I am attempting to build this repo with gitbash in Windows 10 pro. I have cloned the repo to a folder /git/cheatsheets and created /git/cheatsheets/fonts with all the required sub-folders as described in the README. However, when running the python scripts in the scripts directory I am getting a failure of

"findfont: Font family ['Source Code Pro'] not found. Falling back to DejaVu Sans."

I am guessing I have put the fonts folder in the wrong location and/or finding the fonts on the Windows file system is not working as expected.

Any help would be appreciated.

It would be nice to have an automatic building of the PDF and PNG for each commit but I don't know if it is possible with e.g. Travis. It would require a partial TeXLive installation and I'm not sure how to setup.

Also, some have been manually post-processed with pdfcrop (to remove white margin) and this should be automatized.

Attempts to address #28.

• replace 'none' with blackman

Something else to consider would be making the image data the same as what is used in the current matplotlib gallery example for the interpolation methods. To that effect, I could add a commit to this PR.

As has been discussed in #32, there is a need to keep the matplotlib logo in the cheatsheets up to date with the correct version in the tag line. This PR does a couple of things:

• upgrades and pins the matplotlib version, updates the dependencies, and simplifies the requirements specification
• adds a script for generating the matplotlib logo, originally written by @timhoffm
• modifies the script to include "Cheat sheets" and the matplotlib version in the tag line

There may be some small tweaks to make the logo more closely resemble the one by @rougier; well, maybe also a big tweak, as the font is currently Carlito, not Calibri.

If you look at the imagine cheatsheets-2.png linked in the README.md: https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets-2.png

in the interpolations image grid, the "none" version actually is using some sort of interpolation. This makes it seem like "none" and None are different, but they should not be.

When I run the interpolations.py script locally (https://github.com/matplotlib/cheatsheets/blob/master/scripts/interpolations.py) I get the following:

interpolations.pdf

which has the correct top row.

The image in the repo should be updated.

the most important thing on charting is finansal charts that matplotlib didnt work around it. yes it have some libs and things but not sharply have do something for that. please add cheatsheat for finansal and candlistic charts to help us thanks

Most, if not all, np., plt., ax., fig., and mpl. can be safely removed if importing * directly in the main name namespace. Currently there are so many useless repetitions, IMHO.

from matplotpib.pylab import *

would make things much cleaner and simple.

Thanks for the great cheatsheets!

The first page in Matplotlib cheatsheets, chart "style name" missing in "Style" - plot (2,1), that looks like "ggplot"

No need for a list comprehension; just call cmap(list).

Also, use Oranges colormap instead of Blues because the illustrative image is in orange and using Blues is slightly confusing then.

• make room for two additional annotation arrow styles (so that 'wedge' is show again)
• add possibility to set tick labels together with tick positions

• add default color cycler colors (C0 ... C9)
• group synomys (e.g. black, k or aqua, cyan), combining gray and grey into gr[ae]y to save space
• some minor flake8 fixes

I think it's essential to show the default color cycler colors in this plot too. In order to not reduce the size of the color patches I've put color name synonyms together and combined the various ...gray/...grey names to ...gr[ae]y much in the same way as used elsewhere in this cheatsheet.

This requires updating the Sphinx theme (e.g. matplotlib/mpl-third-party#134), but also auditing the plots to make sure they did not break or could be using newer stuff.

see https://github.com/matplotlib/matplotlib/issues/23714

These is some user confusion about when to use ax.plot with np.nan separated data and when something like LineCollection is a better choice. In particular, using the segmented data approach the line will be drawn as a single path which will not show self-intersection for alpha < 1.

Closes #109 Closes #111

Hard to make the subplot_adjusts figure look really good, but here is the update one:

Stairs illustration:

(Both are screen shots of the PDF.)

Not obvious which arguments should be used. I went with those that are (a bit) "special" for stairs.

Very thankful of the cheatsheet, but I think there's a little problem. In 'Axes adjustments' of the second cheatsheet, 'right' directly refers to the padding between subplots' right edge and figure's right edge. However it actually refers to the relative distance between subplots' right edge and figure's left edge. The same also applies to 'top'.