pep8

wafo/MSPPT.py

@@ -30,6 +30,7 @@ MinorVersion = 8
 LibraryFlags = 8
 LCID = 0x0
 
+
 class constants:
     msoAnimAccumulateAlways = 2          # from enum MsoAnimAccumulate
     msoAnimAccumulateNone = 1          # from enum MsoAnimAccumulate

wafo/bitwise.py

@@ -1,6 +1,6 @@
-'''
+"""
 Module extending the bitoperator capabilites of numpy
-'''
+"""
 
 from numpy import (bitwise_and, bitwise_or,
                    bitwise_not, binary_repr,  # @UnusedImport

wafo/covariance/estimation.py

@@ -1,8 +1,8 @@
-'''
+"""
 Created on 10. mai 2014
 
 @author: pab
-'''
+"""
 import numpy as np
 from numpy.fft import fft
 from wafo.misc import nextpow2
@@ -13,7 +13,7 @@ import warnings
 
 
 def sampling_period(t_vec):
-    '''
+    """
     Returns sampling interval
 
      Returns
@@ -24,7 +24,7 @@ def sampling_period(t_vec):
          [m] otherwise
 
      See also
-    '''
+    """
     dt1 = t_vec[1] - t_vec[0]
     n = len(t_vec) - 1
     t = t_vec[-1] - t_vec[0]
@@ -35,7 +35,7 @@ def sampling_period(t_vec):
 
 
 class CovarianceEstimator(object):
-    '''
+    """
     Class for estimating AutoCovariance from timeseries
 
     Parameters
@@ -61,7 +61,7 @@ class CovarianceEstimator(object):
         True if normalize output to one
     dt : scalar
         time-step between data points (default see sampling_period).
-    '''
+    """
     def __init__(self, lag=None, tr=None, detrend=None, window='boxcar',
                  flag='biased', norm=False, dt=None):
         self.lag = lag
@@ -84,24 +84,24 @@ class CovarianceEstimator(object):
         return lag
 
     def tocovdata(self, timeseries):
-        '''
+        """
         Return auto covariance function from data.
 
         Return
         -------
-        R : CovData1D object
+        acf : CovData1D object
             with attributes:
             data : ACF vector length L+1
             args : time lags  length L+1
             sigma : estimated large lag standard deviation of the estimate
                     assuming x is a Gaussian process:
-                     if R(k)=0 for all lags k>q then an approximation
+                    if acf[k]=0 for all lags k>q then an approximation
                     of the variance for large samples due to Bartlett
-                     var(R(k))=1/N*(R(0)^2+2*R(1)^2+2*R(2)^2+ ..+2*R(q)^2)
+                     var(acf[k])=1/N*(acf[0]**2+2*acf[1]**2+2*acf[2]**2+ ..+2*acf[q]**2)
                      for  k>q and where  N=length(x). Special case is
-                     white noise where it equals R(0)^2/N for k>0
+                     white noise where it equals acf[0]**2/N for k>0
             norm : bool
-                If false indicating that R is not normalized
+                If false indicating that auto_cov is not normalized
 
          Example:
          --------
@@ -112,7 +112,7 @@ class CovarianceEstimator(object):
          >>> acf = ts.tocovdata(150)
 
          h = acf.plot()
-        '''
+        """
         lag = self.lag
         window = self.window
         detrend = self.detrend
@@ -142,30 +142,30 @@ class CovarianceEstimator(object):
             x = detrend(x)
 
         nfft = 2 ** nextpow2(n)
-        Rper = abs(fft(x, nfft)) ** 2 / Ncens  # Raw periodogram
-        R = np.real(fft(Rper)) / nfft  # ifft = fft/nfft since Rper is real!
+        raw_periodogram = abs(fft(x, nfft)) ** 2 / Ncens
+        auto_cov = np.real(fft(raw_periodogram)) / nfft  # ifft = fft/nfft since raw_periodogram is real!
 
         if self.flag.startswith('unbiased'):
             # unbiased result, i.e. divide by n-abs(lag)
-            R = R[:Ncens] * Ncens / np.arange(Ncens, 1, -1)
+            auto_cov = auto_cov[:Ncens] * Ncens / np.arange(Ncens, 1, -1)
 
         if self.norm:
-            R = R / R[0]
+            auto_cov = auto_cov / auto_cov[0]
 
         if lag is None:
-            lag = self._estimate_lag(R, Ncens)
+            lag = self._estimate_lag(auto_cov, Ncens)
         lag = min(lag, n - 2)
         if isinstance(window, str) or type(window) is tuple:
             win = get_window(window, 2 * lag - 1)
         else:
             win = np.asarray(window)
-        R[:lag] = R[:lag] * win[lag - 1::]
-        R[lag] = 0
+        auto_cov[:lag] = auto_cov[:lag] * win[lag - 1::]
+        auto_cov[lag] = 0
         lags = slice(0, lag + 1)
         t = np.linspace(0, lag * dt, lag + 1)
-        acf = CovData1D(R[lags], t)
-        acf.sigma = np.sqrt(np.r_[0, R[0] ** 2,
-                            R[0] ** 2 + 2 * np.cumsum(R[1:] ** 2)] / Ncens)
+        acf = CovData1D(auto_cov[lags], t)
+        acf.sigma = np.sqrt(np.r_[0, auto_cov[0] ** 2,
+                            auto_cov[0] ** 2 + 2 * np.cumsum(auto_cov[1:] ** 2)] / Ncens)
         acf.children = [PlotData(-2. * acf.sigma[lags], t),
                         PlotData(2. * acf.sigma[lags], t)]
         acf.plot_args_children = ['r:']

wafo/dctpack.py

@@ -8,7 +8,7 @@ __all__ = ['dct', 'idct', 'dctn', 'idctn', 'dst', 'idst', 'dstn', 'idstn']
 
 
 def dct(x, type=2, n=None, axis=-1, norm='ortho'):  # @ReservedAssignment
-    '''
+    """
     Return the Discrete Cosine Transform of arbitrary type sequence x.
 
     Parameters
@@ -108,7 +108,7 @@ def dct(x, type=2, n=None, axis=-1, norm='ortho'):  # @ReservedAssignment
     'A Fast Cosine Transform in One and Two Dimensions', by J. Makhoul, `IEEE
     Transactions on acoustics, speech and signal processing` vol. 28(1),
     pp. 27-34, http://dx.doi.org/10.1109/TASSP.1980.1163351 (1980).
-    '''
+    """
     return _dct(x, type, n, axis, norm)
 
 
@@ -117,7 +117,7 @@ def dst(x, type=2, n=None, axis=-1, norm='ortho'):  # @ReservedAssignment
 
 
 def idct(x, type=2, n=None, axis=-1, norm='ortho'):  # @ReservedAssignment
-    '''
+    """
     Return the Inverse Discrete Cosine Transform of an arbitrary type sequence.
 
