def landName(event):
titleDict = {"space heat" : "Space Heating Demand (kBtu)",
"cool" : "Space Cooling Demand (kBtu)",
"heat" : "Heating Demand (kBtu)",
"electricity" : "Electricity Demand (kBtu)",
"recover": "Energy Recovery Potential (kBtu)",
"single" : "", "group" : "Total ", "community":"Community "}
typeDict = {"space heat" : dfSpaceHeat, "cool" : dfCool,"heat" : dfHeat,
"recover" : dfRecover, "electricity" : dfElec}
pt = (event.x, event.y)
for key in landDict:
key2list = [int(x) for x in ast.literal_eval(key)]
if geo.pointInPolygon(pt, key2list):
landInit = landDict[key]
bdtype = initialDict[landInit]
hmap.g.create_polygon(tuple(key2list), fill = 'red', tag = 'a')
landSelection.append(landInit)
print 'Selection Set: {0}'.format(landSelection)
print "landuse is {0}".format(landDict[key])
idx = allyear.get()
(num, choice, period, step, title, stat) = plotMethod()
numPeriod = 8760 // step
# plot for single building
if num == "single":
title = '{0} {1}'.format(bdtype, title)
f, axarr = plt.subplots(2, 1, sharex=False, sharey = False)
g1 = typeDict[choice][bdtype][idx:(min(idx+step, 8760))].plot(ax = axarr[0], title = title)
g1.set_xlim(idx, min(idx+step, 8760) - 1)
building = typeDict[choice][bdtype]
if stat == "exact":
sr = pd.Series([building[idx%step + i*step]
for i in range(numPeriod)])
title = '{0} with step {1}'.format(title, period)
else:
if stat == "peak":
sr = pd.Series([max(building[i*step:(i + 1)*step])
for i in range(numPeriod)])
if stat == "total":
sr = pd.Series([sum(building[i*step:(i + 1)*step])
for i in range(numPeriod)])
if stat == "average":
sr = pd.Series([ar.getAve(building[i*step:(i + 1)*step])
for i in range(numPeriod)])
title = '{0} {1} {2}'.format(stat.capitalize(), (period+"ly").capitalize(), title)
g2 = sr.plot(ax = axarr[1],title = title)
g2.set_xlim(0, numPeriod - 1)
g2.axvline(idx//step, color = 'red', linestyle='--')
g2.annotate('current', xy = (idx//step, sr[idx//step]))
else:
title = '{0} {1}'.format(landSelection, titleDict[choice])
f, axarr = plt.subplots(2, 1, sharex=False, sharey = False)
bdtypelst = [initialDict[x] for x in landSelection]
selectDF = pd.DataFrame(typeDict[choice], columns = bdtypelst)
print list(selectDF.columns.values)
selectDF['agg'] = selectDF.sum(axis = 1)
g1 = selectDF['agg'][idx:(min(idx+step, 8760))].plot(ax = axarr[0], title = title)
g1.set_xlim(idx, min(idx+step, 8760) - 1)
if stat == "exact":
sr = pd.Series([selectDF['agg'][idx%step + i*step]
for i in range(numPeriod)])
title = '{0} with step {1}'.format(title, period)
else:
if stat == "peak":
sr = pd.Series([max(selectDF['agg'][i*step:(i+1)*step])
for i in range(numPeriod)])
elif stat == "average":
sr = pd.Series([ar.getAve(selectDF['agg'][i*step:(i+1)*step]) for i in range(numPeriod)])
elif stat == "total":
sr = pd.Series([sum(selectDF['agg'][i*step:(i+1)*step])
for i in range(numPeriod)])
title = '{0} {1} {2}'.format(stat.capitalize(), (period+"ly").capitalize(), title)
g2 = sr.plot(ax = axarr[1], title = title)
g2.set_xlim(0, numPeriod - 1)
g2.axvline(idx//step, color = 'red', linestyle='--')
g2.annotate('current', xy = (idx//step, sr[idx//step]))
plt.xlabel('time')
title = titleDict[num] + titleDict[choice]
plt.ylabel(titleDict[choice])
plt.show()
return
print "invalid selection"
def plotBuilding():
dirname = "energyData/Community_"
fileDict = {"space heat" : "spaceheat.csv", "cool" : "c_elec.csv",
"recover" : "recov.csv", "electricity" : "elec.csv",
"heat" : "heat.csv"}
typeDict = {"space heat" : dfSpaceHeat, "cool" : dfCool,"heat" : dfHeat,
"recover" : dfRecover, "electricity" : dfElec}
(num, choice, period, step, title, stat) = plotMethod()
filename = dirname + fileDict[choice]
df = typeDict[choice]
idx = allyear.get()
numPeriod = 8760 // step
if num == "single":
f, axarr = plt.subplots(4, 4, sharex=True, sharey = True)
for i in range(16):
if stat == "exact":
data = df.ix[idx: min(idx + step, 8760), i]
windowTitle = "Single Building " + title
g = data.plot(ax=axarr[i/4, i%4], title = bdTypelist[i])
g.set_xlim(idx, min(idx + step, 8760) - 1)
else:
if stat == "peak":
data = pd.Series([(df.ix[y * step:(y + 1)*step, i]).max()
for y in range(numPeriod)])
elif stat == "average":
data = pd.Series([(df.ix[y * step:(y + 1)*step, i]).mean()
for y in range(numPeriod)])
elif stat == "total":
data = pd.Series([(df.ix[y * step:(y + 1)*step, i]).sum()
for y in range(numPeriod)])
windowTitle = 'Single Building {0} {1}'.format(stat.capitalize(), title)
g = data.plot(ax=axarr[i/4, i%4], title = bdTypelist[i])
g.set_xlim(0, numPeriod + 1)
else:
f, axarr = plt.subplots(2, 1, sharex = False, sharey = False)
sr = pd.Series(np.genfromtxt(filename, delimiter = ','))
g1 = sr[idx:(min(idx+step, 8760))].plot(ax = axarr[0], title = title)
g1.set_xlim(idx, min(idx+step, 8760) - 1)
if stat == "exact":
sr2 = pd.Series([sr[idx%step + i*step] for i in range(numPeriod)])
title = '{0} with step {1}'.format(title, period)
else:
if stat == "peak":
sr2 = pd.Series([max(sr[i*step:(i + 1)*step])
for i in range(numPeriod)])
if stat == "total":
sr2 = pd.Series([sum(sr[i*step:(i + 1)*step])
for i in range(numPeriod)])
if stat == "average":
sr2 = pd.Series([ar.getAve(sr[i*step:(i + 1)*step])
for i in range(numPeriod)])
title = '{0} {1} {2}'.format(stat.capitalize(), period+"ly", title)
g2 = sr2.plot(ax = axarr[1], title = title)
g2.set_xlim(0, numPeriod - 1)
g2.axvline(idx//step, color = 'red', linestyle='--')
g2.annotate('current', xy = (idx//step, sr[idx//step]))
windowTitle = "Community " + title
f.canvas.set_window_title(windowTitle)
plt.show()
