diff --git a/Final/python files/pretty_graph.py b/Final/python files/pretty_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1e3a0805c35d5e612a2ea88c80b302bf9fff80c
--- /dev/null
+++ b/Final/python files/pretty_graph.py
@@ -0,0 +1,297 @@
+import matplotlib.pyplot as plt
+import matplotlib
+import math
+from matplotlib.gridspec import GridSpec
+import csv
+INPUT = "avgGen_mutHigh_food_2_64.csv"
+INPUT2 = "avgGen_mutHigh_food_32_4.csv"
+INPUT3 = "avgGen_mutLow_food_2_64.csv"
+INPUT4 = "avgGen_mutLow_food_32_4.csv"
+
+INPUTP = "avgGen_mutHigh_pred_45_140.csv"
+INPUTP2 = "avgGen_mutHigh_pred_135_70.csv"
+INPUTP3 = "avgGen_mutLow_pred_45_140.csv"
+INPUTP4 = "avgGen_mutLow_pred_135_70.csv"
+
+INPUTL = "avgDeltaPref.csv"
+INPUTL1 = "conf_1.csv"
+INPUTL2 = "conf_5.csv"
+INPUTL3 = "conf_10.csv"
+INPUTL4 = "conf_15.csv"
+INPUTL5 = "conf_-10_3_1.csv"
+INPUTL6 = "conf_-10_4_3.csv"
+INPUTL7 = "conf_-10_5_5.csv"
+INPUTL8 = "conf_11_3_1.csv"
+INPUTL9 = "conf_11_4_3.csv"
+INPUTL10 = "conf_11_5_5.csv"
+INPUTL11 = "conf_-11_3_1.csv"
+INPUTL12 = "conf_-11_4_3.csv"
+INPUTL13 = "conf_-11_5_5.csv"
+
+
+def parseanddrawfood():
+ global INPUT
+ matplotlib.rcParams.update({'font.size': 5})
+ s = "high mut small clust"
+ i = 0
+ maxis = 8
+ plt.figure(1)
+ height = math.ceil(maxis/2)
+ width = 2
+ gs = GridSpec(height, width)
+ axeslist = []
+ for j in range (maxis):
+ axeslist.append(plt.subplot(gs[j]))
+ for a1,a2 in zip(axeslist[0::2],axeslist[1::2]):
+ speed = []
+ foodpref = []
+ noise = []
+ antipred = []
+ timeneeded = []
+ zoa = []
+ zoo = []
+ blind = []
+ turn = []
+ with open(INPUT,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ speed.append(float(row["Speed"]))
+ foodpref.append(float(row["food pref"]))
+ noise.append(float(row["noise"]))
+ antipred.append(float(row["antipred"]))
+ zoa.append(float(row["zoa"]))
+ zoo.append(float(row["zoo"]))
+ blind.append(float(row["blind"]))
+ turn.append(float(row["turn"]))
+ timeneeded.append(float(row["timeNeeded"]))
+ a1.set_ylabel(s)
+ a2.set_ylabel(s)
+ a1.plot(speed,label = "Speed")
+ a1.plot(foodpref,label = "Foodpref")
+ a1.plot(noise,label = "Noise")
+ a1.plot(antipred,label = "antipred")
+ a2.plot(timeneeded,label = "time")
+ a2.plot(zoa,label = "zoa")
+ a2.plot(zoo,label = "zoo")
+ a2.plot(blind,label = "blind")
+ a2.plot(turn,label= "turn")
+ a1.legend(loc = "upper left")
+ a2.legend(loc = "upper left")
+ if i == 0:
+ INPUT = INPUT2
+ s = "high mut big clust"
+ s = "high mut big clust"
+ elif i == 1:
+ INPUT = INPUT3
+ s = "low mut small clust"
+ s = "low mut small clust"
+ elif i == 2:
+ INPUT = INPUT4
+ s= "low mut big clust"
+ s = "low mut big clust"
+ elif i == 3:
+ True
+ elif i ==4:
+ True
+ i += 1
+ plt.show()
+
+
+def parseanddrawpred():
+ global INPUTP
+ matplotlib.rcParams.update({'font.size': 5})
+ s = "high mut good Pred"
+ i = 0
+ maxis = 8
+ plt.figure(2)
+ height = math.ceil(maxis/2)
+ width = 2
+ gs = GridSpec(height, width)
+ axeslist = []
+ for j in range (maxis):
+ axeslist.append(plt.subplot(gs[j]))
+ for a1,a2 in zip(axeslist[0::2],axeslist[1::2]):
+ speed = []
+ foodpref = []
+ noise = []
+ antipred = []
+ timeneeded = []
+ zoa = []
+ zoo = []
+ blind = []
+ turn = []
+ with open(INPUTP,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ speed.append(float(row["Speed"]))
+ foodpref.append(float(row["food pref"]))
+ noise.append(float(row["noise"]))
+ antipred.append(float(row["antipred"]))
+ zoa.append(float(row["zoa"]))
+ zoo.append(float(row["zoo"]))
+ blind.append(float(row["blind"]))
