diff --git "a/scripts/Section_6-7_Users_Models.ipynb" "b/scripts/Section_6-7_Users_Models.ipynb"
new file mode 100644--- /dev/null
+++ "b/scripts/Section_6-7_Users_Models.ipynb"
@@ -0,0 +1,1683 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0683462e-3b7a-4846-9578-b26cb2624b16",
+   "metadata": {},
+   "source": [
+    "# Section 6.7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73641d67-a971-4c47-a8bc-195932c0cef8",
+   "metadata": {},
+   "source": [
+    "## Step 1: Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "04f9f5e2-0d97-4090-84b5-c2df1d4db448",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:00.953194Z",
+     "iopub.status.busy": "2025-02-08T22:00:00.952679Z",
+     "iopub.status.idle": "2025-02-08T22:00:00.956414Z",
+     "shell.execute_reply": "2025-02-08T22:00:00.955984Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:00.953159Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "17c714a8-201f-4b0d-87fb-f7ebdeab7d5f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:01.239646Z",
+     "iopub.status.busy": "2025-02-08T22:00:01.239316Z",
+     "iopub.status.idle": "2025-02-08T22:00:01.242379Z",
+     "shell.execute_reply": "2025-02-08T22:00:01.241907Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:01.239627Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_dir = Path.cwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d3a14093-e726-4607-9ebe-cc27ba5dd686",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:01.469059Z",
+     "iopub.status.busy": "2025-02-08T22:00:01.468887Z",
+     "iopub.status.idle": "2025-02-08T22:00:01.471699Z",
+     "shell.execute_reply": "2025-02-08T22:00:01.471268Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:01.469043Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "models = current_dir.parent / 'data/CSV/Civiverse-Models_ratios.csv'\n",
+    "agg_user_models =  current_dir.parent / 'data/CSV/model_subsets/Section_6-7/users_model_contributions.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4103e90b-a560-4fdf-ba77-b4233277729b",
+   "metadata": {},
+   "source": [
+    "### Model Data Filtering and Aggregation\n",
+    "\n",
+    "This script filters a dataset of models, excluding those with fewer than five matches, and calculates browsing levels based on NSFW ratios. It then aggregates data by username, sorting by total images, and saves the processed data to \"agg_models.csv\". \n",
+    "Additionally, entries with empty usernames are extracted and saved separately as \"empty_username_images.csv\". Key statistics about the filtering process are printed to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "caeb45b9-9a02-4154-97ca-329e26a2eec6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:02.195735Z",
+     "iopub.status.busy": "2025-02-08T22:00:02.195491Z",
+     "iopub.status.idle": "2025-02-08T22:00:02.482904Z",
+     "shell.execute_reply": "2025-02-08T22:00:02.482079Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:02.195718Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sctmp/lauwag/ipykernel_1497701/2581937833.py:11: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "/sctmp/lauwag/ipykernel_1497701/2581937833.py:27: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows included: 16\n",
+      "Rows excluded: 684\n",
+      "Images with empty usernameHash: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(PosixPath('/shares/weddigen.ki.uzh/laura_wagner/Civitai_page_analysis/Civitai_visualizations/data/CSV/model_subsets/Section_6-7/agg_models.csv'),\n",
+       " PosixPath('/shares/weddigen.ki.uzh/laura_wagner/Civitai_page_analysis/Civitai_visualizations/data/CSV/model_subsets/Section_6-7/models_excluded_due_to_empty_username_images.csv'))"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the dataset\n",
+    "data = pd.read_csv(models)\n",
+    "\n",
+    "# Step 1: Exclude models with `matches` < 5\n",
+    "filtered_data = data[data['matches'] >= 1]\n",
+    "\n",
+    "# Step 2: Perform aggregation on the filtered data\n",
+    "# Rename `usernameHash` to `username`\n",
+    "filtered_data.rename(columns={'usernameHash': 'username'}, inplace=True)\n",
+    "\n",
+    "# Function to calculate browsing levels based on NSFW ratio\n",
+    "def calculate_browsing_level(nsfw_ratio):\n",
+    "    if nsfw_ratio < 0.2:\n",
+    "        return 1\n",
+    "    elif 0.2 <= nsfw_ratio < 0.4:\n",
+    "        return 2\n",
+    "    elif 0.4 <= nsfw_ratio < 0.6:\n",
+    "        return 4\n",
+    "    elif 0.6 <= nsfw_ratio < 0.8:\n",
+    "        return 8\n",
+    "    else:\n",
+    "        return 16\n",
+    "\n",
+    "# Apply browsing level calculation\n",
+    "filtered_data['browsingLevel'] = filtered_data['nsfw_ratio'].apply(calculate_browsing_level)\n",
+    "\n",
+    "# Filter rows where username is empty or NaN\n",
+    "empty_username_data = filtered_data[filtered_data['username'].isna() | (filtered_data['username'] == '')]\n",
+    "\n",
+    "# Save the filtered empty username data\n",
