def load_parameters(filename, process, constraints):
implementation_name = process + ' BSZ'
parameter_names = [
implementation_name + ' ' + coeff_name for coeff_name in a_ff_string
]
# a0_A0 and a0_T2 are not treated as independent parameters!
parameter_names.remove(implementation_name + ' a0_A0')
parameter_names.remove(implementation_name + ' a0_T2')
for parameter_name in parameter_names:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
# get LaTeX representation of coefficient and form factor names
_tex_a = tex_a[parameter_name.split(' ')[-1].split('_')[0]]
_tex_ff = tex_ff[parameter_name.split(' ')[-1].split('_')[-1]]
p.tex = r'$' + _tex_a + r'^{' + _tex_ff + r'}$'
p.description = r'BSZ form factor parametrization coefficient $' + _tex_a + r'$ of $' + _tex_ff + r'$'
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
[central, unc, corr] = get_ffpar(filename)
constraints.add_constraint(
parameter_names,
MultivariateNormalDistribution(central_value=central,
covariance=np.outer(unc, unc) * corr))
def load_parameters(file_res, file_cov, process, constraints):
implementation_name = process + ' SSE'
res_dict = csv_to_dict(file_res)
cov_dict = csv_to_dict(file_cov)
keys_sorted = sorted(res_dict.keys())
res = [res_dict[k] for k in keys_sorted]
# M -> M + M^T - diag(M) since the dictionary contains only the entries above the diagonal
cov = (np.array([[cov_dict.get((k, m), 0) for m in keys_sorted]
for k in keys_sorted]) +
np.array([[cov_dict.get((m, k), 0) for m in keys_sorted]
for k in keys_sorted]) -
np.diag([cov_dict[(k, k)] for k in keys_sorted]))
parameter_names = [
implementation_name + ' ' + coeff_name for coeff_name in keys_sorted
]
for parameter_name in parameter_names:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
constraints.add_constraint(
parameter_names,
MultivariateNormalDistribution(central_value=res, covariance=cov))
def load_parameters(filename, constraints):
f = pkgutil.get_data('flavio.physics', filename)
ff_dict = yaml.load(f)
for parameter_name in ff_dict['parameters']:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
covariance = np.outer(ff_dict['uncertainties'], ff_dict['uncertainties'])*ff_dict['correlation']
if not np.allclose(covariance, covariance.T):
# if the covariance is not symmetric, it is assumed that only the values above the diagonal are present.
# then: M -> M + M^T - diag(M)
covariance = covariance + covariance.T - np.diag(np.diag(covariance))
constraints.add_constraint(ff_dict['parameters'],
MultivariateNormalDistribution(central_value=ff_dict['central_values'], covariance=covariance) )
def load_parameters(file_res, file_cov, process, constraints):
implementation_name = process + ' SSE'
res_dict = csv_to_dict(file_res)
cov_dict = csv_to_dict(file_cov)
keys_sorted = sorted(res_dict.keys())
res = [res_dict[k] for k in keys_sorted]
# M -> M + M^T - diag(M) since the dictionary contains only the entries above the diagonal
cov = ( np.array([[ cov_dict.get((k,m),0) for m in keys_sorted] for k in keys_sorted])
+ np.array([[ cov_dict.get((m,k),0) for m in keys_sorted] for k in keys_sorted])
- np.diag([ cov_dict[(k,k)] for k in keys_sorted]) )
parameter_names = [implementation_name + ' ' + coeff_name for coeff_name in keys_sorted]
for parameter_name in parameter_names:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
constraints.add_constraint(parameter_names,
MultivariateNormalDistribution(central_value=res, covariance=cov ))
def load_parameters(filename, constraints):
f = pkgutil.get_data('flavio.physics', filename)
ff_dict = yaml.load(f)
for parameter_name in ff_dict['parameters']:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
covariance = np.outer(ff_dict['uncertainties'],
ff_dict['uncertainties']) * ff_dict['correlation']
if not np.allclose(covariance, covariance.T):
# if the covariance is not symmetric, it is assumed that only the values above the diagonal are present.
# then: M -> M + M^T - diag(M)
covariance = covariance + covariance.T - np.diag(np.diag(covariance))
constraints.add_constraint(
ff_dict['parameters'],
MultivariateNormalDistribution(central_value=ff_dict['central_values'],
covariance=covariance))
def load_parameters(filename, process, constraints):
implementation_name = process + ' BSZ'
parameter_names = [implementation_name + ' ' + coeff_name for coeff_name in a_ff_string]
# a0_A0 and a0_T2 are not treated as independent parameters!
parameter_names.remove(implementation_name + ' a0_A0')
parameter_names.remove(implementation_name + ' a0_T2')
for parameter_name in parameter_names:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
# get LaTeX representation of coefficient and form factor names
_tex_a = tex_a[parameter_name.split(' ')[-1].split('_')[0]]
_tex_ff = tex_ff[parameter_name.split(' ')[-1].split('_')[-1]]
p.tex = r'$' + _tex_a + r'^{' + _tex_ff + r'}$'
p.description = r'BSZ form factor parametrization coefficient $' + _tex_a + r'$ of $' + _tex_ff + r'$'
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
[central, unc, corr] = get_ffpar(filename)
constraints.add_constraint(parameter_names,
MultivariateNormalDistribution(central_value=central, covariance=np.outer(unc, unc)*corr) )
def load_parameters(file_res, file_cov, process, constraints):
implementation_name = process + ' SSE'
res_dict = csv_to_dict(file_res)
cov_dict = csv_to_dict(file_cov)
keys_sorted = sorted(res_dict.keys())
res = [res_dict[k] for k in keys_sorted]
cov = np.array([[ cov_dict.get((k,m),0) for m in keys_sorted] for k in keys_sorted])
parameter_names = [implementation_name + ' ' + translate_parameters(coeff_name) for coeff_name in keys_sorted]
for parameter_name in parameter_names:
try: # check if parameter object already exists
p = Parameter.get_instance(parameter_name)
except: # otherwise, create a new one
p = Parameter(parameter_name)
_tex_a = tex_a[parameter_name.split(' ')[-1].split('_')[0]]
_tex_ff = tex_ff[parameter_name.split(' ')[-1].split('_')[-1]]
p.tex = r'$' + _tex_a + r'^{' + _tex_ff + r'}$'
p.description = r'SSE form factor parametrization coefficient $' + _tex_a + r'$ of $' + _tex_ff + r'$'
else: # if parameter exists, remove existing constraints
constraints.remove_constraints(parameter_name)
constraints.add_constraint(parameter_names,
MultivariateNormalDistribution(central_value=res, covariance=cov ))
