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Fixed a bug and added more tests

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master
pbrod 9 years ago
parent d6a5d0e807
commit e17d08fa61
3 changed files with 230 additions and 169 deletions
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6
wafo/misc.py
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@ -962,7 +962,7 @@ def findcross(x, v=0.0, kind=None, method='clib'):
if xor(is_odd, kind in ('dw', 'uw')): if xor(is_odd, kind in ('dw', 'uw')):
ind = ind[:-1] ind = ind[:-1]
else: else:
raise ValueError('Unknown wave/crossing definition!') raise ValueError('Unknown wave/crossing definition! {}'.format(kind))
return ind return ind
@ -1973,7 +1973,7 @@ def findtc(x_in, v=None, kind=None):
if (2 * n_tc + 1 < n_c) and (kind in (None, 'tw')): if (2 * n_tc + 1 < n_c) and (kind in (None, 'tw')):
# trough # trough
ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]].argmin() ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1].argmin()
else: # the first is a up-crossing else: # the first is a up-crossing
for i in range(n_tc): for i in range(n_tc):
@ -1985,7 +1985,7 @@ def findtc(x_in, v=None, kind=None):
if (2 * n_tc + 1 < n_c) and (kind in (None, 'cw')): if (2 * n_tc + 1 < n_c) and (kind in (None, 'cw')):
# crest # crest
ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]].argmax() ind[n_c - 2] = x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1].argmax()
return v_ind[:n_c - 1] + ind + 1, v_ind return v_ind[:n_c - 1] + ind + 1, v_ind

266
wafo/objects.py
Unescape Escape View File

@ -21,7 +21,7 @@ from wafo.misc import (nextpow2, findtp, findrfc, findtc, findcross,
detrendma) detrendma)
from wafo.interpolate import stineman_interp from wafo.interpolate import stineman_interp
from wafo.containers import PlotData from wafo.containers import PlotData
from wafo.plotbackend import plotbackend from wafo.plotbackend import plotbackend as plt
from scipy.integrate import trapz from scipy.integrate import trapz
from scipy.signal import welch, lfilter from scipy.signal import welch, lfilter
from scipy.signal.windows import get_window # @UnusedImport from scipy.signal.windows import get_window # @UnusedImport
@ -69,9 +69,9 @@ class LevelCrossings(PlotData):
Examples Examples
-------- --------
>>> import wafo.data >>> import wafo.data as wd
>>> import wafo.objects as wo >>> import wafo.objects as wo
>>> x = wafo.data.sea() >>> x = wd.sea()
>>> ts = wo.mat2timeseries(x) >>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points() >>> tp = ts.turning_points()
@ -80,8 +80,14 @@ class LevelCrossings(PlotData):
>>> lc = mm.level_crossings() >>> lc = mm.level_crossings()
>>> np.allclose(lc.data[:5], [ 0., 1., 2., 2., 3.]) >>> np.allclose(lc.data[:5], [ 0., 1., 2., 2., 3.])
