diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index f5e9ff2a5..c185f9639 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -717,7 +717,7 @@ def histogram(a, bins=10, range=None, normed=False, weights=None, # At this point, if the weights are not integer, floating point, or # complex, we have to use the slow algorithm. if weights is not None and not (np.can_cast(weights.dtype, np.double) or - np.can_cast(weights.dtype, np.complex)): + np.can_cast(weights.dtype, complex)): bins = linspace(mn, mx, bins + 1, endpoint=True) if not iterable(bins): @@ -1541,7 +1541,7 @@ def gradient(f, *varargs, **kwargs): Examples -------- - >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=np.float) + >>> f = np.array([1, 2, 4, 7, 11, 16], dtype=float) >>> np.gradient(f) array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ]) >>> np.gradient(f, 2) @@ -1557,7 +1557,7 @@ def gradient(f, *varargs, **kwargs): Or a non uniform one: - >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=np.float) + >>> x = np.array([0., 1., 1.5, 3.5, 4., 6.], dtype=float) >>> np.gradient(f, x) array([ 1. , 3. , 3.5, 6.7, 6.9, 2.5]) @@ -1565,7 +1565,7 @@ def gradient(f, *varargs, **kwargs): axis. In this example the first array stands for the gradient in rows and the second one in columns direction: - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float)) [array([[ 2., 2., -1.], [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ], [ 1. , 1. , 1. ]])] @@ -1575,7 +1575,7 @@ def gradient(f, *varargs, **kwargs): >>> dx = 2. >>> y = [1., 1.5, 3.5] - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), dx, y) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), dx, y) [array([[ 1. , 1. , -0.5], [ 1. , 1. , -0.5]]), array([[ 2. , 2. , 2. ], [ 2. , 1.7, 0.5]])] @@ -1592,7 +1592,7 @@ def gradient(f, *varargs, **kwargs): The `axis` keyword can be used to specify a subset of axes of which the gradient is calculated - >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float), axis=0) + >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=float), axis=0) array([[ 2., 2., -1.], [ 2., 2., -1.]]) @@ -2600,7 +2600,7 @@ class vectorize(object): >>> out = vfunc([1, 2, 3, 4], 2) >>> type(out[0]) <type 'numpy.int32'> - >>> vfunc = np.vectorize(myfunc, otypes=[np.float]) + >>> vfunc = np.vectorize(myfunc, otypes=[float]) >>> out = vfunc([1, 2, 3, 4], 2) >>> type(out[0]) <type 'numpy.float64'> @@ -3029,7 +3029,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, # Get the product of frequencies and weights w = None if fweights is not None: - fweights = np.asarray(fweights, dtype=np.float) + fweights = np.asarray(fweights, dtype=float) if not np.all(fweights == np.around(fweights)): raise TypeError( "fweights must be integer") @@ -3044,7 +3044,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, "fweights cannot be negative") w = fweights if aweights is not None: - aweights = np.asarray(aweights, dtype=np.float) + aweights = np.asarray(aweights, dtype=float) if aweights.ndim > 1: raise RuntimeError( "cannot handle multidimensional aweights") |