def run(self):
"""Risk plugin for classified polygon hazard on building/structure.
Counts number of building exposed to each hazard zones.
:returns: Impact vector layer building exposed to each hazard zones.
Table with number of buildings affected
:rtype: Vector
"""
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')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# 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_buildings = 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_buildings[vector_hazard_class['name']] = {}
hazard_zone_attribute_index = self.hazard.layer.fieldNameIndex(
self.hazard_class_attribute)
# Check if hazard_zone_attribute exists in hazard_layer
if hazard_zone_attribute_index < 0:
message = (
'Hazard data %s does not contain expected attribute %s ' %
(self.hazard.layer.name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Hazard zone categories from hazard layer
unique_values = self.hazard.layer.uniqueValues(
hazard_zone_attribute_index)
# Values might be integer or float, we should have unicode. #2626
self.hazard_zones = [get_unicode(val) for val in unique_values]
self.buildings = {}
wgs84_extent = QgsRectangle(
self.requested_extent[0], self.requested_extent[1],
self.requested_extent[2], self.requested_extent[3])
# Run interpolation function for polygon2polygon
interpolated_layer = interpolate_polygon_polygon(
self.hazard.layer, self.exposure.layer, wgs84_extent)
new_field = QgsField(self.target_field, QVariant.String)
interpolated_layer.dataProvider().addAttributes([new_field])
interpolated_layer.updateFields()
attribute_names = [
field.name() for field in interpolated_layer.pendingFields()]
target_field_index = interpolated_layer.fieldNameIndex(
self.target_field)
changed_values = {}
if interpolated_layer.featureCount() < 1:
raise ZeroImpactException()
# Extract relevant interpolated data
for feature in interpolated_layer.getFeatures():
# Get the hazard value based on the value mapping in keyword
hazard_value = get_key_for_value(
feature[self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
changed_values[feature.id()] = {target_field_index: hazard_value}
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = feature[self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, feature)
if usage is None:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.hazard_class_mapping.keys():
self.affected_buildings[category][usage] = OrderedDict(
[(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.hazard_class_mapping.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
interpolated_layer.dataProvider().changeAttributeValues(changed_values)
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
categories = self.affected_buildings.keys()
categories.append(self._not_affected_value)
colours = color_ramp(len(categories))
style_classes = []
i = 0
for hazard_zone in self.affected_buildings.keys():
style_class = dict()
style_class['label'] = tr(hazard_zone)
style_class['transparency'] = 0
style_class['value'] = hazard_zone
style_class['size'] = 1
style_class['colour'] = colours[i]
style_classes.append(style_class)
i += 1
# Override style info with new classes and name
style_info = dict(
target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol'
)
# For printing map purpose
map_title = tr('Buildings affected by each hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
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 = Vector(
data=interpolated_layer,
name=tr('Buildings affected by each hazard zone'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Risk plugin for volcano hazard on building/structure.
Counts number of building exposed to each volcano hazard zones.
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
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.name_attribute = self.hazard.keyword('volcano_name_field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# Input checks
if not self.hazard.layer.is_polygon_data:
message = (
'Input hazard must be a polygon. I got %s with '
'layer type %s' %
(self.hazard.name, self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_zone_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
message = (
'Hazard data %s did not contain expected attribute %s ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Get names of volcanoes considered
if self.name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = set()
for row in self.hazard.layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[self.name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
else:
self.volcano_names = tr('Not specified in data')
# 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_buildings = 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_buildings[vector_hazard_class['name']] = {}
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
self.buildings = {}
for i in range(len(features)):
# Get the hazard value based on the value mapping in keyword
hazard_value = get_key_for_value(
features[i][self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
features[i][self.target_field] = get_string(hazard_value)
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = features[i][self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage in [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][
usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
colours = colours[::-1] # flip
colours = colours[:len(self.affected_buildings.keys())]
style_classes = []
i = 0
for category_name in self.affected_buildings.keys():
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(self.affected_buildings.keys()):
i = len(self.affected_buildings.keys()) - 1
style_class['colour'] = colours[i]
i += 1
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='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
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 = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by volcanic hazard zone'),
keywords=impact_layer_keywords,
style_info=style_info
)
self._impact = impact_layer
return impact_layer
def run(self):
"""Tsunami raster impact to buildings (e.g. from Open Street Map)."""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step',
'Impact function is calculating the impact.')
# Thresholds for tsunami hazard zone breakdown.
low_max = self.parameters['low_threshold'].value
medium_max = self.parameters['medium_threshold'].value
high_max = self.parameters['high_threshold'].value
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=self.target_field)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(self.hazard_classes[0], {}),
(self.hazard_classes[1], {}),
(self.hazard_classes[2], {}),
(self.hazard_classes[3], {}),
(self.hazard_classes[4], {})
])
categories = self.affected_buildings.keys()
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i][self.target_field])
if water_depth <= 0:
inundated_status = 0
elif 0 < water_depth <= low_max:
inundated_status = 1 # low
elif low_max < water_depth <= medium_max:
inundated_status = 2 # medium
elif medium_max < water_depth <= high_max:
inundated_status = 3 # high
elif high_max < water_depth:
inundated_status = 4 # very high
# If not a number or a value beside real number.
else:
inundated_status = 0
# Count affected buildings by usage type if available
if (structure_class_field in attribute_names and
structure_class_field):
usage = features[i].get(structure_class_field, None)
else:
usage = get_osm_building_usage(
attribute_names, features[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
# Count all buildings by type
self.buildings[usage] += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
category = categories[inundated_status]
self.affected_buildings[category][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_table = impact_summary = self.html_report()
# For printing map purpose
map_title = tr('Inundated buildings')
legend_title = tr('Inundated structure status')
legend_units = tr('(low, medium, high, and very high)')
style_classes = [
dict(
label=self.hazard_classes[0] + ': 0 m',
value=0,
colour='#00FF00',
transparency=0,
size=1
),
dict(
label=self.hazard_classes[1] + ': 0.1 - %.1f m' % low_max,
value=1,
colour='#FFFF00',
transparency=0,
size=1
),
dict(
label=self.hazard_classes[2] + ': %.1f - %.1f m' % (
low_max + 0.1, medium_max),
value=2,
colour='#FFB700',
transparency=0,
size=1
),
dict(
label=self.hazard_classes[3] + ': %.1f - %.1f m' % (
medium_max + 0.1, high_max),
value=3,
colour='#FF6F00',
transparency=0,
size=1
),
dict(
label=self.hazard_classes[4] + ' > %.1f m' % high_max,
value=4,
colour='#FF0000',
transparency=0,
size=1
),
]
style_info = dict(
target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_title': legend_title,
'legend_units': legend_units,
'buildings_total': total_features,
'buildings_affected': self.total_affected_buildings
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
vector_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords=impact_layer_keywords,
style_info=style_info)
# Create vector layer and return
self._impact = vector_layer
return vector_layer
def run(self, layers=None):
"""Earthquake impact to buildings (e.g. from OpenStreetMap).
:param layers: All the input layers (Hazard Layer and Exposure Layer)
"""
self.validate()
self.prepare(layers)
LOGGER.debug('Running earthquake building impact')
# merely initialize
building_value = 0
contents_value = 0
# Thresholds for mmi breakdown.
t0 = self.parameters['low_threshold']
t1 = self.parameters['medium_threshold']
t2 = self.parameters['high_threshold']
# Class Attribute and Label.
class_1 = {'label': tr('Low'), 'class': 1}
class_2 = {'label': tr('Medium'), 'class': 2}
class_3 = {'label': tr('High'), 'class': 3}
# Extract data
hazard_layer = self.hazard # Depth
exposure_layer = self.exposure # Building locations
# Define attribute name for hazard levels.
hazard_attribute = 'mmi'
# Determine if exposure data have NEXIS attributes.
attribute_names = exposure_layer.get_attribute_names()
if (
'FLOOR_AREA' in attribute_names and
'BUILDING_C' in attribute_names and
'CONTENTS_C' in attribute_names):
self.is_nexis = True
else:
self.is_nexis = False
# Interpolate hazard level to building locations.
interpolate_result = assign_hazard_values_to_exposure_data(
hazard_layer,
exposure_layer,
attribute_name=hazard_attribute
)
# Extract relevant exposure data
# attribute_names = interpolate_result.get_attribute_names()
attributes = interpolate_result.get_data()
interpolate_size = len(interpolate_result)
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('High'), {}),
(tr('Medium'), {}),
(tr('Low'), {})
])
for i in range(interpolate_size):
# Classify building according to shake level
# and calculate dollar losses
if self.is_nexis:
try:
area = float(attributes[i]['FLOOR_AREA'])
except (ValueError, KeyError):
# print 'Got area', attributes[i]['FLOOR_AREA']
area = 0.0
try:
building_value_density = float(attributes[i]['BUILDING_C'])
except (ValueError, KeyError):
# print 'Got bld value', attributes[i]['BUILDING_C']
building_value_density = 0.0
try:
contents_value_density = float(attributes[i]['CONTENTS_C'])
except (ValueError, KeyError):
# print 'Got cont value', attributes[i]['CONTENTS_C']
contents_value_density = 0.0
building_value = building_value_density * area
contents_value = contents_value_density * area
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
if self.is_nexis:
self.affected_buildings[category][usage] = OrderedDict(
[
(tr('Buildings Affected'), 0),
(tr('Buildings value ($M)'), 0),
(tr('Contents value ($M)'), 0)])
else:
self.affected_buildings[category][usage] = OrderedDict(
[
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
try:
mmi = float(attributes[i][hazard_attribute]) # MMI
except TypeError:
mmi = 0.0
if t0 <= mmi < t1:
cls = 1
category = tr('Low')
elif t1 <= mmi < t2:
cls = 2
category = tr('Medium')
elif t2 <= mmi:
cls = 3
category = tr('High')
else:
# Not reported for less than level t0
continue
attributes[i][self.target_field] = cls
self.affected_buildings[
category][usage][tr('Buildings Affected')] += 1
if self.is_nexis:
self.affected_buildings[category][usage][
tr('Buildings value ($M)')] += building_value / 1000000.0
self.affected_buildings[category][usage][
tr('Contents value ($M)')] += contents_value / 1000000.0
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
impact_table = impact_summary = self.generate_html_report()
# Create style
style_classes = [dict(label=class_1['label'], value=class_1['class'],
colour='#ffff00', transparency=1),
dict(label=class_2['label'], value=class_2['class'],
colour='#ffaa00', transparency=1),
dict(label=class_3['label'], value=class_3['class'],
colour='#ff0000', transparency=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Building affected by earthquake')
legend_notes = tr('The level of the impact is according to the '
'threshold the user input.')
legend_units = tr('(mmi)')
legend_title = tr('Impact level')
# Create vector layer and return
result_layer = Vector(
data=attributes,
projection=interpolate_result.get_projection(),
geometry=interpolate_result.get_geometry(),
name=tr('Estimated buildings affected'),
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,
'target_field': self.target_field,
'statistics_type': self.statistics_type,
'statistics_classes': self.statistics_classes},
style_info=style_info)
msg = 'Created vector layer %s' % str(result_layer)
LOGGER.debug(msg)
self._impact = result_layer
return result_layer
def run(self):
"""Risk plugin for volcano hazard on building/structure.
