diff --git a/.sh_history b/.sh_history
index dc30a3a..4c939cd 100644
--- a/.sh_history
+++ b/.sh_history
@@ -173,3 +173,6 @@ ls ../case3
 cd ..
 ls
 ls case1
+ls
+cd case2
+ls
diff --git a/commit_analysis/plotting/ww-bots-plot-script.R b/commit_analysis/plotting/ww-bots-plot-script.R
index d371164..46f79db 100644
--- a/commit_analysis/plotting/ww-bots-plot-script.R
+++ b/commit_analysis/plotting/ww-bots-plot-script.R
@@ -46,35 +46,35 @@ new_unaff_authors <- new_authors_long_df |>
              fill=commit_seniority)) +
   geom_col(position='dodge') +
   labs(x = "Relative Week", y = "Commits", fill="Contributor Tenure (New contributors <= 5 commits before deployment announcement)") +
-  geom_vline(data = long_df |> filter(source == "c1"), 
+  geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -29), 
-             linetype = "dotted", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c1"), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -9), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "dotted", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -4), 
-             linetype = "3313", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c2"), 
-             aes(xintercept = -99), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "3313", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c2"), 
-             aes(xintercept = -4), 
-             linetype = "3313", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c3"), 
+             aes(xintercept = -99), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c2"), 
+             aes(xintercept = -4),   
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c3"), 
              aes(xintercept = -97), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "dotted", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c3"), 
              aes(xintercept = -3), 
-             linetype = "3313", color = "black", linewidth = 1) +
-  geom_text(data = data.frame(source = "c1", relative_week = -40, lengthened_commit_count = 90), 
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_text(data = data.frame(source = "c1", relative_week = -39, lengthened_commit_count = 80), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Opt-In Testing Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
-  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 1) + # Add vertical line at week 0
-  geom_text(data = data.frame(source = "c1", relative_week = 7, lengthened_commit_count = 90), 
+  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 0.5) + # Add vertical line at week 0
+  geom_text(data = data.frame(source = "c2", relative_week = 7, lengthened_commit_count = 80), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Wide Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
-  geom_text(data = data.frame(source = "c3", relative_week = -15, lengthened_commit_count = 100), 
+  geom_text(data = data.frame(source = "c3", relative_week = -15, lengthened_commit_count = 80), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Wide Deployment Announcement"), 
             inherit.aes = FALSE, color = "black", size = 4) +
   scale_fill_manual(values = c("returning_unaff_commit_count" = "#FFC107",  # Color for "Returning Contributors"
@@ -96,14 +96,14 @@ new_unaff_authors <- new_authors_long_df |>
     strip.text = element_text(size = 14)# Increase legend title font size
   ) +
   facet_wrap(~source, nrow = 3, labeller = labeller(source = c(
-    "c1" = "VisualEditor",
-    "c2" = "HTTPS-as-default",
-    "c3" = "HTTP-deprecation"
+    "c1" = "VisualEditor (2013)",
+    "c2" = "HTTPS-as-default (2013)",
+    "c3" = "HTTP-deprecation (2015)"
   )))
 
 new_unaff_authors
 
-ggsave(filename = "ww-0501-bot-commits-faceted.png", plot = new_unaff_authors, width = 15, height = 9, dpi = 800)
+ggsave(filename = "d1-m2-bot-commits-faceted.png", plot = new_unaff_authors, width = 15, height = 9, dpi = 800)
 
 unaff_authors <- new_authors_long_df |>
   ggplot(aes(x=relative_week,
diff --git a/commit_analysis/plotting/ww-plot-script.R b/commit_analysis/plotting/ww-plot-script.R
index 3598d39..fe4fc36 100644
--- a/commit_analysis/plotting/ww-plot-script.R
+++ b/commit_analysis/plotting/ww-plot-script.R
@@ -38,25 +38,37 @@ commit_authors <- long_df |>
              fill=factor(commit_type))) +
   geom_col(position='dodge') +
   labs(x = "Relative Week", y = "Commits", fill="Commit Author") +
-  geom_vline(data = long_df |> filter(source == "c1"), 
+  geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -29), 
-             linetype = "dotted", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c1"), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -9), 
-             linetype = "dotted", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c2"), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c1"), 
+             aes(xintercept = -4), 
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c2"), 
              aes(xintercept = -99), 
-             linetype = "dotted", color = "black", linewidth = 1) +
-  geom_vline(data = long_df |> filter(source == "c3"), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c2"), 
+             aes(xintercept = -4),   
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c3"), 
              aes(xintercept = -97), 
-             linetype = "dotted", color = "black", linewidth = 1) +
-  geom_text(data = data.frame(source = "c1", relative_week = -40, lengthened_commit_count = 50), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c3"), 
+             aes(xintercept = -3), 
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_text(data = data.frame(source = "c1", relative_week = -39, lengthened_commit_count = 50), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Opt-In Testing Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
-  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 1) + # Add vertical line at week 0
-  geom_text(data = data.frame(source = "c1", relative_week = 7, lengthened_commit_count = 50), 
+  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 0.5) + # Add vertical line at week 0
+  geom_text(data = data.frame(source = "c2", relative_week = 7, lengthened_commit_count = 50), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Wide Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
+  geom_text(data = data.frame(source = "c3", relative_week = -15, lengthened_commit_count = 50), 
+            aes(x = relative_week, y = lengthened_commit_count, label = "Wide Deployment Announcement"), 
+            inherit.aes = FALSE, color = "black", size = 4) +
   scale_fill_manual(values = affiliationColors,
                     labels = c("unaff_commit_count" = "Unaffiliated",
                                "wikimedia_commit_count" = "WMF-affiliated")) +
@@ -74,10 +86,10 @@ commit_authors <- long_df |>
     strip.text = element_text(size = 14)# Increase legend title font size
   ) +
   facet_wrap(~source, nrow = 3, labeller = labeller(source = c(
-    "c1" = "VisualEditor (commits to extensions/visualeditor)",
-    "c2" = "HTTPS-as-default (relevant commits to mediawiki/core)",
-    "c3" = "HTTP-deprecation (relevant commits to mediawiki/core)"
+    "c1" = "VisualEditor (2013) [commits to extensions/visualeditor]",
+    "c2" = "HTTPS-as-default (2013) [relevant commits to mediawiki/core]",
+    "c3" = "HTTP-deprecation (2015) [relevant commits to mediawiki/core]"
   )))
 commit_authors
 
