numpy.polynomial.laguerre.lagcompanion(c) [source] Return the companion matrix of c. The usual companion matrix of the Laguerre polynomials is already symmetric when c is a basis Laguerre polynomial, so no scaling is applied. Parameters: c : array_like 1-D array of Laguerre series coefficients ordered from low to high degree. Returns: mat : ndarray Companion matrix of dimensions (deg, deg). Notes

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

numpy.polynomial.laguerre.lagx = array([ 1, -1])

MaskedArray.cumsum(axis=None, dtype=None, out=None) [source] Return the cumulative sum of the elements along the given axis. The cumulative sum is calculated over the flattened array by default, otherwise over the specified axis. Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Parameters: axis : {None, -1, int}, optional Axis along which the sum is computed. The default (axis = None) is to

numpy.not_equal(x1, x2[, out]) = Return (x1 != x2) element-wise. Parameters: x1, x2 : array_like Input arrays. out : ndarray, optional A placeholder the same shape as x1 to store the result. See doc.ufuncs (Section ?Output arguments?) for more details. Returns: not_equal : ndarray bool, scalar bool For each element in x1, x2, return True if x1 is not equal to x2 and False otherwise. See also equal, greater, greater_equal, less, less_equal Examples >>> np.not_equal([1.,

numpy.polynomial.hermite_e.hermeadd(c1, c2) [source] Add one Hermite series to another. Returns the sum of two Hermite series c1 + c2. The arguments are sequences of coefficients ordered from lowest order term to highest, i.e., [1,2,3] represents the series P_0 + 2*P_1 + 3*P_2. Parameters: c1, c2 : array_like 1-D arrays of Hermite series coefficients ordered from low to high. Returns: out : ndarray Array representing the Hermite series of their sum. See also hermesub, hermemul, her

numpy.lookfor(what, module=None, import_modules=True, regenerate=False, output=None) [source] Do a keyword search on docstrings. A list of of objects that matched the search is displayed, sorted by relevance. All given keywords need to be found in the docstring for it to be returned as a result, but the order does not matter. Parameters: what : str String containing words to look for. module : str or list, optional Name of module(s) whose docstrings to go through. import_modules : bool

numpy.ma.average(a, axis=None, weights=None, returned=False) [source] Return the weighted average of array over the given axis. Parameters: a : array_like Data to be averaged. Masked entries are not taken into account in the computation. axis : int, optional Axis along which the average is computed. The default is to compute the average of the flattened array. weights : array_like, optional The importance that each element has in the computation of the average. The weights array can e

numpy.pv(rate, nper, pmt, fv=0.0, when='end') [source] Compute the present value. Given: a future value, fv an interest rate compounded once per period, of which there are nper total a (fixed) payment, pmt, paid either at the beginning (when = {?begin?, 1}) or the end (when = {?end?, 0}) of each period Return: the value now Parameters: rate : array_like Rate of interest (per period) nper : array_like Number of compounding periods pmt : array_like Payment fv : array_like, optional

matrix.setflags(write=None, align=None, uic=None) Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable