Source code for sertit.vectors

# -*- coding: utf-8 -*-
# Copyright 2024, SERTIT-ICube - France, https://sertit.unistra.fr/
# This file is part of sertit-utils project
#     https://github.com/sertit/sertit-utils
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Vectors tools

You can use this only if you have installed sertit[full] or sertit[vectors]
"""
import logging
import os
import re
import shutil
import tarfile
import tempfile
import zipfile
from contextlib import contextmanager
from typing import Any, Generator, Union

import numpy as np
import pandas as pd
from cloudpathlib.exceptions import AnyPathTypeError
from fiona._err import CPLE_AppDefinedError
from fiona.errors import DriverError, UnsupportedGeometryTypeError

from sertit import AnyPath, files, geometry, logs, misc, path, strings
from sertit.types import AnyPathStrType, AnyPathType

try:
    import geopandas as gpd
    from shapely import Polygon, wkt
except ModuleNotFoundError as ex:
    raise ModuleNotFoundError(
        "Please install 'geopandas' to use the rasters package."
    ) from ex

from sertit.logs import SU_NAME

LOGGER = logging.getLogger(SU_NAME)

EPSG_4326 = "EPSG:4326"
WGS84 = EPSG_4326

EXT_TO_DRIVER = {
    ".shp": "ESRI Shapefile",
    ".kml": "KML",
    ".kmz": "KMZ",
    ".json": "GeoJSON",
    ".geojson": "GeoJSON",
    ".gml": "GML",
}

SHP_CO_FILES = [".dbf", ".prj", ".sbn", ".sbx", ".shx", ".sld"]


