コード例 #1
0
    def run(layers):
        """Risk plugin for volcano population impact

        Input
          layers: List of layers expected to contain
              H: Raster layer of volcanic hazard level
              P: Raster layer of population data on the same grid as H
        """

        # Identify hazard and exposure layers
        # Volcanic hazard level [0-1]
        volcanic_hazard_level = get_hazard_layer(layers)
        population = get_exposure_layer(layers)  # Density [people/area]

        # Extract data as numeric arrays
        V = volcanic_hazard_level.get_data(nan=0.0)
        # Population density
        P = population.get_data(nan=0.0, scaling=True)

        # Calculate impact as population exposed to depths > threshold
        I = numpy.where(V > 2.0 / 3, P, 0)

        # Generate text with result for this study
        number_of_people_affected = numpy.nansum(I.flat)
        impact_summary = ('%i people affected by volcanic hazard level greater'
                          ' than 0.667' % number_of_people_affected)

        # Create raster object and return
        R = Raster(I,
                   projection=volcanic_hazard_level.get_projection(),
                   geotransform=volcanic_hazard_level.get_geotransform(),
                   name='People affected',
                   keywords={'impact_summary': impact_summary})
        return R
コード例 #2
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    def run(layers):
        """Risk plugin for earthquake fatalities

        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
        """

        threshold = 1  # Load above which people are regarded affected [kg/m2]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)    # Tephra load [kg/m2]
        population = get_exposure_layer(layers)  # Density [people/km^2]

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth
        P = population.get_data(nan=0.0, scaling=True)  # Population density

        # Calculate impact as population exposed to depths > threshold
        I = numpy.where(D > threshold, P, 0)

        # Generate text with result for this study
        number_of_people_affected = numpy.nansum(I.flat)
        caption = ('%i people affected by ash levels greater '
                   'than %i kg/m^2' % (number_of_people_affected,
                                       threshold))

        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='People affected',
                   keywords={'caption': caption})
        return R
コード例 #3
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    def run(layers):
        """Risk plugin for earthquake fatalities

        Input
          layers: List of layers expected to contain
              H: Raster layer of flood depth
              P: Raster layer of poor household density on the same grid as H
        """

        # Depth above which people are regarded affected [m]
        threshold = 1.0

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)  # Flood inundation [m]
        poor_households = get_exposure_layer(layers)  # Poverty density

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # This is the new generic way of scaling (issue #168 and #172)
        P = poor_households.get_data(nan=0.0, scaling=True)
        I = numpy.where(D > threshold, P, 0)

        # Generate text with result for this study
        total = str(int(sum(P.flat) / 1000))
        count = str(int(sum(I.flat) / 1000))

        # Create report
        iname = inundation.get_name()
        pname = poor_households.get_name()
        impact_summary = ('<b>Apabila terjadi "%s" perkiraan dampak terhadap '
                          '"%s" kemungkinan yang terjadi&#58;</b><br><br><p>' %
                          (iname, pname))

        impact_summary += ('<table border="0" width="320px">')
                   #'   <tr><td><b>%s&#58;</b></td>'
                   #'<td align="right"><b>%s</b></td></tr>'
                   #% ('Jumlah Rumah Tangga Miskin', total))

        impact_summary += ('   <tr><td><b>%s&#58;</b></td>'
                    '<td align="right"><b>%s</b></td></tr>'
                    % ('Jumlah Rumah Tangga Terdampak (x 1000)', count))

        impact_summary += '</table>'

        impact_summary += '<br>'  # Blank separation row
        impact_summary += '<b>Catatan&#58;</b><br>'
        impact_summary += '- Jumlah Rumah Tangga Miskin %s<br>' % total
        impact_summary += '- Jumlah dalam ribuan<br>'
        impact_summary += ('- Rumah Tangga Miskin dalam bahaya ketika '
                    'banjir lebih dari %.1f m. ' % threshold)

        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='People affected',
                   keywords={'impact_summary': impact_summary})
        return R
コード例 #4
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    def run(self, layers):
        """Risk plugin for tsunami population
        """

        thresholds = [0.2, 0.3, 0.5, 0.8, 1.0]
        #threshold = 1  # Depth above which people are regarded affected [m]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)    # Tsunami inundation [m]
        population = get_exposure_layer(layers)  # Population density

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth
        P = population.get_data(nan=0.0, scaling=True)  # Population density

        # Calculate impact as population exposed to depths > 1m
        I_map = numpy.where(D > thresholds[-1], P, 0)

        # Generate text with result for this study
        number_of_people_affected = numpy.nansum(I_map.flat)

        # Do breakdown

        # Create report
        impact_summary = ('<table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                   '   <tr></tr>' % ('Ambang batas', 'Jumlah orang terdampak'))

        counts = []
        for i, threshold in enumerate(thresholds):
            I = numpy.where(D > threshold, P, 0)
            counts.append(numpy.nansum(I.flat))

            impact_summary += '   <tr><td>%s m</td><td>%i</td></tr>' % (
                                 threshold, counts[i])

        impact_summary += '</table>'

        # Create raster object and return
        R = Raster(I_map,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='People affected by more than 1m of inundation',
                   keywords={'impact_summary': impact_summary})
        return R
コード例 #5
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    def run(layers,
            teta=14.05, beta=0.17, zeta=2.15):
        """Risk plugin for earthquake fatalities

        Input
          H: Numerical array of hazard data
          E: Numerical array of exposure data
        """

        # Suppress warnings about invalid value in multiply and divide zero
        # http://comments.gmane.org/gmane.comp.python.numeric.general/43218
        # http://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html
        old_numpy_setting = numpy.seterr(invalid='ignore')
        numpy.seterr(divide='ignore')

        # Identify input layers
        intensity = get_hazard_layer(layers)
        population = get_exposure_layer(layers)

        # Extract data
        H = intensity.get_data(nan=0)
        P = population.get_data(nan=0)

        # Calculate impact
        logHazard = 1 / beta * numpy.log(H / teta)

        # Convert array to be standard floats expected by cdf
        arrayout = numpy.array([[float(value) for value in row]
                               for row in logHazard])
        x = arrayout * P
        F = normal_cdf(x)

        numpy.seterr(**old_numpy_setting)

        # Create new layer and return
        R = Raster(F,
                   projection=population.get_projection(),
                   geotransform=population.get_geotransform(),
                   name='Estimated fatalities')
        return R
コード例 #6
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    def run(layers):
        """Risk plugin for earthquake school damage
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        H = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        shaking = H.get_data()

        # Calculate building damage
        building_damage = []
        for i in range(len(shaking)):
            x = float(shaking[i].values()[0])
            if x < 6.0:
                value = 0.0
            else:
                value = (0.692 * (x ** 4) -
                         15.82 * (x ** 3) +
                         135.0 * (x ** 2) -
                         509.0 * x +
                         714.4)

            building_damage.append({'DAMAGE': value, 'MMI': x})

        # FIXME (Ole): Need helper to generate new layer using
        #              correct spatial reference
        #              (i.e. sensibly wrap the following lines)
        projection = E.get_projection()

        V = Vector(data=building_damage,
                   projection=E.get_projection(),
                   geometry=coordinates)
        return V
コード例 #7
0
ファイル: HKV_flood_study.py プロジェクト: purnamabs/inasafe
    def run(layers):
        """Risk plugin for earthquake fatalities

        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
        """

        # Depth above which people are regarded affected [m]
        threshold = 0.1
        thresholds = [0.1, 0.2, 0.3, 0.5, 0.8, 1.0]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)  # Flood inundation [m]

        # Get population and gender ratio
        population = gender_ratio = None
        for layer in get_exposure_layers(layers):
            keywords = layer.get_keywords()

            if 'datatype' not in keywords:
                population = layer
            else:
                datatype = keywords['datatype']

                if 'ratio' not in datatype:
                    population = layer
                else:
                    # if 'female' in datatype and 'ratio' in datatype:
                    gender_ratio_unit = keywords['unit']

                    msg = ('Unit for gender ratio must be either '
                           '"percent" or "ratio"')
                    if gender_ratio_unit not in ['percent', 'ratio']:
                        raise Exception(msg)

                    gender_ratio = layer

        msg = 'No population layer was found in: %s' % str(layers)
        verify(population is not None, msg)

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > threshold
        if population.get_resolution(native=True, isotropic=True) < 0.0005:
            # Keep this for backwards compatibility just a little while
            # This uses the original custom population set and
            # serves as a reference

