class FloodEvacuationRasterHazardFunction(ContinuousRHContinuousRE,
PopulationExposureReportMixin):
# noinspection PyUnresolvedReferences
"""Risk plugin for flood population evacuation."""
_metadata = FloodEvacuationRasterHazardMetadata()
def __init__(self):
"""Constructor."""
super(FloodEvacuationRasterHazardFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
# Initialize instance attributes for readability (pylint)
self.no_data_warning = False
def notes(self):
"""Return the notes section of the report.
:return: The notes that should be attached to this impact report.
:rtype: safe.messaging.Message
"""
thresholds = self.parameters['thresholds'].value
if get_needs_provenance_value(self.parameters) is None:
needs_provenance = ''
else:
needs_provenance = tr(get_needs_provenance_value(self.parameters))
message = m.Message(style_class='container')
message.add(m.Heading(tr('Notes and assumptions'),
**styles.INFO_STYLE))
checklist = m.BulletedList()
checklist.add(
tr('Total population in the analysis area: %s') %
population_rounding(self.total_population))
checklist.add(
tr('<sup>1</sup>People need evacuation if flood levels '
'exceed %(eps).1f m.') % {'eps': thresholds[-1]})
checklist.add(needs_provenance)
if self.no_data_warning:
checklist.add(
tr('The layers contained "no data" values. This missing data '
'was carried through to the impact layer.'))
checklist.add(
tr('"No data" values in the impact layer were treated as 0 '
'when counting the affected or total population.'))
checklist.add(
tr('All values are rounded up to the nearest integer in '
'order to avoid representing human lives as fractions.'))
checklist.add(
tr('Population rounding is applied to all population '
'values, which may cause discrepancies when adding values.'))
message.add(checklist)
return message
def _tabulate_zero_impact(self):
thresholds = self.parameters['thresholds'].value
message = m.Message()
table = m.Table(style_class='table table-condensed table-striped')
row = m.Row()
label = m.ImportantText(
tr('People in %.1f m of water') % thresholds[-1])
content = '%s' % format_int(self.total_evacuated)
row.add(m.Cell(label))
row.add(m.Cell(content))
table.add(row)
table.caption = self.question
message.add(table)
message = message.to_html(suppress_newlines=True)
return message
def run(self):
"""Risk plugin for flood population evacuation.
Counts number of people exposed to flood levels exceeding
specified threshold.
:returns: Map of population exposed to flood levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds'].value
verify(isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = self.hazard.layer.get_data(nan=True) # Depth
if has_no_data(data):
self.no_data_warning = True
# Calculate impact as population exposed to depths > max threshold
population = self.exposure.layer.get_data(nan=True, scaling=True)
total = int(numpy.nansum(population))
if has_no_data(population):
self.no_data_warning = True
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
thresholds_name = tr('People in >= %.1f m of water') % lo
self.impact_category_ordering.append(thresholds_name)
self._evacuation_category = thresholds_name
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
thresholds_name = tr('People in %.1f m to %.1f m of water' %
(lo, hi))
self.impact_category_ordering.append(thresholds_name)
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.nansum(medium))
self.affected_population[thresholds_name] = val
# Put the deepest area in top #2385
self.impact_category_ordering.reverse()
self.total_population = total
self.unaffected_population = total - self.total_affected_population
# Carry the no data values forward to the impact layer.
impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
impact = numpy.where(numpy.isnan(data), numpy.nan, impact)
# Count totals
evacuated = self.total_evacuated
self.minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Result
impact_summary = self.html_report()
impact_table = impact_summary
total_needs = self.total_needs
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impact.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(interval_classes[i], 'Low')
elif i == 4:
label = create_label(interval_classes[i], 'Medium')
elif i == 7:
label = create_label(interval_classes[i], 'High')
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
# For printing map purpose
map_title = tr('People in need of evacuation')
legend_title = tr('Population Count')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'evacuated': evacuated,
'total_needs': total_needs
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
raster = Raster(
impact,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=tr('Population which %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = raster
return raster
class ClassifiedRasterHazardPopulationFunction(ImpactFunction):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by classified hazard."""
