# -*- coding: utf-8 -*-
# Copyright 2024, 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.
""" Super class for SAR products """
import logging
import os
import tempfile
from abc import abstractmethod
from enum import unique
from string import Formatter
from typing import Union
import geopandas as gpd
import numpy as np
import rasterio
import rioxarray
import xarray as xr
from rasterio import crs
from rasterio.enums import Resampling
from sertit import AnyPath, geometry, misc, path, rasters, snap, strings, types
from sertit.misc import ListEnum
from sertit.types import AnyPathStrType, AnyPathType
from shapely.geometry.polygon import Polygon
from eoreader import EOREADER_NAME, cache, utils
from eoreader.bands import BandNames, SarBand, SarBandMap
from eoreader.bands import SarBandNames as sab
from eoreader.env_vars import (
DEM_PATH,
DSPK_GRAPH,
PP_GRAPH,
SAR_DEF_PIXEL_SIZE,
SNAP_DEM_NAME,
)
from eoreader.exceptions import InvalidProductError, InvalidTypeError
from eoreader.keywords import SAR_INTERP_NA
from eoreader.products.product import Product, SensorType
from eoreader.reader import Constellation
from eoreader.stac import INTENSITY
from eoreader.utils import simplify
LOGGER = logging.getLogger(EOREADER_NAME)
[docs]
@unique
class SnapDems(ListEnum):
"""
DEM available in SNAP for the Terrain Correction module
"""
ACE2_5Min = "ACE2_5Min"
"""
ACE2_5Min, Altimeter Corrected Elevations, Version 2
"""
ACE30 = "ACE30"
"""
ACE30: Altimeter Corrected Elevations
"""
ASTER = "ASTER 1sec GDEM"
"""
ASTER 1sec GDEM: Advanced Spaceborne Thermal Emission and Reflection Radiometer
"""
GLO_30 = "Copernicus 30m Global DEM"
"""
Copernicus 30m Global DEM
"""
GLO_90 = "Copernicus 90m Global DEM"
"""
Copernicus 90m Global DEM
"""
GETASSE30 = "GETASSE30"
"""
GETASSE30: Global Earth Topography And Sea Surface Elevation at 30 arc second resolution
"""
SRTM_1SEC = "SRTM 1Sec HGT"
"""
SRTM 1Sec HGT: Shuttle Radar Topography Mission
"""
SRTM_3SEC = "SRTM 3Sec"
"""
SRTM 3Sec: Shuttle Radar Topography Mission
"""
EXT_DEM = "External DEM"
f"""
External DEM, needs `{DEM_PATH}` to be correctly positioned
"""
[docs]
@unique
class SarProductType(ListEnum):
"""
Generic products types, used to choose a SNAP graph.
"""
CPLX = "COMPLEX"
"""Single Look Complex"""
GDRG = "GROUND"
"""Ground Range"""
OTHER = "OTHER"
"""Other products types, no used in EOReader"""
