def _add_not_exposed(analysis_row, enable_rounding, exposure_unit,
coefficient):
"""Helper to add the `not exposed` item to the legend.
:param analysis_row: The analysis row as a list.
:type analysis_row: list
:param enable_rounding: If we need to do a rounding.
:type enable_rounding: bool
:param exposure_unit: The exposure unit.
:type exposure_unit: safe.definitions.units
:param coefficient: Divide the result after the rounding.
:type coefficient:float
:return: A tuple with the color and the formatted label.
:rtype: tuple
"""
# We add the not exposed class at the end.
not_exposed_field = (hazard_count_field['field_name'] %
not_exposed_class['key'])
try:
value = analysis_row[not_exposed_field]
except KeyError:
# The field might not exist if there is not feature not exposed.
value = 0
value = format_number(value, enable_rounding, coefficient)
label = _format_label(hazard_class=not_exposed_class['name'],
value=value,
exposure_unit=exposure_unit)
return not_exposed_class['color'], label
def number(self):
"""Number to be displayed for the element."""
value = format_number(
self._number,
enable_rounding=True,
is_population=True)
return value
def _add_not_exposed(
analysis_row,
enable_rounding,
is_population,
exposure_unit,
coefficient):
"""Helper to add the `not exposed` item to the legend.
:param analysis_row: The analysis row as a list.
:type analysis_row: list
:param enable_rounding: If we need to do a rounding.
:type enable_rounding: bool
:param is_population: Flag if the number is population. It needs to be
used with enable_rounding.
:type is_population: bool
:param exposure_unit: The exposure unit.
:type exposure_unit: safe.definitions.units
:param coefficient: Divide the result after the rounding.
:type coefficient:float
:return: A tuple with the color and the formatted label.
:rtype: tuple
"""
# We add the not exposed class at the end.
not_exposed_field = (
hazard_count_field['field_name'] % not_exposed_class['key'])
try:
value = analysis_row[not_exposed_field]
except KeyError:
# The field might not exist if there is not feature not exposed.
value = 0
value = format_number(value, enable_rounding, is_population, coefficient)
label = _format_label(
hazard_class=not_exposed_class['name'],
value=value,
exposure_unit=exposure_unit)
return not_exposed_class['color'], label
def minimum_needs_extractor(impact_report, component_metadata):
"""Extracting minimum needs of 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 = {}
extra_args = component_metadata.extra_args
analysis_layer = impact_report.analysis
analysis_keywords = analysis_layer.keywords['inasafe_fields']
debug_mode = impact_report.impact_function.debug_mode
is_rounding = not debug_mode
header = resolve_from_dictionary(extra_args, 'header')
context['header'] = header
# check if displaced is not zero
try:
displaced_field_name = analysis_keywords[displaced_field['key']]
total_displaced = value_from_field_name(
displaced_field_name, analysis_layer)
if total_displaced == 0:
zero_displaced_message = resolve_from_dictionary(
extra_args, 'zero_displaced_message')
context['zero_displaced'] = {
'status': True,
'message': zero_displaced_message
}
return context
except KeyError:
# in case no displaced field
pass
# minimum needs calculation only affect population type exposure
# check if analysis keyword have minimum_needs keywords
have_minimum_needs_field = False
for field_key in analysis_keywords:
if field_key.startswith(minimum_needs_namespace):
have_minimum_needs_field = True
break
if not have_minimum_needs_field:
return context
frequencies = {}
# map each needs to its frequency groups
for field in (minimum_needs_fields + additional_minimum_needs):
need_parameter = field.get('need_parameter')
if isinstance(need_parameter, ResourceParameter):
frequency = need_parameter.frequency
else:
frequency = field.get('frequency')
if frequency:
if frequency not in frequencies:
frequencies[frequency] = [field]
else:
frequencies[frequency].append(field)
needs = []
analysis_feature = analysis_layer.getFeatures().next()
header_frequency_format = resolve_from_dictionary(
extra_args, 'header_frequency_format')
total_header = resolve_from_dictionary(extra_args, 'total_header')
need_header_format = resolve_from_dictionary(
extra_args, 'need_header_format')
# group the needs by frequency
for key, frequency in frequencies.iteritems():
group = {
'header': header_frequency_format.format(frequency=tr(key)),
'total_header': total_header,
'needs': []
}
for field in frequency:
# check value exists in the field
field_idx = analysis_layer.fieldNameIndex(field['field_name'])
if field_idx == -1:
# skip if field doesn't exists
continue
value = format_number(
analysis_feature[field_idx],
enable_rounding=is_rounding,
is_population=True)
if field.get('need_parameter'):
need_parameter = field['need_parameter']
""":type: ResourceParameter"""
name = tr(need_parameter.name)
unit_abbreviation = need_parameter.unit.abbreviation
else:
if field.get('header_name'):
name = field.get('header_name')
else:
name = field.get('name')
need_unit = field.get('unit')
if need_unit:
unit_abbreviation = need_unit.get('abbreviation')
if unit_abbreviation:
header = need_header_format.format(
name=name,
unit_abbreviation=unit_abbreviation)
else:
header = name
item = {
'header': header,
'value': value
}
group['needs'].append(item)
needs.append(group)
context['needs'] = needs
return context
def mmi_detail_extractor(impact_report, component_metadata):
"""Extracting MMI-related analysis result.
This extractor should only be used for EQ Raster with Population.
: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 = {}
analysis_layer = impact_report.analysis
analysis_layer_keywords = analysis_layer.keywords
extra_args = component_metadata.extra_args
use_rounding = impact_report.impact_function.use_rounding
provenance = impact_report.impact_function.provenance
hazard_keywords = provenance['hazard_keywords']
exposure_keywords = provenance['exposure_keywords']
# check if this is EQ raster with population
hazard_type = definition(hazard_keywords['hazard'])
if not hazard_type == hazard_earthquake:
return context
hazard_geometry = hazard_keywords[layer_geometry['key']]
if not hazard_geometry == layer_geometry_raster['key']:
return context
exposure_type = definition(exposure_keywords['exposure'])
if not exposure_type == exposure_population:
return context
header = resolve_from_dictionary(extra_args, 'header')
context['header'] = header
reported_fields = resolve_from_dictionary(extra_args, 'reported_fields')
"""Generate headers."""
table_header = [resolve_from_dictionary(extra_args, 'mmi_header')
] + [v['header'] for v in reported_fields]
"""Extract MMI-related data"""
# mmi is ranged from 1 to 10, which means: [1, 11)
mmi_range = list(range(1, 11))
rows = []
roman_numeral = [
'I', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX', 'X'
]
for i in mmi_range:
columns = [roman_numeral[i - 1]]
for value in reported_fields:
field = value['field']
try:
key_name = field['key'] % (i, )
field_name = analysis_layer_keywords[key_name]
# check field exists
count = value_from_field_name(field_name, analysis_layer)
if not count:
count = 0
except KeyError:
count = 0
count = format_number(count,
use_rounding=use_rounding,
is_population=True)
columns.append(count)
rows.append(columns)
"""Extract total."""
total_footer = [resolve_from_dictionary(extra_args, 'total_header')]
total_fields = resolve_from_dictionary(extra_args, 'total_fields')
for field in total_fields:
try:
field_name = analysis_layer_keywords[field['key']]
total = value_from_field_name(field_name, analysis_layer)
if not total:
total = 0
except KeyError:
total = 0
total = format_number(total,
use_rounding=use_rounding,
is_population=True)
total_footer.append(total)
context['component_key'] = component_metadata.key
context['mmi'] = {
'header': table_header,
'rows': rows,
'footer': total_footer
}
return context
def general_report_extractor(impact_report, component_metadata):
"""Extracting general analysis result 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 = {}
extra_args = component_metadata.extra_args
# figure out analysis report type
hazard_layer = impact_report.hazard
exposure_layer = impact_report.exposure
analysis_layer = impact_report.analysis
provenance = impact_report.impact_function.provenance
debug_mode = impact_report.impact_function.debug_mode
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
# find hazard class
summary = []
analysis_feature = analysis_layer.getFeatures().next()
analysis_inasafe_fields = analysis_layer.keywords['inasafe_fields']
exposure_unit = exposure_type['units'][0]
hazard_header = resolve_from_dictionary(extra_args, 'hazard_header')
if exposure_unit['abbreviation']:
value_header = u'{measure} ({abbreviation})'.format(**exposure_unit)
else:
value_header = u'{name}'.format(**exposure_unit)
# in case there is a classification
if 'classification' in hazard_layer.keywords:
# retrieve hazard classification from hazard layer
hazard_classification = layer_hazard_classification(hazard_layer)
# classified hazard must have hazard count in analysis layer
hazard_stats = []
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'], )
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = analysis_inasafe_fields[field_key_name]
field_index = analysis_layer.fieldNameIndex(field_name)
# Hazard label taken from translated hazard count field
# label, string-formatted with translated hazard class label
hazard_label = hazard_class['name']
hazard_value = format_number(analysis_feature[field_index],
enable_rounding=is_rounded,
is_population=is_population)
stats = {
'key': hazard_class['key'],
'name': hazard_label,
'value': hazard_value
}
except KeyError:
# in case the field was not found
hazard_label = hazard_class['name']
stats = {
'key': hazard_class['key'],
'name': hazard_label,
'value': 0,
}
hazard_stats.append(stats)
# find total field
try:
field_name = analysis_inasafe_fields[total_field['key']]
total = value_from_field_name(field_name, analysis_layer)
total = format_number(total,
enable_rounding=is_rounded,
is_population=is_population)
stats = {
'key': total_field['key'],
'name': total_field['name'],
'as_header': True,
'value': total
}
hazard_stats.append(stats)
except KeyError:
pass
summary.append({
'header_label': hazard_header,
'value_label': value_header,
'rows': hazard_stats
})
# retrieve affected column
report_stats = []
reported_fields = resolve_from_dictionary(extra_args, 'reported_fields')
for item in reported_fields:
header = item['header']
field = item['field']
if field['key'] in analysis_inasafe_fields:
field_index = analysis_layer.fieldNameIndex(field['field_name'])
if field == fatalities_field:
# For fatalities field, we show a range of number
# instead
row_value = fatalities_range(analysis_feature[field_index])
else:
row_value = format_number(analysis_feature[field_index],
enable_rounding=is_rounded,
is_population=is_population)
row_stats = {
'key': field['key'],
'name': header,
'value': row_value
}
report_stats.append(row_stats)
# Give report section
exposure_type = layer_definition_type(exposure_layer)
header_label = exposure_type['name']
summary.append({
'header_label': header_label,
# This should depend on exposure unit
# TODO: Change this so it can take the unit dynamically
'value_label': value_header,
'rows': report_stats
})
header = resolve_from_dictionary(extra_args, ['header'])
table_header_format = resolve_from_dictionary(extra_args,
'table_header_format')
table_header = table_header_format.format(
title=provenance['map_legend_title'],
unit=hazard_classification['classification_unit'])
# Section notes
note_format = resolve_from_dictionary(extra_args,
['concept_notes', 'note_format'])
if is_population:
concepts = resolve_from_dictionary(
extra_args, ['concept_notes', 'population_concepts'])
else:
concepts = resolve_from_dictionary(
extra_args, ['concept_notes', 'general_concepts'])
notes = []
for concept in concepts:
note = note_format.format(**concept)
notes.append(note)
context['header'] = header
context['summary'] = summary
context['table_header'] = table_header
context['notes'] = notes
return context
def generate_classified_legend(
analysis,
exposure,
hazard,
debug_mode):
"""Generate an ordered python structure with the classified symbology.
:param analysis: The analysis layer.
:type analysis: QgsVectorLayer
:param exposure: The exposure layer.
:type exposure: QgsVectorLayer
:param hazard: The hazard layer.
:type hazard: QgsVectorLayer
:param debug_mode: Boolean if run in debug mode.
:type debug_mode: bool
:return: The ordered dictionary to use to build the classified style.