_metadata = ClassifiedRasterHazardPopulationMetadata()
def __init__(self):
super(ClassifiedRasterHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def _tabulate(self, high, low, medium, minimum_needs, no_impact, question,
total_impact):
# Generate impact report for the pdf map
table_body = [
question,
TableRow([
tr('Total Population Affected '),
'%s' % format_int(total_impact)
],
header=True),
TableRow([
tr('Population in High hazard class areas '),
'%s' % format_int(high)
]),
TableRow([
tr('Population in Medium hazard class areas '),
'%s' % format_int(medium)
]),
TableRow([
tr('Population in Low hazard class areas '),
'%s' % format_int(low)
]),
TableRow(
[tr('Population Not Affected'),
'%s' % format_int(no_impact)]),
TableRow(
tr('Table below shows the minimum needs for all '
'evacuated people'))
]
total_needs = evacuated_population_needs(total_impact, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
return table_body, total_needs
def _tabulate_action_checklist(self, table_body, total, no_data_warning):
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('How will warnings be disseminated?')))
table_body.append(TableRow(tr('How will we reach stranded people?')))
table_body.append(TableRow(tr('Do we have enough relief items?')))
table_body.append(
TableRow(
tr('If yes, where are they located and how will we distribute '
'them?')))
table_body.append(
TableRow(
tr('If no, where can we obtain additional relief items from '
'and how will we transport them to here?')))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows the numbers of people in high, medium, and low '
'hazard class areas'),
tr('Total population: %s') % format_int(total)
])
if no_data_warning:
table_body.extend([
tr('The layers contained `no data`. This missing data was '
'carried through to the impact layer.'),
tr('`No data` values in the impact layer were treated as 0 '
'when counting the affected or total population.')
])
return table_body
def run(self, layers=None):
"""Plugin for impact of population as derived by classified hazard.
Input
:param layers: List of layers expected to contain
* hazard_layer: Raster layer of classified hazard
* exposure_layer: Raster layer of population data
Counts number of people exposed to each class of the hazard
Return
Map of population exposed to high class
Table with number of people in each class
"""
self.validate()
self.prepare(layers)
# The 3 classes
# TODO (3.2): shouldnt these be defined in keywords rather? TS
low_class = self.parameters['low_hazard_class']
medium_class = self.parameters['medium_hazard_class']
high_class = self.parameters['high_hazard_class']
# The classes must be different to each other
unique_classes_flag = all(x != y for x, y in list(
itertools.combinations([low_class, medium_class, high_class], 2)))
if not unique_classes_flag:
raise FunctionParametersError(
'There is hazard class that has the same value with other '
'class. Please check the parameters.')
# Identify hazard and exposure layers
hazard_layer = self.hazard # Classified Hazard
exposure_layer = self.exposure # Population Raster
# Extract data as numeric arrays
hazard_data = hazard_layer.get_data(nan=True) # Class
no_data_warning = False
if has_no_data(hazard_data):
no_data_warning = True
# Calculate impact as population exposed to each class
population = exposure_layer.get_data(scaling=True)
# Get all population data that falls in each hazard class
high_hazard_population = numpy.where(hazard_data == high_class,
population, 0)
medium_hazard_population = numpy.where(hazard_data == medium_class,
population, 0)
low_hazard_population = numpy.where(hazard_data == low_class,
population, 0)
affected_population = (high_hazard_population +
medium_hazard_population +
low_hazard_population)
# Carry the no data values forward to the impact layer.
affected_population = numpy.where(numpy.isnan(population), numpy.nan,
affected_population)
affected_population = numpy.where(numpy.isnan(hazard_data), numpy.nan,
affected_population)
# Count totals
total_population = int(numpy.nansum(population))
total_high_population = int(numpy.nansum(high_hazard_population))
total_medium_population = int(numpy.nansum(medium_hazard_population))
total_low_population = int(numpy.nansum(low_hazard_population))
total_affected = int(numpy.nansum(affected_population))
total_not_affected = total_population - total_affected
# check for zero impact
if total_affected == 0:
table_body = [
self.question,
TableRow(
[tr('People affected'),
'%s' % format_int(total_affected)],
header=True)
]
message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(message)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
table_body, total_needs = self._tabulate(
population_rounding(total_high_population),
population_rounding(total_low_population),
population_rounding(total_medium_population), minimum_needs,
population_rounding(total_not_affected), self.question,
population_rounding(total_affected))
impact_table = Table(table_body).toNewlineFreeString()
table_body = self._tabulate_action_checklist(
table_body, population_rounding(total_population), no_data_warning)
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(affected_population.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 0
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('Population affected by each class')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Number of People')
# Create raster object and return
raster_layer = Raster(
data=affected_population,
projection=exposure_layer.get_projection(),
geotransform=exposure_layer.get_geotransform(),
name=tr('Population which %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs
},
style_info=style_info)
self._impact = raster_layer
return raster_layer
class ClassifiedRasterHazardPopulationFunction(ClassifiedRHContinuousRE,
PopulationExposureReportMixin):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by classified hazard."""
