{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Social Network Analysis of Leviticus 17-26\n",
    "\n",
    "This notebook combines the participant references and semantic roles computed in other phases of this research project. The two datatypes are combined to create a social network model of the data and to explore this model by social network analytical tools. The first SNA-measures are given in this notobook, while more detailed studies of participant roles are reserved for other notebooks in this repo.\n",
    "\n",
    "**Content**\n",
    "1. Import of data\n",
    "2. Cross-tabulating participant and semantic roles\n",
    "3. Creation of network model\n",
    "4. Validation of the model\n",
    "5. First social network analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: uncompiled fa2util module.  Compile with cython for a 10-100x speed boost.\n"
     ]
    }
   ],
   "source": [
    "#Dataset path\n",
    "PATH = 'datasets/'\n",
    "\n",
    "import csv, collections, html\n",
    "from operator import itemgetter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from adjustText import adjust_text\n",
    "import networkx as nx\n",
    "import forceatlas2\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  1. Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the Hebrew data and Text-Fabric\n",
    "from tf.app import use\n",
    "A = use('etcbc/bhsa', hoist=globals(), mod='etcbc/heads/tf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.a Import of participant reference data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'{PATH}participants_FINAL.csv')\n",
    "df.columns = ['participant','refs']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The references are transformed to lists and their respective frequencies in the corpus are counted "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_list = []\n",
    "participant_freq = []\n",
    "\n",
    "for row in df.iterrows():\n",
    "    refs = [int(r) for r in row[1].refs.split()]\n",
    "    ref_list.append(refs)\n",
    "    participant_freq.append(len(refs))\n",
    "    \n",
    "df.insert(2, 'ref_list', ref_list)\n",
    "df.insert(3, 'freq', participant_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(f'Number of participants: {len(df)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two functions fetch the participant label from any given word or phrase in the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLabel(ref, df=df):\n",
    "    '''\n",
    "    This function fetches the actor/participant reference from the participant dataframe.\n",
    "    '''\n",
    "    \n",
    "    actor_list = []\n",
    "    \n",
    "    for row in df.iterrows():\n",
    "        if ref in row[1].ref_list:\n",
    "            actor_list.append(row[1].participant)\n",
    "    \n",
    "    return actor_list\n",
    "\n",
    "def Actor(ref, df=df):\n",
    "    '''\n",
    "    This function takes a reference as input and returns the participant label. Phrases are treated differently, becuase \n",
    "    non-verbal phrases require additional measures to find the nominal head of the phrase and return the label for that \n",
    "    particular constituent.\n",
    "    '''\n",
    "    \n",
    "    nom_head = E.nhead.t(ref) #Finding the nominal head(s) of the phrase\n",
    "    \n",
    "    if F.otype.v(ref) == 'word': #Identifying object suffixes\n",
    "        return getLabel(ref, df=df)\n",
    "    \n",
    "    elif F.typ.v(ref) == 'VP':\n",
    "        return getLabel(L.d(ref, 'phrase_atom')[0], df=df)\n",
    "    \n",
    "    elif F.typ.v(ref) == 'PP':\n",
    "        if len(nom_head) > 1:\n",
    "            return getLabel(L.d(ref, 'phrase_atom')[0], df=df)\n",
    "        if nom_head != E.head.t(ref): #If equal, the reference is a simple preposition with a suffix\n",
    "            return getLabel(L.u(nom_head[0], 'phrase_atom')[0], df=df)\n",
    "        else:\n",
    "            if getLabel(E.head.t(ref)[0], df=df):\n",
    "                return getLabel(E.head.t(ref)[0], df=df)\n",
    "            else:\n",
    "                return getLabel(L.u(nom_head[0], 'phrase_atom')[0], df=df)\n",
    "        \n",
    "    elif F.typ.v(ref) in {'NP','PrNP','PPrP','DPrP','CP'}:\n",
    "        return getLabel(L.u(nom_head[0], 'phrase_atom')[0], df=df)\n",
    "        \n",
    "    else:\n",
    "        return \"error\"\n",
    "\n",
    "#Actor(65418)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The participants and their references (tabular view):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRefs(references, limit=10):\n",
    "    \n",
    "    clause_list = set()\n",
    "    for r in references:\n",
    "        if F.otype.v(r) != 'clause':\n",
    "            r = L.u(r, 'clause')[0]\n",
    "        clause_list.add(r)\n",
    "    \n",
    "    clause_list = list(clause_list)\n",
    "    clause_list.sort()\n",
    "\n",
    "    ref_string = '''{}:{}'''.format(T.sectionFromNode(clause_list[0])[1], T.sectionFromNode(clause_list[0])[2])\n",
    "    for cl in clause_list[1:limit]:\n",
    "        bo, ch, ve = T.sectionFromNode(cl)\n",
    "            \n",
    "        prec_cl = clause_list.index(cl)-1\n",
    "        prec_bo, prec_ch, prec_ve = T.sectionFromNode(clause_list[prec_cl])\n",
    "        if ch == prec_ch and ve == prec_ve:\n",
    "            if '×' in ref_string[-3]:\n",
    "                indices = (ref_string.rindex('×'), ref_string.rindex(')'))\n",
    "                existing_value = ref_string[indices[0]+1:indices[1]]\n",
    "                new_value = str(int(existing_value)+1)\n",
    "                ref_string = ref_string[:indices[0]+1] + new_value + ref_string[indices[0]+len(new_value)+1:]\n",
    "            else:\n",
    "                ref_string += f' (×2)'\n",
    "        elif ch == prec_ch:\n",
