{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Stage-in the EO data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Jupyter Notebooks process local Earth Observation data. As such we have prepared a set of Jupyter notebooks that stage-in the EO data required for the implementation of application.\n",
    "\n",
    "The stage-in notebooks are published on a software repository hosted by GitLab at the URL https://gitlab.com/ellip/quick-start/jupyterlab/stage-in. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, the stage-in Jupyter Notebooks are:\n",
    "\n",
    "* Sentinel1 - stage-in\n",
    "* Landsat8 - stage-in\n",
    "* Envisat - stage-in\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Procedure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This step will clone the Stage-in Jupyter Notebooks git repository on the local sandbox and thus provide a simple way to stage-in EO data.\n",
    "\n",
    "* On the JupyterLab Launcher, start a new Terminal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Type the commands below to set the `HOME` to `/workspace` and clone the stage-in notebooks:\n",
    "\n",
    "```bash\n",
    "cd /workspace\n",
    "git clone https://gitlab.com/ellip/quick-start/jupyterlab/stage-in\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create a new folder under `/workspace`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Using the JupyterLab Left Sidebar, browse the **stage-in** folder and open the stage-in Jupyter Notebook **Sentinel1 - stage-in**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Click _Kernel_/_Restart Kernel and Run All Cells_:\n",
    "\n",
    "![Kernel/Restart Kernel and Run All Cells](artifacts/step-3-restart.png \"Kernel/Restart Kernel and Run All Cells\")\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Wait for the conclusion,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note: It can take several minutes to conclude. You can be sure that it has finished when all the cells have number within the squared parentheses. On the other hand, the symbol [*] close to a cell means that the execution of that cell is ongoing.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "* Once completed, use the left panel to inspect the content of the 'data' folder.\n",
    "There is now a Sentinel-1 product downloaded locally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Going further\n",
    "#### Input identifier and input reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Stage-in Jupyter Notebook executed earlier creates two output cells that are worth drawing your attention to:\n",
    "\n",
    "![Ellip Workflows reserved keywords](artifacts/step-3-reserved-keyword.png \"Ellip Workflows reserved keywords\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first cellis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "input_identifier = 'S1A_IW_GRDH_1SDV_20171210T182024_20171210T182049_019644_021603_0A33'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_identifier = 'S1A_IW_GRDH_1SDV_20171210T182024_20171210T182049_019644_021603_0A33'\n"
     ]
    }
   ],
   "source": [
    "print 'input_identifier = \\'%s\\'' % s1_identifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output will be used in the [Step 4 - Implement the data transformation steps](step-4.ipynb).\n",
    "\n",
    "`input_identifier` is thus a reserved keyword in a Jupyter Notebook streaming executable. This variable will be used to open the product using SNAP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second cell is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "input_reference = 'https://catalog.terradue.com/sentinel1/search?format=atom&uid=S1A_IW_GRDH_1SDV_20171210T182024_20171210T182049_019644_021603_0A33'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_reference = 'https://catalog.terradue.com/sentinel1/search?format=atom&uid=S1A_IW_GRDH_1SDV_20171210T182024_20171210T182049_019644_021603_0A33'\n"
     ]
    }
   ],
   "source": [
    "print 'input_reference = \\'%s\\'' % input_reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output will be used in the [Step 4 - Implement the data transformation steps](step-4.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`input_reference` is thus a reserved keyword in a Jupyter Notebook streaming executable. It can be used for querying metadata about the product to the Catalogue.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that there's a Sentinel-1 product staged-in, proceed with [Step 4 - Implement the data transformation steps](step-4.ipynb)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}