Counts number of building exposed to each volcano hazard zones.
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
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.name_attribute = self.hazard.keyword('volcano_name_field')
self.hazard_class_mapping = self.hazard.keyword('value_map')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# Input checks
if not self.hazard.layer.is_polygon_data:
message = (
'Input hazard must be a polygon. I got %s with '
'layer type %s' %
(self.hazard.name, self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_zone_attribute exists in hazard_layer
if (self.hazard_class_attribute
not in self.hazard.layer.get_attribute_names()):
message = (
'Hazard data %s did not contain expected attribute %s ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Get names of volcanoes considered
if self.name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = set()
for row in self.hazard.layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[self.name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
else:
self.volcano_names = tr('Not specified in data')
# 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_buildings = 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_buildings[vector_hazard_class['name']] = {}
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
self.buildings = {}
for i in range(len(features)):
# Get the hazard value based on the value mapping in keyword
hazard_value = get_key_for_value(
features[i][self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
features[i][self.target_field] = get_string(hazard_value)
if (self.exposure_class_attribute
and self.exposure_class_attribute in attribute_names):
usage = features[i][self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage in [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][tr(
'Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
colours = colours[::-1] # flip
colours = colours[:len(self.affected_buildings.keys())]
style_classes = []
i = 0
for category_name in self.affected_buildings.keys():
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(self.affected_buildings.keys()):
i = len(self.affected_buildings.keys()) - 1
style_class['colour'] = colours[i]
i += 1
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='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
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 = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by volcanic hazard zone'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self, layers=None):
"""Classified hazard impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard: Classified Hazard layer
* exposure: Vector layer of structure data on
the same grid as hazard
"""
self.validate()
self.prepare(layers)
# The 3 classes
low_t = self.parameters['low_hazard_class']
medium_t = self.parameters['medium_hazard_class']
high_t = self.parameters['high_hazard_class']
# Extract data
hazard = self.hazard # Classified Hazard
exposure = self.exposure # Building locations
# Determine attribute name for hazard levels
if hazard.is_raster:
hazard_attribute = 'level'
else:
hazard_attribute = None
interpolated_result = assign_hazard_values_to_exposure_data(
hazard, exposure, attribute_name=hazard_attribute, mode='constant')
# Extract relevant exposure data
attribute_names = interpolated_result.get_attribute_names()
attributes = interpolated_result.get_data()
buildings_total = len(interpolated_result)
# Calculate building impact
self.buildings = {}
self.affected_buildings = OrderedDict([(tr('High Hazard Class'), {}),
(tr('Medium Hazard Class'), {}),
(tr('Low Hazard Class'), {})])
for i in range(buildings_total):
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
# Count all buildings by type
self.buildings[usage] += 1
attributes[i][self.target_field] = 0
attributes[i][self.affected_field] = 0
level = float(attributes[i]['level'])
level = float(numpy_round(level))
if level == high_t:
impact_level = tr('High Hazard Class')
elif level == medium_t:
impact_level = tr('Medium Hazard Class')
elif level == low_t:
impact_level = tr('Low Hazard Class')
else:
continue
# Add calculated impact to existing attributes
attributes[i][self.target_field] = {
tr('High Hazard Class'): 3,
tr('Medium Hazard Class'): 2,
tr('Low Hazard Class'): 1
}[impact_level]
attributes[i][self.affected_field] = 1
# Count affected buildings by type
self.affected_buildings[impact_level][usage][tr(
'Buildings Affected')] += 1
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
# Create style
style_classes = [
dict(label=tr('High'),
value=3,
colour='#F31A1C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Medium'),
value=2,
colour='#F4A442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Low'),
value=1,
colour='#EBF442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Not Affected'),
value=None,
colour='#1EFC7C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
impact_table = impact_summary = self.generate_html_report()
# For printing map purpose
map_title = tr('Buildings affected')
legend_units = tr('(Low, Medium, High)')
legend_title = tr('Structure inundated status')
# Create vector layer and return
vector_layer = Vector(data=attributes,
projection=exposure.get_projection(),
geometry=exposure.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary':
impact_summary,
'impact_table':
impact_table,
'target_field':
self.affected_field,
'map_title':
map_title,
'legend_units':
legend_units,
'legend_title':
legend_title,
'buildings_total':
buildings_total,
'buildings_affected':
self.total_affected_buildings
},
style_info=style_info)
self._impact = vector_layer
return vector_layer
def run(self):
"""Risk plugin for classified polygon hazard on building/structure.
Counts number of building exposed to each hazard zones.
:returns: Impact vector layer building exposed to each hazard zones.
Table with number of buildings affected
:rtype: Vector
"""
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')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# 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_buildings = 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_buildings[vector_hazard_class['name']] = {}
hazard_zone_attribute_index = self.hazard.layer.fieldNameIndex(
self.hazard_class_attribute)
# Check if hazard_zone_attribute exists in hazard_layer
if hazard_zone_attribute_index < 0:
message = (
'Hazard data %s does not contain expected attribute %s ' %
(self.hazard.layer.name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Hazard zone categories from hazard layer
unique_values = self.hazard.layer.uniqueValues(
hazard_zone_attribute_index)
# Values might be integer or float, we should have unicode. #2626
self.hazard_zones = [get_unicode(val) for val in unique_values]
self.buildings = {}
wgs84_extent = QgsRectangle(self.requested_extent[0],
self.requested_extent[1],
self.requested_extent[2],
self.requested_extent[3])
# Run interpolation function for polygon2polygon
interpolated_layer = interpolate_polygon_polygon(
self.hazard.layer, self.exposure.layer, wgs84_extent)
new_field = QgsField(self.target_field, QVariant.String)
interpolated_layer.dataProvider().addAttributes([new_field])
interpolated_layer.updateFields()
attribute_names = [
field.name() for field in interpolated_layer.pendingFields()
]
target_field_index = interpolated_layer.fieldNameIndex(
self.target_field)
changed_values = {}
if interpolated_layer.featureCount() < 1:
raise ZeroImpactException()
# Extract relevant interpolated data
for feature in interpolated_layer.getFeatures():
# Get the hazard value based on the value mapping in keyword
hazard_value = get_key_for_value(
feature[self.hazard_class_attribute],
self.hazard_class_mapping)
if not hazard_value:
hazard_value = self._not_affected_value
changed_values[feature.id()] = {target_field_index: hazard_value}
if (self.exposure_class_attribute
and self.exposure_class_attribute in attribute_names):
usage = feature[self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, feature)
if usage is None:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.hazard_class_mapping.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
self.buildings[usage] += 1
if hazard_value in self.hazard_class_mapping.keys():
self.affected_buildings[hazard_value][usage][tr(
'Buildings Affected')] += 1
interpolated_layer.dataProvider().changeAttributeValues(changed_values)
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
categories = self.affected_buildings.keys()
categories.append(self._not_affected_value)
colours = color_ramp(len(categories))
style_classes = []
i = 0
for hazard_zone in self.affected_buildings.keys():
style_class = dict()
style_class['label'] = tr(hazard_zone)
style_class['transparency'] = 0
style_class['value'] = hazard_zone
style_class['size'] = 1
style_class['colour'] = colours[i]
style_classes.append(style_class)
i += 1
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by each hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
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 = Vector(
data=interpolated_layer,
name=tr('Buildings affected by each hazard zone'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self, layers=None):
"""Risk plugin for classified polygon hazard on building/structure.
Counts number of building exposed to each hazard zones.
:param layers: List of layers expected to contain.
