def generate_stats(self):
spkwhole = SpeakerStatistics("whole")
self.statistics["whole"]=spkwhole
for spc in self.get_speech_list():
self.statistics["whole"].add_speech(spc.get_length())
#check if the speakers all have the dot so we don't get the same speaker doubled.
if "." in spc.get_speaker():
speaker = spc.get_speaker()
else:
speaker = spc.get_speaker() + "."
if(speaker not in self.statistics):
spkstat = SpeakerStatistics(speaker)
spkstat.add_speech(spc.get_length())
self.statistics[speaker] = spkstat
else:
spkstat = self.statistics[speaker]
spkstat.add_speech(spc.get_length())
#the medium and mean count for the whole text
for key,value in self.statistics.items():
if(key!="whole"):
self.counts.append(value.get_count())
tools = Tools()
self.median_count = tools.calc_median(self.counts)
self.average_count = tools.calc_average(tools.calc_sum(self.counts),len(self.counts))
def generate_stats(self):
word_count = 0
pause = False
spkwhole = SpeakerStatistics("whole")
self.statistics["whole"]=spkwhole
for spc in self.get_speech_list():
self.statistics["whole"].add_speech(spc.get_length())
#five hundred span and 10 pause
for words in spc.get_words_array():
word_count = word_count + 1
if (word_count % 500 == 0):
word_count = 0
pause = True
if(word_count == 10 and pause == True):
pause = False
word_count = 0
if "." in spc.get_speaker():
speaker = spc.get_speaker()
else:
speaker = spc.get_speaker() + "."
if(pause != True):
if(speaker not in self.statistics):
spkstat = SpeakerStatistics(speaker)
spkstat.add_speech(spc.get_length())
self.statistics[speaker] = spkstat
else:
spkstat = self.statistics[speaker]
spkstat.add_speech(spc.get_length())
#the medium and mean count for the whole text
for key,value in self.statistics.items():
if(key!="whole"):
self.counts.append(value.get_count())
tools = Tools()
self.median_count = tools.calc_median(self.counts)
self.average_count = tools.calc_average(tools.calc_sum(self.counts),len(self.counts))
def set_median(self): tools = Tools() values = self.get_speech_length() self.median = tools.calc_median(values)