def get_impact_comments(fatdict, ecodict, econexposure, event_year, ccode):
"""Create comments for a given event, describing economic and human (fatality) impacts.
:param fatdict:
Dictionary containing country code keys and integer population estimations of human loss.
:param ecodict:
Dictionary containing country code keys and integer population estimations of economic loss.
:param econexposure:
Dictionary containing country code (ISO2) keys, and values of
10 element arrays representing population exposure to MMI 1-10.
Dictionary will contain an additional key 'Total', with value of exposure across all countries.
:param event_year:
Year in which event occurred.
:param ccode:
Two letter country code of epicenter or 'UK' if not a country (usu. ocean).
:returns:
A tuple of two strings which describe the economic and human impacts. The most impactful
of these will be the first string. Under certain situations, the second comment could be blank.
"""
# first, figure out what the alert levels are for each loss result
fatmodel = EmpiricalLoss.fromDefaultFatality()
ecomodel = EmpiricalLoss.fromDefaultEconomic()
fatlevel = fatmodel.getAlertLevel(fatdict)
ecolevel = fatmodel.getAlertLevel(ecodict)
levels = {'green': 0, 'yellow': 1, 'orange': 2, 'red': 3}
rlevels = {0: 'green', 1: 'yellow', 2: 'orange', 3: 'red'}
fat_higher = levels[fatlevel] > levels[ecolevel]
eco_higher = levels[ecolevel] > levels[fatlevel]
gdpcomment = get_gdp_comment(ecodict, ecomodel, econexposure, event_year,
ccode)
if fat_higher:
if fatlevel == 'green':
impact1 = GREEN_FAT_HIGH
elif fatlevel == 'yellow':
impact1 = YELLOW_FAT_HIGH
elif fatlevel == 'orange':
impact1 = ORANGE_FAT_HIGH
elif fatlevel == 'red':
impact1 = RED_FAT_HIGH
if ecolevel == 'green':
impact2 = GREEN_ECON_LOW
elif ecolevel == 'yellow':
impact2 = YELLOW_ECON_LOW
elif ecolevel == 'orange':
impact2 = ORANGE_ECON_LOW
elif ecolevel == 'red':
impact2 = RED_ECON_LOW
impact2 = impact2.replace('[GDPCOMMENT]', gdpcomment)
elif eco_higher:
if ecolevel == 'green':
impact1 = GREEN_ECON_HIGH
elif ecolevel == 'yellow':
impact1 = YELLOW_ECON_HIGH
elif ecolevel == 'orange':
impact1 = ORANGE_ECON_HIGH
elif ecolevel == 'red':
impact1 = RED_ECON_HIGH
if fatlevel == 'green':
impact2 = GREEN_FAT_LOW
elif fatlevel == 'yellow':
impact2 = YELLOW_FAT_LOW
elif fatlevel == 'orange':
impact2 = ORANGE_FAT_LOW
elif fatlevel == 'red':
impact2 = RED_FAT_LOW
impact1 = impact1.replace('[GDPCOMMENT]', gdpcomment)
else:
if fatlevel == 'green':
impact1 = GREEN_FAT_EQUAL
elif fatlevel == 'yellow':
impact1 = YELLOW_FAT_EQUAL
elif fatlevel == 'orange':
impact1 = ORANGE_FAT_EQUAL
elif fatlevel == 'red':
impact1 = RED_FAT_EQUAL
if ecolevel == 'green':
impact2 = GREEN_ECON_EQUAL
elif ecolevel == 'yellow':
impact2 = YELLOW_ECON_EQUAL
elif ecolevel == 'orange':
impact2 = ORANGE_ECON_EQUAL
elif ecolevel == 'red':
impact2 = RED_ECON_EQUAL
impact2 = impact2.replace('[GDPCOMMENT]', gdpcomment)
impact1 = impact1.replace('\n', ' ')
impact2 = impact2.replace('\n', ' ')
return (impact1, impact2)
def test():
# where is this script?
homedir = os.path.dirname(os.path.abspath(__file__))
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)
shapefile = os.path.join(homedir, '..', 'data', 'eventdata', event,
'City_BoundariesWGS84', 'City_Boundaries.shp')
print('Test loading empirical fatality model from XML file...')
fatmodel = EmpiricalLoss.fromDefaultFatality()
print('Passed loading empirical fatality model from XML file.')
