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per.andreas.brodtkorb 14 years ago
parent 4d8ba7aefc
commit 51387dcb5d
6 changed files with 89 additions and 65 deletions
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2
pywafo/src/Wafo.egg-info/PKG-INFO
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@ -1,7 +1,7 @@
Metadata-Version: 1.0 Metadata-Version: 1.0
Name: wafo Name: wafo
Version: 0.1.2 Version: 0.1.2
Summary: UNKNOWN Summary: Statistical analysis and simulation of random waves and random loads
Home-page: http://code.google.com/p/pywafo/ Home-page: http://code.google.com/p/pywafo/
Author: WAFO-group Author: WAFO-group
Author-email: wafo@maths.lth.se Author-email: wafo@maths.lth.se

3
pywafo/src/Wafo.egg-info/SOURCES.txt
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@ -262,14 +262,11 @@ src/wafo/spectrum/models.py
src/wafo/stats/__init__.py src/wafo/stats/__init__.py
src/wafo/stats/core.py src/wafo/stats/core.py
src/wafo/stats/distributions.py src/wafo/stats/distributions.py
src/wafo/stats/distributions_juli2010.py
src/wafo/stats/estimation.py src/wafo/stats/estimation.py
src/wafo/stats/misc.py src/wafo/stats/misc.py
src/wafo/stats/twolumps.py
src/wafo/test/__init__.py src/wafo/test/__init__.py
src/wafo/test/test_gaussian.py src/wafo/test/test_gaussian.py
src/wafo/test/test_misc.py src/wafo/test/test_misc.py
src/wafo/test/test_objects.py
src/wafo/transform/__init__.py src/wafo/transform/__init__.py
src/wafo/transform/core.py src/wafo/transform/core.py
src/wafo/transform/models.py src/wafo/transform/models.py

129
pywafo/src/wafo/kdetools.py
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@ -18,14 +18,14 @@ from scipy.special import gamma
from misc import tranproc, trangood from misc import tranproc, trangood
from itertools import product from itertools import product
_stats_epan=(1. / 5, 3. / 5, np.inf) _stats_epan = (1. / 5, 3. / 5, np.inf)
_stats_biwe=(1. / 7, 5. / 7, 45. / 2), _stats_biwe = (1. / 7, 5. / 7, 45. / 2),
_stats_triw=(1. / 9, 350. / 429, np.inf), _stats_triw = (1. / 9, 350. / 429, np.inf),
_stats_rect=(1. / 3, 1. / 2, np.inf), _stats_rect = (1. / 3, 1. / 2, np.inf),
_stats_tria=(1. / 6, 2. / 3, np.inf), _stats_tria = (1. / 6, 2. / 3, np.inf),
_stats_lapl=(2, 1. / 4, np.inf), _stats_lapl = (2, 1. / 4, np.inf),
_stats_logi=(pi ** 2 / 3, 1. / 6, 1 / 42), _stats_logi = (pi ** 2 / 3, 1. / 6, 1 / 42),
_stats_gaus=(1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi))) _stats_gaus = (1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi)))
@ -100,7 +100,7 @@ class KDE(object):
""" """
def __init__(self, dataset, hs=None, kernel=None,L2=None,alpha=0.0): def __init__(self, dataset, hs=None, kernel=None, L2=None, alpha=0.0):
self.kernel = kernel if kernel else Kernel('gauss') self.kernel = kernel if kernel else Kernel('gauss')
self.hs = hs self.hs = hs
self.L2 = L2 self.L2 = L2
@ -121,21 +121,21 @@ class KDE(object):
h = get_smoothing(self.dataset) h = get_smoothing(self.dataset)
hsiz = h.shape hsiz = h.shape
if (min(hsiz)==1) or (self.d==1): if (min(hsiz) == 1) or (self.d == 1):
if max(hsiz)==1: if max(hsiz) == 1:
h = h*np.ones(self.d) h = h * np.ones(self.d)
else: else:
h.shape = (self.d,) # make sure it has the correct dimension h.shape = (self.d,) # make sure it has the correct dimension
# If h negative calculate automatic values # If h negative calculate automatic values
ind, = np.where(h<=0) ind, = np.where(h <= 0)
for i in ind.tolist(): # for i in ind.tolist(): #
h[i] = get_smoothing(self.dataset[i]) h[i] = get_smoothing(self.dataset[i])
deth = h.prod() deth = h.prod()
self.inv_hs = linalg.diag(1.0/h) self.inv_hs = linalg.diag(1.0 / h)
else: #fully general smoothing matrix else: #fully general smoothing matrix
deth = linalg.det(h) deth = linalg.det(h)
if deth<=0: if deth <= 0:
raise ValueError('bandwidth matrix h must be positive definit!') raise ValueError('bandwidth matrix h must be positive definit!')
