def evaluate_affected_people(self):
"""Calculate the number of people affected on the area
based on the affected area.
Currently we assume the population distribution is
uniform
:raises: ZeroImpactException
"""
for area_id, area_value in self.all_areas_ids.iteritems():
if area_id in self.all_affected_areas:
affected = self.all_affected_areas[area_id]
else:
affected = 0.0
single_total_area = area_value
if area_value:
affected_area_ratio = affected / single_total_area
else:
affected_area_ratio = 0
number_people_affected = (affected_area_ratio *
self.all_areas_population[area_id])
# rounding to float without decimal, we can't have number
# of people with decimal
number_people_affected = round(number_people_affected, 0)
self.affected_population[area_id] = number_people_affected
total_affected_population = self.total_affected_population
unaffected_population = (self.total_population -
self.total_affected_population)
self.unaffected_population = unaffected_population
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
def evaluate_affected_people(self):
"""Calculate the number of people affected on the area
based on the affected area.
Currently we assume the population distribution is
uniform
:raises: ZeroImpactException
"""
for area_id, area_value in self.all_areas_ids.iteritems():
if area_id in self.all_affected_areas:
affected = self.all_affected_areas[area_id]
else:
affected = 0.0
single_total_area = area_value
if area_value:
affected_area_ratio = affected / single_total_area
else:
affected_area_ratio = 0
number_people_affected = (
affected_area_ratio * self.all_areas_population[area_id])
# rounding to float without decimal, we can't have number
# of people with decimal
number_people_affected = round(number_people_affected, 0)
self.affected_population[area_id] = number_people_affected
total_affected_population = self.total_affected_population
unaffected_population = (
self.total_population - self.total_affected_population)
self.unaffected_population = unaffected_population
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
def run(self):
"""Plugin for impact of population as derived by classified hazard.
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
"""
self.validate()
self.prepare()
# The 3 classes
# TODO (3.2): shouldnt these be defined in keywords rather? TS
categorical_hazards = self.parameters['Categorical hazards'].value
low_class = categorical_hazards[0].value
medium_class = categorical_hazards[1].value
high_class = categorical_hazards[2].value
# The classes must be different to each other
unique_classes_flag = all(
x != y for x, y in list(
itertools.combinations(
[low_class, medium_class, high_class], 2)))
if not unique_classes_flag:
raise FunctionParametersError(
'There is hazard class that has the same value with other '
'class. Please check the parameters.')
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Class
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each class
population = self.exposure.layer.get_data(scaling=True)
# Get all population data that falls in each hazard class
high_hazard_population = numpy.where(
hazard_data == high_class, population, 0)
medium_hazard_population = numpy.where(
hazard_data == medium_class, population, 0)
low_hazard_population = numpy.where(
hazard_data == low_class, population, 0)
affected_population = (
high_hazard_population + medium_hazard_population +
low_hazard_population)
# Carry the no data values forward to the impact layer.
affected_population = numpy.where(
numpy.isnan(population),
numpy.nan,
affected_population)
affected_population = numpy.where(
numpy.isnan(hazard_data),
numpy.nan,
affected_population)
# Count totals
self.total_population = int(numpy.nansum(population))
self.affected_population[
tr('Population in High hazard class areas')] = int(
numpy.nansum(high_hazard_population))
self.affected_population[
tr('Population in Medium hazard class areas')] = int(
numpy.nansum(medium_hazard_population))
self.affected_population[
tr('Population in Low hazard class areas')] = int(
numpy.nansum(low_hazard_population))
self.unaffected_population = (
self.total_population - self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
self.minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
total_needs = self.total_needs
impact_table = impact_summary = self.html_report()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(affected_population.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],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
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('Number of people affected in each class')
legend_title = tr('Number of People')
legend_units = tr('(people per cell)')
legend_notes = tr(
'Thousand separator is represented by %s' %
get_thousand_separator())
# Create raster object and return
raster_layer = Raster(
data=affected_population,
projection=self.exposure.layer.get_projection(),
geotransform=self.exposure.layer.get_geotransform(),
name=tr('People that might %s') % (
self.impact_function_manager
.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)
self._impact = raster_layer
return raster_layer
def run(self):
"""Risk plugin for flood population evacuation.
Counts number of people exposed to flood levels exceeding
specified threshold.
