def __init__(self):
super(VolcanoPolygonPopulationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
self.volcano_names = tr('Not specified in data')
def __init__(self):
super(ClassifiedRasterHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(TsunamiEvacuationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ITBFatalityFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.hardcoded_parameters = OrderedDict([
('x', 0.62275231),
('y', 8.03314466), # Model coefficients
# Rates of people displaced for each MMI level
# should be consistent with defined mmi range below. - Hyeuk
('displacement_rate', {
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 1.0,
7: 1.0,
8: 1.0,
9: 1.0,
10: 1.0
}),
# it should be range(2,11) if mmi 10 is included. Otherwise we
# should remove mmi 10 in the displacement_rate as well - Hyeuk
('mmi_range', range(2, 11)),
('step', 0.5),
('calculate_displaced_people', True)
])
self.total_fatalities = None
def __init__(self):
super(TsunamiEvacuationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(TsunamiEvacuationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ClassifiedRasterHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(TsunamiEvacuationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ClassifiedPolygonHazardPopulationFunction, self).__init__()
# Hazard zones are all unique values from the hazard zone attribute
self.hazard_zones = []
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# Set the question of the IF (as the hazard data is not an event)
self.question = tr("In each of the hazard zones how many people might be impacted.")
def __init__(self):
"""Constructor."""
super(FloodEvacuationRasterHazardFunction, self).__init__()
self.target_field = 'population'
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ClassifiedPolygonHazardPopulationFunction, self).__init__()
# Hazard zones are all unique values from the hazard zone attribute
self.hazard_zones = []
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# Set the question of the IF (as the hazard data is not an event)
self.question = tr(
'In each of the hazard zones how many people might be impacted.')
def __init__(self):
super(VolcanoPointPopulationFunction, self).__init__()
self.target_field = 'population'
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# TODO: alternatively to specifying the question here we should
# TODO: consider changing the 'population' metadata concept to 'people'
self.question = (
'In the event of a volcano point how many people might be impacted'
)
def __init__(self):
super(VolcanoPointPopulationFunction, self).__init__()
self.target_field = 'population'
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# TODO: alternatively to specifying the question here we should
# TODO: consider changing the 'population' metadata concept to 'people'
self.question = (
'In the event of a volcano point how many people might be impacted'
)
def __init__(self):
"""Constructor."""
super(FloodEvacuationRasterHazardFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# Initialize instance attributes for readability (pylint)
self.no_data_warning = False
def __init__(self):
"""Constructor."""
super(FloodEvacuationRasterHazardFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# Initialize instance attributes for readability (pylint)
self.no_data_warning = False
def __init__(self):
super(VolcanoPointPopulationFunction, self).__init__()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# TODO: alternatively to specifying the question here we should
# TODO: consider changing the 'population' metadata concept to 'people'
self.question = (
'In the event of a volcano point how many people might be impacted'
)
self.no_data_warning = False
self.volcano_names = tr('Not specified in data')
self.hazard_zone_attribute = 'radius'
def __init__(self):
"""Constructor."""
super(FloodEvacuationVectorHazardFunction, self).__init__()
# Use affected field flag (if False, all polygon will be considered as
# affected)
self.use_affected_field = False
# The 'wet' variable
self.wet = 'wet'
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
"""Constructor."""
super(FloodEvacuationVectorHazardFunction, self).__init__()
# Target field in the impact layer
self.target_field = 'population'
# Use affected field flag (if False, all polygon will be considered as
# affected)
self.use_affected_field = False
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def __init__(self):
"""Constructor."""
super(FloodEvacuationVectorHazardFunction, self).__init__()
# Target field in the impact layer
self.target_field = 'population'
# Use affected field flag (if False, all polygon will be considered as
# affected)
self.use_affected_field = False
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def __init__(self):
super(VolcanoPointPopulationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# TODO: alternatively to specifying the question here we should
# TODO: consider changing the 'population' metadata concept to 'people'
self.question = (
'In the event of a volcano point how many people might be '
'impacted?')
self.no_data_warning = False
# A set of volcano names
self.volcano_names = set()
self.hazard_zone_attribute = 'radius'
def __init__(self):
"""Constructor."""
super(AshRasterPopulationFunction, self).__init__()
PopulationExposureReportMixin.__init__(self)
self.hazard_classes = [
tr('Very Low'),
tr('Low'),
tr('Moderate'),
tr('High'),
tr('Very High'),
]
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def __init__(self):
super(ClassifiedPolygonHazardPolygonPeopleFunction, self).__init__()
PolygonPeopleExposureReportMixin.__init__(self)
# Set the question of the IF (as the hazard data is not an event)
self.question = tr(
'In each of the hazard zones which areas might be affected?')
# Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.all_areas_ids = {}
self.all_affected_areas = {}
self.all_areas_population = {}
self.areas_names = {}
self.hazard_levels = {}
self.hazard_class_mapping = {}
self.hazard_class_field = None
def __init__(self):
super(ClassifiedPolygonHazardPolygonPeopleFunction, self).__init__()
# Set the question of the IF (as the hazard data is not an event)
self.question = tr(
'In each of the hazard zones which areas might be affected.')
# Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.all_areas_ids = {}
self.all_affected_areas = {}
self.all_areas_population = {}
self.areas_names = {}
self.hazard_levels = {}
self.hazard_class_mapping = {}
self.hazard_class_field = None
def __init__(self):
super(ITBFatalityFunction, self).__init__()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.hardcoded_parameters = OrderedDict([
('x', 0.62275231), ('y', 8.03314466), # Model coefficients
# Rates of people displaced for each MMI level
('displacement_rate', {
1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 1.0,
7: 1.0, 8: 1.0, 9: 1.0, 10: 1.0
}),
('mmi_range', range(2, 10)),
('step', 0.5),
# Threshold below which layer should be transparent
('tolerance', 0.01),
('calculate_displaced_people', True)
])
def __init__(self):
super(ITBFatalityFunction, self).__init__()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.hardcoded_parameters = OrderedDict([
('x', 0.62275231), ('y', 8.03314466), # Model coefficients
# Rates of people displaced for each MMI level
# should be consistent with defined mmi range below. - Hyeuk
('displacement_rate', {
2: 0.0, 3: 0.0, 4: 0.0, 5: 0.0, 6: 1.0,
7: 1.0, 8: 1.0, 9: 1.0, 10: 1.0
}),
# it should be range(2,11) if mmi 10 is included. Otherwise we
# should remove mmi 10 in the displacement_rate as well - Hyeuk
('mmi_range', range(2, 11)),
('step', 0.5),
('calculate_displaced_people', True)
])
self.total_fatalities = None
class TsunamiEvacuationFunction(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Impact function for tsunami evacuation.
