mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-09-20 08:57:56 +00:00
293 lines
7.9 KiB
Python
293 lines
7.9 KiB
Python
# Titan Robotics Team 2022: Super Script
|
|
# Written by Arthur Lu & Jacob Levine
|
|
# Notes:
|
|
# setup:
|
|
|
|
__version__ = "1.0.6.001"
|
|
|
|
__changelog__ = """changelog:
|
|
1.0.6.001:
|
|
- fixed multiple bugs
|
|
- works now
|
|
1.0.6.000:
|
|
- added pulldata function
|
|
- service now pulls in, computes data, and outputs data as planned
|
|
1.0.5.003:
|
|
- hotfix: actually pushes data correctly now
|
|
1.0.5.002:
|
|
- more information given
|
|
- performance improvements
|
|
1.0.5.001:
|
|
- grammar
|
|
1.0.5.000:
|
|
- service now iterates forever
|
|
- ready for production other than pulling json data
|
|
1.0.4.001:
|
|
- grammar fixes
|
|
1.0.4.000:
|
|
- actually pushes to firebase
|
|
1.0.3.001:
|
|
- processes data more efficiently
|
|
1.0.3.000:
|
|
- actually processes data
|
|
1.0.2.000:
|
|
- added data reading from folder
|
|
- nearly crashed computer reading from 20 GiB of data
|
|
1.0.1.000:
|
|
- added data reading from file
|
|
- added superstructure to code
|
|
1.0.0.000:
|
|
- added import statements (revolutionary)
|
|
"""
|
|
|
|
__author__ = (
|
|
"Arthur Lu <arthurlu@ttic.edu>, "
|
|
"Jacob Levine <jlevine@ttic.edu>,"
|
|
)
|
|
|
|
import firebase_admin
|
|
from firebase_admin import credentials
|
|
from firebase_admin import firestore
|
|
import analysis
|
|
#import titanlearn
|
|
import visualization
|
|
import os
|
|
import sys
|
|
import warnings
|
|
import glob
|
|
import numpy as np
|
|
import time
|
|
import tbarequest as tba
|
|
import csv
|
|
|
|
|
|
def titanservice():
|
|
|
|
print("[OK] loading data")
|
|
|
|
start = time.time()
|
|
|
|
source_dir = 'data'
|
|
# supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
|
|
file_list = glob.glob(source_dir + '/*.csv')
|
|
data = []
|
|
files = [fn for fn in glob.glob('data/*.csv')
|
|
if not (os.path.basename(fn).startswith('scores') or os.path.basename(fn).startswith('teams') or os.path.basename(fn).startswith('match') or os.path.basename(fn).startswith('notes') or os.path.basename(fn).startswith('observationType') or os.path.basename(fn).startswith('teamDBRef'))] # scores will be handled sperately
|
|
|
|
for i in files:
|
|
data.append(analysis.load_csv(i))
|
|
|
|
# print(files)
|
|
|
|
stats = []
|
|
measure_stats = []
|
|
teams = analysis.load_csv("data/teams.csv")
|
|
scores = analysis.load_csv("data/scores.csv")
|
|
|
|
end = time.time()
|
|
|
|
print("[OK] loaded data in " + str(end - start) + " seconds")
|
|
|
|
# assumes that team number is in the first column, and that the order of teams is the same across all files
|
|
# unhelpful comment
|
|
# for measure in data: #unpacks 3d array into 2ds
|
|
|
|
#measure_stats = []
|
|
|
|
# for i in range(len(measure)): #unpacks into specific teams
|
|
|
|
#ofbest_curve = [None]
|
|
#r2best_curve = [None]
|
|
|
|
#line = measure[i]
|
|
|
|
# print(line)
|
|
|
|
#x = list(range(len(line)))
|
|
#eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
|
|
|
|
#beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
|
|
|
|
#print(eqs, rmss, r2s, overfit)
|
|
|
|
# ofbest_curve.append(beqs)
|
|
# ofbest_curve.append(brmss)
|
|
# ofbest_curve.append(br2s)
|
|
# ofbest_curve.append(boverfit)
|
|
# ofbest_curve.pop(0)
|
|
|
|
# print(ofbest_curve)
|
|
|
|
#beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
|
|
|
|
# r2best_curve.append(beqs)
|
|
# r2best_curve.append(brmss)
|
|
# r2best_curve.append(br2s)
|
|
# r2best_curve.append(boverfit)
|
|
# r2best_curve.pop(0)
|
|
|
|
# print(r2best_curve)
|
|
|
|
#measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0)) + list(analysis.histo_analysis(line, 1, -3, 3)))
|
|
|
|
# stats.append(list(measure_stats))
|
|
nishant = []
|
|
|
|
for i in range(len(scores)):
|
|
|
|
# print(scores)
|
|
|
|
ofbest_curve = [None]
|
|
r2best_curve = [None]
|
|
|
|
line = scores[i]
|
|
|
|
if len(line) < 4:
|
|
|
|
nishant.append('no_data')
|
|
|
|
continue
|
|
|
|
# print(line)
|
|
|
|
# print(line)
|
|
|
|
x = list(range(len(line)))
|
|
eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
|
|
|
|
beqs, brmss, br2s, boverfit = analysis.select_best_regression(
|
|
eqs, rmss, r2s, overfit, "min_overfit")
|
|
|
|
#print(eqs, rmss, r2s, overfit)
|
|
|
|
ofbest_curve.append(beqs)
|
|
ofbest_curve.append(brmss)
|
|
ofbest_curve.append(br2s)
|
|
ofbest_curve.append(boverfit)
|
|
ofbest_curve.pop(0)
|
|
|
|
# print(ofbest_curve)
|
|
|
|
beqs, brmss, br2s, boverfit = analysis.select_best_regression(
|
|
eqs, rmss, r2s, overfit, "max_r2s")
|
|
|
|
r2best_curve.append(beqs)
|
|
r2best_curve.append(brmss)
|
|
r2best_curve.append(br2s)
|
|
r2best_curve.append(boverfit)
|
|
r2best_curve.pop(0)
|
|
|
|
# print(r2best_curve)
|
|
|
|
z = len(scores[0]) + 1
|
|
nis_num = []
|
|
|
|
nis_num.append(eval(str(ofbest_curve[0])))
|
|
nis_num.append(eval(str(r2best_curve[0])))
|
|
|
|
nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2)
|
|
|
|
nishant.append(teams[i] + nis_num)
|
|
|
|
json_out = {}
|
|
score_out = {}
|
|
|
|
for i in range(len(teams)):
|
|
score_out[str(teams[i][0])] = (nishant[i])
|
|
|
|
location = db.collection(u'stats').document(u'stats-noNN')
|
|
# for i in range(len(teams)):
|
|
#general_general_stats = location.collection(teams[i][0])
|
|
|
|
# for j in range(len(files)):
|
|
# json_out[str(teams[i][0])] = (stats[j][i])
|
|
# name = os.path.basename(files[j])
|
|
# general_general_stats.document(name).set({'stats':json_out.get(teams[i][0])})
|
|
|
|
for i in range(len(teams)):
|
|
nnum = location.collection(teams[i][0]).document(
|
|
u'nishant_number').set({'nishant': score_out.get(teams[i][0])})
|
|
|
|
|
|
def pulldata():
|
|
teams = analysis.load_csv('data/teams.csv')
|
|
scores = []
|
|
for i in range(len(teams)):
|
|
team_scores = []
|
|
# print(teams[i][0])
|
|
request_data_object = tba.req_team_matches(
|
|
teams[i][0], 2019, "UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5")
|
|
json_data = request_data_object.json()
|
|
|
|
for match in range(len(json_data) - 1, -1, -1):
|
|
if json_data[match].get('winning_alliance') == "":
|
|
# print(json_data[match])
|
|
json_data.remove(json_data[match])
|
|
|
|
json_data = sorted(json_data, key=lambda k: k.get(
|
|
'actual_time', 0), reverse=False)
|
|
for j in range(len(json_data)):
|
|
if "frc" + teams[i][0] in json_data[j].get('alliances').get('blue').get('team_keys'):
|
|
team_scores.append(json_data[j].get(
|
|
'alliances').get('blue').get('score'))
|
|
elif "frc" + teams[i][0] in json_data[j].get('alliances').get('red').get('team_keys'):
|
|
team_scores.append(json_data[j].get(
|
|
'alliances').get('red').get('score'))
|
|
scores.append(team_scores)
|
|
|
|
with open("data/scores.csv", "w+", newline='') as file:
|
|
writer = csv.writer(file, delimiter=',')
|
|
writer.writerows(scores)
|
|
|
|
|
|
def service():
|
|
|
|
while True:
|
|
|
|
pulldata()
|
|
|
|
start = time.time()
|
|
|
|
print("[OK] starting calculations")
|
|
|
|
fucked = False
|
|
|
|
for i in range(0, 5):
|
|
try:
|
|
titanservice()
|
|
break
|
|
except:
|
|
if (i != 4):
|
|
print("[WARNING] failed, trying " +
|
|
str(5 - i - 1) + " more times")
|
|
else:
|
|
print("[ERROR] failed to compute data, skipping")
|
|
fucked = True
|
|
|
|
end = time.time()
|
|
if (fucked == True):
|
|
|
|
break
|
|
|
|
else:
|
|
|
|
print("[OK] finished calculations")
|
|
|
|
print("[OK] waiting: " + str(300 - (end - start)) + " seconds" + "\n")
|
|
|
|
time.sleep(300 - (end - start)) # executes once every 5 minutes
|
|
|
|
|
|
warnings.simplefilter("ignore")
|
|
# Use a service account
|
|
try:
|
|
cred = credentials.Certificate('keys/firebasekey.json')
|
|
except:
|
|
cred = credentials.Certificate('keys/keytemp.json')
|
|
firebase_admin.initialize_app(cred)
|
|
|
|
db = firestore.client()
|
|
|
|
service() # finally we write something that isn't a function definition
|
|
# titanservice()
|