     Parameters
@@ -152,7 +152,7 @@ def idct(x, type=2, n=None, axis=-1, norm='ortho'):  # @ReservedAssignment
     IDCT of type 1 is the DCT of type 1, IDCT of type 2 is the DCT of type 3,
     and IDCT of type 3 is the DCT of type 2. For the definition of these types,
     see `dct`.
-    '''
+    """
     return _idct(x, type, n, axis, norm)
 
 
@@ -201,7 +201,7 @@ def _raw_dctn(y, type, shape, axes, norm, fun):  # @ReservedAssignment
 
 def dctn(x, type=2, shape=None, axes=None,  # @ReservedAssignment
          norm='ortho'):
-    '''
+    """
     Return the N-D Discrete Cosine Transform of array x.
 
     Parameters
@@ -271,7 +271,7 @@ def dctn(x, type=2, shape=None, axes=None,  # @ReservedAssignment
     See also
     --------
     idctn, dct, idct
-    '''
+    """
     y = np.atleast_1d(x)
     return _raw_dctn(y, type, shape, axes, norm, dct)
 
@@ -284,14 +284,14 @@ def dstn(x, type=2, shape=None, axes=None,  # @ReservedAssignment
 
 def idctn(x, type=2, shape=None, axes=None,  # @ReservedAssignment
           norm='ortho'):
-    '''Return inverse N-D Discrete Cosine Transform of array x.
+    """Return inverse N-D Discrete Cosine Transform of array x.
 
     For description of parameters see `dctn`.
 
     See Also
     --------
     dctn : for detailed information.
-    '''
+    """
     y = np.atleast_1d(x)
     return _raw_dctn(y, type, shape, axes, norm, idct)
 
@@ -317,7 +317,7 @@ def no_leading_ones(x):
 
 
 def shiftdim(x, n=None):
-    '''
+    """
     Shift dimensions
 
     Parameters
@@ -342,7 +342,7 @@ def shiftdim(x, n=None):
     See also
     --------
     reshape, squeeze
-    '''
+    """
     if n is None:
         return x.reshape(no_leading_ones(x.shape))
     elif n >= 0:

wafo/doc/tutorial_scripts/rainflow_example.py

@@ -28,7 +28,8 @@ def plot_varying_symbols(x, y, color='red', size=5):
 
 def damage_vs_S(S, beta, K):
     """
-    calculate the damage 1/N for a given stress S
+    Calculate the damage 1/N for a given stress S
+
     Parameters
     ----------
     S       : Stress [Pa]

wafo/gaussian.py

@@ -32,7 +32,7 @@ __all__ = ['Rind', 'rindmod', 'mvnprdmod', 'mvn', 'cdflomax', 'prbnormtndpc',
 
 class Rind(object):
 
-    '''
+    """
     RIND Computes multivariate normal expectations
 
     Parameters
@@ -152,10 +152,10 @@ class Rind(object):
     Wave Distributions",
     Methodology And Computing In Applied Probability, Volume 8, Number 1,
     pp. 65-91(27)
-    '''
+    """
 
     def __init__(self, **kwds):
-        '''
+        """
         Parameters
         ----------
         method : integer, optional
@@ -217,7 +217,7 @@ class Rind(object):
             number of times to use the regression equation to restrict
             integration area. Nc1c2 = 1,2 is recommended. (default 2)
             (note: works only for method >0)
-        '''
+        """
         self.method = 3
         self.xcscale = 0
         self.abseps = 0
@@ -238,7 +238,7 @@ class Rind(object):
         self.set_constants()
 
     def initialize(self, speed=None):
-        '''
+        """
         Initializes member variables according to speed.
 
         Parameter
@@ -259,7 +259,7 @@ class Rind(object):
         maxpts : Maximum number of function values allowed.
         quadno : Quadrature formulae used in integration of Xd(i)
                 implicitly determining # nodes
-        '''
+        """
         if speed is None:
             return
         self.speed = min(max(speed, 1), 13)
@@ -324,22 +324,22 @@ class Rind(object):
         if xc is None:
             xc = zeros((0, 1))
 
-        BIG, Blo, Bup, xc = atleast_2d(cov, ab, bb, xc)
-        Blo = Blo.copy()
-        Bup = Bup.copy()
+        big, b_lo, b_up, xc = atleast_2d(cov, ab, bb, xc)
+        b_lo = b_lo.copy()
+        b_up = b_up.copy()
 
-        Ntdc = BIG.shape[0]
+        Ntdc = big.shape[0]
         Nc = xc.shape[0]
         if nt is None:
             nt = Ntdc - Nc
 
-        unused_Mb, Nb = Blo.shape
+        unused_Mb, Nb = b_lo.shape
         Nd = Ntdc - nt - Nc
         Ntd = nt + Nd
 
         if indI is None:
             if Nb != Ntd:
-                raise ValueError('Inconsistent size of Blo and Bup')
+                raise ValueError('Inconsistent size of b_lo and b_up')
             indI = r_[-1:Ntd]
 
         Ex, indI = atleast_1d(m, indI)
@@ -354,22 +354,22 @@ class Rind(object):
         #            if INFIN(I) = 1, Ith limits are [Hlo(I), infinity);
         #            if INFIN(I) = 2, Ith limits are [Hlo(I), Hup(I)].
         infinity = 37
-        dev = sqrt(diag(BIG))  # std
+        dev = sqrt(diag(big))  # std
         ind = nonzero(indI[1:] > -1)[0]
         infin = repeat(2, len(indI) - 1)
-        infin[ind] = (2 - (Bup[0, ind] > infinity * dev[indI[ind + 1]]) -
-                      2 * (Blo[0, ind] < -infinity * dev[indI[ind + 1]]))
+        infin[ind] = (2 - (b_up[0, ind] > infinity * dev[indI[ind + 1]]) -
+                      2 * (b_lo[0, ind] < -infinity * dev[indI[ind + 1]]))
 
-        Bup[0, ind] = minimum(Bup[0, ind], infinity * dev[indI[ind + 1]])
-        Blo[0, ind] = maximum(Blo[0, ind], -infinity * dev[indI[ind + 1]])
+        b_up[0, ind] = minimum(b_up[0, ind], infinity * dev[indI[ind + 1]])
+        b_lo[0, ind] = maximum(b_lo[0, ind], -infinity * dev[indI[ind + 1]])
         ind2 = indI + 1
 
