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pep8

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master
pbrod 9 years ago
parent 00f3ce12d8
commit 436a9f9ba9
2 changed files with 105 additions and 105 deletions
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8
wafo/doc/rainflow_example.py
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@ -4,7 +4,6 @@ import logging
import matplotlib.pyplot as plt
import itertools
# import sys
import itertools
log = logging.getLogger(__name__)
log.setLevel(logging.DEBUG)
@ -14,6 +13,7 @@ MARKERS = ('o', 'x', '+', '.', '<', '>', '^', 'v')
def set_windows_title(title='', log=None):
pass
def plot_varying_symbols(x, y, color='red', size=5):
"""
Create a plot with varying symbols
@ -29,7 +29,9 @@ def plot_varying_symbols(x, y, color='red', size=5):
"""
markers = itertools.cycle(MARKERS)
for q, p in zip(x, y):
plt.plot(q, p, marker=markers.next(), linestyle='', color=color, markersize=size)
plt.plot(q, p, marker=markers.next(), linestyle='', color=color,
markersize=size)
def damage_vs_S(S, beta, K):
"""
@ -47,7 +49,7 @@ def damage_vs_S(S, beta, K):
return K * np.power(S, beta)
# Section 4.3.1 Crossing intensity
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import wafo.data as wd
import wafo.objects as wo
import wafo.misc as wm

202
wafo/stats/estimation.py
Unescape Escape View File

@ -36,9 +36,9 @@ arr = asarray
all = alltrue # @ReservedAssignment
def _burr_link(x, logSF, phat, ix):
def _burr_link(x, logsf, phat, ix):
c, d, loc, scale = phat
logp = log(-expm1(logSF))
logp = log(-expm1(logsf))
xn = (x - loc) / scale
if ix == 1:
return -logp / log1p(xn**(-c))
@ -51,28 +51,28 @@ def _burr_link(x, logSF, phat, ix):
raise IndexError('Index to the fixed parameter is out of bounds')
def _expon_link(x, logSF, phat, ix):
def _expon_link(x, logsf, phat, ix):
if ix == 1:
return - (x - phat[0]) / logSF
return - (x - phat[0]) / logsf
if ix == 0:
return x + phat[1] * logSF
return x + phat[1] * logsf
raise IndexError('Index to the fixed parameter is out of bounds')
def _weibull_min_link(x, logSF, phat, ix):
def _weibull_min_link(x, logsf, phat, ix):
c, loc, scale = phat
if ix == 0:
return log(-logSF) / log((x - loc) / scale)
return log(-logsf) / log((x - loc) / scale)
if ix == 1:
return x - scale * (-logSF) ** (1. / c)
return x - scale * (-logsf) ** (1. / c)
if ix == 2:
return (x - loc) / (-logSF) ** (1. / c)
return (x - loc) / (-logsf) ** (1. / c)
raise IndexError('Index to the fixed parameter is out of bounds')
def _exponweib_link(x, logSF, phat, ix):
def _exponweib_link(x, logsf, phat, ix):
a, c, loc, scale = phat
logP = -log(-expm1(logSF))
logP = -log(-expm1(logsf))
xn = (x - loc) / scale
if ix == 0:
return - logP / log(-expm1(-xn**c))
@ -85,7 +85,7 @@ def _exponweib_link(x, logSF, phat, ix):
raise IndexError('Index to the fixed parameter is out of bounds')
def _genpareto_link(x, logSF, phat, ix):
def _genpareto_link(x, logsf, phat, ix):
# Reference
# Stuart Coles (2004)
# "An introduction to statistical modelling of extreme values".
@ -94,27 +94,27 @@ def _genpareto_link(x, logSF, phat, ix):
if ix == 2:
# Reorganizing w.r.t.scale, Eq. 4.13 and 4.14, pp 81 in
# Coles (2004) gives
# link = -(x-loc)*c/expm1(-c*logSF)
# link = -(x-loc)*c/expm1(-c*logsf)
if c != 0.0:
phati = (x - loc) * c / expm1(-c * logSF)
phati = (x - loc) * c / expm1(-c * logsf)
else:
phati = -(x - loc) / logSF
phati = -(x - loc) / logsf
elif ix == 1:
if c != 0:
phati = x + scale * expm1(c * logSF) / c
phati = x + scale * expm1(c * logsf) / c
else:
phati = x + scale * logSF
phati = x + scale * logsf
elif ix == 0:
raise NotImplementedError(
'link(x,logSF,phat,i) where i=0 is not implemented!')
