{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d475d263",
   "metadata": {},
   "source": [
    "# GAWD Dataset Overview\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SwareUG/gh-aw-dataset-builder/blob/main/data_analysis/dataset_overview.ipynb)\n",
    "\n",
    "Overview statistics and cumulative adoption figures for **A Dataset of GitHub Agentic Workflow Histories: Early Adopters**.\n",
    "\n",
    "This notebook follows the compact structure of the AIDev `dataset_overview.ipynb`: setup, utilities, load published Parquet tables, produce summary tables, then plot cumulative trends.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0f25656",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import importlib.util\n",
    "import subprocess\n",
    "import sys\n",
    "from io import BytesIO\n",
    "from pathlib import Path\n",
    "from urllib.request import urlopen\n",
    "\n",
    "REQUIRED_PACKAGES = {\n",
    "    \"pandas\": \"pandas\",\n",
    "    \"pyarrow\": \"pyarrow\",\n",
    "    \"matplotlib\": \"matplotlib\",\n",
    "    \"seaborn\": \"seaborn\",\n",
    "}\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    missing = [\n",
    "        package\n",
    "        for package, import_name in REQUIRED_PACKAGES.items()\n",
    "        if importlib.util.find_spec(import_name) is None\n",
    "    ]\n",
    "    if missing:\n",
    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"-q\", *missing])\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import Image, display\n",
    "\n",
    "DATASET_ID = \"pavtch/gawd\"\n",
    "DATASET_REVISION = \"main\"\n",
    "LOCAL_RELEASE_CANDIDATES = [\n",
    "    Path(\"..\"),\n",
    "    Path(\".\"),\n",
    "    Path(\"../releases/gh-aw-early-adopters-source-history-2026-06-26\"),\n",
    "    Path(\"releases/gh-aw-early-adopters-source-history-2026-06-26\"),\n",
    "    Path(\"../releases/gawd\"),\n",
    "    Path(\"releases/gawd\"),\n",
    "]\n",
    "LOCAL_RELEASE_MIRROR = next(\n",
    "    (\n",
    "        path.resolve()\n",
    "        for path in LOCAL_RELEASE_CANDIDATES\n",
    "        if (path / \"data\").is_dir() and any((path / \"data\").glob(\"*.parquet\"))\n",
    "    ),\n",
    "    None,\n",
    ")\n",
    "USE_LOCAL_MIRROR = \"google.colab\" not in sys.modules and LOCAL_RELEASE_MIRROR is not None\n",
    "\n",
    "\n",
    "def remote_parquet_url(table_name: str) -> str:\n",
    "    return (\n",
    "        f\"https://huggingface.co/datasets/{DATASET_ID}/resolve/\"\n",
    "        f\"{DATASET_REVISION}/data/{table_name}.parquet\"\n",
    "    )\n",
    "\n",
    "\n",
    "def local_parquet_path(table_name: str) -> Path:\n",
    "    if LOCAL_RELEASE_MIRROR is None:\n",
    "        raise FileNotFoundError(\"No local release mirror with a data/ directory was found.\")\n",
    "    return LOCAL_RELEASE_MIRROR / \"data\" / f\"{table_name}.parquet\"\n",
    "\n",
    "\n",
    "def parquet_path(table_name: str) -> str:\n",
    "    if USE_LOCAL_MIRROR:\n",
    "        return str(local_parquet_path(table_name))\n",
    "    return remote_parquet_url(table_name)\n",
    "\n",
    "\n",
    "def read_parquet_table(table_name: str) -> pd.DataFrame:\n",
    "    if USE_LOCAL_MIRROR:\n",
    "        return pd.read_parquet(local_parquet_path(table_name))\n",
    "\n",
    "    try:\n",
    "        with urlopen(remote_parquet_url(table_name)) as response:\n",
    "            return pd.read_parquet(BytesIO(response.read()))\n",
    "    except Exception:\n",
    "        if LOCAL_RELEASE_MIRROR is not None:\n",
    "            return pd.read_parquet(local_parquet_path(table_name))\n",
    "        raise\n",
    "\n",
    "mpl.rcParams.update(\n",
    "    {\n",
    "        \"font.family\": \"serif\",\n",
    "        \"font.serif\": [\"Times New Roman\", \"Times\", \"DejaVu Serif\"],\n",
    "        \"mathtext.fontset\": \"stix\",\n",
    "        \"axes.linewidth\": 1.0,\n",
    "        \"axes.labelsize\": 14,\n",
    "        \"axes.titlesize\": 15,\n",
    "        \"xtick.major.size\": 4,\n",
    "        \"ytick.major.size\": 4,\n",
    "        \"xtick.labelsize\": 12,\n",
    "        \"ytick.labelsize\": 12,\n",