True True
>>> m, s = lc.estimate_mean_and_stdev()
>>> np.allclose([m, s], (0.033974280952584639, 0.48177752818956326))
True
>>> np.allclose((lc.mean, lc.sigma),
... (1.5440875692709283e-09, 0.47295493383306714))
True
h2 = lc.plot() >>> h2 = lc.plot()
''' '''
def __init__(self, *args, **kwds): def __init__(self, *args, **kwds):
@ -97,27 +103,37 @@ class LevelCrossings(PlotData):
self.mean = kwds.get('mean') self.mean = kwds.get('mean')
# self.setplotter(plotmethod='step') # self.setplotter(plotmethod='step')
icmax = self.data.argmax()
if self.data is not None: if self.data is not None:
i_cmax = self.data.argmax()
if self.sigma is None or self.mean is None: if self.sigma is None or self.mean is None:
logcros = where(self.data == 0.0, inf, -log(self.data)) mean, sigma = self.estimate_mean_and_stdev(i_cmax)
logcmin = logcros[icmax]
logcros = sqrt(2 * abs(logcros - logcmin))
logcros[0:icmax + 1] = 2 * logcros[
icmax] - logcros[0:icmax + 1]
ncr = 10
# least square fit
p = polyfit(self.args[ncr:-ncr], logcros[ncr:-ncr], 1)
if self.sigma is None: if self.sigma is None:
# estimated standard deviation of x # estimated standard deviation of x
self.sigma = 1.0 / p[0] self.sigma = sigma
if self.mean is None: if self.mean is None:
self.mean = -p[1] / p[0] # self.args[icmax] self.mean = mean
cmax = self.data[icmax] cmax = self.data[i_cmax]
x = (self.args - self.mean) / self.sigma x = (self.args - self.mean) / self.sigma
y = cmax * exp(-x ** 2 / 2.0) y = cmax * exp(-x ** 2 / 2.0)
self.children = [PlotData(y, self.args)] self.children = [PlotData(y, self.args)]
def estimate_mean_and_stdev(self, i_cmax=None):
"""
Return mean and standard deviation of process x estimated from crossing
"""
if i_cmax is None:
i_cmax = self.data.argmax()
logcros = where(self.data == 0.0, inf, -log(self.data))
logcmin = logcros[i_cmax]
logcros = sqrt(2 * abs(logcros - logcmin))
logcros[0:i_cmax + 1] = 2 * logcros[i_cmax] - logcros[0:i_cmax + 1]
ncr = 10
# least square fit
p = polyfit(self.args[ncr:-ncr], logcros[ncr:-ncr], 1)
sigma = 1.0 / p[0]
mean = -p[1] / p[0] # self.args[i_cmax]
return mean, sigma
def extrapolate(self, u_min=None, u_max=None, method='ml', dist='genpar', def extrapolate(self, u_min=None, u_max=None, method='ml', dist='genpar',
plotflag=0): plotflag=0):
''' '''
@ -137,8 +153,8 @@ class LevelCrossings(PlotData):
expon : Exponential distribution (GPD with k=0) expon : Exponential distribution (GPD with k=0)
rayleigh : truncated Rayleigh distribution rayleigh : truncated Rayleigh distribution
plotflag : scalar integer plotflag : scalar integer
1: Diagnostic plots. (default) 1: Diagnostic plots.
0: Don't plot diagnostic plots. 0: Don't plot diagnostic plots. (default)
Returns Returns
------- -------
@ -155,7 +171,7 @@ class LevelCrossings(PlotData):
H(x) = exp(-x/s), k=0 (expon) H(x) = exp(-x/s), k=0 (expon)
The tails with the survival function of a truncated Rayleigh The tails with the survival function of a truncated Rayleigh
distribution. distribution.
H(x) = exp(-((x+x0).^2-x0^2)/s^2) (rayleigh) H(x) = exp(-((x+x0)**2-x0^2)/s**2) (rayleigh)
where x0 is the distance from the truncation level to where the LC has where x0 is the distance from the truncation level to where the LC has
its maximum. its maximum.
The method 'gpd' uses the GPD. We recommend the use of 'gpd,ml'. The method 'gpd' uses the GPD. We recommend the use of 'gpd,ml'.
@ -165,24 +181,36 @@ class LevelCrossings(PlotData):
Example Example
------- -------
>>> import wafo.data >>> import wafo.data as wd
>>> import wafo.objects as wo >>> import wafo.objects as wo
>>> x = wafo.data.sea() >>> x = wd.sea()
>>> ts = wo.mat2timeseries(x) >>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points() >>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs() >>> mm = tp.cycle_pairs()
>>> lc = mm.level_crossings() >>> lc = mm.level_crossings()
>>> s = x[:,1].std() >>> s = x[:, 1].std()
>>> lc_gpd = lc.extrapolate(-2*s, 2*s) >>> lc_gpd = lc.extrapolate(-2*s, 2*s)
>>> lc_exp = lc.extrapolate(-2*s, 2*s, dist='expon') >>> lc_exp = lc.extrapolate(-2*s, 2*s, dist='expon')
>>> lc_ray = lc.extrapolate(-2*s, 2*s, dist='rayleigh') >>> lc_ray = lc.extrapolate(-2*s, 2*s, dist='rayleigh')
lc.plot() >>> n = 3
lc_gpd.plot() >>> np.allclose([lc_gpd.data[:n], lc_gpd.data[-n:]],
lc_exp.plot() ... [[ 0., 0., 0.], [ 0., 0., 0.]])
