def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'PAGFatalityFunction.',
'name': tr('PAG Fatality Function.'),
'impact': tr('Die or be displaced according Pager model'),
'author': 'Helen Crowley',
'date_implemented': 'N/A',
'overview': tr(
'To assess the impact of earthquake on population based '
'on Population Vulnerability Model Pager'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_earthquake,
'units': [unit_mmi],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def _calculate_total(self):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Total')
if self.target_field is not None:
myName = '%s %s' % (myName, tr(self.target_field).lower())
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'FloodEvacuationFunctionVectorHazard',
'name': tr('Flood Evacuation Function Vector Hazard'),
'impact': tr('Need evacuation'),
'author': 'AIFDR',
'date_implemented': 'N/A',
'overview': tr(
'To assess the impacts of flood inundation '
'in vector format on population.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [hazard_flood],
'units': unit_wetdry,
'layer_constraints': [layer_vector_polygon]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def _calculate_type(self, title, types):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr(title)
if self.target_field is not None:
myName = '%s %s' % (myName, tr(self.target_field).lower())
myResult = 0
if self.type_field is not None:
try:
for building in self.impact_attrs:
if building[self.type_field] in types:
myResult += building[self.target_field]
myResult = int(round(myResult))
except (ValueError, KeyError):
myResult = self.NO_DATA_TEXT
else:
if self.noFeatures:
myResult = 0
else:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_total(self):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Total')
if self.target_field is not None:
myName = '%s %s' % (myName, tr(self.target_field).lower())
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_type(self, title, types):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr(title)
if self.target_field is not None:
myName = '%s %s' % (myName, tr(self.target_field).lower())
myResult = 0
if self.type_field is not None:
try:
for building in self.impact_attrs:
if building[self.type_field] in types:
myResult += building[self.target_field]
myResult = int(round(myResult))
except (ValueError, KeyError):
myResult = self.NO_DATA_TEXT
else:
if self.noFeatures:
myResult = 0
else:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'FloodNativePolygonExperimentalFunction',
'name': tr('Flood Native Polygon Experimental Function'),
'impact': tr('Be-flooded'),
'author': 'Dmitry Kolesov',
'date_implemented': 'N/A',
'overview': tr('N/A'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [hazard_flood],
'units': unit_wetdry,
'layer_constraints': [layer_vector_polygon]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units': [unit_building_type_type],
'layer_constraints': [layer_vector_polygon]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'disabled': True,
'id': 'FloodRasterRoadsExperimentalFunction',
'name': tr('Flood Raster Roads Experimental Function'),
'impact': tr('Be flooded in given thresholds'),
'author': 'Dmitry Kolesov',
'date_implemented': 'N/A',
'overview': tr('N/A'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [hazard_flood, hazard_tsunami],
'units': [unit_metres_depth, unit_feet_depth],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_road,
'units': [unit_road_type_type],
'layer_constraints': [layer_vector_line]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'CategorisedHazardPopulationImpactFunction',
'name': tr('Categorised Hazard Population Impact Function'),
'impact': tr('Be impacted'),
'author': 'AIFDR',
'date_implemented': 'N/A',
'overview': tr(
'To assess the impacts of categorized hazards in raster '
'format on population raster layer.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_all,
'units': [unit_normalised],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'FloodNativePolygonExperimentalFunction',
'name': tr('Flood Native Polygon Experimental Function'),
'impact': tr('Be-flooded'),
'author': 'Dmitry Kolesov',
'date_implemented': 'N/A',
'overview': tr('N/A'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [hazard_flood],
'units': unit_wetdry,
'layer_constraints': [layer_vector_polygon]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units': [unit_building_type_type],
'layer_constraints': [layer_vector_polygon]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'ITBFatalityFunction',
'name': tr('ITB Fatality Function'),
'impact': tr('Die or be displaced'),
'author': 'Hadi Ghasemi',
'date_implemented': 'N/A',
'overview': tr(
'To assess the impact of earthquake on population based '
'on earthquake model developed by ITB'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_earthquake,
'units': [unit_mmi],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def evacuated_population_weekly_needs(population,
minimum_needs=False,
human_names=False):
"""Calculate estimated needs using BNPB Perka 7/2008 minimum bantuan.
