Week7/notebooks/info490w7l2.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Python Plotting\n",
    "\n",
    "While there are a number of different options to plot data by using [Python](http://www.python.org), we will use [matplotlib](http://matplotlib.org), which is the most popular Python plotting package, especially for large data. In this lesson, we will focus on creating basic plots by using matplotlib.\n",
    "\n",
    "\n",
    "Note: When using an [iPython](http://ipython.org) notebook, we will always use the following statement to ensure that all figures generated by matplotlib appear inline within the iPython notebook. If you are writing a Python program outside of iPython, you should not use this line. Instead, the figures will either be displayed by an appropriate matplotlib visualization backend or be saved to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib Basics\n",
    "\n",
    "The matplotlib plottling library provides powerful data visualization functionaility. There are two basic methods for working with matplotlib. The first method is to use the pylab interface, which provides a simple interface to the power of matplotlib by mimicing the matlab approach. The second method is to use the matplotlib pyplot interface, which is the preferred method for Python programs as it provides an object-oriented access to the full matplotlib library. Since we will be focusing on writing Python programs, we will use the pyplot access method.\n",
    "\n",
    "Thus our first step in using matplotlib will be to import that pyplot interface:\n",
    "\n",
    "```python\n",
    "    import matplotlib.pyplot as plt\n",
    "```\n",
    "\n",
    "The second step will be to create `Figure` object that will allow us to control the global appearance of our visualization. This can include, among other things, the size and the resolution (or dots per inch) of the generated figure. We make a Figure object by calling the `figure()` method within our pyplot interface:\n",
    "\n",
    "```python\n",
    "    fig = plt.figure()\n",
    "```\n",
    "\n",
    "Next, we create an `Axes` object, which allows us to make an actual plot within our figure. By separating out these concetps, we can easily add multuple subplots to our figure, although for now we will simply stick to a single plot. We can easily add a subplot to our `figure` object:\n",
    "```python\n",
    "    ax = fig.add_subplot()\n",
    "```\n",
    "\n",
    "Of course, for a quick and simple plot, the matplotlib library proivides a shortcut form that both creates the `Figure` object and creates a single subplot `axes` object:\n",
    "\n",
    "```python\n",
    "    fig, ax = plt.subplots()\n",
    "```\n",
    "We will use this simple technique for now, but in later modules we will switch to the more expressive technique of explicitly creating the `Figure` and `Axes` objects to have more control over the resulting plot.\n",
    "\n",
    "In the following code block, we demonstrate the basic concepts required to create a simple plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "<matplotlib.figure.Figure at 0x7f5afd9e7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this plot is rather boring, we simply have an empty box with labels on the bottom and left-hand sides. This box encloses your default plot area, and the labels show that, by default, your first plot spans zero to one in both the horizontal, or x-axis, and the vertical, or y-axis.\n",
    "\n",
    "Our next step will be to actually display something. But first, I want to breifly discuss our use of the iPython notebook. Since this course will focus on the creation of Python programs, we will renter all matplotlib python commands in each iPython code cell in these notebooks. Technically this is not necessary, since an iPython notebook carries state through the notebook. What this means is that if you define a variable or import apackage in a cell, later cells in the notebook will be able to use that variable or package. This makes a notebook a concise visual representation of your work. However, by doing that, you can't simply cut-n-paste code from an iPython notebook into a Python program.\n",
    "\n",
    "Thus, we will treat each iPython code cell as a seperate Python program and include all relevant Python statements in that cell. We also will explicitly call the matplotlib `show()` method, which generates and sisplays our figure. By default, matplotlib does nto actually create a plot until it is forced to do so. While an iPython notebook will generally do this automatically, this must be done explitlicy within a normal Python program. Later on we will discuss how to save a figure we generate with matplotlib.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data with matplotlib\n",
    "\n",
    "Now, given the `Axes` object, we can simply pass the data we wish to visualize to the `plot()` method. In the following code sample, we use numpy to create an array of linearly spaced values, which will be our independent variable. Next, we define two constants: m and b, that will be used to create our dependent variable. Given these to arrays, we can plot this sequence of points, which are a linear function, by passing them to the `plot()` method.\n",
