{
 "cells": [
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   "source": [
    "# Python API Example - Access Regas Slots\n",
    "\n",
    "This guide is designed to provide an example of how to call the Access Slots API endpoint, and store the data accordingly.\n",
    "\n",
    "__N.B. This guide is just for Access terminal slots data. If you're looking for other API data products (such as contract prices, Freight routes or Netbacks), please refer to their according code example files.__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Have any questions?\n",
    "\n",
    "If you have any questions regarding our API, or need help accessing specific datasets, please contact us at:\n",
    "\n",
    "__data@sparkcommodities.com__\n",
    "\n",
    "or refer to our API website for more information about this endpoint: https://www.sparkcommodities.com/api/lng-access/slot-availability.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing Data\n",
    "\n",
    "Here we define the functions that allow us to retrieve the valid credentials to access the Spark API.\n",
    "\n",
    "This section can remain unchanged for most Spark API users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from base64 import b64encode\n",
    "from urllib.parse import urljoin\n",
    "import requests\n",
    "from io import StringIO\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "try:\n",
    "    from urllib import request, parse\n",
    "    from urllib.error import HTTPError\n",
    "except ImportError:\n",
    "    raise RuntimeError(\"Python 3 required\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "API_BASE_URL = \"https://api.sparkcommodities.com\"\n",
    "\n",
    "\n",
    "def retrieve_credentials(file_path=None):\n",
    "    \"\"\"\n",
    "    Find credentials either by reading the client_credentials file or reading\n",
    "    environment variables\n",
    "    \"\"\"\n",
    "    if file_path is None:\n",
    "        client_id = os.getenv(\"SPARK_CLIENT_ID\")\n",
    "        client_secret = os.getenv(\"SPARK_CLIENT_SECRET\")\n",
    "        if not client_id or not client_secret:\n",
    "            raise RuntimeError(\n",
    "                \"SPARK_CLIENT_ID and SPARK_CLIENT_SECRET environment vars required\"\n",
    "            )\n",
    "    else:\n",
    "        # Parse the file\n",
    "        if not os.path.isfile(file_path):\n",
    "            raise RuntimeError(\"The file {} doesn't exist\".format(file_path))\n",
    "\n",
    "        with open(file_path) as fp:\n",
    "            lines = [l.replace(\"\\n\", \"\") for l in fp.readlines()]\n",
    "\n",
    "        if lines[0] in (\"clientId,clientSecret\", \"client_id,client_secret\"):\n",
    "            client_id, client_secret = lines[1].split(\",\")\n",
    "        else:\n",
    "            print(\"First line read: '{}'\".format(lines[0]))\n",
    "            raise RuntimeError(\n",
    "                \"The specified file {} doesn't look like to be a Spark API client \"\n",
    "                \"credentials file\".format(file_path)\n",
    "            )\n",
    "\n",
    "    print(\">>>> Found credentials!\")\n",
    "    \n",
    "    return client_id, client_secret\n",
    "\n",
    "\n",
    "def do_api_post_query(uri, body, headers):\n",
    "    \"\"\"\n",
    "    OAuth2 authentication requires a POST request with client credentials before accessing the API.\n",
    "    This POST request will return an Access Token which will be used for the API GET request.\n",
    "    \"\"\"\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "\n",
    "    data = json.dumps(body).encode(\"utf-8\")\n",
    "\n",
    "    # HTTP POST request\n",
    "    req = request.Request(url, data=data, headers=headers)\n",
    "    try:\n",
    "        response = request.urlopen(req)\n",
    "    except HTTPError as e:\n",
    "        print(\"HTTP Error: \", e.code)\n",
    "        print(e.read())\n",
    "        sys.exit(1)\n",
    "\n",
    "    resp_content = response.read()\n",
    "\n",
    "    # The server must return HTTP 201. Raise an error if this is not the case\n",
    "    assert response.status == 201, resp_content\n",
    "\n",
    "    # The server returned a JSON response\n",
    "    content = json.loads(resp_content)\n",
    "\n",
    "    return content\n",
    "\n",
    "\n",
    "def do_api_get_query(uri, access_token, format='json'):\n",
    "    \"\"\"\n",
    "    After receiving an Access Token, we can request information from the API.\n",
    "    Supports both JSON (default) and CSV responses.\n",
