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dfbSetSettlingTime to settling time = 29, number of samples = 1
ERROR: An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line statement', (8, 0))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)

/home/pcuser/trunk/python/autotune/autoTune.py in <module>()
    837     a.logSquidTESStatus()

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# implement WirelessKeyboard as K
immutable K
    f::Int
    s::Int
end
# implement InterferesWith as <
>(k::K,K::K)=k.f==K.f
#prepare randomized array
k = [K(f,(j-1)*3+f) for j=1:12,f=1:3]
k9 = [K(f,(j-1)*3+f) for j=1:3,f=1:3] # smaller testing array

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function bench_smith(n,d)
    for i = 1:length(n)
        smith_cdiv(n[i], d[i])
    end
end

function bench_robust(n,d)
    for i = 1:length(n)
        robust_cdiv(n[i], d[i])
    end

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v = dot(a[j:j+length(filter)-1], filter)
	elapsed time: 1.305102964 seconds (640420192 bytes allocated)
v = Base.dot(sub(a,j:j+length(filter)-1), filter)
	elapsed time: 10.830825758 seconds (2561056544 bytes allocated)
v = Base.dot(unsafe_view(a,j:j+length(filter)-1), filter)
	elapsed time: 0.171855427 seconds (420640 bytes allocated)
v = NumericExtensions.dot(unsafe_view(a,j:j+length(filter)-1), filter)
	elapsed time: 0.165605407 seconds (420640 bytes allocated)
v = -a[j]-a[j+1]+a[j+2]+a[j+3]
	elapsed time: 0.050317084 seconds (420208 bytes allocated)

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The archive paper provides 10 example hard complex divions with answers.  It also provides an algorith for measuring the accuracy of the result in "bits", which I have implemented.  The robust cdiv algorithm is said to get 53 bits for all but #8 on the hard problems, where it gets 52 bits. I reproduce all of the results (also the default julia / reproduces results for smith's algorithm), except I get 0 bits on #5.  I've tracked this down to the division of b/c returning zero inside robust_cdiv2, after r is also zero. But it's not clear how to fix it.


cdiv #1 ,53 bits accurate, 1.1125369292536007e-308 - 1.1125369292536007e-308im
cdiv #2 ,53 bits accurate, 8.98846567431158e307 + 0.0im
cdiv #3 ,53 bits accurate, 1.4334366349937947e104 - 3.645561009778199e-304im
cdiv #4 ,53 bits accurate, 8.98846567431158e307 + 0.0im
cdiv #5 ,0 bits accurate, 3.757668132438133e109 - 2.0e-323im
cdiv #6 ,53 bits accurate, 2.0e-323 + 1.048576e6im
cdiv #7 ,53 bits accurate, 3.8981256045591133e289 + 8.174961907852354e295im

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boollist = String[]
bool = ones(10,10)
macro newbool(name, value)
    push!(boollist, repr(name)[2:end])
    bool[length(boollist),:] = eval(value)
end

@newbool shoes rand(10)

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Caclulating and drawing fractals

Multiple dispatch