created nishant only script

data analysis/data/scores.csv

@@ -1,6 +1,40 @@
-35,63,62,18,56,65,42,77,82,63,76,50,47,69,57,65,85,70,38,67,89,74,71,67,70,71,95,77,45
+49,56,63
-46,24,12,23,66,48,43,35,32,42,78,56,70,53,56,43,16,85,46,76,86,77,71,77,76,70,73
+48,58,43,71,44,47,38,61,54,68,46,72,67,61,71,55
-29,63,35,37,26,51,35,58,30,61,45
+46,71,40
-26,62,45,37,42,29,59,44,40
+20,18,45,55,25,46,55,31,26,49,51,56,74
-46,45,71,85,54,57,52,66,47,77,68,75,45,51
+42,61,52,43,67,32,55,44,41,49,47,19,74
-26,15,31,54,37,48,19,69,56
+40,25,43,25,56,68,59,46,72,61,45,45,47,67
+65,43,74
+41,35,35,30,56,39,48,49,71,48,72,44,49,54
+26,47,56,49,12,30,44,52,27,49,61,47,32,55,44,54
+29,63,35,37,26,51,35,58,30,61,45,39,56,55
+35,31,27,49,30,57,35,34,65,56,67
+46,43
+45,62,48
+17,16,25,37,28,33,33,41,42,66,71,33
+19,81,49
+49,47
+24,16,12,20,42,37,42,49,19,43,57
+46,45,71,85,54,57,52,66,47,77,68,75,45,51,66,71,67
+40,23,45,58,37,42,42,43,30,36,40,66,54,55
+45,58,52,49,53,55,60,64,66,43,49,58,82,67,78,71,71,63
+63,57,57,39,51,45,69,64,57,74,74,57,70,64,62,62,81,57
+46,48,55,42,24,40,99,39,36,40,39,66,48
+60,49,54
+24,48,20,54,28,41,35,27,28,32,60,33,33
+43,62,53,38,63,45,71,64,60,70,74,57,70,64,62,60,81
+46,49
+20,15,15,37,63,39,41,39,15,45,33
+31,27,63,12,12,30,45,47,30,51,56,40
+65,62,63
+48,30,27,37,36,53,52,62,50,32,44,57,71,69,73,60,64,46,33,40
+39,62,33
+23,43,43,35,33,29,30,83,38,33,96,79,96,77,81,0,46,47,54
+38,39,20,61,42,68,48,65,48,48,72,44,62,43,54
+25,62,53,54,61,63,42,59,14,53,63,65,72,58,50,46,56
+27,19,35,44,40,43,45,38,51,48,44,51,42
+44,43
+40,31,45,42,27,37,43,46,47,50,55,44,42,57
+66,49,49
+66,43,49
+62,42,48

data analysis/superscript.py

@@ -141,6 +141,12 @@ def titanservice():
 
                 line = scores[i]
 
+                if len(line) < 4:
+
+                    nishant.append('no_data')
+                    
+                    continue
+
                 #print(line)
 
                 #print(line)
@@ -260,6 +266,10 @@ def pulldata():
                     quant_keys.append(key)
 
                     #print(full[i][j].get(key).get('teamDBRef')[5:] in list_teams)
+
+                    print(full[i][j].get(key).get('teamDBRef'))
+
+                    print(list_teams)
                     
                     if full[i][j].get(key).get('teamDBRef')[5:] in list_teams:
                         
@@ -330,10 +340,10 @@ def service():
         fucked = False
         
         for i in range(0, 5):
-            #try:
+            try:
                 titanservice()
                 break
-            #except:
+            except:
                 if (i != 4):
                     print("[WARNING] failed, trying " + str(5 - i - 1) + " more times")
                 else:

data analysis/superscript_nishant_only.py

@@ -0,0 +1,280 @@
+#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'
+    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('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()