lc_ray.plot() True
>>> np.allclose([lc_exp.data[:n], lc_exp.data[-n:]],
... [[ 6.51864195e-12, 7.02339889e-12, 7.56724060e-12],
... [ 1.01040335e-05, 9.70417448e-06, 9.32013956e-06]])
True
>>> np.allclose([lc_ray.data[:n], lc_ray.data[-n:]],
... [[ 1.78925398e-37, 2.61098785e-37, 3.80712964e-37],
... [ 1.28140956e-13, 1.11668143e-13, 9.72878135e-14]])
True
>>> h0 = lc.plot()
>>> h1 = lc_gpd.plot()
>>> h2 = lc_exp.plot()
>>> h3 = lc_ray.plot()
See also See also
@ -209,18 +237,17 @@ class LevelCrossings(PlotData):
u_max = self.args[i.max()] u_max = self.args[i.max()]
lcf, lcx = self.data, self.args lcf, lcx = self.data, self.args
# Extrapolate LC for high levels # Extrapolate LC for high levels
[lc_High, phat_high] = self._extrapolate(lcx, lcf, u_max, lc_High, phat_high = self._extrapolate(lcx, lcf, u_max, u_max - lc_max,
u_max - lc_max, method, dist) method, dist)
# Extrapolate LC for low levels # Extrapolate LC for low levels
[lcEst1, phat_low] = self._extrapolate(-lcx[::-1], lcf[::-1], -u_min, lcEst1, phat_low = self._extrapolate(-lcx[::-1], lcf[::-1], -u_min,
lc_max - u_min, method, dist) lc_max - u_min, method, dist)
lc_Low = lcEst1[::-1, :] # [-lcEst1[::-1, 0], lcEst1[::-1, 1::]] lc_Low = lcEst1[::-1, :] # [-lcEst1[::-1, 0], lcEst1[::-1, 1::]]
lc_Low[:, 0] *= -1 lc_Low[:, 0] *= -1
if plotflag: if plotflag:
plotbackend.semilogx(lcf, lcx, plt.semilogx(lcf, lcx, lc_High[:, 1], lc_High[:, 0],
lc_High[:, 1], lc_High[:, 0], lc_Low[:, 1], lc_Low[:, 0])
lc_Low[:, 1], lc_Low[:, 0])
i_mask = (u_min < lcx) & (lcx < u_max) i_mask = (u_min < lcx) & (lcx < u_max)
f = np.hstack((lc_Low[:, 1], lcf[i_mask], lc_High[:, 1])) f = np.hstack((lc_Low[:, 1], lcf[i_mask], lc_High[:, 1]))
x = np.hstack((lc_Low[:, 0], lcx[i_mask], lc_High[:, 0])) x = np.hstack((lc_Low[:, 0], lcx[i_mask], lc_High[:, 0]))
@ -267,7 +294,7 @@ class LevelCrossings(PlotData):
r = sqrt(-2 * log(1 - 90 / 100)) # 90 # confidence sphere r = sqrt(-2 * log(1 - 90 / 100)) # 90 # confidence sphere
Nc = 16 + 1 Nc = 16 + 1
ang = linspace(0, 2 * pi, Nc) ang = linspace(0, 2 * pi, Nc)
# 90# Circle # 90% Circle
c0 = np.vstack( c0 = np.vstack(
(r * sqrt(D[0]) * sin(ang), r * sqrt(D[1]) * cos(ang))) (r * sqrt(D[0]) * sin(ang), r * sqrt(D[1]) * cos(ang)))
# plot(c0(1,:),c0(2,:)) # plot(c0(1,:),c0(2,:))
@ -309,7 +336,7 @@ class LevelCrossings(PlotData):
# F = -np.expm1(-((xF + offset) ** 2 - offset ** 2) / s ** 2) # F = -np.expm1(-((xF + offset) ** 2 - offset ** 2) / s ** 2)
lcEst = np.vstack((xF + u, lcu * (SF))).T lcEst = np.vstack((xF + u, lcu * (SF))).T
else: else:
raise ValueError() raise NotImplementedError('Unknown distribution {}'.format(dist))
return lcEst, phat return lcEst, phat
# End extrapolate # End extrapolate
@ -373,14 +400,14 @@ class LevelCrossings(PlotData):
>>> lc2 = ts2.turning_points().cycle_pairs().level_crossings() >>> lc2 = ts2.turning_points().cycle_pairs().level_crossings()
import pylab as plt >>> import pylab as plt
h0 = S.plot('b') >>> h0 = S.plot('b')
h1 = Se.plot('r') >>> h1 = Se.plot('r')
h = plt.subplot(211) >>> h = plt.subplot(211)
h2 = lc2.plot() >>> h2 = lc2.plot()
h = plt.subplot(212) >>> h = plt.subplot(212)
h0 = lc.plot() >>> h0 = lc.plot()
""" """
@ -438,8 +465,7 @@ class LevelCrossings(PlotData):
lc1 = self.args lc1 = self.args
mcr = trapz(lc1 * lc, lc1) if self.mean is None else self.mean mcr = trapz(lc1 * lc, lc1) if self.mean is None else self.mean
if self.sigma is None: if self.sigma is None:
scr = trapz(lc1 ** 2 * lc, lc1) scr = sqrt(trapz(lc1 ** 2 * lc, lc1) - mcr ** 2)
scr = sqrt(scr - mcr ** 2)
else: else:
scr = self.sigma scr = self.sigma
lc2 = LevelCrossings(lc, lc1, mean=mcr, sigma=scr, intensity=True) lc2 = LevelCrossings(lc, lc1, mean=mcr, sigma=scr, intensity=True)
@ -582,19 +608,6 @@ class LevelCrossings(PlotData):
return estimate._trdata_lc(self, mean, sigma) return estimate._trdata_lc(self, mean, sigma)
def test_levelcrossings_extrapolate():
import wafo.data
x = wafo.data.sea()
ts = mat2timeseries(x)
tp = ts.turning_points()
mm = tp.cycle_pairs()
lc = mm.level_crossings()
s = x[:, 1].std()
lc_gpd = lc.extrapolate(-2 * s, 2 * s, dist='rayleigh') # @UnusedVariable
class CyclePairs(PlotData): class CyclePairs(PlotData):
''' '''
Container class for Cycle Pairs data objects in WAFO Container class for Cycle Pairs data objects in WAFO
@ -612,12 +625,22 @@ class CyclePairs(PlotData):
>>> ts = wo.mat2timeseries(x) >>> ts = wo.mat2timeseries(x)
>>> tp = ts.turning_points() >>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs() >>> mM = tp.cycle_pairs(kind='min2max')
>>> np.allclose(mm.data[:5], >>> np.allclose(mM.data[:5],
... [ 0.83950546, -0.02049454, -0.04049454, 0.25950546, -0.08049454])
True
>>> np.allclose(mM.args[:5],
... [-1.2004945 , -0.09049454, -0.09049454, -0.16049454, -0.43049454])
True
>>> Mm = tp.cycle_pairs(kind='max2min')
>>> np.allclose(Mm.data[:5],
... [ 0.83950546, -0.02049454, -0.04049454, 0.25950546, -0.08049454]) ... [ 0.83950546, -0.02049454, -0.04049454, 0.25950546, -0.08049454])
True True
>>> np.allclose(Mm.args[:5],
... [-0.09049454, -0.09049454, -0.16049454, -0.43049454, -0.21049454])
True
h1 = mm.plot(marker='x') >>> h1 = mM.plot(marker='x')
''' '''
def __init__(self, *args, **kwds): def __init__(self, *args, **kwds):
@ -666,12 +689,11 @@ class CyclePairs(PlotData):
>>> bv = range(3,9) >>> bv = range(3,9)
>>> D = mm.damage(beta=bv) >>> D = mm.damage(beta=bv)
>>> D >>> np.allclose(D, [ 138.5238799 , 117.56050788, 108.99265423,
array([ 138.5238799 , 117.56050788, 108.99265423, 107.86681126, ... 107.86681126, 112.3791076 , 122.08375071])
112.3791076 , 122.08375071]) True
>>> h = mm.plot(marker='.')
