def test_classification_thresholds(self):
"""Test for classification_thresholds method."""
thresholds = default_classification_thresholds(flood_hazard_classes)
wet_class = flood_hazard_classes['classes'][0]
dry_class = flood_hazard_classes['classes'][1]
expected = {
'dry': [
dry_class['numeric_default_min'],
dry_class['numeric_default_max']
],
'wet': [
wet_class['numeric_default_min'],
wet_class['numeric_default_max']
]
}
self.assertDictEqual(thresholds, expected)
unit_knots_key = unit_knots['key']
thresholds = default_classification_thresholds(
cyclone_au_bom_hazard_classes, unit_knots_key)
category_5_class = cyclone_au_bom_hazard_classes['classes'][0]
category_4_class = cyclone_au_bom_hazard_classes['classes'][1]
category_3_class = cyclone_au_bom_hazard_classes['classes'][2]
category_2_class = cyclone_au_bom_hazard_classes['classes'][3]
category_1_class = cyclone_au_bom_hazard_classes['classes'][4]
tropical_depression_class = cyclone_au_bom_hazard_classes['classes'][5]
expected = {
'tropical_depression': [
tropical_depression_class['numeric_default_min'],
tropical_depression_class['numeric_default_max']
[unit_knots_key]
],
'category_1': [
category_1_class['numeric_default_min'][unit_knots_key],
category_1_class['numeric_default_max'][unit_knots_key]
],
'category_2': [
category_2_class['numeric_default_min'][unit_knots_key],
category_2_class['numeric_default_max'][unit_knots_key]
],
'category_3': [
category_3_class['numeric_default_min'][unit_knots_key],
category_3_class['numeric_default_max'][unit_knots_key]
],
'category_4': [
category_4_class['numeric_default_min'][unit_knots_key],
category_4_class['numeric_default_max'][unit_knots_key]
],
'category_5': [
category_5_class['numeric_default_min'][unit_knots_key],
category_5_class['numeric_default_max']
]
}
self.assertDictEqual(thresholds, expected)
def test_classification_thresholds(self):
"""Test for classification_thresholds method."""
thresholds = default_classification_thresholds(flood_hazard_classes)
wet_class = flood_hazard_classes['classes'][0]
dry_class = flood_hazard_classes['classes'][1]
expected = {
'dry': [
dry_class['numeric_default_min'],
dry_class['numeric_default_max']
],
'wet': [
wet_class['numeric_default_min'],
wet_class['numeric_default_max']
]
}
self.assertDictEqual(thresholds, expected)
unit_knots_key = unit_knots['key']
thresholds = default_classification_thresholds(
cyclone_au_bom_hazard_classes, unit_knots_key)
category_5_class = cyclone_au_bom_hazard_classes['classes'][0]
category_4_class = cyclone_au_bom_hazard_classes['classes'][1]
category_3_class = cyclone_au_bom_hazard_classes['classes'][2]
category_2_class = cyclone_au_bom_hazard_classes['classes'][3]
category_1_class = cyclone_au_bom_hazard_classes['classes'][4]
tropical_depression_class = cyclone_au_bom_hazard_classes['classes'][5]
expected = {
'tropical_depression': [
tropical_depression_class['numeric_default_min'],
tropical_depression_class['numeric_default_max'][
unit_knots_key]
],
'category_1': [
category_1_class['numeric_default_min'][unit_knots_key],
category_1_class['numeric_default_max'][unit_knots_key]
],
'category_2': [
category_2_class['numeric_default_min'][unit_knots_key],
category_2_class['numeric_default_max'][unit_knots_key]
],
'category_3': [
category_3_class['numeric_default_min'][unit_knots_key],
category_3_class['numeric_default_max'][unit_knots_key]
],
'category_4': [
category_4_class['numeric_default_min'][unit_knots_key],
category_4_class['numeric_default_max'][unit_knots_key]
],
'category_5': [
category_5_class['numeric_default_min'][unit_knots_key],
category_5_class['numeric_default_max']
]
}
self.assertDictEqual(thresholds, expected)
def activate_classification(self, exposure, classification=None):
"""Set active to True for classification for the exposure.
If classification = None, all classification set active = False.
:param exposure: Exposure definition.
:type exposure: dict
:param classification: Classification definition.
