def generate_data():
r = pygsl.rng.mt19937()
a = numx.arange(20) / 10.# + .1
y0 = numx.exp(a)
sigma = 0.1 * y0
dy = numx.array(map(r.gaussian, sigma))
return a, y0+dy, sigma
def generate_data():
r = pygsl.rng.mt19937()
a = numx.arange(20) / 10. # + .1
y0 = numx.exp(a)
sigma = 0.1 * y0
tmp = tuple(map(r.gaussian, sigma))
dy = numx.array(tmp)
tmp = y0 + dy
return a, tmp, sigma
def EFunc(self):
x = self._data
t = x-1.0
t2 = t*t
# Necessary as my python does not handle the exp of big numbers
# correctly
if t2 > 700:
tmp = 0
else:
tmp = numx.exp(-t2)
return tmp*numx.sin(8*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