# Add ortho products ?
class _ExtendedFormatter(Formatter):
"""An extended format string formatter
Formatter with extended conversion symbol
"""
def convert_field(self, value, conversion):
"""Extend conversion symbol
Following additional symbol has been added
* l: convert to string and low case
* u: convert to string and up case
default are:
* s: convert with str()
* r: convert with repr()
* a: convert with ascii()
"""
if conversion == "u":
cv_field = str(value).upper()
elif conversion == "l":
cv_field = str(value).lower()
else:
cv_field = super().convert_field(value, conversion)
return cv_field
[docs]
class SarProduct(Product):
"""Super class for SAR Products"""
[docs]
def __init__(
self,
product_path: AnyPathStrType,
archive_path: AnyPathStrType = None,
output_path: AnyPathStrType = None,
remove_tmp: bool = False,
**kwargs,
) -> None:
self.sar_prod_type = None
"""SAR product type, either Single Look Complex or Ground Range"""
self.sensor_mode = None
"""Sensor Mode of the current product"""
self.pol_channels = None
"""Polarization Channels stored in the current product"""
self.snap_filename = None
"""Path used by SNAP to process this product"""
self.nof_swaths = None
"""Number of swaths of the current SAR product"""
# Private attributes
self._band_folder = None
self._raw_band_regex = None
self._snap_no_data = 0
self._raw_no_data = 0
self._need_snap = None
# Calibrate or not
self._calibrate = True
# Initialization from the super class
super().__init__(product_path, archive_path, output_path, remove_tmp, **kwargs)
def _map_bands(self) -> None:
"""
Map bands
"""
self.bands.map_bands(
{
band_name: SarBand(
eoreader_name=band_name,
name=band_name.name,
gsd=self.pixel_size,
id=band_name.value,
asset_role=INTENSITY,
)
for band_name in self.get_existing_bands()
}
)
def _pre_init(self, **kwargs) -> None:
"""
Function used to pre_init the products
(setting needs_extraction and so on)
"""
self.tile_name = None
self.sensor_type = SensorType.SAR
self.bands = SarBandMap()
self.is_ortho = False
self._need_snap = self._need_snap_to_pre_process()
def _post_init(self, **kwargs) -> None:
"""
Function used to post_init the products
(setting product-type, band names and so on)
"""
self._set_sensor_mode()
self.pol_channels = self._get_raw_bands()
[docs]
@cache
def get_snap_version(self):
return snap.get_snap_version()
@cache
def _has_snap_10_or_higher(self) -> bool:
"""True if SNAP version is 10 or higher"""
return misc.compare_version(self.get_snap_version(), "10.0.0", ">=")
def _get_predictor(self) -> int:
"""
Set LZW predictor to 1 in order to SNAP to be able to read this GeoTiff (in despeckle operations mostly).
Else set to 3.
Caused by: javax.imageio.IIOException: Illegal value for Predictor in TIFF file
This is related to JAVA imageio library, not SNAP directly, so be sure to have imageio up-to-date to set 3.
For now, it is unknown to know if imageio handles predictor = 3, so live it to 1.
"""
# If we could know if imageio handles Predictor=3:
# # 3 for float if handled
# return 3 if xxx else 1
# But we cannot:
return 1
def _need_snap_to_pre_process(self):
"""This product needs SNAP for pre-process."""
raw_band_path = self.get_raw_band_paths()
# Cannot use get_default_band here, because of recursion
def_key = list(raw_band_path.keys())[0]
with rasterio.open(raw_band_path[def_key]) as ds:
raw_crs = ds.crs
need_snap = not (raw_crs is not None and raw_crs.is_projected)
return need_snap
[docs]
def get_default_band(self) -> BandNames:
"""
Get default band:
The first existing one between :code:`VV` and :code:`HH` for SAR data.
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.get_default_band()
<SarBandNames.VV: 'VV'>
Returns:
str: Default band
"""
existing_bands = self._get_raw_bands()
if not existing_bands:
raise InvalidProductError(f"No band exists for products: {self.name}")
# The order matters, as we almost always prefer VV and HH
if sab.VV in existing_bands:
default_band = sab.VV
elif sab.HH in existing_bands:
default_band = sab.HH
elif sab.VH in existing_bands:
default_band = sab.VH
elif sab.HV in existing_bands:
default_band = sab.HV
else:
raise InvalidTypeError(f"Invalid bands for products: {existing_bands}")
return default_band
[docs]
def get_default_band_path(self, **kwargs) -> AnyPathType:
"""
Get default band path (the first existing one between :code:`VV` and :code:`HH` for SAR data), ready to use (orthorectified)
.. WARNING:: This functions orthorectifies SAR bands if not existing !
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.get_default_band_path()
Executing processing graph
....10%....20%....30%....40%....50%....60%....70%....80%....90% done.
'20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif'
Args:
kwargs: Additional arguments
Returns:
AnyPathType: Default band path
"""
default_band = self.get_default_band()
band_path = self.get_band_paths([default_band], **kwargs)
return band_path[default_band]
[docs]
@cache
@abstractmethod
def wgs84_extent(self) -> gpd.GeoDataFrame:
"""
Get the WGS84 extent of the file before any reprojection.
This is useful when the SAR pre-process has not been done yet.
