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pep8 + added option for plotting confidence interval in plotesf

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master
Per A Brodtkorb 8 years ago
parent deef6c4994
commit f69683428a
2 changed files with 55 additions and 51 deletions
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102
wafo/misc.py
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@ -185,14 +185,14 @@ def piecewise(condlist, funclist, xi=None, fill_value=0.0, args=(), **kw):
def check_shapes(condlist, funclist):
nc, nf = len(condlist), len(funclist)
if nc not in [nf-1, nf]:
if nc not in [nf - 1, nf]:
raise ValueError("function list and condition list" +
" must be the same length")
check_shapes(condlist, funclist)
condlist = np.broadcast_arrays(*condlist)
if len(condlist) == len(funclist)-1:
if len(condlist) == len(funclist) - 1:
condlist.append(otherwise_condition(condlist))
if xi is None:
arrays = ()
@ -1094,14 +1094,14 @@ def findrfc_astm(tp):
sig_rfc[:,2] Cycle type, half (=0.5) or full (=1.0)
"""
return numba_misc.findrfc_astm(tp)
y1 = atleast_1d(tp).ravel()
sig_rfc, cnr = clib.findrfc3_astm(y1)
# the sig_rfc was constructed too big in rainflow.rf3, so
# reduce the sig_rfc array as done originally by a matlab mex c function
n = len(sig_rfc)
# sig_rfc = sig_rfc.__getslice__(0, n - cnr[0])
# sig_rfc holds the actual rainflow counted cycles, not the indices
return sig_rfc[:n - cnr[0]]
# y1 = atleast_1d(tp).ravel()
# sig_rfc, cnr = clib.findrfc3_astm(y1)
# # the sig_rfc was constructed too big in rainflow.rf3, so
# # reduce the sig_rfc array as done originally by a matlab mex c function
# n = len(sig_rfc)
# # sig_rfc = sig_rfc.__getslice__(0, n - cnr[0])
# # sig_rfc holds the actual rainflow counted cycles, not the indices
# return sig_rfc[:n - cnr[0]]
def findrfc(tp, h=0.0, method='clib'):
@ -1236,15 +1236,15 @@ def nt2cmat(nt, kind=1):
"""
n = len(nt) # Number of discrete levels
if kind == 1:
I = np.r_[0:n-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]
c2 = np.vstack((c, np.zeros((n-1))))
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):
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]
else:
_raise_kind_error(kind)
@ -1303,12 +1303,12 @@ def cmat2nt(cmat, kind=1):
if kind == 1:
csum = np.cumsum
flip = np.fliplr
nt[1:n, :n-1] = flip(csum(flip(csum(cmat[:-1, 1:], axis=0)), axis=1))
nt[1:n, :n - 1] = flip(csum(flip(csum(cmat[:-1, 1:], axis=0)), axis=1))
elif kind == 11: # same as def=1 but using for-loop
# j = np.r_[1:n]
for i in range(1, n):
for j in range(n-1):
nt[i, j] = np.sum(cmat[:i, j+1:n])
for j in range(n - 1):
nt[i, j] = np.sum(cmat[:i, j + 1:n])
else:
_raise_kind_error(kind)
return nt
@ -1340,9 +1340,9 @@ def mctp2tc(f_Mm, utc, param, f_mM=None):
udisc = np.fliplr(u)
ntc = np.sum(udisc >= utc)
n = len(f_Mm)
if ntc > n-1:
if ntc > n - 1:
raise IndexError('index for mean-level out of range, stop')
if param[2]-1 < ntc or ntc < 2:
if param[2] - 1 < ntc or ntc < 2:
raise ValueError('the reference level out of range, stop')
# normalization of frequency matrices
@ -1363,25 +1363,25 @@ def mctp2tc(f_Mm, utc, param, f_mM=None):
Ph = np.fliplr(Ph)
F = np.zeros((n, n))
F[:ntc-1, :(n-ntc)] = f_mM[:ntc-1, :(n-ntc)]
F[:ntc - 1, :(n - ntc)] = f_mM[:ntc - 1, :(n - ntc)]
F = cmat2nt(F)
for i in range(1, ntc):
for j in range(ntc, n-1):
for j in range(ntc, n - 1):
if i < ntc:
Ap = P[i:ntc-1, i+1:ntc]
Bp = Ph[i+1:ntc, i:ntc-1]
dim_p = ntc-i
Ap = P[i:ntc - 1, i + 1:ntc]
Bp = Ph[i + 1:ntc, i:ntc - 1]
dim_p = ntc - i
tempp = zeros((dim_p, 1))
I = np.eye(np.shape(Ap))
