def clean_inasafe_fields(layer):
"""Clean inasafe_fields based on keywords.
1. Must use standard field names.
2. Sum up list of fields' value and put in the standard field name.
3. Remove un-used fields.
:param layer: The layer
:type layer: QgsVectorLayer
"""
fields = []
# Exposure
if layer.keywords['layer_purpose'] == layer_purpose_exposure['key']:
fields = get_fields(
layer.keywords['layer_purpose'], layer.keywords['exposure'])
# Hazard
elif layer.keywords['layer_purpose'] == layer_purpose_hazard['key']:
fields = get_fields(
layer.keywords['layer_purpose'], layer.keywords['hazard'])
# Aggregation
elif layer.keywords['layer_purpose'] == layer_purpose_aggregation['key']:
fields = get_fields(
layer.keywords['layer_purpose'])
# Add displaced_field definition to expected_fields
# for minimum needs calculator.
# If there is no displaced_field keyword, then pass
try:
if layer.keywords['inasafe_fields'][displaced_field['key']]:
fields.append(displaced_field)
except KeyError:
pass
expected_fields = {field['key']: field['field_name'] for field in fields}
# Convert the field name and sum up if needed
new_keywords = {}
for key, val in layer.keywords.get('inasafe_fields').iteritems():
if key in expected_fields:
if isinstance(val, basestring):
val = [val]
sum_fields(layer, key, val)
new_keywords[key] = expected_fields[key]
# Houra, InaSAFE keywords match our concepts !
layer.keywords['inasafe_fields'].update(new_keywords)
to_remove = []
# Remove unnecessary fields (the one that is not in the inasafe_fields)
for field in layer.fields().toList():
if field.name() not in layer.keywords['inasafe_fields'].values():
to_remove.append(field.name())
remove_fields(layer, to_remove)
LOGGER.debug(
'Fields which have been removed from %s : %s'
% (layer.keywords['layer_purpose'], ' '.join(to_remove)))
def update_value_map(layer, exposure_key=None):
"""Assign inasafe values according to definitions for a vector layer.
:param layer: The vector layer.
:type layer: QgsVectorLayer
:param exposure_key: The exposure key.
:type exposure_key: str
:return: The classified vector layer.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = assign_inasafe_values_steps['output_layer_name']
output_layer_name = output_layer_name % layer.keywords['layer_purpose']
keywords = layer.keywords
inasafe_fields = keywords['inasafe_fields']
classification = None
if keywords['layer_purpose'] == layer_purpose_hazard['key']:
if not inasafe_fields.get(hazard_value_field['key']):
raise InvalidKeywordsForProcessingAlgorithm
old_field = hazard_value_field
new_field = hazard_class_field
classification = active_classification(layer.keywords, exposure_key)
elif keywords['layer_purpose'] == layer_purpose_exposure['key']:
if not inasafe_fields.get(exposure_type_field['key']):
raise InvalidKeywordsForProcessingAlgorithm
old_field = exposure_type_field
new_field = exposure_class_field
else:
raise InvalidKeywordsForProcessingAlgorithm
# It's a hazard layer
if exposure_key:
if not active_thresholds_value_maps(keywords, exposure_key):
raise InvalidKeywordsForProcessingAlgorithm
value_map = active_thresholds_value_maps(keywords, exposure_key)
# It's exposure layer
else:
if not keywords.get('value_map'):
raise InvalidKeywordsForProcessingAlgorithm
value_map = keywords.get('value_map')
unclassified_column = inasafe_fields[old_field['key']]
unclassified_index = layer.fields().lookupField(unclassified_column)
reversed_value_map = {}
for inasafe_class, values in list(value_map.items()):
for val in values:
reversed_value_map[val] = inasafe_class
classified_field = QgsField()
classified_field.setType(new_field['type'])
classified_field.setName(new_field['field_name'])
classified_field.setLength(new_field['length'])
classified_field.setPrecision(new_field['precision'])
layer.startEditing()
layer.addAttribute(classified_field)
classified_field_index = layer.fields(). \
lookupField(classified_field.name())
for feature in layer.getFeatures():
attributes = feature.attributes()
source_value = attributes[unclassified_index]
classified_value = reversed_value_map.get(source_value)
if not classified_value:
classified_value = ''
layer.changeAttributeValue(feature.id(), classified_field_index,
classified_value)
layer.commitChanges()
remove_fields(layer, [unclassified_column])
