def insert_data_markov(sentence_id,sentence, channel_id):
# データベースInstanceの作成
psgr = Psgr()
try:
#トランザクション処理開始
psgr.begin()
# messageの形態素解析
analyze = Analyze()
# 分かち書きした文章を取得する
# 吾輩 は 猫 で ある
parse_data = analyze.parse_wakati_sentence(sentence)
w1 = ''
w2 = ''
for data in parse_data:
if data[0] == '':
continue
# 登録用データ↓↓
word = data[0]
if w1 and w2:
values = (sentence_id, w1, w2, word, channel_id)
sqlcom = "INSERT INTO markov_chain (sentence_id, word1, word2, word3, channel_id) VALUES (%s,%s,%s,%s,%s)"
psgr.execute(sqlcom,values)
w1, w2 = w2, word
psgr.commit()
del analyze
except Exception as e:
print (e)
psgr.rollback()
del psgr
def results(request):
# Handle file upload
if request.method == 'POST':
print "Results being called"
form = DocumentForm(request.POST, request.FILES)
if form.is_valid():
newdoc = Document(docfile = request.FILES['docfile'])
newdoc.save()
parser = TextParsing.TextParsing(request.FILES['docfile'].name)
analyzer = Analyze.Analyze()
arr = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(), "trust")
score = analyzer.getAverageConversationScores(parser)
convo = analyzer.getConversationScore(parser)["trust"]
# labels = ['compound', 'neg', 'neu', 'pos']
# ind = np.arange(4)
# width = .5
# plt.bar(ind, score, width)
# plt.ylabel('Normalized Score')
# plt.xticks(ind,labels)
# fig, ax = plt.subplots()
# plot = ax.bar(ind, score, width)
# plt.savefig('ConversationAnalysis/media/graph.png')
# Redirect to the document list after POST
# return HttpResponseRedirect('MainPage.views.main')
else:
form = DocumentForm() # A empty, unbound form
return render(request, 'appTemps/results.html', {'arr': arr, 'score':score, 'convo':convo})
def results(request):
# Handle file upload
global arr, score, emoarr, cwords, arr2, p, cmpd
if request.method == 'POST':
# print "Results being called"
form = DocumentForm(request.POST, request.FILES)
if form.is_valid():
newdoc = Document(docfile=request.FILES['docfile'])
newdoc.save()
parser = TextParsing.TextParsing(request.FILES['docfile'].name)
analyzer = Analyze.Analyze()
analyzer.popDialogEmotion(parser)
analyzer.setDialogSentiment(parser)
p = json.dumps(analyzer.getPersonData(parser),
separators=(',', ':'))
arr2 = parser.plotlyBarFreqDist("everyone")
cmpd = analyzer.plotlyCompoundSenti(parser)
score = analyzer.getAverageConversationScores(parser)
emo1 = analyzer.getConversationScore(parser)["anger"]
emo2 = analyzer.getConversationScore(parser)["anticipation"]
emo3 = analyzer.getConversationScore(parser)["disgust"]
emo4 = analyzer.getConversationScore(parser)["fear"]
emo5 = analyzer.getConversationScore(parser)["joy"]
emo6 = analyzer.getConversationScore(parser)["sadness"]
emo7 = analyzer.getConversationScore(parser)["surprise"]
emo8 = analyzer.getConversationScore(parser)["trust"]
emoarr = [emo1, emo2, emo3, emo4, emo5, emo6, emo7, emo8]
cwords = parser.getNCommonWords(50)
# print freqdist
anger = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"anger")
anticipation = analyzer.plotlyEmotion(parser,
parser.speakerDict.keys(),
"anticipation")
disgust = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"disgust")
fear = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"fear")
joy = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"joy")
sadness = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"sadness")
trust = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"trust")
arr = [anger, anticipation, disgust, fear, joy, sadness, trust]
else:
form = DocumentForm() # A empty, unbound form
return render(
request, 'appTemps/results.html', {
'arr': arr,
'score': score,
'emoarr': emoarr,
'cwords': cwords,
'arr2': arr2,
"person": p,
"form": form,
"documents": documents
})
def analyze(self):
global rawData
global rawTime
global pic
global scan
rawData, rawTime, pic, scan = Analyze.Analyze(scan, protein, nd2_file,
Roi_Data_file,
status_file, time_file,
n)
self.analyze_btn.setText('Analyze scan:%d' % scan)
scan += 1
def open_plot(self, widget):
if not self.project: return
marker = self.project.timeline.ruler.get_playback_marker()
if not marker: return
start, duration = marker
frq, power = self.project.appsinkpipeline.get_spectrum(
start, start + duration)
w = Analyze.Analyze()
w.show_all()
w.plot_spectrum(frq, power)
def vis_plot_evolution(self, viscontrol, semitone):
if not self.project: return
playback_marker = self.project.timeline.ruler.get_playback_marker()
if not playback_marker: return
import scipy.interpolate
start, duration = playback_marker
##########
dialog = gtk.Dialog(
title="interval",
flags=gtk.DIALOG_DESTROY_WITH_PARENT | gtk.DIALOG_MODAL,
buttons=(gtk.STOCK_CANCEL, gtk.RESPONSE_REJECT, gtk.STOCK_OK,
gtk.RESPONSE_ACCEPT))
entry = gtk.Entry()
entry.set_text("0.1")
dialog.vbox.add(entry)
dialog.show_all()
dialog.run()
interval = float(entry.get_text())
dialog.destroy()
#############
# interval = 0.1
delta = interval / 2.
