def test_pop_growth():
homedir = os.path.dirname(
os.path.abspath(__file__)) # where is this script?
print('Testing loading Population Growth from UN spreadsheet...')
pg = PopulationGrowth.fromDefault()
print('Passed loading Population Growth from UN spreadsheet...')
print('Testing getting growth rates for the US...')
rate = pg.getRate(840, 1963)
assert rate == 1.373 / 100.0
allrates = np.array([
1.581, 1.724, 1.373, 0.987, 0.885, 0.948, 0.945, 0.985, 1.035, 1.211,
0.915, 0.907, 0.754
]) / 100.0
starts, usrates = pg.getRates(840)
np.testing.assert_almost_equal(usrates, allrates)
print('Passed getting growth rate for the US...')
# three scenarios to test with regards to population growth rates
# 1: time population data was "collected" is before the event time
tpop = 2015
tevent = 2016
ccode = 840 # US
pop = 1e6
newpop = pg.adjustPopulation(pop, ccode, tpop, tevent)
tpop = 2007
tevent = 2016
ccode = 840
pop = 1e6
# TODO: hand-verify this result!
newpop = pg.adjustPopulation(pop, ccode, tpop, tevent)
# #2: time population data was "collected" is after the event time
tpop = 2016
tevent = 2012
ccode = 840 # US
pop = 1e6
newpop = pg.adjustPopulation(pop, ccode, tpop, tevent)
def test():
print('Testing Northridge exposure check (with GPW data).')
events = ['northridge']
homedir = os.path.dirname(
os.path.abspath(__file__)) # where is this script?
excelfile = os.path.join(homedir, '..', 'data',
'WPP2015_POP_F02_POPULATION_GROWTH_RATE.xls')
for event in events:
shakefile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_grid.xml' % event)
popfile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_gpw.flt' % event)
isofile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_isogrid.bil' % event)
growth = PopulationGrowth.fromDefault()
exp = Exposure(popfile, 2012, isofile)
results = exp.calcExposure(shakefile)
cmpexposure = [
0, 0, 1817, 1767260, 5840985, 5780298, 2738374, 1559657, 4094, 0
]
np.testing.assert_almost_equal(cmpexposure, results['TotalExposure'])
print('Passed Northridge exposure check (with GPW data).')
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 = Grid2D(popdata, geodict.copy())
isogrid = Grid2D(isodata, geodict.copy())
write(popgrid, popfile, 'netcdf')
write(isogrid, isofile, 'netcdf')
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 test():
homedir = os.path.dirname(os.path.abspath(
__file__)) # where is this script?
fatfile = os.path.join(homedir, '..', 'data', 'fatality.xml')
ecofile = os.path.join(homedir, '..', 'data', 'economy.xml')
cityfile = os.path.join(homedir, '..', 'data', 'cities1000.txt')
event = 'northridge'
shakefile = os.path.join(homedir, '..', 'data',
'eventdata', event, '%s_grid.xml' % event)
popfile = os.path.join(homedir, '..', 'data',
'eventdata', event, '%s_gpw.flt' % event)
isofile = os.path.join(homedir, '..', 'data',
'eventdata', event, '%s_isogrid.bil' % event)
urbanfile = os.path.join(homedir, '..', 'data',
'eventdata', 'northridge', 'northridge_urban.bil')
oceanfile = os.path.join(
homedir, '..', 'data', 'eventdata', 'northridge', 'northridge_ocean.json')
oceangridfile = os.path.join(
homedir, '..', 'data', 'eventdata', 'northridge', 'northridge_ocean.bil')
timezonefile = os.path.join(
homedir, '..', 'data', 'eventdata', 'northridge', 'northridge_timezone.shp')
invfile = os.path.join(homedir, '..', 'data', 'semi_inventory.hdf')
colfile = os.path.join(homedir, '..', 'data', 'semi_collapse_mmi.hdf')
casfile = os.path.join(homedir, '..', 'data', 'semi_casualty.hdf')
workfile = os.path.join(homedir, '..', 'data', 'semi_workforce.hdf')
tdir = tempfile.mkdtemp()
basename = os.path.join(tdir, 'output')
exp = Exposure(popfile, 2012, isofile)
results = exp.calcExposure(shakefile)
shakegrid = exp.getShakeGrid()
popgrid = exp.getPopulationGrid()
pdffile, pngfile, mapcities = draw_contour(
shakegrid, popgrid, oceanfile, oceangridfile, cityfile, basename)
