{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58c35970-3e91-47f9-9cf9-663a81039589",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## Using UK Met Office PP files"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a320d6a7-0f8f-489e-9cd2-b8f0332607fe",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "This example uses a small PP file containing global temperature data. \n",
    "\n",
    "We can read this data as a :ref:`file source <data-sources-file>` using the `Iris <https://scitools-iris.readthedocs.io/en/latest/>`_ library under the hood. \n",
    "\n",
    "Please note, PP file support requires the ``scitools-iris`` and ``ncdata`` packages to be installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6625ad75-a1cd-4f5b-a04b-2e1a63aafb89",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cgr/venv/dev/lib/python3.13/site-packages/array_api_compat/torch/_info.py:361: UserWarning: 'mkldnn' is no longer used as device type. So torch.device('mkldnn') will be deprecated and removed in the future. Please use other valid device types instead. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/c10/core/Device.cpp:42.)\n",
      "  a = torch.empty((0,), device=torch.device(device_name, index=i))\n",
      "/Users/cgr/venv/dev/lib/python3.13/site-packages/iris/fileformats/netcdf/saver.py:2857: IrisDeprecation: Saving to netcdf with legacy-style attribute handling for backwards compatibility.\n",
      "This mode is deprecated since Iris 3.8, and will eventually be removed.\n",
      "Please consider enabling the new split-attributes handling mode, by setting 'iris.FUTURE.save_split_attrs = True'.\n",
      "  warn_deprecated(message)\n",
      "/Users/cgr/venv/dev/lib/python3.13/site-packages/ncdata/xarray.py:165: FutureWarning: In a future version, xarray will not decode the variable 'forecast_period' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n",
      "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n",
      "To opt-in to future behavior, set `decode_timedelta=False`.\n",
      "  ds = store_entrypoint.open_dataset(self, **xr_load_kwargs)\n"
     ]
    }
   ],
   "source": [
    "import earthkit.data as ekd\n",
    "\n",
    "# we ensure the example file is available. \n",
    "ekd.download_example_file(\"air_temp.pp\")\n",
    "\n",
    "ds = ekd.from_source(\"file\", \"air_temp.pp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044b5b88-848a-49ee-8d91-5996a1327a48",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The resulting object is an Xarray fieldList."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f21a9370-1ded-4334-af4d-d244b325561b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>variable</th>\n",
       "      <th>level</th>\n",
       "      <th>valid_datetime</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>air_temperature</td>\n",
       "      <td>None</td>\n",
       "      <td>1998-12-01T00:00:00</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variable level       valid_datetime units\n",
       "0  air_temperature  None  1998-12-01T00:00:00     K"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a70b28d-3244-46ee-afdc-27ad31b0d226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73, 96)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f76468d-3bc0-4b86-9b21-9cb3e9a6d4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([254.644, 254.644, 254.644, 254.644, 254.644], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].values[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b189c2e-0981-4ff0-b99b-62e2a60c7c69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'lat': array([[ 89.999985,  89.999985,  89.999985, ...,  89.999985,  89.999985,\n",
       "          89.999985],\n",
       "        [ 87.499985,  87.499985,  87.499985, ...,  87.499985,  87.499985,\n",
       "          87.499985],\n",
       "        [ 84.999985,  84.999985,  84.999985, ...,  84.999985,  84.999985,\n",
       "          84.999985],\n",
       "        ...,\n",
       "        [-84.999954, -84.999954, -84.999954, ..., -84.999954, -84.999954,\n",
       "         -84.999954],\n",
       "        [-87.499954, -87.499954, -87.499954, ..., -87.499954, -87.499954,\n",
       "         -87.499954],\n",
       "        [-89.999954, -89.999954, -89.999954, ..., -89.999954, -89.999954,\n",
       "         -89.999954]], shape=(73, 96), dtype=float32),\n",
       " 'lon': array([[  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ],\n",
       "        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ],\n",
       "        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ],\n",
       "        ...,\n",
       "        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ],\n",
       "        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ],\n",
       "        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,\n",
       "         356.2499  ]], shape=(73, 96), dtype=float32)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].to_latlon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d5789d4-10e9-4283-9773-dc94b0f30f5f",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev",
   "language": "python",
   "name": "dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}