:rtype: OrderedDict
"""
# We need to read the analysis layer to get the number of features.
analysis_row = analysis.getFeatures().next()
# Let's style the hazard class in each layers.
hazard_classification = hazard.keywords['classification']
hazard_classification = definition(hazard_classification)
# Let's check if there is some thresholds:
thresholds = hazard.keywords.get('thresholds')
if thresholds:
hazard_unit = hazard.keywords.get('continuous_hazard_unit')
hazard_unit = definition(hazard_unit)['abbreviation']
else:
hazard_unit = None
exposure = exposure.keywords['exposure']
exposure_definitions = definition(exposure)
exposure_units = exposure_definitions['units']
exposure_unit = exposure_units[0]
coefficient = 1
# We check if can use a greater unit, such as kilometre for instance.
if len(exposure_units) > 1:
# We use only two units for now.
delta = coefficient_between_units(
exposure_units[1], exposure_units[0])
all_values_are_greater = True
# We check if all values are greater than the coefficient
for i, hazard_class in enumerate(hazard_classification['classes']):
field_name = hazard_count_field['field_name'] % hazard_class['key']
try:
value = analysis_row[field_name]
except KeyError:
value = 0
if 0 < value < delta:
# 0 is fine, we can still keep the second unit.
all_values_are_greater = False
if all_values_are_greater:
# If yes, we can use this unit.
exposure_unit = exposure_units[1]
coefficient = delta
classes = OrderedDict()
# In debug mode we don't round number.
enable_rounding = not debug_mode
for i, hazard_class in enumerate(hazard_classification['classes']):
# Get the hazard class name.
field_name = hazard_count_field['field_name'] % hazard_class['key']
# Get the number of affected feature by this hazard class.
try:
value = analysis_row[field_name]
except KeyError:
# The field might not exist if no feature impacted in this hazard
# zone.
value = 0
value = format_number(
value,
enable_rounding,
exposure_definitions['use_population_rounding'],
coefficient)
minimum = None
maximum = None
# Check if we need to add thresholds.
if thresholds:
if i == 0:
minimum = thresholds[hazard_class['key']][0]
elif i == len(hazard_classification['classes']) - 1:
maximum = thresholds[hazard_class['key']][1]
else:
minimum = thresholds[hazard_class['key']][0]
maximum = thresholds[hazard_class['key']][1]
label = _format_label(
hazard_class=hazard_class['name'],
value=value,
exposure_unit=exposure_unit['abbreviation'],
minimum=minimum,
maximum=maximum,
hazard_unit=hazard_unit)
classes[hazard_class['key']] = (hazard_class['color'], label)
if exposure_definitions['display_not_exposed'] or debug_mode:
classes[not_exposed_class['key']] = _add_not_exposed(
analysis_row,
enable_rounding,
exposure_definitions['use_population_rounding'],
exposure_unit['abbreviation'],
coefficient)
return classes
def analysis_detail_extractor(impact_report, component_metadata):
"""Extracting analysis result 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 = {}
extra_args = component_metadata.extra_args
analysis_layer = impact_report.analysis
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
analysis_feature = analysis_layer.getFeatures().next()
exposure_summary_table = impact_report.exposure_summary_table
if exposure_summary_table:
exposure_summary_table_fields = exposure_summary_table.keywords[
'inasafe_fields']
provenance = impact_report.impact_function.provenance
use_rounding = impact_report.impact_function.use_rounding
hazard_keywords = provenance['hazard_keywords']
exposure_keywords = provenance['exposure_keywords']
"""Initializations."""
# Get hazard classification
hazard_classification = definition(
active_classification(hazard_keywords, exposure_keywords['exposure']))
# Get exposure type definition
exposure_type = definition(exposure_keywords['exposure'])
# Only round the number when it is population exposure and we use rounding
is_population = exposure_type is exposure_population
# action for places with poopulation exposure
is_place_with_population = False
if exposure_type is exposure_place:
exposure_fields = exposure_keywords['inasafe_fields']
if exposure_fields.get(population_count_field['key']):
is_place_with_population = True
# Analysis detail 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
# 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
"""Create detail header."""
headers = []
# breakdown header
breakdown_header_template = ''
if breakdown_field == exposure_type_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_type_format')
elif breakdown_field == exposure_class_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_class_format')
# check if there is header type associations
type_header_mapping = resolve_from_dictionary(
extra_args, 'exposure_type_header_mapping')
if exposure_type['key'] in type_header_mapping:
exposure_header = type_header_mapping[exposure_type['key']]
else:
exposure_header = exposure_type['name']
headers.append(breakdown_header_template.format(exposure=exposure_header))
# this is mapping for customizing double header for
# affected/not affected hazard classes
hazard_class_header_mapping = resolve_from_dictionary(
extra_args, 'hazard_class_header_mapping')
# hazard header
# TODO: we need to get affected and not_affected key from
# definitions concept
header_hazard_group = {
'affected': {
'hazards': [],
'total': []
},
'not_affected': {
'hazards': [],
'total': []
}
}
for key, group in header_hazard_group.iteritems():
if key in hazard_class_header_mapping:
header_hazard_group[key].update(hazard_class_header_mapping[key])
affected_header_index = None
for index, hazard_class in enumerate(hazard_classification['classes']):
# the tuple format would be:
# (class name, is it affected, header background color
hazard_class_name = hazard_class['name']
affected = hazard_class.get('affected')
if not affected and not affected_header_index:
affected_header_index = index + 1
affected_status = 'not_affected'
elif affected:
affected_status = 'affected'
else:
affected_status = 'not_affected'
header_hazard_group[affected_status]['hazards'].append(
hazard_class_name)
headers.append(hazard_class_name)
if affected_header_index:
not_affected_header_index = len(hazard_classification['classes']) + 2
else:
affected_header_index = len(hazard_classification['classes']) + 1
not_affected_header_index = affected_header_index + 2
report_fields = []
headers.insert(affected_header_index, total_affected_field['name'])
header_hazard_group['affected']['total'].append(
total_affected_field['name'])
report_fields.append(total_affected_field)
headers.insert(not_affected_header_index, total_not_affected_field['name'])
header_hazard_group['not_affected']['total'].append(
total_not_affected_field['name'])
report_fields.append(total_not_affected_field)
# affected, not affected, population (if applicable), not exposed,
# total header
report_fields += [total_not_exposed_field, total_field]
place_pop_name = resolve_from_dictionary(
extra_args, ['place_with_population', 'header'])
if is_place_with_population:
# we want to change header name for population
duplicated_population_count_field = deepcopy(
exposed_population_count_field)
duplicated_population_count_field['name'] = place_pop_name
report_fields.append(duplicated_population_count_field)
report_fields_index = -2 + -(int(is_place_with_population))
for report_field in report_fields[report_fields_index:]:
headers.append(report_field['name'])
"""Create detail rows."""
details = []
for feat in exposure_summary_table.getFeatures():
row = []
# Get breakdown name
exposure_summary_table_field_name = breakdown_field['field_name']
field_index = exposure_summary_table.fieldNameIndex(
exposure_summary_table_field_name)
class_key = feat[field_index]
row.append(class_key)
# Get hazard count
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'], )
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = exposure_summary_table_fields[field_key_name]
field_index = exposure_summary_table.fieldNameIndex(field_name)
# exposure summary table is in csv format, so the field
# returned is always in text format
count_value = int(float(feat[field_index]))
count_value = format_number(count_value,
use_rounding=use_rounding,
is_population=is_population)
row.append({'value': count_value, 'header_group': group_key})
except KeyError:
# in case the field was not found
# assume value 0
row.append({'value': 0, 'header_group': group_key})
skip_row = False
for field in report_fields:
group_key = None
for key, group in header_hazard_group.iteritems():
if field['name'] in group['total']:
group_key = key
break
field_index = exposure_summary_table.fieldNameIndex(
field['field_name'])
total_count = int(float(feat[field_index]))
total_count = format_number(total_count,
use_rounding=use_rounding,
is_population=is_population)
# we comment below code because now we want to show all rows,
# we can uncomment if we want to remove the rows with zero total
# if total_count == '0' and field == total_affected_field:
# skip_row = True
# break
if group_key:
if field == total_affected_field:
row.insert(affected_header_index, {
'value': total_count,
'header_group': group_key
})
elif field == total_not_affected_field:
row.insert(not_affected_header_index, {
'value': total_count,
'header_group': group_key
})
else:
row.append({
'value': total_count,
'header_group': group_key
})
else:
row.append(total_count)
if skip_row:
continue
details.append(row)
# retrieve classes definitions
exposure_classes_lists = retrieve_exposure_classes_lists(exposure_keywords)
# sort detail rows based on class order
# create function to sort
def sort_classes(_row):
"""Sort method to retrieve exposure class key index."""
# class key is first column
_class_key = _row[0]
# find index in class list
for i, _exposure_class in enumerate(exposure_classes_lists):
if _class_key == _exposure_class['key']:
index = i
break
else:
index = -1
return index
# sort
details = sorted(details, key=sort_classes)
# retrieve breakdown name from classes list
for row in details:
class_key = row[0]
for exposure_class in exposure_classes_lists:
if class_key == exposure_class['key']:
breakdown_name = exposure_class['name']
break
else:
# attempt for dynamic translations
breakdown_name = tr(class_key.capitalize())
# replace class_key with the class name
row[0] = breakdown_name
"""create total footers."""
# create total header
footers = [total_field['name']]
# total for hazard
save_total_affected_field = False
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (hazard_class['key'], )
if not hazard_class.get('affected'):
save_total_affected_field = True
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = analysis_layer_fields[field_key_name]
field_index = analysis_layer.fieldNameIndex(field_name)
count_value = format_number(analysis_feature[field_index],
use_rounding=use_rounding,
is_population=is_population)
except KeyError:
# in case the field was not found
# assume value 0
count_value = '0'
if count_value == '0':
# if total affected for hazard class is zero, delete entire
# column
column_index = len(footers) + int(save_total_affected_field)
# delete header column
headers = headers[:column_index] + headers[column_index + 1:]
for row_idx in range(0, len(details)):
row = details[row_idx]
row = row[:column_index] + row[column_index + 1:]
details[row_idx] = row
# reduce total affected and not affected column index by 1
# since we are removing a column
if group_key == affected_field['field_name']:
affected_header_index -= 1
else:
not_affected_header_index -= 1
continue
footers.append({'value': count_value, 'header_group': group_key})
# for footers
for field in report_fields:
total_count = value_from_field_name(field['field_name'],
analysis_layer)
if not total_count and field['name'] == place_pop_name:
field = population_count_field
field['name'] = place_pop_name
total_count = value_from_field_name(field['field_name'],
analysis_layer)
group_key = None
for key, group in header_hazard_group.iteritems():
if field['name'] in group['total']:
group_key = key
break
total_count = format_number(total_count,
use_rounding=use_rounding,
is_population=is_population)
if group_key:
if field == total_affected_field:
footers.insert(affected_header_index, {
'value': total_count,
'header_group': group_key
})
elif field == total_not_affected_field:
footers.insert(not_affected_header_index, {
'value': total_count,
'header_group': group_key
})
else:
footers.append({
'value': total_count,
'header_group': group_key
})
else:
footers.append(total_count)
header = resolve_from_dictionary(extra_args, 'header')
notes = resolve_from_dictionary(extra_args, 'notes')
context['header'] = header
context['group_border_color'] = resolve_from_dictionary(
extra_args, 'group_border_color')
context['notes'] = notes
breakdown_header_index = 0
# we want to include total affected and not affected as a group
# to its class so len(report_fields) - 2
total_header_index = len(headers) - (len(report_fields) - 2)
context['detail_header'] = {
'header_hazard_group': header_hazard_group,
'breakdown_header_index': breakdown_header_index,
'total_header_index': total_header_index
}
# modify headers to include double header
affected_headers = []
last_group = 0
for i in range(breakdown_header_index, total_header_index):
hazard_class_name = headers[i]
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class_name in group['hazards'] or (hazard_class_name
in group['total']):
group_key = key
break
if group_key and group_key not in affected_headers:
affected_headers.append(group_key)
headers[i] = {
'name': hazard_class_name,
'start': True,
'header_group': group_key,
'colspan': 1
}
last_group = i
header_hazard_group[group_key]['start_index'] = i
elif group_key:
colspan = headers[last_group]['colspan']
headers[last_group]['colspan'] = colspan + 1
headers[i] = {
'name': hazard_class_name,
'start': False,
'header_group': group_key
}
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,
exposure=exposure_header)
table_header = ' '.join(table_header.split())
context['detail_table'] = {
'table_header': table_header,
'headers': headers,
'details': details,
'footers': footers,
}
context['extra_table'] = {}
# extra table for specific exposure if exist
extra_fields = resolve_from_dictionary(extra_args, 'exposure_extra_fields')
if exposure_type['key'] in extra_fields.keys():
# create header for the extra table
extra_table_header_format = resolve_from_dictionary(
extra_args, 'extra_table_header_format')
extra_table_header = extra_table_header_format.format(
exposure=exposure_header)
# headers
headers = []
headers.append(
breakdown_header_template.format(exposure=exposure_header))
current_unit = None
currency_unit = setting('currency', expected_type=str)
for field in extra_fields[exposure_type['key']]:
field_index = exposure_summary_table.fieldNameIndex(
field['field_name'])
if field_index < 0:
LOGGER.debug('Field name not found: %s, field index: %s' %
(field['field_name'], field_index))
continue
units = field.get('units')
if units:
for unit in units:
if currency_unit == unit['key']:
current_unit = unit['name']
break
if not current_unit:
current_unit = units[0]['name']
header_format = '{header} ({unit})'
headers.append(
header_format.format(header=field['header_name'],
unit=current_unit))
# rows
details = []
for feat in exposure_summary_table.getFeatures():
row = []
# Get breakdown name
exposure_summary_table_field_name = breakdown_field['field_name']
field_index = exposure_summary_table.fieldNameIndex(
exposure_summary_table_field_name)
class_key = feat[field_index]
row.append(class_key)
for field in extra_fields[exposure_type['key']]:
field_index = exposure_summary_table.fieldNameIndex(
field['field_name'])
# noinspection PyBroadException
try:
total_count = int(float(feat[field_index]))
except:
LOGGER.debug('Field name not found: %s, field index: %s' %
(field['field_name'], field_index))
continue
total_count = format_number(total_count,
use_rounding=use_rounding,
is_population=is_population)
row.append(total_count)
details.append(row)
details = sorted(details, key=sort_classes)
context['extra_table'] = {
'table_header': extra_table_header,
'headers': headers,
'details': details,
}
return context
def population_infographic_extractor(impact_report, component_metadata):
"""Extracting aggregate result of demographic.
