calc_obj_tim_schema.yml

# Used for calculating objective team in match data in Server
#
# Example:
#
# {
#     "confidence_rating": 1,
#     "team_number": "34",
#     "match_number": 2,
#     "auto_cube_low": 4,
#     "auto_cube_mid": 2,
#     "auto_cube_high": 8,
#     "auto_cone_low": 5,
#     "auto_cone_mid": 2,
#     "auto_cone_high": 2,
#     "auto_charge_attempt": 2,
#     "tele_cube_low": 3,
#     "tele_cube_mid": 7,
#     "tele_cube_high": 7,
#     "tele_cone_low": 7,
#     "tele_cone_mid": 9,
#     "tele_cone_high": 9,
#     "tele_charge_attempt": 6,
#     "intakes_ground": 5,
#     "intakes_station": 5,
#     "intakes_low_row": 6,
#     "auto_total_cubes": 1,
#     "auto_total_cones": 8,
#     "auto_total_gamepieces": 3,
#     "tele_total_cubes": 6,
#     "tele_total_cones": 6,
#     "tele_total_gamepieces": 6,
#     "total_intakes": 4,
#     "total_charge_attempts": 9,
#     "tele_total_gamepiece_low": 1,
#     "incap": 4,
#     "auto_charge_level": "D",
#     "tele_charge_level": "N",
#     "start_position": 2,
#     "preloaded_gamepiece": "B"
# }
#


# Contains information about this schema file
schema_file:
  # Version of schema file
  # Incremented every merged schema change
  version: 14

# Data that doesn't fit into other categorical calculations
data:
  confidence_rating:
    type: int
  team_number:
    type: str
  match_number:
    type: int

# Calculations built around counting timeline actions with certain key:value pairs
timeline_counts:
  auto_cube_low:
    type: int
    action_type: score_cube_low
    in_teleop: false
  auto_cube_mid:
    type: int
    action_type: score_cube_mid
    in_teleop: false
  auto_cube_high:
    type: int
    action_type: score_cube_high
    in_teleop: false
  auto_cone_low:
    type: int
    action_type: score_cone_low
    in_teleop: false
  auto_cone_mid:
    type: int
    action_type: score_cone_mid
    in_teleop: false
  auto_cone_high:
    type: int
    action_type: score_cone_high
    in_teleop: false
  auto_charge_attempt:
    type: int
    action_type: charge_attempt
    in_teleop: false
  tele_cube_low:
    type: int
    action_type: score_cube_low
    in_teleop: true
  tele_cube_mid:
    type: int
    action_type: score_cube_mid
    in_teleop: true
  tele_cube_high:
    type: int
    action_type: score_cube_high
    in_teleop: true
  tele_cone_low:
    type: int
    action_type: score_cone_low
    in_teleop: true
  tele_cone_mid:
    type: int
    action_type: score_cone_mid
    in_teleop: true
  tele_cone_high:
    type: int
    action_type: score_cone_high
    in_teleop: true
  tele_charge_attempt:
    type: int
    action_type: charge_attempt
    in_teleop: true
  intakes_ground:
    type: int
    action_type: intake_ground
  intakes_double:
    type: int
    action_type: intake_double
  intakes_single:
    type: int
    action_type: intake_single
  intakes_low_row:
    type: int
    action_type: intake_low_row
  intakes_mid_row:
    type: int
    action_type: intake_mid_row
  intakes_high_row:
    type: int
    action_type: intake_high_row
  failed_scores:
    type: int
    action_type: score_fail

# Calculations made by adding up other counts
aggregates:
  auto_total_cubes:
    type: int
    counts: [auto_cube_low, auto_cube_mid, auto_cube_high]
  auto_total_cones:
    type: int
    counts: [auto_cone_low, auto_cone_mid, auto_cone_high]
  auto_total_gamepieces:
    type: int
    counts: [auto_total_cubes, auto_total_cones]
  auto_total_gamepieces_low:
    type: int
    counts: [auto_cube_low, auto_cone_low]
  tele_total_cubes:
    type: int
    counts: [tele_cube_low, tele_cube_mid, tele_cube_high]
  tele_total_cones:
    type: int
    counts: [tele_cone_low, tele_cone_mid, tele_cone_high]
  tele_total_gamepieces:
    type: int
    counts: [tele_total_cubes, tele_total_cones]
  total_intakes:
    type: int
    counts: [intakes_station, intakes_ground]
  total_charge_attempts:
    type: int
    counts: [tele_charge_attempt, auto_charge_attempt]
  tele_total_gamepieces_low:
    type: int
    counts: [tele_cube_low, tele_cone_low]

# Calculations built around measuring the time between two timeline actions.
# First item in each list is the start item key:value pair requirements, second item is the end item.
timeline_cycle_time:
  incap:
    type: int
    start_action: start_incap
    end_action: end_incap
    minimum_time: 8
  median_cycle_time:
    type: int
    start_action: score
    end_action: score
    minimum_time: 1


# Robot actions which are one out of a dict of possible actions
# Dict items should be ordered, values are shortened string from qr
categorical_actions:
  auto_charge_level:
    list: ["N", D, E]
    type: str
  tele_charge_level:
    list: ["N", P, D, E]
    type: str
  start_position:
    list: ["0", "1", "2", "3", "4"]
    type: str
  preloaded_gamepiece:
    list: ["U", "O"]
    type: str