:param population: The number of evacuated population.
:type: int, float
:param minimum_needs: Ratios to use when calculating minimum needs.
Defaults to perka 7 as described in assumptions below.
:type minimum_needs: dict
:returns: The weekly needs for the evacuated population.
:rtype: dict
Assumptions:
* 400g rice per person per day
* 2.5L drinking water per person per day
* 15L clean water per person per day
* assume 5 people per family (not in perka - 0.2 people per family)
* 20 people per toilet (0.05 per person)
"""
rice = tr('Rice')
drinking_water = tr('Drinking Water')
water = tr('Water')
family_kits = tr('Family Kits')
toilets = tr('Toilets')
if not minimum_needs:
minimum_needs = default_minimum_needs()
min_rice = minimum_needs[rice]
min_drinking_water = minimum_needs[drinking_water]
min_water = minimum_needs[water]
min_family_kits = minimum_needs[family_kits]
min_toilets = minimum_needs[toilets]
val_rice = int(ceil(population * min_rice))
val_drinking_water = int(ceil(population * min_drinking_water))
val_water = int(ceil(population * min_water))
val_family_kits = int(ceil(population * min_family_kits))
val_toilets = int(ceil(population * min_toilets))
if human_names:
weekly_needs = {
rice: val_rice,
drinking_water: val_drinking_water,
water: val_water,
family_kits: val_family_kits,
toilets: val_toilets}
else:
weekly_needs = {
'rice': val_rice,
'drinking_water': val_drinking_water,
'water': val_water,
'family_kits': val_family_kits,
'toilets': val_toilets}
return weekly_needs
def default_minimum_needs():
"""Helper to get the default minimum needs.
.. note:: Key names will be translated.
"""
rice = tr('Rice')
drinking_water = tr('Drinking Water')
water = tr('Water')
family_kits = tr('Family Kits')
toilets = tr('Toilets')
minimum_needs = OrderedDict([(rice, 2.8), (drinking_water, 17.5),
(water, 105), (family_kits, 0.2),
(toilets, 0.05)])
return minimum_needs
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'FloodBuildingImpactFunction',
'name':
tr('Flood Building Impact Function'),
'impact':
tr('Be flooded'),
'author': ['Ole Nielsen', 'Kristy van Putten'],
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of (flood or tsunami) inundation '
'on building footprints originating from OpenStreetMap '
'(OSM).'),
'categories': {
'hazard': {
'definition':
hazard_definition,
'subcategory': [hazard_flood, hazard_tsunami],
'units':
[unit_wetdry, unit_metres_depth, unit_feet_depth],
'layer_constraints': [
layer_vector_polygon,
layer_raster_numeric,
]
},
'exposure': {
'definition':
exposure_definition,
'subcategory':
exposure_structure,
'units':
[unit_building_type_type, unit_building_generic],
'layer_constraints':
[layer_vector_polygon, layer_vector_point]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'FloodBuildingImpactFunction',
'name': tr('Flood Building Impact Function'),
'impact': tr('Be flooded'),
'author': ['Ole Nielsen', 'Kristy van Putten'],
'date_implemented': 'N/A',
'overview': tr(
'To assess the impacts of (flood or tsunami) inundation '
'on building footprints originating from OpenStreetMap '
'(OSM).'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [
hazard_flood,
hazard_tsunami
],
'units': [
unit_wetdry,
unit_metres_depth,
unit_feet_depth],
'layer_constraints': [
layer_vector_polygon,
layer_raster_numeric,
]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units': [
unit_building_type_type,
unit_building_generic],
'layer_constraints': [
layer_vector_polygon,
layer_vector_point]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'EarthQuakeBuildingImpactFunction',
'name':
tr('Earthquake Building Impact Function'),
'impact':
tr('Be affected'),
'author':
'N/A',
'date_implemented':
'N/A',
'overview':
tr('This impact function will calculate the impact of an '
'earthquake on buildings, reporting how many are expected '
'to be damaged etc.'),
'categories': {
'hazard': {
'definition':
hazard_definition,
'subcategory':
hazard_earthquake,
'units': [unit_mmi],
'layer_constraints':
[layer_vector_polygon, layer_raster_numeric]
},
'exposure': {
'definition':
exposure_definition,
'subcategory':
exposure_structure,
'units':
[unit_building_type_type, unit_building_generic],
'layer_constraints':
[layer_vector_polygon, layer_vector_point]
}
}
}
return dict_meta
def _calculate_weekly_increased_calories(self):
"""Weekly additional kg of rice for pregnant and lactating women
indicator.
