Python Raster.impact_data方法代码示例

# 需要导入模块: from safe.storage.raster import Raster [as 别名]
# 或者: from safe.storage.raster.Raster import impact_data [as 别名]

#.........这里部分代码省略.........
        exposure_data = self.exposure.layer.get_data(nan=True, scaling=True)
        if has_no_data(exposure_data):
            self.no_data_warning = True

        # Make 3 data for each zone. Get the value of the exposure if the
        # exposure is in the hazard zone, else just assign 0
        low_exposure = numpy.where(hazard_data < low_t, exposure_data, 0)
        medium_exposure = numpy.where(
            (hazard_data >= low_t) & (hazard_data < medium_t),
            exposure_data, 0)
        high_exposure = numpy.where(
            (hazard_data >= medium_t) & (hazard_data <= high_t),
            exposure_data, 0)
        impacted_exposure = low_exposure + medium_exposure + high_exposure

        # Count totals
        self.total_population = int(numpy.nansum(exposure_data))
        self.affected_population[
            tr('Population in high hazard areas')] = int(
                numpy.nansum(high_exposure))
        self.affected_population[
            tr('Population in medium hazard areas')] = int(
                numpy.nansum(medium_exposure))
        self.affected_population[
            tr('Population in low hazard areas')] = int(
                numpy.nansum(low_exposure))
        self.unaffected_population = (
            self.total_population - self.total_affected_population)

        # check for zero impact
        if self.total_affected_population == 0:
            message = no_population_impact_message(self.question)
            raise ZeroImpactException(message)

        # Don't show digits less than a 1000
        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]
        total_needs = self.total_needs

        # Style for impact layer
        colours = [
            '#FFFFFF', '#38A800', '#79C900', '#CEED00',
            '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(impacted_exposure.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []

        for i in xrange(len(colours)):
            style_class = dict()
            if i == 1:
                label = create_label(
                    interval_classes[i],
                    tr('Low Population [%i people/cell]' % classes[i]))
            elif i == 4:
                label = create_label(
                    interval_classes[i],
                    tr('Medium Population [%i people/cell]' % classes[i]))
            elif i == 7:
                label = create_label(
                    interval_classes[i],
                    tr('High Population [%i people/cell]' % classes[i]))
            else:
                label = create_label(interval_classes[i])
            style_class['label'] = label
            style_class['quantity'] = classes[i]
            style_class['transparency'] = 0
            style_class['colour'] = colours[i]
            style_classes.append(style_class)

        style_info = dict(
            target_field=None,
            style_classes=style_classes,
            style_type='rasterStyle')

        impact_data = self.generate_data()

        extra_keywords = {
            'map_title': self.metadata().key('map_title'),
            'legend_notes': self.metadata().key('legend_notes'),
            'legend_units': self.metadata().key('legend_units'),
            'legend_title': self.metadata().key('legend_title'),
            'total_needs': total_needs
        }

        impact_layer_keywords = self.generate_impact_keywords(extra_keywords)

        # Create raster object and return
        impact_layer = Raster(
            data=impacted_exposure,
            projection=self.hazard.layer.get_projection(),
            geotransform=self.hazard.layer.get_geotransform(),
            name=self.metadata().key('layer_name'),
            keywords=impact_layer_keywords,
            style_info=style_info)

        impact_layer.impact_data = impact_data
        self._impact = impact_layer
        return impact_layer

# 需要导入模块: from safe.storage.raster import Raster [as 别名]
# 或者: from safe.storage.raster.Raster import impact_data [as 别名]

#.........这里部分代码省略.........
            self.volcano_names = volcano_names[:-2]  # Strip trailing ', '

        # Run interpolation function for polygon2raster
        interpolated_layer, covered_exposure_layer = \
            assign_hazard_values_to_exposure_data(
                self.hazard.layer,
                self.exposure.layer,
                attribute_name=self.target_field
            )

        # Initialise affected population per categories
        for radius in radii:
            category = 'Radius %s km ' % format_int(radius)
            self.affected_population[category] = 0

        if has_no_data(self.exposure.layer.get_data(nan=True)):
            self.no_data_warning = True
        # Count affected population per polygon and total
        for row in interpolated_layer.get_data():
            # Get population at this location
            population = row[self.target_field]
            if not numpy.isnan(population):
                population = float(population)
                # Update population count for this category
                category = 'Radius %s km ' % format_int(
                    row[self.hazard_zone_attribute])
                self.affected_population[category] += population

        # Count totals
        self.total_population = population_rounding(
            int(numpy.nansum(self.exposure.layer.get_data())))

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        # 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])

            style_class['label'] = label
            style_class['quantity'] = classes[i]
            style_class['colour'] = colours[i]
            style_class['transparency'] = 0
            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')

        impact_data = self.generate_data()