+    "empty_username_output_path = current_dir.parent / 'data/CSV/model_subsets/Section_6-7/models_excluded_due_to_empty_username_images.csv'\n",
+    "empty_username_data.to_csv(empty_username_output_path, index=False)\n",
+    "\n",
+    "# Aggregating data by unique creators (username)\n",
+    "aggregated_data = filtered_data.groupby('username').agg(\n",
+    "    total_images=('name', 'count'),  # Total contributions per user\n",
+    "    total_nsfw_true=('browsingLevel', lambda x: (x != 1).sum()),  # Non-SFW contributions\n",
+    "    total_browsingLevel_16=('browsingLevel', lambda x: (x == 16).sum()),  # Browsing level 16\n",
+    "    total_browsingLevel_8=('browsingLevel', lambda x: (x == 8).sum()),  # Browsing level 8\n",
+    "    total_browsingLevel_4=('browsingLevel', lambda x: (x == 4).sum()),  # Browsing level 4\n",
+    "    total_browsingLevel_2=('browsingLevel', lambda x: (x == 2).sum()),  # Browsing level 2\n",
+    "    total_browsingLevel_1=('browsingLevel', lambda x: (x == 1).sum())   # Browsing level 1\n",
+    ").reset_index()\n",
+    "\n",
+    "# Sort the aggregated data by total images in descending order\n",
+    "aggregated_data_sorted = aggregated_data.sort_values(by='total_images', ascending=False)\n",
+    "\n",
+    "# Save the filtered and aggregated data\n",
+    "output_file_path = current_dir.parent / 'data/CSV/model_subsets/Section_6-7/agg_models.csv'\n",
+    "aggregated_data_sorted.to_csv(output_file_path, index=False)\n",
+    "\n",
+    "# Display the counts of included and excluded rows\n",
+    "excluded_count = len(data) - len(filtered_data)\n",
+    "empty_username_count = len(empty_username_data)\n",
+    "print(f\"Rows included: {len(filtered_data)}\")\n",
+    "print(f\"Rows excluded: {excluded_count}\")\n",
+    "print(f\"Images with empty usernameHash: {empty_username_count}\")\n",
+    "\n",
+    "# Display the location of the saved files\n",
+    "output_file_path, empty_username_output_path\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07599912-0efb-432e-bea3-5e064c1d1c72",
+   "metadata": {},
+   "source": [
+    "This script processes a dataset of models, filters out entries with fewer than five matches, and then aggregates the remaining data to analyze browsing levels based on NSFW ratios. It begins by loading two datasets: one containing model information and another comparison dataset. It identifies unique creators before and after filtering, determines the number of excluded creators, and checks how many of them appear in the comparison dataset. The script then calculates a browsing level for each entry based on the NSFW ratio and aggregates data by username, counting total images and categorizing them by browsing levels. Finally, the aggregated data is sorted by total images and saved as \"filtered_aggregated_models.csv\" in the current working directory, while key statistics about the filtering process are printed to the console."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f6a0f2d0-82b8-4e2a-aa42-4465862d64cd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:03.746798Z",
+     "iopub.status.busy": "2025-02-08T22:00:03.746435Z",
+     "iopub.status.idle": "2025-02-08T22:00:03.891113Z",
+     "shell.execute_reply": "2025-02-08T22:00:03.890411Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:03.746778Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows included: 6\n",
+      "Rows excluded: 694\n",
+      "Total unique creators before filtering: 470\n",
+      "Creators excluded after filtering: 464\n",
+      "Creators remaining after filtering: 6\n",
+      "Creators excluded that were present in comparison dataset: 455\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sctmp/lauwag/ipykernel_1497701/3630865961.py:30: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n",
+      "/sctmp/lauwag/ipykernel_1497701/3630865961.py:44: SettingWithCopyWarning:\n",
+      "\n",
+      "\n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('/shares/weddigen.ki.uzh/laura_wagner/Civitai_page_analysis/Civitai_visualizations/data/CSV/model_subsets/Section_6-7/filtered_aggregated_models.csv')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the datasets\n",
+    "models_data = pd.read_csv(models)\n",
+    "comparison_data = pd.read_csv(current_dir.parent / 'data/CSV/model_subsets/Section_6-7/agg_models.csv')\n",
+    "\n",
+    "# Step 1: Get the number of unique creators from models dataset\n",
+    "unique_creators_before = set(models_data['usernameHash'].unique())\n",
+    "\n",
+    "# Step 2: Get the number of unique usernames from the comparison dataset\n",
+    "unique_usernames_comparison = set(comparison_data['username'].unique())\n",
+    "\n",
+    "# Step 3: Exclude models with `matches` < 3\n",
+    "filtered_data = models_data[models_data['matches'] >= 5]\n",
+    "\n",
+    "# Step 4: Get the number of unique creators after filtering\n",
+    "unique_creators_after = set(filtered_data['usernameHash'].unique())\n",
+    "\n",
+    "# Step 5: Calculate excluded creators based on filtering\n",