_metadata = ClassifiedRasterHazardPopulationMetadata()
def __init__(self):
super(ClassifiedRasterHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def notes(self):
"""Return the notes section of the report.
:return: The notes that should be attached to this impact report.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
message.add(m.Heading(tr('Notes and assumptions'),
**styles.INFO_STYLE))
checklist = m.BulletedList()
checklist.add(
tr('Total population in the analysis area: %s') %
population_rounding(self.total_population))
checklist.add(
tr('<sup>1</sup>People need evacuation if they are in a '
'hazard zone.'))
if self.no_data_warning:
checklist.add(
tr('The layers contained "no data" values. This missing data '
'was carried through to the impact layer.'))
checklist.add(
tr('"No data" values in the impact layer were treated as 0 '
'when counting the affected or total population.'))
checklist.add(
tr('All values are rounded up to the nearest integer in '
'order to avoid representing human lives as fractions.'))
checklist.add(
tr('Population rounding is applied to all population '
'values, which may cause discrepancies when adding value.'))
message.add(checklist)
return message
def run(self):
"""Plugin for impact of population as derived by classified hazard.
Counts number of people exposed to each class of the hazard
Return
Map of population exposed to high class
Table with number of people in each class
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
# The 3 classes
# TODO (3.2): shouldnt these be defined in keywords rather? TS
categorical_hazards = self.parameters['Categorical hazards'].value
low_class = categorical_hazards[0].value
medium_class = categorical_hazards[1].value
high_class = categorical_hazards[2].value
# The classes must be different to each other
unique_classes_flag = all(x != y for x, y in list(
itertools.combinations([low_class, medium_class, high_class], 2)))
if not unique_classes_flag:
raise FunctionParametersError(
'There is hazard class that has the same value with other '
'class. Please check the parameters.')
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Class
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each class
population = self.exposure.layer.get_data(scaling=True)
# Get all population data that falls in each hazard class
high_hazard_population = numpy.where(hazard_data == high_class,
population, 0)
medium_hazard_population = numpy.where(hazard_data == medium_class,
population, 0)
low_hazard_population = numpy.where(hazard_data == low_class,
population, 0)
affected_population = (high_hazard_population +
medium_hazard_population +
low_hazard_population)
# Carry the no data values forward to the impact layer.
affected_population = numpy.where(numpy.isnan(population), numpy.nan,
affected_population)
affected_population = numpy.where(numpy.isnan(hazard_data), numpy.nan,
affected_population)
# Count totals
self.total_population = int(numpy.nansum(population))
self.affected_population[tr(
'Population in High hazard class areas')] = int(
numpy.nansum(high_hazard_population))
self.affected_population[tr(
'Population in Medium hazard class areas')] = int(
numpy.nansum(medium_hazard_population))
self.affected_population[tr(
'Population in Low hazard class areas')] = int(
numpy.nansum(low_hazard_population))
self.unaffected_population = (self.total_population -
self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
self.minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
total_needs = self.total_needs
impact_table = impact_summary = self.html_report()
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(affected_population.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('Number of people affected in each class')
legend_title = tr('Number of People')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
raster_layer = Raster(
data=affected_population,
projection=self.exposure.layer.get_projection(),
geotransform=self.exposure.layer.get_geotransform(),
name=tr('People that might %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = raster_layer
return raster_layer
class TsunamiEvacuationFunction(ImpactFunction):
# noinspection PyUnresolvedReferences
"""Impact function for tsunami evacuation."""
