kopia lustrzana https://github.com/gabrielegilardi/SignalFilters
add comments
Gabriele Gilardi
rodzic
afad3296ed
commit
753245f285
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@ -1,14 +1,9 @@
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"""
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Class for filter/smooth data.
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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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References (both from John F. Ehlers):
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[1] "Cycle Analytics for Traders: Advanced Technical Trading Concepts".
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[2] "Signal Analysis, Filters And Trading Strategies".
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X (n_samples, n_series) Dataset to filter
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b (n_b, ) Numerator coefficients
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a (n_a, ) Denominator coefficients
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@ -75,23 +70,26 @@ Y (n_obs, 1) Observation residual (innovation)
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import numpy as np
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from scipy import signal
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import matplotlib.pyplot as plt
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def plot_signals(signals, idx_start=0, idx_end=None):
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def plot_signals(signals, start=0):
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"""
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signals must be all same lenght
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signals must be a list
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"""
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if (idx_end is None):
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idx_end = len(signals[0])
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t = np.arange(idx_start, idx_end)
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names = []
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legend = []
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count = 0
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for signal in signals:
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plt.plot(t, signal[idx_start:idx_end])
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names.append(str(count))
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signal = signal.flatten()
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end = len(signal)
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t = np.arange(start, end)
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plt.plot(t, signal[start:end])
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legend.append('Signal [' + str(count) + ']')
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count += 1
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plt.xlabel('Index')
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plt.ylabel('Value')
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plt.grid(b=True)
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plt.legend(names)
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plt.legend(legend)
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plt.show()
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@ -99,22 +97,23 @@ def filter_data(data, b, a):
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"""
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Applies a filter with transfer response coefficients <a> and <b>.
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"""
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n_samples, n_series = data.shape
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n_samples = len(data)
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nb = len(b)
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na = len(a)
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idx = np.amax([0, nb-1, na-1])
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Y = data.copy()
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for i in range(idx, n_samples):
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tmp = np.zeros(n_series)
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tmp = 0
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for j in range(nb):
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tmp = tmp + b[j] * data[i-j, :] # Numerator term
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tmp += b[j] * data[i-j] # Numerator term
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for j in range(1, na):
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tmp = tmp - a[j] * Y[i-j, :] # Denominator term
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tmp -= a[j] * Y[i-j] # Denominator term
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Y[i, :] = tmp / a[0]
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Y[i] = tmp / a[0]
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return Y, idx
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@ -125,7 +124,7 @@ class Filter:
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"""
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"""
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self.data = np.asarray(data)
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self.n_samples, self.n_series = data.shape
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self.n_samples = len(data)
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def Generic(self, b=1.0, a=1.0):
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"""
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@ -577,8 +576,10 @@ class Filter:
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plt.axhline(-3.0, lw=1.5, ls='--', C='r')
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plt.grid(b=True)
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plt.xlim(np.amin(wf), np.amax(wf))
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plt.xlabel('$omega$ [rad/sample]')
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plt.xlabel(r'$\omega$ [rad/sample]')
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plt.ylabel('$h$ [db]')
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legend = ['Filter', '-3dB']
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plt.legend(legend)
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plt.show()
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def plot_lag(self):
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@ -589,6 +590,6 @@ class Filter:
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plt.plot(wf, gd)
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plt.grid(b=True)
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plt.xlim(np.amin(wf), np.amax(wf))
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plt.xlabel('$omega$ [rad/sample]')
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plt.xlabel(r'$\omega$ [rad/sample]')
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plt.ylabel('$gd$ [samples]')
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plt.show()
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@ -1,500 +0,0 @@
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1399.48,1399.48,1399.48
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1410.49,1410.49,1410.49
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1409.3,1409.3,1409.3
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1410.44,1410.44,1410.44
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1411.13,1411.13,1411.13
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1402.08,1402.08,1402.08
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1413.49,1413.49,1413.49
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1413.17,1413.17,1413.17
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1418.13,1418.13,1418.13
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1418.16,1418.16,1418.16
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1415.51,1415.51,1415.51
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1405.53,1405.53,1405.53
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1403.93,1403.93,1403.93
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1404.11,1404.11,1404.11
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1405.87,1405.87,1405.87
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1402.8,1402.8,1402.8
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1402.22,1402.22,1402.22
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1401.35,1401.35,1401.35
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1394.23,1394.23,1394.23
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1390.99,1390.99,1390.99