This indicator reports the weekly additional kg of rice for pregnant
and lactating women.
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
name = tr('Additional weekly rice kg for pregnant and lactating women')
meta = {
'description':
'Additional rice kg per week for pregnant and '
'lactating women'
}
# weekly Kg rice =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
lact_kg = self.impact_total * self.female_ratio * 2 * 0.033782
preg_kg = self.impact_total * self.female_ratio * 2 * 0.01281
result = lact_kg + preg_kg
try:
result = int(round(result))
except ValueError:
result = self.NO_DATA_TEXT
self._append_result(name, result, meta)
def _calculate_weekly_hygene_packs(self):
"""Weekly requirements of female hygiene packs indicator.
This indicator reports the weekly requirements of female hygiene packs
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
name = tr('Weekly hygiene packs')
meta = {'description': 'Females hygiene packs for weekly use'}
# weekly hygene packs =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
result = self.impact_total * self.female_ratio * 0.7937 * (7 / 7)
try:
result = int(round(result))
except ValueError:
result = self.NO_DATA_TEXT
self._append_result(name, result, meta)
def _calculate_adult(self):
"""Indicator that shows population between 15 and 64 years old.
this indicator reports the amount of young population according to the
set adult_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Adult count')
#FIXME (MB) Shameless hack to deal with issue #368
if self.population_total > 8000000000 or self.population_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.population_total * self.adult_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_total(self):
"""Indicator that shows total population.
This indicator reports the total population.
"""
name = tr('Total')
if self.target_field is not None:
name = '%s %s' % (name, tr(self.target_field).lower())
result = self.impact_total
try:
result = int(round(result))
except ValueError:
result = self.NO_DATA_TEXT
self._append_result(name, result)
def description(self):
"""Describe briefly what the post processor does.
:returns: The translated description.
:rtype: str
"""
return tr('Calculates building types related statistics.')
def _calculate_weekly_hygene_packs(self):
"""Weekly requirements of female hygiene packs indicator.
This indicator reports the weekly requirements of female hygiene packs
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
myName = tr("Weekly hygiene packs")
myMeta = {"description": "Females hygiene packs for weekly use"}
# weekly hygene packs =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
myResult = self.impact_total * self.female_ratio * 0.7937 * (7 / 7)
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult, myMeta)
def _calculate_females(self):
"""Female population count indicator.
this indicator reports the amount of female population according to the
set female_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Female population')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total * self.female_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_elder(self):
"""Indicator that shows population above 64 years old.
this indicator reports the amount of young population according to the
set elder_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Elderly count (affected)')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total * self.elder_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_total(self):
"""Total population indicator.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Total')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
try:
myResult = self.impact_total
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_weekly_hygene_packs(self):
"""Weekly requirements of female hygiene packs indicator.
This indicator reports the weekly requirements of female hygiene packs
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Weekly hygiene packs')
myMeta = {'description': 'Females hygiene packs for weekly use'}
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT, myMeta)
return
#weekly hygene packs =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
myResult = self.impact_total * self.female_ratio * 0.7937 * (7 / 7)
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult, myMeta)
def _calculate_adult(self):
"""Indicator that shows population between 15 and 64 years old.
this indicator reports the amount of young population according to the
set adult_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Adult count')
#FIXME (MB) Shameless hack to deal with issue #368
if self.population_total > 8000000000 or self.population_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.population_total * self.adult_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_weekly_increased_calories(self):
"""Weekly additional kg of rice for pregnant and lactating women
indicator.
