docs/examples/inspecting_grib_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting GRIB data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running Metview through its graphical user interface, the easiest way to inspect a GRIB file is to right-click on it and choose 'examine' from the context menu. Similar functionality is available from the command line: \"`metview -e /path/to/grib/file`\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to inspect GRIB data from within a Python environment. Note that filtering, processing and visualising data is covered in other notebooks and gallery examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import metview as mv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"data_fc.grib\"\n",
    "if mv.exist(filename):\n",
    "    fs = mv.read(filename)\n",
    "else:\n",
    "    fs = mv.gallery.load_dataset(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Metview, GRIB data is represented by a **Fieldset** object. Many Metview operations generate GRIB data, we can retrieve it from MARS, or we can load it directly from disk.\n",
    "\n",
    "GRIB files are composed of messages, one for each field. The [describe()](../api/functions/describe.rst) method groups the GRIB messages by parameter and gives a summary for quick inspection. Here we will see that the Fieldset contains parameters q,t,u,v,z for multiple vertical levels, and tp at surface level at various forecast steps. We will see in a moment what these parameters actually are in case you're not familiar with the letters!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "  \n",
       "                <table>\n",
       "                <tr><th>parameter</th><th>typeOfLevel</th><th>level</th><th>date</th><th>time</th><th>step</th><th>paramId</th><th>class</th><th>stream</th><th>type</th><th>experimentVersionNumber</th></tr>\n",
       "                <tr><th style='text-align: right;'>q</th><td style='text-align: left;'>isobaricInhPa</td><td style='text-align: left;'>100,250,...</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>133</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr><tr><th style='text-align: right;'>t</th><td style='text-align: left;'>isobaricInhPa</td><td style='text-align: left;'>100,250,...</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>130</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr><tr><th style='text-align: right;'>tp</th><td style='text-align: left;'>surface</td><td style='text-align: left;'>0</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>228</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr><tr><th style='text-align: right;'>u</th><td style='text-align: left;'>isobaricInhPa</td><td style='text-align: left;'>100,250,...</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>131</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr><tr><th style='text-align: right;'>v</th><td style='text-align: left;'>isobaricInhPa</td><td style='text-align: left;'>100,250,...</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>132</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr><tr><th style='text-align: right;'>z</th><td style='text-align: left;'>isobaricInhPa</td><td style='text-align: left;'>100,250,...</td><td style='text-align: left;'>20111215</td><td style='text-align: left;'>0</td><td style='text-align: left;'>0,6,...</td><td style='text-align: left;'>129</td><td style='text-align: left;'>od</td><td style='text-align: left;'>oper</td><td style='text-align: left;'>fc</td><td style='text-align: left;'>0001</td></tr>\n",
       "                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look a bit deeper into each parameter via its shortName, for example, temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "  \n",
       "                <table>\n",
       "                <tr></tr>\n",
       "                <tr><th style='text-align: right;'>shortName</th><td style='text-align: left;'>t</td></tr><tr><th style='text-align: right;'>paramId</th><td style='text-align: left;'>130</td></tr><tr><th style='text-align: right;'>typeOfLevel</th><td style='text-align: left;'>isobaricInhPa</td></tr><tr><th style='text-align: right;'>level</th><td style='text-align: left;'>100,250,300,500,700,850,1000</td></tr><tr><th style='text-align: right;'>date</th><td style='text-align: left;'>20111215</td></tr><tr><th style='text-align: right;'>time</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>step</th><td style='text-align: left;'>0,6,12,18,24,30,36,42</td></tr><tr><th style='text-align: right;'>class</th><td style='text-align: left;'>od</td></tr><tr><th style='text-align: right;'>stream</th><td style='text-align: left;'>oper</td></tr><tr><th style='text-align: right;'>type</th><td style='text-align: left;'>fc</td></tr><tr><th style='text-align: right;'>experimentVersionNumber</th><td style='text-align: left;'>0001</td></tr>\n",
       "                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.describe(\"t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the data do not necessarily form a complete hypercube - this summary lists all the values for each 'dimension', but it is not necessarily the case that, for example, all the steps exist for all the levels. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify the parameter via its numeric paramId, for example, total precipitation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "  \n",
       "                <table>\n",
       "                <tr></tr>\n",
       "                <tr><th style='text-align: right;'>shortName</th><td style='text-align: left;'>tp</td></tr><tr><th style='text-align: right;'>paramId</th><td style='text-align: left;'>228</td></tr><tr><th style='text-align: right;'>typeOfLevel</th><td style='text-align: left;'>surface</td></tr><tr><th style='text-align: right;'>level</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>date</th><td style='text-align: left;'>20111215</td></tr><tr><th style='text-align: right;'>time</th><td style='text-align: left;'>0</td></tr><tr><th style='text-align: right;'>step</th><td style='text-align: left;'>0,6,12,18,24,30,36,42</td></tr><tr><th style='text-align: right;'>class</th><td style='text-align: left;'>od</td></tr><tr><th style='text-align: right;'>stream</th><td style='text-align: left;'>oper</td></tr><tr><th style='text-align: right;'>type</th><td style='text-align: left;'>fc</td></tr><tr><th style='text-align: right;'>experimentVersionNumber</th><td style='text-align: left;'>0001</td></tr>\n",
