blob: 70f521fdafca63e2ec589d859549c0e710c5172a [file] [log] [blame]
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;
return (withinStdDev(-a) + withinStdDev(b)) / 2;
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)]
# 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))