This indicator reports the weekly additional kg of rice for pregnant
and lactating women.
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Additional weekly rice kg for pregnant and lactating'
' women')
myMeta = {'description': 'Additional rice kg per week for pregnant and'
' lactating women'}
#weekly Kg rice =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
myLactKg = self.impact_total * self.female_ratio * 2 * 0.033782
myPregKg = self.impact_total * self.female_ratio * 2 * 0.01281
myResult = myLactKg + myPregKg
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult, myMeta)
def categorical_style(target_field,
categories,
no_data_value,
no_data_label,
data_defined_saturation_field=None,
max_impact_value=None):
"""Style with equidistant hue and optional data defined saturation
:param target_field: field name that needs to be classified
:type: target_field, str
:param categories: values of target_field
:type: categories, list
:param no_data_value: value for no data
:type: no_data_value, int, str
:param no_data_label: label for the no data category
:type: no_data_label, str
:param data_defined_saturation_field: field for saturation for the
generated colors.
:type: data_defined_saturation_field, None, str
:param max_impact_value: maximum value in data_defined_saturation_field,
used to normalize saturation values for the generated colors.
:type: max_impact_value, int, float
:returns: a dict with target_field, style_classes and style_type
:rtype: dict
"""
# Create style
classes = []
colors = generate_categorical_color_ramp(len(categories))
for index, category in enumerate(categories):
hsv = colors['hsv'][index]
if category == no_data_value:
label = no_data_label
else:
label = '%s %s' % (tr('Category'), category)
style_class = dict(label=label,
value=category,
colour=colors['hex'][index],
border_color=colors['hex'][index],
border_width=0.8,
transparency=0,
size=1)
if data_defined_saturation_field is not None:
# expr is like 'color_hsv(270.0, "pop"/9807.0*100, 70.0)'
expr = 'color_hsv(%s, "%s"/%s*100, %s)' % (
hsv[0] * 360, data_defined_saturation_field, max_impact_value,
hsv[2] * 100)
style_class.update({'data_defined': {'color': expr}})
classes.append(style_class)
style_info = dict(target_field=target_field,
style_classes=classes,
style_type='categorizedSymbol')
return style_info
def _calculate_categories(self):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
impact_name = tr(self.target_field).lower()
results = {}
for impact_class in self.impact_classes:
results[impact_class] = 0
for feature in self.impact_attrs:
myTarget = feature[self.target_field]
results[myTarget] += 1
for impact_class in self.impact_classes:
result = results[impact_class]
self._append_result('%s %s' % (impact_name, impact_class), result)
def _calculate_categories(self):
"""Indicator that shows total population.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
impact_name = tr(self.target_field).lower()
results = {}
for impact_class in self.impact_classes:
results[impact_class] = 0
for feature in self.impact_attrs:
myTarget = feature[self.target_field]
results[myTarget] += 1
for impact_class in self.impact_classes:
result = results[impact_class]
self._append_result('%s %s' % (impact_name, impact_class), result)
def _calculate_weekly_hygene_packs(self):
"""Weekly requirements of female hygiene packs indicator.
This indicator reports the weekly requirements of female hygiene packs
for further detail refer to the "Sample InaSAFE Actions for Vulnerable
Populations" [27.07.2012] paper
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Weekly hygiene packs')
myMeta = {'description': 'Females hygiene packs for weekly use'}
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT, myMeta)
return
#weekly hygene packs =
# affected pop * fem_ratio * 0.7937 * week / intended day-of-use
myResult = self.impact_total * self.female_ratio * 0.7937 * (7 / 7)
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult, myMeta)
def _calculate_females(self):
"""Female population count indicator.
this indicator reports the amount of female population according to the
set female_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Female population (affected)')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total * self.female_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def _calculate_total(self):
"""Total population indicator.
this indicator reports the total population
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Total')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
try:
myResult = self.impact_total
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def description(self):
"""Describe briefly what the post processor does.