        # Create vector layer and return
        extra_keywords = {
            'target_field': self.target_field,
            'map_title': self.metadata().key('map_title'),
            'legend_notes': self.metadata().key('legend_notes'),
            'legend_units': self.metadata().key('legend_units'),
            'legend_title': self.metadata().key('legend_title'),
            'total_needs': self.total_needs
        }

        impact_layer_keywords = self.generate_impact_keywords(extra_keywords)

        impact_layer = Raster(
            data=covered_exposure_layer.get_data(),
            projection=covered_exposure_layer.get_projection(),
            geotransform=covered_exposure_layer.get_geotransform(),
            name=self.metadata().key('layer_name'),
            keywords=impact_layer_keywords,
            style_info=style_info)

        impact_layer.impact_data = impact_data
        self._impact = impact_layer
        return impact_layer

# 需要导入模块: from safe.storage.raster import Raster [as 别名]
# 或者: from safe.storage.raster.Raster import impact_data [as 别名]

#.........这里部分代码省略.........
            #    displacements > fatalities, displacements - fatalities, 0)

            # Sum up numbers for map
            # We need to use matrices here and not just numbers #2235
            # filter out NaN to avoid overflow additions
            mmi_matches = numpy.nan_to_num(mmi_matches)
            mask += mmi_matches   # Displaced

            # Generate text with result for this study
            # This is what is used in the real time system exposure table
            number_of_exposed[mmi] = exposed
            number_of_displaced[mmi] = displacements
            # noinspection PyUnresolvedReferences
            number_of_fatalities[mmi] = fatalities

        # Total statistics
        total_fatalities_raw = numpy.nansum(
            number_of_fatalities.values(), axis=0)

        # Compute probability of fatality in each magnitude bin
        if (self.__class__.__name__ == 'PAGFatalityFunction') or (
                self.__class__.__name__ == 'ITBBayesianFatalityFunction'):
            prob_fatality_mag = self.compute_probability(total_fatalities_raw)
        else:
            prob_fatality_mag = None

        # Compute number of fatalities
        self.total_population = numpy.nansum(number_of_exposed.values())
        self.total_fatalities = numpy.median(total_fatalities_raw)
        total_displaced = numpy.nansum(number_of_displaced.values())

        # As per email discussion with Ole, Trevor, Hadi, total fatalities < 50
        # will be rounded down to 0 - Tim
        # Needs to revisit but keep it alive for the time being - Hyeuk, Jono
        if self.total_fatalities < 50:
            self.total_fatalities = 0

        affected_population = self.affected_population
        affected_population[tr('Number of fatalities')] = self.total_fatalities
        affected_population[
            tr('Number of people displaced')] = total_displaced
        self.unaffected_population = (
            self.total_population - total_displaced - self.total_fatalities)
        self._evacuation_category = tr('Number of people displaced')

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]
        total_needs = self.total_needs

        # Create style
        colours = ['#EEFFEE', '#FFFF7F', '#E15500', '#E4001B', '#730000']
        classes = create_classes(mask.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []
        for i in xrange(len(interval_classes)):
            style_class = dict()
            style_class['label'] = create_label(interval_classes[i])
            style_class['quantity'] = classes[i]
            style_class['transparency'] = 30
            style_class['colour'] = colours[i]
            style_classes.append(style_class)

        style_info = dict(target_field=None,
                          style_classes=style_classes,
                          style_type='rasterStyle')

        impact_data = self.generate_data()

        extra_keywords = {
            'exposed_per_mmi': number_of_exposed,
            'total_population': self.total_population,
            'total_fatalities': population_rounding(self.total_fatalities),
            'total_fatalities_raw': self.total_fatalities,
            'fatalities_per_mmi': number_of_fatalities,
            'total_displaced': population_rounding(total_displaced),
            'displaced_per_mmi': number_of_displaced,
            'map_title': self.metadata().key('map_title'),
            'legend_notes': self.metadata().key('legend_notes'),
            'legend_units': self.metadata().key('legend_units'),
            'legend_title': self.metadata().key('legend_title'),
            'total_needs': total_needs,
            'prob_fatality_mag': prob_fatality_mag,
        }

        impact_layer_keywords = self.generate_impact_keywords(extra_keywords)

        # Create raster object and return
        impact_layer = Raster(
            mask,
            projection=self.exposure.layer.get_projection(),
            geotransform=self.exposure.layer.get_geotransform(),
            keywords=impact_layer_keywords,
            name=self.metadata().key('layer_name'),
            style_info=style_info)

        impact_layer.impact_data = impact_data
        self._impact = impact_layer
        return impact_layer

# 需要导入模块: from safe.storage.raster import Raster [as 别名]
# 或者: from safe.storage.raster.Raster import impact_data [as 别名]

#.........这里部分代码省略.........
        # merely initialize
        impact = None
        for i, lo in enumerate(thresholds):
            if i == len(thresholds) - 1:
                # The last threshold
                thresholds_name = tr(
                    'People in >= %.1f m of water') % lo
                impact = medium = numpy.where(data >= lo, population, 0)
                self.impact_category_ordering.append(thresholds_name)
                self._evacuation_category = thresholds_name
            else:
                # Intermediate thresholds
                hi = thresholds[i + 1]
                thresholds_name = tr(
                    'People in %.1f m to %.1f m of water' % (lo, hi))
                medium = numpy.where((data >= lo) * (data < hi), population, 0)

