def test_exposure_summary_table(self):
"""Test we can produce the breakdown for the exposure type."""
aggregate_hazard = load_test_vector_layer(
'gisv4', 'intermediate',
'aggregate_classified_hazard_summary.geojson')
aggregate_hazard.keywords['hazard_keywords'] = {
'classification': 'generic_hazard_classes'
}
# I need the number of unique exposure
unique_exposure = read_dynamic_inasafe_field(
aggregate_hazard.keywords['inasafe_fields'], exposure_count_field)
# I need the number of unique hazard
fields = aggregate_hazard.keywords['inasafe_fields']
hazard_class = fields[hazard_class_field['key']]
hazard_class_index = aggregate_hazard.fieldNameIndex(hazard_class)
unique_hazard = aggregate_hazard.uniqueValues(hazard_class_index)
layer = exposure_summary_table(aggregate_hazard, None)
check_inasafe_fields(layer)
self.assertEqual(len(unique_exposure), layer.featureCount())
# We should have
# one column per hazard
# one for the exposure
# one for total affected
# one for total not affected
# one for total not exposed
# one for total
self.assertEqual(layer.fields().count(), len(unique_hazard) + 5)
def test_exposure_summary_table_productivity(self):
"""Test we can produce the breakdown for the exposure type."""
aggregate_hazard = load_test_vector_layer(
'gisv4',
'intermediate',
'summaries',
'land_cover_aggregate_hazard_impacted.geojson')
aggregate_hazard.keywords['hazard_keywords'] = {
'hazard': 'generic',
'classification': 'generic_hazard_classes'
}
aggregate_hazard.keywords['exposure_keywords'] = {
'exposure': 'land_cover'
}
exposure_summary = load_test_vector_layer(
'gisv4',
'intermediate',
'summaries',
'land_cover_exposure_summary.geojson'
)
# I need the number of unique exposure
unique_exposure = read_dynamic_inasafe_field(
aggregate_hazard.keywords['inasafe_fields'],
exposure_count_field)
# I need the number of unique hazard
fields = aggregate_hazard.keywords['inasafe_fields']
hazard_class = fields[hazard_class_field['key']]
hazard_class_index = aggregate_hazard.fields().lookupField(hazard_class)
unique_hazard = aggregate_hazard.uniqueValues(hazard_class_index)
layer = exposure_summary_table(aggregate_hazard, exposure_summary)
check_inasafe_fields(layer)
self.assertEqual(len(unique_exposure), layer.featureCount())
# We should have
# one column per hazard
# 1. one for the exposure
# 2. one for total affected
# 3. one for total not affected
# 4. one for total not exposed
# 5. one for total
# 6. one for affected productivity
# 7. one for affected production cost
# 8. one for affected production value
self.assertEqual(layer.fields().count(), len(unique_hazard) + 8)
def test_exposure_summary_table_productivity(self):
"""Test we can produce the breakdown for the exposure type."""
aggregate_hazard = load_test_vector_layer(
'gisv4',
'intermediate',
'summaries',
'land_cover_aggregate_hazard_impacted.geojson')
aggregate_hazard.keywords['hazard_keywords'] = {
'classification': 'generic_hazard_classes'
}
exposure_summary = load_test_vector_layer(
'gisv4',
'intermediate',
'summaries',
'land_cover_exposure_summary.geojson'
)
# I need the number of unique exposure
unique_exposure = read_dynamic_inasafe_field(
aggregate_hazard.keywords['inasafe_fields'],
exposure_count_field)
# I need the number of unique hazard
fields = aggregate_hazard.keywords['inasafe_fields']
hazard_class = fields[hazard_class_field['key']]
hazard_class_index = aggregate_hazard.fieldNameIndex(hazard_class)
unique_hazard = aggregate_hazard.uniqueValues(hazard_class_index)
layer = exposure_summary_table(aggregate_hazard, exposure_summary)
check_inasafe_fields(layer)
self.assertEqual(len(unique_exposure), layer.featureCount())
# We should have
# one column per hazard
# one for the exposure
# one for total affected
# one for total not affected
# one for total not exposed
# one for total
# one for affected productivity
# one for affected production cost
# one for affected production value
self.assertEqual(layer.fields().count(), len(unique_hazard) + 8)
def aggregation_result_extractor(impact_report, component_metadata):
"""Extracting aggregation result of breakdown from the impact layer.
:param impact_report: the impact report that acts as a proxy to fetch
all the data that extractor needed
:type impact_report: safe.report.impact_report.ImpactReport
:param component_metadata: the component metadata. Used to obtain
information about the component we want to render
:type component_metadata: safe.report.report_metadata.
ReportComponentsMetadata
:return: context for rendering phase
:rtype: dict
.. versionadded:: 4.0
"""
context = {}
"""Initializations"""
extra_args = component_metadata.extra_args
# Find out aggregation report type
exposure_layer = impact_report.exposure
analysis_layer = impact_report.analysis
provenance = impact_report.impact_function.provenance
exposure_summary_table = impact_report.exposure_summary_table
if exposure_summary_table:
exposure_summary_table_fields = exposure_summary_table.keywords[
'inasafe_fields']
aggregation_summary = impact_report.aggregation_summary
aggregation_summary_fields = aggregation_summary.keywords['inasafe_fields']
debug_mode = impact_report.impact_function.debug_mode
"""Filtering report sections"""
# Only process for applicable exposure types
# Get exposure type definition
exposure_type = layer_definition_type(exposure_layer)
# Only round the number when it is population exposure and it is not
# in debug mode
is_rounded = not debug_mode
is_population = exposure_type is exposure_population
# For now aggregation report only applicable for breakable exposure types:
itemizable_exposures_all = [
exposure for exposure in exposure_all
if exposure.get('classifications')
]
if exposure_type not in itemizable_exposures_all:
return context
"""Generating type name for columns"""
type_fields = read_dynamic_inasafe_field(aggregation_summary_fields,
affected_exposure_count_field)
# do not include total, to preserve ordering and proper reference
type_fields.remove('total')
# we need to sort the column
# get the classes lists
# retrieve classes definitions
exposure_classes_lists = retrieve_exposure_classes_lists(exposure_layer)
# sort columns based on class order
# create function to sort
def sort_classes(_type_field):
"""Sort method to retrieve exposure class key index."""
