FR1_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2b5aaa88-e071-4d63-962f-6f800e7c37ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import cmlreaders as cml\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('/home1/djhalp/pybeh_pd')\n",
    "import pybeh_pd as pb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e2f83696-f626-4a38-a423-b513cdf549b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_fr1_events = cml.CMLReader.load_events(experiments=[\"FR1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "321c2e95-acc5-4376-ac5b-32a053cd77c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, -999, 1], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fr1_events.recalled.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d45a14bd-6d3f-412e-88dc-8f4d5b45caa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "events = all_fr1_events.query('type == [\"WORD\", \"REC_WORD\"]')\n",
    "events = events.assign(row_order=range(len(events)))\n",
    "events.rename(columns={'item_name': 'item'}, inplace=True)\n",
    "events = events[events['list'] > 0] # removing practice lists\n",
    "\n",
    "#removing subjects who were presented with the same word multiple times (probably repeated data)\n",
    "word_evs = events.query('type == \"WORD\"')\n",
    "n_word_pres = word_evs.groupby(['subject', 'session', 'list', 'item_num'], as_index=False).size()\n",
    "bad_lists = n_word_pres.query('size >= 2')\n",
    "bad_subs = bad_lists.subject.unique().tolist()\n",
    "\n",
    "#drop subjects without 12 items per list\n",
    "n_list_pres = word_evs.groupby(['subject', 'session', 'list'], as_index=False).size()\n",
    "bad_lists = n_list_pres.query('size != 12')\n",
    "bad_subs += bad_lists.subject.unique().tolist()\n",
    "\n",
    "#drop subjects with no recalls\n",
    "n_types = events.groupby('subject', as_index=False).agg({'type': 'nunique'})\n",
    "no_recall_subs = n_types.query('type != 2')\n",
    "bad_subs += no_recall_subs.subject.unique().tolist()\n",
    "\n",
    "#drop subs with messed up recalls\n",
    "rec_evs = events.query('type == \"REC_WORD\"')\n",
    "joint_evs = word_evs.merge(rec_evs[['subject', 'session', 'list', 'item']], how='left', indicator=True)\n",
    "bad_subs += joint_evs.query('(recalled == 1 and _merge != \"both\") or (recalled == 0 and _merge == \"both\")').subject.unique().tolist()\n",
    "\n",
    "#drop subjects with rare_items\n",
    "item_sub_df = word_evs.groupby(['item'], as_index=False).agg({'subject': 'count'})\n",
    "rare_items = item_sub_df.query('subject < 20').item.unique()\n",
    "bad_subs += word_evs.query('item in @rare_items').subject.unique().tolist()\n",
    "\n",
    "events.query('subject not in @bad_subs', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0e773012-9474-4a78-a9de-eb2f7dbd3ce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fixing serial position in REC_WORD events\n",
    "events.loc[events['type'] == \"REC_WORD\", \"serialpos\"] = np.nan\n",
    "events.sort_values('type', inplace=True)\n",
    "#bfill because WORD will come after REC_WORD after sorting\n",
    "events['serialpos'] = events.groupby(['subject', 'session', 'list', 'item'])['serialpos'].bfill()\n",
    "events.sort_values('row_order', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04c1e16c-6dee-42ad-97e4-1aa097d72d11",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_evs = events.query('type == \"WORD\"')\n",
    "rec_evs = events.query('type == \"REC_WORD\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cac95c74-cbec-4559-afc7-12ec88c0a1f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "spc_sess_df = word_evs.groupby(['subject', 'session', 'serialpos'], as_index=False).agg({'recalled': 'mean'})\n",
    "spc_df = spc_sess_df.groupby(['subject', 'serialpos'], as_index=False).agg({'recalled': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23fac797-8213-4a39-ae10-1b717c32aac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abfb25722d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.relplot(x='serialpos', y='recalled', data=spc_df, kind='line')\n",
    "g.set(xlabel='Serial Position', ylabel='P(recalled)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94f0ca42-7824-4353-b1d1-b235df67bf18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "rec_evs['outpos'] = rec_evs.groupby(['subject', 'session', 'list']).cumcount()\n",
    "n_list_df = word_evs.groupby(['subject', 'session'], as_index=False).agg({'list': 'nunique'}).rename(columns={'list': 'n_lists'})\n",
    "first_recall_sess_df = rec_evs.query('outpos == 0 and serialpos != -999') #don't include intrusions (i.e., serialpos == -999)\n",
    "\n",
    "#make sure there are 0s in serialpos that never get recalled first\n",
    "first_recall_sess_df = first_recall_sess_df.groupby(\n",
    "    ['subject', 'session', 'serialpos']).agg(\n",
    "    {'recalled': 'count'}).unstack(fill_value=0).stack().reset_index()\n",
    "\n",
    "first_recall_sess_df = first_recall_sess_df.merge(n_list_df)\n",
    "first_recall_sess_df['pfr'] = first_recall_sess_df['recalled'] / first_recall_sess_df['n_lists']\n",
