pycequeau.core.utils module

pycequeau.core.utils.GetExtent(raster: Dataset)

Return list of corner coordinates from a gdal Dataset

pycequeau.core.utils.convert_multi_to_poly(geometry: Geometry) → Geometry

_summary_

Parameters:

geometry (ogr.Geometry) – _description_

Returns:

_description_

Return type:

ogr.Geometry

pycequeau.core.utils.convert_slope(slope_file_path: str) → ndarray

pycequeau.core.utils.falls_in_extent(extent: tuple, x: list, y: list) → ndarray

_summary_

Parameters:

• extent (tuple) – _description_

• x (list) – _description_

• y (list) – _description_

Returns:

_description_

Return type:

np.ndarray

pycequeau.core.utils.find_nearest(array: ndarray, value: float) → ndarray

_summary_ https://stackoverflow.com/questions/2566412/find-nearest-value-in-numpy-array :param array: _description_ :type array: np.ndarray :param value: _description_ :type value: float

Returns:

_description_

Return type:

np.ndarray

pycequeau.core.utils.fix_geometry(gdf: GeoDataFrame) → GeoDataFrame

_summary_ https://gis.stackexchange.com/questions/430384/using-shapely-methods-explain-validity-and-make-valid-on-shapefile :param gdf: _description_ :type gdf: gpd.GeoDataFrame

Returns:

_description_

Return type:

gpd.GeoDataFrame

pycequeau.core.utils.get_altitude_point(DEM: Dataset, lat_utm: array, lon_utm: array)

_summary_

Parameters:

• DEM (gdal.Dataset) – _description_

• lat_utm (np.array) – _description_

• lon_utm (np.array) – _description_

Returns:

_description_

Return type:

_type_

pycequeau.core.utils.get_index_list(raster: Dataset, x: ndarray, y: ndarray) → tuple

_summary_

Parameters:

• raster (gdal.Dataset) – _description_

• x (np.ndarray) – _description_

• y (np.ndarray) – _description_

Returns:

_description_

Return type:

tuple

pycequeau.core.utils.get_outlet_point(FAC_path: str)

pycequeau.core.utils.ogr_to_gpd(shp: Dataset) → GeoDataFrame

_summary_ This function converst a input OGR object into a GeoDataFrame The results is taken to be used in the merge function. Or any other funtion that needs geopandas :param shp: _description_ :type shp: ogr.DataSource

Returns:

_description_

Return type:

gpd.GeoDataFrame

pycequeau.core.utils.polygonize_raster(raster_name: str)

_summary_

Parameters:

raster_name (str) – _description_

Returns:

NoData value from the input raster first band.

Return type:

_type_

pycequeau.core.utils.rasterize_feature(gdf: GeoDataFrame, raster_name: str, att: str) → ndarray

_summary_

Parameters:

• gdf (gpd.GeoDataFrame) – _description_

• raster_name (str) – _description_

• att (str) – _description_

Returns:

_description_

Return type:

np.ndarray

pycequeau.core.utils.rasterize_shp(grid_shp: str, ref_name: str, field: str) → ndarray

_summary_

Parameters:

• grid_shp (str) – _description_

• ref_name (str) – _description_

• field (str) – _description_

Returns:

_description_

Return type:

np.ndarray

pycequeau.core.utils.rasterize_shp_as_byte(grid_shp: str, ref_name: str, field: str, name: str) → None

_summary_

Parameters:

• grid_shp (str) – _description_

• ref_name (str) – _description_

• field (str) – _description_

• name (str) – _description_

Returns:

_description_

Return type:

_type_

pycequeau.core.utils.reclassify_landcover(LC_file_path: str, classes_idx: list, output_tif_name: str)

pycequeau.core.utils.regrid_CE(raster: Dataset, shp: Dataset, grid_size: int) → Dataset

_summary_

Parameters:

• raster (gdal.Dataset) – _description_

• shp (ogr.DataSource) – _description_

• grid_size (int) – _description_

Returns:

_description_

Return type:

gdal.Dataset

pycequeau.core.utils.saveGTIFF(ref_TIF: str, data_array: ndarray, output_name: str)