print('Test getting alert level from various losses...')
assert fatmodel.getAlertLevel({'TotalFatalities': 0}) == 'green'
assert fatmodel.getAlertLevel({'TotalFatalities': 5}) == 'yellow'
assert fatmodel.getAlertLevel({'TotalFatalities': 100}) == 'orange'
assert fatmodel.getAlertLevel({'TotalFatalities': 1000}) == 'red'
# 1000 times Earth's population
assert fatmodel.getAlertLevel({'TotalFatalities': 1e13}) == 'red'
print('Passed getting alert level from various losses.')
print('Test retrieving fatality model data from XML file...')
model = fatmodel.getModel('af')
testmodel = LognormalModel('dummy', 11.613073, 0.180683, 8.428822)
assert model == testmodel
print('Passed retrieving fatality model data from XML file.')
print('Testing with known exposures/fatalities for 1994 Northridge EQ...')
exposure = {
'xf':
np.array([
0, 0, 1506.0, 1946880.0, 6509154.0, 6690236.0, 3405381.0,
1892446.0, 5182.0, 0
])
}
fatdict = fatmodel.getLosses(exposure)
testdict = {'xf': 22}
assert fatdict['xf'] == testdict['xf']
print(
'Passed testing with known exposures/fatalities for 1994 Northridge EQ.'
)
print(
'Testing combining G values from all countries that contributed to losses...'
)
fatdict = {'CO': 2.38005147e-01, 'EC': 8.01285916e+02}
zetf = fatmodel.getCombinedG(fatdict)
assert zetf == 2.5
print(
'Passed combining G values from all countries that contributed to losses...'
)
print('Testing calculating probabilities for standard PAGER ranges...')
expected = {'UK': 70511, 'TotalFatalities': 70511}
G = 2.5
probs = fatmodel.getProbabilities(expected, G)
testprobs = {
'0-1': 3.99586017993e-06,
'1-10': 0.00019277654968408576,
'10-100': 0.0041568251597835061,
'100-1000': 0.039995273501147441,
'1000-10000': 0.17297196910604343,
'10000-100000': 0.3382545813262674,
'100000-10000000': 0.44442457847445394
}
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('Testing calculating total fatalities for Northridge...')
expobject = Exposure(popfile, 2012, isofile)
expdict = expobject.calcExposure(shakefile)
fatdict = fatmodel.getLosses(expdict)
testdict = {'XF': 18}
assert fatdict['XF'] == testdict['XF']
print('Passed calculating total fatalities for Northridge...')
print('Testing creating a fatality grid...')
mmidata = expobject.getShakeGrid().getLayer('mmi').getData()
popdata = expobject.getPopulationGrid().getData()
isodata = expobject.getCountryGrid().getData()
fatgrid = fatmodel.getLossGrid(mmidata, popdata, isodata)
print(np.nansum(fatgrid))
print('Passed creating a fatality grid.')
print('Testing assigning fatalities to polygons...')
popdict = expobject.getPopulationGrid().getGeoDict()
shapes = []
f = fiona.open(shapefile, 'r')
for row in f:
shapes.append(row)
f.close()
fatshapes, totfat = fatmodel.getLossByShapes(mmidata, popdata, isodata,
shapes, popdict)
fatalities = 12
for shape in fatshapes:
if shape['id'] == '312': # Los Angeles
cname = shape['properties']['CITY_NAME']
lalosses = shape['properties']['fatalities']
assert lalosses == fatalities
assert cname == 'Los Angeles'
break
print('Passed assigning fatalities to polygons...')
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?
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...')
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, 'TotalDollars': 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])
msg = ('Passed combining G values from all countries that '
'contributed to losses...')
print(msg)
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']
msg = ('Passed testing with known exposures/fatalities for '
'1994 Northridge EQ.')
print(msg)
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 drawImpactScale(lossdict, ranges, losstype, debug=False):
"""Draw a loss impact scale, showing the probabilities that estimated losses fall into one of many bins.
:param lossdict:
Dictionary containing either 'TotalFatalities' or 'TotalDollars', depending on losstype.