* hazard_layer: Hazard layer
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
:returns: Map of building exposed to each hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare(layers)
# Target Field
target_field = 'zone'
# Not affected string in the target field
not_affected_value = 'Not Affected'
# Parameters
hazard_zone_attribute = self.parameters['hazard zone attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard
exposure_layer = self.exposure
# Input checks
if not hazard_layer.is_polygon_data:
message = (
'Input hazard must be a polygon. I got %s with '
'layer type %s' %
(hazard_layer.get_name(), hazard_layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_zone_attribute exists in hazard_layer
if hazard_zone_attribute not in hazard_layer.get_attribute_names():
message = (
'Hazard data %s does not contain expected attribute %s ' %
(hazard_layer.get_name(), hazard_zone_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Find the target field name that has no conflict with default
# target
attribute_names = hazard_layer.get_attribute_names()
target_field = get_non_conflicting_attribute_name(
target_field, attribute_names)
# Hazard zone categories from hazard layer
self.hazard_zones = list(
set(hazard_layer.get_data(hazard_zone_attribute)))
self.buildings = {}
self.affected_buildings = OrderedDict()
for hazard_zone in self.hazard_zones:
self.affected_buildings[hazard_zone] = {}
# Run interpolation function for polygon2polygon
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=None)
# Extract relevant interpolated data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
for i in range(len(features)):
hazard_value = features[i][hazard_zone_attribute]
if not hazard_value:
hazard_value = not_affected_value
features[i][target_field] = hazard_value
usage = get_osm_building_usage(attribute_names, features[i])
if usage is None:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict(
[(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.generate_html_report()
# Create style
categories = self.hazard_zones
categories.append(not_affected_value)
colours = color_ramp(len(categories))
style_classes = []
i = 0
for hazard_zone in self.hazard_zones:
style_class = dict()
style_class['label'] = tr(hazard_zone)
style_class['transparency'] = 0
style_class['value'] = hazard_zone
style_class['size'] = 1
style_class['colour'] = colours[i]
style_classes.append(style_class)
i += 1
# Override style info with new classes and name
style_info = dict(target_field=target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by each hazard zone')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(building)')
legend_title = tr('Building count')
# Create vector layer and return
impact_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by each hazard zone'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': 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, layers=None):
"""Flood impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard_layer: Hazard raster layer of flood
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
"""
self.validate()
self.prepare(layers)
threshold = self.parameters['threshold [m]'] # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Extract data
hazard_layer = self.hazard # Depth
exposure_layer = self.exposure # Building locations
# Determine attribute name for hazard levels
hazard_attribute = 'depth'
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('Number Inundated'), {}),
(tr('Number of Wet Buildings'), {}),
(tr('Number of Dry Buildings'), {})
])
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
# Count all buildings by type
self.buildings[usage] += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
category = [
tr('Number of Dry Buildings'),
tr('Number of Wet Buildings'),
tr('Number Inundated')][inundated_status]
self.affected_buildings[category][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_table = impact_summary = self.generate_html_report()
# Prepare impact layer
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
style_classes = [
dict(
label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1
),
dict(
label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1
),
dict(
label=tr('Inundated (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1
)]
legend_units = tr('(inundated, wet, or dry)')
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Create vector layer and return
vector_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': total_features,
'buildings_affected': self.total_affected_buildings},
style_info=style_info)
self._impact = vector_layer
return vector_layer
def run(self):
"""Risk plugin for volcano hazard on building/structure.
Counts number of building exposed to each volcano hazard zones.
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare()
# Get parameters from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
name_attribute = self.hazard.keyword('volcano_name_field')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# Input checks
if not self.hazard.layer.is_polygon_data:
message = (
'Input hazard must be a polygon. I got %s with '
'layer type %s' %
(self.hazard.name, self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_zone_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
message = (
'Hazard data %s did not contain expected attribute %s ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Get names of volcanoes considered
if name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = set()
for row in self.hazard.layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
else:
self.volcano_names = tr('Not specified in data')
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
# Hazard zone categories from hazard layer
hazard_zone_categories = list(
set(self.hazard.layer.get_data(self.hazard_class_attribute)))
self.buildings = {}
self.affected_buildings = OrderedDict()
for hazard_category in hazard_zone_categories:
self.affected_buildings[hazard_category] = {}
for i in range(len(features)):
hazard_value = features[i][self.hazard_class_attribute]
if not hazard_value:
hazard_value = self._not_affected_value
features[i][self.target_field] = hazard_value
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = features[i][self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage in [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][
usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
category_names = hazard_zone_categories
category_names.append(self._not_affected_value)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
colours = colours[::-1] # flip
colours = colours[:len(category_names)]
style_classes = []
i = 0
for category_name in category_names:
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(category_names):
i = len(category_names) - 1
style_class['colour'] = colours[i]
i += 1
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='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings 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},
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard_layer: Hazard raster layer of flood
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
"""
threshold = self.parameters['threshold [m]'] # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Extract data
hazard_layer = get_hazard_layer(layers) # Depth
exposure_layer = get_exposure_layer(layers) # Building locations
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Determine attribute name for hazard levels
mode = 'grid'
hazard_attribute = 'depth'
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
buildings = {}
# The number of affected buildings
affected_count = 0
# The variable for grid mode
inundated_count = 0
wet_count = 0
dry_count = 0
inundated_buildings = {}
wet_buildings = {}
dry_buildings = {}
# The variable for regions mode
affected_buildings = {}
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
inundated_buildings[key] = 0
wet_buildings[key] = 0
dry_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if inundated_status is 0:
# Count dry buildings by type
dry_buildings[key] += 1
# Count total dry buildings
dry_count += 1
if inundated_status is 1:
# Count inundated buildings by type
inundated_buildings[key] += 1
# Count total dry buildings
inundated_count += 1
if inundated_status is 2:
# Count wet buildings by type
wet_buildings[key] += 1
# Count total wet buildings
wet_count += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
affected_count = inundated_count + wet_count
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
if mode == 'grid':
inundated_buildings['other'] = 0
wet_buildings['other'] = 0
dry_buildings['other'] = 0
elif mode == 'regions':
affected_buildings['other'] = 0
buildings['other'] += x
if mode == 'grid':
inundated_buildings['other'] += inundated_buildings[usage]
wet_buildings['other'] += wet_buildings[usage]
dry_buildings['other'] += dry_buildings[usage]
del buildings[usage]
del inundated_buildings[usage]
del wet_buildings[usage]
del dry_buildings[usage]
elif mode == 'regions':
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate simple impact report
table_body = [
question,
TableRow([
tr('Building type'),
tr('Number Inundated'),
tr('Number of Wet Buildings'),
tr('Number of Dry Buildings'),
tr('Total')
],
header=True),
TableRow([
tr('All'),
format_int(inundated_count),
format_int(wet_count),
format_int(dry_count),
format_int(total_features)
])
]
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type if available
list_type_attribute = [
'TYPE', 'type', 'amenity', 'building_t', 'office', 'tourism',
'leisure', 'building'
]
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
building_list.append([
building_type.capitalize(),
format_int(inundated_buildings[usage]),
format_int(wet_buildings[usage]),
format_int(dry_buildings[usage]),
format_int(buildings[usage])
])
if usage.lower() == 'school':
school_closed = 0
school_closed += inundated_buildings[usage]
school_closed += wet_buildings[usage]
if usage.lower() == 'hospital':
hospital_closed = 0
hospital_closed += inundated_buildings[usage]
hospital_closed += wet_buildings[usage]
# Sort alphabetically
building_list.sort()
table_body.append(
TableRow(tr('Breakdown by building type'), header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
# Action Checklist Section
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(
TableRow(tr('Are the critical facilities still open?')))
table_body.append(
TableRow(
tr('Which structures have warning capacity (eg. sirens, speakers, '
'etc.)?')))
table_body.append(
TableRow(tr('Which buildings will be evacuation centres?')))
table_body.append(
TableRow(tr('Where will we locate the operations centre?')))
table_body.append(
TableRow(
tr('Where will we locate warehouse and/or distribution centres?'
)))
if school_closed > 0:
table_body.append(
TableRow(
tr('Where will the students from the %s closed schools go to '
'study?') % format_int(school_closed)))
if hospital_closed > 0:
table_body.append(
TableRow(
tr('Where will the patients from the %s closed hospitals go '
'for treatment and how will we transport them?') %
format_int(hospital_closed)))
# Notes Section
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
TableRow(
tr('Buildings are said to be inundated when flood levels '
'exceed %.1f m') % threshold))
table_body.append(
TableRow(
tr('Buildings are said to be wet when flood levels '
'are greater than 0 m but less than %.1f m') % threshold))
table_body.append(
TableRow(
tr('Buildings are said to be dry when flood levels '
'are less than 0 m')))
table_body.append(
TableRow(
tr('Buildings are said to be closed if they are inundated or '
'wet')))
table_body.append(
TableRow(tr('Buildings are said to be open if they are dry')))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# Prepare impact layer
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
style_classes = [
dict(label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1),
dict(label=tr('Inundated (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1)
]
legend_units = tr('(inundated, wet, or dry)')
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Create vector layer and return
vector_layer = Vector(data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': total_features,
'buildings_affected': affected_count
},
style_info=style_info)
return vector_layer
def run(self):
"""Classified hazard impact to buildings (e.g. from Open Street Map).
"""
self.validate()
self.prepare()
self.provenance.append_step("Calculating Step", "Impact function is calculating the impact.")
