import numpy

def xyz2srgb(tristimulus, scale=True):
   """
   title::
      xyz2srgb

   description::
      This method converts a provided set of tristimulus values to
      sRGB.  The tristimulus values are assumed to be in the range
      [0, 1] and computed with Illuminant D65.  A non-linearity 
      adjustment for the monitor response is applied (gamma = 2.4).
         (Source: http://www.brucelindbloom.com)

         | R-linear |   |  3.2404542 -1.5371385 -0.4985314 |   | X |
         | G-linear | = | -0.9692660  1.8760108  0.0415560 | * | Y |
         | B-linear |   |  0.0556434 -0.2040259  1.0572252 |   | Z |

      For [R-linear,G-linear,B-linear] <= 0.0031308
         sRGB = 12.92 * RGB-linear

      For [R-linear,G-linear,B-linear] > 0.0031308
         sRGB = ( 1 + a ) * RGB-linear^( 1 / 2.4 ) - a

   attributes::
      tristimulus
         A tuple containing the tristimulus vales X, Y, and Z
      scale
         A boolean indicating whether the provided tristimulus values
         should be divided by 100 prior to use in the computation to make
         sure they are in the range [0, 1]. [default is True]

   returns::
      A tuple containing the sRGB factors computed for the provided 
      tristimulus values.  The factors are in the range [0,1]. 

   author::
      Carl Salvaggio

   copyright::
      Copyright (C) 2015, Rochester Institute of Technology

   license::
      GPL

   version::
      1.0.0

   disclaimer::
      This source code is provided "as is" and without warranties as to 
      performance or merchantability. The author and/or distributors of 
      this source code may have made statements about this source code. 
      Any such statements do not constitute warranties and shall not be 
      relied on by the user in deciding whether to use this source code.
      
      This source code is provided without any express or implied warranties 
      whatsoever. Because of the diversity of conditions and hardware under 
      which this source code may be used, no warranty of fitness for a 
      particular purpose is offered. The user is advised to test the source 
      code thoroughly before relying on it. The user must assume the entire 
      risk of using the source code.
   """

   m = numpy.matrix([[ 3.2404542, -1.5371385, -0.4985314],
                     [-0.9692660,  1.8760108,  0.0415560],
                     [ 0.0556434, -0.2040259,  1.0572252]])

   XYZ = numpy.matrix(tristimulus).T
   if scale:
      XYZ = XYZ / 100.0

   rgbLinear = m * XYZ

   sRGB = rgbLinear

   index = numpy.where(rgbLinear <= 0.0031308)
   if len(index[0]) != 0:
      sRGB[index] = rgbLinear[index] * 12.92

   index = numpy.where(rgbLinear > 0.0031308)
   if len(index[0]) != 0:
      a = 0.055
      sRGB[index] = (1 + a) * numpy.array(rgbLinear[index])**(1 / 2.4) - a

   sRGB = numpy.clip(sRGB, 0, 1)

   return sRGB[0,0], sRGB[1,0], sRGB[2,0]


if __name__ == '__main__':

   import spectral

   filename = '../../examples/data/real1.sp'

   spectrum = spectral.SpectrumFactory.create_from_file(filename)
   tristimulus = spectral.spectrum2xyz(spectrum)
   sRGB = spectral.xyz2srgb(tristimulus)

   print('X = {0}'.format(tristimulus[0]))
   print('Y = {0}'.format(tristimulus[1]))
   print('Z = {0}'.format(tristimulus[2]))
   print('')
   print('R = {0}'.format(sRGB[0]))
   print('G = {0}'.format(sRGB[1]))
   print('B = {0}'.format(sRGB[2]))