def test_pre_processors_nearby_places(self):
"""Test the pre_processors_nearby_places"""
hazard_layer = load_test_raster_layer(
'gisv4', 'hazard', 'earthquake.asc')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# The exposure is not place but buildings
self.assertFalse(
pre_processors_nearby_places['condition'](impact_function))
hazard_layer = load_test_raster_layer(
'gisv4', 'hazard', 'earthquake.asc')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'places.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# EQ on places, it must be OK.
self.assertTrue(
pre_processors_nearby_places['condition'](impact_function))
def test_pre_processors_earthquake_contour(self):
"""Test the pre_processors_earthquake_contour"""
hazard_layer = load_test_raster_layer(
'gisv4', 'hazard', 'earthquake.asc')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
self.assertTrue(
pre_processor_earthquake_contour['condition'](impact_function))
hazard_layer = load_test_raster_layer(
'hazard', 'classified_flood_20_20.asc')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'places.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# not ok, since the hazard is flood, not earthquake
self.assertFalse(
pre_processor_earthquake_contour['condition'](impact_function))
def test_pre_processors_nearby_places(self):
"""Test the pre_processors_nearby_places"""
hazard_layer = load_test_raster_layer('gisv4', 'hazard',
'earthquake.asc')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# The exposure is not place but buildings
self.assertFalse(
pre_processors_nearby_places['condition'](impact_function))
hazard_layer = load_test_raster_layer('gisv4', 'hazard',
'earthquake.asc')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'places.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# EQ on places, it must be OK.
self.assertTrue(
pre_processors_nearby_places['condition'](impact_function))
def test_pre_processors_earthquake_contour(self):
"""Test the pre_processors_earthquake_contour"""
hazard_layer = load_test_raster_layer('gisv4', 'hazard',
'earthquake.asc')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
self.assertTrue(
pre_processor_earthquake_contour['condition'](impact_function))
hazard_layer = load_test_raster_layer('hazard',
'classified_flood_20_20.asc')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'places.geojson')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
# not ok, since the hazard is flood, not earthquake
self.assertFalse(
pre_processor_earthquake_contour['condition'](impact_function))
def test_old_fields_keywords(self):
"""The IF is not ready with we have some wrong inasafe_fields."""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson',
clone=True)
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
# The layer should be fine.
self.assertEqual(PREPARE_SUCCESS, status, message)
# Now, we remove one field
exposure_layer.startEditing()
field = exposure_layer.keywords['inasafe_fields'].values()[0]
index = exposure_layer.fieldNameIndex(field)
exposure_layer.deleteAttribute(index)
exposure_layer.commitChanges()
# It shouldn't be fine as we removed one field which
# was in inasafe_fields
status, message = impact_function.prepare()
self.assertNotEqual(PREPARE_SUCCESS, status, message)
def test_analysis_earthquake_summary(self):
"""Test we can compute summary after an EQ on population."""
hazard = load_test_raster_layer('gisv4', 'hazard', 'earthquake.asc')
exposure = load_test_raster_layer(
'gisv4', 'exposure', 'raster', 'population.asc')
aggregation = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.hazard = hazard
impact_function.exposure = exposure
impact_function.aggregation = aggregation
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
layer = impact_function.analysis_impacted
classification = hazard.keywords['classification']
classes = definition(classification)['classes']
for hazard_class in classes:
field_name = hazard_count_field['field_name'] % hazard_class['key']
message = '%s is not found in the EQ summary layer.' % field_name
self.assertNotEqual(-1, layer.fieldNameIndex(field_name), message)
check_inasafe_fields(impact_function.analysis_impacted)
check_inasafe_fields(impact_function.aggregation_summary)
def test_profiling(self):
"""Test running impact function on test data."""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.run()
self.assertEqual(ANALYSIS_FAILED_BAD_INPUT, status, message)
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
message = impact_function.performance_log_message().to_text()
expected_result = get_control_text('test-profiling-logs.txt')
for line in expected_result:
line = line.replace('\n', '')
if line == '' or line == '-':
continue
self.assertIn(line, message)
# Notes(IS): For some unknown reason I need to do this to make
# test_provenance pass
del hazard_layer
def test_analysis_earthquake_summary(self):
"""Test we can compute summary after an EQ on population."""
hazard = load_test_raster_layer('gisv4', 'hazard', 'earthquake.asc')
exposure = load_test_raster_layer('gisv4', 'exposure', 'raster',
'population.asc')
aggregation = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.hazard = hazard
impact_function.exposure = exposure
impact_function.aggregation = aggregation
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
layer = impact_function.analysis_impacted
classification = hazard.keywords['classification']
classes = definition(classification)['classes']
for hazard_class in classes:
field_name = hazard_count_field['field_name'] % hazard_class['key']
message = '%s is not found in the EQ summary layer.' % field_name
self.assertNotEqual(-1, layer.fieldNameIndex(field_name), message)
check_inasafe_fields(impact_function.analysis_impacted)
check_inasafe_fields(impact_function.aggregation_summary)
def test_old_fields_keywords(self):
"""The IF is not ready with we have some wrong inasafe_fields."""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4',
'exposure',
'building-points.geojson',
clone=True)
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
# The layer should be fine.
self.assertEqual(PREPARE_SUCCESS, status, message)
# Now, we remove one field
exposure_layer.startEditing()
field = exposure_layer.keywords['inasafe_fields'].values()[0]
index = exposure_layer.fieldNameIndex(field)
exposure_layer.deleteAttribute(index)
exposure_layer.commitChanges()
# It shouldn't be fine as we removed one field which
# was in inasafe_fields
status, message = impact_function.prepare()
self.assertNotEqual(PREPARE_SUCCESS, status, message)
def prepare_impact_function(self):
"""Create analysis as a representation of current situation of IFCW."""
# Impact Functions
impact_function = ImpactFunction()
impact_function.callback = self.progress_callback
# Layers
impact_function.hazard = self.parent.hazard_layer
impact_function.exposure = self.parent.exposure_layer
aggregation = self.parent.aggregation_layer
if aggregation:
impact_function.aggregation = aggregation
impact_function.use_selected_features_only = (
setting('useSelectedFeaturesOnly', False, bool))
else:
mode = setting('analysis_extents_mode')
if self.extent.user_extent:
# This like a hack to transform a geometry to a rectangle.
# self.extent.user_extent is a QgsGeometry.
# impact_function.requested_extent needs a QgsRectangle.
wkt = self.extent.user_extent.exportToWkt()
impact_function.requested_extent = wkt_to_rectangle(wkt)
impact_function.requested_extent_crs = self.extent.crs
elif mode == HAZARD_EXPOSURE_VIEW:
impact_function.requested_extent = (
self.iface.mapCanvas().extent())
impact_function.requested_extent_crs = self.extent.crs
# We don't have any checkbox in the wizard for the debug mode.
impact_function.debug_mode = False
return impact_function
def test_profiling(self):
"""Test running impact function on test data."""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.run()
self.assertEqual(ANALYSIS_FAILED_BAD_INPUT, status, message)
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
message = impact_function.performance_log_message().to_text()
expected_result = get_control_text(
'test-profiling-logs.txt')
for line in expected_result:
line = line.replace('\n', '')
if line == '' or line == '-':
continue
self.assertIn(line, message)
# Notes(IS): For some unknown reason I need to do this to make
# test_provenance pass
del hazard_layer
def test_provenance_without_aggregation(self):
"""Test provenance of impact function without aggregation."""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
hazard = definition(hazard_layer.keywords['hazard'])
exposure = definition(exposure_layer.keywords['exposure'])
hazard_category = definition(hazard_layer.keywords['hazard_category'])
expected_provenance = {
'gdal_version': gdal.__version__,
'host_name': gethostname(),
'map_title': get_map_title(hazard, exposure, hazard_category),
'map_legend_title': exposure['layer_legend_title'],
'inasafe_version': get_version(),
'pyqt_version': PYQT_VERSION_STR,
'qgis_version': QGis.QGIS_VERSION,
'qt_version': QT_VERSION_STR,
'user': getpass.getuser(),
'os': readable_os_version(),
'aggregation_layer': None,
'aggregation_layer_id': None,
'exposure_layer': exposure_layer.source(),
'exposure_layer_id': exposure_layer.id(),
'hazard_layer': hazard_layer.source(),
'hazard_layer_id': hazard_layer.id(),
'analysis_question': get_analysis_question(hazard, exposure),
'aggregation_keywords': None,
'exposure_keywords': deepcopy(exposure_layer.keywords),
'hazard_keywords': deepcopy(hazard_layer.keywords),
}
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
self.maxDiff = None
expected_provenance.update({
'action_checklist': impact_function.action_checklist(),
'analysis_extent': impact_function.analysis_extent.exportToWkt(),
'impact_function_name': impact_function.name,
'impact_function_title': impact_function.title,
'notes': impact_function.notes(),
'requested_extent': impact_function.requested_extent,
'data_store_uri': impact_function.datastore.uri_path,
'start_datetime': impact_function.start_datetime,
'end_datetime': impact_function.end_datetime,
'duration': impact_function.duration
})
self.assertDictEqual(expected_provenance, impact_function.provenance)
def test_impact_function_behaviour(self):
"""Test behaviour of impact function."""
hazard_layer = load_test_vector_layer('hazard',
'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
self.assertEqual(impact_function.name, 'Flood Polygon On Roads Line')
self.assertEqual(impact_function.title, 'be affected')
def test_impact_function_behaviour(self):
"""Test behaviour of impact function."""
hazard_layer = load_test_vector_layer(
'hazard', 'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
self.assertEqual(impact_function.name, 'Flood Polygon On Road Line')
self.assertEqual(impact_function.title, 'be affected')
def run_impact_function(cli_arguments):
"""Runs an analysis and delegates producing pdf and .geojson output layers.
