{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d22ddaf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1d442f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "bl_char_stats_dict = defaultdict(int)\n",
    "german_char_stats_dict = defaultdict(int)\n",
    "french_char_stats_dict = defaultdict(int)\n",
    "finnish_char_stats_dict = defaultdict(int)\n",
    "swedish_char_stats_dict = defaultdict(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f813206e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# English\n",
    "with open('bl_stats.pickle', 'rb') as f_p:\n",
    "    bl_char_stats_dict = pickle.load(f_p)\n",
    "\n",
    "# German\n",
    "with open('german_europeana_stats.pickle', 'rb') as f_p:\n",
    "    german_char_stats_dict = pickle.load(f_p)\n",
    "# French\n",
    "with open('french_europeana_stats.pickle', 'rb') as f_p:\n",
    "    french_char_stats_dict = pickle.load(f_p)\n",
    "# Finnish\n",
    "with open('finnish_europeana_stats.pickle', 'rb') as f_p:\n",
    "    finnish_char_stats_dict = pickle.load(f_p)\n",
    "# Swedish\n",
    "with open('swedish_europeana_stats.pickle', 'rb') as f_p:\n",
    "    swedish_char_stats_dict = pickle.load(f_p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98389af",
   "metadata": {},
   "source": [
    "# British Library Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9a9e752",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bl_stats = pd.DataFrame(bl_char_stats_dict.items(), columns=['year', 'length'])\n",
    "df_bl_stats[\"year\"] = pd.to_numeric(df_bl_stats[\"year\"])\n",
    "df_bl_stats_filtered = df_bl_stats[\n",
    "    (df_bl_stats[\"year\"] >= 1800) & (df_bl_stats[\"year\"] <= 1899)\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "bl_plot = sns.barplot(x=\"year\", y=\"length\", data=df_bl_stats_filtered.sort_values(by=['year']))\n",
    "\n",
    "bl_plot.set_title(\"Number of chars per year (BL, English)\")\n",
    "bl_plot.set(ylabel=\"Number of chars\")\n",
    "bl_plot.set(xlabel=\"Year\")\n",
    "bl_plot.set_xticklabels(bl_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = bl_plot.get_figure()\n",
    "fig.savefig(\"./figures/bl_corpus_stats.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6da9b526",
   "metadata": {},
   "source": [
    "# German Europeana Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b93c2dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_german_europeana_stats = pd.DataFrame(german_char_stats_dict.items(), columns=['year', 'length'])\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "\n",
    "german_europeana_plot = sns.barplot(x=\"year\", y=\"length\", data=df_german_europeana_stats.sort_values(by=['year']))\n",
    "\n",
    "german_europeana_plot.set_title(\"Number of chars per year (Europeana, German)\")\n",
    "german_europeana_plot.set(ylabel=\"Number of chars\")\n",
    "german_europeana_plot.set(xlabel=\"Year\")\n",
    "german_europeana_plot.set_xticklabels(german_europeana_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = german_europeana_plot.get_figure()\n",
    "fig.savefig(\"./figures/german_europeana_corpus_stats.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4dbd3bf",
   "metadata": {},
   "source": [
    "# French Europeana Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9991e671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_french_europeana_stats = pd.DataFrame(french_char_stats_dict.items(), columns=['year', 'length'])\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "\n",
    "french_europeana_plot = sns.barplot(x=\"year\", y=\"length\", data=df_french_europeana_stats.sort_values(by=['year']))\n",
    "\n",
    "french_europeana_plot.set_title(\"Number of chars per year (Europeana, French)\")\n",
    "french_europeana_plot.set(ylabel=\"Number of chars\")\n",
    "french_europeana_plot.set(xlabel=\"Year\")\n",
    "french_europeana_plot.set_xticklabels(french_europeana_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = french_europeana_plot.get_figure()\n",
    "fig.savefig(\"./figures/french_europeana_corpus_stats.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1accee04",
   "metadata": {},
   "source": [
    "# Finnish Europeana Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20466e12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_finnish_europeana_stats = pd.DataFrame(finnish_char_stats_dict.items(), columns=['year', 'length'])\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "\n",
    "finnish_europeana_plot = sns.barplot(x=\"year\", y=\"length\",\n",
    "                                     data=df_finnish_europeana_stats.sort_values(by=['year']))\n",
    "\n",
    "finnish_europeana_plot.set_title(\"Number of chars per year (Europeana, Finnish)\")\n",
    "finnish_europeana_plot.set(ylabel=\"Number of chars\")\n",
    "finnish_europeana_plot.set(xlabel=\"Year\")\n",
    "finnish_europeana_plot.set_xticklabels(finnish_europeana_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = finnish_europeana_plot.get_figure()\n",
    "fig.savefig(\"./figures/finnish_europeana_corpus_stats.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c02f842e",
   "metadata": {},
   "source": [
    "# Swedish Europeana Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf672580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_swedish_europeana_stats = pd.DataFrame(swedish_char_stats_dict.items(), columns=['year', 'length'])\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "\n",
    "swedish_europeana_plot = sns.barplot(x=\"year\", y=\"length\",\n",
    "                                     data=df_swedish_europeana_stats.sort_values(by=['year']))\n",
    "\n",
    "swedish_europeana_plot.set_title(\"Number of chars per year (Europeana, Swedish)\")\n",
    "swedish_europeana_plot.set(ylabel=\"Number of chars\")\n",
    "swedish_europeana_plot.set(xlabel=\"Year\")\n",
    "swedish_europeana_plot.set_xticklabels(swedish_europeana_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = swedish_europeana_plot.get_figure()\n",
    "fig.savefig(\"./figures/swedish_europeana_corpus_stats.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a24bb2",
   "metadata": {},
   "source": [
    "# Mixed Corpus Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a133b022",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dicts = [bl_char_stats_dict,\n",
    "             german_char_stats_dict,\n",
    "             french_char_stats_dict,\n",
    "             finnish_char_stats_dict,\n",
    "             swedish_char_stats_dict]\n",
    "\n",
    "all_languages_stats_dict = defaultdict(int)\n",
    "for d in all_dicts:\n",
    "    for k, v in d.items():\n",
    "        # Fix for British Library Corpus, only >= 1800\n",
    "        # Fix for 1945 - 1999\n",
    "        if k and int(k) >= 1800 and int(k) < 1945:\n",
    "            all_languages_stats_dict[k] += v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f40cfc8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all_stats = pd.DataFrame(all_languages_stats_dict.items(), columns=['year', 'length'])\n",
    "\n",
    "plt.figure(figsize=(50, 6))\n",
    "\n",
    "all_plot = sns.barplot(x=\"year\", y=\"length\", data=df_all_stats.sort_values(by=['year']))\n",
    "\n",
    "all_plot.set_title(\"Number of chars per year (All Languages)\")\n",
    "all_plot.set(ylabel=\"Number of chars\")\n",
    "all_plot.set(xlabel=\"Year\")\n",
    "all_plot.set_xticklabels(all_plot.get_xticklabels(), rotation=90)\n",
    "\n",
    "fig = all_plot.get_figure()\n",
    "fig.savefig(\"./figures/all_corpus_stats.png\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}