def run(self, layers):
"""Risk plugin for flood population evacuation
Input
layers: List of layers expected to contain
my_hazard: Raster layer of flood depth
my_exposure: Raster layer of population data on the same grid
as my_hazard
Counts number of people exposed to flood levels exceeding
specified threshold.
Return
Map of population exposed to flood levels exceeding the threshold
Table with number of people evacuated and supplies required
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Flood inundation [m]
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(), my_exposure.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
D = my_hazard.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
P = my_exposure.get_data(nan=0.0, scaling=True)
# Calculate impact to intermediate thresholds
counts = []
# merely initialize
my_impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
my_impact = M = numpy.where(D >= lo, P, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
M = numpy.where((D >= lo) * (D < hi), P, 0)
# Count
val = int(numpy.sum(M))
# Don't show digits less than a 1000
val = round_thousand(val)
counts.append(val)
# Count totals
evacuated = counts[-1]
total = int(numpy.sum(P))
# Don't show digits less than a 1000
total = round_thousand(total)
# Calculate estimated minimum needs
# The default value of each logistic is based on BNPB Perka 7/2008
# minimum bantuan
minimum_needs = self.parameters['minimum needs']
mn_rice = minimum_needs['Rice']
mn_drinking_water = minimum_needs['Drinking Water']
mn_water = minimum_needs['Water']
mn_family_kits = minimum_needs['Family Kits']
mn_toilets = minimum_needs['Toilets']
rice = int(evacuated * mn_rice)
drinking_water = int(evacuated * mn_drinking_water)
water = int(evacuated * mn_water)
family_kits = int(evacuated * mn_family_kits)
toilets = int(evacuated * mn_toilets)
# Generate impact report for the pdf map
table_body = [
question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s*' % format_int(evacuated)],
header=True),
TableRow(tr('* Number is rounded to the nearest 1000'),
header=False),
TableRow(tr('Map shows population density needing evacuation')),
TableRow([tr('Needs per week'), tr('Total')], header=True),
[tr('Rice [kg]'), format_int(rice)],
[tr('Drinking Water [l]'),
format_int(drinking_water)],
[tr('Clean Water [l]'), format_int(water)],
[tr('Family Kits'), format_int(family_kits)],
[tr('Toilets'), format_int(toilets)]
]
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 fractionals.')
])
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)
})
table_body.append(TableRow(s, header=False))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(my_impact) == 0 == numpy.nanmin(my_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(my_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 density')
# Create raster object and return
R = Raster(my_impact,
projection=my_hazard.get_projection(),
geotransform=my_hazard.get_geotransform(),
name=tr('Population which %s') % get_function_title(self),
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
},
style_info=style_info)
return R
def run(self, layers):
"""Risk plugin for flood population evacuation
Input
layers: List of layers expected to contain
my_hazard: Raster layer of flood depth
my_exposure: Raster layer of population data on the same grid
as my_hazard
Counts number of people exposed to flood levels exceeding
specified threshold.
Return
Map of population exposed to flood levels exceeding the threshold
Table with number of people evacuated and supplies required
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Flood inundation [m]
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(),
my_exposure.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
D = my_hazard.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
P = my_exposure.get_data(nan=0.0, scaling=True)
# Calculate impact to intermediate thresholds
counts = []
# merely initialize
my_impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
my_impact = M = numpy.where(D >= lo, P, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
M = numpy.where((D >= lo) * (D < hi), P, 0)
# Count
val = int(numpy.sum(M))
# Don't show digits less than a 1000
val = round_thousand(val)
counts.append(val)
# Count totals
evacuated = counts[-1]
total = int(numpy.sum(P))
# Don't show digits less than a 1000
total = round_thousand(total)
# Calculate estimated minimum needs
# The default value of each logistic is based on BNPB Perka 7/2008
# minimum bantuan
minimum_needs = self.parameters['minimum needs']
tot_needs = evacuated_population_weekly_needs(evacuated, 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%s' % (format_int(evacuated), (
'*' if evacuated >= 1000 else ''))],
header=True),
TableRow(tr('* Number is rounded to the nearest 1000'),
header=False),
TableRow(tr('Map shows population density needing evacuation')),
TableRow(tr('Table below shows the weekly minium needs for all '
'evacuated people')),
TableRow([tr('Needs per week'), tr('Total')], header=True),
[tr('Rice [kg]'), format_int(tot_needs['rice'])],
[tr('Drinking Water [l]'),
format_int(tot_needs['drinking_water'])],
[tr('Clean Water [l]'), format_int(tot_needs['water'])],
[tr('Family Kits'), format_int(tot_needs['family_kits'])],
[tr('Toilets'), format_int(tot_needs['toilets'])]]
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 fractionals.')])
