def get_plugins_as_table(name=None):
"""Retrieve a table listing all plugins and their requirements.
Or just a single plugin if name is passed.
Args: name str optional name of a specific plugin.
Returns: table instance containing plugin descriptive data
Raises: None
"""
table_body = []
header = TableRow([_('Title'), _('ID'), _('Requirements')], header=True)
table_body.append(header)
plugins_dict = dict([(pretty_function_name(p), p)
for p in FunctionProvider.plugins])
if name is not None:
if isinstance(name, basestring):
# Add the names
plugins_dict.update(dict([(p.__name__, p)
for p in FunctionProvider.plugins]))
msg = ('No plugin named "%s" was found. '
'List of available plugins is: %s'
% (name, ', '.join(plugins_dict.keys())))
if name not in plugins_dict:
raise RuntimeError(msg)
plugins_dict = {name: plugins_dict[name]}
else:
msg = ('get_plugins expects either no parameters or a string '
'with the name of the plugin, you passed: '
'%s which is a %s' % (name, type(name)))
raise Exception(msg)
# Now loop through the plugins adding them to the table
for key, func in plugins_dict.iteritems():
for requirement in requirements_collect(func):
row = []
row.append(TableCell(get_function_title(func), header=True))
row.append(key)
row.append(requirement)
table_body.append(TableRow(row))
table = Table(table_body)
table.caption = _('Available Impact Functions')
return table
def test_column(self):
"""Test to retrieve all element in a column.
"""
table_body = []
header = TableRow(['header1', 'header2', 'header3', 'header4'],
header=True)
table_body.append(header)
table_body.append(TableRow([1, 2, 3, 4]))
table_body.append(TableRow(['a', 'b', 'c', 'd']))
table_body.append(TableRow(['x', 'y', 'z', 't']))
html_table = Table(table_body)
expected_result1 = ['header1', 1, 'a', 'x']
expected_result2 = [2, 'b', 'y']
real_result1 = html_table.column(0, True)
real_result2 = html_table.column(1)
message1 = "Expected %s but got %s" % (
expected_result1, real_result1)
message2 = "Expected %s but got %s" % (
expected_result2, real_result2)
assert expected_result1 == real_result1, message1
assert expected_result2 == real_result2, message2
def run(self, layers):
"""Run the impact function.
:param layers: List of layers expected to contain at least:
H: Polygon layer of inundation areas
E: Vector layer of roads
:type layers: list
:returns: A new line layer with inundated roads marked.
:type: safe_layer
"""
target_field = self.parameters['target_field']
road_type_field = self.parameters['road_type_field']
threshold_min = self.parameters['min threshold [m]']
threshold_max = self.parameters['max threshold [m]']
if threshold_min > threshold_max:
message = tr(
'The minimal threshold is greater then the maximal specified '
'threshold. Please check the values.')
raise GetDataError(message)
# Extract data
H = get_hazard_layer(layers) # Flood
E = get_exposure_layer(layers) # Roads
question = get_question(
H.get_name(), E.get_name(), self)
H = H.get_layer()
E = E.get_layer()
# reproject self.extent to the hazard projection
hazard_crs = H.crs()
hazard_authid = hazard_crs.authid()
if hazard_authid == 'EPSG:4326':
viewport_extent = self.extent
else:
geo_crs = QgsCoordinateReferenceSystem()
geo_crs.createFromSrid(4326)
viewport_extent = extent_to_geo_array(
QgsRectangle(*self.extent), geo_crs, hazard_crs)
# Align raster extent and viewport
# assuming they are both in the same projection
raster_extent = H.dataProvider().extent()
clip_xmin = raster_extent.xMinimum()
# clip_xmax = raster_extent.xMaximum()
clip_ymin = raster_extent.yMinimum()
# clip_ymax = raster_extent.yMaximum()
if viewport_extent[0] > clip_xmin:
clip_xmin = viewport_extent[0]
if viewport_extent[1] > clip_ymin:
clip_ymin = viewport_extent[1]
# TODO: Why have these two clauses when they are not used?
# Commenting out for now.
# if viewport_extent[2] < clip_xmax:
# clip_xmax = viewport_extent[2]
# if viewport_extent[3] < clip_ymax:
# clip_ymax = viewport_extent[3]
height = ((viewport_extent[3] - viewport_extent[1]) /
H.rasterUnitsPerPixelY())
height = int(height)
width = ((viewport_extent[2] - viewport_extent[0]) /
H.rasterUnitsPerPixelX())
width = int(width)
raster_extent = H.dataProvider().extent()
xmin = raster_extent.xMinimum()
xmax = raster_extent.xMaximum()
ymin = raster_extent.yMinimum()
ymax = raster_extent.yMaximum()
x_delta = (xmax - xmin) / H.width()
x = xmin
for i in range(H.width()):
if abs(x - clip_xmin) < x_delta:
# We have found the aligned raster boundary
break
x += x_delta
_ = i
y_delta = (ymax - ymin) / H.height()
y = ymin
for i in range(H.width()):
if abs(y - clip_ymin) < y_delta:
# We have found the aligned raster boundary
break
y += y_delta
clip_extent = [x, y, x + width * x_delta, y + height * y_delta]
# Clip and polygonize
small_raster = clip_raster(
H, width, height, QgsRectangle(*clip_extent))
(flooded_polygon_inside, flooded_polygon_outside) = polygonize_gdal(
small_raster, threshold_min, threshold_max)
# Filter geometry and data using the extent
extent = QgsRectangle(*self.extent)
request = QgsFeatureRequest()
request.setFilterRect(extent)
if flooded_polygon_inside is None:
message = tr(
'There are no objects in the hazard layer with "value">%s.'
