{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "22408e0c-ddae-4924-b352-e7d796602c14",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## GRIB: using array fieldlists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05675a78-99c4-404f-aae4-12c1d8ee1ced",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "In this example we will use a GRIB file containing 4 messages. First we ensure the file is available and read it into a fieldlist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "076ae474-e6c4-4a0d-a66c-91b03965627a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import earthkit.data as ekd\n",
    "ekd.download_example_file(\"test4.grib\")\n",
    "ds_in = ekd.from_source(\"file\", \"test4.grib\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "847c90c4-5928-4481-abc9-c2ed9aada29f",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [],
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "Using the :meth:`~data.core.fieldlist.FieldList.to_fieldlist` method we can convert this object into an array fieldlist where each field contains an array (holding the field values) and a :py:class:`~data.readers.grib.metadata.RestrictedGribMetadata` object representing the related metadata. Array fieldlists are entirely stored in memory. The resulting array format is controlled by ``array_namespace`` keyword argument of :meth:`~data.core.fieldlist.FieldList.to_fieldlist`. When using its default value (None) the underlying array format of the original fieldlist is kept. For GRIB data read from a file or stream this will be \"numpy\". "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3374ab00-054b-4aba-a355-f119b619be1e",
   "metadata": {},
   "source": [
    "### Numpy array fieldlist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d99d5aa-8e9e-4c75-8394-188891c75c29",
   "metadata": {},
   "source": [
    "The \"numpy\" fieldlist we generate in the cell below works exactly in the same way as the original one but stores all the data in memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ec4489f-2030-4a55-8292-7f50fb845677",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ds_in.to_fieldlist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "07e0823d-ff1d-43d1-8ffb-e5a6d0616ce4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4106883-8035-4da5-90eb-b83f0d903c4b",
   "metadata": {},
   "source": [
    "### Pytorch array fieldlist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34b8492-60d5-4052-abf9-b02d1a692c1d",
   "metadata": {},
   "source": [
    "For the next example we choose the \"torch\" array namespace. Since pytorch is an optional dependency for earthkit-data we need to ensure it is installed in the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b3b30d9f-0edb-4938-baec-7026acd70192",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install torch --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5d2174d7-0f36-4b20-8ad5-bd93fd12f91b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ds = ds_in.to_fieldlist(array_namespace=\"torch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c108a25-5f41-422f-9adb-98c932205dce",
   "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>centre</th>\n",
       "      <th>shortName</th>\n",
       "      <th>typeOfLevel</th>\n",
       "      <th>level</th>\n",
       "      <th>dataDate</th>\n",
       "      <th>dataTime</th>\n",
       "      <th>stepRange</th>\n",
       "      <th>dataType</th>\n",
       "      <th>number</th>\n",
       "      <th>gridType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>500</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>an</td>\n",
       "      <td>0</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>500</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>an</td>\n",
       "      <td>0</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>an</td>\n",
       "      <td>0</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>an</td>\n",
       "      <td>0</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  centre shortName    typeOfLevel  level  dataDate  dataTime stepRange  \\\n",
       "0   ecmf         t  isobaricInhPa    500  20070101      1200         0   \n",
       "1   ecmf         z  isobaricInhPa    500  20070101      1200         0   \n",
       "2   ecmf         t  isobaricInhPa    850  20070101      1200         0   \n",
       "3   ecmf         z  isobaricInhPa    850  20070101      1200         0   \n",
       "\n",
       "  dataType  number    gridType  \n",
       "0       an       0  regular_ll  \n",
       "1       an       0  regular_ll  \n",
       "2       an       0  regular_ll  \n",
       "3       an       0  regular_ll  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fae43976-e6c9-4520-a28a-13099a44f08d",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### values"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0019f9b6-3607-48df-a018-d8435cdac15e",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "When we use either :py:attr:`Field.values <data.core.fieldlist.Field.values>` or :py:attr:`FieldList.values <data.core.fieldlist.FieldList.values>` now we get a pytorch Tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21a1e8b1-f0e5-4de6-bdbf-e92c5df5989e",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([228.0460, 228.0460, 228.0460, 228.0460, 228.0460, 228.0460, 228.0460,\n",
       "        228.0460, 228.0460, 228.0460], dtype=torch.float64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbd92126-bb5b-47e5-80a7-d4bed3097764",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([65160])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].values.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "714b320c-90ea-4326-bc5f-340dda66daab",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 65160])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.values.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9197df9-3b35-4220-b189-2439de0a4ea9",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### to_array()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe0f41b3-15f6-4a09-a4ef-8b6b36686142",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    ":py:meth:`Field.to_array() <data.core.fieldlist.Field.to_array>` and :py:meth:`FieldList.to_array() <data.core.fieldlist.FieldList.to_array>` return the values based on the underlying namespace. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f60797eb-d578-4638-b1d0-bd18949dd249",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[228.0460, 228.0460],\n",