    "\n",
    "Note that you can change the m and b values and reprocess the cell (either by using the toolbar at the top, or  using the `shift+enter` key combination within the cell)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afb9b0048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x = np.linspace(0,10)\n",
    "y = m * x + b\n",
    "\n",
    "ax.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding text information\n",
    "\n",
    "You have now actually plotted a linear function! But overall the plot remains uninformative, so lets add information to make our plot self-contained. First, we can place descriptive text along both axes. This text is known as an axis label and can be easily added to your plot by calling the appropriate set function on our `Axes` object:\n",
    "\n",
    "```python\n",
    "    ax.set_xlabel(\"X Axis\")\n",
    "    ax.set_ylabel(\"Y Axis\")\n",
    "```\n",
    "\n",
    "Second, most plots benefit from having a title that gives context for the rest of the datain the plot. You also might consider adding your name to your plot, either in the title or within the plot box itself (which will be discussed later).\n",
    "```python\n",
    "    ax.set_title(\"Our first plot!\")\n",
    "```\n",
    "\n",
    "Together, these new methods will improve the readability of our plot, as shown below, where we now have the\n",
    "two-dimensional plot with axes labels and a title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afb935550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x = np.linspace(0,10)\n",
    "y = m * x + b\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\")\n",
    "ax.set_ylabel(\"Y Axis\")\n",
    "\n",
    "ax.set_title(\"Our First Plot!\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restricting the axes range\n",
    "\n",
    "Matplotlib provides you with a great deal of control over the appearance of your plot. Two items you may wish to change include the degree to which your plot fills the subplot box, and how the numbers are dsiplayed on the axes. For the first item, you can change the range displayed in either the x-axis, the y-axis or both by simply setting the limits displayed by matplotlib. For instance, we can change the x-axis to show -2 to 12, and the y-axis to show 5 to -20:\n",
    "\n",
    "```python\n",
    "    ax.set_xlim(-2, 12)\n",
    "    ax.set_ylim(-20, 10)\n",
    "```\n",
    "In this case, we have told matplotlib to change our display to span from -2 to 12 in the x-drection and from -20 to 10 in the y-direction.\n",
    "\n",
    "You also can change how the numbers are displayed on the plot, which are done at specific intervals on the axes, which are known as ticks. Once again, you can control each axis independently and change only one or both by using the approapriate set method:\n",
    "\n",
    "```python\n",
    "    ax.set_xticks(np.arange(0, 15, 5))\n",
    "    ax.set_yticks(np.arange(-15, 10, 5))\n",
    "```\n",
    "In this example, we have used the arange method from numpy to create an array of labels. The x-axis labels will now include 0, 5, and 10, while the y-axis labels will include -15, -10. -5, 0, asnd 5. Of course you can change these values to whatever you like, but be careful as by using too many ticks (and thus numerical labels as well) your plot can become overly full and hard to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xEBERVAxERAQVAxERQcVARESA/w+5CDNE6mQctwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afb91dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x = np.linspace(0,10)\n",
    "y = m * x + b\n",
    "\n",
    "ax.plot(x, y)\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\")\n",
    "ax.set_ylabel(\"Y Axis\")\n",
    "\n",
    "# Change the axis limits displayed in our plot\n",
    "ax.set_xlim(-2, 12)\n",
    "ax.set_ylim(-20, 10)\n",
    "\n",
    "# Change the ticks on each axis and the corresponding numerical values that are displayed\n",
    "ax.set_xticks(np.arange(0, 15, 5))\n",
    "ax.set_yticks(np.arange(-15, 10, 5))\n",
    "    \n",
    "ax.set_title(\"Our Next Plot!\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting multiple functions\n",
    "We are not restricted to plotting only one item within a matplotlib figure. We can either call plot twice with different data, or, alternatively, pass multiple data to the plot method itself.\n",
    "\n",
    "```python\n",
    "    ax.plot(x1, y1)\n",
    "    ax.plot(x2, y2)\n",
    "```\n",
    "or equivalently:\n",
    "```python\n",
    "    ax.plot(x1,y1, x2,y2)\n",
    "```\n",
    "Note that when plotting multiple items, you may need to change your axis limits or ticks to ensure everything is properly displayed. For instance, in the demonstration below, we have changed our y-axis limits to be -20 to 20, and our y-axis labels to now run to 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gMBARERQGIiKCwkAkLzOrNLM3zeyE8PoJ4fX+bWz/JTNrNbPPdOB7/5WZ3VXo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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afb886438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x1 = np.linspace(0,10)\n",
    "y1 = m * x1 + b\n",
    "\n",
