def minimum_needs(self, input_layer, population_name): """Compute minimum needs given a layer and a column containing pop. :param input_layer: InaSAFE layer object assumed to contain population counts :type input_layer: read_layer :param population_name: Attribute name that holds population count :type population_name: str :returns: Layer with attributes for minimum needs as per Perka 7 :rtype: read_layer """ all_attributes = [] for attributes in input_layer.get_data(): # Get population count population = attributes[population_name] # Clean up and turn into integer if population in ['-', None]: displaced = 0 else: if isinstance(population, basestring): population = str(population).replace(',', '') try: displaced = int(population) except ValueError: # noinspection PyTypeChecker,PyArgumentList QtGui.QMessageBox.information( None, self.tr('Format error'), self.tr( 'Please change the value of %1 in attribute ' '%1 to integer format').arg(population).arg( population_name)) raise ValueError # Calculate estimated needs based on BNPB Perka 7/2008 # minimum needs # weekly_needs = { # 'rice': int(ceil(population * min_rice)), # 'drinking_water': int(ceil(population * min_drinking_water)), # 'water': int(ceil(population * min_water)), # 'family_kits': int(ceil(population * min_family_kits)), # 'toilets': int(ceil(population * min_toilets))} # Add to attributes weekly_needs = evacuated_population_weekly_needs(displaced) # Record attributes for this feature all_attributes.append(weekly_needs) output_layer = Vector( geometry=input_layer.get_geometry(), data=all_attributes, projection=input_layer.get_projection()) return output_layer
def test_default_weekly_needs(self): """default calculated needs are as expected """ # 20 Happens to be the smallest number at which integer rounding # won't make a difference to the result result = evacuated_population_weekly_needs(20) assert (result['rice'] == 56 and result['drinking_water'] == 350 and result['water'] == 1340 and result['family_kits'] == 4 and result['toilets'] == 1)
def test_default_weekly_needs(self): """default calculated needs are as expected """ # 20 Happens to be the smallest number at which integer rounding # won't make a difference to the result result = evacuated_population_weekly_needs(20) assert (result['rice'] == 56 and result['drinking_water'] == 350 and result['water'] == 2100 and result['family_kits'] == 4 and result['toilets'] == 1)
def test_arbitrary_weekly_needs(self): """custom need ratios calculated are as expected """ minimum_needs = {'Rice': 4, 'Drinking Water': 3, 'Water': 2, 'Family Kits': 1, 'Toilets': 0.2} result = evacuated_population_weekly_needs(10, minimum_needs) assert (result['rice'] == 40 and result['drinking_water'] == 30 and result['water'] == 20 and result['family_kits'] == 10 and result['toilets'] == 2)
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
class ITBFatalityFunction(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 was used to estimate the model parameters. The objective function (L2G norm) that is minimised during the optimisation is the same as the one used by Jaiswal et al. (2010). The coefficients used in the indonesian model are x=0.62275231, y=8.03314466, zeta=2.15 Allen, T. I., Wald, D. J., Earle, P. S., Marano, K. D., Hotovec, A. J., Lin, K., and Hearne, M., 2009. An Atlas of ShakeMaps and population exposure catalog for earthquake loss modeling, Bull. Earthq. Eng. 7, 701-718. Jaiswal, K., and Wald, D., 2010. An empirical model for global earthquake fatality estimation, Earthq. Spectra 26, 1017-1037. Caveats and limitations: The current model is the result of the above mentioned workshop and reflects the best available information. However, the current model has a number of issues listed below and is expected to evolve further over time. 1 - The model is based on limited number of observed fatality rates during 4 past fatal events. 2 - The model clearly over-predicts the fatality rates at intensities higher than VIII. 3 - The model only estimates the expected fatality rate for a given intensity level; however the associated uncertainty for the proposed model is not addressed. 4 - There are few known mistakes in developing the current model: - rounding MMI values to the nearest 0.5, - Implementing Finite-Fault models of candidate events, and - consistency between selected GMPEs with those in use by BMKG. These issues will be addressed by ITB team in the final report. Note: Because of these caveats, decisions should not be made solely on the information presented here and should always be verified by ground truthing and other reliable information sources. :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') synopsis = tr( 'To asses the impact of earthquake on population based on earthquake ' 'model developed by ITB') citations = tr( ' * Indonesian Earthquake Building-Damage and Fatality Models and ' ' Post Disaster Survey Guidelines Development Bali, 27-28 ' ' February 2012, 54pp.\n' ' * 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.\n' ' * Jaiswal, K., and Wald, D., 2010. An empirical model for ' ' global earthquake fatality estimation, Earthq. Spectra ' ' 26, 1017-1037.\n') limitation = tr( ' - The model is based on limited number of observed fatality ' ' rates during 4 past fatal events. \n' ' - The model clearly over-predicts the fatality rates at ' ' intensities higher than VIII.\n' ' - The model only estimates the expected fatality rate ' ' for a given intensity level; however the associated ' ' uncertainty for the proposed model is not addressed.\n' ' - There are few known mistakes in developing the current ' ' model:\n\n' ' * rounding MMI values to the nearest 0.5,\n' ' * Implementing Finite-Fault models of candidate events, and\n' ' * consistency between selected GMPEs with those in use by ' ' BMKG.\n') actions = tr( 'Provide details about the population will be die or displaced') detailed_description = tr( 'This model was developed by Institut Teknologi Bandung (ITB) ' 'and implemented by Dr. Hadi Ghasemi, Geoscience Australia\n' 'Algorithm:\n' 'In this study, the same functional form as Allen (2009) is ' 'adopted o express fatality rate as a function of intensity ' '(see Eq. 10 in the report). The Matlab built-in function ' '(fminsearch) for Nelder-Mead algorithm was used to estimate ' 'the model parameters. The objective function (L2G norm) that ' 'is minimized during the optimisation is the same as the one ' 'used by Jaiswal et al. (2010).