Source code for eoreader.utils

# -*- coding: utf-8 -*-
# Copyright 2022, SERTIT-ICube - France,
# This file is part of eoreader project
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
""" Utils: mostly getting directories relative to the project """
import logging
import os
import platform
from pathlib import Path
from typing import Union

import numpy as np
import pandas as pd
import xarray as xr
from cloudpathlib import AnyPath, CloudPath
from lxml import etree
from rasterio.control import GroundControlPoint
from rasterio.enums import Resampling
from rasterio.rpc import RPC
from sertit import rasters

from eoreader.env_vars import USE_DASK
from eoreader.keywords import prune_keywords

EOREADER_NAME = "eoreader"
LOGGER = logging.getLogger(EOREADER_NAME)

[docs]def get_src_dir() -> Union[CloudPath, Path]: """ Get src directory. Returns: str: Root directory """ return AnyPath(__file__).parent
[docs]def get_root_dir() -> Union[CloudPath, Path]: """ Get root directory. Returns: str: Root directory """ return get_src_dir().parent
[docs]def get_data_dir() -> Union[CloudPath, Path]: """ Get data directory. Returns: str: Data directory """ data_dir = get_src_dir().joinpath("data") if not data_dir.is_dir() or not list(data_dir.iterdir()): data_dir = None # Last resort try if platform.system() == "Linux": data_dirs = AnyPath("/usr", "local", "lib").glob("**/eoreader/data") else: data_dirs = AnyPath("/").glob("**/eoreader/data") # Look for non empty directories for ddir in data_dirs: if len(os.listdir(ddir)) > 0: data_dir = ddir break if not data_dir: raise FileNotFoundError("Impossible to find the data directory.") return data_dir
[docs]def get_split_name(name: str) -> list: """ Get split name (with _). Removes empty index. Args: name (str): Name to split Returns: list: Split name """ return [x for x in name.split("_") if x]
# flake8: noqa
[docs]def use_dask(): """Use Dask or not""" # Check environment variable use_dask = os.getenv(USE_DASK, "0").lower() in ("1", "true") # Check installed libs if use_dask: try: import dask import distributed except ImportError: use_dask = False return use_dask
[docs]def read( path: Union[str, CloudPath, Path], resolution: Union[tuple, list, float] = None, size: Union[tuple, list] = None, resampling: Resampling = Resampling.nearest, masked: bool = True, indexes: Union[int, list] = None, **kwargs, ) -> xr.DataArray: """ Overload of :code:`` managing DASK in EOReader's way. .. code-block:: python >>> raster_path = "path/to/raster.tif" >>> xds1 = read(raster_path) >>> # or >>> with as dst: >>> xds2 = read(dst) >>> xds1 == xds2 True Args: path (Union[str, CloudPath, Path]): Path to the raster resolution (Union[tuple, list, float]): Resolution of the wanted band, in dataset resolution unit (X, Y) size (Union[tuple, list]): Size of the array (width, height). Not used if resolution is provided. resampling (Resampling): Resampling method masked (bool): Get a masked array indexes (Union[int, list]): Indexes to load. Load the whole array if None. **kwargs: Optional keyword arguments to pass into rioxarray.open_rasterio(). Returns: Union[XDS_TYPE]: Masked xarray corresponding to the raster data and its meta data """ if use_dask(): chunks = True else: chunks = None return path, resolution=resolution, size=size, resampling=resampling, masked=masked, indexes=indexes, chunks=chunks, **prune_keywords(**kwargs), )