steps = 1.0 * duration / delta
x = []
y = []
for step in xrange(int(steps)):
pos = start + step * delta
frq, power = self.project.appsinkpipeline.get_spectrum(
pos - interval / 2., pos + interval / 2.)
# frq, power = self.project.appsinkpipeline.get_spectrum(pos,pos+interval)
spline = scipy.interpolate.InterpolatedUnivariateSpline(
frq, power, None, [None, None], 1)
lower = Math.semitone_to_frequency(semitone - 0.5)
upper = Math.semitone_to_frequency(semitone + 0.5)
total = spline.integral(lower, upper)
x.append(pos)
y.append(total)
w = Analyze.Analyze()
w.show_all()
w.simple_plot(x, y)
def measure_start(self):
if self.adjust == 1: #Adjust gain?
self.meas_adjust()
else:
self.date = ephem.now().tuple() #Date for FITS-file
self.receiver.start()
self.sig_time = 0
self.ref_time = 0
self.totpowTime = 0
self.config.set('CTRL', 'abort', '0')
self.config.set('CTRL', 'state', 'integrating')
with open(self.configfil, 'wb') as configfile:
self.config.write(configfile)
index = 0
start = time.time()
while index < self.loops:
self.set_index(index)
if self.switched == 1:
self.measure_switch_in()
else:
self.loops = 1
self.measure_tot_pow()
self.counter = 0
self.sigCount = 0
self.refCount = 0
if int(self.config.get('CTRL', 'abort')) != 1:
tc = Analyze(self.sigCount, self.refCount, index,
self.fftSize, self.c_freq, self.samp_rate,
self.switched, self.user)
index += 1
stop = time.time()
print "Total time: "
print stop - start
edit = 0
if int(self.config.get('CTRL', 'abort')) != 1:
td = Finalize(index, self.fftSize, self.c_freq, self.samp_rate,
edit, self.sig_time, self.ref_time,
self.switched, self.totpowTime, self.user,
self.date)
self.receiver.stop()
self.receiver.wait()
files = glob.glob('/tmp/ramdisk/*')
for f in files:
if f.endswith(self.index):
os.remove(f)
else:
continue
self.config.set('CTRL', 'state', 'ready')
with open(self.configfil, 'wb') as configfile:
self.config.write(configfile)
def analyze_message(sentence, sentence_id,channel_id):
psgr = Psgr()
cur = psgr.getCur()
# messageの解析
try:
psgr.begin()
analyze = Analyze()
parse_data = analyze.parse_sentence(sentence)
for data in parse_data:
detail_array = ['*'] * 9
detail_array[:len(data[1].split(','))] = data[1].split(',')
# 登録用データ↓↓
# word = data[0] # Surfaceデータが取れないので。
word = detail_array[6]
part_of_speech = detail_array[0]
if part_of_speech == "BOS/EOS":
continue
part_of_speech_detail1 = detail_array[1]
part_of_speech_detail2 = detail_array[2]
part_of_speech_detail3 = detail_array[3]
conjugate1 = detail_array[4]
conjugate2 = detail_array[5]
original = detail_array[6]
pronunciation1 = detail_array[7]
pronunciation2 = detail_array[8]
# 単語の登録
values = (word,part_of_speech,part_of_speech_detail1,part_of_speech_detail2,part_of_speech_detail3,conjugate1,conjugate2,original,pronunciation1,pronunciation2)
sqlcom = "INSERT INTO words (word,part_of_speech,part_of_speech_detail1,part_of_speech_detail2,part_of_speech_detail3,conjugate1,conjugate2,original,pronunciation1,pronunciation2) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) RETURNING word_id;"
psgr.execute(sqlcom,values)
word_id = psgr.lastrowid()
# 単語と文章を繋ぐ
values = (sentence_id,word_id,channel_id)
sqlcom = "INSERT INTO sentence_word (sentence_id, word_id, channel_id) VALUES (%s,%s,%s) RETURNING sentence_word_id;"
psgr.execute(sqlcom,values)
except Exception as err:
print(err)
psgr.rollback()
# コミット
psgr.dbCommit()
del psgr
def main():
if len(sys.argv)==1:
print '\n/-----\\\n|ERROR|===>>> Try inputs of the form python Analyze.py foo.p where foo.p is a pickle... not literally.\n\-----/\n'
sys.exit(0)
