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Per.Andreas.Brodtkorb 15 years ago
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5 changed files with 1721 additions and 1727 deletions
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69
pywafo/src/wafo/SpecData1D.mm
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1
pywafo/src/wafo/plotbackend.py
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@ -12,6 +12,7 @@ if False:
else: else:
try: try:
from matplotlib import pyplot as plotbackend from matplotlib import pyplot as plotbackend
plotbackend.interactive(True)
print('wafo.wafodata: plotbackend is set to matplotlib.pyplot') print('wafo.wafodata: plotbackend is set to matplotlib.pyplot')
except: except:
print('wafo: Unable to load matplotlib.pyplot as plotbackend') print('wafo: Unable to load matplotlib.pyplot as plotbackend')

113
pywafo/src/wafo/stats/distributions.py
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@ -236,107 +236,6 @@ class rv_frozen_old(object):
return self.dist.pmf(k, *self.args, **self.kwds) return self.dist.pmf(k, *self.args, **self.kwds)
def ecross(t,f,ind,v):
''' Extracts exact level v crossings
Parameters
-----------
t,f : vectors
arguments and functions values, respectively.
ind : scalar or vector of integers
indices to level v crossings as found by findcross.
v : scalar or vector (of size(ind))
defining the level(s) to cross.
Returns
--------
t0 : vector
exact level v crossings.
Description
-----------
ECROSS interpolates t and f linearly to find the exact level v
crossings, i.e., the points where f(t0) = v
Example
-------
>>> from matplotlib import pylab as plb
>>> t = plb.linspace(0,7*pi,250)
>>> x = sin(t);
>>> ind = findcross(x,0.75)
>>> t0 = ecross(t,x,ind,0.75);
>>> plb.plot(t,x,'.',t[ind],x[ind],'r.',t, ones(t.shape)*.75, t0,ones(t0.shape)*0.75,'g.')
See also
--------
findcross
'''
# Tested on: Python 2.5
# revised pab Feb2004
# By pab 18.06.2001
return t[ind]+(v-f[ind])*(t[ind+1]-t[ind])/(f[ind+1]-f[ind])
def findcross(x,v=0.0):
'''
Return indices to level v up and downcrossings of a vector
Parameters
----------
x : vector
sampled values.
v : scalar
crossing level. (Default 0).
Returns
-------
ind : vector of integers
indices to the crossings in the original sequence x.
Example
-------
>>> from matplotlib import pylab as plb
>>> v = 0.75
>>> t = plb.linspace(0,7*pi,250); x = sin(t);
>>> ind = findcross(x,v)
>>> plb.plot(t,x,'.',t[ind],x[ind],'r.', t, ones(t.shape)*v)
See also
---------
crossdef
'''
xn = numpy.atleast_1d(x)
xn = numpy.int8(numpy.sign(xn.ravel()-v))
ind = None
n = len(xn)
if n>1:
iz, = numpy.nonzero(xn==0)
if any(iz):
# Trick to avoid turning points on the crossinglevel.
if iz[0]==0:
if len(iz)==n:
#warning('All values are equal to crossing level!')
return ind
diz = numpy.diff(iz)
ix = iz((diz>1).argmax())
if not any(ix):
ix = iz[-1]
#x(ix) is a up crossing if x(1:ix) = v and x(ix+1) > v.
#x(ix) is a downcrossing if x(1:ix) = v and x(ix+1) < v.