# Value from layer's keywords
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword("structure_class_field")
except KeywordNotFoundError:
structure_class_field = None
# The 3 classes
categorical_hazards = self.parameters["Categorical hazards"].value
low_t = categorical_hazards[0].value
medium_t = categorical_hazards[1].value
high_t = categorical_hazards[2].value
# Determine attribute name for hazard levels
if self.hazard.layer.is_raster:
hazard_attribute = "level"
else:
hazard_attribute = None
interpolated_result = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer, attribute_name=hazard_attribute, mode="constant"
)
# Extract relevant exposure data
attribute_names = interpolated_result.get_attribute_names()
attributes = interpolated_result.get_data()
buildings_total = len(interpolated_result)
# Calculate building impact
self.buildings = {}
self.affected_buildings = OrderedDict(
[(tr("High Hazard Class"), {}), (tr("Medium Hazard Class"), {}), (tr("Low Hazard Class"), {})]
)
for i in range(buildings_total):
if structure_class_field and structure_class_field in attribute_names:
usage = attributes[i][structure_class_field]
else:
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = "unknown"
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([(tr("Buildings Affected"), 0)])
# Count all buildings by type
self.buildings[usage] += 1
attributes[i][self.target_field] = 0
attributes[i][self.affected_field] = 0
level = float(attributes[i]["level"])
level = float(numpy_round(level))
if level == high_t:
impact_level = tr("High Hazard Class")
elif level == medium_t:
impact_level = tr("Medium Hazard Class")
elif level == low_t:
impact_level = tr("Low Hazard Class")
else:
continue
# Add calculated impact to existing attributes
attributes[i][self.target_field] = {
tr("High Hazard Class"): 3,
tr("Medium Hazard Class"): 2,
tr("Low Hazard Class"): 1,
}[impact_level]
attributes[i][self.affected_field] = 1
# Count affected buildings by type
self.affected_buildings[impact_level][usage][tr("Buildings Affected")] += 1
# Consolidate the small building usage groups < 25 to other
# Building threshold #2468
postprocessors = self.parameters["postprocessors"]
building_postprocessors = postprocessors["BuildingType"][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Create style
style_classes = [
dict(
label=tr("High"),
value=3,
colour="#F31A1C",
transparency=0,
size=2,
border_color="#969696",
border_width=0.2,
),
dict(
label=tr("Medium"),
value=2,
colour="#F4A442",
transparency=0,
size=2,
border_color="#969696",
border_width=0.2,
),
dict(
label=tr("Low"),
value=1,
colour="#EBF442",
transparency=0,
size=2,
border_color="#969696",
border_width=0.2,
),
dict(
label=tr("Not Affected"),
value=None,
colour="#1EFC7C",
transparency=0,
size=2,
border_color="#969696",
border_width=0.2,
),
]
style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type="categorizedSymbol")
impact_table = impact_summary = self.html_report()
# For printing map purpose
map_title = tr("Buildings affected")
legend_title = tr("Structure inundated status")
legend_units = tr("(Low, Medium, High)")
extra_keywords = {
"impact_summary": impact_summary,
"impact_table": impact_table,
"target_field": self.affected_field,
"map_title": map_title,
"legend_units": legend_units,
"legend_title": legend_title,
"buildings_total": buildings_total,
"buildings_affected": self.total_affected_buildings,
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
vector_layer = Vector(
data=attributes,
projection=self.exposure.layer.get_projection(),
geometry=self.exposure.layer.get_geometry(),
name=tr("Estimated buildings affected"),
keywords=impact_layer_keywords,
style_info=style_info,
)
self._impact = vector_layer
return vector_layer
def run(self):
"""Earthquake impact to buildings (e.g. from OpenStreetMap)."""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
LOGGER.debug('Running earthquake building impact')
# merely initialize
building_value = 0
contents_value = 0
# Thresholds for mmi breakdown.
t0 = self.parameters['low_threshold'].value
t1 = self.parameters['medium_threshold'].value
t2 = self.parameters['high_threshold'].value
# Class Attribute and Label.
class_1 = {'label': tr('Low'), 'class': 1}
class_2 = {'label': tr('Medium'), 'class': 2}
class_3 = {'label': tr('High'), 'class': 3}
# Define attribute name for hazard levels.
hazard_attribute = 'mmi'
# Determine if exposure data have NEXIS attributes.
attribute_names = self.exposure.layer.get_attribute_names()
if ('FLOOR_AREA' in attribute_names and 'BUILDING_C' in attribute_names
and 'CONTENTS_C' in attribute_names):
self.is_nexis = True
else:
self.is_nexis = False
# Interpolate hazard level to building locations.
interpolate_result = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute)
# Extract relevant exposure data
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
attributes = interpolate_result.get_data()
interpolate_size = len(interpolate_result)
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('High'), {}),
(tr('Medium'), {}),
(tr('Low'), {}),
])
removed = []
for i in range(interpolate_size):
# Classify building according to shake level
# and calculate dollar losses
if self.is_nexis:
try:
area = float(attributes[i]['FLOOR_AREA'])
except (ValueError, KeyError):
# print 'Got area', attributes[i]['FLOOR_AREA']
area = 0.0
try:
building_value_density = float(attributes[i]['BUILDING_C'])
except (ValueError, KeyError):
# print 'Got bld value', attributes[i]['BUILDING_C']
building_value_density = 0.0
try:
contents_value_density = float(attributes[i]['CONTENTS_C'])
except (ValueError, KeyError):
# print 'Got cont value', attributes[i]['CONTENTS_C']
contents_value_density = 0.0
building_value = building_value_density * area
contents_value = contents_value_density * area
if (structure_class_field in attribute_names
and structure_class_field):
usage = attributes[i].get(structure_class_field, None)
else:
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
if self.is_nexis:
self.affected_buildings[category][usage] = OrderedDict(
[(tr('Buildings Affected'), 0),
(tr('Buildings value ($M)'), 0),
(tr('Contents value ($M)'), 0)])
else:
self.affected_buildings[category][usage] = \
OrderedDict([(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
try:
mmi = float(attributes[i][hazard_attribute]) # MMI
except TypeError:
mmi = 0.0
if t0 <= mmi < t1:
cls = 1
category = tr('Low')
elif t1 <= mmi < t2:
cls = 2
category = tr('Medium')
elif t2 <= mmi:
cls = 3
category = tr('High')
else:
# Not reported for less than level t0
continue
attributes[i][self.target_field] = cls
self.affected_buildings[category][usage][tr(
'Buildings Affected')] += 1
if self.is_nexis:
self.affected_buildings[category][usage][tr(
'Buildings value ($M)')] += building_value / 1000000.0
self.affected_buildings[category][usage][tr(
'Contents value ($M)')] += contents_value / 1000000.0
# remove un-categorized element
removed.reverse()
geometry = interpolate_result.get_geometry()
for i in range(0, len(removed)):
del attributes[removed[i]]
del geometry[removed[i]]
if len(attributes) < 1:
raise ZeroImpactException()
# Consolidate the small building usage groups < 25 to other
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
impact_table = impact_summary = self.html_report()
# Create style
style_classes = [
dict(label=class_1['label'],
value=class_1['class'],
colour='#ffff00',
transparency=1),
dict(label=class_2['label'],
value=class_2['class'],
colour='#ffaa00',
transparency=1),
dict(label=class_3['label'],
value=class_3['class'],
colour='#ff0000',
transparency=1)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Building affected by earthquake')
legend_notes = tr(
'The level of the impact is according to the threshold the user '
'input.')
legend_units = tr('(mmi)')
legend_title = tr('Impact level')
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,
'target_field': self.target_field,
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
result_layer = Vector(data=attributes,
projection=interpolate_result.get_projection(),
geometry=geometry,
name=tr('Estimated buildings affected'),
keywords=impact_layer_keywords,
style_info=style_info)
msg = 'Created vector layer %s' % str(result_layer)
LOGGER.debug(msg)
self._impact = result_layer
return result_layer
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard_layer: Hazard raster layer of flood
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
"""
# Extract data
hazard_layer = get_hazard_layer(layers) # Depth
exposure_layer = get_exposure_layer(layers) # Building locations
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Determine attribute name for hazard levels
hazard_attribute = None
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
buildings = {}
# The number of affected buildings
affected_count = 0
# The variable for regions mode
affected_buildings = {}
for i in range(total_features):
# Use interpolated polygon attribute
atts = features[i]
# FIXME (Ole): Need to agree whether to use one or the
# other as this can be very confusing!
# For now look for 'affected' first
if 'affected' in atts:
# E.g. from flood forecast
# Assume that building is wet if inside polygon
# as flagged by attribute Flooded
res = atts['affected']
if res is None:
inundated_status = False
else:
inundated_status = bool(res)
elif 'FLOODPRONE' in atts:
res = atts['FLOODPRONE']
if res is None:
inundated_status = False
else:
inundated_status = res.lower() == 'yes'
elif DEFAULT_ATTRIBUTE in atts:
# Check the default attribute assigned for points
# covered by a polygon
res = atts[DEFAULT_ATTRIBUTE]
if res is None:
inundated_status = False
else:
inundated_status = res
else:
# there is no flood related attribute
message = ('No flood related attribute found in %s. I was '
'looking for either "affected", "FLOODPRONE" or '
'"inapolygon". 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)
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
affected_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if inundated_status is True:
# Count affected buildings by type
affected_buildings[key] += 1
# Count total affected buildings
affected_count += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = int(inundated_status)
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
affected_buildings['other'] = 0
buildings['other'] += x
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate simple impact report
table_body = [
question,
TableRow([tr('Building type'),
tr('Number flooded'),
tr('Total')],
header=True),
TableRow([
tr('All'),
format_int(affected_count),
format_int(total_features)
])
]
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type if available
list_type_attribute = [
'TYPE', 'type', 'amenity', 'building_t', 'office', 'tourism',
'leisure', 'building'
]
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
building_list.append([
building_type.capitalize(),
format_int(affected_buildings[usage]),
format_int(buildings[usage])
])
if usage.lower() == 'school':
school_closed = affected_buildings[usage]
if usage.lower() == 'hospital':
hospital_closed = affected_buildings[usage]
# Sort alphabetically
building_list.sort()
table_body.append(
TableRow(tr('Breakdown by building type'), header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
# Action Checklist Section
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(
TableRow(tr('Are the critical facilities still open?')))
table_body.append(
TableRow(
tr('Which structures have warning capacity (eg. sirens, speakers, '
'etc.)?')))
table_body.append(
TableRow(tr('Which buildings will be evacuation centres?')))
table_body.append(
TableRow(tr('Where will we locate the operations centre?')))
table_body.append(
TableRow(
tr('Where will we locate warehouse and/or distribution centres?'
)))
if school_closed > 0:
table_body.append(
TableRow(
tr('Where will the students from the %s closed schools go to '
'study?') % format_int(school_closed)))
if hospital_closed > 0:
table_body.append(
TableRow(
tr('Where will the patients from the %s closed hospitals go '
'for treatment and how will we transport them?') %
format_int(hospital_closed)))
# Notes Section
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
TableRow(
tr('Buildings are said to be flooded when in regions marked '
'as affected')))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# Prepare impact layer
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
style_classes = [
dict(label=tr('Not Inundated'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(label=tr('Inundated'),
value=1,
colour='#F31A1C',
ztransparency=0,
size=1)
]
legend_units = tr('(inundated or not inundated)')
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Create vector layer and return
vector_layer = Vector(data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': total_features,
'buildings_affected': affected_count
},
style_info=style_info)
return vector_layer
def run(self):
"""Counts number of building exposed to each volcano hazard zones.
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step',
'Impact function is calculating the impact.')
