def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(10, nbreak)
b.knots_uniform(x[0], x[-1])
X = numx.zeros((N, ncoeffs))
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
res_x = x[::N / 64]
X = b.eval_vector(res_x)
res_y, res_y_err = multifit.linear_est_matrix(X, c, cov)
pylab.plot(x, y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(10, nbreak)
b.knots_uniform(x[0], x[-1])
X = numx.zeros((N, ncoeffs))
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
res_x = x[::N/64]
X = b.eval_vector(res_x)
res_y, res_y_err = multifit.linear_est_matrix(X, c, cov)
pylab.plot(x,y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(4, nbreak)
k = b.get_internal_knots()
pygsl.set_debug_level(10)
b.knots(k)
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
b.set_coefficients_and_covariance_matrix(c, cov)
res_x = x[:: N / 64]
res_y, res_y_err = b.eval_dep_yerr_vector(res_x)
# res_y = b.eval_dep_vector(res_x)
print res_y
pylab.plot(x, y, "-")
pylab.errorbar(res_x, res_y, fmt="g-", xerr=res_y_err)
def run():
N = 1024
x = numx.arange(N) * (numx.pi * 2 / N)
y = numx.sin(x)
b = bspline(4, nbreak)
k = b.get_internal_knots()
pygsl.set_debug_level(10)
b.knots(k)
X = b.eval_vector(x)
c, cov, chisq = multifit.linear(X, y)
b.set_coefficients_and_covariance_matrix(c, cov)
res_x = x[::N / 64]
res_y, res_y_err = b.eval_dep_yerr_vector(res_x)
#res_y = b.eval_dep_vector(res_x)
print(res_y)
pylab.plot(x, y, '-')
pylab.errorbar(res_x, res_y, fmt='g-', xerr=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 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
