def showSignIn():
error = ''
try:
if request.method == "POST":
name = request.form['inputName']
password = sha256_crypt.encrypt(str(request.form['inputPassword']))
c, conn = connection()
data = c.execute("SELECT * FROM doctor WHERE docname = (%s)", name)
data = c.fetchone()[4]
if sha256_crypt.verify(password, data):
session['logged_in'] = True
session['username'] = name
flash("You are now logged in")
return redirect(url_for("main"))
else:
print data
error = "Invalid credentials, try again."
gc.collect()
return render_template("signin.html", error=error)
except Exception as e:
# flash(e)
error = "Invalid credentials, try again."
return render_template("signin.html", error=error)
def get_users():
# Query DB:
cur, conn = connection()
q_list_one = "SELECT * FROM users"
cur.execute(q_list_one)
users = cur.fetchall()
users_dict = []
for user in users:
user_dict = {
'Id': user[0],
'IdUniq': user[1],
'Name': user[2],
'Age': user[3],
'Date': user[4],
'Imei': user[5],
}
users_dict.append(user_dict)
return json.dumps(users_dict)
def showSignUp():
try:
if request.method == "POST":
name = request.form['inputName']
email = request.form['inputEmail']
password = sha256_crypt.encrypt(str(request.form['inputPassword']))
c, conn = connection()
x = c.execute("SELECT * FROM doctor WHERE docname = (%s)", name)
today = datetime.datetime.today().strftime("%m/%d/%Y")
if int(x) > 0:
flash("That username is already taken, please choose another")
return render_template('signup.html')
else:
c.execute(
"INSERT INTO doctor (doc_id, docname, email, password, created_at) VALUES (%s, %s, %s, %s, %s)",
(name, name, email, password, today))
conn.commit()
flash("Thanks for registering!")
c.close()
conn.close()
gc.collect()
session['logged_in'] = True
session['username'] = name
return redirect(url_for('main'))
return render_template("signup.html")
except Exception as e:
return (str(e))
return render_template('signup.html')
def dashboard(userid):
# Declarations
global starttime, endtime, myPath, numbPauses, loudness, dur, madmax, maxDBFS, MPA, numOfFtrs, phoneCallFtrFinal, wavo, jsonFeatures, nf
starttime = ""
endtime = ""
myPath = ""
numbPauses = 0
loudness = 0
nf = 0
dur = 0
madmax = 0
maxDBFS = 0
MPA = 0
numOfFtrs = 0
wavo = ""
jsonFeatures = {}
phoneCallFtrFinal = pd.DataFrame()
# Ftp connection
FtpHostName = "**********************"
FtpUser = "******"
FtpPassword = "******"
ftp = FTP(FtpHostName)
ftp.login(FtpUser, FtpPassword)
path = "****************************"
# forms
form = DateForm()
if form.validate_on_submit():
starttime = form.dt.data.strftime('%x')
#endtime = form.dt.data.strftime('%x')
########################################
########################################
# Accelerometer CSV data manipulation and features extraction
#########################################
#########################################
#########################################
# SQL queries
curCSV, conn = connection()
reqCSV = "SELECT * FROM files AS f WHERE f.uid = %s and f.type = 'CSV' "
curCSV.execute(reqCSV, userid)
CSVfiles = curCSV.fetchall()
def magnitude(activity):
x2 = activity['xAxis'] * activity['xAxis']
y2 = activity['yAxis'] * activity['yAxis']
z2 = activity['zAxis'] * activity['zAxis']
m2 = x2 + y2 + z2
m = m2.apply(lambda x: math.sqrt(x))
return m
def windows(df, size=100):
start = 0
while start < df.count():
yield start, start + size
start += (size / 2)
def jitter(axis, start, end):
j = float(0)
for i in xrange(start, min(end, axis.count())):
if start != 0:
j += abs(axis[i] - axis[i - 1])
return j / (end - start)
def mean_crossing_rate(axis, start, end):
cr = 0
m = axis.mean()
for i in xrange(start, min(end, axis.count())):
