gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebook\n",
    "\n",
    "[Jupyter Notebook](http://jupyter.org/) that evolved out of iPython and is aimed at providing a platform for easy sharing, interaction, and development of open-source software, standards and services.  Althought primarily and originally used for phyton interactions, you can interact with multiple programming languages using Jupyter (although orignially expanded from iPython to includ Julia, Pythion, and R).  Jupyter notebooks is a great way to create, test, and share live code, as well as equations and visualizations with markdown text.  \n",
    "\n",
    "To inlcude more languages, check out [kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) that are supported and their [installation instructions](https://ipython.readthedocs.io/en/latest/install/kernel_install.html).\n",
    "\n",
    "# Installation\n",
    "\n",
    "To get started, [download and install](http://jupyter.readthedocs.io/en/latest/install.html) Jupyter.  Installation of [Anaconda](https://www.anaconda.com/download/) is reccomened by Jupyter becuase it will also cover your python and notebook installation at the same time.  If you prefer, you can also install Jupyter with pip, but this is only suggested for advanced Python users.  There is extensive documentation to help with installation and download availble on the Jupyter and Anaconda pages.\n",
    "\n",
    "Once installed, launch the Anacdona Navigator and launch Jupter notebooks.  Also open the Anaconda Prompt window.  We will use this window to install extra widgets.\n",
    "\n",
    "In your Anaconda Prompt window, type the statement below and press enter, continue by following the prompts.\n",
    "\n",
    "```\n",
    "conda install -c conda-forge ipyleaflet\n",
    "```\n",
    "\n",
    "In your Anaconda Prompt window, type the statement below and press enter, continue by following the prompts.\n",
    "\n",
    "```\n",
    "conda install -c conda-forge bqplot\n",
    "```\n",
    "\n",
    "In your Anaconda Prompt window, type the statement below and press enter, continue by following the prompts.\n",
    "\n",
    "```\n",
    "conda install -c conda-forge pythreejs\n",
    "```\n",
    "\n",
    "Check out this [cheatsheet](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Jupyter_Notebook_Cheat_Sheet.pdf) by DataCamp to help with the interface.\n",
    "\n",
    "## Help Dropdown\n",
    "\n",
    "Spend some time going through these help features.\n",
    "\n",
    "## Markdown\n",
    "\n",
    "Markdown can be easily used in Jupyter.  Simply change the dropdown for the cell to \"Markdown\".  If you need a refresher, here is another good [cheatsheet](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf).  \n",
    "\n",
    "## Special Commands\n",
    "Special commands can change shell to run different commands, for example you can run bash commands and magic commands directly within a new cell.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /Users/ldsfoxmac/anaconda:\n",
      "#\n",
      "_license                  1.1                      py36_1  \n",
      "alabaster                 0.7.10                   py36_0  \n",
      "anaconda                  custom                   py36_0  \n",
      "anaconda-client           1.6.3                    py36_0  \n",
      "anaconda-navigator        1.6.2                    py36_0  \n",
      "anaconda-project          0.6.0                    py36_0  \n",
      "appnope                   0.1.0                    py36_0  \n",
      "appscript                 1.0.1                    py36_0  \n",
      "asn1crypto                0.22.0                   py36_0  \n",
      "astroid                   1.4.9                    py36_0  \n",
      "astropy                   1.3.2               np112py36_0  \n",
      "babel                     2.4.0                    py36_0  \n",
      "backports                 1.0                      py36_0  \n",
      "beautifulsoup4            4.6.0                    py36_0  \n",
      "bitarray                  0.8.1                    py36_0  \n",
      "blaze                     0.10.1                   py36_0  \n",
      "bleach                    1.5.0                    py36_0  \n",
      "bokeh                     0.12.5                   py36_1  \n",
      "boto                      2.46.1                   py36_0  \n",
      "bottleneck                1.2.1               np112py36_0  \n",
      "cffi                      1.10.0                   py36_0  \n",
      "chardet                   3.0.3                    py36_0  \n",
      "click                     6.7                      py36_0  \n",
      "cloudpickle               0.2.2                    py36_0  \n",
      "clyent                    1.2.2                    py36_0  \n",
      "colorama                  0.3.9                    py36_0  \n",
      "conda                     4.3.29                   py36_0    conda-forge\n",
      "conda-env                 2.6.0                         0    conda-forge\n",
      "contextlib2               0.5.5                    py36_0  \n",
      "cryptography              1.8.1                    py36_0  \n",
      "curl                      7.52.1                        0  \n",
      "cycler                    0.10.0                   py36_0  \n",