    "            ref_string += f', {ve}'\n",
    "        else:\n",
    "            ref_string += f'; {ch}:{ve}'\n",
    "    \n",
    "    if len(clause_list) > limit:\n",
    "        return f'{ref_string} ...'\n",
    "    else:\n",
    "        return f'{ref_string}'\n",
    "    \n",
    "getRefs(df[df.participant == 'KL/ >JC/'].ref_list.item(), limit=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ref_dict = collections.defaultdict()\n",
    "\n",
    "for p in list(df.participant):\n",
    "    ref_dict[label_gloss[p]] = getRefs(df[df.participant == p].ref_list.item(), limit=1000)\n",
    "    \n",
    "df_ref = pd.DataFrame([ref_dict]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)  # or 1000\n",
    "df_ref.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref.to_csv('participant_Biblical_references.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.b Import agency ranks of participants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ranks_df = pd.read_csv(f'{PATH}role_ranks.csv', index_col=0)\n",
    "ranks_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function is defined to return the agency of any given reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Agency(ref, colname, df=ranks_df):\n",
    "    \n",
    "    if ref in list(df.index):\n",
    "        return df[df.index == ref][colname].item()\n",
    "\n",
    "#Agency(68032, 'new_rank')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Cross-tabulating participants and roles\n",
    "\n",
    "This section cross-tabulates the participant and role data to calculate the mean agency of each participant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "actor_list = [Actor(ph) for ph in list(ranks_df.index)]\n",
    "ranks_df.insert(8, 'Actor', actor_list) #The actor is inserted as a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross-tabulation of the data to count how often each participant obtains a certain agency level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dic = collections.defaultdict(lambda: collections.defaultdict(int))\n",
    "\n",
    "for row in ranks_df.iterrows():\n",
    "    for n in row[1].Actor:\n",
    "        dic[n][row[1].new_rank] += 1\n",
    "        \n",
    "agency_df = pd.DataFrame(dic).fillna(0).astype('Int64').T\n",
    "agency_df = agency_df[[5,4,3,1,0,-1,-2]]\n",
    "agency_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(df.participant)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean agency is calculated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "agency_mean = []\n",
    "\n",
    "for row in agency_df.iterrows():\n",
    "    n=0\n",
    "    total = 0\n",
    "    for v in row[1]:\n",
    "        total += (v * agency_df.columns[n])\n",
    "        n+=1\n",
    "    agency_mean.append(round(total/row[1].sum(), 3))\n",
    "    \n",
    "agency_df.insert(7, 'mean', agency_mean)\n",
    "\n",
    "#Inserting labels\n",
    "labels = [label_gloss[l] if l in label_gloss else l for l in list(agency_df.index)]\n",
    "agency_df.insert(0, 'label', labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "top_agency = agency_df[agency_df.sum(axis=1) > 20]\n",
    "top_agency.sort_values(by='mean', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(agency_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Creating nodes and edges\n",
    "\n",
    "The network model combines participant data and semantic roles. The primary principle is to isolate those clauses where at least two participants occur (they can be identical) which means that isolated participants are ignored. Secondly, the edges are made from the participant with the highest agency level toward the participant with the lowest agency level within the same clause. We can assume that the participant with the highest agency level is also most active in the event and therefore the source of the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def createEdges(colname, df=df, ranks_df=ranks_df, verb_list = [], relation='function', label_text='gloss', mode=str()):\n",
    "    '''\n",
    "    Input: dictionary of actors + nodes (references), plus preferred text type, that is, English gloss (default)\n",
    "        or transcription of the Hebrew lexeme (= trans)\n",
    "        colname is name of the rank column (usually \"rank\" or \"new_rank\")\n",
    "    Output: dictionary of edges and labels\n",
    "    '''\n",
    "    \n",
    "    error_list = []\n",
    "    \n",
    "    #Finding intersection between nodes\n",
    "    clause_node_list = []\n",
    "    for i, row in df.iterrows():\n",
    "        refs = [int(r) for r in row.refs.split()]\n",
    "        clause_node_list += list(set([L.u(n, 'clause')[0] for n in refs]))\n",
    "        \n",
    "    #Intersections are calculated by counting the frequency of unique clauses. If a clause appears more than once, there is\n",
    "    #an intersection\n",
    "    counter = collections.Counter(clause_node_list)\n",
    "    intersection = [n for n in counter if counter[n] > 1]\n",
    "         \n",
    "    edges = []\n",
    "    \n",
    "    if intersection:\n",
    "        \n",
    "        for cl in intersection: #Looping over clauses with intersecting actors\n",
    "            \n",
    "            clause_inventory = []\n",
    "            pred = False\n",
    "            \n",
    "            for ph in L.d(cl, 'phrase'):\n",
    "                ph_info = {}\n",
    "                sfx_info = {} #Directory for object suffixes\n",
    "                \n",
    "                rank = Agency(ph, colname, ranks_df)\n",
    "                \n",
    "                #Get verb gloss if Predicate\n",
    "                if F.function.v(ph) in {'Pred','PreS','PreO','PtcO','PreC'}:\n",
    "                    pred = True\n",
    "                    \n",
    "                    #Finding verb gloss:\n",