.. versionadded:: 3.2
:param cli_arguments: User inputs.
:type cli_arguments: CommandLineArguments
"""
hazard = get_layer(cli_arguments.hazard, 'Hazard Layer')
exposure = get_layer(cli_arguments.exposure, 'Exposure Layer')
aggregation = None
if cli_arguments.aggregation:
aggregation = get_layer(cli_arguments.aggregation, 'Aggregation Layer')
# Set up impact function
impact_function = ImpactFunction()
impact_function.hazard = hazard
impact_function.exposure = exposure
impact_function.aggregation = aggregation
# Set the datastore
impact_function.datastore = Folder(cli_arguments.output_dir)
impact_function.datastore.default_vector_format = 'geojson'
# Set the extent
if cli_arguments.extent:
impact_function.requested_extent_crs = \
QgsCoordinateReferenceSystem(4326)
try:
impact_function.requested_extent = QgsRectangle(
float(cli_arguments.extent[0]),
float(cli_arguments.extent[1]),
float(cli_arguments.extent[2]),
float(cli_arguments.extent[3])
)
except AttributeError:
print "Extent is not valid..."
pass
# Prepare impact function
status, message = impact_function.prepare()
if status != PREPARE_SUCCESS:
print message.to_text()
return status, message, None
status, message = impact_function.run()
if status != ANALYSIS_SUCCESS:
print message.to_text()
return status, message, None
return status, message, impact_function
def test_raster_post_minimum_needs_value_generation(self):
"""Test minimum needs postprocessors on raster exposure.
Minimum needs postprocessors is defined to only generate values
when exposure contains population data.
Especially important to test, since on raster exposure the population
field is generated on the fly.
The postprocessors need to expect generated population field exists.
"""
# # #
# Test with raster exposure data with population_exposure_count
# exists.
# # #
hazard_layer = load_test_raster_layer(
'hazard', 'tsunami_wgs84.tif')
exposure_layer = load_test_raster_layer(
'exposure', 'pop_binary_raster_20_20.asc')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
# minimum needs fields should exists in the results
self._check_minimum_fields_exists(impact_function)
# TODO: should include demographic postprocessor value too
expected_value = {
u'total_affected': 9.208200000039128,
u'minimum_needs__rice': 25,
u'minimum_needs__toilets': 0,
u'minimum_needs__drinking_water': 161,
u'minimum_needs__clean_water': 616,
u'male': 4,
u'female': 4,
u'youth': 2,
u'adult': 6,
u'elderly': 0,
u'total': 162.7667000000474,
u'minimum_needs__family_kits': 1,
u'total_not_affected': 153.55850000000828,
}
self._check_minimum_fields_value(expected_value, impact_function)
def test_raster_post_minimum_needs_value_generation(self):
"""Test minimum needs postprocessors on raster exposure.
Minimum needs postprocessors is defined to only generate values
when exposure contains population data.
Especially important to test, since on raster exposure the population
field is generated on the fly.
The postprocessors need to expect generated population field exists.
"""
# # #
# Test with raster exposure data with population_exposure_count
# exists.
# # #
hazard_layer = load_test_raster_layer(
'hazard', 'tsunami_wgs84.tif')
exposure_layer = load_test_raster_layer(
'exposure', 'pop_binary_raster_20_20.asc')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
# minimum needs fields should exists in the results
self._check_minimum_fields_exists(impact_function)
# TODO: should include demographic postprocessor value too
expected_value = {
u'total_affected': 9.208200000039128,
u'minimum_needs__rice': 25,
u'minimum_needs__toilets': 0,
u'minimum_needs__drinking_water': 161,
u'minimum_needs__clean_water': 616,
u'male': 4,
u'female': 4,
u'youth': 2,
u'adult': 6,
u'elderly': 0,
u'total': 162.7667000000474,
u'minimum_needs__family_kits': 1,
u'total_not_affected': 153.55850000000828,
}
self._check_minimum_fields_value(expected_value, impact_function)
def test_vector_post_minimum_needs_value_generation(self):
"""Test minimum needs postprocessors on vector exposure.
Test with vector exposure data with population_count_field exists.
Minimum needs postprocessors is defined to only generate values when
exposure contains population data.
"""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'tsunami_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'population.geojson')
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
# minimum needs fields should exists in the results
self._check_minimum_fields_exists(impact_function)
expected_value = {
u'population': 69,
u'total': 9.0,
u'minimum_needs__rice': 491,
u'minimum_needs__clean_water': 11763,
u'minimum_needs__toilets': 8,
u'minimum_needs__drinking_water': 3072,
u'minimum_needs__family_kits': 35,
u'male': 34,
u'female': 34,
u'youth': 17,
u'adult': 45,
u'elderly': 6,
u'total_affected': 6.0,
}
self._check_minimum_fields_value(expected_value, impact_function)
def test_vector_post_minimum_needs_value_generation(self):
"""Test minimum needs postprocessors on vector exposure.
Test with vector exposure data with population_count_field exists.
Minimum needs postprocessors is defined to only generate values when
exposure contains population data.
"""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'tsunami_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'population.geojson')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
# minimum needs fields should exists in the results
self._check_minimum_fields_exists(impact_function)
expected_value = {
u'population': 69,
u'total': 9.0,
u'minimum_needs__rice': 491,
u'minimum_needs__clean_water': 11763,
u'minimum_needs__toilets': 8,
u'minimum_needs__drinking_water': 3072,
u'minimum_needs__family_kits': 35,
u'male': 34,
u'female': 34,
u'youth': 17,
u'adult': 45,
u'elderly': 6,
u'total_affected': 6.0,
}
self._check_minimum_fields_value(expected_value, impact_function)
def impact_function_setup(command_line_arguments,
hazard,
exposure,
aggregation=None):
"""Sets up an analysis object.
.. versionadded:: 3.2
:param command_line_arguments: User inputs.
:type command_line_arguments: CommandLineArguments
:param hazard: Hazard layer
:type hazard: QgsLayer
:param exposure: Exposure Layer
:type exposure: QgsLayer
:param aggregation: Aggregation Layer
:type aggregation: QgsLayer
:raises: Exception
"""
# IF
impact_function = ImpactFunction()
impact_function.hazard = hazard
impact_function.exposure = exposure
impact_function.aggregation = aggregation
impact_function.map_canvas = CANVAS
# QSetting context
settings = QSettings()
crs = settings.value('inasafe/user_extent_crs', '', type=str)
impact_function.requested_extent_crs = QgsCoordinateReferenceSystem(crs)
try:
impact_function.requested_extent = QgsRectangle(
float(command_line_arguments.extent[0]),
float(command_line_arguments.extent[1]),
float(command_line_arguments.extent[2]),
float(command_line_arguments.extent[3]))
except AttributeError:
print "No extents"
pass
return impact_function
def impact_function_setup(
command_line_arguments, hazard, exposure, aggregation=None):
"""Sets up an analysis object.
.. versionadded:: 3.2
:param command_line_arguments: User inputs.
:type command_line_arguments: CommandLineArguments
:param hazard: Hazard layer
:type hazard: QgsLayer
:param exposure: Exposure Layer
:type exposure: QgsLayer
:param aggregation: Aggregation Layer
:type aggregation: QgsLayer
:raises: Exception
"""
# IF
impact_function = ImpactFunction()
impact_function.hazard = hazard
impact_function.exposure = exposure
impact_function.aggregation = aggregation
impact_function.map_canvas = CANVAS
# QSetting context
settings = QSettings()
crs = settings.value('inasafe/user_extent_crs', '', type=str)
impact_function.requested_extent_crs = QgsCoordinateReferenceSystem(crs)
try:
impact_function.requested_extent = QgsRectangle(
float(command_line_arguments.extent[0]),
float(command_line_arguments.extent[1]),
float(command_line_arguments.extent[2]),
float(command_line_arguments.extent[3])
)
except AttributeError:
print "No extents"
pass
return impact_function
def run_task(self, task_item, status_item, count=0, index=''):
"""Run a single task.
