def test_resample_rmd(make_data_path):
infile = make_data_path('gro_delta.txt')
ui.load_ascii_with_errors(1, infile, delta=True, func=rms)
base = ui.get_data(1)
data = Data1DAsymmetricErrs(2, base.x, base.y, base.elo, base.ehi,
base.staterror, base.syserror)
resample_data(data, RESAMPLE_BENCH, RESULTS_BENCH_RMS)
def test_AsymmetricErrors_resample_avg(self):
ui.load_ascii_with_errors(1, self.gro_delta_fname, delta=True)
tmp = ui.get_data(1)
data = Data1DAsymmetricErrs(1, tmp.x, tmp.y, tmp.elo,
tmp.ehi, tmp.staterror, tmp.syserror)
self.resample_data(data, self._resample_bench,
self._results_bench_avg)
def test_constructor_rms(make_data_path):
infile = make_data_path('gro_delta.txt')
ui.load_ascii_with_errors(1, infile, delta=True, func=rms)
base = ui.get_data(1)
data = Data1DAsymmetricErrs(2, base.x, base.y, base.elo, base.ehi,
base.staterror, base.syserror)
fit_asymmetric_err(RESULTS_BENCH_RMS, data)
def test_AsymmetricErrs_rms(self):
ui.load_ascii_with_errors(1, self.gro_delta_fname, func=self.rms,
delta=True)
tmp = ui.get_data(1)
data = Data1DAsymmetricErrs(2, tmp.x, tmp.y, tmp.elo,
tmp.ehi, tmp.staterror, tmp.syserror)
self.fit_asymmetric_err(self._results_bench_rms, data)
def test_zero_case():
"""Check what happens when values can be near -1. See #740"""
xs = np.arange(1, 6)
ys = np.asarray([0.5, -0.5, 0.3, 0.2, -0.1])
dyl = np.asarray([2, 2, 2, 2, 2])
dyh = np.asarray([2, 2, 2, 2, 2])
data = Data1DAsymmetricErrs('zero', xs, ys, dyl, dyh)
mdl = Const1D('flat')
bestfit = Fit(data, mdl).fit()
rd = ReSampleData(data, mdl)
# Both approaches should give the same results (as setting
# niter explicitly).
#
# res = rd.call(niter=10, seed=47)
res = rd(niter=10, seed=47)
# Technically this depends on random chance, but it is rather
# unlikely we'd get 10 -1 values here. Relax the -1 value and
# just ensure we have more than 1 unique value here (can probably
# assert nvs == 10 but technically we allow repeated values).
#
vs = np.unique(res['flat.c0'])
nvs = len(vs)
assert nvs > 1
# Minimal testing of the other return values. We do assume that
# we have not found a better fit than the fit.
#
samples = res['samples']
stats = res['statistic']
assert samples.shape == (10, 5)
assert stats.shape == (10, )
assert (stats >= bestfit.statval).all()