    "                    for w in L.d(ph, 'word'):\n",
    "                        if F.sp.v(w) == 'verb':\n",
    "                            pred_gloss, pred_lex = F.gloss.v(L.u(w, 'lex')[0]), F.lex.v(w)\n",
    "                \n",
    "                #If the phrase is annotated with a rank (agency), it is fetched.\n",
    "                if rank or rank == 0:\n",
    "                    \n",
    "                    ph_info['ref'] = ph\n",
    "                    ph_info['function'] = F.function.v(ph)\n",
    "                    ph_info['rank'] = rank\n",
    "                    \n",
    "                    clause_inventory.append(ph_info)\n",
    "                    \n",
    "                #If object suffix, the suffix info is stored separately and added to the clause inventory\n",
    "                if F.function.v(ph) in {'PreO','PtcO'}:\n",
    "                    for w in L.d(ph, 'word'):\n",
    "                        if F.sp.v(w) == 'verb' and (Agency(w, colname, ranks_df) or Agency(w, colname, ranks_df) == 0):\n",
    "                            sfx_info['ref'] = w\n",
    "                            sfx_info['function'] = F.function.v(ph)\n",
    "                            sfx_info['rank'] = Agency(w, colname, ranks_df)\n",
    "                                \n",
    "                            clause_inventory.append(sfx_info)\n",
    "            \n",
    "            if pred == True and pred_lex!= 'HJH[' and len(clause_inventory) > 1:\n",
    "                ranked = sorted(clause_inventory, key=itemgetter('rank'), reverse = True)                    \n",
    "                    \n",
    "                #Getting Actor and labels\n",
    "                Actor_ref = ranked[0]['ref']\n",
    "                Actor_rank = ranked[0]['rank']\n",
    "                Actors = Actor(Actor_ref, df=df) #A list of Actors\n",
    "                \n",
    "                if Actors == 'error':\n",
    "                    error_list.append((cl, Actor_ref))\n",
    "                        \n",
    "                #Creating edges from Actor to Undergoer(s)\n",
    "                for Undergoer in ranked[1:]:\n",
    "                    Undergoer_ref = Undergoer['ref']\n",
    "                    Undergoer_rank = Undergoer['rank']\n",
    "                    Undergoers = Actor(Undergoer_ref, df=df)\n",
    "                    \n",
    "                    if Undergoers == 'error':\n",
    "                        error_list.append((cl, Undergoer_ref))\n",
    "                    \n",
    "                    if (Actors and Undergoers) and (Undergoers != 'error') and (Actors != 'error'):\n",
    "                        \n",
    "                        ref = '{} {}:{}'.format(*T.sectionFromNode(cl))\n",
    "                        \n",
    "                        for A in Actors:\n",
    "                            for U in Undergoers:\n",
    "                                \n",
    "                                if mode == 'one-mode':\n",
    "                                    edge = (A, Actor_ref, Actor_rank, U, Undergoer_ref, Undergoer_rank, pred_gloss, cl, ref)\n",
    "                                    edges.append(edge)\n",
    "                                elif mode == 'two-mode':\n",
    "                                    Actor_edge = (A, Actor_ref, Actor_rank, pred_gloss, cl, ref)\n",
    "                                    Undergoer_edge = (pred_gloss, U, Undergoer_ref, Undergoer_rank, cl, ref)\n",
    "                                    edges.append(Actor_edge), edges.append(Undergoer_edge)\n",
    "                                else:\n",
    "                                    print(\"You need to specify mode\")\n",
    "                                \n",
    "        return edges, error_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two models are created to account for two versions of the agency data. The 'old' data does not account negations in the clause, while the 'new' data involves a recalculation of the agency (NB: the recalculation is done in another notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_edges = createEdges(colname='rank',df=df, mode='one-mode')\n",
    "print(len(old_edges[0]))\n",
    "\n",
    "#With new ranks because of negatives (e.g. Agent -> Frustrative)\n",
    "new_edges = createEdges(colname='new_rank',df=df,mode='one-mode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "old_edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore errors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = old_edges[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for e in errors:\n",
    "    A.pretty(e[0], highlights={e[1]:'gold'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both errors concern adverbial phrases, both referring to a location, so they are not important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will remove edges for which both the Actor and Undergoer i 0 (Neutral) in Agency. In these cases, there is no interaction so those relations are not important:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeNeutral(edge_list):\n",
    "    upd_edge_list = []\n",
    "    \n",
    "    for e in edge_list[0]:\n",
    "        Actor_rank = e[2]\n",
    "        Undergoer_rank = e[5]\n",
    "        \n",
    "        if Actor_rank == 0 and Undergoer_rank == 0:\n",
    "            continue\n",
    "        else:\n",
    "            upd_edge_list.append(e)\n",
    "            \n",
    "    return upd_edge_list\n",
    "            \n",
    "old_edges = removeNeutral(old_edges)\n",
    "new_edges = removeNeutral(new_edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Validation and export of the network model\n",
    "\n",
    "#### 4a. Validation\n",
    "\n",
    "Before the final export the edges need review. Several issues need validation:\n",