\n' 'The coefficients used in the indonesian model are x=0.62275231, ' 'y=8.03314466, zeta=2.15') 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 }), ('mmi_range', range(2, 10)), ('step', 0.5), # 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'])]) }), ('MinimumNeeds', { 'on': True }) ])), ('minimum needs', default_minimum_needs()) ]) def fatality_rate(self, mmi): """ ITB method to compute fatality rate :param mmi: """ # As per email discussion with Ole, Trevor, Hadi, mmi < 4 will have # a fatality rate of 0 - Tim if mmi < 4: return 0 x = self.parameters['x'] y = self.parameters['y'] return numpy.power(10.0, x * mmi - y) def run(self, layers): """Indonesian Earthquake Fatality Model Input: :param layers: List of layers expected to contain, my_hazard: Raster layer of MMI ground shaking my_exposure: Raster layer of population density """ 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 my_hazard = intensity.get_data() # Ground Shaking my_exposure = 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 = self.parameters['mmi_range'] number_of_exposed = {} number_of_displaced = {} number_of_fatalities = {} # Calculate fatality rates for observed Intensity values (my_hazard # based on ITB power model R = numpy.zeros(my_hazard.shape) for mmi in mmi_range: # Identify cells where MMI is in class i and # count population affected by this shake level I = numpy.where((my_hazard > mmi - self.parameters['step']) * (my_hazard <= mmi + self.parameters['step']), my_exposure, 0) # Calculate expected number of fatalities per level fatality_rate = self.fatality_rate(mmi) 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)) # noinspection PyExceptionInherit 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) # noinspection PyUnresolvedReferences 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(my_exposure.flat) / 1000) * 1000) # Compute number of fatalities fatalities = int( round(numpy.nansum(number_of_fatalities.values()) / 1000)) * 1000 # As per email discussion with Ole, Trevor, Hadi, total fatalities < 50 # will be rounded down to 0 - Tim if fatalities < 50: fatalities = 0 # Compute number of people displaced due to building collapse displaced = int( round(numpy.nansum(number_of_displaced.values()) / 1000)) * 1000 # Generate impact report table_body = [question] # Add total fatality estimate s = format_int(fatalities) table_body.append( TableRow([tr('Number of fatalities'), s], header=True)) if self.parameters['calculate_displaced_people']: # Add total estimate of people displaced s = format_int(displaced) table_body.append( TableRow([tr('Number of people displaced'), s], header=True)) else: displaced = 0 # Add estimate of total population in area s = format_int(int(total)) table_body.append( TableRow([tr('Total number of people'), s], header=True)) # Calculate estimated needs based on BNPB Perka 7/2008 minimum bantuan # FIXME: Refactor and share minimum_needs = self.parameters['minimum needs'] needs = evacuated_population_weekly_needs(displaced, minimum_needs) # Generate impact report for the pdf map table_body = [ question, TableRow([tr('Fatalities'), '%s' % format_int(fatalities)], header=True), TableRow([tr('People displaced'), '%s' % format_int(displaced)], header=True), TableRow( tr('Map shows density estimate of ' 'displaced population')), TableRow([tr('Needs per week'), tr('Total')], header=True), [tr('Rice [kg]'), format_int(needs['rice'])], [tr('Drinking Water [l]'), format_int(needs['drinking_water'])], [tr('Clean Water [l]'), format_int(needs['water'])], [tr('Family Kits'), format_int(needs['family_kits'])], TableRow(tr('Action Checklist:'), header=True) ] if fatalities > 0: table_body.append( tr('Are there enough victim identification ' 'units available for %s people?') % format_int(fatalities)) if displaced > 0: table_body.append( tr('Are there enough shelters and relief items ' 'available for %s people?') % format_int(displaced)) table_body.append( TableRow( tr('If yes, where are they located and ' 'how will we distribute them?'))) table_body.append( TableRow( tr('If no, where can we obtain ' 'additional relief items from and ' 'how will we transport them?'))) # Extend impact report for on-screen display table_body.extend([ TableRow(tr('Notes'), header=True), tr('Total population: %s') % format_int(total), tr('People are considered to be displaced if ' 'they experience and survive a shake level' 'of more than 5 on the MMI scale '), tr('Minimum needs are defined in BNPB ' 'regulation 7/2008'), tr('The fatality calculation assumes that ' 'no fatalities occur for shake levels below 4 ' 'and fatality counts of less than 50 are ' 'disregarded.'), tr('All values are rounded up to the nearest ' 'integer in order to avoid representing human ' 'lives as fractionals.') ]) 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.')) # Result impact_summary = Table(table_body).toNewlineFreeString() impact_table = impact_summary # check for zero impact if numpy.nanmax(R) == 0 == numpy.nanmin(R): table_body = [ question, TableRow([tr('Fatalities'), '%s' % format_int(fatalities)], header=True) ] my_message = Table(table_body).toNewlineFreeString() raise ZeroImpactException(my_message) # Create style colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000'] classes = create_classes(R.flat[:], len(colours)) interval_classes = humanize_class(classes) style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) style_class['quantity'] = classes[i] if i == 0: transparency = 100 else: transparency = 30 style_class['transparency'] = transparency style_class['colour'] = colours[i] style_classes.append(style_class) style_info = dict(target_field=None, style_classes=style_classes, style_type='rasterStyle') # For printing map purpose map_title = tr('Earthquake impact to population') legend_notes = tr('Thousand separator is represented by %s' % get_thousand_separator()) legend_units = tr('(people per cell)') legend_title = tr('Population density') # Create raster object and return L = Raster(R, projection=population.get_projection(), geotransform=population.get_geotransform(), keywords={ 'impact_summary': impact_summary, 'total_population': total, 'total_fatalities': fatalities, 'fatalites_per_mmi': number_of_fatalities, 'exposed_per_mmi': number_of_exposed, 'displaced_per_mmi': number_of_displaced, 'impact_table': impact_table, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title }, name=tr('Estimated displaced population per cell'), style_info=style_info) return L