self.inv_hs = linalg.inv(h) self.inv_hs = linalg.inv(h)
self.hs = h self.hs = h
@ -188,9 +188,9 @@ class KDE(object):
diff = self.dataset - points[:, i, np.newaxis] diff = self.dataset - points[:, i, np.newaxis]
tdiff = np.dot(self.inv_hs, diff) tdiff = np.dot(self.inv_hs, diff)
tmp = self.kernel(tdiff) tmp = self.kernel(tdiff)
result[i] = tmp.sum(axis=-1) result[i] = tmp.sum(axis= -1)
result /= (self._norm_factor*self.kernel.norm_factor(d,self.n)) result /= (self._norm_factor * self.kernel.norm_factor(d, self.n))
return result return result
@ -481,7 +481,7 @@ class _Kernel(object):
return 1.0 return 1.0
def norm_kernel(self, x): def norm_kernel(self, x):
X = np.atleast_2d(x) X = np.atleast_2d(x)
return self._kernel(X)/self.norm_factor(*X.shape) return self._kernel(X) / self.norm_factor(*X.shape)
def kernel(self, x): def kernel(self, x):
return self._kernel(np.atleast_2d(x)) return self._kernel(np.atleast_2d(x))
__call__ = kernel __call__ = kernel
@ -538,7 +538,7 @@ class _KernelRectangular(_Kernel):
return np.where(np.all(np.abs(x) <= self.r, axis=0), 1, 0.0) return np.where(np.all(np.abs(x) <= self.r, axis=0), 1, 0.0)
def norm_factor(self, d=1, n=None): def norm_factor(self, d=1, n=None):
r = self.r r = self.r
return (2*r) ** d return (2 * r) ** d
mkernel_rectangular = _KernelRectangular(stats=_stats_rect) mkernel_rectangular = _KernelRectangular(stats=_stats_rect)
class _KernelTriangular(_Kernel): class _KernelTriangular(_Kernel):
@ -565,7 +565,7 @@ class _KernelLaplace(_Kernel):
absX = np.abs(x) absX = np.abs(x)
return exp(-absX.sum(axis=0)) return exp(-absX.sum(axis=0))
def norm_factor(self, d=1, n=None): def norm_factor(self, d=1, n=None):
return 2**d return 2 ** d
mkernel_laplace = _KernelLaplace(stats=_stats_lapl) mkernel_laplace = _KernelLaplace(stats=_stats_lapl)
class _KernelLogistic(_Kernel): class _KernelLogistic(_Kernel):
@ -825,8 +825,8 @@ class Kernel(object):
return a * linalg.sqrtm(covA) * n * (-1. / (d + 4)) return a * linalg.sqrtm(covA) * n * (-1. / (d + 4))
def norm_factor(self, d=1,n=None): def norm_factor(self, d=1, n=None):
return self.kernel.norm_factor(n,d) return self.kernel.norm_factor(n, d)
def evaluate(self, X): def evaluate(self, X):
return self.kernel(np.atleast_2d(X)) return self.kernel(np.atleast_2d(X))
__call__ = evaluate __call__ = evaluate
@ -981,7 +981,17 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None):
out[s] = func(vals[s]) out[s] = func(vals[s])
return out return out
def bitget(int_type, offset):
'''
Returns the value of the bit at the offset position in int_type.
Example
-------
>>> bitget(5, np.r_[0:4])
array([1, 0, 1, 0])
'''
mask = (1 << offset)
return (int_type & mask) != 0
def gridcount(data, X): def gridcount(data, X):
''' '''
GRIDCOUNT D-dimensional histogram using linear binning. GRIDCOUNT D-dimensional histogram using linear binning.