# We transfer keywords to the output.
# We add new class field
inasafe_fields[new_field['key']] = new_field['field_name']
# and we remove hazard value field
inasafe_fields.pop(old_field['key'])
layer.keywords = keywords
layer.keywords['inasafe_fields'] = inasafe_fields
if exposure_key:
value_map_key = 'value_maps'
else:
value_map_key = 'value_map'
if value_map_key in list(layer.keywords.keys()):
layer.keywords.pop(value_map_key)
layer.keywords['title'] = output_layer_name
if classification:
layer.keywords['classification'] = classification
check_layer(layer)
return layer
def zonal_stats(raster, vector):
"""Reclassify a continuous raster layer.
Issue https://github.com/inasafe/inasafe/issues/3190
The algorithm will take care about projections.
We don't want to reproject the raster layer.
So if CRS are different, we reproject the vector layer and then we do a
lookup from the reprojected layer to the original vector layer.
:param raster: The raster layer.
:type raster: QgsRasterLayer
:param vector: The vector layer.
:type vector: QgsVectorLayer
:return: The output of the zonal stats.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = zonal_stats_steps['output_layer_name']
exposure = raster.keywords['exposure']
if raster.crs().authid() != vector.crs().authid():
layer = reproject(vector, raster.crs())
# We prepare the copy
output_layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, output_layer)
else:
layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, layer)
input_band = layer.keywords.get('active_band', 1)
analysis = QgsZonalStatistics(
layer,
raster,
'exposure_',
input_band,
QgsZonalStatistics.Sum)
result = analysis.calculateStatistics(None)
LOGGER.debug(tr('Zonal stats on %s : %s' % (raster.source(), result)))
output_field = exposure_count_field['field_name'] % exposure
if raster.crs().authid() != vector.crs().authid():
output_layer.startEditing()
field = create_field_from_definition(
exposure_count_field, exposure)
output_layer.addAttribute(field)
new_index = output_layer.fields().lookupField(field.name())
old_index = layer.fields().lookupField('exposure_sum')
for feature_input, feature_output in zip(
layer.getFeatures(), output_layer.getFeatures()):
output_layer.changeAttributeValue(
feature_input.id(), new_index, feature_input[old_index])
output_layer.commitChanges()
layer = output_layer
else:
fields_to_rename = {
'exposure_sum': output_field
}
if qgis_version() >= 21600:
rename_fields(layer, fields_to_rename)
else:
copy_fields(layer, fields_to_rename)
remove_fields(layer, list(fields_to_rename.keys()))
layer.commitChanges()
# The zonal stats is producing some None values. We need to fill these
# with 0. See issue : #3778
# We should start a new editing session as previous fields need to be
# committed first.
layer.startEditing()
request = QgsFeatureRequest()
expression = '\"%s\" is None' % output_field
request.setFilterExpression(expression)
request.setFlags(QgsFeatureRequest.NoGeometry)
index = layer.fields().lookupField(output_field)
for feature in layer.getFeatures():
if feature[output_field] is None:
layer.changeAttributeValue(feature.id(), index, 0)
layer.commitChanges()
layer.keywords = raster.keywords.copy()
layer.keywords['inasafe_fields'] = vector.keywords['inasafe_fields'].copy()
layer.keywords['inasafe_default_values'] = (
raster.keywords['inasafe_default_values'].copy())
key = exposure_count_field['key'] % raster.keywords['exposure']
# Special case here, one field is the exposure count and the total.
layer.keywords['inasafe_fields'][key] = output_field
layer.keywords['inasafe_fields'][total_field['key']] = output_field
layer.keywords['exposure_keywords'] = raster.keywords.copy()
layer.keywords['hazard_keywords'] = vector.keywords[
'hazard_keywords'].copy()
layer.keywords['aggregation_keywords'] = (
vector.keywords['aggregation_keywords'])
layer.keywords['layer_purpose'] = (
layer_purpose_aggregate_hazard_impacted['key'])
layer.keywords['title'] = output_layer_name
check_layer(layer)
return layer
def rename_remove_inasafe_fields(layer):
"""Loop over fields and rename fields which are used in InaSAFE.