[docs] def to_utm_crs(lon: float, lat: float) -> "CRS": # noqa: F821 """ Find the EPSG code of the UTM CRS from a lon/lat in WGS84. DEPRECATED: use :code:`estimate_utm_crs` instead. https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.estimate_utm_crs.html .. code-block:: python >>> to_utm_crs(lon=7.8, lat=48.6) # Strasbourg <Derived Projected CRS: EPSG:32632> Name: WGS 84 / UTM zone 32N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - bounds: (6.0, 0.0, 12.0, 84.0) Coordinate Operation: - name: UTM zone 32N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Args: lon (float): Longitude (WGS84, epsg:4326) lat (float): Latitude (WGS84, epsg:4326) Returns: CRS: UTM CRS """ # Manage the case with centroids etc. that are already written as arrays try: point = gpd.points_from_xy([lon], [lat]) except ValueError: point = gpd.points_from_xy(lon, lat) return gpd.GeoDataFrame(geometry=point, crs=EPSG_4326).estimate_utm_crs()
[docs] def corresponding_utm_projection(lon: float, lat: float) -> str: """ Find the EPSG code of the UTM CRS from a lon/lat in WGS84. DEPRECATED: use :code:`estimate_utm_crs` instead. https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.estimate_utm_crs.html .. code-block:: python >>> to_utm_crs(lon=7.8, lat=48.6) # Strasbourg <Derived Projected CRS: EPSG:32632> Name: WGS 84 / UTM zone 32N Axis Info [cartesian]: - E[east]: Easting (metre) - N[north]: Northing (metre) Area of Use: - bounds: (6.0, 0.0, 12.0, 84.0) Coordinate Operation: - name: UTM zone 32N - method: Transverse Mercator Datum: World Geodetic System 1984 ensemble - Ellipsoid: WGS 84 - Prime Meridian: Greenwich Args: lon (float): Longitude (WGS84, epsg:4326) lat (float): Latitude (WGS84, epsg:4326) Returns: CRS: UTM CRS """ logs.deprecation_warning( "Deprecated, use 'to_utm_crs' instead, which directly returs a CRS instead of a string." ) return to_utm_crs(lon, lat).to_string()
[docs] def get_geodf(geom: Union[Polygon, list, gpd.GeoSeries], crs: str) -> gpd.GeoDataFrame: """ Get a GeoDataFrame from a geometry and a crs .. code-block:: python >>> poly = Polygon(((0., 0.), (0., 1.), (1., 1.), (1., 0.), (0., 0.))) >>> geodf = get_geodf(poly, crs=WGS84) >>> print(geodf) geometry 0 POLYGON ((0.00000 0.00000, 0.00000 1.00000, 1.... Args: geom (Union[Polygon, list]): List of Polygons, or Polygon or bounds crs (str): CRS of the polygon Returns: gpd.GeoDataFrame: Geometry as a geodataframe """ if isinstance(geom, list): if isinstance(geom[0], Polygon): pass else: try: geom = [geometry.from_bounds_to_polygon(*geom)] except TypeError as ex: raise TypeError( "Give the extent as 'left', 'bottom', 'right', and 'top'" ) from ex elif isinstance(geom, Polygon): geom = [geom] elif isinstance(geom, gpd.GeoSeries): geom = geom.geometry else: raise TypeError("geometry should be a list or a Polygon.") return gpd.GeoDataFrame(geometry=geom, crs=crs)
[docs] def set_kml_driver() -> None: """ Set KML driver for Fiona data (use it at your own risks !) .. code-block:: python >>> path = "path/to/kml.kml" >>> gpd.read_file(path) fiona.errors.DriverError: unsupported driver: 'LIBKML' >>> set_kml_driver() >>> gpd.read_file(path) Name ... geometry 0 CC679_new_AOI2_3 ... POLYGON Z ((45.03532 32.49765 0.00000, 46.1947... [1 rows x 12 columns] """ import fiona drivers = fiona.drvsupport.supported_drivers if "LIBKML" not in drivers: drivers["LIBKML"] = "rw" if "KML" not in drivers: # Just in case drivers["KML"] = "rw"
[docs] def get_aoi_wkt(aoi_path: AnyPathStrType, as_str: bool = True) -> Union[str, Polygon]: """ Get AOI formatted as a WKT from files that can be read by Fiona (like shapefiles, ...) or directly from a WKT file. The use of KML has been forced (use it at your own risks !). See: https://fiona.readthedocs.io/en/latest/fiona.html#fiona.open It is assessed that: - only **one** polygon composes the AOI (as only the first one is read) - it should be specified in lat/lon (WGS84) if a WKT file is provided .. code-block:: python >>> path = "path/to/vec.geojson" # OK with ESRI Shapefile, geojson, WKT, KML... >>> get_aoi_wkt(path) 'POLYGON Z ((46.1947755465253067 32.4973553439109324 0.0000000000000000, 45.0353174370802520 32.4976496856158974 0.0000000000000000, 45.0355748149750283 34.1139970085580018 0.0000000000000000, 46.1956059695554089 34.1144793800670882 0.0000000000000000, 46.1947755465253067 32.4973553439109324 0.0000000000000000))' Args: aoi_path (AnyPathStrType): Absolute or relative path to an AOI. Its format should be WKT or