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)})
table_body.append(TableRow(s, header=False))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(my_impact) == 0 == numpy.nanmin(my_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(my_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 density')
# Create raster object and return
R = Raster(my_impact,
projection=my_hazard.get_projection(),
geotransform=my_hazard.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},
style_info=style_info)
return R
def run(self, layers):
"""Plugin for impact of population as derived by categorised hazard
Input
layers: List of layers expected to contain
my_hazard: Raster layer of categorised hazard
my_exposure: 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 = 1
medium_t = 0.66
low_t = 0.34
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Categorised Hazard
my_exposure = get_exposure_layer(layers) # Population Raster
question = get_question(my_hazard.get_name(), my_exposure.get_name(),
self)
# Extract data as numeric arrays
C = my_hazard.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
P = my_exposure.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))
medium = int(numpy.sum(M)) - int(numpy.sum(H))
low = int(numpy.sum(L)) - int(numpy.sum(M))
total_impact = high + medium + low
# Don't show digits less than a 1000
total = round_thousand(total)
total_impact = round_thousand(total_impact)
high = round_thousand(high)
medium = round_thousand(medium)
low = round_thousand(low)
# 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 density in high or medium '
'hazard area'),
tr('Total population: %s') % format_int(total)
])
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 Density')
# Create raster object and return
R = Raster(M,
projection=my_hazard.get_projection(),
geotransform=my_hazard.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
},
style_info=style_info)
return R
def run(self, layers):
"""Risk plugin for volcano population evacuation
Input
layers: List of layers expected to contain
H: Vector polygon layer of volcano impact zones
P: Raster layer of population data on the same grid as H
Counts number of people exposed to volcano event.
Return
Map of population exposed to the volcano hazard zone.
Table with number of people evacuated and supplies required.
"""
# Identify hazard and exposure layers
H = get_hazard_layer(layers) # Flood inundation
E = get_exposure_layer(layers)
question = get_question(H.get_name(),
E.get_name(),
self)
# Input checks
if not H.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% H.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. '
'I got %s with layer '
'type %s' % (H.get_name(),
H.get_geometry_name()))
if not (H.is_polygon_data or H.is_point_data):
raise Exception(msg)
if H.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
centers = H.get_geometry()
attributes = H.get_data()
rad_m = [x * 1000 for x in radii] # Convert to meters
H = make_circular_polygon(centers,
rad_m,
attributes=attributes)
# NOTE (Sunni) : I commented out this one because there will be
# a permission problem on windows
#H.write_to_file('Evac_zones_%s.shp' % str(radii)) # To check
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
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
attributes = H.get_data()
# Get names of volcanos considered
if name_attribute in H.get_attribute_names():
D = {}
for att in H.get_data():
# Run through all polygons and get unique names
D[att[name_attribute]] = None
volcano_names = ''
for name in D:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
if not category_title in H.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (H.get_name(), category_title))
raise InaSAFEError(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(H, E,
attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = H.get_data()
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
cat = attr[category_title]
categories[cat] = 0
# Count affected population per polygon and total
evacuated = 0
for attr in P.get_data():
# 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
cat = new_attributes[poly_id][category_title]
categories[cat] += pop
# Count totals
total = int(numpy.sum(E.get_data(nan=0)))
# Don't show digits less than a 1000
total = round_thousand(total)
# Count number and cumulative for each zone
cum = 0
pops = {}
cums = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
pop = int(categories[key])
pop = round_thousand(pop)
cum += pop
cum = round_thousand(cum)
pops[name] = pop
cums[name] = cum
# Use final accumulation as total number needing evac
evacuated = cum
# Calculate estimated needs based on BNPB Perka
# 7/2008 minimum bantuan
# FIXME (Ole): Refactor into one function to be shared
rice = int(evacuated * 2.8)
drinking_water = int(evacuated * 17.5)
water = int(evacuated * 67)
family_kits = int(evacuated / 5)
toilets = int(evacuated / 20)
# Generate impact report for the pdf map
blank_cell = ''
table_body = [question,
TableRow([tr('Volcanos 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(pops[name]),
format_int(cums[name])]))
table_body.extend([TableRow(tr('Map shows population affected in '
