def setUp(self): self.dim = 100 self.param = 2 self.a = 1000.1 self.b = 100 self.x = Numeric.arange(self.dim) x = self.x self.y = self.a + self.b * self.x self.w = Numeric.ones((self.dim,)) self.ws = multifit.linear_workspace(self.dim, self.param) self.X = Numeric.transpose(Numeric.array((Numeric.ones(self.dim,), x)))
def setUp(self): self.dim = 100 self.param = 2 self.a = 1000.1 self.b = 100 self.x = Numeric.arange(self.dim) x = self.x self.y = self.a + self.b * self.x self.w = Numeric.ones((self.dim, )) self.ws = multifit.linear_workspace(self.dim, self.param) self.X = Numeric.transpose(Numeric.array( (Numeric.ones(self.dim, ), x)))
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