: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 = {}
extra_args = component_metadata.extra_args
"""Initializations"""
hazard_layer = impact_report.hazard
analysis_layer = impact_report.analysis
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
icons = component_metadata.extra_args.get('icons')
# this report sections only applies if it is a population report.
population_fields = [
population_count_field['key'],
exposure_count_field['key'] % (exposure_population['key'], ),
] + [f['key'] for f in minimum_needs_fields]
for item in population_fields:
if item in analysis_layer_fields:
break
else:
return context
# We try to get total affected field
# if it didn't exists, check other fields to show
total_affected_fields = [
total_affected_field['key'],
# We might want to check other fields, but turn it off until further
# discussion
population_count_field['key'],
exposure_count_field['key'] % (exposure_population['key'], ),
]
for item in total_affected_fields:
if item in analysis_layer_fields:
total_affected = value_from_field_name(analysis_layer_fields[item],
analysis_layer)
total_affected_field_used = item
break
else:
return context
if displaced_field['key'] in analysis_layer_fields:
total_displaced = value_from_field_name(
analysis_layer_fields[displaced_field['key']], analysis_layer)
else:
return context
sections = OrderedDict()
"""People Section"""
# Take default value from definitions
people_header = resolve_from_dictionary(extra_args,
['sections', 'people', 'header'])
people_items = resolve_from_dictionary(extra_args,
['sections', 'people', 'items'])
# create context for affected infographic
sub_header = resolve_from_dictionary(people_items[0], 'sub_header')
# retrieve relevant header based on the fields we showed.
sub_header = sub_header[total_affected_field_used]
affected_infographic = PeopleInfographicElement(
header=sub_header,
icon=icons.get(total_affected_field['key']),
number=total_affected)
# create context for displaced infographic
sub_header = resolve_from_dictionary(people_items[1], 'sub_header')
sub_header_note_format = resolve_from_dictionary(people_items[1],
'sub_header_note_format')
rate_description_format = resolve_from_dictionary(
people_items[1], 'rate_description_format')
rate_description = []
hazard_classification = layer_hazard_classification(hazard_layer)
for hazard_class in hazard_classification['classes']:
displacement_rate = hazard_class.get('displacement_rate', 0)
if displacement_rate:
rate_description.append(
rate_description_format.format(**hazard_class))
rate_description_string = ', '.join(rate_description)
sub_header_note = sub_header_note_format.format(
rate_description=rate_description_string)
displaced_infographic = PeopleInfographicElement(
header=sub_header,
header_note=sub_header_note,
icon=icons.get(displaced_field['key']),
number=total_displaced)
sections['people'] = {
'header': people_header,
'items': [affected_infographic, displaced_infographic]
}
"""Vulnerability Section"""
# Take default value from definitions
vulnerability_items = resolve_from_dictionary(
extra_args, ['sections', 'vulnerability', 'items'])
vulnerability_section_header = resolve_from_dictionary(
extra_args, ['sections', 'vulnerability', 'header'])
vulnerability_section_sub_header_format = resolve_from_dictionary(
extra_args, ['sections', 'vulnerability', 'sub_header_format'])
infographic_elements = []
for group in vulnerability_items:
fields = group['fields']
group_header = group['sub_group_header']
bootstrap_column = group['bootstrap_column']
element_column = group['element_column']
headers = group['headers']
elements = []
for field, header in zip(fields, headers):
field_key = field['key']
try:
field_name = analysis_layer_fields[field_key]
value = value_from_field_name(field_name, analysis_layer)
except KeyError:
# It means the field is not there
continue
if value:
value_percentage = value * 100.0 / total_displaced
else:
value_percentage = 0
infographic_element = PeopleVulnerabilityInfographicElement(
header=header,
icon=icons.get(field_key),
number=value,
percentage=value_percentage)
elements.append(infographic_element)
if elements:
infographic_elements.append({
'group_header': group_header,
'bootstrap_column': bootstrap_column,
'element_column': element_column,
'items': elements
})
total_displaced_rounded = format_number(total_displaced,
enable_rounding=True,
is_population=True)
sections['vulnerability'] = {
'header':
vulnerability_section_header,
'small_header':
vulnerability_section_sub_header_format.format(
number_displaced=total_displaced_rounded),
'items':
infographic_elements
}
"""Minimum Needs Section"""
minimum_needs_header = resolve_from_dictionary(
extra_args, ['sections', 'minimum_needs', 'header'])
empty_unit_string = resolve_from_dictionary(
extra_args, ['sections', 'minimum_needs', 'empty_unit_string'])
items = []
for item in minimum_needs_fields:
need = item['need_parameter']
if isinstance(need, ResourceParameter):
needs_count = value_from_field_name(item['field_name'],
analysis_layer)
if need.unit.abbreviation:
unit_string = '{unit}/{frequency}'.format(
unit=need.unit.abbreviation, frequency=need.frequency)
else:
unit_string = empty_unit_string
item = PeopleMinimumNeedsInfographicElement(header=item['name'],
icon=icons.get(
item['key']),
number=needs_count,
unit=unit_string)
items.append(item)
# TODO: get from impact function provenance later
needs_profile = NeedsProfile()
sections['minimum_needs'] = {
'header': minimum_needs_header,
'small_header': needs_profile.provenance,
'items': items,
}
"""Population Charts"""
population_donut_path = impact_report.component_absolute_output_path(
'population-chart-png')
css_label_classes = []
try:
population_chart_context = impact_report.metadata.component_by_key(
'population-chart').context['context']
"""
:type: safe.report.extractors.infographic_elements.svg_charts.
DonutChartContext
"""
for pie_slice in population_chart_context.slices:
label = pie_slice['label']
if not label:
continue
css_class = label.replace(' ', '').lower()
css_label_classes.append(css_class)
except KeyError:
population_chart_context = None
sections['population_chart'] = {
'img_path': resource_url(population_donut_path),
'context': population_chart_context,
'css_label_classes': css_label_classes
}
context['brand_logo'] = resource_url(
resources_path('img', 'logos', 'inasafe-logo-white.png'))
context['sections'] = sections
context['title'] = analysis_layer.title() or value_from_field_name(
analysis_name_field['field_name'], analysis_layer)
return context
def analysis_detail_extractor(impact_report, component_metadata):
"""Extracting analysis result 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 = {}
extra_args = component_metadata.extra_args
analysis_layer = impact_report.analysis
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
analysis_feature = next(analysis_layer.getFeatures())
exposure_summary_table = impact_report.exposure_summary_table
if exposure_summary_table:
exposure_summary_table_fields = exposure_summary_table.keywords[
'inasafe_fields']
provenance = impact_report.impact_function.provenance
use_rounding = impact_report.impact_function.use_rounding
hazard_keywords = provenance['hazard_keywords']
exposure_keywords = provenance['exposure_keywords']
"""Initializations."""
# Get hazard classification
hazard_classification = definition(
active_classification(hazard_keywords, exposure_keywords['exposure']))
# Get exposure type definition
exposure_type = definition(exposure_keywords['exposure'])
# Only round the number when it is population exposure and we use rounding
is_population = exposure_type is exposure_population
# action for places with poopulation exposure
is_place_with_population = False
if exposure_type is exposure_place:
exposure_fields = exposure_keywords['inasafe_fields']
if exposure_fields.get(population_count_field['key']):
is_place_with_population = True
# Analysis detail 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
# 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
"""Create detail header."""
headers = []
# breakdown header
breakdown_header_template = ''
if breakdown_field == exposure_type_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_type_format')
elif breakdown_field == exposure_class_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_class_format')
# check if there is header type associations
type_header_mapping = resolve_from_dictionary(
extra_args, 'exposure_type_header_mapping')
if exposure_type['key'] in type_header_mapping:
exposure_header = type_header_mapping[exposure_type['key']]
else:
exposure_header = exposure_type['name']
headers.append(
breakdown_header_template.format(exposure=exposure_header))
# this is mapping for customizing double header for
# affected/not affected hazard classes
hazard_class_header_mapping = resolve_from_dictionary(
extra_args,
'hazard_class_header_mapping')
# hazard header
# TODO: we need to get affected and not_affected key from
# definitions concept
header_hazard_group = {
'affected': {
'hazards': [],
'total': []
},
'not_affected': {
'hazards': [],
'total': []
}
}
for key, group in list(header_hazard_group.items()):
if key in hazard_class_header_mapping:
header_hazard_group[key].update(hazard_class_header_mapping[key])
affected_header_index = None
for index, hazard_class in enumerate(hazard_classification['classes']):
# the tuple format would be:
# (class name, is it affected, header background color
hazard_class_name = hazard_class['name']
affected = hazard_class.get('affected')
if not affected and not affected_header_index:
affected_header_index = index + 1
affected_status = 'not_affected'
elif affected:
affected_status = 'affected'
else:
affected_status = 'not_affected'
header_hazard_group[affected_status]['hazards'].append(
hazard_class_name)
headers.append(hazard_class_name)
if affected_header_index:
not_affected_header_index = len(hazard_classification['classes']) + 2
else:
affected_header_index = len(hazard_classification['classes']) + 1
not_affected_header_index = affected_header_index + 2
report_fields = []
headers.insert(affected_header_index, total_affected_field['name'])
header_hazard_group['affected']['total'].append(
total_affected_field['name'])
report_fields.append(total_affected_field)
headers.insert(not_affected_header_index, total_not_affected_field['name'])
header_hazard_group['not_affected']['total'].append(
total_not_affected_field['name'])
report_fields.append(total_not_affected_field)
# affected, not affected, population (if applicable), not exposed,
# total header
report_fields += [total_not_exposed_field, total_field]
place_pop_name = resolve_from_dictionary(
extra_args, ['place_with_population', 'header'])
if is_place_with_population:
# we want to change header name for population
duplicated_population_count_field = deepcopy(
exposed_population_count_field)
duplicated_population_count_field['name'] = place_pop_name
report_fields.append(duplicated_population_count_field)
report_fields_index = -2 + -(int(is_place_with_population))
for report_field in report_fields[report_fields_index:]:
headers.append(report_field['name'])
"""Create detail rows."""
details = []
for feat in exposure_summary_table.getFeatures():
row = []
# Get breakdown name
exposure_summary_table_field_name = breakdown_field['field_name']
field_index = exposure_summary_table.fields().lookupField(
exposure_summary_table_field_name)
class_key = feat[field_index]
row.append(class_key)
# Get hazard count
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'], )
group_key = None
for key, group in list(header_hazard_group.items()):
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = exposure_summary_table_fields[field_key_name]
field_index = exposure_summary_table.fields() \
.lookupField(field_name)
# exposure summary table is in csv format, so the field
# returned is always in text format
count_value = int(float(feat[field_index]))
count_value = format_number(
count_value,
use_rounding=use_rounding,
is_population=is_population)
row.append({
'value': count_value,
'header_group': group_key
})
except KeyError:
# in case the field was not found
# assume value 0
row.append({
'value': 0,
'header_group': group_key
})
skip_row = False
for field in report_fields:
group_key = None
for key, group in list(header_hazard_group.items()):
if field['name'] in group['total']:
group_key = key
break
field_index = exposure_summary_table.fields().lookupField(
field['field_name'])
total_count = int(float(feat[field_index]))
total_count = format_number(
total_count,
use_rounding=use_rounding,
is_population=is_population)
# we comment below code because now we want to show all rows,
# we can uncomment if we want to remove the rows with zero total
# if total_count == '0' and field == total_affected_field:
# skip_row = True
# break
if group_key:
if field == total_affected_field:
row.insert(
affected_header_index,
{
'value': total_count,
'header_group': group_key
})
elif field == total_not_affected_field:
row.insert(
not_affected_header_index,
{
'value': total_count,
'header_group': group_key
})
else:
row.append({
'value': total_count,
'header_group': group_key
})
else:
row.append(total_count)
if skip_row:
continue
details.append(row)
# retrieve classes definitions
exposure_classes_lists = retrieve_exposure_classes_lists(exposure_keywords)
# sort detail rows based on class order
# create function to sort
def sort_classes(_row):
"""Sort method to retrieve exposure class key index."""
# class key is first column
_class_key = _row[0]
# find index in class list
for i, _exposure_class in enumerate(exposure_classes_lists):
if _class_key == _exposure_class['key']:
index = i
break
else:
index = -1
return index
# sort
details = sorted(details, key=sort_classes)
# retrieve breakdown name from classes list
for row in details:
class_key = row[0]
for exposure_class in exposure_classes_lists:
if class_key == exposure_class['key']:
breakdown_name = exposure_class['name']
break
else:
# attempt for dynamic translations
breakdown_name = tr(class_key.capitalize())
# replace class_key with the class name
row[0] = breakdown_name
"""create total footers."""