:returns: Map of population exposed to flood levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds'].value
verify(isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = self.hazard.layer.get_data(nan=True) # Depth
if has_no_data(data):
self.no_data_warning = True
# Calculate impact as population exposed to depths > max threshold
population = self.exposure.layer.get_data(nan=True, scaling=True)
total = int(numpy.nansum(population))
if has_no_data(population):
self.no_data_warning = True
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
thresholds_name = tr('People in >= %.1f m of water') % lo
self.impact_category_ordering.append(thresholds_name)
self._evacuation_category = thresholds_name
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
thresholds_name = tr('People in %.1f m to %.1f m of water' %
(lo, hi))
self.impact_category_ordering.append(thresholds_name)
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.nansum(medium))
self.affected_population[thresholds_name] = val
# Put the deepest area in top #2385
self.impact_category_ordering.reverse()
self.total_population = total
self.unaffected_population = total - self.total_affected_population
# Carry the no data values forward to the impact layer.
impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
impact = numpy.where(numpy.isnan(data), numpy.nan, impact)
# Count totals
evacuated = self.total_evacuated
self.minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Result
impact_summary = self.html_report()
impact_table = impact_summary
total_needs = self.total_needs
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
message = no_population_impact_message(self.question)
raise ZeroImpactException(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]
style_class['transparency'] = 0
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
# For printing map purpose
map_title = tr('People in need of evacuation')
legend_title = tr('Population Count')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
extra_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
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
raster = Raster(
impact,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=tr('Population which %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = raster
return raster
def run(self):
"""Plugin for impact of population as derived by continuous hazard.
Hazard is reclassified into 3 classes based on the extrema provided
as impact function parameters.
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
"""
thresholds = [
p.value for p in self.parameters['Categorical thresholds'].value
]
# Thresholds must contain 3 thresholds
if len(thresholds) != 3:
raise FunctionParametersError(
'The thresholds must consist of 3 values.')
# Thresholds must monotonically increasing
monotonically_increasing_flag = all(
x < y for x, y in zip(thresholds, thresholds[1:]))
if not monotonically_increasing_flag:
raise FunctionParametersError(
'Each threshold should be larger than the previous.')
# The 3 categories
low_t = thresholds[0]
medium_t = thresholds[1]
high_t = thresholds[2]
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Category
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each category
exposure_data = self.exposure.layer.get_data(nan=True, scaling=True)
if has_no_data(exposure_data):
self.no_data_warning = True
# Make 3 data for each zone. Get the value of the exposure if the
# exposure is in the hazard zone, else just assign 0
low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
medium_exposure = numpy.where(
(hazard_data >= low_t) & (hazard_data < medium_t), exposure_data,
0)
high_exposure = numpy.where(
(hazard_data >= medium_t) & (hazard_data <= high_t), exposure_data,
0)
impacted_exposure = low_exposure + medium_exposure + high_exposure
# Count totals
self.total_population = int(numpy.nansum(exposure_data))
self.affected_population[tr('Population in high hazard zones')] = int(
numpy.nansum(high_exposure))
self.affected_population[tr(
'Population in medium hazard zones')] = int(
numpy.nansum(medium_exposure))
self.affected_population[tr('Population in low hazard zones')] = int(
numpy.nansum(low_exposure))
self.unaffected_population = (self.total_population -
self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Don't show digits less than a 1000
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
total_needs = self.total_needs
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impacted_exposure.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],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'map_title': self.map_title(),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'total_needs': total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
impact_layer = Raster(
data=impacted_exposure,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=self.map_title(),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Plugin for impact of population as derived by continuous hazard.
Hazard is reclassified into 3 classes based on the extrema provided
as impact function parameters.
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
"""
thresholds = [
p.value for p in self.parameters['Categorical thresholds'].value]
# Thresholds must contain 3 thresholds
if len(thresholds) != 3:
raise FunctionParametersError(
'The thresholds must consist of 3 values.')
# Thresholds must monotonically increasing
monotonically_increasing_flag = all(
x < y for x, y in zip(thresholds, thresholds[1:]))
if not monotonically_increasing_flag:
raise FunctionParametersError(
'Each threshold should be larger than the previous.')
