AusClimateService / AusClimateService/plotting_maps

Apply Dask to parallelise acs_regional_stats

Abierto
#34 0 comentarios 0 reacciones 0 asignados Ver en GitHub
Lenguaje dominante
Jupyter Notebook
Estrellas
8
Forks
1
Métricas de merge de PR
Sin PR fusionados en 30 d

Descripción

`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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Línea de trabajo

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.

Escrito por el modelo de indexación a partir del texto del issue.

Evaluación

Stack tecnológico
jupyter-notebook
Área
data, performance
Tipo de issue
Nueva funcionalidad
Dificultad
4/5
Tiempo estimado
3-5 días
Estado de actividad
Estancado
Claridad
Bastante claro
Aptitud para principiantes
35/100

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