pyuncertainnumber.characterisation.utils¶

Classes¶

`EnhancedJSONEncoder`	a template for jsonify general (dataclass) object
`PBAEncoder`	a bespoke JSON encoder for the PBA object
`UNEncoder`	a bespoke JSON encoder for the UncertainNumber object

Functions¶

`save2Vis`(figname)	a shortcut function to save plot to visualization dir
`tranform_ecdf`(s[, display])	plot the CDF return the quantile
`pl_pcdf`(dist[, ax, title])
`pl_ecdf`(s[, ax, return_value])	plot the empirical CDF given samples
`to_database`(dict_list, db_name, col_name)
`cd_root_dir`([depth])
`sgnumber`(→ list)	significant digits representation
`initial_list_checking`(text)	detects if a string representation of a list
`bad_list_checking`(text)	detects if a syntactically wrong specification of a list
`PlusMinus_parser`(txt)
`parser4`(text)
`percentage_finder`(txt)
`percentage_converter`(txt)	convert a percentage into a float number
`get_concise_repr`(a_dict)
`array2list`(a_dict)	convert an array from a dictionary into a list
`entries_to_remove`(remove_entries, the_dict)

Module Contents¶

pyuncertainnumber.characterisation.utils.save2Vis(figname)¶: a shortcut function to save plot to visualization dir

Note

We simply assume that every repo will have a ‘visulizations’ dir under the root directory

pyuncertainnumber.characterisation.utils.tranform_ecdf(s, display=False, **kwargs)¶

plot the CDF return the quantile

Parameters:: s – sample

pyuncertainnumber.characterisation.utils.pl_pcdf(dist: type[scipy.stats.rv_continuous | scipy.stats.rv_discrete], ax=None, title=None, **kwargs)¶

pyuncertainnumber.characterisation.utils.pl_ecdf(s, ax=None, return_value=False, **kwargs)¶

plot the empirical CDF given samples

Parameters:: s (array-like) – sample which can be either raw data or deviates as a representation of dist construct

pyuncertainnumber.characterisation.utils.to_database(dict_list, db_name, col_name)¶

pyuncertainnumber.characterisation.utils.cd_root_dir(depth=0)¶

pyuncertainnumber.characterisation.utils.sgnumber(user_input: str) → list¶

significant digits representation

number plus or minus its significant-digit imprecision

pyuncertainnumber.characterisation.utils.initial_list_checking(text)¶: detects if a string representation of a list

pyuncertainnumber.characterisation.utils.bad_list_checking(text)¶: detects if a syntactically wrong specification of a list

pyuncertainnumber.characterisation.utils.PlusMinus_parser(txt)¶

pyuncertainnumber.characterisation.utils.parser4(text)¶

pyuncertainnumber.characterisation.utils.percentage_finder(txt)¶

pyuncertainnumber.characterisation.utils.percentage_converter(txt)¶: convert a percentage into a float number

Note

force only 1 percentage

class pyuncertainnumber.characterisation.utils.EnhancedJSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.JSONEncoder

a template for jsonify general (dataclass) object

#TODO Interval object in not json serializable

default(o)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)

class pyuncertainnumber.characterisation.utils.PBAEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.JSONEncoder

a bespoke JSON encoder for the PBA object

default(o)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)

class pyuncertainnumber.characterisation.utils.UNEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)¶

Bases: json.JSONEncoder

a bespoke JSON encoder for the UncertainNumber object

Note

Currently I’m treating the JSON data represent of a UN object

the same as the __repr__ method. But this can be changed later on to show more explicitly the strucutre of pbox or distribution # TODO prettify the JSON output to be explicit e.g. ‘essence’: ‘interval’, ‘interval_initialisation’: [2, 3] to shown as ‘interval’ with lower end and upper end distribution to shown as the type and parameters; e.g. ‘distribution’: ‘normal’, ‘parameters’: [2, 3]

default(o)¶

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default like this:

def default(self, o):
    try:
        iterable = iter(o)
    except TypeError:
        pass
    else:
        return list(iterable)
    # Let the base class default method raise the TypeError
    return super().default(o)

pyuncertainnumber.characterisation.utils.get_concise_repr(a_dict)¶

pyuncertainnumber.characterisation.utils.array2list(a_dict)¶: convert an array from a dictionary into a list

pyuncertainnumber.characterisation.utils.entries_to_remove(remove_entries, the_dict)¶