{
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   "source": [
    "## list-of-dicts: defining geography"
   ]
  },
  {
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   "id": "150bca58-93b3-4ae9-b7fe-b5350cbc117b",
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   "source": [
    "This example demonstrates the various ways to define the values and the geography for :ref:`data-sources-lod` fieldlists. "
   ]
  },
  {
   "cell_type": "raw",
   "id": "756ea884-74b9-4a46-a7f0-3a306648700b",
   "metadata": {
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   "source": [
    "First, we create a helper method to generate the input data."
   ]
  },
  {
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   "execution_count": 1,
   "id": "49a5d9d0-2b27-4662-941f-2e7fc89b1059",
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   "source": [
    "import numpy as np\n",
    "import earthkit.data as ekd\n",
    "\n",
    "def build_lod(data):\n",
    "    prototype = {\n",
    "            **data,\n",
    "            \"valid_datetime\": \"2018-08-01T09:00:00Z\",\n",
    "            \"param\": \"t\", \n",
    "        }\n",
    "    \n",
    "    lod = [\n",
    "            {\"level\": 500, **prototype},\n",
    "            {\"level\": 850, **prototype},\n",
    "        ]\n",
    "    return lod"
   ]
  },
  {
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   "id": "5e0e6d2a-77f2-4829-b03f-809b5073771f",
   "metadata": {
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     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### Disctinct latitudes-longitudes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df0ee6f1-5681-401d-a00e-3f67b15aa064",
   "metadata": {
    "editable": true,
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     "slide_type": ""
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    "tags": []
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   "source": [
    "For structured grids, the latitudes and longitudes can be specified as distinct values, the 2D field structure will be automatically built from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdde9002-e8fe-47d1-9ff6-5fb9928c9328",
   "metadata": {
    "editable": true,
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>level</th>\n",
       "      <th>base_datetime</th>\n",
       "      <th>valid_datetime</th>\n",
       "      <th>step</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>t</td>\n",
       "      <td>500</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-08-01T09:00:00+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t</td>\n",
       "      <td>850</td>\n",
       "      <td>None</td>\n",
       "      <td>2018-08-01T09:00:00+00:00</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  param  level base_datetime             valid_datetime  step number\n",
       "0     t    500          None  2018-08-01T09:00:00+00:00  None   None\n",
       "1     t    850          None  2018-08-01T09:00:00+00:00  None   None"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = {\"latitudes\": np.array([10.0, 0.0, -10.0]),\n",
    "        \"longitudes\": np.array([20, 40.0]),\n",
    "        \"values\": np.array([1, 2, 3, 4, 5, 6])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d745b047-ea0a-4acd-b4e2-d9a9d406fcc4",
   "metadata": {
    "editable": true,
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    {
     "data": {
      "text/plain": [
       "((3, 2),\n",
       " {'lat': array([[ 10.,  10.],\n",
       "         [  0.,   0.],\n",
       "         [-10., -10.]]),\n",
       "  'lon': array([[20., 40.],\n",
       "         [20., 40.],\n",
       "         [20., 40.]])},\n",
       " array([[1, 2],\n",
       "        [3, 4],\n",
       "        [5, 6]]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ea54ff-4164-4621-8c3a-9780c3ae95dc",
   "metadata": {
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    "tags": []
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   "source": [
    "We can define the same data by using the actual shape for the \"values\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89640c9d-3e6b-402d-8192-d243f23d6ad6",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 2),\n",
       " {'lat': array([[ 10.,  10.],\n",
       "         [  0.,   0.],\n",
       "         [-10., -10.]]),\n",
       "  'lon': array([[20., 40.],\n",
       "         [20., 40.],\n",
       "         [20., 40.]])},\n",
       " array([[1, 2],\n",
       "        [3, 4],\n",
       "        [5, 6]]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"latitudes\": np.array([10.0, 0.0, -10.0]),\n",
    "        \"longitudes\": np.array([20, 40.0]),\n",
