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@ -4,61 +4,98 @@ Created on 5. aug. 2010
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@author: pab
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"""
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import unittest
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from numpy.testing import TestCase, assert_array_almost_equal
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import wafo.data # @UnusedImport
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import numpy as np # @UnusedImport
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import wafo.objects as wo
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import wafo.spectrum.models as sm
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import wafo.transform.models as tm
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class TestObjects(TestCase):
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def test_timeseries(self):
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x = wafo.data.sea()
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ts = wo.mat2timeseries(x)
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assert_array_almost_equal(ts.sampling_period(), 0.25)
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S = ts.tospecdata()
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assert_array_almost_equal(S.data[:10],
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[0.00913087, 0.00881073, 0.00791944,
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0.00664244, 0.00522429, 0.00389816,
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0.00282753, 0.00207843, 0.00162678,
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0.0013916])
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def test_timeseries():
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'''
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>>> import wafo.data
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>>> import wafo.objects as wo
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>>> x = wafo.data.sea()
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>>> ts = wo.mat2timeseries(x)
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>>> ts.sampling_period()
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0.25
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Estimate spectrum
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>>> S = ts.tospecdata()
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>>> S.data[:10]
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array([ 0.00913087, 0.00881073, 0.00791944, 0.00664244, 0.00522429,
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0.00389816, 0.00282753, 0.00207843, 0.00162678, 0.0013916 ])
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Estimated covariance function
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>>> rf = ts.tocovdata(lag=150)
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>>> rf.data[:10]
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array([ 0.22368637, 0.20838473, 0.17110733, 0.12237803, 0.07024054,
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0.02064859, -0.02218831, -0.0555993 , -0.07859847, -0.09166187])
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'''
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def test_timeseries_trdata():
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'''
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>>> import wafo.spectrum.models as sm
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>>> import wafo.transform.models as tm
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>>> from wafo.objects import mat2timeseries
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>>> Hs = 7.0
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>>> Sj = sm.Jonswap(Hm0=Hs)
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>>> S = Sj.tospecdata() #Make spectrum object from numerical values
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>>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, sigma=Hs/4, ysigma=Hs/4)
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>>> xs = S.sim(ns=2**20, iseed=10)
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>>> ts = mat2timeseries(xs)
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>>> g0, gemp = ts.trdata(monitor=True) # Monitor the development
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rf = ts.tocovdata(lag=150)
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assert_array_almost_equal(rf.data[:10],
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[0.22368637, 0.20838473, 0.17110733,
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0.12237803, 0.07024054, 0.02064859,
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-0.02218831, -0.0555993, -0.07859847,
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-0.09166187])
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def test_timeseries_trdata(self):
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Hs = 7.0
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Sj = sm.Jonswap(Hm0=Hs)
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S = Sj.tospecdata() # Make spectrum object from numerical values
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S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, sigma=Hs/4, ysigma=Hs/4)
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xs = S.sim(ns=2**20, iseed=10)
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ts = wo.mat2timeseries(xs)
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g0, _gemp = ts.trdata(monitor=False) # Not Monitor the development
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# Equal weight on all points
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>>> g1, gemp = ts.trdata(method='mnonlinear', gvar=0.5 )
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g1, _gemp = ts.trdata(method='mnonlinear', gvar=0.5)
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# Less weight on the ends
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>>> g2, gemp = ts.trdata(method='nonlinear', gvar=[3.5, 0.5, 3.5])
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>>> 1.2 < S.tr.dist2gauss() < 1.6
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True
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>>> 1.65 < g0.dist2gauss() < 2.05
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True
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>>> 0.54 < g1.dist2gauss() < 0.95
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True
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>>> 1.5 < g2.dist2gauss() < 1.9
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True
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'''
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if __name__ == '__main__':
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import doctest
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doctest.testmod()
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g2, _gemp = ts.trdata(method='nonlinear', gvar=[3.5, 0.5, 3.5])
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self.assert_(1.2 < S.tr.dist2gauss() < 1.6)
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self.assert_(1.65 < g0.dist2gauss() < 2.05)
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self.assert_(0.54 < g1.dist2gauss() < 0.95)
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self.assert_(1.5 < g2.dist2gauss() < 1.9)
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def test_cycles_and_levelcrossings(self):
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x = wafo.data.sea()
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ts = wo.mat2timeseries(x)
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tp = ts.turning_points()
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assert_array_almost_equal(tp.data[:10],
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[-1.200495, 0.839505, -0.090495, -0.020495,
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-0.090495, -0.040495, -0.160495, 0.259505,
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-0.430495, -0.080495]
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)
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mm = tp.cycle_pairs()
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assert_array_almost_equal(mm.data[:10],
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[0.839505, -0.020495, -0.040495, 0.259505,
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-0.080495, -0.080495, 0.349505, 0.859505,
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0.009505, 0.319505])
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lc = mm.level_crossings()
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assert_array_almost_equal(lc.data[:10],
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[0., 1., 2., 2., 3., 4.,
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5., 6., 7., 9.])
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def test_levelcrossings_extrapolate(self):
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x = wafo.data.sea()
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ts = wo.mat2timeseries(x)
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tp = ts.turning_points()
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mm = tp.cycle_pairs()
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lc = mm.level_crossings()
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s = x[:, 1].std()
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lc_gpd = lc.extrapolate(-2 * s, 2 * s, dist='rayleigh')
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assert_array_almost_equal(lc_gpd.data[:10],
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[1.789254e-37, 2.610988e-37,
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3.807130e-37, 5.546901e-37,
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8.075384e-37, 1.174724e-36,
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1.707531e-36, 2.480054e-36,
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3.599263e-36, 5.219466e-36])
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if __name__ == "__main__":
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# import sys;sys.argv = ['', 'Test.testName']
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unittest.main()
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