[Scipy-svn] r6433 - trunk/scipy/stats

[scipy-svn at scipy.org]

Author: oliphant
Date: 2010-05-29 22:51:49 -0500 (Sat, 29 May 2010)
New Revision: 6433

Modified:
   trunk/scipy/stats/_support.py
   trunk/scipy/stats/distributions.py
   trunk/scipy/stats/stats.py
Log:
A few cleanups.

Modified: trunk/scipy/stats/_support.py
===================================================================
--- trunk/scipy/stats/_support.py	2010-05-30 03:11:17 UTC (rev 6432)
+++ trunk/scipy/stats/_support.py	2010-05-30 03:51:49 UTC (rev 6433)
@@ -134,8 +134,7 @@
 
 Format:  adm (a,criterion)   where criterion is like 'x[2]==37'\n"""
 
-    function = 'lines = filter(lambda x: '+criterion+',a)'
-    exec(function)
+    lines = eval('filter(lambda x: '+criterion+',a)')
     try:
         lines = np.array(lines)
     except:

Modified: trunk/scipy/stats/distributions.py
===================================================================
--- trunk/scipy/stats/distributions.py	2010-05-30 03:11:17 UTC (rev 6432)
+++ trunk/scipy/stats/distributions.py	2010-05-30 03:51:49 UTC (rev 6433)
@@ -1423,15 +1423,26 @@
         if (Narg < self.numargs):
             start = self._fitstart(data)  # get distribution specific starting locations
             args += start[Narg:]
-        # location and scale are at the end
-        x0 = args + map(kwds.get, ['loc', 'scale'], self.fit_loc_scale(data))
+        # location and scale
+        loc0, scale0 = None, None
+        if 'loc' not in kwds or 'scale' not in kwds:
+            loc0, scale0 = self.fit_loc_scale(data, *args)
+        loc = kwds.get('loc', loc0)
+        scale = kwds.get('scale', scale0)
+
+        if not np.isfinite(scale):
+            scale = np.sqrt(data.std())
+        if not np.isfinite(loc):
+            loc = data.mean()
+
+        x0 = args + (loc, scale)
         return optimize.fmin(self.nnlf,x0,args=(ravel(data),),disp=0)
 
     def fit_loc_scale(self, data, *args):
         """
         Estimate loc and scale parameters from data using 1st and 2nd moments
         """
-        mu, mu2 = self.stats(*args,**{'moments':'mv'})
+        mu, mu2 = self.stats(*args,moments='mv')
         muhat = st.nanmean(data)
         mu2hat = st.nanstd(data)
         Shat = sqrt(mu2hat / mu2)
@@ -1574,6 +1585,7 @@
         return 0.0, 1.0, 0.0, 0.0
     def _entropy(self):
         return 0.5*(log(2*pi)+1)
+    def fit(self, data, *args, **kwds):
         return arr(data).mean(), arr(data).std(ddof=0)
 norm = norm_gen(name='norm',longname='A normal',extradoc="""
 
@@ -4861,7 +4873,7 @@
 
         signature = inspect.getargspec(self._stats.im_func)
         if (signature[2] is not None) or ('moments' in signature[0]):
-            mu, mu2, g1, g2 = self._stats(*args,**{'moments':moments})
+            mu, mu2, g1, g2 = self._stats(*args,moments=moments)
         else:
             mu, mu2, g1, g2 = self._stats(*args)
         if g1 is None:

Modified: trunk/scipy/stats/stats.py
===================================================================
--- trunk/scipy/stats/stats.py	2010-05-30 03:11:17 UTC (rev 6432)
+++ trunk/scipy/stats/stats.py	2010-05-30 03:51:49 UTC (rev 6433)
@@ -2,7 +2,7 @@
 #
 # Disclaimer
 #
-# This software is provided "as-is".  There are no expressed or implied
+# This software is provided "as-is".  There are no exprgoessed or implied
 # warranties of any kind, including, but not limited to, the warranties
 # of merchantability and fittness for a given application.  In no event
 # shall Gary Strangman be liable for any direct, indirect, incidental,
@@ -1777,18 +1777,6 @@
 
 
 
-def zmap(scores, compare, axis=0):
-    """
-Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to
-array passed to compare (e.g., [time,x,y]).  Assumes collapsing over dim 0
-of the compare array.
-
-"""
-    mns = np.mean(compare,axis)
-    sstd = samplestd(compare,0)
-    return (scores - mns) / sstd
-
-
 #####################################
 #######  TRIMMING FUNCTIONS  #######
 #####################################