-        return rindmod.rind(BIG, Ex, xc, nt, ind2, Blo, Bup, infin, seed)
+        return rindmod.rind(big, Ex, xc, nt, ind2, b_lo, b_up, infin, seed)
 
 
 def test_rind():
-    ''' Small test function
-    '''
+    """ Small test function
+    """
     n = 5
     Blo = -inf
     Bup = -1.2
@@ -398,7 +398,7 @@ def test_rind():
 
 
 def cdflomax(x, alpha, m0):
-    '''
+    """
     Return CDF for local maxima for a zero-mean Gaussian process
 
     Parameters
@@ -449,14 +449,14 @@ def cdflomax(x, alpha, m0):
     See also
     --------
     spec2mom, spec2bw
-    '''
+    """
     c1 = 1.0 / (sqrt(1 - alpha ** 2)) * x / sqrt(m0)
     c2 = alpha * c1
     return cdfnorm(c1) - alpha * exp(-x ** 2 / 2 / m0) * cdfnorm(c2)
 
 
-def prbnormtndpc(rho, a, b, D=None, df=0, abseps=1e-4, IERC=0, HNC=0.24):
-    '''
+def prbnormtndpc(rho, a, b, d=None, df=0, abseps=1e-4, ierc=0, hnc=0.24):
+    """
     Return Multivariate normal or T probability with product correlation.
 
     Parameters
@@ -469,13 +469,13 @@ def prbnormtndpc(rho, a, b, D=None, df=0, abseps=1e-4, IERC=0, HNC=0.24):
         vector of lower and upper integration limits, respectively.
         Note: any values greater the 37 in magnitude, are considered as
         infinite values.
-    D : array-like
+    d : array-like
         vector of means (default zeros(size(rho)))
     df = Degrees of freedom, NDF<=0 gives normal probabilities (default)
     abseps = absolute error tolerance. (default 1e-4)
-    IERC   = 1 if strict error control based on fourth derivative
+    ierc   = 1 if strict error control based on fourth derivative
              0 if error control based on halving the intervals (default)
-    HNC   = start interval width of simpson rule (default 0.24)
+    hnc   = start interval width of simpson rule (default 0.24)
 
     Returns
     -------
@@ -529,20 +529,20 @@ def prbnormtndpc(rho, a, b, D=None, df=0, abseps=1e-4, IERC=0, HNC=0.24):
     Charles Dunnett (1989)
     "Multivariate normal probability integrals with product correlation
     structure", Applied statistics, Vol 38,No3, (Algorithm AS 251)
-    '''
+    """
 
-    if D is None:
-        D = zeros(len(rho))
+    if d is None:
+        d = zeros(len(rho))
     # Make sure integration limits are finite
-    A = np.clip(a - D, -100, 100)
-    B = np.clip(b - D, -100, 100)
+    aa = np.clip(a - d, -100, 100)
+    bb = np.clip(b - d, -100, 100)
 
-    return mvnprdmod.prbnormtndpc(rho, A, B, df, abseps, IERC, HNC)
+    return mvnprdmod.prbnormtndpc(rho, aa, bb, df, abseps, ierc, hnc)
 
 
 def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True,
                 usebreakpoints=False):
-    '''
+    """
     Return Multivariate Normal probabilities with product correlation
 
     Parameters
@@ -600,7 +600,7 @@ def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True,
     Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU,
     Trondheim, Norway.
 
-    '''
+    """
     # Call fortran implementation
     val, err, ier = mvnprdmod.prbnormndpc(rho, a, b, abserr, relerr,
                                           usebreakpoints, usesimpson)
@@ -612,7 +612,7 @@ def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True,
 
 _ERRORMESSAGE = {}
 _ERRORMESSAGE[0] = ''
-_ERRORMESSAGE[1] = '''
+_ERRORMESSAGE[1] = """
     Maximum number of subdivisions allowed has been achieved. one can allow
     more subdivisions by increasing the value of limit (and taking the
     according dimension adjustments into account). however, if this yields
@@ -623,32 +623,32 @@ _ERRORMESSAGE[1] = '''
     the vector points. If necessary an appropriate special-purpose
     integrator must be used, which is designed for handling the type of
     difficulty involved.
-    '''
-_ERRORMESSAGE[2] = '''
+    """
+_ERRORMESSAGE[2] = """
     the occurrence of roundoff error is detected, which prevents the requested
-    tolerance from being achieved. The error may be under-estimated.'''
+    tolerance from being achieved. The error may be under-estimated."""
 
-_ERRORMESSAGE[3] = '''
+_ERRORMESSAGE[3] = """
     Extremely bad integrand behaviour occurs at some points of the integration
-    interval.'''
-_ERRORMESSAGE[4] = '''
+    interval."""
+_ERRORMESSAGE[4] = """
     The algorithm does not converge. Roundoff error is detected in the
     extrapolation table. It is presumed that the requested tolerance cannot be
     achieved, and that the returned result is the best which can be obtained.
-    '''
-_ERRORMESSAGE[5] = '''
+    """
+_ERRORMESSAGE[5] = """
      The integral is probably divergent, or slowly convergent.
      It must be noted that divergence can occur with any other value of ier>0.
-     '''
-_ERRORMESSAGE[6] = '''the input is invalid because:
+     """
+_ERRORMESSAGE[6] = """the input is invalid because:
         1) npts2 < 2
         2) break points are specified outside the integration range
         3) (epsabs<=0 and epsrel<max(50*rel.mach.acc.,0.5d-28))
-        4) limit < npts2.'''
+        4) limit < npts2."""
 
 
 def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
-    '''
+    """
     Multivariate Normal probability by Genz' algorithm.
 