'link(x,logsf,phat,i) where i=0 is not implemented!')
else:
raise IndexError('Index to the fixed parameter is out of bounds')
return phati
def _genextreme_link(x, logSF, phat, ix):
def _genextreme_link(x, logsf, phat, ix):
c, loc, scale = phat
loglogP = log(-log(-expm1(logSF)))
loglogP = log(-log(-expm1(logsf)))
if ix == 2:
# link = -(x-loc)*c/expm1(c*log(-logP))
if c != 0.0:
@ -126,40 +126,40 @@ def _genextreme_link(x, logSF, phat, ix):
return x + scale * loglogP
if ix == 0:
raise NotImplementedError(
'link(x,logSF,phat,i) where i=0 is not implemented!')
'link(x,logsf,phat,i) where i=0 is not implemented!')
raise IndexError('Index to the fixed parameter is out of bounds')
def _genexpon_link(x, logSF, phat, ix):
def _genexpon_link(x, logsf, phat, ix):
a, b, c, loc, scale = phat
xn = (x - loc) / scale
fact1 = (xn + expm1(-c * xn) / c)
if ix == 0:
return b * fact1 + logSF # a
return b * fact1 + logsf # a
if ix == 1:
return (a - logSF) / fact1 # b
return (a - logsf) / fact1 # b
if ix in [2, 3, 4]:
raise NotImplementedError('Only implemented for index in [0,1]!')
raise IndexError('Index to the fixed parameter is out of bounds')
def _rayleigh_link(x, logSF, phat, ix):
def _rayleigh_link(x, logsf, phat, ix):
if ix == 1:
return x - phat[0] / sqrt(-2.0 * logSF)
return x - phat[0] / sqrt(-2.0 * logsf)
if ix == 0:
return x - phat[1] * sqrt(-2.0 * logSF)
return x - phat[1] * sqrt(-2.0 * logsf)
raise IndexError('Index to the fixed parameter is out of bounds')
def _trunclayleigh_link(x, logSF, phat, ix):
def _trunclayleigh_link(x, logsf, phat, ix):
c, loc, scale = phat
if ix == 0:
xn = (x - loc) / scale
return - 2 * logSF / xn - xn / 2.0
return - 2 * logsf / xn - xn / 2.0
if ix == 2:
return x - loc / (sqrt(c * c - 2 * logSF) - c)
return x - loc / (sqrt(c * c - 2 * logsf) - c)
if ix == 1:
return x - scale * (sqrt(c * c - 2 * logSF) - c)
return x - scale * (sqrt(c * c - 2 * logsf) - c)
raise IndexError('Index to the fixed parameter is out of bounds')
@ -240,9 +240,9 @@ class Profile(object):
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
# Better 90% CI for phat.par[i=0]
>>> Lp = Profile(phat, i=0)
>>> Lp.plot()
>>> phat_ci = Lp.get_bounds(alpha=0.1)
>>> profile_phat_i = Profile(phat, i=0)
>>> profile_phat_i.plot()
>>> phat_ci = profile_phat_i.get_bounds(alpha=0.1)
'''
def __init__(self, fit_dist, i=None, pmin=None, pmax=None, n=100,
@ -558,16 +558,13 @@ def plot_all_profiles(phats, plot=None):
n = len(indices)
for j, k in enumerate(indices):
plt.subplot(n, 1, j+1)
Lp1 = Profile(phats, i=k)
profile_phat_k = Profile(phats, i=k)
m = 0
while hasattr(Lp1, 'best_par') and m < 7:
phats.fit(*Lp1.best_par)
# phats = FitDistribution(dist, data, args=Lp1.best_par,
# method=method, search=True)
Lp1 = Profile(phats, i=k)
while hasattr(profile_phat_k, 'best_par') and m < 7:
phats.fit(*profile_phat_k.best_par)
profile_phat_k = Profile(phats, i=k)
m += 1
Lp1.plot()
profile_phat_k.plot()
if j != 0:
plt.title('')
if j != n//2:
@ -600,9 +597,9 @@ class ProfileQuantile(Profile):
alpha : real scalar
confidence coefficent (default 0.05)
link : function connecting the x-quantile and the survival probability
(SF) with the fixed distribution parameter, i.e.:
self.par[i] = link(x, logSF, self.par, i), where
logSF = log(Prob(X>x;phat)).
(sf) with the fixed distribution parameter, i.e.:
self.par[i] = link(x, logsf, self.par, i), where
logsf = log(Prob(X>x;phat)).
(default is fetched from the LINKS dictionary)
Returns
@ -625,8 +622,8 @@ class ProfileQuantile(Profile):
Lmax : Maximum value of profile function
alpha_cross_level :
PROFILE is a utility function for making inferences either on a particular
component of the vector phat or the quantile, x, or the probability, SF.
ProfileQuantile is a utility function for making inferences on the
quantile, x.
This is usually more accurate than using the delta method assuming
asymptotic normality of the ML estimator or the MPS estimator.
