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 test_evacuated_population_needs(self):
"""Test evacuated_population_needs function."""
water = ResourceParameter()
water.name = 'Water'
water.unit.name = 'litre'
water.unit.abbreviation = 'l'
water.unit.plural = 'litres'
water.frequency = 'weekly'
water.maximum_allowed_value = 10
water.minimum_allowed_value = 0
water.value = 5
rice = ResourceParameter()
rice.name = 'Rice'
rice.unit.name = 'kilogram'
rice.unit.abbreviation = 'kg'
rice.unit.plural = 'kilograms'
rice.frequency = 'daily'
rice.maximum_allowed_value = 1
rice.minimum_allowed_value = 0
rice.value = 0.5
total_needs = evacuated_population_needs(
10,
[water.serialize(), rice.serialize()]
)
self.assertEqual(total_needs['weekly'][0]['name'], 'Water')
self.assertEqual(total_needs['weekly'][0]['amount'], 50)
self.assertEqual(total_needs['weekly'][0]['table name'], 'Water [l]')
self.assertEqual(total_needs['daily'][0]['name'], 'Rice')
self.assertEqual(total_needs['daily'][0]['amount'], 5)
self.assertEqual(total_needs['daily'][0]['table name'], 'Rice [kg]')
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_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 test_evacuated_population_needs(self):
"""Test evacuated_population_needs function."""
water = ResourceParameter()
water.name = 'Water'
water.unit.name = 'litre'
water.unit.abbreviation = 'l'
water.unit.plural = 'litres'
water.frequency = 'weekly'
water.maximum_allowed_value = 10
water.minimum_allowed_value = 0
water.value = 5
rice = ResourceParameter()
rice.name = 'Rice'
rice.unit.name = 'kilogram'
rice.unit.abbreviation = 'kg'
rice.unit.plural = 'kilograms'
rice.frequency = 'daily'
rice.maximum_allowed_value = 1
rice.minimum_allowed_value = 0
rice.value = 0.5
total_needs = evacuated_population_needs(
10, [water.serialize(), rice.serialize()])
self.assertEqual(total_needs['weekly'][0]['name'], 'Water')
self.assertEqual(total_needs['weekly'][0]['amount'], 50)
self.assertEqual(total_needs['weekly'][0]['table name'], 'Water [l]')
self.assertEqual(total_needs['daily'][0]['name'], 'Rice')
self.assertEqual(total_needs['daily'][0]['amount'], 5)
self.assertEqual(total_needs['daily'][0]['table name'], 'Rice [kg]')
def total_needs(self):
"""Get the total minimum needs based on the total evacuated.
:returns: Total minimum needs.
:rtype: dict
"""
total_population_evacuated = self.total_evacuated
return evacuated_population_needs(
total_population_evacuated, self.minimum_needs)
def total_needs(self):
"""Get the total minimum needs based on the total evacuated.
:returns: Total minimum needs.
:rtype: dict
"""
total_population_evacuated = self.total_evacuated
return evacuated_population_needs(total_population_evacuated,
self.minimum_needs)
def test_default_needs(self):
"""default calculated needs are as expected
"""
minimum_needs = [
parameter.serialize() for parameter in default_minimum_needs()]
# 20 Happens to be the smallest number at which integer rounding
# won't make a difference to the result
result = evacuated_population_needs(20, minimum_needs)['weekly']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert (result['Rice [kg]'] == 56
and result['Drinking Water [l]'] == 350
and result['Clean Water [l]'] == 1340
and result['Family Kits'] == 4)
result = evacuated_population_needs(10, minimum_needs)['single']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert result['Toilets'] == 1
def _tabulate(self, affected_population, evacuated, minimum_needs,
question, rounding, rounding_evacuated):
# People Affected
table_body = [
question,
TableRow(
[tr('People affected'), '%s*' % (
format_int(int(affected_population)))],
header=True)]
if self.use_affected_field:
table_body.append(
TableRow(
tr('* People are considered to be affected if they are '
'within the area where the value of the hazard field ('
'"%s") is "%s"') %
(self.parameters['affected_field'],
self.parameters['affected_value'])))
else:
table_body.append(
TableRow(
tr('* People are considered to be affected if they are '
'within any polygons.')))
table_body.append(
TableRow([TableCell(
tr('* Number is rounded up to the nearest %s') % rounding,
col_span=2)]))
# People Needing Evacuation
table_body.append(
TableRow([tr('People needing evacuation'), '%s*' % (
format_int(int(evacuated)))], header=True))
table_body.append(TableRow(
[TableCell(
tr('* Number is rounded up to the nearest %s') %
rounding_evacuated, col_span=2)]))
table_body.append(
TableRow(
[tr('Evacuation threshold'), '%s%%' % format_int(
self.parameters['evacuation_percentage'])], header=True))
table_body.append(
TableRow(tr('Table below shows the weekly minimum needs for all '
'evacuated people')))
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'])]))
return table_body, total_needs
def test_default_needs(self):
"""default calculated needs are as expected
"""
minimum_needs = [
parameter.serialize() for parameter in default_minimum_needs()
]
# 20 Happens to be the smallest number at which integer rounding
# won't make a difference to the result
result = evacuated_population_needs(20, minimum_needs)['weekly']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert (result['Rice [kg]'] == 56
and result['Drinking Water [l]'] == 350
and result['Clean Water [l]'] == 1340
and result['Family Kits'] == 4)
result = evacuated_population_needs(10, minimum_needs)['single']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert result['Toilets'] == 1
def test_arbitrary_needs(self):
"""custom need ratios calculated are as expected
"""
minimum_needs = [
parameter.serialize() for parameter in default_minimum_needs()]
minimum_needs[0]['value'] = 4
minimum_needs[1]['value'] = 3
minimum_needs[2]['value'] = 2
minimum_needs[3]['value'] = 1
minimum_needs[4]['value'] = 0.2
result = evacuated_population_needs(10, minimum_needs)['weekly']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert (result['Rice [kg]'] == 40
and result['Drinking Water [l]'] == 30
and result['Clean Water [l]'] == 20
and result['Family Kits'] == 10)
result = evacuated_population_needs(10, minimum_needs)['single']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert result['Toilets'] == 2
def test_arbitrary_needs(self):
"""custom need ratios calculated are as expected
"""
minimum_needs = [
parameter.serialize() for parameter in default_minimum_needs()
]
minimum_needs[0]['value'] = 4
minimum_needs[1]['value'] = 3
minimum_needs[2]['value'] = 2
minimum_needs[3]['value'] = 1
minimum_needs[4]['value'] = 0.2
result = evacuated_population_needs(10, minimum_needs)['weekly']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert (result['Rice [kg]'] == 40
and result['Drinking Water [l]'] == 30
and result['Clean Water [l]'] == 20
and result['Family Kits'] == 10)
result = evacuated_population_needs(10, minimum_needs)['single']
result = OrderedDict([[r['table name'], r['amount']] for r in result])
assert result['Toilets'] == 2
def total_needs(self):
"""Get the total minimum needs based on the total evacuated.
