Merge pull request #44 from dougalsutherland/py3k

Python 3 support; closes #19.

Makefile

@@ -1,5 +1,7 @@
 # Bottleneck Makefile 
 
+PYTHON=python
+
 srcdir := bottleneck/src
 
 help:
@@ -17,7 +19,7 @@ help:
 all: clean pyx cfiles build test
 
 pyx:
-	python -c "from bottleneck.src.makepyx import makepyx; makepyx()"
+	${PYTHON} -c "from bottleneck.src.makepyx import makepyx; makepyx()"
 
 cfiles:
 	cython ${srcdir}/func/32bit/func.pyx
@@ -29,23 +31,23 @@ build: funcs moves
 
 funcs:
 	rm -rf ${srcdir}/../func.so
-	python ${srcdir}/func/setup.py build_ext --inplace
+	${PYTHON} ${srcdir}/func/setup.py build_ext --inplace
 
 moves:
 	rm -rf ${srcdir}/../move.so
-	python ${srcdir}/move/setup.py build_ext --inplace
+	${PYTHON} ${srcdir}/move/setup.py build_ext --inplace
 
 test:
-	python -c "import bottleneck;bottleneck.test(extra_argv=['--processes=4'])"
+	${PYTHON} -c "import bottleneck;bottleneck.test(extra_argv=['--processes=4'])"
 
 bench:
-	python -c "import bottleneck; bottleneck.bench()"
+	${PYTHON} -c "import bottleneck; bottleneck.bench()"
 
 sdist: pyx cfiles
 	rm -f MANIFEST
 	git status
 	find -name *.c
-	python setup.py sdist
+	${PYTHON} setup.py sdist
 
 # Phony targets for cleanup and similar uses
 

README.rst

@@ -228,7 +228,7 @@ Install
 Requirements:
 
 ======================== ====================================================
-Bottleneck               Python 2.5, 2.6, 2.7; NumPy 1.5.1 or 1.6.0
+Bottleneck               Python 2.6, 2.7, or 3.2; NumPy 1.5.1 or 1.6.0
 Unit tests               nose
 Compile                  gcc or MinGW
 Optional                 SciPy 0.8.0 or 0.9.0 (portions of benchmark)

RELEASE.rst

@@ -17,6 +17,7 @@ Bottleneck 0.6.0
 
 **Enhancements**
 
+- Python 3 support -- only 3.2 tested, but earlier versions may work
 - Upgrade numpydoc from 0.3.1 to 0.4 to support Sphinx 1.0.1
 
 **Bug fixes**

bottleneck/__init__.py

@@ -1,21 +1,22 @@
+from __future__ import absolute_import
 
 # Supported dtypes
 dtypes = ['int32', 'int64', 'float32', 'float64']
 
-import slow
+from . import slow
 
 # If you bork the build (e.g. by messing around with the templates),
 # you still want to be able to import Bottleneck so that you can
 # rebuild using the templates. So try to import the compiled Bottleneck
 # functions to the top level, but move on if not successful.
 try:
-    from func import (nansum, nanmax, nanmin, nanmean, nanstd, nanvar, median,
+    from .func import (nansum, nanmax, nanmin, nanmean, nanstd, nanvar, median,
                       nanmedian, nanargmin, nanargmax, rankdata, nanrankdata,
                       ss, partsort, argpartsort, replace)
 except:
     pass
 try:
-    from move import (move_sum, move_nansum,
+    from .move import (move_sum, move_nansum,
                       move_mean, move_nanmean,
                       move_std, move_nanstd,
                       move_min, move_nanmin,
@@ -32,4 +33,4 @@
     test = Tester().test
     del Tester
 except (ImportError, ValueError):
-    print "No Bottleneck unit testing available."
+    print("No Bottleneck unit testing available.")

bottleneck/benchmark/autotimeit.py

@@ -14,5 +14,5 @@ def autoscaler(timer, mintime):
         if time > mintime:
             return number, time 
         number *= 10
-    raise RuntimeError, 'function is too fast to test'    
+    raise RuntimeError('function is too fast to test')
 

bottleneck/benchmark/bench.py

@@ -1,10 +1,10 @@
 
 # For support of python 2.5
-from __future__ import with_statement
+from __future__ import absolute_import, with_statement
 
 import numpy as np
 import bottleneck as bn
-from autotimeit import autotimeit
+from .autotimeit import autotimeit
 
