# Copyright 2025, 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.
"""
COSMO-SkyMed products.
More info `here <https://egeos.my.salesforce.com/sfc/p/#1r000000qoOc/a/69000000JXxZ/WEEbowzi5cmY8vLqyfAAMKZ064iN1eWw_qZAgUkTtXI>`_.
"""
import logging
import os
import tempfile
from datetime import datetime
from enum import unique
from typing import Union
import geopandas as gpd
import numpy as np
import rasterio
from lxml import etree
from lxml.builder import E
from rasterio import merge
from sertit import AnyPath, misc, path, rasters_rio, strings, vectors
from sertit.misc import ListEnum
from sertit.types import AnyPathStrType, AnyPathType
from shapely.geometry import Polygon, box
from eoreader import DATETIME_FMT, EOREADER_NAME, cache, utils
from eoreader.exceptions import InvalidProductError
from eoreader.products import SarProduct, SarProductType
from eoreader.products.product import OrbitDirection
LOGGER = logging.getLogger(EOREADER_NAME)
[docs]
@unique
class CosmoProductType(ListEnum):
"""
COSMO-SkyMed (both generations) products types.
The product classed are not specified here.
More info
`here <https://egeos.my.salesforce.com/sfc/p/#1r000000qoOc/a/69000000JXxZ/WEEbowzi5cmY8vLqyfAAMKZ064iN1eWw_qZAgUkTtXI>`_.
"""
RAW = "RAW"
"""Level 0"""
SCS = "SCS"
"""Level 1A, Single-look Complex Slant"""
DGM = "DGM"
"""Level 1B, Detected Ground Multi-look"""
GEC = "GEC"
"""Level 1C, Geocoded Ellipsoid Corrected"""
GTC = "GTC"
"""Level 1D, Geocoded Terrain Corrected"""
[docs]
class CosmoProduct(SarProduct):
"""
Class for COSMO-SkyMed (both generations) Products
More info
`here <https://egeos.my.salesforce.com/sfc/p/#1r000000qoOc/a/69000000JXxZ/WEEbowzi5cmY8vLqyfAAMKZ064iN1eWw_qZAgUkTtXI>`_.
"""
[docs]
def __init__(
self,
product_path: AnyPathStrType,
archive_path: AnyPathStrType = None,
output_path: AnyPathStrType = None,
remove_tmp: bool = False,
**kwargs,
) -> None:
try:
product_path = AnyPath(product_path)
self._img_path = next(product_path.glob("*.h5"))
except (IndexError, StopIteration) as ex:
raise InvalidProductError(
f"Image file (*.h5) not found in {product_path}"
) from ex
# Initialization from the super class
super().__init__(product_path, archive_path, output_path, remove_tmp, **kwargs)
def _pre_init(self, **kwargs) -> None:
"""
Function used to pre_init the products
(setting needs_extraction and so on)
"""
# Private attributes
self._raw_band_regex = "*_{}_*.h5"
self._band_folder = self.path
self.snap_filename = self._img_path.name
# SNAP cannot process its archive
self.needs_extraction = True
# Get the number of swaths of this product
with rasterio.open(str(self._img_path)) as raw_h5:
sub_ds = [s.split("//")[-1] for s in raw_h5.subdatasets]
# Never more than 10 swaths
self.nof_swaths = len(set(s.split("/")[0] for s in sub_ds if "S0" in s))
# Pre init done by the super class
super()._pre_init(**kwargs)
[docs]
@cache
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"1011117-766193"
>>> prod = Reader().open(path)
>>> prod.wgs84_extent()
geometry
0 POLYGON ((108.09797 15.61011, 108.48224 15.678...
Returns:
gpd.GeoDataFrame: WGS84 extent as a gpd.GeoDataFrame
"""
root, _ = self.read_mtd()
# Open extent coordinates
try:
def from_str_to_arr(geo_coord: str):
return np.array(strings.str_to_list(geo_coord), dtype=float)[:2][::-1]
bl_corner = from_str_to_arr(root.findtext(".//GeoCoordBottomLeft"))
br_corner = from_str_to_arr(root.findtext(".//GeoCoordBottomRight"))
tl_corner = from_str_to_arr(root.findtext(".//GeoCoordTopLeft"))
tr_corner = from_str_to_arr(root.findtext(".//GeoCoordTopRight"))
if bl_corner is None:
raise InvalidProductError("Invalid XML: missing extent.")