def run(self, layers): """Risk plugin for volcano population evacuation. :param layers: List of layers expected to contain where two layers should be present. * hazard_layer: Vector polygon layer of volcano impact zones * exposure_layer: Raster layer of population data on the same grid as hazard_layer Counts number of people exposed to volcano event. :returns: Map of population exposed to the volcano hazard zone. The returned dict will include a table with number of people evacuated and supplies required. :rtype: dict :raises: * Exception - When hazard layer is not vector layer * RadiiException - When radii are not valid (they need to be monotonically increasing) """ # Identify hazard and exposure layers hazard_layer = get_hazard_layer(layers) # Volcano KRB exposure_layer = get_exposure_layer(layers) question = get_question( hazard_layer.get_name(), exposure_layer.get_name(), self) # Input checks if not hazard_layer.is_vector: msg = ('Input hazard %s was not a vector layer as expected ' % hazard_layer.get_name()) raise Exception(msg) msg = ('Input hazard must be a polygon or point layer. I got %s with ' 'layer type %s' % (hazard_layer.get_name(), hazard_layer.get_geometry_name())) if not (hazard_layer.is_polygon_data or hazard_layer.is_point_data): raise Exception(msg) data_table = hazard_layer.get_data() if hazard_layer.is_point_data: # Use concentric circles radii = self.parameters['distance [km]'] centers = hazard_layer.get_geometry() rad_m = [x * 1000 for x in radii] # Convert to meters hazard_layer = buffer_points(centers, rad_m, data_table=data_table) category_title = 'Radius' category_header = tr('Distance [km]') category_names = radii name_attribute = 'NAME' # As in e.g. the Smithsonian dataset else: # Use hazard map category_title = 'KRB' category_header = tr('Category') # FIXME (Ole): Change to English and use translation system category_names = ['Kawasan Rawan Bencana III', 'Kawasan Rawan Bencana II', 'Kawasan Rawan Bencana I'] name_attribute = 'GUNUNG' # As in e.g. BNPB hazard map # Get names of volcanoes considered if name_attribute in hazard_layer.get_attribute_names(): volcano_name_list = [] # Run through all polygons and get unique names for row in data_table: volcano_name_list.append(row[name_attribute]) volcano_names = '' for name in volcano_name_list: volcano_names += '%s, ' % name volcano_names = volcano_names[:-2] # Strip trailing ', ' else: volcano_names = tr('Not specified in data') # Check if category_title exists in hazard_layer if not category_title in hazard_layer.get_attribute_names(): msg = ('Hazard data %s did not contain expected ' 'attribute %s ' % (hazard_layer.get_name(), category_title)) # noinspection PyExceptionInherit raise InaSAFEError(msg) # Find the target field name that has no conflict with default target attribute_names = hazard_layer.get_attribute_names() new_target_field = get_non_conflicting_attribute_name( self.target_field, attribute_names) self.target_field = new_target_field # Run interpolation function for polygon2raster interpolated_layer = assign_hazard_values_to_exposure_data( hazard_layer, exposure_layer, attribute_name=self.target_field) # Initialise data_table of output dataset with all data_table # from input polygon and a population count of zero new_data_table = hazard_layer.get_data() categories = {} for row in new_data_table: row[self.target_field] = 0 category = row[category_title] categories[category] = 0 # Count affected population per polygon and total for row in interpolated_layer.get_data(): # Get population at this location population = float(row[self.target_field]) # Update population count for associated polygon poly_id = row['polygon_id'] new_data_table[poly_id][self.target_field] += population # Update population count for each category category = new_data_table[poly_id][category_title] categories[category] += population # Count totals total = int(numpy.sum(exposure_layer.get_data(nan=0))) # Don't show digits less than a 1000 total = round_thousand(total) # Count number and cumulative for each zone cumulative = 0 all_categories_population = {} all_categories_cumulative = {} for name in category_names: if category_title == 'Radius': key = name * 1000 # Convert to meters else: key = name # prevent key error population = int(categories.get(key, 0)) population = round_thousand(population) cumulative += population cumulative = round_thousand(cumulative) all_categories_population[name] = population all_categories_cumulative[name] = cumulative # Use final accumulation as total number needing evacuation evacuated = cumulative # Calculate estimated minimum needs minimum_needs = self.parameters['minimum needs'] total_needs = evacuated_population_weekly_needs( evacuated, minimum_needs) # Generate impact report for the pdf map blank_cell = '' table_body = [question, TableRow([tr('Volcanoes considered'), '%s' % volcano_names, blank_cell], header=True), TableRow([tr('People needing evacuation'), '%s' % format_int(evacuated), blank_cell], header=True), TableRow([category_header, tr('Total'), tr('Cumulative')], header=True)] for name in category_names: table_body.append( TableRow([name, format_int(all_categories_population[name]), format_int(all_categories_cumulative[name])])) table_body.extend([ TableRow(tr( 'Map shows the number of people affected in each of volcano ' 'hazard polygons.')), TableRow( [tr('Needs per week'), tr('Total'), blank_cell], header=True), [tr('Rice [kg]'), format_int(total_needs['rice']), blank_cell], [ tr('Drinking Water [l]'), format_int(total_needs['drinking_water']), blank_cell], [tr('Clean Water [l]'), format_int(total_needs['water']), blank_cell], [tr('Family Kits'), format_int(total_needs['family_kits']), blank_cell], [tr('Toilets'), format_int(total_needs['toilets']), blank_cell]]) impact_table = Table(table_body).toNewlineFreeString() # Extend impact report for on-screen display table_body.extend( [TableRow(tr('Notes'), header=True), tr('Total population %s in the exposure layer') % format_int( total), tr('People need evacuation if they are within the ' 'volcanic hazard zones.')]) population_counts = [x[self.target_field] for x in new_data_table] impact_summary = Table(table_body).toNewlineFreeString() # check for zero impact if numpy.nanmax(population_counts) == 0 == numpy.nanmin( population_counts): table_body = [ question, TableRow([tr('People needing evacuation'), '%s' % format_int(evacuated), blank_cell], header=True)] my_message = Table(table_body).toNewlineFreeString() raise ZeroImpactException(my_message) # Create style colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600', '#FF0000', '#7A0000'] classes = create_classes(population_counts, len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) if i == 0: transparency = 100 style_class['min'] = 0 else: transparency = 30 style_class['min'] = classes[i - 1] style_class['transparency'] = transparency style_class['colour'] = colours[i] style_class['max'] = classes[i] style_classes.append(style_class) # Override style info with new classes and name style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type='graduatedSymbol') # For printing map purpose map_title = tr('People affected by volcanic hazard zone') legend_notes = tr('Thousand separator is represented by %s' % get_thousand_separator()) legend_units = tr('(people)') legend_title = tr('Population count') # Create vector layer and return impact_layer = Vector( data=new_data_table, projection=hazard_layer.get_projection(), geometry=hazard_layer.get_geometry(as_geometry_objects=True), name=tr('People affected by volcanic hazard zone'), keywords={'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title}, style_info=style_info) return impact_layer
def run(self, layers): """Risk plugin for flood population evacuation Input: layers: List of layers expected to contain my_hazard : Vector polygon layer of flood depth my_exposure : Raster layer of population data on the same grid as my_hazard Counts number of people exposed to areas identified as flood prone Return Map of population exposed to flooding Table with number of people evacuated and supplies required """ # Identify hazard and exposure layers my_hazard = get_hazard_layer(layers) # Flood inundation my_exposure = get_exposure_layer(layers) question = get_question(my_hazard.get_name(), my_exposure.get_name(), self) # Check that hazard is polygon type if not my_hazard.is_vector: msg = ('Input hazard %s was not a vector layer as expected ' % my_hazard.get_name()) raise Exception(msg) msg = ('Input hazard must be a polygon layer. I got %s with layer ' 'type %s' % (my_hazard.get_name(), my_hazard.get_geometry_name())) if not my_hazard.is_polygon_data: raise Exception(msg) # Run interpolation function for polygon2raster P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure, attribute_name='population') # Initialise attributes of output dataset with all attributes # from input polygon and a population count of zero new_attributes = my_hazard.get_data() category_title = 'affected' # FIXME: Should come from keywords deprecated_category_title = 'FLOODPRONE' categories = {} for attr in new_attributes: attr[self.target_field] = 0 try: cat = attr[category_title] except KeyError: cat = attr['FLOODPRONE'] categories[cat] = 0 # Count affected population per polygon, per category and total affected_population = 0 for attr in P.get_data(): affected = False if 'affected' in attr: res = attr['affected'] if res is None: x = False else: x = bool(res) affected = x elif 'FLOODPRONE' in attr: # If there isn't an 'affected' attribute, res = attr['FLOODPRONE'] if res is not None: affected = res.lower() == 'yes' elif 'Affected' in attr: # Check the default attribute assigned for points # covered by a polygon res = attr['Affected'] if res is None: x = False else: x = res affected = x else: # there is no flood related attribute msg = ('No flood related attribute found in %s. ' 'I was looking fore either "Flooded", "FLOODPRONE" ' 'or "Affected". The latter should have been ' 'automatically set by call to ' 'assign_hazard_values_to_exposure_data(). ' 'Sorry I can\'t help more.') raise Exception(msg) if affected: # Get population at this location pop = float(attr['population']) # Update population count for associated polygon poly_id = attr['polygon_id'] new_attributes[poly_id][self.target_field] += pop # Update population count for each category try: cat = new_attributes[poly_id][category_title] except KeyError: cat = new_attributes[poly_id][deprecated_category_title] categories[cat] += pop # Update total affected_population += pop affected_population = round_thousand(affected_population) # Estimate number of people in need of evacuation evacuated = (affected_population * self.parameters['evacuation_percentage'] / 100.0) total = int(numpy.sum(my_exposure.get_data(nan=0, scaling=False))) # Don't show digits less than a 1000 total = round_thousand(total) evacuated = round_thousand(evacuated) # Calculate estimated minimum needs minimum_needs = self.parameters['minimum needs'] tot_needs = evacuated_population_weekly_needs(evacuated, minimum_needs) # Generate impact report for the pdf map table_body = [question, TableRow([tr('People affected'), '%s%s' % (format_int(int(affected_population)), ('*' if affected_population >= 1000 else ''))], header=True), TableRow([tr('People needing evacuation'), '%s%s' % (format_int(int(evacuated)), ('*' if evacuated >= 1000 else ''))], header=True), TableRow([ TableCell( tr('* Number is rounded to the nearest 1000'), col_span=2)], header=False), TableRow([tr('Evacuation threshold'), '%s%%' % format_int( self.parameters['evacuation_percentage'])], header=True), TableRow(tr('Map shows population affected in each flood' ' prone area')), TableRow(tr('Table below shows the weekly minium needs ' 'for all evacuated people')), TableRow([tr('Needs per week'), tr('Total')], header=True), [tr('Rice [kg]'), format_int(tot_needs['rice'])], [tr('Drinking Water [l]'), format_int(tot_needs['drinking_water'])], [tr('Clean Water [l]'), format_int(tot_needs['water'])], [tr('Family Kits'), format_int(tot_needs[ 'family_kits'])], [tr('Toilets'), format_int(tot_needs['toilets'])]] impact_table = Table(table_body).toNewlineFreeString() table_body.append(TableRow(tr('Action Checklist:'), header=True)) table_body.append(TableRow(tr('How will warnings be disseminated?'))) table_body.append(TableRow(tr('How will we reach stranded people?'))) table_body.append(TableRow(tr('Do we have enough relief items?'))) table_body.append(TableRow(tr('If yes, where are they located and how ' 'will we distribute them?'))) table_body.append(TableRow(tr('If no, where can we obtain additional ' 'relief items from and how will we ' 'transport them to here?'))) # Extend impact report for on-screen display table_body.extend([TableRow(tr('Notes'), header=True), tr('Total population: %s') % format_int(total), tr('People need evacuation if in area identified ' 'as "Flood Prone"'), tr('Minimum needs are defined in BNPB ' 'regulation 7/2008')]) impact_summary = Table(table_body).toNewlineFreeString() # Create style # Define classes for legend for flooded population counts colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600', '#FF0000', '#7A0000'] population_counts = [x['population'] for x in new_attributes] classes = create_classes(population_counts, len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) if i == 0: transparency = 100 style_class['min'] = 0 else: transparency = 0 style_class['min'] = classes[i - 1] style_class['transparency'] = transparency style_class['colour'] = colours[i] style_class['max'] = classes[i] style_classes.append(style_class) # Override style info with new classes and name style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type='graduatedSymbol') # For printing map purpose map_title = tr('People affected by flood prone areas') legend_notes = tr('Thousand separator is represented by \'.