docs/examples/academy/26. Inspecting Cargo Movements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 6\n",
    "\n",
    "## Video 26: Inspecting Cargo Movements\n",
    "**Python for the Energy Industry**\n",
    "\n",
    "In this lesson, we take a closer look at the structure of a Cargo Movement. \n",
    "\n",
    "[Cargo movements documentation.](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "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": [
    "Let's start with a basic query to the SDK, for all Cargo Movements that are currently loading. We then look at one of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cargo_movement_id': '00a8b809a62f1b844d9586da3bad219e00a8b809a62f1b844d9586da3bad219e',\n",
       " 'quantity': 263385,\n",
       " 'status': 'loading_state',\n",
       " 'vessels': [{'id': '17fa9b4bcddf35051ba9aa2d1b04c6b4bba7eeefaa522a45dd07ec8e894416bb',\n",
       "   'mmsi': 248159000,\n",
       "   'imo': 9391957,\n",
       "   'name': 'ZAPPHIRE',\n",
       "   'dwt': 47329,\n",
       "   'cubic_capacity': 52466,\n",
       "   'vessel_class': 'handymax',\n",
       "   'corporate_entities': [{'id': 'bd551ce360a4175771b740a4efcce179d37992bf532c84358a115f78a2354198',\n",
       "     'label': 'So.Co.Mar',\n",
       "     'layer': 'effective_controller',\n",
       "     'probability': 1,\n",
       "     'source': 'external'}],\n",
       "   'start_timestamp': '2021-02-08T04:19:05+0000',\n",
       "   'fixture_fulfilled': False,\n",
       "   'voyage_id': '9bd55e30686409e92e0aee35598af38614c0053da7efe4afb9b8d950c297227d',\n",
       "   'tags': [{'tag': 'vessel_coated_tag'}],\n",
       "   'status': 'vessel_status_laden_known',\n",
       "   'year': 2010,\n",
       "   'scrubber': [],\n",
       "   'flag': [{'tag': 'vessel_flag_tag',\n",
       "     'flag': 'MT',\n",
       "     'flag_country': '80dd61da7ce1edccaa43d2d60207c482e397bdd7f7efe7ad2a222d35dde2bc8c'}]}],\n",
       " 'product': [{'id': '5de0b00094e0fd7542c10f9f8a71b4008d55750f21dc905cda9b0f7f5f76bc08',\n",
       "   'layer': 'group',\n",
       "   'probability': 0.8644994,\n",
       "   'source': 'model',\n",
       "   'label': 'Dirty Petroleum Products'},\n",
       "  {'id': '1c107b4317bc2c85fb6c13cd7b28e8e0a02ec7fecc68afc2b68ca0545c835e1c',\n",
       "   'layer': 'group_product',\n",
       "   'probability': 0.8644994,\n",
       "   'source': 'model',\n",
       "   'label': 'Fuel Oil'},\n",
       "  {'id': '3fe831fb60183af885bc789b0224adfecfd3d911e9d2549981c1d7c092d30154',\n",
       "   'layer': 'category',\n",
       "   'probability': 0.8644994,\n",
       "   'source': 'model',\n",
       "   'label': 'High Sulphur Fuel Oil'}],\n",
       " 'events': [{'event_type': 'cargo_port_load_event',\n",
       "   'location': [{'id': 'ee1de4914cc26e8f1326b49793b089131870d478714c07e0c99c56cb307704c5',\n",
       "     'layer': 'country',\n",
       "     'label': 'Italy',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '29005fdc7273f96d4598adc2a8619b796875bd652b4c5a2b97b3283b92ca8dc8',\n",
       "     'layer': 'port',\n",
       "     'label': 'Augusta [IT]',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'f39d455f5d38907394d6da3a91da4e391f9a34bd6a17e826d6042761067e88f4',\n",
       "     'layer': 'region',\n",
       "     'label': 'Europe',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '676c3cff1dffd971ae51cd350f231394eeb0ab94eff981432f6ee55c5219b913',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'UKC-Med region',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'a0a9eba4c7b8c1e853c207f32d67c4959b6197c39785e037abad42e90824c088',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'West Mediterranean',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '6aa9a4fb76ca378e75eff85f458c90f7fd42778c26c957c417490c097161a3c5',\n",
       "     'layer': 'trading_region',\n",
       "     'label': 'Mediterranean (incl. North Africa)',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'ee1de4914cc26e8f1326b49793b089131870d478714c07e0c99c56cb307704c5',\n",
       "     'layer': 'trading_subregion',\n",
       "     'label': 'Italy',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '5e5deb2ca3810bf061e7cc540bb6a352651ab1f8b2feced92d285320a43b1468',\n",
       "     'layer': 'terminal',\n",
       "     'label': 'Augusta Refinery ESSO',\n",
       "     'source': 'model',\n",
       "     'probability': 1}],\n",
       "   'probability': 1,\n",
       "   'pos': [15.193458283160899, 37.20789046681706],\n",
       "   'start_timestamp': '2021-02-08T04:19:05+0000',\n",
       "   'end_timestamp': '2021-02-09T16:15:06+0000'}],\n",
       " 'parent_ids': []}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# basic query\n",
    "cm_query = v.CargoMovements().search(\n",
    "    filter_activity=\"loading_state\",\n",
    "    filter_time_min=now,\n",
    "    filter_time_max=now)\n",
    "\n",
    "# remember - cm_query is a *list* of cargo movements\n",
