def test_godt_2008():
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'godt_2008.ini')
conf = ConfigObj(conf_file)
conf['godt_2008']['divfactor'] = '1.'
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
conf = correct_config_filepaths(data_path, conf)
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
maplayers = godt2008(shakefile, conf)
pgrid = maplayers['model']['grid']
test_data = pgrid.getData()
if changetarget:
# To change target data:
pgrd = GMTGrid(pgrid.getData(), pgrid.getGeoDict())
pgrd.save(
os.path.join(datadir, 'loma_prieta', 'targets', 'godt_2008.grd'))
# Load target
target_file = os.path.join(datadir, 'loma_prieta', 'targets',
'godt_2008.grd')
target_grid = GMTGrid.load(target_file)
target_data = target_grid.getData()
# Assert
np.testing.assert_allclose(target_data, test_data, rtol=1e-3)
def test_zhu2015():
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'zhu_2015.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
# Check slopefile trimming
conf['zhu_2015']['slopefile'] = 'global_gted_maxslope_30c.flt'
conf = correct_config_filepaths(data_path, conf)
# Run with divfactor of 1
conf['zhu_2015']['divfactor'] = '1.'
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
lm = LM.LogisticModel(shakefile, conf, saveinputs=True)
maplayers = lm.calculate()
pgrid = maplayers['model']['grid']
test_data = pgrid.getData()
if changetarget:
# To change target data:
pgrd = GMTGrid(pgrid.getData(), pgrid.getGeoDict())
pgrd.save(
os.path.join(datadir, 'loma_prieta', 'targets', 'zhu2015.grd'))
# Load target
target_file = os.path.join(datadir, 'loma_prieta', 'targets',
'zhu2015.grd')
target_grid = GMTGrid.load(target_file)
target_data = target_grid.getData()
# Assert
np.testing.assert_allclose(target_data, test_data, rtol=1e-3)
def test_jessee_2018():
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'jessee_2018.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
conf = correct_config_filepaths(data_path, conf)
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
undertainty_file = os.path.join(datadir, 'loma_prieta', 'uncertainty.xml')
lm = LM.LogisticModel(shakefile,
conf,
saveinputs=True,
uncertfile=undertainty_file)
maplayers = lm.calculate()
pgrid = maplayers['model']['grid']
stdgrid = maplayers['std']['grid']
test_data = pgrid.getData()
test_data_std = stdgrid.getData()
if changetarget:
# To change target data:
pgrd = GMTGrid(pgrid.getData(), pgrid.getGeoDict())
pgrd.save(
os.path.join(datadir, 'loma_prieta', 'targets', 'jessee_2018.grd'))
stdgrd = GMTGrid(stdgrid.getData(), stdgrid.getGeoDict())
stdgrd.save(
os.path.join(datadir, 'loma_prieta', 'targets',
'jessee_2018_std.grd'))
# Load target
target_file = os.path.join(datadir, 'loma_prieta', 'targets',
'jessee_2018.grd')
target_grid = GMTGrid.load(target_file)
target_data = target_grid.getData()
std_file = os.path.join(datadir, 'loma_prieta', 'targets',
'jessee_2018_std.grd')
target_grid_std = GMTGrid.load(std_file)
target_data_std = target_grid_std.getData()
# Assert
np.testing.assert_allclose(target_data, test_data, rtol=1e-3)
np.testing.assert_allclose(target_data_std, test_data_std, rtol=1e-3)
def basic_test():
