def overbookingCurves(self, network, orgs=None, dests=None, flights=None,
cabins=None, bcs=None, date_ranges=None, normalized=True):
""" Plots overbooking curves for some subset of the data.
Overbooking is defined where AUTH > CAP. We plot overbooking as a
ratio between AUTH and CAP. Overbooking varies with time.
"""
df = network.f.getDrillDown(orgs=orgs, dests=dests, flights=flights,
cabins=cabins, bcs=bcs, date_ranges=date_ranges)
fltbk = network.f.getUniqueFlightsAndBookings(df)
plt.figure()
if normalized:
for g, d in fltbk:
# normalized AUTH == OVERBOOKED
AUTH = np.array(d.sort(columns='KEYDAY', ascending=False)['AUTH'])
# ignore time series that are not overbooked
if not Utils.isOverbooked(AUTH):
continue
KEYDAY = np.array(-d.sort(columns='KEYDAY', ascending=False)['KEYDAY'])
plt.plot(KEYDAY, AUTH)
else:
for g, d in fltbk:
AUTH = np.array(d.sort(columns='KEYDAY', ascending=False)['AUTH'])
CAP = float(d.iloc[0]['CAP'])
OVRBKD = AUTH/CAP
# ignore time series that are not overbooked
if not Utils.isOverbooked(OVRBKD):
continue
KEYDAY = np.array(-d.sort(columns='KEYDAY', ascending=False)['KEYDAY'])
plt.plot(KEYDAY, OVRBKD)
title = Utils.createTitleForFeatures(orgs,dests,flights,cabins,bcs,date_ranges)
plt.title(title)
plt.xlabel('-KEYDAY')
plt.ylabel('Percentage Overbooked: AUTH / CAP')
plt.show()
def overbookingVsCabinLoadFactor(self, network, orgs=None, dests=None, flights=None,
cabins=None, bcs=None, date_ranges=None,
normalized=True, subplots=True):
""" Plots how overbooking varies with Cabin load factor. Final Cabin Load Factor
for a particular flight booking class is binned into three separate categories:
Overbooked: CLF > 1
Underbooked: CLF < .8
Optimumly booked: .8 < CLF < 1
"""
df = network.f.getDrillDown(orgs=orgs, dests=dests, flights=flights,
cabins=cabins, bcs=bcs, date_ranges=date_ranges)
fltbk = network.f.getUniqueFlightsAndBookings(df)
# TODO: allow for countFinalCabinLoadFactor to use normalized data
CLF_dict = network.countFinalCabinLoadFactor()
fig = plt.figure()
# preparing to capture the legend handles
legend_over = None
legend_under = None
legend_optimum = None
n_over = 0
n_under = 0
n_optimum = 0
if normalized:
for g, d in fltbk:
# normalized AUTH == OVERBOOKED
AUTH = np.array(d.sort(columns='KEYDAY', ascending=False)['AUTH'])
# ignore time series that are not overbooked
if not Utils.isOverbooked(AUTH):
continue
KEYDAY = np.array(-d.sort(columns='KEYDAY', ascending=False)['KEYDAY'])
DATE = d.iloc[0]['DATE']
FLT = d.iloc[0]['FLT']
ORG = d.iloc[0]['ORG']
DES = d.iloc[0]['DES']
#TODO: See CLF_dict (above)
CABIN_LOAD_FACTOR = CLF_dict[(DATE, FLT, ORG, DES)]
if CABIN_LOAD_FACTOR > 1:
plt.plot(KEYDAY, AUTH, 'r')
elif CABIN_LOAD_FACTOR < .95:
plt.plot(KEYDAY, AUTH, 'y')
else:
plt.plot(KEYDAY, AUTH, 'g')
else:
for g, d in fltbk:
AUTH = np.array(d.sort(columns='KEYDAY', ascending=False)['AUTH'])
CAP = float(d.iloc[0]['CAP'])
OVRBKD = AUTH/CAP
# ignore time series that are not overbooked
if not Utils.isOverbooked(OVRBKD):
continue
KEYDAY = np.array(-d.sort(columns='KEYDAY', ascending=False)['KEYDAY'])
DATE = d.iloc[0]['DATE']
FLT = d.iloc[0]['FLT']
ORG = d.iloc[0]['ORG']
DES = d.iloc[0]['DES']
CABIN_LOAD_FACTOR = CLF_dict[(DATE, FLT, ORG, DES)]
if CABIN_LOAD_FACTOR > 1:
plt.subplot(311) if subplots else None
if not legend_over:
legend_over, = plt.plot(KEYDAY, OVRBKD , 'r')
else:
plt.plot(KEYDAY, OVRBKD , 'r')
n_over += 1
elif CABIN_LOAD_FACTOR < .95:
plt.subplot(313) if subplots else None
if not legend_under:
legend_under, = plt.plot(KEYDAY, OVRBKD, 'y')
else:
plt.plot(KEYDAY, OVRBKD, 'y')
n_under += 1
else:
plt.subplot(312) if subplots else None
if not legend_optimum:
legend_optimum, = plt.plot(KEYDAY, OVRBKD, 'g')
else:
plt.plot(KEYDAY, OVRBKD, 'g')
n_optimum += 1
title = Utils.createTitleForFeatures(orgs,dests,flights,cabins,bcs,date_ranges)
plt.subplot(311) if subplots else None
plt.suptitle(title)
plt.xlabel('-KEYDAY')
plt.ylabel('Percentage Overbooked: AUTH / CAP')
plt.subplot(311)
plt.ylim([.25,2])
plt.subplot(312)
plt.ylim([.25,2])
plt.subplot(313)
plt.ylim([.25,2])
plt.legend([legend_over, legend_optimum, legend_under],
["Cabin Load Factor > 1, n={}".format(n_over),
"Optimum Cabin Load Factor,n={}".format(n_optimum),
"Cabin Load Factor < .95, n={}".format(n_under)
])
plt.show()