# Hazard Zone Attribute
hazard_zone_attribute = 'radius'
# Parameters
radii = self.parameters['distances'].value
# Get parameters from layer's keywords
volcano_name_attribute = self.hazard.keyword('volcano_name_field')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# Input checks
if not self.hazard.layer.is_point_data:
message = (
'Input hazard must be a vector point layer. I got %s '
'with layer type %s' % (
self.hazard.name, self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Make hazard layer by buffering the point
centers = self.hazard.layer.get_geometry()
features = self.hazard.layer.get_data()
hazard_layer = buffer_points(
centers,
radii,
hazard_zone_attribute,
data_table=features)
# Category names for the impact zone
category_names = radii
# In kilometers
self._affected_categories_volcano = [
tr('Radius %.1f km') % key for key in radii[::]]
# Get names of volcanoes considered
if volcano_name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = set()
for row in hazard_layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[volcano_name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
# Find the target field name that has no conflict with the attribute
# names in the hazard layer
hazard_attribute_names = hazard_layer.get_attribute_names()
target_field = get_non_conflicting_attribute_name(
self.target_field, hazard_attribute_names)
# Run interpolation function for polygon2polygon
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, self.exposure.layer)
# Extract relevant interpolated layer data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
self.buildings = {}
self.affected_buildings = OrderedDict()
for category in radii:
self.affected_buildings[category] = {}
# Iterate the interpolated building layer
for i in range(len(features)):
hazard_value = features[i][hazard_zone_attribute]
if not hazard_value:
hazard_value = self._not_affected_value
features[i][target_field] = hazard_value
# Count affected buildings by usage type if available
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = features[i][self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage is [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][
usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Adding 'km'
affected_building_keys = self.affected_buildings.keys()
for key in affected_building_keys:
self.affected_buildings[tr('Radius %.1f km' % key)] = \
self.affected_buildings.pop(key)
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
colours = colours[::-1] # flip
colours = colours[:len(category_names)]
style_classes = []
i = 0
for category_name in category_names:
style_class = dict()
style_class['label'] = tr('Radius %s km') % tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(category_names):
i = len(category_names) - 1
style_class['colour'] = colours[i]
i += 1
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic buffered point')
legend_title = tr('Building count')
legend_units = tr('(building)')
legend_notes = tr(
'Thousand separator is represented by %s' %
get_thousand_separator())
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': 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 = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by volcanic buffered point'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self):
"""Flood impact to buildings (e.g. from Open Street Map)."""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
threshold = self.parameters['threshold'].value # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Determine attribute name for hazard levels
hazard_attribute = 'depth'
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([(tr('Flooded'), {}),
(tr('Wet'), {}),
(tr('Dry'), {})])
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
if (structure_class_field in attribute_names
and structure_class_field):
usage = features[i].get(structure_class_field, None)
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
# Count all buildings by type
self.buildings[usage] += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
category = [tr('Dry'), tr('Flooded'), tr('Wet')][inundated_status]
self.affected_buildings[category][usage][tr(
'Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Generate simple impact report
impact_table = impact_summary = self.html_report()
# For printing map purpose
map_title = tr('Flooded buildings')
legend_title = tr('Flooded structure status')
legend_units = tr('(flooded, wet, or dry)')
style_classes = [
dict(label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1),
dict(label=tr('Flooded (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_title': legend_title,
'legend_units': legend_units,
'buildings_total': total_features,
'buildings_affected': self.total_affected_buildings
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
vector_layer = Vector(data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords=impact_layer_keywords,
style_info=style_info)
# Create vector layer and return
self._impact = vector_layer
return vector_layer
def run(self, layers=None):
"""Counts number of building exposed to each volcano hazard zones.
:param layers: List of layers expected to contain.
* hazard_layer: Hazard layer of volcano
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare(layers)
# Target Field
target_field = 'zone'
# Hazard Zone Attribute
hazard_zone_attribute = 'radius'
# Not Affected Value
not_affected_value = 'Not Affected'
# Parameters
radii = self.parameters['distances [km]']
volcano_name_attribute = self.parameters['volcano name attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard # Volcano hazard layer
exposure_layer = self.exposure # Building exposure layer
# Input checks
if not hazard_layer.is_point_data:
message = (
'Input hazard must be a vector point layer. I got %s '
'with layer type %s' %
(hazard_layer.get_name(), hazard_layer.get_geometry_name()))
raise Exception(message)
# Make hazard layer by buffering the point
centers = hazard_layer.get_geometry()
features = hazard_layer.get_data()
radii_meter = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(centers,
radii_meter,
hazard_zone_attribute,
data_table=features)
# Category names for the impact zone
category_names = radii_meter
category_names.append(not_affected_value)
# Get names of volcanoes considered
if volcano_name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = set()
for row in hazard_layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[volcano_name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
else:
self.volcano_names = tr('Not specified in data')
# Find the target field name that has no conflict with the attribute
# names in the hazard layer
hazard_attribute_names = hazard_layer.get_attribute_names()
target_field = get_non_conflicting_attribute_name(
target_field, hazard_attribute_names)
# Run interpolation function for polygon2polygon
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=None)
# Extract relevant interpolated layer data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
self.buildings = {}
self.affected_buildings = OrderedDict()
for category in radii_meter:
self.affected_buildings[category] = {}
# Iterate the interpolated building layer
for i in range(len(features)):
hazard_value = features[i][hazard_zone_attribute]
if not hazard_value:
hazard_value = not_affected_value
features[i][target_field] = hazard_value
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][tr(
'Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.generate_html_report()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
colours = colours[::-1] # flip
colours = colours[:len(category_names)]
style_classes = []
i = 0
for category_name in category_names:
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(category_names):
i = len(category_names) - 1
style_class['colour'] = colours[i]
i += 1
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic buffered point')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(building)')
legend_title = tr('Building count')
# Create vector layer and return
impact_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by volcanic buffered point'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': 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):
"""Classified hazard impact to buildings (e.g. from Open Street Map).
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
# Value from layer's keywords
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
# The 3 classes
categorical_hazards = self.parameters['Categorical hazards'].value
low_t = categorical_hazards[0].value
medium_t = categorical_hazards[1].value
high_t = categorical_hazards[2].value
# Determine attribute name for hazard levels
if self.hazard.layer.is_raster:
hazard_attribute = 'level'
else:
hazard_attribute = None
interpolated_result = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute,
mode='constant')
# Extract relevant exposure data
attribute_names = interpolated_result.get_attribute_names()
attributes = interpolated_result.get_data()
buildings_total = len(interpolated_result)
# Calculate building impact
self.buildings = {}
self.affected_buildings = OrderedDict([(tr('High Hazard Class'), {}),
(tr('Medium Hazard Class'), {}),
(tr('Low Hazard Class'), {})])
for i in range(buildings_total):
if (structure_class_field
and structure_class_field in attribute_names):
usage = attributes[i][structure_class_field]
else:
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)
])
# Count all buildings by type
self.buildings[usage] += 1
attributes[i][self.target_field] = 0
attributes[i][self.affected_field] = 0
level = float(attributes[i]['level'])
level = float(numpy_round(level))
if level == high_t:
impact_level = tr('High Hazard Class')
elif level == medium_t:
impact_level = tr('Medium Hazard Class')
elif level == low_t:
impact_level = tr('Low Hazard Class')
else:
continue
# Add calculated impact to existing attributes
attributes[i][self.target_field] = {
tr('High Hazard Class'): 3,
tr('Medium Hazard Class'): 2,
tr('Low Hazard Class'): 1
}[impact_level]
attributes[i][self.affected_field] = 1
# Count affected buildings by type
self.affected_buildings[impact_level][usage][tr(
'Buildings Affected')] += 1
# Consolidate the small building usage groups < 25 to other
# Building threshold #2468
postprocessors = self.parameters['postprocessors']
building_postprocessors = postprocessors['BuildingType'][0]
self.building_report_threshold = building_postprocessors.value[0].value
self._consolidate_to_other()
# Create style
style_classes = [
dict(label=tr('High'),
value=3,
colour='#F31A1C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Medium'),
value=2,
colour='#F4A442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Low'),
value=1,
colour='#EBF442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Not Affected'),
value=None,
colour='#1EFC7C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
impact_table = impact_summary = self.html_report()
# For printing map purpose
map_title = tr('Buildings affected')
legend_title = tr('Structure inundated status')
legend_units = tr('(Low, Medium, High)')
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.affected_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': buildings_total,
'buildings_affected': self.total_affected_buildings
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create vector layer and return
vector_layer = Vector(data=attributes,
projection=self.exposure.layer.get_projection(),
geometry=self.exposure.layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = vector_layer
return vector_layer
def run(self):
"""Risk plugin for volcano hazard on building/structure.
Counts number of building exposed to each volcano hazard zones.