 
@@ -713,7 +713,7 @@ def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
     See also
     --------
     prbnormndpc, Rind
-    '''
+    """
 
     m, n = correl.shape
     Na = len(a)
@@ -787,7 +787,7 @@ _X20 = [-0.9931285991850949e+00, -0.9639719272779138e+00,
 
 
 def cdfnorm2d(b1, b2, r):
-    '''
+    """
     Returnc Bivariate Normal cumulative distribution function
 
     Parameters
@@ -831,7 +831,7 @@ def cdfnorm2d(b1, b2, r):
     Drezner, z and g.o. Wesolowsky, (1989),
     "On the computation of the bivariate normal integral",
     Journal of statist. comput. simul. 35, pp. 101-107,
-    '''
+    """
     # Translated into Python
     # Per A. Brodtkorb
     #
@@ -956,7 +956,7 @@ def fi(x):
 
 
 def prbnorm2d(a, b, r):
-    '''
+    """
     Returns Bivariate Normal probability
 
     Parameters
@@ -986,7 +986,7 @@ def prbnorm2d(a, b, r):
     cdfnorm2d,
     cdfnorm,
     prbnormndpc
-    '''
+    """
     infinity = 37
     lower = np.asarray(a)
     upper = np.asarray(b)

wafo/numba_misc.py

@@ -1,8 +1,8 @@
-'''
+"""
 Created on 6. okt. 2016
 
 @author: pab
-'''
+"""
 from __future__ import absolute_import, division
 from numba import guvectorize, jit, float64, int64, int32, int8, void
 import numpy as np
@@ -66,8 +66,8 @@ def _findcross(ind, y):
 
 
 def findcross(xn):
-    '''Return indices to zero up and downcrossings of a vector
-    '''
+    """Return indices to zero up and downcrossings of a vector
+    """
     ind = np.empty(len(xn), dtype=np.int64)
     m = _findcross(ind, xn)
     return ind[:m]
@@ -150,9 +150,9 @@ _findrfc_lt = _make_findrfc(a_lt_b, a_le_b)
 def _findrfc(ind, y, h):
     n = len(y)
     t_start = 0
-    NC = n // 2 - 1
+    nc = n // 2 - 1
     ix = 0
-    for i in range(NC):
+    for i in range(nc):
         Tmi = t_start + 2 * i
         Tpl = t_start + 2 * i + 2
         xminus = y[2 * i]
@@ -174,7 +174,7 @@ def _findrfc(ind, y, h):
             # goto L180 continue
         else:
             j = i + 1
-            while (j < NC):
+            while (j < nc):
                 if (y[2 * j + 1] >= y[2 * i + 1]):
                     break  # goto L170
                 if((y[2 * j + 2] <= xplus)):

wafo/spectrum/models.py

@@ -57,7 +57,7 @@ def sech(x):
 
 
 def _gengamspec(wn, N=5, M=4):
-    ''' Return Generalized gamma spectrum in dimensionless form
+    """ Return Generalized gamma spectrum in dimensionless form
 
     Parameters
     ----------
@@ -106,7 +106,7 @@ def _gengamspec(wn, N=5, M=4):
     Torsethaugen, K. (2004)
     "Simplified Double Peak Spectral Model for Ocean Waves"
     In Proc. 14th ISOPE
-    '''
+    """
     w = atleast_1d(wn)
     S = zeros_like(w)
 
@@ -132,7 +132,7 @@ class ModelSpectrum(object):
         self.type = 'ModelSpectrum'
 
     def tospecdata(self, w=None, wc=None, nw=257):
-        '''
+        """
         Return SpecData1D object from ModelSpectrum
 
         Parameter
@@ -148,7 +148,7 @@ class ModelSpectrum(object):
         -------
         S : SpecData1D object
             member attributes of model spectrum are copied to S.workspace
-        '''
+        """
 
         if w is None:
             if wc is None:
@@ -165,8 +165,8 @@ class ModelSpectrum(object):
         return S
 
     def chk_seastate(self):
-        ''' Check if seastate is valid
-        '''
+        """ Check if seastate is valid
+        """
 
         if self.Hm0 < 0:
             raise ValueError('Hm0 can not be negative!')
@@ -185,7 +185,7 @@ class ModelSpectrum(object):
 
 class Bretschneider(ModelSpectrum):
 
-    '''
+    """
     Bretschneider spectral density model
 
     Member variables
@@ -237,7 +237,7 @@ class Bretschneider(ModelSpectrum):
     --------
     Jonswap,
     Torsethaugen
-    '''
+    """
 
     def __init__(self, Hm0=7.0, Tp=11.0, N=5, M=4, chk_seastate=True, **kwds):
         self.type = 'Bretschneider'
@@ -249,8 +249,8 @@ class Bretschneider(ModelSpectrum):
             self.chk_seastate()
 
     def __call__(self, wi):
-        ''' Return Bretschnieder spectrum
-        '''
+        """ Return Bretschnieder spectrum
+        """
         w = atleast_1d(wi)
         if self.Hm0 > 0:
             wp = 2 * pi / self.Tp
@@ -262,7 +262,7 @@ class Bretschneider(ModelSpectrum):
 
 
 def jonswap_peakfact(Hm0, Tp):
-    ''' Jonswap peakedness factor, gamma, given Hm0 and Tp
+    """ Jonswap peakedness factor, gamma, given Hm0 and Tp
 
     Parameters
     ----------
@@ -313,7 +313,7 @@ def jonswap_peakfact(Hm0, Tp):
     See also
     --------
     jonswap
-    '''
+    """
     Hm0, Tp = atleast_1d(Hm0, Tp)
 
     x = Tp / sqrt(Hm0)
@@ -332,7 +332,7 @@ def jonswap_peakfact(Hm0, Tp):
 
 def jonswap_seastate(u10, fetch=150000., method='lewis', g=9.81,
                      output='dict'):
-    '''
+    """
     Return Jonswap seastate from windspeed and fetch
 
     Parameters
@@ -400,7 +400,7 @@ def jonswap_seastate(u10, fetch=150000., method='lewis', g=9.81,
     A parametric wave prediction model.
     J. phys. oceanogr. Vol 6, pp 200-228
 
-    '''
+    """
 
     # The following formulas are from Lewis and Allos 1990:
     zeta = g * fetch / (u10 ** 2)  # dimensionless fetch, Table 1
@@ -440,7 +440,7 @@ def jonswap_seastate(u10, fetch=150000., method='lewis', g=9.81,
 
 class Jonswap(ModelSpectrum):
 