'Please check the value or use other extent.' % (
threshold_min, ))
raise GetDataError(message)
# reproject the flood polygons to exposure projection
exposure_crs = E.crs()
exposure_authid = exposure_crs.authid()
if hazard_authid != exposure_authid:
flooded_polygon_inside = reproject_vector_layer(
flooded_polygon_inside, E.crs())
flooded_polygon_outside = reproject_vector_layer(
flooded_polygon_outside, E.crs())
# Clip exposure by the extent
# extent_as_polygon = QgsGeometry().fromRect(extent)
# no need to clip since It is using a bbox request
# line_layer = clip_by_polygon(
# E,
# extent_as_polygon
# )
# Find inundated roads, mark them
line_layer = split_by_polygon_in_out(
E,
flooded_polygon_inside,
flooded_polygon_outside,
target_field, 1, request)
target_field_index = line_layer.dataProvider().\
fieldNameIndex(target_field)
# Generate simple impact report
epsg = get_utm_epsg(self.extent[0], self.extent[1])
output_crs = QgsCoordinateReferenceSystem(epsg)
transform = QgsCoordinateTransform(E.crs(), output_crs)
road_len = flooded_len = 0 # Length of roads
roads_by_type = dict() # Length of flooded roads by types
roads_data = line_layer.getFeatures()
road_type_field_index = line_layer.fieldNameIndex(road_type_field)
for road in roads_data:
attributes = road.attributes()
road_type = attributes[road_type_field_index]
if road_type.__class__.__name__ == 'QPyNullVariant':
road_type = tr('Other')
geom = road.geometry()
geom.transform(transform)
length = geom.length()
road_len += length
if road_type not in roads_by_type:
roads_by_type[road_type] = {'flooded': 0, 'total': 0}
roads_by_type[road_type]['total'] += length
if attributes[target_field_index] == 1:
flooded_len += length
roads_by_type[road_type]['flooded'] += length
table_body = [
question,
TableRow(
[
tr('Road Type'),
tr('Flooded in the threshold (m)'),
tr('Total (m)')],
header=True),
TableRow([tr('All'), int(flooded_len), int(road_len)]),
TableRow(tr('Breakdown by road type'), header=True)]
for t, v in roads_by_type.iteritems():
table_body.append(
TableRow([t, int(v['flooded']), int(v['total'])])
)
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('Roads inundated')
style_classes = [
dict(
label=tr('Not Inundated'), value=0,
colour='#1EFC7C', transparency=0, size=0.5),
dict(
label=tr('Inundated'), value=1,
colour='#F31A1C', transparency=0, size=0.5)]
style_info = dict(
target_field=target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Convert QgsVectorLayer to inasafe layer and return it
line_layer = Vector(
data=line_layer,
name=tr('Flooded roads'),
keywords={
'impact_summary': impact_summary,
'map_title': map_title,
'target_field': target_field},
style_info=style_info)
return line_layer
def run(self, layers):
"""Earthquake impact to buildings (e.g. from OpenStreetMap).
:param layers: All the input layers (Hazard Layer and Exposure Layer)
"""
LOGGER.debug('Running earthquake building impact')
# merely initialize
building_value = 0
contents_value = 0
# Thresholds for mmi breakdown.
t0 = self.parameters['low_threshold']
t1 = self.parameters['medium_threshold']
t2 = self.parameters['high_threshold']
# Class Attribute and Label.
class_1 = {'label': tr('Low'), 'class': 1}
class_2 = {'label': tr('Medium'), 'class': 2}
class_3 = {'label': tr('High'), 'class': 3}
# Extract data
hazard_layer = get_hazard_layer(layers) # Depth
exposure_layer = get_exposure_layer(layers) # Building locations
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Define attribute name for hazard levels.
hazard_attribute = 'mmi'
# Determine if exposure data have NEXIS attributes.
attribute_names = exposure_layer.get_attribute_names()
if ('FLOOR_AREA' in attribute_names and 'BUILDING_C' in attribute_names
and 'CONTENTS_C' in attribute_names):
is_nexis = True
else:
is_nexis = False
# Interpolate hazard level to building locations.
interpolate_result = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
# attribute_names = interpolate_result.get_attribute_names()
attributes = interpolate_result.get_data()
interpolate_size = len(interpolate_result)
# Calculate building impact
lo = 0
me = 0
hi = 0
building_values = {}
contents_values = {}
for key in range(4):
building_values[key] = 0
contents_values[key] = 0
for i in range(interpolate_size):
# Classify building according to shake level
# and calculate dollar losses
if is_nexis:
try:
area = float(attributes[i]['FLOOR_AREA'])
except (ValueError, KeyError):
# print 'Got area', attributes[i]['FLOOR_AREA']
area = 0.0
try:
building_value_density = float(attributes[i]['BUILDING_C'])
except (ValueError, KeyError):
# print 'Got bld value', attributes[i]['BUILDING_C']
building_value_density = 0.0
try:
contents_value_density = float(attributes[i]['CONTENTS_C'])
except (ValueError, KeyError):
# print 'Got cont value', attributes[i]['CONTENTS_C']
contents_value_density = 0.0
building_value = building_value_density * area
contents_value = contents_value_density * area
try:
x = float(attributes[i][hazard_attribute]) # MMI
except TypeError:
x = 0.0
if t0 <= x < t1:
lo += 1
cls = 1
elif t1 <= x < t2:
me += 1
cls = 2
elif t2 <= x:
hi += 1
cls = 3
else:
# Not reported for less than level t0
cls = 0
attributes[i][self.target_field] = cls
if is_nexis:
# Accumulate values in 1M dollar units
building_values[cls] += building_value
contents_values[cls] += contents_value
if is_nexis:
# Convert to units of one million dollars
for key in range(4):
building_values[key] = int(building_values[key] / 1000000)
contents_values[key] = int(contents_values[key] / 1000000)
if is_nexis:
# Generate simple impact report for NEXIS type buildings
table_body = [
question,
TableRow([
tr('Hazard Level'),
tr('Buildings Affected'),
tr('Buildings value ($M)'),
tr('Contents value ($M)')
],
header=True),
TableRow([
class_1['label'],
format_int(lo),
format_int(building_values[1]),
format_int(contents_values[1])
]),
TableRow([
class_2['label'],
format_int(me),
format_int(building_values[2]),
format_int(contents_values[2])
]),
TableRow([
class_3['label'],
format_int(hi),
format_int(building_values[3]),
format_int(contents_values[3])
])
]
else:
# Generate simple impact report for unspecific buildings
table_body = [
question,
TableRow([tr('Hazard Level'),
tr('Buildings Affected')],
header=True),
TableRow([class_1['label'], format_int(lo)]),
TableRow([class_2['label'], format_int(me)]),
TableRow([class_3['label'], format_int(hi)])
]
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
tr('High hazard is defined as shake levels greater '
'than %i on the MMI scale.') % t2)
table_body.append(
tr('Medium hazard is defined as shake levels '
'between %i and %i on the MMI scale.') % (t1, t2))
table_body.append(
tr('Low hazard is defined as shake levels '
'between %i and %i on the MMI scale.') % (t0, t1))
if is_nexis:
table_body.append(
tr('Values are in units of 1 million Australian '
'Dollars'))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# Create style
style_classes = [
dict(label=class_1['label'],
value=class_1['class'],
colour='#ffff00',
transparency=1),
dict(label=class_2['label'],
value=class_2['class'],
colour='#ffaa00',
transparency=1),
dict(label=class_3['label'],
value=class_3['class'],
colour='#ff0000',
transparency=1)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# For printing map purpose
map_title = tr('Building affected by earthquake')
legend_notes = tr('The level of the impact is according to the '
'threshold the user input.')
legend_units = tr('(mmi)')
legend_title = tr('Impact level')
# Create vector layer and return
result_layer = Vector(data=attributes,
projection=interpolate_result.get_projection(),
geometry=interpolate_result.get_geometry(),
name=tr('Estimated buildings affected'),
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,
'target_field': self.target_field,
'statistics_type': self.statistics_type,
'statistics_classes': self.statistics_classes
},
style_info=style_info)
msg = 'Created vector layer %s' % str(result_layer)
LOGGER.debug(msg)
return result_layer
def run(self, layers=None):
"""Plugin for impact of population as derived by categorised hazard.