@ -1016,11 +1026,13 @@ def gridcount(data, X):
>>> import numpy as np >>> import numpy as np
>>> import wafo.kdetools as wk >>> import wafo.kdetools as wk
>>> import pylab as plb >>> import pylab as plb
>>> N = 500; >>> N = 20;
>>> data = np.random.rayleigh(1,N) >>> data = np.random.rayleigh(1,N)
>>> x = np.linspace(0,max(data)+1,50) >>> x = np.linspace(0,max(data)+1,50)
>>> dx = x[1]-x[0] >>> dx = x[1]-x[0]
>>> c = wk.gridcount(data,x) >>> c = wk.gridcount(data,x)
>>> h = plb.plot(x,c,'.') # 1D histogram >>> h = plb.plot(x,c,'.') # 1D histogram
>>> h1 = plb.plot(x,c/dx/N) # 1D probability density plot >>> h1 = plb.plot(x,c/dx/N) # 1D probability density plot
>>> np.trapz(x,c/dx/N) >>> np.trapz(x,c/dx/N)
@ -1043,7 +1055,6 @@ def gridcount(data, X):
if d != d1: if d != d1:
raise ValueError('Dimension 0 of data and X do not match.') raise ValueError('Dimension 0 of data and X do not match.')
dx = np.diff(x[:, :2], axis=1) dx = np.diff(x[:, :2], axis=1)
xlo = x[:, 0] xlo = x[:, 0]
xup = x[:, -1] xup = x[:, -1]
@ -1061,47 +1072,46 @@ def gridcount(data, X):
abs = np.abs abs = np.abs
if d == 1: if d == 1:
x.shape = (-1,) x.shape = (-1,)
c = (accum(binx, (x[binx + 1] - data), size=[inc, ]) + c = (accum(binx, (x[binx + 1] - dat), size=[inc, ]) +
accum(binx, (data - x[binx]), size=[inc, ])) / w accum(binx, (dat - x[binx]), size=[inc, ])) / w
elif d == 2: elif d == 2:
b2 = binx[1] b2 = binx[1]
b1 = binx[0] b1 = binx[0]
c_ = np.c_ c_ = np.c_
stk = np.vstack stk = np.vstack
c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) + c = (accum(c_[b1, b2] , abs(np.prod(stk([X[0, b1 + 1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - data, axis=0)), size=[inc, inc]) + accum(c_[b1 + 1, b2] , abs(np.prod(stk([X[0, b1], X[1, b2 + 1]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - data, axis=0)), size=[inc, inc]) + accum(c_[b1 , b2 + 1], abs(np.prod(stk([X[0, b1 + 1], X[1, b2]]) - dat, axis=0)), size=[inc, inc]) +
accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - data, axis=0)), size=[inc, inc])) / w accum(c_[b1 + 1, b2 + 1], abs(np.prod(stk([X[0, b1], X[1, b2]]) - dat, axis=0)), size=[inc, inc])) / w
else: # % d>2 else: # % d>2
raise ValueError('Not implemented for d>2')
Nc = csiz.prod() Nc = csiz.prod()
c = np.zeros((Nc, 1)) c = np.zeros((Nc,))
fact2 = inc * np.arange(d) fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int)
fact1 = csiz.cumprod() / inc fact1 = np.asarray(np.reshape(csiz.cumprod() / inc, (d, -1)), dtype=int)
#fact1 = fact1(ones(n,1),:); #fact1 = fact1(ones(n,1),:);
# for ir in xrange(2**(d-1)): bt0 = [0, 0]
# bt0[:,:,1] = bitget(ir,1:d) X1 = X.ravel()
# bt0[:,:,2] = 1-bt0[:,:,1] for ir in xrange(2 ** (d - 1)):
# for ix in range(2): bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1))
# one = mod(ix,2)+1; bt0[1] = 1 - bt0[0]
# two = mod(ix+1,2)+1; for ix in xrange(2):
# # Convert to linear index (faster than sub2ind) one = np.mod(ix, 2)
# b1 = sum((binx + bt0(ones(n,1),:,one)-1).*fact1,2)+1; #%linear index to c two = np.mod(ix + 1, 2)
# bt2 = bt0(:,:,two) + fact2; # Convert to linear index
# b2 = binx + bt2(ones(n,1),:); #% linear index to X b1 = np.sum((binx + bt0[one]) * fact1, axis=0) #linear index to c
# bt2 = bt0[two] + fact2
# c = c + accum(b1,abs(prod(X(b2)-data,2)),[Nc,1]); b2 = binx + bt2 # linear index to X
# #c = c + accum([b1,ones(n,1)],abs(prod(X(b2)-data,2)),[Nc,1]); c += accum(b1, abs(np.prod(X1[b2] - dat, axis=0)), size=(Nc,))
# #[len,bin,val] = bincount(b1,abs(prod(X(b2)-data,2)));
# #c(bin) = c(bin)+val; c = np.reshape(c / w, csiz)
# # TODO: check that the flipping of axis is correct
# #end T = range(d); T[-2],T[-1] = T[-1], T[-2]
# #end c = c.transpose(*T)
# c = reshape(c/w,csiz);
#end if d == 2: # make sure c is stored in the same way as meshgrid