 __all__ = ['bench']
 
@@ -55,47 +55,47 @@ def bench(mode='fast', dtype='float64', axis=0,
     tab = '    '
 
     # Header
-    print 'Bottleneck performance benchmark'
-    print "%sBottleneck  %s" % (tab, bn.__version__)
-    print "%sNumpy (np)  %s" % (tab, np.__version__)
+    print('Bottleneck performance benchmark')
+    print("%sBottleneck  %s" % (tab, bn.__version__))
+    print("%sNumpy (np)  %s" % (tab, np.__version__))
     if SCIPY:
-        print "%sScipy (sp)  %s" % (tab, sp.__version__)
+        print("%sScipy (sp)  %s" % (tab, sp.__version__))
     else:
-        print "%sScipy (sp)  Cannot import, skipping scipy benchmarks" % tab
-    print "%sSpeed is NumPy or SciPy time divided by Bottleneck time" % tab
+        print("%sScipy (sp)  Cannot import, skipping scipy benchmarks" % tab)
+    print("%sSpeed is NumPy or SciPy time divided by Bottleneck time" % tab)
     tup = (tab, dtype, axis)
-    print "%sNaN means one-third NaNs; %s and axis=%s are used" % tup 
+    print("%sNaN means one-third NaNs; %s and axis=%s are used" % tup )
     if mode == 'fast':
-        print "%sHigh-level functions used (mode='fast')" % tab
+        print("%sHigh-level functions used (mode='fast')" % tab)
     elif mode == 'faster':    
-        print "%sLow-level functions used (mode='faster')" % tab
+        print("%sLow-level functions used (mode='faster')" % tab)
 
-    print
+    print('')
     header = [" "*14]
     for nan in nans:
         if nan:
             header.append("NaN".center(11))
         else:
             header.append("no NaN".center(11))
-    print "".join(header)       
+    print("".join(header))
     header = ["".join(str(shape).split(" ")).center(11) for shape in shapes]
     header = [" "*14] + header
-    print "".join(header)
+    print("".join(header))
 
     suite = benchsuite(mode, shapes, dtype, axis, nans)
     for test in suite:
         name = test["name"].ljust(12)
         fmt = name + "%10.2f" + "%11.2f"*(len(shapes) - 1)
         if test['scipy_required'] and not SCIPY:
-            print "%s%s" % (name, "requires SciPy")
+            print("%s%s" % (name, "requires SciPy"))
         else:
             speed = timer(test['statements'], test['setups'])
-            print fmt % tuple(speed)
+            print(fmt % tuple(speed))
 
-    print
-    print 'Reference functions:'
+    print('')
+    print('Reference functions:')
     for test in suite:
-        print "%s%s" % (test["name"].ljust(15), test['ref'])
+        print("%s%s" % (test["name"].ljust(15), test['ref']))
 
