def pullPitScoutingData():
homeDir = os.path.expanduser('~')
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mri2019.firebaseapp.com",
"databaseURL": "https://mri2019.firebaseio.com",
"storageBucket": "mri2019.appspot.com",
}
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
pit_file = os.path.join(homeDir, 'MRI-2019Server/config/pitscouting.xlsx')
wb = openpyxl.load_workbook(pit_file)
data = wb['Form Responses 1']
rows = [
row for row in data.iter_rows(
min_row=2, min_col=1, max_col=8, values_only=True)
]
for row in rows:
if not row[1]:
break
teamNumber = row[1]
pitscouting = {'drivetrain': row[2], 'length': row[6], 'width': row[7]}
database.child("teams").child(teamNumber).child('pitscouting').set(
pitscouting)
print(f'{teamNumber} pitscouting uploaded to Firebase')
print("All pitscouting data pulled")
def get_sykes_data():
homeDir = os.path.expanduser('~')
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mri2019.firebaseapp.com",
"databaseURL": "https://mri2019.firebaseio.com",
"storageBucket": "mri2019.appspot.com",
}
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
sykes_file = os.path.join(homeDir, 'MRI-2019Server/config/sykes.xlsx')
wb = openpyxl.load_workbook(sykes_file)
pred_contrib = wb['Sheet1']
print("loaded")
rows = [row for row in pred_contrib.iter_rows(min_row=2, max_col=5, values_only=True)]
for row in rows:
if not row[0]:
break
teamNumber = row[0]
sykesData = {
'teamName': row[1],
'elo': row[2],
'opr': row[4]
}
database.child("teams").child(teamNumber).child("sykes").set(sykesData)
print(f'{teamNumber} Sykes uploaded to Firebase')
print('All Sykes data pulled')
def calculate_timd(compressed_timd, timd_name, test=False):
decompressed_timd = decompression.decompress_timd(compressed_timd)
if decompressed_timd['header']['isNoShow']:
pass
else:
decompressed_timd['calculated'] = calculate_statistics(
decompressed_timd)
decompressed_timd['climb'] = calculate_climb(decompressed_timd)
if not test:
print(f'{timd_name} decompressed')
homeDir = os.path.expanduser('~')
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mri2019.firebaseapp.com",
"databaseURL": "https://mri2019.firebaseio.com",
"storageBucket": "mri2019.appspot.com",
}
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
# Save data in local cache
if not os.path.exists(
os.path.join(homeDir, 'MRI-2019Server/cache/TIMDs')):
os.makedirs(os.path.join(homeDir, 'MRI-2019Server/cache/TIMDs'))
with open(
os.path.join(homeDir,
f'MRI-2019Server/cache/TIMDs/{timd_name}.json'),
'w') as file:
json.dump(decompressed_timd, file)
print(f'{timd_name} cached')
database.child("TIMDs").child(timd_name).set(decompressed_timd)
database.child("decompedTIMDs").child(timd_name).set(compressed_timd)
print(f'{timd_name} uploaded to Firebase\n')
return decompressed_timd
def calculate_team(team_number, last_timd, is_json=False):
if is_json is not False:
team = is_json
timds = team['timds']
else:
try:
team = get_team(team_number)
timds = get_timds(team_number)
# TODO Fix in master
except IndexError:
team = {'teamNumber': last_timd['team_number']}
timds = [last_timd]
team['timds'] = sorted(timds,
key=lambda timd: timd['header']['matchNumber'])
num_matches = len(timds)
num_no_shows = len([timd for timd in timds if timd['header']['isNoShow']])
timds = [timd for timd in timds if not timd['header']['isNoShow']]
l3m_timds = sorted(timds,
key=lambda timd: timd['header']['matchNumber'])[-3:]
team_abilities = {}
team_abilities['groundCargoPickup'] = True if len(
cycles.filter_timeline_actions(timds,
actionType='intake',
actionPiece='cargo',
actionPlace='ground')) > 0 else False
team_abilities['groundHatchPickup'] = True if len(
cycles.filter_timeline_actions(timds,
actionType='intake',
actionPiece='hatch',
actionPlace='ground')) > 0 else False
team_abilities['climbHab2'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level2')) > 0 else False
team_abilities['climbHab3'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level3')) > 0 else False
team_abilities['placeLevel2'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='place', placeLevel='level2')) > 0 else False
team_abilities['placeLevel3'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='place', placeLevel='level3')) > 0 else False
team_abilities['startLevel2'] = True if len(
[timd for timd in timds
if timd['header']['startLevel'] == 'hab2']) > 0 else False