-ggsave(filename = "ww-0501-commits-faceted.png", plot = commit_authors, width = 15, height = 9, dpi = 800)
+ggsave(filename = "d1-m2-commits-faceted.png", plot = commit_authors, width = 15, height = 9, dpi = 800)
diff --git a/m2-figures/d1-m2-bot-commits-faceted.png b/m2-figures/d1-m2-bot-commits-faceted.png
new file mode 100644
index 0000000..db2430f
Binary files /dev/null and b/m2-figures/d1-m2-bot-commits-faceted.png differ
diff --git a/m2-figures/d1-m2-commits-faceted.png b/m2-figures/d1-m2-commits-faceted.png
new file mode 100644
index 0000000..d3c4b8a
Binary files /dev/null and b/m2-figures/d1-m2-commits-faceted.png differ
diff --git a/m2-figures/d1-m2-tasks-faceted.png b/m2-figures/d1-m2-tasks-faceted.png
new file mode 100644
index 0000000..2838b46
Binary files /dev/null and b/m2-figures/d1-m2-tasks-faceted.png differ
diff --git a/mgaughan-rstudio-server_25681892.out b/mgaughan-rstudio-server_26402644.out
similarity index 65%
rename from mgaughan-rstudio-server_25681892.out
rename to mgaughan-rstudio-server_26402644.out
index bd7c2ac..f2163d3 100644
--- a/mgaughan-rstudio-server_25681892.out
+++ b/mgaughan-rstudio-server_26402644.out
@@ -1,18 +1,17 @@
 1. SSH tunnel from your workstation using the following command:
 
-   ssh -N -L 8787:n3439:39175 mjilg@klone.hyak.uw.edu
+   ssh -N -L 8787:n3439:38329 mjilg@klone.hyak.uw.edu
 
    and point your web browser to http://localhost:8787
 
 2. log in to RStudio Server using the following credentials:
 
    user: mjilg
-   password: twImEJor5ex498HTzJjx
+   password: YXXLCjS/064zAiagiRdx
 
 When done using RStudio Server, terminate the job by:
 
 1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
 2. Issue the following command on the login node:
 
