numpy.identity(n, dtype=None) [source] Return the identity array. The identity array is a square array with ones on the main diagonal. Parameters: n : int Number of rows (and columns) in n x n output. dtype : data-type, optional Data-type of the output. Defaults to float. Returns: out : ndarray n x n array with its main diagonal set to one, and all other elements 0. Examples >>> np.identity(3) array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]])

numpy.i0(x) [source] Modified Bessel function of the first kind, order 0. Usually denoted . This function does broadcast, but will not ?up-cast? int dtype arguments unless accompanied by at least one float or complex dtype argument (see Raises below). Parameters: x : array_like, dtype float or complex Argument of the Bessel function. Returns: out : ndarray, shape = x.shape, dtype = x.dtype The modified Bessel function evaluated at each of the elements of x. Raises: TypeError: array

numpy.hypot(x1, x2[, out]) = Given the ?legs? of a right triangle, return its hypotenuse. Equivalent to sqrt(x1**2 + x2**2), element-wise. If x1 or x2 is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument. (See Examples) Parameters: x1, x2 : array_like Leg of the triangle(s). out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output.

numpy.hstack(tup) [source] Stack arrays in sequence horizontally (column wise). Take a sequence of arrays and stack them horizontally to make a single array. Rebuild arrays divided by hsplit. Parameters: tup : sequence of ndarrays All arrays must have the same shape along all but the second axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. vstack Stack arrays in sequence vertically (row w

numpy.hsplit(ary, indices_or_sections) [source] Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the split documentation. hsplit is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension. See also split Split an array into multiple sub-arrays of equal size. Examples >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.],

numpy.histogramdd(sample, bins=10, range=None, normed=False, weights=None) [source] Compute the multidimensional histogram of some data. Parameters: sample : array_like The data to be histogrammed. It must be an (N,D) array or data that can be converted to such. The rows of the resulting array are the coordinates of points in a D dimensional polytope. bins : sequence or int, optional The bin specification: A sequence of arrays describing the bin edges along each dimension. The number of

numpy.histogram2d(x, y, bins=10, range=None, normed=False, weights=None) [source] Compute the bi-dimensional histogram of two data samples. Parameters: x : array_like, shape (N,) An array containing the x coordinates of the points to be histogrammed. y : array_like, shape (N,) An array containing the y coordinates of the points to be histogrammed. bins : int or array_like or [int, int] or [array, array], optional The bin specification: If int, the number of bins for the two dimension

numpy.histogram(a, bins=10, range=None, normed=False, weights=None, density=None) [source] Compute the histogram of a set of data. Parameters: a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If bins is an int, it defines the number of equal-width bins in the given range (10, by default). If bins is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. New

numpy.hanning(M) [source] Return the Hanning window. The Hanning window is a taper formed by using a weighted cosine. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. Returns: out : ndarray, shape(M,) The window, with the maximum value normalized to one (the value one appears only if M is odd). See also bartlett, blackman, hamming, kaiser Notes The Hanning window is defined as The Hanning was named for Julius von Hann, an A

numpy.hamming(M) [source] Return the Hamming window. The Hamming window is a taper formed by using a weighted cosine. Parameters: M : int Number of points in the output window. If zero or less, an empty array is returned. Returns: out : ndarray The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd). See also bartlett, blackman, hanning, kaiser Notes The Hamming window is defined as The Hamming was named for R. W. Hammin