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare()
# Get parameters from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
name_attribute = self.hazard.keyword('volcano_name_field')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
# Input checks
if not self.hazard.layer.is_polygon_data:
message = (
'Input hazard must be a polygon. I got %s with '
'layer type %s' %
(self.hazard.name, self.hazard.layer.get_geometry_name()))
raise Exception(message)
# Check if hazard_zone_attribute exists in hazard_layer
if (self.hazard_class_attribute not in
self.hazard.layer.get_attribute_names()):
message = (
'Hazard data %s did not contain expected attribute %s ' %
(self.hazard.name, self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Get names of volcanoes considered
if name_attribute in self.hazard.layer.get_attribute_names():
volcano_name_list = set()
for row in self.hazard.layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
else:
self.volcano_names = tr('Not specified in data')
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer, self.exposure.layer)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
# Hazard zone categories from hazard layer
hazard_zone_categories = list(
set(self.hazard.layer.get_data(self.hazard_class_attribute)))
self.buildings = {}
self.affected_buildings = OrderedDict()
for hazard_category in hazard_zone_categories:
self.affected_buildings[hazard_category] = {}
for i in range(len(features)):
hazard_value = features[i][self.hazard_class_attribute]
if not hazard_value:
hazard_value = self._not_affected_value
features[i][self.target_field] = hazard_value
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = features[i][self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, features[i])
if usage in [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][
usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.generate_html_report()
category_names = hazard_zone_categories
category_names.append(self._not_affected_value)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
colours = colours[::-1] # flip
colours = colours[:len(category_names)]
style_classes = []
i = 0
for category_name in category_names:
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(category_names):
i = len(category_names) - 1
style_class['colour'] = colours[i]
i += 1
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='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings 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},
style_info=style_info)
self._impact = impact_layer
return impact_layer
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard_layer: Hazard layer of flood
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
"""
threshold = self.parameters['threshold [m]'] # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Extract data
hazard_layer = get_hazard_layer(layers) # Depth
exposure_layer = get_exposure_layer(layers) # Building locations
question = get_question(
hazard_layer.get_name(),
exposure_layer.get_name(),
self)
# Determine attribute name for hazard levels
if hazard_layer.is_raster:
mode = 'grid'
hazard_attribute = 'depth'
else:
mode = 'regions'
hazard_attribute = None
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
buildings = {}
# The number of affected buildings
affected_count = 0
# The variable for grid mode
inundated_count = 0
wet_count = 0
dry_count = 0
inundated_buildings = {}
wet_buildings = {}
dry_buildings = {}
# The variable for regions mode
affected_buildings = {}
if mode == 'grid':
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
inundated_buildings[key] = 0
wet_buildings[key] = 0
dry_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if inundated_status is 0:
# Count dry buildings by type
dry_buildings[key] += 1
# Count total dry buildings
dry_count += 1
if inundated_status is 1:
# Count inundated buildings by type
inundated_buildings[key] += 1
# Count total dry buildings
inundated_count += 1
if inundated_status is 2:
# Count wet buildings by type
wet_buildings[key] += 1
# Count total wet buildings
wet_count += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
elif mode == 'regions':
for i in range(total_features):
# Use interpolated polygon attribute
atts = features[i]
# FIXME (Ole): Need to agree whether to use one or the
# other as this can be very confusing!
# For now look for 'affected' first
if 'affected' in atts:
# E.g. from flood forecast
# Assume that building is wet if inside polygon
# as flagged by attribute Flooded
res = atts['affected']
if res is None:
inundated_status = False
else:
inundated_status = bool(res)
elif 'FLOODPRONE' in atts:
res = atts['FLOODPRONE']
if res is None:
inundated_status = False
else:
inundated_status = res.lower() == 'yes'
elif DEFAULT_ATTRIBUTE in atts:
# Check the default attribute assigned for points
# covered by a polygon
res = atts[DEFAULT_ATTRIBUTE]
if res is None:
inundated_status = False
else:
inundated_status = res
else:
# there is no flood related attribute
message = (
'No flood related attribute found in %s. I was '
'looking for either "affected", "FLOODPRONE" or '
'"inapolygon". 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)
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
affected_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if inundated_status is True:
# Count affected buildings by type
affected_buildings[key] += 1
# Count total affected buildings
affected_count += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = int(inundated_status)
else:
message = (tr('Unknown hazard type %s. Must be either "depth" or '
'"grid"') % mode)
raise Exception(message)
if mode == 'grid':
affected_count = inundated_count + wet_count
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
if mode == 'grid':
inundated_buildings['other'] = 0
wet_buildings['other'] = 0
dry_buildings['other'] = 0
elif mode == 'regions':
affected_buildings['other'] = 0
buildings['other'] += x
if mode == 'grid':
inundated_buildings['other'] += inundated_buildings[usage]
wet_buildings['other'] += wet_buildings[usage]
dry_buildings['other'] += dry_buildings[usage]
del buildings[usage]
del inundated_buildings[usage]
del wet_buildings[usage]
del dry_buildings[usage]
elif mode == 'regions':
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate simple impact report
table_body = []
if mode == 'grid':
table_body = [
question,
TableRow([tr('Building type'),
tr('Number Inundated'),
tr('Number of Wet Buildings'),
tr('Number of Dry Buildings'),
tr('Total')], header=True),
TableRow(
[tr('All'),
format_int(inundated_count),
format_int(wet_count),
format_int(dry_count),
format_int(total_features)])]
elif mode == 'regions':
table_body = [
question,
TableRow([tr('Building type'),
tr('Number flooded'),
tr('Total')], header=True),
TableRow([tr('All'),
format_int(affected_count),
format_int(total_features)])]
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type if available
list_type_attribute = [
'TYPE', 'type', 'amenity', 'building_t', 'office',
'tourism', 'leisure', 'building']
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
if mode == 'grid':
building_list.append([
building_type.capitalize(),
format_int(inundated_buildings[usage]),
format_int(wet_buildings[usage]),
format_int(dry_buildings[usage]),
format_int(buildings[usage])])
elif mode == 'regions':
building_list.append([
building_type.capitalize(),
format_int(affected_buildings[usage]),
format_int(buildings[usage])])
if usage.lower() == 'school':
school_closed = 0
if mode == 'grid':
school_closed += inundated_buildings[usage]
school_closed += wet_buildings[usage]
elif mode == 'regions':
school_closed = affected_buildings[usage]
if usage.lower() == 'hospital':
hospital_closed = 0
if mode == 'grid':
hospital_closed += inundated_buildings[usage]
hospital_closed += wet_buildings[usage]
elif mode == 'regions':
hospital_closed = affected_buildings[usage]
# Sort alphabetically
building_list.sort()
table_body.append(TableRow(tr('Breakdown by building type'),
header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
# Action Checklist Section
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(
tr('Are the critical facilities still open?')))
table_body.append(TableRow(
tr('Which structures have warning capacity (eg. sirens, speakers, '
'etc.)?')))
table_body.append(TableRow(
tr('Which buildings will be evacuation centres?')))
table_body.append(TableRow(
tr('Where will we locate the operations centre?')))
table_body.append(TableRow(
tr('Where will we locate warehouse and/or distribution centres?')))
if school_closed > 0:
table_body.append(TableRow(
tr('Where will the students from the %s closed schools go to '
'study?') % format_int(school_closed)))
if hospital_closed > 0:
table_body.append(TableRow(
tr('Where will the patients from the %s closed hospitals go '
'for treatment and how will we transport them?') %
format_int(hospital_closed)))
# Notes Section
table_body.append(TableRow(tr('Notes'), header=True))
if mode == 'grid':
table_body.append(TableRow(
tr('Buildings are said to be inundated when flood levels '
'exceed %.1f m') % threshold))
table_body.append(TableRow(
tr('Buildings are said to be wet when flood levels '
'are greater than 0 m but less than %.1f m') % threshold))
table_body.append(TableRow(
tr('Buildings are said to be dry when flood levels '
'are less than 0 m')))
table_body.append(TableRow(
tr('Buildings are said to be closed if they are inundated or '
'wet')))
table_body.append(TableRow(
tr('Buildings are said to be open if they are dry')))
else:
table_body.append(TableRow(
tr('Buildings are said to be flooded when in regions marked '
'as affected')))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# Prepare impact layer
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
legend_units = ''
style_classes = []
if mode == 'grid':
style_classes = [
dict(
label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1
),
dict(
label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1
),
dict(
label=tr('Inundated (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1
)]
legend_units = tr('(inundated, wet, or dry)')
elif mode == 'regions':
style_classes = [
dict(
label=tr('Not Inundated'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(
label=tr('Inundated'),
value=1,
colour='#F31A1C',
ztransparency=0, size=1)]
legend_units = tr('(inundated or not inundated)')
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Create vector layer and return
vector_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': total_features,
'buildings_affected': affected_count},
style_info=style_info)
return vector_layer
def run(self):
"""Risk plugin for classified polygon hazard on building/structure.
Counts number of building exposed to each hazard zones.
:returns: Map of building exposed to each hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare()
# Value from layer's keywords
self.hazard_class_attribute = self.hazard.keyword('field')
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
self.exposure_class_attribute = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
self.exposure_class_attribute = None
hazard_zone_attribute_index = self.hazard.layer.fieldNameIndex(
self.hazard_class_attribute)
# Check if hazard_zone_attribute exists in hazard_layer
if hazard_zone_attribute_index < 0:
message = (
'Hazard data %s does not contain expected attribute %s ' %
(self.hazard.layer.name(), self.hazard_class_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(message)
# Hazard zone categories from hazard layer
self.hazard_zones = self.hazard.layer.uniqueValues(
hazard_zone_attribute_index)
self.buildings = {}
self.affected_buildings = OrderedDict()
for hazard_zone in self.hazard_zones:
self.affected_buildings[hazard_zone] = {}
wgs84_extent = QgsRectangle(
self.requested_extent[0], self.requested_extent[1],
self.requested_extent[2], self.requested_extent[3])
# Run interpolation function for polygon2polygon
interpolated_layer = interpolate_polygon_polygon(
self.hazard.layer, self.exposure.layer, wgs84_extent)
new_field = QgsField(self.target_field, QVariant.String)
interpolated_layer.dataProvider().addAttributes([new_field])
interpolated_layer.updateFields()
attribute_names = [
field.name() for field in interpolated_layer.pendingFields()]
target_field_index = interpolated_layer.fieldNameIndex(
self.target_field)
changed_values = {}
if interpolated_layer.featureCount() < 1:
raise ZeroImpactException()
# Extract relevant interpolated data
for feature in interpolated_layer.getFeatures():
hazard_value = feature[self.hazard_class_attribute]
if not hazard_value:
hazard_value = self._not_affected_value
changed_values[feature.id()] = {target_field_index: hazard_value}
if (self.exposure_class_attribute and
self.exposure_class_attribute in attribute_names):
usage = feature[self.exposure_class_attribute]
else:
usage = get_osm_building_usage(attribute_names, feature)
if usage is None:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict(
[(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
interpolated_layer.dataProvider().changeAttributeValues(changed_values)
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.html_report()
# Create style
categories = self.hazard_zones
categories.append(self._not_affected_value)
colours = color_ramp(len(categories))
style_classes = []
i = 0
for hazard_zone in self.hazard_zones:
style_class = dict()
style_class['label'] = tr(hazard_zone)
style_class['transparency'] = 0
style_class['value'] = hazard_zone
style_class['size'] = 1
style_class['colour'] = colours[i]
style_classes.append(style_class)
i += 1
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by each hazard zone')
legend_title = tr('Building count')
legend_units = tr('(building)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
# Create vector layer and return
impact_layer = Vector(
data=interpolated_layer,
name=tr('Buildings affected 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, layers=None):
"""Earthquake impact to buildings (e.g. from OpenStreetMap).
