def main():
col = columns.Columns()
print('Reading data...')
data = np.loadtxt(sys.argv[1], dtype=float, delimiter=',', skiprows=9)
sc = data[:,col.selectColumn('E: Skin Cond')] # skin conductivity
scf = filters.lowpass(2,0.01,sc)
scd = filters.detrend(scf)
# Make plots
fig = plt.figure(1)
ax1 = fig.add_subplot(211)
plt.plot(range(0,sc.size),sc, 'b-')
plt.plot(range(0,sc.size),scf, 'r-',linewidth=2)
plt.plot(range(0,sc.size),scd, 'g-',linewidth=2)
ax1.axes.get_xaxis().set_visible(False)
ax1 = fig.add_subplot(212)
plt.plot(range(0,sc.size),sc-scf, 'b-')
sp = np.fft.fft(sc)
freq = np.fft.fftfreq(sc.shape[-1])
plt.figure(2)
plt.plot(freq, sp.real, freq, sp.imag)
plt.show()
def main():
col = columns.Columns()
print('Reading data...')
data = np.loadtxt(sys.argv[1], dtype=float, delimiter=',', skiprows=9)
sc = data[:, col.selectColumn('E: Skin Cond')] # skin conductivity
scf = filters.lowpass(2, 0.01, sc)
scd = filters.detrend(scf)
# Make plots
fig = plt.figure(1)
ax1 = fig.add_subplot(211)
plt.plot(range(0, sc.size), sc, 'b-')
plt.plot(range(0, sc.size), scf, 'r-', linewidth=2)
plt.plot(range(0, sc.size), scd, 'g-', linewidth=2)
ax1.axes.get_xaxis().set_visible(False)
ax1 = fig.add_subplot(212)
plt.plot(range(0, sc.size), sc - scf, 'b-')
sp = np.fft.fft(sc)
freq = np.fft.fftfreq(sc.shape[-1])
plt.figure(2)
plt.plot(freq, sp.real, freq, sp.imag)
plt.show()
def mc_ar1_spectrum(self,
data=None,
N=1000,
filter_type=None,
filter_cutoff=None,
spectrum='mtm'):
""" calculates the Monte-Carlo spectrum
with 1, 2.5, 5, 95, 97.5, 99 percentiles
input:
x .. time series
spectrum .. spectral density estimation function
N .. number of MC simulations
filter_type ..
filter_cutoff ..
spectrum .. 'mtm': multi-taper method, 'per': periodogram, 'Welch'
output:
mc_spectrum ..
"""
if data is None: data = np.array(self.ts)
assert type(data) == np.ndarray
AM = ARMA(endog=data, order=(1, 0)).fit()
phi, std = AM.arparams[0], np.sqrt(AM.sigma2)
mc = self.mc_ar1_ARMA(phi=phi, std=std, n=len(data), N=N)
if filter_type is not None:
assert filter_type in ['lowpass', 'chebychev']
assert type(filter_cutoff) == int
assert filter_cutoff > 1
n = int(
filter_cutoff / 2
) + 1 # datapoints to remove from either end due to filter edge effects
mc = mc[:, n:-n]
if filter_type == 'lowpass': mc = lowpass(mc.T, filter_cutoff).T
elif filter_type == 'chebychev':
mc = chebychev(mc.T, filter_cutoff).T
if spectrum == 'mtm': freq, _ = self.mtspectrum()
elif spectrum == 'per': freq, _ = self.periodogram()
elif spectrum == 'Welch': freq, _ = self.Welch()
mc_spectra = np.zeros((N, len(freq)))
# mc_spectra = np.zeros((N, int(len(mc[0,:])/2)+1))#int(self.len/2)+1))
for i in range(N):
if spectrum == 'mtm':
freq, mc_spectra[i, :] = self.mtspectrum(data=mc[i, :])
elif spectrum == 'per':
freq, mc_spectra[i, :] = self.periodogram(data=mc[i, :])
elif spectrum == 'Welch':
freq, mc_spectra[i, :] = self.Welch(data=mc[i, :])
mc_spectrum = {}
mc_spectrum['median'] = np.median(mc_spectra, axis=0)
mc_spectrum['freq'] = freq
for p in [1, 2.5, 5, 95, 97.5, 99]:
mc_spectrum[str(p)] = np.percentile(mc_spectra, p, axis=0)
return mc_spectrum
def filter_timeseries(self, data, filter_type, filter_cutoff):
assert filter_type in ['lowpass', 'chebychev']
assert type(filter_cutoff) == int
assert filter_cutoff > 1
n = int(
filter_cutoff / 2
) + 1 # datapoints to remove from either end due to filter edge effects
if filter_type == 'lowpass': data = lowpass(data, filter_cutoff)[n:-n]
elif filter_type == 'chebychev':
data = chebychev(data, filter_cutoff)[n:-n]
return data
def rotateAcceleration(accel, angles):
#angles: 0:roll, 1:pitch, 2:yaw
#accel: 0:x, 1:y, 2:z
#gravity = rotateGravity(angles)
#accel[0] -= gravity[0]
#accel[1] -= gravity[1]
#accel[2] -= gravity[2]
rotAccel = [[0.0 for i in range(len(accel[0]))] for i in range(3)]
roll = [math.radians(i) for i in angles[0]]
pitch = [math.radians(i) for i in angles[1]]
yaw = [math.radians(i) for i in angles[2]]
alpha = 0.05
accel[0] = filters.lowpass(filters.highpass(accel[0], alpha), 0.2)
accel[1] = filters.lowpass(filters.highpass(accel[1], alpha), 0.2)
accel[2] = filters.lowpass(filters.highpass(accel[2], alpha), 0.2)
for k in range(len(accel[0])):
R = RtoGround(roll[k], pitch[k], yaw[k])
for i in range(3):
for j in range(3):
rotAccel[i][k] += R[i][j] * accel[j][k]
return rotAccel
def rotateAcceleration(accel, angles):
#angles: 0:roll, 1:pitch, 2:yaw
#accel: 0:x, 1:y, 2:z
#gravity = rotateGravity(angles)
#accel[0] -= gravity[0]
#accel[1] -= gravity[1]
#accel[2] -= gravity[2]
rotAccel = [[0.0 for i in range(len(accel[0]))] for i in range(3)]
roll = [math.radians(i) for i in angles[0]]
pitch = [math.radians(i) for i in angles[1]]
yaw = [math.radians(i) for i in angles[2]]
alpha = 0.05
accel[0] = filters.lowpass(filters.highpass(accel[0], alpha), 0.2)
accel[1] = filters.lowpass(filters.highpass(accel[1], alpha), 0.2)
accel[2] = filters.lowpass(filters.highpass(accel[2], alpha), 0.2)
for k in range(len(accel[0])):
R = RtoGround(roll[k], pitch[k], yaw[k])
for i in range(3):
for j in range(3):
rotAccel[i][k] += R[i][j] * accel[j][k]
return rotAccel
# df_PDOsplit = pd.DataFrame(standardize.transform(df_PDOsplit['PDO'].values.reshape(-1,1)),
# index=df_PDOsplit.index, columns=['PDO'])
df_PDOsplit = df_PDOsplit[['PDO']].apply(standardize_on_train,
args=[df_PDO.loc[0]['TrainIsTrue']],
result_type='broadcast')
# Butter Lowpass
yr = 2
dates = df_PDOsplit.index
freqraw = (dates[1] - dates[0]).days
window = int(yr*functions_pp.get_oneyr(dates).size) # 2 year
fig, ax = plt.subplots(1,1)
ax.plot_date(dates, df_PDOsplit.values, label=f'raw ({freqraw} daymeans)',
alpha=.2, linestyle='solid', marker=None)
ax.plot_date(dates, filters.lowpass(df_PDOsplit, period=window), label='Butterworth',
linestyle='solid', linewidth=1, marker=None)
df_PDOrm = df_PDOsplit.rolling(window=window, center=True, min_periods=1).mean()
# ax.plot_date(dates, filters.lowpass(df_PDOrm, period=window), label='Butterworth on rolling mean',
# linestyle='solid', linewidth=1, marker=None)
ax.plot_date(dates, df_PDOrm,
label='rolling mean', color='green', linestyle='solid', linewidth=1, marker=None)
ax.legend()
#%%
import wrapper_PCMCI as wPCMCI
args=[df_PDO.loc[0]['TrainIsTrue']],
result_type='broadcast')
# Butter Lowpass
dates = df_PDOsplit.index
freqraw = (dates[1] - dates[0]).days
ls = ['solid', 'dotted', 'dashdot', 'dashed']
fig, ax = plt.subplots(1,1, figsize=(10,5))
list_dfPDO = [df_PDOsplit]
lowpass_yrs = [.25, .5, 1.0, 2.0]
for i, yr in enumerate(lowpass_yrs):
window = int(yr*functions_pp.get_oneyr(dates).size) # 2 year
if i ==0:
ax.plot_date(dates, df_PDOsplit.values, label=f'Raw ({freqraw} day means)',
alpha=.3, linestyle='solid', marker=None)
df_PDObw = pd.Series(filters.lowpass(df_PDOsplit, period=window).squeeze(),
index=dates, name=f'PDO{yr}bw')
ax.plot_date(dates, df_PDObw, label=f'Butterworth {yr}-year low-pass',
color='red',linestyle=ls[i], linewidth=1, marker=None)
df_PDOrm = df_PDOsplit.rolling(window=window, closed='right', min_periods=window).mean()
df_PDOrm = df_PDOrm.rename({'PDO':f'PDO{yr}rm'}, axis=1)
ax.plot_date(dates, df_PDOrm,
label=f'Rolling mean {yr}-year low-pass (closed right)', color='green',linestyle=ls[i],
linewidth=1, marker=None)
list_dfPDO.append(df_PDObw) ; list_dfPDO.append(df_PDOrm)
ax.legend()
filepath = os.path.join(path_out_main, 'Low-pass_filter.pdf')
plt.savefig(filepath, bbox_inches='tight')
df_PDOs = pd.concat(list_dfPDO,axis=1)
# Prove we don't need synchronized carrier oscilators
initial_carrier_phase = random.random() * 2 * math.pi
last_angle = 0.0
istream = []
qstream = []
for n in range(0, input_src.getnframes()):
inputsgn = struct.unpack('h', input_src.readframes(1))[0] / 32768.0
# I/Q demodulation, not unlike QAM
carrier = 2 * math.pi * FM_CARRIER * (n / 44100.0) + initial_carrier_phase
istream.append(inputsgn * math.cos(carrier))
qstream.append(inputsgn * -math.sin(carrier))
istream = filters.lowpass(istream, 1500)
qstream = filters.lowpass(qstream, 1500)
last_output = 0
for n in range(0, len(istream)):
i = istream[n]
q = qstream[n]
# Determine phase (angle) of I/Q pair
angle = math.atan2(q, i)
# Change of angle = baseband signal
# Were you rushing or were you dragging?!
angle_change = last_angle - angle