_metadata = TsunamiEvacuationMetadata()
def __init__(self):
super(TsunamiEvacuationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def _tabulate(self, counts, evacuated, minimum_needs, question, rounding,
thresholds, total, no_data_warning):
# noinspection PyListCreation
table_body = [
question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s*' % format_int(evacuated)],
header=True),
TableRow(
tr('* Number is rounded up to the nearest %s') % rounding),
TableRow(tr('Map shows the numbers of people needing evacuation'))
]
total_needs = evacuated_population_needs(evacuated, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(TableRow(tr('How will warnings be disseminated?')))
table_body.append(TableRow(tr('How will we reach stranded people?')))
table_body.append(TableRow(tr('Do we have enough relief items?')))
table_body.append(
TableRow(
tr('If yes, where are they located and how '
'will we distribute them?')))
table_body.append(
TableRow(
tr('If no, where can we obtain additional relief items from and how '
'will we transport them to here?')))
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Total population: %s') % format_int(total),
tr('People need evacuation if tsunami levels exceed %(eps).1f m') %
{
'eps': thresholds[-1]
},
tr('Minimum needs are defined in BNPB regulation 7/2008'),
tr('All values are rounded up to the nearest integer in order to '
'avoid representing human lives as fractions.')
])
if len(counts) > 1:
table_body.append(TableRow(tr('Detailed breakdown'), header=True))
for i, val in enumerate(counts[:-1]):
s = (tr('People in %(lo).1f m to %(hi).1f m of water: %(val)i')
% {
'lo': thresholds[i],
'hi': thresholds[i + 1],
'val': format_int(val[0])
})
table_body.append(TableRow(s))
if no_data_warning:
table_body.extend([
tr('The layers contained `no data`. This missing data was '
'carried through to the impact layer.'),
tr('`No data` values in the impact layer were treated as 0 '
'when counting the affected or total population.')
])
return table_body, total_needs
def run(self, layers=None):
"""Risk plugin for tsunami population evacuation.
:param layers: List of layers expected to contain
hazard_layer: Raster layer of tsunami depth
exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to tsunami levels exceeding
specified threshold.
:returns: Map of population exposed to tsunami levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
self.validate()
self.prepare(layers)
# Identify hazard and exposure layers
hazard_layer = self.hazard # Tsunami inundation [m]
exposure_layer = self.exposure
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds [m]']
verify(isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=True) # Depth
no_data_warning = False
if has_no_data(data):
no_data_warning = True
# Calculate impact as population exposed to depths > max threshold
population = exposure_layer.get_data(nan=True, scaling=True)
if has_no_data(population):
no_data_warning = True
# Calculate impact to intermediate thresholds
counts = []
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.nansum(medium))
# Sensible rounding
val, rounding = population_rounding_full(val)
counts.append([val, rounding])
# Carry the no data values forward to the impact layer.
impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
impact = numpy.where(numpy.isnan(data), numpy.nan, impact)
# Count totals
evacuated, rounding = counts[-1]
total = int(numpy.nansum(population))
# Don't show digits less than a 1000
total = population_rounding(total)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body, total_needs = self._tabulate(counts, evacuated,
minimum_needs, self.question,
rounding, thresholds, total,
no_data_warning)
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
table_body = [
self.question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s' % format_int(evacuated)],
header=True)
]
my_message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(my_message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impact.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(interval_classes[i], 'Low')
elif i == 4:
label = create_label(interval_classes[i], 'Medium')
elif i == 7:
label = create_label(interval_classes[i], 'High')
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 0
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People in need of evacuation')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create raster object and return
raster = Raster(
impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'evacuated': evacuated,
'total_needs': total_needs
},
style_info=style_info)
self._impact = raster
return raster
class ContinuousHazardPopulationFunction(ContinuousRHContinuousRE,
PopulationExposureReportMixin):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by continuous hazard."""
_metadata = ContinuousHazardPopulationMetadata()
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.no_data_warning = False
def notes(self):
"""Return the notes section of the report.
:return: The notes that should be attached to this impact report.