if start != 0:
p = axis[i - 1] > m
c = axis[i] > m
if p != c:
cr += 1
return float(cr) / (end - start - 1)
def window_summary(axis, start, end):
acf = stattools.acf(axis[start:end])
acv = stattools.acovf(axis[start:end])
sqd_error = (axis[start:end] - axis[start:end].mean())**2
return [
jitter(axis, start, end),
mean_crossing_rate(axis, start, end),
axis[start:end].mean(),
axis[start:end].std(),
axis[start:end].var(),
axis[start:end].min(),
axis[start:end].max(),
acf.mean(), # mean auto correlation
acf.std(), # standard deviation auto correlation
acv.mean(), # mean auto covariance
acv.std(), # standard deviation auto covariance
skew(axis[start:end]),
kurtosis(axis[start:end]),
math.sqrt(sqd_error.mean())
]
def features(activity):
for (start, end) in windows(activity['timestamp']):
features = []
for axis in ['xAxis', 'yAxis', 'zAxis', 'magnitude']:
features += window_summary(activity[axis], start, end)
yield features
COLUMNS = ['timestamp', 'xAxis', 'yAxis', 'zAxis']
features_dict = []
for csvfile in CSVfiles:
Activity = pd.read_csv(path + csvfile[1], header=None,
names=COLUMNS)[:3000]
Activity['magnitude'] = magnitude(Activity)
with open(
'/home/ahmed/Desktop/flaskTherapio/AccFtr/' + csvfile[1][17:] +
'_Features.csv', 'w') as out:
rows = csv.writer(out)
for f in features(Activity):
rows.writerow(f)
ActivityDataFeature = pd.read_csv(
'/home/ahmed/Desktop/flaskTherapio/AccFtr/' + csvfile[1][17:] +
'_Features.csv',
header=None)
# return ActivityDataFeature.reset_index().to_json(orient='index')
# CSVDATA = json.dumps(csv_files_dict)
########################################
########################################
# Phone calls processing and features extraction
#########################################
#########################################
#########################################
curWAV, conn = connection()
reqWAV = "SELECT * FROM files AS f WHERE f.uid = %s and f.type = 'WAV' "
curWAV.execute(reqWAV, userid)
WAVfiles = curWAV.fetchall()
wavs_dict = []
for wavf in WAVfiles:
wavf_dict = {
'Id': wavf[0],
'filepath': wavf[1],
'type': wavf[2],
'createdat': wavf[3],
'uid': wavf[4]
}
wavs_dict.append(wavf_dict)
#print wavs_dict.__len__()
print len(WAVfiles)
ind = 0
patho = "/home/ahmed/Desktop/flaskTherapio/"
for wavfile in WAVfiles:
ind += 1
print "checking database for the " + str(ind) + " time"
dirPath = wavfile[1][1:17]
wavName = wavfile[1][17:]
wavdate = wavfile[3][1:10]
#print dirPath
print wavName
print wavdate
ftp.cwd("/" + dirPath)
print "done changing directory"
audiofilematch = '*.wav'
cc = 0
error = False
for filename in ftp.nlst(
audiofilematch): # Loop - looking for WAV files
if error == False:
cc += 1
print "checking FTP for the " + str(cc) + " time"
print "comparing " + wavName + " and " + filename
print wavName == filename
fhandle = open(filename, 'wb')
print 'Getting ' + filename
os.chdir("/home/ahmed/Desktop/flaskTherapio/")
ftp.retrbinary('RETR ' + filename, fhandle.write)
print "stored"
fhandle.close()
if filename == wavName:
print " just do this shit here"
sound = AudioSegment.from_file(patho + filename)
sound.export(patho + "PhoneCallsFtr/" + filename + ".wav",
format="wav")
loudness = sound.rms
#number of Frames
nf = sound.frame_count()
# Value of loudness
loudness = sound.rms
#duration
dur = sound.duration_seconds
#max
madmax = sound.max
#max possible amplitude
MPA = sound.max_possible_amplitude
#max dbfs
maxDBFS = sound.max_dBFS
samplewidth = sound.sample_width
today = datetime.datetime.today().strftime("%m/%d/%Y")
blahblah = patho + "PhoneCallsFtr/" + filename + ".wav"
print "Blah blah path : ======> " + blahblah
[Fs, x] = audioBasicIO.readAudioFile(blahblah)