            # Count
            val = int(numpy.nansum(medium))
            self.affected_population[thresholds_name] = val

        # Put the deepest area in top #2385
        self.impact_category_ordering.reverse()

        # 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
        self.total_population = int(numpy.nansum(population))
        self.unaffected_population = (
            self.total_population - self.total_affected_population)

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        # check for zero impact
        if numpy.nanmax(impact) == 0 == numpy.nanmin(impact):
            message = m.Message()
            message.add(self.question)
            message.add(tr('No people in %.1f m of water') % thresholds[-1])
            message = message.to_html(suppress_newlines=True)
            raise ZeroImpactException(message)

        # Create style
        colours = [
            '#FFFFFF', '#38A800', '#79C900', '#CEED00',
            '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(impact.flat[:], len(colours))
        interval_classes = humanize_class(classes)
        style_classes = []

        for i in xrange(len(colours)):
            style_class = dict()
            if i == 1:
                label = create_label(interval_classes[i], 'Low')
            elif i == 4:
                label = create_label(interval_classes[i], 'Medium')
            elif i == 7:
                label = create_label(interval_classes[i], 'High')
            else:
                label = create_label(interval_classes[i])
            style_class['label'] = label
            style_class['quantity'] = classes[i]
            style_class['transparency'] = 0
            style_class['colour'] = colours[i]
            style_classes.append(style_class)

        style_info = dict(
            target_field=None,
            style_classes=style_classes,
            style_type='rasterStyle')

        impact_data = self.generate_data()

        extra_keywords = {
            'map_title': self.metadata().key('map_title'),
            'legend_notes': self.metadata().key('legend_notes'),
            'legend_units': self.metadata().key('legend_units'),
            'legend_title': self.metadata().key('legend_title'),
            'evacuated': self.total_evacuated,
            'total_needs': self.total_needs
        }

        impact_layer_keywords = self.generate_impact_keywords(extra_keywords)

        # Create raster object and return
        impact_layer = Raster(
            impact,
            projection=self.hazard.layer.get_projection(),
            geotransform=self.hazard.layer.get_geotransform(),
            name=self.metadata().key('layer_name'),
            keywords=impact_layer_keywords,
            style_info=style_info)

        impact_layer.impact_data = impact_data
        self._impact = impact_layer
        return impact_layer

# 需要导入模块: from safe.storage.raster import Raster [as 别名]
# 或者: from safe.storage.raster.Raster import impact_data [as 别名]

#.........这里部分代码省略.........
                    (grid_point - covered_exposure_top_left) / (
                        covered_exposure_dimension)).astype(int)
                new_covered_exposure_data[index[1]][index[0]] = 0

        # Estimate number of people in need of evacuation
        if self.use_affected_field:
            affected_population = tr(
                'People within hazard field ("%s") of value "%s"') % (
                    self.hazard_class_attribute,
                    ','.join([
                        unicode(hazard_class) for
                        hazard_class in self.hazard_class_mapping[self.wet]
                    ]))
        else:
            affected_population = tr('People within any hazard polygon.')

        self.affected_population[affected_population] = (
            total_affected_population)

        self.total_population = int(
            numpy.nansum(self.exposure.layer.get_data(scaling=False)))
        self.unaffected_population = (
            self.total_population - self.total_affected_population)

        self.minimum_needs = [
            parameter.serialize() for parameter in
            filter_needs_parameters(self.parameters['minimum needs'])
        ]

        # Create style
        colours = ['#FFFFFF', '#38A800', '#79C900', '#CEED00',
                   '#FFCC00', '#FF6600', '#FF0000', '#7A0000']
        classes = create_classes(
            new_covered_exposure_data.flat[:], len(colours))

        # check for zero impact
        if total_affected_population == 0:
            message = no_population_impact_message(self.question)
            raise ZeroImpactException(message)

        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])

            style_class['label'] = label
            style_class['quantity'] = classes[i]
            style_class['colour'] = colours[i]
            style_class['transparency'] = 0
            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')

        impact_data = self.generate_data()

        extra_keywords = {
            'target_field': self.target_field,
            'map_title': self.metadata().key('map_title'),
            'legend_notes': self.metadata().key('legend_notes'),
            'legend_units': self.metadata().key('legend_units'),
            'legend_title': self.metadata().key('legend_title'),
            'affected_population': total_affected_population,
            'total_population': self.total_population,
            'total_needs': self.total_needs
        }

        impact_layer_keywords = self.generate_impact_keywords(extra_keywords)

        # Create raster layer and return
        impact_layer = Raster(
            data=new_covered_exposure_data,
            projection=covered_exposure.get_projection(),
            geotransform=covered_exposure.get_geotransform(),
            name=self.metadata().key('layer_name'),
            keywords=impact_layer_keywords,
            style_info=style_info)

        impact_layer.impact_data = impact_data
        self._impact = impact_layer
        return impact_layer