From 1e76b650eadcad998ae86f12ba9f7a909921639f Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 19 Feb 2019 09:44:51 -0600
Subject: [PATCH 01/31] superscript.py - v 1.0.2.000

changelog:
- added data reading from folder
- nearly crashed computer reading from 20 GiB of data
---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25058 -> 25075 bytes
 .../__pycache__/titanlearn.cpython-37.pyc     | Bin 4864 -> 4864 bytes
 .../__pycache__/visualization.cpython-37.pyc  | Bin 3908 -> 3908 bytes
 data analysis/analysis.py                     |   1 +
 data analysis/data/dicc.csv                   |   2 ++
 data analysis/superscript.py                  |  18 ++++++++++++++++--
 6 files changed, 19 insertions(+), 2 deletions(-)
 create mode 100644 data analysis/data/dicc.csv

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index b9e6f3452fa0920d42d52dd0d05318c076174733..e4e72e41c130d3785f6a1a16953342d71ec15747 100644
GIT binary patch
delta 2456
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delta 2465
zcmai0Yiv|i5Z<}Fdo3+nYFlV4wq0$tY^zvWEJ1C$E{Gz<*3uWZ&+B$us@+HDUTS%%
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diff --git a/data analysis/__pycache__/titanlearn.cpython-37.pyc b/data analysis/__pycache__/titanlearn.cpython-37.pyc
index 4bc942f5b8a9484fe92cf658f6f6f07989469dbb..534544d2aa385ef5f79ac87e816fa44a91ae641c 100644
GIT binary patch
delta 20
ZcmZorYf$5M;^pOH00QxVl#Se9g#aig1WW(`

delta 20
acmZorYf$5M;^pOH00QsdF&nwR3IPBuZ3QC$

diff --git a/data analysis/__pycache__/visualization.cpython-37.pyc b/data analysis/__pycache__/visualization.cpython-37.pyc
index cdbce53bde8c22e8f344e4a0d8c0ac9b16528f0b..14d8a833780cfc3c127f6781287714e54268fd29 100644
GIT binary patch
delta 20
acmX>icSMfciI<m)0SF`lQZ{m1@&f=bs{{rB

delta 20
acmX>icSMfciI<m)0SMSW#%$!a<OcvU%LI)8

diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index 6fc315a3..fbafcf7c 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -411,6 +411,7 @@ class objectives:
 def load_csv(filepath):
     with open(filepath, newline = '') as csvfile:
         file_array = list(csv.reader(csvfile))
+        csvfile.close()
     return file_array
 
 def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
diff --git a/data analysis/data/dicc.csv b/data analysis/data/dicc.csv
new file mode 100644
index 00000000..da813b68
--- /dev/null
+++ b/data analysis/data/dicc.csv	
@@ -0,0 +1,2 @@
+1,2,3
+4,5,6
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 2e7c1fd1..0690ef10 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -4,9 +4,12 @@
 #Notes:
 #setup:
 
-__version__ = "1.0.1.000"
+__version__ = "1.0.2.000"
 
 __changelog__ = """changelog:
+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
@@ -22,5 +25,16 @@ __author__ = (
 import analysis
 import titanlearn
 import visualization
+import os
+import glob
 
-data = analysis.load_csv("data/data.csv")
\ No newline at end of file
+#get all the data
+source_dir = 'data'
+file_list = glob.glob(source_dir + '/*.CSV')
+data = []
+for file_path in file_list:
+    data.append(analysis.load_csv(file_path))
+
+#unhelpful comment
+#for d in data:
+#    print (d)
\ No newline at end of file

From 7fbc838e846d8ed0fa5e5ac8a59bfeca3862aff9 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 19 Feb 2019 13:21:06 -0600
Subject: [PATCH 02/31] qwertyuiop

---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25075 -> 25075 bytes
 data analysis/superscript.py                  |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index e4e72e41c130d3785f6a1a16953342d71ec15747..0b7fb33f6d3fc0e8ebcfa0b5785ff27292a3e019 100644
GIT binary patch
delta 21
bcmex-nDO&rMlL5_UM>b8sQ28+^)e9vP|XIc

delta 21
bcmex-nDO&rMlL5_UM>b8;8)nl^)e9vO<)ES

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 0690ef10..03223149 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -36,5 +36,5 @@ for file_path in file_list:
     data.append(analysis.load_csv(file_path))
 
 #unhelpful comment
-#for d in data:
+#for d in data: #unpacks 3d array into 2d
 #    print (d)
\ No newline at end of file

From f40ff03cb29b3a24502e315462529b722dd899d2 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 20 Feb 2019 11:44:11 -0600
Subject: [PATCH 03/31] superscript.py - v 1.0.3.000

changelog:
- actually processes data
---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25075 -> 25075 bytes
 data analysis/data/aSetOfData.csv             |   1 +
 data analysis/data/data.csv                   | 101 +-----------------
 data analysis/data/dicc.csv                   |   2 -
 data analysis/data/teams.csv                  |   1 +
 data analysis/superscript.py                  |  37 +++++--
 6 files changed, 33 insertions(+), 109 deletions(-)
 create mode 100644 data analysis/data/aSetOfData.csv
 delete mode 100644 data analysis/data/dicc.csv
 create mode 100644 data analysis/data/teams.csv

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 0b7fb33f6d3fc0e8ebcfa0b5785ff27292a3e019..5ac2626d5723fec4a205adc4701ff0bab0d92870 100644
GIT binary patch
delta 22
ccmex-nDO&rMs6owUM>b8NNvg8$o(P_09aB7I{*Lx

delta 22
ccmex-nDO&rMs6owUM>b8sQ1j-$o(P_09T<082|tP

diff --git a/data analysis/data/aSetOfData.csv b/data analysis/data/aSetOfData.csv
new file mode 100644
index 00000000..ce148ada
--- /dev/null
+++ b/data analysis/data/aSetOfData.csv	
@@ -0,0 +1 @@
+2022,4,4,4,4
2011,5,5,5,5,5
1101,6,6,6,6,6,6
821374,7,7,7
5,8,8,8
\ No newline at end of file
diff --git a/data analysis/data/data.csv b/data analysis/data/data.csv
index 2fff51ba..05fe9505 100644
--- a/data analysis/data/data.csv	
+++ b/data analysis/data/data.csv	
@@ -1,100 +1 @@
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--0.8110344162534986,-2.2104890658052723,-3.6276303542899218,1.2695954895164263,-1.1193473578528916,9.17713861293543,-4.856573542241643,-5.756118147768068,0.5332671972431982,-4.456586424229336,-7.140592149264373,-4.2033940962228105,-7.81307888633876,4.784553928089881,-7.622942483504254,-8.19935060533816,-7.847562538933213,-7.440247496072693,-5.263217199694008,-1.9240701454927454,-2.435303217168048,6.955905305458195,-1.0130668073388343,8.177122474477187,0.30060810861921183,-9.834439419678901,8.0001693532904,-8.198740354415364,4.80948159837223,-1.8347150073508427,2.289172744871479,-5.144022545699993,-2.148292609159985,0.21283145570462558,9.434067709253572,-2.2699881523040695,-4.662825456953234,1.040259853383409,-7.510554468456876,8.003377222763724,-1.6454378891143833,-3.90006087056358,0.32770357567370567,0.12112531862051767,-7.604478297123663,-6.118272052233486,3.822814924035587,2.7685000903266896,9.270003837721795,2.7010039400530026,-5.253910421139063,-1.8816714261749201,2.5931919110431174,-7.10691873319967,7.628219335031098,-1.251787603299137,7.385756372481442,-3.797777849372636,8.41230565034494,-0.9383785054575338,-3.1669193220664305,6.7428403195739115,1.3280259000342873,8.367506041853858,-6.5858843021382345,4.364637825238322,3.3944995031247664,8.970196846462951,-7.002270777695292,-4.027542754799056,-4.090340583278245,-4.5960370162028275,-8.980047294948237,7.103150699735188,-3.766314758573337,-3.7909502678864726,-8.525642608941844,-0.9931227770637658,3.20986566157492,-3.664391418102804,-2.413012083051571,2.0301569493649403,-0.5350335884653372,-8.282732163876167,-3.249293970192399,6.732831705764774,7.038498011668331,-6.370215911026782,-4.976593486357608,-4.969183409153279,6.1150465956048485,-9.940089363978316,9.924842355598312,-1.375675510005001,-2.5035978824116745,-4.32806284170465,8.880761839264764,3.232356698297645,-2.6973723109856174,3.9352460389242303
--2.1342958590327044,2.3676493594110255,3.2351604081317458,4.050136213716883,-0.3917609094130192,0.2592746436527431,6.396662963467882,-0.9575222883996872,5.44075456196958,-4.303118088139417,-4.059633951861732,-0.059554306423354575,1.7431002678445964,-8.484290577217596,2.8219151882004887,1.464813047935948,0.14718927940822013,-0.015001113713937642,4.152058830911127,-5.263410299752131,-9.183341010380968,-7.022287055206142,-3.4172501435917884,7.427376688475842,-6.184526136949684,-6.6942388973656675,-5.714318874517195,-0.008389160553221586,9.24262487973419,0.5219330841068892,0.9730118169596693,-3.8524379978185337,-4.145530465100482,2.433444620695454,0.8224805333487772,-8.269702163901462,-9.489889375195656,0.4399896224600752,-1.491937686725258,8.059008141267924,-6.242982250488276,3.3584883072962857,5.5662562084657665,6.609733586625289,0.8820801304724988,0.9130453910870369,7.210864545375813,-8.333448342638544,-6.401137599232647,-2.083684679944053,-9.321289608882072,4.2213782337401256,-0.8421591655432294,0.22134131646782151,9.076023502237366,-5.4143350248939,8.675734541338386,7.956664452967704,-3.075919525438109,9.867907237925941,-0.7695293853003999,-6.340177982495567,-2.403744339820701,4.243685582946949,7.208857961402792,9.20648037365089,9.431863338841275,4.544975191406149,8.546964629782398,2.557316973456878,-5.065851511109325,-1.7897132220284497,-0.80799609535263,-3.7936290508662545,8.24744800087527,7.7780967933890075,6.013689348282536,4.474975822474388,-4.0926837904865305,2.4222936189369992,4.164671196793979,9.034838789889662,8.646020668767346,-2.983774127010421,3.0641392810791164,-8.640996407690206,-5.378676245167984,4.627631442474367,1.5470415538619875,6.796226129577054,-8.74466927442172,0.22028404032345605,-3.8580014405262553,-5.291748711955693,9.383633752843203,3.4002438960583703,-2.3499528021715665,3.972764111678533,-3.5632989508211406,8.924620910325022
+2022,1,2,3,4
2011,5,6,7,8
1101,1000,2000,3000,4000
821374,0,0,0,0
5,3,5,6
\ No newline at end of file
diff --git a/data analysis/data/dicc.csv b/data analysis/data/dicc.csv
deleted file mode 100644
index da813b68..00000000
--- a/data analysis/data/dicc.csv	
+++ /dev/null
@@ -1,2 +0,0 @@
-1,2,3
-4,5,6
diff --git a/data analysis/data/teams.csv b/data analysis/data/teams.csv
new file mode 100644
index 00000000..a49e3650
--- /dev/null
+++ b/data analysis/data/teams.csv	
@@ -0,0 +1 @@
+2022
2011
1101
821374
5
\ No newline at end of file
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 03223149..a66d00e1 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -1,12 +1,13 @@
-
 #Titan Robotics Team 2022: Data Analysis Script
 #Written by Arthur Lu & Jacob Levine
 #Notes:
 #setup:
 