-      scancel -f 25681892
-slurmstepd: error: *** JOB 25681892 ON n3439 CANCELLED AT 2025-05-01T23:08:23 DUE TO TIME LIMIT ***
+      scancel -f 26402644
diff --git a/phab_analysis/case2/040425_phab_comments.ipynb b/phab_analysis/case2/040425_phab_comments.ipynb
index 2fffdc0..4a3c3da 100644
--- a/phab_analysis/case2/040425_phab_comments.ipynb
+++ b/phab_analysis/case2/040425_phab_comments.ipynb
@@ -80,13 +80,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_55861/3758790231.py:41: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_76053/3758790231.py:41: 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",
-      "/tmp/ipykernel_55861/3758790231.py:44: SettingWithCopyWarning: \n",
+      "/tmp/ipykernel_76053/3758790231.py:44: 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",
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
    "metadata": {},
    "outputs": [
@@ -172,6 +172,29 @@
     "print(f\"Unique speakers: {unique_speakers}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b9229ca3-afb9-4eec-a173-f30be8c4729b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "given_date = pd.Timestamp(\"2013-08-28\").tz_localize(None)\n",
+    "task_phab_df['timestamp'] = pd.to_datetime(task_phab_df['timestamp'], unit='s').dt.tz_localize(None)\n",
+    "task_phab_df['week_bin'] = ((task_phab_df['timestamp'] - given_date).dt.days // 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "24205386-d18f-4fb7-b37d-e81c0a5ba532",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_phab_df\n",
+    "task_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/phab_tasks.csv\", index=False)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -1024,7 +1047,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/phab_analysis/case2/041525_resolved_phab_comments.ipynb b/phab_analysis/case2/041525_resolved_phab_comments.ipynb
deleted file mode 100644
index fa7434b..0000000
--- a/phab_analysis/case2/041525_resolved_phab_comments.ipynb
+++ /dev/null
@@ -1,803 +0,0 @@
-{
- "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/case2/041325_coref_rel_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",
-    "    # expanded dictionary for relevancy\n",
-    "    # http, login, SSL, TLS, certificate \n",
-    "    for word in text.split():\n",
-    "        if \"://\" not in word.lower():\n",
-    "            #http\n",
-    "            if \"http\" in word.lower():\n",
-    "                return True\n",
-    "            #login\n",
-    "            if \"login\" in word.lower():\n",
-    "                return True\n",
-    "            #ssl\n",
-    "            if \"ssl\" in word.lower():\n",
-    "                return True\n",
-    "            #tls\n",
-    "            if \"tls\" in word.lower():\n",
-    "                return True\n",
-    "            #cert\n",
-    "            if word.lower().startswith(\"cert\"):\n",
-    "                return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def is_migrated(comment_text):\n",
-    "    if pd.isnull(comment_text):\n",
-    "        return False\n",
-    "    text = comment_text.strip()\n",
-    "    if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
-    "        return True \n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
-    "#phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
-    "\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",
-    "#filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379975444)]\n",
-    "\n",
-    "#removing headless conversations\n",
-    "task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
-    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
-    "filtered_phab_df = phab_df[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",
-    "'''\n",
-    "# filter out the sourceforge migration \n",
-    "# Originally from: http://sourceforge.net in the task task_summary\n",
-    "migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\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",
-    "task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
-    "mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
-    "'''\n",
-    "#comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
-    "#task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
-    "comment_phab_df = filtered_phab_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unique conversation_ids: 382\n",
-      "Unique ids: 1838\n",
-      "Unique speakers: 189\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": 7,
-   "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": 8,
-   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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": 10,
-   "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": 11,
-   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "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": 13,
-   "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": 14,
-   "id": "828fb57a-e152-42ef-9c60-660648898532",
-   "metadata": {},
-   "outputs": [],
-   "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": 15,
-   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
-   "metadata": {},
-   "outputs": [],
-   "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": 16,
-   "id": "1889034d-bc93-495f-bdc4-961d193d3e08",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def token_http_relevant(word):\n",
-    "    # expanded dictionary for relevancy\n",
-    "    # http, login, SSL, TLS, certificate \n",
-    "    if \"://\" not in word.lower():\n",
-    "        #http\n",
-    "        if \"http\" in word.lower():\n",
-    "            return True\n",
-    "        #login\n",
-    "        if \"login\" in word.lower():\n",
-    "            return True\n",
-    "        #ssl\n",
-    "        if \"ssl\" in word.lower():\n",
-    "            return True\n",
-    "        #tls\n",
-    "        if \"tls\" in word.lower():\n",
-    "            return True\n",
-    "        #cert\n",
-    "        if word.lower().startswith(\"cert\"):\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "09ddcbfc-b856-40ca-ad61-13577795d94b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "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 token_http_relevant(token):\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 token_http_relevant(token):\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": 19,
-   "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": 35,
-   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
-      ]
-     },
-     "execution_count": 35,
-     "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": 21,
-   "id": "2274795e-c64d-43e4-b0f5-a19b5b8ba2c8",
-   "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>comment_id</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>wmfAffil</th>\n",
-       "      <th>token</th>\n",
-       "      <th>dependency</th>\n",
-       "      <th>head</th>\n",
-       "      <th>depth</th>\n",
-       "      <th>children</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>115</td>\n",
-       "      <td>2013-10-11 09:04:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>use_api_login</td>\n",
-       "      <td>dobj</td>\n",
-       "      <td>use_api</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>157</td>\n",
-       "      <td>2013-10-07 08:09:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>use_api_login</td>\n",
-       "      <td>dobj</td>\n",
-       "      <td>use_api</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>177</td>\n",
-       "      <td>2013-10-04 17:56:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>certainly</td>\n",
-       "      <td>advmod</td>\n",
-       "      <td>require</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>247</td>\n",
-       "      <td>2013-09-27 22:15:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Login</td>\n",
-       "      <td>ROOT</td>\n",
-       "      <td>Login</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>426</td>\n",
-       "      <td>2013-09-01 11:26:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>HTTP</td>\n",
-       "      <td>compound</td>\n",
-       "      <td>login</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1463</th>\n",
-       "      <td>45300</td>\n",
-       "      <td>2013-08-01 17:35:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>certain</td>\n",
-       "      <td>amod</td>\n",
-       "      <td>commands</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1464</th>\n",
-       "      <td>45300</td>\n",
-       "      <td>2013-08-01 17:35:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>certain</td>\n",
-       "      <td>amod</td>\n",
-       "      <td>commands</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>45373</td>\n",
-       "      <td>2013-07-27 13:30:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>certain</td>\n",
-       "      <td>amod</td>\n",
-       "      <td>element</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1466</th>\n",
-       "      <td>46078</td>\n",
-       "      <td>2013-06-18 21:17:00+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>HTTP</td>\n",
-       "      <td>compound</td>\n",
-       "      <td>Error</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1467</th>\n",
-       "      <td>46086</td>\n",
-       "      <td>2013-06-19 23:31:02+00:00</td>\n",
-       "      <td>False</td>\n",
-       "      <td>HTTP</td>\n",
-       "      <td>compound</td>\n",
-       "      <td>Error</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1468 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      comment_id                 timestamp  wmfAffil          token  \\\n",
-       "0            115 2013-10-11 09:04:00+00:00     False  use_api_login   \n",
-       "1            157 2013-10-07 08:09:00+00:00     False  use_api_login   \n",
-       "2            177 2013-10-04 17:56:00+00:00     False      certainly   \n",
-       "3            247 2013-09-27 22:15:00+00:00     False          Login   \n",
-       "4            426 2013-09-01 11:26:00+00:00     False           HTTP   \n",
-       "...          ...                       ...       ...            ...   \n",
-       "1463       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
-       "1464       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
-       "1465       45373 2013-07-27 13:30:00+00:00     False        certain   \n",
-       "1466       46078 2013-06-18 21:17:00+00:00     False           HTTP   \n",
-       "1467       46086 2013-06-19 23:31:02+00:00     False           HTTP   \n",
-       "\n",
-       "     dependency      head  depth  children  \n",
-       "0          dobj   use_api      1         6  \n",
-       "1          dobj   use_api      1         4  \n",
-       "2        advmod   require      2         1  \n",
-       "3          ROOT     Login      0         4  \n",
-       "4      compound     login      4         0  \n",
-       "...         ...       ...    ...       ...  \n",
-       "1463       amod  commands      5         0  \n",
-       "1464       amod  commands      5         0  \n",
-       "1465       amod   element      8         0  \n",
-       "1466   compound     Error      2         0  \n",
-       "1467   compound     Error      3         0  \n",
-       "\n",
-       "[1468 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "resolved_dependency_relations_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_79236/963914566.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",
-      "/tmp/ipykernel_79236/963914566.py:5: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
-      "  resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
-      "/tmp/ipykernel_79236/963914566.py:20: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
-      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
-      "/tmp/ipykernel_79236/963914566.py:20: 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",
-      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
-      "/tmp/ipykernel_79236/963914566.py:39: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
-      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
-      "/tmp/ipykernel_79236/963914566.py:39: 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",
-      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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 Relevant Term 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 Relevant Term 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')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/phab_analysis/case2/c2_resolved_phab.ipynb b/phab_analysis/case2/c2_resolved_phab.ipynb
new file mode 100644
index 0000000..232fa8e
--- /dev/null
+++ b/phab_analysis/case2/c2_resolved_phab.ipynb
@@ -0,0 +1,1160 @@
+{
+ "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/case2/051825_coref_rel_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",
+    "    # expanded dictionary for relevancy\n",
+    "    # http, login, SSL, TLS, certificate \n",
+    "    for word in text.split():\n",
+    "        if \"://\" not in word.lower():\n",
+    "            #http\n",
+    "            if \"http\" in word.lower():\n",
+    "                return True\n",
+    "            #login\n",
+    "            if \"login\" in word.lower():\n",
+    "                return True\n",
+    "            #ssl\n",
+    "            if \"ssl\" in word.lower():\n",
+    "                return True\n",
+    "            #tls\n",
+    "            if \"tls\" in word.lower():\n",
+    "                return True\n",
+    "            #cert\n",
+    "            if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
+    "                return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_migrated(comment_text):\n",
+    "    if pd.isnull(comment_text):\n",
+    "        return False\n",
+    "    text = comment_text.strip()\n",
+    "    if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
+    "        return True \n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c05f8b0d-ae4c-4cd5-8832-edb54e36ed9a",
+   "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>task_title</th>\n",
+       "      <th>comment_text</th>\n",
+       "      <th>date_created</th>\n",
+       "      <th>speaker</th>\n",
+       "      <th>meta.affil</th>\n",
+       "      <th>conversation_id</th>\n",
+       "      <th>comment_type</th>\n",
+       "      <th>status</th>\n",
+       "      <th>meta.gerrit</th>\n",
+       "      <th>id</th>\n",
+       "      <th>reply_to</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>is_relevant</th>\n",
+       "      <th>is_migrated</th>\n",
+       "      <th>text</th>\n",
+       "      <th>resolved_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>time data error</td>\n",
+       "      <td>After last update via SVN bot does not work, s...</td>\n",
+       "      <td>1381482240</td>\n",
+       "      <td>PHID-USER-wwnv7nzuscfuc2xfjwbq</td>\n",
+       "      <td>False</td>\n",
+       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
+       "      <td>task_description</td>\n",
+       "      <td>resolved</td>\n",
+       "      <td>False</td>\n",
+       "      <td>115</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013-10-11 09:04:00+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>After last update via SVN bot does not work, s...</td>\n",
+       "      <td>After last update via SVN bot does not work, s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>time data error</td>\n",
+       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
+       "      <td>1381484030</td>\n",
+       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
+       "      <td>False</td>\n",
+       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>118</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>2013-10-11 09:33:50+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
+       "      <td>SVN r10320 is https://gerrit.wikimedia.org/r/8...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>time data error</td>\n",
+       "      <td>see also bug 55399</td>\n",
+       "      <td>1381483747</td>\n",
+       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
+       "      <td>False</td>\n",
+       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>119</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2013-10-11 09:29:07+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>see also bug 55399</td>\n",
+       "      <td>see also bug 55399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>time data error</td>\n",
+       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
+       "      <td>1381483651</td>\n",
+       "      <td>PHID-USER-xezsyhikbr7hjrig2ofp</td>\n",
+       "      <td>False</td>\n",
+       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>120</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>2013-10-11 09:27:31+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
+       "      <td>It's a mess with these timestamps. Without tha...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>time data error</td>\n",
+       "      <td>When I go back from SVN revision 10320 to 1031...</td>\n",
+       "      <td>1381482504</td>\n",
+       "      <td>PHID-USER-wwnv7nzuscfuc2xfjwbq</td>\n",
+       "      <td>False</td>\n",
+       "      <td>PHID-TASK-qjt5coghg7n62wamkubq</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>121</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>2013-10-11 09:08:24+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>When I go back from SVN revision 10320 to 1031...</td>\n",
+       "      <td>When I go back from SVN revision 10320 to 1031...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6510</th>\n",
+       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
+       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
+       "      <td>1385163660</td>\n",
+       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
+       "      <td>task_description</td>\n",
+       "      <td>duplicate</td>\n",
+       "      <td>False</td>\n",
+       "      <td>155659</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2013-11-22 23:41:00+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
+       "      <td>Intention:\\nRe-use a reference.\\n\\n\\nActual Re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6511</th>\n",
+       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
+       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
+       "      <td>1385399054</td>\n",
+       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
+       "      <td>True</td>\n",
+       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>155661</td>\n",
+       "      <td>155660.0</td>\n",
+       "      <td>2013-11-25 17:04:14+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
+       "      <td>Speaking as an extensive editor, I just find t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6512</th>\n",
+       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
+       "      <td>I realize that any automagic system will have ...</td>\n",
+       "      <td>1385397795</td>\n",
+       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>155662</td>\n",
+       "      <td>155661.0</td>\n",
+       "      <td>2013-11-25 16:43:15+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>I realize that any automagic system will have ...</td>\n",
+       "      <td>I realize that any automagic system will have ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6513</th>\n",
+       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
+       "      <td>Why humans need to be able to remember the ref...</td>\n",
+       "      <td>1385397298</td>\n",
+       "      <td>PHID-USER-uf3buojo4ceizjywvyn5</td>\n",
+       "      <td>True</td>\n",
+       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>155663</td>\n",
+       "      <td>155662.0</td>\n",
+       "      <td>2013-11-25 16:34:58+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Why humans need to be able to remember the ref...</td>\n",
+       "      <td>Why humans need to be able to remember the ref...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6514</th>\n",
+       "      <td>VisualEditor:  Automatic naming scheme for ref...</td>\n",
+       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
+       "      <td>1385394470</td>\n",
+       "      <td>PHID-USER-ydswvwhh5pm4lshahjje</td>\n",
+       "      <td>True</td>\n",
+       "      <td>PHID-TASK-j3rfh4pmjx4pel7dk2tn</td>\n",
+       "      <td>task_subcomment</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>155664</td>\n",
+       "      <td>155663.0</td>\n",
+       "      <td>2013-11-25 15:47:50+00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
+       "      <td>(In reply to comment #0)\\n&gt; The ref naming sch...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6515 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             task_title  \\\n",
+       "0                                       time data error   \n",
+       "1                                       time data error   \n",
+       "2                                       time data error   \n",
+       "3                                       time data error   \n",
+       "4                                       time data error   \n",
+       "...                                                 ...   \n",
+       "6510  VisualEditor:  Automatic naming scheme for ref...   \n",
+       "6511  VisualEditor:  Automatic naming scheme for ref...   \n",
+       "6512  VisualEditor:  Automatic naming scheme for ref...   \n",
+       "6513  VisualEditor:  Automatic naming scheme for ref...   \n",