any format read by Fiona, like shapefiles. as_str (bool): If True, return WKT as a str, otherwise as a shapely geometry Returns: Union[str, Polygon]: AOI formatted as a WKT stored in lat/lon """ aoi_path = AnyPath(aoi_path) if not aoi_path.is_file(): raise FileNotFoundError(f"AOI file {aoi_path} does not exist.") if aoi_path.suffix == ".wkt": try: with open(aoi_path, "r") as aoi_f: aoi = wkt.load(aoi_f) except Exception as ex: raise ValueError("AOI WKT cannot be read") from ex else: try: # Open file aoi_file = read(aoi_path, crs=EPSG_4326) # Get envelope polygon geom = aoi_file["geometry"] if len(geom) > 1: LOGGER.warning( "Your AOI contains several polygons. Only the first will be treated !" ) polygon = geom[0].convex_hull # Convert to WKT aoi = wkt.loads(str(polygon)) except Exception as ex: raise ValueError("AOI cannot be read by Fiona") from ex # Convert to string if needed if as_str: aoi = wkt.dumps(aoi) LOGGER.debug("Specified AOI in WKT: %s", aoi) return aoi
[docs] def shapes_to_gdf(shapes: Generator, crs: str) -> gpd.GeoDataFrame: """ Convert rasterio shapes to geodataframe Args: shapes (Generator): Shapes from rasterio crs: Wanted CRS of the vector. If None, using naive or origin CRS. Returns: gpd.GeoDataFrame: Shapes as a GeoDataFrame """ def _to_polygons(val: Any) -> Polygon: """ Convert to polygon (to be used in pandas) -> convert the geometry column Args: val (Any): Pandas value that has a "coordinates" field Returns: Polygon: Pandas value as a Polygon """ # Donut cases if len(val["coordinates"]) > 1: poly = Polygon(val["coordinates"][0], val["coordinates"][1:]) else: poly = Polygon(val["coordinates"][0]) pass return poly # Convert results to pandas (because of invalid geometries) and save it pd_results = pd.DataFrame(shapes, columns=["geometry", "raster_val"]) if not pd_results.empty: # Convert to proper polygons(correct geometries) pd_results.geometry = pd_results.geometry.apply(_to_polygons) # Convert to geodataframe with correct geometry gdf = gpd.GeoDataFrame(pd_results, geometry=pd_results.geometry, crs=crs) # Return valid geometries gdf = geometry.make_valid(gdf) return gdf
[docs] def write(gdf: gpd.GeoDataFrame, path: AnyPathStrType, **kwargs) -> None: """ Write vector to disk, managing the common drivers automatically. Args: gdf (gpd.GeoDataFrame): GeoDataFrame to write on disk path (AnyPathStrType): Where to write on disk. """ path = AnyPath(path) driver = kwargs.pop("driver", None) if not driver: driver = EXT_TO_DRIVER.get(path.suffix) if driver == "KML": set_kml_driver() elif driver == "KMZ": raise NotImplementedError("Impossible to write a KMZ for now.") gdf.to_file(str(path), driver=driver, **kwargs)
[docs] def copy(src_path: AnyPathStrType, dst_path: AnyPathStrType) -> AnyPathType: """ Copy vector (handles shapefiles additional files) Args: src_path (AnyPathStrType): Source Path dst_path (AnyPathStrType): Destination Path (file or folder) Returns: AnyPathType: Path to copied vector """ src_path = AnyPath(src_path) dst_path = AnyPath(dst_path) if not dst_path.is_file(): dst_path = files.copy(src_path, dst_path) # Add files that come with shape shp_co_files = [ file for file in src_path.parent.glob(f"{path.get_filename(src_path)}.*") if file.suffix in SHP_CO_FILES ] for co_file in shp_co_files: files.copy(co_file, dst_path.with_suffix(co_file.suffix)) return dst_path
[docs] def read( vector_path: AnyPathStrType, crs: Any = None, archive_regex: str = None, window: Any = None, **kwargs, ) -> gpd.GeoDataFrame: """ Read any vector: - if KML/KMZ: sets correctly the drivers and open layered KML (you may need :code:`ogr2ogr` to make it work !) - if archive (only zip or tar), use a regex to look for the vector inside the archive. You can use this `site <https://regexr.com/>`_ to build your regex. - if GML: manages the empty errors Handles a lot of exceptions and have fallback mechanisms with ogr2ogr (if in PATH) .. code-block:: python >>> # Usual >>> path = 'D:/path/to/vector.geojson' >>> vectors.read(path, crs=WGS84) Name ... geometry 0 Sentinel-1 Image Overlay ... POLYGON ((0.85336 42.24660, -2.32032 42.65493,... >>> # Archive >>> arch_path = 'D:/path/to/zip.zip' >>> vectors.read(arch_path, archive_regex=r".