:param task_item: Table task_item containing task name / details.
:type task_item: QTableWidgetItem
:param status_item: Table task_item that holds the task status.
:type status_item: QTableWidgetItem
:param count: Count of scenarios that have been run already.
:type count:
:param index: The index for the table item that will be run.
:type index: int
:returns: Flag indicating if the task succeeded or not.
:rtype: bool
"""
self.enable_busy_cursor()
for layer_group in self.layer_group_container:
layer_group.setItemVisibilityChecked(False)
# set status to 'running'
status_item.setText(self.tr('Running'))
# .. see also:: :func:`appendRow` to understand the next 2 lines
variant = task_item.data(QtCore.Qt.UserRole)
value = variant[0]
result = True
if isinstance(value, str):
filename = value
# run script
try:
self.run_script(filename)
# set status to 'OK'
status_item.setText(self.tr('Script OK'))
except Exception as e: # pylint: disable=W0703
# set status to 'fail'
status_item.setText(self.tr('Script Fail'))
LOGGER.exception(
'Running macro failed. The exception: ' + str(e))
result = False
elif isinstance(value, dict):
# start in new project if toggle is active
if self.start_in_new_project:
self.iface.newProject()
# create layer group
group_name = value['scenario_name']
self.layer_group = self.root.addGroup(group_name)
self.layer_group_container.append(self.layer_group)
# Its a dict containing files for a scenario
success, parameters = self.prepare_task(value)
if not success:
# set status to 'running'
status_item.setText(self.tr('Please update scenario'))
self.disable_busy_cursor()
return False
directory = self.output_directory.text()
if self.scenario_directory_radio.isChecked():
directory = self.source_directory.text()
output_directory = os.path.join(directory, group_name)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
# If impact function parameters loaded successfully, initiate IF.
impact_function = ImpactFunction()
impact_function.datastore = Folder(output_directory)
impact_function.datastore.default_vector_format = "geojson"
impact_function.hazard = parameters[layer_purpose_hazard['key']]
impact_function.exposure = (
parameters[layer_purpose_exposure['key']])
if parameters[layer_purpose_aggregation['key']]:
impact_function.aggregation = (
parameters[layer_purpose_aggregation['key']])
elif parameters['extent']:
impact_function.requested_extent = parameters['extent']
impact_function.crs = parameters['crs']
prepare_status, prepare_message = impact_function.prepare()
if prepare_status == PREPARE_SUCCESS:
LOGGER.info('Impact function ready')
status, message = impact_function.run()
if status == ANALYSIS_SUCCESS:
status_item.setText(self.tr('Analysis Success'))
impact_layer = impact_function.impact
if impact_layer.isValid():
layer_list = [
impact_layer,
impact_function.analysis_impacted,
parameters[layer_purpose_hazard['key']],
parameters[layer_purpose_exposure['key']],
parameters[layer_purpose_aggregation['key']]]
QgsProject.instance().addMapLayers(
layer_list, False)
for layer in layer_list:
self.layer_group.addLayer(layer)
map_canvas = QgsProject.instance().mapLayers()
for layer in map_canvas:
# turn of layer visibility if not impact layer
if map_canvas[layer].id() == impact_layer.id():
self.set_layer_visible(
map_canvas[layer], True)
else:
self.set_layer_visible(
map_canvas[layer], False)
# we need to set analysis_impacted as an active layer
# because we need to get all qgis variables that we
# need from this layer for infographic.
if self.iface:
self.iface.setActiveLayer(
impact_function.analysis_impacted)
report_directory = os.path.join(
output_directory, 'output')
# generate map report and impact report
try:
error_code, message = (
impact_function.generate_report(
all_default_report_components,
report_directory))
except BaseException:
status_item.setText(
self.tr('Report failed to generate.'))
else:
LOGGER.info('Impact layer is invalid')
elif status == ANALYSIS_FAILED_BAD_INPUT:
LOGGER.info('Bad input detected')
elif status == ANALYSIS_FAILED_BAD_CODE:
LOGGER.info(
'Impact function encountered a bug: %s' % message)
else:
LOGGER.warning('Impact function not ready')
send_error_message(self, prepare_message)
else:
LOGGER.exception('Data type not supported: "%s"' % value)
result = False
self.disable_busy_cursor()
return result
def test_provenance_without_aggregation(self):
"""Test provenance of impact function without aggregation."""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
hazard = definition(hazard_layer.keywords['hazard'])
exposure = definition(exposure_layer.keywords['exposure'])
hazard_category = definition(hazard_layer.keywords['hazard_category'])
expected_provenance = {
provenance_gdal_version['provenance_key']:
gdal.__version__,
provenance_host_name['provenance_key']:
gethostname(),
provenance_map_title['provenance_key']:
get_map_title(hazard, exposure, hazard_category),
provenance_map_legend_title['provenance_key']:
exposure['layer_legend_title'],
provenance_user['provenance_key']:
getpass.getuser(),
provenance_os['provenance_key']:
readable_os_version(),
provenance_pyqt_version['provenance_key']:
PYQT_VERSION_STR,
provenance_qgis_version['provenance_key']:
QGis.QGIS_VERSION,
provenance_qt_version['provenance_key']:
QT_VERSION_STR,
provenance_inasafe_version['provenance_key']:
get_version(),
provenance_aggregation_layer['provenance_key']:
None,
provenance_aggregation_layer_id['provenance_key']:
None,
provenance_exposure_layer['provenance_key']:
exposure_layer.source(),
provenance_exposure_layer_id['provenance_key']:
exposure_layer.id(),
provenance_hazard_layer['provenance_key']:
hazard_layer.source(),
provenance_hazard_layer_id['provenance_key']:
hazard_layer.id(),
provenance_analysis_question['provenance_key']:
get_analysis_question(hazard, exposure),
provenance_aggregation_keywords['provenance_key']:
None,
provenance_exposure_keywords['provenance_key']:
deepcopy(exposure_layer.keywords),
provenance_hazard_keywords['provenance_key']:
deepcopy(hazard_layer.keywords),
}
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
self.maxDiff = None
expected_provenance.update({
provenance_action_checklist['provenance_key']:
impact_function.action_checklist(),
provenance_analysis_extent['provenance_key']:
impact_function.analysis_extent.exportToWkt(),
provenance_impact_function_name['provenance_key']:
impact_function.name,
provenance_impact_function_title['provenance_key']:
impact_function.title,
provenance_notes['provenance_key']:
impact_function.notes(),
provenance_requested_extent['provenance_key']:
impact_function.requested_extent,
provenance_data_store_uri['provenance_key']:
impact_function.datastore.uri_path,
provenance_start_datetime['provenance_key']:
impact_function.start_datetime,
provenance_end_datetime['provenance_key']:
impact_function.end_datetime,
provenance_duration['provenance_key']:
impact_function.duration
})
self.assertDictContainsSubset(expected_provenance,
impact_function.provenance)
output_layer_provenance_keys = [
provenance_layer_exposure_summary['provenance_key'],
provenance_layer_aggregate_hazard_impacted['provenance_key'],
provenance_layer_aggregation_summary['provenance_key'],
provenance_layer_analysis_impacted['provenance_key'],
provenance_layer_exposure_summary_table['provenance_key']
]
for key in output_layer_provenance_keys:
self.assertIn(key, impact_function.provenance.keys())
def test_ratios_with_raster_exposure(self):
"""Test if we can add defaults to a raster exposure.
See ticket #3851 how to manage ratios with a raster exposure.