:param layers: List of layers expected to contain
* hazard_layer: Raster layer of categorised hazard
* exposure_layer: Raster layer of population data
Counts number of people exposed to each category of the hazard
:returns:
Map of population exposed to high category
Table with number of people in each category
"""
self.validate()
self.prepare(layers)
thresholds = self.parameters['Categorical thresholds']
# Thresholds must contain 3 thresholds
if len(thresholds) != 3:
raise FunctionParametersError(
'The thresholds must consist of 3 values.')
# Thresholds must monotonically increasing
monotonically_increasing_flag = all(
x < y for x, y in zip(thresholds, thresholds[1:]))
if not monotonically_increasing_flag:
raise FunctionParametersError(
'Each threshold should be larger than the previous.')
# The 3 categories
low_t = thresholds[0]
medium_t = thresholds[1]
high_t = thresholds[2]
# Identify hazard and exposure layers
hazard_layer = self.hazard # Categorised Hazard
exposure_layer = self.exposure # Population Raster
# Extract data as numeric arrays
hazard_data = hazard_layer.get_data(nan=True) # Category
no_data_warning = False
if has_no_data(hazard_data):
no_data_warning = True
# Calculate impact as population exposed to each category
exposure_data = exposure_layer.get_data(nan=True, scaling=True)
if has_no_data(exposure_data):
no_data_warning = True
# Make 3 data for each zone. Get the value of the exposure if the
# exposure is in the hazard zone, else just assign 0
low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
medium_exposure = numpy.where(
(hazard_data >= low_t) & (hazard_data < medium_t), exposure_data,
0)
high_exposure = numpy.where(
(hazard_data >= medium_t) & (hazard_data <= high_t), exposure_data,
0)
impacted_exposure = low_exposure + medium_exposure + high_exposure
# Count totals
total = int(numpy.nansum(exposure_data))
low_total = int(numpy.nansum(low_exposure))
medium_total = int(numpy.nansum(medium_exposure))
high_total = int(numpy.nansum(high_exposure))
total_impact = high_total + medium_total + low_total
# Check for zero impact
if total_impact == 0:
table_body = [
self.question,
TableRow(
[tr('People impacted'),
'%s' % format_int(total_impact)],
header=True)
]
message = Table(table_body).toNewlineFreeString()
raise ZeroImpactException(message)
# Don't show digits less than a 1000
total = population_rounding(total)
total_impact = population_rounding(total_impact)
low_total = population_rounding(low_total)
medium_total = population_rounding(medium_total)
high_total = population_rounding(high_total)
minimum_needs = [
parameter.serialize()
for parameter in self.parameters['minimum needs']
]
table_body = self._tabulate(high_total, low_total, medium_total,
self.question, total_impact)
impact_table = Table(table_body).toNewlineFreeString()
table_body, total_needs = self._tabulate_notes(minimum_needs,
table_body, total,
total_impact,
no_data_warning)
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in each hazard areas (low, medium, high)')
# Style for impact layer
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
classes = create_classes(impacted_exposure.flat[:], len(colours))
interval_classes = humanize_class(classes)
style_classes = []
for i in xrange(len(colours)):
style_class = dict()
if i == 1:
label = create_label(
interval_classes[i],
tr('Low Population [%i people/cell]' % classes[i]))
elif i == 4:
label = create_label(
interval_classes[i],
tr('Medium Population [%i people/cell]' % classes[i]))
elif i == 7:
label = create_label(
interval_classes[i],
tr('High Population [%i people/cell]' % classes[i]))
else:
label = create_label(interval_classes[i])
style_class['label'] = label
style_class['quantity'] = classes[i]
if i == 0:
transparency = 100
else:
transparency = 0
style_class['transparency'] = transparency
style_class['colour'] = colours[i]
style_classes.append(style_class)
style_info = dict(target_field=None,
style_classes=style_classes,
style_type='rasterStyle')
# Create raster object and return
raster_layer = Raster(
data=impacted_exposure,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population might %s') %
(self.impact_function_manager.get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'total_needs': total_needs
},
style_info=style_info)
self._impact = raster_layer
return raster_layer
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 test_cell_header(self):
"""Test we can make a cell as a <th> element"""
cell = TableCell('Foo', header=True)
row = TableRow([cell])
table = Table(row)
del table
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map)
"""
threshold = 1.0 # Flood threshold [m]
# Extract data
H = get_hazard_layer(layers) # Depth
E = get_exposure_layer(layers) # Building locations
question = get_question(H.get_name(), E.get_name(), self)
# Determine attribute name for hazard levels
if H.is_raster:
mode = 'grid'
hazard_attribute = 'depth'
else:
mode = 'regions'
hazard_attribute = None
# Interpolate hazard level to building locations
I = assign_hazard_values_to_exposure_data(
H, E, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = I.get_attribute_names()
attributes = I.get_data()
N = len(I)
# Calculate building impact
count = 0
buildings = {}
affected_buildings = {}
for i in range(N):
if mode == 'grid':
# Get the interpolated depth
x = float(attributes[i]['depth'])
x = x >= threshold
elif mode == 'regions':
# Use interpolated polygon attribute
atts = attributes[i]
# FIXME (Ole): Need to agree whether to use one or the
# other as this can be very confusing!
# For now look for 'Flooded first'
if 'Flooded' in atts:
# E.g. from flood forecast
# Assume that building is wet if inside polygon
# as flagged by attribute Flooded
res = atts['Flooded']
if res is None:
x = False
else:
x = res
elif 'FLOODPRONE' in atts:
res = atts['FLOODPRONE']
if res is None:
x = False
else:
x = res.lower() == 'yes'
elif 'Affected' in atts:
# Check the default attribute assigned for points
# covered by a polygon
res = atts['Affected']
if res is None:
x = False
else:
x = res
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)
else:
msg = (tr('Unknown hazard type %s. '
'Must be either "depth" or "grid"') % mode)
raise Exception(msg)
# Count affected buildings by usage type if available
if 'type' in attribute_names:
usage = attributes[i]['type']
else:
usage = None
if 'amenity' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['amenity']
if 'building_t' in attribute_names and (usage is None
or usage == 0):
usage = attributes[i]['building_t']
if 'office' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['office']
if 'tourism' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['tourism']
if 'leisure' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['leisure']
if 'building' in attribute_names and (usage is None or usage == 0):
usage = attributes[i]['building']
if usage == 'yes':
usage = 'building'
#LOGGER.debug('usage ')
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
affected_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if x is True:
# Count affected buildings by type
affected_buildings[key] += 1
# Count total affected buildings
count += 1
# Add calculated impact to existing attributes
attributes[i][self.target_field] = x
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
affected_buildings['other'] = 0
buildings['other'] += x
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate csv file of results
## fid = open('C:\dki_table_%s.csv' % H.get_name(), 'wb')
## fid.write('%s, %s, %s\n' % (tr('Building type'),
## tr('Temporarily closed'),
## tr('Total')))
## fid.write('%s, %i, %i\n' % (tr('All'), count, N))
# Generate simple impact report
table_body = [
question,
TableRow([tr('Building type'),
tr('Number flooded'),
tr('Total')],
header=True),
TableRow([tr('All'), count, N])
]
## fid.write('%s, %s, %s\n' % (tr('Building type'),
## tr('Temporarily closed'),
## tr('Total')))
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type is available
list_type_attribute = [
'type', 'amenity', 'building_t', 'office', 'tourism', 'leisure',
'building'
]
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
#==============================================================
# print ('WARNING: %s could not be translated'
# % building_type)
#==============================================================
# FIXME (Sunni) : I change affected_buildings[usage] to string
# because it will be replace with   in html
building_list.append([
building_type.capitalize(),
str(affected_buildings[usage]), buildings[usage]
])
if building_type == 'school':
school_closed = affected_buildings[usage]
if building_type == 'hospital':
hospital_closed = affected_buildings[usage]
## fid.write('%s, %i, %i\n' % (building_type.capitalize(),
## affected_buildings[usage],
## buildings[usage]))
# Sort alphabetically
building_list.sort()
#table_body.append(TableRow([tr('Building type'),
# tr('Temporarily closed'),
# tr('Total')], header=True))
table_body.append(
TableRow(tr('Breakdown by building type'), header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
## fid.close()
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(
TableRow(tr('Are the critical facilities still '
'open?')))