    "pfr_df = first_recall_sess_df.groupby(['subject', 'serialpos'], as_index=False).agg({'pfr': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7354208c-de95-417c-9702-558b433ab8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abfe6f19cd0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.relplot(x='serialpos', y='pfr', data=pfr_df, kind='line')\n",
    "g.set(xlabel='Serial Position', ylabel='P(recalled first)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd6850af-c2b7-4f65-a488-43fa5009d3c2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pb_crp = events.groupby('subject').apply(pb.pd_crp, \n",
    "                                itemno_column='item_num').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "95c8809f-367a-4805-ba1c-6621ea84c3cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n",
      "  overwrite_input=overwrite_input, interpolation=interpolation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abfe233cad0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='lag', y='prob', kind='point', \n",
    "                  data=pb_crp.query('lag <= 5 and lag >= -5'))\n",
    "g.set(xlabel='Lag', ylabel='P(transition)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4cb792b0-d1a7-4e3d-80da-a141cf09f574",
   "metadata": {},
   "outputs": [],
   "source": [
    "#probably the correct way to do it (aggregating at all levels), but as far as I know not how we have done it in the past -- takes a little while to run\n",
    "pb_crp_list = events.groupby(['subject', 'session', 'list']).apply(pb.pd_crp, \n",
    "                                itemno_column='item_num').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "822666ef-e700-43ce-b01a-71065d113348",
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_crp_sess = pb_crp_list.groupby(['subject', 'session', 'lag']).agg({'prob': 'mean'}).reset_index()\n",
    "pb_crp2 = pb_crp_sess.groupby(['subject', 'lag']).agg({'prob': 'mean'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5aa31cf7-5de5-4b8b-87ea-01b381d376e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n",
      "  overwrite_input=overwrite_input, interpolation=interpolation\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abfee233c50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='lag', y='prob', kind='point', \n",
    "                  data=pb_crp2.query('lag <= 5 and lag >= -5'))\n",
    "g.set(xlabel='Lag', ylabel='P(transition)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ab2f331d-7071-435c-bf00-c5740a481177",
   "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>subject</th>\n",
       "      <th>temp_fact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>0.625303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1002P</td>\n",
       "      <td>0.602389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R1003P</td>\n",
       "      <td>0.697490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R1006P</td>\n",
       "      <td>0.674832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R1010J</td>\n",
       "      <td>0.603109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>R1467M</td>\n",
       "      <td>0.626045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>R1542J</td>\n",
       "      <td>0.562895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>R1569T</td>\n",
       "      <td>0.758442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>R1571T</td>\n",
       "      <td>0.741228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>R1572T</td>\n",
       "      <td>0.395809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  temp_fact\n",
       "0    R1001P   0.625303\n",
       "1    R1002P   0.602389\n",
       "2    R1003P   0.697490\n",
       "3    R1006P   0.674832\n",
       "4    R1010J   0.603109\n",
       "..      ...        ...\n",
       "239  R1467M   0.626045\n",
       "240  R1542J   0.562895\n",
       "241  R1569T   0.758442\n",
       "242  R1571T   0.741228\n",
       "243  R1572T   0.395809\n",
       "\n",
       "[244 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_temp_fact = events.groupby(\"subject\").apply(pb.pd_temp_fact,\n",
    "                                                   itemno_column='item_num', \n",
    "                                                   list_index=['subject', 'session', 'list'], \n",
    "                                                   pres_type=\"WORD\", rec_type=\"REC_WORD\", type_column='type'\n",
    "                                                   ).to_frame(name='temp_fact').reset_index()\n",
    "\n",
    "pb_temp_fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c630b1c-8910-42c4-9da1-8cd3e9e7ec62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2abff2e34150>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(x='temp_fact', data=pb_temp_fact)\n",
    "g.set(xlabel='Temporal Factor Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5435512c-6db7-470b-81f3-a20832decc12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace item nums so they are consistent across subjects\n",
    "item_col = 'item'\n",
    "item_num_col = 'item_num'\n",
    "item_num_df = events.query('type == \"WORD\"').drop_duplicates(subset=item_col, ignore_index=True\n",
    "                                    )[item_col].reset_index().rename(columns={'index': item_num_col})\n",
