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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tutorial 2: Data Plots\n",
    "\n",
    "This tutorial will talk about how to visualise the distributions that have been built in Tutorial 1. \n",
    "\n",
    "**NOTE FOR CONTRIBUTORS: Always clear all output before committing (``Cell`` > ``All Output`` > ``Clear``)**!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Magic\n",
    "%matplotlib inline\n",
    "# Reload modules whenever they change\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# Make clusterking package available even without installation\n",
    "import sys\n",
    "sys.path = [\"../../\"] + sys.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import clusterking as ck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we load the data created in Tutorial 1 in the folder output/cluster/ with the name tutorial_basics and pass it to the Data class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = ck.Data(\"output/tutorial_basics.sql\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The possible columns to be plotted can be found using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.par_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also improve the styling of the variable on the x axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.configure_variable(\"q2\", axis_label=r\"$q^2\\ [\\mathrm{GeV}^2]$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to visualise our created data: Let's start by drawing the histograms corresponding to the benchmark points of each clusters by typing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add more sample points to the plot (in addition to the benchmark point):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist(clusters=[1, 2], nlines=5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save plots using the usual matplotlib syntax, e.g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = d.plot_dist();\n",
    "fig.savefig(\"output/plots/test.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the minima and maxima of all clusters is achieved with the plot_minmax method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist_minmax();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same plot for clusters 2 and 3 only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist_minmax(clusters=[2,3]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Box plots can be produced using  the box_plot method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist_box();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing clusters 0 and 2 only:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d.plot_dist_box(clusters=[0, 2]);"
   ]
  }
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