# The 3 categories
low_t = thresholds[0]
medium_t = thresholds[1]
high_t = thresholds[2]
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Category
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each category
exposure_data = self.exposure.layer.get_data(nan=True, scaling=True)
if has_no_data(exposure_data):
self.no_data_warning = True
# Make 3 data for each zone. Get the value of the exposure if the
# exposure is in the hazard zone, else just assign 0
low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
medium_exposure = numpy.where(
(hazard_data >= low_t) & (hazard_data < medium_t),
exposure_data, 0)
high_exposure = numpy.where(
(hazard_data >= medium_t) & (hazard_data <= high_t),
exposure_data, 0)
impacted_exposure = low_exposure + medium_exposure + high_exposure
# Count totals
self.total_population = int(numpy.nansum(exposure_data))
self.affected_population[
tr('Population in high hazard areas')] = int(
numpy.nansum(high_exposure))
self.affected_population[
tr('Population in medium hazard areas')] = int(
numpy.nansum(medium_exposure))
self.affected_population[
tr('Population in low hazard areas')] = int(
numpy.nansum(low_exposure))
self.unaffected_population = (
self.total_population - self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Don't show digits less than a 1000
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
total_needs = self.total_needs
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(impacted_exposure.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],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'map_title': self.metadata().key('map_title'),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'total_needs': total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
impact_layer = Raster(
data=impacted_exposure,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=self.metadata().key('layer_name'),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Run volcano population evacuation Impact Function.
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)
"""
# Parameters
self.hazard_class_attribute = self.hazard.keyword('field')
name_attribute = self.hazard.keyword('volcano_name_field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
if has_no_data(self.exposure.layer.get_data(nan=True)):
self.no_data_warning = True
# Input checks
if not self.hazard.layer.is_polygon_data:
message = tr(
'Input hazard must be a polygon layer. I got %s with layer '
'type %s' % (
self.hazard.layer.get_name(),
self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
message = tr(
'Hazard data %s did not contain expected attribute ''%s ' % (
self.hazard.layer.get_name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
features = self.hazard.layer.get_data()
# Get names of volcanoes considered
if name_attribute in self.hazard.layer.get_attribute_names():
# Run through all polygons and get unique names
for row in features:
self.volcano_names.add(row[name_attribute])
# Retrieve the classification that is used by the hazard layer.
vector_hazard_classification = self.hazard.keyword(
'vector_hazard_classification')
# Get the dictionary that contains the definition of the classification
vector_hazard_classification = definition(vector_hazard_classification)
# Get the list classes in the classification
vector_hazard_classes = vector_hazard_classification['classes']
# Initialize OrderedDict of affected buildings
self.affected_population = OrderedDict()
# Iterate over vector hazard classes
for vector_hazard_class in vector_hazard_classes:
# Check if the key of class exist in hazard_class_mapping
if vector_hazard_class['key'] in self.hazard_class_mapping.keys():
# Replace the key with the name as we need to show the human
# friendly name in the report.
self.hazard_class_mapping[vector_hazard_class['name']] = \
self.hazard_class_mapping.pop(vector_hazard_class['key'])
# Adding the class name as a key in affected_building
self.affected_population[vector_hazard_class['name']] = 0
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field)
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_value = get_key_for_value(
row[self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
self.affected_population[hazard_value] += population
# Count totals
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
self.unaffected_population = (
self.total_population - self.total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(
covered_exposure_layer.get_data().flat[:], 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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = 0
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'target_field': self.target_field,
'map_title': self.map_title(),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'total_needs': self.total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=self.map_title(),
keywords=impact_layer_keywords,
style_info=style_info
)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Risk plugin for flood population evacuation.
Counts number of people exposed to flood levels exceeding
specified threshold.
:returns: Map of population exposed to flood levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds'].value
verify(
isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = self.hazard.layer.get_data(nan=True) # Depth
if has_no_data(data):
self.no_data_warning = True
# Calculate impact as population exposed to depths > max threshold
population = self.exposure.layer.get_data(nan=True, scaling=True)
total = int(numpy.nansum(population))
if has_no_data(population):
self.no_data_warning = True
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
thresholds_name = tr(
'People in >= %.1f m of water') % lo
self.impact_category_ordering.append(thresholds_name)
self._evacuation_category = thresholds_name
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
thresholds_name = tr(
'People in %.1f m to %.1f m of water' % (lo, hi))
self.impact_category_ordering.append(thresholds_name)
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.nansum(medium))
self.affected_population[thresholds_name] = val
# Put the deepest area in top #2385
self.impact_category_ordering.reverse()
self.total_population = total
self.unaffected_population = total - self.total_affected_population
# Carry the no data values forward to the impact layer.
impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
impact = numpy.where(numpy.isnan(data), numpy.nan, impact)
# Count totals
evacuated = self.total_evacuated
self.minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
total_needs = self.total_needs
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
message = no_population_impact_message(self.question)
raise ZeroImpactException(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]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'map_title': self.metadata().key('map_title'),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'evacuated': evacuated,
'total_needs': total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
impact_layer = Raster(
impact,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=self.metadata().key('layer_name'),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Risk plugin for flood population evacuation.