    "\n",
    "* Are all relevant clauses included?\n",
    "* Are the participants annotated correctly?\n",
    "* Are the roles annotated correctly?\n",
    "\n",
    "The review is carried out manually but assisted by an interface and colorcoding. 'Green' signals that the clause is included in the network, 'salmon' signals absence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_verse = T.nodeFromSection(('Leviticus',17,1))\n",
    "last_verse = T.nodeFromSection(('Leviticus',26,46))\n",
    "\n",
    "clauses = range(L.d(first_verse, 'clause')[0], L.d(last_verse, 'clause')[0]+2)\n",
    "verbal_clauses = []\n",
    "for cl in clauses:\n",
    "    pred = False\n",
    "    for ph in L.d(cl, 'phrase'):\n",
    "        if F.function.v(ph) in {'Pred','PreS','PreO','PtcO','PreC'}:\n",
    "            pred = True\n",
    "            for w in L.d(ph, 'word'):\n",
    "                if F.sp.v(w) == 'verb' and F.lex.v(w) != 'HJH[':\n",
    "                    verbal_clauses.append(cl)\n",
    "\n",
    "print(f'Number of clauses to review: {len(verbal_clauses)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validate(clauses, edges, n):\n",
    "    print(f'Nr {n}: {clauses[n]}')\n",
    "    \n",
    "    df = pd.DataFrame(edges)\n",
    "    edge_clauses = list(df[7])\n",
    "    \n",
    "    if clauses[n] in edge_clauses:\n",
    "        subset = df[df[7] == clauses[n]]\n",
    "        \n",
    "        for i, row in subset.iterrows():\n",
    "            print(f'Actor: {row[0]} - Agency: {row[2]}')\n",
    "            print(f'Undergoer: {row[3]} - Agency: {row[5]}\\n')\n",
    "        \n",
    "        A.pretty(clauses[n], highlights={clauses[n]:'lightgreen'})\n",
    "    \n",
    "    else:\n",
    "        A.pretty(clauses[n], highlights={clauses[n]:'salmon'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "validate(verbal_clauses, old_edges, n)\n",
    "n+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### -----Update: All corrections made -----\n",
    "\n",
    "\n",
    "\n",
    "**Lev 17**\n",
    "* BN >HRN added to participant: Need to be listed as part-whole relations across the entire text\n",
    "* 'MN QRB/ <M/ ->JC >JC' added to Nodes\n",
    "* KHN added to roles\n",
    "* 'F<JR=' added to Nodes\n",
    "* 'B TWK/ -BJT JFR>L' and 'B TWK/ -BJT JFR>L#2' added to Nodes\n",
    "* L KM added to Roles\n",
    "\n",
    "**Lev 18**\n",
    "* '<RWH >CH >B -2ms' added to Nodes\n",
    "* '<RWH >CH >X -2ms' added to Nodes\n",
    "* '<RWH/ ->CH#2' corrected in Nodes\n",
    "* 'MN QRB/ <M/ -NPC' added in Nodes\n",
    "* 'L PNH/ -BN JFR>L' added in Nodes\n",
    "    \n",
    "**Lev 19**\n",
    "* '>B >JC' and >M >JC added to participants: Need to be listed as part-whole relations across the entire text\n",
    "* '<NJ' added to participants\n",
    "* 'B CM/ -JHWH' and '>T CM/ >LHJM/ -2ms' added to Nodes\n",
    "* 'XRC=/' added to Nodes\n",
    "* 'PNH/ DL/' and 'PNH/ GDWL/' added to Nodes\n",
    "* '>T BN/ <M/ -2ms' changed in collocations\n",
    "* Changing Aktionsart of QWM (4_Export_Aktionsart)\n",
    "* 'PNH/ ZQN/' added to Nodes\n",
    "\n",
    "**Lev 20**\n",
    "* '>T CM/ QDC/ -JHWH' added to Nodes\n",
    "* '>JC' changed role\n",
    "* '>T >CH/ >JC/' changed role\n",
    "* 'MN QRB/ <M/ ->JC', 'MN QRB/ <M/ -KL', 'MN QRB/ <M/ -NPC', and 'MN QRB/ <M/ -CNJM -' added to Nodes\n",
    "\n",
    "**Lev 21**\n",
    "* Passive corrected in 2c_Instigation\n",
    "* MCH changed in participants\n",
    "* '>CH_2' added in participants\n",
    "* BN JFR>L added in participants: Need to be listed as part-whole relations across the entire text\n",
    "\n",
    "**Lev 22**\n",
    "* NPC_2 added in participants (Aaron's offspring)\n",
    "* NPC_3 added in participants (A chattel-slave)\n",
    "* Make sure that the compound reference \">HRN BN >HRN\" is only deleted if the references have been succesfully distributed to either >HRN or BN >HRN\n",
    "* BN JFR>L changed in participants\n",
    "* Hypernyms accross the text: >JC (top-level) refers both to GR and a native\n",
    "* '>JC#2' added to Nodes: Refers to \"any man\" within the household of the priest\n",
    "\n",
    "**Lev 23**\n",
    "* Hypernyms accross the text: 'L H <NJ/ W L H GR/' refers to the poor and the stranger. If this hypernym is removed before the reference are distributed to the involved participants, the references will be missing.\n",
    "\n",
    "**Lev 24**\n",
    "* BN >CH changed in Affectedness\n",
    "* 3mp removed from Participants (one instance)\n",
    "* JHWH changed in Affectedness\n",
    "\n",
    "**Lev 25**\n",
    "* When '<BD -2ms>MH -2ms' is removed (because it is a hypernym) the participants are missing. Hypernyms need to be constructed on top-level before removal.\n",
    "* Skip clauses with HJH? They are not interactions\n",
    "* 'JD ->JC', 'JD ->X -2ms' and 'JD GR TWCB' added to Nodes (as synonyms)\n",
    "* '>X -2ms' changed in Affectedness\n",
    "* GR TWCB#2 has been changed in Participants to distinguish \"your brother\" from \"foreigners\" although they are sometimes given the same label.\n",
    "\n",
    "**Lev 26**\n",
    "* NPL changed in Akstionsart to stative\n",
    "* XMC changed in Nodes from XMC/\n",
    "* PNH changed in Affectedness\n",
    "* 'L PNH/ >JB[ -<M' changed in Nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4b. Export\n",
    "\n",
    "#### Export nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(new_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "old_df = pd.DataFrame(old_edges)\n",
    "old_df.insert(3, 'new_rank_Actor', new_df.iloc[:,2])\n",
    "old_df.insert(7, 'new_rank_Undergoer', new_df.iloc[:,5])\n",
    "old_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The labels (generated from the ETCBC-transliteration) will be replaced more readable ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_gloss = {'>CH BN -2ms': 'daughter-in-law',\n",
