shamo.core.distributions.normal.DistTruncNormal¶

class shamo.core.distributions.normal.DistTruncNormal(mu, sigma, lower, upper)[source]¶

Bases: shamo.core.distributions.abc.DistABC

A truncated normal distribution.

Parameters

mufloat: The mean of the distribution.
sigmafloat: The standard deviation of the distribution.
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.
`mu`	Return the mean of the distribution.
`salib_bounds`	Return the bounds of the distribution in SALib.
`salib_name`	Return the name of the distribution in SALib.
`sigma`	Return the standard deviation of the distribution.
`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.TruncNormal: 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: The loaded distribution.

property lower¶

Return the lower bound of the distribution.

Returns

float: The lower bound of the distribution.

property mu¶

Return the mean of the distribution.

mufloat: The mean 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 sigma¶

Return the standard deviation of the distribution.

sigmafloat: The standard deviation of the distribution.

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¶