    "        \"values\": np.array([[1, 2], [3, 4], [5, 6]])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4b846d-4e9d-4467-9d6a-6245e3b75655",
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   "source": [
    "Alternatively, the \"disctinctLatitudes\" and \"distinctLongitudes\" keys can be used. The result will be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b575c6cc-46ef-4f80-a2ae-8321640f7d5f",
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    "editable": true,
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    {
     "data": {
      "text/plain": [
       "((3, 2),\n",
       " {'lat': array([[ 10.,  10.],\n",
       "         [  0.,   0.],\n",
       "         [-10., -10.]]),\n",
       "  'lon': array([[20., 40.],\n",
       "         [20., 40.],\n",
       "         [20., 40.]])},\n",
       " array([[1, 2],\n",
       "        [3, 4],\n",
       "        [5, 6]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"distinctLatitudes\": np.array([10.0, 0.0, -10.0]),\n",
    "        \"distinctLongitudes\": np.array([20, 40.0]),\n",
    "        \"values\": np.array([1, 2, 3, 4, 5, 6])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993d527b-e4bd-41a4-864d-d19ff19f382d",
   "metadata": {
    "editable": true,
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    },
    "tags": []
   },
   "source": [
    "#### Full latitudes and longitudes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4240441e-acb7-4a7d-aca6-b5d2a5a0ba43",
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   "source": [
    "The other supported case is when the \"latitudes\" and \"longitudes\" contain all the points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c5dbae-93b2-4551-8476-50ec286c27a2",
   "metadata": {
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    },
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   },
   "source": [
    "This example is the same as the distinct case above but the \"latitudes\" and \"longitudes\" now contain all the grid points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3cd11de-3468-4130-a135-2f800d370441",
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    {
     "data": {
      "text/plain": [
       "((6,),\n",
       " {'lat': array([ 10.,  10.,   0.,   0., -10., -10.]),\n",
       "  'lon': array([20., 40., 20., 40., 20., 40.])},\n",
       " array([1, 2, 3, 4, 5, 6]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"latitudes\": np.array([10., 10., 0., 0., -10.0, -10.0]),\n",
    "        \"longitudes\": np.array([20, 40.0, 20., 40., 20., 40.]),\n",
    "        \"values\": np.array([1, 2, 3, 4, 5, 6])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01321a54-5642-45d9-900b-18d6383208d0",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "We can introduce the 2D structure by using a proper shape either in the \"latitudes\"/\"longitudes\" or the \"values\". The field's shape will be the one with the higher dimensionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fe935f37-745c-4d88-924c-383b5ba510c6",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 2),\n",
       " {'lat': array([[ 10.,  10.],\n",
       "         [  0.,   0.],\n",
       "         [-10., -10.]]),\n",
       "  'lon': array([[20., 40.],\n",
       "         [20., 40.],\n",
       "         [20., 40.]])},\n",
       " array([[1, 2],\n",
       "        [3, 4],\n",
       "        [5, 6]]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"latitudes\": np.array([[10., 10.], [0., 0.], [-10.0, -10.0]]),\n",
    "        \"longitudes\": np.array([[20.0, 40.], [20., 40.], [20., 40.]]),\n",
    "        \"values\": np.array([1, 2, 3, 4, 5, 6])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14dba07-316b-4610-8d14-68badef494d3",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "In this mode we are free to use arbitrary \"latitudes\" and \"longitudes\" without having any structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "875234f9-43fa-4bb5-bc00-cca1c11fa0f9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
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    {
     "data": {
      "text/plain": [
       "((5,),\n",
       " {'lat': array([10., 10.,  6.,  6.,  6.]),\n",
       "  'lon': array([ 20.,  40., -40.,   0.,  40.])},\n",
       " array([1, 2, 3, 4, 5]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"latitudes\": np.array([10., 10., 6., 6., 6.]),\n",
    "        \"longitudes\": np.array([20, 40.0, -40., 0., 40.]),\n",
    "        \"values\": np.array([1, 2, 3, 4, 5])}\n",
    "\n",
    "ds = ekd.from_source(\"list-of-dicts\",  build_lod(data))\n",
    "ds[0].shape, ds[0].to_latlon(), ds[0].to_numpy()"
   ]
  },
  {
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