       "                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.describe(228)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`fs` is a Fieldset object representing the GRIB file. It is essentially a list of GRIB messages.\n",
    "The [ls()](../api/functions/ls.rst) method displays information on a per-message basis. We can call it with the whole Fieldset, but that gives us a very long listing! So let's call it on the first 10 fields using Python's 'slice' operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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: center;\">\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>gridType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</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>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        centre shortName  typeOfLevel    level  dataDate  dataTime stepRange  \\\n",
       "Message                                                                        \n",
       "0        ecmf       t     isobaricInhPa  1000   20111215      0         0      \n",
       "1        ecmf       z     isobaricInhPa  1000   20111215      0         0      \n",
       "2        ecmf       u     isobaricInhPa  1000   20111215      0         0      \n",
       "3        ecmf       v     isobaricInhPa  1000   20111215      0         0      \n",
       "4        ecmf       q     isobaricInhPa  1000   20111215      0         0      \n",
       "5        ecmf       t     isobaricInhPa   850   20111215      0         0      \n",
       "6        ecmf       z     isobaricInhPa   850   20111215      0         0      \n",
       "7        ecmf       u     isobaricInhPa   850   20111215      0         0      \n",
       "8        ecmf       v     isobaricInhPa   850   20111215      0         0      \n",
       "9        ecmf       q     isobaricInhPa   850   20111215      0         0      \n",
       "\n",
       "        dataType   gridType   \n",
       "Message                       \n",
       "0          fc     regular_ll  \n",
       "1          fc     regular_ll  \n",
       "2          fc     regular_ll  \n",
       "3          fc     regular_ll  \n",
       "4          fc     regular_ll  \n",
       "5          fc     regular_ll  \n",
       "6          fc     regular_ll  \n",
       "7          fc     regular_ll  \n",
       "8          fc     regular_ll  \n",
       "9          fc     regular_ll  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[:10].ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select a different range of fields and add extra ecCodes keys to get more information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\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>gridType</th>\n",
       "      <th>name</th>\n",
       "      <th>units</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Temperature</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Geopotential</td>\n",
       "      <td>m**2 s**-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>U component of wind</td>\n",
       "      <td>m s**-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>V component of wind</td>\n",
       "      <td>m s**-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Specific humidity</td>\n",
       "      <td>kg kg**-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Temperature</td>\n",
       "      <td>K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Geopotential</td>\n",
       "      <td>m**2 s**-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>U component of wind</td>\n",
       "      <td>m s**-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>V component of wind</td>\n",
       "      <td>m s**-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>Specific humidity</td>\n",
       "      <td>kg kg**-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        centre shortName  typeOfLevel    level  dataDate  dataTime stepRange  \\\n",
       "Message                                                                        \n",
       "0        ecmf       t     isobaricInhPa  1000   20111215      0        36      \n",
       "1        ecmf       z     isobaricInhPa  1000   20111215      0        36      \n",
       "2        ecmf       u     isobaricInhPa  1000   20111215      0        36      \n",
       "3        ecmf       v     isobaricInhPa  1000   20111215      0        36      \n",
       "4        ecmf       q     isobaricInhPa  1000   20111215      0        36      \n",
       "5        ecmf       t     isobaricInhPa   850   20111215      0        36      \n",
       "6        ecmf       z     isobaricInhPa   850   20111215      0        36      \n",
       "7        ecmf       u     isobaricInhPa   850   20111215      0        36      \n",
       "8        ecmf       v     isobaricInhPa   850   20111215      0        36      \n",
       "9        ecmf       q     isobaricInhPa   850   20111215      0        36      \n",
       "\n",
       "        dataType   gridType          name            units     \n",
       "Message                                                        \n",
       "0          fc     regular_ll          Temperature           K  \n",
       "1          fc     regular_ll         Geopotential  m**2 s**-2  \n",
       "2          fc     regular_ll  U component of wind     m s**-1  \n",
       "3          fc     regular_ll  V component of wind     m s**-1  \n",
       "4          fc     regular_ll    Specific humidity   kg kg**-1  \n",
       "5          fc     regular_ll          Temperature           K  \n",
       "6          fc     regular_ll         Geopotential  m**2 s**-2  \n",
       "7          fc     regular_ll  U component of wind     m s**-1  \n",
       "8          fc     regular_ll  V component of wind     m s**-1  \n",
       "9          fc     regular_ll    Specific humidity   kg kg**-1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[210:220].ls(extra_keys=['name', 'units'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add some keys that compute statistics per field (this will take longer, as the values will be decoded and the statistics computed - they are not stored in the GRIB header)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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: center;\">\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>gridType</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ecmf</td>\n",
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       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
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       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>250.973</td>\n",
       "      <td>312.973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>-2506.38</td>\n",
       "      <td>3061.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>-14.0416</td>\n",
       "      <td>19.7084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
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       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>-16.6044</td>\n",
       "      <td>19.3956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>8.95455e-05</td>\n",
       "      <td>0.0198649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>245.139</td>\n",
       "      <td>302.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>10344.5</td>\n",
       "      <td>16136.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>-16.2513</td>\n",
       "      <td>26.9987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>-21.4716</td>\n",
       "      <td>25.5284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>8.98526e-05</td>\n",
       "      <td>0.0155317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        centre shortName  typeOfLevel    level  dataDate  dataTime stepRange  \\\n",
       "Message                                                                        \n",
       "0        ecmf       t     isobaricInhPa  1000   20111215      0        36      \n",
       "1        ecmf       z     isobaricInhPa  1000   20111215      0        36      \n",
       "2        ecmf       u     isobaricInhPa  1000   20111215      0        36      \n",
       "3        ecmf       v     isobaricInhPa  1000   20111215      0        36      \n",
       "4        ecmf       q     isobaricInhPa  1000   20111215      0        36      \n",
       "5        ecmf       t     isobaricInhPa   850   20111215      0        36      \n",
       "6        ecmf       z     isobaricInhPa   850   20111215      0        36      \n",
       "7        ecmf       u     isobaricInhPa   850   20111215      0        36      \n",
       "8        ecmf       v     isobaricInhPa   850   20111215      0        36      \n",
       "9        ecmf       q     isobaricInhPa   850   20111215      0        36      \n",
       "\n",
       "        dataType   gridType      min         max      \n",
       "Message                                               \n",
       "0          fc     regular_ll      250.973    312.973  \n",
       "1          fc     regular_ll     -2506.38    3061.62  \n",
       "2          fc     regular_ll     -14.0416    19.7084  \n",
       "3          fc     regular_ll     -16.6044    19.3956  \n",
       "4          fc     regular_ll  8.95455e-05  0.0198649  \n",
       "5          fc     regular_ll      245.139    302.639  \n",
       "6          fc     regular_ll      10344.5    16136.5  \n",
       "7          fc     regular_ll     -16.2513    26.9987  \n",
       "8          fc     regular_ll     -21.4716    25.5284  \n",
       "9          fc     regular_ll  8.98526e-05  0.0155317  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[210:220].ls(extra_keys=['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use type qualifiers (s=string, l=long, d=double) to give more control over how the keys are displayed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
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       "      <th>centre</th>\n",
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       "      <th>gridType</th>\n",
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       "    <tr>\n",
       "      <th>Message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ecmf</td>\n",
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       "      <th>1</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
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       "      <td>regular_ll</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
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       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>z</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>u</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>v</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>q</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        centre shortName  typeOfLevel    level  dataDate  dataTime stepRange  \\\n",
       "Message                                                                        \n",
       "0        ecmf       t     isobaricInhPa  1000   20111215      0        36      \n",
       "1        ecmf       z     isobaricInhPa  1000   20111215      0        36      \n",
       "2        ecmf       u     isobaricInhPa  1000   20111215      0        36      \n",
       "3        ecmf       v     isobaricInhPa  1000   20111215      0        36      \n",
       "4        ecmf       q     isobaricInhPa  1000   20111215      0        36      \n",
       "5        ecmf       t     isobaricInhPa   850   20111215      0        36      \n",
       "6        ecmf       z     isobaricInhPa   850   20111215      0        36      \n",
       "7        ecmf       u     isobaricInhPa   850   20111215      0        36      \n",
       "8        ecmf       v     isobaricInhPa   850   20111215      0        36      \n",
       "9        ecmf       q     isobaricInhPa   850   20111215      0        36      \n",
       "\n",
       "        dataType   gridType  centre:l  \n",
       "Message                                \n",
       "0          fc     regular_ll   98.0    \n",
       "1          fc     regular_ll   98.0    \n",
       "2          fc     regular_ll   98.0    \n",
       "3          fc     regular_ll   98.0    \n",
       "4          fc     regular_ll   98.0    \n",
       "5          fc     regular_ll   98.0    \n",
       "6          fc     regular_ll   98.0    \n",
       "7          fc     regular_ll   98.0    \n",
       "8          fc     regular_ll   98.0    \n",
       "9          fc     regular_ll   98.0    "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[210:220].ls(extra_keys=['centre:l'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add a filter to show only those messages whose keys match all the given conditions. Note that these must be valid ecCodes keys - 'shortName' or 'paramId' can be used for the parameter, but 'param' cannot, since it is not a key. Keys do not need to be displayed in order to be used as a filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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: center;\">\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>gridType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Message</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>1000</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>850</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>700</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>500</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>300</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>250</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>ecmf</td>\n",
       "      <td>t</td>\n",
       "      <td>isobaricInhPa</td>\n",
       "      <td>100</td>\n",
       "      <td>20111215</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>fc</td>\n",
       "      <td>regular_ll</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        centre shortName  typeOfLevel    level  dataDate  dataTime stepRange  \\\n",
       "Message                                                                        \n",
       "35       ecmf       t     isobaricInhPa  1000   20111215      0         6      \n",
       "40       ecmf       t     isobaricInhPa   850   20111215      0         6      \n",
       "45       ecmf       t     isobaricInhPa   700   20111215      0         6      \n",
       "50       ecmf       t     isobaricInhPa   500   20111215      0         6      \n",
       "55       ecmf       t     isobaricInhPa   300   20111215      0         6      \n",
       "60       ecmf       t     isobaricInhPa   250   20111215      0         6      \n",
       "65       ecmf       t     isobaricInhPa   100   20111215      0         6      \n",
       "\n",
       "        dataType   gridType   \n",
       "Message                       \n",
       "35         fc     regular_ll  \n",
       "40         fc     regular_ll  \n",
       "45         fc     regular_ll  \n",
       "50         fc     regular_ll  \n",
       "55         fc     regular_ll  \n",
       "60         fc     regular_ll  \n",
       "65         fc     regular_ll  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.ls(filter={'shortName':'t', 'step':6, 'centre:s':'ecmf'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the output from [ls()](../api/functions/ls.rst) is a pandas dataframe, allowing further manipulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index(['centre', 'shortName', 'typeOfLevel', 'level', 'dataDate', 'dataTime',\n",
      "       'stepRange', 'dataType', 'gridType'],\n",
      "      dtype='object')\n",
      "Message\n",
      "0    t\n",
      "1    z\n",
      "2    u\n",
      "3    v\n",
      "4    q\n",
      "5    t\n",
      "6    z\n",
      "7    u\n",
      "8    v\n",
      "9    q\n",
      "Name: shortName, dtype: object\n"
     ]
    }
   ],
   "source": [
    "p = fs[:10].ls()\n",
    "print(type(p))\n",
    "print(p.columns)\n",
    "print(p['shortName'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting more specific keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To extract just the keys you want from the GRIB fields, use [grib_get_long()](../api/functions/grib_get.rst#grib_get_long), [grib_get_double()](../api/functions/grib_get.rst#grib_get_double) and [grib_get_string()](../api/functions/grib_get.rst#grib_get_string) to extract single keys, and [grib_get()](../api/functions/grib_get.rst) for multiple keys. These examples show how."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ecmf', 'ecmf', 'ecmf', 'ecmf', 'ecmf']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[:5].grib_get_string('centre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[98.0, 98.0, 98.0, 98.0, 98.0]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[:5].grib_get_long('centre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[850.0, 850.0, 700.0, 700.0, 700.0]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[43:48].grib_get_double('level')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When extracting mutliple keys, it is more efficient to use [grib_get()](../api/functions/grib_get.rst) to get them all in one go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['ecmf', '6', '850'],\n",
       " ['ecmf', '6', '850'],\n",
       " ['ecmf', '6', '700'],\n",
       " ['ecmf', '6', '700'],\n",
       " ['ecmf', '6', '700']]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[43:48].grib_get(['centre', 'step', 'level'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the [grib_get()](../api/functions/grib_get.rst) method does not specify a type for each key, we can add `:l`, `:d` or `:s` to each one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[98.0, '6', '850'],\n",
       " [98.0, '6', '850'],\n",
       " [98.0, '6', '700'],\n",
       " [98.0, '6', '700'],\n",
       " [98.0, '6', '700']]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[43:48].grib_get(['centre:l', 'step', 'level'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above command will return a list per field. We can instead tell it to group the results by key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[98.0, 98.0, 98.0, 98.0, 98.0],\n",
       " ['6', '6', '6', '6', '6'],\n",
       " ['850', '850', '700', '700', '700']]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs[43:48].grib_get(['centre:l', 'step', 'level'], 'key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick plot\n",
    "Let's plot the first field to see the geographic extents of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mv.setoutput('jupyter')\n",
    "mv.plot(fs[0], mv.mcont(contour_automatic_setting='ecmwf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}