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Matplotlib chooses the wrong font for unrecognized weights #8550

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@Rufflewind

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@Rufflewind

Bug report

Bug summary

With a lot of the modern fonts containing exotic font weights like “Thin” or “Ultra bold”, matplotlib will sometimes pick the wrong font weight because it assumes that any font weight that it doesn’t recognize is either normal (400) or bold (700), even though there is a “Regular” available. Ultimately which one matplotlib picks seems to be actually kind of random, probably depending on the order in which the fonts appear in the font cache.

This is what the logic in font_manager.py looks like:

weight = next((w for w in weight_dict if sfnt4.find(w) >= 0), None)
if not weight:
    if font.style_flags & ft2font.BOLD:
        weight = 700
    else:
        weight = 400

sfnt4 appears to be a string that contains the full name of the font, including the weight. The problem with this approach is that if it doesn’t recognize the weight, it will simply assume the weight is either 400 or 700. A “Thin” font that was mistakenly labeled 400 might get picked over a “Regular” font.

Here are all the weights currently recognized:

weight_dict = {
    'ultralight' : 100,
    'light'      : 200,
    'normal'     : 400,
    'regular'    : 400,
    'book'       : 400,
    'medium'     : 500,
    'roman'      : 500,
    'semibold'   : 600,
    'demibold'   : 600,
    'demi'       : 600,
    'bold'       : 700,
    'heavy'      : 800,
    'extra bold' : 800,
    'black'      : 900}

I imagine we could add “Thin” and some others into the list, but I don’t think this solves the fundamental problem that fonts with unrecognized weights are presumed “Regular”/“Bold” and do not have lower precedence than fonts that are definitely “Regular”/“Bold”.

TL;DR: It would be nice to have a more robust way to detect font weights and handle unknown font weights.

Matplotlib version

  • Operating System: Arch Linux
  • Matplotlib Version: HEAD
  • Python Version: 3.6.0
  • Jupyter Version (if applicable): n/a
  • Other Libraries: n/a

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