extent_wgs84 = gpd.GeoDataFrame(
geometry=[Polygon([tl_corner, tr_corner, br_corner, bl_corner])],
crs=vectors.WGS84,
)
except ValueError as exc:
def from_str_to_arr(geo_coord: str):
str_list = [
it
for it in strings.str_to_list(geo_coord, additional_separator="\n")
if "+" not in it
]
# Create tuples of 2D coords
coord_list = []
coord = [0.0, 0.0]
for it_id, it in enumerate(str_list):
if it_id % 3 == 0:
# Invert lat and lon
coord[1] = float(it)
elif it_id % 3 == 1:
# Invert lat and lon
coord[0] = float(it)
elif it_id % 3 == 2:
# Z coordinates: do not store it
# Append the last coordinates
coord_list.append(coord.copy())
# And reinit it
coord = [0.0, 0.0]
return coord_list
bl_corners = from_str_to_arr(root.findtext(".//GeoCoordBottomLeft"))
br_corners = from_str_to_arr(root.findtext(".//GeoCoordBottomRight"))
tl_corners = from_str_to_arr(root.findtext(".//GeoCoordTopLeft"))
tr_corners = from_str_to_arr(root.findtext(".//GeoCoordTopRight"))
if not bl_corners:
raise InvalidProductError("Invalid XML: missing extent.") from exc
assert (
len(bl_corners) == len(br_corners) == len(tl_corners) == len(tr_corners)
)
polygons = [
Polygon(
[
tl_corners[coord_id],
tr_corners[coord_id],
br_corners[coord_id],
bl_corners[coord_id],
]
)
for coord_id in range(len(bl_corners))
]
extents_wgs84 = gpd.GeoDataFrame(
geometry=polygons,
crs=vectors.WGS84,
)
extent_wgs84 = gpd.GeoDataFrame(
geometry=[box(*extents_wgs84.total_bounds)],
crs=vectors.WGS84,
)
return extent_wgs84
def _set_product_type(self) -> None:
"""Set products type"""
# Get MTD XML file
root, _ = self.read_mtd()
# DGM_B, or SCS_B -> remove last 2 characters
prod_type = root.findtext(".//ProductType")[:-2]
if not prod_type:
raise InvalidProductError("mode not found in metadata!")
self.product_type = CosmoProductType.from_value(prod_type)
if self.product_type == CosmoProductType.GTC:
self.sar_prod_type = SarProductType.ORTHO
elif self.product_type == CosmoProductType.GEC:
self.sar_prod_type = SarProductType.GEOCODED
elif self.product_type == CosmoProductType.DGM:
self.sar_prod_type = SarProductType.GRD
elif self.product_type == CosmoProductType.SCS:
self.sar_prod_type = SarProductType.CPLX
else:
raise NotImplementedError(
f"{self.product_type.value} product type is not available for {self.name}"
)
[docs]
def get_datetime(self, as_datetime: bool = False) -> Union[str, datetime]:
"""
Get the product's acquisition datetime, with format :code:`YYYYMMDDTHHMMSS` <-> :code:`%Y%m%dT%H%M%S`
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"1011117-766193"
>>> prod = Reader().open(path)
>>> prod.get_datetime(as_datetime=True)
datetime.datetime(2020, 10, 28, 22, 46, 25)
>>> prod.get_datetime(as_datetime=False)
'20201028T224625'
Args:
as_datetime (bool): Return the date as a datetime.datetime. If false, returns a string.
Returns:
Union[str, datetime.datetime]: Its acquisition datetime
"""
if self.datetime is None:
# Get MTD XML file
root, _ = self.read_mtd()
# Open identifier
acq_date = root.findtext(".//SceneSensingStartUTC")
if not acq_date:
raise InvalidProductError("SceneSensingStartUTC not found in metadata!")
# Convert to datetime
# 2020-10-28 22:46:24.808662850
# To many milliseconds (strptime accepts max 6 digits) -> needs to be cropped
date = datetime.strptime(acq_date[:-3], "%Y-%m-%d %H:%M:%S.%f")
else:
date = self.datetime
if not as_datetime:
date = date.strftime(DATETIME_FMT)
return date
def _get_name_constellation_specific(self) -> str:
"""
Set product real name from metadata
Returns:
str: True name of the product (from metadata)
"""
# Get MTD XML file
root, _ = self.read_mtd()
# Open identifier
name = path.get_filename(root.findtext(".//ProductName"))
if not name:
raise InvalidProductError("ProductName not found in metadata!")