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.wgs84_extent()
Name ... geometry
0 Sentinel-1 Image Overlay ... POLYGON ((0.85336 42.24660, -2.32032 42.65493,...
[1 rows x 12 columns]
Returns:
gpd.GeoDataFrame: WGS84 extent as a gpd.GeoDataFrame
"""
raise NotImplementedError
[docs]
@cache
def extent(self) -> gpd.GeoDataFrame:
"""
Get UTM extent of the tile
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.utm_extent()
Name ... geometry
0 Sentinel-1 Image Overlay ... POLYGON ((817914.501 4684349.823, 555708.624 4...
[1 rows x 12 columns]
Returns:
gpd.GeoDataFrame: Extent in UTM
"""
# Get WGS84 extent
extent_wgs84 = self.wgs84_extent()
# Convert to UTM
return extent_wgs84.to_crs(self.crs())
[docs]
@cache
def crs(self) -> crs.CRS:
"""
Get UTM projection
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.utm_crs()
CRS.from_epsg(32630)
Returns:
crs.CRS: CRS object
"""
# Get WGS84 extent
extent_wgs84 = self.wgs84_extent()
# Estimate UTM from extent
return extent_wgs84.estimate_utm_crs()
@abstractmethod
def _set_sensor_mode(self) -> None:
"""
Set SAR sensor mode
"""
raise NotImplementedError
[docs]
def get_band_paths(
self, band_list: list, pixel_size: float = None, **kwargs
) -> dict:
"""
Return the paths of required bands.
.. WARNING:: This functions orthorectifies and despeckles SAR bands if not existing !
.. code-block:: python
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.get_band_paths([VV, HH])
{
<SarBandNames.VV: 'VV'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif'
}
>>> # HH doesn't exist
Args:
band_list (list): List of the wanted bands
pixel_size (float): Band pixel size
kwargs: Other arguments used to load bands
Returns:
dict: Dictionary containing the path of each queried band
"""
band_paths = {}
for band in band_list:
if self.bands[band] is None:
raise InvalidProductError(
f"Non existing band ({band.name}) for {self.name}"
)
try:
# Try to load orthorectified bands
band_id = self.bands[band].id
band_paths[band] = path.get_file_in_dir(
self._get_band_folder(),
f"*{self.condensed_name}_{band_id}.tif",
exact_name=True,
)
except FileNotFoundError:
speckle_band = sab.corresponding_speckle(band)
if speckle_band in self.pol_channels:
if sab.is_despeckle(band):
# Check if existing speckle ortho band
try:
path.get_file_in_dir(
self._get_band_folder(),
f"*{self.condensed_name}_{self.bands[speckle_band].id}.tif",
exact_name=True,
)
except FileNotFoundError:
self._pre_process_sar(speckle_band, pixel_size, **kwargs)
# Despeckle the noisy band
band_paths[band] = self._despeckle_sar(speckle_band, **kwargs)
else:
band_paths[band] = self._pre_process_sar(
band, pixel_size, **kwargs
)
return band_paths
[docs]
def get_raw_band_paths(self, **kwargs) -> dict:
"""
Return the existing band paths (as they come with the archived products).
Args:
**kwargs: Additional arguments
Returns:
dict: Dictionary containing the path of every band existing in the raw products
"""
extended_fmt = _ExtendedFormatter()
band_paths = {}
for band in sab.speckle_list():
band_regex = extended_fmt.format(self._raw_band_regex, band.value)
if self.is_archived:
if self.path.suffix == ".zip":
try:
band_paths[band] = path.get_archived_rio_path(
self.path, band_regex.replace("*", ".*") + "$", as_list=True
)[0]
# Get as a list but keep only the first item (S1-SLC with 3 swaths)
except FileNotFoundError:
continue
else:
raise InvalidProductError(
f"Only zipped products can be processed without extraction: {self.path}"
)
else:
try:
band_paths[band] = path.get_file_in_dir(
self._band_folder, band_regex, exact_name=True, get_list=True
)[0]
# Get as a list but keep only the first item (S1-SLC with 3 swaths)
except FileNotFoundError:
continue
return band_paths
def _get_raw_bands(self) -> list:
"""
Return the existing band paths (as they come with th archived products).
