AusClimateService / AusClimateService/plotting_maps

Apply Dask to parallelise acs_regional_stats

Aperta
#34 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub
Lingua principale
Jupyter Notebook
Stelle
8
Fork
1
Metriche di merge delle PR
Nessuna PR unita negli ultimi 30g

Descrizione

`acs_regional_stats` can be very memory intensive to run, particularly over many regions and many timesteps.
We should develop an example of running `acs_regional_stats` for many years of daily data to produce area averaged timeseries for regions. Currently, this is [possible](https://github.com/AusClimateService/plotting_maps/blob/main/example_notebooks/FAQ_example_timeseries_stats.ipynb), but will take several minutes to calculate.
Dask is likely to be able to achieve this by calculating area averages per file.
Previous development has focused on reducing memory usage through other clever means, such as implementing `chunks` to reduce the number of timesteps loaded into the memory to calculate stats over each time. This could be parallelised, but it is not currently.

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Direzione di ricerca

Start with example_notebooks/FAQ_example_timeseries_stats.ipynb and the acs_regional_stats entry point, then run the current multi-year daily calculation to establish its memory use and runtime. Develop the requested Dask-based example that calculates area averages per file, and verify that it produces regional time series while reducing the current several-minute, memory-intensive workload.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
jupyter-notebook
Ambito
data, performance
Tipo di issue
Funzionalità
Difficoltà
4/5
Tempo stimato
3-5 giorni
Stato di attività
Ferma
Chiarezza
Abbastanza chiara
Idoneità per principianti
35/100

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