+       "6514  VisualEditor:  Automatic naming scheme for ref...   \n",
+       "\n",
+       "                                           comment_text  date_created  \\\n",
+       "0     After last update via SVN bot does not work, s...    1381482240   \n",
+       "1     SVN r10320 is https://gerrit.wikimedia.org/r/8...    1381484030   \n",
+       "2                                    see also bug 55399    1381483747   \n",
+       "3     It's a mess with these timestamps. Without tha...    1381483651   \n",
+       "4     When I go back from SVN revision 10320 to 1031...    1381482504   \n",
+       "...                                                 ...           ...   \n",
+       "6510  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...    1385163660   \n",
+       "6511  Speaking as an extensive editor, I just find t...    1385399054   \n",
+       "6512  I realize that any automagic system will have ...    1385397795   \n",
+       "6513  Why humans need to be able to remember the ref...    1385397298   \n",
+       "6514  (In reply to comment #0)\\n> The ref naming sch...    1385394470   \n",
+       "\n",
+       "                             speaker  meta.affil  \\\n",
+       "0     PHID-USER-wwnv7nzuscfuc2xfjwbq       False   \n",
+       "1     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
+       "2     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
+       "3     PHID-USER-xezsyhikbr7hjrig2ofp       False   \n",
+       "4     PHID-USER-wwnv7nzuscfuc2xfjwbq       False   \n",
+       "...                              ...         ...   \n",
+       "6510  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
+       "6511  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
+       "6512  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
+       "6513  PHID-USER-uf3buojo4ceizjywvyn5        True   \n",
+       "6514  PHID-USER-ydswvwhh5pm4lshahjje        True   \n",
+       "\n",
+       "                     conversation_id      comment_type     status  \\\n",
+       "0     PHID-TASK-qjt5coghg7n62wamkubq  task_description   resolved   \n",
+       "1     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
+       "2     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
+       "3     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
+       "4     PHID-TASK-qjt5coghg7n62wamkubq   task_subcomment        NaN   \n",
+       "...                              ...               ...        ...   \n",
+       "6510  PHID-TASK-j3rfh4pmjx4pel7dk2tn  task_description  duplicate   \n",
+       "6511  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
+       "6512  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
+       "6513  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
+       "6514  PHID-TASK-j3rfh4pmjx4pel7dk2tn   task_subcomment        NaN   \n",
+       "\n",
+       "      meta.gerrit      id  reply_to                  timestamp  is_relevant  \\\n",
+       "0           False     115       NaN  2013-10-11 09:04:00+00:00         True   \n",
+       "1           False     118     117.0  2013-10-11 09:33:50+00:00         True   \n",
+       "2           False     119     118.0  2013-10-11 09:29:07+00:00         True   \n",
+       "3           False     120     119.0  2013-10-11 09:27:31+00:00         True   \n",
+       "4           False     121     120.0  2013-10-11 09:08:24+00:00         True   \n",
+       "...           ...     ...       ...                        ...          ...   \n",
+       "6510        False  155659       NaN  2013-11-22 23:41:00+00:00         True   \n",
+       "6511        False  155661  155660.0  2013-11-25 17:04:14+00:00         True   \n",
+       "6512        False  155662  155661.0  2013-11-25 16:43:15+00:00         True   \n",
+       "6513        False  155663  155662.0  2013-11-25 16:34:58+00:00         True   \n",
+       "6514        False  155664  155663.0  2013-11-25 15:47:50+00:00         True   \n",
+       "\n",
+       "      is_migrated                                               text  \\\n",
+       "0           False  After last update via SVN bot does not work, s...   \n",
+       "1           False  SVN r10320 is https://gerrit.wikimedia.org/r/8...   \n",
+       "2           False                                 see also bug 55399   \n",
+       "3           False  It's a mess with these timestamps. Without tha...   \n",
+       "4           False  When I go back from SVN revision 10320 to 1031...   \n",
+       "...           ...                                                ...   \n",
+       "6510        False  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...   \n",
+       "6511        False  Speaking as an extensive editor, I just find t...   \n",
+       "6512        False  I realize that any automagic system will have ...   \n",
+       "6513        False  Why humans need to be able to remember the ref...   \n",
+       "6514        False  (In reply to comment #0)\\n> The ref naming sch...   \n",
+       "\n",
+       "                                          resolved_text  \n",
+       "0     After last update via SVN bot does not work, s...  \n",
+       "1     SVN r10320 is https://gerrit.wikimedia.org/r/8...  \n",
+       "2                                    see also bug 55399  \n",
+       "3     It's a mess with these timestamps. Without tha...  \n",
+       "4     When I go back from SVN revision 10320 to 1031...  \n",
+       "...                                                 ...  \n",
+       "6510  Intention:\\nRe-use a reference.\\n\\n\\nActual Re...  \n",
+       "6511  Speaking as an extensive editor, I just find t...  \n",
+       "6512  I realize that any automagic system will have ...  \n",
+       "6513  Why humans need to be able to remember the ref...  \n",
+       "6514  (In reply to comment #0)\\n> The ref naming sch...  \n",
+       "\n",
+       "[6515 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phab_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
+    "phab_df['isGerrit'] = phab_df['speaker'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
+    "\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'] <  1385596799) & (phab_df['date_created'] > 1315008000)]\n",
+    "\n",
+    "#removing headless conversations\n",
+    "task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
+    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
+    "filtered_phab_df = phab_df[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",
+    "'''\n",
+    "# filter out the sourceforge migration \n",
+    "# Originally from: http://sourceforge.net in the task task_summary\n",
+    "migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\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",
+    "task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
+    "mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
+    "'''\n",
+    "#comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
+    "#task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
+    "comment_phab_df = filtered_phab_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique conversation_ids: 1074\n",
+      "Unique ids: 6515\n",
+      "Unique speakers: 305\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": 9,
+   "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": 10,
+   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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": 12,
+   "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": 13,
+   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 25,
+   "id": "1b51f395-aaa9-4bf2-9c67-c1bc4640a89a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "comment_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/051825_coref_resolved_dep_trees.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": 16,
+   "id": "828fb57a-e152-42ef-9c60-660648898532",
+   "metadata": {},
+   "outputs": [],
+   "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": 17,
+   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
+   "metadata": {},
+   "outputs": [],
+   "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": 18,
+   "id": "09ddcbfc-b856-40ca-ad61-13577795d94b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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",
+    "        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",
+    "        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": 20,
+   "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": 35,
+   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
+      ]
+     },
+     "execution_count": 35,
+     "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": 21,
+   "id": "2274795e-c64d-43e4-b0f5-a19b5b8ba2c8",
+   "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>comment_id</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>wmfAffil</th>\n",
+       "      <th>token</th>\n",
+       "      <th>dependency</th>\n",
+       "      <th>head</th>\n",
+       "      <th>depth</th>\n",
+       "      <th>children</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>115</td>\n",
+       "      <td>2013-10-11 09:04:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>use_api_login</td>\n",
+       "      <td>dobj</td>\n",
+       "      <td>use_api</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>157</td>\n",
+       "      <td>2013-10-07 08:09:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>use_api_login</td>\n",
+       "      <td>dobj</td>\n",
+       "      <td>use_api</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>177</td>\n",
+       "      <td>2013-10-04 17:56:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>certainly</td>\n",
+       "      <td>advmod</td>\n",
+       "      <td>require</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>247</td>\n",
+       "      <td>2013-09-27 22:15:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>Login</td>\n",
+       "      <td>ROOT</td>\n",
+       "      <td>Login</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>426</td>\n",
+       "      <td>2013-09-01 11:26:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>compound</td>\n",
+       "      <td>login</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1463</th>\n",
+       "      <td>45300</td>\n",
+       "      <td>2013-08-01 17:35:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>certain</td>\n",
+       "      <td>amod</td>\n",
+       "      <td>commands</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1464</th>\n",
+       "      <td>45300</td>\n",
+       "      <td>2013-08-01 17:35:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>certain</td>\n",
+       "      <td>amod</td>\n",
+       "      <td>commands</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1465</th>\n",
+       "      <td>45373</td>\n",