            P = population.get_data(nan=0.0)  # Population density
            pixel_area = 2500
            I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
        else:
            # This is the new generic way of scaling (issue #168 and #172)
            P = population.get_data(nan=0.0, scaling=True)
            I = numpy.where(D > threshold, P, 0)

        if gender_ratio is not None:
            # Extract gender ratio at each pixel (as ratio)
            G = gender_ratio.get_data(nan=0.0)
            if gender_ratio_unit == 'percent':
                G /= 100

            # Calculate breakdown
            P_female = P * G
            P_male = P - P_female

            I_female = I * G
            I_male = I - I_female

        # Generate text with result for this study
        total = str(int(sum(P.flat) / 1000))
        count = str(int(sum(I.flat) / 1000))

        # Create report
        impact_summary = ('<table border="0" width="320px">'
                   '   <tr><td><b>%s&#58;</b></td>'
                   '<td align="right"><b>%s</b></td></tr>'
                   % ('Jumlah Penduduk', total))
        if gender_ratio is not None:
            total_female = str(int(sum(P_female.flat) / 1000))
            total_male = str(int(sum(P_male.flat) / 1000))

            impact_summary += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Wanita', total_female))
            impact_summary += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Pria', total_male))
            impact_summary += '<tr><td>&nbsp;</td></tr>'  # Blank row

        impact_summary += ('   <tr><td><b>%s&#58;</b></td>'
                    '<td align="right"><b>%s</b></td></tr>'
                    % ('Perkiraan Jumlah Terdampak (> %.1fm)' % threshold,
                       count))

        if gender_ratio is not None:
            affected_female = str(int(sum(I_female.flat) / 1000))
            affected_male = str(int(sum(I_male.flat) / 1000))

            impact_summary += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Wanita', affected_female))
            impact_summary += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Pria', affected_male))

        impact_summary += '</table>'

        impact_summary += '<br>'  # Blank separation row
        impact_summary += 'Catatan&#58; Semua nomor x 1000'

        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='People affected',
                   keywords={'impact_summary': impact_summary})
        return R
コード例 #8
0
    def run(layers,
            a=0.97429, b=11.037):
        """Risk plugin for earthquake fatalities

        Input
          layers: List of layers expected to contain
              H: Raster layer of MMI ground shaking
              P: Raster layer of population data on the same grid as H
        """

        # Identify input layers
        intensity = get_hazard_layer(layers)

        # Get population and gender ratio
        population = gender_ratio = None
        for layer in get_exposure_layers(layers):
            keywords = layer.get_keywords()

            if 'datatype' not in keywords:
                population = layer
            else:
                datatype = keywords['datatype']

                if not 'ratio' in datatype:
                    population = layer
                else:
                    # 'female' in datatype and 'ratio' in datatype:
                    gender_ratio_unit = keywords['unit']

                    msg = ('Unit for gender ratio must be either '
                           '"percent" or "ratio"')
                    assert gender_ratio_unit in ['percent', 'ratio'], msg

                    gender_ratio = layer

        msg = 'No population layer was found in: %s' % str(layers)
        assert population is not None, msg

        # Extract data
        H = intensity.get_data(nan=0)
        P = population.get_data(nan=0)
        #print
        #print 'Population', population.get_name()

        # Calculate impact
        F = 10 ** (a * H - b) * P

        if gender_ratio is not None:
            # Extract gender ratio at each pixel (as ratio)
            G = gender_ratio.get_data(nan=0)
            if gender_ratio_unit == 'percent':
                G /= 100

            # Calculate breakdown
            P_female = P * G
            P_male = P - P_female

            F_female = F * G
            F_male = F - F_female

        # Generate text with result for this study
        count = numpy.nansum(F.flat)
        total = numpy.nansum(P.flat)

        # Create report
        caption = ('<table border="0" width="320px">'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   % ('Jumlah Penduduk', int(total)))
        if gender_ratio is not None:
            caption += ('        <tr><td>%s&#58;</td><td>%i</td></tr>'
                        % (' - Wanita', int(numpy.nansum(P_female.flat))))
            caption += ('        <tr><td>%s&#58;</td><td>%i</td></tr>'
                        % (' - Pria', int(numpy.nansum(P_male.flat))))
        caption += ('   <tr><td>%s&#58;</td><td>%i</td></tr>'
                    % ('Perkiraan Orang Meninggal', int(count)))

        if gender_ratio is not None:
            caption += ('        <tr><td>%s&#58;</td><td>%i</td></tr>'
                        % (' - Wanita', int(numpy.nansum(F_female.flat))))
            caption += ('        <tr><td>%s&#58;</td><td>%i</td></tr>'
                        % (' - Pria', int(numpy.nansum(F_male.flat))))

        caption += '</table>'

        # Create new layer and return
        R = Raster(F,
                   projection=population.get_projection(),
                   geotransform=population.get_geotransform(),
                   name='Estimated fatalities',
                   keywords={'caption': caption})
        return R
コード例 #9
0
    def run(self, layers):
        """Risk plugin for earthquake school damage
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        keywords = E.get_keywords()
        if 'datatype' in keywords:
            datatype = keywords['datatype']
            if datatype.lower() == 'osm':
                # Map from OSM attributes to the guideline classes (URM and RM)
                E = osm2bnpb(E, target_attribute=self.vclass_tag)
            elif datatype.lower() == 'sigab':
                # Map from SIGAB attributes to the guideline classes
                # (URM and RM)
                E = sigab2bnpb(E)
            else:
                E = unspecific2bnpb(E, target_attribute=self.vclass_tag)
        else:
            E = unspecific2bnpb(E, target_attribute=self.vclass_tag)

        # Interpolate hazard level to building locations
        H = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        shaking = H.get_data()
        N = len(shaking)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate building damage
        count3 = 0
        count2 = 0
        count1 = 0
        count_unknown = 0
        building_damage = []
        for i in range(N):
            mmi = float(shaking[i].values()[0])

            building_class = E.get_data(self.vclass_tag, i)
            lo, hi = damage_parameters[building_class]

            if numpy.isnan(mmi):
                # If we don't know the shake level assign Not-a-Number
                damage = numpy.nan
                count_unknown += 1
            elif mmi < lo:
                damage = 1  # Low
                count1 += 1
            elif lo <= mmi < hi:
                damage = 2  # Medium
                count2 += 1
            elif mmi >= hi:
                damage = 3  # High
                count3 += 1
            else:
                msg = 'Undefined shakelevel %s' % str(mmi)
                raise Exception(msg)

            # Collect shake level and calculated damage
            result_dict = {self.target_field: damage,
                           'MMI': mmi}

            # Carry all orginal attributes forward
            for key in attributes:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            building_damage.append(result_dict)

        # Create report
        caption = ('<table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                    '   <tr></tr>'
                    '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (10-25%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (25-50%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (50-100%%)&#58;</td><td>%i</td></tr>'
                    % (_('Buildings'), _('Total'),
                       _('All'), N,
                       _('Low damage'), count1,
                       _('Medium damage'), count2,
                       _('High damage'), count3))
        caption += ('   <tr><td>%s (NaN)&#58;</td><td>%i</td></tr>'
                    % ('Unknown', count_unknown))
        caption += '</table>'

        # Create style
        style_classes = [dict(label=_('Low damage'), min=0.5, max=1.5,
                              colour='#fecc5c', transparency=1),
                         dict(label=_('Medium damage'), min=1.5, max=2.5,
                              colour='#fd8d3c', transparency=1),
                         dict(label=_('High damage'), min=2.5, max=3.5,
                              colour='#f31a1c', transparency=1)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        V = Vector(data=building_damage,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated damage level',
                   keywords={'caption': caption},
                   style_info=style_info)

        return V
コード例 #10
0
    def run(self, layers):
        """Risk plugin for earthquake fatalities

        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
        """

        # Depth above which people are regarded affected [m]
        threshold = 0.1  # Threshold [m]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)  # Flood inundation [m]
        population = get_exposure_layer(layers)

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > threshold
        if population.get_resolution(native=True, isotropic=True) < 0.0005:
            # Keep this for backwards compatibility just a little while
            # This uses the original custom population set and
            # serves as a reference

            P = population.get_data(nan=0.0)  # Population density
            pixel_area = 2500
            I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
        else:
            # This is the new generic way of scaling (issue #168 and #172)
            P = population.get_data(nan=0.0, scaling=True)
            I = numpy.where(D > threshold, P, 0)

