def generate_classified_legend(
analysis,
exposure,
hazard,
debug_mode):
"""Generate an ordered python structure with the classified symbology.
:param analysis: The analysis layer.
:type analysis: QgsVectorLayer
:param exposure: The exposure layer.
:type exposure: QgsVectorLayer
:param hazard: The hazard layer.
:type hazard: QgsVectorLayer
:param debug_mode: Boolean if run in debug mode.
:type debug_mode: bool
:return: The ordered dictionary to use to build the classified style.
:rtype: OrderedDict
"""
# We need to read the analysis layer to get the number of features.
analysis_row = analysis.getFeatures().next()
# Let's style the hazard class in each layers.
hazard_classification = hazard.keywords['classification']
hazard_classification = definition(hazard_classification)
# Let's check if there is some thresholds:
thresholds = hazard.keywords.get('thresholds')
if thresholds:
hazard_unit = hazard.keywords.get('continuous_hazard_unit')
hazard_unit = definition(hazard_unit)['abbreviation']
else:
hazard_unit = None
exposure = exposure.keywords['exposure']
exposure_definitions = definition(exposure)
exposure_units = exposure_definitions['units']
exposure_unit = exposure_units[0]
coefficient = 1
# We check if can use a greater unit, such as kilometre for instance.
if len(exposure_units) > 1:
# We use only two units for now.
delta = coefficient_between_units(
exposure_units[1], exposure_units[0])
all_values_are_greater = True
# We check if all values are greater than the coefficient
for i, hazard_class in enumerate(hazard_classification['classes']):
field_name = hazard_count_field['field_name'] % hazard_class['key']
try:
value = analysis_row[field_name]
except KeyError:
value = 0
if 0 < value < delta:
# 0 is fine, we can still keep the second unit.
all_values_are_greater = False
if all_values_are_greater:
# If yes, we can use this unit.
exposure_unit = exposure_units[1]
coefficient = delta
classes = OrderedDict()
# In debug mode we don't round number.
enable_rounding = not debug_mode
for i, hazard_class in enumerate(hazard_classification['classes']):
# Get the hazard class name.
field_name = hazard_count_field['field_name'] % hazard_class['key']
# Get the number of affected feature by this hazard class.
try:
value = analysis_row[field_name]
except KeyError:
# The field might not exist if no feature impacted in this hazard
# zone.
value = 0
value = format_number(
value,
enable_rounding,
exposure_definitions['use_population_rounding'],
coefficient)
minimum = None
maximum = None
# Check if we need to add thresholds.
if thresholds:
if i == 0:
minimum = thresholds[hazard_class['key']][0]
elif i == len(hazard_classification['classes']) - 1:
maximum = thresholds[hazard_class['key']][1]
else:
minimum = thresholds[hazard_class['key']][0]
maximum = thresholds[hazard_class['key']][1]
label = _format_label(
hazard_class=hazard_class['name'],
value=value,
exposure_unit=exposure_unit['abbreviation'],
minimum=minimum,
maximum=maximum,
hazard_unit=hazard_unit)
classes[hazard_class['key']] = (hazard_class['color'], label)
if exposure_definitions['display_not_exposed'] or debug_mode:
classes[not_exposed_class['key']] = _add_not_exposed(
analysis_row,
enable_rounding,
exposure_definitions['use_population_rounding'],
exposure_unit['abbreviation'],
coefficient)
return classes
def generate_classified_legend(analysis, exposure, hazard, debug_mode):
"""Generate an ordered python structure with the classified symbology.
:param analysis: The analysis layer.
:type analysis: QgsVectorLayer
:param exposure: The exposure layer.
:type exposure: QgsVectorLayer
:param hazard: The hazard layer.
:type hazard: QgsVectorLayer
:param debug_mode: Boolean if run in debug mode.
:type debug_mode: bool
:return: The ordered dictionary to use to build the classified style.
:rtype: OrderedDict
"""
# We need to read the analysis layer to get the number of features.
analysis_row = analysis.getFeatures().next()
# Let's style the hazard class in each layers.
hazard_classification = hazard.keywords['classification']
hazard_classification = definition(hazard_classification)
# Let's check if there is some thresholds:
thresholds = hazard.keywords.get('thresholds')
if thresholds:
hazard_unit = hazard.keywords.get('continuous_hazard_unit')
hazard_unit = definition(hazard_unit)['abbreviation']
else:
hazard_unit = None
exposure = exposure.keywords['exposure']
exposure_definitions = definition(exposure)
exposure_units = exposure_definitions['units']
exposure_unit = exposure_units[0]
coefficient = 1
# We check if can use a greater unit, such as kilometre for instance.
if len(exposure_units) > 1:
# We use only two units for now.
delta = coefficient_between_units(exposure_units[1], exposure_units[0])
all_values_are_greater = True
# We check if all values are greater than the coefficient
for i, hazard_class in enumerate(hazard_classification['classes']):
field_name = hazard_count_field['field_name'] % hazard_class['key']
try:
value = analysis_row[field_name]
except KeyError:
value = 0
if 0 < value < delta:
# 0 is fine, we can still keep the second unit.
all_values_are_greater = False
if all_values_are_greater:
# If yes, we can use this unit.
exposure_unit = exposure_units[1]
coefficient = delta
classes = OrderedDict()
# In debug mode we don't round number.
enable_rounding = not debug_mode
for i, hazard_class in enumerate(hazard_classification['classes']):
# Get the hazard class name.
field_name = hazard_count_field['field_name'] % hazard_class['key']
# Get the number of affected feature by this hazard class.
try:
value = analysis_row[field_name]
except KeyError:
# The field might not exist if no feature impacted in this hazard
# zone.
value = 0
value = format_number(value, enable_rounding,
exposure_definitions['use_population_rounding'],
coefficient)
minimum = None
maximum = None
# Check if we need to add thresholds.
if thresholds:
if i == 0:
minimum = thresholds[hazard_class['key']][0]
elif i == len(hazard_classification['classes']) - 1:
maximum = thresholds[hazard_class['key']][1]
else:
minimum = thresholds[hazard_class['key']][0]
maximum = thresholds[hazard_class['key']][1]
label = _format_label(hazard_class=hazard_class['name'],
value=value,
exposure_unit=exposure_unit['abbreviation'],
minimum=minimum,
maximum=maximum,
hazard_unit=hazard_unit)
classes[hazard_class['key']] = (hazard_class['color'], label)
if exposure_definitions['display_not_exposed'] or debug_mode:
classes[not_exposed_class['key']] = _add_not_exposed(
analysis_row, enable_rounding,
exposure_definitions['use_population_rounding'],
exposure_unit['abbreviation'], coefficient)
return classes