# class key is the type field name
# find index in class list
for i, _exposure_class in enumerate(exposure_classes_lists):
if _type_field == _exposure_class['key']:
index = i
break
else:
index = -1
return index
# sort
type_fields = sorted(type_fields, key=sort_classes)
# generate type_header_labels for column header
type_header_labels = []
for type_name in type_fields:
type_label = tr(type_name.capitalize())
type_header_labels.append(type_label)
"""Generating values for rows"""
# generate rows of values for values of each column
rows = []
aggregation_name_index = aggregation_summary.fieldNameIndex(
aggregation_name_field['field_name'])
total_field_index = aggregation_summary.fieldNameIndex(
total_affected_field['field_name'])
type_field_index = []
for type_name in type_fields:
field_name = affected_exposure_count_field['field_name'] % type_name
type_index = aggregation_summary.fieldNameIndex(field_name)
type_field_index.append(type_index)
for feat in aggregation_summary.getFeatures():
total_affected_value = format_number(feat[total_field_index],
enable_rounding=is_rounded,
is_population=is_population)
if total_affected_value == '0':
# skip aggregation type if the total affected is zero
continue
item = {
# Name is the header for each row
'name': feat[aggregation_name_index],
# Total is the total for each row
'total': total_affected_value
}
# Type values is the values for each column in each row
type_values = []
for idx in type_field_index:
affected_value = format_number(feat[idx],
enable_rounding=is_rounded)
type_values.append(affected_value)
item['type_values'] = type_values
rows.append(item)
"""Generate total for footers"""
# calculate total values for each type. Taken from exposure summary table
type_total_values = []
# Get affected field index
affected_field_index = exposure_summary_table.fieldNameIndex(
total_affected_field['field_name'])
# Get breakdown field
breakdown_field = None
# I'm not sure what's the difference
# It is possible to have exposure_type_field or exposure_class_field
# at the moment
breakdown_fields = [exposure_type_field, exposure_class_field]
for field in breakdown_fields:
if field['key'] in exposure_summary_table_fields:
breakdown_field = field
break
breakdown_field_name = breakdown_field['field_name']
breakdown_field_index = exposure_summary_table.fieldNameIndex(
breakdown_field_name)
# Fetch total affected for each breakdown name
value_dict = {}
for feat in exposure_summary_table.getFeatures():
# exposure summary table is in csv format, so the field returned is
# always in text format
affected_value = int(float(feat[affected_field_index]))
affected_value = format_number(affected_value,
enable_rounding=is_rounded,
is_population=is_population)
value_dict[feat[breakdown_field_index]] = affected_value
if value_dict:
for type_name in type_fields:
affected_value_string_formatted = value_dict[type_name]
if affected_value_string_formatted == '0':
# if total affected for breakdown type is zero
# current column index
column_index = len(type_total_values)
# cut column header
type_header_labels = (type_header_labels[:column_index] +
type_header_labels[column_index + 1:])
# cut all row values for the column
for item in rows:
type_values = item['type_values']
item['type_values'] = (type_values[:column_index] +
type_values[column_index + 1:])
continue
type_total_values.append(affected_value_string_formatted)
"""Get the super total affected"""
# total for affected (super total)
analysis_feature = analysis_layer.getFeatures().next()
field_index = analysis_layer.fieldNameIndex(
total_affected_field['field_name'])
total_all = format_number(analysis_feature[field_index],
enable_rounding=is_rounded)
"""Generate and format the context"""
aggregation_area_default_header = resolve_from_dictionary(
extra_args, 'aggregation_area_default_header')
header_label = (aggregation_summary.title()
or aggregation_area_default_header)
table_header_format = resolve_from_dictionary(extra_args,
'table_header_format')
# check unit
units = exposure_type['units']
if units:
unit = units[0]
abbreviation = unit['abbreviation']
if abbreviation:
unit_string = '({abbreviation})'.format(abbreviation=abbreviation)
else:
unit_string = ''
else:
unit_string = ''
table_header = table_header_format.format(
title=provenance['map_legend_title'], unit=unit_string)
table_header = ' '.join(table_header.split())
section_header = resolve_from_dictionary(extra_args, 'header')
notes = resolve_from_dictionary(extra_args, 'notes')
total_header = resolve_from_dictionary(extra_args, 'total_header')
total_in_aggregation_header = resolve_from_dictionary(
extra_args, 'total_in_aggregation_header')
context['header'] = section_header
context['notes'] = notes
context['aggregation_result'] = {
'table_header': table_header,
'header_label': header_label,
'type_header_labels': type_header_labels,
'total_label': total_header,
'total_in_aggregation_area_label': total_in_aggregation_header,
'rows': rows,
'type_total_values': type_total_values,
'total_all': total_all,
}
return context
def aggregation_summary(aggregate_hazard, aggregation, callback=None):
"""Compute the summary from the aggregate hazard to the analysis layer.
Source layer :
| haz_id | haz_class | aggr_id | aggr_name | total_feature |
Target layer :
| aggr_id | aggr_name |
Output layer :
| aggr_id | aggr_name | count of affected features per exposure type
:param aggregate_hazard: The layer to aggregate vector layer.
