118 lines
2.6 KiB
Python
118 lines
2.6 KiB
Python
import seaborn as sb
|
|
import pandas as pd
|
|
import scipy
|
|
import matplotlib.pyplot as plt
|
|
|
|
data_uy=pd.read_csv('data/m.csv')
|
|
# head(data_uy)
|
|
|
|
correlation_result=scipy.stats.pearsonr(data_uy['u'], data_uy['y'])
|
|
print(correlation_result)
|
|
|
|
p = sb.lmplot(
|
|
data=data_uy,
|
|
x='u',
|
|
y='y',
|
|
fit_reg=True,
|
|
height=8,
|
|
order=2,
|
|
)
|
|
p.set(title='Correlation UY')
|
|
p.savefig('res/uy-correlation-analysis.png')
|
|
plt.clf()
|
|
|
|
lp = sb.lineplot(data=data_uy['u'])
|
|
lp.set(
|
|
title='Signal u',
|
|
xlabel='kT',
|
|
ylabel='u',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/signal_u.png')
|
|
plt.clf()
|
|
|
|
lp = sb.lineplot(data=data_uy['y'])
|
|
lp.set(
|
|
title='Signal y',
|
|
xlabel='kT',
|
|
ylabel='y',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/signal_y.png')
|
|
plt.clf()
|
|
|
|
imf=pd.read_csv('data/impulse_func.csv')
|
|
lp = sb.lineplot(data=imf, legend=False, linewidth=2.5)
|
|
lp.set(
|
|
title='Impulse Function Estimate (gCustom)',
|
|
xlabel='time (s)',
|
|
ylabel='amplitude',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/impulse_func_estimate.png')
|
|
plt.clf()
|
|
|
|
acr=pd.read_csv('data/autocorrelation_u.csv')
|
|
lp = sb.lineplot(data=acr, legend=False, linewidth=2.5)
|
|
lp.set(
|
|
title='Autocorrelation of u (Ruu)',
|
|
xlabel='shift',
|
|
ylabel='Ruu',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/autocorrelation_u.png')
|
|
plt.clf()
|
|
|
|
acr=pd.read_csv('data/autocorrelation_y.csv')
|
|
lp = sb.lineplot(data=acr, legend=False, linewidth=2.5)
|
|
lp.set(
|
|
title='Autocorrelation of y (Ryy)',
|
|
xlabel='shift',
|
|
ylabel='Ryy',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/autocorrelation_y.png')
|
|
plt.clf()
|
|
|
|
mcr=pd.read_csv('data/mutual_correlation_uy.csv')
|
|
lp = sb.lineplot(data=mcr, legend=False, linewidth=2.5)
|
|
lp.set(
|
|
title='Mutual correlation of uy (Ruy)',
|
|
xlabel='shift',
|
|
ylabel='Ruy',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/mutual_correlation_uy.png')
|
|
plt.clf()
|
|
|
|
mcr=pd.read_csv('data/mutual_correlation_yu.csv')
|
|
lp = sb.lineplot(data=mcr, legend=False, linewidth=2.5)
|
|
lp.set(
|
|
title='Mutual correlation of yu (Ryu)',
|
|
xlabel='shift',
|
|
ylabel='Ryu',
|
|
)
|
|
fig = lp.get_figure()
|
|
fig.subplots_adjust(top=.97)
|
|
fig.savefig('res/mutual_correlation_yu.png')
|
|
plt.clf()
|
|
|
|
# plot comparison
|
|
imf=pd.read_csv('data/impulse_func.csv')
|
|
imfM=pd.read_csv('data/ircra.csv')
|
|
|
|
plt.plot(range(0, len(imf)), imf, label='gCustom')
|
|
plt.plot(range(0, len(imfM)), imfM, label='gSIT')
|
|
plt.legend(loc="upper right")
|
|
plt.title('Impulse Function Estimate Comparison')
|
|
plt.xlabel('time (s)')
|
|
plt.ylabel('amplitude')
|
|
plt.savefig('res/impulse_func_estimate_comparison.png')
|
|
plt.close()
|