\'') legend_units = tr('(people per polygon)') legend_title = tr('Population Count') # Create vector layer and return V = Vector(data=new_attributes, projection=my_hazard.get_projection(), geometry=my_hazard.get_geometry(), name=tr('Population affected by flood prone areas'), keywords={'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title}, style_info=style_info) return V
def run(self, layers): """Plugin for impact of population as derived by categorised hazard. Input layers: List of layers expected to contain hazard_layer: Raster layer of categorised hazard exposure_layer: Raster layer of population data Counts number of people exposed to each category of the hazard Return Map of population exposed to high category Table with number of people in each category """ # The 3 category high_t = self.parameters['Categorical thresholds'][2] medium_t = self.parameters['Categorical thresholds'][1] low_t = self.parameters['Categorical thresholds'][0] # Identify hazard and exposure layers hazard_layer = get_hazard_layer(layers) # Categorised Hazard exposure_layer = get_exposure_layer(layers) # Population Raster question = get_question( hazard_layer.get_name(), exposure_layer.get_name(), self) # Extract data as numeric arrays C = hazard_layer.get_data(nan=0.0) # Category # Calculate impact as population exposed to each category P = exposure_layer.get_data(nan=0.0, scaling=True) H = numpy.where(C <= high_t, P, 0) M = numpy.where(C < medium_t, P, 0) L = numpy.where(C < low_t, P, 0) # Count totals total = int(numpy.sum(P)) high = int(numpy.sum(H)) - int(numpy.sum(M)) medium = int(numpy.sum(M)) - int(numpy.sum(L)) low = int(numpy.sum(L)) total_impact = high + medium + low # Don't show digits less than a 1000 total = round_thousand(total) total_impact = round_thousand(total_impact) high = round_thousand(high) medium = round_thousand(medium) low = round_thousand(low) # Calculate estimated minimum needs minimum_needs = self.parameters['minimum needs'] tot_needs = evacuated_population_weekly_needs( total_impact, minimum_needs) # Generate impact report for the pdf map table_body = [ question, TableRow([tr('People impacted '), '%s' % format_int(total_impact)], header=True), TableRow([tr('People in high hazard area '), '%s' % format_int(high)], header=True), TableRow([tr('People in medium hazard area '), '%s' % format_int(medium)], header=True), TableRow([tr('People in low hazard area'), '%s' % format_int(low)], header=True)] impact_table = Table(table_body).toNewlineFreeString() # Extend impact report for on-screen display table_body.extend([ TableRow(tr('Notes'), header=True), tr('Map shows population density in high or medium hazard area'), tr('Total population: %s') % format_int(total), TableRow(tr( 'Table below shows the weekly minimum needs for all ' 'affected people')), TableRow([tr('Needs per week'), tr('Total')], header=True), [tr('Rice [kg]'), format_int(tot_needs['rice'])], [tr('Drinking Water [l]'), format_int(tot_needs['drinking_water'])], [tr('Clean Water [l]'), format_int(tot_needs['water'])], [tr('Family Kits'), format_int(tot_needs['family_kits'])], [tr('Toilets'), format_int(tot_needs['toilets'])] ]) impact_summary = Table(table_body).toNewlineFreeString() map_title = tr('People in high hazard areas') # Generate 8 equidistant classes across the range of flooded population # 8 is the number of classes in the predefined flood population style # as imported # noinspection PyTypeChecker classes = numpy.linspace( numpy.nanmin(M.flat[:]), numpy.nanmax(M.flat[:]), 8) # Modify labels in existing flood style to show quantities style_classes = style_info['style_classes'] style_classes[1]['label'] = tr('Low [%i people/cell]') % classes[1] style_classes[4]['label'] = tr('Medium [%i people/cell]') % classes[4] style_classes[7]['label'] = tr('High [%i people/cell]') % classes[7] style_info['legend_title'] = tr('Population Density') # Create raster object and return raster_layer = Raster( M, projection=hazard_layer.get_projection(), geotransform=hazard_layer.get_geotransform(), name=tr('Population which %s') % ( get_function_title(self).lower()), keywords={ 'impact_summary': impact_summary, 'impact_table': impact_table, 'map_title': map_title}, style_info=style_info) return raster_layer
def run(self, layers): """Risk plugin for flood population evacuation Input: layers: List of layers expected to contain my_hazard : Vector polygon layer of flood depth my_exposure : Raster layer of population data on the same grid as my_hazard Counts number of people exposed to areas identified as flood prone Return Map of population exposed to flooding Table with number of people evacuated and supplies required """ # Identify hazard and exposure layers my_hazard = get_hazard_layer(layers) # Flood inundation my_exposure = get_exposure_layer(layers) question = get_question(my_hazard.get_name(), my_exposure.get_name(), self) # Check that hazard is polygon type if not my_hazard.is_vector: msg = ('Input hazard %s was not a vector layer as expected ' % my_hazard.get_name()) raise Exception(msg) msg = ('Input hazard must be a polygon layer. I got %s with layer ' 'type %s' % (my_hazard.get_name(), my_hazard.get_geometry_name())) if not my_hazard.is_polygon_data: raise Exception(msg) # Run interpolation function for polygon2raster P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure, attribute_name='population') # Initialise attributes of output dataset with all attributes # from input polygon and a population count of zero new_attributes = my_hazard.get_data() category_title = 'affected' # FIXME: Should come from keywords deprecated_category_title = 'FLOODPRONE' categories = {} for attr in new_attributes: attr[self.target_field] = 0 try: cat = attr[category_title] except KeyError: cat = attr['FLOODPRONE'] categories[cat] = 0 # Count affected population per polygon, per category and total affected_population = 0 for attr in P.get_data(): affected = False if 'affected' in attr: res = attr['affected'] if res is None: x = False else: x = bool(res) affected = x elif 'FLOODPRONE' in attr: # If there isn't an 'affected' attribute, res = attr['FLOODPRONE'] if res is not None: affected = res.lower() == 'yes' elif 'Affected' in attr: # Check the default attribute assigned for points # covered by a polygon res = attr['Affected'] if res is None: x = False else: x = res affected = x else: # there is no flood related attribute msg = ('No flood related attribute found in %s. ' 'I was looking fore either "Flooded", "FLOODPRONE" ' 'or "Affected". The latter should have been ' 'automatically set by call to ' 'assign_hazard_values_to_exposure_data(). ' 'Sorry I can\'t help more.') raise Exception(msg) if affected: # Get population at this location pop = float(attr['population']) # Update population count for associated polygon poly_id = attr['polygon_id'] new_attributes[poly_id][self.target_field] += pop # Update population count for each category try: cat = new_attributes[poly_id][category_title] except KeyError: cat = new_attributes[poly_id][deprecated_category_title] categories[cat] += pop # Update total affected_population += pop affected_population = round_thousand(affected_population) # Estimate number of people in need of evacuation evacuated = (affected_population * self.parameters['evacuation_percentage'] / 100.0) total = int(numpy.sum(my_exposure.get_data(nan=0, scaling=False))) # Don't show digits less than a 1000 total = round_thousand(total) evacuated = round_thousand(evacuated) # Calculate estimated minimum needs minimum_needs = self.parameters['minimum needs'] tot_needs = evacuated_population_weekly_needs(evacuated, minimum_needs) # Generate impact report for the pdf map table_body = [ question, TableRow([ tr('People affected'), '%s%s' % (format_int(int(affected_population)), ('*' if affected_population >= 1000 else '')) ], header=True), TableRow([ tr('People needing evacuation'), '%s%s' % (format_int(int(evacuated)), ('*' if evacuated >= 1000 else '')) ], header=True), TableRow([ TableCell(tr('* Number is rounded to the nearest 1000'), col_span=2) ], header=False), TableRow([ tr('Evacuation threshold'), '%s%%' % format_int(self.parameters['evacuation_percentage']) ], header=True), TableRow( tr('Map shows population affected in each flood' ' prone area')), TableRow( tr('Table below shows the weekly minium needs ' 'for all evacuated people')), TableRow([tr('Needs per week'), tr('Total')], header=True), [tr('Rice [kg]'), format_int(tot_needs['rice'])], [ tr('Drinking Water [l]'), format_int(tot_needs['drinking_water']) ], [tr('Clean Water [l]'), format_int(tot_needs['water'])], [tr('Family Kits'), format_int(tot_needs['family_kits'])], [tr('Toilets'), format_int(tot_needs['toilets'])] ] impact_table = Table(table_body).toNewlineFreeString() table_body.append(TableRow(tr('Action Checklist:'), header=True)) table_body.append(TableRow(tr('How will warnings be disseminated?'))) table_body.append(TableRow(tr('How will we reach stranded people?'))) table_body.append(TableRow(tr('Do we have enough relief items?'))) table_body.append( TableRow( tr('If yes, where are they located and how ' 'will we distribute them?'))) table_body.append( TableRow( tr('If no, where can we obtain additional ' 'relief items from and how will we ' 'transport them to here?'))) # Extend impact report for on-screen display table_body.extend([ TableRow(tr('Notes'), header=True), tr('Total population: %s') % format_int(total), tr('People need evacuation if in area identified ' 'as "Flood Prone"'), tr('Minimum needs are defined in BNPB ' 'regulation 7/2008') ]) impact_summary = Table(table_body).toNewlineFreeString() # Create style # Define classes for legend for flooded population counts colours = [ '#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600', '#FF0000', '#7A0000' ] population_counts = [x['population'] for x in new_attributes] classes = create_classes(population_counts, len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) if i == 0: transparency = 100 style_class['min'] = 0 else: transparency = 0 style_class['min'] = classes[i - 1] style_class['transparency'] = transparency style_class['colour'] = colours[i] style_class['max'] = classes[i] style_classes.append(style_class) # Override style info with new classes and name style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type='graduatedSymbol') # For printing map purpose map_title = tr('People affected by flood prone areas') legend_notes = tr('Thousand separator is represented by \'.\'') legend_units = tr('(people per polygon)') legend_title = tr('Population Count') # Create vector layer and return V = Vector(data=new_attributes, projection=my_hazard.get_projection(), geometry=my_hazard.get_geometry(), name=tr('Population affected by flood prone areas'), keywords={ 'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title }, style_info=style_info) return V
def run(self, layers): """Risk plugin for volcano population evacuation :param layers: List of layers expected to contain where two layers should be present. * my_hazard: Vector polygon layer of volcano impact zones * my_exposure: Raster layer of population data on the same grid as my_hazard Counts number of people exposed to volcano event. :returns: Map of population exposed to the volcano hazard zone. The returned dict will include a table with number of people evacuated and supplies required. :rtype: dict """ # Identify hazard and exposure layers my_hazard = get_hazard_layer(layers) # Volcano KRB my_exposure = get_exposure_layer(layers) question = get_question(my_hazard.get_name(), my_exposure.get_name(), self) # Input checks if not my_hazard.is_vector: msg = ('Input hazard %s was not a vector layer as expected ' % my_hazard.get_name()) raise Exception(msg) msg = ('Input hazard must be a polygon or point layer. I got %s with ' 'layer type %s' % (my_hazard.get_name(), my_hazard.get_geometry_name())) if not (my_hazard.is_polygon_data or my_hazard.is_point_data): raise Exception(msg) if my_hazard.is_point_data: # Use concentric circles radii = self.parameters['distance [km]'] centers = my_hazard.get_geometry() attributes = my_hazard.get_data() rad_m = [x * 1000 for x in radii] # Convert to meters my_hazard = make_circular_polygon(centers, rad_m, attributes=attributes) category_title = 'Radius' category_header = tr('Distance [km]') category_names = radii name_attribute = 'NAME' # As in e.g. the Smithsonian dataset else: # Use hazard map category_title = 'KRB' category_header = tr('Category') # FIXME (Ole): Change to English and use translation system category_names = [ 'Kawasan Rawan Bencana III', 'Kawasan Rawan Bencana II', 'Kawasan Rawan Bencana I' ] name_attribute = 'GUNUNG' # As in e.g. BNPB hazard map attributes = my_hazard.get_data() # Get names of volcanos considered if name_attribute in my_hazard.get_attribute_names(): D = {} for att in my_hazard.get_data(): # Run through all polygons and get unique names D[att[name_attribute]] = None volcano_names = '' for name in D: volcano_names += '%s, ' % name volcano_names = volcano_names[:-2] # Strip trailing ', ' else: volcano_names = tr('Not specified in data') if not category_title in my_hazard.get_attribute_names(): msg = ('Hazard data %s did not contain expected ' 'attribute %s ' % (my_hazard.get_name(), category_title)) # noinspection PyExceptionInherit raise InaSAFEError(msg) # Run interpolation function for polygon2raster P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure, attribute_name='population') # Initialise attributes of output dataset with all attributes # from input polygon and a population count of zero new_attributes = my_hazard.get_data() categories = {} for attr in new_attributes: attr[self.target_field] = 0 cat = attr[category_title] categories[cat] = 0 # Count affected population per polygon and total evacuated = 0 for attr in P.get_data(): # Get population at this location pop = float(attr['population']) # Update population count for associated polygon poly_id = attr['polygon_id'] new_attributes[poly_id][self.target_field] += pop # Update population count for each category cat = new_attributes[poly_id][category_title] categories[cat] += pop # Count totals total = int(numpy.sum(my_exposure.get_data(nan=0))) # Don't show digits less than a 1000 total = round_thousand(total) # Count number and cumulative for each zone cum = 0 pops = {} cums = {} for name in category_names: if category_title == 'Radius': key = name * 1000 # Convert to meters else: key = name # prevent key error pop = int(categories.get(key, 0)) pop = round_thousand(pop) cum += pop cum = round_thousand(cum) pops[name] = pop cums[name] = cum # Use final accumulation as total number needing evac evacuated = cum tot_needs = evacuated_population_weekly_needs(evacuated) # Generate impact report for the pdf map blank_cell = '' table_body = [ question, TableRow( [tr('Volcanos considered'), '%s' % volcano_names, blank_cell], header=True), TableRow([ tr('People needing evacuation'), '%s' % format_int(evacuated), blank_cell ], header=True), TableRow( [category_header, tr('Total'), tr('Cumulative')], header=True) ] for name in category_names: table_body.append( TableRow( [name, format_int(pops[name]), format_int(cums[name])])) table_body.extend([ TableRow( tr('Map shows population affected in ' 'each of volcano hazard polygons.')), TableRow([tr('Needs per week'), tr('Total'), blank_cell], header=True), [tr('Rice [kg]'), format_int(tot_needs['rice']), blank_cell], [ tr('Drinking Water [l]'), format_int(tot_needs['drinking_water']), blank_cell ], [ tr('Clean Water [l]'), format_int(tot_needs['water']), blank_cell ], [ tr('Family Kits'), format_int(tot_needs['family_kits']), blank_cell ], [tr('Toilets'), format_int(tot_needs['toilets']), blank_cell] ]) impact_table = Table(table_body).toNewlineFreeString() # Extend impact report for on-screen display table_body.extend([ TableRow(tr('Notes'), header=True), tr('Total population %s in the exposure layer') % format_int(total), tr('People need evacuation if they are within the ' 'volcanic hazard zones.') ]) population_counts = [x[self.target_field] for x in new_attributes] impact_summary = Table(table_body).toNewlineFreeString() # check for zero impact if numpy.nanmax(population_counts) == 0 == numpy.nanmin( population_counts): table_body = [ question, TableRow([ tr('People needing evacuation'), '%s' % format_int(evacuated), blank_cell ], header=True) ] my_message = Table(table_body).toNewlineFreeString() raise ZeroImpactException(my_message) # Create style colours = [ '#FFFFFF', '#38A800', '#79C900', '#CEED00', '#FFCC00', '#FF6600', '#FF0000', '#7A0000' ] classes = create_classes(population_counts, len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class['label'] = create_label(interval_classes[i]) if i == 0: transparency = 100 style_class['min'] = 0 else: transparency = 30 style_class['min'] = classes[i - 1] style_class['transparency'] = transparency style_class['colour'] = colours[i] style_class['max'] = classes[i] style_classes.append(style_class) # Override style info with new classes and name style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type='graduatedSymbol') # For printing map purpose map_title = tr('People affected by volcanic hazard zone') legend_notes = tr('Thousand separator is represented by %s' % get_thousand_separator()) legend_units = tr('(people)') legend_title = tr('Population count') # Create vector layer and return V = Vector(data=new_attributes, projection=my_hazard.get_projection(), geometry=my_hazard.get_geometry(as_geometry_objects=True), name=tr('Population affected by volcanic hazard zone'), keywords={ 'impact_summary': impact_summary, 'impact_table': impact_table, 'target_field': self.target_field, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title }, style_info=style_info) return V
def run(self, layers): """Risk plugin for 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), keywords={ 'impact_summary': impact_summary, 'impact_table': impact_table, 'map_title': map_title, 'legend_notes': legend_notes, 'legend_units': legend_units, 'legend_title': legend_title }, style_info=style_info) return R
