def test_exp_probs():
np.random.seed(2001)
inst_params = {"scale": 20 * mcfpd_to_gps}
time = simulation_classes.Time()
gas_field = simulation_classes.GasField()
test_ad = AD(time, gas_field, detection_model_name="exponential")
test_fluxes = np.array([5, 10, 20, 50, 100, 120, 2000]) * mcfpd_to_gps
probs = test_ad.get_exp_probabilities(test_fluxes, inst_params=inst_params)
truth_probs = [
0.221199, 0.393469, 0.632121, 0.917915, 0.993262, 0.997521, 1.
]
np.testing.assert_array_almost_equal(probs, truth_probs)
def test_legacy_AD():
np.random.seed(2001)
inst_params = {} # legacy has no parameters.
time = simulation_classes.Time()
gas_field = simulation_classes.GasField()
test_ad = AD(time, gas_field)
test_fluxes = np.array([5, 10, 20, 50, 100, 120, 2000]) * mcfpd_to_gps
probs = test_ad.get_legacy_probabilities(test_fluxes,
inst_params=inst_params)
truth_probs = [
0.99001, 0.99006, 0.990161, 0.990462, 0.990964, 0.991165, 1.
]
np.testing.assert_array_almost_equal(probs, truth_probs)
def test_exp_erf_probs():
np.random.seed(2001)
inst_params = {
"center": 35 * mcfpd_to_gps,
"width": 21 * mcfpd_to_gps,
"scale": 3 * mcfpd_to_gps
}
time = simulation_classes.Time()
gas_field = simulation_classes.GasField()
test_ls = AD(time, gas_field)
test_fluxes = np.array([5, 10, 20, 50, 100, 120, 2000]) * mcfpd_to_gps
probs = test_ls.get_exp_erf_probabilities(test_fluxes, inst_params)
truth_probs = [
0.011896, 0.034174, 0.134452, 0.79465, 0.97044, 0.97531, 0.998501
]
np.testing.assert_array_almost_equal(probs, truth_probs)
def field_simulation(gas_field=None,
atm=None,
input_leaks=None,
dir_out='Results',
time=None,
econ_set=None,
tech_dict=None,
detection_techs=None,
display_status=True):
"""
field_simulation generates a single realization of scenario. The scenario is defined by the input values.
gas_field a GasField object
atm an Atmosphere object
input_leaks a list of leaks to be generated at each timestep
dir_out directory name in which to save results
time a Time object
econ_set a FinanceSettings object
tech_dict a dict of detection technology objects
detection_techs a list of detection technology identifying strings
"""
# -------------- Define settings --------------
# time defines parameters related to time in the model. Time units are days.
if time is None:
time = simulation_classes.Time()
if gas_field is None:
gas_field = simulation_classes.GasField()
leak_list = copy.deepcopy(gas_field.initial_leaks)
# Note: econ_settings are not used during the simulation, but are saved for use in post simulation data processing
if econ_set is None:
econ_set = simulation_classes.FinanceSettings()
if atm is None:
atm = simulation_classes.Atmosphere(time.n_timesteps)
if detection_techs is None:
detection_techs = ['fid.FID', 'null.Null', 'ir.AIR', 'ir.MIR', 'dd.DD']
new_leaks, no_repair_leakage = [], []
if tech_dict is None:
tech_dict = dict()
for tech in detection_techs:
[tech_mod, tech_class] = tech.split(sep='.')
