Filling In The Interop Packages and Rosenbrock

By: JuliaDiffEq

Re-posted from:

In the 2.0 state of the ecosystem post
it was noted that, now that we have a clearly laid out and expansive common API,
the next goal is to fill it in. This set of releases tackles the lowest hanging
fruits in that battle. Specifically, the interop packages were setup to be as
complete in their interfaces as possible, and the existing methods which could
expand were expanded. Time for specifics.

Exploring Fibonacci Fractions with Julia

By: perfectionatic

Re-posted from:

Recently, I came across a fascinating blog and video from Mind you Decisions. It is about how a fraction
would show the Fibonacci numbers in order when looking at its decimal output.

On a spreadsheet and most standard programming languages, such output can not be attained due to the limited precision for floating point numbers. If you try this on R or Python, you will get an output of 1e-48.
Wolfram alpha,however,allows arbitrary precision.

In Julia by default we get a little better that R and Python

julia> 1/999999999999999999999998999999999999999999999999
julia> typeof(ans)

We observe here that we are getting the first few Fibonacci numbers 1, 1, 2, 3. We need more precision to get more numbers. Julia has arbitrary precision arithmetic baked into the language. We can crank up the precision of the BigFloat type on demand. Of course, the higher the precision, the slower the computation and the greater the memory we use. We do that by setprecision.

julia> setprecision(BigFloat,10000)

Reevaluating, we get

julia> 1/999999999999999999999998999999999999999999999999

That is looking much better. However it we be nice if we could extract the Fibonacci numbers that are buried in that long decimal. Using the approach in the original blog. We define a function


and calculate the decimal


Here we use the non-standard string literal big"..." to insure proper interpretation of our input. Using BigFloat(1e-24)) would first construct at floating point with limited precision and then do the conversion. The initial loss of precision will not be recovered in the conversion, and hence the use of big. Now we extract our Fibonacci numbers by this function

function extract_fib(a)
   for i=1:div(length(x)-24,24)

Here we first convert our very long decimal number of a string and they we exploit the fact the Fibonacci numbers occur in blocks that 24 digits in length. We get out output in an array of BigInt. We want to compare the output with exact Fibonacci numbers, we just do a quick and non-recursive implementation.

function fib(n)
    for i=3:n+1

Now we compare…

for i in eachindex(fib_frac)
     println(fib_exact[i], " ", fib_exact[i]-fib_frac[i])

We get a long sequence, we just focused here on when the discrepancy happens.

184551825793033096366333 0
298611126818977066918552 0
483162952612010163284885 0
781774079430987230203437 -1
1264937032042997393488322 999999999999999999999998
2046711111473984623691759 1999999999999999999999997

The output shows that just before the extracted Fibonacci number exceeds 24 digits, a discrepancy occurs. I am not quite sure why, but this was a fun exploration. Julia allows me to do mathematical explorations that would take one or even two orders of magnitude of effort to do in any other language.

Where are the Julians?

By: Júlio Hoffimann

Re-posted from:


  • Pan and zoom with the mouse.
  • Click on a bubble to open profiles on GitHub.
  • Alt + click to remove a bubble.

You may need to unblock popups in your browser to have multiple profiles opening as tabs. Removing a bubble can be useful for revealing other bubbles.

Julians are presented in decreasing order of contributions. An arc is drawn between locations X and Y in the map whenever a Julian in X and a Julian in Y have contributed to a common package.

Want to be on the map?

If your nickname is listed below and you want to appear on the map, please consider typing your address on GitHub:


The data was extracted from METADATA. It only includes members
of the community that have contributed to a registered Julia package (e.g. issues, pull requests) up
until 16-May-2017.

The Jupyter notebook used for data extraction is available in our
JuliaGraphsTutorials repository.


  • Russia and China have an unexpectedly low number of bubbles.
  • The number of outgoing arcs from India is great.
  • Less developed countries are slowly adopting the language.

Say hello to a Julian near you. #JuliansInTheGlobe