+    "excluded_creators = unique_creators_before - unique_creators_after\n",
+    "num_excluded_creators = len(excluded_creators)\n",
+    "num_total_creators = len(unique_creators_before)\n",
+    "num_remaining_creators = len(unique_creators_after)\n",
+    "\n",
+    "# Step 6: Compare with usernames in the second dataset\n",
+    "excluded_creators_in_comparison = unique_creators_before - unique_usernames_comparison\n",
+    "num_excluded_creators_in_comparison = len(excluded_creators_in_comparison)\n",
+    "\n",
+    "# Step 7: Perform aggregation on the filtered data\n",
+    "filtered_data.rename(columns={'usernameHash': 'username'}, inplace=True)\n",
+    "\n",
+    "def calculate_browsing_level(nsfw_ratio):\n",
+    "    if nsfw_ratio < 0.2:\n",
+    "        return 1\n",
+    "    elif 0.2 <= nsfw_ratio < 0.4:\n",
+    "        return 2\n",
+    "    elif 0.4 <= nsfw_ratio < 0.6:\n",
+    "        return 4\n",
+    "    elif 0.6 <= nsfw_ratio < 0.8:\n",
+    "        return 8\n",
+    "    else:\n",
+    "        return 16\n",
+    "\n",
+    "filtered_data['browsingLevel'] = filtered_data['nsfw_ratio'].apply(calculate_browsing_level)\n",
+    "\n",
+    "aggregated_data = filtered_data.groupby('username').agg(\n",
+    "    total_images=('name', 'count'),\n",
+    "    total_nsfw_true=('browsingLevel', lambda x: (x != 1).sum()),\n",
+    "    total_browsingLevel_16=('browsingLevel', lambda x: (x == 16).sum()),\n",
+    "    total_browsingLevel_8=('browsingLevel', lambda x: (x == 8).sum()),\n",
+    "    total_browsingLevel_4=('browsingLevel', lambda x: (x == 4).sum()),\n",
+    "    total_browsingLevel_2=('browsingLevel', lambda x: (x == 2).sum()),\n",
+    "    total_browsingLevel_1=('browsingLevel', lambda x: (x == 1).sum())\n",
+    ").reset_index()\n",
+    "\n",
+    "aggregated_data_sorted = aggregated_data.sort_values(by='total_images', ascending=False)\n",
+    "\n",
+    "output_file_path = current_dir.parent / 'data/CSV/model_subsets/Section_6-7/filtered_aggregated_models.csv'\n",
+    "aggregated_data_sorted.to_csv(output_file_path, index=False)\n",
+    "\n",
+    "# Display the counts of included and excluded rows\n",
+    "excluded_count = len(models_data) - len(filtered_data)\n",
+    "print(f\"Rows included: {len(filtered_data)}\")\n",
+    "print(f\"Rows excluded: {excluded_count}\")\n",
+    "\n",
+    "# Display the counts of included and excluded creators\n",
+    "print(f\"Total unique creators before filtering: {num_total_creators}\")\n",
+    "print(f\"Creators excluded after filtering: {num_excluded_creators}\")\n",
+    "print(f\"Creators remaining after filtering: {num_remaining_creators}\")\n",
+    "\n",
+    "# Display the number of excluded creators that were also in the comparison file\n",
+    "print(f\"Creators excluded that were present in comparison dataset: {num_excluded_creators_in_comparison}\")\n",
+    "\n",
+    "# Display the location of the saved file\n",
+    "output_file_path\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c59e84e-84a1-4980-be01-4199d8d50c95",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T11:04:25.785429Z",
+     "iopub.status.busy": "2025-02-08T11:04:25.783967Z",
+     "iopub.status.idle": "2025-02-08T11:04:25.791236Z",
+     "shell.execute_reply": "2025-02-08T11:04:25.789538Z",
+     "shell.execute_reply.started": "2025-02-08T11:04:25.785367Z"
+    }
+   },
+   "source": [
+    "## Step 2: Sankey Diagram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "43d4ea1a-3217-4204-a663-9b9113366dde",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:05.519854Z",
+     "iopub.status.busy": "2025-02-08T22:00:05.519416Z",
+     "iopub.status.idle": "2025-02-08T22:00:05.522866Z",
+     "shell.execute_reply": "2025-02-08T22:00:05.522360Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:05.519833Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import plotly.graph_objects as go\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1942a2bc-28dc-42e4-ab4c-62f0f7676500",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:05.878506Z",
+     "iopub.status.busy": "2025-02-08T22:00:05.878061Z",
+     "iopub.status.idle": "2025-02-08T22:00:05.881215Z",
+     "shell.execute_reply": "2025-02-08T22:00:05.880736Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:05.878480Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_dir = Path.cwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6a0b6a25-ea53-4787-9356-a28693b193a3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:06.156325Z",
+     "iopub.status.busy": "2025-02-08T22:00:06.155819Z",
+     "iopub.status.idle": "2025-02-08T22:00:06.159128Z",
+     "shell.execute_reply": "2025-02-08T22:00:06.158690Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:06.156302Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_dir = current_dir.parent / 'plots/'\n",
+    "agg_user_models =  current_dir.parent / 'data/CSV/model_subsets/Section_6-7/filtered_aggregated_models.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "0e77776b-ad98-4c18-8209-5e590d5ee5f8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-02-08T22:00:06.801852Z",
