def analysis(self):
query = request.params['query']
if query.strip() == "":
return render('/index.mako')
conn = solr.SolrConnection('http://localhost:8983/solr')
# Grab polarized data from Solr
params = {'q': sanitize(query) + " sentiment:[0.0 TO 1.0]", 'rows': 10}
good_results = conn.query(**(params))
params = {
'q': sanitize(query) + " sentiment:[-1.0 TO 0.0]",
'rows': 10
}
bad_results = conn.query(**(params))
# Grab data from Solr
params = {'q': sanitize(query), 'rows': 500, 'start': 0}
results = conn.query(**(params))
conn.close()
# Do ngrams analysis
goodText = ''.join(hit['content'] for hit in good_results.results)
badText = ''.join(hit['content'] for hit in bad_results.results)
c.goodTerms, c.badTerms = ngrams.main(goodText, badText,
ngrams.getWordsForDisplay)
c.goodCount = good_results.numFound
c.badCount = bad_results.numFound
c.goodResults = good_results
c.badResults = bad_results
c.query = query
if len(results) == 0:
return render('/noresults.mako')
c.results = results
c.service = "analysis"
return render('/analysis.mako')
def getBlogPost(self, filter):
conn = solr.SolrConnection('http://localhost:8983/solr')
print filter
parsedFilter = SearchFilter()
parsedFilter.decode(filter)
params = parsedFilter.toSolr()
params['q'] = params['q'] + " sentiment:[0.75 TO 1.0]"
good_response = conn.query(**(params))
params = parsedFilter.toSolr()
params['q'] = params['q'] + " sentiment:[0.0 TO 0.25]"
bad_response = conn.query(**(params))
conn.close()
goodText = ""
badText = ""
results = good_response.results + bad_response.results
for hit in results:
if hit['sentiment'] == True:
goodText += hit['content']
elif hit['sentiment'] == False:
badText += hit['content']
q, p = ngrams.main(goodText, badText, ngrams.getWordsForDisplay)
return (q, p, [(hit['title'], hit['content'], hit['sentiment']) for hit in results if hit['content']])
def polarize(self):
c.service = "polarize"
query = request.params['query']
if query.strip() == "":
return render('/index.mako')
conn = solr.SolrConnection('http://localhost:8983/solr')
# Grab data from Solr
params = {'q': sanitize(query) + " sentiment:[0.0 TO 1.0]", 'rows': 10}
good_results = conn.query(**(params))
params = {
'q': sanitize(query) + " sentiment:[-1.0 TO 0.0]",
'rows': 10
}
bad_results = conn.query(**(params))
conn.close()
if not (good_results or bad_results):
c.query = query
return render('/noresults.mako')
# Do ngrams analysis
goodText = ''.join(hit['content'] for hit in good_results.results)
badText = ''.join(hit['content'] for hit in bad_results.results)
q, p = ngrams.main(goodText, badText, ngrams.getWordsForDisplay)
# Send params to context
c.goodTerms = q #[i[0] for i in q]
c.badTerms = p #[i[0] for i in p]
c.goodResults = good_results
c.badResults = bad_results
c.query = query
return render('/polarize.mako')
def analysis(self):
query = request.params['query']
if query.strip() == "":
return render('/index.mako')
conn = solr.SolrConnection('http://localhost:8983/solr')
# Grab polarized data from Solr
params = {'q': sanitize(query) + " sentiment:[0.0 TO 1.0]", 'rows' : 10}
good_results = conn.query(**(params))
params = {'q': sanitize(query) + " sentiment:[-1.0 TO 0.0]", 'rows' : 10}
bad_results = conn.query(**(params))
# Grab data from Solr
params = {'q': sanitize(query), 'rows' : 500, 'start': 0}
results = conn.query(**(params))
conn.close()
# Do ngrams analysis
goodText = ''.join(hit['content'] for hit in good_results.results)
badText = ''.join(hit['content'] for hit in bad_results.results)
c.goodTerms, c.badTerms = ngrams.main(goodText, badText, ngrams.getWordsForDisplay)
c.goodCount = good_results.numFound
c.badCount = bad_results.numFound
c.goodResults = good_results
c.badResults = bad_results
c.query = query
if len(results) == 0:
return render('/noresults.mako')
c.results = results
c.service = "analysis"
return render('/analysis.mako')
def getBlogPost(self, filter):
conn = solr.SolrConnection('http://localhost:8983/solr')
print filter
parsedFilter = SearchFilter()
parsedFilter.decode(filter)
params = parsedFilter.toSolr()
params['q'] = params['q'] + " sentiment:[0.75 TO 1.0]"
good_response = conn.query(**(params))
params = parsedFilter.toSolr()
params['q'] = params['q'] + " sentiment:[0.0 TO 0.25]"
bad_response = conn.query(**(params))
conn.close()
goodText = ""
badText = ""
results = good_response.results + bad_response.results
for hit in results:
if hit['sentiment'] == True:
goodText += hit['content']
elif hit['sentiment'] == False:
badText += hit['content']
q, p = ngrams.main(goodText, badText, ngrams.getWordsForDisplay)
return (q, p, [(hit['title'], hit['content'], hit['sentiment'])
for hit in results if hit['content']])
def polarize(self):
c.service = "polarize"
query = request.params['query']
if query.strip() == "":
return render('/index.mako')
conn = solr.SolrConnection('http://localhost:8983/solr')
# Grab data from Solr
params = {'q': sanitize(query) + " sentiment:[0.0 TO 1.0]", 'rows' : 10}
good_results = conn.query(**(params))
params = {'q': sanitize(query) + " sentiment:[-1.0 TO 0.0]", 'rows' : 10}
bad_results = conn.query(**(params))
conn.close()
if not (good_results or bad_results):
c.query = query
return render('/noresults.mako')
# Do ngrams analysis
goodText = ''.join(hit['content'] for hit in good_results.results)
badText = ''.join(hit['content'] for hit in bad_results.results)
q, p = ngrams.main(goodText, badText, ngrams.getWordsForDisplay)
# Send params to context
c.goodTerms = q #[i[0] for i in q]
c.badTerms = p #[i[0] for i in p]
c.goodResults = good_results
c.badResults = bad_results
c.query = query
return render('/polarize.mako')
def main(language=None, corpus=None, datafolder=None, filename=None,
maxwordtokens=0, use_corpus=True):
print("\n*****************************************************\n"
"Running the phon.py program now...\n")
infilename, corpusName = get_wordlist_path_corpus_stem(language, corpus,
datafolder, filename, maxwordtokens, use_corpus)
if not infilename.exists():
if use_corpus:
if maxwordtokens:
warning = " ({} tokens)".format(maxwordtokens)
else:
warning = ""
print("\nWordlist for {}{} not found.\n"
"ngrams.py is now run.\n".format(corpus, warning))
ngrams.main(language=language, corpus=corpus,
datafolder=datafolder, filename=filename,
maxwordtokens=maxwordtokens)
else:
sys.exit("\nThe specified wordlist ""\n"
"is not found.".format(infilename))
if filename:
outfolder = Path(Path(filename).parent, "phon")
else:
outfolder = Path(datafolder, language, 'phon')
if not outfolder.exists():
outfolder.mkdir(parents=True)
outfilenamePhones = Path(outfolder, corpusName + "_phones.txt")
outfilenameBiphones = Path(outfolder, corpusName + "_biphones.txt")
outfilenameTriphones = Path(outfolder, corpusName + "_triphones.txt")
phoneDict = Counter()
triphoneDict = Counter()
biphoneDict = Counter()
sep = "\t"
print('Reading the wordlist file now...')