:type classification: dict
"""
if self.layer_mode == layer_mode_continuous:
selected_unit = self.parent.step_kw_unit.selected_unit()['key']
target = self.thresholds.get(exposure['key'])
if target is None:
self.thresholds[exposure['key']] = {}
target = self.thresholds.get(exposure['key'])
else:
selected_unit = None
target = self.value_maps.get(exposure['key'])
if target is None:
self.value_maps[exposure['key']] = {}
target = self.value_maps.get(exposure['key'])
if classification is not None:
if classification['key'] not in target:
if self.layer_mode == layer_mode_continuous:
default_classes = default_classification_thresholds(
classification, selected_unit)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
else:
default_classes = default_classification_value_maps(
classification)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
return
for classification_key, value in target.items():
if classification is None:
value['active'] = False
continue
if classification_key == classification['key']:
value['active'] = True
else:
value['active'] = False
def activate_classification(self, exposure, classification=None):
"""Set active to True for classification for the exposure.
If classification = None, all classification set active = False.
:param exposure: Exposure definition.
:type exposure: dict
:param classification: Classification definition.
:type classification: dict
"""
if self.layer_mode == layer_mode_continuous:
selected_unit = self.parent.step_kw_unit.selected_unit()['key']
target = self.thresholds.get(exposure['key'])
if target is None:
self.thresholds[exposure['key']] = {}
target = self.thresholds.get(exposure['key'])
else:
selected_unit = None
target = self.value_maps.get(exposure['key'])
if target is None:
self.value_maps[exposure['key']] = {}
target = self.value_maps.get(exposure['key'])
if classification is not None:
if classification['key'] not in target:
if self.layer_mode == layer_mode_continuous:
default_classes = default_classification_thresholds(
classification, selected_unit)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
else:
default_classes = default_classification_value_maps(
classification)
target[classification['key']] = {
'classes': default_classes,
'active': True
}
return
for classification_key, value in target.items():
if classification is None:
value['active'] = False
continue
if classification_key == classification['key']:
value['active'] = True
else:
value['active'] = False
def create_keyword_file(self, algorithm):
"""Create keyword file for the raster file created.
Basically copy a template from keyword file in converter data
and add extra keyword (usually a title)
:param algorithm: Which re-sampling algorithm to use.
valid options are 'nearest' (for nearest neighbour), 'invdist'
(for inverse distance), 'average' (for moving average). Defaults
to 'nearest' if not specified. Note that passing re-sampling alg
parameters is currently not supported. If None is passed it will
be replaced with 'nearest'.
:type algorithm: str
"""
keyword_io = KeywordIO()
# Set thresholds for each exposure
mmi_default_classes = default_classification_thresholds(
earthquake_mmi_scale)
mmi_default_threshold = {
earthquake_mmi_scale['key']: {
'active': True,
'classes': mmi_default_classes
}
}
generic_default_classes = default_classification_thresholds(
generic_hazard_classes)
generic_default_threshold = {
generic_hazard_classes['key']: {
'active': True,
'classes': generic_default_classes
}
}
threshold_keyword = {}
for exposure in exposure_all:
# Not all exposure is supported by earthquake_mmi_scale
if exposure in earthquake_mmi_scale['exposures']:
threshold_keyword[exposure['key']] = mmi_default_threshold
else:
threshold_keyword[exposure['key']] = generic_default_threshold
extra_keywords = {
extra_keyword_earthquake_latitude['key']:
self.latitude,
extra_keyword_earthquake_longitude['key']:
self.longitude,
extra_keyword_earthquake_magnitude['key']:
self.magnitude,
extra_keyword_earthquake_depth['key']:
self.depth,
extra_keyword_earthquake_description['key']:
self.description,
extra_keyword_earthquake_location['key']:
self.location,
extra_keyword_earthquake_event_time['key']:
self.time.strftime('%Y-%m-%dT%H:%M:%S'),
extra_keyword_time_zone['key']:
self.time_zone,
extra_keyword_earthquake_x_minimum['key']:
self.x_minimum,
extra_keyword_earthquake_x_maximum['key']:
self.x_maximum,
extra_keyword_earthquake_y_minimum['key']:
self.y_minimum,
extra_keyword_earthquake_y_maximum['key']:
self.y_maximum,
extra_keyword_earthquake_event_id['key']:
self.event_id
}
for key, value in self.extra_keywords.items():