:author AIFDR
:rating 4
:param requires category=='hazard' and \
subcategory=='tsunami' and \
layertype=='raster' and \
unit=='m'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for TsunamiEvacuationFunction.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'TsunamiEvacuationFunction',
'name':
tr('Tsunami Evacuation Function'),
'impact':
tr('Need evacuation'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of tsunami inundation '
'in raster format on population.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': [hazard_tsunami],
'units': [unit_feet_depth, unit_metres_depth],
'layer_constraints': [layer_raster_continuous]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
title = tr('Need evacuation')
defaults = get_defaults()
# Function documentation
synopsis = tr('To assess the impacts of tsunami inundation in raster '
'format on population.')
actions = tr(
'Provide details about how many people would likely need to be '
'evacuated, where they are located and what resources would be '
'required to support them.')
detailed_description = tr(
'The population subject to inundation exceeding a threshold '
'(default 0.7m) is calculated and returned as a raster layer. In '
'addition the total number and the required needs in terms of the '
'BNPB (Perka 7) are reported. The threshold can be changed and even '
'contain multiple numbers in which case evacuation and needs are '
'calculated using the largest number with population breakdowns '
'provided for the smaller numbers. The population raster is resampled '
'to the resolution of the hazard raster and is rescaled so that the '
'resampled population counts reflect estimates of population count '
'per resampled cell. The resulting impact layer has the same '
'resolution and reflects population count per cell which are affected '
'by inundation.')
hazard_input = tr(
'A hazard raster layer where each cell represents tsunami depth '
'(in meters).')
exposure_input = tr(
'An exposure raster layer where each cell represent population count.')
output = tr(
'Raster layer contains population affected and the minimum needs '
'based on the population affected.')
limitation = tr(
'The default threshold of 0.7 meter was selected based on consensus, '
'not hard evidence.')
# Configurable parameters
# TODO: Share the mimimum needs and make another default value
parameters = OrderedDict([
('thresholds [m]', [0.7]),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}),
('MinimumNeeds', {
'on': True
}),
])), ('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def run(self, layers):
"""Risk plugin for tsunami population evacuation.
:param layers: List of layers expected to contain
hazard_layer: Raster layer of tsunami depth
exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to tsunami levels exceeding
specified threshold.
:returns: Map of population exposed to tsunami levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Tsunami inundation [m]
exposure_layer = get_exposure_layer(layers)
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds [m]']
verify(isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
population = exposure_layer.get_data(nan=0.0, scaling=True)
# Calculate impact to intermediate thresholds
counts = []
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.sum(medium))
# Sensible rounding
val, rounding = population_rounding_full(val)
counts.append([val, rounding])
# Count totals
evacuated, rounding = counts[-1]
total = int(numpy.sum(population))
# Don't show digits less than a 1000
total = population_rounding(total)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
# noinspection PyListCreation
table_body = [
question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s*' % format_int(evacuated)],
header=True),
TableRow(
tr('* Number is rounded up to the nearest %s') % rounding),
TableRow(tr('Map shows the numbers of people needing evacuation'))
]
total_needs = evacuated_population_needs(evacuated, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('How will warnings be disseminated?')))
table_body.append(TableRow(tr('How will we reach stranded people?')))
table_body.append(TableRow(tr('Do we have enough relief items?')))
table_body.append(
TableRow(
tr('If yes, where are they located and how '
'will we distribute them?')))
table_body.append(
TableRow(
tr('If no, where can we obtain additional relief items from and how '
'will we transport them to here?')))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Total population: %s') % format_int(total),
tr('People need evacuation if tsunami levels exceed %(eps).1f m') %
{
'eps': thresholds[-1]
},
tr('Minimum needs are defined in BNPB regulation 7/2008'),
tr('All values are rounded up to the nearest integer in order to '
'avoid representing human lives as fractions.')
])
if len(counts) > 1:
table_body.append(TableRow(tr('Detailed breakdown'), header=True))
for i, val in enumerate(counts[:-1]):
s = (tr('People in %(lo).1f m to %(hi).1f m of water: %(val)i')
% {
'lo': thresholds[i],
'hi': thresholds[i + 1],
'val': format_int(val[0])
})
table_body.append(TableRow(s))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
table_body = [
question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s' % format_int(evacuated)],
header=True)
]
my_message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(my_message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impact.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(interval_classes[i], 'Low')
elif i == 4:
label = create_label(interval_classes[i], 'Medium')
elif i == 7:
label = create_label(interval_classes[i], 'High')
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 0
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People in need of evacuation')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create raster object and return
raster = Raster(impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'evacuated': evacuated,
'total_needs': total_needs
},
style_info=style_info)
return raster
def __init__(self):
super(VolcanoPolygonPopulationFunction, self).__init__()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
self.volcano_names = tr('Not specified in data')
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
class ITBFatalityFunction(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Indonesian Earthquake Fatality Model.
This model was developed by Institut Teknologi Bandung (ITB) and
implemented by Dr. Hadi Ghasemi, Geoscience Australia.
Reference:
Indonesian Earthquake Building-Damage and Fatality Models and
Post Disaster Survey Guidelines Development,
Bali, 27-28 February 2012, 54pp.