:param layers: All the input layers (Hazard Layer and Exposure Layer)
"""
self.validate()
self.prepare(layers)
LOGGER.debug("Running earthquake building impact")
# merely initialize
building_value = 0
contents_value = 0
# Thresholds for mmi breakdown.
t0 = self.parameters["low_threshold"]
t1 = self.parameters["medium_threshold"]
t2 = self.parameters["high_threshold"]
# Class Attribute and Label.
class_1 = {"label": tr("Low"), "class": 1}
class_2 = {"label": tr("Medium"), "class": 2}
class_3 = {"label": tr("High"), "class": 3}
# Extract data
hazard_layer = self.hazard # Depth
exposure_layer = self.exposure # Building locations
# Define attribute name for hazard levels.
hazard_attribute = "mmi"
# Determine if exposure data have NEXIS attributes.
attribute_names = exposure_layer.get_attribute_names()
if "FLOOR_AREA" in attribute_names and "BUILDING_C" in attribute_names and "CONTENTS_C" in attribute_names:
self.is_nexis = True
else:
self.is_nexis = False
# Interpolate hazard level to building locations.
interpolate_result = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute
)
# Extract relevant exposure data
# attribute_names = interpolate_result.get_attribute_names()
attributes = interpolate_result.get_data()
interpolate_size = len(interpolate_result)
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([(tr("High"), {}), (tr("Medium"), {}), (tr("Low"), {})])
for i in range(interpolate_size):
# Classify building according to shake level
# and calculate dollar losses
if self.is_nexis:
try:
area = float(attributes[i]["FLOOR_AREA"])
except (ValueError, KeyError):
# print 'Got area', attributes[i]['FLOOR_AREA']
area = 0.0
try:
building_value_density = float(attributes[i]["BUILDING_C"])
except (ValueError, KeyError):
# print 'Got bld value', attributes[i]['BUILDING_C']
building_value_density = 0.0
try:
contents_value_density = float(attributes[i]["CONTENTS_C"])
except (ValueError, KeyError):
# print 'Got cont value', attributes[i]['CONTENTS_C']
contents_value_density = 0.0
building_value = building_value_density * area
contents_value = contents_value_density * area
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = "unknown"
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
if self.is_nexis:
self.affected_buildings[category][usage] = OrderedDict(
[
(tr("Buildings Affected"), 0),
(tr("Buildings value ($M)"), 0),
(tr("Contents value ($M)"), 0),
]
)
else:
self.affected_buildings[category][usage] = OrderedDict([(tr("Buildings Affected"), 0)])
self.buildings[usage] += 1
try:
mmi = float(attributes[i][hazard_attribute]) # MMI
except TypeError:
mmi = 0.0
if t0 <= mmi < t1:
cls = 1
category = tr("Low")
elif t1 <= mmi < t2:
cls = 2
category = tr("Medium")
elif t2 <= mmi:
cls = 3
category = tr("High")
else:
# Not reported for less than level t0
continue
attributes[i][self.target_field] = cls
self.affected_buildings[category][usage][tr("Buildings Affected")] += 1
if self.is_nexis:
self.affected_buildings[category][usage][tr("Buildings value ($M)")] += building_value / 1000000.0
self.affected_buildings[category][usage][tr("Contents value ($M)")] += contents_value / 1000000.0
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
impact_table = impact_summary = self.generate_html_report()
# Create style
style_classes = [
dict(label=class_1["label"], value=class_1["class"], colour="#ffff00", transparency=1),
dict(label=class_2["label"], value=class_2["class"], colour="#ffaa00", transparency=1),
dict(label=class_3["label"], value=class_3["class"], colour="#ff0000", transparency=1),
]
style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type="categorizedSymbol")
# For printing map purpose
map_title = tr("Building affected by earthquake")
legend_notes = tr("The level of the impact is according to the " "threshold the user input.")
legend_units = tr("(mmi)")
legend_title = tr("Impact level")
# Create vector layer and return
result_layer = Vector(
data=attributes,
projection=interpolate_result.get_projection(),
geometry=interpolate_result.get_geometry(),
name=tr("Estimated buildings affected"),
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,
"target_field": self.target_field,
"statistics_type": self.statistics_type,
"statistics_classes": self.statistics_classes,
},
style_info=style_info,
)
msg = "Created vector layer %s" % str(result_layer)
LOGGER.debug(msg)
self._impact = result_layer
return result_layer
def run(self):
"""Flood impact to buildings (e.g. from Open Street Map)."""
self.validate()
self.prepare()
threshold = self.parameters['threshold'].value # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Determine attribute name for hazard levels
hazard_attribute = 'depth'
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('Number Inundated'), {}),
(tr('Number of Wet Buildings'), {}),
(tr('Number of Dry Buildings'), {})
])
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
if (structure_class_field in attribute_names and
structure_class_field):
usage = features[i].get(structure_class_field, None)
else:
usage = get_osm_building_usage(
attribute_names, features[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
# Count all buildings by type
self.buildings[usage] += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
category = [
tr('Number of Dry Buildings'),
tr('Number Inundated'),
tr('Number of Wet Buildings')][inundated_status]
self.affected_buildings[category][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_table = impact_summary = self.generate_html_report()
# For printing map purpose
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
legend_units = tr('(inundated, wet, or dry)')
style_classes = [
dict(
label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1
),
dict(
label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1
),
dict(
label=tr('Inundated (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1
)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
vector_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_title': legend_title,
'legend_units': legend_units,
'buildings_total': total_features,
'buildings_affected': self.total_affected_buildings},
style_info=style_info)
# Create vector layer and return
self._impact = vector_layer
return vector_layer
def run(self):
"""Earthquake impact to buildings (e.g. from OpenStreetMap)."""
self.validate()
self.prepare()
LOGGER.debug('Running earthquake building impact')
# merely initialize
building_value = 0
contents_value = 0
# Thresholds for mmi breakdown.
t0 = self.parameters['low_threshold'].value
t1 = self.parameters['medium_threshold'].value
t2 = self.parameters['high_threshold'].value
# Class Attribute and Label.
class_1 = {'label': tr('Low'), 'class': 1}
class_2 = {'label': tr('Medium'), 'class': 2}
class_3 = {'label': tr('High'), 'class': 3}
# Define attribute name for hazard levels.
hazard_attribute = 'mmi'
# Determine if exposure data have NEXIS attributes.
attribute_names = self.exposure.layer.get_attribute_names()
if (
'FLOOR_AREA' in attribute_names and
'BUILDING_C' in attribute_names and
'CONTENTS_C' in attribute_names):
self.is_nexis = True
else:
self.is_nexis = False
# Interpolate hazard level to building locations.
interpolate_result = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute
)
# Extract relevant exposure data
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
attributes = interpolate_result.get_data()
interpolate_size = len(interpolate_result)
# Building breakdown
self.buildings = {}
# Impacted building breakdown
self.affected_buildings = OrderedDict([
(tr('High'), {}),
(tr('Medium'), {}),
(tr('Low'), {})
])
removed = []
for i in range(interpolate_size):
# Classify building according to shake level
# and calculate dollar losses
if self.is_nexis:
try:
area = float(attributes[i]['FLOOR_AREA'])
except (ValueError, KeyError):
# print 'Got area', attributes[i]['FLOOR_AREA']
area = 0.0
try:
building_value_density = float(attributes[i]['BUILDING_C'])
except (ValueError, KeyError):
# print 'Got bld value', attributes[i]['BUILDING_C']
building_value_density = 0.0
try:
contents_value_density = float(attributes[i]['CONTENTS_C'])
except (ValueError, KeyError):
# print 'Got cont value', attributes[i]['CONTENTS_C']
contents_value_density = 0.0
building_value = building_value_density * area
contents_value = contents_value_density * area
if (structure_class_field in attribute_names and
structure_class_field):
usage = attributes[i].get(structure_class_field, None)
else:
usage = get_osm_building_usage(
attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
if self.is_nexis:
self.affected_buildings[category][usage] = OrderedDict(
[
(tr('Buildings Affected'), 0),
(tr('Buildings value ($M)'), 0),
(tr('Contents value ($M)'), 0)])
else:
self.affected_buildings[category][usage] = \
OrderedDict([(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
try:
mmi = float(attributes[i][hazard_attribute]) # MMI
except TypeError:
mmi = 0.0
if t0 <= mmi < t1:
cls = 1
category = tr('Low')
elif t1 <= mmi < t2:
cls = 2
category = tr('Medium')
elif t2 <= mmi:
cls = 3
category = tr('High')
else:
# Not reported for less than level t0
removed.append(i)
continue
attributes[i][self.target_field] = cls
self.affected_buildings[
category][usage][tr('Buildings Affected')] += 1
if self.is_nexis:
self.affected_buildings[category][usage][
tr('Buildings value ($M)')] += building_value / 1000000.0
self.affected_buildings[category][usage][
tr('Contents value ($M)')] += contents_value / 1000000.0
# remove uncategorized element
removed.reverse()
geometry = interpolate_result.get_geometry()
for i in range(0, len(removed)):
del attributes[removed[i]]
del geometry[removed[i]]
if len(attributes) < 1:
raise ZeroImpactException()
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
impact_table = impact_summary = self.html_report()
# Create style
style_classes = [dict(label=class_1['label'], value=class_1['class'],
colour='#ffff00', transparency=1),
dict(label=class_2['label'], value=class_2['class'],
colour='#ffaa00', transparency=1),
dict(label=class_3['label'], value=class_3['class'],
colour='#ff0000', transparency=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Building affected by earthquake')
legend_notes = tr('The level of the impact is according to the '
'threshold the user input.')
legend_units = tr('(mmi)')
legend_title = tr('Impact level')
# Create vector layer and return
result_layer = Vector(
data=attributes,
projection=interpolate_result.get_projection(),
geometry=geometry,
name=tr('Estimated buildings affected'),
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,
'target_field': self.target_field,
'statistics_type': self.statistics_type,
'statistics_classes': self.statistics_classes},
style_info=style_info)
msg = 'Created vector layer %s' % str(result_layer)
LOGGER.debug(msg)
self._impact = result_layer
return result_layer
def run(self):
"""Classified hazard impact to buildings (e.g. from Open Street Map).
