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@ -211,14 +211,23 @@ def nt2cmat(nt, kind=1):
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if kind == 1:
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I = np.r_[0:n - 1]
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J = np.r_[1:n]
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c = nt[I+1][:, J-1] - nt[I][:, J-1] - nt[I+1][:, J] + nt[I][:, J]
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c = nt[I + 1][:,
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J - 1] - nt[I][:,
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J - 1] - nt[I + 1][:,
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J] + nt[I][:,
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J]
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c2 = np.vstack((c, np.zeros((n - 1))))
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cmat = np.hstack((np.zeros((n, 1)), c2))
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elif kind == 11: # same as def=1 but using for-loop
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cmat = np.zeros((n, n))
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j = np.r_[1:n]
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for i in range(n - 1):
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cmat[i, j] = nt[i+1, j-1] - nt[i, j-1] - nt[i+1, j] + nt[i, j]
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cmat[i,
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j] = nt[i + 1,
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j - 1] - nt[i,
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j - 1] - nt[i + 1,
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j] + nt[i,
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j]
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else:
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_raise_kind_error(kind)
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return cmat
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@ -604,7 +613,6 @@ def mc2rfc(f_xy, paramv=None, paramu=None):
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if paramv is None:
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paramv = (-1, 1, N)
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if paramu is None:
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paramu = paramv
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@ -617,8 +625,10 @@ def mc2rfc(f_xy, paramv=None, paramu=None):
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for i in range(N):
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Spm = Sminus[i] + Splus[i] - dd[i]
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if Spm > 0:
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Max_rfc[i]=(Splus[i]-dd[i])*(Splus[i]-dd[i])/(1-dd[i]/Spm)/Spm
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Min_rfc[i]=(Sminus[i]-dd[i])*(Sminus[i]-dd[i])/(1-dd[i]/Spm)/Spm
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Max_rfc[i] = (Splus[i] - dd[i]) * \
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(Splus[i] - dd[i]) / (1 - dd[i] / Spm) / Spm
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Min_rfc[i] = (Sminus[i] - dd[i]) * \
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(Sminus[i] - dd[i]) / (1 - dd[i] / Spm) / Spm
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norm[i] = Spm
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# end if
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# end for
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@ -671,7 +681,6 @@ def mc2rfc(f_xy, paramv=None, paramu=None):
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AA = np.rot90(AA)
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AA = AA - 0.5 * np.diag(np.diag(AA))
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for j in range(nA):
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if normA[j] != 0:
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AA[j, :] = AA[j, :] / normA[j]
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@ -680,7 +689,8 @@ def mc2rfc(f_xy, paramv=None, paramu=None):
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# end for
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fx = 0.
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if np.max(np.abs(e)) > 1e-7 and np.max(np.abs(NN)) > 1e-7*MA_rfc[0]:
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if np.max(
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np.abs(e)) > 1e-7 and np.max(np.abs(NN)) > 1e-7 * MA_rfc[0]:
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I = np.eye(np.shape(AA))
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if nA == 1:
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@ -695,7 +705,7 @@ def mc2rfc(f_xy, paramv=None, paramu=None):
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# end
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# end
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m0 = np.maximum(0, Min_rfc[N] - sum(f_rfc[N - k + 1:N, 0]))
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M0 = np.maximum(0, Max_rfc[N-k]-sum(f_rfc[N-k, 1:k]));
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M0 = np.maximum(0, Max_rfc[N - k] - sum(f_rfc[N - k, 1:k]))
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f_rfc[N - k, 0] = min(m0, M0)
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# n_loops_left=N-k+1
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# end for
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@ -851,7 +861,7 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None):
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F = cmat
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shape = np.shape(F)
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if ux is None:
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ux = np.arange(shape[1]); # Antalet kolumner
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ux = np.arange(shape[1]) # Antalet kolumner
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if uy is None:
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uy = np.arange(shape[0]) # Antalet rader
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@ -861,17 +871,25 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None):
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if method in [5, 15]:
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clevels = Fmax * np.r_[0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0]
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else: # 4, 14
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clevels = Fmax*np.r_[0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8]
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clevels = Fmax * np.r_[0.005,
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0.01,
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0.02,
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0.05,
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0.1,
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0.2,
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0.4,
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0.6,
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0.8]
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# Make sure ux and uy are row vectors
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ux = ux.ravel();
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uy = uy.ravel();
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ux = ux.ravel()
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uy = uy.ravel()
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n = len(F)
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from matplotlib import pyplot as plt
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if method == 1: # mesh
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F = np.flipud(F.T); # Vrid cykelmatrisen for att plotta rett
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F = np.flipud(F.T) # Vrid cykelmatrisen for att plotta rett
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plt.mesh(ux, np.fliplr(uy), F)
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plt.xlabel('min')
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plt.ylabel('Max')
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@ -1087,6 +1105,7 @@ def cmatplot(cmat, ux=None, uy=None, method=1, clevels=None):
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#
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# end % _cmatplot
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def arfm2mctp(Frfc):
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"""
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ARFM2MCTP Calculates the markov matrix given an asymmetric rainflow matrix.
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@ -1148,11 +1167,10 @@ def arfm2mctp(Frfc):
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# The cond. prob. pS1, pS2, pS3, pH1, pH2, pH3 are calculated using
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# the elementary cond. prob. C, E, R, D, E3, Ch, Eh, Rh, Dh, E3h.
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# T(1,:)=clock;
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N = np.sum(Frfc)
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Frfc = Frfc/N;
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Frfc = Frfc / N
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n = len(Frfc) # Number of levels
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Per A Brodtkorb
8 years ago