CLIWOC_missing_code_tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "nutritional-indie",
   "metadata": {},
   "source": [
    "## Generating CLIWOC missing code tables "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "grateful-coalition",
   "metadata": {},
   "source": [
    "The Climatological Database for the World's Oceans 1750-1850 ([CLIWOC](https://stvno.github.io/page/cliwoc/)) has valuable information on its supplemental data stored in the [IMMA](https://icoads.noaa.gov/e-doc/imma/R3.0-imma1.pdf) format under the C99 column. \n",
    "\n",
    "We have successfully extracted this information with the [mdf_reader()](https://git.noc.ac.uk/brecinosrivas/mdf_reader) tool, but several important variables are missing their code tables. \n",
    "\n",
    "List of variables: \n",
    "\n",
    "- Ship types\n",
    "- latitude indicator\n",
    "- longitude indicator,\n",
    "- air temperature units\n",
    "- sst units\n",
    "- air pressure units\n",
    "- units of attached thermometer\n",
    "- longitude units\n",
    "- Barometer type\n",
    "\n",
    "According to the [documentation](https://stvno.github.io/page/cliwoc/) of this deck (730) there are up to 20 different ways of writing down the air pressure but the code tables are not available anymore on the website. Therefore, we extracted from the supplemental data all possible entries for those fields which are missing a code table. We count each entry in order to construct a code table for that particular variable.\n",
    "\n",
    "The code to extract multiple variables from the CLIWOC supplemental data can be found [here](https://git.noc.ac.uk/brecinosrivas/mdf_reader/-/blob/master/tests/gather_stats_c99.py)\n",
    "\n",
    "\n",
    "### Set up "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "described-wallet",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "# PARAMS for plots\n",
    "from matplotlib import rcParams\n",
    "sns.set_style(\"whitegrid\")\n",
    "rcParams['axes.labelsize'] = 14\n",
    "rcParams['xtick.labelsize'] = 14\n",
    "rcParams['ytick.labelsize'] = 14\n",
    "rcParams['legend.fontsize'] = 14\n",
    "rcParams['legend.title_fontsize'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ranging-ferry",
   "metadata": {},
   "source": [
    "We stored the statistics per year in python pickle dictionaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "favorite-maria",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paths to data\n",
    "dirs = '/Users/brivas/c3s_work/mdf_reader/tests/data/133-730/133-730'\n",
    "file_names = sorted(os.listdir(dirs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "recent-knife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1662.pkl', '1663.pkl', '1677.pkl', '1699.pkl', '1745.pkl']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_names[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "traditional-terminal",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_values(dic, key, year):\n",
    "    \"\"\"\n",
    "    Get individual sets of values from the pickle df\n",
    "    Params:\n",
    "    ------\n",
    "    dic: python dictionary containing all variables stats per year\n",
    "    key: variable name \n",
    "    year: year to extract\n",
    "    Returns:\n",
    "    --------\n",
    "    indexes: these are the variable types (e.g. barque or nan)\n",
    "    series.values: these are the counts of how many that variable name gets repeated\n",
    "    year: year to sample\n",
    "    \"\"\"\n",
    "    series = dic[key]\n",
    "    indexes = series.index.values\n",
    "    year = np.repeat(year, len(indexes))\n",
    "    return indexes, series.values, year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "forward-context",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exptract_year_arrays(path_to_file, key):\n",
    "    \"\"\"\n",
    "    Reads pickle file and extracts the variable arrays per year\n",
    "    Parms:\n",
    "    -----\n",
    "    path_to_file: path to the pickle file\n",
    "    key: variable to extract\n",
    "    Returns:\n",
    "    --------\n",
    "    df: dataframe from get_df\n",
    "    \n",
    "    \"\"\"\n",
    "    with open(path_to_file, 'rb') as handle:\n",
    "        base = os.path.basename(path_to_file)\n",
    "        year = os.path.splitext(base)[0]\n",
    "        dic_pickle = pickle.load(handle)\n",
    "        df = get_values(dic_pickle, key, year)\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ongoing-franchise",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_data_frame(list_of_files, main_directory, key):\n",
    "    \n",
    "    # Define empty arrays to store the data \n",
    "    years=np.array([])\n",
    "    types_of_var = np.array([])\n",
    "    counts_var = np.array([])\n",
    "    \n",
    "    for file in list_of_files:\n",
    "        full_path = os.path.join(main_directory, file)\n",
    "        var_type, count, year_f = exptract_year_arrays(full_path, key)\n",
    "        years = np.concatenate([years, year_f])\n",
    "        types_of_var = np.concatenate([types_of_var, var_type])\n",
    "        counts_var = np.concatenate([counts_var, count])\n",
    "    \n",
    "    dataset = pd.DataFrame({'Year': years, \n",
    "                            key: types_of_var, 'Count': counts_var})\n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sufficient-jacob",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/brivas/c3s_work/mdf_reader/tests/data/133-730/133-730'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "settled-tribune",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of variables names stored in the pickle files \n",
    "dic_keys = ['ship_types', \n",
    "            'lan_inds', # in a silly mistake I wrote lat wrong in the output data set. Oh well\n",
    "            'lon_inds', \n",
    "            'at_units', \n",
    "            'sst_units', \n",
    "            'ap_units', \n",
    "            'bart_units', \n",
    "            'lon_units', \n",
    "            'baro_types']\n",
    "\n",
    "df_ships = make_data_frame(file_names, dirs, dic_keys[0]).dropna()\n",
    "df_lati = make_data_frame(file_names, dirs, dic_keys[1]).dropna()\n",
    "df_loni = make_data_frame(file_names, dirs, dic_keys[2]).dropna()\n",
    "df_atu = make_data_frame(file_names, dirs, dic_keys[3]).dropna()\n",
    "df_sstu = make_data_frame(file_names, dirs, dic_keys[4]).dropna()\n",
    "df_apu = make_data_frame(file_names, dirs, dic_keys[5]).dropna()\n",
    "df_bartu = make_data_frame(file_names, dirs, dic_keys[6]).dropna()\n",
    "df_lonu = make_data_frame(file_names, dirs, dic_keys[7]).dropna()\n",
    "df_barot = make_data_frame(file_names, dirs, dic_keys[8]).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pressing-discovery",
   "metadata": {},
   "source": [
    "- Ship types \n",
    "\n",
    "Something might be off here in the `mdf_reader()` .json file because some of the ship types seem cut off. Also I guess the plot that I had before made more sense with less data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "occasional-abuse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GALJOOT\n",
      "NAVIO\n",
      "5TH RATE\n",
      "6TH RATE\n",
      "FRIGATE\n",
      "SHIP O.T. LINE\n",
      "SLOOP\n",
      "FREGAT\n",
      "4TH RATE\n",
      "SNOW\n",
      "3RD RATE\n",
      "FR�GATE\n",
      "FREGATE\n",
      "FREGATTE\n",
      "2ND RATE\n",
      "NAV�O\n",
      "SNAUW\n",
      "BOMB VESSEL, SL\n",
      "OORLOGSSCHIP\n",
      "BOMB/EXPLORATIO\n",
      "OORLOGSSNAUW\n",
      "SLOOP (?)\n",
      "STORESHIP\n",
      "BRIK\n",
      "PAQUEBOTE\n",
      "CUTTER\n",
      "FRAGATA\n",
      "FRAGATA CORREO\n",
      "PAQUEBOT\n",
      "BALANDRA\n",
      "BARK\n",
      "BERGANTIN\n",
      "SLOOP, THREE MA\n",
      "6TH RATE FRIGAT\n",
      "SPIEGELRETOURSC\n",
      "TRANSPORT\n",
      "EXPLORATION VES\n",
      "MERCHANT BRIG\n",
      "CHAMBEQU�N\n",
      "BUQUE\n",
      "FRAGATA DE GUER\n",
      "FIRESHIP\n",
      "SNAAUW\n",
      "NAV�O DE LA REA\n",
      "BRIG\n",
      "ADVIJSJAGT\n",
      "KOTTER\n",
      "7TH RATE\n",
      "BRIGANTIJN\n",
      "8TH RATE\n",
      "CORVETTE\n",
      "COTTER\n",
      "GABARRE\n",
      "BRIG/SLOOP\n",
      "PINK\n",
      "BARGENTIJN\n",
      "HOEKERSCHIP\n",
      "L'AVISO\n",
      "FLUTE\n",
      "GOLETA GUARDA C\n",
      "HOEKER\n",
      "CORVETA\n",
      "FLUIT\n",
      "POLACRA\n",
      "WHALER\n",
      "PAKKETBOOT (BRI\n",
      "ARMED STORESHIP\n",
      "SLOEP\n",
      "SCHOENER\n",
      "PACKET SHIP\n",
      "KORVET\n",
      "STORE SHIP\n",
      "TROOP SHIP\n",
      "CORVET\n",
      "LINIESCHIP\n",
      "KORVET V OORLOG\n",
      "BRIK VAN OORLOG\n",
      "KOOPVAARDER\n",
      "FREGATSCHIP\n",
      "STEAMPOWERED WA\n",
      "TRANSPORTSCHIP\n",
      "GOLETA\n",
      "KORVET VAN OORL\n",
      "BRICK\n",
      "ORVET         M\n",
      "STEAMER\n",
      "SCHOENERBRIK\n",
      "MISTICO\n",
      "STOOMSCHIP\n",
      "FALUCHO\n"
     ]
    }
   ],
   "source": [
    "types_of_ships = df_ships.ship_types.unique()\n",
    "for t in types_of_ships:\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "waiting-syndicate",
   "metadata": {},
   "source": [
    "For example that `ORVET         M` ? It looks that maybe that string does not belong to the ship_types field? \n",
    "- AT units \n",
    "\n",
    "This works perfect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "communist-selection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CELSIUS', 'FAHRENHEIT', 'REAMUR', 'REAUMUR', 'AHRENHEIT'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_atu.at_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unnecessary-binary",
   "metadata": {},
   "source": [
    "- SST units \n",
    "\n",
    "This one not so much, and from all the years only those had data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "welsh-legend",
   "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: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>sst_units</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1772</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1773</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1820</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1837</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1838</td>\n",
       "      <td>D</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>1847</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1852</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1853</td>\n",
       "      <td>FAHRENHEIT</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Year   sst_units  Count\n",
       "31   1772  FAHRENHEIT    2.0\n",
       "33   1773  FAHRENHEIT    1.0\n",
       "81   1820  FAHRENHEIT    3.0\n",
       "99   1837  FAHRENHEIT   15.0\n",
       "101  1838           D    1.0\n",
       "111  1847  FAHRENHEIT   16.0\n",
       "117  1852  FAHRENHEIT   32.0\n",
       "119  1853  FAHRENHEIT   51.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sstu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-rings",
   "metadata": {},
   "source": [
    "- Air pressure units\n",
    "\n",
    "This one I am not sure ...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "breeding-header",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['INCHES MERCURY', 'DLS12FRANS', 'DLS100', 'DLS10', 'DLS1004',\n",
       "       'DLS1204F', 'DLS1200R', 'DLS1204R', 'INCHES MERCURY60', 'DLS1104R',\n",
       "       'DLS1200', 'DLS12R', 'MILLIMETERS MERCURY', 'RHINE INCHE MERCURY',\n",
       "       'DLS1004A', 'LS1004A', 'DLS12', 'DLS1204', 'DLS1004R', 'DLS10R',\n",
       "       'AMST INCHES MERCURY'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apu.ap_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spiritual-bulgarian",
   "metadata": {},
   "source": [
    "- Attached thermometer units\n",
    "\n",
    "Here I think there might also be a mistake on the .json file since the `U` character seems suspicious."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "blank-pregnancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['U', 'FAHRENHEIT'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bartu.bart_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-shelf",
   "metadata": {},
   "source": [
    "- Longitude Units\n",
    "\n",
    "These seems ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "beginning-posting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['360 DEGREES', '180 DEGREES', 'UNKNOWN', '180 GRADEN',\n",
       "       '80 DEGREES'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lonu.lon_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "formal-appreciation",
   "metadata": {},
   "source": [
    "- Barometer types:\n",
    "\n",
    "This also looks ok!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fatty-demographic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MERCURY', 'SYMPISIOMETER', 'ANEROID'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_barot.baro_types.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "seven-international",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(20,10))\n",
    "\n",
    "\n",
    "g = sns.histplot(data=df_lati, x=\"Year\", hue=\"lan_inds\", \n",
    "                 multiple=\"stack\", palette='deep', ax=ax[0], legend=True)\n",
    "plt.setp(g.get_xticklabels(), rotation=45)\n",
    "ax[0].grid(False)\n",
    "\n",
    "xticks=ax[0].xaxis.get_major_ticks()\n",
    "for i in range(len(xticks)):\n",
    "    if i%2==1:\n",
    "        xticks[i].set_visible(False)\n",
    "        \n",
    "ax[0].set_title('Coordinates indicator', fontdict={'fontsize': 16, 'fontweight': 'medium'})\n",
    "ax[0].set_xlabel('')\n",
    "        \n",
    "\n",
    "p = sns.histplot(data=df_loni, x=\"Year\", hue=\"lon_inds\", \n",
    "                 multiple=\"stack\", palette='colorblind', ax=ax[1], legend=True)\n",
    "plt.setp(p.get_xticklabels(), rotation=45)\n",
    "ax[1].grid(False)\n",
    "\n",
    "xticks=ax[1].xaxis.get_major_ticks()\n",
    "for i in range(len(xticks)):\n",
    "    if i%2==1:\n",
    "        xticks[i].set_visible(False)\n",
    "         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fossil-victim",
   "metadata": {},
   "source": [
    "Code table for lat and lon indicators, according to this [information](https://stvno.github.io/page/cliwoc/):\n",
    "\n",
    "```\n",
    "{\n",
    "\t\"1\":\"originates from dead reckoning\",\n",
    "\t\"2\":\"originates from true navigation (bearing/distance, celestial)\",\n",
    "\t\"3\":\"Interpolated manually\",\n",
    "\t\"4\":\"Interpolated\",\n",
    "\t\"5\":\"Inserted actual position (ports, islands, etc.)\",\n",
    "\t\"6\":\"Missing\"\n",
    "}\n",
    "```\n",
    "\n",
    "Is it worth using the coordinates from the supplemental metadata or should I use the imma.core lat and lon?"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}