    "               '>DM': 'human_being',\n",
    "               'GR': 'sojourner',\n",
    "               '>CH#2': 'woman_in_menstruation',\n",
    "               '>X -2ms': 'brother',\n",
    "               'BFR/ BN/ -<M': 'children',\n",
    "               '>T BT/ BN/ ->CH W >T BT/ BT/ ->CH': 'granddaughter_of_woman',\n",
    "               'MLK=': 'idols',\n",
    "               'NPC#3': 'slave',\n",
    "               '<M': 'foreign_nations',\n",
    "               '>T >CH/ >JC/': \"fellow's_wife\",\n",
    "               'ZR': 'lay-person',\n",
    "               '>B -2ms': 'father',\n",
    "               'JHWH': 'YHWH',\n",
    "               '2mp_sfx': '2MPl',\n",
    "               '>HRN': 'Aaron',\n",
    "               'MN >JC/ ->CH': 'husband',\n",
    "               'DWDH -2ms': 'aunt-in-law',\n",
    "               '>CH >M ->CH': 'woman_and_her_mother',\n",
    "               'RDP': 'no-one',\n",
    "               'BN >CH': 'blasphemer',\n",
    "               'XRC=/': 'deaf',\n",
    "               'BN JFR>L': 'Israelites',\n",
    "               'C>R >B -2ms': 'aunt',\n",
    "               'KL': 'group_of_people',\n",
    "               '<RWH/ -<RWH -2ms': 'granddaughter',\n",
    "               'PNH/ ZQN/': 'elderly',\n",
    "               'BTWLH/': 'virgin',\n",
    "               '>JC >JC': 'an_Israelite',\n",
    "               'BN ->X -2ms': 'son_of_brother',\n",
    "               'QNH': 'purchaser',\n",
    "               '>JC >CH': 'man/woman',\n",
    "               '<RWH/ >CH/ W BT/ ->CH': 'woman_and_her_daughter',\n",
    "               '3mp': 'witnesses',\n",
    "               '>L MCPXT/ ->JC': 'clan',\n",
    "               'BT >B -2msBT >M -2ms': 'sister',\n",
    "               'PNH/ GDWL/': 'rich',\n",
    "               '>XD': \"brother's_brother\",\n",
    "               '>T== ZKR=/': 'male',\n",
    "               '2ms': '2MSg',\n",
    "               '>XWT ->CH': 'sister_of_woman',\n",
    "               'BN TWCB': 'sons_of_sojourners',\n",
    "               '>M -2ms': 'mother',\n",
    "               'L >JC/': 'man',\n",
    "               'ZR< ->JC': 'offspring',\n",
    "               'PNH/ DL/': 'poor',\n",
    "               'L PNH/ <WR/': 'blind',\n",
    "               '>CH': 'woman',\n",
    "               '>CH >B -2ms': \"father's_wife\",\n",
    "               'MCH=': 'Moses',\n",
    "               'BN >HRN': \"Aaron's_sons\",\n",
    "               'BT -2ms': 'daughter',\n",
    "               'CPXH': 'handmaid',\n",
    "               'C>R -HW>': 'relative',\n",
    "               '>LMNH GRC XLL': 'widowed/expelled/defiled_woman',\n",
    "               'HM': 'remnants',\n",
    "               '>T PGR/ -<M': 'corpse',\n",
    "               'DWD ->X -2ms': \"brother's_uncle\",\n",
    "               'B <M/ -2ms': 'kinsmen',\n",
    "               'BN -2ms': 'son',\n",
    "               '>X >B -2ms': \"father's_brother\",\n",
    "               'MYRJM': 'Egyptians',\n",
    "               'KL/ >JC/': 'blemished_man',\n",
    "               'TWCB KHN FKJR': 'resident_laborer',\n",
    "               'JLJD BJT -KHN': 'resident_with_priest',\n",
    "               'CLMJT': 'Shelomith',\n",
    "               'G>L': 'redeemer',\n",
    "               'LWJ': 'Levite',\n",
    "               'MN MCPXT/ -BN TWCB': \"sojourner's descendants\",\n",
    "               '<FR= >CH': 'ten_women',\n",
    "               '>B -HM': 'ancestors',\n",
    "               'J<QB/': 'Jacob',\n",
    "               'JYXQ/': 'Isaac',\n",
    "               '>BRHM/': 'Abraham'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_df = old_df\n",
    "\n",
    "Source = []\n",
    "Target = []\n",
    "\n",
    "for n, row in edges_df.iterrows():\n",
    "    source = row[0]\n",
    "    target = row[3]\n",
    "    \n",
    "    Source.append(label_gloss[source])\n",
    "    Target.append(label_gloss[target])\n",
    "    \n",
    "edges_df.insert(1, 'Source', Source)\n",
    "edges_df.insert(6, 'Target', Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "edges_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weight of the ties between the participants is defined as the difference between Actor and Undergoer Rank. We create time stamps to include original rank and new rank (new rank takes negations into account): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "old_weight = (edges_df[2]-edges_df[5])**2\n",
    "new_weight = (edges_df['new_rank_Actor']-edges_df['new_rank_Undergoer'])**2\n",
    "\n",
    "#Insert Weight: calculated as the difference between the Actor rank and the Undergoer rank\n",
    "edges_df.insert(12, 'old_weight', old_weight)\n",
    "edges_df.insert(13, 'new_weight', new_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We produce two files, one for dynamic networks and one for static networks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "static = edges_df[['Source','new_rank_Actor',1,'Target','new_rank_Undergoer',4,6,'new_weight',7,8]]\n",
    "static.columns = ['Source','Source_agency','Source_ph','Target','Target_agency','Target_ph','Label','Weight','Clause','Ref']\n",
    "\n",
    "#Export\n",
    "static.to_excel('Lev17-26.edges.Static_New.xlsx', index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.c Compare with older datasets\n",
    "\n",
    "For the sake of consistency, it is possible to easily compare the changes that are made in new models in comparison to old ones. This helps to update the data without going through a manual validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_old = pd.read_excel('Lev17-26.edges.Static.xlsx')\n",
