From 30bf392f801dc73733ff559a451d88db69ce792e Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Tue, 6 Sep 2016 10:49:30 +0200 Subject: [PATCH] Replaced methods with function calls --- wafo/spectrum/core.py | 48 +++++++++++++++++++++++-------------------- 1 file changed, 26 insertions(+), 22 deletions(-) diff --git a/wafo/spectrum/core.py b/wafo/spectrum/core.py index 4ecf5dd..67e6e48 100644 --- a/wafo/spectrum/core.py +++ b/wafo/spectrum/core.py @@ -5,7 +5,7 @@ import numpy as np from numpy import (pi, inf, zeros, ones, where, nonzero, flatnonzero, ceil, sqrt, exp, log, arctan2, tanh, cosh, sinh, random, atleast_1d, - minimum, diff, isnan, any, r_, conj, mod, + minimum, diff, isnan, r_, conj, mod, hstack, vstack, interp, ravel, finfo, linspace, arange, array, nan, newaxis, sign) from numpy.fft import fft @@ -56,7 +56,7 @@ def _set_seed(iseed): if iseed is not None: try: random.set_state(iseed) - except: + except KeyError: random.seed(iseed) @@ -592,7 +592,8 @@ class SpecData1D(PlotData): print('theoretical. Solution:') raise ValueError('use larger dt or sparser grid for spectrum.') - def _check_cov_matrix(self, acfmat, nt, dt): + @staticmethod + def _check_cov_matrix(acfmat, nt, dt): eps0 = 0.0001 if nt + 1 >= 5: cc2 = acfmat[0, 0] - acfmat[4, 0] * (acfmat[4, 0] / acfmat[0, 0]) @@ -751,7 +752,7 @@ class SpecData1D(PlotData): Correct it with resample, for example.''' raise ValueError(txt) d_w = abs(diff(freq, n=2, axis=0)) - if any(d_w > 1.0e-8): + if np.any(d_w > 1.0e-8): txt = '''Not equidistant frequencies/wave numbers in spectrum. Correct it with resample, for example.''' raise ValueError(txt) @@ -995,18 +996,18 @@ class SpecData1D(PlotData): Hw12 = Hw1 - Hw2 maxHw12 = max(abs(Hw12)) if trace == 1: - plotbackend.figure(1), + plotbackend.figure(1) plotbackend.semilogy(freq, Hw1, 'r') plotbackend.title('Hw') - plotbackend.figure(2), + plotbackend.figure(2) plotbackend.semilogy(freq, abs(Hw12), 'r') plotbackend.title('Hw-HwOld') # pause(3) - plotbackend.figure(1), + plotbackend.figure(1) plotbackend.semilogy(freq, Hw1, 'b') plotbackend.title('Hw') - plotbackend.figure(2), + plotbackend.figure(2) plotbackend.semilogy(freq, abs(Hw12), 'b') plotbackend.title('Hw-HwOld') # figtile @@ -1079,7 +1080,7 @@ class SpecData1D(PlotData): S = self.copy() S.normalize() - m, unused_mtxt = self.moment(nr=4, even=True) + m = self.moment(nr=4, even=True)[0] A = sqrt(m[0] / m[1]) if paramt is None: @@ -1103,7 +1104,7 @@ class SpecData1D(PlotData): if verbose: print('The level u for Gaussian process = %g' % u) - unused_t0, tn, Nt = paramt + tn, Nt = paramt[1:] t = linspace(0, tn / A, Nt) # normalized times # Transform amplitudes to Gaussian levels: @@ -1210,7 +1211,7 @@ class SpecData1D(PlotData): S = self.copy() S.normalize() - m, unused_mtxt = self.moment(nr=2, even=True) + m = self.moment(nr=2, even=True)[0] A = sqrt(m[0] / m[1]) if self.tr is None: @@ -1303,7 +1304,8 @@ class SpecData1D(PlotData): pdf.options = opts return pdf - def _covinput_t_pdf(self, pt, R): + @staticmethod + def _covinput_t_pdf(pt, R): """ Return covariance matrix for Tc or Tt period problems @@ -1764,7 +1766,7 @@ class SpecData1D(PlotData): (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn)) (indI(4)=Nt+2); for i\in (indI(4)+1,indI(5)], Y(i)<0 (deriv. X'(ts)) ''' - R0, _R1, R2, _R3, R4 = R[:, :5].T + R0, R2, R4 = R[:, :5:2].T covinput = self._covinput_mmt_pdf Ntime = len(R0) Nx0 = max(1, len(hg)) @@ -2121,7 +2123,8 @@ class SpecData1D(PlotData): # end return pdf, err, terr, options - def _covinput_mmt_pdf(self, BIG, R, tn, ts, tnold=-1): + @staticmethod + def _covinput_mmt_pdf(BIG, R, tn, ts, tnold=-1): """ COVINPUT Sets up the covariance matrix @@ -2340,7 +2343,8 @@ class SpecData1D(PlotData): return dens - def _cleanup(self, *files): + @staticmethod + def _cleanup(*files): '''Removes files from harddisk if they exist''' for f in files: if os.path.exists(f): @@ -3095,7 +3099,7 @@ class SpecData1D(PlotData): xs = acf.sim(ns=ns, cases=Nstep) for iy in range(1, xs.shape[-1]): ts = TimeSeries(xs[:, iy], xs[:, 0].ravel()) - g, _tmp = ts.trdata(method, **opt) + g = ts.trdata(method, **opt)[0] test1.append(g.dist2gauss()) if verbose: print('finished %d of %d ' % (ix + 1, rep)) @@ -3304,7 +3308,7 @@ class SpecData1D(PlotData): wnNew = Cnf2dt / dt # % New Nyquist frequency dWn = wnNew - wnOld doInterpolate = dWn > 0 or w[1] > 0 or ( - nfft != n) or dt != dTold or any(abs(diff(w, axis=0)) > 1.0e-8) + nfft != n) or dt != dTold or np.any(abs(diff(w, axis=0)) > 1.0e-8) if doInterpolate > 0: S1 = self.data @@ -3341,7 +3345,7 @@ class SpecData1D(PlotData): wnc = wnNew # specfun = lambda xi : stineman_interp(xi, w, S1) specfun = interpolate.interp1d(w, S1, kind='cubic') - x, unused_y = discretize(specfun, 0, wnc) + x = discretize(specfun, 0, wnc)[0] dwMin = minimum(min(diff(x)), dwMin) newNfft = 2 ** nextpow2(ceil(wnNew / dwMin)) + 1 @@ -3396,7 +3400,7 @@ class SpecData1D(PlotData): >>> np.allclose(Sn.moment(2)[0], [1.0, 1.0]) True ''' - mom, unused_mtext = self.moment(nr=4, even=True) + mom = self.moment(nr=4, even=True)[0] m0 = mom[0] m2 = mom[1] m4 = mom[2] @@ -3448,7 +3452,7 @@ class SpecData1D(PlotData): array([ 0.73062845, 0.34476034, 0.68277527, 2.90817052]) ''' - m, unused_mtxt = self.moment(nr=4, even=False) + m = self.moment(nr=4, even=False)[0] if isinstance(factors, str): factors = [factors] fact_dict = dict(alpha=0, eps2=1, eps4=3, qp=3, Qp=3) @@ -3594,7 +3598,7 @@ class SpecData1D(PlotData): nfact = atleast_1d(nfact) - if any((nfact > 14) | (nfact < 0)): + if np.any((nfact > 14) | (nfact < 0)): raise ValueError('Factor outside range (0,...,14)') # vari = self.freqtype @@ -3656,7 +3660,7 @@ class SpecData1D(PlotData): # if nargout>1, # covariance between the moments: # COV(mi,mj |T=t0) = int f^(i+j)*S(f)^2 df/T - mij, unused_mijtxt = self.moment(nr=8, even=False, j=1) + mij = self.moment(nr=8, even=False, j=1)[0] for ix, tmp in enumerate(mij): mij[ix] = tmp / T / ((2. * pi) ** (ix - 1.0))