Beispiel #1
0
    def clustering_process(self, records, zoom, pix_x, pix_y):
        """
        Iterate records and create point clusters
        We use a simple method that for every point, that is not within any
        cluster, calculate it's 'catchment' area and add it to the cluster
        If a point is within a cluster 'catchment' area increase point
        count for that cluster and recalculate clusters minimum bbox

        :param records: list of records.
        :type records: list

        :param zoom: zoom level of map
        :type zoom: int

        :param pix_x: pixel x of icon
        :type pix_x: int

        :param pix_y: pixel y of icon
        :type pix_y: int
        """

        cluster_points = []
        for record in records:
            # get x,y of site
            centroid = record.get_centroid()
            if not centroid:
                continue
            x = centroid.x
            y = centroid.y

            # check every point in cluster_points
            for pt in cluster_points:
                if 'minbbox' not in pt:
                    pt['minbbox'] = pt['bbox']

                if within_bbox((x, y), pt['minbbox']):
                    # it's in the cluster 'catchment' area
                    pt['count'] += 1
                    pt['minbbox'] = update_min_bbox((x, y), pt['minbbox'])
                    break

            else:
                # point is not in the catchment area of any cluster
                x_range, y_range = overlapping_area(zoom, pix_x, pix_y, y)
                bbox = (x - x_range * 1.5, y - y_range * 1.5,
                        x + x_range * 1.5, y + y_range * 1.5)
                serializer = LocationSiteClusterSerializer(record)
                new_cluster = {
                    'count': 1,
                    'bbox': bbox,
                    'coordinates': [x, y],
                    'record': serializer.data
                }

                cluster_points.append(new_cluster)

        return cluster_points
Beispiel #2
0
    def clustering_process(
            collection_records,
            site_records,
            zoom,
            pix_x,
            pix_y,
            cluster_points=[],
            sites=[]):
        """
        Iterate records and create point clusters
        We use a simple method that for every point, that is not within any
        cluster, calculate it's 'catchment' area and add it to the cluster
        If a point is within a cluster 'catchment' area increase point
        count for that cluster and recalculate clusters minimum bbox

        :param collection_records: collection records.
        :type collection_records: search query set

        :param site_records: site records.
        :type site_records: search query set

        :param zoom: zoom level of map
        :type zoom: int

        :param pix_x: pixel x of icon
        :type pix_x: int

        :param pix_y: pixel y of icon
        :type pix_y: int
        """
        for collection in GetCollectionAbstract.queryset_gen(
                collection_records):
            # get x,y of site
            try:
                x = collection.site.geometry_point.x
                y = collection.site.geometry_point.y
                location_site = collection.site
                if collection.site.id in sites:
                    continue
                sites.append(collection.site_id)
            except (ValueError, AttributeError):
                continue

            # check every point in cluster_points
            for pt in cluster_points:
                if 'minbbox' not in pt:
                    pt['minbbox'] = pt['bbox']

                if within_bbox((x, y), pt['minbbox']):
                    # it's in the cluster 'catchment' area
                    pt['count'] += 1
                    pt['minbbox'] = update_min_bbox(
                        (x, y), pt['minbbox'])
                    break

            else:
                # point is not in the catchment area of any cluster
                x_range, y_range = overlapping_area(
                    zoom, pix_x, pix_y, y)
                bbox = (
                    x - x_range * 1.5, y - y_range * 1.5,
                    x + x_range * 1.5, y + y_range * 1.5
                )
                serializer = LocationSiteClusterSerializer(
                    location_site)
                new_cluster = {
                    'count': 1,
                    'bbox': bbox,
                    'coordinates': [x, y],
                    'record': serializer.data
                }

                cluster_points.append(new_cluster)

        return cluster_points, sites