Fix metrics on Windows.

tools/generate_fir_coeff.py

Index: tools/generate_fir_coeff.py
===================================================================
--- tools/generate_fir_coeff.py	(revision 0)
+++ tools/generate_fir_coeff.py	(working copy)
@@ -0,0 +1,119 @@
+#!/usr/bin/python
+
+'''
+Copyright 2013 Google Inc.
+
+Use of this source code is governed by a BSD-style license that can be
+found in the LICENSE file.
+'''
+
+import math
+import pprint
+
+def withinStdDev(n):
+  """Returns the percent of samples within n std deviations of the normal."""
+  return math.erf(n / math.sqrt(2))
+
+def withinStdDevRange(a, b):
+  """Returns the percent of samples within the std deviation range a, b"""
+  if b < a:
+    return 0;
+
+  if a < 0:
+    if b < 0:
+      return (withinStdDev(-a) - withinStdDev(-b)) / 2;
+    else:
+      return (withinStdDev(-a) + withinStdDev(b)) / 2;
+  else:
+    return (withinStdDev(b) - withinStdDev(a)) / 2;
+
+
+#We have a bunch of smudged samples which represent the average coverage of a range.
+#We have a 'center' which may not line up with those samples.
+#From the 'center' we want to make a normal approximation where '5' sample width out we're at '3' std deviations.
+#The first and last samples may not be fully covered.
+
+#This is the sub-sample shift for each set of FIR coefficients (the centers of the lcds in the samples)
+#Each subpxl takes up 1/3 of a pixel, so they are centered at x=(i/n+1/2n), or 1/6, 3/6, 5/6 of a pixel.
+#Each sample takes up 1/4 of a pixel, so the results fall at (x*4)%1, or 2/3, 0, 1/3 of a sample.
+samples_per_pixel = 4
+subpxls_per_pixel = 3
+#sample_offsets is (frac, int) in sample units.
+sample_offsets = [math.modf((float(subpxl_index)/subpxls_per_pixel + 1.0/(2.0*subpxls_per_pixel))*samples_per_pixel) for subpxl_index in range(subpxls_per_pixel)]
+
+#How many samples to consider to the left and right of the subpxl center.
+sample_units_width = 5
+
+#The std deviation at sample_units_width.
+std_dev_max = 3
+
+#The target sum is in some fixed point representation.
+#Values larger the 1 in fixed point simulate ink spread.
+target_sum = 0x110
+
+for sample_offset, sample_align in sample_offsets:
+  coeffs = []
+  coeffs_rounded = []
+
+  #We start at sample_offset - sample_units_width
+  current_sample_left = sample_offset - sample_units_width
+  current_std_dev_left = -std_dev_max
+
+  done = False
+  while not done:
+    current_sample_right = math.floor(current_sample_left + 1)
+    if current_sample_right > sample_offset + sample_units_width:
+      done = True
+      current_sample_right = sample_offset + sample_units_width
+    current_std_dev_right = current_std_dev_left + ((current_sample_right - current_sample_left) / sample_units_width) * std_dev_max
+
+    coverage = withinStdDevRange(current_std_dev_left, current_std_dev_right)
+    coeffs.append(coverage * target_sum)
+    coeffs_rounded.append(int(round(coverage * target_sum)))
+
+    current_sample_left = current_sample_right
+    current_std_dev_left = current_std_dev_right
+
+  # Now we have the numbers we want, but our rounding needs to add up to target_sum.
+  delta = 0
+  coeffs_rounded_sum = sum(coeffs_rounded)
+  if coeffs_rounded_sum > target_sum:
+    # The coeffs add up to too much. Subtract 1 from the ones which were rounded up the most.
+    delta = -1
+
+  if coeffs_rounded_sum < target_sum:
+    # The coeffs add up to too little. Add 1 to the ones which were rounded down the most.
+    delta = 1
+
+  if delta:
+    print "Initial sum is 0x%0.2X, adjusting." % (coeffs_rounded_sum,)
+    coeff_diff = [(coeff_rounded - coeff) * delta
+                  for coeff, coeff_rounded in zip(coeffs, coeffs_rounded)]
+
+    class IndexTracker:
+      def __init__(self, index, item):
+        self.index = index
+        self.item = item
+      def __lt__(self, other):
+        return self.item < other.item
+      def __repr__(self):
+        return "arr[%d] == %s" % (self.index, repr(self.item))
+
+    coeff_pkg = [IndexTracker(i, diff) for i, diff in enumerate(coeff_diff)]
+    coeff_pkg.sort()
+
+    # num_elements_to_force_round had better be < (2 * sample_units_width + 1) or
+    # * our math was wildy wrong
+    # * an awful lot of the curve is out side our sample
+    # either is pretty bad, and probably means the results will not be useful.
+    num_elements_to_force_round = abs(coeffs_rounded_sum - target_sum)
+    for i in xrange(num_elements_to_force_round):
+      print "Adding %d to index %d to force round %f." % (delta, coeff_pkg[i].index, coeffs[coeff_pkg[i].index])
+      coeffs_rounded[coeff_pkg[i].index] += delta
+
+  print "Prepending %d 0x00 for allignment." % (sample_align,)
+  coeffs_rounded_aligned = ([0] * int(sample_align)) + coeffs_rounded
+
+  print ', '.join(["0x%0.2X" % coeff_rounded for coeff_rounded in coeffs_rounded_aligned])
+  print sum(coeffs), hex(sum(coeffs_rounded))
+  print