# create total header
footers = [total_field['name']]
# total for hazard
save_total_affected_field = False
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'],)
if not hazard_class.get('affected'):
save_total_affected_field = True
group_key = None
for key, group in list(header_hazard_group.items()):
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = analysis_layer_fields[field_key_name]
field_index = analysis_layer.fields().lookupField(field_name)
count_value = format_number(
analysis_feature[field_index],
use_rounding=use_rounding,
is_population=is_population)
except KeyError:
# in case the field was not found
# assume value 0
count_value = '0'
if count_value == '0':
# if total affected for hazard class is zero, delete entire
# column
column_index = len(footers) + int(save_total_affected_field)
# delete header column
headers = headers[:column_index] + headers[column_index + 1:]
for row_idx in range(0, len(details)):
row = details[row_idx]
row = row[:column_index] + row[column_index + 1:]
details[row_idx] = row
# reduce total affected and not affected column index by 1
# since we are removing a column
if group_key == affected_field['field_name']:
affected_header_index -= 1
else:
not_affected_header_index -= 1
continue
footers.append({
'value': count_value,
'header_group': group_key
})
# for footers
for field in report_fields:
total_count = value_from_field_name(
field['field_name'], analysis_layer)
if not total_count and field['name'] == place_pop_name:
field = population_count_field
field['name'] = place_pop_name
total_count = value_from_field_name(
field['field_name'], analysis_layer)
group_key = None
for key, group in list(header_hazard_group.items()):
if field['name'] in group['total']:
group_key = key
break
total_count = format_number(
total_count,
use_rounding=use_rounding,
is_population=is_population)
if group_key:
if field == total_affected_field:
footers.insert(
affected_header_index,
{
'value': total_count,
'header_group': group_key
})
elif field == total_not_affected_field:
footers.insert(
not_affected_header_index,
{
'value': total_count,
'header_group': group_key
})
else:
footers.append({
'value': total_count,
'header_group': group_key
})
else:
footers.append(total_count)
header = resolve_from_dictionary(
extra_args, 'header')
notes = resolve_from_dictionary(
extra_args, 'notes')
context['component_key'] = component_metadata.key
context['header'] = header
context['group_border_color'] = resolve_from_dictionary(
extra_args, 'group_border_color')
context['notes'] = notes
breakdown_header_index = 0
# we want to include total affected and not affected as a group
# to its class so len(report_fields) - 2
total_header_index = len(headers) - (len(report_fields) - 2)
context['detail_header'] = {
'header_hazard_group': header_hazard_group,
'breakdown_header_index': breakdown_header_index,
'total_header_index': total_header_index
}
# modify headers to include double header
affected_headers = []
last_group = 0
for i in range(breakdown_header_index, total_header_index):
hazard_class_name = headers[i]
group_key = None
for key, group in list(header_hazard_group.items()):
if hazard_class_name in group['hazards'] or (
hazard_class_name in group['total']):
group_key = key
break
if group_key and group_key not in affected_headers:
affected_headers.append(group_key)
headers[i] = {
'name': hazard_class_name,
'start': True,
'header_group': group_key,
'colspan': 1
}
last_group = i
header_hazard_group[group_key]['start_index'] = i
elif group_key:
colspan = headers[last_group]['colspan']
headers[last_group]['colspan'] = colspan + 1
headers[i] = {
'name': hazard_class_name,
'start': False,
'header_group': group_key
}
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,
exposure=exposure_header)
table_header = ' '.join(table_header.split())
context['detail_table'] = {
'table_header': table_header,
'headers': headers,
'details': details,
'footers': footers,
}
context['extra_table'] = {}
# extra table for specific exposure if exist
extra_fields = resolve_from_dictionary(extra_args, 'exposure_extra_fields')
if exposure_type['key'] in list(extra_fields.keys()):
# create header for the extra table
extra_table_header_format = resolve_from_dictionary(
extra_args, 'extra_table_header_format')
extra_table_header = extra_table_header_format.format(
exposure=exposure_header)
# headers
headers = []
headers.append(
breakdown_header_template.format(exposure=exposure_header))
current_unit = None
currency_unit = setting('currency', expected_type=str)
for field in extra_fields[exposure_type['key']]:
field_index = exposure_summary_table.fields().lookupField(
field['field_name'])
if field_index < 0:
LOGGER.debug(
'Field name not found: %s, field index: %s' % (
field['field_name'], field_index))
continue
units = field.get('units')
if units:
for unit in units:
if currency_unit == unit['key']:
current_unit = unit['name']
break
if not current_unit:
current_unit = units[0]['name']
header_format = '{header} ({unit})'
headers.append(header_format.format(
header=field['header_name'], unit=current_unit))
# rows
details = []
for feat in exposure_summary_table.getFeatures():
row = []
# Get breakdown name
exposure_summary_table_field_name = breakdown_field['field_name']
field_index = exposure_summary_table.fields().lookupField(
exposure_summary_table_field_name)
class_key = feat[field_index]
row.append(class_key)
for field in extra_fields[exposure_type['key']]:
field_index = exposure_summary_table.fields().lookupField(
field['field_name'])
# noinspection PyBroadException
try:
total_count = int(float(feat[field_index]))
except BaseException:
LOGGER.debug(
'Field name not found: %s, field index: %s' % (
field['field_name'], field_index))
continue
total_count = format_number(
total_count,
use_rounding=use_rounding,
is_population=is_population)
row.append(total_count)
details.append(row)
details = sorted(details, key=sort_classes)
context['extra_table'] = {
'table_header': extra_table_header,
'headers': headers,
'details': details,
}
return context
def create_section_without_aggregation(
aggregation_summary, analysis_layer, postprocessor_fields,
section_header,
units_label=None,
debug_mode=False,
extra_component_args=None):
"""Create demographic section context without aggregation.
:param aggregation_summary: Aggregation summary
:type aggregation_summary: qgis.core.QgsVectorlayer
:param analysis_layer: Analysis layer
:type analysis_layer: qgis.core.QgsVectorLayer
:param postprocessor_fields: Postprocessor fields to extract
:type postprocessor_fields: list[dict]
:param section_header: Section header text
:type section_header: qgis.core.QgsVectorLayer
:param units_label: Unit label for each column
:type units_label: list[str]
:param debug_mode: flag for debug_mode, affect number representations
:type debug_mode: bool
:param extra_component_args: extra_args passed from report component
metadata
:type extra_component_args: dict
:return: context for gender section
:rtype: dict
"""
aggregation_summary_fields = aggregation_summary.keywords[
'inasafe_fields']
analysis_layer_fields = analysis_layer.keywords[
'inasafe_fields']
enable_rounding = not debug_mode
# retrieving postprocessor
postprocessors_fields_found = []
for output_field in postprocessor_fields['fields']:
if output_field['key'] in aggregation_summary_fields:
postprocessors_fields_found.append(output_field)
if not postprocessors_fields_found:
return {}
# figuring out displaced field
try:
displaced_field_name = analysis_layer_fields[
displaced_field['key']]
displaced_field_name = aggregation_summary_fields[
displaced_field['key']]
except KeyError:
# no displaced field, can't show result
return {}
"""Generating header name for columns"""
# First column header is aggregation title
total_population_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_population_header'])
columns = [
section_header,
total_population_header,
]
"""Generating values for rows"""
row_values = []
for idx, output_field in enumerate(postprocessors_fields_found):
name = output_field.get('header_name')
if not name:
name = output_field.get('name')
row = []
if units_label or output_field.get('unit'):
unit = None
if units_label:
unit = units_label[idx]
elif output_field.get('unit'):
unit = output_field.get('unit').get('abbreviation')
if unit:
header_format = u'{name} [{unit}]'
else:
header_format = u'{name}'
header = header_format.format(
name=name,
unit=unit)
else:
header_format = u'{name}'
header = header_format.format(name=name)
row.append(header)
# if no aggregation layer, then aggregation summary only contain one
# feature
field_name = analysis_layer_fields[output_field['key']]
value = value_from_field_name(
field_name,
analysis_layer)
value = format_number(
value,
enable_rounding=enable_rounding,
is_population=True)
row.append(value)
row_values.append(row)
return {
'columns': columns,
'rows': row_values,
}
def create_section_with_aggregation(
aggregation_summary, analysis_layer, postprocessor_fields,
section_header,
units_label=None,
debug_mode=False,
extra_component_args=None):
"""Create demographic section context with aggregation breakdown.
:param aggregation_summary: Aggregation summary
:type aggregation_summary: qgis.core.QgsVectorlayer
:param analysis_layer: Analysis layer
:type analysis_layer: qgis.core.QgsVectorLayer
:param postprocessor_fields: Postprocessor fields to extract
:type postprocessor_fields: list[dict]
:param section_header: Section header text
:type section_header: qgis.core.QgsVectorLayer
:param units_label: Unit label for each column
:type units_label: list[str]
:param debug_mode: flag for debug_mode, affect number representations
:type debug_mode: bool
:param extra_component_args: extra_args passed from report component
metadata
:type extra_component_args: dict
:return: context for gender section
:rtype: dict
.. versionadded:: 4.0
"""
aggregation_summary_fields = aggregation_summary.keywords[
'inasafe_fields']
analysis_layer_fields = analysis_layer.keywords[
'inasafe_fields']
enable_rounding = not debug_mode
# retrieving postprocessor
postprocessors_fields_found = []
if type(postprocessor_fields) is dict:
output_fields = postprocessor_fields['fields']
else:
output_fields = postprocessor_fields
for output_field in output_fields:
if output_field['key'] in aggregation_summary_fields:
postprocessors_fields_found.append(output_field)
if not postprocessors_fields_found:
return {}
# figuring out displaced field
# no displaced field, can't show result
if displaced_field['key'] not in analysis_layer_fields:
return {}
if displaced_field['key'] not in aggregation_summary_fields:
return {}
"""Generating header name for columns"""
# First column header is aggregation title
default_aggregation_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'aggregation_header'])
total_population_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_population_header'])
columns = [
aggregation_summary.title() or default_aggregation_header,
total_population_header,
]
row_values = []
group_fields_found = []
start_group_header = True
for idx, output_field in enumerate(postprocessors_fields_found):
name = output_field.get('header_name')
if not name:
name = output_field.get('name')
if units_label or output_field.get('unit'):
unit = None
if units_label:
unit = units_label[idx]
elif output_field.get('unit'):
unit = output_field.get('unit').get('abbreviation')
if unit:
header_format = u'{name} [{unit}]'
else:
header_format = u'{name}'
header = header_format.format(
name=name,
unit=unit)
else:
header_format = u'{name}'
header = header_format.format(name=name)
if type(postprocessor_fields) is dict:
try:
group_header = postprocessor_fields['group_header']
group_fields = postprocessor_fields['group']['fields']
if output_field in group_fields:
group_fields_found.append(output_field)
else:
columns.append(header)
continue
except KeyError:
group_fields_found.append(output_field)
header_dict = {
'name': header,
'group_header': group_header,
'start_group_header': start_group_header
}
start_group_header = False
columns.append(header_dict)
"""Generating values for rows"""
for feature in aggregation_summary.getFeatures():
aggregation_name_index = aggregation_summary.fieldNameIndex(
aggregation_name_field['field_name'])
displaced_field_name = aggregation_summary_fields[
displaced_field['key']]
displaced_field_index = aggregation_summary.fieldNameIndex(
displaced_field_name)
aggregation_name = feature[aggregation_name_index]
total_displaced = feature[displaced_field_index]
if not total_displaced or isinstance(total_displaced, QPyNullVariant):
# skip if total displaced null
continue
total_displaced = format_number(
feature[displaced_field_index],
enable_rounding=enable_rounding,
is_population=True)
row = [
aggregation_name,
total_displaced,
]
if total_displaced == '0' and not debug_mode:
continue
for output_field in postprocessors_fields_found:
field_name = aggregation_summary_fields[output_field['key']]
field_index = aggregation_summary.fieldNameIndex(field_name)
value = feature[field_index]
value = format_number(
value,
enable_rounding=enable_rounding,
is_population=True)
row.append(value)
row_values.append(row)
"""Generating total rows """
total_displaced_field_name = analysis_layer_fields[
displaced_field['key']]
value = value_from_field_name(
total_displaced_field_name, analysis_layer)
value = format_number(
value,
enable_rounding=enable_rounding,
is_population=True)
total_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_header'])
totals = [
total_header,
value
]
for output_field in postprocessors_fields_found:
field_name = analysis_layer_fields[output_field['key']]
value = value_from_field_name(field_name, analysis_layer)
value = format_number(
value,
enable_rounding=enable_rounding,
is_population=True)
totals.append(value)
default_notes = resolve_from_dictionary(
extra_component_args, ['defaults', 'notes'])
if type(default_notes) is not list:
default_notes = [default_notes]
try:
notes = default_notes + postprocessor_fields['group']['notes']
except (TypeError, KeyError):
notes = default_notes
pass
return {
'notes': notes,
'header': section_header,
'columns': columns,
'rows': row_values,
'totals': totals,
'group_header_colspan': len(group_fields_found)
}
def create_section_with_aggregation(
aggregation_summary, analysis_layer, postprocessor_fields,
section_header,
units_label=None,
debug_mode=False,
extra_component_args=None):
"""Create demographic section context with aggregation breakdown.