:type aggregate_hazard: QgsVectorLayer
:param aggregation: The aggregation vector layer where to write statistics.
:type aggregation: QgsVectorLayer
:param callback: A function to all to indicate progress. The function
should accept params 'current' (int), 'maximum' (int) and 'step' (str).
Defaults to None.
:type callback: function
:return: The new aggregation layer with summary.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = summary_2_aggregation_steps['output_layer_name']
processing_step = summary_2_aggregation_steps['step_name']
source_fields = aggregate_hazard.keywords['inasafe_fields']
target_fields = aggregation.keywords['inasafe_fields']
target_compulsory_fields = [
aggregation_id_field,
aggregation_name_field,
]
check_inputs(target_compulsory_fields, target_fields)
# Missing exposure_count_field
source_compulsory_fields = [
aggregation_id_field,
aggregation_name_field,
hazard_id_field,
hazard_class_field,
affected_field,
]
check_inputs(source_compulsory_fields, source_fields)
pattern = exposure_count_field['key']
pattern = pattern.replace('%s', '')
unique_exposure = read_dynamic_inasafe_field(source_fields,
exposure_count_field)
absolute_values = create_absolute_values_structure(aggregate_hazard,
['aggregation_id'])
flat_table = FlatTable('aggregation_id', 'exposure_class')
aggregation_index = source_fields[aggregation_id_field['key']]
# We want to loop over affected features only.
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
expression = '\"%s\" = \'%s\'' % (affected_field['field_name'], tr('True'))
request.setFilterExpression(expression)
for area in aggregate_hazard.getFeatures(request):
for key, name_field in source_fields.iteritems():
if key.endswith(pattern):
aggregation_id = area[aggregation_index]
exposure_class = key.replace(pattern, '')
value = area[name_field]
flat_table.add_value(value,
aggregation_id=aggregation_id,
exposure_class=exposure_class)
# We summarize every absolute values.
for field, field_definition in absolute_values.iteritems():
value = area[field]
if not value or isinstance(value, QPyNullVariant):
value = 0
field_definition[0].add_value(
value,
aggregation_id=area[aggregation_index],
)
shift = aggregation.fields().count()
aggregation.startEditing()
add_fields(aggregation, absolute_values, [total_affected_field],
unique_exposure, affected_exposure_count_field)
aggregation_index = target_fields[aggregation_id_field['key']]
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
for area in aggregation.getFeatures(request):
aggregation_value = area[aggregation_index]
total = 0
for i, val in enumerate(unique_exposure):
sum = flat_table.get_value(aggregation_id=aggregation_value,
exposure_class=val)
total += sum
aggregation.changeAttributeValue(area.id(), shift + i, sum)
aggregation.changeAttributeValue(area.id(),
shift + len(unique_exposure), total)
for i, field in enumerate(absolute_values.itervalues()):
value = field[0].get_value(aggregation_id=aggregation_value, )
target_index = shift + len(unique_exposure) + 1 + i
aggregation.changeAttributeValue(area.id(), target_index, value)
aggregation.commitChanges()
aggregation.keywords['title'] = layer_purpose_aggregation_summary['name']
if qgis_version() >= 21800:
aggregation.setName(aggregation.keywords['title'])
else:
aggregation.setLayerName(aggregation.keywords['title'])
aggregation.keywords['layer_purpose'] = (
layer_purpose_aggregation_summary['key'])
check_layer(aggregation)
return aggregation
def exposure_summary_table(aggregate_hazard,
exposure_summary=None,
callback=None):
"""Compute the summary from the aggregate hazard to analysis.
Source layer :
| haz_id | haz_class | aggr_id | aggr_name | exposure_count |
Output layer :
| exp_type | count_hazard_class | total |
:param aggregate_hazard: The layer to aggregate vector layer.
:type aggregate_hazard: QgsVectorLayer
:param exposure_summary: The layer impact layer.
:type exposure_summary: QgsVectorLayer
:param callback: A function to all to indicate progress. The function
should accept params 'current' (int), 'maximum' (int) and 'step' (str).
Defaults to None.
:type callback: function
:return: The new tabular table, without geometry.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = summary_4_exposure_summary_table_steps[
'output_layer_name']
source_fields = aggregate_hazard.keywords['inasafe_fields']
source_compulsory_fields = [
aggregation_id_field, aggregation_name_field, hazard_id_field,
hazard_class_field, affected_field, total_field
]
check_inputs(source_compulsory_fields, source_fields)
absolute_values = create_absolute_values_structure(aggregate_hazard,
['all'])
hazard_class = source_fields[hazard_class_field['key']]
hazard_class_index = aggregate_hazard.fields().lookupField(hazard_class)
unique_hazard = aggregate_hazard.uniqueValues(hazard_class_index)
unique_exposure = read_dynamic_inasafe_field(source_fields,
exposure_count_field)
flat_table = FlatTable('hazard_class', 'exposure_class')
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
for area in aggregate_hazard.getFeatures():
hazard_value = area[hazard_class_index]
for exposure in unique_exposure:
key_name = exposure_count_field['key'] % exposure
field_name = source_fields[key_name]
exposure_count = area[field_name]
if not exposure_count:
exposure_count = 0
flat_table.add_value(exposure_count,
hazard_class=hazard_value,
exposure_class=exposure)
