{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides a brief overview of the matplotlib plotting library in python which is popular for generating 2D plots. The [matplotlib gallery](https://matplotlib.org/gallery/index.html) has extensive examples which can be customized for your requirements.  \n",
    "\n",
    "The figure in matplotlib is the area where everything is drawn on. Figures comprise of Axes or subplots. Axes or subplots are the area within the Figure where actual plotting occurs. Figures can have multiple axes's or subplots. Axes have xaxis and yaxis.  \n",
    "\n",
    "Let us look at a simple plotting example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrLHHYt8krSplZbYC3w+Ysimsc+aUZoEB0LGj7TzsM/NM5nC5Qw4JHUkYZWWw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6ede0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 5 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "fig = plt.figure() \n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, color='red')\n",
    "ax.set(xlim=[0, 16.0], ylim=[-1.2, 1.2], title='An Example', ylabel='Sine Curve', xlabel='x values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, you are creating a figure in the line fig = plt.figure().  \n",
    "\n",
    "In the line, ax = fig.add_subplot(111), you are adding a subplot. The 111 indicates number of rows, number of column, and the first subplot. It is not really needed in this case.\n",
    "\n",
    "In line ax.plot(), you are generating the plot. You are setting the parameters of the plot in the line ax.set(). Note that you have control over all aspects of the plot, the x axis limits, y axis limits, ticks etc.\n",
    "\n",
    "The plot will not be displayed until you run the plt.show() command. If you want to save the figure as a png file, use the savefig command. You can save it in other formats like pdf also. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 5 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "fig = plt.figure() \n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, color='red')\n",
    "ax.set(xlim=[0, 16.0], ylim=[-1.2, 1.2], title='An Example', ylabel='Sine Curve', xlabel='x values')\n",
    "fig.savefig(\"trial.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the figure size by providing a figsize = (width, height) argument where both width and height are in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrLHHYt8krSplZbYC3w+Ysimsc+aUZoEB0LGj7TzsM/NM5nC5Qw4JHUkYZWWw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x888d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YJnC+Yv0weVGpoJQMfF5UKJaysqSukmPt08aOleLqa9dMR0J+sXs3cPo0E7gb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x86f6978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 5 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "fig = plt.figure(figsize=(10.0, 8.0)) \n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, color='red')\n",
    "ax.set(xlim=[0, 16.0], ylim=[-1.2, 1.2], title='An Example', ylabel='Sine Curve', xlabel='x values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at an example where you have two subplots or axes in the same figure. Note that the scatter() command provides the scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5+LSLio9oJVtyV1QgxmXFd/x4OOccFb754I03fFt6XJ574Avfp56CV18NnURE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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8101cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 5 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "x1 = np.arange(0, 10)\n",
    "y1 = x1 + 5.0\n",
    "\n",
    "fig = plt.figure(figsize=(16.0, 6.0)) \n",
    "\n",
    "ax1 = fig.add_subplot(121) #indicates 1 row, 2 columns, first subplot\n",
    "ax1.plot(x, y, color='red')\n",
    "ax1.set(xlim=[0, 16.0], ylim=[-1.2, 1.2], title='Plot 1', ylabel='Sine Curve', xlabel='x values')\n",
    "\n",
    "ax2 = fig.add_subplot(122) #indicates 1 row, 2 columns, second subplot\n",
    "ax2.scatter(x1, y1, color='green')\n",
    "ax2.set(xlim=[0, 12.0], ylim=[0, 16], title='Plot 2', ylabel='y values', xlabel='x values')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want them plotted one below the other see the changes in the add_subplot line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06QI0bcoA4Lv8fKBjR+DYY11XEky5ucDBgxYCKFiS4ZUtAKXr1csG97IFK3iS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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8319470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(0, 5 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "x1 = np.arange(0, 10)\n",
    "y1 = x1 + 5.0\n",
    "\n",
    "fig = plt.figure(figsize=(8.0, 12.0)) \n",
    "\n",
    "ax1 = fig.add_subplot(211) #indicates 2 rows, 1 column, first subplot\n",
    "ax1.plot(x, y, color='red')\n",
    "ax1.set(xlim=[0, 16.0], ylim=[-1.2, 1.2], title='Plot 1', ylabel='Sine Curve', xlabel='x values')\n",
    "\n",
    "ax2 = fig.add_subplot(212) #indicates 2 row, 1 columns, second subplot\n",
    "ax2.scatter(x1, y1, color='green')\n",
    "ax2.set(xlim=[0, 12.0], ylim=[0, 16], title='Plot 2', ylabel='y values', xlabel='x values')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to plot a histogram using the hist() command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8113240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mu = 100 # mean of distribution\n",
    "sigma = 15 # standard deviation of distribution\n",
    "z = np.random.randn(10000) # generates 100000 standard normal realizations\n",
    "x = mu + sigma*z \n",
    "fig = plt.figure() \n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(x, bins = 10, normed=1)\n",
    "ax.set(title='Histogram', ylabel='Probability', xlabel='x values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you remove normed=1 you get the frequency instead of probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x58e6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "mu = 100 # mean of distribution\n",
    "sigma = 15 # standard deviation of distribution\n",
    "z = np.random.randn(10000) # generates 100000 standard normal realizations\n",
    "x = mu + sigma*z \n",
    "fig = plt.figure() \n",
    "ax = fig.add_subplot(111)\n",
    "ax.hist(x, bins = 10)\n",
    "ax.set(title='Histogram', ylabel='Frequency', xlabel='x values')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [matplotlib gallery](https://matplotlib.org/gallery/index.html) has lot of examples along with the source code. If you are looking for a specific type of plot, go through the gallery and pick one closest to yours. Use that as a basis to develop your plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}