-__version__ = "1.0.2.000"
+__version__ = "1.0.3.000"
 
 __changelog__ = """changelog:
+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
@@ -30,11 +31,33 @@ import glob
 
 #get all the data
 source_dir = 'data'
-file_list = glob.glob(source_dir + '/*.CSV')
+file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
 data = []
-for file_path in file_list:
-    data.append(analysis.load_csv(file_path))
+files = [fn for fn in glob.glob('data/*.csv') 
+         if not os.path.basename(fn).startswith('teams')]
 
+#for file_path in file_list:
+#	if not os.path.basename(file_list).startswith("teams")
+#		data.append(analysis.load_csv(file_path))
+
+for i in files:
+	data.append(analysis.load_csv(i))
+
+stats = []
+measure_stats = []
+teams = analysis.load_csv("data/teams.csv")
+
+#assumes that team number is in the first column, and that the order of teams is the same across all files
 #unhelpful comment
-#for d in data: #unpacks 3d array into 2d
-#    print (d)
\ No newline at end of file
+for measure in data: #unpacks 3d array into 2ds
+	for i in range(len(measure)): #unpacks into specific teams
+		line = measure[i]
+		line.pop(0) #removes team identifier
+		measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0)))
+	stats.append(list(measure_stats))
+		
+print (stats)
+#	print(d)
+
+#print (stats)
+

From 6c665b10df0c6614009f3797bbe37412905754a5 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 20 Feb 2019 22:59:17 -0600
Subject: [PATCH 04/31] superscript.py - v 1.

changelog:
- processes data more efficiently
---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25075 -> 25209 bytes
 data analysis/analysis.py                     |  88 ++++++++++--------
 data analysis/data/aSetOfData.csv             |   6 +-
 data analysis/data/bdata.csv                  |   5 +
 data analysis/data/data.csv                   |   6 +-
 .../keys/titanscoutandroid_firebase.json      |  12 +++
 data analysis/superscript.py                  |  63 +++++++++++--
 7 files changed, 134 insertions(+), 46 deletions(-)
 create mode 100644 data analysis/data/bdata.csv
 create mode 100644 data analysis/keys/titanscoutandroid_firebase.json

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 5ac2626d5723fec4a205adc4701ff0bab0d92870..72c9c2ce5a0b8ba9a148daeb11308fc5e0ca6553 100644
GIT binary patch
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diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index fbafcf7c..bdc32b3f 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -423,7 +423,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
 
         data_t = []
 
-        for i in range (0, len(data) - 1, 1):
+        for i in range (0, len(data), 1):
             data_t.append(float(data[i]))
     
         _mean = mean(data_t)
@@ -559,12 +559,15 @@ def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th perc
 def histo_analysis(hist_data, delta, low_bound, high_bound):
 
     if hist_data == 'debug':
-        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number for standard deviations')
+        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and higher bounds in number of standard deviations')
 
     derivative = []
 
-    for i in range(0, len(hist_data) - 1, 1):
-        derivative.append(float(hist_data[i + 1]) - float(hist_data [i]))
+    for i in range(0, len(hist_data), 1):
+        try:
+            derivative.append(float(hist_data[i - 1]) - float(hist_data [i]))
+        except:
+            pass
 
     derivative_sorted = sorted(derivative, key=int)
     mean_derivative = basic_stats(derivative_sorted,"1d", 0)[0]
@@ -701,7 +704,7 @@ def tanh_regression(x, y):
     
 def r_squared(predictions, targets): # assumes equal size inputs
 
-    return metrics.r2_score(targets, predictions)
+    return metrics.r2_score(np.array(targets), np.array(predictions))
 
 def rms(predictions, targets): # assumes equal size inputs
 
@@ -724,11 +727,11 @@ def calc_overfit(equation, rms_train, r2_train, x_test, y_test):
         z = x_test[i]
 
         exec("vals.append(" + equation + ")")
-        
+
     r2_test = r_squared(vals, y_test)
     rms_test = rms(vals, y_test)
 
-    return rms_train - rms_test, r2_train - r2_test
+    return r2_train - r2_test
 
 def strip_data(data, mode):
 
@@ -747,7 +750,10 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
         raise error("resolution must be int")
 
     x_train = x
-    y_train = y
+    y_train = []
+
+    for i in range(len(y)):
+        y_train.append(float(y[i]))
 
     x_test = []
     y_test = []
@@ -756,7 +762,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
         index = random.randint(0, len(x) - 1)
 
         x_test.append(x[index])
-        y_test.append(y[index])
+        y_test.append(float(y[index]))
 
         x_train.pop(index)
         y_train.pop(index)
@@ -769,10 +775,13 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
     r2s = []
 
     for i in range (0, _range + 1, 1):
-    	x, y, z = poly_regression(x_train, y_train, i)
-    	eqs.append(x)
-    	rmss.append(y)
-    	r2s.append(z)
+        try:
+            x, y, z = poly_regression(x_train, y_train, i)
+            eqs.append(x)
+            rmss.append(y)
+            r2s.append(z)
+        except:
+            pass
 
     for i in range (1, 100 * resolution + 1):
         try:
@@ -792,11 +801,14 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
         except:
             pass
 
-    x, y, z = tanh_regression(x_train, y_train)
+    try:
+        x, y, z = tanh_regression(x_train, y_train)
 
-    eqs.append(x)
-    rmss.append(y)
-    r2s.append(z)
+        eqs.append(x)
+        rmss.append(y)
+        r2s.append(z)
+    except:
+        pass
     
     for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data
         if r2s[i] == 1:
@@ -815,38 +827,38 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
     overfit = []
 
     for i in range (0, len(eqs), 1):
+
         overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test))
             
     return eqs, rmss, r2s, overfit
 
 def select_best_regression(eqs, rmss, r2s, overfit, selector):
 
-	b_eq = ""
-	b_rms = 0
-	b_r2 = 0
-	b_overfit = 0
+    b_eq = ""
+    b_rms = 0
+    b_r2 = 0
+    b_overfit = 0
 
-	ind = 0
+    ind = 0
 
-	if selector == "min_overfit":
+    if selector == "min_overfit":
 
-		ind = np.argmax(overfit)
+        ind = np.argmin(overfit)
 
-		b_eq = eqs[ind]
-		b_rms = rmss[ind]
-		b_r2 = r2s[ind]
-		b_overfit = overfit[ind]
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
 
-	if selector == "max_rmss":
+    if selector == "max_r2s":
 
-		ind = np.argmax(rmss)
+        ind = np.argmax(r2s)
+        b_eq = eqs[ind]
+        b_rms = rmss[ind]
+        b_r2 = r2s[ind]
+        b_overfit = overfit[ind]
 
-		b_eq = eqs[ind]
-		b_rms = rmss[ind]
-		b_r2 = r2s[ind]
-		b_overfit = overfit[ind]
-
-	return b_eq, b_rms, b_r2, b_overfit
+    return b_eq, b_rms, b_r2, b_overfit
 
 def p_value(x, y): #takes 2 1d arrays
 	
@@ -878,7 +890,7 @@ def basic_analysis(data): #assumes that rows are the independent variable and co
 
 def benchmark(x, y):
 
-    start_g = time.time()
+    start_g = time.time() 
     generate_data("data/data.csv", x, y, -10, 10)
     end_g = time.time()
 
@@ -1092,4 +1104,4 @@ def stdev(data, xbar=None):
     try:
         return var.sqrt()
     except AttributeError:
-        return math.sqrt(var)
\ No newline at end of file
+        return math.sqrt(var)
diff --git a/data analysis/data/aSetOfData.csv b/data analysis/data/aSetOfData.csv
index ce148ada..643d2d23 100644
--- a/data analysis/data/aSetOfData.csv	
+++ b/data analysis/data/aSetOfData.csv	
@@ -1 +1,5 @@
-2022,4,4,4,4
2011,5,5,5,5,5
1101,6,6,6,6,6,6
821374,7,7,7
5,8,8,8
\ No newline at end of file
+2022,4,5,10,15,8,954978,84,74
+2011,3,6,9,12,15,7,856,9
+1101,4,16,32,64,128,2,4234,-56
+821374,7,8,9,79.33333333,170.3333333,636647,5874.666667,-121
+5,4,8,8,103.8333333,230.3333333,1114135,7949.666667,-186
diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
new file mode 100644
index 00000000..9b11493e
--- /dev/null
+++ b/data analysis/data/bdata.csv	
@@ -0,0 +1,5 @@
+-1.6007632575405175,3.094243071050249,4.583936305454976,7.237782504548598,-5.521589121433712,-2.9078766762969295,3.2751982610806145,3.669074500924099,6.194289395001171,-9.70395566576695,4.8593751222388235,-6.67094228013908,-4.144180498130141,-7.257062237714525,5.71197645401875,9.945491401171964,-2.7707351521125396,1.4555780053661849,2.2075220439544054,-7.918108528363314,0.6886619609074511,8.379762449827606,5.964876797994396,3.037103869702946,4.796017042338738,8.846961658102465,-8.238704985679558,4.530303869837741,-5.320585260639417,2.7702239357820417,-0.7832177468135537,9.256314936398226,-8.116813655472663,7.315710420609928,3.410242694024994,-8.10603846276264,-8.07153698072364,2.152085496135115,-3.0275379228407058,6.830181793819328,-4.29878946762698,9.088682964786567,-3.0116161280634612,2.111794120861081,1.1271566641715474,-0.35857566057631374,7.051881933450716,8.624850467592807,-9.140779075977711,-4.07202265033866,-2.246723549679377,-7.541531711756095,-2.4168165528412366,-2.7333638278084376,6.443589792007607,0.7905247877923074,0.30456961697296947,-3.990848124445174,-7.9156804373061345,9.162025498895577,-8.064463486673983,5.011434008083688,-8.958579081781028,6.271439236252764,-4.796406036363702,-3.485792580154259,-7.626666268770766,-8.897222191721939,-7.458882322500758,1.3507042532359428,-9.984387735385813,4.709713126273524,5.937388844487694,-6.1309191195400725,-5.786143582459921,8.250793995102445,7.991120247882058,-0.829775210764117,-2.892320256799506,5.713355349767738,-4.291238046532964,5.388711926791316,4.6898933351779135,8.518246578598415,5.737025842395383,-5.025250884552543,5.4223386703040095,-0.7664691196370974,-1.6417217237143138,0.15989165426631047,1.291931036048135,-1.2477263925023596,-8.385094769816812,6.680157017788698,9.200962867930386,4.438716964910279,0.05664451323347208,2.9767752824462654,9.862390452775337,-4.638188933411371
+-4.19886514948963,2.116259832903374,9.784780806656713,-6.0262515990655245,2.259850780278308,5.311171798094897,3.164512882950646,4.350057506858846,7.693618498503586,-1.0820047325626003,6.008893820630796,6.959235712776991,-2.96741010662738,-5.39553133393701,7.687926117869168,-2.214421295339248,-6.563678258550183,3.948911670515244,7.853521046343122,-6.343085246017093,2.56780495027966,-3.232660420633284,2.709342092804402,-8.913853676046354,2.628378579583048,-8.864398454413786,-6.211965077084363,8.779612488518271,1.5594177199635304,-9.772430538411676,3.7679334925591945,-2.8048191086500314,-8.586986980341946,-2.4926295457304093,-4.852547317818196,9.507065105074783,-3.566533536225176,8.196094739800053,2.2637934849874988,6.6155224065612614,-4.0133698637219,-5.911477528422056,2.6049117208119554,-1.5479147894596945,2.594762318154176,-6.7868329999599535,3.113136409844607,9.663942737830045,-7.31890030980084,-4.294092860785357,-4.288178288293631,-2.37970443013519,4.835033320384534,2.9259330858634556,-5.997674611760145,0.4356291979954996,4.836551398108565,9.782096211874464,-9.585544246376953,8.271693526960206,-2.2178050388803605,2.641436188891788,-6.023516481952649,9.792991730773505,-1.0952307943365458,6.379482453639433,7.876314648269901,3.822529107554015,8.882663756536772,5.4924638799010985,4.836648299842416,-1.6546484549283047,-3.758054369040682,-7.098680827220005,4.286604047728407,9.684572924081003,-9.275783902012751,-2.186978098830334,4.343146232468751,4.555468474798047,-4.42979277112862,5.520571646152758,-1.2990764806053807,0.08646762204405789,9.36820098083734,8.882177934124492,-9.05491849330825,7.675706379209121,0.7238793778833781,-2.8640931838596835,3.350602837405848,-6.7814627959535585,-2.0879701330108595,8.692522507298118,0.20922204583939852,-7.3232930928669475,-1.6927613308994474,3.9410921827987355,2.6057819490036422,-3.527273801049575
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+9.459443630130309,7.016111056779078,0.9921215455341255,-6.550347195636091,-1.1500161987948836,5.025520665751312,0.11820321329195416,-6.098011869080235,-7.059777773821221,9.918747974386516,0.5740936358815105,-6.074495459736933,-2.8562420467646437,2.7452132300248024,0.3961615673831034,-8.187709255602321,-2.223162885896828,2.02308233068765,2.173861910822211,7.467631143535776,-6.869303813419824,5.515660829109667,-1.1854728450344822,-7.244540221731035,-2.47741275462168,1.7005021142373273,-2.6435277014190124,2.914197127354111,-5.321090657621217,-3.405385672515453,-7.2390700266735974,-2.2543430838554563,0.5810520220327327,-3.662068584206102,-0.712093105965403,-7.033992941249638,8.947547833409896,-9.761463728561306,6.032536612477848,8.373114739282794,-9.188001492119549,-0.792249220135929,-2.286873900824819,9.12725915348836,9.847383433934262,-4.308017192739209,0.06416205096922312,8.989864884806554,-9.575146040499348,-1.4826049056824822,1.2457815252508393,-5.805141241281417,-8.534557544196815,3.76303051544658,-6.315166870161935,-0.8469444777475754,2.2715479997134445,7.940699113699367,2.0789925133343736,-4.539705528462939,-9.929346289529747,0.08885331451507206,6.586247829508064,7.617817013533614,7.1824527157653435,4.756418030566145,5.607378052322925,7.6483759904306226,-3.347173860635875,-6.528030394514559,7.727623706277711,-7.00049327736375,0.10327904242857677,7.5340647071513445,6.334205397937531,5.577405123406242,9.299609203382204,8.843550733594356,2.856351085240723,0.7394015204831224,-3.8778018128261316,3.6571621070359726,-8.185370601499404,5.933614053714207,-2.8773431938656158,8.796763464112626,-8.643277711461923,6.9056764000134265,-3.3536123917078537,-5.92664281813291,-6.513654020344379,2.1204729995170943,4.571033504057793,1.7282198881806465,-8.785991084685492,-0.5406625828468776,5.042946509631578,-6.18310119331984,-7.877843185844262,5.173734304035298
diff --git a/data analysis/data/data.csv b/data analysis/data/data.csv
index 05fe9505..14b7ad59 100644
--- a/data analysis/data/data.csv	
+++ b/data analysis/data/data.csv	
@@ -1 +1,5 @@
-2022,1,2,3,4
2011,5,6,7,8
1101,1000,2000,3000,4000
821374,0,0,0,0
5,3,5,6
\ No newline at end of file
+2022,1,2,3,4,5,6,7,8
+2011,5,6,7,8,9,10,11,12
+1101,1000,2000,3000,4000,5000,6000,7000,8000
+821374,0,0,0,0,0,0,0,1
+5,3,5,6,7,8.5,9.8,11.1,12.4
diff --git a/data analysis/keys/titanscoutandroid_firebase.json b/data analysis/keys/titanscoutandroid_firebase.json
new file mode 100644
index 00000000..e629f076
--- /dev/null
+++ b/data analysis/keys/titanscoutandroid_firebase.json	
@@ -0,0 +1,12 @@
+{
+  "type": "service_account",
+  "project_id": "titanscoutandroid",
+  "private_key_id": "4fa61815e17c0b5c4dfe48445d87f8fc6d07a5d8",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nSBMA/OHEodv06XqibcgovA==\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
+  "client_id": "114864465329268712237",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
+}
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index a66d00e1..8ecea846 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -6,6 +6,8 @@
 __version__ = "1.0.3.000"
 
 __changelog__ = """changelog:
+1.0.3.001:
+        - processes data more efficiently
 1.0.3.000:
 	- actually processes data
 1.0.2.000:
@@ -23,13 +25,26 @@ __author__ = (
     "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 glob
+import numpy as np
+
+# Use a service account
+cred = credentials.Certificate('keys/titanscoutandroid_firebase.json')
+firebase_admin.initialize_app(cred)
+
+db = firestore.client()
 
 #get all the data
+
+analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
+
 source_dir = 'data'
 file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
 data = []
@@ -50,14 +65,50 @@ teams = analysis.load_csv("data/teams.csv")
 #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]
-		line.pop(0) #removes team identifier
-		measure_stats.append(teams[i] + list(analysis.basic_stats(line, 0, 0)))
-	stats.append(list(measure_stats))
+
+		#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)
 		
-print (stats)
-#	print(d)
+		ofbest_curve.append(beqs)
+		ofbest_curve.append(brmss)
+		ofbest_curve.append(br2s)
+		ofbest_curve.append(boverfit)
+		ofbest_curve.pop(0)
 
-#print (stats)
+		#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)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
+
+	stats.append(list(measure_stats))
+	
+json_out = {}
+		
+for i in range(len(stats)):
+        json_out[files[i]]=stats[i]
+
+db.collection(u'stats').document(u'stats-noNN').set(json_out)

From d14fa4fa675c945c1c848509cc6ac479394483c2 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 21 Feb 2019 12:33:13 -0600
Subject: [PATCH 05/31] Update bdata.csv

---
 data analysis/data/bdata.csv | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
index 9b11493e..385ce867 100644
--- a/data analysis/data/bdata.csv	
+++ b/data analysis/data/bdata.csv	
@@ -1,5 +1,5 @@
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From 4a7761947c4de440a1edcc922e328cea076434df Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Fri, 22 Feb 2019 12:23:49 -0600
Subject: [PATCH 06/31] security patch

---
 data analysis/keys/titanscoutandroid_firebase.json | 12 ------------
 1 file changed, 12 deletions(-)
 delete mode 100644 data analysis/keys/titanscoutandroid_firebase.json

diff --git a/data analysis/keys/titanscoutandroid_firebase.json b/data analysis/keys/titanscoutandroid_firebase.json
deleted file mode 100644
index e629f076..00000000
--- a/data analysis/keys/titanscoutandroid_firebase.json	
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "type": "service_account",
-  "project_id": "titanscoutandroid",
-  "private_key_id": "4fa61815e17c0b5c4dfe48445d87f8fc6d07a5d8",
-  "private_key": "-----BEGIN PRIVATE KEY-----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\nSBMA/OHEodv06XqibcgovA==\n-----END PRIVATE KEY-----\n",
-  "client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
-  "client_id": "114864465329268712237",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
-}

From 8d92716ef270057d75c483e45b0e10c6926de213 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 26 Feb 2019 19:10:53 -0600
Subject: [PATCH 07/31] Update .gitignore

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 8a6040ad..60243c5b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
 
 benchmark_data.csv
 
+data analysis/keys/firebasekey.json

From a8f3598bbe0b25debda8adaed327c7f8a27df19f Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 26 Feb 2019 19:39:56 -0600
Subject: [PATCH 08/31] superscript.py - v 1.0.4.000

changelog:
- actually pushes to firebase
---
 data analysis/data/bdata.csv | 10 +++++-----
 data analysis/superscript.py | 24 ++++++++++++++----------
 2 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
index 385ce867..6f961f2a 100644
--- a/data analysis/data/bdata.csv	
+++ b/data analysis/data/bdata.csv	
@@ -1,5 +1,5 @@
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diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 8ecea846..0c98034a 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -3,21 +3,23 @@
 #Notes:
 #setup:
 
-__version__ = "1.0.3.000"
+__version__ = "1.0.4.000"
 
 __changelog__ = """changelog:
+1.0.4.000:
+    - actually pushes to firebase
 1.0.3.001:
-        - processes data more efficiently
+    - processes data more efficiently
 1.0.3.000:
-	- actually processes data
+    - actually processes data
 1.0.2.000:
-	- added data reading from folder
-	- nearly crashed computer reading from 20 GiB of data
+    - 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
+    - added data reading from file
+    - added superstructure to code
 1.0.0.000:
-	- added import statements (revolutionary)
+    - added import statements (revolutionary)
 """ 
 
 __author__ = (
@@ -36,7 +38,7 @@ import glob
 import numpy as np
 
 # Use a service account
-cred = credentials.Certificate('keys/titanscoutandroid_firebase.json')
+cred = credentials.Certificate('keys/firebasekey.json')
 firebase_admin.initialize_app(cred)
 
 db = firestore.client()
@@ -109,6 +111,8 @@ for measure in data: #unpacks 3d array into 2ds
 json_out = {}
 		
 for i in range(len(stats)):
-        json_out[files[i]]=stats[i]
+        json_out[files[i]]=str(stats[i])
+
+print(json_out)
 
 db.collection(u'stats').document(u'stats-noNN').set(json_out)

From 57eda3815b9e5f1d1bc768b3fd13767decac2f5f Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 26 Feb 2019 23:18:26 -0600
Subject: [PATCH 09/31] superscript.py - v 1.0.4.001

changelog:
- grammar fixes
---
 .../__pycache__/superscript.cpython-37.pyc    | Bin 0 -> 2402 bytes
 data analysis/superscript.py                  | 120 +++++++++---------
 2 files changed, 62 insertions(+), 58 deletions(-)
 create mode 100644 data analysis/__pycache__/superscript.cpython-37.pyc

diff --git a/data analysis/__pycache__/superscript.cpython-37.pyc b/data analysis/__pycache__/superscript.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8cb176ff939de69c7144f7b7ba414f15bcc9e744
GIT binary patch
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HcmV?d00001

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 0c98034a..f2c45db3 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -1,11 +1,13 @@
-#Titan Robotics Team 2022: Data Analysis Script
+#Titan Robotics Team 2022: Super Script
 #Written by Arthur Lu & Jacob Levine
 #Notes:
 #setup:
 
-__version__ = "1.0.4.000"
+__version__ = "1.0.4.001"
 
 __changelog__ = """changelog:
+1.0.4.001:
+    - grammar fixes
 1.0.4.000:
     - actually pushes to firebase
 1.0.3.001:
@@ -37,82 +39,84 @@ import os
 import glob
 import numpy as np
 
-# Use a service account
-cred = credentials.Certificate('keys/firebasekey.json')
-firebase_admin.initialize_app(cred)
+def titanservice():
+    
+    # Use a service account
+    cred = credentials.Certificate('keys/firebasekey.json')
+    firebase_admin.initialize_app(cred)
 
-db = firestore.client()
+    db = firestore.client()
 
-#get all the data
+    #get all the data
 
-analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
+    analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
 
-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')]
+    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')]
 
-#for file_path in file_list:
-#	if not os.path.basename(file_list).startswith("teams")
-#		data.append(analysis.load_csv(file_path))
+    #for file_path in file_list:
+    #	if not os.path.basename(file_list).startswith("teams")
+    #		data.append(analysis.load_csv(file_path))
 
-for i in files:
-	data.append(analysis.load_csv(i))
+    for i in files:
+            data.append(analysis.load_csv(i))
 
-stats = []
-measure_stats = []
-teams = analysis.load_csv("data/teams.csv")
+    stats = []
+    measure_stats = []
+    teams = analysis.load_csv("data/teams.csv")
 
-#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
+    #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 = []
+            measure_stats = []
 
-	for i in range(len(measure)): #unpacks into specific teams
+            for i in range(len(measure)): #unpacks into specific teams
 
-		ofbest_curve = [None]
-		r2best_curve = [None]
+                    ofbest_curve = [None]
+                    r2best_curve = [None]
 
-		line = measure[i]
+                    line = measure[i]
 
-		#print(line)
+                    #print(line)
 
-		x = list(range(len(line)))
-		eqs, rmss, r2s, overfit = analysis.optimize_regression(x, line, 10, 1)
+                    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")
+                    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(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)
+                    #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)
-		
-		measure_stats.append(teams[i] + ["|"] +  list(analysis.basic_stats(line, 0, 0)) + ["|"] + list(analysis.histo_analysis(line, 1, -3, 3)) + ["|"] + ofbest_curve + ["|"] + r2best_curve)
+                    #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)
 
-	stats.append(list(measure_stats))
-	
-json_out = {}
-		
-for i in range(len(stats)):
-        json_out[files[i]]=str(stats[i])
+            stats.append(list(measure_stats))
+            
+    json_out = {}
+                    
+    for i in range(len(stats)):
+            json_out[files[i]]=str(stats[i])
 
-print(json_out)
+    print(json_out)
 
-db.collection(u'stats').document(u'stats-noNN').set(json_out)
+    db.collection(u'stats').document(u'stats-noNN').set(json_out)

From 61832e90151ca7a09616c43a8ab6f4a8abb1ec2b Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 27 Feb 2019 13:38:24 -0600
Subject: [PATCH 10/31] superscript.py - v 1.0.5.000

changelog:
- service now iterates forever
- ready for production other than pulling json data
---
 data analysis/data/bdata.csv | 10 +++---
 data analysis/superscript.py | 68 +++++++++++++++++++++++++++++-------
 2 files changed, 61 insertions(+), 17 deletions(-)

diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
index 6f961f2a..efcf8294 100644
--- a/data analysis/data/bdata.csv	
+++ b/data analysis/data/bdata.csv	
@@ -1,5 +1,5 @@
--4.9249668189769995,-2.352811503488086,6.112647665258532,0.33496472578524106,0.20521681743994868,8.992532238575293,7.458273188238607,-8.432626054850502,5.372451223330504,8.133479642509677,9.613643551236748,-6.28607348259123,5.1229960559135925,1.1490144224910637,5.725583739510011,4.251502494617657,8.8991784475948,-8.004384747173637,-5.392871068769239,9.719275562391342,9.068094344693883,-8.05257009558867,1.6106217487913295,-0.22721855930308266,4.4689234841090375,4.296787587754832,4.4833305993165435,6.832477770446523,-0.9317753609118302,6.854927783035741,9.239698695908071,-6.766629254056086,-0.8247611745479411,0.8546100433211521,-9.790723876655765,-1.7654513276654988,-0.3087277207234891,-8.015731224952209,-5.448218287856572,1.7895130906871088,-3.0986113942066495,7.628320682084684,9.824039172670325,3.458089474036468,8.060706493837387,0.44841594941993534,3.6499557554286284,-5.891357611691451,-1.3154398694118274,3.5743814680902872,-7.789706444400268,4.172473876055996,4.416283696293899,9.425554825035913,0.28143212907267845,7.062946911216393,2.8479505888635437,-3.528077533110949,4.83454622455686,-7.954611726147571,-9.887540739045274,-6.649397799285721,-4.247426629845441,4.26297716163614,-0.5089932876430243,-0.9384679664276714,-9.563759542999533,3.4234643950176533,-8.139424123415166,-6.844676934909173,-7.7239579990567115,8.423359498722803,4.652863140015189,-7.6030386045826575,-8.092808468928318,-6.8664615781376925,-7.111505064629298,-1.8143906468595699,-9.31721764956907,5.4159592399097,-0.2195843514933582,-2.4965009818539112,4.128600146559155,-2.4875600640665745,-7.2949908650381,4.887589625425823,-4.9190075902245205,1.8538248779542243,-6.540399933776451,-5.849697307943355,0.3270664867084463,-3.359049176069142,-2.7654379163281657,3.5925915728273345,3.9343684739511637,-1.4765454329985257,1.0389309062618057,0.47916876898467464,2.919986033639086,-6.349049514611398
--4.6870954909314655,9.960065962499975,5.9122684944905295,-5.695304051638073,5.254325301049336,-5.215259389015749,-2.6345521758058315,-5.737224569312072,-5.484379065547829,-9.249272820655925,2.484677017734329,9.048350151248233,5.55996569219978,2.345383840602956,-0.6534625208059257,-7.383126548052175,-5.329066286794819,0.1336957159106582,-4.229190593046683,-0.15974415114943596,6.267182352092313,-9.76724396491003,9.18025061352418,-2.482394457695209,-0.7094974045311311,4.705125896491527,2.8092569404536576,0.23952538156108716,6.371599100835752,7.787627752074613,-3.038965249468262,-5.230464044169234,-1.8997558790444717,4.034201333716521,-2.518487462352514,-6.927042405566299,2.6785180421485535,-2.587260072160749,-9.110059130651852,-4.322922893339108,8.432897494236933,-3.8901391233076925,-2.8298183844550966,1.928754661321399,2.719590844679276,-8.812658863823499,0.6961088399814148,1.8852161061105033,6.769102966018572,5.682844121009527,8.013036547650895,-8.1135250005979,5.085250356025684,-0.19412648951139033,-8.630954397339924,3.8316624378717616,-2.528086625415149,-0.7228162892177714,1.5622753075799896,9.148665961640347,1.1547059931096335,-8.296411734117646,-5.528108037246133,6.009716998966542,-5.690951033397087,-4.995711592904599,-7.7762826214190905,0.6487782103399802,8.828020157138276,0.5225986323162619,8.431282678429735,-6.12645474844564,-0.0004429193427419875,0.04978332821876563,-7.774275287365593,-6.947979741065855,-4.999655534007161,7.2497666307213,-2.9512970192716903,2.116442096867214,7.7713419667619235,1.9843088598705538,8.684486102016603,-4.0808439513713495,6.695265743136922,3.528034413199496,-3.4124279487970233,6.999254305283561,-7.57187421030145,-0.7947709521685464,-1.1446191881730279,0.0630751411099606,9.04634619345487,5.764435722869997,1.4804502683091378,-8.08249927190686,-6.461724684750294,9.346772458205816,-9.529057647708076,2.597496433905979
--4.336504323000437,-7.226300533844892,-5.625521574444312,0.2790215191896088,-1.771248961896383,-4.239354085883162,8.722603223586681,7.034983578139265,-4.738770982739724,2.3474987256030015,-1.5286821232759387,3.716685978626167,-0.366576408089772,-7.204942051035516,0.7779810341596178,-7.296800342206451,-8.259193020253342,4.004171277527242,8.329575326270081,-0.12014119508343057,0.8767497676752267,-4.000714006092128,-4.656478530729656,-4.780792880533484,6.514182928800871,1.214362229146687,-0.3574767161695718,-0.41430786032810474,-1.6321267662541228,-6.058370187548407,-3.156742295480073,-4.526667876599659,-1.689370135894885,9.888798822370909,1.272213309843922,1.7155441263680515,-3.6203193271260226,1.835414980608638,2.5830050930342523,3.6596828313189427,-1.1984322170490085,5.746243472688693,6.758981735554169,6.160225034078945,5.517355177127239,1.9191217510570588,2.914224151202598,6.722180528019162,-3.9303525557162278,-4.661626483104142,-8.236189838216594,7.954602564106711,5.176856194678114,-0.5577079765543917,4.175342314110257,5.552840618695241,8.479835408304492,9.889293524424026,0.8460714340278326,-7.724731044256654,1.5176133286002589,-9.578070074866087,-7.489270624790758,5.298227147069497,-4.912415431395585,-4.404576024703967,-7.453894300315671,1.09968710966481,3.3202555176826127,8.276613940359052,7.637833151966355,-3.775669242713448,1.6881619795662992,-2.516843043135859,3.871453813836471,2.747326766037446,7.806801599503405,9.643281722897786,-4.995039858315707,-4.960373805784355,6.18077162398837,4.8126183903840705,-3.6349266166615006,5.8827893971273415,-6.486789262181977,1.8854552229681403,1.4242142253385577,-0.5255468109010426,-9.836167348627857,3.7903872112523267,5.422147123983411,-5.329336523071322,8.762052150643914,-1.4096868384938315,5.8174674898759235,0.5457732038131198,5.445721719225578,-7.962055846100555,-1.8212402304687725,-9.865194544046108
--5.8468894367452044,-5.817889646416845,-6.5836201578218905,-1.201555263846899,-1.4828382517480048,-3.8152101509635123,7.798303680696538,-8.59750280351106,-7.680882862961386,-4.229133747560803,8.172982800508343,-8.710877844668003,-7.1018871388343685,-8.421030176694487,-9.811116430398549,2.4751608142389436,-7.227581210521428,7.422328389364363,-2.092130083518149,5.468062844011492,5.977057523328426,-7.95462343085978,4.425878508487951,5.970563419997589,-1.776809786297715,-2.2684698408750297,-4.6515394258825316,-5.154275163093278,8.302699219039049,-7.2250504824047095,-1.5643265473399062,-5.298020253647044,-0.5233369255301401,1.5914732069917008,7.126325211127014,6.255638441435742,-1.369262588265217,-1.5783873223121532,-2.208247104259595,-0.708625300773086,-9.542956037155887,1.2562919631909182,2.300978853236975,-4.086041001500023,3.0740223286478745,8.43645637170496,-1.0398560771043215,-7.627547458901489,0.9005628469605078,7.6279207742622575,-2.0110241530446142,6.053193899922686,2.3966301298102817,5.892633072467737,2.8410633890462496,1.0446659127515439,-2.7519344202486913,-6.692856743506368,0.9162099695605086,-8.903228534166896,7.578275188107192,-1.8452727861094935,-1.6742046795863512,3.262387343013664,6.459238060181416,-9.828162003460054,-6.450839077750878,-8.188575885096764,9.413344833613657,1.00580923980662,6.6647023306010595,-5.921508869926364,-6.003021011814069,6.699763876229486,1.3690635575652372,1.5530021338939175,3.0866187195665766,-6.508397475019958,-3.77826019671261,6.8601472289073655,-7.164024151588664,-5.864210561367697,-8.68258806786833,1.8471145702530656,-9.28317658061636,-0.9236958209628163,3.7313259642596677,2.161049222685259,-6.94180906999132,-2.055818032325048,-9.09186520524633,-0.33512011823171584,-6.562075987660753,3.3795624694671904,-8.405533823742726,0.7992557981457509,-4.835215668553639,-1.6867898121384641,-9.775858804374087,5.554035396617223
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diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index f2c45db3..9a89f425 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -3,9 +3,12 @@
 #Notes:
 #setup:
 
