mw-lifecycle-analysis/text_analysis/case2/040425_phab_comments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/0402_https1_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def http_relevant(text):\n",
    "    if pd.isnull(text):\n",
    "        return False\n",
    "\n",
    "    for word in text.split():\n",
    "        if word.lower() == \"http\" or word.lower() == \"https\":\n",
    "            return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/1288881096.py:35: 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",
      "  mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n"
     ]
    }
   ],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "#cleaning df\n",
    "phab_df['id'] = phab_df.index + 1\n",
    "#may have to build out the reply_to column \n",
    "phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
    "phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
    "\n",
    "phab_df = phab_df.rename(columns={\n",
    "    'AuthorPHID': 'speaker',\n",
    "    'TaskPHID': 'conversation_id',\n",
    "    'WMFaffil':'meta.affil',\n",
    "    'isGerrit': 'meta.gerrit'\n",
    "})\n",
    "\n",
    "# after 12-1-2012 before 12-1-2013\n",
    "phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
    "filtered_phab_df = phab_df[(phab_df['date_created'] < 1385856000) & (phab_df['date_created'] > 1354320000)]\n",
    "\n",
    "#removing headless conversations\n",
    "task_phab_df = filtered_phab_df[filtered_phab_df['comment_type']==\"task_description\"]\n",
    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
    "filtered_phab_df = filtered_phab_df[filtered_phab_df['conversation_id'].isin(headed_task_phids)]\n",
    "\n",
    "#removing gerrit comments \n",
    "mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
    "\n",
    "#cut down to only the data that is relevant (mentions http)\n",
    "relevant_conversation_ids = task_phab_df[\n",
    "    task_phab_df['comment_text'].apply(http_relevant) |\n",
    "    task_phab_df['task_title'].apply(http_relevant)\n",
    "]['conversation_id'].unique()\n",
    "\n",
    "task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "\n",
    "#comment_phab_df = mid_comment_phab_df[mid_comment_phab_df['is_relevant'] == True]\n",
    "comment_phab_df = mid_comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 6139\n",
      "Unique ids: 26300\n",
      "Unique speakers: 506\n"
     ]
    }
   ],
   "source": [
    "unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
    "unique_ids = len(comment_phab_df['id'].unique())\n",
    "unique_speakers = len(comment_phab_df['speaker'].unique())\n",
    "\n",
    "print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
    "print(f\"Unique ids: {unique_ids}\")\n",
    "print(f\"Unique speakers: {unique_speakers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d226d781-b002-4842-a3ae-92d4851a5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = str(text)\n",
    "    text = text.replace('*', ' ')\n",
    "    text = text.replace('-', ' ')\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/2783900859.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",
      "  comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(text):\n",
    "    doc = nlp(text)\n",
    "    dependency_trees = []\n",
    "    \n",
    "    for sentence in doc.sents:\n",
    "        for token in sentence:\n",
    "            token_info = (\n",
    "                token.text, \n",
    "                token.lemma_, \n",
    "                token.dep_, \n",
    "                token.head.text, \n",
    "                list(token.ancestors), \n",
    "                list(token.subtree), \n",
    "                list(token.children)\n",
    "            )\n",
    "            dependency_trees.append(token_info)\n",
    "        \n",
    "    return dependency_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/2805711855.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",
      "  comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get VAD scores\n",
    "import numpy as np\n",
    "#https://saifmohammad.com/WebPages/nrc-vad.html\n",
    "column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
    "vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
    "vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
    "\n",
    "def vad_scoring(dependency_tree):\n",
    "    valence = []\n",
    "    arousal = []\n",
    "    dominance = []\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            valence.append(vad_dict[lemma]['Valence'])\n",
    "            arousal.append(vad_dict[lemma]['Arousal'])\n",
    "            dominance.append(vad_dict[lemma]['Domination'])\n",
    "\n",
    "    # Compute average scores across the comment\n",
    "    avg_valence = np.mean(valence) if valence else 0\n",
    "    avg_arousal = np.mean(arousal) if arousal else 0\n",
    "    avg_dominance = np.mean(dominance) if dominance else 0\n",
    "\n",
    "    return [avg_valence, avg_arousal, avg_dominance]\n",
    "\n",
    "def dominance_prevail(dependency_tree):\n",
    "    dominant_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Domination'] >= 0.75:\n",
    "                dominant_words += 1\n",
    "            if vad_dict[lemma]['Domination'] <= 0.25:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "\n",
    "def arousal_prevail(dependency_tree):\n",
    "    arousal_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Arousal'] >= 0.75:\n",
    "                arousal_words += 1\n",
    "            if vad_dict[lemma]['Arousal'] <= 0.25:\n",
    "                arousal_words += 1\n",
    "    return arousal_words\n",
    "\n",
    "def valence_prevail(dependency_tree):\n",
    "    valence_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Valence'] >= 0.75:\n",
    "                valence_words += 1\n",
    "            if vad_dict[lemma]['Valence'] <= 0.25:\n",
    "                valence_words += 1\n",
    "    return valence_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/2858732056.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",
      "  comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
      "/tmp/ipykernel_19468/2858732056.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",
      "  comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
      "/tmp/ipykernel_19468/2858732056.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",
      "  comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
      "/tmp/ipykernel_19468/2858732056.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",
      "  comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)\n"
     ]
    }
   ],
   "source": [
    "#establishing per-comment VAD scores \n",
    "comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
    "comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
    "comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
    "comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/335308388.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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_19468/335308388.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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_19468/335308388.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",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
