opencharts/ccsds_eccodes.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "id": "1c3ef1cc-72fc-44ec-b7d3-d707dab0e7f6",
   "metadata": {},
   "source": [
    "![ECMWF_Master_Logo_RGB.png](attachment:f7b0801c-f593-4b71-ae2e-60e5f1f7d2c6.png)\n",
    "# CCSDS compression in ECMWF Real-time open data \n",
    "Authors Milana Vučković and Shahram Najm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "519335b5-fb2d-42fd-b0b1-5ff5fc268863",
   "metadata": {},
   "source": [
    "In June 2023 ECMWF will update its IFS forecast model to version 48r1. This is a major model change with several updates which are described [here](https://confluence.ecmwf.int/display/FCST/Implementation+of+IFS+Cycle+48r1).  \n",
    "\n",
    "With 48r1 all **gridded GRIB2 \"raw\"** data will use the [new CCSDS packing type](https://codes.ecmwf.int/grib/format/grib2/templates/5/42/).  \n",
    "The end users will not see any change as long as they use **the latest version of ecCodes** library."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a423c53-90b5-4ea1-abb4-5767d9eb6a64",
   "metadata": {},
   "source": [
    "## Libraries to download and work with GRIB data\n",
    "\n",
    "There are many tools to work with GRIB data. Here you will find how to inspect new ECMWF open data using [**ecCodes**](https://confluence.ecmwf.int/display/ECC/ecCodes+Home) Python library.  \n",
    "\n",
    "If you are looking at this notebook on MyBinder, it should be already installed. \n",
    "\n",
    "If you are using this notebook individually and don't have ecCodes and [**ecmwf.opendata**](https://github.com/ecmwf/ecmwf-opendata) Client, please use the next cell to install them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de83cf1b-be9a-4f09-83be-3529bab10819",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install ecmwf-opendata eccodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a836527-806a-4ef4-83fe-412ece165d6a",
   "metadata": {},
   "source": [
    "We can now import open data api client and eccodes.  \n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note:</b> Make sure that you have <b>ecCodes version 2.15</b> or higher for the decoding of grib data, otherwise it will not work.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "682e7807-d303-4b16-9af6-62ed58546ca6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found: ecCodes v2.27.0\n"
     ]
    }
   ],
   "source": [
    "from ecmwf.opendata import Client\n",
    "from eccodes import *\n",
    "\n",
    "version = codes_get_api_version()\n",
    "print(f\"Found: ecCodes v{version}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c05ebdbe-00b0-4729-8cfd-854de5d5d9e0",
   "metadata": {},
   "source": [
    "## Download data\n",
    "\n",
    "To download test (esuite) data, set the source to '**ecmwf-esuites**'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c929b942-f4bf-4106-95da-e800587747da",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(\"ecmwf-esuites\", beta=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69a6957e-fa93-40e2-a51d-bf97c3637a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT = \"ccsds.grib2\"\n",
    "OUTPUT = \"simple.grib2\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ec660b-55cb-458a-8a45-eea0f754d124",
   "metadata": {},
   "source": [
    "Test forecast is usually available a bit late, so just in case you are running this early in the morning, let's set the date parameter to yesterday.  \n",
    "This is still experimental data, so if the data is not available, set the date to a few days ago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "179f75fe-8ff6-4ca6-a66a-e296f61f32ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "20230328000000-12h-oper-fc.grib2:   0%|          | 0.00/249k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ecmwf.opendata.client.Result at 0x7fe401868f40>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.retrieve(\n",
    "    date=-2,\n",
    "    time=0,\n",
    "    step=12,\n",
    "    stream=\"oper\",\n",
    "    type=\"fc\",\n",
    "    levtype=\"pl\",\n",
    "    levelist=[850],\n",
    "    param='t',\n",
    "    target=INPUT\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "276890a8-39e1-4969-9d05-3cac2eabd3d4",
   "metadata": {},
   "source": [
    "## Inspect the data\n",
    "First we open the data we have just downloaded and check the packing type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8292857c-3c66-4b3d-9129-b91003536400",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(INPUT, \"rb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6b507fc-592a-4f63-a483-fc404aec29ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "packingType = grid_ccsds\n"
     ]
    }
   ],
   "source": [
    "gid = codes_grib_new_from_file(f)\n",
    "print(f'packingType = {codes_get(gid, \"packingType\")}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a3a043-9fa1-4a36-a528-a9ef3cb763eb",
   "metadata": {},
   "source": [
    "Let's now inspect some values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c89e63f5-c616-46c3-8641-dfc2ec516c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 252.99560546875)\n",
      "(101, 252.99560546875)\n",