    "x2 = x1\n",
    "y2 = -1 * y1\n",
    "\n",
    "# we can either plot each set of data separately as shown, or plot them all at \n",
    "# once by calling ax.plot(x1, y1, x2, y2)\n",
    "\n",
    "ax.plot(x1, y1)\n",
    "ax.plot(x2, y2)\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\")\n",
    "ax.set_ylabel(\"Y Axis\")\n",
    "\n",
    "# Change the axis limits displayed in our plot\n",
    "ax.set_xlim(-2, 12)\n",
    "ax.set_ylim(-20, 20)\n",
    "\n",
    "# Change the ticks on each axis and the corresponding numerical values that are displayed\n",
    "ax.set_xticks(np.arange(0, 15, 5))\n",
    "ax.set_yticks(np.arange(-15, 20, 5))\n",
    "    \n",
    "ax.set_title(\"Our Final Plot!\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should notice how matplotlib changed the plot color for the two functions beign displayed. While this was done automatically in this case, you can explitlcy change the color, the linestyle, the linewidth, and many other plot characteristcs should you so choose. We will cover some of these topics in later modules, but you are encouraged to look at the matplotlib documentation (and [example gallery](http://matplotlib.org/gallery.html)) to learn more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving your Plot\n",
    "To this point, you have made several different plots and had them displayed on the screen. In same cases, however, you might want to save your plot to a file, which can then be displayed online, printed out, or submitted as part of a programming assignment. To save a figure, you call the `savefig()` method. This method takes several parameters, the most important of which is the file name for your saved plot. In this case, matplotlib will take the file extension you provide and select the appropriate rendering method. For example, if your filename ends in 'pdf' the plot will be created using a PDF rendering function, while if you use 'png' a different renderer will be used.\n",
    "\n",
    "When saving your plot, either a PDF or PNG is preferred as these formats are portable vector graphics formats. This means that the instructions to make your plot are stored in the file as opposed to the actual image of the plot. As a result, your new plot can be made larger or smaller without affecting the image quality.\n",
    "\n",
    "In the example below, we save our plot by using a PDF renderer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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gMBARERQGIiKCwkAkLzOrNLM3zeyE8PoJ4fX+bWz/JTNrNbPPdOB7/5WZ3VXo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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5afb7e0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x1 = np.linspace(0,10)\n",
    "y1 = m * x1 + b\n",
    "\n",
    "x2 = x1\n",
    "y2 = -1 * y1\n",
    "\n",
    "# we can either plot each set of data separately as shown, or plot them all at \n",
    "# once by calling ax.plot(x1, y1, x2, y2)\n",
    "\n",
    "ax.plot(x1, y1)\n",
    "ax.plot(x2, y2)\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\")\n",
    "ax.set_ylabel(\"Y Axis\")\n",
    "\n",
    "# Change the axis limits displayed in our plot\n",
    "ax.set_xlim(-2, 12)\n",
    "ax.set_ylim(-20, 20)\n",
    "\n",
    "# Change the ticks on each axis and the corresponding numerical values that are displayed\n",
    "ax.set_xticks(np.arange(0, 15, 5))\n",
    "ax.set_yticks(np.arange(-15, 20, 5))\n",
    "    \n",
    "ax.set_title(\"Our Final Plot!\")\n",
    "plt.savefig('test.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how in the previous example, your plot was still displayed inline as before. However, if you now navigate to the directoy where this iPython notebook is saved, you will see a new file called 'test.pdf'. Open that file, you will see your new figure!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Seaborn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the MatPlotLib library provides powerful plotting functionality to\n",
    "the Python programmer, the MatPlotLib API can be rather daunting for\n",
    "beginners and the basic color schemes and plot styles are not the most\n",
    "visually appealing. For anyone who has been exposed to the work and\n",
    "ideas of [Edward Tufte][i], the importance of making good data\n",
    "visualizations can not be understated. As a result, other libraries have\n",
    "been developed that build on the MatPlotLib legacy and provide both\n",
    "improved visual aesthetics as well as new plot functions. Of these, one\n",
    "of the most interesting is the [Seaborn][1]library. \n",
    "\n",
    "Searbon is easy to start using, yet introduces a number of new powerful\n",
    "plot styles along with statistical functionality that can make difficult\n",
    "plotting tasks simple. Seaborn also simplifies the task of removing plot\n",
    "clutter and you also can choose from different color palettes that have\n",
    "already been screened to maximize the visual impact of your new plots.\n",
    "Seaborn can be easily imported into your Python program, by using\n",
    "`import seaborn as sns`, and with one function call to `sns.set()` your\n",
    "plot colors and styles can be improved. The `set` method takes a number\n",