shutil.rmtree(tdir)
popyear = 2012
shake_tuple = getHeaderData(shakefile)
tsunami = shake_tuple[1]['magnitude'] >= TSUNAMI_MAG_THRESH
semi = SemiEmpiricalFatality.fromDefault()
semi.setGlobalFiles(popfile, popyear, urbanfile, isofile)
semiloss, resfat, nonresfat = semi.getLosses(shakefile)
popgrowth = PopulationGrowth.fromDefault()
econexp = EconExposure(popfile, 2012, isofile)
fatmodel = EmpiricalLoss.fromDefaultFatality()
expobject = Exposure(popfile, 2012, isofile, popgrowth)
expdict = expobject.calcExposure(shakefile)
fatdict = fatmodel.getLosses(expdict)
econexpdict = econexp.calcExposure(shakefile)
ecomodel = EmpiricalLoss.fromDefaultEconomic()
ecodict = ecomodel.getLosses(expdict)
shakegrid = econexp.getShakeGrid()
pagerversion = 1
cities = Cities.loadFromGeoNames(cityfile)
impact1 = '''Red alert level for economic losses. Extensive damage is probable
and the disaster is likely widespread. Estimated economic losses are less
than 1% of GDP of Italy. Past events with this alert level have required
a national or international level response.'''
impact2 = '''Orange alert level for shaking-related fatalities. Significant
casualties are likely.'''
structcomment = '''Overall, the population in this region resides in structures
that are a mix of vulnerable and earthquake resistant construction. The predominant
vulnerable building types are unreinforced brick with mud and mid-rise nonductile
concrete frame with infill construction.'''
histeq = [1, 2, 3]
struct_comment = '''Overall, the population in this region resides
in structures that are resistant to earthquake
shaking, though some vulnerable structures
exist.'''
secondary_comment = '''Recent earthquakes in this area have caused secondary hazards
such as landslides that might have contributed to losses.'''
hist_comment = ''''A magnitude 7.1 earthquake 240 km east of this event struck Reventador: Ecuador
on March 6, 1987 (UTC), with estimated population exposures of 14,000 at intensity VIII and 2,000
at intensity IX or greater, resulting in a reported 5,000 fatalities.'''.replace('\n', '')
location = 'At the top of the world.'
is_released = True
doc = PagerData()
eventcode = shakegrid.getEventDict()['event_id']
versioncode = eventcode
doc.setInputs(shakegrid, timezonefile, pagerversion,
versioncode, eventcode, tsunami, location, is_released)
doc.setExposure(expdict, econexpdict)
doc.setModelResults(fatmodel, ecomodel,
fatdict, ecodict,
semiloss, resfat, nonresfat)
doc.setComments(impact1, impact2, struct_comment,
hist_comment, secondary_comment)
doc.setMapInfo(cityfile, mapcities)
doc.validate()
# let's test the property methods
tdoc(doc, shakegrid, impact1, impact2,
expdict, struct_comment, hist_comment)
# see if we can save this to a bunch of files then read them back in
try:
tdir = tempfile.mkdtemp()
doc.saveToJSON(tdir)
newdoc = PagerData()
newdoc.loadFromJSON(tdir)
tdoc(newdoc, shakegrid, impact1, impact2,
expdict, struct_comment, hist_comment)
# test the xml saving method
xmlfile = doc.saveToLegacyXML(tdir)
except Exception as e:
assert 1 == 2
finally:
shutil.rmtree(tdir)
def test():
event = 'northridge'
homedir = os.path.dirname(
os.path.abspath(__file__)) #where is this script?
xmlfile = os.path.join(homedir, '..', 'data', 'economy.xml')
growthfile = os.path.join(homedir, '..', 'data',
'WPP2015_POP_F02_POPULATION_GROWTH_RATE.xls')
gdpfile = os.path.join(homedir, '..', 'data',
'API_NY.GDP.PCAP.CD_DS2_en_excel_v2.xls')
shakefile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_grid.xml' % event)
popfile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_gpw.flt' % event)
isofile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'%s_isogrid.bil' % event)
shapefile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'City_BoundariesWGS84', 'City_Boundaries.shp')
print('Test loading economic exposure from inputs...')
