numpy.ma.atleast_2d(*arys) = View inputs as arrays with at least two dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns: res, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned. Notes The function is applied to both the _dat

chararray.flat A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python?s built-in iterator object. See also flatten Return a copy of the array collapsed into one dimension. flatiter Examples >>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type

numpy.atleast_3d(*arys) [source] View inputs as arrays with at least three dimensions. Parameters: arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved. Returns: res1, res2, ... : ndarray An array, or tuple of arrays, each with a.ndim >= 3. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape (N,)

ndarray.__setstate__(version, shape, dtype, isfortran, rawdata) For unpickling. Parameters: version : int optional pickle version. If omitted defaults to 0. shape : tuple dtype : data-type isFortran : bool rawdata : string or list a binary string with the data (or a list if ?a? is an object array)

numpy.cosh(x[, out]) = Hyperbolic cosine, element-wise. Equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). Parameters: x : array_like Input array. Returns: out : ndarray Output array of same shape as x. Examples >>> np.cosh(0) 1.0 The hyperbolic cosine describes the shape of a hanging cable: >>> import matplotlib.pyplot as plt >>> x = np.linspace(-4, 4, 1000) >>> plt.plot(x, np.cosh(x)) >>> plt.show() (Source code, png, pdf

numpy.polynomial.hermite_e.herme2poly(c) [source] Convert a Hermite series to a polynomial. Convert an array representing the coefficients of a Hermite series, ordered from lowest degree to highest, to an array of the coefficients of the equivalent polynomial (relative to the ?standard? basis) ordered from lowest to highest degree. Parameters: c : array_like 1-D array containing the Hermite series coefficients, ordered from lowest order term to highest. Returns: pol : ndarray 1-D arra

numpy.testing.rundocs(filename=None, raise_on_error=True) [source] Run doctests found in the given file. By default rundocs raises an AssertionError on failure. Parameters: filename : str The path to the file for which the doctests are run. raise_on_error : bool Whether to raise an AssertionError when a doctest fails. Default is True. Notes The doctests can be run by the user/developer by adding the doctests argument to the test() call. For example, to run all tests (including doctest

numpy.ma.clump_unmasked(a) [source] Return list of slices corresponding to the unmasked clumps of a 1-D array. (A ?clump? is defined as a contiguous region of the array). Parameters: a : ndarray A one-dimensional masked array. Returns: slices : list of slice The list of slices, one for each continuous region of unmasked elements in a. See also flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, notmasked_contiguous, clump_masked Notes New in version 1.4.0. Examples &

numpy.newbuffer(size) Return a new uninitialized buffer object. Parameters: size : int Size in bytes of returned buffer object. Returns: newbuffer : buffer object Returned, uninitialized buffer object of size bytes.

numpy.ma.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