:rtype: safe.messaging.Message
"""
message = m.Message(style_class='container')
message.add(m.Heading(tr('Notes and assumptions'),
**styles.INFO_STYLE))
checklist = m.BulletedList()
checklist.add(
tr('Total population in the analysis area: %s') %
population_rounding(self.total_population))
checklist.add(
tr('<sup>1</sup>People need evacuation if they are in a '
'hazard zone.'))
checklist.add(
tr('Map shows the numbers of people in high, medium, '
'and low hazard class areas.'))
if self.no_data_warning:
checklist.add(
tr('The layers contained "no data" values. This missing data '
'was carried through to the impact layer.'))
checklist.add(
tr('"No data" values in the impact layer were treated as 0 '
'when counting the affected or total population.'))
checklist.add(
tr('All values are rounded up to the nearest integer in '
'order to avoid representing human lives as fractions.'))
checklist.add(
tr('Population rounding is applied to all population '
'values, which may cause discrepancies when adding value.'))
message.add(checklist)
return message
def run(self):
"""Plugin for impact of population as derived by continuous hazard.
Hazard is reclassified into 3 classes based on the extrema provided
as impact function parameters.
Counts number of people exposed to each category of the hazard
:returns:
Map of population exposed to high category
Table with number of people in each category
"""
self.validate()
self.prepare()
self.provenance.append_step(
'Calculating Step', 'Impact function is calculating the impact.')
thresholds = [
p.value for p in self.parameters['Categorical thresholds'].value
]
# Thresholds must contain 3 thresholds
if len(thresholds) != 3:
raise FunctionParametersError(
'The thresholds must consist of 3 values.')
# Thresholds must monotonically increasing
monotonically_increasing_flag = all(
x < y for x, y in zip(thresholds, thresholds[1:]))
if not monotonically_increasing_flag:
raise FunctionParametersError(
'Each threshold should be larger than the previous.')
# The 3 categories
low_t = thresholds[0]
medium_t = thresholds[1]
high_t = thresholds[2]
# Extract data as numeric arrays
hazard_data = self.hazard.layer.get_data(nan=True) # Category
if has_no_data(hazard_data):
self.no_data_warning = True
# Calculate impact as population exposed to each category
exposure_data = self.exposure.layer.get_data(nan=True, scaling=True)
if has_no_data(exposure_data):
self.no_data_warning = True
# Make 3 data for each zone. Get the value of the exposure if the
# exposure is in the hazard zone, else just assign 0
low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
medium_exposure = numpy.where(
(hazard_data >= low_t) & (hazard_data < medium_t), exposure_data,
0)
high_exposure = numpy.where(
(hazard_data >= medium_t) & (hazard_data <= high_t), exposure_data,
0)
impacted_exposure = low_exposure + medium_exposure + high_exposure
# Count totals
self.total_population = int(numpy.nansum(exposure_data))
self.affected_population[tr('Population in high hazard areas')] = int(
numpy.nansum(high_exposure))
self.affected_population[tr(
'Population in medium hazard areas')] = int(
numpy.nansum(medium_exposure))
self.affected_population[tr('Population in low hazard areas')] = int(
numpy.nansum(low_exposure))
self.unaffected_population = (self.total_population -
self.total_affected_population)
# check for zero impact
if self.total_affected_population == 0:
message = no_population_impact_message(self.question)
raise ZeroImpactException(message)
# Don't show digits less than a 1000
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
total_needs = self.total_needs
impact_table = impact_summary = self.html_report()
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impacted_exposure.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['transparency'] = 0
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People in each hazard areas (low, medium, high)')
legend_title = tr('Number of People')
legend_units = tr('(people per cell)')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
extra_keywords = {
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs
}
self.set_if_provenance()
impact_layer_keywords = self.generate_impact_keywords(extra_keywords)
# Create raster object and return
raster_layer = Raster(
data=impacted_exposure,
projection=self.hazard.layer.get_projection(),
geotransform=self.hazard.layer.get_geotransform(),
name=tr('Population might %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords=impact_layer_keywords,
style_info=style_info)
self._impact = raster_layer
return raster_layer
class ContinuousHazardPopulationFunction(ImpactFunction):
# noinspection PyUnresolvedReferences
"""Plugin for impact of population as derived by continuous hazard."""