:param layer: The layer
:type layer: QgsVectorLayer
"""
# Exposure
if layer.keywords['layer_purpose'] == layer_purpose_exposure['key']:
fields = get_fields(
layer.keywords['layer_purpose'], layer.keywords['exposure'])
# Hazard
elif layer.keywords['layer_purpose'] == layer_purpose_hazard['key']:
fields = get_fields(
layer.keywords['layer_purpose'], layer.keywords['hazard'])
# Aggregation
elif layer.keywords['layer_purpose'] == layer_purpose_aggregation['key']:
fields = get_fields(
layer.keywords['layer_purpose'])
# Add displaced_field definition to expected_fields
# for minimum needs calculator.
# If there is no displaced_field keyword, then pass
try:
if layer.keywords['inasafe_fields'][displaced_field['key']]:
fields.append(displaced_field)
except KeyError:
pass
expected_fields = {field['key']: field['field_name'] for field in fields}
# Rename fields
to_rename = {}
new_keywords = {}
for key, val in layer.keywords.get('inasafe_fields').iteritems():
if key in expected_fields:
if expected_fields[key] != val:
to_rename[val] = expected_fields[key]
new_keywords[key] = expected_fields[key]
copy_fields(layer, to_rename)
to_remove = to_rename.keys()
LOGGER.debug(
'Fields which have been renamed from %s :' % (
layer.keywords['layer_purpose']))
for old_name, new_name in to_rename.iteritems():
LOGGER.debug('%s -> %s' % (old_name, new_name))
# Houra, InaSAFE keywords match our concepts !
layer.keywords['inasafe_fields'].update(new_keywords)
# Remove unnecessary fields
for field in layer.fields().toList():
if field.name() not in expected_fields.values():
if field.name() not in to_remove:
to_remove.append(field.name())
remove_fields(layer, to_remove)
LOGGER.debug(
'Fields which have been removed from %s : %s'
% (layer.keywords['layer_purpose'], ' '.join(to_remove)))
def update_value_map(layer, exposure_key=None, callback=None):
"""Assign inasafe values according to definitions for a vector layer.
:param layer: The vector layer.
:type layer: QgsVectorLayer
:param exposure_key: The exposure key.
:type exposure_key: str
:param callback: A function to all to indicate progress. The function
should accept params 'current' (int), 'maximum' (int) and 'step' (str).
Defaults to None.
:type callback: function
:return: The classified vector layer.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = assign_inasafe_values_steps['output_layer_name']
processing_step = assign_inasafe_values_steps['step_name']
output_layer_name = output_layer_name % layer.keywords['layer_purpose']
keywords = layer.keywords
inasafe_fields = keywords['inasafe_fields']
classification = None
if keywords['layer_purpose'] == layer_purpose_hazard['key']:
if not inasafe_fields.get(hazard_value_field['key']):
raise InvalidKeywordsForProcessingAlgorithm
old_field = hazard_value_field
new_field = hazard_class_field
classification = active_classification(layer.keywords, exposure_key)
elif keywords['layer_purpose'] == layer_purpose_exposure['key']:
if not inasafe_fields.get(exposure_type_field['key']):
raise InvalidKeywordsForProcessingAlgorithm
old_field = exposure_type_field
new_field = exposure_class_field
else:
raise InvalidKeywordsForProcessingAlgorithm
# It's a hazard layer
if exposure_key:
if not active_thresholds_value_maps(keywords, exposure_key):
raise InvalidKeywordsForProcessingAlgorithm
value_map = active_thresholds_value_maps(keywords, exposure_key)
# It's exposure layer
else:
if not keywords.get('value_map'):
raise InvalidKeywordsForProcessingAlgorithm
value_map = keywords.get('value_map')
unclassified_column = inasafe_fields[old_field['key']]
unclassified_index = layer.fieldNameIndex(unclassified_column)
reversed_value_map = {}
for inasafe_class, values in value_map.iteritems():
for val in values:
reversed_value_map[val] = inasafe_class
classified_field = QgsField()
classified_field.setType(new_field['type'])
classified_field.setName(new_field['field_name'])
classified_field.setLength(new_field['length'])
classified_field.setPrecision(new_field['precision'])
layer.startEditing()
layer.addAttribute(classified_field)
classified_field_index = layer.fieldNameIndex(classified_field.name())
for feature in layer.getFeatures():
attributes = feature.attributes()
source_value = attributes[unclassified_index]
classified_value = reversed_value_map.get(source_value)
if not classified_value:
classified_value = ''
layer.changeAttributeValue(
feature.id(), classified_field_index, classified_value)
layer.commitChanges()
remove_fields(layer, [unclassified_column])
