def __init__(self, master):
Frame.__init__(self, master)
self.grid(column=4, row=10)
self.create_widgets()
self.recorder = Recording.Recording(wave_output_filename, CHANNELS,
RATE, CHUNK)
self.recordingtest = False
def __init__(self, master):
Frame.__init__(self, master)
self.grid(column=1, row=5)
self.create_widgets()
self.recorder = Recording.Recording(wave_output_filename, CHANNELS,
RATE, CHUNK)
self.processor = emotionProcessor.EmotionProcessor(
wave_output_filename)
self.recordingtest = False
def recordAudio(self):
if (self.recordingtest == True):
print("Already Recording!")
else:
self.recorder = Recording.Recording(wave_output_filename, CHANNELS,
RATE, CHUNK)
self.recorder.startAudio()
self.emotionalPrediction.set("Recording..")
self.recordingtest = True
return self
from connections.postoffice import MasterConnection, ConnectionPostOfficeEnd
import Recording
import BCI
import PostOffice
if __name__ == "__main__":
main_connection = ConnectionPostOfficeEnd.MainConnection()
connections = MasterConnection.MasterConnection()
recording = Recording.Recording()
bci_controller = BCI.BCI(connections, main_connection, recording)
PostOffice.PostOffice(main_connection, connections, bci_controller)
usbPath = "/media/pi/VIDEOS"
localPath = "/home/pi/Documents/localVids"
# Main
fileNameList = []
duration = 0
startTime = 0
stopTime = 0
# Device Initialization
x = di.DeviceInitialization()
while (x == -1):
x = di.DeviceInitialization()
print("SETUP COMPLETE \n")
while (True):
# Recording
duration, fileNameList = rc.Recording()
#print("Done Recording")
#print("in driver Duration : ",duration)
#print("in driver FNL : ",fileNameList)
# Processing
vr = pro.Processing(fileNameList, duration)
#print("verify processing return :",vr)
print("Done Procesing")
# Finish
di.finishInitialization()
print("OUTPUT A1 : ", outputA1)
if (outputA1 == 0):
print("UNIT TEST A1 DEVICE INITIALIZATION STATUS : PASS")
elif (ouputA1 == -1):
print("UNIT TEST A1 DEVICE INITIALIZATION STATUS : FAIL")
print("\n")
# UNIT TEST : A2, A3
print("UNIT TEST A2, A3 : RECORDING")
print("INPUTS : None")
print(
"EXPECTED OUTPUTS : 1. duration (float object) 2. fileNameList (list object)"
)
print("\n")
# duration, fnl
outputA23_1, outputA23_2 = Recording.Recording()
print("\n")
print("OUTPUTS : ", outputA23_1, outputA23_2)
if ((type(outputA23_1) == float) and (type(outputA23_2) == list)):
print("UNIT TEST A2, A3 RECORDING STATUS : PASS")
else:
print("UNIT TEST A2, A3 RECORDING STATUS : FAIL")
print("\n")
# UNIT TEST A4
print("UNIT TEST A4 : PROCESSING")
print("INPUTS : 1. fileNameList (list object) 2. duration (float object)")
print("EXPECTED OUTPUT : True")
print("\n")
inputA4_1 = outputA23_2
def __init__(self,
pid,
eventfile,
datafile,
fixfile,
segfile,
log_time_offset=None,
aoifile=None,
prune_length=None,
require_valid_segs=True,
auto_partition_low_quality_segments=False,
rpsdata=None,
export_pupilinfo=False):
"""Inits BasicParticipant class
Args:
pid: Participant id
eventfile: a string containing the name of the "Event-Data.tsv" file for this participant
datafile: a string containing the name of the "All-Data.tsv" file for this participant
fixfile: a string containing the name of the "Fixation-Data.tsv" file for this participant
segfile: a string containing the name of the '.seg' file for this participant
log_time_offset: If not None, an integer indicating the time offset between the
external log file and eye tracking logs
aoifile: If not None, a string containing the name of the '.aoi' file
with definitions of the "AOI"s.
prune_length: If not None, an integer that specifies the time
interval (in ms) from the beginning of each Segment in which
samples are considered in calculations. This can be used if,
for example, you only wish to consider data in the first
1000 ms of each Segment. In this case (prune_length = 1000),
all data beyond the first 1000ms of the start of the "Segment"s
will be disregarded.
auto_partition_low_quality_segments: a boolean indicating whether EMDAT should
split the "Segment"s which have low sample quality, into two new
sub "Segment"s discarding the largest gap of invalid samples.
