02_tools-and-packages/01_plot-packages.ipynb

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   "source": [
    "# Required to load webpages\n",
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "[Table of contents](../toc.ipynb)\n",
    "\n",
    "# Plotting packages\n",
    "\n",
    "* Until now, we were just able to return results via `print()` command.\n",
    "* However, as humans depend very much on vision, data visualization is urgently needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "## Matplotlib\n",
    "\n",
    "<img src=\"https://github.com/matplotlib/matplotlib/raw/master/doc/_static/logo2.png\" alt=\"Matplotlib\" width=\"350\" align=\"right\">\n",
    "\n",
    "* Very common and widely used is the [matplotlib](https://matplotlib.org/) package.\n",
    "* Matplotlib provides with `pyplot` module very similar plotting commands as Matlab.\n",
    "* Matplotlib is packaged and available through pip and conda.\n",
    "* Let's create our first plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* The first line of code import the pyplot module, which is very convenient for most simple plots.\n",
    "* The second line is a special Jupyter magic command to present the plots instantly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Basic plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [],
   "source": [
    "# First, we create some data to plot, which is not that handy with lists.\n",
    "x = [i for i in range(-20, 20)]\n",
    "y = [x[i]**2 for i in range(0,40)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
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   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now the plot command\n",
    "plt.plot(x, y)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here the same plot with axis labels\n",
    "plt.plot(x, y)\n",
    "plt.xlabel(\"samples\")\n",
    "plt.ylabel(\"$x^2$\") # You can use LaTeX's math support\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some line style arguments and a title\n",
    "plt.plot(x, y, \"k--\")\n",
    "plt.xlabel(\"samples\")\n",
    "plt.ylabel(\"$x^2$\") # You can use LaTeX's math support\n",
    "plt.title(\"My parabola\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Multiple plots in one figure\n",
    "\n",
    "To present two time series, we will create an additional list of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import math\n",
    "y2 = [math.sin(x[i]) for i in range(0,40)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x, y, 'o-')\n",
    "plt.title('A tale of 2 subplots')\n",
    "plt.ylabel('$x^2$')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, y2, '.-')\n",
    "plt.xlabel('samples')\n",
    "plt.ylabel('$\\sin{x}$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* I guess you got the concept here.\n",
    "* Also basic statistical plots are simple to generate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Generate random numbers in a list\n",
    "import random\n",
    "\n",
    "y3 = [random.gammavariate(alpha=1.2, beta=2.3) for i in range(0, 5000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y3, bins=40)\n",
    "plt.xlabel('Data')\n",
    "plt.ylabel('Probability density')\n",
    "plt.title(r'Histogram of Gammadist: $\\alpha=1.2$, $\\beta=2.3$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Exercise:  Matplotlib (10 minutes)\n",
    "\n",
    "<img src=\"../_static/exercise.png\" alt=\"Exercise\" width=\"75\" align=\"left\">\n",
    "\n",
    "\n",
    "Here the task:\n",
    "\n",
    "* Activate your local Python environment and install matplotlib with `conda install matplotlib`.\n",
    "* Either work in Jupyter notebook, in Ipython shell, or Pycharm.\n",
    "* Create a list of 1500 data points.\n",
    "* Add a second list of 1500 data points with Gaussian distribution $\\mathcal{N}\\sim(0, 1)$.\n",
    "* Create a figure with a histogram of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Solution\n",
    "\n",
    "Please find one possible solution in [`solution_plotting.py`](solution_plotting.py) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run solution_plotting.py\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### More plot types\n",
    "\n",
    "* Many other plots are as simple as the examples above.\n",
    "* Other typical plots are \n",
    "  * Bar plots\n",
    "  * 3D plots\n",
    "  * Pie charts\n",
    "  * Scatter plots\n",
    "  * ...\n",
    "* You can find many more plot examples in the [Matlotlib gallery](https://matplotlib.org/gallery/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://matplotlib.org/gallery/index.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc70636a1d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://matplotlib.org/gallery/index.html', width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seaborn\n",
    "\n",
    "* Seaborn is a statistical plot library for Python.\n",
    "* It is based on matplotlib.\n",
    "* Please find it's documentation here [https://seaborn.pydata.org/](https://seaborn.pydata.org/).\n",
    "* You can install it with `conda install seaborn`.\n",
    "* Next comes a snapshot of seaborn's example gallery [https://seaborn.pydata.org/examples/index.html](https://seaborn.pydata.org/examples/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://seaborn.pydata.org/examples/index.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc70636a588>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://seaborn.pydata.org/examples/index.html', width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Bokeh\n",
    "\n",
    "<img src=\"https://static.bokeh.org/logos/logotype.svg\" alt=\"Bokeh\" width=\"350\" align=\"right\">\n",
    "\n",
    "* Add to \"static\" plots, interactive plots and dashboards can be build with Bokeh library.\n",
    "* Interactive plots are ideal if you want to visualize large data sets.\n",
    "* Real time information is visible like server load,...\n",
    "* Boekh is easy to install via conda and pip.\n",
    "* Please find here the Bokeh gallery [https://docs.bokeh.org/en/latest/docs/gallery.html](https://docs.bokeh.org/en/latest/docs/gallery.html).\n",
    "* And in next cell, an interactive plot of bokeh website is presented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://demo.bokeh.org/crossfilter\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc706373c18>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://demo.bokeh.org/crossfilter', width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotly\n",
    "\n",
    "* Plotly is another very simple to use interactive plot library.\n",
    "* You can find more detail in [https://plot.ly/python/](https://plot.ly/python/).\n",
    "* Next, some examples from plotly gallery are presented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"600\"\n",
       "            src=\"https://plot.ly/python/\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fc7063ac160>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IFrame(src='https://plot.ly/python/', width=700, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## I a nutshell\n",
    "\n",
    "* There are many very powerful plot libraries available for Python.\n",
    "* The chance that someone wrote a template for your plotting task is very high.\n",
    "* Just look in the galleries and you will find a great starting point."
   ]
  }
 ],
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