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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate requirements.txt\n",
    "\n",
    "`requirements.txt` is a common name for a Python requirements file. This is a specially formatted list of packages that are required for software to run correctly.\n",
    "\n",
    "Some of our notebooks, like [Met AP.ipynb](./Met%20API.ipynb), define their requirements right in the notebook using Margo.\n",
    "\n",
    "We can extract those values with a command line tool called `margo-tool`. Since we can run shell commands from inside a notebook using `!`, we'll demonstrate this approach inside a notebook. You can do this on a command line if you prefer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: margo-tool extract [-h] -i NOTEBOOK_FILE [-f DECLARATION_FORMAT]\r\n",
      "                          [-p PROPERTY]\r\n",
      "\r\n",
      "optional arguments:\r\n",
      "  -h, --help            show this help message and exit\r\n",
      "  -i NOTEBOOK_FILE, --input NOTEBOOK_FILE\r\n",
      "  -f DECLARATION_FORMAT, --format DECLARATION_FORMAT\r\n",
      "  -p PROPERTY, --property PROPERTY\r\n",
      "                        The property to extract\r\n"
     ]
    }
   ],
   "source": [
    "# Display help for `margo-tool extract` subcommand\n",
    "\n",
    "!margo-tool extract --help"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo \"# Met API.ipynb\" > requirements.txt\n",
    "!margo-tool extract -i \"Met API.ipynb\" -p requirements.txt -f raw >> requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the contents of the file with the cat command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Met API.ipynb\r\n",
      "matplotlib==3.3.4\r\n",
      "requests==2.25.1\r\n"
     ]
    }
   ],
   "source": [
    "! cat requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do this for the rest of the `.ipynb` in this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "!echo \"\\n# Intro to Margo Loader.ipynb\" >> requirements.txt\n",
    "!margo-tool extract -i \"Importing Notebooks.ipynb\" -p requirements.txt -f raw >> requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Met API.ipynb\r\n",
      "matplotlib==3.3.4\r\n",
      "requests==2.25.1\r\n",
      "\r\n",
      "# Intro to Margo Loader.ipynb\r\n",
      "margo-loader\r\n"
     ]
    }
   ],
   "source": [
    "! cat requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preving this notebook from being imported\n",
    "# :: not-a-module ::"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next lesson\n",
    "\n",
    "So far we've learned how to use Margo Loader to import notebooks; how to annotate notebooks with Margo notes so they are more usful to Margo Loader; and how to use Margo notes with a different application, `margo-tool`, to integrate notebooks with pip.\n",
    "\n",
    "The property we assigned in other notebooks and extract in this notebook was called `requirements.txt`, but you can use any name and `margo-tool` can extract it. You can even extract data stored as JSON or YAML.\n",
    "\n",
    "To review the Margo \"keywords\" we've learned so far, checko out [Margo keyword reference.ipynb](./Margo%20keyword%20reference.ipynb)."
   ]
  }
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