with infilename.open() as f:
lines = f.readlines()
for line in lines:
if not line or line.startswith("#"):
continue
line = line.strip().casefold()
phones, *rest = line.split()
try:
freq = int(rest[0])
except (ValueError, IndexError):
freq = 1
phones = "#{}#".format(phones) # add word boundaries
lenPhones = len(phones)
for i in range(lenPhones-2):
phone1 = phones[i]
phone2 = phones[i+1]
phone3 = phones[i+2]
phoneDict[phone3] += freq
if i == 0:
phoneDict[phone1] += freq
phoneDict[phone2] += freq
biphone = phone1 + sep + phone2
biphoneDict[biphone] += freq
biphone = phone2 + sep + phone3
triphone = phone1 + sep + phone2 + sep + phone3
triphoneDict[triphone] += freq
biphoneDict[biphone] += freq
print("\nCompleted counting phones, biphones, and triphones.")
intro_string = "# data source: {}".format(str(infilename))
phonesSorted = sorted_alphabetized(phoneDict.items(),
key=lambda x: x[1], reverse=True)
biphonesSorted = sorted_alphabetized(biphoneDict.items(),
key=lambda x: x[1], reverse=True)
triphonesSorted = sorted_alphabetized(triphoneDict.items(),
key=lambda x: x[1], reverse=True)
#--------------------------------------------------------------------------#
# generate .txt output files
#--------------------------------------------------------------------------#
with outfilenamePhones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(phonesSorted)), file=f)
print("# token count: {}".format(str(sum(phoneDict.values()))), file=f)
for (phone, freq) in phonesSorted:
print(phone + sep + str(freq), file=f)
with outfilenameBiphones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(biphonesSorted)), file=f)
print("# token count: {}".format(str(sum(biphoneDict.values()))),
file=f)
for (biphone, freq) in biphonesSorted:
print(biphone + sep + str(freq), file=f)
with outfilenameTriphones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(triphonesSorted)), file=f)
print("# token count: {}".format(str(sum(triphoneDict.values()))),
file=f)
for (triphone, freq) in triphonesSorted:
print(triphone + sep + str(freq), file=f)
#--------------------------------------------------------------------------#
# generate .json output files
#--------------------------------------------------------------------------#
outfilenamePhones_json = changeFilenameSuffix(outfilenamePhones, '.json')
with outfilenamePhones_json.open('w') as f:
json_pdump(phoneDict, f, key=lambda x:x[1], reverse=True)
outfilenameBiphones_json = changeFilenameSuffix(outfilenameBiphones, '.json')
with outfilenameBiphones_json.open('w') as f:
json_pdump(biphoneDict, f, key=lambda x:x[1], reverse=True)
outfilenameTriphones_json = changeFilenameSuffix(outfilenameTriphones, '.json')
with outfilenameTriphones_json.open('w') as f:
json_pdump(triphoneDict, f, key=lambda x:x[1], reverse=True)
print('phone, biphone and triphone files ready')
stdout_list("Output files:",
outfilenamePhones, outfilenameBiphones, outfilenameTriphones,
outfilenamePhones_json, outfilenameBiphones_json, outfilenameTriphones_json)
def main(language=None,
corpus=None,
datafolder=None,
filename=None,
maxwordtokens=0,
use_corpus=True):
print("\n*****************************************************\n"
"Running the phon.py program now...\n")
infilename, corpusName = get_wordlist_path_corpus_stem(
language, corpus, datafolder, filename, maxwordtokens, use_corpus)
if not infilename.exists():
if use_corpus:
if maxwordtokens:
warning = " ({} tokens)".format(maxwordtokens)
else:
warning = ""
print("\nWordlist for {}{} not found.\n"
"ngrams.py is now run.\n".format(corpus, warning))
ngrams.main(language=language,
corpus=corpus,
datafolder=datafolder,
filename=filename,
maxwordtokens=maxwordtokens)
else:
sys.exit("\nThe specified wordlist "
"\n"
"is not found.".format(infilename))
if filename:
outfolder = Path(Path(filename).parent, "phon")
else:
outfolder = Path(datafolder, language, 'phon')
if not outfolder.exists():
outfolder.mkdir(parents=True)
outfilenamePhones = Path(outfolder, corpusName + "_phones.txt")
outfilenameBiphones = Path(outfolder, corpusName + "_biphones.txt")
outfilenameTriphones = Path(outfolder, corpusName + "_triphones.txt")
phoneDict = Counter()
triphoneDict = Counter()
biphoneDict = Counter()
sep = "\t"
print('Reading the wordlist file now...')
with infilename.open() as f:
lines = f.readlines()
for line in lines:
if not line or line.startswith("#"):
continue
line = line.strip().casefold()
phones, *rest = line.split()
try:
freq = int(rest[0])
except (ValueError, IndexError):
freq = 1
phones = "#{}#".format(phones) # add word boundaries
lenPhones = len(phones)
for i in range(lenPhones - 2):
phone1 = phones[i]
phone2 = phones[i + 1]
phone3 = phones[i + 2]
phoneDict[phone3] += freq
if i == 0:
phoneDict[phone1] += freq
phoneDict[phone2] += freq
biphone = phone1 + sep + phone2
biphoneDict[biphone] += freq
biphone = phone2 + sep + phone3
triphone = phone1 + sep + phone2 + sep + phone3
triphoneDict[triphone] += freq
biphoneDict[biphone] += freq
print("\nCompleted counting phones, biphones, and triphones.")
