def test_cadel_ground_motion(self):
eqrm_dir = determine_eqrm_path()
cadel_dir = join(eqrm_dir, "..", "test_cadell", "Cadell")
natcadell_loc = join(cadel_dir, "natcadell.csv")
# Silently return from the test if the data set does not exist.
# The data is in python_eqrm
if not exists(natcadell_loc):
return
default_input_dir = join(eqrm_dir, "resources", "data", "")
sites = Structures.from_csv(
natcadell_loc, "", "", default_input_dir, eqrm_dir=eqrm_dir, buildings_usage_classification="FCB"
)
magnitudes = array([7.2])
cadell_periods_loc = join(cadel_dir, "Cadell_periods.csv")
periods = csv.reader(open(cadell_periods_loc)).next()
periods = array([float(v) for v in periods])
num_periods = len(periods)
num_sites = len(sites.latitude)
assert allclose(periods, [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5])
cadell_gm_loc = join(cadel_dir, "Cadell_ground_motions_precision.csv")
SA = self.ground_motions_from_csv(cadell_gm_loc, num_periods, num_sites)
# SA = SA[0:1,...]
# set up damage model
csm_use_variability = None
csm_standard_deviation = None
damage_model = Damage_model(sites, SA, periods, magnitudes, csm_use_variability, csm_standard_deviation)
damage_model.csm_use_variability = False
damage_model.csm_standard_deviation = 0.3
point = damage_model.get_building_displacement()
# point is SA,SD
# SA should by of shape
# (number of buildings,number of events,number of samples).
# print point[0].shape
# check that SA is the right shape
assert point[0].shape == (num_sites, 1)
# check that SD is the same shape as SA
assert point[1].shape == point[0].shape
point = (point[0][..., 0], point[1][..., 0])
# collapse out sample dimension so it matches the shape of matlab
cadell_bd_loc = join(cadel_dir, "Cadell_building_displacements.csv")
matlab_point = open(cadell_bd_loc)
matlab_point = array([[float(p) for p in mpoint.split(",")] for mpoint in matlab_point])
matlab_point = (matlab_point[:, 1], matlab_point[:, 0])
assert allclose(point, matlab_point, 5e-3)
assert allclose(point, matlab_point, 1e-2)
# check that we are 1% of matlabs SA and SD
assert allclose(point, matlab_point, 5e-3)
def calc_total_loss(self, SA, eqrm_flags, event_set_Mw):
"""
Calculate the economic loss and damage state at a site.
eqrm_flags high level controlling object
SA array of Spectral Acceleration, in g, with axis;
sites, events, periods
the site axis usually has a size of 1
event_set_Mw array of Mw, 1D, dimension (events)
(used only by buildings)
Returns a tuple (total_loss, damage_model) where:
total_loss a 4 long list of dollar loss. The loss categories are;
(structure_loss, nsd_loss, accel_loss, contents_loss)
These dollar losses have the dimensions of;
(site, event)
damage_model an instance of the damage model.
used in risk.py to get damage states.
"""
# note: damage_model has an object called capacity_spectrum_model
# buried inside, which will now calculate capacity curves
# parameters
# csm_params are parameters for the capacity_spectrum_model
csm_params = {
'csm_damping_regimes': eqrm_flags.csm_damping_regimes,
'csm_damping_modify_Tav': eqrm_flags.csm_damping_modify_Tav,
'csm_damping_use_smoothing': eqrm_flags.csm_damping_use_smoothing,
'rtol': eqrm_flags.csm_SDcr_tolerance_percentage / 100.0,
'csm_damping_max_iterations':
eqrm_flags.csm_damping_max_iterations,
'sdtcap': # FIXME sdt -> std
eqrm_flags.csm_standard_deviation,
'csm_use_variability': eqrm_flags.csm_use_variability,
'csm_variability_method': eqrm_flags.csm_variability_method,
'csm_hysteretic_damping': eqrm_flags.csm_hysteretic_damping,
'atten_override_RSA_shape': eqrm_flags.atten_override_RSA_shape,
'atten_cutoff_max_spectral_displacement':
eqrm_flags.atten_cutoff_max_spectral_displacement,
'loss_min_pga': eqrm_flags.loss_min_pga
}
damage_model = Damage_model(self,
SA,
eqrm_flags.atten_periods,
event_set_Mw,
eqrm_flags.csm_use_variability,
float(eqrm_flags.csm_standard_deviation),
csm_params=csm_params)
