def test_get_current_conditions():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
rep = Dm.repair.Repair(repair_delay=0)
prob_points, detect_probs = ex_prob_detect_arrays()
tech = Dm.comp_survey.CompSurvey(
time,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean', 'wind speed': 'mean'},
detection_probability_points=prob_points,
detection_probabilities=detect_probs,
site_queue=[]
)
# pd.DataFrame initializes a new DataFrame to avoid chained indexing warnings
emissions = pd.DataFrame(gas_field.emissions.get_current_emissions(time))
n_em = 100
emissions.loc[:, 'flux'] = np.linspace(0.1, 10, n_em)
em_indexes = np.linspace(0, n_em - 1, n_em, dtype=int)
ret = tech.get_current_conditions(time, gas_field, emissions, em_indexes)
if np.any(ret[:, 0] != emissions.flux):
raise ValueError("get_current_conditions not returning the correct values")
if np.any(ret[:, 1] != np.mean(gas_field.met['wind speed'][8:17])):
raise ValueError("get_current_conditions not returning the correct values")
def test_choose_sites():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
rep = Dm.repair.Repair(repair_delay=0)
# wind_dirs_mins = np.zeros(gas_field.n_sites)
# wind_dirs_maxs = np.ones(gas_field.n_sites) * 90
tech = Dm.comp_survey.CompSurvey(
time,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
op_envelope={
# 'wind speed': {'class': 1, 'min': 1, 'max': 10},
# 'wind direction': {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
},
site_queue=[],
detection_probability_points=[1, 2],
detection_probabilities=[0, 1]
)
tech.site_queue = list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int))
siteinds = tech.choose_sites(gas_field, time, 10)
if not (siteinds == np.linspace(0, 9, 10, dtype=int)).all():
raise ValueError("choose_sites() is not selecting the correcting sites")
tech.site_queue = []
siteinds = tech.choose_sites(gas_field, time, 10)
if siteinds:
raise ValueError("choose_sites() fails for empty site_queue queue")
def test_site_survey():
gas_field = basic_gas_field()
time = sc.Time(delta_t=1, end_time=10, current_time=0)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.474)) / (1.36 * np.sqrt(2))) for f
in points])
rep = Dm.repair.Repair(repair_delay=0)
comp_temp = Dm.comp_survey.CompSurvey(
time,
site_queue=[],
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
tech = Dm.site_survey.SiteSurvey(
time,
survey_interval=50,
sites_per_day=100,
ophrs={'begin': 8, 'end': 17},
site_cost=100,
dispatch_object=comp_temp,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
emissions = gas_field.emissions.get_current_emissions(time)
np.random.seed(0)
# test detect_prob_curve
detect = tech.detect_prob_curve(time, gas_field, [0, 1, 2], emissions)
if detect != np.array([0]):
raise ValueError("SiteSurvey.detect_prob_curve not returning expected sites.")
# test sites_surveyed with empty queue
sites_surveyed = tech.sites_surveyed(gas_field, time)
if sites_surveyed:
raise ValueError("sites_surveyed returning sites when it should not")
# test detect
np.random.seed(0)
tech.site_queue = [0, 1, 2]
tech.detect(time, gas_field, emissions)
if tech.deployment_count.get_sum_val(0, 1) != 3:
raise ValueError("SiteSurvey is not counting deployments correctly")
if tech.detection_count.get_sum_val(0, 1) != 1:
raise ValueError("SiteSurvey is not counting detections correctly.")