extra_keywords[key] = value
# Delete empty element.
empty_keys = []
for key, value in extra_keywords.items():
if value is None:
empty_keys.append(key)
for empty_key in empty_keys:
extra_keywords.pop(empty_key)
keywords = {
'hazard': hazard_earthquake['key'],
'hazard_category': hazard_category_single_event['key'],
'keyword_version': inasafe_keyword_version,
'layer_geometry': layer_geometry_raster['key'],
'layer_mode': layer_mode_continuous['key'],
'layer_purpose': layer_purpose_hazard['key'],
'continuous_hazard_unit': unit_mmi['key'],
'classification': earthquake_mmi_scale['key'],
'thresholds': threshold_keyword,
'extra_keywords': extra_keywords,
'active_band': 1
}
if self.algorithm_name:
layer_path = os.path.join(
self.output_dir,
'%s-%s.tif' % (self.output_basename, algorithm))
else:
layer_path = os.path.join(self.output_dir,
'%s.tif' % self.output_basename)
# append title and source to the keywords file
if len(self.title.strip()) == 0:
keyword_title = self.output_basename
else:
keyword_title = self.title
keywords['title'] = keyword_title
hazard_layer = QgsRasterLayer(layer_path, keyword_title)
if not hazard_layer.isValid():
raise InvalidLayerError()
keyword_io.write_keywords(hazard_layer, keywords)
def setup_left_panel(self):
"""Setup the UI for left panel.
Generate all exposure, combobox, and edit button.
"""
hazard = self.parent.step_kw_subcategory.selected_subcategory()
left_panel_heading = QLabel(tr('Classifications'))
left_panel_heading.setFont(big_font)
self.left_layout.addWidget(left_panel_heading)
inner_left_layout = QGridLayout()
row = 0
for exposure in exposure_all:
special_case = False
# Filter out unsupported exposure for the hazard
if exposure in hazard['disabled_exposures']:
# Remove from the storage if the exposure is disabled
if self.layer_mode == layer_mode_continuous:
if exposure['key'] in self.thresholds:
self.thresholds.pop(exposure['key'])
else:
if exposure['key'] in self.value_maps:
self.value_maps.pop(exposure['key'])
continue
# Trick for EQ raster for population #3853
if exposure == exposure_population and hazard == hazard_earthquake:
if is_raster_layer(self.parent.layer):
if self.layer_mode == layer_mode_continuous:
self.use_default_thresholds = True
special_case = True
# Set classification for EQ Raster for Population
self.thresholds[exposure_population['key']] = {
earthquake_mmi_scale['key']: {
'classes': default_classification_thresholds(
earthquake_mmi_scale),
'active': True
}
}
# Add label
# Hazard on Exposure Classifications
label = tr(
'{hazard_name} on {exposure_name} Classifications').format(
hazard_name=hazard['name'],
exposure_name=exposure['name']
)
exposure_label = QLabel(label)
# Add combo box
exposure_combo_box = QComboBox()
hazard_classifications = hazard.get('classifications')
exposure_combo_box.addItem(tr('No classifications'))
exposure_combo_box.setItemData(
0, None, Qt.UserRole)
current_index = 0
i = 0
# Iterate through all available hazard classifications
for hazard_classification in hazard_classifications:
# Skip if the classification is not for the exposure
if 'exposures' in hazard_classification:
if exposure not in hazard_classification['exposures']:
continue
exposure_combo_box.addItem(hazard_classification['name'])
exposure_combo_box.setItemData(
i + 1, hazard_classification, Qt.UserRole)
if self.layer_mode == layer_mode_continuous:
current_hazard_classifications = self.thresholds.get(
exposure['key'])
else:
current_hazard_classifications = self.value_maps.get(
exposure['key'])
if current_hazard_classifications:
current_hazard_classification = \
current_hazard_classifications.get(
hazard_classification['key'])
if current_hazard_classification:
is_active = current_hazard_classification.get('active')
if is_active:
current_index = i + 1
i += 1
# Set current classification
exposure_combo_box.setCurrentIndex(current_index)
# Add edit button
exposure_edit_button = QPushButton(tr('Edit'))
# For special case. Raster EQ on Population.
if special_case:
mmi_index = exposure_combo_box.findText(
earthquake_mmi_scale['name'])
exposure_combo_box.setCurrentIndex(mmi_index)
exposure_combo_box.setEnabled(False)
exposure_edit_button.setEnabled(False)
tool_tip_message = tr(
'InaSAFE use default classification for Raster Earthquake '
'hazard on population.')
