242 lines
8.8 KiB
Python
242 lines
8.8 KiB
Python
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'''generate py modules with test cases and results from mlabwrap
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currently matlab: princomp, garchar, garchma
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'''
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from numpy import array
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xo = array([[ -419, -731, -1306, -1294],
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[ 6, 529, -200, -437],
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[ -27, -833, -6, -564],
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[ -304, -273, -502, -739],
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[ 1377, -912, 927, 280],
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[ -375, -517, -514, 49],
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[ 247, -504, 123, -259],
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[ 712, 534, -773, 286],
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[ 195, -1080, 3256, -178],
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[ -854, 75, -706, -1084],
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[-1219, -612, -15, -203],
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[ 550, -628, -483, -2686],
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[ -365, 1376, -1266, 317],
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[ -489, 544, -195, 431],
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[ -656, 854, 840, -723],
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[ 16, -1385, -880, -460],
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[ 258, -2252, 96, 54],
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[ 2049, -750, -1115, 381],
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[ -65, 280, -777, 416],
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[ 755, 82, -806, 1027],
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[ -39, -170, -2134, 743],
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[ -859, 780, 746, -133],
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[ 762, 252, -450, -459],
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[ -941, -202, 49, -202],
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[ -54, 115, 455, 388],
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[-1348, 1246, 1430, -480],
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[ 229, -535, -1831, 1524],
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[ -651, -167, 2116, 483],
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[-1249, -1373, 888, -1092],
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[ -75, -2162, 486, -496],
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[ 2436, -1627, -1069, 162],
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[ -63, 560, -601, 587],
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[ -60, 1051, -277, 1323],
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[ 1329, -1294, 68, 5],
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[ 1532, -633, -923, 696],
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[ 669, 895, -1762, -375],
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[ 1129, -548, 2064, 609],
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[ 1320, 573, 2119, 270],
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[ -213, -412, -2517, 1685],
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[ 73, -979, 1312, -1220],
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[-1360, -2107, -237, 1522],
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[ -645, 205, -543, -169],
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[ -212, 1072, 543, -128],
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[ -352, -129, -605, -904],
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[ 511, 85, 167, -1914],
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[ 1515, 1862, 942, 1622],
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[ -465, 623, -495, -89],
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[-1396, -979, 1758, 128],
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[ -255, -47, 980, 501],
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[-1282, -58, -49, -610],
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[ -889, -1177, -492, 494],
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[ 1415, 1146, 696, -722],
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[ 1237, -224, -1609, -64],
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[ -528, -1625, 231, 883],
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[ -327, 1636, -476, -361],
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[ -781, 793, 1882, 234],
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[ -506, -561, 1988, -810],
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[-1233, 1467, -261, 2164],
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[ 53, 1069, 824, 2123],
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[-1200, -441, -321, 339],
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[ 1606, 298, -995, 1292],
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[-1740, -672, -1628, -129],
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[-1450, -354, 224, -657],
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[-2556, 1006, -706, -1453],
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[ -717, -463, 345, -1821],
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[ 1056, -38, -420, -455],
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[ -523, 565, 425, 1138],
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[-1030, -187, 683, 78],
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[ -214, -312, -1171, -528],
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[ 819, 736, -265, 423],
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[ 1339, 351, 1142, 579],
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[ -387, -126, -1573, 2346],
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[ 969, 2, 327, -134],
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[ 163, 227, 90, 2021],
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[ 1022, -1076, 174, 304],
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[ 1042, 1317, 311, 880],
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[ 2018, -840, 295, 2651],
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[ -277, 566, 1147, -189],
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[ 20, 467, 1262, 263],
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[ -663, 1061, -1552, -1159],
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[ 1830, 391, 2534, -199],
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[ -487, 752, -1061, 351],
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[-2138, -556, -367, -457],
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[ -868, -411, -559, 726],
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[ 1770, 819, -892, -363],
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[ 553, -736, -169, -490],
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[ 388, -503, 809, -821],
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[ -516, -1452, -192, 483],
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[ 493, 2904, 1318, 2591],
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[ 175, 584, -1001, 1675],
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[ 1316, -1596, -460, 1500],
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[ 1212, 214, -644, -696],
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[ -501, 338, 1197, -841],
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[ -587, -469, -1101, 24],
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[-1205, 1910, 659, 1232],
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[ -150, 398, 594, 394],
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[ 34, -663, 235, -334],
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[-1580, 647, 239, -351],
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[-2177, -345, 1215, -1494],
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[ 1923, 329, -152, 1128]])
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x = xo/1000.
