{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 4 - Basic Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will explore the basic plot interface using ``matplotlib.pyplot``. We will just scratch the surface of this vastly capable package: you can find out more about matplotlib [here](https://matplotlib.org/).\n",
    "\n",
    "This notebook a matplotlib version of [this web tutorial](http://jakevdp.github.io/mpl_tutorial/tutorial_pages/tut1.html#)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first plot: the matplotlib interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use matplotlib, we will need to import it. Since matplotlib is built on and designed to work with numpy, we should import this too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make some simple data to plot: a sinusoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(0, 20, 0.02)  # 100 evenly-spaced values from 0 to 50\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing the plot: Axes Limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's play around with this a bit: first we can change the axis limits using ``xlim()`` and ``ylim()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, y)\n",
    "plt.xlim(5, 15)\n",
    "plt.ylim(-1.2, 1.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing the plot: Axes Labels and Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can label the axes and add a title:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, y)\n",
    "\n",
    "plt.xlabel('this is x!')\n",
    "plt.ylabel('this is y!')\n",
    "plt.title('My First Plot')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Labels can also be rendered using LaTeX symbols:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.sin(2 * np.pi * x)\n",
    "plt.plot(x, y)\n",
    "plt.title(r'$\\sin(2 \\pi x)$')  # the `r` before the string indicates a \"raw string\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing the plot: Line Styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can vary the line color or the line symbol:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(x, y, '-r')  # solid red line ('r' comes from RGB color scheme)\n",
    "plt.xlim(0, 10)\n",
    "plt.ylim(-1.2, 1.2)\n",
    "\n",
    "plt.xlabel('this is x!')\n",
    "plt.ylabel('this is y!')\n",
    "plt.title('My First Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other options for the color characters are:\n",
    "\n",
    "     'r' = red\n",
    "     'g' = green\n",
    "     'b' = blue\n",
    "     'c' = cyan\n",
    "     'm' = magenta\n",
    "     'y' = yellow\n",
    "     'k' = black\n",
    "     'w' = white\n",
    "\n",
    "Options for line styles are\n",
    "\n",
    "     '-' = solid\n",
    "     '--' = dashed\n",
    "     ':' = dotted\n",
    "     '-.' = dot-dashed\n",
    "     '.' = points\n",
    "     'o' = filled circles\n",
    "     '^' = filled triangles\n",
    "\n",
    "and many, many more.\n",
    "\n",
    "For more information, view the documentation of the plot function.  In IPython, this can be\n",
    "accomplished using the ``?`` functionality:\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also see the online version of this help: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cusomizing the Plot: Legends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple lines can be shown on the same plot.  In this case, you can use a legend\n",
    "to label the two lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0, 20, 0.02)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "plt.plot(x, y1, '-b', label='sine')\n",
    "plt.plot(x, y2, '-r', label='cosine')\n",
    "plt.legend(loc='upper right')\n",
    "plt.ylim(-1.5, 2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Linestyles & Plot Customization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are two sets of arrays ``x1, y1``, and ``x2, y2``.  Create a plot where\n",
    "``x1`` and ``y1`` are represented by blue circles, and ``x2`` and ``y2`` are\n",
    "represented by a dotted black line.  Label the symbols \"sampled\" and\n",
    "\"continuous\", and add a legend.  Adjust the y limits to suit your taste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.arange(0, 10, 0.5)\n",
    "y1 = np.sin(x1)\n",
    "\n",
    "x2 = np.arange(0, 10, 0.01)\n",
    "y2 = np.sin(x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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