numpy.invert(x[, out]) = Compute bit-wise inversion, or bit-wise NOT, element-wise. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~. For signed integer inputs, the two?s complement is returned. In a two?s-complement system negative numbers are represented by the two?s complement of the absolute value. This is the most common method of representing signed integers on computers [R32]. A N-bit

numpy.intersect1d(ar1, ar2, assume_unique=False) [source] Find the intersection of two arrays. Return the sorted, unique values that are in both of the input arrays. Parameters: ar1, ar2 : array_like Input arrays. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns: intersect1d : ndarray Sorted 1D array of common and unique elements. See also numpy.lib.arraysetops Module with a number of oth

numpy.interp(x, xp, fp, left=None, right=None, period=None) [source] One-dimensional linear interpolation. Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points. Parameters: x : array_like The x-coordinates of the interpolated values. xp : 1-D sequence of floats The x-coordinates of the data points, must be increasing if argument period is not specified. Otherwise, xp is internally sorted after normalizing the periodic boundarie

numpy.insert(arr, obj, values, axis=None) [source] Insert values along the given axis before the given indices. Parameters: arr : array_like Input array. obj : int, slice or sequence of ints Object that defines the index or indices before which values is inserted. New in version 1.8.0. Support for multiple insertions when obj is a single scalar or a sequence with one element (similar to calling insert multiple times). values : array_like Values to insert into arr. If the type of val

numpy.inner(a, b) Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes. Parameters: a, b : array_like If a and b are nonscalar, their last dimensions must match. Returns: out : ndarray out.shape = a.shape[:-1] + b.shape[:-1] Raises: ValueError If the last dimension of a and b has different size. See also tensordot Sum products over arbitrary axes. dot Generalised

numpy.info(object=None, maxwidth=76, output=', mode 'w' at 0x402ae078>, toplevel='numpy') [source] Get help information for a function, class, or module. Parameters: object : object or str, optional Input object or name to get information about. If object is a numpy object, its docstring is given. If it is a string, available modules are searched for matching objects. If None, information about info itself is returned. maxwidth : int, optional Printing width. output : file like obje

numpy.indices(dimensions, dtype=) [source] Return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0,1,... varying only along the corresponding axis. Parameters: dimensions : sequence of ints The shape of the grid. dtype : dtype, optional Data type of the result. Returns: grid : ndarray The array of grid indices, grid.shape = (len(dimensions),) + tuple(dimensions). See also mgrid, meshgrid Notes The output shape is obtained b

numpy.in1d(ar1, ar2, assume_unique=False, invert=False) [source] Test whether each element of a 1-D array is also present in a second array. Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise. Parameters: ar1 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of ar1. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calcul

numpy.imag(val) [source] Return the imaginary part of the elements of the array. Parameters: val : array_like Input array. Returns: out : ndarray Output array. If val is real, the type of val is used for the output. If val has complex elements, the returned type is float. See also real, angle, real_if_close Examples >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.imag array([ 2., 4., 6.]) >>> a.imag = np.array([8, 10, 12]) >>> a array([ 1. +8.j, 3

class numpy.iinfo(type) [source] Machine limits for integer types. Parameters: int_type : integer type, dtype, or instance The kind of integer data type to get information about. See also finfo The equivalent for floating point data types. Examples With types: >>> ii16 = np.iinfo(np.int16) >>> ii16.min -32768 >>> ii16.max 32767 >>> ii32 = np.iinfo(np.int32) >>> ii32.min -2147483648 >>> ii32.max 2147483647 With instances: >&g