"""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'tsunami_vector.geojson')
exposure_layer = load_test_raster_layer(
'gisv4', 'exposure', 'raster', 'population.asc')
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
for layer in impact_function.outputs:
if layer.keywords['layer_purpose'] == (
layer_purpose_analysis_impacted['key']):
analysis = layer
if layer.keywords['layer_purpose'] == (
layer_purpose_aggregate_hazard_impacted['key']):
impact = layer
# We check in the impact layer if we have :
# female default ratio with the default value
index = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, index)
unique_values = impact.uniqueValues(index)
self.assertEqual(1, len(unique_values))
female_ratio = unique_values[0]
# female displaced count and youth displaced count
self.assertNotEqual(
-1, impact.fieldNameIndex(
female_displaced_count_field['field_name']))
self.assertNotEqual(
-1, impact.fieldNameIndex(
youth_displaced_count_field['field_name']))
# Check that we have more than 0 female displaced in the analysis layer
index = analysis.fieldNameIndex(
female_displaced_count_field['field_name'])
female_displaced = analysis.uniqueValues(index)[0]
self.assertGreater(female_displaced, 0)
# Let's check computation
index = analysis.fieldNameIndex(
displaced_field['field_name'])
displaced_population = analysis.uniqueValues(index)[0]
self.assertEqual(
int(displaced_population * female_ratio), female_displaced)
# Check that we have more than 0 youth displaced in the analysis layer
index = analysis.fieldNameIndex(
female_displaced_count_field['field_name'])
value = analysis.uniqueValues(index)[0]
self.assertGreater(value, 0)
# Let do another test with the special aggregation layer
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'tsunami_vector.geojson')
exposure_layer = load_test_raster_layer(
'gisv4', 'exposure', 'raster', 'population.asc')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid_ratios.geojson')
# This aggregation layer has :
# * a field for female ratio : 1, 0.5 and 0
# * use global default for youth ratio
# * do not ust for adult ratio
# * use custom 0.75 for elderly ratio
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.aggregation = aggregation_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We should have a female_ratio with many values
index = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(3, len(values))
# We should have a youth_ratio with global default
index = impact.fieldNameIndex(youth_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(1, len(values))
# We should not have an adult_ratio
index = impact.fieldNameIndex(adult_ratio_field['field_name'])
self.assertEqual(-1, index)
# We should have a elderly_ratio = 0.75
index = impact.fieldNameIndex(elderly_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(1, len(values))
self.assertEqual(0.75, values[0])
def test_provenance_without_aggregation(self):
"""Test provenance of impact function without aggregation."""
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
hazard = definition(hazard_layer.keywords['hazard'])
exposure = definition(exposure_layer.keywords['exposure'])
hazard_category = definition(hazard_layer.keywords['hazard_category'])
expected_provenance = {
provenance_gdal_version['provenance_key']: gdal.__version__,
provenance_host_name['provenance_key']: gethostname(),
provenance_map_title['provenance_key']: get_map_title(
hazard, exposure, hazard_category),
provenance_map_legend_title['provenance_key']: exposure[
'layer_legend_title'],
provenance_user['provenance_key']: getpass.getuser(),
provenance_os['provenance_key']: readable_os_version(),
provenance_pyqt_version['provenance_key']: PYQT_VERSION_STR,
provenance_qgis_version['provenance_key']: QGis.QGIS_VERSION,
provenance_qt_version['provenance_key']: QT_VERSION_STR,
provenance_inasafe_version['provenance_key']: get_version(),
provenance_aggregation_layer['provenance_key']: None,
provenance_aggregation_layer_id['provenance_key']: None,
provenance_exposure_layer['provenance_key']:
exposure_layer.source(),
provenance_exposure_layer_id['provenance_key']:
exposure_layer.id(),
provenance_hazard_layer['provenance_key']: hazard_layer.source(),
provenance_hazard_layer_id['provenance_key']: hazard_layer.id(),
provenance_analysis_question['provenance_key']:
get_analysis_question(hazard, exposure),
provenance_aggregation_keywords['provenance_key']: None,
provenance_exposure_keywords['provenance_key']:
deepcopy(exposure_layer.keywords),
provenance_hazard_keywords['provenance_key']: deepcopy(
hazard_layer.keywords),
}
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
self.maxDiff = None
expected_provenance.update({
provenance_action_checklist['provenance_key']:
impact_function.action_checklist(),
provenance_analysis_extent['provenance_key']:
impact_function.analysis_extent.exportToWkt(),
provenance_impact_function_name['provenance_key']:
impact_function.name,
provenance_impact_function_title['provenance_key']:
impact_function.title,
provenance_notes['provenance_key']: impact_function.notes(),
provenance_requested_extent['provenance_key']: impact_function.
requested_extent,
provenance_data_store_uri['provenance_key']: impact_function.
datastore.uri_path,
provenance_start_datetime['provenance_key']: impact_function.
start_datetime,
provenance_end_datetime['provenance_key']:
impact_function.end_datetime,
provenance_duration['provenance_key']: impact_function.duration
})
self.assertDictContainsSubset(
expected_provenance, impact_function.provenance)
output_layer_provenance_keys = [
provenance_layer_exposure_summary['provenance_key'],
provenance_layer_aggregate_hazard_impacted['provenance_key'],
provenance_layer_aggregation_summary['provenance_key'],
provenance_layer_analysis_impacted['provenance_key'],
provenance_layer_exposure_summary_table['provenance_key']
]
for key in output_layer_provenance_keys:
self.assertIn(key, impact_function.provenance.keys())
def test_earthquake_population_without_aggregation(self):
"""Testing Earthquake in Population without aggregation.
.. versionadded:: 4.0
"""
output_folder = self.fixtures_dir('../output/earthquake_population')
# Classified vector with building-points
shutil.rmtree(output_folder, ignore_errors=True)
hazard_layer = load_test_raster_layer(
'hazard', 'earthquake.tif')
exposure_layer = load_test_raster_layer(
'exposure', 'pop_binary_raster_20_20.asc')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
report_metadata = ReportMetadata(
metadata_dict=standard_impact_report_metadata_html)
impact_report = ImpactReport(
IFACE,
report_metadata,
impact_function=impact_function)
impact_report.output_folder = output_folder
return_code, message = impact_report.process_components()
self.assertEqual(
return_code, ImpactReport.REPORT_GENERATION_SUCCESS, message)
"""Checking generated context"""
empty_component_output_message = 'Empty component output'
# Check Analysis Summary
analysis_summary = impact_report.metadata.component_by_key(
general_report_component['key'])
""":type: safe.report.report_metadata.Jinja2ComponentsMetadata"""
expected_context = {
'table_header': (
u'Estimated Number of people affected per MMI intensity'),
'header': u'General Report',
'summary': [
{
'header_label': u'Hazard Zone',
'rows': [
{'value': 0, 'name': u'X', 'key': 'X'},
{'value': 0, 'name': u'IX', 'key': 'IX'},
{'value': '200', 'name': u'VIII', 'key': 'VIII'},
{'value': 0, 'name': u'VII', 'key': 'VII'},
{'value': 0, 'name': u'VI', 'key': 'VI'},
{'value': 0, 'name': u'V', 'key': 'V'},
{'value': 0, 'name': u'IV', 'key': 'IV'},
{'value': 0, 'name': u'III', 'key': 'III'},
{'value': 0, 'name': u'II', 'key': 'II'},
{'value': 0, 'name': u'I', 'key': 'I'},
{
'as_header': True,
'key': 'total_field',
'name': u'Total',
'value': '200'
}
],
'value_label': u'Count'
},
{
'header_label': u'Population',
'rows': [
{
'value': '200',
'name': u'Affected',
'key': 'total_affected_field',
}, {
'key': 'total_not_affected_field',
'name': u'Not Affected',
'value': '0'
}, {
'key': 'total_not_exposed_field',
'name': u'Not Exposed',
'value': '0'},
{
'value': '200',
'name': u'Displaced',
'key': 'displaced_field'
}, {
'value': '0 - 100',
'name': u'Fatalities',
'key': 'fatalities_field'
}],
'value_label': u'Count'
}
],
'notes': [
'Exposed People: People who are present in hazard zones and '
'are thereby subject to potential losses. In InaSAFE, people '
'who are exposed are those people who are within the extent '
'of the hazard.',
'Affected People: People who are affected by a hazardous '
'event. People can be affected directly or indirectly. '
'Affected people may experience short-term or long-term '
'consequences to their lives, livelihoods or health and in '
'the economic, physical, social, cultural and environmental '
'assets. In InaSAFE, people who are killed during the event '
'are also considered affected.',
'Displaced People: Displaced people are people who, for '
'different reasons and circumstances because of risk or '
'disaster, have to leave their place of residence. '
'In InaSAFE, demographic and minimum needs reports are based '
'on displaced / evacuated people.'
]
}
actual_context = analysis_summary.context
self.assertDictEqual(expected_context, actual_context)
self.assertTrue(
analysis_summary.output, empty_component_output_message)
report_metadata = ReportMetadata(
metadata_dict=infographic_report)
infographic_impact_report = ImpactReport(
IFACE,
report_metadata,
impact_function=impact_function)
infographic_impact_report.output_folder = output_folder
return_code, message = infographic_impact_report.process_components()
self.assertEqual(
return_code, ImpactReport.REPORT_GENERATION_SUCCESS, message)
# check population pie chart if we have 100% donut slice
population_chart_svg = (
infographic_impact_report.metadata.component_by_key(
population_chart_svg_component['key'])
)
expected_slices = [
{
'value': 200,
'show_label': True,
'center': (224.0, 128.0),
'stroke_opacity': 1,
'path': 'M128.000000,0.000000a128.000000,128.000000 0 0 1 '
'0.000000,256.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,-128.000000Z',
'percentage': 100,
'label': u'VIII',
'stroke': u'#ff7000',
'label_position': (256, 0),
'fill': u'#ff7000'
}, {
'value': 100,
'show_label': False,
'center': (32.0, 128.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'-0.000000,-256.000000l0.000000,64.000000a64.000000,'
'64.000000 0 0 0 0.000000,128.000000Z',
'percentage': 50.0,
'label': '',
'stroke': u'#ff7000',
'label_position': (256, 0),
'fill': u'#ff7000'
}, {
'value': 0,
'show_label': False,
'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0,
'label': u'Total Not Affected',
'stroke': '#fff',
'label_position': (256, 0),
'fill': u'#1a9641'
}]
actual_context = population_chart_svg.context['context']
actual_slices = actual_context.slices
self.assertEqual(expected_slices, actual_slices)
self.assertTrue(
population_chart_svg.output, empty_component_output_message)
shutil.rmtree(output_folder, ignore_errors=True)
def test_ratios_with_vector_exposure(self):
"""Test if we can add defaults to a vector exposure."""