    "comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "\n",
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "pattern = r'\\b(http|https)\\b'\n",
    "\n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['comment_text']\n",
    "    timestamp = row['timestamp']\n",
    "    comment_id = row['id']\n",
    "    conversation_id = row['conversation_id']\n",
    "    WMFaffil = row['meta.affil']\n",
    "    \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''        \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            resolved_dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''\n",
    "#resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations)            \n",
    "dependency_relations_df = pd.DataFrame(dependency_relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82498686-14f4-40c8-9e33-27b31f115b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now analysis/plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/119938314.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",
      "  task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
      "/tmp/ipykernel_19468/119938314.py:7: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_19468/119938314.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",
      "  task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
    "#task_phab_df = task_phab_df[task_phab_df['is_relevant'] == True]\n",
    "task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
    "#task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
    "\n",
    "task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "wmf_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] == True)]\n",
    "wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "other_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] != True)]\n",
    "other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "unaff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] != True)]\n",
    "unaff_new_tasks = unaff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "aff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] == True)]\n",
    "aff_new_tasks = aff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total', marker='o')\n",
    "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "#sns.lineplot(x=aff_new_tasks.index, y=aff_new_tasks.values, color='#c7756a',linestyle=\"dotted\", label=\"WMF-affiliated new authors\", marker='x')\n",
    "#sns.lineplot(x=unaff_new_tasks.index, y=unaff_new_tasks.values, color='#5da2d8', linestyle=\"dotted\", label=\"Nonaffiliated new authors\", marker='x')\n",
    "\n",
    "plt.title('New Phabricator Tasks Indexed with HTTPS')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Unique taskPHIDs')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#plt.savefig('031825_new_tasks_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/3743952880.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",
      "  unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
      "/tmp/ipykernel_19468/3743952880.py:17: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_19468/3743952880.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
      "/tmp/ipykernel_19468/3743952880.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task_phab_df = phab_df[phab_df['comment_type'] == \"task_description\"]\n",
    "unaff_tasks_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
    "# Rank speaker's task values within each group\n",
    "unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "\n",
    "# Filter dates 08-01-2013 to 09-30-2013\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1380499200) & (unaff_tasks_phab_df['date_created'] > 1375315200)]\n",
    "# Bin the speakers based on the number of tasks they created\n",
    "bins = [0, 6, 26, 51, float('inf')]\n",
    "labels = ['0-5', '6-25', '26-50', '51+']\n",
    "min_speakers_task = unaff_tasks_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
    "min_speakers_task = min_speakers_task.rename(columns={'speakers_task': 'min_speakers_task'})\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
    "unaff_tasks_phab_df['task_bins'] = pd.cut(unaff_tasks_phab_df['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Calculate the weekly breakdown of binned speakers_task values\n",
    "unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
    "\n",
    "speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
    "\n",
    "# Reshape the DataFrame for use with Seaborn\n",
    "weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
    "speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(data=weekly_breakdown, x='week', y='count', hue='task_bins', palette='colorblind')\n",
    "#sns.barplot(data=speaker_breakdown, x='week', y='speakers', hue='task_bins', palette='colorblind')\n",
    "plt.title(\"08-01-2013 to 09-30-2013 Weekly Unaffiliated Task Creation by Contirbutor Tenure\")\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Tasks')\n",
    "plt.legend(title=\"Contributor had created # tasks by 08-01-2013:\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('031625_weekly_tasks_by_history.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/2708736932.py:27: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
      "/tmp/ipykernel_19468/2708736932.py:28: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
      "/tmp/ipykernel_19468/2708736932.py:35: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
      "/tmp/ipykernel_19468/2708736932.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comments for each date group:\n",
      "date_group\n",
      "Before announcement                      10038\n",
      "After announcement, before deployment     3471\n",
      "After deployment                         12791\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group:\n",
      "date_group\n",
      "Before announcement                      342\n",
      "After announcement, before deployment    249\n",
      "After deployment                         398\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False          9165\n",
      "                                                      True            873\n",
      "After announcement, before deployment  False          False          3212\n",
      "                                                      True            259\n",
      "After deployment                       False          False         12095\n",
      "                                                      True            696\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         341\n",
      "                                                      True           67\n",
      "After announcement, before deployment  False          False         243\n",
      "                                                      True           49\n",
      "After deployment                       False          False         393\n",
      "                                                      True           77\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         24472\n",
