Exemple #1
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def polygon_2_raster(polygon: gpd.GeoDataFrame, rst_fn: str, out_fn: str,
                     out_shape: List[int]) -> None:
    '''This function take in a geopandas object of polygons and converts them into a raster
        image based on the 'temp_rast' raster

    Input:
    polygon -- the geopandas object of polygons
    rst_fn -- the filename of the raster that will be the template upon which the polygons will be burned
    out_fn -- the output file name that the raster of the polygons will be written to
    out_shape -- a two element list of the x and y shape of the output raster image

    Output:

    '''

    with rio.open(rst_fn, 'r') as rst:
        meta = rst.meta.copy()
        meta.update(compress='lzw')

        with rio.open(out_fn, 'w', **meta) as out:

            #we create a generator of geom, value pairs to use in the features.rasterize function
            shapes = ((feature['geometry'], 1)
                      for feature in polygon.iterfeatures())

            labels = features.rasterize(shapes=shapes,
                                        out_shape=out_shape,
                                        transform=meta['transform'],
                                        fill=0,
                                        dtype='uint16',
                                        all_touched=False)
            out.write(labels, indexes=1)
Exemple #2
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    def test_geodataframe_iterfeatures(self):
        df = self.df.iloc[:1].copy()
        df.loc[0, "BoroName"] = np.nan
        # when containing missing values
        # null: ouput the missing entries as JSON null
        result = list(df.iterfeatures(na="null"))[0]["properties"]
        assert result["BoroName"] is None
        # drop: remove the property from the feature.
        result = list(df.iterfeatures(na="drop"))[0]["properties"]
        assert "BoroName" not in result.keys()
        # keep: output the missing entries as NaN
        result = list(df.iterfeatures(na="keep"))[0]["properties"]
        assert np.isnan(result["BoroName"])

        # test for checking that the (non-null) features are python scalars and
        # not numpy scalars
        assert type(df.loc[0, "Shape_Leng"]) is np.float64
        # null
        result = list(df.iterfeatures(na="null"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float
        # drop
        result = list(df.iterfeatures(na="drop"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float
        # keep
        result = list(df.iterfeatures(na="keep"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float

        # when only having numerical columns
        df_only_numerical_cols = df[["Shape_Leng", "Shape_Area", "geometry"]]
        assert type(df_only_numerical_cols.loc[0, "Shape_Leng"]) is np.float64
        # null
        result = list(df_only_numerical_cols.iterfeatures(na="null"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float
        # drop
        result = list(df_only_numerical_cols.iterfeatures(na="drop"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float
        # keep
        result = list(df_only_numerical_cols.iterfeatures(na="keep"))[0]
        assert type(result["properties"]["Shape_Leng"]) is float

        with pytest.raises(
                ValueError,
                match="GeoDataFrame cannot contain duplicated column names."):
            df_with_duplicate_columns = df[[
                "Shape_Leng", "Shape_Leng", "Shape_Area", "geometry"
            ]]
            list(df_with_duplicate_columns.iterfeatures())

        # geometry not set
        df = GeoDataFrame({
            "values": [0, 1],
            "geom": [Point(0, 1), Point(1, 0)]
        })
        with pytest.raises(AttributeError):
            list(df.iterfeatures())