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
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step',
'Impact function is calculating the impact.')
# Get parameters from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
# Get the IF parameters
self._evacuation_percentage = (
self.parameters['evacuation_percentage'].value)
# Check that hazard is polygon type
if not self.hazard.layer.is_polygon_data:
message = (
'Input hazard must be a polygon layer. I got %s with layer '
'type %s' % (
self.hazard.name,
self.hazard.layer.get_geometry_name()))
raise Exception(message)
if has_no_data(self.exposure.layer.get_data(nan=True)):
self.no_data_warning = True
# Check that affected field exists in hazard layer
if (self.hazard_class_attribute in
self.hazard.layer.get_attribute_names()):
self.use_affected_field = True
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field)
# Data for manipulating the covered_exposure layer
new_covered_exposure_data = covered_exposure.get_data()
covered_exposure_top_left = numpy.array([
covered_exposure.get_geotransform()[0],
covered_exposure.get_geotransform()[3]])
covered_exposure_dimension = numpy.array([
covered_exposure.get_geotransform()[1],
covered_exposure.get_geotransform()[5]])
# Count affected population per polygon, per category and total
total_affected_population = 0
for attr in interpolated_layer.get_data():
affected = False
if self.use_affected_field:
row_affected_value = attr[self.hazard_class_attribute]
if row_affected_value is not None:
affected = get_key_for_value(
row_affected_value, self.hazard_class_mapping)
else:
# assume that every polygon is affected (see #816)
affected = self.wet
if affected == self.wet:
# Get population at this location
population = attr[self.target_field]
if not numpy.isnan(population):
population = float(population)
total_affected_population += population
else:
# If it's not affected, set the value of the impact layer to 0
grid_point = attr['grid_point']
index = numpy.floor(
(grid_point - covered_exposure_top_left) / (
covered_exposure_dimension)).astype(int)
new_covered_exposure_data[index[1]][index[0]] = 0
# Estimate number of people in need of evacuation
if self.use_affected_field:
affected_population = tr(
'People within hazard field ("%s") of value "%s"') % (
self.hazard_class_attribute,
','.join([
unicode(hazard_class) for
hazard_class in self.hazard_class_mapping[self.wet]
]))
else:
affected_population = tr('People within any hazard polygon.')
self.affected_population[affected_population] = (
total_affected_population)
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data(scaling=False)))
self.unaffected_population = (
self.total_population - self.total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
impact_table = impact_summary = self.html_report()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(
new_covered_exposure_data.flat[:], len(colours))
# check for zero impact
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = 0
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People affected by flood prone areas')
legend_title = tr('Population Count')
legend_units = tr('(people per polygon)')
legend_notes = tr(
'Thousand separator is represented by %s' %
get_thousand_separator())
extra_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': total_affected_population,
'total_population': self.total_population,
'total_needs': self.total_needs
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
impact_layer = Raster(
data=new_covered_exposure_data,
projection=covered_exposure.get_projection(),
geotransform=covered_exposure.get_geotransform(),
name=tr('People affected by flood prone areas'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Run the impact function.