:returns: Total minimum needs.
:rtype: dict
"""
total_population_evacuated = sum(self.affected_population.values())
self.minimum_needs = [
parameter.serialize() for parameter in
filter_needs_parameters(self.parameters['minimum needs'])
]
return evacuated_population_needs(
total_population_evacuated, self.minimum_needs)
def total_needs(self):
"""Get the total minimum needs based on the total evacuated.
:returns: Total minimum needs.
:rtype: dict
"""
total_population_evacuated = sum(self.affected_population.values())
self.minimum_needs = [
parameter.serialize() for parameter in filter_needs_parameters(
self.parameters['minimum needs'])
]
return evacuated_population_needs(total_population_evacuated,
self.minimum_needs)
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_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):
"""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
"""
# The 3 classes
low_t = self.parameters['low_hazard_class']
medium_t = self.parameters['medium_hazard_class']
high_t = self.parameters['high_hazard_class']
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Classified Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=0.0) # Class
# Calculate impact as population exposed to each class
population = exposure_layer.get_data(nan=0.0, scaling=True)
if high_t == 0:
hi = numpy.where(0, population, 0)
else:
hi = numpy.where(data == high_t, population, 0)
if medium_t == 0:
med = numpy.where(0, population, 0)
else:
med = numpy.where(data == medium_t, population, 0)
if low_t == 0:
lo = numpy.where(0, population, 0)
else:
lo = numpy.where(data == low_t, population, 0)
if high_t == 0:
impact = numpy.where((data == low_t) + (data == medium_t),
population, 0)
elif medium_t == 0:
impact = numpy.where((data == low_t) + (data == high_t),
population, 0)
elif low_t == 0:
impact = numpy.where((data == medium_t) + (data == high_t),
population, 0)
else:
impact = numpy.where(
(data == low_t) + (data == medium_t) + (data == high_t),
population, 0)
# Count totals
total = int(numpy.sum(population))
high = int(numpy.sum(hi))
medium = int(numpy.sum(med))
low = int(numpy.sum(lo))
total_impact = int(numpy.sum(impact))
# Perform population rounding based on number of people
no_impact = population_rounding(total - total_impact)
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# 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'])
]))
impact_table = Table(table_body).toNewlineFreeString()
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)
])
impact_summary = Table(table_body).toNewlineFreeString()
# 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 = 30
else:
transparency = 30
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(impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(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)
return raster_layer
class ITBFatalityFunction(ImpactFunction):
# noinspection PyUnresolvedReferences
"""Indonesian Earthquake Fatality Model.
This model was developed by Institut Teknologi Bandung (ITB) and
implemented by Dr. Hadi Ghasemi, Geoscience Australia.
Reference:
Indonesian Earthquake Building-Damage and Fatality Models and
Post Disaster Survey Guidelines Development,
Bali, 27-28 February 2012, 54pp.
Algorithm:
In this study, the same functional form as Allen (2009) is adopted
to express fatality rate as a function of intensity (see Eq. 10 in the
report). The Matlab built-in function (fminsearch) for Nelder-Mead
algorithm was used to estimate the model parameters. The objective
function (L2G norm) that is minimised during the optimisation is the
same as the one used by Jaiswal et al. (2010).
The coefficients used in the indonesian model are
x=0.62275231, y=8.03314466, zeta=2.15
Allen, T. I., Wald, D. J., Earle, P. S., Marano, K. D., Hotovec, A. J.,
Lin, K., and Hearne, M., 2009. An Atlas of ShakeMaps and population
exposure catalog for earthquake loss modeling, Bull. Earthq. Eng. 7,
701-718.
Jaiswal, K., and Wald, D., 2010. An empirical model for global earthquake
fatality estimation, Earthq. Spectra 26, 1017-1037.
Caveats and limitations:
The current model is the result of the above mentioned workshop and
reflects the best available information. However, the current model
has a number of issues listed below and is expected to evolve further
over time.
1 - The model is based on limited number of observed fatality
rates during 4 past fatal events.
2 - The model clearly over-predicts the fatality rates at
intensities higher than VIII.
3 - The model only estimates the expected fatality rate for a given
intensity level; however the associated uncertainty for the proposed
model is not addressed.
4 - There are few known mistakes in developing the current model:
- rounding MMI values to the nearest 0.5,
- Implementing Finite-Fault models of candidate events, and
- consistency between selected GMPEs with those in use by BMKG.
These issues will be addressed by ITB team in the final report.
Note: Because of these caveats, decisions should not be made solely on
the information presented here and should always be verified by ground
truthing and other reliable information sources.
"""
_metadata = ITBFatalityMetadata()
def __init__(self):
super(ITBFatalityFunction, self).__init__()
# AG: Use the proper minimum needs, update the parameters
self.parameters = add_needs_parameters(self.parameters)
self.hardcoded_parameters = OrderedDict([
('x', 0.62275231), ('y', 8.03314466), # Model coefficients
# Rates of people displaced for each MMI level
('displacement_rate', {
1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 1.0,
7: 1.0, 8: 1.0, 9: 1.0, 10: 1.0
}),
('mmi_range', range(2, 10)),
('step', 0.5),
# Threshold below which layer should be transparent
('tolerance', 0.01),
('calculate_displaced_people', True)
])
def fatality_rate(self, mmi):
"""ITB method to compute fatality rate.
:param mmi:
"""
# As per email discussion with Ole, Trevor, Hadi, mmi < 4 will have
# a fatality rate of 0 - Tim
if mmi < 4:
return 0
x = self.hardcoded_parameters['x']
y = self.hardcoded_parameters['y']
# noinspection PyUnresolvedReferences
return numpy.power(10.0, x * mmi - y)
def run(self, layers=None):
"""Indonesian Earthquake Fatality Model.
