def run_wav2(wav0):
conn = psycopg2.connect(
user="******",
password="******",
host="test-heroku.cs0ubczlcaiz.us-west-1.rds.amazonaws.com",
port="5432",
database="test-aws")
cur = conn.cursor()
print('\nProcessing Run Wave 2...\n')
# wav = os.path.join('data/new_data/fft/' + string2 + '.png')
# wav0 = os.listdir('data/new_data/')[0]
wav = wav0.strip('data/new_data/')
wav1 = wav.strip('.wav') + '.png'
# wav = os.listdir('data/new_data/')[0]
# wav = next(join('data/new_data/', f) for f in os.listdir('data/new_data/'))
# print('Wav0: \n', wav0)
# print('Wav: \n', wav)
# print('Wav1: \n', wav1)
word = wav.split('-')
# print('Word: \n', word)
mea_date = word[0]
# print('Measure Date: \n', mea_date)
disp_date = mea_date[0:4] + '-' + mea_date[4:6] + '-' + mea_date[
6:8] + ' ' + mea_date[8:10] + ':' + mea_date[10:12] + ':' + mea_date[
12:14]
# print('Display Date: \n', disp_date)
dev_id = word[1]
# print('Dev ID: \n', dev_id)
qr_code0 = word[3]
qr_code = qr_code0.strip('.wav')
# print('RQ Code: \n', qr_code)
loc = word[2]
# print('Location: \n', loc)
print('\nProcessing Prob...')
conf = testModel()
p # rint('Conf-2: ',conf)
# run_prob (dev_id)
print('\nFinished Prob.')
# path = '/Users/andy/Projects/flow-ez/data'
# print("Moving file:")
# print(os.listdir(path))
# source = '/Users/andy/Projects/flow-ez/data/new_data'
# destination = '/Users/andy/Projects/flow-ez/data/old_data'
# dest = shutil.move(source, destination, copy_function=copy2)
# print("Finished Moving file:")
# print(os.listdir(path))
# print("Destination path:", dest)
print('\nProcessing Pulse...')
plt.figure(dpi=2400)
my_pulse(wav0)
print('\nFinished Pulse.')
print('\nProcessing FFT...')
my_fft(wav0)
print('\nFinished FFT.')
if (conf > 50):
res = 'Abnormal'
else:
res = 'Normal'
first_name = names.get_first_name()
last_name = names.get_last_name()
shutil.copy('static/trend/20191101101100-003004802801-UP-00000001.png',
'static/trend/' + wav1)
cur.execute("BEGIN TRANSACTION;")
# cur.execute("Update data_table (mea_date,dev_id,qr_code,loc,pulse,fft,trend) VALUES (?,?,?,?,?,?,?)",(mea_date,dev_id,qr_code,loc,wav1,wav1,wav1) where (last_name='West'))
# cur.execute("UPDATE data_table set mea_date=?,disp_date=?,dev_id=?,qr_code=?,loc=?,pulse=?,fft=?,trend=?,res=?,prob=? WHERE dev_id=?", (mea_date,disp_date,dev_id,qr_code,loc,wav1,wav1,wav1,res,conf,dev_id,) )
cur.execute(
"INSERT INTO data_table (first_name,last_name,mea_date,disp_date,dev_id,qr_code,loc,pulse,fft,trend,res,prob) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)",
(first_name, last_name, mea_date, disp_date, dev_id, qr_code, loc,
wav1, wav1, wav1, res, conf))
cur.execute("COMMIT TRANSACTION;")
cur.close
conn.close()
#source = os.listdir("/Users/andy/Projects/flow-ez/data/new_data/")
#destination = "/Users/andy/Projects/flow-ez/data/old_data/"
source = os.listdir("/AboveCare/Projects/flow-ez/data/new_data/")
destination = "/AboveCare/Projects/flow-ez/data/old_data/"
for files in source:
if files.endswith('.wav'):
shutil.move("/AboveCare/Projects/flow-ez/data/new_data/" + files,
destination)
print('\nWave Files Moved.')
# my_psp (wav0) ## Leadtek's data has "RuntimeWarning: divide by zero" issue
def afm_dno_solver_rk4(K, ep, mu, Llx, tf, Mval, dt):
KT = 2 * K
# Find the wave numbers to implement the 2/3 de-aliasing throughout
Kc = int(np.floor(2. * K / 3.))
