Creating publication-quality geometric figures in Python

There is a way to generate vector images with matplotlob, outputting with the library io to a vector image (SVG) with this approach.

I personally tried to run the code of the approach (generate a vectorial histogram) in that webpage as a python file, and it worked.

The code:

    import numpy as np
    import matplotlib.pyplot as plt
    import xml.etree.ElementTree as ET
    from io import BytesIO
    import json
    plt.rcParams['svg.fonttype'] = 'none'
    # Apparently, this `register_namespace` method is necessary to avoid garbling
    # the XML namespace with ns0.
    ET.register_namespace("", "http://www.w3.org/2000/svg")
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    # --- Create histogram, legend and title ---
    plt.figure()
    r = np.random.randn(100)
    r1 = r + 1
    labels = ['Rabbits', 'Frogs']
    H = plt.hist([r, r1], label=labels)
    containers = H[-1]
    leg = plt.legend(frameon=False)
    plt.title("From a web browser, click on the legend\n"
            "marker to toggle the corresponding histogram.")
    # --- Add ids to the svg objects we'll modify
    hist_patches = {}
    for ic, c in enumerate(containers):
        hist_patches['hist_%d' % ic] = []
        for il, element in enumerate(c):
            element.set_gid('hist_%d_patch_%d' % (ic, il))
            hist_patches['hist_%d' % ic].append('hist_%d_patch_%d' % (ic, il))
    # Set ids for the legend patches
    for i, t in enumerate(leg.get_patches()):
        t.set_gid('leg_patch_%d' % i)
    # Set ids for the text patches
    for i, t in enumerate(leg.get_texts()):
        t.set_gid('leg_text_%d' % i)
    # Save SVG in a fake file object.
    f = BytesIO()
    plt.savefig(f, format="svg")
    # Create XML tree from the SVG file.
    tree, xmlid = ET.XMLID(f.getvalue())
    # --- Add interactivity ---
    # Add attributes to the patch objects.
    for i, t in enumerate(leg.get_patches()):
        el = xmlid['leg_patch_%d' % i]
        el.set('cursor', 'pointer')
        el.set('onclick', "toggle_hist(this)")
    # Add attributes to the text objects.
    for i, t in enumerate(leg.get_texts()):
        el = xmlid['leg_text_%d' % i]
        el.set('cursor', 'pointer')
        el.set('onclick', "toggle_hist(this)")
    # Create script defining the function `toggle_hist`.
    # We create a global variable `container` that stores the patches id
    # belonging to each histogram. Then a function "toggle_element" sets the
    # visibility attribute of all patches of each histogram and the opacity
    # of the marker itself.
    script = """
    <script type="text/ecmascript">
    <![CDATA[
    var container = %s
    function toggle(oid, attribute, values) {
        /* Toggle the style attribute of an object between two values.
        Parameters
        ----------
        oid : str
        Object identifier.
        attribute : str
        Name of style attribute.
        values : [on state, off state]
        The two values that are switched between.
        */
        var obj = document.getElementById(oid);
        var a = obj.style[attribute];
        a = (a == values[0] || a == "") ? values[1] : values[0];
        obj.style[attribute] = a;
        }
    function toggle_hist(obj) {
        var num = obj.id.slice(-1);
        toggle('leg_patch_' + num, 'opacity', [1, 0.3]);
        toggle('leg_text_' + num, 'opacity', [1, 0.5]);

        var names = container['hist_'+num]

        for (var i=0; i < names.length; i++) {
            toggle(names[i], 'opacity', [1, 0])
        };
        }
    ]]>
    </script>
    """ % json.dumps(hist_patches)
    # Add a transition effect
    css = tree.getchildren()[0][0]
    css.text = css.text + "g {-webkit-transition:opacity 0.4s ease-out;" + \
        "-moz-transition:opacity 0.4s ease-out;}"
    # Insert the script and save to file.
    tree.insert(0, ET.XML(script))
    ET.ElementTree(tree).write("svg_histogram.svg")

Previously, you need to pip install the required libraries on the top lines, and it successfully saved a SVG file with a plot (you can read the file and zoomwant in the histogram and you will get no pixels, as the image is generated with mathematicals functions).

It (obviously for our time) uses python 3.

You then could import the SVG image within your TeX document for the publication rendering.

I hope it may help.

Greetings,
Javier.