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Removed unused code. Refactored some code

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Per A Brodtkorb 9 years ago
parent f112d95b97
commit 1e285749b7
1 changed files with 2332 additions and 2402 deletions
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wafo/objects.py
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@ -1120,54 +1120,7 @@ class TimeSeries(PlotData):
norm=norm, dt=dt)
return estimate_cov(self)
def _specdata(self, L=None, tr=None, method='cov', detrend=detrend_mean,
window='parzen', noverlap=0, pad_to=None):
"""
Obsolete: Delete?
Return power spectral density by Welches average periodogram method.
Parameters
----------
NFFT : int, scalar
if len(data) < NFFT, it will be zero padded to `NFFT`
before estimation. Must be even; a power 2 is most efficient.
detrend : function
window : vector of length NFFT or function
To create window vectors see numpy.blackman, numpy.hamming,
numpy.bartlett, scipy.signal, scipy.signal.get_window etc.
noverlap : scalar int
gives the length of the overlap between segments.
Returns
-------
S : SpecData1D
Power Spectral Density
Notes
-----
The data vector is divided into NFFT length segments. Each segment
is detrended by function detrend and windowed by function window.
noverlap gives the length of the overlap between segments. The
absolute(fft(segment))**2 of each segment are averaged to compute Pxx,
with a scaling to correct for power loss due to windowing.
Reference
---------
Bendat & Piersol (1986) Random Data: Analysis and Measurement
Procedures, John Wiley & Sons
"""
dt = self.sampling_period()
yy = self.data.ravel() if tr is None else tr.dat2gauss(
self.data.ravel())
yy = detrend(yy) if hasattr(detrend, '__call__') else yy
S, f = psd(yy, Fs=1. / dt, NFFT=L, detrend=detrend, window=window,
noverlap=noverlap, pad_to=pad_to, scale_by_freq=True)
fact = 2.0 * pi
w = fact * f
return _wafospec.SpecData1D(S / fact, w)
def _get_bandwidth_and_dof(self, wname, n, L, dt):
def _get_bandwidth_and_dof(self, wname, n, L, dt, ftype='w'):
'''Returns bandwidth (rad/sec) and degrees of freedom
used in chi^2 distribution
'''
@ -1177,6 +1130,8 @@ class TimeSeries(PlotData):
bartlett=3).get(wname, np.nan) * n/L)
Be = dict(parzen=1.33, hanning=1,
bartlett=1.33).get(wname, np.nan) * 2 * pi / (L*dt)
if ftype == 'f':
Be = Be / (2 * pi) # bandwidth in Hz
return Be, dof
def tospecdata(self, L=None, tr=None, method='cov', detrend=detrend_mean,
@ -1223,6 +1178,10 @@ class TimeSeries(PlotData):
>>> import wafo.objects as wo
>>> x = wd.sea()
>>> ts = wo.mat2timeseries(x)
>>> S0 = ts.tospecdata(method='psd')
>>> np.allclose(S0.data[21:25],
... ( 0.2543896 , 0.26366755, 0.23372824, 0.19459349))
True
>>> S = ts.tospecdata()
>>> np.allclose(S.data[21:25],
... (0.00207876, 0.0025113 , 0.00300008, 0.00351852))
@ -1284,15 +1243,13 @@ class TimeSeries(PlotData):
else:
raise ValueError('Unknown method (%s)' % method)
Be, v = self._get_bandwidth_and_dof(window, n, L, dt)
Be, dof = self._get_bandwidth_and_dof(window, n, L, dt, ftype)
spec.Bw = Be
if ftype == 'f':
spec.Bw = Be / (2 * pi) # bandwidth in Hz
if alpha is not None:
# Confidence interval constants
spec.CI = [v / _invchi2(1 - alpha / 2, v),
v / _invchi2(alpha / 2, v)]
spec.CI = [dof / _invchi2(1 - alpha / 2, dof),
dof / _invchi2(alpha / 2, dof)]
spec.tr = tr
spec.L = L
@ -1554,15 +1511,8 @@ class TimeSeries(PlotData):
--------
wafo.definitions
'''
dT = self.sampling_period()
if rate > 1:
dT = dT / rate
t0, tn = self.args[0], self.args[-1]
n = len(self.args)
ti = linspace(t0, tn, int(rate * n))
xi = interp1d(self.args, self.data.ravel(), kind='cubic')(ti)
else:
ti, xi = self.args, self.data.ravel()
dT = self.sampling_period()/np.maximum(rate, 1)
xi, ti = self._interpolate(rate)
tc_ind, z_ind = findtc(xi, v=0, kind='tw')
tc_a = xi[tc_ind]
@ -1581,7 +1531,7 @@ class TimeSeries(PlotData):
Tcb[(Tcb == 0)] = dT # avoiding division by zero
return dict(Ac=Ac, At=At, Hu=Hu, Hd=Hd, Tu=Tu, Td=Td, Tcf=Tcf, Tcb=Tcb)
def wave_height_steepness(self, method=1, rate=1, g=None):
def wave_height_steepness(self, kind='Vcf', rate=1, g=None):
'''
Returns waveheights and steepnesses from data.
