def calculate_third_pick_ability(calculated_data):
"""Calculates the relative third pick score for a team.
calculated_data is the dictionary of calculated_data calculated for
a team."""
# Weights for how much each aspect of the robot is considered for a
# third pick
level_1_weight = 0.9
level_2_weight = 1.1
level_3_weight = 1.1
sandstorm_weight = 1.0
climbing_weight = 0.2
# Scores for points scored on level 1, 2, and 3.
level_1_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL1'] +
3 * calculated_data['avgCargoScoredTeleL1']) * level_1_weight
level_2_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL2'] +
3 * calculated_data['avgCargoScoredTeleL2']) * level_2_weight
level_3_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL3'] +
3 * calculated_data['avgCargoScoredTeleL3']) * level_3_weight
# Scores for points gained during sandstorm.
sand_score = max([
float(utils.no_none_get(calculated_data, 'habLineSuccessL1', 0)) * 3 /
100,
float(utils.no_none_get(calculated_data, 'habLineSuccessL2', 0)) * 6 /
100
])
sand_score += calculated_data['avgPanelsScoredSandstorm'] * 5
sand_score += calculated_data['avgCargoScoredSandstorm'] * 3
sand_score *= sandstorm_weight
# Scores for points scored in the endgame.
end_game_score = max([
3 * float(calculated_data.get('climbSuccessL1', 0)) / 100,
6 * float(calculated_data.get('climbSuccessL2', 0)) / 100,
12 * float(calculated_data.get('climbSuccessL3', 0)) / 100
])
end_game_score *= climbing_weight
# A third pick robot must have a driver ability greater than 0
# (average) and must score an average of more than 1 cargo per match.
if (calculated_data['normalizedDriverAbility'] <= 0) or \
(calculated_data['avgCargoScored'] <= 1):
return 0
# Adds all the previous scores together to get a full third pick score.
return sand_score + level_1_teleop_score + level_2_teleop_score + level_3_teleop_score + end_game_score
def calculate_first_pick_ability(calculated_data):
"""Calculates the relative first pick score for a team.
calculated_data is the dictionary of calculated_data calculated for
a team."""
# Weights for how much each aspect of the robot is considered for a
# first pick.
level_1_weight = 0.9
level_2_weight = 1.1
level_3_weight = 1.1
sandstorm_weight = 1.0
climbing_weight = 0.2
# Scores for points scored on level 1, 2, and 3.
level_1_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL1'] +
3 * calculated_data['avgCargoScoredTeleL1']) * level_1_weight
level_2_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL2'] +
3 * calculated_data['avgCargoScoredTeleL2']) * level_2_weight
level_3_teleop_score = (
2 * calculated_data['avgPanelsScoredTeleL3'] +
3 * calculated_data['avgCargoScoredTeleL3']) * level_3_weight
# Scores for points gained during sandstorm.
sand_score = max([
float(utils.no_none_get(calculated_data, 'habLineSuccessL1', 0)) * 3 /
100,
float(utils.no_none_get(calculated_data, 'habLineSuccessL2', 0)) * 6 /
100
])
sand_score += calculated_data['avgPanelsScoredSandstorm'] * 5
sand_score += calculated_data['avgCargoScoredSandstorm'] * 3
sand_score *= sandstorm_weight
# Scores for points scored in the endgame.
end_game_score = max([
3 * float(calculated_data.get('climbSuccessL1', 0)) / 100,
6 * float(calculated_data.get('climbSuccessL2', 0)) / 100,
12 * float(calculated_data.get('climbSuccessL3', 0)) / 100
])
end_game_score *= climbing_weight
# Adds all the previous scores together to get a full first pick score.
return sand_score + level_1_teleop_score + level_2_teleop_score + level_3_teleop_score + end_game_score
def calculate_second_pick_ability(calculated_data, max_da, min_da):
"""Calculates the relative second pick score for a team.
calculated_data is the dictionary of calculated_data calculated for
a team.
max_da is the maximum driver ability in the competition, this is
used to weight driver ability.
min_da is the minimum driver ability in the competition, this is
used to weight driver ability."""
# Weights for how much each aspect of the robot is considered for a
# second pick.
climbing_weight = 0.25
cargo_weight = 0.5
panels_weight = 1.0
sandstorm_weight = 1.0
driving_weight = 18.0
defense_weight = 3.5
# Scores for points gained during sandstorm.
sand_score = max([
float(utils.no_none_get(calculated_data, 'habLineSuccessL1', 0)) * 3 /
100,
float(utils.no_none_get(calculated_data, 'habLineSuccessL2', 0)) * 6 /
100
])
sand_score += calculated_data['avgPanelsScoredSandstorm'] * 5
sand_score += calculated_data['avgCargoScoredSandstorm'] * 3
sand_score *= sandstorm_weight
# Scores for points scored on level 1.
level_1_teleop_score = calculated_data[
'avgPanelsScoredTeleL1'] * 2 * panels_weight
level_1_teleop_score += calculated_data[
'avgCargoScoredTeleL1'] * 3 * cargo_weight
# Scores for points scored in the endgame.
end_game_score = max([
3 * float(calculated_data.get('climbSuccessL1', 0)) / 100,
6 * float(calculated_data.get('climbSuccessL2', 0)) / 100,
12 * float(calculated_data.get('climbSuccessL3', 0)) / 100
])
end_game_score *= climbing_weight
# If the max_da is 0.0, all the zscores are equal and there is no
# point to weighting it.
if max_da == 0.0:
driver_ability = 0.0
else:
# Otherwise, takes the previously calculated driverAbility and
# weights it into the pick ability. Scales all driverAbilities
# between 0 and 'driving_weight'.
driver_ability = driving_weight * \
(calculated_data['normalizedDriverAbility'] - min_da)/(max_da - min_da)
# When the average rank defense is None, the defense_ability should
# be 0, because the team didn't play defense.
if calculated_data.get('avgRankDefense') is None:
defense_ability = 0
else:
# Score for defense, based around the lowest being 1, and the
# highest being 2 times the defense weight + 1. Example: If the
# weight is 5, the defense ability for an avgRankDefense of 1,
# 2, and 3 would be 1, 6, and 11 respectively.
defense_ability = (float(calculated_data['avgRankDefense']) * \
defense_weight) - (defense_weight - 1)
return sand_score + level_1_teleop_score + end_game_score + driver_ability + defense_ability