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1365,1365,1365
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1375.32,1375.32,1375.32
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1379.32,1379.32,1379.32
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1385.3,1385.3,1385.3
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1385.97,1385.97,1385.97
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1360.02,1360.02,1360.02
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1337.89,1337.89,1337.89
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1338.31,1338.31,1338.31
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1350.52,1350.52,1350.52
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1362.66,1362.66,1362.66
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1376.51,1376.51,1376.51
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1372.78,1372.78,1372.78
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1363.67,1363.67,1363.67
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1353.64,1353.64,1353.64
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1356.78,1356.78,1356.78
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1334.76,1334.76,1334.76
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1341.45,1341.45,1341.45
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1341.47,1341.47,1341.47
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1352.46,1352.46,1352.46
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1354.68,1354.68,1354.68
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1367.58,1367.58,1367.58
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1374.02,1374.02,1374.02
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1365.51,1365.51,1365.51
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1362.16,1362.16,1362.16
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1329.04,1329.04,1329.04
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1331.85,1331.85,1331.85
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1319.99,1319.99,1319.99
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1313.72,1313.72,1313.72
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1335.02,1335.02,1335.02
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1325.51,1325.51,1325.51
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1355.69,1355.69,1355.69
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1357.98,1357.98,1357.98
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1344.78,1344.78,1344.78
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1342.84,1342.84,1342.84
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1329.1,1329.1,1329.1
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1314.88,1314.88,1314.88
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1324.18,1324.18,1324.18
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1308.93,1308.93,1308.93
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1325.66,1325.66,1325.66
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1314.99,1314.99,1314.99
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1315.13,1315.13,1315.13
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1285.5,1285.5,1285.5
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1278.18,1278.18,1278.18
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1278.04,1278.04,1278.04
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1310.33,1310.33,1310.33
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1313.32,1313.32,1313.32
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1332.42,1332.42,1332.42
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1317.82,1317.82,1317.82
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1320.68,1320.68,1320.68
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1318.86,1318.86,1318.86
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1316.63,1316.63,1316.63
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1315.99,1315.99,1315.99
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1295.22,1295.22,1295.22
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1304.86,1304.86,1304.86
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1324.8,1324.8,1324.8
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1330.66,1330.66,1330.66
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1338.35,1338.35,1338.35
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1353.39,1353.39,1353.39
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1357.99,1357.99,1357.99
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1354.58,1354.58,1354.58
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1363.72,1363.72,1363.72
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1369.58,1369.58,1369.58
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1369.1,1369.1,1369.1
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1391.57,1391.57,1391.57
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1402.31,1402.31,1402.31
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1405.82,1405.82,1405.82
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1397.91,1397.91,1397.91
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1403.36,1403.36,1403.36
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1399.98,1399.98,1399.98
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1390.69,1390.69,1390.69
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1371.97,1371.97,1371.97
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1366.94,1366.94,1366.94
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1378.53,1378.53,1378.53
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1376.92,1376.92,1376.92
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1385.14,1385.14,1385.14
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1390.78,1390.78,1390.78
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1369.57,1369.57,1369.57
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1370.26,1370.26,1370.26
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1387.57,1387.57,1387.57
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1368.71,1368.71,1368.71
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1358.59,1358.59,1358.59
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1382.2,1382.2,1382.2
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1398.08,1398.08,1398.08
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1398.96,1398.96,1398.96
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1413.38,1413.38,1413.38
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1419.04,1419.04,1419.04
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1408.47,1408.47,1408.47
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1403.28,1403.28,1403.28
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1405.54,1405.54,1405.54
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1412.52,1412.52,1412.52
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1416.51,1416.51,1416.51
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1397.11,1397.11,1397.11
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1392.78,1392.78,1392.78
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1402.89,1402.89,1402.89
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1405.52,1405.52,1405.52
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1409.75,1409.75,1409.75
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1404.17,1404.17,1404.17
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1402.6,1402.6,1402.6
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1394.28,1394.28,1394.28
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1395.95,1395.95,1395.95
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1371.09,1371.09,1371.09
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1370.87,1370.87,1370.87
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1365.91,1365.91,1365.91
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1352.63,1352.63,1352.63
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1343.36,1343.36,1343.36
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1364.33,1364.33,1364.33
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1369.63,1369.63,1369.63
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1374.09,1374.09,1374.09
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1365.68,1365.68,1365.68
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1372.18,1372.18,1372.18
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||||
1367.59,1367.59,1367.59
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1365.74,1365.74,1365.74
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||||
1363.46,1363.46,1363.46
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||||
1357.66,1357.66,1357.66
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||||
1362.21,1362.21,1362.21
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1361.23,1361.23,1361.23
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||||
