def plot_pareto_scenarios(generations, headers, relative, savelocation):
xs =[]
ys =[]
zs =[]
fig = plt.figure()
counter = 0
for header in headers:
pathX = sFn.pathX(header)
pop, eps, testedPop = sFn.readCheckPoint(pathX, generations[counter], 0)
Area_buildings = pd.read_csv(pathX.pathRaw+'//'+'Total.csv',usecols=['Af']).values.sum()
[x, y, z] = map(np.array, zip( *[ind.fitness.values for ind in pop] ) )
ax = fig.add_subplot(111)
if relative == True:
z[:] = [a / Area_buildings for a in z]
x[:] = [b / Area_buildings for b in x]
y[:] = [c / Area_buildings for c in y]
xs.extend(x)
ys.extend(y)
zs.extend(z)
counter +=1
if relative == True:
TAC = 'TAC [EU/m2.yr]'
CO2 = 'CO2 [kg-CO2/m2.yr]'
PEN = 'PEN [MJ/m2.yr]'
ax.set_ylim([10,50])
finallocation = savelocation+"pareto_m2.png"
else:
var = 1000000
zs[:] = [x / var for x in zs]
xs[:] = [x / var for x in xs]
ys[:] = [x / var for x in ys]
TAC = 'TAC [Mio EU/yr]'
CO2 = 'CO2 [kton-CO2/yr]'
PEN = 'PEN [TJ/yr]'
ax.set_xlim([2.5,7.0])
finallocation = savelocation+"pareto.png"
cm = plt.get_cmap('jet')
cNorm = matplotlib.colors.Normalize(vmin=min(zs), vmax=max(zs))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
ax.scatter(xs, ys, c=scalarMap.to_rgba(zs), s=50, alpha =0.8)
ax.set_xlabel(TAC)
ax.set_ylabel(CO2)
scalarMap.set_array(zs)
fig.colorbar(scalarMap, label=PEN)
plt.grid(True)
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams.update({'font.size':12})
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig(finallocation)
plt.clf()
def plot_epsilon_scenarios(scenarios, generations, headers):
colors = ['b','r','y','c']
fig = plt.figure()
counter = 0
for scenario in scenarios:
epsNorm = norm.normalize_epsIndicator(sFn.pathX(headers[counter]), generations[counter])
xList = [ 2 + x for x in range(len(epsNorm)) ]
ax = fig.add_subplot(111)
ax.plot(xList, epsNorm, colors[counter], label=scenario)
ax.set_ylabel('Normalized epsilon indicator')
ax.set_xlabel('Generation No.')
plt.legend(scenarios, loc='upper right')
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams['font.size'] = 12
counter +=1
plt.show()
def plot_buildings_connected_scenarios(scenarios, generations, headers):
counter = 0
data = [1, 2, 3, 4]
colors = ['b', 'r', 'y', 'c']
fig = plt.figure()
for header in headers:
pathX = sFn.pathX(header)
BuildCon = []
BuildCon2 = []
for i in range(generations[counter]):
i += 1
pop, eps, testedPop, ntwList, fitness = sFn.readCheckPoint(
pathX, i, 0)
buildCon = []
buildCon2 = []
for ind in pop:
buildCon.extend([np.average(ind[21:]) * 100])
buildCon2.append([np.average(ind[21:]) * 100])
BuildCon.extend(buildCon)
BuildCon2.append(np.average(buildCon2))
data[counter] = BuildCon
# individual lines per generation
xList = [1 + x for x in range(len(BuildCon2))]
ax = fig.add_subplot(111)
ax.plot(xList, BuildCon2, colors[counter], label=scenarios)
ax.set_xlim(1, generations[counter])
ax.set_ylabel('Percentage of buildings connected (%)')
ax.set_xlabel('Generation No.')
#ax.set_xticks(np.arange(1,generations[counter]+1,2))
counter += 1
plt.legend(scenarios, loc='upper left')
plt.rcParams['figure.figsize'] = (6, 4)
plt.rcParams['font.size'] = 12
plt.show()
# box plot with all data)
fig = plt.figure(figsize=(6, 4))
plt.rcParams['font.size'] = 12
plt.boxplot(data, 1, 'rs', 0)
plt.yticks([1, 2, 3, 4], scenarios)
plt.xlabel("Percentage of buildings connected (%)")
plt.show()
return data
def plot_buildings_connected_scenarios(scenarios, generations, headers):
counter =0
data = [1,2,3,4]
colors = ['b','r','y','c']
fig = plt.figure()
for header in headers:
pathX = sFn.pathX(header)
BuildCon = []
BuildCon2 = []
for i in range(generations[counter]):
i += 1
pop, eps, testedPop = sFn.readCheckPoint(pathX, i, 0)
buildCon = []
buildCon2 = []
for ind in pop:
buildCon.extend([np.average(ind[21:])*100])
buildCon2.append([np.average(ind[21:])*100])
BuildCon.extend(buildCon)
BuildCon2.append(np.average(buildCon2))
data[counter] = BuildCon
# individual lines per generation
xList = [ 1 + x for x in range(len(BuildCon2)) ]
ax = fig.add_subplot(111)
ax.plot(xList, BuildCon2, colors[counter], label=scenarios)
ax.set_xlim(1,generations[counter])
ax.set_ylabel('Percentage of buildings connected (%)')
ax.set_xlabel('Generation No.')
#ax.set_xticks(np.arange(1,generations[counter]+1,2))
counter +=1
plt.legend(scenarios, loc='upper left')
plt.rcParams['figure.figsize'] = (6,4)
plt.rcParams['font.size'] = 12
plt.show()
# box plot with all data)
fig = plt.figure(figsize = (6,4))
plt.rcParams['font.size'] = 12
plt.boxplot(data,1,'rs',0)
plt.yticks([1, 2, 3, 4], scenarios)
plt.xlabel("Percentage of buildings connected (%)")
plt.show()
return data