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  • tommylees112 · 3 ✖

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  • How to select using `.where()` on a timestamp `coordinate` for forecast data with 5 dimensions · 3 ✖

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  • NONE · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
508995115 https://github.com/pydata/xarray/issues/3053#issuecomment-508995115 https://api.github.com/repos/pydata/xarray/issues/3053 MDEyOklzc3VlQ29tbWVudDUwODk5NTExNQ== tommylees112 21049064 2019-07-07T12:17:12Z 2019-07-07T12:17:12Z NONE

Thanks closing!

 
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   How to select using `.where()` on a timestamp `coordinate` for forecast data with 5 dimensions 462010865
508544556 https://github.com/pydata/xarray/issues/3053#issuecomment-508544556 https://api.github.com/repos/pydata/xarray/issues/3053 MDEyOklzc3VlQ29tbWVudDUwODU0NDU1Ng== tommylees112 21049064 2019-07-04T17:29:25Z 2019-07-04T17:29:25Z NONE

This is the greatest thing since sliced bread thankyou @spencerkclark !!

I have been referring to this constantly for the last week :D 

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   How to select using `.where()` on a timestamp `coordinate` for forecast data with 5 dimensions 462010865
506730153 https://github.com/pydata/xarray/issues/3053#issuecomment-506730153 https://api.github.com/repos/pydata/xarray/issues/3053 MDEyOklzc3VlQ29tbWVudDUwNjczMDE1Mw== tommylees112 21049064 2019-06-28T13:17:04Z 2019-06-28T13:17:42Z NONE

Thanks! Your assumption was correct, apologies for the mistake!

This might be asking for too much but is there any way I can keep track of the forecast_horizon or initialisation_date? The only issue I can foresee is that I won't be able to distinguish between the forecasts for the same date, even though those initialised closest to the valid_time will likely have more information in them than those initialised a long time before.

What I'm asking is can I add a new dimension to my original ds so that I have the OPTION to select by either valid_time/time or by forecast_horizon/initialisation_date

So I would be looking for something like:

<xarray.Dataset> Dimensions: (forecast_horizon: 24, initialisation_date: 12, lat: 36, lon: 45, number: 51, time: 288) Coordinates: * lon (lon) float64 33.5 33.7 33.9 34.1 34.3 ... 41.65 41.85 42.05 42.25 * lat (lat) float64 -5.175 -5.176 -5.177 -5.177 ... -5.2 -5.201 -5.202 * number (number) int64 0 1 2 3 4 5 6 7 8 9 ... 42 43 44 45 46 47 48 49 50 * initialisation_date (initialisation_date) datetime64[ns] 2018-01-31 ... 2018-12-31 * forecast_horizon (forecast_horizon) timedelta64[ns] 28 days ... 215 days * time (time) datetime64[ns] 2018-04-02 2018-04-03 ... 2018-04-30 Data variables: precip (forecast_horizon, initialisation_date, number, lat, lon, time) float64 0.2684 0.8408 ... 1.7 -0.383

 
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   How to select using `.where()` on a timestamp `coordinate` for forecast data with 5 dimensions 462010865

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