-__version__ = "1.0.4.001"
+__version__ = "1.0.5.000"
 
 __changelog__ = """changelog:
+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:
@@ -36,20 +39,13 @@ import analysis
 import titanlearn
 import visualization
 import os
+import sys
+import warnings
 import glob
 import numpy as np
+import time
 
 def titanservice():
-    
-    # Use a service account
-    cred = credentials.Certificate('keys/firebasekey.json')
-    firebase_admin.initialize_app(cred)
-
-    db = firestore.client()
-
-    #get all the data
-
-    analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
 
     source_dir = 'data'
     file_list = glob.glob(source_dir + '/*.csv') #supposedly sorts by alphabetical order, skips reading teams.csv because of redundancy
@@ -117,6 +113,54 @@ def titanservice():
     for i in range(len(stats)):
             json_out[files[i]]=str(stats[i])
 
-    print(json_out)
+    #print(json_out)
 
     db.collection(u'stats').document(u'stats-noNN').set(json_out)
+
+def service():
+
+    while True:
+
+        print("\n")
+
+        start = time.time()
+
+        print("[OK] starting calculations")
+
+        fucked = False
+        
+        for i in range(0, 5):
+            try:
+                titanservice()
+                break
+            except:
+                if (i != 4):
+                    print("[WARN] failed, trying " + str(5 - i - 1) + " more times")
+                else:
+                    print("[ERR] 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")
+
+        time.sleep(300 - (end - start)) #executes once every 5 minutes
+
+warnings.simplefilter("ignore")
+# Use a service account
+cred = credentials.Certificate('keys/firebasekey.json')
+firebase_admin.initialize_app(cred)
+
+db = firestore.client()
+
+#get all the data
+
+analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
+service() #finally we write something that isn't a function definition

From 109fa745f17239c5845163eb7f947c0e4fd6236f Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 27 Feb 2019 13:43:33 -0600
Subject: [PATCH 11/31] superscript.py - v 1.0.5.001

changelog:
- grammar
---
 data analysis/data/bdata.csv | 10 +++++-----
 data analysis/superscript.py |  6 ++----
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
index efcf8294..e86499e9 100644
--- a/data analysis/data/bdata.csv	
+++ b/data analysis/data/bdata.csv	
@@ -1,5 +1,5 @@
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diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 9a89f425..acb5c3c2 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -121,8 +121,6 @@ def service():
 
     while True:
 
-        print("\n")
-
         start = time.time()
 
         print("[OK] starting calculations")
@@ -135,7 +133,7 @@ def service():
                 break
             except:
                 if (i != 4):
-                    print("[WARN] failed, trying " + str(5 - i - 1) + " more times")
+                    print("[WAR] failed, trying " + str(5 - i - 1) + " more times")
                 else:
                     print("[ERR] failed to compute data, skipping")
                     fucked = True
@@ -149,7 +147,7 @@ def service():
             
             print("[OK] finished calculations")
 
-        print("[OK] waiting: " + str(300 - (end - start)) + " seconds")
+        print("[OK] waiting: " + str(300 - (end - start)) + " seconds" + "\n")
 
         time.sleep(300 - (end - start)) #executes once every 5 minutes
 

From 0e634ee5e015966cac455d82065e27218d385c5f Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 27 Feb 2019 14:00:29 -0600
Subject: [PATCH 12/31] superscript.py - v 1.0.5.002

changelog:
- more information given
- performance improvements
---
 data analysis/data/bdata.csv | 10 +++++-----
 data analysis/superscript.py | 16 +++++++++++-----
 2 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/data analysis/data/bdata.csv b/data analysis/data/bdata.csv
index e86499e9..a2160ba8 100644
--- a/data analysis/data/bdata.csv	
+++ b/data analysis/data/bdata.csv	
@@ -1,5 +1,5 @@
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diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index acb5c3c2..0ffc9720 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -3,9 +3,11 @@
 #Notes:
 #setup:
 
-__version__ = "1.0.5.000"
+__version__ = "1.0.5.001"
 
 __changelog__ = """changelog:
+1.0.5.001:
+    - grammar
 1.0.5.000:
     - service now iterates forever
     - ready for production other than pulling json data
@@ -46,17 +48,17 @@ import numpy as np
 import time
 
 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')]
 
-    #for file_path in file_list:
-    #	if not os.path.basename(file_list).startswith("teams")
-    #		data.append(analysis.load_csv(file_path))
-
     for i in files:
             data.append(analysis.load_csv(i))
 
@@ -64,6 +66,10 @@ def titanservice():
     measure_stats = []
     teams = analysis.load_csv("data/teams.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

From 243bbb48b1b97f5b78a724db7010a3cac71f6414 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 27 Feb 2019 14:01:25 -0600
Subject: [PATCH 13/31] Update superscript.py

---
 data analysis/superscript.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 0ffc9720..047db8ab 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -3,9 +3,12 @@
 #Notes:
 #setup:
 
-__version__ = "1.0.5.001"
+__version__ = "1.0.5.002"
 
 __changelog__ = """changelog:
+1.0.5.002:
+    - more information given
+    - performance improvements
 1.0.5.001:
     - grammar
 1.0.5.000:

From c79fb5adb649bbead875bbaeef960f0ed5e12000 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 28 Feb 2019 09:04:37 -0600
Subject: [PATCH 14/31] Update superscript.py

---
 data analysis/superscript.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 047db8ab..974c942d 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -126,6 +126,10 @@ def titanservice():
 
     db.collection(u'stats').document(u'stats-noNN').set(json_out)
 
+def pulldata():
+    #TODO
+    pass
+
 def service():
 
     while True:
@@ -167,7 +171,4 @@ firebase_admin.initialize_app(cred)
 
 db = firestore.client()
 
-#get all the data
-
-analysis.generate_data("data/bdata.csv", 100, 5, -10, 10)
 service() #finally we write something that isn't a function definition

From b9e89c8c9176620c02e65126251a00e8253502d9 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 28 Feb 2019 13:13:52 -0600
Subject: [PATCH 15/31] bassbsabjasb

---
 .gitignore                          | 3 ++-
 data analysis/keys/firebasekey.json | 0
 2 files changed, 2 insertions(+), 1 deletion(-)
 create mode 100644 data analysis/keys/firebasekey.json

diff --git a/.gitignore b/.gitignore
index 60243c5b..2c6ba44a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 
 benchmark_data.csv
 
-data analysis/keys/firebasekey.json
+
+data analysis/keys/keytemp.json
diff --git a/data analysis/keys/firebasekey.json b/data analysis/keys/firebasekey.json
new file mode 100644
index 00000000..e69de29b

From 206ebd1b0c6c61a3794f8b36568ed41bfbcbb239 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 28 Feb 2019 13:28:29 -0600
Subject: [PATCH 16/31] sfasf

---
 data analysis/keys/firebasekey.json | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/data analysis/keys/firebasekey.json b/data analysis/keys/firebasekey.json
index e69de29b..b7a073ed 100644
--- a/data analysis/keys/firebasekey.json	
+++ b/data analysis/keys/firebasekey.json	
@@ -0,0 +1,12 @@
+{
+  "type": "service_account",
+  "project_id": "titanscoutandroid",
+  "private_key_id": "e7cde706a13a6fade555cce2bc46ee53f05a0b11",
+  "private_key": "-----BEGIN PRIVATE KEY-----\(gottem)/B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\nvJCSRvjRtxyFafuX4YQzWu0=\n-----END PRIVATE KEY-----\n",
+  "client_email": "firebase-adminsdk-wpsvx@titanscoutandroid.iam.gserviceaccount.com",
+  "client_id": "114864465329268712237",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/firebase-adminsdk-wpsvx%40titanscoutandroid.iam.gserviceaccount.com"
+}
\ No newline at end of file

