def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
:param params: Parameters to pass to the post processor.
:type params: dict
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.minimum_needs is not None:
self._raise_error('clear needs to be called before setup')
try:
evacuation_percentage = 1
if 'function_params' in params.keys():
function_params = params['function_params']
if 'evacuation_percentage' in function_params.keys():
evacuation_percentage = function_params[
'evacuation_percentage'].value
evacuation_percentage /= 100.0 # make it decimal
self.impact_total = int(
round(params['impact_total'] * evacuation_percentage))
except (ValueError, TypeError):
self.impact_total = self.NO_DATA_TEXT
self.minimum_needs = filter_needs_parameters(
params['function_params']['minimum needs'])
def __init__(self):
"""
Constructor for AgePostprocessor postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
:param params: dict of parameters to pass to the post processor
"""
AbstractPostprocessor.setup(self, None)
if (self.impact_total is not None or
self.impact_attrs is not None or
self.target_field is not None or
self.valid_type_fields is not None or
self.type_fields is not None):
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.impact_attrs = params['impact_attrs']
self.target_field = params['target_field']
self.valid_type_fields = params['key_attribute']
# find which attribute field has to be used
self.type_fields = []
try:
for key in self.impact_attrs[0].iterkeys():
if key.lower() in self.valid_type_fields:
self.type_fields.append(key)
except IndexError:
pass
if len(self.type_fields) == 0:
self.type_fields = None
self.no_features = False
# there are no features in this postprocessing polygon
if self.impact_attrs == []:
self.no_features = True
def __init__(self):
"""
Constructor for AgePostprocessor postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
# Integer with total number of features/meters affected.
self.impact_total = None
# List of features
self.impact_attrs = None
# Name the field in the impact layer for the result.
self.target_field = None
# The name of field in the exposure layer.
self.valid_type_fields = None
# The value mapping for the exposure layer.
self.value_mapping = None
# Bool to know if there are some features. We will be computed later.
self.no_features = None
# Find which attribute field has to be used
self.type_fields = None
# Dictionary key - display name for the mapping.
self._labels = {}
# The categories order.
self._order = None
def setup(self, params):
"""Concrete implementation to ensure needed parameters are initialized.
:param params: Dict of parameters to pass to the post processor.
:type params: dict
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
try:
# either all 3 ratio are custom set or we use defaults
self.youth_ratio = params['youth_ratio']
self.adult_ratio = params['adult_ratio']
self.elderly_ratio = params['elderly_ratio']
ratios_total = (self.youth_ratio + self.adult_ratio +
self.elderly_ratio)
if ratios_total > 1:
self._raise_error('Age ratios should sum up to 1. Found: '
'%s + %s + %s = %s ' %
(self.youth_ratio, self.adult_ratio,
self.elderly_ratio, ratios_total))
except KeyError:
self._log_message('either all 3 age ratio are custom set or we'
' use defaults')
defaults = get_defaults()
self.youth_ratio = defaults['YOUTH_RATIO']
self.adult_ratio = defaults['ADULT_RATIO']
self.elderly_ratio = defaults['ELDERLY_RATIO']
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
:param params: Parameters to pass to the post processor.
:type params: dict
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.minimum_needs is not None:
self._raise_error('clear needs to be called before setup')
try:
evacuation_percentage = 1
if 'function_params' in params.keys():
function_params = params['function_params']
if 'evacuation_percentage' in function_params.keys():
evacuation_percentage = function_params[
'evacuation_percentage']
evacuation_percentage /= 100.0 # make it decimal
self.impact_total = int(round(params['impact_total'] *
evacuation_percentage))
except (ValueError, TypeError):
self.impact_total = self.NO_DATA_TEXT
self.minimum_needs = params['function_params']['minimum needs']
def clear(self):
"""Clear properly parameters.