    "        \"legend.fontsize\": 11,\n",
    "        \"legend.title_fontsize\": 11,\n",
    "        \"figure.dpi\": 160,\n",
    "    }\n",
    ")\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "NOTEBOOK_DIR = Path(\"data_analysis\") if Path(\"data_analysis\").is_dir() else Path(\".\")\n",
    "FIG_DIR = NOTEBOOK_DIR / \"figs\"\n",
    "FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "def save_and_show(fig: plt.Figure, out: Path) -> None:\n",
    "    png_out = out.with_suffix(\".png\")\n",
    "    pdf_out = out.with_suffix(\".pdf\")\n",
    "    fig.savefig(png_out, bbox_inches=\"tight\", dpi=300)\n",
    "    fig.savefig(pdf_out, bbox_inches=\"tight\")\n",
    "    plt.close(fig)\n",
    "    display(Image(filename=str(png_out)))\n",
    "    print(\"Wrote\", png_out)\n",
    "    print(\"Wrote\", pdf_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c1315c89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------------------- Utils -----------------------\n",
    "def load_tables() -> dict[str, pd.DataFrame]:\n",
    "    return {\n",
    "        \"repository\": read_parquet_table(\"repository\"),\n",
    "        \"source_markdown_file_snapshot\": read_parquet_table(\"source_markdown_file_snapshot\"),\n",
    "        \"source_markdown_file_version\": read_parquet_table(\"source_markdown_file_version\"),\n",
    "        \"source_markdown_file_history\": read_parquet_table(\"source_markdown_file_history\"),\n",
    "        \"lock_file_snapshot\": read_parquet_table(\"lock_file_snapshot\"),\n",
    "    }\n",
    "\n",
    "\n",
    "def build_version_events(tables: dict[str, pd.DataFrame]) -> pd.DataFrame:\n",
    "    events = (\n",
    "        tables[\"source_markdown_file_version\"].merge(\n",
    "            tables[\"source_markdown_file_snapshot\"],\n",
    "            on=\"source_markdown_file_snapshot_id\",\n",
    "            how=\"inner\",\n",
    "            validate=\"many_to_one\",\n",
    "        )\n",
    "        .merge(\n",
    "            tables[\"repository\"],\n",
    "            on=\"repository_id\",\n",
    "            how=\"left\",\n",
    "            validate=\"many_to_one\",\n",
    "        )\n",
    "    )\n",
    "    events[\"committed_at\"] = pd.to_datetime(events[\"committed_at\"], utc=True, errors=\"coerce\")\n",
    "    events = events.dropna(subset=[\"committed_at\"]).copy()\n",
    "    events[\"committed_month\"] = (\n",
    "        events[\"committed_at\"].dt.tz_convert(None).dt.to_period(\"M\").dt.to_timestamp()\n",
    "    )\n",
    "    return events.sort_values([\"repository_id\", \"committed_at\", \"rank\"])\n",
    "\n",
    "\n",
    "def build_adoption_table(version_events: pd.DataFrame) -> pd.DataFrame:\n",
    "    adoption = (\n",
    "        version_events.groupby(\"repository_id\", as_index=False)\n",
    "        .agg(\n",
    "            repo_full_name=(\"repo_full_name\", \"first\"),\n",
    "            main_language=(\"main_language\", \"first\"),\n",
    "            license=(\"license\", \"first\"),\n",
    "            stars=(\"stars\", \"first\"),\n",
    "            forks=(\"forks\", \"first\"),\n",
    "            adoption_date=(\"committed_at\", \"first\"),\n",
    "            first_source_path=(\"path\", \"first\"),\n",
    "            observed_versions=(\"source_markdown_file_version_id\", \"count\"),\n",
    "            observed_source_paths=(\"path\", \"nunique\"),\n",
    "        )\n",
    "        .sort_values(\"adoption_date\")\n",
    "        .reset_index(drop=True)\n",
    "    )\n",
    "    adoption[\"adoption_month\"] = (\n",
    "        adoption[\"adoption_date\"].dt.tz_convert(None).dt.to_period(\"M\").dt.to_timestamp()\n",
    "    )\n",
    "    return adoption\n",
    "\n",
    "\n",
    "def monthly_cumulative(adoption_df: pd.DataFrame) -> pd.DataFrame:\n",
    "    monthly = (\n",
    "        adoption_df.groupby(\"adoption_month\", as_index=False)\n",
    "        .agg(new_repositories=(\"repository_id\", \"count\"))\n",
    "        .sort_values(\"adoption_month\")\n",
    "    )\n",
    "    monthly[\"cumulative_repositories\"] = monthly[\"new_repositories\"].cumsum()\n",