xn[0:ix] = -xn[ix+1]
iz = iz[ix::]
for ix in iz.tolist():
xn[ix] = xn[ix-1]
#% indices to local level crossings ( without turningpoints)
#ind, = numpy.nonzero(xn[:n-1]*xn[1:] < 0)
ind, = (xn[:n-1]*xn[1:] < 0).nonzero()
return ind
def stirlerr(n): def stirlerr(n):
""" """
Return error of Stirling approximation, Return error of Stirling approximation,
@ -400,6 +299,7 @@ def stirlerr(n):
y[n15] = (S0 - (S1 - (S2 - (S3 - S4 / nn15) / nn15) / nn15) / nn15) / n1[n15] y[n15] = (S0 - (S1 - (S2 - (S3 - S4 / nn15) / nn15) / nn15) / nn15) / n1[n15]
return y return y
def bd0(x, npr): def bd0(x, npr):
""" """
Return deviance term x*log(x/npr) + npr - x Return deviance term x*log(x/npr) + npr - x
@ -5734,12 +5634,15 @@ Skellam distribution
""" """
) )
if __name__=='__main__':
def main():
import matplotlib
matplotlib.interactive(True)
#nbinom(10, 0.75).rvs(3) #nbinom(10, 0.75).rvs(3)
bernoulli(0.75).rvs(3) t = bernoulli(0.75).rvs(3)
x = np.r_[5, 10] x = np.r_[5, 10]
npr = np.r_[9, 9] npr = np.r_[9, 9]
bd0(x,npr) t2 = bd0(x, npr)
#Examples MLE and better CI for phat.par[0] #Examples MLE and better CI for phat.par[0]
R = weibull_min.rvs(1, size=100); R = weibull_min.rvs(1, size=100);
phat = weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan]) phat = weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan])
@ -5777,3 +5680,5 @@ if __name__=='__main__':
r = genpareto.rvs(0, size=100) r = genpareto.rvs(0, size=100)
pht = genpareto.fit(r, 1, par_fix=[0, 0, nan]) pht = genpareto.fit(r, 1, par_fix=[0, 0, nan])
lp = pht.profile() lp = pht.profile()
if __name__ == '__main__':
main()

86
pywafo/src/wafo/stats/estimation.py
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@ -8,6 +8,8 @@ from __future__ import division
from wafo.plotbackend import plotbackend from wafo.plotbackend import plotbackend
from wafo.misc import ecross, findcross, JITImport from wafo.misc import ecross, findcross, JITImport
from scipy.misc.ppimport import ppimport
import numdifftools import numdifftools
from scipy.linalg import pinv2 from scipy.linalg import pinv2
@ -16,12 +18,13 @@ from scipy.linalg import pinv2
from scipy import optimize from scipy import optimize
# Trick to avoid error due to circular import # Trick to avoid error due to circular import
#_WAFOCOV = ppimport('wafo.covariance') #_WAFOCOV = ppimport('wafo.covariance')
_WAFODIST = JITImport('wafo.stats.distributions') _WAFODIST = ppimport('wafo.stats.distributions')
#_WAFODIST = JITImport('wafo.stats.distributions')
from numpy import alltrue, arange, \ from numpy import alltrue, arange, \
ravel, ones, sum, \ ravel, ones, sum, \
zeros, log, sqrt, exp zeros, log, sqrt, exp
from numpy import atleast_1d, any, vectorize, asarray, nan, inf, pi from numpy import atleast_1d, any, asarray, nan, inf, pi, reshape, repeat, product, ndarray
import numpy import numpy
import numpy as np import numpy as np
@ -38,13 +41,17 @@ floatinfo = np.finfo(float)
#arr = atleast_1d #arr = atleast_1d
arr = asarray arr = asarray
all = alltrue all = alltrue
def valarray(shape, value=nan, typecode=None):
"""Return an array of all value.