intro_string = "# data source: {}".format(str(infilename))
phonesSorted = sorted_alphabetized(phoneDict.items(),
key=lambda x: x[1],
reverse=True)
biphonesSorted = sorted_alphabetized(biphoneDict.items(),
key=lambda x: x[1],
reverse=True)
triphonesSorted = sorted_alphabetized(triphoneDict.items(),
key=lambda x: x[1],
reverse=True)
#--------------------------------------------------------------------------#
# generate .txt output files
#--------------------------------------------------------------------------#
with outfilenamePhones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(phonesSorted)), file=f)
print("# token count: {}".format(str(sum(phoneDict.values()))), file=f)
for (phone, freq) in phonesSorted:
print(phone + sep + str(freq), file=f)
with outfilenameBiphones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(biphonesSorted)), file=f)
print("# token count: {}".format(str(sum(biphoneDict.values()))),
file=f)
for (biphone, freq) in biphonesSorted:
print(biphone + sep + str(freq), file=f)
with outfilenameTriphones.open('w') as f:
print(intro_string, file=f)
print("# type count: {}".format(len(triphonesSorted)), file=f)
print("# token count: {}".format(str(sum(triphoneDict.values()))),
file=f)
for (triphone, freq) in triphonesSorted:
print(triphone + sep + str(freq), file=f)
#--------------------------------------------------------------------------#
# generate .json output files
#--------------------------------------------------------------------------#
outfilenamePhones_json = changeFilenameSuffix(outfilenamePhones, '.json')
with outfilenamePhones_json.open('w') as f:
json_pdump(phoneDict, f, key=lambda x: x[1], reverse=True)
outfilenameBiphones_json = changeFilenameSuffix(outfilenameBiphones,
'.json')
with outfilenameBiphones_json.open('w') as f:
json_pdump(biphoneDict, f, key=lambda x: x[1], reverse=True)
outfilenameTriphones_json = changeFilenameSuffix(outfilenameTriphones,
'.json')
with outfilenameTriphones_json.open('w') as f:
json_pdump(triphoneDict, f, key=lambda x: x[1], reverse=True)
print('phone, biphone and triphone files ready')
stdout_list("Output files:", outfilenamePhones, outfilenameBiphones,
outfilenameTriphones, outfilenamePhones_json,
outfilenameBiphones_json, outfilenameTriphones_json)
def main(language=None,
corpus=None,
datafolder=None,
filename=None,
maxwordtypes=1000,
nNeighbors=9,
nEigenvectors=11,
create_WordToContexts=False,
create_ContextToWords=False,
mincontexts=3,
usesigtransforms=True):
print("\n*****************************************************\n"
"Running the manifold.py program now...\n")
if filename:
corpusStem = Path(filename).stem
infolder = Path(Path(filename).parent, 'ngrams')
outfolder = Path(Path(filename).parent, 'neighbors')
outcontextsfolder = Path(Path(filename).parent, 'word_contexts')
else:
corpusStem = Path(corpus).stem
infolder = Path(datafolder, language, 'ngrams')
outfolder = Path(datafolder, language, 'neighbors')
outcontextsfolder = Path(datafolder, language, 'word_contexts')
if not outfolder.exists():
outfolder.mkdir(parents=True)
if not outcontextsfolder.exists():
outcontextsfolder.mkdir(parents=True)
infileWordsname = Path(infolder, corpusStem + '_words.txt')
infileBigramsname = Path(infolder, corpusStem + '_bigrams.txt')
infileTrigramsname = Path(infolder, corpusStem + '_trigrams.txt')
if (not infileWordsname.exists()) or \
(not infileBigramsname.exists()) or \
(not infileTrigramsname.exists()):
print("Error in locating n-gram data files.\n"
"The program now creates them.\n")
ngrams.main(language=language,
corpus=corpus,
datafolder=datafolder,
filename=filename)
if usesigtransforms:
if filename:
infolderlxa = Path(Path(filename).parent, 'lxa')
else:
infolderlxa = Path(datafolder, language, 'lxa')
sigtransform_json_fname = Path(
infolderlxa, corpusStem + "_WordToSigtransforms.json")
try:
WordToSigtransforms = json_pload(sigtransform_json_fname.open())
except FileNotFoundError:
print("The file \"{}\" is not found.\n"
"The program now creates it.\n".format(
sigtransform_json_fname))
lxa5.main(language=language,
corpus=corpus,
datafolder=datafolder,
filename=filename)
WordToSigtransforms = json_pload(sigtransform_json_fname.open())