      "cython                    0.25.2                   py36_0  \n",
      "cytoolz                   0.8.2                    py36_0  \n",
      "dask                      0.14.3                   py36_1  \n",
      "datashape                 0.5.4                    py36_0  \n",
      "decorator                 4.0.11                   py36_0  \n",
      "distributed               1.16.3                   py36_0  \n",
      "docutils                  0.13.1                   py36_0  \n",
      "entrypoints               0.2.2                    py36_1  \n",
      "et_xmlfile                1.0.1                    py36_0  \n",
      "fastcache                 1.0.2                    py36_1  \n",
      "flask                     0.12.2                   py36_0  \n",
      "flask-cors                3.0.2                    py36_0  \n",
      "freetype                  2.5.5                         2  \n",
      "get_terminal_size         1.0.0                    py36_0  \n",
      "gevent                    1.2.1                    py36_0  \n",
      "greenlet                  0.4.12                   py36_0  \n",
      "h5py                      2.7.0               np112py36_0  \n",
      "hdf5                      1.8.17                        1  \n",
      "heapdict                  1.0.0                    py36_1  \n",
      "html5lib                  0.999                    py36_0  \n",
      "icu                       54.1                          0  \n",
      "idna                      2.5                      py36_0  \n",
      "imagesize                 0.7.1                    py36_0  \n",
      "ipykernel                 4.6.1                    py36_0  \n",
      "ipyleaflet                0.4.0                    py36_0    conda-forge\n",
      "ipython                   5.3.0                    py36_0  \n",
      "ipython_genutils          0.2.0                    py36_0  \n",
      "ipywidgets                7.0.1                      py_2    conda-forge\n",
      "isort                     4.2.5                    py36_0  \n",
      "itsdangerous              0.24                     py36_0  \n",
      "jbig                      2.1                           0  \n",
      "jdcal                     1.3                      py36_0  \n",
      "jedi                      0.10.2                   py36_2  \n",
      "jinja2                    2.9.6                    py36_0  \n",
      "jpeg                      9b                            0  \n",
      "jsonschema                2.6.0                    py36_0  \n",
      "jupyter                   1.0.0                    py36_3  \n",
      "jupyter_client            5.0.1                    py36_0  \n",
      "jupyter_console           5.1.0                    py36_0  \n",
      "jupyter_core              4.3.0                    py36_0  \n",
      "lazy-object-proxy         1.2.2                    py36_0  \n",
      "libiconv                  1.14                          0  \n",
      "libpng                    1.6.27                        0  \n",
      "libtiff                   4.0.6                         3  \n",
      "libxml2                   2.9.4                         0  \n",
      "libxslt                   1.1.29                        0  \n",
      "llvmlite                  0.18.0                   py36_0  \n",
      "locket                    0.2.0                    py36_1  \n",
      "lxml                      3.7.3                    py36_0  \n",
      "markupsafe                0.23                     py36_2  \n",
      "matplotlib                2.0.2               np112py36_0  \n",
      "mistune                   0.7.4                    py36_0  \n",
      "mkl                       2017.0.1                      0  \n",
      "mkl-service               1.1.2                    py36_3  \n",
      "mpmath                    0.19                     py36_1  \n",
      "msgpack-python            0.4.8                    py36_0  \n",
      "multipledispatch          0.4.9                    py36_0  \n",
      "navigator-updater         0.1.0                    py36_0  \n",
      "nbconvert                 5.1.1                    py36_0  \n",
      "nbformat                  4.3.0                    py36_0  \n",
      "networkx                  1.11                     py36_0  \n",
      "nltk                      3.2.3                    py36_0  \n",
      "nose                      1.3.7                    py36_1  \n",
      "notebook                  5.0.0                    py36_0  \n",
      "numba                     0.33.0              np112py36_0  \n",
      "numexpr                   2.6.2               np112py36_0  \n",
      "numpy                     1.12.1                   py36_0  \n",
      "numpydoc                  0.6.0                    py36_0  \n",
      "odo                       0.5.0                    py36_1  \n",
      "olefile                   0.44                     py36_0  \n",
      "openpyxl                  2.4.7                    py36_0  \n",
      "openssl                   1.0.2l                        0  \n",
      "packaging                 16.8                     py36_0  \n",
      "pandas                    0.20.1              np112py36_0  \n",
      "pandocfilters             1.4.1                    py36_0  \n",