      "(201, 252.99560546875)\n",
      "(301, 252.99560546875)\n",
      "(401, 252.99560546875)\n",
      "(501, 252.99560546875)\n",
      "(601, 252.99560546875)\n",
      "(701, 252.99560546875)\n",
      "(801, 252.99560546875)\n",
      "(901, 253.40185546875)\n",
      "(1001, 253.08935546875)\n",
      "(1101, 253.18310546875)\n",
      "(1201, 253.83935546875)\n",
      "(1301, 254.43310546875)\n",
      "(1401, 254.33935546875)\n",
      "(1501, 253.58935546875)\n",
      "(1601, 253.02685546875)\n",
      "(1701, 253.21435546875)\n",
      "(1801, 254.46435546875)\n",
      "(1901, 253.65185546875)\n"
     ]
    }
   ],
   "source": [
    "values_ccsds = codes_get_values(gid)\n",
    "num_vals = len(values_ccsds)\n",
    "for i in range(0,2000,100):\n",
    "    print((i + 1, values_ccsds[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02438b6a-0f50-4772-ace8-7e800ecea490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "405900 values found in ccsds.grib2\n"
     ]
    }
   ],
   "source": [
    "print(f\"{num_vals} values found in {INPUT}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35f7882e-d2e4-46f2-8b2f-b46c18183d65",
   "metadata": {},
   "source": [
    "We can check what happens if we convert to simple packing. It is two lines of code to convert using eccodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "26d16970-9803-41a5-9451-1042128f49ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "packingType = grid_simple\n"
     ]
    }
   ],
   "source": [
    "codes_set(gid, \"packingType\", \"grid_simple\")\n",
    "codes_set_values(gid, values_ccsds)\n",
    "\n",
    "print(f'packingType = {codes_get(gid, \"packingType\")}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776d04c4-9984-461a-bee5-bbf9939548d6",
   "metadata": {},
   "source": [
    "Just to make sure our values didn't change, we can print the same points again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a072ff0a-54ee-4575-8b8c-add26bf91e08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 252.99560546875)\n",
      "(101, 252.99560546875)\n",
      "(201, 252.99560546875)\n",
      "(301, 252.99560546875)\n",
      "(401, 252.99560546875)\n",
      "(501, 252.99560546875)\n",
      "(601, 252.99560546875)\n",
      "(701, 252.99560546875)\n",
      "(801, 252.99560546875)\n",
      "(901, 253.40185546875)\n",
      "(1001, 253.08935546875)\n",
      "(1101, 253.18310546875)\n",
      "(1201, 253.83935546875)\n",
      "(1301, 254.43310546875)\n",
      "(1401, 254.33935546875)\n",
      "(1501, 253.58935546875)\n",
      "(1601, 253.02685546875)\n",
      "(1701, 253.21435546875)\n",
      "(1801, 254.46435546875)\n",
      "(1901, 253.65185546875)\n"
     ]
    }
   ],
   "source": [
    "values_simple = codes_get_values(gid)\n",
    "for i in range(0,2000,100):\n",
    "    print((i + 1, values_simple[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ace41e-5ee0-40af-a526-6b78ad18d42b",
   "metadata": {},
   "source": [
    "Finally we write the output into new file and close."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e50625c2-f8cb-4b65-aa8c-2c3161a90a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "fout = open(OUTPUT, \"wb\")\n",
    "codes_write(gid, fout)\n",
    "codes_release(gid)\n",
    "f.close()\n",
    "fout.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "733989d9-db64-49d7-8496-1ca043bc4e3f",
   "metadata": {},
   "source": [
    "Now we can use grib_ls to compare the two files as well.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c152643c-d4bb-4fd2-9f4e-b53f6dc48d22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ \"messages\" : [ \n",
      "  {\n",
      "    \"packingType\": \"grid_ccsds\",\n",
      "    \"max\": 304.089,\n",
      "    \"min\": 233.558,\n",
      "    \"avg\": 273.89\n",
      "  },\n",
      "  {\n",
      "    \"packingType\": \"grid_simple\",\n",
      "    \"max\": 304.089,\n",
      "    \"min\": 233.558,\n",
      "    \"avg\": 273.89\n",
      "  }\n",
      "]}\n"
     ]
    }
   ],
   "source": [
    "!grib_ls -j -p packingType,max,min,avg ccsds.grib2 simple.grib2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e499ea2-90f9-4ffb-b54f-d00a200714b7",
   "metadata": {},
   "source": [
    "We can also compare the size of the files to see the benefit of the compression.  \n",
    "In this case, the simple packed file is double the size of the CCSDS packed one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0dcfd60c-5f29-4dac-975b-c3a64c1a365d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--  1 mamv  staff  254950 30 Mar 14:17 ccsds.grib2\n",
      "-rw-r--r--  1 mamv  staff  609046 30 Mar 14:17 simple.grib2\n"
     ]
    }
   ],
   "source": [
    "!ls -l *.grib2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71955650-075f-4ca9-9155-50183f7d7ddc",
   "metadata": {},
   "source": [
    "If you are an advanced user, you can have a look at how to do the same things using [**Metview**](https://metview.readthedocs.io/en/latest/index.html) and [**xarray**](https://docs.xarray.dev/en/stable/) in [**this notebook**](ccsds.ipynb)."
   ]
  }
 ],
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