        # Generate text with result for this study
        total = str(int(numpy.sum(P) / 1000))
        count = str(int(numpy.sum(I) / 1000))

        # Create report
        iname = inundation.get_name()
        pname = population.get_name()
        impact_summary = ('<table class="table table-striped condensed">')
        impact_summary += ('<caption>Apabila terjadi "%s" '
                    'perkiraan dampak terhadap "%s" '
                    'kemungkinan yang terjadi&#58;</caption>' % (iname,
                                                                 pname))
        impact_summary += ('<tbody>')
        impact_summary += ('   <tr><th>%s&#58;</th>'
                    '<td align="right">%s</td></tr>'
                    % ('Terdampak (x 1000)', count))
        impact_summary += ('</tbody>')
        impact_summary += '</table>'

        impact_summary += '<br>'  # Blank separation row
        impact_summary += '<span class="label label-success">'
        impact_summary += 'Catatan&#58;</span>'
        impact_summary += '<ul>'
        impact_summary += '  <li>Jumlah penduduk Jakarta %s</li>' % total
        impact_summary += '  <li>Jumlah dalam ribuan</li>'
        impact_summary += (' <li>Penduduk dianggap terdampak ketika '
                           'banjir lebih dari %.1f m.</li>' % threshold)
        impact_summary += '</ul>'

        impact_table = ('<table class="table table-striped condensed'
                        ' bordered-table">'
                 '  <caption>Jumlah Penduduk Yang Mungkin Dievakuasi</caption>'
                 '  <thead>'
                 '    <tr>'
                 '      <th rowspan="2">Wilayah</th>'
                 '      <th colspan="2">Jumlah Penduduk</th>'
                 '      <th rowspan="2" colspan="2" width="100px">'
                            'Jumlah Penduduk yang Mungkin dievakuasi</th>'
                 '    </tr>'
                 '    <tr>'
                 '      <th>Perempuan</th>'
                 '      <th>Laki-Laki</th>'
                 '    </tr>'
                 '  </thead>'
                 '  <tbody>'
                 '    <tr>'
                 '      <td>Jakarta Barat</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '    <tr>'
                 '      <td>Jakarta Pusat</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '    <tr>'
                 '      <td>Jakarta Seletan</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '    <tr>'
                 '      <td>Jakarta Timur</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '    <tr>'
                 '      <td>Jakarta Utara</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '    <tr>'
                 '      <td class="align-right">Total</td>'
                 '      <td>87510</td>'
                 '      <td>93076</td>'
                 '      <td>180586</td>'
                 '    </tr>'
                 '  </tbody>'
                 '  <caption>Sumber: Badan Pusat Statistik</caption>'
                 '</table>')
        map_title = 'Penduduk yang Mungkin dievakuasi'

        style_info['legend_title'] = 'Kepadatan Penduduk'
        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='Penduduk yang %s' % (self.plugin_name.lower()),
                   keywords={'impact_summary': impact_summary,
                             'impact_table': impact_table,
                             'map_title': map_title},
                   style_info=style_info)
        return R
コード例 #11
0
    def run(layers):
        """Risk plugin for tephra impact
        """

        # Extract data
        H = get_hazard_layer(layers)  # Ash load
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        H = H.interpolate(E, attribute_name="load")

        # Calculate building damage
        count3 = 0
        count2 = 0
        count1 = 0
        count0 = 0
        result = []
        for i in range(len(E)):

            # -------------------
            # Extract parameters
            # -------------------
            load = H.get_data("load", i)

            # ------------------------
            # Compute damage level
            # ------------------------

            # FIXME: The thresholds have been greatly reduced
            # for the purpose of demonstration. Any real analyis
            # should bring them back to 0, 90, 150, 300
            if 0.01 <= load < 0.5:
                # Loss of crops and livestock
                impact = 0
                count0 += 1
            elif 0.5 <= load < 2.0:
                # Cosmetic damage
                impact = 1
                count1 += 1
            elif 2.0 <= load < 10.0:
                # Partial building collapse
                impact = 2
                count2 += 1
            elif load >= 10.0:
                # Complete building collapse
                impact = 3
                count3 += 1
            else:
                impact = 0
                count0 += 1

            result.append({"DAMAGE": impact, "ASHLOAD": load})

        # Create report
        impact_summary = (
            '<font size="3"> <table border="0" width="320px">'
            "   <tr><th><b>%s</b></th><th><b>%s</b></th></th>"
            "   <tr></tr>"
            "   <tr><td>%s&#58;</td><td>%i</td></tr>"
            "   <tr><td>%s&#58;</td><td>%i</td></tr>"
            "   <tr><td>%s&#58;</td><td>%i</td></tr>"
            "   <tr><td>%s&#58;</td><td>%i</td></tr>"
            "</table></font>"
            % (
                "Beban abu",
                "Gedung dampak",
                "< 0.5 kg/m2",
                count0,
                "0.5 - 2 kg/m2",
                count1,
                "2 - 10 kg/m2",
                count2,
                "> 10 kg/m2",
                count3,
            )
        )
        #'</table>' %
        # ('Beban abu', 'Gedung dampak',
        # 'Gangguan (< 90 kg/m2)', count0,
        # 'Kerusakan kosmetik (90 - 150 kg/m2', count1,
        # 'parsial runtuhnya (150 - 300 kg/m2', count2,
        # 'runtuhnya lengkap (> 300 kg/m2', count3))

        V = Vector(
            data=result,
            projection=E.get_projection(),
            geometry=E.get_geometry(),
            name="Estimated ashload damage",
            keywords={"impact_summary": impact_summary},
        )
        return V
コード例 #12
0
    def run(self, layers):
        """Risk plugin for tsunami population
        """

        threshold = 1.0  # Flood threshold [m]

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        E = get_exposure_layer(layers)  # Building locations

        # FIXME (Ole): interpolate does not carry original name through,
        # so get_name gives "Vector Layer" :-)

        # Interpolate hazard level to building locations
        I = H.interpolate(E)

        # Extract relevant numerical data
        attributes = I.get_data()
        N = len(I)

        # List attributes to carry forward to result layer
        attribute_names = E.get_attribute_names()

        # Calculate population impact
        count = 0
        building_impact = []
        for i in range(N):
            if H.is_raster:
                # Get the interpolated depth
                x = float(attributes[i].values()[0])
                x = x > threshold
            elif H.is_vector:
                # Use interpolated polygon attribute
                x = attributes[i]['Affected']

            # Tag and count
            if x is True:
                affected = 1
                count += 1
            else:
                affected = 0

            # Collect depth and calculated damage
            result_dict = {self.target_field: x}

            # Carry all original attributes forward
            # FIXME (Ole): Make this part of the interpolation (see issue #101)
            for key in attribute_names:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            building_impact.append(result_dict)

        # Create report
        Hname = H.get_name()
        Ename = E.get_name()
        caption = _('<b>In case of "%s" the estimated impact to "%s" '
                   'the possibility of &#58;</b><br><br><p>' % (Hname,
                                                                Ename))
        caption += ('<table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                    '   <tr></tr>'
                    '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                    '</table>' % (_('Building'), _('Number of'),
                                  _('All'), N,
                                  _('Closed'), count,
                                  _('Opened'), N - count))

        caption += '<br>'  # Blank separation row
        caption += '<b>' + _('Assumption') + '&#58;</b><br>'
        caption += _('Buildings that will need to closed when flooding'
                   'more than %.1f m' % threshold)

        # Create style
        style_classes = [dict(label=_('Opened'), min=0, max=0,
                              colour='#1EFC7C', transparency=0),
                         dict(label=_('Closed'), min=1, max=1,
                              colour='#F31A1C', transparency=0)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        V = Vector(data=building_impact,
                   projection=E.get_projection(),
                   geometry=E.get_geometry(),
                   name=_('Estimated buildings affected'),
                   keywords={'caption': caption},
                   style_info=style_info)
        return V
コード例 #13
0
    def run(self, layers):
        """Risk plugin for tsunami population
        """

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        Hi = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = Hi.get_geometry()
        depth = Hi.get_data()
        N = len(depth)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate building impact according to guidelines
        count3 = 0
        count1 = 0
        count0 = 0
        population_impact = []
        for i in range(N):

            if H.is_raster:
                # Get depth
                dep = float(depth[i].values()[0])