# Note, aggregate slight, medium, critical damage
# Compute building damage and loss (LOTS done here!)
total_loss = \
damage_model.aggregated_building_loss(
ci=eqrm_flags.loss_regional_cost_index_multiplier,
loss_aus_contents=eqrm_flags.loss_aus_contents)
return (total_loss, damage_model)
def test_building_response(self):
#Test that building response is the same as matlab
periods=array([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,
1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9,
3])
SA = array([0.017553049, 0.028380350, 0.036142210, 0.037701113,
0.039325398, 0.038083417, 0.036880517, 0.036190107,
0.035512489, 0.035088679, 0.034669917, 0.033162774,
0.030871523, 0.027841184, 0.025094836, 0.022850476,
0.021322256, 0.019895084, 0.018562342, 0.017317833,
0.016160874, 0.015195155, 0.014287144, 0.013433394,
0.012630662, 0.011875899, 0.011166239, 0.010498986,
0.009871606, 0.009281717, 0.008727078, 0.008140645,
0.007593619, 0.007083352, 0.006607374, 0.006163380])
SA = SA[newaxis, newaxis, :]
magnitudes = array([6.5])
Btype = 'RM2L'
eqrm_dir = determine_eqrm_path()
default_input_dir = join(eqrm_dir, 'resources', 'data', '')
building_parameters = \
building_params_from_csv(building_classification_tag = '',
damage_extent_tag = '',
default_input_dir=default_input_dir)
# Pull the parameters out:
b_index = where([(bt == Btype) for bt in
building_parameters['structure_classification']])
new_bp = {}
for key in building_parameters:
try:
new_bp[key] = building_parameters[key][b_index]
except:
new_bp[key] = building_parameters[key]
structures = Structures(latitude=[-31], longitude=[150],
building_parameters=new_bp,
#bridge_parameters={},
FCB_USAGE=array([111]),
STRUCTURE_CLASSIFICATION=array([Btype]),
STRUCTURE_CATEGORY=array(['BUILDING']))
building_parameters = structures.building_parameters
# All the same for this type anyway
csm_use_variability = None
csm_standard_deviation = None
damage_model = Damage_model(structures, SA, periods, magnitudes,
csm_use_variability, csm_standard_deviation)
# set up the capacity model
capacity_spectrum_model = Capacity_spectrum_model(periods, magnitudes,
building_parameters)
capacity_spectrum_model.smooth_damping = True
capacity_spectrum_model.use_displacement_corner_period = True
capacity_spectrum_model.damp_corner_periods = True
capacity_spectrum_model.use_exact_area = True
capacity_spectrum_model.rtol = 0.01
capacity_spectrum_model.csm_damping_max_iterations = 7
###########################################################
damage_model.capacity_spectrum_model = capacity_spectrum_model
# Warning, point is not used
point = damage_model.get_building_displacement()
# matlab values
SAcr = 0.032208873
SDcr = 0.97944026
assert allclose(point[0], SAcr)
assert allclose(point[1], SDcr)
assert allclose(point, [[[SAcr]], [[SDcr]]])
def calc_total_loss(self, SA, eqrm_flags, event_set_Mw):
"""
Calculate the economic loss and damage state at a site.
eqrm_flags high level controlling object
SA array of Spectral Acceleration, in g, with axis;
sites, events, periods
the site axis usually has a size of 1
event_set_Mw array of Mw, 1D, dimension (events)
(used only by buildings)
Returns a tuple (total_loss, damage_model) where:
total_loss a 4 long list of dollar loss. The loss categories are;
(structure_loss, nsd_loss, accel_loss, contents_loss)
These dollar losses have the dimensions of;
(site, event)
damage_model an instance of the damage model.
used in risk.py to get damage states.
"""
# note: damage_model has an object called capacity_spectrum_model
# buried inside, which will now calculate capacity curves
# parameters
# csm_params are parameters for the capacity_spectrum_model
csm_params = {'csm_damping_regimes':
eqrm_flags.csm_damping_regimes,
'csm_damping_modify_Tav':
eqrm_flags.csm_damping_modify_Tav,
'csm_damping_use_smoothing':
eqrm_flags.csm_damping_use_smoothing,
'rtol':
eqrm_flags.csm_SDcr_tolerance_percentage / 100.0,
'csm_damping_max_iterations':
eqrm_flags.csm_damping_max_iterations,
'sdtcap': # FIXME sdt -> std
eqrm_flags.csm_standard_deviation,
'csm_use_variability':
eqrm_flags.csm_use_variability,
'csm_variability_method':
eqrm_flags.csm_variability_method,
'csm_hysteretic_damping':
eqrm_flags.csm_hysteretic_damping,
'atten_override_RSA_shape':
eqrm_flags.atten_override_RSA_shape,
'atten_cutoff_max_spectral_displacement':
eqrm_flags.atten_cutoff_max_spectral_displacement,
'loss_min_pga': eqrm_flags.loss_min_pga}
damage_model = Damage_model(self, SA, eqrm_flags.atten_periods,
event_set_Mw,
eqrm_flags.csm_use_variability,
float(eqrm_flags.csm_standard_deviation),
csm_params=csm_params)
# Note, aggregate slight, medium, critical damage
# Compute building damage and loss (LOTS done here!)
total_loss = \
damage_model.aggregated_building_loss(
ci=eqrm_flags.loss_regional_cost_index_multiplier,
loss_aus_contents=eqrm_flags.loss_aus_contents)
return (total_loss, damage_model)
def test_cadel_ground_motion(self):
eqrm_dir = determine_eqrm_path()
cadel_dir = join(eqrm_dir,'..','test_cadell', 'Cadell')
natcadell_loc = join(cadel_dir, 'natcadell.csv')