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class HoldIt:
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def __init__(self, name):
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self.name = name
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def save(self, what=None, filename=None, header=True, useinstant=True,
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comment=None):
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if what is None:
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what = (i for i in self.__dict__ if i[0] != '_')
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if header:
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txt = ['import numpy as np\nfrom numpy import array\n\n']
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if useinstant:
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txt.append('from statsmodels.tools.testing import Holder\n\n')
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else:
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txt = []
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if useinstant:
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txt.append('%s = Holder()' % self.name)
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prefix = '%s.' % self.name
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else:
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prefix = ''
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if comment is not None:
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txt.append(f"{prefix}comment = '{comment}'")
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for x in what:
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txt.append(f'{prefix}{x} = {repr(getattr(self,x))}')
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txt.extend(['','']) #add empty lines at end
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if filename is not None:
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with open(filename, 'a+', encoding="utf-8") as fd:
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fd.write('\n'.join(txt))
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return txt
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def generate_princomp(xo, filen='testsave.py'):
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# import mlabwrap only when run as script
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from mlabwrap import mlab
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np.set_printoptions(precision=14, linewidth=100)
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data = HoldIt('data')
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data.xo = xo
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data.save(filename='testsave.py', comment='generated data, divide by 1000')
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res_princomp = HoldIt('princomp1')
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res_princomp.coef, res_princomp.factors, res_princomp.values = \
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mlab.princomp(x, nout=3)
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res_princomp.save(filename=filen, header=False,
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comment='mlab.princomp(x, nout=3)')
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res_princomp = HoldIt('princomp2')
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res_princomp.coef, res_princomp.factors, res_princomp.values = \
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mlab.princomp(x[:20,], nout=3)
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np.set_printoptions(precision=14, linewidth=100)
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res_princomp.save(filename=filen, header=False,
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comment='mlab.princomp(x[:20,], nout=3)')
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res_princomp = HoldIt('princomp3')
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res_princomp.coef, res_princomp.factors, res_princomp.values = \
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mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)
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np.set_printoptions(precision=14, linewidth=100)
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res_princomp.save(filename=filen, header=False,
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comment='mlab.princomp(x[:20,]-x[:20,].mean(0), nout=3)')
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def generate_armarep(filen='testsave.py'):
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# import mlabwrap only when run as script
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from mlabwrap import mlab
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res_armarep = HoldIt('armarep')
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res_armarep.ar = np.array([1., -0.5, +0.8])
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res_armarep.ma = np.array([1., -0.6, 0.08])
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res_armarep.marep = mlab.garchma(-res_armarep.ar[1:], res_armarep.ma[1:], 20)
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res_armarep.arrep = mlab.garchar(-res_armarep.ar[1:], res_armarep.ma[1:], 20)
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res_armarep.save(filename=filen, header=False,
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comment=("''mlab.garchma(-res_armarep.ar[1:], res_armarep.ma[1:], 20)\n" +
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"mlab.garchar(-res_armarep.ar[1:], res_armarep.ma[1:], 20)''"))
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def exampletest(res_armarep):
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from statsmodels.sandbox import tsa
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arrep = tsa.arma_impulse_response(res_armarep.ma, res_armarep.ar, nobs=21)[1:]
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marep = tsa.arma_impulse_response(res_armarep.ar, res_armarep.ma, nobs=21)[1:]
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assert_array_almost_equal(res_armarep.marep.ravel(), marep, 14)
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#difference in sign convention to matlab for AR term
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assert_array_almost_equal(-res_armarep.arrep.ravel(), arrep, 14)
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if __name__ == '__main__':
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from mlabwrap import mlab
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import savedrvs
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xo = savedrvs.rvsdata.xar2
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x100 = xo[-100:]/1000.
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x1000 = xo/1000.
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filen = 'testsavetls.py'
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res_pacf = HoldIt('mlpacf')
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res_pacf.comment = 'mlab.parcorr(x, [], 2, nout=3)'
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res_pacf.pacf100, res_pacf.lags100, res_pacf.bounds100 = \
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mlab.parcorr(x100, [], 2, nout=3)
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res_pacf.pacf1000, res_pacf.lags1000, res_pacf.bounds1000 = \
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mlab.parcorr(x1000, [], 2, nout=3)
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res_pacf.save(filename=filen, header=True)
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res_acf = HoldIt('mlacf')
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res_acf.comment = 'mlab.autocorr(x, [], 2, nout=3)'
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res_acf.acf100, res_acf.lags100, res_acf.bounds100 = \
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mlab.autocorr(x100, [], 2, nout=3)
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res_acf.acf1000, res_acf.lags1000, res_acf.bounds1000 = \
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mlab.autocorr(x1000, [], 2, nout=3)
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res_acf.save(filename=filen, header=False)
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res_ccf = HoldIt('mlccf')
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res_ccf.comment = 'mlab.crosscorr(x[4:], x[:-4], [], 2, nout=3)'
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res_ccf.ccf100, res_ccf.lags100, res_ccf.bounds100 = \
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mlab.crosscorr(x100[4:], x100[:-4], [], 2, nout=3)
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res_ccf.ccf1000, res_ccf.lags1000, res_ccf.bounds1000 = \
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mlab.crosscorr(x1000[4:], x1000[:-4], [], 2, nout=3)
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res_ccf.save(filename=filen, header=False)
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res_ywar = HoldIt('mlywar')
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res_ywar.comment = "mlab.ar(x100-x100.mean(), 10, 'yw').a.ravel()"
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mbaryw = mlab.ar(x100-x100.mean(), 10, 'yw')
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res_ywar.arcoef100 = np.array(mbaryw.a.ravel())
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mbaryw = mlab.ar(x1000-x1000.mean(), 20, 'yw')
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res_ywar.arcoef1000 = np.array(mbaryw.a.ravel())
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res_ywar.save(filename=filen, header=False)
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