FWIW I can't tell any difference between colors in different backends. I tried agg, cairo, several interactive backends and ipython inline in Ubuntu.
Goyo 2014-05-17 6:14 GMT+02:00 Michael Goerz <go...@physik.uni-kassel.de>: > On Fri, 16 May 2014, Eric Firing wrote: >> On 2014/05/16 4:41 PM, Michael Goerz wrote: >> >While playing around for an afternoon with colors in matplotlib, I came >> >across some inconsistencies. I was trying to define colors in sRGB (my >> >understanding is that sRGB as a standardized color space is >> >device-independent). Giving RGB values to matplotlib, I checked the >> >output with a color picker -- Color Maker from the Mac App Store, set >> >to the sRGB mode, but the built-in DigitalColor Meter gives the same >> >result. It turns out that the colors I see in the output are not what I >> >specified. This makes playing around with colors, a bit difficult: >> >http://nbviewer.ipython.org/gist/goerz/9aab94f5322d5a457625 >> >As far as I can tell, Matplotlib does not specify in >> >which color model in interprets RGB values (this should ideally be >> >stated in the documentation). Also, the output in different backends >> >(IPython notebook, and interactive GUI) gave different results. As a >> >minimal example, consider the following: >> Matplotlib doesn't interpret RGB values; that is left to the display >> device. Matplotlib simply writes the RGB values in a file, >> unmodified, or sends them via a GUI backend to a display, also >> unmodified *by mpl*. What you put in, is what it puts out. >> >> Thanks for pointing out the DigitalColor Meter--I hadn't noticed it. >> >> What I see with your example is that the MacOSX backend *display* is >> interpreting RGB as sRGB, and the qt4agg backend *display* is >> interpreting RGB as native values (based on matching with >> DigitalColor Meter). >> >> I haven't tried this with inline plots in the notebook, but mpl is >> merely feeding RGB values to the browser, just as would occur when >> displaying a png, and it is up to the browser to decide how to >> convert them into pixel intensity. >> >> I don't see that there is a bug here. > Fair enough, so that would imply that there is a difference between the > png streams produced by matplotlib and the "direct" PIL image. I might > be seeing the problems discussed here: https://hsivonen.fi/png-gamma/ > It might be that the matplotlib png stream is "untagged", whereas the > PIL image is tagged properly as sRGB. I'd have to decode the base64 > data from the notebook to check this. > > Beyond that it seems quite tricky to understands how in a given > application, or matplotlib backend in this case, a given RGB color > input yields a specific output on my screen, and how to make this > consistent (consistency obviously being more important than > accurateness with respect to some "real" color model). Maybe this just > goes to show what I realized these last couple of days, that colors are > a huge can of worms. > > >> > import pylab >> > import numpy as np >> > import matplotlib >> > #%matplotlib inline >> > # draw line with sRGB color 228, 26, 28 >> > pylab.plot(np.linspace(0,10, 10), np.linspace(0,20,10), linewidth=10, >> > color=(0.894, 0.102, 0.110)) >> > pylab.show() >> > # Colorpicker shows 210, 46, 13 in GUI view >> > # Colorpicker shows 229, 39, 27 in iPython Notebook >> > >> >In the IPython notebook, direct PIL graphics do seem to give the right >> >colors cf. In [5] in the linked IPython notebook (even though PIL also >> >does not explicitly specify its color model). Just matplotlib seems >> >off. Of course, I'm doing some slightly crazy stuff in the Notebook, >> >displaying every matplotlib output through PIL, but I cecked that >> >`%matplotlib inline` actually produces the same colors. >> > >> >Should I file this on github as a bug report? >> > >> >Best, >> >Michael >> > >> >P.S.: please CC for direct replies > > ------------------------------------------------------------------------------ > "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE > Instantly run your Selenium tests across 300+ browser/OS combos. > Get unparalleled scalability from the best Selenium testing platform available > Simple to use. Nothing to install. Get started now for free." > http://p.sf.net/sfu/SauceLabs > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ "Accelerate Dev Cycles with Automated Cross-Browser Testing - For FREE Instantly run your Selenium tests across 300+ browser/OS combos. Get unparalleled scalability from the best Selenium testing platform available Simple to use. Nothing to install. Get started now for free." http://p.sf.net/sfu/SauceLabs _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
