diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/arraysetops.py | 2 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 11 | ||||
| -rw-r--r-- | numpy/lib/shape_base.py | 18 | ||||
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 24 | ||||
| -rw-r--r-- | numpy/lib/tests/test_type_check.py | 8 | ||||
| -rw-r--r-- | numpy/lib/utils.py | 2 |
6 files changed, 27 insertions, 38 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 7bd666029..b98517f3d 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -179,7 +179,7 @@ def test_unique1d_speed( plot_results = False ): dt1s.append( dt1 ) dt2s.append( dt2 ) - assert numpy.alltrue( b == c ) + assert numpy.alltrue( b == c) print nItems diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index d8cd30f2c..a202f67cb 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -108,17 +108,16 @@ def average(a, axis=None, weights=None, returned=False): """average(a, axis=None weights=None, returned=False) Average the array over the given axis. If the axis is None, average - over all dimensions of the array. Equivalent to a.mean(axis), but - with a default axis of 0 instead of None. + over all dimensions of the array. Equivalent to a.mean(axis) If an integer axis is given, this equals: a.sum(axis) * 1.0 / len(a) If axis is None, this equals: - a.sum(axis) * 1.0 / product(a.shape) + a.sum(axis) * 1.0 / product(a.shape,axis=0) If weights are given, result is: - sum(a * weights) / sum(weights), + sum(a * weights,axis) / sum(weights,axis), where the weights must have a's shape or be 1D with length the size of a in the given axis. Integer weights are converted to Float. Not specifying weights is equivalent to specifying @@ -541,9 +540,9 @@ def extract(condition, arr): """Return the elements of ravel(arr) where ravel(condition) is True (in 1D). - Equivalent to compress(ravel(condition), ravel(arr)). + Equivalent to compress(ravel(condition), ravel(arr),0). """ - return _nx.take(ravel(arr), nonzero(ravel(condition))[0]) + return _nx.take(ravel(arr), nonzero(ravel(condition))[0],axis=0) def place(arr, mask, vals): """Similar to putmask arr[mask] = vals but the 1D array vals has the diff --git a/numpy/lib/shape_base.py b/numpy/lib/shape_base.py index df0e9876d..03db2570a 100644 --- a/numpy/lib/shape_base.py +++ b/numpy/lib/shape_base.py @@ -32,7 +32,7 @@ def apply_along_axis(func1d,axis,arr,*args): if isscalar(res): outarr = zeros(outshape,asarray(res).dtype) outarr[ind] = res - Ntot = product(outshape) + Ntot = product(outshape,axis=0) k = 1 while k < Ntot: # increment the index @@ -48,7 +48,7 @@ def apply_along_axis(func1d,axis,arr,*args): k += 1 return outarr else: - Ntot = product(outshape) + Ntot = product(outshape,axis=0) holdshape = outshape outshape = list(arr.shape) outshape[axis] = len(res) @@ -326,12 +326,7 @@ def array_split(ary,indices_or_sections,axis = 0): Caveats: Currently, the default for axis is 0. This means a 2D array is divided into multiple groups - of rows. This seems like the appropriate default, but - we've agreed most other functions should default to - axis=-1. Perhaps we should use axis=-1 for consistency. - However, we could also make the argument that NumPy - works on "rows" by default. sum() sums up rows of - values. split() will split data into rows. Opinions? + of rows. This seems like the appropriate default, """ try: Ntotal = ary.shape[axis] @@ -391,12 +386,7 @@ def split(ary,indices_or_sections,axis=0): Caveats: Currently, the default for axis is 0. This means a 2D array is divided into multiple groups - of rows. This seems like the appropriate default, but - we've agreed most other functions should default to - axis=-1. Perhaps we should use axis=-1 for consistency. - However, we could also make the argument that NumPy - works on "rows" by default. sum() sums up rows of - values. split() will split data into rows. Opinions? + of rows. This seems like the appropriate default """ try: len(indices_or_sections) except TypeError: diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index fdb2f270f..b548ce386 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -42,11 +42,11 @@ class test_all(NumpyTestCase): class test_average(NumpyTestCase): def check_basic(self): y1 = array([1,2,3]) - assert(average(y1) == 2.) + assert(average(y1,axis=0) == 2.) y2 = array([1.,2.,3.]) - assert(average(y2) == 2.) + assert(average(y2,axis=0) == 2.) y3 = [0.,0.,0.] - assert(average(y3) == 0.) + assert(average(y3,axis=0) == 0.) y4 = ones((4,4)) y4[0,1] = 0 @@ -117,7 +117,7 @@ class test_amin(NumpyTestCase): class test_ptp(NumpyTestCase): def check_basic(self): a = [3,4,5,10,-3,-5,6.0] - assert_equal(ptp(a),15.0) + assert_equal(ptp(a,axis=0),15.0) b = [[3,6.0, 9.0], [4,10.0,5.0], [8,3.0,2.0]] @@ -132,7 +132,7 @@ class test_cumsum(NumpyTestCase): float32,float64,complex64,complex128]: a = array(ba,ctype) a2 = array(ba2,ctype) - assert_array_equal(cumsum(a), array([1,3,13,24,30,35,39],ctype)) + assert_array_equal(cumsum(a,axis=0), array([1,3,13,24,30,35,39],ctype)) assert_array_equal(cumsum(a2,axis=0), array([[1,2,3,4],[6,8,10,13], [16,11,14,18]],ctype)) assert_array_equal(cumsum(a2,axis=1), @@ -153,7 +153,7 @@ class test_prod(NumpyTestCase): self.failUnlessRaises(ArithmeticError, prod, a2, 1) self.failUnlessRaises(ArithmeticError, prod, a) else: - assert_equal(prod(a),26400) + assert_equal(prod(a,axis=0),26400) assert_array_equal(prod(a2,axis=0), array([50,36,84,180],ctype)) assert_array_equal(prod(a2,axis=-1),array([24, 1890, 600],ctype)) @@ -305,35 +305,35 @@ class test_filterwindows(NumpyTestCase): w=hanning(10) assert_array_almost_equal(w,flipud(w),7) #check known value - assert_almost_equal(sum(w),4.500,4) + assert_almost_equal(sum(w,axis=0),4.500,4) def check_hamming(self): #check symmetry w=hamming(10) assert_array_almost_equal(w,flipud(w),7) #check known value - assert_almost_equal(sum(w),4.9400,4) + assert_almost_equal(sum(w,axis=0),4.9400,4) def check_bartlett(self): #check symmetry w=bartlett(10) assert_array_almost_equal(w,flipud(w),7) #check known value - assert_almost_equal(sum(w),4.4444,4) + assert_almost_equal(sum(w,axis=0),4.4444,4) def check_blackman(self): #check symmetry w=blackman(10) assert_array_almost_equal(w,flipud(w),7) #check known value - assert_almost_equal(sum(w),3.7800,4) + assert_almost_equal(sum(w,axis=0),3.7800,4) class test_trapz(NumpyTestCase): def check_simple(self): r=trapz(exp(-1.0/2*(arange(-10,10,.1))**2)/sqrt(2*pi),dx=0.1) #check integral of normal equals 1 - assert_almost_equal(sum(r),1,7) + assert_almost_equal(sum(r,axis=0),1,7) class test_sinc(NumpyTestCase): def check_simple(self): @@ -348,7 +348,7 @@ class test_histogram(NumpyTestCase): v=rand(n) (a,b)=histogram(v) #check if the sum of the bins equals the number of samples - assert(sum(a)==n) + assert(sum(a,axis=0)==n) #check that the bin counts are evenly spaced when the data is from a linear function (a,b)=histogram(linspace(0,10,100)) assert(all(a==10)) diff --git a/numpy/lib/tests/test_type_check.py b/numpy/lib/tests/test_type_check.py index b95760c8b..fba89e4c1 100644 --- a/numpy/lib/tests/test_type_check.py +++ b/numpy/lib/tests/test_type_check.py @@ -91,7 +91,7 @@ class test_iscomplex(NumpyTestCase): def check_fail(self): z = array([-1,0,1]) res = iscomplex(z) - assert(not sometrue(res)) + assert(not sometrue(res,axis=0)) def check_pass(self): z = array([-1j,1,0]) res = iscomplex(z) @@ -125,7 +125,7 @@ class test_isnan(NumpyTestCase): def check_goodvalues(self): z = array((-1.,0.,1.)) res = isnan(z) == 0 - assert_all(alltrue(res)) + assert_all(alltrue(res,axis=0)) def check_posinf(self): assert_all(isnan(array((1.,))/0.) == 0) def check_neginf(self): @@ -145,7 +145,7 @@ class test_isfinite(NumpyTestCase): def check_goodvalues(self): z = array((-1.,0.,1.)) res = isfinite(z) == 1 - assert_all(alltrue(res)) + assert_all(alltrue(res,axis=0)) def check_posinf(self): assert_all(isfinite(array((1.,))/0.) == 0) def check_neginf(self): @@ -165,7 +165,7 @@ class test_isinf(NumpyTestCase): def check_goodvalues(self): z = array((-1.,0.,1.)) res = isinf(z) == 0 - assert_all(alltrue(res)) + assert_all(alltrue(res,axis=0)) def check_posinf(self): assert_all(isinf(array((1.,))/0.) == 1) def check_posinf_scalar(self): diff --git a/numpy/lib/utils.py b/numpy/lib/utils.py index db7c00db6..deca8fa06 100644 --- a/numpy/lib/utils.py +++ b/numpy/lib/utils.py @@ -126,7 +126,7 @@ def who(vardict=None): namestr = name original=1 shapestr = " x ".join(map(str, var.shape)) - bytestr = str(var.itemsize*product(var.shape)) + bytestr = str(var.itemsize*product(var.shape,axis=0)) sta.append([namestr, shapestr, bytestr, var.dtype.name, original]) |