    "data_new = pd.read_excel('Lev17-26.edges.Static_New.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(data_new)-len(data_old)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### i. Check if edges have been removed or added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_edges1 = []\n",
    "review_edges2 = []\n",
    "\n",
    "for n, row in data_new.iterrows(): #Added edges\n",
    "    if row.Clause in list(data_old.Clause):\n",
    "        subset_old = data_old[data_old.Clause == row.Clause]\n",
    "        match = False\n",
    "        for n1, row1 in subset_old.iterrows():\n",
    "            if row1.Source == row.Source and row1.Target == row.Target and row1.Label == row.Label:\n",
    "                match = True\n",
    "        if not match:\n",
    "            review_edges1.append(row.Clause)        \n",
    "    else:\n",
    "        review_edges1.append(row.Clause) #Clause is added in new dataset\n",
    "        \n",
    "for n, row in data_old.iterrows(): #removed edges\n",
    "    if row.Clause in list(data_old.Clause):\n",
    "        subset_new = data_new[data_new.Clause == row.Clause]\n",
    "        match = False\n",
    "        for n1, row1 in subset_new.iterrows():\n",
    "            if row1.Source == row.Source and row1.Target == row.Target and row1.Label == row.Label:\n",
    "                match = True\n",
    "        if not match:\n",
    "            review_edges2.append(row.Clause)        \n",
    "    else:\n",
    "        review_edges2.append(row.Clause) #Clause is added in new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#review_edges1\n",
    "#review_edges2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ii. Check if identical edges have same weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_edges3 = []\n",
    "\n",
    "for n, row in data_new.iterrows():\n",
    "    if row.Clause in list(data_old.Clause):\n",
    "        subset_old = data_old[data_old.Clause == row.Clause]\n",
    "        match = False\n",
    "        for n1, row1 in subset_old.iterrows():\n",
    "            if row1.Source == row.Source and row1.Target == row.Target and row1.Label == row.Label and row1.Weight == row.Weight:\n",
    "                match = True\n",
    "        if not match:\n",
    "            review_edges3.append(row.Clause)        \n",
    "    else:\n",
    "        review_edges3.append(row.Clause) #Clause is added in new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_edges3 = [e for e in review_edges3 if e not in review_edges1 and e not in review_edges2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "review_edges3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('Lev17-26.edges.Static.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "participants = list(set(list(data.Source) + list(data.Target)))\n",
    "\n",
    "for p in list(df_ref.index):\n",
    "    if p not in participants:\n",
    "        print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Social Network Analysis\n",
    "\n",
    "The network model can now be explored with SNA-tools, in this case NetworkX.\n",
    "\n",
    "### 5.a Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Source</th>\n",
       "      <th>Source_agency</th>\n",
       "      <th>Source_ph</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target_agency</th>\n",
       "      <th>Target_ph</th>\n",
       "      <th>Label</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>5</td>\n",
       "      <td>690343</td>\n",
       "      <td>YHWH</td>\n",
       "      <td>0</td>\n",
       "      <td>690347</td>\n",
       "      <td>swing</td>\n",
       "      <td>25</td>\n",
       "      <td>440323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>690383</td>\n",
       "      <td>Moses</td>\n",
       "      <td>-1</td>\n",
       "      <td>690384</td>\n",
       "      <td>speak</td>\n",
       "      <td>36</td>\n",
       "      <td>440335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Israelites</td>\n",
       "      <td>5</td>\n",
       "      <td>690397</td>\n",
       "      <td>YHWH</td>\n",
       "      <td>-1</td>\n",
       "      <td>690399</td>\n",
       "      <td>approach</td>\n",
       "      <td>36</td>\n",
       "      <td>440341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>690402</td>\n",
       "      <td>Moses</td>\n",
       "      <td>-1</td>\n",
       "      <td>690403</td>\n",
       "      <td>speak</td>\n",
       "      <td>36</td>\n",
       "      <td>440342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Israelites</td>\n",
       "      <td>5</td>\n",
       "      <td>690415</td>\n",
       "      <td>YHWH</td>\n",
       "      <td>-1</td>\n",
       "      <td>690417</td>\n",
       "      <td>approach</td>\n",
       "      <td>36</td>\n",
       "      <td>440347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Source  Source_agency  Source_ph Target  Target_agency  Target_ph  \\\n",
       "0  Aaron's_sons              5     690343   YHWH              0     690347   \n",
       "1          YHWH              5     690383  Moses             -1     690384   \n",
       "2    Israelites              5     690397   YHWH             -1     690399   \n",
       "3          YHWH              5     690402  Moses             -1     690403   \n",
       "4    Israelites              5     690415   YHWH             -1     690417   \n",
       "\n",
       "      Label  Weight  Clause  \n",
       "0     swing      25  440323  \n",
       "1     speak      36  440335  \n",
       "2  approach      36  440341  \n",
       "3     speak      36  440342  \n",
       "4  approach      36  440347  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel('Lev17-26.edges.Static.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Source</th>\n",
       "      <th>Source_agency</th>\n",
       "      <th>Source_ph</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target_agency</th>\n",
