I've tried all of the plotting packages. Winston is a nice start but still a little rough around the edges. I couldn't get a colorbar, for example, and the fonts aren't as well rendered as in Matplotlib.
ImageView is nice for peeking at things, but it doesn't produce plot annotations, AFAIK. I use ImageView when developing. On Tuesday, December 16, 2014 10:36:17 PM UTC-6, Isaiah wrote: > > Tim Holy's ImageView package is another one to look at. Performance is > very good with the Gtk backend (streaming video works well). > > On Tue, Dec 16, 2014 at 6:39 PM, Johan Sigfrids <johan.s...@gmail.com > <javascript:>> wrote: > >> 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 <david...@gmail.com> >>>> 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 >>>>> >>>>> >