      "partd                     0.3.8                    py36_0  \n",
      "path.py                   10.3.1                   py36_0  \n",
      "pathlib2                  2.2.1                    py36_0  \n",
      "patsy                     0.4.1                    py36_0  \n",
      "pep8                      1.7.0                    py36_0  \n",
      "pexpect                   4.2.1                    py36_0  \n",
      "pickleshare               0.7.4                    py36_0  \n",
      "pillow                    4.1.1                    py36_0  \n",
      "pip                       9.0.1                    py36_1  \n",
      "ply                       3.10                     py36_0  \n",
      "prompt_toolkit            1.0.14                   py36_0  \n",
      "psutil                    5.2.2                    py36_0  \n",
      "ptyprocess                0.5.1                    py36_0  \n",
      "py                        1.4.33                   py36_0  \n",
      "py-d3                     0.2.7                     <pip>\n",
      "pycosat                   0.6.2                    py36_0  \n",
      "pycparser                 2.17                     py36_0  \n",
      "pycrypto                  2.6.1                    py36_6  \n",
      "pycurl                    7.43.0                   py36_2  \n",
      "pyflakes                  1.5.0                    py36_0  \n",
      "pygments                  2.2.0                    py36_0  \n",
      "pylint                    1.6.4                    py36_1  \n",
      "pyodbc                    4.0.16                   py36_0  \n",
      "pyopenssl                 17.0.0                   py36_0  \n",
      "pyparsing                 2.1.4                    py36_0  \n",
      "pyqt                      5.6.0                    py36_1  \n",
      "pytables                  3.3.0               np112py36_0  \n",
      "pytest                    3.0.7                    py36_0  \n",
      "python                    3.6.1                         2  \n",
      "python-dateutil           2.6.0                    py36_0  \n",
      "python.app                1.2                      py36_4  \n",
      "pythreejs                 0.3.0                    py36_0    conda-forge\n",
      "pytz                      2017.2                   py36_0  \n",
      "pywavelets                0.5.2               np112py36_0  \n",
      "pyyaml                    3.12                     py36_0  \n",
      "pyzmq                     16.0.2                   py36_0  \n",
      "qt                        5.6.2                         2  \n",
      "qtawesome                 0.4.4                    py36_0  \n",
      "qtconsole                 4.3.0                    py36_0  \n",
      "qtpy                      1.2.1                    py36_0  \n",
      "readline                  6.2                           2  \n",
      "requests                  2.14.2                   py36_0  \n",
      "rope                      0.9.4                    py36_1  \n",
      "ruamel_yaml               0.11.14                  py36_1  \n",
      "scikit-image              0.13.0              np112py36_0  \n",
      "scikit-learn              0.18.1              np112py36_1  \n",
      "scipy                     0.19.0              np112py36_0  \n",
      "seaborn                   0.7.1                    py36_0  \n",
      "setuptools                27.2.0                   py36_0  \n",
      "simplegeneric             0.8.1                    py36_1  \n",
      "singledispatch            3.4.0.3                  py36_0  \n",
      "sip                       4.18                     py36_0  \n",
      "six                       1.10.0                   py36_0  \n",
      "snowballstemmer           1.2.1                    py36_0  \n",
      "sortedcollections         0.5.3                    py36_0  \n",
      "sortedcontainers          1.5.7                    py36_0  \n",
      "sphinx                    1.5.6                    py36_0  \n",
      "spyder                    3.1.4                    py36_0  \n",
      "sqlalchemy                1.1.9                    py36_0  \n",
      "sqlite                    3.13.0                        0  \n",
      "statsmodels               0.8.0               np112py36_0  \n",
      "sympy                     1.0                      py36_0  \n",
      "tblib                     1.3.2                    py36_0  \n",
      "terminado                 0.6                      py36_0  \n",
      "testpath                  0.3                      py36_0  \n",
      "tk                        8.5.18                        0  \n",
      "toolz                     0.8.2                    py36_0  \n",
      "tornado                   4.5.1                    py36_0  \n",
      "traitlets                 4.3.2                    py36_0  \n",
      "unicodecsv                0.14.1                   py36_0  \n",
      "unixodbc                  2.3.4                         0  \n",
      "wcwidth                   0.1.7                    py36_0  \n",
      "werkzeug                  0.12.2                   py36_0  \n",
      "wheel                     0.29.0                   py36_0  \n",
      "widgetsnbextension        3.0.3                    py36_2    conda-forge\n",