                # Classify buildings according to depth
                if dep >= 3:
                    affected = 3  # FIXME: Colour upper bound is 100 but
                    count3 += 1          # does not catch affected == 100
                elif 1 <= dep < 3:
                    affected = 2
                    count1 += 1
                else:
                    affected = 1
                    count0 += 1
            elif H.is_vector:
                dep = 0  # Just put something here
                cat = depth[i]['Affected']
                if cat is True:
                    affected = 3
                    count3 += 1
                else:
                    affected = 1
                    count0 += 1

            # Collect depth and calculated damage
            result_dict = {self.target_field: affected,
                           'DEPTH': dep}

            # Carry all original attributes forward
            # FIXME: This should be done in interpolation. Check.
            #for key in attributes:
            #    result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            population_impact.append(result_dict)

        # Create report
        if H.is_raster:
            impact_summary = ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % ('ketinggian tsunami', 'Jumlah gedung',
                                     '< 1 m', count0,
                                     '1 - 3 m', count1,
                                     '> 3 m', count3))
        else:
            impact_summary = ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % ('Terdampak oleh tsunami', 'Jumlah gedung',
                                     'Terdampak', count3,
                                     'Tidak terdampak', count0,
                                     'Semua', N))

        # Create style
        style_classes = [dict(label=_('OK'), min=0, max=2,
                              colour='#1EFC7C', transparency=0),
                         dict(label=_('Flooded'), min=2, max=4,
                              colour='#F31A1C', transparency=0)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        if Hi.is_line_data:
            name = 'Roads flooded'
        elif Hi.is_point_data:
            name = 'Buildings flooded'

        V = Vector(data=population_impact,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   keywords={'impact_summary': impact_summary},
                   geometry_type=Hi.geometry_type,
                   name=name,
                   style_info=style_info)
        return V
コード例 #14
0
    def run(self, layers):
        """Separate exposed elements by depth [m]:
        < 1     Rendah
        1 - 3   Sedang
        > 3     Tinggi
        """

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        I = H.interpolate(E, attribute_name='depth')

        # Extract relevant numerical data
        attributes = I.get_data()
        N = len(I)

        # List attributes to carry forward to result layer
        attribute_names = E.get_attribute_names()

        # Calculate building impact
        rendah = 0
        sedang = 0
        tinggi = 0
        building_impact = []
        for i in range(N):

            # Get the interpolated depth
            x = float(attributes[i]['depth'])

            # Assign impact level (nilai) depending on depth and count
            if x < 1:
                nilai = 1
                rendah += 1
            elif 1 <= x < 3:
                nilai = 2
                sedang += 1
            else:
                nilai = 3
                tinggi += 1

            # Record depth and impact level for this feature
            building_impact.append({'depth': x,
                                    self.target_field: nilai})

        # Create summary report
        Hname = H.get_name()
        Ename = E.get_name()
        table = ('<b>In case of "%s" the estimated impact to "%s" '
                  'the possibility of &#58;</b><br><br><p>' % (Hname,
                                                               Ename))
        table += ('<table border="0" width="320px">'
                  '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                  '   <tr></tr>'
                  '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                  '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                  '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                  '   <tr><td>%s &#58;</td><td>%i</td></tr>'
                  '</table>' % (('Ketinggian Banjir'), ('Jumlah gedung'),
                                ('All'), N,
                                ('< 1 m'), rendah,
                                ('1 - 3 m'), sedang,
                                ('> 3 m'), tinggi))

        table += '<br>'  # Blank separation row
        table += '<b>' + ('Based on BNPB Perka 2 - 2012') + '</b><br>'

        # Create style
        style_classes = [dict(label=('< 1 m'), min=1, max=1,
                              colour='#00FF00',  # Green
                              transparency=0, size=1),
                         dict(label=('1 - 3 m'), min=2, max=2,
                              colour='#FFFF00',  # Yellow
                              transparency=0, size=1),
                         dict(label=('> 3 m'), min=3, max=3,
                              colour='#FF0000',  # Red
                              transparency=0, size=1)]

        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        V = Vector(data=building_impact,
                   projection=E.get_projection(),
                   geometry=E.get_geometry(),
                   name=('Estimated buildings affected'),
                   keywords={'impact_summary': table},
                   style_info=style_info)
        return V
コード例 #15
0
    def run(self, layers,
            x=0.62275231, y=8.03314466, zeta=2.15):
        """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

        """

        # Define percentages of people being displaced at each mmi level
        displacement_rate = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0,
                             7: 0.1, 8: 0.5, 9: 0.75, 10: 1.0}

        # 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()  # 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 Exception, e:
                msg = 'mmi = %i, I = %s, Error msg: %s' % (mmi, str(I), str(e))
                fid = open('C:\\error_message.txt', 'wb')
                fid.write(msg)
                fid.close()

            # Sum up numbers for map
            R += F   # Fatalities
            #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)
コード例 #16
0
    def run(self, layers):
        """Risk plugin for earthquake fatalities

        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
        """

        # Depth above which people are regarded affected [m]
        threshold = 1.5  # Threshold [m]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)  # Flood inundation [m]
        population = get_exposure_layer(layers)

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > threshold
        if population.get_resolution(native=True, isotropic=True) < 0.0005:
            # Keep this for backwards compatibility just a little while
            # This uses the original custom population set and
            # serves as a reference

            P = population.get_data(nan=0.0)  # Population density
            pixel_area = 2500
            I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
        else:
            # This is the new generic way of scaling (issue #168 and #172)
            P = population.get_data(nan=0.0, scaling=True)
            I = numpy.where(D > threshold, P, 0)

        # Generate text with result for this study
        total = str(int(numpy.sum(P) / 1000))
        count = str(int(numpy.sum(I) / 1000))

        # Create report
        iname = inundation.get_name()
        pname = population.get_name()
        impact_summary = ('<b>Apabila terjadi "%s" perkiraan dampak '
                          'terhadap "%s" kemungkinan yang terjadi&#58;'
                          '</b><br><br><p>' % (iname, pname))
        impact_summary += ('<table border="0" width="320px">')
        impact_summary += ('   <tr><td><b>%s&#58;</b></td>'
                    '<td align="right"><b>%s</b></td></tr>'
                    % ('Meninggal (x 1000)', count))

        impact_summary += '</table>'

        impact_summary += '<br>'  # Blank separation row
        impact_summary += '<b>Catatan&#58;</b><br>'
        impact_summary += '- Jumlah penduduk Jakarta %s<br>' % total
        impact_summary += '- Jumlah dalam ribuan<br>'
        impact_summary += ('- Penduduk dianggap meninggal ketika '
                           'banjir lebih dari %.1f m.' % threshold)

        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='Penduduk yang %s' % (self.plugin_name.lower()),
                   keywords={'impact_summary': impact_summary},
                   style_info=style_info)

        return R
コード例 #17
0
    def run(self, layers):
        """Risk plugin for earthquake fatalities

        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
        """

        # Depth above which people are regarded affected [m]
        threshold = 1.0  # Threshold [m]

        # Identify hazard and exposure layers
        inundation = get_hazard_layer(layers)  # Flood inundation [m]

        # Get population and gender ratio
        population = gender_ratio = None
        for layer in get_exposure_layers(layers):
            keywords = layer.get_keywords()

            if 'datatype' not in keywords:
                population = layer
            else:
                datatype = keywords['datatype']

                if 'ratio' not in datatype:
                    population = layer
                else:
                    #if 'female' in datatype and 'ratio' in datatype:
                    gender_ratio_unit = keywords['unit']

                    msg = ('Unit for gender ratio must be either '
                           '"percent" or "ratio"')
                    assert gender_ratio_unit in ['percent', 'ratio'], msg

                    gender_ratio = layer

        msg = 'No population layer was found in: %s' % str(layers)
        assert population is not None, msg

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > threshold
        if population.get_resolution(native=True, isotropic=True) < 0.0005:
            # Keep this for backwards compatibility just a little while
            # This uses the original custom population set and
            # serves as a reference

            P = population.get_data(nan=0.0)  # Population density
            pixel_area = 2500
            I = numpy.where(D > threshold, P, 0) / 100000.0 * pixel_area
        else:
            # This is the new generic way of scaling (issue #168 and #172)
            P = population.get_data(nan=0.0, scaling=True)
            I = numpy.where(D > threshold, P, 0)

        if gender_ratio is not None:
            # Extract gender ratio at each pixel (as ratio)
            G = gender_ratio.get_data(nan=0.0)
            if gender_ratio_unit == 'percent':
                G /= 100