From 605183f3fe986485db84fe3e030b9e0279e3cd22 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 28 Feb 2019 13:39:35 -0600
Subject: [PATCH 17/31] Update superscript.py

---
 data analysis/superscript.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 974c942d..3eaad286 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -146,9 +146,9 @@ def service():
                 break
             except:
                 if (i != 4):
-                    print("[WAR] failed, trying " + str(5 - i - 1) + " more times")
+                    print("[WARNING] failed, trying " + str(5 - i - 1) + " more times")
                 else:
-                    print("[ERR] failed to compute data, skipping")
+                    print("[ERROR] failed to compute data, skipping")
                     fucked = True
 
         end = time.time()

From 5889978f1d4cad42b8325bf10622edd082e88ebd Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Fri, 1 Mar 2019 12:18:38 -0600
Subject: [PATCH 18/31] dumb

---
 .../__pycache__/titanlearn.cpython-37.pyc     | Bin 4864 -> 5554 bytes
 data analysis/superscript.py                  |   2 +
 data analysis/titanlearn.py                   |  42 ++++++++++++++----
 3 files changed, 35 insertions(+), 9 deletions(-)

diff --git a/data analysis/__pycache__/titanlearn.cpython-37.pyc b/data analysis/__pycache__/titanlearn.cpython-37.pyc
index 534544d2aa385ef5f79ac87e816fa44a91ae641c..18fa10d52f2f52bc0d5d14fd4cd87b4cebee0726 100644
GIT binary patch
delta 2156
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zLPO`4I~+gY^G=m9cP2jW^Y-|>YsB{jy`660^wX{zpO{4NJ3@bb1PK3o>s3~-=w~oX
O)|mTKd`v#6o%##x>;3Qm

delta 1601
zcmZvcOK;p%6vywi$Da2)nVE!yOqw*wq)j@M0wsm2ZTd)yNCPcNv5KO}Tqkkn$(=h<
zGk6rF>4F7O6B$;p@NAISwtFNtDB>en6)QI7D<A>qc!ma5Sn|(*|IfYW_*`H6>6tI5
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z**Gf^ViKDO+NJHFbs?PF4{ecl*bLZGmoQqA_<bSocy<&fG5LVDBh}S@%mcA9#%#4q
zz`xx074RRszSs4q`o0SO_pa}A{fO|}0p{&eN*K9EAQIdOnZbQK+$E;Ud<RlHdPpBF
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z?4+5%LSjG+nz0ULfiwY?=+HFjpr6`Fi;O)aB5dDp##k^-Om9c-QZ&s_m$DEGAIMNf
zW~3xT=^vn={Ia0um4e}y1y2(Hf8Izxudko?r+tyKhfN=grU^dr8Z6F7<YV#x7RY`L
zdd<T_MD39-IqBJqn9y@aY6oDcN8L!&ejTqTvp@fJjhKG7h{UsyYYZCjcO~rI&i1mw
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z10ytLjRa>ijshFBzUQcgdL6f=Qs+ih@Ql?CuBk~!uIj@2J^0n^DVXS}u&e7$;Yfu#
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zpT_a_93qc+192YFTMJZgA}%1_LZtA!;|qw4H5?xwe;1^qG(@3T;%}p^BHlq<L|n3d
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zaUTwF|98ISWqum(rVzN=VBjmu`9VBD^HYQyM)hwvWj!4@ueuv;#QI}kGViX-`!K?Z
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E04nB*AOHXW

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 3eaad286..b9ca508b 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -134,6 +134,8 @@ def service():
 
     while True:
 
+        pulldata()
+
         start = time.time()
 
         print("[OK] starting calculations")
diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 9fab3a11..2b895610 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -35,7 +35,7 @@ import math
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 #linear_nn: creates a fully connected network given params
-def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="softmax"):
+def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
     if act_fn.lower()=="tanh":
         k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
         for i in range(num_hidden):
@@ -72,7 +72,7 @@ def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="softm
     return torch.nn.Sequential(k)
 
 #train_sgd_simple: trains network using SGD
-def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
     model=net.to(device)
     data=data.to(device)
     ground=ground.to(device)
@@ -81,7 +81,7 @@ def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnra
     losses=[]
     dev_losses=[]
     if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
+        loss_fn = torch.nn.MSELoss()
     elif loss.lower()=="cross entropy":
         loss_fn = torch.nn.CrossEntropyLoss()
     elif loss.lower()=="nll":
@@ -96,21 +96,34 @@ def train_sgd_simple(net, data, ground, dev=None, devg=None, iters=1000, learnra
         if i%testevery==0:
             with torch.no_grad():
                 output = model(data)
-                ap = metrics.average_precision_score(ground.numpy(), output.numpy())
+                if evalType == "ap":
+                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
+                if evalType == "regression":
+                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
                 losses.append(ap)
                 print(str(i)+": "+str(ap))
                 plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
                 if dev != None:
                     output = model(dev)
-                    ap = metrics.average_precision_score(devg.numpy(), output.numpy())
-                    dev_losses.append(ap)
-                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    print(evalType)
+                    if evalType == "ap":
+                        
+                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    elif evalType == "regression":
+                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ev)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
+
+                    
                 if graphsaveloc != None:
                     plt.savefig(graphsaveloc+".pdf")
         with torch.enable_grad():
             optimizer.zero_grad()
             output = model(data)
             loss = loss_fn(output, ground)
+            print(loss.item())
             loss.backward()
             optimizer.step()
     if modelsaveloc != None:
@@ -128,7 +141,7 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     losses=[]
     dev_losses=[]
     if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss(reduction='sum')
+        loss_fn = torch.nretyuoipufdyun.MSELoss(reduction='sum')
     elif loss.lower()=="cross entropy":
         loss_fn = torch.nn.CrossEntropyLoss()
     elif loss.lower()=="nll":
@@ -138,7 +151,7 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     else: 
         warnings.warn("Did not specify a valid loss function. Returning nothing.")
         return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
+    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
     itercount=0
     for i in range(epoch):
         print("EPOCH "+str(i)+" OF "+str(epoch-1))
@@ -176,3 +189,14 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
         torch.save(model, modelsaveloc)
     plt.show()
     return model
+
+def retyuoipufdyu():
+    
+    data = torch.tensor([[ 1.,  2.,  5.,  2.,  5.],
+        [27.,  8.,  4.,  6., 10.],
+        [12., 12., 12.,  5.,  6.],
+        [10., 12., 10., 20.,  2.],
+        [ 1.,  2.,  3.,  4.,  5.]])
+    ground = torch.tensor([15., 55., 47., 54., 15.])
+    model = linear_nn(5, 10, 1, 3, act_fn = "relu")
+    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-2)

From 145e48fc89c38d212f6bd899087a3a230869410f Mon Sep 17 00:00:00 2001
From: jlevine18 <jacoblevine18@gmail.com>
Date: Fri, 1 Mar 2019 12:25:41 -0600
Subject: [PATCH 19/31] Update titanlearn.py

---
 data analysis/titanlearn.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 2b895610..1340e49c 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -141,7 +141,7 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     losses=[]
     dev_losses=[]
     if loss.lower()=="mse":
-        loss_fn = torch.nretyuoipufdyun.MSELoss(reduction='sum')
+        loss_fn = torch.nn.MSELoss()
     elif loss.lower()=="cross entropy":
         loss_fn = torch.nn.CrossEntropyLoss()
     elif loss.lower()=="nll":

From 852521e09aafa1c6b516caae1748095594d2d330 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Fri, 1 Mar 2019 13:49:33 -0600
Subject: [PATCH 20/31] Update titanlearn.py

---
 data analysis/titanlearn.py | 21 +++++++++------------
 1 file changed, 9 insertions(+), 12 deletions(-)

diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 2b895610..08b5fafc 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -30,6 +30,7 @@ from sklearn import metrics
 import numpy as np
 import matplotlib.pyplot as plt
 import math
+from sklearn import datasets
 
 #enable CUDA if possible
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@@ -112,8 +113,8 @@ def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=100
                         dev_losses.append(ap)
                         plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
                     elif evalType == "regression":
-                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ev)
+                        ap = metrics.explained_variance_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
                         plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
 
                     
@@ -190,13 +191,9 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
     plt.show()
     return model
 
-def retyuoipufdyu():
-    
-    data = torch.tensor([[ 1.,  2.,  5.,  2.,  5.],
-        [27.,  8.,  4.,  6., 10.],
-        [12., 12., 12.,  5.,  6.],
-        [10., 12., 10., 20.,  2.],
-        [ 1.,  2.,  3.,  4.,  5.]])
-    ground = torch.tensor([15., 55., 47., 54., 15.])
-    model = linear_nn(5, 10, 1, 3, act_fn = "relu")
-    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-2)
+data = datasets.load_diabetes()
+print(data["data"], data["target"])
+ground = torch.tensor(data["target"]).to(torch.float)
+data = torch.tensor(data["data"]).to(torch.float)
+model = linear_nn(10, 100, 1, 20, act_fn = "tanh")
+model = train_sgd_simple(model,"regression", data, ground, learnrate=1e-4)

From d3b8329164e6d0ebf894b0fcbc4b50ee03afb8e7 Mon Sep 17 00:00:00 2001
From: jlevine18 <jacoblevine18@gmail.com>
Date: Sat, 2 Mar 2019 08:18:28 -0600
Subject: [PATCH 21/31] tl.py

