def check_autocorrelation(
device="Sr20R21", alpha="0", phi="0", z="10", p=(-0.1, 0.1), component="vx", plot_name="test.png"
):
from time_series_functions import get_autocorrelation, butter_lowpass_filter
from matplotlib import pyplot as plt
import seaborn as sns
from numpy import arange
sns.__version__
time_series = load_time_series(device=device, alpha=alpha, phi=phi, z=z, p=p)
time_series.vx = butter_lowpass_filter(time_series.vx, cutoff=2000, fs=10000)
time_series.vy = butter_lowpass_filter(time_series.vy, cutoff=2000, fs=10000)
time_series.vz = butter_lowpass_filter(time_series.vz, cutoff=2000, fs=10000)
autocorr_u = get_autocorrelation(time_series.vx)
autocorr_v = get_autocorrelation(time_series.vy)
autocorr_w = get_autocorrelation(time_series.vz)
if plot_name:
fig = plt.figure()
plt.plot(arange(len(autocorr_u)), autocorr_u / autocorr_u.max(), label="$u$", alpha=0.6)
plt.plot(arange(len(autocorr_u)), autocorr_v / autocorr_v.max(), label="$v$", alpha=0.6)
plt.plot(arange(len(autocorr_u)), autocorr_w / autocorr_w.max(), label="$w$", alpha=0.6)
plt.xlabel("Lag [time steps (1:1/10000 s)]")
plt.ylabel("Autocorrelation")
plt.xlim(0, 20)
plt.legend(loc="upper right")
plt.savefig(plot_name)
fig.clear()
return autocorr_u, autocorr_v
def check_autocorrelation(device="Sr20R21",
alpha='0',
phi='0',
z='10',
p=(-0.1, 0.1),
component='vx',
plot_name='test.png'):
from time_series_functions import get_autocorrelation, butter_lowpass_filter
from matplotlib import pyplot as plt
import seaborn as sns
from numpy import arange
sns.__version__
time_series = load_time_series(device=device,
alpha=alpha,
phi=phi,
z=z,
p=p)
time_series.vx = butter_lowpass_filter(time_series.vx,
cutoff=2000,
fs=10000)
time_series.vy = butter_lowpass_filter(time_series.vy,
cutoff=2000,
fs=10000)
time_series.vz = butter_lowpass_filter(time_series.vz,
cutoff=2000,
fs=10000)
autocorr_u = get_autocorrelation(time_series.vx)
autocorr_v = get_autocorrelation(time_series.vy)
autocorr_w = get_autocorrelation(time_series.vz)
if plot_name:
fig = plt.figure()
plt.plot(arange(len(autocorr_u)),
autocorr_u / autocorr_u.max(),
label='$u$',
alpha=0.6)
plt.plot(arange(len(autocorr_u)),
autocorr_v / autocorr_v.max(),
label='$v$',
alpha=0.6)
plt.plot(arange(len(autocorr_u)),
autocorr_w / autocorr_w.max(),
label='$w$',
alpha=0.6)
plt.xlabel("Lag [time steps (1:1/10000 s)]")
plt.ylabel("Autocorrelation")
plt.xlim(0, 20)
plt.legend(loc='upper right')
plt.savefig(plot_name)
fig.clear()
return autocorr_u, autocorr_v
def plot_time_series(device="Sr20R21",alpha='0',phi='0',z='10',
p=(-0.1,0.1),component='vx',plot_name='test.png'):
from matplotlib import pyplot as plt
import seaborn as sns
from time_series_functions import butter_lowpass_filter
sns.__version__
time_series = load_time_series(device=device,alpha=alpha,
phi=phi,z=z,p=p)
time_series_low_passed = butter_lowpass_filter(time_series.vx,
cutoff=2000,fs=10000)
fig = plt.figure()
plt.plot(time_series.t,time_series.vx,label='$u$',alpha=0.6)
plt.plot(time_series.t,time_series_low_passed,
label='Low passed $u$',alpha=1.0,lw=3,color='k')
#plt.plot(time_series.t,time_series.vy,label='$v$',alpha=0.6)
#plt.plot(time_series.t,time_series.vz,label='$w$',alpha=0.6)
plt.xlabel("t [s]")
plt.ylabel("Velocity [m/s]")
plt.xlim(0,500/10000.)
plt.ylim(-5,22)
plt.legend(loc='lower right')
plt.savefig(plot_name)
fig.clear()
