modules/02_Python_Jupyter/Intro_iPython.ipynb

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   "source": [
    "# IPython: beyond plain Python\n",
    "\n",
    "Original notebook from Fernando Perez  \n",
    "Modified slightly by David Shean  \n",
    "January 17, 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "## First things first: running code, getting help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:\n",
    "    \n",
    "- `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).\n",
    "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Hi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "### Getting help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%quickref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "collections.OrderedDict?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "collections.OrderedDict??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "An IPython quick reference card:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Tab completion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tab completion, especially for attributes, is a convenient way to explore the structure of any object you’re dealing with. Simply type `object_name.<TAB>` to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "collections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "## The interactive workflow: input, output, history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "2+10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "_+10"
   ]
  },
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     "slide_type": "slide"
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   "source": [
    "You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "10+20;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "_"
   ]
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   "source": [
    "The output is stored in `_N` and `Out[N]` variables:"
   ]
  },
  {
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   "source": [
    "_11 == Out[11]"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Previous inputs are available, too:"
   ]
  },
  {
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     "slide_type": "-"
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   "outputs": [],
   "source": [
    "In[11]"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%history -n 1-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Exercise**\n",
    "\n",
    "Use `%history?` to have a look at `%history`'s magic documentation, and write the last 10 lines of history to a file named `log.py`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Accessing the underlying operating system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = !ls \n",
    "print(\"files this directory:\")\n",
    "print(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo $files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo {files[0].upper()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all this is available even in multiline blocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "for i,f in enumerate(files):\n",
    "    if f.endswith('ipynb'):\n",
    "        !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n",
    "    else:\n",
    "        print('--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beyond Python: magic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface.  They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:\n",
    "    \n",
    "- To provide an namespace for controlling IPython itself and exposing other system-oriented functionality that is separate from your Python variables and functions.  This lets you have a `cd` command accessible as a magic regardless of whether you have a Python `cd` variable.\n",
    "\n",
    "- To expose a calling mode that requires minimal verbosity and typing while working interactively.  Thus the inspiration taken from the classic Unix shell style for commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line vs cell magics:\n",
    "\n",
    "Magics can be applied at the single-line level or to entire cells. Line magics are identified with a single `%` prefix, while cell magics use `%%` and can only be used as the first line of the cell (since they apply to the entire cell). Some magics, like the convenient `%timeit` that ships built-in with IPython, can be called in either mode, while others may be line- or cell-only (you can see all magics with `%lsmagic`).\n",
    "\n",
    "Let's see this with some `%timeit` examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%timeit list(range(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%timeit\n",
    "list(range(10))\n",
    "list(range(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Line magics can be used even inside code blocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 5):\n",
    "    size = i*100\n",
    "    print('size:', size, end=' ')\n",
    "    %timeit list(range(size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Magics can do anything they want with their input, so it doesn't have to be valid Python (note that the below may not work on a Windows machine, depending on how you are running Jupyter on it):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "echo \"My shell is:\" $SHELL\n",
    "echo \"My disk usage is:\"\n",
    "df -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting cell magic: create any file you want locally from the notebook:"
   ]
  }
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