# WordToSigtransforms just read into the program; to be used soon...
print('Reading word list...', flush=True)
mywords = GetMyWords(infileWordsname, corpus)
print("Word file is", infileWordsname, flush=True)
print("Number of neighbors to find for each word type: ", nNeighbors)
print('Corpus has', len(mywords), 'word types', flush=True)
lenMywords = len(mywords)
if lenMywords > maxwordtypes:
nWordsForAnalysis = maxwordtypes
else:
nWordsForAnalysis = lenMywords
print('number of words for analysis adjusted to', nWordsForAnalysis)
analyzedwordlist = list(mywords.keys())[:nWordsForAnalysis]
worddict = {w: analyzedwordlist.index(w) for w in analyzedwordlist}
corpusName = corpusStem + '_' + str(nWordsForAnalysis) + '_' + str(
nNeighbors)
outfilenameNeighbors = Path(outfolder, corpusName + "_neighbors.txt")
outfilenameSharedcontexts = Path(outfolder, corpusName + \
"_shared_contexts.txt")
outfilenameNeighborGraph = Path(outfolder, corpusName + "_neighbors.gexf")
outfilenameImportantContextToWords = Path(outfolder, corpusName + \
"_ImportantContextToWords.txt")
outWordToContexts_json = Path(outcontextsfolder, corpusName + \
"_WordToContexts.json")
outContextToWords_json = Path(outcontextsfolder, corpusName + \
"_ContextToWords.json")
print("Reading bigrams/trigrams and computing context array...",
flush=True)
context_array, contextdict, \
WordToContexts, ContextToWords = GetContextArray(nWordsForAnalysis,
worddict, infileBigramsname, infileTrigramsname, mincontexts)
print("Computing shared context master matrix...", flush=True)
CountOfSharedContexts = context_array.dot(context_array.T).todense()
del context_array
print("Computing diameter...", flush=True)
Diameter = Normalize(nWordsForAnalysis, CountOfSharedContexts)
print("Computing incidence graph...", flush=True)
incidencegraph = compute_incidence_graph(nWordsForAnalysis, Diameter,
CountOfSharedContexts)
del CountOfSharedContexts
print("Computing mylaplacian...", flush=True)
mylaplacian = compute_laplacian(nWordsForAnalysis, Diameter,
incidencegraph)
del Diameter
del incidencegraph
print("Computing eigenvectors...", flush=True)
myeigenvalues, myeigenvectors = GetEigenvectors(mylaplacian)
del mylaplacian
del myeigenvalues
print('Computing distances between words...', flush=True)
# take first N columns of eigenvector matrix
coordinates = myeigenvectors[:, :nEigenvectors]
wordsdistance = compute_words_distance(nWordsForAnalysis, coordinates)
del coordinates
print('Computing nearest neighbors now... ', flush=True)
closestNeighbors = compute_closest_neighbors(wordsdistance, nNeighbors)
WordToNeighbors_by_str = OrderedDict()
WordToNeighbors = dict()
for wordno in range(nWordsForAnalysis):
line = closestNeighbors[wordno]
word_idx, neighbors_idx = line[0], line[1:]
word = analyzedwordlist[word_idx]
neighbors = [analyzedwordlist[idx] for idx in neighbors_idx]
WordToNeighbors_by_str[word] = neighbors
WordToNeighbors[word_idx] = neighbors_idx
del closestNeighbors
with outfilenameNeighbors.open('w') as f:
print("# language: {}\n# corpus: {}\n"
"# Number of word types analyzed: {}\n"
"# Number of neighbors: {}\n".format(language, corpus,
nWordsForAnalysis,
nNeighbors),
file=f)
for word, neighbors in WordToNeighbors_by_str.items():
print(word, " ".join(neighbors), file=f)
neighbor_graph = GetMyGraph(WordToNeighbors_by_str)
# output manifold as gexf data file
nx.write_gexf(neighbor_graph, str(outfilenameNeighborGraph))
# output manifold as json for d3 visualization
manifold_json_data = json_graph.node_link_data(neighbor_graph)
outfilenameManifoldJson = Path(outfolder, corpusName + "_manifold.json")
json.dump(manifold_json_data, outfilenameManifoldJson.open("w"), indent=2)
WordToNeighbors_json = changeFilenameSuffix(outfilenameNeighbors, ".json")
json_pdump(WordToNeighbors_by_str,
WordToNeighbors_json.open("w"),
asis=True)
print("Computing shared contexts among neighbors...", flush=True)
WordToSharedContextsOfNeighbors, \
ImportantContextToWords = compute_WordToSharedContextsOfNeighbors(
nWordsForAnalysis, WordToContexts,
WordToNeighbors, ContextToWords,
nNeighbors, mincontexts)
output_WordToSharedContextsOfNeighbors(outfilenameSharedcontexts,
WordToSharedContextsOfNeighbors,
worddict, contextdict,
nWordsForAnalysis)
output_ImportantContextToWords(outfilenameImportantContextToWords,
ImportantContextToWords, contextdict,
worddict)
outputfilelist = [
outfilenameNeighbors, outfilenameNeighborGraph, WordToNeighbors_json,
outfilenameSharedcontexts, outfilenameImportantContextToWords,
outfilenameManifoldJson
]
if create_WordToContexts:
outputfilelist.append(outWordToContexts_json)
json_pdump(WordToContexts,
outWordToContexts_json.open("w"),
key=lambda x: len(x[1]),
reverse=True)
if create_ContextToWords:
outputfilelist.append(outContextToWords_json)
json_pdump(ContextToWords,
outContextToWords_json.open("w"),
key=lambda x: len(x[1]),
reverse=True)
stdout_list("Output files:", *outputfilelist)
def main(language=None, corpus=None, datafolder=None, filename=None,
MinimumStemLength=4, MinimumAffixLength=1, SF_threshold=3,
maxwordtokens=0, use_corpus=True):
print("\n*****************************************************\n"
"Running the tries.py program now...\n")
#--------------------------------------------------------------------##
# read wordlist
#--------------------------------------------------------------------##
print("reading wordlist...", flush=True)
wordlist_path, corpusName = get_wordlist_path_corpus_stem(language, corpus,
datafolder, filename, maxwordtokens, use_corpus)
print("wordlist file path:\n{}\n".format(wordlist_path))
if not wordlist_path.exists():
if use_corpus:
if maxwordtokens:
warning = " ({} tokens)".format(maxwordtokens)
else:
warning = ""
print("\nWordlist for {}{} not found.\n"
"ngrams.py is now run.\n".format(corpus, warning))
ngrams.main(language=language, corpus=corpus,
datafolder=datafolder, filename=filename,
maxwordtokens=maxwordtokens)
else:
sys.exit("\nThe specified wordlist ""\n"
"is not found.".format(wordlist_path))
wordFreqDict = read_word_freq(wordlist_path)
wordlist = sorted(wordFreqDict.keys())
reversedwordlist = sorted([x[::-1] for x in wordlist])
#--------------------------------------------------------------------##
# output settings
#--------------------------------------------------------------------##
if filename:
outfolder = Path(Path(filename).parent, "tries")
