Mathematica FAIL

* Ray & I discussed strategy w.r.t. how to proceed with FEDM board testing. We'll wait a few more days to hopefully get documentation from Sachin/Vaibhav, since they have been ill.

* Took forms to Lee hall to get Xmath past EIT approval. The secretary said, the guy who does the approvals is supposed to be in tomorrow.

* Since the result from Mathematica was wrong, I manually constructed a symmetrized version of the distribution of frequency drift values we'd expect to see as a result of quantization error; & updated Monday's notes accordingly. Today planning to numerically calculate the distribution in C to make sure the curve is correct.

* Checked our NetBeans installation to see if it had C/C++ support installed. No; apparently I only installed it with Java support before. Uninstalling & reinstalling to add support for other languages. In the meantime, maybe I should just use Python...

* Had trouble registering NetBeans with Oracle, so Python won the day. Here is my Python code:

def dist(d):
if d > 100:
return 0.25*(2-(d/100))**2;
elif d < -100:
return 0.25*(2+(d/100))**2;
else:
return 0.25*(2-(d/100)**2);

count = {}

for d in range(-200, 200):
count[d] = 0

for x1 in range(-50,50):
for x2 in range(-50,50):
for x3 in range(-50,50):
y1 = x2 - x1
y2 = x3 - x2
d = y2 - y1
count[d] = count[d] + 1

tot = 0
totd = 0
for d in range(-200,200):
print(d, count[d], dist(d), sep='\t')
tot = tot + count[d]
totd = totd + dist(d)

print("TOT", tot, totd)

The two columns, when normalized, are identical, except for tiny discrepancies which can be chalked up to the imprecision that's inherent to the numerical integration. So, this confirms my previous belief that the solution from Mathematica was correct except for the gap.