table_body.append(
TableRow(
tr('Which structures have warning capacity '
'(eg. sirens, speakers, etc.)?')))
table_body.append(
TableRow(tr('Which buildings will be evacuation '
'centres?')))
table_body.append(
TableRow(tr('Where will we locate the operations '
'centre?')))
table_body.append(
TableRow(
tr('Where will we locate warehouse and/or '
'distribution centres?')))
if school_closed > 0:
table_body.append(
TableRow(
tr('Where will the students from the %d'
' closed schools go to study?') % school_closed))
if hospital_closed > 0:
table_body.append(
TableRow(
tr('Where will the patients from the %d'
' closed hospitals go for treatment '
'and how will we transport them?') % hospital_closed))
table_body.append(TableRow(tr('Notes'), header=True))
assumption = tr('Buildings are said to be flooded when ')
if mode == 'grid':
assumption += tr('flood levels exceed %.1f m') % threshold
else:
assumption += tr('in regions marked as affected')
table_body.append(assumption)
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = tr('Buildings inundated')
# Create style
style_classes = [
dict(label=tr('Not Flooded'),
min=0,
max=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(label=tr('Flooded'),
min=1,
max=1,
colour='#F31A1C',
transparency=0,
size=1)
]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
V = Vector(data=attributes,
projection=I.get_projection(),
geometry=I.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'target_field': self.target_field
},
style_info=style_info)
return V
class ITBFatalityFunctionConfigurable(FunctionProvider):
"""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 is 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.
:author Hadi Ghasemi
:rating 3
:param requires category=='hazard' and \
subcategory=='earthquake' and \
layertype=='raster' and \
unit=='MMI'
:param requires category=='exposure' and \
subcategory=='population' and \
layertype=='raster'
"""
title = tr('Die or be displaced')
defaults = get_defaults()
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
}),
# Threshold below which layer should be transparent
('tolerance', 0.01),
('calculate_displaced_people', True),
('postprocessors',
OrderedDict([
('Gender', {
'on': True
}),
('Age', {
'on':
True,
'params':
OrderedDict([('youth_ratio', defaults['YOUTH_RATIO']),
('adult_ratio', defaults['ADULT_RATIO']),
('elder_ratio', defaults['ELDER_RATIO'])])
})
]))
])
def run(self, layers):
"""Indonesian Earthquake Fatality Model
Input
layers: List of layers expected to contain
H: Raster layer of MMI ground shaking
P: Raster layer of population density
"""
# Establish model coefficients
x = self.parameters['x']
y = self.parameters['y']
# Define percentages of people being displaced at each mmi level
displacement_rate = self.parameters['displacement_rate']
# Tolerance for transparency
tolerance = self.parameters['tolerance']
# Extract input layers
intensity = get_hazard_layer(layers)
population = get_exposure_layer(layers)
question = get_question(intensity.get_name(), population.get_name(),
self)
# Extract data grids
H = intensity.get_data() # Ground Shaking
P = population.get_data(scaling=True) # Population Density
# Calculate population affected by each MMI level
# FIXME (Ole): this range is 2-9. Should 10 be included?
mmi_range = range(2, 10)
number_of_exposed = {}
number_of_displaced = {}
number_of_fatalities = {}
# Calculate fatality rates for observed Intensity values (H
# based on ITB power model
R = numpy.zeros(H.shape)
for mmi in mmi_range:
# Identify cells where MMI is in class i
mask = (H > mmi - 0.5) * (H <= mmi + 0.5)
# Count population affected by this shake level
I = numpy.where(mask, P, 0)
# Calculate expected number of fatalities per level
fatality_rate = numpy.power(10.0, x * mmi - y)
F = fatality_rate * I
# Calculate expected number of displaced people per level
try:
D = displacement_rate[mmi] * I
except KeyError, e:
msg = 'mmi = %i, I = %s, Error msg: %s' % (mmi, str(I), str(e))
raise InaSAFEError(msg)
# Adjust displaced people to disregard fatalities.
# Set to zero if there are more fatalities than displaced.
D = numpy.where(D > F, D - F, 0)
# Sum up numbers for map
R += D # 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(I.flat)
number_of_displaced[mmi] = numpy.nansum(D.flat)
number_of_fatalities[mmi] = numpy.nansum(F.flat)