"""
self.validate()
self.prepare()
# Value from layer's keywords
# Try to get the value from keyword, if not exist, it will not fail,
# but use the old get_osm_building_usage
try:
structure_class_field = self.exposure.keyword(
'structure_class_field')
except KeywordNotFoundError:
structure_class_field = None
# The 3 classes
categorical_hazards = self.parameters['Categorical hazards'].value
low_t = categorical_hazards[0].value
medium_t = categorical_hazards[1].value
high_t = categorical_hazards[2].value
# Determine attribute name for hazard levels
if self.hazard.layer.is_raster:
hazard_attribute = 'level'
else:
hazard_attribute = None
interpolated_result = assign_hazard_values_to_exposure_data(
self.hazard.layer,
self.exposure.layer,
attribute_name=hazard_attribute,
mode='constant')
# Extract relevant exposure data
attribute_names = interpolated_result.get_attribute_names()
attributes = interpolated_result.get_data()
buildings_total = len(interpolated_result)
# Calculate building impact
self.buildings = {}
self.affected_buildings = OrderedDict([
(tr('High Hazard Class'), {}),
(tr('Medium Hazard Class'), {}),
(tr('Low Hazard Class'), {})
])
for i in range(buildings_total):
if (structure_class_field and
structure_class_field in attribute_names):
usage = attributes[i][structure_class_field]
else:
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
# Count all buildings by type
self.buildings[usage] += 1
attributes[i][self.target_field] = 0
attributes[i][self.affected_field] = 0
level = float(attributes[i]['level'])
level = float(numpy_round(level))
if level == high_t:
impact_level = tr('High Hazard Class')
elif level == medium_t:
impact_level = tr('Medium Hazard Class')
elif level == low_t:
impact_level = tr('Low Hazard Class')
else:
continue
# Add calculated impact to existing attributes
attributes[i][self.target_field] = {
tr('High Hazard Class'): 3,
tr('Medium Hazard Class'): 2,
tr('Low Hazard Class'): 1
}[impact_level]
attributes[i][self.affected_field] = 1
# Count affected buildings by type
self.affected_buildings[impact_level][usage][
tr('Buildings Affected')] += 1
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
# Create style
style_classes = [dict(label=tr('High'),
value=3,
colour='#F31A1C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Medium'),
value=2,
colour='#F4A442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Low'),
value=1,
colour='#EBF442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Not Affected'),
value=None,
colour='#1EFC7C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
impact_table = impact_summary = self.html_report()
# For printing map purpose
map_title = tr('Buildings affected')
legend_title = tr('Structure inundated status')
legend_units = tr('(Low, Medium, High)')
# Create vector layer and return
vector_layer = Vector(
data=attributes,
projection=self.exposure.layer.get_projection(),
geometry=self.exposure.layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.affected_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': buildings_total,
'buildings_affected': self.total_affected_buildings},
style_info=style_info)
self._impact = vector_layer
return vector_layer
def run(self, layers=None):
"""Counts number of building exposed to each volcano hazard zones.
:param layers: List of layers expected to contain.
* hazard_layer: Hazard layer of volcano
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
:returns: Map of building exposed to volcanic hazard zones.
Table with number of buildings affected
:rtype: dict
"""
self.validate()
self.prepare(layers)
# Target Field
target_field = 'zone'
# Hazard Zone Attribute
hazard_zone_attribute = 'radius'
# Not Affected Value
not_affected_value = 'Not Affected'
# Parameters
radii = self.parameters['distances [km]']
volcano_name_attribute = self.parameters['volcano name attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard # Volcano hazard layer
exposure_layer = self.exposure # Building exposure layer
# Input checks
if not hazard_layer.is_point_data:
message = (
'Input hazard must be a vector point layer. I got %s '
'with layer type %s' % (
hazard_layer.get_name(), hazard_layer.get_geometry_name()))
raise Exception(message)
# Make hazard layer by buffering the point
centers = hazard_layer.get_geometry()
features = hazard_layer.get_data()
radii_meter = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(
centers,
radii_meter,
hazard_zone_attribute,
data_table=features)
# Category names for the impact zone
category_names = radii_meter
category_names.append(not_affected_value)
# Get names of volcanoes considered
if volcano_name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = set()
for row in hazard_layer.get_data():
# Run through all polygons and get unique names
volcano_name_list.add(row[volcano_name_attribute])
self.volcano_names = ', '.join(volcano_name_list)
# Find the target field name that has no conflict with the attribute
# names in the hazard layer
hazard_attribute_names = hazard_layer.get_attribute_names()
target_field = get_non_conflicting_attribute_name(
target_field, hazard_attribute_names)
# Run interpolation function for polygon2polygon
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=None)
# Extract relevant interpolated layer data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
self.buildings = {}
self.affected_buildings = OrderedDict()
for category in radii_meter:
self.affected_buildings[category] = {}
# Iterate the interpolated building layer
for i in range(len(features)):
hazard_value = features[i][hazard_zone_attribute]
if not hazard_value:
hazard_value = not_affected_value
features[i][target_field] = hazard_value
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is [None, 'NULL', 'null', 'Null', 0]:
usage = tr('Unknown')
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][
usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
self.buildings[usage] += 1
if hazard_value in self.affected_buildings.keys():
self.affected_buildings[hazard_value][usage][
tr('Buildings Affected')] += 1
# Lump small entries and 'unknown' into 'other' category
self._consolidate_to_other()
# Generate simple impact report
impact_summary = impact_table = self.generate_html_report()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
colours = colours[::-1] # flip
colours = colours[:len(category_names)]
style_classes = []
i = 0
for category_name in category_names:
style_class = dict()
style_class['label'] = tr(category_name)
style_class['transparency'] = 0
style_class['value'] = category_name
style_class['size'] = 1
if i >= len(category_names):
i = len(category_names) - 1
style_class['colour'] = colours[i]
i += 1
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Buildings affected by volcanic buffered point')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(building)')
legend_title = tr('Building count')
# Create vector layer and return
impact_layer = Vector(
data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Buildings affected by volcanic buffered point'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': 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, layers=None):
"""Classified hazard impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard: Classified Hazard layer
* exposure: Vector layer of structure data on
the same grid as hazard
"""
self.validate()
self.prepare(layers)
# The 3 classes
low_t = self.parameters['low_hazard_class']
medium_t = self.parameters['medium_hazard_class']
high_t = self.parameters['high_hazard_class']
# Extract data
hazard = self.hazard # Classified Hazard
exposure = self.exposure # Building locations
# Determine attribute name for hazard levels
if hazard.is_raster:
hazard_attribute = 'level'
else:
hazard_attribute = None
interpolated_result = assign_hazard_values_to_exposure_data(
hazard,
exposure,
attribute_name=hazard_attribute,
mode='constant')
# Extract relevant exposure data
attribute_names = interpolated_result.get_attribute_names()
attributes = interpolated_result.get_data()
buildings_total = len(interpolated_result)
# Calculate building impact
self.buildings = {}
self.affected_buildings = OrderedDict([
(tr('High Hazard Class'), {}),
(tr('Medium Hazard Class'), {}),
(tr('Low Hazard Class'), {})
])
for i in range(buildings_total):
usage = get_osm_building_usage(attribute_names, attributes[i])
if usage is None or usage == 0:
usage = 'unknown'
if usage not in self.buildings:
self.buildings[usage] = 0
for category in self.affected_buildings.keys():
self.affected_buildings[category][usage] = OrderedDict([
(tr('Buildings Affected'), 0)])
# Count all buildings by type
self.buildings[usage] += 1
attributes[i][self.target_field] = 0
attributes[i][self.affected_field] = 0
level = float(attributes[i]['level'])
level = float(numpy_round(level))
if level == high_t:
impact_level = tr('High Hazard Class')
elif level == medium_t:
impact_level = tr('Medium Hazard Class')
elif level == low_t:
impact_level = tr('Low Hazard Class')
else:
continue
# Add calculated impact to existing attributes
attributes[i][self.target_field] = {
tr('High Hazard Class'): 3,
tr('Medium Hazard Class'): 2,
tr('Low Hazard Class'): 1
}[impact_level]
attributes[i][self.affected_field] = 1
# Count affected buildings by type
self.affected_buildings[impact_level][usage][
tr('Buildings Affected')] += 1
# Consolidate the small building usage groups < 25 to other
self._consolidate_to_other()
# Create style
style_classes = [dict(label=tr('High'),
value=3,
colour='#F31A1C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Medium'),
value=2,
colour='#F4A442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Low'),
value=1,
colour='#EBF442',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2),
dict(label=tr('Not Affected'),
value=None,
colour='#1EFC7C',
transparency=0,
size=2,
border_color='#969696',
border_width=0.2)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
impact_table = impact_summary = self.generate_html_report()
# For printing map purpose
map_title = tr('Buildings affected')
legend_units = tr('(Low, Medium, High)')
legend_title = tr('Structure inundated status')
# Create vector layer and return
vector_layer = Vector(
data=attributes,
projection=exposure.get_projection(),
geometry=exposure.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.affected_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': buildings_total,
'buildings_affected': self.total_affected_buildings},
style_info=style_info)
self._impact = vector_layer
return vector_layer