    "item_num_df['item_num'] = item_num_df['item_num'] + 1\n",
    "events_new = events.merge(item_num_df, on=item_col, suffixes=('', '_new'), \n",
    "                          how='left', sort=False)#.sort_values('mstime')\n",
    "events_new.fillna({'item_num_new': -999}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2c9d8001-c746-4238-bf6d-4c945946d3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim.downloader\n",
    "word2vec_vectors = gensim.downloader.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b8473ce5-a5b2-46a8-bde0-bae2f69bc3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def word_similarity(df, col1, col2, keyed_vector=None):\n",
    "    try:\n",
    "        return keyed_vector.similarity(df[col1].lower(), df[col2].lower())\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25b1e07a-e248-4466-8753-9b8dbd5b4c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all pairs of items\n",
    "items = item_num_df.item.values\n",
    "sem_sim_df = pd.MultiIndex.from_product([items, items], names=['wordpool_item_1', 'wordpool_item_2']).to_frame(index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9ddba187-bc99-4b6e-a0a0-5f6f4238c3a8",
   "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>wordpool_item_1</th>\n",
       "      <th>wordpool_item_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>HOUSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>HAWK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>WAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>GEESE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>STAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147451</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>HAZE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147452</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>THIEF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147453</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>SPHINX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147454</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>QUAIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147455</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>FLASK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147456 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       wordpool_item_1 wordpool_item_2\n",
       "0                HOUSE           HOUSE\n",
       "1                HOUSE            HAWK\n",
       "2                HOUSE            WAND\n",
       "3                HOUSE           GEESE\n",
       "4                HOUSE            STAR\n",
       "...                ...             ...\n",
       "147451           FLASK            HAZE\n",
       "147452           FLASK           THIEF\n",
       "147453           FLASK          SPHINX\n",
       "147454           FLASK           QUAIL\n",
       "147455           FLASK           FLASK\n",
       "\n",
       "[147456 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem_sim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "443f0751-c0ea-423d-860c-26de5889942c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute similarity of all pairs\n",
    "sem_sim_df['similarity'] = sem_sim_df.apply(word_similarity, \n",
    "               axis=1, col1='wordpool_item_1', \n",
    "               col2='wordpool_item_2', \n",
    "               keyed_vector=word2vec_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5f9e349e-c11c-450d-9abb-b51bb784ff29",
   "metadata": {},
   "outputs": [],
   "source": [
    "sem_sim_df.to_csv('/scratch/djh/fr1_sem_sim_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d5a3316f-3d83-41e4-9543-a1aab2d2903f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sem_sim_df = pd.read_csv('/scratch/djh/fr1_sem_sim_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1492d170-47a9-4c2f-8d1a-c2c18ef70b8e",
   "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>wordpool_item_1</th>\n",
       "      <th>wordpool_item_2</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>HOUSE</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>HAWK</td>\n",
       "      <td>0.113100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>WAND</td>\n",
       "      <td>0.093160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>GEESE</td>\n",
       "      <td>0.057868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOUSE</td>\n",
       "      <td>STAR</td>\n",
       "      <td>0.063717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147451</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>HAZE</td>\n",
       "      <td>0.127924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147452</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>THIEF</td>\n",
       "      <td>0.166890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147453</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>SPHINX</td>\n",
       "      <td>0.183773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147454</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>QUAIL</td>\n",
       "      <td>0.099602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147455</th>\n",
       "      <td>FLASK</td>\n",
       "      <td>FLASK</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147456 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       wordpool_item_1 wordpool_item_2  similarity\n",
       "0                HOUSE           HOUSE    1.000000\n",
       "1                HOUSE            HAWK    0.113100\n",
       "2                HOUSE            WAND    0.093160\n",
       "3                HOUSE           GEESE    0.057868\n",
       "4                HOUSE            STAR    0.063717\n",
       "...                ...             ...         ...\n",
       "147451           FLASK            HAZE    0.127924\n",
       "147452           FLASK           THIEF    0.166890\n",
       "147453           FLASK          SPHINX    0.183773\n",
       "147454           FLASK           QUAIL    0.099602\n",
       "147455           FLASK           FLASK    1.000000\n",
       "\n",
       "[147456 rows x 3 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem_sim_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8a5adec3-1b8f-4df0-a7cf-74f9416dc44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "sem_sim_num_df = sem_sim_df.merge(\n",
    "    item_num_df, left_on='wordpool_item_1', right_on='item').merge(\n",
    "    item_num_df, left_on='wordpool_item_2', right_on='item', suffixes=('_1', '_2')).drop(columns=['wordpool_item_1', 'wordpool_item_2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7414a870-0597-4087-8a16-a40f18763828",
   "metadata": {},
   "outputs": [],
   "source": [
    "sem_sims = pd.pivot_table(sem_sim_num_df, index='item_num_1', columns='item_num_2', values='similarity', dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a1183727-8e80-4fab-bd37-27e76ca89cc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pb_sem_crp = events_new.groupby('subject').apply(pb.pd_sem_crp, \n",
    "                                itemno_column='item_num_new', sem_sims=sem_sims).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1121a0d2-f6de-4d22-87d1-49f4a4fd8aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac119b28a50>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='sem_bin', y='prob', kind='point', \n",
    "                  data=pb_sem_crp)\n",
    "g.set(xlabel='Similarity Bin', ylabel='P(transition)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1962d276-facb-42f7-a0d5-7dce697ee5f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac119d586d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using average bin value\n",
    "pb_sem_crp['avg_sem_bin_mean'] = pb_sem_crp.groupby('sem_bin')['sem_bin_mean'].transform('mean')\n",
    "g = sns.relplot(x='avg_sem_bin_mean', y='prob', data=pb_sem_crp, kind='line')\n",
    "g.set(xlabel='Similarity', ylabel='P(transition)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "846bf074-a63a-43e4-a137-eafe89973f63",
   "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>subject</th>\n",
       "      <th>sem_fact</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1001P</td>\n",
       "      <td>0.550296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1002P</td>\n",
       "      <td>0.548142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R1003P</td>\n",
       "      <td>0.545138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R1006P</td>\n",
       "      <td>0.550895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R1010J</td>\n",
       "      <td>0.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>R1467M</td>\n",
       "      <td>0.647275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>R1542J</td>\n",
       "      <td>0.570793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>R1569T</td>\n",
       "      <td>0.579774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>R1571T</td>\n",
       "      <td>0.640936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>R1572T</td>\n",
       "      <td>0.651736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  sem_fact\n",
       "0    R1001P  0.550296\n",
       "1    R1002P  0.548142\n",
       "2    R1003P  0.545138\n",
       "3    R1006P  0.550895\n",
       "4    R1010J  0.650000\n",
       "..      ...       ...\n",
       "239  R1467M  0.647275\n",
       "240  R1542J  0.570793\n",
       "241  R1569T  0.579774\n",
       "242  R1571T  0.640936\n",
       "243  R1572T  0.651736\n",
       "\n",
       "[244 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb_sem_dist_fact = events_new.groupby(\"subject\").apply(pb.pd_dist_fact,\n",
    "                                                       rec_itemnos=None, \n",
    "                                                       itemno_column='item_num_new', \n",
    "                                                       list_index=['subject', 'session', 'list'], \n",
    "                                                       dist_mat=sem_sims, \n",
    "                                                       sim_columns=None, \n",
    "                                                       is_similarity=True, \n",
    "                                                       dist_columns=None,\n",
    "                                                       skip_first_n=0,\n",
    "                                                       pres_type=\"WORD\", rec_type=\"REC_WORD\", type_column='type', ret_counts=False\n",