Input:
:param layers: List of layers expected to contain,
hazard: Raster layer of MMI ground shaking
exposure: Raster layer of population count
"""
self.validate()
self.prepare(layers)
displacement_rate = self.hardcoded_parameters['displacement_rate']
# Tolerance for transparency
tolerance = self.hardcoded_parameters['tolerance']
# Extract input layers
intensity = self.hazard
population = self.exposure
# Extract data grids
hazard = intensity.get_data() # Ground Shaking
exposure = population.get_data(scaling=True) # Population Density
# Calculate people affected by each MMI level
# FIXME (Ole): this range is 2-9. Should 10 be included?
mmi_range = self.hardcoded_parameters['mmi_range']
number_of_exposed = {}
number_of_displaced = {}
number_of_fatalities = {}
# Calculate fatality rates for observed Intensity values (hazard
# based on ITB power model
mask = numpy.zeros(hazard.shape)
for mmi in mmi_range:
# Identify cells where MMI is in class i and
# count people affected by this shake level
mmi_matches = numpy.where(
(hazard > mmi - self.hardcoded_parameters['step']) * (
hazard <= mmi + self.hardcoded_parameters['step']),
exposure, 0)
# Calculate expected number of fatalities per level
fatality_rate = self.fatality_rate(mmi)
fatalities = fatality_rate * mmi_matches
# Calculate expected number of displaced people per level
try:
displacements = displacement_rate[mmi] * mmi_matches
except KeyError, e:
msg = 'mmi = %i, mmi_matches = %s, Error msg: %s' % (
mmi, str(mmi_matches), str(e))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Adjust displaced people to disregard fatalities.
# Set to zero if there are more fatalities than displaced.
displacements = numpy.where(
displacements > fatalities, displacements - fatalities, 0)
# Sum up numbers for map
mask += displacements # Displaced
# Generate text with result for this study
# This is what is used in the real time system exposure table
number_of_exposed[mmi] = numpy.nansum(mmi_matches.flat)
number_of_displaced[mmi] = numpy.nansum(displacements.flat)
# noinspection PyUnresolvedReferences
number_of_fatalities[mmi] = numpy.nansum(fatalities.flat)
# Set resulting layer to NaN when less than a threshold. This is to
# achieve transparency (see issue #126).
mask[mask < tolerance] = numpy.nan
# Total statistics
total, rounding = population_rounding_full(numpy.nansum(exposure.flat))
# Compute number of fatalities
fatalities = population_rounding(numpy.nansum(
number_of_fatalities.values()))
# As per email discussion with Ole, Trevor, Hadi, total fatalities < 50
# will be rounded down to 0 - Tim
if fatalities < 50:
fatalities = 0
# Compute number of people displaced due to building collapse
displaced = population_rounding(numpy.nansum(
number_of_displaced.values()))
# Generate impact report
table_body = [self.question]
# Add total fatality estimate
s = format_int(fatalities)
table_body.append(TableRow([tr('Number of fatalities'), s],
header=True))
if self.hardcoded_parameters['calculate_displaced_people']:
# Add total estimate of people displaced
s = format_int(displaced)
table_body.append(TableRow([tr('Number of people displaced'), s],
header=True))
else:
displaced = 0
# Add estimate of total population in area
s = format_int(int(total))
table_body.append(TableRow([tr('Total number of people'), s],
header=True))
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
self.question, TableRow(
[tr('Fatalities'), '%s' % format_int(fatalities)],
header=True),
TableRow(
[tr('People displaced'), '%s' % format_int(displaced)],
header=True),
TableRow(tr('Map shows the estimation of displaced population'))]
total_needs = evacuated_population_needs(
displaced, 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('Provenance'), header=True))
table_body.append(TableRow(self.parameters['provenance']))
table_body.append(TableRow(tr('Action Checklist:'), header=True))
if fatalities > 0:
table_body.append(tr('Are there enough victim identification '
'units available for %s people?') %
format_int(fatalities))
if displaced > 0:
table_body.append(tr('Are there enough shelters and relief items '
'available for %s people?')
% format_int(displaced))
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?')))
# Extend impact report for on-screen display
table_body.extend([TableRow(tr('Notes'), header=True),
tr('Total population: %s') % format_int(total),
tr('People are considered to be displaced if '
'they experience and survive a shake level'
'of more than 5 on the MMI scale '),
tr('Minimum needs are defined in BNPB '
'regulation 7/2008'),
tr('The fatality calculation assumes that '
'no fatalities occur for shake levels below 4 '
'and fatality counts of less than 50 are '
'disregarded.'),
tr('All values are rounded up to the nearest '
'integer in order to avoid representing human '
'lives as fractions.')])
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(tr('Fatality model is from '
'Institute of Teknologi Bandung 2012.'))
table_body.append(
tr('Population numbers rounded up to the nearest %s.') % rounding)
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(mask) == 0 == numpy.nanmin(mask):
table_body = [
self.question,
TableRow([tr('Fatalities'), '%s' % format_int(fatalities)],
header=True)]
my_message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(my_message)
# Create style
colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000']
classes = create_classes(mask.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 30
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('Earthquake impact to population')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population Count')
# Create raster object and return
raster = Raster(
mask,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
keywords={
'impact_summary': impact_summary,
'total_population': total,
'total_fatalities': fatalities,
'fatalities_per_mmi': number_of_fatalities,
'exposed_per_mmi': number_of_exposed,
'displaced_per_mmi': number_of_displaced,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs},
name=tr('Estimated displaced population per cell'),
style_info=style_info)
self._impact = raster
return raster
def run(self, layers):
"""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
"""
# The 3 category
high_t = self.parameters['Categorical thresholds'][2]
medium_t = self.parameters['Categorical thresholds'][1]
low_t = self.parameters['Categorical thresholds'][0]
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Categorised Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Extract data as numeric arrays
C = hazard_layer.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
P = exposure_layer.get_data(nan=0.0, scaling=True)
H = numpy.where(C <= high_t, P, 0)
M = numpy.where(C < medium_t, P, 0)
L = numpy.where(C < low_t, P, 0)
# Count totals
total = int(numpy.sum(P))
high = int(numpy.sum(H)) - int(numpy.sum(M))
medium = int(numpy.sum(M)) - int(numpy.sum(L))
low = int(numpy.sum(L))
total_impact = high + medium + low
# Don't show digits less than a 1000
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# 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)]
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows population count in high or medium hazard area'),
tr('Total population: %s') % format_int(total),
TableRow(tr(
'Table below shows the minimum needs for all '
'affected 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'])]))
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in high hazard areas')
# Generate 8 equidistant classes across the range of flooded population
# 8 is the number of classes in the predefined flood population style
# as imported
# noinspection PyTypeChecker
classes = numpy.linspace(
numpy.nanmin(M.flat[:]), numpy.nanmax(M.flat[:]), 8)
# Modify labels in existing flood style to show quantities
style_classes = style_info['style_classes']
style_classes[1]['label'] = tr('Low [%i people/cell]') % classes[1]
style_classes[4]['label'] = tr('Medium [%i people/cell]') % classes[4]
style_classes[7]['label'] = tr('High [%i people/cell]') % classes[7]
style_info['legend_title'] = tr('Population Count')
# Create raster object and return
raster_layer = Raster(
M,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') % (
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)
return raster_layer
def run(self, layers):
"""Plugin for impact of population as derived by categorised hazard.