# We summarize every absolute values.
for field, field_definition in list(absolute_values.items()):
value = area[field]
if not value:
value = 0
field_definition[0].add_value(value, all='all')
tabular = create_memory_layer(output_layer_name, QgsWkbTypes.NullGeometry)
tabular.startEditing()
field = create_field_from_definition(exposure_type_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][exposure_type_field['key']] = (
exposure_type_field['field_name'])
hazard_keywords = aggregate_hazard.keywords['hazard_keywords']
hazard = hazard_keywords['hazard']
classification = hazard_keywords['classification']
exposure_keywords = aggregate_hazard.keywords['exposure_keywords']
exposure = exposure_keywords['exposure']
hazard_affected = {}
for hazard_class in unique_hazard:
if (hazard_class == '' or hazard_class is None or
(hasattr(hazard_class, 'isNull') and hazard_class.isNull())):
hazard_class = 'NULL'
field = create_field_from_definition(hazard_count_field, hazard_class)
tabular.addAttribute(field)
key = hazard_count_field['key'] % hazard_class
value = hazard_count_field['field_name'] % hazard_class
tabular.keywords['inasafe_fields'][key] = value
hazard_affected[hazard_class] = post_processor_affected_function(
exposure=exposure,
hazard=hazard,
classification=classification,
hazard_class=hazard_class)
field = create_field_from_definition(total_affected_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_affected_field['key']] = (
total_affected_field['field_name'])
# essentially have the same value as NULL_hazard_count
# but with this, make sure that it exists in layer so it can be used for
# reporting, and can be referenced to fields.py to take the label.
field = create_field_from_definition(total_not_affected_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_not_affected_field['key']] = (
total_not_affected_field['field_name'])
field = create_field_from_definition(total_not_exposed_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_not_exposed_field['key']] = (
total_not_exposed_field['field_name'])
field = create_field_from_definition(total_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_field['key']] = (
total_field['field_name'])
summarization_dicts = {}
if exposure_summary:
summarization_dicts = summarize_result(exposure_summary)
sorted_keys = sorted(summarization_dicts.keys())
for key in sorted_keys:
summary_field = summary_rules[key]['summary_field']
field = create_field_from_definition(summary_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][summary_field['key']] = (
summary_field['field_name'])
# Only add absolute value if there is no summarization / no exposure
# classification
if not summarization_dicts:
# For each absolute values
for absolute_field in list(absolute_values.keys()):
field_definition = definition(absolute_values[absolute_field][1])
field = create_field_from_definition(field_definition)
tabular.addAttribute(field)
key = field_definition['key']
value = field_definition['field_name']
tabular.keywords['inasafe_fields'][key] = value
for exposure_type in unique_exposure:
feature = QgsFeature()
attributes = [exposure_type]
total_affected = 0
total_not_affected = 0
total_not_exposed = 0
total = 0
for hazard_class in unique_hazard:
if hazard_class == '' or hazard_class is None:
hazard_class = 'NULL'
value = flat_table.get_value(hazard_class=hazard_class,
exposure_class=exposure_type)
attributes.append(value)
if hazard_affected[hazard_class] == not_exposed_class['key']:
total_not_exposed += value
elif hazard_affected[hazard_class]:
total_affected += value
else:
total_not_affected += value
total += value
attributes.append(total_affected)
attributes.append(total_not_affected)
attributes.append(total_not_exposed)
attributes.append(total)
if summarization_dicts:
for key in sorted_keys:
attributes.append(summarization_dicts[key].get(
exposure_type, 0))
else:
for i, field in enumerate(absolute_values.values()):
value = field[0].get_value(all='all')
attributes.append(value)
feature.setAttributes(attributes)
tabular.addFeature(feature)
# Sanity check ± 1 to the result. Disabled for now as it seems ± 1 is
# not enough. ET 13/02/17
# total_computed = (
# total_affected + total_not_affected + total_not_exposed)
# if not -1 < (total_computed - total) < 1:
# raise ComputationError
tabular.commitChanges()
tabular.keywords['title'] = layer_purpose_exposure_summary_table['name']
if qgis_version() >= 21800:
tabular.setName(tabular.keywords['title'])
else:
tabular.setLayerName(tabular.keywords['title'])
tabular.keywords['layer_purpose'] = layer_purpose_exposure_summary_table[
'key']
check_layer(tabular, has_geometry=False)
return tabular
def multi_exposure_analysis_summary(analysis, intermediate_analysis):
"""Merge intermediate analysis into one analysis summary.
List of analysis layers like:
| analysis_id | count_hazard_class | affected_count | total |
Target layer :
| analysis_id |
Output layer :
| analysis_id | count_hazard_class | affected_count | total |
:param analysis: The target vector layer where to write statistics.
:type analysis: QgsVectorLayer
:param intermediate_analysis: List of analysis layer for a single exposure.
:type intermediate_analysis: list
:return: The new target layer with summary.