"""
AbstractPostprocessor.clear(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.minimum_needs = None
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.no_features = None
self.type_fields = None
self.valid_type_fields = None
self.fields_values = OrderedDict([
('Medical', ['Clinic/Doctor', 'Hospital']),
('Schools', ['School', 'University/College', ]),
('Places of worship', ['Place of Worship - Unitarian',
'Place of Worship - Islam',
'Place of Worship - Buddhist',
'Place of Worship']),
('Residential', ['Residential']),
('Government', ['Government']),
('Public Building', ['Public Building']),
('Fire Station', ['Fire Station']),
('Police Station', ['Police Station']),
('Supermarket', ['Supermarket']),
('Commercial', ['Commercial']),
('Industrial', ['Industrial']),
('Utility', ['Utility']),
('Sports Facility', ['Sports Facility']),
('Other', [])])
self.known_types = []
self._update_known_types()
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.no_features = None
self.type_fields = None
self.valid_type_fields = None
self.fields_values = OrderedDict([
('Medical', ['Clinic/Doctor', 'Hospital']),
('Schools', [
'School',
'University/College',
]),
('Places of worship', [
'Place of Worship - Unitarian', 'Place of Worship - Islam',
'Place of Worship - Buddhist', 'Place of Worship'
]), ('Residential', ['Residential']),
('Government', ['Government']),
('Public Building', ['Public Building']),
('Fire Station', ['Fire Station']),
('Police Station', ['Police Station']),
('Supermarket', ['Supermarket']), ('Commercial', ['Commercial']),
('Industrial', ['Industrial']), ('Utility', ['Utility']),
('Sports Facility', ['Sports Facility']), ('Other', [])
])
self.known_types = []
self._update_known_types()
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
# Integer with total number of features/meters affected.
self.impact_total = None
# List of features
self.impact_attrs = None
# Name the field in the impact layer for the result.
self.target_field = None
# The name of field in the exposure layer.
self.valid_type_fields = None
# The value mapping for the exposure layer.
self.value_mapping = None
# Bool to know if there are some features. We will be computed later.
self.no_features = None
# Find which attribute field has to be used
self.type_fields = None
# Dictionary key - display name for the mapping.
self._labels = {}
# The categories order.
self._order = None
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.minimum_needs = None
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
Args:
params: dict of parameters to pass to the post processor
Returns:
None
Raises:
None
"""
AbstractPostprocessor.setup(self, None)
if (self.impact_total is not None or self.impact_attrs is not None
or self.target_field is not None
or self.type_field is not None):
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.impact_attrs = params['impact_attrs']
self.target_field = params['target_field']
#find which attribute field has to be used
try:
for key in self.impact_attrs[0].iterkeys():
if key in self.valid_type_fields:
self.type_field = key
break
except IndexError:
pass
#there are no features in this postprocessing polygon
if self.impact_attrs == []:
self.noFeatures = True
else:
self.noFeatures = False
def clear(self):
"""Clear the parameters.
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.female_ratio = None
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.no_features = None
self.fields_values = {
'Medical': ['Clinic/Doctor', 'Hospital'],
'Schools': ['School', 'University/College', ],
'Places of worship': ['Place of Worship - Unitarian',
'Place of Worship - Islam',
'Place of Worship - Buddhist',
'Place of Worship'],
'Residential': ['Residential'],
'Government': ['Government'],
'Public Building': ['Public Building'],
'Fire Station': ['Fire Station'],
'Police Station': ['Police Station'],
'Supermarket': ['Supermarket'],
'Commercial': ['Commercial'],
'Industrial': ['Industrial'],
'Utility': ['Utility'],
'Sports Facility': ['Sports Facility'], }
self.type_fields = None
self.valid_type_fields = ['type']
def clear(self):
"""Clear the parameters.
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.female_ratio = None
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
:param params: dict of parameters to pass to the post processor
"""
AbstractPostprocessor.setup(self, None)
if (self.impact_total is not None or self.impact_attrs is not None
or self.target_field is not None
or self.valid_type_fields is not None
or self.type_fields is not None):
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.impact_attrs = params['impact_attrs']
self.target_field = params['target_field']
self.valid_type_fields = params['key_attribute']
# find which attribute field has to be used
self.type_fields = []
try:
for key in self.impact_attrs[0].iterkeys():
if key.lower() in self.valid_type_fields:
self.type_fields.append(key)
except IndexError:
pass
if len(self.type_fields) == 0:
self.type_fields = None
self.no_features = False
# there are no features in this postprocessing polygon
if self.impact_attrs == []:
self.no_features = True
def clear(self):
"""Clear properly parameters.
"""
AbstractPostprocessor.clear(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
Args:
params: dict of parameters to pass to the post processor
Returns:
None
Raises:
None
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
try:
#either all 3 ratio are custom set or we use defaults
self.youth_ratio = params['youth_ratio']
self.adult_ratio = params['adult_ratio']
self.elder_ratio = params['elder_ratio']
except KeyError:
self._log_message('either all 3 age ratio are custom set or we'
' use defaults')
defaults = get_defaults()
self.youth_ratio = defaults['YOUTH_RATIO']
self.adult_ratio = defaults['ADULT_RATIO']
self.elder_ratio = defaults['ELDER_RATIO']
def __init__(self):
"""
Constructor for MinimumNeedsPostprocessor postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.minimum_needs = None
def __init__(self):
"""
Constructor for MinimumNeedsPostprocessor postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.minimum_needs = None
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_classes
"""
AbstractPostprocessor.__init__(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def clear(self):
"""concrete implementation that ensures needed parameters are cleared.
