def NIST_runner(dataset, method='leastsq', chi_atol=1e-5,
val_rtol=1e-2, err_rtol=0.01):
NIST_dataset = ReadNistData(dataset)
x, y = (NIST_dataset['x'], NIST_dataset['y'])
if dataset == 'Nelson':
y = np.log(y)
params = NIST_dataset['start']
fitfunc = Models[dataset][0]
fitter = CurveFitter(fitfunc, (x, y), params)
result = fitter.fit(method, params)
assert_allclose(result.chisqr, NIST_dataset['sum_squares'], atol=chi_atol)
thisval = values(result.params)
certval = NIST_dataset['cert_values']
assert_allclose(thisval, certval, rtol=val_rtol)
if result.errorbars:
thiserr = np.array([result.params[par].stderr for par in result.params])
certerr = NIST_dataset['cert_stderr']
assert_allclose(thiserr, certerr, rtol=err_rtol)
def _write_results(f, emcee_result):
# the flatchain is what we're interested in.
# make an output array
# hopefully the chain has been burned and thinned enough.
output = np.zeros((np.size(emcee_result.flatchain,
0), len(emcee_result.params)))
gen = pgen(emcee_result.params, emcee_result.flatchain)
for row in output:
pars = next(gen)
row[:] = values(pars)[:]
np.savetxt(f, output, header=' '.join(names(emcee_result.params)))
def _write_results(f, emcee_result):
# the flatchain is what we're interested in.
# make an output array
# hopefully the chain has been burned and thinned enough.
output = np.zeros((np.size(emcee_result.flatchain, 0),
len(emcee_result.params)))
gen = pgen(emcee_result.params, emcee_result.flatchain)
for row in output:
pars = next(gen)
row[:] = values(pars)[:]
np.savetxt(f, output, header=' '.join(names(emcee_result.params)))