'each of volcano hazard polygons.')),
TableRow([tr('Needs per week'), tr('Total'),
blank_cell],
header=True),
[tr('Rice [kg]'), format_int(rice), blank_cell],
[tr('Drinking Water [l]'),
format_int(drinking_water), blank_cell],
[tr('Clean Water [l]'), format_int(water),
blank_cell],
[tr('Family Kits'), format_int(family_kits),
blank_cell],
[tr('Toilets'), format_int(toilets),
blank_cell]])
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),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People affected by volcanic hazard zone')
# Define classes for legend for flooded population counts
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
population_counts = [x[self.target_field] for x in new_attributes]
cls = [0] + numpy.linspace(1,
max(population_counts),
len(colours)).tolist()
# Define style info for output polygons showing population counts
style_classes = []
for i, colour in enumerate(colours):
lo = cls[i]
hi = cls[i + 1]
if i == 0:
label = tr('0')
else:
label = tr('%i - %i') % (lo, hi)
entry = dict(label=label, colour=colour, min=lo, max=hi,
transparency=50, size=1)
style_classes.append(entry)
# Override style info with new classes and name
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
legend_title=tr('Population Count'))
# Create vector layer and return
V = Vector(data=new_attributes,
projection=H.get_projection(),
geometry=H.get_geometry(as_geometry_objects=True),
name=tr('Population affected by volcanic hazard zone'),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'target_field': self.target_field},
style_info=style_info)
return V
def run(self, layers):
"""Risk plugin for flood population evacuation
Input:
layers: List of layers expected to contain
my_hazard : Vector polygon layer of flood depth
my_exposure : Raster layer of population data on the same
grid as my_hazard
Counts number of people exposed to areas identified as flood prone
Return
Map of population exposed to flooding
Table with number of people evacuated and supplies required
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Flood inundation
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(),
my_exposure.get_name(),
self)
# Check that hazard is polygon type
if not my_hazard.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% my_hazard.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon layer. I got %s with layer '
'type %s' % (my_hazard.get_name(),
my_hazard.get_geometry_name()))
if not my_hazard.is_polygon_data:
raise Exception(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure,
attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = my_hazard.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:
cat = attr['FLOODPRONE']
categories[cat] = 0
# 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:
# there is no flood related attribute
msg = ('No flood related attribute found in %s. '
'I was looking fore 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(msg)
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
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
affected_population = round_thousand(affected_population)
# Estimate number of people in need of evacuation
evacuated = (affected_population *
self.parameters['evacuation_percentage']
/ 100.0)
total = int(numpy.sum(my_exposure.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
total = round_thousand(total)
evacuated = round_thousand(evacuated)
# Calculate estimated minimum needs
minimum_needs = self.parameters['minimum needs']
tot_needs = evacuated_population_weekly_needs(evacuated, minimum_needs)
# Generate impact report for the pdf map
table_body = [question,
TableRow([tr('People affected'),
'%s%s' % (format_int(int(affected_population)),
('*' if affected_population >= 1000
else ''))],
header=True),
TableRow([tr('People needing evacuation'),
'%s%s' % (format_int(int(evacuated)),
('*' if evacuated >= 1000 else ''))],
header=True),
TableRow([
TableCell(
tr('* Number is rounded to the nearest 1000'),
col_span=2)],
header=False),
TableRow([tr('Evacuation threshold'),
'%s%%' % format_int(
self.parameters['evacuation_percentage'])],
header=True),
TableRow(tr('Map shows population affected in each flood'
' prone area')),
TableRow(tr('Table below shows the weekly minium needs '
'for all evacuated people')),
TableRow([tr('Needs per week'), tr('Total')],
header=True),
[tr('Rice [kg]'), format_int(tot_needs['rice'])],
[tr('Drinking Water [l]'),
format_int(tot_needs['drinking_water'])],
[tr('Clean Water [l]'), format_int(tot_needs['water'])],
[tr('Family Kits'), format_int(tot_needs[
'family_kits'])],
[tr('Toilets'), format_int(tot_needs['toilets'])]]
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 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 = 100
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
V = Vector(data=new_attributes,
projection=my_hazard.get_projection(),
geometry=my_hazard.get_geometry(),
name=tr('Population 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},
style_info=style_info)
return V
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]']
centers = hazard_layer.get_geometry()
rad_m = [x * 1000 for x in radii] # Convert to meters