popgrowth = PopulationGrowth.fromDefault()
econexp = EconExposure(popfile, 2012, isofile)
print('Passed loading economic exposure from inputs...')
print('Test loading empirical fatality model from XML file...')
ecomodel = EmpiricalLoss.fromDefaultEconomic()
print('Passed loading empirical fatality model from XML file.')
print('Testing calculating probabilities for standard PAGER ranges...')
expected = {'UK': 6819.883892 * 1e6}
G = 2.5
probs = ecomodel.getProbabilities(expected, G)
testprobs = {
'0-1': 0.00020696841425738358,
'1-10': 0.0043200811319132086,
'10-100': 0.041085446477813294,
'100-1000': 0.17564981840854255,
'1000-10000': 0.33957681768639003,
'10000-100000': 0.29777890303065313,
'100000-10000000': 0.14138196485040311
}
for key, value in probs.items():
np.testing.assert_almost_equal(value, testprobs[key])
print(
'Passed combining G values from all countries that contributed to losses...'
)
print('Test retrieving economic model data from XML file...')
model = ecomodel.getModel('af')
testmodel = LognormalModel('dummy',
9.013810,
0.100000,
4.113200,
alpha=15.065400)
assert model == testmodel
print('Passed retrieving economic model data from XML file.')
print('Testing with known exposures/losses for 1994 Northridge EQ...')
exposure = {
'xf':
np.array([
0, 0, 556171936.807, 718990717350.0, 2.40385709638e+12,
2.47073141687e+12, 1.2576210799e+12, 698888019337.0, 1913733716.16,
0.0
])
}
expodict = ecomodel.getLosses(exposure)
testdict = {'xf': 25945225582}
assert expodict['xf'] == testdict['xf']
print(
'Passed testing with known exposures/fatalities for 1994 Northridge EQ.'
)
print('Testing calculating total economic losses for Northridge...')
expdict = econexp.calcExposure(shakefile)
ecomodel = EmpiricalLoss.fromDefaultEconomic()
lossdict = ecomodel.getLosses(expdict)
testdict = {'XF': 23172277187}
assert lossdict['XF'] == testdict['XF']
print('Passed calculating total economic losses for Northridge...')
print('Testing creating a economic loss grid...')
mmidata = econexp.getShakeGrid().getLayer('mmi').getData()
popdata = econexp.getEconPopulationGrid().getData()
isodata = econexp.getCountryGrid().getData()
ecogrid = ecomodel.getLossGrid(mmidata, popdata, isodata)
ecosum = 23172275857.094917
assert np.nansum(ecogrid) == ecosum
print('Passed creating a economic loss grid.')
print('Testing assigning economic losses to polygons...')
popdict = econexp.getPopulationGrid().getGeoDict()
shapes = []
f = fiona.open(shapefile, 'r')
for row in f:
shapes.append(row)
f.close()
ecoshapes, toteco = ecomodel.getLossByShapes(mmidata, popdata, isodata,
shapes, popdict)
ecoshapes = sorted(ecoshapes,
key=lambda shape: shape['properties']['dollars_lost'],
reverse=True)
lalosses = 17323352577
for shape in ecoshapes:
if shape['id'] == '312': #Los Angeles
cname = shape['properties']['CITY_NAME']
dollars = shape['properties']['dollars_lost']
assert lalosses == dollars
assert cname == 'Los Angeles'
print('Passed assigning economic losses to polygons...')
def test():
homedir = os.path.dirname(os.path.abspath(__file__)) #where is this script?
