def UpperLimit(self,
fp,
fn,
delta,
mx_min,
mx_max,
num_steps,
output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({
'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file
} for mx in mx_list)
upper_limit = np.array(
par.parmap(self.MaximumGapUpperBoundSHM, kwargs,
processes)).flatten()
upper_limit = 1. / conversion_factor * mx_list * upper_limit
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
result = np.log10(np.transpose([mx_list, upper_limit]))
return result[result[:, 1] != np.inf]
def UpperLimit(self,
fp,
fn,
delta,
mx_min,
mx_max,
num_steps,
output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
# mx_list= np.array([2.891, 3.029, 3.175, 3.327, 3.486, 3.654, 3.829, 4.012, 4.205, 4.406, \
# 4.617, 4.839, 5.071, 5.314, 5.569, 5.836, 6.116, 6.409, 6.716, 7.038, \
# 7.376, 7.729, 8.1, 8.488, 8.895, 9.322, 9.769, 10.237, 10.728, \
# 11.242, 11.781, 12.346, 12.938, 13.558, 14.208, 14.89, 15.603, \
# 16.352, 17.136, 17.957, 18.818, 19.72, 20.666, 21.657, 22.695, \
# 23.783, 24.924, 26.119, 27.371, 28.683, 30.058, 31.5, 33.01, 34.593, \
# 36.251, 37.989, 39.811])
kwargs = ({
'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file
} for mx in mx_list)
upper_limit = np.array(
par.parmap(self.MaximumGapUpperBoundSHM, kwargs,
processes)).flatten()
upper_limit = 1. / conversion_factor * mx_list * upper_limit
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
result = np.log10(np.transpose([mx_list, upper_limit]))
return result[result[:, 1] != np.inf]
def UpperLimit(self, fp, fn, delta, mx_min, mx_max, num_steps, output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
# mx_list= np.array([2.891, 3.029, 3.175, 3.327, 3.486, 3.654, 3.829, 4.012, 4.205, 4.406, \
# 4.617, 4.839, 5.071, 5.314, 5.569, 5.836, 6.116, 6.409, 6.716, 7.038, \
# 7.376, 7.729, 8.1, 8.488, 8.895, 9.322, 9.769, 10.237, 10.728, \
# 11.242, 11.781, 12.346, 12.938, 13.558, 14.208, 14.89, 15.603, \
# 16.352, 17.136, 17.957, 18.818, 19.72, 20.666, 21.657, 22.695, \
# 23.783, 24.924, 26.119, 27.371, 28.683, 30.058, 31.5, 33.01, 34.593, \
# 36.251, 37.989, 39.811])
kwargs = ({'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file}
for mx in mx_list)
upper_limit = np.array(par.parmap(self.MaximumGapUpperBoundSHM, kwargs, processes)
).flatten()
upper_limit = 1./conversion_factor * mx_list * upper_limit
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
result = np.log10(np.transpose([mx_list, upper_limit]))
return result[result[:, 1] != np.inf]
def UpperLimit(self, fp, fn, delta, mx_min, mx_max, num_steps, output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
Input:
fp, fn: float
Couplings to proton and neutron.
delta: float
DM mass split.
mx_min, mx_max: float
Minimum and maximum DM mass.
num_steps: int
Number of steps in the range of DM mass values.
output_file: string
Name of output file where the result will be printed.
Returns:
upperlimit: ndarray
Table with the first row giving the base-10 log of the DM mass, and
the second row giving the base-10 log of the upper limit in cross-section.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta}
for mx in mx_list)
upper_limit = np.array(par.parmap(self._MinusLogLikelihood, kwargs, processes)).flatten()
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
result = np.log10(np.transpose([mx_list, 10.0**-upper_limit]))
result = result[result[:, 1] != np.inf]
with open(output_file, 'ab') as f_handle:
np.savetxt(f_handle, result)
return result
def UpperLimit(self, fp, fn, delta, mx_min, mx_max, num_steps, output_file,
processes=None):
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file}
for mx in mx_list)
upper_limit = np.array(par.parmap(self.RegionSHM, kwargs, processes))
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
return upper_limit
def UpperLowerLists(self, CL, output_file, output_file_lower, output_file_upper,
num_mx=1000, processes=None):
print('CL =', CL, 'chi2 =', chi_squared(2, CL))
self.logL_target = self.logL_max - chi_squared(2, CL)/2
table = np.transpose(np.loadtxt(output_file))
num_dimensions = table.shape[0]
num_bins = (num_dimensions - 3)//3
print('num_dimensions =', num_dimensions)
print('num_bins =', num_bins)
mx_list = table[0]
sigma_fit_interp = interpolate.interp1d(mx_list, table[1])
log_likelihood_max_interp = interpolate.interp1d(mx_list, table[2])
predicted_interp = [interpolate.interp1d(mx_list, table[i])
for i in range(3, 3 + num_bins)]
data_interp = [interpolate.interp1d(mx_list, table[i])
for i in range(3 + num_bins, 3 + 2 * num_bins)]
error_interp = [interpolate.interp1d(mx_list, table[i])
for i in range(3 + 2 * num_bins, 3 + 3 * num_bins)]