       "        [228.6085, 228.5792]], dtype=torch.float64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].to_array()[:2,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4d053fa-2acf-4949-9bbd-a0b2ccf30318",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 181, 360])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.to_array().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04cd31df-34cd-47a9-90cc-833b9805bd55",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 65160])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.to_array(flatten=True).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87cff063-95af-45b9-bae1-91cb28ca23ea",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "#### to_numpy()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "23eba4ad-a0f3-4f94-aff3-83b7a0dd07d6",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    ":py:meth:`Field.to_numpy() <data.core.fieldlist.Field.to_numpy>` and :py:meth:`FieldList.to_numpy() <data.core.fieldlist.FieldList.to_numpy>` still return ndarrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b60eea0f-0da1-48f1-a64e-f1b75e36a737",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[228.04600525, 228.04600525],\n",
       "       [228.60850525, 228.57920837]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[0].to_numpy()[:2,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae6bd7cd-b043-4993-b715-e5ea2531490a",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 181, 360)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.to_numpy().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0efdd2b-e078-43e2-936e-35db3a645a6c",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "### Building array fieldlists with from_array()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3cdcbb75-86b1-4c38-b667-73ac563c8d97",
   "metadata": {
    "editable": true,
    "raw_mimetype": "text/restructuredtext",
    "slideshow": {
     "slide_type": ""
    },
    "tags": [],
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "We can build a new array fieldlist straight from metadata and array values using :meth:`~data.core.fieldlist.FieldList.from_array`. This can be used for computations when we want to alter the values and store the result in a new FieldList."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a92db7cb-e2e1-472e-92b6-0f42360d2105",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>centre</th>\n",
       "      <th>shortName</th>\n",
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       "      <th>0</th>\n",
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       "      <td>isobaricInhPa</td>\n",
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       "      <td>20070101</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
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       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20070101</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>an</td>\n",
       "      <td>0</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  centre shortName    typeOfLevel  level  dataDate  dataTime stepRange  \\\n",
       "0   ecmf         t  isobaricInhPa    500  20070101      1200         0   \n",
       "1   ecmf         z  isobaricInhPa    500  20070101      1200         0   \n",
       "2   ecmf         t  isobaricInhPa    850  20070101      1200         0   \n",
       "3   ecmf         z  isobaricInhPa    850  20070101      1200         0   \n",
       "\n",
       "  dataType  number    gridType  \n",
       "0       an       0  regular_ll  \n",
       "1       an       0  regular_ll  \n",
       "2       an       0  regular_ll  \n",
       "3       an       0  regular_ll  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md = ds.metadata()\n",
    "v = ds.to_array() + 2\n",
    "r1 = ekd.FieldList.from_array(v, md)\n",
    "r1.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920f6b98-f0e6-4ffc-a5e4-f8f643c23f76",
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   "source": [
    "As expected, the values in *r1* are now differing by 2 from the ones in the original fieldlist (*r*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b78ea8a-64e9-4bfa-9d11-8b390995994b",
   "metadata": {
    "editable": true,
    "slideshow": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([230.0460, 230.0460, 230.0460, 230.0460, 230.0460, 230.0460, 230.0460,\n",
       "        230.0460, 230.0460, 230.0460], dtype=torch.float64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1[0].values[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48f6f5a4-1304-43ef-b357-563cfa071309",
   "metadata": {},
   "source": [
    "### Building an array fieldlist in a loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4b086a5-406d-4d1c-bd93-1edbde84bf81",
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   "source": [
    "md = ds.metadata()\n",
    "v = ds.to_array() + 2\n",
    "\n",
    "r1 = ekd.SimpleFieldList()\n",
    "for k in range(len(md)):\n",
    "    r1.append(ekd.ArrayField(v[k], md[k]))\n",
    "r1.ls()"
   ]
  },
  {
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   "id": "78b1c896-a892-4192-99a1-bc9dc1cfcd4f",
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   "source": [
    "### Saving to GRIB"
   ]
  },
  {
   "cell_type": "raw",
   "id": "dc2332e6-434c-406f-9f03-567e16afc876",
   "metadata": {
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   "source": [
    "We can save array fieldlists into GRIB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ceea7b3-5059-4378-9a26-d282caa3b74a",
   "metadata": {
    "editable": true,
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   "outputs": [
    {
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       "  centre shortName    typeOfLevel  level  dataDate  dataTime stepRange  \\\n",
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     "metadata": {},
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   ],
   "source": [
    "path = \"_from_pytroch.grib\"\n",
    "r1.to_target(\"file\", path)\n",
    "ds1 = ekd.from_source(\"file\", path)\n",
    "ds1.ls()"
   ]
  }
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