On: Converting my PhD thesis into HTML (2021)

[hoosieree]

I mostly use matplotlib+seaborn in a python code block, and tell matplotlib to write pdf output. Then I'll include the pdf with a link. Here's a sketch:

    # first run this code block:

    #+begin_src python
    # some code to generate a plot at images/plot.pdf
    #+end_src

    # then put the image where you want it:

    #+CAPTION: Some plot or other
    #+NAME: fig:asdfhjkl
    #+ATTR_LATEX: :width 0.7\textwidth
    [[file:./images/plot.pdf]]

You can put some incantation on the top of your .org file to make it run all the code blocks before exporting, but I usually just run them manually if I need to make a change to a figure.

Here's a full example from one of my papers. You can see I made quite a few revisions with all the commented-out lines. Note the :results none :exports none arguments to the org-babel code block, which makes the code itself invisible in the resulting paper.

    #+begin_src python :results none :exports none
    import matplotlib.pyplot as plt
    import seaborn as sns

    def get_loss(filename):
    loss = []
    with open(filename) as f:
        for line in f:
        loss.append(float(line.split(' = ')[-1]))
    return loss

    data = {}
    # data['NLP (P=0.1)'] = get_loss( '../sample-programs/loss-epochs-nlp-0.1-30.txt')
    # data['NLP (P=0.2)'] = get_loss( '../sample-programs/loss-epochs-nlp-0.2-30.txt')
    # data['NLP (P=0.4)'] = get_loss( '../sample-programs/loss-epochs-nlp-0.4-30.txt')
    # data['NLP (P=0.8)'] = get_loss( '../sample-programs/loss-epochs-nlp-0.8-30.txt')
    data['NLP (P=0.1)'] = get_loss( '../sample-programs/loss-epochs-both-0.1-30.txt')
    data['NLP (P=0.2)'] = get_loss( '../sample-programs/loss-epochs-both-0.2-30.txt')
    data['NLP (P=0.4)'] = get_loss( '../sample-programs/loss-epochs-both-0.4-30.txt')
    # data['NLP (P=0.8)'] = get_loss( '../sample-programs/loss-epochs-both-0.8-30.txt')
    data['Plain'] = get_loss( '../sample-programs/loss-epochs-30.txt')
    sns.lineplot(data=data, palette='deep')
    # TODO add plots for combined Plain+NLP at other probabilities
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    plt.legend()

    filename = 'images/loss-plot.pdf'
    plt.savefig(filename)
    return filename
    #+end_src

    #+CAPTION: Training loss for data augmentation
    #+LABEL: fig:loss
    #+ATTR_LATEX: :width 0.5\textwidth
    #+RESULTS: fig:loss
    [[file:./images/loss-plot.pdf]]