      "wrapt                     1.10.10                  py36_0  \n",
      "xlrd                      1.0.0                    py36_0  \n",
      "xlsxwriter                0.9.6                    py36_0  \n",
      "xlwings                   0.10.4                   py36_0  \n",
      "xlwt                      1.2.0                    py36_0  \n",
      "xz                        5.2.2                         1  \n",
      "yaml                      0.1.6                         0  \n",
      "zict                      0.1.2                    py36_0  \n",
      "zlib                      1.2.8                         3  \n"
     ]
    }
   ],
   "source": [
    "!conda list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Magic commands](http://ipython.readthedocs.io/en/stable/interactive/magics.html)- Single percent sign means the cells arguments come from the same line, 2 mean the entire cell is used for the entire argument.  lsmagic command can be used to list all other commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cat": "Other",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "colors": "BasicMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "cp": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "lf": "Other",
        "lk": "Other",
        "ll": "Other",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "lx": "Other",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "man": "KernelMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "mv": "Other",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "profile": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rm": "Other",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib examples can be found [here](https://matplotlib.org/gallery/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HfyPw1xHxZOA9wGoASQuAs4HnAYcAL5d0SJ4NNcuT9zrz4+vhVNdAOfiI+GHb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\nZmYN5QBvZtZQ/x9iNtNPKatR3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ddea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fixing random state for reproducibility\n",
    "np.random.seed(19680801)\n",
    "\n",
    "\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(10*np.random.randn(100), 10*np.random.randn(100), 'o')\n",
    "ax.set_title('Using hyphen instead of Unicode minus')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+UoYq8Gl2kPO/2syomAsJNO9CW/8OBqefWs/VKNp6lKgprDJ9xINhUTTpJBne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qMrboUNTdz/xg42Hmn9RU96yocE/x1SmJmkf/6TtqI+ND/0EE0PPYStpZn3r7qKx5tdPFfdxLIW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\nDIFlYGBgYNAh+H9gD6t6hhqdkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ded710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import collections, colors, transforms\n",
    "import numpy as np\n",
    "\n",
    "nverts = 50\n",
    "npts = 100\n",
    "\n",
    "# Make some spirals\n",
    "r = np.arange(nverts)\n",
    "theta = np.linspace(0, 2*np.pi, nverts)\n",
    "xx = r * np.sin(theta)\n",
    "yy = r * np.cos(theta)\n",
    "spiral = list(zip(xx, yy))\n",
    "\n",
    "# Make some offsets\n",
    "# Fixing random state for reproducibility\n",
    "rs = np.random.RandomState(19680801)\n",
    "\n",
    "\n",
    "xo = rs.randn(npts)\n",
    "yo = rs.randn(npts)\n",
    "xyo = list(zip(xo, yo))\n",
    "\n",
    "# Make a list of colors cycling through the default series.\n",
    "colors = [colors.to_rgba(c)\n",
    "          for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2)\n",
    "fig.subplots_adjust(top=0.92, left=0.07, right=0.97,\n",
    "                    hspace=0.3, wspace=0.3)\n",
    "((ax1, ax2), (ax3, ax4)) = axes  # unpack the axes\n",
    "\n",
    "\n",
    "col = collections.LineCollection([spiral], offsets=xyo,\n",
    "                                 transOffset=ax1.transData)\n",
    "trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)\n",
    "col.set_transform(trans)  # the points to pixels transform\n",
    "# Note: the first argument to the collection initializer\n",
    "# must be a list of sequences of x,y tuples; we have only\n",
    "# one sequence, but we still have to put it in a list.\n",
    "ax1.add_collection(col, autolim=True)\n",
    "# autolim=True enables autoscaling.  For collections with\n",
    "# offsets like this, it is neither efficient nor accurate,\n",
    "# but it is good enough to generate a plot that you can use\n",
    "# as a starting point.  If you know beforehand the range of\n",
    "# x and y that you want to show, it is better to set them\n",
    "# explicitly, leave out the autolim kwarg (or set it to False),\n",
    "# and omit the 'ax1.autoscale_view()' call below.\n",
    "\n",
    "# Make a transform for the line segments such that their size is\n",
    "# given in points:\n",
    "col.set_color(colors)\n",
    "\n",
    "ax1.autoscale_view()  # See comment above, after ax1.add_collection.\n",
    "ax1.set_title('LineCollection using offsets')\n",
    "\n",
    "\n",
    "# The same data as above, but fill the curves.\n",
    "col = collections.PolyCollection([spiral], offsets=xyo,\n",
    "                                 transOffset=ax2.transData)\n",
    "trans = transforms.Affine2D().scale(fig.dpi/72.0)\n",
    "col.set_transform(trans)  # the points to pixels transform\n",
    "ax2.add_collection(col, autolim=True)\n",