---
 data analysis/titanlearn.py | 400 ++++++++++++++++++------------------
 1 file changed, 201 insertions(+), 199 deletions(-)

diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 0fff749d..0578a875 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -1,199 +1,201 @@
-#Titan Robotics Team 2022: ML Module
-#Written by Arthur Lu & Jacob Levine
-#Notes:
-#   this should be imported as a python module using 'import titanlearn'
-#   this should be included in the local directory or environment variable
-#   this module has not been optimized for multhreaded computing
-#   this module learns from its mistakes far faster than 2022's captains
-#setup:
-
-__version__ = "1.0.0.001"
-
-#changelog should be viewed using print(analysis.__changelog__)
-__changelog__ = """changelog:
-1.0.0.xxx:
-    -added generation of ANNS, basic SGD training"""
-__author__ = (
-    "Arthur Lu <arthurlu@ttic.edu>, "
-    "Jacob Levine <jlevine@ttic.edu>,"
-    )
-__all__ = [
-    'linear_nn',
-    'train_sgd_minibatch',
-    'train_sgd_simple'
-    ]
-#imports
-import torch
-import warnings
-from collections import OrderedDict
-from sklearn import metrics
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-from sklearn import datasets
-
-#enable CUDA if possible
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-#linear_nn: creates a fully connected network given params
-def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
-    if act_fn.lower()=="tanh":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
-        
-    elif act_fn.lower()=="sigmoid":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('sig0', torch.nn.Sigmoid())])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
-
-    elif act_fn.lower()=="relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('relu0', torch.nn.ReLU())])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
-
-    elif act_fn.lower()=="leaky relu":
-        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('lre0', torch.nn.LeakyReLU())])
-        for i in range(num_hidden):
-            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
-    else:
-        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
-        return None
-
-    if end.lower()=="softmax":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
-    elif end.lower()=="none":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
-    elif end.lower()=="sigmoid":
-        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
-    else:
-        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
-        return None
-    
-    return torch.nn.Sequential(k)
-
-#train_sgd_simple: trains network using SGD
-def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss()
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else: 
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
-    for i in range(iters):
-        if i%testevery==0:
-            with torch.no_grad():
-                output = model(data)
-                if evalType == "ap":
-                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
-                if evalType == "regression":
-                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
-                losses.append(ap)
-                print(str(i)+": "+str(ap))
-                plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
-                if dev != None:
-                    output = model(dev)
-                    print(evalType)
-                    if evalType == "ap":
-                        
-                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ap)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
-                    elif evalType == "regression":
-                        ap = metrics.explained_variance_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ap)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
-
-                    
-                if graphsaveloc != None:
-                    plt.savefig(graphsaveloc+".pdf")
-        with torch.enable_grad():
-            optimizer.zero_grad()
-            output = model(data)
-            loss = loss_fn(output, ground)
-            print(loss.item())
-            loss.backward()
-            optimizer.step()
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
-
-#train_sgd_minibatch: same as above, but with minibatches
-def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
-    model=net.to(device)
-    data=data.to(device)
-    ground=ground.to(device)
-    if dev != None:
-        dev=dev.to(device)
-    losses=[]
-    dev_losses=[]
-    if loss.lower()=="mse":
-        loss_fn = torch.nn.MSELoss()
-    elif loss.lower()=="cross entropy":
-        loss_fn = torch.nn.CrossEntropyLoss()
-    elif loss.lower()=="nll":
-        loss_fn = torch.nn.NLLLoss()
-    elif loss.lower()=="poisson nll":
-        loss_fn = torch.nn.PoissonNLLLoss()
-    else: 
-        warnings.warn("Did not specify a valid loss function. Returning nothing.")
-        return None
-    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
-    itercount=0
-    for i in range(epoch):
-        print("EPOCH "+str(i)+" OF "+str(epoch-1))
-        batches=math.ceil(data.size()[0].item()/batchsize)
-        for j in range(batches):
-            batchdata=[]
-            batchground=[]
-            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
-                batchdata.append(data[k])
-                batchground.append(ground[k])
-            batchdata=torch.stack(batchdata)
-            batchground=torch.stack(batchground)
-            if itercount%testevery==0:
-                with torch.no_grad():
-                    output = model(data)
-                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
-                    losses.append(ap)
-                    print(str(i)+": "+str(ap))
-                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
-                    if dev != None:
-                        output = model(dev)
-                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
-                        dev_losses.append(ap)
-                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
-                    if graphsaveloc != None:
-                        plt.savefig(graphsaveloc+".pdf")
-            with torch.enable_grad():
-                optimizer.zero_grad()
-                output = model(batchdata)
-                loss = loss_fn(output, ground)
-                loss.backward()
-                optimizer.step()
-            itercount +=1
-    if modelsaveloc != None:
-        torch.save(model, modelsaveloc)
-    plt.show()
-    return model
-
-data = datasets.load_diabetes()
-print(data["data"], data["target"])
-ground = torch.tensor(data["target"]).to(torch.float)
-data = torch.tensor(data["data"]).to(torch.float)
-model = linear_nn(10, 100, 1, 20, act_fn = "tanh")
-model = train_sgd_simple(model,"regression", data, ground, learnrate=1e-4)
+#Titan Robotics Team 2022: ML Module
+#Written by Arthur Lu & Jacob Levine
+#Notes:
+#   this should be imported as a python module using 'import titanlearn'
+#   this should be included in the local directory or environment variable
+#   this module has not been optimized for multhreaded computing
+#   this module learns from its mistakes far faster than 2022's captains
+#setup:
+
+__version__ = "1.0.0.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+1.0.0.xxx:
+    -added generation of ANNS, basic SGD training"""
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>, "
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+__all__ = [
+    'linear_nn',
+    'train_sgd_minibatch',
+    'train_sgd_simple'
+    ]
+#imports
+import torch
+import warnings
+from collections import OrderedDict
+from sklearn import metrics, datasets
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+#enable CUDA if possible
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+#linear_nn: creates a fully connected network given params
+def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
+    if act_fn.lower()=="tanh":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
+
+    elif act_fn.lower()=="sigmoid":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
+
+    elif act_fn.lower()=="relu":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
+
+    elif act_fn.lower()=="leaky relu":
+        k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
+        for i in range(num_hidden):
+            k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
+    else:
+        warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
+        return None
+
+    if end.lower()=="softmax":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
+    elif end.lower()=="none":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
+    elif end.lower()=="sigmoid":
+        k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
+    else:
+        warnings.warn("Did not specify a valid final activation function. Returning nothing.")
+        return None
+
+    return torch.nn.Sequential(k)
+
+#train_sgd_simple: trains network using SGD
+def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+    model=net.to(device)
+    data=data.to(device)
+    ground=ground.to(device)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
+    for i in range(iters):
+        if i%testevery==0:
+            with torch.no_grad():
+                output = model(data)
+                if evalType == "ap":
+                    ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
+                if evalType == "regression":
+                    ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
+                losses.append(ap)
+                print(str(i)+": "+str(ap))
+                plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
+                if dev != None:
+                    output = model(dev)
+                    print(evalType)
+                    if evalType == "ap":
+
+                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    elif evalType == "regression":
+                        ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ev)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
+
+
+                if graphsaveloc != None:
+                    plt.savefig(graphsaveloc+".pdf")
+        with torch.enable_grad():
+            optimizer.zero_grad()
+            output = model(data)
+            loss = loss_fn(output, ground)
+            print(loss.item())
+            loss.backward()
+            optimizer.step()
+    if modelsaveloc != None:
+        torch.save(model, modelsaveloc)
+    plt.show()
+    return model
+
+#train_sgd_minibatch: same as above, but with minibatches
+def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
+    model=net.to(device)
+    data=data.to(device)
+    ground=ground.to(device)
+    if dev != None:
+        dev=dev.to(device)
+    losses=[]
+    dev_losses=[]
+    if loss.lower()=="mse":
+        loss_fn = torch.nn.MSELoss()
+    elif loss.lower()=="cross entropy":
+        loss_fn = torch.nn.CrossEntropyLoss()
+    elif loss.lower()=="nll":
+        loss_fn = torch.nn.NLLLoss()
+    elif loss.lower()=="poisson nll":
+        loss_fn = torch.nn.PoissonNLLLoss()
+    else:
+        warnings.warn("Did not specify a valid loss function. Returning nothing.")
+        return None
+    optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
+    itercount=0
+    for i in range(epoch):
+        print("EPOCH "+str(i)+" OF "+str(epoch-1))
+        batches=math.ceil(data.size()[0].item()/batchsize)
+        for j in range(batches):
+            batchdata=[]
+            batchground=[]
+            for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
+                batchdata.append(data[k])
+                batchground.append(ground[k])
+            batchdata=torch.stack(batchdata)
+            batchground=torch.stack(batchground)
+            if itercount%testevery==0:
+                with torch.no_grad():
+                    output = model(data)
+                    ap = metrics.average_precision_score(ground.numpy(), output.numpy())
+                    losses.append(ap)
+                    print(str(i)+": "+str(ap))
+                    plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
+                    if dev != None:
+                        output = model(dev)
+                        ap = metrics.average_precision_score(devg.numpy(), output.numpy())
+                        dev_losses.append(ap)
+                        plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
+                    if graphsaveloc != None:
+                        plt.savefig(graphsaveloc+".pdf")
+            with torch.enable_grad():
+                optimizer.zero_grad()
+                output = model(batchdata)
+                loss = loss_fn(output, ground)
+                loss.backward()
+                optimizer.step()
+            itercount +=1
+    if modelsaveloc != None:
+        torch.save(model, modelsaveloc)
+    plt.show()
+    return model
+
+def retyuoipufdyu():
+
+    data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
+    ground = datasets.fetch_california_housing()['target']
+    ground=torch.tensor(ground).to(torch.float)
+    model = linear_nn(8, 100, 1, 20, act_fn = "relu")
+    print(model)
+    return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
+retyuoipufdyu()

From 8fecc5b5828497d8e4684bdf34a12859b872b46b Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sat, 2 Mar 2019 16:34:12 -0600
Subject: [PATCH 22/31] Create README.md

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 00000000..e7e10dbb
--- /dev/null
+++ b/README.md
@@ -0,0 +1,3 @@
+# tr2022-strategy
+Titan Robotics 2022 Strategy Team Repository
+TIT SCOUT

From a917ed6b3776db079ca9165cd5edf1ab60ec5d8a Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sat, 2 Mar 2019 16:34:48 -0600
Subject: [PATCH 23/31] ryiop

---
 .gitignore                                    |   1 +
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25209 -> 25209 bytes
 2 files changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 2c6ba44a..03801333 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@ benchmark_data.csv
 
 
 data analysis/keys/keytemp.json
+data analysis/__pycache__/analysis.cpython-37.pyc
diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 72c9c2ce5a0b8ba9a148daeb11308fc5e0ca6553..1af64efa316b59ee1feb15521a67f8c57d0fe71b 100644
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From 027937876d718c19bbca592d0ee9e03faa6b3c8d Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sat, 2 Mar 2019 19:47:31 -0600
Subject: [PATCH 24/31] titanscouting app v 1.0.0.002

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index 03801333..f52c5638 100644
--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,5 @@ benchmark_data.csv
 
 data analysis/keys/keytemp.json
 data analysis/__pycache__/analysis.cpython-37.pyc
+apps/android/source/app/src/main/res/drawable-v24/uuh.png
+apps/android/source/app/src/main/java/com/example/titanscouting/tits.java

From 93587bf4c7710f0904eb6ba0e2c7fedef4fc0fb4 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sat, 2 Mar 2019 19:58:00 -0600
Subject: [PATCH 25/31] titanscouting app v 1.0.0.003

simple bug fix

From 515b92b356002ea08841044a44b76622d51a52b9 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sat, 2 Mar 2019 20:18:51 -0600
Subject: [PATCH 26/31] Update analysis.py