#silence removal ---- into segments
segments = aS.silenceRemoval(x,
Fs,
0.030,
0.030,
smoothWindow=0.3,
Weight=0.6,
plot=True)
numbPauses = len(segments) - 1
if numbPauses:
error = False
else:
error = True
print "num pauses : " + str(numbPauses)
# SELECT * FROM `phoneCallFeatures` WHERE (`created_at` BETWEEN 60 AND 1500) AND (uid = '5795028d168257.04609170')
# SQL query to insert phone call features to DB
insertFtrReq = """INSERT INTO phoneCallFeatures
(uid,
npause,
loudness,
maxA,
created_at)
VALUES (%s,
%s,
%s,
%s,
%s)""", (userid,
numbPauses,
loudness,
madmax, wavdate)
curInsertFtr, conn = connection()
curInsertFtr.execute(*insertFtrReq)
conn.commit()
# short term features extraction
fw = audioFeatureExtraction.stFeatureExtraction(
x, Fs, 0.1 * Fs, 0.1 * Fs)
print fw
# Mid term feature extraction
#F1 = audioFeatureExtraction.mtFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs,0.050*Fs, 0.050*Fs)
mypath = "/home/ahmed/Desktop/flaskTherapio/PhoneCallsFtr/"
if not os.path.isdir(mypath):
os.makedirs(mypath)
numpy.savetxt(mypath + wavName + "_AllStFtrs.csv",
fw,
delimiter=",")
#numpy.savetxt("00_AllMidFtrs.csv", F1, delimiter=",")
# read features in a data frame
phoneCallFtrPrime = pd.read_csv(mypath + wavName +
"_AllStFtrs.csv")
COLUMNS = []
for i in range(0, len(phoneCallFtrPrime.columns)):
COLUMNS.append('Frame' + str(i + 1))
COLUMNS.append('label')
phoneCallFtrFinal = pd.read_csv(mypath + wavName +
"_AllStFtrs.csv",
header=None,
names=COLUMNS)
numOfFtrs = len(phoneCallFtrFinal["label"])
phoneCallFtrFinal.label = phoneCallFtrFinal.label.astype(
float).fillna(0)
# phoneCallFtrFinal.label = phoneCallFtrFinal.label.astype(int).fillna(0)
for j in range(0, numOfFtrs):
phoneCallFtrFinal.iloc[j]['label'] = 0
#jsonFeatures = phoneCallFtrFinal.reset_index().to_json(orient='index')
#jsonFeatures = phoneCallFtrFinal.to_json(orient=None)
display(phoneCallFtrFinal.head())
#print "JSON Features of every audio ----------------------------------------------"
#print jsonFeatures
else:
print " Moving to the next file"
curPhone, conn = connection()
reqPhoneCallFtr = """SELECT * FROM phoneCallFeatures WHERE
created_at BETWEEN %s AND %s
AND uid = %s""", (starttime, today, userid)
curPhone.execute(*reqPhoneCallFtr)
calls = curPhone.fetchall()
calls_dict = []
for call in calls:
call_dict = {
'Id': call[0],
'IdUniq': call[1],
'npause': call[2],
'loudness': call[3],
'maxA': call[4],
'createdat': call[5],
}
calls_dict.append(call_dict)
#callJsonData = json.dumps(calls_dict)
#LoadJsonData = json.loads(callJsonData)
#print "JSON calls data ----------------------------------------------"
#print LoadJsonData
#print LoadJsonData['loudness']
return render_template('dashboard.html',
userid=userid,
loudness=loudness,
nf=nf,
dur=dur,
maxDBFS=maxDBFS,
madmax=madmax,
MPA=MPA,
jsonFeatures=jsonFeatures,
starttime=starttime,
form=form)
def extractAudio():
# Declarations
global userid, starttime, endtime, myPath, numbPauses, loudness, dur, madmax, maxDBFS, MPA, numOfFtrs, phoneCallFtrFinal, wavo, jsonFeatures, nf
userid = ""
starttime = ""
endtime = ""
myPath = ""
numbPauses = 0
loudness = 0
nf = 0
dur = 0
madmax = 0
maxDBFS = 0
MPA = 0
numOfFtrs = 0
wavo = ""
jsonFeatures = {}
phoneCallFtrFinal = pd.DataFrame()
# getting the parameters
if request.method == 'POST':
userid = request.args.get('userid')
#userid = request.form['userid']
print " Android must Have invoked this"
# Ftp connection
FtpHostName = "****************"
FtpUser = "******"
FtpPassword = "******"
ftp = FTP(FtpHostName)
ftp.login(FtpUser, FtpPassword)
path = "************************"
########################################