Input
: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
Return
Map of population exposed to high category
Table with number of people in each category
"""
# The 3 category
high_t = self.parameters['high_thresholds']
medium_t = self.parameters['medium_thresholds']
low_t = self.parameters['low_thresholds']
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Categorised Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
population = exposure_layer.get_data(nan=0.0, scaling=True)
if high_t == 0:
hi = numpy.where(0, population, 0)
else:
hi = numpy.where(data == high_t, population, 0)
if medium_t == 0:
med = numpy.where(0, population, 0)
else:
med = numpy.where(data == medium_t, population, 0)
if low_t == 0:
lo = numpy.where(0, population, 0)
else:
lo = numpy.where(data == low_t, population, 0)
if high_t == 0:
impact = numpy.where(
(data == low_t) +
(data == medium_t),
population, 0)
elif medium_t == 0:
impact = numpy.where(
(data == low_t) +
(data == high_t),
population, 0)
elif low_t == 0:
impact = numpy.where(
(data == medium_t) +
(data == high_t),
population, 0)
else:
impact = numpy.where(
(data == low_t) +
(data == medium_t) +
(data == high_t),
population, 0)
# Count totals
total = int(numpy.sum(population))
high = int(numpy.sum(hi))
medium = int(numpy.sum(med))
low = int(numpy.sum(lo))
total_impact = int(numpy.sum(impact))
# Perform population rounding based on number of people
no_impact = population_rounding(total - total_impact)
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# 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 risk areas '),
'%s' % format_int(high)]),
TableRow([tr('Population in Medium risk areas '),
'%s' % format_int(medium)]),
TableRow([tr('Population in Low risk 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'])]))
impact_table = Table(table_body).toNewlineFreeString()
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 areas'),
tr('Total population: %s') % format_int(total)
])
impact_summary = Table(table_body).toNewlineFreeString()
# 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 = 30
else:
transparency = 30
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 category')
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(
impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') % (
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)
return raster_layer
def run(self, layers=None):
"""Run volcano point population evacuation Impact Function.
:param layers: List of layers expected to contain where two layers
should be present.
* hazard_layer: Vector point layer.
* exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to volcano event.
:returns: Map of population exposed to the volcano hazard zone.
The returned dict will include a table with number of people
evacuated and supplies required.
:rtype: dict
:raises:
* Exception - When hazard layer is not vector layer
* RadiiException - When radii are not valid (they need to be
monotonically increasing)
"""
self.validate()
self.prepare(layers)
# Parameters
radii = self.parameters['distance [km]']
name_attribute = self.parameters['volcano name attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard
exposure_layer = self.exposure
# Input checks
if not hazard_layer.is_point_data:
msg = (
'Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' %
(hazard_layer.get_name(), hazard_layer.get_geometry_name()))
raise Exception(msg)
data_table = hazard_layer.get_data()
# Use concentric circles
category_title = 'Radius'
category_header = tr('Distance [km]')
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(centers,
rad_m,
category_title,
data_table=data_table)
# Get names of volcanoes considered
if name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in data_table:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for radius in volcano_name_list:
volcano_names += '%s, ' % radius
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
# Find the target field name that has no conflict with default target
attribute_names = hazard_layer.get_attribute_names()
new_target_field = get_non_conflicting_attribute_name(
self.target_field, attribute_names)
self.target_field = new_target_field
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
hazard_layer,
exposure_layer,
attribute_name=self.target_field
)
# Initialise affected population per categories
affected_population = {}
for radius in rad_m:
affected_population[radius] = 0
nan_warning = False
if has_no_data(exposure_layer.get_data(nan=True)):
nan_warning = True
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this category
category = row[category_title]
affected_population[category] += population
# Count totals
total_population = population_rounding(
int(numpy.nansum(exposure_layer.get_data())))
# Count cumulative for each zone
total_affected_population = 0
cumulative_affected_population = {}
for radius in rad_m:
population = int(affected_population.get(radius, 0))
total_affected_population += population
cumulative_affected_population[radius] = total_affected_population
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
blank_cell = ''
table_body = [
self.question,
TableRow(
[tr('Volcanoes considered'),
'%s' % volcano_names, blank_cell],
header=True),
TableRow([
tr('People needing evacuation'),
'%s' %
format_int(population_rounding(total_affected_population)),
blank_cell
],
header=True),
TableRow(
[category_header,
tr('Total'), tr('Cumulative')], header=True)
]
for radius in rad_m:
table_body.append(
TableRow([
radius,
format_int(population_rounding(
affected_population[radius])),
format_int(
population_rounding(
cumulative_affected_population[radius]))
]))
table_body.extend([
TableRow(
tr('Map shows the number of people affected in each of volcano '
'hazard polygons.'))
])
total_needs = evacuated_population_needs(total_affected_population,
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'])
]))
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Total population %s in the exposure layer') %
format_int(total_population),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')
])
if nan_warning:
table_body.extend([
tr('The population layer 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.')
