NF - abs routine to deal with largest -ve int

Default abs leaves largest negative int from int dtypes as still
negative.  This is particularly confusing for np.allclose.

Author: matthew-brett

nibabel/casting.py

@@ -355,6 +355,46 @@ def floor_exact(val, flt_type):
     return sign * faval
 
 
+def int_abs(arr):
+    """ Absolute values of array taking care of max negative int values
+
+    Parameters
+    ----------
+    arr : array-like
+
+    Returns
+    -------
+    abs_arr : array
+        array the same shape as `arr` in which all negative numbers have been
+        changed to positive numbers with the magnitude.
+
+    Examples
+    --------
+    This kind of thing is confusing in base numpy:
+
+    >>> import numpy as np
+    >>> np.abs(np.int8(-128))
+    -128
+
+    ``int_abs`` fixes that:
+
+    >>> int_abs(np.int8(-128))
+    128
+    >>> int_abs(np.array([-128, 127], dtype=np.int8))
+    array([128, 127], dtype=uint8)
+    >>> int_abs(np.array([-128, 127], dtype=np.float32))
+    array([ 128.,  127.], dtype=float32)
+    """
+    arr = np.array(arr, copy=False)
+    dt = arr.dtype
+    if dt.kind == 'u':
+        return arr
+    if dt.kind != 'i':
+        return np.absolute(arr)
+    out = arr.astype(np.dtype(dt.str.replace('i', 'u')))
+    return np.choose(arr < 0, (arr, arr * -1), out=out)
+
+
 def floor_log2(x):
     """ floor of log2 of abs(`x`)
 

nibabel/tests/test_casting.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from ..casting import (float_to_int, shared_range, CastingError, int_to_float,
-                       as_int)
+                       as_int, int_abs)
 
 from numpy.testing import (assert_array_almost_equal, assert_array_equal)
 
@@ -121,3 +121,18 @@ def test_casting():
     assert_array_equal(float_to_int(np.nan, np.int16), [0])
     # Test nans give error if not nan2zero
     assert_raises(CastingError, float_to_int, np.nan, np.int16, False)
+
+
+def test_int_abs():
+    for itype in np.sctypes['int']:
+        info = np.iinfo(itype)
+        in_arr = np.array([info.min, info.max], dtype=itype)
+        idtype = np.dtype(itype)
+        udtype = np.dtype(idtype.str.replace('i', 'u'))
+        assert_equal(udtype.kind, 'u')
+        assert_equal(idtype.itemsize, udtype.itemsize)
+        mn, mx = in_arr
+        e_mn = as_int(mx) + 1 # as_int needed for numpy 1.4.1 casting
+        assert_equal(int_abs(mx), mx)
+        assert_equal(int_abs(mn), e_mn)
+        assert_array_equal(int_abs(in_arr), [e_mn, mx])