else:
outfolder = Path(datafolder, language, "tries")
if not outfolder.exists():
outfolder.mkdir(parents=True)
outfile_SF_name = Path(outfolder, corpusName + "_SF.txt")
outfile_trieLtoR_name = Path(outfolder, corpusName + "_trieLtoR.txt")
outfile_trieRtoL_name = Path(outfolder, corpusName + "_trieRtoL.txt")
outfile_PF_name = Path(outfolder, corpusName + "_PF.txt")
outfile_Signatures_name = Path(outfolder, corpusName + "_Signatures.txt")
#--------------------------------------------------------------------##
# Find breaks in words (left-to-right and right-to-left)
#--------------------------------------------------------------------##
print("finding breaks in words...", flush=True)
breaks_LtoR = findBreaksInWords(wordlist, MinimumStemLength)
breaks_RtoL = findBreaksInWords(reversedwordlist, MinimumStemLength)
#--------------------------------------------------------------------##
# Break up each word (left-to-right and right-to-left)
#--------------------------------------------------------------------##
WordsBrokenLtoR = BreakUpEachWord(wordlist, breaks_LtoR)
WordsBrokenRtoL = BreakUpEachWord(reversedwordlist, breaks_RtoL)
#--------------------------------------------------------------------------#
# Compute successors and predecessors
#--------------------------------------------------------------------------#
print("computing successors and predecessors...", flush=True)
successors = GetSuccessors(wordlist, WordsBrokenLtoR)
OutputSuccessors(outfile_SF_name, successors, SF_threshold)
predecessors = GetSuccessors(reversedwordlist, WordsBrokenRtoL)
OutputSuccessors(outfile_PF_name, predecessors, SF_threshold, reverse=True)
outfile_SF_name_json = changeFilenameSuffix(outfile_SF_name, ".json")
json_pdump(successors, outfile_SF_name_json.open("w"))
outfile_PF_name_json = changeFilenameSuffix(outfile_PF_name, ".json")
json_pdump(predecessors, outfile_PF_name_json.open("w"))
print("printing signatures...", flush=True)
OutputSignatures1(outfile_Signatures_name, successors)
#--------------------------------------------------------------------------#
# Print tries (left-to-right, right-to-left)
#--------------------------------------------------------------------------#
print("printing tries...", flush=True)
OutputTrie(outfile_trieLtoR_name, wordlist, WordsBrokenLtoR)
OutputTrie(outfile_trieRtoL_name, reversedwordlist, WordsBrokenRtoL, reverse=True)
outfile_trieLtoR_name_json = changeFilenameSuffix(outfile_trieLtoR_name, ".json")
json_pdump(WordsBrokenLtoR, outfile_trieLtoR_name_json.open("w"))
outfile_trieRtoL_name_json = changeFilenameSuffix(outfile_trieRtoL_name, ".json")
json_pdump(WordsBrokenRtoL, outfile_trieRtoL_name_json.open("w"))
stdout_list("Output files:", outfile_SF_name, outfile_PF_name,
outfile_trieLtoR_name, outfile_trieRtoL_name,
outfile_Signatures_name,
outfile_SF_name_json, outfile_PF_name_json,
outfile_trieLtoR_name_json,
outfile_trieRtoL_name_json)
try:
goodText += hit['content']
except Exception , e:
print 'good_results error', e
pass
for hit in bad_results:
try:
badText += hit['content']
except Exception , e:
print 'bad_results error', e
pass
#goodText = ''.join(hit['content'] for hit in good_results)
#badText = ''.join(hit['content'] for hit in bad_results)
c.goodTerms, c.badTerms = ngrams.main(goodText, badText,
ngrams.getWordsForDisplay)
c.goodResults = good_results
c.neutralResults = neutral_results
c.badResults = bad_results
return render('/analysis.mako')
def custom(self):
c.service = "search"
c.query = request.params['query']
if c.query.strip() == "":
return render('/index.mako')
self.startAsyncSearch(c.query)
def main(language=None,
corpus=None,
datafolder=None,
filename=None,
MinimumStemLength=4,
MinimumAffixLength=1,
SF_threshold=3,
maxwordtokens=0,
use_corpus=True):
print("\n*****************************************************\n"
"Running the tries.py program now...\n")
#--------------------------------------------------------------------##
# read wordlist
#--------------------------------------------------------------------##
print("reading wordlist...", flush=True)
wordlist_path, corpusName = get_wordlist_path_corpus_stem(
language, corpus, datafolder, filename, maxwordtokens, use_corpus)
print("wordlist file path:\n{}\n".format(wordlist_path))
if not wordlist_path.exists():
if use_corpus:
if maxwordtokens:
warning = " ({} tokens)".format(maxwordtokens)
else:
warning = ""
print("\nWordlist for {}{} not found.\n"
"ngrams.py is now run.\n".format(corpus, warning))
ngrams.main(language=language,
corpus=corpus,
datafolder=datafolder,
filename=filename,
maxwordtokens=maxwordtokens)
else:
sys.exit("\nThe specified wordlist "
"\n"
"is not found.".format(wordlist_path))
wordFreqDict = read_word_freq(wordlist_path)
wordlist = sorted(wordFreqDict.keys())
reversedwordlist = sorted([x[::-1] for x in wordlist])
#--------------------------------------------------------------------##
# output settings
#--------------------------------------------------------------------##
if filename:
outfolder = Path(Path(filename).parent, "tries")
else:
outfolder = Path(datafolder, language, "tries")
if not outfolder.exists():
outfolder.mkdir(parents=True)
outfile_SF_name = Path(outfolder, corpusName + "_SF.txt")
outfile_trieLtoR_name = Path(outfolder, corpusName + "_trieLtoR.txt")
outfile_trieRtoL_name = Path(outfolder, corpusName + "_trieRtoL.txt")
outfile_PF_name = Path(outfolder, corpusName + "_PF.txt")
outfile_Signatures_name = Path(outfolder, corpusName + "_Signatures.txt")
#--------------------------------------------------------------------##
# Find breaks in words (left-to-right and right-to-left)
#--------------------------------------------------------------------##
print("finding breaks in words...", flush=True)
breaks_LtoR = findBreaksInWords(wordlist, MinimumStemLength)
breaks_RtoL = findBreaksInWords(reversedwordlist, MinimumStemLength)
#--------------------------------------------------------------------##
# Break up each word (left-to-right and right-to-left)
#--------------------------------------------------------------------##
WordsBrokenLtoR = BreakUpEachWord(wordlist, breaks_LtoR)
WordsBrokenRtoL = BreakUpEachWord(reversedwordlist, breaks_RtoL)
#--------------------------------------------------------------------------#
# Compute successors and predecessors
#--------------------------------------------------------------------------#
