def run(layers):
"""Risk plugin for earthquake fatalities
Input
layers: List of layers expected to contain
H: Raster layer of flood depth
P: Raster layer of population data on the same grid as H
"""
threshold = 1 # Load above which people are regarded affected [kg/m2]
# Identify hazard and exposure layers
inundation = layers[0] # Tephra load [kg/m2]
population = layers[1] # Population density [people/km^2]
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
P = population.get_data(nan=0.0, scaling=True) # Population density
# Calculate impact as population exposed to depths > threshold
I = numpy.where(D > threshold, P, 0)
# Generate text with result for this study
number_of_people_affected = numpy.nansum(I.flat)
caption = ('%i people affected by ash levels greater '
'than %i kg/m^2' % (number_of_people_affected, threshold))
# Create raster object and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='People affected',
keywords={'caption': caption})
return R
def run(layers, teta=14.05, beta=0.17, zeta=2.15):
"""Risk plugin for earthquake fatalities
Input
H: Numerical array of hazard data
E: Numerical array of exposure data
"""
# Identify input layers
intensity = layers[0]
population = layers[1]
# Extract data
H = intensity.get_data(nan=0)
P = population.get_data(nan=0)
# Calculate impact
logHazard = 1 / beta * scipy.log(H / teta)
# Convert array to be standard floats expected by cdf
arrayout = numpy.array([[float(value) for value in row]
for row in logHazard])
F = scipy.stats.norm.cdf(arrayout * P)
# Create new layer and return
R = Raster(F,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
name='Estimated fatalities')
return R
def read_layer(filename):
"""Read spatial layer from file.
This can be either raster or vector data.
"""
_, ext = os.path.splitext(filename)
if ext in ['.asc', '.tif']:
return Raster(filename)
elif ext in ['.shp', '.gml']:
return Vector(filename)
else:
msg = ('Could not read %s. '
'Extension "%s" has not been implemented' % (filename, ext))
raise Exception(msg)
def run(self, layers):
"""Risk plugin for tsunami population
"""
thresholds = [0.2, 0.3, 0.5, 0.8, 1.0]
#threshold = 1 # Depth above which people are regarded affected [m]
# Identify hazard and exposure layers
inundation = layers[0] # Tsunami inundation [m]
population = layers[1] # Population density
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
P = population.get_data(nan=0.0, scaling=True) # Population density
# Calculate impact as population exposed to depths > 1m
I_map = numpy.where(D > thresholds[-1], P, 0)
# Generate text with result for this study
number_of_people_affected = numpy.nansum(I_map.flat)
# Do breakdown
# Create report
caption = ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
' <tr></tr>' % ('Ambang batas', 'Jumlah orang terdampak'))
counts = []
for i, threshold in enumerate(thresholds):
I = numpy.where(D > threshold, P, 0)
counts.append(numpy.nansum(I.flat))
caption += ' <tr><td>%s m</td><td>%i</td></tr>' % (threshold,
counts[i])
caption += '</table>'
# Create raster object and return
R = Raster(I_map,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='People affected by more than 1m of inundation',
keywords={'caption': caption})
return R
def write_raster_data(data, projection, geotransform, filename, keywords=None):
"""Write array to raster file with specified metadata and one data layer
Input:
data: Numpy array containing grid data
projection: WKT projection information
geotransform: 6 digit vector
(top left x, w-e pixel resolution, rotation,
top left y, rotation, n-s pixel resolution).
See e.g. http://www.gdal.org/gdal_tutorial.html
filename: Output filename
keywords: Optional dictionary
Note: The only format implemented is GTiff and the extension must be .tif
"""
R = Raster(data, projection, geotransform, keywords=keywords)
R.write_to_file(filename)
def run(layers, a=0.97429, b=11.037):
"""Risk plugin for earthquake fatalities
Input
layers: List of layers expected to contain
H: Raster layer of MMI ground shaking
P: Raster layer of population data on the same grid as H
"""
# Identify input layers
intensity = layers[0]
population = layers[1]
# Extract data
H = intensity.get_data(nan=0)
P = population.get_data(nan=0)
# Calculate impact
F = 10**(a * H - b) * P
# Generate text with result for this study
count = numpy.nansum(F.flat)
total = numpy.nansum(P.flat)
# Create report
caption = ('<table border="0" width="320px">'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
'</table>' % ('Jumlah Penduduk', int(total),
'Perkiraan Orang Meninggal', int(count)))
# Create new layer and return
R = Raster(F,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
name='Estimated fatalities',
keywords={'caption': caption})
return R
def run(layers):
"""Calculate population exposed to different levels of ground shaking
Input
layers: List of layers expected to contain
H: Raster layer of MMI ground shaking
P: Raster layer of population density
"""
# Identify input layers
intensity = layers[0]
population = layers[1]
# Extract data
H = intensity.get_data(nan=0)
P = population.get_data(nan=0)
# Calculate exposure to MMI impact
mmi_classes = range(1, 11) # MMI classes considered (1-10)
# Form result as keyword strings
mmi_str = str(mmi_classes)[1:-1] # Get rid of []
count_str = ''
for i in mmi_classes:
# Identify cells where MMI is in class i
mask = (H >= i - 0.5) * (H < i + 0.5)
# Count population affected by this shake level
count = round(numpy.nansum(P[mask]))
if numpy.isnan(count):
count = 0
# Update keyword string
count_str += '%i ' % count
# Calculate fatality map (FIXME (Ole): Need to replaced by USGS model)
a = 0.97429
b = 11.037
F = 10 ** (a * H - b) * P
# Generate text with result for this study
count = numpy.nansum(F.flat)
total = numpy.nansum(P.flat)
# Create report
caption = ('<table border="0" width="320px">'
' <tr><td>%s:</td><td>%i</td></tr>'
' <tr><td>%s:</td><td>%i</td></tr>'
'</table>' % ('Jumlah Penduduk', int(total),
'Perkiraan Orang Meninggal', int(count)))
# Create new layer and return
R = Raster(F,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
name='Estimated fatalities',
keywords={'caption': caption,
'mmi-classes': mmi_str,
'affected-population': count_str})
return R
def run(layers, teta=14.05, beta=0.17, zeta=2.15):
"""Risk plugin for earthquake fatalities
Input
H: Numerical array of hazard data
E: Numerical array of exposure data
Algorithm and coefficients are from:
An Empirical Model for Global Earthquake Fatality Estimation.
