developmentseed / sat-gbdx

Library and CLI to search and access GBDX imagery
MIT License
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Metadata not found for catalog ID 1040050008DC1E00 #7

Open Rub21 opened 6 years ago

Rub21 commented 6 years ago

I am facing this error:

[root@c7bb480d499b data]# sat-gbdx load TUR25/2018/scenes-filtered.geojson --download full
1 scenes found
Order status for 1040050008DC1E00: delivered, s3://receiving-dgcs-tdgplatform-com/057926017010_01_003
RDA error: All factories fail for the operation 'DigitalGlobeStrip' - Metadata not found for catalog ID 1040050008DC1E00
[root@c7bb480d499b data]#

The metadata: scenes-filtered.geojson.zip

The thumbnails looks ok: 👇 image

cc. @matthewhanson

chelm commented 6 years ago

It looks 1040050008DC1E00 is still not ordered inside the DG catalog. Could there be an issue with the ordering interface here?

matthewhanson commented 6 years ago

@chelm I don't think so, the order status given is the output from gbdxtools.ordering.status:

Order status for 1040050008DC1E00: delivered, s3://receiving-dgcs-tdgplatform-com/057926017010_01_003
RDA error: All factories fail for the operation 'DigitalGlobeStrip' - Metadata not found for catalog ID 1040050008DC1E00

Which says it's been delivered and includes the location, but then there's that RDA error.

chelm commented 6 years ago

One thing to watch out for is lag of some undetermined about of time between the image being delivered and RDA having completed its ingest.

The way i check for completed orders and their readiness in RDA is pinging: https://rda.geobigdata.io/v1/stripMetadata/1040050008DC1E00 If that exists, then i trust it'll work.

This is some code i used to see that a single DGAcquisition item exists for that CatalogID, which means its not ordered yet (strange). If it were ordered AND ready for RDA you'd see IDAHOImage items that RDA creates.

from shapely.geometry import box
from gbdxtools import Interface
gbdx = Interface()

bbox = [-180, -90, 180, 90] 

query = 'attributes.catalogID.keyword:1040050008DC1E00'
aoi = box(*bbox).wkt
recs = gbdx.vectors.query(aoi, query, count=1000)
print(len(recs))

for a in recs:
    print a['properties']['attributes']['catalogID'], a['properties']['item_type']