"""
# Range for ash hazard
group_parameters = self.parameters['group_threshold']
unaffected_max = group_parameters.value_map[
'unaffected_threshold'].value
very_low_max = group_parameters.value_map['very_low_threshold'].value
low_max = group_parameters.value_map['low_threshold'].value
medium_max = group_parameters.value_map['moderate_threshold'].value
high_max = group_parameters.value_map['high_threshold'].value
# Extract hazard data as numeric arrays
ash = self.hazard.layer.get_data(nan=True) # Thickness
if has_no_data(ash):
self.no_data_warning = True
# Extract exposure data as numeric arrays
population = self.exposure.layer.get_data(nan=True, scaling=True)
if has_no_data(population):
self.no_data_warning = True
# Create 5 data for each hazard level. Get the value of the exposure
# if the exposure is in the hazard zone, else just assign 0
unaffected_exposure = numpy.where(ash < unaffected_max, population, 0)
very_low_exposure = numpy.where(
(ash >= unaffected_max) & (ash < very_low_max), population, 0)
low_exposure = numpy.where(
(ash >= very_low_max) & (ash < low_max), population, 0)
medium_exposure = numpy.where(
(ash >= low_max) & (ash < medium_max), population, 0)
high_exposure = numpy.where(
(ash >= medium_max) & (ash < high_max), population, 0)
very_high_exposure = numpy.where(ash >= high_max, population, 0)
impacted_exposure = (
very_low_exposure +
low_exposure +
medium_exposure +
high_exposure +
very_high_exposure
)
# Count totals
self.total_population = int(numpy.nansum(population))
self.affected_population[
tr('Population in very low hazard zone')] = int(
numpy.nansum(very_low_exposure))
self.affected_population[
tr('Population in low hazard zone')] = int(
numpy.nansum(low_exposure))
self.affected_population[
tr('Population in medium hazard zone')] = int(
numpy.nansum(medium_exposure))
self.affected_population[
tr('Population in high hazard zone')] = int(
numpy.nansum(high_exposure))
self.affected_population[
tr('Population in very high hazard zone')] = int(
numpy.nansum(very_high_exposure))
self.unaffected_population = int(
numpy.nansum(unaffected_exposure))
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Don't show digits less than a 1000
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
total_needs = self.total_needs
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(impacted_exposure.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],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'map_title': self.map_title(),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'total_needs': total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
impact_layer = Raster(
data=impacted_exposure,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=self.map_title(),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Run classified population evacuation Impact Function.
Counts number of people exposed to each hazard zones.
:returns: Map of population exposed to each 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
"""
# Value from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
# TODO: Remove check to self.validate (Ismail)
# Input checks
message = tr(
'Input hazard must be a polygon layer. I got %s with layer type '
'%s' % (self.hazard.name, self.hazard.layer.get_geometry_name()))
if not self.hazard.layer.is_polygon_data:
raise Exception(message)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute
not in self.hazard.layer.get_attribute_names()):
message = tr(
'Hazard data %s does not contain expected hazard '
'zone attribute "%s". Please change it in the option. ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Retrieve the classification that is used by the hazard layer.
vector_hazard_classification = self.hazard.keyword(
'vector_hazard_classification')
# Get the dictionary that contains the definition of the classification
vector_hazard_classification = definition(vector_hazard_classification)
# Get the list classes in the classification
vector_hazard_classes = vector_hazard_classification['classes']
# Initialize OrderedDict of affected buildings
self.affected_population = OrderedDict()
# Iterate over vector hazard classes
for vector_hazard_class in vector_hazard_classes:
# Check if the key of class exist in hazard_class_mapping
if vector_hazard_class['key'] in self.hazard_class_mapping.keys():
# Replace the key with the name as we need to show the human
# friendly name in the report.
self.hazard_class_mapping[vector_hazard_class['name']] = \
self.hazard_class_mapping.pop(vector_hazard_class['key'])
# Adding the class name as a key in affected_building
self.affected_population[vector_hazard_class['name']] = 0
# Interpolated layer represents grid cell that lies in the polygon
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field
)
# Count total affected population per hazard zone
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_value = get_key_for_value(
row[self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
else:
self.affected_population[hazard_value] += population
# Count total population from exposure layer
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
# Count total affected population
total_affected_population = self.total_affected_population
self.unaffected_population = (self.total_population -
total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
# check for zero impact
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(covered_exposure_layer.get_data().flat[:],
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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = 0
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'target_field': self.target_field,
'map_title': self.map_title(),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title')
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=self.map_title(),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Plugin for impact of population as derived by classified hazard.
Counts number of people exposed to each class of the hazard
:returns: Map of population exposed to high class
Table with number of people in each class
"""
# The 3 classes
# TODO (3.2): shouldnt these be defined in keywords rather? TS
categorical_hazards = self.parameters['Categorical hazards'].value
low_class = categorical_hazards[0].value
medium_class = categorical_hazards[1].value
high_class = categorical_hazards[2].value
# The classes must be different to each other
unique_classes_flag = all(x != y for x, y in list(
itertools.combinations([low_class, medium_class, high_class], 2)))
if not unique_classes_flag:
raise FunctionParametersError(
'There is hazard class that has the same value with other '
'class. Please check the parameters.')