+     "iopub.status.busy": "2025-02-08T22:00:06.801530Z",
+     "iopub.status.idle": "2025-02-08T22:00:06.965504Z",
+     "shell.execute_reply": "2025-02-08T22:00:06.965010Z",
+     "shell.execute_reply.started": "2025-02-08T22:00:06.801830Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/sctmp/lauwag/ipykernel_1497701/356657099.py:31: FutureWarning:\n",
+      "\n",
+      "The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "arrangement": "fixed",
+         "link": {
+          "color": [
+           "blueviolet",
+           "silver"
+          ],
+          "customdata": [
+           "<b>33.3% of models (2)</b>",
+           "<b>66.7% of models (4)</b>"
+          ],
+          "hovertemplate": "%{customdata}<extra></extra>",
+          "source": [
+           0,
+           0
+          ],
+          "target": [
+           6,
+           10
+          ],
+          "value": [
+           33.33333333333333,
+           66.66666666666666
+          ]
+         },
+         "node": {
+          "color": [
+           "black",
+           "black",
+           "black",
+           "black",
+           "black",
+           "black",
+           "blueviolet",
+           "crimson",
+           "coral",
+           "rosybrown",
+           "silver"
+          ],
+          "hovertemplate": "%{label}",
+          "label": [
+           "<b>1+ Contributions</b><br><b>100.0% of creators (6)</b><br><b>100.0% of models (6)</b><br><b>100.0% of NSFW models (2)</b>",
+           "<b>0+ Contributions</b><br><b>0.0% of creators (0)</b><br><b>0.0% of models (0)</b><br><b>0.0% of NSFW models (0)</b>",
+           "<b>2+ Contributions</b><br><b>0.0% of creators (0)</b><br><b>0.0% of models (0)</b><br><b>0.0% of NSFW models (0)</b>",
+           "<b>10+ Contributions</b><br><b>0.0% of creators (0)</b><br><b>0.0% of models (0)</b><br><b>0.0% of NSFW models (0)</b>",
+           "<b>100+ Contributions</b><br><b>0.0% of creators (0)</b><br><b>0.0% of models (0)</b><br><b>0.0% of NSFW models (0)</b>",
+           "<b>1000+ Contributions</b><br><b>0.0% of creators (0)</b><br><b>0.0% of models (0)</b><br><b>0.0% of NSFW models (0)</b>",
+           "<b>0.8-1</b><br><b>33.3% of models (2)</b>",
+           "<b>0.6-0.8</b><br><b>0.0% of models (0)</b>",
+           "<b>0.4-0.6</b><br><b>0.0% of models (0)</b>",
+           "<b>0.2-0.4</b><br><b>0.0% of models (0)</b>",
+           "<b>0.0-0.2</b><br><b>66.7% of models (4)</b>"
+          ],
+          "line": {
+           "color": "black",
+           "width": 0.5
+          },
+          "pad": 20,
+          "thickness": 30,
+          "x": [
+           0,
+           0,
+           0,
+           0,
+           0,
+           0,
+           1,
+           1,
+           1,
+           1,
+           1
+          ],
+          "y": [
+           0.9166666666666666,
+           0.75,
+           0.5833333333333333,
+           0.41666666666666663,
+           0.25,
+           0.08333333333333337,
+           0.9,
+           0.7,
+           0.5,
+           0.30000000000000004,
+           0.09999999999999998
+          ]
+         },
+         "type": "sankey"
+        }
+       ],
+       "layout": {
+        "font": {
+         "size": 12
+        },
+        "height": 900,
+        "template": {
+         "data": {
+          "bar": [
+           {
+            "error_x": {
+             "color": "#2a3f5f"
+            },
+            "error_y": {
+             "color": "#2a3f5f"
+            },
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "bar"
+           }
+          ],
+          "barpolar": [
+           {
+            "marker": {
+             "line": {
+              "color": "#E5ECF6",
+              "width": 0.5
+             },
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "barpolar"
+           }
+          ],
+          "carpet": [
+           {
+            "aaxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "baxis": {
+             "endlinecolor": "#2a3f5f",
+             "gridcolor": "white",
+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
+            },
+            "type": "carpet"
+           }
+          ],
+          "choropleth": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "choropleth"
+           }
+          ],
+          "contour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "contour"
+           }
+          ],
+          "contourcarpet": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "contourcarpet"
+           }
+          ],
+          "heatmap": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmap"
+           }
+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "heatmapgl"
+           }
+          ],
+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
+             }
+            },
+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2d"
+           }
+          ],
+          "histogram2dcontour": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "histogram2dcontour"