########################################
# Phone calls processing and features extraction
#########################################
#########################################
#########################################
# SQL queries
curWAV, conn = connection()
reqWAV = "SELECT * FROM files AS f WHERE f.uid = %s and f.type = 'WAV' "
curWAV.execute(reqWAV, userid)
WAVfiles = curWAV.fetchall()
wavs_dict = []
for wavf in WAVfiles:
wavf_dict = {
'Id': wavf[0],
'filepath': wavf[1],
'type': wavf[2],
'createdat': wavf[3],
'uid': wavf[4]
}
wavs_dict.append(wavf_dict)
#print wavs_dict.__len__()
print len(WAVfiles)
ind = 0
patho = "/home/ahmed/Desktop/flaskTherapio/"
for wavfile in WAVfiles:
ind += 1
print "checking database for the " + str(ind) + " time"
dirPath = wavfile[1][1:17]
wavName = wavfile[1][17:]
wavdate = wavfile[3][1:10]
#print dirPath
print wavName
print wavdate
ftp.cwd("/" + dirPath)
print "done changing directory"
audiofilematch = '*.wav'
cc = 0
for filename in ftp.nlst(
audiofilematch): # Loop - looking for WAV files
cc += 1
print "checking FTP for the " + str(cc) + " time"
print "comparing " + wavName + " and " + filename
print wavName == filename
fhandle = open(filename, 'wb')
print 'Getting ' + filename
os.chdir("/home/ahmed/Desktop/flaskTherapio/")
ftp.retrbinary('RETR ' + filename, fhandle.write)
print "stored"
fhandle.close()
if filename == wavName:
print " just do this shit here"
sound = AudioSegment.from_file(patho + filename)
sound.export(patho + "PhoneCallsFtr/" + filename + ".wav",
format="wav")
loudness = sound.rms
#number of Frames
nf = sound.frame_count()
# Value of loudness
loudness = sound.rms
#duration
dur = sound.duration_seconds
#max
madmax = sound.max
#max possible amplitude
MPA = sound.max_possible_amplitude
#max dbfs
maxDBFS = sound.max_dBFS
samplewidth = sound.sample_width
today = datetime.datetime.today().strftime("%m/%d/%Y")
blahblah = patho + "PhoneCallsFtr/" + filename + ".wav"
print "Blah blah path : ======> " + blahblah
[Fs, x] = audioBasicIO.readAudioFile(blahblah)
#silence removal ---- into segments
segments = aS.silenceRemoval(x,
Fs,
0.030,
0.030,
smoothWindow=0.3,
Weight=0.6,
plot=True)
numbPauses = len(segments) - 1
if numbPauses:
error = False
else:
error = True
print "num pauses : " + str(numbPauses)
# SELECT * FROM `phoneCallFeatures` WHERE (`created_at` BETWEEN 60 AND 1500) AND (uid = '5795028d168257.04609170')
# SQL query to insert phone call features to DB
insertFtrReq = """INSERT INTO phoneCallFeatures
(uid,
npause,
loudness,
maxA,
created_at)
VALUES (%s,
%s,
%s,
%s,
%s)""", (userid,
numbPauses,
loudness,
madmax, wavdate)
curInsertFtr, conn = connection()
curInsertFtr.execute(*insertFtrReq)
conn.commit()
# short term features extraction
fw = audioFeatureExtraction.stFeatureExtraction(
x, Fs, 0.1 * Fs, 0.1 * Fs)
print fw
# Mid term feature extraction
#F1 = audioFeatureExtraction.mtFeatureExtraction(x, Fs, 0.050*Fs, 0.050*Fs,0.050*Fs, 0.050*Fs)
mypath = "/home/ahmed/Desktop/flaskTherapio/PhoneCallsFtr/"
if not os.path.isdir(mypath):
os.makedirs(mypath)
numpy.savetxt(mypath + wavName + "_AllStFtrs.csv",
fw,
delimiter=",")
#numpy.savetxt("00_AllMidFtrs.csv", F1, delimiter=",")
# read features in a data frame
phoneCallFtrPrime = pd.read_csv(mypath + wavName +
"_AllStFtrs.csv")
COLUMNS = []
for i in range(0, len(phoneCallFtrPrime.columns)):
COLUMNS.append('Frame' + str(i + 1))
COLUMNS.append('label')
phoneCallFtrFinal = pd.read_csv(mypath + wavName +
"_AllStFtrs.csv",
header=None,
names=COLUMNS)
numOfFtrs = len(phoneCallFtrFinal["label"])
phoneCallFtrFinal.label = phoneCallFtrFinal.label.astype(
float).fillna(0)
