Index: doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py
===================================================================
--- doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py	(revision bb9b8f0065bcafadbafc5494b6d3578b24719733)
+++ doc/theses/colby_parsons_MMAth/benchmarks/mutex_stmt/plotData.py	(revision bb9b8f0065bcafadbafc5494b6d3578b24719733)
@@ -0,0 +1,112 @@
+import os
+import sys
+import time
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticks
+import math
+from scipy import stats as st
+import numpy as np
+from enum import Enum
+from statistics import median
+
+import matplotlib
+matplotlib.use("pgf")
+matplotlib.rcParams.update({
+    "pgf.texsystem": "pdflatex",
+    'font.family': 'serif',
+    'text.usetex': True,
+    'pgf.rcfonts': False,
+})
+
+readfile = open(sys.argv[1], "r")
+
+machineName = ""
+
+if len(sys.argv) > 2:
+    machineName = sys.argv[2]
+
+# first line has num times per experiment
+line = readfile.readline()
+numTimes = int(line)
+
+# second line has processor args
+line = readfile.readline()
+procs = []
+for val in line.split():
+    procs.append(int(val))
+
+# 3rd line has num locks args
+line = readfile.readline()
+locks = []
+for val in line.split():
+    locks.append(int(val))
+
+# 4th line has number of variants
+line = readfile.readline()
+names = line.split()
+numVariants = len(names)
+
+lines = (line.rstrip() for line in readfile) # All lines including the blank ones
+lines = (line for line in lines if line) # Non-blank lines
+
+nameSet = False
+currLocks = -1 # default val
+count = 0
+procCount = 0
+currVariant = 0
+name = "Aggregate Lock"
+var_name = ""
+sendData = [0.0 for j in range(numVariants)]
+data = [[0.0 for i in range(len(procs))] for j in range(numVariants)]
+bars = [[[0.0 for i in range(len(procs))],[0.0 for k in range(len(procs))]] for j in range(numVariants)]
+tempData = [0.0 for i in range(numTimes)]
+for idx, line in enumerate(lines):
+    # print(line)
+    
+    if currLocks == -1:
+        lineArr = line.split()
+        currLocks = lineArr[-1]
+        continue
+
+    if line[0:5] == "cores":
+        continue
+
+    if not nameSet:
+        nameSet = True
+        continue
+    
+    lineArr = line.split()
+    tempData[count] = float(lineArr[-1])
+    count += 1
+    if count == numTimes:
+        currMedian = median( tempData )
+        data[currVariant][procCount] = currMedian
+        lower, upper = st.t.interval(0.95, numTimes - 1, loc=np.mean(tempData), scale=st.sem(tempData))
+        bars[currVariant][0][procCount] = currMedian - lower
+        bars[currVariant][1][procCount] = upper - currMedian
+        count = 0
+        procCount += 1
+
+        if procCount == len(procs):
+            procCount = 0
+            nameSet = False
+            currVariant += 1
+
+            if currVariant == numVariants:
+                fig, ax = plt.subplots()
+                plt.title(name + " Benchmark: " + str(currLocks) + " Locks")
+                plt.ylabel("Throughput (entries)")
+                plt.xlabel("Cores")
+                for idx, arr in enumerate(data):
+                    plt.errorbar( procs, arr, [bars[idx][0], bars[idx][1]], capsize=2, marker='o' )
+                plt.yscale("log")
+                ax.get_yaxis().set_major_formatter(ticks.ScalarFormatter())
+                plt.xticks(procs)
+                ax.legend(names)
+                # fig.savefig("plots/" + machineName + name + "_" + str(currLocks) + ".png")
+                plt.savefig("plots/" + machineName + name + "_" + str(currLocks) + ".pgf")
+                fig.clf()
+
+                # reset
+                currLocks = -1
+                currVariant = 0