From 52c3ddd2d65f6e739bd80dfbc7c089e8a981f80d Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 21 Mar 2019 17:25:16 -0500
Subject: [PATCH] WORKING!!!!

---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25263 -> 25263 bytes
 data analysis/superscript.py                  |  64 ++++++++++--------
 2 files changed, 35 insertions(+), 29 deletions(-)

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 57580d6183d862f285e1b60de1d82e3fe7d5e924..4932fad96cd40a4f3305c4831f7141351ad4ec66 100644
GIT binary patch
delta 21
bcmZ2~lyUu0MlL5_UM>b85aQp+wKNF;LWBiA

delta 21
bcmZ2~lyUu0MlL5_UM>b8=;qkSwKNF;MK=Yo

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 8086e05d..4f8f3dcf 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -67,11 +67,13 @@ 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') 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
+             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")
@@ -127,49 +129,53 @@ def titanservice():
         stats.append(list(measure_stats))
         nishant = []
             
-        for i in range(len(scores)):
+    for i in range(len(scores)):
 
-                    ofbest_curve = [None]
-                    r2best_curve = [None]
+                #print(scores)
 
-                    line = measure[i]
+                ofbest_curve = [None]
+                r2best_curve = [None]
 
-                    #print(line)
+                line = scores[i]
 
-                    x = list(range(len(line)))
-                    eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
+                #print(line)
 
-                    beqs, brmss, br2s, boverfit = analysis.select_best_regression(eqs, rmss, r2s, overfit, "min_overfit")
+                #print(line)
 
-                    #print(eqs, rmss, r2s, overfit)
+                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)
+                ofbest_curve.append(beqs)
+                ofbest_curve.append(brmss)
+                ofbest_curve.append(br2s)
+                ofbest_curve.append(boverfit)
+                ofbest_curve.pop(0)
 
-                    #print(ofbest_curve)
+                #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(brmss)
-                    r2best_curve.append(br2s)
-                    r2best_curve.append(boverfit)
-                    r2best_curve.pop(0)
+                r2best_curve.append(beqs)
+                r2best_curve.append(brmss)
+                r2best_curve.append(br2s)
+                r2best_curve.append(boverfit)
+                r2best_curve.pop(0)
 
-                    #print(r2best_curve)
+                #print(r2best_curve)
                     
-                    z = len(scores[0]) + 1
-                    nis_num = []
+                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(str(ofbest_curve[0])))
+                nis_num.append(eval(str(r2best_curve[0])))
 
-                    nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2)
+                nis_num.append((eval(ofbest_curve[0]) + eval(r2best_curve[0])) / 2)
 
-                    nishant.append(teams[i] + nis_num)
+                nishant.append(teams[i] + nis_num)
                 
     json_out = {}
     score_out = {}