If True, check that the input arrays do not contain nans of infs,Īnd raise a ValueError if they do. Pcov(absolute_sigma=False) = pcov(absolute_sigma=True) * chisq(popt)/(M-N) check_finite bool, optional Match the sample variance of the residuals after the fit. Reduced chisq for the optimal parameters popt when using the This constant is set by demanding that the The returned parameter covariance matrix pcov is based on scaling If False (default), only the relative magnitudes of the sigma values matter. If True, sigma is used in an absolute sense and the estimated parameterĬovariance pcov reflects these absolute values. None (default) is equivalent of 1-D sigma filled with ones. R = ydata - f(xdata, *popt), then the interpretation of sigma sigma None or M-length sequence or MxM array, optionalĭetermines the uncertainty in ydata. Initial values will all be 1 (if the number of parameters for theįunction can be determined using introspection, otherwise a Initial guess for the parameters (length N). The dependent data, a length M array - nominally f(xdata. Should usually be an M-length sequence or an (k,M)-shaped array forįunctions with k predictors, and each element should be floatĬonvertible if it is an array like object. The independent variable where the data is measured. Variable as the first argument and the parameters to fit as Use non-linear least squares to fit a function, f, to data.Īssumes ydata = f(xdata, *params) + eps. curve_fit ( f, xdata, ydata, p0 = None, sigma = None, absolute_sigma = False, check_finite = True, bounds = (-inf, inf), method = None, jac = None, *, full_output = False, ** kwargs ) #
Statistical functions for masked arrays ( K-means clustering and vector quantization (
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