@ -1590,7 +1540,7 @@ class TimeSeries(PlotData):
rate : scalar integer
interpolation rate. Interpolates with spline if greater than one.
method : scalar integer (default 1)
kind : scalar integer (default 1)
0 max(Vcf, Vcb) and corresponding wave height Hd or Hu in H
1 crest front (rise) speed (Vcf) in S and wave height Hd in H.
-1 crest back (fall) speed (Vcb) in S and waveheight Hu in H.
@ -1602,7 +1552,7 @@ class TimeSeries(PlotData):
for zero-upcrossing waves.
Returns
-------
S, H = Steepness and the corresponding wave height according to method
S, H = Steepness and the corresponding wave height according to kind
The parameters are calculated as follows:
@ -1631,7 +1581,7 @@ class TimeSeries(PlotData):
... [[ 0.10140867, 0.06141156], [ 0.42, 0.78]],
... [[ 0.01821413, 0.01236672], [ 0.42, 0.78]]]
>>> for i in range(-3,4):
... S, H = ts.wave_height_steepness(method=i)
... S, H = ts.wave_height_steepness(kind=i)
... np.allclose((S[:2],H[:2]), true_SH[i+3])
True
True
@ -1651,71 +1601,63 @@ class TimeSeries(PlotData):
wafo.definitions
'''
dT = self.sampling_period()
dT = self.sampling_period() / np.maximum(rate, 1)
if g is None:
g = gravity() # % acceleration of gravity
g = gravity() # acceleration of gravity
if rate > 1:
dT = dT / rate
t0, tn = self.args[0], self.args[-1]
n = len(self.args)
ti = linspace(t0, tn, int(rate * n))
xi = interp1d(self.args, self.data.ravel(), kind='cubic')(ti)
else:
ti, xi = self.args, self.data.ravel()
xi, ti = self._interpolate(rate)
tc_ind, z_ind = findtc(xi, v=0, kind='tw')
tc_a = xi[tc_ind]
tc_t = ti[tc_ind]
Ac = tc_a[1::2] # crest amplitude
At = -tc_a[0::2] # trough amplitude
if (0 <= method and method <= 2):
defnr = dict(maxVcfVcb=0, Vcf=1, Vcb=-1, Scf=2, Scb=-2, StHd=3,
StHu=-3).get(kind, kind)
if 0 <= defnr <= 2:
# time between zero-upcrossing and crest [s]
tu = ecross(ti, xi, z_ind[1:-1:2], v=0)
Tcf = tc_t[1::2] - tu
Tcf[(Tcf == 0)] = dT # avoiding division by zero
if (0 >= method and method >= -2):
if -2 <= defnr <= 0:
# time between crest and zero-downcrossing [s]
td = ecross(ti, xi, z_ind[2::2], v=0)
Tcb = td - tc_t[1::2]
Tcb[(Tcb == 0)] = dT
# % avoiding division by zero
if method == 0:
if defnr == 0:
# max(Vcf, Vcr) and the corresponding wave height Hd or Hu in H
Hu = Ac + At[1:]
Hd = Ac + At[:-1]
T = np.where(Tcf < Tcb, Tcf, Tcb)
S = Ac / T
H = np.where(Tcf < Tcb, Hd, Hu)