:rtype: QgsVectorLayer
.. versionadded:: 4.3
"""
analysis.startEditing()
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
target_id = next(analysis.getFeatures(request)).id()
for analysis_result in intermediate_analysis:
exposure = analysis_result.keywords['exposure_keywords']['exposure']
iterator = analysis_result.getFeatures(request)
feature = next(iterator)
source_fields = analysis_result.keywords['inasafe_fields']
# Dynamic fields
hazards = read_dynamic_inasafe_field(source_fields, hazard_count_field)
for hazard_zone in hazards:
field = create_field_from_definition(exposure_hazard_count_field,
exposure, hazard_zone)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
value = feature[analysis_result.fields().lookupField(
hazard_count_field['field_name'] % hazard_zone)]
analysis.changeAttributeValue(target_id, index, value)
# keywords
key = exposure_hazard_count_field['key'] % (exposure, hazard_zone)
value = exposure_hazard_count_field['field_name'] % (exposure,
hazard_zone)
analysis.keywords['inasafe_fields'][key] = value
# total affected
source_index = analysis_result.fields().lookupField(
total_affected_field['field_name'])
field = create_field_from_definition(exposure_total_affected_field,
exposure)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
analysis.changeAttributeValue(target_id, index, feature[source_index])
# keywords
key = exposure_total_affected_field['key'] % exposure
value = exposure_total_affected_field['field_name'] % exposure
analysis.keywords['inasafe_fields'][key] = value
# total not affected
source_index = analysis_result.fields().lookupField(
total_not_affected_field['field_name'])
field = create_field_from_definition(exposure_total_not_affected_field,
exposure)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
analysis.changeAttributeValue(target_id, index, feature[source_index])
# keywords
key = exposure_total_not_affected_field['key'] % exposure
value = exposure_total_not_affected_field['field_name'] % exposure
analysis.keywords['inasafe_fields'][key] = value
# total exposed
source_index = analysis_result.fields().lookupField(
total_exposed_field['field_name'])
field = create_field_from_definition(exposure_total_exposed_field,
exposure)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
analysis.changeAttributeValue(target_id, index, feature[source_index])
# keywords
key = exposure_total_exposed_field['key'] % exposure
value = exposure_total_exposed_field['field_name'] % exposure
analysis.keywords['inasafe_fields'][key] = value
# total not exposed
source_index = analysis_result.fields().lookupField(
total_not_exposed_field['field_name'])
field = create_field_from_definition(exposure_total_not_exposed_field,
exposure)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
analysis.changeAttributeValue(target_id, index, feature[source_index])
# keywords
key = exposure_total_not_exposed_field['key'] % exposure
value = exposure_total_not_exposed_field['field_name'] % exposure
analysis.keywords['inasafe_fields'][key] = value
# total
source_index = analysis_result.fields().lookupField(
total_field['field_name'])
field = create_field_from_definition(exposure_total_field, exposure)
analysis.addAttribute(field)
index = analysis.fields().lookupField(field.name())
analysis.changeAttributeValue(target_id, index, feature[source_index])
# keywords
key = exposure_total_field['key'] % exposure
value = exposure_total_field['field_name'] % exposure
analysis.keywords['inasafe_fields'][key] = value
analysis.commitChanges()
analysis.keywords['title'] = (
layer_purpose_analysis_impacted['multi_exposure_name'])
analysis.keywords['layer_purpose'] = layer_purpose_analysis_impacted['key']
# Set up the extra keywords so everyone knows it's a
# multi exposure analysis result.
extra_keywords = {
extra_keyword_analysis_type['key']: MULTI_EXPOSURE_ANALYSIS_FLAG
}
analysis.keywords['extra_keywords'] = extra_keywords
if qgis_version() >= 21600:
analysis.setName(analysis.keywords['title'])
else:
analysis.setLayerName(analysis.keywords['title'])
return analysis
def multi_exposure_aggregation_summary(aggregation, intermediate_layers):
"""Merge intermediate aggregations into one aggregation summary.
Source layer :
| aggr_id | aggr_name | count of affected features per exposure type
Target layer :
| aggregation_id | aggregation_name |
Output layer :
| aggr_id | aggr_name | count of affected per exposure type for each
:param aggregation: The target vector layer where to write statistics.
:type aggregation: QgsVectorLayer
:param intermediate_layers: List of aggregation layer for a single exposure
:type intermediate_layers: list
:return: The new target layer with summary.
:rtype: QgsVectorLayer
.. versionadded:: 4.3
"""
target_index_field_name = (
aggregation.keywords['inasafe_fields'][aggregation_id_field['key']])
aggregation.startEditing()
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
for layer in intermediate_layers:
source_fields = layer.keywords['inasafe_fields']
exposure = layer.keywords['exposure_keywords']['exposure']
unique_exposure = read_dynamic_inasafe_field(
source_fields, affected_exposure_count_field,
[total_affected_field])
field_map = {}
for exposure_class in unique_exposure:
field = create_field_from_definition(
exposure_affected_exposure_type_count_field,
name=exposure,
sub_name=exposure_class)
aggregation.addAttribute(field)
source_field_index = layer.fields().lookupField(
affected_exposure_count_field['field_name'] % exposure_class)
target_field_index = aggregation.fields().lookupField(field.name())
field_map[source_field_index] = target_field_index
# Total affected field
field = create_field_from_definition(exposure_total_not_affected_field,
exposure)
aggregation.addAttribute(field)
source_field_index = layer.fields().lookupField(
total_affected_field['field_name'])
target_field_index = aggregation.fields().lookupField(field.name())
field_map[source_field_index] = target_field_index
# Get Aggregation ID from original feature
index = (layer.fields().lookupField(
source_fields[aggregation_id_field['key']]))
for source_feature in layer.getFeatures(request):
target_expression = QgsFeatureRequest()
target_expression.setFlags(QgsFeatureRequest.NoGeometry)
expression = '\"{field_name}\" = {id_value}'.format(
field_name=target_index_field_name,
id_value=source_feature[index])
target_expression.setFilterExpression(expression)
iterator = aggregation.getFeatures(target_expression)
target_feature = next(iterator) # It must return only 1 feature.
for source_field, target_field in list(field_map.items()):
aggregation.changeAttributeValue(target_feature.id(),
target_field,
source_feature[source_field])
try:
next(iterator)
except StopIteration:
# Everything is fine, it's normal.
pass
else:
# This should never happen ! IDs are duplicated in the
# aggregation layer.
raise Exception(
'Aggregation IDs are duplicated in the aggregation layer. '
'We can\'t make any joins.')
aggregation.commitChanges()
aggregation.keywords['title'] = (
layer_purpose_aggregation_summary['multi_exposure_name'])
aggregation.keywords['layer_purpose'] = (
layer_purpose_aggregation_summary['key'])
# Set up the extra keywords so everyone knows it's a
# multi exposure analysis result.
extra_keywords = {
extra_keyword_analysis_type['key']: MULTI_EXPOSURE_ANALYSIS_FLAG
}
aggregation.keywords['extra_keywords'] = extra_keywords
if qgis_version() >= 21600:
aggregation.setName(aggregation.keywords['title'])
else:
aggregation.setLayerName(aggregation.keywords['title'])
return aggregation
def aggregation_result_extractor(impact_report, component_metadata):
"""Extracting aggregation result of breakdown from the impact layer.
