using `cog_translate` on netCDF4 with improper georeferencing

I am trying to use cog_translate to convert some netCDF4 datasets to COG, and it appears these files do not have proper georeferencing.

I have modified cogeo.py to accept vrt_params, so that I can manually pass the src_crs and src_transform via the WarpedVRTReaderBase mixin of WarpedVRT, as you can see here:

https://github.com/ryanjdillon/rio-cogeo/blob/cog_translate_vrt_params/rio_cogeo/cogeo.py#L87-L99

Then I’m passing the following vrt_params:

vrt_params = dict(src_transform=Affine(gdal_geotransform), src_crs='EPSG:4326')

This get’s me past the CRS is None error, but it doesn’t seem to acknowledge the Affine transform I pass to src_transform, resulting in the following error:

CPLE_AppDefinedError: The transformation is already “north up” or a transformation between pixel/line and georeferenced coordinates cannot be computed for /home/ryan/data/webstep/kvt/NVE_Sorlandet/NVE_Sorlandet_wslev0_xyt_synth.nc4. There is no affine transformation and no GCPs. Specify transformation option SRC_METHOD=NO_GEOTRANSFORM to bypass this check.

Any suggestions on how I might get this to work?

[vincentsarago]

@ryanjdillon Long time no see 😄 I finally had to work with netcdf recently and thanks to some recent change in rio-cogeo we can now pass Memory or VRT files to cog_translate.

• Latest version

def translate(
    src_path,
    cog_path,
    cogeo_profile="deflate",
    cogeo_options={"blockxsize": 128, "blockysize": 128, "predictor": 2, "zlevel": 7},
):
    """Convert a netcdf to COG."""
    config = dict(GDAL_NUM_THREADS="ALL_CPUS", GDAL_TIFF_OVR_BLOCKSIZE="128")

    with rasterio.Env(**config):
        with rasterio.open(src_path) as src_dst:
            with WarpedVRT(
                src_dst,
                crs="epsg:4326",
                warp_mem_limit=0.1,   # Reduce chunk size, see: https://github.com/OSGeo/gdal/issues/1989
                num_threads=8
            ) as vrt:
                profile = cog_profiles.get(cogeo_profile)
                profile.update(cogeo_options)
                cog_translate(
                    vrt,
                    cog_path,
                    profile,
                    config=config,
                    in_memory=True,
                    quiet=True,
                )

• First version

def translate(
    src_path,
    cog_path,
    cogeo_profile="deflate",
    cogeo_options={"blockxsize": 128, "blockysize": 128, "predictor": 2, "zlevel": 7},
):
    """Convert a netcdf to COG."""
    config = dict(GDAL_NUM_THREADS="ALL_CPUS", GDAL_TIFF_OVR_BLOCKSIZE="128")

    with rasterio.Env(**config):
        with rasterio.open(src_path) as src_dst:
            dst_transform, dst_width, dst_height = calculate_default_transform(
                src_dst.crs, "epsg:4326", src_dst.width, src_dst.height, *src_dst.bounds
            )
            meta = dict(
                driver="GTiff",
                dtype=src_dst.dtypes[0],
                count=src_dst.count,
                height=dst_height,
                width=dst_width,
                crs="epsg:4326",
                transform=dst_transform,
                nodata=-999,
                tiled=True,
                compress="deflate",
                blockxsize=256,
                blockysize=256,
            )
            with MemoryFile() as memfile:
                with memfile.open(**meta) as mem:
                    reproject(
                        rasterio.band(src_dst, 1),
                        rasterio.band(mem, 1),
                        resampling=rasterio.enums.Resampling.nearest,
                        warp_mem_limit=1,  # Reduce chunk size, see: https://github.com/OSGeo/gdal/issues/1989
                        num_threads=8,
                    )
                    mem.update_tags(**src_dst.tags(1))
                    profile = cog_profiles.get(cogeo_profile)
                    profile.update(cogeo_options)
                    cog_translate(
                        mem,
                        cog_path,
                        profile,
                        config=config,
                        in_memory=True,
                        quiet=True,
                    )

since we can use MemoryFile or VRT, we can now fix the data upfront and pass it to cog_translate so I think we can close this issue.

[vincentsarago]

🤔 thanks @ryanjdillon,

I also tried using cog_validate on the generated tiff, and it bugged out when src.get_tag_item(“BLOCK_OFFSET_0_0”, “TIFF”, bidx=1) was None.

Can you checkout what https://svn.osgeo.org/gdal/trunk/gdal/swig/python/samples/validate_cloud_optimized_geotiff.py gives you ?

I could submit a PR if you want to look over / incorporate these changes

Allowing vrt_params as input might complicated a lot the code (especially with the changes from https://github.com/cogeotiff/rio-cogeo/pull/62). That said, I think we could document it and tell how to generate a proper temporary file from the netCDF4 before passing it to rio-cogeo.

I’m no expert with netCDF so I’m may be missing some knowledge here