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CaseDataBase.py
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CaseDataBase.py
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#!/usr/bin/env python
# coding: utf-8
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
#Convention: 1-Good, 0-Bad
#Color coding: impact the action has on the environment/community
#Case 1: Tulip planting (0.8, 0.8, 0.5) / yellow for the environment
#Case 2: Oktoberfest trash (0.7, 0.8, 0.1) / red for the community
#Case 3: Car shaming (0.5, 0.3, 0.5) / yellow for community
#Case 4: Tire slashing - honest users (0.8, 0.8, 0.2) / red for the community - car fascism
#Case 4.5: Tire slashing - cheaters (0.3, 0.3, 0.2) / potentially bad for the community but cheaters make it yellow
#Case 5: Cycling scam (0.7, 0.4, 0.9) / potentially green for the environment but cheaters make it yellow
#Case 5.5: Cycling to work (0.9, 0.9, 1) / green for the environment
#Case 6: Scool Isar trash cleaning (1, 1, 1) / green for the environment
#Case 7: No flying aggresion (1, 0.4, 1) / potentially green for the environment, PAT robustness makes it yellow
#Case 7.5: No flying token (0.9, 0.8, 1) / green for the environment - flaying fascism
#Case 8: Killer Birds (1, 0.8, 1) / potentially green but because of the uncaped PAT, red
#Case 9: Bird feeder token (0.9, 1, 1) / green for the environment
#Case 10: Feed the homeless - signature (0.9, 0.9, 0.9) / potentially green for the community but the PAT proof is too strict
#Case 11: Feed the homeless (0.9, 0.4, 0.9) / green for the community
#Case 12: Feed the homeless in Grunwald (0.2, 0.7, 1) /pottentially green for the community but there are no homeless people in Grunwald
#Case 13: Beggar cartel (0.1, 0.3, 0.9) /red for the community
#Case 14: Tree token - false (-0.1, -0.1, 0.1)
#Red Stories: C2, C4, C8, C13 #social conflict/mistrust in the Token system/ecosystem colapse
#Yellow Stories: C1, C3, C4.5, C5, C7, C10, C12
#Green Stories: C5.5, C6, C7.5, C9, C11
#Black Storie: C14 #impact unknown
lines = True
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# C2 C4 C8 C13
xr =[0.7, 0.8, 1, 0.1]
yr =[0.8, 0.8, 0.8, 0.3]
zr =[0.1, 0.2, 1, 0.9]
ax.scatter(xr, yr, zr, c='r', marker='o', label='bad impact')
ax.text(xr[0], yr[0], zr[0], '%s' %('C2'), size=7, zorder=1)
ax.text(xr[1], yr[1], zr[1], '%s' %('C4'), size=7, zorder=1)
ax.text(xr[2], yr[2], zr[2], '%s' %('C8'), size=7, zorder=1, color='purple')
ax.text(xr[3], yr[3], zr[3], '%s' %('C13'), size=7, zorder=1)
# C1 C3 C4.5 C5 C7 C10 C12
xy =[0.8, 0.5, 0.3, 0.7, 1, 0.9, 0.2]
yy =[0.8, 0.3, 0.3, 0.4, 0.4, 0.9, 0.7]
zy =[0.5, 0.5, 0.2, 0.9, 1, 0.9, 1]
ax.scatter(xy, yy, zy, c='y', marker='o', label='neutral impact')
ax.text(xy[0], yy[0], zy[0], '%s' %('C1'), size=7, zorder=1)
ax.text(xy[1], yy[1], zy[1], '%s' %('C3'), size=7, zorder=1)
ax.text(xy[2], yy[2], zy[2], '%s' %('C4.5'), size=7, zorder=1)
ax.text(xy[3], yy[3], zy[3], '%s' %('C5'), size=7, zorder=1)
ax.text(xy[4], yy[4], zy[4], '%s' %('C7'), size=7, zorder=1)
ax.text(xy[5], yy[5], zy[5], '%s' %('C10'), size=7, zorder=1, color='purple')
ax.text(xy[6], yy[6], zy[6], '%s' %('C12'), size=7, zorder=1, color='purple')
# C5.5 C6 C7.5 C9 C11
xg =[0.9, 1, 0.9, 0.9, 0.9]
yg =[0.9, 1, 0.8, 1, 0.4]
zg =[ 1, 1, 1, 1, 0.9]
ax.scatter(xg, yg, zg, c='g', marker='o', label='good impact')
ax.text(xg[0], yg[0], zg[0], '%s' %('C5.5'), size=7, zorder=1)
ax.text(xg[1], yg[1], zg[1], '%s' %('C6'), size=7, zorder=1)
ax.text(xg[2], yg[2], zg[2], '%s' %('C7.5'), size=7, zorder=1)
ax.text(xg[3], yg[3], zg[3], '%s' %('C9'), size=7, zorder=1)
ax.text(xg[4], yg[4], zg[4], '%s' %('C11'), size=7, zorder=1, color='purple')
# C14
xb =[-0.1, -0.1, -0.1, -0.1]
yb =[-0.1, -0.1, -0.1, -0.1]
zb =[0, 0.1, 0.2, 0.3]
ax.scatter(xb, yb, zb, c='black', marker='o', label='unknown impact')
ax.text(xb[3], yb[3], zb[3], '%s' %('C14-Fake tokens'), size=7, zorder=1)
#-------------------------lines------------------------------
if lines:
#from C4 to C4.5
l1 = [0.8, 0.3]
l2 = [0.8, 0.3]
l3 = [0.2, 0.2]
ax.plot(l1, l2, l3, c='b')
#from C5 to C5.5
l1 = [0.7, 0.9]
l2 = [0.4, 0.9]
l3 = [0.9, 1]
ax.plot(l1, l2, l3, c='b')
#from C7 to C7.5
l1 = [1, 0.9]
l2 = [0.4, 0.8]
l3 = [1, 1]
ax.plot(l1, l2, l3, c='b')
#from C8 to C9
l1 = [1, 0.9]
l2 = [0.8, 1]
l3 = [1, 1]
ax.plot(l1, l2, l3, c='purple')
ax.set_xlim(-0.1, 1, 10)
ax.set_ylim(1, -0.1, 10)
ax.set_zlim(-0.1, 1, 10)
ax.legend(loc='upper left', frameon = False)
plt.title(" Story Map")
ax.set_xlabel('User alignment with Fin4')
ax.set_ylabel('Token design robustness')
ax.set_zlabel('Token Creator intent')
plt.show()