picklename=sys.argv[1]
SET=[]
x=np.array([0.0000000020128,0.000000002108307,0.000000002205033,0.000000002304373,0.000000002404483,0.000000002506767,0.000000002609444,0.000000002714023,0.000000002819564,0.000000002925106,0.000000003032267,0.000000003139197,0.000000003247665,0.000000003356696,0.000000003465344,0.00000000357534,0.000000003684857,0.000000003795656,0.000000003905897,0.000000004017367,0.000000004129093,0.000000004240215,0.000000004352506,0.000000004464105])
x=[i*10**9 for i in x]
basename='./20130506_06/A'
filenumbers=range(101)[40:71]#[6:20]
filenumbers=[('%03d'%filenumber) for filenumber in filenumbers]
filenames=[basename+filenumber+'.dat' for filenumber in filenumbers]
#GET RID OF THIS NEXT!
#filenames=filenames[12:15]
for filename in filenames: print filename
print ''
for filename in filenames:
success=False
while success==False:
TR=Analyze.Analyze(filename)
succ=False
while succ==False:
IN=raw_input("y to accept, or 'skip' or 'repeat': ")
if IN=='y':
succ=True
SET.append(TR)
success=True
elif IN=='skip':
succ=True
success=True
elif IN=='repeat':
succ=True
elif IN!='':
print "What!?"
pickle.dump(SET,open( picklename, 'w'))
pb.show()
def doubleresults(request):
global arr, score, emoarr, cwords, arr2, p, cmpd
form = DocumentForm(request.POST, request.FILES)
if form.is_valid():
newdoc = Document(docfile=request.FILES['docfile'])
newdoc.save()
parser = TextParsing.TextParsing(request.FILES['docfile'].name)
analyzer = Analyze.Analyze()
analyzer.popDialogEmotion(parser)
analyzer.setDialogSentiment(parser)
anger = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"anger")
anticipation = analyzer.plotlyEmotion(parser,
parser.speakerDict.keys(),
"anticipation")
disgust = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"disgust")
fear = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"fear")
joy = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(), "joy")
sadness = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"sadness")
trust = analyzer.plotlyEmotion(parser, parser.speakerDict.keys(),
"trust")
arr1 = [anger, anticipation, disgust, fear, joy, sadness, trust]
cmpd1 = analyzer.plotlyCompoundSenti(parser)
return render(
request, 'appTemps/doubleresults.html', {
'arr': arr,
'score': score,
'emoarr': emoarr,
'cwords': cwords,
'arr2': arr2,
"person": p,
'arr1': arr1,
'cmpd1': cmpd1,
'cmpd': cmpd
})
def test(self, widget):
if not self.project: return
marker = self.project.timeline.ruler.get_playback_marker()
if not marker: return
start, duration = marker
power = numpy.array(
self.project.appsinkpipeline.get_data(start,
start + duration))**2.0
rate = self.project.appsinkpipeline.caps[0]["rate"]
delta_t = self.builder.get_object("delta_t").get_value()
decay = self.builder.get_object("decay").get_value()
separation = self.builder.get_object("beat_separation").get_value()
# delta_t = 0.01
# decay = 0.5 # time needed to get to 1/e
# k*decay = 1
# power(t) = exp(-1)*power(t-decay)
# power(t) = exp(-k*decay)*power(t-decay)
# power(t) = exp(-k*delta_t)*power(t-delta_t)
decay_per_chunk = numpy.exp(-delta_t / decay)
samples = int(rate * delta_t)
limit = numpy.average(power[0:samples])
t = []
level = []
lim = []
tp1 = []
w = Analyze.Analyze()
for i in xrange(1, int(len(power) / samples)):
limit *= decay_per_chunk
chunk = power[samples * i:samples * (i + 1)]
avg_power = numpy.average(chunk)
power_spectrum = Math.windowed_fft(chunk)
bands = len(power_spectrum)
frqs = 0.5 * (numpy.arange(bands) + 0.5) * rate / bands
time = delta_t * i + start
min_frq_idx = numpy.min(numpy.nonzero(frqs > 80.))