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.type_fields = None
self.valid_type_fields = None
def clear(self):
"""concrete implementation that ensures needed parameters are cleared.
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.type_fields = None
self.valid_type_fields = None
def __init__(self):
"""
Constructor for MinimumNeedsPostprocessor postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.minimum_needs = None
self._description = tr('Aggregates minimum needs.')
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_classes
"""
AbstractPostprocessor.__init__(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def __init__(self):
"""
Constructor for AgePostprocessor postprocessor class.
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.youth_ratio = None
self.adult_ratio = None
self.elderly_ratio = None
self.impact_total = None
def __init__(self):
"""
Constructor for AgePostprocessor postprocessor class.
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.youth_ratio = None
self.adult_ratio = None
self.elderly_ratio = None
self.impact_total = None
def setup(self, params):
"""Initialise parameters.
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.female_ratio is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.female_ratio = params['female_ratio']
if self.female_ratio > 1:
self._raise_error('Female ratio should be lower max 1. Found: '
'%s ' % self.female_ratio)
def setup(self, params):
"""Initialise needed parameters.
"""
AbstractPostprocessor.setup(self, None)
if (self.impact_classes is not None or self.impact_attrs is not None
or self.target_field is not None):
self._raise_error('clear needs to be called before setup')
self.impact_classes = params['impact_classes']
self.impact_attrs = params['impact_attrs']
self.target_field = params['target_field']
self._log_message(self.impact_attrs)
def process(self):
"""concrete implementation it takes care of the needed parameters being
available and performs all the indicators calculations
"""
AbstractPostprocessor.process(self)
if self.impact_total is None or self.minimum_needs is None:
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor' %
self.__class__.__name__)
else:
self._calculate_needs()
def process(self):
"""Performs all the indicators calculations.
"""
AbstractPostprocessor.process(self)
if (self.impact_classes is None or self.impact_attrs is None
or self.target_field is None):
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor' %
self.__class__.__name__)
else:
self._calculate_categories()
def setup(self, params):
"""Initialise parameters.
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.female_ratio is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.female_ratio = params['female_ratio']
if self.female_ratio > 1:
self._raise_error('Female ratio should be lower max 1. Found: '
'%s ' % self.female_ratio)
def process(self):
"""concrete implementation it takes care of the needed parameters being
available and performs all the indicators calculations
"""
AbstractPostprocessor.process(self)
if self.impact_total is None or self.minimum_needs is None:
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor'
% self.__class__.__name__)
else:
self._calculate_needs()
def setup(self, params):
"""Initialise needed parameters.
"""
AbstractPostprocessor.setup(self, None)
if (self.impact_classes is not None or
self.impact_attrs is not None or
self.target_field is not None):
self._raise_error('clear needs to be called before setup')
self.impact_classes = params['impact_classes']
self.impact_attrs = params['impact_attrs']
self.target_field = params['target_field']
self._log_message(self.impact_attrs)
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
:param params: Parameters to pass to the post processor.
:type params: dict
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.minimum_needs is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = int(round(params['impact_total']))
self.minimum_needs = params['function_params']['minimum needs']
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.clear(self)
self.impact_total = None
def process(self):
"""Performs all the indicator calculations.
"""
AbstractPostprocessor.process(self)
if self.impact_total is None:
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor' %
self.__class__.__name__)
else:
self._calculate_total()
self._calculate_youth()
self._calculate_adult()
self._calculate_elderly()
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.type_fields = None
self.valid_type_fields = ['AMENITY', 'TYPE']
self.fields_values = {'Hospitals': ['medical', 'clinic', 'hospital'],
'Places of worship': ['place_of_worship'],
'Schools': ['school']}
def process(self):
"""Performs all the indicators calculations.
"""
AbstractPostprocessor.process(self)
if (self.impact_classes is None or
self.impact_attrs is None or
self.target_field is None):
self._log_message(
'%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor'
% self.__class__.__name__)
else:
self._calculate_categories()
def __init__(self):
"""
Constructor for postprocessor class,
It takes care of defining self.impact_total
"""
AbstractPostprocessor.__init__(self)
self.impact_total = None
self.impact_attrs = None
self.target_field = None
self.type_field = None
self.valid_type_fields = ['amenity', 'type']
self.types = {'Hospitals': ['medical', 'clinic', 'hospital'],
'Places of worship': ['place_of_worship'],
'Schools': ['school']}
def process(self):
"""Concrete implementation that performs all indicators calculations.