+           }
+          ],
+          "mesh3d": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "type": "mesh3d"
+           }
+          ],
+          "parcoords": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "parcoords"
+           }
+          ],
+          "pie": [
+           {
+            "automargin": true,
+            "type": "pie"
+           }
+          ],
+          "scatter": [
+           {
+            "fillpattern": {
+             "fillmode": "overlay",
+             "size": 10,
+             "solidity": 0.2
+            },
+            "type": "scatter"
+           }
+          ],
+          "scatter3d": [
+           {
+            "line": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatter3d"
+           }
+          ],
+          "scattercarpet": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattercarpet"
+           }
+          ],
+          "scattergeo": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergeo"
+           }
+          ],
+          "scattergl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattergl"
+           }
+          ],
+          "scattermapbox": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scattermapbox"
+           }
+          ],
+          "scatterpolar": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolar"
+           }
+          ],
+          "scatterpolargl": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterpolargl"
+           }
+          ],
+          "scatterternary": [
+           {
+            "marker": {
+             "colorbar": {
+              "outlinewidth": 0,
+              "ticks": ""
+             }
+            },
+            "type": "scatterternary"
+           }
+          ],
+          "surface": [
+           {
+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
+            },
+            "colorscale": [
+             [
+              0,
+              "#0d0887"
+             ],
+             [
+              0.1111111111111111,
+              "#46039f"
+             ],
+             [
+              0.2222222222222222,
+              "#7201a8"
+             ],
+             [
+              0.3333333333333333,
+              "#9c179e"
+             ],
+             [
+              0.4444444444444444,
+              "#bd3786"
+             ],
+             [
+              0.5555555555555556,
+              "#d8576b"
+             ],
+             [
+              0.6666666666666666,
+              "#ed7953"
+             ],
+             [
+              0.7777777777777778,
+              "#fb9f3a"
+             ],
+             [
+              0.8888888888888888,
+              "#fdca26"
+             ],
+             [
+              1,
+              "#f0f921"
+             ]
+            ],
+            "type": "surface"
+           }
+          ],
+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
+           }
+          ]
+         },
+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
+          },
+          "autotypenumbers": "strict",
+          "coloraxis": {
+           "colorbar": {
+            "outlinewidth": 0,
+            "ticks": ""
+           }
+          },
+          "colorscale": {
+           "diverging": [
+            [
+             0,
+             "#8e0152"
+            ],
+            [
+             0.1,
+             "#c51b7d"
+            ],
+            [
+             0.2,
+             "#de77ae"
+            ],
+            [
+             0.3,
+             "#f1b6da"
+            ],
+            [
+             0.4,
+             "#fde0ef"
+            ],
+            [
+             0.5,
+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
+           "sequential": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ],
+           "sequentialminus": [
+            [
+             0,
+             "#0d0887"
+            ],
+            [
+             0.1111111111111111,
+             "#46039f"
+            ],
+            [
+             0.2222222222222222,
+             "#7201a8"
+            ],
+            [
+             0.3333333333333333,
+             "#9c179e"
+            ],
+            [
+             0.4444444444444444,
+             "#bd3786"
+            ],
+            [
+             0.5555555555555556,
+             "#d8576b"
+            ],
+            [
+             0.6666666666666666,
+             "#ed7953"
+            ],
+            [
+             0.7777777777777778,
+             "#fb9f3a"
+            ],
+            [
+             0.8888888888888888,
+             "#fdca26"
+            ],
+            [
+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "<b>Sankey Diagram: Creator Bins → Browsing Groups</b>"
+        },
+        "width": 1300
+       }
+      },
+      "image/png": "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",
+      "text/html": [