hazard_layer = buffer_points(centers, rad_m, data_table=data_table)
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
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 not category_title 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 = int(numpy.sum(exposure_layer.get_data(nan=0)))
# Don't show digits less than a 1000
total = round_thousand(total)
# 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))
population = round_thousand(population)
cumulative += population
cumulative = round_thousand(cumulative)
all_categories_population[name] = population
all_categories_cumulative[name] = cumulative
# Use final accumulation as total number needing evacuation
evacuated = cumulative
# Calculate estimated minimum needs
minimum_needs = self.parameters['minimum needs']
total_needs = evacuated_population_weekly_needs(
evacuated, 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.')),
TableRow(
[tr('Needs per week'), tr('Total'), blank_cell], header=True),
[tr('Rice [kg]'), format_int(total_needs['rice']), blank_cell], [
tr('Drinking Water [l]'),
format_int(total_needs['drinking_water']),
blank_cell],
[tr('Clean Water [l]'), format_int(total_needs['water']),
blank_cell],
[tr('Family Kits'), format_int(total_needs['family_kits']),
blank_cell],
[tr('Toilets'), format_int(total_needs['toilets']), blank_cell]])
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),
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)')
legend_title = tr('Population count')
# 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},
style_info=style_info)
return impact_layer
def run(self, layers):
"""Risk plugin for flood population evacuation
Input:
layers: List of layers expected to contain
my_hazard : Vector polygon layer of flood depth
my_exposure : Raster layer of population data on the same
grid as my_hazard
Counts number of people exposed to areas identified as flood prone
Return
Map of population exposed to flooding
Table with number of people evacuated and supplies required
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Flood inundation
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(), my_exposure.get_name(),
self)
# Check that hazard is polygon type
if not my_hazard.is_vector:
msg = ('Input hazard %s was not a vector layer as expected ' %
my_hazard.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon layer. I got %s with layer '
'type %s' %
(my_hazard.get_name(), my_hazard.get_geometry_name()))
if not my_hazard.is_polygon_data:
raise Exception(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(my_hazard,
my_exposure,
attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = my_hazard.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:
cat = attr['FLOODPRONE']
categories[cat] = 0
# 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:
# there is no flood related attribute
msg = ('No flood related attribute found in %s. '
'I was looking fore 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(msg)
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
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
affected_population = round_thousand(affected_population)
# Estimate number of people in need of evacuation
evacuated = (affected_population *
self.parameters['evacuation_percentage'] / 100.0)
total = int(numpy.sum(my_exposure.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
total = round_thousand(total)
evacuated = round_thousand(evacuated)
# Calculate estimated minimum needs
minimum_needs = self.parameters['minimum needs']
tot_needs = evacuated_population_weekly_needs(evacuated, minimum_needs)
# Generate impact report for the pdf map
table_body = [
question,
TableRow([
tr('People affected'),
'%s%s' % (format_int(int(affected_population)),
('*' if affected_population >= 1000 else ''))
],
header=True),
TableRow([
tr('People needing evacuation'),
'%s%s' % (format_int(int(evacuated)),
('*' if evacuated >= 1000 else ''))
],
header=True),
TableRow([
TableCell(tr('* Number is rounded to the nearest 1000'),
col_span=2)
],
header=False),
TableRow([
tr('Evacuation threshold'),
'%s%%' % format_int(self.parameters['evacuation_percentage'])
],
header=True),
TableRow(
tr('Map shows population affected in each flood'
' prone area')),
TableRow(
tr('Table below shows the weekly minium needs '
'for all evacuated people')),
TableRow([tr('Needs per week'), tr('Total')], header=True),
[tr('Rice [kg]'), format_int(tot_needs['rice'])],
[
tr('Drinking Water [l]'),
format_int(tot_needs['drinking_water'])
], [tr('Clean Water [l]'),
format_int(tot_needs['water'])],
[tr('Family Kits'),
format_int(tot_needs['family_kits'])],
[tr('Toilets'), format_int(tot_needs['toilets'])]
]
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 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 = 100
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
V = Vector(data=new_attributes,
projection=my_hazard.get_projection(),
geometry=my_hazard.get_geometry(),
name=tr('Population 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
},
style_info=style_info)
return V
def run(self, layers):
"""Plugin for impact of population as derived by categorised hazard.
