diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 52 |
1 files changed, 26 insertions, 26 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index d1786969d..52c8cb4d0 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -58,36 +58,36 @@ class TestAverage(NumpyTestCase): assert_almost_equal(y5.mean(1), average(y5, 1)) y6 = matrix(rand(5,5)) - assert_array_equal(y6.mean(0), average(y6,0)) - + assert_array_equal(y6.mean(0), average(y6,0)) + def check_weights(self): y = arange(10) w = arange(10) assert_almost_equal(average(y, weights=w), (arange(10)**2).sum()*1./arange(10).sum()) - + y1 = array([[1,2,3],[4,5,6]]) w0 = [1,2] actual = average(y1,weights=w0,axis=0) desired = array([3.,4.,5.]) assert_almost_equal(actual, desired) - - + + w1 = [0,0,1] desired = array([3., 6.]) assert_almost_equal(average(y1, weights=w1, axis=1), desired) # This should raise an error. Can we test for that ? # assert_equal(average(y1, weights=w1), 9./2.) - - + + # 2D Case w2 = [[0,0,1],[0,0,2]] desired = array([3., 6.]) assert_array_equal(average(y1, weights=w2, axis=1), desired) - + assert_equal(average(y1, weights=w2), 5.) - - + + def check_returned(self): y = array([[1,2,3],[4,5,6]]) @@ -97,23 +97,23 @@ class TestAverage(NumpyTestCase): avg, scl = average(y, 0, returned=True) assert_array_equal(scl, array([2.,2.,2.])) - + avg, scl = average(y, 1, returned=True) assert_array_equal(scl, array([3.,3.])) - + # With weights w0 = [1,2] avg, scl = average(y, weights=w0, axis=0, returned=True) assert_array_equal(scl, array([3., 3., 3.])) - + w1 = [1,2,3] avg, scl = average(y, weights=w1, axis=1, returned=True) assert_array_equal(scl, array([6., 6.])) - + w2 = [[0,0,1],[1,2,3]] avg, scl = average(y, weights=w2, axis=1, returned=True) assert_array_equal(scl, array([1.,6.])) - + class TestSelect(NumpyTestCase): def _select(self,cond,values,default=0): @@ -433,7 +433,7 @@ class TestHistogram(NumpyTestCase): (a,b)=histogram(v) #check if the sum of the bins equals the number of samples assert(sum(a,axis=0)==n) - #check that the bin counts are evenly spaced when the data is from a + #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)) @@ -443,7 +443,7 @@ class TestHistogramdd(NumpyTestCase): x = array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \ [.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]]) H, edges = histogramdd(x, (2,3,3), range = [[-1,1], [0,3], [0,3]]) - answer = asarray([[[0,1,0], [0,0,1], [1,0,0]], [[0,1,0], [0,0,1], + answer = asarray([[[0,1,0], [0,0,1], [1,0,0]], [[0,1,0], [0,0,1], [0,0,1]]]) assert_array_equal(H,answer) # Check normalization @@ -451,12 +451,12 @@ class TestHistogramdd(NumpyTestCase): H, edges = histogramdd(x, bins = ed, normed = True) assert(all(H == answer/12.)) # Check that H has the correct shape. - H, edges = histogramdd(x, (2,3,4), range = [[-1,1], [0,3], [0,4]], + H, edges = histogramdd(x, (2,3,4), range = [[-1,1], [0,3], [0,4]], normed=True) - answer = asarray([[[0,1,0,0], [0,0,1,0], [1,0,0,0]], [[0,1,0,0], + answer = asarray([[[0,1,0,0], [0,0,1,0], [1,0,0,0]], [[0,1,0,0], [0,0,1,0], [0,0,1,0]]]) assert_array_almost_equal(H, answer/6., 4) - # Check that a sequence of arrays is accepted and H has the correct + # Check that a sequence of arrays is accepted and H has the correct # shape. z = [squeeze(y) for y in split(x,3,axis=1)] H, edges = histogramdd(z, bins=(4,3,2),range=[[-2,2], [0,3], [0,2]]) @@ -473,7 +473,7 @@ class TestHistogramdd(NumpyTestCase): def check_shape_3d(self): # All possible permutations for bins of different lengths in 3D. - bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4), + bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4), (4, 5, 6)) r = rand(10,3) for b in bins: @@ -482,11 +482,11 @@ class TestHistogramdd(NumpyTestCase): def check_shape_4d(self): # All possible permutations for bins of different lengths in 4D. - bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4), - (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6), - (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7), - (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5), - (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5), + bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4), + (5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6), + (7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7), + (4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5), + (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5), (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4)) r = rand(10,4) |