:param ranges:
Ordered Dictionary of probability of losses over ranges :
'0-1' (green alert)
'1-10' (yellow alert)
'10-100' (yellow alert)
'100-1000' (orange alert)
'1000-10000' (red alert)
'10000-100000' (red alert)
'100000-10000000' (red alert)
:param losstype:
String, one of 'fatality' or 'economic'.
:returns:
Matplotlib figure containing plot showing probabilities of loss falling into one of the bins listed above.
:raises:
PagerException if input range OrderedDict list of keys is not complete, or
if ranges is not an OrderedDict.
"""
req_keys = [
'0-1', '1-10', '10-100', '100-1000', '1000-10000', '10000-100000',
'100000-10000000'
]
if not isinstance(ranges, OrderedDict):
raise PagerException('Input ranges must be an OrderedDict instance.')
for key in req_keys:
if key not in ranges:
raise PagerException('Input ranges dictionary must have keys: %s' %
str(req_keys))
height = WIDTH / ASPECT
f = plt.figure(figsize=(WIDTH, height))
renderer = _find_renderer(f)
ax = plt.gca()
plt.axis([0, 1, 0, 1])
if not debug:
plt.axis('off')
# reserve the left edge of the figure for the "sponge ball" - colored circle indicating most likely alert level.
starting_left_edge = 11 / 63
bottom_edge = 7 / 23
bottom_bar_height = 3 / 23
bar_width = 7 / 63
barcolors = [GREEN, YELLOW, YELLOW, ORANGE, RED, RED, RED]
ticklabels = [1, 10, 100, 1000, 10000, 100000]
wfactor = 0
ticklens = [0.03, 0.09, 0.03, 0.09, 0.03, 0.09]
text_widths = []
inv = ax.transData.inverted()
for ticklabel in ticklabels:
t = plt.text(0.5,
0.5,
format(ticklabel, ",d"),
weight='normal',
size=12)
dxmin, dymin, dwidth, dheight = t.get_window_extent(
renderer=renderer).bounds
dxmax = dxmin + dwidth
dymax = dymin + dheight
dataxmin, dataymin = inv.transform((dxmin, dymin))
dataxmax, dataymax = inv.transform((dxmax, dymax))
text_widths.append((format(ticklabel, ",d"), dataxmax - dataxmin))
t.remove()
# draw the bottom bars indicating where the alert levels are
for barcolor in barcolors:
left_edge = starting_left_edge + bar_width * wfactor
rect = Rectangle((left_edge, bottom_edge),
bar_width,
bottom_bar_height,
fc=barcolor,
ec='k')
ax.add_patch(rect)
if wfactor < len(barcolors) - 1:
ticklen = ticklens[wfactor]
ticklabel = text_widths[wfactor][0]
twidth = text_widths[wfactor][1]
plt.plot([left_edge + bar_width, left_edge + bar_width],
[bottom_edge - ticklen, bottom_edge], 'k')
plt.text(left_edge + (bar_width) - (twidth / 2.0),
bottom_edge - (ticklen + 0.07),
ticklabel,
weight='normal',
size=12)
wfactor += 1
# now draw the top bars
bottom_edge_bar_top = 10.5 / 23
total_height = (23 - 10.5) / 23
wfactor = 0
fdict = {'weight': 'normal', 'size': 12}
imax = np.array(list(ranges.values())).argmax()
for rkey, pvalue in ranges.items():
if pvalue < 0.03:
wfactor += 1
continue
barcolor = barcolors[wfactor]
left_edge = starting_left_edge + bar_width * wfactor
bar_height = (pvalue * total_height)
lw = 1
zorder = 1
bottom_value, top_value = [int(v) for v in rkey.split('-')]
if losstype == 'fatality':
expected = lossdict['TotalFatalities']
else:
expected = lossdict['TotalDollars'] / 1e6
if expected >= bottom_value and expected < top_value:
lw = 3
zorder = 100
rect = Rectangle((left_edge, bottom_edge_bar_top),
bar_width,
bar_height,
fc=barcolor,
ec='k',
lw=lw)
rect.set_zorder(zorder)
ax.add_patch(rect)
ptext = '%i%%' % np.round(pvalue * 100)
plt.text(left_edge + bar_width / 2.7,
bottom_edge_bar_top + bar_height + 0.02,
ptext,
fontdict=fdict)