:param impact_report: the impact report that acts as a proxy to fetch
all the data that extractor needed
:type impact_report: safe.report.impact_report.ImpactReport
:param component_metadata: the component metadata. Used to obtain
information about the component we want to render
:type component_metadata: safe.report.report_metadata.
ReportComponentsMetadata
:return: context for rendering phase
:rtype: dict
.. versionadded:: 4.0
"""
context = {}
"""Initializations"""
extra_args = component_metadata.extra_args
# Find out aggregation report type
exposure_layer = impact_report.exposure
analysis_layer = impact_report.analysis
provenance = impact_report.impact_function.provenance
exposure_summary_table = impact_report.exposure_summary_table
if exposure_summary_table:
exposure_summary_table_fields = exposure_summary_table.keywords[
'inasafe_fields']
aggregation_summary = impact_report.aggregation_summary
aggregation_summary_fields = aggregation_summary.keywords[
'inasafe_fields']
debug_mode = impact_report.impact_function.debug_mode
"""Filtering report sections"""
# Only process for applicable exposure types
# Get exposure type definition
exposure_type = layer_definition_type(exposure_layer)
# Only round the number when it is population exposure and it is not
# in debug mode
is_rounded = not debug_mode
is_population = exposure_type is exposure_population
# For now aggregation report only applicable for breakable exposure types:
itemizable_exposures_all = [
exposure for exposure in exposure_all
if exposure.get('classifications')]
if exposure_type not in itemizable_exposures_all:
return context
"""Generating type name for columns"""
type_fields = read_dynamic_inasafe_field(
aggregation_summary_fields, affected_exposure_count_field)
# do not include total, to preserve ordering and proper reference
type_fields.remove('total')
# we need to sort the column
# get the classes lists
# retrieve classes definitions
exposure_classes_lists = retrieve_exposure_classes_lists(exposure_layer)
# sort columns based on class order
# create function to sort
def sort_classes(_type_field):
"""Sort method to retrieve exposure class key index."""
# class key is the type field name
# find index in class list
for i, _exposure_class in enumerate(exposure_classes_lists):
if _type_field == _exposure_class['key']:
index = i
break
else:
index = -1
return index
# sort
type_fields = sorted(type_fields, key=sort_classes)
# generate type_header_labels for column header
type_header_labels = []
for type_name in type_fields:
type_label = tr(type_name.capitalize())
type_header_labels.append(type_label)
"""Generating values for rows"""
# generate rows of values for values of each column
rows = []
aggregation_name_index = aggregation_summary.fieldNameIndex(
aggregation_name_field['field_name'])
total_field_index = aggregation_summary.fieldNameIndex(
total_affected_field['field_name'])
type_field_index = []
for type_name in type_fields:
field_name = affected_exposure_count_field['field_name'] % type_name
type_index = aggregation_summary.fieldNameIndex(field_name)
type_field_index.append(type_index)
for feat in aggregation_summary.getFeatures():
total_affected_value = format_number(
feat[total_field_index],
enable_rounding=is_rounded,
is_population=is_population)
if total_affected_value == '0':
# skip aggregation type if the total affected is zero
continue
item = {
# Name is the header for each row
'name': feat[aggregation_name_index],
# Total is the total for each row
'total': total_affected_value
}
# Type values is the values for each column in each row
type_values = []
for idx in type_field_index:
affected_value = format_number(
feat[idx],
enable_rounding=is_rounded)
type_values.append(affected_value)
item['type_values'] = type_values
rows.append(item)
"""Generate total for footers"""
# calculate total values for each type. Taken from exposure summary table
type_total_values = []
# Get affected field index
affected_field_index = exposure_summary_table.fieldNameIndex(
total_affected_field['field_name'])
# Get breakdown field
breakdown_field = None
# I'm not sure what's the difference
# It is possible to have exposure_type_field or exposure_class_field
# at the moment
breakdown_fields = [
exposure_type_field,
exposure_class_field
]
for field in breakdown_fields:
if field['key'] in exposure_summary_table_fields:
breakdown_field = field
break
breakdown_field_name = breakdown_field['field_name']
breakdown_field_index = exposure_summary_table.fieldNameIndex(
breakdown_field_name)
# Fetch total affected for each breakdown name
value_dict = {}
for feat in exposure_summary_table.getFeatures():
# exposure summary table is in csv format, so the field returned is
# always in text format
affected_value = int(float(feat[affected_field_index]))
affected_value = format_number(
affected_value,
enable_rounding=is_rounded,
is_population=is_population)
value_dict[feat[breakdown_field_index]] = affected_value
if value_dict:
for type_name in type_fields:
affected_value_string_formatted = value_dict[type_name]
if affected_value_string_formatted == '0':
# if total affected for breakdown type is zero
# current column index
column_index = len(type_total_values)
# cut column header
type_header_labels = (
type_header_labels[:column_index] +
type_header_labels[column_index + 1:])
# cut all row values for the column
for item in rows:
type_values = item['type_values']
item['type_values'] = (
type_values[:column_index] +
type_values[column_index + 1:])
continue
type_total_values.append(affected_value_string_formatted)
"""Get the super total affected"""
# total for affected (super total)
analysis_feature = analysis_layer.getFeatures().next()
field_index = analysis_layer.fieldNameIndex(
total_affected_field['field_name'])
total_all = format_number(
analysis_feature[field_index],
enable_rounding=is_rounded)
"""Generate and format the context"""
aggregation_area_default_header = resolve_from_dictionary(
extra_args, 'aggregation_area_default_header')
header_label = (
aggregation_summary.title() or aggregation_area_default_header)
table_header_format = resolve_from_dictionary(
extra_args, 'table_header_format')
# check unit
units = exposure_type['units']
if units:
unit = units[0]
abbreviation = unit['abbreviation']
if abbreviation:
unit_string = '({abbreviation})'.format(abbreviation=abbreviation)
else:
unit_string = ''
else:
unit_string = ''
table_header = table_header_format.format(
title=provenance['map_legend_title'],
unit=unit_string)
table_header = ' '.join(table_header.split())
section_header = resolve_from_dictionary(extra_args, 'header')
notes = resolve_from_dictionary(extra_args, 'notes')
total_header = resolve_from_dictionary(extra_args, 'total_header')
total_in_aggregation_header = resolve_from_dictionary(
extra_args, 'total_in_aggregation_header')
context['header'] = section_header
context['notes'] = notes
context['aggregation_result'] = {
'table_header': table_header,
'header_label': header_label,
'type_header_labels': type_header_labels,
'total_label': total_header,
'total_in_aggregation_area_label': total_in_aggregation_header,
'rows': rows,
'type_total_values': type_total_values,
'total_all': total_all,
}
return context
def aggregation_summary(aggregate_hazard, aggregation, callback=None):
"""Compute the summary from the aggregate hazard to the analysis layer.
