import wafo.spectrum.models as sm
import wafo.transform.models as wtm
import wafo.objects as wo
from wafo.spectrum import SpecData1D
import numpy as np
from numpy import NAN
from numpy.testing import assert_array_almost_equal, assert_array_equal
import unittest
def slow(f):
f.slow = True
return f
class TestSpectrumHs7(unittest.TestCase):
def setUp(self):
self.Sj = sm.Jonswap(Hm0=7.0, Tp=11)
self.S = self.Sj.tospecdata()
def test_tocovmatrix(self):
acfmat = self.S.tocov_matrix(nr=3, nt=256, dt=0.1)
vals = acfmat[:2, :]
true_vals = np.array([[3.06073383, 0.0000000, -1.67748256, 0.],
[3.05235423, -0.1674357, -1.66811444,
0.18693242]])
assert_array_almost_equal(vals, true_vals)
def test_tocovdata(self):
Nt = len(self.S.data) - 1
acf = self.S.tocovdata(nr=0, nt=Nt)
vals = acf.data[:5]
true_vals = np.array(
[3.06090339, 2.22658399, 0.45307391, -1.17495501, -2.05649042])
assert_array_almost_equal(vals, true_vals)
assert((np.abs(vals - true_vals) < 1e-6).all())
def test_to_t_pdf(self):
f = self.S.to_t_pdf(pdef='Tc', paramt=(0, 10, 51), speed=7, seed=100)
vals = ['%2.3f' % val for val in f.data[:10]]
truevals = ['0.000', '0.014', '0.027', '0.040',
'0.050', '0.059', '0.067', '0.073', '0.077', '0.082']
for t, v in zip(truevals, vals):
assert(t == v)
# estimated error bounds
vals = ['%2.4f' % val for val in f.err[:10]]
truevals = ['0.0000', '0.0003', '0.0003', '0.0004',
'0.0006', '0.0008', '0.0016', '0.0019', '0.0020', '0.0021']
for t, v in zip(truevals, vals):
assert(t == v)
@slow
def test_sim(self):
S = self.S
import scipy.stats as st
x2 = S.sim(20000, 20)
truth1 = [0, np.sqrt(S.moment(1)[0]), 0., 0.]
funs = [np.mean, np.std, st.skew, st.kurtosis]
for fun, trueval in zip(funs, truth1):
res = fun(x2[:, 1::], axis=0)
m = res.mean()
sa = res.std()
assert(np.abs(m - trueval) < sa)
@slow
def test_sim_nl(self):
S = self.S
import scipy.stats as st
x2, _x1 = S.sim_nl(ns=20000, cases=40)
truth1 = [0, np.sqrt(S.moment(1)[0][0])] + S.stats_nl(moments='sk')
truth1[-1] = truth1[-1] - 3
# truth1
# [0, 1.7495200310090633, 0.18673120577479801, 0.061988521262417606]
funs = [np.mean, np.std, st.skew, st.kurtosis]
for fun, trueval in zip(funs, truth1):
res = fun(x2.data, axis=0)
m = res.mean()
sa = res.std()
# trueval, m, sa
assert(np.abs(m - trueval) < 2 * sa)
def test_stats_nl(self):
S = self.S
me, va, sk, ku = S.stats_nl(moments='mvsk')
assert(me == 0.0)
assert_array_almost_equal(va, 3.0608203389019537)
assert_array_almost_equal(sk, 0.18673120577479801)
assert_array_almost_equal(ku, 3.0619885212624176)
def test_testgaussian(self):
Hs = self.Sj.Hm0
S0 = self.S
# ns =100; dt = .2
# x1 = S0.sim(ns, dt=dt)
S = S0.copy()
me, _va, sk, ku = S.stats_nl(moments='mvsk')
S.tr = wtm.TrHermite(
mean=me, sigma=Hs / 4, skew=sk, kurt=ku, ysigma=Hs / 4)
ys = wo.mat2timeseries(S.sim(ns=2 ** 13))
g0, _gemp = ys.trdata()
t0 = g0.dist2gauss()
t1 = S0.testgaussian(ns=2 ** 13, test0=None, cases=50)
assert(sum(t1 > t0) < 5)
class TestSpectrumHs5(unittest.TestCase):
def setUp(self):
self.Sj = sm.Jonswap(Hm0=5.0)
self.S = self.Sj.tospecdata()
def test_moment(self):
S = self.S
vals, txt = S.moment()
true_vals = [1.5614600345079888, 0.95567089481941048]
true_txt = ['m0', 'm0tt']
assert_array_almost_equal(vals, true_vals)
for tv, v in zip(true_txt, txt):
assert(tv == v)
def test_nyquist_freq(self):
S = self.S
assert_array_almost_equal(S.nyquist_freq(), 3.0)
def test_sampling_period(self):
S = self.S
assert_array_almost_equal(S.sampling_period(), 1.0471975511965976)
def test_normalize(self):
S = self.S
mom, txt = S.moment(2)
assert_array_almost_equal(mom,
[1.5614600345079888, 0.95567089481941048])
assert_array_equal(txt, ['m0', 'm0tt'])
vals, _txt = S.moment(2)
true_vals = [1.5614600345079888, 0.95567089481941048]
assert_array_almost_equal(vals, true_vals)
Sn = S.copy()
Sn.normalize()
# Now the moments should be one
new_vals, _txt = Sn.moment(2)
assert_array_almost_equal(new_vals, np.ones(2))
def test_characteristic(self):
S = self.S
ch, R, txt = S.characteristic(1)
assert_array_almost_equal(ch, 8.59007646)
assert_array_almost_equal(R, 0.03040216)
self.assert_(txt == ['Tm01'])
ch, R, txt = S.characteristic([1, 2, 3]) # fact a vector of integers
assert_array_almost_equal(ch, [8.59007646, 8.03139757, 5.62484314])
assert_array_almost_equal(R,
[[0.03040216, 0.02834263, NAN],
[0.02834263, 0.0274645, NAN],
[NAN, NAN, 0.01500249]])
assert_array_equal(txt, ['Tm01', 'Tm02', 'Tm24'])
ch, R, txt = S.characteristic('Ss') # fact a string
assert_array_almost_equal(ch, [0.04963112])
assert_array_almost_equal(R, [[2.63624782e-06]])
assert_array_equal(txt, ['Ss'])
# fact a list of strings
ch, R, txt = S.characteristic(['Hm0', 'Tm02'])
assert_array_almost_equal(ch,
[4.99833578, 8.03139757])
assert_array_almost_equal(R, [[0.05292989, 0.02511371],
[0.02511371, 0.0274645]])
assert_array_equal(txt, ['Hm0', 'Tm02'])
class TestSpectrumHs3(unittest.TestCase):
def test_bandwidth(self):
Sj = sm.Jonswap(Hm0=3, Tp=7)
w = np.linspace(0, 4, 256)
S = SpecData1D(Sj(w), w) # Make spectrum object from numerical values
vals = S.bandwidth([0, 1, 2, 3])
true_vals = [0.73062845, 0.34476034, 0.68277527, 2.90817052]
assert_array_almost_equal(vals, true_vals)
if __name__ == '__main__':
import nose
nose.run()