data.datasets.electricity_demand_timeseries.cts_buildings.create_synthetic_buildings()

Complexity

Conditions

6

Size

Total Lines	52
Code Lines	29

Duplication

Lines	0
Ratio	0 %

Importance

Changes

0

from geoalchemy2.shape import to_shape
def create_synthetic_buildings(df, points=None, crs="EPSG:3035"):
    """
    Synthetic buildings are generated around points.
    """
    if isinstance(points, str) and points in df.columns:
        points = df[points]
    elif isinstance(points, gpd.GeoSeries):
        pass
    else:
        raise ValueError("Points are of the wrong type")

    # Create building using a square around point
    edge_length = 5
    df["geom_building"] = points.buffer(distance=edge_length / 2, cap_style=3)

    if "geom_point" not in df.columns:
        df["geom_point"] = df["geom_building"].centroid

    # TODO Check CRS
    df = gpd.GeoDataFrame(
        df,
        crs=crs,
        geometry="geom_building",
    )

    # TODO remove after implementation of egon_building_id
    df.rename(columns={"id": "egon_building_id"}, inplace=True)

    # get max number of building ids from synthetic residential table
    with db.session_scope() as session:
        max_synth_residential_id = session.execute(
            func.max(OsmBuildingsSynthetic.id)
        ).scalar()
    max_synth_residential_id = int(max_synth_residential_id)

    # create sequential ids
    df["egon_building_id"] = range(
        max_synth_residential_id + 1,
        max_synth_residential_id + df.shape[0] + 1,
    )

    df["area"] = df["geom_building"].area
    # set building type of synthetic building
    df["building"] = "cts"
    # TODO remove in #772
    df = df.rename(
        columns={
            # "zensus_population_id": "cell_id",
            "egon_building_id": "id",
        }
    )
    return df