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    "# Module 6\n",
    "\n",
    "## Video 27: Multiple Vessels in a Movement\n",
    "**Python for the Energy Industry**\n",
    "\n",
    "Sometimes, a Cargo Movement will contain information on multiple vessels. There are two reasons this might happen:\n",
    "- The cargo is loaded to, or unloaded from, a floating storage vessel\n",
    "- A ship to ship transfer (STS) occurs\n",
    "\n",
    "Let's find some example Cargo Movements where this is the case.\n",
    "\n",
    "[Cargo Movements documentation](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import pandas as pd\n",
    "import vortexasdk as v\n",
    "now = datetime.utcnow()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start as before with a simple query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_query = v.CargoMovements().search(\n",
    "    filter_activity=\"any_activity\",\n",
    "    filter_time_min=now,\n",
    "    filter_time_max=now)\n",
    "\n",
    "print(len(cm_query))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find Cargo Movements with STS events by getting those there multiple vessels are detailed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_vessel = [q for q in cm_query if len(q['vessels']) > 1]\n",
    "print(len(multi_vessel))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Let's take a look at the types of event happening in each of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cm in multi_vessel:\n",
    "    event_types = [e['event_type'] for e in cm['events']]\n",
    "    print(event_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STS Events\n",
    "\n",
    "We'll look in more detail at those Cargo Movements with STS events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sts_movements = [cm for cm in multi_vessel if 'cargo_sts_event' in [e['event_type'] for e in cm['events']]]\n",
    "print(len(sts_movements))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the vessels in one of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sts_movements[0]['vessels'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sts_movements[0]['vessels'][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " And also the STS event:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sts_movements[0]['events'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the data on movements with STS transfers in a DataFrame. The easiest way to do this is to convert our initial search into a DataFrame using the `to_df()` function, and then drop all movements with no STS event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "required_columns = [\"vessels.0.name\",\"vessels.0.vessel_class\",\"vessels.1.name\",\"vessels.1.vessel_class\",\"product.group.label\",\"quantity\",\n",
    "                    \"events.cargo_port_load_event.0.location.port.label\",\"events.cargo_sts_event.0.location.sts_zone.label\",\n",
    "                    \"events.cargo_port_unload_event.0.location.port.label\"]\n",
    "\n",
    "new_labels = [\"vessel0\",\"vessel0_class\",\"vessel1\",\"vessel1_class\",\"product_group\",\"quantity\",\"loading_port\",\"sts_location\",\"unloading_port\"]\n",
    "\n",
    "relabel = dict(zip(required_columns,new_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_df = cm_query.to_df(columns=required_columns).rename(relabel,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_df_multi = cm_df.dropna(subset=['loading_port','sts_location','unloading_port'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_df_multi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Expand the range of the Cargo Movements - say to a ~2 week period. Can you find any movements involving 3 or more vessels? If so, isolate and have a look at these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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