Returns:
list: List of existing bands in the raw products (vv, hh, vh, hv)
"""
band_paths = self.get_raw_band_paths()
return list(band_paths.keys())
[docs]
def get_existing_band_paths(self) -> dict:
"""
Return the existing orthorectified band paths (including despeckle bands).
.. WARNING:: This functions orthorectifies SAR bands if not existing !
.. WARNING:: This functions despeckles SAR bands if not existing !
.. code-block:: python
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_band_paths()
Executing processing graph
....10%....20%....30%....40%....50%....60%....70%....80%....90% done.
Executing processing graph
....10%....20%....30%....40%....50%....60%....70%....80%....90% done.
{
<SarBandNames.VV: 'VV'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV.tif',
<SarBandNames.VH: 'VH'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VH.tif',
<SarBandNames.VV_DSPK: 'VV_DSPK'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VV_DSPK.tif',
<SarBandNames.VH_DSPK: 'VH_DSPK'>: '20191215T060906_S1_IW_GRD/20191215T060906_S1_IW_GRD_VH_DSPK.tif'
}
Returns:
dict: Dictionary containing the path of every orthorectified bands
"""
return self.get_band_paths(self.get_existing_bands())
[docs]
def get_existing_bands(self) -> list:
"""
Return the existing orthorectified bands (including despeckle bands).
.. code-block:: python
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.get_existing_bands()
[<SarBandNames.VV: 'VV'>,
<SarBandNames.VH: 'VH'>,
<SarBandNames.VV_DSPK: 'VV_DSPK'>,
<SarBandNames.VH_DSPK: 'VH_DSPK'>]
Returns:
list: List of existing bands in the products
"""
# Get raw bands (maximum number of bands)
raw_bands = self._get_raw_bands()
existing_bands = raw_bands + [
sab.corresponding_despeckle(band) for band in raw_bands
]
return existing_bands
# unused band_name (compatibility reasons)
# pylint: disable=W0613
def _read_band(
self,
band_path: AnyPathType,
band: BandNames = None,
pixel_size: Union[tuple, list, float] = None,
size: Union[list, tuple] = None,
**kwargs,
) -> xr.DataArray:
"""
Read band from disk.
.. WARNING::
Invalid pixels are not managed here
Args:
band_path (AnyPathType): Band path
band (BandNames): Band to read
pixel_size (Union[tuple, list, float]): Size of the pixels of the wanted band, in dataset unit (X, Y)
size (Union[tuple, list]): Size of the array (width, height). Not used if pixel_size is provided.
kwargs: Other arguments used to load bands
Returns:
xr.DataArray: Band xarray
"""
# TODO: check if that works
# In case of data that doesn't have any known pixel_size
if self.pixel_size < 0.0:
with rasterio.open(band_path) as ds:
self.pixel_size = ds.res[0]
try:
if pixel_size < 0.0:
pixel_size = self.pixel_size
except TypeError:
pass
return utils.read(
band_path,
pixel_size=pixel_size,
size=size,
resampling=Resampling.bilinear,
as_type=np.float32,
**kwargs,
)
def _load_bands(
self,
bands: Union[list, BandNames],
pixel_size: float = None,
size: Union[list, tuple] = None,
**kwargs,
) -> dict:
"""
Load bands as numpy arrays with the same pixel size (and same metadata).
Args:
bands (list, BandNames): List of the wanted bands
pixel_size (float): Band pixel size in meters
size (Union[tuple, list]): Size of the array (width, height). Not used if pixel_size is provided.
kwargs: Other arguments used to load bands
Returns:
dict: Dictionary {band_name, band_xarray}
"""
# Return empty if no band are specified
if not bands:
return {}
# Get band paths
bands = types.make_iterable(bands)
if pixel_size is None and size is not None:
pixel_size = self._pixel_size_from_img_size(size)
band_paths = self.get_band_paths(bands, pixel_size, **kwargs)
# Open bands and get array (resampled if needed)
band_arrays = {}
for band_name, band_path in band_paths.items():
# Read SAR band
band_arrays[band_name] = self._read_band(
band_path, pixel_size=pixel_size, size=size, **kwargs
)
return band_arrays
def _pre_process_sar(self, band: sab, pixel_size: float = None, **kwargs) -> str:
"""
Pre-process SAR data (geocoding...)