])
impact_summary = Table(table_body).toNewlineFreeString()
# check for zero impact
if total_affected_population == 0:
table_body = [
self.question,
TableRow([
tr('People needing evacuation'),
'%s' % format_int(total_affected_population), blank_cell
],
header=True)
]
message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(message)
# Create style
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(covered_exposure_layer.get_data().flat[:],
len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
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])
if i == 0:
transparency = 100
else:
transparency = 0
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = transparency
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People affected by the buffered point volcano')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People affected by the buffered point volcano'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'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 = impact_layer
return impact_layer
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):
"""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
"""
# The 3 category
high_t = self.parameters['Categorical thresholds'][2]
medium_t = self.parameters['Categorical thresholds'][1]
low_t = self.parameters['Categorical thresholds'][0]
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Categorised Hazard
exposure_layer = get_exposure_layer(layers) # Population Raster
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Extract data as numeric arrays
C = hazard_layer.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
P = exposure_layer.get_data(nan=0.0, scaling=True)
H = numpy.where(C <= high_t, P, 0)
M = numpy.where(C < medium_t, P, 0)
L = numpy.where(C < low_t, P, 0)
# Count totals
total = int(numpy.sum(P))
high = int(numpy.sum(H)) - int(numpy.sum(M))
medium = int(numpy.sum(M)) - int(numpy.sum(L))
low = int(numpy.sum(L))
total_impact = high + medium + low
# Don't show digits less than a 1000
total = population_rounding(total)
total_impact = population_rounding(total_impact)
high = population_rounding(high)
medium = population_rounding(medium)
low = population_rounding(low)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# 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)
]
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([
TableRow(tr('Notes'), header=True),
tr('Map shows population count in high or medium hazard area'),
tr('Total population: %s') % format_int(total),
TableRow(
tr('Table below shows the minimum needs for all '
'affected 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'])
]))
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in high hazard areas')
# Generate 8 equidistant classes across the range of flooded population
# 8 is the number of classes in the predefined flood population style
# as imported
# noinspection PyTypeChecker
classes = numpy.linspace(numpy.nanmin(M.flat[:]),
numpy.nanmax(M.flat[:]), 8)
# Modify labels in existing flood style to show quantities
style_classes = style_info['style_classes']
style_classes[1]['label'] = tr('Low [%i people/cell]') % classes[1]
style_classes[4]['label'] = tr('Medium [%i people/cell]') % classes[4]
style_classes[7]['label'] = tr('High [%i people/cell]') % classes[7]
style_info['legend_title'] = tr('Population Count')
# Create raster object and return
raster_layer = Raster(M,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') %
(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)
return raster_layer
def _tabulate(self, affected_population, evacuated, minimum_needs,
question, rounding, rounding_evacuated):
# People Affected
table_body = [
question,
TableRow([
tr('People affected'),
'%s*' % (format_int(int(affected_population)))
],
header=True)
]
if self.use_affected_field:
table_body.append(
TableRow(
tr('* People are considered to be affected if they are '
'within the area where the value of the hazard field ('
'"%s") is "%s"') % (self.parameters['affected_field'],
self.parameters['affected_value'])))
else:
table_body.append(
TableRow(
tr('* People are considered to be affected if they are '
'within any polygons.')))
table_body.append(
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
rounding,
col_span=2)
]))
# People Needing Evacuation
table_body.append(
TableRow([
tr('People needing evacuation'),
'%s*' % (format_int(int(evacuated)))
],
header=True))
table_body.append(
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
rounding_evacuated,
col_span=2)
]))
table_body.append(
TableRow([
tr('Evacuation threshold'),
'%s%%' % format_int(self.parameters['evacuation_percentage'])
],
header=True))
table_body.append(
TableRow(
tr('Table below shows the weekly minimum needs for all '
'evacuated people')))
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'])
]))
return table_body, total_needs
def run(self, layers=None):
"""Run volcano point population evacuation Impact Function.
:param layers: List of layers expected to contain where two layers
should be present.
* hazard_layer: Vector point layer.
* exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to volcano event.
:returns: Map of population exposed to the volcano hazard zone.
The returned dict will include a table with number of people
evacuated and supplies required.
:rtype: dict
:raises:
* Exception - When hazard layer is not vector layer
* RadiiException - When radii are not valid (they need to be
monotonically increasing)
"""
self.validate()
self.prepare(layers)
# Parameters
radii = self.parameters['distance [km]']
name_attribute = self.parameters['volcano name attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard
exposure_layer = self.exposure
# Input checks
if not hazard_layer.is_point_data:
msg = (
'Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' % (hazard_layer.get_name(),
hazard_layer.get_geometry_name()))
raise Exception(msg)
data_table = hazard_layer.get_data()
# Use concentric circles
category_title = 'Radius'
category_header = tr('Distance [km]')
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(
centers, rad_m, category_title, data_table=data_table)
# Get names of volcanoes considered
if name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in data_table:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for radius in volcano_name_list:
volcano_names += '%s, ' % radius
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
# Find the target field name that has no conflict with default target
attribute_names = hazard_layer.get_attribute_names()
new_target_field = get_non_conflicting_attribute_name(
self.target_field, attribute_names)
self.target_field = new_target_field
# Run interpolation function for polygon2raster
interpolated_layer, covered_exposure_layer = \
assign_hazard_values_to_exposure_data(
hazard_layer,
exposure_layer,
attribute_name=self.target_field
)
# Initialise affected population per categories
affected_population = {}
for radius in rad_m:
affected_population[radius] = 0
nan_warning = False
if has_no_data(exposure_layer.get_data(nan=True)):
nan_warning = True
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = row[self.target_field]
if not numpy.isnan(population):
population = float(population)
# Update population count for this category
category = row[category_title]
affected_population[category] += population
# Count totals
total_population = population_rounding(
int(numpy.nansum(exposure_layer.get_data())))
# Count cumulative for each zone
total_affected_population = 0
cumulative_affected_population = {}
for radius in rad_m:
population = int(affected_population.get(radius, 0))
total_affected_population += population
cumulative_affected_population[radius] = total_affected_population
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
blank_cell = ''
table_body = [
self.question,
TableRow(
[tr('Volcanoes considered'),
'%s' % volcano_names,
blank_cell],
header=True),
TableRow(
[tr('People needing evacuation'),
'%s' % format_int(
population_rounding(total_affected_population)),
blank_cell],
header=True),
TableRow(
[category_header,
tr('Total'),
tr('Cumulative')],
header=True)]
for radius in rad_m:
table_body.append(
TableRow(
[radius,
format_int(
population_rounding(
affected_population[radius])),
format_int(
population_rounding(
cumulative_affected_population[radius]))]))
table_body.extend([
TableRow(tr(
'Map shows the number of people affected in each of volcano '
'hazard polygons.'))])
total_needs = evacuated_population_needs(
total_affected_population, 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'])]))
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend(
[TableRow(tr('Notes'), header=True),
tr('Total population %s in the exposure layer') % format_int(
total_population),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
if nan_warning:
table_body.extend([
tr('The population layer 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.')
])
impact_summary = Table(table_body).toNewlineFreeString()
# check for zero impact
if total_affected_population == 0:
table_body = [
self.question,
TableRow(
[tr('People needing evacuation'),
'%s' % format_int(total_affected_population),
blank_cell],
header=True)]
message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(message)
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
classes = create_classes(
covered_exposure_layer.get_data().flat[:], len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
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])
if i == 0:
transparency = 100
else:
transparency = 0
style_class['label'] = label
style_class['quantity'] = classes[i]
style_class['colour'] = colours[i]
style_class['transparency'] = transparency
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(
target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# For printing map purpose
map_title = tr('People affected by the buffered point volcano')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create vector layer and return
impact_layer = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
name=tr('People affected by the buffered point volcano'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'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 = impact_layer
return impact_layer
def run(self, layers):
"""Risk plugin for volcano population evacuation.
:param layers: List of layers expected to contain where two layers
should be present.
* hazard_layer: Vector polygon layer of volcano impact zones
* exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to volcano event.
:returns: Map of population exposed to the volcano hazard zone.
The returned dict will include a table with number of people
evacuated and supplies required.