Kuc = KT - Kc + 1
Kc = Kc + 1
ftrans = my_fft.my_fft(KT)
eta0 = pyfftw.empty_aligned(KT, dtype='complex128')
q0 = pyfftw.empty_aligned(KT, dtype='complex128')
etan = pyfftw.empty_aligned(KT, dtype='complex128')
qn = pyfftw.empty_aligned(KT, dtype='complex128')
un = pyfftw.empty_aligned(2 * KT, dtype='complex128')
k1 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
k2 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
k3 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
k4 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
svec = pyfftw.empty_aligned(2 * KT, dtype='complex128')
G0 = pyfftw.empty_aligned(KT, dtype='float64')
qh2 = pyfftw.empty_aligned(KT, dtype='complex128')
Xmesh = pyfftw.empty_aligned(KT, dtype='float64')
Kmesh = pyfftw.empty_aligned(KT, dtype='float64')
tnh = pyfftw.empty_aligned(KT, dtype='float64')
mDk = pyfftw.empty_aligned(KT, dtype='float64')
L1 = pyfftw.empty_aligned(KT, dtype='float64')
Zs = np.zeros([KT, KT], dtype=np.float64)
Is = np.identity(KT, dtype=np.float64)
dx = 2. * Llx / KT
Xmesh[:] = np.arange(-Llx, Llx, dx)
Kmesh[:] = np.pi / Llx * (np.concatenate(
(np.arange(0, K + 1), np.arange(-K + 1, 0)), 0))
nmax = int(np.round(tf / dt))
mDk[:] = mu * Kmesh
tnh[:] = np.tanh(mDk)
L1[:] = Kmesh * np.tanh(mu * Kmesh) / mu
Lop = np.concatenate((np.concatenate(
(Zs, np.diag(L1)), axis=1), np.concatenate((-Is, Zs), axis=1)),
axis=0)
eLdt2 = expm(dt * Lop / 2.)
eLdt = np.matmul(eLdt2, eLdt2)
eta0[:] = np.cos(np.pi * Xmesh / Llx)
q0[:] = np.sin(np.pi * Xmesh / Llx)
etan[:] = ftrans.fft(eta0)
qn[:] = ftrans.fft(q0)
etan[Kc - 1:Kuc] = 0
qn[Kc - 1:Kuc] = 0
un[:] = np.concatenate((etan, qn))
G0[:] = ftrans.ifft(L1 * qn).real
for jj in xrange(nmax):
G0[:] = ftrans.ifft(L1 * qn).real
k1[:] = dt * nonlinearity(K, etan, qn, G0, ep, mu, Kmesh, mDk, tnh,
Mval, ftrans)
svec[:] = np.matmul(eLdt2, (un + .5 * k1))
qh2[:] = svec[KT:]
qh2[Kc - 1:Kuc] = 0.
G0[:] = ftrans.ifft(L1 * qh2).real
k2[:] = dt * nonlinearity(K, svec[0:KT], svec[KT:], G0, ep, mu, Kmesh,
mDk, tnh, Mval, ftrans)
svec[:] = np.matmul(eLdt2, un) + .5 * k2
qh2[:] = svec[KT:]
qh2[Kc - 1:Kuc] = 0.
G0[:] = ftrans.ifft(L1 * qh2).real
k3[:] = dt * nonlinearity(K, svec[0:KT], svec[KT:], G0, ep, mu, Kmesh,
mDk, tnh, Mval, ftrans)
svec[:] = np.matmul(eLdt, un) + np.matmul(eLdt2, k3)
qh2[:] = svec[KT:]
qh2[Kc - 1:Kuc] = 0.
G0[:] = ftrans.ifft(L1 * qh2).real
k4[:] = dt * nonlinearity(K, svec[0:KT], svec[KT:], G0, ep, mu, Kmesh,
mDk, tnh, Mval, ftrans)
un[:] = (np.matmul(eLdt, (un + k1 / 6.)) +
np.matmul(eLdt2, (k2 + k3) / 3.) + k4 / 6.)
etan = un[0:KT]
qn = un[KT:]
etan[Kc - 1:Kuc] = 0
qn[Kc - 1:Kuc] = 0
un[:] = np.concatenate((etan, qn))
return ftrans.ifft(etan).real
def afm_dno_solver(K, ep, mu, Llx, tf, Mval, dt):
KT = 2 * K
# Find the wave numbers to implement the 2/3 de-aliasing throughout
Kc = int(np.floor(2. * K / 3.))