h = mm.plot(marker='.') >>> h = plt.plot(bv, D, 'x-')
h = plt.plot(bv,D,'x-')
See also See also
-------- --------
@ -730,14 +752,10 @@ class CyclePairs(PlotData):
LevelCrossings LevelCrossings
""" """
if isinstance(kind, str): defnr = dict(u=0, uM=1, umM=2, um=3).get(kind, kind)
t = dict(u=0, uM=1, umM=2, um=3)
defnr = t.get(kind, 1)
else:
defnr = kind
if defnr not in [1, 2, 3, 4]: if defnr not in [1, 2, 3, 4]:
raise ValueError('kind must be one of (1,2,3,4).') raise ValueError('kind must be one of (1, 2, 3, 4, "u", "uM",'
' "umM", "um"). Got kind = {}'.format(kind))
m, M = self.get_minima_and_maxima() m, M = self.get_minima_and_maxima()
@ -847,11 +865,13 @@ class TurningPoints(PlotData):
>>> np.allclose(tph.data[:5], >>> np.allclose(tph.data[:5],
... [-0.16049454, 0.25950546, -0.43049454, -0.08049454, -0.42049454]) ... [-0.16049454, 0.25950546, -0.43049454, -0.08049454, -0.42049454])
True True
>>> np.allclose(tph.args[:5],
... [ 7.05, 7.8 , 9.8 , 11.8 , 12.8 ])
True
>>> hs = ts1.plot()
hs = ts1.plot() >>> hp = tp.plot('ro')
hp = tp.plot('ro') >>> hph = tph.plot('k.')
hph = tph.plot('k.')
See also See also
--------- ---------
@ -896,7 +916,7 @@ class TurningPoints(PlotData):
... 0.25950546, -0.08049454]) ... 0.25950546, -0.08049454])
True True
h = mM.plot(marker='x') >>> h = mM.plot(marker='x')
See also See also
@ -1205,9 +1225,15 @@ class TimeSeries(PlotData):
Example Example
------- -------
x = load('sea.dat'); >>> import wafo.data as wd
S = dat2spec(x); >>> import wafo.objects as wo
specplot(S) >>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> S = ts.tospecdata()
>>> np.allclose(S.data[21:25],
... (0.00207876, 0.0025113 , 0.00300008, 0.00351852))
True
>>> h = S.plot()
See also See also
-------- --------
@ -1717,7 +1743,7 @@ class TimeSeries(PlotData):
# sorting crossings and tp in sequence # sorting crossings and tp in sequence
index = sort(r_[index, v_ind]) index = sort(r_[index, v_ind])
else: else:
raise ValueError('Unknown pdef option!') raise ValueError('Unknown pdef option! {}'.format(str(pdef)))
return index return index
def _get_start_index(self, pdef, down_crossing_or_max): def _get_start_index(self, pdef, down_crossing_or_max):
@ -2221,7 +2247,7 @@ class TimeSeries(PlotData):
>>> x = wafo.data.sea() >>> x = wafo.data.sea()
>>> ts150 = wafo.objects.mat2timeseries(x[:150,:]) >>> ts150 = wafo.objects.mat2timeseries(x[:150,:])
h = ts150.plot_wave('r-', sym2='bo') >>> h = ts150.plot_wave('r-', sym2='bo')
See also See also
-------- --------
@ -2272,12 +2298,12 @@ class TimeSeries(PlotData):
if np.max(abs(xn[indg])) > 5 * sigma: if np.max(abs(xn[indg])) > 5 * sigma:
XlblTxt = XlblTxt + ' (Spurious data since max > 5 std.)' XlblTxt = XlblTxt + ' (Spurious data since max > 5 std.)'
plot = plotbackend.plot plot = plt.plot
subplot = plotbackend.subplot subplot = plt.subplot
figs = [] figs = []
for unused_iz in range(nfig): for unused_iz in range(nfig):
figs.append(plotbackend.figure()) figs.append(plt.figure())
plotbackend.title('Surface elevation from mean water level (MWL).') plt.title('Surface elevation from mean water level (MWL).')
