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## Jupyter notebook server at Nevis | ||||||||

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Gnuplot is a graphing utility for visualizing mathematical functions and data interactively. There are Gnuplot cell magics that let you use Gnuplot graphics to create plots within some of the other kernels on this page, such as Julia and Octave.
## Julia | ||||||||

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< < | Julia is a high-level, high-performance dynamic programming language developed at MIT for technical computing. It combines the ease-of-use of Python with the speed of Fortran. Here's a Julia tutorial, though the plotting examples won't work in Jupyter unless you use PyPlot); e.g.: | |||||||

> > | Julia is a high-level, high-performance dynamic programming language developed at MIT for technical computing. It combines the ease-of-use of Python with the speed of Fortran. Here's a Julia tutorial, though the plotting examples may not work in Jupyter unless you use PyPlot); e.g.: | |||||||

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< < | using PyPlot x=linspace(0,2*pi,1000) y=sin(3x + 3cos(2x)) plot(x,y,color="red",linewidth=2.0,linestyle="--") title("plot of oscillatory function") xlabel("the x axis") | |||||||

> > | using Pkg
Pkg.add("PyPlot")
x = range(0; stop=2*pi, length=1000);
# The "." after the Base function tells Julia it's operating on a Vector
y = sin.(3 * x + 4 * cos.(2 * x));
PyPlot.plot(x, y, color="red", linewidth=2.0, linestyle="--")
PyPlot.title("A sinusoidally modulated sinusoid")
Here's another little example:
# You probably won't need to add these packages, since WilliamSeligman has # already done so on the notebook server. using Pkg Pkg.add("Plots") Pkg.add("DifferentialEquations") using DifferentialEquations f(u,p,t) = 1.01*u u0 = 1/2 tspan = (0.0,1.0) prob = ODEProblem(f,u0,tspan) sol = solve(prob, Tsit5(), reltol=1e-8, abstol=1e-8) Plots.plot(sol,linewidth=5,title="Solution to the linear ODE with a thick line", xaxis="Time (t)",yaxis="u(t) (in μm)",label="My Thick Line!") # legend=false Plots.plot!(sol.t, t->0.5*exp(1.01t),lw=3,ls=:dash,label="True Solution!") | |||||||

## Octave |

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