1358.04,1358.04,1358.04
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1343.23,1343.23,1343.23
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||||
1350.5,1350.5,1350.5
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||||
1351.77,1351.77,1351.77
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||||
1342.64,1342.64,1342.64
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1351.95,1351.95,1351.95
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1349.96,1349.96,1349.96
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1347.05,1347.05,1347.05
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1344.33,1344.33,1344.33
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||||
1344.9,1344.9,1344.9
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1325.54,1325.54,1325.54
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||||
1324.09,1324.09,1324.09
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||||
1312.41,1312.41,1312.41
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1313.01,1313.01,1313.01
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1316.33,1316.33,1316.33
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1318.43,1318.43,1318.43
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1326.06,1326.06,1326.06
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1314.65,1314.65,1314.65
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||||
1316,1316,1316
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1315.38,1315.38,1315.38
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||||
1314.5,1314.5,1314.5
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1308.04,1308.04,1308.04
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||||
1293.67,1293.67,1293.67
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||||
1289.09,1289.09,1289.09
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||||
1295.5,1295.5,1295.5
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||||
1292.48,1292.48,1292.48
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||||
1292.08,1292.08,1292.08
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1280.7,1280.7,1280.7
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1277.81,1277.81,1277.81
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1281.06,1281.06,1281.06
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1277.3,1277.3,1277.3
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1277.06,1277.06,1277.06
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1257.6,1257.6,1257.6
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1263.02,1263.02,1263.02
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1249.64,1249.64,1249.64
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1265.43,1265.43,1265.43
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1265.33,1265.33,1265.33
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1254,1254,1254
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1243.72,1243.72,1243.72
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1241.3,1241.3,1241.3
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1205.35,1205.35,1205.35
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1219.66,1219.66,1219.66
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1215.75,1215.75,1215.75
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1211.82,1211.82,1211.82
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1225.73,1225.73,1225.73
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1236.47,1236.47,1236.47
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1255.19,1255.19,1255.19
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1234.35,1234.35,1234.35
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1261.01,1261.01,1261.01
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1258.47,1258.47,1258.47
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1257.08,1257.08,1257.08
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1244.28,1244.28,1244.28
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1244.58,1244.58,1244.58
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1246.96,1246.96,1246.96
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1195.19,1195.19,1195.19
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1192.55,1192.55,1192.55
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1158.67,1158.67,1158.67
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1161.79,1161.79,1161.79
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1188.04,1188.04,1188.04
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||||
1192.98,1192.98,1192.98
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||||
1215.65,1215.65,1215.65
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1216.13,1216.13,1216.13
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1236.91,1236.91,1236.91
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1257.81,1257.81,1257.81
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||||
1251.78,1251.78,1251.78
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||||
1263.85,1263.85,1263.85
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||||
1239.7,1239.7,1239.7
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||||
1229.1,1229.1,1229.1
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||||
1275.92,1275.92,1275.92
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||||
1261.12,1261.12,1261.12
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||||
1253.23,1253.23,1253.23
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||||
1261.15,1261.15,1261.15
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||||
1237.9,1237.9,1237.9
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1218.28,1218.28,1218.28
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||||
1253.3,1253.3,1253.3
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1285.09,1285.09,1285.09
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1284.59,1284.59,1284.59
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1242,1242,1242
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1229.05,1229.05,1229.05
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1254.19,1254.19,1254.19
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1238.25,1238.25,1238.25
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1215.39,1215.39,1215.39
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1209.88,1209.88,1209.88
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1225.38,1225.38,1225.38
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1200.86,1200.86,1200.86
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1224.58,1224.58,1224.58
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1203.66,1203.66,1203.66
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1207.25,1207.25,1207.25
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1195.54,1195.54,1195.54
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1194.89,1194.89,1194.89
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1155.46,1155.46,1155.46
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1164.97,1164.97,1164.97
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1144.03,1144.03,1144.03
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1123.95,1123.95,1123.95
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1099.23,1099.23,1099.23
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1131.42,1131.42,1131.42
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1160.4,1160.4,1160.4
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1151.06,1151.06,1151.06
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||||
1175.38,1175.38,1175.38
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||||
1162.95,1162.95,1162.95
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1136.43,1136.43,1136.43
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1129.56,1129.56,1129.56
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1166.76,1166.76,1166.76
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1202.09,1202.09,1202.09
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1204.09,1204.09,1204.09
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||||
1216.01,1216.01,1216.01
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||||
1209.11,1209.11,1209.11
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||||
1188.68,1188.68,1188.68
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1172.87,1172.87,1172.87
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||||
1162.27,1162.27,1162.27
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1154.23,1154.23,1154.23
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1185.9,1185.9,1185.9
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1198.62,1198.62,1198.62
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||||
1165.24,1165.24,1165.24
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||||
1173.97,1173.97,1173.97
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1204.42,1204.42,1204.42