Args:
band (sbn): Band to preprocess
pixel_size (float): Pixel size
kwargs: Additional arguments
Returns:
str: Band path
"""
# pixel_size (use SNAP default pixel size for terrain correction)
def_pixel_size = float(os.environ.get(SAR_DEF_PIXEL_SIZE, 0))
pixel_size = (
pixel_size
if (pixel_size and pixel_size != self.pixel_size)
else def_pixel_size
)
if not self._need_snap:
# Set the nodata and write the image where they belong
arr = utils.read(
self.get_raw_band_paths(**kwargs)[band],
pixel_size=pixel_size if pixel_size != 0 else None,
masked=False,
)
arr = arr.where(arr != self._raw_no_data, np.nan)
file_path = os.path.join(
self._get_band_folder(writable=True),
f"{self.condensed_name}_{band.name}.tif",
)
utils.write(
arr,
file_path,
dtype=np.float32,
nodata=self._snap_no_data,
predictor=self._get_predictor(),
)
return file_path
else:
# Create target dir (tmp dir)
with tempfile.TemporaryDirectory() as tmp_dir:
# Use dimap for speed and security (i.e. GeoTiff's broken georef)
pp_target = os.path.join(tmp_dir, f"{self.condensed_name}")
pp_dim = pp_target + ".dim"
# Pre-process graph
if PP_GRAPH not in os.environ:
if self.constellation_id == Constellation.S1.name:
sat = "s1"
if self.sensor_mode.value == "SM":
sat += "_sm"
elif not self._calibrate:
sat = "no_calib"
else:
sat = "sar"
spt = "grd" if self.sar_prod_type == SarProductType.GDRG else "cplx"
pp_graph = utils.get_data_dir().joinpath(
f"{spt}_{sat}_preprocess_default.xml"
)
else:
pp_graph = AnyPath(os.environ[PP_GRAPH]).resolve()
if not pp_graph.is_file() or not pp_graph.suffix == ".xml":
FileNotFoundError(f"{pp_graph} cannot be found.")
# Command line
if not os.path.isfile(pp_dim):
res_deg = (
pixel_size / 10.0 * 8.983152841195215e-5
) # Approx, shouldn't be used
# Manage DEM name
try:
dem_name = SnapDems.from_value(
os.environ.get(SNAP_DEM_NAME, SnapDems.GLO_30)
)
except AttributeError as ex:
raise ValueError(
f"{SNAP_DEM_NAME} should be chosen among {SnapDems.list_values()}"
) from ex
# Manage DEM path
if dem_name == SnapDems.EXT_DEM:
dem_path = os.environ.get(DEM_PATH)
if not dem_path:
raise ValueError(
f"You specified '{dem_name.value}' but you didn't give any DEM path. "
f"Please set the environment variable {DEM_PATH} "
f"or change {SNAP_DEM_NAME} to an acceptable SNAP DEM."
)
else:
dem_path = ""
# Download cloud path to cache
prod_path = kwargs.get("prod_path")
if prod_path is None:
if path.is_cloud_path(self.path):
LOGGER.debug(
f"Caching {self.path} to {os.path.join(tmp_dir, self.path.name)}"
)
if self.path.is_dir():
prod_path = os.path.join(
tmp_dir, self.path.name, self.snap_filename
)
self.path.download_to(
os.path.join(tmp_dir, self.path.name)
)
else:
prod_path = (
self.path.fspath
) # In tmp file, no need to download_to
else:
prod_path = self.path.joinpath(self.snap_filename)
# Create SNAP CLI
cmd_list = snap.get_gpt_cli(
pp_graph,
[
f"-Pfile={strings.to_cmd_string(prod_path)}",
f"-Pcalib_pola={strings.to_cmd_string(band.name)}",
f"-Pdem_name={strings.to_cmd_string(dem_name.value)}",
f"-Pdem_path={strings.to_cmd_string(dem_path)}",
f"-Pcrs={self.crs()}",
f"-Pres_m={pixel_size}",
f"-Pres_deg={res_deg}",
f"-Pout={strings.to_cmd_string(pp_dim)}",
],
display_snap_opt=LOGGER.level == logging.DEBUG,
)
# Pre-process SAR images according to the given graph
LOGGER.debug("Pre-process SAR image")
try:
misc.run_cli(cmd_list)
except RuntimeError as ex:
raise RuntimeError("Something went wrong with SNAP!") from ex
# Convert DIMAP images to GeoTiff
LOGGER.debug("Converting DIMAP to GeoTiff")
return self._write_sar(pp_dim, band.value, **kwargs)
def _despeckle_sar(self, band: sab, **kwargs) -> str:
"""
Pre-process SAR data (geocode...)
