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   "source": [
    "## GRIB: generating time series"
   ]
  },
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   "source": [
    "We will read temperature data from a GRIB file containing forecast steps. First, we ensure the example file is available, then read it with :ref:`from_source() <data-sources-file>`."
   ]
  },
  {
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   "execution_count": 1,
   "id": "87e4f240-b145-4c4d-9906-38db9a7b3a99",
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    "editable": true,
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   "outputs": [],
   "source": [
    "import earthkit.data as ekd\n",
    "from earthkit.geo import nearest_point_haversine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ff42074-f9af-4f1b-a7c3-3d19ae54cf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "ekd.download_example_file(\"time_series.grib\")\n",
    "ds = ekd.from_source(\"file\", \"time_series.grib\").sel(param=\"t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35f59b4-586c-42b5-a24f-5a8c50db98aa",
   "metadata": {},
   "source": [
    "Our data contains 4 steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a635a172-ca2c-446d-b61e-7851821747f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 3, 6, 9]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.metadata(\"step\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24fd9b22-becd-404d-a240-0bde0d952d16",
   "metadata": {},
   "source": [
    "We define a **reference point** and get the index of the nearest gridpoint. We utilise the fact that all the fields have the same grid, so we need not do it field by field. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2578089a-c895-44a0-be5d-e6f5d6f30998",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latlon = ds.to_latlon()\n",
    "lat = latlon[\"lat\"]\n",
    "lon = latlon[\"lon\"]\n",
    "\n",
    "p_ref = (51.45, -0.97)\n",
    "idx, dist = nearest_point_haversine(p_ref, (lat, lon))\n",
    "idx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f1d0e8-41da-4a8a-b3a6-ea8e0c50b420",
   "metadata": {},
   "source": [
    "With the resulting index we can get the values at the nearest gridpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8b8cc37-efab-4870-bc54-20ebb6c8faa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[280.44058228],\n",
       "       [280.31297302],\n",
       "       [280.2789917 ],\n",
       "       [280.08499146]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = ds.values[:,idx]\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f4738a-48f7-488d-8534-631c6422ae27",
   "metadata": {},
   "source": [
    "We extract the datetime for each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4809ecd5-837e-4d36-b411-cced61c306f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = ds.metadata(\"valid_datetime\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571d3091-d881-4060-b7f7-89da3be302a8",
   "metadata": {},
   "source": [
    "With this we can now print out the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb68fa7b-dc43-4b71-a843-8454ced5b4cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-12-21T12:00:00 [280.44058228]\n",
      "2020-12-21T15:00:00 [280.31297302]\n",
      "2020-12-21T18:00:00 [280.2789917]\n",
      "2020-12-21T21:00:00 [280.08499146]\n"
     ]
    }
   ],
   "source": [
    "for v1, v2 in zip(t, v):\n",
    "    print(v1, v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb92e03b-3ab7-4470-a423-ad3fca9262cb",
   "metadata": {},
   "source": [
    "### "
   ]
  }
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