NormalDistribution

class pharmpy.model.NormalDistribution(name, level, mean, variance)

Bases: Distribution

Normal distribution for one random variable

Parameters:

• name (str) – Name of the random variable

• level (str) – Name of the variability level

• mean (expression or number) – Mean of the random variable

• variance (expression or number) – Variance of the random variable

Example

>>> from pharmpy.model import NormalDistribution, Parameter
>>> omega = Parameter.create('OMEGA_CL', 0.1)
>>> dist = NormalDistribution.create("IIV_CL", "IIV", 0, omega.symbol)
>>> dist
IIV_CL ~ N(0, OMEGA_CL)

Attributes Summary

free_symbols	Free symbols including random variable itself
level	Name of VariabilityLevel of the random variables
mean
names	Names of random variables of distribution
parameter_names	List of names of all parameters used in definition
variance

Methods Summary

create(name, level, mean, variance)
evalf(parameters)
from_dict(d)
get_covariance(name1, name2)
get_variance(name)
latex_string([aligned])
replace(**kwargs)	Replace properties and create a new NormalDistribution
subs(d)	Substitute expressions
to_dict()

Attributes Documentation

free_symbols

Free symbols including random variable itself

level

mean

names

parameter_names

List of names of all parameters used in definition

variance

Methods Documentation

classmethod create(name, level, mean, variance)

evalf(parameters)

classmethod from_dict(d)

get_covariance(name1, name2)

get_variance(name)

latex_string(aligned=False)

replace(**kwargs)

Replace properties and create a new NormalDistribution

subs(d)

Substitute expressions

Parameters:

d (dict) – Dictionary of from: to pairs for substitution

Examples

>>> import sympy
>>> from pharmpy.model import NormalDistribution, Parameter
>>> omega = Parameter.create("OMEGA_CL", 0.1)
>>> dist = NormalDistribution.create("IIV_CL", "IIV", 0, omega.symbol)
>>> dist = dist.subs({omega.symbol: sympy.Symbol("OMEGA_NEW")})
>>> dist
IIV_CL ~ N(0, OMEGA_NEW)

to_dict()