Args:
band (sbn): Band to despeckle
kwargs: Additional arguments
Returns:
str: Despeckled path
"""
# Create target dir (tmp dir)
with tempfile.TemporaryDirectory() as tmp_dir:
# Out files
target_file = os.path.join(tmp_dir, f"{self.condensed_name}")
dspk_dim = target_file + ".dim"
# Despeckle graph
if DSPK_GRAPH not in os.environ:
dspk_graph = utils.get_data_dir().joinpath("sar_despeckle_default.xml")
else:
dspk_graph = AnyPath(os.environ[DSPK_GRAPH]).resolve()
if not dspk_graph.is_file() or not dspk_graph.suffix == ".xml":
FileNotFoundError(f"{dspk_graph} cannot be found.")
# Create command line and run it
if not os.path.isfile(dspk_dim):
dspk_path = self.get_band_paths([band], **kwargs)[band]
cmd_list = snap.get_gpt_cli(
dspk_graph,
[f"-Pfile={dspk_path}", f"-Pout={dspk_dim}"],
display_snap_opt=False,
)
# Pre-process SAR images according to the given graph
LOGGER.debug(f"Despeckling {band.name}")
try:
misc.run_cli(cmd_list)
except RuntimeError as ex:
raise RuntimeError("Something went wrong with SNAP!") from ex
# Convert DIMAP images to GeoTiff
out = self._write_sar(dspk_dim, band.value.upper(), dspk=True, **kwargs)
return out
def _write_sar(self, dim_path: str, pol: str, dspk=False, **kwargs) -> str:
"""
Write SAR image on disk.
Args:
dim_path (str): DIMAP path
pol (str): Polarization name
kwargs: Additional arguments
Returns:
str: SAR path
"""
def interp_na(array, dim):
try:
array = array.interpolate_na(dim=dim, limit=10, keep_attrs=True)
except ValueError:
try:
# ValueError: Index 'y' must be monotonically increasing
dim_idx = getattr(array, dim)
reversed_dim_idx = list(reversed(dim_idx))
array = array.reindex(**{dim: reversed_dim_idx})
array = array.interpolate_na(dim=dim, limit=10, keep_attrs=True)
array = array.reindex(**{dim: dim_idx})
except ValueError:
pass
return array
# Get the .img path(s)
try:
imgs = utils.get_dim_img_path(dim_path, f"*{pol}*")
except FileNotFoundError:
imgs = utils.get_dim_img_path(dim_path) # Maybe not the good name
# Manage cases where multiple swaths are ortho independently
if len(imgs) > 1:
mos_path, exists = self._get_out_path(
path.get_filename(dim_path) + "_mos.vrt"
)
if not exists:
# Get .img file path (readable by rasterio)
# Useful for PAZ SC data (multiswath)
rasters.merge_vrt(imgs, mos_path)
else:
mos_path = imgs[0]
# Open SAR image and convert it to a clean geotiff
with rioxarray.open_rasterio(mos_path) as arr:
arr = arr.where(arr != self._snap_no_data, np.nan)
# Interpolate if needed (interpolate na works only 1D-like, sadly)
# DSPK step in done on already interpolated data
if not dspk and kwargs.get(SAR_INTERP_NA, False):
arr = interp_na(arr, dim="y")
arr = interp_na(arr, dim="x")
# Get suffix
suffix = kwargs.get("suffix")
# Save the file as the terrain-corrected image
file_path = os.path.join(
self._get_band_folder(writable=True),
f"{path.get_filename(dim_path)}_{pol}{'_DSPK' if dspk else ''}{f'_{suffix}' if suffix else ''}.tif",
)