            # Calculate breakdown
            P_female = P * G
            P_male = P - P_female

            I_female = I * G
            I_male = I - I_female

        # Generate text with result for this study
        total = str(int(numpy.sum(P) / 1000))
        count = str(int(numpy.sum(I) / 1000))

        # Create report
        iname = inundation.get_name()
        pname = population.get_name()
        caption = ('<b>Apabila terjadi "%s" perkiraan dampak terhadap "%s" '
                   'kemungkinan yang terjadi&#58;</b><br><br><p>' % (iname,
                                                                     pname))
        caption += ('<table border="0" width="320px">')
                   #'   <tr><td><b>%s&#58;</b></td>'
                   #'<td align="right"><b>%s</b></td></tr>'
                   #% ('Jumlah Penduduk', total))
        # if gender_ratio is not None:
        #     total_female = str(int(sum(P_female.flat) / 1000))
        #     total_male = str(int(sum(P_male.flat) / 1000))

        #     caption += ('        <tr><td>%s&#58;</td>'
        #                 '<td align="right">%s</td></tr>'
        #                 % (' - Wanita', total_female))
        #     caption += ('        <tr><td>%s&#58;</td>'
        #                 '<td align="right">%s</td></tr>'
        #                 % (' - Pria', total_male))
        #    caption += '<tr><td>&nbsp;</td></tr>'  # Blank separation row

        caption += ('   <tr><td><b>%s&#58;</b></td>'
                    '<td align="right"><b>%s</b></td></tr>'
                    % ('Perlu Evakuasi (x 1000)', count))

        if gender_ratio is not None:
            affected_female = str(int(sum(I_female.flat) / 1000))
            affected_male = str(int(sum(I_male.flat) / 1000))

            caption += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Wanita', affected_female))
            caption += ('        <tr><td>%s&#58;</td>'
                        '<td align="right">%s</td></tr>'
                        % (' - Pria', affected_male))

        caption += '</table>'

        caption += '<br>'  # Blank separation row
        caption += '<b>Catatan&#58;</b><br>'
        caption += '- Jumlah penduduk Jakarta %s<br>' % total
        caption += '- Jumlah dalam ribuan<br>'
        caption += ('- Penduduk dianggap perlu dievakuasi ketika '
                    'banjir lebih dari %.1f m.' % threshold)

        # Create raster object and return
        R = Raster(I,
                   projection=inundation.get_projection(),
                   geotransform=inundation.get_geotransform(),
                   name='Penduduk yang %s' % (self.plugin_name.lower()),
                   keywords={'caption': caption})
        return R
コード例 #18
0
    def run(self, layers):
        """Risk plugin for Padang building survey
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        datatype = E.get_keywords()['datatype']
        if datatype.lower() == 'osm':
            # Map from OSM attributes to the padang building classes
            E = osm2padang(E)
            vclass_tag = 'VCLASS'
        elif datatype.lower() == 'sigab':
            E = sigab2padang(E)
            vclass_tag = 'VCLASS'
        else:
            vclass_tag = 'TestBLDGCl'

        # Interpolate hazard level to building locations
        H = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        shaking = H.get_data()
        N = len(shaking)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate building damage
        count50 = 0
        count25 = 0
        count10 = 0
        count0 = 0
        building_damage = []
        for i in range(N):
            mmi = float(shaking[i].values()[0])

            building_class = E.get_data(vclass_tag, i)

            building_type = str(int(building_class))
            damage_params = damage_curves[building_type]
            beta = damage_params['beta']
            median = damage_params['median']
            percent_damage = cdf(mmi, mu=median, sigma=beta) * 100

            # Collect shake level and calculated damage
            result_dict = {self.target_field: percent_damage,
                           'MMI': mmi}

            # Carry all orginal attributes forward
            for key in attributes:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            building_damage.append(result_dict)

            # Calculate statistics
            if percent_damage < 10:
                count0 += 1

            if 10 <= percent_damage < 25:
                count10 += 1

            if 25 <= percent_damage < 50:
                count25 += 1

            if 50 <= percent_damage:
                count50 += 1

        # Create report
        caption = ('<font size="3"> <table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                    '   <tr></tr>'
                    '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (<10%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (10-25%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (25-50%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (50-100%%)&#58;</td><td>%i</td></tr>'
                    '</table></font>' % (_('Buildings'), _('Total'),
                                  _('All'), N,
                                  _('No damage'), count0,
                                  _('Low damage'), count10,
                                  _('Medium damage'), count25,
                                  _('High damage'), count50))

        # Create vector layer and return
        V = Vector(data=building_damage,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated pct damage',
                   keywords={'caption': caption})
        return V
コード例 #19
0
    def run(self, layers):
        """Risk plugin for Padang building survey
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        datatype = E.get_keywords()['datatype']
        vclass_tag = 'VCLASS'
        if datatype.lower() == 'osm':
            # Map from OSM attributes to the padang building classes
            Emap = osm2padang(E)
        elif datatype.lower() == 'sigab':
            Emap = sigab2padang(E)
        elif datatype.lower() == 'padang':
            Emap = padang2itb(E)
        else:
            Emap = E

        # Interpolate hazard level to building locations
        Hi = H.interpolate(Emap, attribute_name='MMI')

        # Extract relevant numerical data
        coordinates = Emap.get_geometry()
        shaking = Hi.get_data()
        N = len(shaking)

        # List attributes to carry forward to result layer
        attributes = Emap.get_attribute_names()

        # Calculate building damage
        count50 = 0
        count25 = 0
        count10 = 0
        count0 = 0
        building_damage = []
        for i in range(N):
            mmi = float(shaking[i]['MMI'])

            building_class = Emap.get_data(vclass_tag, i)

            building_type = str(int(building_class))
            damage_params = damage_curves[building_type]
            beta = damage_params['beta']
            median = damage_params['median']
            percent_damage = lognormal_cdf(mmi,
                                           median=median,
                                           sigma=beta) * 100

            # Collect shake level and calculated damage
            result_dict = {self.target_field: percent_damage,
                           'MMI': mmi}

            # Carry all orginal attributes forward
            for key in attributes:
                result_dict[key] = Emap.get_data(key, i)

            # Record result for this feature
            building_damage.append(result_dict)

            # Debugging
            #if percent_damage > 0.01:
            #    print mmi, percent_damage

            # Calculate statistics
            if percent_damage < 10:
                count0 += 1

            if 10 <= percent_damage < 33:
                count10 += 1

            if 33 <= percent_damage < 66:
                count25 += 1

            if 66 <= percent_damage:
                count50 += 1

        # Create report
        Hname = H.get_name()
        Ename = E.get_name()
        impact_summary = ('<b>In case of "%s" the estimated impact to '
                           '"%s" '
                           'is&#58;</b><br><br><p>' % (Hname, Ename))
        impact_summary += ('<table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                    '   <tr></tr>'
                    '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (<10%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (10-33%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (33-66%%)&#58;</td><td>%i</td></tr>'
                    '   <tr><td>%s (66-100%%)&#58;</td><td>%i</td></tr>'
                    '</table></font>' % (_('Buildings'), _('Total'),
                                  _('All'), N,
                                  _('No damage'), count0,
                                  _('Low damage'), count10,
                                  _('Medium damage'), count25,
                                  _('High damage'), count50))
        impact_summary += '<br>'  # Blank separation row
        impact_summary += '<b>' + _('Assumption') + '&#58;</b><br>'
        # This is the proper text:
        #_('Levels of impact are defined by post 2009 '
        #  'Padang earthquake survey conducted by Geoscience '
        #  'Australia and Institute of Teknologi Bandung.'))
        #_('Unreinforced masonry is assumed where no '
        #  'structural information is available.'))
        impact_summary += _('Levels of impact are defined by post 2009 '
                            'Padang earthquake survey conducted by Geoscience '
                            'Australia and Institute of Teknologi Bandung.')
        impact_summary += _('Unreinforced masonry is assumed where no '
                            'structural information is available.')
        # Create style
        style_classes = [dict(label=_('No damage'), min=0, max=10,
                              colour='#00ff00', transparency=1),
                         dict(label=_('Low damage'), min=10, max=33,
                              colour='#ffff00', transparency=1),
                         dict(label=_('Medium damage'), min=33, max=66,
                              colour='#ffaa00', transparency=1),
                         dict(label=_('High damage'), min=66, max=100,
                              colour='#ff0000', transparency=1)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        V = Vector(data=building_damage,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated pct damage',
                   keywords={'impact_summary': impact_summary},
                   style_info=style_info)
        return V
コード例 #20
0
    def run(layers):
        """Calculate population exposed to different levels of ground shaking