wfactor += 1
# now draw the sponge ball on the left
cx = 0.105
cy = 0.6
# because our axes is not equal, assuming a circle will be drawn as a circle doesn't work.
x0, y0 = ax.transAxes.transform((0, 0)) # lower left in pixels
x1, y1 = ax.transAxes.transform((1, 1)) # upper right in pixes
dx = x1 - x0
dy = y1 - y0
maxd = max(dx, dy)
width = .11 * maxd / dx
height = .11 * maxd / dy
# choose the spongeball color based on the expected total losses from lossdict
sponge_dict = {
'green': GREEN,
'yellow': YELLOW,
'orange': ORANGE,
'red': RED
}
if losstype == 'fatality':
lossmodel = EmpiricalLoss.fromDefaultFatality()
alert_level = lossmodel.getAlertLevel(lossdict)
else:
lossmodel = EmpiricalLoss.fromDefaultEconomic()
alert_level = lossmodel.getAlertLevel(lossdict)
spongecolor = sponge_dict[alert_level]
spongeball = Ellipse((cx, cy), width, height, fc=spongecolor, ec='k', lw=2)
ax.add_patch(spongeball)
font = {'style': 'italic'}
# draw units at bottom
if losstype == 'fatality':
plt.text(0.5, 0.07, 'Fatalities', fontdict=font)
if losstype == 'economic':
plt.text(0.45, 0.07, 'USD (Millions)', fontdict=font)
return f
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'
def main(pargs, config):
# get the users home directory
homedir = os.path.expanduser("~")
# handle cancel messages
if pargs.cancel:
# we presume that pargs.gridfile in this context is an event ID.
msg = _cancel(pargs.gridfile, config)
print(msg)
return True
# what kind of thing is gridfile?
is_file = os.path.isfile(pargs.gridfile)
is_url, url_gridfile = _is_url(pargs.gridfile)
is_pdl, pdl_gridfile = _check_pdl(pargs.gridfile, config)
if is_file:
gridfile = pargs.gridfile
elif is_url:
gridfile = url_gridfile
elif is_pdl:
gridfile = pdl_gridfile
else:
print("ShakeMap Grid file %s does not exist." % pargs.gridfile)
return False
pager_folder = os.path.join(homedir, config["output_folder"])
pager_archive = os.path.join(homedir, config["archive_folder"])
admin = PagerAdmin(pager_folder, pager_archive)
# stdout will now be logged as INFO, stderr will be logged as WARNING
mail_host = config["mail_hosts"][0]
mail_from = config["mail_from"]
developers = config["developers"]
logfile = os.path.join(pager_folder, "pager.log")
plog = PagerLogger(logfile, developers, mail_from, mail_host, debug=pargs.debug)
logger = plog.getLogger()
try:
eid = None
pager_version = None
# get all the basic event information and print it, if requested
shake_tuple = getHeaderData(gridfile)
eid = shake_tuple[1]["event_id"].lower()
etime = shake_tuple[1]["event_timestamp"]
if not len(eid):
eid = shake_tuple[0]["event_id"].lower()
network = shake_tuple[1]["event_network"].lower()
if network == "":
network = "us"
if not eid.startswith(network):
eid = network + eid
# Create a ComcatInfo object to hopefully tell us a number of things about this event
try:
ccinfo = ComCatInfo(eid)
location = ccinfo.getLocation()
tsunami = ccinfo.getTsunami()
authid, allids = ccinfo.getAssociatedIds()
authsource, othersources = ccinfo.getAssociatedSources()
except: # fail over to what we can determine locally
location = shake_tuple[1]["event_description"]
tsunami = shake_tuple[1]["magnitude"] >= TSUNAMI_MAG_THRESH
authid = eid
authsource = network
allids = []
# location field can be empty (None), which breaks a bunch of things
if location is None:
location = ""
# Check to see if user wanted to override default tsunami criteria
if pargs.tsunami != "auto":
if pargs.tsunami == "on":
tsunami = True
else:
tsunami = False
# check to see if this event is a scenario
is_scenario = False
shakemap_type = shake_tuple[0]["shakemap_event_type"]
if shakemap_type == "SCENARIO":
is_scenario = True
# if event is NOT a scenario and event time is in the future,
# flag the event as a scenario and yell about it.
if etime > datetime.datetime.utcnow():
is_scenario = True
logger.warning(
"Event origin time is in the future! Flagging this as a scenario."
