alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-12-27 09:59:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

superscript.py - v 1.0.4.001

Browse Source 
changelog:
- grammar fixes
This commit is contained in:
ltcptgeneral 2019-02-26 23:18:26 -06:00
parent a8f3598bbe
commit 57eda3815b
2 changed files with 62 additions and 58 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
data analysis/__pycache__/superscript.cpython-37.pyc Normal file
View File

Binary file not shown.

112
data analysis/superscript.py
View File

@ -1,11 +1,13 @@
#Titan Robotics Team 2022: Data Analysis Script #Titan Robotics Team 2022: Super Script
#Written by Arthur Lu & Jacob Levine #Written by Arthur Lu & Jacob Levine
#Notes: #Notes:
#setup: #setup:
__version__ = "1.0.4.000" __version__ = "1.0.4.001"
__changelog__ = """changelog: __changelog__ = """changelog:
1.0.4.001:
- grammar fixes
1.0.4.000: 1.0.4.000:
- actually pushes to firebase - actually pushes to firebase
1.0.3.001: 1.0.3.001:
@ -37,82 +39,84 @@ import os
import glob import glob
import numpy as np import numpy as np
# Use a service account def titanservice():
cred = credentials.Certificate('keys/firebasekey.json')
firebase_admin.initialize_app(cred)
db = firestore.client() # Use a service account
cred = credentials.Certificate('keys/firebasekey.json')
firebase_admin.initialize_app(cred)
#get all the data db = firestore.client()
analysis.generate_data("data/bdata.csv", 100, 5, -10, 10) #get all the data
source_dir = 'data' analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
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')]
#for file_path in file_list: source_dir = 'data'
# if not os.path.basename(file_list).startswith("teams") file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
# data.append(analysis.load_csv(file_path)) data = []
files = [fn for fn in glob.glob('data/*.csv')
if not os.path.basename(fn).startswith('teams')]
for i in files: #for file_path in file_list:
data.append(analysis.load_csv(i)) # if not os.path.basename(file_list).startswith("teams")
# data.append(analysis.load_csv(file_path))
stats = [] for i in files:
measure_stats = [] data.append(analysis.load_csv(i))
teams = analysis.load_csv("data/teams.csv")
#assumes that team number is in the first column, and that the order of teams is the same across all files stats = []
#unhelpful comment measure_stats = []
for measure in data: #unpacks 3d array into 2ds teams = analysis.load_csv("data/teams.csv")
measure_stats = [] #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
for i in range(len(measure)): #unpacks into specific teams measure_stats = []
ofbest_curve = [None] for i in range(len(measure)): #unpacks into specific teams
r2best_curve = [None]
line = measure[i] ofbest_curve = [None]
r2best_curve = [None]
#print(line) line = measure[i]
x = list(range(len(line))) #print(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") x = list(range(len(line)))
eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
#print(eqs, rmss, r2s, overfit) beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
ofbest_curve.append(beqs) #print(eqs, rmss, r2s, overfit)
ofbest_curve.append(brmss)
ofbest_curve.append(br2s)
ofbest_curve.append(boverfit)
ofbest_curve.pop(0)
#print(ofbest_curve) ofbest_curve.append(beqs)
ofbest_curve.append(brmss)
ofbest_curve.append(br2s)
ofbest_curve.append(boverfit)
ofbest_curve.pop(0)
beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s") #print(ofbest_curve)
r2best_curve.append(beqs) beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
r2best_curve.append(brmss)
r2best_curve.append(br2s)
r2best_curve.append(boverfit)
r2best_curve.pop(0)
#print(r2best_curve) r2best_curve.append(beqs)
r2best_curve.append(brmss)
r2best_curve.append(br2s)
r2best_curve.append(boverfit)
r2best_curve.pop(0)
measure_stats.append(teams[i] + ["|"] + list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve) #print(r2best_curve)
stats.append(list(measure_stats)) measure_stats.append(teams[i] + ["|"] + list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
json_out = {} stats.append(list(measure_stats))
for i in range(len(stats)): json_out = {}
json_out[files[i]]=str(stats[i])
print(json_out) for i in range(len(stats)):
json_out[files[i]]=str(stats[i])
db.collection(u'stats').document(u'stats-noNN').set(json_out) print(json_out)
db.collection(u'stats').document(u'stats-noNN').set(json_out)