study_GK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Greek Study Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "import collections\n",
    "from datetime import datetime\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from iMahir import loadStudy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_dir = os.path.expanduser('~/')\n",
    "greek = os.path.join(home_dir, 'Documents/vocabulary/greek.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon 30 Dec 2019 09:55:56 GMT\r\n"
     ]
    }
   ],
   "source": [
    "! date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing TF...\n",
      "   |     0.00s No structure info in otext, the structure part of the T-API cannot be used\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       "@font-face {\n",
       "  font-family: \"SBL_BLit\";\n",
       "  src:\n",
       "    local(\"SBL_BLit.ttf\"),\n",
       "    url(\"https://github.com/annotation/text-fabric/blob/master/tf/server/static/fonts/SBL_BLit.ttf?raw=true\");\n",
       "}\n",
       ".features {\n",
       "    font-family: monospace;\n",
       "    font-size: medium;\n",
       "    font-weight: bold;\n",
       "    color: #0a6611;\n",
       "    display: flex;\n",
       "    flex-flow: column nowrap;\n",
       "    padding: 0.1em;\n",
       "    margin: 0.1em;\n",
       "    direction: ltr;\n",
       "}\n",
       ".features div,.features span {\n",
       "    padding: 0;\n",
       "    margin: -0.1rem 0;\n",
       "}\n",
       ".features .f {\n",
       "    font-family: sans-serif;\n",
       "    font-size: x-small;\n",
       "    font-weight: normal;\n",
       "    color: #5555bb;\n",
       "}\n",
       ".features .xft {\n",
       "  color: #000000;\n",
       "  background-color: #eeeeee;\n",
       "  font-size: medium;\n",
       "  margin: 0.1em 0em;\n",
       "}\n",
       ".features .xft .f {\n",
       "  color: #000000;\n",
       "  background-color: #eeeeee;\n",
       "  font-style: italic;\n",
       "  font-size: small;\n",
       "  font-weight: normal;\n",
       "}\n",
       ".verse {\n",
       "    display: flex;\n",
       "    flex-flow: row wrap;\n",
       "    direction: ltr;\n",
       "}\n",
       ".vl {\n",
       "    display: flex;\n",
       "    flex-flow: column nowrap;\n",
       "    justify-content: flex-end;\n",
       "    align-items: flex-end;\n",
       "    direction: ltr;\n",
       "    width: 100%;\n",
       "}\n",
       ".outeritem {\n",
       "    display: flex;\n",
       "    flex-flow: row wrap;\n",
       "    direction: ltr;\n",
       "}\n",
       ".word {\n",
       "    padding: 0.1em;\n",
       "    margin: 0.1em;\n",
       "    border-radius: 0.1em;\n",
       "    border: 1px solid #cccccc;\n",
       "    display: flex;\n",
       "    flex-flow: column nowrap;\n",
       "    direction: ltr;\n",
       "    background-color: #ffffff;\n",
       "}\n",
       ".occs {\n",
       "    font-size: x-small;\n",
       "}\n",
       ".tr,.tr a:visited,.tr a:link {\n",
       "    font-family: sans-serif;\n",
       "    font-size: large;\n",
       "    color: #000044;\n",
       "    direction: ltr;\n",
       "    text-decoration: none;\n",
       "}\n",
       ".trb,.trb a:visited,.trb a:link {\n",
       "    font-family: sans-serif;\n",
       "    font-size: normal;\n",
       "    direction: ltr;\n",
       "    text-decoration: none;\n",
       "}\n",
       ".grk,.grk a:visited,.sy a:link {\n",
       "    font-family: \"SBL_BLit\", serif;\n",
       "    font-size: large;\n",
       "    color: #111111;\n",
       "    direction: ltr;\n",
       "    text-decoration: none;\n",
       "}\n",
       ".vn {\n",
       "  font-size: small !important;\n",
       "  padding-right: 1em;\n",
       "}\n",
       ".nd {\n",
       "    font-family: monospace;\n",
       "    font-size: x-small;\n",
       "    color: #999999;\n",
       "}\n",
       ".hl {\n",
       "    background-color: #ffee66;\n",
       "}\n",
       "\n",
       "tr.tf, td.tf, th.tf {\n",
       "  text-align: left;\n",
       "}\n",
       "\n",
       "span.hldot {\n",
       "\tbackground-color: var(--hl-strong);\n",
       "\tborder: 0.2rem solid var(--hl-rim);\n",
       "\tborder-radius: 0.4rem;\n",
       "\t/*\n",
       "\tdisplay: inline-block;\n",
       "\twidth: 0.8rem;\n",
       "\theight: 0.8rem;\n",
       "\t*/\n",
       "}\n",
       "span.hl {\n",
       "\tbackground-color: var(--hl-strong);\n",
       "\tborder-width: 0;\n",
       "\tborder-radius: 0.1rem;\n",
       "\tborder-style: solid;\n",
       "}\n",
       "\n",
       "span.hlup {\n",
       "\tborder-color: var(--hl-dark);\n",
       "\tborder-width: 0.1rem;\n",
       "\tborder-style: solid;\n",
       "\tborder-radius: 0.2rem;\n",
       "  padding: 0.2rem;\n",
       "}\n",
       "\n",
       ":root {\n",
       "\t--hl-strong:        hsla( 60, 100%,  70%, 0.9  );\n",
       "\t--hl-rim:           hsla( 55, 100%,  60%, 0.9  );\n",
       "\t--hl-dark:          hsla( 55, 100%,  40%, 0.9  );\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tischendorf GNT Greek ready for study.\n",
      "this is session 10:\n",
      "score 3: 76 terms\n",
      "score 1: 2 terms\n",