# First test, if we do not provide an aggregation,
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'population.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
# Let's remove one field from keywords.
# We monkey patch keywords for testing after `prepare` & before `run`.
fields = impact_function.exposure.keywords['inasafe_fields']
del fields[female_count_field['key']]
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We check the field exist after the IF with only one value.
field = impact.fieldNameIndex(
female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(1, len(unique_ratio), unique_ratio)
self.assertEqual(
unique_ratio[0], female_ratio_default_value['default_value'])
# Second test, if we provide an aggregation without a default ratio 0.2
expected_ratio = 1.0
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'population.geojson')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.debug_mode = True
impact_function.prepare()
# The `prepare` reads keywords from the file.
impact_function.aggregation.keywords['inasafe_default_values'] = {
elderly_ratio_field['key']: expected_ratio
}
fields = impact_function.exposure.keywords['inasafe_fields']
del fields[female_count_field['key']]
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We check the field exist after the IF with only original values.
field = impact.fieldNameIndex(
female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(3, len(unique_ratio), unique_ratio)
# We check the field exist after the IF with only one value.
field = impact.fieldNameIndex(
elderly_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(1, len(unique_ratio), unique_ratio)
self.assertEqual(expected_ratio, unique_ratio[0])
# Third test, if we provide an aggregation with a ratio and the
# exposure has a count, we should a have a ratio from the exposure
# count.
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'population.geojson')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.aggregation = aggregation_layer
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# Check that we have don't have only one unique value since the ratio
# depends on the "population / female count" and we should have at
# least different ratios.
field = impact.fieldNameIndex(
female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertNotEqual(1, len(unique_ratio), unique_ratio)
def test_ratios_with_raster_exposure(self):
"""Test if we can add defaults to a raster exposure.
See ticket #3851 how to manage ratios with a raster exposure.
"""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'tsunami_vector.geojson')
exposure_layer = load_test_raster_layer('gisv4', 'exposure', 'raster',
'population.asc')
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
for layer in impact_function.outputs:
if layer.keywords['layer_purpose'] == (
layer_purpose_analysis_impacted['key']):
analysis = layer
if layer.keywords['layer_purpose'] == (
layer_purpose_aggregate_hazard_impacted['key']):
impact = layer
# We check in the impact layer if we have :
# female default ratio with the default value
index = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, index)
unique_values = impact.uniqueValues(index)
self.assertEqual(1, len(unique_values))
female_ratio = unique_values[0]
# female displaced count and youth displaced count
self.assertNotEqual(
-1,
impact.fieldNameIndex(female_displaced_count_field['field_name']))
self.assertNotEqual(
-1,
impact.fieldNameIndex(youth_displaced_count_field['field_name']))
# Check that we have more than 0 female displaced in the analysis layer
index = analysis.fieldNameIndex(
female_displaced_count_field['field_name'])
female_displaced = analysis.uniqueValues(index)[0]
self.assertGreater(female_displaced, 0)
# Let's check computation
index = analysis.fieldNameIndex(displaced_field['field_name'])
displaced_population = analysis.uniqueValues(index)[0]
self.assertEqual(int(displaced_population * female_ratio),
female_displaced)
# Check that we have more than 0 youth displaced in the analysis layer
index = analysis.fieldNameIndex(
female_displaced_count_field['field_name'])
value = analysis.uniqueValues(index)[0]
self.assertGreater(value, 0)
# Let do another test with the special aggregation layer
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'tsunami_vector.geojson')
exposure_layer = load_test_raster_layer('gisv4', 'exposure', 'raster',
'population.asc')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid_ratios.geojson')
# This aggregation layer has :
# * a field for female ratio : 1, 0.5 and 0
# * use global default for youth ratio
# * do not ust for adult ratio
# * use custom 0.75 for elderly ratio
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.aggregation = aggregation_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We should have a female_ratio with many values
index = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(3, len(values))
# We should have a youth_ratio with global default
index = impact.fieldNameIndex(youth_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(1, len(values))
# We should not have an adult_ratio
index = impact.fieldNameIndex(adult_ratio_field['field_name'])
self.assertEqual(-1, index)
# We should have a elderly_ratio = 0.75
index = impact.fieldNameIndex(elderly_ratio_field['field_name'])
self.assertNotEqual(-1, index)
values = impact.uniqueValues(index)
self.assertEqual(1, len(values))
self.assertEqual(0.75, values[0])
def test_provenance_without_aggregation(self):
"""Test provenance of impact function without aggregation."""
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
hazard = definition(hazard_layer.keywords['hazard'])
exposure = definition(exposure_layer.keywords['exposure'])
hazard_category = definition(hazard_layer.keywords['hazard_category'])
expected_provenance = {
'gdal_version': gdal.__version__,
'host_name': gethostname(),
'map_title': get_map_title(hazard, exposure, hazard_category),
'map_legend_title': exposure['layer_legend_title'],
'inasafe_version': get_version(),
'pyqt_version': PYQT_VERSION_STR,
'qgis_version': QGis.QGIS_VERSION,
'qt_version': QT_VERSION_STR,
'user': getpass.getuser(),
'os': readable_os_version(),
'aggregation_layer': None,
'aggregation_layer_id': None,
'exposure_layer': exposure_layer.source(),
'exposure_layer_id': exposure_layer.id(),
'hazard_layer': hazard_layer.source(),
'hazard_layer_id': hazard_layer.id(),
'analysis_question': get_analysis_question(hazard, exposure),
'aggregation_keywords': None,
'exposure_keywords': deepcopy(exposure_layer.keywords),
'hazard_keywords': deepcopy(hazard_layer.keywords),
}
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
self.maxDiff = None
expected_provenance.update({
'action_checklist':
impact_function.action_checklist(),
'analysis_extent':
impact_function.analysis_extent.exportToWkt(),
'impact_function_name':
impact_function.name,
'impact_function_title':
impact_function.title,
'notes':
impact_function.notes(),
'requested_extent':
impact_function.requested_extent,
'data_store_uri':
impact_function.datastore.uri_path,
'start_datetime':
impact_function.start_datetime,
'end_datetime':
impact_function.end_datetime,
'duration':
impact_function.duration
})
self.assertDictEqual(expected_provenance, impact_function.provenance)
def run_task(self, task_item, status_item, count=0, index=''):
"""Run a single task.
:param task_item: Table task_item containing task name / details.
:type task_item: QTableWidgetItem
:param status_item: Table task_item that holds the task status.
:type status_item: QTableWidgetItem
:param count: Count of scenarios that have been run already.
:type count:
:param index: The index for the table item that will be run.
:type index: int
:returns: Flag indicating if the task succeeded or not.
:rtype: bool
"""
self.enable_busy_cursor()
for layer_group in self.layer_group_container:
layer_group.setVisible(False)
# set status to 'running'
status_item.setText(self.tr('Running'))
# .. see also:: :func:`appendRow` to understand the next 2 lines
variant = task_item.data(QtCore.Qt.UserRole)
value = variant[0]
result = True
if isinstance(value, str):
filename = value
# run script
try:
self.run_script(filename)
# set status to 'OK'
status_item.setText(self.tr('Script OK'))
except Exception as e: # pylint: disable=W0703
# set status to 'fail'
status_item.setText(self.tr('Script Fail'))
LOGGER.exception('Running macro failed. The exception: ' +
str(e))
result = False
elif isinstance(value, dict):
# start in new project if toggle is active
if self.start_in_new_project:
self.iface.newProject()
# create layer group
group_name = value['scenario_name']
self.layer_group = self.root.addGroup(group_name)
self.layer_group_container.append(self.layer_group)
# Its a dict containing files for a scenario
success, parameters = self.prepare_task(value)
if not success:
# set status to 'running'
status_item.setText(self.tr('Please update scenario'))
self.disable_busy_cursor()