# WARNING: Set nodata to 0 here as it is the value wanted by SNAP !
# SNAP < 10.0.0 fails with classic predictor !!! Set the predictor to the default value (1) !!!
# Caused by: javax.imageio.IIOException: Illegal value for Predictor in TIFF file
utils.write(
arr,
file_path,
dtype=np.float32,
nodata=self._snap_no_data,
predictor=self._get_predictor(),
)
return file_path
def _compute_hillshade(
self,
dem_path: str = "",
pixel_size: Union[float, tuple] = None,
size: Union[list, tuple] = None,
resampling: Resampling = Resampling.bilinear,
) -> AnyPathType:
"""
Compute Hillshade mask
Args:
dem_path (str): DEM path, using EUDEM/MERIT DEM if none
pixel_size (Union[float, tuple]): Pixel size in meters. If not specified, use the product pixel size.
resampling (Resampling): Resampling method
size (Union[tuple, list]): Size of the array (width, height). Not used if pixel_size is provided.
Returns:
AnyPathType: Hillshade mask path
"""
raise InvalidProductError("Impossible to compute hillshade mask for SAR data.")
def _has_cloud_band(self, band: BandNames) -> bool:
"""
Does this product has the specified cloud band ?
.. code-block:: python
>>> from eoreader.reader import Reader
>>> from eoreader.bands import *
>>> path = r"S1A_IW_GRDH_1SDV_20191215T060906_20191215T060931_030355_0378F7_3696.zip"
>>> prod = Reader().open(path)
>>> prod.has_cloud_band(CLOUDS)
False
"""
return False
def _get_condensed_name(self) -> str:
"""
Get products condensed name ({acq_datetime}_{constellation}_{polarization}_{sensor_mode}_{product_type}).
Returns:
str: Condensed name
"""
pol_chan = [pol.value for pol in self.pol_channels]
return f"{self.get_datetime()}_{self.constellation.name}_{'_'.join(pol_chan)}_{self.sensor_mode.name}_{self.product_type.value}"
def _update_attrs_constellation_specific(
self, xarr: xr.DataArray, bands: list, **kwargs
) -> xr.DataArray:
"""
Update attributes of the given array (constellation specific)
Args:
xarr (xr.DataArray): Array whose attributes need an update
bands (list): Array name (as a str or a list)
Returns:
xr.DataArray: Updated array/dataset
"""
return xarr
def _to_repr_constellation_specific(self) -> list:
"""
Representation specific to the constellation
Returns:
list: Representation list (constellation specific)
"""
return [
f"\torbit direction: {self.get_orbit_direction().value}",
]
def _fallback_wgs84_extent(self, extent_file_name: str = None) -> gpd.GeoDataFrame:
"""
Fallback for wgs84 extent (slower than to read a file, but should work in almsot any case)
Args:
extent_file_name (str): Extent file name, if existing
Returns:
gpd.GeoDataFrame: WGS84 extent
"""
with rasterio.open(self.get_raw_band_paths()[self.get_default_band()]) as ds:
if ds.crs is not None:
extent_wgs84 = gpd.GeoDataFrame(
geometry=[geometry.from_bounds_to_polygon(*ds.bounds)],
crs=ds.crs,
)
elif ds.gcps is not None:
gcps, crs = ds.gcps
corners = geometry.from_bounds_to_polygon(*ds.bounds).exterior.coords
extent_poly = Polygon(
[rasterio.transform.from_gcps(gcps) * corner for corner in corners]
)
extent_wgs84 = gpd.GeoDataFrame(geometry=[extent_poly], crs=crs)
else:
if extent_file_name:
name = f"({extent_file_name}) "
else:
name = ""
raise InvalidProductError(
f"Extent file {name}not found in {self.path}. "
"Default band isn't georeferenced and have no GCPs. "
"It is therefore impossible to determine quickly the extent of this product. "
"Please write an issue on GitHub!"
)
return extent_wgs84
