From 3472a720e15642d1764800d978a29b9ff5b092fb Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Wed, 7 Jun 2017 09:13:02 +0200 Subject: [PATCH] pep8 --- wafo/markov.py | 204 +++++++++++++++++++++++++++---------------------- 1 file changed, 111 insertions(+), 93 deletions(-) diff --git a/wafo/markov.py b/wafo/markov.py index ced5cc2..1cb5103 100644 --- a/wafo/markov.py +++ b/wafo/markov.py @@ -27,14 +27,14 @@ def nt2fr(nt, kind=0): """ n = len(nt) - fr_raw = (nt[:n-1, :][:, n-1]+nt[1:,:][:, 1:]- - nt[:n-1,:][:, 1:] - nt[1:,:][:, :n-1]) + fr_raw = (nt[:n - 1, :][:, n - 1] + nt[1:, :][:, 1:] - + nt[:n - 1, :][:, 1:] - nt[1:, :][:, :n - 1]) fr = np.zeros(np.shape(nt)) - if kind==0: - fr[:n-1,:n-1] = fr_raw + if kind == 0: + fr[:n - 1, :n - 1] = fr_raw else: - fr[1:,1:] = fr_raw + fr[1:, 1:] = fr_raw return fr @@ -61,7 +61,7 @@ def fr2nt(fr): m1 = np.cumsum(np.cumsum(fr, axis=1) - fr) m2 = np.zeros((n, n)) - m2[1:, 1:] = m1[:n-2, :][:, 1:] + m2[1:, 1:] = m1[:n - 2, :][:, 1:] nt = np.fliplr(np.triu(np.fliplr(m2), 0)) return nt @@ -109,11 +109,11 @@ def _raw_iter(fun2rfc, frfc, fmM_0=None, k=1, epsilon=1e-5): for _i in range(k): fmM_old = fmM frfc = fun2rfc(fmM) - fmM = fmM_old+(frfc0-frfc) + fmM = fmM_old + (frfc0 - frfc) fmM = np.maximum(0, fmM) # check = [k-i+1, sum(sum(abs(fmM_old-fmM))), sum(sum(frfc0))/sum(sum(fmM))]; # disp(['iteration step, accuracy ', num2str(check(1:2))]) - converged = not np.sum(np.abs(fmM_old-fmM)) > epsilon + converged = not np.sum(np.abs(fmM_old - fmM)) > epsilon if converged: break @@ -211,14 +211,23 @@ def nt2cmat(nt, kind=1): if kind == 1: I = np.r_[0:n - 1] J = np.r_[1:n] - c = nt[I+1][:, J-1] - nt[I][:, J-1] - nt[I+1][:, J] + nt[I][:, J] + c = nt[I + 1][:, + J - 1] - nt[I][:, + J - 1] - nt[I + 1][:, + J] + nt[I][:, + J] c2 = np.vstack((c, np.zeros((n - 1)))) cmat = np.hstack((np.zeros((n, 1)), c2)) elif kind == 11: # same as def=1 but using for-loop cmat = np.zeros((n, n)) j = np.r_[1:n] for i in range(n - 1): - cmat[i, j] = nt[i+1, j-1] - nt[i, j-1] - nt[i+1, j] + nt[i, j] + cmat[i, + j] = nt[i + 1, + j - 1] - nt[i, + j - 1] - nt[i + 1, + j] + nt[i, + j] else: _raise_kind_error(kind) return cmat @@ -361,15 +370,15 @@ def mctp2tc(f_Mm, utc, param, f_mM=None): return tempp def _make_tempm(P, Ph, j, ntc, n): - Am = P[ntc-1:j, ntc:j+1] - Bm = Ph[ntc:j+1, ntc-1:j] + Am = P[ntc - 1:j, ntc:j + 1] + Bm = Ph[ntc:j + 1, ntc - 1:j] dim_m = j - ntc + 1 tempm = np.zeros((dim_m, 1)) Im = np.eye(len(Am)) if j == n - 1: - em = P[ntc-1:j, n] + em = P[ntc - 1:j, n] else: - em = np.sum(P[ntc-1:j, j + 1:n], axis=1) + em = np.sum(P[ntc - 1:j, j + 1:n], axis=1) # end if max(abs(em)) > 1e-10: if dim_m == 1: @@ -394,7 +403,7 @@ def mctp2tc(f_Mm, utc, param, f_mM=None): # normalization of frequency matrices f_Mm = _normalize_rows(f_Mm) P = np.fliplr(f_Mm) - Ph = np.rot90(np.fliplr(f_mM*1.0), -1) + Ph = np.rot90(np.fliplr(f_mM * 1.0), -1) Ph = _normalize_rows(Ph) Ph = np.fliplr(Ph) @@ -403,34 +412,34 @@ def mctp2tc(f_Mm, utc, param, f_mM=None): F = cmat2nt(F) for i in range(1, ntc): - for j in range(ntc-1, n - 1): - if i < ntc-1: + for j in range(ntc - 1, n - 1): + if i < ntc - 1: tempp = _make_tempp(P, Ph, i, ntc) b = np.dot(np.dot(tempp.T, f_mM[i:ntc - 1, n - j - 2::-1]), np.ones((n - j - 1, 1))) # end - if j > ntc-1: + if j > ntc - 1: tempm = _make_tempm(P, Ph, j, ntc, n) c = np.dot(np.dot(np.ones((1, i)), - f_mM[:i, n - ntc-1:n - j - 2:-1]), + f_mM[:i, n - ntc - 1:n - j - 2:-1]), tempm) # end - if (j > ntc-1) and (i < ntc-1): + if (j > ntc - 1) and (i < ntc - 1): a = np.dot(np.dot(tempp.T, f_mM[i:ntc - 1, n - ntc - 1:-1:n - j + 1]), tempm) F[i, n - j - 1] = F[i, n - j - 1] + a + b + c # end - if (j == ntc-1) and (i < ntc-1): + if (j == ntc - 1) and (i < ntc - 1): F[i, n - ntc] = F[i, n - ntc] + b for k in range(ntc): F[i, n - k - 1] = F[i, n - ntc] # end # end - if (j > ntc-1) and (i == ntc-1): + if (j > ntc - 1) and (i == ntc - 1): F[i, n - j - 1] = F[i, n - j - 1] + c - for k in range(ntc-1, n): - F[k, n - j - 1] = F[ntc-1, n - j - 1] + for k in range(ntc - 1, n): + F[k, n - j - 1] = F[ntc - 1, n - j - 1] # end # end # end @@ -580,7 +589,7 @@ def mctp2rfc(fmM, fMm=None): return f_rfc -def mc2rfc(f_xy, paramv=None, paramu=None): +def mc2rfc(f_xy, paramv=None, paramu=None): """ MC2RFC Calculates a rainflow matrix given a Markov chain with kernel f_xy; f_rfc = f_xy + F_mc(f_xy). @@ -604,7 +613,6 @@ def mc2rfc(f_xy, paramv=None, paramu=None): if paramv is None: paramv = (-1, 1, N) - if paramu is None: paramu = paramv @@ -615,43 +623,45 @@ def mc2rfc(f_xy, paramv=None, paramu=None): Min_rfc = np.zeros((N, 1)) norm = np.zeros((N, 1)) for i in range(N): - Spm=Sminus[i] + Splus[i]-dd[i] + Spm = Sminus[i] + Splus[i] - dd[i] if Spm > 0: - Max_rfc[i]=(Splus[i]-dd[i])*(Splus[i]-dd[i])/(1-dd[i]/Spm)/Spm - Min_rfc[i]=(Sminus[i]-dd[i])*(Sminus[i]-dd[i])/(1-dd[i]/Spm)/Spm - norm[i]=Spm + Max_rfc[i] = (Splus[i] - dd[i]) * \ + (Splus[i] - dd[i]) / (1 - dd[i] / Spm) / Spm + Min_rfc[i] = (Sminus[i] - dd[i]) * \ + (Sminus[i] - dd[i]) / (1 - dd[i] / Spm) / Spm + norm[i] = Spm # end if # end for - #cross=zeros(N,1) - #for i=2:N + # cross=zeros(N,1) + # for i=2:N # cross(N-i+1)=cross(N-i+2)+Sminus(N-i+2)-Splus(N-i+2) - #end + # end - f_rfc= np.zeros((N,N)) - f_rfc[N-1, 1] = Max_rfc[N-1] - f_rfc[1, N-1] = Min_rfc[2] + f_rfc = np.zeros((N, N)) + f_rfc[N - 1, 1] = Max_rfc[N - 1] + f_rfc[1, N - 1] = Min_rfc[2] - for k in range(2, N-1): - for i in range(1, k-1): + for k in range(2, N - 1): + for i in range(1, k - 1): # AAe= f_xy(1:N-k,1:k-i) # SAAe=sum(sum(AAe)) - AA = f_xy[N-k:N-k+i, :][:, k-i:k] - #RAA = f_rfc(N-k+1:N-k+i,k-i+1:k) - RAA = f_rfc[N-k:N-k+i, :][:, k-i:k] + AA = f_xy[N - k:N - k + i, :][:, k - i:k] + # RAA = f_rfc(N-k+1:N-k+i,k-i+1:k) + RAA = f_rfc[N - k:N - k + i, :][:, k - i:k] nA = len(AA) # MA = Splus(N-k+1:N-k+i) - MA = Splus[N-k:N-k+i] - mA = Sminus[N-k:N-k+i] - normA = norm[N-k:N-k+i] - MA_rfc = Max_rfc[N-k:N-k+i] + MA = Splus[N - k:N - k + i] + mA = Sminus[N - k:N - k + i] + normA = norm[N - k:N - k + i] + MA_rfc = Max_rfc[N - k:N - k + i] # mA_rfc=Min_rfc(k-i+1:k) SA = np.sum(AA) SRA = np.sum(RAA) SMA_rfc = sum(MA_rfc) SMA = np.sum(MA) - DRFC = SA-SMA-SRA+SMA_rfc + DRFC = SA - SMA - SRA + SMA_rfc NT = MA_rfc[0] - np.sum(RAA[0, :]) @@ -662,50 +672,50 @@ def mc2rfc(f_xy, paramv=None, paramu=None): NT = np.maximum(NT, 0) - if NT > 1e-6*MA_rfc[0]: + if NT > 1e-6 * MA_rfc[0]: - NN = MA - np.sum(AA,axis=1).T - e = (np.fliplr(mA)-np.sum(AA)).T + NN = MA - np.sum(AA, axis=1).T + e = (np.fliplr(mA) - np.sum(AA)).T e = np.flipud(e) - AA = AA + np.flipud(np.rot90(AA,-1)) + AA = AA + np.flipud(np.rot90(AA, -1)) AA = np.rot90(AA) - AA = AA - 0.5*np.diag(np.diag(AA)) - + AA = AA - 0.5 * np.diag(np.diag(AA)) for j in range(nA): - if normA[j] !=0: - AA[j,:] = AA[j, :] / normA[j] + if normA[j] != 0: + AA[j, :] = AA[j, :] / normA[j] e[j] = e[j] / normA[j] # end if # end for fx = 0. - if np.max(np.abs(e)) > 1e-7 and np.max(np.abs(NN)) > 1e-7*MA_rfc[0]: + if np.max( + np.abs(e)) > 1e-7 and np.max(np.abs(NN)) > 1e-7 * MA_rfc[0]: I = np.eye(np.shape(AA)) - if nA==1: - fx = NN/(1-AA)*e + if nA == 1: + fx = NN / (1 - AA) * e else: # TODO CHECK this - fx = NN*np.linalg.solve((I-AA), e)[0] # (I-AA)\e + fx = NN * np.linalg.solve((I - AA), e)[0] # (I-AA)\e # end # end - f_rfc[N-k, k-i] = DRFC + fx - #end + f_rfc[N - k, k - i] = DRFC + fx + # end # end - m0 = np.maximum(0, Min_rfc[N]-sum(f_rfc[N-k+1:N, 0])) - M0 = np.maximum(0, Max_rfc[N-k]-sum(f_rfc[N-k, 1:k])); - f_rfc[N-k, 0] = min(m0, M0) + m0 = np.maximum(0, Min_rfc[N] - sum(f_rfc[N - k + 1:N, 0])) + M0 = np.maximum(0, Max_rfc[N - k] - sum(f_rfc[N - k, 1:k])) + f_rfc[N - k, 0] = min(m0, M0) # n_loops_left=N-k+1 # end for for k in range(1, N): - M0 = max(0, Max_rfc[0] -sum(f_rfc[0, N-k+1:N])) - m0 = max(0, Min_rfc[k] - sum(f_rfc[1:k, N-k])) - f_rfc[0, N-k] = min(m0, M0) + M0 = max(0, Max_rfc[0] - sum(f_rfc[0, N - k + 1:N])) + m0 = max(0, Min_rfc[k] - sum(f_rfc[1:k, N - k])) + f_rfc[0, N - k] = min(m0, M0) # end for - f_rfc = f_rfc + np.rot90(np.diag(dd),-1) + f_rfc = f_rfc + np.rot90(np.diag(dd), -1) # clf # subplot(1,2,2) @@ -785,26 +795,26 @@ def mktestmat(param=(-1, 1, 32), x0=None, s=None, lam=1, numsubzero=0): # Toolbox: Rainflow Cycles for Switching Processes V.1.0, 2-Oct-1997 if x0 is None: - x0 = np.ones((2,)) * (param[1]+param[0])/2 + x0 = np.ones((2,)) * (param[1] + param[0]) / 2 if s is None: - s = (param[1]-param[0])/4 + s = (param[1] - param[0]) / 4 if np.isinf(numsubzero): - numsubzero = -(param[2]+1) + numsubzero = -(param[2] + 1) u = np.linspace(*param) n = param[2] # F - min-Max matrix - F = np.zeros((n,n)) - S = 1/2*s**2*np.array([[lam**2+1, lam**2-1], - [lam**2-1, lam**2+1]]) + F = np.zeros((n, n)) + S = 1 / 2 * s**2 * np.array([[lam**2 + 1, lam**2 - 1], + [lam**2 - 1, lam**2 + 1]]) Sm1 = np.linalg.pinv(S) - for i in range(min(n-1-numsubzero, n)): + for i in range(min(n - 1 - numsubzero, n)): for j in range(max(i + numsubzero, 0), n): dx = np.array([u[i], u[j]]) - x0 - F[i, j] = np.exp(-1/2*np.dot(np.dot(dx, Sm1), dx)) + F[i, j] = np.exp(-1 / 2 * np.dot(np.dot(dx, Sm1), dx)) Fh = F.T # Time-reversible Max-min matrix return F, Fh @@ -851,7 +861,7 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None): F = cmat shape = np.shape(F) if ux is None: - ux = np.arange(shape[1]); # Antalet kolumner + ux = np.arange(shape[1]) # Antalet kolumner if uy is None: uy = np.arange(shape[0]) # Antalet rader @@ -860,32 +870,40 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None): Fmax = np.max(F) if method in [5, 15]: clevels = Fmax * np.r_[0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0] - else: # 4, 14 - clevels = Fmax*np.r_[0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8] + else: # 4, 14 + clevels = Fmax * np.r_[0.005, + 0.01, + 0.02, + 0.05, + 0.1, + 0.2, + 0.4, + 0.6, + 0.8] # Make sure ux and uy are row vectors - ux = ux.ravel(); - uy = uy.ravel(); + ux = ux.ravel() + uy = uy.ravel() n = len(F) from matplotlib import pyplot as plt if method == 1: # mesh - F = np.flipud(F.T); # Vrid cykelmatrisen for att plotta rett + F = np.flipud(F.T) # Vrid cykelmatrisen for att plotta rett plt.mesh(ux, np.fliplr(uy), F) plt.xlabel('min') plt.ylabel('Max') # view(-37.5-90,30); #v = axis; - #plt.axis([min(ux) max(ux) min(uy) max(uy) v[5:6]]); + # plt.axis([min(ux) max(ux) min(uy) max(uy) v[5:6]]); elif method == 2: # surf F = np.flipud(F.T) # Vrid cykelmatrisen for att plotta rett - plt.surf(ux, np.fliplr(uy),F) + plt.surf(ux, np.fliplr(uy), F) plt.xlabel('min') plt.ylabel('Max') # view(-37.5-90,30); #v = axis; - #plt.axis([min(ux) max(ux) min(uy) max(uy) v(5:6)]); + # plt.axis([min(ux) max(ux) min(uy) max(uy) v(5:6)]); # elseif method == 3 # pcolor # F = flipud(F'); # F1 = [F zeros(length(uy),1); zeros(1,length(ux)+1)]; @@ -1087,6 +1105,7 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None): # # end % _cmatplot + def arfm2mctp(Frfc): """ ARFM2MCTP Calculates the markov matrix given an asymmetric rainflow matrix. @@ -1148,11 +1167,10 @@ def arfm2mctp(Frfc): # The cond. prob. pS1, pS2, pS3, pH1, pH2, pH3 are calculated using # the elementary cond. prob. C, E, R, D, E3, Ch, Eh, Rh, Dh, E3h. - # T(1,:)=clock; N = np.sum(Frfc) - Frfc = Frfc/N; + Frfc = Frfc / N n = len(Frfc) # Number of levels @@ -1170,20 +1188,20 @@ def arfm2mctp(Frfc): # Probability of minimum and of maximun MIN = np.sum(np.triu(Frfc).T, axis=0) + np.sum(np.tril(Frfc), axis=0) - MAX = np.sum(np.triu(Frfc), axis=0) + np.sum(np.tril(Frfc).T, axis=0) + MAX = np.sum(np.triu(Frfc), axis=0) + np.sum(np.tril(Frfc).T, axis=0) # Calculate rainflow matrix - F = np.zeros((n,n)) - EYE = np.eye((n,n)) + F = np.zeros((n, n)) + EYE = np.eye((n, n)) #fprintf(1,'Calculating row '); - for k in range(n-1): # k = subdiagonal - # fprintf(1,'-%1d',i); + for k in range(n - 1): # k = subdiagonal + # fprintf(1,'-%1d',i); - for i in range(n-k): # i = minimum + for i in range(n - k): # i = minimum - j = i + k + 1 # maximum; + j = i + k + 1 # maximum; # pS = Frfc(i,j); # Standing cycle # pH = Frfc(j,i); # Hanging cycle