-
Notifications
You must be signed in to change notification settings - Fork 187
/
plot_fc_gauss.py
executable file
·50 lines (40 loc) · 1.27 KB
/
plot_fc_gauss.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
"""
Compute numerical solution for Gaussian with a boundary at the origin.
Produces Fig. 10 from the Feldman & Cousins paper.
"""
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
from gammapy.stats import (
fc_construct_acceptance_intervals_pdfs,
fc_fix_limits,
fc_get_limits,
)
sigma = 1
n_sigma = 10
n_bins_x = 1000
step_width_mu = 0.005
mu_min = 0
mu_max = 8
cl = 0.90
x_bins = np.linspace(-n_sigma * sigma, n_sigma * sigma, n_bins_x, endpoint=True)
mu_bins = np.linspace(mu_min, mu_max, mu_max / step_width_mu + 1, endpoint=True)
matrix = [
dist / sum(dist)
for dist in (norm(loc=mu, scale=sigma).pdf(x_bins) for mu in mu_bins)
]
acceptance_intervals = fc_construct_acceptance_intervals_pdfs(matrix, cl)
LowerLimitNum, UpperLimitNum, _ = fc_get_limits(mu_bins, x_bins, acceptance_intervals)
fc_fix_limits(LowerLimitNum, UpperLimitNum)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.plot(UpperLimitNum, mu_bins, ls="-", color="red")
plt.plot(LowerLimitNum, mu_bins, ls="-", color="red")
plt.grid(True)
ax.xaxis.set_ticks(np.arange(-10, 10, 1))
ax.xaxis.set_ticks(np.arange(-10, 10, 0.2), True)
ax.yaxis.set_ticks(np.arange(0, 8, 0.2), True)
ax.set_xlabel(r"Measured Mean x")
ax.set_ylabel(r"Mean $\mu$")
plt.axis([-2, 4, 0, 6])
plt.show()