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Searching in a catalog for xrfreq='YS' will not match the entries where that frequency is written as AS-JAN. Same thing for QS vs QS-APR vs QS-JUL vs QS-OCT (and the other 2 groups).
Potential Solution
I see three solutions:
In search, expand the requested list with all equivalent xrfreq.
Normalize all frequencies that come in the catalog.
Number 2 is my favorite, but the issue is that we don't control all the ways to add entries to the catalog. I guess the normalization could be run upon loading the catalog, as well as in update.
Additional context
xr.infer_freq always returns : AS-JAN, QS-OCT, QS-NOV or QS-DEC. compute_indicators will usually use infer_freq to find the frequency to use in the catalog. Thus, if one passes freq='YS', there's an inconsistency between the output catalog and the input yaml. Only fixing the annual case is easy, but it becomes inelegant when we want to fix the seasonal cases too.
Contribution
I would be willing/able to open a Pull Request to contribute this feature.
The text was updated successfully, but these errors were encountered:
Addressing a Problem?
Searching in a catalog for
xrfreq='YS'
will not match the entries where that frequency is written asAS-JAN
. Same thing forQS
vsQS-APR
vsQS-JUL
vsQS-OCT
(and the other 2 groups).Potential Solution
I see three solutions:
search
, expand the requested list with all equivalentxrfreq
.Number 2 is my favorite, but the issue is that we don't control all the ways to add entries to the catalog. I guess the normalization could be run upon loading the catalog, as well as in
update
.Additional context
xr.infer_freq
always returns :AS-JAN
,QS-OCT
,QS-NOV
orQS-DEC
.compute_indicators
will usually useinfer_freq
to find the frequency to use in the catalog. Thus, if one passesfreq='YS'
, there's an inconsistency between the output catalog and the input yaml. Only fixing the annual case is easy, but it becomes inelegant when we want to fix the seasonal cases too.Contribution
The text was updated successfully, but these errors were encountered: