+++ /dev/null
-#!/usr/bin/env python
-
-import math, numpy
-
-"""
-
-const float REF_PT = (7120.0 - 1520.0) / 8000.0 * (100.0 / 55.0) - log10(0.18);
-
-const float LUT_1D[11][2] = {
- {-0.190000000000000, -6.000000000000000},
- { 0.010000000000000, -2.721718645000000},
- { 0.028000000000000, -2.521718645000000},
- { 0.054000000000000, -2.321718645000000},
- { 0.095000000000000, -2.121718645000000},
- { 0.145000000000000, -1.921718645000000},
- { 0.220000000000000, -1.721718645000000},
- { 0.300000000000000, -1.521718645000000},
- { 0.400000000000000, -1.321718645000000},
- { 0.500000000000000, -1.121718645000000},
- { 0.600000000000000, -0.926545676714876}
-};
-
- // Convert Channel Independent Density values to Relative Log Exposure values
- float logE[3];
- if ( cid[0] <= 0.6) logE[0] = interpolate1D( LUT_1D, cid[0]);
- if ( cid[1] <= 0.6) logE[1] = interpolate1D( LUT_1D, cid[1]);
- if ( cid[2] <= 0.6) logE[2] = interpolate1D( LUT_1D, cid[2]);
-
- if ( cid[0] > 0.6) logE[0] = ( 100.0 / 55.0) * cid[0] - REF_PT;
- if ( cid[1] > 0.6) logE[1] = ( 100.0 / 55.0) * cid[1] - REF_PT;
- if ( cid[2] > 0.6) logE[2] = ( 100.0 / 55.0) * cid[2] - REF_PT;
-"""
-
-
-def interpolate1D(x, xp, fp):
- return numpy.interp(x, xp, fp)
-
-LUT_1D_xp = [-0.190000000000000,
- 0.010000000000000,
- 0.028000000000000,
- 0.054000000000000,
- 0.095000000000000,
- 0.145000000000000,
- 0.220000000000000,
- 0.300000000000000,
- 0.400000000000000,
- 0.500000000000000,
- 0.600000000000000]
-
-LUT_1D_fp = [-6.000000000000000,
- -2.721718645000000,
- -2.521718645000000,
- -2.321718645000000,
- -2.121718645000000,
- -1.921718645000000,
- -1.721718645000000,
- -1.521718645000000,
- -1.321718645000000,
- -1.121718645000000,
- -0.926545676714876]
-
-REF_PT = (7120.0 - 1520.0) / 8000.0 * (100.0 / 55.0) - math.log(0.18, 10.0)
-
-def cid_to_rle(x):
- if x <= 0.6:
- return interpolate1D(x, LUT_1D_xp, LUT_1D_fp)
- return (100.0 / 55.0) * x - REF_PT
-
-def WriteSPI1D(filename, fromMin, fromMax, data):
- f = file(filename,'w')
- f.write("Version 1\n")
- f.write("From %s %s\n" % (fromMin, fromMax))
- f.write("Length %d\n" % len(data))
- f.write("Components 1\n")
- f.write("{\n")
- for value in data:
- f.write(" %s\n" % value)
- f.write("}\n")
- f.close()
-
-def Fit(value, fromMin, fromMax, toMin, toMax):
- if fromMin == fromMax:
- raise ValueError("fromMin == fromMax")
- return (value - fromMin) / (fromMax - fromMin) * (toMax - toMin) + toMin
-
-NUM_SAMPLES = 2**12
-RANGE = (-0.19, 3.0)
-data = []
-for i in xrange(NUM_SAMPLES):
- x = i/(NUM_SAMPLES-1.0)
- x = Fit(x, 0.0, 1.0, RANGE[0], RANGE[1])
- data.append(cid_to_rle(x))
-
-WriteSPI1D('adx_cid_to_rle.spi1d', RANGE[0], RANGE[1], data)
-