print("computing successors and predecessors...", flush=True)
successors = GetSuccessors(wordlist, WordsBrokenLtoR)
OutputSuccessors(outfile_SF_name, successors, SF_threshold)
predecessors = GetSuccessors(reversedwordlist, WordsBrokenRtoL)
OutputSuccessors(outfile_PF_name, predecessors, SF_threshold, reverse=True)
outfile_SF_name_json = changeFilenameSuffix(outfile_SF_name, ".json")
json_pdump(successors, outfile_SF_name_json.open("w"))
outfile_PF_name_json = changeFilenameSuffix(outfile_PF_name, ".json")
json_pdump(predecessors, outfile_PF_name_json.open("w"))
print("printing signatures...", flush=True)
OutputSignatures1(outfile_Signatures_name, successors)
#--------------------------------------------------------------------------#
# Print tries (left-to-right, right-to-left)
#--------------------------------------------------------------------------#
print("printing tries...", flush=True)
OutputTrie(outfile_trieLtoR_name, wordlist, WordsBrokenLtoR)
OutputTrie(outfile_trieRtoL_name,
reversedwordlist,
WordsBrokenRtoL,
reverse=True)
outfile_trieLtoR_name_json = changeFilenameSuffix(outfile_trieLtoR_name,
".json")
json_pdump(WordsBrokenLtoR, outfile_trieLtoR_name_json.open("w"))
outfile_trieRtoL_name_json = changeFilenameSuffix(outfile_trieRtoL_name,
".json")
json_pdump(WordsBrokenRtoL, outfile_trieRtoL_name_json.open("w"))
stdout_list("Output files:", outfile_SF_name, outfile_PF_name,
outfile_trieLtoR_name, outfile_trieRtoL_name,
outfile_Signatures_name, outfile_SF_name_json,
outfile_PF_name_json, outfile_trieLtoR_name_json,
outfile_trieRtoL_name_json)
def main(language=None,
corpus=None,
datafolder=None,
filename=None,
MinimumStemLength=4,
MaximumAffixLength=3,
MinimumNumberofSigUses=5,
maxwordtokens=0,
use_corpus=True):
print("\n*****************************************************\n"
"Running the lxa5.py program now...\n")
# -------------------------------------------------------------------------#
# decide suffixing or prefixing
# -------------------------------------------------------------------------#
suffix_languages = {
"english", "french", "hungarian", "turkish", "russian", "german",
"spanish", 'test'
}
prefix_languages = {"swahili"}
if str(language).casefold() in prefix_languages:
FindSuffixesFlag = False # prefixal
else:
FindSuffixesFlag = True # suffixal
wordlist_path, corpus_stem = get_wordlist_path_corpus_stem(
language, corpus, datafolder, filename, maxwordtokens, use_corpus)
print("wordlist file path:\n{}\n".format(wordlist_path))
if not wordlist_path.exists():
if use_corpus:
if maxwordtokens:
warning = " ({} tokens)".format(maxwordtokens)
else:
warning = ""
print("\nWordlist for {}{} not found.\n"
"ngrams.py is now run.\n".format(corpus, warning))
ngrams.main(language=language,
corpus=corpus,
datafolder=datafolder,
filename=filename,
maxwordtokens=maxwordtokens)
else:
sys.exit("\nThe specified wordlist "
"\n"
"is not found.".format(wordlist_path))
wordFreqDict = read_word_freq(wordlist_path)
wordlist = sorted(wordFreqDict.keys())
if filename:
outfolder = Path(Path(filename).parent, "lxa")
else:
outfolder = Path(datafolder, language, 'lxa')
if not outfolder.exists():
outfolder.mkdir(parents=True)
# TODO -- filenames not yet used in main()
outfile_Signatures_name = str(outfolder) + corpus_stem + "_Signatures.txt"
outfile_SigTransforms_name = str(
outfolder) + corpus_stem + "_SigTransforms.txt"
outfile_FSA_name = str(outfolder) + corpus_stem + "_FSA.txt"
outfile_FSA_graphics_name = str(
outfolder) + corpus_stem + "_FSA_graphics.png"
# -------------------------------------------------------------------------#
# create: BisigToTuple
# (key: tuple of bisig | value: set of (stem, word1, word2)
# StemToWords (key: stem | value: set of words)
# SigToStems (key: tuple of sig | value: set of stems )
# StemToSig (key: str of stem | value: tuple of sig )
# WordToSigs (key: str of word | value: set of sigs )
# AffixToSigs (key: str of affix | value: set of sigs )
# -------------------------------------------------------------------------#
BisigToTuple = MakeBiSignatures(wordlist, MinimumStemLength,
MaximumAffixLength, FindSuffixesFlag)
print("BisigToTuple ready", flush=True)
StemToWords = MakeStemToWords(BisigToTuple, MinimumNumberofSigUses)
print("StemToWords ready", flush=True)
SigToStems = MakeSigToStems(StemToWords, MaximumAffixLength,
MinimumNumberofSigUses, FindSuffixesFlag)
print("SigToStems ready", flush=True)
StemToSig = MakeStemToSig(SigToStems)
print("StemToSig ready", flush=True)
WordToSigs = MakeWordToSigs(StemToWords, StemToSig)
print("WordToSigs ready", flush=True)
WordToSigtransforms = MakeWordToSigtransforms(WordToSigs)
print("WordToSigtransforms ready", flush=True)
AffixToSigs = MakeAffixToSigs(SigToStems)
print("AffixToSigs ready", flush=True)
# -------------------------------------------------------------------------#
# generate graphs for several dicts
# -------------------------------------------------------------------------#
# GenerateGraphFromDict(StemToWords, outfolder, 'StemToWords.gexf')
# GenerateGraphFromDict(SigToStems, outfolder, 'SigToStems.gexf')
# GenerateGraphFromDict(WordToSigs, outfolder, 'WordToSigs.gexf')
# GenerateGraphFromDict(StemToSig, outfolder, 'StemToSig.gexf')
# -------------------------------------------------------------------------#
# -------------------------------------------------------------------------#
# output stem file
# -------------------------------------------------------------------------#
stemfilename = Path(outfolder, '{}_StemToWords.txt'.format(corpus_stem))
OutputLargeDict(stemfilename,
StemToWords,
key=lambda x: len(x[1]),
reverse=True,
min_cell_width=25,
howmanyperline=5)
print('===> stem file generated:', stemfilename, flush=True)
# -------------------------------------------------------------------------#
# output affix file
# -------------------------------------------------------------------------#
affixfilename = Path(outfolder, '{}_AffixToSigs.txt'.format(corpus_stem))
OutputLargeDict(affixfilename,
AffixToSigs,
min_cell_width=25,
key=lambda x: len(x[1]),
reverse=True,
howmanyperline=5,
SignatureValues=True)
print('===> affix file generated:', affixfilename, flush=True)
# -------------------------------------------------------------------------#
# output SigToStems
# -------------------------------------------------------------------------#
SigToStems_outfilename = Path(outfolder, corpus_stem + "_SigToStems.txt")