    "of parameters like `style` or `font` that you can also specify\n",
    "explicitly, as shown later in this Notebook.\n",
    "\n",
    "-----\n",
    "[i]: http://www.edwardtufte.com/tufte/\n",
    "[1]: http://web.stanford.edu/~mwaskom/software/seaborn/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "To demonstrate the impact of these two Python statements, we can simply\n",
    "repeat one of the first Python visualization examples from earlier in\n",
    "this IPython Notebook. In this case we are simply plotting two lines,\n",
    "along with labels for the `x` and `y` axes and a title for the plot.\n",
    "Compare this version with the same plot created by only using the\n",
    "MatPlotLib library.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Start using the Seaborn library. Note that this will remain in effect in your \n",
    "# IPython Notebook until the Kernel is restarted or you issue an reset() call.\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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MUCgDndDUqf9+t8Pqu3e9X2oMDfZnnnmG8vJy2tvbWbp0KU8++SSPPvooTz31FG+++eaV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\nklgwNZEjZ5rJL6ng4OkmDp5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5af24d9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Repeat this plot, but now with Seaborn default style\n",
    "\n",
    "# First we need to import matplotlib and numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x1 = np.linspace(0,10)\n",
    "y1 = m * x1 + b\n",
    "\n",
    "x2 = x1\n",
    "y2 = -1 * y1\n",
    "\n",
    "# we can either plot each set of data separately as shown, or plot them all at \n",
    "# once by calling ax.plot(x1, y1, x2, y2)\n",
    "\n",
    "ax.plot(x1, y1)\n",
    "ax.plot(x2, y2)\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\")\n",
    "ax.set_ylabel(\"Y Axis\")\n",
    "\n",
    "# Change the axis limits displayed in our plot\n",
    "ax.set_xlim(-2, 12)\n",
    "ax.set_ylim(-20, 20)\n",
    "\n",
    "# Change the ticks on each axis and the corresponding numerical values that are displayed\n",
    "ax.set_xticks(np.arange(0, 15, 5))\n",
    "ax.set_yticks(np.arange(-15, 20, 5))\n",
    "    \n",
    "ax.set_title(\"Our Final Plot!\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "\n",
    "In the previous plot, you saw how by simply using the _Seaborn_ plotting\n",
    "library, the plot appearance changed. This is one of the primary\n",
    "benefits of using Seaborn, in that the color schemes and plotting styles\n",
    "have already been well-thought out. In this case, a grey background with\n",
    "soft lines at the tick marks are used along with a specific font type.\n",
    "However, Seaborn provides the capability of changing the plot\n",
    "appearance, both by using other predefined styles as well as specific\n",
    "features in the plot.\n",
    "\n",
    "In the following code cell, we demonstrate how to change the overall\n",
    "appearance of the plot by using a white style with tick marks, and a\n",
    "specific Seaborn context, which can take one of four predefined types:\n",
    "\n",
    "- `notebook`\n",
    "- `paper`\n",
    "- `talk`\n",
    "- `poster`\n",
    "\n",
    "which can be used in a `with` statement. We also use the `despine`\n",
    "method, which can remove the box appearance of the plot, and we change\n",
    "the fontsize, resulting a very different plot appearance.\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PT5PHodXq7XbJ7JvVPvO3v/ztOXeA+dtT/odzjuOH33cgpzwPAgTE+HXFA52GIdQ9yC7y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\nIhLN/wNkr7vhOVOHYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5af24e9940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 24 # Font Size\n",
    "\n",
    "# Now we create our figure and axes for the plot we will make.\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Now we generate something to plot. In this case, we will plot a straight line.\n",
    "# You can change the constants: m and b, in the equation below to get a different plot.\n",
    "\n",
    "m = -2\n",
    "b = 5\n",
    "x1 = np.linspace(0,10)\n",
    "y1 = m * x1 + b\n",
    "\n",
    "x2 = x1\n",
    "y2 = -1 * y1\n",
    "\n",
    "# we can either plot each set of data separately as shown, or plot them all at \n",
    "# once by calling ax.plot(x1, y1, x2, y2)\n",
    "\n",
    "linestyle = [':', '--', '-']\n",
    "\n",
    "\n",
    "ax.plot(x1, y1, label = 'Plot 1', ls = ':')\n",
    "ax.plot(x2, y2, label = 'Plot 2', ls = '--')\n",
    "\n",
    "# Set our axis labels\n",
    "ax.set_xlabel(\"X Axis\", size=fs)\n",
    "ax.set_ylabel(\"Y Axis\", size=fs)\n",
    "\n",
    "plt.legend(loc='upper left', fontsize=fs)\n",
    "\n",
    "# Change the axis limits displayed in our plot\n",
    "ax.set_xlim(-2, 12)\n",
    "ax.set_ylim(-20, 20)\n",
    "\n",
    "ax.set_title(\"Our Final Plot!\")\n",
    "\n",
    "# Now Seaborn specific modifications\n",
    "\n",
    "sns.set_context(\"poster\")\n",
    "\n",
    "sns.despine(offset=10, trim=True)\n",
    "sns.set_style(\"white\")\n",
    "sns.set_style(\"ticks\")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}