if tech.deployment_cost.get_sum_val(0, 1) != 300:
raise ValueError("SiteSurvey is not calculating survey costs correctly")
if tech.dispatch_object.site_queue != [0]:
raise ValueError("site_detect.detect not updating dispatch object sites to survey correctly")
# test action and sites_surveyed with
tech.action(list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)))
if tech.site_queue != list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)):
raise ValueError("action is not updating site_queue as expected")
# test sites_surveyed with full queue
sites_surveyed = tech.sites_surveyed(gas_field, time)
if (sites_surveyed != np.linspace(0, 99, 100, dtype=int)).any():
raise ValueError("sites_surveyed not identifying the correct sites")
def test_site_monitor():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
rep = Dm.repair.Repair(repair_delay=0)
ogi = Dm.comp_survey.CompSurvey(
time,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
op_envelope={
# 'wind speed': {'class': 1, 'min': 1, 'max': 10},
# 'wind direction': {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
},
site_queue=[],
detection_probability_points=[1, 2],
detection_probabilities=[0, 1]
)
cm = Dm.site_monitor.SiteMonitor(
time,
time_to_detect_points=np.array([0.99, 1.0, 1.01]),
time_to_detect_days=[np.infty, 1, 0],
detection_variables={'flux': 'mean'},
dispatch_object=ogi,
capital=1000,
ophrs={'begin': 0, 'end': 24},
site_queue=list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int))
)
site_inds = list(range(0, 10))
emissions = copy.copy(gas_field.emissions.get_current_emissions(time))
detect = cm.detect_prob_curve(time, gas_field, site_inds, emissions)
must_detect = [0, 9]
must_not_detect = [2, 7, 8]
for md in must_detect:
if md not in detect:
raise ValueError("site_monitor.detect_prob_curve not flagging the correct sites")
for mnd in must_not_detect:
if mnd in detect:
raise ValueError("site_monitor.detect_prob_curve flagging sites that it should not")
ttd_list = []
ttd = 0
time.delta_t = 0.1
for ind in range(1000):
ttd += time.delta_t
detect = cm.detect_prob_curve(time, gas_field, site_inds, emissions)
if 1 in detect:
ttd_list.append(ttd)
ttd = 0
if np.abs(np.mean(ttd_list) - 1) > 0.5:
raise ValueError("Mean time to detection deviates from expected value by >5 sigma in site_monitor test")
if cm.deployment_cost.get_sum_val(0, 1) != 1000:
raise ValueError("SiteMonitor deployment cost is not calculated correctly.")
cm.detect(time, gas_field, emissions)
def test_results_analysis():
gf = basic_gas_field()
timeobj = feast.EmissionSimModules.simulation_classes.Time(delta_t=1,
end_time=2)
rep = Dm.repair.Repair(repair_delay=0)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([
np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2)))
for f in points
])
for ind in range(3):
ogi = Dm.comp_survey.CompSurvey(timeobj,
survey_interval=50,
survey_speed=150,
ophrs={
'begin': 8,
'end': 17
},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs,
site_queue=[])
ogi_survey = Dm.ldar_program.LDARProgram(copy.deepcopy(gf),
{'ogi': ogi})
sc = Esm.simulation_classes.Scenario(
time=timeobj, gas_field=gf, ldar_program_dict={'ogi': ogi_survey})
# todo: test 'json' save method
# sc2 = copy.deepcopy(sc)
sc.run(display_status=False,
dir_out='ResultsTemp',
save_method='pickle')
null_npv, emissions, techs = raf.results_analysis('ResultsTemp', 0.08,
2e-4)
if len(null_npv.keys()) != 4:
raise ValueError(
"results analysis function returning the wrong number of keys")
if null_npv['Finding'].shape != (1, 3):
raise ValueError(
"results analysis function returning the wrong number of realizations or LDAR programs in "
+ "null npv")
if emissions.shape != (2, 2, 3):
raise ValueError(
"results analysis function returning the wrong number of realizations or "
+ "LDAR programs in emissions")
for f in os.listdir('ResultsTemp'):
os.remove(os.path.join('ResultsTemp', f))
os.rmdir('ResultsTemp')
def test_gasfield_leak_maker():
gf = basic_gas_field()
new_leaks = lcf.Emission()
time = sc.Time()
time.current_time = 10
np.random.seed(0)
gf.emission_maker(100, new_leaks, 'Fugitive', 0, time,
gf.sites['basic pad']['parameters'])
for f in new_leaks.emissions.flux:
if f not in gf.comp_dict['Fugitive'].emission_params.leak_sizes['All']:
raise ValueError("unexpected flux value")
if np.abs(
np.mean(new_leaks.emissions.end_time) -
1 / gf.comp_dict['Fugitive'].null_repair_rate) > 100:
# Note: this is a probabilistic test, but in 1e7 iterations the test never failed randomly.
raise ValueError("unexpected endtime distribution")
return None
def test_check_op_envelope():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
rep = Dm.repair.Repair(repair_delay=0)
wind_dirs_mins = np.zeros(gas_field.n_sites)
wind_dirs_maxs = np.ones(gas_field.n_sites) * 90
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2))) for f
in points])
tech = Dm.comp_survey.CompSurvey(
time,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
op_envelope={
'wind speed': {'class': 1, 'min': 1, 'max': 10},
'wind direction': {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
},
site_queue=[],
detection_probability_points=points,
detection_probabilities=probs
)
op_env = tech.check_op_envelope(gas_field, time, 0)
if op_env != 'site fail':
raise ValueError("check_op_envelope is not returning 'site fail' as expected")
if len(tech.op_env_site_fails.get_vals(0, 1)) != 1 or tech.op_env_site_fails.get_vals(0, 1)[0] != 1:
raise ValueError("DetectionMethod.op_env_site_fails is not updated correctly")
wind_dirs_mins = np.zeros([gas_field.n_sites, 2])
wind_dirs_mins[:, 1] = 145
wind_dirs_maxs = np.ones([gas_field.n_sites, 2]) * 90
wind_dirs_maxs[:, 1] += 145
tech.op_envelope['wind direction'] = {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
op_env = tech.check_op_envelope(gas_field, time, 0)
if op_env != 'site pass':
raise ValueError("check_op_envelope is not passing as expected")
tech.op_envelope['wind speed']['max'] = [2]
op_env = tech.check_op_envelope(gas_field, time, 0)
if op_env != 'field fail':
raise ValueError("check_op_envelope is not retruning 'field fail' as expected")
if tech.op_env_field_fails.get_sum_val(0, 1) != 1:
raise ValueError("DetectionMethod.op_env_field_fails is not updated correctly")
def test_sitedetect_sites_surveyed():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
wind_dirs_mins = np.zeros(gas_field.n_sites)
wind_dirs_maxs = np.ones(gas_field.n_sites) * 90
wind_dirs_maxs[50] = 270
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.474)) / (1.36 * np.sqrt(2))) for f
in points])
comp_survey = Dm.comp_survey.CompSurvey(
time,
dispatch_object=None,
survey_interval=None,
survey_speed=100,
labor=100,
site_queue=[],
detection_probability_points=[1, 2],
detection_probabilities=[0, 1],
ophrs={'begin': 8, 'end': 17}
)
tech = Dm.site_survey.SiteSurvey(
time,
survey_interval=50,
sites_per_day=100,
ophrs={'begin': 8, 'end': 17},
site_cost=100,
dispatch_object=comp_survey,
op_envelope={
'wind speed': {'class': 1, 'min': 1, 'max': 10},
'wind direction': {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
},
detection_probability_points=points,
detection_probabilities=probs
)
tech.site_queue = list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int))
np.random.seed(0)
sites_surveyed = tech.sites_surveyed(gas_field, time)
if sites_surveyed != [50]:
raise ValueError("sites_surveyed is not returning sites correctly")
def test_comp_survey():
gas_field = basic_gas_field()
time = sc.Time(delta_t=1, end_time=10, current_time=0)
rep = Dm.repair.Repair(repair_delay=0)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2))) for f
in points])
tech = Dm.comp_survey.CompSurvey(
time,
site_queue=list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)),
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
emissions = gas_field.emissions.get_current_emissions(time)
tech.action(list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)))
tech.detect(time, gas_field, emissions)
if tech.deployment_count.get_sum_val(0, 1) != 14:
raise ValueError("deployment count is not updated correctly in comp_survey")
if np.abs(tech.deployment_cost.get_sum_val(0, 1) - 9 * 100) > 1e-5:
raise ValueError("CompSurvey is not calculating deployment costs correctly")
if np.max(emissions.site_index[emissions.index.isin(rep.to_repair)]) != 13:
raise ValueError("tech.detect repairing emissions at incorrect sites")
