def calc_wstat_sherpa(mu_sig, n_on, n_off, alpha):
import sherpa.stats as ss
from sherpa.astro.data import DataPHA
from sherpa.models import Const1D
wstat = ss.WStat()
model = Const1D()
model.c0 = mu_sig
data = DataPHA(counts=np.atleast_1d(n_on),
name='dummy',
channel=np.atleast_1d(1),
backscal=1,
exposure=1)
background = DataPHA(counts=np.atleast_1d(n_off),
name='dummy background',
channel=np.atleast_1d(1),
backscal=np.atleast_1d(1. / alpha),
exposure=1)
data.set_background(background, 1)
# Docstring for ``calc_stat``
# https://github.com/sherpa/sherpa/blob/fe8508818662346cb6d9050ba676e23318e747dd/sherpa/stats/__init__.py#L219
stat = wstat.calc_stat(model=model, data=data)
print("Sherpa stat: {}".format(stat[0]))
print("Sherpa fvec: {}".format(stat[1]))
def test_case(args):
import sherpa.stats as ss
mu_sig = float(args[1])
n_on = float(args[2])
n_off = float(args[3])
alpha = float(args[4])
model, data = get_data(mu_sig, n_on, n_off, alpha)
wstat, cash, cstat = ss.WStat(), ss.Cash(), ss.CStat()
print('wstat: {}'.format(wstat.calc_stat(model=model, data=data)[0]))
print('cash: {}'.format(cash.calc_stat(model=model, data=data)[0]))
print('cstat: {}'.format(cstat.calc_stat(model=model, data=data)[0]))
def calc_wstat_sherpa():
data, model, staterr, off_vec, alpha = get_test_data()
import sherpa.stats as ss
wstat = ss.WStat()
# We assume equal exposure
bkg = dict(bkg=off_vec,
exposure_time=[1, 1],
backscale_ratio=1. / alpha,
data_size=len(model))
stat = wstat.calc_stat(data, model, staterror=staterr, bkg=bkg)
print("Sherpa stat: {}".format(stat[0]))
print("Sherpa fvec: {}".format(stat[1]))
def statistic(self, stat):
import sherpa.stats as s
if isinstance(stat, six.string_types):
if stat.lower() == 'cstat':
stat = s.CStat()
elif stat.lower() == 'wstat':
stat = s.WStat()
else:
raise ValueError("Undefined stat string: {}".format(stat))
if not isinstance(stat, s.Stat):
raise ValueError("Only sherpa statistics are supported")
self._stat = stat
def test_wstat():
import sherpa.stats as ss
sherpa_stat = ss.WStat()
data, model, staterror, off_vec = get_test_data()
alpha = np.ones(len(data)) * 0.2
statsvec = gammapy_stats.wstat(n_on=data,
mu_sig=model,
n_off=off_vec,
alpha=alpha,
extra_terms=True)
# This is how sherpa wants the background (found by trial and error)
bkg = dict(bkg=off_vec,
exposure_time=[1, 1],
backscale_ratio=1. / alpha,
data_size=len(data))
# Check for one bin first
test_bin = 0
bkg_testbin = dict(bkg=off_vec[test_bin],
exposure_time=[1, 1],
backscale_ratio=1. / alpha[test_bin],
data_size=1)
desired_testbin, fvec = sherpa_stat.calc_stat(
data[test_bin],
model[test_bin],
staterror=staterror[test_bin],
bkg=bkg_testbin)
actual_testbin = statsvec[test_bin]
# assert_allclose(actual_testbin, desired_testbin)
# Now check total stat for all bins
desired, fvec = sherpa_stat.calc_stat(data,
model,
staterror=staterror,
bkg=bkg)
actual = np.sum(statsvec)
print(fvec)
assert_allclose(actual, desired)
def statistic(self, stat):
"""Set Statistic to be used in the fit
Parameters
----------
stat : `~sherpa.stats.Stat`, str
Statistic
"""
import sherpa.stats as s
if isinstance(stat, six.string_types):
if stat == 'cash':
stat = s.Cash()
elif stat == 'wstat':
stat = s.WStat()
else:
raise ValueError("Undefined stat string: {}".format(stat))
if not isinstance(stat, s.Stat):
raise ValueError("Only sherpa statistics are supported")
self._stat = stat
# clear.
#
@requires_data
@requires_fits
@pytest.mark.parametrize("stat",
[None,
stats.Chi2(),
stats.Chi2ConstVar(),
stats.Chi2DataVar(),
stats.Chi2Gehrels(),
stats.Chi2ModVar(),
stats.Chi2XspecVar(),
stats.LeastSq(),
stats.Cash(),
stats.CStat(),
stats.WStat()])
def test_astro_data_plot_with_stat_simple(make_data_path, stat):
from sherpa.astro import io
infile = make_data_path('3c273.pi')
pha = io.read_pha(infile)
# tweak the data set so that we aren't using the default
# options (it shouldn't matter for this test but just
# in case).
#
# Note that background subtraction would normally be an issue
# for some of the stats (e.g. WStat), but this shouldn't
# trigger a problem here.
#
# clear.
#
@requires_data
@requires_fits
@pytest.mark.parametrize("stat", [
None,
stats.Chi2(),
stats.Chi2ConstVar(),
stats.Chi2DataVar(),
stats.Chi2Gehrels(),
stats.Chi2ModVar(),
stats.Chi2XspecVar(),
stats.LeastSq(),
stats.Cash(),
stats.CStat(),
stats.WStat(),
])
def test_astro_data_plot_with_stat_simple(make_data_path, stat):
from sherpa.astro import io
infile = make_data_path('3c273.pi')
pha = io.read_pha(infile)
# tweak the data set so that we aren't using the default
# options (it shouldn't matter for this test but just
# in case).
#
# Note that background subtraction would normally be an issue
# for some of the stats (e.g. WStat), but this shouldn't
# trigger a problem here.
