bluemira.fuel_cycle.timeline_tools

Distribution and timeline utilities

Classes

UniformLearningStrategy	Uniform learning strategy
UserSpecifiedLearningStrategy	User-specified learning strategy to hard-code the operational availabilities at
GompertzLearningStrategy	Gompertz learning strategy.
LogNormalAvailabilityStrategy	Log-normal distribution strategy
TruncNormAvailabilityStrategy	Truncated normal distribution strategy
ExponentialAvailabilityStrategy	Exponential distribution strategy

Module Contents

class bluemira.fuel_cycle.timeline_tools.UniformLearningStrategy

Bases: LearningStrategy

Uniform learning strategy

static generate_phase_availabilities(lifetime_op_availability: float, op_durations: collections.abc.Iterable[float]) → collections.abc.Iterable[float]

Generate operational availabilities for the specified phase durations.

Parameters:

• lifetime_op_availability (float) – Operational availability averaged over the lifetime

• op_durations (collections.abc.Iterable[float]) – Durations of the operational phases [fpy]

Returns:

Operational availabilities at each operational phase

Return type:

collections.abc.Iterable[float]

class bluemira.fuel_cycle.timeline_tools.UserSpecifiedLearningStrategy(operational_availabilities: collections.abc.Iterable[float])

Bases: LearningStrategy

User-specified learning strategy to hard-code the operational availabilities at each operational phase.

Parameters:

operational_availabilities (collections.abc.Iterable[float])

operational_availabilities

generate_phase_availabilities(lifetime_op_availability: float, op_durations: collections.abc.Iterable[float]) → collections.abc.Iterable[float]

Generate operational availabilities for the specified phase durations.

Parameters:

• lifetime_op_availability (float) – Lifetime operational availability

• op_durations (collections.abc.Iterable[float]) – Durations of the operational phases [fpy]

Returns:

Operational availabilities at each operational phase

Raises:

FuelCycleError – Number of phases should be equal to the number of operational availabilities

Return type:

collections.abc.Iterable[float]

class bluemira.fuel_cycle.timeline_tools.GompertzLearningStrategy(learn_rate: float, min_op_availability: float, max_op_availability: float)

Bases: LearningStrategy

Gompertz learning strategy.

Parameters:

• learn_rate (float)

• min_op_availability (float)

• max_op_availability (float)

learn_rate

min_op_a

max_op_a

_f_op_availabilities(t, x, arg_dates)

generate_phase_availabilities(lifetime_op_availability: float, op_durations: collections.abc.Iterable[float]) → collections.abc.Iterable[float]

Generate operational availabilities for the specified phase durations.

Parameters:

• lifetime_op_availability (float) – Operational availability averaged over the lifetime

• op_durations (collections.abc.Iterable[float]) – Durations of the operational phases [fpy]

Returns:

Operational availabilities at each operational phase

Raises:

FuelCycleError – Input lifetimes must be in range

Return type:

collections.abc.Iterable[float]

class bluemira.fuel_cycle.timeline_tools.LogNormalAvailabilityStrategy(sigma: float, rng_seed: int | numpy.random.SeedSequence = RNGSeeds.timeline_tools_lognorm.value)

Bases: OperationalAvailabilityStrategy

Log-normal distribution strategy

Parameters:

• sigma (float) – Standard deviation of the underlying normal distribution

• rng_seed (int | numpy.random.SeedSequence) – random number generator seed for the normal distribution

sigma

generate_distribution(n: int, integral: float) → numpy.ndarray

Generate a log-normal distribution with a specified number of entries and integral.

Parameters:

• n (int) – Number of entries in the distribution

• integral (float) – Integral of the distribution

Returns:

The distribution of size n and of the correct integral value

Return type:

numpy.ndarray

class bluemira.fuel_cycle.timeline_tools.TruncNormAvailabilityStrategy(sigma: float, rng_seed: int | numpy.random.SeedSequence = RNGSeeds.timeline_tools_truncnorm.value)

Bases: OperationalAvailabilityStrategy

Truncated normal distribution strategy

Parameters:

• sigma (float) – Standard deviation of the underlying normal distribution

• rng_seed (int | numpy.random.SeedSequence) – random number generator seed for the normal distribution

sigma

generate_distribution(n: int, integral: float) → numpy.ndarray

Generate a truncated normal distribution with a specified number of entries and integral.

Parameters:

• n (int) – Number of entries in the distribution

• integral (float) – Integral of the distribution

Returns:

The distribution of size n and of the correct integral value

Return type:

numpy.ndarray

class bluemira.fuel_cycle.timeline_tools.ExponentialAvailabilityStrategy(lambdda: float, rng_seed: int | numpy.random.SeedSequence = RNGSeeds.timeline_tools_expo.value)

Bases: OperationalAvailabilityStrategy

Exponential distribution strategy

Parameters:

• lambdda (float) – Rate of the distribution

• rng_seed (int | numpy.random.SeedSequence) – random number generator seed for the exponential distribution

lambdda

generate_distribution(n: int, integral: float) → numpy.ndarray

Generate an exponential distribution with a specified number of entries and integral.

Parameters:

• n (int) – Number of entries in the distribution

• integral (float) – Integral of the distribution

Returns:

The distribution of size n and of the correct integral value

Return type:

numpy.ndarray