exposure_label.setToolTip(tool_tip_message)
exposure_combo_box.setToolTip(tool_tip_message)
exposure_edit_button.setToolTip(tool_tip_message)
else:
if current_index == 0:
# Disable if there is no classification chosen.
exposure_edit_button.setEnabled(False)
exposure_edit_button.clicked.connect(
partial(self.edit_button_clicked,
edit_button=exposure_edit_button,
exposure_combo_box=exposure_combo_box,
exposure=exposure))
exposure_combo_box.currentIndexChanged.connect(
partial(
self.classifications_combo_box_changed,
exposure=exposure,
exposure_combo_box=exposure_combo_box,
edit_button=exposure_edit_button))
# Arrange in layout
inner_left_layout.addWidget(exposure_label, row, 0)
inner_left_layout.addWidget(exposure_combo_box, row, 1)
inner_left_layout.addWidget(exposure_edit_button, row, 2)
# Adding to step's attribute
self.exposures.append(exposure)
self.exposure_combo_boxes.append(exposure_combo_box)
self.exposure_edit_buttons.append(exposure_edit_button)
self.exposure_labels.append(label)
if special_case:
self.special_case_index = len(self.exposures) - 1
row += 1
self.left_layout.addLayout(inner_left_layout)
# To push the inner_left_layout up
self.left_layout.addStretch(1)
def setup_left_panel(self):
"""Setup the UI for left panel.
Generate all exposure, combobox, and edit button.
"""
hazard = self.parent.step_kw_subcategory.selected_subcategory()
left_panel_heading = QLabel(tr('Classifications'))
left_panel_heading.setFont(big_font)
self.left_layout.addWidget(left_panel_heading)
inner_left_layout = QGridLayout()
row = 0
for exposure in exposure_all:
special_case = False
# Filter out unsupported exposure for the hazard
if exposure in hazard['disabled_exposures']:
# Remove from the storage if the exposure is disabled
if self.layer_mode == layer_mode_continuous:
if exposure['key'] in self.thresholds:
self.thresholds.pop(exposure['key'])
else:
if exposure['key'] in self.value_maps:
self.value_maps.pop(exposure['key'])
continue
# Trick for EQ raster for population #3853
if exposure == exposure_population and hazard == hazard_earthquake:
if is_raster_layer(self.parent.layer):
if self.layer_mode == layer_mode_continuous:
self.use_default_thresholds = True
special_case = True
# Set classification for EQ Raster for Population
self.thresholds[exposure_population['key']] = {
earthquake_mmi_scale['key']: {
'classes': default_classification_thresholds(
earthquake_mmi_scale),
'active': True
}
}
# Add label
# Hazard on Exposure Classifications
label = tr(
'{hazard_name} on {exposure_name} Classifications').format(
hazard_name=hazard['name'],
exposure_name=exposure['name']
)
exposure_label = QLabel(label)
# Add combo box
exposure_combo_box = QComboBox()
hazard_classifications = hazard.get('classifications')
exposure_combo_box.addItem(tr('No classifications'))
exposure_combo_box.setItemData(
0, None, Qt.UserRole)
current_index = 0
i = 0
# Iterate through all available hazard classifications
for hazard_classification in hazard_classifications:
# Skip if the classification is not for the exposure
if 'exposures' in hazard_classification:
if exposure not in hazard_classification['exposures']:
continue
exposure_combo_box.addItem(hazard_classification['name'])
exposure_combo_box.setItemData(
i + 1, hazard_classification, Qt.UserRole)
if self.layer_mode == layer_mode_continuous:
current_hazard_classifications = self.thresholds.get(
exposure['key'])
else:
current_hazard_classifications = self.value_maps.get(
exposure['key'])
if current_hazard_classifications:
current_hazard_classification = \
current_hazard_classifications.get(
hazard_classification['key'])
if current_hazard_classification:
is_active = current_hazard_classification.get('active')
if is_active:
current_index = i + 1
i += 1
# Set current classification
exposure_combo_box.setCurrentIndex(current_index)
# Add edit button
exposure_edit_button = QPushButton(tr('Edit'))
# For special case. Raster EQ on Population.
if special_case:
mmi_index = exposure_combo_box.findText(
earthquake_mmi_scale['name'])
exposure_combo_box.setCurrentIndex(mmi_index)
exposure_combo_box.setEnabled(False)
exposure_edit_button.setEnabled(False)
tool_tip_message = tr(
'InaSAFE use default classification for Raster Earthquake '
'hazard on population.')