+       "<div>                            <div id=\"d6c206e9-0583-458b-ae34-cb19e461ad08\" class=\"plotly-graph-div\" style=\"height:900px; width:1300px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"d6c206e9-0583-458b-ae34-cb19e461ad08\")) {                    Plotly.newPlot(                        \"d6c206e9-0583-458b-ae34-cb19e461ad08\",                        [{\"arrangement\":\"fixed\",\"link\":{\"color\":[\"blueviolet\",\"silver\"],\"customdata\":[\"\\u003cb\\u003e33.3% of models (2)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e66.7% of models (4)\\u003c\\u002fb\\u003e\"],\"hovertemplate\":\"%{customdata}\\u003cextra\\u003e\\u003c\\u002fextra\\u003e\",\"source\":[0,0],\"target\":[6,10],\"value\":[33.33333333333333,66.66666666666666]},\"node\":{\"color\":[\"black\",\"black\",\"black\",\"black\",\"black\",\"black\",\"blueviolet\",\"crimson\",\"coral\",\"rosybrown\",\"silver\"],\"hovertemplate\":\"%{label}\",\"label\":[\"\\u003cb\\u003e1+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e100.0% of creators (6)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e100.0% of models (6)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e100.0% of NSFW models (2)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of creators (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of NSFW models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e2+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of creators (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of NSFW models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e10+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of creators (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of NSFW models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e100+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of creators (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of NSFW models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e1000+ Contributions\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of creators (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of NSFW models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0.8-1\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e33.3% of models (2)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0.6-0.8\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0.4-0.6\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0.2-0.4\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e0.0% of models (0)\\u003c\\u002fb\\u003e\",\"\\u003cb\\u003e0.0-0.2\\u003c\\u002fb\\u003e\\u003cbr\\u003e\\u003cb\\u003e66.7% of models (4)\\u003c\\u002fb\\u003e\"],\"line\":{\"color\":\"black\",\"width\":0.5},\"pad\":20,\"thickness\":30,\"x\":[0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0],\"y\":[0.9166666666666666,0.75,0.5833333333333333,0.41666666666666663,0.25,0.08333333333333337,0.9,0.7,0.5,0.30000000000000004,0.09999999999999998]},\"type\":\"sankey\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"title\":{\"text\":\"\\u003cb\\u003eSankey Diagram: Creator Bins \\u2192 Browsing Groups\\u003c\\u002fb\\u003e\"},\"font\":{\"size\":12},\"width\":1300,\"height\":900},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('d6c206e9-0583-458b-ae34-cb19e461ad08');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# =============================================================================\n",
+    "# 1. LOAD DATA & ASSIGN CREATOR BINS\n",
+    "# =============================================================================\n",
+    "input_path =  agg_user_models\n",
+    "output_path = plot_dir / 'Section_6-7_model_contributions_sankey.svg'\n",
+    "\n",
+    "# Read the CSV file into a DataFrame.\n",
+    "df = pd.read_csv(input_path)\n",
+    "\n",
+    "# Define bins for \"total_images\":\n",
+    "# Bins: [0,1), [1,2), [2,10), [10,100), [100,1000), [1000,∞)\n",
+    "bins = [0, 1, 2, 10, 100, 1000, float('inf')]\n",
+    "bin_labels_list = [0, 1, 2, 10, 100, 1000]\n",
+    "df['creator_bin'] = pd.cut(df['total_images'], bins=bins, right=False, labels=bin_labels_list)\n",
+    "\n",
+    "# Overall number of creators (each row represents one creator)\n",
+    "overall_creators = len(df)\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 2. AGGREGATE DATA BY CREATOR BIN\n",
+    "# =============================================================================\n",
+    "# Sum the counts for each creator_bin and for each browsing level.\n",
+    "agg_columns = {\n",
+    "    'total_browsingLevel_1': 'sum',\n",
+    "    'total_browsingLevel_2': 'sum',\n",
+    "    'total_browsingLevel_4': 'sum',\n",
+    "    'total_browsingLevel_8': 'sum',\n",
+    "    'total_browsingLevel_16': 'sum',\n",
+    "    'total_images': 'sum'\n",
+    "}\n",
+    "flow_data = df.groupby('creator_bin').agg(agg_columns).reset_index()\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 3. MAP BROWSING LEVELS TO NEW GROUPS & ASSIGN COLORS\n",
+    "# =============================================================================\n",
+    "browsing_groups = {\n",
+    "    \"0.0-0.2\": \"total_browsingLevel_1\",\n",
+    "    \"0.2-0.4\": \"total_browsingLevel_2\",\n",
+    "    \"0.4-0.6\": \"total_browsingLevel_4\",\n",