Algorithm:
In this study, the same functional form as Allen (2009) is adopted
to express fatality rate as a function of intensity (see Eq. 10 in the
report). The Matlab built-in function (fminsearch) for Nelder-Mead
algorithm was used to estimate the model parameters. The objective
function (L2G norm) that is minimised during the optimisation is the
same as the one used by Jaiswal et al. (2010).
The coefficients used in the indonesian model are
x=0.62275231, y=8.03314466, zeta=2.15
Allen, T. I., Wald, D. J., Earle, P. S., Marano, K. D., Hotovec, A. J.,
Lin, K., and Hearne, M., 2009. An Atlas of ShakeMaps and population
exposure catalog for earthquake loss modeling, Bull. Earthq. Eng. 7,
701-718.
Jaiswal, K., and Wald, D., 2010. An empirical model for global earthquake
fatality estimation, Earthq. Spectra 26, 1017-1037.
Caveats and limitations:
The current model is the result of the above mentioned workshop and
reflects the best available information. However, the current model
has a number of issues listed below and is expected to evolve further
over time.
1 - The model is based on limited number of observed fatality
rates during 4 past fatal events.
2 - The model clearly over-predicts the fatality rates at
intensities higher than VIII.
3 - The model only estimates the expected fatality rate for a given
intensity level; however the associated uncertainty for the proposed
model is not addressed.
4 - There are few known mistakes in developing the current model:
- rounding MMI values to the nearest 0.5,
- Implementing Finite-Fault models of candidate events, and
- consistency between selected GMPEs with those in use by BMKG.
These issues will be addressed by ITB team in the final report.
Note: Because of these caveats, decisions should not be made solely on
the information presented here and should always be verified by ground
truthing and other reliable information sources.
:author Hadi Ghasemi
:rating 3
:param requires category=='hazard' and \
subcategory=='earthquake' and \
layertype=='raster' and \
unit=='MMI'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for ITB Fatality function.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'ITBFatalityFunction',
'name':
tr('ITB Fatality Function'),
'impact':
tr('Die or be displaced'),
'author':
'Hadi Ghasemi',
'date_implemented':
'N/A',
'overview':
tr('To assess the impact of earthquake on population based '
'on earthquake model developed by ITB'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': [hazard_earthquake],
'units': [unit_mmi],
'layer_constraints': [layer_raster_continuous]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
title = tr('Die or be displaced')
synopsis = tr(
'To assess the impact of earthquake on population based on earthquake '
'model developed by ITB')
citations = tr(
' * Indonesian Earthquake Building-Damage and Fatality Models and '
' Post Disaster Survey Guidelines Development Bali, 27-28 '
' February 2012, 54pp.\n'
' * Allen, T. I., Wald, D. J., Earle, P. S., Marano, K. D., '
' Hotovec, A. J., Lin, K., and Hearne, M., 2009. An Atlas '
' of ShakeMaps and population exposure catalog for '
' earthquake loss modeling, Bull. Earthq. Eng. 7, 701-718.\n'
' * Jaiswal, K., and Wald, D., 2010. An empirical model for '
' global earthquake fatality estimation, Earthq. Spectra '
' 26, 1017-1037.\n')
limitation = tr(
' - The model is based on limited number of observed fatality '
' rates during 4 past fatal events. \n'
' - The model clearly over-predicts the fatality rates at '
' intensities higher than VIII.\n'
' - The model only estimates the expected fatality rate '
' for a given intensity level; however the associated '
' uncertainty for the proposed model is not addressed.\n'
' - There are few known mistakes in developing the current '
' model:\n\n'
' * rounding MMI values to the nearest 0.5,\n'
' * Implementing Finite-Fault models of candidate events, and\n'
' * consistency between selected GMPEs with those in use by '
' BMKG.\n')
actions = tr(
'Provide details about the population will be die or displaced')
detailed_description = tr(
'This model was developed by Institut Teknologi Bandung (ITB) '
'and implemented by Dr. Hadi Ghasemi, Geoscience Australia\n'
'Algorithm:\n'
'In this study, the same functional form as Allen (2009) is '
'adopted o express fatality rate as a function of intensity '
'(see Eq. 10 in the report). The Matlab built-in function '
'(fminsearch) for Nelder-Mead algorithm was used to estimate '
'the model parameters. The objective function (L2G norm) that '
'is minimized during the optimisation is the same as the one '
'used by Jaiswal et al. (2010).\n'
'The coefficients used in the indonesian model are x=0.62275231, '
'y=8.03314466, zeta=2.15')
defaults = get_defaults()
parameters = OrderedDict([
('x', 0.62275231),
('y', 8.03314466), # Model coefficients
# Rates of people displaced for each MMI level
('displacement_rate', {
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
6: 1.0,
7: 1.0,
8: 1.0,
9: 1.0,
10: 1.0
}),
('mmi_range', range(2, 10)),
('step', 0.5),
# Threshold below which layer should be transparent
('tolerance', 0.01),
('calculate_displaced_people', True),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}), ('MinimumNeeds', {
'on': True
})
])),
('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def fatality_rate(self, mmi):
"""ITB method to compute fatality rate.
:param mmi:
"""
# As per email discussion with Ole, Trevor, Hadi, mmi < 4 will have
# a fatality rate of 0 - Tim
if mmi < 4:
return 0
x = self.parameters['x']
y = self.parameters['y']
# noinspection PyUnresolvedReferences
return numpy.power(10.0, x * mmi - y)
def run(self, layers):
"""Indonesian Earthquake Fatality Model.