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||||
1218.89,1218.89,1218.89
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1212.92,1212.92,1212.92
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1210.08,1210.08,1210.08
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1176.8,1176.8,1176.8
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1159.27,1159.27,1159.27
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1177.6,1177.6,1177.6
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1162.35,1162.35,1162.35
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||||
1123.82,1123.82,1123.82
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1123.53,1123.53,1123.53
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1140.65,1140.65,1140.65
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1193.89,1193.89,1193.89
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1192.76,1192.76,1192.76
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1204.49,1204.49,1204.49
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1178.81,1178.81,1178.81
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1172.64,1172.64,1172.64
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1120.76,1120.76,1120.76
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1172.53,1172.53,1172.53
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1119.46,1119.46,1119.46
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1199.38,1199.38,1199.38
|
||||
1200.07,1200.07,1200.07
|
||||
1260.34,1260.34,1260.34
|
||||
1254.05,1254.05,1254.05
|
||||
1286.94,1286.94,1286.94
|
||||
1292.28,1292.28,1292.28
|
||||
1300.67,1300.67,1300.67
|
||||
1304.89,1304.89,1304.89
|
||||
1331.94,1331.94,1331.94
|
||||
1337.43,1337.43,1337.43
|
||||
1345.02,1345.02,1345.02
|
||||
1343.8,1343.8,1343.8
|
||||
1325.84,1325.84,1325.84
|
||||
1326.73,1326.73,1326.73
|
||||
1305.44,1305.44,1305.44
|
||||
1316.14,1316.14,1316.14
|
||||
1308.87,1308.87,1308.87
|
||||
1317.72,1317.72,1317.72
|
||||
1313.64,1313.64,1313.64
|
||||
1319.49,1319.49,1319.49
|
||||
1343.8,1343.8,1343.8
|
||||
1353.22,1353.22,1353.22
|
||||
1339.22,1339.22,1339.22
|
||||
1337.88,1337.88,1337.88
|
||||
1339.67,1339.67,1339.67
|
||||
1320.64,1320.64,1320.64
|
||||
1307.41,1307.41,1307.41
|
||||
1296.67,1296.67,1296.67
|
||||
1280.1,1280.1,1280.1
|
||||
1268.45,1268.45,1268.45
|
||||
1283.5,1283.5,1283.5
|
||||
1287.14,1287.14,1287.14
|
||||
1295.52,1295.52,1295.52
|
||||
1278.36,1278.36,1278.36
|
||||
1271.5,1271.5,1271.5
|
||||
1267.64,1267.64,1267.64
|
||||
1265.42,1265.42,1265.42
|
||||
1287.87,1287.87,1287.87
|
||||
1271.83,1271.83,1271.83
|
||||
1270.98,1270.98,1270.98
|
||||
1289,1289,1289
|
||||
1279.56,1279.56,1279.56
|
||||
1284.94,1284.94,1284.94
|
||||
1286.17,1286.17,1286.17
|
||||
1300.16,1300.16,1300.16
|
||||
1312.94,1312.94,1312.94
|
||||
1314.55,1314.55,1314.55
|
||||
1345.2,1345.2,1345.2
|
||||
1331.1,1331.1,1331.1
|
||||
1325.69,1325.69,1325.69
|
||||
1320.47,1320.47,1320.47
|
||||
1316.28,1316.28,1316.28
|
||||
1317.37,1317.37,1317.37
|
||||
1333.27,1333.27,1333.27
|
||||
1343.6,1343.6,1343.6
|
||||
1340.68,1340.68,1340.68
|
||||
1328.98,1328.98,1328.98
|
||||
1329.47,1329.47,1329.47
|
||||
1337.77,1337.77,1337.77
|
||||
1348.65,1348.65,1348.65
|
||||
1342.08,1342.08,1342.08
|
||||
1357.16,1357.16,1357.16
|
||||
1346.29,1346.29,1346.29
|
||||
1340.2,1340.2,1340.2
|
||||
1335.1,1335.1,1335.1
|
||||
1347.32,1347.32,1347.32
|
||||
1356.62,1356.62,1356.62
|
||||
1361.22,1361.22,1361.22
|
||||
1363.61,1363.61,1363.61
|
||||
1360.48,1360.48,1360.48
|
||||
1355.66,1355.66,1355.66
|
||||
1347.24,1347.24,1347.24
|
||||
1335.25,1335.25,1335.25
|
||||
1337.38,1337.38,1337.38
|
||||
1330.36,1330.36,1330.36
|
||||
1312.62,1312.62,1312.62
|
||||
1305.14,1305.14,1305.14
|
||||
1319.68,1319.68,1319.68
|
||||
1314.52,1314.52,1314.52
|
||||
1314.41,1314.41,1314.41
|
||||
1314.16,1314.16,1314.16
|
||||
1324.46,1324.46,1324.46
|
||||
1328.17,1328.17,1328.17
|
||||
1333.51,1333.51,1333.51
|
||||
1335.54,1335.54,1335.54
|
||||
1332.63,1332.63,1332.63
|
||||
1332.87,1332.87,1332.87
|
||||
1332.41,1332.41,1332.41
|
||||
1325.83,1325.83,1325.83
|
||||
1328.26,1328.26,1328.26
|
||||
1319.44,1319.44,1319.44
|
||||
1310.19,1310.19,1310.19
|
||||
1313.8,1313.8,1313.8
|
||||
1309.66,1309.66,1309.66
|
||||
1297.54,1297.54,1297.54
|
||||
1293.77,1293.77,1293.77
|
||||
1298.38,1298.38,1298.38
|
||||
1279.2,1279.2,1279.2
|
||||
1273.72,1273.72,1273.72
|
||||
1256.88,1256.88,1256.88
|
||||
1281.87,1281.87,1281.87
|
||||
1296.39,1296.39,1296.39
|
||||
1304.28,1304.28,1304.28
|
||||
1295.11,1295.11,1295.11
|
||||
1320.02,1320.02,1320.02
|
||||
1321.82,1321.82,1321.82
|
||||
1310.13,1310.13,1310.13
|
||||
1321.15,1321.15,1321.15
|
||||
1330.97,1330.97,1330.97
|
||||
1308.44,1308.44,1308.44
|
||||
1306.33,1306.33,1306.33
|
||||
1327.22,1327.22,1327.22
|
||||
1319.88,1319.88,1319.88
|
||||
1306.1,1306.1,1306.1
|
||||
1307.4,1307.4,1307.4
|
||||
1315.44,1315.44,1315.44
|
||||
1343.01,1343.01,1343.01
|
||||
1340.43,1340.43,1340.43
|
||||
1336.32,1336.32,1336.32
|
||||
1328.01,1328.01,1328.01
|
||||
1332.32,1332.32,1332.32
|
||||
1329.15,1329.15,1329.15
|
||||
1321.87,1321.87,1321.87
|
||||
1320.88,1320.88,1320.88
|
||||
1324.57,1324.57,1324.57
|
||||
1319.05,1319.05,1319.05
|
||||
1310.87,1310.87,1310.87
|
||||
1307.1,1307.1,1307.1
|
||||
1304.03,1304.03,1304.03
|
||||
1307.59,1307.59,1307.59
|
||||
1286.12,1286.12,1286.12
|
||||
1276.34,1276.34,1276.34
|
||||
1299.54,1299.54,1299.54
|
||||
1296.63,1296.63,1296.63
|
||||
1291.18,1291.18,1291.18
|
||||
1290.84,1290.84,1290.84
|
||||
1283.35,1283.35,1283.35
|
||||
1280.26,1280.26,1280.26
|
||||
1281.92,1281.92,1281.92
|
||||
1295.02,1295.02,1295.02
|
||||
1293.24,1293.24,1293.24
|
||||
1283.76,1283.76,1283.76
|
||||
1285.96,1285.96,1285.96
|
||||
1274.48,1274.48,1274.48
|
||||
1269.75,1269.75,1269.75
|
||||
1271.5,1271.5,1271.5
|
||||
1273.85,1273.85,1273.85
|
||||
1276.56,1276.56,1276.56
|
||||
1270.2,1270.2,1270.2
|
||||
1271.89,1271.89,1271.89
|
||||
1257.64,1257.64,1257.64
|
||||
1257.88,1257.88,1257.88
|
||||
1259.78,1259.78,1259.78
|
||||
1258.51,1258.51,1258.51
|
||||
1257.54,1257.54,1257.54
|
||||
1256.77,1256.77,1256.77
|
||||
1258.84,1258.84,1258.84
|
||||
1254.6,1254.6,1254.6
|
||||
1247.08,1247.08,1247.08
|
||||
1243.91,1243.91,1243.91
|
||||
1242.87,1242.87,1242.87
|
||||
1235.23,1235.23,1235.23
|
||||
1241.59,1241.59,1241.59
|
||||
1240.46,1240.46,1240.46
|
||||
1240.4,1240.4,1240.4
|
||||
1233,1233,1233
|
||||
1228.28,1228.28,1228.28
|
||||
1223.75,1223.75,1223.75
|
||||
1223.12,1223.12,1223.12
|
||||
1224.71,1224.71,1224.71
|
||||
1221.53,1221.53,1221.53
|
||||
1206.07,1206.07,1206.07
|
||||
1180.55,1180.55,1180.55
|
||||
1187.76,1187.76,1187.76
|
||||
1189.4,1189.4,1189.4
|
||||
1198.35,1198.35,1198.35
|
||||
1180.73,1180.73,1180.73
|
||||
1197.84,1197.84,1197.84
|
||||
1199.73,1199.73,1199.73
|
||||
1196.69,1196.69,1196.69
|
||||
1178.59,1178.59,1178.59
|
||||
1178.34,1178.34,1178.34
|
||||
1197.75,1197.75,1197.75
|
||||
1199.21,1199.21,1199.21
|
||||
1213.54,1213.54,1213.54
|
||||
1218.71,1218.71,1218.71
|
||||
1213.4,1213.4,1213.4
|
||||
1223.25,1223.25,1223.25
|
||||
1225.85,1225.85,1225.85
|
||||
1221.06,1221.06,1221.06
|
||||
1197.96,1197.96,1197.96
|
||||
1193.57,1193.57,1193.57
|
||||
1184.38,1184.38,1184.38
|
||||
1183.26,1183.26,1183.26
|
||||
1183.78,1183.78,1183.78
|
||||
1182.45,1182.45,1182.45
|
||||
1185.64,1185.64,1185.64
|
||||
1185.62,1185.62,1185.62
|
||||
1183.08,1183.08,1183.08
|
||||
1180.26,1180.26,1180.26
|
||||
1178.17,1178.17,1178.17
|
||||
1165.9,1165.9,1165.9
|
||||
1184.71,1184.71,1184.71
|
||||
1176.19,1176.19,1176.19
|
||||
1173.81,1173.81,1173.81
|
||||
1178.1,1178.1,1178.1
|
||||
1169.77,1169.77,1169.77
|
||||
1165.32,1165.32,1165.32
|
||||
1165.15,1165.15,1165.15
|
||||
1158.06,1158.06,1158.06
|
||||
1159.97,1159.97,1159.97
|
||||
1160.75,1160.75,1160.75
|
||||
1137.03,1137.03,1137.03
|
||||
1146.24,1146.24,1146.24
|
||||
1141.2,1141.2,1141.2
|
||||
1144.73,1144.73,1144.73
|
||||
1147.7,1147.7,1147.7
|
||||
1142.16,1142.16,1142.16
|
||||
1148.67,1148.67,1148.67
|
||||
1124.83,1124.83,1124.83
|
||||
1134.28,1134.28,1134.28
|
||||
1139.78,1139.78,1139.78
|
||||
1142.71,1142.71,1142.71
|
||||
1125.59,1125.59,1125.59
|
||||
1124.66,1124.66,1124.66
|
||||
1125.07,1125.07,1125.07
|
||||
1121.1,1121.1,1121.1
|
||||
1121.9,1121.9,1121.9
|
||||
1109.55,1109.55,1109.55
|
||||
1104.18,1104.18,1104.18
|
||||
|
|
|
@ -1,5 +1,5 @@
|
|||
"""
|
||||
Utility functions for ????.
|
||||
Signal Filtering/Smoothing and Generation of Synthetic Time-Series.
|
||||
|
||||
Copyright (c) 2020 Gabriele Gilardi
|
||||
"""
|
||||
|
|
@ -7,6 +7,200 @@ Copyright (c) 2020 Gabriele Gilardi
|
|||
import numpy as np
|
||||
|
||||
|
||||
def synthetic_wave(P, A=None, phi=None, num=1000):
|
||||
"""
|
||||
Generates a multi-sine wave given a periods, amplitudes, and phases.
