{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![title](header.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib is the \"grandfather\" data visualisation tool for data in Python - it offers unparalleled control over graphs and diagrams for data, and lets us annotate and customise figures to our heart's content. Matplotlib is built upon for other important modules we'll use later, such as Seaborn, which is more used for statistical visualisation.\n",
"\n",
"All the documentation for Matplotlib can be found [here](http://matplotlib.org/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recap - Functional Plotting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create quick and dirty graphs using the functional method as we've seen before. This method is simpler but won't allow us to customise our plots as much.\n",
"\n",
"First, we need to import our modules:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we're running in a notebook, we can run the following line of code to save us from writing plt.show() to give us our graphs. Spyder automatically shows plots, so we don't need this line of code and everything will still work as normal."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then graph any data we want using the plt.plot command. Here we will use the np.linspace function to generate a numpy array called x with 101 equally spaced points between 0 and 10, and another numpy array called y that is simply x squared. (Remember numpy arrays can be operated on and the operation will apply element-wise)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = np.linspace(0,10,101)\n",
"y = x**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then plot the graph of x versus y by using the plt.plot() function:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x,y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! From here we could add more plots, linestyles, x labels, y labels, titles and more using various methods:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Graph of x squared and x cubed')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nISIiCVFwiIhIQv4/mNiACvv75B8AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x,x**2,x,x**3)\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"plt.title(\"Graph of x squared and x cubed\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create multiple plots on one output. These are called subplots, and to do this we use the plt.subplot() function - this takes 3 arguments - the number of rows, the number of columns, and then the plot number we're refering to. This may seem like an ugly way to do things, and we'll see a better way to do it later with the object orientated method."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xd4VGX6//H3kwYkoRNCCRCqFKUZ\nKYKAICBFBAQEBZGl2VBWd/2Cu2tdXXZdcRcFV0QUadJEQIoCoiJCIITQBUILaSQQQgKBtHl+f2Tw\nh5BAkinnzJn7dV1cM5k5mfPhzD13zjynKa01QgghrMvH6ABCCCFcSxq9EEJYnDR6IYSwOGn0Qghh\ncdLohRDC4qTRCyGExUmjF0IIi5NGL4QQFieNXgghLM7P6AAA1apV0+Hh4UbHEBa1e/fuc1rrECPm\nLbUtXKm4tW2KRh8eHk5UVJTRMYRFKaVOGzVvqW3hSsWtbRm6EUIIi5NGL4QQFieNXgghLE4avRBC\nWJw0eiGEsLjbNnql1FylVIpS6sB1j1VRSm1USh2z31a2P66UUjOUUrFKqX1KqbauDC+EK5Sk5oXw\nBMVZo/8cePCGx6YAm7XWjYHN9p8B+gCN7f8mAB85J6YQbvU5xa95IUzvto1ea/0TkHbDww8D8+z3\n5wEDr3v8C11gB1BJKVXTWWGFKMyvyRn8Z9NRLlzOccrrlbDmhXCZhfsWsuzgModfp7Rj9KFa6yQA\n+211++O1gTPXTRdvf+wmSqkJSqkopVRUampqKWMIASt2xzNzSyxKuXQ2RdX8TaS2haMysjMYtXIU\nI1eOZN7eeTh6bW9nb4wt7KNWaEKt9WytdYTWOiIkxJCj04UF5Ns0q/cm0rVJdSoFBhgdB5DaFo6J\njI+k9f9as2j/It7o9gZfD/8a5eBaTGkb/dlrQzL22xT74/FAneumCwMSSx9PiFvbeTKNsxnZPNy6\nlqtnVVTNC+EU+bZ8/rH1H3Sa2wmbtvHTkz/xatdX8fNx/Ew1pW30q4HR9vujgVXXPf6Efe+bDsDF\na193hXCF1XsTCAzw5YFmoS6fFYXXvBAOS8xMpNeCXrzy/SsMaT6EmKdi6FS3k9Ne/7Z/KpRSi4Fu\nQDWlVDzwGjANWKqUGgvEAUPtk68D+gKxQBYwxmlJhbhBdl4+a/cl0btFDcoF+DrtdUtY80I4ZO3R\ntTy56kmycrP4dMCnjGk9xuGhmhvdttFrrUcU8VSPQqbVwLOOhhKiOH44kkrG1TynD9uUpOaFKK2c\n/BymbprK9B3TaRnakiVDltC0WlOXzMsUpykWojRWxSRQLTiAzo2qGR1FiBI5ceEEjy5/lKjEKJ69\n51n+3evflPUr67L5SaMXHinjai6bDqfwWLu6+PnKmTyE51h+aDljV4/FR/mwYtgKBjcb7PJ5SqMX\nHmnDgWRy8mzu2NtGCKe4mneVl759iVlRs2hfuz1fDvmS8Erhbpm3NHrhkb7ek0B41UBa16lkdBQh\nbis2LZZhy4axJ3kPf+r4J97p8Q7+vv5um780euFxki5eYfuJ87zQo7HT904QwtmWHVzG2NVj8ff1\nZ82INfRv0t/tGWRwU3ic1TGJaA0DWxd6dg0hTCE7L5vn1z/PsOXDaFG9BXsm7jGkyYOs0QsPtHJP\nAm3qViK8WpDRUYQo1On00wxdNpRdibv4Y4c/Mu2BaQT4GneKDmn0wqMcSszg1+RM3nq4hdFRhCjU\n+mPrefyrx8nX+W7bq+Z2ZOhGeJSVe+Lx81H0ayl72whzybfl8+qWV+m7qC91K9Zl94TdpmjyIGv0\nwoPk2zSrYhLpdkd1qgSZ40yVQgCcyzrHYyseY+OJjYxpPYaZfWdSzr+c0bF+I41eeIxtsedIyczm\nkbayEVaYx66EXQxZNoSzl87yyUOfMK7tOKMj3USGboTH+Co6ngpl/ejerMhrfgjhVnOi59D5s84A\n/PyHn03Z5EHW6IWHuJSdx7cHzzK4bW3K+DnvTJVClEZ2XjaT1k/ik+hP6NmgJ4seWUS1QPOec0ka\nvfAI6/cncSU3n8EybCMMFp8RzyNLH2Fnwk6mdp7KW/e/ha+PuVc+pNELj7AiOp7wqoG0rVvZ6CjC\ni209vZUhy4aQlZvFV8O+YlCzQUZHKhYZoxemF38hix0n0hjcNkxOeSAMobVm5s6ZdP+iO5XKVmLn\nuJ0e0+RB1uiFB1gZnQDAoDYybCPcLzsvm2fWPsPcmLn0b9KfBYMWULFsRaNjlYg0emFqWmtWRMfT\noUEV6lQJNDqO8DJJmUkMXjqYHfE7+Ot9f+WN+9/AR3neQIg0emFqu09f4NT5LJ7r3tjoKMLL7ErY\nxcAlA7l49SLLhy7nkeaPGB2p1DzvT5PwKst3xxMY4EufO2sYHUV4kYX7FnLfZ/cR4BvAL2N/8egm\nD9LohYldycnnm31J9L2rJkFl5MuncD2btjF101RGrhxJh7AO7Bq/i5ahLY2O5TD59AjT2nAwiUvZ\neQy5O8zoKMILZGZnMnLlSFYfWc3Euycyo88MQ08t7EzS6IVpLYuKp26VQNqFVzE6irC40+mneWjx\nQxxKPcQHfT7g2XuetdSuvNLohSmdScvil+Pn+eMDTfDxsc4HTpjP9jPbGbhkINl52ax7fB29GvYy\nOpLTyRi9MKUV0fEoBY/cLfvOC9dZtH8R98+7n/IB5dkxboclmzxIoxcmZLNplu+Op1PDaoRVln3n\nhfNprXnjhzd4/KvHaR/WnshxkTSt1tToWC4jQzfCdHacOE/8hSv8ufcdRkcRFpSdl824NeNYsG8B\no1uNZvZDsy2z0bUo0uiF6SyNOkP5sn70biH7zgvnOp91nkFLBrE1bitvd3+bqZ2nWmqja1Gk0QtT\nuXgll/UHkhkaEUZZf3Of+lV4lti0WPou7EvcxTgWP7KY4XcONzqS20ijF6ayOiaB7Dwbj0bUNTqK\nsJDtZ7Yz4MsB2LSNTU9sonPdzkZHciuHNsYqpf6olDqolDqglFqslCqrlKqvlIpUSh1TSi1RSll7\n8Es41ZKoMzSrWYE7a1cwOkqhCqt5ozOJW1t5eCXdv+hOxTIV2T52u9c1eXCg0SulagPPAxFa6zsB\nX2A48E/gfa11Y+ACMNYZQYX1HUy8yIGEDB6NMOd5529R88KkPoj8gEeWPkKr0FZsH7udJlWbGB3J\nEI7uXukHlFNK+QGBQBLQHVhuf34eMNDBeQgvsWTXGQL8fBho7vPO31jziQbnEYWwaRsvb3yZ5zc8\nz4A7BvD96O8JCQoxOpZhSt3otdYJwL+BOAoa/EVgN5Cutc6zTxYPmPpTK8zham4+K/ck0OfOGlQK\nNOdoX2E1r7X+zthU4kY5+Tk8sfIJ3v3lXZ6JeIYVw1YQ6O/dx2M4MnRTGXgYqA/UAoKAPoVMqov4\n/QlKqSilVFRqamppYwiLWH8gicyreTx6Tx2joxSpsJpXSo0sZDqpbYNkZmfSb1E/Fu5fyNvd3+bD\nvh+a/sLd7uDI0M0DwEmtdarWOhf4CrgXqGT/WgsQRhFfbbXWs7XWEVrriJAQ7/1KJQos3nmGelUD\n6VC/qtFRbqWomv8dqW1jpFxOodu8bmw5uYXPHv6MV+57xZTbeozgSKOPAzoopQJVwdLsARwCtgBD\n7NOMBlY5FlFY3fHUS+w8mcaj99Qx+wnMCqv5wwZnEsDJCyfpNLcTh1MPs2r4Kp5s/aTRkUzFkTH6\nSAo2ukYD++2vNRv4P+BFpVQsUBX41Ak5hYUt2XUGPx9l+vPO36LmhYH2nd3HvXPv5XzWeTY/sZl+\nTfoZHcl0HDpgSmv9GvDaDQ+fANo58rrCe2Tn5bN8dzwPNAulennz75JeRM0Lg2yL20b/xf0J8g9i\n65ittKjewuhIpiRnrxSG2njoLGmXcxjezrwbYYU5rT+2np7ze1I9qDrb/rBNmvwtSKMXhlq8M47a\nlcrRpbFstBTFt+TAEgZ8OYCm1ZqydcxW6lWqZ3QkU5NGLwxz6txltsWeZ0Q702+EFSYyJ3oOI1aM\noENYB7aM3kL1oOpGRzI9afTCMIt3xeHroxgaIcM2onje3/4+49eMp3ej3nw78lsqlq1odCSPII1e\nGCI7L59lUfH0bBZKaAXzb4QVxtJa89aPb/Hidy8ypPkQVg1f5fVHu5aEnKZYGGLDgWTSLufweAc5\nHbG4Na01UzdP5Z/b/skTrZ7g0wGf4ucjraskZGkJQyyMjKNulUA6NaxmdBRhYlprJm+YzIydM3jq\n7qeY2W8mPkoGIkpKlphwu6NnM9l5Mo3H2teVjbCiSDZt4+m1TzNj5wwmt5/MrH6zpMmXkqzRC7db\nFBlHgK8PQ01+JKwwjk3bGL96PHNj5jKl0xTe6fGOnLfGAfLnUbhVVk4eK3bH0/euGlQNLmN0HGFC\n+bZ8/rDqD8yNmcvfuvxNmrwTyBq9cKtVMYlkZufxeAc5wEXcLN+Wz5hVY5i/bz5vdHuDV7u+anQk\nS5BGL9xGa8387adpWqM8EfUqGx1HmMz1Tf6t+9/ir13+anQky5ChG+E20XHpHErKYFTHevJVXPyO\nNHnXkkYv3Gb+9lMEl/FjYGu5uqT4/2zaxrg146TJu5A0euEW5y5ls25/MkPuDiOojIwYigI2bWPi\nmol8HvM5r3V9TZq8i0ijF26xZNcZcvJtjJSNsMJOa83z659nzp45vNL5FV7rKqf5dxVp9MLl8vJt\nLNxxmk6NqtKoerDRcYQJaK3588Y/M3PXTP7U8U/8vfvfZbuNC0mjFy636fBZEi9e5YmO4UZHESbx\n+g+v897293j2nmf5V89/SZN3MWn0wuU+/+UUtSuV44FmoUZHESbw7rZ3efOnN/lD6z8wo88MafJu\nII1euNSR5Ex2nEhjVMd6+Mp5bbzex1Ef8/Kml3m0xaPMfmi2nLvGTWQpC5f6/JdTlPHz4VG5uIjX\nW7x/MU+vfZp+jfsxf9B8fH18jY7kNaTRC5dJz8ph5Z54BrWpTeWgAKPjCAOtO7aOJ75+gi71urBs\n6DL8ff2NjuRVpNELl1my6wxXc22Mvjfc6CjCQNvitjFk6RBahrZk9YjVlPMvZ3QkryONXrhEXr6N\nL7afpn39KjSrWcHoOMIgB1IO0H9xf+pUrMOGxzdQoYzUghGk0QuX2HjoLAnpVxjTqb7RUYRBTqef\npveC3gT6B/LtyG8JCQoxOpLXkmPRhUt8tu0UYZXL0bO57FLpjc5lnaP3gt5k5WaxdcxWwiuFGx3J\nq8kavXC6AwkX2XkqjSfvDZddKr1QVm4WDy1+iFPpp1g9fDV3Vr/T6EheT9bohdPN/fkkQQG+DLtH\ndqn0Nvm2fEasGEFkfCQrhq3gvnr3GR1JIGv0wslSMq6yZl8iQyPqUKGs7ELnTbTWTFo/idVHVjOj\nzwwGNRtkdCRhJ41eONUX20+TZ9OM6RRudBThZv/+5d98FPURf773zzzX7jmj44jrSKMXTnMlJ58F\nkafp2SyUelWDjI4j3Gj5oeW8vOllhrUYxrQHphkdR9zAoUavlKqklFqulPpVKXVYKdVRKVVFKbVR\nKXXMfisXB/USK6LjSc/KZXyXBkZHcZnCat7oTEbbEb+DUStHcW+de5k3cJ6cv8aEHH1H/gts0Fo3\nBVoBh4EpwGatdWNgs/1nYXE2m2buzydpGVbR6hf+Lqzmvdap9FMMWDyA2uVr8/WjX1PWr6zRkUQh\nSt3olVIVgC7ApwBa6xytdTrwMDDPPtk8YKCjIYX5bf41hRPnLjPuvgaWPe3sLWreK2VkZ9B/UX9y\nbbmsfWytHBBlYo6s0TcAUoHPlFJ7lFJzlFJBQKjWOgnAflu9sF9WSk1QSkUppaJSU1MdiCHM4JOf\nTlC7Ujn63lnD6CiuVFTN/4431Pa13SiPnD/C8qHLuaPaHUZHErfgSKP3A9oCH2mt2wCXKcEwjdZ6\nttY6QmsdERIiawKeLOZMOjtPpTGmUzh+vpYeny1WzXtDbb+88WXWHVvHh30+pEeDHkbHEbfhyKcy\nHojXWkfaf15OwYfgrFKqJoD9NsWxiMLsZv90nPJl/Rjerq7RUVytqJr3Kp/t+YzpO6Yzqd0kJkZM\nNDqOKIZSN3qtdTJwRil17TtbD+AQsBoYbX9sNLDKoYTC1E6du8yGA8mM7FCP4DLWPtD6FjXvNbaf\n2c5Ta5/igQYPML33dKPjiGJy9JM5CViolAoATgBjKPjjsVQpNRaIA4Y6OA9hYnN+PoGfjw9jvOec\n84XVvFdIyEhg8NLB1KlQhyVDluDnY+0/7Fbi0DultY4BIgp5SgbtvMC5S9ksi4pnYJtaVK/gHbvV\n3aLmLS07L5tHlj5CZnYmG0dtpEq5KkZHEiUgf5JFqX3xyyly8m1M6NLQ6CjCxSatn0RkQsGJyuRs\nlJ7H0rtICNe5nJ3HFzsKTnfQqHqw0XGEC32y+xM+if6EVzq/wuBmg42OI0pBGr0olcU740jPyuWp\nbrI2b2W7Enbx3Prn6NWwF2/e/6bRcUQpSaMXJZaTZ2PO1pN0aFCFtnUtfboDr3Y+6zxDlg2hZnBN\nFg1ehK+Pr9GRRClJoxcl9vWeBJIzrvJUV1mbtyqbtjFq5SiSLyWzfNhyqgZWNTqScIBsjBUlkm/T\nfPTjcVrUqkDXJtY86lPAtJ+nsT52PR/1+4iIWl63k5HlyBq9KJH1B5I4ee4yz97fyLInL/N2P576\nkb9t+Rsj7hzBxLvlyFcrkEYvik1rzcwtx2kQEkTvFpY+eZnXSrmcwogVI2hUpREf9/9Y/phbhDR6\nUWzf/5rC4aQMnunWCF8faQBWY9M2Rn89mrQraSwdspTyZcobHUk4iYzRi2LRWvPB97GEVS7Hw61r\nGR1HuMD07dPZELuBWX1n0apGK6PjCCeSNXpRLNtizxNzJp2JXRvib+1TEXulqMQopm6eyuBmg3kq\n4imj4wgnk0+sKJYZ3x+jRoWyDIsIMzqKcLJLOZd4bMVj1AyuyZyH5si4vAVJoxe3FXniPDtPpjGx\nawPK+MlBM1YzecNkYtNimT9oPpXLyQFwViSNXtzWjO+PUS24DCOsf2ERr7Py8Eo+3fMpUzpPoWt4\nV6PjCBeRRi9uadepNLbFnueprg0o6y9r81aSfCmZ8WvG07ZmW17v9rrRcYQLSaMXt/TfTceoFhzA\n4+3rGR1FOJHWmvFrxnM59zLzB80nwDfA6EjChaTRiyJFnUrj59hzTOjSgHIBsjZvJZ/FfMY3R79h\nWo9pNA9pbnQc4WLS6EWR3t90lGrBAYzsIGvzVhJ3MY7JGybTLbwbk9pPMjqOcANp9KJQO09eG5tv\nSGCAHFdnFVprxq0eh03bmDtgLj5KWoA3kE+wuInWmve+O0JI+TIyNm8xc6LnsPHERmb1nUX9yvWN\njiPcRP6ci5v8cvw8kSfTeLZbQxmbt5D4jHj+tPFP3B9+PxMj5KyU3kQavfida2vzNSqUZbjsN28Z\nWmueWfsMufm5fPLQJzJk42Xk3Ra/s+VICtFx6Tzfo7HsN28hyw4tY83RNbx1/1s0rCJXBvM20ujF\nb2w2zXvfHaVulUCGyjltLOPClQs8v/557q55Ny90eMHoOMIAsjFW/GbdgSQOJmbw3tBWcoZKC5m6\neSqpWamse3wdfj7ykfdG8mkWAOTl25j+3VGahAYzsE1to+MIJ9l+Zjsf7/6Yye0n07ZmW6PjCINI\noxcArIiO58S5y7zU6w65epRF5NnyeHrt04RVCOON+98wOo4wkHyPE1zNzef9jcdoXacSvZqHGh1H\nOMmHOz9k79m9rBi2guCAYKPjCAPJGr1g3i+nSM64yv892FQuOmERyZeSee2H1+jdsDeDmg4yOo4w\nmDR6L3cxK5dZPxyna5MQOjasanQc4SRTNk3hSu4VZvSZIX+8heONXinlq5Tao5T6xv5zfaVUpFLq\nmFJqiVJKzn9qYrN+iCXjai5T+jQ1OorHuLHmzSYyPpJ5e+fxYscXaVK1idFxhAk4Y43+BeDwdT//\nE3hfa90YuACMdcI8hAskpl/hs19OMahNbZrVrGB0HE9yY82bhk3beH7D89QMrslf7vuL0XGESTjU\n6JVSYUA/YI79ZwV0B5bbJ5kHDHRkHsJ1/v3dEQBe7ClrfcV1Y82bzaL9i9iZsJN/9PgH5cuUNzqO\nMAlH1+j/A7wM2Ow/VwXStdZ59p/jAdkp24QOJl5k5Z4ExnQKJ6xyoNFxPMmNNW8aWblZTN08lbY1\n2zKq1Sij4wgTKXWjV0r1B1K01ruvf7iQSXURvz9BKRWllIpKTU0tbQxRClpr3ll3mIrl/HmmWyOj\n43iMImq+sOkMqe3/7PgP8RnxTO81XU5aJn7HkWroBAxQSp0CvqRgyOY/QCWl1LX988OAxMJ+WWs9\nW2sdobWOCAkJcSCGKKktR1LYFnueF3o0pmI5f6PjeJKbal4pteDGiYyo7dTLqUz7eRoD7hhA1/Cu\nbpmn8BylbvRa66la6zCtdTgwHPhea/04sAUYYp9sNLDK4ZTCaXLzbby99jD1qwXJRUVKqIiaH2lw\nLADe3vo2l3MvM63HNKOjCBNyxfe7/wNeVErFUjBm/6kL5iFKaVFkHMdTLzO1T1MC/OTrvRWcvHCS\nWbtmMbbNWJqFNDM6jjAhp5wCQWv9A/CD/f4JoJ0zXlc418WsXN7fdJR7G1alp5zqwCHX17zRXv/x\ndXx9fHmt62tGRxEmJat0XuT9TUfJuJLLX/s1l6MlLeJQ6iHm753PpHaTqF1BdnAThZNG7yWOnc1k\n/o7TjGhXl+a15OAoq3j9h9cJCgji5U4vGx1FmJg0ei+gtebNbw4RFOArB0dZyL6z+1h2aBmT20+m\nWmA1o+MIE5NG7wW+O3SWrcfOMfmBJlQNLmN0HOEkb/74JhXKVODFji8aHUWYnDR6i7uam8/f1x6i\nSWgwozrK7pRWcSDlACsOr+CF9i9QuVxlo+MIk5MLj1jc/348zpm0Kywa116uA2sh72x9h+CAYCZ3\nmGx0FOEB5JNvYXHns/joh+P0b1mTexvJGK5VxKbFsuTgEp6JeIYq5aoYHUd4AGn0FvbGmoP4+ij+\n0k8OorGSd7e9i7+PP3/s+EejowgPIY3eojYeOsvmX1OY/EBjalYsZ3Qc4STJl5L5fO/nPNn6SWoE\n1zA6jvAQ0ugtKCsnj9dXH6RJaDBjOtU3Oo5wog8iPyA3P5eXOr5kdBThQWRjrAX9d/MxEtKvsHRi\nR9kAayGXcy7zUdRHDGo2iMZVGxsdR3gQ6QIW82tyBp9uPcmwiDDa1ZcNdVYyb+88Lly9IGvzosSk\n0VuIzaaZ+tV+KpTzZ2of2QBrJTZt47+R/6Vd7XZ0DOtodBzhYWToxkIWRp5mT1w67z/aispBAUbH\nEU703fHvOHr+KAsHL5QT0okSkzV6i0hMv8I/NxzhvsbVGNhazmJoNR/u/JDQoFCGNB9y+4mFuIE0\negvQWvPqqgPk2zTvDLpL1vgs5uSFk6w7to4Jd08gwFe+qYmSk0ZvAav3JrLpcAov9WpCnSqBRscR\nTjZ792yUUoxvO97oKMJDSaP3cOcvZfPGmkO0qlNJ9pm3oJz8HObGzKV/k/7UqVjH6DjCQ0mj93Cv\nrj5I5tVc3h3SEl8fGbKxmjVH1pByOYUJbScYHUV4MGn0Hmzd/iTW7kti8gNNaBJa3ug4wgXmxsyl\ndvnaPNjoQaOjCA8mjd5DnbuUzd++PsBdtSsysUsDo+MIF0jMTGRD7AZGtxqNr4+v0XGEB5NG74G0\n1rzy1X4ys/N4b1gr/OQ0B5a0YN8CbNrGk62fNDqK8HDSITzQV9EJfHfoLC/1lCEbq9Ja88XeL+gY\n1lHOayMcJo3ew8RfyOL11QdpF16FcffJkI1V7Tu7j4OpBxnVcpTRUYQFSKP3IPk2zUtL92LTmveG\ntZK9bCxs4f6F+Pn4MbTFUKOjCAuQRu9BZv90gsiTabw2oIUcGGVhNm1jycEl9G7Ym2qBcglI4Thp\n9B5iX3w60zceoc+dNRh6d5jRcYQLRcZHEncxjkdbPGp0FGER0ug9wOXsPJ5fvIdqwWX4x2A5l43V\nLT+0HH8ffwbcMcDoKMIi5DTFHuBvqw4Ql5bF4vEdqBQoJ7WyMq01Kw6voGfDnlQsW9HoOMIiZI3e\n5Fbsjuer6ASe79GY9g2qGh1HuFhMcgynL55mcNPBRkcRFiKN3sSOnc3kr18foF39KkzqLvtSe4NV\nR1bho3xk2EY4VakbvVKqjlJqi1LqsFLqoFLqBfvjVZRSG5VSx+y3lZ0X13tk5eTx7KJoAgN8+WBE\nG9mV0gSKqnlnWnN0DR3DOhISFOLslxZezJE1+jzgJa11M6AD8KxSqjkwBdistW4MbLb/LEpAa81f\nVh7gWMol/jO8NaEVyhodSRQoquadIjEzkeikaPo36e+slxQCcKDRa62TtNbR9vuZwGGgNvAwMM8+\n2TxgoKMhvc3CyDhW7klgco8m3NdY1uzM4hY17xTrj60HoF/jfs56SSEAJ43RK6XCgTZAJBCqtU6C\ngg8GUL2I35mglIpSSkWlpqY6I4YlRMdd4I01B+l2RwiTujcyOo4owg01f+NzpartDcc3ULt8be6s\nfqfTcgoBTmj0SqlgYAUwWWudUdzf01rP1lpHaK0jQkJkrRUgJfMqzyyIpkbFsvzn0db4yLi8Kd2u\n5ktT2/m2fDad2ETvhr3lOAnhdA41eqWUPwUFv1Br/ZX94bNKqZr252sCKY5F9A45eTaeWRBN+pUc\nPh4ZIfvLm1QRNe+w3Um7Sb+aTs+GPZ31kkL8xpG9bhTwKXBYaz39uqdWA6Pt90cDq0ofzztorXl1\n1QGiTl/g3SGtaF6rgtGRRCFuUfMO+/7k9wDcH36/M19WCMCxNfpOwCigu1Iqxv6vLzAN6KmUOgb0\ntP8sbmHeL6f4ctcZnr2/IQ+1qmV0HFG0omreYVtObaFFSAtCg0Od8XJC/E6pT4Ggtf4ZKGowsUdp\nX9fb/HAkhTe/OUTP5qG81PMOo+OIW7hNzZdabn4u2+K2yZWkhMvIkbEG+jU5g+cW7aFpjQqy8dWL\nxSTHcDn3MvfVvc/oKMKipNEb5GzGVcZ8tougMr7MGR1BUBk5v5y32nZmGwCd63Y2OImwKmn0Bsi8\nmsuTn+0i40ouc5+8h1qVyhmmiGV6AAALd0lEQVQdSRhoe/x26lWsR+0KTjv2SojfkUbvZjl5Np5e\nEM3Rs5nMGnk3LWrJqWi93Y74HXQI62B0DGFh0ujdyGbTvLRsLz/HnmPa4Lvo2kQOFPN2yZeSibsY\nR/va7Y2OIixMGr2baK15bfVB1uxNZEqfpgyNqGN0JGECUYlRAETUijA4ibAyafRu8u63R5i/4zQT\nuzRgYpcGRscRJhGdFI1C0aZmG6OjCAuTRu8GH2w+xqwfjjOiXV2m9Gkq5zIRv4lJjqFx1cYEBwQb\nHUVYmDR6F/vfj8d5b+NRBrWpzdsD75QmL35n79m9tAptZXQMYXHS6F3o4x+PM239rzzUqhbvDmkp\nB0SJ37mUc4kTF05wV/W7jI4iLE6O0nGRmVtieffbI/RvWZP3h7XCz1f+porfO5R6CEDOPy9cThq9\nk2mtmb7xKB98H8vDrWvx3lBp8qJwh1MPA9A8xGlXIxSiUNLonchm07z5zSE+/+UUw++pw9uD7pKL\neosiHTl/BD8fPxpUlr2whGtJo3eSnDwbf16+l1UxiYy/rz6v9G0mG17FLR09f5QGlRvg7+tvdBRh\ncdLonSDjai7PLIjm59hzvPzgHTzdtaE0eXFbsWmxNKoi1wUWrieN3kEJ6VcY+/kuYlMu8e6QlnLE\nqygWrTUnLpygS70uRkcRXkAavQP2xF1g/Be7yc7N5/Mx7ejcuJrRkYSHuHD1Apk5mYRXCjc6ivAC\n0uhLacXueKau3E9ohTIsHt+exqHljY4kPMjp9NMA1KtYz+AkwhtIoy+hnDwb76w7zOe/nKJjg6rM\nfLwtVYICjI4lPMyZjDMA1KkoQ33C9aTRl0BC+hWeWxTNnrh0xnauz5Q+TfGXfeRFKSRmJgJQu7xc\nbES4njT6Yvr2YDIvL99Hvk0z87G29GtZ0+hIwoMlZSahUIQGhxodRXgBafS3cTk7j7+vPcTinWe4\nq3ZFZoxoQ/1qQUbHEh4u+VIyIUEh+PnIR1C4nlTZLew4cZ4/L99L/IUrPNW1IS/2bEKAnwzVCMel\nZKUQEihXGBPuIY2+EBev5PKvDb+yMDKOelUDWTKhI+3qVzE6lrCQ81nnCQmSRi/cQxr9dbTWrIpJ\n5O9rD5N2OZtxnevzYq8mBAbIYhLOlXYljSZVmxgdQ3gJ6WB2MWfS+fs3h4g6fYGWYRX57Ml7uCus\notGxhEWlX02nctnKRscQXsLrG/2J1Eu8t/Eoa/clUS04gGmD72JYRB25SIhwqYzsDCqUqWB0DOEl\nvLbRx6ZkMmvLcb6OSaCsvy+TujdiYteGBJfx2kUi3ERrzaWcS5QvI0dTC/fwqq6mtSbyZBpztp5k\n0+GzlPX3YWzn+kzo0pCQ8mWMjie8xNW8q2g0gf6BRkcRXsIrGn3a5Ry+3pPAl7viOHr2ElWCAni+\neyNG3xtO1WBp8MK9ruRdAaCcXzmDkwhv4ZJGr5R6EPgv4AvM0VpPc8V8buViVi6bfz3L2n1J/Hg0\nlTybplVYRf41pCUDWtWirL+vuyMJC3BGbWfnZQNQ1q+sc8MJUQSnN3qllC8wE+gJxAO7lFKrtdaH\nnD2v62Xn5XMg4SLbj5/np2Pn2H36Avk2Tc2KZRnbuT6D2tamaQ3Z+CVKz1m1nWvLBZArSwm3ccUa\nfTsgVmt9AkAp9SXwMOBwo9dacyk7j7MZ2Zy5kMXJ1MscS8nkUGIGh5Myycm3AdCiVgWe6tqAB5qF\n0iqskuxBI5zFKbWdZ8sDwFfJt0rhHq5o9LWBM9f9HA+0L80L/XFJDHvj08nL12Tl5JNxNZecPNvv\npqlYzp9mNcszplM4bepW5p7wyjLuLlzFKbWttQbAR8npNIR7uKLRF7b6rG+aSKkJwASAunXrFvpC\ndaoEkptvw89HUS7Ajwrl/KgaFED18mUJq1yOelWDqBYcINdnFe7ilNoO9A9kaPOh1KskFx0R7uGK\nRh8PXH81hTAg8caJtNazgdkAERERN31YAF7sKYeIC1NxSm3XLF+TpUOXuiqjEDdxxXfHXUBjpVR9\npVQAMBxY7YL5COFuUtvCIzl9jV5rnaeUeg74loJd0OZqrQ86ez5CuJvUtvBULtmPXmu9DljnitcW\nwkhS28ITyWZ/IYSwOGn0QghhcdLohRDC4qTRCyGExUmjF0IIi1PXDsc2NIRSqcDpIp6uBpxzY5xb\nMUsWs+QA82S5VY56WmtDrsTtIbVtlhxgnixmyQFOqG1TNPpbUUpFaa0jjM4B5slilhxgnixmyVES\nZslslhxgnixmyQHOySJDN0IIYXHS6IUQwuI8odHPNjrAdcySxSw5wDxZzJKjJMyS2Sw5wDxZzJID\nnJDF9GP0QgghHOMJa/RCCCEcYJpGr5R6UCl1RCkVq5SaUsjzZZRSS+zPRyqlwl2QoY5SaotS6rBS\n6qBS6oVCpummlLqolIqx/3vV2Tmum9cppdR++3yiCnleKaVm2JfJPqVUWxfluOO6/2+MUipDKTX5\nhmlcslyUUnOVUilKqQPXPVZFKbVRKXXMflu5iN8dbZ/mmFJqtDPyOMPtat3F8y60xpVSryulEq57\n//q6IctN9V3c99bJOQqtb3ctk5LUeKk/81prw/9RcMrX40ADIADYCzS/YZpngP/Z7w8HlrggR02g\nrf1+eeBoITm6Ad+4abmcAqrd4vm+wHoKrnzUAYh003uVTMH+uy5fLkAXoC1w4LrH/gVMsd+fAvyz\nkN+rApyw31a236/sjvetGMvvlrXu4vkXWuPA68Cf3Lwsbqrv4ry3bnh/koF67lomJanx0n7mzbJG\n/9tFl7XWOcC1iy5f72Fgnv3+cqCHcvI1BLXWSVrraPv9TOAwBdcJNauHgS90gR1AJaVUTRfPswdw\nXGtd1EFATqW1/glIu+Hh62thHjCwkF/tDWzUWqdprS8AG4EHXRa0+IpT6y7jATVenPfWldxa31Di\nGi/VZ94sjb6wiy7fWHy/TaO1zgMuAlVdFcg+NNQGiCzk6Y5Kqb1KqfVKqRauykDB9Ui/U0rtVgXX\nIb1RcZabsw0HFhfxnLuWS6jWOgkKGhdQvZBpjFg2xWGaXIXU+HP24YC57hgyofD6Ls5760o31re7\nl8k1RS2HUtWPWRp9cS66XKwLMzuDUioYWAFM1lpn3PB0NAXDFq2AD4CvXZHBrpPWui3QB3hWKdXl\nxqiF/I7LdqNSBZfPGwAsK+Rpdy6X4nDrsikBU+QqpMY/AhoCrYEk4D03xLhdfbtVIfVtxDK5nVLV\nj1kafXEuuvzbNEopP6AiN3/dcZhSyp+CD8BCrfVXNz6vtc7QWl+y318H+Culqjk7h/31E+23KcBK\nCr72X69YF6t2oj5AtNb67I1PuHO5AGevfV2136YUMo27l01xGZ6rsBrXWp/VWudrrW3AJ9xca05X\nRH0X5711ld/VtxHL5DpFLYdS1Y9ZGn1xLrq8Gri258