:param aggregation_summary: Aggregation summary
:type aggregation_summary: qgis.core.QgsVectorlayer
:param analysis_layer: Analysis layer
:type analysis_layer: qgis.core.QgsVectorLayer
:param postprocessor_fields: Postprocessor fields to extract
:type postprocessor_fields: list[dict]
:param section_header: Section header text
:type section_header: qgis.core.QgsVectorLayer
:param units_label: Unit label for each column
:type units_label: list[str]
:param debug_mode: flag for debug_mode, affect number representations
:type debug_mode: bool
:param extra_component_args: extra_args passed from report component
metadata
:type extra_component_args: dict
:return: context for gender section
:rtype: dict
.. versionadded:: 4.0
"""
aggregation_summary_fields = aggregation_summary.keywords[
'inasafe_fields']
analysis_layer_fields = analysis_layer.keywords[
'inasafe_fields']
enable_rounding = not debug_mode
# retrieving postprocessor
postprocessors_fields_found = []
for output_field in postprocessor_fields:
if output_field['key'] in aggregation_summary_fields:
postprocessors_fields_found.append(output_field)
if not postprocessors_fields_found:
return {}
# figuring out displaced field
# no displaced field, can't show result
if displaced_field['key'] not in analysis_layer_fields:
return {}
if displaced_field['key'] not in aggregation_summary_fields:
return {}
"""Generating header name for columns"""
# First column header is aggregation title
default_aggregation_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'aggregation_header'])
total_population_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_population_header'])
columns = [
aggregation_summary.title() or default_aggregation_header,
total_population_header,
]
row_values = []
for idx, output_field in enumerate(postprocessors_fields_found):
name = output_field['name']
if units_label or output_field.get('unit'):
unit = None
if units_label:
unit = units_label[idx]
elif output_field.get('unit'):
unit = output_field.get('unit').get('abbreviation')
if unit:
header_format = u'{name} [{unit}]'
else:
header_format = u'{name}'
header = header_format.format(
name=name,
unit=unit)
else:
header_format = u'{name}'
header = header_format.format(name=name)
columns.append(header)
"""Generating values for rows"""
for feature in aggregation_summary.getFeatures():
aggregation_name_index = aggregation_summary.fieldNameIndex(
aggregation_name_field['field_name'])
displaced_field_name = aggregation_summary_fields[
displaced_field['key']]
displaced_field_index = aggregation_summary.fieldNameIndex(
displaced_field_name)
aggregation_name = feature[aggregation_name_index]
total_displaced = feature[displaced_field_index]
if not total_displaced or isinstance(total_displaced, QPyNullVariant):
# skip if total displaced null
continue
total_displaced = format_number(
feature[displaced_field_index],
enable_rounding=enable_rounding)
row = [
aggregation_name,
total_displaced,
]
if total_displaced == '0' and not debug_mode:
continue
for output_field in postprocessors_fields_found:
field_name = aggregation_summary_fields[output_field['key']]
field_index = aggregation_summary.fieldNameIndex(field_name)
value = feature[field_index]
value = format_number(
value,
enable_rounding=enable_rounding)
row.append(value)
row_values.append(row)
"""Generating total rows """
total_displaced_field_name = analysis_layer_fields[
displaced_field['key']]
value = value_from_field_name(
total_displaced_field_name, analysis_layer)
value = format_number(
value,
enable_rounding=enable_rounding)
total_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_header'])
totals = [
total_header,
value
]
for output_field in postprocessors_fields_found:
field_name = analysis_layer_fields[output_field['key']]
value = value_from_field_name(field_name, analysis_layer)
value = format_number(
value,
enable_rounding=enable_rounding)
totals.append(value)
notes = resolve_from_dictionary(
extra_component_args, ['defaults', 'notes'])
return {
'notes': notes,
'header': section_header,
'columns': columns,
'rows': row_values,
'totals': totals,
}
def population_infographic_extractor(impact_report, component_metadata):
"""Extracting aggregate result of demographic.
: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 = {}
extra_args = component_metadata.extra_args
"""Initializations"""
hazard_layer = impact_report.hazard
analysis_layer = impact_report.analysis
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
icons = component_metadata.extra_args.get('icons')
# this report sections only applies if it is a population report.
population_fields = [
population_count_field['key'],
exposure_count_field['key'] % (exposure_population['key'], ),
] + [f['key'] for f in minimum_needs_fields]
for item in population_fields:
if item in analysis_layer_fields:
break
else:
return context
# We try to get total affected field
# if it didn't exists, check other fields to show
total_affected_fields = [
total_affected_field['key'],
# We might want to check other fields, but turn it off until further
# discussion
population_count_field['key'],
exposure_count_field['key'] % (exposure_population['key'], ),
]
for item in total_affected_fields:
if item in analysis_layer_fields:
total_affected = value_from_field_name(
analysis_layer_fields[item],
analysis_layer)
total_affected_field_used = item
break
else:
return context
if displaced_field['key'] in analysis_layer_fields:
total_displaced = value_from_field_name(
analysis_layer_fields[displaced_field['key']],
analysis_layer)
else:
return context
sections = OrderedDict()
"""People Section"""
# Take default value from definitions
people_header = resolve_from_dictionary(
extra_args, ['sections', 'people', 'header'])
people_items = resolve_from_dictionary(
extra_args, ['sections', 'people', 'items'])
# create context for affected infographic
sub_header = resolve_from_dictionary(
people_items[0], 'sub_header')
# retrieve relevant header based on the fields we showed.
sub_header = sub_header[total_affected_field_used]
affected_infographic = PeopleInfographicElement(
header=sub_header,
icon=icons.get(
total_affected_field['key']),
number=total_affected)
# create context for displaced infographic
sub_header = resolve_from_dictionary(
people_items[1], 'sub_header')
sub_header_note_format = resolve_from_dictionary(
people_items[1], 'sub_header_note_format')
rate_description_format = resolve_from_dictionary(
people_items[1], 'rate_description_format')
rate_description = []
hazard_classification = layer_hazard_classification(hazard_layer)
for hazard_class in hazard_classification['classes']:
displacement_rate = hazard_class.get('displacement_rate', 0)
if displacement_rate:
rate_description.append(
rate_description_format.format(**hazard_class))
rate_description_string = ', '.join(rate_description)
sub_header_note = sub_header_note_format.format(
rate_description=rate_description_string)
displaced_infographic = PeopleInfographicElement(
header=sub_header,
header_note=sub_header_note,
icon=icons.get(
displaced_field['key']),
number=total_displaced)
sections['people'] = {
'header': people_header,
'items': [
affected_infographic,
displaced_infographic
]
}
"""Vulnerability Section"""
# Take default value from definitions
vulnerability_items = resolve_from_dictionary(
extra_args,
['sections', 'vulnerability', 'items'])
vulnerability_section_header = resolve_from_dictionary(
extra_args,
['sections', 'vulnerability', 'header'])
vulnerability_section_sub_header_format = resolve_from_dictionary(
extra_args,
['sections', 'vulnerability', 'sub_header_format'])
infographic_elements = []
for group in vulnerability_items:
fields = group['fields']
group_header = group['sub_group_header']
bootstrap_column = group['bootstrap_column']
element_column = group['element_column']
headers = group['headers']
elements = []
for field, header in zip(fields, headers):
field_key = field['key']
try:
field_name = analysis_layer_fields[field_key]
value = value_from_field_name(
field_name, analysis_layer)
except KeyError:
# It means the field is not there
continue
if value:
value_percentage = value * 100.0 / total_displaced
else:
value_percentage = 0
infographic_element = PeopleVulnerabilityInfographicElement(
header=header,
icon=icons.get(field_key),
number=value,
percentage=value_percentage
)
elements.append(infographic_element)
if elements:
infographic_elements.append({
'group_header': group_header,
'bootstrap_column': bootstrap_column,
'element_column': element_column,
'items': elements
})
total_displaced_rounded = format_number(
total_displaced,
enable_rounding=True,
is_population=True)
sections['vulnerability'] = {
'header': vulnerability_section_header,
'small_header': vulnerability_section_sub_header_format.format(
number_displaced=total_displaced_rounded),
'items': infographic_elements
}
"""Minimum Needs Section"""
minimum_needs_header = resolve_from_dictionary(
extra_args,
['sections', 'minimum_needs', 'header'])
empty_unit_string = resolve_from_dictionary(
extra_args,
['sections', 'minimum_needs', 'empty_unit_string'])
items = []
for item in minimum_needs_fields:
need = item['need_parameter']
if isinstance(need, ResourceParameter):
needs_count = value_from_field_name(
item['field_name'], analysis_layer)
if need.unit.abbreviation:
unit_string = '{unit}/{frequency}'.format(
unit=need.unit.abbreviation,
frequency=need.frequency)
else:
unit_string = empty_unit_string
item = PeopleMinimumNeedsInfographicElement(
header=item['name'],
icon=icons.get(
item['key']),
number=needs_count,
unit=unit_string)
items.append(item)
# TODO: get from impact function provenance later
needs_profile = NeedsProfile()
sections['minimum_needs'] = {
'header': minimum_needs_header,
'small_header': needs_profile.provenance,
'items': items,
}
"""Population Charts"""
population_donut_path = impact_report.component_absolute_output_path(
'population-chart-png')
css_label_classes = []
try:
population_chart_context = impact_report.metadata.component_by_key(
'population-chart').context['context']
"""
:type: safe.report.extractors.infographic_elements.svg_charts.
DonutChartContext
"""
for pie_slice in population_chart_context.slices:
label = pie_slice['label']
if not label:
continue
css_class = label.replace(' ', '').lower()
css_label_classes.append(css_class)
except KeyError:
population_chart_context = None
sections['population_chart'] = {
'img_path': resource_url(population_donut_path),
'context': population_chart_context,
'css_label_classes': css_label_classes
}
context['brand_logo'] = resource_url(
resources_path('img', 'logos', 'inasafe-logo-white.png'))
context['sections'] = sections
context['title'] = analysis_layer.title() or value_from_field_name(
analysis_name_field['field_name'], analysis_layer)
return context
def multi_exposure_general_report_extractor(impact_report, component_metadata):
"""Extracting general analysis result 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:: 4.3
"""
context = {}
extra_args = component_metadata.extra_args
multi_exposure = impact_report.multi_exposure_impact_function
analysis_layer = impact_report.analysis
provenances = [
impact_function.provenance
for impact_function in (multi_exposure.impact_functions)
]
debug_mode = multi_exposure.debug
population_exist = False
hazard_keywords = provenances[0]['hazard_keywords']
hazard_header = resolve_from_dictionary(extra_args, 'hazard_header')
reported_fields = resolve_from_dictionary(extra_args, 'reported_fields')
# Summarize every value needed for each exposure and extract later to the
# context.
summary = []
map_legend_titles = []
hazard_classifications = {}
exposures_stats = []
for provenance in provenances:
map_legend_title = provenance['map_legend_title']
map_legend_titles.append(map_legend_title)
exposure_stats = {}
exposure_keywords = provenance['exposure_keywords']
exposure_type = definition(exposure_keywords['exposure'])
exposure_stats['exposure'] = exposure_type
# 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
if is_population:
population_exist = True
analysis_feature = next(analysis_layer.getFeatures())
analysis_inasafe_fields = analysis_layer.keywords['inasafe_fields']
exposure_unit = exposure_type['units'][0]
if exposure_unit['abbreviation']:
value_header = '{measure} ({abbreviation})'.format(
measure=map_legend_title,
abbreviation=exposure_unit['abbreviation'])
else:
value_header = '{name}'.format(name=map_legend_title)
exposure_stats['value_header'] = value_header
# Get hazard classification
hazard_classification = definition(
active_classification(hazard_keywords,
exposure_keywords['exposure']))
hazard_classifications[exposure_type['key']] = hazard_classification
# in case there is a classification
if hazard_classification:
classification_result = {}
reported_fields_result = {}
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = exposure_hazard_count_field['key'] % (
exposure_type['key'], hazard_class['key'])
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that
# particular class
field_name = analysis_inasafe_fields[field_key_name]
field_index = analysis_layer.fields() \
.lookupField(field_name)
hazard_value = format_number(analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
except KeyError:
# in case the field was not found
hazard_value = 0
classification_result[hazard_class['key']] = hazard_value
# find total field
try:
field_key_name = exposure_total_exposed_field['key'] % (
exposure_type['key'])
field_name = analysis_inasafe_fields[field_key_name]
total = value_from_field_name(field_name, analysis_layer)
total = format_number(total,
use_rounding=is_rounded,
is_population=is_population)
classification_result[total_exposed_field['key']] = total
except KeyError:
pass
exposure_stats['classification_result'] = classification_result
for item in reported_fields:
field = item.get('field')
multi_exposure_field = item.get('multi_exposure_field')
row_value = '-'
if multi_exposure_field:
field_key = (multi_exposure_field['key'] %
(exposure_type['key']))
field_name = (multi_exposure_field['field_name'] %
(exposure_type['key']))
if field_key in analysis_inasafe_fields:
field_index = analysis_layer.fields() \
.lookupField(field_name)
row_value = format_number(analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
elif field in [displaced_field, fatalities_field]:
if field['key'] in analysis_inasafe_fields and is_population:
field_index = analysis_layer.fields().lookupField(
field['name'])
if field == fatalities_field:
# For fatalities field, we show a range of number
# instead
row_value = fatalities_range(
analysis_feature[field_index])
else:
row_value = format_number(
analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
reported_fields_result[field['key']] = row_value
exposure_stats['reported_fields_result'] = reported_fields_result
exposures_stats.append(exposure_stats)
