Joe, Eric
Thanks to both for your further comments.
I made a new notebook, this time using open source data so it can be
downloaded and followed step by step.
The html version in nbviewer is here:
http://nbviewer.ipython.org/urls/dl.dropbox.com/s/2pfhla9rn66lsbv/surface_shading.ipynb/%3Fdl%3D0
Data is here:
https://www.dropbox.com/s/p87bojlnmad9p9j/Penobscot_HorB.txt?dl=0
The first method suggested by titusjan on stackoverflow is essentially the
same as the matplotlib.colors blend_soft_ligh suggested by Joe as it uses
the pegtop algorithm. It works nicely with the data.
The second method suggested by titusjan replaces value in hsv space with
intensity as suggested. Eric you will notce I did include the line
img_array = plt.get_cmap('cubehelix')(data_n) and yet the colormapping is
not working.
I am very keen to sort out if this is a bug in the software or a problem
in my code, and if there is a way to make it work. The reason is that this
method would allow blending three pieces of information, to create a
figure like the top one in here:
https://books.google.ca/books?id=dP2iACuzq34C&q=figure+20#v=snippet&q=a%20time%20slice%20through%20a%20survey%20acquired%20over%20the%20Central%20Basin%20Platform%2C%20Texas%2C%20U.S.A.%2C%20using%20a%203D&f=false
Any further insight would be really appreciated.
Matteo
On Fri, May 22, 2015 8:28 am, Joe Kington wrote:
> I think you're asking how to blend a custom intensity image with an rgb
> image. (I'm traveling and just have my phone, so you'll have to excuse my
> lack of examples.)
>
> There are several ways to do this. Basically, it's analogous to "blend
> modes" in Photoshop etc.
>
> Have a look at the matplotlib.colors.LightSource.blend_overlay and
> blend_soft_light functions in the current github head. (And also
> http://matplotlib.org/devdocs/examples/specialty_plots/topographic_hillsh
> ading.html )
>
>
> If you're working with 1.4.x, though, you won't have those functions.
>
>
> However, the math is very simple. Have a look at the code in those
> functions in the github head. It's basically a one liner.
>
> You'll need both the 4-band rgba image and the 1 band intensity/hillshade
> image to be floating point arrays scaled from 0-1. However, this is the
> default in matplotlib.
>
> How that helps a bit, and sorry again for the lack of examples!
> Joe
> 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-geoscient
> ists-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-shadin
>>> g- 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/sur
>>> fa 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
>>>
>>>
>>>
>>
>>
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