{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import matplotlib as plt\n",
    "from vortexasdk import CargoMovements, Geographies, Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-21 08:37:23,295 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['singapore']}\n",
      "2020-01-21 08:37:23,414 vortexasdk.client — INFO — 24 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n"
     ]
    }
   ],
   "source": [
    "singapore = [g.id for g in Geographies().search(term='singapore').to_list() if 'port' in g.layer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-21 08:37:24,251 vortexasdk.operations — INFO — Searching Products with params: {'term': ['fuel oil'], 'ids': [], 'product_parent': [], 'allowTopLevelProducts': True}\n",
      "2020-01-21 08:37:24,364 vortexasdk.client — INFO — 1 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n"
     ]
    }
   ],
   "source": [
    "fuel_oil = [p.id for p in Products().search(term='fuel oil').to_list()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-21 08:37:24,697 vortexasdk.operations — INFO — Searching CargoMovements with params: {'filter_activity': 'unloading_state', 'filter_time_min': '2019-01-01T00:00:00.000Z', 'filter_time_max': '2019-12-31T00:00:00.000Z', 'cm_unit': 'b', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['1c107b4317bc2c85fb6c13cd7b28e8e0a02ec7fecc68afc2b68ca0545c835e1c'], 'filter_vessels': [], 'filter_destinations': ['1b79e18416d358d7e07b978abcab3f17e2ca75085a6d70ce1811cf4eaeaea886'], 'filter_origins': [], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading from API:   0%|          | 0/1574 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-21 08:37:26,202 vortexasdk.client — INFO — 1574 Results to retreive. Sending 4 post requests in parallel using 6 threads.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading from API: 2000it [00:02, 878.83it/s]              \n"
     ]
    }
   ],
   "source": [
    "df = CargoMovements()\\\n",
    "        .search(filter_activity='unloading_state',\n",
    "            filter_destinations=singapore,\n",
    "            filter_products=fuel_oil,\n",
    "            filter_time_min=datetime(2019, 1, 1),\n",
    "            filter_time_max=datetime(2019, 12, 31))\\\n",
    "        .to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamp_col = 'events.cargo_port_unload_event.0.start_timestamp'\n",
    "df['month'] = pd.to_datetime(df[timestamp_col]).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d6bf650>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_aggregate = df.groupby(by='month')['quantity'].sum()\n",
    "\n",
    "monthly_aggregate.plot(title='Singapore Fuel Oil Imports ahead of IMO 2020', ylim=(0, 3.5e7), figsize=(15, 5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python-sdk",
   "language": "python",
   "name": "python-sdk"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}