for ix in range(nsub): for ix in range(nsub):
if nsub > 1: if nsub > 1:
subplot(nsub, 1, ix) subplot(nsub, 1, ix)
@ -2287,11 +2313,11 @@ class TimeSeries(PlotData):
if len(ind2) > 0: if len(ind2) > 0:
plot(tn2[ind2] * dT, xn2[ind2], sym2) plot(tn2[ind2] * dT, xn2[ind2], sym2)
plot(h_scale * dT, [0, 0], 'k-') plot(h_scale * dT, [0, 0], 'k-')
# plotbackend.axis([h_scale*dT, v_scale]) # plt.axis([h_scale*dT, v_scale])
for iy in [-2, 2]: for iy in [-2, 2]:
plot(h_scale * dT, iy * sigma * ones(2), ':') plot(h_scale * dT, iy * sigma * ones(2), ':')
ind = ind + Ns ind = ind + Ns
plotbackend.xlabel(XlblTxt) plt.xlabel(XlblTxt)
return figs return figs
@ -2315,7 +2341,7 @@ class TimeSeries(PlotData):
>>> x = wafo.data.sea() >>> x = wafo.data.sea()
>>> ts = wafo.objects.mat2timeseries(x[0:500,...]) >>> ts = wafo.objects.mat2timeseries(x[0:500,...])
h = ts.plot_sp_wave(np.r_[6:9,12:18]) >>> h = ts.plot_sp_wave(np.r_[6:9,12:18])
See also See also
-------- --------
@ -2350,75 +2376,33 @@ class TimeSeries(PlotData):
Nsub = min(6, int(ceil(Nsub / Nfig))) Nsub = min(6, int(ceil(Nsub / Nfig)))
figs = [] figs = []
for unused_iy in range(Nfig): for unused_iy in range(Nfig):
figs.append(plotbackend.figure()) figs.append(plt.figure())
for ix in range(Nsub): for ix in range(Nsub):
plotbackend.subplot(Nsub, 1, mod(ix, Nsub) + 1) plt.subplot(Nsub, 1, mod(ix, Nsub) + 1)
ind = r_[tz_idx[2 * wave_idx[ix] - 1]:tz_idx[ ind = r_[tz_idx[2 * wave_idx[ix] - 1]:tz_idx[
2 * wave_idx[ix] + 2 * Nwp[ix] - 1]] 2 * wave_idx[ix] + 2 * Nwp[ix] - 1]]
# indices to wave # indices to wave
plotbackend.plot(self.args[ind], self.data[ind], *args, **kwds) plt.plot(self.args[ind], self.data[ind], *args, **kwds)
plotbackend.hold('on') plt.hold('on')
xi = [self.args[ind[0]], self.args[ind[-1]]] xi = [self.args[ind[0]], self.args[ind[-1]]]
plotbackend.plot(xi, [0, 0]) plt.plot(xi, [0, 0])
if Nwp[ix] == 1: if Nwp[ix] == 1:
plotbackend.ylabel('Wave %d' % wave_idx[ix]) plt.ylabel('Wave %d' % wave_idx[ix])
else: else:
plotbackend.ylabel( plt.ylabel(
'Wave %d - %d' % (wave_idx[ix], 'Wave %d - %d' % (wave_idx[ix],
wave_idx[ix] + Nwp[ix] - 1)) wave_idx[ix] + Nwp[ix] - 1))
plotbackend.xlabel('Time [sec]') plt.xlabel('Time [sec]')
# wafostamp # wafostamp
return figs return figs
def main():
import wafo
ts = wafo.objects.mat2timeseries(wafo.data.sea())
_S = ts.tospecdata(method='psd')
tp = ts.turning_points()
mm = tp.cycle_pairs()
lc = mm.level_crossings()
lc.plot()
T = ts.wave_periods(vh=0.0, pdef='c2c') # @UnusedVariable
# main()
import wafo.spectrum.models as sm
Sj = sm.Jonswap()
S = Sj.tospecdata()
R = S.tocovdata()
x = R.sim(ns=1000, dt=0.2) # @UnusedVariable
S.characteristic(['hm0', 'tm02'])
ns = 1000
dt = .2
x1 = S.sim(ns, dt=dt)
ts = TimeSeries(x1[:, 1], x1[:, 0])
tp = ts.turning_points(0.0)
x = np.arange(-2, 2, 0.2)
# Plot 2 objects in one call
d2 = PlotData(np.sin(x), x, xlab='x', ylab='sin', title='sinus')
d0 = d2.copy()