Source layer :
| haz_id | haz_class | aggr_id | aggr_name | total_feature |
Target layer :
| aggr_id | aggr_name |
Output layer :
| aggr_id | aggr_name | count of affected features per exposure type
:param aggregate_hazard: The layer to aggregate vector layer.
:type aggregate_hazard: QgsVectorLayer
:param aggregation: The aggregation vector layer where to write statistics.
:type aggregation: QgsVectorLayer
:param callback: A function to all to indicate progress. The function
should accept params 'current' (int), 'maximum' (int) and 'step' (str).
Defaults to None.
:type callback: function
:return: The new aggregation layer with summary.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = summary_2_aggregation_steps['output_layer_name']
processing_step = summary_2_aggregation_steps['step_name']
source_fields = aggregate_hazard.keywords['inasafe_fields']
target_fields = aggregation.keywords['inasafe_fields']
target_compulsory_fields = [
aggregation_id_field,
aggregation_name_field,
]
check_inputs(target_compulsory_fields, target_fields)
# Missing exposure_count_field
source_compulsory_fields = [
aggregation_id_field,
aggregation_name_field,
hazard_id_field,
hazard_class_field,
affected_field,
]
check_inputs(source_compulsory_fields, source_fields)
pattern = exposure_count_field['key']
pattern = pattern.replace('%s', '')
unique_exposure = read_dynamic_inasafe_field(
source_fields, exposure_count_field)
absolute_values = create_absolute_values_structure(
aggregate_hazard, ['aggregation_id'])
flat_table = FlatTable('aggregation_id', 'exposure_class')
aggregation_index = source_fields[aggregation_id_field['key']]
# We want to loop over affected features only.
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
expression = '\"%s\" = \'%s\'' % (
affected_field['field_name'], tr('True'))
request.setFilterExpression(expression)
for area in aggregate_hazard.getFeatures(request):
for key, name_field in source_fields.iteritems():
if key.endswith(pattern):
aggregation_id = area[aggregation_index]
exposure_class = key.replace(pattern, '')
value = area[name_field]
flat_table.add_value(
value,
aggregation_id=aggregation_id,
exposure_class=exposure_class
)
# We summarize every absolute values.
for field, field_definition in absolute_values.iteritems():
value = area[field]
if not value or isinstance(value, QPyNullVariant):
value = 0
field_definition[0].add_value(
value,
aggregation_id=area[aggregation_index],
)
shift = aggregation.fields().count()
aggregation.startEditing()
add_fields(
aggregation,
absolute_values,
[total_affected_field],
unique_exposure,
affected_exposure_count_field)
aggregation_index = target_fields[aggregation_id_field['key']]
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
for area in aggregation.getFeatures(request):
aggregation_value = area[aggregation_index]
total = 0
for i, val in enumerate(unique_exposure):
sum = flat_table.get_value(
aggregation_id=aggregation_value,
exposure_class=val
)
total += sum
aggregation.changeAttributeValue(area.id(), shift + i, sum)
aggregation.changeAttributeValue(
area.id(), shift + len(unique_exposure), total)
for i, field in enumerate(absolute_values.itervalues()):
value = field[0].get_value(
aggregation_id=aggregation_value,
)
target_index = shift + len(unique_exposure) + 1 + i
aggregation.changeAttributeValue(
area.id(), target_index, value)
aggregation.commitChanges()
aggregation.keywords['title'] = layer_purpose_aggregation_summary['name']
if qgis_version() >= 21800:
aggregation.setName(aggregation.keywords['title'])
else:
aggregation.setLayerName(aggregation.keywords['title'])
aggregation.keywords['layer_purpose'] = (
layer_purpose_aggregation_summary['key'])
check_layer(aggregation)
return aggregation
def exposure_summary_table(
aggregate_hazard, exposure_summary=None, callback=None):
"""Compute the summary from the aggregate hazard to analysis.
Source layer :
| haz_id | haz_class | aggr_id | aggr_name | exposure_count |
Output layer :
| exp_type | count_hazard_class | total |
:param aggregate_hazard: The layer to aggregate vector layer.
:type aggregate_hazard: QgsVectorLayer
:param exposure_summary: The layer impact layer.
:type exposure_summary: QgsVectorLayer
:param callback: A function to all to indicate progress. The function
should accept params 'current' (int), 'maximum' (int) and 'step' (str).
Defaults to None.