:rtype: dict
:raises:
* Exception - When hazard layer is not vector layer
* RadiiException - When radii are not valid (they need to be
monotonically increasing)
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Volcano KRB
exposure_layer = get_exposure_layer(layers)
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Input checks
if not hazard_layer.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% hazard_layer.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' % (hazard_layer.get_name(),
hazard_layer.get_geometry_name()))
if not (hazard_layer.is_polygon_data or hazard_layer.is_point_data):
raise Exception(msg)
data_table = hazard_layer.get_data()
if hazard_layer.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(
centers, rad_m, category_title, data_table=data_table)
else:
# Use hazard map
category_title = 'KRB'
category_header = tr('Category')
# FIXME (Ole): Change to English and use translation system
category_names = ['Kawasan Rawan Bencana III',
'Kawasan Rawan Bencana II',
'Kawasan Rawan Bencana I']
name_attribute = 'GUNUNG' # As in e.g. BNPB hazard map
# Get names of volcanoes considered
if name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in data_table:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for name in volcano_name_list:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
# Check if category_title exists in hazard_layer
if category_title not in hazard_layer.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (hazard_layer.get_name(), category_title))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Find the target field name that has no conflict with default target
attribute_names = hazard_layer.get_attribute_names()
new_target_field = get_non_conflicting_attribute_name(
self.target_field, attribute_names)
self.target_field = new_target_field
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=self.target_field)
# Initialise data_table of output dataset with all data_table
# from input polygon and a population count of zero
new_data_table = hazard_layer.get_data()
categories = {}
for row in new_data_table:
row[self.target_field] = 0
category = row[category_title]
categories[category] = 0
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = float(row[self.target_field])
# Update population count for associated polygon
poly_id = row['polygon_id']
new_data_table[poly_id][self.target_field] += population
# Update population count for each category
category = new_data_table[poly_id][category_title]
categories[category] += population
# Count totals
total_population = population_rounding(
int(numpy.sum(exposure_layer.get_data(nan=0))))
# Count number and cumulative for each zone
cumulative = 0
all_categories_population = {}
all_categories_cumulative = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
population = int(categories.get(key, 0))
cumulative += population
# I'm not sure whether this is the best place to apply rounding?
all_categories_population[name] = population_rounding(population)
all_categories_cumulative[name] = population_rounding(cumulative)
# Use final accumulation as total number needing evacuation
evacuated = population_rounding(cumulative)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
blank_cell = ''
table_body = [question,
TableRow([tr('Volcanoes considered'),
'%s' % volcano_names, blank_cell],
header=True),
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell],
header=True),
TableRow([category_header,
tr('Total'), tr('Cumulative')],
header=True)]
for name in category_names:
table_body.append(
TableRow([name,
format_int(all_categories_population[name]),
format_int(all_categories_cumulative[name])]))
table_body.extend([
TableRow(tr(
'Map shows the number of people affected in each of volcano '
'hazard polygons.'))])
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'])]))
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend(
[TableRow(tr('Notes'), header=True),
tr('Total population %s in the exposure layer') % format_int(
total_population),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
population_counts = [x[self.target_field] for x in new_data_table]
impact_summary = Table(table_body).toNewlineFreeString()
# check for zero impact
if numpy.nanmax(population_counts) == 0 == numpy.nanmin(
population_counts):
table_body = [
question,
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell], 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(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 100
style_class['min'] = 0
else:
transparency = 30
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by volcanic hazard zone')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create vector layer and return
impact_layer = Vector(
data=new_data_table,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(as_geometry_objects=True),
name=tr('People affected by volcanic hazard zone'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs},
style_info=style_info)
return impact_layer
def run(self, layers):
"""Risk plugin for volcano population evacuation.
:param layers: List of layers expected to contain where two layers
should be present.
* hazard_layer: Vector polygon layer of volcano impact zones
* exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to volcano event.
:returns: Map of population exposed to the volcano hazard zone.
The returned dict will include a table with number of people
evacuated and supplies required.
:rtype: dict
:raises:
* Exception - When hazard layer is not vector layer
* RadiiException - When radii are not valid (they need to be
monotonically increasing)
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Volcano KRB
exposure_layer = get_exposure_layer(layers)
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Input checks
if not hazard_layer.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% hazard_layer.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' % (hazard_layer.get_name(),
hazard_layer.get_geometry_name()))
if not (hazard_layer.is_polygon_data or hazard_layer.is_point_data):
raise Exception(msg)
data_table = hazard_layer.get_data()
if hazard_layer.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(
centers, rad_m, category_title, data_table=data_table)
else:
# Use hazard map
category_title = 'KRB'
category_header = tr('Category')
# FIXME (Ole): Change to English and use translation system
category_names = ['Kawasan Rawan Bencana III',
'Kawasan Rawan Bencana II',
'Kawasan Rawan Bencana I']
name_attribute = 'GUNUNG' # As in e.g. BNPB hazard map
# Get names of volcanoes considered
if name_attribute in hazard_layer.get_attribute_names():
volcano_name_list = []
# Run through all polygons and get unique names
for row in data_table:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for name in volcano_name_list:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
# Check if category_title exists in hazard_layer
if category_title not in hazard_layer.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (hazard_layer.get_name(), category_title))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Find the target field name that has no conflict with default target
attribute_names = hazard_layer.get_attribute_names()
new_target_field = get_non_conflicting_attribute_name(
self.target_field, attribute_names)
self.target_field = new_target_field
# Run interpolation function for polygon2raster
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=self.target_field)
# Initialise data_table of output dataset with all data_table
# from input polygon and a population count of zero
new_data_table = hazard_layer.get_data()
categories = {}
for row in new_data_table:
row[self.target_field] = 0
category = row[category_title]
categories[category] = 0
# Count affected population per polygon and total
for row in interpolated_layer.get_data():
# Get population at this location
population = float(row[self.target_field])
# Update population count for associated polygon
poly_id = row['polygon_id']
new_data_table[poly_id][self.target_field] += population
# Update population count for each category
category = new_data_table[poly_id][category_title]
categories[category] += population
# Count totals
total_population = population_rounding(
int(numpy.sum(exposure_layer.get_data(nan=0))))
# Count number and cumulative for each zone
cumulative = 0
all_categories_population = {}
all_categories_cumulative = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
population = int(categories.get(key, 0))
cumulative += population
# I'm not sure whether this is the best place to apply rounding?
all_categories_population[name] = population_rounding(population)
all_categories_cumulative[name] = population_rounding(cumulative)
# Use final accumulation as total number needing evacuation
evacuated = population_rounding(cumulative)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
blank_cell = ''
table_body = [question,
TableRow([tr('Volcanoes considered'),
'%s' % volcano_names, blank_cell],
header=True),
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell],
header=True),
TableRow([category_header,
tr('Total'), tr('Cumulative')],
header=True)]
for name in category_names:
table_body.append(
TableRow([name,
format_int(all_categories_population[name]),
format_int(all_categories_cumulative[name])]))
table_body.extend([
TableRow(tr(
'Map shows the number of people affected in each of volcano '
'hazard polygons.'))])
