geospatial_data_converter/kml_tricks.py

import zipfile
from io import StringIO

import bs4
import geopandas as gpd
import lxml # nosec
import pandas as pd
from shapely.geometry import (
 Point,
 LineString,
 Polygon,
 MultiPoint,
 MultiLineString,
 MultiPolygon,
 LinearRing,
)


def parse_descriptions_to_geodf(geodf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
 """Parses Descriptions from Google Earth file to a GeoDataFrame object"""

 dataframes = []

 # Iterate over descriptions and extract data
 for desc in geodf["Description"]:
 desc_as_io = StringIO(desc)

 # Try to read the description into a DataFrame
 parsed_html = pd.read_html(desc_as_io)
 try:
 temp_df = parsed_html[1].T
 except IndexError:
 temp_df = parsed_html[0].T

 # Set DataFrame header and remove the first row
 temp_df.columns = temp_df.iloc[0]
 temp_df = temp_df.iloc[1:]

 dataframes.append(temp_df)

 # Combine all DataFrames
 combined_df = pd.concat(dataframes, ignore_index=True)

 # Add geometry data
 combined_df["geometry"] = geodf["geometry"]

 # Create a GeoDataFrame with the combined data and original CRS
 result_geodf = gpd.GeoDataFrame(combined_df, crs=geodf.crs)

 return result_geodf


def swap_coordinates(geometry):
 """
 Swap the latitude and longitude of a shapely Point, LineString, Polygon,
 MultiPoint, MultiLineString, MultiPolygon, or LinearRing geometry.

 Parameters:
 - geometry: Shapely geometry (Point, LineString, Polygon, MultiPoint,
 MultiLineString, MultiPolygon, or LinearRing)

 Returns:
 - Shapely geometry with swapped coordinates
 """

 def swap_coords(coords):
 return [(coord[1], coord[0]) for coord in coords]

 if isinstance(geometry, Point):
 return Point([geometry.y, geometry.x])
 elif isinstance(geometry, MultiPoint):
 return MultiPoint(
 [Point(swap_coords(point.coords)) for point in geometry.geoms],
 )
 elif isinstance(geometry, LineString):
 return LineString(swap_coords(geometry.coords))
 elif isinstance(geometry, MultiLineString):
 return MultiLineString(
 [LineString(swap_coords(line.coords)) for line in geometry.geoms],
 )
 elif isinstance(geometry, Polygon):
 exterior_coords = swap_coords(geometry.exterior.coords)
 interior_coords = [
 swap_coords(interior.coords) for interior in geometry.interiors
 ]
 return Polygon(exterior_coords, interior_coords)
 elif isinstance(geometry, MultiPolygon):
 return MultiPolygon([swap_coordinates(poly) for poly in geometry.geoms])
 elif isinstance(geometry, LinearRing):
 return LinearRing(swap_coords(geometry.coords))
 else:
 raise ValueError("Unsupported geometry type")


def load_kmz_as_geodf(file_path: str) -> gpd.GeoDataFrame:
 """Loads a KMZ file into a GeoPandas DataFrame, assuming the KMZ contains one KML file"""

 # Open the KMZ file
 with zipfile.ZipFile(file_path, "r") as kmz:
 # List all KML files in the KMZ
 kml_files = [file for file in kmz.namelist() if file.endswith(".kml")]

 # Ensure there's only one KML file in the KMZ
 if len(kml_files) != 1:
 raise IndexError(
 "KMZ contains more than one KML. Please extract or convert to multiple KMLs.",
 )

 # Read the KML file into a GeoDataFrame
 geodf = gpd.read_file(
 f"zip://{file_path}/{kml_files[0]}",
 driver="KML",
 engine="pyogrio",
 )

 return geodf


def load_ge_file(file_path: str) -> gpd.GeoDataFrame:
 """Loads a KML or KMZ file and parses its descriptions into a GeoDataFrame"""
 if file_path.endswith(".kml"):
 return parse_descriptions_to_geodf(
 gpd.read_file(file_path, driver="KML", engine="pyogrio"),
 )
 elif file_path.endswith(".kmz"):
 return parse_descriptions_to_geodf(load_kmz_as_geodf(file_path))
 raise ValueError("The file must have a .kml or .kmz extension.")


def extract_data_from_kml_code(kml_code: str) -> pd.DataFrame:
 """Extracts data from KML code into a DataFrame using SimpleData tags, excluding embedded tables in feature descriptions"""

 # Parse the KML source code
 soup = bs4.BeautifulSoup(kml_code, features="xml")

 # Find all SchemaData tags (representing rows)
 schema_data_tags = soup.find_all("schemadata")

 # Create a generator that yields a dictionary for each row, containing the Placemark name and each SimpleData field
 row_dicts = (
 {
 "Placemark_name": tag.parent.parent.find("name").text
 if tag.parent.parent.find("name")
 else "[no name]",
 **{field.get("name"): field.text for field in tag.find_all("simpledata")},
 }
 for tag in schema_data_tags
 )

 # Convert the row dictionaries into a DataFrame
 df = pd.DataFrame(row_dicts)

 return df


def extract_kml_code_from_file(file_path: str) -> str:
 """Extracts KML source code from a Google Earth file (KML or KMZ)"""

 file_extension = file_path.lower().split(".")[-1]

 if file_extension == "kml":
 with open(file_path, "r") as kml_file:
 kml_code = kml_file.read()
 elif file_extension == "kmz":
 with zipfile.ZipFile(file_path) as kmz_file:
 # Find all KML files in the KMZ
 kml_files = [
 file for file in kmz_file.namelist() if file.lower().endswith(".kml")
 ]

 if len(kml_files) != 1:
 raise IndexError(
 "KMZ file contains more than one KML. Please extract or convert to multiple KMLs.",
 )

 with kmz_file.open(kml_files[0]) as kml_file:
 # Decode the KML file's content from bytes to string
 kml_code = kml_file.read().decode()
 else:
 raise ValueError("The input file must have a .kml or .kmz extension.")

 return kml_code


def extract_data_from_ge_file(file_path: str) -> gpd.GeoDataFrame:
 """Extracts data from a Google Earth file (KML or KMZ) into a GeoDataFrame using SimpleData tags, excluding embedded tables in feature descriptions"""
 data_df = extract_data_from_kml_code(extract_kml_code_from_file(file_path))

 if file_path.endswith(".kmz"):
 ge_file_gdf = load_kmz_as_geodf(file_path)
 else:
 ge_file_gdf = gpd.read_file(file_path, driver="KML", engine="pyogrio")

 geo_df = gpd.GeoDataFrame(
 data_df,
 geometry=ge_file_gdf["geometry"],
 crs=ge_file_gdf.crs,
 )
 geo_df["geometry"] = geo_df["geometry"].apply(swap_coordinates)
 return geo_df


def load_ge_data(file_path: str) -> gpd.GeoDataFrame:
 """Extracts data from a Google Earth file (KML or KMZ) and handles errors due to parsing issues"""

 kml_code = extract_kml_code_from_file(file_path)

 # Choose the extraction method based on the presence of SimpleData or SimpleField tags in the KML code
 primary_func, fallback_func = (
 (extract_data_from_ge_file, load_ge_file)
 if any(tag in kml_code.lower() for tag in ("<simpledata", "<simplefield"))
 else (load_ge_file, extract_data_from_ge_file)
 )

 try:
 data_df = primary_func(file_path)
 except (
 pd.errors.ParserError,
 lxml.etree.ParserError,
 lxml.etree.XMLSyntaxError,
 ValueError,
 ):
 data_df = fallback_func(file_path)

 return data_df