d0.data = d0.data * 0.9
d1 = d2.copy()
d1.data = d1.data * 1.2
d1.children = [d0]
d2.children = [d1]
d2.plot()
print('Done')
def test_docstrings(): def test_docstrings():
import doctest import doctest
print('Testing docstrings in %s' % __file__) print('Testing docstrings in %s' % __file__)
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
if __name__ == '__main__': if __name__ == '__main__':
test_docstrings() test_docstrings()
# main()
# test_levelcrossings_extrapolate()

127
wafo/tests/test_objects.py
Unescape Escape View File

@ -14,11 +14,13 @@ import wafo.spectrum.models as sm
import wafo.transform.models as tm import wafo.transform.models as tm
class TestObjects(TestCase): class TestTimeSeries(TestCase):
def test_timeseries(self): def setUp(self):
x = wafo.data.sea() x = wafo.data.sea()
ts = wo.mat2timeseries(x) self.ts = wo.mat2timeseries(x)
def test_timeseries(self):
ts = self.ts
assert_array_almost_equal(ts.sampling_period(), 0.25) assert_array_almost_equal(ts.sampling_period(), 0.25)
S = ts.tospecdata() S = ts.tospecdata()
@ -54,12 +56,57 @@ class TestObjects(TestCase):
self.assert_(0.54 < g1.dist2gauss() < 0.95) self.assert_(0.54 < g1.dist2gauss() < 0.95)
self.assert_(1.5 < g2.dist2gauss() < 1.9) self.assert_(1.5 < g2.dist2gauss() < 1.9)
def test_cycles_and_levelcrossings(self): def test_timeseries_wave_periods(self):
true_t = ([-0.69, -0.86, -1.05],
[0.42, 0.78, 1.37],
[0.09, 0.51, -0.85],
[-0.27, -0.08, 0.32],
[3.84377468, 3.60707656, 3.90490909],
[6.25273295, 6.35295202, 6.11978685],
[2.48364668, 2.49282402, 2.50553431],
[3.76908628, 3.860128, 3.61425254],
[-5.05027968, -5.41405436, -5.35113091],
[7.53392635, 7.90687837, 7.85666522],
[-0.2811934, -0.86392635, -0.87687837],
[4.05027968, 4.72405436, 4.49113092],
[2.03999996, 0.07, 0.05],
[-0.93, -0.07, -0.12],
[1.10999996, 0., -0.07],
[-0.86, -0.02, 0.3],
[0.93, -0.8, -0.2])
pdefs = ['t2c', 'c2t', 't2t', 'c2c',
'd2d', 'u2u', 'd2u', 'u2d',
'd2t', 't2u', 'u2c', 'c2d',
'm2M', 'M2m', 'm2m', 'M2M', 'all',
]
ts = wo.TimeSeries(self.ts.data[0:400, :], self.ts.args[:400])
for pdef, truth in zip(pdefs, true_t):
T, _ix = ts.wave_periods(vh=0.0, pdef=pdef)
# print(T[:3, 0])
assert_array_almost_equal(T[:3, 0], truth)
true_t2 = ([1.10999996, 0., - 0.07],
[-0.02, 0.3, - 0.34],
[6.10295202, 5.86978685, 6.08501107],
[6.25273295, 6.35295202, 6.11978685],
[-0.27, -0.08, 0.32],
[-0.27, -0.08, 0.32])
wdefs = ['mw', 'Mw', 'dw', 'uw', 'tw', 'cw', ]
for wdef, truth in zip(wdefs, true_t2):
pdef = '{0}2{0}'.format(wdef[0].lower())
T, _ix = ts.wave_periods(vh=0.0, pdef=pdef, wdef=wdef)
# print(T[:3, 0])
assert_array_almost_equal(T[:3, 0], truth)
class TestObjects(TestCase):
def setUp(self):
x = wafo.data.sea() x = wafo.data.sea()
ts = wo.mat2timeseries(x) self.ts = wo.mat2timeseries(x)
tp = ts.turning_points() def test_cycles_and_levelcrossings(self):
tp = self.ts.turning_points()
assert_array_almost_equal(tp.data[:10], assert_array_almost_equal(tp.data[:10],