max_frq_idx = numpy.max(numpy.nonzero(frqs < 1000.))
min_frq = frqs[min_frq_idx]
max_frq = frqs[max_frq_idx]
print frqs[0], min_frq, max_frq, frqs[-1]
total_power1 = numpy.trapz(power_spectrum[min_frq_idx:max_frq_idx],
frqs[min_frq_idx:max_frq_idx])
tp1.append(total_power1)
# if avg_power>=limit*(1.0+separation):
# if avg_power>=limit+separation:
# w.add_line(time, color="g")
if avg_power >= limit:
limit = avg_power
t.append(time)
level.append(avg_power)
lim.append(limit)
w.show_all()
w.simple_plot(numpy.array(t), numpy.array(level), color="r")
w.simple_plot(numpy.array(t), numpy.array(tp1), color="b")
w.simple_plot(numpy.array(t), numpy.array(lim), color="g")
# markers
for tap in self.project.timeline.rhythm.taps:
time = tap.get_time()
if not start < time and time < start + duration: continue
if type(tap.weight) == float: pass
else:
w.add_line(time, color="r")
import Cut
import brightness1
import Analyze
import Action
import BrightAnallyze
from PIL import Image
from PIL import ImageStat
P_SERVO = 22
fPWM = 50
a = 10
b = 2
IN1 = 11
IN2 = 13
IN3 = 15
IN21 = 12
IN22 = 16
IN23 = 18
c = [0]
c2 = [0]
setup()
while True:
setDirection(80)
a = input("please input")
take_photo()
X, Y, Z = Analyze(a)
print(X, Y, Z)
Action(X, Y)
BrightAnallyze(Z)
def main():
# utf-8に変更
set_default_encoding('utf-8')
# PsgrのInstance作成
# ツイートの取得
# twt = Twapi()
# api = twt.twitter_api
# status = api.home_timeline()[0]
#
# sentence = status.text
# tweet_id = status.id
# user_id = status.user.screen_name
# user_name = status.user.name
sentence = "すもももももももものうち"
tweet_id = 760430632317038592
user_id = "snow_moment09"
user_name = "雪村刹那"
# データベースInstanceの作成
psgr = Psgr()
try:
#トランザクション処理開始
psgr.begin()
values = (sentence, tweet_id, user_id, user_name)
sqlcom = "INSERT INTO sentences (sentence,tweet_id,user_id,user_name) VALUES (%s,%s,%s,%s) RETURNING sentence_id;"
psgr.execute(sqlcom, values)
sentence_id = psgr.lastrowid()
# テキストの解析
analyze = Analyze()
# print analyze.get_version()
parse_data = analyze.parse_sentence(sentence.encode('utf-8'))
for data in parse_data:
# print data[0],'\t',data[1].split(',')[0]
detail_array = ['*'] * 9
detail_array[:len(data[1].split(','))] = data[1].split(',')
# 登録用データ↓↓
word = data[0]
part_of_speech = detail_array[0]
if part_of_speech == "BOS/EOS":
continue
part_of_speech_detail1 = detail_array[1]
part_of_speech_detail2 = detail_array[2]
part_of_speech_detail3 = detail_array[3]
conjugate1 = detail_array[4]
conjugate2 = detail_array[5]
original = detail_array[6]
pronunciation1 = detail_array[7]
pronunciation2 = detail_array[8]
values = (word, part_of_speech, part_of_speech_detail1,
part_of_speech_detail2, part_of_speech_detail3,
conjugate1, conjugate2, original, pronunciation1,
pronunciation2)
sqlcom = "INSERT INTO words (word,part_of_speech,part_of_speech_detail1,part_of_speech_detail2,part_of_speech_detail3,conjugate1,conjugate2,original,pronunciation1,pronunciation2) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) RETURNING word_id;"
psgr.execute(sqlcom, values)
word_id = psgr.lastrowid()
values = (sentence_id, word_id)
sqlcom = "INSERT INTO sentence_word (sentence_id,word_id) VALUES (%s,%s) RETURNING sentence_word_id;"
psgr.execute(sqlcom, values)
psgr.commit()
del analyze
except Exception as e:
print e
psgr.rollback()
del psgr