        Input
          layers: List of layers expected to contain
              H: Raster layer of MMI ground shaking
              P: Raster layer of population density
        """

        # Identify input layers
        intensity = get_hazard_layer(layers)
        population = get_exposure_layer(layers)

        # Extract data
        H = intensity.get_data(nan=0)
        P = population.get_data(nan=0)

        # Calculate exposure to MMI impact
        mmi_classes = range(1, 11)  # MMI classes considered (1-10)

        # Form result as keyword strings
        mmi_str = str(mmi_classes)[1:-1]  # Get rid of []
        count_str = ''

        for i in mmi_classes:
            # Identify cells where MMI is in class i
            mask = (H >= i - 0.5) * (H < i + 0.5)

            # Count population affected by this shake level
            count = round(numpy.nansum(P[mask]))
            if numpy.isnan(count):
                count = 0

            # Update keyword string
            count_str += '%i ' % count

        # Calculate fatality map (FIXME (Ole): Need to replaced by USGS model)
        a = 0.97429
        b = 11.037
        F = 10 ** (a * H - b) * P

        # Generate text with result for this study
        count = numpy.nansum(F.flat)
        total = numpy.nansum(P.flat)

        # Create report
        caption = ('<table border="0" width="320px">'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '</table>' % ('Jumlah Penduduk', int(total),
                                 'Perkiraan Orang Meninggal', int(count)))

        # Create new layer and return
        R = Raster(F,
                   projection=population.get_projection(),
                   geotransform=population.get_geotransform(),
                   name='Estimated fatalities',
                   keywords={'caption': caption,
                             'mmi-classes': mmi_str,
                             'affected-population': count_str})
        return R
    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
        """

        # Depth above which people are regarded affected [m]
        threshold = 1.0  # Threshold [m]

        # 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)

        # Extract data as numeric arrays
        D = inundation.get_data(nan=0.0)  # Depth

        # Calculate impact as population exposed to depths > threshold
        P = population.get_data(nan=0.0, scaling=True)
        I = numpy.where(D > threshold, P, 0)
        M = numpy.where(D > 0.5, P, 0)
        L = numpy.where(D > 0.3, P, 0)

        # Count totals
        total = int(numpy.sum(P))
        evacuated = int(numpy.sum(I))
        medium = int(numpy.sum(M)) - int(numpy.sum(I))
        low = int(numpy.sum(L)) - int(numpy.sum(M))

        # Don't show digits less than a 1000
        if total > 1000:
            total = total // 1000 * 1000
        if evacuated > 1000:
            evacuated = evacuated // 1000 * 1000
        if medium > 1000:
            medium = medium // 1000 * 1000
        if low > 1000:
            low = low // 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([_('People needing evacuation'),
                                '%i' % evacuated],
                               header=True),
                      TableRow(_('Map shows population density needing '
                                 'evacuation'))]
                      #,
##                      TableRow([_('People in 50cm to 1m of water '),
##                                '%i' % medium],
##                               header=True),
##                      TableRow([_('People in 30cm to 50cm of water'),
##                                '%i' % low],
##                               header=True)]
##                      TableRow([_('Needs per week'), _('Total')],
##                               header=True),
##                      [_('Rice [kg]'), int(rice)],
##                      [_('Drinking Water [l]'), int(drinking_water)],
##                      [_('Clean Water [l]'), int(water)],
##                      [_('Family Kits'), int(family_kits)],
##                      [_('Toilets'), int(toilets)]]
        impact_table = Table(table_body).toNewlineFreeString()

        # Extend impact report for on-screen display
        table_body.extend([TableRow(_('Notes:'), header=True),
                           _('Total population: %i') % total,
                           _('People need evacuation if flood levels '
                             'exceed %(eps)i m') % {'eps': threshold},
                           _('People in 50cm to 1m of water: %i') % medium,
                           _('People in 30cm to 50cm of water: %i') % low])
##                           _('Minimum needs are defined in BNPB '
##                             'regulation 7/2008')])
        impact_summary = Table(table_body).toNewlineFreeString()
        map_title = _('People in need of evacuation')
        style_info['legend_title'] = _('Population Density')

        # 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
コード例 #22
0
    def run(self, layers, x=0.62275231, y=8.03314466, zeta=2.15):
        """Gender specific earthquake impact model

        Input
          layers: List of layers expected to contain
              H: Raster layer of MMI ground shaking
              P: Raster layer of population density

        """

        # Define percentages of people being displaced at each mmi level
        displacement_rate = {1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0.1, 8: 0.5, 9: 0.75, 10: 1.0}

        # 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()  # 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_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

            # Sum up fatalities to create map
            R += F

            # 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_fatalities[mmi] = numpy.nansum(F.flat)

        # Set resulting layer to zero when less than a threshold. This is to
        # achieve transparency (see issue #126).
        R[R < 1] = numpy.nan

        # Total statistics
        total = numpy.nansum(P.flat)

        # Compute number of fatalities
        fatalities = numpy.nansum(number_of_fatalities.values())

        # Compute number of people displaced due to building collapse
        displaced = 0
        for mmi in mmi_range:
            displaced += displacement_rate[mmi] * number_of_exposed[mmi]
        displaced_women = displaced * 0.52  # Could be made province dependent
        displaced_pregnant_women = displaced_women * 0.01387  # CHECK

        # Generate impact report
        table_body = [question]

        # Add total fatality estimate
        s = str(int(fatalities)).rjust(10)
        table_body.append(TableRow([_("Number of fatalities"), s], header=True))

        # Add total estimate of people displaced
        s = str(int(displaced)).rjust(10)
        table_body.append(TableRow([_("Number of people displaced"), s], header=True))
        s = str(int(displaced_women)).rjust(10)
        table_body.append(TableRow([_("Number of women displaced"), s], header=True))
        s = str(int(displaced_pregnant_women)).rjust(10)
        table_body.append(TableRow([_("Number of pregnant women displaced"), s], header=True))

        table_body.append(TableRow(_("Action Checklist:"), header=True))
        table_body.append(_("Are enough shelters available for %i women?") % displaced_women)
        table_body.append(
            _("Are enough facilities available to assist %i " "pregnant women?") % displaced_pregnant_women
        )

        table_body.append(TableRow(_("Notes:"), header=True))

        table_body.append(_("Fatality model is from " "Institute of Teknologi Bandung 2012."))

        impact_summary = Table(table_body).toNewlineFreeString()
        impact_table = impact_summary
        map_title = _("Earthquake impact to population")

        # 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=_("Estimated fatalities"),
            style_info=style_info,
        )

        # Maybe return a shape file with contours instead
        return L
コード例 #23
0
    def run(layers,
            teta=14.05, beta=0.17, zeta=2.15):
        """Risk plugin for earthquake fatalities

        Input
          H: Numerical array of hazard data
          E: Numerical array of exposure data

        Algorithm and coefficients are from:

        An Empirical Model for Global Earthquake Fatality Estimation.
        Kishor Jaiswal and David Wald.
        Earthquake Spectra, Volume 26, No. 4, pages 1017-1037, November 2010.


        teta=14.05, beta=0.17, zeta=2.1  # Coefficients for Indonesia.