      "score 0: 25 terms\n",
      "total: 103\n"
     ]
    }
   ],
   "source": [
    "grk = loadStudy(greek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following scores were changed \n",
      "\n",
      "duration:  0:05:20.336632\n",
      "\n",
      "seconds per term: 3.11\n"
     ]
    }
   ],
   "source": [
    "grk.learn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "### Notes\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Size of Known Term Set (Scores 3-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all sessions and session data\n",
    "sessions = [sd for sd in grk.set_data['stats'] if 'score_counts' in sd]\n",
    "score_lengths = [s['score_counts']['3']+s['score_counts']['4'] for s in sessions]\n",
    "dates = [datetime.strptime(sd['date'], '%Y-%m-%d %H:%M:%S.%f') for sd in sessions]\n",
    "date_labels = [datetime.strftime(time, format='%d-%m_%H:%M') for time in dates]\n",
    "date2nlearned = dict(zip(date_labels, score_lengths)) # get dict for references\n",
    "\n",
    "# plot this data only with cutoff\n",
    "cutoff = -30 # max amount\n",
    "plt_sessions = sessions[cutoff:]\n",
    "plt_scores = score_lengths[cutoff:]\n",
    "plt_dates = date_labels[cutoff:]\n",
    "\n",
    "# calculate cycle lines\n",
    "cycle_bounds = []\n",
    "last_cycle = None\n",
    "for i, sd in enumerate(plt_sessions):\n",
    "    cycle = sd['cycle']\n",
    "    last_cycle = cycle if not last_cycle else last_cycle\n",
    "    if last_cycle != cycle:\n",
    "        cycle_bounds.append(i-0.5)\n",
    "        last_cycle = cycle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make the plot\n",
    "x = np.arange(len(plt_scores))\n",
    "plt.figure(figsize=(13, 7))\n",
    "plt.plot(x, plt_scores, linestyle='dotted', color='lightblue')\n",
    "plt.scatter(x, plt_scores, color='darkblue')\n",
    "plt.xticks(x, plt_dates, rotation=90, size=10)\n",
    "plt.yticks(size=12)\n",
    "plt.ylabel('# of Learned Terms', size=16)\n",
    "plt.xlabel('Study Session Date', size=16)\n",
    "plt.title(f'total learned terms: {plt_scores[-1]}', size=16)\n",
    "for bound in cycle_bounds:\n",
    "    plt.axvline(bound, color='green', linestyle='dotted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unlearned Terms by Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "47     1\n",
       "60     1\n",
       "43     1\n",
       "24     1\n",
       "22     1\n",
       "21     1\n",
       "18     1\n",
       "19     2\n",
       "17     3\n",
       "16     6\n",
       "15     8\n",
       "14    23\n",
       "13    35\n",
       "12    65\n",
       "11    69\n",
       "10    82\n",
       "9     90\n",
       "8    105\n",
       "7    115\n",
       "6    159\n",
       "5    209\n",
       "4    301\n",
       "3    405\n",
       "2    730\n",
       "1   1649"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get terms left to learn\n",
    "to_learn = collections.Counter()\n",
    "for i in grk.set_data['term_queues']['0']:\n",
    "    term_lexs = grk.set_data['terms_dict'][i]['source_lexemes']\n",
    "    for term in term_lexs:\n",
    "        freq = grk.F.freq_lex.v(term)\n",
    "        to_learn[freq] += 1           \n",
    "to_learn = pd.DataFrame.from_dict(to_learn, orient='index').sort_values(by=0)\n",
    "\n",
    "# make the plot\n",
    "nbars = np.arange(to_learn.shape[0])\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.barplot(nbars, to_learn[0], color='darkred')\n",
    "plt.xticks(nbars, labels=list(str(i) for i in to_learn.index), size=8)\n",
    "plt.yticks(size=12)\n",
    "plt.ylabel('N-Terms', size=16)\n",
    "plt.xlabel('Freq', size=16)\n",
    "plt.title(f'total unlearned terms: {to_learn[0].sum()}', size=16)\n",
    "plt.show()\n",
    "display(to_learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Push Updates to Github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "! cp /Users/cody/Documents/vocabulary/greek.json sample_vocab/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[master c38adaf] Latest updates\n",
      " 1 file changed, 203 insertions(+), 184 deletions(-)\n"
     ]
    }
   ],
   "source": [
    "! git add sample_vocab/greek.json; git commit -m \"Latest updates\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[master ebe17d0] Latest session\r\n",
      " 1 file changed, 16 insertions(+), 16 deletions(-)\r\n"
     ]
    }
   ],
   "source": [
    "! git add study_GK.ipynb; git commit -m \"Latest session\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enumerating objects: 11, done.\n",
      "Counting objects: 100% (11/11), done.\n",
      "Delta compression using up to 4 threads\n",
      "Compressing objects: 100% (7/7), done.\n",
      "Writing objects: 100% (7/7), 49.45 KiB | 4.12 MiB/s, done.\n",
      "Total 7 (delta 3), reused 0 (delta 0)\n",
      "remote: Resolving deltas: 100% (3/3), completed with 2 local objects.\u001b[K\n",
      "To https://github.com/codykingham/Mahir\n",
      "   dda7d63..ebe17d0  master -> master\n"
     ]
    }
   ],
   "source": [
    "! git push origin master"
   ]
  }
 ],
 "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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}