# After finish summarizing value, then proceed to context extraction.
# find total value and labels for each exposure
value_headers = []
total_values = []
for exposure_stats in exposures_stats:
# label
value_header = exposure_stats['value_header']
value_headers.append(value_header)
# total value
classification_result = exposure_stats['classification_result']
total_value = classification_result[total_exposed_field['key']]
total_values.append(total_value)
classifications = list(hazard_classifications.values())
is_item_identical = (classifications.count(
classifications[0]) == len(classifications))
if classifications and is_item_identical:
hazard_stats = []
for hazard_class in classifications[0]['classes']:
values = []
for exposure_stats in exposures_stats:
classification_result = exposure_stats['classification_result']
value = classification_result[hazard_class['key']]
values.append(value)
stats = {
'key': hazard_class['key'],
'name': hazard_class['name'],
'numbers': values
}
hazard_stats.append(stats)
total_stats = {
'key': total_exposed_field['key'],
'name': total_exposed_field['name'],
'as_header': True,
'numbers': total_values
}
hazard_stats.append(total_stats)
summary.append({
'header_label': hazard_header,
'value_labels': value_headers,
'rows': hazard_stats
})
# if there are different hazard classifications used in the analysis,
# we will create a separate table for each hazard classification
else:
hazard_classification_groups = {}
for exposure_key, hazard_classification in (iter(
list(hazard_classifications.items()))):
exposure_type = definition(exposure_key)
if hazard_classification['key'] not in (
hazard_classification_groups):
hazard_classification_groups[hazard_classification['key']] = [
exposure_type
]
else:
hazard_classification_groups[
hazard_classification['key']].append(exposure_type)
for hazard_classification_key, exposures in (iter(
list(hazard_classification_groups.items()))):
custom_headers = []
custom_total_values = []
# find total value and labels for each exposure
for exposure_stats in exposures_stats:
if exposure_stats['exposure'] not in exposures:
continue
# label
value_header = exposure_stats['value_header']
custom_headers.append(value_header)
# total value
classification_result = exposure_stats['classification_result']
total_value = classification_result[total_exposed_field['key']]
custom_total_values.append(total_value)
hazard_stats = []
hazard_classification = definition(hazard_classification_key)
for hazard_class in hazard_classification['classes']:
values = []
for exposure_stats in exposures_stats:
if exposure_stats['exposure'] not in exposures:
continue
classification_result = exposure_stats[
'classification_result']
value = classification_result[hazard_class['key']]
values.append(value)
stats = {
'key': hazard_class['key'],
'name': hazard_class['name'],
'numbers': values
}
hazard_stats.append(stats)
total_stats = {
'key': total_exposed_field['key'],
'name': total_exposed_field['name'],
'as_header': True,
'numbers': custom_total_values
}
hazard_stats.append(total_stats)
summary.append({
'header_label': hazard_header,
'value_labels': custom_headers,
'rows': hazard_stats
})
reported_fields_stats = []
for item in reported_fields:
field = item.get('field')
values = []
for exposure_stats in exposures_stats:
reported_fields_result = exposure_stats['reported_fields_result']
value = reported_fields_result[field['key']]
values.append(value)
stats = {
'key': field['key'],
'name': item['header'],
'numbers': values
}
reported_fields_stats.append(stats)
header_label = resolve_from_dictionary(extra_args,
['reported_fields_header'])
summary.append({
'header_label': header_label,
'value_labels': value_headers,
'rows': reported_fields_stats
})
header = resolve_from_dictionary(extra_args, ['header'])
combined_map_legend_title = ''
for index, map_legend_title in enumerate(map_legend_titles):
combined_map_legend_title += map_legend_title
if not (index + 1) == len(map_legend_titles):
combined_map_legend_title += ', '
table_header_format = resolve_from_dictionary(extra_args,
'table_header_format')
table_header = table_header_format.format(
title=combined_map_legend_title,
unit=classifications[0]['classification_unit'])
# Section notes
note_format = resolve_from_dictionary(extra_args,
['concept_notes', 'note_format'])
concepts = resolve_from_dictionary(extra_args,
['concept_notes', 'general_concepts'])
if population_exist:
concepts += resolve_from_dictionary(
extra_args, ['concept_notes', 'population_concepts'])
notes = []
for concept in concepts:
note = note_format.format(**concept)
notes.append(note)
context['component_key'] = component_metadata.key
context['header'] = header
context['summary'] = summary
context['table_header'] = table_header
context['notes'] = notes
return context
def create_section_with_aggregation(aggregation_summary,
analysis_layer,
postprocessor_fields,
section_header,
units_label=None,
use_rounding=True,
extra_component_args=None):
"""Create demographic section context with aggregation breakdown.
:param aggregation_summary: Aggregation summary
:type aggregation_summary: qgis.core.QgsVectorlayer
:param analysis_layer: Analysis layer
:type analysis_layer: qgis.core.QgsVectorLayer
:param postprocessor_fields: Postprocessor fields to extract
:type postprocessor_fields: list[dict]
:param section_header: Section header text
:type section_header: qgis.core.QgsVectorLayer
:param units_label: Unit label for each column
:type units_label: list[str]
:param use_rounding: flag for rounding, affect number representations
:type use_rounding: bool
:param extra_component_args: extra_args passed from report component
metadata
:type extra_component_args: dict
:return: context for gender section
:rtype: dict
.. versionadded:: 4.0
"""
aggregation_summary_fields = aggregation_summary.keywords['inasafe_fields']
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
# retrieving postprocessor
postprocessors_fields_found = []
if isinstance(postprocessor_fields, dict):
output_fields = postprocessor_fields['fields']
else:
output_fields = postprocessor_fields
for output_field in output_fields:
if output_field['key'] in aggregation_summary_fields:
postprocessors_fields_found.append(output_field)
if not postprocessors_fields_found:
return {}
# figuring out displaced field
# no displaced field, can't show result
if displaced_field['key'] not in analysis_layer_fields:
return {}
if displaced_field['key'] not in aggregation_summary_fields:
return {}
"""Generating header name for columns."""
# First column header is aggregation title
default_aggregation_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'aggregation_header'])
total_population_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_population_header'])
columns = [
aggregation_summary.title() or default_aggregation_header,
total_population_header,
]
row_values = []
group_fields_found = []
start_group_header = True
for idx, output_field in enumerate(postprocessors_fields_found):
name = output_field.get('header_name')
if not name:
name = output_field.get('name')
if units_label or output_field.get('unit'):
unit = None
if units_label:
unit = units_label[idx]
elif output_field.get('unit'):
unit = output_field.get('unit').get('abbreviation')
if unit:
header_format = '{name} [{unit}]'
else:
header_format = '{name}'
header = header_format.format(name=name, unit=unit)
else:
header_format = '{name}'
header = header_format.format(name=name)
if isinstance(postprocessor_fields, dict):
try:
group_header = postprocessor_fields['group_header']
group_fields = postprocessor_fields['group']['fields']
if output_field in group_fields:
group_fields_found.append(output_field)
else:
columns.append(header)
continue
except KeyError:
group_fields_found.append(output_field)
header_dict = {
'name': header,
'group_header': group_header,
'start_group_header': start_group_header
}
start_group_header = False
columns.append(header_dict)
"""Generating values for rows."""
for feature in aggregation_summary.getFeatures():
aggregation_name_index = aggregation_summary.fields().lookupField(
aggregation_name_field['field_name'])
displaced_field_name = aggregation_summary_fields[
displaced_field['key']]
displaced_field_index = aggregation_summary.fields().lookupField(
displaced_field_name)
aggregation_name = feature[aggregation_name_index]
total_displaced = feature[displaced_field_index]
if not total_displaced or total_displaced is None:
# skip if total displaced null
continue
total_displaced = format_number(feature[displaced_field_index],
use_rounding=use_rounding,
is_population=True)
row = [
aggregation_name,
total_displaced,
]
if total_displaced == '0' and not use_rounding:
continue
for output_field in postprocessors_fields_found:
field_name = aggregation_summary_fields[output_field['key']]
field_index = aggregation_summary.fields().lookupField(field_name)
value = feature[field_index]
value = format_number(value,
use_rounding=use_rounding,
is_population=True)
row.append(value)
row_values.append(row)
"""Generating total rows."""
total_displaced_field_name = analysis_layer_fields[displaced_field['key']]
value = value_from_field_name(total_displaced_field_name, analysis_layer)
value = format_number(value, use_rounding=use_rounding, is_population=True)
total_header = resolve_from_dictionary(extra_component_args,
['defaults', 'total_header'])
totals = [total_header, value]
for output_field in postprocessors_fields_found:
field_name = analysis_layer_fields[output_field['key']]
value = value_from_field_name(field_name, analysis_layer)
value = format_number(value,
use_rounding=use_rounding,
is_population=True)
totals.append(value)
default_notes = resolve_from_dictionary(extra_component_args,
['defaults', 'notes'])
if not isinstance(default_notes, list):
default_notes = [default_notes]
try:
notes = default_notes + postprocessor_fields['group']['notes']
except (TypeError, KeyError):
notes = default_notes
pass
return {
'notes': notes,
'header': section_header,
'columns': columns,
'rows': row_values,
'totals': totals,
'group_header_colspan': len(group_fields_found)
}
def create_section_without_aggregation(aggregation_summary,
analysis_layer,
postprocessor_fields,
section_header,
units_label=None,
use_rounding=True,
extra_component_args=None):
"""Create demographic section context without aggregation.
:param aggregation_summary: Aggregation summary
:type aggregation_summary: qgis.core.QgsVectorlayer
:param analysis_layer: Analysis layer
:type analysis_layer: qgis.core.QgsVectorLayer
:param postprocessor_fields: Postprocessor fields to extract
:type postprocessor_fields: list[dict]
:param section_header: Section header text
:type section_header: qgis.core.QgsVectorLayer
:param units_label: Unit label for each column
:type units_label: list[str]
:param use_rounding: flag for rounding, affect number representations
:type use_rounding: bool
:param extra_component_args: extra_args passed from report component
metadata
:type extra_component_args: dict
:return: context for gender section
:rtype: dict
"""
aggregation_summary_fields = aggregation_summary.keywords['inasafe_fields']
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
# retrieving postprocessor
postprocessors_fields_found = []
for output_field in postprocessor_fields['fields']:
if output_field['key'] in aggregation_summary_fields:
postprocessors_fields_found.append(output_field)
if not postprocessors_fields_found:
return {}
# figuring out displaced field
try:
analysis_layer_fields[displaced_field['key']]
aggregation_summary_fields[displaced_field['key']]
except KeyError:
# no displaced field, can't show result
return {}
"""Generating header name for columns."""
# First column header is aggregation title
total_population_header = resolve_from_dictionary(
extra_component_args, ['defaults', 'total_population_header'])
columns = [
section_header,
total_population_header,
]
"""Generating values for rows."""