    "\n",
    "# taking a look at a single Cargo Movement\n",
    "cm_query[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a lot of information here. Note that this is a 'dictionary' structure, so we can put out the top level keys, and their type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['cargo_movement_id', 'quantity', 'status', 'vessels', 'product', 'events', 'parent_ids'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_query[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<class 'str'>, <class 'int'>, <class 'str'>, <class 'list'>, <class 'list'>, <class 'list'>, <class 'list'>]\n"
     ]
    }
   ],
   "source": [
    "print([type(cm_query[0][cmk]) for cmk in cm_query[0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three of these keys correspond to individual vales which we can print out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cargo_movement_id: 00a8b809a62f1b844d9586da3bad219e00a8b809a62f1b844d9586da3bad219e\n",
      "quantity: 263385\n",
      "status: loading_state\n"
     ]
    }
   ],
   "source": [
    "print('cargo_movement_id:', cm_query[0]['cargo_movement_id'])\n",
    "print('quantity:', cm_query[0]['quantity'])\n",
    "print('status:', cm_query[0]['status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remaning keys are lists, which we now now describe in turn.\n",
    "\n",
    "`vessels` contains a `VesselEntity` for each vessel involved in the Cargo Movement. In general, there can be multiple vessels, but we shall touch on this later. The Cargo Movement we're looking only contains one `VesselEntity`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vessels = cm_query[1]['vessels']\n",
    "len(vessels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'c101bba621fa1a4f3a1d1f57c3b800f6976def9448a31e42677ddbb0ea705604',\n",
       " 'mmsi': 538003436,\n",
       " 'imo': 9515436,\n",
       " 'name': 'M.STAR',\n",
       " 'dwt': 314016,\n",
       " 'cubic_capacity': 344553,\n",
       " 'vessel_class': 'vlcc_plus',\n",
       " 'corporate_entities': [{'id': 'd4a185c32e0e38bb5fe45b23f6d880f35af53a6e4337e74648515832875f24f4',\n",
       "   'label': 'SK',\n",
       "   'layer': 'charterer',\n",
       "   'probability': 1,\n",
       "   'source': 'external'},\n",
       "  {'id': '1ed67e6d2c516dc0867162777e1b2e042ed8f4eed6602865402930d853cf1f66',\n",
       "   'label': 'SK GROUP',\n",
       "   'layer': 'effective_controller',\n",
       "   'probability': 1,\n",
       "   'source': 'external'}],\n",
       " 'start_timestamp': '2021-02-07T23:02:19+0000',\n",
       " 'fixture_id': '29e6c45ded5d41ee57a6dc46881e2998477e99d329edd8b9d0f867ef343ea431',\n",
       " 'fixture_fulfilled': True,\n",
       " 'voyage_id': '2192d7a78c8ccf617212a1856e4bd43b97992ddd9053ff0dcd35872b9fa68219',\n",
       " 'tags': [],\n",
       " 'status': 'vessel_status_laden_known',\n",
       " 'year': 2008,\n",
       " 'scrubber': [{'tag': 'vessel_scrubber_tag',\n",
       "   'scrubber': '478fca39000c49d6',\n",
       "   'planned': False}],\n",
       " 'flag': [{'tag': 'vessel_flag_tag',\n",
       "   'flag': 'MH',\n",
       "   'flag_country': 'e084d7507dab6604894c203b3834cd7ce8f16385daeae56e202cd9e930f788d2'}]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vessels[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`products` contains Product Entries, which each describe one layer of the product tree (Group, Group Product, Category, and Grade). Not all products specify a Grade. We can view the Product Entries in a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>5de0b00094e0fd7542c10f9f8a71b4008d55750f21dc90...</td>\n",
       "      <td>group</td>\n",
       "      <td>0.864499</td>\n",
       "      <td>model</td>\n",
       "      <td>Dirty Petroleum Products</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Fuel Oil</td>\n",
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       "      <td>model</td>\n",
       "      <td>High Sulphur Fuel Oil</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                                  id          layer  \\\n",
       "0  5de0b00094e0fd7542c10f9f8a71b4008d55750f21dc90...          group   \n",
       "1  1c107b4317bc2c85fb6c13cd7b28e8e0a02ec7fecc68af...  group_product   \n",
       "2  3fe831fb60183af885bc789b0224adfecfd3d911e9d254...       category   \n",
       "\n",
       "   probability source                     label  \n",
       "0     0.864499  model  Dirty Petroleum Products  \n",
       "1     0.864499  model                  Fuel Oil  \n",
       "2     0.864499  model     High Sulphur Fuel Oil  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = cm_query[0]['product']\n",
    "pd.DataFrame(products)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`events` contains Cargo Events, which each describe a specific event happening to the cargo at a specific time. These can be:\n",
    "- cargo_port_load_event\n",
    "- cargo_port_unload_event\n",