Kuc = KT - Kc + 1
Kc = Kc + 1
ftrans = my_fft.my_fft(KT)
eta0 = pyfftw.empty_aligned(KT, dtype='complex128')
q0 = pyfftw.empty_aligned(KT, dtype='complex128')
eta1 = pyfftw.empty_aligned(KT, dtype='complex128')
eta2 = pyfftw.empty_aligned(KT, dtype='complex128')
q1 = pyfftw.empty_aligned(KT, dtype='complex128')
q2 = pyfftw.empty_aligned(KT, dtype='complex128')
etan = pyfftw.empty_aligned(KT, dtype='complex128')
qn = pyfftw.empty_aligned(KT, dtype='complex128')
G0 = pyfftw.empty_aligned(KT, dtype='complex128')
etanm1 = pyfftw.empty_aligned(KT, dtype='complex128')
qnm1 = pyfftw.empty_aligned(KT, dtype='complex128')
etanp1 = pyfftw.empty_aligned(KT, dtype='complex128')
qnp1 = pyfftw.empty_aligned(KT, dtype='complex128')
nlvecn = pyfftw.empty_aligned(KT, dtype='complex128')
nlvecq = pyfftw.empty_aligned(KT, dtype='complex128')
nln = pyfftw.empty_aligned(2 * KT, dtype='complex128')
nlnm1 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
nlnm2 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
nlnm3 = pyfftw.empty_aligned(2 * KT, dtype='complex128')
Xmesh = pyfftw.empty_aligned(KT, dtype='complex128')
Kmesh = pyfftw.empty_aligned(KT, dtype='complex128')
tnh = pyfftw.empty_aligned(KT, dtype='complex128')
mDk = pyfftw.empty_aligned(KT, dtype='complex128')
L1 = pyfftw.empty_aligned(KT, dtype='complex128')
Linvd = pyfftw.empty_aligned(KT, dtype='complex128')
Linv12 = pyfftw.empty_aligned(KT, dtype='complex128')
Linv21 = pyfftw.empty_aligned(KT, dtype='complex128')
dx = 2. * Llx / KT
Xmesh[:] = np.arange(-Llx, Llx, dx)
Kmesh[:] = np.pi / Llx * np.concatenate(
(np.arange(0, K + 1), np.arange(-K + 1, 0)), 0)
nmax = int(np.round(tf / dt))
mDk[:] = mu * Kmesh
tnh[:] = np.tanh(mDk)
L1[:] = Kmesh * np.tanh(mu * Kmesh) / mu
Linvd[:] = (np.ones(KT, dtype='complex128') + 9. * dt**2 / 16. * L1)**(-1)
Linv12[:] = 3. * dt / 4. * L1 * Linvd
Linv21[:] = -3. * dt / 4. * Linvd
eta0[:] = np.cos(np.pi * Xmesh / Llx)
q0[:] = np.sin(np.pi * Xmesh / Llx)
etan[:] = ftrans.fft(eta0)
qn[:] = ftrans.fft(q0)
etan[Kc - 1:Kuc] = 0
qn[Kc - 1:Kuc] = 0
G0[:] = ftrans.ifft(L1 * qn).real
etanm1[:] = etan
qnm1[:] = qn
nln[:] = nonlinearity(K, etan, qn, G0, ep, mu, Kmesh, mDk, tnh, Mval,
ftrans)
nlnm1[:] = nln
nlnm2[:] = nlnm1
nlnm3[:] = nlnm2
for jj in xrange(nmax):
G0[:] = ftrans.ifft(L1 * qn).real
nln[:] = nonlinearity(K, etan, qn, G0, ep, mu, Kmesh, mDk, tnh, Mval,
ftrans)
nlvecn[:] = 55. / 24. * nln[0:KT] - 59. / 24. * nlnm1[
0:KT] + 37. / 24. * nlnm2[0:KT] - 3. / 8. * nlnm3[0:KT]
nlvecq[:] = 55. / 24. * nln[KT:2 * KT] - 59. / 24. * nlnm1[
KT:2 * KT] + 37. / 24. * nlnm2[KT:2 * KT] - 3. / 8. * nlnm3[KT:2 *
KT]
eta1[:] = Linvd * (etan + etanm1 / 3. + dt * nlvecn)
eta2[:] = Linv12 * (qn + qnm1 / 3. + dt * nlvecq)
q1[:] = Linvd * (qn + qnm1 / 3. + dt * nlvecq)
q2[:] = Linv21 * (etan + etanm1 / 3. + dt * nlvecn)
etanp1[:] = -etanm1 / 3. + eta1 + eta2
qnp1[:] = -qnm1 / 3. + q1 + q2
etanm1[:] = etan
etan[:] = etanp1
qnm1[:] = qn
qn[:] = qnp1
nlnm3[:] = nlnm2
nlnm2[:] = nlnm1
nlnm1[:] = nln
return ftrans.ifft(etan).real
Mval = 14
params = [K, ep, mu, Llx, Mval, dt, dts, Nens, sig]
Xfloats = np.array([-.1, .1])
xint = np.zeros(2 * Xfloats.size, dtype='float64')
xint[0:2 * Xfloats.size:2] = Xfloats
xint[1:2 * Xfloats.size:2] = ep * (np.cos(np.pi * Xfloats / Llx))
nindt = int(np.round(dts / dt))
nmax = int(np.round(tf / dt))
nsamp = int(np.round(nmax / nindt))
Ndat = xint.size / 2
fft = my_fft.my_fft(KT)
Kmesh = empty_aligned(KT, dtype='float64')
tnh = empty_aligned(KT, dtype='float64')
mDk = empty_aligned(KT, dtype='float64')
L1 = empty_aligned(KT, dtype='float64')
# Build forcast and analysis matrices.
xf = np.zeros([2 * (KT + Ndat), Nens], dtype='float64')
# Build time invariant vectors and matrices associated with model
# computations.
Xmesh = np.linspace(-Llx, Llx, KT, endpoint=False)
Kmesh[:] = np.pi / Llx * (np.concatenate(
(np.arange(0, K + 1), np.arange(-K + 1, 0)), 0))