Kishor Jaiswal and David Wald.
Earthquake Spectra, Volume 26, No. 4, pages 1017-1037, November 2010.
teta=14.05, beta=0.17, zeta=2.1 # Coefficients for Indonesia.
"""
# Identify input layers
intensity = layers[0]
population = layers[1]
print intensity.get_resolution()
print population.get_resolution()
# Extract data
H = intensity.get_data(nan=0) # Ground Shaking
P = population.get_data(nan=0) # Population Density
import cPickle
name = intensity.get_name()
print name
fid = open('/home/nielso/population_%s.pck' % name, 'wb')
cPickle.dump(P, fid)
fid.close()
fid = open('/home/nielso/intensity_%s.pck' % name, 'wb')
cPickle.dump(H, fid)
fid.close()
# Calculate population affected by each MMI level
mmi_range = range(2, 10)
number_of_people_affected = {}
for mmi in mmi_range:
mask = numpy.logical_and(mmi - 0.5 < H, H <= mmi + 0.5)
I = numpy.where(mask, P, 0)
# Generate text with result for this study
number_of_people_affected[mmi] = numpy.nansum(I.flat)
# Calculate impact according to equation (1) in the Kishor and Wald 2010
logHazard = 1 / beta * scipy.log(H / teta)
# Convert array to be standard floats expected by cdf
arrayout = numpy.array([[float(value) for value in row]
for row in logHazard])
F = scipy.stats.norm.cdf(arrayout * P)
# Stats
total = numpy.nansum(P.flat)
fatalities = numpy.nansum(F)
print 'Total', total
print 'Estimated fatalities', fatalities
print 'Min', numpy.amin(F)
print 'Max', numpy.amax(F)
# Generate text with result for this study
caption = generate_exposure_table(mmi_range, number_of_people_affected)
caption += generate_fatality_table(fatalities)
# Create new layer and return
R = Raster(F,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
keywords={'caption': caption},
name='Estimated fatalities')
return R
def run(layers):
"""Risk plugin for earthquake fatalities
Input
layers: List of layers expected to contain
H: Raster layer of flood depth
P: Raster layer of population data on the same grid as H
"""
# Depth above which people are regarded affected [m]
threshold = 0.1
thresholds = [0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
# Identify hazard and exposure layers
inundation = layers[0] # Flood inundation [m]
population = layers[1] # Population density [people/100000 m^2]
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > threshold
if population.get_resolution(native=True, isotropic=True) < 0.0005:
# Keep this for backwards compatibility just a little while
# This uses the original custom population set and
# serves as a reference
P = population.get_data(nan=0.0) # Population density
pixel_area = 2500
I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
else:
# This is the new generic way of scaling (issue #168 and #172)
P = population.get_data(nan=0.0, scaling=True)
I = numpy.where(D > threshold, P, 0)
# Generate text with result for this study
number_of_people_affected = numpy.nansum(I.flat)
caption = ('%i people affected by flood levels greater '
'than %i cm' % (number_of_people_affected, threshold * 100))
# Create report
caption = ('<table border="0" width="320px">'
' <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
' <tr></tr>' % ('Min flood levels', 'People affected'))
counts = []
for i, threshold in enumerate(thresholds):
I_tmp = numpy.where(D > threshold, P, 0)
counts.append(numpy.nansum(I_tmp.flat))
caption += ' <tr><td>%s m</td><td>%i</td></tr>' % (threshold,
counts[i])
caption += '</table>'
# Create raster object and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='People affected',
keywords={'caption': caption})
return R