_metadata = ContinuousHazardPopulationMetadata()
def __init__(self):
super(ContinuousHazardPopulationFunction, self).__init__()
self.impact_function_manager = ImpactFunctionManager()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
def _tabulate(self, high, low, medium, question, total_impact):
# Generate impact report for the pdf map
table_body = [
question,
TableRow([tr('People impacted '),
'%s' % format_int(total_impact)],
header=True),
TableRow(
[tr('People in high hazard area '),
'%s' % format_int(high)],
header=True),
TableRow([
tr('People in medium hazard area '),
'%s' % format_int(medium)
],
header=True),
TableRow([tr('People in low hazard area'),
'%s' % format_int(low)],
header=True)
]
return table_body
def _tabulate_notes(self, minimum_needs, table_body, total, total_impact,
no_data_warning):
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows population count in high, medium, and low hazard '
'area.'),
tr('Total population: %s') % format_int(total),
TableRow(
tr('Table below shows the minimum needs for all '
'affected people'))
])
if no_data_warning:
table_body.extend([
tr('The layers contained `no data`. This missing data was '
'carried through to the impact layer.'),
tr('`No data` values in the impact layer were treated as 0 '
'when counting the affected or total population.')
])
total_needs = evacuated_population_needs(total_impact, minimum_needs)
for frequency, needs in total_needs.items():
table_body.append(
TableRow([
tr('Needs should be provided %s' % frequency),
tr('Total')
],
header=True))
for resource in needs:
table_body.append(
TableRow([
tr(resource['table name']),
format_int(resource['amount'])
]))
return table_body, total_needs
def run(self, layers=None):
"""Plugin for impact of population as derived by categorised hazard.
:param layers: List of layers expected to contain
* hazard_layer: Raster layer of categorised hazard
* exposure_layer: Raster layer of population data
Counts number of people exposed to each category of the hazard
:returns:
Map of population exposed to high category
Table with number of people in each category
"""
self.validate()
self.prepare(layers)
thresholds = self.parameters['Categorical thresholds']
# Thresholds must contain 3 thresholds
if len(thresholds) != 3:
raise FunctionParametersError(
'The thresholds must consist of 3 values.')
# Thresholds must monotonically increasing
monotonically_increasing_flag = all(
x < y for x, y in zip(thresholds, thresholds[1:]))
if not monotonically_increasing_flag:
raise FunctionParametersError(
'Each threshold should be larger than the previous.')
# The 3 categories
low_t = thresholds[0]
medium_t = thresholds[1]
high_t = thresholds[2]
# Identify hazard and exposure layers
hazard_layer = self.hazard # Categorised Hazard
exposure_layer = self.exposure # Population Raster
# Extract data as numeric arrays
hazard_data = hazard_layer.get_data(nan=True) # Category
no_data_warning = False
if has_no_data(hazard_data):
no_data_warning = True
# Calculate impact as population exposed to each category
exposure_data = exposure_layer.get_data(nan=True, scaling=True)
if has_no_data(exposure_data):
no_data_warning = True
# Make 3 data for each zone. Get the value of the exposure if the
# exposure is in the hazard zone, else just assign 0
low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
medium_exposure = numpy.where(
(hazard_data >= low_t) & (hazard_data < medium_t), exposure_data,
0)
high_exposure = numpy.where(
(hazard_data >= medium_t) & (hazard_data <= high_t), exposure_data,
0)
impacted_exposure = low_exposure + medium_exposure + high_exposure
# Count totals
total = int(numpy.nansum(exposure_data))
low_total = int(numpy.nansum(low_exposure))
medium_total = int(numpy.nansum(medium_exposure))
high_total = int(numpy.nansum(high_exposure))
total_impact = high_total + medium_total + low_total
# Check for zero impact
if total_impact == 0:
table_body = [
self.question,
TableRow(
[tr('People impacted'),
'%s' % format_int(total_impact)],
header=True)
]
message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(message)
# Don't show digits less than a 1000
total = population_rounding(total)
total_impact = population_rounding(total_impact)
low_total = population_rounding(low_total)
medium_total = population_rounding(medium_total)
high_total = population_rounding(high_total)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
table_body = self._tabulate(high_total, low_total, medium_total,
self.question, total_impact)
impact_table = Table(table_body).toNewlineFreeString()
table_body, total_needs = self._tabulate_notes(minimum_needs,
table_body, total,
total_impact,
no_data_warning)
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in each hazard areas (low, medium, high)')
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impacted_exposure.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 0
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# Create raster object and return
raster_layer = Raster(
data=impacted_exposure,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population might %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'total_needs': total_needs
},
style_info=style_info)
self._impact = raster_layer
return raster_layer