:type callback: function
:return: The new tabular table, without geometry.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = summary_4_exposure_summary_table_steps[
'output_layer_name']
source_fields = aggregate_hazard.keywords['inasafe_fields']
source_compulsory_fields = [
aggregation_id_field,
aggregation_name_field,
hazard_id_field,
hazard_class_field,
affected_field,
total_field
]
check_inputs(source_compulsory_fields, source_fields)
absolute_values = create_absolute_values_structure(
aggregate_hazard, ['all'])
hazard_class = source_fields[hazard_class_field['key']]
hazard_class_index = aggregate_hazard.fields().lookupField(hazard_class)
unique_hazard = aggregate_hazard.uniqueValues(hazard_class_index)
unique_exposure = read_dynamic_inasafe_field(
source_fields, exposure_count_field)
flat_table = FlatTable('hazard_class', 'exposure_class')
request = QgsFeatureRequest()
request.setFlags(QgsFeatureRequest.NoGeometry)
for area in aggregate_hazard.getFeatures():
hazard_value = area[hazard_class_index]
for exposure in unique_exposure:
key_name = exposure_count_field['key'] % exposure
field_name = source_fields[key_name]
exposure_count = area[field_name]
if not exposure_count:
exposure_count = 0
flat_table.add_value(
exposure_count,
hazard_class=hazard_value,
exposure_class=exposure
)
# We summarize every absolute values.
for field, field_definition in list(absolute_values.items()):
value = area[field]
if not value:
value = 0
field_definition[0].add_value(
value,
all='all'
)
tabular = create_memory_layer(output_layer_name, QgsWkbTypes.NullGeometry)
tabular.startEditing()
field = create_field_from_definition(exposure_type_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][exposure_type_field['key']] = (
exposure_type_field['field_name'])
hazard_keywords = aggregate_hazard.keywords['hazard_keywords']
hazard = hazard_keywords['hazard']
classification = hazard_keywords['classification']
exposure_keywords = aggregate_hazard.keywords['exposure_keywords']
exposure = exposure_keywords['exposure']
hazard_affected = {}
for hazard_class in unique_hazard:
if (hazard_class == '' or hazard_class is None
or (hasattr(hazard_class, 'isNull')
and hazard_class.isNull())):
hazard_class = 'NULL'
field = create_field_from_definition(hazard_count_field, hazard_class)
tabular.addAttribute(field)
key = hazard_count_field['key'] % hazard_class
value = hazard_count_field['field_name'] % hazard_class
tabular.keywords['inasafe_fields'][key] = value
hazard_affected[hazard_class] = post_processor_affected_function(
exposure=exposure,
hazard=hazard,
classification=classification,
hazard_class=hazard_class
)
field = create_field_from_definition(total_affected_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_affected_field['key']] = (
total_affected_field['field_name'])
# essentially have the same value as NULL_hazard_count
# but with this, make sure that it exists in layer so it can be used for
# reporting, and can be referenced to fields.py to take the label.
field = create_field_from_definition(total_not_affected_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_not_affected_field['key']] = (
total_not_affected_field['field_name'])
field = create_field_from_definition(total_not_exposed_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_not_exposed_field['key']] = (
total_not_exposed_field['field_name'])
field = create_field_from_definition(total_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][total_field['key']] = (
total_field['field_name'])
summarization_dicts = {}
if exposure_summary:
summarization_dicts = summarize_result(exposure_summary)
sorted_keys = sorted(summarization_dicts.keys())
for key in sorted_keys:
summary_field = summary_rules[key]['summary_field']
field = create_field_from_definition(summary_field)
tabular.addAttribute(field)
tabular.keywords['inasafe_fields'][
summary_field['key']] = (
summary_field['field_name'])
# Only add absolute value if there is no summarization / no exposure
# classification
if not summarization_dicts:
# For each absolute values
for absolute_field in list(absolute_values.keys()):
field_definition = definition(absolute_values[absolute_field][1])
field = create_field_from_definition(field_definition)
tabular.addAttribute(field)
key = field_definition['key']
value = field_definition['field_name']
tabular.keywords['inasafe_fields'][key] = value
for exposure_type in unique_exposure:
feature = QgsFeature()
attributes = [exposure_type]
total_affected = 0
total_not_affected = 0
total_not_exposed = 0
total = 0
for hazard_class in unique_hazard:
if hazard_class == '' or hazard_class is None:
hazard_class = 'NULL'
value = flat_table.get_value(
hazard_class=hazard_class,
exposure_class=exposure_type
)
attributes.append(value)
if hazard_affected[hazard_class] == not_exposed_class['key']:
total_not_exposed += value
elif hazard_affected[hazard_class]:
total_affected += value
else:
total_not_affected += value
total += value
attributes.append(total_affected)
attributes.append(total_not_affected)
attributes.append(total_not_exposed)
attributes.append(total)
if summarization_dicts:
for key in sorted_keys:
attributes.append(summarization_dicts[key].get(
exposure_type, 0))
else:
for i, field in enumerate(absolute_values.values()):
value = field[0].get_value(
all='all'
)
attributes.append(value)
feature.setAttributes(attributes)
tabular.addFeature(feature)
# Sanity check ± 1 to the result. Disabled for now as it seems ± 1 is
# not enough. ET 13/02/17
# total_computed = (
# total_affected + total_not_affected + total_not_exposed)
# if not -1 < (total_computed - total) < 1:
# raise ComputationError
tabular.commitChanges()
tabular.keywords['title'] = layer_purpose_exposure_summary_table['name']
if qgis_version() >= 21800:
tabular.setName(tabular.keywords['title'])
else:
tabular.setLayerName(tabular.keywords['title'])
tabular.keywords['layer_purpose'] = layer_purpose_exposure_summary_table[
'key']
check_layer(tabular, has_geometry=False)
return tabular
