numpy.polynomial.hermite_e.hermevander(x, deg) [source] Pseudo-Vandermonde matrix of given degree. Returns the pseudo-Vandermonde matrix of degree deg and sample points x. The pseudo-Vandermonde matrix is defined by where 0 <= i <= deg. The leading indices of V index the elements of x and the last index is the degree of the HermiteE polynomial. If c is a 1-D array of coefficients of length n + 1 and V is the array V = hermevander(x, n), then np.dot(V, c) and hermeval(x, c) are the

numpy.fv(rate, nper, pmt, pv, when='end') [source] Compute the future value. Given: a present value, pv 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 at the end of the nper periods Parameters: rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of

numpy.polynomial.chebyshev.chebsub(c1, c2) [source] Subtract one Chebyshev series from another. Returns the difference of two Chebyshev series c1 - c2. The sequences of coefficients are from lowest order term to highest, i.e., [1,2,3] represents the series T_0 + 2*T_1 + 3*T_2. Parameters: c1, c2 : array_like 1-D arrays of Chebyshev series coefficients ordered from low to high. Returns: out : ndarray Of Chebyshev series coefficients representing their difference. See also chebadd, c

Simple financial functions fv(rate, nper, pmt, pv[, when]) Compute the future value. pv(rate, nper, pmt[, fv, when]) Compute the present value. npv(rate, values) Returns the NPV (Net Present Value) of a cash flow series. pmt(rate, nper, pv[, fv, when]) Compute the payment against loan principal plus interest. ppmt(rate, per, nper, pv[, fv, when]) Compute the payment against loan principal. ipmt(rate, per, nper, pv[, fv, when]) Compute the interest portion of a payment. irr(values) Retu

numpy.polynomial.chebyshev.cheb2poly(c) [source] Convert a Chebyshev series to a polynomial. Convert an array representing the coefficients of a Chebyshev 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 Chebyshev series coefficients, ordered from lowest order term to highest. Returns: pol : ndarray 1-D

MaskedArray.__iand__ x.__iand__(y) <==> x&=y

Chebyshev.trim(tol=0) [source] Remove trailing coefficients Remove trailing coefficients until a coefficient is reached whose absolute value greater than tol or the beginning of the series is reached. If all the coefficients would be removed the series is set to [0]. A new series instance is returned with the new coefficients. The current instance remains unchanged. Parameters: tol : non-negative number. All trailing coefficients less than tol will be removed. Returns: new_series : ser

numpy.ma.masked_equal(x, value, copy=True) [source] Mask an array where equal to a given value. This function is a shortcut to masked_where, with condition = (x == value). For floating point arrays, consider using masked_values(x, value). See also masked_where Mask where a condition is met. masked_values Mask using floating point equality. Examples >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_equal(a, 2) m

class numpy.core.defchararray.chararray [source] Provides a convenient view on arrays of string and unicode values. Note The chararray class exists for backwards compatibility with Numarray, it is not recommended for new development. Starting from numpy 1.4, if one needs arrays of strings, it is recommended to use arrays of dtype object_, string_ or unicode_, and use the free functions in the numpy.char module for fast vectorized string operations. Versus a regular Numpy array of type str

numpy.nanargmax(a, axis=None) [source] Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ValueError is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs. Parameters: a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns: index_array : ndarray An array of indices or a single index value. See also argmax, nanargmin Examples >>