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'] = 16\n",
    "rcParams['legend.title_fontsize'] = 16"
   ]
  },
  {
   "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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "occasional-abuse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['GALJOOT', 'NAVIO', '5TH RATE', '6TH RATE', 'FRIGATE',\n",
       "       'SHIP O.T. LINE', 'SLOOP', 'FREGAT', '4TH RATE', 'SNOW',\n",
       "       '3RD RATE', 'FR�GATE', 'FREGATE', 'FREGATTE', '2ND RATE', 'NAV�O',\n",
       "       'SNAUW', 'BOMB VESSEL, SL', 'OORLOGSSCHIP', 'BOMB/EXPLORATIO',\n",
       "       'OORLOGSSNAUW', 'SLOOP (?)', 'STORESHIP', 'BRIK', 'PAQUEBOTE',\n",
       "       'CUTTER', 'FRAGATA', 'FRAGATA CORREO', 'PAQUEBOT', 'BALANDRA',\n",
       "       'BARK', 'BERGANTIN', 'SLOOP, THREE MA', '6TH RATE FRIGAT',\n",
       "       'SPIEGELRETOURSC', 'TRANSPORT', 'EXPLORATION VES', 'MERCHANT BRIG',\n",
       "       'CHAMBEQU�N', 'BUQUE', 'FRAGATA DE GUER', 'FIRESHIP', 'SNAAUW',\n",
       "       'NAV�O DE LA REA', 'BRIG', 'ADVIJSJAGT', 'KOTTER', '7TH RATE',\n",
       "       'BRIGANTIJN', '8TH RATE', 'CORVETTE', 'COTTER', 'GABARRE',\n",
       "       'BRIG/SLOOP', 'PINK', 'BARGENTIJN', 'HOEKERSCHIP', \"L'AVISO\",\n",
       "       'FLUTE', 'GOLETA GUARDA C', 'HOEKER', 'CORVETA', 'FLUIT',\n",
       "       'POLACRA', 'WHALER', 'PAKKETBOOT (BRI', 'ARMED STORESHIP', 'SLOEP',\n",
       "       'SCHOENER', 'PACKET SHIP', 'KORVET', 'STORE SHIP', 'TROOP SHIP',\n",
       "       'CORVET', 'LINIESCHIP', 'KORVET V OORLOG', 'BRIK VAN OORLOG',\n",
       "       'KOOPVAARDER', 'FREGATSCHIP', 'STEAMPOWERED WA', 'TRANSPORTSCHIP',\n",
       "       'GOLETA', 'KORVET VAN OORL', 'BRICK', 'STEAMER', 'SCHOENERBRIK',\n",
       "       'MISTICO', 'STOOMSCHIP', 'FALUCHO'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_of_ships = df_ships.ship_types.unique()\n",
    "types_of_ships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "phantom-modem",
   "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",
      "STEAMER\n",
      "SCHOENERBRIK\n",
      "MISTICO\n",
      "STOOMSCHIP\n",
      "FALUCHO\n"
     ]
    }
   ],
   "source": [
    "for t in types_of_ships:\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "waiting-syndicate",
   "metadata": {},
   "source": [
    "- AT units "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "communist-selection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CELSIUS', 'FAHRENHEIT', 'REAMUR', 'REAUMUR'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_atu.at_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unnecessary-binary",
   "metadata": {},
   "source": [
    "- SST units "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "welsh-legend",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FAHRENHEIT'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sstu.sst_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollow-rings",
   "metadata": {},
   "source": [
    "- Air pressure units\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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', 'DLS12', 'DLS1204', 'DLS1004R', 'DLS10R',\n",
       "       'AMST INCHES MERCURY'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_apu.ap_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spiritual-bulgarian",
   "metadata": {},
   "source": [
    "- Attached thermometer units\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "blank-pregnancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FAHRENHEIT'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bartu.bart_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "early-shelf",
   "metadata": {},
   "source": [
    "- Longitude Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "beginning-posting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['360 DEGREES', '180 DEGREES', 'UNKNOWN', '180 GRADEN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lonu.lon_units.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "formal-appreciation",
   "metadata": {},
   "source": [
    "- Barometer types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fatty-demographic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MERCURY', 'SYMPISIOMETER', 'ANEROID'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_barot.baro_types.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "desperate-compilation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "g = sns.histplot(data=df_barot, x=\"Year\", hue=\"baro_types\", \n",
    "                 multiple=\"stack\", palette='deep', ax=ax, legend=True)\n",
    "ax.grid(False)\n",
    "plt.setp(g.get_xticklabels(), rotation=45)\n",
    "plt.title('Barometer types', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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
}