    "    \"\"\"\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "\n",
    "    if format == 'json':\n",
    "        headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"Accept\": \"application/json\",\n",
    "        }\n",
    "    elif format == 'csv':\n",
    "        headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"Accept\": \"text/csv\",\n",
    "        }\n",
    "    else:\n",
    "        raise ValueError(\"The format parameter only takes 'csv' or 'json' as inputs\")\n",
    "\n",
    "    print(\"Fetching {}\".format(url))\n",
    "\n",
    "    # HTTP GET request\n",
    "    req = request.Request(url, headers=headers)\n",
    "    try:\n",
    "        response = request.urlopen(req)\n",
    "    except HTTPError as e:\n",
    "        print(\"HTTP Error: \", e.code)\n",
    "        print(e.read())\n",
    "        sys.exit(1)\n",
    "\n",
    "    resp_content = response.read()\n",
    "\n",
    "    # The server must return HTTP 200. Raise an error if this is not the case\n",
    "    assert response.status == 200, resp_content\n",
    "\n",
    "    # Storing response based on requested format\n",
    "    if format == 'json':\n",
    "        content = json.loads(resp_content)\n",
    "    elif format == 'csv':\n",
    "        content = resp_content\n",
    "\n",
    "    return content\n",
    "\n",
    "\n",
    "def get_access_token(client_id, client_secret):\n",
    "\n",
    "    payload = \"{}:{}\".format(client_id, client_secret).encode()\n",
    "    headers = {\n",
    "        \"Authorization\": b64encode(payload).decode(),\n",
    "        \"Accept\": \"application/json\",\n",
    "        \"Content-Type\": \"application/json\",\n",
    "    }\n",
    "    body = {\n",
    "        \"grantType\": \"clientCredentials\",\n",
    "    }\n",
    "\n",
    "    content = do_api_post_query(uri=\"/oauth/token/\", body=body, headers=headers)\n",
    "\n",
    "    print(\n",
    "        \">>>> Successfully fetched an access token {}****, valid {} seconds.\".format(\n",
    "            content[\"accessToken\"][:5], content[\"expiresIn\"]\n",
    "        )\n",
    "    )\n",
    "\n",
    "    return content[\"accessToken\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N.B. Credentials\n",
    "\n",
    "Here we call the above functions, and input the file path to our credentials.\n",
    "\n",
    "N.B. You must have downloaded your client credentials CSV file before proceeding. Please refer to the API documentation if you have not dowloaded them already.  Instructions for downloading your credentials can be found here:\n",
    "\n",
    "https://www.sparkcommodities.com/api/request/authentication.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert file path to your client credentials here\n",
    "client_id, client_secret = retrieve_credentials(file_path=\"/tmp/client_credentials.csv\")\n",
    "\n",
    "# Authenticate:\n",
    "access_token = get_access_token(client_id, client_secret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Latest Slot Release\n",
    "\n",
    "Here we call the latest slot release and print it in a readable format. This is done using the URL:\n",
    "\n",
    "__/beta/terminal-slots/releases/latest/__\n",
    "\n",
    "\n",
    "We then save the entire dataset as a local variable called `latest`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get the latest slot release\n",
    "def get_latest_slots():\n",
    "    uri = urljoin(API_BASE_URL,'/beta/terminal-slots/releases/latest/')\n",
    "    headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"accept\": \"text/csv\"\n",
    "        }\n",
    "    response = requests.get(uri, headers=headers)\n",
    "    if response.status_code == 200:\n",
    "        df = response.content.decode('utf-8')\n",
    "        df = pd.read_csv(StringIO(df))\n",
    "    else:\n",
    "        print('Bad Request')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call latest slots function\n",
    "latest = get_latest_slots()\n",
    "latest.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Slot Release by Release Date\n",
    "\n",
    "Here we call the slot data by choosing a specific date and print it in a readable format. This is done using the URL:\n",
    "\n",
    "__/beta/terminal-slots/releases/{date}/__ where __date__ is the release date, in the \"YYYY-MM-DD\" string format.\n",
    "\n",
    "\n",
    "We then save the entire dataset as a local variable called `release_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get the slot releases for a specific date\n",
    "def get_slot_releases(date):\n",
