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Final/python files/pretty_graph.py

+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()