# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Feldman Cousins algorithm to compute parameter confidence limits."""
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import numpy as np
__all__ = [
"fc_find_acceptance_interval_gauss",
"fc_find_acceptance_interval_poisson",
"fc_construct_acceptance_intervals_pdfs",
"fc_get_limits",
"fc_fix_limits",
"fc_find_limit",
"fc_find_average_upper_limit",
"fc_construct_acceptance_intervals",
]
log = logging.getLogger(__name__)
[docs]def fc_find_acceptance_interval_gauss(mu, sigma, x_bins, alpha):
r"""
Analytical acceptance interval for Gaussian with boundary at the origin.
.. math :: \int_{x_{min}}^{x_{max}} P(x|mu)\mathrm{d}x = alpha
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
mu : float
Mean of the Gaussian
sigma : float
Width of the Gaussian
x_bins : array-like
Bins in x
alpha : float
Desired confidence level
Returns
-------
(x_min, x_max) : tuple of floats
Acceptance interval
"""
from scipy import stats
dist = stats.norm(loc=mu, scale=sigma)
x_bin_width = x_bins[1] - x_bins[0]
p = []
r = []
for x in x_bins:
p.append(dist.pdf(x) * x_bin_width)
# This is the formula from the FC paper
if mu == 0 and sigma == 1:
if x < 0:
r.append(np.exp(mu * (x - mu * 0.5)))
else:
r.append(np.exp(-0.5 * np.power((x - mu), 2)))
# This is the more general formula
else:
# Implementing the boundary condition at zero
mu_best = max(0, x)
prob_mu_best = stats.norm.pdf(x, loc=mu_best, scale=sigma)
# probMuBest should never be zero. Check it just in case.
if prob_mu_best == 0.0:
r.append(0.0)
else:
r.append(p[-1] / prob_mu_best)
p = np.asarray(p)
r = np.asarray(r)
if sum(p) < alpha:
raise ValueError(
"X bins don't contain enough probability to reach "
"desired confidence level for this mu!"
)
rank = stats.rankdata(-r, method="dense")
index_array = np.arange(x_bins.size)
rank_sorted, index_array_sorted = zip(*sorted(zip(rank, index_array)))
index_min = index_array_sorted[0]
index_max = index_array_sorted[0]
p_sum = 0
for i in range(len(rank_sorted)):
if index_array_sorted[i] < index_min:
index_min = index_array_sorted[i]
if index_array_sorted[i] > index_max:
index_max = index_array_sorted[i]
p_sum += p[index_array_sorted[i]]
if p_sum >= alpha:
break
return x_bins[index_min], x_bins[index_max] + x_bin_width
[docs]def fc_find_acceptance_interval_poisson(mu, background, x_bins, alpha):
r"""Analytical acceptance interval for Poisson process with background.
.. math :: \int_{x_{min}}^{x_{max}} P(x|mu)\mathrm{d}x = alpha
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
mu : float
Mean of the signal
background : float
Mean of the background
x_bins : array-like
Bins in x
alpha : float
Desired confidence level
Returns
-------
(x_min, x_max) : tuple of floats
Acceptance interval
"""
from scipy import stats
dist = stats.poisson(mu=mu + background)
x_bin_width = x_bins[1] - x_bins[0]
p = []
r = []
for x in x_bins:
p.append(dist.pmf(x))
# Implementing the boundary condition at zero
muBest = max(0, x - background)
probMuBest = stats.poisson.pmf(x, mu=muBest + background)
# probMuBest should never be zero. Check it just in case.
if probMuBest == 0.0:
r.append(0.0)
else:
r.append(p[-1] / probMuBest)
p = np.asarray(p)
r = np.asarray(r)
if sum(p) < alpha:
raise ValueError(
"X bins don't contain enough probability to reach "
"desired confidence level for this mu!"