    "col.set_color(colors)\n",
    "\n",
    "\n",
    "ax2.autoscale_view()\n",
    "ax2.set_title('PolyCollection using offsets')\n",
    "\n",
    "# 7-sided regular polygons\n",
    "\n",
    "col = collections.RegularPolyCollection(\n",
    "    7, sizes=np.abs(xx) * 10.0, offsets=xyo, transOffset=ax3.transData)\n",
    "trans = transforms.Affine2D().scale(fig.dpi / 72.0)\n",
    "col.set_transform(trans)  # the points to pixels transform\n",
    "ax3.add_collection(col, autolim=True)\n",
    "col.set_color(colors)\n",
    "ax3.autoscale_view()\n",
    "ax3.set_title('RegularPolyCollection using offsets')\n",
    "\n",
    "\n",
    "# Simulate a series of ocean current profiles, successively\n",
    "# offset by 0.1 m/s so that they form what is sometimes called\n",
    "# a \"waterfall\" plot or a \"stagger\" plot.\n",
    "\n",
    "nverts = 60\n",
    "ncurves = 20\n",
    "offs = (0.1, 0.0)\n",
    "\n",
    "yy = np.linspace(0, 2*np.pi, nverts)\n",
    "ym = np.max(yy)\n",
    "xx = (0.2 + (ym - yy)/ym)**2 * np.cos(yy - 0.4)*0.5\n",
    "segs = []\n",
    "for i in range(ncurves):\n",
    "    xxx = xx + 0.02*rs.randn(nverts)\n",
    "    curve = list(zip(xxx, yy*100))\n",
    "    segs.append(curve)\n",
    "\n",
    "col = collections.LineCollection(segs, offsets=offs)\n",
    "ax4.add_collection(col, autolim=True)\n",
    "col.set_color(colors)\n",
    "ax4.autoscale_view()\n",
    "ax4.set_title('Successive data offsets')\n",
    "ax4.set_xlabel('Zonal velocity component (m/s)')\n",
    "ax4.set_ylabel('Depth (m)')\n",
    "# Reverse the y-axis so depth increases downward\n",
    "ax4.set_ylim(ax4.get_ylim()[::-1])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe src=\"https://giphy.com/embed/3o7qE32pRVNYJYKGBO\" width=\"480\" height=\"269\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/veep-3o7qE32pRVNYJYKGBO\">via GIPHY</a></p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe src=\"https://giphy.com/embed/3o7qE32pRVNYJYKGBO\" width=\"480\" height=\"269\" frameBorder=\"0\" class=\"giphy-embed\" allowFullScreen></iframe><p><a href=\"https://giphy.com/gifs/veep-3o7qE32pRVNYJYKGBO\">via GIPHY</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python \n",
    "\n",
    "To get started, let's practice some very basic python scripts.  Start with trying to print \"Hello World\".  Once you have entered your script hit Shift+Enter.  This will execute your code and create or send you to the next cell.  Once you have done that try checking out a [Cheatsheet](https://ehmatthes.github.io/pcc/cheatsheets/README.html) to help get you running with another basic code snippet.\n",
    "\n",
    "Don't be afraid to try something new.  You won't break the shell, it will simply return an error if something is wrong.\n",
    "\n",
    "There are many cheatsheets out there if you are uncomfortable writing your own snippet.  Just do a quick google search for a function and copy/paste/execute in a cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n",
      "this is neat\n"
     ]
    }
   ],
   "source": [
    "print ('hello world');\n",
    "print('this is neat')\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is cool!\n"
     ]
    }
   ],
   "source": [
    "print('This is cool!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabcabc'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"abc\"*52"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For this next example, we will import the [pandas](http://pandas.pydata.org/) library.  Most python scripts use some sort of library or module that contains global variables, source files, and functions.  These libraries or modules can be called and executed with your code.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a   b   c\n",
       "0    0  10  20\n",
       "1    1  11  21\n",
       "2    2  12  22\n",
       "3    3  13  23\n",
       "4    4  14  24\n",
       "5    5  15  25\n",
       "6    6  16  26\n",
       "7    7  17  27\n",
       "8    8  18  28\n",
       "9    9  19  29\n",
       "10  10  20  30"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame()\n",
    "a=range(11)\n",
    "b=range(10,21)\n",
    "c=range(20,31)\n",
    "df['a']=a\n",
    "df['b']=b\n",
    "df['c']=c\n",
    "\n",
    "df\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The [math](https://docs.python.org/3/library/math.html) functions can be called by saying \"import math\" before your code.  This way jupyter, and any python script for that matter, knows to use those functions in the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.999999900000001\n",
      "6.9999999\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# Input list.\n",
    "values = [0.9999999, 1, 2, 3]\n",
    "\n",
    "# Sum values in list.\n",
    "r = sum(values)\n",
    "print(r)\n",
    "\n",
    "# Sum values with fsum.\n",
    "r = math.fsum(values)\n",
    "print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n",
      "4.0\n",
      "10.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "value1 = 9\n",
    "value2 = 16\n",
    "value3 = 100\n",
    "\n",
    "# Use sqrt method.\n",
    "print(math.sqrt(value1))\n",
    "print(math.sqrt(value2))\n",