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Class
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each class
population = self.exposure.layer.get_data(scaling=True)
# Get all population data that falls in each hazard class
high_hazard_population = numpy.where(hazard_data == high_class,
population, 0)
medium_hazard_population = numpy.where(hazard_data == medium_class,
population, 0)
low_hazard_population = numpy.where(hazard_data == low_class,
population, 0)
affected_population = (high_hazard_population +
medium_hazard_population +
low_hazard_population)
# Carry the no data values forward to the impact layer.
affected_population = numpy.where(numpy.isnan(population), numpy.nan,
affected_population)
affected_population = numpy.where(numpy.isnan(hazard_data), numpy.nan,
affected_population)
# Count totals
self.total_population = int(numpy.nansum(population))
self.affected_population[tr('Population in low hazard zone')] = int(
numpy.nansum(low_hazard_population))
self.affected_population[tr('Population in medium hazard zone')] = int(
numpy.nansum(medium_hazard_population))
self.affected_population[tr('Population in high hazard zone')] = int(
numpy.nansum(high_hazard_population))
self.unaffected_population = (self.total_population -
self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
self.minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
total_needs = self.total_needs
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(affected_population.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],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
impact_data = self.generate_data()
extra_keywords = {
'map_title': self.map_title(),
'legend_notes': self.metadata().key('legend_notes'),
'legend_units': self.metadata().key('legend_units'),
'legend_title': self.metadata().key('legend_title'),
'total_needs': total_needs
}
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
impact_layer = Raster(
data=affected_population,
projection=self.exposure.layer.get_projection(),
geotransform=self.exposure.layer.get_geotransform(),
name=self.map_title(),
keywords=impact_layer_keywords,
style_info=style_info)
impact_layer.impact_data = impact_data
self._impact = impact_layer
return impact_layer
def run(self):
"""Run classified population evacuation Impact Function.
Counts number of people exposed to each hazard zones.
:returns: Map of population exposed to each 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
"""
self.validate()
self.prepare()
# Value from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
# Input checks
msg = ('Input hazard must be a polygon layer. I got %s with '
'layer type %s' % (
self.hazard.name, self.hazard.layer.get_geometry_name()))
if not self.hazard.layer.is_polygon_data:
raise Exception(msg)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
msg = ('Hazard data %s does not contain expected hazard '
'zone attribute "%s". Please change it in the option. ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Get unique hazard zones from the layer attribute
self.hazard_zones = list(
set(self.hazard.layer.get_data(self.hazard_class_attribute)))
# Interpolated layer represents grid cell that lies in the polygon
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field
)
# Initialise total population affected by each hazard zone
for hazard_zone in self.hazard_zones:
self.affected_population[hazard_zone] = 0
# Count total affected population per hazard zone
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_zone = row[self.hazard_class_attribute]
self.affected_population[hazard_zone] += population
# Count total population from exposure layer
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
# Count total affected population
total_affected_population = self.total_affected_population
self.unaffected_population = (
self.total_population - total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
# check for zero impact
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
impact_table = impact_summary = self.html_report()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(
covered_exposure_layer.get_data().flat[:], 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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
if i == 0:
transparency = 100
else:
transparency = 0
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = transparency
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People impacted by each hazard zone')
legend_title = tr('Population')
legend_units = tr('(people per cell)')
legend_notes = tr(
'Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People impacted by each 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},
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Run volcano population evacuation Impact Function.