-    '''
+    """
     Jonswap spectral density model
 
     Member variables
@@ -520,7 +520,7 @@ class Jonswap(ModelSpectrum):
      Measurements of Wind-Wave Growth and Swell Decay during the Joint
      North Sea Project (JONSWAP).
      Ergansungsheft, Reihe A(8), Nr. 12, Deutschen Hydrografischen Zeitschrift.
-    '''
+    """
 
     def __init__(self, Hm0=7.0, Tp=11.0, gamma=None, sigmaA=0.07, sigmaB=0.09,
                  Ag=None, N=5, M=4, method='integration', wnc=6.0,
@@ -552,17 +552,17 @@ class Jonswap(ModelSpectrum):
         gam = self.gamma
         outsideJonswapRange = Tp > 5 * sqrt(Hm0) or Tp < 3.6 * sqrt(Hm0)
         if outsideJonswapRange:
-            txt0 = '''
+            txt0 = """
             Hm0=%g,Tp=%g is outside the JONSWAP range.
             The validity of the spectral density is questionable.
-            ''' % (Hm0, Tp)
+            """ % (Hm0, Tp)
             warnings.warn(txt0)
 
         if gam < 1 or 7 < gam:
-            txt = '''
+            txt = """
             The peakedness factor, gamma, is possibly too large.
             The validity of the spectral density is questionable.
-            '''
+            """
             warnings.warn(txt)
 
     def _localspec(self, wn):
@@ -617,8 +617,8 @@ class Jonswap(ModelSpectrum):
         self.Ag = 1.0 / area
 
     def _pre_calculate_ag(self):
-        ''' PRECALCULATEAG Precalculate normalization.
-        '''
+        """ PRECALCULATEAG Precalculate normalization.
+        """
         if self.gamma == 1:
             self.Ag = 1.0
             self.method = 'parametric'
@@ -631,8 +631,8 @@ class Jonswap(ModelSpectrum):
             norm_ag()
 
     def peak_e_factor(self, wn):
-        ''' PEAKENHANCEMENTFACTOR
-        '''
+        """ PEAKENHANCEMENTFACTOR
+        """
         w = maximum(atleast_1d(wn), 0.0)
         sab = where(w > 1, self.sigmaB, self.sigmaA)
 
@@ -641,8 +641,8 @@ class Jonswap(ModelSpectrum):
         return Gf
 
     def __call__(self, wi):
-        ''' JONSWAP spectral density
-        '''
+        """ JONSWAP spectral density
+        """
         w = atleast_1d(wi)
         if (self.Hm0 > 0.0):
 
@@ -660,7 +660,7 @@ class Jonswap(ModelSpectrum):
 
 
 def phi1(wi, h, g=9.81):
-    ''' Factor transforming spectra to finite water depth spectra.
+    """ Factor transforming spectra to finite water depth spectra.
 
     Input
     -----
@@ -692,7 +692,7 @@ def phi1(wi, h, g=9.81):
      1 spectral form.'
       J. Geophys. Res., Vol 90, No. C1, pp 975-986
 
-    '''
+    """
     w = atleast_1d(wi)
     if h == inf:  # % special case infinite water depth
         return ones_like(w)
@@ -709,7 +709,7 @@ def phi1(wi, h, g=9.81):
 
 class Tmaspec(Jonswap):
 
-    ''' JONSWAP spectrum for finite water depth
+    """ JONSWAP spectrum for finite water depth
 
     Member variables
     ----------------
@@ -783,7 +783,7 @@ class Tmaspec(Jonswap):
     Ergansungsheft, Reihe A(8), Nr. 12, deutschen Hydrografischen
     Zeitschrift.
 
-    '''
+    """
 
     def __init__(self, Hm0=7.0, Tp=11.0, gamma=None, sigmaA=0.07, sigmaB=0.09,
                  Ag=None, N=5, M=4, method='integration', wnc=6.0,
@@ -808,7 +808,7 @@ class Tmaspec(Jonswap):
 
 class Torsethaugen(ModelSpectrum):
 
-    '''
+    """
     Torsethaugen  double peaked (swell + wind) spectrum model
 
     Member variables
@@ -881,7 +881,7 @@ class Torsethaugen(ModelSpectrum):
     'A two peak wave spectral model.'
     In proceedings OMAE, Glasgow
 
-    '''
+    """
 
     def __init__(self, Hm0=7, Tp=11, method='integration', wnc=6, gravity=9.81,
                  chk_seastate=True, **kwds):
@@ -899,26 +899,26 @@ class Torsethaugen(ModelSpectrum):
         self._init_spec()
 
     def __call__(self, w):
-        ''' TORSETHAUGEN spectral density
-        '''
+        """ TORSETHAUGEN spectral density
+        """
         return self.wind(w) + self.swell(w)
 
     def _chk_extra_param(self):
         Hm0 = self.Hm0
         Tp = self.Tp
         if Hm0 > 11 or Hm0 > max((Tp / 3.6) ** 2, (Tp - 2) * 12 / 11):
-            txt0 = '''Hm0 is outside the valid range.
-                    The validity of the spectral density is questionable'''
+            txt0 = """Hm0 is outside the valid range.
+                    The validity of the spectral density is questionable"""
             warnings.warn(txt0)
 
         if Tp > 20 or Tp < 3:
-            txt1 = '''Tp is outside the valid range.
-                    The validity of the spectral density is questionable'''
+            txt1 = """Tp is outside the valid range.
+                    The validity of the spectral density is questionable"""
             warnings.warn(txt1)
 
     def _init_spec(self):
-        ''' Initialize swell and wind part of Torsethaugen spectrum
-        '''
+        """ Initialize swell and wind part of Torsethaugen spectrum
+        """
         monitor = 0
         Hm0 = self.Hm0
         Tp = self.Tp
@@ -1050,7 +1050,7 @@ class Torsethaugen(ModelSpectrum):
 
 class McCormick(Bretschneider):
 
-    ''' McCormick spectral density model
+    """ McCormick spectral density model
 
     Member variables
     ----------------
@@ -1095,7 +1095,7 @@ class McCormick(Bretschneider):
     M.E. McCormick (1999)
     "Application of the Generic Spectral Formula to Fetch-Limited Seas"
     Marine Technology Society, Vol 33, No. 3, pp 27-32
-    '''
+    """
 
     def __init__(self, Hm0=7, Tp=11, Tz=None, M=None, chk_seastate=True):
         self.type = 'McCormick'
@@ -1122,7 +1122,7 @@ class McCormick(Bretschneider):
 
 class OchiHubble(ModelSpectrum):
 
-    ''' OchiHubble bimodal spectral density model.
+    """ OchiHubble bimodal spectral density model.
 
     Member variables
     ----------------
@@ -1171,7 +1171,7 @@ class OchiHubble(ModelSpectrum):
     'On six-parameter wave spectra.'
     In Proc. 15th Conf. Coastal Engng., Vol.1, pp301-328
 
-    '''
+    """
 
     def __init__(self, Hm0=7, par=1, chk_seastate=True):
         self.type = 'Ochi Hubble'
@@ -1253,7 +1253,7 @@ class OchiHubble(ModelSpectrum):
 
 class Wallop(Bretschneider):
 
-    '''Wallop spectral density model.
+    """Wallop spectral density model.
 
