DataFusionTools.d_series_parser

DataFusionTools.clustering module

class datafusiontools.d_series_parser.clustering.ClusteringLayers(points: Union[List, None, numpy.array] = None, normalized_points: Union[List, None, numpy.array] = None, clusters: Union[List, None, numpy.array] = None, global_polygon_list: Union[List, None, numpy.array] = None, simplified_polygons: Union[List, None, numpy.array] = None, extracted_value_per_polygon: Union[List, None, numpy.array] = None, extracted_std_per_polygon: Union[List, None, numpy.array] = None)

Bases: datafusiontools._core.base_class.BaseClass

Class that clusters points with a certain value to a polygon list

Parameters

• points – List of initial points

• normalized_points – List of normalized points

• clusters – Clusters of points after the analysis

• global_polygon_list – Created polygon list with a dense points in edges

• extracted_value_per_polygon – Value extracted per polygon

• extracted_std_per_polygon – Std extracted per polygon

cluster_2d_surface_agglomerative_clusterin(points: numpy.ndarray, cluster_variables: List[str], spatial_connectivity_methods: Any, k_candidates: int = 10, run_dash_app: bool = False)

Function that clusters a surface depending on the x, y coordinates and the values given by the user.

Parameters

• points – list of x,y locations along with the value that will facilitate the clustering

• k_candidates – number of k means candidates

• run_dash_app – value that determines if the user want to run a dash application with an evaluation of the

clustering :param cluster_variables: List of variables to cluster with these can be [“x”, “y”, “value”] :param spatial_connectivity_methods: Spatial weights as defined in the libpysal library (for more information https://pysal.org/libpysal/api.html)

:returns list of clustered polygons

cluster_with_schc_method(n_of_clusters: int, encopassing_shape: shapely.geometry.polygon.Polygon, cluster_variables: List[str], spatial_connectivity_methods: Any) → pandas.core.frame.DataFrame

Function that clusters points based on the Spatially Constrained Hierarchical Clustering method. More on that method can be read on https://geographicdata.science/book/notebooks/10_clustering_and_regionalization.html

Parameters

• n_of_clusters – Number of clusters that are required

• encopassing_shape – 2D slice encopassing shape

• cluster_variables – List of variables to cluster with these can be [“x”, “y”, “value”]

• spatial_connectivity_methods – Spatial weights as defined in the libpysal library (for more information https://pysal.org/libpysal/api.html)

clusters: Union[List, None, numpy.array] = None

denormalise_coordinates(x: List, y: List)

Denormalize point data with the min max technique.

Parameters

• x – List of x coordinates

• y – List of y coordinates

Returns

De-normalized tuple of (x, y) coordinates

evaluation_plots(polygons_dataframe)

Function that creates evaluation plots as a Dash app for the 2D clustering method.

Parameters

polygons_dataframe – Dataframe with the clustered polygons

extracted_std_per_polygon: Union[List, None, numpy.array] = None

extracted_value_per_polygon: Union[List, None, numpy.array] = None

get_encompassing_shape() → shapely.geometry.polygon.Polygon

Function that gets the encompassing shape of a geometry.

get_index_of_polygon_list(poly: shapely.geometry.polygon.Polygon, polygon_list: List[shapely.geometry.polygon.Polygon])

Function that returns the index of a polygon if it part of a list

Parameters

• poly – Polygon that the function will search for

• polygon_list – List of polygons that for the index to be found

Returns

the index that the polygon corresponds to

get_value_per_polygon()

Function that calculates values and standard deviation per polygon.

global_polygon_list: Union[List, None, numpy.array] = None

intersect_all_polygon_combinations(polygon_list: List[shapely.geometry.polygon.Polygon]) → List[shapely.geometry.polygon.Polygon]

Function that intersects all every polygon in a list with all other polygons.

Parameters

polygon_list – List of shapely polygons.

normalise_data()

Normalize point data with the min max technique.

normalized_points: Union[List, None, numpy.array] = None

points: Union[List, None, numpy.array] = None

simplified_polygons: Union[List, None, numpy.array] = None

simplify_polygons()

Function that simplifies exterior of polygons using the douglas-peucker algorithm

datafusiontools.d_series_parser.clustering.alpha_shape(points, alpha, only_outer=True) → set

Compute the alpha shape (concave hull) of a set of points.

Parameters

• points – np.array of shape (n,2) points.

• alpha – alpha value.

• only_outer – boolean value to specify if we keep only the outer border or also inner edges.

Returns

set of (i,j) pairs representing edges of the alpha-shape. (i,j) are the indices in the points array.

datafusiontools.d_series_parser.clustering.find_edges_with(i: int, edge_set: Dict) → Tuple[List, List]

Function that finds edges where point i is included.

Parameters

• i – Lookup point index

• edge_set – List of all edges

Returns

First and second edges were the point is included

datafusiontools.d_series_parser.clustering.stitch_boundaries(edges: Dict) → List

Function that stitches edges (list of lines) into consecutive edges.

Parameters

• edges – List

• edges – List

Returns

List of sorted boundaries

DataFusionTools.d_stability_parser module

class datafusiontools.d_series_parser.d_stability_parser.DStabilityModel

Bases: object

Class that creates a D-Stability model.

static create_model(polygon_list: List, filename: str, soil_list: Optional[List] = None)

Function that creates and saves a D-Stability model based on a polygon list.

Parameters

• polygon_list – List of shapely polygons

• filename – Name of D-Stability file that is serialized.

• polygon_list – List of shapely polygons

• filename – Name of D-Stability file that is serialized

Returns

A geolib D-Stability model

static define_default_soil()

Function that adds a default soil layer to the geolib model.