team_abilities['placeHatch'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='place', actionPiece='hatch')) > 0 else False
team_abilities['placeCargo'] = True if len(
cycles.filter_timeline_actions(
timds, actionType='place', actionPiece='cargo')) > 0 else False
team['team_abilities'] = team_abilities
totals = {
'cargoPlaced':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
actionPiece='cargo')),
'hatchesPlaced':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
actionPiece='hatch')),
'cyclesLevel1':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
placeLevel='level1')),
'cyclesLevel2':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
placeLevel='level2')),
'cyclesLevel3':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
placeLevel='level3')),
'cyclesRocket':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
actionPlace='rocket')),
'cyclesCargoShip':
len(
cycles.filter_timeline_actions(timds,
actionType='place',
actionPlace='cargoShip')),
'timeDefending':
sum([timd['calculated']['timeDefending'] for timd in timds]),
'timeIncap':
sum([timd['calculated']['timeIncap'] for timd in timds])
}
for average_data_field, timd_data_field in TOTAL_AVERAGE_DATA_FIELDS.items(
):
totals[average_data_field] = stats.avg(
[timd['calculated'].get(timd_data_field) for timd in timds])
team['totals'] = totals
l3ms = {}
for l3m_average_data_field, timd_data_field in L3M_AVERAGE_DATA_FIELDS.items(
):
l3ms[l3m_average_data_field] = stats.avg(
[timd['calculated'].get(timd_data_field) for timd in l3m_timds])
for success_data_field, filters in PERCENT_SUCCESS_DATA_FIELDS.items():
l3ms['l3ms' + success_data_field] = stats.percent_success_place(
l3m_timds, **filters)
team['l3ms'] = l3ms
p75s = {}
for p75_data_field, timd_data_field in P75_DATA_FIELDS.items():
p75s[p75_data_field] = stats.p75(
[timd['calculated'].get(timd_data_field) for timd in timds])
team['p75s'] = p75s
SDs = {}
for SD_data_field, timd_data_field in SD_DATA_FIELDS.items():
SDs[SD_data_field] = stats.SD(
[timd['calculated'].get(timd_data_field) for timd in timds])
team['SDs'] = SDs
maxes = {}
for max_data_field, timd_data_field in MAX_DATA_FIELDS.items():
maxes[max_data_field] = stats.maximum(
[timd['calculated'].get(timd_data_field) for timd in timds])
team['maxes'] = maxes
percentages = {}
for success_data_field, filters in PERCENT_SUCCESS_DATA_FIELDS.items():
percentages[success_data_field] = stats.percent_success_place(
timds, **filters)
percentages['hatchPercentageOfCycles'] = sum([
timd['calculated']['hatchesScored'] for timd in timds
]) / sum([timd['calculated']['totalCycles'] for timd in timds]) if sum(
[timd['calculated']['totalCycles'] for timd in timds]) != 0 else 0
percentages['hatchPercentageOfCycles'] = round(
100 * (1 - percentages['hatchPercentageOfCycles']))
percentages['cargoPercentageOfCycles'] = sum([
timd['calculated']['cargoScored'] for timd in timds
]) / sum([timd['calculated']['totalCycles'] for timd in timds]) if sum(
[timd['calculated']['totalCycles'] for timd in timds]) != 0 else 0
percentages['cargoPercentageOfCycles'] = round(
100 * (1 - percentages['cargoPercentageOfCycles']))
percentages['percentMatchesStartHab1'] = len([
timd for timd in timds if timd['header']['startLevel'] == 'hab1'
]) / len(timds) if len(timds) is not 0 else 0
percentages['percentMatchesStartHab1'] = round(
100 * (percentages['percentMatchesStartHab1']))
percentages['percentMatchesStartHab2'] = len([
timd for timd in timds if timd['header']['startLevel'] == 'hab2'
]) / len(timds) if len(timds) is not 0 else 0
percentages['percentMatchesStartHab2'] = round(
100 * (percentages['percentMatchesStartHab2']))
percentages['leftHab'] = round(
100 * (len([timd for timd in timds if timd['header']['leftHab']]) /
len(timds))) if len(timds) is not 0 else 0
percentages['hab3ClimbSuccessRate'] = round(
100 * (len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level3')) / len(
cycles.filter_timeline_actions(
timds, actionType='climb', attemptedClimb='level3')))
) if len(
cycles.filter_timeline_actions(
timds, actionType='climb', attemptedClimb='level3')) != 0 else None
percentages['hab2ClimbSuccessRate'] = round(
100 * (len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level2')) / len(
cycles.filter_timeline_actions(
timds, actionType='climb', attemptedClimb='level2')))
) if len(
cycles.filter_timeline_actions(
timds, actionType='climb', attemptedClimb='level2')) != 0 else None
percentages['percentMatchesClimbHab3'] = round(
