56 lines
1.1 KiB
Python
56 lines
1.1 KiB
Python
import seaborn as sb
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import pandas as pd
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import scipy
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import matplotlib.pyplot as plt
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data_uy=pd.read_csv('data/m.csv')
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# head(data_uy)
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correlation_result=scipy.stats.pearsonr(data_uy['u'], data_uy['y'])
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print(correlation_result)
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p = sb.lmplot(
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data=data_uy,
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x='u',
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y='y',
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fit_reg=True,
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height=8,
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)
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p.set(title='Correlation UY')
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p.savefig('res/uy-correlation-analysis.png')
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plt.clf()
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lp = sb.lineplot(data=data_uy['u'])
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lp.set(
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title='Signal u',
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xlabel='samples',
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ylabel='u',
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)
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fig = lp.get_figure()
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fig.subplots_adjust(top=.97)
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fig.savefig('res/signal_u.png')
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plt.clf()
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lp = sb.lineplot(data=data_uy['y'])
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lp.set(
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title='Signal y',
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xlabel='samples',
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ylabel='y',
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)
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fig = lp.get_figure()
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fig.subplots_adjust(top=.97)
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fig.savefig('res/signal_y.png')
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plt.clf()
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imf=pd.read_csv('data/impulse_func.csv')
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lp = sb.lineplot(data=imf, legend=False, linewidth=2.5)
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lp.set(
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title='Impulse Function Estimate',
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xlabel='time (s)',
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ylabel='y',
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)
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fig = lp.get_figure()
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fig.subplots_adjust(top=.97)
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fig.savefig('res/impulse_func_estimate.png')
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plt.clf()
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