Package: python-scipy
Version: 0.3.2-6
Severity: normal
Tags: patch
There are typographic bugs in stats.py that make two functions fail. In
addition, the calculation for nanstd() provides obviously wrong answers.
I have not done any formal checking for the correction attached below.
In the distributions.py file, the calculation of est_loc_scale() uses
a sibling method wrongly and references an import by the wrong name.
In the same file, nnlf() method passes its own object twice to _nnlf()
and its parameter parsing is incompatible with the fit() method's needs.
The patch below is on directory /lib/python*/site-packages/scipy/stats
*** stats.py 2005-08-10 10:05:38.000000000 -0700
--- stats.py.orig 2005-08-10 09:31:59.000000000 -0700
***************
*** 241,249 ****
x = x.copy()
Norig = x.shape[axis]
factor = 1.0-sum(isnan(x),axis)*1.0/Norig
! n = N.sum(isnan(x),axis)
N.putmask(x,isnan(x),0)
! return mean(x,axis)/factor
def nanstd(x,axis=-1,bias=0):
"""Compute the standard deviation over the given axis ignoring nans
--- 241,249 ----
x = x.copy()
Norig = x.shape[axis]
factor = 1.0-sum(isnan(x),axis)*1.0/Norig
! n = N-sum(isnan(x),axis)
N.putmask(x,isnan(x),0)
! return stats.mean(x,axis)/factor
def nanstd(x,axis=-1,bias=0):
"""Compute the standard deviation over the given axis ignoring nans
***************
*** 253,266 ****
Norig = x.shape[axis]
n = Norig - sum(isnan(x),axis)*1.0
factor = n/Norig
N.putmask(x,isnan(x),0)
! mn = expand_dims(mean(x,axis),axis)
! y = ss(x-mn,axis)
if bias:
! y /= n
else:
! y /= n-1
! return sqrt(y)
def _nanmedian(arr1d): # This only works on 1d arrays
cond = 1-isnan(arr1d)
--- 253,268 ----
Norig = x.shape[axis]
n = Norig - sum(isnan(x),axis)*1.0
factor = n/Norig
+ n = N-sum(isnan(x),axis)
N.putmask(x,isnan(x),0)
! m1 = stats.mean(x,axis)
! m1c = m1/factor
! m2 = stats.mean((x-m1c)**2.0,axis)
if bias:
! m2c = m2/factor
else:
! m2c = m2*Norig/(n-1.0)
! return m2c
def _nanmedian(arr1d): # This only works on 1d arrays
cond = 1-isnan(arr1d)
*** distributions.py.orig 2005-08-10 09:25:14.000000000 -0700
--- distributions.py 2005-08-10 10:32:11.000000000 -0700
***************
*** 730,740 ****
#
try:
x = args[-1]
! loc = args[-2]
! scale = args[-3]
! args = args[:-3]
except IndexError:
! raise ValueError, "Not enough input arguments."
if not self._argcheck(*args) or scale <= 0:
return inf
x = arr((x-loc) / scale)
--- 730,746 ----
#
try:
x = args[-1]
! args = args[:-1]
except IndexError:
! raise ValueError, "Need shape and data arguments."
! try:
! if len(args) == 1:
! args = args[0]
! loc = args[-1]
! scale = args[-2]
! args = args[:-2]
! except IndexError:
! raise ValueError, "Not enough shape arguments."
if not self._argcheck(*args) or scale <= 0:
return inf
x = arr((x-loc) / scale)
***************
*** 743,749 ****
return inf
else:
N = len(x)
! return self._nnlf(self, x, *args) + N*log(scale)
def fit(self, data, *args, **kwds):
loc0, scale0 = map(kwds.get, ['loc', 'scale'],[0.0, 1.0])
--- 749,755 ----
return inf
else:
N = len(x)
! return self._nnlf(x, *args) + N*log(scale)
def fit(self, data, *args, **kwds):
loc0, scale0 = map(kwds.get, ['loc', 'scale'],[0.0, 1.0])
***************
*** 758,766 ****
return optimize.fmin(self.nnlf,x0,args=(ravel(data),),disp=0)
def est_loc_scale(self, data, *args):
! mu, mu2, g1, g2 = self.stats(*args,**{'moments':'mv'})
! muhat = stats.nanmean(data)
! mu2hat = stats.nanstd(data)
Shat = sqrt(mu2hat / mu2)
Lhat = muhat - Shat*mu
return Lhat, Shat
--- 764,772 ----
return optimize.fmin(self.nnlf,x0,args=(ravel(data),),disp=0)
def est_loc_scale(self, data, *args):
! mu, mu2 = self.stats(*args,**{'moments':'mv'})
! muhat = st.nanmean(data)
! mu2hat = st.nanstd(data)
Shat = sqrt(mu2hat / mu2)
Lhat = muhat - Shat*mu
return Lhat, Shat
-- System Information:
Debian Release: testing/unstable
APT prefers testing
APT policy: (500, 'testing')
Architecture: i386 (i686)
Shell: /bin/sh linked to /bin/bash
Kernel: Linux 2.4.23
Locale: LANG=en_US, LC_CTYPE=en_US (charmap=ISO-8859-1)
Versions of packages python-scipy depends on:
ii python2.3-scipy 0.3.2-6 scientific tools for Python 2.3
python-scipy recommends no packages.
-- no debconf information