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)
"""
self.validate()
self.prepare()
# Parameters
self.hazard_class_attribute = self.hazard.keyword('field')
name_attribute = self.hazard.keyword('volcano_name_field')
if has_no_data(self.exposure.layer.get_data(nan=True)):
self.no_data_warning = True
# Input checks
if not self.hazard.layer.is_polygon_data:
msg = ('Input hazard must be a polygon layer. I got %s with '
'layer type %s' % (self.hazard.layer.get_name(),
self.hazard.layer.get_geometry_name()))
raise Exception(msg)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
msg = ('Hazard data %s did not contain expected attribute %s ' % (
self.hazard.layer.get_name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
features = self.hazard.layer.get_data()
hazard_zone_categories = list(
set(self.hazard.layer.get_data(self.hazard_class_attribute)))
# Get names of volcanoes considered
if name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in features:
volcano_name_list.append(row[name_attribute])
self.volcano_names = ', '.join(set(volcano_name_list))
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field)
# Initialise total affected per category
for hazard_zone in hazard_zone_categories:
self.affected_population[hazard_zone] = 0
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this category
category = row[self.hazard_class_attribute]
self.affected_population[category] += population
# Count totals
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
self.unaffected_population = (
self.total_population - self.total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
impact_table = impact_summary = self.html_report()
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(
covered_exposure_layer.get_data().flat[:], 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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
if i == 0:
transparency = 100
else:
transparency = 0
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = transparency
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People affected by Volcano Hazard Zones')
legend_title = tr('Population')
legend_units = tr('(people per cell)')
legend_notes = tr(
'Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People affected by volcano hazard zones'),
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': self.total_needs},
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Run volcano population evacuation Impact Function.
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)
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
# Parameters
self.hazard_class_attribute = self.hazard.keyword('field')
name_attribute = self.hazard.keyword('volcano_name_field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
if has_no_data(self.exposure.layer.get_data(nan=True)):
self.no_data_warning = True
# Input checks
if not self.hazard.layer.is_polygon_data:
message = tr(
'Input hazard must be a polygon layer. I got %s with layer '
'type %s' % (self.hazard.layer.get_name(),
self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute
not in self.hazard.layer.get_attribute_names()):
message = tr(
'Hazard data %s did not contain expected attribute '
'%s ' %
(self.hazard.layer.get_name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
features = self.hazard.layer.get_data()
# Get names of volcanoes considered
if name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in features:
volcano_name_list.append(row[name_attribute])
self.volcano_names = ', '.join(set(volcano_name_list))
# Retrieve the classification that is used by the hazard layer.
vector_hazard_classification = self.hazard.keyword(
'vector_hazard_classification')
# Get the dictionary that contains the definition of the classification
vector_hazard_classification = definition(vector_hazard_classification)
# Get the list classes in the classification
vector_hazard_classes = vector_hazard_classification['classes']
# Initialize OrderedDict of affected buildings
self.affected_population = OrderedDict()
# Iterate over vector hazard classes
for vector_hazard_class in vector_hazard_classes:
# Check if the key of class exist in hazard_class_mapping
if vector_hazard_class['key'] in self.hazard_class_mapping.keys():
# Replace the key with the name as we need to show the human
# friendly name in the report.
self.hazard_class_mapping[vector_hazard_class['name']] = \
self.hazard_class_mapping.pop(vector_hazard_class['key'])
# Adding the class name as a key in affected_building
self.affected_population[vector_hazard_class['name']] = 0
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field)
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_value = get_key_for_value(
row[self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
self.affected_population[hazard_value] += population
# Count totals
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
self.unaffected_population = (self.total_population -
self.total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
impact_table = impact_summary = self.html_report()
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(covered_exposure_layer.get_data().flat[:],
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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = 0
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People affected by Volcano Hazard Zones')
legend_title = tr('Population')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
extra_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': self.total_needs
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People affected by volcano hazard zones'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Run classified population evacuation Impact Function.
Counts number of people exposed to each hazard zones.
:returns: Map of population exposed to each 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
"""
self.validate()
self.prepare()
self.provenance.append_step("Calculating Step", "Impact function is calculating the impact.")
# Value from layer's keywords
self.hazard_class_attribute = self.hazard.keyword("field")
self.hazard_class_mapping = self.hazard.keyword("value_map")
# TODO: Remove check to self.validate (Ismail)
# Input checks
message = tr(
"Input hazard must be a polygon layer. I got %s with layer type "
"%s" % (self.hazard.name, self.hazard.layer.get_geometry_name())
)
if not self.hazard.layer.is_polygon_data:
raise Exception(message)
# Check if hazard_class_attribute exists in hazard_layer
if self.hazard_class_attribute not in self.hazard.layer.get_attribute_names():
message = (
"Hazard data %s does not contain expected hazard "
'zone attribute "%s". Please change it in the option. '
% (self.hazard.name, self.hazard_class_attribute)
)
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Retrieve the classification that is used by the hazard layer.
