wtf2

data analysis/data/SandstormCargoShipBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,1.0
+0.0

data analysis/data/SandstormCargoShipHatches.csv

@@ -0,0 +1,3 @@
+1.0
+0.0,0.0
+0.0

data analysis/data/SandstormLowerRocketBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,0.0
+0.0

data analysis/data/SandstormLowerRocketHatches.csv

@@ -0,0 +1,3 @@
+1.0
+0.0,0.0
+0.0

data analysis/data/SandstormUpperRocketBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,0.0
+0.0

data analysis/data/SandstormUpperRocketHatches.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,0.0
+0.0

data analysis/data/TeleopCargoShipBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,5.0
+0.0

data analysis/data/TeleopCargoShipHatches.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,1.0
+1.0

data analysis/data/TeleopLowerRocketBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,4.0
+0.0

data analysis/data/TeleopLowerRocketHatches.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,0.0
+0.0

data analysis/data/TeleopUpperRocketBalls.csv

@@ -0,0 +1,3 @@
+0.0
+0.0,0.0
+0.0

data analysis/data/TeleopUpperRocketHatches.csv

@@ -0,0 +1,3 @@
+13.0
+0.0,0.0
+0.0

data analysis/data/cargoBalls.csv

@@ -0,0 +1,3 @@
+10.0
+0.0,7.0
+7.0

data analysis/data/hatchPanels.csv

@@ -0,0 +1,3 @@
+1.0
+0.0,7.0
+0.0

data analysis/data/match.csv

@@ -0,0 +1,3 @@
+match-23
+match-18,match-3
+match-18

data analysis/data/notes.csv

@@ -0,0 +1,3 @@
+yeeee 
+,si papa
+nine 

data analysis/data/observationType.csv

@@ -0,0 +1,3 @@
+Quantitative
+Quantitative,Quantitative
+Quantitative

data analysis/data/scores.csv

@@ -1,3 +1,3 @@
 29,63,35,37,26,51,35,58,30,61,45
-35
+26,62,45,37,42,29,59,44,40
-63,61,45
+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

data analysis/data/teamDBRef.csv

@@ -0,0 +1,3 @@
+team-2022
+team-16,team-16
+team-2451

data analysis/superscript.py

@@ -67,7 +67,7 @@ def titanservice():
     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'))] #scores will be handled sperately
+             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))
@@ -87,42 +87,46 @@ def titanservice():
 
         measure_stats = []
 
-        for i in range(len(measure)): #unpacks into specific teams
+        for i in range(len(measure) - 1): #unpacks into specific teams
 
-                    ofbest_curve = [None]
+                    print(i)
-                    r2best_curve = [None]
+                    print(measure)
+                    print(len(measure))
+
+                    #ofbest_curve = [None]
+                    #r2best_curve = [None]
 
                     line = measure[i]
 
                     #print(line)
 
-                    x = list(range(len(line)))
+                    #x = list(range(len(line)))
-                    eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
+                    #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")
+                    #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(beqs)
-                    ofbest_curve.append(brmss)
+                    #ofbest_curve.append(brmss)
-                    ofbest_curve.append(br2s)
+                    #ofbest_curve.append(br2s)
-                    ofbest_curve.append(boverfit)
+                    #ofbest_curve.append(boverfit)
-                    ofbest_curve.pop(0)
+                    #ofbest_curve.pop(0)
 
                     #print(ofbest_curve)
 
-                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
+                    #beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "max_r2s")
 
-                    r2best_curve.append(beqs)
+                    #r2best_curve.append(beqs)
-                    r2best_curve.append(brmss)
+                    #r2best_curve.append(brmss)
-                    r2best_curve.append(br2s)
+                    #r2best_curve.append(br2s)
-                    r2best_curve.append(boverfit)
+                    #r2best_curve.append(boverfit)
-                    r2best_curve.pop(0)
+                    #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)) + ofbest_curve + 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 = []
@@ -197,9 +201,11 @@ def pulldata():
         #print(teams[i][0])
         request_data_object = tba.req_team_matches(teams[i][0], 2019, "UDvKmPjPRfwwUdDX1JxbmkyecYBJhCtXeyVk9vmO2i7K0Zn4wqQPMfzuEINXJ7e5")
         json_data = request_data_object.json()
-        #for i in json_data:
 
-           # if i
+        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)):
@@ -213,6 +219,89 @@ def pulldata():
         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:
@@ -226,10 +315,10 @@ 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: