Thanks Stefan.  That helps clarify some of the dtypes to me; however I still have a few confusions in regard to color data.  I should have specified this more in my OP.

I am trying to create a program where all color data is stored as RGB.  This requires a validator that does flexible to_rgb() conversion.  I want the users to have flexibility, so it should accept names like "aqua" as well as RGB tuples.  I realize now that imshow() will do its own conversions, but still don't quite understand exactly what constraints I need to impose on users for all the various use cases.  For example, if a user enters a single integer (say 239), is there a de-facto way to rgb-convert this?  I've tried to exhause the scenarious below; any case with question marks is still unclear to me. 

INPUT TYPE   INPUT EXAMPLE    HANDLER    DESIRED OUTPUT

-----------------------------------------------------------------------------------------------------

hex string         '#0FF000'      ColorConverter.to_rgb()    (.2, .4, .5)

name string      'purple   '      ColorConverter.to_rgb()     (.1, .8, .3)

< 1 float tuple   '    (.5, .2, .4)          PASS                   (.5, .2, .4)

> 1 float/int tuple   (30,  28, 90)       ????                         ????

int                           140          (Digital channel?)          (140, 140, 140)???

float                        39.5               (Error??)                   ???

I read on wiki that a RGB tuple with elements > 1 can be interpreted as a "Digital Channel", so perhaps just leave these as is.  The tough cases for me are really when a user enters a single Int or Float.  Of course, I could just raise an exception if there's no de-facto way to handle this...  

On Tue, Dec 31, 2013 at 6:15 AM, Stéfan van der Walt <stefan@sun.ac.za> wrote:

Hi Adam

On Mon, 30 Dec 2013 22:37:43 -0800, Adam Hughes wrote:
> I noticed recently that matplotlib.colors limits RGB values to a range (0 -
> 1), while in scikit image, RGB values can be much larger.  For example:
>

> *test = np.zeros( (500,500,3) )*
>
> *test[:,:,0]=50*
> *test[:,:,1]=19*
> *test[:,:,2]=25*
>
> *imshow(test); *

>
> Produces a teal background.  I was curious how the color teal is derived
> from this?  I tried normalizing to 255 and and 50 but neither seemed to
> produce the same teal color.

Here's a write-up of the data-type and range representation that scikit-image
uses:

http://scikit-image.org/docs/0.9.x/user_guide/data_types.html

When visualizing data with Matplotlib, note that data is normalized by
default, so you have to specify "vmin" and "vmax" to correctly display your
generated background.

Regards
Stéfan

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