OutputLargeDict(SigToStems_outfilename,
SigToStems,
key=lambda x: len(x[1]),
reverse=True,
howmanyperline=5,
SignatureKeys=True)
SigToStems_outfilename_json = changeFilenameSuffix(SigToStems_outfilename,
".json")
json_pdump(SigToStems,
SigToStems_outfilename_json.open("w"),
key=lambda x: len(x[1]),
reverse=True)
print('===> output file generated:', SigToStems_outfilename, flush=True)
print('===> output file generated:',
SigToStems_outfilename_json,
flush=True)
# -------------------------------------------------------------------------#
# output WordToSigs
# -------------------------------------------------------------------------#
WordToSigs_outfilename = Path(outfolder, corpus_stem + "_WordToSigs.txt")
OutputLargeDict(WordToSigs_outfilename,
WordToSigs,
key=lambda x: len(x[1]),
reverse=True,
min_cell_width=25,
SignatureValues=True)
WordToSigs_outfilename_json = changeFilenameSuffix(WordToSigs_outfilename,
".json")
json_pdump(WordToSigs,
WordToSigs_outfilename_json.open("w"),
key=lambda x: len(x[1]),
reverse=True)
print('===> output file generated:', WordToSigs_outfilename, flush=True)
print('===> output file generated:',
WordToSigs_outfilename_json,
flush=True)
# -------------------------------------------------------------------------#
# output WordToSigtransforms
# -------------------------------------------------------------------------#
WordToSigtransforms_outfilename = Path(
outfolder, corpus_stem + "_WordToSigtransforms.txt")
OutputLargeDict(WordToSigtransforms_outfilename,
WordToSigtransforms,
min_cell_width=25,
sigtransforms=True,
key=lambda x: len(x[1]),
reverse=True)
print('===> output file generated:',
WordToSigtransforms_outfilename,
flush=True)
WordToSigtransforms_outfilename_json = changeFilenameSuffix(
WordToSigtransforms_outfilename, ".json")
json_pdump(WordToSigtransforms,
WordToSigtransforms_outfilename_json.open("w"),
key=lambda x: len(x[1]),
reverse=True)
print('===> output file generated:',
WordToSigtransforms_outfilename_json,
flush=True)
# -------------------------------------------------------------------------#
# output the most freq word types not in any induced paradigms {the, of..}
# -------------------------------------------------------------------------#
wordFreqDict_sorted = sorted_alphabetized(wordFreqDict.items(),
key=lambda x: x[1],
reverse=True)
mostFreqWordsNotInSigs_outfilename = Path(
outfolder, corpus_stem + "_mostFreqWordsNotInSigs.txt")
with mostFreqWordsNotInSigs_outfilename.open('w') as f:
for (word, freq) in wordFreqDict_sorted:
if word not in WordToSigs:
print(word, freq, file=f)
else:
break
print('===> output file generated:',
mostFreqWordsNotInSigs_outfilename,
flush=True)
# -------------------------------------------------------------------------#
# output the word types in induced paradigms
# -------------------------------------------------------------------------#
WordsInSigs_outfilename = Path(outfolder, corpus_stem + "_WordsInSigs.txt")
with WordsInSigs_outfilename.open('w') as f:
for (word, freq) in wordFreqDict_sorted:
if word in WordToSigs:
print(word, freq, file=f)
print('===> output file generated:', WordsInSigs_outfilename, flush=True)
# -------------------------------------------------------------------------#
# output the word types NOT in induced paradigms
# -------------------------------------------------------------------------#
WordsNotInSigs_outfilename = Path(outfolder,
corpus_stem + "_WordsNotInSigs.txt")
with WordsNotInSigs_outfilename.open('w') as f:
for (word, freq) in wordFreqDict_sorted:
if word not in WordToSigs:
print(word, freq, file=f)
print('===> output file generated:',
WordsNotInSigs_outfilename,
flush=True)
try:
goodText += hit['content']
except Exception, e:
print 'good_results error', e
pass
for hit in bad_results:
try:
badText += hit['content']
except Exception, e:
print 'bad_results error', e
pass
#goodText = ''.join(hit['content'] for hit in good_results)
#badText = ''.join(hit['content'] for hit in bad_results)
c.goodTerms, c.badTerms = ngrams.main(goodText, badText,
ngrams.getWordsForDisplay)
c.goodResults = good_results
c.neutralResults = neutral_results
c.badResults = bad_results
return render('/analysis.mako')
def custom(self):
c.service = "search"
c.query = request.params['query']
if c.query.strip() == "":
return render('/index.mako')
self.startAsyncSearch(c.query)
def main(
language=None,
corpus=None,
datafolder=None,
filename=None,
maxwordtypes=1000,
nNeighbors=9,
nEigenvectors=11,
create_WordToContexts=False,
create_ContextToWords=False,
mincontexts=3,
usesigtransforms=True,
):
print("\n*****************************************************\n" "Running the manifold.py program now...\n")
if filename:
corpusStem = Path(filename).stem
infolder = Path(Path(filename).parent, "ngrams")
outfolder = Path(Path(filename).parent, "neighbors")
outcontextsfolder = Path(Path(filename).parent, "word_contexts")
else:
corpusStem = Path(corpus).stem
infolder = Path(datafolder, language, "ngrams")
outfolder = Path(datafolder, language, "neighbors")
outcontextsfolder = Path(datafolder, language, "word_contexts")
if not outfolder.exists():
outfolder.mkdir(parents=True)
if not outcontextsfolder.exists():
outcontextsfolder.mkdir(parents=True)
infileWordsname = Path(infolder, corpusStem + "_words.txt")
infileBigramsname = Path(infolder, corpusStem + "_bigrams.txt")
infileTrigramsname = Path(infolder, corpusStem + "_trigrams.txt")
if (not infileWordsname.exists()) or (not infileBigramsname.exists()) or (not infileTrigramsname.exists()):
print("Error in locating n-gram data files.\n" "The program now creates them.\n")
ngrams.main(language=language, corpus=corpus, datafolder=datafolder, filename=filename)
if usesigtransforms:
if filename:
infolderlxa = Path(Path(filename).parent, "lxa")
else:
infolderlxa = Path(datafolder, language, "lxa")
sigtransform_json_fname = Path(infolderlxa, corpusStem + "_WordToSigtransforms.json")
try:
WordToSigtransforms = json_pload(sigtransform_json_fname.open())
except FileNotFoundError:
print('The file "{}" is not found.\n' "The program now creates it.\n".format(sigtransform_json_fname))
lxa5.main(language=language, corpus=corpus, datafolder=datafolder, filename=filename)
WordToSigtransforms = json_pload(sigtransform_json_fname.open())