    "    monthly[\"month_label\"] = monthly[\"adoption_month\"].dt.strftime(\"%Y-%m\")\n",
    "    return monthly\n",
    "\n",
    "\n",
    "def plot_cumulative(monthly: pd.DataFrame, title: str, fname_stub: str) -> None:\n",
    "    fig, ax = plt.subplots(figsize=(10, 6))\n",
    "    plot_df = monthly.copy()\n",
    "    plot_df[\"month_index\"] = range(len(plot_df))\n",
    "    x = plot_df[\"month_index\"]\n",
    "    y = plot_df[\"cumulative_repositories\"].astype(int)\n",
    "\n",
    "    ax.fill_between(x, y, color=\"#4C78A8\", alpha=0.58)\n",
    "    ax.plot(x, y, color=\"black\", linewidth=1.8, marker=\"o\", markersize=5)\n",
    "    for xpos, value in zip(x, y, strict=True):\n",
    "        ax.annotate(\n",
    "            f\"{value}\",\n",
    "            xy=(xpos, value),\n",
    "            xytext=(0, 8),\n",
    "            textcoords=\"offset points\",\n",
    "            ha=\"center\",\n",
    "            va=\"bottom\",\n",
    "            fontsize=11,\n",
    "            fontweight=\"bold\",\n",
    "            color=\"black\",\n",
    "        )\n",
    "\n",
    "    ax.set_title(title, pad=16, weight=\"bold\")\n",
    "    ax.set_xlabel(\"Month\", fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"Number of Repositories\", fontweight=\"bold\")\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(plot_df[\"month_label\"], rotation=45, ha=\"right\")\n",
    "    ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.45)\n",
    "    ax.grid(axis=\"x\", visible=False)\n",
    "    sns.despine(ax=ax)\n",
    "    fig.tight_layout()\n",
    "\n",
    "    out = FIG_DIR / f\"{fname_stub}.png\"\n",
    "    save_and_show(fig, out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "859ab539",
   "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>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>published repositories</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>observed adopters</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>source markdown versions</td>\n",
       "      <td>2820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>first observed adoption</td>\n",
       "      <td>2025-10-03 20:05:08+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>latest observed adoption</td>\n",
       "      <td>2026-06-07 18:40:49+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     metric                      value\n",
       "0    published repositories                        262\n",
       "1         observed adopters                        262\n",
       "2  source markdown versions                       2820\n",
       "3   first observed adoption  2025-10-03 20:05:08+00:00\n",
       "4  latest observed adoption  2026-06-07 18:40:49+00:00"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load published parquet tables\n",
    "tables = load_tables()\n",
    "version_events = build_version_events(tables)\n",
    "adoption_df = build_adoption_table(version_events)\n",
    "\n",
    "\n",
    "summary = pd.DataFrame(\n",
    "    [\n",
    "        {\"metric\": \"published repositories\", \"value\": tables[\"repository\"][\"repository_id\"].nunique()},\n",
    "        {\"metric\": \"observed adopters\", \"value\": adoption_df[\"repository_id\"].nunique()},\n",
    "        {\"metric\": \"source markdown versions\", \"value\": len(version_events)},\n",
    "        {\"metric\": \"first observed adoption\", \"value\": adoption_df[\"adoption_date\"].min()},\n",
    "        {\"metric\": \"latest observed adoption\", \"value\": adoption_df[\"adoption_date\"].max()},\n",
    "    ]\n",
    ")\n",
    "summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1a6e80e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote data_analysis/figs/gawd_cumulative_all_repositories.png\n",
      "Wrote data_analysis/figs/gawd_cumulative_all_repositories.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ---- Figure 1: all repositories ----\n",
    "monthly_all = monthly_cumulative(adoption_df)\n",
    "plot_cumulative(\n",
    "    monthly_all,\n",
    "    title=\"Observed GH-AW Adoption by Repository\",\n",
    "    fname_stub=\"gawd_cumulative_all_repositories\",\n",
    ")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}