)
rank = stats.rankdata(-r, method="dense")
index_array = np.arange(x_bins.size)
rank_sorted, index_array_sorted = zip(*sorted(zip(rank, index_array)))
index_min = index_array_sorted[0]
index_max = index_array_sorted[0]
p_sum = 0
for i in range(len(rank_sorted)):
if index_array_sorted[i] < index_min:
index_min = index_array_sorted[i]
if index_array_sorted[i] > index_max:
index_max = index_array_sorted[i]
p_sum += p[index_array_sorted[i]]
if p_sum >= alpha:
break
return x_bins[index_min], x_bins[index_max] + x_bin_width
[docs]def fc_construct_acceptance_intervals_pdfs(matrix, alpha):
r"""Numerically choose bins a la Feldman Cousins ordering principle.
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
matrix : array-like
A list of x PDFs for increasing values of mue.
alpha : float
Desired confidence level
Returns
-------
distributions_scaled : ndarray
Acceptance intervals (1 means inside, 0 means outside)
"""
number_mus = len(matrix)
distributions_scaled = np.asarray(matrix)
distributions_re_scaled = np.asarray(matrix)
summed_propability = np.zeros(number_mus)
# Step 1:
# For each x, find the greatest likelihood in the mu direction.
# greatest_likelihood is an array of length number_x_bins.
greatest_likelihood = np.amax(distributions_scaled, axis=0)
# Set to some value if none of the bins has an entry to avoid
# division by zero
greatest_likelihood[greatest_likelihood == 0] = 1
# Step 2:
# Scale all entries by this value
distributions_re_scaled /= greatest_likelihood
# Step 3 (Feldman Cousins Ordering principle):
# For each mu, get the largest entry
largest_entry = np.argmax(distributions_re_scaled, axis=1)
# Set the rank to 1 and add probability
for i in range(number_mus):
distributions_re_scaled[i][largest_entry[i]] = 1
summed_propability[i] += np.sum(
np.where(distributions_re_scaled[i] == 1, distributions_scaled[i], 0)
)
distributions_scaled[i] = np.where(
distributions_re_scaled[i] == 1, 1, distributions_scaled[i]
)
# Identify next largest entry not yet ranked. While there are entries
# smaller than 1, some bins don't have a rank yet.
while np.amin(distributions_re_scaled) < 1:
# For each mu, this is the largest rank attributed so far.
largest_rank = np.amax(distributions_re_scaled, axis=1)
# For each mu, this is the largest entry that is not yet a rank.
largest_entry = np.where(
distributions_re_scaled < 1, distributions_re_scaled, -1
)
# For each mu, this is the position of the largest entry that is not yet a rank.
largest_entry_position = np.argmax(largest_entry, axis=1)
# Invalidate indices where there is no maximum (every entry is already a rank)
largest_entry_position = [
largest_entry_position[i]
if largest_entry[i][largest_entry_position[i]] != -1
else -1
for i in range(len(largest_entry_position))
]
# Replace the largest entry with the highest rank so far plus one
# Add the probability
for i in range(number_mus):
if largest_entry_position[i] == -1:
continue
distributions_re_scaled[i][largest_entry_position[i]] = largest_rank[i] + 1
if summed_propability[i] < alpha:
summed_propability[i] += distributions_scaled[i][
largest_entry_position[i]
]
distributions_scaled[i][largest_entry_position[i]] = 1
else:
distributions_scaled[i][largest_entry_position[i]] = 0
return distributions_scaled
[docs]def fc_get_limits(mu_bins, x_bins, acceptance_intervals):
r"""Find lower and upper limit from acceptance intervals.
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
mu_bins : array-like
The bins used in mue direction.
x_bins : array-like
The bins of the x distribution
acceptance_intervals : array-like
The output of fc_construct_acceptance_intervals_pdfs.
Returns
-------
lower_limit : array-like
Feldman Cousins lower limit x-coordinates
upper_limit : array-like
Feldman Cousins upper limit x-coordinates
x_values : array-like
All the points that are inside the acceptance intervals
"""
upper_limit = []
lower_limit = []
x_values = []
number_mu = len(mu_bins)
number_bins_x = len(x_bins)
for mu in range(number_mu):
upper_limit.append(-1)
lower_limit.append(-1)
x_values.append([])
acceptance_interval = acceptance_intervals[mu]
for x in range(number_bins_x):
# This point lies in the acceptance interval
if acceptance_interval[x] == 1:
x_value = x_bins[x]
x_values[-1].append(x_value)
# Upper limit is first point where this condition is true
if upper_limit[-1] == -1:
upper_limit[-1] = x_value
# Lower limit is first point after this condition is not true
if x == number_bins_x - 1:
lower_limit[-1] = x_value
else:
lower_limit[-1] = x_bins[x + 1]
return lower_limit, upper_limit, x_values
[docs]def fc_fix_limits(lower_limit, upper_limit):
r"""Push limits outwards as described in the FC paper.