fatfile = os.path.join(homedir,'..','data','fatality.xml')
ecofile = os.path.join(homedir,'..','data','economy.xml')
cityfile = os.path.join(homedir,'..','data','cities1000.txt')
event = 'northridge'
shakefile = os.path.join(homedir,'..','data','eventdata',event,'%s_grid.xml' % event)
popfile = os.path.join(homedir,'..','data','eventdata',event,'%s_gpw.flt' % event)
isofile = os.path.join(homedir,'..','data','eventdata',event,'%s_isogrid.bil' % event)
urbanfile = os.path.join(homedir,'..','data','eventdata','northridge','northridge_urban.bil')
oceanfile = os.path.join(homedir,'..','data','eventdata','northridge','northridge_ocean.json')
invfile = os.path.join(homedir,'..','data','semi_inventory.hdf')
colfile = os.path.join(homedir,'..','data','semi_collapse_mmi.hdf')
casfile = os.path.join(homedir,'..','data','semi_casualty.hdf')
workfile = os.path.join(homedir,'..','data','semi_workforce.hdf')
tdir = tempfile.mkdtemp()
outfile = os.path.join(tdir,'output.pdf')
pngfile,mapcities = draw_contour(shakefile,popfile,oceanfile,cityfile,outfile,make_png=True)
shutil.rmtree(tdir)
popyear = 2012
semi = SemiEmpiricalFatality.fromDefault()
semi.setGlobalFiles(popfile,popyear,urbanfile,isofile)
semiloss,resfat,nonresfat = semi.getLosses(shakefile)
popgrowth = PopulationGrowth.fromDefault()
econexp = EconExposure(popfile,2012,isofile)
fatmodel = EmpiricalLoss.fromDefaultFatality()
expobject = Exposure(popfile,2012,isofile,popgrowth)
expdict = expobject.calcExposure(shakefile)
fatdict = fatmodel.getLosses(expdict)
econexpdict = econexp.calcExposure(shakefile)
ecomodel = EmpiricalLoss.fromDefaultEconomic()
ecodict = ecomodel.getLosses(expdict)
shakegrid = econexp.getShakeGrid()
pagerversion = 1
cities = Cities.loadFromGeoNames(cityfile)
impact1 = '''Red alert level for economic losses. Extensive damage is probable
and the disaster is likely widespread. Estimated economic losses are less
than 1% of GDP of Italy. Past events with this alert level have required
a national or international level response.'''
impact2 = '''Orange alert level for shaking-related fatalities. Significant
casualties are likely.'''
structcomment = '''Overall, the population in this region resides in structures
that are a mix of vulnerable and earthquake resistant construction. The predominant
vulnerable building types are unreinforced brick with mud and mid-rise nonductile
concrete frame with infill construction.'''
histeq = [1,2,3]
struct_comment = '''Overall, the population in this region resides
in structures that are resistant to earthquake
shaking, though some vulnerable structures
exist.'''
secondary_comment = '''Recent earthquakes in this area have caused secondary hazards
such as landslides that might have contributed to losses.'''
hist_comment = ''''A magnitude 7.1 earthquake 240 km east of this event struck Reventador: Ecuador
on March 6, 1987 (UTC), with estimated population exposures of 14,000 at intensity VIII and 2,000
at intensity IX or greater, resulting in a reported 5,000 fatalities.'''.replace('\n','')
doc = PagerData()
doc.setInputs(shakegrid,pagerversion,shakegrid.getEventDict()['event_id'])
doc.setExposure(expdict,econexpdict)
doc.setModelResults(fatmodel,ecomodel,
fatdict,ecodict,
semiloss,resfat,nonresfat)
doc.setComments(impact1,impact2,struct_comment,hist_comment,secondary_comment)
doc.setMapInfo(cityfile,mapcities)
doc.validate()
eventinfo = doc.getEventInfo()
assert eventinfo['mag'] == shakegrid.getEventDict()['magnitude']
imp1,imp2 = doc.getImpactComments()
assert imp1 == impact1 and imp2 == impact2
version = doc.getSoftwareVersion()
elapsed = doc.getElapsed()
exp = doc.getTotalExposure()
assert np.isclose(np.array(exp),expdict['TotalExposure']).all()
hist_table = doc.getHistoricalTable()
assert hist_table[0]['EventID'] == '199206281505'
scomm = doc.getStructureComment()
assert scomm == struct_comment
hcomm = doc.getHistoricalComment()
assert hcomm == hist_comment
citytable = doc.getCityTable()
assert citytable.iloc[0]['name'] == 'Santa Clarita'
summary = doc.getSummaryAlert()
assert summary == 'yellow'