)
if is_scenario:
if re.search("scenario", location.lower()) is None:
location = "Scenario " + location
# create the event directory (if it does not exist), and start logging there
logger.info("Creating event directory")
event_folder = admin.createEventFolder(authid, etime)
# Stop processing if there is a "stop" file in the event folder
stopfile = os.path.join(event_folder, "stop")
if os.path.isfile(stopfile):
fmt = '"stop" file found in %s. Stopping processing, returning with 1.'
logger.info(fmt % (event_folder))
return True
pager_version = get_pager_version(event_folder)
version_folder = os.path.join(event_folder, "version.%03d" % pager_version)
os.makedirs(version_folder)
event_logfile = os.path.join(version_folder, "event.log")
# this will turn off the global rotating log file
# and switch to the one in the version folder.
plog.setVersionHandler(event_logfile)
# Copy the grid.xml file to the version folder
# sometimes (usu when testing) the input grid isn't called grid.xml. Rename it here.
version_grid = os.path.join(version_folder, "grid.xml")
shutil.copyfile(gridfile, version_grid)
# Check to see if the tsunami flag has been previously set
tsunami_toggle = {"on": 1, "off": 0}
tsunami_file = os.path.join(event_folder, "tsunami")
if os.path.isfile(tsunami_file):
tsunami = tsunami_toggle[open(tsunami_file, "rt").read().strip()]
# get the rest of the event info
etime = shake_tuple[1]["event_timestamp"]
elat = shake_tuple[1]["lat"]
elon = shake_tuple[1]["lon"]
emag = shake_tuple[1]["magnitude"]
# get the year of the event
event_year = shake_tuple[1]["event_timestamp"].year
# find the population data collected most closely to the event_year
pop_year, popfile = _get_pop_year(
event_year, config["model_data"]["population_data"]
)
logger.info("Population year: %i Population file: %s\n" % (pop_year, popfile))
# Get exposure results
logger.info("Calculating population exposure.")
isofile = config["model_data"]["country_grid"]
expomodel = Exposure(popfile, pop_year, isofile)
exposure = None
exposure = expomodel.calcExposure(gridfile)
# incidentally grab the country code of the epicenter
numcode = expomodel._isogrid.getValue(elat, elon)
if np.isnan(numcode):
cdict = None
else:
cdict = Country().getCountry(int(numcode))
if cdict is None:
ccode = "UK"
else:
ccode = cdict["ISO2"]
logger.info("Country code at epicenter is %s" % ccode)
# get fatality results, if requested
logger.info("Calculating empirical fatalities.")
fatmodel = EmpiricalLoss.fromDefaultFatality()
fatdict = fatmodel.getLosses(exposure)
# get economic results, if requested
logger.info("Calculating economic exposure.")
econexpmodel = EconExposure(popfile, pop_year, isofile)
ecomodel = EmpiricalLoss.fromDefaultEconomic()
econexposure = econexpmodel.calcExposure(gridfile)
ecodict = ecomodel.getLosses(econexposure)
shakegrid = econexpmodel.getShakeGrid()
# Get semi-empirical losses
logger.info("Calculating semi-empirical fatalities.")
urbanfile = config["model_data"]["urban_rural_grid"]
if not os.path.isfile(urbanfile):
raise PagerException("Urban-rural grid file %s does not exist." % urbanfile)
semi = SemiEmpiricalFatality.fromDefault()
semi.setGlobalFiles(popfile, pop_year, urbanfile, isofile)
semiloss, resfat, nonresfat = semi.getLosses(gridfile)
# get all of the other components of PAGER
logger.info("Getting all comments.")
# get the fatality and economic comments
impact1, impact2 = get_impact_comments(
fatdict, ecodict, econexposure, event_year, ccode
)
# get comment describing vulnerable structures in the region.
struct_comment = get_structure_comment(resfat, nonresfat, semi)
# get the comment describing historic secondary hazards
secondary_comment = get_secondary_comment(elat, elon, emag)
# get the comment describing historical comments in the region
historical_comment = get_historical_comment(elat, elon, emag, exposure, fatdict)
# generate the probability plots
logger.info("Drawing probability plots.")
fat_probs_file, eco_probs_file = _draw_probs(
fatmodel, fatdict, ecomodel, ecodict, version_folder
)
# generate the exposure map
exposure_base = os.path.join(version_folder, "exposure")
logger.info("Generating exposure map...")