# Set resulting layer to NaN when less than a threshold. This is to
# achieve transparency (see issue #126).
R[R < tolerance] = numpy.nan
# Total statistics
total = int(round(numpy.nansum(P.flat) / 1000) * 1000)
# Compute number of fatalities
fatalities = int(
round(numpy.nansum(number_of_fatalities.values()) / 1000)) * 1000
# Compute number of people displaced due to building collapse
displaced = int(
round(numpy.nansum(number_of_displaced.values()) / 1000)) * 1000
# Generate impact report
table_body = [question]
# Add total fatality estimate
s = str(int(fatalities)).rjust(10)
table_body.append(
TableRow([tr('Number of fatalities'), s], header=True))
if self.parameters['calculate_displaced_people']:
# Add total estimate of people displaced
s = str(int(displaced)).rjust(10)
table_body.append(
TableRow([tr('Number of people displaced'), s], header=True))
else:
displaced = 0
# Add estimate of total population in area
s = str(int(total)).rjust(10)
table_body.append(
TableRow([tr('Total number of people'), s], header=True))
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
rice = displaced * 2.8
drinking_water = displaced * 17.5
water = displaced * 67
family_kits = displaced / 5
toilets = displaced / 20
# Generate impact report for the pdf map
table_body = [
question,
TableRow([tr('Fatalities'), '%i' % fatalities], header=True),
TableRow([tr('People displaced'),
'%i' % displaced], header=True),
TableRow(
tr('Map shows density estimate of '
'displaced population')),
TableRow([tr('Needs per week'), tr('Total')], header=True),
[tr('Rice [kg]'), int(rice)],
[tr('Drinking Water [l]'),
int(drinking_water)], [tr('Clean Water [l]'),
int(water)],
[tr('Family Kits'), int(family_kits)],
[tr('Toilets'), int(toilets)]
]
impact_table = Table(table_body).toNewlineFreeString()
table_body.append(TableRow(tr('Action Checklist:'), header=True))
if fatalities > 0:
table_body.append(
tr('Are there enough victim identification '
'units available for %i people?') % fatalities)
if displaced > 0:
table_body.append(
tr('Are there enough shelters and relief items '
'available for %i people?') % 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: %i') % 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')
])
impact_summary = Table(table_body).toNewlineFreeString()
map_title = tr('People in need of evacuation')
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 to nearest 1000.'))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = tr('Earthquake impact to population')
# Create style info dynamically
classes = numpy.linspace(numpy.nanmin(R.flat[:]),
numpy.nanmax(R.flat[:]), 5)
style_classes = [
dict(colour='#EEFFEE',
quantity=classes[0],
transparency=100,
label=tr('%.2f people/cell') % classes[0]),
dict(colour='#FFFF7F', quantity=classes[1], transparency=30),
dict(colour='#E15500',
quantity=classes[2],
transparency=30,
label=tr('%.2f people/cell') % classes[2]),
dict(colour='#E4001B', quantity=classes[3], transparency=30),
dict(colour='#730000',
quantity=classes[4],
transparency=30,
label=tr('%.2f people/cell') % classes[4])
]
style_info = dict(target_field=None, style_classes=style_classes)
# Create new layer and return
L = Raster(R,
projection=population.get_projection(),
geotransform=population.get_geotransform(),
keywords={
'impact_summary': impact_summary,
'total_population': total,
'total_fatalities': fatalities,
'impact_table': impact_table,
'map_title': map_title
},
name=tr('Estimated displaced population'),
style_info=style_info)
# Maybe return a shape file with contours instead
return L
def run(self, layers=None):
"""Run volcano population evacuation Impact Function.
: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)
"""
self.validate()
self.prepare(layers)
# Parameters
hazard_zone_attribute = self.parameters['hazard zone attribute']
name_attribute = self.parameters['volcano name attribute']
# Identify hazard and exposure layers
hazard_layer = self.hazard
exposure_layer = self.exposure
nan_warning = False
if has_no_data(exposure_layer.get_data(nan=True)):
nan_warning = True
# Input checks
if not hazard_layer.is_polygon_data:
msg = ('Input hazard must be a polygon layer. I got %s with '
'layer type %s' % (hazard_layer.get_name(),
hazard_layer.get_geometry_name()))
raise Exception(msg)
# Check if hazard_zone_attribute exists in hazard_layer
if hazard_zone_attribute not in hazard_layer.get_attribute_names():
msg = ('Hazard data %s did not contain expected attribute %s ' % (
hazard_layer.get_name(), hazard_zone_attribute))
# noinspection PyExceptionInherit
raise InaSAFEError(msg)
features = hazard_layer.get_data()
category_header = tr('Category')
hazard_zone_categories = list(
set(hazard_layer.get_data(hazard_zone_attribute)))
# 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 features:
volcano_name_list.append(row[name_attribute])
volcano_names = ''
for hazard_zone in volcano_name_list:
volcano_names += '%s, ' % hazard_zone
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 total affected per category
affected_population = {}
for hazard_zone in hazard_zone_categories:
affected_population[hazard_zone] = 0
# 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[hazard_zone_attribute]
affected_population[category] += population
# Count totals
total_population = population_rounding(
int(numpy.nansum(exposure_layer.get_data())))
# Count number and cumulative for each zone
total_affected_population = 0
cumulative_affected_population = {}
for hazard_zone in hazard_zone_categories:
population = int(affected_population.get(hazard_zone, 0))
total_affected_population += population
cumulative_affected_population[hazard_zone] = \
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 hazard_zone in hazard_zone_categories:
table_body.append(
TableRow(
[hazard_zone,
format_int(
population_rounding(
affected_population[hazard_zone])),
format_int(
population_rounding(
cumulative_affected_population[hazard_zone]))]))
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 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 = Raster(
data=covered_exposure_layer.get_data(),
projection=covered_exposure_layer.get_projection(),
geotransform=covered_exposure_layer.get_geotransform(),
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)
self._impact = impact_layer
return impact_layer
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.
: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):
"""Impact function 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
inundation = get_hazard_layer(layers) # Flood inundation [m]
population = get_exposure_layer(layers)
question = get_question(inundation.get_name(), population.get_name(),
self)
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
threshold = self.parameters['threshold']
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
P = population.get_data(nan=0.0, scaling=True)
# Create new array with positive population counts only for
# pixels where inundation exceeds threshold.
I = numpy.where(D >= threshold, P, 0)
# Count population thus exposed to inundation
evacuated = int(numpy.sum(I))
# Count total population
total = int(numpy.sum(P))
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
# 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([('People in %.1f m of water' % threshold),
'%s' % evacuated],
header=True),
TableRow('Map shows population density needing '
'evacuation'),
TableRow(['Needs per week', 'Total'], header=True),
['Rice [kg]', rice], ['Drinking Water [l]', drinking_water],
['Clean Water [l]', water], ['Family Kits', family_kits],
['Toilets', toilets]
]
impact_table = Table(table_body).toNewlineFreeString()
# Extend impact report for on-screen display
table_body.extend([
TableRow('Notes', header=True),
'Total population: %s' % total,
'People need evacuation if flood levels '
'exceed %(eps).1f m' % {
'eps': threshold
}, 'Minimum needs are defined in BNPB '
'regulation 7/2008'
])
impact_summary = Table(table_body).toNewlineFreeString()
map_title = '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(I.flat[:]),
numpy.nanmax(I.flat[:]), 8)
# Define 8 colours - on for each class
colours = [
'#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600',
'#FF0000', '#7A0000'
]
# Create style associating each class with a colour and transparency.
style_classes = []
for i, cls in enumerate(classes):
if i == 0:
# Smallest class has 100% transparency
transparency = 100
else:
# All the others are solid
transparency = 0
# Create labels for three of the classes
if i == 1:
label = 'Low [%.2f people/cell]' % cls
elif i == 4:
label = 'Medium [%.2f people/cell]' % cls
elif i == 7:
label = 'High [%.2f people/cell]' % cls
else:
label = ''
# Style dictionary for this class
d = dict(colour=colours[i],
quantity=cls,
transparency=transparency,
label=label)
style_classes.append(d)
# Create style info for impact layer
style_info = dict(
target_field=None, # Only for vector data
legend_title='Population Density',
style_classes=style_classes)
# Create raster object and return
R = Raster(I,
projection=inundation.get_projection(),
geotransform=inundation.get_geotransform(),
name='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
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.')