    "print(math.sqrt(value3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Widgets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f57ee9729dc450690e9ed79157c13bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "VBox(children=(Figure(axes=[ColorAxis(scale=ColorScale()), Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(color=array([-0.92080358,  0.66131463, -1.15502926,  0.17181704, -0.20885214,\n",
       "       -0.78020657, -0.75757116, -0.30707455,  0.79767188, -0.11388828,\n",
       "       -0.03741131,  0.57575427,  0.34310406, -0.15033144, -1.41436716,\n",
       "        1.17438084,  0.79621954,  1.83544861, -0.04367038,  1.95792013,\n",
       "        0.30512626, -0.93479621,  0.77883453,  1.12034671, -0.55958301,\n",
       "        0.02024866,  0.14948414,  0.38045536,  0.55602769, -1.48587755,\n",
       "       -0.30966972,  1.14682758, -2.22628793, -0.01868683, -1.22546395,\n",
       "        0.19291983, -0.89589231,  1.33200798, -0.39921825,  0.66031392,\n",
       "        0.82179602,  0.04992955, -0.29885536,  0.88439786,  1.36950129,\n",
       "        0.70619888, -1.48131896, -0.83766929, -0.87865748,  1.12455759,\n",
       "       -1.21701596,  0.4988658 ,  0.1196914 , -0.12980889,  0.55804309,\n",
       "        0.98123251, -1.22683628, -0.96349552,  2.0233898 ,  1.20880133,\n",
       "       -1.22152388,  1.70038495,  0.53889937,  0.8892342 ,  0.64824317,\n",
       "       -1.50366936, -1.26259231, -0.55420107, -0.41399107, -0.80629169,\n",
       "        1.93669078,  0.21621095,  0.83609083, -0.81297184, -0.31152421,\n",
       "        1.00112687,  2.57789196, -0.55222308,  0.78894433, -1.34733951,\n",
       "       -1.10417978,  0.58918534, -1.47593328,  1.62689503, -0.40797588,\n",
       "       -0.0163866 , -1.06600227,  0.19377703,  0.91352616,  1.17692785,\n",
       "        0.66025823, -0.73797367,  0.60339786,  0.39366487,  0.04759423,\n",
       "       -0.88562508,  0.12474733,  0.4994319 ,  0.42198137,  0.72914632]), colors=['DeepSkyBlue'], interactions={'hover': 'tooltip'}, scales={'color': ColorScale(), 'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, x=array([-0.50839487,  0.40902234,  0.29362687, -1.70359295, -0.88415618,\n",
       "       -1.30811772, -0.60286989,  1.14533805,  1.28120236,  1.60813938,\n",
       "       -0.39242788, -0.28123915,  1.14613255,  0.65744746,  1.89030347,\n",
       "       -0.75474109, -0.20746336,  0.28381004,  0.63923225, -0.50692161,\n",
       "        0.21820075,  0.69453919,  0.73281621, -0.23520301, -1.82633425,\n",
       "        1.08779877,  1.9709905 , -1.34499236, -0.24141251, -1.01395497,\n",
       "       -0.63152733, -1.38951453,  0.37426594, -0.0177653 ,  0.12603512,\n",
       "       -0.76364211,  0.00685846, -1.26609802,  0.12910363, -0.84210023,\n",
       "       -0.03444252,  0.41194038,  1.5774241 , -0.7072426 , -1.44401456,\n",
       "        0.85531354, -0.72513653,  1.06208078, -0.10794949,  0.21895383,\n",
       "       -0.289016  ,  0.5663468 , -1.10541524,  0.37317916,  1.10340668,\n",
       "        1.39030982, -0.07399826, -0.70435728, -0.80933725, -0.72756176,\n",
       "       -2.07507434,  0.60154026,  0.87935637, -1.07985375,  1.74785044,\n",
       "        0.61495366,  0.1054298 ,  0.56165161,  0.46956111, -0.2370975 ,\n",
       "       -1.84757975,  1.05556122, -0.21791525,  0.5431496 ,  0.69430854,\n",
       "       -0.03803793,  0.38401293,  0.7381508 ,  1.46669954,  0.22806511,\n",
       "        0.12704251, -0.47968971, -0.77709491, -2.8022127 ,  1.33178059,\n",
       "        0.2479916 , -1.01296684, -1.60827385, -0.53689753,  0.06905545,\n",
       "       -0.24618267, -0.96166908, -0.45936993, -2.19407161, -0.20734592,\n",
       "        0.02039082, -0.6654234 ,  1.1897945 ,  1.15846388, -0.78029378]), y=array([ 1.17346007, -1.69942203,  1.96877927,  0.05101407,  0.78221436,\n",
       "       -0.53441841,  0.26195333,  0.68458291,  0.43227682, -1.30571029,\n",
       "       -0.33189957,  0.99862082, -0.69965994, -0.24663308,  0.64363166,\n",
       "        0.17448592, -0.25395933,  1.93252753,  0.77682442, -1.30003683,\n",
       "        2.38152905,  1.96254004, -0.9159685 , -0.08441864,  1.91875221,\n",
       "        0.15910805, -0.50267876, -0.78677168, -0.46112502,  0.83544892,\n",
       "        1.00079852,  0.46888025,  0.4863489 , -0.71110877, -0.46714679,\n",
       "       -0.61933855,  0.65856619,  0.82376153,  1.95146585,  0.2703958 ,\n",
       "        1.71049534,  0.26736572, -0.42141226,  0.22463631, -0.11224119,\n",
       "       -0.17192094, -0.2567092 , -2.52686273,  0.87230848, -0.78311873,\n",
       "       -0.54677003,  0.91520514, -0.75304147, -0.83928534,  1.28298544,\n",
       "       -1.08579265, -2.13592437,  0.270047  ,  2.1709094 , -0.93479361,\n",
       "        0.53419711,  0.70477123, -0.16650836, -0.72168663, -0.28439006,\n",
       "       -0.91985519,  0.94299437,  0.57411557,  0.43107412,  1.74521959,\n",
       "        0.01753072, -1.32559819,  2.08779279, -1.37260717,  1.13542975,\n",
       "       -1.02581887,  1.24653578, -0.5817143 , -0.63541504,  2.37560608,\n",
       "       -0.79270062,  0.06085948, -0.61524186, -1.52469132,  1.54561418,\n",
       "        0.81676675, -0.01572718,  2.17310553, -0.36297459,  0.33791837,\n",
       "       -0.05587323,  0.51013096,  0.70042445, -1.02578013, -1.04519471,\n",