return False
# If impact function parameters loaded successfully, initiate IF.
impact_function = ImpactFunction()
impact_function.hazard = parameters[layer_purpose_hazard['key']]
impact_function.exposure = (
parameters[layer_purpose_exposure['key']])
if parameters[layer_purpose_aggregation['key']]:
impact_function.aggregation = (
parameters[layer_purpose_aggregation['key']])
elif parameters['extent']:
impact_function.requested_extent = parameters['extent']
impact_function.requested_extent_crs = parameters['crs']
prepare_status, prepare_message = impact_function.prepare()
if prepare_status == PREPARE_SUCCESS:
LOGGER.info('Impact function ready')
status, message = impact_function.run()
if status == ANALYSIS_SUCCESS:
status_item.setText(self.tr('Analysis Success'))
impact_layer = impact_function.impact
if impact_layer.isValid():
layer_list = [
impact_layer,
parameters[layer_purpose_hazard['key']],
parameters[layer_purpose_exposure['key']],
parameters[layer_purpose_aggregation['key']]]
QgsMapLayerRegistry.instance().addMapLayers(
layer_list, False)
for layer in layer_list:
self.layer_group.addLayer(layer)
map_canvas = QgsMapLayerRegistry.instance().mapLayers()
for layer in map_canvas:
# turn of layer visibility if not impact layer
if map_canvas[layer].id() == impact_layer.id():
self.legend.setLayerVisible(
map_canvas[layer], True)
else:
self.legend.setLayerVisible(
map_canvas[layer], False)
# generate map report and impact report
try:
# this line is to save the impact report in default
# InaSAFE directory.
generate_impact_report(impact_function, self.iface)
generate_impact_map_report(
impact_function,
self.iface)
# this line is to save the report in user specified
# directory.
self.generate_pdf_report(
impact_function,
self.iface,
group_name)
except:
status_item.setText(
self.tr('Report failed to generate.'))
else:
LOGGER.info('Impact layer is invalid')
elif status == ANALYSIS_FAILED_BAD_INPUT:
LOGGER.info('Bad input detected')
elif status == ANALYSIS_FAILED_BAD_CODE:
LOGGER.info('Impact function encountered a bug')
else:
LOGGER.warning('Impact function not ready')
send_error_message(self, prepare_message)
else:
LOGGER.exception('Data type not supported: "%s"' % value)
result = False
self.disable_busy_cursor()
return result
def run_scenario(scenario, use_debug=False):
"""Run scenario.
:param scenario: Dictionary of hazard, exposure, and aggregation.
:type scenario: dict
:param use_debug: If we should use debug_mode when we run the scenario.
:type use_debug: bool
:returns: Tuple(status, Flow dictionary, outputs).
:rtype: list
"""
if os.path.exists(scenario['exposure']):
exposure_path = scenario['exposure']
elif os.path.exists(standard_data_path('exposure', scenario['exposure'])):
exposure_path = standard_data_path('exposure', scenario['exposure'])
elif os.path.exists(
standard_data_path(*(scenario['exposure'].split('/')))):
exposure_path = standard_data_path(*(scenario['exposure'].split('/')))
else:
raise IOError('No exposure file')
if os.path.exists(scenario['hazard']):
hazard_path = scenario['hazard']
elif os.path.exists(standard_data_path('hazard', scenario['hazard'])):
hazard_path = standard_data_path('hazard', scenario['hazard'])
elif os.path.exists(standard_data_path(*(scenario['hazard'].split('/')))):
hazard_path = standard_data_path(*(scenario['hazard'].split('/')))
else:
raise IOError('No hazard file')
if not scenario['aggregation']:
aggregation_path = None
else:
if os.path.exists(scenario['aggregation']):
aggregation_path = scenario['aggregation']
elif os.path.exists(
standard_data_path('aggregation', scenario['aggregation'])):
aggregation_path = standard_data_path('aggregation',
scenario['aggregation'])
elif os.path.exists(
standard_data_path(*(scenario['aggregation'].split('/')))):
aggregation_path = standard_data_path(
*(scenario['aggregation'].split('/')))
else:
raise IOError('No aggregation file')
impact_function = ImpactFunction()
impact_function.debug_mode = use_debug
layer = QgsVectorLayer(hazard_path, 'Hazard', 'ogr')
if not layer.isValid():
layer = QgsRasterLayer(hazard_path, 'Hazard')
impact_function.hazard = layer
layer = QgsVectorLayer(exposure_path, 'Exposure', 'ogr')
if not layer.isValid():
layer = QgsRasterLayer(exposure_path, 'Exposure')
impact_function.exposure = layer
if aggregation_path:
impact_function.aggregation = QgsVectorLayer(aggregation_path,
'Aggregation', 'ogr')
status, message = impact_function.prepare()
if status != 0:
return status, message, None
status, message = impact_function.run()
if status != 0:
return status, message, None
for layer in impact_function.outputs:
if layer.type() == QgsMapLayer.VectorLayer:
check_inasafe_fields(layer)
return status, impact_function.state, impact_function.outputs
def run_scenario(scenario, use_debug=False):
"""Run scenario.
:param scenario: Dictionary of hazard, exposure, and aggregation.
:type scenario: dict
:param use_debug: If we should use debug_mode when we run the scenario.
:type use_debug: bool
:returns: Tuple(status, Flow dictionary, outputs).
:rtype: list
"""
if os.path.exists(scenario['exposure']):
exposure_path = scenario['exposure']
elif os.path.exists(standard_data_path('exposure', scenario['exposure'])):
exposure_path = standard_data_path('exposure', scenario['exposure'])
elif os.path.exists(
standard_data_path(*(scenario['exposure'].split('/')))):
exposure_path = standard_data_path(*(scenario['exposure'].split('/')))
else:
raise IOError('No exposure file')
if os.path.exists(scenario['hazard']):
hazard_path = scenario['hazard']
elif os.path.exists(standard_data_path('hazard', scenario['hazard'])):
hazard_path = standard_data_path('hazard', scenario['hazard'])
elif os.path.exists(standard_data_path(*(scenario['hazard'].split('/')))):
hazard_path = standard_data_path(*(scenario['hazard'].split('/')))
else:
raise IOError('No hazard file')
if not scenario['aggregation']:
aggregation_path = None
else:
if os.path.exists(scenario['aggregation']):
aggregation_path = scenario['aggregation']
elif os.path.exists(standard_data_path(
'aggregation', scenario['aggregation'])):
aggregation_path = standard_data_path(
'aggregation', scenario['aggregation'])
elif os.path.exists(
standard_data_path(*(scenario['aggregation'].split('/')))):
aggregation_path = standard_data_path(
*(scenario['aggregation'].split('/')))
else:
raise IOError('No aggregation file')
impact_function = ImpactFunction()
impact_function.debug_mode = use_debug
layer = QgsVectorLayer(hazard_path, 'Hazard', 'ogr')
if not layer.isValid():
layer = QgsRasterLayer(hazard_path, 'Hazard')
impact_function.hazard = layer
layer = QgsVectorLayer(exposure_path, 'Exposure', 'ogr')
if not layer.isValid():
layer = QgsRasterLayer(exposure_path, 'Exposure')
impact_function.exposure = layer
if aggregation_path:
impact_function.aggregation = QgsVectorLayer(
aggregation_path, 'Aggregation', 'ogr')
status, message = impact_function.prepare()
if status != 0:
return status, message, None
status, message = impact_function.run()
if status != 0:
return status, message, None
for layer in impact_function.outputs:
if layer.type() == QgsMapLayer.VectorLayer:
check_inasafe_fields(layer)
return status, impact_function.state, impact_function.outputs
def test_earthquake_population_without_aggregation(self):
"""Testing Earthquake in Population without aggregation.
