def METHOD_NAME(self, params=None, param_jit=None, nTests=100):
    from PYME.localization.FitFactories import import_fit_factory
    if not params:
        params = self.md['Test.DefaultParams']
    if not param_jit:
        param_jit = self.md['Test.ParamJitter']
    self.fitMod = import_fit_factory(self.fitModule)
    self.res = numpy.empty(nTests, self.fitMod.FitResultsDType)
    ps = numpy.zeros((nTests, len(params)), 'f4')
    rs=11
    md2 = copy.copy(self.md)
    if 'Test.PSFFile' in self.md.getEntryNames():
        md2['PSFFile'] = self.md['Test.PSFFile']
    for i in range(nTests):
        p = np.array(params) + np.array(param_jit)*(2*np.random.rand(len(param_jit)) - 1)
        p[0] = abs(p[0])
        ps[i, :] = p
        self.data, self.x0, self.y0, self.z0 = self.fitMod.FitFactory.evalModel(p, md2, roiHalfSize=rs)#, roiHalfSize= roiHalfWidth))
        #print self.data.shape
        #from PYME.DSView import View3D
        #View3D(self.data)
        # note that the noisemaker converts from photons to ADUs, so intensity related parameters in fit will differ from model
        self.d2 = self.noiseM.noisify(self.data)
        #print self.d2.min(), self.d2.max(), self.data.min(), self.data.max()
        #print self.d2.shape
        bg = self.bg*self.md.Camera.TrueEMGain/self.md.Camera.ElectronsPerCount
        #print bg, self.md.Camera.ADOffset
        self.fitFac = self.fitMod.FitFactory(np.atleast_3d(self.d2), self.md, background = bg + self.md.Camera.ADOffset)
        self.res[i] = self.fitFac.FromPoint(rs, rs)#, roiHalfSize=rs)
    self.ps = ps.view(self.res['fitResults'].dtype)
    #self.calcMEs()
    #return ps.view(self.res['fitResults'].dtype), self.res