Parameters in appletree

In general, all parameters including fixed parameters and fit parameters are handled by appletree.Parameter, which has many useful attributes

class appletree.Parameter(parameter_config)

Bases: object

Parameter handler to update parameters and calculate prior.

__getitem__(keys)

__getitem__, keys can be str/list/set.

__init__(parameter_config)

Initialization.

Parameters

parameter_config – can be either * str: the json file name where the config is stored. * dict: config dictionary.

check_parameter_exist(keys, return_not_exist=False)

Check whether the keys exist in parameters.

Parameters

• keys – Parameter names. Can be a single str, or a list of str.

• return_not_exist – If False, function will return a bool if all keys exist. If True, function will additionally return the list of the not existing keys.

get_all_parameter()

Return all parameters as a dict.

get_parameter(keys)

Return parameter values.

Parameters

keys – Parameter names. Can be a single str, or a list of str.

init_parameter(seed=None)

Initializing parameters by sampling prior. sampling from the. initial guess. If the prior is free, then.

Parameters

seed – integer, sent to np.random.seed(seed)

property log_prior

Return log prior.

If any parameter is out of allowed_range return -np.inf.

property parameter_fit

Return sorted list of parameters name waiting for fitting.

property parameter_fit_array

Return non-fixed parameters, ordered by self._parameter_fit.

sample_init()

Samping parameters from initial guess clipped by the allowed_range and set self._parameter_dict.

sample_prior()

Sampling parameters from prior and set self._parameter_dict.

If the prior is free, then sampling from the initial guess.

set_parameter(keys, vals=None)

Set parameter values.

Parameters

• keys – Parameter names. Can be either * str: vals must be int or float. * list: vals must have the same length. * dict: vals will be overwritten as keys.values().

• vals – Values to be set.

We recommend to initialize Parameter from a json file. An example of parameter config file is the ER parameter file, in which the dictionary is structured as

{
    param_name_0: config_of_param_0,
    param_name_1: config_of_param_1,
    ...
}

For each config_of_param, it should be a dictionary containing the following items:

• “prior_type”: can be either

  • “norm”: normal prior, prior_args are mean and std.

  • “uniform”: uniform prior, prior_args are lower and upper.

  • “free”: free prior, no prior_args required.

  • “fixed”: it won’t be considered as a fit parameter, prior_args is val.

• “prior_args”: a dictionary like {arg_name : arg_value} which goes into prior.

• “allowed_range”: a list like [lower_boundary, upper_boundary], above which parameters will be clipped and have -np.inf log prior.

• “init_mean”: the gaussian mean as the initial guess of the MCMC walkers. The random initialzation of MCMC will be clipped by “allowed_range”.

• “init_std”: the gaussian std as the initial guess of the MCMC walkers.

• “unit”: the unit of the parameter, only for documentation purpose.

• “doc”: the addtional docstring for the parameter.

For example,

{
    "w": {
        "prior_type": "norm",
        "prior_args": {
            "mean": 0.0137,
            "std": 0.0002
        },
        "allowed_range": [
            0,
            1.0
        ],
        "init_mean": 0.0137,
        "init_std": 0.0002,
        "unit": "keV",
        "doc": "Mean energy to generate a quanta in liquid xenon"
    },
    "fano": {
        "prior_type": "fixed",
        "prior_args": {
            "val": 0.059
        },
        "allowed_range": null,
        "init_mean": null,
        "init_std": null,
        "unit": "1",
        "doc": "Fano factor which describes the fluctuation of num of quanta"
    },
}

All non-fixed parameters are called fit parameters in appletree, and will be the parameters that MCMC samples. appletree.Plugin needs a dictionary of parameters. With the Parameter class, it can be simply obtained by Parameter.get_all_parameter().