    "    uri = urljoin(API_BASE_URL, f'/beta/terminal-slots/releases/{date}/')\n",
    "    headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"accept\": \"text/csv\"\n",
    "        }\n",
    "\n",
    "    response = requests.get(uri, headers=headers)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        df = response.content.decode('utf-8')\n",
    "        df = pd.read_csv(StringIO(df))\n",
    "        return df\n",
    "\n",
    "    elif response.content == b'{\"errors\":[{\"code\":\"object_not_found\",\"detail\":\"Object not found\"}]}':\n",
    "        print('Bad Date')\n",
    "        return None\n",
    "    \n",
    "    else:\n",
    "        print('Bad Request')\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling slot release function\n",
    "release_df = get_slot_releases(\"2024-10-22\")\n",
    "release_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Terminal List\n",
    "\n",
    "Here we call the list of terminals and their uuid, and print it in a readable format. This is done using the URL:\n",
    "\n",
    "__'beta/terminal-slots/terminals/'__\n",
    "\n",
    "\n",
    "We then save the entire dataset as a local variable called `terminal_list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get the list of terminals and their uuids (as well as their start and latest release date)\n",
    "def get_terminal_list():\n",
    "    uri = urljoin(API_BASE_URL,'beta/terminal-slots/terminals/')\n",
    "    headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"accept\": \"text/csv\"\n",
    "        }\n",
    "    response = requests.get(uri, headers=headers)\n",
    "    if response.status_code == 200:\n",
    "        df = response.content.decode('utf-8')\n",
    "        df = pd.read_csv(StringIO(df))\n",
    "    else:\n",
    "        print('Bad Request')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call terminal list function\n",
    "terminal_list = get_terminal_list()\n",
    "terminal_list.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching Slots Data specific to one terminal\n",
    "\n",
    "Now that we can see all the terminal data available to us, we can start to define what terminal we want to call slots data for (by referring to `terminal_list` above).\n",
    "\n",
    "The first step is to choose which terminal uuid (`my_uuid`), then the request will return all the historical data available for that terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to collect and store historical slots for one specific terminal\n",
    "def get_individual_terminal(terminal_uuid):\n",
    "    uri = urljoin(API_BASE_URL, f'/beta/terminal-slots/terminals/{terminal_uuid}/')\n",
    "    headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"accept\": \"text/csv\"\n",
    "        }\n",
    "    response = requests.get(uri, headers=headers)\n",
    "    if response.status_code == 200:\n",
    "        df = response.content.decode('utf-8')\n",
    "        df = pd.read_csv(StringIO(df))\n",
    "        return df\n",
    "\n",
    "    elif response.content == b'{\"errors\":[{\"code\":\"object_not_found\",\"detail\":\"Object not found\"}]}':\n",
    "        print('Bad Terminal Request')\n",
    "        return None\n",
    "    else:\n",
    "        print('Bad Request')\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call individual terminal function (Dragon)\n",
    "my_uuid = terminal_list[terminal_list['TerminalName']=='Dragon']['TerminalUUID'].tolist()[0]\n",
    "dragon_hist_df = get_individual_terminal(my_uuid)\n",
    "dragon_hist_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Historical Slots\n",
    "\n",
    "Here we collect all the historical slots by iterating over each available terminal. This is done using the URL:\n",
    "\n",
    "__/beta/terminal-slots/terminals/{terminal_uuid}/__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to collect and store each terminal's historical slots data\n",
    "def get_all_terminal_data(terminal_list):\n",
    "    terminals_all = pd.DataFrame()\n",
    "    for i in range(len(terminal_list)):\n",
    "        print(terminal_list['TerminalName'].loc[i])\n",
    "        terminal_df = get_individual_terminal(terminal_list['TerminalUUID'].loc[i])\n",
    "        time.sleep(0.1)\n",
    "        terminals_all = pd.concat([terminals_all,terminal_df])\n",
    "    return terminals_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling all terminal data function\n",
    "all_terminal_historical = get_all_terminal_data(terminal_list)\n",
    "all_terminal_historical.head()"
   ]
  }
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