     Member variables
     ----------------
@@ -1304,7 +1304,7 @@ class Wallop(Bretschneider):
     Huang, N.E., Long, S.R., Tung, C.C, Yuen, Y. and Bilven, L.F. (1981)
     "A unified two parameter wave spectral model for a generous sea state"
     J. Fluid Mechanics, Vol.112, pp 203-224
-    '''
+    """
 
     def __init__(self, Hm0=7, Tp=11, N=None, chk_seastate=True):
         self.type = 'Wallop'
@@ -1325,7 +1325,7 @@ class Wallop(Bretschneider):
 
 
 class Spreading(object):
-    '''
+    """
     Directional spreading function.
 
     Parameters
@@ -1488,7 +1488,7 @@ class Spreading(object):
     NB! The generally strong frequency dependence in directional spread
     makes it questionable to run load tests of ships and structures with a
     directional spread independent of frequency (Krogstad and Barstow, 1999).
-    '''
+    """
 # Parameterization of B
 #    def = 2 Donelan et al freq. parametrization for 'sech2'
 #    def = 3 Banner freq. parametrization for 'sech2'
@@ -1560,10 +1560,10 @@ class Spreading(object):
         return atleast_1d(self.theta0).flatten()
 
     def chk_input(self, theta, w=1, wc=1):
-        ''' CHK_INPUT
+        """ CHK_INPUT
 
         CALL [s_par,TH,phi0,Nt] = inputchk(theta,w,wc)
-        '''
+        """
 
         wn = atleast_1d(w / wc)
         theta = theta.ravel()
@@ -1579,7 +1579,7 @@ class Spreading(object):
         return s, TH, phi0, Nt
 
     def cos2s(self, theta, w=1, wc=1):  # [D, phi0] =
-        ''' COS2S spreading function
+        """ COS2S spreading function
 
         cos2s(theta,w) =  N(S)*[cos((theta-theta0)/2)]^(2*S)  (0 < S)
 
@@ -1599,7 +1599,7 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
         S, TH, phi0 = self.chk_input(theta, w, wc)[:3]
 
         gammaln = sp.gammaln
@@ -1609,7 +1609,7 @@ class Spreading(object):
         return D, phi0
 
     def poisson(self, theta, w=1, wc=1):  # [D,phi0] =
-        ''' POISSON spreading function
+        """ POISSON spreading function
 
         poisson(theta,w) =   N(X)/(1-2*X*cos(theta-theta0)+X^2)  (0 < X < 1)
 
@@ -1629,14 +1629,14 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
         X, TH, phi0 = self.chk_input(theta, w, wc)[:3]
 
         D = (1 - X ** 2.) / (1. - (2. * cos(TH) - X) * X) / (2. * pi)
         return D, phi0
 
     def wrap_norm(self, theta, w=1, wc=1):
-        ''' Wrapped Normal spreading function
+        """ Wrapped Normal spreading function
 
         wnormal(theta,w) = N(D1)*[1 +
                         2*sum exp(-(n*D1)^2/2)*cos(n*(theta-theta0))]  (0 < D1)
@@ -1657,7 +1657,7 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
 
         par, TH, phi0, Nt = self.chk_input(theta, w, wc)
 
@@ -1678,7 +1678,7 @@ class Spreading(object):
         return D, phi0
 
     def sech2(self, theta, w=1, wc=1):
-        '''SECH2 directonal spreading function
+        """SECH2 directonal spreading function
 
             sech2(theta,w) = N(B)*0.5*B*sech(B*(theta-theta0))^2 (0 < B)
 
@@ -1698,7 +1698,7 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
 
         B, TH, phi0 = self.chk_input(theta, w, wc)[:3]
         NB = tanh(pi * B)  # % Normalization factor.
@@ -1708,7 +1708,7 @@ class Spreading(object):
         return D, phi0
 
     def mises(self, theta, w=1, wc=1):
-        '''Mises spreading function
+        """Mises spreading function
 
         mises(theta,w) =  N(K)*exp(K*cos(theta-theta0))       (0 < K)
 
@@ -1728,7 +1728,7 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
 
         K, TH, phi0 = self.chk_input(theta, w, wc)[:3]
 
@@ -1736,7 +1736,7 @@ class Spreading(object):
         return D, phi0
 
     def box(self, theta, w=1, wc=1):
-        ''' Box car spreading function
+        """ Box car spreading function
 
             box(theta,w) =  N(A)*I( -A < theta-theta0 < A)      (0 < A < pi)
 
@@ -1756,7 +1756,7 @@ class Spreading(object):
             The principal direction of D is always along the x-axis.
         phi0 : real scalar
             Parameter defining the actual principal direction of D.
-        '''
+        """
 
         A, TH, phi0 = self.chk_input(theta, w, wc)[:3]
         D = ((-A <= TH) & (TH <= A)) / (2. * A)
@@ -1765,7 +1765,7 @@ class Spreading(object):
 # Local sub functions
 
     def fourier2distpar(self, r1):
-        ''' Fourier coefficients to distribution parameter
+        """ Fourier coefficients to distribution parameter
 
         Parameters
         ----------
@@ -1790,14 +1790,14 @@ class Spreading(object):
                  Poisson spreading  : R1 = X
                  sech-2 spreading   : R1 = pi/(2*B*sinh(pi/(2*B))
                  Wrapped Normal     : R1 = exp(-D1^2/2)
-        '''
+        """
         fourierfun = self._fourierdispatch.get(self.type[0])
         return fourierfun(r1)
 
     @staticmethod
     def fourier2x(r1):
-        ''' Returns the solution of r1 = x.
-        '''
+        """ Returns the solution of r1 = x.
+        """
         X = r1
         if any(X >= 1):
             raise ValueError('POISSON spreading: X value must be less than 1')
@@ -1805,8 +1805,8 @@ class Spreading(object):
 
     @staticmethod
     def fourier2a(r1):
-        ''' Returns the solution of R1 = sin(A)/A.
-        '''
+        """ Returns the solution of R1 = sin(A)/A.
+        """
         A0 = flipud(linspace(0, pi + 0.1, 1025))
         funA = interp1d(sinc(A0 / pi), A0)
         A0 = funA(r1.ravel())
@@ -1837,9 +1837,9 @@ class Spreading(object):
 
     @staticmethod
     def fourier2k(r1):
-        '''
+        """
         Returns the solution of R1 = besseli(1,K)/besseli(0,K),
-        '''
+        """
         def fun0(x):
             return sp.ive(1, x) / sp.ive(0, x)
 
