Added tests to stats. Added nanXXXX functions. Fixed kurtosis and skew to handle biased and unbiased estimates to match MATLAB.

teoliphant · teoliphant · commit cf5c05a45d57 · 2002-11-01T06:41:33.000Z
diff --git a/scipy_base/__init__.py b/scipy_base/__init__.py
@@ -37,6 +37,7 @@
 Useful functions
 ==================
 select           --  Extension of where to multiple conditions and choices
+unique           --  Pull out unique entries in a sequence
 linspace         --  Evenly spaced samples in linear space
 logspace         --  Evenly spaced samples in logarithmic space
 fix              --  Round x to nearest integer towards zero
@@ -55,6 +56,9 @@
 
 Shape manipulation
 ===================
+apply_over_axes  --  Apply a function over multiple axes, output is same shape
+apply_along_axis --  Apply a function for 1d arrays repeatedly for Nd array
+expand_dims      --  Return array with NewAxis applied before given axis
 squeeze          --  Return a with length-one dimensions removed.
 atleast_1d       --  Force arrays to be > 1D
 atleast_2d       --  Force arrays to be > 2D
diff --git a/scipy_base/function_base.py b/scipy_base/function_base.py
@@ -3,13 +3,15 @@
 N = Numeric
 from Numeric import *
 from scipy_base.fastumath import *
+inf = PINF
 import _compiled_base
 from type_check import ScalarType, isscalar
 
 __all__ = ['round','any','all','logspace','linspace','fix','mod',
            'select','trim_zeros','amax','amin','ptp','cumsum',
            'prod','cumprod','diff','angle','unwrap','sort_complex',
-           'disp','unique']
+           'disp','unique','takemask','nansum','nanmax','nanargmax',
+           'nanargmin','nanmin']
 
 round = Numeric.around
 any = Numeric.sometrue
@@ -264,6 +266,47 @@ def unique(inseq):
         set[item] = None
     return asarray(set.keys())
 
+def takemask(arr,mask):
+    """1D array of those elements of ravel(arr) where ravel(mask) is true.
+    """
+    return N.take(ravel(arr), nonzero(ravel(mask)))
+
+def nansum(x,axis=-1):
+    """Sum the array over the given axis treating nans as missing values.
+    """
+    x = N.asarray(x).copy()
+    N.putmask(x,isnan(x),0)
+    return N.sum(x,axis)
+
+def nanmin(x,axis=-1):
+    """Find the minimium over the given axis ignoring nans.
+    """
+    x = N.asarray(x).copy()
+    N.putmask(x,isnan(x),inf)
+    return amin(x,axis)
+
+def nanargmin(x,axis=-1):
+    """Find the indices of the minimium over the given axis ignoring nans.
+    """
+    x = N.asarray(x).copy()
+    N.putmask(x,isnan(x),inf)
+    return argmin(x,axis)
+    
+
+def nanmax(x,axis=-1):
+    """Find the maximum over the given axis ignoring nans.
+    """
+    x = asarray(x).copy()
+    putmask(x,isnan(x),-inf)
+    return amax(x,axis)
+
+def nanargmax(x,axis=-1):
+    """Find the maximum over the given axis ignoring nans.
+    """
+    x = asarray(x).copy()
+    putmask(x,isnan(x),-inf)
+    return argmax(x,axis)
+
 import sys
 def disp(mesg, device=None, linefeed=1):
     """Display a message to device (default is sys.stdout) with(out) linefeed.
diff --git a/scipy_base/shape_base.py b/scipy_base/shape_base.py
@@ -1,11 +1,73 @@
 import Numeric
 from Numeric import *
+from type_check import isscalar
 
 __all__ = ['atleast_1d','atleast_2d','atleast_3d','vstack','hstack',
            'column_stack','dstack','array_split','split','hsplit',
-           'vsplit','dsplit','squeeze','apply_over_axes','expand_dims']
-
+           'vsplit','dsplit','squeeze','apply_over_axes','expand_dims',
+           'apply_along_axis']
 
+def apply_along_axis(func1d,axis,arr,*args):
+    """ Execute func1d(arr[i],*args) where func1d takes 1-D arrays
+        and arr is an N-d array.  i varies so as to apply the function
+        along the given axis for each 1-d subarray in arr.
+    """
+    nd = Numeric.rank(arr)
+    if axis < 0: axis += nd
+    if (axis >= nd):
+        raise ValueError, "axis must be less than the rank; "+\
+              "axis=%d, rank=%d." % (axis,)
+    ind = [0]*(nd-1)
+    dims = Numeric.shape(arr)
+    i = zeros(nd,'O')
+    indlist = range(nd)
+    indlist.remove(axis)
+    i[axis] = slice(None,None)
+    outshape = take(shape(arr),indlist)
+    put(i,indlist,ind)
+    res = func1d(arr[i],*args)
+    #  if res is a number, then we have a smaller output array
+    if isscalar(res):
+        outarr = zeros(outshape,asarray(res).typecode())
+        outarr[ind] = res
+        Ntot = product(outshape)
+        k = 1
+        while k < Ntot:
+            # increment the index
+            ind[-1] += 1
+            n = -1
+            while (ind[n] >= outshape[n]) and (n > (1-nd)):
+                ind[n-1] += 1
+                ind[n] = 0
+                n -= 1
+            put(i,indlist,ind)
+            res = func1d(arr[i],*args)
+            outarr[ind] = res
+            k += 1
+        return outarr
+    else:
+        Ntot = product(outshape)
+        holdshape = outshape
+        outshape = list(shape(arr))
+        outshape[axis] = len(res)
+        outarr = zeros(outshape,asarray(res).typecode())
+        outarr[i] = res
+        k = 1
+        while k < Ntot:
+            # increment the index
+            ind[-1] += 1
+            n = -1
+            while (ind[n] >= holdshape[n]) and (n > (1-nd)):
+                ind[n-1] += 1
+                ind[n] = 0
+                n -= 1
+            put(i,indlist,ind)
+            res = func1d(arr[i],*args)
+            outarr[i] = res
+            k += 1
+        return outarr
+        
+     
 def apply_over_axes(func, a, axes):
     """Apply a function over multiple axes, keeping the same shape
     for the resulting array.
@@ -21,8 +83,7 @@ def apply_over_axes(func, a, axes):
     return val
 
 def expand_dims(a, axis):
-    """Expand the shape of a to include a length 1 dimension before the given
-    axis.
+    """Expand the shape of a by including NewAxis before given axis.
     """
     a = asarray(a)
     shape = a.shape
diff --git a/scipy_test/testing.py b/scipy_test/testing.py
@@ -343,8 +343,16 @@ def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=1):
     """
     msg = '\nItems are not equal to %d significant digits:\n' % significant
     msg += err_msg
-    sc_desired = desired/pow(10,math.floor(math.log10(abs(desired))))
-    sc_actual = actual/pow(10,math.floor(math.log10(abs(actual))))
+    actual, desired = map(float, (actual, desired))
+    # Normalized the numbers to be in range (-10.0,10.0)
+    try:
+        sc_desired = desired/pow(10,math.floor(math.log10(abs(desired))))
+    except ZeroDivisionError:
+        sc_desired = 0.0
+    try:
+        sc_actual = actual/pow(10,math.floor(math.log10(abs(actual))))
+    except ZeroDivisionError:
+        sc_actual = 0.0
     try:
         if ( verbose and len(repr(desired)) < 100 and len(repr(actual)) ):
             msg =  msg \