|
||||
|
||||
P (n, ) Periods
|
||||
A (n, ) Amplitudes
|
||||
phi (n, ) Phases (rad)
|
||||
t (num, ) Time
|
||||
f (num, ) Multi-sine wave
|
||||
|
||||
The default value for the amplitudes is 1 and for the phases is zero. The
|
||||
time goes from zero to the largest period.
|
||||
"""
|
||||
n_waves = len(P) # Number of waves
|
||||
P = np.asarray(P)
|
||||
|
||||
# Amplitudes
|
||||
if (A is None):
|
||||
A = np.ones(n_waves) # Default is 1
|
||||
else:
|
||||
A = np.asarray(A)
|
||||
|
||||
# Phases
|
||||
if (phi is None):
|
||||
phi = np.zeros(n_waves) # Default is 0
|
||||
else:
|
||||
phi = np.asarray(phi)
|
||||
|
||||
# Time
|
||||
t = np.linspace(0.0, np.amax(P), num=num)
|
||||
|
||||
# Add up all the sine waves
|
||||
f = np.zeros(len(t))
|
||||
for i in range(n_waves):
|
||||
f = f + A[i] * np.sin(2.0 * np.pi * t / P[i] + phi[i])
|
||||
|
||||
return t, f
|
||||
|
||||
|
||||
def synthetic_FFT(X, n_reps=1):
|
||||
"""
|
||||
Generates surrogates of the time-serie X using the phase-randomized
|
||||
Fourier-transform algorithm.
|
||||
|
||||
X (n, ) Original time-series
|
||||
X_fft (n, ) FFT of the original time-series
|
||||
X_synt_fft (n_reps, n) FFT of the synthetic time-series
|
||||
X_synt (n_reps, n) Synthetic time-series
|
||||
|
||||
Input array X needs to be a 1D array (of any shape).
|
||||
"""
|
||||
X = X.flatten() # Reshape to (n, )
|
||||
n = len(X)
|
||||
|
||||
# The number of samples must be odd
|
||||
if ((n % 2) == 0):
|
||||
print("Warning: data reduced by one (even number of samples)")
|
||||
n = n - 1
|
||||
X = X[0:n, :]
|
||||
|
||||
# FFT of the original time-serie
|
||||
X_fft = np.fft.fft(X)
|
||||
|
||||
# Parameters
|
||||
half_len = (n - 1) // 2
|
||||
idx1 = np.arange(1, half_len+1, dtype=int) # 1st half
|
||||
idx2 = np.arange(half_len+1, n, dtype=int) # 2nd half
|
||||
|
||||
# Generate the random phases
|
||||
phases = np.random.rand(n_reps, half_len)
|
||||
phases1 = np.exp(2.0 * np.pi * 1j * phases)
|
||||
phases2 = np.conj(np.flipud(phases1))
|
||||
|
||||
# FFT of the synthetic time-series (1st sample is unchanged)
|
||||
X_synt_fft = np.zeros((n_reps, n), dtype=complex)
|
||||
X_synt_fft[:, 0] = X_fft[0]
|
||||
X_synt_fft[:, idx1] = X_fft[idx1] * phases1 # 1st half
|
||||
X_synt_fft[:, idx2] = X_fft[idx2] * phases2 # 2nd half
|
||||
|
||||
# Synthetic time-series
|
||||
X_synt = np.real(np.fft.ifft(X_synt_fft, axis=1))
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
def synthetic_sampling(X, n_reps=1, replace=True):
|
||||
"""
|
||||
Generates surrogates of the time-serie X using randomized-sampling
|
||||
(bootstrap) with or without replacement.
|
||||
|
||||
X (n, ) Original time-series
|
||||
idx (n_reps, n) Random index of X
|
||||
X_synt (n_reps, n) Synthetic time-series
|
||||
|
||||
Input array X needs to be a 1D array (of any shape).
|
||||
"""
|
||||
X = X.flatten() # Reshape to (n, )
|
||||
n = len(X)
|
||||
|
||||
# Sampling with replacement
|
||||
if (replace):
|
||||
idx = np.random.randint(0, n, size=(n_reps, n))
|
||||
|
||||
# Sampling without replacement
|
||||
else:
|
||||
idx = np.argsort(np.random.rand(n_reps, n), axis=1)
|
||||
|
||||
# Synthetic time-series
|
||||
X_synt = X[idx]
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
def synthetic_MEboot(X, n_reps=1, alpha=0.1, bounds=False, scale=False):
|
||||
"""
|
||||
Generates surrogates of the time-serie X using the maximum entropy
|
||||
bootstrap algorithm.
|
||||
|
||||
X (n, ) Original time-series
|
||||
idx (n, ) Original order of X
|
||||
y (n, ) Sorted original time-series
|
||||
z (n+1, ) Intermediate points
|
||||
mt (n, ) Interval means
|
||||
t_w (n_reps, n) Random new points
|
||||
w_int (n_reps, n) Interpolated new points
|
||||
w_corr (n_reps, n) Interpolated new points with corrections for first
|
||||
and last interval
|
||||
X_synt (n_reps, n) Synthetic time-series
|
||||
|
||||
Input array X needs to be a 1D array (of any shape).