])
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):
"""Impact plugin for hazard impact
"""
# Extract data
H = get_hazard_layer(layers) # Value
E = get_exposure_layer(layers) # Building locations
question = get_question(H.get_name(),
E.get_name(),
self)
# Interpolate hazard level to building locations
H = assign_hazard_values_to_exposure_data(H, E,
attribute_name='hazard_lev',
mode='constant')
# Extract relevant numerical data
coordinates = H.get_geometry()
category = H.get_data()
N = len(category)
# List attributes to carry forward to result layer
#attributes = E.get_attribute_names()
# Calculate building impact according to guidelines
count2 = 0
count1 = 0
count0 = 0
building_impact = []
for i in range(N):
# Get category value
val = float(category[i]['hazard_lev'])
# Classify buildings according to value
## if val >= 2.0 / 3:
## affected = 2
## count2 += 1
## elif 1.0 / 3 <= val < 2.0 / 3:
## affected = 1
## count1 += 1
## else:
## affected = 0
## count0 += 1
## FIXME it would be good if the affected were words not numbers
## FIXME need to read hazard layer and see category or keyword
if val == 3:
affected = 3
count2 += 1
elif val == 2:
affected = 2
count1 += 1
elif val == 1:
affected = 1
count0 += 1
else:
affected = 'None'
# Collect depth and calculated damage
result_dict = {self.target_field: affected,
'CATEGORY': val}
# Record result for this feature
building_impact.append(result_dict)
# Create impact report
# Generate impact summary
table_body = [question,
TableRow([tr('Category'), tr('Affected')],
header=True),
TableRow([tr('High'), format_int(count2)]),
TableRow([tr('Medium'), format_int(count1)]),
TableRow([tr('Low'), format_int(count0)]),
TableRow([tr('All'), format_int(N)])]
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(tr('Categorised hazard has only 3'
' classes, high, medium and low.'))
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
map_title = tr('Categorised hazard impact on buildings')
#FIXME it would be great to do categorized rather than grduated
# Create style
style_classes = [dict(label=tr('Low'), min=1, max=1,
colour='#1EFC7C', transparency=0, size=1),
dict(label=tr('Medium'), min=2, max=2,
colour='#FFA500', transparency=0, size=1),
dict(label=tr('High'), min=3, max=3,
colour='#F31A1C', transparency=0, size=1)]
style_info = dict(target_field=self.target_field,
style_classes=style_classes)
# Create vector layer and return
name = 'Buildings Affected'
V = Vector(data=building_impact,
projection=E.get_projection(),
geometry=coordinates,
geometry_type=E.geometry_type,
keywords={'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'target_field': self.target_field,
'statistics_type': self.statistics_type,
'statistics_classes': self.statistics_classes},
name=name,
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: Vector polygon layer of flood depth
P: Raster layer of population data on the same grid as H
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
H = get_hazard_layer(layers) # Flood inundation
E = get_exposure_layer(layers)
question = get_question(H.get_name(),
E.get_name(),
self)
# Check that hazard is polygon type
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 layer. I got %s with layer '
'type %s' % (H.get_name(),
H.get_geometry_name()))
if not H.is_polygon_data:
raise Exception(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()
category_title = 'FLOODPRONE' # FIXME: Should come from keywords
categories = {}
for attr in new_attributes:
attr[self.target_field] = 0
cat = attr[category_title]
categories[cat] = 0
# Count affected population per polygon, per category and total
evacuated = 0
for attr in P.get_data():
affected = False
if 'FLOODPRONE' in attr:
res = attr['FLOODPRONE']
if res is not None:
affected = res.lower() == 'yes'
else:
# If there isn't a flood prone attribute,
# assume that building is wet if inside polygon
# as flag by generic attribute AFFECTED
res = attr['Affected']
if res is not None:
affected = res
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
cat = new_attributes[poly_id][category_title]
categories[cat] += pop
# Update total
evacuated += pop
# Count totals
total = int(numpy.sum(E.get_data(nan=0, scaling=False)))
# Don't show digits less than a 1000
if total > 1000:
total = total // 1000 * 1000
if evacuated > 1000:
evacuated = evacuated // 1000 * 1000
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
rice = evacuated * 2.8
drinking_water = evacuated * 17.5
water = evacuated * 67
family_kits = evacuated / 5
toilets = evacuated / 20
# Generate impact report for the pdf map
table_body = [question,
TableRow([tr('People needing evacuation'),
'%i' % evacuated],
header=True),
TableRow(tr('Map shows population affected in each flood'
' prone area ')),
TableRow([tr('Needs per week'), tr('Total')],
header=True),
[tr('Rice [kg]'), int(rice)],
[tr('Drinking Water [l]'), int(drinking_water)],
[tr('Clean Water [l]'), int(water)],
[tr('Family Kits'), int(family_kits)],
[tr('Toilets'), 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: %i') % 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()
map_title = tr('People affected by flood prone areas')
# 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]
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')
transparency = 100
else:
label = tr('%i - %i') % (lo, hi)
transparency = 0
entry = dict(label=label, colour=colour, min=lo, max=hi,
transparency=transparency, 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(),
name=tr('Population affected by flood prone areas'),
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):
"""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
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
inundation = get_hazard_layer(layers) # Flood inundation [m]
population = get_exposure_layer(layers)
question = get_question(inundation.get_name(), population.get_name(),
self)
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds']
verify(isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
D = inundation.get_data(nan=0.0) # Depth
# Calculate impact as population exposed to depths > max threshold
P = population.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
I = 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
if val > 1000:
val = val // 1000 * 1000
counts.append(val)
# Count totals
evacuated = counts[-1]
total = int(numpy.sum(P))
# Don't show digits less than a 1000
if total > 1000:
total = total // 1000 * 1000
# Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan
# FIXME: Refactor and share
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
table_body = [
question,
TableRow([(tr('People in %.1f m of water') % thresholds[-1]),
'%s' % format_int(evacuated)],
header=True),
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(I.flat[:]),
numpy.nanmax(I.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]
# 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(I,
projection=inundation.get_projection(),
geotransform=inundation.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 get_plugins_as_table(dict_filter=None):
"""Retrieve a table listing all plugins and their requirements.
Or just a single plugin if name is passed.
