record.take() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

record.compress() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

numpy.roll(a, shift, axis=None) [source] Roll array elements along a given axis. Elements that roll beyond the last position are re-introduced at the first. Parameters: a : array_like Input array. shift : int The number of places by which elements are shifted. axis : int, optional The axis along which elements are shifted. By default, the array is flattened before shifting, after which the original shape is restored. Returns: res : ndarray Output array, with the same shape as a.

numpy.asarray_chkfinite(a, dtype=None, order=None) [source] Convert the input to an array, checking for NaNs or Infs. Parameters: a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {?C?, ?F?}, optional Whether to use row-major (C-style) or col

generic.__reduce__()

numpy.ma.getdata(a, subok=True) [source] Return the data of a masked array as an ndarray. Return the data of a (if any) as an ndarray if a is a MaskedArray, else return a as a ndarray or subclass (depending on subok) if not. Parameters: a : array_like Input MaskedArray, alternatively a ndarray or a subclass thereof. subok : bool Whether to force the output to be a pure ndarray (False) or to return a subclass of ndarray if appropriate (True, default). See also getmask Return the ma

numpy.argpartition(a, kth, axis=-1, kind='introselect', order=None) [source] Perform an indirect partition along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in partitioned order. New in version 1.8.0. Parameters: a : array_like Array to sort. kth : int or sequence of ints Element index to partition by. The kth element will be in its final sorted position and all smaller ele

recarray.ptp(axis=None, out=None) Peak to peak (maximum - minimum) value along a given axis. Refer to numpy.ptp for full documentation. See also numpy.ptp equivalent function

numpy.testing.assert_almost_equal(actual, desired, decimal=7, err_msg='', verbose=True) [source] Raises an AssertionError if two items are not equal up to desired precision. Note It is recommended to use one of assert_allclose, assert_array_almost_equal_nulp or assert_array_max_ulp instead of this function for more consistent floating point comparisons. The test is equivalent to abs(desired-actual) < 0.5 * 10**(-decimal). Given two objects (numbers or ndarrays), check that all elements

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