kopia lustrzana https://github.com/gabrielegilardi/SignalFilters
100 wiersze
2.5 KiB
Python
100 wiersze
2.5 KiB
Python
"""
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Signal Filtering/Smoothing and Generation of Synthetic Time-Series.
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Copyright (c) 2020 Gabriele Gilardi
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ToDo:
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- add comments to the code
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- in comments write what filters do
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- is necessary to copy X for Y untouched?
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- decide default values in functions
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"""
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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import filters as flt
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import synthetic as syn
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# Added to avoid message warning "The group delay is singular at frequencies
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# [....], setting to 0" when plotting the lag for SMA filters.
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np.seterr(all='ignore')
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np.random.seed(1294404794)
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# Read data to filter
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if len(sys.argv) != 2:
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print("Usage: python test.py <data_file>")
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sys.exit(1)
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data_file = sys.argv[1] + '.csv'
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# Read data from a csv file (one time-series each column)
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data = np.loadtxt(data_file, delimiter=',')
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t, f = syn.synthetic_wave([1., 2., 3.], A=None, phi=None, num=1000)
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plt.plot(t,f)
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plt.show()
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# spx = flt.Filter(data)
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# res = spx.EMA(N=10)
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# signals = [spx.data, res[0:400]]
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# flt.plot_signals(signals, start=100)
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# spx.plot_frequency()
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# spx.plot_lag()
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# sigma_x = 0.1
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# sigma_v = 0.1 * np.ones(n_samples)
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# res = spx.Kalman(sigma_x=sigma_x, sigma_v=sigma_v, dt=1.0, abg_type="abg")
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# alpha = 0.5
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# beta = 0.005
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# gamma = 0.0
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# Yc, Yp = spx.ABG(alpha=alpha, beta=beta, gamma=gamma, dt=1.0)
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# signals = (spx.data[:, 0], Yc[:, 0], Yp[:, 0])
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# utl.plot_signals(signals, 0, 50)
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# t, f = syn.synthetic_wave([1., 2., 3.], A=None, phi=None, num=100)
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# plt.plot(t,f)
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# plt.show()
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# aa = np.array([
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# [ 0.8252, 0.2820],
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# [ 1.3790, 0.0335],
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# [-1.0582, -1.3337],
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# [-0.4686, 1.1275],
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# [-0.2725, 0.3502],
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# [ 1.0984, -0.2991],
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# [-0.2779, 0.0229],
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# [ 0.7015, -0.2620],
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# [-2.0518, -1.7502],
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# [-0.3538, -0.2857],
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# [-0.8236, -0.8314],
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# [-1.5771, -0.9792],
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# [ 0.5080, -1.1564]])
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# synt_data = syn.synthetic_FFT(aa[0:5,0], n_reps=1)
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# print(synt_data)
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# plt.plot(synt_data1)
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# plt.plot(synt_data2)
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# plt.plot(data)
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# names = ['syn1', 'syn2', 'spx']
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# plt.legend(names)
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# plt.show()
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# percent = False
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# print(data[0:10, :])
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# bb = syn.value2diff(data, percent)
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# print(bb[0:10, :])
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# cc = syn.diff2value(bb, percent)
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# print(cc[0:10, :]+1399.48)
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# i = np.arange(aa.shape[1])
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# bb[:, i] = aa[idx[:, i], i]
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# aa = np.array([4, 12, 36, 20, 8])
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# bb = syn.synthetic_sampling(aa, n_reps=2, replace=True)
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# print(bb)
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# aa = np.array([4, 12, 36, 20, 8])
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# W = syn.synthetic_MEboot(aa, n_reps=1, alpha=0.1, bounds=False, scale=False)
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# print('W=')
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# print(W)
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