def run(self, layers): """Risk plugin for volcano population evacuation :param layers: List of layers expected to contain where two layers should be present. * my_hazard: Vector polygon layer of volcano impact zones * my_exposure: Raster layer of population data on the same grid as my_hazard Counts number of people exposed to volcano event. :returns: Map of population exposed to the volcano hazard zone. The returned dict will include a table with number of people evacuated and supplies required. :rtype: dict """ # Identify hazard and exposure layers my_hazard = get_hazard_layer(layers) # Volcano KRB my_exposure = get_exposure_layer(layers) question = get_question(my_hazard.get_name(), my_exposure.get_name(), self) # Input checks if not my_hazard.is_vector: msg = "Input hazard %s was not a vector layer as expected " % my_hazard.get_name() raise Exception(msg) msg = "Input hazard must be a polygon or point layer. I got %s with " "layer type %s" % ( my_hazard.get_name(), my_hazard.get_geometry_name(), ) if not (my_hazard.is_polygon_data or my_hazard.is_point_data): raise Exception(msg) if my_hazard.is_point_data: # Use concentric circles radii = self.parameters["distance [km]"] centers = my_hazard.get_geometry() attributes = my_hazard.get_data() rad_m = [x * 1000 for x in radii] # Convert to meters my_hazard = make_circular_polygon(centers, rad_m, attributes=attributes) category_title = "Radius" category_header = tr("Distance [km]") category_names = radii name_attribute = "NAME" # As in e.g. the Smithsonian dataset else: # Use hazard map category_title = "KRB" category_header = tr("Category") # FIXME (Ole): Change to English and use translation system category_names = ["Kawasan Rawan Bencana III", "Kawasan Rawan Bencana II", "Kawasan Rawan Bencana I"] name_attribute = "GUNUNG" # As in e.g. BNPB hazard map attributes = my_hazard.get_data() # Get names of volcanos considered if name_attribute in my_hazard.get_attribute_names(): D = {} for att in my_hazard.get_data(): # Run through all polygons and get unique names D[att[name_attribute]] = None volcano_names = "" for name in D: volcano_names += "%s, " % name volcano_names = volcano_names[:-2] # Strip trailing ', ' else: volcano_names = tr("Not specified in data") if not category_title in my_hazard.get_attribute_names(): msg = "Hazard data %s did not contain expected " "attribute %s " % (my_hazard.get_name(), category_title) # noinspection PyExceptionInherit raise InaSAFEError(msg) # Run interpolation function for polygon2raster P = assign_hazard_values_to_exposure_data(my_hazard, my_exposure, attribute_name="population") # Initialise attributes of output dataset with all attributes # from input polygon and a population count of zero new_attributes = my_hazard.get_data() categories = {} for attr in new_attributes: attr[self.target_field] = 0 cat = attr[category_title] categories[cat] = 0 # Count affected population per polygon and total evacuated = 0 for attr in P.get_data(): # Get population at this location pop = float(attr["population"]) # Update population count for associated polygon poly_id = attr["polygon_id"] new_attributes[poly_id][self.target_field] += pop # Update population count for each category cat = new_attributes[poly_id][category_title] categories[cat] += pop # Count totals total = int(numpy.sum(my_exposure.get_data(nan=0))) # Don't show digits less than a 1000 total = round_thousand(total) # Count number and cumulative for each zone cum = 0 pops = {} cums = {} for name in category_names: if category_title == "Radius": key = name * 1000 # Convert to meters else: key = name # prevent key error pop = int(categories.get(key, 0)) pop = round_thousand(pop) cum += pop cum = round_thousand(cum) pops[name] = pop cums[name] = cum # Use final accumulation as total number needing evac evacuated = cum tot_needs = evacuated_population_weekly_needs(evacuated) # Generate impact report for the pdf map blank_cell = "" table_body = [ question, TableRow([tr("Volcanoes considered"), "%s" % volcano_names, blank_cell], header=True), TableRow([tr("People needing evacuation"), "%s" % format_int(evacuated), blank_cell], header=True), TableRow([category_header, tr("Total"), tr("Cumulative")], header=True), ] for name in category_names: table_body.append(TableRow([name, format_int(pops[name]), format_int(cums[name])])) table_body.extend( [ TableRow(tr("Map shows population affected in " "each of volcano hazard polygons.")), TableRow([tr("Needs per week"), tr("Total"), blank_cell], header=True), [tr("Rice [kg]"), format_int(tot_needs["rice"]), blank_cell], [tr("Drinking Water [l]"), format_int(tot_needs["drinking_water"]), blank_cell], [tr("Clean Water [l]"), format_int(tot_needs["water"]), blank_cell], [tr("Family Kits"), format_int(tot_needs["family_kits"]), blank_cell], [tr("Toilets"), format_int(tot_needs["toilets"]), blank_cell], ] ) impact_table = Table(table_body).toNewlineFreeString() # Extend impact report for on-screen display table_body.extend( [ TableRow(tr("Notes"), header=True), tr("Total population %s in the exposure layer") % format_int(total), tr("People need evacuation if they are within the " "volcanic hazard zones."), ] ) population_counts = [x[self.target_field] for x in new_attributes] impact_summary = Table(table_body).toNewlineFreeString() # check for zero impact if numpy.nanmax(population_counts) == 0 == numpy.nanmin(population_counts): table_body = [ question, TableRow([tr("People needing evacuation"), "%s" % format_int(evacuated), blank_cell], header=True), ] my_message = Table(table_body).toNewlineFreeString() raise ZeroImpactException(my_message) # Create style colours = ["#FFFFFF", "#38A800", "#79C900", "#CEED00", "#FFCC00", "#FF6600", "#FF0000", "#7A0000"] classes = create_classes(population_counts, len(colours)) interval_classes = humanize_class(classes) # Define style info for output polygons showing population counts style_classes = [] for i in xrange(len(colours)): style_class = dict() style_class["label"] = create_label(interval_classes[i]) if i == 0: transparency = 100 style_class["min"] = 0 else: transparency = 30 style_class["min"] = classes[i - 1] style_class["transparency"] = transparency style_class["colour"] = colours[i] style_class["max"] = classes[i] style_classes.append(style_class) # Override style info with new classes and name style_info = dict(target_field=self.target_field, style_classes=style_classes, style_type="graduatedSymbol") # For printing map purpose map_title = tr("People affected by volcanic hazard zone") legend_notes = tr("Thousand separator is represented by %s" % get_thousand_separator()) legend_units = tr("(people)") legend_title = tr("Population count") # Create vector layer and return V = Vector( data=new_attributes, projection=my_hazard.get_projection(), geometry=my_hazard.get_geometry(as_geometry_objects=True), name=tr("Population affected by volcanic hazard zone"), keywords={ "impact_summary": impact_summary, "impact_table": impact_table, "target_field": self.target_field, "map_title": map_title, "legend_notes": legend_notes, "legend_units": legend_units, "legend_title": legend_title, }, style_info=style_info, ) return V