="Calculate FL for all pairs of segments")
parser.add_argument('-c', '--context_type', type=str, default='Canonical', help="How to deal with variable pronunciations. Options are 'Canonical', 'MostFrequent', 'SeparatedTokens', or 'Weighted'. See documentation for details.")
parser.add_argument('-a', '--algorithm', default='minpair', help='Algorithm to use for calculating functional load: "minpair" for minimal pair count or "deltah" for change in entropy. Defaults to minpair.')
parser.add_argument('-f', '--frequency_cutoff', type=float, default=0, help='Minimum frequency of words to consider as possible minimal pairs or contributing to lexicon entropy.')
parser.add_argument('-r', '--relative_count', type=check_bool, default=True, help='For minimal pair FL: whether or not to divide the number of minimal pairs by the number of possible minimal pairs (words with either segment).')
parser.add_argument('-d', '--distinguish_homophones', action='store_true', help="For minimal pair FL: if False, then you'll count sock~shock (sock=clothing) and sock~shock (sock=punch) as just one minimal pair; but if True, you'll overcount alternative spellings of the same word, e.g. axel~actual and axle~actual. False is the value used by Wedel et al.")
parser.add_argument('-t', '--type_or_token', default='token', help='For change in entropy FL: specifies whether entropy is based on type or token frequency.')
parser.add_argument('-e', '--relative_fl', action='store_true', help="If True, calculate the relative FL of a single segment by averaging across the functional loads of it and all other segments.")
parser.add_argument('-s', '--sequence_type', default='transcription', help="The attribute of Words to calculate FL over. Normally this will be the transcription, but it can also be the spelling or a user-specified tier.")
parser.add_argument('-q', '--environment_lhs', default=None, help="Left hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni.")
parser.add_argument('-w', '--environment_rhs', default=None, help="Right hand side of environment filter. Format: positions separated by commas, groups by slashes, e.g. m/n,i matches mi or ni.")
parser.add_argument('-x', '--separate_pairs', action='store_true', help="If present, calculate FL for each pair in the pairs file separately.")
parser.add_argument('-o', '--outfile', help='Name of output file')
args = parser.parse_args()
####
corpus = load_binary(args.corpus_file_name)
if args.context_type == 'Canonical':
corpus = CanonicalVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'MostFrequent':
corpus = MostFrequentVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'SeparatedTokens':
corpus = SeparatedTokensVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
elif args.context_type == 'Weighted':
corpus = WeightedVariantContext(corpus, args.sequence_type, args.type_or_token, frequency_threshold=args.frequency_cutoff)
if not args.environment_lhs and not args.environment_rhs:
environment_filter = None
else:
if args.environment_lhs:
split_lhs = [tuple(pos.split('/')) for pos in args.environment_lhs.split(',')]
else:
split_lhs = None
if args.environment_rhs:
split_rhs = [tuple(pos.split('/')) for pos in args.environment_rhs.split(',')]
else:
split_rhs = None
environment_filter = EnvironmentFilter([], split_lhs, split_rhs)
if args.all_pairwise_fls:
result = all_pairwise_fls(corpus, relative_fl=args.relative_fl, algorithm=args.algorithm, relative_count=args.relative_count,
distinguish_homophones=args.distinguish_homophones, environment_filter=environment_filter)
else:
if args.relative_fl != True:
try:
with open(args.pairs_file_name_or_segment) as segpairs_or_segment_file:
segpairs_or_segment = [line for line in csv.reader(segpairs_or_segment_file, delimiter='\t') if len(line) > 0]
except FileNotFoundError:
raise FileNotFoundError("Did not find the segment pairs file even though 'relative_fl' is set to false. If calculating the relative FL of a single segement, please set 'relative_fl' to True. Otherwise, specify correct filename.")
else:
segpairs_or_segment = args.pairs_file_name_or_segment
if args.algorithm == 'minpair':
if args.relative_fl:
result = relative_minpair_fl(corpus, segpairs_or_segment, relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filter=environment_filter)
else:
if args.separate_pairs:
result = []
for pair in segpairs_or_segment:
result.append(minpair_fl(corpus, [pair], relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filter=environment_filter))
else:
result = minpair_fl(corpus, segpairs_or_segment, relative_count=bool(args.relative_count), distinguish_homophones=args.distinguish_homophones, environment_filter=environment_filter)
elif args.algorithm == 'deltah':
if args.relative_fl:
result = relative_deltah_fl(corpus, segpairs_or_segment, environment_filter=environment_filter)
else:
if args.separate_pairs:
result = []
for pair in segpairs_or_segment:
result.append(deltah_fl(corpus, [pair], environment_filter=environment_filter))
else:
result = deltah_fl(corpus, segpairs_or_segment, environment_filter=environment_filter)
else:
raise Exception('-a / --algorithm must be set to either \'minpair\' or \'deltah\'.')
if args.outfile:
with open(args.outfile, 'w') as outfile:
if type(result) != list:
outstr = 'result\t' + '\t'.join([a for a in vars(args)]) + '\n' + str(result) + '\t' + '\t'.join([str(getattr(args, a)) for a in vars(args)])
outfile.write(outstr)
else:
outstr = 'result\tsegment(s)\t' + '\t'.join([a for a in vars(args)]) + '\n'
for element in result:
outstr += str(element[1]) + '\t' + str(element[0]) + '\t' + '\t'.join([str(getattr(args,a)) for a in vars(args)]) + '\n'
outfile.write(outstr)
else:
print('No output file name provided.')
print('The functional load of the given inputs is {}.'.format(str(result)))
def setUpClass(cls):
corpus = load_binary(
r'C:\Users\Scott\Documents\GitHub\CorpusTools\corpustools\lemurian.corpus'
)
inventory = InventoryModel(corpus.inventory, copy_mode=True)
cls.dialog = FLDialog(main, None, corpus, inventory, False)