{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Overview\n",
    "\n",
    "This tutorial will give some examples of plotting and plotting features in Julia, as well as providing references to some relevant resources. The main plotting library is `Plots.jl`, but there are some others that provide useful features.\n",
    "\n",
    "## Some Resources\n",
    "\n",
    "* `Plots.jl` [useful tips](https://docs.juliaplots.org/latest/basics/#Useful-Tips)\n",
    "* `Plots.jl` [examples](https://docs.juliaplots.org/latest/generated/gr/)\n",
    "* [Plot attributes](http://docs.juliaplots.org/latest/generated/attributes_plot/)\n",
    "* [Axis attributes](http://docs.juliaplots.org/latest/generated/attributes_axis/#Axis)\n",
    "* [Color names](http://juliagraphics.github.io/Colors.jl/stable/namedcolors/)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Packages\n",
    "\n",
    "To run this notebook, you can download or view the [`Project.toml`](https://raw.githubusercontent.com/vsrikrish/climate-risk-analysis/gh-pages/tutorials/notebooks/julia-plots/Project.toml) and [`Manifest.toml`](https://raw.githubusercontent.com/vsrikrish/climate-risk-analysis/gh-pages/tutorials/notebooks/julia-plots/Manifest.toml). You can also just add packages as needed to the environment, which is by default set to exist in the path of the notebook file."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using Pkg\n",
    "Pkg.activate(@__DIR__)\n",
    "Pkg.instantiate()"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Demos"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using Plots\n",
    "using Random # hide\n",
    "Random.seed!(1) # hide"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Line Plots\n",
    "To generate a basic line plot, use `plot`."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "y = rand(5)\n",
    "plot(y, label=\"original data\", legend=:topright)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "There's a lot of customization here that can occur, a lot of which is discussed in the docs or can be found with some Googling."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Adding Plot Elements\n",
    "\n",
    "Now we can add some other lines and point markers."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "y2 = rand(5)\n",
    "y3 = rand(5)\n",
    "plot!(y2, label=\"new data\")\n",
    "scatter!(y3, label=\"even more data\")"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "Remember that an exclamation mark (!) at the end of a function name means that function modifies an object in-place, so `plot!` and `scatter!` modify the current plotting object, they don't create a new plot."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Removing Plot Elements\n",
    "\n",
    "Sometimes we want to remove legends, axes, grid lines, and ticks."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "plot!(legend=false, axis=false, grid=false, ticks=false)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Aspect Ratio\n",
    "\n",
    "If we want to have a square aspect ratio, use `ratio = 1`."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "v = rand(5)\n",
    "plot(v, ratio=1, legend=false)\n",
    "scatter!(v)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Heatmaps"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "A heatmap is effectively a plotted matrix with colors chosen according to the values. Use `clim` to specify a fixed range for the color limits."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "A = rand(10, 10)\n",
    "heatmap(A, clim=(0, 1), ratio=1, legend=false, axis=false, ticks=false)\n",
    "\n",
    "\n",
    "M = [ 0 1 0; 0 0 0; 1 0 0]\n",
    "whiteblack = [RGBA(1,1,1,0), RGB(0,0,0)]\n",
    "heatmap(c=whiteblack, M, aspect_ratio = 1, ticks=.5:3.5, lims=(.5,3.5), gridalpha=1, legend=false, axis=false, ylabel=\"i\", xlabel=\"j\")"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Custom Colors"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using Colors\n",
    "\n",
    "mycolors = [colorant\"lightslateblue\",colorant\"limegreen\",colorant\"red\"]\n",
    "A = [i for i=50:300, j=1:100]\n",
    "heatmap(A, c=mycolors, clim=(1,300))"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plotting Areas Under Curves"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "y = rand(10)\n",
    "plot(y, fillrange= y.*0 .+ .5, label= \"above/below 1/2\", legend =:top)\n",
    "\n",
    "\n",
    "x = LinRange(0,2,100)\n",
    "y1 = exp.(x)\n",
    "y2 = exp.(1.3 .* x)\n",
    "plot(x, y1, fillrange = y2, fillalpha = 0.35, c = 1, label = \"Confidence band\", legend = :topleft)\n",
    "\n",
    "\n",
    "x = -3:.01:3\n",
    "areaplot(x, exp.(-x.^2/2)/√(2π),alpha=.25,legend=false)\n",
    "\n",
    "\n",
    "M = [1 2 3; 7 8 9; 4 5 6; 0 .5 1.5]\n",
    "areaplot(1:3, M, seriescolor = [:red :green :blue ], fillalpha = [0.2 0.3 0.4])\n",
    "\n",
    "\n",
    "using SpecialFunctions\n",
    "f = x->exp(-x^2/2)/√(2π)\n",
    "δ = .01\n",
    "plot()\n",
    "x = √2 .* erfinv.(2 .*(δ/2 : δ : 1) .- 1)\n",
    "areaplot(x, f.(x), seriescolor=[ :red,:blue], legend=false)\n",
    "plot!(x, f.(x),c=:black)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plotting Shapes"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "rectangle(w, h, x, y) = Shape(x .+ [0,w,w,0], y .+ [0,0,h,h])\n",
    "circle(r,x,y) = (θ = LinRange(0,2π,500); (x.+r.*cos.(θ), y.+r.*sin.(θ)))\n",
    "plot(circle(5,0,0), ratio=1, c=:red, fill=true)\n",
    "plot!(rectangle(5*√2,5*√2,-2.5*√2,-2.5*√2),c=:white,fill=true,legend=false)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Plotting Distributions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The [`StatsPlots.jl`](https://github.com/JuliaPlots/StatsPlots.jl/blob/master/README.md) package is very useful for making various plots of probability distributions."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using Distributions, StatsPlots\n",
    "plot(Normal(2, 5))\n",
    "\n",
    "\n",
    "scatter(LogNormal(0.8, 1.5))"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can also use this functionality to plot distributions of data in tabular data structures like `DataFrames`."
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "using DataFrames\n",
    "dat = DataFrame(a = 1:10, b = 10 .+ rand(10), c = 10 .* rand(10))\n",
    "@df dat density([:b :c], color=[:black :red])"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Editing Plots Manually"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "pl = plot(1:4,[1, 4, 9, 16])\n",
    "\n",
    "\n",
    "pl.attr"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "pl.series_list[1]"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "pl[:size]=(300,200)"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "pl"
   ],
   "metadata": {},
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Log-Scaled Axes"
   ],
   "metadata": {}
  },
  {
   "outputs": [],
   "cell_type": "code",
   "source": [
    "xx = .1:.1:10\n",
    "plot(xx.^2, xaxis=:log, yaxis=:log)\n",
    "\n",
    "\n",
    "plot(exp.(x), yaxis=:log)"
   ],
   "metadata": {},
   "execution_count": null
  }
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