+       "      <td>2013-07-27 13:30:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>certain</td>\n",
+       "      <td>amod</td>\n",
+       "      <td>element</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1466</th>\n",
+       "      <td>46078</td>\n",
+       "      <td>2013-06-18 21:17:00+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>compound</td>\n",
+       "      <td>Error</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1467</th>\n",
+       "      <td>46086</td>\n",
+       "      <td>2013-06-19 23:31:02+00:00</td>\n",
+       "      <td>False</td>\n",
+       "      <td>HTTP</td>\n",
+       "      <td>compound</td>\n",
+       "      <td>Error</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1468 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      comment_id                 timestamp  wmfAffil          token  \\\n",
+       "0            115 2013-10-11 09:04:00+00:00     False  use_api_login   \n",
+       "1            157 2013-10-07 08:09:00+00:00     False  use_api_login   \n",
+       "2            177 2013-10-04 17:56:00+00:00     False      certainly   \n",
+       "3            247 2013-09-27 22:15:00+00:00     False          Login   \n",
+       "4            426 2013-09-01 11:26:00+00:00     False           HTTP   \n",
+       "...          ...                       ...       ...            ...   \n",
+       "1463       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
+       "1464       45300 2013-08-01 17:35:00+00:00     False        certain   \n",
+       "1465       45373 2013-07-27 13:30:00+00:00     False        certain   \n",
+       "1466       46078 2013-06-18 21:17:00+00:00     False           HTTP   \n",
+       "1467       46086 2013-06-19 23:31:02+00:00     False           HTTP   \n",
+       "\n",
+       "     dependency      head  depth  children  \n",
+       "0          dobj   use_api      1         6  \n",
+       "1          dobj   use_api      1         4  \n",
+       "2        advmod   require      2         1  \n",
+       "3          ROOT     Login      0         4  \n",
+       "4      compound     login      4         0  \n",
+       "...         ...       ...    ...       ...  \n",
+       "1463       amod  commands      5         0  \n",
+       "1464       amod  commands      5         0  \n",
+       "1465       amod   element      8         0  \n",
+       "1466   compound     Error      2         0  \n",
+       "1467   compound     Error      3         0  \n",
+       "\n",
+       "[1468 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resolved_dependency_relations_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_44915/3534785199.py:8: 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",
+      "/tmp/ipykernel_44915/3534785199.py:9: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
+      "  resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
+      "/tmp/ipykernel_44915/3534785199.py:24: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
+      "/tmp/ipykernel_44915/3534785199.py:24: 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",
+      "  filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
+      "/tmp/ipykernel_44915/3534785199.py:45: 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",
+      "  resolved_filtered_dependencies['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\n",
+      "/tmp/ipykernel_44915/3534785199.py:46: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
+      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
+      "/tmp/ipykernel_44915/3534785199.py:46: 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",
+      "  resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
+    "#pattern = r'\\b(contributor|community|volunteer)\\b'\n",
+    "#pattern = r'\\b(WMF|Foundation|Wikimedia)\\b'\n",
+    "pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
+    "#pattern = r'\\b(http|https)\\b'\n",
+    "#pattern = r'\\b(auth)\\b'\n",
+    "\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.scatterplot(data=wmf_filtered_dependencies, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', 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.scatterplot(data=other_filtered_dependencies, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
+    "#sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
+    "ax0.set_title(f'Depth of {pattern} 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['timestamp'] = pd.to_datetime(resolved_filtered_dependencies['timestamp'], utc=True)\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.scatterplot(data=resolved_wmf_filtered_dependencies, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', 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.scatterplot(data=resolved_other_filtered_dependencies, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='o')\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(f'Depth of {pattern} 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')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/phab_analysis/case2/coref_resolution-https.ipynb b/phab_analysis/case2/coref_resolution-https.ipynb
index d3dc4eb..ec74fd1 100644
--- a/phab_analysis/case2/coref_resolution-https.ipynb
+++ b/phab_analysis/case2/coref_resolution-https.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "f6448c6f-2b5d-45f5-a32e-b3b47c16ef85",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "e30e81ad",
    "metadata": {},
    "outputs": [],
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "f359805f",
    "metadata": {},
    "outputs": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "ffd0b263",
    "metadata": {},
    "outputs": [
@@ -175,17 +175,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "f32f6eed-3aeb-4b05-8d40-7ed85e7235c5",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<spacy_experimental.coref.span_resolver_component.SpanResolver at 0x1495ecba4bb0>"
+       "<spacy_experimental.coref.span_resolver_component.SpanResolver at 0x14cab225fd00>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "a5b062d8-2d26-4a3e-a84c-ba0eaf6eb436",
    "metadata": {},
    "outputs": [],
@@ -220,26 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "424d35e0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "John is frustrated with the VisualEditor project, he thinks it doesn't work."
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "999e1656-0036-4ba2-bedf-f54493f67790",
    "metadata": {},
    "outputs": [],
@@ -285,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "be476647-624b-4e95-ab62-9c6b08f85368",
    "metadata": {},
    "outputs": [],
@@ -298,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "a9628b54-a1df-49cd-a365-9cba59de3421",
    "metadata": {},
    "outputs": [
@@ -308,7 +289,7 @@
        "'i hate ve.interface, ve.interface always messes up i browser'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,54 +315,13 @@
       "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
       "  \"\"\"Entry point for launching an IPython kernel.\n",
       "Token indices sequence length is longer than the specified maximum sequence length for this model (911 > 512). Running this sequence through the model will result in indexing errors\n",
-      "Token indices sequence length is longer than the specified maximum sequence length for this model (911 > 512). Running this sequence through the model will result in indexing errors\n",
-      "Token indices sequence length is longer than the specified maximum sequence length for this model (904 > 512). Running this sequence through the model will result in indexing errors\n"
+      "Token indices sequence length is longer than the specified maximum sequence length for this model (911 > 512). Running this sequence through the model will result in indexing errors\n"
      ]
     }
    ],
    "source": [
     "comment_phab_df['text'] = comment_phab_df['comment_text'].apply(str)\n",
-    "comment_phab_df['resolved_text'] = comment_phab_df['text'].apply(resolving_comment)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2b583feb-1c62-4c96-9ba0-2996d72e70d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "46088",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 46088",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_61233/1116300830.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomment_phab_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'resolved_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m46088\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/gscratch/scrubbed/mjilg/envs/coref2-notebook/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 46088"
-     ]
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "92bf47ae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "comment_phab_df['resolved_text'] = comment_phab_df['text'].apply(resolving_comment)\n",
     "comment_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/051825_coref_rel_phab_comments.csv\", index=False)"
    ]
   }
@@ -402,7 +342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.12"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,
diff --git a/phab_analysis/case3/041525_phab_comments.ipynb b/phab_analysis/case3/041525_phab_comments.ipynb
index 3a8be4c..e843832 100644
--- a/phab_analysis/case3/041525_phab_comments.ipynb
+++ b/phab_analysis/case3/041525_phab_comments.ipynb
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
    "metadata": {},
    "outputs": [
@@ -172,6 +172,29 @@
     "print(f\"Unique speakers: {unique_speakers}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0ef35632-ed07-478e-94ab-525169b82783",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "given_date = pd.Timestamp(\"2015-07-02\").tz_localize(None)\n",
+    "task_phab_df['timestamp'] = pd.to_datetime(task_phab_df['timestamp'], unit='s').dt.tz_localize(None)\n",
+    "task_phab_df['week_bin'] = ((task_phab_df['timestamp'] - given_date).dt.days // 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1e7bda13-4c2d-413e-b3c6-9c4b38e6cb07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_phab_df\n",
+    "task_phab_df.to_csv(\"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/phab_tasks.csv\", index=False)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
diff --git a/phab_analysis/case3/050825_join_resolved_files.ipynb b/phab_analysis/case3/050825_join_resolved_files.ipynb
index 2733270..c842306 100644
--- a/phab_analysis/case3/050825_join_resolved_files.ipynb
+++ b/phab_analysis/case3/050825_join_resolved_files.ipynb
@@ -1168,7 +1168,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.11"
+   "version": "3.7.12"
   }
  },
  "nbformat": 4,
diff --git a/ww-task-plot-script.R b/ww-task-plot-script.R
index 2a788c5..b5cff38 100644
--- a/ww-task-plot-script.R
+++ b/ww-task-plot-script.R
@@ -3,6 +3,9 @@ library(tidyverse)
 c1_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/phab_tasks.csv"
 c1_input_df <- read.csv(c1_count , header = TRUE) 
 