Input:
:param layers: List of layers expected to contain,
hazard: Raster layer of MMI ground shaking
exposure: Raster layer of population count
"""
displacement_rate = self.parameters['displacement_rate']
# Tolerance for transparency
tolerance = self.parameters['tolerance']
# Extract input layers
intensity = get_hazard_layer(layers)
population = get_exposure_layer(layers)
question = get_question(intensity.get_name(), population.get_name(),
self)
# Extract data grids
hazard = intensity.get_data() # Ground Shaking
exposure = population.get_data(scaling=True) # Population Density
# Calculate people affected by each MMI level
# FIXME (Ole): this range is 2-9. Should 10 be included?
mmi_range = self.parameters['mmi_range']
number_of_exposed = {}
number_of_displaced = {}
number_of_fatalities = {}
# Calculate fatality rates for observed Intensity values (hazard
# based on ITB power model
mask = numpy.zeros(hazard.shape)
for mmi in mmi_range:
# Identify cells where MMI is in class i and
# count people affected by this shake level
mmi_matches = numpy.where(
(hazard > mmi - self.parameters['step']) *
(hazard <= mmi + self.parameters['step']), exposure, 0)
# Calculate expected number of fatalities per level
fatality_rate = self.fatality_rate(mmi)
fatalities = fatality_rate * mmi_matches
# Calculate expected number of displaced people per level
try:
displacements = displacement_rate[mmi] * mmi_matches
except KeyError, e:
msg = 'mmi = %i, mmi_matches = %s, Error msg: %s' % (
mmi, str(mmi_matches), str(e))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Adjust displaced people to disregard fatalities.
# Set to zero if there are more fatalities than displaced.
displacements = numpy.where(displacements > fatalities,
displacements - fatalities, 0)
# Sum up numbers for map
mask += displacements # Displaced
# Generate text with result for this study
# This is what is used in the real time system exposure table
number_of_exposed[mmi] = numpy.nansum(mmi_matches.flat)
number_of_displaced[mmi] = numpy.nansum(displacements.flat)
# noinspection PyUnresolvedReferences
number_of_fatalities[mmi] = numpy.nansum(fatalities.flat)
# Set resulting layer to NaN when less than a threshold. This is to
# achieve transparency (see issue #126).
mask[mask < tolerance] = numpy.nan
# Total statistics
total, rounding = population_rounding_full(numpy.nansum(exposure.flat))
# Compute number of fatalities
fatalities = population_rounding(
numpy.nansum(number_of_fatalities.values()))
# As per email discussion with Ole, Trevor, Hadi, total fatalities < 50
# will be rounded down to 0 - Tim
if fatalities < 50:
fatalities = 0
# Compute number of people displaced due to building collapse
displaced = population_rounding(
numpy.nansum(number_of_displaced.values()))
# Generate impact report
table_body = [question]
# Add total fatality estimate
s = format_int(fatalities)
table_body.append(
TableRow([tr('Number of fatalities'), s], header=True))
if self.parameters['calculate_displaced_people']:
# Add total estimate of people displaced
s = format_int(displaced)
table_body.append(
TableRow([tr('Number of people displaced'), s], header=True))
else:
displaced = 0
# Add estimate of total population in area
s = format_int(int(total))
table_body.append(
TableRow([tr('Total number of people'), s], header=True))
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow([tr('Fatalities'),
'%s' % format_int(fatalities)],
header=True),
TableRow([tr('People displaced'),
'%s' % format_int(displaced)],
header=True),
TableRow(tr('Map shows the estimation of displaced population'))
]
total_needs = evacuated_population_needs(displaced, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
table_body.append(TableRow(tr('Provenance'), header=True))
table_body.append(TableRow(self.parameters['provenance']))
table_body.append(TableRow(tr('Action Checklist:'), header=True))
if fatalities > 0:
table_body.append(
tr('Are there enough victim identification '
'units available for %s people?') % format_int(fatalities))
if displaced > 0:
table_body.append(
tr('Are there enough shelters and relief items '
'available for %s people?') % format_int(displaced))
table_body.append(
TableRow(
tr('If yes, where are they located and '
'how will we distribute them?')))
table_body.append(
TableRow(
tr('If no, where can we obtain '
'additional relief items from and '
'how will we transport them?')))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Total population: %s') % format_int(total),
tr('People are considered to be displaced if '
'they experience and survive a shake level'
'of more than 5 on the MMI scale '),
tr('Minimum needs are defined in BNPB '
'regulation 7/2008'),
tr('The fatality calculation assumes that '
'no fatalities occur for shake levels below 4 '
'and fatality counts of less than 50 are '
'disregarded.'),
tr('All values are rounded up to the nearest '
'integer in order to avoid representing human '
'lives as fractions.')
])
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
tr('Fatality model is from '
'Institute of Teknologi Bandung 2012.'))
table_body.append(
tr('Population numbers rounded up to the nearest %s.') % rounding)
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(mask) == 0 == numpy.nanmin(mask):
table_body = [
question,
TableRow([tr('Fatalities'),
'%s' % format_int(fatalities)],
header=True)
]
my_message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(my_message)
# Create style
colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000']
classes = create_classes(mask.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 30
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('Earthquake impact to population')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population Count')
# Create raster object and return
raster = Raster(mask,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
keywords={
'impact_summary': impact_summary,
'total_population': total,
'total_fatalities': fatalities,
'fatalities_per_mmi': number_of_fatalities,
'exposed_per_mmi': number_of_exposed,
'displaced_per_mmi': number_of_displaced,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs
},
name=tr('Estimated displaced population per cell'),
style_info=style_info)
return raster
def __init__(self):
super(VolcanoPolygonPopulationFunction, self).__init__()
self.target_field = 'population'
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
class PAGFatalityFunction(ITBFatalityFunction):
# noinspection PyUnresolvedReferences
"""Population Vulnerability Model Pager.
Loss ratio(MMI) = standard normal distrib( 1 / BETA * ln(MMI/THETA)).
Reference:
Jaiswal, K. S., Wald, D. J., and Hearne, M. (2009a).
Estimating casualties for large worldwide earthquakes using an empirical
approach. U.S. Geological Survey Open-File Report 2009-1136.