 def timer(statements, setups):
     speed = []
@@ -343,7 +343,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_sum"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_sum(a, window=w, axis=AXIS)"
@@ -352,7 +352,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_sum(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_sum as scipy_move_sum
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_sum(a, window=w, axis=AXIS)
         func, a = bn.move.move_sum_selector(a, axis=AXIS)
     """
@@ -364,7 +364,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_nansum"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_nansum(a, window=w, axis=AXIS)"
@@ -373,7 +373,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_nansum(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_nansum as scipy_move_nansum
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_nansum(a, window=w, axis=AXIS)
         func, a = bn.move.move_nansum_selector(a, axis=AXIS)
     """
@@ -385,7 +385,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_mean"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_mean(a, window=w, axis=AXIS)"
@@ -394,7 +394,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_mean(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_mean as scipy_move_mean
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_mean(a, window=w, axis=AXIS)
         func, a = bn.move.move_mean_selector(a, axis=AXIS)
     """
@@ -406,7 +406,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_nanmean"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_nanmean(a, window=w, axis=AXIS)"
@@ -415,7 +415,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_nanmean(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_nanmean as scipy_move_nanmean
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_nanmean(a, window=w, axis=AXIS)
         func, a = bn.move.move_nanmean_selector(a, axis=AXIS)
     """
@@ -427,7 +427,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_std"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_std(a, window=w, axis=AXIS)"
@@ -436,7 +436,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_std(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_std as scipy_move_std
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_std(a, window=w, axis=AXIS)
         func, a = bn.move.move_std_selector(a, axis=AXIS)
     """
@@ -448,7 +448,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_nanstd"
     run['ref'] = "sp.ndimage.convolve1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_nanstd(a, window=w, axis=AXIS)"
@@ -457,7 +457,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_nanstd(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_nanstd as scipy_move_nanstd
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_nanstd(a, window=w, axis=AXIS)
         func, a = bn.move.move_nanstd_selector(a, axis=AXIS)
     """
@@ -469,7 +469,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_max"
     run['ref'] = "sp.ndimage.maximum_filter1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_max(a, window=w, axis=AXIS)"
@@ -478,7 +478,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_max(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_max as scipy_move_max
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_max(a, window=w, axis=AXIS)
         func, a = bn.move.move_max_selector(a, axis=AXIS)
     """
@@ -490,7 +490,7 @@ def getsetups(setup, shapes, nans):
     run = {}
     run['name'] = "move_nanmax"
     run['ref'] = "sp.ndimage.maximum_filter1d based, "
-    run['ref'] += "window=a.shape[%s]/5" % axis
+    run['ref'] += "window=a.shape[%s] // 5" % axis
     run['scipy_required'] = True
     if mode == 'fast':
         code = "bn.move_nanmax(a, window=w, axis=AXIS)"
@@ -499,7 +499,7 @@ def getsetups(setup, shapes, nans):
     run['statements'] = [code, "scipy_move_nanmax(a, window=w, axis=AXIS)"] 
     setup = """
         from bottleneck.slow.move import move_nanmax as scipy_move_nanmax
-        w = a.shape[AXIS] / 5
+        w = a.shape[AXIS] // 5
         ignore = bn.slow.move_nanmax(a, window=w, axis=AXIS)
         func, a = bn.move.move_nanmax_selector(a, axis=AXIS)
     """

bottleneck/slow/__init__.py

@@ -1,3 +1,2 @@
-
-from func import *
-from move import *
+from bottleneck.slow.func import *
+from bottleneck.slow.move import *

bottleneck/slow/func.py

@@ -70,7 +70,7 @@ def nanstd(arr, axis=None, ddof=0):
         if axis < 0:
             axis += arr.ndim
         if (axis < 0) or (axis >= arr.ndim):
-            raise ValueError, "axis(=%d) out of bounds" % axis
+            raise ValueError("axis(=%d) out of bounds" % axis)
     else:
         # Older versions of scipy choke on axis=None
         arr = arr.ravel()
@@ -107,6 +107,7 @@ def nanargmax(arr, axis=None):
 
 def rankdata(arr, axis=None):
     "Slow rankdata function used for unaccelerated ndim/dtype combinations."
+    arr = np.asarray(arr)
     if axis is None:
         arr = arr.ravel()
         axis = 0
@@ -122,6 +123,7 @@ def rankdata(arr, axis=None):
 
 def nanrankdata(arr, axis=None):
     "Slow nanrankdata function used for unaccelerated ndim/dtype combinations."
+    arr = np.asarray(arr)
     if axis is None:
         arr = arr.ravel()
         axis = 0
@@ -419,12 +421,12 @@ def scipy_rankdata(a):
     sumranks = 0
     dupcount = 0
     newarray = np.zeros(n, float)
-    for i in xrange(n):
+    for i in range(n):
         sumranks += i
         dupcount += 1
         if i==n-1 or svec[i] != svec[i+1]:
             averank = sumranks / float(dupcount) + 1
-            for j in xrange(i-dupcount+1,i+1):
+            for j in range(i-dupcount+1,i+1):
                 newarray[ivec[j]] = averank
             sumranks = 0
             dupcount = 0