def run_pelicun(DL_input_path,
EDP_input_path,
DL_method,
realization_count,
BIM_file,
EDP_file,
DM_file,
DV_file,
output_path=None,
detailed_results=True,
coupled_EDP=False,
log_file=True,
event_time=None,
ground_failure=False,
auto_script_path=None,
resource_dir=None):
DL_input_path = os.path.abspath(DL_input_path) # BIM file
EDP_input_path = os.path.abspath(EDP_input_path) # dakotaTab
# If the output dir was not specified, results are saved in the directory of
# the input file.
if output_path is None:
output_path = ntpath.dirname(DL_input_path)
# delete output files from previous runs
files = os.listdir(output_path)
for filename in files:
if (filename[-3:] == 'csv') and (('DL_summary' in filename) or
('DMG' in filename) or
('DV_' in filename) or
('EDP' in filename)):
try:
os.remove(posixpath.join(output_path, filename))
except:
pass
# If the event file is specified, we expect a multi-stripe analysis...
try:
# Collect stripe and rate information for every event
with open(DL_input_path, 'r') as f:
event_list = json.load(f)['Events'][0]
df_event = pd.DataFrame(columns=['name', 'stripe', 'rate', 'IM'],
index=np.arange(len(event_list)))
for evt_i, event in enumerate(event_list):
df_event.iloc[evt_i] = [
event['name'], event['stripe'], event['rate'], event['IM']
]
# Create a separate EDP input for each stripe
EDP_input_full = pd.read_csv(EDP_input_path,
sep='\s+',
header=0,
index_col=0)
# EDP_input_full.to_csv(EDP_input_path[:-4]+'_1.out', sep=' ')
stripes = df_event['stripe'].unique()
EDP_files = []
IM_list = []
num_events = []
num_collapses = []
for stripe in stripes:
events = df_event[df_event['stripe'] == stripe]['name'].values
EDP_input = EDP_input_full[EDP_input_full['MultipleEvent'].isin(
events)]
EDP_files.append(EDP_input_path[:-4] + '_{}.out'.format(stripe))
EDP_input.to_csv(EDP_files[-1], sep=' ')
IM_list.append(
df_event[df_event['stripe'] == stripe]['IM'].values[0])
# record number of collapses and number of events per stripe
PID_columns = [col for col in list(EDP_input)
if 'PID' in col] # list of column headers with PID
num_events.append(EDP_input.shape[0])
count = 0
for row in range(num_events[-1]):
print(row)
for col in PID_columns:
if EDP_input.iloc[row][
col] >= 0.20: # TODO: PID collapse limit as argument
count += 1
break
num_collapses.append(count)
# fit lognormal distribution to all points by maximum likelihood estimation (MLE)
theta, beta = lognormal_MLE(IM_list, num_events, num_collapses)
beta_adj = np.sqrt(
beta**2 + 0.35**2
) # TODO: adjust dispersion by 0.35 to account for modeling uncertainty
print("theta: " + str(theta))
print("beta_adj: " + str(beta_adj))
# write BIM file with new probability of collapse for each IM
DL_files = []
for i in range(len(stripes)):
DL_input_stripe = update_collapsep(DL_input_path, stripes[i],
theta, beta_adj, IM_list[i])
DL_files.append(DL_input_stripe)
except: # run analysis for single IM
stripes = [1]
EDP_files = [EDP_input_path]
DL_files = [DL_input_path]
# run the analysis and save results separately for each stripe
#print(stripes, EDP_files)
for s_i, stripe in enumerate(stripes):
DL_input_path = DL_files[s_i]
# read the type of assessment from the DL input file
with open(DL_input_path, 'r') as f:
DL_input = json.load(f)
# check if the DL input file has information about the loss model
if 'DamageAndLoss' in DL_input:
pass
else:
# if the loss model is not defined, give a warning
print(
'WARNING No loss model defined in the BIM file. Trying to auto-populate.'
