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  {
   "cell_type": "markdown",
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   "source": [
    "# Interannual Variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c0739ac-412e-45cf-b8fd-85013ce915ad",
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   "source": [
    "import huracanpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad64ec3-1075-4eaa-be21-e7d1531d063b",
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   "outputs": [],
   "source": [
    "# Load the data. Here: IBTrACS\n",
    "data = huracanpy.load(source=\"ibtracs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab728dab-439e-4280-81d2-4a1a183ca824",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the basins\n",
    "data[\"basin\"] = huracanpy.info.basin(data.lon, data.lat)\n",
    "# Equivalent to data = data.hrcn.add_basin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa6dc3f-cfe6-4433-b08c-3ac8c8612413",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select the basin you're interested in (here: North Atlantic -- NATL)\n",
    "## Option 1: Get only the points within this basin\n",
    "NATL1 = data.where(data.basin == \"NATL\", drop=True)\n",
    "## Option 2: Select tracks that have at least one point in this basin\n",
    "### 2a : calling hurcanpy\n",
    "NATL2a = huracanpy.trackswhere(data, data.track_id, lambda t: (t.basin == \"NATL\").any())\n",
    "### 2b : Using the accessor\n",
    "NATL2b = data.hrcn.trackswhere(lambda t: (t.basin == \"NATL\").any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5923a55c-7cd1-4087-ad8b-b6d6c38e09fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot interannual variability\n",
    "fig, axs = plt.subplots(3, figsize=[15, 5], sharex=True)\n",
    "\n",
    "## Frequency\n",
    "gen = huracanpy.calc.gen_vals(\n",
    "    NATL1, NATL1.time, NATL1.track_id\n",
    ")  # eq. to gen = data.hrcn.get_gen_vals()\n",
    "freq = gen.groupby(\"season\").count().time\n",
    "freq.plot(ax=axs[0], label=\"frequency\")\n",
    "\n",
    "## TC days\n",
    "tcd = NATL1.groupby(\"season\").apply(\n",
    "    lambda s: huracanpy.calc.track_duration(\n",
    "        s.time, s.track_id\n",
    "    ).sum()  # eq. to s.hrcn.get_track_duration().sum()\n",
    "    / 24  # Conversion because track_duration is in h\n",
    ")\n",
    "tcd.plot(ax=axs[1], label=\"TC days\")\n",
    "\n",
    "## ACE\n",
    "ace = NATL1.groupby(\"season\").apply(\n",
    "    lambda s: huracanpy.tc.ace(s.wind).sum()  # eq. to s.hrcn.get_ace().sum()\n",
    ")\n",
    "ace.plot(ax=axs[2], label=\"ACE\")\n",
    "\n",
    "# Labels\n",
    "axs[0].set_ylabel(\"#TC\")\n",
    "axs[1].set_ylabel(\"TC Days\")\n",
    "axs[2].set_ylabel(\"ACE\")\n",
    "for ax in axs:\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_xticks(range(1980, 2022, 5))\n",
    "    ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb19ad9f-6e2e-40ee-9711-ddcfccbd472b",
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   "source": []
  }
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