100 * (len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level3')) /
len(timds))) if len(timds) is not 0 else 0
percentages['percentMatchesClimbHab2'] = round(
100 * (len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level2')) /
len(timds))) if len(timds) is not 0 else 0
percentages['percentMatchesClimbHab1'] = round(
100 * (len(
cycles.filter_timeline_actions(
timds, actionType='climb', actualClimb='level1')) /
len(timds))) if len(timds) is not 0 else 0
percentages['percentOfTotalTeleopDefending'] = round(
100 * (team['totals']['timeDefending'] /
(len(timds) * 135))) if len(timds) is not 0 else 0
percentages['percentOfTimeIncap'] = round(
100 * (team['totals']['timeIncap'] /
(len(timds) * 150))) if len(timds) is not 0 else 0
percentages['percentOfMatchesNoShow'] = round(
100 * (num_no_shows / num_matches + num_no_shows))
team['percentages'] = percentages
cycle_times = {}
for average_cycle_data_field, filters in AVERAGE_CYCLE_TIME_DATA_FIELDS.items(
):
cycle_times[average_cycle_data_field] = cycles.cycle_time_calculations(
cycles.create_cycle_list(timds, 'place', 'intake'), stats.avg,
**filters)
for l3m_average_cycle_data_field, filters in L3M_AVERAGE_CYCLE_TIME_DATA_FIELDS.items(
):
cycle_times[
l3m_average_cycle_data_field] = cycles.cycle_time_calculations(
cycles.create_cycle_list(l3m_timds, 'place', 'intake'),
stats.avg, **filters)
for p75_cycle_data_field, filters in P75_CYCLE_TIME_DATA_FIELDS.items():
cycle_times[p75_cycle_data_field] = cycles.cycle_time_calculations(
cycles.create_cycle_list(timds, 'place', 'intake'), stats.p75,
**filters)
for SD_cycle_data_field, filters in SD_CYCLE_TIME_DATA_FIELDS.items():
cycle_times[SD_cycle_data_field] = cycles.cycle_time_calculations(
cycles.create_cycle_list(timds, 'place', 'intake'), stats.SD,
**filters)
team['cycle_times'] = cycle_times
team['rankings'] = calculations.calculateRankings.calculate_rankings(
int(team_number), team)
print(f'{team_number} calculated')
homeDir = os.path.expanduser('~')
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mri2019.firebaseapp.com",
"databaseURL": "https://mri2019.firebaseio.com",
"storageBucket": "mri2019.appspot.com",
}
if is_json is False:
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
# team['pitscouting'] = dict(database.child("teams").child(team_number).child('pitscouting').get().val())
# team['sykes'] = dict(database.child("teams").child(team_number).child('sykes').get().val())
# Save data in local cache
if not os.path.exists(
os.path.join(homeDir, 'MRI-2019Server/cache/teams')):
os.makedirs(os.path.join(homeDir, 'MRI-2019Server/cache/teams'))
with open(
os.path.join(homeDir,
f'MRI-2019Server/cache/teams/{team_number}.json'),
'w') as file:
json.dump(team, file)
print(f'{team_number} cached')
database.child("teams").child(team_number).set(team)
print(f'{team_number} uploaded to Firebase\n')
return team
import tbapy
import os
import json
import pyrebase
# File with functions which return info such as API keys and passwords
import sensitiveInfo
homeDir = os.path.expanduser('~')
# Firebase setup
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mri2019.firebaseapp.com",
"databaseURL": "https://mri2019.firebaseio.com",
"storageBucket": "mri2019.appspot.com",
}
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
# Setup for tbapy
tba = tbapy.TBA(sensitiveInfo.tba_api_key())
event = "2019mnri"
# Get a list of all qualifying matches at an event
try:
matches = [
match for match in tba.event_matches(event, simple=True)
if match['comp_level'] == 'qm'
]
print(matches[0])
def calculate_team(team_number, last_timd, is_json=False):
if is_json is not False:
team = is_json
timds = team['timds']
else:
try:
team = get_team(team_number)
timds = get_timds(team_number)
except IndexError:
team = {'teamNumber': last_timd['team_number']}
timds = [last_timd]
team['timds'] = sorted(timds, key=lambda timd: timd['header']['matchNumber'])
num_matches = len(timds)
num_no_shows = len([timd for timd in timds if timd['header']['noShow']])
timds = [timd for timd in timds if not timd['header']['noShow']]
l3m_timds = sorted(timds, key=lambda timd: timd['header']['matchNumber'])[-3:]
totals = {'cellsScoredHighTeleop': stats.total_filter_values(stats.filter_timeline_actions(timds, actionType='shoot', actionTime='teleop'), 'outerPort', 'innerPort'),
'cellsScoredLowTeleop': stats.total_filter_values(stats.filter_timeline_actions(timds, actionType='shoot', actionTime='teleop'), 'lowerGoal'),