)
EDP_input_path = EDP_files[s_i]
# and try to auto-populate the loss model using the BIM information
DL_input, DL_input_path = auto_populate(
DL_input_path, EDP_input_path, DL_method, realization_count,
coupled_EDP, event_time, ground_failure, auto_script_path)
DL_method = DL_input['DamageAndLoss']['_method']
stripe_str = '' if len(stripes) == 1 else str(stripe) + '_'
# Copy the resources to the specified location - if needed
if resource_dir is not None:
resource_dir = os.path.abspath(resource_dir)
AT = DL_method_to_AT[DL_method]
resources = get_required_resources(DL_input_path, AT)
# take each resource file
for name, resource in resources.items():
resource_filename = os.path.basename(resource)
# copy it to the designated location
if ((AT == 'HAZUS_HU') and (name == 'component')):
# hurricane assessments require two files
temp_resource = resource.replace('.hdf', '_FL.hdf')
temp_filename = os.path.basename(temp_resource)
new_path = os.path.join(resource_dir, temp_filename)
if not os.path.exists(new_path):
shutil.copy(temp_resource, new_path)
temp_resource = resource.replace('.hdf', '_HU.hdf')
temp_filename = os.path.basename(temp_resource)
new_path = os.path.join(resource_dir, temp_filename)
if not os.path.exists(new_path):
shutil.copy(temp_resource, new_path)
new_path = new_path[:-7] + '.hdf'
else:
new_path = os.path.join(resource_dir, resource_filename)
if not os.path.exists(new_path):
shutil.copy(resource, new_path)
# and update the DL config file to point to that location
if name == 'component':
DL_input['DamageAndLoss']['ComponentDataFolder'] = new_path
elif name == 'population':
DL_input['DamageAndLoss']['LossModel']['Inhabitants'][
'PopulationDataFile'] = new_path
elif name == 'combination':
DL_input['DamageAndLoss']['CombinationDataFile'] = new_path
with open(DL_input_path, 'w') as f:
json.dump(DL_input, f, indent=2)
log_msg('Running damage and loss simulation...')
if DL_method == 'FEMA P58':
A = FEMA_P58_Assessment(log_file=log_file)
elif DL_method in ['HAZUS MH EQ', 'HAZUS MH', 'HAZUS MH EQ IM']:
A = HAZUS_Assessment(hazard='EQ', log_file=log_file)
elif DL_method == 'HAZUS MH HU':
A = HAZUS_Assessment(hazard='HU', log_file=log_file)
elif DL_method == 'HAZUS MH FL':
A = HAZUS_Assessment(hazard='FL', log_file=log_file)
A.read_inputs(
DL_input_path, EDP_files[s_i],
verbose=False) # make DL inputs into array of all BIM files
A.define_random_variables()
A.define_loss_model()
A.calculate_damage()
A.calculate_losses()
A.aggregate_results()
A.save_outputs(output_path,
BIM_file,
EDP_file,
DM_file,
DV_file,
stripe_str,
detailed_results=detailed_results)
return 0
def run_pelicun(DL_input_path,
EDP_input_path,
DL_method,
realization_count,
EDP_file,
DM_file,
DV_file,
output_path=None,
detailed_results=True,
log_file=True):
DL_input_path = os.path.abspath(DL_input_path) # BIM file
EDP_input_path = os.path.abspath(EDP_input_path) # dakotaTab
# If the output dir was not specified, results are saved in the directory of
# the input file.
if output_path is None:
output_path = ntpath.dirname(DL_input_path)
# delete output files from previous runs
files = os.listdir(output_path)
for filename in files:
if (filename[-3:] == 'csv') and (('DL_summary' in filename) or
('DMG' in filename) or
('DV_' in filename) or
('EDP' in filename)):
try:
os.remove(posixpath.join(output_path, filename))
except:
pass
# If the event file is specified, we expect a multi-stripe analysis...
try:
# Collect stripe and rate information for every event
with open(DL_input_path, 'r') as f:
event_list = json.load(f)['Events'][0]
df_event = pd.DataFrame(columns=['name', 'stripe', 'rate', 'IM'],
index=np.arange(len(event_list)))
for evt_i, event in enumerate(event_list):
df_event.iloc[evt_i] = [
event['name'], event['stripe'], event['rate'], event['IM']
]
# Create a separate EDP input for each stripe
EDP_input_full = pd.read_csv(EDP_input_path,
sep='\s+',
header=0,
index_col=0)
# EDP_input_full.to_csv(EDP_input_path[:-4]+'_1.out', sep=' ')
stripes = df_event['stripe'].unique()
EDP_files = []
IM_list = []
num_events = []
num_collapses = []
for stripe in stripes:
events = df_event[df_event['stripe'] == stripe]['name'].values
EDP_input = EDP_input_full[EDP_input_full['MultipleEvent'].isin(
events)]
EDP_files.append(EDP_input_path[:-4] + '_{}.out'.format(stripe))
EDP_input.to_csv(EDP_files[-1], sep=' ')
IM_list.append(
df_event[df_event['stripe'] == stripe]['IM'].values[0])
# record number of collapses and number of events per stripe
PID_columns = [col for col in list(EDP_input)
if 'PID' in col] # list of column headers with PID
num_events.append(EDP_input.shape[0])
count = 0
for row in range(num_events[-1]):
print(row)
for col in PID_columns:
if EDP_input.iloc[row][
col] >= 0.20: # TODO: PID collapse limit as argument
count += 1
break
num_collapses.append(count)
# fit lognormal distribution to all points by maximum likelihood estimation (MLE)
theta, beta = lognormal_MLE(IM_list, num_events, num_collapses)
beta_adj = np.sqrt(
beta**2 + 0.35**2
) # TODO: adjust dispersion by 0.35 to account for modeling uncertainty
print("theta: " + str(theta))
print("beta_adj: " + str(beta_adj))
# write BIM file with new probability of collapse for each IM
DL_files = []
for i in range(len(stripes)):
DL_input_stripe = update_collapsep(DL_input_path, stripes[i],
theta, beta_adj, IM_list[i])
DL_files.append(DL_input_stripe)
except: # run analysis for single IM
stripes = [1]
EDP_files = [EDP_input_path]
DL_files = [DL_input_path]
# run the analysis and save results separately for each stripe
#print(stripes, EDP_files)
for s_i, stripe in enumerate(stripes):
DL_input_path = DL_files[s_i]
# read the type of assessment from the DL input file
with open(DL_input_path, 'r') as f:
DL_input = json.load(f)
# check if the DL input file has information about the loss model
if 'DamageAndLoss' in DL_input:
pass
else:
# if the loss model is not defined, give a warning
print(
'WARNING No loss model defined in the BIM file. Trying to auto-populate.'
)
EDP_input_path = EDP_files[s_i]
# and try to auto-populate the loss model using the BIM information
DL_input, DL_input_path = auto_populate(DL_input_path,
EDP_input_path, DL_method,
realization_count)
DL_method = DL_input['DamageAndLoss']['_method']
stripe_str = '' if len(stripes) == 1 else str(stripe) + '_'
if DL_method == 'FEMA P58':
A = FEMA_P58_Assessment(log_file=log_file)
elif DL_method in ['HAZUS MH EQ', 'HAZUS MH']:
A = HAZUS_Assessment(hazard='EQ', log_file=log_file)
elif DL_method == 'HAZUS MH HU':
A = HAZUS_Assessment(hazard='HU', log_file=log_file)
A.read_inputs(
DL_input_path, EDP_files[s_i],
verbose=False) # make DL inputs into array of all BIM files
A.define_random_variables()
A.define_loss_model()
A.calculate_damage()
A.calculate_losses()
A.aggregate_results()
A.save_outputs(output_path,
EDP_file,
DM_file,
DV_file,
stripe_str,
detailed_results=detailed_results)
return 0