I'm not sure whether I'm correctly understanding you. Let's consider a hypothetical engineering performance-versus-requirements scatter plot or contour plot of a performance metric that takes positive values. I'd like to map values to colors so that anything below 50 gets solid red, values from 50 to 70 get a color between red and orange (linear interpolation), values from 70 to 90 get a color between orange and yellow, values from 90 to 105 get a color between yellow and green, and anything greater than or equal to 105 gets solid green. Is this considered "smooth variation"? If so, how would I implement something like this?
Thanks! Phillip Eric Firing wrote: > Phillip M. Feldman wrote: >> Eric and Reinier- >> >> It seems to me that continuous (piecewise-linear) colormaps could >> work in much the same fashion. One would specify n boundary colors >> and n thresholds (for continuous colormaps, I believe that the number >> of thresholds and colors must be the same), and for any value between >> two thresholds, the colors associated with the bounding thresholds >> would be automatically interpolated. What do you think? > > How does this differ from LinearSegmentedColormap.from_list()? I > guess what you are getting at is the quantization problem I mentioned > in connection with discrete colormaps. But it is not a problem when > the colors are linearly interpolated--that is, smoothly varying from > one end of the map to the other. It is only a problem when there are > jumps. > > Eric > >> >> Phillip >> >> Eric Firing wrote: >>> What does allow you to specify the transitions exactly (to within >>> the limits of double precision) is this: >>> >>> cmap = ListedColormap(['r','g','b']) >>> norm = BoundaryNorm([1.5+1.0/3, 1.5+2.0/3], cmap.N) >> > > ------------------------------------------------------------------------------ Come build with us! The BlackBerry(R) Developer Conference in SF, CA is the only developer event you need to attend this year. Jumpstart your developing skills, take BlackBerry mobile applications to market and stay ahead of the curve. Join us from November 9 - 12, 2009. Register now! http://p.sf.net/sfu/devconference _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