+ turn.append(float(row["turn"]))
+ timeneeded.append(float(row["timeNeeded"]))
+ a1.set_ylabel(s)
+ a2.set_ylabel(s)
+ a1.plot(speed,label = "Speed")
+ a1.plot(foodpref,label = "Foodpref")
+ a1.plot(noise,label = "Noise")
+ a1.plot(antipred,label = "antipred")
+ #a2.plot(timeneeded,label = "time")
+ a2.plot(zoa,label = "zoa")
+ a2.plot(zoo,label = "zoo")
+ a2.plot(blind,label = "blind")
+ a2.plot(turn,label= "turn")
+ a1.legend(loc = "upper left")
+ a2.legend(loc = "upper left")
+ if i == 0:
+ INPUTP= INPUTP2
+ s = "high mut bad Pred"
+ elif i == 1:
+ INPUTP = INPUTP3
+ s = "low mut good Pred"
+ elif i == 2:
+ INPUTP = INPUTP4
+ s = "low mut bad Pred"
+ elif i == 3:
+ True
+ elif i ==4:
+ True
+ i += 1
+
+ plt.show()
+
+
+def parseanddrawleader():
+ global INPUTL
+ matplotlib.rcParams.update({'font.size': 5})
+ s = "high mut good Pred"
+ maxis = 8
+ plt.figure(2)
+ height = math.ceil(maxis/2)
+ width = 2
+ gs = GridSpec(height, width)
+ axeslist = []
+ i= 1
+ INPUTL = INPUTL1
+ j= 0
+ for j in range (maxis):
+ axeslist.append(plt.subplot(gs[j]))
+ for a in axeslist:
+ avgdeltaangle = []
+ confusion1 = []
+ confusion2 = []
+ confusion3 = []
+ confusion4 = []
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ if i == 0:
+ avgdeltaangle.append(float(row[0]))
+ if i >= 1 and i<= 4:
+ confusion1.append(float(row['Confusion']))
+ if i == 1:
+ INPUTL = INPUTL2
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion2.append(float(row['Confusion']))
+ INPUTL = INPUTL3
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion3.append(float(row['Confusion']))
+ INPUTL = INPUTL4
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion4.append(float(row['Confusion']))
+ if i == 2:
+ INPUTL = INPUTL6
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion2.append(float(row['Confusion']))
+ INPUTL = INPUTL7
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion3.append(float(row['Confusion']))
+ if i == 3:
+ INPUTL = INPUTL9
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion2.append(float(row['Confusion']))
+ INPUTL = INPUTL10
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion3.append(float(row['Confusion']))
+ if i == 4:
+ INPUTL = INPUTL12
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion2.append(float(row['Confusion']))
+ INPUTL = INPUTL13
+ with open(INPUTL,'r') as csvfile:
+ dreader = csv.DictReader(csvfile)
+ for row in dreader:
+ confusion3.append(float(row['Confusion']))
+
+ if i == 0:
+ a.plot(avgdeltaangle)
+ INPUTL = INPUTL1
+
+ elif i == 1:
+ a.plot(confusion1,label = "confusion1")
+ a.plot(confusion2,label = "confusion5")
+ a.plot(confusion3,label = "confusion10")
+ a.plot(confusion4,label = "confusion15")
+ confusion1= []
+ confusion2=[]
+ confusion3=[]
+ confusion4=[]
+ a.legend(loc = "upper left")
+ INPUTL = INPUTL5
+ elif i == 2:
+ a.plot(confusion1)
+ a.plot(confusion2)
+ a.plot(confusion3)
+ a.plot(confusion4)
+ confusion1= []
+ confusion2=[]
+ confusion3=[]
+ confusion4=[]
+ a.legend(loc = "upper left")
+ INPUTL = INPUTL8
+ elif i == 3:
+ a.plot(confusion1)
+ a.plot(confusion2)
+ a.plot(confusion3)
+ a.plot(confusion4)
+ confusion1= []
+ confusion2=[]
+ confusion3=[]
+ confusion4=[]
+ a.legend(loc = "upper left")
+ INPUTL = INPUTL11
+ elif i == 4:
+ a.plot(confusion1)
+ a.plot(confusion2)
+ a.plot(confusion3)
+ a.plot(confusion4)
+ confusion1= []
+ confusion2=[]
+ confusion3=[]
+ confusion4=[]
+ a.legend(loc = "upper left")
+ a.set_ylabel(s)
+ i += 1
+ plt.show()
+
+parseanddrawleader()
+parseanddrawfood()
+parseanddrawpred()