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119 lines
2.9 KiB
Python
119 lines
2.9 KiB
Python
#Titan Robotics Team 2022: Data Analysis Script
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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#setup:
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__version__ = "1.0.4.000"
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__changelog__ = """changelog:
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1.0.4.000:
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- actually pushes to firebase
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1.0.3.001:
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- processes data more efficiently
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1.0.3.000:
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- actually processes data
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1.0.2.000:
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- added data reading from folder
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- nearly crashed computer reading from 20 GiB of data
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1.0.1.000:
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- added data reading from file
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- added superstructure to code
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1.0.0.000:
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- added import statements (revolutionary)
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"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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import firebase_admin
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from firebase_admin import credentials
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from firebase_admin import firestore
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import analysis
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import titanlearn
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import visualization
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import os
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import glob
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import numpy as np
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# Use a service account
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cred = credentials.Certificate('keys/firebasekey.json')
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firebase_admin.initialize_app(cred)
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db = firestore.client()
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#get all the data
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analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
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source_dir = 'data'
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file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
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data = []
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files = [fn for fn in glob.glob('data/*.csv')
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if not os.path.basename(fn).startswith('teams')]
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#for file_path in file_list:
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# if not os.path.basename(file_list).startswith("teams")
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# data.append(analysis.load_csv(file_path))
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for i in files:
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data.append(analysis.load_csv(i))
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stats = []
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measure_stats = []
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teams = analysis.load_csv("data/teams.csv")
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#assumes that team number is in the first column, and that the order of teams is the same across all files
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#unhelpful comment
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for measure in data: #unpacks 3d array into 2ds
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measure_stats = []
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for i in range(len(measure)): #unpacks into specific teams
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ofbest_curve = [None]
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r2best_curve = [None]
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line = measure[i]
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#print(line)
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x = list(range(len(line)))
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eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
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beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
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#print(eqs, rmss, r2s, overfit)
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ofbest_curve.append(beqs)
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ofbest_curve.append(brmss)
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ofbest_curve.append(br2s)
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ofbest_curve.append(boverfit)
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ofbest_curve.pop(0)
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#print(ofbest_curve)
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beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
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r2best_curve.append(beqs)
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r2best_curve.append(brmss)
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r2best_curve.append(br2s)
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r2best_curve.append(boverfit)
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r2best_curve.pop(0)
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#print(r2best_curve)
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measure_stats.append(teams[i] + ["|"] + list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
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stats.append(list(measure_stats))
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json_out = {}
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for i in range(len(stats)):
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json_out[files[i]]=str(stats[i])
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print(json_out)
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db.collection(u'stats').document(u'stats-noNN').set(json_out)
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