rpsdata: rest pupil sizes for all scenes if available
Yields:
a BasicParticipant object
"""
Participant.__init__(self, pid, eventfile, datafile, fixfile, segfile,
log_time_offset, aoifile, prune_length,
require_valid_segs,
auto_partition_low_quality_segments,
rpsdata) #calling the Participan's constructor
print "reading the files"
self.features = {}
rec = Recording.Recording(datafile,
fixfile,
event_file=eventfile,
media_offset=params.MEDIA_OFFSET)
print "Done!"
scenelist, self.numofsegments = partition_Basic(segfile)
print "partition done!"
if aoifile != None:
aois = Recording.read_aois_Tobii(aoifile)
else:
aois = None
self.features['numofsegments'] = self.numofsegments
self.segments, self.scenes = rec.process_rec(
scenelist=scenelist,
aoilist=aois,
prune_length=prune_length,
require_valid_segs=require_valid_segs,
auto_partition_low_quality_segments=
auto_partition_low_quality_segments,
rpsdata=rpsdata,
export_pupilinfo=export_pupilinfo)
Segments = self.segments
self.whole_scene = Scene('P' + str(pid), [],
rec.all_data,
rec.fix_data,
event_data=rec.event_data,
Segments=self.segments,
aoilist=aois,
prune_length=prune_length,
require_valid=require_valid_segs,
export_pupilinfo=export_pupilinfo)
self.scenes.insert(0, self.whole_scene)
for sc in self.scenes:
sc.clean_memory()
def main(morphFileFolder, outputFolder, embeddings_file, positive_words,
negatve_words):
print "loading embedding vectors..."
e = Embeddings(embeddings_file)
print "done!"
positive_df = pd.read_csv(positive_words)
negative_df = pd.read_csv(negatve_words)
positive_df["vector"] = positive_df["word"].apply(lambda x: e.get(x))
negative_df["vector"] = negative_df["word"].apply(lambda x: e.get(x))
files = [
f for f in listdir(morphFileFolder) if isfile(join(morphFileFolder, f))
]
recordings = []
for f in files:
recordings.append(Recording.Recording(join(morphFileFolder, f)))
questionSummaries = []
for r in recordings:
for i in xrange(len(r.questions)):
if len(questionSummaries) < (i + 1):
questionSummaries.append(Question.Question([]))
questionSummaries[i].mergeWith(r.questions[i])
#specific metrics comparison across all questions
nouns = {}
verbs = {}
adjectives = {}
adverbs = {}
content = {}
person_1 = {}
person_2 = {}
person_3 = {}
for i, q in enumerate(questionSummaries):
norm_pos = counter2normDictionary(q.pos, q.word_count)
norm_per = counter2normDictionary(q.person, q.word_count)
nouns[i + 1] = norm_pos["noun"]
verbs[i + 1] = norm_pos["verb"]
adjectives[i + 1] = norm_pos["adjective"]
adverbs[i + 1] = norm_pos["adverb"]
content[i + 1] = norm_pos["noun"] + norm_pos["verb"] + norm_pos[
"adjective"] + norm_pos["adverb"]
person_1[i + 1] = norm_per["1"]
person_2[i + 1] = norm_per["2"]
person_3[i + 1] = norm_per["3"]
print "Question " + ` (i + 1) ` + ", avg word count: " + ` (
q.word_count / len(questionSummaries)) `
counter2hist(nouns, 'Nouns', outputFolder)
counter2hist(verbs, 'Verbs', outputFolder)
counter2hist(adjectives, 'Adjectives', outputFolder)
counter2hist(adverbs, 'Adverbs', outputFolder)
counter2hist(content, 'Content words', outputFolder)
counter2hist(person_1, '1st person', outputFolder)
counter2hist(person_2, '2nd person', outputFolder)
counter2hist(person_3, '3rd person', outputFolder)
#raw metrics for each question
sentiment_scores = {}
for i, q in enumerate(questionSummaries):
positive_score = calculate_sentiment_score(q.words, positive_df, e)
negative_score = calculate_sentiment_score(q.words, negative_df, e)
print "Question " + ` (
i + 1
) ` + ", Positive: " + ` positive_score ` + ", Negative: " + ` negative_score ` + ", Overall: " + ` (
positive_score / negative_score) `
sentiment_scores[i + 1] = (positive_score / negative_score)
buildWordCloud(q.contentWords, True, 'Question ' + `
(i + 1) ` + ' Content Word Cloud', outputFolder)
counter2hist(counter2normDictionary(q.pos, q.word_count),
'Question ' + ` (i + 1) ` + ' POS', outputFolder)
counter2hist(counter2normDictionary(q.person, q.word_count),
'Question ' + ` (i + 1) ` + ' Person', outputFolder)
counter2hist(sentiment_scores, 'Sentiment scores', outputFolder)