       "      <th>Target_ph</th>\n",
       "      <th>Label</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>0</td>\n",
       "      <td>689852</td>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>-2</td>\n",
       "      <td>689851</td>\n",
       "      <td>cut</td>\n",
       "      <td>4</td>\n",
       "      <td>440159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>689937</td>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>-1</td>\n",
       "      <td>65535</td>\n",
       "      <td>be holy</td>\n",
       "      <td>36</td>\n",
       "      <td>440190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>690020</td>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>-1</td>\n",
       "      <td>65653</td>\n",
       "      <td>be holy</td>\n",
       "      <td>36</td>\n",
       "      <td>440215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>690168</td>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>-1</td>\n",
       "      <td>65928</td>\n",
       "      <td>be holy</td>\n",
       "      <td>36</td>\n",
       "      <td>440264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>YHWH</td>\n",
       "      <td>5</td>\n",
       "      <td>690170</td>\n",
       "      <td>Aaron's_sons</td>\n",
       "      <td>-1</td>\n",
       "      <td>690171</td>\n",
       "      <td>go out</td>\n",
       "      <td>36</td>\n",
       "      <td>440265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source  Source_agency  Source_ph        Target  Target_agency  Target_ph  \\\n",
       "153   YHWH              0     689852  Aaron's_sons             -2     689851   \n",
       "155   YHWH              5     689937  Aaron's_sons             -1      65535   \n",
       "158   YHWH              5     690020  Aaron's_sons             -1      65653   \n",
       "182   YHWH              5     690168  Aaron's_sons             -1      65928   \n",
       "185   YHWH              5     690170  Aaron's_sons             -1     690171   \n",
       "\n",
       "       Label  Weight  Clause  \n",
       "153      cut       4  440159  \n",
       "155  be holy      36  440190  \n",
       "158  be holy      36  440215  \n",
       "182  be holy      36  440264  \n",
       "185   go out      36  440265  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.Source == 'YHWH') & (data.Target == \"Aaron's_sons\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.MultiGraph()\n",
    "\n",
    "for n, row in data.iterrows():\n",
    "    if row['Source'] != row['Target']: #Ignoring selfloops, e,g., \"YHWH\" > \"YHWH\"\n",
    "        G.add_edge(row.Source, row.Target)\n",
    "    \n",
    "pos = { i : (random.random(), random.random()) for i in G.nodes()}\n",
    "l = forceatlas2.forceatlas2_networkx_layout(G, pos, niter=2000, gravity=30, scalingRatio=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight = collections.Counter(G.edges())\n",
    "\n",
    "for u, v, d in G.edges(data=True):\n",
    "    d['weight'] = weight[u, v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "\n",
    "nx.draw_networkx(G, l, node_color='gold', node_size=[n[1]*10 for n in G.degree()], \n",
    "                 edge_color='grey', width=[d['weight']/3 for _, _, d in G.edges(data=True)], font_size=16)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.margins(x=0.1, y=0.1)\n",
    "\n",
    "plt.savefig('images/7.2.png', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export for cover page:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,15))\n",
    "\n",
    "nx.draw_networkx(G, l, node_color='black', alpha=0.5, node_size=[n[1]*10 for n in G.degree()], \n",
    "                 edge_color='grey', font_size=16, with_labels=False, arrows=True, arrowsize=0.1)\n",
    "\n",
    "\n",
    "plt.axis('off')\n",
    "plt.margins(x=0.1, y=0.1)\n",
    "plt.savefig('images/cover.png', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of nodes and edges:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'Nodes: {len(G.nodes())}\\nEdges: {len(G.edges())}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having created the edges and computed a multiple directed graph (MultiDiGraph), we can now explore the resulting network. We will begin with a general inspection:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.b Cohesion and network density\n",
    "\n",
    "One of the simplest measures of cohession (\"knittedness\") is probably density. Density is simply the number of ties in the network proportional to the possible number of ties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G1 = nx.Graph()\n",
    "\n",
    "for n, row in data.iterrows():  \n",
    "    G1.add_edge(row.Source, row.Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'Number of edges: {len(G1.edges)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx.density(G1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Density is sensitive to the size of the network, and large networks tend to have lower density than small networks, simply because it is more realistic for a member of a small network to be connected with most of the remaining participants than in a large network.\n",
    "\n",
    "Therefore, another approach is average degree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree = G.degree()\n",
    "sum_degree = sum(dict(degree).values())\n",
    "print(f'Average degree: {sum_degree/len(G.nodes())}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.MultiDiGraph()\n",
    "\n",
    "for n, row in data.iterrows():  \n",
    "    G.add_edge(row.Source, row.Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outdegree_sequence = collections.Counter(sorted([d for n, d in G.out_degree()], reverse=True))\n",
    "indegree_sequence = collections.Counter(sorted([d for n, d in G.in_degree()], reverse=True))\n",
    "\n",
    "outdegree_df = pd.DataFrame(outdegree_sequence, index=[0]).T\n",
