diff --git a/data analysis/superscript.py b/data analysis/superscript.py
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+#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()