+    "    \"0.6-0.8\": \"total_browsingLevel_8\",\n",
+    "    \"0.8-1\":   \"total_browsingLevel_16\"\n",
+    "}\n",
+    "\n",
+    "# Fixed colors for each browsing group.\n",
+    "color_mapping = {\n",
+    "    \"0.0-0.2\": \"silver\",\n",
+    "    \"0.2-0.4\": \"rosybrown\",\n",
+    "    \"0.4-0.6\": \"coral\",\n",
+    "    \"0.6-0.8\": \"crimson\",\n",
+    "    \"0.8-1\":   \"blueviolet\"\n",
+    "}\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 4. COMPUTE NSFW COUNTS & OVERALL TOTALS\n",
+    "# =============================================================================\n",
+    "# Define NSFW as any contribution from levels 0.2 to 1 (i.e. all groups except \"0.0-0.2\").\n",
+    "flow_data['nsfw_count'] = flow_data.apply(\n",
+    "    lambda row: sum([row[col] for grp, col in browsing_groups.items() if grp != \"0.0-0.2\"]),\n",
+    "    axis=1\n",
+    ")\n",
+    "\n",
+    "# Overall total of all models (from all bins).\n",
+    "overall_total_models = flow_data['total_images'].sum()\n",
+    "\n",
+    "print(overall_total_models)\n",
+    "\n",
+    "# Overall total of NSFW models (levels 0.2–1 across all bins).\n",
+    "overall_nsfw_total = flow_data['nsfw_count'].sum()\n",
+    "\n",
+    "# Also, count the number of creators per bin (from the original DataFrame).\n",
+    "creator_counts = df['creator_bin'].value_counts().to_dict()\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 5. PREPARE LEFT (CREATOR BIN) NODES & SORT BY INFLOW SIZE\n",
+    "# =============================================================================\n",
+    "# For each creator bin, we build a node with:\n",
+    "# - Total models in that bin (used for sorting, highest first)\n",
+    "# - Percentage of overall creators (with gross number)\n",
+    "# - Percentage of overall models (with gross number)\n",
+    "# - Percentage of overall NSFW models (with gross number)\n",
+    "left_nodes = []\n",
+    "for _, row in flow_data.iterrows():\n",
+    "    bin_label = row['creator_bin']\n",
+    "    bin_total_models = row['total_images']\n",
+    "    pct_models = (bin_total_models / overall_total_models * 100) if overall_total_models > 0 else 0\n",
+    "    nsfw_count = row['nsfw_count']\n",
+    "    nsfw_pct = (nsfw_count / overall_nsfw_total * 100) if overall_nsfw_total > 0 else 0\n",
+    "    count_creators = creator_counts.get(bin_label, 0)\n",
+    "    left_nodes.append({\n",
+    "        'bin': bin_label,\n",
+    "        'total': bin_total_models,\n",
+    "        'pct_models': pct_models,\n",
+    "        'nsfw_pct': nsfw_pct,\n",
+    "        'nsfw_count': nsfw_count,\n",
+    "        'count_creators': count_creators\n",
+    "    })\n",
+    "\n",
+    "# Sort left nodes by total models (largest inflow on top)\n",
+    "left_nodes = sorted(left_nodes, key=lambda x: x['total'], reverse=True)\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 6. PREPARE RIGHT (BROWSING GROUP) NODES & SORT BY CUSTOM ORDER\n",
+    "# =============================================================================\n",
+    "# For each browsing group, sum its contributions across all bins.\n",
+    "right_nodes = []\n",
+    "for group_label, col in browsing_groups.items():\n",
+    "    group_total = flow_data[col].sum()\n",
+    "    pct_models = (group_total / overall_total_models * 100) if overall_total_models > 0 else 0\n",
+    "    right_nodes.append({\n",
+    "        'group': group_label,\n",
+    "        'total': group_total,\n",
+    "        'pct_models': pct_models,\n",
+    "        'color': color_mapping[group_label]\n",
+    "    })\n",
+    "\n",
+    "# Define a custom order for outflows.\n",
+    "# (Here we assume the desired top-down order is:\n",
+    "#  0.8-1, then 0.6-0.8, then 0.4-0.6, then 0.2-0.4, then 0.0-0.2)\n",
+    "custom_right_order = [\"0.8-1\", \"0.6-0.8\", \"0.4-0.6\", \"0.2-0.4\", \"0.0-0.2\"]\n",
+    "\n",
+    "# Sort right nodes by the custom order (regardless of their total values)\n",
+    "right_nodes = sorted(right_nodes, key=lambda x: custom_right_order.index(x['group']))\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 7. BUILD SANKEY LINKS DIRECTLY BETWEEN LEFT & RIGHT NODES\n",
+    "# =============================================================================\n",
+    "# Create a link for each combination of creator bin (left node) and browsing group (right node)\n",
+    "# if the contribution is greater than zero.\n",
+    "links = []\n",
+    "for i, left in enumerate(left_nodes):\n",
+    "    # Get the aggregated row corresponding to this bin.\n",
+    "    row = flow_data[flow_data['creator_bin'] == left['bin']].iloc[0]\n",
+    "    for j, right in enumerate(right_nodes):\n",
+    "        # Identify the column corresponding to this browsing group.\n",
+    "        col = browsing_groups[right['group']]\n",
+    "        value = row[col]\n",
+    "        if value > 0:\n",
+    "            # Express the link value as a percentage of overall models.\n",
+    "            pct_value = (value / overall_total_models * 100)\n",