|
||||
"""
|
||||
X = X.flatten() # Reshape to (n, )
|
||||
n = len(X)
|
||||
|
||||
# Sort the time series keeping track of the original order
|
||||
idx = np.argsort(X)
|
||||
y = X[idx]
|
||||
|
||||
# Trimmed mean
|
||||
g = int(np.floor(n * alpha))
|
||||
r = n * alpha - g
|
||||
m_trm = ((1.0 - r) * (y[g] + y[n-g-1]) + y[g+1:n-g-1].sum()) \
|
||||
/ (n * (1.0 - 2.0 * alpha))
|
||||
|
||||
# Intermediate points
|
||||
z = np.zeros(n+1)
|
||||
z[0] = y[0] - m_trm
|
||||
z[1:-1] = (y[0:-1] + y[1:]) / 2.0
|
||||
z[n] = y[n-1] + m_trm
|
||||
|
||||
# Interval means
|
||||
mt = np.zeros(n)
|
||||
mt[0] = 0.75 * y[0] + 0.25 * y[1]
|
||||
mt[1:n-1] = 0.25 * y[0:n-2] + 0.5 * y[1:n-1] + 0.25 * y[2:n]
|
||||
mt[n-1] = 0.25 * y[n-2] + 0.75 * y[n-1]
|
||||
|
||||
# Generate randomly new points and sort them
|
||||
t_w = np.random.rand(n_reps, n)
|
||||
t_w = np.sort(t_w, axis=1)
|
||||
|
||||
# Interpolate the new points
|
||||
t = np.linspace(0.0, 1.0, num=n+1)
|
||||
w_int = np.interp(t_w, t, z)
|
||||
|
||||
# Correct the new points in the first and last interval to satisfy
|
||||
# the mass constraint
|
||||
idw = (np.floor_divide(t_w, 1.0 / n)).astype(int)
|
||||
corr = np.where(idw == 0, mt[idw] - (z[idw] + z[idw+1]) / 2.0, 0.0)
|
||||
w_corr = w_int + corr
|
||||
if (n > 1):
|
||||
corr = np.where(idw == n-1, mt[idw] - (z[idw] + z[idw+1]) / 2.0, 0.0)
|
||||
w_corr += corr
|
||||
|
||||
# Enforce limits (if requested)
|
||||
if (bounds):
|
||||
w_corr = np.fmin(np.fmax(w_corr, z[0]), z[n])
|
||||
|
||||
# Recovery the time-dependency of the original time-series
|
||||
X_synt = np.zeros((n_reps, n))
|
||||
X_synt[:, idx] = w_corr
|
||||
|
||||
# Scale to force equal variance (if requested)
|
||||
if (scale):
|
||||
var_z = np.diff(z) ** 2.0 / 12.0
|
||||
X_mean = X.mean(axis=0)
|
||||
var_ME = (((mt - X_mean) ** 2).sum() + var_z.sum()) / n
|
||||
std_X = X.std(ddof=1)
|
||||
std_ME = np.sqrt(var_ME)
|
||||
k_scale = std_X / std_ME - 1.0
|
||||
X_synt = X_synt + k_scale * (X_synt - X_mean)
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
def normalize_data(X, param=(), ddof=0):
|
||||
"""
|
||||
If mu and sigma are not defined, returns a column-normalized version of
|
||||
|
|
@ -63,235 +257,61 @@ def scale_data(X, param=()):
|
|||
|
||||
def value2diff(X, percent=True):
|
||||
"""
|
||||
from value to difference in abs or %
|
||||
dx is reduced by 1 row
|
||||
Returns the 1st discrete difference of array X.
|
||||
|
||||
X (n, ) Original dataset
|
||||
dX (n-1, ) 1st discrete differences
|
||||
|
||||
Notes:
|
||||
- the discrete difference can be calculated in percent or in value.
|
||||
- array dX is one element shorter than array X.
|
||||
- array X needs to be a 1D array (of any shape).
|
||||
"""
|
||||
# Difference in percent
|
||||
X = X.flatten() # Reshape to (n, )
|
||||
|
||||
# Discrete difference in percent
|
||||
if (percent):
|
||||
dX = X[1:, :] / X[:-1, :] - 1.0
|
||||
|
||||
# Difference in value
|
||||
dX = X[1:] / X[:-1] - 1.0
|
||||
|
||||
# Discrete difference in value
|
||||
else:
|
||||
dX = X[1:, :] - X[:-1, :]
|
||||
|
||||
dX = X[1:] - X[:-1]
|
||||
|
||||
return dX
|
||||
|
||||
|
||||
def diff2value(dX, percent=True):
|
||||
def diff2value(dX, X0, percent=True):
|
||||
"""
|
||||
from difference in abs or % to value
|
||||
X is increased by one row
|
||||
Returns array X from the 1st discrete difference.
|
||||
|
||||
Value from percent: first row set to one. If X0 defines the starting
|
||||
values, then X0*X would be the ???
|
||||
dX (n, ) Discrete differences
|
||||
X0 scalar Initial value
|
||||
X (n+1, ) Original dataset ?????
|
||||
|
||||
Notes:
|
||||
- the discrete difference can be in percent or in value.
|
||||
- array X is one element longer than array dX.
|
||||
- array dX needs to be a 1D array (of any shape).
|
||||
|
||||
Value from difference: first row set to zero. If X0 defines the starting
|
||||
values, then X0+X would be the ???
|
||||
If the discrete difference is in percent, the first column of X is set to
|
||||
one. The original array is X[0] * X
|
||||
|
||||
If the discrete difference is in value, the first column of X is set to
|
||||
zero. X0+X
|
||||
- array X needs to be a 1D array (of any shape).
|
||||
"""
|
||||
n_rows, n_cols = dX.shape
|
||||
X = np.zeros((n_rows+1, n_cols))
|
||||
dX = dX.flatten() # Reshape to (n, )
|
||||
n = len(dX)
|
||||
X = np.zeros(n+1)
|
||||
|
||||
# Value from percent
|
||||
# X[0, :] = 1
|
||||
# X[1, :] = X[0, :] * (1 + dX[1, :]) = (1 + dX[1, :])
|
||||
# X[2, :] = X[1, :] * (1 + dX[2, :])
|
||||
# = X[0, :] * (1 + dX[1, :]) * (1 + dX[2, :])
|
||||
# = (1 + dX[1, :]) * (1 + dX[2, :])
|
||||
# ....
|
||||
# Discrete difference in percent
|
||||
if (percent):
|
||||
X[0, :] = 1.0
|
||||
X[1:, :] = np.cumprod((1.0 + dX), axis=0)
|
||||
|
||||
# Value from difference
|
||||
# X[0, :] = 0
|
||||
# X[1, :] = X[0, :] + dX[1, :] = dX[1, :]
|
||||
# X[2, :] = X[0, :] + dX[1, :] + dX[2, :] = dX[1, :] + dX[2, :]
|
||||
# ....
|
||||
X[0] = X0
|
||||
X[1:] = np.cumprod(1.0 + dX) * X0 # !!!! check
|
||||
|
||||
# Discrete difference in value
|
||||
else:
|
||||
# First row already set to zero
|
||||
X[1:, :] = np.cumsum(dX, axis=0)
|
||||
|
||||
X[0] = X0
|
||||
X[1:] = np.cumsum(dX) + X0 # !!!!!c check
|
||||
|
||||
return X
|
||||
|
||||
|
||||
def synthetic_wave(P, A=None, phi=None, num=1000):
|
||||
"""
|
||||
Generates a multi-sinewave.
