# -*- coding: utf-8 -*-
# Copyright 2023, SERTIT-ICube - France, https://sertit.unistra.fr/
# This file is part of eoreader project
# https://github.com/sertit/eoreader
#
# 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.
""" Utils: mostly getting directories relative to the project """
import logging
import os
import platform
import warnings
from functools import wraps
from pathlib import Path
from typing import Callable, Union
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
from cloudpathlib import AnyPath, CloudPath
from lxml import etree
from rasterio import errors
from rasterio.enums import Resampling
from rasterio.errors import NotGeoreferencedWarning
from rasterio.rpc import RPC
from sertit import rasters
from eoreader import EOREADER_NAME
from eoreader.bands import is_index, is_sat_band, to_str
from eoreader.env_vars import TILE_SIZE, USE_DASK
from eoreader.exceptions import InvalidProductError
from eoreader.keywords import _prune_keywords
LOGGER = logging.getLogger(EOREADER_NAME)
DEFAULT_TILE_SIZE = 2048
UINT16_NODATA = rasters.UINT16_NODATA
[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 indexes.
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, "1").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:`sertit.rasters.read()` managing DASK in EOReader's way.
.. code-block:: python
>>> raster_path = "path/to/raster.tif"
>>> xds1 = read(raster_path)
>>> # or
>>> with rasterio.open(raster_path) 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:
xr.DataArray: Masked xarray corresponding to the raster data and its metadata
"""
window = kwargs.get("window")
# Always use chunks
tile_size = int(os.getenv(TILE_SIZE, DEFAULT_TILE_SIZE))
if use_dask():
chunks = kwargs.get("chunks", [1, tile_size, tile_size])
# LOGGER.debug(f"Current chunking: {chunks}")
else:
# LOGGER.debug("Dask use is not enabled. No chunk will be used, but you may encounter memory overflow errors.")
chunks = None
try:
# Disable georef warnings here as the SAR/Sentinel-3 products are not georeferenced
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=NotGeoreferencedWarning)
return rasters.read(
path,
resolution=resolution,
resampling=resampling,
masked=masked,
indexes=indexes,
size=size if window is None else None,
window=window,
chunks=chunks,
**_prune_keywords(additional_keywords=["window", "chunks"], **kwargs),
)
except errors.RasterioIOError as ex:
if (str(path).endswith("jp2") or str(path).endswith("tif")) and path.exists():
raise InvalidProductError(f"Corrupted file: {path}") from ex
else:
raise
[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`
"""
lock = None
if use_dask():
from distributed import Lock, get_client
try:
lock = Lock("rio", client=get_client())
except ValueError:
pass
# Reset the long name as a list to write it down
previous_long_name = xds.attrs.get("long_name")
if previous_long_name and xds.rio.count > 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(["window"], **kwargs))
# Set back the previous long name
if previous_long_name and xds.rio.count > 1:
xds.attrs["long_name"] = previous_long_name
[docs]def quick_xml_to_dict(element: etree._Element) -> tuple:
"""
Convert a lxml root to a nested dict (quick and dirty)
https://lxml.de/FAQ.html#how-can-i-map-an-xml-tree-into-a-dict-of-dicts:
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.iat[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}")
[docs]def simplify(footprint_fct: Callable):
"""
Simplify footprint decorator
Args:
footprint_fct (Callable): Function to decorate
Returns:
Callable: decorated function
"""
@wraps(footprint_fct)
def simplify_wrapper(self):
"""Simplify footprint wrapper"""
footprint = footprint_fct(self)
return simplify_footprint(footprint, self.resolution)
return simplify_wrapper
[docs]def stack_dict(
bands: list, band_dict: dict, save_as_int: bool, nodata: float, **kwargs
) -> (xr.DataArray, type):
"""
Stack a dictionnary containing bands in a DataArray
Args:
bands (list): List of bands (to keep the right order of the stack)
band_dict (dict): Dict containing the bands as xr.DataArray {band_name, band}
save_as_int (bool): Convert stack to uint16 to save disk space (and therefore multiply the values by 10.000)
nodata (float): Nodata value
Returns:
(xr.DataArray, type): Stack as a DataArray and its dtype
"""
# Convert into dataset with str as names
LOGGER.debug("Stacking")
data_vars = {}
coords = band_dict[bands[0]].coords
for key in bands:
data_vars[to_str(key)[0]] = (
band_dict[key].coords.dims,
band_dict[key].data,
)
# Set memory free (for big stacks)
band_dict[key].close()
band_dict[key] = None
# Create dataset, with dims well-ordered
stack = (
xr.Dataset(
data_vars=data_vars,
coords=coords,
)
.to_stacked_array(new_dim="z", sample_dims=("x", "y"))
.transpose("z", "y", "x")
)
# Save as integer
dtype = np.float32
if save_as_int:
scale = 10000
stack_min = np.nanmin(stack.data)
if np.round(stack_min * 1000) / 1000 < -0.1:
LOGGER.warning(
f"Cannot convert the stack to uint16 as it has negative values ({stack_min} < -0.1). Keeping it in float32."
)
else:
if stack_min < 0:
LOGGER.warning(
"Small negative values ]-0.1, 0] have been found. Clipping to 0."
)
stack = stack.copy(data=np.clip(stack.data, a_min=0, a_max=None))
# Scale to uint16, fill nan and convert to uint16
dtype = np.uint16
for b_id, band in enumerate(bands):
# SCALING
# NOT ALL bands need to be scaled, only:
# - Satellite bands
# - index
if is_sat_band(band) or is_index(band):
if np.nanmax(stack[b_id, ...]) > UINT16_NODATA / scale:
LOGGER.debug(
"Band not in reflectance, keeping them as is (the values will be rounded)"
)
else:
stack[b_id, ...] = stack[b_id, ...] * scale
# Fill no data (done here to avoid RAM saturation)
stack[b_id, ...] = stack[b_id, ...].fillna(nodata)
if dtype == np.float32:
# Set nodata if needed (NaN values are already set)
if stack.rio.encoded_nodata != nodata:
stack = stack.rio.write_nodata(nodata, encoded=True, inplace=True)
return stack, dtype