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'])]))
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend(
[TableRow(tr('Notes'), header=True),
tr('Total population %s in the exposure layer') % format_int(
total_population),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
population_counts = [x[self.target_field] for x in new_data_table]
impact_summary = Table(table_body).toNewlineFreeString()
# check for zero impact
if numpy.nanmax(population_counts) == 0 == numpy.nanmin(
population_counts):
table_body = [
question,
TableRow([tr('People needing evacuation'),
'%s' % format_int(evacuated),
blank_cell], 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(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 100
style_class['min'] = 0
else:
transparency = 30
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by volcanic hazard zone')
legend_notes = tr('Thousand separator is represented by %s' %
get_thousand_separator())
legend_units = tr('(people per cell)')
legend_title = tr('Population')
# Create vector layer and return
impact_layer = Vector(
data=new_data_table,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(as_geometry_objects=True),
name=tr('People affected by volcanic hazard zone'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'total_needs': total_needs},
style_info=style_info)
return impact_layer
def run(self, layers):
"""Risk plugin for flood population evacuation.
:param layers: List of layers expected to contain
* hazard_layer : Vector polygon layer of flood depth
* exposure_layer : Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to areas identified as flood prone
:returns: Map of population exposed to flooding Table with number of
people evacuated and supplies required.
:rtype: tuple
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Flood inundation
exposure_layer = get_exposure_layer(layers)
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# Check that hazard is polygon type
if not hazard_layer.is_vector:
message = (
'Input hazard %s was not a vector layer as expected ' %
hazard_layer.get_name())
raise Exception(message)
message = (
'Input hazard must be a polygon layer. I got %s with layer type '
'%s' % (hazard_layer.get_name(), hazard_layer.get_geometry_name()))
if not hazard_layer.is_polygon_data:
raise Exception(message)
# Run interpolation function for polygon2raster
combined = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = hazard_layer.get_data()
category_title = 'affected' # FIXME: Should come from keywords
deprecated_category_title = 'FLOODPRONE'
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
try:
title = attr[category_title]
except KeyError:
try:
title = attr['FLOODPRONE']
categories[title] = 0
except KeyError:
pass
# Count affected population per polygon, per category and total
affected_population = 0
for attr in combined.get_data():
affected = False
if 'affected' in attr:
res = attr['affected']
if res is None:
x = False
else:
x = bool(res)
affected = x
elif 'FLOODPRONE' in attr:
# If there isn't an 'affected' attribute,
res = attr['FLOODPRONE']
if res is not None:
affected = res.lower() == 'yes'
elif 'Affected' in attr:
# Check the default attribute assigned for points
# covered by a polygon
res = attr['Affected']
if res is None:
x = False
else:
x = res
affected = x
else:
# assume that every polygon is affected (see #816)
affected = True
# there is no flood related attribute
# message = ('No flood related attribute found in %s. '
# 'I was looking for either "Flooded", "FLOODPRONE" '
# 'or "Affected". The latter should have been '
# 'automatically set by call to '
# 'assign_hazard_values_to_exposure_data(). '
# 'Sorry I can\'t help more.')
# raise Exception(message)
if affected:
# Get population at this location
pop = float(attr['population'])
# Update population count for associated polygon
poly_id = attr['polygon_id']
new_attributes[poly_id][self.target_field] += pop
# Update population count for each category
if len(categories) > 0:
try:
title = new_attributes[poly_id][category_title]
except KeyError:
title = new_attributes[poly_id][
deprecated_category_title]
categories[title] += pop
# Update total
affected_population += pop
# Estimate number of people in need of evacuation
evacuated = (
affected_population *
self.parameters['evacuation_percentage'] /
100.0)
affected_population, rounding = population_rounding_full(
affected_population)
total = int(numpy.sum(exposure_layer.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
total = population_rounding(total)
evacuated, rounding_evacuated = population_rounding_full(evacuated)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow(
[tr('People affected'), '%s*' % (
format_int(int(affected_population)))],
header=True),
TableRow(
[TableCell(
tr('* Number is rounded up to the nearest %s') % (
rounding),
col_span=2)]),
TableRow([tr('People needing evacuation'), '%s*' % (
format_int(int(evacuated)))], header=True),
TableRow(
[TableCell(
tr('* Number is rounded up to the nearest %s') % (
rounding_evacuated),
col_span=2)]),
TableRow([tr('Evacuation threshold'), '%s%%' % format_int(
self.parameters['evacuation_percentage'])], header=True),
TableRow(tr(
'Map shows the number of people affected in each flood prone '
'area')),
TableRow(tr(
'Table below shows the weekly minimum needs for all '
'evacuated people'))]
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'])]))
impact_table = Table(table_body).toNewlineFreeString()
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(
'If yes, where are they located and how will we distribute '
'them?'))
table_body.append(TableRow(
'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 in the area identified as '
'"Flood Prone"'),
tr('Minimum needs are defined in BNPB regulation 7/2008')])
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
# Define classes for legend for flooded population counts
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
population_counts = [x['population'] for x in new_attributes]
classes = create_classes(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 0
style_class['min'] = 0
else:
transparency = 0
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by flood prone areas')
legend_notes = tr('Thousand separator is represented by \'.\'')
legend_units = tr('(people per polygon)')
legend_title = tr('Population Count')
# Create vector layer and return
vector_layer = Vector(
data=new_attributes,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(),
name=tr('People affected by flood prone areas'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'affected_population': affected_population,
'total_population': total,
'total_needs': total_needs},
style_info=style_info)
return vector_layer
def run(self, layers):
"""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
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Tsunami inundation [m]
exposure_layer = get_exposure_layer(layers)
question = get_question(
hazard_layer.get_name(), exposure_layer.get_name(), self)
# 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=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
population = exposure_layer.get_data(nan=0.0, scaling=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.sum(medium))
# Sensible rounding
val, rounding = population_rounding_full(val)
counts.append([val, rounding])
# Count totals
evacuated, rounding = counts[-1]
total = int(numpy.sum(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
# 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))
# 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 = [
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') % (
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)
return raster
def run(self, layers):
"""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
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Tsunami inundation [m]
exposure_layer = get_exposure_layer(layers)
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# 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=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
population = exposure_layer.get_data(nan=0.0, scaling=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.sum(medium))
# Sensible rounding
val, rounding = population_rounding_full(val)
counts.append([val, rounding])
# Count totals
evacuated, rounding = counts[-1]
total = int(numpy.sum(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
# 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))
# 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 = [
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') %
(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)
return raster
def run(self, layers):
"""Risk plugin for flood population evacuation.
