Have you tried Winston? https://github.com/nolta/Winston.jl
On Tuesday, December 16, 2014 11:15:42 PM UTC+2, David Smith wrote: > > Thank you. > > I feel like Julia has matured enough finally to start migrating all of my > MRI research over to it. So far I have found no barriers whatsoever. I > recommend it enthusiastically to all of my colleagues. They are probably > getting tired of hearing about it. ;-) > > My biggest wish-list item (as a medical imager) would be native Julia > plotting that is similar to Matplotlib. I'd rather not have to require > that people have Python alongside Julia. Makes Julia sound less mature. I > tried Gadfly, but when most of what you plot is images, Gadfly makes less > sense. (Maybe something is missing in the grammar that includes images as > something separate from rectbins.) I also got bit pretty hard by the pair > borking bug in Color.jl, which was very annoying. > > On Tuesday, December 16, 2014 1:42:16 PM UTC-6, Isaiah wrote: > >> This is exciting! Congratulations on the release. >> >> On Tue, Dec 16, 2014 at 1:50 PM, David Smith <[email protected]> wrote: >>> >>> A few of us around here do medical imaging research, so I'm announcing >>> the release of DCEMRI.jl, a Julia module for processing dynamic contrast >>> enhanced magnetic resonance imaging (MRI) data. >>> >>> http://github.com/davidssmith/DCEMRI.jl >>> >>> To install, >>> >>> julia> Pkg.add("DCEMRI") >>> >>> To run a quick demo, >>> >>> julia> using DCEMRI >>> >>> julia> demo() >>> >>> To rerun the validations, >>> >>> julia> validate() >>> >>> (Validation can take a while, because the phantoms use a ridiculously >>> large number of time points, and the Levenberg-Marquardt fitting scales >>> poorly with number of measurements.) >>> >>> When you run these functions, PyPlot will show the resulting images >>> after the run is complete, and pdfs of the images will be saved in the >>> module directory by default, or another place if you specify. >>> >>> The models included currently are the standard and extended Tofts-Kety, >>> and both have been validated against the test phantoms provided by the >>> Quantitative Imaging Biomarkers Association. The execution speed is the >>> fastest of any code I've tried, by about an order of magnitude, on a >>> per-processor basis. You can fit a typical slice of in vivo data in about >>> 1-2 seconds on a decent machine. >>> >>> Several modes of operation are supported, including file-based >>> processing and passing data as function arguments and parameters as kwargs. >>> See the demo and the validation functions for examples of usage. Parallel >>> processing is supported, using either function parameters or by starting >>> julia with the '-p <n>' flag. I also have a command-line script and a >>> (simplistic) Matlab interface function. >>> >>> The code currently uses PyPlot for plotting, so you need Matplotlib >>> installed, and that is not handled automatically, but all of the Julia >>> dependencies are. >>> >>> A paper on the code is in press at PeerJ ( >>> https://peerj.com/preprints/670/). >>> >>> Let me know what you think. >>> >>> Cheers, >>> Dave >>> >>>