# Silently return from the test if the data set does not exist.
# The data is in python_eqrm
if not exists(natcadell_loc):
return
default_input_dir = join(eqrm_dir,'resources',
'data', '')
sites=Structures.from_csv(natcadell_loc,
'',
'',
default_input_dir,
eqrm_dir=eqrm_dir,
buildings_usage_classification='FCB')
magnitudes=array([7.2])
cadell_periods_loc = join(cadel_dir, 'Cadell_periods.csv')
periods=csv.reader(open(cadell_periods_loc)).next()
periods=array([float(v) for v in periods])
num_periods=len(periods)
num_sites=len(sites.latitude)
assert allclose(periods,[0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,
0.9,1,1.5,2,2.5,3,3.5,4,4.5,5])
cadell_gm_loc = join(cadel_dir, 'Cadell_ground_motions_precision.csv')
SA=self.ground_motions_from_csv(cadell_gm_loc,
num_periods,num_sites)
#SA = SA[0:1,...]
# set up damage model
csm_use_variability = None
csm_standard_deviation = None
damage_model=Damage_model(sites,SA,periods,magnitudes,
csm_use_variability, csm_standard_deviation)
damage_model.csm_use_variability=False
damage_model.csm_standard_deviation=0.3
point=damage_model.get_building_displacement()
# point is SA,SD
# SA should by of shape
# (number of buildings,number of events,number of samples).
# print point[0].shape
# check that SA is the right shape
assert point[0].shape==(num_sites,1)
# check that SD is the same shape as SA
assert point[1].shape== point[0].shape
point = (point[0][...,0],point[1][...,0])
#collapse out sample dimension so it matches the shape of matlab
cadell_bd_loc = join(cadel_dir, 'Cadell_building_displacements.csv')
matlab_point=open(cadell_bd_loc)
matlab_point=array([[float(p) for p in mpoint.split(',')]
for mpoint in matlab_point])
matlab_point=(matlab_point[:,1],matlab_point[:,0])
assert allclose(point,matlab_point,5e-3)
assert allclose(point,matlab_point,1e-2)
# check that we are 1% of matlabs SA and SD
assert allclose(point,matlab_point,5e-3)
def test_building_response(self):
#Test that building response is the same as matlab
periods=array([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5,
0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,
1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9,
3])
SA = array([0.017553049, 0.028380350, 0.036142210, 0.037701113,
0.039325398, 0.038083417, 0.036880517, 0.036190107,
0.035512489, 0.035088679, 0.034669917, 0.033162774,
0.030871523, 0.027841184, 0.025094836, 0.022850476,
0.021322256, 0.019895084, 0.018562342, 0.017317833,
0.016160874, 0.015195155, 0.014287144, 0.013433394,
0.012630662, 0.011875899, 0.011166239, 0.010498986,
0.009871606, 0.009281717, 0.008727078, 0.008140645,
0.007593619, 0.007083352, 0.006607374, 0.006163380])
SA = SA[newaxis, newaxis, :]
magnitudes = array([6.5])
Btype = 'RM2L'
eqrm_dir = determine_eqrm_path()
default_input_dir = join(eqrm_dir, 'resources', 'data', '')
building_parameters = \
building_params_from_csv(building_classification_tag = '',
damage_extent_tag = '',
default_input_dir=default_input_dir)
# Pull the parameters out:
b_index = where([(bt == Btype) for bt in
building_parameters['structure_classification']])
new_bp = {}
for key in building_parameters:
try:
new_bp[key] = building_parameters[key][b_index]
except:
new_bp[key] = building_parameters[key]
structures = Structures(latitude=[-31], longitude=[150],
building_parameters=new_bp,
#bridge_parameters={},
FCB_USAGE=array([111]),
STRUCTURE_CLASSIFICATION=array([Btype]),
STRUCTURE_CATEGORY=array(['BUILDING']))
building_parameters = structures.building_parameters
# All the same for this type anyway
csm_use_variability = None
csm_standard_deviation = None
damage_model = Damage_model(structures, SA, periods, magnitudes,
csm_use_variability, csm_standard_deviation)
# set up the capacity model
capacity_spectrum_model = Capacity_spectrum_model(periods, magnitudes,
building_parameters)
capacity_spectrum_model.smooth_damping = True
capacity_spectrum_model.use_displacement_corner_period = True
capacity_spectrum_model.damp_corner_periods = True
capacity_spectrum_model.use_exact_area = True
capacity_spectrum_model.rtol = 0.01
capacity_spectrum_model.csm_damping_max_iterations = 7
###########################################################
damage_model.capacity_spectrum_model = capacity_spectrum_model
# Warning, point is not used
point = damage_model.get_building_displacement()
# matlab values
SAcr = 0.032208873
SDcr = 0.97944026
assert allclose(point[0], SAcr)
assert allclose(point[1], SDcr)
assert allclose(point, [[[SAcr]], [[SDcr]]])