row_values = []
for idx, output_field in enumerate(postprocessors_fields_found):
name = output_field.get('header_name')
if not name:
name = output_field.get('name')
row = []
if units_label or output_field.get('unit'):
unit = None
if units_label:
unit = units_label[idx]
elif output_field.get('unit'):
unit = output_field.get('unit').get('abbreviation')
if unit:
header_format = '{name} [{unit}]'
else:
header_format = '{name}'
header = header_format.format(name=name, unit=unit)
else:
header_format = '{name}'
header = header_format.format(name=name)
row.append(header)
# if no aggregation layer, then aggregation summary only contain one
# feature
field_name = analysis_layer_fields[output_field['key']]
value = value_from_field_name(field_name, analysis_layer)
value = format_number(value,
use_rounding=use_rounding,
is_population=True)
row.append(value)
row_values.append(row)
return {
'columns': columns,
'rows': row_values,
}
def general_report_extractor(impact_report, component_metadata):
"""Extracting general analysis result 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 = {}
extra_args = component_metadata.extra_args
# figure out analysis report type
hazard_layer = impact_report.hazard
exposure_layer = impact_report.exposure
analysis_layer = impact_report.analysis
provenance = impact_report.impact_function.provenance
debug_mode = impact_report.impact_function.debug_mode
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
# find hazard class
summary = []
analysis_feature = analysis_layer.getFeatures().next()
analysis_inasafe_fields = analysis_layer.keywords['inasafe_fields']
exposure_unit = exposure_type['units'][0]
hazard_header = resolve_from_dictionary(extra_args, 'hazard_header')
if exposure_unit['abbreviation']:
value_header = u'{measure} ({abbreviation})'.format(**exposure_unit)
else:
value_header = u'{name}'.format(**exposure_unit)
# in case there is a classification
if 'classification' in hazard_layer.keywords:
# retrieve hazard classification from hazard layer
hazard_classification = layer_hazard_classification(hazard_layer)
# classified hazard must have hazard count in analysis layer
hazard_stats = []
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'], )
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = analysis_inasafe_fields[field_key_name]
field_index = analysis_layer.fieldNameIndex(field_name)
# Hazard label taken from translated hazard count field
# label, string-formatted with translated hazard class label
hazard_label = hazard_class['name']
hazard_value = format_number(
analysis_feature[field_index],
enable_rounding=is_rounded,
is_population=is_population)
stats = {
'key': hazard_class['key'],
'name': hazard_label,
'value': hazard_value
}
except KeyError:
# in case the field was not found
hazard_label = hazard_class['name']
stats = {
'key': hazard_class['key'],
'name': hazard_label,
'value': 0,
}
hazard_stats.append(stats)
# find total field
try:
field_name = analysis_inasafe_fields[total_field['key']]
total = value_from_field_name(field_name, analysis_layer)
total = format_number(
total, enable_rounding=is_rounded, is_population=is_population)
stats = {
'key': total_field['key'],
'name': total_field['name'],
'as_header': True,
'value': total
}
hazard_stats.append(stats)
except KeyError:
pass
summary.append({
'header_label': hazard_header,
'value_label': value_header,
'rows': hazard_stats
})
# retrieve affected column
report_stats = []
reported_fields = resolve_from_dictionary(
extra_args, 'reported_fields')
for item in reported_fields:
header = item['header']
field = item['field']
if field['key'] in analysis_inasafe_fields:
field_index = analysis_layer.fieldNameIndex(
field['field_name'])
if field == fatalities_field:
# For fatalities field, we show a range of number
# instead
row_value = fatalities_range(analysis_feature[field_index])
else:
row_value = format_number(
analysis_feature[field_index],
enable_rounding=is_rounded,
is_population=is_population)
row_stats = {
'key': field['key'],
'name': header,
'value': row_value
}
report_stats.append(row_stats)
# Give report section
exposure_type = layer_definition_type(exposure_layer)
header_label = exposure_type['name']
summary.append({
'header_label': header_label,
# This should depend on exposure unit
# TODO: Change this so it can take the unit dynamically
'value_label': value_header,
'rows': report_stats
})
header = resolve_from_dictionary(extra_args, ['header'])
table_header_format = resolve_from_dictionary(
extra_args, 'table_header_format')
table_header = table_header_format.format(
title=provenance['map_legend_title'],
unit=hazard_classification['classification_unit'])
context['header'] = header
context['summary'] = summary
context['table_header'] = table_header
return context
def number(self):
"""Number to be displayed for the element."""
value = format_number(self._number,
enable_rounding=True,
is_population=True)
return value
def analysis_detail_extractor(impact_report, component_metadata):
"""Extracting analysis result 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 = {}
extra_args = component_metadata.extra_args
hazard_layer = impact_report.hazard
exposure_layer = impact_report.exposure
analysis_layer = impact_report.analysis
analysis_layer_fields = analysis_layer.keywords['inasafe_fields']
analysis_feature = analysis_layer.getFeatures().next()
exposure_summary_table = impact_report.exposure_summary_table
if exposure_summary_table:
exposure_summary_table_fields = exposure_summary_table.keywords[
'inasafe_fields']
provenance = impact_report.impact_function.provenance
debug_mode = impact_report.impact_function.debug_mode
"""Initializations"""
# Get hazard classification
hazard_classification = layer_hazard_classification(hazard_layer)
# 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_rounding = not debug_mode
# Analysis detail 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
# 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
"""Create detail header"""
headers = []
# breakdown header
breakdown_header_template = ''
if breakdown_field == exposure_type_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_type_format')
elif breakdown_field == exposure_class_field:
breakdown_header_template = resolve_from_dictionary(
extra_args, 'breakdown_header_class_format')
# check if there is header type associations
type_header_mapping = resolve_from_dictionary(
extra_args, 'exposure_type_header_mapping')
if exposure_type['key'] in type_header_mapping:
exposure_header = type_header_mapping[exposure_type['key']]
else:
exposure_header = exposure_type['name']
headers.append(breakdown_header_template.format(exposure=exposure_header))
# this is mapping for customizing double header for
# affected/not affected hazard classes
hazard_class_header_mapping = resolve_from_dictionary(
extra_args, 'hazard_class_header_mapping')
# hazard header
# TODO: we need to get affected and not_affected key from
# definitions concept
header_hazard_group = {
'affected': {
'hazards': []
},
'not_affected': {
'hazards': []
}
}
for key, group in header_hazard_group.iteritems():
if key in hazard_class_header_mapping:
header_hazard_group[key].update(hazard_class_header_mapping[key])
for hazard_class in hazard_classification['classes']:
# the tuple format would be:
# (class name, is it affected, header background color
hazard_class_name = hazard_class['name']
if hazard_class.get('affected'):
affected_status = 'affected'
else:
affected_status = 'not_affected'
header_hazard_group[affected_status]['hazards'].append(
hazard_class_name)
headers.append(hazard_class_name)
# affected, not affected, not exposed, total header
report_fields = [
total_affected_field, total_not_affected_field,
total_not_exposed_field, total_field
]
for report_field in report_fields:
headers.append(report_field['name'])
"""Create detail rows"""
details = []
for feat in exposure_summary_table.getFeatures():
row = []
# Get breakdown name
exposure_summary_table_field_name = breakdown_field['field_name']
field_index = exposure_summary_table.fieldNameIndex(
exposure_summary_table_field_name)
class_key = feat[field_index]
row.append(class_key)
# Get hazard count
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (
hazard_class['key'], )
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = exposure_summary_table_fields[field_key_name]
field_index = exposure_summary_table.fieldNameIndex(field_name)
# exposure summary table is in csv format, so the field
# returned is always in text format
count_value = int(float(feat[field_index]))
count_value = format_number(count_value,
enable_rounding=is_rounding)
row.append({'value': count_value, 'header_group': group_key})
except KeyError:
# in case the field was not found
# assume value 0
row.append({'value': 0, 'header_group': group_key})
skip_row = False
for field in report_fields:
field_index = exposure_summary_table.fieldNameIndex(
field['field_name'])
total_count = int(float(feat[field_index]))
total_count = format_number(total_count,
enable_rounding=is_rounding)
if total_count == '0' and field == total_affected_field:
skip_row = True
break
row.append(total_count)
if skip_row:
continue
details.append(row)
# retrieve classes definitions
exposure_classes_lists = retrieve_exposure_classes_lists(exposure_layer)
# sort detail rows based on class order
# create function to sort
def sort_classes(_row):
"""Sort method to retrieve exposure class key index."""
# class key is first column
_class_key = _row[0]
# find index in class list
for i, _exposure_class in enumerate(exposure_classes_lists):
if _class_key == _exposure_class['key']:
index = i
break
else:
index = -1
return index
# sort
details = sorted(details, key=sort_classes)
# retrieve breakdown name from classes list
for row in details:
class_key = row[0]
for exposure_class in exposure_classes_lists:
if class_key == exposure_class['key']:
breakdown_name = exposure_class['name']
break
else:
# attempt for dynamic translations
breakdown_name = tr(class_key.capitalize())
# replace class_key with the class name
row[0] = breakdown_name
"""create total footers"""
# create total header
footers = [total_field['name']]
# total for hazard
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = hazard_count_field['key'] % (hazard_class['key'], )
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class['name'] in group['hazards']:
group_key = key
break
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that particular
# class
field_name = analysis_layer_fields[field_key_name]
field_index = analysis_layer.fieldNameIndex(field_name)
count_value = format_number(analysis_feature[field_index],
enable_rounding=is_rounding)
except KeyError:
# in case the field was not found
# assume value 0
count_value = '0'
if count_value == '0':
# if total affected for hazard class is zero, delete entire
# column
column_index = len(footers)
# delete header column
headers = headers[:column_index] + headers[column_index + 1:]
for row_idx in range(0, len(details)):
row = details[row_idx]
row = row[:column_index] + row[column_index + 1:]
details[row_idx] = row
continue
footers.append({'value': count_value, 'header_group': group_key})
# for footers
for field in report_fields:
total_count = value_from_field_name(field['field_name'],
analysis_layer)
total_count = format_number(total_count, enable_rounding=is_rounding)
footers.append(total_count)
header = resolve_from_dictionary(extra_args, 'header')
notes = resolve_from_dictionary(extra_args, 'notes')
context['header'] = header
context['group_border_color'] = resolve_from_dictionary(
extra_args, 'group_border_color')
context['notes'] = notes
breakdown_header_index = 0
total_header_index = len(headers) - len(report_fields)
context['detail_header'] = {
'header_hazard_group': header_hazard_group,
'breakdown_header_index': breakdown_header_index,
'total_header_index': total_header_index
}
# modify headers to include double header
affected_headers = []
last_group = 0
for i in range(breakdown_header_index, total_header_index):
hazard_class_name = headers[i]
group_key = None
for key, group in header_hazard_group.iteritems():
if hazard_class_name in group['hazards']:
group_key = key
break
if group_key and group_key not in affected_headers:
affected_headers.append(group_key)
headers[i] = {
'name': hazard_class_name,
'start': True,
'header_group': group_key,
'colspan': 1
}
last_group = i
header_hazard_group[group_key]['start_index'] = i
elif group_key:
colspan = headers[last_group]['colspan']
headers[last_group]['colspan'] = colspan + 1
headers[i] = {
'name': hazard_class_name,
'start': False,
'header_group': group_key
}
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,
exposure=exposure_header)
table_header = ' '.join(table_header.split())
context['detail_table'] = {
'table_header': table_header,
'headers': headers,
'details': details,
'footers': footers,
}
return context
def mmi_detail_extractor(impact_report, component_metadata):
"""Extracting MMI-related analysis result.
This extractor should only be used for EQ Raster with Population.
: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 = {}
analysis_layer = impact_report.analysis
analysis_layer_keywords = analysis_layer.keywords
extra_args = component_metadata.extra_args
use_rounding = impact_report.impact_function.use_rounding
provenance = impact_report.impact_function.provenance
hazard_keywords = provenance['hazard_keywords']
exposure_keywords = provenance['exposure_keywords']
# check if this is EQ raster with population
hazard_type = definition(hazard_keywords['hazard'])
if not hazard_type == hazard_earthquake:
return context
hazard_geometry = hazard_keywords[layer_geometry['key']]
if not hazard_geometry == layer_geometry_raster['key']:
return context
exposure_type = definition(exposure_keywords['exposure'])
if not exposure_type == exposure_population:
return context
header = resolve_from_dictionary(extra_args, 'header')
context['header'] = header
reported_fields = resolve_from_dictionary(extra_args, 'reported_fields')
"""Generate headers."""
table_header = [
resolve_from_dictionary(extra_args, 'mmi_header')
] + [v['header'] for v in reported_fields]
"""Extract MMI-related data"""
# mmi is ranged from 1 to 10, which means: [1, 11)
mmi_range = list(range(1, 11))
rows = []
roman_numeral = [
'I',
'II',
'III',
'IV',
'V',
'VI',
'VII',
'VIII',
'IX',
'X'
]
for i in mmi_range:
columns = [roman_numeral[i - 1]]
for value in reported_fields:
field = value['field']
try:
key_name = field['key'] % (i, )
field_name = analysis_layer_keywords[key_name]
# check field exists
count = value_from_field_name(field_name, analysis_layer)
if not count:
count = 0
except KeyError:
count = 0
count = format_number(
count,
use_rounding=use_rounding,
is_population=True)
columns.append(count)
rows.append(columns)
"""Extract total."""
total_footer = [
resolve_from_dictionary(extra_args, 'total_header')
]
total_fields = resolve_from_dictionary(extra_args, 'total_fields')
for field in total_fields:
try:
field_name = analysis_layer_keywords[field['key']]
total = value_from_field_name(field_name, analysis_layer)
if not total:
total = 0
except KeyError:
total = 0
total = format_number(
total,
use_rounding=use_rounding,
is_population=True)
total_footer.append(total)
context['component_key'] = component_metadata.key
context['mmi'] = {
'header': table_header,
'rows': rows,
'footer': total_footer
}
return context
def generate_classified_legend(analysis, exposure, hazard, debug_mode):
"""Generate an ordered python structure with the classified symbology.
:param analysis: The analysis layer.
:type analysis: QgsVectorLayer
:param exposure: The exposure layer.
:type exposure: QgsVectorLayer
:param hazard: The hazard layer.
:type hazard: QgsVectorLayer
:param debug_mode: Boolean if run in debug mode.
:type debug_mode: bool
:return: The ordered dictionary to use to build the classified style.