QQ4Htt3zrhLPYx/0+Bw1rr6UVMU+PatgGl\nVDsKluF5Z+awv3aQUqr8tftAL+DADZOtBp6wb4nvAFy89nXPRUZQxLCNu5aL3fW1MBpYVcg03wK9\nlFKV7V+5e9kfM5qhFxgvqsZvGOcdxM215uwcRdV3cd5bV/ldfbt7mdygqOVQus+8q7col2DLc18K\n9gA4DvzF/tibwAD7/bIUfKWKBXYCDVyQoTMFX4P2ATH2f32Bp4Cn7NM8BxykYG+JHcC9LloeDezz\n2Guf37Vlcn0WBcy0L7P9QIQL359AChp3xesec/lyoeCDlwTkUrA2M5aCbTObgWP22yr2aSOAOdf9\n7h/s9RILjDG6xm9V626cd1E1Pt9eQ/vszaSmi3MUVd+FvrduWC6F1bdblkkJa7xUn3k5MlYIISzO\nLEM3QgghXEQavRBCWJw0eiGEsDhp9EIIYXHS6IUQwuKk0QshhMVJoxdCCIuTRi+EEBb3/wBmhOEx\n2044jAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1,2,1)\n",
"plt.plot(x,x**2)\n",
"\n",
"plt.subplot(1,2,2)\n",
"plt.plot(x**2,x,'g')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Object Orientated - There has to be a better way!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem with the functional method is that it's a bit of a mess: Commands can be a bit hard to work with and understand at a glance, and problems often happen because of ambiguity in code. Python works on an \"object orientetated\" philosophy so it makes sense for us to use a similar methodology for graphing. The object orientated way of creating plots works by creating figure objects and calling methods on it.\n",
"\n",
"To start, we need to create a figure:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Figures are a blank canvas for us to work on. To add axis, we use the add.axes() method. We can always look at our figure by just running it as a line of code:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAd0AAAFDCAYAAAB/UdRdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0QWAIaILAENEFwCG/C89W5iskzQcrAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"axes = fig.add_axes([0,0,1,1])\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Techinically, the \"add_axes\" method takes in one arguement - a list. This list has 4 elements - the x position with respect to the left of the axis (in percent), the y position with respect to the bottom of the axis (also in percent) - so here, 0,0 just means we don't want white space - and width and height (so here, 1,1 means width 1, height 1).\n",
"\n",
"Now if we want to plot something on these axis, we call the \"plot\" method with respect to the axes:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAdsAAAFCCAYAAAC5E3e/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"axes.plot(x,y)\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also add x labels, y lables, and more using the following methods:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAFdCAYAAAAnlZX0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niE/9P50XNj6Syz4kAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"axes.set_xlabel('X')\n",
"axes.set_ylabel('Y')\n",
"axes.set_title('x vs x squared')\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's see why the object orientated way of creating graphs is so much more powerful:\n",
"\n",
"To start, we can create graphs within graphs:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Inverse')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAFdCAYAAAAnlZX0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0khZ0pIkjZQlLUnSSP0NOmPwATfI2agAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"\n",
"axes1 = fig.add_axes([0,0,1,1])\n",
"axes2 = fig.add_axes([0.1,0.6,0.4,0.3]) #Creating axes within our axes\n",
"\n",
"axes1.plot(x,y)\n",
"axes2.plot(y,x)\n",
"\n",
"axes1.set_xlabel('X')\n",
"axes1.set_ylabel('Y')\n",
"axes1.set_title('x vs x squared')\n",
"\n",