    "indegree_df = pd.DataFrame([indegree_sequence]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_df = pd.concat([indegree_df, outdegree_df], axis=1, sort=False)\n",
    "degree_df.columns = ['indegree','outdegree']\n",
    "degree_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "\n",
    "plt.bar(degree_df.index, degree_df.indegree, width=0.33)\n",
    "plt.bar(degree_df.index+0.33, degree_df.outdegree, color='tomato', width=0.33)\n",
    "\n",
    "ax.legend(labels=['indegree', 'outdegree'], fontsize=14)\n",
    "plt.ylabel(\"Count\", size=14)\n",
    "plt.xlabel(\"Degree\", size=14)\n",
    "plt.xticks(size=12)\n",
    "plt.yticks(size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(G.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "indegree_cum = [n/len(G.nodes())*100 for n in np.cumsum(degree_df.fillna(0).indegree)]\n",
    "outdegree_cum = [n/len(G.nodes())*100 for n in np.cumsum(degree_df.fillna(0).outdegree)]\n",
    "degree_df.insert(2, \"indegree_cum (%)\", indegree_cum)\n",
    "degree_df.insert(3, \"outdegree_cum (%)\", outdegree_cum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most connected participants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "top_degree = sorted(dict(degree).items(), key=itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A cummulative view:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_degree = pd.DataFrame(top_degree)\n",
    "cum_degree.columns = ['participant','degree']\n",
    "\n",
    "degree_cum = [n/(len((G.edges()))*2)*100 for n in np.cumsum(cum_degree.degree)]\n",
    "cum_degree.insert(2, \"degree_cum (%)\", degree_cum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cum_degree.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updated graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(15,7))\n",
    "ax2 = ax1.twinx()\n",
    "ax1.tick_params(axis='both', which='major', labelsize=16)\n",
    "\n",
    "ax1.bar(degree_df.index, degree_df.indegree, width=0.33)\n",
    "ax1.bar(degree_df.index+0.33, degree_df.outdegree, color='tomato', width=0.33)\n",
    "\n",
    "ax2.plot(degree_df.index, degree_df['indegree_cum (%)'], linestyle='--', alpha=0.5, linewidth=2)\n",
    "ax2.plot(degree_df.index, degree_df['outdegree_cum (%)'], linestyle='--', alpha=0.5, linewidth=2)\n",
    "\n",
    "ax1.legend(frameon=1, labels=['indegree', 'outdegree'], fontsize=16, facecolor='white', framealpha=1, loc='right')\n",
    "ax1.set_ylabel(\"Count\", size=20)\n",
    "ax2.set_ylabel(\"Cumulative %\", size=20)\n",
    "ax1.set_xlabel(\"\")\n",
    "plt.xticks(size=16)\n",
    "plt.yticks(size=16)\n",
    "plt.savefig(f'images/7.3.png', bbox_inches='tight', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.degree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.in_degree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.c Reciprocity\n",
    "\n",
    "Reciprocity concerns whether an interaction from one actor to another is returned, or whether the relation is one-sided. A simple measure of reciprocity is to count the number of reciprocal ties and divide these by the total number of ties. For this analysis, we are not interested in the weights of the edges but simply the binary value (connected or not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digraph = nx.DiGraph()\n",
    "\n",
    "for n, row in data.iterrows():\n",
    "    digraph.add_edge(row.Source, row.Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "round(nx.reciprocity(digraph)*100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reci_df = pd.DataFrame([nx.reciprocity(digraph, digraph.nodes())]).T.sort_values(by=0, ascending=False).head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "\n",
    "plt.bar(reci_df.index, reci_df[0])\n",
    "plt.ylabel(\"fraction\", size=20)\n",
    "plt.xticks(size=16, rotation=45, ha='right')\n",
    "plt.yticks(size=16)\n",
    "plt.savefig(f'images/7.4.png', bbox_inches='tight', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reci_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.d Centrality\n",
    "\n",
    "We use 4 measures for measuring the centrality of individual nodes. That will give an image of core and periphery of the network. The four measures are Degree, Closeness, Betweenness, and Eigenvector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "indegree = nx.in_degree_centrality(digraph)\n",
    "outdegree = nx.out_degree_centrality(digraph)\n",
    "betweenness = nx.betweenness_centrality(digraph)\n",
    "pagerank = nx.pagerank(digraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "centrality = pd.DataFrame([indegree, outdegree, betweenness, pagerank]).T\n",
    "centrality.columns = ['indegree','outdegree','betweeness','pagerank']\n",
    "centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top five scores for centrality measures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top(measure, df=centrality):\n",
    "    return df.sort_values(by=measure, ascending=False)[measure][:10]\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(15,7), sharey=True)\n",
    "fig.tight_layout(pad=1.0)\n",
    "\n",
    "ax1.bar(top('outdegree').index, top('outdegree'))\n",
    "ax1.set_title(\"Outdegree\", size=22)\n",
    "ax2.bar(top('indegree').index, top('indegree'))\n",
    "ax2.set_title(\"Indegree\", size=22)\n",
    "ax3.bar(top('betweeness').index, top('betweeness'))\n",
    "ax3.set_title(\"Betweenness\", size=22)\n",
    "ax4.bar(top('pagerank').index, top('pagerank'))\n",
    "ax4.set_title(\"PageRank\", size=22)\n",
    "\n",
    "for ax in fig.axes:\n",
    "    plt.sca(ax)\n",
    "    plt.xticks(rotation=45, ha='right', size=18)\n",
    "    plt.yticks(size=18)\n",
    "\n",
    "plt.savefig(f'images/7.5.png', bbox_inches='tight', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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