"""
out = reshape(repeat([value], product(shape, axis=0), axis=0), shape)
if typecode is not None:
out = out.astype(typecode)
if not isinstance(out, ndarray):
out = asarray(out)
return out
# Frozen RV class # Frozen RV class
class rv_frozen(object): class rv_frozen(object):
@ -240,7 +247,7 @@ class Profile(object):
self.i_free = nonzero(isfree) self.i_free = nonzero(isfree)
self.Lmax = Lmax self.Lmax = Lmax
self.alpha_Lrange = 0.5*chi2.isf(self.alpha,1) self.alpha_Lrange = 0.5 * _WAFODIST.chi2.isf(self.alpha, 1)
self.alpha_cross_level = Lmax - self.alpha_Lrange self.alpha_cross_level = Lmax - self.alpha_Lrange
lowLevel = self.alpha_cross_level - self.alpha_Lrange / 7.0 lowLevel = self.alpha_cross_level - self.alpha_Lrange / 7.0
@ -337,7 +344,7 @@ class Profile(object):
pcov = self.fit_dist.par_cov[i_notfixed, :][:, i_notfixed] pcov = self.fit_dist.par_cov[i_notfixed, :][:, i_notfixed]
pvar = sum(numpy.dot(drl, pcov) * drl) pvar = sum(numpy.dot(drl, pcov) * drl)
p_crit = norm.isf(self.alpha/2.0)*sqrt(numpy.ravel(pvar))*1.5 p_crit = _WAFODIST.norm.isf(self.alpha / 2.0) * sqrt(numpy.ravel(pvar)) * 1.5
if self.pmin == None: if self.pmin == None:
self.pmin = p_opt - 5.0 * p_crit self.pmin = p_opt - 5.0 * p_crit
if self.pmax == None: if self.pmax == None:
@ -359,10 +366,10 @@ class Profile(object):
prb = exp(self.logSF) prb = exp(self.logSF)
return self.fit_dist.dist.isf(prb, *mphat); return self.fit_dist.dist.isf(prb, *mphat);
def _myprbfun(phatnotfixed): def _myprbfun(self, phatnotfixed):
mphat = self._par.copy() mphat = self._par.copy()
mphat[self.i_notfixed] = phatnotfixed; mphat[self.i_notfixed] = phatnotfixed;
return self.fit_dist.dist.sf(self.x,*mphat); return self.fit_dist.dist.sf(self.x, *mphat)
def _nlogfun(self, free_par, fix_par): def _nlogfun(self, free_par, fix_par):
@ -385,7 +392,7 @@ class Profile(object):
if alpha < self.alpha: if alpha < self.alpha:
raise ValueError('Unable to return CI with alpha less than %g' % self.alpha) raise ValueError('Unable to return CI with alpha less than %g' % self.alpha)
cross_level = self.Lmax - 0.5*chi2.isf(alpha,1) cross_level = self.Lmax - 0.5 * _WAFODIST.chi2.isf(alpha, 1)
ind = findcross(self.data, cross_level) ind = findcross(self.data, cross_level)
N = len(ind) N = len(ind)
if N == 0: if N == 0:
@ -529,7 +536,7 @@ class FitDistribution(rv_frozen):
self.par_cov[:, :] = nan self.par_cov[:, :] = nan
pvar = numpy.diag(self.par_cov) pvar = numpy.diag(self.par_cov)
zcrit = -norm.ppf(self.alpha/2.0) zcrit = -_WAFODIST.norm.ppf(self.alpha / 2.0)
self.par_lower = self.par - zcrit * sqrt(pvar) self.par_lower = self.par - zcrit * sqrt(pvar)
self.par_upper = self.par + zcrit * sqrt(pvar) self.par_upper = self.par + zcrit * sqrt(pvar)
@ -614,8 +621,6 @@ class FitDistribution(rv_frozen):
return Profile(self, **kwds) return Profile(self, **kwds)
def plotfitsumry(self): def plotfitsumry(self):
''' Plot various diagnostic plots to asses the quality of the fit. ''' Plot various diagnostic plots to asses the quality of the fit.
@ -771,7 +776,7 @@ class FitDistribution(rv_frozen):
dx = numpy.diff(x, axis=0) dx = numpy.diff(x, axis=0)
tie = (dx == 0) tie = (dx == 0)
if any(tie): if any(tie):
disp('P-value is on the conservative side (i.e. too large) due to ties in the data!') print('P-value is on the conservative side (i.e. too large) due to ties in the data!')