---
 data analysis/analysis.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index bdc32b3f..ece02176 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -16,34 +16,34 @@ __changelog__ = """changelog:
 1.0.8.004:
     - removed a few unused dependencies
 1.0.8.003:
-	- added p_value function
+    - added p_value function
 1.0.8.002:
-	- updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
+    - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001
 1.0.8.001:
-	- refactors
-	- bugfixes
+    - refactors
+    - bugfixes
 1.0.8.000:
-	- depreciated histo_analysis_old
-	- depreciated debug
-	- altered basic_analysis to take array data instead of filepath
-	- refactor
-	- optimization
+    - depreciated histo_analysis_old
+    - depreciated debug
+    - altered basic_analysis to take array data instead of filepath
+    - refactor
+    - optimization
 1.0.7.002:
-	- bug fixes
+    - bug fixes
 1.0.7.001:
-	- bug fixes
+    - bug fixes
 1.0.7.000:
-	- added tanh_regression (logistical regression)
-	- bug fixes
+    - added tanh_regression (logistical regression)
+    - bug fixes
 1.0.6.005:
-	- added z_normalize function to normalize dataset
-	- bug fixes
+    - added z_normalize function to normalize dataset
+    - bug fixes
 1.0.6.004:
-	- bug fixes
+    - bug fixes
 1.0.6.003:
-	- bug fixes
+    - bug fixes
 1.0.6.002:
-	- bug fixes
+    - bug fixes
 1.0.6.001:
     - corrected __all__ to contain all of the functions
 1.0.6.000:

From 035712c1eaa230993f4efec67a61c515cb7368fe Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Sun, 3 Mar 2019 22:50:29 -0600
Subject: [PATCH 27/31] 1

---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25209 -> 25263 bytes
 .../__pycache__/titanlearn.cpython-37.pyc     | Bin 5554 -> 5410 bytes
 data analysis/superscript.py                  |   3 +--
 3 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 1af64efa316b59ee1feb15521a67f8c57d0fe71b..1a78a7f09ae17e8f194b7afba15a48540d331b49 100644
GIT binary patch
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diff --git a/data analysis/__pycache__/titanlearn.cpython-37.pyc b/data analysis/__pycache__/titanlearn.cpython-37.pyc
index 18fa10d52f2f52bc0d5d14fd4cd87b4cebee0726..7be8234faea11aaf82a9ac31044af43a6124769a 100644
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diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index b9ca508b..90b23298 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -41,7 +41,6 @@ import firebase_admin
 from firebase_admin import credentials
 from firebase_admin import firestore
 import analysis
-import titanlearn
 import visualization
 import os
 import sys
@@ -168,7 +167,7 @@ def service():
 
 warnings.simplefilter("ignore")
 # Use a service account
-cred = credentials.Certificate('keys/firebasekey.json')
+cred = credentials.Certificate('keys/keytemp.json')
 firebase_admin.initialize_app(cred)
 
 db = firestore.client()

From 98a2519f42907e29cddead6d2e3b133f0f8dd2eb Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Mon, 4 Mar 2019 16:38:40 -0600
Subject: [PATCH 28/31] lotta bug fixes

---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25263 -> 25263 bytes
 .../__pycache__/titanlearn.cpython-37.pyc     | Bin 5410 -> 5402 bytes
 data analysis/analysis.py                     |   2 +-
 data analysis/data/scores.csv                 |   1 +
 data analysis/superscript.py                  |  82 ++++++++++++++++--
 data analysis/titanlearn.py                   |   2 +-
 6 files changed, 77 insertions(+), 10 deletions(-)
 create mode 100644 data analysis/data/scores.csv

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 1a78a7f09ae17e8f194b7afba15a48540d331b49..299a28cf7ccd6caaba8ba8ed590dbf634c5151c3 100644
GIT binary patch
delta 35
ncmZ2~lyUu0Ms6owUM>b85a_7g$o;{M9mHl}c(9qteXTSAsyPV`

delta 35
ncmZ2~lyUu0Ms6owUM>b8Xtk`~$o;{Meb!76xVf3heXTSA&rb}(

diff --git a/data analysis/__pycache__/titanlearn.cpython-37.pyc b/data analysis/__pycache__/titanlearn.cpython-37.pyc
index 7be8234faea11aaf82a9ac31044af43a6124769a..5877577c18768ca5750ac68d5f70139d561abe6b 100644
GIT binary patch
delta 36
qcmZ3aHA{=viI<m)0SKxbYhxHE@=7u;n5aH~;~6~>My1KWMN|Qz4GHl8

delta 46
zcmbQGwMdKCiI<m)0SGkaR>d$(<dtMxHc@>(TdH_710%!6V|pTtDwDs8s4{VI0092w
B3kv`M

diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index ece02176..c3cc4fd0 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -758,7 +758,7 @@ def optimize_regression(x, y, _range, resolution):#_range in poly regression is
     x_test = []
     y_test = []
 
-    for i in range (0, math.floor(len(x) * 0.4), 1):
+    for i in range (0, math.floor(len(x) * 0.5), 1):
         index = random.randint(0, len(x) - 1)
 
         x_test.append(x[index])
diff --git a/data analysis/data/scores.csv b/data analysis/data/scores.csv
new file mode 100644
index 00000000..5562ec8a
--- /dev/null
+++ b/data analysis/data/scores.csv	
@@ -0,0 +1 @@
+2022, 21, 23, 39, 50, 89, 97, 191, 213, 233, 236, 272, 289, 308, 310, 314, 317, 329, 355, 428, 436
2011, 1, 25, 34, 106, 112, 132, 146, 167, 184, 223, 256, 267, 304, 337, 339, 370, 411, 460, 485, 494
1101, 7, 11, 55, 81, 92, 144, 151, 157, 165, 174, 177, 220, 241, 312, 320, 341, 343, 347, 419, 473
821374, 6, 9, 11, 28, 31, 194, 200, 201, 246, 298, 340, 351, 355, 364, 388, 395, 414, 422, 429, 442
5, 33, 73, 102, 103, 137, 183, 200, 212, 218, 242, 249, 264, 294, 302, 310, 378, 400, 408, 457, 473
\ No newline at end of file
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 90b23298..4371e8b7 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -41,6 +41,7 @@ import firebase_admin
 from firebase_admin import credentials
 from firebase_admin import firestore
 import analysis
+import titanlearn
 import visualization
 import os
 import sys
@@ -59,7 +60,7 @@ 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')]
+             if not (os.path.basename(fn).startswith('teams'))] #scores will be handled sperately
 
     for i in files:
             data.append(analysis.load_csv(i))
@@ -67,6 +68,7 @@ def titanservice():
     stats = []
     measure_stats = []
     teams = analysis.load_csv("data/teams.csv")
+    scores = analysis.load_csv("data/scores.csv")
 
     end = time.time()
 
@@ -111,19 +113,82 @@ def titanservice():
                     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)) + ofbest_curve + r2best_curve)
 
             stats.append(list(measure_stats))
-            
-    json_out = {}
+            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)
                     
-    for i in range(len(stats)):
-            json_out[files[i]]=str(stats[i])
+                    ofbest_curve.append(beqs)
+                    ofbest_curve.append(brmss)
+                    ofbest_curve.append(br2s)
+                    ofbest_curve.append(boverfit)
+                    ofbest_curve.pop(0)
 
-    #print(json_out)
+                    #print(ofbest_curve)
 
-    db.collection(u'stats').document(u'stats-noNN').set(json_out)
+                    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 = {}
+
+    #print(stats)
+                    
+    for i in range(len(teams)):
+            json_out[str(teams[i][0])] = (stats[0][i])
+
+    for i in range(len(teams)):
+            score_out[str(teams[i][0])] = (nishant[i])
+
+    print(json_out)
+
+    #print(json_out.get('5'))
+
+    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)):
+            general_general_stats.document(files[j]).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])})
+
+    #general_general_stats.collection().document('stats').set()
+    #db.collection(u'stats').document(u'stats-noNN').set(score_out)
 
 def pulldata():
     #TODO
@@ -173,3 +238,4 @@ firebase_admin.initialize_app(cred)
 db = firestore.client()
 
 service() #finally we write something that isn't a function definition
+#titanservice()
diff --git a/data analysis/titanlearn.py b/data analysis/titanlearn.py
index 0578a875..a32bceaa 100644
--- a/data analysis/titanlearn.py	
+++ b/data analysis/titanlearn.py	
@@ -198,4 +198,4 @@ def retyuoipufdyu():
     model = linear_nn(8, 100, 1, 20, act_fn = "relu")
     print(model)
     return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
-retyuoipufdyu()
+#retyuoipufdyu()

From 79bb1f27f9007b51865629033ac7c2d48555ee7c Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Tue, 5 Mar 2019 13:36:47 -0600
Subject: [PATCH 29/31] Update superscript.py

---
 data analysis/superscript.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index 4371e8b7..d14582a9 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -119,6 +119,7 @@ def titanservice():
 
             stats.append(list(measure_stats))
             nishant = []
+            
             for i in range(len(scores)):
 
                     ofbest_curve = [None]

From f836b59d2ed35cbdc5bb068a35f0a75f3c790f68 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Wed, 6 Mar 2019 20:05:50 -0600
Subject: [PATCH 30/31] 12

---
 .../__pycache__/analysis.cpython-37.pyc       | Bin 25263 -> 25608 bytes
 data analysis/analysis.py                     |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/data analysis/__pycache__/analysis.cpython-37.pyc b/data analysis/__pycache__/analysis.cpython-37.pyc
index 299a28cf7ccd6caaba8ba8ed590dbf634c5151c3..5d9f4727015df54b08bf11e72f6cc3c6d6673271 100644
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diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index c3cc4fd0..e9fe227c 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -152,7 +152,7 @@ from sklearn import *
 #import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002
 import time
 import torch
-
+    
 class error(ValueError):
     pass
 

From 8b1837d19478ca0fff8327f570bc59da18e3eef7 Mon Sep 17 00:00:00 2001
From: ltcptgeneral <35508619+ltcptgeneral@users.noreply.github.com>
Date: Thu, 7 Mar 2019 09:14:20 -0600
Subject: [PATCH 31/31] hotfix 2000

---
 data analysis/analysis.py    | 2 +-
 data analysis/superscript.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/data analysis/analysis.py b/data analysis/analysis.py
index e9fe227c..479f79de 100644
--- a/data analysis/analysis.py	
+++ b/data analysis/analysis.py	
@@ -1104,4 +1104,4 @@ def stdev(data, xbar=None):
     try:
         return var.sqrt()
     except AttributeError:
-        return math.sqrt(var)
+        return math.sqrt(var)
\ No newline at end of file
diff --git a/data analysis/superscript.py b/data analysis/superscript.py
index d14582a9..68a99a36 100644
--- a/data analysis/superscript.py	
+++ b/data analysis/superscript.py	
@@ -78,9 +78,9 @@ def titanservice():
     #unhelpful comment
     for measure in data: #unpacks 3d array into 2ds
 
-            measure_stats = []
+        measure_stats = []
 
-            for i in range(len(measure)): #unpacks into specific teams
+        for i in range(len(measure)): #unpacks into specific teams
 
                     ofbest_curve = [None]
                     r2best_curve = [None]