elif method == 1: # extracting crest front velocity [m/s] and
elif defnr == 1: # extracting crest front velocity [m/s] and
# Zero-downcrossing wave height [m]
H = Ac + At[:-1] # Hd
S = Ac / Tcf
elif method == -1: # extracting crest rear velocity [m/s] and
elif defnr == -1: # extracting crest rear velocity [m/s] and
# Zero-upcrossing wave height [m]
H = Ac + At[1:] # Hu
S = Ac / Tcb
# crest front steepness in S and the wave height Hd in H.
elif method == 2:
elif defnr == 2:
H = Ac + At[:-1] # Hd
Td = diff(ecross(ti, xi, z_ind[::2], v=0))
S = 2 * pi * Ac / Td / Tcf / g
# crest back steepness in S and the wave height Hu in H.
elif method == -2:
elif defnr == -2:
H = Ac + At[1:]
Tu = diff(ecross(ti, xi, z_ind[1::2], v=0))
S = 2 * pi * Ac / Tu / Tcb / g
elif method == 3: # total steepness in S and the wave height Hd in H
# for zero-doewncrossing waves.
elif defnr == 3: # total steepness in S and the wave height Hd in H
# for zero-downcrossing waves.
H = Ac + At[:-1]
# Period zero-downcrossing waves
Td = diff(ecross(ti, xi, z_ind[::2], v=0))
S = 2 * pi * H / Td ** 2 / g
# total steepness in S and the wave height Hu in H for
elif method == -3:
elif defnr == -3:
# zero-upcrossing waves.
H = Ac + At[1:]
# Period zero-upcrossing waves
@ -1776,6 +1718,17 @@ class TimeSeries(PlotData):
step = 2
return step
def _interpolate(self, rate):
if rate > 1: # interpolate with spline
n = ceil(self.data.size * rate)
ti = linspace(self.args[0], self.args[-1], n)
x = stineman_interp(ti, self.args, self.data.ravel())
# xi = interp1d(self.args, self.data.ravel(), kind='cubic')(ti)
else:
x = self.data.ravel()
ti = self.args
return x, ti
def wave_periods(self, vh=None, pdef='d2d', wdef=None, index=None, rate=1):
"""
Return sequence of wave periods/lengths from data.
@ -1852,30 +1805,7 @@ class TimeSeries(PlotData):
findcross, perioddef
"""
# % This is a more flexible version than the dat2hwa or tp2wa routines.
# % There is a secret option: if pdef='all' the function returns
# % all the waveperiods 'd2t', 't2u', 'u2c' and 'c2d' in sequence.
# % It is up to the user to extract the right waveperiods.
# % If the first is a down-crossing then the first is a 'd2t' waveperiod.
# % If the first is a up-crossing then the first is a 'u2c' waveperiod.
# %
# % Example:
# % [T ind]=dat2wa(x,0,'all') %returns all waveperiods
# % nn = length(T)
# % % want to extract all t2u waveperiods
# % if x(ind(1),2)>0 % if first is down-crossing
# % Tt2u=T(2:4:nn)
# % else % first is up-crossing
# % Tt2u=T(4:4:nn)
# % end
if rate > 1: # % interpolate with spline
n = ceil(self.data.size * rate)
ti = linspace(self.args[0], self.args[-1], n)
x = stineman_interp(ti, self.args, self.data.ravel())
else:
x = self.data
ti = self.args
x, ti = self._interpolate(rate)
if vh is None:
if pdef[0] in ('m', 'M'):
@ -1899,7 +1829,7 @@ class TimeSeries(PlotData):
dist = 1
nn = len(index)
# New call: (pab 28.06.2001)
if pdef[0] in ('u', 'd'):
t0 = ecross(ti, x, index[start:(nn - dist):step], vh)
else: # min, Max, trough, crest or all crossings wanted

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