'timeDefending': sum([timd['calculated']['timeDefending'] for timd in timds]),
'timeIncap': sum([timd['calculated']['timeIncap'] for timd in timds])}
for average_data_field, timd_data_field in TOTAL_AVERAGE_DATA_FIELDS.items():
totals[average_data_field] = stats.avg([timd['calculated'].get(timd_data_field) for timd in timds])
totals['cellsScored'] = totals['avgCellsScoredTele'] + totals['avgCellsScoredAuto']
team['totals'] = totals
team_abilities = {}
team_abilities['shootCellsHigh'] = True if totals['cellsScoredHighTeleop'] > 0 else False
team_abilities['shootCellsLow'] = True if totals['cellsScoredLowTeleop'] > 0 else False
team_abilities['climb'] = True if len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='hanging')) > 0 else False
team_abilities['wheelSpunInMatch'] = True if len(stats.filter_timeline_actions(timds, actionType='wheel')) > 0 else False
team_abilities['moveOffLineAuto'] = True if len([timd for timd in timds if timd['header']['leftLine']]) > 0 else False
team['team_abilities'] = team_abilities
l3ms = {}
for l3m_average_data_field, timd_data_field in L3M_AVERAGE_DATA_FIELDS.items():
l3ms[l3m_average_data_field] = stats.avg([timd['calculated'].get(timd_data_field) for timd in l3m_timds])
team['l3ms'] = l3ms
p75s = {}
for p75_data_field, timd_data_field in P75_DATA_FIELDS.items():
p75s[p75_data_field] = stats.p75([timd['calculated'].get(timd_data_field) for timd in timds])
team['p75s'] = p75s
SDs = {}
for SD_data_field, timd_data_field in SD_DATA_FIELDS.items():
SDs[SD_data_field] = stats.SD([timd['calculated'].get(timd_data_field) for timd in timds])
team['SDs'] = SDs
maxes = {}
for max_data_field, timd_data_field in MAX_DATA_FIELDS.items():
maxes[max_data_field] = stats.maximum([timd['calculated'].get(timd_data_field) for timd in timds])
team['maxes'] = maxes
percentages = {}
for success_data_field, filters in PERCENT_SUCCESS_DATA_FIELDS.items():
percentages[success_data_field] = stats.percent_success_shooting(timds, 'teleop', 'shoot', *filters)
percentages['leftInitLine'] = round(100 * (len([timd for timd in timds if timd['header']['leftLine']]) / len(timds))) if len(timds) is not 0 else 0
percentages['climbSuccessRate'] = round(100 * (len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='hanging')) / len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='fell')))) if len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='fell')) != 0 else None
percentages['levelClimbPercentage'] = round(100 * (len(stats.filter_timeline_actions(timds, actionType='climb', levelClimb=True)) / len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='hanging')))) if len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='hanging')) != 0 else None
percentages['parkPercentage'] = round(100 * (len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='parked')) / len(timds))) if len(timds) is not 0 else 0
percentages['climbPercentage'] = round(100 * (len(stats.filter_timeline_actions(timds, actionType='climb', climbHeight='hanging')) / len(timds))) if len(timds) is not 0 else 0
percentages['percentOfTotalTeleopDefending'] = round(100 * (team['totals']['timeDefending'] / (len(timds) * 135))) if len(timds) is not 0 else 0
percentages['percentOfTimeIncap'] = round(100 * (team['totals']['timeIncap'] / (len(timds) * 150))) if len(timds) is not 0 else 0
percentages['percentOfMatchesNoShow'] = round(100 * (num_no_shows / num_matches + num_no_shows))
team['percentages'] = percentages
# team['rankings'] = calculations.calculateRankings.calculate_rankings(int(team_number), team)
print(f'{team_number} calculated')
homeDir = os.path.expanduser('~')
pyrebase_config = {
"apiKey": sensitiveInfo.firebase_api_key(),
"authDomain": "mndu2-2020.firebaseapp.com",
"databaseURL": "https://mndu2-2020.firebaseio.com",
"storageBucket": "mndu2-2020.appspot.com"
}
if is_json is False:
firebase = pyrebase.initialize_app(pyrebase_config)
database = firebase.database()
# Save data in local cache
if not os.path.exists(os.path.join(homeDir, 'MNDU2-2020Server/cache/teams')):
os.makedirs(os.path.join(homeDir, 'MNDU2-2020Server/cache/teams'))
with open(os.path.join(homeDir, f'MNDU2-2020Server/cache/teams/{team_number}.json'), 'w') as file:
json.dump(team, file)
print(f'{team_number} cached')
database.child("teams").child(team_number).set(team)
print(f'{team_number} uploaded to Firebase\n')
return team