numpy.polynomial.hermite_e.hermezero = array([0])

MaskedArray.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) [source] Return the sum along diagonals of the array. Refer to numpy.trace for full documentation. See also numpy.trace equivalent function

matrix.argmax(axis=None, out=None) [source] Indexes of the maximum values along an axis. Return the indexes of the first occurrences of the maximum values along the specified axis. If axis is None, the index is for the flattened matrix. Parameters: See `numpy.argmax` for complete descriptions See also numpy.argmax Notes This is the same as ndarray.argmax, but returns a matrix object where ndarray.argmax would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,

numpy.ma.hsplit(ary, indices_or_sections) = 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. Notes The function is applied to both the _data and the _mask, if any. Examples >>> x = np.arange(16.0).reshape(4, 4) >>>

Polynomial.cutdeg(deg) [source] Truncate series to the given degree. Reduce the degree of the series to deg by discarding the high order terms. If deg is greater than the current degree a copy of the current series is returned. This can be useful in least squares where the coefficients of the high degree terms may be very small. New in version 1.5.0. Parameters: deg : non-negative int The series is reduced to degree deg by discarding the high order terms. The value of deg must be a non-

numpy.polynomial.laguerre.lagmul(c1, c2) [source] Multiply one Laguerre series by another. Returns the product of two Laguerre series c1 * c2. The arguments are sequences of coefficients, from lowest order ?term? to highest, e.g., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Laguerre series coefficients ordered from low to high. Returns: out : ndarray Of Laguerre series coefficients representing their product. See also lagadd, lag

RandomState.rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1). Parameters: d0, d1, ..., dn : int, optional The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. Returns: out : ndarray, shape (d0, d1, ..., dn) Random values. See also random Notes This is a convenience function. If you want an interface

ndindex.next() [source] Standard iterator method, updates the index and returns the index tuple. Returns: val : tuple of ints Returns a tuple containing the indices of the current iteration.

numpy.ma.vstack(tup) = Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by vsplit. Parameters: tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns: stacked : ndarray The array formed by stacking the given arrays. See also stack Join a sequence of arrays along a new axis. hstack Stack arrays i

numpy.find_common_type(array_types, scalar_types) [source] Determine common type following standard coercion rules. Parameters: array_types : sequence A list of dtypes or dtype convertible objects representing arrays. scalar_types : sequence A list of dtypes or dtype convertible objects representing scalars. Returns: datatype : dtype The common data type, which is the maximum of array_types ignoring scalar_types, unless the maximum of scalar_types is of a different kind (dtype.kind)