OK, I understand.
Could you suggest a way to reduce that 3D array to a 2D array and plot it with a specific colormap, while preserving the shading? I did something similar in Matlab https://mycarta.wordpress.com/2012/04/05/visualization-tips-for-geoscientists-matlab-part-ii/ But it took using some custom functions and a ton of asking and tinkering, and I'm not quite at that level with matplotlib, so any suggestion would be appreciated Thanks, Matteo On Thu, May 21, 2015 4:10 pm, Eric Firing wrote: > > Colormapping occurs only when you give imshow a 2-D array of numbers to > be mapped; when you feed it a 3-D array of RGB values, it simply shows > those colors. For colormapping to occur, it must be done on a 2-D array > as a step leading up to the generation of your img_array. > > Eric > On 2015/05/21 5:50 AM, Matteo Niccoli wrote: > >> I posted a question on stackoverflow about creating with making my own >> shading effect (I want to use horizontal gradient for the shading). >> http://stackoverflow.com/questions/30310002/issue-creating-map-shading- >> in-matplotlib-imshow-by-setting-opacity-to-data-gradi >> >> >> Unfortunately I cannot share the data because I am using it for a >> manuscripts, but my notebook with full code listing and plots, here: >> http://nbviewer.ipython.org/urls/dl.dropbox.com/s/2pfhla9rn66lsbv/surfa >> ce_shading.ipynb/%3Fdl%3D0 >> >> The shading using gradient is implemented in two ways as suggested in >> the answer. What I do not understand is why the last plot comes out with >> a rainbow-like colors, when I did specify cubehelix as colormap. >> >> hsv = cl.rgb_to_hsv(img_array[:, :, :3]) hsv[:, :, 2] = tdx_n >> rgb = cl.hsv_to_rgb(hsv) plt.imshow(rgb[4:-3,4:-3], cmap='cubehelix') >> plt.show() >> >> >> Am I doing something wrong or is this unexpected behavior; is there a >> workaround? > >> >> Thanks >> Matteo >> >> > > > ------------------------------------------------------------------------- > ----- > One dashboard for servers and applications across Physical-Virtual-Cloud > Widest out-of-the-box monitoring support with 50+ applications > Performance metrics, stats and reports that give you Actionable Insights > Deep dive visibility with transaction tracing using APM Insight. > http://ad.doubleclick.net/ddm/clk/290420510;117567292;y > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > ------------------------------------------------------------------------------ One dashboard for servers and applications across Physical-Virtual-Cloud Widest out-of-the-box monitoring support with 50+ applications Performance metrics, stats and reports that give you Actionable Insights Deep dive visibility with transaction tracing using APM Insight. http://ad.doubleclick.net/ddm/clk/290420510;117567292;y _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
