def document_stuff(
directory_location, input_file_name, marker_list, output_file_name, var_x
): # writes the final info to a csv file in this order: [DOC ID, speaker, replier, speaker words to replier total, replier words to speaker total, marker, conditional number, speaker marker number, reply marker number, replier utterance number]
global ordered_utterance_list
global convo_dict
global sparsity_measure
global output_almost
global final_counter
global alignment_dict
global possible_conversation_list
global speaker_list
initialize()
if Stemmed == False:
get_childes_files(directory_location, input_file_name)
# if extractRoles: #comment this and next line out eventually
# return
else:
get_childes_stemmed(directory_location, input_file_name)
determine_speakers(ordered_utterance_list)
determine_possible_conversations(speaker_list)
squisher(ordered_utterance_list)
convo_grouper(squished_dict)
calculate_sparsity(speaker_list, convo_dict)
utterances = convo_converter(corpus, input_file_name, convo_dict, marker_list)
if useCategories:
catdict = alignment.makeCatDict(marker_list, useREs)
# pprint(catdict)
for i in range(0, len(utterances)):
utterances[i]["msgMarkers"] = alignment.determineCategories(utterances[i]["msgTokens"], catdict, useREs)
utterances[i]["replyMarkers"] = alignment.determineCategories(utterances[i]["replyTokens"], catdict, useREs)
# pprint(utterances[0]["msgMarkers"])
marker_list = list(catdict.keys())
results = alignment.calculateAlignments(utterances, marker_list, 1, output_file_name, var_x, "CHILDES")
def document_stuff(
directory_location, input_file_name, marker_list, output_file_name, var_x
): # writes the final info to a csv file in this order: [DOC ID, speaker, replier, speaker words to replier total, replier words to speaker total, marker, conditional number, speaker marker number, reply marker number, replier utterance number]
global ordered_utterance_list
global convo_dict
global sparsity_measure
global output_almost
global final_counter
global alignment_dict
global possible_conversation_list
global speaker_list
initialize()
if Stemmed == False:
get_childes_files(directory_location, input_file_name)
#if extractRoles: #comment this and next line out eventually
# return
else:
get_childes_stemmed(directory_location, input_file_name)
determine_speakers(ordered_utterance_list)
determine_possible_conversations(speaker_list)
squisher(ordered_utterance_list)
convo_grouper(squished_dict)
calculate_sparsity(speaker_list, convo_dict)
utterances = convo_converter(corpus, input_file_name, convo_dict,
marker_list)
if (useCategories):
catdict = alignment.makeCatDict(marker_list, useREs)
#pprint(catdict)
for i in range(0, len(utterances)):
utterances[i]["msgMarkers"] = alignment.determineCategories(
utterances[i]["msgTokens"], catdict, useREs)
utterances[i]["replyMarkers"] = alignment.determineCategories(
utterances[i]["replyTokens"], catdict, useREs)
#pprint(utterances[0]["msgMarkers"])
marker_list = list(catdict.keys())
results = alignment.calculateAlignments(utterances, marker_list, 1,
output_file_name, var_x, 'CHILDES')
def document_stuff(directory_location, input_file_name, marker_list, output_file_name, corpus): # writes the final info to a csv file in this order: [DOC ID, speaker, replier, speaker words to replier total, replier words to speaker total, marker, conditional number, speaker marker number, reply marker number, replier utterance number] global ordered_utterance_list global convo_dict global sparsity_measure global output_almost global final_counter global alignment_dict global possible_conversation_list global speaker_list initialize() if Stemmed == False: get_childes_files(directory_location, input_file_name) if Stemmed == True: get_childes_stemmed(directory_location, input_file_name) determine_speakers(ordered_utterance_list) magic_marker_grabber(ordered_utterance_list, 'CHI') determine_possible_conversations(speaker_list) squisher(ordered_utterance_list) convo_grouper(squished_dict) calculate_sparsity(speaker_list, convo_dict) utterances = convo_converter(corpus, input_file_name, convo_dict, marker_list) results = alignment.calculateAlignments(utterances, marker_list, 0, output_file_name, var_x)
#Shuffling tweets if any shuffling has been requested
if (someShuffling):
logger1.log(rows[0])
rows = shuffleUtterances(rows, shuffleIds, shuffleTweets, shuffleMarkers,
combineMsgReply)
logger1.log(rows[0])
#Adding user info & extracting markers from messages
utterances = transformCSV(markers, users, rows)
#If we're using categories, re-process markers into their category labels before calculating alignment
if (useCategories):
catdict = alignment.makeCatDict(markers, useREs)
pprint(catdict)
for i in range(0, len(utterances)):
utterances[i]["msgMarkers"] = alignment.determineCategories(
utterances[i]["msgMarkers"], catdict, useREs)
utterances[i]["replyMarkers"] = alignment.determineCategories(
utterances[i]["replyMarkers"], catdict, useREs)
pprint(utterances[0]["msgMarkers"])
markers = list(catdict.keys())
#Calculate alignment, print & finish
results = alignment.calculateAlignments(utterances,
markers,
smoothing,
outputFile,
shouldWriteHeader,
corpusType='Twitter')
logger1.finish(start)
#Reading in user info and tweets
logger1.log("Reading user info...")
users = readUserInfo()
logger1.log("Reading messages...")
result = readCSV(inputFile, users, numMarkers)
rows = result["rows"]
markers = result["markers"]
#Shuffling tweets if any shuffling has been requested
if(someShuffling):
logger1.log(rows[0])
rows = shuffleUtterances(rows, shuffleIds, shuffleTweets, shuffleMarkers, combineMsgReply)
logger1.log(rows[0])
#Adding user info & extracting markers from messages
utterances = transformCSV(markers, users, rows)
#If we're using categories, re-process markers into their category labels before calculating alignment
if (useCategories):
catdict = alignment.makeCatDict(markers,useREs)
pprint(catdict)
for i in range(0,len(utterances)):
utterances[i]["msgMarkers"] = alignment.determineCategories(utterances[i]["msgMarkers"],catdict,useREs)
utterances[i]["replyMarkers"] = alignment.determineCategories(utterances[i]["replyMarkers"],catdict,useREs)
pprint(utterances[0]["msgMarkers"])
markers = list(catdict.keys())
#Calculate alignment, print & finish
results = alignment.calculateAlignments(utterances, markers, smoothing, outputFile, shouldWriteHeader,corpusType='Twitter')
logger1.finish(start)