return name
@cache
def _read_mtd(self) -> (etree._Element, dict):
"""
Read metadata and outputs the metadata XML root and its namespaces as a dict
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"1001513-735093"
>>> prod = Reader().open(path)
>>> prod.read_mtd()
(<Element DeliveryNote at 0x2454ad4ee88>, {})
Returns:
(etree._Element, dict): Metadata XML root and its namespaces
"""
try:
mtd_from_path = "DFDN_*.h5.xml"
return self._read_mtd_xml(mtd_from_path)
except InvalidProductError:
try:
field_map = {
# ProductInfo
"ProductName": "Product Filename",
# "ProductId": ,
"MissionId": "Mission ID",
# "UniqueIdentifier": ,
"ProductGenerationDate": "Product Generation UTC",
# "UserRequestId": ,
# "ServiceRequestName": ,
# ProductDefinitionData
"ProductType": "Product Type",
"SceneSensingStartUTC": "Scene Sensing Start UTC",
"SceneSensingStopUTC": "Scene Sensing Stop UTC",
# "GeoCoordTopRightEN": ,
"GeoCoordSceneCentre": "Scene Centre Geodetic Coordinates",
"SatelliteId": "Satellite ID",
"AcquisitionMode": "Acquisition Mode",
"LookSide": "Look Side",
"ProjectionId": "Projection ID",
"DeliveryMode": "Delivery Mode",
"AcquisitionStationId": "Acquisition Station ID",
# ProcessingInfo
# "ProcessingLevel":,
# ProductCharacteristics
"AzimuthGeometricResolution": "Azimuth Geometric Resolution",
"GroundRangeGeometricResolution": "Ground Range Geometric Resolution",
}
sbi_field_map = {
"GeoCoordBottomLeft": "Bottom Left Geodetic Coordinates",
"GeoCoordBottomRight": "Bottom Right Geodetic Coordinates",
"GeoCoordTopLeft": "Top Left Geodetic Coordinates",
"GeoCoordTopRight": "Top Right Geodetic Coordinates",
# "GeoCoordTopRightEN": "Top Right East-North",
"NearLookAngle": "Near Look Angle",
"FarLookAngle": "Far Look Angle",
}
def h5_to_str(h5_val):
str_val = str(h5_val)
str_val = str_val.replace("[", "")
str_val = str_val.replace("]", "")
return str_val
import h5netcdf
with h5netcdf.File(str(self._img_path)) as netcdf_ds:
# Create XML attributes
global_attr = []
for xml_attr, h5_attr in field_map.items():
try:
global_attr.append(
E(xml_attr, h5_to_str(netcdf_ds.attrs[h5_attr]))
)
except KeyError:
# CSG products don't have their ProductName in the h5 file...
if xml_attr == "ProductName":
global_attr.append(
E(xml_attr, path.get_filename(self._img_path))
)
try:
# CSK products
sbi = netcdf_ds.groups["S01"].variables["SBI"]
except KeyError:
# CSG products
sbi = netcdf_ds.groups["S01"].variables["IMG"]
for xml_attr, h5_attr in sbi_field_map.items():
global_attr.append(E(xml_attr, h5_to_str(sbi.attrs[h5_attr])))
mtd = E.s3_global_attributes(*global_attr)
mtd_el = etree.fromstring(
etree.tostring(
mtd,
pretty_print=True,
xml_declaration=True,
encoding="UTF-8",
)
)
return mtd_el, {}
except KeyError as exc:
raise InvalidProductError(
"Missing the XML metadata file. Cannot read the product."
) from exc
[docs]
def get_quicklook_path(self) -> str:
"""
Get quicklook path if existing.
Returns:
str: Quicklook path
"""
qlk_path, qlk_exists = self._get_out_path(f"{self.condensed_name}_QLK.png")
if not qlk_exists:
with rasterio.open(str(self._img_path)) as ds:
quicklook_paths = [subds for subds in ds.subdatasets if "QLK" in subds]
if len(quicklook_paths) == 0:
LOGGER.warning(f"No quicklook found in {self.condensed_name}")
else:
utils.write(
utils.read(quicklook_paths[0]),
qlk_path,
dtype=np.uint8,
nodata=255,
driver="PNG",
)
if len(quicklook_paths) > 1:
LOGGER.info(
"For now, only the quicklook of the first swath is taken into account."
)
return str(qlk_path)
[docs]
@cache
def get_orbit_direction(self) -> OrbitDirection:
"""
Get cloud cover as given in the metadata
.. code-block:: python
>>> from eoreader.reader import Reader
>>> path = r"S2A_MSIL1C_20200824T110631_N0209_R137_T30TTK_20200824T150432.SAFE.zip"
>>> prod = Reader().open(path)
>>> prod.get_orbit_direction().value
"DESCENDING"
Returns:
OrbitDirection: Orbit direction (ASCENDING/DESCENDING)
"""
with rasterio.open(str(self._img_path)) as h5_xarr:
# Get the orbit direction
try:
od = OrbitDirection.from_value(h5_xarr.tags().get("Orbit_Direction"))
except TypeError as exc:
raise InvalidProductError(
"'Orbit_Direction' not found in h5 tags!"