:author Helen Crowley
:rating 3
:param requires category=='hazard' and \
subcategory=='earthquake' and \
layertype=='raster' and \
unit=='MMI'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ITBFatalityFunction.Metadata):
"""Metadata for PAG Fatality Function.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'PAGFatalityFunction',
'name':
tr('PAG Fatality Function'),
'impact':
tr('Die or be displaced according Pager model'),
'author':
'Helen Crowley',
'date_implemented':
'N/A',
'overview':
tr('To assess the impact of earthquake on population based '
'on Population Vulnerability Model Pager'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': [hazard_earthquake],
'units': [unit_mmi],
'layer_constraints': [layer_raster_continuous]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
synopsis = tr('To assess the impact of earthquake on population based on '
'Population Vulnerability Model Pager')
citations = tr(
' * Jaiswal, K. S., Wald, D. J., and Hearne, M. (2009a). '
' Estimating casualties for large worldwide earthquakes using '
' an empirical approach. U.S. Geological Survey Open-File '
' Report 2009-1136.')
limitation = ''
detailed_description = ''
title = tr('Die or be displaced according Pager model')
defaults = get_defaults()
parameters = OrderedDict([
('Theta', 11.067),
('Beta', 0.106), # Model coefficients
# Rates of people displaced for each MMI level
('displacement_rate', {
1: 0,
1.5: 0,
2: 0,
2.5: 0,
3: 0,
3.5: 0,
4: 0,
4.5: 0,
5: 0,
5.5: 0,
6: 1.0,
6.5: 1.0,
7: 1.0,
7.5: 1.0,
8: 1.0,
8.5: 1.0,
9: 1.0,
9.5: 1.0,
10: 1.0
}),
('mmi_range', list(numpy.arange(2, 10, 0.5))),
('step', 0.25),
# Threshold below which layer should be transparent
('tolerance', 0.01),
('calculate_displaced_people', True),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}), ('MinimumNeeds', {
'on': True
})
])),
('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
# noinspection PyPep8Naming
def fatality_rate(self, mmi):
"""Pager method to compute fatality rate.
:param mmi: MMI
:returns: Fatality rate
"""
N = math.sqrt(2 * math.pi)
THETA = self.parameters['Theta']
BETA = self.parameters['Beta']
x = math.log(mmi / THETA) / BETA
return math.exp(-x * x / 2.0) / N
class VolcanoPolygonHazardPopulation(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Impact function for volcano hazard zones impact on population.
:author AIFDR
:rating 4
:param requires category=='hazard' and \
subcategory in ['volcano'] and \
layertype=='vector'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for Volcano Polygon Hazard Population.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'VolcanoPolygonHazardPopulation',
'name': tr('Volcano Polygon Hazard Population'),
'impact': tr('Need evacuation'),
'author': 'AIFDR',
'date_implemented': 'N/A',
'overview': tr('To assess the impacts of volcano eruption '
'on population.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': [hazard_volcano],
'units': [unit_volcano_categorical],
'layer_constraints': [
layer_vector_polygon,
layer_vector_point
]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
title = tr('Need evacuation')
target_field = 'population'
defaults = get_defaults()
# Function documentation
synopsis = tr('To assess the impacts of volcano eruption on population.')
actions = tr(
'Provide details about how many population would likely be affected '
'by each hazard zones.')
hazard_input = tr(
'A hazard vector layer can be polygon or point. If polygon, it must '
'have "KRB" attribute and the valuefor it are "Kawasan Rawan '
'Bencana I", "Kawasan Rawan Bencana II", or "Kawasan Rawan Bencana '
'III."If you want to see the name of the volcano in the result, you '
'need to add "NAME" attribute for point data or "GUNUNG" attribute '
'for polygon data.')
exposure_input = tr(
'An exposure raster layer where each cell represent population count.')
output = tr(
'Vector layer contains people affected and the minimum needs '
'based on the number of people affected.')
parameters = OrderedDict([
('distance [km]', [3, 5, 10]),
('minimum needs', default_minimum_needs()),
('postprocessors', OrderedDict([
('Gender', {'on': True}),
('Age', {
'on': True,
'params': OrderedDict([
('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])}),
('MinimumNeeds', {'on': True})
])),
('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def run(self, layers):
"""Risk plugin for volcano population evacuation.
:param layers: List of layers expected to contain where two layers
should be present.
* hazard_layer: Vector polygon layer of volcano impact zones
* exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to volcano event.
:returns: Map of population exposed to the volcano hazard zone.
The returned dict will include a table with number of people
evacuated and supplies required.
:rtype: dict
:raises:
* Exception - When hazard layer is not vector layer
* RadiiException - When radii are not valid (they need to be
monotonically increasing)
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Volcano KRB
exposure_layer = get_exposure_layer(layers)
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Input checks
if not hazard_layer.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% hazard_layer.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' % (hazard_layer.get_name(),
hazard_layer.get_geometry_name()))
if not (hazard_layer.is_polygon_data or hazard_layer.is_point_data):
raise Exception(msg)
data_table = hazard_layer.get_data()
if hazard_layer.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(
centers, rad_m, category_title, data_table=data_table)
else:
# Use hazard map
category_title = 'KRB'
category_header = tr('Category')
# FIXME (Ole): Change to English and use translation system
category_names = ['Kawasan Rawan Bencana III',
'Kawasan Rawan Bencana II',
'Kawasan Rawan Bencana I']
name_attribute = 'GUNUNG' # As in e.g. BNPB hazard map
# Get names of volcanoes considered
if name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in data_table:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for name in volcano_name_list:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
# Check if category_title exists in hazard_layer
if category_title not in hazard_layer.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (hazard_layer.get_name(), category_title))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Find the target field name that has no conflict with default target
attribute_names = hazard_layer.get_attribute_names()
new_target_field = get_non_conflicting_attribute_name(
self.target_field, attribute_names)
self.target_field = new_target_field
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=self.target_field)
# Initialise data_table of output dataset with all data_table
# from input polygon and a population count of zero
new_data_table = hazard_layer.get_data()
categories = {}
for row in new_data_table:
row[self.target_field] = 0
category = row[category_title]
categories[category] = 0
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = float(row[self.target_field])
# Update population count for associated polygon
poly_id = row['polygon_id']
new_data_table[poly_id][self.target_field] += population
# Update population count for each category
category = new_data_table[poly_id][category_title]
categories[category] += population
# Count totals
total_population = population_rounding(
int(numpy.sum(exposure_layer.get_data(nan=0))))
# Count number and cumulative for each zone
cumulative = 0
all_categories_population = {}
all_categories_cumulative = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
population = int(categories.get(key, 0))