if i == 2:
e = Ph[i+1:ntc, 0]
e = Ph[i + 1:ntc, 0]
else:
e = np.sum(Ph[i+1:ntc, 1:i-1], axis=1)
e = np.sum(Ph[i + 1:ntc, 1:i - 1], axis=1)
if max(abs(e)) > 1e-10:
if dim_p == 1:
tempp[0] = (Ap/(1-Bp*Ap)*e)
tempp[0] = (Ap / (1 - Bp * Ap) * e)
else:
rh = I - np.dot(Bp, Ap)
tempp = np.dot(Ap, linalg.solve(rh, e))
@ -1390,49 +1390,52 @@ def mctp2tc(f_Mm, utc, param, f_mM=None):
# end
# end
elif j > ntc:
Am = P[ntc:j-1, ntc+1:j]
Bm = Ph[ntc+1:j, ntc:j-1]
dim_m = j-ntc
Am = P[ntc:j - 1, ntc + 1:j]
Bm = Ph[ntc + 1:j, ntc:j - 1]
dim_m = j - ntc
tempm = zeros((dim_m, 1))
Im = np.eye(np.shape(Am))
if j == n-1:
em = P[ntc:j-1, n]
if j == n - 1:
em = P[ntc:j - 1, n]
else:
em = np.sum(P[ntc:j-1, j+1:n], axis=1)
em = np.sum(P[ntc:j - 1, j + 1:n], axis=1)
# end
if max(abs(em)) > 1e-10:
if dim_m == 1:
tempm[0, 0] = (Bm/(1-Am*Bm)*em)
tempm[0, 0] = (Bm / (1 - Am * Bm) * em)
else:
tempm = np.dot(Bm,
linalg.lstsq(Im-np.dot(Am, Bm), em)[0])
rh = Im - np.dot(Am, Bm)
tempm = np.dot(Bm, linalg.lstsq(rh, em)[0])
# end
# end
# end
if (j > ntc) and (i < ntc):
a = np.dot(np.dot(tempp.T, f_mM[i:ntc-1, n-ntc:-1:n-j+1]),
a = np.dot(np.dot(tempp.T,
f_mM[i:ntc - 1, n - ntc:-1:n - j + 1]),
tempm)
b = np.dot(np.dot(tempp.T, f_mM[i:ntc-1, n-j:-1:1]),
ones((n-j, 1)))
c = np.dot(np.dot(ones((1, i-1)), f_mM[1:i-1, n-ntc:-1:n-j+1]),
b = np.dot(np.dot(tempp.T, f_mM[i:ntc - 1, n - j:-1:1]),
ones((n - j, 1)))
c = np.dot(np.dot(ones((1, i - 1)),
f_mM[1:i - 1, n - ntc:-1:n - j + 1]),
tempm)
F[i, n-j+1] = F[i, n-j+1] + a + b + c
F[i, n - j + 1] = F[i, n - j + 1] + a + b + c
# end
if (j == ntc) and (i < ntc):
b = np.dot(np.dot(tempp.T, f_mM[i:ntc-1, n-j:-1:1]),
ones((n-j, 1)))
F[i, n-j+1] = F[i, n-j+1] + b
b = np.dot(np.dot(tempp.T, f_mM[i:ntc - 1, n - j:-1:1]),
ones((n - j, 1)))
F[i, n - j + 1] = F[i, n - j + 1] + b
for k in range(ntc):
F[i, n-k+1] = F[i, n-ntc+1]
F[i, n - k + 1] = F[i, n - ntc + 1]
# end
# end
if (j > ntc) and (i == ntc):
c = np.dot(np.dot(ones((1, i-1)), f_mM[1:i-1, n-ntc:-1:n-j+1]),
c = np.dot(np.dot(ones((1, i - 1)),
f_mM[1:i - 1, n - ntc:-1:n - j + 1]),
tempm)
F[i, n-j+1] = F[i, n-j+1]+c
F[i, n - j + 1] = F[i, n - j + 1] + c
for k in range(ntc, n):
F[k, n-j+1] = F[ntc, n-j+1]
F[k, n - j + 1] = F[ntc, n - j + 1]
# end
# end
# end
@ -1830,7 +1833,7 @@ def findtc(x_in, v=None, kind=None):
if (2 * n_tc + 1 < n_c) and (kind in (None, 'tw', 'cw')):
# trough or crest
ind[n_c - 2] = f1(x[v_ind[n_c - 2] + 1:v_ind[n_c - 1]+1])
ind[n_c - 2] = f1(x[v_ind[n_c - 2] + 1:v_ind[n_c - 1] + 1])
return v_ind[:n_c - 1] + ind + 1, v_ind
@ -3109,7 +3112,8 @@ def test_docstrings():
# np.set_printoptions(precision=6)
import doctest
print('Testing docstrings in %s' % __file__)
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE|doctest.ELLIPSIS)
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE |
doctest.ELLIPSIS)
if __name__ == "__main__":

4
wafo/stats/estimation.py
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@ -1407,7 +1407,7 @@ class FitDistribution(rv_frozen):
except Exception:
pass
def plotesf(self, symb1='r-', symb2='b.', axis=None):
def plotesf(self, symb1='r-', symb2='b.', axis=None, plot_ci=False):
''' Plot Empirical and fitted Survival Function
The purpose of the plot is to graphically assess whether
@ -1422,7 +1422,7 @@ class FitDistribution(rv_frozen):
axis.semilogy(self.data, sf, symb2,
self.data, self.sf(self.data), symb1)
if True:
if plot_ci:
low = int(np.log10(1.0/n)-0.7) - 1
sf1 = np.logspace(low, -0.5, 7)[::-1]
ci1 = self.ci_sf(sf, alpha=0.05, i=2)

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