*map-overlay.kml") Name ... geometry 0 Sentinel-1 Image Overlay ... POLYGON ((0.85336 42.24660, -2.32032 42.65493,... Args: vector_path (AnyPathStrType): Path to vector to read. In case of archive, path to the archive. crs: Wanted CRS of the vector. If None, using naive or origin CRS. archive_regex (str): [Archive only] Regex for the wanted vector inside the archive window (Any): Anything that can be returned as a bbox (i.e. path, gpd.GeoPandas, Iterable, ...). In case of an iterable, assumption is made it corresponds to geographic bounds. Mimics 'rasters.read(..., window=)'. If given, 'bbox' is ignored. **kwargs: Additional arguments used in gpd.read_file Returns: gpd.GeoDataFrame: Read vector as a GeoDataFrame """ # Default values gpd_vect_path = str(vector_path) arch_path = None # -- Manage window and convert it to a bbox if window is not None: try: bbox = read(window) except (FileNotFoundError, TypeError): # Convert ndarray to tuple if isinstance(window, np.ndarray): bbox = tuple(window) else: bbox = window kwargs["bbox"] = bbox # -- Manage the path formatting (create the path to be read by GeoPandas, the archive path if needed, ...) try: vector_path = AnyPath(vector_path) # Manage formatted archive file (fsspec style for example) if "!" in str(vector_path): split_vect = str(vector_path).split("!") archive_regex = ".*{0}".format(split_vect[1].replace(".", r"\.")) vector_path = AnyPath(split_vect[0]) # Manage archive case if vector_path.suffix in [".tar", ".zip"]: if path.is_cloud_path(vector_path): vector_path = AnyPath(vector_path.fspath) prefix = vector_path.suffix[-3:] file_list = path.get_archived_file_list(vector_path) try: regex = re.compile(archive_regex) arch_path = list(filter(regex.match, file_list))[0] # Different template if on cloud or not... (only tested with S3) if path.is_cloud_path(vector_path): gpd_vect_path = f"{prefix}+{vector_path}!{arch_path}" else: gpd_vect_path = f"{prefix}://{vector_path}!{arch_path}" except IndexError: raise FileNotFoundError( f"Impossible to find vector {archive_regex} in {path.get_filename(vector_path)}" ) # Don't read tar.gz archives (way too slow) elif vector_path.suffixes == [".tar", ".gz"]: raise TypeError( ".tar.gz files are too slow to be read from inside the archive. Please extract them instead." ) except AnyPathTypeError: pass # Check existence of the file (here and not before to handle fsspec cases with '!') if not AnyPath(vector_path).exists(): raise FileNotFoundError(f"Non existing vector: {vector_path}") # Read vector return _read_vector_core(gpd_vect_path, vector_path, arch_path, crs, **kwargs)
def _read_vector_core( gpd_vect_path: str, raw_path: AnyPathStrType, arch_path: str, crs, **kwargs ): """ Read vector (core function) with correctly formatted paths. Handles a lot of exceptions, reads KML, and have fallback mechanisms with ogr2ogr (if in PATH) Args: gpd_vect_path (str): Resolved vector path (readable by geopandas) raw_path (AnyPathStrType): Path to vector to read. In case of archive, path to the archive. arch_path (str): If archived vector, path to the vector file inside the archive (from the root of the archive) crs: Wanted CRS of the vector. If None, using naive or origin CRS. **kwargs: Other arguments Returns: gpd.GeoDataFrame: Read vector as a GeoDataFrame """ tmp_dir = None # -- Open vector try: # Discard some weird error concerning a NULL pointer that outputs a ValueError (as we already except it) fiona_logger = logging.getLogger("fiona") fiona_logger.setLevel(logging.CRITICAL) # Manage KML driver if gpd_vect_path.endswith(".kml") or gpd_vect_path.endswith(".kmz"): vect = _read_kml(gpd_vect_path, raw_path, arch_path, tmp_dir, **kwargs) else: vect = gpd.read_file(gpd_vect_path, **kwargs) # Manage naive geometries try: if vect.crs and crs: vect = vect.to_crs(crs) except AttributeError: # Pyogrio don't create crs columns for dbf files for example pass # Set fiona logger back to what it was fiona_logger.setLevel(logging.INFO) except DriverError: raise except (ValueError, UnsupportedGeometryTypeError) as ex: if "Use a.any() or a.all()" in str(ex): raise # Do not print warning for null layer elif "Null layer" not in str(ex): LOGGER.warning(ex) vect = gpd.GeoDataFrame(geometry=[], crs=crs) except CPLE_AppDefinedError as ex: # Last try to read this vector # Needs ogr2ogr here if shutil.which("ogr2ogr"): # Open as geojson tmp_dir = tempfile.TemporaryDirectory() vect_path_gj = ogr2geojson(raw_path, tmp_dir.name, arch_path) vect = gpd.read_file(vect_path_gj, **kwargs) vect.crs = None else: # Do not print warning for null layer if "Null layer" not in str(ex): LOGGER.warning(ex) vect = gpd.GeoDataFrame(geometry=[], crs=crs) # Clean if needed if tmp_dir: tmp_dir.cleanup() return vect def _read_kml( gpd_vect_path: str, raw_path: AnyPathStrType, arch_path: str = None, tmp_dir=None, **kwargs, ) -> gpd.GeoDataFrame: """ Reader of KML data Args: gpd_vect_path (str): Resolved vector path (readable by geopandas) raw_path (AnyPathStrType): Path to vector to read. In case of archive, path to the archive. arch_path: If archived vector, path to the vector file inside the archive (from the root of the archive) tmp_dir: Temporary directory **kwargs: Additional arguments used in gpd.read_file Returns: gpd.GeoDataFrame: KML as a geopandas GeoDataFrame """ set_kml_driver() vect = gpd.GeoDataFrame() # Document tags in KML file are separate layers for GeoPandas. # When you try to get the KML content, you actually get the first layer. # So you need for loop for iterating over layers. # https://gis.stackexchange.com/questions/328525/geopandas-read-file-only-reading-first-part-of-kml/328554 import fiona driver = "KML" if gpd_vect_path.endswith(".kml") else "KMZ" for layer in fiona.listlayers(gpd_vect_path): try: vect_layer = gpd.read_file( gpd_vect_path, driver=driver, layer=layer, **kwargs ) if not vect_layer.empty: # KML files are always in WGS84 (and does not contain this information) vect_layer.crs = EPSG_4326 vect = pd.concat([vect, vect_layer]) except ValueError: pass # Except Null Layer # Workaround for archived KML -> they may be empty # Convert KML to GeoJSON # Needs ogr2ogr here if vect.empty: if shutil.which("ogr2ogr"): # Open the geojson if not tmp_dir: tmp_dir = tempfile.TemporaryDirectory() # KML should be downloaded to work with ogr2ogr if path.is_cloud_path(raw_path): raw_path = AnyPath(raw_path).fspath vect_path_gj = ogr2geojson(raw_path, tmp_dir.name, arch_path) vect = gpd.read_file(vect_path_gj, **kwargs) else: # Try reading it in a basic manner LOGGER.warning( "Missing `ogr2ogr` in your PATH, your KML may be incomplete. " "(KML files can contain unsupported data structures, nested folders etc.)" ) try: vect = gpd.read_file(gpd_vect_path, **kwargs) except Exception: # Force set CRS to empty vector vect.crs = EPSG_4326 return vect
[docs] def ogr2geojson( vector_path: AnyPathStrType, out_dir: AnyPathStrType, arch_vect_path: str = None, ) -> str: """ Wrapper of ogr2ogr function, converting the input vector to GeoJSON. Args: vector_path (AnyPathStrType): Path to vector to read. In case of archive, path to the archive. out_dir (AnyPathStrType): Output directory arch_vect_path: If archived vector, path to the vector file inside the archive (from the root of the archive) Returns: str: Converted file Args: vector_path (AnyPathStrType): Path to vector to read. In case of archive, path to the archive. out_dir (AnyPathStrType): Output directory arch_vect_path: If archived vector, path to the vector file inside the archive (from the root of the archive) Returns: str: Converted file """ assert shutil.which("ogr2ogr") # Needs ogr2ogr here out_dir = str(out_dir) vector_path = str(vector_path) if vector_path.endswith(".zip"): with zipfile.ZipFile(vector_path, "r") as zip_ds: vect_path = zip_ds.extract(arch_vect_path, out_dir) elif vector_path.endswith(".tar"): with tarfile.open(vector_path, "r") as tar_ds: tar_ds.extract(arch_vect_path, out_dir) vect_path = os.path.join(out_dir, arch_vect_path) else: vect_path = vector_path vect_path_gj = os.path.join( out_dir, os.path.basename(vect_path).replace(path.get_ext(vect_path), "geojson"), ) cmd_line = [ "ogr2ogr", "-fieldTypeToString DateTime", # Disable warning "-f GeoJSON", strings.to_cmd_string(vect_path_gj), # dst strings.to_cmd_string(vect_path), # src ] try: misc.run_cli(cmd_line) except RuntimeError as ex: raise RuntimeError(f"Something went wrong with ogr2ogr: {ex}") from ex return vect_path_gj
[docs] @contextmanager def utm_crs(gdf: gpd.GeoDataFrame) -> None: """ Change temporary the CRS of a vector, ie when computing area based statistics / features (centroid....) which need a meter-based CRS. WARNING: the modifications (other than CRS) on the yielded GeoDataFrame will be kept! .. code-block:: python >>> vect = vectors.read(vectors_path().joinpath("aoi.kml")) >>> with vectors.utm_crs(vect) as utm_vect: >>> utm_centroid = utm_vect.centroid >>> utm_vect["centroid_utm"] = utm_centroid >>> vect["centroid_utm"].equals(c2) True Args: gdf (str): GeoDataFrame to copnvert """ src_crs = None if not gdf.crs.is_projected: src_crs = gdf.crs gdf.to_crs(gdf.estimate_utm_crs(), inplace=True) try: yield gdf finally: if src_crs is not None: gdf.to_crs(src_crs, inplace=True)