+    "            links.append({\n",
+    "                'source': i,                         # index of left node\n",
+    "                'target': len(left_nodes) + j,         # index of right node (offset by left node count)\n",
+    "                'value': pct_value,\n",
+    "                'raw_value': value,\n",
+    "                'hover': f\"<b>{pct_value:.1f}% of models ({value})</b>\",\n",
+    "                'color': right['color']               # color based on the browsing group\n",
+    "            })\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 8. CREATE NODE LABELS WITH PERCENTAGES & GROSS NUMBERS (ALL BOLD)\n",
+    "# =============================================================================\n",
+    "# Left node labels: now renamed as \"<bin>+ Contributions\" (instead of \"Bin <bin>\")\n",
+    "# and showing:\n",
+    "#   - Percentage of creators (and gross number)\n",
+    "#   - Percentage of models (and gross number)\n",
+    "#   - Percentage of NSFW models (and gross number)\n",
+    "left_labels = []\n",
+    "for left in left_nodes:\n",
+    "    pct_creators = (left['count_creators'] / overall_creators * 100) if overall_creators > 0 else 0\n",
+    "    label = (\n",
+    "        f\"<b>{left['bin']}+ Contributions</b><br>\"\n",
+    "        f\"<b>{pct_creators:.1f}% of creators ({left['count_creators']})</b><br>\"\n",
+    "        f\"<b>{left['pct_models']:.1f}% of models ({left['total']})</b><br>\"\n",
+    "        f\"<b>{left['nsfw_pct']:.1f}% of NSFW models ({left['nsfw_count']})</b>\"\n",
+    "    )\n",
+    "    left_labels.append(label)\n",
+    "\n",
+    "# Right node labels: include the browsing group and its percentage and gross number of models.\n",
+    "right_labels = []\n",
+    "for right in right_nodes:\n",
+    "    label = (\n",
+    "        f\"<b>{right['group']}</b><br>\"\n",
+    "        f\"<b>{right['pct_models']:.1f}% of models ({right['total']})</b>\"\n",
+    "    )\n",
+    "    right_labels.append(label)\n",
+    "\n",
+    "# Combine left and right node labels.\n",
+    "node_labels = left_labels + right_labels\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 9. DEFINE NODE POSITIONS\n",
+    "# =============================================================================\n",
+    "# Set left nodes at x = 0.0 and right nodes at x = 1.0.\n",
+    "num_left = len(left_nodes)\n",
+    "num_right = len(right_nodes)\n",
+    "node_x = [0.0] * num_left + [1.0] * num_right\n",
+    "\n",
+    "# Evenly space nodes vertically.\n",
+    "node_y_left = [1 - (i + 0.5) / num_left for i in range(num_left)]\n",
+    "node_y_right = [1 - (i + 0.5) / num_right for i in range(num_right)]\n",
+    "node_y = node_y_left + node_y_right\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 10. PREPARE LINK DATA FOR THE SANKEY DIAGRAM\n",
+    "# =============================================================================\n",
+    "link_source = [link['source'] for link in links]\n",
+    "link_target = [link['target'] for link in links]\n",
+    "link_value = [link['value'] for link in links]\n",
+    "link_colors = [link['color'] for link in links]\n",
+    "link_hover = [link['hover'] for link in links]\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 11. BUILD THE SANKEY DIAGRAM WITH PLOTLY\n",
+    "# =============================================================================\n",
+    "fig = go.Figure(go.Sankey(\n",
+    "    arrangement=\"fixed\",  # we specify custom node positions\n",
+    "    node=dict(\n",
+    "        pad=20,               # space between nodes\n",
+    "        thickness=30,         # node thickness\n",
+    "        line=dict(color=\"black\", width=0.5),\n",
+    "        label=node_labels,\n",
+    "        # Left nodes use a default (black) color; right nodes use their assigned colors.\n",
+    "        color=[\"black\"] * num_left + [node['color'] for node in right_nodes],\n",
+    "        x=node_x,\n",
+    "        y=node_y,\n",
+    "        hovertemplate='%{label}'\n",
+    "    ),\n",
+    "    link=dict(\n",
+    "        source=link_source,\n",
+    "        target=link_target,\n",
+    "        value=link_value,\n",
+    "        color=link_colors,\n",
+    "        customdata=link_hover,\n",
+    "        hovertemplate='%{customdata}<extra></extra>'\n",
+    "    )\n",
+    "))\n",
+    "\n",
+    "fig.update_layout(\n",
+    "    title_text=\"<b>Sankey Diagram: Creator Bins → Browsing Groups</b>\",\n",
+    "    font_size=12,\n",
+    "    width=1300,\n",
+    "    height=900\n",
+    ")\n",
+    "\n",
+    "# =============================================================================\n",
+    "# 12. SAVE & DISPLAY THE FIGURE\n",
+    "# =============================================================================\n",
+    "fig.write_image(output_path, format='svg')\n",
+    "fig.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24d7ad3d-74fa-4a61-9010-7e63a96d0407",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}