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The instructions here assume a unix environment. Adjust as appropriate if on OSX or Windows. We also use team 23 as an example for the naming; please use your own group number from the group assignment excel sheet

NOTE: the file hierarchy and ways to run things mentioned above are just an example. You are not required to conform to the hierarchy or way of running scripts as long as its something reasonable. The expectation is for any competent person to be able to read the README and without much extra fiddling around, run the code to generate your reported results.

  1. Please ensure your code is all contained within a top-level directory using your (zero-padded) team name — e.g. 23, or 08, etc.

    An example file hierarchy might look like

    $ tree 23
    23
    ├── README.md
    ├── report
    │   ├── lineno.sty
    │   ├── nicefrac.sty
    │   ├── nips_2016.sty
    │   ├── refs.bib
    │   └── report.tex
    ├── run-analysis.py
    ├── run-models.py
    └── src
        ├── __init__.py
        └── models.py
    
    2 directories, 10 files
    

    Please include the latex source for your report as a subfolder in your source archive. So if you’re team 23, you’d have a path like 23/report within which all your tex sources live. Your zip archive would then also include the tex source in addition to the code for your models and results.

  2. Please include a README.md (markdown) or README.txt (ascii text) This file should provide full details of
    • what software / packages are needed, if in python, you can also include a requirements.txt file as is normally done. if relevant, please specify version numbers for packages used
    • where the data needs to be put, specify hardcoded paths/sub-paths (e.g. 23/data) or you could have your run scripts take the paths as arguments
    • how to run the scripts and/or notebooks; for shell scripts, please ensure they are bash only

    For example, the instructions could be something like

    - Put the data under `23/data`
    - setup a python3 virtual env and install the following packages
      numpy, scipy, pandas, matplotlib
    - Run the models using `bash run-models`
    - Run the analysis using `bash run-analysis`
    - View the results in 23/results
    

    Or if you’re using jupyter notebooks, it could just be instructions to start a server and run the notebook.

    Or some combination of such things.

    Basically, we need to be able to follow your instructions and reproduce your results The key here is simplicity. The easier you make it for us to understand things, the better. The less ambiguous you make any instructions, the better.

  3. zip your files recursively with the top-level directory and create an archive using the (zero-padded) team name as the file name. The will require you to be outside the top-level directory for your project.

    $ zip -r 23.zip 23
      adding: 23/ (stored 0%)
      adding: 23/run-analysis.py (stored 0%)
      adding: 23/run-models.py (stored 0%)
      adding: 23/README.md (stored 0%)
      adding: 23/report/ (stored 0%)
      adding: 23/report/report.tex (deflated 51%)
      adding: 23/report/lineno.sty (deflated 71%)
      adding: 23/report/nips_2016.sty (deflated 70%)
      adding: 23/report/nicefrac.sty (deflated 68%)
      adding: 23/report/refs.bib (deflated 39%)
      adding: 23/src/ (stored 0%)
      adding: 23/src/__init__.py (stored 0%)
      adding: 23/src/models.py (stored 0%)
    

    This should create a file called 23.zip with your project and latex source code. Please check that the archive is not corrupted and that all the files are there by extracting to a different location locally for verification.

    Upload the single zip file containing all your code and instructions to the supplementary-material assignment on Learn [Assessment -> Coursework Submission -> Coursework Supplementary Submission]