expected_detected_ids = [54, 55, 75, 66, 87, 62, 58, 84, 5, 25, 43, 71, 13, 79, 97]
for ind in range(len(rep.to_repair)):
if expected_detected_ids[ind] != rep.to_repair[ind]:
raise ValueError("tech.detect not detecting the correct emissions")
if len(expected_detected_ids) != len(rep.to_repair):
raise ValueError("tech.detect not detecting the correct emissoins")
rep.repair(time, gas_field.emissions)
expected_repaired = gas_field.emissions.emissions.index.isin(expected_detected_ids)
if np.max(gas_field.emissions.emissions.end_time[expected_repaired]) != 0:
raise ValueError("rep.repair not adjusting the end times correctly")
def test_comp_survey_emitters_surveyed():
gas_field = basic_gas_field()
gas_field.met_data_path = 'TMY-DataExample.csv'
time = sc.Time(delta_t=1, end_time=10, current_time=0)
gas_field.met_data_maker()
rep = Dm.repair.Repair(repair_delay=0)
wind_dirs_mins = np.zeros(gas_field.n_sites)
wind_dirs_maxs = np.ones(gas_field.n_sites) * 90
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2))) for f
in points])
tech = Dm.comp_survey.CompSurvey(
time,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
op_envelope={
'wind speed': {'class': 1, 'min': 1, 'max': 10},
'wind direction': {'class': 2, 'min': wind_dirs_mins, 'max': wind_dirs_maxs}
},
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs,
site_queue=[]
)
tech.action(list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)))
emissions = gas_field.emissions.get_current_emissions(time)
emitter_inds = tech.emitters_surveyed(time, gas_field, emissions)
if emitter_inds:
# emitter_inds is expected to be []
raise ValueError("CompSurvey.emitters_surveyed is not returning expected emitter indexes")
wind_dirs_maxs[11] = 200
emitter_inds = tech.emitters_surveyed(time, gas_field, emissions)
if emitter_inds != [71]:
# The wind direction op envelope was updated to pass at site 11 only. Site 11 has one emission at index 71.
raise ValueError("CompSurvey.emitters_surveyed is not returning expected indexes")
def test_gas_field():
gf = basic_gas_field()
if gf.n_sites != 100:
raise ValueError(
"gas_field.__init__() is defining gf.n_sites incorrectly.")
if gf.n_comps != 10000:
raise ValueError(
"gas_field.__init__() is not defining gf.n_comps correctly")
if gf.sites['basic pad']['parameters'].site_inds != [0, 100]:
raise ValueError(
"gas_field.__init__() is not defining gf.site_inds correctly")
comp = gf.sites['basic pad']['parameters'].comp_dict['Fugitive'][
'parameters']
new_emissions = gf.emissions.emissions[
gf.emissions.emissions.start_time > 0]
if len(new_emissions.flux) > comp.emission_production_rate * 2 * gf.n_comps * 5 or \
len(new_emissions.flux) == 0:
raise ValueError(
"gas_field.__init__() is not generating new leaks as expected")
timeobj = feast.EmissionSimModules.simulation_classes.Time(delta_t=1,
end_time=2)
if gf.met['solar intensity'][10] != 226:
raise ValueError(
"gas_field.met_data_maker is not loading TMY data correctly.")
timeobj.current_time = 10 / 24
timeobj.delta_t = 1 / 24
met_dat = gf.get_met(timeobj, ['wind speed', 'solar intensity'])
if met_dat['wind speed'] != 2.6 or met_dat['solar intensity'] != 226:
raise ValueError("gas_field.get_met not returning the right values")
timeobj.current_time = 365
timeobj.delta_t = 1
met_dat = gf.get_met(timeobj, ['wind speed', 'solar Intensity'],
interp_modes=['max', 'min'],
ophrs={
'begin': 9,
'end': 17
})
if met_dat['solar intensity'] != 0:
raise ValueError(
"gas_field.get_met not returning the correct values when interp mode is min"
)
if met_dat['wind speed'] != 4.1:
raise ValueError(
"gas_field.get_met not returning the correct values when max is specified"
)
wd = gf.get_met(timeobj, ['wind direction'],
interp_modes=['mean'],
ophrs={
'begin': 10,
'end': 11
})
gf.met_data_maker(100)
wd2 = gf.get_met(timeobj, ['wind direction'],
interp_modes=['mean'],
ophrs={
'begin': 10,
'end': 11
})
if wd2 == wd:
raise ValueError(
"gas_field.met_data_maker is not changing the start hour correctly"
)
def test_scenario_run():
gas_field = basic_gas_field()