Args:
* dict_filter = dictionary that contains filters
- id = list_id
- title = list_title
- category : list_category
- subcategory : list_subcategory
- layertype : list_layertype
- datatype : list_datatype
- unit: list_unit
- disabled : list_disabled # not included
Returns:
* table contains plugins match with dict_filter
Raises: None
"""
if dict_filter is None:
dict_filter = {'id': [],
'title': [],
'category': [],
'subcategory': [],
'layertype': [],
'datatype': [],
'unit': []}
table_body = []
# use this list for avoiding wrong order in dict
atts = ['category', 'subcategory', 'layertype',
'datatype', 'unit']
header = TableRow([tr('Title'), tr('ID'), tr('Category'),
tr('Sub Category'), tr('Layer type'), tr('Data type'),
tr('Unit')],
header=True)
table_body.append(header)
plugins_dict = dict([(pretty_function_name(p), p)
for p in FunctionProvider.plugins])
not_found_value = 'N/A'
for key, func in plugins_dict.iteritems():
for requirement in requirements_collect(func):
dict_found = {'title': False,
'id': False,
'category': False,
'subcategory': False,
'layertype': False,
'datatype': False,
'unit': False}
dict_req = parse_single_requirement(str(requirement))
# If the impact function is disabled, do not show it
if dict_req.get('disabled', False):
continue
for myKey in dict_found.iterkeys():
myFilter = dict_filter.get(myKey, [])
if myKey == 'title':
myValue = str(get_function_title(func))
elif myKey == 'id':
myValue = str(key)
else:
myValue = dict_req.get(myKey, not_found_value)
if myFilter != []:
for myKeyword in myFilter:
if type(myValue) == type(str()):
if myValue == myKeyword:
dict_found[myKey] = True
break
elif type(myValue) == type(list()):
if myKeyword in myValue:
dict_found[myKey] = True
break
else:
if myValue.find(str(myKeyword)) != -1:
dict_found[myKey] = True
break
else:
dict_found[myKey] = True
add_row = True
for found_value in dict_found.itervalues():
if not found_value:
add_row = False
break
if add_row:
row = []
row.append(TableCell(get_function_title(func), header=True))
row.append(key)
for myKey in atts:
myValue = pretty_string(dict_req.get(myKey,
not_found_value))
row.append(myValue)
table_body.append(TableRow(row))
cw = 100 / 7
table_col_width = [str(cw) + '%', str(cw) + '%', str(cw) + '%',
str(cw) + '%', str(cw) + '%', str(cw) + '%',
str(cw) + '%']
table = Table(table_body, col_width=table_col_width)
table.caption = tr('Available Impact Functions')
return table
def run(self, layers):
"""Flood impact to buildings (e.g. from Open Street Map).
:param layers: List of layers expected to contain.
* hazard_layer: Hazard raster layer of flood
* exposure_layer: Vector layer of structure data on
the same grid as hazard_layer
"""
threshold = self.parameters['threshold [m]'] # Flood threshold [m]
verify(isinstance(threshold, float),
'Expected thresholds to be a float. Got %s' % str(threshold))
# Extract data
hazard_layer = get_hazard_layer(layers) # Depth
exposure_layer = get_exposure_layer(layers) # Building locations
question = get_question(hazard_layer.get_name(),
exposure_layer.get_name(), self)
# Determine attribute name for hazard levels
mode = 'grid'
hazard_attribute = 'depth'
# Interpolate hazard level to building locations
interpolated_layer = assign_hazard_values_to_exposure_data(
hazard_layer, exposure_layer, attribute_name=hazard_attribute)
# Extract relevant exposure data
attribute_names = interpolated_layer.get_attribute_names()
features = interpolated_layer.get_data()
total_features = len(interpolated_layer)
buildings = {}
# The number of affected buildings
affected_count = 0
# The variable for grid mode
inundated_count = 0
wet_count = 0
dry_count = 0
inundated_buildings = {}
wet_buildings = {}
dry_buildings = {}
# The variable for regions mode
affected_buildings = {}
for i in range(total_features):
# Get the interpolated depth
water_depth = float(features[i]['depth'])
if water_depth <= 0:
inundated_status = 0 # dry
elif water_depth >= threshold:
inundated_status = 1 # inundated
else:
inundated_status = 2 # wet
# Count affected buildings by usage type if available
usage = get_osm_building_usage(attribute_names, features[i])
if usage is not None and usage != 0:
key = usage
else:
key = 'unknown'
if key not in buildings:
buildings[key] = 0
inundated_buildings[key] = 0
wet_buildings[key] = 0
dry_buildings[key] = 0
# Count all buildings by type
buildings[key] += 1
if inundated_status is 0:
# Count dry buildings by type
dry_buildings[key] += 1
# Count total dry buildings
dry_count += 1
if inundated_status is 1:
# Count inundated buildings by type
inundated_buildings[key] += 1
# Count total dry buildings
inundated_count += 1
if inundated_status is 2:
# Count wet buildings by type
wet_buildings[key] += 1
# Count total wet buildings
wet_count += 1
# Add calculated impact to existing attributes
features[i][self.target_field] = inundated_status
affected_count = inundated_count + wet_count
# Lump small entries and 'unknown' into 'other' category
for usage in buildings.keys():
x = buildings[usage]
if x < 25 or usage == 'unknown':
if 'other' not in buildings:
buildings['other'] = 0
if mode == 'grid':
inundated_buildings['other'] = 0
wet_buildings['other'] = 0
dry_buildings['other'] = 0
elif mode == 'regions':
affected_buildings['other'] = 0
buildings['other'] += x
if mode == 'grid':
inundated_buildings['other'] += inundated_buildings[usage]
wet_buildings['other'] += wet_buildings[usage]
dry_buildings['other'] += dry_buildings[usage]
del buildings[usage]
del inundated_buildings[usage]
del wet_buildings[usage]
del dry_buildings[usage]
elif mode == 'regions':
affected_buildings['other'] += affected_buildings[usage]
del buildings[usage]
del affected_buildings[usage]
# Generate simple impact report
table_body = [
question,
TableRow([
tr('Building type'),
tr('Number Inundated'),
tr('Number of Wet Buildings'),
tr('Number of Dry Buildings'),
tr('Total')
],
header=True),
TableRow([
tr('All'),
format_int(inundated_count),
format_int(wet_count),
format_int(dry_count),
format_int(total_features)
])
]
school_closed = 0
hospital_closed = 0
# Generate break down by building usage type if available
list_type_attribute = [
'TYPE', 'type', 'amenity', 'building_t', 'office', 'tourism',
'leisure', 'building'
]
intersect_type = set(attribute_names) & set(list_type_attribute)
if len(intersect_type) > 0:
# Make list of building types
building_list = []
for usage in buildings:
building_type = usage.replace('_', ' ')
# Lookup internationalised value if available
building_type = tr(building_type)
building_list.append([
building_type.capitalize(),
format_int(inundated_buildings[usage]),
format_int(wet_buildings[usage]),
format_int(dry_buildings[usage]),
format_int(buildings[usage])
])
if usage.lower() == 'school':
school_closed = 0
school_closed += inundated_buildings[usage]
school_closed += wet_buildings[usage]
if usage.lower() == 'hospital':
hospital_closed = 0
hospital_closed += inundated_buildings[usage]
hospital_closed += wet_buildings[usage]
# Sort alphabetically
building_list.sort()
table_body.append(
TableRow(tr('Breakdown by building type'), header=True))
for row in building_list:
s = TableRow(row)
table_body.append(s)
# Action Checklist Section
table_body.append(TableRow(tr('Action Checklist:'), header=True))
table_body.append(
TableRow(tr('Are the critical facilities still open?')))
table_body.append(
TableRow(
tr('Which structures have warning capacity (eg. sirens, speakers, '
'etc.)?')))
table_body.append(
TableRow(tr('Which buildings will be evacuation centres?')))
table_body.append(
TableRow(tr('Where will we locate the operations centre?')))
table_body.append(
TableRow(
tr('Where will we locate warehouse and/or distribution centres?'
