{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try out the VortexaSDK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's import our requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import vortexasdk as v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the tonne miles of the New Wisdom vessel over 2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll need to enter your Vortexa API key when prompted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_wisdom = [g.id for g in v.Vessels().search(\"NEW WISDOM\").to_list()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = v.TonneMilesBreakdown().search(\n",
    "    unit='b',\n",
    "    breakdown_frequency='month',\n",
    "    filter_vessels=new_wisdom,\n",
    "    filter_time_min=datetime(2018, 1, 1),\n",
    "    filter_time_max=datetime(2018, 12, 31)\n",
    ").to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it! You've successfully loaded data using the Vortexa SDK. Check out https://vortechsa.github.io/python-sdk/ for more examples"
   ]
  }
 ],
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