:returns: The translated description.
:rtype: str
"""
return tr('Calculates building types related statistics.')
def _calculate_total(self):
"""Indicator that shows total population.
This indicator reports the total population.
"""
name = tr('Total')
if self.target_field is not None:
name = '%s %s' % (name, tr(self.target_field).lower())
result = self.impact_total
try:
result = int(round(result))
except ValueError:
result = self.NO_DATA_TEXT
self._append_result(name, result)
def _calculate_elderly(self):
"""Indicator that shows population above 64 years old.
this indicator reports the amount of young population according to the
set elderly_ratio
Args:
None
Returns:
None
Raises:
None
"""
myName = tr('Elderly count (affected)')
#FIXME (MB) Shameless hack to deal with issue #368
if self.impact_total > 8000000000 or self.impact_total < 0:
self._append_result(myName, self.NO_DATA_TEXT)
return
myResult = self.impact_total * self.elderly_ratio
try:
myResult = int(round(myResult))
except ValueError:
myResult = self.NO_DATA_TEXT
self._append_result(myName, myResult)
def default_minimum_needs():
"""Helper to get the default minimum needs.
.. note:: Key names will be translated.
"""
rice = tr('Rice')
drinking_water = tr('Drinking Water')
water = tr('Water')
family_kits = tr('Family Kits')
toilets = tr('Toilets')
minimum_needs = OrderedDict([
(rice, 2.8),
(drinking_water, 17.5),
(water, 105),
(family_kits, 0.2),
(toilets, 0.05)])
return minimum_needs
def interpolate_raster_vector_points(source,
target,
layer_name=None,
attribute_name=None,
mode='linear'):
"""Interpolate from raster layer to point data
Args:
* source: Raster data set (grid)
* target: Vector data set (points)
* layer_name: Optional name of returned interpolated layer.
If None the name of target is used for the returned layer.
* attribute_name: Name for new attribute.
If None (default) the name of layer source is used
* mode: 'linear' or 'constant' - determines whether interpolation
from grid to points should be bilinear or piecewise constant
Output
I: Vector data set; points located as target with values
interpolated from source
"""
msg = ('There are no data points to interpolate to. Perhaps zoom out '
'and try again')
verify(len(target) > 0, msg)
# Input checks
verify(source.is_raster)
verify(target.is_vector)
verify(target.is_point_data)
# Get raster data and corresponding x and y axes
A = source.get_data(nan=True)
longitudes, latitudes = source.get_geometry()
verify(len(longitudes) == A.shape[1])
verify(len(latitudes) == A.shape[0])
# Get vector point geometry as Nx2 array
coordinates = numpy.array(target.get_geometry(), dtype='d', copy=False)
# Get original attributes
attributes = target.get_data()
# Create new attribute and interpolate
try:
values = interpolate_raster(longitudes,
latitudes,
A,
coordinates,
mode=mode)
except (BoundsError, InaSAFEError), e:
msg = (tr('Could not interpolate from raster layer %(raster)s to '
'vector layer %(vector)s. Error message: %(error)s') % {
'raster': source.get_name(),
'vector': target.get_name(),
'error': str(e)
})
raise InaSAFEError(msg)
def safe_to_qgis_layer(layer):
"""Helper function to make a QgsMapLayer from a safe read_layer layer.
:param layer: Layer object as provided by InaSAFE engine.
:type layer: read_layer
:returns: A validated QGIS layer or None. Returns None when QGIS is not
available.
:rtype: QgsMapLayer, QgsVectorLayer, QgsRasterLayer, None
:raises: Exception if layer is not valid.