      "               True           1828\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         504\n",
      "               True          112\n",
      "Name: speaker, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nplot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'new_commenter\\', scatter=False, legend=False, palette=palette)\\nplot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\\nplot1.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\\nplot1.add_legend(title=\"Comment publication timestamp:\")\\nfig1 = plot1.fig\\n# Plot for arousal_wc\\nplot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\\nplot2.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot2.add_legend(title=\"Comment publication timestamp:\")\\n#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\\n\\nplot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\\nplot3.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot3.add_legend(title=\"Comment publication timestamp:\")\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [\n",
    "    pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-28 00:00:01+00:00'),\n",
    "    pd.Timestamp('2100-08-28 00:00:01+00:00')\n",
    "]\n",
    "labels = ['Before announcement', 'After announcement, before deployment', 'After deployment']\n",
    "\n",
    "#creating variables of interest\n",
    "affective_comment_phab_df = comment_phab_df\n",
    "affective_comment_phab_df['date_group'] = pd.cut(affective_comment_phab_df['timestamp'], bins=bins, labels=labels, right=False)\n",
    "affective_comment_phab_df['speakers_comment'] = affective_comment_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "#all comments prior to june 1 2013\n",
    "subset_comment_phab_df = affective_comment_phab_df[affective_comment_phab_df['date_created'] <= 1370044800]\n",
    "#getting counts \n",
    "comment_counts = subset_comment_phab_df.groupby('speaker')['speakers_comment'].max().reset_index()\n",
    "comment_counts = comment_counts.rename(columns={'speakers_comment': 'pre_june_2013_comments'})\n",
    "#merge back \n",
    "affective_comment_phab_df = affective_comment_phab_df.merge(comment_counts, on='speaker', how='left')\n",
    "affective_comment_phab_df['pre_june_2013_comments'] = affective_comment_phab_df['pre_june_2013_comments'].fillna(0)\n",
    "\n",
    "affective_comment_phab_df['new_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] <= 10\n",
    "affective_comment_phab_df['est_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] > 50\n",
    "\n",
    "palette = ['#31449c', '#4a7c85', '#c5db68']\n",
    "\n",
    "comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
    "speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
    "\n",
    "print(\"Number of comments for each date group:\")\n",
    "print(comment_counts)\n",
    "print(\"\\nNumber of speakers for each date group:\")\n",
    "print(speaker_counts)\n",
    "\n",
    "comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each date group and engaged commenter subgroup:\")\n",
    "print(comment_counts_engaged)\n",
    "print(\"\\nNumber of speakers for each date group and engaged commenter subgroup:\")\n",
    "print(speaker_counts_engaged)\n",
    "\n",
    "comment_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(comment_counts_wmf)\n",
    "print(\"\\nNumber of speakers for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(speaker_counts_wmf)\n",
    "\n",
    "#comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
    "#fig, axes = plt.subplots(2, 1, figsize=(10, 12), sharex=True)\n",
    "affective_comment_phab_df['polarized_wc'] = affective_comment_phab_df['dominant_wc'] + affective_comment_phab_df['valence_wc'] + affective_comment_phab_df['arousal_wc'] \n",
    "plot1 = sns.lmplot(data=affective_comment_phab_df, x=\"date_created\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", row='est_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Established Author: {row_name}\", col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "'''\n",
    "plot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row='new_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "# Plot for arousal_wc\n",
    "plot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\n",
    "plot2.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")\n",
    "#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\n",
    "\n",
    "plot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\n",
    "plot3.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot3.add_legend(title=\"Comment publication timestamp:\")\n",
    "'''\n",
    "# Show plots\n",
    "#fig1.savefig('031725_engaged_commenter_D_scoring_fig.png')\n",
    "#plot2.fig.savefig('031725_engaged_commenter_A_scoring_fig.png')\n",
    "#plot3.fig.savefig('031725_engaged_commenter_V_scoring_fig.png')\n",
    "#plt.savefig('031625_engaged_commenter_VAD_scoring_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14b13d824340>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
    "plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/1559616732.py:4: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'resolved_dependency_relations_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(WMF|Foundation)\\b'\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(bots)\\b'\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filtered_dependencies \u001b[38;5;241m=\u001b[39m dependency_relations_df[dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[0;32m----> 5\u001b[0m resolved_filtered_dependencies \u001b[38;5;241m=\u001b[39m \u001b[43mresolved_dependency_relations_df\u001b[49m[resolved_dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      8\u001b[0m gs \u001b[38;5;241m=\u001b[39m GridSpec(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, height_ratios\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'resolved_dependency_relations_df' is not defined"
     ]
    }
   ],
   "source": [
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots)\\b'\n",
    "filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
    "\n",
    "# Main plot: Token depth by timestamp\n",
    "'''\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
    "ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Dependency Type')\n",
    "ax0.legend().set_visible(False)\n",
    "'''\n",
    "# Calculate the median depth over time\n",
    "filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax0.set_title('Median Depth of \"VE\" in Phabricator Sentence Dependency Trees')\n",
    "ax0.set_ylabel('Median Depth')\n",
    "ax0.set_xlabel('')\n",
    "\n",
    "# Calculate the median depth over time\n",
    "resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
    "resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
    "resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax1 = plt.subplot(gs[1])\n",
    "sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax1.set_title('Median Depth of \"VE\" in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
    "ax1.set_ylabel('Median Depth')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "\n",
    "#plt.savefig('031625_VE_depth_fig.png')"
   ]
  }
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