oceanfile = config["model_data"]["ocean_vectors"]
oceangrid = config["model_data"]["ocean_grid"]
cityfile = config["model_data"]["city_file"]
borderfile = config["model_data"]["border_vectors"]
shake_grid = expomodel.getShakeGrid()
pop_grid = expomodel.getPopulationGrid()
pdf_file, png_file, mapcities = draw_contour(
shake_grid,
pop_grid,
oceanfile,
oceangrid,
cityfile,
exposure_base,
borderfile,
is_scenario=is_scenario,
)
logger.info("Generated exposure map %s" % pdf_file)
# figure out whether this event has been "released".
is_released = _get_release_status(
pargs,
config,
fatmodel,
fatdict,
ecomodel,
ecodict,
shake_tuple,
event_folder,
)
# Create a data object to encapsulate everything we know about the PAGER
# results, and then serialize that to disk in the form of a number of JSON files.
logger.info("Making PAGER Data object.")
doc = PagerData()
timezone_file = config["model_data"]["timezones_file"]
elapsed = pargs.elapsed
doc.setInputs(
shakegrid,
timezone_file,
pager_version,
shakegrid.getEventDict()["event_id"],
authid,
tsunami,
location,
is_released,
elapsed=elapsed,
)
logger.info("Setting inputs.")
doc.setExposure(exposure, econexposure)
logger.info("Setting exposure.")
doc.setModelResults(
fatmodel, ecomodel, fatdict, ecodict, semiloss, resfat, nonresfat
)
logger.info("Setting comments.")
doc.setComments(
impact1, impact2, struct_comment, historical_comment, secondary_comment
)
logger.info("Setting map info.")
doc.setMapInfo(cityfile, mapcities)
logger.info("Validating.")
doc.validate()
# if we have determined that the event is a scenario (origin time is in the future)
# and the shakemap is not flagged as such, set the shakemap type in the
# pagerdata object to be 'SCENARIO'.
if is_scenario:
doc.setToScenario()
json_folder = os.path.join(version_folder, "json")
os.makedirs(json_folder)
logger.info("Saving output to JSON.")
doc.saveToJSON(json_folder)
logger.info("Saving output to XML.")
doc.saveToLegacyXML(version_folder)
logger.info("Creating onePAGER pdf...")
onepager_pdf, error = create_onepager(doc, version_folder)
if onepager_pdf is None:
raise PagerException("Could not create onePAGER output: \n%s" % error)
# copy the contents.xml file to the version folder
contentsfile = get_data_path("contents.xml")
if contentsfile is None:
raise PagerException("Could not find contents.xml file.")
shutil.copy(contentsfile, version_folder)
# send pdf as attachment to internal team of PAGER users
if not is_released and not is_scenario:
message_pager(config, onepager_pdf, doc)
# run transfer, as appropriate and as specified by config
# the PAGER product eventsource and eventsourcecode should
# match the input ShakeMap settings for these properties.
# This can possibly cause confusion if a regional ShakeMap is
# trumped with one from NEIC, but this should happen less often
# than an NEIC origin being made authoritative over a regional one.
eventsource = network
eventsourcecode = eid
res, msg = transfer(
config,
doc,
eventsourcecode,
eventsource,
version_folder,
is_scenario=is_scenario,
)
logger.info(msg)
if not res:
logger.critical('Error transferring PAGER content. "%s"' % msg)
print("Created onePAGER pdf %s" % onepager_pdf)
logger.info("Created onePAGER pdf %s" % onepager_pdf)
logger.info("Done.")
return True
except Exception as e:
f = io.StringIO()
traceback.print_exc(file=f)
msg = e
msg = "%s\n %s" % (str(msg), f.getvalue())
hostname = socket.gethostname()
msg = msg + "\n" + "Error occurred on %s\n" % (hostname)
if gridfile is not None:
msg = msg + "\n" + "Error on file: %s\n" % (gridfile)
if eid is not None:
msg = msg + "\n" + "Error on event: %s\n" % (eid)
if pager_version is not None:
msg = msg + "\n" + "Error on version: %i\n" % (pager_version)
f.close()
logger.critical(msg)
logger.info("Sent error to email")
return False