cumulative += population
# I'm not sure whether this is the best place to apply rounding?
all_categories_population[name] = population_rounding(population)
all_categories_cumulative[name] = population_rounding(cumulative)
# Use final accumulation as total number needing evacuation
evacuated = population_rounding(cumulative)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
blank_cell = ''
table_body = [question,
TableRow([tr('Volcanoes considered'),
'%s' % volcano_names, blank_cell],
header=True),
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell],
header=True),
TableRow([category_header,
tr('Total'), tr('Cumulative')],
header=True)]
for name in category_names:
table_body.append(
TableRow([name,
format_int(all_categories_population[name]),
format_int(all_categories_cumulative[name])]))
table_body.extend([
TableRow(tr(
'Map shows the number of people affected in each of volcano '
'hazard polygons.'))])
total_needs = evacuated_population_needs(
evacuated, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(TableRow(
[
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(TableRow([
tr(resource['table name']),
format_int(resource['amount'])]))
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend(
[TableRow(tr('Notes'), header=True),
tr('Total population %s in the exposure layer') % format_int(
total_population),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
population_counts = [x[self.target_field] for x in new_data_table]
impact_summary = Table(table_body).toNewlineFreeString()
# check for zero impact
if numpy.nanmax(population_counts) == 0 == numpy.nanmin(
population_counts):
table_body = [
question,
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell], header=True)]
my_message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(my_message)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 100
style_class['min'] = 0
else:
transparency = 30
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by volcanic hazard zone')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create vector layer and return
impact_layer = Vector(
data=new_data_table,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(as_geometry_objects=True),
name=tr('People affected by volcanic hazard zone'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs},
style_info=style_info)
return impact_layer
class FloodEvacuationFunctionVectorHazard(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Impact function for vector flood evacuation.
:author AIFDR
:rating 4
:param requires category=='hazard' and \
subcategory=='flood' and \
layertype=='vector'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for FloodEvacuationFunctionVectorHazard.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'FloodEvacuationFunctionVectorHazard',
'name':
tr('Flood Evacuation Function Vector Hazard'),
'impact':
tr('Need evacuation'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of flood inundation '
'in vector format on population.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': [hazard_flood],
'units': [unit_wetdry],
'layer_constraints': [layer_vector_polygon]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
title = tr('Need evacuation')
# Function documentation
synopsis = tr('To assess the impacts of flood inundation in vector '
'format on population.')
actions = tr(
'Provide details about how many people would likely need to be '
'evacuated, where they are located and what resources would be '
'required to support them.')
detailed_description = tr(
'The population subject to inundation is determined whether in an '
'area which affected or not. You can also set an evacuation '
'percentage to calculate how many percent of the total population '
'affected to be evacuated. This number will be used to estimate needs'
' based on BNPB Perka 7/2008 minimum bantuan.')
hazard_input = tr(
'A hazard vector layer which has attribute affected the value is '
'either 1 or 0')
exposure_input = tr(
'An exposure raster layer where each cell represent population count.')
output = tr('Vector layer contains people affected and the minimum needs '
'based on evacuation percentage.')
target_field = 'population'
defaults = get_defaults()
# Configurable parameters
# TODO: Share the mimimum needs and make another default value
parameters = OrderedDict([
('evacuation_percentage', 1), # Percent of affected needing evacuation
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}),
('MinimumNeeds', {
'on': True
}),
])),
('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def run(self, layers):
"""Risk plugin for flood population evacuation.
:param layers: List of layers expected to contain
* hazard_layer : Vector polygon layer of flood depth
* exposure_layer : Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to areas identified as flood prone
:returns: Map of population exposed to flooding Table with number of
people evacuated and supplies required.
:rtype: tuple
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Flood inundation
exposure_layer = get_exposure_layer(layers)
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Check that hazard is polygon type
if not hazard_layer.is_vector:
message = ('Input hazard %s was not a vector layer as expected ' %
hazard_layer.get_name())
raise Exception(message)
message = (
'Input hazard must be a polygon layer. I got %s with layer type '
'%s' % (hazard_layer.get_name(), hazard_layer.get_geometry_name()))
if not hazard_layer.is_polygon_data:
raise Exception(message)
# Run interpolation function for polygon2raster
combined = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = hazard_layer.get_data()
category_title = 'affected' # FIXME: Should come from keywords
deprecated_category_title = 'FLOODPRONE'
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
try:
title = attr[category_title]
except KeyError:
try:
title = attr['FLOODPRONE']
categories[title] = 0
except KeyError:
pass
# Count affected population per polygon, per category and total
affected_population = 0
for attr in combined.get_data():
affected = False
if 'affected' in attr:
res = attr['affected']
if res is None:
x = False
else:
x = bool(res)
affected = x
elif 'FLOODPRONE' in attr:
# If there isn't an 'affected' attribute,
res = attr['FLOODPRONE']
if res is not None:
affected = res.lower() == 'yes'
elif 'Affected' in attr:
# Check the default attribute assigned for points
# covered by a polygon
res = attr['Affected']
if res is None:
x = False
else:
x = res
affected = x
else:
# assume that every polygon is affected (see #816)
affected = True
# there is no flood related attribute
# message = ('No flood related attribute found in %s. '
# 'I was looking for either "Flooded", "FLOODPRONE" '
# 'or "Affected". The latter should have been '
# 'automatically set by call to '
# 'assign_hazard_values_to_exposure_data(). '
# 'Sorry I can\'t help more.')
# raise Exception(message)
if affected:
# Get population at this location
pop = float(attr['population'])
# Update population count for associated polygon
poly_id = attr['polygon_id']
new_attributes[poly_id][self.target_field] += pop
# Update population count for each category
if len(categories) > 0:
try:
title = new_attributes[poly_id][category_title]
except KeyError:
title = new_attributes[poly_id][
deprecated_category_title]
categories[title] += pop
# Update total
affected_population += pop
# Estimate number of people in need of evacuation
evacuated = (affected_population *
self.parameters['evacuation_percentage'] / 100.0)
affected_population, rounding = population_rounding_full(
affected_population)
total = int(numpy.sum(exposure_layer.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
total = population_rounding(total)
evacuated, rounding_evacuated = population_rounding_full(evacuated)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow([
tr('People affected'),
'%s*' % (format_int(int(affected_population)))
],
header=True),
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
(rounding),
col_span=2)
]),
TableRow([
tr('People needing evacuation'),
'%s*' % (format_int(int(evacuated)))
],
header=True),
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
(rounding_evacuated),
col_span=2)
]),
TableRow([
tr('Evacuation threshold'),
'%s%%' % format_int(self.parameters['evacuation_percentage'])
],
header=True),
TableRow(
tr('Map shows the number of people affected in each flood prone '
'area')),
TableRow(
tr('Table below shows the weekly minimum needs for all '
'evacuated people'))
]
total_needs = evacuated_population_needs(evacuated, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
impact_table = Table(table_body).toNewlineFreeString()
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('How will warnings be disseminated?')))
table_body.append(TableRow(tr('How will we reach stranded people?')))
table_body.append(TableRow(tr('Do we have enough relief items?')))
table_body.append(
TableRow(
'If yes, where are they located and how will we distribute '
'them?'))
table_body.append(
TableRow(
'If no, where can we obtain additional relief items from and '
'how will we transport them to here?'))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Total population: %s') % format_int(total),
tr('People need evacuation if in the area identified as '
'"Flood Prone"'),
tr('Minimum needs are defined in BNPB regulation 7/2008')
])
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
# Define classes for legend for flooded population counts
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
population_counts = [x['population'] for x in new_attributes]
classes = create_classes(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 0
style_class['min'] = 0
else:
transparency = 0
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by flood prone areas')
legend_notes = tr('Thousand separator is represented by \'.\'')
legend_units = tr('(people per polygon)')
legend_title = tr('Population Count')
# Create vector layer and return
vector_layer = Vector(data=new_attributes,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(),
name=tr('People affected by flood prone areas'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'affected_population': affected_population,
'total_population': total,
'total_needs': total_needs
},
style_info=style_info)
return vector_layer
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
class ClassifiedHazardPopulationImpactFunction(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by classified hazard.
:author ESSC
:rating 3
:param requires category=='hazard' and \
layertype=='raster' and \
data_type=='classified' and \
unit=='classes'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for Classified Hazard Population Impact Function.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'ClassifiedHazardPopulationImpactFunction',
'name':
tr('Classified Hazard Population Impact Function'),
'impact':
tr('Be impacted by each class'),
'author':
'Dianne Bencito',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of classified hazards in raster '
'format on population raster layer.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': hazard_all,
'units': [unit_classified],
'layer_constraints': [layer_raster_classified]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
# Function documentation
title = tr('Be affected by each hazard class')
synopsis = tr(
'To assess the impacts of classified hazards in raster format on '
'population raster layer.')
actions = tr(
'Provide details about how many people would likely be affected for '
'each hazard class.')
hazard_input = tr(
'A hazard raster layer where each cell represents the class of the '
'hazard. There should be 3 classes: e.g. 1, 2, and 3.')
exposure_input = tr(
'An exposure raster layer where each cell represent population count.')
output = tr(
'Map of population exposed to high class and a table with number '
'of people in each class')
detailed_description = tr(
'This function will use the class from the hazard layer that has been '
'identified by the user which one is low, medium, or high from the '
'parameter that user input. After that, this impact function will '
'calculate the people will be affected per each class for class in '
'the hazard layer. Finally, it will show the result and the total of '
'people that will be affected for the hazard given.')
limitation = tr('The number of classes is three.')
# Configurable parameters
defaults = get_defaults()
parameters = OrderedDict([
('low_hazard_class', 1.0), ('medium_hazard_class', 2.0),
('high_hazard_class', 3.0),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}),
('MinimumNeeds', {
'on': True
}),
])), ('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def run(self, layers):
"""Plugin for impact of population as derived by classified hazard.
Input
:param layers: List of layers expected to contain
* hazard_layer: Raster layer of classified hazard
* exposure_layer: Raster layer of population data
Counts number of people exposed to each class of the hazard
Return
Map of population exposed to high class
Table with number of people in each class
"""
# The 3 classes
low_t = self.parameters['low_hazard_class']
medium_t = self.parameters['medium_hazard_class']
high_t = self.parameters['high_hazard_class']
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Classified Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=0.0) # Class
# Calculate impact as population exposed to each class
population = exposure_layer.get_data(nan=0.0, scaling=True)
if high_t == 0:
hi = numpy.where(0, population, 0)
else:
hi = numpy.where(data == high_t, population, 0)
if medium_t == 0:
med = numpy.where(0, population, 0)
else:
med = numpy.where(data == medium_t, population, 0)
if low_t == 0:
lo = numpy.where(0, population, 0)
else:
lo = numpy.where(data == low_t, population, 0)
if high_t == 0:
impact = numpy.where((data == low_t) + (data == medium_t),
population, 0)
elif medium_t == 0:
impact = numpy.where((data == low_t) + (data == high_t),
population, 0)
elif low_t == 0:
impact = numpy.where((data == medium_t) + (data == high_t),
population, 0)
else:
impact = numpy.where(
(data == low_t) + (data == medium_t) + (data == high_t),
population, 0)
# Count totals
total = int(numpy.sum(population))
high = int(numpy.sum(hi))
medium = int(numpy.sum(med))
low = int(numpy.sum(lo))
total_impact = int(numpy.sum(impact))
# Perform population rounding based on number of people
no_impact = population_rounding(total - total_impact)
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow([
tr('Total Population Affected '),
'%s' % format_int(total_impact)
],
header=True),
TableRow([
tr('Population in High hazard class areas '),
'%s' % format_int(high)
]),
TableRow([
tr('Population in Medium hazard class areas '),
'%s' % format_int(medium)
]),
TableRow([
tr('Population in Low hazard class areas '),
'%s' % format_int(low)
]),
TableRow(
[tr('Population Not Affected'),
'%s' % format_int(no_impact)]),
TableRow(
tr('Table below shows the minimum needs for all '
'evacuated people'))
]
total_needs = evacuated_population_needs(total_impact, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
impact_table = Table(table_body).toNewlineFreeString()
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('How will warnings be disseminated?')))
