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June 26, 2020 10:56
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using DifferentialEquations, ArgParse | |
function buchstab_model(dω,ω,h,p,u) | |
dω[1] = ( h(p, u-1)[1] - ω[1] ) / u | |
end | |
function buchstabtab(up, res=1, prec=40) | |
setprecision(prec) do | |
buchstab_history(p, u) = 1/u | |
buchstab_initial = [1/BigFloat(2)] | |
buchstab_problem = DDEProblem(buchstab_model, buchstab_initial, buchstab_history, | |
(BigFloat(2), BigFloat(up)+3); constant_lags=[1]) | |
alg = MethodOfSteps(Tsit5()) | |
buchstab = solve(buchstab_problem, alg) | |
return [(x, buchstab(x)[1]) for x in (BigFloat(2):BigFloat(res):BigFloat(up))] | |
end | |
end | |
s = ArgParseSettings() | |
@add_arg_table s begin | |
"--resolution", "-r" | |
help = "Evaluate at points spaced by this resolution" | |
arg_type = BigFloat | |
default = 1/BigFloat(10) | |
"--precision", "-p" | |
help = "Use this much floating point precision" | |
arg_type = Int | |
default = 40 | |
"bound" | |
help = "Evaluate up to bound" | |
arg_type = Int | |
required = true | |
end | |
args = parse_args(ARGS, s) | |
prec = args["precision"] | |
table = buchstabtab(args["bound"], args["resolution"], prec) | |
for (x,bx) in table | |
print(x, "\t", bx, "\n") | |
end |
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2.0 | 0.50 | |
---|---|---|
2.0999999999985 | 0.52157630634701 | |
2.2000000000007 | 0.5374190833445 | |
2.2999999999993 | 0.54885393980112 | |
2.4000000000015 | 0.55686477484232 | |
2.5 | 0.56218584807357 | |
2.5999999999985 | 0.56538554056715 | |
2.7000000000007 | 0.56689986002948 | |
2.7999999999993 | 0.56706706796285 | |
2.9000000000015 | 0.56615605923344 | |
3.0 | 0.56438242347758 | |
3.0999999999985 | 0.56266459268045 | |
3.2000000000007 | 0.56163933379867 | |
3.3000000000029 | 0.5610950057744 | |
3.4000000000015 | 0.56086563559256 | |
3.5 | 0.56083091827531 | |
3.5999999999985 | 0.56091500129514 | |
3.7000000000007 | 0.56105937735629 | |
3.8000000000029 | 0.56121827356947 | |
3.9000000000015 | 0.56135885112963 | |
4.0 | 0.56145797496356 | |
4.1000000000058 | 0.56150891681955 | |
4.1999999999971 | 0.56152564479453 | |
4.3000000000029 | 0.56152133876913 | |
4.4000000000015 | 0.56150650212385 | |
4.5 | 0.56148896174727 | |
4.6000000000058 | 0.56147386802786 | |
4.6999999999971 | 0.56146369486032 | |
4.8000000000029 | 0.5614582396438 | |
4.9000000000015 | 0.56145515950448 | |
5.0 | 0.5614543076872 | |
5.1000000000058 | 0.56145486287096 | |
5.1999999999971 | 0.56145602209926 | |
5.3000000000029 | 0.56145734204711 | |
5.4000000000015 | 0.56145850755092 | |
5.5 | 0.56145933161042 | |
5.6000000000058 | 0.56145975538766 | |
5.6999999999971 | 0.56145984820796 | |
5.8000000000029 | 0.5614598069069 | |
5.9000000000015 | 0.56145975164327 | |
6.0 | 0.56145966531403 | |
6.0999999999985 | 0.56145958617253 | |
6.2000000000044 | 0.56145952622683 | |
6.3000000000029 | 0.56145948360427 | |
6.4000000000015 | 0.56145945614844 | |
6.5 | 0.56145944142099 | |
6.5999999999985 | 0.5614594366989 | |
6.7000000000044 | 0.56145943897809 | |
6.8000000000029 | 0.56145944497257 | |
6.9000000000015 | 0.56145945111166 | |
7.0 | 0.56145945486787 |
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