        """

        # Identify input layers
        intensity = get_hazard_layer(layers)
        population = get_exposure_layer(layers)

        print intensity.get_resolution()
        print population.get_resolution()

        # Extract data
        H = intensity.get_data(nan=0)   # Ground Shaking
        P = population.get_data(nan=0)  # Population Density

        import cPickle
        name = intensity.get_name()
        print name
        fid = open('/home/nielso/population_%s.pck' % name, 'wb')
        cPickle.dump(P, fid)
        fid.close()

        fid = open('/home/nielso/intensity_%s.pck' % name, 'wb')
        cPickle.dump(H, fid)
        fid.close()

        # Calculate population affected by each MMI level
        mmi_range = range(2, 10)
        number_of_people_affected = {}
        for mmi in mmi_range:
            mask = numpy.logical_and(mmi - 0.5 < H,
                                     H <= mmi + 0.5)
            I = numpy.where(mask, P, 0)

            # Generate text with result for this study
            number_of_people_affected[mmi] = numpy.nansum(I.flat)

        # Calculate impact according to equation (1) in the
        # Kishor and Wald 2010
        logHazard = 1 / beta * numpy.log(H / teta)

        # Convert array to be standard floats expected by cdf
        arrayout = numpy.array([[float(value) for value in row]
                               for row in logHazard])
        F = cdf(arrayout * P)

        # Stats
        total = numpy.nansum(P.flat)
        fatalities = numpy.nansum(F)
        print 'Total', total
        print 'Estimated fatalities', fatalities
        print 'Min', numpy.amin(F)
        print 'Max', numpy.amax(F)

        # Generate text with result for this study
        caption = generate_exposure_table(mmi_range,
                                          number_of_people_affected)
        caption += generate_fatality_table(fatalities)

        # Create new layer and return
        R = Raster(F,
                   projection=population.get_projection(),
                   geotransform=population.get_geotransform(),
                   keywords={'caption': caption},
                   name='Estimated fatalities')
        return R
コード例 #24
0
    def run(self, layers,
            a=0.97429, b=11.037):
        """Risk plugin for earthquake fatalities

        Input
          layers: List of layers expected to contain
                  H: Raster layer of MMI ground shaking
                  E: Polygon population data
          a: Parameter for Allen impact function
          b: Parameter for Allen impact function
        """

        # Identify input layers
        H = get_hazard_layer(layers)   # Intensity
        E = get_exposure_layer(layers)  # Exposure - population counts

        # Interpolate hazard level to building locations
        H = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()  # Stay with polygons
        shaking = H.get_data()
        N = len(shaking)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate fatilities
        count = 0
        total = 0

        result_feature_set = []
        for i in range(N):
            mmi = float(shaking[i].values()[0])
            if mmi < 0.0:
                # FIXME: Hack until interpolation is fixed
                mmi = 0.0

            population_count = E.get_data('Jumlah_Pen', i)

            # Calculate impact
            F = 10 ** (a * mmi - b) * population_count

            # Collect shake level and calculated damage
            result_dict = {self.target_field: F,
                           'MMI': mmi}

            # Carry all orginal attributes forward
            for key in attributes:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            result_feature_set.append(result_dict)

            # Calculate statistics
            if not numpy.isnan(F):
                count += F
            total += population_count

        # Create report
        impact_summary = ('<table border="0" width="320px">'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '</table>' % ('Jumlah Penduduk', int(total),
                                 'Perkiraan Orang Meninggal', int(count)))

        # Create vector layer and return
        V = Vector(data=result_feature_set,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated fatalities',
                   keywords={'impact_summary': impact_summary})

        return V
コード例 #25
0
    def run(layers):
        """Risk plugin for tsunami building damage
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        H = H.interpolate(E, attribute_name='depth')

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        inundation = H.get_data()

        # Calculate
        N = len(H)
        impact = []
        for i in range(N):

            #-------------------
            # Extract parameters
            #-------------------
            depth = float(inundation[i]['depth'])
            shore_distance = E.get_data('SHORE_DIST', i)

            # FIXME: Get rid of the type casting when
            #        issue #66 is done
            number_of_people_in_building = int(E.get_data('NEXIS_PEOP', i))
            wall_type = E.get_data('WALL_TYPE', i)
            contents_value = E.get_data('CONT_VALUE', i)
            structure_value = E.get_data('STR_VALUE', i)

            #------------------------
            # Compute people affected
            #------------------------
            if 0.01 < depth < 1.0:
                people_affected = number_of_people_in_building
            else:
                people_affected = 0

            if depth >= 1.0:
                people_severely_affected = number_of_people_in_building
            else:
                people_severely_affected = 0

            #----------------------------------------
            # Compute impact on buldings and contents
            #----------------------------------------
            depth_floor = depth - 0.3  # Adjust for floor height

            if depth_floor >= 0.0:
                buildings_inundated = 1
            else:
                buildings_inundated = 0

            if depth_floor < 0.0:
                structural_damage = contents_damage = 0.0
            else:
                # Water is deep enough to cause damage
                if wall_type in struct_damage_curve:
                    curve = struct_damage_curve[wall_type]
                else:
                    # Establish default for unknown wall type
                    curve = struct_damage_curve['Brick veneer']

                structural_damage = curve(depth_floor)
                contents_damage = contents_damage_curve(depth_floor)

            #---------------
            # Compute losses
            #---------------
            structural_loss = structural_damage * structure_value
            contents_loss = contents_damage * contents_value

            #-------
            # Return
            #-------
            impact.append({'NEXIS_PEOP': number_of_people_in_building,
                           'PEOPLE_AFFECTED': people_affected,
                           'PEOPLE_SEV_AFFECTED': people_severely_affected,
                           'STRUCT_INUNDATED': buildings_inundated,
                           'STRUCT_DAMAGE_fraction': structural_damage,
                           'CONTENTS_DAMAGE_fraction': contents_damage,
                           'STRUCT_LOSS_AUD': structural_loss,
                           'CONTENTS_LOSS_AUD': contents_loss,
                           'DEPTH': depth})

        # FIXME (Ole): Need helper to generate new layer using
        #              correct spatial reference
        #              (i.e. sensibly wrap the following lines)
        V = Vector(data=impact, projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated tsunami impact')
        return V
コード例 #26
0
    def run(self, layers):
        """Impact plugin for hazard impact
        """

        # Extract data
        H = get_hazard_layer(layers)    # Value
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        H = H.interpolate(E, attribute_name='hazard_level')

        # 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_level'])

            # 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

            # Collect depth and calculated damage
            result_dict = {self.target_field: affected,
                           'CATEGORY': val}

            # Record result for this feature
            building_impact.append(result_dict)

        # Create report
        #FIXME: makes the output format the same as all other results

        impact_summary = ('<table border="0" width="320px">'
                   '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                   '   <tr></tr>'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                   '</table>' % (_('Category'), _('Affected'),
                                 _('Low'), count0,
                                 _('Medium'), count1,
                                 _('High'), count2))

        # Create style
        style_classes = [dict(label=_('Low'), min=0, max=0,
                              colour='#1EFC7C', transparency=0, size=1),
                         dict(label=_('Medium'), min=1, max=1,
                              colour='#FFA500', transparency=0, size=1),
                         dict(label=_('High'), min=2, max=2,
                              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,
                   keywords={'impact_summary': impact_summary},
                   geometry_type=H.geometry_type,
                   name=name,
                   style_info=style_info)
        return V
コード例 #27
0
    def run(self, layers):
        """Risk plugin for tsunami population
        """

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        R = get_exposure_layer(layers)  # Building locations

        # Make the delta 10 times the size of the resolution.
        delta = abs(H.get_geotransform()[1]) * 10
        min_value, max_value = H.get_extrema()

        E = convert_line_to_points(R, delta)

        # Interpolate hazard level to building locations
        H = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        depth = H.get_data()
        N = len(depth)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        #print attributes
        #print 'Number of population points', N

        # Calculate population impact
        road_impact = []
        num_classes = 10
        classes = range(num_classes)
        difference = (max_value - min_value) / num_classes

        for i in range(N):
            dep = float(depth[i].values()[0])
            affected = classes[0]
            for level in classes:
                normalized_depth = dep - min_value
                level_value = level * difference
                if normalized_depth > level_value:
                    affected = level

            # Collect depth and calculated damage
            result_dict = {'AFFECTED': affected,
                           'DEPTH': dep}

            # Carry all original attributes forward
            for key in attributes:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            road_impact.append(result_dict)

        # Create report
        impact_summary = ('')

        # Create vector layer and return
        V = Vector(data=road_impact,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimated roads affected',
                   keywords={'impact_summary': impact_summary})
        return V
コード例 #28
0
    def run(self, layers):
        """Risk plugin for tsunami population
        """

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        Hi = H.interpolate(E, attribute_name='depth')

        # Extract relevant numerical data
        coordinates = Hi.get_geometry()
        depth = Hi.get_data()
        N = len(depth)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate building impact according to guidelines
        count3 = 0
        count1 = 0
        count0 = 0
        population_impact = []
        for i in range(N):

            if H.is_raster:
                # Get depth
                dep = float(depth[i]['depth'])