@@ -1858,8 +1858,8 @@ class Spreading(object):
         return K
 
     def fourier2b(self, r1):
-        ''' Returns the solution of R1 = pi/(2*B*sinh(pi/(2*B)).
-        '''
+        """ Returns the solution of R1 = pi/(2*B*sinh(pi/(2*B)).
+        """
         B0 = hstack((linspace(_EPS, 5, 513), linspace(5.0001, 100)))
         funB = interp1d(self._r1ofsech2(B0), B0)
 
@@ -1879,8 +1879,8 @@ class Spreading(object):
         return B
 
     def fourier2d(self, r1):
-        ''' Returns the solution of R1 = exp(-D**2/2).
-        '''
+        """ Returns the solution of R1 = exp(-D**2/2).
+        """
         r = clip(r1, 0., 1.0)
         return where(r <= 0, inf, sqrt(-2.0 * log(r)))
 
@@ -1936,7 +1936,7 @@ class Spreading(object):
         return atleast_1d(self.s_a)
 
     def spread_parameter_s(self, wn):
-        ''' Return spread parameter, S, equivalent for the COS2S function
+        """ Return spread parameter, S, equivalent for the COS2S function
 
         Parameters
         ----------
@@ -1947,7 +1947,7 @@ class Spreading(object):
         -------
         S   : ndarray
             spread parameter of COS2S functions
-        '''
+        """
 
         spread = dict(b=self._banner_spread,
                       d=self._donelan_spread,
@@ -1972,14 +1972,14 @@ class Spreading(object):
 
     @staticmethod
     def _donelan(wn):
-        ''' High frequency decay of B of sech2 paramater
-        '''
+        """ High frequency decay of B of sech2 paramater
+        """
         return 10.0 ** (-0.4 + 0.8393 * exp(-0.567 * log(wn ** 2)))
 
     @staticmethod
     def _r1ofsech2(B):
-        ''' R1OFSECH2   Computes R1 = pi./(2*B.*sinh(pi./(2*B)))
-        '''
+        """ R1OFSECH2   Computes R1 = pi./(2*B.*sinh(pi./(2*B)))
+        """
         realmax = finfo(float).max
         tiny = 1. / realmax
         x = clip(2. * B, tiny, realmax)
@@ -1988,7 +1988,7 @@ class Spreading(object):
                      -2. * xk / (exp(xk) * expm1(-2. * xk)))
 
     def tospecdata2d(self, specdata=None, theta=None, wc=0.52, nt=51):
-        '''
+        """
          MKDSPEC Make a directional spectrum
                  frequency spectrum times spreading function
 
@@ -2020,7 +2020,7 @@ class Spreading(object):
          h = SD.plot()
 
          See also  spreading, rotspec, jonswap, torsethaugen
-        '''
+        """
 
         if specdata is None:
             specdata = Jonswap().tospecdata()

wafo/stats/core.py

@@ -19,9 +19,9 @@ arr = asarray
 
 
 def now():
-    '''
+    """
     Return current date and time as a string
-    '''
+    """
     return strftime("%a, %d %b %Y %H:%M:%S", gmtime())
 
 
@@ -66,7 +66,7 @@ def _invnorm(q):
 
 
 def edf(x, method=2):
-    '''
+    """
     Returns Empirical Distribution Function (EDF).
 
     Parameters
@@ -87,7 +87,7 @@ def edf(x, method=2):
     >>> h = F.plot()
 
      See also edf, pdfplot, cumtrapz
-    '''
+    """
     z = atleast_1d(x)
     z.sort()
 
@@ -105,7 +105,7 @@ def edf(x, method=2):
 
 
 def edfcnd(x, c=None, method=2):
-    '''
+    """
     Returns empirical Distribution Function CoNDitioned that X>=c (EDFCND).
 
     Parameters
@@ -128,7 +128,7 @@ def edfcnd(x, c=None, method=2):
     >>> h = F.plot()
 
      See also edf, pdfplot, cumtrapz
-    '''
+    """
     z = atleast_1d(x)
     if c is None:
         c = floor(min(z.min(), 0))
@@ -146,7 +146,7 @@ def edfcnd(x, c=None, method=2):
 
 def reslife(data, u=None, umin=None, umax=None, nu=None, nmin=3, alpha=0.05,
             plotflag=False):
-    '''
+    """
     Return Mean Residual Life, i.e., mean excesses vs thresholds
 
     Parameters
@@ -196,7 +196,7 @@ def reslife(data, u=None, umin=None, umax=None, nu=None, nmin=3, alpha=0.05,
     ---------
     genpareto
     fitgenparrange, disprsnidx
-    '''
+    """
     if u is None:
         sd = np.sort(data)
         n = len(data)
@@ -252,7 +252,7 @@ def reslife(data, u=None, umin=None, umax=None, nu=None, nmin=3, alpha=0.05,
 def dispersion_idx(
     data, t=None, u=None, umin=None, umax=None, nu=None, nmin=10, tb=1,
         alpha=0.05, plotflag=False):
-    '''Return Dispersion Index vs threshold
+    """Return Dispersion Index vs threshold
 
     Parameters
     ----------
@@ -329,7 +329,7 @@ def dispersion_idx(
     Cunnane, C. (1979) Note on the poisson assumption in
     partial duration series model. Water Resource Research, 15\bold{(2)}
          :489--494.}
-    '''
+    """
 
     n = len(data)
     if t is None:
@@ -407,7 +407,7 @@ def dispersion_idx(
 
 
 def decluster(data, t=None, thresh=None, tmin=1):
-    '''
+    """
     Return declustered peaks over threshold values
 
     Parameters
@@ -442,7 +442,7 @@ def decluster(data, t=None, thresh=None, tmin=1):
     See also
     --------
     fitgenpar, findpot, extremalidx
-    '''
+    """
     if t is None:
         t = np.arange(len(data))
     i = findpot(data, t, thresh, tmin)
@@ -450,7 +450,7 @@ def decluster(data, t=None, thresh=None, tmin=1):
 
 
 def findpot(data, t=None, thresh=None, tmin=1):
-    '''
+    """
     Retrun indices to Peaks over threshold values
 
     Parameters
@@ -490,7 +490,7 @@ def findpot(data, t=None, thresh=None, tmin=1):
     See also
     --------
     fitgenpar, decluster, extremalidx
-    '''
+    """
     Data = arr(data)
     if t is None:
         ti = np.arange(len(Data))
@@ -538,36 +538,35 @@ def findpot(data, t=None, thresh=None, tmin=1):
     return Ie
 
 
-def _find_ok_peaks(Ye, Te, Tmin):
-    '''
-    Return indices to the largest maxima that are at least Tmin
-    distance apart.
-    '''
-    Ny = len(Ye)
+def _find_ok_peaks(y, t, t_min):
+    """
+    Return indices to the largest maxima that are at least t_min distance apart.
+    """
+    num_y = len(y)
 