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
lower_limit : array-like
Feldman Cousins lower limit x-coordinates
upper_limit : array-like
Feldman Cousins upper limit x-coordinates
"""
all_fixed = False
while not all_fixed:
all_fixed = True
for j in range(1, len(upper_limit)):
if upper_limit[j] < upper_limit[j - 1]:
upper_limit[j - 1] = upper_limit[j]
all_fixed = False
for j in range(1, len(lower_limit)):
if lower_limit[j] < lower_limit[j - 1]:
lower_limit[j] = lower_limit[j - 1]
all_fixed = False
[docs]def fc_find_limit(x_value, x_values, y_values):
r"""
Find the limit for a given x measurement
For more information see :ref:`documentation <feldman_cousins>`
Parameters
----------
x_value : float
The measured x value for which the upper limit is wanted.
x_values : array-like
The x coordinates of the confidence belt.
y_values : array-like
The y coordinates of the confidence belt.
Returns
-------
limit : float
The Feldman Cousins limit
"""
if x_value > max(x_values):
raise ValueError("Measured x outside of confidence belt!")
# Loop through the x-values in reverse order
for i in reversed(range(len(x_values))):
current_x = x_values[i]
# The measured value sits on a bin edge. In this case we want the upper
# most point to be conservative, so it's the first point where this
# condition is true.
if x_value == current_x:
return y_values[i]
# If the current value lies between two bins, take the higher y-value
# in order to be conservative.
if x_value > current_x:
return y_values[i + 1]
[docs]def fc_find_average_upper_limit(x_bins, matrix, upper_limit, mu_bins, prob_limit=1e-5):
r"""
Function to calculate the average upper limit for a confidence belt
For more information see :ref:`documentation <feldman_cousins>`
Parameters
----------
x_bins : array-like
Bins in x direction
matrix : array-like
A list of x PDFs for increasing values of mue
(same as for fc_construct_acceptance_intervals_pdfs).
upper_limit : array-like
Feldman Cousins upper limit x-coordinates
mu_bins : array-like
The bins used in mue direction.
prob_limit : float
Probability value at which x values are no longer considered for the
average limit.
Returns
-------
average_limit : float
Average upper limit
"""
average_limit = 0
number_points = len(x_bins)
for i in range(number_points):
# Bins with very low probability will not contribute to average limit
if matrix[0][i] < prob_limit:
continue
try:
limit = fc_find_limit(x_bins[i], upper_limit, mu_bins)
except:
log.warning("Warning: Calculation of average limit incomplete!")
log.warning("Add more bins in mu direction or decrease prob_limit.")
return average_limit
average_limit += matrix[0][i] * limit
return average_limit
[docs]def fc_construct_acceptance_intervals(distribution_dict, bins, alpha):
r"""Convenience function that calculates the PDF for the user.
For more information see :ref:`documentation <feldman_cousins>`.
Parameters
----------
distribution_dict : dict
Keys are mu values and value is an array-like list of x values
bins : array-like
The bins the x distribution will have
alpha : float
Desired confidence level
Returns
-------
acceptance_intervals : ndarray
Acceptance intervals (1 means inside, 0 means outside)
"""
distributions_scaled = []
# Histogram gets rid of the last bin, so add one extra
bin_width = bins[1] - bins[0]
new_bins = np.concatenate((bins, np.array([bins[-1] + bin_width])), axis=0)
# Histogram and normalise each distribution so it is a real PDF
for _, distribution in sorted(distribution_dict.items()):
entries = np.histogram(distribution, bins=new_bins)[0]
integral = float(sum(entries))
distributions_scaled.append(entries / integral)
acceptance_intervals = fc_construct_acceptance_intervals_pdfs(
distributions_scaled, alpha
)
return acceptance_intervals