Plotting

Author

Marie-Hélène Burle

It can be convenient to plot directly in the REPL (for instance when using SSH).

using UnicodePlots
histogram(randn(1000), nbins=40)

Precompiling UnicodePlots
  ✓ MarchingCubes
  ✓ UnicodePlots
  2 dependencies successfully precompiled in 36 seconds. 42 already precompiled.
Precompiling IntervalSetsExt
  ✓ UnicodePlots → IntervalSetsExt
  1 dependency successfully precompiled in 1 seconds. 46 already precompiled.
Precompiling FreeTypeExt
  ✓ UnicodePlots → FreeTypeExt
  1 dependency successfully precompiled in 2 seconds. 53 already precompiled.

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                                 Frequency

Most of the time however, you will want to make nicer looking graphs. There are many options to plot in Julia.

Plots is a convenient Julia package which allows to use the same code with several graphing backends such as the GR framework (great for speed), Plotly.js (allows interaction with your graphs in a browser), or PyPlot. The default backend is the GR framework.

StatsPlots is an enhanced version with added stats functionality.

Example:

# First run takes time as the package needs to compile
using StatsPlots
StatsPlots.histogram(randn(1000), bins=40)

Precompiling StatsPlots
  ✓ Arpack_jll
  ✓ Widgets
  ✓ Distances
  ✓ Arpack
  ✓ SentinelArrays
  ✓ Distances → DistancesSparseArraysExt
  ✓ TableOperations
  ✓ Distances → DistancesChainRulesCoreExt
  ✓ MultivariateStats
  ✓ NearestNeighbors
  ✓ Clustering
  ✓ StatsPlots
  12 dependencies successfully precompiled in 8 seconds. 225 already precompiled.

Here, we need to explicitly run StatsPlots.histogram rather than histogram to prevent a conflict with the function of the same name from the package UnicodePlots.