"""
AbstractPostprocessor.process(self)
if (self.impact_total is None or self.impact_attrs is None
or self.value_mapping is None or self.target_field is None):
self._log_message(
'%s not all params have been correctly initialized, setup '
'needs to be called before process. Skipping this '
'postprocessor.' % self.__class__.__name__)
else:
for title in self.value_mapping:
self._calculate_type(title)
self.translate_results()
def process(self):
"""Setup parameters parameters and performs all the calculations.
"""
AbstractPostprocessor.process(self)
if self.impact_total is None or self.female_ratio is None:
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor' %
self.__class__.__name__)
else:
self._calculate_total()
self._calculate_females()
self._calculate_weekly_hygene_packs()
self._calculate_weekly_increased_calories()
def process(self):
"""Performs all the indicator calculations.
"""
AbstractPostprocessor.process(self)
if self.impact_total is None:
self._log_message(
'%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor'
% self.__class__.__name__)
else:
self._calculate_total()
self._calculate_youth()
self._calculate_adult()
self._calculate_elderly()
def process(self):
"""Concrete implementation that performs all indicators calculations.
"""
AbstractPostprocessor.process(self)
if (self.impact_total is None or self.impact_attrs is None
or self.target_field is None):
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor' %
self.__class__.__name__)
else:
self._calculate_total()
for title, field_values in self.fields_values.iteritems():
self._calculate_type(title, field_values)
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.clear(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def clear(self):
"""concrete implementation it takes care of the needed parameters being
properly cleared
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.clear(self)
self.impact_classes = None
self.impact_attrs = None
self.target_field = None
def process(self):
"""Concrete implementation that performs all indicators calculations.
"""
AbstractPostprocessor.process(self)
if self.impact_total is None or self.impact_attrs is None or self.target_field is None:
self._log_message(
"%s not all params have been correctly "
"initialized, setup needs to be called before "
"process. Skipping this postprocessor" % self.__class__.__name__
)
else:
self._calculate_total()
for title, field_values in self.fields_values.iteritems():
self._calculate_type(title, field_values)
def process(self):
"""Setup parameters parameters and performs all the calculations.
"""
AbstractPostprocessor.process(self)
if self.impact_total is None or self.female_ratio is None:
self._log_message('%s not all params have been correctly '
'initialized, setup needs to be called before '
'process. Skipping this postprocessor'
% self.__class__.__name__)
else:
self._calculate_total()
self._calculate_females()
self._calculate_weekly_hygene_packs()
self._calculate_weekly_increased_calories()
def process(self):
"""concrete implementation it takes care of the needed parameters being
available and performs all the indicators calculations
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.process(self)
if self.impact_total is None:
self._raise_error('setup needs to be called before process')
self._calculate_total()
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
Args:
params: Dict of parameters to pass to the post processor
Returns:
None
Raises:
None
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.female_ratio is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.female_ratio = params['female_ratio']
def setup(self, params):
"""concrete implementation it takes care of the needed parameters being
initialized
Args:
params: Dict of parameters to pass to the post processor
Returns:
None
Raises:
None
"""
AbstractPostprocessor.setup(self, None)
if self.impact_total is not None or self.female_ratio is not None:
self._raise_error('clear needs to be called before setup')
self.impact_total = params['impact_total']
self.female_ratio = params['female_ratio']
def process(self):
"""Concrete implementation that performs all indicators calculations.
"""
AbstractPostprocessor.process(self)
if (self.impact_total is None or
self.impact_attrs is None or
self.value_mapping is None or
self.target_field is None):
self._log_message(
'%s not all params have been correctly initialized, setup '
'needs to be called before process. Skipping this '
'postprocessor.' % self.__class__.__name__)
else:
for title in self.value_mapping:
self._calculate_type(title)
self.translate_results()
def process(self):
"""concrete implementation it takes care of the needed parameters being
available and performs all the indicators calculations
Args:
None
Returns:
None
Raises:
None
"""
AbstractPostprocessor.process(self)
if self.population_total is None or self.female_ratio is None:
self._raise_error('setup needs to be called before process')
self._calculate_total()
self._calculate_females()
self._calculate_weekly_hygene_packs()
self._calculate_weekly_increased_calories()