Input
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['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 = round_thousand(total)
total_impact = round_thousand(total_impact)
high = round_thousand(high)
medium = round_thousand(medium)
low = round_thousand(low)
# Calculate estimated minimum needs
minimum_needs = self.parameters['minimum needs']
tot_needs = evacuated_population_weekly_needs(
total_impact, 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 density in high or medium hazard area'),
tr('Total population: %s') % format_int(total),
TableRow(tr(
'Table below shows the weekly minimum needs for all '
'affected people')),
TableRow([tr('Needs per week'), tr('Total')], header=True),
[tr('Rice [kg]'), format_int(tot_needs['rice'])],
[tr('Drinking Water [l]'), format_int(tot_needs['drinking_water'])],
[tr('Clean Water [l]'), format_int(tot_needs['water'])],
[tr('Family Kits'), format_int(tot_needs['family_kits'])],
[tr('Toilets'), format_int(tot_needs['toilets'])]
])
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 Density')
# 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},
style_info=style_info)
return raster_layer
def run(self, layers):
"""Plugin for impact of population as derived by categorised hazard
Input
layers: List of layers expected to contain
my_hazard: Raster layer of categorised hazard
my_exposure: 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 = 1
medium_t = 0.66
low_t = 0.34
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Categorised Hazard
my_exposure = get_exposure_layer(layers) # Population Raster
question = get_question(my_hazard.get_name(),
my_exposure.get_name(),
self)
# Extract data as numeric arrays
C = my_hazard.get_data(nan=0.0) # Category
# Calculate impact as population exposed to each category
P = my_exposure.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))
medium = int(numpy.sum(M)) - int(numpy.sum(H))
low = int(numpy.sum(L)) - int(numpy.sum(M))
total_impact = high + medium + low
# Don't show digits less than a 1000
total = round_thousand(total)
total_impact = round_thousand(total_impact)
high = round_thousand(high)
medium = round_thousand(medium)
low = round_thousand(low)
# 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 density in high or medium '
'hazard area'),
tr('Total population: %s') % format_int(total)])
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 Density')
# Create raster object and return
R = Raster(M,
projection=my_hazard.get_projection(),
geotransform=my_hazard.get_geotransform(),
name=tr('Population which %s') % get_function_title(self),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title},
style_info=style_info)
return R
def run(self, layers):
"""Risk plugin for volcano population evacuation
:param layers: List of layers expected to contain where two layers
should be present.
* my_hazard: Vector polygon layer of volcano impact zones
* my_exposure: Raster layer of population data on the same grid as
my_hazard
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
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Volcano KRB
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(), my_exposure.get_name(),
self)
# Input checks
if not my_hazard.is_vector:
msg = ('Input hazard %s was not a vector layer as expected ' %
my_hazard.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' %
(my_hazard.get_name(), my_hazard.get_geometry_name()))
if not (my_hazard.is_polygon_data or my_hazard.is_point_data):
raise Exception(msg)
if my_hazard.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
centers = my_hazard.get_geometry()
attributes = my_hazard.get_data()
rad_m = [x * 1000 for x in radii] # Convert to meters
my_hazard = make_circular_polygon(centers,
rad_m,
attributes=attributes)
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
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
attributes = my_hazard.get_data()
# Get names of volcanos considered
if name_attribute in my_hazard.get_attribute_names():
D = {}
for att in my_hazard.get_data():
# Run through all polygons and get unique names
D[att[name_attribute]] = None
volcano_names = ''
for name in D:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
if not category_title in my_hazard.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (my_hazard.get_name(), category_title))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(my_hazard,
my_exposure,
attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = my_hazard.get_data()
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
cat = attr[category_title]
categories[cat] = 0
# Count affected population per polygon and total
evacuated = 0
for attr in P.get_data():
# 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
cat = new_attributes[poly_id][category_title]
categories[cat] += pop
# Count totals
total = int(numpy.sum(my_exposure.get_data(nan=0)))
# Don't show digits less than a 1000
total = round_thousand(total)
# Count number and cumulative for each zone
cum = 0
pops = {}
cums = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
pop = int(categories.get(key, 0))
pop = round_thousand(pop)
cum += pop
cum = round_thousand(cum)
pops[name] = pop
cums[name] = cum
# Use final accumulation as total number needing evac
evacuated = cum
tot_needs = evacuated_population_weekly_needs(evacuated)
# Generate impact report for the pdf map
blank_cell = ''
