Paper figures - #199
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AI-assisted implementation!
This PR adds meteograms for publication, centralises some ploting style
things, uses long names for variables and tries out some colors :)
Any feedback and changes welcome!
To run only plotting:
```
evalml publication config/Varda-Single_paper.yaml -- \
--allowed-rules publication_all publication_meteogram publication_figures \
data_download_eckit_geo_grids \
--forcerun publication_meteogram publication_figures
```
Current version of the plots:
<img width="2957" height="1685" alt="image"
src="https://github.com/user-attachments/assets/218f849e-4bd3-41d2-b19d-124dfe156e50"
/>
<img width="2335" height="2056" alt="image"
src="https://github.com/user-attachments/assets/25d1d544-996a-4adf-869c-54ae054cfcf8"
/>
---------
Co-authored-by: Jonas Bhend <jonas.bhend@meteoswiss.ch>
Co-authored-by: Daniele Nerini <daniele.nerini@meteoswiss.ch>
Co-authored-by: Michele Cattaneo <44707621+MicheleCattaneo@users.noreply.github.com>
Co-authored-by: Jonas Bhend <jonasbhend@users.noreply.github.com>
) AI assisted implementation! - Derive the jretrievedwh obs marker from --station inside the script instead of passing a separate --obs argument. The marker value contains '=' (e.g. jretrievedwh:locations=KLO), which marimo's cli_args drops, so obs silently fell back to the KLO default and any non-KLO station was broken (also via the snakemake rule). The rule no longer passes --obs. - Import `time` in the entry cell and thread it through: the timing log lines used time.perf_counter() but `time` was never imported (NameError at runtime).
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AI assisted implementation! Changes the plotting workflow for publication to be able to generate them from the snakemake workflow as well as interactively rendering through a manifest . A documentation is currently available in `docs/publication_figures.md`
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Switch the scoremap skill palette to ColorBrewer RdBu, reserving the deep ends for out-of-range extremes and lighter tiers for in-range bins, and extend the skill levels from ±0.35 to ±0.55 at 0.10 spacing.
This PR adds a standalone script that plots an areal-mean meteogram over a region polygon, styled to match the paper figures. It masks the truth points inside a region shapefile, averages over them at each valid time, and plots the series, reusing `publication_style` (`line_style`, `param_label`), `publication.mplstyle`, and the lead-time x-axis of `publication_meteogram` so it reads consistently with the other paper figures. The script is standalone (not wired to snakemake) and truth-only: it reads the analysis zarr directly, with no inference or verification. It works for any region shapefile and parameter. For the Valais precipitation case study it plots the areal-mean hourly precipitation of the ICON-CH1 analysis over the Valais region. The Valais shapefile is on store at `/store_new/mch/msopr/ml/regions/cantons/valais.shp`. It can also be generated with the included `make_region_shapefile.py`, which pulls the canton boundary from GADM and reprojects it to EPSG:2056: ``` python workflow/scripts/make_region_shapefile.py --canton Valais --outfn valais.shp ``` ## Output With ``` python workflow/scripts/plot_meteogram_region.py \ --truth /store_new/mch/msopr/ml/datasets/mch-ich1-1km-2024-2025-1h-pl13-v1.0.zarr \ --truth_label "ICON-CH1 analysis" \ --shapefile /store_new/mch/msopr/ml/regions/cantons/valais.shp \ --date 202506271800 --steps 0/120/1 --param TOT_PREC --outfn output/results/valais.png ``` you get: <img width="1560" height="585" alt="valais_final" src="https://github.com/user-attachments/assets/779cef33-3894-473b-8d6f-890379f0659a" />
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…n (backward_transform_filter only)
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WIP on figures used for Varda-Single publication