|
||||
P = [P_1, P_2, ... P_n] Periods
|
||||
A = [A_1, A_2, ... A_n] Amplitudes
|
||||
phi = [phi_1, phi_2, ... phi_n] Phases (rad)
|
||||
|
||||
Default amplitudes are ones
|
||||
Default phases are zeros
|
||||
Time is from 0 to largest period (default 1000 steps)
|
||||
"""
|
||||
n_waves = len(P)
|
||||
P = np.asarray(P)
|
||||
|
||||
# Define amplitudes
|
||||
if (A is None):
|
||||
A = np.ones(n_waves)
|
||||
else:
|
||||
A = np.asarray(A)
|
||||
|
||||
# Define phases
|
||||
if (phi is None):
|
||||
phi = np.zeros(n_waves)
|
||||
else:
|
||||
phi = np.asarray(phi)
|
||||
|
||||
# Add all the waves
|
||||
t = np.linspace(0.0, np.amax(P), num=num)
|
||||
f = np.zeros(len(t))
|
||||
for i in range(n_waves):
|
||||
f = f + A[i] * np.sin(2.0 * np.pi * t / P[i] + phi[i])
|
||||
|
||||
return t, f
|
||||
|
||||
|
||||
def synthetic_FFT(X, multiv=False):
|
||||
"""
|
||||
- univariate and single time-series
|
||||
- univariate and multi-time series (can be used to generate multi from same)
|
||||
- multi-variate multi-time series
|
||||
|
||||
generate more than n_samples, remome multi-time series for uni-variate
|
||||
use 3d matrix looping on the time-series for multi-variate
|
||||
|
||||
"""
|
||||
n_samples, n_series = X.shape
|
||||
|
||||
# The number of samples must be odd (if the number is even drop the last value)
|
||||
if ((n_samples % 2) == 0):
|
||||
print("Warning: data reduced by one (even number of samples)")
|
||||
n_samples = n_samples - 1
|
||||
X = X[0:n_samples, :]
|
||||
|
||||
# FFT of the original data
|
||||
X_fft = np.fft.fft(X, axis=0)
|
||||
|
||||
# Parameters
|
||||
half_len = (n_samples - 1) // 2
|
||||
idx1 = np.arange(1, half_len+1, dtype=int)
|
||||
idx2 = np.arange(half_len+1, n_samples, dtype=int)
|
||||
|
||||
# If multivariate the random phases is the same
|
||||
if (multiv):
|
||||
phases = np.random.rand(half_len, 1)
|
||||
phases1 = np.tile(np.exp(2.0 * np.pi * 1j * phases), (1, n_series))
|
||||
phases2 = np.conj(np.flipud(phases1))
|
||||
|
||||
# If univariate the random phases is different
|
||||
else:
|
||||
phases = np.random.rand(half_len, n_series)
|
||||
phases1 = np.exp(2.0 * np.pi * 1j * phases)
|
||||
phases2 = np.conj(np.flipud(phases1))
|
||||
|
||||
# FFT of the synthetic data
|
||||
synt_fft = X_fft.copy()
|
||||
synt_fft[idx1, :] = X_fft[idx1, :] * phases1
|
||||
synt_fft[idx2, :] = X_fft[idx2, :] * phases2
|
||||
|
||||
# Inverse FFT of the synthetic data
|
||||
X_synt = np.real(np.fft.ifft(synt_fft, axis=0))
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
def synthetic_sampling(X, n_reps=1, replace=True):
|
||||
"""
|
||||
X must be (n_samples, 1)
|
||||
"""
|
||||
n_samples = X.shape[0]
|
||||
|
||||
# Sampling with replacement
|
||||
if (replace):
|
||||
idx = np.random.randint(0, n_samples, size=(n_samples, n_reps))
|
||||
i = np.arange(n_reps)
|
||||
Xe = np.tile(X,(1, n_reps))
|
||||
X_synt = Xe[idx, i]
|
||||
|
||||
# Sampling without replacement
|
||||
else:
|
||||
idx = np.zeros(n_samples, n_reps)
|
||||
for j in range(n_reps):
|
||||
idx[:, j] = np.random.permutation(n_samples)
|
||||
i = np.arange(n_reps)
|
||||
Xe = np.tile(X,(1, n_reps))
|
||||
X_synt = Xe[idx, i]
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
def synthetic_MEboot(X, alpha=0.1, bounds=True, scale=False):
|
||||
"""
|
||||
"""
|
||||
n_samples, n_series = X.shape
|
||||
X_synt = np.zeros_like(X)
|
||||
|
||||
# Loop over time-series iterate over number of desired series or set parallel
|
||||
# if possile
|
||||
n = n_samples
|
||||
for ts in range(n_series):
|
||||
|
||||
# Sort the time series keeping track of the original position
|
||||
idx = np.argsort(X[:, ts])
|
||||
Y = X[idx, ts]
|
||||
|
||||
# Compute the trimmed mean
|
||||
# trimmed_mean = np.abs(np.diff(x)).mean()
|
||||
g = int(np.floor(n * alpha))
|
||||
r = n * alpha - g
|
||||
m_trm = ((1.0 - r) * (Y[g] + Y[n-g-1]) + Y[g+1:n-g-1].sum()) \
|
||||
/ (n * (1.0 - 2.0 * alpha))
|
||||
|
||||
# Compute the intermediate points
|
||||
Z = np.zeros(n+1)
|
||||
Z[0] = Y[0] - m_trm
|
||||
Z[1:-1] = (Y[0:-1] + Y[1:]) / 2.0
|
||||
Z[n] = Y[n-1] + m_trm
|
||||
|
||||
# Compute the interval means
|
||||
mt = np.zeros(n)
|
||||
mt[0] = 0.75 * Y[0] + 0.25 * Y[1]
|
||||
mt[1:n-1] = 0.25 * Y[0:n-2] + 0.5 * Y[1:n-1] + 0.25 * Y[2:n]
|
||||
mt[n-1] = 0.25 * Y[n-2] + 0.75 * Y[n-1]
|
||||
|
||||
# Randomly generate new points
|
||||
# t_w = np.random.rand(n)
|
||||
t_w = np.array([0.12, 0.83, 0.53, 0.59, 0.11])
|
||||
t_w = np.sort(t_w)
|
||||
|
||||
# Interpolate new points
|
||||
t = np.linspace(0.0, 1.0, num=n+1)
|
||||
w_int = np.interp(t_w, t, Z)
|
||||
|
||||
# Correct the new points (first and last interval) to satisfy mass constraint
|
||||
idw = (np.floor_divide(t_w, 1.0 / n)).astype(int)
|
||||
corr = np.where(idw == 0, mt[idw] - (Z[idw] + Z[idw+1]) / 2.0, 0.0)
|
||||
w_corr = w_int + corr
|
||||
if (n > 1):
|
||||
corr = np.where(idw == n-1, mt[idw] - (Z[idw] + Z[idw+1]) / 2.0, 0.0)
|
||||
w_corr += corr
|
||||
|
||||
# Enforce limits
|
||||
if (bounds):
|
||||
w_corr = np.fmin(np.fmax(w_corr, Z[0]), Z[n])
|
||||
|
||||
# Recovery the time-dependencies (done all togheter?)
|
||||
X_synt = np.zeros(n)
|
||||
X_synt[idx] = w_corr
|
||||
|
||||
# Scale (done all together?)
|
||||
if (scale):
|
||||
var_Z = np.diff(Z) ** 2.0 / 12.0
|
||||
X_mean = X.mean(axis=0)
|
||||
var_ME = (((mt - X_mean) ** 2).sum() + var_Z.sum()) / n
|
||||
std_X= X.std(axis=0, ddof=1)
|
||||
std_ME = np.sqrt(var_ME)
|
||||
k_scale = std_X / std_ME - 1.0
|
||||
X_synt = X_synt + k_scale * (X_synt - X_mean)
|
||||
|
||||
return X_synt
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,20 +1,14 @@
|
|||
"""
|
||||
Filters for time series.
|
||||
Signal Filtering/Smoothing and Generation of Synthetic Time-Series.
|
||||
|
||||
Copyright (c) 2020 Gabriele Gilardi
|
||||
|
||||
|
||||
ToDo:
|
||||
- add comments to the code
|
||||
- in comments write what filters do
|
||||
- is necessary to copy X for Y untouched?
|
||||
- decide default values in functions
|
||||
- why lag plot gives errors
|
||||
- fix plotting function
|
||||
- example for alpha-beta-gamma using variable sigma as in financial time series
|
||||
(see Ehler)
|
||||
- example using noisy multi-sine-waves
|
||||
- vectors must be ( .., 1)
|
||||
- synt remove multi-series unless multi-variate and add number of istances (fft 3 D)
|
||||
"""
|
||||
|
||||
import sys
|
||||
|
|
@ -24,26 +18,32 @@ import matplotlib.pyplot as plt
|
|||
import filters as flt
|
||||
import synthetic as syn
|
||||
|
||||
# Added to avoid message warning "The group delay is singular at frequencies
|
||||
# [....], setting to 0" when plotting the lag for SMA filters.
|
||||
np.seterr(all='ignore')
|
||||
np.random.seed(1294404794)
|
||||
|
||||
# Read data to filter
|
||||
if len(sys.argv) != 2:
|
||||
print("Usage: python test.py <data_file>")
|
||||
sys.exit(1)
|
||||
data_file = sys.argv[1] + '.csv'
|
||||
|
||||
# Read data from a csv file
|
||||
# Read data from a csv file (one time-series each column)
|
||||
data = np.loadtxt(data_file, delimiter=',')
|
||||
n_samples = data.shape[0]
|
||||
data = data.reshape(n_samples, -1)
|
||||
|
||||
np.random.seed(1294404794)
|
||||
t, f = syn.synthetic_wave([1., 2., 3.], A=None, phi=None, num=1000)
|
||||
plt.plot(t,f)
|
||||
plt.show()
|
||||
|
||||
# spx = flt.Filter(data)
|
||||
# res = spx.EMA(N=10)
|
||||
# signals = [spx.data, res[0:400]]
|
||||
# flt.plot_signals(signals, start=100)
|
||||
|
||||
# spx.plot_frequency()
|
||||
# spx.plot_lag()
|
||||
|
||||
# res, bb, aa = spx.SincFunction(2, 50)
|
||||
# print(bb)
|
||||
# print(aa)
|
||||
# utl.plot_frequency_response(bb, aa)
|
||||
# utl.plot_lag_response(bb, aa)
|
||||
# sigma_x = 0.1
|
||||
# sigma_v = 0.1 * np.ones(n_samples)
|
||||
# res = spx.Kalman(sigma_x=sigma_x, sigma_v=sigma_v, dt=1.0, abg_type="abg")
|
||||
|
|
@ -71,8 +71,9 @@ np.random.seed(1294404794)
|
|||
# [-0.8236, -0.8314],
|
||||
# [-1.5771, -0.9792],
|
||||
# [ 0.5080, -1.1564]])
|
||||
# synt_data1 = syn.synthetic_FFT(data, False)
|
||||
# synt_data2 = syn.synthetic_FFT(data, False)
|
||||
# synt_data = syn.synthetic_FFT(aa[0:5,0], n_reps=1)
|
||||
# print(synt_data)
|
||||
|
||||
# plt.plot(synt_data1)
|
||||
# plt.plot(synt_data2)
|
||||
# plt.plot(data)
|
||||
|
|
@ -85,30 +86,14 @@ np.random.seed(1294404794)
|
|||
# print(bb[0:10, :])
|
||||
# cc = syn.diff2value(bb, percent)
|
||||
# print(cc[0:10, :]+1399.48)
|
||||
# aa = np.arange(10,28).reshape(6,3)
|
||||
# print(aa)
|
||||
# idx = np.zeros_like(aa)
|
||||
# bb = np.zeros_like(aa)
|
||||
# for i in range(aa.shape[1]):
|
||||
# idx[:, i] = np.random.permutation(aa.shape[0])
|
||||
# print(idx)
|
||||
|
||||
# i = np.arange(aa.shape[1])
|
||||
# bb[:, i] = aa[idx[:, i], i]
|
||||
# bb = syn.synthetic_boot(aa, replace=False)
|
||||
# aa = np.array([4, 12, 36, 20, 8])
|
||||
# bb = syn.synthetic_sampling(aa, n_reps=2, replace=True)
|
||||
# print(bb)
|
||||
# aa = np.array([4, 12, 36, 20, 8]).reshape(5, 1)
|
||||
# W = syn.synthetic_MEboot(aa, alpha=0.1, bounds=False, scale=True)
|
||||
# print(bb.sum())
|
||||
# print('W=', W)
|
||||
# aa = np.array([4, 12, 36, 20, 8])
|
||||
# W = syn.synthetic_MEboot(aa, n_reps=1, alpha=0.1, bounds=False, scale=False)
|
||||
# print('W=')
|
||||
# print(W)
|
||||
|
||||
n = 8
|
||||
aa = np.arange(n).reshape(n,1) * 1.1
|
||||
print(aa)
|
||||
idx = np.random.randint(0, n, size=(n, 3))
|
||||
print(idx)
|
||||
i = np.arange(3)
|
||||
# print(i)
|
||||
bb = np.tile(aa,(1, 3))
|
||||
# print(bb)
|
||||
cc = bb[idx, i]
|
||||
print(cc)
|
||||
Ładowanie…
Reference in New Issue