"""
try:
from qgis.core import QgsVectorLayer, QgsRasterLayer
except ImportError:
return None
# noinspection PyUnresolvedReferences
message = tr(
'Input layer must be a InaSAFE spatial object. I got %s'
) % (str(type(layer)))
if not hasattr(layer, 'is_inasafe_spatial_object'):
raise Exception(message)
if not layer.is_inasafe_spatial_object:
raise Exception(message)
# Get associated filename and symbolic name
filename = layer.get_filename()
name = layer.get_name()
qgis_layer = None
# Read layer
if layer.is_vector:
qgis_layer = QgsVectorLayer(filename, name, 'ogr')
elif layer.is_raster:
qgis_layer = QgsRasterLayer(filename, name)
# Verify that new qgis layer is valid
if qgis_layer.isValid():
return qgis_layer
else:
# noinspection PyUnresolvedReferences
message = tr('Loaded impact layer "%s" is not valid') % filename
raise Exception(message)
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'EarthQuakeBuildingImpactFunction',
'name': tr('Earthquake Building Impact Function'),
'impact': tr('Be affected'),
'author': 'N/A',
'date_implemented': 'N/A',
'overview': tr(
'This impact function will calculate the impact of an '
'earthquake on buildings, reporting how many are expected '
'to be damaged etc.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_earthquake,
'units': [unit_mmi],
'layer_constraints': [
layer_vector_polygon,
layer_raster_numeric
]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units': [
unit_building_type_type,
unit_building_generic],
'layer_constraints': [
layer_vector_polygon,
layer_vector_point
]
}
}
}
return dict_meta
def safe_to_qgis_layer(layer):
"""Helper function to make a QgsMapLayer from a safe read_layer layer.
:param layer: Layer object as provided by InaSAFE engine.
:type layer: read_layer
:returns: A validated QGIS layer or None. Returns None when QGIS is not
available.
:rtype: QgsMapLayer, QgsVectorLayer, QgsRasterLayer, None
:raises: Exception if layer is not valid.
"""
try:
from qgis.core import QgsVectorLayer, QgsRasterLayer
except ImportError:
return None
# noinspection PyUnresolvedReferences
message = tr('Input layer must be a InaSAFE spatial object. I got %s') % (
str(type(layer)))
if not hasattr(layer, 'is_inasafe_spatial_object'):
raise Exception(message)
if not layer.is_inasafe_spatial_object:
raise Exception(message)
# Get associated filename and symbolic name
filename = layer.get_filename()
name = layer.get_name()
qgis_layer = None
# Read layer
if layer.is_vector:
qgis_layer = QgsVectorLayer(filename, name, 'ogr')
elif layer.is_raster:
qgis_layer = QgsRasterLayer(filename, name)
# Verify that new qgis layer is valid
if qgis_layer.isValid():
return qgis_layer
else:
# noinspection PyUnresolvedReferences
message = tr('Loaded impact layer "%s" is not valid') % filename
raise Exception(message)
def _calculate_type(self, title, fields_values):
"""Indicator that shows total population.
this indicator reports the building by type. the logic is:
- look for the fields that occurs with a name included in
self.valid_type_fields
- look in those fields for any of the values of self.fields_values
- if a record has one of the valid fields with one of the valid
fields_values then it is considered affected
Args:
None
Returns:
None
Raises:
None
"""
title = tr(title)
if self.target_field is not None:
title = '%s %s' % (title, tr(self.target_field).lower())
result = 0
if self.type_fields is not None:
try:
for building in self.impact_attrs:
for type_field in self.type_fields:
building_type = building[type_field]
if building_type in fields_values:
result += building[self.target_field]
break
elif self._is_unknown_type(building_type):
self._update_known_types(building_type)
result = int(round(result))
except (ValueError, KeyError):
result = self.NO_DATA_TEXT
else:
if self.no_features:
result = 0
else:
result = self.NO_DATA_TEXT
self._append_result(title, result)
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'VolcanoPolygonHazardPopulation',
'name':
tr('Volcano Polygon Hazard Population'),
'impact':
tr('Need evacuation'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of volcano eruption '
'on population.'),
'categories': {
'hazard': {
'definition':
hazard_definition,
'subcategory':
hazard_volcano,
'units': [unit_volcano_categorical],
'layer_constraints':
[layer_vector_polygon, layer_vector_point]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def interpolate_raster_vector_points(source, target,
layer_name=None,
attribute_name=None,
mode='linear'):
"""Interpolate from raster layer to point data
Args:
* source: Raster data set (grid)
* target: Vector data set (points)
* layer_name: Optional name of returned interpolated layer.