)))
if school_closed > 0:
table_body.append(
TableRow(
tr('Where will the students from the %s closed schools go to '
'study?') % format_int(school_closed)))
if hospital_closed > 0:
table_body.append(
TableRow(
tr('Where will the patients from the %s closed hospitals go '
'for treatment and how will we transport them?') %
format_int(hospital_closed)))
# Notes Section
table_body.append(TableRow(tr('Notes'), header=True))
table_body.append(
TableRow(
tr('Buildings are said to be inundated when flood levels '
'exceed %.1f m') % threshold))
table_body.append(
TableRow(
tr('Buildings are said to be wet when flood levels '
'are greater than 0 m but less than %.1f m') % threshold))
table_body.append(
TableRow(
tr('Buildings are said to be dry when flood levels '
'are less than 0 m')))
table_body.append(
TableRow(
tr('Buildings are said to be closed if they are inundated or '
'wet')))
table_body.append(
TableRow(tr('Buildings are said to be open if they are dry')))
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# Prepare impact layer
map_title = tr('Buildings inundated')
legend_title = tr('Structure inundated status')
style_classes = [
dict(label=tr('Dry (<= 0 m)'),
value=0,
colour='#1EFC7C',
transparency=0,
size=1),
dict(label=tr('Wet (0 m - %.1f m)') % threshold,
value=2,
colour='#FF9900',
transparency=0,
size=1),
dict(label=tr('Inundated (>= %.1f m)') % threshold,
value=1,
colour='#F31A1C',
transparency=0,
size=1)
]
legend_units = tr('(inundated, wet, or dry)')
style_info = dict(target_field=self.target_field,
style_classes=style_classes,
style_type='categorizedSymbol')
# Create vector layer and return
vector_layer = Vector(data=features,
projection=interpolated_layer.get_projection(),
geometry=interpolated_layer.get_geometry(),
name=tr('Estimated buildings affected'),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'target_field': self.target_field,
'map_title': map_title,
'legend_units': legend_units,
'legend_title': legend_title,
'buildings_total': total_features,
'buildings_affected': affected_count
},
style_info=style_info)
return vector_layer
def test_table_with_colalign(self):
"""Table columns can be right justified"""
# First with default alignment
actual_result = Table(['12', '3000', '5'])
expected_strings = [
'<td colspan="100%">12</td>', '<td colspan="100%">3000</td>',
'<td colspan="100%">5</td>'
]
for s in expected_strings:
message = ('Did not find expected string "%s" in result: %s' %
(s, actual_result))
assert s in str(actual_result).strip(), message
# Then using explicit alignment (all right justified)
# FIXME (Ole): This does not work if e.g. col_align has
# different strings: col_align = ['right', 'left', 'center']
actual_result = Table(['12', '3000', '5'],
col_align=['right', 'right', 'right'])
expected_strings = [
('<td colspan="100%" align="right" style="text-align: '
'right;">12</td>'),
('<td colspan="100%" align="right" style="text-align: '
'right;">3000</td>'),
('<td colspan="100%" align="right" style="text-align: '
'right;">5</td>')
]
for s in expected_strings:
message = ('Did not find expected string "%s" in result: %s' %
(s, actual_result))
assert s in str(actual_result).strip(), message
# Now try at the TableRow level
# FIXME (Ole): Breaks tables!
# row = TableRow(['12', '3000', '5'],
# col_align=['right', 'right', 'right'])
# actual_result = Table(row)
# print actual_result
# This breaks too - what's going on?
# row = TableRow(['12', '3000', '5'])
# actual_result = Table(row)
# print actual_result
# Try at the cell level
cell_1 = TableCell('12', align='right')
cell_2 = TableCell('3000', align='right')
cell_3 = TableCell('5', align='right')
row = TableRow([cell_1, cell_2, cell_3])
# print row # OK
# This is OK
for cell in [cell_1, cell_2, cell_3]:
msg = 'Wrong cell alignment %s' % cell
assert 'align="right"' in str(cell), msg
table = Table(row)
self.html += str(table)
self.writeHtml('table_column_alignment')
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
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.
: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=None):
"""Risk plugin for flood population evacuation.
:param layers: List of layers expected to contain
hazard_layer: Raster layer of flood depth
exposure_layer: Raster layer of population data on the same grid
as hazard_layer
Counts number of people exposed to flood levels exceeding
specified threshold.
:returns: Map of population exposed to flood levels exceeding the
threshold. Table with number of people evacuated and supplies
required.
:rtype: tuple
"""
self.validate()
self.prepare(layers)
# Identify hazard and exposure layers
hazard_layer = self.hazard # Flood inundation
exposure_layer = self.exposure
# Determine depths above which people are regarded affected [m]
# Use thresholds from inundation layer if specified
thresholds = self.parameters['thresholds [m]']
verify(
isinstance(thresholds, list),
'Expected thresholds to be a list. Got %s' % str(thresholds))
# Extract data as numeric arrays
data = hazard_layer.get_data(nan=True) # Depth
no_data_warning = False
if has_no_data(data):
no_data_warning = True
# Calculate impact as population exposed to depths > max threshold
population = exposure_layer.get_data(nan=True, scaling=True)
if has_no_data(population):
no_data_warning = True
# Calculate impact to intermediate thresholds
counts = []
# merely initialize
impact = None
for i, lo in enumerate(thresholds):
if i == len(thresholds) - 1:
# The last threshold
impact = medium = numpy.where(data >= lo, population, 0)
else:
# Intermediate thresholds
hi = thresholds[i + 1]
medium = numpy.where((data >= lo) * (data < hi), population, 0)
# Count
val = int(numpy.nansum(medium))
counts.append(val)
# Carry the no data values forward to the impact layer.
impact = numpy.where(numpy.isnan(population), numpy.nan, impact)
impact = numpy.where(numpy.isnan(data), numpy.nan, impact)
# Count totals
evacuated, rounding_evacuated = population_rounding_full(counts[-1])
total = int(numpy.nansum(population))
# Don't show digits less than a 1000
total = population_rounding(total)
minimum_needs = [
parameter.serialize() for parameter in
self.parameters['minimum needs']
]
# Generate impact report for the pdf map
# noinspection PyListCreation
table_body, total_needs = self._tabulate(
counts,
evacuated,
minimum_needs,
self.question,
rounding_evacuated,
thresholds,
total,
no_data_warning)
# Result
impact_summary = Table(table_body).toNewlineFreeString()
impact_table = impact_summary
# check for zero impact
if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
table_body = self._tabulate_zero_impact(
evacuated, self.question, table_body, thresholds)
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 Count')
# Create raster object and return
raster = Raster(
impact,
projection=hazard_layer.get_projection(),
geotransform=hazard_layer.get_geotransform(),
name=tr('Population which %s') % (
self.impact_function_manager
.get_function_title(self).lower()),
keywords={
'impact_summary': impact_summary,
'impact_table': impact_table,
'map_title': map_title,
'legend_notes': legend_notes,
'legend_units': legend_units,
'legend_title': legend_title,
'evacuated': evacuated,
'total_needs': total_needs},
style_info=style_info)
self._impact = raster
return raster