:param layers: List of layers expected to contain
* hazard_layer : Vector polygon layer of flood depth
* exposure_layer : Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to areas identified as flood prone
:returns: Map of population exposed to flooding Table with number of
people evacuated and supplies required.
:rtype: tuple
"""
# Identify hazard and exposure layers
hazard_layer = get_hazard_layer(layers) # Flood inundation
exposure_layer = get_exposure_layer(layers)
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Check that hazard is polygon type
if not hazard_layer.is_vector:
message = ('Input hazard %s was not a vector layer as expected ' %
hazard_layer.get_name())
raise Exception(message)
message = (
'Input hazard must be a polygon layer. I got %s with layer type '
'%s' % (hazard_layer.get_name(), hazard_layer.get_geometry_name()))
if not hazard_layer.is_polygon_data:
raise Exception(message)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(hazard_layer,
exposure_layer,
attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = hazard_layer.get_data()
category_title = 'affected' # FIXME: Should come from keywords
deprecated_category_title = 'FLOODPRONE'
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
try:
cat = attr[category_title]
except KeyError:
try:
cat = attr['FLOODPRONE']
categories[cat] = 0
except KeyError:
pass
# Count affected population per polygon, per category and total
affected_population = 0
for attr in P.get_data():
affected = False
if 'affected' in attr:
res = attr['affected']
if res is None:
x = False
else:
x = bool(res)
affected = x
elif 'FLOODPRONE' in attr:
# If there isn't an 'affected' attribute,
res = attr['FLOODPRONE']
if res is not None:
affected = res.lower() == 'yes'
elif 'Affected' in attr:
# Check the default attribute assigned for points
# covered by a polygon
res = attr['Affected']
if res is None:
x = False
else:
x = res
affected = x
else:
# assume that every polygon is affected (see #816)
affected = True
# there is no flood related attribute
# message = ('No flood related attribute found in %s. '
# 'I was looking for either "Flooded", "FLOODPRONE" '
# 'or "Affected". The latter should have been '
# 'automatically set by call to '
# 'assign_hazard_values_to_exposure_data(). '
# 'Sorry I can\'t help more.')
# raise Exception(message)
if affected:
# Get population at this location
pop = float(attr['population'])
# Update population count for associated polygon
poly_id = attr['polygon_id']
new_attributes[poly_id][self.target_field] += pop
# Update population count for each category
if len(categories) > 0:
try:
cat = new_attributes[poly_id][category_title]
except KeyError:
cat = new_attributes[poly_id][
deprecated_category_title]
categories[cat] += pop
# Update total
affected_population += pop
# Estimate number of people in need of evacuation
evacuated = (affected_population *
self.parameters['evacuation_percentage'] / 100.0)
affected_population, rounding = population_rounding_full(
affected_population)
total = int(numpy.sum(exposure_layer.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
total = population_rounding(total)
evacuated, rounding_evacuated = population_rounding_full(evacuated)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
# Generate impact report for the pdf map
table_body = [
question,
TableRow([
tr('People affected'),
'%s*' % (format_int(int(affected_population)))
],
header=True),
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
(rounding),
col_span=2)
]),
TableRow([
tr('People needing evacuation'),
'%s*' % (format_int(int(evacuated)))
],
header=True),
TableRow([
TableCell(tr('* Number is rounded up to the nearest %s') %
(rounding_evacuated),
col_span=2)
]),
TableRow([
tr('Evacuation threshold'),
'%s%%' % format_int(self.parameters['evacuation_percentage'])
],
header=True),
TableRow(
tr('Map shows the number of people affected in each flood prone '
'area')),
TableRow(
tr('Table below shows the weekly minimum needs for all '
'evacuated people'))
]
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'])
]))
impact_table = Table(table_body).toNewlineFreeString()
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 in the area identified as '
'"Flood Prone"'),
tr('Minimum needs are defined in BNPB regulation 7/2008')
])
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
# Define classes for legend for flooded population counts
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
population_counts = [x['population'] for x in new_attributes]
classes = create_classes(population_counts, len(colours))
interval_classes = humanize_class(classes)
# Define style info for output polygons showing population counts
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
style_class['label'] = create_label(interval_classes[i])
if i == 0:
transparency = 0
style_class['min'] = 0
else:
transparency = 0
style_class['min'] = classes[i - 1]
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_class['max'] = classes[i]
style_classes.append(style_class)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='graduatedSymbol')
# For printing map purpose
map_title = tr('People affected by flood prone areas')
legend_notes = tr('Thousand separator is represented by \'.\'')
legend_units = tr('(people per polygon)')
legend_title = tr('Population Count')
# Create vector layer and return
vector_layer = Vector(data=new_attributes,
projection=hazard_layer.get_projection(),
geometry=hazard_layer.get_geometry(),
name=tr('People affected by flood prone areas'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'affected_population': affected_population,
'total_population': total,
'total_needs': total_needs
},
style_info=style_info)
return vector_layer
def _tabulate(
self,
counts,
evacuated,
minimum_needs,
question,
rounding_evacuated,
thresholds,
total,
no_data_warning):
"""Create a tabulated output.
:param counts: The counts breakdown.
:param evacuated: The total.
:param minimum_needs: The minimum needs breakdown.
:param question: The impact question.
:param rounding_evacuated: The rounding that was applied.
:param thresholds: The thresholds that where used.
:param total: The impact total.
:param no_data_warning: Flag to warn about nodata in layer data.
:return:
"""
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_evacuated)),
TableRow(tr('Map shows the numbers of people needing evacuation')),
TableRow(tr(
'Table shows the weekly minimum needs for all evacuated people'
))]
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 flood 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.'),
tr('All affected people are assumed to be evacuated.')])
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.')
])
if len(counts) > 1:
table_body.append(TableRow(tr('Detailed breakdown'), header=True))
for i, val in enumerate(counts):
if i == len(thresholds) - 1:
# The last interval
s = (tr('People in >= %(lo).1f m of water: %(val)s') % {
'lo': thresholds[i],
'val': format_int(val)})
else:
s = tr(
'People in %(lo).1f m to %(hi).1f m of water: '
'%(val)s') % {
'lo': thresholds[i],
'hi': thresholds[i + 1],
'val': format_int(val)}
table_body.append(TableRow(s))
return table_body, total_needs