"axes2.set_xlabel('X')\n",
"axes2.set_ylabel('Y')\n",
"axes2.set_title('Inverse')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also create subplots as before using a shortcut:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npCYMviQ18f+GmWq6NWLIwgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This combines the \"fig\" and \"axes\" call together assuming we want the large plot by using tuple unpacking. We can add arguements to the subplot method to create more plots:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD8CAYAAAB6paOMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nM7PMuRCYmWXu/wE1nMzlQ6VPgAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2,2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that all the numbers are a little bit squished together - we can fix this using the plt.tight_layout() function:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAEYCAYAAAAeWvJ8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nasogkiQ1ZRBJkpr6f/XC8aSt83JNAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(2,2)\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! For now, to learn how to access these axes let's work with a 2x1 subplot grid.\n",
"\n",
"If we call the \"axes\" object after using the subplot command, we can see that \"axes\" is just a list of axes objects:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJgx8SWrCwJekJv4PcgCmcLyIQvoAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1,2)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([,\n",
" ], dtype=object)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"axes #Notice the square brackets? It's a list (technically a numpy array)!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This means we can iterate over it, but more importantly, we can select what axes we want by using indexing:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"axes[0].plot(x,x)\n",
"axes[1].plot(x,x**2)\n",
"\n",
"fig\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Say we don't want to use two axes and we'd rather use one set of axes with two lines. We already know how to do this:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEicUgEQEYlT/w/82YKbdnvyEwAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(x,x)\n",
"ax.plot(x,x**2)\n",
"ax.plot(x,x**3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How can we tell at a glance which plot is which? A legend would be helpful here - to do this we need to edit out code a little bit:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(x,x, label='Linear')\n",
"ax.plot(x,x**2, label='Squared')\n",
"ax.plot(x,x**3, label='Cubed')\n",
"\n",
"ax.legend()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice how we need to label our plots within their respective methods, and then call the legend method. A similar method is used to change the color and linestyles of these plots:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.plot(x,x, label='Linear', color = \"purple\", lw=3,ls=':')\n",
"ax.plot(x,x**2, label='Squared', color = \"pink\", lw =3,ls='-.')\n",
"ax.plot(x,x**3, label='Cubed', color = \"blue\", lw=3,ls='--')\n",
"\n",
"ax.legend()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are hundreds of different plot options out there, as well as the option for 3d plots, contour plots, log scaling and more! To check these out, take a look at the documentation [here](http://matplotlib.org/)\n",
"\n",
"Finally, it's worth noting that we can use the object orientated method for scatter graphs, boxplots and histograms.\n",