+c2_count <- "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/phab_tasks.csv"
+c2_input_df <- read.csv(c2_count , header = TRUE) 
+
 c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/phab_tasks.csv"
 c3_input_df <- read.csv(c3_count , header = TRUE) 
 
@@ -10,17 +13,16 @@ c1_unique_counts <- c1_input_df %>%
   group_by(meta.affil, week_bin) %>%
   summarise(unique_count = n_distinct(conversation_id), .groups = "drop")
 
+c2_unique_counts <- c2_input_df %>%
+  group_by(meta.affil, week_bin) %>%
+  summarise(unique_count = n_distinct(conversation_id), .groups = "drop")
+
 c3_unique_counts <- c3_input_df %>%
   group_by(meta.affil, week_bin) %>%
   summarise(unique_count = n_distinct(conversation_id), .groups = "drop")
 
 c1_unique_counts <- c1_unique_counts%>% mutate(source = "c1")
-c2_unique_counts <- data.frame(
-  meta.affil = rep("False", 117), 
-  week_bin = -103:13,          
-  unique_count = rep(0, 117),  
-  source = rep("c2", 117)       
-)
+c2_unique_counts <- c2_unique_counts %>% mutate(source = "c2")
 c3_unique_counts <- c3_unique_counts %>% mutate(source = "c3")
 