# We transfer keywords to the output.
# We add new class field
inasafe_fields[new_field['key']] = new_field['field_name']
# and we remove hazard value field
inasafe_fields.pop(old_field['key'])
layer.keywords = keywords
layer.keywords['inasafe_fields'] = inasafe_fields
if exposure_key:
value_map_key = 'value_maps'
else:
value_map_key = 'value_map'
if value_map_key in layer.keywords.keys():
layer.keywords.pop(value_map_key)
layer.keywords['title'] = output_layer_name
if classification:
layer.keywords['classification'] = classification
check_layer(layer)
return layer
def zonal_stats(raster, vector):
"""Reclassify a continuous raster layer.
Issue https://github.com/inasafe/inasafe/issues/3190
The algorithm will take care about projections.
We don't want to reproject the raster layer.
So if CRS are different, we reproject the vector layer and then we do a
lookup from the reprojected layer to the original vector layer.
:param raster: The raster layer.
:type raster: QgsRasterLayer
:param vector: The vector layer.
:type vector: QgsVectorLayer
:return: The output of the zonal stats.
:rtype: QgsVectorLayer
.. versionadded:: 4.0
"""
output_layer_name = zonal_stats_steps['output_layer_name']
exposure = raster.keywords['exposure']
if raster.crs().authid() != vector.crs().authid():
layer = reproject(vector, raster.crs())
# We prepare the copy
output_layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, output_layer)
else:
layer = create_memory_layer(
output_layer_name,
vector.geometryType(),
vector.crs(),
vector.fields()
)
copy_layer(vector, layer)
input_band = layer.keywords.get('active_band', 1)
analysis = QgsZonalStatistics(
layer,
raster,
'exposure_',
input_band,
QgsZonalStatistics.Sum)
result = analysis.calculateStatistics(None)
LOGGER.debug(tr('Zonal stats on %s : %s' % (raster.source(), result)))
output_field = exposure_count_field['field_name'] % exposure
if raster.crs().authid() != vector.crs().authid():
output_layer.startEditing()
field = create_field_from_definition(
exposure_count_field, exposure)
output_layer.addAttribute(field)
new_index = output_layer.fields().lookupField(field.name())
old_index = layer.fields().lookupField('exposure_sum')
for feature_input, feature_output in zip(
layer.getFeatures(), output_layer.getFeatures()):
output_layer.changeAttributeValue(
feature_input.id(), new_index, feature_input[old_index])
output_layer.commitChanges()
layer = output_layer
else:
fields_to_rename = {
'exposure_sum': output_field
}
if qgis_version() >= 21600:
rename_fields(layer, fields_to_rename)
else:
copy_fields(layer, fields_to_rename)
remove_fields(layer, list(fields_to_rename.keys()))
layer.commitChanges()
# The zonal stats is producing some None values. We need to fill these
# with 0. See issue : #3778
# We should start a new editing session as previous fields need to be
# committed first.
layer.startEditing()
request = QgsFeatureRequest()
expression = '\"%s\" is None' % output_field
request.setFilterExpression(expression)
request.setFlags(QgsFeatureRequest.NoGeometry)
index = layer.fields().lookupField(output_field)
for feature in layer.getFeatures():
if feature[output_field] is None:
layer.changeAttributeValue(feature.id(), index, 0)
layer.commitChanges()
layer.keywords = raster.keywords.copy()
layer.keywords['inasafe_fields'] = vector.keywords['inasafe_fields'].copy()
layer.keywords['inasafe_default_values'] = (
raster.keywords['inasafe_default_values'].copy())
key = exposure_count_field['key'] % raster.keywords['exposure']
# Special case here, one field is the exposure count and the total.
layer.keywords['inasafe_fields'][key] = output_field
layer.keywords['inasafe_fields'][total_field['key']] = output_field
layer.keywords['exposure_keywords'] = raster.keywords.copy()
layer.keywords['hazard_keywords'] = vector.keywords[
'hazard_keywords'].copy()
layer.keywords['aggregation_keywords'] = (
vector.keywords['aggregation_keywords'])
layer.keywords['layer_purpose'] = (
layer_purpose_aggregate_hazard_impacted['key'])
layer.keywords['title'] = output_layer_name
check_layer(layer)
return layer
