generic.argmin() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

HermiteE.integ(m=1, k=[], lbnd=None) [source] Integrate. Return a series instance that is the definite integral of the current series. Parameters: m : non-negative int The number of integrations to perform. k : array_like Integration constants. The first constant is applied to the first integration, the second to the second, and so on. The list of values must less than or equal to m in length and any missing values are set to zero. lbnd : Scalar The lower bound of the definite integra

Chebyshev.copy() [source] Return a copy. Returns: new_series : series Copy of self.

classmethod Laguerre.fromroots(roots, domain=[], window=None) [source] Return series instance that has the specified roots. Returns a series representing the product (x - r[0])*(x - r[1])*...*(x - r[n-1]), where r is a list of roots. Parameters: roots : array_like List of roots. domain : {[], None, array_like}, optional Domain for the resulting series. If None the domain is the interval from the smallest root to the largest. If [] the domain is the class domain. The default is []. wind

numpy.ma.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False) [source] Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error. Parameters: x : array_like, shape (M,) x-coordinates of the M sample points (x[i], y[i]). y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinate

Fanaticism consists of redoubling your efforts when you have forgotten your aim. ? George Santayana An authority is a person who can tell you more about something than you really care to know. ? Unknown This Chapter attempts to explain the logic behind some of the new pieces of code. The purpose behind these explanations is to enable somebody to be able to understand the ideas behind the implementation somewhat more easily than just staring at the code. Perhaps in this way, the algorithms ca

RandomState.triangular(left, mode, right, size=None) Draw samples from the triangular distribution. The triangular distribution is a continuous probability distribution with lower limit left, peak at mode, and upper limit right. Unlike the other distributions, these parameters directly define the shape of the pdf. Parameters: left : scalar Lower limit. mode : scalar The value where the peak of the distribution occurs. The value should fulfill the condition left <= mode <= right.

matrix.ctypes An object to simplify the interaction of the array with the ctypes module. This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library. Parameters: None Returns: c : Python object Possessing attributes data, shape, strides, etc. S

busdaycalendar.weekmask A copy of the seven-element boolean mask indicating valid days.

Chebyshev.roots() [source] Return the roots of the series polynomial. Compute the roots for the series. Note that the accuracy of the roots decrease the further outside the domain they lie. Returns: roots : ndarray Array containing the roots of the series.