Comments (5)
The first step is concluded in #67. We have a new climatology with mean and standard deviation and we can generate it from any new dataset. We still need:
- Loop on season and regions for global_mean
- Heatmap for global_mean output
from ecmean4.
This is completed in #68
from ecmean4.
from ecmean4.
We still need to update the docs
from ecmean4.
This has been completed in #68
from ecmean4.
Related Issues (20)
- Including sea ice area in global mean HOT 2
- Correct the parsing of the global_mean() and performance_indices()
- Interpolation with CDO to replace xESMF HOT 3
- Non-cmor variables HOT 2
- Revisiting the file-handling functions HOT 1
- Revisiting the `component` and its files in the interface YAML files HOT 1
- Coherence and alignment of `global_mean` and `performance_indices` HOT 1
- global mean with no standard deviation does not show up in the figure HOT 1
- Coverage is not considering the call to `subprocess.run()`
- variables that cannot be processed HOT 1
- Installing from PyPI causes dependency errors HOT 8
- oceanic mask HOT 1
- Midlat regions are misleading
- Global Mean for `tas`: CRU data does not include Antarctica
- Evaporation has a flipped sign in EC-Earth4 data
- Coverage is changing results and taking foreever
- Matplotlib 3.8.0 and seaborn 1.12.2 does not plot annotations in heatmap HOT 1
- Slow down of the `performance_indices` due to `xesmf>=0.8` version HOT 1
- gridfile and areafile in template are used with * if they contain year1 or year2
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