numpy.apply_along_axis(func1d, axis, arr, *args, **kwargs) [source] Apply a function to 1-D slices along the given axis. Execute func1d(a, *args) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis. Parameters: func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of arr along the specified axis. axis : integer Axis along which arr is sliced. arr : ndarray Input array. args : any Additional arguments to func1d. kwargs: any Add

numpy.append(arr, values, axis=None) [source] Append values to the end of an array. Parameters: arr : array_like Values are appended to a copy of this array. values : array_like These values are appended to a copy of arr. It must be of the correct shape (the same shape as arr, excluding axis). If axis is not specified, values can be any shape and will be flattened before use. axis : int, optional The axis along which values are appended. If axis is not given, both arr and values are f

numpy.any(a, axis=None, out=None, keepdims=False) [source] Test whether any array element along a given axis evaluates to True. Returns single boolean unless axis is not None Parameters: a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical OR reduction is performed. The default (axis = None) is to perform a logical OR over all the dimensions of the input array. axis may be negative, in wh

numpy.angle(z, deg=0) [source] Return the angle of the complex argument. Parameters: z : array_like A complex number or sequence of complex numbers. deg : bool, optional Return angle in degrees if True, radians if False (default). Returns: angle : ndarray or scalar The counterclockwise angle from the positive real axis on the complex plane, with dtype as numpy.float64. See also arctan2, absolute Examples >>> np.angle([1.0, 1.0j, 1+1j]) # in radians array([

numpy.amin(a, axis=None, out=None, keepdims=False) [source] Return the minimum of an array or minimum along an axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of

numpy.amax(a, axis=None, out=None, keepdims=False) [source] Return the maximum of an array or maximum along an axis. Parameters: a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of

numpy.alterdot() [source] Change dot, vdot, and inner to use accelerated BLAS functions. Typically, as a user of Numpy, you do not explicitly call this function. If Numpy is built with an accelerated BLAS, this function is automatically called when Numpy is imported. When Numpy is built with an accelerated BLAS like ATLAS, these functions are replaced to make use of the faster implementations. The faster implementations only affect float32, float64, complex64, and complex128 arrays. Further

numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) [source] Returns True if two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b. If either array contains one or more NaNs, False is returned. Infs are treated as equal if they are in the same place and of the same sig

numpy.all(a, axis=None, out=None, keepdims=False) [source] Test whether all array elements along a given axis evaluate to True. Parameters: a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical AND reduction is performed. The default (axis = None) is to perform a logical AND over all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first

numpy.add(x1, x2[, out]) = Add arguments element-wise. Parameters: x1, x2 : array_like The arrays to be added. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: add : ndarray or scalar The sum of x1 and x2, element-wise. Returns a scalar if both x1 and x2 are scalars. Notes Equivalent to x1 + x2 in terms of array broadcasting. Examples >>> np.add(1.0, 4.0) 5.0 >>> x1 = np.arange(9.0).reshap