if d == 2: #% make sure c is stored in the same way as meshgrid
c = c.T c = c.T
elif d == 3: elif d == 3:
c = c.transpose(1, 0, 2) c = c.transpose(1, 0, 2)
@ -1119,7 +1129,7 @@ def test_kde():
f = kde(x) f = kde(x)
#plb.hist(data.ravel()) #plb.hist(data.ravel())
plb.plot(x,f) plb.plot(x, f)
plb.show() plb.show()
def test_gridcount(): def test_gridcount():
@ -1129,7 +1139,8 @@ def test_gridcount():
N = 500; N = 500;
data = np.random.rayleigh(1, size=(2, N)) data = np.random.rayleigh(1, size=(2, N))
x = np.linspace(0, max(data.ravel()) + 1, 10) x = np.linspace(0, max(data.ravel()) + 1, 10)
X = np.vstack((x, x)) X = np.vstack((x, x))
dx = x[1] - x[0] dx = x[1] - x[0]
c = wk.gridcount(data, X) c = wk.gridcount(data, X)
h = plb.contourf(x, x, c) h = plb.contourf(x, x, c)
@ -1147,4 +1158,4 @@ def main():
if __name__ == '__main__': if __name__ == '__main__':
#main() #main()
#test_gridcount() #test_gridcount()
test_kde() test_kde()

16
pywafo/src/wafo/stats/distributions.py
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@ -216,6 +216,22 @@ Check accuracy of cdf and ppf
Random number generation Random number generation
>>> R = %(name)s.rvs(%(shapes)s, size=100) >>> R = %(name)s.rvs(%(shapes)s, size=100)
Compare ML and MPS method
>>> phat = %(name)s.fit2(R, method='ml');
>>> phat.plotfitsummary(); plt.figure(plt.gcf().number+1)
>>> phat2 = %(name)s.fit2(R, method='mps')
>>> phat2.plotfitsummary(); plt.figure(plt.gcf().number+1)
Fix loc=0 and estimate shapes and scale
>>> phat3 = %(name)s.fit2(R, scale=1, floc=0, method='mps')
>>> phat3.plotfitsummary(); plt.figure(plt.gcf().number+1)
Accurate confidence interval with profile loglikelihood
>>> lp = phat3.profile()
>>> lp.plot()
>>> pci = lp.get_bounds()
""" """
_doc_default = ''.join([_doc_default_longsummary, _doc_default = ''.join([_doc_default_longsummary,

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pywafo/src/wafo/stats/estimation.py
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@ -713,7 +713,7 @@ class FitDistribution(rv_frozen):
'''Compute covariance '''Compute covariance
''' '''
somefixed = (self.par_fix != None) and any(isfinite(self.par_fix)) somefixed = (self.par_fix != None) and any(isfinite(self.par_fix))
H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data)) #H1 = numpy.asmatrix(self.dist.hessian_nnlf(self.par, self.data))
H = numpy.asmatrix(self.dist.hessian_nlogps(self.par, self.data)) H = numpy.asmatrix(self.dist.hessian_nlogps(self.par, self.data))
self.H = H self.H = H
try: try:

2
pywafo/src/wafo/stats/tests/test_estimation.py
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@ -7,7 +7,7 @@ Created on 19. nov. 2010
import wafo.stats as ws import wafo.stats as ws
from wafo.stats.estimation import Profile, FitDistribution from wafo.stats.estimation import Profile, FitDistribution
from numpy import log, array from numpy import log, array
def test_profile(): def test_fit_and_profile():
''' '''
# MLE # MLE
import wafo.stats as ws import wafo.stats as ws

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