-    I = np.argsort(-Ye)  # sort in descending order
+    i = np.argsort(-y)  # sort in descending order
 
-    Te1 = Te[I]
-    oOrder = zeros(Ny, dtype=int)
-    oOrder[I] = range(Ny)  # indices to the variables original location
+    tis = t[i]
+    o_order = zeros(num_y, dtype=int)
+    o_order[i] = range(num_y)  # indices to the variables original location
 
-    isTooClose = zeros(Ny, dtype=bool)
+    is_too_close = zeros(num_y, dtype=bool)
 
-    pool = zeros((Ny, 2))
-    T_range = np.hstack([-Tmin, Tmin])
-    K = 0
-    for i, ti in enumerate(Te1):
-        isTooClose[i] = np.any((pool[:K, 0] <= ti) & (ti <= pool[:K, 1]))
-        if not isTooClose[i]:
-            pool[K] = ti + T_range
-            K += 1
+    pool = zeros((num_y, 2))
+    t_range = np.hstack([-t_min, t_min])
+    k = 0
+    for i, ti in enumerate(tis):
+        is_too_close[i] = np.any((pool[:k, 0] <= ti) & (ti <= pool[:k, 1]))
+        if not is_too_close[i]:
+            pool[k] = ti + t_range
+            k += 1
 
-    iOK, = where(1 - isTooClose[oOrder])
-    return iOK
+    i_ok, = where(1 - is_too_close[o_order])
+    return i_ok
 
 
 def declustering_time(t):
-    '''
+    """
     Returns minimum distance between clusters.
 
     Parameters
@@ -589,7 +588,7 @@ def declustering_time(t):
     >>> tc = declustering_time(Ie)
     >>> tc
     21
-    '''
+    """
     t0 = arr(t)
     nt = len(t0)
     if nt < 2:
@@ -606,7 +605,7 @@ def declustering_time(t):
 
 
 def interexceedance_times(t):
-    '''
+    """
     Returns interexceedance times of data
 
     Parameters
@@ -624,12 +623,12 @@ def interexceedance_times(t):
     >>> interexceedance_times(t)
     array([1, 3, 5])
 
-    '''
+    """
     return np.diff(np.sort(t))
 
 
 def extremal_idx(ti):
-    '''
+    """
     Returns Extremal Index measuring the dependence of data
 
     Parameters
@@ -671,7 +670,7 @@ def extremal_idx(ti):
     Journal of the Royal Statistical society: Series B
     (Statistical Methodology) 54 (2), 545-556
     doi:10.1111/1467-9868.00401
-    '''
+    """
     t = arr(ti)
     tmax = t.max()
     if tmax <= 1:
@@ -693,7 +692,7 @@ def _logitinv(x):
 
 class RegLogit(object):
 
-    '''
+    """
     REGLOGIT Fit ordinal logistic regression model.
 
       CALL model = reglogit (options)
@@ -756,7 +755,7 @@ class RegLogit(object):
       b21.compare(b2)
 
      See also regglm, reglm, regnonlm
-    '''
+    """
 
     #% Original for MATLAB written by Gordon K Smyth <gks@maths.uq.oz.au>,
     #% U of Queensland, Australia, on Nov 19, 1990.  Last revision Aug 3,
@@ -840,7 +839,7 @@ class RegLogit(object):
         return y, X
 
     def fit(self, y, X=None, theta0=None, beta0=None):
-        '''
+        """
         Member variables
       .df           : degrees of freedom for error.
       .params       : estimated model parameters
@@ -858,7 +857,7 @@ class RegLogit(object):
       .d2L          : Hessian matrix (double derivative of log-likelihood)
       .dL           : First derivative of loglikelihood w.r.t. THETA and BETA.
 
-        '''
+        """
         self.family = 'multinomial'
         self.link = 'logit'
         y, X = self.check_xy(y, X)
@@ -1016,7 +1015,7 @@ class RegLogit(object):
             self.summary()
 
     def compare(self, object2):
-        ''' Compare  small LOGIT versus large one
+        """ Compare  small LOGIT versus large one
 
         CALL     [pvalue] = compare(object2)
 
@@ -1026,7 +1025,7 @@ class RegLogit(object):
         model, and the residuals from the larger model.
 
         See also fitls
-        '''
+        """
 
         try:
             if self.numvar > object2.numvar:
@@ -1156,7 +1155,7 @@ class RegLogit(object):
         #end % summary
 
     def predict(self, Xnew=None, alpha=0.05, fulloutput=False):
-        '''LOGIT/PREDICT Predict from a fitted LOGIT object
+        """LOGIT/PREDICT Predict from a fitted LOGIT object
 
          CALL [y,ylo,yup] = predict(Xnew,options)
 
@@ -1167,7 +1166,7 @@ class RegLogit(object):
          options  = options struct defining the calculation
                 .alpha : confidence coefficient (default 0.05)
                 .size  : size if binomial family (default 1).
-        '''
+        """
 
         [_mx, nx] = self.X.shape
         if Xnew is None:
@@ -1233,10 +1232,10 @@ class RegLogit(object):
         return y
 
     def loglike(self, beta, y, x, z, z1, numout=3):
-        '''
+        """
         [dev, dl, d2l, p] = loglike( y ,x,beta,z,z1)
         Calculates likelihood for the ordinal logistic regression model.
-        '''
+        """
         # Author: Gordon K. Smyth <gks@maths.uq.oz.au>
         zx = np.hstack((z, x))
         z1x = np.hstack((z1, x))
@@ -1248,10 +1247,10 @@ class RegLogit(object):
         p = g - g1
         dev = -2 * np.log(p).sum()
 
-        '''[dl, d2l] = derivatives of loglike(beta, y, x, z, z1)
+        """[dl, d2l] = derivatives of loglike(beta, y, x, z, z1)
         % Called by logistic_regression.  Calculates derivates of the
         % log-likelihood for ordinal logistic regression model.
-        '''
+        """
         # Author: Gordon K. Smyth <gks@maths.uq.oz.au>
         # Description: Derivates of log-likelihood in logistic regression
 

wafo/tests/test_objects.py

@@ -7,8 +7,7 @@ Created on 5. aug. 2010
 
 import unittest
 from numpy.testing import TestCase, assert_array_almost_equal
-import wafo.data  # @UnusedImport
-import numpy as np  # @UnusedImport
+import wafo.data
 import wafo.objects as wo
 import wafo.spectrum.models as sm
 import wafo.transform.models as tm