Re: [Matplotlib-users] XKCD style graphs?

On Thu, Oct 4, 2012 at 3:35 PM, Pierre Haessig <pie...@cr...> wrote:
> Le 04/10/2012 16:03, Jason Grout a écrit :
>> f@r means f(r)
>>
>> a~ImageConvolve~b means ImageConvolve(a,b)  (~ treats an operator as infix)
>>
>> Table[..., {2}] means [... for i in range(2)]
>>
>> #+1& is a lambda function lambda x: x+1
>>
>> So I think it goes something like:
>>
>> def xkcdDistort(p):
>>      r = ImagePad(Rasterize(p), 10, Padding='White')
>>      (ix, iy) = [ImageConvolve(RandomImage([-1,1], ImageDimensions(r)),
>>                                GaussianMatrix(10))
>>                  for i in range(2)]
>>      return ImagePad(ImageTransformation(r,
>>                  lambda coord: (coord[0]+15*ImageValue(ix, coord),
>>                                 coord[1]+15*ImageValue(iy, coord)),
>>                   DataRange='Full'),
>>                -5)
> Thanks a lot!
>
> It's the first time I encounter Mathematica syntax. Some of these
> functional notations are not so easy to follow for my unexperienced eyes
> but it makes this Mathematica code nicely compact.
>
> So I think this code indeed resamples the rastered plot image on a
> shaken coordinate grid. I kind of understand that the noise on
> coordinates is spatially smoothed by a 10px Gaussian Point Spread
> Function (if I understand correctly...)
>
> Best,
> Pierre

Adding Gaussian noise to each point on a function doesn't look nice.
That's why I produced a random function in Fourier space first. That
way, random functions still have some sense of smoothness.

-- 
Damon McDougall
http://www.damon-is-a-geek.com
B2.39
Mathematics Institute
University of Warwick
Coventry
West Midlands
CV4 7AL
United Kingdom