suboptimumg.compsim.models#

Pydantic models for competition simulation data structures.

class suboptimumg.compsim.models.CompetitionData[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`accel`	`ContinuousTrackData` \| `DiscreteTrackData`	Yes
`autoX`	`ContinuousTrackData` \| `DiscreteTrackData`	Yes
`endurance`	`ContinuousTrackData` \| `DiscreteTrackData`	Yes
`scoring`	`CompetitionScoring`	Yes
`skidpad`	`ContinuousTrackData` \| `DiscreteTrackData`	Yes
`vehicle_model`	`VehicleModel`	Yes

Parameters:

vehicle_model (VehicleModel)
accel (ContinuousTrackData | DiscreteTrackData)
skidpad (ContinuousTrackData | DiscreteTrackData)
autoX (ContinuousTrackData | DiscreteTrackData)
endurance (ContinuousTrackData | DiscreteTrackData)
scoring (CompetitionScoring)

accel: ContinuousTrackData | DiscreteTrackData#

autoX: ContinuousTrackData | DiscreteTrackData#

endurance: ContinuousTrackData | DiscreteTrackData#

scoring: CompetitionScoring#

skidpad: ContinuousTrackData | DiscreteTrackData#

vehicle_model: VehicleModel#

class suboptimumg.compsim.models.CompetitionResults[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`accel`	`EventResults`	Yes
`autoX`	`EventResults`	Yes
`efficiency_points`	`float`	Yes
`endu_consumed_energy`	`float`	Yes
`endurance`	`EventResults`	Yes
`skidpad`	`EventResults`	Yes

Parameters:

accel (EventResults)
skidpad (EventResults)
autoX (EventResults)
endurance (EventResults)
efficiency_points (float)
endu_consumed_energy (float)

accel: EventResults#

autoX: EventResults#

efficiency_points: float#

endu_consumed_energy: float#

endurance: EventResults#

skidpad: EventResults#

property total_points: float#

class suboptimumg.compsim.models.CompetitionScoring[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`accel`	`EventScoring`	Yes
`autoX`	`EventScoring`	Yes
`efficiency`	`EfficiencyScoring`	Yes
`endurance`	`EventScoring`	Yes
`skidpad`	`EventScoring`	Yes

Parameters:

accel (EventScoring)
skidpad (EventScoring)
autoX (EventScoring)
endurance (EventScoring)
efficiency (EfficiencyScoring)

accel: EventScoring#

autoX: EventScoring#

efficiency: EfficiencyScoring#

endurance: EventScoring#

skidpad: EventScoring#

class suboptimumg.compsim.models.EfficiencyScoring[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`co2_min`	`float`	Yes
`co2_scaling`	`float`	Yes
`eff_max`	`float`	Yes
`eff_min_co2_your`	`float`	Yes
`fastest_lap_time`	`float`	Yes
`max_eff_points`	`float`	Yes
`max_time_scale`	`float`	Yes
`num_endurance_laps`	`int`	Yes

Parameters:

num_endurance_laps (int)
max_eff_points (float)
co2_scaling (float)
fastest_lap_time (float)
co2_min (float)
eff_max (float)
max_time_scale (float)
eff_min_co2_your (float)

co2_min: float#

co2_scaling: float#

eff_max: float#

eff_min_co2_your: float#

fastest_lap_time: float#

max_eff_points: float#

max_time_scale: float#

num_endurance_laps: int#

class suboptimumg.compsim.models.EventResults[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`lapsim_results`	`LapsimResults`	Yes
`points`	`float`	Yes
`tyour`	`float`	Yes

Parameters:

points (float)
tyour (float)
lapsim_results (LapsimResults)

lapsim_results: LapsimResults#

points: float#

tyour: float#

class suboptimumg.compsim.models.EventScoring[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`event_best_time`	`float`	Yes
`max_point_gain`	`float`	Yes
`min_points`	`float`	Yes
`scale_time_ratio`	`float`	Yes
`worst_time_ratio`	`float`	Yes

Parameters:

event_best_time (float)
worst_time_ratio (float)
min_points (float)
max_point_gain (float)
scale_time_ratio (float)

event_best_time: float#

max_point_gain: float#

min_points: float#

scale_time_ratio: float#

worst_time_ratio: float#

class suboptimumg.compsim.models.InternalData[source]#

Bases: BaseModel

Fields#
Field	Type	Required
`cumulative_dist`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`initial_velocity`	`float`	Yes
`per_seed`	`List`[`InternalDataSeedInfo`]	Yes
`seed_idx_list`	`ndarray`[`tuple`[`Any`, …], `dtype`[`integer`]]	Yes
`seed_v_list`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`v_max_profile`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes

Parameters:

initial_velocity (float)
v_max_profile (ndarray[tuple[Any, ...], dtype[float64]])
seed_idx_list (ndarray[tuple[Any, ...], dtype[integer]])
cumulative_dist (ndarray[tuple[Any, ...], dtype[float64]])
seed_v_list (ndarray[tuple[Any, ...], dtype[float64]])
per_seed (List[InternalDataSeedInfo])

cumulative_dist: ndarray[tuple[Any, ...], dtype[float64]]#

initial_velocity: float#

per_seed: List[InternalDataSeedInfo]#

seed_idx_list: ndarray[tuple[Any, ...], dtype[integer]]#

seed_v_list: ndarray[tuple[Any, ...], dtype[float64]]#

v_max_profile: ndarray[tuple[Any, ...], dtype[float64]]#

class suboptimumg.compsim.models.InternalDataSeedInfo[source]#

Bases: BaseModel

Fields#
Field	Type	Required	Default
`acc_max_post`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	No	`None`
`acc_proposal`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	No	`None`
`grown_forward_mask`	`ndarray`[`tuple`[`Any`, …], `dtype`[`bool`]]	No	`None`
`p_proposal`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	No	`None`
`slower_mask`	`ndarray`[`tuple`[`Any`, …], `dtype`[`bool`]]	No	`None`
`v_max_post`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	No	`None`
`v_proposal`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	No	`None`

Parameters:

v_proposal (ndarray[tuple[Any, ...], dtype[float64]])
acc_proposal (ndarray[tuple[Any, ...], dtype[float64]])
grown_forward_mask (ndarray[tuple[Any, ...], dtype[bool]])
slower_mask (ndarray[tuple[Any, ...], dtype[bool]])
v_max_post (ndarray[tuple[Any, ...], dtype[float64]])
acc_max_post (ndarray[tuple[Any, ...], dtype[float64]])
p_proposal (ndarray[tuple[Any, ...], dtype[float64]])

acc_max_post: ndarray[tuple[Any, ...], dtype[float64]]#

acc_proposal: ndarray[tuple[Any, ...], dtype[float64]]#

grown_forward_mask: ndarray[tuple[Any, ...], dtype[bool]]#

p_proposal: ndarray[tuple[Any, ...], dtype[float64]]#

slower_mask: ndarray[tuple[Any, ...], dtype[bool]]#

v_max_post: ndarray[tuple[Any, ...], dtype[float64]]#

v_proposal: ndarray[tuple[Any, ...], dtype[float64]]#

class suboptimumg.compsim.models.LapsimResults[source]#

Bases: BaseModel

Fields#
Field	Type	Required	Default
`internal_data`	`InternalData` \| `None`	No	`None`
`lap_accs`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`lap_dxs`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`lap_eff_motor_torques`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`lap_powers`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`lap_t`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes
`lap_vels`	`ndarray`[`tuple`[`Any`, …], `dtype`[`float64`]]	Yes

Parameters:

lap_t (ndarray[tuple[Any, ...], dtype[float64]])
lap_dxs (ndarray[tuple[Any, ...], dtype[float64]])
lap_vels (ndarray[tuple[Any, ...], dtype[float64]])
lap_accs (ndarray[tuple[Any, ...], dtype[float64]])
lap_powers (ndarray[tuple[Any, ...], dtype[float64]])
lap_eff_motor_torques (ndarray[tuple[Any, ...], dtype[float64]])
internal_data (InternalData | None)

internal_data: InternalData | None#

lap_accs: ndarray[tuple[Any, ...], dtype[float64]]#

lap_dxs: ndarray[tuple[Any, ...], dtype[float64]]#

lap_eff_motor_torques: ndarray[tuple[Any, ...], dtype[float64]]#

lap_powers: ndarray[tuple[Any, ...], dtype[float64]]#

lap_t: ndarray[tuple[Any, ...], dtype[float64]]#

lap_vels: ndarray[tuple[Any, ...], dtype[float64]]#

write_csv(filepath)[source]#

Parameters:: filepath (str | Path)
Return type:: None