 combined_df <- bind_rows(c1_unique_counts, c2_unique_counts, c3_unique_counts)
@@ -37,28 +39,34 @@ commit_authors <- combined_df |>
   ggplot(aes(x=week_bin, 
              y=unique_count, 
              fill=factor(meta.affil))) +
-  geom_col(position='dodge') +
-  labs(x = "Relative Week", y = "Tasks", fill="Task Author") +
+  geom_col(position='dodge2') +
+  labs(x = "Relative Week", y = "New Tasks Created", fill="Task Author") +
   geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -29), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "dotted", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -9), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "dotted", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c1"), 
              aes(xintercept = -4), 
-             linetype = "3313", color = "black", linewidth = 1) +
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c2"), 
+             aes(xintercept = -99), 
+             linetype = "dotted", color = "black", linewidth = 0.5) +
+  geom_vline(data = combined_df |> filter(source == "c2"), 
+             aes(xintercept = -4),   
+             linetype = "3313", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c3"), 
              aes(xintercept = -97), 
-             linetype = "dotted", color = "black", linewidth = 1) +
+             linetype = "dotted", color = "black", linewidth = 0.5) +
   geom_vline(data = combined_df |> filter(source == "c3"), 
              aes(xintercept = -3), 
-             linetype = "3313", color = "black", linewidth = 1) +
-  geom_text(data = data.frame(source = "c1", relative_week = -40, lengthened_commit_count = 130), 
+             linetype = "3313", color = "black", linewidth = 0.5) +
+  geom_text(data = data.frame(source = "c1", relative_week = -39, lengthened_commit_count = 130), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Opt-In Testing Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
-  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 1) + # Add vertical line at week 0
-  geom_text(data = data.frame(source = "c1", relative_week = 7, lengthened_commit_count = 130), 
+  geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 0.5) + # Add vertical line at week 0
+  geom_text(data = data.frame(source = "c2", relative_week = 7, lengthened_commit_count = 130), 
             aes(x = relative_week, y = lengthened_commit_count, label = "Wide Deployment"), 
             inherit.aes = FALSE, color = "black", size = 4) +
   geom_text(data = data.frame(source = "c3", relative_week = -15, lengthened_commit_count = 130), 
@@ -81,10 +89,10 @@ commit_authors <- combined_df |>
     strip.text = element_text(size = 14)# Increase legend title font size
   ) +
   facet_wrap(~source, nrow = 3, labeller = labeller(source = c(
-    "c1" = "VisualEditor",
-    "c2" = "HTTPS-as-default",
-    "c3" = "HTTP-deprecation"
+    "c1" = "VisualEditor (2013)",
+    "c2" = "HTTPS-as-default (2013)",
+    "c3" = "HTTP-deprecation (2015)"
   )))
 commit_authors
 
-ggsave(filename = "ww-0501-tasks-faceted.png", plot = commit_authors, width = 15, height = 9, dpi = 800)
+ggsave(filename = "d1-m2-tasks-faceted.png", plot = commit_authors, width = 15, height = 9, dpi = 800)