@ -641,9 +638,9 @@ class ProfileQuantile(Profile):
>>> x = phat.isf(sf)
# 80% CI for x
>>> Lx = ProfileQuantile(phat, x)
>>> Lx.plot()
>>> x_ci = Lx.get_bounds(alpha=0.2)
>>> profile_x = ProfileQuantile(phat, x)
>>> profile_x.plot()
>>> x_ci = profile_x.get_bounds(alpha=0.2)
'''
def __init__(self, fit_dist, x, i=None, pmin=None, pmax=None, n=100,
alpha=0.05, link=None):
@ -709,9 +706,9 @@ class ProfileProbability(Profile):
alpha : real scalar
confidence coefficent (default 0.05)
link : function connecting the x-quantile and the survival probability
(SF) with the fixed distribution parameter, i.e.:
self.par[i] = link(x, logSF, self.par, i), where
logSF = log(Prob(X>x;phat)).
(sf) with the fixed distribution parameter, i.e.:
self.par[i] = link(x, logsf, self.par, i), where
logsf = log(Prob(X>x;phat)).
(default is fetched from the LINKS dictionary)
Returns
@ -734,7 +731,7 @@ class ProfileProbability(Profile):
Lmax : Maximum value of profile function
alpha_cross_level :
PROFILE is a utility function for making inferences the survival
ProfileProbability is a utility function for making inferences the survival
probability (sf).
This is usually more accurate than using the delta method assuming
asymptotic normality of the ML estimator or the MPS estimator.
@ -749,9 +746,9 @@ class ProfileProbability(Profile):
>>> sf = 1./990
# 80% CI for sf
>>> Lsf = ProfileProbability(phat, np.log(sf))
>>> Lsf.plot()
>>> sf_ci = Lsf.get_bounds(alpha=0.2)
>>> profile_logsf = ProfileProbability(phat, np.log(sf))
>>> profile_logsf.plot()
>>> logsf_ci = profile_logsf.get_bounds(alpha=0.2)
'''
def __init__(self, fit_dist, logsf, i=None, pmin=None, pmax=None, n=100,
alpha=0.05, link=None):
@ -776,8 +773,8 @@ class ProfileProbability(Profile):
def _myprbfun(self, phatnotfixed):
mphat = self._par.copy()
mphat[self.i_notfixed] = phatnotfixed
logSF = self.fit_dist.dist.logsf(self.x, *mphat)
return np.where(np.isfinite(logSF), logSF, np.nan)
logsf = self.fit_dist.dist.logsf(self.x, *mphat)
return np.where(np.isfinite(logsf), logsf, np.nan)
def _get_variance(self):
i_notfixed = self.i_notfixed
@ -865,30 +862,30 @@ class FitDistribution(rv_frozen):
>>> R = ws.weibull_min.rvs(1,size=100);
>>> phat = FitDistribution(ws.weibull_min, R, 1, scale=1, floc=0.0)
#Plot various diagnostic plots to asses quality of fit.
# Plot various diagnostic plots to asses quality of fit.
>>> phat.plotfitsummary()
#phat.par holds the estimated parameters
#phat.par_upper upper CI for parameters
#phat.par_lower lower CI for parameters
#Better CI for phat.par[0]
>>> Lp = phat.profile(i=0)
>>> Lp.plot()
>>> p_ci = Lp.get_bounds(alpha=0.1)
# phat.par holds the estimated parameters
# phat.par_upper upper CI for parameters
# phat.par_lower lower CI for parameters
>>> SF = 1./990
>>> x = phat.isf(SF)
# Better 90% CI for phat.par[0]
>>> profile_phat_i = phat.profile(i=0)
>>> profile_phat_i.plot()
>>> p_ci = profile_phat_i.get_bounds(alpha=0.1)
# CI for x
>>> Lx = phat.profile_quantile(x=x)
>>> Lx.plot()
>>> x_ci = Lx.get_bounds(alpha=0.2)
>>> sf = 1./990
>>> x = phat.isf(sf)
# CI for logSF=log(SF)
>>> Lsf = phat.profile_probability(log(SF))
>>> Lsf.plot()
>>> sf_ci = Lsf.get_bounds(alpha=0.2)
# 80% CI for x
>>> profile_x = phat.profile_quantile(x=x)
>>> profile_x.plot()
>>> x_ci = profile_x.get_bounds(alpha=0.2)
# 80% CI for logsf=log(sf)
>>> profile_logsf = phat.profile_probability(log(sf))
>>> profile_logsf.plot()
>>> sf_ci = profile_logsf.get_bounds(alpha=0.2)
'''
def __init__(self, dist, data, args=(), method='ML', alpha=0.05,
@ -1021,9 +1018,9 @@ class FitDistribution(rv_frozen):
else:
kwds[key] = val
args = list(args)
Nargs = len(args)
nargs = len(args)
fixedn = []
names = ['f%d' % n for n in range(Nargs - 2)] + ['floc', 'fscale']
names = ['f%d' % n for n in range(nargs - 2)] + ['floc', 'fscale']
x0 = []
for n, key in enumerate(names):
if key in kwds:
@ -1038,7 +1035,7 @@ class FitDistribution(rv_frozen):
func = fitfun
restore = None
else:
if len(fixedn) == Nargs:
if len(fixedn) == nargs:
raise ValueError("All parameters fixed. " +
"There is nothing to optimize.")