       "       -0.40802812,  0.88572294, -0.92815148, -0.76502623, -1.36942313]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0), title='Scatter plot with colors'), Toolbar(figure=Figure(axes=[ColorAxis(scale=ColorScale()), Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(color=array([-0.92080358,  0.66131463, -1.15502926,  0.17181704, -0.20885214,\n",
       "       -0.78020657, -0.75757116, -0.30707455,  0.79767188, -0.11388828,\n",
       "       -0.03741131,  0.57575427,  0.34310406, -0.15033144, -1.41436716,\n",
       "        1.17438084,  0.79621954,  1.83544861, -0.04367038,  1.95792013,\n",
       "        0.30512626, -0.93479621,  0.77883453,  1.12034671, -0.55958301,\n",
       "        0.02024866,  0.14948414,  0.38045536,  0.55602769, -1.48587755,\n",
       "       -0.30966972,  1.14682758, -2.22628793, -0.01868683, -1.22546395,\n",
       "        0.19291983, -0.89589231,  1.33200798, -0.39921825,  0.66031392,\n",
       "        0.82179602,  0.04992955, -0.29885536,  0.88439786,  1.36950129,\n",
       "        0.70619888, -1.48131896, -0.83766929, -0.87865748,  1.12455759,\n",
       "       -1.21701596,  0.4988658 ,  0.1196914 , -0.12980889,  0.55804309,\n",
       "        0.98123251, -1.22683628, -0.96349552,  2.0233898 ,  1.20880133,\n",
       "       -1.22152388,  1.70038495,  0.53889937,  0.8892342 ,  0.64824317,\n",
       "       -1.50366936, -1.26259231, -0.55420107, -0.41399107, -0.80629169,\n",
       "        1.93669078,  0.21621095,  0.83609083, -0.81297184, -0.31152421,\n",
       "        1.00112687,  2.57789196, -0.55222308,  0.78894433, -1.34733951,\n",
       "       -1.10417978,  0.58918534, -1.47593328,  1.62689503, -0.40797588,\n",
       "       -0.0163866 , -1.06600227,  0.19377703,  0.91352616,  1.17692785,\n",
       "        0.66025823, -0.73797367,  0.60339786,  0.39366487,  0.04759423,\n",
       "       -0.88562508,  0.12474733,  0.4994319 ,  0.42198137,  0.72914632]), colors=['DeepSkyBlue'], interactions={'hover': 'tooltip'}, scales={'color': ColorScale(), 'x': LinearScale(), 'y': LinearScale()}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, tooltip_style={'opacity': 0.9}, x=array([-0.50839487,  0.40902234,  0.29362687, -1.70359295, -0.88415618,\n",
       "       -1.30811772, -0.60286989,  1.14533805,  1.28120236,  1.60813938,\n",
       "       -0.39242788, -0.28123915,  1.14613255,  0.65744746,  1.89030347,\n",
       "       -0.75474109, -0.20746336,  0.28381004,  0.63923225, -0.50692161,\n",
       "        0.21820075,  0.69453919,  0.73281621, -0.23520301, -1.82633425,\n",
       "        1.08779877,  1.9709905 , -1.34499236, -0.24141251, -1.01395497,\n",
       "       -0.63152733, -1.38951453,  0.37426594, -0.0177653 ,  0.12603512,\n",
       "       -0.76364211,  0.00685846, -1.26609802,  0.12910363, -0.84210023,\n",
       "       -0.03444252,  0.41194038,  1.5774241 , -0.7072426 , -1.44401456,\n",
       "        0.85531354, -0.72513653,  1.06208078, -0.10794949,  0.21895383,\n",
       "       -0.289016  ,  0.5663468 , -1.10541524,  0.37317916,  1.10340668,\n",
       "        1.39030982, -0.07399826, -0.70435728, -0.80933725, -0.72756176,\n",
       "       -2.07507434,  0.60154026,  0.87935637, -1.07985375,  1.74785044,\n",
       "        0.61495366,  0.1054298 ,  0.56165161,  0.46956111, -0.2370975 ,\n",
       "       -1.84757975,  1.05556122, -0.21791525,  0.5431496 ,  0.69430854,\n",
       "       -0.03803793,  0.38401293,  0.7381508 ,  1.46669954,  0.22806511,\n",
       "        0.12704251, -0.47968971, -0.77709491, -2.8022127 ,  1.33178059,\n",
       "        0.2479916 , -1.01296684, -1.60827385, -0.53689753,  0.06905545,\n",
       "       -0.24618267, -0.96166908, -0.45936993, -2.19407161, -0.20734592,\n",
       "        0.02039082, -0.6654234 ,  1.1897945 ,  1.15846388, -0.78029378]), y=array([ 1.17346007, -1.69942203,  1.96877927,  0.05101407,  0.78221436,\n",
       "       -0.53441841,  0.26195333,  0.68458291,  0.43227682, -1.30571029,\n",
       "       -0.33189957,  0.99862082, -0.69965994, -0.24663308,  0.64363166,\n",
       "        0.17448592, -0.25395933,  1.93252753,  0.77682442, -1.30003683,\n",
       "        2.38152905,  1.96254004, -0.9159685 , -0.08441864,  1.91875221,\n",
       "        0.15910805, -0.50267876, -0.78677168, -0.46112502,  0.83544892,\n",
       "        1.00079852,  0.46888025,  0.4863489 , -0.71110877, -0.46714679,\n",
       "       -0.61933855,  0.65856619,  0.82376153,  1.95146585,  0.2703958 ,\n",
       "        1.71049534,  0.26736572, -0.42141226,  0.22463631, -0.11224119,\n",
       "       -0.17192094, -0.2567092 , -2.52686273,  0.87230848, -0.78311873,\n",
       "       -0.54677003,  0.91520514, -0.75304147, -0.83928534,  1.28298544,\n",
       "       -1.08579265, -2.13592437,  0.270047  ,  2.1709094 , -0.93479361,\n",
       "        0.53419711,  0.70477123, -0.16650836, -0.72168663, -0.28439006,\n",
       "       -0.91985519,  0.94299437,  0.57411557,  0.43107412,  1.74521959,\n",
       "        0.01753072, -1.32559819,  2.08779279, -1.37260717,  1.13542975,\n",
       "       -1.02581887,  1.24653578, -0.5817143 , -0.63541504,  2.37560608,\n",
       "       -0.79270062,  0.06085948, -0.61524186, -1.52469132,  1.54561418,\n",
       "        0.81676675, -0.01572718,  2.17310553, -0.36297459,  0.33791837,\n",
       "       -0.05587323,  0.51013096,  0.70042445, -1.02578013, -1.04519471,\n",