mx_list = np.logspace(np.log10(mx_list[0]), np.log10(mx_list[-1]), num=num_mx)
sigma_fit = np.array([sigma_fit_interp(mx) for mx in mx_list])
logL_max = np.array([log_likelihood_max_interp(mx) for mx in mx_list])
predicted = np.array([[p(mx) for p in predicted_interp] for mx in mx_list])
data = np.array([[d(mx) for d in data_interp] for mx in mx_list])
error = np.array([[err(mx) for err in error_interp] for mx in mx_list])
kwargs = ({'mx': mx_list[index],
'logL_max': logL_max[index],
'sigma': sigma_fit[index],
'pred': predicted[index],
'data': data[index],
'err': error[index]
}
for index in range(len(mx_list)))
limits = [l for l in par.parmap(self._UpperLowerLists, kwargs,
processes=processes, verbose=False) if l]
if limits:
limits = np.transpose(limits, axes=(1, 0, 2))
limit_low = limits[0]
limit_high = limits[1]
else:
limit_low = limit_high = []
return [np.array(limit_low), np.array(limit_high)]
def UpperLimit(self, fp, fn, delta, mx_min, mx_max, num_steps, output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file}
for mx in mx_list)
upper_limit = np.array(par.parmap(self._GaussianUpperBoundSHM, kwargs, processes)
).flatten()
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
result = np.log10(np.transpose([mx_list, upper_limit]))
return result[result[:, 1] != np.inf]
def UpperLimit(self,
fp,
fn,
delta,
mx_min,
mx_max,
num_steps,
output_file,
processes=None):
""" Computes the upper limit in cross-section as a function of DM mass mx.
Input:
fp, fn: float
Couplings to proton and neutron.
delta: float
DM mass split.
mx_min, mx_max: float
Minimum and maximum DM mass.
num_steps: int
Number of steps in the range of DM mass values.
output_file: string
Name of output file where the result will be printed.
Returns:
upperlimit: ndarray
Table with the first row giving the base-10 log of the DM mass, and
the second row giving the base-10 log of the upper limit in cross-section.
"""
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({
'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta
} for mx in mx_list)
upper_limit = np.array(
par.parmap(self._PoissonUpperBoundSHM, kwargs, processes))
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
upper_limit = 1 / conversion_factor * mx_list * upper_limit
result = np.log10(np.transpose([mx_list, upper_limit]))
result = result[result[:, 1] != np.inf]
with open(output_file, 'ab') as f_handle:
np.savetxt(f_handle, result)
return result
def UpperLimit(self,
fp,
fn,
delta,
mx_min,
mx_max,
num_steps,
output_file,
processes=None):
mx_list = np.logspace(np.log10(mx_min), np.log10(mx_max), num_steps)
kwargs = ({
'mx': mx,
'fp': fp,
'fn': fn,
'delta': delta,
'output_file': output_file
} for mx in mx_list)
upper_limit = np.array(par.parmap(self.RegionSHM, kwargs, processes))
print("mx_list = ", mx_list)
print("upper_limit = ", upper_limit)
return upper_limit
def UpperLowerLists(self,
CL,
output_file,
output_file_lower,
output_file_upper,
num_mx=1000,
processes=None):
print('CL =', CL, 'chi2 =', chi_squared(2, CL))
self.logL_target = self.logL_max - chi_squared(2, CL) / 2
table = np.transpose(np.loadtxt(output_file))
num_dimensions = table.shape[0]
num_bins = (num_dimensions - 3) // 3
print('num_dimensions =', num_dimensions)
print('num_bins =', num_bins)
mx_list = table[0]
sigma_fit_interp = interpolate.interp1d(mx_list, table[1])
log_likelihood_max_interp = interpolate.interp1d(mx_list, table[2])
predicted_interp = [
interpolate.interp1d(mx_list, table[i])
for i in range(3, 3 + num_bins)
]
data_interp = [
interpolate.interp1d(mx_list, table[i])
for i in range(3 + num_bins, 3 + 2 * num_bins)
]
error_interp = [
interpolate.interp1d(mx_list, table[i])
for i in range(3 + 2 * num_bins, 3 + 3 * num_bins)
]
mx_list = np.logspace(np.log10(mx_list[0]),
np.log10(mx_list[-1]),
num=num_mx)
sigma_fit = np.array([sigma_fit_interp(mx) for mx in mx_list])
logL_max = np.array([log_likelihood_max_interp(mx) for mx in mx_list])
predicted = np.array([[p(mx) for p in predicted_interp]
for mx in mx_list])
data = np.array([[d(mx) for d in data_interp] for mx in mx_list])
error = np.array([[err(mx) for err in error_interp] for mx in mx_list])
kwargs = ({
'mx': mx_list[index],
'logL_max': logL_max[index],
'sigma': sigma_fit[index],
'pred': predicted[index],
'data': data[index],
'err': error[index]
} for index in range(len(mx_list)))
limits = [
l for l in par.parmap(self._UpperLowerLists,
kwargs,
processes=processes,
verbose=False) if l
]
if limits:
limits = np.transpose(limits, axes=(1, 0, 2))
limit_low = limits[0]
limit_high = limits[1]
else:
limit_low = limit_high = []
return [np.array(limit_low), np.array(limit_high)]