@ -1047,7 +1044,7 @@ class FitDistribution(rv_frozen):
# This allows the non-fixed values to vary, but
# we still call self.nnlf with all parameters.
i = 0
for n in range(Nargs):
for n in range(nargs):
if n not in fixedn:
args[n] = theta[i]
i += 1
@ -1189,7 +1186,8 @@ class FitDistribution(rv_frozen):
return -np.sum(logD[finiteD], axis=0) + penalty
return -np.sum(logD, axis=0)
def _get_optimizer(self, kwds):
@staticmethod
def _get_optimizer(kwds):
optimizer = kwds.pop('optimizer', optimize.fmin)
# convert string to function in scipy.optimize
if not callable(optimizer) and isinstance(optimizer, string_types):
@ -1203,14 +1201,14 @@ class FitDistribution(rv_frozen):
def _fit_start(self, data, args, kwds):
dist = self.dist
Narg = len(args)
if Narg > dist.numargs + 2:
narg = len(args)
if narg > dist.numargs + 2:
raise ValueError("Too many input arguments.")
if (Narg < dist.numargs + 2) or not ('loc' in kwds and
if (narg < dist.numargs + 2) or not ('loc' in kwds and
'scale' in kwds):
# get distribution specific starting locations
start = dist._fitstart(data)
args += start[Narg:]
args += start[narg:]
loc = kwds.pop('loc', args[-2])
scale = kwds.pop('scale', args[-1])
args = args[:-2] + (loc, scale)
@ -1315,9 +1313,9 @@ class FitDistribution(rv_frozen):
>>> x = phat.isf(sf)
# 80% CI for x
>>> Lx = phat.profile_quantile(x)
>>> Lx.plot()
>>> x_ci = Lx.get_bounds(alpha=0.2)
>>> profile_x = phat.profile_quantile(x)
>>> profile_x.plot()
>>> x_ci = profile_x.get_bounds(alpha=0.2)
'''
return ProfileQuantile(self, x, **kwds)
@ -1335,9 +1333,9 @@ class FitDistribution(rv_frozen):
>>> log_sf = np.log(1./990)
# 80% CI for log_sf
>>> Lsf = phat.profile_probability(log_sf)
>>> Lsf.plot()
>>> log_sf_ci = Lsf.get_bounds(alpha=0.2)
>>> profile_logsf = phat.profile_probability(log_sf)
>>> profile_logsf.plot()
>>> log_sf_ci = profile_logsf.get_bounds(alpha=0.2)
'''
return ProfileProbability(self, log_sf, **kwds)
@ -1392,10 +1390,10 @@ class FitDistribution(rv_frozen):
Other distribution types will introduce deviations in the plot.
'''
n = len(self.data)
SF = (arange(n, 0, -1)) / n
sf = (arange(n, 0, -1)) / n
plt.semilogy(
self.data, SF, symb2, self.data, self.sf(self.data), symb1)
# plt.plot(self.data,SF,'b.',self.data,self.sf(self.data),'r-')
self.data, sf, symb2, self.data, self.sf(self.data), symb1)
# plt.plot(self.data,sf,'b.',self.data,self.sf(self.data),'r-')
plt.xlabel('x')
plt.ylabel('F(x) (%s)' % self.dist.name)
plt.title('Empirical SF plot')
@ -1575,9 +1573,9 @@ def test1():
x = phat.isf(sf)
# 80% CI for x
Lx = ProfileQuantile(phat, x)
Lx.plot()
# x_ci = Lx.get_bounds(alpha=0.2)
profile_x = ProfileQuantile(phat, x)
profile_x.plot()
# x_ci = profile_x.get_bounds(alpha=0.2)
plt.figure(5)
@ -1585,9 +1583,9 @@ def test1():
x = phat.isf(sf)
# 80% CI for x
Lsf = ProfileProbability(phat, np.log(sf))
Lsf.plot()
# logsf_ci = Lsf.get_bounds(alpha=0.2)
profile_logsf = ProfileProbability(phat, np.log(sf))
profile_logsf.plot()
# logsf_ci = profile_logsf.get_bounds(alpha=0.2)
plt.show('hold')

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