Output results- editor

openEO Platform
1

Dear,
Is it possible to specify what kind of data visualizes in the editor?
From this input for example:
datacube = session.load_collection(
“SENTINEL2_L2A”,
spatial_extent={“west”: 2, “south”: 42, “east”: 2.15, “north”: 42.15},
temporal_extent=[“2019-01-01T00:00:00.000Z”, “2019-01-31T23:59:59.999Z”],
bands=[“B04”, “B08”,“SCL”],
properties={“eo:cloud_cover”: lambda cc:lte(cc, 80 )}
)
Only I wanted to visualitze an output image, for example datacube2 = datacube.max_time() or other kind of process.
Is possible to define in if job.job_id:
print(job.job_id)
print(job.start_job())
print (job.describe_job())
else:
print(“Job ID is None”)

If so, could you let me know how.

Any help would be appreciated

Thank you
Imma

2

I’m not sure I completely understand because you mention “editor” (which usually means https://editor.openeo.cloud/), but from your code snippets I infer that you are working in a jupyter notebook environment. Is that correct?

Anyway, this kind of easy/automatic visualization is still an experimental feature. For now I would recommend to download the job result (e.g. with job.get_results().download_files("output-folder") ) as local files and work from these (e.g. visualize through rasterio/xarrio in your notebook, or use a tool like QGIS)

3

@i.serra if you would like to have an interactive look at the data itself, you could indeed use the Web Editor (editor.open.cloud), where you could define a process graph and directly see the result. This is an example using the extent you have provided and taking the minimum over the month of January 2019:

image
image1630×744 140 KB

4

Yes, it’s correct I am working in a Jupyter notebook, but I would to visualize the data in https://editor.openeo.cloud/

5

Ok, yes I will do with as local files and work from these (e.g. visualize through rasterio/xarrio in notebook

6

I tried to visualize in the Web Editor (editor.open.cloud), I got the following message:

image

Thank you

7

Could you please share the full code you are using? Or the process graph? You could get it from the web editor for instance:

image
image1682×728 40.5 KB

or using the python client with:

print(datacube.to_json())
or
datacube.print_json()

From the message I understand that the platform generates multiple files as a result and therefore the viewer might not be able to visualize them correctly.

@m.mohr what happens if we have multiple files as results and we try to visualize them?

8

The Editor tries to open as many tabs/maps as there are files available, here 9.
As opening multiple COGs might be demanding for slower computers the warning above is shown.
There’s a feature request to show them on a single map, but this will take a bit more time:

9

ok, instead of visulasize 9 images.
I have done this, as example:
datacube = datacube.reduce_dimension(dimension=“t”, reducer=“mean”)

{
“process_graph”: {
“loadcollection1”: {
“arguments”: {
“bands”: [
“B04”,
“B08”,
“SCL”
],
“id”: “SENTINEL2_L2A_SENTINELHUB”,
“properties”: {
“eo:cloud_cover”: {
“process_graph”: {
“lte1”: {
“arguments”: {
“x”: {
“from_parameter”: “value”
},
“y”: 80
},
“process_id”: “lte”,
“result”: true
}
}
}
},
“spatial_extent”: {
“crs”: “epsg:32631”,
“east”: 433953,
“north”: 4672292,
“south”: 4666780,
“west”: 429770
},
“temporal_extent”: [
“2019-01-01T00:00:00Z”,
“2019-01-31T23:59:59Z”
]
},
“process_id”: “load_collection”
},
“reducedimension1”: {
“arguments”: {
“data”: {
“from_node”: “loadcollection1”
},
“dimension”: “t”,
“reducer”: {
“process_graph”: {
“mean1”: {
“arguments”: {
“data”: {
“from_parameter”: “data”
}
},
“process_id”: “mean”,
“result”: true
}
}
}
},
“process_id”: “reduce_dimension”,
“result”: true
}
}
}

i got a one image :

image
image1453×616 200 KB

Thank you

10

ok, thank you very much for your answers