mmidata = np.array([[7, 8, 8, 8, 7], [8, 9, 9, 9, 8], [8, 9, 10, 9, 8],
[8, 9, 9, 8, 8], [7, 8, 8, 6, 5]],
dtype=np.float32)
popdata = np.ones_like(mmidata) * 1e7
isodata = np.array(
[[4, 4, 4, 4, 4], [4, 4, 4, 4, 4], [4, 4, 156, 156, 156],
[156, 156, 156, 156, 156], [156, 156, 156, 156, 156]],
dtype=np.int32)
shakefile = get_temp_file_name()
popfile = get_temp_file_name()
isofile = get_temp_file_name()
geodict = GeoDict({
'xmin': 0.5,
'xmax': 4.5,
'ymin': 0.5,
'ymax': 4.5,
'dx': 1.0,
'dy': 1.0,
'nx': 5,
'ny': 5
})
layers = OrderedDict([
('mmi', mmidata),
])
event_dict = {
'event_id': 'us12345678',
'magnitude': 7.8,
'depth': 10.0,
'lat': 34.123,
'lon': -118.123,
'event_timestamp': datetime.utcnow(),
'event_description': 'foo',
'event_network': 'us'
}
shake_dict = {
'event_id': 'us12345678',
'shakemap_id': 'us12345678',
'shakemap_version': 1,
'code_version': '4.5',
'process_timestamp': datetime.utcnow(),
'shakemap_originator': 'us',
'map_status': 'RELEASED',
'shakemap_event_type': 'ACTUAL'
}
unc_dict = {'mmi': (1, 1)}
shakegrid = ShakeGrid(layers, geodict, event_dict, shake_dict, unc_dict)
shakegrid.save(shakefile)
popgrid = GMTGrid(popdata, geodict.copy())
isogrid = GMTGrid(isodata, geodict.copy())
popgrid.save(popfile)
isogrid.save(isofile)
ratedict = {
4: {
'start': [2010, 2012, 2014, 2016],
'end': [2012, 2014, 2016, 2018],
'rate': [0.01, 0.02, 0.03, 0.04]
},
156: {
'start': [2010, 2012, 2014, 2016],
'end': [2012, 2014, 2016, 2018],
'rate': [0.02, 0.03, 0.04, 0.05]
}
}
popgrowth = PopulationGrowth(ratedict)
popyear = datetime.utcnow().year
exposure = Exposure(popfile, popyear, isofile, popgrowth=popgrowth)
expdict = exposure.calcExposure(shakefile)
modeldict = [
LognormalModel('AF', 11.613073, 0.180683, 1.0),
LognormalModel('CN', 10.328811, 0.100058, 1.0)
]
fatmodel = EmpiricalLoss(modeldict)
# for the purposes of this test, let's override the rates
# for Afghanistan and China with simpler numbers.
fatmodel.overrideModel(
'AF',
np.array([0, 0, 0, 0, 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0],
dtype=np.float32))
fatmodel.overrideModel(
'CN',
np.array([0, 0, 0, 0, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3, 0],
dtype=np.float32))
print('Testing very basic fatality calculation...')
fatdict = fatmodel.getLosses(expdict)
# strictly speaking, the afghanistant fatalities should be 462,000 but floating point precision dictates otherwise.
testdict = {'CN': 46111, 'AF': 461999, 'TotalFatalities': 508110}
for key, value in fatdict.items():
assert value == testdict[key]
print('Passed very basic fatality calculation...')
print('Testing grid fatality calculations...')
mmidata = exposure.getShakeGrid().getLayer('mmi').getData()
popdata = exposure.getPopulationGrid().getData()
isodata = exposure.getCountryGrid().getData()
fatgrid = fatmodel.getLossGrid(mmidata, popdata, isodata)
assert np.nansum(fatgrid) == 508111
print('Passed grid fatality calculations...')
# Testing modifying rates and stuffing them back in...
chile = LognormalModel('CL', 19.786773, 0.259531, 0.0)
rates = chile.getLossRates(np.arange(5, 10))
modrates = rates * 2 # does this make event twice as deadly?
