# shamo.core.distributions.uniform.DistUniform¶

class shamo.core.distributions.uniform.DistUniform(lower, upper)[source]

A uniform distribution.

Parameters
lowerfloat

The lower bound of the distribution.

upperfloat

The upper bound of the distribution.

Methods

 clear copy fromkeys Create a new dictionary with keys from iterable and values set to value. get Return the value for key if key is in the dictionary, else default. items keys load Load a distribution from its dict representation. pop If key is not found, d is returned if given, otherwise KeyError is raised popitem Remove and return a (key, value) pair as a 2-tuple. setdefault Insert key with a value of default if key is not in the dictionary. update If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k] values

Attributes

 TYPE_CONSTANT TYPE_NORMAL TYPE_TRUNC_NORMAL TYPE_UNIFORM dist Return the actual distribution. dist_type Return the type of the distribution. expect Return the expected value of the distribution. lower Return the lower bound of the distribution. salib_bounds Return the bounds of the distribution in SALib. salib_name Return the name of the distribution in SALib. uniform_dist Return a uniform distribution used for sampling. upper Return the upper bound of the distribution.
clear() → None. Remove all items from D.
copy() → a shallow copy of D
property dist

Return the actual distribution.

Returns
chaospy.Uniform

The actual distribution.

property dist_type

Return the type of the distribution.

Returns
str

The type of the distribution.

property expect

Return the expected value of the distribution.

Returns
float

The expected value of the distribution.

fromkeys(iterable, value=None, /)

Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)

Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D’s items
keys() → a set-like object providing a view on D’s keys
static load(dist_type, **kwargs)

Load a distribution from its dict representation.

Returns
DistABC

property lower

Return the lower bound of the distribution.

Returns
float

The lower bound of the distribution.

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised

popitem(/)

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

property salib_bounds

Return the bounds of the distribution in SALib.

Returns
list [float]

The bounds of the distribution in SALib.

property salib_name

Return the name of the distribution in SALib.

Returns
str

The name of the distribution in SALib.

setdefault(key, default=None, /)

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

property uniform_dist

Return a uniform distribution used for sampling.

Returns
chaospy.Uniform

The uniform distribution.

update([E, ]**F) → None. Update D from dict/iterable E and F.

If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

property upper

Return the upper bound of the distribution.

Returns
float

The upper bound of the distribution.

values() → an object providing a view on D’s values