    "- cargo_fso_load_event\n",
    "- cargo_fso_unload_event\n",
    "- cargo_sts_event\n",
    "- cargo_fixture_event\n",
    "- cargo_storage_event\n",
    "\n",
    "In our example, the two events are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cargo_port_load_event']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = cm_query[1]['events']\n",
    "[e['event_type'] for e in events]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see some high level information about these events by putting them into a DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_type</th>\n",
       "      <th>location</th>\n",
       "      <th>probability</th>\n",
       "      <th>pos</th>\n",
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       "      <td>[{'id': '6253047d839a51684f2ab6e3a4fa9956f582c...</td>\n",
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      "text/plain": [
       "              event_type                                           location  \\\n",
       "0  cargo_port_load_event  [{'id': '6253047d839a51684f2ab6e3a4fa9956f582c...   \n",
       "\n",
       "   probability                                      pos  \\\n",
       "0            1  [48.32317414902807, 29.142766459317272]   \n",
       "\n",
       "            start_timestamp             end_timestamp  \n",
       "0  2021-02-07T23:02:19+0000  2021-02-09T14:00:10+0000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'location' entries are themselves dictionaries, which we can expand into a DataFrame to see the different layers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>region</td>\n",
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       "      <td>trading_block</td>\n",
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       "      <td>trading_block</td>\n",
       "      <td>OPEC + Russia</td>\n",
       "      <td>model</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0899599f74faadb7ba7eb65205ee5c20cb434367a6e720...</td>\n",
       "      <td>trading_region</td>\n",
       "      <td>MEG/AG</td>\n",
       "      <td>model</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6253047d839a51684f2ab6e3a4fa9956f582c73dfbbb27...</td>\n",
       "      <td>trading_subregion</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>model</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1d4d3d8565ea23172f9ea72d3daebc642e835171bccf26...</td>\n",
       "      <td>terminal</td>\n",
       "      <td>KNPC SBMs</td>\n",
       "      <td>model</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  id              layer  \\\n",
       "0  6253047d839a51684f2ab6e3a4fa9956f582c73dfbbb27...            country   \n",
       "1  f8c1ff7397acf5d2e353a369fa399b310591680c7a862c...               port   \n",
       "2  80aa9e4f3014c3d96559c8e642157edbb2b684ea0144ed...             region   \n",
       "3  0899599f74faadb7ba7eb65205ee5c20cb434367a6e720...    shipping_region   \n",
       "4  5057dafe08229da478858da705209d61d88db8dcaadf83...      trading_block   \n",
       "5  a7536c48714140c7ba8e8895cdcccc12ebb2c4813720d6...      trading_block   \n",
       "6  0899599f74faadb7ba7eb65205ee5c20cb434367a6e720...     trading_region   \n",
       "7  6253047d839a51684f2ab6e3a4fa9956f582c73dfbbb27...  trading_subregion   \n",
       "8  1d4d3d8565ea23172f9ea72d3daebc642e835171bccf26...           terminal   \n",
       "\n",
       "                 label source  probability  \n",
       "0               Kuwait  model            1  \n",
       "1  Mina Al Ahmadi [KW]  model            1  \n",
       "2          Middle East  model            1  \n",
       "3               MEG/AG  model            1  \n",
       "4                 OPEC  model            1  \n",
       "5        OPEC + Russia  model            1  \n",
       "6               MEG/AG  model            1  \n",
       "7               Kuwait  model            1  \n",
       "8            KNPC SBMs  model            1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(events[0]['location'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A point to keep in mind**\n",
    "\n",
    "In this lesson we have dived into the structure of a Cargo Movement. While important for understanding, in practice it is often simpler to just use cm_query.to_df('all') to convert all these records to a familiar tabular form, without paying attention to the structure. This is true of other endpoints as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "The Vortexa SDK also offers a `VesselMovements` endpoint. Vessel Movements can be searched for in a similar way to Cargo Movements, but they have some differences. Do a query for Vessel Movements, and inspect the structure of a Vessel Movement to identify the differences.\n",
    "\n",
    "[Vessel Movements documentation.](https://vortechsa.github.io/python-sdk/endpoints/vessel_movements/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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