diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 66 |
1 files changed, 36 insertions, 30 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 3cc607f93..6e1c3583d 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -440,110 +440,116 @@ class TestSinc(TestCase): class TestHistogram(TestCase): def setUp(self): - warnings.simplefilter('ignore', FutureWarning) - + warnings.simplefilter('ignore', Warning) + def tearDown(self): warnings.resetwarnings() - def test_simple(self): + def test_simple_old(self): n=100 v=rand(n) - (a,b)=histogram(v) + (a,b)=histogram(v, new=False) #check if the sum of the bins equals the number of samples assert_equal(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)) + (a,b)=histogram(linspace(0,10,100), new=False) assert_array_equal(a, 10) - def test_simple_new(self): + def test_simple(self): n=100 v=rand(n) - (a,b)=histogram(v, new=True) + (a,b)=histogram(v) #check if the sum of the bins equals the number of samples assert_equal(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), new=True) + (a,b)=histogram(linspace(0,10,100)) assert_array_equal(a, 10) - def test_normed_new(self): + def test_one_bin(self): + # Ticket 632 + hist,edges = histogram([1,2,3,4],[1,2]) + assert_array_equal(hist,[2, ]) + assert_array_equal(edges,[1,2]) + + def test_normed(self): # Check that the integral of the density equals 1. n = 100 v = rand(n) - a,b = histogram(v, normed=True, new=True) + a,b = histogram(v, normed=True) area = sum(a*diff(b)) assert_almost_equal(area, 1) # Check with non constant bin width v = rand(n)*10 bins = [0,1,5, 9, 10] - a,b = histogram(v, bins, normed=True, new=True) + a,b = histogram(v, bins, normed=True) area = sum(a*diff(b)) assert_almost_equal(area, 1) - def test_outliers_new(self): + def test_outliers(self): # Check that outliers are not tallied a = arange(10)+.5 # Lower outliers - h,b = histogram(a, range=[0,9], new=True) + h,b = histogram(a, range=[0,9]) assert_equal(h.sum(),9) # Upper outliers - h,b = histogram(a, range=[1,10], new=True) + h,b = histogram(a, range=[1,10]) assert_equal(h.sum(),9) # Normalization - h,b = histogram(a, range=[1,9], normed=True, new=True) + h,b = histogram(a, range=[1,9], normed=True) assert_equal((h*diff(b)).sum(),1) # Weights w = arange(10)+.5 - h,b = histogram(a, range=[1,9], weights=w, normed=True, new=True) + h,b = histogram(a, range=[1,9], weights=w, normed=True) assert_equal((h*diff(b)).sum(),1) - h,b = histogram(a, bins=8, range=[1,9], weights=w, new=True) + h,b = histogram(a, bins=8, range=[1,9], weights=w) assert_equal(h, w[1:-1]) - def test_type_new(self): + def test_type(self): # Check the type of the returned histogram a = arange(10)+.5 - h,b = histogram(a, new=True) + h,b = histogram(a) assert(issubdtype(h.dtype, int)) - h,b = histogram(a, normed=True, new=True) + h,b = histogram(a, normed=True) assert(issubdtype(h.dtype, float)) - h,b = histogram(a, weights=ones(10, int), new=True) + h,b = histogram(a, weights=ones(10, int)) assert(issubdtype(h.dtype, int)) - h,b = histogram(a, weights=ones(10, float), new=True) + h,b = histogram(a, weights=ones(10, float)) assert(issubdtype(h.dtype, float)) - def test_weights_new(self): + def test_weights(self): v = rand(100) w = ones(100)*5 - a,b = histogram(v,new=True) - na,nb = histogram(v, normed=True, new=True) - wa,wb = histogram(v, weights=w, new=True) - nwa,nwb = histogram(v, weights=w, normed=True, new=True) + a,b = histogram(v) + na,nb = histogram(v, normed=True) + wa,wb = histogram(v, weights=w) + nwa,nwb = histogram(v, weights=w, normed=True) assert_array_almost_equal(a*5, wa) assert_array_almost_equal(na, nwa) # Check weights are properly applied. v = linspace(0,10,10) w = concatenate((zeros(5), ones(5))) - wa,wb = histogram(v, bins=arange(11),weights=w, new=True) + wa,wb = histogram(v, bins=arange(11),weights=w) assert_array_almost_equal(wa, w) # Check with integer weights - wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1], new=True) + wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1]) assert_array_equal(wa, [4,5,0,1]) - wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1], normed=True, new=True) + wa, wb = histogram([1,2,2,4], bins=4, weights=[4,3,2,1], normed=True) assert_array_equal(wa, array([4,5,0,1])/10./3.*4) class TestHistogramdd(TestCase): |