tech_dict[tech_class] = eval('Dm.' + tech_mod.lower() + '.' +
tech_class + '(time, gas_field)')
elif 'Null' not in tech_dict:
tech_dict['Null'] = Dm.null.Null(time, gas_field)
# -------------- Run the simulation --------------
n_leaks = []
for time.time_index in range(0, time.n_timesteps):
time.current_time += time.delta_t
if display_status and time.current_time % int(
time.end_time / 10) < time.delta_t:
print("Currently evaluating time step " + str(time.time_index) +
" of " + str(time.n_timesteps))
if input_leaks is None:
new_leaks.append(
gas_field.leak_size_maker(new_leak_count(time, gas_field),
gas_field))
else:
new_leaks.append(input_leaks[time.time_index])
no_repair_leakage.append(sum(leak_list.flux))
leak_list.extend(new_leaks[-1])
# Loop through each LDAR program:
for tech_obj in tech_dict.values():
n_leaks.append(tech_obj.leaks.n_leaks)
tech_obj.leakage.append(sum(tech_obj.leaks.flux))
tech_obj.detection(time, gas_field, atm)
tech_obj.leaks.extend(new_leaks[-1])
# -------------- Save results --------------
results = simulation_classes.Results(time, gas_field, tech_dict, leak_list,
no_repair_leakage, atm, econ_set,
new_leaks, n_leaks)
save_results(dir_out, results)
def field_simulation(gas_field=None, atm=None, input_leaks=None, dir_out="Results", time=None,
econ_set=None, tech_dict=None, detection_techs=None, display_status=True,
label=None, runID=None, fullResults=False):
"""
field_simulation generates a single realization of scenario. The scenario is defined by the
input values.
gas_field a GasField object
atm an Atmosphere object
input_leaks a list of leaks to be generated at each timestep
dir_out directory name in which to save results
time a Time object
econ_set a FinanceSettings object
tech_dict a dict of detection technology objects
detection_techs a list of detection technology identifying strings
label a string to include in the filenames of the realization results
runID a number to include in the filenames of the realization results
"""
# -------------- Define settings --------------
# time defines parameters related to time in the model. Time units are days.
if time is None:
time = simulation_classes.Time()
if gas_field is None:
gas_field = simulation_classes.GasField()
leak_list = copy.deepcopy(gas_field.initial_leaks)
# Note: econ_settings are not used during the simulation, but are saved for use in post
# simulation data processing
if econ_set is None:
econ_set = simulation_classes.FinanceSettings()
if atm is None:
atm = simulation_classes.Atmosphere(time.n_timesteps)
if detection_techs is None:
detection_techs = ["FID", "Null", "AIR", "MIR", "DD", "AD"]
else:
print("Running {}".format(detection_techs))
new_leaks, no_repair_leakage = [], []
if tech_dict is None:
tech_dict = dict()
for tech in detection_techs:
tech_dict[tech] = DETECTION_CLASSES[tech](time, gas_field)
elif "Null" not in tech_dict:
print("Warning: Null tech not in dict. Adding it.")
tech_dict["Null"] = Dm.null.Null(time, gas_field)
# --- Establish base conditions
# print("field_simulation run {}".format(runID))
# print("field_simulation: Timesteps: {}. Current time: {} Time index: {}".format(
# time.n_timesteps,
# time.current_time,
# time.time_index))
# -------------- Run the simulation --------------
n_leaks = []
for time.time_index in range(0, time.n_timesteps):
time.current_time += time.delta_t
# if display_status and time.current_time % int(time.end_time / 10) < time.delta_t:
# print("Currently evaluating time step " + str(time.time_index)
# + " of " + str(time.n_timesteps))
if input_leaks is None: # and LEAKSTHERE_ALREADY_WHY NOT THERE:
new_leaks.append(make_leaks(new_leak_count(time, gas_field), gas_field))
# print("New leaks on Day {:.1f}: {}".format(time.current_time,
# len(new_leaks[-1].flux)))
else:
new_leaks.append(input_leaks[time.time_index])
no_repair_leakage.append(sum(leak_list.flux))
leak_list.extend(new_leaks[-1])
# Loop through each LDAR program:
for tech_obj in tech_dict.values():
# the following is a terrible idea, it should be 2-D
# just append the number of leaks for this tech for this step in a long list.
n_leaks.append(tech_obj.leaks.n_leaks)
# append leakage in this step (not necessarily total daily leakage) for this tech
tech_obj.leakage.append(sum(tech_obj.leaks.flux))
# Find and remove leaks
tech_obj.detection(time, gas_field, atm)
# Tack on the new leaks for this time step
tech_obj.leaks.extend(new_leaks[-1])
# -------------- Save results --------------
results = simulation_classes.Results(time, gas_field, tech_dict, leak_list, no_repair_leakage,
atm, econ_set, new_leaks, n_leaks)
save_results(dir_out, results, label=label, runID=runID, fullResults=fullResults)