ndarray.shape Tuple of array dimensions. Notes May be used to ?reshape? the array, as long as this would not require a change in the total number of elements Examples >>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]]) &

Hermite.linspace(n=100, domain=None) [source] Return x, y values at equally spaced points in domain. Returns the x, y values at n linearly spaced points across the domain. Here y is the value of the polynomial at the points x. By default the domain is the same as that of the series instance. This method is intended mostly as a plotting aid. New in version 1.5.0. Parameters: n : int, optional Number of point pairs to return. The default value is 100. domain : {None, array_like}, optiona

numpy.testing.assert_allclose(actual, desired, rtol=1e-07, atol=0, equal_nan=False, err_msg='', verbose=True) [source] Raises an AssertionError if two objects are not equal up to desired tolerance. The test is equivalent to allclose(actual, desired, rtol, atol). It compares the difference between actual and desired to atol + rtol * abs(desired). New in version 1.5.0. Parameters: actual : array_like Array obtained. desired : array_like Array desired. rtol : float, optional Relative t

numpy.random.random(size=None) Return random floats in the half-open interval [0.0, 1.0). Results are from the ?continuous uniform? distribution over the stated interval. To sample multiply the output of random_sample by (b-a) and add a: (b - a) * random_sample() + a Parameters: size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Returns: out : float or

recarray.newbyteorder(new_order='S') Return the array with the same data viewed with a different byte order. Equivalent to: arr.view(arr.dtype.newbytorder(new_order)) Changes are also made in all fields and sub-arrays of the array data type. Parameters: new_order : string, optional Byte order to force; a value from the byte order specifications below. new_order codes can be any of: ?S? - swap dtype from current to opposite endian {?<?, ?L?} - little endian {?>?, ?B?} - big endian {

poly1d.integ(m=1, k=0) [source] Return an antiderivative (indefinite integral) of this polynomial. Refer to polyint for full documentation. See also polyint equivalent function

Hermite.has_samewindow(other) [source] Check if windows match. New in version 1.6.0. Parameters: other : class instance The other class must have the window attribute. Returns: bool : boolean True if the windows are the same, False otherwise.

HermiteE.__call__(arg) [source]

numpy.polynomial.laguerre.lagone = array([1])

numpy.random.rayleigh(scale=1.0, size=None) Draw samples from a Rayleigh distribution. The and Weibull distributions are generalizations of the Rayleigh. Parameters: scale : scalar Scale, also equals the mode. Should be >= 0. size : int or tuple of ints, optional Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned. Notes The probability density function for the Rayleigh distribution is