table_body = [
question,
TableRow(
[tr('Volcanos 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(pops[name]),
format_int(cums[name])]))
table_body.extend([
TableRow(
tr('Map shows population affected in '
'each of volcano hazard polygons.')),
TableRow([tr('Needs per week'),
tr('Total'), blank_cell],
header=True),
[tr('Rice [kg]'),
format_int(tot_needs['rice']), blank_cell],
[
tr('Drinking Water [l]'),
format_int(tot_needs['drinking_water']), blank_cell
],
[
tr('Clean Water [l]'),
format_int(tot_needs['water']), blank_cell
],
[
tr('Family Kits'),
format_int(tot_needs['family_kits']), blank_cell
], [tr('Toilets'),
format_int(tot_needs['toilets']), blank_cell]
])
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),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')
])
population_counts = [x[self.target_field] for x in new_attributes]
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)')
legend_title = tr('Population count')
# Create vector layer and return
V = Vector(data=new_attributes,
projection=my_hazard.get_projection(),
geometry=my_hazard.get_geometry(as_geometry_objects=True),
name=tr('Population 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
},
style_info=style_info)
return V
def run(self, layers):
"""Risk plugin for volcano population evacuation
Input
layers: List of layers expected to contain
my_hazard: Vector polygon layer of volcano impact zones
my_exposure: Raster layer of population data on the same grid as
my_hazard
Counts number of people exposed to volcano event.
Return
Map of population exposed to the volcano hazard zone.
Table with number of people evacuated and supplies required.
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Volcano KRB
my_exposure = get_exposure_layer(layers)
question = get_question(
my_hazard.get_name(), my_exposure.get_name(), self)
# Input checks
if not my_hazard.is_vector:
msg = ('Input hazard %s was not a vector layer as expected '
% my_hazard.get_name())
raise Exception(msg)
msg = ('Input hazard must be a polygon or point layer. I got %s with '
'layer type %s' % (my_hazard.get_name(),
my_hazard.get_geometry_name()))
if not (my_hazard.is_polygon_data or my_hazard.is_point_data):
raise Exception(msg)
if my_hazard.is_point_data:
# Use concentric circles
radii = self.parameters['distance [km]']
centers = my_hazard.get_geometry()
attributes = my_hazard.get_data()
rad_m = [x * 1000 for x in radii] # Convert to meters
my_hazard = make_circular_polygon(
centers, rad_m, attributes=attributes)
category_title = 'Radius'
category_header = tr('Distance [km]')
category_names = radii
name_attribute = 'NAME' # As in e.g. the Smithsonian dataset
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
attributes = my_hazard.get_data()
# Get names of volcanos considered
if name_attribute in my_hazard.get_attribute_names():
D = {}
for att in my_hazard.get_data():
# Run through all polygons and get unique names
D[att[name_attribute]] = None
volcano_names = ''
for name in D:
volcano_names += '%s, ' % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr('Not specified in data')
if not category_title in my_hazard.get_attribute_names():
msg = ('Hazard data %s did not contain expected '
'attribute %s ' % (my_hazard.get_name(), category_title))
raise InaSAFEError(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(
my_hazard, my_exposure, attribute_name='population')
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = my_hazard.get_data()
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
cat = attr[category_title]
categories[cat] = 0
# Count affected population per polygon and total
evacuated = 0
for attr in P.get_data():
# 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
cat = new_attributes[poly_id][category_title]
categories[cat] += pop
# Count totals
total = int(numpy.sum(my_exposure.get_data(nan=0)))
# Don't show digits less than a 1000
total = round_thousand(total)
# Count number and cumulative for each zone
cum = 0
pops = {}
cums = {}
for name in category_names:
if category_title == 'Radius':
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
pop = int(categories.get(key, 0))
pop = round_thousand(pop)
cum += pop
cum = round_thousand(cum)
pops[name] = pop
cums[name] = cum
# Use final accumulation as total number needing evac
evacuated = cum
# Calculate estimated needs based on BNPB Perka
# 7/2008 minimum bantuan
# FIXME (Ole): Refactor into one function to be shared
rice = int(evacuated * 2.8)
drinking_water = int(evacuated * 17.5)
water = int(evacuated * 67)
family_kits = int(evacuated / 5)
toilets = int(evacuated / 20)
# Generate impact report for the pdf map
blank_cell = ''
table_body = [question,
TableRow([tr('Volcanos 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(pops[name]),
format_int(cums[name])]))
table_body.extend([TableRow(tr('Map shows population affected in '
'each of volcano hazard polygons.')),
TableRow([tr('Needs per week'), tr('Total'),
blank_cell],
header=True),
[tr('Rice [kg]'), format_int(rice), blank_cell],
[tr('Drinking Water [l]'),
format_int(drinking_water), blank_cell],
[tr('Clean Water [l]'), format_int(water),
blank_cell],
[tr('Family Kits'), format_int(family_kits),
blank_cell],
[tr('Toilets'), format_int(toilets),
blank_cell]])
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),
tr('People need evacuation if they are within the '
'volcanic hazard zones.')])