exposure_label.setToolTip(tool_tip_message)
exposure_combo_box.setToolTip(tool_tip_message)
exposure_edit_button.setToolTip(tool_tip_message)
else:
if current_index == 0:
# Disable if there is no classification chosen.
exposure_edit_button.setEnabled(False)
exposure_edit_button.clicked.connect(
partial(self.edit_button_clicked,
edit_button=exposure_edit_button,
exposure_combo_box=exposure_combo_box,
exposure=exposure))
exposure_combo_box.currentIndexChanged.connect(
partial(
self.classifications_combo_box_changed,
exposure=exposure,
exposure_combo_box=exposure_combo_box,
edit_button=exposure_edit_button))
# Arrange in layout
inner_left_layout.addWidget(exposure_label, row, 0)
inner_left_layout.addWidget(exposure_combo_box, row, 1)
inner_left_layout.addWidget(exposure_edit_button, row, 2)
# Adding to step's attribute
self.exposures.append(exposure)
self.exposure_combo_boxes.append(exposure_combo_box)
self.exposure_edit_buttons.append(exposure_edit_button)
self.exposure_labels.append(label)
if special_case:
self.special_case_index = len(self.exposures) - 1
row += 1
self.left_layout.addLayout(inner_left_layout)
# To push the inner_left_layout up
self.left_layout.addStretch(1)
def create_keyword_file(self, algorithm):
"""Create keyword file for the raster file created.
Basically copy a template from keyword file in converter data
and add extra keyword (usually a title)
:param algorithm: Which re-sampling algorithm to use.
valid options are 'nearest' (for nearest neighbour), 'invdist'
(for inverse distance), 'average' (for moving average). Defaults
to 'nearest' if not specified. Note that passing re-sampling alg
parameters is currently not supported. If None is passed it will
be replaced with 'nearest'.
:type algorithm: str
"""
keyword_io = KeywordIO()
# Set thresholds for each exposure
mmi_default_classes = default_classification_thresholds(
earthquake_mmi_scale
)
mmi_default_threshold = {
earthquake_mmi_scale['key']: {
'active': True,
'classes': mmi_default_classes
}
}
generic_default_classes = default_classification_thresholds(
generic_hazard_classes
)
generic_default_threshold = {
generic_hazard_classes['key']: {
'active': True,
'classes': generic_default_classes
}
}
threshold_keyword = {}
for exposure in exposure_all:
# Not all exposure is supported by earthquake_mmi_scale
if exposure in earthquake_mmi_scale['exposures']:
threshold_keyword[exposure['key']] = mmi_default_threshold
else:
threshold_keyword[
exposure['key']] = generic_default_threshold
extra_keywords = {
extra_keyword_earthquake_latitude['key']: self.latitude,
extra_keyword_earthquake_longitude['key']: self.longitude,
extra_keyword_earthquake_magnitude['key']: self.magnitude,
extra_keyword_earthquake_depth['key']: self.depth,
extra_keyword_earthquake_description['key']: self.description,
extra_keyword_earthquake_location['key']: self.location,
extra_keyword_earthquake_event_time['key']: self.time.strftime(
'%Y-%m-%dT%H:%M:%S'),
extra_keyword_time_zone['key']: self.time_zone,
extra_keyword_earthquake_x_minimum['key']: self.x_minimum,
extra_keyword_earthquake_x_maximum['key']: self.x_maximum,
extra_keyword_earthquake_y_minimum['key']: self.y_minimum,
extra_keyword_earthquake_y_maximum['key']: self.y_maximum,
extra_keyword_earthquake_event_id['key']: self.event_id
}
for key, value in list(self.extra_keywords.items()):
extra_keywords[key] = value
# Delete empty element.
empty_keys = []
for key, value in list(extra_keywords.items()):
if value is None:
empty_keys.append(key)
for empty_key in empty_keys:
extra_keywords.pop(empty_key)
keywords = {
'hazard': hazard_earthquake['key'],
'hazard_category': hazard_category_single_event['key'],
'keyword_version': inasafe_keyword_version,
'layer_geometry': layer_geometry_raster['key'],
'layer_mode': layer_mode_continuous['key'],
'layer_purpose': layer_purpose_hazard['key'],
'continuous_hazard_unit': unit_mmi['key'],
'classification': earthquake_mmi_scale['key'],
'thresholds': threshold_keyword,
'extra_keywords': extra_keywords,
'active_band': 1
}
if self.algorithm_name:
layer_path = os.path.join(
self.output_dir, '%s-%s.tif' % (
self.output_basename, algorithm))
else:
layer_path = os.path.join(
self.output_dir, '%s.tif' % self.output_basename)
# append title and source to the keywords file
if len(self.title.strip()) == 0:
keyword_title = self.output_basename
else:
keyword_title = self.title
keywords['title'] = keyword_title
hazard_layer = QgsRasterLayer(layer_path, keyword_title)
if not hazard_layer.isValid():
raise InvalidLayerError()
keyword_io.write_keywords(hazard_layer, keywords)
