aces/luts/unbuild/adx_cid_to_rle.py

   1 #!/usr/bin/env python
   2
   3 import math, numpy
   4
   5 """
   6
   7 const float REF_PT = (7120.0 - 1520.0) / 8000.0 * (100.0 / 55.0) - log10(0.18);
   8
   9 const float LUT_1D[11][2] = {
  10         {-0.190000000000000, -6.000000000000000},
  11         { 0.010000000000000, -2.721718645000000},
  12         { 0.028000000000000, -2.521718645000000},
  13         { 0.054000000000000, -2.321718645000000},
  14         { 0.095000000000000, -2.121718645000000},
  15         { 0.145000000000000, -1.921718645000000},
  16         { 0.220000000000000, -1.721718645000000},
  17         { 0.300000000000000, -1.521718645000000},
  18         { 0.400000000000000, -1.321718645000000},
  19         { 0.500000000000000, -1.121718645000000},
  20         { 0.600000000000000, -0.926545676714876}
  21 };
  22
  23         // Convert Channel Independent Density values to Relative Log Exposure values
  24         float logE[3];
  25         if ( cid[0] <= 0.6) logE[0] = interpolate1D( LUT_1D, cid[0]);
  26         if ( cid[1] <= 0.6) logE[1] = interpolate1D( LUT_1D, cid[1]);
  27         if ( cid[2] <= 0.6) logE[2] = interpolate1D( LUT_1D, cid[2]);
  28
  29         if ( cid[0] > 0.6) logE[0] = ( 100.0 / 55.0) * cid[0] - REF_PT;
  30         if ( cid[1] > 0.6) logE[1] = ( 100.0 / 55.0) * cid[1] - REF_PT;
  31         if ( cid[2] > 0.6) logE[2] = ( 100.0 / 55.0) * cid[2] - REF_PT;
  32 """
  33
  34
  35 def interpolate1D(x, xp, fp):
  36     return numpy.interp(x, xp, fp)
  37
  38 LUT_1D_xp = [-0.190000000000000,
  39               0.010000000000000,
  40               0.028000000000000,
  41               0.054000000000000,
  42               0.095000000000000,
  43               0.145000000000000,
  44               0.220000000000000,
  45               0.300000000000000,
  46               0.400000000000000,
  47               0.500000000000000,
  48               0.600000000000000]
  49
  50 LUT_1D_fp = [-6.000000000000000,
  51              -2.721718645000000,
  52              -2.521718645000000,
  53              -2.321718645000000,
  54              -2.121718645000000,
  55              -1.921718645000000,
  56              -1.721718645000000,
  57              -1.521718645000000,
  58              -1.321718645000000,
  59              -1.121718645000000,
  60              -0.926545676714876]
  61
  62 REF_PT = (7120.0 - 1520.0) / 8000.0 * (100.0 / 55.0) - math.log(0.18, 10.0)
  63
  64 def cid_to_rle(x):
  65     if x <= 0.6:
  66         return interpolate1D(x, LUT_1D_xp, LUT_1D_fp)
  67     return (100.0 / 55.0) * x - REF_PT
  68
  69 def WriteSPI1D(filename, fromMin, fromMax, data):
  70     f = file(filename,'w')
  71     f.write("Version 1\n")
  72     f.write("From %s %s\n" % (fromMin, fromMax))
  73     f.write("Length %d\n" % len(data))
  74     f.write("Components 1\n")
  75     f.write("{\n")
  76     for value in data:
  77         f.write("        %s\n" % value)
  78     f.write("}\n")
  79     f.close()
  80
  81 def Fit(value, fromMin, fromMax, toMin, toMax):
  82     if fromMin == fromMax:
  83         raise ValueError("fromMin == fromMax")
  84     return (value - fromMin) / (fromMax - fromMin) * (toMax - toMin) + toMin
  85
  86 NUM_SAMPLES = 2**12
  87 RANGE = (-0.19, 3.0)
  88 data = []
  89 for i in xrange(NUM_SAMPLES):
  90     x = i/(NUM_SAMPLES-1.0)
  91     x = Fit(x, 0.0, 1.0, RANGE[0], RANGE[1])
  92     data.append(cid_to_rle(x))
  93
  94 WriteSPI1D('adx_cid_to_rle.spi1d', RANGE[0], RANGE[1], data)
  95