def from_yaml_dict(cls, data, pname='parameters', instance=None, *args, **kwargs):
"""Class method: load constraint from a dictionary or list of dicts.
If it is a dictionary, it should have the form:
```{
'metadata': {...}, # optional, do set attributes of the instance
'arguments': {...}, # optional, to specify keyword arguments for instantiation,
'constraints': [...], # required, the list of constraints
}
Alternatively, the list of constraints can be directly given.
This list should have elements in one of the two possible forms:
1. Dictionary as returned by `Probability.get_dict`:
```{
pname: [...], # required, list of constrained parameters
'values': {
'distribution': '...', # required, string identifying ProbabilityDistribution, e.g. 'normal'
'...': '...', # required, any arguments for the instantiation of the ProbabilityDistribution
}
}
```
2. String representing one or several (to be convolved) constraints:
```{
'my_parameter': '1.0 ± 0.2 ± 0.1 e-3'
}
"""
if isinstance(data, dict):
constraints = data['constraints']
meta = data.get('metadata', {})
arguments = data['arguments']
kwargs.update(arguments)
inst = instance or cls(*args, **kwargs)
for m in meta:
inst.__dict__[m] = meta[m]
else:
inst = instance or cls(*args, **kwargs)
constraints = data.copy()
for c in constraints:
if pname not in c:
if 'values' not in c and len(c) == 1:
# this means we probably have a constraint of the
# form parameter: constraint_string
for k, v in c.items(): # this loop runs only once
inst.set_constraint(k, v)
break # just to be sure
continue
else:
# in this case something is clearly wrong. Mabye the
# wrong "pname" was used.
raise ValueError('Key ' + pname + ' not found. '
'Please check the `pname` argument.')
else:
parameters = [tuple(p) if isinstance(p, list) else p for p in c[pname]]
pds = dict2dist(c['values'])
combined_pd = convolve_distributions(pds)
inst.add_constraint(parameters, combined_pd)
return inst
def set_constraint(self, parameter, constraint_string=None,
constraint_dict=None):
r"""Set the constraint on a parameter/observable by specifying a string
or a dictionary. If several constraints (e.g. several types of
uncertainty) are given, the total constraint will be the convolution
of the individual distributions. Existing constraints will be removed.
Arguments:
- parameter: parameter string (or tuple)
- constraint_string: string specifying the constraint that can be e.g.
of the form `'1.55(3)(1)'` or `'4.0±0.1'`.
- constraint_dict: dictionary or list of several dictionaries of the
form `{'distribution': 'distribution_name', 'arg1': val1, ...}`, where
'distribution_name' is a string name associated to each probability
distribution (see `flavio.statistics.probability.class_from_string`)
and `'arg1'`, `val1` are argument/value pairs of the arguments of
the distribution class's constructor (e.g.`central_value`,
`standard_deviation` for a normal distribution).
`constraint_string` and `constraint_dict` must not be present
simultaneously.
"""
if constraint_string is not None and constraint_dict is not None:
raise ValueError("constraint_string and constraint_dict cannot"
" be used at the same time.")
if constraint_string is not None:
pds = constraints_from_string(constraint_string)
elif constraint_dict is not None:
pds = dict2dist(constraint_dict)
else:
raise TypeError("Either constraint_string or constraint_dict have"
" to be specified.")
combined_pd = convolve_distributions(pds)
self.add_constraint([parameter], combined_pd)
def from_yaml_dict(cls, data, pname='parameters', instance=None, *args, **kwargs):
"""Class method: load constraint from a dictionary or list of dicts.
If it is a dictionary, it should have the form:
```{
'metadata': {...}, # optional, do set attributes of the instance
'arguments': {...}, # optional, to specify keyword arguments for instantiation,
'constraints': [...], # required, the list of constraints
}
Alternatively, the list of constraints can be directly given.
This list should have elements in one of the two possible forms:
1. Dictionary as returned by `Probability.get_dict`:
```{
pname: [...], # required, list of constrained parameters
'values': {
'distribution': '...', # required, string identifying ProbabilityDistribution, e.g. 'normal'
'...': '...', # required, any arguments for the instantiation of the ProbabilityDistribution
}
}
```
2. String representing one or several (to be convolved) constraints:
```{
'my_parameter': '1.0 ± 0.2 ± 0.1 e-3'
}
"""
if isinstance(data, dict):
constraints = data['constraints']
meta = data.get('metadata', {})
arguments = data['arguments']
kwargs.update(arguments)
inst = instance or cls(*args, **kwargs)
for m in meta:
inst.__dict__[m] = meta[m]
else:
inst = instance or cls(*args, **kwargs)
constraints = data.copy()
for c in constraints:
if pname not in c:
if 'values' not in c and len(c) == 1:
# this means we probably have a constraint of the
# form parameter: constraint_string
for k, v in c.items(): # this loop runs only once
inst.set_constraint(k, v)
break # just to be sure
continue
else:
# in this case something is clearly wrong. Mabye the
# wrong "pname" was used.
raise ValueError('Key ' + pname + ' not found. '
'Please check the `pname` argument.')
else:
parameters = [tuple(p) if isinstance(p, list) else p for p in c[pname]]
pds = dict2dist(c['values'])
combined_pd = convolve_distributions(pds)
inst.add_constraint(parameters, combined_pd)
return inst
def set_constraint(self, parameter, constraint_string):
"""Set the constraints on a parameter/observable by specifying a string
that can be e.g. of the form 1.55(3)(1) or 4.0±0.1. Existing
constraints will be removed."""
pds = constraints_from_string(constraint_string)
combined_pd = convolve_distributions(pds)
self.add_constraint([parameter], combined_pd)
def set_constraint(self, parameter, constraint_string=None,
constraint_dict=None):
r"""Set the constraint on a parameter/observable by specifying a string
or a dictionary. If several constraints (e.g. several types of
uncertainty) are given, the total constraint will be the convolution
of the individual distributions. Existing constraints will be removed.
Arguments:
- parameter: parameter string (or tuple)
- constraint_string: string specifying the constraint that can be e.g.
of the form `'1.55(3)(1)'` or `'4.0±0.1'`.
- constraint_dict: dictionary or list of several dictionaries of the
form `{'distribution': 'distribution_name', 'arg1': val1, ...}`, where
'distribution_name' is a string name associated to each probability
distribution (see `flavio.statistics.probability.class_from_string`)
and `'arg1'`, `val1` are argument/value pairs of the arguments of
the distribution class's constructor (e.g.`central_value`,
`standard_deviation` for a normal distribution).
`constraint_string` and `constraint_dict` must not be present
simultaneously.
"""
if constraint_string is not None and constraint_dict is not None:
raise ValueError("constraint_string and constraint_dict cannot"
" be used at the same time.")
if constraint_string is not None:
pds = constraints_from_string(constraint_string)
elif constraint_dict is not None:
pds = dict2dist(constraint_dict)
else:
raise TypeError("Either constraint_string or constraint_dict have"
" to be specified.")
combined_pd = convolve_distributions(pds)
self.add_constraint([parameter], combined_pd)