table_body.append(TableRow(tr('How will we reach stranded people?')))
table_body.append(TableRow(tr('Do we have enough relief items?')))
table_body.append(
TableRow(
tr('If yes, where are they located and how will we distribute '
'them?')))
table_body.append(
TableRow(
tr('If no, where can we obtain additional relief items from '
'and how will we transport them to here?')))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows the numbers of people in high, medium, and low '
'hazard class areas'),
tr('Total population: %s') % format_int(total)
])
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impact.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(interval_classes[i], 'Low')
elif i == 4:
label = create_label(interval_classes[i], 'Medium')
elif i == 7:
label = create_label(interval_classes[i], 'High')
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 30
else:
transparency = 30
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('Population affected by each class')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Number of People')
# Create raster object and return
raster_layer = Raster(impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs
},
style_info=style_info)
return raster_layer
class ContinuousHazardPopulationImpactFunction(FunctionProvider):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by continuous hazard.
:author AIFDR
:rating 2
:param requires category=='hazard' and \
layertype=='raster' and \
data_type=='continuous'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
class Metadata(ImpactFunctionMetadata):
"""Metadata for Continuous Hazard Population Impact Function.
.. versionadded:: 2.1
We only need to re-implement get_metadata(), all other behaviours
are inherited from the abstract base class.
"""
@staticmethod
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'ContinuousHazardPopulationImpactFunction',
'name':
tr('Continuous Hazard Population Impact Function'),
'impact':
tr('Be impacted'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of continuous hazards in raster '
'format on population raster layer.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategories': hazard_all, # already a list
'units': [],
'layer_constraints': [layer_raster_continuous]
},
'exposure': {
'definition': exposure_definition,
'subcategories': [exposure_population],
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_continuous]
}
}
}
return dict_meta
# Function documentation
title = tr('Be impacted')
synopsis = tr(
'To assess the impacts of continuous hazards in raster format on '
'population raster layer.')
actions = tr(
'Provide details about how many people would likely need to be '
'impacted for each category.')
hazard_input = tr(
'A hazard raster layer where each cell represents the level of the '
'hazard. The hazard has continuous value of hazard level.')
exposure_input = tr(
'An exposure raster layer where each cell represent population count.')
output = tr(
'Map of population exposed to high category and a table with number '
'of people in each category')
detailed_description = tr(
'This function will categorised the continuous hazard level into 3 '
'category based on the threshold that has been input by the user.'
'After that, this function will calculate how many people will be '
'impacted per category for all categories in the hazard layer.')
limitation = tr('The number of categories is three.')
# Configurable parameters
defaults = get_defaults()
parameters = OrderedDict([
('Categorical thresholds', [0.34, 0.67, 1]),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elderly_ratio', defaults['ELDERLY_RATIO'])])
}),
('MinimumNeeds', {
'on': True
}),
])), ('minimum needs', default_minimum_needs()),
('provenance', default_provenance())
])
parameters = add_needs_parameters(parameters)
def run(self, layers):
"""Plugin for impact of population as derived by categorised hazard.
:param layers: List of layers expected to contain
* hazard_layer: Raster layer of categorised hazard
* exposure_layer: Raster layer of population data
Counts number of people exposed to each category of the hazard
:returns:
Map of population exposed to high category
Table with number of people in each category
"""
# The 3 category
high_t = self.parameters['Categorical thresholds'][2]
medium_t = self.parameters['Categorical thresholds'][1]
low_t = self.parameters['Categorical thresholds'][0]
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Categorised Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Extract data as numeric arrays
C = hazard_layer.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
P = exposure_layer.get_data(nan=0.0, scaling=True)
H = numpy.where(C <= high_t, P, 0)
M = numpy.where(C < medium_t, P, 0)
L = numpy.where(C < low_t, P, 0)
# Count totals
total = int(numpy.sum(P))
high = int(numpy.sum(H)) - int(numpy.sum(M))
medium = int(numpy.sum(M)) - int(numpy.sum(L))
low = int(numpy.sum(L))
total_impact = high + medium + low
# Don't show digits less than a 1000
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow([tr('People impacted '),
'%s' % format_int(total_impact)],
header=True),
TableRow(
[tr('People in high hazard area '),
'%s' % format_int(high)],
header=True),
TableRow([
tr('People in medium hazard area '),
'%s' % format_int(medium)
],
header=True),
TableRow([tr('People in low hazard area'),
'%s' % format_int(low)],
header=True)
]
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows population count in high or medium hazard area'),
tr('Total population: %s') % format_int(total),
TableRow(
tr('Table below shows the minimum needs for all '
'affected people'))
])
total_needs = evacuated_population_needs(total_impact, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in high hazard areas')
# Generate 8 equidistant classes across the range of flooded population
# 8 is the number of classes in the predefined flood population style
# as imported
# noinspection PyTypeChecker
classes = numpy.linspace(numpy.nanmin(M.flat[:]),
numpy.nanmax(M.flat[:]), 8)
# Modify labels in existing flood style to show quantities
style_classes = style_info['style_classes']
style_classes[1]['label'] = tr('Low [%i people/cell]') % classes[1]
style_classes[4]['label'] = tr('Medium [%i people/cell]') % classes[4]
style_classes[7]['label'] = tr('High [%i people/cell]') % classes[7]
style_info['legend_title'] = tr('Population Count')
# Create raster object and return
raster_layer = Raster(M,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'total_needs': total_needs
},
style_info=style_info)
return raster_layer
