def test_cash_errors(n_on, mu_bkg, result):
stat = CashCountsStatistic(n_on, mu_bkg)
errn = stat.compute_errn()
errp = stat.compute_errp()
assert_allclose(errn, result[0], atol=1e-5)
assert_allclose(errp, result[1], atol=1e-5)
"""Example plot showing the profile of the Cash statistic and its connection to excess errors."""
import numpy as np
import matplotlib.pyplot as plt
from gammapy.stats import CashCountsStatistic
count_statistic = CashCountsStatistic(n_on=13, mu_bkg=5.5)
excess = count_statistic.excess
errn = count_statistic.compute_errn(1.0)
errp = count_statistic.compute_errp(1.0)
errn_2sigma = count_statistic.compute_errn(2.0)
errp_2sigma = count_statistic.compute_errp(2.0)
# We compute the Cash statistic profile
mu_signal = np.linspace(-1.5, 25, 100)
stat_values = count_statistic._stat_fcn(mu_signal)
xmin, xmax = -1.5, 25
ymin, ymax = -42, -28.0
plt.figure(figsize=(5, 5))
plt.plot(mu_signal, stat_values, color="k")
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xlabel(r"Number of expected signal event, $\mu_{sig}$")
plt.ylabel(r"Cash statistic value, TS ")
plt.hlines(
count_statistic.TS_max + 1,
xmin=excess + errn,
xmax=excess + errp,