mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-26 17:49:09 +00:00
Create superscript.py
This commit is contained in:
parent
ef336eb454
commit
d3e98391d4
352
data analysis/superscript.py
Normal file
352
data analysis/superscript.py
Normal file
@ -0,0 +1,352 @@
|
||||
#Titan Robotics Team 2022: Super Script
|
||||
#Written by Arthur Lu & Jacob Levine
|
||||
#Notes:
|
||||
#setup:
|
||||
|
||||
__version__ = "1.0.6.000"
|
||||
|
||||
__changelog__ = """changelog:
|
||||
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'
|
||||
file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
|
||||
data = []
|
||||
files = [fn for fn in glob.glob('data/*.csv')
|
||||
if not (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))
|
||||
|
||||
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
|
||||
|
||||
print(i)
|
||||
print(measure)
|
||||
print(len(measure))
|
||||
|
||||
#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)):
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
list_teams = teams
|
||||
teams=db.collection('data').document('team-2022').collection("Central 2019").get()
|
||||
full=[]
|
||||
tms=[]
|
||||
for team in teams:
|
||||
|
||||
tms.append(team.id)
|
||||
reports=db.collection('data').document('team-2022').collection("Central 2019").document(team.id).collection("matches").get()
|
||||
|
||||
for report in reports:
|
||||
data=[]
|
||||
data.append(db.collection('data').document('team-2022').collection("Central 2019").document(team.id).collection("matches").document(report.id).get().to_dict())
|
||||
full.append(data)
|
||||
|
||||
quant_keys = []
|
||||
|
||||
out = []
|
||||
var = {}
|
||||
|
||||
for i in range(len(full)):
|
||||
for j in range(len(full[i])):
|
||||
for key in list(full[i][j].keys()):
|
||||
|
||||
if "Quantitative" in key:
|
||||
|
||||
quant_keys.append(key)
|
||||
|
||||
if full[i][j].get(key).get('teamDBRef')[5:] in list_teams:
|
||||
|
||||
var = {}
|
||||
measured_vars = []
|
||||
|
||||
for k in range(len(list(full[i][j].get(key).keys()))):
|
||||
|
||||
individual_keys = list(full[i][j].get(key).keys())
|
||||
|
||||
var[individual_keys[k]] = full[i][j].get(key).get(individual_keys[k])
|
||||
|
||||
out.append(var)
|
||||
|
||||
sorted_out = []
|
||||
|
||||
for i in out:
|
||||
|
||||
j_list = []
|
||||
|
||||
key_list = []
|
||||
|
||||
sorted_keys = sorted(i.keys())
|
||||
|
||||
for j in sorted_keys:
|
||||
|
||||
key_list.append(i[j])
|
||||
|
||||
j_list.append(j)
|
||||
|
||||
sorted_out.append(key_list)
|
||||
|
||||
var_index = 0
|
||||
team_index = 0
|
||||
|
||||
big_out = []
|
||||
|
||||
for j in range(len(i)):
|
||||
big_out.append([])
|
||||
for t in range(len(list_teams)):
|
||||
big_out[j].append([])
|
||||
|
||||
for i in sorted_out:
|
||||
|
||||
team_index = list_teams.index(sorted_out[sorted_out.index(i)][j_list.index('teamDBRef')][5:])
|
||||
|
||||
for j in range(len(i)):
|
||||
|
||||
big_out[j][team_index].append(i[j])
|
||||
|
||||
for i in range(len(big_out)):
|
||||
|
||||
with open('data/' + j_list[i] + '.csv', "w+", newline = '') as file:
|
||||
|
||||
writer = csv.writer(file, delimiter = ',')
|
||||
writer.writerows(big_out[i])
|
||||
|
||||
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()
|
Loading…
Reference in New Issue
Block a user