If None the name of target is used for the returned layer.
* attribute_name: Name for new attribute.
If None (default) the name of layer source is used
* mode: 'linear' or 'constant' - determines whether interpolation
from grid to points should be bilinear or piecewise constant
Output
I: Vector data set; points located as target with values
interpolated from source
"""
msg = ('There are no data points to interpolate to. Perhaps zoom out '
'and try again')
verify(len(target) > 0, msg)
# Input checks
verify(source.is_raster)
verify(target.is_vector)
verify(target.is_point_data)
# Get raster data and corresponding x and y axes
A = source.get_data(nan=True)
longitudes, latitudes = source.get_geometry()
verify(len(longitudes) == A.shape[1])
verify(len(latitudes) == A.shape[0])
# Get vector point geometry as Nx2 array
coordinates = numpy.array(target.get_geometry(),
dtype='d',
copy=False)
# Get original attributes
attributes = target.get_data()
# Create new attribute and interpolate
try:
values = interpolate_raster(longitudes, latitudes, A,
coordinates, mode=mode)
except (BoundsError, InaSAFEError), e:
msg = (tr('Could not interpolate from raster layer %(raster)s to '
'vector layer %(vector)s. Error message: %(error)s')
% {'raster': source.get_name(),
'vector': target.get_name(),
'error': str(e)})
raise InaSAFEError(msg)
def evacuated_population_weekly_needs(population, minimum_needs=False):
"""Calculate estimated needs using BNPB Perka 7/2008 minimum bantuan.
:param population: The number of evacuated population.
:type: int, float
:param minimum_needs: Ratios to use when calculating minimum needs.
Defaults to perka 7 as described in assumptions below.
:type minimum_needs: dict
:returns: The weekly needs for the evacuated population.
:rtype: dict
Assumptions:
* 400g rice per person per day
* 2.5L drinking water per person per day
* 15L clean water per person per day
* assume 5 people per family (not in perka - 0.2 people per family)
* 20 people per toilet (0.05 per person)
"""
rice = tr('Rice')
drinking_water = tr('Drinking Water')
water = tr('Water')
family_kits = tr('Family Kits')
toilets = tr('Toilets')
if not minimum_needs:
minimum_needs = default_minimum_needs()
min_rice = minimum_needs[rice]
min_drinking_water = minimum_needs[drinking_water]
min_water = minimum_needs[water]
min_family_kits = minimum_needs[family_kits]
min_toilets = minimum_needs[toilets]
weekly_needs = {
'rice': int(ceil(population * min_rice)),
'drinking_water': int(ceil(population * min_drinking_water)),
'water': int(ceil(population * min_water)),
'family_kits': int(ceil(population * min_family_kits)),
'toilets': int(ceil(population * min_toilets))
}
return weekly_needs
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'CategoricalHazardBuildingImpactFunction',
'name':
tr('Categorised Hazard Building Impact Function'),
'impact':
tr('Be impacted'),
'author':
'Dianne Bencito',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of categorized hazards in raster '
'format on building vector layer.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_all,
'units': [unit_categorised],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units':
[unit_building_type_type, unit_building_generic],
'layer_constraints': [layer_vector_polygon]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id': 'VolcanoBuildingImpact',
'name': tr('Volcano Building Impact'),
'impact': tr('Be affected'),
'author': 'AIFDR',
'date_implemented': 'N/A',
'overview': tr('To assess the impacts of volcano eruption '
'on building.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_volcano,
'units': [unit_volcano_categorical],
'layer_constraints': [
layer_vector_polygon,
layer_vector_point
]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_structure,
'units': [
unit_building_type_type,
unit_building_generic],
'layer_constraints': [
layer_vector_polygon,
layer_vector_point]
}
}
}
return dict_meta
def get_postprocessor_human_name(postprocessor):
"""
Returns the human readable name of post processor
:param postprocessor: Machine name of the postprocessor
:returns : Human readable name for the postprocessor.