impact_summary = Table(table_body).toNewlineFreeString()
# Create style
colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
'#FFCC00', '#FF6600', '#FF0000', '#7A0000']
population_counts = [x[self.target_field] 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 = 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 \'.\'')
legend_units = tr('(people)')
legend_title = tr('Population count')
# Create vector layer and return
V = Vector(data=new_attributes,
projection=my_hazard.get_projection(),
geometry=my_hazard.get_geometry(as_geometry_objects=True),
name=tr('Population 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},
style_info=style_info)
return V
def run(self, layers):
"""Risk plugin for volcano population evacuation
:param layers: List of layers expected to contain where two layers
should be present.
* my_hazard: Vector polygon layer of volcano impact zones
* my_exposure: Raster layer of population data on the same grid as
my_hazard
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
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Volcano KRB
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(), my_exposure.get_name(), self)
# Input checks
if not my_hazard.is_vector:
msg = "Input hazard %s was not a vector layer as expected " % my_hazard.get_name()
raise Exception(msg)
msg = "Input hazard must be a polygon or point layer. I got %s with " "layer type %s" % (
my_hazard.get_name(),
my_hazard.get_geometry_name(),
)
if not (my_hazard.is_polygon_data or my_hazard.is_point_data):
raise Exception(msg)
if my_hazard.is_point_data:
# Use concentric circles
radii = self.parameters["distance [km]"]
centers = my_hazard.get_geometry()
attributes = my_hazard.get_data()
rad_m = [x * 1000 for x in radii] # Convert to meters
my_hazard = make_circular_polygon(centers, rad_m, attributes=attributes)
category_title = "Radius"
category_header = tr("Distance [km]")
category_names = radii
name_attribute = "NAME" # As in e.g. the Smithsonian dataset
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
attributes = my_hazard.get_data()
# Get names of volcanos considered
if name_attribute in my_hazard.get_attribute_names():
D = {}
for att in my_hazard.get_data():
# Run through all polygons and get unique names
D[att[name_attribute]] = None
volcano_names = ""
for name in D:
volcano_names += "%s, " % name
volcano_names = volcano_names[:-2] # Strip trailing ', '
else:
volcano_names = tr("Not specified in data")
if not category_title in my_hazard.get_attribute_names():
msg = "Hazard data %s did not contain expected " "attribute %s " % (my_hazard.get_name(), category_title)
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
# Run interpolation function for polygon2raster
P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure, attribute_name="population")
# Initialise attributes of output dataset with all attributes
# from input polygon and a population count of zero
new_attributes = my_hazard.get_data()
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
cat = attr[category_title]
categories[cat] = 0
# Count affected population per polygon and total
evacuated = 0
for attr in P.get_data():
# 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
cat = new_attributes[poly_id][category_title]
categories[cat] += pop
# Count totals
total = int(numpy.sum(my_exposure.get_data(nan=0)))
# Don't show digits less than a 1000
total = round_thousand(total)
# Count number and cumulative for each zone
cum = 0
pops = {}
cums = {}
for name in category_names:
if category_title == "Radius":
key = name * 1000 # Convert to meters
else:
key = name
# prevent key error
pop = int(categories.get(key, 0))
pop = round_thousand(pop)
cum += pop
cum = round_thousand(cum)
pops[name] = pop
cums[name] = cum
# Use final accumulation as total number needing evac
evacuated = cum
tot_needs = evacuated_population_weekly_needs(evacuated)
# 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(pops[name]), format_int(cums[name])]))
table_body.extend(
[
TableRow(tr("Map shows population affected in " "each of volcano hazard polygons.")),
TableRow([tr("Needs per week"), tr("Total"), blank_cell], header=True),
[tr("Rice [kg]"), format_int(tot_needs["rice"]), blank_cell],
[tr("Drinking Water [l]"), format_int(tot_needs["drinking_water"]), blank_cell],
[tr("Clean Water [l]"), format_int(tot_needs["water"]), blank_cell],
[tr("Family Kits"), format_int(tot_needs["family_kits"]), blank_cell],
[tr("Toilets"), format_int(tot_needs["toilets"]), blank_cell],
]
)
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),
tr("People need evacuation if they are within the " "volcanic hazard zones."),
]
)
population_counts = [x[self.target_field] for x in new_attributes]
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)")
legend_title = tr("Population count")
# Create vector layer and return
V = Vector(
data=new_attributes,
projection=my_hazard.get_projection(),
geometry=my_hazard.get_geometry(as_geometry_objects=True),
name=tr("Population 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,
},
style_info=style_info,
)
return V
def run(self, layers):
"""Risk plugin for flood population evacuation
Input
layers: List of layers expected to contain
H: Raster layer of flood depth
P: Raster layer of population data on the same grid as H
Counts number of people exposed to flood levels exceeding
specified threshold.