:rtype: OrderedDict
"""
# We need to read the analysis layer to get the number of features.
analysis_row = analysis.getFeatures().next()
# Let's style the hazard class in each layers.
hazard_classification = hazard.keywords['classification']
hazard_classification = definition(hazard_classification)
# Let's check if there is some thresholds:
thresholds = hazard.keywords.get('thresholds')
if thresholds:
hazard_unit = hazard.keywords.get('continuous_hazard_unit')
hazard_unit = definition(hazard_unit)['abbreviation']
else:
hazard_unit = None
exposure = exposure.keywords['exposure']
exposure_definitions = definition(exposure)
exposure_units = exposure_definitions['units']
exposure_unit = exposure_units[0]
coefficient = 1
# We check if can use a greater unit, such as kilometre for instance.
if len(exposure_units) > 1:
# We use only two units for now.
delta = coefficient_between_units(exposure_units[1], exposure_units[0])
all_values_are_greater = True
# We check if all values are greater than the coefficient
for i, hazard_class in enumerate(hazard_classification['classes']):
field_name = hazard_count_field['field_name'] % hazard_class['key']
try:
value = analysis_row[field_name]
except KeyError:
value = 0
if 0 < value < delta:
# 0 is fine, we can still keep the second unit.
all_values_are_greater = False
if all_values_are_greater:
# If yes, we can use this unit.
exposure_unit = exposure_units[1]
coefficient = delta
classes = OrderedDict()
# In debug mode we don't round number.
enable_rounding = not debug_mode
for i, hazard_class in enumerate(hazard_classification['classes']):
# Get the hazard class name.
field_name = hazard_count_field['field_name'] % hazard_class['key']
# Get the number of affected feature by this hazard class.
try:
value = analysis_row[field_name]
except KeyError:
# The field might not exist if no feature impacted in this hazard
# zone.
value = 0
value = format_number(value, enable_rounding,
exposure_definitions['use_population_rounding'],
coefficient)
minimum = None
maximum = None
# Check if we need to add thresholds.
if thresholds:
if i == 0:
minimum = thresholds[hazard_class['key']][0]
elif i == len(hazard_classification['classes']) - 1:
maximum = thresholds[hazard_class['key']][1]
else:
minimum = thresholds[hazard_class['key']][0]
maximum = thresholds[hazard_class['key']][1]
label = _format_label(hazard_class=hazard_class['name'],
value=value,
exposure_unit=exposure_unit['abbreviation'],
minimum=minimum,
maximum=maximum,
hazard_unit=hazard_unit)
classes[hazard_class['key']] = (hazard_class['color'], label)
if exposure_definitions['display_not_exposed'] or debug_mode:
classes[not_exposed_class['key']] = _add_not_exposed(
analysis_row, enable_rounding,
exposure_definitions['use_population_rounding'],
exposure_unit['abbreviation'], coefficient)
return classes
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_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 minimum_needs_extractor(impact_report, component_metadata):
"""Extracting minimum needs of 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 = {}
extra_args = component_metadata.extra_args
analysis_layer = impact_report.analysis
analysis_keywords = analysis_layer.keywords['inasafe_fields']
debug_mode = impact_report.impact_function.debug_mode
is_rounding = not debug_mode
header = resolve_from_dictionary(extra_args, 'header')
context['header'] = header
# check if displaced is not zero
try:
displaced_field_name = analysis_keywords[displaced_field['key']]
total_displaced = value_from_field_name(displaced_field_name,
analysis_layer)
if total_displaced == 0:
zero_displaced_message = resolve_from_dictionary(
extra_args, 'zero_displaced_message')
context['zero_displaced'] = {
'status': True,
'message': zero_displaced_message
}
return context
except KeyError:
# in case no displaced field
pass
# minimum needs calculation only affect population type exposure
# check if analysis keyword have minimum_needs keywords
have_minimum_needs_field = False
for field_key in analysis_keywords:
if field_key.startswith(minimum_needs_namespace):
have_minimum_needs_field = True
break
if not have_minimum_needs_field:
return context
frequencies = {}
# map each needs to its frequency groups
for field in (minimum_needs_fields + additional_minimum_needs):
need_parameter = field.get('need_parameter')
if isinstance(need_parameter, ResourceParameter):
frequency = need_parameter.frequency
else:
frequency = field.get('frequency')
if frequency:
if frequency not in frequencies:
frequencies[frequency] = [field]
else:
frequencies[frequency].append(field)
needs = []
analysis_feature = analysis_layer.getFeatures().next()
header_frequency_format = resolve_from_dictionary(
extra_args, 'header_frequency_format')
total_header = resolve_from_dictionary(extra_args, 'total_header')
need_header_format = resolve_from_dictionary(extra_args,
'need_header_format')
# group the needs by frequency
for key, frequency in frequencies.iteritems():
group = {
'header': header_frequency_format.format(frequency=tr(key)),
'total_header': total_header,
'needs': []
}
for field in frequency:
# check value exists in the field
field_idx = analysis_layer.fieldNameIndex(field['field_name'])
if field_idx == -1:
# skip if field doesn't exists
continue
value = format_number(analysis_feature[field_idx],
enable_rounding=is_rounding,
is_population=True)
if field.get('need_parameter'):
need_parameter = field['need_parameter']
""":type: ResourceParameter"""
name = tr(need_parameter.name)
unit_abbreviation = need_parameter.unit.abbreviation
else:
if field.get('header_name'):
name = field.get('header_name')
else:
name = field.get('name')
need_unit = field.get('unit')
if need_unit:
unit_abbreviation = need_unit.get('abbreviation')
if unit_abbreviation:
header = need_header_format.format(
name=name, unit_abbreviation=unit_abbreviation)
else:
header = name
item = {'header': header, 'value': value}
group['needs'].append(item)
needs.append(group)
context['needs'] = needs
return context
def multi_exposure_general_report_extractor(impact_report, component_metadata):
"""Extracting general analysis result 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:: 4.3
"""
context = {}
extra_args = component_metadata.extra_args
multi_exposure = impact_report.multi_exposure_impact_function
analysis_layer = impact_report.analysis
provenances = [
impact_function.provenance for impact_function in (
multi_exposure.impact_functions)]
debug_mode = multi_exposure.debug
population_exist = False
hazard_keywords = provenances[0]['hazard_keywords']
hazard_header = resolve_from_dictionary(extra_args, 'hazard_header')
reported_fields = resolve_from_dictionary(extra_args, 'reported_fields')
# Summarize every value needed for each exposure and extract later to the
# context.
summary = []
map_legend_titles = []
hazard_classifications = {}
exposures_stats = []
for provenance in provenances:
map_legend_title = provenance['map_legend_title']
map_legend_titles.append(map_legend_title)
exposure_stats = {}
exposure_keywords = provenance['exposure_keywords']
exposure_type = definition(exposure_keywords['exposure'])
exposure_stats['exposure'] = exposure_type
# 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
if is_population:
population_exist = True
analysis_feature = next(analysis_layer.getFeatures())
analysis_inasafe_fields = analysis_layer.keywords['inasafe_fields']
exposure_unit = exposure_type['units'][0]
if exposure_unit['abbreviation']:
value_header = '{measure} ({abbreviation})'.format(
measure=map_legend_title,
abbreviation=exposure_unit['abbreviation'])
else:
value_header = '{name}'.format(name=map_legend_title)
exposure_stats['value_header'] = value_header
# Get hazard classification
hazard_classification = definition(
active_classification(hazard_keywords,
exposure_keywords['exposure']))
hazard_classifications[exposure_type['key']] = hazard_classification
# in case there is a classification
if hazard_classification:
classification_result = {}
reported_fields_result = {}
for hazard_class in hazard_classification['classes']:
# hazard_count_field is a dynamic field with hazard class
# as parameter
field_key_name = exposure_hazard_count_field['key'] % (
exposure_type['key'], hazard_class['key'])
try:
# retrieve dynamic field name from analysis_fields keywords
# will cause key error if no hazard count for that
# particular class
field_name = analysis_inasafe_fields[field_key_name]
field_index = analysis_layer.fields() \
.lookupField(field_name)
hazard_value = format_number(
analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
except KeyError:
# in case the field was not found
hazard_value = 0
classification_result[hazard_class['key']] = hazard_value
# find total field
try:
field_key_name = exposure_total_exposed_field['key'] % (
exposure_type['key'])
field_name = analysis_inasafe_fields[field_key_name]
total = value_from_field_name(field_name, analysis_layer)
total = format_number(
total, use_rounding=is_rounded,
is_population=is_population)
classification_result[total_exposed_field['key']] = total
except KeyError:
pass
exposure_stats['classification_result'] = classification_result
for item in reported_fields:
field = item.get('field')
multi_exposure_field = item.get('multi_exposure_field')
row_value = '-'
if multi_exposure_field:
field_key = (
multi_exposure_field['key'] % (exposure_type['key']))
field_name = (
multi_exposure_field['field_name'] % (
exposure_type['key']))
if field_key in analysis_inasafe_fields:
field_index = analysis_layer.fields() \
.lookupField(field_name)
row_value = format_number(
analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
elif field in [displaced_field, fatalities_field]:
if field['key'] in analysis_inasafe_fields and is_population:
field_index = analysis_layer.fields(
).lookupField(field['name'])
if field == fatalities_field:
# For fatalities field, we show a range of number
# instead
row_value = fatalities_range(
analysis_feature[field_index])
else:
row_value = format_number(
analysis_feature[field_index],
use_rounding=is_rounded,
is_population=is_population)
reported_fields_result[field['key']] = row_value
exposure_stats['reported_fields_result'] = reported_fields_result
exposures_stats.append(exposure_stats)
# After finish summarizing value, then proceed to context extraction.
# find total value and labels for each exposure
value_headers = []
total_values = []
for exposure_stats in exposures_stats:
# label
value_header = exposure_stats['value_header']
value_headers.append(value_header)
# total value
classification_result = exposure_stats['classification_result']
total_value = classification_result[total_exposed_field['key']]
total_values.append(total_value)
classifications = list(hazard_classifications.values())
is_item_identical = (
classifications.count(
classifications[0]) == len(classifications))
if classifications and is_item_identical:
hazard_stats = []
for hazard_class in classifications[0]['classes']:
values = []
for exposure_stats in exposures_stats:
classification_result = exposure_stats['classification_result']
value = classification_result[hazard_class['key']]
values.append(value)
stats = {
'key': hazard_class['key'],
'name': hazard_class['name'],
'numbers': values
}
hazard_stats.append(stats)
total_stats = {
'key': total_exposed_field['key'],
'name': total_exposed_field['name'],
'as_header': True,
'numbers': total_values
}
hazard_stats.append(total_stats)
summary.append({
'header_label': hazard_header,
'value_labels': value_headers,
'rows': hazard_stats
})
# if there are different hazard classifications used in the analysis,
# we will create a separate table for each hazard classification
else:
hazard_classification_groups = {}
for exposure_key, hazard_classification in (
iter(list(hazard_classifications.items()))):
exposure_type = definition(exposure_key)
if hazard_classification['key'] not in (
hazard_classification_groups):
hazard_classification_groups[hazard_classification['key']] = [
exposure_type]
else:
hazard_classification_groups[
hazard_classification['key']].append(exposure_type)
for hazard_classification_key, exposures in (
iter(list(hazard_classification_groups.items()))):
custom_headers = []
custom_total_values = []
# find total value and labels for each exposure
for exposure_stats in exposures_stats:
if exposure_stats['exposure'] not in exposures:
continue
# label
value_header = exposure_stats['value_header']
custom_headers.append(value_header)
# total value
classification_result = exposure_stats['classification_result']
total_value = classification_result[total_exposed_field['key']]
custom_total_values.append(total_value)
hazard_stats = []
hazard_classification = definition(hazard_classification_key)
for hazard_class in hazard_classification['classes']:
values = []
for exposure_stats in exposures_stats:
if exposure_stats['exposure'] not in exposures:
continue
classification_result = exposure_stats[
'classification_result']
value = classification_result[hazard_class['key']]
values.append(value)
stats = {
'key': hazard_class['key'],
'name': hazard_class['name'],
'numbers': values
}
hazard_stats.append(stats)
total_stats = {
'key': total_exposed_field['key'],
'name': total_exposed_field['name'],
'as_header': True,
'numbers': custom_total_values
}
hazard_stats.append(total_stats)
summary.append({
'header_label': hazard_header,
'value_labels': custom_headers,
'rows': hazard_stats
})
reported_fields_stats = []
for item in reported_fields:
field = item.get('field')
values = []
for exposure_stats in exposures_stats:
reported_fields_result = exposure_stats['reported_fields_result']
value = reported_fields_result[field['key']]
values.append(value)
stats = {
'key': field['key'],
'name': item['header'],
'numbers': values
}
reported_fields_stats.append(stats)
header_label = resolve_from_dictionary(
extra_args, ['reported_fields_header'])
summary.append({
'header_label': header_label,
'value_labels': value_headers,
'rows': reported_fields_stats
})
header = resolve_from_dictionary(extra_args, ['header'])
combined_map_legend_title = ''
for index, map_legend_title in enumerate(map_legend_titles):
combined_map_legend_title += map_legend_title
if not (index + 1) == len(map_legend_titles):
combined_map_legend_title += ', '
table_header_format = resolve_from_dictionary(
extra_args, 'table_header_format')
table_header = table_header_format.format(
title=combined_map_legend_title,
unit=classifications[0]['classification_unit'])
# Section notes
note_format = resolve_from_dictionary(
extra_args, ['concept_notes', 'note_format'])
concepts = resolve_from_dictionary(
extra_args, ['concept_notes', 'general_concepts'])
if population_exist:
concepts += resolve_from_dictionary(
extra_args, ['concept_notes', 'population_concepts'])
notes = []
for concept in concepts:
note = note_format.format(**concept)
notes.append(note)
context['component_key'] = component_metadata.key
context['header'] = header
context['summary'] = summary
context['table_header'] = table_header
context['notes'] = notes
return context