Source code for sas.plottools.fittings

"""
"""
from scipy import optimize


[docs]class Parameter: """ Class to handle model parameters """ def __init__(self, model, name, value=None): self.model = model self.name = name if not value == None: self.model.setParam(self.name, value)
[docs] def set(self, value): """ Set the value of the parameter """ self.model.setParam(self.name, value)
def __call__(self): """ Return the current value of the parameter """ return self.model.getParam(self.name)
[docs]def sasfit(model, pars, x, y, err_y , qmin=None, qmax=None): """ Fit function :param model: sas model object :param pars: list of parameters :param x: vector of x data :param y: vector of y data :param err_y: vector of y errors """ def f(params): """ Calculates the vector of residuals for each point in y for a given set of input parameters. :param params: list of parameter values :return: vector of residuals """ i = 0 for p in pars: p.set(params[i]) i += 1 residuals = [] for j in range(len(x)): if x[j] >= qmin and x[j] <= qmax: residuals.append((y[j] - model.runXY(x[j])) / err_y[j]) return residuals def chi2(params): """ Calculates chi^2 :param params: list of parameter values :return: chi^2 """ sum = 0 res = f(params) for item in res: sum += item * item return sum p = [param() for param in pars] out, cov_x, info, mesg, success = optimize.leastsq(f, p, full_output=1) # Calculate chi squared if len(pars) > 1: chisqr = chi2(out) elif len(pars) == 1: chisqr = chi2([out]) return chisqr, out, cov_x
[docs]def calcCommandline(event): #Testing implementation # Fit a Line model from LineModel import LineModel line = LineModel() cstA = Parameter(line, 'A', event.cstA) cstB = Parameter(line, 'B', event.cstB) y = line.run() chisqr, out, cov = sasfit(line, [cstA, cstB], event.x, y, 0) # print "Output parameters:", out print "The right answer is [70.0, 1.0]" print chisqr, out, cov