def run(): r = rng.rng() bw = bspline(4, nbreak) # Data to be fitted x = 15. / (N - 1) * numx.arange(N) y = numx.cos(x) * numx.exp(0.1 * x) sigma = .1 w = 1.0 / sigma**2 * numx.ones(N) dy = r.gaussian(sigma, N) y = y + dy # use uniform breakpoints on [0, 15] bw.knots_uniform(0.0, 15.0) X = numx.zeros((N, ncoeffs)) for i in range(N): B = bw.eval(x[i]) X[i, :] = B # do the fit c, cov, chisq = multifit.wlinear(X, w, y, multifit.linear_workspace(N, ncoeffs)) # output the smoothed curve res_y = [] res_y_err = [] for i in range(N): B = bw.eval(x[i]) yi, yi_err = multifit.linear_est(B, c, cov) res_y.append(yi) res_y_err.append(yi_err) #print yi, yerr res_y = numx.array(res_y) res_y_err = numx.array(res_y_err) return ( x, y, ), (x, res_y), res_y_err
def run(): r = rng.rng() bw = bspline(4, nbreak) # Data to be fitted x = 15. / (N-1) * numx.arange(N) y = numx.cos(x) * numx.exp(0.1 * x) sigma = .1 w = 1.0 / sigma**2 * numx.ones(N) dy = r.gaussian(sigma, N) y = y + dy # use uniform breakpoints on [0, 15] bw.knots_uniform(0.0, 15.0) X = numx.zeros((N, ncoeffs)) for i in range(N): B = bw.eval(x[i]) X[i,:] = B # do the fit c, cov, chisq = multifit.wlinear(X, w, y, multifit.linear_workspace(N, ncoeffs)) # output the smoothed curve res_y = [] res_y_err = [] for i in range(N): B = bw.eval(x[i]) yi, yi_err = multifit.linear_est(B, c, cov) res_y.append(yi) res_y_err.append(yi_err) #print yi, yerr res_y = numx.array(res_y) res_y_err = numx.array(res_y_err) return (x, y,), (x, res_y), res_y_err
def test_wlinear(self): c, cov, chisq = multifit.wlinear(self.X, self.w, self.y, self.ws) assert(Numeric.absolute(c[0] - self.a) < self._eps) assert(Numeric.absolute(c[1] - self.b) < self._eps)
def test_wlinear(self): c, cov, chisq = multifit.wlinear(self.X, self.w, self.y, self.ws) assert (Numeric.absolute(c[0] - self.a) < self._eps) assert (Numeric.absolute(c[1] - self.b) < self._eps)