Return
Map of population exposed to flood levels exceeding the threshold
Table with number of people evacuated and supplies required
"""
# Identify hazard and exposure layers
my_hazard = get_hazard_layer(layers) # Flood inundation [m]
my_exposure = get_exposure_layer(layers)
question = get_question(my_hazard.get_name(),
my_exposure.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
D = my_hazard.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
P = my_exposure.get_data(nan=0.0, scaling=True)
# Calculate impact to intermediate thresholds
counts = []
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
my_impact = M = numpy.where(D >= lo, P, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
M = numpy.where((D >= lo) * (D < hi), P, 0)
# Count
val = int(numpy.sum(M))
# Don't show digits less than a 1000
val = round_thousand(val)
counts.append(val)
# Count totals
evacuated = counts[-1]
total = int(numpy.sum(P))
# Don't show digits less than a 1000
total = round_thousand(total)
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
# FIXME: Refactor and share
# 400g per person per day
rice = int(evacuated * 2.8)
# 2.5L per person per day
drinking_water = int(evacuated * 17.5)
# 15L per person per day
water = int(evacuated * 105)
# assume 5 people per family (not in perka)
family_kits = int(evacuated / 5)
# 20 people per toilet
toilets = int(evacuated / 20)
# Generate impact report for the pdf map
table_body = [question,
TableRow([(tr('People in %.1f m of water') %
thresholds[-1]),
'%s*' % format_int(evacuated)],
header=True),
TableRow(tr('* Number is rounded to the nearest 1000'),
header=False),
TableRow(tr('Map shows population density needing '
'evacuation')),
TableRow([tr('Needs per week'), tr('Total')],
header=True),
[tr('Rice [kg]'), format_int(rice)],
[tr('Drinking Water [l]'), format_int(drinking_water)],
[tr('Clean Water [l]'), format_int(water)],
[tr('Family Kits'), format_int(family_kits)],
[tr('Toilets'), format_int(toilets)]]
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 flood levels '
'exceed %(eps).1f m') % {'eps': thresholds[-1]},
tr('Minimum needs are defined in BNPB '
'regulation 7/2008')])
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)})
table_body.append(TableRow(s, header=False))
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in need of evacuation')
# Generate 8 equidistant classes across the range of flooded population
# 8 is the number of classes in the predefined flood population style
# as imported
classes = numpy.linspace(numpy.nanmin(my_impact.flat[:]),
numpy.nanmax(my_impact.flat[:]), 8)
# Work out how many decimals to use
# Modify labels in existing flood style to show quantities
style_classes = style_info['style_classes']
style_classes[1]['label'] = tr('Low [%.2f people/cell]') % classes[1]
style_classes[4]['label'] = tr('Medium [%.2f people/cell]')\
% classes[4]
style_classes[7]['label'] = tr('High [%.2f people/cell]') % classes[7]
# Override associated quantities in colour style
for i in range(len(classes)):
if i == 0:
transparency = 100
else:
transparency = 0
style_classes[i]['quantity'] = classes[i]
style_classes[i]['transparency'] = transparency
# Title
style_info['legend_title'] = tr('Population Density')
# Create raster object and return
R = Raster(my_impact,
projection=my_hazard.get_projection(),
geotransform=my_hazard.get_geotransform(),
name=tr('Population which %s') % get_function_title(self),
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title},
style_info=style_info)
return R