T = self.nlogps(theta, x) T = self.nlogps(theta, x)
@ -788,7 +793,7 @@ class FitDistribution(rv_frozen):
C1 = m - sqrt(0.5 * n * v) C1 = m - sqrt(0.5 * n * v)
C2 = sqrt(v / (2.0 * n)) C2 = sqrt(v / (2.0 * n))
Tn = (T + 0.5 * k * isParUnKnown - C1) / C2 # chi2 with n degrees of freedom Tn = (T + 0.5 * k * isParUnKnown - C1) / C2 # chi2 with n degrees of freedom
pvalue = chi2.sf(Tn,n) pvalue = _WAFODIST.chi2.sf(Tn, n)
return pvalue return pvalue
def nlogps(self, theta, x): def nlogps(self, theta, x):
@ -830,7 +835,7 @@ class FitDistribution(rv_frozen):
if (any(cond0)): if (any(cond0)):
return inf return inf
else: else:
linfo = numpy.finfo(float) #linfo = numpy.finfo(float)
realmax = floatinfo.machar.xmax realmax = floatinfo.machar.xmax
lowertail = True lowertail = True
@ -857,7 +862,7 @@ class FitDistribution(rv_frozen):
i_tie = nonzero(tie) i_tie = nonzero(tie)
tiedata = x[i_tie] tiedata = x[i_tie]
logD[(I_tie[0]+1,)]= log(self.dist._pdf(tiedata,*args)) + log(scale) logD[(i_tie[0] + 1,)] = log(self.dist._pdf(tiedata, *args)) + log(scale)
finiteD = numpy.isfinite(logD) finiteD = numpy.isfinite(logD)
nonfiniteD = 1 - finiteD nonfiniteD = 1 - finiteD
@ -884,15 +889,16 @@ class FitDistribution(rv_frozen):
return inf return inf
x = arr((x - loc) / scale) x = arr((x - loc) / scale)
cond0 = (x <= self.a) | (self.b <= x) cond0 = (x <= self.a) | (self.b <= x)
newCall = False # newCall = False
if newCall: # if newCall:
goodargs = argsreduce(1-cond0, *((x,))) # goodargs = argsreduce(1-cond0, *((x,)))
goodargs = tuple(goodargs + list(args)) # goodargs = tuple(goodargs + list(args))
N = len(x) # N = len(x)
Nbad = sum(cond0) # Nbad = sum(cond0)
xmin = floatinfo.machar.xmin # xmin = floatinfo.machar.xmin
return self._nnlf(*goodargs) + N*log(scale) + Nbad*100.0*log(xmin) # return self._nnlf(*goodargs) + N*log(scale) + Nbad*100.0*log(xmin)
elif (any(cond0)): # el
if (any(cond0)):
return inf return inf
else: else:
N = len(x) N = len(x)
@ -957,16 +963,17 @@ class FitDistribution(rv_frozen):
#pcov = -pinv(H); #pcov = -pinv(H);
return - H return - H
def main():
if __name__=='__main__':
#nbinom(10, 0.75).rvs(3) #nbinom(10, 0.75).rvs(3)
bernoulli(0.75).rvs(3) import matplotlib
matplotlib.interactive(True)
t = _WAFODIST.bernoulli(0.75).rvs(3)
x = np.r_[5, 10] x = np.r_[5, 10]
npr = np.r_[9, 9] npr = np.r_[9, 9]
bd0(x,npr) t2 = _WAFODIST.bd0(x, npr)
#Examples MLE and better CI for phat.par[0] #Examples MLE and better CI for phat.par[0]
R = weibull_min.rvs(1, size=100); R = _WAFODIST.weibull_min.rvs(1, size=100);
phat = weibull_min.fit(R,1,1,par_fix=[nan,0,nan]) phat = _WAFODIST.weibull_min.fit(R, 1, 1, par_fix=[nan, 0, nan])
Lp = phat.profile(i=0) Lp = phat.profile(i=0)
Lp.plot() Lp.plot()
Lp.get_CI(alpha=0.1) Lp.get_CI(alpha=0.1)
@ -990,14 +997,17 @@ if __name__=='__main__':
t = zipf(2) t = zipf(2)
t.ppf(0.5) t.ppf(0.5)
import pylab as plb import pylab as plb
rice.rvs(1) _WAFODIST.rice.rvs(1)
x = plb.linspace(-5, 5) x = plb.linspace(-5, 5)
y = genpareto.cdf(x,0) y = _WAFODIST.genpareto.cdf(x, 0)
#plb.plot(x,y) #plb.plot(x,y)
#plb.show() #plb.show()
on = ones((2, 3)) on = ones((2, 3))
r = genpareto.rvs(0,size=100) r = _WAFODIST.genpareto.rvs(0, size=100)
pht = genpareto.fit(r,1,par_fix=[0,0,nan]) pht = _WAFODIST.genpareto.fit(r, 1, par_fix=[0, 0, nan])
lp = pht.profile() lp = pht.profile()
if __name__ == '__main__':
main()

9
pywafo/src/wafo/test_ppimport.py
Unescape Escape View File

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from scipy.misc.ppimport import ppimport
st = ppimport('scitools')
def main():
t = st.numpytools.linspace(0,1)
print(t)
if __name__ == '__main__':
main()
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