Improve documentation 2 #959
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Hello @yerbol-akhmetov! Great work and a very needed improvement. My feeling is that your idea regarding structuring works very well.
Have gone through the PR and added some comments, which mostly relate to the points which have been updated quite recently but not yet documented properly. Feel free please to disagree and improve. That is a really tricky part, and I'm happy to discuss how can it be made more clear.
Thanks a lot for tackling this.
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Hello @yerbol-akhmetov! Looks almost perfect. Great work!
Have gone through the updated version and added some comments, mostly quite minor suggestions for improvements. The only significant point left is to check consistency in a description when a custom cutout is needed.
My general feeling is that we are quite close to finalise :)
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doc/customization_steps.rst
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| .. _customization_steps: | ||
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| 3. General Guidelines for Modeling |
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As a refence, as discussed today, it may be good to revise this steps as a preliminary introduction for the overall full tutorial, while moving more advanced concepts into the relative steps.
Great Yerbol :D This seems the last step to finalize. Congratulations!
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Nice @yerbol-akhmetov :D
Minor elements on my side :) @ekatef what about you?
doc/customization_basic1.rst
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| 2. Basic customization | ||
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| A good starting point to customize your model are settings of the default configuration file `config.default`. You may want to do a reserve copy of your current configuration file and then overwrite it by a default configuration: |
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Could make sense to add an extension to the file name: config.default.yaml (not crucial, just for consistency)
doc/customization_basic1.rst
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| .. note:: | ||
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| Ensure that the selected data range aligns with the dates available in the cutout dataset. If the weather data within the cutouts corresponds to the year 2013, then the range of snapshots should fall within that same year. |
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Wouldn't date range instead of data range be more specific?
doc/customization_basic1.rst
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| prediction_year: 2030 | ||
| scale: 1 | ||
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| The `weather_year` value corresponds to the weather data which was used to generate the electricity demand profiles for a selected area while `prediction_year` correspond to the point of a `Shared Socioeconomic Pathways (SSP) <https://en.wikipedia.org/wiki/Shared_Socioeconomic_Pathways>`__ trajectory. PyPSA-Earth uses SSP2-2.6 scenario within the Shared Socioeconomic Pathways framework, which is characterized by medium challenges to mitigation and adaptation efforts resulting in a global warming of approximately 2.6°C by the end of the 21st century. |
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Could make sense to fix a typo: -> prediction_year corresponds
doc/customization_run.rst
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| Choose the solver | ||
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| To solve full optimization problem, it is important to pick a solver in `config.yaml` file. For instance, this tutorial uses the open-source solver glpk and does not rely |
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Isn't an article needed here, like a full optimization problem?
An absolutely fantastic work @yerbol-akhmetov @davide-f!!! According to my impression, the PR was still in a good shape, while thanks to Davide's review, the documentation has become brilliant. I really like the idea to include a part which relates to validation. Have added a couple very minor comments and a question on adding a short explanation for essence of the Looking forward to see the PR merged :D |
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@ekatef I have no more comments, as you are satisfied, feel free to merge :) |
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Merged, which means a dramatic increase in quality of our docs 🎉 🎉 🎉 @yerbol-akhmetov thanks a lot for the awesome contribution. @davide-f thank you so much for the fantastic support :D |
Changes proposed in this Pull Request
Good day, here I am addressing documentation improvements as a draft PR.
Checklist
doc/configtables/*.csvand line references are adjusted indoc/configuration.rstanddoc/tutorial.rst.doc/release_notes.rstis amended in the format of previous release notes, including reference to the requested PR.