                # Classify buildings according to depth
                if dep >= 3:
                    affected = 3  # FIXME: Colour upper bound is 100 but
                    count3 += 1          # does not catch affected == 100
                elif 1 <= dep < 3:
                    affected = 2
                    count1 += 1
                else:
                    affected = 1
                    count0 += 1
            elif H.is_vector:
                dep = 0  # Just put something here
                cat = depth[i]['Affected']
                if cat is True:
                    affected = 3
                    count3 += 1
                else:
                    affected = 1
                    count0 += 1

            # Collect depth and calculated damage
            result_dict = {self.target_field: affected,
                           'DEPTH': dep}

            # Carry all original attributes forward
            # FIXME: This should be done in interpolation. Check.
            #for key in attributes:
            #    result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            population_impact.append(result_dict)

        # Create report
        Hname = H.get_name()
        Ename = E.get_name()
        if H.is_raster:
            impact_summary = ('<b>In case of "%s" the estimated impact to '
                           '"%s" '
                           'is&#58;</b><br><br><p>' % (Hname, Ename))
            impact_summary += ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></tr>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % (_('Impact'), _('Number of buildings'),
                                     _('Low'), count0,
                                     _('Medium'), count1,
                                     _('High'), count3))
        else:
            impact_summary = ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></tr>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % ('Terdampak oleh tsunami',
                                     'Jumlah gedung',
                                     'Terdampak', count3,
                                     'Tidak terdampak', count0,
                                     'Semua', N))

        impact_summary += '<br>'  # Blank separation row
        impact_summary += '<b>' + _('Assumption') + '&#58;</b><br>'
        impact_summary += ('Levels of impact are defined by BNPB\'s '
                            '<i>Pengkajian Risiko Bencana</i>')
        impact_summary += ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></tr>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%s&#58;</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%s&#58;</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%s&#58;</td></tr>'
                       '</table>' % (_('Impact'), _('Tsunami height'),
                                     _('Low'), '<1 m',
                                     _('Medium'), '1-3 m',
                                     _('High'), '>3 m'))

        # Create style
        style_classes = [dict(label='< 1 m', min=0, max=1,
                              colour='#1EFC7C', transparency=0, size=1),
                         dict(label='1 - 3 m', min=1, max=2,
                              colour='#FFA500', transparency=0, size=1),
                         dict(label='> 3 m', min=2, max=4,
                              colour='#F31A1C', transparency=0, size=1)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        if Hi.is_line_data:
            name = 'Roads flooded'
        elif Hi.is_point_data:
            name = 'Buildings flooded'

        V = Vector(data=population_impact,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   keywords={'impact_summary': impact_summary},
                   geometry_type=Hi.geometry_type,
                   name=name,
                   style_info=style_info)
        return V
コード例 #29
0
    def run(self, layers):
        """Risk plugin for tsunami population
        """

        # Extract data
        H = get_hazard_layer(layers)    # Depth
        E = get_exposure_layer(layers)  # Building locations

        # Interpolate hazard level to building locations
        Hi = H.interpolate(E)

        # Extract relevant numerical data
        coordinates = E.get_geometry()
        depth = Hi.get_data()
        N = len(depth)

        # List attributes to carry forward to result layer
        attributes = E.get_attribute_names()

        # Calculate building impact according to guidelines
        count3 = 0
        count1 = 0
        count0 = 0
        population_impact = []
        for i in range(N):

            if H.is_raster:
                # Get depth
                dep = float(depth[i].values()[0])

                # Classify buildings according to depth
                if dep >= 3:
                    affected = 3  # FIXME: Colour upper bound is 100 but
                    count3 += 1          # does not catch affected == 100
                elif 1 <= dep < 3:
                    affected = 2
                    count1 += 1
                else:
                    affected = 1
                    count0 += 1
            elif H.is_vector:
                dep = 0  # Just put something here
                cat = depth[i]['Affected']
                if cat is True:
                    affected = 3
                    count3 += 1
                else:
                    affected = 1
                    count0 += 1

            # Collect depth and calculated damage
            result_dict = {self.target_field: affected,
                           'DEPTH': dep}

            # Carry all original attributes forward
            for key in attributes:
                result_dict[key] = E.get_data(key, i)

            # Record result for this feature
            population_impact.append(result_dict)

        # Create report
        if H.is_raster:
            caption = ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % ('ketinggian tsunami', 'Jumlah gedung',
                                     '< 1 m', count0,
                                     '1 - 3 m', count1,
                                     '> 3 m', count3))
        else:
            caption = ('<table border="0" width="320px">'
                       '   <tr><th><b>%s</b></th><th><b>%s</b></th></th>'
                       '   <tr></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '   <tr><td>%s&#58;</td><td>%i</td></tr>'
                       '</table>' % ('Terdampak oleh tsunami', 'Jumlah gedung',
                                     'Terdampak', count3,
                                     'Tidak terdampak', count0,
                                     'Semua', N))

        # Create vector layer and return
        V = Vector(data=population_impact,
                   projection=E.get_projection(),
                   geometry=coordinates,
                   name='Estimate of buildings affected',
                   keywords={'caption': caption})
        return V
コード例 #30
0
    def run(self, layers):
        """Risk plugin for Padang building survey
        """

        # Extract data
        H = get_hazard_layer(layers)    # Ground shaking
        E = get_exposure_layer(layers)  # Building locations

        question = get_question(H.get_name(),
                                E.get_name(),
                                self)

        # Map from different kinds of datasets to Padang vulnerability classes
        datatype = E.get_keywords()['datatype']
        vclass_tag = 'VCLASS'
        if datatype.lower() == 'osm':
            # Map from OSM attributes
            Emap = osm2padang(E)
        elif datatype.lower() == 'sigab':
            # Map from SIGAB attributes
            Emap = sigab2padang(E)
        else:
            Emap = E

        # Interpolate hazard level to building locations
        I = H.interpolate(Emap, attribute_name='MMI')

        # Extract relevant numerical data
        attributes = I.get_data()
        N = len(I)

        # Calculate building damage
        count_high = count_medium = count_low = count_none = 0
        for i in range(N):
            mmi = float(attributes[i]['MMI'])

            building_type = Emap.get_data(vclass_tag, i)
            damage_params = damage_curves[building_type]
            beta = damage_params['beta']
            median = damage_params['median']
            percent_damage = lognormal_cdf(mmi,
                                           median=median,
                                           sigma=beta) * 100

            # Add calculated impact to existing attributes
            attributes[i][self.target_field] = percent_damage

            # Calculate statistics
            if percent_damage < 10:
                count_none += 1

            if 10 <= percent_damage < 33:
                count_low += 1

            if 33 <= percent_damage < 66:
                count_medium += 1

            if 66 <= percent_damage:
                count_high += 1

        # Generate impact report
        table_body = [question,
                      TableRow([_('Buildings'), _('Total')],
                               header=True),
                      TableRow([_('All'), N]),
                      TableRow([_('No damage'), count_none]),
                      TableRow([_('Low damage'), count_low]),
                      TableRow([_('Medium damage'), count_medium]),
                      TableRow([_('High damage'), count_high])]

        table_body.append(TableRow(_('Notes:'), header=True))
        table_body.append(_('Levels of impact are defined by post 2009 '
                            'Padang earthquake survey conducted by Geoscience '
                            'Australia and Institute of Teknologi Bandung.'))
        table_body.append(_('Unreinforced masonry is assumed where no '
                            'structural information is available.'))

        impact_summary = Table(table_body).toNewlineFreeString()
        impact_table = impact_summary
        map_title = _('Earthquake damage to buildings')

        # Create style
        style_classes = [dict(label=_('No damage'), min=0, max=10,
                              colour='#00ff00', transparency=1),
                         dict(label=_('Low damage'), min=10, max=33,
                              colour='#ffff00', transparency=1),
                         dict(label=_('Medium damage'), min=33, max=66,
                              colour='#ffaa00', transparency=1),
                         dict(label=_('High damage'), min=66, max=100,
                              colour='#ff0000', transparency=1)]
        style_info = dict(target_field=self.target_field,
                          style_classes=style_classes)

        # Create vector layer and return
        V = Vector(data=attributes,
                   projection=E.get_projection(),
                   geometry=E.get_geometry(),
                   name='Estimated pct damage',
                   keywords={'impact_summary': impact_summary,
                             'impact_table': impact_table,
                             'map_title': map_title},
                   style_info=style_info)
        return V