vector_hazard_classification = self.hazard.keyword("vector_hazard_classification")
# Get the dictionary that contains the definition of the classification
vector_hazard_classification = definition(vector_hazard_classification)
# Get the list classes in the classification
vector_hazard_classes = vector_hazard_classification["classes"]
# Initialize OrderedDict of affected buildings
self.affected_population = OrderedDict()
# Iterate over vector hazard classes
for vector_hazard_class in vector_hazard_classes:
# Check if the key of class exist in hazard_class_mapping
if vector_hazard_class["key"] in self.hazard_class_mapping.keys():
# Replace the key with the name as we need to show the human
# friendly name in the report.
self.hazard_class_mapping[vector_hazard_class["name"]] = self.hazard_class_mapping.pop(
vector_hazard_class["key"]
)
# Adding the class name as a key in affected_building
self.affected_population[vector_hazard_class["name"]] = 0
# Interpolated layer represents grid cell that lies in the polygon
interpolated_layer, covered_exposure_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer, attribute_name=self.target_field
)
# Count total affected population per hazard zone
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_value = get_key_for_value(row[self.hazard_class_attribute], self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
self.affected_population[hazard_value] += population
# Count total population from exposure layer
self.total_population = int(numpy.nansum(self.exposure.layer.get_data()))
# Count total affected population
total_affected_population = self.total_affected_population
self.unaffected_population = self.total_population - total_affected_population
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(self.parameters["minimum needs"])
]
# check for zero impact
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
impact_table = impact_summary = self.html_report()
# Create style
colours = ["#FFFFFF", "#38A800", "#79C900", "#CEED00", "#FFCC00", "#FF6600", "#FF0000", "#7A0000"]
classes = create_classes(covered_exposure_layer.get_data().flat[:], 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 == 1:
label = create_label(interval_classes[i], tr("Low Population [%i people/cell]" % classes[i]))
elif i == 4:
label = create_label(interval_classes[i], tr("Medium Population [%i people/cell]" % classes[i]))
elif i == 7:
label = create_label(interval_classes[i], tr("High Population [%i people/cell]" % classes[i]))
else:
label = create_label(interval_classes[i])
style_class["label"] = label
style_class["quantity"] = classes[i]
style_class["colour"] = colours[i]
style_class["transparency"] = 0
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=None, style_classes=style_classes, style_type="rasterStyle")
# For printing map purpose
map_title = tr("People impacted by each hazard zone")
legend_title = tr("Population")
legend_units = tr("(people per cell)")
legend_notes = tr("Thousand separator is represented by %s" % get_thousand_separator())
extra_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,
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr("People impacted by each hazard zone"),
keywords=impact_layer_keywords,
style_info=style_info,
)
self._impact = impact_layer
return impact_layer
def run(self):
"""Run classified population evacuation Impact Function.
Counts number of people exposed to each hazard zones.
:returns: Map of population exposed to each 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
"""
self.validate()
self.prepare()
# Value from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
# Input checks
msg = ('Input hazard must be a polygon layer. I got %s with '
'layer type %s' %
(self.hazard.name, self.hazard.layer.get_geometry_name()))
if not self.hazard.layer.is_polygon_data:
raise Exception(msg)
# Check if hazard_class_attribute exists in hazard_layer
if (self.hazard_class_attribute
not in self.hazard.layer.get_attribute_names()):
msg = ('Hazard data %s does not contain expected hazard '
'zone attribute "%s". Please change it in the option. ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Get unique hazard zones from the layer attribute
self.hazard_zones = list(
set(self.hazard.layer.get_data(self.hazard_class_attribute)))
# Interpolated layer represents grid cell that lies in the polygon
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field
)
# Initialise total population affected by each hazard zone
for hazard_zone in self.hazard_zones:
self.affected_population[hazard_zone] = 0
# Count total affected population per hazard zone
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this hazard zone
hazard_zone = row[self.hazard_class_attribute]
self.affected_population[hazard_zone] += population
# Count total population from exposure layer
self.total_population = int(
numpy.nansum(self.exposure.layer.get_data()))
# Count total affected population
total_affected_population = self.total_affected_population
self.unaffected_population = (self.total_population -
total_affected_population)
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
# check for zero impact
if total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
impact_table = impact_summary = self.html_report()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(covered_exposure_layer.get_data().flat[:],
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 == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
if i == 0:
transparency = 100
else:
transparency = 0
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = transparency
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People impacted by each hazard zone')
legend_title = tr('Population')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People impacted by each 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
},
style_info=style_info)
self._impact = impact_layer
return impact_layer