"\n",
"To start, let's take a scatter plot looking at a (albeit fake) positive correlation."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots()\n",
"\n",
"\n",
"randomData = 0.4 * np.random.randn(101) + 0.5 #Nice way to fake a correlation - 0.4 is the slope, 0.5 is the intercept.\n",
"\n",
"axes.scatter(x,randomData + x)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's look at how to create a histogram representing a normal distribution:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1., 15., 76., 156., 249., 267., 152., 61., 20., 4.]),\n",
" array([-3.38632307, -2.71377809, -2.04123311, -1.36868813, -0.69614314,\n",
" -0.02359816, 0.64894682, 1.3214918 , 1.99403678, 2.66658176,\n",
" 3.33912675]),\n",
" )"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots()\n",
"\n",
"data = np.random.randn(1001)\n",
"\n",
"axes.hist(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally, let's look at boxplots:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'boxes': [,\n",
" ,\n",
" ],\n",
" 'caps': [,\n",
" ,\n",
" ,\n",
" ,\n",
" ,\n",
" ],\n",
" 'fliers': [,\n",
" ,\n",
" ],\n",
" 'means': [],\n",
" 'medians': [,\n",
" ,\n",
" ],\n",
" 'whiskers': [,\n",
" ,\n",
" ,\n",
" ,\n",
" ,\n",
" ]}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots()\n",
"\n",
"\n",
"data = [np.random.normal(0,1,100),np.random.normal(0,2,100),np.random.normal(0,3,100)]\n",
"\n",
"axes.boxplot(data)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice how the boxplot and histogram methods also output a lot of other useful information about the plots to access later if we want to. If we don't want these, we can just run fig again in another cell, or omit %matplotlib inline:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Worked Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to create a graph that looks at what happens to the graph of ex, 2x, 3x, x2 and x3. Soon we will be able to import data so we're not working the mathematical functions all the time!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeIAAAFDCAYAAADmqQ3oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline\n",
"\n",
"x = np.linspace(0,10,101)\n",
"\n",
"\n",
"fig = plt.figure()\n",
"axes = fig.add_axes([0,0,1,1])\n",
"\n",
"axes.plot(x, np.exp(x), label = \"Exponential\")\n",
"axes.plot(x, 2**x, label = \"2 Power\")\n",
"axes.plot(x, 3**x, label = \"3 Power\")\n",
"axes.plot(x, x**2, label = \"Squared\")\n",
"axes.plot(x, x**3, label = \"Cubed\")\n",
"\n",
"axes.set_ylim((0,1000))\n",
"axes.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mini Project"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try graphing the sin(), cos() and tan() functions on one graph, adding a legend, title and axis. Then, try using the subplot command to put them all on different plots."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More Resources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* http://www.matplotlib.org - The project web page for matplotlib.\n",
"* https://github.com/matplotlib/matplotlib - The source code for matplotlib.\n",
"* http://matplotlib.org/gallery.html - A large gallery showcaseing various types of plots matplotlib can create. Highly recommended! \n",
"* http://www.loria.fr/~rougier/teaching/matplotlib - A good matplotlib tutorial.\n",
"* http://scipy-lectures.github.io/matplotlib/matplotlib.html - Another good matplotlib reference.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}