.. versionadded:: 4.0
"""
output_folder = self.fixtures_dir('../output/earthquake_population')
# Classified vector with building-points
shutil.rmtree(output_folder, ignore_errors=True)
hazard_layer = load_test_raster_layer(
'hazard', 'earthquake.tif')
exposure_layer = load_test_raster_layer(
'exposure', 'pop_binary_raster_20_20.asc')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
report_metadata = ReportMetadata(
metadata_dict=standard_impact_report_metadata_html)
impact_report = ImpactReport(
IFACE,
report_metadata,
impact_function=impact_function)
impact_report.output_folder = output_folder
return_code, message = impact_report.process_component()
self.assertEqual(
return_code, ImpactReport.REPORT_GENERATION_SUCCESS, message)
"""Checking generated context"""
empty_component_output_message = 'Empty component output'
# Check Analysis Summary
analysis_summary = impact_report.metadata.component_by_key(
general_report_component['key'])
""":type: safe.report.report_metadata.Jinja2ComponentsMetadata"""
expected_context = {
'table_header': u'Estimated Number of people',
'header': u'General Report',
'summary': [
{
'header_label': u'Hazard Zone',
'rows': [{'value': '0', 'name': u'X', 'key': 'X'},
{'value': '0', 'name': u'IX', 'key': 'IX'},
{'value': '200', 'name': u'VIII', 'key': 'VIII'},
{'value': '0', 'name': u'VII', 'key': 'VII'},
{'value': '0', 'name': u'VI', 'key': 'VI'},
{'value': '0', 'name': u'V', 'key': 'V'},
{'value': '0', 'name': u'IV', 'key': 'IV'},
{'value': '0', 'name': u'III', 'key': 'III'},
{'value': '0', 'name': u'II', 'key': 'II'},
{'value': '0', 'name': u'I', 'key': 'I'}],
'value_label': u'Count'
},
{
'header_label': u'Population',
'rows': [{'value': '200',
'name': u'Displaced',
'key':
'displaced_field'},
{'value': '0 - 100',
'name':
u'Fatalities',
'key':
'fatalities_field'}],
'value_label': u'Count'
}
]
}
actual_context = analysis_summary.context
self.assertDictEqual(expected_context, actual_context)
self.assertTrue(
analysis_summary.output, empty_component_output_message)
# check population pie chart if we have 100% donut slice
population_chart_svg = impact_report.metadata.component_by_key(
population_chart_svg_component['key'])
expected_slices = [
{'value': 0, 'show_label': False, 'center': (128.0, 32.0),
'stroke_opacity': 1,
'path': 'M128.000000,0.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'X', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#dd0000'},
{'value': 0, 'show_label': False, 'center': (128.0, 32.0),
'stroke_opacity': 1,
'path': 'M128.000000,0.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'IX', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#ff0000'},
{'value': 200, 'show_label': True, 'center': (224.0, 128.0),
'stroke_opacity': 1,
'path': 'M128.000000,0.000000a128.000000,128.000000 0 0 1 '
'0.000000,256.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,-128.000000Z',
'percentage': 100, 'label': u'VIII', 'stroke': u'#ff7000',
'label_position': (256, 0), 'fill': u'#ff7000'},
{'value': 100, 'show_label': False, 'center': (32.0, 128.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'-0.000000,-256.000000l0.000000,64.000000a64.000000,'
'64.000000 0 0 0 0.000000,128.000000Z',
'percentage': 50.0, 'label': '', 'stroke': u'#ff7000',
'label_position': (256, 0), 'fill': u'#ff7000'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'VII', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#ffa800'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'VI', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#fff000'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'V', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#aaffff'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'IV', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#55ffff'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'III', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#00cfff'},
{'value': 0, 'show_label': False, 'center': (128.0, 224.0),
'stroke_opacity': 1,
'path': 'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage': 0.0, 'label': u'II', 'stroke': '#fff',
'label_position': (256, 0), 'fill': u'#209fff'}]
actual_context = population_chart_svg.context['context']
actual_slices = actual_context.slices
self.assertEqual(expected_slices, actual_slices)
self.assertTrue(
population_chart_svg.output, empty_component_output_message)
shutil.rmtree(output_folder, ignore_errors=True)
def test_minimum_extent(self):
"""Test we can compute the minimum extent in the IF."""
# Without aggregation layer
hazard_layer = load_test_vector_layer('hazard',
'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent without an aggregation layer is '
'failing.')
self.assertTrue(
compare_wkt(
'Polygon (('
'106.8080099999999959 -6.19531000000000009, '
'106.8080099999999959 -6.16752599999999962, '
'106.83456946836641066 -6.16752599999999962, '
'106.83456946836641066 -6.19531000000000009, '
'106.8080099999999959 -6.19531000000000009))',
impact_function.analysis_extent.exportToWkt()), message)
# Without aggregation layer but with a requested_extent
hazard_layer = load_test_vector_layer('hazard',
'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.requested_extent = wkt_to_rectangle(
'POLYGON (('
'106.772279 -6.237576, '
'106.772279 -6.165415, '
'106.885165 -6.165415, '
'106.885165 -6.237576, '
'106.772279 -6.237576'
'))')
impact_function.requested_extent_crs = QgsCoordinateReferenceSystem(
4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent without an aggregation layer but '
'with a requested extent is failing.')
self.assertTrue(
compare_wkt(
'Polygon (('
'106.8080099999999959 -6.19531000000000009, '
'106.8080099999999959 -6.16752599999999962, '
'106.83456946836641066 -6.16752599999999962, '
'106.83456946836641066 -6.19531000000000009, '
'106.8080099999999959 -6.19531000000000009))',
impact_function.analysis_extent.exportToWkt()), message)
# With an aggregation layer, without selection
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'building-points.geojson')
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.use_selected_features_only = False
impact_function.aggregation.select(0)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent with an aggregation layer is '
'failing.')
self.assertTrue(
compare_wkt(
'Polygon ((106.9033179652593617 -6.18324454090033182, '
'106.90331796525939012 -6.2725478115989306, '
'106.72365490843547775 -6.2725478115989306, '
'106.72365490843547775 -6.18324645462287137, '
'106.72365490843547775 -6.09392810187095257, '
'106.81348643684744104 -6.09392810187095257, '
'106.9033179652593617 -6.09392810187095257, '
'106.9033179652593617 -6.18324454090033182))',
impact_function.analysis_extent.exportToWkt()), message)
# With an aggregation layer, with selection
impact_function.use_selected_features_only = True
impact_function.aggregation = aggregation_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent with an aggregation layer and '
'a selection is failing.')
self.assertTrue(
compare_wkt(
'Polygon ((106.72365490843547775 -6.09392810187095257, '
'106.81348643684744104 -6.09392810187095257, '
'106.81348643684744104 -6.18324645462287137, '
'106.72365490843547775 -6.18324645462287137, '
'106.72365490843547775 -6.09392810187095257))',
impact_function.analysis_extent.exportToWkt()), message)
def test_ratios_with_vector_exposure(self):
"""Test if we can add defaults to a vector exposure."""
# First test, if we do not provide an aggregation,
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'population.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.prepare()
# Let's remove one field from keywords.
# We monkey patch keywords for testing after `prepare` & before `run`.
fields = impact_function.exposure.keywords['inasafe_fields']
del fields[female_count_field['key']]
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We check the field exist after the IF with only one value.
field = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(1, len(unique_ratio), unique_ratio)
self.assertEqual(unique_ratio[0],
female_ratio_default_value['default_value'])
# Second test, if we provide an aggregation without a default ratio 0.2
expected_ratio = 1.0
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'population.geojson')
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.debug_mode = True
impact_function.prepare()
# The `prepare` reads keywords from the file.
impact_function.aggregation.keywords['inasafe_default_values'] = {
elderly_ratio_field['key']: expected_ratio
}
fields = impact_function.exposure.keywords['inasafe_fields']
del fields[female_count_field['key']]
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# We check the field exist after the IF with only original values.
field = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(3, len(unique_ratio), unique_ratio)
# We check the field exist after the IF with only one value.
field = impact.fieldNameIndex(elderly_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertEqual(1, len(unique_ratio), unique_ratio)
self.assertEqual(expected_ratio, unique_ratio[0])
# Third test, if we provide an aggregation with a ratio and the
# exposure has a count, we should a have a ratio from the exposure
# count.
hazard_layer = load_test_vector_layer('gisv4', 'hazard',
'classified_vector.geojson')
exposure_layer = load_test_vector_layer('gisv4', 'exposure',
'population.geojson')
aggregation_layer = load_test_vector_layer('gisv4', 'aggregation',
'small_grid.geojson')
# Set up impact function
impact_function = ImpactFunction()
impact_function.debug_mode = True
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.aggregation = aggregation_layer
impact_function.prepare()
status, message = impact_function.run()
self.assertEqual(ANALYSIS_SUCCESS, status, message)
impact = impact_function.impact
# Check that we have don't have only one unique value since the ratio
# depends on the "population / female count" and we should have at
# least different ratios.
field = impact.fieldNameIndex(female_ratio_field['field_name'])
self.assertNotEqual(-1, field)
unique_ratio = impact.uniqueValues(field)
self.assertNotEqual(1, len(unique_ratio), unique_ratio)
def test_earthquake_population_without_aggregation(self):
"""Testing Earthquake in Population without aggregation.
