Metadata-Version: 2.1
Name: moepy
Version: 0.0.6
Summary: Code and analysis used for calculating the merit order effect of renewables on price and carbon intensity of electricity markets
Home-page: https://github.com/AyrtonB/Merit-Order-Effect
Author: Ayrton Bourn
Author-email: ayrtonbourn@outlook.com
License: UNKNOWN
Description: # Merit-Order-Effect
        
        Code and analysis used for calculating the merit order effect of renewables on price and carbon intensity of electricity markets
        
        <br>
        
        ### Repo Publishing - To Do
        
        Notebook Polishing Changes:
        - [x] Add docstrings (can be one-liners unless shown in the user-guides or likely to be used often)
        - [x] Add a mini sentence or two at the top of each nb explaining what it's about
        - [x] Ensure there is a short explanation above each code block
        - [x] Move input data to a raw dir
        - [ ] Check all module imports are included in settings.ini
        - [x] Re-run all of the notebooks at the end to check that everything works sequentially
        
        Completed Notebooks:
        - [x] Retrieval
        - [x] EDA
        - [x] LOWESS (start with the biggy)
        - [x] Price Surface Estimation
        - [x] Price MOE
        - [x] Carbon Surface Estimation and MOE
        - [x] Prediction and Confidence Intervals
        - [x] Hyper-Parameter Tuning
        - [x] Tables and Figures
        
        New Code:
        - [ ] Separate the binder and development `environment.yml` files
        - [ ] Re-attempt LIGO fitting example as part of a user-guide
        - [ ] Add in the prediction and confidence interval plots
        - [ ] Add a lot more to the EDA examples
        - [ ] Every week re-run a single analysis (could be in the user-guide) and show the generated fit at the top of the ReadMe
        - [ ] Try to speed things up, e.g. with Numba ([one person has already started doing this](https://gist.github.com/agramfort/850437#gistcomment-3437320))
        - [ ] Get the models saved on S3 or figshare and pulled into binder via a postBuild script
        
        External/ReadMe
        - [ ] Add the GH action for version assignment triggering pypi push and zenodo update
        - [ ] Just before the paper is published set the version to 1.0.0 and have a specific Binder link that builds from that version as stored in the Zenodo archive
        - [ ] Could link the zotero collection
        - [ ] Add citations for both the external data I use and the resulting time-series I generate
        - [ ] Add bibtex citation examples for both the paper and the code (could use [this](https://citation-file-format.github.io/cff-initializer-javascript/))
        - [ ] Publish the latest version to PyPi
        - [ ] Mention the new module in the [gist](https://gist.github.com/agramfort/850437) that some of the basic regression code was inspired by 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