    "                                                       ).to_frame(name='sem_fact').reset_index()\n",
    "\n",
    "pb_sem_dist_fact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "72431e8a-24a8-444c-b670-f8367317324d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac119d91c90>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(x='sem_fact', data=pb_sem_dist_fact)\n",
    "g.set(xlabel='Semantic Factor Score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0a30f2be-7831-46c4-8f34-5e3ccee54694",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "rec_evs['repeat'] = rec_evs.duplicated(subset=['subject', 'session', 'list', 'item'])\n",
    "rec_evs['correct'] = (rec_evs['repeat'] == False) & (rec_evs['intrusion'] == 0)\n",
    "rec_evs['next_rectime'] = rec_evs.groupby(['subject', 'session', 'list']).rectime.shift(-1)\n",
    "rec_evs['next_correct'] = rec_evs.groupby(['subject', 'session', 'list']).correct.shift(-1)\n",
    "rec_evs['irt'] = rec_evs['next_rectime'] - rec_evs['rectime']\n",
    "rec_evs.loc[(rec_evs['correct'] == False) | (rec_evs['next_correct'] == False), 'irt'] = np.nan\n",
    "rec_evs['correct_outpos'] = rec_evs.groupby(['subject', 'session', 'list'])['correct'].cumsum()\n",
    "rec_evs['n_correct'] = rec_evs.groupby(['subject', 'session', 'list'])['correct_outpos'].transform('max')\n",
    "rec_evs['irt_s'] = rec_evs['irt'] / 1000\n",
    "rec_evs['next_serialpos'] = rec_evs.groupby(['subject', 'session', 'list']).serialpos.shift(-1)\n",
    "rec_evs['transition'] = rec_evs['next_serialpos'] - rec_evs['serialpos']\n",
    "rec_evs.loc[(rec_evs['correct'] == False) | (rec_evs['next_correct'] == False), 'transition'] = np.nan\n",
    "rec_evs['next_item'] = rec_evs.groupby(['subject', 'session', 'list']).item.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4c0a4db7-a0f0-4e78-af6d-4edf450cc798",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_evs = rec_evs.merge(sem_sim_df, how='left', left_on=['item', 'next_item'], \n",
    "                        right_on=['wordpool_item_1', 'wordpool_item_2'])\n",
    "rec_evs.loc[(rec_evs['correct'] == False) | (rec_evs['next_correct'] == False), 'similarity'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f2c22ebb-afa8-4408-936d-9cedb98cb4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rec_evs['similarity_bin'] = pd.qcut(rec_evs.similarity, 10, labels=np.arange(0, 10)).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2e1bda6d-f27a-4390-bb2a-1e594be94532",
   "metadata": {},
   "outputs": [],
   "source": [
    "irt_sess_df = rec_evs.groupby(['subject', 'session', 'correct_outpos', 'n_correct'], as_index=False).agg({'irt_s': 'mean'})\n",
    "irt_df = irt_sess_df.groupby(['subject', 'correct_outpos', 'n_correct'], as_index=False).agg({'irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4472e47e-3d6d-4c2d-acce-fbde160613a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/seaborn/algorithms.py:98: RuntimeWarning: Mean of empty slice\n",
      "  boot_dist.append(f(*sample, **func_kwargs))\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1376: RuntimeWarning: All-NaN slice encountered\n",
      "  overwrite_input=overwrite_input, interpolation=interpolation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 414.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='correct_outpos', y='irt_s', hue='n_correct', data=irt_df.query('n_correct > 1 and n_correct < 10'), kind='point')\n",
    "g.set(xlabel='Output Position', ylabel='IRT (s)', title='FR1')\n",
    "g._legend.set_title('Total Recalled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "13811dcc-726b-4ac7-bffa-987779d6fe78",
   "metadata": {},
   "outputs": [],
   "source": [
    "crl_sess_df = rec_evs.groupby(['subject', 'session', 'transition'], as_index=False).agg({'irt_s': 'mean'})\n",
    "crl_df = crl_sess_df.groupby(['subject', 'transition'], as_index=False).agg({'irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e10f3b6d-ae42-4e98-a0ae-23b9290d6f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac119fee490>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='transition', y='irt_s', data=crl_df.query('abs(transition) < 6'),\n",
    "                order=np.arange(-5, 6),\n",
    "                kind='point')\n",
    "g.set(xlabel='Lag', ylabel='IRT (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f5ba9471-695f-4e27-8369-4d2c1a35e095",
   "metadata": {},
   "outputs": [],
   "source": [
    "sem_crl_sess_df = rec_evs.groupby(['subject', 'session', 'similarity_bin'], as_index=False).agg({'irt_s': 'mean'})\n",
    "sem_crl_df = sem_crl_sess_df.groupby(['subject', 'similarity_bin'], as_index=False).agg({'irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1e20b221-decf-45fd-9e85-91fbdee424b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ac119fee250>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='similarity_bin', y='irt_s', data=sem_crl_df,\n",
    "                kind='point')\n",
    "g.set(xlabel='Similarity', ylabel='IRT (s)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aa34eba-53be-43ff-942a-f8db25db760b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "workshop",
   "language": "python",
   "name": "workshop"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}