timeobj = feast.EmissionSimModules.simulation_classes.Time(delta_t=1, end_time=2)
rep = Dm.repair.Repair(repair_delay=0)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2))) for f
in points])
ogi = Dm.comp_survey.CompSurvey(
timeobj,
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs,
site_queue=[]
)
ogi_no_survey = Dm.comp_survey.CompSurvey(
timeobj,
survey_interval=None,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs,
site_queue=[]
)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.474)) / (1.36 * np.sqrt(2))) for f
in points])
plane_survey = Dm.site_survey.SiteSurvey(
timeobj,
survey_interval=50,
sites_per_day=200,
site_cost=100,
dispatch_object=ogi_no_survey,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
ogi_survey = Dm.ldar_program.LDARProgram(
copy.deepcopy(gas_field), {'ogi': ogi}
)
tech_dict = {
'plane': plane_survey,
'ogi': ogi_no_survey
}
tiered_survey = Dm.ldar_program.LDARProgram(
gas_field, tech_dict
)
scenario = sc.Scenario(time=timeobj, gas_field=gas_field, ldar_program_dict={'tiered': tiered_survey,
'ogi': ogi_survey})
scenario.run(dir_out='ResultsTemp', display_status=False, save_method='pickle')
with open('ResultsTemp/realization0.p', 'rb') as f:
res = pickle.load(f)
if res.ldar_program_dict['tiered'].emissions_timeseries[-1] >= \
res.ldar_program_dict['ogi'].emissions_timeseries[-1]:
raise ValueError("Scenario.run is not returning emission reductions as expected")
if res.ldar_program_dict['ogi'].emissions_timeseries[-1] >= res.ldar_program_dict['Null'].emissions_timeseries[-1]:
raise ValueError("Scenario.run is not returning emission reductions as expected")
for f in os.listdir('ResultsTemp'):
os.remove(os.path.join('ResultsTemp', f))
os.rmdir('ResultsTemp')
def test_ldar_program():
gas_field = basic_gas_field()
time = sc.Time(delta_t=1, end_time=10, current_time=0)
rep = Dm.repair.Repair(repair_delay=0)
points = np.logspace(-3, 1, 100)
probs = 0.5 + 0.5 * np.array([np.math.erf((np.log(f) - np.log(0.02)) / (0.8 * np.sqrt(2))) for f
in points])
probs[0] = 0
ogi = Dm.comp_survey.CompSurvey(
time,
site_queue=list(np.linspace(0, gas_field.n_sites - 1, gas_field.n_sites, dtype=int)),
survey_interval=50,
survey_speed=150,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
ogi_no_survey = Dm.comp_survey.CompSurvey(
time,
site_queue=[],
survey_interval=None,
survey_speed=100,
ophrs={'begin': 8, 'end': 17},
labor=100,
dispatch_object=rep,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
points = [0.5, 0.6]
probs = [0, 1]
probs[0] = 0
plane_survey = Dm.site_survey.SiteSurvey(
time,
survey_interval=50,
sites_per_day=200,
site_cost=100,
dispatch_object=ogi_no_survey,
detection_variables={'flux': 'mean'},
detection_probability_points=points,
detection_probabilities=probs
)
# test __init__
ogi_survey = Dm.ldar_program.LDARProgram(
copy.deepcopy(gas_field), {'ogi': ogi}
)
em = ogi_survey.emissions.get_current_emissions(time)
if np.sum(em.flux) != 100:
raise ValueError("Unexpected emission rate in LDAR program initialization")
# test end_emissions
ogi_survey.emissions.emissions.loc[0, 'end_time'] = 0
# test action
ogi_survey.action(time, gas_field)
em = ogi_survey.emissions.get_current_emissions(time)
if np.sum(em.flux) != 84:
raise ValueError("Unexpected emission rate after LDAR program action")
# test combined program
tech_dict = {
'plane': plane_survey,
'ogi': ogi_no_survey
}
tiered_survey = Dm.ldar_program.LDARProgram(
gas_field, tech_dict
)
# test action
np.random.seed(0)
tiered_survey.action(time, gas_field)
em = tiered_survey.emissions.get_current_emissions(time)
if np.sum(em.flux) != 86:
raise ValueError("Unexpected emission rate after LDAR program action with tiered survey")
time.current_time = 1
tiered_survey.calc_rep_costs(time)
if tiered_survey.repair_cost.time_value[0][0] != 0 or len(tiered_survey.repair_cost.time_value) != 14 or \
tiered_survey.repair_cost.time_value[-1][-1] != 2:
raise ValueError("tiered_survey.calc_rep_costs is not calculating the correct repair costs")