# roughly the exposures from 2015-9-16 CL event
expo_pop = np.array(
[0, 0, 0, 1047000, 7314000, 1789000, 699000, 158000, 0, 0])
mmirange = np.arange(5, 10)
chile_deaths = chile.getLosses(expo_pop[4:9], mmirange)
chile_double_deaths = chile.getLosses(expo_pop[4:9],
mmirange,
rates=modrates)
print('Chile model fatalities: %f' % chile_deaths)
print('Chile model x2 fatalities: %f' % chile_double_deaths)
def model_test_simple():
A = 4 #ccode for afghanistan
J = 392 #ccode for japan
R = 1 #rural code
U = 2 #urban code
#create a 5x5 population data set with 1000 people in each cell
popdata = np.ones((5, 5)) * 1000.0
#create a mixed grid of afghanistan and japan (have very different inventory,collapse, and fatality rates.)
isodata = np.array([[A, A, A, A, A], [A, A, A, A, A], [A, A, A, J, J],
[J, J, J, J, J], [J, J, J, J, J]],
dtype=np.int16)
#make a mix of urban and rural cells
urbdata = np.array([[R, R, R, R, R], [R, U, U, U, R], [R, U, U, U, U],
[U, U, U, R, R], [R, R, R, R, R]],
dtype=np.int16)
mmidata = np.array([[6, 7, 8, 9, 6], [7, 8, 9, 6, 7], [8, 9, 6, 6, 7],
[8, 9, 6, 7, 8], [9, 6, 7, 8, 9]],
dtype=np.float32)
homedir = os.path.dirname(
os.path.abspath(__file__)) #where is this script?
invfile = os.path.join(homedir, '..', 'data', 'semi_inventory.hdf')
colfile = os.path.join(homedir, '..', 'data', 'semi_collapse_mmi.hdf')
fatfile = os.path.join(homedir, '..', 'data', 'semi_casualty.hdf')
workfile = os.path.join(homedir, '..', 'data', 'semi_workforce.hdf')
growthfile = os.path.join(homedir, '..', 'data',
'WPP2015_POP_F02_POPULATION_GROWTH_RATE.xls')
geodict = GeoDict({
'xmin': 0.5,
'xmax': 4.5,
'ymin': 0.5,
'ymax': 4.5,
'dx': 1.0,
'dy': 1.0,
'nx': 5,
'ny': 5
})
popgrid = GMTGrid(popdata, geodict)
isogrid = GMTGrid(isodata, geodict)
urbgrid = GMTGrid(urbdata, geodict)
popyear = 2016
layers = {'mmi': mmidata}
eventdict = {
'event_id': '1234',
'magnitude': 7.5,
'lat': 34.2,
'lon': 118.2,
'depth': 10.0,
'event_timestamp': datetime(2016, 1, 1, 0, 0, 0),
'event_description': 'test data',
'event_network': 'us'
}
shakedict = {
'event_id': '1234',
'shakemap_id': '1234',
'shakemap_version': 1,
'code_version': '1.0',
'process_timestamp': datetime.utcnow(),
'shakemap_originator': 'us',
'map_status': 'RELEASED',
'shakemap_event_type': 'SCENARIO'
}
uncdict = {'mmi': (1.0, 1)}
mmigrid = ShakeGrid(layers, geodict, eventdict, shakedict, uncdict)
popfile = isofile = urbfile = shakefile = ''
try:
#make some temporary files
f, popfile = tempfile.mkstemp()
os.close(f)
f, isofile = tempfile.mkstemp()
os.close(f)
f, urbfile = tempfile.mkstemp()
os.close(f)
f, shakefile = tempfile.mkstemp()
os.close(f)
popgrid.save(popfile)
isogrid.save(isofile)
urbgrid.save(urbfile)
mmigrid.save(shakefile)
semi = SemiEmpiricalFatality.fromDefault()
losses, resfat, nonresfat = semi.getLosses(shakefile)
assert losses == 85
print('Semi-empirical model calculations appear to be done correctly.')
except:
print(
'There is an error attempting to do semi-empirical loss calculations.'
)
finally:
files = [popfile, isofile, urbfile, shakefile]
for fname in files:
if os.path.isfile(fname):
os.remove(fname)
def make_test_semi_model(ccode, timeofday, density, popvalue, mmi):
"""Run the semi-empirical model for a single value of input. Intended for testing purposes.
:param ccode:
Two letter ISO country code ('US', 'JP', etc.) to be used to extract inventory, collapse rates, etc.