[docs]def write(xds: xr.DataArray, path: Union[str, CloudPath, Path], **kwargs) -> None: """ Overload of :code:`sertit.rasters.write()` managing DASK in EOReader's way. .. code-block:: python >>> raster_path = "path/to/raster.tif" >>> raster_out = "path/to/out.tif" >>> # Read raster >>> xds = read(raster_path) >>> # Rewrite it >>> write(xds, raster_out) Args: xds (xr.DataArray): Path to the raster or a rasterio dataset or a xarray path (Union[str, CloudPath, Path]): Path where to save it (directories should be existing) **kwargs: Overloading metadata, ie :code:`nodata=255` or :code:`dtype=np.uint8` """ if use_dask(): from distributed import Lock, get_client lock = Lock("rio", client=get_client()) else: lock = None # Reset the long name as a list to write it down previous_long_name = xds.attrs.get("long_name") if previous_long_name and > 1: try: xds.attrs["long_name"] = xds.attrs.get( "long_name", xds.attrs.get("name", "") ).split(" ") except AttributeError: pass # Write rasters.write(xds, path=path, lock=lock, **prune_keywords(**kwargs)) # Set back the previous long name if previous_long_name and > 1: xds.attrs["long_name"] = previous_long_name
[docs]def create_gcps(lon: xr.DataArray, lat: xr.DataArray, alt: xr.DataArray) -> list: """ Create GCPs from an array of longitude, latitude and altitude (based on Sentinel-3 geocoding). Args: lon (xr.DataArray): Longitude array lat (xr.DataArray): Latitude array alt (xr.DataArray): Altitude array Returns: list: List of GroundControlPoints """ gcps = [] assert == == # Get the GCPs coordinates nof_gcp_x = np.linspace(0, - 1, dtype=int) nof_gcp_y = np.linspace(0, - 1, dtype=int) # Create the GCP sequence gcp_id = 0 for x in nof_gcp_x: for y in nof_gcp_y: curr_lon =[0, y, x] curr_lat =[0, y, x] curr_alt =[0, y, x] if ( not np.isnan(curr_lon) and not np.isnan(curr_lat) and not np.isnan(curr_alt) ): gcps.append( GroundControlPoint( row=y, col=x,[0, y, x],[0, y, x],[0, y, x], id=gcp_id, ) ) gcp_id += 1 return gcps
[docs]def quick_xml_to_dict(element: etree._Element) -> tuple: """ Convert a lxml root to a nested dict (quick and dirty) How can I map an XML tree into a dict of dicts? Note that this beautiful quick-and-dirty converter expects children to have unique tag names and will silently overwrite any data that was contained in preceding siblings with the same name. For any real-world application of xml-to-dict conversion, you would better write your own, longer version of this. Args: element (etree._Element): Element to convert into a dict Returns: : XML as a nested dict """ return element.tag, dict(map(quick_xml_to_dict, element)) or element.text
[docs]def open_rpc_file(path: Union[CloudPath, Path]) -> RPC: """ Create a rasterio RPC object from a :code:`.rpc` file. Used for Vision-1 product Args: path: Path of the RPC file Returns: RPC: RPC object """ def to_float(pd_table, field) -> float: pd_field = pd_table.T[field] val = None for val in pd_field.values[0].split(" "): if val: break return float(val) def to_list(pd_table, field) -> list: pd_list = pd_table[pd_table.index.str.contains(field)].values return [float(val[0]) for val in pd_list] try: rpcs_file = pd.read_csv( path, delimiter=":", names=["name", "value"], index_col=0 ) height_off = to_float(rpcs_file, "HEIGHT_OFF") height_scale = to_float(rpcs_file, "HEIGHT_SCALE") lat_off = to_float(rpcs_file, "LAT_OFF") lat_scale = to_float(rpcs_file, "LAT_SCALE") line_den_coeff = to_list(rpcs_file, "LINE_DEN_COEFF") line_num_coeff = to_list(rpcs_file, "LINE_NUM_COEFF") line_off = to_float(rpcs_file, "LINE_OFF") line_scale = to_float(rpcs_file, "LINE_SCALE") long_off = to_float(rpcs_file, "LONG_OFF") long_scale = to_float(rpcs_file, "LONG_SCALE") samp_den_coeff = to_list(rpcs_file, "SAMP_DEN_COEFF") samp_num_coeff = to_list(rpcs_file, "SAMP_NUM_COEFF") samp_off = to_float(rpcs_file, "SAMP_OFF") samp_scale = to_float(rpcs_file, "SAMP_SCALE") return RPC( height_off, height_scale, lat_off, lat_scale, line_den_coeff, line_num_coeff, line_off, line_scale, long_off, long_scale, samp_den_coeff, samp_num_coeff, samp_off, samp_scale, err_bias=None, err_rand=None, ) except KeyError as msg: raise KeyError(f"Invalid RPC file, missing key: {msg}")