flownet.parameters.probability_distributions module

Defines the different probability distributions. These are used by the parameter class definitions.

class flownet.parameters.probability_distributions.Constant(const)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

property ert_gen_kw: str

string representing an ERT “CONST CONSTANT” distribution keyword for use in GEN_KW

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution

Providing a value for either the mean, the mode, the minimum or the maximum will change the constant value, but ONLY one value can be provided.

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.LogNormalDistribution(mean, stddev)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

property ert_gen_kw: str

string representing an ERT “LOGNORMAL MEAN STDDEV” distribution keyword for use in GEN_KW

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution

In the lognormal distribution one or both of the mean and the standard deviation can be changed. Providing any other value as input here (mode, minimum, maximum) will have no effect.

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.LogUniformDistribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

_find_dist_minmax(mean_val: float, min_val: Optional[float] = None, max_val: Optional[float] = None) → float

Find the distribution min or max for a loguniform distribution, assuming only one of these and the mean are given

Parameters

• min_val – minimum value for the distribution

• max_val – maximum value for the distribution

• mean_val – mean value for the distribution

Returns

missing value (minimum if maximum is provided as input, maximum if minimum is provided)

property ert_gen_kw: str

string representing an ERT “LOGUNIF MIN MAX” distribution keyword for use in GEN_KW

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution

The following input combinations will make changes to the distribution:

• Giving a new minimum value as input will trigger a calculation of a new mean and stddev

• Giving a new maximum value as input will trigger a calculation of a new mean and stddev

• Giving a new mean value as input requires a new minimum OR maximum value to be defined also

  • A new mean value and a new minimum value will trigger an update of the maximum value and the stddev

  • A new mean value and a new maximum value will trigger an update of the minimum value and the stddev

Providing values for stddev or mode has no effect here, since in the loguniform distribution the mode is equal to the minimum value, and the stddev is caluculated from the minimum and maximum values

Providing a new mean, a new minimum and a new maximum value (all three of them) will trigger an error

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.NormalDistribution(mean, stddev)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

property ert_gen_kw: str

string representing an ERT “NORMAL MEAN STDDEV” distribution keyword for use in GEN_KW

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution.

In the normal distribution one or both of the mean and the standard deviation can be changed. Providing any other value as input here (mode, minimum, maximum) will have no effect.

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.ProbabilityDistribution(name)

Bases: abc.ABC

_abc_impl = <_abc_data object>

abstract property ert_gen_kw

A string representing what ERT needs in GEN_KW

abstract update_distribution(minimum: Optional[float], maximum: Optional[float], mean: Optional[float], mode: Optional[float], stddev: Optional[float])

Function to update parameters for the various distributions

class flownet.parameters.probability_distributions.TriangularDistribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

defined: bool

property ert_gen_kw: str

string representing an ERT “TRIANGULAR MIN MODE MAX” distribution keyword for use in GEN_KW

maximum: float

mean: float

minimum: float

mode: float

name: str

stddev: float

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution

The following input combinations will make changes to the distribution:

• Giving a new minimum value as input will trigger a calculation of a new mean and stddev

• Giving a new maximum value as input will trigger a calculation of a new mean and stddev

• Giving a new mode as input will trigger a calculation of a new mean and stddev

• Giving a new mean value as input requires a new minimum/maximum OR mode/maximum OR minimum/mode values

to be defined also:

• A new mean, minimum and mode will trigger an update of the maximum value and the stddev

• A new mean, mode and maximum value will trigger an update of the minimum value and the stddev

• A new mean, minimum and maximum value will trigger an update of the mode and the stddev

Providing values for stddev has no effect here.

Providing a new mean, minimum, maximum and mode (all four of them) will trigger an error

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.TruncatedNormalDistribution(mean: float, stddev, minimum, maximum)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

_abc_impl = <_abc_data object>

defined: bool

property ert_gen_kw: str

string representing an ERT “TRUNCATED_NORMAL MEAN STDDEV” distribution keyword for use in GEN_KW

maximum: float

mean: float

minimum: float

mode: float

name: str

stddev: float

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution

In the truncated normal distribution one or more of the following can be changed:

*The mean *The standard deviation *The minimum value *The maximum value

Providing a value for the mode has no effect here

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing

class flownet.parameters.probability_distributions.UniformDistribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None)

Bases: flownet.parameters.probability_distributions.ProbabilityDistribution

The UniformDistribution class

The class is initialized by providing ONLY two of the following inputs different from None:

• The minimum value of the uniform distribution

• The mean value of the uniform distribution

• The maximum value of the uniform distribution

Parameters

• minimum (float) – The minimum value of the distribution

• mean (float) – The mean value of the distribution

• maximum (float) – The maximum value of the distribution

_abc_impl = <_abc_data object>

defined: bool

property ert_gen_kw: str

string representing an ERT “UNIFORM MIN MAX” distribution keyword for use in GEN_KW

maximum: float

mean: float

minimum: float

mode: float

name: str

stddev: float

update_distribution(minimum: Optional[float] = None, maximum: Optional[float] = None, mean: Optional[float] = None, mode: Optional[float] = None, stddev: Optional[float] = None)

Function that updates the parameters that defines the probability distribution.

The following input combinations will make changes to the distribution:

• Giving a new minimum value as input will trigger calculation of a new mean and standard deviation

• Giving a new maximum value as input will trigger calculation of a new mean and standard deviation

• Giving a new mean value as input requires a new minimum OR maximum value to be defined also

  • A new mean value and a new minimum value will trigged an update of the maximum value and the stddev

  • A new mean value and a new maximum value will trigged an update of the minimum value and the stddev

Providing values for stddev or mode has no effect here, since the uniform distribution has no mode, and the stddev is calculated from the minimum and maximum values

Providing a new mean, a new minimum and a new maximum value (all three of them) will trigger an error

Parameters

• minimum – The minimum values of the updated distribution

• mean – The mean value of the updated distribution

• mode – The mode of the updated distribution

• maximum – The maximum value of the updated distribution

• stddev – The standard deviation of the updated distribution

Returns

Nothing