def time_comparaison():
import precleaning as pcl
import time
p = prm.ask_params()
startdate, enddate, countries, perpetrators, h3_level, freq = p.get_params(
)
def choose_attack():
exposure = input("Choose attack type : [t]errorism, [c]onventional > ")
if exposure == "t":
return "terrorism"
elif exposure == "c":
return "conventional"
else:
return choose_attack()
attack_type = choose_attack()
events = pcl.get_events(p)
events.index = [i for i in range(len(events.index))]
E = get_exposure(events, attack_type, p, freq)
t = time.perf_counter()
F = fast_get_exposure(events, attack_type, p, freq)
print("Fast exposure: ", time.perf_counter() - t)
def main():
import precleaning as pcl
p = prm.ask_params()
startdate, enddate, countries, perpetrators, h3_level, freq = p.get_params(
)
def choose_attack():
exposure = input("Choose attack type : [t]errorism, [c]onventional > ")
if exposure == "t":
return "terrorism"
elif exposure == "c":
return "conventional"
else:
return choose_attack()
attack_type = choose_attack()
events = pcl.get_events(p)
E = get_exposure(events, attack_type, p, freq)
def str_countries(countries):
res = str(countries[0])
for i in range(1, len(countries)):
res += "-" + str(countries[i])
return res
E.to_csv(
f"../exposures/exposure_{str_countries(countries)}_{attack_type}_{startdate}_{enddate}_{freq}.csv"
)
def main():
import precleaning as pcl
import time
p = prm.ask_params()
startdate, enddate, countries, _, _, freq = p.get_params()
def choose_attack():
exposure = input(
"Choose attack type : [t]errorism | [c]onventional > ")
if exposure == "t":
return "terrorism"
elif exposure == "c":
return "conventional"
else:
return choose_attack()
attack_type = choose_attack()
events = pcl.get_events(p)
t = time.perf_counter()
E = get_exposure(events, attack_type, p, freq)
t = time.perf_counter() - t
print(f"Exposure computed in {t} seconds.\n")
print(E)
def str_countries(countries):
res = str(countries[0])
for i in range(1, len(countries)):
res += "-" + str(countries[i])
return res
# On enregistre le résultat
save = (input("Save ? y/N > ") == "y") or False
if save:
E.to_csv(
f"../exposures/exposure-numba_{str_countries(countries)}_{attack_type}_{startdate}_{enddate}_{freq}.csv"
)
def main():
p = prm.ask_params()
df = get_events(p)
print(df)
def main():
p = parameters.ask_params()
startdate, enddate, countries, _, _, freq = p.get_params()
def ask_use_numba():
ask = input("Use numba ? Y/n > ").lower() or "y"
if ask == "y":
return (True, "-numba")
elif ask == "n":
return (False, "")
else:
return ask_use_numba()
use_numba, numba_str = ask_use_numba()
events = precleaning.get_events(p)
def str_countries(countries):
res = str(countries[0])
for i in range(1, len(countries)):
res += "-" + str(countries[i])
return res
if use_numba:
print("\nComputing exposure to conventional warfare...")
Ec = exposure_numba.get_exposure(events, "conventional", p, freq)
print("Done !")
print("\nSaved exposure to conventional warfare to " + f"../exposures/exposure{numba_str}_{str_countries(countries)}_conventional_{startdate}_{enddate}_{freq}.csv")
Ec.to_csv(f"../exposures/exposure{numba_str}_{str_countries(countries)}_conventional_{startdate}_{enddate}_{freq}.csv")
print("\nComputing exposure to terrorism...")
Et = exposure_numba.get_exposure(events, "terrorism", p, freq)
print("Done !")
print("\nSaved exposure to terrorism to " + f"../exposures/exposure{numba_str}_{str_countries(countries)}_terrorism_{startdate}_{enddate}_{freq}.csv")
Et.to_csv(f"../exposures/exposure{numba_str}_{str_countries(countries)}_terrorism_{startdate}_{enddate}_{freq}.csv")
else:
print("\nComputing exposure to conventional warfare...")
Ec = exposure.get_exposure(events, "conventional", p, freq)
print("Done !")
print("\nSaved exposure to conventional warfare to " + f"../exposures/exposure{numba_str}_{str_countries(countries)}_conventional_{startdate}_{enddate}_{freq}.csv")
Ec.to_csv(f"../exposures/exposure{numba_str}_{str_countries(countries)}_conventional_{startdate}_{enddate}_{freq}.csv")
print("\nComputing exposure to terrorism...")
Et = exposure.get_exposure(events, "terrorism", p, freq)
print("Done !")
print("\nSaved exposure to terrorism to " + f"../exposures/exposure{numba_str}_{str_countries(countries)}_terrorism_{startdate}_{enddate}_{freq}.csv")
Et.to_csv(f"../exposures/exposure{numba_str}_{str_countries(countries)}_terrorism_{startdate}_{enddate}_{freq}.csv")
print("\nComputing exposition to conventional warfare probabilities...")
C = observation.get_probabilities(Ec)
print("Done !")
print("\nComputing exposition to terrorism probabilities...")
T = observation.get_probabilities(Et)
print("Done !")
print(f"\nThe median of C is : {C.median().median()}")
print(f"The median of T is : {T.median().median()}")
m = float(input("Enter a value for m (default is 0.15) > ") or 0.15)
xs = float(input("Enter a value for xs (default is 0.01) > ") or 0.01)
print("\nComputing observations...")
O = observation.get_observation(Et,Ec,C,T,m,xs)
print("Done !")
print("\nSaved observations to " + f"../observations/observation{numba_str}_{str_countries(countries)}_{startdate}_{enddate}_{freq}.csv")
O.to_csv(f"../observations/observation{numba_str}_{str_countries(countries)}_{startdate}_{enddate}_{freq}.csv")
pi = get_pi(countries[0])
print("\nApplying Viterbi algorithm to estimate territorial control...")
states_df = get_states(O, pi)
print("Done !")
print("\nSaved control to " + f"../controls/controls{numba_str}_{str_countries(countries)}_{startdate}_{enddate}_{freq}.csv")
states_df.to_csv(f"../controls/controls{numba_str}_{str_countries(countries)}_{startdate}_{enddate}_{freq}.csv")
print("\nCreating figures (in ../figures)...")
figures.plot_control(figures.to_gdf(states_df.T), countries[0])
print("Done !")