.. versionadded:: 4.0
"""
output_folder = self.fixtures_dir('../output/earthquake_population')
# Classified vector with building-points
shutil.rmtree(output_folder, ignore_errors=True)
hazard_layer = load_test_raster_layer('hazard', 'earthquake.tif')
exposure_layer = load_test_raster_layer('exposure',
'pop_binary_raster_20_20.asc')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.crs = QgsCoordinateReferenceSystem(4326)
impact_function.prepare()
return_code, message = impact_function.run()
self.assertEqual(return_code, ANALYSIS_SUCCESS, message)
report_metadata = ReportMetadata(
metadata_dict=standard_impact_report_metadata_html)
impact_report = ImpactReport(IFACE,
report_metadata,
impact_function=impact_function)
impact_report.output_folder = output_folder
return_code, message = impact_report.process_components()
self.assertEqual(return_code, ImpactReport.REPORT_GENERATION_SUCCESS,
message)
"""Checking generated context."""
empty_component_output_message = 'Empty component output'
# Check Analysis Summary
analysis_summary = impact_report.metadata.component_by_key(
general_report_component['key'])
""":type: safe.report.report_metadata.Jinja2ComponentsMetadata"""
expected_context = {
'table_header':
(u'Estimated Number of people affected per MMI intensity'),
'header':
u'General Report',
'summary': [{
'header_label':
u'Hazard Zone',
'rows': [{
'numbers': ['0'],
'name': u'X',
'key': 'X'
}, {
'numbers': ['0'],
'name': u'IX',
'key': 'IX'
}, {
'numbers': ['200'],
'name': u'VIII',
'key': 'VIII'
}, {
'numbers': ['0'],
'name': u'VII',
'key': 'VII'
}, {
'numbers': ['0'],
'name': u'VI',
'key': 'VI'
}, {
'numbers': ['0'],
'name': u'V',
'key': 'V'
}, {
'numbers': ['0'],
'name': u'IV',
'key': 'IV'
}, {
'numbers': ['0'],
'name': u'III',
'key': 'III'
}, {
'numbers': ['0'],
'name': u'II',
'key': 'II'
}, {
'numbers': ['0'],
'name': u'I',
'key': 'I'
}, {
'as_header': True,
'key': 'total_exposed_field',
'name': u'Total Exposed',
'numbers': ['200']
}],
'value_labels': [u'Count']
}, {
'header_label':
u'Population',
'rows': [{
'numbers': ['200'],
'name': u'Affected',
'key': 'total_affected_field',
}, {
'key': 'total_not_affected_field',
'name': u'Not Affected',
'numbers': ['0']
}, {
'key': 'total_not_exposed_field',
'name': u'Not Exposed',
'numbers': ['0']
}, {
'numbers': ['200'],
'name': u'Displaced',
'key': 'displaced_field'
}, {
'numbers': ['0 - 100'],
'name': u'Fatalities',
'key': 'fatalities_field'
}],
'value_labels': [u'Count']
}],
'notes': [
u'Exposed People: People who are present in hazard zones and '
u'are thereby subject to potential losses. In InaSAFE, people '
u'who are exposed are those people who are within the extent '
u'of the hazard.',
u'Affected People: People who are affected by a hazardous '
u'event. People can be affected directly or indirectly. '
u'Affected people may experience short-term or long-term '
u'consequences to their lives, livelihoods or health and in '
u'the economic, physical, social, cultural and environmental '
u'assets. In InaSAFE, people who are killed during the event '
u'are also considered affected.',
u'Displaced People: Displaced people are people who, for '
u'different reasons and circumstances because of risk or '
u'disaster, have to leave their place of residence. '
u'In InaSAFE, demographic and minimum needs reports are based '
u'on displaced / evacuated people.'
]
}
actual_context = analysis_summary.context
self.assertDictEqual(expected_context, actual_context)
self.assertTrue(analysis_summary.output,
empty_component_output_message)
report_metadata = ReportMetadata(metadata_dict=infographic_report)
infographic_impact_report = ImpactReport(
IFACE, report_metadata, impact_function=impact_function)
infographic_impact_report.output_folder = output_folder
return_code, message = infographic_impact_report.process_components()
self.assertEqual(return_code, ImpactReport.REPORT_GENERATION_SUCCESS,
message)
# check population pie chart if we have 100% donut slice
population_chart_svg = (
infographic_impact_report.metadata.component_by_key(
population_chart_svg_component['key']))
expected_slices = [{
'value':
200,
'show_label':
True,
'center': (224.0, 128.0),
'stroke_opacity':
1,
'path':
'M128.000000,0.000000a128.000000,128.000000 0 0 1 '
'0.000000,256.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,-128.000000Z',
'percentage':
100,
'label':
u'VIII',
'stroke':
u'#ff7000',
'label_position': (256, 0),
'fill':
u'#ff7000'
}, {
'value':
100,
'show_label':
False,
'center': (32.0, 128.0),
'stroke_opacity':
1,
'path':
'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'-0.000000,-256.000000l0.000000,64.000000a64.000000,'
'64.000000 0 0 0 0.000000,128.000000Z',
'percentage':
50.0,
'label':
'',
'stroke':
u'#ff7000',
'label_position': (256, 0),
'fill':
u'#ff7000'
}, {
'value':
0,
'show_label':
False,
'center': (128.0, 224.0),
'stroke_opacity':
1,
'path':
'M128.000000,256.000000a128.000000,128.000000 0 0 1 '
'0.000000,0.000000l-0.000000,-64.000000a64.000000,'
'64.000000 0 0 0 0.000000,0.000000Z',
'percentage':
0.0,
'label':
u'Total Not Affected',
'stroke':
'#fff',
'label_position': (256, 0),
'fill':
u'#1a9641'
}]
actual_context = population_chart_svg.context['context']
actual_slices = actual_context.slices
self.assertEqual(expected_slices, actual_slices)
self.assertTrue(population_chart_svg.output,
empty_component_output_message)
shutil.rmtree(output_folder, ignore_errors=True)
def test_minimum_extent(self):
"""Test we can compute the minimum extent in the IF."""
# Without aggregation layer
hazard_layer = load_test_vector_layer(
'hazard', 'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent without an aggregation layer is '
'failing.')
self.assertTrue(
compare_wkt(
'Polygon (('
'106.8080099999999959 -6.19531000000000009, '
'106.8080099999999959 -6.16752599999999962, '
'106.83456946836641066 -6.16752599999999962, '
'106.83456946836641066 -6.19531000000000009, '
'106.8080099999999959 -6.19531000000000009))',
impact_function.analysis_extent.exportToWkt()),
message
)
# Without aggregation layer but with a requested_extent
hazard_layer = load_test_vector_layer(
'hazard', 'flood_multipart_polygons.shp')
exposure_layer = load_test_vector_layer('exposure', 'roads.shp')
impact_function = ImpactFunction()
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.requested_extent = wkt_to_rectangle(
'POLYGON (('
'106.772279 -6.237576, '
'106.772279 -6.165415, '
'106.885165 -6.165415, '
'106.885165 -6.237576, '
'106.772279 -6.237576'
'))')
impact_function.requested_extent_crs = QgsCoordinateReferenceSystem(
4326)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent without an aggregation layer but '
'with a requested extent is failing.')
self.assertTrue(
compare_wkt(
'Polygon (('
'106.8080099999999959 -6.19531000000000009, '
'106.8080099999999959 -6.16752599999999962, '
'106.83456946836641066 -6.16752599999999962, '
'106.83456946836641066 -6.19531000000000009, '
'106.8080099999999959 -6.19531000000000009))',
impact_function.analysis_extent.exportToWkt()),
message
)
# With an aggregation layer, without selection
hazard_layer = load_test_vector_layer(
'gisv4', 'hazard', 'classified_vector.geojson')
exposure_layer = load_test_vector_layer(
'gisv4', 'exposure', 'building-points.geojson')
aggregation_layer = load_test_vector_layer(
'gisv4', 'aggregation', 'small_grid.geojson')
impact_function = ImpactFunction()
impact_function.aggregation = aggregation_layer
impact_function.exposure = exposure_layer
impact_function.hazard = hazard_layer
impact_function.use_selected_features_only = False
impact_function.aggregation.select(0)
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent with an aggregation layer is '
'failing.')
self.assertTrue(
compare_wkt(
'Polygon ((106.9033179652593617 -6.18324454090033182, '
'106.90331796525939012 -6.2725478115989306, '
'106.72365490843547775 -6.2725478115989306, '
'106.72365490843547775 -6.18324645462287137, '
'106.72365490843547775 -6.09392810187095257, '
'106.81348643684744104 -6.09392810187095257, '
'106.9033179652593617 -6.09392810187095257, '
'106.9033179652593617 -6.18324454090033182))',
impact_function.analysis_extent.exportToWkt()),
message
)
# With an aggregation layer, with selection
impact_function.use_selected_features_only = True
impact_function.aggregation = aggregation_layer
status, message = impact_function.prepare()
self.assertEqual(PREPARE_SUCCESS, status, message)
message = (
'Test about the minimum extent with an aggregation layer and '
'a selection is failing.')
self.assertTrue(
compare_wkt(
'Polygon ((106.72365490843547775 -6.09392810187095257, '
'106.81348643684744104 -6.09392810187095257, '
'106.81348643684744104 -6.18324645462287137, '
'106.72365490843547775 -6.18324645462287137, '
'106.72365490843547775 -6.09392810187095257))',
impact_function.analysis_extent.exportToWkt()),
message
)