) from exc
return od
def _pre_process_sar(
self, pre_processed_path, band, pixel_size: float = None, **kwargs
) -> AnyPathType:
"""
Pre-process SAR data (geocoding...)
Args:
band (sbn): Band to preprocess
pixel_size (float): Pixl size
kwargs: Additional arguments
Returns:
AnyPathType: Band path
"""
if self.product_type == CosmoProductType.GTC:
ortho_path = self.get_band_path(band, writable=True, **kwargs)
with rasterio.open(str(self._img_path)) as ds:
img_paths = [subds for subds in ds.subdatasets if "IMG" in subds]
if len(img_paths) == 0:
LOGGER.warning(f"No image found in {self.condensed_name}")
else:
utils.write(
utils.read(img_paths[0]),
ortho_path,
dtype=np.float32,
nodata=self._snap_no_data,
predictor=self._get_predictor(),
driver="GTiff", # SNAP doesn't handle COGs very well apparently
**utils._prune_keywords(
additional_keywords=["dtype", "nodata", "predictor", "driver"],
**kwargs,
),
)
if len(img_paths) > 1:
LOGGER.info(
"For now, only the image of the first swath is taken into account."
)
return ortho_path
elif misc.compare_version(self.get_snap_version(), "11.0.0", ">="):
return super()._pre_process_sar(pre_processed_path, band, **kwargs)
else:
if self.nof_swaths == 1:
return super()._pre_process_sar(pre_processed_path, band, **kwargs)
else:
LOGGER.warning(
"SNAP (before version 11.0.0) doesn't handle multiswath Cosmo-SkyMed products. This is a workaround. See https://github.com/sertit/eoreader/issues/78"
)
# For every swath, pre-process the swath array alone
pp_swath_path = []
import h5netcdf
with h5netcdf.File(str(self._img_path), phony_dims="access") as raw_h5:
for group in raw_h5.groups:
with tempfile.TemporaryDirectory() as tmp_dir:
LOGGER.debug(f"Processing {group}")
# Create a mock-up of a COSMO product with only one swath and handled by SNAP
prod_path = os.path.join(
tmp_dir, f"{path.get_filename(self._img_path)}.h5"
)
with h5netcdf.File(
prod_path, "w", phony_dims="access"
) as group_h5:
# Basic layer
group_h5.attrs.update(raw_h5.attrs)
# Change the swath to S01 as it is the only one read by SNAP (and is mandatory for the file to be recognized)
new_group = "S01"
group_h5.create_group(new_group)
group_h5.groups[new_group].attrs.update(
raw_h5.groups[group].attrs
)
# Copy all variables
for var_name in raw_h5.groups[group].variables:
var = raw_h5.groups[group].variables[var_name]
group_h5.groups[new_group].create_variable(
f"/{new_group}/{var_name}",
dimensions=var.dimensions,
dtype=var.dtype,
data=var,
chunks=var.chunks,
)
group_h5.groups[new_group].variables[
var_name
].attrs.update(var.attrs)
# Copy all groups
for grp_name in raw_h5.groups[group].groups:
grp = raw_h5.groups[group].groups[grp_name]
if (
grp_name
not in group_h5.groups[new_group].groups
):
group_h5.groups[new_group].create_group(
grp_name
)
group_h5.groups[new_group].groups[
grp_name
].attrs.update(grp.attrs)
# Pre-process swath
pp_swath_path.append(
super()._pre_process_sar(
pre_processed_path,
band,
prod_path=prod_path,
suffix=group,
**kwargs,
)
)
# Merge the swaths
LOGGER.debug("Merging the swaths")
pp_path = self.get_band_path(band, writable=True, **kwargs)
# Force GTiff to be used in SNAP
# Don't use rasters.merge_gtiff because off the predictor and the nodata...
try:
pp_ds = [rasterio.open(str(p)) for p in pp_swath_path]
merged_array, merged_transform = merge.merge(pp_ds, **kwargs)
merged_meta = pp_ds[0].meta.copy()
merged_meta.update(
{
"driver": "GTiff",
"height": merged_array.shape[1],
"width": merged_array.shape[2],
"transform": merged_transform,
}
)
finally:
for ds in pp_ds:
ds.close()
# Write
# 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
rasters_rio.write(
merged_array,
merged_meta,
pp_path,
nodata=self._snap_no_data,
predictor=self._get_predictor(),
driver="GTiff", # SNAP doesn't handle COGs very well apparently
)
return pp_path