:param timeofday:
One of 'day','night' - used to determine residential/non-residental population distribution and casualty rates.
:param density:
One of semimodel.URBAN (2) or semimodel.RURAL (1).
:param popvalue:
Scalar population value to multiply by inventory, collapse, and fatality rates.
:param mmi:
MMI value used to extract collapse rates in given country code.
:returns:
Tuple of:
1) Total number of fatalities
2) Dictionary of residential fatalities per building type, per country.
3) Dictionary of non-residential fatalities per building type, per country.
"""
country = Country()
cdict = country.getCountry(ccode)
ucode = cdict['ISON']
geodict = GeoDict({
'xmin': 0.5,
'xmax': 4.5,
'ymin': 0.5,
'ymax': 4.5,
'dx': 1.0,
'dy': 1.0,
'nx': 5,
'ny': 5
})
if timeofday == 'day':
etime = datetime(2016, 1, 1, 12, 0, 0) #noon
elif timeofday == 'transit':
etime = datetime(2016, 1, 1, 18, 0, 0) #6 pm
else:
etime = datetime(2016, 1, 1, 0, 0, 0) #midnight
eventdict = {
'event_id': '1234',
'magnitude': 7.5,
'lat': 0.0,
'lon': 0.0,
'depth': 10.0,
'event_timestamp': etime,
'event_description': 'test data',
'event_network': 'us'
}
shakedict = {
'event_id': '1234',
'shakemap_id': '1234',
'shakemap_version': 1,
'code_version': '1.0',
'process_timestamp': datetime.utcnow(),
'shakemap_originator': 'us',
'map_status': 'RELEASED',
'shakemap_event_type': 'SCENARIO'
}
uncdict = {'mmi': (1.0, 1)}
popdata = np.ones((2, 2), dtype=np.float32) * (popvalue) / 4
isodata = np.ones((2, 2), dtype=np.int16) * ucode
urbdata = np.ones((2, 2), dtype=np.int16) * density
mmidata = np.ones((2, 2), dtype=np.float32) * mmi
geodict = GeoDict({
'xmin': 0.5,
'xmax': 1.5,
'ymin': 0.5,
'ymax': 1.5,
'dx': 1.0,
'dy': 1.0,
'nx': 2,
'ny': 2
})
popgrid = GMTGrid(popdata, geodict)
isogrid = GMTGrid(isodata, geodict)
urbgrid = GMTGrid(urbdata, geodict)
popyear = 2016
layers = {'mmi': mmidata}
mmigrid = ShakeGrid(layers, geodict, eventdict, shakedict, uncdict)
popfile = isofile = urbfile = shakefile = ''
popsum = None
newresfat = None
newnresfat = None
try:
#make some temporary files
f, popfile = tempfile.mkstemp()
os.close(f)
f, isofile = tempfile.mkstemp()
os.close(f)
f, urbfile = tempfile.mkstemp()
os.close(f)
f, shakefile = tempfile.mkstemp()
os.close(f)
popgrid.save(popfile)
isogrid.save(isofile)
urbgrid.save(urbfile)
mmigrid.save(shakefile)
semi = SemiEmpiricalFatality.fromDefault()
semi.setGlobalFiles(popfile, popyear, urbfile, isofile)
t, resfat, nonresfat = semi.getLosses(shakefile)
popsum = 0
newresfat = {ccode: {}}
newnonresfat = {ccode: {}}
for key, value in resfat[ccode].items():
if value < 1:
value = np.floor(value)
newresfat[ccode][key] = value / 4.0
popsum += value / 4.0
for key, value in nonresfat[ccode].items():
newnonresfat[ccode][key] = value / 4.0
if value < 1:
value = np.floor(value)
popsum += value / 4.0
popsum = int(popsum)
finally:
files = [popfile, isofile, urbfile, shakefile]
for fname in files:
if os.path.isfile(fname):
os.remove(fname)
return (popsum, newresfat, newnonresfat)