[-1.200495, 0.839505, -0.090495, -0.020495, [-1.200495, 0.839505, -0.090495, -0.020495,
-0.090495, -0.040495, -0.160495, 0.259505, -0.090495, -0.040495, -0.160495, 0.259505,
@ -71,29 +118,59 @@ class TestObjects(TestCase):
[0.839505, -0.020495, -0.040495, 0.259505, [0.839505, -0.020495, -0.040495, 0.259505,
-0.080495, -0.080495, 0.349505, 0.859505, -0.080495, -0.080495, 0.349505, 0.859505,
0.009505, 0.319505]) 0.009505, 0.319505])
true_lcs = (([0., 1., 2., 2., 3., 4., 5., 6., 7., 9.],
lc = mm.level_crossings() [-1.7504945, -1.4404945, -1.4204945, -1.4004945,
-1.3704945, -1.3204945, -1.2704945, -1.2604945,
assert_array_almost_equal(lc.data[:10], -1.2504945, -1.2004945]),
[0., 1., 2., 2., 3., 4., ([0., 1., 2., 3., 3., 4., 5., 6., 7., 9.],
5., 6., 7., 9.]) [-1.7504945, -1.4404945, -1.4204945, -1.4004945,
-1.3704945, -1.3204945, -1.2704945, -1.2604945,
-1.2504945, -1.2004945]),
([1., 2., 3., 4., 4., 5., 6., 7., 9., 11.],
[-1.7504945, -1.4404945, -1.4204945, -1.4004945,
-1.3704945, -1.3204945, -1.2704945, -1.2604945,
-1.2504945, -1.2004945]),
([1., 2., 3., 3., 4., 5., 6., 7., 9., 11.],
[-1.7504945, -1.4404945, -1.4204945, -1.4004945,
-1.3704945, -1.3204945, -1.2704945, -1.2604945,
-1.2504945, -1.2004945]))
for i, true_lc in enumerate(true_lcs):
true_count, true_levels = true_lc
lc = mm.level_crossings(kind=i+1)
assert_array_almost_equal(lc.data[:10], true_count)
assert_array_almost_equal(lc.args[:10], true_levels)
def test_levelcrossings_extrapolate(self): def test_levelcrossings_extrapolate(self):
x = wafo.data.sea() tp = self.ts.turning_points()
ts = wo.mat2timeseries(x)
tp = ts.turning_points()
mm = tp.cycle_pairs() mm = tp.cycle_pairs()
lc = mm.level_crossings() lc = mm.level_crossings()
s = x[:, 1].std() s = lc.sigma # x[:, 1].std()
lc_gpd = lc.extrapolate(-2 * s, 2 * s, dist='rayleigh') ix = slice(0, 1000, 100)
assert_array_almost_equal(lc_gpd.data[:10], lc_ray = lc.extrapolate(-2 * s, 2 * s, dist='rayleigh')
[1.789254e-37, 2.610988e-37,
3.807130e-37, 5.546901e-37, assert_array_almost_equal(lc_ray.data[ix],
8.075384e-37, 1.174724e-36, [1.78925398e-37, 9.61028192e-23,
1.707531e-36, 2.480054e-36, 2.05282628e-11, 1.74389448e-03,
3.599263e-36, 5.219466e-36]) 5.89169345e+01, 5.240000e+02,
6.72609651e+01, 4.46086175e-01,
2.23463577e-04, 8.45526153e-09])
lc_exp = lc.extrapolate(-2 * s, 2 * s, dist='expon')
lc_gpd = lc.extrapolate(-2 * s, 2 * s, dist='genpareto')
assert_array_almost_equal(lc_exp.data[ix],
[6.51864195e-12, 1.13025876e-08,
1.95974080e-05, 3.39796881e-02,
5.89169345e+01, 5.24000000e+02,
6.43476951e+01, 1.13478843e+00,
2.00122906e-02, 3.52921977e-04])
assert_array_almost_equal(lc_gpd.data[ix],
[0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00,
5.89169345e+01, 5.24000000e+02,
6.80484770e+01, 1.41019390e-01,
0.00000000e+00, 0.00000000e+00])
if __name__ == "__main__": if __name__ == "__main__":

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