# WordToSigtransforms just read into the program; to be used soon...
print("Reading word list...", flush=True)
mywords = GetMyWords(infileWordsname, corpus)
print("Word file is", infileWordsname, flush=True)
print("Number of neighbors to find for each word type: ", nNeighbors)
print("Corpus has", len(mywords), "word types", flush=True)
lenMywords = len(mywords)
if lenMywords > maxwordtypes:
nWordsForAnalysis = maxwordtypes
else:
nWordsForAnalysis = lenMywords
print("number of words for analysis adjusted to", nWordsForAnalysis)
analyzedwordlist = list(mywords.keys())[:nWordsForAnalysis]
worddict = {w: analyzedwordlist.index(w) for w in analyzedwordlist}
corpusName = corpusStem + "_" + str(nWordsForAnalysis) + "_" + str(nNeighbors)
outfilenameNeighbors = Path(outfolder, corpusName + "_neighbors.txt")
outfilenameSharedcontexts = Path(outfolder, corpusName + "_shared_contexts.txt")
outfilenameNeighborGraph = Path(outfolder, corpusName + "_neighbors.gexf")
outfilenameImportantContextToWords = Path(outfolder, corpusName + "_ImportantContextToWords.txt")
outWordToContexts_json = Path(outcontextsfolder, corpusName + "_WordToContexts.json")
outContextToWords_json = Path(outcontextsfolder, corpusName + "_ContextToWords.json")
print("Reading bigrams/trigrams and computing context array...", flush=True)
context_array, contextdict, WordToContexts, ContextToWords = GetContextArray(
nWordsForAnalysis, worddict, infileBigramsname, infileTrigramsname, mincontexts
)
print("Computing shared context master matrix...", flush=True)
CountOfSharedContexts = context_array.dot(context_array.T).todense()
del context_array
print("Computing diameter...", flush=True)
Diameter = Normalize(nWordsForAnalysis, CountOfSharedContexts)
print("Computing incidence graph...", flush=True)
incidencegraph = compute_incidence_graph(nWordsForAnalysis, Diameter, CountOfSharedContexts)
del CountOfSharedContexts
print("Computing mylaplacian...", flush=True)
mylaplacian = compute_laplacian(nWordsForAnalysis, Diameter, incidencegraph)
del Diameter
del incidencegraph
print("Computing eigenvectors...", flush=True)
myeigenvalues, myeigenvectors = GetEigenvectors(mylaplacian)
del mylaplacian
del myeigenvalues
print("Computing distances between words...", flush=True)
# take first N columns of eigenvector matrix
coordinates = myeigenvectors[:, :nEigenvectors]
wordsdistance = compute_words_distance(nWordsForAnalysis, coordinates)
del coordinates
print("Computing nearest neighbors now... ", flush=True)
closestNeighbors = compute_closest_neighbors(wordsdistance, nNeighbors)
WordToNeighbors_by_str = OrderedDict()
WordToNeighbors = dict()
for wordno in range(nWordsForAnalysis):
line = closestNeighbors[wordno]
word_idx, neighbors_idx = line[0], line[1:]
word = analyzedwordlist[word_idx]
neighbors = [analyzedwordlist[idx] for idx in neighbors_idx]
WordToNeighbors_by_str[word] = neighbors
WordToNeighbors[word_idx] = neighbors_idx
del closestNeighbors
with outfilenameNeighbors.open("w") as f:
print(
"# language: {}\n# corpus: {}\n"
"# Number of word types analyzed: {}\n"
"# Number of neighbors: {}\n".format(language, corpus, nWordsForAnalysis, nNeighbors),
file=f,
)
for word, neighbors in WordToNeighbors_by_str.items():
print(word, " ".join(neighbors), file=f)
neighbor_graph = GetMyGraph(WordToNeighbors_by_str)
# output manifold as gexf data file
nx.write_gexf(neighbor_graph, str(outfilenameNeighborGraph))
# output manifold as json for d3 visualization
manifold_json_data = json_graph.node_link_data(neighbor_graph)
outfilenameManifoldJson = Path(outfolder, corpusName + "_manifold.json")
json.dump(manifold_json_data, outfilenameManifoldJson.open("w"), indent=2)
WordToNeighbors_json = changeFilenameSuffix(outfilenameNeighbors, ".json")
json_pdump(WordToNeighbors_by_str, WordToNeighbors_json.open("w"), asis=True)
print("Computing shared contexts among neighbors...", flush=True)
WordToSharedContextsOfNeighbors, ImportantContextToWords = compute_WordToSharedContextsOfNeighbors(
nWordsForAnalysis, WordToContexts, WordToNeighbors, ContextToWords, nNeighbors, mincontexts
)
output_WordToSharedContextsOfNeighbors(
outfilenameSharedcontexts, WordToSharedContextsOfNeighbors, worddict, contextdict, nWordsForAnalysis
)
output_ImportantContextToWords(outfilenameImportantContextToWords, ImportantContextToWords, contextdict, worddict)
outputfilelist = [
outfilenameNeighbors,
outfilenameNeighborGraph,
WordToNeighbors_json,
outfilenameSharedcontexts,
outfilenameImportantContextToWords,
outfilenameManifoldJson,
]
if create_WordToContexts:
outputfilelist.append(outWordToContexts_json)
json_pdump(WordToContexts, outWordToContexts_json.open("w"), key=lambda x: len(x[1]), reverse=True)
if create_ContextToWords:
outputfilelist.append(outContextToWords_json)
json_pdump(ContextToWords, outContextToWords_json.open("w"), key=lambda x: len(x[1]), reverse=True)
stdout_list("Output files:", *outputfilelist)