# phoneCallFtrFinal.label = phoneCallFtrFinal.label.astype(int).fillna(0)
for j in range(0, numOfFtrs):
phoneCallFtrFinal.iloc[j]['label'] = 0
#jsonFeatures = phoneCallFtrFinal.reset_index().to_json(orient='index')
#jsonFeatures = phoneCallFtrFinal.to_json(orient=None)
display(phoneCallFtrFinal.head())
#print "JSON Features of every audio ----------------------------------------------"
#print jsonFeatures
print "this script has been called and data has been handled"
return jsonify({'USER ID': userid}), 201
def extractCSV():
global myPath, userid
# Declarations
userid = ""
myPath = ""
jsonFeatures = {}
if request.method == "POST":
#userid = request.form['userid']
userid = request.args.get('userid')
print " Android must Have invoked this CSV shit"
# Ftp connection
FtpHostName = "*************"
FtpUser = "******"
FtpPassword = "******"
ftp = FTP(FtpHostName)
ftp.login(FtpUser, FtpPassword)
path = "*********************"
########################################
########################################
# Accelerometer CSV data manipulation and features extraction
#########################################
#########################################
#########################################
# SQL queries
#con = mdb.connect(hostname, username, password, database)
curCSV, conn = connection()
reqCSV = "SELECT * FROM files AS f WHERE f.uid = %s and f.type = 'CSV' "
curCSV.execute(reqCSV, userid)
CSVfiles = curCSV.fetchall()
def magnitude(activity):
x2 = activity['xAxis'] * activity['xAxis']
y2 = activity['yAxis'] * activity['yAxis']
z2 = activity['zAxis'] * activity['zAxis']
m2 = x2 + y2 + z2
m = m2.apply(lambda x: math.sqrt(x))
return m
def windows(df, size=100):
start = 0
while start < df.count():
yield start, start + size
start += (size / 2)
def jitter(axis, start, end):
j = float(0)
for i in xrange(start, min(end, axis.count())):
if start != 0:
j += abs(axis[i] - axis[i - 1])
return j / (end - start)
def mean_crossing_rate(axis, start, end):
cr = 0
m = axis.mean()
for i in xrange(start, min(end, axis.count())):
if start != 0:
p = axis[i - 1] > m
c = axis[i] > m
if p != c:
cr += 1
return float(cr) / (end - start - 1)
def window_summary(axis, start, end):
acf = stattools.acf(axis[start:end])
acv = stattools.acovf(axis[start:end])
sqd_error = (axis[start:end] - axis[start:end].mean())**2
return [
jitter(axis, start, end),
mean_crossing_rate(axis, start, end),
axis[start:end].mean(),
axis[start:end].std(),
axis[start:end].var(),
axis[start:end].min(),
axis[start:end].max(),
acf.mean(), # mean auto correlation
acf.std(), # standard deviation auto correlation
acv.mean(), # mean auto covariance
acv.std(), # standard deviation auto covariance
skew(axis[start:end]),
kurtosis(axis[start:end]),
math.sqrt(sqd_error.mean())
]
def features(activity):
for (start, end) in windows(activity['timestamp']):
features = []
for axis in ['xAxis', 'yAxis', 'zAxis', 'magnitude']:
features += window_summary(activity[axis], start, end)
yield features
COLUMNS = ['timestamp', 'xAxis', 'yAxis', 'zAxis']
features_dict = []
for csvfile in CSVfiles:
Activity = pd.read_csv(path + csvfile[1],
header=None,
names=COLUMNS)[:3000]
Activity['magnitude'] = magnitude(Activity)
with open(
'/home/ahmed/Desktop/flaskTherapio/AccFtr/' +
csvfile[1][17:] + '_Features.csv', 'w') as out:
rows = csv.writer(out)
for f in features(Activity):
rows.writerow(f)
ActivityDataFeature = pd.read_csv(
'/home/ahmed/Desktop/flaskTherapio/AccFtr/' + csvfile[1][17:] +
'_Features.csv',
header=None)
# return ActivityDataFeature.reset_index().to_json(orient='index')
# CSVDATA = json.dumps(csv_files_dict)
return "Shit must be invoked by android and the user id is " + str(
userid)