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()