       "       -0.40802812,  0.88572294, -0.92815148, -0.76502623, -1.36942313]))], scale_x=LinearScale(allow_padding=False, max=1.0, min=0.0), scale_y=LinearScale(allow_padding=False, max=1.0, min=0.0), title='Scatter plot with colors'))))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import bqplot.pyplot as plt\n",
    "\n",
    "size = 100\n",
    "\n",
    "plt.figure(title='Scatter plot with colors')\n",
    "plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4dcfc7245b646e7bfddc05788d209e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Map</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another notebook frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Map(center=[36.146956, -86.779788], layers=(TileLayer(options=['attribution', 'detect_retina', 'max_zoom', 'min_zoom', 'opacity', 'tile_size']),), layout=Layout(align_self='stretch', height='400px'), options=['attribution_control', 'bounce_at_zoom_limits', 'box_zoom', 'center', 'close_popup_on_click', 'double_click_zoom', 'dragging', 'inertia', 'inertia_deceleration', 'inertia_max_speed', 'keyboard', 'keyboard_pan_offset', 'keyboard_zoom_offset', 'max_zoom', 'min_zoom', 'scroll_wheel_zoom', 'tap', 'tap_tolerance', 'touch_zoom', 'world_copy_jump', 'zoom', 'zoom_animation_threshold', 'zoom_control', 'zoom_start'], zoom=10)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipyleaflet import Map \n",
    "\n",
    "Map(center=[36.146956, -86.779788], zoom=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: './europe_110.geo.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-6326f91275b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# geojson layer with hover handler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./europe_110.geo.json'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfeature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'features'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './europe_110.geo.json'"
     ]
    }
   ],
   "source": [
    "from ipyleaflet import Map import json\n",
    "\n",
    "Map(center=[36.146956, -86.779788], zoom=10)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pythreejs'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-0ede01a8a748>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpythreejs\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m f = \"\"\"\n\u001b[1;32m      4\u001b[0m function f(origu,origv) {\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m//\u001b[0m \u001b[0mscale\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mv\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mranges\u001b[0m \u001b[0mI\u001b[0m \u001b[0mwant\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pythreejs'"
     ]
    }
   ],
   "source": [
    "from pythreejs import *\n",
    "\n",
    "f = \"\"\"\n",
    "function f(origu,origv) {\n",
    "    // scale u and v to the ranges I want: [0, 2*pi]\n",
    "    var u = 2*Math.PI*origu;\n",
    "    var v = 2*Math.PI*origv;\n",
    "    \n",
    "    var x = Math.sin(u);\n",
    "    var y = Math.cos(v);\n",
    "    var z = Math.cos(u+v);\n",
    "    \n",
    "    return new THREE.Vector3(x,y,z)\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "surf_g = ParametricGeometry(func=f);\n",
    "surf = Mesh(geometry=surf_g, material=LambertMaterial(color='green', side='FrontSide'))\n",
    "surf2 = Mesh(geometry=surf_g, material=LambertMaterial(color='yellow', side='BackSide'))\n",
    "scene = Scene(children=[surf, surf2, AmbientLight(color='#777777')])\n",
    "c = PerspectiveCamera(position=[2.5, 2.5, 2.5], up=[0, 0, 1],\n",
    "                      children=[DirectionalLight(color='white',\n",
    "                                                 position=[3, 5, 1],\n",
    "                                                 intensity=0.6)])\n",
    "Renderer(camera=c, scene=scene, controls=[OrbitControls(controlling=c)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equations\n",
    "\n",
    " You can use [LaTex](https://www.latex-project.org/) within Jupyter.  Check out the LaTex [cheatsheet](https://wch.github.io/latexsheet/)!\n",
    " \n",
    " One alternative to LaTeX is [prettyPy](https://github.com/charliekawczynski/prettyPy) or [sympy](http://www.sympy.org/en/index.html).  These are all great ways to write out equations, although the pros and cons of each need to be weighed before using.  For example prettyPy does not evaluate expressions but you also don't have to initialize variables.\n",
    " \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math, Latex\n",
    "display(Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "\\begin{eqnarray}\n",
       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
       "\\end{eqnarray}"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Latex\n",
    "Latex(r\"\"\"\\begin{eqnarray}\n",
    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
    "\\end{eqnarray}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extra Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out these [cool Jupyter notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks#statistics-machine-learning-and-data-science).\n",
    "\n",
    "Most resources used to build this tutorial were taken from sourced cheatsheets, documentation, and stack overflow.  \n"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

Introduction to Jupyter Notebook.ipynb