:rtype: str
"""
# Sunni : translate it first
human_name_translated = tr(AVAILABLE_POSTPTOCESSORS[postprocessor])
return human_name_translated
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'PAGFatalityFunction.',
'name':
tr('PAG Fatality Function.'),
'impact':
tr('Die or be displaced according Pager model'),
'author':
'Helen Crowley',
'date_implemented':
'N/A',
'overview':
tr('To assess the impact of earthquake on population based '
'on Population Vulnerability Model Pager'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_earthquake,
'units': [unit_mmi],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'disabled': True,
'id': 'FloodRasterRoadsExperimentalFunction',
'name': tr('Flood Raster Roads Experimental Function'),
'impact': tr('Be flooded in given thresholds'),
'author': 'Dmitry Kolesov',
'date_implemented': 'N/A',
'overview': tr('N/A'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [
hazard_flood,
hazard_tsunami
],
'units': [
unit_metres_depth,
unit_feet_depth
],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_road,
'units': [unit_road_type_type],
'layer_constraints': [layer_vector_line]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'TsunamiEvacuationFunction',
'name':
tr('Tsunami Evacuation Function'),
'impact':
tr('Need evacuation'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of tsunami inundation '
'in raster format on population.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': [hazard_tsunami],
'units': [unit_feet_depth, unit_metres_depth],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary.
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'CategorisedHazardPopulationImpactFunction',
'name':
tr('Categorised Hazard Population Impact Function'),
'impact':
tr('Be impacted'),
'author':
'AIFDR',
'date_implemented':
'N/A',
'overview':
tr('To assess the impacts of categorized hazards in raster '
'format on population raster layer.'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_all,
'units': [unit_normalised],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def get_metadata():
"""Return metadata as a dictionary
This is a static method. You can use it to get the metadata in
dictionary format for an impact function.
:returns: A dictionary representing all the metadata for the
concrete impact function.
:rtype: dict
"""
dict_meta = {
'id':
'ITBFatalityFunction',
'name':
tr('ITB Fatality Function'),
'impact':
tr('Die or be displaced'),
'author':
'Hadi Ghasemi',
'date_implemented':
'N/A',
'overview':
tr('To assess the impact of earthquake on population based '
'on earthquake model developed by ITB'),
'categories': {
'hazard': {
'definition': hazard_definition,
'subcategory': hazard_earthquake,
'units': [unit_mmi],
'layer_constraints': [layer_raster_numeric]
},
'exposure': {
'definition': exposure_definition,
'subcategory': exposure_population,
'units': [unit_people_per_pixel],
'layer_constraints': [layer_raster_numeric]
}
}
}
return dict_meta
def description(self):
"""Describe briefly what the post processor does.
Args:
None
Returns:
Str the translated description
Raises:
Errors are propagated
"""
return tr('Calculates age related statistics.')
def description(self):
"""Describe briefly what the post processor does.
Args:
None
Returns:
Str the translated description
Raises:
Errors are propagated
"""
return tr('Calculates generic aggregation statistics')
def description(self):
"""Describe briefly what the post processor does.
Args:
None
Returns:
Str the translated description
Raises:
Errors are propagated
"""
return tr('Aggregates minimum needs.')
def get_postprocessor_human_name(postprocesor):
"""
Returns the human readable name of post processor
Args:
* postprocessor: Machine name of the postprocessor
Returns:
str with the human readable name
"""
# Sunni : translete it first
human_name_translated = tr(AVAILABLE_POSTPTOCESSORS[postprocesor])
return human_name_translated
def description(self):
"""Describe briefly what the post processor does.
Args:
None
Returns:
Str the translated description
Raises:
Errors are propagated
"""
return tr('Calculates building types related statistics.')